VOLUME 5(1)Edited by
Zakaria Hossain
Akira Kobayashi
Shinya Inazumi

GEOMATE 2015 OSAKA, JAPANGEOTECHNIQUE, CONSTRUCTION MATERIALS AND ENVIRONMENTiPROCEEDINGS OF FIFTH INTERNATIONAL CONFERENCE – GEOMATE 2015
GEOTECHNIQUE, CONSTRUCTION MATERIALS AND ENVIRONMENT OSAKA, JAPAN
16-18 NOVEMBER, 2015

Geotechnique, Construction
Materials and Environment
Edited by

Zakaria Hossain
Department of Environmental Science and Technology
Graduate School of Bioresources
Mie University, Japan

Akira Kobayashi
Department of Civil, Environmental and Applied System Engineering
Faculty of Environmental and Urban Engineering
Kansai University, Japan

Shinya Inazumi
Department of Civil Engineering
National Institute of Technology
Akashi College, JapanTHE GEOMATE INTERNATIONAL SOCIETYii

Copyright @ 2015 by The GEOMATE International Society
All rights reserved. In principle, no part of this publication or the information contained herein may be
reproduced in any form or by any means, translated in any language, stored in any data base or retrieval
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Disclaimer: The editors and the publisher have tried their best effort to ensure the integrity and the quality ofthis publication and information herein. However, they give no warranty of any kind, expressed or impliedwith regard to the material contained in this book, and will not be liable in any event for the consequences of
its use.
Published by:
The GEOMATE International Society
Tsu city, Mie, Japan
E-mail: society@geomate.org
http://www.geomate.org/

ISBN Number: 978-4-9905958-4-5 C3051

iii

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

Table of Contents

PrefaceOrganization

xiii
xiv

ID Keynote Papers

1

1k

BEARING CAPACITY OF AN OPEN-ENDED PILE
Yoshiaki Kikuchi

2

2k

INTRODUCING ADVANCED TOPICS IN GEOTECHNICAL ENGINEERING TEACHING
Jim Shiau

14

4k

FRP STRENGTHENED REINFORCED CONCRETE PILES UNDER STATIC LATERAL LOADS – A FIELD
STUDY
K. Muthukkumaran

25

33

Technical Papers
ID

Geotechnique

34

5108

A NEW LOOK AT PAVEMENT MANAGEMENTJohn Yeaman, Stuart Yeaman and John W Yeaman

35

5117

SPATIAL ANALYSIS OF COLLAPSE-RELATED SOIL PARAMETERS
M. Mohammad Al

41

5119

A 3D INVESTIGATION INTO UNDRAINED CLAY SINKHOLE FORMATION
Brian Lamb and Jim Shiau

48

5122

EFFECT OF LIQUID ALKALINE ACTIVATOR CONTENT ON UNIT WEIGHT AND STRENGTH OF A WATERTREATMENT SLUDGE–FLY ASH LIGHTWEIGHT CELLULAR GEOPOLYMER
Cherdsak Suksiripattanapong, Suksun Horpibulsuk and Arul Arulrajah

54

5127

PREDICTING THE STRESS-STRAIN BEHAVIOR OF MINE TAILINGS USING MODIFIED HYPERBOLIC
MODEL
Mary Ann Q. Adajar and Mark Albert H. Zarco

60

5128

STABILITY OF UNDRAINED TWIN CIRCULAR TUNNELS
Jay Lobwein, Mathew Sams and Jim Shiau

66

5132

RELATING THE TUNNEL SETTLEMENT PARAMETER WITH VOLUME LOSS
Mathew Sams and Jim Shiau

72

iv

5134

EFFICIENCY OF SAND-NATURAL EXPANSIVE CLAY MIXTURE AS A HYDRAULIC BARRIER
Abdullah A. Shaker and Tamer Y. Elkady

78

5149

MECHANICAL PROPERTY OF SAND CEMENTED WITH CALCIUM PHOSPHATE COMPOUNDS USING
PLANT–DERIVED UREASE
R.A.N. Dilrukshi, Jun Watanabe and Satoru Kawasaki

84

5160

EFFECTS OF BAGASSE ASH AND HYDRATED LIME ADDITION ON ENGINEERING PROPERTIES OF
EXPANSIVE SOIL
Liet Chi Dang, Hayder Hasan, Behzad Fatahi, Robert Jones and Hadi Khabbaz

90

5161

CLAY-SHALE MATERIALS AS LOW-COST LANDFILL LINERS: AN INTEGRATED GEOCHEMICAL AND
GEOTECHNICAL ASSESSMENTS
Moshood N.Tijani, Jean-Frank Wagner and Matthew E. Nton

96

5163

LABORATORY-SCALE MACRODISPERSION IN HETEROGENEOUS POROUS FORMATION AND ITS
QUANTIFICATION USING SPATIAL AND TEMPORAL MOMENT APPROACHES
Kazuya Inoue, Tomoki Kurasawa and Tsutomu Tanaka

102

5168

STRESS-STRAIN BEHAVIOR OF GEOSYNTHETIC REINFORCED SOIL USING A MODIFIED CBR TEST
Danny Useche Infante, Gonzalo Aiassa Martinez , Pedro Arrúa and Marcelo Eberhardt

108

5169

INFLUENCE OF DIFFERENT INITIAL DENSITIES IN THE HIDROMECHANICAL BEHAVIOR OF STABILIZEDLOESSIC SOILS
Alercia Biga Carolina, Arrúa Pedro, Eberhardt Marcelo and Aiassa Martinez Gonzalo

114

5174

CASE STUDY ON DEFORMATION ANALYSIS DURING EARTHQUAKE OF IRRIGATION PONDEMBANKMENT
Minoru Yamanaka, Takuya Sanei, Katsuhiko Koizumi and Shuichi Hasegawa

120

5175

IMAGE PROCESSING FOR GEOTECHNICAL LABORATORY MEASUREMENTS
Erica Elice Saloma Uy and Tirawat Boonyatee

125

5185

CORAL SAND SOLIDIFICATION TEST THROUGH MICROBIAL CARBONATE PRECIPITATION USING
PARARHODOBACTER SP.
Md. Nakibul Hasan Khan, Suguru Shimazaki and Satoru Kawasaki

131

5186

NUMERICAL MODELING OF ROCKING OF SHALLOW FOUNDATIONS SUBJECTED TO SLOW CYCLICLOADING WITH CONSIDERATION OF SOIL-STRUCTURE INTERACTION
S. Mohsen Haeri and Aria Fathi

138

5187

FUZZY LOGIC APPLICATION TO COAXIAL AND NON-COAXIAL COMPONENTS OF SHEAR STRENGTH
AND CONSOLIDATION OF SOILS
J.Rajaraman, K.Thiruvenkatasamy and S.Narasimha Rao

144

5191

DEVELOPMENT OF A PROBABILISTIC LIQUEFACTION POTENTIAL MAP FOR METRO MANILA
Chua Renz Anderson D. and Dungca Jonathan R.

152

5193

EFFECT OF RICE STRAW LENGTH ON MECHANICAL PROPERTIES OF QUICK LIME AND RICE HUSKASH STABILIZED SOIL
Ayaka Oya, Satoru Iida , Masamitsu Fujimoto, Ryoichi Fukagawa and Der-Her Lee

159

5195

GIS BASED PROBABILISTIC HAZARD RISK ASSESSMENT OF ROAD ACCIDENTS
Ather Ashraf and Hania Arif

165

5197

PREDICTIONS FROM STANDARD PENETRATING TEST USING ARTIFICIAL NEURAL NETWORK
A.G. Mahgoub, Dahlia H. Hafez and Mostafa A. Abu Kiefa

171

5198

COMPRESSIBILITY BEHAVIOR OF SIDOARJO MUD VOLCANO (LUSI)Luky Handoko, Noriyuki Yasufuku, Ryohei Ishikura and Ahmad Rifa

177

v

5203

AN EXACT FINITE ELEMENT MODEL FOR AXIALLY LOADED PILE IN ELASTO-PLASTIC SOIL
C. Buachart, C. Hansapinyo, J. Tanun, N. Tanakhum and W. Sommanawat

182

5213

LIQUEFACTION ASSESSMENT BY MICROTREMOR MEASUREMENTS IN BABOL CITY
Asskar Janalizadeh Choobbasti, Sadegh Rezaei and Mehran Naghizadeh

188

5216

STUDIES ON CYCLIC LOADING OF RECYCLED CONCRETE AGGREGATE WITH ADDITION OF RUBBER
CHIPS
Wojciech Sas and Andrzej Głuchowski Alojzy Szymański

193

5218

ULTIMATE BEARING CAPACITY OF FOOTING ON SANDY SOIL AGAINST COMBINED LOAD OFVERTICAL, HORIZONTAL AND MOMENT LOADS
Du L. Nguye, S. Ohtsuka and K. Kaneda

199

5223

THE PROPAGATION BEHAVIOR OF PILE-DRIVING-INDUCED VIBRATION DONE ON SOIL AT VARYINGDISTANCES AND ITS EFFECTS ON EXISTING STRUCTURES
D.Y. Acosta, M.B. Juego, H.M. Sanchez and I.S. Sanchez

205

5224

SOCIAL MEDIA GIS TO SUPPORT THE UTILIZATION
Kayoko Yamamoto and Shun Fujita

211

5225

CLAY-CEMENT STABILIZED CRUSHED ROCK BASE FOR WESTERN AUSTRALIAN ROADS: STRENGTH
PROPERTY INVESTIGATION
Peerapong Jitsangiam and Matthew Merrin

226

5231

APPLICATION OF FUZZY SET THEORY TO SEISMIC VULNERABILITY ASSESSMENT
Kshama S. Roy, Md. Jakiul Hassan, Kamrul Islam and Naveel Islam

232

5232

GNOMON AS SOURCE OF INFORMATION ON PLANET RHYTHMS
Alina Paranina and Roman Paranin

238

5235

LONG TERM PERFORMANCE PREDICTION OF ROAD EMBANKMENT ON ESTUARINE DEPOSITS: A
CASE STUDY IN SOUTHEAST QUEENSLAND
Jia Xi and Barry Wai-Choo Kok

244

5236

EVALUATION OF INFILTRATION BEHAVIORS USING ADVECTIVE DIFFUSION ANALYSIS ON
RADIOACTIVE SUBSTANCES IN SOILS
Yoji Ueyama and Shinya Inazumi

250

5237

FLOWABILITY EVALUATION OF FLUIDIZED TREATING SOILS BASED ON MOVING PARTICLESIMULATION
Masaki Kaneko, Shinya Inazumi, Yuji Shigematsu and Hiroshi Kawamura

254

5238

PREDICTION OF SPACE DISTRIBUTION FOR SOIL SURVEY VALUES BASED ON GEOSTASTICS
Kohei Urakami, Shinya Inazumi, Kozo Okita and Tsuyoshi Okita

260

5249

ALTERNATIVE MATERIALS OF SUB BASE (RESERVOIR BASE) FOR POROUS PAVEMENT
Yulvi Zaika1 and Ludfi Djakfar

265

5250

CONSOLIDATION ANALYSIS OF VIETNAM SOFT MARINE CLAY BY FINITE DIFFERENCE METHOD WITH
APPLICATION OF CONSTANT RATE OF STRAIN CONSOLIDATION TEST
Cong-Oanh Nguyen and Thanh Thi Tran

271

5251

SIMULATION OF DAMPING RATIO AND ELASTIC MODULUS CHANGES DURING THE EARTHQUAKEOCCURRENCE IN A SANDY SLOPE BY NUMERICAL MODELING
Fardin Jafarzadeh, Sina Heidari and Hadi Farahi Jahromi

277

5252

FURTHER DEVELOPMENTS IN THE AASHTOLIV EQUATIONS TO FORWARD-CALCULATE SUBGRADE
AND PAVEMENT MODULIMoshe Livneh

284

vi

OF DISASTER INFORMATION

5253

LIQUEFACTION POTENTIAL ASSESSMENT BASED ON LABORATORY TEST
Abdul Hakam, Febrin Anas Ismail and Fauzan

291

5269

STRENGTH PROPERTIES OF ROAD BASE MATERIALS BLENDED WITH WASTE LIMESTONES
Jonathan R. Dungca and Lydia Francisca T. Dychangco

295

5272

BEARING CAPACITY OF STRIP FOOTINGS ON CLAYS WITH INCREASING SHEAR STRENGTH
Seyednima Salimi Eshkevari, Andrew J. Abbo

301

5274

DRAINAGE SYSTEM OF PRAMBANAN TEMPLE YARD USING NO-FINE CONCRETE OF VOLCANIC ASH
AND BANTAK MERAPI
Ahmad Rifa’i, Nor Puji Lestari and Noriyuki Yasufuku

307

5275

MODELING OF EVAPORATION PROCESSES UNDER THE GROUND
B. Liu, A. Kobayashi, Y. Tsukada and M. Chijimatsu

313

5278

A COMPARISON OF THE BASE RESISTANCE OF OPEN-ENDED PILES EVALUATED BY DIFFERENTDESIGN METHODS
Janaka J. Kumara, Yoshiaki Kikuchi and Takashi Kurashina

319

5281

MEASUREMENT OF ARITHMETICAL MEAN ROUGHNESS OF CONCRETE SURFACE BY TRANSCEIVER
TYPE AERIAL ULTRASONIC SENSOR
Nagaoka Seiya, Okajima Kenji, Ishiguro Satoru, Ito Ryouei, Watanabe Ken and Ito Tesu

325

5284

OVERTURNING AND SLIDING STABILITY OF THE GEOTEXTILE REINFORCED TIDE WALL FOR
OVERFLOW
Naoki Oyama, Yutaka hashizume and Kenji Kaneko

331

5285

DEVELOPMENT OF GEOCELL REINFORCEMENT METHOT TO PREVENT THE SCOURING OF THE
FOUNDATION OF TIDE WALL
Yuataka Hashizume , Naoki Oyama and Kenji Kaneko

337

5287

EFFECT OF SOIL LIQUEFACTION ON THE NATURAL FREQUENCY AND DAMPING OF PILE-SUPPORTEDSTRUCTURES
Domenico Lombardi and Subhamoy Bhattacharya

341

5288

ESTIMATION OF DISTRIBUTION OF CONE PENETRATION RESISTANCE INSIDE EARTH-FILLS WITH USE
OF GEOSTATISTICAL METHOD
Kazunari Imaide, Shin-ichi Nishimura and Takayuki Shuku

346

5297

BEARING CAPACITY OF STRIP FOOTINGS ON DESICCATED CLAY CRUSTS
Qiang Xie, Andrew J. Abbo, Daniel W. Wilson and Andrei V. Lyamin

352

5300

NUMERICAL EXPERIMENT FOR VIRTUAL PLASTER MODEL TESTS SIMULATING BLOCK SHEAR TESTS
Tatsuro Nishiyama and Takashi Hasegawa

358

5301

CREEP AND RELAXATION BEHAVIOR OF HIGHLY ORGANIC SOIL
Shoji Kamao

364

5303

INVESTIGATION OF SEISMIC BEHAVIOR OF EARTH DAM EMBANKMENTS BY USING FEM AND
CENTRIFUGAL LOADING TESTS
Shohei Tsurui , Takahiro Sugano, Akira Kobayashi and Akira Murakami

370

5320

A CYCLIC ELASTOPLASTICITY MODEL OF UNSATURATED SOILS
Tomotaka Sato, Hirotaka Saito and Yuji Kohgo

377

5329

GEOTECHNICAL HAZARD ANALYSIS OF RIVER EMBANKMENT OF BANGLADESH AND ITSPROTECTABILITY
Md. Wasif Zaman, Md. Yousuf Rumi, Md. Salequzzaman and H. M. Shahin

383

vii

5335

AXIAL RESISTANCES OF OPEN-ENDED PILES DRIVEN INTO GRAVEL LAYERS: DYNAMIC LOADINGTESTS OF PILES AT ROAD BRIDGE CONSTRUCTION SITE IN MIZUSHIMA PORT
Takaaki Mizutani

389

5337

LABORATORY INVESTIGATION ON THE EFFECTS OF OVERBURDEN PRESSURE, WATER, AND TIME ONSLAKING INDUCED MATERIAL PROPERTY DEGRADATION OF COAL MINE SPOIL
Chaminda Gallage, Golam Mostofa, David Vosolo and Jayasiri Rajapakse

395

5341

LIQUEFACTION ANALYSIS OF RECLAIMED GROUND IN URAYASU CITY CONSIDERING MAIN SHOCK
AND AFTERSHOCK OF THE 2011 OFF THE COAST OF TOHOKU EARTHQUAKE
Sayuri Kimoto, Hiroshi Matsuoka, Hirokazu Yui, Fusao Oka

401

5346

THE PERFORMANCES OF THREE SOIL RETAINING STRUCTURES: GABION, MODULAR BLOCK AND
GEOCELL
Stathas Dionysios and Wang Jui Pin

407

5349

DEVELOPMENT OF CAPACITANCE DISPLACEMENT MONITORING SYSTEM AND ITS PERFORMANCETESTS
Lin Wenli,, Katsutoshi ueno, Shen Mingrong, Ryosuke Uzuoka and Hisashi Suzuki

413

5350

THE CHARACTERISTIC OF SAND-KAOLIN CLAY MIXTURE AS ARTIFICIAL MATERIAL FOR LABORATORYSOIL TESTING
Purwana Y.M. and Nikraz H

421

5353

ESTIMATING UREASE ACTIVITY IN MICROORGANISM OF PEAT BASED ON ELECTRIC CONDUCTIVITY
Atsuko Sato, Satoru Kawasaki, Toshiro Hata and Takahiro Yamanashi

426

5357

STUDY OF SPH SIMULATION ON FACE COLLAPSE BEHAVIOUR AND EFFECT OF STABILIZATIONMETHOD AROUND TUNNEL PORTAL ZONE
T. Matsuo, K. Mori, N. Hiraoka, M. Sun, Ha H. Bui and R. Fukagawa

430

5360

3D PRINTING OF GRANULAR MATERIAL AND ITS APPLICATION IN SOIL MECHANICS
Satoshi Matsumura, Takaaki Kobayashi and Takaaki Mizutani

436

5364

EFFECT OF SOIL MOISTURE CONDITIONS ON SEISMIC STABILITY OF EMBANKMENT SLOPE
Koichi Sumi, Nobutaka Hiraoka, Masamitsu Fujimoto and Ryoichi Fukagawa

442

5367

A METHOD FOR JUDGING THE RISK OF SLOPE FAILURE BASED ON PRECIPITATION AND SOIL
MOISTURE CHARACTERISTICS
Tetsuya Iida, Masamitsu Fujimoto, Nobuyuki Kumakura and Daisuke Takasaki

448

5368

EFFECT OF ANALYSIS FOR THE INCLINATION ANGLE OF THE BACK SUPPORT OF INCLINED EARTHRETAINING STRUCTURE
Minsu Seo, Jongchul Im, Changyoung Kim, Wooje Lee and Jaewon Yoo

454

5369

CHARACTERISTICS OF SUBSURFACE WATER MOVEMENTS IN SOIL LAYERS ON THE HILLSLOPEBEHIND KIYOMIZU TEMPLE
Yuuki Arimitsu, Masamitsu Fujimoto, Nobutaka Hiraoka,Toru Danjo and Yuko Ishida

459

5372

SEDIMENT DISASTER RISK EVALUATION WITH THE USE OF SLOPE STABILITY ANALYSIS AT
YOKOGAKI-TOGE PASS
Tsuyoshi Kibayashi, Yuko Ishida Tatsuo Konegawa, Masamitsu Fujimoto and Ryoichi Fukagawa

465

5374

DYNAMIC RESPONSE OF SINGLE PILE LOCATED IN SOFT CLAY UNDERLAY BY SAND
Deendayal Rathod, K. Muthukkumaran and T.G. Sitharam

471

5378

INFLUENCE OF MEASURED AND ANALYSIS RAINFALLS ON SEEPAGE: NUMERICAL SIMULATION ON
SLOPE AT THE YOKOGAKI-TOGE PASS
Yuko Ishida, Tsuyoshi Kibayashi , Tatsuo Konegawa, Masamitsu Fujimoto and Ryoichi Fukagawa

475

5385

EXPERIMETAL MEASUREMENT OF SOIL-PRESTRESSED FOUNDATION INTERACTION
Radim Cajka, Petr Mynarcik and Jana Labudkova

481

viii

5389

A NEW RHEOMETRICAL EQUIPEMENT FOR THE CHARACTERIZATION OF LARGE GRANULAR
MATERIALS INVOLVED IN FAST LANDSLIDES
Anna Maria Pellegrino, Anna Scotto di Santolo, Aldo Evangelista and Leonardo Schippa

487

5394

UNDRAINED BEHAVIOUR OF JOHOR SAND IN CYCLIC TRIAXIAL TEST
Aminaton Marto, Choy Soon Tan, Ahmad Mahir Makhtar and Siti Norafida Jusoh

493

5395

INVESTIGATING THE EFFECT OF CHANGING GEOMETRIC AND RESISTANCE PROPERTIES OF
MULTI-TIERED REINFORCED SOIL WALL, ON IT’S BEHAVIOR
Mohammad Reza Atrchian and Ahmad Armani

499

5396

THE MONITORING OF ELECTROKINETIC STABILISATION TECHNIQUE ON BATU PAHAT MARINE CLAY
Saiful Azhar Ahmad Tajudin,, Nurul Syakeera Nordin, Aminaton Marto, Ian Jefferson, Aziman
Madun and Mohd Hazreek Zainal Abidin

506

5397

USING 3DEC TO SIMULATE FRACTURES AND CANISTERS INTERSECTIONS AND THEIR APPLICATIONSFOR REPOSITORY LAYOUT
Chi-Jen Chen, Yuan-Chieh Wu and Meng-Hsiu Hsieh

512

5398

EVALUATION OF GEOTECHNICAL CORRELATIONS USING ATIC METHOD
Waled A. Daoud, Kiyonobu Kasama, Naser M. Saleh and Abdelazim M. Negm

516

ID

Construction Materials

523

5105

STRUCTURE OF SUPERCONDUCTING CABLE COMPONENTS
Svetlana Barannikova, Anatoly Malinovsky and Dmitrii Pestsov

524

5107

A FRESH APPROACH TO CHARACTERISATION OF HOT MIX ASPHALTS
Yeaman J. and Yeaman S.D

530

5124

A STUDY RELATING SOLAR RADIATION, ROAD SURFACE TEMPERATURE AND TEMPERATURE AT
DEPTH IN HOT MIX ASPHALTJohn Yeaman

536

5130

EXPERIMENTAL STUDY ON THE EPS-BASED SHOCK ABSORBING MATERIAL FOR THE ROCK-SHED
Shen-Hsiang Hsu, Koji Maegawa and Li-Hsien Chen

541

5133

A STUDY FOR SEISMIC IMPROVEMENT OF CONCRETE FRAME WITH PERFORATED WALLSA CASE OF A STAIRCASE WITH GIRDERS AND SLABS AT MID-STORY
Ryota Ishii and Tetsuya Ohmura

548

5142

EVALUATING CONCRETE PROPERTIES USING RECYCLED GLASS SAND FOR RIGID PAVEMENTAPPLICATIONS
Hadi Khabbaz and Abraham Khoury

553

5178

AN EXPERIMENTAL INVESTIGATION OF PHYSICAL, CHEMICAL AND RHEOLOGICAL PROPERTIES OF
RAP MODIFIED ASPHALT BINDER
Ashwini Shanbhag, Dinesh S V, H S Lalithamba and Anand Tadas

559

5192

EXPERIMENTAL STUDY ON LOAD-DEFORMATION-TEMPERATURE BEHAVIOUR OF POLYMERGEOGRID
Thitapan Chantachot , Warat Kongkitkul and Fumio Tatsuoka

567

5199

MANAGING VALORIZATION OF DREDGED SEDIMENTS VIA AN INNOVATIVE MATHEMATICAL MODELUSED FOR CIVIL ENGINEERING APPLICATION
M. Benzerzour, N. Abriak, .Khezami and R. Mansi

574

5217

AN INVESTIGATION OF POLYURETHANE FOAM WITH A VAPOUR PHASE CORROSION INHIBITOR AS
CORROSION PROTECTION SYSTEM
Steve Hecker and Vanissorn Vimonsatit

580

ix

5230

USE OF DESIGN OF EXPERIMENT IN SEISMIC VULNERABILITY ASSESMENT FOR EXISTING RC
BUILDINGS BY JAPANESE METHOD
Naveel Islam, Kshama S. Roy and Kamrul Islam

586

5263

THE ROLE OF POLYPROPYLENE FIBERS IN IMPROVING HIGHLY PLASTIC CLAY INTEGRITY
Muawia A Dafalla , Arif Ali Baig Moghal and A. Obaid

592

5296

PARAMETRIC STUDY OF REINFORCED CONCRETE SLABS UNDER BLAST LOADS
Mujtaba M. N. Shuaib, Osama M. A. Daoud

597

5304

SOME ASPECTS OF PHYSICAL AND MECHANICAL PROPERTIES OF SAWDUST CONCRETE
A.S.M. Abdul Awal, A.A.K. Mariyana and M.Z. Hossain

604

5336

DAMAGE INVESTIGATION AND RETROFITTING OF WEST SUMATRA GOVERNOR’S OFFICE BUILDING
Fauzan Febrin Anas Ismail and Zaidir Nugrafindo Yanto

610

5352

HORIZONTAL PERMEABILITY OF CLAY MIXTURES UNDER LARGE SHEAR DEFORMATIONS
Po- Hao Hsu, Yohei Katayama, Takashi Tsuchida and Athapaththu A.M.R.G

615

5376

EXPERIMENTAL STUDY OF INFLUENCE OF SEAWATER ON STRENGTH OF CONCRETE STRUCTURES
Moshood N.Tijani, James I. Inim and Saheed O. Adetu

621

ID

Environment

627

5112

5137

5138

POTENTIAL USE OF SILT AND SLUDGE AS LANDFILL CAPPING MATERIAL
Hamidi Abdul Aziz, Wong Chin Yik, Muhd. Harris Ramli and Salem S. Abu Amr
OPTIMAL DESIGN OF RAIN GAUGE NETWORK IN JOHOR BY USING GEOSTATISTICS AND PARTICLE
SWARM OPTIMIZATION
Mohd Khairul Bazli Mohd Aziz, Fadhilah Yusof, Zalina Mohd Daud, Zulkifli Yusop4, Mohammad Afif
Kasno
EFFECTIVENESS OF JACKFRUIT SEED STARCH AS COAGULANT AID IN LANDFILL LEACHATE
TREATMENT PROCESS
Mohd SuffianYusoff , Noor Aina Mohamad Zuki and Mohd Faiz Muaz Ahmad Zamri

628

634

642

5140

WATER QUALITY AND COMPOSITIONS OF THE PHYTOPLANKTON AND ZOOPLANKTON BEFORE AND
AFTER BUILDING A BULKHEAD MAINTENANCE CONSTRUCTION IN LAKE FUKAMI-IKE, JAPAN
Hiromi Suda , Masaaki Tanaka, Maki Oyagi , Megumi Nobori and Akihiko Yagi

647

5143

NEW MICROBIAL FUNCTION BASED REINFORCEMENT METHOD FOR SLOPES
Toshiro Hata; Kazuhiro Kaneda; and Tomohiro Tanikawa

653

5147

ANALYSIS OF EVACUATION BEHAVIORS IN DIFFERENT AREAS BEFORE AND AFTER THE GREAT EAST
JAPAN EARTHQUAKE
Tetsuo Morita, Shinya Tsukada and Akira Yuzawa

657

5148

AN APPROACH TO ASSESS PERFORMANCE OF SEWER PIPES SUBJECTED TO BOULDER IMPACT
Thayalan Nall and TaeWoo Eom

662

5150

STUDY ON ASSESSING THE VALUE OF THE TENGUIWA IRRIGATION CANAL
Shinya Tsukada, Tetsuo Morita and Akira Yuzawa

668

5151

THE EFFECT OF FLOODING ON THE INTEGRITY OF ROAD SUBGRADE
Abdul Naser Abdul Ghani, Nur Izyan Roslan and Ahmad Hilmy Abdul Hamid

672

x

5154

5155

5156

ARSENIC RELEASE PROCESSES INTO CONFINED AQUIFERS OF THE SEINO BASINS, NOBI PLAIN,
JAPAN
Yusuke Kawai, Masahiko Katoh, Ryuki Mori, Yu Otake, and Takeshi Sato
ARSENIC FIXATION STUDIES ON LIME STABILIZED SEMI ARID SOILS
Arif Ali Baig Moghal, Krishna R Reddy, Syed Abu Sayeed Mohammed, Mosleh Ali Al-Shamrani andWaleed M Zahid
INVESTIGATION ON THE ARSENIC POLLUTION AROUND THE DOMESTIC WASTE DISPOSAL SITE INSUZUKA CITY
Masaaki Takahashi and Yukimasa Takemoto

678

685

691

5158

CHANGES OF CHLOROPHYLL-A, BACTERIOCHLOROPHYLL-C AND DOC BEFORE AND AFTERREVETMENT WORK IN LAKE FUKAMI-IKE, JAPAN
Megumi Nobori, Hiromi Suda, Maki Oyagi, Akiko Yokoyama, Akihiko Yagi

695

5159

GENETIC DIVERSITY OF RESTORED ENDANGERED SPECIES, PENTHORUM CHINENSE IN THE
RIVERBED
Michiko Masuda and Fumitake Nishimura

701

5164

EARTH QUAKE AND SPATIAL TSUNAMI MODELLING FOR THE SOUTH JAVA COASTAL AREA –INDONESIA
Agus Hartoko, Muhamad Helmi, Mujahid Sukarno and Hariyadi MT

708

5173

MODELLING OF PERMEABILITY CHARACTERISTICS OF SOIL-FLY ASH-BENTONITE CUT-OFF WALL
USING RESPONSE SURFACE METHOD
Joenel G. Galupino and Jonathan R. Dungca

714

5177

VEGETATION RECOVERY PROCESS ON LANDSLIDE STEEP SLOPE AFTER Alnus sieboldiana AND
Miscanthus condensatus PLANTING WITH SIMPLE TERRACING WORK IN MIKURA-JIMA ISLAND,
JAPAN
Tetsuo Okano and Teruo Arase

720

5180

QUANTIFICATION OF PATHOGENIC BACTERIA IN FLOOD WATER IN MALAYSIA
Zummy Dahria Mohamed Basri, Zulhafizal Othman and Marfiah Ab. Wahid

726

5182

CONVERSION OF A DECOMMISSIONED OXIDATION LAGOON INTO A FUNCTIONAL WETLAND
Jeram Mallis, Rhiannon Hamilton, Scott Rayburg, Melissa Neave

730

5184

DESIGNING URBAN RIVERS TO MAXIMISE THEIR GEOMORPHIC AND ECOLOGIC DIVERSITY
Melissa Neave and Scott Rayburg

736

5189

CHANGES IN DEPTH AND THE SEDIMENT RATE BEFORE AND AFTER THE LAKESHOREDEVELOPMENT IN LAKE FUKAMI-IKE, JAPANMaki Oyagi, Megumi Nobori, Hiromi Ssuda, Akiko Yokoyama and Akihiko Yagi

5190

5202

5226

MODELING OF FLUID INTRUSION INTO POROUS MEDIA WITH MIXED WETTABILITIES USING PORE
NETWORK
Junichiro Takeuchi, Wataru Sumii and Masayuki Fujihara
DEVELOPMENT OF TERRESTRIAL AND HYDROPHYTIC FLORA IN RELATION TO WATERMANAGEMENT IN OBASUTE-OIKE IRRIGATION POND IN JAPANTeruo Arase, Tetsuo Okano, Yoshiyuki Uchikawa and Taizo Uchida
EFFECTS OF WATER FLOW RATE ON RIVER WATER PURIFICATION BY ATTACHED GROWTHMICROORGANISM ON RIVER BED - AN EXPERIMENTAL STUDY ABOUT NITRIFICATION Fumitake Nishimura and Michiko Masuda

742

748

755

759

5227

BIODIESEL INDUSTRY WASTE RECOVERY IN AGRICULTURE
Mariana Ferreira Rabelo Fernandes, Essaid Bilal , Regynaldo Arruda Sampaio, Jatnel Alonso Lazo
Luiz Arnaldo Fernandes, Adolf Heinrich Horn and Najla Lassoued

764

5248

LESSONS FROM THE INTERNATIONAL COMPARISON OF CONTAMINATED LAND POLICIES WITH RISKGOVERNANCE IN JAPAN, THE NETHERLANDS, AND THE UK
Tomoko Miyagawa, Noriko Otsuka and Hirokazu Abe

771

xi

5265

OCEAN DECONTAMINATION: REMOVAL EFFICIEMCY OF RADIOACTIVE CESIUM FROM OCEANSLUDGE BY USING MICRO BUBBLES AND ACTIVATING MICROORGANISMS
Kyoichi Okamoto and Takeshi Toyama

777

5280

THE CHOICE FACTOR OF THE SLOPE PROTECTION METHOD OF RICE TERRACE
Shoko Nishiwaki, Kenji Okaima

782

5286

OXYGEN AND HYDROGEN ISOTOPE STUDY FOR PRECIPITATION SAMPLED AT OSAKA AND MATSUEIN JAPAN
Tetsuya Fukano and Hiroyuki Ii

788

5292

IMPORTANCE OF ROOM VENTILATION FOR MAINTAINING HEALTHY CO2 CONCENTRATIONS
Hiroyuki Ii, Kazuki Taguchi and Masahiro Yamashita

791

5293

OPTIMUM CONDITIONS OF OYSTER SHELLS AS MATERIAL COVERING SEDIMENT AND
INHABITATION SIMULATION OF INTERNAL LOAD USING ECOSYSTEM MODEL
Masaaki Kondo, Yuji Kato and Takamitsu Kajisa

797

5294

ESTIMATION OF USEFUL SPECIES OF RIVER INSECTS AND WATER PLANTS AS AN INDEX OF METALCONTAMINATION IN THE KINOKAWA RIVER CATCHMENT
Takuma Kubohara and Hiroyuki Ii

804

5295

ESTIMATION OF EVAPORATION RATE OF SURFACE WATER TO USING HYDROGEN AND OXYGENISOTOPIC RATIO
Masahiro Yamashita and Hiroyuki Ii

810

5305

EXAMINING THE IMPACT OF THE 120-DAY WINDS ON EVAPOTRANSPIRATION CONSIDERING PANEVAPORATION COEFFECIENT IN WEST REGION OF AFGHANISTAN
Homayoon Ganji, Takamitsu Kajisa, Masaaki Kondo and Behroze Rostami

816

5309

HEAVY METAL CONCENTRATIONS OF SEAWEED FROM OSAKA BAY TO SOUTH OF KII PENINSULA,JAPAN
Hiroyuki Ii and Miho Kohara

822

5322

DISTRIBUTION CHARACTERISTICS OF THE LOAD AMOUNT OF NUTRIENTS, INCLUDING AT THE
FLOODS TIME IN YAMATO RIVER BASIN
Masanobu Taniguchi, Hiroyuki Ii and Tatemasa Hirata

828

5351

SEDIMENT IMPROVEMENT MATERIALS COMPARISON APPLIED TO EUTROPHICATED SEASIDE PARK
POND
Kazuhito Murakami, Saki Agatsuma and Akiko Inoue-Kohama

834

5362

RISK-BASED ENVIRONMENTAL PREPAREDNESS FOR DEALING WITH DEBRIS AND WASTE FROMGREAT TSUNAMI
K. Kitazume, T. Ozaki and T. Morioka

840

5370

MATHEMATICAL MODELING OF CONTAMINANT FLOW IN CLOSED RESERVOIRValeriy Perminov and Alexander Goudov

844

5382

FUZZY RANKING FOR LANDFILL SITE SELECTION IN INDIAN CONTEXT
Dr. J.E.M. Macwan and Hecson Christian

847

5384

TOURISM PLANNING PROPOSAL FOR VISAKHAPATNAM CITY, ANDHRA PRADESH, INDIA
Krupesh A. Chauhan and Y. Pavan Teja

851

5399

SOLID WASTE MANAGEMENT IN DHAKA CITY CORPORATION
Md. Aminul Islam and Zakaria Hossain

857

5196

INFLUENCE OF SOIL REPLACEMENT ON BEARING CAPACITY AND DEFORMATION OF SOFT CLAY IN
ARABIAN GULF
Nuri al- Mohamadi and Vahik K. Bedrosian862

Authors index

867

xii

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

PrefaceOn behalf of the GEOMATE 2015 Organizing Committee, we would like to welcome you in attending theInternational Conference on Geotechnique, Construction Materials and Environment held at the Osaka
International Convention Center, Japan in conjunction with Kansai University, Japan Geotechnical Society,
GEOMATE International Society, AOI-Engineering, Useful Plant Spread Society, HOJUN and Glorious
International.
On Friday 11 March 2011, at 14:46 Japan Standard Time, the north east of Japan was struck and severely
damaged by a series of powerful earthquakes which also caused a major tsunami. This conference wasdedicated to the tragic victims of the Tohoku-Kanto earthquake and tsunami disasters. The 2015 Geomate
conference covers three major themes with 17 specific themes including:
The conference covers three major themes with 17 specific themes including:●
Advances in Composite Materials●
Computational Mechanics●
Foundation and Retaining Walls●
Slope Stability●
Soil Dynamics●
Soil-Structure Interaction●
Pavement Technology●
Tunnels and Anchors●
Site Investigation and Rehabilitation●
Ecology and Land Development●
Water Resources Planning●
Environmental Management●
Public Health and Rehabilitation●
Earthquake and Tsunami Issues●
Safety and Reliability●
Geo-Hazard Mitigation●
Case History and Practical Experience
This year we have received many paper submissions from different countries all over the world, including
Albania, Algeria, Australia, Bahrain, Bangladesh, Brazil, Canada, China, Colombia, Czech Republic, Egypt,France, Hong Kong, India, Indonesia, Iran, Israel, Japan, Jordan, Malaysia, Nigeria, Pakistan, Poland,
Romania, Russia, Saudi Arabia, South Korea, Sudan, Taiwan, Thailand, Tunisia, United Arab Emirates, UK,
USA and Vietnam. The technical papers were selected from the vast number of contributions submitted after
a review of the abstracts. The final papers in the proceedings have been peer reviewed rigorously and revised
as necessary by the authors. It relies on the solid cooperation of numerous people to organize a conference of
this size. Hence, we appreciate everyone who support as well as participate in the joint conferences.
Last but not least, we would like to express our gratitude to all the authors, session chairs, reviewers,
participants, institutions and companies for their contribution to GEOMATE 2015. We hope you enjoy theconference and find this experience inspiring and helpful in your professional field. We look forward toseeing you at our upcoming conference next year.
Best regards,
Prof. Dr. Akira Kobayashi, Conference Chairman
A/Prof. Dr. Shinya Inazumi, Conference Vice-Chairman
Prof. Dr. Md. Zakaria Hossain, Conference General Secretaryxiii

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

Organization
Conference Honorary Chairmen:
Emeritus Professor Dr. Sohji Inoue, Mie University, Japan
Emeritus Professor Dr. Teruo Nakai, Nagoya Institute of Technology, Japan
Emeritus Professor Dr. Fusao Oka, Kyoto University, Japan
Scientific Committees:
Conference Chairman: Professor Dr. Akira Kobayashi, Kansai University, Japan
Conference Vice Chairman: Associate Professor Dr. Shinya Inazumi, National Institute of Technology,
Akashi, Japan
Conference Organizing Committee:
Professor Dr. Akira Kobayashi, Kansai University, Japan
Professor Dr. Tatsuaki Nishigata, Kansai Univ., Japan
Associate Professor Dr. Shinya Inazumi, National Institute of Technology, Akashi, Japan
Professor Dr. Akira Murakami, Kyoto University, Japan
Professor Dr. Kazuhiro Oda, Osaka University, Japan
Emeritus Professor Dr. Sohji Inoue, Mie University, Japan
Professor Dr. Toshinori Sakai, Mie University, Japan
Professor Dr. Zakaria Hossain, Mie University, Japan
National Advisory Committee:
Professor Dr. Fumio Tatsuoka, Tokyo University of Science, Japan
Professor Dr. Junichiro Takeuchi, Kyoto University, Japan
Professor Dr. Kazunori Uchida, Kobe University, Japan
Professor Dr. Kingshuk Roy, Nihon University, Japan
Associate Professor Dr. M.S. Hossain, Islamic University of Technology, Bangladesh
Associate Professor Dr. Sayuri Kimoto, Kyoto University, Japan
Associate Professor Dr. Inoue Kazuya, Kobe University, Japan
Professor Dr. Jing-Cai Jiang, University of Tokushima, Japan
Professor Dr. Noriyuki Yasufuku, Kyushu University, Japan
Professor Dr. Masuya Hiroshi, Kanazawa University, Japan
Professor Dr. Toshihiro Morii, Niigata University, Japan
Professor Dr. Nishimura Tomoyoshi, Ashikaga Institute of Technology, Japan
Professor Dr. Yoshihiro Kimura, Tohoku University, Japan
Professor Dr. Kimitoshi Hayano, Yokohama National Univ., Japan
Associate Professor Dr. Kenichiro Nakarai, Hiroshima University, Japan
Professor Dr. Hiroyuki Ii, Prof. Wakayama University, Japan
International Advisory Committee:
Professor Dr. Sai Vanapalli, University of Ottawa, Canada
Professor Dr. Musharraf Zaman, University of Oklahama, USA
Professor Dr. Rafiqul Tarefder, University of New Mexico, USA
Professor Dr. M. Bouassida, National School of Engineering of Tunis
Professor Dr. L.R. Austriaco, Angles Univ. Found., Philippines
Professor Dr. A.S.M. Abdul Awal, University Technology Malaysia
Professor Dr. M. Ibn Ibrahimy, International Islamic University, Malaysia
Professor Dr. Mohammad Shariful Islam, BUniversity of Engineering and Technology, BangladeshxivProfessor Dr. Bujang B.K. Huat, University Putra Malaysia
Professor Dr. Nemy Banthia, University of British Columbia, Canada
Professor Dr. Ian Jefferson, University of Birmingham, UK
Professor Dr. John Bolander, University of California, USA
Professor Dr. Shamsul Chowdhury, Roosevelt University, USA
Professor Dr. Isabel Pinto, University of Coimbra, Portugal
Professor Dr. Mark Jaksa, University of Adelaide, Australia
Associate Professor Dr. Jim Shiau, University of Southern Queensland, Australia
Professor Dr. Hj. Ramli Bin Hj. Nazir, University Technology Malaysia
Associate Professor Dr. Raja Rizwan Hussain, King Saud University, Saudi Arabia
International Technical Program Committee:
Prof. Sai Vanapalli, University of Ottawa, Canada
Prof. Alaa Masoud, Tanta University, Egypt
Prof. Aly Ahmed, Beni-Suef University, Egypt
Prof. Aminaton Marto, Universiti Teknologi Malaysia, Malaysia
Prof. Bandari Shankar, Osmania University, India
Prof. Bashir Ahmed Mir, National Institute of Technology, India
Prof. Hamidi Abdul, Aziz, Universiti Sains Malaysia (USM), Malaysia
Prof. Hussein Elarabi, University of Khartoum, Sudan
Prof. M. Shohidullah Miah, International Univ. of Business Agriculture &Tech., Bangladesh
Prof. Michele, Casagrande, Pontifical University Catholic of Rio De Janeiro, Brazil
Prof. Radim Cajka, Technical University of Ostrava, Czech RepublicProf. Rajaraman Jambunathan, AMET University, India
Prof. Reshma Chandran T., Global Institute of Architecture, India
Prof. Roslan Hashim, University of Malaya, Malaysia
Prof. Seyed Naser Moghaddas Tafreshi, K.N. Toosi University of Technology, IranProf. Valeriy Perminov, Tomsk Polytechnic University, Russia
A/ Prof. Abdoullah Namdar, Sichuan University, China
A/ Prof. Abdul Naser Abdul Ghani, Universiti Sains Malaysia, Malaysia
A/ Prof. Alaeddinne Eljamassi, Islamic University of Gaza, Palestine
A/ Prof. Alina Paranina, Herzen State Pedagogical University of Russia, Russia
A/ Prof. Ashraf Elmoustafa, Ain Shams Univeristy, Egypt
A/ Prof. Bindu C S, Cochin University of Science& Technology, India
A/ Prof. Chee-Ming Chan, Universiti Tun Hussein Onn Malaysia, Malaysia
A/ Prof. Chidanand Naik, Anjuman Instistute of Technology and Management, India
A/ Prof. Dahlia Hafez, Cairo University, Egypt
A/ Prof. Deepa G Nair, Isfahan University of Technology, India
A/ Prof. Dolrerdee Hormdee, Khon Kaen University, Thailand
A/ Prof. Farhad Behnamfar, Isfahan University of Technology, Iran
A/ Prof. Gabriela B. Cazacu, Geotech Dobrogea, Romania
A/ Prof. Hadi Khabbaz, University of Technology, Sydney (UTS), Australia
A/ Prof. Homayoon Ganji, Herat Univercity Badghis, Afghanistan
A/ Prof. Hudson Jackson, US Coast Guard Academy, United States
A/ Prof. Inazumi Shinya, National Institute of Technology, Akashi, Japan
A/ Prof. John Smith, RMIT University, Australia
A/ Prof. Kasinathan Muthukkumaran, National Institute of Technology, India
A/ Prof. Lindrianasari Lindrianasari, Universitas Lampung, Indonesia
A/ Prof. Mahdi Karkush, Baghdad University, Iraq
A/ Prof. Mohamed Redha Menani, Batna University, Algeria
A/ Prof. Navid Khayat, Ahvaz Branch, Islamic Azad University, Iran
A/ Prof. Salam, Bash AlMaliky, AlMustansiriya University, Iraq
A/ Prof. Shailesh Kumar Jha, Indian Institute of Technology, India
Dr. Abdul Karim M. Zein, University of Khartoum, Sudan
Dr. Abdull Halim Abdul, Universiti Teknologi MARA, Malaysia
Dr. Afshin Asadi, University Putra Malaysia, Malaysia
Dr. Ahmad Safuan A Rashid, Univeristi Teknologi Malaysia, Malaysia

xvDr. Akindele Okewale, Federal University of Petroleum Resources, Nigeria
Dr. Akinola Johnson Olarewaju, Federal Polytechnic Ilaro, Nigeria
Dr. Ali Sobhanmanesh, Universiti Teknologi Malaysia (UTM), Malaysia
Dr. Alireza Bahiraie, Semnan University, Iran
Dr. Allan Manalo, Centre of Excellence in Engineered Fibre Composites, Australia
Dr. Aniza Ibrahim, National Defence University of Malaysia, Malaysia
Dr. Arif Ali Baig Moghal, King Saud University, Saudi Arabia
Dr. Aslan S. Hokmabadi, University of Technology, Sydney (UTS), Australia
Dr. Ather Ashraf, PUCIT, Old Campus Punjab University, Pakistan
Dr. Atsuko Sato, Cicil Engineering Research Institute for Cold Region, Japan
Dr. Ben-Hur Silva, Military Institute of Engineering, United States
Dr. David Thorpe, University of Southern Queensland, Australia
Dr. Delsye Ching Lee Teo, Universiti Malaysia Sarawak, Malaysia
Dr. Domenico Lombardi, University of Manchester, United Kingdom
Dr. Ehsan, Jorat Newcastle University, United Kingdom
Dr. Ganesh Kumar Shanmugam, National Institute of Ocean Technology, India
Dr. Helsin Wang, Institute of Bridge Engineering, Taiwan
Dr. Hossein Moayedi, Kermanshah University of Technology, IranDr. Hossein MolaAbasi, Babol University of Technology, Iran
Dr. James Hambleton, University of Newcastle, Australia
Dr. James Hambleton, University of Newcastle, Australia
Dr. Janaka Kumara, Tokyo University of Science, Japan
Dr. Jirayut, Suebsuk Rajamangala University of Technology Isan, Thailand
Dr. Juhyung Lee, Korea Institute of Civil Engineering and Building Technology, South Korea
Dr. Jun Sugawara, Advision - WorleyParsons Group, Australia
Dr. Lamia Touiti Bouebdellah, Ecole Nationale d'Ingénieurs de Gabes, Tunisia
Dr. Luky Handoko, Universitas Atma Jaya Yogyakarta, Indonesia
Dr. M. Mohammad Ali, California Public Utilities Commission, United States
Dr. Marfiah Ab.Wahid, Universiti Technologi Mara, Malaysia
Dr. Maryam Naeimi, Semnan University, Iran
Dr. Mehdi Mokhberi, Islamic Azad University, Shiraz, Iran
Dr. Neelima Satyam, International Institute of Information Technology, India
Dr. Nor Zurairahetty Mohd Yunus, Univeristi Teknologi Malaysia, Malaysia
Dr. Siavash Zamiran, Southern Illinois University Carbondale, United States
Dr. Subha Vishnudas, Cochin University of Science and Technology, India
Dr. Sunggi Jin, SK E&C, South Korea
Dr. Sunil Pusadkar, Govt. College of Engineering, Amravati, India
Dr. Usama Juniansyah Fauzi, University of Tokyo, Japan
Dr. Yusep Muslih Purwana, Sebelas Maret University, Indonesia
Conference Correspondence:Prof. Dr. Zakaria Hossain, General Secretary,Dept. of Env. Sci. & Tech., Mie University, Japan,
Prof. Dr. Akira Kobayashi (Chairman)
Dept. of Civil and Environmental Engineering, Kansai University, Osaka, Japan,
A/Prof. Dr. Sinya Inazumi (Vice-Chairman),National Ins. of Tech., Akashi, Japan,
E-mail: conference@geomate.org
Tel & Fax: +81-59-231-9578
Editorial Committee and Executive Committee:
Prof. Dr. Zakaria Hossain
Prof. Dr. Akira Kobayashi
A/Prof. Dr. Sinya Inazumi

xviKeynote Paper

1Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

BEARING CAPACITY OF AN OPEN-ENDED PILE

1

Yoshiaki Kikuchi 1
Professor, Department of Civil Engineering, Tokyo University of Science, Japan

ABSTRACTIn recent years, large-diameter steel pipe piles embedded deeply have come to be often used in port
construction in Japan. This is greatly different from how the situation used to be. However, the procedure used toestimate the bearing capacity of a pile has hardly changed. In this paper, the bearing capacity mechanism of an
open-ended pile was examined. Plugging phenomenon was studied in field loading tests, laboratory experiments
both on a large and small scale. The following conclusions were presented in this report: (1) Many loading test
results have shown that the plugging effect is mainly affected by pile diameter. (2) From the field pile loading
test results, it was shown that a pile with secondary critical resistance, which is considered to show ultimate
bearing capacity, had a very low plugging condition. (3) Inner friction that causes plugging was mostly
mobilized in 2D from the pile tip.
Keywords: Open-Ended Pile, Toe Bearing Capacity, Plugging Effect, Inner Friction

the ratio of embedded depth to stiff layer (L B ) to thepile diameter (d I ), is less than 5, η equals 0.16
(L B /d I ), and if L B /d I is larger than 5, then η is equal
to 0.8.
Thus, it has been assumed that the condition of
the plugging effect depends on the ratio of theembedded depth to the diameter of the pile. If theembedded depth is smaller than the regulation value,
the toe bearing capacity of the open-ended pile isreduced at a fixed rate which bears no relation to thecharacteristics of the ground in the construction site.
This can be one of the causes of underestimating thetoe bearing capacity of open-ended piles.

INTRODUCTION
The pile foundations of port facilities in Japan
have been constructed by pile driving with steel pipe
piles for the past 50 years. Although pile diametersand pile lengths have changed a lot in this time, thedesign method has hardly changed. However,
evaluating the toe bearing capacity of a pile has
always been a problem. Because of the use of openended piles with a large diameter in recent years,
there have come to be many uncertainties about thetoe bearing capacity. This problem has been taken
into account in the design as a plugging ratio.
However, the argument made in the method of
estimating a plugging ratio is not fully encompassing.
Under these circumstances, this paper introduces thelatest method of examining the toe bearing capacityof an open-ended pile.

Pu

PREVIOUS STUDIESTo precisely estimate the toe bearing capacity of
open-ended piles, it is important to evaluate theplugging effect at their bottom end. Therefore, manystudies on the mechanism of the plugging of openended piles have already been done both analyticallyand experimentally. The results of those studies havesuggested several types of algorithms, and they havebeen adopted in several design codes in Japan, suchas the Specifications for highway bridges [1] andRecommendation for the design of building
foundations [2]. In the Recommendation for thedesign of building foundations, the toe bearing
capacity of an open-ended pile is evaluated to beequal to ηR PC , in which R PC indicates the toe
bearing capacity of a closed-ended pile, and η is acoefficient of the plugging effect. If L B /d I , which is

RF

RfI

dI
RP
Fig. 1 Diagram of the bearing capacity of an openended pile.

Considering the toe bearing resistance of an
open-ended pile, total resistance will be the sum of
the resistance of the annular part, R P , and inner
friction between the pile and the soil, R fI as shown in
Fig. 1. Considering this, the theoretical plugging
2

GEOMATE- Osaka, Nov. 16-18, 2015

effect, η, will be the ratio of the sum of the toe
bearing capacity of an open-ended pile and that of aclosed-ended pile，R PC [2].
(1)

In this case, estimating R fI is the most importantand difficult point in estimating η.
TOE BEARING CAPACITY ESTIMATIONMETHODS AND MEASURED TOE BEARING
CAPACITY IN THE FIELD
The bearing capacity of a single pile iscommonly divided into toe bearing capacity andshaft resistance force [3].
The calculation formulae for static bearing
capacity of piles used in the port construction
standards of Japan (JPTS), and Eurocode7 (EC7)and the standard for the U.S. Army Corps ofEngineers (USCOE) as typical examples of thecalculation formulae, are compared in Table 1 [4][6]. As shown in the table, in every standard, thebearing capacity calculation condition of a pile isdivided into a sandy layer and a clayey layer.
Moreover, bearing capacity is separated into shaft
resistance and toe resistance.
Table 1 Comparison of static bearing capacity
calculation methods in each design codeSandy
groundClayeygroundBaseresistanceq p (kN/m2)
Shaft
resistanceq s (kN/m2)
Baseresistanceq p (kN/m2)
Shaft
resistanceq s (kN/m2)

JPTS

Eurocode
7 (EC7)

USCOE

300N

N q σ v0 'N q σ v0 '2N

K s σ v0 'tanδK s σ v0 'tanδ

6c u

9c u + σ v0 '

9c u

cu

αc u

αc u

pil
e
red

Coefficient of bearing capacity Nq

Internal friction angle φ'

ile

RPC

Bo

RP + R fI

Dr
ive
np

η=

This comparison shows that bearing capacity
estimation for a pile in clayey ground is similar in
the three codes. However, the bearing capacity
estimation methods for a pile in sandy ground differ
greatly. The method in JPTS is originally what
Meyerhof [7] proposed from the relationshipbetween unit toe resistance and SPT-N value (q p =400 N). However, the constant of the equation ismodified in many instances in Japan [8] and shaft
resistance is proposed based on many measurement
examples [8]. Thus, the bearing capacity estimation
technique in JPTS mainly relies on experiential
results.

lf
ra
ne
e
G

c
Lo

l
fai
al

lu
ai

re

ure

Fig. 2. Relationship between Nq and φ proposed byresearchers.
The coefficient of bearing capacity N q for
estimating toe bearing capacity of a pile in sandy
ground used in EC7 and USCOE is calculated by
supposing the failure mechanism of the groundaround the pile toe. The bearing capacity coefficientN q is determined from the internal friction angle φ.
Although this method is rational, the coefficient ofbearing capacity varies with the assumption of a
failure mechanism that is more than 10 times greater.
For this reason, the relationship between Nq and φproposed by many researchers varies greatly (Fig. 2)[9]. Moreover, there is a difference in theassumption of effective overburden pressure. In EC7,
it is assumed that all overburden pressures are
effective. On the other hand, in USCOE, it is
proposed that the effective overburden pressure inbearing capacity calculation of a deeply embedded
pile should be reduced from the total overburdenpressure according to the proposal by Meyerhof [6].
Shaft resistance force is premised on the

In JPTS, the SPT-N value is used for estimating
the bearing capacity of piles in sandy ground. For
piles in clayey ground, the shear strength from an
unconfined compression test is used. A similar
estimation method is used for the bearing capacity ofpiles both in EC7 and USCOE. That is, the toe
bearing capacity of a pile in sandy ground isestimated by the product of the coefficient ofbearing capacity and the overburden pressure. The
shaft resistance in sandy ground is assumed to be
proportional to the normal stress at the pile surface.
The bearing capacity of the pile in clayey ground isestimated based on the undrained shear strength ineach code.
3

GEOMATE- Osaka, Nov. 16-18, 2015

frictional resistance along a shaft, and such anassumption is rational. However, it is difficult toprecisely determine the earth pressure coefficient at
the pile shaft.

From this reason, I don't consider USCOE method
hereafter.
Other methods for estimating the bearing
capacity of piles are presented. One of them is thecavity extension theory. This theory used for
estimating the bearing capacity of a pile wasoriginally proposed by Vesic [12]. Yasufuku et al.
[13] revised it as follows:

Coefficient of base resistance N

q

250
USCOE
200

qp =

150

EC7

100

3(1 + sin φcv ' )[I rr ]( 4 sin φcv '/(3(1+sin φcv ')))  3 − 2 sin φcv ' σ v 0 '(1 − sin φcv ' )(3 − sin φcv ' )
3



(3)

3G
Ir =
(3 − sin φcv' )σ v' 0 tan φcv'

(4)

50

0

0

10

20

30

40

(2)

Ir
I rr =
1 + I r ∆ av

where q p : unit bearing capacity of pile toe(kN/m2)
I rr : reduced rigidity index
I r : rigidity index
φcv ' : critical state friction angle (degrees), which

50Internal friction angle (deg)Fig. 3. N q used in EC7 and USCOE.

can be φcv ' = 30 + ∆φ1 + ∆φ 2 . Values of ∆φ1 and∆φ 2 can be referred from Table 2.

Coefficients of bearing capacity N q shown in Fig.
3 are used in EC7 and USCOE. They give moderate
numbers comparing the variance of N q shown in Fig.
2. Internal friction angle should be defined whencalculating N q . Here, the following equation is used
for estimating the internal friction angle from the
SPT-N value and is introduced in the present JPTS.
This equation is constructed from two proposals by
Meyerhof [10], [11] about the relationship between
SPT-N value and relative density and between
relative density and internal friction angle. If thisequation is used, φ will become smaller if the depth
increases under the condition of the SPT-N value
independent from the depth.

φ = 25 + 3.2

100 N
p v' 0 + 70

∆ av : average volumetric strain for plastic zone
around a cavity. It is a function of I r :∆ av = 50( I r ) −1.8 .
G: shear stiffness, which can be calculated fromG = 7000 N0.72(kN/m2). N is the SPT-N value arounda pile toe.
Figure 4 shows the difference in estimated toe
bearing capacity using these four methods. Here, the
SPT-N value is assumed to be 50 independent of thedepth. Effective unit weight of the ground isassumed to be 7 kN/m3. In this case, according to the
USCOE code, if the embedded depth is at least 20
times greater than the pile diameter, the effective
overburden pressure in bearing capacity estimation
will become constant at 20Dγ'.
Table 2. ∆φ 1 and ∆φ 2 for sandy soil and gravel(a) ∆φ 1
Angularity∆φ 1 (º)
Round0Sub- angular2
Angular4(b) ∆φ 2
Grading of soil∆φ2(º)
Uniform (Uc < 2)0Moderate grading(2 < Uc < 6)
2
Well graded (6 < Uc)
4(1)

where N: SPT-N value and p' v0 : effective
overburden pressure at sounding. Maximum of SPTN should be 50.
Although the toe bearing capacity per unit area isdetermined by only the SPT-N value using themethod in JPTS, it is determined from the SPT-N
value and effective unit volume weight in EC7 and
USCOE. Furthermore, in USCOE, the maximum
effective overburden pressure is considered to beaffected by the pile diameter and ground conditions.
In equation USCOE, overburden pressure applied to
calculate toe bearing capacity is constant when thepenetration depth exceeds 20 times of pile diameter.
This assumption is quite conservative and gives too
small bearing capacity when deeply embedded piles.

The estimated toe bearing capacity using JPTS isindependent of the embedded depth, as shown in Fig.
4. This has an odd effect in that the bearing capacity
does not change in the depth direction. EC7 shows
the highest bearing capacity at almost all depths.
4

GEOMATE- Osaka, Nov. 16-18, 2015

However, estimated bearing capacities by the threemethods become almost the same when theembedded depth of the piles is 15–40 m and theyshow a big difference when the embedded depth isdeeper.

or that the JPTS results are preferable for estimating
measured data.
Unit base bearing capacity (kN/m2)
0

10

30

20

0

0

Measured
JPTS
EC7
Cavity Ex.

10

SPT-N=50γ'= 7 kN/m3

20

30

Cavity Ex.
-40

Embedded depth (m)Embedded depth of pile toe GL(m)

-20

EC7

-60

40

50

60

-80
70

80

-100

JPTS

Pile diameter less than 1000mm
90a) Pile diameter 1000 mm or less

-120
5

10

15

20

25

30

Unit base bearing capacity (MN/m2)

Unit base bearing capacity (kN/m2)

Fig. 4. Comparison of toe bearing capacity in JPTS,
EC7, and Cavity Expantion.

0

10

20

30

0
Measured

10

JPTS
EC7

The estimated values should be compared withmeasured values to discuss the validity of themethods. Figure 5 a) shows a comparison between
the measured toe bearing capacity per unit area andthe estimated value in the case of a pile diameter of1000 mm or less. The measured data used for thiscomparison were from the Japanese Association for
Steel Pipe Piles [14] and the latest data. This resultshows that the measured values are smaller than theestimated values. The estimated values by JPTS andthe cavity expansion method become closer to themeasured values compared to those by EC7.
However, when the embedded depth of the pile
becomes deep, the values using JPTS were
underestimated compared to the measured values.
Figure 5 b) shows the same type of comparison
for the case of a pile diameter of 1000 mm or more.
It turns out that the measured values are
considerably smaller than the estimated values on
the whole. This is because forming of the plug of apile toe portion becomes inadequate and the bearing
capacity is smaller than the calculated bearing
capacity based on a closed-end pile, when the pile
diameter becomes large.
Since the toe resistance at the plug portion of an
open-ended pile may decrease if the pile diameter isincreased, it is possible that the resistance at the real
portion of the pile will differ from that of the plug
portion even if the pile diameter is 1000 mm or less.
When considered in this way, there is no reason tobelieve that even the EC7 results are overestimatedCavity Ex.

20

Embedded depth (m)

30

40

50

60

70

80
Pile diameter more than 1000ｍｍ
90b) Pile diameter 1000 mm or more
Fig. 5.
Comparison between measured andestimated bearing capacity.
Most piles presently used in port facilities are
deeply embedded large-diameter steel pipe piles.
Popular pile diameters are around from 1400mm to
1600mm. Embedded length of piles are up to 90m.
When estimating the bearing capacity of these typesof piles, the toe bearing capacity poses a big
problem, referred to as the problem of plugging ratio.
Figure 6 provides a reference for considering theplugging of a large-diameter steel pipe pile. The
figure shows the relationship between the pile
diameter and the ratio of measured toe resistanceand the estimation results of JPTS. Since theembedded depth is not considered in the figure, the5

GEOMATE- Osaka, Nov. 16-18, 2015

data shows some variation, but it is clear that there isa tendency for the toe bearing capacity of the pile
per unit area to be small when the pile diameter islarge. This means that the resistance per unit area ofthe portion of a plug becomes small when the pile
diameter becomes large.

Measured toe bearing capacity/（ 0.3NAp)

1.6
1.4
1.2

Fig. 7. View of static axial compressive load test.

1

0.8
0.6
0.4
0.2
0
0.5

1

1.5

2

Pile diameter (ｍ)Fig. 6. Relationship between pile diameter and theratio of measured and estimated bearing
capacity.
EXAMINATION OF BEARING CAPACITYMECHANISM OF OPEN-ENDED PILESThe Tokyo Port Seaside Road was designed sothat the Tokyo Gate Bridge would cross over
Channel No. 3 of the port. The subject of this study
is a steel-pipe-sheet pile foundation that featureslarge-diameter steel pipe piles serving as the pierfoundation between the principal meridians of theTokyo Gate Bridge. As its bearing capacity has notbeen fully proved, load tests (including a static axial
compressive load test, rapid load test, and dynamic
load test) were conducted, as shown in Fig. 7, toobtain information about the bearing mechanism of
the foundation piles at the site of the planned bridge
construction, and the test results were used fordesigning the bridge. The characteristics of verticalbearing capacity obtained from in-situ tests on actual
size large-diameter open-ended steel pipe piles are
discussed.

Fig. 8 Overview of soil boring log and embedmentof test piles.
The results of static axial compressive load tests
are presented here. In the tests, steel pipe piles 1,500
mm in diameter were hammer-driven into a sandy
gravel layer and a sandy layer, in order to obtain thecharacteristics of bearing capacity under two
conditions (in sandy gravel layer and sandy layer).
In these load tests, the axial force acting on a pile
was measured by attaching strain gauges to the pile;at the same time, the settlement of the pile (both at
the toe and the head) was measured using settlementgauges. Multi-stage repeated loading was conductedin accordance with the static axial compressive load
test method proposed by the Japanese GeotechnicalSociety [15]. Particular care was taken when a virginload was applied; that is, the load was maintainedfor 30 min or longer to wait for settlement of the pile,
before moving to the next loading stage.

Overview of Static Axial Compressive Load Test
Figure 8 shows the ground conditions where thetests were conducted and the embedded depth of test
piles. It can be seen from the figure that the water
depth at this site is AP-7 m. A soft, cohesive
alluvium layer continues down to AP-40 m,
followed by laminated ground (alternating sandy andcohesive layers) down to AP-65 m, and then adiluvial sandy gravel layer (Tokyo gravel layer)
appears around AP-69 m. Following a few weaklayers in the sandy gravel layer around AP-80 m, asolid sandy layer appears around AP-82 m.

Vertical Bearing Capacity
Figure 9 shows the axial force distribution when
6

GEOMATE- Osaka, Nov. 16-18, 2015a virgin load was applied to Pile No. 4. What should
be noted here is the axial force distribution aroundthe lower end of the test pile. As the lower end was
being embedded into the sandy gravel layer, thesubgrade resistance increased, with a large change in
the axial force in the depth direction. At the point
where the pile head load exceeded 22,000 kN, theaxial force distribution showed a large bend aroundthe pile toe. This suggests that transferred axial pileforce rapidly decreased toward the pile toe.

in Figs. 12 to 14. Although the embedded length
differed from Pile No. 4, the pile was inserted into a
stiff sandy layer. The features of the axial force
distribution were similar to the results of the load
test on Pile No. 4, such that the transferred axial pileforce rapidly decreased toward the pile toe when theload applied to the pile head was close to the bearing
capacity.
35

Axial force (MN)
0

5

10

15

20

30

25

30

Pile head

35
25

20

20

Load (MN)

10

Depth (AP-m)

30

15

Pile toe

40
10

50
No.4

5

60
No.4

0

70

0

50

100

150

200

250

300

Displacement (mm)80

Fig. 11 Relationship between load and displacement
on Pile No. 4.

Fig. 9. Axial force distribution in Pile No. 4 load
test.

Axial force (MN)

Measured Axial Force (MN)
0

5

10

15

0

25

-64

10

-65

20

Depth (AP-m)

No.4

-67
-68
-69
-70
-71

5

10

15

20

25

30

35

40

30

-66
Depth AP(m)

20

40
50
60
70
No.5

Point A

80

-72
-73

90

Point B

Fig. 12. Axial force distribution in Pile No. 5 load
test.

-74

Fig. 10.

End Bearing Capacity of Pile

Axial force distribution at the base ofPile No. 4.

The axial force distribution around a pile end
under the secondary critical resistance is shown in
Figs. 10 and 13. Axial force suddenly became
smaller than at the upper part at the point where thestrain gauge was set near the lower pile end. The
point showing a sharp change in axial force was 1D(D: pile diameter) above the lower pile end.

Figure 10 shows an enlarged view of thesituation around the pile toe. Here, assuming theload at Point B in Fig. 10 to be the toe resistance,load settlement curves for both the head and lower
end of the pile can be obtained as shown in Fig. 11.
The loading test results for Pile No. 5 are shown7

GEOMATE- Osaka, Nov. 16-18, 2015a sharp change in axial force might have beencaused by skin friction at the inner side of the pile.
As shown in Fig. 15, the total bearing capacity at thepile toe could be obtained by extrapolating themeasured values of axial force within the assumed
bearing layer to the level of the lower pile end (Point
B).

Measured Axial Force (MN)
0

5

10

15

20

25

-77
-78

Depth AP(m)

-79
-80

No.5

-81

Table 3. Critical resistance (MN).

-82

Pile No. 4
20
32

Primary critical resistance
Secondary critical resistance-83

Pile No. 5
26
36

-84
-85

Pile No.5

Point B

Axial force

FrictionG

Pile No.4
Axial force
FrictionE-86
Point A
-87

Outer

Outer

D

Fig. 13.

Axial force distribution at the base ofPile No. 5.

Inner

F

Outsidefriction

Pile head

30

Fig.

25
Load (MN)

Insidefriction

No.5

35

15.

Relationship between
distribution and friction.

axial

force

20

Assuming that the axial force at Point A obtainedby extrapolating the measured value that curved at
the pile end should be the resistance of the pile body,the difference between the axial force at Point B andthe axial force at Point A could be regarded as theplug resistance due to inner friction. Figures 16 and17 show the relationship between total baseresistance, inner friction resistance, and resistance ofthe pile body. In both figures, at the secondary
critical resistance, the total base resistance shows anincrease. The resistance of the pile body reachednearly the maximum level when the settlement of
the pile end was about 50 mm, about 3% of diameter,while inner friction resistance was still increasing.
Yamagata and Nagai [16] and Nicola andRandolph [17] proposed an equation to obtain theultimate resistance of a unit sectional area, based on
their assumption that the base resistance of the openended pile can be expressed as the sum of resistanceat the sectional area of the lower end of a pile, At,
and inner friction.
Yamagata and Nagai [16] proposed the following
equation to estimate this resistance from the SPT-N
value: q t = 0.4NA t (MN/m2).
Nicola and Randolph [17] proposed a correlation
between the resistance of the real portion of an openended pile, q t , and CPT resistance, q c :15
10

Pile toe

5
0
0

50

100

150

200

250

Displacement (mm)

Fig.

1D
B

Pile
body

40

A

C

14.

Relationship between load
displacement on Pile No. 5.

andBased on the relationship between vertical axial
compressive load and pile displacement shown in
Figs. 11 and 14, the primary and secondary criticalresistance (when the toe displacement is 10% of thepile diameter) were obtained as shown in Table 3.
Focusing on the phenomena at the pile toe of
No.4 pile, ground solidity did not change around thelower pile end, Judging from the test results for Pile
No. 5 that was more deeply embedded, it was
unlikely that peripheral friction became large only atthis point (see Fig. 15). Moreover, the results of acone penetration test (CPT) showed that thepenetration resistance of the inner soil at the pilebase clearly became larger than the outer groundafter the loading test. Thus, it can be concluded that8

GEOMATE- Osaka, Nov. 16-18, 2015

 λm( wt / D) qc At
qt = 
 ( wt / D) + c 

The axial forces at Point A on Pile No. 4 and No.
5 in Figs 10 and 13 under the secondary critical loadcondition were 8.2 and 4.6 MN, respectively, as
shown in Figs. 16 and 17. Both loads were close to
the Rt values shown in Table 4.
It might appear less accurate to assume that theend bearing capacity of a pile at Point C, where was
the nearest axial force measuring point from the piletoe, is the sum of the resistance of the sectional areaof the pile end and the inner friction, and that theload at Point B is the resistance of the sectional areaof the pile toe, as shown in Fig. 15. However, theresults coincide relatively well with the existing
proposals, so the above assumption can beconsidered to be fairly correct. Table 5 shows theresults obtained from load tests. Inner friction
resistance was estimated to be 7.5 MN on Pile No. 4(accounting for 48% of the end bearing capacity ofthe pile) and 6.7 MN on Pile No. 5 (accounting for
59% of its bearing capacity). Inner friction
resistance showed similar values whether the
bearing layer was the sandy gravel layer or the sandy
layer. Thus, it can be said that the difference in pilebase resistance under the secondary critical load in asandy gravel layer and a sandy layer largely depends
on the difference in pile body resistance.
As shown in Table 4, the pile body resistancedepends on the penetration resistance (CPTqc value
or converted SPT-N value) of a relatively limitedarea around the pile base. However, the inner
friction in a sandy gravel layer and a sandy layer canapparently be determined independently regardlessof the penetration resistance.

(6)

where m = 0.7, c = 0.015, wt/D: normalized
settlement of the pile toe.
λ: = 1.75 − (σ'v/200) (σ'v ≤ 200 kPa)
= 0.75(σ'v ≥ 200 kPa)
20
Embedded to AP-73.5m
Total toe resistance

Resitatance (MN)

15

Resistance of pile real part

10

5
Resistance of inner surface friction

0
0

50

100

150

200

Settelement of pile toe (mm)Fig. 16. Resistance at base of Pile No. 4.
20
Embedded to AP-86m

Resistance (MN)

15

Table 4. Resistance at sectional area of pile end Rt(MN)

Total toe resistance
10

Proposed by
Resistance of inner surface friction

Rt proposed
equation
Pile No.
Converted SPTN value
CPT: qc value(MPa)
Resistance Rt
Load at Point B

5
Resistance of pile real part

0
0

50

100

150

200

Settelment of pile toe (mm)Fig. 17. Resistance at base of Pile No. 5.

Yamagata andNagai (1973)

Nicola andRandolph (1997)

0.4NAt0.46qcAt

4

5

4

5

125

68

-

-

-

-

97

50

8.6
8.2

4.7
4.6

7.7
8.2

4.0
4.6

Table 5. Bearing capacity at pile toe based on
results of static axial compression load
test (MN)

When converting the equation proposed by
Nicola and Randolph under the test pile embedment
condition and the secondary critical load condition,
the resistance of the unit sectional area qb can beobtained from the cone penetration resistance qc as
follows: qb = 0.46qc (kN/m2). Thus, the ultimate
resistance Rt of the sectional area of a pile can beobtained using the proposed equations, based on the
SPT-N value measured at the pile base and the CPT
results, as shown in Table 4.

Pile No.
Bearing capacity at pile toe

9

4
15.7

5
11.3

GEOMATE- Osaka, Nov. 16-18, 2015

pile end and inner plate, dTP, was a parameter of thisstudy.

60

Unit resistance (MPa)50

Annular No.4(MPa)Annular No.5(MPa)40

Inner No.4(MPa)
Inner No.5(MPa)

30
20
10
0
0

50
100
150
Settlement of pile toe (mm)200

Fig. 18. Unit resistances of the annular and innerparts of a pile. Each unit resistance is theresistance divided by the cross-sectionalarea.
The loading test results show that the toe
resistance could be divided into inner friction andresistance of the pile body. It is also shown that the
maximum resistance of the pile body can be reachedwith a relatively small settlement, whereas a large
settlement is required for exhibiting inner resistance.
Figure 18 shows unit resistances for the annularpart and inner friction. The unit toe resistance of theannular part is Rt/At, where At is the cross-sectional
area of the steel. The unit toe resistance due to inner
friction was RfI/(Ap-At). Ap was the total crosssectional area and Ap-At is the inner cross-sectional
area of the pile. The intensities of the unit frictional
resistance for both piles were almost the same,
although the unit annular resistances were different.

Fig. 19. Diagram of the pile used in this study.

EXPERIMENT FOR MEASURING INNERFRICTION OF MODEL OPEN-ENDED PILE
Material and Experimental Procedure
Fig. 20. Set-up of the penetration experiment withlarge-diameter pile.

The model ground was made of Souma sand #4.
Dried Souma sand was pluviated into a container,
which was 2 m in length, 2 m in width and 1.5 m indepth, through a pipe with a diameter of 3 cm. The
height of the sand fall was kept at 30 cm while thesample was being prepared. The relative density ofthe model ground was about 40%.
After the ground preparation was completed,model piles were driven into the ground. The model
pile, which was a triple-tube pile with a length of1656 mm with the inner plate, was made of steel asshown in Fig. 19. The outer pile diameter was 24.6
cm and the inner pile diameter, D, was 20 cm. The
cross-sectional area ratio of the inner pile andannular part was 0.66:0.34. The position of the innerplate could be changed from the bottom of the model
pile to 40 cm above the pile end and also the innerplate could be removed. The distance between the

The model piles were penetrated into the model
ground statically at a speed of 10 mm a minute. Fourload cells were used to measure the penetration
resistances of various components such as outersurface resistance, annular part resistance, innersurface resistance, and inner plate resistance. Eightearth pressure transducers were attached to the innerplate to measure the earth pressure acting on theplate. The penetration depth of the pile wasmeasured with a retractable displacement meter.
Figure 20 shows the set-up of the experiment.
Results
Figure 21 shows the relationship between

10GEOMATE- Osaka, Nov. 16-18, 2015

penetration resistance and penetration depth. Figure
21 (a) shows the results obtained with a closedended pile; on the other hand Fig. 21 (c) shows thoseobtained for an open-ended pile. Figure 21 (b) shows
those obtained for dTP = 20 cm. In each figure, LT
represents the total resistance, Rf the outer friction
resistance, Rp the annular part resistance, RI the
inner plate resistance, and RfI the inner friction
resistance.
Figure 21 (b) shows how RI increased after 210
mm of penetration and RI, RfI rapidly increased and
Rp decreased after about 10 mm of the following
penetration. These results mean the inner plate cameinto contact with the soil at penetration of around
210 mm and the pile was fully plugged at
penetration of around 220 mm.
In Fig. 21 (c), clear plugging was not observed but
a small vibration of the penetration resistance wasobserved after a penetration of about 730 mm and it
shows the generation of plugging.
Load (kN)20
400Penetration depth (mm)Penetration depth (mm)

200
400
Rf

600

Rp

RI
Rp
1000

(a) dTP= 0 cm
Load (kN)
20
40

Penetration depth (mm)

LT

Rf

400

LT

600
800

RI Rp

Rp

600
800

LT

Rf

RfI

Rp

0%

Resistance share

20%

40%

60%

80%

100%

No
2D
1.5D
1D
0.5D
0D

Inner plate resistance

Inner friction

Tip resistanceOuter friction

Figure 22 shows the sharing of each resistancecomponent at a penetration depth of 700 mm in each
inner plate condition. In these experimental
conditions, the outer friction was negligible. The
share of annular part resistance was about 40% ineach experimental condition except no inner plate
condition and 0D of dTP condition. These resultsmean that the reaction of the ground on the pile toe
was almost uniform, as the cross-sectional area ratioof the inner pile and annular part was 0.66:0.34. The
share of inner friction increased as dTP increased.
When dTP was 2.0D, the share of inner plate
resistance was less than 5% and the share of inner
friction was about 54%. In this experimentcondition, if an inner plate exists, the inner friction
observed can represent the inner friction with
sufficient plugging. Inner friction required to cause
plugging was mostly mobilized in 2D from the pile
toe.
Vertical stress acting on the horizontal plane of

60

0
200

Rf

RfI

Fig. 22. Resistance share of each inner plate
condition at a penetration depth of 700 mm.
At around this penetration depth, almost
full plugging was observed in the openended condition.

800

0

400

(c) No inner plate.
Fig. 21. Penetration resistance change with depth.

LT

RI

60

200

1000

0

LT

40

LT

60

Rf

20

0

Height of inner plate from the tip

0

Load (kN)

RfI

RI

Rf
Rp

RfI

1000

(b) dTP= 20 cm

11

GEOMATE- Osaka, Nov. 16-18, 2015

fitting the experimental data. Friction angle between
the pile wall and the sand, δ will be (2/3)φ - φ. As φis about 38 degree, δ will be 26 – 38 degree, then µ
is considered to be between 0.49 and 0.78. As aresult, Kh will be between 0.44 and 0.69.
The equation can simulate the tendency of thestress acting on the inner plate to be reduced.
The difference in inner friction between different
tip plate distances may present the inner friction ofthat area in the condition of full plugging.

inside of the pile in each height can be assumed to
be uniform in a very simple way of consideration.
This way is consideration is the same as Yamahara’s[18], [19]. The force balance of the thin soil discwith dx thickness inside the pile will be drawn asshown in equation (6) µK hσ 1 Dπ  ⋅ dx∆σ = γ t 1 +AsAs = D 2π / 4(6)Where σ1 is the vertical stress acting on the lower
surface of the soil, ∆σ is the vertical stress difference
acting on the lower and the upper surface of the soilin dx, µ is the friction coefficient between the pileand soil, Kh is the horizontal earth pressure
coefficient on the pile surface, D is the inner pile
diameter, dx is the thickness of the soil considering,and As is the horizontal cross-sectional area inside
the pile.
From equation (6), equation (7) can be drawn
when x axis is vertical axis, x=0 is at the pile toe,and x is positive in the pile.
γ D   4µK h
 γ D
(7)
(− x)  − t
σ =  σ 0 + t  exp4µK h  D
 4µK h

CONCLUSIONSThis paper introduced the problem of theestimation of open-ended large diameter piles. Alsopresented was an international comparison of
methods for evaluating bearing capacity used indesign, which is particularly important for openended piles. Then this paper introduced a series offield loading test results which was conducted forlarge diameter long embedded open-ended piles. The
results gave us strong impression for the importanceof researching inner friction of open-ended piles.
From this reason, this paper finally introduced aseries of laboratory model experiment for estimating
inner friction of fully plugged piles.
Following are the conclusions reached in thisstudy:(1) Comparison between measured toe bearing
capacities of open-ended piles and three major
methods for evaluating the toe bearing capacity
shows difficulties of estimation of large diameterdeep embedded open-ended piles. Difficulty consistsfrom two part; one is difficulty of evaluation of toe
bearing capacity of deep embedded closed-ended
piles and the other is evaluation of plugging ratio oflarge diameter open-ended piles.
(2) From the field loading test, it was concluded thata sharp change in the axial force might have beencaused by skin friction of the inner side of the pile
only one or two times the length of the pile diameterfrom the pile toe.
(3) From the field pile loading tests results, a very
low level of plugging was performed at secondary
critical resistance, which is considered to show
ultimate toe bearing capacity.
(4) Inner friction for plugging was mostly mobilizedin 2D from the pile toe when the pile with an outer
diameter of 24.6 cm and inner diameter of 20 cm,
penetrated from the surface to a depth of about 3.5
times the pile diameter.
This paper consists of some previously presented
contents. I reconstructed this paper for thispresentation.

Where σ0 is the vertical stress acting on thehorizontal plane of the pile toe, σ is the verticalstress acting on the horizontal plane at x upper thanthe pile toe.
50

Distance from the pile tip (cm)σ =  σ 0 +40



γ t D   4µK h γ D exp(− x)  − t
4µK h D 4µK h30

Penetration
depth: 700 mm

20

10

0
0.0

0.5

1.0

Average pressure
acting on the inner plate (MN/m2)

Fig.23. Average vertical stress acting on the innerplate.
Figure 23 shows a comparison between theexperimental results and equation (7). In this figure,the data used were those measured at a penetration
depth of 700 mm. At this penetration depth, the total
resistance of the pile in each case was almost thesame. The dotted line shows the calculation resultsfrom equation (7). In this case µKh was 0.34 for

ACKNOWLEDGEMENTS
The research work presented in this keynote
papers was conducted a lot of colleagues. I will not
12GEOMATE- Osaka, Nov. 16-18, 2015

show all the name of my colleagues, but I sincerely
thank to all the colleagues involved in this research.

[10] Meyerhof, G.G., “Discussion on soil properties
and their measurement”, Discussion 2. Proc. of
the IV International Conference on SMFE, Vol.
III, ASCE, 1957, p. 110.
[11] Meyerhof, G.G., “Compaction of sands andbearing capacity of piles”. Journal of SMFE,
Vol. 85, No. SM6, ASCE, 1959, pp. 1-29.
[12] Vesic, A.S., “Expansion of Cavities in Infinite
Soil Mass”. Journal of SMFE, Vol. 98, No.
SM3, ASCE, 1972, pp. 265-290.
[13] Yasufuku, N., H. Ochiai and S. Ohno, “PileEnd-bearing Capacity of Sand Related to Soil
Compressibility”. Soils and Foundations, Vol.
41, No. 4, Japanese Geotechnical Society, 2001,
pp. 59-71.
[14] Japanese Association for Steel Pipe Piles, “Steelpipe pile – its design and construction”, 2000, p.
130 (in Japanese).
[15] Japanese Geotechnical Society, “Standard for
Method for Static Axial Compressive Load Testof Single Piles”, JGS1811-2002, 2002.
[16] Yamagata, K. and K. Nagai, “Consideration ofan open ended steel pipe pile (No. 2)”, Journalof Architecture and Building Engineering. No.
213, 1973. (in Japanese).
[17] Nicola, A. and Randolph, M.F., “Plugging
behaviour of driven and jacked piles in sand”,
Geotechnique Vol. 47, No. 4, 1997, pp. 841-856.
[18] Yamahara, H., "Plugging effects and bearing
mechanism of steel pipe piles", Trans. of theArchitectural Inst. of Japan, 96, 1964, pp. 28-35(in Japanese).
[19] Yamahara, H., "Plugging effects and bearing
mechanism of steel pipe piles (Part 2)", Trans.
of the Architectural Inst. of Japan, 97, 1964, pp.
34-41. (in Japanese).

REFERENCES[1] Japan Road Association, "Specifications andComments for Highway Bridges", Vol. IV
Substructures. (in Japanese), 2002.
[2] Architectural
InstituteofJapan,“Recommendations for design of building
Foundations”, 2001, p. 207. (in Japanese)[3] Fleming, W.G.K., A.J. Weltman, M.F.
Randolph and W.K. Elson, “Piling Engineering
2nd edition”, Blackie, Glasgow, 1992.
[4] Overseas Coastal Area Development Institute ofJapan, “Technical standards and commentaries
for port and harbour facilities in Japan”,
Daikokusha, 2002, pp. 286-287.
[5] Orr, T.L.L, E.R. Farrell, “Geotechnical Design
to Eurocode 7”, Springer, 1999, pp. 106-113.
[6] ASCE, “Design of Pile Foundations. TechnicalEngineering and Design Guides as adapted fromthe US Army Corps of Engineers”. No. 1,
ASCE, 1993, pp. 17-22.
[7] Meyerhof, G.G., “Penetration tests and bearing
capacity of cohesionless soils”. Journal of
SMFE, Vol. 82, No. SM1, ASCE, 1956, pp. 110.
[8] Yamagata, K., “Allowable bearing capacities ofa pile constructed in various installation
methods”, Tsuchi to Kiso, Vol. 23, No. 7,
Japanese Geotechnical Society , 1975, pp. 13-20.
(in Japanese).
[9] Japanese Geotechnical Society, “3.4 Pile
foundations”, Hand book for GeotechnicalEngineering, Japanese Geotechnical Society,1999, pp. 783-872. (in Japanese).

13Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

INTRODUCING ADVANCED TOPICS IN GEOTECHNICALENGINEERING TEACHING
Jim Shiau, PhD (Newcastle)
School of Civil Engineering and Surveying, University of Southern Queensland, Australia

ABSTRACTThis paper demonstrates a strategy to integrate research into geotechnical engineering teaching at advanced
postgraduate level. It involves the use of simple small-scale physical models, the introduction of particle image
velocimetry (PIV), and a numerical model of a tunnel heading. The effectiveness of using a project-basedlearning (PBL) assignment is also presented. Based on staff experience and student feedback, it is concluded that
this is an effective and satisfying way to engage students with the subject matter. It is hoped that discussing anddisplaying some of these advanced topics will encourage further student interest in geotechnical engineeringresearch.
Keywords: Tunnel, Heading, Modelling, PIV, Education

student.
Physical modelling has long been used bygeotechnical engineering researchers with successfor a number of years. It is an effective way ofsimplifying and visualizing a particular problem [6]and will likely always be necessary at some level tovalidate increasingly popular numerical modelling.
It is particularly useful in assisting the transition
from basic soil mechanics into the analysis anddesign of geotechnical structures [7-9] who
successfully demonstrated a number of flow net
problems and concluded that such inexpensive
development can be used efficiently to complement
the theoretical approach and serve to visualise
phenomena of geo-structural failures in twodimensional space.
The concept of Project Based Learning (PBL) is
not new, but rarely can be found in geotechnicalengineering teaching. PBL is referred to as anintegrated approach to teaching, in which students
learn the required material in the course of a realistic
problem. This is opposed to the traditional deductive
method, where students are taught fundamentals andanalytical approaches, and then move onto applying
them to textbook problems. The critical difference
between the two is the motivation and enthusiasmthat the student feels towards the content. In the
integrated approach, the theory and background isbrought in as needed for the problem, and thus thestudent is able to connect to the material better [1-2]When PBL is combined with modern technology
it may appear a very interesting and unique approachto teaching. However, the concept that people learn
best through active engagement is not new, and isoften credited to philosophers Socrates and Aristotle.
Its relevance to modern universities is often credited
to [10], who recommended more interactive
approaches to engineering teaching. While praise of

INTRODUCTION
The current methods of engineering education
are under significant pressure to reform. Increasing
complaints from industry regarding graduates being
inadequately prepared, high failure rates among
most engineering programs, and results from anumber of engineering education research studies
showing problems with traditional teaching
methods; this is forcing a push towards different
approaches.
The complex nature of geotechnical engineeringin general leads to a heavy reliance on computermodelling both in research and development as wellas in practice, and this makes it difficult to teachusing conventional ‘chalk, book and talk’ methods
that have been found lacking by [1] who stated that
it is too inflexible, and the amount of students thatwere responding poorly was increasing as more
people were going to university. This is consistent
with [2] who state that student centric methods,
where the teacher responds to students, were shown
to be more effective as opposed to teacher centric
methods where students respond to the teacher.
Changing technology, increasing reliance on
computers and software, and expectations ofstudents and industry are different [3]. Where
students used to use textbooks and libraries, they
now use the internet. Everyone expects to be able touse a computer, or even a smartphone, and results
are expected over a much shorter period. With thelimited time allocated to geotechnical engineeringby some universities, finding efficient ways ofteaching is critical [4]. The benefits of hands-on
teaching and using laboratory work have been
widely published. Both teaching staff and students
can benefit from such an approach [5], as it is moreenjoyable for the staff and more engaging for the14

GEOMATE- Osaka, Nov. 16-18, 2015

PBL
isfairlywidespread,
unsuccessful
implementation can lead to very high student
disapproval if the problem is inappropriate, and/or
the required theory and background information
isn’t easier enough to access. The difficulty ofcreating a suitable assignment dissuades many staff,
particularly in the engineering area where thepotential gains are actually very high.
There is demand for alternative assessment ofgeotechnical engineering due to its complexity, andthe need to be realistic and relevant for its industry.
It is the objective of this paper to demonstrate thesuccessful development of a tunnel heading modeland a PBL assignment based on the 2009 Shanghai
building collapse; both of which are ready to be
adopted in any advanced geotechnical engineeringcourse.

movement of the soil in these models. The sand usedwill be kept constant and the properties of this sandwill then be used in future numerical modellingusing FLAC.

EXAMPLE 1: TUNNEL HEADING MODEL

Fig. 1

Schematic Diagram of tunnel heading
during construction

Fig. 2

Photograph of the tunnel heading model

BackgroundPhysical modelling of tunnels is generally split
based on whether it has been done in a centrifuge(ng) or under normal conditions (1g). The former
can more accurately model realistically larger
structures; the model can be scaled up by a factor ofn. It is, however, expensive and time-consuming.
The latter are easy to develop and cost-effective,
more suited for qualitative and educational purposes.
These simple models can give valuable insight into
the stability and settlement problems.
The aim of this example is to demonstrate a way
in which physical modelling can be used to enrich atechnical course. It is believed that showing some
aspects of an advanced topic such as tunnelling andPIV may help to reinforce earlier learnings such as
the concept of a soil model, and show thepossibilities and breadth of the geotechnical area.

Physical models are used themselves to study the
behaviour of soil and its impacts, but quite
commonly also to verify numerical models. If thesoil of interest is sand, using 1g scale models isparticularly attractive, as there is no need for acentrifuge as the soil fails under its own self weight.
The models can be relatively simple to construct andoperate, whilst still yielding good results. Therefore
for this exercise, 1g scale models have been used to
conduct the physical modelling.
The aim of the experimental tests was to gain anunderstanding of the failure mechanisms in front of
the tunnel face, and to examine the settlement
characteristics of the soil with varying overburden(C/D ratios). To do this, the modelling tanks needed
to be designed and constructed and the properties ofthe sand in question needed to be identified. The
design of the models draws inspiration from other r
external research projects, namely from [15]-[17]. A
picture of the models used is shown in Fig. 2.
A total of five boxes were constructed to cater forthe six C/D cases that were to be examined. They are

Problem statement and the physical model
Tunnelling has undergone significant researchusing physical modelling; a comprehensive review
can be found in [11]. The critical geotechnicalaspects for tunnel design as discussed by [12]-[14]are: stability during construction, ground movements,
and the determination of structural forces for thelining design.
Fig. 1 shows the problem definition: C is theoverburden height, D is the tunnel diameter, σ s is thesurface surcharge, σ t is the tunnel support pressure, γ
the unit weight and φ is the angle of internal friction.
The purpose of this model is to investigate tunnel
heading stability and soil movement upon collapse
in cohesionless soil over a range of cover todiameter ratios (C/D).
To do this, the following methods will be used:physical modelling using scale models with atransparent face, and PIV to demonstrate the15

GEOMATE- Osaka, Nov. 16-18, 2015

50 cm long and 5cm in breadth, with a totallytransparent Perspex front panel. The crank handle
rotates the screw which slowly withdraws theheading. The tunnel has been approximated to arectangle to simplify construction. The sand was
blended with coloured sand (for the PIV), and wasconstant for all experiments. The density wasdetermined to be 1800 kg/m3, and the friction angle
to be 35 degrees.
The procedure for the physical modelling was
kept as similar as possible through the six cases. The
heading is slowly withdrawn using the crank handle.
The screw that has been used is calibrated for
1mm/revolution. Thus, the handle was rotated at
approximately 1 revolution every two seconds,
which therefore leads to the heading being retracted
at 0.5mm/sec. This is stopped when the sand is nolonger touching the heading, and this was consideredas the total failure of the tunnel heading. After this,measurements were taken of settlement parameters:
S max , L, and B as shown in fig. 3.

Fig. 3the program that was selected was GeoPIV, createdby [17].

Fig. 4

Definition of Settlement Parameters

A PIV analysis of the C/D=3 caseAs the analysis software requires images rather
than a video file, the first step once the testing iscompleted is to export images out of the video. A
freeware program named “VirtualDub” has beenused for this purpose. As the raw footage has beenrecorded at 50 frames per second (fps), only every
100th frame is exported, this corresponds to one
image every two seconds. More frames may increase
accuracy but there will be diminishing returns themore frames that are included, and this will also
dramatically increase the computing time. Then
Adobe Photoshop was used for a slight adjustment to
the levels to increase spatial variation in brightness
and contrast across the image.
From the physical modelling results, therelationship among maximum settlement (S max ), C/D,
and the position (B) of this maximum settlement canthen be studied. Figs. 6 and 7 show the PIV analysesof cases with C/D = 3 and 7 respectively. These PIV
plots of the physical modelling appear to somewhat
confirm these observations.
Using the PIV analyses in such a qualitative
manner means other observations can be maderegarding the failure behaviour. For instance, thefailure mechanism observed using an animation of

While the heading is being retracted, a full HD
camera is recording the soil movement frombeginning to end. This footage is then used for theparticle image velocimetry.
Particle Image Velocimetry (PIV)PIV is a technique that allows investigation ofplane displacement patterns [14]. It was first used in
fluid dynamics to demonstrate flow fields, but it has
since become widely used in various fields, fromaerospace, thermodynamics, and also geotechnicalresearch. It is non-invasive, requires relatively
minimal setup, and can analyse a soil sample on a
grain-scale level [14]. This combined with rapid
technological advances in computing during the lastfew decades, has meant that PIV has become very
widely used in the geotechnical area.
In this research project, a qualitative investigation
using PIV of the tunnel face collapse is used to
identify displacement patterns and demonstrate
failure behaviour. Layers of coloured sand wereused in the physical modelling to better allow PIV totrack the soil particles. There are many possible
software alternatives that can do PIV analysis, but

16GEOMATE- Osaka, Nov. 16-18, 2015

all the frames analysed using PIV is consistent withwhat is described in [10], of a staged failure.

Fig. 6(C/D=2)

Fig. 5

A typical mesh of a tunnel heading

The model assumes a Mohr-Coulomb material,and 2D plane strain conditions. While the real soil
conditions and tunnel/soil interactions are threedimensional, the model uses these assumptions for
simplicity, such that the tool could be used in the
preliminary stages. The tunnel lining is assumed tobe rigid and soil conditions homogenous having
uniform properties with increasing depth. The
problem is similarly defined using fig. 1.
The tunnel heading problem can be approachedby making use of a pressure relaxation technique
whereby the internal pressure (σ t ) is gradually
reduced until a point of failure is detected. During
the operation of a tunnelling machine, it is the slurryor earth pressures at the tunnel heading that iscontrolled. Therefore, the use of a pressure
relaxation technique would provide a more realistic
result when compared to other methods such as thedisplacement method, as the relaxation method
better reflects the conditions at the cutter head.
When the heading pressure is relaxed, theheading stability decreases until yielding occurs at
the point of instability. This point is also where asudden change in tunnel face movement takes place.
While reaching this point of collapse would be
avoided in practice, it does provide a meaningful
bound to designers in understanding the sensitivityof the soils response to changes in face pressuresduring construction.
The overall heading stability is commonly
presented using the dimensionless quantities γD/S u
and (σ s - σ t )/S u and in conjunction with thegeometry ratio C/D. These dimensionless quantities
would help to define the point of instability,
providing designers and TBM operators withminimum heading pressures to prevent collapse andto estimate the sensitivity of the soil response to thetunnel boring and changes to face pressures.

A PIV analysis of the C/D=7 case

The numerical model
Geotechnical problems in reality are very difficult
to model with 100% accuracy because of the numberof variables involved. Although numericalmodelling has significantly improved in quality andquantity over the past few decades, it is still
somewhat bound by these restrictions. This
combined with the need for numerical modelling tobe verified means that physical modelling is still alarge part of geotechnical research.
A numerical model has been developed for
simulating a tunnel heading [9]. The developed
FLAC model uses the built-in FISH language that
automatically generates grid dimensions, solutioncommands and outputs relevant plots for prescribed
condition. The developed model is user friendly with
only limited soil parameters and dimension inputs
required by the designer to achieve relatively
accurate and meaningful results for preliminary
design and construction purposes. A typical meshgenerated by FLAC using the developed script isshown in fig. 6.

17

GEOMATE- Osaka, Nov. 16-18, 2015

The aforementioned point of collapse isidentified by examining the force history plot, which
describes how convergent the solution is, in thatparticular stage. A convergent solution indicates thatthe retaining soil forces (shear strength and internalpressure) have reached equilibrium with thedestabilising forces (weight). As this numericalmethod gradually reduces the internal pressure, thereis eventually a stage where it is reduced enough foran equilibrium to be unattainable. A typical history
plot is shown in fig. 7. After approximately 80,000
steps, an equilibrium can’t be found, this is
identified as the collapse stage.

Fig. 7•••lining forces), and define the problems
that engineers face.
Discuss stability and settlement – howthese are measured, and how are they
managed in tunnel boring machines(TBM’s)Brief explanation of the 1g model in thelaboratory, PIV analysis and numericalmodelling techniques using FLAC. Show
what to expect in the following weeks.
A number of journal and conference
papers are given to students for reading
activity at home.

Typical unbalanced force history plot

Figs 8-12 present typical plots of velocity vector,
plasticity indicator, shear strain rate (SSR), verticaldisplacement contour, and principal stress tensor.
These plots are useful for student learning inobserving and comparing model responses. They
also provide an increased understanding for soil
behaviour and the failure mechanisms of a tunnel
heading collapse

Fig. 8 Velocity vector plot at collapse for C/D=2,
φ=40°Classroom integration
The above developments were implemented into
an advanced geotechnical engineering course over afour week period, where each week constitutes athree hour session. The contents of these four weeks
are given below. Week 1 is an introductory lecture
covering some of the required material; basics oftunnelling and a discussion about physical modellingin geotechnical engineering. In week 2, the physicalmodels are actually used by the students, and a briefdemonstration of the PIV analysis as well as thecomputer modelling is conducted. Week 3 is for the
PIV analysis and the computer simulations, while
week 4 involves an activity to complete a groupreport which is due two weeks after.
Week 1: There is a short lecture including:•Fig. 9Introduction to tunnelling: history,
methods of construction, discussion ofdesign criteria (stability, settlement,
18

Plasticity indicator plot at collapse for
C/D=2, φ=40°

GEOMATE- Osaka, Nov. 16-18, 2015

Week 2: Class meets in the soil laboratory:• Five groups are formed (one for eachbox), and they are each set the task ofinvestigating a different C/D (between 1
and 7)• Students are asked to draw diagrams
showing deformed shapes and failure
planes, and participate in a discussion
with questions such as: what is the effectof C/D? What effect would the strengthof sand have? What are the assumptions
involved?
• A demonstration of the PIV process –series of images extracted from video,
mesh generation, processing, and postprocessing (vector animation, final
picture)• A demonstration of the numerical modelusing FLAC. Show input of parameters,
explain operation, analysis of outputs –identify the collapse stage.
• Both PIV and FLAC script are given to
students for preparing Week 3 activities.

Fig. 10 Shear strain rate (SSR) plot at collapse
for C/D=2, φ=40°

Week 3: Class meets in the computer laboratory:• Complete PIV analysis using the images
captured in Week 2. Does the PIV matchwith observations?
• Perform stability and settlement analysis
using FLAC and the developed script
provided by the lecturer. Complete
parametric studies on the effects of depth
ratio C/D and the frictional angle of sandφ.
Week 4: Class meets in the tutorial room:• Students begin a group technical reportof approximately 15 pages summarising
all their findings.
• There is no fixed structure of this report,
it needs to demonstrate that they havelearnt and understood from this subject
matter.

Fig. 11 Vertical displacement contour plot at
collapse for C/D=2, φ=40°

Summary
A four week teaching activity was developed tofacilitate student learning in tunnel analysis anddesign. This activity involves physical modelling ingeotechnical engineering, using advanced computing
techniques (PIV), and employing a numerical model(FLAC). This development would suit a part of a
postgraduate course; it could also suit a short tunnel
engineering course for practising engineers. It canalso be tailored to fit an advanced undergraduate
course.
Fig. 12 Principal stress tensor plot at collapse
for C/D=2, φ=40°

19

GEOMATE- Osaka, Nov. 16-18, 2015

According to some student feedback and generalobservation of the activity as it is in progress,
outcomes from this activity seem to be quite positive.
Using a more interactive class style such as thisleads to better student engagement than a traditionalclassroom method. These extra activities also
introduce students to tunnelling and some of themethods of geotechnical research. It is hoped that
this helps increase awareness and interest in the fieldof geotechnical engineering.

nearby creek; the weight of the earth caused theriver bank to collapse, which, in turn, allowed water

EXAMPLE 2: PROJECT BASED LEARNING
BackgroundIn a traditional assignment, students may be given
a very basic ‘textbook’ version of a problem that
normally requires the employment of a procedureand formulae taught in class, by hand or by the useof a standard spreadsheet, or possibly very basic
usage of software to solve.
One of the problems here is that geotechnicalengineering deals with a high level of uncertainty,
where judgment and simplifications frequently need
to be made in order to be able to produce a solution.
These problems also have no context, and so
students can be left wondering what the linkage is toreal design and construction. Students can only see
the “trees”, but not the forest. Besides, students are
not able to experience that important part oftechnical writing experience.
A novel PBL assignment was developed with theconcept behind the development and the required
process as follows:• Pick a problem which is relevant to thecontent and difficulty of the course.
• It must have enough information for students
to research.
• The software must be available and easy
enough to learn to do the problem before thedue date.
• There must be enough ‘room’ in the problem
for HD students to achieve better than A
students.

Fig. 13 Shanghai building collapse(Source: <http://www.wsj-asia.com>
to seep into the ground, creating a muddy
foundation for the building that toppled.”
A team of experts and government officials
examined the cause of collapse and prepared areport that was made available from CIV3403 Study
Desk. It was noted that a theoretical analysis was
not included in the report.
The assigned task was to conduct slope stability
analyses using computer software Slope/W or/and
FLAC/Slope and prepare a technical report tosupport the final outcome of investigation.

The rubric
Students were provided with a comprehensive
rubric which was to be used to mark the assignments.
This matrix identified the main headings, such aspresentation, research and theory, and assignment
requirements, and set out under 15 subheadings, thelevels of return which would justify a grade of HD,
A, B, C or F. This clearly told the students the levelsof effort staff were expecting. A complete marking
rubric can be found in the appendix of this paper. As
demonstrated in the marking rubric in the Appendix,
marks were awarded for:

The PBL assignment
The developed one-page assignment given to
students is shown below.

••

At around 5:30am on June 27 2009, an
unoccupied 13-storey building in the Minhang
district of Shanghai city toppled over killing one
worker.
It was reported in the Wall Street Journal that:“According to the Shanghai Daily, initialinvestigations attributed the accident to the
excavations for the construction of a garage under
the collapsed building. Large quantities of earth
were removed and dumped in a landfill next to a••the quality of the problem definition and its
justification;
the approach with which they were trying tosolve the problem;
the quality of the computer modelling; andthe quality and organization of the technicalreport.

Other than this very little information about the
problem was given, and thus the first step was forthe students to redefine the problem in terms which
made it solvable within the limitations of theirknowledge and the available software. This required
20

GEOMATE- Osaka, Nov. 16-18, 2015a substantial amount of research on the geometry
and properties of the building site, soil properties,
and groundwater conditions. Many aspects of theserequired assumptions and simplifications to be made,and thus there was no absolute correct answer.

whereby students are able to provide feedback
online. Questions that are asked include commenting
on effective and ineffective aspects of the course, as
well as general suggestions for the course.
Positive responses

Reflection
As might be expected, a wide range of qualityof assignments was submitted, with more than 150
scripts in each of three years. As well as the reportfrom the Shanghai Daily, a number of othernewspaper reports are available on the internet, anda common thread among many was sensational
reporting for maximum news benefit, with afrequent lack of technical justification. It wasnecessary for the students to be able to sift throughthese, and extract the useful factual data without
being biased by the unsupported conclusions.
Since first setting the assignment, someauthoritative technical publications have appearedon the same subject, so it was also necessary forstudents to assimilate these, but not to plagiarize theresults and conclusions, often from more advanced
software than they had available.
Responses ranged from about 12 to over 100
pages, with one or two each year failing to submit areasonable effort, but the vast majority having a fairgo.
The PBL approach also highlighted somefundamental misunderstandings amongst thestudents, and one of the most common arose fromthe confusion surrounding undrained shear strength,“undrained cohesion”, and apparent cohesion. Looseterminology and use of symbols in published papers
was a major contributor, which would not affectthose well versed in geotechnical engineering, but isa trap which easily ensnares those still engaged in
the learning process.
Another similar problem frequently arose
because of the special use which we, as geotechnicalengineers, have given to words which occur ineveryday use. This is particularly true of drained
behavior, and saturated soils. Students may notactually read all the text book material assigned tothem, find it hard to understand how a soil can be
simultaneously saturated and drained. Aspects suchas this made marking quite difficult, because it is so
fundamental to the subsequent analysis that failure
to grasp these concepts makes most of any
subsequent analysis meaningless.
Since there are no “exact answers”, it isnecessary for markers to read every script, whichtakes considerable time and effort. With over 150
assignments to mark in a short period, it has also
been essential to share the workload. Means need to
be found to ensure that a consistent scheme is being
applied.
Student responses
The university has a feedback procedure

I am working in a very active geotechnicalenvironment, and the comments are very positivefrom my professional geotechnical colleague:seniors, juniors and a PhD.
The assignment was great, which helped me with
understanding the course material better, andproblems were realistic. It’s great experience tomake the connection between theories and the reallife scenario, which gave me a taste of my future
career.
For a subject that I thought I would just ‘do’ and try
and pass to get it out of the way, I ended up reallyenjoying this subject. And I have also found myself
wanting to understand these areas more andwanting to investigate the areas in my own time. I
now have an interest in it!
Over all I think this course was fantastic. I actuallyremember the contents of the assignments and I
actually understood them! I think back to so manyother courses and I don't remember hardly anything
to do with the assignments!
Negative responses
Trying to learn the geotechnical packages was alittle hard, need a session dedicated to introducing
the software.
Assignment 2 needs a bit more direction. It had goodintentions, and while I know it was meant to test
student’s ability for analysis and problem solving, I
was always a little confused.
Summary
A PBL assignment has been developed for anundergraduate geotechnical engineering course. This
assignment follows an integrated approach wherestudents are given a realistic problem and learn thegeotechnical and other aspects in the process ofsolving it. Students learn geotechnical fundamentals
and concepts, the process of problem definition,technical writing, and are introduced to thecommercially used software packages.
The challenges in setting up this type ofassignment are considerable: finding a suitableproblem that fulfils the required conditions is not aneasy task, initial student disapproval can be
dissuading, and marking can be more timeconsuming. Despite this, it is recommended for21

GEOMATE- Osaka, Nov. 16-18, 2015teaching and research. International Conference
on Physical Modelling in Geotechnics, HongKong.
[8] Shiau, J. S., and Hobson, M. (2011). Teaching
Earth pressure theory using physical models: an
example in civil engineering. International
Engineering and Technology Education
Conference. Kuala Lumpur, Malaysia.
[9] Shiau, J. S., Sams, M. S., & Kemp, R. J. (2014).
Physical modelling and PIV analyses of anunderground tunnel heading. In GeotechnicalAspects of Underground Construction in SoftGround, Seoul, 61-66.
[10] Dewey, J. (2007). Experience and Education.
Simon and Schuster.
[11] Peck, R. B. (1969). Deep excavations andtunnelling in soft ground. In Proc. 7th int. conf.
on SMFE (pp. 225-290).
[12] Ward, W. H., & Pender, M. J. (1981).
Tunnelling in soft ground: general report.
In Proceedings of the 10th International
Conference on Soil Mechanics and Foundation
Engineering, Stockholm (Vol. 4, pp. 261-275).
[13] Mair, R. J., & Taylor, R. N. (1997). Themelecture: Bored tunneling in the urban
environment. of XIV ICSMFE [131], 2353-2385.
[14] Kirsch, A. (2010). Experimental investigation of
the face stability of shallow tunnels insand. Acta Geotechnica, 5(1), 43-62.
[15] Chen, R. P., Tang, L. J., Ling, D. S., & Chen, Y.
M. (2011). Face stability analysis of shallow
shield tunnels in dry sandy ground using thediscrete element method. Computers andGeotechnics, 38(2), 187-195.
[16] Chen, R. P., Li, J., Kong, L. G., & Tang, L. J.
(2013). Experimental study on face instabilityof shield tunnel in sand. Tunnelling andUnderground Space Technology, 33, 12-21.
[17] White, D. J., Take, W. A., & Bolton, M. D.
(2003). Soil deformation measurement using
particle image velocimetry (PIV) andphotogrammetry. Geotechnique, 53(7), 619-631.
[18] Itasca Consulting Group Inc (2003). “FLAC
User’s Manual”. Minneapolis, USA.

geotechnical teaching as it is more suitable andrelevant than traditional classroom teaching. Student
feedback and response from the course has beenvery positive. It is more engaging for the student,
and more satisfying for the staff.
ACKNOWLEDGEMENT
The author would like to thank his project students
for their direct and indirect efforts in the past 12
years at University of Southern Queensland. Special
thanks are given to his recent students Mathew Sams,
Michael Hobson, Brian Lamb and Ryan Kemp, as
well as his colleague Dr. Stephen Buttling.
REFERENCES[1] Felder, R. M., and Silverman, L. K. (1988).
Learning and teaching styles in engineering
education. Engineering education, 78(7), 674681.
[2] Prince, M., & Felder, R. (2007). The many facesof inductive teaching and learning. Journal ofCollege Science Teaching, 36(5), 14.
[3] Quintela, A., & Santana, T. (2007). Teaching
geotechnical works using professional practice.
Paper
presented
attheProceedings,
International Conference on Engineering
Education (ICEE), Coimbra, Portugal.
[4] Haury, D. and Rillero, P. (1995). North Central
Regional Educational Laboratory. Perspectivesof Hands-On Science Teaching. Viewed
05/02. <http://www.ncrel.org/sdrs/areas/issues/c
ontent/cntareas/science/eric/eric-2.htm>[5] Wartman, J. (2006). Geotechnical physicalmodeling for education: Learning theory
approach. Journal of Professional Issues inEngineering Education and Practice, 132(4),288-296.
[6] Meguid, M. A., Saada, O., Nunes, M. A., &Mattar, J. (2008). Physical modeling of tunnels
in soft ground: a review. Tunnelling andUnderground Space Technology, 23(2), 185198.
[7] Shiau, J. S., Selvan, P. and Ayers, R. (2006).
Developing physical models for geotechnical

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GEOMATE- Osaka, Nov. 16-18, 2015

APPENDIX: MARKING RUBRIC GIVEN TO STUDENTS

Technical Matter(80%)

Research(10%)

Presentation(10%)

Component(% of marks allocated)

F
0-49%

C
50-64%

B
65-74%

A
75-84%

HD
85-100%Marking requisites

No

Overall presentation

No effort

Poor

Adequate

Good

Excellent

Language skills

UnacceptableBasic – many
mistakes

Adequate –somemistakes

Good – few
mistakesHigh level –very few
mistakes

Evidence of research

None

Some – notbeyond theset text

Adequate –
attempt has
been madeGood –
decentresearch

Excellent –extensive
research

Quality of resources

None orunacceptable

Basic

Adequate

Good

High level

Knowledge of study
materials

None

LimitedAdequate

Considerable

Excellent

Reference list

None orunacceptable

Basic

Adequate

Good – arange ofsources

Excellent –authoritative
sources

Executive Summary

None orunacceptable

Basic – notclear

Adequate –providesenough info.

Good

Excellent –clear,
concise, withrequired info

Table of Contents

None

Introduction andbackground

None orunacceptable

Problem Definition*(discussion,
justification, etc.)
Discussion of methods(FLAC/slope andSlope/w)

Yes

Basic

Comprehensive

BasicAdequate
informationGood

Excellent –provides all
required
information

Inadequate little to no
discussion

Basic level of
discussion

Adequate –many aspects
are covered

Good – most
aspects are
discussed

Excellent –all required
aspects
defined andjustified

Little or no
discussion on
topic

Basic
discussion on
topic

Adequate
discussion on
topic

Good
discussion
on topicHigh level –extensive
research isevident

23

GEOMATE- Osaka, Nov. 16-18, 2015

Numerical Analysis of
the problem*

None,
unacceptable, manyaspects
incorrect

Supplied computer files

No

Conclusions andrecommendations

Basic
analysis,
simplistic
approach

Adequateanalysis,inconclusive
approachGood
analysis, agood
approach

Excellent,approach iscomprehensi
ve

Yes

None

Basic

*High weighting given to these sections

24

Adequate

Good – hasmade many
conclusions

Excellent –problem has
been solved,
recommenda
tions given

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

FRP STRENGTHENED REINFORCED CONCRETE PILES UNDERSTATIC LATERAL LOADS – A FIELD STUDY

Dr. K. Muthukkumaran
Associate Professor, Department of Civil Engineering, National Institute of Technology, Trichy, India
Dr. M. Murugan
Former Ph.D. Scholar, Department of Civil Engineering, National Institute of Technology, Trichy, India
Dr. C. Natarajan
Professor, Department of Civil Engineering, National Institute of Technology, Trichy, India

ABSTRACT
Piles could be damaged under major lateral loading environment for a long period. The sources of lateralloads are lateral earth pressure, wind loads, seismic loads, impact loads from ships (berthing and mooring)eccentric loads on columns, river current and mud movement loads in alluvial settings (foundations subject toscour), ocean wave forces, slope movements, and cable forces on transmission towers. Hence it is important toimprove the lateral load capacity of pile foundation. In recent years, Fiber Reinforced Polymer (FRP) jacketinghas become popular to retrofit existing deficient piles. The lightweight, high strength and corrosion resistance of
FRPs made them particularly suitable for repair. This paper presents the experimental study on Glass Fibre
Reinforced Polymer (GFRP) strengthened Reinforced Concrete (RC) piles subjected to lateral loads. The effectsof GFRP strengthening on RC piles were studied.
Keywords: Strengthening, RC piles, GFRP, Lateral Loads

and Nanni (2000) studied the flexural strengtheningof bridge piers using FRP composites. Test results
indicate that FRP strengthening technique iseffective in increasing the flexural capacity of thepiers. Test results also indicate that the capacity andfailure modes of the bridge piers are closely related
to the superstructure/substructure interaction and thepier boundary conditions. Chaallal and Shahawy(2000) studied the performance of fiber-reinforced
polymer-wrapped reinforced concrete column under
combined axial-flexural loading. Results indicatethat the strength capacity of beam-columns
improved significantly as a result of the combined
action of the longitudinal and the transverse weavesof the bidirectional composite fabric. Richard et al.
(2003) investigated the retrofit of square concrete
columns with carbon fiber reinforced polymer forseismic resistance. Results indicate that added
confinement with CFRP at critical locations
enhanced ductility, energy dissipation capacity andstrength of all substandard members. Bousias et al.
(2004) studied the effectiveness of fibre reinforced
polymers in retrofitting of rectangular reinforced
concrete columns. Wrapping with FRP is found to
greatly improve seismic performance of columns
that suffer from both lack of seismic detailing andreinforcement corrosion. Sen and Mullins (2007)
studied the application of FRP composites forunderwater piles repair. They suggested that the bi-

INTRODUCTION
Foundation of any structure plays an importantrole in safety and satisfactory performance of thestructure as it transmits the loads from structure toearth. Piles are structural elements in a foundation
which have the function of transferring load fromthe superstructure through weak compressible strata
or through water, onto stiffer or more compact andless compressible soils or onto rock. Pile
foundations are often necessary to support large
structures when the surface soil conditions are notstrong enough to support the structure with shallow
foundations. Piles are also subjected to significant
amounts of lateral loads and overturning moments
besides axial loads when used under tall chimneys,
high-rise structures, and coastal and offshore
structures. Piles are also designed for lateral loads,
when they are required to restrain forces causing
sliding or overturning of structures. Lateral loads arein the order of 10-15% of the vertical loads in thecase of onshore structures, whereas coastal andoffshore structures these lateral loads can exceed
30% of the vertical loads. Therefore, proper
attention has to be given in designing such pile
structures under lateral loads. In recent years, many
investigations have addressed the externally bonded
GFRP composites for strengthening and retrofitting
the piles subjected to heavy lateral loads. Alkhrdaji
25

GEOMATE- Osaka, Nov. 16-18, 2015

directional material should be preferred over unidirectional material and carbon fibre over glass fibre.
Ilki et al. (2008) studied the axial behavior of RC
columns retrofitted with FRP composites. The study
concluded that the external confinement of columns
with CFRP sheets resulted in an increase in thestrength and ductility. Bournas and Triantafillou(2009) studied the flexural strengthening of
reinforced concrete columns with near-surfacemounted (NSM) FRP and stainless steel. The results
demonstrate that NSM FRP and stainless steel
reinforcement is a viable solution towards enhancing
the flexural resistance of RC columns subjected toseismic loads. Ilki et al. (2009) investigated theseismic performance of reinforced concrete columns
constructed with low quality of concrete andinsufficient transverse reinforcement before andafter retrofitting. The test results showed that allreference specimens, which were not retrofitted,
failed with a premature loss of performance either
due to buckling of longitudinal reinforcement or lossof bond, while retrofitted ones exhibited asignificantly superior performance, particularly interms of ductility. Purushotham Reddy et al. (2009)
studied the retrofitting of RC Piles using GFRP
Composites. The authors concluded that the axial
and lateral load carrying capacity of the GFRP
retrofitted pile increases with the conventional pile.
Sangeetha and Sumathi (2010) investigated the
behavior of Glass fibre wrapped concrete columns
under uniaxial compression. The study concluded
that confinement increased the strength of theconcrete columns loaded axially.
Goksu et al. (2012) investigated the possibility ofusing CFRP reinforcement for the flexural retrofit of
low-strength reinforced members under reversed
cyclic loading conditions. The longitudinal CFRP
reinforcement effectively contributes to the reversed
cyclic flexural capacity of the existing low-strength
member. The laterally load pile behaviour under
different
conditions
werestudiedby
Muthukkumaran et al. (2008), Almas Begum &Muthukkumaran (2009) and Muthukkumaran (2015).

1

 EI  5
T = 
 k1 

(1)

in which, EI is the flexural rigidity of pile and k 1
is the co-efficient of subgrade reaction of soil.
Failure of a short rigid pile occurs when thelateral resistance of the soil this been exceeded. The
failure mechanisms of short rigid pile for freeheaded and fixed head condition are shown in Figs.
1a and 1b respectively. In case of long flexible pile,the failure is associated when the moment at one ormore points exceeds the moment of resistance andthe failure takes place by formation of one or two
plastic hinges along the pile length. The failuremodes of long flexible pile for free headed and fixedhead condition are shown in Figs. 1c and 1d
respectively.
In case of long flexible-free headed pile, thefailure takes place by formation of one plastic hinge
along the pile length. This distance from groundlevel is termed as depth of fixity. Generally thelateral strength of piles can be improved byincreasing the stiffness of the soil around the pile bycompaction (or special grouting) or by increasing thepile stiffness for the portion above depth of fixity. In
this paper, the performance of long flexible-freeheaded piles subjected to lateral loads was studied
by increasing the pile stiffness by GFRP
confinement.

1a. Free headed pileShort rigid pile

1b. Fixed head pileShort rigid pile

1c.Free headed pile-longflexible pile

1d. Fixed head pile-longflexible pile

FAILURE MECHANISMS
Collapse of a laterally loaded pile foundation
occurs when a failure mechanism forms in each pile
within a pile group. The pile behavior is dependent
on the characteristic length of the pile. When thelength of the pile exceeds 4T, the pile is considered
as long pile and when the pile length is ≤ 2T, the pileis considered as short rigid pile (IS 2911). Where, T
is the relative stiffness factor

Fig. 1. Typical failure modes in short rigid piles andlong flexible piles

26

GEOMATE- Osaka, Nov. 16-18, 2015

MATERIAL PROPERTIES

Table 2. Engineering properties of the soil

Concrete

Depth (m)
4.75
mm
%
425
μ
Passing

The characteristic compressive strength of
concrete used for the study was 30 MPa. The mix
ratio adopted for casting the specimens was 1: 1.204:
2.755 (Cement: Fine aggregate: Coarse Aggregate)with water-cement ratio of 0.385. The compressive
strength of cubes after 28 days water curing was
41.44 MPa.

Atterberg
LimitsReinforcement
The yield strength of steel used for the study was
415 MPa. Six numbers of 8 mm bars were used as
longitudinal reinforcement with a cover thickness of
40 mm. Bars of 6 mm dia. at 175 mm spacing wereused as lateral reinforcement (ties).

1

2

3

99

97

94

64

64

62

75 μ

49

44

41

LL (%)47

46

39

PL (%)26

26

23

Ip (%)

21

20

16

2

UCC

(kN/m )

894.671028.091126.19

SPT
value

(N)

26

28

31

Type of
soil

(IS
1498)

SC

SC

SC

CASTING OF RC PILES
Properties of Glass Fibre Reinforced Polymers
A pile of 230 mm dia. and 4 m length (3 m
below and 1 m above the ground level (GL)) ofbored-and-cast-in-situ (reinforced concrete piles)
were cast in the field already surveyed. The center tocenter spacing between the piles are 1.18 m and all
the test piles were located at a distance of 1.5 mfrom the reaction pile which is located at the centeras shown in Fig. 2. Theoretical lateral strength of
230 mm dia. pile is 25 kN. The following steps were
followed to cast the piles in the field.

Glass fibre reinforced polymers were used in the
study. Properties of GFRP materials are given in the
Table 1.
Table 1 Properties of GFRP materials
Properties

GFRP
Unidirectional

Bidirectional

920

750

0.90

0.6

1.5

1

3400

3400

73000

73000

Weight offibre, g/m2
Fibre
thickness,mm
Nominalthickness
per layer,mm
Fibre tensile
strength,
N/mm2
Tensilemodulus,
N/mm2

Fig. 2. Pile layout
BoringPower auger equipment was used for the drilling
work in bored-and-cast-in-situ piles. Boring was
carried out up to the depth of 3 m from ground level.
Fig. 3 shows the power auger boring at the field.

Soil Characteristics
Disturbed and undisturbed soil samples werecollected from the field to study the engineering
properties of soil. Engineering properties of the soil
are listed in Table 2. The soil was classified asclayey sand (SC) according to IS 1498 – 1970.

Concreting
Pre-fabricated reinforcements were inserted in
the boreholes and concreting was done. Fig. 4 shows
the concreting of RC piles in the field.

27

GEOMATE- Osaka, Nov. 16-18, 2015

CuringRubbing

Piles were covered with gunny bags for water
curing immediately after demoulding since gunnybags can hold water for long time. Fig. 5 shows thewater curing of concrete piles.

The surfaces of the RC piles were rubbed withsand paper to remove loose and deleterious material
from the surface with a silicon carbide water-proof
paper sheet.
Primer Coating
The mixed material of Nitowrap 30 epoxy
primer was applied over the prepared and cleaned
surface.

Fig. 3.
boring

Power

auger

Saturant Coating

Fig. 4. Concreting

The saturant system used in this work was madeof two parts, namely, resin and hardener. The
components were thoroughly hand mixed for 3
minutes before application.
GFRP wrapping
The first coat of saturant was applied over theprimer coat and GFRP sheet was then wrapped
directly on the surface. GFRP layer was wrappedaround the pile with an overlap of ¼ of the perimeterto avoid sliding or debonding of fibres during tests
and to ensure the development of full strength. Fig. 6
shows the wrapping process done on piles.

Fig. 5. Curing of RC piles
GLASS FIBRE
WRAPPING

REINFORCED

POLYMER

After sufficient water curing for 28 days, the RC
piles were prepared for GFRP wrapping. The
following steps were followed for FRP wrapping

Earth Filling

Soil Excavation

Excavated soil was filled in layer by layer.
Sufficient compaction was done for each layer toachieve the same density as obtained during
excavation. Fig. 7 shows the filling of excavated soil.

Soil around the pile was excavated for FRP
wrapping. For laterally loaded piles, the depth of
fixity is important for the designer since themaximum bending moment will be at depth of fixity.
The pile is mainly designed for maximum bending
moment. In the present study, the depth of fixity wasestimated as 1.23 m below ground level based on thesoil properties. The depth of excavation (1.5 m)
made was slightly higher than the depth of fixity
ChisellingFig. 6. GFRP wrapping

Chiselling was carried out to make the unevenconcrete surface as even surface using chisel andhammer.

Fig. 7. Filling
excavated soilof

EXPERIMENTAL SET UP AND PROCEDURE
Lateral Load Application

Plastering

The lateral load test was performed inaccordance with IS : 2911(Part 4) - 1985. The pile
was loaded horizontally at a distance of 0.85 m
above ground level. The lateral load to the pile wasapplied through a hydraulic jack, which was placed
between the supporting and testing piles. The

Cement mortar was applied over the unevenconcrete surface to make even for GFRP wrapping.

28

GEOMATE- Osaka, Nov. 16-18, 2015

reaction was obtained from the central supporting
pile. The capacity of the jack is 200 kN and ram
diameter is 75 mm with 150 mm ram travel. The
jack is connected to a hydraulic pump. The load
applied is displayed on pressure gauge and alsomeasured through proving ring.

GFRP Strengthened RC Piles Subjected to Static
Lateral Loads
Experimental investigations have been conducted
on 4 RC piles. Out of the 4 piles, one reference pile
was tested without any wrapping and the remaining
3 piles were wrapped with GFRP composites ofvarying configurations. The static lateral load test
was performed in accordance with IS : 2911 (Part 4)- 1985. At each stage of loading, lateraldisplacement of piles were measured. Displacement
at ground level was interpolated from the three dialreadings since the three dial gauge readings werelinearly varied. Lateral load versus ground level
displacement of piles is shown in Fig. 9. Lateral loadcorresponds to 5 mm and 12 mm displacements atground level were calculated from the Fig. 9 and it istabulated in Table 3. As per IS : 2911(Part 4) - 1985,
safe load of the pile was taken as 50% of final load
at which the total displacement increases to 12 mm
or final load at which the total displacement
corresponds to 5 mm at ground level. However, thesafe load of the pile is taken as the minimum of theabove two. In all the cases it was observed that thefirst condition is the minimum

Installation of Dial Gauges
Three dial gauges were installed to measure thelateral displacement of pile. Dial gauges used forthese tests were accurate to 0.01 mm. Dial gauges
were clamped on specially fabricated steel framewith 30 cm apart from one another. The steel frame
was fixed in the ground in such a way that there wasno deflection in the steel frame. One dial gauge wasplaced behind the loading point.
Loading Procedure / Loading Sequence
The loading sequence adopted in the test is inaccordance with IS : 2911(Part 4) - 1985 section 6.2.
Maintained Load Method was adopted in the test. In
this method application of increment of test load andtaking of measurement or displacement in each stageof loading was maintained till rate of displacementof the pile either 0.1 mm in first 30 minutes or 0.2
mm in first one hour or till 2 hour whichever occur
first. In the static lateral load test, the load wasapplied in increment of 20% of the working load
until it reaches ultimate lateral load. After this stage,
the load was released and then brought to “no load”
condition. Each load step was maintained as
mentioned above. The pile lateral movement
readings were taken at 0, 15 and 30 minutes after
each load step. In the cyclic lateral load test, eachload was applied in increment of 20% of theworking load until it reaches ultimate lateral load.
After each load step, the load was released and thenbrought to “no load” condition then the next
increment load was applied. Each load step wasmaintained as mentioned above. Test set up is shownin Fig. 8

50Load (kN)

40
30
Unconfined
Uni-GFRP-L
Uni-GFRP-C
Bi-GFRP20
10
0
0

3 6 9 12 15 18 21 24 27 30
Lateral displacement at GL (mm)Fig. 9. Lateral displacement of piles at ground level
Soil Behavior around the Pile
In the analysis of soil-pile interaction underlateral load, the behavior of soil around a pile is an
important parameter which has a great influence on
the results. Most of the research about piles underlateral loads has to date been performed toinvestigate the load-bearing capacity of the piles,
pile deflection, pile rotation and internal forces
created in the pile. Only a few physical models havebeen created to investigate the behavior of the soil
around the pile and its deformation pattern.
Therefore it is necessary to investigate the soil
deformation pattern around laterally loaded piles andpile-soil interaction in order to improve the level ofknowledge on this subject. It can be observed that
there is a cap between pile and soil surface in theloading side of the ground. This cap is extended tosome depth below ground level depends on the typeof FRP wrapping. It was also noticed that the top
surface of the ground around the pile opposite to

Fig. 8. Test setup

RESULTS AND DISCUSSIONS

29

GEOMATE- Osaka, Nov. 16-18, 2015

loading slightly heaved. Considering Fig. 10 the
maximum horizontal displacement occurs in the
ground level and decreases gradually with depth. At
greater depths (depths about 2 to 3D, where D is thediameter of pile) the displacements of soil aroundthe pile are negligible which was confirmed byvisual observation as well as measurement by thinrod.

pile is created. Fig. 10 shows that in front of alaterally loaded pile a passive zone is establishedand the propagation of this zone is purely depend on
the imposed displacement, since the soil condition is
identical for all pile locations.
Effects of Pile Stiffness on Soil Deformation
Patterns around a Pile

Table 3. Lateral load carrying capacities of tested
piles
50 % of
Load
Load
Load
correspcorrespSa
correspondingondingUlti
feonding12
to5mate to
to
12
loa
mm
mm
Type
Loa
mm
d
displaced, P u displace
displace(kment at(kN) ment at
ment at
N)
GL
GL
GL
(kN)
(kN)
(kN)
Uncon
12.
fined
28.5 24.26
12.13
13.81
13
pile
Uni40.2
17.
GFRP
35.12
17.56
20.29
7
56
-L
Uni32.7
14.
GFRP
28.96
14.48
15.45
7
48
-C
Bi35.8
16.
32.37
16.18
18.16
GFRP 118a) Unconfinedc) Bi-GFRP

In flexible piles, the pile stiffness is an effective
parameter in lateral load capacity. This parametersignificantly influences the deformation of thesurrounding soil. From the observation it can be seenthat the mobilized depth of the strain wedge wasincreased and the mobilized angle was decreased
with increasing pile stiffness.
Failure Mode of Piles
The failure of all piles occurred below theground level, at a distance of about 1.4 to 2.4 times
the diameter of the pile. The failure modes of all thepiles were long pile failures that occurred bybending moments, as shown in Fig. 11a) Unconfinedb) Uni-CFRP-Cc) Bi-GFRPd) Uni-CFRP-Lb) Uni-CFRP-Cd) Uni-CFRP-L

Fig. 11. Failure mode of piles

Fig. 10. Soil behavior around the pile

Under lateral loading, the unconfined pile failed
at 0.32 m from the ground level which is about 1.4
times its diameter. Whereas, unidirectional GFRP
confined piles with fibres along the length andcircumference of piles failed at 0.54 m and 0.51 m(about 2.3 to 2.4 times diameter) respectively. The
displacement of soil around the pile (width anddepth of gap) was measured by visual observation as

Shear Strains around a Laterally Loaded Pile
It can be observed that the maximum shear strain
occurred in pile-adjacent soils near the surface. A
triangular strain wedge to the side of the displaced

30

GEOMATE- Osaka, Nov. 16-18, 2015

was 26% than unconfined pile. This clearly indicatesthat the GFRP strengthening is an effective method
for strengthening of existing piles.

well as measurement by thin rod. The width anddepth of gap depends on soil type and loading
condition. This experimental study was conducted
on C - φ soil. Table 4 presents the gap width &depth and the depth of pile failure from ground level.
It shows very clearly that the GFRP confined piles
are taken much more lateral load than unconfined
piles. It is also noticed that the GFRP confined piles
depth of fixity is about 1.6 to 1.8 times more than
unconfined piles which means that the GFRP
confined piles moment carrying capacity is almost
more than 1.5 times than unconfined piles.

REFERENCES[1] Almas Begum, N. & Muthukkumaran, K.
(2009). Experimental investigation on single
model pile in sloping ground under lateral load.
International
Journalof
GeotechnicalEngineering 3, No.1, 133-146.
[2] Alkhrdaji, T., and Nanni, A. (2000). “Flexuralstrengthening of bridge piers using FRP
composites.” ASCE structures congress 2000,
M. Elgaaly, ed., Philadelphia.
[3] Bournas, D. A., and Triantafillou, T. C. (2009).
“Flexural strengthening of reinforced concrete
columns with near surface mounted FRP or
stainless steel.” ACI Struct. J., Vol. 106, No. 4,
pp.495–505.
[4] Bousias, S.N., Triantafillou, T.C., Fardis, M.N.,
Spathis, L. and O’Regan, B.A. (2004). “Fiberreinforced polymer retrofitting of rectangularreinforced concrete columns with or without
corrosion”, ACI Structural Journal, Vol. 101,
No. 4, pp. 512–520[5] Chaallal, O. and Shahawy, M. (2000).
“Performance of fiber-reinforced polymerwrapped reinforced concrete column under
combinedaxial-flexuralloading”,
ACI
Structural Journal, Vol. 97, No. 4, pp. 659–668.
[6] Goksu, C., Polat, A. and Ilki, A. (2012).
“Attempt for seismic retrofit of existing
substandard RC members under reversed cyclic
flexural effects”, Vol. 16, No. 3, pp. 286-299.
[7] Iacobucci, R.D., Sheikh, S.A. and Bayrak, O.
(2003). “Retrofit of square concrete columns
with carbon fiber-reinforced polymer forseismic resistance”, ACI Structural Journal, Vol.
100, No. 6, pp. 785–794.
[8] Ilki, A., Peker, O., Karamuk, E., Demir, C., andKumbasar, N. (2008). “FRP retrofit of low andmedium strength circular and rectangularreinforced concrete columns.” Journal of
Materials in Civil Engineering, Vol. 20, No. 2 ,pp.169-188.
[9] Ilki, A., Demir, C., Bedirhanoglu, I. andKumbasar, N. (2009). “Seismic retrofit of brittle
and low strength RC columns using fiber
reinforced
polymerandcementitious
composites”,
Advances
in
StructuralEngineering, Vol. 12, No. 3, pp. 325-347

Table 4. Gap formation and pile failure
Type
of
piles
Maxi
mum
width
of gap
at GL
(cm)
Maxi
mum
depth
of gap
from
GL
(cm)
Pile
failure(depth
from
GL in
cm)

Unconfined
pile

UnidirectionalGFRP-L

UnidirectionalGFRP-C

Bidirectiona
l
GFRP

1.8

2

1.9

1.8

42

67

58

71

32
(1.39D)54(2.32D)51(2.19D)

56
(2.41
D)

CONCLUSIONS
Based on the experimental results, the following
conclusions are made.
1. The GFRP wrapped piles lateral capacity issignificantly higher than the conventional pile of thesame depth and diameter. The GFRP wrapped piles
depth of fixity is about 60 to 70% higher thanconventional concrete piles. It shows that the
moment carrying capacity of GFRP wrapped piles
are much more than the conventional concrete piles.
2. GFRP piles with fibre orientation along thelength of the pile indicated about 41% more lateralstrength than unconfined pile. However, theincrement in the lateral load carrying capacity was
only about 15% for fibres oriented in circumferential
direction. The percentage increase in lateral strengthof pile strengthened with bidirectional GFRP mat
31

GEOMATE- Osaka, Nov. 16-18, 2015[10] IS : 2911 (Part 1/Sec 2) - 1979 Code of practicefor design and construction of pile foundations.
Bureau of Indian standards.
[11] IS : 2911 (Part 4) - 1985 Code of practice fordesign and construction of pile foundations.
Bureau of Indian standards.
[12] IS : 1498 - 1970 Classification andidentification of soils for general engineering
purposes. Bureau of Indian standards.
[13] Muthukkumaran, K., Sundaravadivelu, R. &Gandhi, S.R. (2008). Effect of slope on P-Y
curves due to surcharge load. Soils andFoundations 48, No.3, 361-365.
[14] Muthukkumaran, K. (2014). Effect of slope andloading direction on laterally loaded piles incohesionless soil.
International Journal of
Geomechanics, ASCE, 14(1): 1-7.

[15] Purushotham Reddy, B., Alagusundaramoorthy,P.,
andSundaravadivelu,
R.
(2009).
“Retrofitting of RC Piles using GFRP
Composites.” KSCE Journal of Civil
Engineering, 2009, Vol. 13, No.1, pp. 39-47.
[16] Sangeetha, P., and Sumathi, R. (2010).
“Behaviour of glass fibre wrapped concrete
columns
underuniaxial
compression.”International Journal of Advanced Engineering
Technology, Vol. 1, No. 1, pp.74-83.
[17] Sen, R., and Mullins, G. (2007). “Application of
FRP composites for underwater piles repair.”
Journal of Composites: Part B 38 (2007)
pp.751–758.

32

Technical Paper

33

Geotechnique Paper

34

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

A NEW LOOK AT PAVEMENT MANAGEMENTJohn Yeaman, Stuart Yeaman2 and John W Yeaman3
Queensland Pavement Centre, University of The Sunshine Coast, Australia 2 Jacobs Engineering,, Australia,
3
Pavement Management Services, Australia

1

ABSTRACT
Pavement Management embraces the full range of engineering activities (Design, Construction, Operation,
Maintenance and Renewal), throughout the life cycle. Over the past 4 years, The Queensland Pavement Centre at
the University of the Sunshine Coast, Australia, has been studying the various parameters that influence
pavement behaviour and those parameters that impact the whole of life of a road pavement under real operational
conditions. By 2015 five sites have been installed in operational pavements, designed to monitor the
performance of critical parameters as a function of material properties, climatic changes and the complete
spectrum of traffic usage. One site (Sippy Downs Drive – East) has been operational for over 1000 days. This
paper is based on the findings from that study. The paper will address a design aspect of Pavement Management,in particular the critical strains as measured in the subject pavement every minute of every day for the past two
years and demonstrate how the performance parameters can be utilised in a simulation method to predict wholeof life performance based on operational data, obtained from real life, rather than assumed parameters.
The configuration of other sites and a test suite for installation and analysis are recommended.
Keywords: Transfer Function, Layer Temperature, Design, Simulation, Field Sampling

Mathematics and computing power mean that we nolonger have the need to design pavement for one
season (The Mean Monthly Average Pavement
Temperature), One Load (An equivalent Standard
Axle) and one material (defined by Stiffness at one
temperature and one time of loading).
This paper will examine will examine the use
mathematical simulation process , using statistical
data from data collected from operational pavements
and materials to determine the probability of apavement meeting its whole of life expectancy, forthe conditions pertinent to its use..

INTRODUCTION
The Engineering Science of Pavement
Management has stagnated for the past 20 years,
with practioners reducing the Science to becoming
an inventory of the stock of road assets and a meansof justifying decisions that are often based on very
little knowledge of the performance of the asset
under an extensive range of conditions andmanufacturing methods.
Pavement Management, as many people understandit, does not involve the fundamental requirements ofa triple bottom line in engineering, engineering,
economics and sustainability, embracing theprinciples of risk management.
Pavement Management must consider thecomponent paradigms:

•
•
•
•
•

THE
FUNDAMENTAL
ISSUESOF
PAVEMENT MANAGEMENT
Construction
The pavement constructor can make or break thedesign, particularly when it comes to preparation of
the subgrade and compaction of the structural layers.
Many industry associations go to great lengths totrain construction operators in the correct use of
equipment, but if the material supplied is a little
segregated, cold , or containing “ off -spec”
materials the resultant variability in the as built
quality can easily make the finished product lessthan that assumed for the design.
Structure
We assume one structure for the design, butnatural materials have a latent coefficient ofvariability in the order of thirty percent for almost allof their properties. Manufactured materials such as

DesignConstruction
Operation
Maintenance
RenewalWhilst all of these aspects are necessary for theeffective management of pavements throughout thelife cycle, the design of a pavement to meet theconditions of its use must be paramount, since if thedesign is unsuitable or simply incorrect then theremainder of the management process is useless35

GEOMATE- Osaka, Nov. 16-18 2015

quarried and crushed products, Hot Mix Asphalt(HMA) and Concrete have much lower coefficientsof variation and can be laid with great accuracy.
Layer thicknesses and compaction can be assumed
with a lot more certainty but what about the
subgrade stiffness? We use a modulus (stiffness)
mostly calculated from a Californian Bearing Ratio(CBR) test. If we are fortunate to have determined
the Resilient Modulus of the Subgrade material,
what is its variability across the project?
Environment
Solar Radiation is a critical parameter impacting
on the pavement’s environment. The quantum ofsolar radiation drives the pavement surface
temperature, oxidation and polymerisation of thebitumen binders in HMA mixtures and surface
treatments. The predominate pavements adopted in
Queensland.
Fortunately Solar Radiation is anunique commodity in that it can be calculated forany point on the earth’s surface for any time of the
day and day of the year, for example at the Sippy
Downs Campus – Latitude 26° 43’23”South ;Longitude 153° 04’02”East ,Solar Radiation is at its
peak at 12:03 pm every day. Moisture content also
has a serious impact on pavement performance. In
Queensland we are not subjected to Freeze Thaw
cycles but the State is subject to “drought andflooding rains”. Pavements can be dried by theenvironment to well under optimum and can betotally inundated for weeks on end.
Traffic
Over the past few years we have become moreaware of the impact of heavy trucks and vehicle
speed on the performance of pavements. It will beshown that often Four Wheel Drives, Armoured
Cars ,innocent looking Coffee Carts and mobile
food vans are exerting stresses onto a pavement asmuch as a Bus or Truck.

Vb is the Voids filled by bitumen in the target HMA
Smix (|E*| the Stiffness of the HMA obtained from
the Mixture Master Curveμε is the horizontal tensile strain at the base of the
HMA layer
Other transfer functions such as those developed inreference [2] are equally suitable for the local
conditions
𝑵𝑵𝑵𝑵 = 𝟏𝟏𝟏𝟏. 𝟒𝟒 (𝟏𝟏𝟏𝟏. 𝟒𝟒(𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝝁𝝁𝝁𝝁−𝟑𝟑.𝟐𝟐𝟐𝟐𝟐𝟐 )𝑬𝑬−𝟎𝟎.𝟔𝟔𝟔𝟔𝟔𝟔
Equation 2
Where symbols are as defined above
For this study we will only discuss the influence of
the Critical horizontal strain με.
DESCRIPTIONOF
SIPPY
PAVEMENT MONITORING SITE

During the latter part of 2012, Sunshine Coast
Council (SCC), undertook a upgrade of a majorarterial road, adjacent to the University Campus. It
was suggested by SCC officers that this would
provide an opportunity to instrument the road anddetermine some of the critical parameters underoperational conditions. The description andequipment at the site is described in Reference [3] apaper prepared following an extreme weather event
shortly after the road as opened to traffic.
Following this event, Pavement Management
Services (PMS) added to the equipment at the site toenhance further study and analysis capabilityAirtemperature
Relative
Humidity

Weather
Station

Maintenance
The timing, treatment, quality and methods will
have a significant impact on the performance of apavement throughout its life. Serviceability (water
proofing, riding quality) Safety (skid resistance,
potholes, rutting) and Structural Capacity (ability tocarry changing loads, fatigue cracking andpermanent deformation) all can be modified bymaintenance, provided that the cause not the effectof the problem is rectified

In
Pavement

Horizontal
Strain Gauge@ 75mm
Vertical Strain
Gauge
@
650mm
Thermocouple
on Surface
Thermocouple@ 75mmMoisture
Gauge @
Moisture
Gauge @
CameraWind
Direction
Solar
Radiation
Solar panels provide electrical energy
Data are collected every minute 24/7 and transferred
dally at 6:am to the researcher’s computer
The camera records a photograph of the road surface
every day at 10:00 am and a photograph of any vehicle
that exceeds a nominated strain level
Table 1 Elements of Pavement Monitoring Site at
Sippy Downs Drive

𝟔𝟔𝟔𝟔𝟔𝟔𝟔𝟔(𝟎𝟎.𝟖𝟖𝟖𝟖𝟖𝟖𝑽𝑽𝑽𝑽+𝟏𝟏.𝟎𝟎𝟎𝟎 𝟓𝟓
𝑺𝑺 𝑴𝑴𝑴𝑴𝑴𝑴𝟎𝟎.𝟑𝟑𝟑𝟑 𝝁𝝁𝝁𝝁

AtmosphericPressure
RainfallWind Speed

THE RELATIONSHIP BETWEEN CRITICALSTRAINS AND FATIGUE CRACKING
For the purposes of this paper we will adopt thetransfer function adopted throughout Australia
Reference[1]
Nf= �

DOWNS

� Equation 1

Where
Nf = the number of cycles to fatigue failure
36

GEOMATE- Osaka, Nov. 16-18 2015

RELATIONSHIPBETWEENLAYER
TEMPERATURE
AND
HORIZONTALTENSILE STRAIN
The surface layer temperature has a major
influence on the strains obtained under operational
conditions, even diurnally. In general, surface layer
temperature rises to a maximum around 3:30 pm ona summers day, this is around 3 hours after peaksolar radiation (figure 4) to be noted here that ingeneral the Strains as measured in the pavement are
general positive in format which indicates that thesurface layer is in compression and there is unlikely
to be development of fatigue failure during thisperiod.
On the other hand, during a winter cycle (19 June2014) it can be seen that again peak strain occurs
about 3:30pm about 3 hours after peak solar
radiation (figure 3), but here the Strain lies in thenegative zone of the chart, signifying that thepavement is likely to be developing fatigue strain.)-103,480

Table 2 Seasonal Comparison between Strain 1
and surface layer Temperature
Summer

Area
Under
Curve
Mean
Standard
Deviation

80

40

60
40

20

0
-20

0

-60

Area underCurve
76,355

Dec 12
Dec 13
Dec 14

10

-40

MEAN
STRAIN
53

Temperature

STRAIN

Temp.

76,35554,357-109,408

30,77153

37.7°C-76

21.3°C

29.5

6.05

11.53

3.54 °C

Table 3 Strain and temperature parameters 1
year apart

30

20

STRAIN

A comparison of daily results for the same date 1
year apart fails to yield a significant trend at thistime (750 days) (table 3). What we can see here isthe variability in surface layer temperature, which
may explain some of the differences

50

100

WinterAnnual Drift

60

120

22.3°C

These results can be compared in table 2, where it
can be observed the major differences between
parameters as a function of season.
One must therefore query the efficacy of using one
strain in the transfer function (Equation 3 orEquation 4) to calculate the Cycles to failure

Figure 1 Relationship between Strain 1 and
Surface Layer Temperature for 20 December
2014
140

-72

Area
under
Curve
9,511
-7827
76,355MEAN

Standard
Deviation

6.6
-5.4
53

15.1
29.7
29.5

Mean
Pavement
Temperature
34.3°C
40.9°C
37.7°CIt was therefore decided to examine the strain
distribution month by month. Figure 3 provides thisinformation

Mean Temperature
37.7

Figure 2 Relationship between Strain 1 and
Surface Layer temperature for 19th June 2014
40

55

20

50

0

45

-20

40

-40

35

-60

30

-80

25

-100

20

-120

15

Area under
Curve

MEAN
STRAIN

Figure 3 Distribution of Strain Data over 750
days
From Figure 3 , it can be seen that range in strain
values, in fact, varies month by month. With strain
values at a minimum (largest negativity) in June ofMean Temperature

37

GEOMATE- Osaka, Nov. 16-18 2015

each year to date. It is interesting to note the slightincrease in strain range (greater positivity) in
December of each year. Is this the permanent set of
Hooke’s Law?

The range for the strain during this period is best
illustrated by a Histogram and a Cumulative
Distribution function (Figure 10) from which it canbe observed that the 90th percentile of strain 1 results
fall within the range -70 to +10 Micro strain

STRAIN DISTRIBUTION WITH SEASONA statistical analysis (Histogram and Cumulative
Distribution function) of all results between 20thDecember 2012 (the first day opened to traffic) and20th December 2014 are given in Figure 7
Analysis of this data provides the following
statistics:
MEAN = -1.4 μεStandard Deviation = 42.37 με
Number of samples = 30,240

100%

25%

90%
80%

Percentage of total

Cumulative percentage

20%

70%

15%

60%
50%

10%

40%
30%

5%

20%
10%

0%
-100
-90
-80
-70
-50
-40
-30
-20
-10
0
10
20
30
40
50
60
60
70
80
90
100

0%
percent
軸ラベル

Cum %

Figure 6 Distribution of Strain 1 without Traffic
July to October 2014
Further extension of this data without traffic(between 11 July and 3 October 2014) can beillustrated in the following Figure 5

Figure 4 Distribution of Horizontal strain dataover 750 days
ABERRATIONS TO STRAIN TEMPERATURE
RELATIONSHIPIn the period July to October 2014 that section ofroad containing the monitoring station was closed
due to new construction being undertaken to thewest of the monitoring site. This provided a unique
opportunity to observe the strain behaviour without
traffic and only influenced by pavement temperature.
The relationship between Strain and temperature inthis period is shown in Figure 10

Superimposing the Temperature profile for the same
period demonstrates the impact of temperature on
the pavement as a function of Surface layer
temperature

STRAIN v TEMPERATURE
Sans Traffic

50

0

5

10

15

20

STRAIN (0

Figure 7 Daily Range of Strain (without traffic)

-50

25

30

35

40

45

y = 0.0313x2 + 1.4493x - 95.321
R² = 0.8916

-100
S1
-150

多項式 (S1)
Temperature

Figure 5 Relationship between Strain 1 andTemperature (without traffic) July to October
2014

38

These figures illustrate, not only, the narrow band ofstrains occurring in a pavement, (0 to -70 με at the90th percentile range but also the fact that there issome strain developed in a pavement by thermalchanges only

GEOMATE- Osaka, Nov. 16-18 2015

The camera is also configured to take a photographof the same area should the measured strain exceed
160 με at either gauge.

In figure 10 we can see that a similar vehicle, bus,
makes a different impact on the pavement at adifferent time of the day. In the first photo, when the
pavement is relatively cool (8:00 am and 27°C) thebus triggers a reaction in the Strain 1 gauge (theupper one) of around 200 με, but around 3:00 pm
when the temperature is at its maximum (around
45 °C) and the pavement is considerably less stiff, asimilar bus triggers a reaction in Strain Gauge 2 (thelower one) of around -60 με. The stiffnesses from
the Master Curve Catalogue for this subject HMA
are
Speed = 45kph, Temperature = 25°C Dynamic
Modulus = 11,400 MPa
Speed = 45kph, Temperature = 45°C the Dynamic
Modulus =4,350 MPa
This further highlights the risk associated with
continuing to use one, only, strain in the transfer
equation – Equation 3

Figure 8 Daily Range of strain (without Traffic)
with daily range of temperature superimposed
DISTRIBUTION OF SURFACE LAYERTEMPERATURE
Clearly there is a very strong relationshipbetween horizontal strain and temperature with amuch greater effect that many of us realise; ofsignificance is the maximum value for surface layer.
Surface layer temperature over two full years ofstudy has indicated that the surface layer
temperature at this site exceeds 50°C less than 2% of
the time. Figure 8 following illustrates the
distribution of Surface Layer (top 75mm) over 750
days

Figure 10 Comparison of field measured Strains
for Similar Vehicles at different temperature
conditions
D is the fraction of life consumed by cycles at thedifferent strain levels. In general if the damage
fraction reaches 1 failure occurs.
This linear summation of cycle ratios isfundamental to pavement analysis

Figure 9 Distribution of Surface Layer
Temperature for Latitude 26° 43'23"S;Longitude 153° 04'02"E
From Dec 2012 to Dec 2014

THE EFFECT OF TRAFFIC
The camera at the monitoring site is configured
to take a photograph of the pavement surface at
10:00 am daily. These photographs are scanned
visually to assess any visible deterioration in the
pavement. To date there has only been one issue
with cracking,, which on investigation was found to
be a blocked sub soil drain. A significant outcome
for the efficacy of the technology

PAVEMENT ANALYSIS BY SIMULATION
Monte Carlo Simulation is a computation methodthat is based on taking random readings from aphysical law or mathematical model numerous times
in order to obtain a numerical integration, an
optimum value or a probability distribution.
In this application by Jopson Reference [4], Monte
Carlo Simulations were used to determine the values
for Nf using the statistical data for εt (Figure 7)
39

GEOMATE- Osaka, Nov. 16-18 2015

above. The HMA Master Curves were determinedby analysis of the HMA taken from the failed
section of the road and compacted in the Shear Box
compactor and the real life data obtained from thePavement Monitoring Site.
These values together with volumetric properties ofthe HMA (obtained from the HMA testing such as
Voids filled by Bitumen (Vb) were used within theTransfer function (Equation 3) to determine thenumber of cycles to failure (nf). The damage factor(Nf calculated by this method divided but the designlife).
The probability density function was determined todetermine the probability of failure within the design
life.
This analysis provided a predictive time to failure
under actual traffic of 6 years compared with anactual result of 7 years

and determine the stiffness of the mix at any
temperature and time of loading.
Solar Radiation and Surface Temperature
The solar radiation for the proposed site can becalculated for any minute of any day for the analysis
period, from this the surface temperature (taking into
account, Albedo, Emissivity and Conductivity forthe nominated HMA can be provided
ANALYSIS
The Damage factor for each iteration isdetermined by simulation via the input data and thetransfer function
The Design thickness
The design thickness is that one that is obtainedat D= 1 (vide Equation 7) from the application of
Miners Hypothesis

INPUTS FOR SIMULATION ANALYSISThe following inputs were required to start thesystemCONCLUSION
The use of Field data from a permanent
pavement monitoring site, together with laboratory
CHARACTERIZATION of materials can be
adopted to provide a fully mechanistic approach topavement analysis and design

Analysis Period
The analysis period is the “whole of life” desired“for the pavement. This will determine the numberof iterations in the simulation. For Example if thedesired life is 20 years the process will be iterated 10
Million times (20 years X 365.25 days per year x
1440 minutes per day).

REFERENCES[1] Austroads “Guide to Pavement TechnologyPart2 : Pavement Structural Design , 2008 pp 76[2] Tanquist, BA August 1, 2001, “Development ofa Quick Reliability Method for MechanisticEmpirical Asphalt Pavement. , Minnesota
Department of Transportation[3] Fairweather H and Yeaman J 2014 “A studyof the Parameters Affecting the Performance ofRoads Under an Extreme Rainfall Event” Int. J
of GEOMATE , Sept, 2014 Vol. 7, No. 1 (SINo.13) pp 99 -960.

Pavement Thickness
A value chosen by the designer, throughexperience, as to the approximate thickness requiredby the site conditions and the traffic spectrum. This
will be tested by simulation and increased or
decreased in the analysis until the desired thickness
for the conditions is met.
PERFORMANCE PARAMETERS
Traffic Spectrum
The traffic data from the proposed site together
with typical strains obtained for the spectrum ofvehicles provides statistics as to the strains to beexerted on the pavement, (vide Figure 7).

[4] Jopson K “A forensic analysis of premature
pavement failure of a road pavement using
Asphalt Master Curves and Computer
simulation-“Final Year Thesis , University of
the Sunshine Coast, Australia

Asphalt Master Curve
A suitable mixture provided by the supplier isnominated and the Master Curve obtained for that
mixture. This data is used to populate Equation 3.

40

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

SPATIAL ANALYSIS OF COLLAPSE-RELATED SOIL PARAMETERS

M. Mohammad Ali
California Public Utilities Commission. 180 Promenade Circle #115, Sacramento, CA 95834, USA.

ABSTRACT
Application of various techniques for spatial analysis to the field of civil engineering is well documented. In thispaper a case study is presented, where kriging is applied for estimating collapsing soil parameters, which
exceeds a critical threshold value for high, medium and low collapse susceptibility, with the estimation variance,at certain depth of Tucson, Arizona area. The estimate is based on existing criteria with spatial analysis used tobuild up model variograms and utilizing its parameter in the linear interpolation technique of simple kriging.
.
Keywords: Collapsing soil, geostatistics, variogram, krigging,
geological process. A soil deposit in a region may
be either residual or transported. Also a transported
soil may be either alluvial (stream borne) or aeolian(wind borne) or colluvial (gravity transported).
When alluvial soils are deposited in an arid or asemi-arid environment, they develop larger voids
and undergo a large decrease in bulk volume uponsaturation or load application and are known as
collapsing soils. However, it is difficult to identify
collapse susceptible soils with this definition due tothe existence of many different types of clay
minerals and many other factors that contribute to
the collapse phenomenon. Therefore, geostatisticalmethods in analyzing collapsing soil parameter
would provide an optimum solution.

INTRODUCTION
Analyzing and providing an appropriate
interpretation of data obtained from thephenomenon whose distribution is spatial in natureare challenging tasks. Due to availability of fastcomputing system such studies are becoming
common in the fields of health, environment,
geology, engineering, and many others. Besides
visual perception of the spatial distribution of thephenomenon, the analysis is useful to translate theexisting patterns in to objective and measureable
quantities by estimating parameters at an unknown
location. Since the emphasis of spatial analysis is tomeasure properties and relationship, taking in toaccount the spatial localization of the phenomenon
under study, such analysis is being applied in
geotechnical field [7], and significant amount ofworks have been published in the literature [6] [3][4] . Geostatistical technique of simple kriging canbe used with the geostatistical model to develop thecontour of estimated parameters with the contour of
estimation variance for parameters of a soil deposit
having vertical as well as horizontal anisotropy.

In this study geostatistical techniques of simple
kriging were applied to selected collapsing soil
parameters for soil in Tucson, Arizona. Previous
works on this topic was limited only to studies
involving either specific areas or specific soil
parameters. The purpose of this study was to gather
as much information as possible from reliable
sources and to use this data with geostatisticaltechnique to estimate parameters at un-sampled
locations with the determination of estimation
variance.

In general, parameters of a soil deposit vary due tothe inherent properties from its formation in the
MATHEMATICAL DETAILS:

estimator Zˆ ( x 0 ) of Z(x 0 ) based on a stochastic
Kriging is a geostatistical technique to interpolate
the value Z(x 0 ) of a random field Z(x) at an
unobserved location x 0 from observations z i =Z(x i ),
i= 1,….n of the random field at nearby locations
x 1 ,…,x n . Kriging computes the best linear un biased

model of the spatial dependence quantified either
by the variogram γ(x,y) or by the expectation
μ(x)=E[Z(x)] and the covariance function c(x,y) of
the random field.

41

GEOMATE- Osaka, Nov. 16-18, 2015

The kriging estimator is given by a linear

condition. Therefore, the method of calculating
weights depend upon the type of kriging.Common
types of kring are: simple kriging, ordinary kriging,
universal kriging,indicator kriging, Multipleindicator kriging,disjunctive kriging, andLognormal
kriging.

nZˆ ( x0 ) = ∑ wi x0 Z ( xi ) of the

combinationi =1

observed value z i =Z(x i ) with weights w i (x 0 ),i=1,….,n chosen such that the variance, known as
kriging variance or kriging error is as given by
Eqn.(1) is minimized subject to the unbiasedness
condition given in Eqn (2):σ ( x0 ) :2k(= Var Zˆ ( x0 − Z ( x0
n

n

i =1

i =1

= ∑∑

Simple kriging
Simple kriging is mathematically the simplest, but
the least general. It assumes the expectation of therandom field to be known, and relies on acovariance function. However in most applications
neither the expectation nor the covariance is known
beforehand.

)n∑ w ( x ) w ( x ) + Var ( Z ( x ))
i

j =1

0

j

0

0

n

− 2∑ wi ( x0 )c ( xi , x0 )............................(1)i =1Assumptionn

E[ Zˆ ( x ) − Z ( x ) = ∑ wi ( x0 ) µ ( xi ) − µ ( x0 ) = 0...........(2)The practical assumptions for the application of
simple kriging are i) wide sense stationary of the
field, ii) The expectation is zero everywhere:µ(x)=0,iii)
Knowncovariancefunctionc(x,y)=Cov[Z(x),Z(y)].

i =1

The kriging variance must not be confused with thevariance as given by Eqn. (3) f the kriging predictorZˆ ( x0 ) itself.
Simple kriging equation(

Var Zˆ ( x 0)

The kriging weights of simple kriging have nounbiasedness condition and are given by simple
kriging equation system:

)

 n
 n n
= Var  ∑ wi Z ( xi )  = ∑∑ wi w j c( xi , x j )...(3) i =1
 i =1 j =1

 w1   c( x1 , x1 )  c( x1 , x0 ) 
  




  = w   c( x , x )  c( x , x nn  n  n 1

Types of kriging

Depending on the schochastic properties of therandom field different types of kriging apply. The
type of kriging determines the linear constraint on
the weights wi implied by the unbiasedness(

Var Zˆ ( x0 ) − Z ( x0 )−1

 c( x1 , x0 ) .......( 4) c( x , x )  n 0 )−1′c( x , x ) c( x , x )  c( x1 , xn )   c( x1 , x0 ) = c( x0 , x0 ) −  1 0   1 1
 
......(6)










 

Var ( Z ( x0 ) ) c( x , x )   c( x , x )  c( x , x )   c( x , x ) 
n
n  
n
1  
n
n 1


0

Simple kriging interpolation:var ( Z ′( x ) )The interpolation by simple kriging is given byZˆ ( x 0 ) =′−1
 z 1   c ( x1 , x1 )  c ( x1 , x n   c ( x1 , x 0 
  
 

`.......(5)    
 z   c( x , x )  c( x , x )   c( x , x ) 
1
n
n
n  
n
n 
 n 

Which leads to the generalized least squares versionof the Gauss- Marcov theorem [4]:Var (Z ( x0 ) ) =Var Zˆ ( x0 ) + Var Zˆ ( x0 ) − Z ( x0 ) .........(7)(The kriging error is given by:
42

)

(

)

GEOMATE- Osaka, Nov. 16-18, 2015

The additional parameter µ is a Lagrange multiplierused in the minimization of the kriging error σ2 k (x)to honor the unbiasedness condition.

Ordinary kriging equation

The kriging weights of ordinary kriging fulfill theunbiasedness condition
The interpolation by ordinary kriging is given byn∑ λ = 1..................................(8)i =1′
 λ1   Z ( x1 )  Zˆ ( x*) =      .........................(10)  λn   Z ( x n ) iand are given by the ordinary kriging equation
system: λ1   γ ( x1 , x1 )     λ  = λ(x , x )n
1
 n  
1
µ 

−1

 λ ( x1 , x n ) 1   λ ( x1 , x * 
 



 


.....(9) λ ( x n , x n ) 1   λ ( x n , x*) 

 

1
0  
1
The kriging error is given by′ λ1   λ ( x1 , x*) 
 







var Zˆ ( x*) − Z ( x*) =   .......(11)λn λ ( xn , x*)   

1
µ


(

)

The parameter C p is known as collapse parameterand is obtained from consolidation test as shown in
Fig1. [9]Collapse Criteria and Related Parameters:

In arid regions soil deposits become partiallysaturated with large voids due to high evaporation
rate. Application of loads on such soils causes small
deformation at low degree of saturation. However
as soon as the soil becomes saturated, large
deformations take place due to the collapse of theintergranular structure and the phenomenon isreferred to as collapse. In general, if the dry densityof the soil is sufficiently low to give a void spacelarger than that required to hold the liquid limit
water content, then collapse upon saturation is
likely. Otherwise collapse generally occurs only
when the soil is loaded.

C p (%) =e1 − e2∆e∆H× 100 =
× 100 =
× 100....(12)H0
1 + e01 + e0
Where e 1, e 2 are initial and final void ratio uponwetting, ∆e and ∆H are changes in void ratio andsample height after saturation under a pressure of
200 kPa, and H 0 is the initial height of sample.

Collapsing soils has been recognized in Africa, partof Asia, Europe as well as in the United States. In
the United States the severity of the problem has
been observed in the Midwestern and Western
United States, where soil deposits are generally
either aeolian or alluvial.
Many criteria for predicting the collapsing potentialof a soil are available in the literature ( Ref fromASCE). Some of the criteria are derived
theoretically from consolidation test results andsome are empirical. The methods for evaluating
collapse susceptibility vary from simple to very
complex. Considerable effort has been given to
establish criteria for predicting the collapse
potential and the critical values for severity of a soil.
Some of the more commonly used criteria aredescribed below.

Fig. 1. Typical collapse potential in one
dimensional consolidation test

43

GEOMATE- Osaka, Nov. 16-18, 2015

two categories: established criteria, such as C p , R,and collapse-related soil parameters, such as,
γ d ,w 0 ,e 0 ,n 0 ,s 0 and PL.. Analysis on selectedparameters are presented in this paper.

[3].
R, known as Gibb’s parameter is obtained from thefollowing relation:

Modelling of variogram:
Modelling of variogram is the first and mostimportant step in applying the technique of
kriging ,which is the method used here forobtaining unbiased estimate of parameters in un
sampled location. A considerable amount ofcomputation is necessary to obtain an adequate
estimate of the variogram because of the empiricaland subjective nature of the estimation process [2].
Parameters of interest with critical values are listed
in Table 1. The various data sets containing
number of data points of the parameters are listed inTable 2.
Table 2. Data sets used in the
Analysis
Data set Range of
NumberNumber depth, mof Data1
0.0-0.3
1252
0.3-0.6286
3
0.6-0.9254
4
0.9-1.2
100
5
1.2-1.8
1046
1.8-12.2
123
7
0.0-12.2
219

Table 1.Critical Values for and HighCollapse (HC) Medium Collapse (MC) andNon-Collapsing(NC),
Soil Parameters.
Para
mete
rs
Cp
(%)
R

High
Collapsing (HC)>5e0≥ 0.82

γd,
pcf
(kN/
m3)

≤
90(14.3)

R=

≥ 1.4

γw 1
−
γ d Gswl

MediumCollapsing (MC)
2 < Cp≤5
1.0 ≤ R
<1.4
0.67 ≤ e 0<0.82
90(14.3)< γd≤99( 15.
6)

NonCollapsing(N
C)
≤2
< 1.0
< 0.67>99(15.6)

Representative variograms were obtained for eachof the parameters in each of the seven data sets, but
only few are presented here. Since modeling of avariogram is, in part, an art requiring somesubjective judgment, multiple trials are usuallynecessary in order to obtain a satisfactory
variogram.
The important parameters for avariograms are the range of influence, a, and the sill
C, .
Nested Model:........................(13)

Where γ d is the dry unit weight, w l is the liquid
limit moisture content, γ w is the unit weight ofwater, and G s is the specific gravity of soil solids.

Denisov (1964) criteria of collapse susceptibility isexpressed as the ratio e/e LL .If the ratio is greaterthan 1 then the soil is collapse susceptible.

𝜸𝜸(𝒉𝒉) = 𝑪𝑪 �

Where e LL is the void ratio at liquid limit and e isthe void ratio at natural moisture content.

= C + C0

Beside the established criteria for soil collapse,
there are other parameters that contribute tocollapse phenomenon. These related parameters
are : initial dry unit weight (γ d ), initial moisture
content (w 0 ), initial void ratio (e 0 ), initial porosity(n 0 ), initial degree of saturation (s 0 ) and plastic
limit (PL). Specific cut-off values for selectedparameters are given below in Table 1

𝟑𝟑𝒉𝒉 𝟏𝟏𝒉𝒉𝟑𝟑−
� + 𝑪𝑪𝟎𝟎 𝒇𝒇𝒇𝒇𝒇𝒇 𝒉𝒉
𝟐𝟐𝒂𝒂 𝟐𝟐𝒂𝒂𝟑𝟑< 𝒂𝒂(𝟏𝟏𝟏𝟏)
for h ≥ aandγ(0) = C 0

for h = 0

In geostatistical modeling, the most commonly usedmodel is the nested models as shown in Figure 2.
This model bears the same significance as theNormal distribution bears to statistics.

For this study field and laboratory test data werecollected from local consulting engineers’ offices
and from the reports of previous researchers[3].
The raw data were reduced to obtain parameters in
44

GEOMATE- Osaka, Nov. 16-18, 2015

Fig.5. Semi-variogram and fitted pure nugget
model for e 0 of Data Set 5
Fig.3. The Nested Model (Spherical with Nugget)

Fitting a Model
Constructing a variogram is to find theoreticalmodel that best fits the experimental variogram.
The choice is often limited to linear or sphericalmodels, with a spherical model being the mostcommon as the parameters are estimatedsubjectively. However, it is important that thechosen theoretical model y(h) fits well to theexperimental semi-variogram within the model’slimits of reliability [10] The choice of a theoreticalmodel is generally made by examining theexperimental variogram and taking in to accountthe fact that variograms are subject to significant
fluctuations at large distances. Since most of theexperimental variograms could be approximated bya spherical model, such a model was fitted to all thecomputed variograms in this study.

Variograms were obtained for the analysis for all
the parameters , however
few of them arepresented here. Figures 3 and 4 show thevariograms for Cp and γ d of data set 5, respectively.
In several cases a pure “nugget effect” model was
obtained, indicating a complete lack of geologicalstructure, Figure 5, shows such a model for theparameter e 0 of data set 6.

The key parameters of the selected spherical model,
after cross-validation with different trial models for
Data set 1 is presented in Table 3. The nugget value
C 0 which is the estimate of y at h=0, provide anindication of short distance variation. The greater
the value of C 0 , the greater is the variance of thedata set. Some of the model, e 0 , for example showsa “pure nugget” effect indicating lack of spatial
correlation.

Fig.3. Semi-variogram and fitted equation
for C p of Data Set 5

Table 3. Parameters of Spherical Models
fitted to Data Set 2
Data
Parameter Nugget Range, Sill C
Set
C0
a
Cp
18.5
γd
12.5
35.0
100.0
n0
32.4
35.0
45.0
s0
2
102.0
30.0
148.0
w0
0.002
25.0
0.0025
e0
0.042
30
0.053
The range , a, of the variogram can be interpreted
as the diameter of the zone of influence which
represents the average maximum distance overFig.4. Semi-variogram and fitted equation for γ dof Data Set 5
45

GEOMATE- Osaka, Nov. 16-18, 2015

which a soil property is spatially related. In ourstudy this distance was found to be 5.5 to 8 miles
which is large relative to the distance over whichsoils are usually sampled for laboratory tests for aparticular project, This suggest that , geostatisticalconcept can be applied successfully to the study ofgeotechnical problems.

required over a volume or an area. Universal
kriging is an optimal method of interpolation thatapplies in all cases where drift must be taken in toaccount because of lack of data to make stationaryor quasi-stationary estimates.`
In this study, variograms were estimated for eachcollapse criterion and collapse related soil
parameters (Table 1) using a discrete number ofvalues obtained from test data at incremental
distances corresponding to sampling locations
throughout the area. These variograms (Table 3) arethen used in conjunction with ordinary kriging toestimate values of the parameters at un-sampled
locations. Simple kriging [10] was then utilized to
produce contour plots of estimated probability andassociated kriging variance for each parameter ineach data set.

There are three methods of krigging: punctual,
block, and universal. Puntual kriging, which
provides estimates for values of a random variable
at points where there is no drift, has two forms:
simple kriging if the mean value of the variable is
known, and ordinary kriging, if the mean value is
not known. Drift is defined as a non-stationary
expectation of a random function. Block kriging isused when an estimation of the spatial average isResults and discussion
Results of kriging analyses showing contour of
estimated valur of soils parameter γ d (pcf) within 5
ft. (0.30 m) of surface having high collapse
susceptibility based on collapse criteria withestimation variance are shown are in Fig 6 (a) and6(b). If the critical values for any specific areas are
known, areas containing high. Medium or low
collapse susceptible soils can be obtained from thesecontour plots with known confidence. Areas where
no data points are available are easily detected bydiscontinuities in the contour lines. Similar plots
were developed for all of the other collapse criteria
and collapse-related soil parameters for each of theseven data sets, however, they are not presented here
due to space limitations.

(a)

(b)

Fig.6. Contour plots of a) Estimated value of γ din pcf, and b) Estimation variance
CONCLUSION

1.

2.

3.

Collapsing soil parameters can beconsidered as regionalized variable and theconcept of geostatistics may be applicable
where a large amount of data are available
from reliable sources.
Linear estimation method of kiriging was
found to be a valuable tool forcharacterizing and modeling the spatial
variability of geotechnical parameters.
The parameters under investigation can bebest be fitted by a spherical model
variogram4.

Simple Kriging method provides a meansfor estimating soil parameters at an unsampled location with known variance of
estimation. Therefore, the method can beapplied to estimate any collapsing soil
parameter in an area with a known degreeof confidence.
This information isextremely valuable to planners andGovernment officials.

[2] Ali, M.M., E.A. Nowatzki, D.E. Myers, (1989),“Geostatistical Methods to Predict CollapsingSoil”, Proc. XII ICSMFE, Rio de Janeiro, Brazil,Vol. VII, No.2, August, pp. 567-570.
[3] Andrew Richmond (2003) Financially Efficient
Ore Selection Incorporating Grade Uncertainty,
Mathematical Geology, Vol 35, N.2p 195-215,

REFERENCES[1] Ali, M.M., E.A. Nowatzki, D.E. Myers, (1989),“Geostatistical Techniques to Estimate Collapserelated Soil Parameters”, Proc. 25th symposium
on Engineering Geology and GeotechnicalEngineering, Reno, Nevada, March, pp. 289-296.

46

GEOMATE- Osaka, Nov. 16-18, 2015

[4]

ChilesJP,
P
Delfiner(1999)
Geostatistics,Modeling Spatial Uncertainty,
Willey Seriesin Probability And Statistics.
[5] Clark I, (1979) Practical geostatistics, Applied
Science Publishing, Ltd, Essex, London.
[6] David JC, (2002) Statistical and Data analysis in
Gelogy, 3rd Ed, New Rork, John Wiley & Sons.
[7] Hammah, RE, and Curran, JH (2006)
Geostatistics in Geotechnical Engineering: Fador Empowering, GeoCongress, GeotechnicalEngineering in the Information Technology Age,
Proceedings, Atlanta, USA.
[8] Harman, H.H (1969) Modern Factor Analysis,
2nd Edition, Univ. of Chicago press. Chicago,
Illinois.
[9] Jennings, JE and Knight K. (1957) The
additional Settlement of foundations due tocollapse of sandy subsoils on wetting.
Proceedings,4th ICSMF.
[10] Journel, AJ and Huijbregts, C,J. (1978). Mining
Geostatistics, Academic Press, New York,
Matheron, G.(1963) Principles of Geostatistics.
Econ. Geology, 58, 1246-1266.

47

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

A 3D INVESTIGATION INTO UNDRAINED CLAY SINKHOLEFORMATION
Brian Lamb1 and Jim Shiau1
School of Civil Engineering and Surveying, University of Southern Queensland, Australia

1

ABSTRACTThe main focus of this paper is to use 3D analysis to investigate sinkhole formation behavior. This iscompleted by analysing different square sinkhole cavity opening sizes and overburden properties with trapdoor
analysis. The research is conducted by closely examining sinkhole structures through finite difference software
FLAC 3D. Once the cavity trapdoor roof is released, the effect of the trapdoor on the overbuden of the sinkhole
can be analysed. The sinkholes models are computed via the strength reduction method, which can produce thefactor of safety of the formation along with the final overburden displacements and sinkhole shape. The
completion of 3D numerical models allows for numerous sinkhole simulations, with varying material propertiesand different overburden depth to cavity width ratios. It is expected that this research would increase theunderstanding of the phenomena of sinkholes by making use of three dimensional numerical simulations.
Keywords: 3D Investigation, Sinkhole, Geotechnical, Trapdoor, FLAC
the process of non-cohesive soil material falling into
underground cracks or soil material being washedinto cracks. These cracks are caused by weatheringof the underground rock.

INTRODUCTION
Sinkholes are a global phenomenon which canhave devastating consequences. A sinkhole is a holeor depression viewable on the ground surface.
Sinkholes often make headlines for causing
structural damage, damage to property or simply
because of their size and their immediate
unpredictable impact. Current well known sinkholecases include the three examples which formed in
Siberia, being up to 80m wide and 70m deep.
Locations such as Florida are known for theirlikeliness of spontaneous sinkhole collapse due tothe limestone geology present [1]. The nature of how
sinkholes occur spontaneously makes them of
constant public interest.
They have many different causes but all are
based on the overburden soil pressures increasing toa breaking point, were then the overburden material
collapse. The material above an underground cavity
is known as the overburden. This collapse into theunderground cavity leaves, in many cases, a conicalshape and circular ground opening. The shape of theextent of the sinkhole appears to be influenced
mainly by the cavity roofs aspect ratio. If the overall
width and length of the cavity roof has an aspectratio of around 1:1, it is hypothesised no matter theactual shape of the cavity roof then the surface
extent of the sinkhole will be principally circular.
Causes of sinkhole failure include those
produced from manmade activities such as collapseof underground mines, tunnels and pipelines as wellas manmade ground disturbances such as drillingand compaction. Natural causes of sinkholes include
the suffusion of carbonate rocks, most commonly
limestone [2]. Other soil materials that lead tosuffusion are chalk, gypsum and basalt. Suffusion is

3D FLAC MODEL

Fig. 1 Idealised trapdoor cavity sinkhole in 3D space
While physical soil conditions and overburden to
trapdoor cavity interactions have high complexity,
the model uses simplification and assumptions to aid
with predicting theoretical soil responses within the
trapdoor cavity opening and overburden soil. Figure
1 shows the idealised trapdoor cavity in 3D space.
The height (h) representing the height of theoverburden material above the cavity and width (w)representing the length or diameter of the cavity roof.
(l) represents the length or depth of the trapdoor
cavity in the x-direction as shown in Figure 1. The
sinkhole model assumes the use of Mohr-Coulomb
failure criteria with set soil characteristics of massdensity, elastic modulus, Poisson’s ratio, cohesion
and friction angle. The overburden soil properties
are assumed to be homogenous throughout theheight of the overburden. Artificial boundary48

GEOMATE- Osaka, Nov. 16-18, 2014

conditions are required to allow for the explicit finite
difference to function. This goes against the endlesscontinuum of soil strata in which sinkholes form inreal life. Artificial boundaries are necessary to set
the quantity of overburden to be tested, which has alarge control on computation time. However theseboundaries can be set to minimise their effect on the
final results. Undrained clay was analysed. The
major characteristic of the undrained clay was thenon-zero cohesion and zero friction angle.
Plotted were the maximum shear strain rate plots,
displacement and velocity contour plots, maximum
unbalanced force ratio history and z-displacement
history. Each material has six different cases,
ranging from shallow to deep each case had its ownheight to width ratio. The trapdoor width to length
ratio is 2w:1L for each case. Therefore the resultspresent half symmetry of a square trapdoor. The
results are presented in a dimensionless ratio, FoS =f(h/w, c u /γh)Fig. 3 3D Undrained clay case 2 (h/w=10), Picture 1
Max Shear Strain Rate, Picture 2 Displacement
Contours.
Figure 3 shows the 3D plots for shear strain rate,and displacement contours for undrained clay case 2.
The shear strain rate plot indicates a curved failure
plain, with a major amount of the shear strain occur
at the cavity extents.
The maximum displacement in the displacement
plot occurs over the cavity opening. Minimum
displacement occurs again on the sides of the funnel
shape where the displacement becomes zero. The
funnel shape created in 3D matches sinkholes which
form in reality. The 3D model left a circular extent
on the surface despite the cavity roof representing asquare. As discussed it is thought that the main
contributing factor to the shape of the surface extent
is the aspect ratio. Neglecting the form of the shape,
if the cavity shape falls into an aspect ratio of around
1:1, then the surface deformation extent should becircular. The aspect ratio can be defined as width (w)x length (l) of a square surrounding the extents ofthe cavity roof. The aspect ratio of the cavities hereare 1:1 (w:l) with the cross-section taken
representing a whole sinkhole. The maximum
displacement appears to form a dome shape above
the cavity opening. The displacement contours thenopen up to create the funnel shape. The contours
have a smaller magnitude than case 1, due to thesmaller cavity size here.

Fig. 2 Generated 3D mesh
RESULTS AND DISCUSSION
The trapdoor cavity opening can cause structural
failure and the soil body will reach a new deformed
equilibrium state. The change in location of aparticle from its original location is the shear strain.
Shear strain rate is an indication of the rate of
deformation and thus is also a representation of theslip failure plane of sinkholes.
The displacement contours plots indicate areas ofsimilar displacements. The displacement for eachcase is at its maximum at the geometrical center.
Figure 3 shows example plots.

49

GEOMATE- Osaka, Nov. 16-18, 2014

Effect of Depth Ratio h/w
Figure 4 is a plot of the undrained clay factor ofsafety for both 3D and 2D for comparison. Both 2D
and 3D have near linear trends. With increasing(h/w) ratio the factor of safety increased. Also notethat the same points for both 2D and 3D results fallon the same side of the trend line. 2D underestimates
the factor of safety by roughly 30% when comparedto the 3D results. This is because 2D cross-sections
have no constraint in length (l) going into the model.
On the other hand 3D models have their model
dimension fully specified.
As the width decreases the factor of safety
increases. 3D analysis appears to have a more
realistic factor of safety calculated than the 2D
analysis. 2D analysis underestimates the factor ofsafety, this is due to the fact with 2D analysis the
cross section taken has no given depth or length (l)into the page. With 3D analysis the cavity openinghas given dimension (wl) and thus the 3D model ismore constrained, which produces a higher factor ofsafety. The factor of safety for 3D analysis isroughly 30% greater than the 2D results, thesematches previous 2D to 3D analysis results.

As expected smaller the h/w ratio larger the
displacement contours extent and value. The
displacement contours acted very similarly to those
calculated in 2D. This circular extend of thedisplacement contours also matches that of two
physical model tests [3], [4]. As displacement
contours represent areas of similar displacement,
they also can represent the possible locations of slip
planes.
For case 2 of the undrained clay six deformation
factors (DF) plots were generated to illustrate thebehaviour of the collapse of this modelled sinkhole.
The deformation factor is a built in changeable
attribute of a plot which allows the user to see how
the soil body will deform and has the ability to
exaggerate the deformation as shown in the supplied
plots to see how the soil body behaves.
Table 1 Factor of Safety for Undrained Clay (h = 10,
w/l = 2, square) Strength Ratio c u /γh = 1.58
case
1
2
3
4
5
6

h/w
1.667
2
2.5
3.333
5
10

FOS (3D)
0.82
1
1.06
1.32
1.41
1.86

FOS (2D)
0.51
0.592
0.682
0.795
0.947
1.189

Table 1 shows the undrained clay factor of safety
results for both the 2D and 3D calculations. Both thefactor of safety for 2D and 3D analysis follows avery similar trend as expected, this is also shown in
Figure 4.
2
1.8
1.6
1.4

FOS

1.2
1

0.8

3D FOS

0.6

2D FOS0.4

線形 (3D FOS)

0.2

線形 (2D FOS)

0

1.00

3.00

5.00

7.00

(h/w) Ratio

Fig. 5 Case 1 (h/w) = 1.667, displacement contours
of top, front and 3D view

9.00

Fig. 4 FOS 3D & 2D plot w/l = 2, c u /γh = 1.58

50

GEOMATE- Osaka, Nov. 16-18, 2014

Figure 5, 6 and 7 show the displacement contours
for cases 1, 3 and 5 resectively. As this h/w ratio
increases the magnitude and extent of thedisplacement contours become smaller. This is as
expected as smaller cavity roof should produce
smalleer displacment magnitudes. It is evident fromthe top view of the displacement contours that themagnitude of the contours increased with theincreasing cavity opening area.
Investigation of Effect of Shear Strength Ratio
As an additional study, four cases were examined.
All parameters for the undrained clay were kept
constant apart for the cohesion. Case 1 has cohesionof 10kPa, case 2 c u = 20kPa, case 3 c u = 30kPa andcase 4 c u = 40kPa. Effectively here is a study of theundrained strength ratio. The undrained strength
ratio is defined as γD/c u , a dimensional less ratio
where γ represents the self-weight of the soil, D
represents the cavity opening w and c u represents theuniform undrained shear strength. As the strength
ratio is dimensionless, it can be redefined as c u /γD,this way when conducting this parametric study andincrease the cohesion of the soil from 10 to 40, theratio will increase with the factor of safety. However
if the ratio was left as defined as γD/c u then thestrength ratio would decrease as the cohesion isincreased. Undrained strength ratio = γD/c u
redefined as = c u /γDFig. 6 Case 3 (h/w) = 2.5, displacement contours of
top, front and 3D view

Table 2 FOS results Effect of Shear Strength Ratio,
h/wl = 10/(5x2.5)
case
1

Strengthratio (c u /γD)
1.05

3D FOS
0.33

2

2.11

0.66

3

3.16

1.00

4

4.21

1.33

Table 2 shows the factor of safety results for thisinvestigation. Note again the trapdoor width tolength ratio is always w=2l. As expected the factorof safety increases linearly as the cohesion isincreased. This is due to the increased undrained
shear strength increases the stability of theoverburden. Therefore increase of factor of safety
should generate displacement contours with lesser
and lesser displacement magnitude.

Fig. 7 Case 5 (h/w) = 5, displacement contours of
top, front and 3D view

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GEOMATE- Osaka, Nov. 16-18, 2014

Case - 3; c u /γD = 3.16; FOS = 1.00

Factor of Saftey (FOS)

1.4
1.2
1
0.8
0.6
0.4
0.2
0

0

2

4

6

Case - 4; c u /γD = 4.21; FOS = 1.33

Strength Ratio (cu/γD)Figure 8 Effect of cohesion on the FOS of theundrained clay plot
Figure 8 depicts the factor of safety plot vs theundrained strength ratio in terms of c u /γD. By linear
interpolation, Figure 8 should give the factor ofsafety for any sinkhole by this analysis with a cavity
ratio of w/l = 2 with an undrained strength ratio
defined as c u /γD.
Case – 1; c u /γD = 1.05; FOS = 0.33

Figure 10 Effect of Shear Strength
displacement contour plots h/w=2, w/l=2

Figure 9 & 10 shows the displacement contours
for the four cases studied in this section. As stated
all cases have the same cavity opening, instead thisstudy is for the effect of the cohesion. This also canbe taken as the effect of changing the undrained
strength ratio. All the displacement contour plots
exhibit funnel shape contours. The maximum
displacement is occurring over the cavity opening.
For the weakest case, case 1 with a factor of safetyof 0.33 the displacement contours shows a uniqueformation. The contours show a localised cavingaround the cavity opening. They also show achimney type failure with the same displacement
magnitude as the localised caving in around thecavity opening. The overall displacement contour
extent of case 1 is very wide.
Every case has a circular surface extent. Thisshows that the undrained strength ratio does notaffect the shape of the surface extent of the sinkhole
in a homogenous soil body.

Case - 2; c u /γD = 2.11; FOS = 0.66

Figure 9 Effect of Shear Strength
displacement contour plots h/w=2, w/l=2

Ratio,

Ratio,

52

GEOMATE- Osaka, Nov. 16-18, 2014

analysis is roughly 30% greater than the 2D results,
these matches previous 2D to 3D analysis results.

CONCLUSION
Trapdoor openings can cause structural failure,causing the soil body to reach a new deformed
equilibrium state. This was successfully shown withthe shear strain rates plots, measuring deformation.
As expected, smaller the h/w ratio, larger the
displacement contours extent and value. The
displacement contours acted very similarly to those
calculated in 2D. These results can be found in [3],[4]. Displacement contours have circular extents,
which match existing sinkholes. Future work is
required to determine if the circular surface
displacement extents is irrespective of the cavity
trapdoor opening.
As the trapdoor cavity width decreases the factorof safety increases. 3D analysis appears to have amore realistic factor of safety calculated than the 2D
analysis. 2D analysis underestimates the factor ofsafety, this is due to the fact 2D analysis neglects
depth or length of a 3D problem. The 3D models
constrained by all three dimensions, producing ahigher factor of safety. The factor of safety for 3D

REFERENCES[1] Zisman, ED 2005, 'Sinkhole Case Studies-Is it
or Isn’t it a Sinkhole?', Sinkholes and theEngineering and Environmental Impacts of
Karst.
[2] Augarde, CE Lyamin, AV Sloan, SW 2003,'Prediction of Undrained Sinkhole Collapse',
Journal of Geotechnical and Geoenvironmental
Engineering, pp. p.197-205, viewed 20
November 2013.
[3] Lamb, B 2014, ‘Physical and NumericalInvestigation into Sinkhole Collapse’, BENG
(honours) Thesis, University of Southern
Queensland.
[4] Lamb, B and Shiau, J 2014, ‘Physical andNumerical Investigation into Sinkhole Collapse’,FourthInternational
Conferenceon
Geotechnique, Construction Materials andEnvironment, Brisbane, Australia.

53Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

EFFECT OF LIQUID ALKALINE ACTIVATOR CONTENT ON UNITWEIGHT AND STRENGTH OF A WATER TREATMENT SLUDGE–FLY
ASH LIGHTWEIGHT CELLULAR GEOPOLYMER

Cherdsak Suksiripattanapong1, Suksun Horpibulsuk2 and Arul Arulrajah3
School of Civil Engineering, Faculty of Engineering and Architecture, Rajamangala University ofTechnology Suvarnabhumi Suphan Buri Cumpus, Thailand
2
School of Civil Engineering, Institute of Engineering, Suranaree University of Technology, Thailand
3
Faculty of Science, Engineering and Technology, Swinburne University of Technology, Australia
1

ABSTRACT
A lightweight water treatment sludge-fly ash geopolymer is investigated in this research with the intention todevelop an alternative green construction and building materials, without the usage of Portland cement as a
cementing agent. Two waste by-products namely water treatment sludge from the Bang Khen water treatment
plants of the Metropolitan Water Work Authority of Thailand (MWA) and Fly Ash (FA) from the Mae Moh
power plants of the Electricity Generating Authority of Thailand (EGAT) were used in this research. The liquidalkaline activator, L used was a mixture of sodium silicate solution (Na2SiO3) and sodium hydroxide solution(NaOH). This article investigates the effect of liquid alkaline activator on unit weight and strength of lightweightwater treatment sludge-fly ash geopolymer. The various influential factors studied are mixing ingredient (aircontent and Na2SiO3/NaOH) and heat condition. Test results show that the influence of liquid alkaline activatoron unit weight is classified into two zone: (zone 1 is the amount of L less than 1.0LL and zone 2 is the amount of
L greater than 1.0LL). For zone 1, the unit weight of lightweight water treatment sludge-FA geopolymer isconstant although the amount of L increases. For zone 2, the unit weight of lightweight water treatment sludgeFA geopolymer decreases significantly as amount of L increases. The maximum compressive strengths oflightweight sludge-FA geopolymers are at L of 1.0LL for all the Na2SiO3/NaOH ratios, heat duration and aircontent.
Keywords: Lightweight, Geopolymer, Sludge, Fly Ashof sludge, which in the past have been disposed tolandfills.
Alkali-activated alumino-silicate cement, known
as ‘geopolymers’ has become increasingly popular
in recent years as an environmental friendly
alternative to ordinary Portland cement [1].
Geopolymers are furthermore touted for their highperformance (high strength and durability), low CO2
emission and low energy consumption. Silica richmaterials such as clay or kaolin [2], fly ash, andbottom ash [3] can be used as a precursor to reactwith the liquid alkaline activator.
Fly Ash (FA) derived from coal-fired electricity
generation provides the greatest opportunity forcommercial utilization of this technology due to theplentiful worldwide raw material supply [4,5].
Palomo et al [6] found that the different FA
activated with 8-12 M NaOH cured at 85oC for 24hours produced a material with compressive strengthof 35-40 MPa and about 90 MPa if sodium silicate(Na2SiO3) is added to the NaOH solution. Xie &Yunping [7] reported that the hardening process of
FA activated with Na2SiO3 is mainly attributed to
the gel-like reaction products that bind FA particles

INTRODUCTION
Water production requires the extraction of waterfrom natural sources. The water treatment process
results in a muddy sludge by-product. The clarifiersystem employed in water treatment plants results inthe sludge flocculating and falling in the bottom of
the treatment tank. This method is similar to thatused by other water treatment plants worldwide The
liquid sludge is subsequently drained to sludge
lagoons for disposal. The increasing demand oftreated water produced by the Metropolitan
Waterworks Authority of Thailand (MWA) has
resulted in increasing quantities of sludge byproducts generated annually. For MWA, the water
treatment sludge is generated with the maximum
capacity of 300 tons per day in the dry season andabout 700 tons per day in the wet season. With thecontinuous increase in water demand due to growing
population in many developed and developing
countries including Thailand, the quantity of water
treatment sludge is subsequently increasing at everincreasing rate and hence the urgent need to find asustainable reuse option for the growing stockpiles
54

GEOMATE- Osaka, Nov. 16-18, 2015

together. FA is extensively used as a precursor for
geopolymers in Thailand [8, 9]
Sukmak et al. [10, 11] previously investigatedthe possibility of using FA as a raw material andsilty clay as aggregates to develop the clay-FA
geopolymer brick. The liquid alkaline activator (L)was a mixture of Na2SiO3 and NaOH. The suitable
ingredient for the clay-FA geopolymer is
Na2SiO3/NaOH ratio of 0.7 and L/FA ratio of 0.6,
which are lower than those of FA geopolymer. The
7-day strength of the clay-FA geopolymer is greaterthan 10 MPa, suitable as bearing masonry unit
according to the Thailand Industrial Standard. The
strength requirement is 2.5 MPa for non-bearing and
7.0 MPa bearing masonry units. It has been
illustrated that the durability against sulfate attack ofclay-FA geoplymer is better than that of claycement; i.e., there is no major change in the
microstructure and pH of clay-FA geopolymer when
exposed to sulfate solutions [12].
Recently, Suksiripattanapong et al. [13]investigated strength development in water treatment
sludge-FA geopolymer. The optimum ingredientproviding maximum unit weight and strength is
NaOH/Na2SiO3 ratio of 80:20 and L/FA ratio of 1.3,
irrespective of heat condition and curing time. The
optimum heat temperature and duration for the
optimum ingredient are 75oC and 72 hours,
respectively. The durability against wet-dry ofsludge-FA geoplymer is better than that of claycement [14]. The research develops a newlightweight Cellular (LC) FA-sludge geopolymer asa sustainable lightweight masonry unit. The air foam
is used to make this LC FA-sludge geoplymer.
This research investigates the effect of liquidalkaline activator, L on unit weight and strength oflightweight cellular sludge-FA geopolymer. The
scanning electron microscopy (SEM) analysis isundertaken to analyze the role of L on unit weightand strength. This research will enable WTS
traditionally destined for landfill to be used as anaggregate in the lightweight geopolymer masonry
product, which is significant from engineering,
economical and environmental perspectives.

2.60. The liquid limit is 64% and is classified as a
non-plastic material.
FA was obtained from the Mae Moh power
plant of the Electricity Generating Authority ofThailand (EGAT) in the northern region of Thailand.
Table 1 summarizes the chemical composition of FA
using X-ray ﬂuorescence (XRF). Total amount of
the major components (SiO2, Al2O3 and Fe2O3) are
67.31% while the CaO content is 30.24%; therefore,
it is classified as Class C. Figures 1 shows the grain
size distribution curve of FA, which was tested bylaser particle size analysis. It is shown that thesludge particles are larger than the FA ones. The
average grain size of FA is 13.25 micron. The
specific gravity of FA is 2.35. The morphology ofthe sludge and the FA is shown in Figure 2. The FA
particles are fine and spherical whereas the sludge
particles are irregular in shape. The liquid alkaline
activator (L) is a mixture of Na2SiO3, which consistsof 9% Na2O and 30% SiO2 by weight, and NaOH
with a concentration of 10 molars.
100
SludgeFly ashPercent finer (%)

80

60

40

20

0
0.1

Fig. 11

10100Particle Diameter ( µ m)

100

Grain size distribution of Sludge and FA.

Table 1 Chemical composition of sludge and ﬂy ash.
Chemical composition (%)
SiO2
Al2O3
Fe2O3
CaO
MgO
SO3
Na2O
K2O
LOI

MATERIALS AND METHODS
Materials
The sludge was collected from the Bang Khenwater treatment plant of the Metropolitan
Waterworks Authority of Thailand. The sludge
consists of 0.15% sand, 99.65% silt and only 0.20%
clay. The grain size distribution, mineral andchemical compositions of the sludge were obtained
from laser particle size analysis and X-ray
Fluorescence (XRF) analysis as shown in Figure 1
and Table 1, respectively. The specific gravity is

Sludge
61.84
24.80
9.52
0.60
N.D.
0.59
N.D.
1.90
0.75

Fly ash
47.51
13.14
6.66
30.24
N.D.
N.D.
0.41
1.63
0.42

Air foam agent, Sika Poro 40 from Sika
(Thailand) Company limited, were used in this study.
55

GEOMATE- Osaka, Nov. 16-18, 2015

state decreases with decreasing Na2SiO3/NaOH ratio(increase of NaOH) until the minimum L/FA ratio is1.92 at Na2SiO3/NaOH ratio of 80:20. Beyond thisminimum, L/FA ratio at liquid limit state increases
with decreasing Na2SiO3/NaOH ratio.
To prepare lightweight sludge-FA, the input L
contents in the lightweight sludge-FA vary from 0.8
to 1.5 times LL, in which LL for each
Na2SiO3/NaOH ratio can be determined from Figure
3. The air form was added into the sludge-FA-L
mixture and mixed for 5 minutes. The air content(Ac) values were 100, 300 and 500% by total
volume of dry soil (Vs). The Vs value wasdetermined from the dry weight of sludge and FA(Ws) and specific gravity values of sludge and FA.
The air form was then added into this sludge-FA
geopolymer mixture and mixed for additional 5
minutes. The uniform lightweight sludge-FA
geopolymer was transferred to containers of
50x50x50 mm. The samples were dismantled,wrapped within vinyl sheet and then heated at 65οC
for heat durations of 24, 72 and 120 hours. After
heating, the samples were subsequently cured atroom temperature (27-30οC). Unit weight andstrengths of lightweight sludge-FA geopolymer
samples were measured after 7 days of curing inaccordance with ASTM C138 and ASTM C69-09,
respectively.

Sika Poro 40 is a blend of anionic surfactants andfoam stabilizers. It is a liquid air entraining agent
used in various types of mortar, concrete andcementitious material. The air foam was prepared bymixing the foaming agent with water at a ratio of
1:50 by weight.

Sludge2.2

Fig. 2

Scanning electron microscopy
images of sludge and FA.

L/FA ratio at liquid limit state

FA
(SEM)

Sample Preparation
The LC sludge-FA geopolymer sample is acombination of sludge, FA, liquid alkaline activator(Na2SiO3 and NaOH) and air content (Ac). The
sludge/FA ratio was fixed at 70:30. The
Na2SiO3/NaOH ratios were 90:10, 80:20 an d 70:30.
The air-dried sludge and FA were mixed for 5
minutes in a mixer to ensure homogeneity of the
mixture. The mixer was stopped and the mixture wasactivated by the liquid alkaline activator and mixed
for additional 5 minutes. The mixture with various
Na2SiO3/NaOH ratios was taken to determine L
content at liquid limit state (LL). LL was determinedusing Casagrande’s method, which is the same fordetermination of liquid limit of soil and used as areference for mixing lightweight sludge-FA atvarious Na2SiO3/NaOH ratios to ensure that thesludge-FA-L mixture before mixing with air foam
had similar flowability [15]. Figure 3 shows therelationship between L/FA ratio at liquid limit state
and Na2SiO3/NaOH ratios. The test result shows that
L/FA ratio at liquid limit state varies with
Na2SiO3/NaOH ratio which is between 1.92 and 2.04
by fly ash content. The L/FA ratio at liquid limit

2.1

2

1.9

1.8

1.7
100 : 090 : 10 80 : 20 70 : 30 60 : 40 50 : 50

Na2SiO3 : NaOH

Fig. 3

Relationshipbetween
Na2SiO3/NaOH

L/FA

and

UNIT WEIGHT AND STRENGTH OF
LIGHTWEIGHTWATER
TREATMENTSLUDGE-FA GEOPOLYMER
Figure 4 shows the relationships between 7-day
unit weight versus liquid alkaline activator of LC
sludge-FA geopolymer for Na2SiO3/NaOH ratios of
90:10, 80:20 and 70:30. The samples were heated at
65oC for 24, 72 and 120 hours. The amount of liquidalkaline activator is 0.8LL, 0.9LL, 1.0LL, 1.1LL,
1.3LL and 1.5LL where LL is liquid alkaline
activator at liquid limit. The air content (Ac) values
56

GEOMATE- Osaka, Nov. 16-18, 2015

varied between 100 and 500% by volume of dry soil
(Vs). The test result shows that the unit weight of LC
sludge-FA geopolymers decreases as air content and
L increases. The L on unit weight is classified into
two zones: (zone 1 is L less than 1.0LL and zone 2 isL greater than 1.0LL). For zone 1, the unit weight of
LC sludge-FA geopolymer is constant although the
L content increases. For zone 2, the unit weight oflightweight water treatment sludge-FA geopolymer
decreases significantly as the L content increases.
The L content at liquid limit is designated astransitional liquid alkaline activator.

L increases. It is because the excessive liquidalkaline activator resulting in the bleeding in sample[16]. The maximum compressive strengths of LC
sludge-FA geopolymers are at L of 1.0LL for all theNa2SiO3/NaOH ratios, heat duration and air content.
Compressive strengths on wet side (1.1LL, 1.3LL
and 1.5LL) and dry side (0.8LL and 0.9LL) of
transitional L are similar. Even through the increaseof L greater than transitional L results in thedecreases in unit weight of LC sludge-FA
geopolymer, the compressive strength of this sample
decreases. In order to reduce cost (reduction of L),
the increase of air content is the most appropriate to
reduce unit weight and provides the highcompressive strength. For example, at the same
Na2SiO3/NaOH ratio of 90:10 and unit weight (13.53
kN/m3), the sample with L = 1.0LL and Ac = 300%
has 10.33 MPa strength while the sample with L =1.5LL and Ac = 100% has 3.84 MPa strength.

oSludge : FA = 70 : 30, Heat temperature = 65 C, Curing = 7 days
Heat duration
24 hours;
72 hours;
120 hours

20
Na2SiO3 / NaOH = 90 : 1015
A c = 100 %A c = 300 %10

Sludge : FA = 70 : 30, Heat temperature = 65 oC, Curing = 7 days
Heat duration
24 hours;
72 hours;
120 hours

5
20Transitionalliquid alkaline activator

20
Transitional liquid alkaline activator

Na2SiO3 / NaOH = 80 : 20

A c = 100 %

10

5
20

10
A c = 100 %A c = 300 %Transitionalliquid alkaline activator

5 A c = 300 %A c = 500 %A c = 500 %Na2SiO3 / NaOH = 70 : 30
15
A c = 100 %10

A c = 300 %A c = 500 %Transitionalliquid alkaline activator

5
0.6 LL

0.8 LL

1.0 LL

1.2 LL

1.4 LL

1.6 LL20
0
Na2SiO3 / NaOH = 80 : 20
15
10A c = 100 %5 A c = 300 %020

1.8 LLA c = 500 %Na2SiO3 / NaOH = 70 : 30

Liquid alkaline activator, L

Fig. 4

Na2SiO3 / NaOH = 90 : 10

15

15

7-day strength (MPa)Unit weight (kN/m3)

A c = 500 %15

Relationship between unit weight andLiquid alkaline activator of lightweightsludge-FA geopolymer at different air
content and Na2SiO3/NaOH.

10
A c = 100 %5 A c = 300 %A c = 500 %00.6 LL

Effect of L on 7-day strength of LC sludge-FA
geopolymer is shown in Figure 5. The amount of L
was 0.8LL, 0.9LL, 1.0LL, 1.1LL, 1.3LL and 1.5LL
at Na2SiO3/NaOH ratio of 90:10 80:20 and 70:30
and air content of 100, 300 and 500%. The samples
were heated at 65oC for 24, 72 and 120 hours,
respectively. The test result shows that the 7-day
strength of LC sludge-FA geopolymer depends on
the L content for all Na2SiO3/NaOH ratios, heat
duration and air content. The 7-day strength of LC
sludge-FA geopolymer increases with increasing L
until the maximum strength is attained at atransitional L value. Beyond this transitional L value,
the compressive strength decreases as the amount of

0.8 LL

1.0 LL

1.2 LL

1.4 LL

1.6 LL

1.8 LL

Liquid alkaline activator, L

Fig. 5

Relationship between compressive strengthand Liquid alkaline activator of lightweightsludge-FA geopolymer at different air
content and Na2SiO3/NaOH.

Figure 6 shows the scanning electron microscopy(SEM) images of 7-day cured geopolymer samples
with Na2SiO3/NaOH ratio of 80:20 and air content of
300% for different L (0.8LL to 1.5LL). The samples
were heated at 65oC for 72 hours. The SEM images
show that the low L content (0.8LL and 0.9LL) was
57

GEOMATE- Osaka, Nov. 16-18, 2015

not enough to manufacture homogenous sample
though air content is greater than 300%. In addition,
the low L content is not sufficient to dissolve silica
and alumina oxide from FA for producing
geopolymerization products. On the other hand (Lcontent greater than transitional L), the FA particles
are etched obviously (reaction alkali dissolution).
Furthermore, it is found that the holes in FA
particles are filled with smaller FA particles andwelded by the geopolymerization products (Sodium
aluminosilicate hydrate, N-A-S-H), which is typicalof geopolymerization reaction [3]. The SEM images(L = 1.1LL, 1.3LL and 1.5LL) show that the L
content is enough to react with FA. Even through theamount of geopolymerization products increases as
the L content increases, the compressive strength ofFig. 6lightweight sludge-FA geopolymer decreases asamount of L increases (Figure. 5). The decrease ofthis strength is due to the loss of the pore fluid
between structure of sludge and FA particle while
the samples were heated. The excessive amount of L
causes shrinkage resulting in the micro-cracks(Figure. 6) (L=1.1LL, 1.3LL and 1.5LL) this reportis in agreement with Sukmak et al. [10,11]. When
compared SEM images of sample at L of 1.0LL and
SEM images of samples at L of 0.8LL and 0.90LL,
the geopolymerization products are clearly observedon FA particle for L of 1.0LL. This result isagreement with the compressive strength oflightweight sludge-FA geopolymer (Figure. 5)
which gives maximum compressive strength.

SEM images of the lightweight sludge-FA geopolymer at curing time of 7 days at Na2SiO3/NaOH ratioof 80/20 and heat temperature of 65 oC for 72 hours air content of 300% for different L.
less than 1.0LL and zone 2 is L greater than 1.0LL).
For zone 1, the unit weight of LC sludge-FA
geopolymer is essentially constant although the L
increases. The L at liquid limit is designated astransitional liquid alkaline activator. For zone 2, theunit weight of LC sludge-FA geopolymer decreases
as amount of L increases clearly.
2. The 7-day strength of LC sludge-FA

CONCLUSIONSThe effect of liquid alkaline activator on unit
weight and strength of lightweight sludge-FA
geopolymer is researched in this study. The
conclusions can be drawn as follows.
1. The influence of liquid alkaline activator onunit weight is classified into two zone: (zone 1 is L

58

GEOMATE- Osaka, Nov. 16-18, 2015

geopolymer increases with the increase in L andgives maximum compressive strength when L ratiois 1.0LL which is Transitional L. Beyond thismaximum value, compressive strength of LC sludgeFA geopolymer decreases as the L increases. It isbecause the excess liquid alkaline activator results inthe bleeding in sample. The maximum compressive
strengths of lightweight sludge-FA geopolymers areat L of 1.0LL for all the Na2SiO3/NaOH ratios, heat
duration and air content.
3. The low L content is insufficient to dissolve
silica and alumina oxide from FA for producing
geopolymerization products. On the other hand(amount of L greater than transitional L), the FA
particles are etched obviously (reaction alkali
dissolution). In addition, it is found that the holes in
FA particles are filled with smaller FA particles andwelded by the geopolymerization products (Sodium
aluminosilicate hydrate, N-A-S-H) which is typicalof geopolymerization reaction.

metals in geopolymerized fly ash”, Metall
Mater Trans B Vol.29, 1998, pp.283-91.
[6] Palomo A., Grutzek M.W. & Blanco M.T.,“Alkali-activated ﬂy ashes. A cement for the
future”, Cement and Concrete Research Vol.29,
1999, pp.1323-1329.
[7] Xie Z. & Yunping X., “Hardening mechanismsof an alkaline-activated class F fly ash”,
Cement and Concrete Research Vol.31, 2001,
pp.1245-1249.
[8] Chindaprasirt
C.,
Chareerat
T.
&
Sirivivatnanon V., “Workability and strengthof coarse high calcium fly ash geopolymer”
Cement and Concrete Composites, Vol.29,
2007, pp.224-229.
[9] Sata V., Sathonsaowaphak A. & Chindaprasirt
P., “Resistance of lignite bottom ash
geopolymer mortar to sulfate and sulfuric acidattack” Cement and Concrete Composites
Vol.34, 2012, pp.700-708[10] Sukmak P., Horppibulsuk S. & Shen S.L.
“Strength development in clay-fly ash
geopolymer”, Construction and building
Materials Vol.40, 2013, pp.566-574.
[11] Sukmak P., Horppibulsuk S., Shen S.L.,
Chindaprasirt P. & Suksiripattqanapong C.
“Factors influencing strength development inclay-fly ash geopolymer”, Construction and
Building Materials Volo.40, 2013, pp.11251136.
[12] Sukmak P., Silva P-D., Horpibulsuk S. &Chindaprasirt P., “Sulphate resistance of clayPortland cement and clay-high calcium fly ash
geopolymer”, Journal of Materials in Civil
Engineeing, 2014, pp.04014158(1-11).
[13] Suksiripattanapong C., Horpibulsuk S.,
Chanprasert P., Sukmak P. & Arulrajah A.,“Compressive strength development in fly ash
geopolymer masonsy units manufactured fromwater treatment sludge”, Construction and
Building Materials, 2015, Vol.82, pp.20-30.
[14] Horpibulsuk S., Suksiripattanapong C.,
Samingthong W., Rachan R. & Arulrajah A.,“Durability against wet-dry cycles of water
treatment sludge-fly ash geopolymer”, Journalof Materials in Civil Engineeing (Accepted for
publication)[15] Neramitkornburi A, Horpibulsuk S, Shen SL,
Arulrajah A and Disfani MM, Engineering
properties of lightweight cellular cemented
clay-fly ash material. Soils and Foundations
2014; 55: doi: 10.1016/j.sandf.2015.02.020.
[16] Posi, P., Teerachanwit, C., Tanutong, C.,
Limkamoltip, S., Lertnimoolchai, S., Sata, V.,
Chindaprasirt, P. “Lightweight geopolymer
concrete containing aggregate from recycle
lightweight block.” Materials and Design
Vol.52, 2013, pp.580–586.

ACKNOWLEDGEMENTS
This work was financially supported byMetropolitan Waterworks Authority of Thailand in
the fiscal year 2013, the Thailand Research Fundunder the TRF Senior Research Scholar program
Grant No. RTA5680002. The financial support fromthe Office of Higher Education Commission under
NRU project of Thailand, as well as Suranaree
University of Technology are also very muchappreciated. The first author also acknowledges thefinancial support from the Rajamangala Universityof Technology Suvarnabhumi.
REFERENCES[1] Davidovits J., “Geopolymers”, Journal of
Thermal Analysis and Calorimetry Vol. 37,
1991, pp. 1633-1656.
[2] Buchwald A. & Kaps Ch., “Property
controlling influences on the generation ofgeopolymeric binders based on clay”,
Geopolymer (Melbournd Australia), 2002.
[3] Davidovits J., Buzzi L., Rocher R., Gimeno D.,
Marini C.. & Tocco S., “Geopolymericcenment based on low cost geologic material”,
results from the European Researh project
GEOCIS-TEM. In: Davidovits et al., editors.,
Proceedings of the second international
conference gropolymere Vol. 99, 1999, pp.8396.
[4] Mohapatra R. & Rao J.R., “Some aspects ofcharacterisation, utilisation and environmental
effects of fly ash”, Journal of Chemical
Technology and Biotechnology Vol.76, 2001,
pp.9-26.
[5] Van Jaarsveld J., Van Deventer J. & Lorenzen
L., “Factors affecting the immobilization of59

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

PREDICTING THE STRESS-STRAIN BEHAVIOR OF MINE TAILINGSUSING MODIFIED HYPERBOLIC MODEL
Mary Ann Q. Adajar1 and Mark Albert H. Zarco2
Associate Professor, De La Salle University, Manila, Philippines
2
Professor, University of the Philippines, Quezon City, Philippines
1

ABSTRACTThe shear strength behavior of mine tailings was investigated through direct shear test to determine its
applicability as embankment material. Tailings in dry condition manifested a strong particle interlock as
indicated by high critical state friction angle ranging from 36.6º to 38.4º. Friction angles at failure of saturated
tailings were lower at an average of 4o as compared to those obtained in dry condition. Modified hyperbolicmodel was formulated to predict the shear stress against shear strain and volumetric strain against shear strain
responses of tailings to different stresses. The modified hyperbolic model provides a good approximation to thestress-strain and volumetric strain-shear strain responses measured during the tests of tailings that exhibited aductile failure and compressive volumetric strain. However, the model does not give a good prediction of stressstrain response for specimens that exhibited brittle failure with dilative volumetric strain. The model cannot
capture the strain softening phenomena, but it can be used to model the behavior leading to the strain softening
as well as during the ductile stage.
Keywords: Mine Tailings, Shear Strength, Friction Angle, Stress-strain Behavior, Modified Hyperbolic Modelof its compaction behavior, compressibility andhydrocompression
settlement
describes
its
consolidation behavior [4]. This research study
investigates the shear strength behavior of minetailings through direct shear test. The determinationof the shear strength parameters and understandingof the stress-strain response to different stresses are
essential steps to completely evaluate the suitabilityof mine tailings for geotechnical application.
Different methods have been developed to model
the stress-strain behavior of soils prior to failure.
The catastrophic failure of Merriesspruit gold
tailings dam in South Africa led to the study ofFourie, AB, et.al. on the stress-strain behavior of
Merriesspruit tailings under undrained loading toestablish the steady state line using critical state soil
mechanics [5]. Onur, M. et.al. determined the shear
strength parameters of saturated clayey soils under
unconsolidated undrained condition using the MohrCoulomb model with the aid of PLAXIS software
program [6]. Hyperbolic stress-strain equations weredeveloped by Duncan and Chang as a means formodeling the isotropic nonlinear elastic stress-strain
behavior of soils prior to failure [7]. In this study,
the critical state model and a modified version of the
Duncan and Chang hyperbolic model were used todescribe the stress-strain and volume change
behavior of tailings. The shear strength behavior of
tailings must be established because this is adetermining factor for the stability of earth structures
built using this waste material.

INTRODUCTION
The abundance of solid wastes in theenvironment can pose risk to the health of the
exposed population and damage to the environment.
One way to respond to this environmental issue is tofind useful application for these wastes materials.
Numerous studies have been undertaken to
determine the applicability of solid wastes as analternative construction material. Ceramic waste andquarry dust aggregates were used as a possible
replacement for conventional crushed stone coarse
and fine aggregates [1]. Ashes from a wastewater
treatment facility that incinerates its treatment
sludge were evaluated for possible use as partial clay
substitute for brick manufacture and as a soft soil
stabilizing admixture [2].
In the Philippines, one of the most abundantsolid wastes found in the environment are wastes
from mining operations called mine tailings. The
most common environmental issue associated withmining activities is the disposal of the enormous
amounts of tailings regularly produced from mining
operations. The use of mine tailings as embankment
materials is one possible option that alleviate
disposal problem and reduce the negativeenvironmental effects. To evaluate the applicability
mine tailings as embankment materials, thoroughstudy of its geotechnical characteristics is needed.
Geotechnicalcharacterization
includesthedetermination of its physical properties andhydraulic conductivity characteristics [3]. A study
60

GEOMATE- Osaka, Nov. 16-18, 2015

reaching a peak value at low shear strains and thendecreases with increasing shear strains indicatingstrain softening until the critical shear stress is
attained. This indicates that specimen failed inbrittle manner, typical for dense sands and overconsolidated clays.
The determination of brittle
failure is potentially important when dealing withembankment and slopes as this leads to progressive
failure which may be sudden and catastrophic. At
higher normal effective stresses and relative densityof 90%, all the tailing samples behaved like loose
sands or normally consolidated clays. There is a
gradual increase in shear stresses as the shear strain
increases until an approximately constant shear
stress is attained.
The stress-strain behavior
suggests that tailings failed in ductile manner. For
test specimen with relative densities of 60% and80%, the peak shear stress was not observed even atlower stresses for all the tailing samples. The
samples behaved like a ductile material at every
normal effective stress.

EXPERIMENTAL PROGRAM
Mine tailing samples came from three (3) mining
sites in the Philippines namely: wastes fromconcrete aggregate quarry in Cavite and designatedas TS#1, gold mine tailings from gold processing
plant in Davao del Norte and designated as TS#2,
and gold mine tailings from mining site in Aroroy,
Masbate and designated as TS#3. These tailings are
non-plastic, considered as fine-grained consisting offine sands and silts. The physical properties,
hydraulic conductivity, and consolidation behaviorof the tailing samples are discussed in Adajar andZarco (2013, 2014) [3][4]. In this study, direct shear
test was carried out with samples in dry andsaturated conditions following the procedures
described in ASTM D3080. Samples are prepared atinitial relative densities of 60% (medium dense),
80% (dense) and 90% (very dense) both in dry andfully saturated conditions.
For dry condition, test runs for each tailingsamples used five (5) different vertical stresses of
13.63 KPa, 20.44 KPa, 27.25 KPa, 54.50 KPa and87.50 KPa. The first two vertical stresses (13.63
KPa and 20.44 KPa) represent the low-stressconditions. The other three (3) vertical stresses of
27.25 KPa, 54.50 KPa and 87.50 KPa correspond tothe overburden pressure in the field at the depth of
1.5m, 3.0m and 4.5m, respectively. The horizontal
stress was applied at a fast rate of 1.25mm/min.
For the saturated condition, four (4) verticalstresses (13.63 kPa, 20.44 kPa, 27.25 kPa and 54.50
kPa) were used. The sample was allowed to comeinto drained equilibrium under the application ofthese vertical stresses before subjecting to horizontal
load. A very slow strain rate of 0.12mm/min wasused to simulate the drained condition.
.
SHEAR STRENGTH PROPERTIES
Stress-strain and Volume Change Behavior of
Tailings in Dry Condition
The typical stress-strain and volume change
relationship of dry tailing samples is presented in
Figs. 1. The stress-strain plot shows the typicalresponse of soil to shearing forces at monotonic
loading. The shear stress at failure is greater for
specimens subjected to greater normal effective
stress (σ’) as compared to specimens with lower
normal effective stress. The shear stress at failure,termed as the critical state shear stress is describedas the shear strength at which continued shearing
occurs without change in shear stress and volume fora given normal effective stress. The three (3) tailingsamples exhibited similar stress-strain behavior. It
can be noted that at very dense condition (Dr = 90%)
and lower normal effective stresses (σ’ ≤ 27.25 KPa),
the specimen showed a rapid increase in shear stressFig. 1 Typical stress-strain and volumetric strainshear strain plots of dry tailing
The volumetric strain plot shows that tailings
followed a well-established behavior trends for
granular materials. As the normal effective stress
was increased, the tendency for the specimen todilate was decreased. Specimens achieved greater
compressive volume change as the normal effective
stress is increased.
Samples that exhibited peak
shear stress showed an initial compressive behavior
61

GEOMATE- Osaka, Nov. 16-18, 2015

and then continued to increase in volume with shear
strain manifesting a dilative behavior. This behavior
arises because tailing, like soil, is essentially aparticulate material. The particles must take up asuitable arrangement of packing before continued
shearing can take place. If the particles are initiallymore densely packed, some loosening whichcorresponds to an increase in volume change, that is
dilation, will have to occur before the critical shear
can takes place.

It is expected that all soils reach an
approximately constant shear stress irrespective of
their initial state. In the case of saturated tailings inthis study, the samples continued to gain strength
until the end of the test. The ductile behavior
manifested by tailings is preferable as this will notlead to progressive collapse. Many of the design
procedure in geotechnical engineering assume thatthe soil can be relied on to behave in a ductile
manner, where it will undergo continued
deformation at constant load. This is in contrast toa brittle material, which at failure breaks and loses it
load carrying capacity entirely.

Stress-strain and Volume Change Behavior of
Saturated Tailings

Friction Angles

The typical stress-strain and volume change
behavior of saturated tailings are shown in Fig. 2.
All specimens exhibited the same trend in stressstrain behavior. Peak shear stress was not observed
even at very dense condition and low normal
stresses. The samples behaved like loose sands andnormally consolidated clays where strain hardening
was observed indicating a ductile failure. The
increase in shear strength was coupled withdecreased in volume. This compressive behavior
increased as the normal effective stress is also
increased. The rearrangement of soil particles into a
denser configuration was easily facilitated becauseof the presence of moisture, thus resulted to ductile
and compressive behavior of the saturated tailings.

Tailings in dry condition reached a critical statein which unlimited shear strain could be applied
without further changes in volume, normal effective
stress or shear stress. Test results revealed that thecritical state reached by the specimen depends on thenormal effective stress at which it is sheared. The
initial void ratio, as expressed by relative densities,
has no significant effect on the critical shear strength.
The critical state shear stress when plotted on agraph of τ against σ’ lies in a straight line with slopeτ=tan (ϕ cs ) and can be described by equation
σ'tanϕ cs . This line represents the failure line ortermed as critical state line (CSL) and ϕ cs is called
the critical state friction angle. Soil states below the
CSL will cause the soil to behave in a ductile
manner and is desirable in engineering design. Soilstates above the CSL and bounded by the peak shear
strength envelope will cause the soil to behave inbrittle manner which can cause sudden failure orcollapse. Although high shear strengths (peak) are
observed in this region, there is no guarantee that thehigh shear strengths will be uniformly mobilized at`the same time and this will present high safety risk
which should be avoided. High values of friction
angles are obtained from the tests indicating that dry
tailings have strong particle interlock resulting tohigh shear strength.
For tailings in saturated condition, the shear
stress corresponding to 15% horizontal displacement
or approximately equal to 29% horizontal strain wasconsidered as the shear stress at failure and friction
angle obtained using this shear strength is referred toas friction angle at failure (ϕ f ). Similar to the test
results in dry condition, the values of shear strength
at failure of each tailings is unaffected by its initial
void ratio. The friction angles at failure for saturated
tailings were lower at an average of 4o as comparedto those obtained in dry condition. The moistureserves as lubricant that reduces frictional resistance
between particles, therefore, it resulted to lower
values of friction angles.
Cohesion values arealmost zero. The saturated tailings did not exhibit
cohesive behavior since samples are classified as

Fig. 2 Typical stress-strain and volumetric strainshear strain plots of saturated tailings
62

GEOMATE- Osaka, Nov. 16-18, 2015

non-plastic. The friction angles in both dry andsaturated condition are summarized in Table 2
together with some values of friction angles fromliterature. The values of friction angles derived fromthis study are within the range of values obtained
from studies of other researchers.

relationship measured during the test is hyperbolic,the transformed diagram is a straight line. The
hyperbolic parameters a and b of Eq. (1) is theintercept and slope of this straight line, respectively.
The intercept a of this straight line on the γ/τ axis is
the reciprocal of initial shear modulus, Gi of thetailing sample while the slope of the line, b is thereciprocal of the asymptotic shear stress, τ ult .
The variation of the initial shear modulus, Gi in
response to change in normal effective pressure canbe represented using the power law approach as
suggested by Janbu:

Table 2 Friction angle at failure of tailings
Type of
Tailings

Friction angle at failureDrycondition

SourceSaturatedcondition

ϕ cs (deg.) ϕ f (deg.)
TS#1
TS#2
TS#3
Gold
SlimesFine Coal
RefuseCopperSlimes

38.4
34.4
36.8
32.5
36.6
33.3
20 – 40.5

This study
This study
This study
Vick [8]22 – 39

Vick [8]24 – 37

Shamsai [9]

σ '
G=
K ⋅ Pa  i Pa 

Strain hardening manifested in all test runs ofsaturated tailings. The stress-strain curves reflect an
asymptotic behavior, as such, hyperbolic fitting
techniques were applied to characterize the stressstrain and volume-change behavior of tailings insaturated condition determined from direct shear
tests. The hyperbolic model is one of the mostfrequently used models for predicting the behaviorof soils especially if one wishes to apply the finite
element method to describe the non-linear
movement within soil masses. The Duncan andChang [7] hyperbolic model was developed basedon data obtained from the triaxial tests. In this study,
the stress-strain data are obtained from direct shear
test; hence, there is a need to modify the method in
the hyperbolic model to obtain the hyperbolic
parameters from the direct shear test.
The modified model approximates the stressstrain behavior from direct shear tests by thefollowing hyperbolic relation:γ
a + bγ(2)

The parameters K (shear modulus number) and n(shear modulus exponent) describing initial shear
modulus (Gi) are obtained from a best-fit straightline drawn through data points of the logarithmic
diagram showing the values of normalized shear
modulus (Gi/Pa) against the values of normalized
normal effective stress (σ’/Pa), where thenormalizing parameter Pa is the atmosphericpressure equal to 101.325 KPa.
The failure ratio R f that relates the asymptotic
shear stress with shear stress at failure is defined as
the ratio of the shear stress at failure, τ f to theasymptotic shear stress, τ ult . Since the stress-strain
plot exhibited an asymptotic behavior, the value ofshear stress at failure, τ f from stress-strain plot of
the test is taken as the shear stress at 15% horizontal
displacement.
The variation of angle of internal friction ϕ’ withrespect to normal stress, σ’ is described in terms ofhyperbolic parameters ϕ o and Δϕ. The angle ofinternal friction, ϕ’ is given by the expression,

The Modified Hyperbolic Model

τ=

n

σ '
φ =' φo − ∆φ log   Pa 

(3)

The parameter ϕ o is the value of angle of internal
friction, ϕ’ at σ’ equal to Pa and Δϕ is the reduction
in ϕ’ for a ten-fold increase in σ’ which are obtained
from best-fit line through data points on the plot of ϕ’vs. logarithm of normalized σ’/Pa.
The volumetric strain vs. shear strain behavior
can also be approximated by hyperbolic equation of
the form,(1)

where τ is the shear stress, γ is the shear strain, aand b are parameters evaluated from the tests data.
The stress-strain data is represented in atransformed plot where the value of shear strain, γmeasured during the test is divided by thecorresponding value of shear stress, τ and plottedagainst the shear strain (γ/τ vs. γ). If the stress-strainεv =γ
α + βγ(4)where ε v is the volumetric strain, α and β arevolumetric strain parameters that can be determined

63

GEOMATE- Osaka, Nov. 16-18, 2015

from test data presented in transformed plot of γ/ε v
vs. γ. The value of α is the intercept of the best fit
line of the data points while the value of β is theslope of the plot and represents the asymptotic valueof ε v . The parameters α and β vary with normal
effective stress and can be represented by thefollowing expressions:

σ '
α = K a Pa   Pa mσ '
β = K b Pa   Pa 

r

strain parameters α and β for specimens thatexhibited dilation. The volumetric strain against
shear strain curve of dilatant samples is not
hyperbolic and the transformed plot produced
negative values of α and β, thus, the determinationof Ka, m, Kb and r using the normalized α and β inlogarithmic diagram was not possible. It wasobserved that the modified hyperbolic model does
not give a good prediction of the stress-strain
response for dry tailings. This is because the stressstrain curves do not exhibit an asymptotic trend.

(5)

Table 3 Hyperbolic parameters describing stressstrain response from test data of tailings insaturated condition

(6)

Type
of
tailing

The values of K a and m are determined in alogarithmic diagram of normalized α/Pa versus
normalized σ’/Pa while the values of K b and r are
determined in a logarithmic diagram of normalized
β/Pa versus normalized σ’/Pa. The parameters K a
and K b (volumetric strain numbers) are the values ofnormalized α and β, respectively, for a confining
stress of 1 atm. The slope of the best fit line is thevalue of parameters m and r (volumetric strain
exponents).
After the hyperbolic parameter values are
determined, the stress-strain and volumetric strainshear strain responses of tailings as a function of
normal effective stress, σ’ can be predicted using thefollowing expressions:
The shear stress is given by equation,

τ=

γ

TS#1

TS#2

TS#3

Type
of
Tailing

The volumetric strain is calculated using theequation,

γ

σ '
σ '
K a Pa   + γ K b Pa   Pa 
 Pa 

r

K

n

36.11
28.39
23.84
17.84
15.95
13.91
34.69
18.11
15.39

(7)

γ 
+ Rf  nτ f σ ' K ⋅ Pa   Pa 

m

90
80
60
90
80
60
90
80
60

Shear modulus hyperbolic parametersØo(deg.)

1.20
1.04
1.02
0.894
0.842
0.831
1.115
0.917
0.604

31.61
31.75
32.13
30.89
31.22
30.55
30.97
31.81
31.68

ΔØ

Rf

(deg.)
8.09
5.85
4.70
4.42
3.29
4.18
6.85
2.86
2.67

0.897
0.888
0.902
0.882
0.868
0.856
0.899
0.892
0.907

Table 4 Hyperbolic parameters describing change involumetric strain of tailings in saturated
condition

1

εv =

Dr
(%)

(8)

The hyperbolic parameters to describe the shear
modulus (Gi) and volumetric strain parameters (αand β) of tailings in saturated condition arepresented in Table 3 and Table 4, respectively.
The modified hyperbolic model was also applied
to tailings in dry condition. This is to verify if the
proposed model is applicable to samples with stressstrain and volumetric strain- shear strain curves that
do not exhibit an asymptotic trend. It can be notedthat it was not possible to obtain the hyperbolic
parameters (Ka, m, Kb, r) to describe the volumetricDr(%)

Volumetricstrainparameter, αVolumetricstrainparameter, β

Ka

m

Kb

r

90

0.0357-1.1301.369-0.307

TS#1

80
60
90

0.02120.02200.0094-0.911-0.997-1.080

0.822
0.625
0.281

-0.350-0.342-0.188

TS#2

80
60
90

0.01490.01200.0139-0.759-0.639-1.024

0.247
0.223
0.342

-0.280-0.280-0.191

TS#3

80
60

0.01070.0071-1.013-0.868

0.221
0.220

-0.357-0.372Using the determined hyperbolic parameters, themodel’s response to the test data was compared withexperimental data using Eqs. (7) and (8). The
comparison of the test data and the calculated
hyperbolic response is shown in Fig 3.
The comparison showed that the modified
hyperbolic model provides a good approximation to
the stress-strain response measured during the tests
for tailings with ductile behavior since the stress64

GEOMATE- Osaka, Nov. 16-18, 2015

strain curve shows a hyperbolic trend. However, themodel does not give a good prediction of stressstrain response for specimens that exhibited brittle
failure.
The model cannot capture the strain
softening phenomena, but it can be used to model
the behavior leading to the strain softening as well as
during the ductile stage.
The volumetric strainshear strain response using the modified hyperbolicmodel also compares fairly well with the test data
for specimens with compressive volumetric strain.
However, the model does not apply to dilatantsamples because the present model cannot accountfor the change in sign of the volumetric strain.

Saturated tailings exhibited strain hardening
indicating a ductile failure with contractive
volumetric strain. Friction angles at failure werelower at an average of 4o as compared to thoseobtained in dry condition.
The modified hyperbolic model provides a good
approximation to the stress-strain response measured
during the tests of tailings that exhibited a ductile
failure. However, the model cannot capture thestrain softening phenomena, but it can be used tomodel the behavior leading to the strain softening aswell as during the ductile stage.
The volumetric strain-shear strain response using
the modified hyperbolic model also compares fairly
well with the test data specifically for specimens
which have compressive volumetric strain.
However, the model does not apply to dilatantsamples.
REFERENCES[1] Abdullah, M, et.al., “Concrete with ceramic
wastes and quarry dust aggregates”, 5th Annual
Conf. Management in Construction Researchers
Asso., Malaysia, 2006, pp.383 – 388.
[2] Tempest, BQ and Pando, MA, “Characterizationand demonstration of reuse applications ofsewage sludge ash”, Int. J. of GEOMATE,
Vol. 4, No. 2 (Sl. No. 8), June 2013, pp. 552559.
[3] Adajar, MQ and Zarco, MH, “An empiricalmodel for predicting hydraulic conductivity ofmine tailings”, Int. J. of GEOMATE, Vol. 7, No.
2 (Sl. No. 14), Dec., 2014, pp. 1054-1061.
[4] Adajar, MQ and Zarco, MH “Estimatinghydrocompression settlement of mine tailings”
Philippine Engineering J., Vol. 34, No.1, June
2013, pp. 11 – 30.
[5] Fourie, AB and Papageorgiou, G, “Defining anappropriate steady state line for Merriespruit
gold tailings”, Canadian Geotechnical J., Vol.
38, 2001, pp. 695-706.
[6] Onur, MI, et.al., “Modelling of shear strengthparameters of saturated clayey soils”, Int. J. of
GEOMATE, Vol. 7, No. 2 (Sl. No. 14), Dec.
2014, pp. 1107-1110.
[7] Duncan, J and Chang, C, “Nonlinear analysis ofstress and strain in soils”, Proc. of the American
Society of Civil Engineers, Vol. 96, 1970, pp.
1629 – 1653.
[8] Vick, SG, Planning, design, and analysis of
tailings dams, John Wiley & Sons, New York,1990.
[9] Shamsai, A, et.al., “Geotechnical characteristics
of copper mine tailings: a case study”,
Geotechnical Geological Engineering J., Vol.
25, 2007, pp. 591-602.

Fig. 3 Comparison of hyperbolic model and test
data for saturated tailing.
CONCLUSION
The shear strength behavior of tailings fromaggregate quarry and gold mining sites in thePhilippines were investigated through direct shear
tests. Experimental results showed that:Dry tailings at very dense initial state and lower
normal stresses exhibited peak shear strength andstrain softening with dilative behavior, indicatingthat tailing samples failed in brittle manner. At
relative densities lower than 90%, samples reached
the critical state even at lower normal stresses
indicating that tailings have ductile and contractive
behavior.

65

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

STABILITY OF UNDRAINED TWIN CIRCULAR TUNNELS
Jay Lobwein1, Mathew Sams1 and Jim Shiau1
Faculty of Engineering and Surveying, University of Southern Queensland, Australia

1

ABSTRACTThis paper investigates the stability of horizontally aligned 2D twin circular tunnels in tresca material underplane strain conditions. Finite element limit analysis based on classical plasticity theory is used to determine
rigorous upper and lower bounds from Optum G2. The upper and lower Factor of Safety (FoS) bounds are definedin terms of gravitational accelerations in an unsupported greenfield problem. A new unit factor of safety design
stability chart is developed as a function of dimensionless parameters, including the depth ratio, strength ratio andspacing ratio. Amplified factor of safety design stability charts are illustrated and compared for shallow, medium
and deep twin tunnels. It was found the greater the depth, the lesser the FoS. Additionally, increased depth ratios,presents tunnel stability interaction, reducing the spacing ratio impact on the failure. These charts can be used in
the preliminary stages of design for practicing engineers.

Keywords: Circular Tunnel, Twin Tunnels, Undrained Clay, FOS, Design Chart

across numerous experimental and theoretical studies
for tunnel stability, predominantly modelling circularheadings. The experimental studies were conductedusing a centrifuge which enhanced the gravity until
tunnel collapse [3]–[5]. The definition of the stability
number was extended using a number ofdimensionless parameters [5].
Basing the stability number on dimensionlessparameters, stability charts are produced. Thesestability design charts are based on a unit factor ofsafety for all cases. This study will determine factorof safety values to produce new stability charts fortwin circular tunnels. Upper and lower boundsolutions were produced using limit analysis for thestability number shown below in Eq. (2). The stability
number was regarded as a function of the depth ratio
and strength ratio for a homogenous soil.

INTRODUCTION
Subsurface infrastructures are becoming more
important with the growing demand of transportation
systems in major cities around the world. Tunnel
construction is a key focus in the advancement ofunderground construction. There are number ofdifferent tunnel designs evolving based on
construction techniques, soil settlement and tunnel
stability.
Many research studies have been conducted in thefield of tunnel stability for different types of soils andgeometries. Early studies by Broms and Bennermark[1] introduced a stability number as shown below in
Eq. (1).
DN=

σs −σt +γ(C+ 2 )
Su

(1)

N=

Where σs is the uniform surcharge pressure on thesurface and σt is the internal normal tunnel pressure,
C is the cover depth above the tunnel roof, D is thetunnel diameter, γ corresponds to the unit weight, and
Su is the undrained shear strength of the soil.
Commonly, from early stages the stability number
was formulated by increasing the surcharge pressure
whilst setting the tunnel pressure to zero in an active
collapse mechanism.
An active tunnel collapse mechanism is triggeredby the surface pressure and self-weight of the soil,
whilst the internal tunnel pressure is the resistanceagainst tunnel failure. For a passive tunnel collapse,
the tunnel pressure is increased in a ‘blow out’situation, whilst the self-weight and surface pressure
are the resistance against failure [2].
The work by Broms and Bennermark [1] extended

σs −σtSu

C γD= f( ,

D Su

)

(2)

Upper and lower bounds on the stability
parameter were extensively described by Lyamin and
Sloan [6], [7]. These numerical techniques modelled
arbitrary geometries, varying soil layers, and complex
loading conditions. However, most importantly thecontinual refinement of the limit analysis using upperand lower bounds increased the accuracy of thecollapse loads, and hence the accuracy of the design
stability charts for tunnels.
With further refinement of the finite element limit
analysis (FELA) techniques, numerous geotechnicalengineering problems have been solved rigorously
through upper and lower bounds [8]. Thisdevelopment has led to a number of studies in thefield of tunnel stability for circular, square and66

GEOMATE- Osaka, Nov. 15-18, 2015

heading tunnel profiles [9]–[12].
Studies by Wilson et al. [9], [10] investigated thestability of circular and square tunnel headings,
respectively. The shear strength was assumed toincrease linearly with depth. In this instance, thestability problem is defined with an extra
dimensionless variable, as shown in Eq. (3).
N=σs −σtsu

C γD ρD= f( ,

D su

,

Su

)

N=

σs −σtsu

C γD S= f( ,, )D Cu D(4)

The dual tunnels were investigated using FELAand compared with rigid block analysis under planestrain conditions with uniform tresca material. The
true collapse loads, were found to differ by at most
5% and presented in stability design charts. Similar to
the work completed by [9]–[12], the failure of thestructure was induced by uniform surface surcharge
for upper and lower bounds.
The ultimate purpose of this paper is to study theparameters of the unsupported-greenfield twin tunnel
problem. A factor of safety approach is used, and apractical range of dimensionless parameters has been
selected.

(3)

Where ρ is the strength factor which determines
the rate of strength increase with depth. Both studies
modelled ρ incrementally, from zero (homogeneouscase) to one. For square tunnels, D refers to the side
length instead of the diameter.
Semi-analytical rigid block mechanisms wereused as a comparison to the numerical formulation of
the upper and lower bound theorems. Both upper andlower bounds were bracketed to a true collapse load
to within 5% for all cases considered.
A similar approach was opted for wide
rectangular tunnels, with an additional dimensionlessparameter, B/D, representing the aspect ratio of thetunnel, where B is the width and D is the depth [11].
Furthermore, an infinitely long flat wall wasmodelled by Augarde et al. [12], where only the faceof the heading was free to move and subject to a
uniform surface pressure (σs). This model can also be
assumed to be the longitudinal section of a tunnel.
The tunnel floor and roof were assumed to be fixed ina rigid lining situation. This plain strain heading
problem presented a number of stability design charts
similar to studies completed by Wilson et al. [9], [10].
In relation to twin tunneling stability analysis,
only a limited amount of study has been completed.
Reference [13] investigated the finite element
optimization of the tunnel positioning in threedifferent configurations; aligned horizontally,
vertically and inclined. It concludes that the best
design in soft soil is the horizontal side-by-side
configuration, based on lining forces and settlement.
Therefore, this paper will only investigate thehorizontal alignment.
By defining the problem with two tunnels, anothervariable is introduced, that being the tunnel spacing.
This spacing distance is given by S and is normalizedwith respect to the tunnel diameter D, which together
establish the dimensionless spacing ratio parameter
defined as S/D.
In-line with the studies completed on various
tunnel profiles, Wilson et al. [14], [15] extended thetheory into square and circular tunnels respectively.
Both studies presented 2D horizontally aligned dual
tunnels for undrained homogenous soil. Shown below
in Eq. (4) is the stability number for the dual circulartunnels, with the problem now also being dependent
on tunnel spacing.

PROBLEM DEFINITION
This paper investigates only the homogeneouscase for varying twin circular tunnels. Tresca material
is used for all models of analysis, where the angle offriction (φ) is zero. Fig. 1 shows the problem in thisstudy. The soil properties include the unit weight γ,
and undrained shear strength Su. The geometry of thesystem is described by the cover depth C, the tunnel
diameters D, and the tunnel spacing S.
The surcharge surface pressure σs and internaltunnel pressure σt are both ignored. With thisunsupported greenfield condition, the stability of thesystem would then simplify considerably, as
expressed in [15], but would need to be defined usinga factor of safety approach, as described in Eq. (5).
The developed FoS is a function of the depth ratio
C/D, strength ratio Su/γD and the spacing ratio S/D.
C S

SFoS = ( , u , )D γD D

Fig. 1(5)

Problem Definition

The system is described using dimensionlessratios as described in Eq. (5). A range has been
selected based on its practicality; this study willcalculate a factor of safety for: C/D = 1–6, Su/γD =0.05–2, and S/D = 1–14. Other Tresca material
67

GEOMATE- Osaka, Nov. 15-18, 2015

properties that had minimal effect on the factor ofsafety outcome included: Poisson’s ratio ν = 0.49 andYoung’s Modulus E = 30MPa.

factor of safety.
Table 1 illustrates the procedure for an upper andlower bound twin circular tunnel with a depth ratio
C/D = 1 and spacing ratio S/D = 3 for strength ratios
0.05, 0.5, 1.0, 1.5 and 2.0. The constant values are thediameter D = 3m and unit weight of the soil γ =18kN/m 3 used throughout all calculations. The
averaged stability numbers calculated are 0.630 for
the lower bound and 0.579 for the upper bound.

FELA MODELLING
Optum G2 is a finite element software used forstrength and deformation analysis of geotechnicalboundary value problems. The program differs bycommuting rigorous upper and lower bounds to an
exact limit load or strength reduction factor.
Each 2D model was restrained in the base andsides in both the x and y dimensions. There wasallowance of enough model space between eachtunnel, base and sides. This ensured the assumptionof an infinitely wide and constant soil medium;
however this might not be the case in reality. Shownin Fig. 2 identifies the model space and boundary
restraints for a twin tunnel profile of depth ratio C/D= 1 and spacing ratio S/D = 3. Over allowance of themodel would deteriorate the accuracy of the finite
element limit analysis, as more elements would needto cover the entire soil area.

Fig. 2

Table 1
SR
0.05
0.5
1.0
1.5
2.0

Average stability number calculations
FoS(LB)FoS(UB)

N(LB)

N(UB)

0.079
0.794
1.589
2.383
3.177

0.086
0.864
1.728
2.592
3.455

0.633
0.630
0.629
0.629
0.630

0.581
0.579
0.579
0.579
0.579

RESULTS AND DISCUSSION
Collating all the averaged stability numbers
across a range of depth and spacing values, a new
design stability chart is shown in Fig. 3. This chart isbased on an exact bound unit factor of safety with apercent difference equal to 11.7 percent. The exact
bound is found by averaging the upper and lower
bounds to calculate a true collapse FoS. Averaging
the stability number across a range of strength ratios,
simplifies the unit factor of safety chart as a functionof the depth ratio, strength ratio and spacing ratio.

Typical twin tunnel mesh (C/D = 1, S/D = 3)

The number of finite elements used was 1000 foreach model for all cases. Mesh adaptivity was enabledwith three iterations starting at 1000 elements. Fig. 2
shows 10,000 mesh elements with mesh adaptivity of
three iterations starting at 1000 elements to illustrate
the failure mechanism for twin circular tunnels.
The FoS value was developed by prompting thegravity multiplier. The gravity multiplier is iterated in
the software code until a state of collapse is reached,whilst all other properties are constant. The upper andlower bound gravity multiplier values are essentiallythe factor of safety values. For example, if a lower
bound gravity multiplier is 1.5, then the gravity on themodel would have to be amplified by 1.5 times thegravity (9.81m/s2) to produce a tunnel collapse.
Therefore, the initial unit weight of the soil has
increased also by 1.5.
The strength ratio was developed with respect tothe diameter. The undrained soil shear strength was
ranged from 2.7kPa–108kPa for the ranging strength
ratios. The range of strength ratios are used to verify
the factor of safety values.
Subsequently, each strength ratio value is divided
by the calculated factor of safety value to produce a
new stability number. The stability number isaveraged out across the range of strength ratios forconstant depth and spacing ratios. It is important tonote that the stability number calculated is for a unit

Fig. 3

Conditions at collapse state.

By inspection, the spacing ratio was increased
until a singular tunnel failure occurred for therespective depth ratio. The failure was governed
either by an upper or lower bound failure, with anadditional space ratio calculated to confirm andextend the singular failure. A linear line of best fit waselected to dissect the two failure zones.
This chart can be used to determine whether aparticular twin tunnel profile will fail or not. This isbased on required information based on the depth,
spacing, diameter and soil properties of the dual
tunnels. Due to the fact soil can behave in an
uncertain and complex matter, it would be rare for anengineer to base dual tunnels on a FoS = 1.
68

GEOMATE- Osaka, Nov. 15-18, 2015

Consequently, an extension of the design stability
chart is completed.
Table 2 identifies the lower bound factor of safetyvalues for varying strength and spacing ratios, at aconstant depth ratio = 3. By inspection a unit factor ofsafety value is achieved when the strength ratio
approximately equaled to 1.1 and the spacing ratio =5 at a constant depth ratio = 3. This figure can also be
identified by inspection on the design stability chart
presented in Fig. 3.
Table 2effect on the tunnel stability. For the shallow tunnels,a steep linear gradient is projected up until singular
failure is induced, where all the strength ratios sharply
plateau over the failure boundary when S/D = 5.
There is no initial tunnel stability interaction between
the tunnels at close spacing ratios.
Fig. 5 presents the design stability chart for a twin
circular tunnel depth ratio C/D = 3, for varying
spacing and strength ratios with increasing upper andlower bound strength ratios. The limiting spacing
ratio for dual stability is S/D = 9 triggered by both
upper and lower bounds. This chart is presented as a
medium depth dual tunnel profile.
Comparably, the extent of the factor of safetyvalues is less with FoS maximizing at 2.5, when thestrength ratio Su/γD = 2 and the spacing ratio S/D = 9.
There is a rising curved plateau over the failureboundary line when S/D = 9 for all strength ratios.
Furthermore, there is minor tunnel stability
interaction when the spacing ratio is less than 2,
where the tunnels interact with each other under
reducing conditions.

Lower Bound FoS values (C/D=3)

SR

S/D=1

S/D=3

S/D=5

S/D=7

0.05
0.3
0.7
1.1
1.5
2.0

0.045
0.267
0.624
0.980
1.336
1.782

0.044
0.262
0.612
0.961
1.311
1.748

0.049
0.296
0.692
1.087
1.482
1.976

0.053
0.32
0.747
1.174
1.600
2.134

Design Stability Charts
Three design stability charts were constructed for
depth ratios 1, 3 and 6 to identify the comparisons
between shallow, medium and deep twin tunnel
stability behavior. Both upper and lower bound FoS
values are determined for each chart until singular
failure is induced. Furthermore, each design stability
chart illustrates the failure boundary between twinand singular tunnel failure.
It can be noticed for all charts that the spacing
between the tunnels and the strength of the soil has asignificant effect on the factor of safety values.

Fig. 4

Design stability chart (C/D = 1)

Fig. 5

Design stability chart (C/D = 3)

Fig. 6

Design stability chart (C/D = 6)

The deep design stability chart for a twin circulartunnel with a depth ratio C/D = 6, for varying spacing
and strength ratios is illustrated in Fig. 6. The limiting
spacing ratio for dual stability is S/D = 14 triggered
only by the lower bound.
The FoS values have the lowest extent of tunnels
maximizing at 1.7, for a strength ratio Su/γD = 2.0 andspacing ratio S/D = 14. The stability tunnel
interaction is more prominent for spacing ratios lessthan 3. The rising FoS plateau is further reduced until

Shown in Fig. 4 is the shallow stability chart witha twin tunnel depth ratio C/D = 1, for varying spacing
and strength ratios. The limiting spacing ratio for dual
stability is S/D = 5 for both upper and lower bounds.
The shallow design stability chart shows thelargest extent of factor of safety values, maximizing
at a FoS = 4, when the strength ratio Su/γD = 2 andthe spacing ratio S/D = 5. This concludes that for
shallow tunnels the strength ratio has much greater
69

GEOMATE- Osaka, Nov. 15-18, 2015

the failure boundary when the spacing ratio S/D = 14.
At this point for all strength ratios, only a singularlower bound failure is induced which can be
identified as a drop in the FoS results along the failureboundary line. This indicates a slight tunnel stability
interaction just prior to singular collapse
Referring to all the design charts, the greater thedepth and/or strength ratios, the less the FoS.
Furthermore, the greater the depth and/or strength
ratios the less the spacing ratio impacts the failure andthe more tunnel stability.

medium and deep tunneling (Figs. 10 to 12). The blue
vectors indicate the major principle stresses, whilst
the red vectors indicate the minor principle stresses.
For all cases, the vector fields are similar. The major
principle vectors are actively shown in the symmetricecliptic contour area, whilst the minor principle
vectors are predominantly shown in the pillar areas
closer to the surface. With greater depth the stresscomplexities are increased around the tunnel voids.

Plasticity and Shear Strain Rate
The plastic multiplier is a direct measure of theplastic strain increment. The contour plots shown in(Figs. 7 to 9) indicate the failure mechanism for eachtunnel configuration, with a spacing ratio just prior to
singular collapse. Each tunnel depth illustrates asymmetrical elliptic failure contour that branchesfrom the inner aspect of the walls to a center point. As
the tunnel depth increases, the ecliptic failure contour
engulfs the tunnel roof and base entirely. The shallow
tunnel failure mechanism is positioned in the pillar
and extending from both sides shown in Fig. 7. The
pillar is positioned at a distance equal to the diameter.
As the tunnel is increased with depth the pillar width
is extended to a distance six times the diameter fromthe external walls for a deep tunnel shown in Fig. 9.
Lastly, the tunnel depth increases, the complexity isenlarged by floor heave.

Fig. 10

Principle stress vectors (C/D = 1, S/D = 4).

Fig. 11

Principle stress vectors (C/D = 3, S/D = 8).

Fig. 12

Principle stress vectors (C/D = 6, S/D = 13).

Displacement Vectors
Fig. 7

Plastic multiplier plot (C/D = 1, S/D = 4)

Fig. 8

Plastic dissipation (C/D = 3, S/D = 8)

Fig. 9

Plastic dissipation (C/D = 6, S/D = 13).

The displacement vectors represent the
deformation in the x and y direction for each tunnel
void. Deformations for shallow, medium and deeptunnels are shown in (Figs 13 to 15). For a shallow
configuration, the major displacements are projectedon the inner wall and invert of the void towards thecenter of the tunnel. There is no displacement on thecrown and the external invert of the tunnel. As thedepth increases, the vector field surrounding thetunnel void becomes greater.

Principle Elemental Stresses
The principle stresses were plotted for shallow,

Fig. 1370

Displacement vectors (C/D = 1, S/D = 4)

GEOMATE- Osaka, Nov. 15-18, 2015

Fig. 14

Displacement vectors (C/D = 3, S/D = 8)

Fig. 15

Displacement vectors (C/D = 6, S/D = 13).

Conference on Soil Mechanics andFundamentals Engineering, Mexico, 1969, vol.
3, pp. 225–290.
[5] Davis EH Gunn MJ Mair RJ & Seneviratne HN,“The stability of shallow tunnels andunderground openings in cohesive material”,
Geotechnique, 1980, vol. 30, no. 4, pp. 397–
416.
[6] Lyamin AV & Sloan SW, “Lower bound limit
analysis using non-linear programming”,
International Journal for Numerical Methods inEngineering, 2002a, vol. 55, no. 5, pp. 573–611.
[7] Lyamin AV & Sloan SW, “Upper bound limit
analysis using linear finite elements and non‐linear programming”, International Journal for
Numerical and Analytical Methods inGeomechanics, 2002b, vol. 26, no. 2, pp. 181–
216.
[8] Sloan SW, “Geotechnical stability analysis”,
Geotechnique, 2013, vol. 63, pp. 531–572.
[9] Wilson DW Abbo AJ Sloan SW Lyamin AV,“Undrained stability of a square tunnel where
the shear strength increases linearly withdepth”, Computers and Geotechnics, 2013, vol.
49, pp. 314–325.
[10] Wilson DW Abbo AJ Sloan SW Lyamin AV,“Undrained stability of a circular tunnel where
the shear strength increases linearly withdepth”, Canadian Geotechnical Journal, 2011,vol. 48, pp. 1328–1342.
[11] Abbo AJ Wilson DW Sloan SW Lyamin AV,“Undrained stability of wide rectangulartunnels”, Computers and Geotechnics, 2013,vol. 53, pp. 46–59.
[12] Augarde CE Lyamin AV Sloan SW, “Stabilityof an undrained plane strain heading revised”,
Computers and Geotechnics, 2003, vol. 30, pp.
419–430.
[13] Chehade F Shahrour I, “Numerical analysis of
the interaction between twin-tunnels: Influenceof the relative position and construction
procedure”, Tunneling and Underground SpaceTechnology, 2008, vol. 23, pp. 210–214.
[14] Wilson DW Abbo AJ Sloan SW Lyamin AV,“Undrained Stability of Dual Circular Tunnels”,
International Journal of Geomechanics, 2014,vol. 14, pp. 69-77.
[15] Wilson DW Abbo AJ Sloan SW Lyamin AV,“Undrained Stability of Dual Square Tunnels”,
12th International Association for Computer
Methods and Advances in Geomechanics
Conference, 2008, Goa, India, pp. 4284–429.

CONCLUSION
The stability of twin circular heading tunnels
horizontally aligned in tresca material under planestrain conditions were investigated using FELA. The
upper and lower FoS bounds were defined in terms ofgravitational accelerations in an unsupported
greenfield analysis using the software Optum G2.
The factor of safety results are a function of three
dimensional properties; depth ratio C/D, strength
ratio Su/γD and spacing ratio S/D. A new unit factorof safety stability chart was created to determine thefailure. Design stability charts were developed for
depth ratios 1, 3 and 6 to compare the failure behaviorof the different tunnel depths.
It was concluded the greater the depth, the less the
FoS. Additionally, the greater the depth the less the
spacing ratio impacts the failure and the more tunnel
stability interaction. Further work includes to modelsquare twin tunnel stability and to further investigate
the stability tunnel interaction for undrained clay.
REFERENCES[1] Broms BB & Bennermark H, “Stability of clay
at vertical openings”, Journal of the Soil
Mechanic and Foundations Division,
Proceedings of the American Society of Civil
Engineers, 1967, vol. 93, pp. 71–93.
[2] Assadi A & Sloan S, “Undrained Stability of
Shallow Square Tunnel”, Journal of
Geotechnical Engineering, 1991, vol. 117, pp.
1152–1173.
[3] Atkinson JH & Cairncross AM, “Collapse of ashallow tunnel in a Mohr-Coulomb material”,
Cambridge, pp. 202–06.
[4] Peck RB, “Deep excavations and tunneling insoft ground”, Proceedings of 7th International
71

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

RELATING THE TUNNEL SETTLEMENT PARAMETER WITH
VOLUME LOSS
Mathew Sams and Jim Shiau
School of Civil Engineering and Surveying, University of Southern Queensland, Australia

ABSTRACTThis paper describes the development, verification and use of a numerical model for investigating settlement
induced by circular tunnels in cohesive soils. The model aims to simulate the movement and relaxation of thesoil around the shield and lining annulus that occurs due to the overcutting and grouting of the tunnel void by atunnel boring machine. To achieve this, the model uses a pressure relaxation technique that progressively
reduces the tunnel support pressure from a specific amount until a point of failure is detected. For three separate
stages before this point of failure, the surface settlement data is exported for analysis. Using a regression of thecommonly used Gaussian equation on the settlement data, i x values can be determined for each case. This isdone for a number of geometry and soil ratios which will cover the most practical range for soft cohesive soils.
The results of this study are quite positive, settlement results compare well with previous experimental andobservational results. Design charts using dimensionless ratios have therefore been presented.
Keywords: Tunnel, Settlement, Undrained, Cohesive, Volume losschanges in stress resulting from tunnel construction
by determining analytical solutions for these
problems. ([5], [6], [7], and [8])However, with the rapid development ofcomputer technology, numerical modelling using
finite element or finite difference methods has
become the preferred method for geotechnicaldesign and analysis. These models are generallycompared to field observations and experimental
results for validation. In some cases however,
empirical and semi-empirical methods are still
applicable, and indeed quite capable. For tunnel
settlement in particular, the empirical method is stillwidely used, due to its suitability and ease of use.
([9] and [10])This empirical method for estimating surface
settlements generally follows a Gaussian distribution
curve, as in Eq. (1). This approach was firstsuggested by [11], who observed that it matchedsettlement patterns of deep excavations remarkably
well. For the particular application to tunnels,
research by [1] indicated a close fit withexperimental and observational results. This method
requires the input of a trough parameter (i x ) which
influences the physical width of the profile, and also
relates the volume loss and the maximum settlement,
as shown in Eq. (2).
In practice, a target volume loss (V s ) will likely
be known (based on experience or client specified);
this and the estimated i x can be used to predict a S max ,which can then be used in Eq. (1).

INTRODUCTION
Population increases, increased urbanisation, andthe rapid development of emerging countries have
driven research into the better management oftransport services and infrastructure. Tunnels haveincreasingly become a common solution; withlimited scope for changes above ground, better useof the underground space is an effective solution.
With the development of tunnel boring machines(TBM’s) over the past few decades tunnels can now
be produced increasingly difficult ground conditions,such as very soft ground. In such conditions where
the soil mechanics are more critical, responsibility
increasingly falls to the geotechnical engineer.
These problems arise in modern tunnel boringmachines (TBM’s) because of the small amount ofovercutting, resulting in the inevitable delay between
when the tunnel is bored and when the lining and thegrout is installed. During this period, there is always
a degree of soil movement.
The three primary design criteria of undergroundtunnels from a geotechnical perspective are: stability
during construction, long and short term settlement,
and determination of lining structural loading as
discussed by [1], [2], [3], and [4]. This paper
considers the settlement problem.
SETTLEMENT ANALYSIS OF TUNNELS
Surface settlement induced by tunnelling is acomplex phenomenon that is dependent on manyfactors such as soil and groundwater conditions,tunnelling dimensions and construction techniques.
Therefore, much modern tunnelling research has
been given to better predict the soils response to𝑆𝑆𝑥𝑥 = 𝑆𝑆𝑚𝑚𝑚𝑚𝑚𝑚 𝑒𝑒
72

−

𝑥𝑥2

2𝑖𝑖𝑥𝑥 2

𝑉𝑉𝑠𝑠 = √2𝜋𝜋 𝑖𝑖𝑥𝑥 𝑆𝑆𝑚𝑚𝑚𝑚𝑚𝑚

(1)
(2)

GEOMATE- Osaka, Nov. 16-18, 2015

PROBLEM DEFINITION
Figure 1 shows the nature of this equation. D isthe diameter of the tunnel, H is the to-axis tunnel
depth, C is the overburden, S x is the settlement
profile at the surface, S max is the maximum verticalsettlement, and i x is the trough width parameter
which, physically, is the distance from the tunnel
axis to the point of inflection of the curve.

The circular tunnel problem is shown in Fig. 2.
In this study, only greenfield settlement has been
analyzed. Thus, the surcharge load (σ s ) is set to 0
kPa. The soil is considered as homogenous
undrained clay following the Mohr-Coulomb model.
The system will be described in terms of
dimensionless ratios: C/D, γD/s u , and E/s u .
This paper will study the following parametricrange: C/D = 1 - 5, γD/ S u = 1.5 - 6, and E/s u = 100 800. Using this approach, the results can be studied
methodically, and practical design charts employing
these ratios can be produced that should cover apractical range.

Fig. 1 Typical settlement profile of a tunnel

Further examination of this method has been
extensive. Centrifuge modelling has been one of themethods used to test its adequacy, with results from[12], [13], [3], [10], and [14]. It has also been
extensively compared with measurements fromconstructed tunnels in [15], [16], [17], and [18],reporting settlement profiles of the shape suggested
by a Gaussian equation.
Estimations of the inflection point parameter, i xhave been attempted, the most notably by [19], [3],[20] in Eq.’s (3), (4), and (5) respectively. However,these only take into account the geometry of thesystem, volume loss and soil strength aren’t
definable parameters. The most widely used method
is the one suggested by [17], which throughanalysing data collected from tunnels in London
suggested that i x is linearly proportional to the toaxis tunnel depth, H, as in Eq. (6).

𝑖𝑖𝑥𝑥 = 0.5𝐷𝐷0.2 𝐻𝐻 0.8

𝐶𝐶 0.8𝑖𝑖𝑥𝑥 = 0.75𝐷𝐷 �𝐷𝐷�
𝐻𝐻

𝑖𝑖𝑥𝑥 = 0.29 �𝐷𝐷 � + 0.5
𝑖𝑖𝑥𝑥 = 𝑘𝑘𝑘𝑘

Fig. 2 Problem Definition

The problem is modelled using 2D plane strain
conditions in FLAC [22]. Despite the fact thattunneling is a three-dimensional activity, transverse
settlement under greenfield conditions can bemodelled quite accurately with this simplification[23].
3D numerical programing is much more complex
requiring more parameters which sometimes can bedifficult to determine in practice. Three-dimensional
analysis is also much more time consuming andcomputationally demanding. For simplicity, thetunnel can be reasonably considered to be very long
and at a consistent depth. It is the focus of this paper
to study 2D transverse surface settlement.

(3)
(4)
(5)

PRESSURE RELAXATION TECHNIQUE(6)With the development of powerful computersover the last two decades, numerical modelling has
proceeded to become a dominant technique forproblem resolution. The finite difference method is
one such technique that has been successfully usedin the past for modelling tunnels using shear strength
reduction method [24], and it has again been used inthis study. Using FLAC, the problem is solved by apressure relaxation method [25], developed in thispaper using the built-in program language FISH.
After defining boundary conditions, soil
properties and tunnel geometry, the developed

This equation wouldn’t be suitable for very
shallow cases (C/D < 1), as the diameter would
become a more dominant parameter. However, thisequation allows the coefficient of proportionality (k)to vary with other parameters such as volume loss
and soil type. Commonly assumed values of k range
from 0.4 for stiff clays to approximately 0.7 for soft
clays [21]. However, these haven’t been thoroughly
defined using dimensionless parameters.

73

GEOMATE- Osaka, Nov. 16-18, 2015

model slowly reduces the internal supportingpressure from a set amount, at each relaxation step.
In this study, the model was set to fully relax fromthe starting amount in 1% increments. At each ofthese relaxation steps, the surface settlement data isrecorded.
A typical finite difference mesh of the problem
in this study is shown in Fig. 3. The boundary
conditions shown in the figure are important asthey ensure that the entire soil mass is modelledaccurately despite using a finite mesh. It should
be noted that the soil domain size for each of thecases was chosen so that the failure zone of the soil
body is placed well within the domain. Using Fig. 2,
L = 1.5D and W = 4C are adopted in all analyses ofthe paper.

stage was 52% relaxation; the stages being analyzed
are: 5%, 13%, and 26%).

Fig. 4 Unbalanced force history plot (C/D=3, γD/ S u =4,
E/s u =200)

Fig. 3 Typical mesh of the problem (C/D=2)

Fig. 5 Plasticity (left) and velocity (right) plots at thestage of collapse (C/D =1, γD/ S u =3)

The internal pressure σ t , is reduced bymultiplying the at-rest pressure, where no movement
occurs, by a reduction factor which is based on thenumber and range of relaxation steps. At eachsubsequent relaxation step, the internal pressure isless than the at-rest pressure, and consequently thesoil moves into the tunnel void until the internal
forces in the soil reach equilibrium, balanced orotherwise.
In the elastic state, internal forces have reached abalanced state (nodal unbalanced force approacheszero), no more movement takes place and thecircular tunnel is considered to be stable. Once theinternal pressure is reduced to the extent where theinternal forces are no longer sufficient to retain theearth pressures, nodal forces become unbalanced andthe tunnel is considered to be unstable.
The failure point occurs when the unbalanced
forces fail to reach zero equilibrium during aparticular relaxation stage. This point of instability isquite abrupt and can be identified relatively easily
by observing the unbalanced force history (Fig. 4).
It can also be clearly observed using plasticity
indicator and velocity plots (Fig. 5).
The surface settlement data is recorded for every
relaxation step. Thus, settlement analysis can bedone for any arbitrary step. In this study, the analysis
has been based on the relaxation steps of 10%, 25%,
and 50% of the collapse stage (i.e. if the collapse

The volume loss can then be back-calculated byintegrating the surface settlement data. It should be
noted that this numerical model controls the internaltunnel pressure; the volume loss isn’t a controlledparameter.
RESULTS AND DISCUSSION
As previously discussed, these settlement
profiles are commonly represented by the Gaussian
equation, as in Eq. (1). In this study, this equation
has been used for a regression with the datacollected at the collapse stage of each of the cases.
This has been done using MATLAB, and the curve
fitting toolbox. A typical example of this is shown in
Fig. 6. It was found that using this equation to model
settlement can be considered accurate, with r2 valuesof greater than 0.97 achieved for all cases, where an
r2 of one would indicate a perfect fit. The example
shown in Fig. 6 is for C/D = 4, γD/S u = 3, and thisparticular example has r2 = 0.987.
In Fig. 7, the depth ratio (C/D) is varied and thestrength ratio (γD/S u ) and Young’s modulus (E) are
kept constant. The profiles are as expected, with theshallow case producing a narrow but deep trough,that become shallower and wider as C/D increases.
In Fig. 8, C/D and E are kept constant, and γD/S u isvaried. Once again, the trend is as expected, when74

GEOMATE- Osaka, Nov. 16-18, 2015

the strength ratio is increased (i.e. soils become
weaker), the settlement at the point of collapse isgreater. Fig. 9 similarly shows the impact ofYoung’s modulus, with the stiffer soils (higher E)
having proportionately lower settlement.

relaxation continues, the displacement pattern
becomes more as expected. The y-displacement
contours show similar behavior.

Fig. 9 Settlement profiles with varying E/ S u
Fig. 6 Typical regression analysis (C/D =4, γD/S u = 3)

Fig. 10 Displacement vector and y-displacement
contour of 10% of collapse (C/D=1, γD/S u =3,
E/S u =200)

Fig. 7 Settlement profiles with varying C/D
Fig. 11 Displacement vector and y-displacement
contour of 25% of collapse (C/D=1, γD/S u =3,
E/S u =200)

Fig. 12 Displacement vector and y-displacement
contour of 50% of collapse (C/D=1, γD/S u =3,
E/S u =200)

Fig. 8 Settlement profiles with varying γD/S u

Fig. 13 contains a comparison with experimental
results by [1], and suggested equations by [19], [3],and [20]. These equations are given in Eq.’s (3), (4),and (5). It should be noted that these equations are
based on γD/S u ≈1.8, γD/S u =2.6, and γD/S u ≈3
respectively.
These equations appear to match quite well with

Figures 10-12 show displacement vectors and ydisplacement contours of a particular case (C/D=1,
γD/S u =3, E/S u =200) during 10%, 25%, and 50% of
the collapse relaxation, as previously discussed. In
Fig. 10, there is too much internal pressure being
applied, resulting in some local blowout; as75

GEOMATE- Osaka, Nov. 16-18, 2015

k =0.5 could be used for clay. That study used clay
with γD/S u =2.6. If this strength ratio is used in Eq.
(7), a result of k =0.55 is obtained. Therefore, it canbe said that the results of this study and this
proposed equation correlate quite well with [3]. It
should be noted however, that this equation is forsoft clay, using for harder clay (γD/S u < 1) will resultin unrealistically low settlement trough parameters(k).

the results of this study, appearing in approximately
in the expected positions according to the strength
ratio (γD/S u ) with which they were obtained.

6
D/su = 1.5
D/su = 2
D/su = 3

5

D/su = 4
D/su = 5
D/su = 6

Peck (1969) Hard clay
Peck (1969) Medium clay
Clough and Schmidt (1981)
Mair and Taylor (1997)
Lee et al (1999)

ix /D

4

𝒊𝒊𝒙𝒙

3

𝑯𝑯

2

𝜸𝜸𝜸𝜸

= 𝒌𝒌 = 𝟎𝟎. 𝟓𝟓𝟓𝟓𝟓𝟓 � � + 𝟎𝟎. 𝟎𝟎𝟎𝟎
𝒔𝒔𝒖𝒖

(7)

1

0
1

2

3

4

5

6

H/D

Fig. 13 Comparison with previous results

Fig. 15 Equation for practical estimation of k

CONCLUSION
A pressure relaxation approach has beendeveloped to simulate a circular tunnel and therelaxation of the soil that occurs during construction.
The stage of relaxation that induces collapse can be
determined using outputs from the FISH script that
automatically generates the mesh and outputs
surface settlement data for each relaxation step.
Using this, the settlement data at three previous ‘precollapse’ stages are exported to MATLAB where a
Gaussian curve is fitted. This allows reliable
estimation of i x for all cases.
This study compares favorably with previous
published results, and it is concluded that volume
loss makes only a small difference to the resulting
trough parameter. By using the O’Reilly and New
relationship, a k value can be selected based purely
on the soil strength ratio, γD/S u .

Fig. 14 Comparison with O’Reilly’s and New equation(eq. 6)Using the widely known equation from [17], theresults can be compared, as in Fig. 14. These results
concur strongly with this equation, with the stronger
soils achieving the lowest k (0.29 for γD/S u =1.5),
and the weaker soils achieving the highest k (0.96
for γD/S u =6). Therefore, the general use of k = 0.5
for clay may not be a good approach; will be very
conservative for strong soils and very unconservative for weak clay.
From Fig. 14, it also becomes clear that a k value
could be reasonably selected based solely on thestrength ratio. For each γD/S u , there is somevariation being caused by differing levels of volume
loss, but its impact is relatively small. The overallvalues of k for each γD/S u are taken from Fig. 14,
and graphed against γD/S u , as shown in Fig. 15. An
equation describing the relationship between k andγD/S u can then be acquired, as shown in Eq. (7).
Reference [3] supported a recommendation that

REFERENCES[1] Peck, R.B., (1969). “Deep Excavations andTunneling in Soft Ground”. In: Proceedings of the
7th International Conference on Soil Mechanics andFoundation Engineering, Mexico City, State-of-theart Volume, pp. 225-290.
[2] Ward, W., & Pender, M. (1981). “Tunnelling in soft
ground: general report”. Paper presented at theProceedings of the 10th International Conference on

76

GEOMATE- Osaka, Nov. 16-18, 2015

Soil Mechanics and Foundation Engineering,
Stockholm.
[3] Mair, R. J., and Taylor, R. N. (1997). “Themelecture: Bored tunnelling in the urban environment”.
In Proc., 14th Int. Conf. on Soil Mechanics andFoundation Engineering, Hamburg, Balkema,
Rotterdam, Netherlands, 2353–2385.
[4] Mair, R. (2008). “Tunnelling and geotechnics: newhorizons”. Geotechnique, 58(9), 695-736.
[5] Rowe, R. K., Lo, K. Y., & Kack, G. J. (1983). “A
method of estimating surface settlement abovetunnels constructed in soft ground”. Canadiangeotechnical journal, 20(1), 11-22.
[6] Sagaseta, C. (1987). “Analysis of undraind soil
deformation due to ground loss”. Geotechnique,37(3), 301-320.
[7] Loganathan, N., & Poulos, H. G. (1998). “Analyticalprediction for tunneling-induced ground movements
inclays”.
Journalof
Geotechnical&Geoenvironmental Engineering, 124(9), 846.
[8] Pinto, F., & Whittle, A. J. (2013). “Groundmovements due to shallow tunnels in soft ground. I:
Analytical solutions”. Journal of Geotechnical andGeoenvironmental Engineering, 140(4), 04013040.
[9] Gunn, M. J. (1993). “The prediction of surface
settlement profiles due to tunneling”. In Predictive
Soil Mechanics. Proceedings of the Wroth Memorial
Symposium, 27-29 July, St Catherines College,
Oxford.
[10] Taylor, R. (1998). “Modelling of Tunnel Behaviour”.
Proceedings of the ICE-Geotechnical Engineering,131(3), 127-132.
[11] Martos, F. (1958). “Concerning an approximate
equation of the subsidence trough and its time
factors”. In International strata control congress,
Leipzig, 191-205.
[12] Atkinson, J. H., & Potts, D. M. (1977). “Subsidence
above shallow tunnels in soft ground”. Journal of thegeotechnical engineering division, 103(4), 307-325.
[13] Mair, R. J. (1979). “Centrifugal modelling of tunnel
construction in soft clay”. PhD Thesis.
[14] Osman, A. S., Mair, R. J., & Bolton, M. D. (2006).
“On the kinematics of 2D tunnel collapse inundrained clay”. Géotechnique, 56(9), 585-595.

[15] Attewell, P., & Farmer, I. (1974). “Grounddeformations resulting from shield tunnelling in
London Clay”. Canadian Geotechnical Journal, 11(3),380-395.
[16] Cording, E. J., and Hansmire, W. H. (1975).
“Displacement around soft ground tunnels”. General
Report: Session IV, Tunnels in soil. In Proc., 5thPanamerican Congr. on Soil Mech. and Found. Engrg.
[17] O'reilly, M. P., & New, B. M. (1982). “Settlements
above tunnels in the United Kingdom-theirmagnitude and prediction”. In Tunneling ’82. 173181.
[18] Rankin, W. (1988). “Ground movements resulting
from urban tunnelling: predictions and effects”.
Geological Society, London, Engineering Geology
Special Publications, 5(1), 79-92.
[19] Clough, G. W., & Schmidt, B. (1977). “Design andperformance of excavations and tunnels in soft clay”.
Dept. of Civil Engineering, Stanford University.
[20] Lee, C.J., Wu, B.R., Chiou, S.Y., (1999). “Soilmovements around a tunnel in soft soils”.
Proceedings of the National Science Council, Part A:
Physical Science and Engineering 23 (2), 235–247.
[21] Guglielmetti, V., Grasso, P., Mahtab, A., & Xu, S.
(Eds.). (2008). “Mechanized tunnelling in urban
areas: design methodology and construction control”.
CRC Press.
[22] Itasca Consulting Group Inc (2003). “FLAC User’s
Manual”. Minneapolis, USA.
[23] Ghaboussi, J., Ranken, R. E., & Karshenas, M.
(1978). “Analysis of subsidence over soft-groundtunnels”. Paper presented at the Evaluation andPrediction of Subsidence.
[24] Shiau, J.S., Lamb, B. R., and Sams, M.S. (2015).
“Stability Charts for Unsupported Circular Tunnels in
Cohesive Soils”. Submitted for consideration.
[25] Panet, M., & Guenot, A. (1983). “Analysis ofconvergence behind the face of a tunnel”. Tunnelling82, proceedings of the 3rd international symposium,
Brighton, 7–11 June 1982, P197–204. Publ London:
IMM, 1982. Paper presented at the International
Journal of Rock Mechanics and Mining Sciences &Geomechanics
Abstracts.

77

FifthInternational Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan,Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

EFFICIENCY OF SAND-NATURAL EXPANSIVE CLAY MIXTURE AS
A HYDRAULIC BARRIER

1

2

Abdullah A. Shaker1 and Tamer Y. Elkady 1 2Eng. AbdullahBugshan Research Chair in Expansive Soils Civil Engineering Department, College ofEngineering, King Saud University, Riyadh, Saudi Arabia

Soil Mechanics and Foundation Research Laboratory, Faculty of Engineering, Cairo University, Giza,
Egypt
ABSTRACTThe aim of this study is to evaluate the hydraulic performance of a sand-natural expansive clay mixture incomparison with sand-bentonite mixture. Natural expansive clay used in this study was obtained from Al-Qatif
region, Saudi Arabia. The saturated hydraulic conductivity was evaluated for different mixtures of sand-Al Qatif
clay with clay contents ranging from 10% to 60% clay. Similarly, the saturated hydraulic conductivity of sandbentonite mixtures with different bentonite contents (0%, 5%, 10% and 20%) was also evaluated. All hydraulicconductivity tests were performed under different confining pressures. Test results revealed that sand-Al-Qatif
clay mixtures can perform adequately as a hydraulic barrier; however, with Al-Qatif clay contents greater thanthat used for sand-bentonite mixtures. This is attributed to difference between clay mineralogy between Al-Qatif
clay and bentonite. In addition a quantitative approach is proposed to assess the hydraulic conductivity of sandAl-Qatif clay mixtures in comparison to that for sand-bentonite mixtures. Finally,soil fabric of mixtures was
examined using scanning electron microscope (SEM) technique to observe differences in fabric between sandAl-Qatif clay and sand-bentonite mixtures.
Keywords: Expansive clay, Hydraulic conductivity, Sand, Mixture, Barrier

considered. Daniel (1993) and Rowe et al (1995)
suggested specifications for liner material in which
the coefficient of hydraulic conductivity (cm/s) was
put as (10-6< k <10-8).
The aim of this paper is to investigate theefficiency of a mixture of sand-Al Qatif (natural)
expansive clay as a hydraulic barrier.The main
efficiency criteria considered in this study ishydraulic conductivity. Test parameters consideredin this study included clay content (10%, 20%, 30%,
40% and 60% by dry weight of sand and confiningpressure (50, 100, 200 and 400 kPa). The relative
performance of the proposed sand-Al-Qatif clay
mixture with respect to sand bentonite mixtures withbentonite contents ranging from 5% to 20% by dry
weight of sand was evaluated. In addition, SEM
technique was used to examine the different fabrics
emerged for sand-Al-Qatif clay and sand-bentonite
mixtures at different clay contents.

INTRODUCTION
The rapid growth of oil industry in the easternregion of Saudi Arabia generates enormous
quantities of hazardous wastes. In order to avoid theground water contamination with these hazardous
wastes provision of proper hydraulic barriers is ofutmost importance. These barriers are usuallyconstructed using sand-clay mixtures.Current
practices employ construction of hydraulic barriers
using mixture of high plastic expansive clay withsand. Bentonite is commonly used in these mixtures
because of its high swelling potential that provide
better sealing characteristics with small clay content(Chapuis. 1990).
The call for utilizing local resources is always
well received as it provides an alternative to costly
imported foreign materials.Several researchersinvestigated the use of natural clays as a hydraulicbarrier. Rawas et al (2006) investigated the use of
Oman shale in liners. Obrike et al (2009)
investigated the use of some type of shale in waste
disposal landfills in Nigeria. Langdon et al (1996)
studied the permeability of clay liners of somegeological formations and different depositional
basins in Turkey.
General specifications of hydraulic barriers canvary from one project to another. Some tolerance on
the amount of water to be allowed to pass can be

MATERIALS
The sand used in this study was locallyprocessedsand which is typically used in concrete constructionin Saudi Arabia. The grain size distribution of sandis presented in Fig. 1 and its specific gravity is
2.66.Values corresponding to coefficients of
uniformity and curvature are C u = 1.737 andC c =1.078;respectively.
78

GEOMATE- Osaka, Nov. 16-18, 2015

The naturalexpansive clay used in this study was
sampledfrom the town of Al-Qatif. Al-Qatif, which
is a historic, coastal oasis region located on thewestern shoreline of the Arabian Gulf in the Eastern
Provinceof
Saudi
Arabia.Geotechnicalandmineralogical characterization of this expansive clay
was documented by Dhowian et al. (1985),
Abduljawad (1992) and Al-Shamrani et al. (2010).
This expansive clay is considered highly expansive
in nature due to the presence of high smectite
mineral content (Azam, 2003). Soil samples wereobtained from a test pit excavated to a depth ofabout three meters. Samples were transferred tolaboratory for complete geotechnical, chemicalandmineralogical characterization. Tables 1 and 2
summarize the Geotechnical and chemical properties
of Al-Qatif clay, respectively. Figure 2 shows the XRay diffraction X-Ray diffraction (XRD) intensity
diagram. From this figure (Fig. 2), it is apparent thatthe main swelling clay minerals are montmorillonite
and attapulgite (palygorskite).
The bentonite used in this study was sodium typeobtained by a local supplier. Bentonite iscommercially used as drilling mud for boring
activities in Saudi Arabia. Tables 1 and 2 summarize
the geotechnicaland chemical characteristics ofbentonite, respectively. The mineralogical analysis
of bentonite using XRD technique indicated that themain clay mineral is montmorillonite minerals along
withquartz, feldspar and hematite minerals as shownin Fig. 3.

Percent Finer (%)

CaO
FeO
ZnO

4000

0.01

A A: Quartz

3500

B: Montmorillonite
C: Attapulgite
D: Dolomit
E : Muccovite
F : Illite
G : Hallovsite

Intensity

3000
2500
2000
1500
1000

C

500

C CE

F

0

BD E

D DD

F CF GC

222
42
2 θ Degrees
Figure 2: X-Ray diffraction of Al-Qatif clay

Bentonite

1200

2.70
480
50
430
--

A: Quartz
B: Montmorillonite
C: Feldspar
D: Hematite

B

1000
800

---

600

B

A

B

400

C

D

200

CH refers to clay with high plasticity0

Table 2 Chemical composition of Al-Qatif clayand
bentonite
Al-Qatif
Clay
0.5
4.92
15.82
55.86
5.22

0.1

grain size (mm)(1)

ChemicalCompoundNa 2 O
MgO
Al 2 O 3
SiO 2
K 2O

1

Figure 1: Grain size distribution of sandIntensity

Al-Qatif
Clay
Index parameters
Specific gravity, G s
2.75
Liquid limit, LL (%)
140
Plastic limit, PL (%)
45
Plasticity index, PI (%)
95
USCS
CH1
Swelling Characteristics
Swelling
550-600
pressure(kN/m2)
Swelling potential(%)
16-18

3.87
21.36
0.92

100
90
80
70
60
50
40
30
20
10
0
10

Table 1 Geotechnical characteristics of Al-Qatif
clayand bentonite
Property

1.74
9.39
---

2

22

42

2 θ Degrees

Bentonite

Figure 3: X-Ray diffraction of bentonite

0.83
3.07
14.38
47.25
0.57

TESTING METHODS
Sample Preparation of Mixtures

79

GEOMATE- Osaka, Nov. 16-18, 2015

In this study, two types of mixtures were
fabricated for the laboratory testing program. The
first was sand-Al-Qatif expansive clay mixtures withclay contents of 0%, 10%, 20%, 30%, 40% and 60%
by dry weight of sand. The second mixtures weresand-bentonite with bentonite contents of 0%, 5%,
10% and 20% by dry weight of sand. The sandbentonite mixture was used to provide basis ofcomparison for the sand-Al-Qatif clay mixtures.
The natural expansive clay (Al-Qatif) obtained
from the field was air-dried, pulverized and sieved
using sieve No. 40 to form clay powder. Desired
proportions of oven-dried sand and powder clay
(whether Al-Qatif or bentonite) were hand mixed
under dry conditions to obtain a homogenous blend.
Desired water content was then added and mixed
thoroughly and stored in plastic bags overnight in
humid environment to allow time for the soil
particles to hydrate as recommended by ASTM D
698 (2000) Test samples were statically compacted
to initial molding conditions corresponding tomaximum dry unit weight and optimum moisture
content for each sand-clay mixture. Final dimensionsof samples were 70 mm in diameter and 34 mm highfor hydraulic conductivity tests.

10
20
30
40
60

0

PL (%)PI (%)

0

NA

NA

NA

5

NA

NA

NA

10

36

23.6

12.4

20

62

27

35

19

Specific
Gravity
2.66
2.67
2.68
2.7

0%
10%
20%
30%
40%
60%

18
17
16
15
14
13
12
0

Table 4 Atterberg limits of sand-Al-Qatif clay
mixturesClay content (%)

LL (%)

Dry unit weight (KN/m3 )

2.66
2.67
2.67
2.68
2.68
2.69

Bentonite
content (%)

The compaction curves for all sand-Al-Qatif
mixtures and sand-bentonite mixtureswere carriedout in accordance with standard proctor compaction
method (ASTM D698 - 00). Figures 4 and 5 show
the variation of compaction curves as a function ofclay content for sand-Al-Qatif clay and sandbentonite mixtures; respectively.For sand-Al-Qatif
clay mixtures (Fig. 4), it is apparent that as, the clay
content increases,the maximum dry unit weightincreasesreaching a peak value between 20% and
30% clay content. However, as the clay content
increased beyond this value (i.e., 25%), themaximum dry unit weight decreases with increase inclay content. Similar trend was observed for sandbentonite mixtures (Figure 5) with the peak value ofmaximum dry unit weight observed at 10%
bentonite content. These compaction curves will be
used to determine the initial molding conditions
considered in this study.

Table 3 Specific gravity values for different
sand-Al-Qatif clay and sand-bentonite mixtures
Bentonite
Content
(%)
0
5
10
20

NA
11.4
17.5
23.1
37.8

Compaction Curves for Mixtures

The basic index properties of sand-Al-Qatif
clayand sand-bentonitemixtures were evaluated for
different clay contents. Tests performed included
specific gravity tests (ASTM D854-02) andAtterberg limits (ASTM D 4318-00). Specificgravity values are summarized in Table 3.As shownin Table 3, a notable increase in specific gravity withincrease in clay content. The liquid limit, plastic
limit and plasticity index values for sand-Al-Qatif
clay and sand-bentonite mixtures at different clay
contents, are presented in Tables 4and 5;
respectively.

Specific
Gravity

NA
17.6
18.5
19.9
22.2

Table 5Atterberg limits of sand-bentonite mixtures

Index Properties of Mixtures

Clay
Content
(%)
0
10
20
30
40
60

NA
29
36
43
60

LL (%)NA

PL (%)
NA

10

20

30

40

Moisture content (%)

Figure 4: Compaction curves of sand-Al-Qatif
claymixtures with different clay contents

PI (%)NA
80

GEOMATE- Osaka, Nov. 16-18, 201519
18

Dry unit weight (KN/m3)

barrier (K=10-7 cm/s) as shown in Fig. 6.
To quantitatively assess the hydraulicconductivity of sand-Al-Qatif clay mixtures incomparison to that for sand-bentonite mixtures, a
simple index termed hydraulic conductivity
performance ratio (HCPR) was proposed and isdefined as follow:

0%B
5%B
10%B
20%B

17
16
15

(HCPR) =

14
13
10

20

30

𝐿𝐿𝐿𝐿𝐿𝐿(𝐾𝐾 𝑜𝑜𝑜𝑜 sand−bentonite )(1)

The HCPR of sand-Al-Qatif clay mixtures are
summarized in Table 6 for samples at 10% and 20%
clay content. From this table, it is noted that thehydraulic conductivity performance ratio depends on
confining pressure and ranges between 0.48 and 0.59.

120

𝐿𝐿𝐿𝐿𝐿𝐿(𝐾𝐾 𝑜𝑜𝑜𝑜 sand−Al_Qatif clay )40

Moisture content (%)
Figure 5: Compaction curves ofsandbentonitemixtures with different bentonite contents

Table 6 Hydraulic conductivity performance ratio ofsand-Al-Qatif claymixture at 10% and 20% clay
content

Hydraulic Conductivity Test
Thesaturated hydraulic conductivities of sandAl-Qatif clayand sand-bentonitemixtures were
evaluated using flexible wall constant head
permeameter. Confining pressures considered in thistest series rangedfrom 50 to 400 kPa.Tests were
performed under constant tin accordance withASTM D5084 (2003). This procedure includes four
test stages: specimens soaking, backpressure
saturation,
consolidation
andpermeation.
Permeation through the test samples was conductedunder a constant hydraulic gradient (i)of 30.

Confining
Pressure (kPa)

Performance ratio (HCPR) of
mixture

50
100
200
400

10%

20%

0.57
0.57
0.56
0.59

0.48
0.48
0.5
0.5

Hydraulic Conductivity, K (cm/s)

1.E-03DISCUSSION AND TEST RESULTS
A comparison of hydraulic conductivities of
evaluated for sand-Al-Qatif clayand sand-bentonite
mixtures are presented in Fig.6. From this figure, it
is observed that the hydraulic conductivity of sandbentonite mixtures is much lower than that for sandAl-Qatif claymixtures. This is attributed todifferences in clay mineralogy between Al-Qatif andbentonite clay. In other words, bentonite has a highswelling ability due to the presence of highpercentage of montmorillonite mineral as comparedto Al-Qatif clay.This swelling ability promote thethe better sealing of sand voids even under low clay
content.
Furthermore, it is noted that sand-bentonite
mixtures with 5% bentonite content can produce a
hydraulic conductivity equivalent to that of 30% AlQatif clay content, also the 10%, 20% bentonite
content can produce a hydraulic conductivity
equivalent to that of 40%, 60% Al Qatif clay
content; respectively. Sand-Al-Qatif clay mixtureswith clay contents greater than 40% can match thehydraulic conductivity requirementsof hydraulic

50 kPa100 kPa
200 kPa
400 kPa
50 kPa100 kPa
200 kPa
400 kPa

Sand-Qatif

1.E-04
1.E-05
1.E-06line of hydraulicbarrierrequirements

1.E-07
1.E-08

SandBentonite

1.E-09

0

20

40

60

80

Clay Content (%)

Figure 6: Variation of hydraulic conductivity ofsand-Al-Qatif clay mixtures and sandbentonitemixtures with clay content
SEM Investigation
The fabric of sand-Al-Qatif clayand sandbentonite mixtures was observed using scanningelectron microscope (SEM) technique. SEM was
performed using Joel apparatus (Model JSM-6380
LA). Micrographs of SEM investigations for both81

GEOMATE- Osaka, Nov. 16-18, 2015

mixtures at 20% clay content are shown in Figs 7
and 8 for sand-Al-Qatif clayand sand-bentonite
mixtures; respectively. From these micrographs, it
was observed that, for sand-Al-Qatif claymixtures
clay partially filled the void and coated the sandgrains while grain-to-grain contacts are maintained
which, in turn, control the behavior of the mixture.
Micrographs for sand-bentonite mixture with sameclay content (i.e., 20%) showed that the clay particle
assemblages were observed to occupy the voids
between sand grains and develop connectors
between sand grains. This leads to a significant
reduction in hydraulic conductivity of sandbentonite mixtures Compared to the hydraulicconductivity of sand-Al-Qatif claymixture at sameclay content.

Based on this investigation, the following
conclusions can be derived:
1- The clay content had a significant impact on
the hydraulic conductivity of sand-Al-Qatif
clay mixtures; whereas, confining pressure
was observed to have a pronounced effect on
K at clay contents greater than 30%.
2- The hydraulic conductivity of sandbentonite mixtures was to be much lower
than that for sand-Al-Qatif clay mixtures.
This difference is attributed to the difference
in clay mineralogy of Al-Qatif and bentoniteclay.
3- The HCPR proposed in this study can beused as a quantitative approach to evaluate
the hydraulic conductivity of sand-naturalclay mixtures with respect to that of sandbentonite mixtures. The HCPRfor sand-AlQatif clay ranged between 0.48 and 0.59.
4- The SEM investigation for sand-Al-Qatif
clay mixtures showed thatthe part of the clay
particles reside in the voids between thesand grains at clay contents 20%. As forsand-bentonite mixtures at the same clay
content observed, the gaps between sandgrains are hardly visible and filled with clay
causing a significant decrease in hydraulicconductivity.
5- Al-Qatif clay was found to satisfy thehydraulic conductivity requirements forliners when 40 % clay or more is used.

Sand grain

Figure 7: SEM of sand-Al-Qatif claymixtures
mixture with 20% clay content

ACKNOWLEDGEMENTS
This paper was financially supported by King
Saud University, Vice Deanship of Scientific
Research Chairs.
REFERENCES[1] Abduljauwad, S N, "Characterization andchemical treatment of Expansive clay in
AlQatif.", International Journal of RockMechanics
andMiningSciences&Geomechanics Abstracts, Volume 29, Issue 4,
July 1992, Page 226.
[2] Al-Shamrani, M.A., Mutaz,E., Puppala, A.J.,
and Dafalla, M.A. "Characterization of
Problematic
Expansive
Soils
from
Mineralogical and Swell Characterization
Studies.".American
Societyof
Civil
Engineers,Geoflorida, Florida, 2010, No.199, pp.
793-802.
[3] ASTM D854-02. “Standard test methods for
specific gravity of soils.” Annual book ofClay particle

Figure 8: SEM of sand-bentonite mixtures mixture
with 20% clay content

CONCLUSIONS
82

GEOMATE- Osaka, Nov. 16-18, 2015

ASTM standards, section 4, vol. 04.08, Soil androck; Building stones, ASTM, PA[4] ASTM D 698-00. “Standard Test Methods forLaboratory Compaction Characteristics of Soil
Using Standard Effort.” Annual Book of ASTM
Standards, Section 4, vol. 04.08, Soil and Rock;
Building Stones, ASTM, PA.
[5] ASTM D4318-00. “Standard Test Methods for
Liquid Limit, Plastic Limit, and Plasticity Index
of Soils.” Annual Book of ASTM Standards,
Section 4, vol. 04.08, Soil and Rock; Building
Stones, ASTM, PA.
[6] ASTM D5084-03. “Standard Test Methods for
Measurement of HydraulicConductivity of
Saturated Porous Materials Using a Flexible
Wall Permeameter.”American Society for
Testing and Materials, West Conshocken, PA.
[7] Azam. S, Influence of mineralogy on swellingand consolidation of soils in eastern Saudi
Arabia. Can Geotech J, 2003, 40(5):964–975.
[8] Chapuis,
R.R.
“Sand-bentonite
liners;
Predicting permeability from laboratory tests.”
Canadian Geotechnical Journal, 1990, 27, 47–
57.
[9] Daniel, D.E, "Geotechnical practice for waste
disposal."Chapman and Hall, London, 1993.
[10] Dhowian, A.W., Ruwiah, I., AndErol, A."TheDistribution and Evaluation of the Expansive
Soils in Saudi Arabia."2nd Saudi Engineering
Conference, KFUPM, Dhahran, Vol.4, 1985.
[11] Langdon, N.J., Al Hussaini, M.J.,Walden,P.J.,
and Sangha, C.M, “An assessment of
permeability of clay liners: two case histories”.
Geologicalsociety,London,
Engineering
Geology Special publications, 1996.
[12] Obrike, S.E., Osadebe, C.C., and Omoniyi, S.S,
"Geotechnical analysis of two Nigerian soils foruse as clay liners."Bulletin of Engineering
Geology and the Environment, 2009, Volume
68, issue 3, p. 417 – 419.
[13] Rawas,A.A.,Yahiae, A.M., Abdulaziz, S.A., andSalem, A. "sand–attapulgite clay mixtures as alandfill liner."Geotechnical and GeologicalEngineering, 2006, 24: 1365–1383.
[14] Rowe RK, Quigley RM, Booker JR,"Clayey
barrier systems for waste disposal facilities". E
and FN Spon, 1995.

83

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

MECHANICAL PROPERTY OF SAND CEMENTED WITH CALCIUM
PHOSPHATE COMPOUNDS USING PLANT–DERIVED UREASE

1

R.A.N. Dilrukshi1, Jun Watanabe1 and Satoru Kawasaki2
Graduate School of Engineering, Hokkaido University, Sapporo 060-8628, Japan
2
Faculty of Engineering, Hokkaido University, Sapporo 060-8628, Japan
ABSTRACTThe effect of plant-derived urease enzyme to induce the precipitation of calcium phosphate compounds(CPCs) and hence to improve the unconfined compressive strength (UCS) of sand was examined as a novel, ecofriendly ground improvement method. Initially, Toyoura sand test pieces were cemented only from CPC solution.
Furthermore, another sand test pieces were cemented by different concentrations of urea, concentration fixedplant seed extract (Watermelon) to obtain optimal cementation and different concentrations of CPC solutions
made from calcium and phosphate stock solutions. All test pieces were cured up to 28 days in an airtightcontainer at high humidity at 25oC. The UCS tests and scanning electron microscope (SEM) observations of sandtest pieces were carried out. The UCS of test pieces cemented with CPC and plant extract were significantly
higher than that of test pieces cemented without plant extract and increases with time. The best ratio of Ca: P in
CPC solution was 0.75 M: 1.5 M, reaching a maximum UCS of 125.6 kPa after 28 days of curing. In addition,
pH concentration was measured after UCS test and it has increased with time. Results indicate that the best pH
for optimal cementation is 8.0. A specific crystal structure could not be identified from SEM observations in thesegments of the test pieces cemented with CPC in all cases in this study. These results suggest that the additionof plant extract to CPC significantly enhances the mechanical properties of sand.
Keywords: Plant-based Urease, Calcium Phosphate Compound, Unconfined Compressive Strength, Ground
Improvement

excavation of cement improved ground. Therefore,
in recent years, biogrout- based ground improvement
practices have been introduced.
The process of using biological means to obtainground improvement is known as biogrouting [2].
Different mineral formation mechanisms are
involved in the formation of biogrouts. Carbonate
precipitation using urea and ureolytic bacteria [3] orurea and purified/crude extracts of plant species
having urease activity [4]-[6] or using glucose andyeast [7], iron/manganese compound precipitation
though iron-oxidizing bacteria [8], siloxane bondformation using glucose and yeast [9], calcium
phosphate compound (CPC) based chemical grouts(CPC-Chem) formation by its self-setting
mechanism [10], CPC biogrout (CPC-Bio)formation in relation to the addition of ureolytic
microorganisms and an ammonia source to CPCChem [11]. The solubility of CPC-Bio which is
generated as a result of the biological action isdependent on its pH (Fig.1 and Table 1) [12]. CPCChem is easy to obtain, safe to handle, non-toxic,and can be recycled in the form of a fertilizer. Theseadvantages make it suitable for geotechnicalapplications [10]. When CPC-Chem was converted
to CPC-Bio by the addition of urease producing
microorganisms and an ammonia source, theunconfined compressive strength (UCS) increased
from 42.9 kPa to 57.6 kPa [11].

INTRODUCTION
In recent years, development of infrastructureswith scarcity of useful land has become a major
issue and the engineers are compelled to resolve theproblem. Possible alternative solutions may be oneof among as avoid the particular land, removal/
replace unsuitable soils, designing the planned
structures (flexible/ rigid), and modifying existing
ground using ground improvement techniques.
However, currently, the engineers are focusing on
modifyingexisting
groundusing
groundimprovement techniques by ensuring sustainability
in land use. Ground improvement refers to atechnique that improves the engineering propertiesof the soil mass treated and hence to enhancebearing capacity of soil for successful foundation
design and use as a countermeasure against natural
disasters, including ground liquefaction of saturated
sand.
Currently, ground improvement techniques suchas grouting via cement, chemical, compaction,
fracture and jet, micro piles, jacked piers, driven
piers, ground anchors, shoring, soil nailing, vibro
compaction, concrete columns, piers, etc. arepracticing widely [1]. However, these techniques are
related with major environmental issues such aslarge CO 2 emissions during cement production, andhigh energy cost for cement production and re-84

GEOMATE- Osaka, Nov. 16-18, 2015

strength for mitigating ground liquefaction [14].
SELECTIONOFA
UREASE
SOURCEPRECIPITATION

The importance of urease enzyme is to catalyze
the reaction of urea hydrolysis to form ammonium
ion (NH 4 +) and carbonate ion (CO 3 2-) (Eq. (1)). The
NH 4 + and CO 3 2- produced from this reaction
represent the final products of the reaction. However
NH 4 + ions actually start out as NH 3 . When NH 3
reacts with water, it creates OH- ions, which raise
the pH of the system. A number of common plantfamilies are very rich in urease; for example, melons
and squash, the pine family, bean varities [15] such
as Jack beans (Canavalia ensiformis), soybean(Glycine max) leaf and seed, pigweed (Chenopodium
album) and mulberry leaf (Morus alba) [16].

Fig. 1 Solubility phase diagrams for the ternary
system, Ca(OH)2-H3PO4-H2O, at 250C, showing thesolubility isotherms of CaHPO4 (DCPA),CaHPO4.2H2O (DCPD), Ca8H2(PO4)6. 5H2O (OCP),(α-TCP),β-Ca3(PO4)2(β-TCP),
α-Ca3(PO4)2
Ca4(PO4)2O (TTCP), and Ca10(PO4)6.(OH)2 (HA)
[12].

CO(NH 2 ) 2

Table 1 Biologically relevant calcium
orthophosphates [13]
Ca/P
ratio
0.5
0.5
1
1
1.33
1.5
1.5
1.22.2
1.51.67

1.67
2

CompoundPLANT-DERIVED
FOR
CPC

+
(1)

2H 2 O→2NH 4 ++

CO 3 2-In this study, we investigated urease active plant
species to increase the pH that is favorable for CPC
precipitation. Hence, initially, we studied three typesof plant seeds in cucurbit family such as watermelon,
melon and pumpkin. Out of them, a solution madeby dissolving 0.08 g of urea in 3.44 mL ofwatermelon seed extract (prepared by soaking 1g ofcrushed seeds about 30 min. in 10 mL of distilledwater) showed pH value ranging from 8.5 to a
constant value of 9.5 within 1 hour. This range of
pH is favorable for CPC precipitation (See Fig. 1),
and therefore, watermelon seeds were selected forfurther investigations of this study.

Abbreviation

Monocalcium phosphate
MCPM
monohydrate (Ca(H 2 PO 4 ) 2 .H 2 O )Monocalcium phosphate
MCPA
anhydrate (Ca(H 2 PO 4 ) 2 )Dicalcium phosphate dihydrate
DCPD(CaHPO 4 .2H 2 O)
Dicalcium phosphate anhydrate
DCPA(CaHPO 4 )Octacalcium phosphate
OCP(Ca 8 (HPO 4 ) 2 (PO 4 ) 4 .5H 2 O )A-tricalcium phosphate
α-TCP(α- Ca 3 (PO 4 ) 2 )B-tricalcium phosphateβ-TCPβ- Ca 3 (PO 4 ) 2
Amorphous calcium phosphate
ACP(Ca x (PO 4 ) y .nH 2 O)
Calcium-deficienthydroxyapatite(Ca 10―x
CDHA(HPO 4 ) X (PO 4 ) 6―x (OH) 2―x )(0＜x＜1)
Hydroxyapatite
HA(Ca 10 (PO 4 ) 6 (OH) 2 )Tetracalcium phosphate
TTCP(Ca 4 (PO 4 ) 2 O)

MATERIALS AND METHODS
Chemical reagents such as calcium acetateCa(CH 3 COO) 2 (CA), and dipotassium phosphateK 2 HPO 4 (DPP) was used as CPC chemicals. Urea
was selected as the ammonia source and watermelon
seeds (Citrullus vulgaris) that leftover as food waste
were used for catalyzing the hydrolysis of urea. Seed
extract was prepared by soaking crushed seeds inwater or another liquid as required, about 30 min.
and collecting the filtrate. Toyoura sand withparticle density ρ s = 2.64 g/cm3, minimum density
ρ min = 1.335 g/cm3, maximum density ρ max = 1.645
g/cm3, mean diameter D 50 = 170 μm was used for
UCS test specimen preparation. The final
concentrations of CA: DPP = 0.75 M: 1.5 M and
CA: DPP = 0.75 M: 0.75 M, which have yielded
largest UCS at the initial investigation were used forfurther investigations of this study.
Initially, the pH variability in relation to different

The purpose of this study was to discover a plant
species that contains urease activity unrelated toureolytic microorganisms. This activity can then be
used to increase the pH that is favorable for CPC
precipitation by catalyzing the hydrolysis of urea,
which can then use to precipitate CPC and increase
the UCS of small scale, cylindrical Toyoura sandtest pieces more than 100 kPa, which is the required

85

GEOMATE- Osaka, Nov. 16-18, 2015

concentrations of plant seed extract (different solidliquid ratios) and urea was investigated to ascertain
optimal concentrations for maximum CPC
precipitation. Solid-liquid ratio (dry weight ofcrushed seeds, (g) /volume of liquid, (mL)) ranging
from 0.0005 to 0.005 were selected and prepared ina CA solution with 0.75 M. Amount of urea ranging
from 0.08 g to 0.8 g was selected and dissolved in a
DPP solution (0.75 M and 1.5 M). Small scale
sample mixtures consisting of 15 g of Toyoura sand,1.72 mL of plant seed extract prepared using CA
solution and 1.72 mL urea solution made bydissolving urea in 1.72 mL DPP solution were used
for the pH determination. Hence, the final volume ofthe solution was 3.44 mL.
Ca2+ ion concentration in a CPC solution gives
an indication about CPC precipitation. A low Ca2+
ion concentration in the solution indicates high CPC
precipitation rather than high Ca2+ ion concentration.
Therefore, Ca2+ ion concentration in a mixture
consisted with CPC- Chem, seed extract and urea
was measured with time using a Ca2+ ion meter to
get an indication about CPC precipitation with time.
Best solution mixture that gives favorable pH fromfirst investigation explained in above paragraph wasconsidered for assessing Ca2+ ions. According to that,
5 mL of plant seed extract having best solid-liquidratio, was prepared using CA solution with 0.75 M
and 5 mL urea solution was made by dissolving urea
in 5 mL of DPP solution (0.75 M and 1.5 M). Hence,the volume of the final solution was 10 mL. Ca2+ ion
concentration of prepared solution was measured
just after mixing, after 1 day, 3 days, 7 days, 14 days,and 28 days. Controls were prepared using only CPC
solution for the comparison purpose and measured
Ca2+ ion concentration with time.
Urease activity test was conducted according to amethod based on conductivity [17]. Conductivity
was measured using a conductivity meter toinvestigate the change in urease activity using
different solid-liquid ratios of seed extract and urea.
Urea dissolved in 10 mL of distilled water was
mixed with 10 mL of seed extract and the final
mixture with the volume of 20 mL was used formeasuring conductivity and hence to calculate
urease activity of watermelon seed extract contactedwith urea.
After completing basic investigations, such as
pH variability test, Ca2+ ion measurement and urease
activity test, best solid-liquid ratio and amount of
urea were selected to prepare of test specimens for
UCS test. As mentioned above, CA with 0.75 M and
DPP with 0.75 M and 1.5 M concentrations wereselected to prepare of test specimens for UCS test.
Crushed seeds were soaked in the concentration
known CA solution by maintaining required solidliquid ratio. The required weight of urea, which had
been weighted in advance, was then dissolved in theconcentration known DPP solution. A volume of

36.65 mL from both the seed extract in the CA
solution and the urea in the DPP solution were added
to 320.09 g of Toyoura sand. The mixture was
uniformly mixed in a stainless steel ball for 2 min
and then divided into quarters. Each quarter wasthen placed in to a plastic mold (inner diameter φ =5 cm, height h = 10 cm). The inner wall of eachmold was covered with an overhead projector sheet(0.1 mm thick) to avoid any disturbance of the test
pieces during their removal from the mold. The
mixture was tamped down 30 times by a handrammer when each quarter was filled into the mold.
Finally, the upper edges of the test pieces wereslightly trimmed so that they were flat, and covered
with Parafilm M (Structure Probe, Inc., West
Chester, PA) to avoid desiccation. The molded test
pieces were subsequently cured in an airtightcontainer at high humidity. After curing, test pieces
were removed carefully from the mold, and the UCSof the test pieces was measured with a UCS
apparatus T266-31100 (Seiken-sha Co. Ltd., Japan)
at an axial strain rate of 1% /min. All test pieces
were prepared, cured and tested at 25 oC. Two test
pieces were made for each test case under same
condition, in order to investigate the repeatability ofmeasurements of UCS test. The pH of the test pieces
was calculated as an average of three measurements(at the top, bottom, and middle of each test piece)
using a pH Spear (Eutech Instruments Pte., Ltd.,Singapore).
RESULTS AND DISCUSSION
According to the results of pH variability withdifferent amount of urea and solid-liquid ratio, pH
was increased and became constant with time and it
has shown favorable pH for CPC precipitation, inthe mixture of 0.08 g of urea and 0.005 of solidliquid ratio.
Ca2+ ion concentration of the mixture of CPC
solution consisted with 0.233 g urea (0.08 g*5
mL/1.72 mL) instead of 0.08 g of urea used in pH
variability test, and 0.005 of solid-liquid ratio wasdecreased gradually with the time and that indicatedthe increment of the precipitation of CPC with time.
The behavior of control solutions were similar anddecrement rate with the time was low compared withthe solution consisted with urea and seed extract.
Furthermore, this investigation showed that CPC
precipitation was closely related with pH. As an
example, Ca2+ ion concentration with time in the test
case only with CPC (CA = DPP = 0.75 M) and thetest case with CPC (CA = DPP = 0.75 M), urea andseed extract are shown in Fig. 2. According to theFig. 2, low Ca2+ ion concentration (high CPC
precipitation) could be expected with urea and seedextract rather than using CPC only. The reason to
get high precipitation is the availability of favorable
pH for CPC precipitation. (See Fig. 1). Moreover,86GEOMATE- Osaka, Nov. 16-18, 2015

the precipitation of CPC could be observed in thesolution only with CPC. However the amount andthe rate of precipitation were low compared with
CPC having urea and seed extract due to low pH(pH around 6). There is a tendency to solubilize
CPC at pH around 6 rather than the pH in the rangeof 8-9 (See Fig.1).
Urease activity values of the mixture ofwatermelon seed extract and urea are presented inTable 2.

Table 2 Quantitative values of urease activity
Case
No.
1
2
3
4
5
6
7

Effect of pH on UCS

Fig.4

Temporal variation of UCS

Solid/liquid
ratio

Conductivity(mS/cm)

0.005
0.005
0.005
0.005
0.005
0.00250.00050.04380.06170.06080.07220.07980.01490.0013

Ureaseactivity(u/L)
520.54733.28722.58858.06948.39177.0815.45

As shown in Table 2, when the urea content
increased then the urease activity decreased andwhen the solid-liquid ratio of the seed extract wasincreased, the urease activity increased. Furthermore,
best combination of urea and seed extract (0.08 gof urea/1.72 mL and 0.005 of solid/liquid ratio)
selected from pH variability test showed a highurease activity from urease activity test (Case No. 4
in Table 2).In Case No. 4, weight of urea was 0.465
g instead of 0.08 g to match the concentration of
urea (0.08 g*10 mL/1.72 mL). It is clear that theproperty of urease activity supported to increase the
pH by catalyzing urea in to NH4+ and CO32-.

Fig.2 Temporal variation of Ca2+ ion concentration

Fig.3

Weightofurea (g)
4.65
2.33
0.93
0.465
0.0465
4.65
4.65

87

GEOMATE- Osaka, Nov. 16-18, 2015

DCPD is higher than that of DCPA for the samevalue of pH and this could thus cause a decrease in
UCS after 14 days rather than 7 days, even if bothcases indicated nearly the same pH values (See Fig.
4 (A)). In case of CA = 0.75 M and DPP = 1.5 M,
the pH value of the test pieces increased and pH ofaround 8 was observed after 28 days. This favorable
pH caused to precipitate maximum CPC. In thisrespect, the UCS value increased with time and had
a value of more than 100 kPa after 28 days byachieving our goal.
According to the compressive stress (σ) – strain(ε) curves for the test pieces made using 8.54 g ofurea, (Fig. 5) to study the temporal variations of the
UCS, the two stress- strain curves obtained for two
identical test pieces made at each test case showedalmost the same behavior in terms of shape.
Furthermore, the compressive strain (ε) was
decreased and moved leftward, and Young’s
modulus increased with the increments of UCS. A
comparison of all the stress (σ) - strain (ε) curves inrelation to the maximum UCS values, shows a
distinctive peak at approximately 2% of the failure
strain. The above discussion confirms that the aim of
this study was achieved after 28 day curing periodwith CA: DPP = 0.75 M: 1.5 M in relation to theuse of seed extract at solid-liquid ratio of 0.005 and8.54 g of urea for 320.09 g of Toyoura sand.

Finally, the UCS test was conducted for the test
specimens made using best combination of urea andseed extract. According to that, 1.71 g (0.08g *36.65 mL /1.72 mL) was used to prepare the first
UCS test pieces and later, more test pieces weremade by changing urea in the range of 1.71 g-17.07
g to observe the behavior of UCS. As shown in Fig.3(A), while using CA = DPP = 0.75 M, the incrementof urea caused to increase the UCS up to certain
level and afterward increment of urea caused todecrease the UCS. When the maximum UCS
achieved was decreased by adding more urea, the pHof the sample has changed from around 8 to morethan 9. It was clear that an increase of urea caused
the release of greater number of NH 4 + ions inrelation to urea hydrolysis, and that caused anincrease in the pH of the specimen that was
unfavorable for CPC precipitation. It was thus
ascertained that the UCS value has been decreased
after a certain level of urea was used. The maximum
value of UCS was obtained after 7 days with 8.54 gof urea. At the same way, while using CA: DPP =0.75 M: 1.5 M, the behavior of UCS was observedby changing the amount of urea. The maximum
average value of UCS was obtained after 7 days with
8.54 g of urea (Fig.3 (B)) as in the case with CA =DPP = 0.75 M. We therefore further considered
temporal variations in values of the UCS using 8.54gof urea for both cases (CA = DPP = 0.75 M and CA= 0.75 M, DPP = 1.5 M) (Figs. 4 (A) and 4 (B)). In
the case of CA = DPP = 0.75 M, the UCS began togradually decreased after 14 days and dramaticallydecreased after 28 days. The pH of the test pieces
after 28 days was more than 9. That was unfavorable
for precipitating CPC relevant to CA = DPP = 0.75
M that means Ca/P = 1. The possible precipitates are
DCPA and/or DCPD (See Table 1). Unfavorable pH
caused to decrease the UCS value dramatically after
28 days. Furthermore, the decrease of the UCS after
14 days was not related to the value of pH. As we
mentioned earlier, it is possible to precipitate two
CPCs in the case of Ca/P ratio of 1. The solubility of

CONCLUSION
The efficacy of plant-derived urease enzyme(watermelon seed extract) to induce the precipitationof CPC-Bio and hence to improve the UCS ofsand was examined in small scale, cylindrical,
laboratory samples. The results show that,- the UCS of more than 100 kPa wasachieved after 28 days, due to generated
CPC-Bio with the help of CA: DPP = 0.75
M: 1.5 M in relation to the use ofwatermelon seed extract at a solid-liquidratio of 0.005 and 8.54 g of urea for320.09g of Toyoura sand.

Fig.5 Compressive stress (σ) - compressive strain (ε) curves of test pieces with different curing
time (Day) – (Solid/liquid ratio =0.005)
88

GEOMATE- Osaka, Nov. 16-18, 2015-

the pH was the governing factor to
precipitate CPC as CPC-Bio andwatermelon seed extract having urease
activity helps to increase the pH bycatalyzing the process of urea hydrolysis
and best pH observed for optimal
cementation was around 8.0.
According to the results of this study, planturease- induced CPC precipitation technique has thepotential to be utilized as an environmental friendly
grouting material to respond to soil liquefaction by
improving the ground. In addition to thegeotechnical engineering issues, knowledge gatheredfrom this technique may also provide satisfactory
solutions for solving problems in the fields ofgeoenvironmental and rock engineering. However,
the relationship between the strength and the various
CPC precipitation parameters such as concentrationand pH of reaction mixture, and long term curing
time should be examined and understood moreclearly in further to facilitate practical applications.

[7] Kawasaki S, Murao A, Hiroyoshi N, Tsunekawa
M and Kaneko K, “ Fundamental study on
novel grout cementing due to microbial
metabolism”, J. of the Japan Society ofEngineering Geology, Vol. 47, 2006, pp. 2–12
(in Japanese with English abstract).
[8] Weaver TJ, Burbank M, Lewis A, Crawford R
and Williams B, “Bio- induced calcite, iron andmanganese precipitation for geotechnicalengineering applications” in Proc. GeoFrontiers, 2011, pp. 3975–3983.
[9] Terajima R, Shimada S, Oyama T and
Kawasaki S, “Fundamental study of siliceous
biogrout for eco-friendly soil improvement”, J.
of of the Japan Society of Civil Engineers,
Ser.C (Geosphere Engineering), Vol. 65, 2009,
pp.120–130 (in Japanese with English abstract).
[10] Akiyama M and Kawasaki S, “Novel grout
material using calcium phosphate compounds:in vitro evaluation of crystal precipitation andstrength reinforcement”, J. of the Japan Societyof Engineering Geology, Vol. 125, 2012a, pp.
119–128.
[11] Akiyama M and Kawasaki S, “Microbiallymediated sand solidification using calcium
phosphate compounds”, J. of the Japan Societyof Engineering Geology, Vol. 137-138, 2012b,
pp. 29-39.
[12] Tung MS, “Calcium phosphates: structure,composition, solubility, and stability, in: A.
Zahid (Ed.), Calcium phosphates in biologicaland industrial systems”, Kluwer Academic
Publishers, Norwell, 1998, pp. 1-19.
[13] Dorozhkin SV and Epple M, “Biological andmedical significance of calcium phosphates”,
Angewandte Chemie International Edition, Vol.
41, 2002, pp.3130–3146.
[14] Yamazaki H, Maeda K, Takahashi K, Zen K
and Hayashi K, “Technical Note of Port andHarbour Research Institute”, Vol. 905, 1998, pp.
1-29 (in Japanese).
[15] Das N, Kayastha AM and Srivastava PK,“Purification and characterization of urease
from dehusked pigeonpea (Cajanus cajan L.)
seeds”, J. of Phytochemistry, Vol.61, Issue.5,
Nov. 2002, pp.513-521.
[16] Sirko A and Brodzik R, “Plant ureases: Rolesand regulation - Review”, J. of Acta Biochimica
Polonica, Vol. 47 (4), 2000, pp.1189-1195.
[17] Whiffin VS, “Microbial CaCO 3 precipitation
for the production of biocement”, PhD.
dissertation, School of Biological Science &Biotechnology, Murdoch University, Perth,
Australia, 2004, 154 pp.

ACKNOWLEDGEMENTS
The work was partly supported by a Grant-inAid for Scientific Research (No. 24300299) from theJapanese Ministry of Education, Sports, Science, andTechnology; this support is gratefully acknowledged.
REFERENCES[1] Karol RH, “Chemical grouting and soil
stabilization”, 3rd ed., CRC Press, Boca Raton,
FL, 2003.
[2] Van Paassen LA, Harkes MP, Van Zwieten GA,
Van der Zon WH, Van der Star WRL and Van
Loosdrecht MCM, in Proc. 17th Int. Conf. on
Soil Mechanics and Geotechnical Engineering,
2009, pp. 2328-2333.
[3] Harkes MP, Van Paassen LA, Booster JL,
Whiffin VS and Van Loosdrecht MCM,
“Ecological Engineering”, Vol. 36, 2010, pp.
112-117.
[4] Park SS, Choi SG and Nam IH, “Effect ofPlant-Induced Calcite Precipitation on theStrength of Sand”, J. of Materials in Civil
Engineering, 2014, pp 1-5 (06014017).
[5] Neupane D, Yasuhara H, Kinoshita N and Unno
T, “Applicability of Enzymatic Calcium
Carbonate Precipitation as a Soil-Strengthening
Technique”,
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of
GeotechnicalandGeoenvironmental Engineering, Vol. 139 No.
12, Dec. 2013, pp. 2201-2211.
[6] Nam IH, Chon CM, Jung KY, Choi SG, Choi H
and Park SS, “Calcite Precipitation by Ureolytic
Plant (Canavalia ensiformis) Extracts as
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Engineering, (0000) 00(0), 2014, pp 1-6.

89

GEOMATEJapan,Conference
Nov. 16-18,
Fifth Osaka,
Internationalon 2015
Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

EFFECTS OF BAGASSE ASH AND HYDRATED LIME ADDITION ON
ENGINEERING PROPERTIES OF EXPANSIVE SOIL

1,2

Liet Chi Dang1, Hayder Hasan2, Behzad Fatahi3, Robert Jones4 and Hadi Khabbaz5
PhD Candidate, School of Civil and Environmental Engineering, University of Technology Sydney (UTS),
Sydney, Australia
3
Senior Lecturer in Geotechnical Engineering, School of Civil and Environmental Engineering,
University of Technology Sydney (UTS), Sydney, Australia
4
Senior Infrastructure Engineer, Arup, Brisbane, QLD 4001, Australia
5
Associate Professor of Geotechnical Engineering, School of Civil and Environmental Engineering,
University of Technology Sydney (UTS), Sydney, Australia

ABSTRACTThe main objective of this research is to investigate the influence of hydrated lime and bagasse ash on
engineering properties of expansive soil obtained from an array of laboratory tests. Bagasse ash is a readily
available waste by-product of the sugar-cane refining industry causing the adverse impacts on environment.
Bagasse ash is considered in this investigation in order to evaluate the potential benefits of its pozzolanicmaterial for stabilisation of expansive soil. The preparation of stabilised soil specimens was conducted bychanging the bagasse ash contents from 0 to 25% by dry weight of expansive soil along with hydrated lime
dosage was increased. The bearing capacity and shrinkage properties of stabilised expansive soil were examinedthrough a series of experimental tests including linear shrinkage and California bearing ratio (CBR) after various
curing periods of 3, 7 and 28 days. The results reveal that the additions of hydrated lime and bagasse ash
improved the strength and bearing capacity of stabilised expansive soil remarkably, and meanwhile significantly
reduced the linear shrinkage of treated expansive soil. Hence, the application of hydrated lime and bagasse ash as
reinforcing material can not only enhance the engineering properties of expansive soil, but also facilitate to copewith environmental issues and provide trivial cost construction materials for sustainable development.
Keywords: Expansive Soil, Hydrated Lime, Bagasse Ash, Linear Shrinkage, Strength and CBRtechniques have been suggested for dealing
problematic soil such as the application of sandcushion technique, belled piers and granular pileanchors. In addition, chemical stabilisation is themost popular method utilized to enhance thephysical and mechanical properties of problematicsoils consisting of soft soil and expansive soil. The
chemical ground improvement approach is a proventechnique in improving engineering properties ofproblematic soils and is highly applicable for lightly
loaded structures such as road pavement and lowrise residential buildings [2].
In recent years, a growing quantity of laboratory
and field experiments have been carried out andextensive studies have been conducted on
problematic soil using various additives such as
cement [3], [4], and lime [5]. Several by-products
including fly ash [6], rice husk ash, bagasse ash [7],just to name a few have been investigated by using
each alone or in combination with other additives.
Hence, the potential for using by-products as
chemical treatment of soft soil and expansive soil isthe most promising. Although a growing number ofinvestigations have been undertaken so as toreinforce problematic soil using waste by-products
to diminish the effects of the swelling-shrinkage

INTRODUCTION
Expansive soils are fine grained soil ordecomposed rocks that show huge volume change
when exposed to the fluctuations of moisture content.
Swelling-shrinkage behaviour is likely to take placenear ground surface where it is directly subjected toseasonal and environmental ups and downs. The
expansive soils are most likely to be unsaturated andhave montmorillonite clay minerals. Most of severe
damage in relation to expansive soils is depended on
the amount of monovalent cations absorbed to theclay minerals.
Construction of residential buildings and othercivil engineering structures such as highways,
bridges, airports, seaports on expansive soil is highly
risky in that such soil is susceptible to cycles ofdrying and wetting, inducing shrinkage and swelling
behaviour under building foundations, which resultsin cracking to structural and none structural elementsof those structures. The average annual cost ofdamage to structures due to shrinkage and swellingis estimated about £400 million in the UK, $15
billion in the USA, and many billions of dollars
worldwide [1].
An increasing number of ground improvement
90

GEOMATE- Osaka, Japan, Nov. 16-18, 2015

characteristics and enhance the mechanicalproperties, there are still not adequate studies on theinfluence of waste by-products, particularly inbagasse ash stabilised expansive soil.
Bagasse ash is an abundant fibrous waste productderived from sugar-refining industry and readily
available for use without costs. This material isincreasingly warned to pose problem to theenvironment that requires public attention andresearchers on its safe disposal and searching forchances to use as recycled material as well. However,bagasse ash is considered as pozzolanic stuff rich inamorphous silica, which is effectively employed
together with hydrated lime in improving theengineering properties of expansive soil. Therefore,
it has become a focus of interest in recent years.
Manikandan and Moganraj [8] have performed
several studies on bagasse ash in searching of
beneficial approaches to stabilise expansive soil.
Based on the test results, they indicated that bagasse
ash inclusion caused significant modification andimprovement in the engineering properties of
expansive soil.
Nevertheless, more investigations are essential inorder to provide a comprehensive understanding of
the engineering properties of black soil improved by
combination of hydrated lime and bagasse ash in
ground improvement. Expectantly, a couple of chief
objectives associated with adopting industrial waste
byproducts in line with diminishing hydrated lime
dosage are likely to be acquired concurrently if theuse of hydrated lime-bagasse ash inclusions treatedexpansive soil can give rise to the appreciableimprovement of shrink-swell behavior andmechanical properties.
In this paper, an array of laboratory experiments
including linear shrinkage, compaction, and CBRtests have been performed on untreated and treatedexpansive soil samples with different hydrate lime
and bagasse ash contents after different curing timeof 3, 7 and 28 days. Outcomes of this experimental
investigation were analysed to obtain a better
understanding of the effects of hydrated limebagasse ash additions on the shrinkage potential andengineering behavior of expansive soil.

soil was classified as clay of high compressibility(CH) according to the Unified Soil Classification
System. The specific gravity of solids (Gs) was
2.62-2.65. The grain size distribution showed that
0.1% of particles were in the range of gravel, 18.3%
in the range of sand and 81.6% were fine-grainedmaterial (silt/clay). Atterberg limits of the fineportion of material were about 86% liquid limit (LL)and 37% plastic limit (PL), which yielded to aplasticity index (PI) of 49%. The average linear
shrinkage and natural moisture content of thesamples was 21.7% and 30.8%, respectively. Basedon the high linear shrinkage and plasticity index, thesoil can be classified as highly expansive soil.
Table 1 Characteristics of natural soil
Characteristics
Gravel (%)
Sand (%)
Silt/Clay (%)
Natural water content (%)
Liquid limit (%)
Plastic limit (%)
Plasticity index (%)
Linear shrinkage (%)
Specific gravity
USCS classification of the soil

Value
0.1
18.3
81.6
30.8
86
37
49
21.7
2.62-2.65
CH

Lime
Hydrated lime has 75–80% of calcium hydroxide
and 7% silica that was used in this investigation. The
Hydrated lime is locally purchased from Cement
Australia supplier, one of the most widely used
construction materials in Australia.
Bagasse Ash
Bagasse ash was collected during cleaning
operation of boiler from ISIS Central Sugar Mill
Company Limited, Queensland, Australia. The
bagasse ash was taken from this company at aboiling temperature of 700-800°C. Table 2 provides
the similarly physical and chemical properties ofbagasse ash employed in this study, which aresimilar to the bagasse ash utilized in the previous
research performed by Anumpam et al. [9]. The
bagasse ash used for this research was carefully
sieved and passed through 0.425mm aperture sieveto eliminate undesirable materials.

MATERIALS AND EXPERIMENTAL
PROGRAM
Materials
Soil
The soil samples used in this study for current
experimental tests were collected from the specificarea of Queensland, Australia. The soil was airdried and broken into pieces in the laboratory. Table
1 shows the physical properties of the soil used inthis investigation. In term of sizes of particles, theMixing of Materials
Soil samples with particles size smaller than 2.36mm were prepared by mixing bagasse ash or
hydrated lime combined with bagasse ash at a ratioof 1:3 in the ranges shown in Table 3. Following this91

GEOMATE- Osaka, Japan, Nov. 16-18, 2015

preparation, the specimens were mixed thoroughly.
A mechanical mixer was used for the mixing of the
expansive soil with hydrated lime and bagasse ash.
After mixing of the material, the specimens wereprepared for the conventional geotechnicalexperiments, including compaction and California
bearing ratio tests in order to determine the optimum
moisture contents, the maximum dry densities of
selected admixtures and observe the stress – strain
behaviour of treated and untreated expansive soil
samples.

specified in accordance with in AS 1289 [10].
California Bearing Ratio Test
Unsoaked CBR tests were conducted on
untreated and treated expansive soil in accordingwith the method specified in AS 1289 [10] in order
to examine the strength and bearing capacity of
expansive soil as the subgrade materials in supportof road and highway systems. Soaked CBR is notreported in this paper. Right after conducting the
mixture process of expansive soil mixed withbagasse ash and hydrated lime, untreated and treatedsamples were shaped in a cylindrical metal mould ofknown volume, with 152 mm in internal diameterand 178 mm in height, at the maximum dry density(MDD) and optimum moisture content (OMC). In
order to ensure uniform compaction, the samples
were compacted in five equal layers by pressing thespacer disk of 61 mm in thickness with help of
testing machine in order to obtain the targeted dry
density. Then, the prepared samples were sealed byusing plastic wrap to prevent moisture change, andafterward curing for various periods of 7 and 28
days at a controlled room environment of 250C
temperature and relatively 80% humidity. After
sample preparation and curing for different periodsof 7 and 28 days, the samples were subjected to anannular surcharge of 4.5 kg put on the top and
immediately set up in the conventional unconfinedcompression apparatus without soaking. The
machine was set at a load rate of 1 mm/min, and thiswas kept consistent for all samples tested. An S-type
load cell was used as a transducer to converting theforce into an electrical signal, readable on the loadcells display. A data logger was used to transfer thedata to a readable output. An LVDT displacementtransducer was set up against the bearing block of
the machine to measure the vertical displacement of
the samples under the applied load. The LVDT
reading was used to plot the load-penetration curvethat was commonly used to calculate the CBRvalues. The CBR values of untreated and treatedexpansive soil specimens were computed based on
2.5 mm penetration instead of 5.0 mm penetration,as penetration of 2.5 mm always gave rise to higher
CBR values than that of 5.0 mm penetration. For
each type of mixtures, the CBR value was obtained
as the average of three CBR tests.

Table 2 Characteristics of bagasse ash after
Anumpam et al. [9]Physical properties
Chemical properties
Property
Value Components % by weight
Specific gravity 2.38 Ignition loss
2.11
Liquid limit (%)
41
SiO2
65.27
Plastic limit (%) None
Al2O3
3.11
Optimum
48
Fe2O3
2.10
moisture content(%)
Maximum dry
1.27
CaO
11.16
density (g/cm3)
Lime Reactivity
32
MgO
1.27
2
(kg/cm )Table 3 Summary of mixes used in this study
Mix No.
1
2
3
4
5
6
7
8
9

Bagasse Ash (%)
0
6
10
18
25
4.5
7.5
13.5
18.25

Hydrated Lime (%)
0
0
0
0
0
1.5
2.5
4.5
6.25

Experimental Procedure
Linear Shrinkage
In this investigation, portion of a soil sample ofat least 250g from the material passing the 425 µm
sieve by drying method was air dried until it wasdried enough to permit crumbling of the soil
aggregation, which has been prepared in accordingwith the procedure prescribed in AS 1289 [10] forthe preparation of disturbed soil samples for
Atterberg limits and linear shrinkage. Then, thelinear shrinkage values of untreated and treatedexpansive soil specimens were determined as

RESULTS AND DISCUSSIONS
Effects on Linear Shrinkage
Fig. 1 provides a comparison of linear shrinkage
improvement of hydrated lime and bagasse ash
additives stabilised expansive soil after curing
period of 7 days. As can be seen in Fig. 1, thegrowing amount of hydrated lime and bagasse ash
92GEOMATE- Osaka, Japan, Nov. 16-18, 2015

inclusions from 0 up to 25% at a given curing period
was found to decrease the linear shrinkage
remarkably. Particularly, when bagasse ash contents
was increased from 0 up to 25%, the liner shrinkage
of bagasse ash mixed with expansive soil drasticallyplunged by a substantial amount of 46% after only
curing for 7 days in comparison with that of virginsoil specimen. The effects of hydrated lime-bagasse
ash on linear shrinkage of treated expansive soil,
moreover, was more pronounced than that ofbagasse ash mixed with expansive soil. For example,
when the 25% hydrated lime-bagasse ash addition at
a ratio of 1:3 was ultilised to stabilise expansive soil,
there was a remarkable reduction of linear shrinkage
of approximately 80% compared to that of original
expansive soil specimen. Hence, it is noteworthy tostate that the additions of bagasse ash and hydrated
lime-bagasse ash admixtures yielded the strongly
positive effects of reducing linear shrinkage of
expansive soil even with the application of bagasse
ash stabilisation of expansive soil adopted only.

linear shrinkage of stabilised expansive soil after
curing time prolonged than that of 6 % hydrated
lime-bagasse ash combination. However, thedecrease in linear shrinkage of hydrated limebagasse ash combinations treated expansive soil inline with increasing curing time was so much lower
and more pronounced than their bagasse ashstabilised counterparts with further increasingstabilisers contents up to 25%. Subsequently, the 28
days final linear shrinkage of treated expansive soil
shown in Fig. 3 fell down dramatically by almost
84% compared to that of original expansive soil
when hydrated lime-bagasse ash combination
contents were increased from 0 to 25%, whereas thedrop of linear shrinkage of bagasse ash stabilised
expansive soil as illustrated in Fig. 2 was roughly
half in comparison with that of untreated soil
specimen when bagasse ash inclusions were forced
to rise up to 25%. It can be noted that the increasing
curing time together with increasing additive
contents obviously caused the substantial influenceon linear shrinkage of bagasse ash and hydrated
lime-bagasse ash admixtures treated expansive soil.
Consequently, the use of the growing amount ofeither bagasse ash stabilisation or hydrated limebagasse ash treatment could be likely beneficial
since these stabilisers could provide considerably
positive effects of coping with expansive soil interms of reducing linear shrinkage and cracking,which cause the most damage for superstructuresand roads built directly on it.

Fig.1 Influence of different bagasse ash andhydrated lime-bagasse ash combination on linear
shrinkage of expansive soil after 7 days curing
In addition, Fig. 2 and Fig. 3 depict the effects ofbagasse ash and hydrated lime-bagasse ash
admixtures on linear shrinkage of stabilised
expansive soil in line with curing time prolonged.
Overall, the increase in bagasse ash and hydrated
lime-bagasse ash inclusions up to 25% led to thedecrease in linear shrinkage of expansive soil
enormously with increasing curing periods of 3, 7,
28 days. It is obviously observed that the decrease inlinear shrinkage was significant, speedy during thefirst 7-day curing period for both bagasse ash andhydrated lime-bagasse ash treated expansive soil
specimens for most of particular additive contents.
And then there was a marginally reduction of linear
shrinkage of stabilised expansive soil when the
curing time was further sustained up to 28 days. To
be more specific, as plotted in Fig. 2 and Fig. 3, theaddition of 6% bagasse ash caused lower decrease in

Fig. 2 Linear shrinkage of expansive soil mixed withvarious bagasse ash contents for different curingtime
The significant improvement in linear shrinkage
could be attributed to the flocculation andaggregation phenomena of clay particles induced by
the presence of free lime in bagasse ash that caused
a decrease in surface of clay particles, then formed
the clay particles coarser, and eventually enhanced
the friction and strength of treated expansive soil. As
a result, the finer clay particles were replaced by93

GEOMATE- Osaka, Japan, Nov. 16-18, 2015

relative coarser particles that could be one of the key
factors resulting in the considerable decrease inlinear shrinkage with increasing the additives
contents and age.

7-day curing, respectively. This obviously
demonstrates that the surge of CBR values was not
only with increasing bagasse as additions but also
curing time prolonged. The increase in CBR values
may be due to cementation and pozzolanic reactions
in form of frictional resistance contributed frombagasse ash. This agreed well with the early reports
presented by researchers [7], [9].

Fig. 3 Linear shrinkage of hydrated lime-bagasse ash
treated expansive soil with different additives
contents and curing time
Fig. 4 Influence of bagasse ash admixtures on
average unsoaked CBR of treated expansive soil
with various curing time

Effects on the California Bearing Ratio (CBR)
The strength and bearing capacity of subgrade
materials are essential factors in pavement
engineering. In order to evaluate the strength ofpavement resisting repetitive point loads by traffic
vehicles, the CBR test is one of the most common
tests used to assess the quality of base and subgrade
materials for highway and road construction anddesign purposes. Fig. 4 and Fig. 5 represent thevariation of CBR values of expansive soil specimens
stabilised with different bagasse ash contents andhydrated lime-bagasse ash combinations from 0 up
to 25% along with various curing periods of 7 and
28 days.
In general, the figures depict the appreciableimprovement in CBR values with increase inbagasse ash and hydrated lime-bagasse ash
combinations together with the increase of age. To
be more specific, the CBR values of bagasse ash
treated expansive soil plotted in Fig. 4 increased
substantially with the additive amount increased andcuring time prolonged. For instance, in comparison
with the CBR value of parent soil specimen, theCBR values of bagasse ash stabilised expansive soil
increased from 7.1% up to 11.5% with increasing
bagasse ash additions from 0 to 25% after curing for
7 days. The amount of CBR increase was almost
62% compared with the CBR of parent soil
specimen. In addition, with the curing time increased
from 7 days up to 28 days, the CBR value of bagasse
ash treated expansive soil at a content of 25%
increased significantly by approximately 83%, 15%compared with that of untreated expansive soil andsame bagasse as content treated expansive soil afterFig. 5 Influence of hydrated lime and bagasse ash
admixtures on average unsoaked CBR of treatedexpansive soil after curing period of 7 days
Figure 5 indicates the general effects of bagasseash and hydrated lime-bagasse ash combinations on
the CBR gain of treated expansive soil for varying
hydrated lime-bagasse ash contents after curing for 7
days. As illustrated in Fig. 5, the CBR of treatedexpansive soil specimens rose drastically with anincreasing amount of stabilisers up to 25% adopted
in order to treat expansive soil for a given curing
period. To illustrate this, the addition of 10%
percentage of hydrated lime-bagasse ash generated
the 7-day curing CBR increased by a factor of 3.7 inaverage, whereas with 25% content of the hydrated
94

GEOMATE- Osaka, Japan, Nov. 16-18, 2015

lime-bagasse ash addition at a ratio of 1:3 after
curing for 7 days, the average CBR was significantly
increased by a factor of 8.8 in comparison with thatof original soil specimen, respectively. Additionally,in order to compare with the same content andcuring time of bagasse ash stabilised expansive soil
specimens, the 25% combination of hydrated limebagasse treatment of expansive soil after curing
period of 7 days gave rise to the average CBR
surged up by a great factor of 5.5. Hence, it isimportant to note that the combinations of hydrated
lime-bagasse
ashtreatedexpansive
soil
incontrovertibly yielded higher CBR values thanbagasse ash alone. This was in conformity with theprevious investigations carried out by Osinubi [7].

ACKNOWLEDGEMENTS
The results presented in this paper are part of an
ongoing research at University of Technology
Sydney (UTS) supported by Arup Pty Ltd,
Queensland Department of Transport and Main
Roads (TMR), ARRB Group Ltd and Australian
Sugar Milling Council (ASMC). The authors
gratefully acknowledge their supports.
REFERENCES[1] Jones, L.D. & Jefferson, I., 'Expansive soils',
ICE manual of geotechnical engineering, ICE
Publishing, London, 2012, pp. 413-41.
[2] Khabbaz, H. & Fatahi, B., 'Stabilisation of
Closed Landfill Sites by Fly Ash Using Deep
Mixing Method', Grouting and Deep Mixing
2012, eds Johnsen, L.F., Bruce, D.A. & Byle,
M.J., ASCE, USA, 2012, pp. 417-26.
[3] Fatahi, B., Engelbert, D., Mujic, S. & Khabbaz,
H. 2012, 'Assessment of Surcharging on
Strength and Stiffness of Cement Treated Clays',
Grouting and Deep Mixing 2012, eds Johnsen,
L.F., Bruce, D.A. & Byle, M.J., ASCE, USA,2012, pp. 272-80.
[4] Fatahi, B., Khabbaz, H. & Fatahi, B.,'Mechanical characteristics of soft clay treatedwith fibre and cement', Geosynthetics
International, 2012, vol. 19, pp. 252-62.
[5] Bell, F.G., 'Lime stabilization of clay minerals
and soils', Engineering Geology, 1996, vol. 42,
no. 4, pp. 223-37.
[6] Fatahi, B. & Khabbaz, H., 'Influence of fly ash
and quicklime addition on behaviour ofmunicipal solid wastes', Journal of Soils andSediments, 2013, vol. 13, no. 7, pp. 1201-12.
[7] Osinubi, K.J., Bafyau, V. & Eberemu, A.O.,'Bagasse Ash Stabilization of Lateritic Soil', inE. Yanful (ed.), Appropriate Technologies forEnvironmental Protection in the Developing
World, Springer Netherlands, 2009, pp. 271-80.
[8] Manikandan,A.
&
Moganraj,
M.,'Consolidation and Rebound Characteristics ofExpansive Soil by Using Lime and Bagasse
Ash', International Journal of Research in
Engineering and Technology, 2014, vol. 03, no.
04, pp. 403-11.
[9] Anupam, A.K., Kumar, P. & Ransinchung, G.D.,'Use of Various Agricultural and Industrial
Waste Materials in Road Construction',
Procedia - Social and Behavioral Sciences,
2013, vol. 104, pp. 264-73.
[10] AS 1289, Methods of testing soils forengineering purposes, Standards Australia,
Sydney, Australia, 2001.

CONCLUSIONS
The following conclusions can be drawn basedon the experimental results
The increasing amount of bagasse ash andhydrated lime-bagasse ash inclusions from 0 up to
25% gave rise to the significant decrease in linear
shrinkage when curing time was increased. The
effects of hydrated lime-bagasse ash combinations
on linear shrinkage of treated expansive soil was
more pronounced than that of bagasse ash mixed
with expansive soil. As a result, the application of
hydrated lime-bagasse ash treatment could be likely
the most promising approach in terms of
construction materials being adopted in ground
improvement.
There was the enormous improvement in CBRvalues with increase in bagasse ash and hydrated
lime-bagasse ash combinations from 0 up to 25% inline with curing time prolonged. Overall, thehydrated lime-bagasse ash inclusion stabilised
expansive soil could be satisfied the requirements bymost of specifications for either subgrade or evensubbase course materials for road and highway
construction purposes on the basis of CBR.
This investigation based on experimental tests
exhibits the additions of bagasse ash and hydrated
lime-bagasse ash stabilised expansive soil enhanceengineering properties and remarkably diminish
linear shrinkage of treated soil samples. Hence, thehydrated lime-bagasse ash combination should be
widely utilised in expansive soil treatment that
undeniably helps not only impede the influence ofwaste by-product bagasse ash on the environmental
issues but also provide novel construction materials
for sustainable development together with atremendous amount construction cost saving on thebasis of decrease in dosages of conventional
stabilisers comprising of lime, cement.

95

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

CLAY-SHALE MATERIALS AS LOW-COST LANDFILL LINERS:AN INTEGRATED GEOCHEMICAL AND GEOTECHNICALASSESSMENTS
Moshood N.TIJANI1, Jean-Frank WAGNER2 and Matthew E. NTON1
Department of Geology, University of Ibadan, 20005 Ibadan - Nigeria
2
Fachbereich Geographie/Geowissenschaften (FB VI), Universität Trier, D-54286 Trier - Germany
1

ABSTRACT
Increasing rate of wastes generation due to urbanization and industrialization, as well as the need for proper
waste disposal management has been the concerns of many low income countries like Nigeria. Hence, this study
assessed the geotechnical and chemical characteristics in respect of suitability of selected clay-shale deposits in
Nigeria as low cost landfill liners for waste disposal. Twenty four (24) clay-shale samples were subjected toengineering tests, mineralogical XRD and geochemical analyses. Apart from normal kaolinitic clay, the XRD
analyses revealed smectitic and mixed layer clays with Liquid Limit (LL) of 58.3 – 116.5 (av. 75.4) and 170.4 –
173.2 (av. 171.8) respectively while the Plasticity Index (PI) ranged from 20.3 – 51.6 (av. 31.8) and 80.9 – 93.3
(av. 87.1) respectively. Methylene blue adsorption index (MBI) ranges from ≈10 to 18.6 meq/100g for both
smectictic and mixed layer clay-shales with corresponding surface area of 0.8−1.5m2/g, suggesting thedominance of active clay minerals. In addition, the geochemical analyses show that the clay-shale materials
contain significant amount of Al 2 O 3 with average value of 17.0 and 15.9% respectively while Fe 2 O 3 hasaverage value of 8.2 and 6.5% respectively, suggesting Fe-rich smectitic clays. The overall evaluation revealed
that the clay-shale materials are chemically and geotechnically suitable for application as landfill liners subject toappropriate beneficiation /amendment such as mixing with cement and other binding materials.
KEYWORDS: Geochemical, Geotechnical Assessment, Landfill, Clay-Shale, Liner Materialsof the compacted clay liners in order to satisfy theabove requirements [1], [2], [5].
Another major constraint to the development ofproperly engineered landfills is the high cost ofimported synthetic liners in local markets ofdeveloping countries like Nigeria, which therefore
calls for alternative local sources of materials forlandfill liner. Consequently, crushed shale / clayshale deposits appear to be inexpensive alternativethat can be utilized to contain leachate in landfill andprotect the underlying groundwater resources. This
study, therefore, assesses the geotechnical andgeochemical characteristics of shale (clay-shale)
units from southern part of Nigeria, for suitability as
low cost compacted clay liner (CCL) and ascomponents of landfill barrier system in waste
disposal systems.

INTRODUCTION
Increasing urbanization and industrialization
have led to an increase in the flow of goods andservices in many low income developing nations,
resulting to increase in the volume and varieties ofgenerated wastes. Thus, the environmental
implications of the increasing rate of solid waste
production and the need for proper waste disposal
management has been the concerns of many low
income countries like Nigeria. In addition, safe
containment of wastes and control of leachate fromlandfill or waste-dump sites are paramount in the
mitigation of groundwater pollution. Hence, proper
design and careful selection of lining materials arevital parts of engineered waste disposal system [1].
For economical reasons, natural clays and clayshales available within a reasonable hauling distance,
can be used as landfill liners while compacted
natural clays are also widely used as landfill liners
due to their low hydraulic conductivity and highcontaminant attenuation [2], [3]. In other words,
compacted clay liner (CCL) must have a low
hydraulic conductivity to control leachates from thewastes and must have sufficient shear strength toaccommodate possible settlement [2], [4]. Therefore,
strict specifications are usually imposed on theselection of a liner material, design and construction

GEOLOGICAL SETTING OF STUDY AREA
The three major rock types - igneous,
metamorphic and sedimentary - abound in Nigeria.
Igneous and metamorphic rocks constitute thePrecambrian crystalline Basement Complex units
and underlie the physical foundation of the country,
while the Cretaceous-Tertiary sedimentary units,
which lie unconformably on the Basement Complex
constitute the sedimentary basins as depressions
between basement landmass in the south, northeast
96

GEOMATE- Osaka, Nov. 16-18, 2015

and northwest [6], [7]. The Basement Complex andthe sedimentary basins are equally dispersed in
Nigeria with the basement rocks most extensive innorthern-central, south-western parts and along theeastern margin of Nigeria (Fig.1).

laboratory tests. Most of the samples were either informs of soft rock when wet or in blocky compact
and laminated form. Physical appearance indicatesthat the colour of the sampled clay-shale varied fromlight grey, to whitish and light brown as well as light
reddish when iron-stained. After drying in thelaboratory, the samples were disaggregated andsieved to obtain fine-grained materials with amaximum size of about 4.75 mm (sieve number 4).
In this preliminary study, representative samplesof the selected clay/shale units were subjected totests such as Atterberg consistency limits (liquid
limit, plastic limit, shrinkage) and hydrometer tests
while methylene blue adsorption index (MBI) was
estimated following standard method [8]. For thechemical studies, oxides of major elements (SiO 2 ,Al 2 O 3 , Fe 2 O 3 , CaO, MgO, K 2 O, Na 2 O and TiO 2 )were analysed using a Philips PYE UNICAM SP9
atomic absorption spectrophotometer (AAS) anddetermination of the loss on ignition (LOI) using
Heraus muffle furnace with sequential heating up to
1,050oC.
The mineralogical analyses for the identificationof clay minerals via X-ray diffraction were carriedout using a Siemens D500 Diffractometer with CuK alpha radiation. Powdered as well as texturalpreparations of samples were measured in threeforms; untreated, treated with ethylene glycol andheated to 550oC. The resulting diffractogram charts
were analysed using the DIFFRAC-plus Evaluation
programme (EVA), version 6.0 rev. 0. The criteria
for the mineral identification as in [9] and [10] arepresented in Table 1.

Fig. 1: Geology of Nigeria indicating Sampling
Locations (Inset: Map of Africa)

Table 1: Criteria for Identification of Major Clay
Minerals

The sedimentary basins occupy the central Xshaped area of the country and underlie the southernpart as well as northwestern (Sokoto Basin) andnorth-eastern areas (Bornu Basin). The PrecambrianBasement units consist of gneisses, granites,
migmatitic and granitic gneisses, quartzites, slightly
migmatised to unmigmatised meta-sedimentary
schists and dioritic rocks [6]. The Cretaceous andTertiary sedimentary units consist of alternating
sequence of rock units ranging from shales,
limestones, mudstone / siltstone intercalated withsandy units in places. The occurrences of extensive
clay-shale and mudstones in many of the sedimentarybasins (Fig. 1) offer an opportunity of local sourcingof materials for this study.

Minerals Basal
Glycolationd-spacing, ÅEffect(Untreated)
Kaolinite (d 1 =7.1,
No change /d 2 =3.5, d 3 =reaction
10.4
Illite (d 1 = 10.0,
No change
d 2 = 5.0)
Palygor. (d 1 =10.5,
No change
d 2 = 4.5, d 3 = 3.2)
SmectitePeak change(d ≈ 11.9−15.3)
d ≈16.6−17.2
Vermiculite
No change(d ≈ 13.9 − 14.3)
Chlorite (d 1 =14.3,
No change
d 2 = 7.1)
Palygor = Palygorskite

MATERIALS AND METHODSFor the purpose of this study, selected shale /clay-shale samples were collected from different
lithologic units within the sedimentary basins in
Nigeria (Fig. 1). The samples were obtained fromroad cut sections and in excavation pits ofsand/laterite quarry sites. Efforts were made tocollect fresh un-weathered samples for the

Heating
EffectDegraded@
550o CNo reaction
Degraded @600o C
Change tod ≈ 9.5−10.0
Change to d
≈ 9.8−10.0
Change tod ≈ 13.8

All the geotechnical, chemical and mineralogicaltests, following standard laboratory procedures, aswell as follow-up data evaluation and interpretations
97

GEOMATE- Osaka, Nov. 16-18, 2015

were carried out at the Department of Geology,
University of Trier, Germany.
RESULTS AND DISCUSSIONS

and intermediate compressibility (ML) for thekaolinitic clay-shales while the smectitic and mixed
layer clay-shale materials exhibited high plasticity as
inorganic clays (CL-OH) of higher compressibility.

Grain-size Distribution and Index Properties
Table 2: Geotechnical characteristics of the clayshale materials (in %)The grain-size distribution curves for the
representative samples of the clay-shales used in thisstudy are presented in Fig. 2. The curves revealed
that the crushed clay-shales are generally clayey
with percentage fines (silt and clay) ranges from90−98% for most of the samples. However, theproportion of clay fractions (<0.002mm) are in therange of 60−75% for the more plastic (smectitic)
samples, 50−55% for mixed layer clay-shalematerials and 15−30% for silty (kaolinitic) samples.
The values are more than minimum of 20%
recommended for liner materials, especially for thesmectitic and mixed layer clay-shale materials [11].

ParaMin. Max.
metersSmectitic Clays (N=10)
LL
58.3 116.5
PL
32.2
64.9
PI
20.3
51.6
SL
25.6
52.0
Mixed layer Clays (N=4)
LL
170.4 173.2
PL
79.9
89.6
PI
80.9
93.3
SL
56.6
69.3
Kaolinitic Clays (N=10)
LL
38.8
75.0
PL
21.9
48.6
PI
10.5
29.6
SL
16.3
42.1

Mean

Std.
Dev.

75.4
43.4
32.0
35.4

19.9
10.4
10.4
8.4

171.8
84.7
87.1
63.0

2.0
6.8
8.8
9.0

52.3
32.2
20.2
27.2

10.9
8.0
6.1
7.8

Therefore, with possible minimal beneficiation,these characteristics will be suitable for capping andbottom liner materials or as components of such, in
landfill system.
Fig. 2: Representative Grain size Distribution
Curves for the clay-Shale Samples.
The NRA (1992) define suitable materials as
those clays with a liquid of 30>(LL)<90% andplasticity index of 20>(PI)<65%. For this study, theresults of the Atterberge limits as presented in Table
2 revealed Liquid Limit (LL) of 58.3 – 116.5 (av.
76.2) and 170.4 – 173.2 (av. 171.8 0) for smectitic
and mixed layer clay materials respectively, while
the Plasticity Index (PI) ranged from 20.3 – 51.6 (av.
31.8) and 80.9 – 93.3 (av. 87.1) respectively.
However, the kaolinitic clay materials revealed LL
of 38.8 – 75 (av. 54.6) and PI of 10.5 – 29.6 (av.
20.7), suggesting little expansive properties
desirable of landfill liner materials. The implicationis that the relatively low plasticity index for thekaolinitic clay will enhanced leachate attack fromthe landfill as a result of its potential highpermeability.
As shown in the Casagrande Plasticity Chart (Fig.
3), nearly all the clay-shale samples plot below the"A" line. In other words, the clay-shale materials can
be classified as inorganic silts of medium plasticity

Fig. 3: Casagrande chart classification of the clayshales
Although a moderately high plasticity index is
considered important in the selection of a suitable
liner material. However, under extremely highplasticity (PI>65%) the liner material becomessticky and difficult to work with when wet andforms hard lumps when dry and at the same time98

GEOMATE- Osaka, Nov. 16-18, 2015

more susceptible to desiccation [1], [3], [5]. In thisstudy, the PI values of 10-30% for kaolinitic clayshales and 20-52% for smectitic clay-shales arewithin the recommended range of 20-65% for mostof the samples, especially for smectitic clay-shalematerials. However, the values of 80-93% for themixed-layer clay-shales are extreme enough as towarrant beneficiation with other materials, before
application as liner materials.

values of 7.8 – 15.5wt.% (av. 12.0%). Furthermore,
the analyzed clay-shales contain significant amountof Al 2 O 3, with respective average values of 17.0%
15.9% and 24.5% for smectitic, mixed layer andkaolinitic clay materials.
Table 3c: Geochemical Characteristics of thekaolinitic Clays (N=10)
Parameters
Min Max.
(%)
SiO 2
47.8 62.8
TiO 2
1.01 3.17
Al 2 O 3
17.0 30.9
Fe 2 O 3
1.62 7.79
MnO
0.01 0.04
CaO
0.01 0.36
MgO
0.07 2.08
Na 2 O
0.02 1.02
K2O
0.15 2.16
LOI
8.18 15.5
LOI=Loss on Ignition

Chemical Characterization
The summary of the results of geochemical
analyses are presented in Table 3a-c. The results
revealed that the smectitic clay-shales haverelatively lower SiO 2 of 38.2 – 63.1wt.% (±8.4%)
compared to values of 47.8 – 68.1 wt.% for themixed layer and kaolinitic clay materials.
Table 3a: Geochemical Characteristics of thesmectitic Clays (N=10)
Parameters
(%)
SiO 2
TiO 2
Al 2 O 3
Fe 2 O 3
MnO
CaO
MgO
Na 2 O
K2O
LOI

Min

Max.

Mean

38.2
0.87
11.5
6.37
0.02
0.01
0.81
0.04
0.21
10.1

63.1
1.52
23.2
10.9
0.17
17.2
2.82
0.32
1.90
21.5

53.1
1.16
17.0
8.19
0.08
4.50
1.52
0.12
0.80
13.4

Std.
Dev.
8.44
0.21
4.31
1.40
0.05
6.04
0.58
0.10
0.58
4.11

Min

Max.

Mean

48.84
1.00
12.5
5.07
0.04
0.57
0.62
0.04
0.17
7.78

68.0
1.26
18.4
8.21
0.08
3.61
4.25
0.44
2.07
15.5

59.6
1.11
15.91
6.54
0.06
1.48
2.37
0.16
1.05
12.1

56.7
1.89
24.5
3.56
0.02
0.09
0.51
0.22
0.91
12.0

Std.
Dev.
4.52
0.64
5.04
2.03
0.01
0.12
0.63
0.32
0.64
2.72

However, Fe 2 O 3 have average value of 8.2%,
6.5%, and 3.6% respectively, suggesting Fe-richassemblages for smectitic, mixed layer and kaolinitic
clay-shale materials. Also average values of 4.5%
and 1.5% CaO for smectitic and mixed layer clays
respectively compared to 0.1% CaO for kaolinitic
clay-shales suggest the dominance of Camontmorillonite as also supported by the XRD
results.
Mineralogical Characterization
The X-ray diffraction (XRD) analyses of the clayshale samples revealed the general presence ofquartz (usually at d = 3.34A°) as the principal nonclay mineral in all the analyzed samples while
calcite (CaCO 3 ) also occur in a couple of thesamples. The representative X-ray patterns presented
in Fig. 4 revealed the presence of kaolinite in all
samples by its basal spacings at 7.1 and 3.5A°. In
addition, the oriented mounts of the samples confirm
the presence of divalent-cation saturated smectite(Fig. 4b and c) for the mixed-layer and smectitic
clay-shale materials. The basal spacing, d 001 of thesmectite mineral ranges from 14.45–15.31A°,
suggesting the dominance of Ca-montmorillonite.
In both cases, such basal spacing (d 001 ) are
expanded to about 17.0 A° under glycolation (Fig.
4b and c), and clearly distinguished the observed Camontmorillonite from chlorite and vermiculitegroups. The X-ray pattern for the smectitic clayshale materials also exhibit two weak bands at d =10.5 and 4.5A° (Fig. 4b), indicating the presence of
palygorskite while a weak peak at d = ≈5.0A° (Fig.

Table 3b: Geochemical Characteristics of the mixed
layer Clays (N=4)
Parameters
(%)
SiO 2
TiO 2
Al 2 O 3
Fe 2 O 3
MnO
CaO
MgO
Na 2 O
K2O
LOI

Mean

Std.
Dev.
9.09
0.11
2.78
1.39
0.02
1.43
1.93
0.19
0.78
3.92

The loss on ignition (LOI) values range between
10.1 and 21.5 wt.% (av. 13.4%) for smectitic clay
while the mixed layer and kaolinitic clays exhibited99

GEOMATE- Osaka, Nov. 16-18, 20154c) also suggest the presence of illite in the mixed
layer clay-shale materials. Moreover, these resultscorrelated well with the estimated activities of theclay-shales and are also clearly consistent with theresults of XRD analyses presented earlier.

region of an expanded plasticity chart (high liquid
limit and high PI) as shown earlier in Fig. 3.
Table 4: Geochemical Characteristics of thekaolinitic Clays (N=10)
ParaActiMBI
SA
%
meter
vity (meq/100g) (m2/g) Mont.
Smectitic Clay (N=10)
Min.
1.32
10.0
0.78
32.3
Max.
1.92
18.6
1.46
60.1
Mean
1.71
14.7
1.15
47.5
Std. Dev. 0.23
2.63
0.21
8.48
Mixed layer Clay (N=4)
Min.
0.96
9.75
0.76
31.50
Max.
1.81
17.4
1.36
56.05
Mean
1.49
13.5
1.05
43.45
Std. Dev. 0.38
3.19
0.25
10.31
Kaolinitic Clay (N=10)
Min.
0.51
1.77
0.14
5.71
Max.
0.72
5.80
0.45
18.7
Mean
0.60
3.59
0.28
11.6
Std. Dev. 0.10
1.92
0.15
6.19
MBI = Methylene blue adsorption index
Mont. = % Montmorillonite
SA= Surface Area; %Activity provides an indirect indication of thetype of clay minerals present, e.g. Allophones, Illite,
Kaolinite, and Halloysite, do exhibit an activity of
0.5, while Illites and Smectites are characterized byan activity of 1 [15]. In this study, based on the grain
size distribution and Atterberg limits, the estimatedactivity as presented in Table 4 range from
0.51−0.72 for the kaolinitic clays; suggesting thedominance of inactive clays with low CEC and little
or no swelling potential. However, values of0.96−1.92 for smectitic and mixed layer clay-shales
indicate normal to predominantly active clay
materials with relatively higher swell susceptibility
and higher CEC. These results correlated well withthe estimated activities of the clay-shales and arealso clearly consistent with the results of XRD
analyses, index properties and Atterberg limits (LL,
PL and PI) presented earlier; thus it can be inferredthat the study clay-shales are suitable as liner
materials.

Fig. 4: Representative XRD patterns of the clayshale samples under different treatments(A= Kaolinitic; B= Mixed layer; C= Smectitic)

Methylene Blue Index and Activity
In the field of clay chemistry, the adsorption of
methylene blue to the edges, external surfaces, andaccessible inter-layer regions of clay minerals
dispersed in an aqueous solution is often used tomeasure CEC and specific surface area of clay
minerals [12], [13]. In this study, the results ofMethylene blue adsorption index, MBI (an estimateof CEC) and surface area of the clay-shale materials
are presented in Table 4 alongside the activity.
As shown in Table 4, MBI (an estimate of CEC)
range from ≈10 to 18.6 meq/100g for both smectictic
and mixed layer clay-shales with corresponding
surface area of 0.8−1.5m2/g suggesting thedominance of swelling clay minerals as also
revealed by the estimated montmorillonite content of
31.5 to 60.1%. However, the MBI value of 1.8−5.8
meq/100g and surface area of 0.14−0.45m2/g forkaolinitic clay-shale materials is a confirmation of
the dominance of inactive clay minerals as reflectedby the low values (15.1 - 18.1%) of estimatedmontmorillonite content.
Clay minerals with high plasticity also generally
have higher activity and corresponding high CEC[14]. These tend to plot in the upper right hand

SUMMARY AND CONCLUSIONS
The results of the geotechnical tests conductedon the clay-shale samples revealed that smectitic andmixed layer clays have Liquid Limit (LL) of 58.3 –116.5 (av. 75.4) and 170.4 – 173.2 (av. 171.8)
respectively while the plasticity index (PI) ranged
from 20.3 – 51.6 (av. 31.8) and 80.9 – 93.3 (av.
87.1) respectively. With percentage fines of morethan 85%, percentage clay fraction of 60−75%(smectitic) and 50−55% (mixed-layer), most of thesamples satisfy the basic requirements of clay liners
100GEOMATE- Osaka, Nov. 16-18, 2015

according to the standard specifications (Daniel,1993). The mineralogical analyses revealed thepresence of kaolinite in all samples by its basal
spacings at 7.1 and 3.5A°, however, the oriented
mounts of the samples confirm the presence ofdivalent-cation saturated smectite for the mixedlayer and smectitic clay-shale materials. These areconsistent with the geochemical data with SiO 2 of
38.2 – 63.1wt.% (±8.4%) and average Al 2 O 3 of
17.0% for smectitic clay-shales and SiO 2 of 47.8 –68.1 wt.% and Al 2 O 3 of 5.9% and 24.5%
respectively for mixed layer and kaolinitic clay
materials. Nonetheless, average value of 8.2%, 6.5%,
and 3.6% respectively, suggests Fe-rich assemblages.
In summary, based on the results of thegeotechnical, mineralogical and chemical propertiesof the study clay-shales were generally within therange suitable for use as liners or component
materials. However, higher plasticity index of 8093% for the mixed-layer clay-shales are extreme
enough as to warrant amendment or blending withother materials in order to obtain suitable plasticity
property and shrinkage susceptibility, before
application as liner materials. Further studies inrespect of permeability, compaction behaviour andsorption characteristics of the clay-shales arerecommended.

Landfill Liner Material. Current Advances in
Civil Engineering (CACE); Vol. 2(40); 2014.
pp. 102-117.
[4] Mitchell, J.K., Bray, J.D., Mitchell, R.A.
Material interactions in solid waste landfills.
Proceedings of a Specialty Geoenvironment
2000 Conference. ASCE, New Orleans, LA,
USA, 1995; pp. 568– 590.
[5] Rowe, R.K., Quigley, R.M. and Booker, J.R.
“Clayey barrier systems for waste disposal
facilities”. E & FN Spon Lond, 1995; 390 pp.
[6] Kogbe, C.A. The Cretaceous and Paleocene
sediments of southern Nigeria.In. CA. Kogbe.
(Ed). 2nd ed. Lagos: Elizabeth Publishers, 1989;
273-286.
[7] Obaje N. G. Geology and Mineral Resources of
Nigeria, Lecture Notes in Earth Sciences 120,
DOI 10.1007/978-3-540-92685-6 1, SpringerVerlag Berlin-Heidelberg, 2009.
[8] Hang, P. and Brindley, G.. Methylene blue
adsorption by clay minerals. Determination of
surface areas and cation exchange capacities.
Clay and Clay Minerals. 18: 1970; pp.203-212.
[9] Brindley, G.W., and Brown, G. (Eds.). Crystal
Structures of Clay Minerals and Their X-ray
Identification. Mineralogical Society, London.
1980.
[10] Moore, D.M., Reynolds Jr., R.C.,. X-ray
Diffraction and the Identification and Analysisof Clay Minerals. Oxford University Press,
Oxford; 1997.
[11] National River Authority North West Region,
Earthworks to landfill sites, 1989.
[12] Omotoso, D. “Clay Characterization in the Oil
Sand Industry.” 2nd CONRAD Clay Workshop
Presentation, Edmonton, March, 2011.
[13] Bergaya, F., Theng, B., and Lagaly, G. (Eds).
Handbook of Clay Science. 1. Elseviers. 2006;
1223p.
[14] Boxill, L. Potential for Use of Methylene Blue
Index testing to Enhance Geotechnical
Characterization of Oil Sands Ores and Tailings.
Proceedings Tailings and Mine Waste;
Vancouver, BC, November 6 to 9, 2011.
[15] Sobkowicz, J. and Boswell, J. “Influence ofClay Mineralogy on the Geotechnical Stabilityof Tailings Deposits." 2nd CONRAD ClayWorkshop Presentation, Edmonton, March,
2011.

ACKNOWLEDGMENTS
The first author acknowledged, with manythanks, the assistance and supports of Dr. Oscar
Baeza-Urrea and Mrs. Jutta Moschansky-Koster of
the Fachbereich Geographie/Geowissenschaften(FB-VI), Universität Trier, Germany, during thelaboratory phase of this study.

REFERENCES[1] Daniel, D.E. "Landfill and impoundments”. In:
Daniel DE (ed) Geotechnical practice for waste
disposal. Chapman & Hall, London, 1993. pp
97–112.
[2] Mohamedzein A; Yahia E., Amer A. A.,
Mohammed Y. A., Ahmed Q., Abdul-Hamid A.
Assessment of crushed shales for use ascompacted landfill liners. Engineering Geology
80; 2005; pp. 271–281.

[3] Nayak, S., Sunil, B.M., and Allamaprabhu, K.
Assessment of Blended Lithomargic Clay as
101

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

LABORATORY-SCALE MACRODISPERSION IN HETEROGENEOUS
POROUS FORMATION AND ITS QUANTIFICATION USING SPATIAL
AND TEMPORAL MOMENT APPROACHES
Kazuya INOUE1, Tomoki KURASAWA1 and Tsutomu TANAKA1
1
Graduate School of Agricultural Science, Kobe University, Japan;

ABSTRACT
Solute transport in groundwater is significantly influenced by the heterogeneity associated with spatial
distribution of the hydraulic conductivity. Understanding solute transport behavior in heterogeneous flow fields
is fundamental to develop appropriate models in aquifer management. In this study, laboratory two-dimensional
tracer experiments were carried out in a 1 m length, 1 m height and 0.03 m thickness sandbox to identify
macrodispersion in heterogeneous porous formation. Dye tracer of Brilliant Blue FCF mixed with NaCl wasapplied as a pulse type source and subsequent transport behavior of dye was recorded with digital camera. In
addition to the spatial moment approach, temporal moment approach was also applied to estimate thelongitudinal macrodispersivity by means of NaCl concentrations at an observation point in the flow field. The
experimental results indicated that longitudinal and transverse macrodispersivity were larger than those inhomogeneous formation. Moreover, it was revealed that both macrodispersivities in longitudinal and lateral
directions have a non-dependency of the regional flow rates.
Keywords: Macrodispersion, Spatial moment, Temporal moment, Dye tracer experiment, Heterogeneitydifficulty to obtain detailed and comprehensive data
sets directly in the field has led to the developmentof well-controlled laboratory experiments as useful
tool to investigate macrodispersion process. In
addition, image analysis techniques have become an
attractive tool for a detailed monitoring of laboratory
experiments at very high spatial and temporal
resolution [3].
Macrodispersivities are often identified in thefield by monitoring breakthrough curves at a limited
number of observation wells [4]. However, field
tracer test, in general, based on breakthrough curves
cannot infer the transverse dispersivity. Contrary to
the temporal moments, a snapshot of a cloud ofsolute particles in time expresses the dispersive
variation in the aquifers, providing the estimates ofspatial moments corresponding to the heterogeneityof concern [5].
The objectives of this study are to identify thelaboratory-scale
longitudinalandtransverse
macrodispersivities using a non-invasive techniquein conjunction with a dye tracer and to elucidate thesolute transport behavior in two-dimensional
heterogeneous porous formations under saturated
conditions. Spatial and temporal moment approaches
linked with image data of dye tracer behavior andvariations of NaCl concentration are utilized to relyon the difference between the two approaches.

INTRODUCTION
The phenomenon of dispersive mixing of solutein aquifers has been the subject of considerable
research interest during the past few decades due toconcerns on public health. In homogeneous porous
media under laboratory conditions, the classicaltheory describing this spreading or dispersion ofsolute, based on Fickian process and flow, combines
mass conservation and the time rate of change forthe flux of a contaminant moving through a porous
medium. On the other hand, geological media often
possess very complex spatial patterns of hydraulicconductivity, leading to extremely complex solutetransport behavior. That is, the Fick’s law
assumptions are no longer valid and the classicaltheory does not predict the amount of mixing. Thisphenomenon referred to as macrodispersion [1].
Understanding the characteristic behind suchtransport pathways related to macrodispersion is a
crucial step to ensuring the reliable prediction of acontaminant behavior in a laboratory or a field. Field
and laboratory scale experimental studies have been
extensively conducted to examine macrodispersion
in heterogeneous porous media. Several field
experiments have also provided considerable insight
into the transport process as affected by both smalland large-scale variations in the hydraulic properties[2]. The problem of field study is, however, that
discrete point sampling provides only limited
sampling spatially, and time required to collectsamples limits sampling temporally. Such a

EXPERIMENTAL METHODS
Materials and Experimental Apparatus

102

GEOMATE- Osaka, Nov. 16-18, 2015

Fig. 3 The fraction distribution of the seventeen
different materials.
out in a two-dimensional horizontal laboratory tank(100 cm × 100 cm × 3 cm) packed with silica sandto create a heterogeneous medium with well-defined
statistical properties. The flow tank was constructedfrom 1.5 cm acrylic and glass plates for the front andback respectively and stainless steel for the bothsides and bottom. At the front side, the glass plate
provides the opportunity of visual observation of
migrating dye tracer, while 6 manometers
established at the perforated acrylic plate wereplaced in the flow tank to determine piezometricheads. Constant head water reservoirs connected tothe upstream and downstream ends of the tank wereused to control the water flow. Schematic diagram ofexperimental apparatus is shown in Fig. 1.
The area referred to as the heterogeneous
formation shown in Fig. 1 was the primal area tocreate heterogeneous porous formation using
seventeen different sands reflecting heterogeneous
field sites. Hydraulic conductivity of each material
was individually measured with a constant headcolumn test. The hydraulic conductivity distribution
assumed to follow a log-normal probability density
function and was characterized by the geometricmean value of 0.0859 cm/s and the geometricvariance of 0.3074 cm2/s2. The hydraulicconductivity distribution exhibits spatially-correlated
structure defined by an exponential covariancefunction with correlation scale of 6 cm. The
generated heterogeneous formation generated fromblock kriging and the fraction distribution of theseventeen different materials are shown on Fig. 2
and Fig. 3, respectively. Addition to theheterogeneous formation, as the base case of solutetransport in homogeneous porous media, soilmaterial ( ln K = −2.13 ) comprised a homogeneous
porous formation.

Fig. 1 Schematic diagram of experimental
apparatus: (a) Plane view and (b) vertical view.

Fig. 2 The generated heterogeneous formation.
Flow and transport observations are routinely
made using dye tracers that allow visual qualitative,
and in some cases quantitative, evaluations of plumeevolution. For the purpose of visualization of solutetransport phenomena, in this study, Brilliant Blue
FCF, which is a synthetic color having the nature of
readily visible in soils, moderate mobile, and low
toxicity [6], was used as a dye tracer. In particular,
the advantage of Brilliant Blue FCF is its goodvisibility and against the color of the soil’s
background and its low toxicity [6]. NaCl was also
mixed with dye tracer solution of Brilliant Blue FCF
in order to observe the solute concentration of NaClin a flow field. The initial concentrations of Brilliant
Blue FCF and NaCl were adjusted to 0.40 mg/cm3
and 5.0 mg/cm3, respectively. The initialconcentration of dye tracer was determined to be
low enough to avoid density-induced flow effects.
The intermediate-scale experiments were carriedExperimental procedure
Packing of the materials with 24 and 16 cells in103

GEOMATE- Osaka, Nov. 16-18, 2015

Table 1 Experimental cases.
Case

Injection port

Flow rate(cm3/min)

Homogeneous

1 point (c)

297
297

Heterogeneous

3 points (b,c,d)5 points(a,b,c,d,e)
1 point (c)3 points (b,c,d)5 points(a,b,c,d,e)5 points(a,b,c,d,e)5 points(a,b,c,d,e)

297

297
297

101
184
297

material ( ln K = −1.18 ) in the same manner in 6 cm
layers.
After packing, water was applied to the flow tankunder a specific hydraulic gradient controlled byconstant head water reservoirs at the upstream anddownstream sides, while maintaining saturated
condition of porous media. A steady saturated flow
field was established in the flow tank whenfluctuations in the observed drainage rate, which
was effluent from the constant head water reservoir
at the downstream side, and piezometer reading atwater pressure measurement points could become
negligible. In tracer experiments, to examine theeffect of variation of flow rate on macrodispersion
phenomena, the experiments were carried out forflow rates of 101, 184 and 297 cm3/min.
After steady flow field was established, dyetracer of Brilliant Blue FCF mixed with NaCl was
released from one injection port of c, three injection
ports of b, c and d or five injection ports from a to e
whose locations are depicted in Fig. 1. Experimental
cases are listed in Table 1. The solution with 25 cm3in each injection port was injected for 30 seconds.
During the experiment, the profiles of solute
migration were recorded using a digital camera
approximately 100 cm located away from the waterflow tank.

Fig. 4 Representative images of dye tracer
transport in heterogeneous formation.

Fig. 5 Example of relation between the pixel
intensity and the dye tracer concentration.
the x and z directions, respectively, was carried out
under saturated conditions in order to avoid air
entrapment. Each sand cell had 3 cm and 3 cm inboth directions. The prescribed packing structures
were established to transparent sheets described allsand cell locations and attached to the glass plate offlow tank. As the first step before creating
heterogeneous porous packing, as shown in Fig. 1,
the soil material with -1.18 of the geometric mean of
the hydraulic conductivity was filled in 7.5 cm layer
at the bottom of the tank through the process ofcompaction. In heterogeneous packing, the materials
comprising each cell were packed in 3 cm layers
using narrow dividers with the thickness of 1 cm toestablish sharp connectivity between cells. The
dividers were removed as packing progressed. The
rest of upper area was filled by the same soil

Transport process and image calibration
Fig. 4 shows the typical image obtained during
the tracer experiments. Each of the pixels
representing an image has a pixel intensity which
describes how bright that pixel is. Data recorded by
the digital camera successfully indicated different
pixel intensities in dye tracer distributions,suggesting different concentrations of the dye tracer.
In order to establish a relationship between the color
intensity and concentration, calibrations were104

GEOMATE- Osaka, Nov. 16-18, 2015

where A L is the longitudinal macrodispersivity, A T is
the transverse macrodispersivity and ξc is the travel
distance of the center of tracer plume in the mean
flow direction at a given time t.

performed and analyzed with solutes of known
concentrations of dye. Example of regression curveis shown in Fig. 5.
Spatial Moment Approach

Temporal Moment Approach
Spatial moments of aqueous concentrations
distributed in space are calculated based on thespatial moment approach [7]. Spatial moments arecalculated from digital image at given times asfollows:

M ij (t ) = ∫∞∞∫ c( x, z, t ) x z dxdzij

The temporal moment approach was used tocharacterize the breakthrough data at NaClmeasurement location. The jth normalized absolutetemporal moments, μj (x) , and the jth central
temporal moments around the mean, 𝜇𝜇𝑗𝑗′ (x), are very
useful descriptors of breakthrough curves (BTCs).
The first-order temporal moment corresponds to themean arrival time of solute, while the second-ordertemporal moment is an analogy with the statisticaldispersion [9]

(1)

−∞ −∞

where x and z are the Cartesian coordinates, c is thesolute concentration, t is the time, M ij is the spatial
moments associated with the distribution of tracer
plume at a certain time, and i and j are the spatial
order in the x and z coordinates, respectively. Basedon the regression curves relevant to the dyeconcentration and pixel intensity, the pixel intensity
distribution can be converted to a concentration
distribution by the calibration, providing an analogy
between Eq.(1) and Eq.(2) [8]M ij (t ) = ∫∞∞∫ t c ( x, z, t )dtµ ( x, z ) =∫ c ( x, z, t )dt
0∫ (t − µ ) c ( x, z, t )dtµ ' ( x, z ) =∫ c ( x, z, t )dtij

AL (ζ p ) =(3)

1 σ xx
,
2 ξc

AT =

1 σ zz2 ξc

ζ p µ 2' (ζ p )2 ( µ1 (ζ p )) 2

(8)

RESULTS AND DISCUSSION
Longitudinal Macrodispersivity(4)

Longitudinal macrodispersivity values obtained
from spatial and temporal moments inheterogeneous and homogeneous formations areshown in Fig. 6 as a function of the mean
displacement distance of dye tracer distribution.
Longitudinal macrodispersivity estimates inhomogeneous formation remain constant during thecourse of solute transport, whereas longitudinalmacrodispersivity estimates in heterogeneous
formation exhibit the increase tendency with theincrease of displacement distance. This is attributed

where σ ij is the second order spatial moments.
Longitudinal and transverse macrodispersivities
from spatial moments of the distributed tracer plumeare calculated as [8]

AL =

m

where ζ𝒑𝒑 is the distance from the source of soluteinjection, μ1 (x) is the first normalized absolutetemporal moments and 𝝁𝝁′𝟐𝟐 (x) is the second central
temporal moments around the mean.

where x c and z c are the centroid locations of plumeconcentration distribution in the x and z coordinates,
respectively. The second order spatial moments arealso computed as follows.

M 11− xc z c 
M 00

M 02
2 
− zc 
M 00
(7)

where 𝒄𝒄𝒎𝒎 (x,z,t) represents the time variation of the
NaCl concentrations at the monitoring locations, andj is the nonnegative integer corresponding to theorder of concern. Longitudinal macrodispersivities
using temporal moments are calculated aswhere H(x,z) is the area per unit pixel and I(x,z,t) is
the intensity at a corresponding pixel. The centroidof concentration distribution is calculated as thenormalized first-order spatial moment.

 M 20− xc2
M
σ ij =  00
 M 11
 M − z c xc
 00

m

∞

0

(2)

M
z c = 01
M 00

j

1

0

∫ H ( x, z ) I ( x, z, t ) x z dxdz

(6)

m

∞

−∞ −∞

M
xc = 10 ,
M 00

∞

j

j

∞

j

m

0

(5)

105

GEOMATE- Osaka, Nov. 16-18, 2015

Fig. 8 Variation of spatial moment of σzz in eachformation

Fig. 6 Longitudinal macrodispersivity variation
identified with spatial and temporal moments inheterogeneous and homogeneous formation.

regional flow direction. Contrary to this, inheterogeneous formation except for the case with 3
injection points, a marked difference between
longitudinal macrodispersivity estimates fromspatial and temporal moments appears. This
indicates that single monitoring location is
insufficient to capture the primal characteristics ofsolute transport as breakthrough curves and to
identify a reliable estimate.
Transverse Macrodispersivity
The results of transverse macrodispersivity
estimates obtained from spatial moments are shownin Fig. 7 as a function of the displacement distanceof solute. The values of the transverse
macrodispersivity indicate a dependency on thenumber of injection ports in the z direction and show
the decrease tendency. To clarify this variation, theresults of the second spatial moments in the z
direction σ zz as a function of elapsed time are shownin Fig. 8. The results of σ zz exhibit a dependency
on the plume-scale in the z direction. The
geometrical elongation of the initial tracer
distribution in the z direction provides a higher valueof σ zz . In addition, the velocity component in the z
direction is relatively small to that in the x direction.
Therefore, as shown in Fig. 8, the results of σ zz
remain constant during the solute transport, resulting
in larger values of the transverse dispersivity
associated with the source length in the z direction
and a decrease tendency of the transverse
macrodispersivity.
The
differenceofestimates
betweenhomogeneous and heterogeneous porous formations
appears in transverse macrodispersivity variation
shown in Fig. 7, especially in larger displacement
distance. This is because the shapes of tracer plumes
become complex and irregular according to thepreferential pathlines of solute in heterogeneous
formation,
leadingtothetransverse

Fig. 7 Transverse macrodispersivity variation
identified with spatial moments in heterogeneousand homogeneous formation.
to the distribution of hydraulic conductivity
comprising flow fields and exhibits the evidence
occurringmacrodispersion
phenomenainheterogeneous porous formation. Hence, theseresults demonstrate that the scale effect ofmacrodispersion can be observed under controlledlaboratory conditions. In heterogeneous formation,
longitudinal macrodispersivity values obtained fromspatial moments show a dependency on the numberof injection ports, or on the initial plume length in
the z direction. This is due to the difference of theinherent heterogeneity associated with the solutetransport pathway.
In homogeneous formation, longitudinalmacrodispersivity
estimatesusing
temporalmomentsarealmostidentical
tothemacrodispersivity estimates obtained from spatial
moments. This may suggest that single monitoring
point leads to a plausible result in homogeneous
porous formations if a direction from a soluteinjection port to a monitoring point is parallel to the106

GEOMATE- Osaka, Nov. 16-18, 2015

4.

heterogeneous formation, a marked difference
betweenlongitudinalmacrodispersivity
estimates from spatial and temporal moments
appeared.
Longitudinal and transverse macrodispersivities
have a non-dependency of the regional flow
rates.

REFERENCES[1] Gelhar LW, Welty C, Rehfeldt KW, "A criticalreview of data on field-scale dispersion in
aquifers", Water Resour. Res., 28(7), 1992,
pp.1955-74.
[2] Sudicky EA, "A natural gradient experiment on
solute transport in a sand aquifer: Spatial
variability of hydraulic conductivity and its rolein the dispersion process", Water Resour. Res.,
22(13), 1986, pp.2069-82.
[3] McNeil JD, Oldenborger GA, Schincariol RA,"Quantitative
imagingofcontaminantdistributions in heterogeneous porous media
laboratory experiments", J. Contam. Hydrol., 84,2006, pp36-54.
[4] Adams EE, Gelhar LW, "Field study ofdispersion in a heterogeneous aquifer 2. Spatial
moments analysis", Water Resour. Res. 28(12),1992, pp.3293-307.
[5] Fadili A, Ababou R, Lenormand R, "Dispersive
particle transport: identification of macroscale
behavior in heterogeneous stratified subsurface
flows", Math. Geol., 31(7), 1999, pp.793-840.
[6] Flury M and Flühler H, "Tracer characteristics
of brilliant blue FCF", Soil Sci. Soc. Am. J., 59,
1995, pp.22-27.
[7] Tompson AFB, Gelhar LW, "Numericalsimulation of solute transport in threedimensional, randomly, heterogeneous porous
media", Water Resour. Res., 26(10), 1990,
pp.2541-62.
[8] Inoue K, Kobayashi A, Suzuki K, Takenouti R,
Tanaka T, "A new approach for estimating
macrodispersivity using dye tracer and spatial
moment analysis", Annual J. Hydraulic Eng.,
JSCE, 55, 2011, pp.613-618 (in Japanese).
[9] Fernàndez-Garcia D, Illangasekare TH, Rajaram
H, "Difference in the scale-dependence ofdispersivity estimated from temporal and spatial
moments in chemically and physicallyheterogeneous porous media", Water Resour.
Res., 28, 2005, pp.745-59.
[10] Zinn B, Meigs LC, Harvey CF, Haggerty R,
Peplinski WJ, Von Schwerin CF, "Experimental
visualization of solute transport and masstransfer
processintwo-dimensional
conductivity fields with connected region ofhigh conductivity", Environ. Sci. Technol, 38,
2004, pp.3916-3926.

Fig. 9 Macrodispersivity estimates obtained fromspatial moments at various flow rates.
macrodispersivity estimates in heterogeneous
formation relative to those in homogeneous
formation.
Effect of Flow Rate
In order to examine the effect of variation offlow rate on macrodispersion phenomena, threeexperimental cases with the initial dye release fromfive injection ports were performed at various flow
rates of 101, 184 and 297 cm3/min. Fig. 9 shows
macrodispersivity estimates obtained from spatial
moments at three flow rates. Longitudinal andtransverse macrodispersivities have a nondependency of flow rate. This agrees with earlier
work [10], suggesting the reliability of estimates.
CONCLUSIONSIn this study, the behavior of macrodispersion inheterogeneous and homogeneous formations wasassessed through intermediate-scale solute transport
experiments.
Longitudinalandtransverse
macrodispersivities were identified using spatial andtemporal moment approaches using the dye tracer
distribution and NaCl concentration, respectively.
The following findings have been clarified.
1. Longitudinal and transverse macrodispersivity
estimates obtained from spatial moments of the
distributed dye tracer plume in heterogeneous
formation were larger than those inhomogeneous formation.
2. Longitudinal and transverse macrodispersivity
values obtained from spatial moments inheterogeneous formation show a dependency on
the plume-scale in the z direction.
3. In homogeneous formation, longitudinalmacrodispersivity estimates using temporal
moments are almost identical to themacrodispersivity estimates obtained fromspatial moments. On the other hand, in107

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

STRESS-STRAIN BEHAVIOR OF GEOSYNTHETIC REINFORCED
SOIL USING A MODIFIED CBR TEST

1

Danny Useche Infante1,2, Gonzalo Aiassa Martinez 1, Pedro Arrúa 1 and Marcelo Eberhardt1
Facultad Regional Córdoba, Universidad Tecnológica Nacional, Argentina; 2 Doctoral Fellow CONICET,
Argentina

ABSTRACTIn previous works several experimental investigations aimed at establishing the behavior of granular soils
reinforced with geosynthetics have been conducted. It has been found that the capacity of the soil improves due
to a tensile force given by the geosynthetic that thus provides a soil reinforcement function and therefore allows
a better distribution of pressure in the soil. Many of these studies report the use of CBR tests to characterize thebehavior of reinforced soil. The CBR test measures the vertical deformation with application of vertical loads, bywhich it is possible to characterize the stress-strain behavior of the material. In this paper, modified CBR tests
are performed on samples of granular soils reinforced with geogrids in order to establish the behavior ofreinforced soil at the load application surface from foundations; in this case the displacements are restricted in
the cylinder edges of CBR test. The results are compared with those obtained by CBR testing performed without
restricting displacements at the edges and without including any type of reinforcement, which were documented
in previous work of the authors.
Keywords: Shallow Foundations, Geosynthetics, Reinforced Soil, CBR

INTRODUCTION

MATERIALS

A considerable amount of literature has been
published on improvements achieved by including
geosynthetics in the soil mass with shallow
foundations: [1]-[3]. Stress-strain behavior of
geogrids reinforced soil, with use of the CBR test,
has been studied in [4]. In research presented at [5],the authors conducted an investigation with amodified CBR device to characterize the behavior ofsoil-geotextile system and the mechanism ofreinforced.
In this paper, an experimental study using the
CBR test is performed to determine the stress-strain
behavior of sand with the inclusion of geosynthetics.
In particular the case is studied where thegeosynthetic is anchored to the CBR mold, in orderto reproduce the condition in which thegeosynthetic is pressed by compacted ground and atension force occurs when the geosynthetic deforms
by the application of loads.
Subsequently, the results are compared withsamples without anchoring of the geosynthetic to themold. Therefore, it is possible to establish the
improvement achieved in the soil mass by
including geosynthetics anchored and unanchoredto the mold. Some results for specimens without
geosynthetic and geosynthetic layers not anchored tothe mold were reported in [6], and are considered inthis work to determine the effect of fixing thegeosynthetic to the mold in stress-strain behavior ofa soil-geosynthetic system.

Sand
In tests, natural sand of Córdoba Argentina isused. This soil is commonly utilized inshallow foundations of structures and embankments
bases. The Granulometric distribution of the sand ispresented in Fig. 1, the sand is classified by the
USCS system as SW (Well graded sand) withuniformity coefficient C u =8.9 and curvature
coefficient C c =1.7.

Fig. 1

108

Granulometric distribution of the sand.

GEOMATE- Osaka, Nov. 16-18, 2015

The first 4 samples were prepared in the moldused to standard proctor compaction test, preparing
samples with 152.4 mm of diameter and 110 mm ofheight (see [6]). The geogrid was cut into thecircular bore size of the mold and introduced into thesoil mass as the geometry shown in Figs. 2b, 2c and
2d. For specimens 5 and 6, two rings with 152.4 mmof diameter and 53 mm of height were used in orderto press the geosynthetic and thus restrict movementof the geosynthetic on the edge of the mold; for ageosynthetic layer halfway the sample, the height ofthe specimen after compaction was 106 mm (see Fig.
2e) and for the sample with a geosynthetic layer on
the upper third part the height of the specimen after
compaction was 82 mm (see Fig. 2f). Compaction of
the material was using a 5.5 lb hammer (2.5 kg) withdifferent layers according to the sample volume or
the purpose of conserving constant compactionenergy as can be seen in Table 2. Load readings ofevery 0.2 mm piston settlement up to a depth of 20
mm were taken. This was followed by readings
every 1 mm until completing a depth of 25 mm,
where the trial ends. The piston used to transfer theload to the soil mass had a diameter of 50.8 mm. The
dry density of samples γ d were 19 kN/m3 with avariation of ± 1.0 kN/m3 while the moisture content
was 4% with a variation of ± 1%.

Geosynthetic
In the soil mass uniaxial geogrids are used,commercialized by CORIPA S.A, a local
company. Table 1 shows the physical andmechanicalproperties
ofthegeogridused. These geogrids are flexible meshwith shaped orthogonal grid made up of woven
filaments of polyvinyl alcohol (PVA) covered with apolymeric coating, with high tensile strength, a highstiffness modulus and low susceptibility to yield.
Table 1 Mechanical properties of geogrids [7].
Property
Tensile strength (to def. 5%)
Functional modulus (def. 5%)
Strain to breakTensile strength (to break)Tensile strength to yield for
120 years ≤ 30ºC
Mesh opening

Unit
kN/m
kN/m
%
kN/m

Value
35
700
5
35

kN/m

23

mmXmm

20X30

LOAD TEST
A modified CBR type tests were conducted oncompacted sand samples. The compacted specimens
were reinforced and unreinforced by geosynthetic.
Some samples were also prepared with geosynthetic
anchored to the edge of the mold (Table 2).

(a)

(b)

(c)

(d)

(e)

(f)

Table 2 Tests conducted.
No.
Description of theSpecimentest
1
Without geosynthetics
2

3
4

5

6

Compactionof the sample
Three layers,
55 blows each
A geosynthetic layer
Three layers,
on the upper third part 55 blows eachof the sample
A geosynthetic layer
Three layers,
halfway the sample
55 blows each
Two layers ofThree layers,
geosynthetics on the 55 blows each
upper third part ofthe sample
A geosynthetic layer Two layers 83
halfway the sample
blows eachanchored in the edgeof the moldA geosynthetic layer
Three layers,
on the upper third
42 blows eachpart of the sample
anchored in the edgeof the moldFig. 2

Scheme of the tests described in Table 2,(a) Specimen No. 1, (b) Specimen No. 2,(c) Specimen No. 3, (d) Specimen No. 4,(e) Specimen No. 5, (f) Specimen No. 6.

EXPERIMENT RESULTS
Stress vs. Settlement graphics are performed foreach specimen by obtaining values shown in Fig. 3.
In these curves different behaviors for small andlarge deformations were observed. Growth in theslope of the curve is presented from 2.5 mm
settlement typical behavior of sands in CBR type
tests. From Fig. 4 to Fig. 7 stress-strain behavior forsmall, medium and high deformations is shown.
Subsequently, vertical displacements produced in
the geosynthetic for each of the reinforced soil
specimens are presented.
109

GEOMATE- Osaka, Nov. 16-18, 2015

In Fig. 6, the curves for deformation between 5mm and 15 mm can be seen, the growth shown from
2.5 mm settlement remains. A small improvement isshown for specimen 3 (geosynthetic used halfway
the sample unanchored to the mold), which had
shown no increase with small deformations. Fig. 7
shows the results for high strains, namely strainsfrom 15 mm to 25 mm; in this case the linear trendof the data is maintained.

12000
Specimen 1
Specimen 2

10000

Specimen 3
Specimen 4

Stress (KPa)

8000

Specimen 5
Specimen 6

6000
4000
2000
0

0

5

10

15

20

25

Settlement (mm)7000
Specimen 1
6000
5000

The values obtained for deformations below 1
mm are shown in Fig. 4. There can be seen someerratic data and in some specimens improvement isachieved. It is also shown no significant
improvement for specimen 3 corresponding to thegeosynthetic layer halfway. In Fig. 5, deformation
behavior between 1 mm and 5 mm is presented. The
data trend is shown and the improvement is seen
with the inclusion of geosynthetics, except when alayer of geosynthetic is used halfway the sample(Specimen No. 3). From 2.5 mm settlement anaccelerated growth curve is shown, marking
stiffening.

Fig. 6

Stress (KPa)

7

8

Specimen 1
Specimen 3
Specimen 5

11000
10000

9

10

11

12

13

14

15

Specimen 2
Specimen 4
Specimen 6

Stress (KPa)

9000

Specimen 2

8000
7000
6000
5000

Specimen 3
Specimen 4

4000

Specimen 5

3000

15

16

17

18

19

20

21

22

23

24

25

Settlement (mm)Fig. 7
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Fig. 4Furthermore, vertical relative deformations in thegeosynthetic were measured. These measurements
were made from the upper horizontal plane of thespecimen, every 2 cm from the central axis
considering the directions shown in Fig 8. Before
measuring, the soil above the layer of geosynthetic
in the specimen was removed. Vertical profiles were
performed along the coordinate axes for each layerof geosynthetic, obtaining the resulting deformationin the geogrid at the end of the test. The results ofthese measurements show some small initial
deformations caused by soil compaction, these arelocated at different points of geogrid and are mostevident in the samples in which the geosynthetic is
not anchored to the mold. The vertical profiles forthe different layers of geosynthetic according to thedifferent geometric configurations (see Fig. 2 andTable 2) can be seen in Fig. 9 and Fig. 10.

Stress vs. Settlement for the specimens
tested to lower strains than 1 mm.

2000
Specimen 1

1800

Specimen 2

1600

Specimen 3

1400

Specimen 4

1200

Specimen 5

1000

Specimen 6

800
600
400
200
1

1.5

2

2.5

3

3.5

4

4.5

Stress vs. Settlement for the specimens
tested to strains between 15 mm and 25 mm.

1

Settlement (mmStress (KPa)

6

Stress vs. Settlement for the specimens
tested to strains between 5 mm and 15 mm.

12000

50

5

Settlement (mmFig. 5

5

Settlement (mm)1000Specimen 6

3000

0

Specimen 60Specimen 5

1000

Specimen 1

150

Specimen 4

4000

2000

250

200

Specimen 2
Specimen 3

Stress vs. Settlement for the specimens
tested.

Stress (KPa)

Fig. 3Stress vs. Settlement for the specimens
tested to strains between 1 mm and 5 mm.
110

y-axisGEOMATE- Osaka, Nov. 16-18, 20158

Fig. 10 Geosynthetic vertical displacement (y-axis).
ANALYSES AND DISCUSSION

6
4
2

-8

-6

-4

x-axis0-2

In order to estimate the improvement of thesoil produced by the inclusion of geogrids, a
modified BCR (Bearing Capacity Ratio) as well as
the index SR (Stress Ratio) are calculated for eachof the samples tested.

2

4

6

8

-2

BCR (modified Bearing Capacity Ratio)-4

With the results of the experiment the BCR(modifiedBearing
Capacity
Ratio)
wascalculated and the data obtained from the samples
anchored and unanchored to the mold werecompared. As can be seen when thegeosynthetic is pressed by the mold, a tension force
occurs in the geosynthetic achieving a much greater
improvement in the soil. The BCR was defined in[8] as the ratio of the ultimate bearing capacity ofreinforced soil and ultimate bearing capacity of
unreinforced soil. In the present study, the ratio isperformed for vertical load between reinforced andunreinforced samples at the same settlement. So, wedefined it as modified BCR as follows:-6-8Fig. 8Axes (Unit: cm) used to measure thevertical displacements in the geosynthetic.

The vertical displacements along the xaxis for the specimens with geosynthetics can beseen in Fig. 9 and the displacements along the y-axis
are
shown
in
Fig.
10.
In
these
curves, greater deformation can be observed in
specimen 6, in which the displacements ofgeosynthetic are restricted in the edges, where
the geogrid layer presses the mold. The top layer ofthespecimen
4(wheretwolayersof geosynthetic are used in the upper third part of thesample) also shows greater deformations than 1.5
cm, proving that a tensile force occurs in thegeosynthetic that results in an improvement in soil
behavior.

𝐵𝐵𝐵𝐵𝐵𝐵 =

-6.0

-4.0

Horizontal distance (cm)
-2.0
0.0
2.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0

Specimen 2
Specimen 4 (top layer)
Specimen 5

Fig. 9

4.0

6.0

8.0
7.62

Specimen 3
Specimen 4 (bottom layer)
Specimen 6

Geosynthetic vertical displacement (x-axis).
VERTICAL DEFORMATION IN Y AXIS (cm)

--8.0
7.62

-6.0

-4.0

Horizontal distance (cm)
-2.0
0.0
2.0
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0

Specimen 2
Specimen 4 (top layer)
Specimen 5

4.0

6.0

(1)

where 𝑞𝑞(𝑅𝑅) and 𝑞𝑞(𝑈𝑈) are the values of the appliedloadduringthetest for reinforced and unreinforced soil respectively
for the same settlement value.
The modified BCR is plotted against the ratio ofsettlement (s) and the width of the foundation (B) forthe purpose determining the improvement in soil
produced for different values of (s/B).
Fig. 11 provides the BCR versus the relation
(s/B)
for
specimens
2
and
6
with
a geosynthetic layer included in the upper thirdpart of the sample unanchored (2) and anchored (6)to the mold (see Table 2). Evidently, a further
increase in the BCR occurs when the geosynthetic ispressed by the mold (Specimen 6). A peak value canalso be seen for BCR when the ratio(s/B)
iscloserto
0.08, namely when Settlement reaches piston 4 mm.
The results, for the specimens with ageosynthetic layer halfway the sample, are presented
in Fig. 12. In this case can be seen thatwhen the geogrid is included in the soil without
being anchored to the mold edges, nosignificant increase occurs in the BCR, however
when the geosynthetic is anchored to themold, this value increases reaching a maximum
value when the ratio (s/B) is near to 0.08 as
occurs in the samples with a geosynthetic layer inthe upper third part.
Fig. 13 shows the curve for specimen 4 in

VERTICAL DEFORMATION IN X AXIS (cm)
--8.0
7.62

𝑞𝑞(𝑅𝑅)

𝑞𝑞(𝑈𝑈)

8.0
7.62

Specimen 3
Specimen 4 (bottom layer)
Specimen 6111

GEOMATE- Osaka, Nov. 16-18, 2015

which two layers of geosynthetic in the upper thirdpart of the sample are included without
any anchor. It shows similar behavior to theother specimens tested with a peak in the value ofthe BCR when the value of the ratio (s/B) is close
to 0.08. The sample in which a larger value of
BCR is reached corresponds to specimen 6, with ageosynthetic layer on the upper third part of thesample anchored to the mold (see Fig. 2 and Fig. 11).

part of the sample unanchored to the mold.

Index calculation SR (Stress Ratio)
The SR (Stress Ratio) index relates the stressvalues obtained in load tests with standard values tothe same deformation. It was calculated for eachspecimen as follows:

3

𝑺𝑺𝑺𝑺 =

2.5

BCR

2

1
Specimen 2 (Unanchored)
Specimen 6 (Anchored)

0
0

5
100

0.1

0

0.2

0.3

0.4

∗ 𝟏𝟏𝟏𝟏𝟏𝟏

(2)

This
index
is
calculatedforstressvalues corresponding to 2.5 mm and 5.0 mm ofsettlement
obtainedfromstresssettlement curves, which are divided between CBRtest standard stress of 1000 Psi (6900 kPa) and 1500
psi (10300 kPa) respectively. In other words,
with this analysis small deformations are measured.

1.5

0.5

𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺 (𝒌𝒌𝒌𝒌𝒌𝒌)

𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺 𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺 (𝒌𝒌𝒌𝒌𝒌𝒌)

0.5

(s/B)

20.00

Fig. 11 BCR
vs.
(s/B)
for
a geosynthetic layer included in the upper
third part of the sample; with geogrid
unanchored(Specimen
2)
and with geogrid anchored (Specimen 6).

18.00
16.00

SR (%)

14.00
12.00
10.00
8.00

SR (2.5 mm)

6.00

SR (5.0 mm)

4.00

2.5

2.00
0.00

2

BCR

1.5
1

Fig. 14 SR for the specimens tested.

Specimen 3 (Unanchored)
Specimen 5 (Anchored)

0.5

In Fig. 14, SR results are shown for each of thesamples tested. It can be seen that no appreciable
increase occurs in the SR rate for specimen 3.
This
means
that
includingageogridlayer
halfway
thesample produced no improvement in thesoil, although when the geogrid layer is pressed by
the mold (the displacements are restricted in theedge of the mold) a considerable increase occurs
in the SR as is shown in the values
obtained for specimen 5.
The results for the other samples show asignificant
increaseinthe SR using geogrid, especially when they areanchored to the edge of the mold. These results are
similar to those achieved calculating themodified BCR.

0
0

0.1

0.2

0.3

0.4

0.5

(s/B)

Fig. 12 BCR vs. (s/B) for samples with a layerof geosynthetic in halfway; with geogrid unanchored(Specimen
3)
and with geogrid anchored (Specimen 5).
3
2.5

BCR

2
1.5
1
Specimen 4 (Unanchored)

0.5
0
0

0.1

0.2

0.3

0.4

CONCLUSIONS

0.5

(s/B)

In this paper, modified CBR test was
performed on compacted sand samples in order
todeterminetheeffectoftheFig. 13 BCR vs. (s/B) for the specimen 4, with twolayers of geosynthetic in the upper third112

GEOMATE- Osaka, Nov. 16-18, 2015

geosynthetic inclusion on the behavior of soilgeosynthetic systems. From the experimental
results, the following can be concluded:
For settlements, s/B, around 8%, results show asignificant increase when geosynthetics are includedin the soil mass. The calculated values of modified
BCR show that for deformations near to 4 mm apeak value occurs in the BCR, which results in an
appreciable improvement in soil behavior.
The values of SR (Stress Ratio) show asignificant increase particularly when thegeosynthetic is anchored to the mold edges.
The
improvement
obtainedwhen
thegeosynthetic layer is boundary anchored by the mold,is better than when it is left unanchored. The tension
force developed by the geogrid when it isanchored to the mold can clearly be seen, therefore it
is important to establish a sufficient anchorage
length in geosynthetic, enough to ensure that thisforce occurs. In the present tests, a size of geogrid
three times the width of the foundation (3B) wasused.
For large settlements (s/B > 10%), the results
showedthat
althoughtheimprovementthat occurs is below the peak, an asymptotic
behavior occurs in the BCR, which is >1 (see Figs.
11, 12 and 13). The behavior of modified BCR vs.
s/B presents a curve with two differentiated zones
denominated as pre-peak and post-peak. It isestimated that this can be provisionally explained asfollows: (1) First, the development of tension in thegeosynthetic, and friction interaction sand-geogrid,
during the first settlements produces an increased inmodified BCR reaching a maximum at optimum
combination between compacted soil andgeosynthetic effect, (2) then the curve drops tostabilize at a given value of BCR, higher than one(asymptote) where, possibly, have been reaching a
residual friction interaction sand-geogrid. However,it will be necessary to increase the experimental
study and numerical analysis of the behavior, inorder to confirm this explanation. For this, direct
shear tests are planning, where sand-geogrid
interaction can be studied.
In addition, larger scale tests are planned in orderto validate the present results and establish the
optimum value of the anchorage length as well as
the overlapping layers.
Furthermore, other variables such as soil

moisture, shape and size of the foundation should beconsidered. Also experiments with biaxial geogrids
should be considered, as well as different forms ofgeometric configuration of the soil-geosynthetic
system.
ACKNOWLEDGEMENTS
The authors would like to thank the
NationalScientific and Technical Research Council(CONICET), the National Technological University(UTN), the Regional Faculty of Córdoba (Argentina)
and CORIPA S.A. for the geogrid samples provided.
REFERENCES[1] Chen, X.; Zhang, J. y Li, Z. “Shear behaviour ofa geogrid-reinforced coarse-grained soil basedon large-scale triaxial tests”. Geotextiles andGeomembranes, 42, 4, 2014, pp. 312-328.
[2] Kumar, S.; Solanki, C.H. and Pandey, B.K.
“Behaviour of prestressed geotextile-reinforced
fine sand bed supporting an embedded squarefooting” Int. J. of GEOMATE. Vol. 8, No. 2,
2015, pp. 1257-1262.
[3] Marto, A.; Oghabi, M. and Eisazadeh, A. “Theeffect of geogrid reinforcement on bearing
capacity properties of soil under static load; areview”. Electronic Journal of GeotechnicalEngineering, 18, 2013, pp. 1881-1898.
[4] Duncan-Williams, E. and Attoh-Okine, N. O.
“Effect of geogrid in granular base strength - Anexperimental investigation”. Construction and
Building Materials, 22, 2008, pp. 2180-2184.
[5] Bergado, D.T.; Youwai, S.; Hai C.N. andVoottipruex, P. “Interaction of nonwoven
needle-punched geotextiles under axisymmetricloading
conditions”.
Geotextiles
andGeomembranes, 19, 5, 2001, pp. 299-328.
[6] Aiassa, G.M.; Useche, D.J.; Arrua, P. andEberhardt, M. “Investigación experimental delcomportamiento de cimentaciones superficiales
sobre arenas reforzadas con geosintéticos”. XV
Congreso Panamericano de Mecánica de Suelose Ingeniería Geotécnica. (accepted 2015).
[7] CORIPA:
www.coripa.com.ar/productos/4geogrillas.
[8] Das, B.M. Shallow Foundations: Bearing
Capacity and Settlement. CRC Press, 1999.

113

FifthInternational Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan,Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

INFLUENCE OF DIFFERENT INITIAL DENSITIES IN THE
HIDROMECHANICAL BEHAVIOR OF STABILIZED LOESSIC SOILS
Alercia Biga Carolina1, Arrúa Pedro2, Eberhardt Marcelo3, Aiassa Martinez Gonzalo4
Grupo de Investigación y transferencia en Geotecnia, Estructuras y Fundaciones (GIGEF),
Departamento de Ingeniería Civil, Facultad Regional Córdoba, Universidad Tecnológica Nacional,
Argentina
1,2,3,4

ABSTRACT
Loessic soil is widely recognized for its metastable structure which, in a randomica way, presents soil
particles linked together by soluble salts bridges , forming an open mineral skeleton, which therefore, in presenceof cycles wetting, cause a dissolving of these links causing an internal slippage and thereby resulting a new
macroscopic structural reorganization. The arrangement of the porous medium and its porosity, characterize thesoil hydromechanical response. Thus, the geotechnical properties are significantly modified by using different
compaction methods and the addition of natural agents as sodium motmorillonite.
This paper presents a series of tests using remoulded samples, based on the influence of different initialcompaction densities and different percentages of bentonite, in the hydraulic and mechanical behavior.
Infiltration assays were performed with the falling head technique, by using a flexible wall permeameter. Also, auniversal compression machine was used to execute unconfined compressive tests.
The experimental results are compared to those obtained in natural soil samples remoulded at different densities.
The comparatives show that, increasing the packing density in conjunction with the addition of bentonite,achieve greatly improve the hydraulic and mechanical properties of loessic soils, getting geotechnical structures
more resistants and less permeable hydraulically.

Keywords: Permeability coefficient, Bentonite, Axial stress, Flexible wall permeameter, Loessic soil.

The mineralogical structure is composed byvolcanic origin materials and is alkaline in nature.
Grain-size distribution includes fine sand (1%-10%),silt (50%-80%) and clay (2%-15%) that have been
deposited by wind action in areas of low energy[4][5]. In nature the loess has an open structure [6],low unit weight, and it is highly dependent on
external conditions. Loessic soils are characterized
by volumetric instability due to wetting and stressstate changes. [7] The purpose of the addition of
bentonite is to stabilize the silty loess due to low
technical qualities for application in construction
works. [8][9]This paper presents an experimental study
characterizing the mechanical and hydraulic loessresponse improved with bentonite. The variables
affecting the samples manufacturing were theinfluence of bentonite content, water content, anddry unit weight. They were analyzed to the
optimization of infiltration and compression tests.

INTRODUCTION
Loess is aeolian sediment and is one of the mostextensive surficial deposits on the surface of the
Earth. Loess has come to be regarded as one of themost important archives of long-term dust
deposition and Quaternary climate change. [1]Loess soils are usually found in arid or semiarid
climates and their physical and chemical
characteristics mainly depends on their geologicalorigin.
South American loess presents a broad
geographic distribution extending across the Chaco–
Pampean Plain of Argentina and neighboring areasof Uruguay, southern Brazil (Rio Grande do Sul),
Paraguay, and the eastern Bolivia lowlands [2]The largest area of loess soils is concentrated incentral Argentina.
The accumulation of loess has been explained
through a combined mechanism of fluvial andaeolian transport from the Andes to the eastern
lowlands. [2]
It is estimated a volume of 34000-45000 cubic
kilometers for the Argentine Pampas loess,estimating an average thickness of 30-40 meters anda deposition period of 2.5 million years.[3]Argentinean loessic soils Performance
Due to its random structure and its low unit
weight, the stability and strength of the soil mass ishighly dependent on the strength of the bonds and114

GEOMATE-Osaka, Nov. 16-18, 2015

groups of soil were selected, (a) specimens withdifferent dry unit weight with a 3% content ofbentonite respect to the dry unit weight of soil. They
called SB1-1, SB1-2, SB1-3 and SB1-4. (b)Specimens with different dry unit weight with a 6%
content of bentonite respect to the dry unit weight ofsoil. They called SB2-1, SB2-2, SB2-3 and SB 2-4.
(c) ) specimens with different dry unit weight with a
9% content of bentonite respect to the dry unit
weight of soil. They called SB3-1, SB3-2, SB3-3
and SB 3-4 and the last group was only made ofloessic soil. They called S1, S2, S3 and S4.
Specimens were 0.07 m in diameter and 0.14m in
high. Static compaction method was used to prepare
samples, in cylindrical metallic molds. The
specimen test was built and compacted in threelayers of equal thickness. Extraction of samples was
performed using a hydraulic jack. In order toconserve water content, plastic bags were used.

contacts between particles. Wetting and dissolutionof salts that make contact bridges altering the
behaviour of the soil mass.
Hydraulic conductivity is affected by thisphenomenon. There are numerous factors that
control this conductivity like porosity, degree ofsaturation, particle size, structure, and properties ofthe permeant fluid [10].
That is why it is important to know, how thesefactors affect the soil behaviour, to understand thegeotechnical soils response against different
environmental conditions.
It has been analyzed the behaviour of Cordoba
loessic soil by adding materials such as bentonite. It
has been reported changes in values of mechanicaland hydraulic response obtained experimentally in
Cordoba loess soils. [11][12].
MATERIALS

Table 1. Materials properties

Loessic soil
The soil used was obtained from boreholes at 1m
depth, in Ciudad Universitaria, Córdoba. Table 1
presents the principals geotechnical properties of thesoil used in this work.
Bentonite

Properties

Loess

γ d (kN/m3)

12.2-14.5

Bentonite--

γ (kN/ m3)

14.9-16.8---

LL (%)

20.8-32.2

301

IP (%)

0-8

231

Gs

2.68

2.71

Ps 200 (%)

96

100

0,002 mm

4

85

SUCS

CL-ML

CH

Ss (m2 /g)

1731(*)

Ph

>8

7-7.5

Clay content<

Sodium bentonite was used. It comes from
deposits from Pellegrini Lake, Black River, locatedat north of Patagonia. Composed of 92% of
motmorillonite, and lower percentages of quartz,
gibbsite, feldspar, calcite and zeolites. It has a highsodium content product as a result of presence ofsoluble salts and cations retained in the thickness ofthe diffuse double layer. The magnesium comesfrom octahedral positions of the clay structure, fromsoluble salts and exchangeable cations. Generally
show high proportions of iron between 4% and 6%.
It has a high cation exchange capacity, which varies
between 76 and 97 meq/100g. The exchangeable
ions are Na+, Ca++, Mg++ and K+, with apredominance of sodium cations, hence its
classification [13]. It has high plasticity and highswelling capacity. Table 1 presents the principals
geotechnical properties of the soils used in this work.

Sc (%)0.38< 0.1
Note: γ = natural unit weight, LL = Liquid limit, IP = Plasticity
index, Gs = Specific gravity, Ps = passing sieve, Ss = Specificsurface, Sc = salt content. (*) [14]INFILTRATION TEST
A flexible wall permeameter was used to evaluate
hydraulic properties of samples. Falling head
permeability test was conducted [15]. Fig.1
Loess-bentonite samples were infiltrated inunsaturated and saturated condition. At the ends ofeach sample, filter paper and porous stones wereused. Porous stones were saturated during 24 hours.
During infiltration tests, the gradient was 10 [15].
Pressures for the camera, upper and lower head weretaken as 117 kPa, 114 kPa and 100 kPa respectively.
Deaerated water was used as permeant fluid.

METHODS
The materials were collected on trays at 20ºC
during 24 hs. Soil passing sieve 100 was used. Soilhas been drying at 105 ºC during 24 hs. The loessic
soil comparison specimen was four different dry
units weight, γ d 1 (kN/m3) = 1.4, γ d 2 (kN/m3) = 1.5,
γ d 3 (kN/m3) = 1.6 and γ d 4 (kN/m3) = 1.7, and thecompaction water content was ω ini (%) = 20. Three

In unsatured condition, the infiltration rate (I r ) is
adopted (Eq. (1)).

115

GEOMATE-Osaka, Nov. 16-18, 2015

Ir =ΔV (t )Δt A

(1)

Where ∆V: volume infiltrate during time ∆t, A:
cross section specimen area. Under saturated
condition the permeability parameter k is obtainedwith Eq. (2).

Vu (t ) − Vl (t1 ) 
PB1 + 1


aL
a
k=
Ln 

Vu (t2 ) − Vl (t2 ) 
A Δt 
 PB2 +

a



(2)

Where a: area of burette, L: length of sample, A:
area of sample, ∆t: lapsed time, PBi: bias pressure,V u (ti): volume reading of upper burette at time i,
V l (ti): volume reading of lower burette at time i. The
saturation level was calculated as B = [(u 2 -u 1 )/(σ 2 σ 1 )]. Where u 2 - u 1 : increase in pore pressure, σ 2 σ 1 : increase in cell pressure. We consider that, B at
98% is saturation condition [16]. At the end ofexperiment, the water content was established ineach sample.

Fig.2 Mechanical Press for unconfined compression
tests

Axial strain was determinate by using Eq. (3):

ε=

∆l
l0

(3)

Where: Δl: length change of specimen as read fromdeformation indicator, mm, and L0: initial length of
the specimen, mm.
The compressive strength s c, for a given applied
load was calculated by Eq. (4):

σc =

P
A

(4)

Where P: given applied load, kpa, and A:
corresponding cross sectional area mm2.
At the end of each test, it proceeded to takemoisture from each sample.
Fig.1 Flexible Wall Permeameter. (a) Infiltration cell.
(b) Transfer tank. (c) pressure-volume panel

RESULTS DISCUSSIONUNCONFINED COMPRESSION (UC)

Infiltration test

For UC test a mechanical press was used
instrumented with a load cell with a capacity of
50kN and a digital comparator for recording
displacements with a precision of 0.001 mm, to a
constant deformation rate of 2.4 mm/min. UC tests
was used to evaluate the stress–strain characteristics
and the stiffness properties of the Soil-AdditiveMixture. Fig. 2
Unconfined compression strengths were determinate
on loess- bentonite samples and loess samples.

The incorporation of fine material produced adecreased on infiltration and on permeability
coefficient too, on soil bentonite samples. [17] [18][19]. Figure 3 and Fig. 4 show the experimental
results obtained for each soil bentonite mixture
remolded at different dry unit weight.

116

GEOMATE-Osaka, Nov. 16-18, 2015

60
SB 1-2 (3%)…… CL-ML : S2 (0%)
SB 2-2 (6%)
SB 3-2 (9%)

2.5
2.0
1.5
1.0
0.5

SB 3-2SB 3-3SB 3-4

40
30
20
10

0.0
0

20

40

60

80

100

0

120

0

Time (min)SB 1-3 (3%)…… CL-ML : S3 (0%)
SB 2-3 (6%)
SB 3-3 (9%)

2.0

The results were: for γ d1 = 1,4 kN/m3 , k SB1 =9,4 x 10 -9 m/s, k SB2 = 6,64 x 10 -9 m/s, k SB3 = 1,48
x 10 -8 m/s and k S = 4,15 x 10 -8 m/s, for γ d2 = 1,5
kN/m3 , k SB1 = 7,10 x 10 -9 m/s, k SB2 = 6,65 x 10 9m/s, k SB3 = 3,11 x 10 -9 m/s and k S = 9,91 x 10 9
m/s. For γ d3 = 1,6 kN/m3 , k SB1 = 4,21 x 10 -9 m/s,
k SB2 = 1,59 x 10 -9 m/s, k SB3 = 1,56 x 10 -9 m/s andk S = 4,88 x 10 -10 m/s. For γ d4 = 1,7 kN/m3 ,k SB1 =1,28 x 10 -9 m/s, k SB2 = 1,79 x 10 -9 m/s, k SB3 =3,14 x 10 -10 m/s and k S = 3,36 x 10 -10 m/s.

1.5
1.0
0.5
0.0
20

40

60

800 1000 1200 1400 1600 1800 2000

In saturated condition, an increase of bentonite
on soil mixtures produced a decrece on infiltration
volume. Similar behaviour was observed, in terms ofincrease in the dry unit weight.

3.0

0

400 600

Fig.7 Infiltration test on soil /bentonite samples
with different dry unit weight and 9% ofbentonite content.

Figure 5, Fig. 6 and Fig. 7 shown how influence aparticular dry unit weight to infiltration rate.

2.5

200

Time (seg)Fig. 3 Infiltration test on soil /bentonite samples with
1.5 kN/m3 dry unit weight.

Infiltration (cm)SB 3-150
Infiltration (cm3)

Infiltration (cm)

3.0

80

100

120

Time (min)Fig.4 Infiltration test on soil /bentonite samples with
1.6 kN/m3 dry unit weight.
60
SB 1-1

Infiltration (cm3)50

SB 1-2

SB 1-3SB 1-4A possible explanation for this behaviour is due
to high cation exchange capacity sodium bentonite
which has, comes from the presence of Na + ions,which become attractive to water molecules. The
swelling process of these ions modifies theconsistency of the mixture and changing its texture,affecting the hydraulic conductivity. This behaviouris accentuated by increasing the percentage of
bentonite on soil -bentonite mixtures. [15]

40
30
20
10
0
0

200

400

600

800 1000 1200 1400 1600 1800 2000
Time (seg)Fig.5 Infiltration test on soil /bentonite samples withdifferent dry unit weight and 3% of bentonite
content.

Unconfined Compression (UC)The results of unconfined compression are
shown on Fig.8. The graphic shows that mechanicalbehavior has a better performance in relation with
the increase of bentonite content.
Higher compression resistance is achieved insamples with 9% of bentonite than samples without
bentonite. The compressive strength increased by
25% in samples with addition of bentonite withrespect to samples without addition, for maximum
experimental dry unit weight near 1.7 kN / m3.

60
SB 2-1

Infiltration (cm3)50

SB 2-2SB 2-3SB 2-4

40
30
20
10
0
0

200

400

600

800 1000 1200 1400 1600 1800 2000
Time (seg)Fig.6 Infiltration test on soil /bentonite samples withdifferent dry unit weight and 6% of bentonite
content.

117

GEOMATE-Osaka, Nov. 16-18, 2015

Unconfined compression: a) The compressive
strength increased by 25% in samples with addition
of 9% of bentonite with respect to samples without
addition, for maximum experimental dry density of
about 1.7 kN / m3.b) The maximum unconfinedcompression strength registered was in samples withdry densities greater than 1.65 kN / m3, with values
close to 100 kN / m2, and contributions from 9%
bentonite.
This represented an increase of 5.3 times thecompressive strength obtained for soil samples
without addition of bentonite and molded with a dry
density of 1.4 kN / m3.

Unconfined compression (kN/m2)

100
SB 3-1
SB 3-2SB 3-3SB 3-4
S1
S2
S3
S4

90
80
70
60
50
40
30
20
10
0
0.0

1.0

2.0

3.0

4.0

5.0

6.0

Relative strain (%)

Fig.8 Unconfined compression tests on samples with
9% bentonite

ACKNOWLEDGEMENTS

Densities close to 1.7 g / cm3, and bentonite
contributions greater than 3%, produced changes inthe stiffness of the specimen. It was observed aductile behaviour with relative deformations greaterthan 5% was observed.
The maximum unconfined compression strength
registered was in samples with dry densities greaterthan 1.65 kN / m3, with values close to 100 kN / m2,
and contributions from 9% bentonite.
This represented an increase of 5.3 times thecompressive strength obtained for soil samples
without addition of bentonite and moulded with adry density of 1.4 kN / m3.

The authors thanks the support of Universidad
Tecnológica Nacional Facultad Regional Córdoba,
Ministerio de Ciencia y Tecnología, Gobierno de laProvincia de Córdoba (PID-GRF), and FONCyT
Agencia Nacional de Promoción Científica yTecnológica (PICT).
REFERENCES[1] Muhs R. Daniel, Stephen R. Casttle, Onn
Crouvi, Denis-Didier Rousseau, Jimin Sun, andMarcelo A. Zárate, Loess Records, Ed Springer
Sciencie, USA, 2014, ch 16.
[2] Marcelo A. Zarate, Loess of southern SouthAmerica, Quaternary Science. Vol. 22. 2003
pp.1987–2006.
[3] Bloom, A.,“Some Questions about the Pampean
Loess”.Paper for the Department of Geography.
University of Leicester Nº 23, 1992. pp 17-18.
[4] Teruggi, M.E. “The Nature and Origin ofArgentinean Loess”. Journal of Sedimentology
and Petrology, 27(3), 1957.pp 323-332.
[5] Moll, L.L. y Rocca, R.J. “Properties of loess inthe center of Argentina”. Memoria. XI Pan
American Conference on Soil Mechanics andFoundation Engineering. Viña del Mar, Chile.
Vol. I.1991. pp. 1-14.
[6] Aiassa G, Zeballos M, Arrúa P and Terzariol R,“Infiltración en suelos limosos compactados”,
XIX Congreso Argentino de Mecánica deSuelos e Ingeniería Geotécnica, Octubre 2008,
La Plata, Argentina[7] Arrúa P, Aiassa G., and Eberhardt M. “Behavior
of collapsible loessic soil after interparticle
cementation”, International J. of GEOMATE,
Vol. 1, November-December 2011, pp. 130–135[8] Arrúa, P.; Aiassa, G. and Eberhardt, M. Loesssoil stabilized with cement for civil engineering
applications. International Journal of Earth
Sciences and Engineering. ISSN: 0974-5904.
Vol. 5, No 1, 2012.pp.10-18

CONCLUSION
This paper has presented a study loess soil bentonite mixtures and has revised the importance ofbentonite content and its influence on themechanical and hydraulically performance ofadditive mixtures. Water content, dry unit weight,
and bentonite percentages have been studied. It has
been identified the main results as follows:
Hydraulic properties: a) Increased design dry densityfrom 1.4 kN / m3 to 1.7 kN / m3, on samples withthe same bentonite percentage, it caused a decreaseon the infiltration volume about 90%. The lowest
rate infiltration was recorded on samples with a dry
unit weight about 1.65 kN / m3 and with 9%
bentonite addition, registered a decreced of 75% incomparison with soil without bentonite. b) The useof compaction techniques to enhance natural dry
density of silty loessic- soils without addition of
bentonite, and designing samples with dry densities
near 1.7 kN / m3, generate a decreced on
permeability coefficient about two orders ofmagnitude, reaching values in the order of
3.14 x 10-10 m/s. This behavior also visualized on
samples with 9% of bentonite and higher density
about 1.6 kN / m3.

118

GEOMATE-Osaka, Nov. 16-18, 2015

[9] Aiassa G. and Arrúa P, “Desempeño de barreras
sanitarias
simplesdesueloloessicocompactado”, Revista Tecnología y Ciencia,
Año 8, Noviembre 2009, pp. 26–40.
[10] Mitchell K. James. Fundamentals of Soils
Behavior. Editorial John Wiley and Sons. Inc.
1993.
[11] Alercia Biga, I. C ; Arrúa P., Eberhardt M.G;
Aiassa Martinez G.A.; “Hydraulic Conductivity
inLoessic
Stabilized
Soil”,“FourthInternational Conference on Geotechnique,
Construction Materials and Environment,
Brisbane”, Australia (2014), ISBN 978-49905958-3-8 C3051.
[12] Nieva, P.M. y Francisca, F.M. “Conductividad
hidráulica de mezclas de limo-bentonita
compactadas”. En: Francisca, F. (ed.), Desafíosy avances de la geotecnia joven en Sudamérica,
Vol. 1, 2009, pp. 193-196.
[13] Santamarina J., Klein K, Wang Y., and Prencke
E. Specific surface: determination and relevance.
2002 Published on the NRC Research Press
Web site at http://cgj.nrc.ca[14] Hyang-Sig Ahn, Ho Young Jo, “Influence of
exchangeable cations on hydraulic conductivityof compacted bentonite.” Journal of Applied
Clay Science, Vol 44, 2009. pp.144-150.
[15] ASTM. D 5084. Standard Test Methods for
Measurement of Hydraulic Conductivity of
Satured porous Materials using a Flexible Wall
Permeameter.
[16] Fredlund, D,G; Rahardjo, H; Soils Mechanics
for unsatured soils. John wiley and Sons.. NY.
USA. 1993[17] Cuisinier,O; Auriol, J.Cl;Le Borgne, T; Deneele,
D; “ Microestructure and hidraulic conductivityof a compacted lime-treated soil”, Journal ofEngineering Geology. Vol. 123, (2011).187-193.
[18] Qiong Wang ,Yu-Jun Cui, Anh Minh Tang,
Jean-Dominique Barnichon, Simona Saba, WeiMin Ye “Hydraulic conductivity andmicrostructurechangesofcompacted
bentonite/sand mixture during hydration”.
Journal of Engineering Geology. Vol 164,(2013) ,67-76.
[19] India Sudhakar M. Rao, Ravi K., “Hydromechanical characterization of Barmer 1
bentonite from Rajasthan, India”. Journal of

Nuclear Engineering and Design. Vol 265.
(2013). 330-340.

119

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

CASE STUDY ON DEFORMATION ANALYSIS DURING
EARTHQUAKE OF IRRIGATION POND EMBANKMENT
Minoru Yamanaka1, Takuya Sanei2, Katsuhiko Koizumi2 and Shuichi Hasegawa1
1
Facultyof Engineering, Kagawa University, Japan
2
Graduate School of Engineering, Kagawa University, Japan

ABSTRACTA failure damage of irrigation pond embankment by earthquake was worried seriously after the 2011 off thepacific Coast of Tohoku Earthquake, Japan. It is very important to confirm a safety against sliding failure ofirrigation pond embankment which is made from soil material. In order to clear a deformation of irrigation pond
embankment during an earthquake, an influence of earthquake acceleration and ground water table inside thepond embankment by the geotechnical software ALID was discussed mainly in this paper. As a result, it could
be clear that deformation of embankment model becomes larger as well as value of acceleration and height ofground water table. And more it turned out of influence of the location of the sandy layer with a possibility ofliquefaction.
Keywords: Soil Structure, Earthquake, Deformation, Numerical Analysis

INTRODUCTION

and can't indicate deformation shape in detail for thewhole embankment.
The authors were discussed using by the analysis
for liquefaction induced deformation program ALID
on deformation during earthquake of pond
embankment5). In this paper, deformation properties
for the pond embankment was discussed in detail bychanging liquefaction layer, earthquake accelerationand ground water level using the ALID.

Some irrigation pond embankments were serious
damaged by washout or large subsidence during the2011 off the pacific Coast of Tohoku Earthquake,Japan1),2). About 210,000 ponds are distributed inJapan. Pond embankment, which is earth structure,causes slip failure by seismic ground motion, or it
may occur a large settlement and deformation by aliquefaction of foundation ground during anearthquake. Serious damage occurrence to a pond
embankment by the big earthquake in the near futureis worried, therefore it is necessary to evaluate anearthquake-resistant of pond embankment by
numerical
deformation
analysiswhich
isconsiderable an influence of seismic ground motion.
The deformation amount can't be estimated bycircler slip stable analysis which is generally used.
Newmark method3),4) is very useful as an estimation
method of deformation amount. But the Newmark
method just analyzes the slipping length of circularsurface which indicates the smallest safety factor,

ANALYSIS MODEL AND CONDITION
Figure 1 shows the analysis model of irrigation
pond embankment, and the model sets a low
permeability soil ② at front zone of embankment.
Each size of model used a standard length forconstructing a new irrigation embankment.
Seismic accelerations of 5 steps, which ranges
from 150 to 600 gal, loads on the upper surface oflayer ④ and ⑤ . To evaluate influence todeformation amount by the difference in thelocations of the liquefaction layer distributed underthe embankment,

Table 1 Input data used in the analysis
Soil Layer

Deformation Condition

�𝑵𝑵① Compacted Sandy Soil-A Decrease of G, Liquefaction

14.5② Compacted Sandy Soil-B Decrease of G, Liquefaction③ Cohesive Soil (Old Fill)④c' (kN/m2) φ’ (°) 𝜸𝜸𝐭𝐭 (kN/m3) G (kN/m2)
R

33.6

28.2

20.6

18,000

16.7

16.7

29.8

20.9

18,000Decrease of G, Undrained

6.0

26.9

25.5

20.3

6,316

Alluvial Clayey Soil

Decrease of G, Undrained

10.0

19.0

26.8

16.8

10,526⑤

Alluvial Sandy Soil

Decrease of G, Liquefaction

4.0

13.0

28.1

18.9

4,210

⑥

Diluvia Gravel Soil

Coupled Element

50.0

38.8

41.0

19.2

52632

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GEOMATE- Osaka, Nov. 16-18, 2015

element of stiffness G. the G depends on theliquefaction resistance ratio FL. and the FL changes
on the fine content Fc 6). This analysis used ALID;
the Analysis for Liquefaction Induced Deformation
program.
The input data was decided by using some soillaboratory tests and geotechnical survey results. The
strength parameter c’ and φ’ was decided from theconsolidated undrained shear test; CU-bar test, and
the G was translated by from the N-value bystandard penetration test.

Fig.1 Analysis model
Case-1 is set the alluvial sandy soil layer ⑤ with apossibility of the liquefaction at the right side underthe embankment model, and Case-2 is set the
alluvial sandy soil layer ⑤ at the left side.
Table 1 shows the condition for deformation andthe input data for each layer of the model. For the
deformation condition under each ground watertable inside the embankment model, the sandy layer
was defined as the liquefaction element, and theclayey layer was defined as undrained element. On
the other hand, for the deformation condition upper
each ground water table, both of the sandy layer andthe clayey layer were defined as the decreasing

10
50

Deformation of embankment for Case-1
Figure 2 shows the maximum shear strain counterfor each height of water level and seismic
acceleration a=300 gal for Case-1; the sandy layer⑤ with a possibility of the liquefaction at the rightside under the embankment model. For the waterlevel = 18 m, which equivalent to the full water level
F.W.L, in Fig.2(a), the shape showing the maximum(a) Full water level F.W.L; 18 m(a) Full water level F.W.L; 18 m(b) Water level; 12 m(b) Water level; 12 m(c) Water level; 6 m(c) Water level; 6 m(d) Water level; 0 m(d) Water level; 0 m

Maximum shear strain (%)

15

ANALYSIS RESULTS

Fig.2 Maximum shear strain contour(Case-1, a = 300 gal)

121

Fig.3 Deformation vector(Case-1, a = 300 gal)

GEOMATE- Osaka, Nov. 16-18, 2015

shear strain is distributed from the top ofembankment to the sand layer ⑤ such as a circle
slip surface. In particular the maximum shear strainof the sand layer ⑤ is over 10 %, it seems that theembankment slips largely such a circular shapebecause the sand layer occurs large-decreasing of G
by liquefaction of the layer ⑤. In the other hand, forin Fig.2 (b), (c), (d) the shape of maximum shear
strain and its amount is almost same as Fig.1 (a)although the water level falls.
Figure 3 shows the deformation vector for eachwater level and seismic acceleration a=300 gal for
Case-1. For the water level = 18 m in Fig.3(a), thedeformation vector directs toward to the downstreamside of embankment, and the length of vector at the
sandy layer ⑤ is larger. The maximum length of
deformation vector is about 0.5 m at the toe ofembankment slope. Amount and direction of
deformation vector for condition with other watertable in Fig. 3 (b),(c),(d) seems to almost same,
therefore the deformation of embankment is notdifferent by the height of water lever for a=300 gal.
Deformation of embankment for Case-2(a) Full water level F.W.L; 18(a) Full water level F.W.L; 18 m(b) Water level; 12(b) Water level; 12(c) Water level; 6 m
15

10

5

(d) Water level; 00Maximum shear strain (%)(c) Water level; 6 m(d) Water level; 0 m

Fig.5 Deformation vector(Case-2, a = 300 gal)

Fig.4 Maximum Shear strain contour(Case-2, a = 300 gal)122

GEOMATE- Osaka, Nov. 16-18, 2015(a) Case-(b) CaseFig.6 Relationship between acceleration and settlement of crown

Figure 4 shows the maximum shear strain counterfor Case-2; the sandy sol layer ⑤ with a possibilityof the liquefaction at the left side. For the water level= 18 m in Fig.4 (a), the range of the maximum shear
strain is concentrating to the sandy layer ⑤, and therange is not reaching to the upper part ofembankment. For in Fig.4 (b), (c), d) the amount of
maximum shear strain at the sandy layer ⑤ isincreasing as well as a decline of the water level, andthe range is expanding to the upper part ofembankment. The range looks like a circular slip
surface.
Figure 5 shows the deformation vector for eachwater level for Case-2. The vector directs toward thelower left side, the length of vector is increasing
toward the same side. The length of vector indicatesmaximum at the sandy layer ⑤ for the water level =0 m in Fig.5 (d), the deformation of the toe ofembankment slope is very large by 0.45 m
Figure 6 shows the relationship between
acceleration a and settlement of embankment crown.
For Case-1 in Fig.6 (a), the amount of settlement isincreasing slightly from a=150gal to 200 gal, but theamount of settlement is almost constant at 13 cm
over a=200 gal. For Case-2 in Fig.6 (b), thesettlement is 25 cm already at a=150 gal for thewater level =18 m, and is constant almost froma=150 gal to 600 gal. Therefore it is obvious that theliquefaction occurs to the sandy layer ⑤ for a=150
and it occurs at the condition of all acceleration for
the water level 18m. And more the settlement ofcrown is increasing as well as a decline of the waterlevel for Case-2.

right side under the embankment, the deformation
vector directs toward to the downstream side ofembankment, and the length of the vector the
deformation of embankment is not different by theheight of water lever for a=300 gal.
2) For Case-2 which is set the alluvial sandy soil
layer at the left side, the amount of maximum shear
strain at the sandy soil layer is increasing as well asa decline of the water level, and the range is
expanding to the upper part of embankment. The
range looks like a circular slip surface.
3) The amount of settlement at embankment crownis different from the height of water level, and theamount of Case-2 is bigger than one of Case-1.
REFERENCES[1] Suzuki H, “Study on damage factors to earth
dams for irrigation due to earthquake motions In the Case of the 2011 offshore Pacific Coast
Tohoku Earthquake-”, Bulletin of the National
Institute for Rural Engineering, No.54, 2015,
pp.73-137 (in Japanese with English abstract)[2] Abbas M, Takada S and Sepand S, “Fujinuma
dam performance during 2011 Tohoku
Earthquake, Japan and Failure mechanism by
FEM”, Proc. of the 15th World Conf. on
Earthquake Engineering, 2012, CD-ROM, 8p.
[3] Newmark N.M, “Effects of Earthquakes on
Dams and Embankments”, Geotechnique,Vol.15, No.2, 1965, pp.139-159.
[4] Yasuda S. and Adachi K “Estimation of Sliding
Displacementof
Embankments
duringEarthquakesbyNewmark’s
Method”,
Geotechnical Engineering Magazine, JGS, 2010,
pp.52-53 (in Japanese)[5] Sanei T, Yamanaka M, Hasegawa S andKoizumi K, “Analysis Case Study on Dynamic
Deformation of Pond Embankment by using
ALID”, Proc. of the 49th Japan National Conf.

CONCLUSION
The following results can be obtained in this study.
1) For Case-1 which is set the alluvial sandy soil
layer with a possibility of the liquefaction at the123

GEOMATE- Osaka, Nov. 16-18, 2015

on Geotechnical Engineering, 2014, pp.13931394 (in Japanese)[6] Yasuda S, Yoshida N, Adachi K, Kiku H, Gose
S and Masuda T, “A simplified practical method
for evaluating liquefaction-induced flow”,

Journalof
GeotechnicalEngineering,
JSCE,No.638/ Ⅲ -49,1999, pp.71-89 (inJapanese
with
EnglishAbstract).

124

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

IMAGE PROCESSING FOR GEOTECHNICAL LABORATORYMEASUREMENTS
1

Erica Elice Saloma Uy1 and Tirawat Boonyatee2
Civil Engineering, De La Salle University, Philippines; 2 Chulalongkorn University, Thailand

ABSTRACTThis research is an effort to apply image processing techniques for non-contact determination of 2D and 3D
displacements of specimens in a triaxial apparatus. For the 2D measurement, a calibration procedure is applied tocorrect image distortions before the positions of interested points are tracked by the pyramidal optical flow
algorithm (Lucas and Kanade, 1981). By considering the change of position, the displacement of an interested
point can be determined. The results are compared with the measurements obtained from LVDTs and are wellagreed. For 3D measurement, calibration, rectification, correspondence and 3D re-projection are performed. It is
verified by an isotropic compression test of a cylindrical sponge in a triaxial apparatus. The estimateddisplacement and discharge of water are compared with the measured ones and are well agreed. The repeatability
and resolution of the developed system are found to be in the order of 0.006 cm and 0.004 cm, respectively.
Keywords: Image Processing, Non-Contact Measurement, Optical Flow, Stereo Vision Systemthrough the pictures taken. Furthermore, monitoringof the progression of failure zones within the sample
as it is being tested is possible. In order to integrate
this system, Labview is used as the software todevelop programs that can implement image
processing, optical flow and monitoring.

INTRODUCTION
Geotechnical measurements have provided thelife and blood for advances in modern geotechnicalengineering [1]. Since the development of soil
mechanics in 1930s, innovations have been made toachieve more precise, accurate and reliablemeasurements. Over the years, several techniques inmeasuring the displacement of soil are developed.
Techniques such as full-field measurements, which
can also be referred to as a non-contact measurement,
began to flourish by providing better understanding
compared to point-wise measurements. In contrast toconventional sensors such as linear variable
differential transducers (LVDTs), deformation canbe determined without any direct contact with thetarget of measurement [2]. Image processing is
becoming a trend especially in the field of
GeotechnicalEngineeringindetermining
measurements. The advent of digital image
processing resulted in powerful measuring
techniques such as Digital photogrammetry, particle
image velocimetry (PIV) and digital image
correlation (DIC) [3],[4].
This study is an effort to develop a system thatincorporates non-contact measurement throughimage processing. The system aims to determine thedisplacement and volume change of soil specimens
in a triaxial apparatus. Lucas and Kanade pyramidal
optical flow algorithm is applied to track the
movement of the soil [5], [6]. Non-contact
measurements are made with respect to 2D and 3D
axes. Triaxial tests are performed to validate thedisplacements and volume change the systemmeasured. The advantage of this technique is thaton-line and off-line measurement can be done

OPTICAL FLOW ALGORITHM
Optical flow can be defined as the changes ingray values that occur within the image planethrough successive images [5].
It has thedimensions of velocity were it can be denoted as
v = (u, v) where u and v are the x and y componentsof the optical flow vector at a point. When the
optical flow is obtained from two successive images,
the optical flow vector will appear as a displacementvector d = (d x , d y ) where d x and d y are the x and ycomponents of the displacement vector at a point.
An optical flow algorithm has the capability inestimating the changes in motion of a certain point
within the image under the assumption thatbrightness is constant among the subsequent images[7]. Lucas and Kanade pyramidal optical flow
algorithm is a type of feature based approach. The
method establishes correspondence of feature points
between the varying images at a certain time interval.
Furthermore, it analyzes two grayscale images, I and
J, having x = ( x, y ) as the pixel location within theimage plane. Thus, I ( x ) = I ( x, y ) and J ( x ) = J ( x, y ) .
Let image I ( x ) be the first image while image J ( x )the second image. When a point of concern,s = ( sx , s y ) , is established in the first image the  
algorithm will track its location t= s + d in the125GEOMATE- Osaka, Nov. 16-18, 2015
second image. I ( s ) and J (t ) must be almost equaland have a similar brightness in the two dimensional
neighborhood. Equation (1) defines the expression
used to track the points and determine the imagedisplacement d where it is the vector that minimizes
the residual function ε (d ) [5].

focal length, baseline or separation between the twocameras and image resolution. The cube is usedsince its straight edges can be easily detected byimage processing routines. The cube is 9.9x7.5x6
cm. The parameters were varied until they provide agood coverage area of the object of concern. From
this test, the cameras must be placed at the back of
the apparatus at 113 cm. Cameras are mounted on
the wall so that it can have stable support. A focal
length of 55 mm and base line of 20 cm is used tohave a better view of the object as it is being tested.
An image resolution of 1920x1280 pixels is
implemented since it can capture the whole object
with less distortion.

ε (d ) = ε (d x , d y )uy + ωy(1)
ux + ωx[ I ( x, y ) − J ( x + d x , y + d y )]2∑∑x=ux − ωx y =uy −ωy2D IMAGE PROCESSING

whereω x , ω y = arbitrary numbers that ranges from 1, 2, 3

Accuracy Checkor more pixels
A rubber cylinder with a height of 12.7 cm and adiameter of 5.8 cm is used to check the accuracy ofthe system. An angle bar is attached on theperimeters of the specimen to serve as the support
for the LVDT. Three strain gauges are also attachedat the top, middle and bottom part of the specimen.
Lucas and Kanade pyramidal optical flow algorithm
is used to track the movements of marked points. 10
sets of readings up to a displacement of 1.27 cm are
performed in steps of 0.5 mm and 1 mm. From thetest, a resolution of 0.2 mm is obtained. Strains arederived from displacements obtained from image
processing and compared to the readings from astrain gage. Unfortunately, there is a large
discrepancy because of the difference in the size ofgage length used. The strain gage measures at asingle point over a gage length of 6 mm while thestrain is computed over a wider distance.

CALIBRATIONStage 1: 2D Camera Calibration
Prior to the calibration for the stereo visionsystem, 2D camera calibration is performed toeliminate the distortion. Two Canon 650D cameras
with 18-55 mm f/3.5-5.6 IS II Kit Lens are used andpointed parallel towards the triaxial apparatus. This
stage involves the determination of the internal andexternal parameters of cameras. The focal length,
optical center and distortion coefficients are known
as the internal parameters. Tangential and radial
distortion coefficients are the most common types
encountered in calibrating a camera. On the other
hand for the external parameters, it is composed of arotation matrix and translation vector. Presented inthis section are procedures for calibrating a single
camera which were implemented in Labview.

Triaxial Test
Two Bangkok soft clay samples taken from
depths of 6 to 7 m (BH1) and 9 to 10 m (BH2) were
tested. A grid of dots is drawn on the membrane toserve as targets. A ruler is placed inside the triaxial
cell to serve as a reference during the test.
Consolidated drained compression unloading triaxial
test is performed having a cell pressure of 50 kPa for
BH1 and 100 kPa for BH2. For the compression
unloading part of the test, it is done by increasing thedeviator stress while reducing the cell pressure sothat the mean effective stress decreased. Pictures aretaken every hour with the use of timer remotecontrols.
The displacements, external and internal, isobtained through image processing and compared to
the reading from a LVDT. The externalmeasurement is made by selecting a region ofinterest at the top most part of the triaxial cell. It canbe observed from Fig. 1 and 2 that the results of both1.) A panel of dotted grid having a spacing of 2
cm center to center is placed in front of the object ofconcern. The camera must capture different
orientations of the panel in a range of + 20 degrees.
Caution must be made to ensure that the first image
is in the field of view of the camera so that the dots
can be visibly seen.
2.) Threshold is performed to extract the gridfeature of the calibration panel from pictures takenfrom the previous step.
3.) The calibration axis is established at theupper left corner of the grid.
4.) The internal, distortion and externalparameters are estimated.
Stage 2: Calibration for 3D Measurement
In this stage a cube is placed inside the triaxial
cell together with water to determine the appropriate
126

GEOMATE- Osaka, Nov. 16-18, 2015

measurement techniques are well agreed. For theinternal measurement, the whole region within the
coverage of the cameras is tracked. Among theentire region tracked, the area close to the pedestal
has the best fit as seen in Fig. 3 and 4. From the test,
failures occurred at the mid-section. For the region
near the top cap, a small movement was observed.
These observations can be clearly seen in Fig. 5 and
6.

0.14

Load, kN

0.12
0.1
0.08

LVDT

0.06

Image Processing Left
Camera BH2 Bottom
Image Processing Right
Camera BH2 Bottom

0.04
0.02

Deviator Stress, kPa

50

0
0

4

6

8

Displacement, mm
30

Fig. 4

Image Processing Right 50kPa
LVDT 50kPa

20

Load vs displacement curve for BH2

Color Mapping

Image Processing Left 50kPa
10

To further monitor the behavior of the soil thedisplacement and strain field were calculated. A
color magnification mapping technique is applied todetermine the areas where large deformations occur.
This technique is inspired from the research done at
Massachusetts Institute of Technology. The pixel
values are amplified to reveal hidden information
within the image [8]. Their technique can be referred
to as Eulerian video magnification. From this, a
program is developed in Labview where a color map
containing amplified values is plotted and overlaid
to the image. This served as an early detection ofcritical zone of the soil as seen in Fig. 5 and 6. Threebase colors in the program are red, green and blue.
Red represents a large displacement while blue is fora small displacement. The program developed has acapability of showing only large displacements andstrains by making small values transparent. The
strain is computed from the change in displacement
between two points dividing by the length between
them. The grid size used for this computation is 1x1
pixel or 0.26x0.26 mm.

00Fig. 1

0.03

0.06
0.09
0.12
Strain, mm/mm

0.15

Stress strain curve for BH1
70

Deviator Stress, kPa

2

40

60
50
40
LVDT 100kPa

30

Image Processing Right 100kPa

20

Image Processing Left 100kPa

10
0
0Fig. 2

0.03
0.06
0.09
Strain, mm/mm

0.12

Stress strain curve for BH2

0.08

Load, kN

0.06

LVDT

0.04

Image Processing Left
Camera BH1 Bottom
Image Processing Right
Camera BH1 Bottom

0.02
0
0
Fig. 32

4
6Displacement, mm

Fig. 5 Color mapping, displacement field (above)and strian field (below), for BH1 from the left
camera at day 1, 2 and end of the test.

8

Load vs displacement curve for BH1

127

GEOMATE- Osaka, Nov. 16-18, 2015

In Fig. 6, it shows that the sample BH2 tends tohave a large movement at the middle in the first few
days of the test. This would mean that the soil isundergoing a bulging failure. During the shearing
stage, the triaxial cell is being pushed from thebottom. Therefore large displacements can also be
expected near the base. Large strain localizations arevisible where large displacements occurred. Fromthe color map, non-homogeneous movement is seenas the soil experiences failure.

rectification error should always be satisfied. This
maintains the accuracy of the system to performmapping.
Table 1 Error statistics of stereo calibration
Error Statistics
Max Projection Error
Calibration Quality
Max Rectification Error
Rectification Quality

Result
1.99
0.83
1.45
0.9

Stereo Correspondence
Stereo correspondence is the stage when thematch between the field of view of the left and rightimage is mapped [10]. A disparity map is obtained
during this step. The overlapping view of the twocameras produces an almost equal disparity value
thus the disparity image would highlight the objects
within that region. To achieve this, the object ofconcern must be the dominant one in the scene.
Furthermore, a uniform light condition is essential to
avoid errors such as shadows form other objects inthe scene.
Stereo correspondence is performed along theoverlapping view through sum of absolutedifferences (SAD) window. The SAD algorithm isan area-based correspondence algorithm. It
computes the intensity differences for each center
pixel (i,j) in a window v x by v y [11]. To have a better
correspondence, a bigger window size is advisable.

Fig. 6 Color mapping, displacement field (above)and strian field (below), for BH2 from the left
camera at day 1, 2 and end of the test.
STEREO VISION SYSTEM
Stereo vision system utilizes two cameras todetermine the 3D position of a desired point.
Calibration

Depth Mapping

For the stereo vision calibration there are twophases. First, the cameras are calibrated
independently. The process is similar for calibratinga camera that measures 2D deformation. Second,
stereo calibration is performed. This process isdefined as the computation of the geometrical
relationship between the two cameras [9]. Error
statistics, as shown in Table 1, are also obtained tocheck if the calibration data is valid. The calibration
quality and the rectification should be within therange of 0.7 to 1.0. Having a calibration quality of
1.0 would mean that the system is perfectly
calibrated. On the other hand the maximum
rectification error should not exceed 1.5.

The depth map can be obtained through theprocess called reprojection. It is performed at aparticular reference rectified image. For the
program’s case the left rectified image served as thereference. The reprojection matrix (Q), Eq. (3), isused to reproject the 2D coordinates at the rectified
image together with the corresponding disparity
value into its 3D position [9].
X 
x
Y 
 y
 = Q ⋅  
Z 
d 
 
 
W 
1 

Rectification

1

0

Q = 00Stereo image rectification is a process when theimage planes produced by the left and right camera
are being aligned [9]. This process comes right after
the stereo calibration since distortion should first be
corrected.
It
helps
simplify
thestereocorrespondence computation. From the error
statistics obtained from the stereo calibration, the

128

(2)

0

0

1

0

0

0

0 −1

Tx

−c x



−c y


f


(c x − c,x ) 
Tx 


(3)

GEOMATE- Osaka, Nov. 16-18, 2015where
c x = horizontal distance from the principal point to
the optical center on the image plane of the leftimage
c y = vertical distance from the principal point to theoptical center on the image plane of the left imagef = focal length
T x = baselinec' x = horizontal distance from the principal point to
the optical center on the image plane of the rightimage
W = weightwater. Six trials are made in determining thecapacity of the system. The amount of water flowing
in and out of the sponge is calculated by threedifferent techniques.
For the first technique, the reference image istaken before the beginning of test. In this manner the
cumulative volume of water can be measured. Thismethod only worked for a certain amount of time
because it became difficult to estimate movementsfrom significantly different image pair. For thesecond technique, the reference image is taken rightafter the cell pressure and back pressure has been
applied. The volume obtained from this technique isalso a cumulative one. Similarly the algorithm
worked over a limited period. In the third technique,the change in volume between successive images isdetermined. For all methods, percentage errors in arange of 1.16 to 8.86% are observed. When the sizeof patch is varied to examine its influence on thecalculated result, no distinct trend was observed. Alarge amount of water is introduced to the sponge todetermine its limitation. Using the second method,the volume of inflow calculated is 43.16 cc while themeasured volume is 45.88 cc and error of 6.1% isobserved.
The capacity of the system to measure at
displacements is also determined. 3 Unconsolidated
undrained tests are performed using Bangkok clay.
Unfortunately, only 1 trial can be analyzed because
the images of the other trials are not in goodcondition. An error of 1.77 to 10.14% is observed
from the measured external displacements. Theinternal displacements on the other hand aremeasured at the midsection of the soil sample since
it is the critical area. Fluctuations are encounteredwhen the displacements and strains are computed.
To further investigate on this, displacements at thepedestal, 1/3 from the base and 2/3 from the base
was obtained and compared with the result from the
LVDT. From Fig. 7, it can be seen that fluctuations
started at a deviator stress greater than 15 kPa at 2/3
from the base. To have a better understanding on
what occurred during the test full-field monitoring
was performed. Displacement fields are shown in
Fig. 7 and it can be seen that there are localizations
at the area 2/3 from the base. Due to this, it is
impossible to measure the internal displacements forthis data.

Accuracy Check
Validation for depth reading
To thoroughly check the accuracy of the system,a small card board was attached on a micrometer. It
is placed on the triaxial base at a distance of 105 cm
from the cameras. Ten readings are made when the
cardboard was moved in steps of 0.05, 0.1, 0.2, 0.4,
0.6, and 0.8 cm along the micrometer axis. The
standard deviation from both increments arecomputed and used to express the repeatability ofthe system. The coefficient of variation of thesystem is found to be around 0.006381 to 0.078066.
The repeatability and accuracy of the system isdetermined from the standard deviation and it isfound to be 0.006 cm and 0.004 cm, respectively.
Validation for depth reading under tilting planes
In order to determine the capacity of the system
to read depth changes under tilting planes, a lasertransducer was used to get the profile of a deformed
sample and it is compared to the result of the system.
Oil clay is used as a sample since it is easy tomanipulate. Readings are made at the center, 40ºfrom the center and boundary of the sample. The
profile is read for 10 times at the center and 40º fromthe center of the sample. For the profile at theboundary of the sample, only 3 readings are made.
The measurements from the stereo vision system andthe laser transducer are compared. From the test,
errors at the center has a range of 0.39 to 0.90 mm,
errors at 40º from the center has a range of 0.64 to
4.62 mm, and errors at the boundary has a range of
0.79 to 1.87 mm. Large errors are observed from thereadings at 40º from the center. Stereo
correspondence search is difficult to perform at thisarea since it is at the maximum camera coverage.
Triaxial Test
Initially tests are planned to be done on Bangkok
Clay samples but a sponge is used instead to avoid
non-homogeneous mode of deformation. The use of
sponge provided a better control on the flow of129

GEOMATE- Osaka, Nov. 16-18, 2015

Geotechnical Engineering Profession, 2000, pp.
42-60.
[2] Viggiani, G. amd Hall, SA, “Full-field
Measurements, A New Tool for Laboratory
Experimental Geomechanics”, Paper presented
at the Proceedings of the 4th International
Symposium on Deformation Characteristics ofGeomaterials (Burns SE, Mayne PW andSantamarina JC (Eds.)), 2008, pp.3-26.
[3] Xing, ZX, Wells, LG, Yaping, J and Shearer,SA, “Using Image Analysis to Measure TwoDimensional Soil Deformation”, Journal of
Terramechanics, Vol 34, 1997, pp. 73-82.
[4] Khosravi, M, Tang, L, Pipatpongsa, T,
Takemura, J, and Doncommul, P, “Performanceof Counterweight Balance in Stability ofUndercut Slope Evaluated by PhysicalModeling”,
International
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Geotechnical Engineering, Vol 6, 2012, pp.
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[5] Fernando, WSP, Udawatta, L, and Pathirana, P,“Identification of Moving Obstacles with
Pyramidal Lucas and Kanade Optical Flow andK Means Clustering”, Proceedings on the 2007
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111-117.
[6] Lucas, BD and Kanade, T, “An Iterative Image
Registration Technique with an Application to
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[7] Mahalingam, V, Bhattacharya, K, Ranganathan,N, Chakravarthula, H, Murphy, RR and Pratt,
KS “A VLSI Architecture and Algorithm for
Lucas-Kanade
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OpticalFlow
Computation”, IEEE Transaction on Very Large
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[8] Wu, HY, Rubinstein, M, Shih, E, Guttag, J,
Durand, F, and Freeman, WT, “Eulerian Video
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[11] Kuhl, K. “Comparison of Stereo Matching
Algorithms for Mobile Robots”, The Universityof Western Australia, Australia, 2005

Deviator Stress, kPa

25
20
15
10
5
0
0

LVDT
At Pedestal
At 1/3
At 2/32
4
6Displacement, mm

8Fig. 7 Stress-displacement plot for Bangkok Clay
CONCLUSION
A non-contact measurement system wasdeveloped and it had the capacities to perform 2D
and 3D measurements through image processing.
Lukas and Kanade pyramidal optical flow algorithm
is applied in both measurements to track the
movement of interested points.
The 2D measuring system’s resolution is foundto be 0.2 mm. When the readings from the system
are compared to those from LVDT, for a
consolidated drained unloading test errors ranging
from 5 to 10% are observed. For the 3D measuringsystem, when an unconsolidated undrained test is
performed errors ranging from 1.77 to 10.14% areobserved. When measuring out of plane movements,
the repeatability and resolution is found to be 0.006
cm and 0.004 cm, respectively. For the readings ofdepth at titling planes the profile from the system iscompared with a laser transducer. Large differences
can be observed at 40º from the center at a range of
0.64 to 4.62 mm. Errors ranging from 1.16 to 8.86%
are obtained when computed volumes are comparedwith measured values. The system provides anoption to compute the volume of flow between any
image pair. From these, it can be concluded that thesystem has a good capability in obtaining 2D and 3D
measurements. It can be applied in monitoring the
behavior of the soil during the test such as tomonitor the development of localization failures insoil. However, it is only limited to the area viewableby the cameras.

REFERENCES[1] Marr WA, “Advances and Retreats inGeotechnical Measurements”, Judgment andInnovation: The Heritage and Future of the130

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

CORAL SAND SOLIDIFICATION TEST THROUGH MICROBIALCARBONATE PRECIPITATION USING PARARHODOBACTER SP.
Md. Nakibul Hasan Khan1, Suguru Shimazaki1 and Satoru Kawasaki2
1
Graduate School of Engineering, Hokkaido University, Japan
2
Faculty of Engineering, Hokkaido University, Japan

ABSTRACTThe coastal erosion has been a problem in associate with manmade construction. The maintenance andmanagement is expensive for repair and rebuild the coast. Compared to the concrete structure coral sandsolidification would considered to minimize cost. The present experimental study was conducted to coral sandsolidification through microbial carbonate precipitation (MCP) using Pararhodobacter sp. Ureolytic bacteria;
Pararhodobacter sp. was isolated from the beachrock in Nago, Okinawa, Japan. ZoBell2216E as a medium formarine bacteria was used for the culture of the bacteria. Suitability for the use in MCP syringe test, growthproperties of the bacteria were observed in various cultural conditions. MCP sand solidification test was carriedin 30mL Terumo syringe injected bacterial culture fluid. Strength of the specimen was measured by needle
penetration test. The maximum value of the absorbance at bacterial growth curve was constant if the added
amount of the medium is 100mL. The correlation coefficient of determination for absorbance measurements andviable cell count measurements was obtained 0.5478. In MCP syringe test, pH of the specimen was decreased
and Ca2+ concentration was increase with time. The estimated value of unconfined comprehensive strength(UCS) also increased with time. The maximum value of UCS of the specimen was 12MPa. The estimated UCS
values of the specimen produced by sand solidification test for 14 days curing time have been achieved.
Keywords: Sand Solidification, Unconfined Compressive Strength, Microbial Carbonate Precipitation,
Pararhodobacter sp

Kawasaki (2014) [3], focused on the possibility ofpromoting solidification by microbial processes,
specifically urea decomposition by microorganisms.
In this study, the microbial induced calcium
carbonate precipitation (MICP) method was utilized,
which relies on the microbial metabolism of urea
that generates carbon dioxide and precipitates
CaCO 3 [4]. This low environmental impact method
was assessed to determine its efficiency as analternative means to alleviate coastal erosion. In
order to create an artificial rock, the present study
was conducted with the following two purposes:(a) To solidify coral sand with an unconfinedcompressive strength (UCS) of 10 MPa or more,through the MICP method using ureolytic
bacteria, Pararhodobacter sp..
(b) To examine the growth features of thePararhodobacter sp. which used in theexperiment.

INTRODUCTION
Erosion of the sandy shore is often used to referto changes in the coastline due to the collapse of thesediment balance. Coastal erosion has been asignificant problem globally due to anthropogenic
changes along the coastline. In order to prevent, or atleast minimize damage from erosion, a combinationof various structures and processes has been
traditionallyused,
includingembankments,
revetments, jetties, artificial reefs, offshore
breakwaters, and sand bypassing [1].
The use of inexpensive alternative materials
should be considered, to cope with the increase incosts related to the maintenance and management of
concrete structures. In this regards, we focused onalternative materials to replace beachrock to reduce
lifecycle costs associated with the currently
employed methods. Beachrock forms much more
quickly than other sedimentary rocks within theintertidal zone and is composed of coastal sedimentsthat have been cemented together mainly by CaCO 3[2] . By using the processes that solidify beach sand,we hypothesized that could create a highly durable
artificial beachrock that would be comparable inefficacy to the existing concrete structures.
For the formation of beachrock various factors
have been considered. Among them, Danjo and

MATERIALS AND METHODS
Absorbance Measuring Test
Materials
The test bacterium was Pararhodobacter sp., anureolytic bacterium isolated from the soil near131

GEOMATE- Osaka, Nov. 16-18, 2015

beachrock in Sumuide, Nago, Okinawa, Japan. This
strain was isolated from soil samples using artificial
seawater (Akuamarine, Yashima Drug Company,
Osaka, Japan) (Table 1). The strain was cultured on
ZoBell2216E medium (polypeptone 5.0 g/L, yeast
extract 1.0 g/L and FePO 4 0.1 g/L, in artificial
seawater, pH 7.6~7.8), which is often used for
growth of marine bacteria.

respect to previous study [1].
(a) The initial mass of the microbe when added to
the liquid culture medium was varied: 0.01 g,
0.1 g, or 1.0 g (the previously used mass was
0.1 g).
(b) The volume of liquid culture media was varied:
100 mL, 150 mL, or 200 mL (as opposed to thepreviously used volume of 100 mL).
(c) The shaking speed at the time of culture wasvaried: 80 rpm and 160 rpm (the previously
used speed was 80 rpm).

Table 1 Composition of artificial seawater
Reagent
MgCl 2 ∙ 6H 2 O
CaCl 2 ∙ 2H 2 O
SrCl 2 ∙ 6H 2 O
KCl
NaHCO 3KBr
H 3 BO 3
NaF
NaCl
Na 2 SO 4

g/20L
222.23
30.7
0.85
13.89
4.02
2.01
0.54
0.06
490.6881.88

MCP Test in Syringe
Materials
Pararhodobacter sp. and coral sand (the grain
size distribution of the coral sand is 0.07 ~ 4.0 mm)
from Okinawa, Japan, were used for the syringe
solidification test. In addition, artificial seawater(Table 1) was used to recreate similar conditions tothose in which the original beachrock was formed.
Methods
First, the bacterium was grown up under various
experimental conditions listed above. These cultures,
ZoBell2216E culture solution, were shaken for 2days at 30°C. Then, 40 g of coral sand, dried at
110°C for at least 2 days, was placed in a 35mL
syringe (diameter 2.5cm × height 7cm).
Subsequently, 16mL of the culture medium(ZoBEll2216E) and 20mL of the solution forconsolidation (this was the composition, mainly urea
and CaCl 2 , used for all of the various test
conditions) was sequentially injected into thesyringe. The solution for consolidation was theninjected and drained once a day. The pH values andCa2+ ion concentrations were measured every 3 days.
The state of the syringe solidification test is shownin Fig. 1b. After 14 days of curing, the UCS of thespecimen was estimated using a needle penetration
device (SH-70, Maruto Testing Machine Company,
Tokyo, Japan).

Fig. 1 State of (a) liquid culture and (b) sandsolidification tests in the syringe.
Methods
The bacterial strain was cultured in liquidmedium under various conditions. The change incell density over time for each culture condition wasquantified by measuring the absorbance (opticaldensity) of the suspension using a spectrophotometerat 600-nm wavelength (OD 600 ) [5], [6]. For each
culture condition, the absorbance was read and theviable cell count was determined using the plate
dilution method simultaneously. Finally, the growth
curve of the strain in each culture condition was
obtained to investigate the effects of various
conditions on bacterial growth.

Experimental conditions
Considering the effect of conditions on the UCSof specimen, two test conditions were utilized: one
with regular and other without regular, the reinjection of the culture solution. For regular test
condition, the mass of the bacterial culture used was
0.01 g, 0.1 g, or 1.0 g; the concentrations of urea and
CaCl 2 (solution for consolidation) used were 0.3 M,
or 0.5 M. The standard conditions (0.1 g/0.3 M)
were the same as those used by Inagaki et al. (2011)[7]. The test period was 14 days. In the experiment

Experimental condition
In order to shorten the total time for culturing thebacterial strain used in the experiment, the following
three conditions were set up and analyzed, with132

GEOMATE- Osaka, Nov. 16-18, 2015

where the culture solution was re-injected, the massof the bacteria (culture solution) was 1.0 g and urea
(solutionfor
consolidation)
andCaCl 2concentrations were 0.5 M each. The test period was
also 14 days, where the culture solution repeatedly
injected at 7th day. Both the pH and Ca2+ ion
concentrations were measured daily.

When more bacterial culture was added at the
beginning of the experiment, the maximum value ofthe absorbance was obtained (Fig. 3), where culture
solution was used 100mL. Since the absorbancemeasurement was based on the turbidity of theculture solution, this result is expected. However, as
the same concentration of nutrients is available inthe media, we predicted that the difference in celldensities would reach a peak regardless of theamount of cells initially added. Unlike the expected
test result, regardless of the amount of strain added,
the absorbance was nearly constant at approximately
80–100h (Fig. 3). It is difficult to explain the actual
cause of the above result; the possible reasons could
lie in the nutrients, source of dissolved oxygen andmedium, but these factors were not examined.
However, when the medium composition was thesame, the absorbance read at the various time points
were comparable regardless added amount of strain.
This result is considered advantageous as it allows
for the artificial control of the cell density in themedium used. The absorbance increased with afaster shaking speed, the maximum value of theabsorbance was obtained when shaken at 160 rpm incomparison to 80 rpm (Fig. 3). The increase in
shaking speed was considered to be effective inshortening overall time needed for the experiment totake place. When shaken at 160 rpm, the absorbancepeaked approximately 24h after the start of theculture, indicating that the three-day growth is notnecessary in the future.

RESULTS AND DISCUSSION
Absorbance Measuring Test
The absorbance values, which indicate thegrowth rate of the bacterium, under various culture
conditions are described below.

Fig. 2 The absorbance measurement results focusedon the amount of liquid medium.
Importance of the volume of the culture solution
The results obtained using various volumes ofculture media (100 mL, 150 mL, and 200 mL) andshaking speeds (80 rpm) are shown in Fig. 2 where
microbial strain was 0.1g. With regard to shaking at
80 rpm, the absorption values for all threeexperiments increased with a decrease in the volume
of culture medium. However, the absorbance values
measured in 150mL and 200mL volumes at 80 rpm
experiments were drastically different. In addition,
the time required to reach the maximum absorbance
was between 48 and 60 h in 100mL. In this case, itappears that the nutrient source (oxygen) was rapidly
and homogeneously dispersed in the culture. If
homogenization of nutrients and increased supply ofoxygen in the culture fluid is assumed important, it
is also explains why a decrease in absorbance isobserved when the volume of the culture media isincreased.

Fig. 3 The absorbance measurement results focusedon the amount of strain added and shaking speed.
MCP Test in Syringe
The liquid culture medium of 100 mL and shakethe culture at 160 rpm, which would provide ideal
absorbance readings as well as result in optimum
bacterial growth was used for syringe test. The pH
and temperature for the optimal growth of thebacteria was 7.0–8.5 and 30–40°C, respectively. The
above conditions were implemented for all aspects

Effects of the amount of strain added and shaking
speed
133

GEOMATE- Osaka, Nov. 16-18, 2015

during the solidification test.

was approximately 2 MPa, regardless of the added
amount of culture added. In the cases of 0.5 M, theestimated UCS value tended to be higher than thecase of 0.3 M. When the amount of bacterial strain
added was 0.01 g and 0.1 g for 0.5 M urea and
CaCl 2 solutions, the UCS showed 3~4 MPa. The
specimens were significantly solidified in all
experiments using 0.5 M, where the estimated UCS
at some points exceeded 7 MPa. The estimated UCS
tended to decreases from the top to the bottom of thespecimen.

Role of pH and Ca2+ concentrations
The pH of all specimens during the test periodtended to be lower than the pH at the beginning of
the experiment (Fig. 4). However, when 1.0 g of theculture was added, regardless of the amount of urea
and CaCl 2 added, the pH increased from days 3
through 6. The Ca2+ concentration was at 2.0 g/L orless in all cases when measured after 3 days (Fig. 5).
After the 6th day, the Ca2+ concentration tended toincrease markedly when the urea and CaCl 2 solution
added was at 0.3 M and 0.5 M. The concentration of
Ca2+, when the 0.3 M urea and CaCl 2 solution wereused, did not change over time and continued to be
consumed until the Ca2+ was present inconcentrations above 10.0 g/L. On the other hand,
the most dramatic increase in Ca2+concentration
when using 0.5 M (0.01 g/0.5 M), during which theconsumption of Ca2+ at the 12-day time point was
reduced to 7.0 g/L. As can be gathered from Fig. 4
and Fig. 5, the Ca2+ concentration increases when the
pH lowers. The diminution of urea hydrolysis withdecreasing cell density is a potential cause of pH
reduction. Therefore, in the specimen, if there arehigh concentrations of urea or CaCl 2 at the
beginning, the amount of carbon dioxide produced
by urea decomposition does not reduce Ca2+
consumption, in fact, eventually Ca2+ concentration
could have increased. Alternatively, the precipitationof CaCO 3 for MICP begins at the cell surface ofbacteria [8], [9], strains induce precipitation of
CaCO 3 at a crystal nucleus.

Fig. 5 Ca2+ concentration of effluent over time ofdifferent test cases in the syringe.

Fig. 6 Needle penetration test results of specimen insyringe in various test conditions.

Fig. 4 The pH of the effluent over time course ofdifferent test cases in the syringe.
Estimated UCS value

Relationship between Ca2+ concentration and theestimated UCS in the specimen

The summarized results of the needle penetration
test for each case are shown in Fig. 6. The estimated
UCS value of the 0.3 M urea and CaCl 2 experiments

From Fig. 6, in the case of 0.5 M solutions of
urea and CaCl 2 , the estimated UCS value of thespecimen ranged from 3 to 4 MPa; the intensity
134

GEOMATE- Osaka, Nov. 16-18, 2015

distribution tended to decrease from the top to thebottom of the specimen. In this case, the Ca2+
concentration in the effluent demonstrated atendency to increase in the latter half of the study
period (Fig. 5). The collected effluent was estimatedto have a pH and Ca2+ concentration of the lower
part of the specimen. Furthermore, since this strain
was cultured in aerobic conditions, it is possible thatoxygen concentration played a role in strain activity.
Since it can be considered that the bottom of thespecimen approaches anaerobic conditions, thelower portion of the specimen may demonstrate
reduced strain activity when compared to the top of
the specimen. Therefore, the estimated UCS value ofthe specimen was expected to decrease from the top
to bottom. On the other hand, from Fig. 6, in thecase of 0.3 M urea/CaCl 2 , the estimated UCS of thespecimen were approximately 2 MPa. In addition to
the concentration of the urea and CaCl 2 , the amountof Ca2+ in the effluent was generally constantthroughout the test period (Fig. 5). The
concentration of Ca2+ in the original solidification
promoting solution was consumed nearly 90% by
the precipitate as CaCO 3 . Therefore, it was expectedthat the estimated UCS values of the specimen
should also remain constant, which is consistent
with the results obtained above.

injected test remained between approximately
6.9~7.5, and did not continue to decrease (Fig. 7).
The concentration of the Ca2+on effluent in the reinjection test is shown in Fig. 8. The Ca2+
concentration in the re-injection test wasapproximately constant during the entire test period(Fig. 8). Overall, in the standard tests (without reinjection) the Ca2+ concentration increased from the
beginning to the end of the experiment, which was
not seen when cultures were re-injected.
The results demonstrated in Fig. 7 and Fig. 8,
indicate that if the re-injection of the culture was not
performed during the study in the syringe
solidification test, both the pH and Ca2+
concentrations in the effluent decreased andincreased, respectively. This trend was observed
regardless of the cell density of the liquid culture
injected. However, if the re-injection of the culture
is completed during the study, the pH wasapproximately 7.0~7.3, and the Ca2+ concentration
was constant at approximately 1.0 g/L throughout
the course of the experiment (Fig. 7 and Fig. 8). The
pH rise of in the effluent due to urease activity ofstrain increased during re-injection of the culture
liquid. At the same time, the Ca2+ concentration
decreases also to be considered due to the increase incell concentration. After subsequent re-injection test
that had remained high cell concentration in thespecimen as compared to the previous test, evenlonger observed increase and maintenance of pH byurease activity. The precipitation of CaCO 3 wasincreased and persisted, Ca2+ concentration remain
constant.

Fig. 7 Changes of pH in the effluent over time in the
re-injection test in the syringe.
pH and Ca2+ concentration of effluent in the reinjection test
Fig. 8 Ca2+ concentration of effluent over time in the
re-injection test in syringe.

The pH of effluent in the re-injection test (1.0 gof bacterial culture was also re-injected on 7th day) isshown in Fig. 7. In the re-injection test, the pH wasmaintained higher as compared to the previous test,
without re-injection. The pH begins to decrease after
5th day, and at 7th days it was reduced to a value
close to the previous test. However, when carryingout the re-injection of the culture on day 7, pH wasthe maximum value during the study after 8 days
thereafter decreased. However, the pH in the re-

Estimated UCS value in the re-injection test
In the re-injected 14 days test period, the UCS
was approximately 8.0~13.0 MPa (Fig. 9), this value
is greater than the maximum value at the time of theprevious test. The UCS demonstrated a minor
decreasing tendency from the upper half to the lower
135

GEOMATE- Osaka, Nov. 16-18, 2015

half of the specimen (Fig. 9). Specimens ofimmediately after removal of the syringe test and there-injection syringe test is shown in Fig. 10. In the
re-injection test results of the syringe shows theestimated UCS value was double that recorded using
the without re-injection test. In addition, we also
observed variation in solidification, which had
increased the UCS value regardless of the positionof the specimen, indicating that the specimen washeterogeneous and lacked problems in solidification.
For this reason, there was a possibility that celldensity in the specimen was not constant. From Fig.
9, the estimated UCS value in re-injection test was
shown by about 2 times the needle penetration test
as compared to the without reinjection. By
increasing the cell concentration at 7th day, for theamount of precipitation of CaCO 3 and trends of Ca2+
concentration was kept constant, it is considered thatthe estimated UCS value increases. Therefore,
although the estimated UCS value of the specimen
was expected to be constant from the top to thebottom, variations were observed in the results.

Fig. 10 appearance of specimens immediately after
removal of (a) the syringe test and (b) the reinjection syringe test (photo left specimen top).
CONCLUSION
In this study, results demonstrated that the specimen
solidified up to 13 MPa UCS after 14 days of curing
using a microbial strain of Pararhodobacter sp..
Based on the findings from this study, for futureattempts of the MICP method using this strain to
obtain the ideal conditions for solidification in the
future, four suggestions are outlined below:(1) To facilitate solidification in a short period, theurea and CaCl 2 concentration should preferably
be 0.5 M.
(2) For the well progress of solidification of thespecimen, the pH in the specimen should be 7.0
or higher, and the Ca2+ concentration should bemaintained at1.0 g/L.
(3) When the pH and Ca2+ concentrations in thespecimen are out of the above range, the process
can be improved by injecting more of theculture again.
REFERENCES[1]. T. Danjo, S. Kawasaki, S. Shimazaki and K.
Koizuka: Coral sand solidification test using
ureolytic bacteria. Geomechanics from Micro to
Macro (2013) 1521-1526.
[2]. T. Danjo and S. Kawasaki: A Study of theFormation Mechanism of Beachrock in
Okinawa, Japan: Toward Making Artificial
Rock. International Journal of Geomate 5(1)(2013) 633–638.
[3]. T. Danjo and S. Kawasaki: Formation
Mechanisms of Beachrocks in Okinawa andIshikawa, Japan, with a Focus on Cements.
Materials Transactions 55(3) (2014) 493-500.
[4]. V. S. Whiffin, L. A. Van Paassen and M. P.
Harkes: Microbial Carbonate Precipitation as a
Soil Improvement Technique. Geomicrobiology
Journal 24 (2007) 417-423.
[5]. H. L. Ehrlich: Geomicrobiology. Marcel Dekker,
New York (2002).
[6]. J. K. Fredrickson and M. Fletcher: Subsurface
microbiology and biogeochemistry. Wiley-Liss,
Hoboken, NJ (2001).
[7]. Y. Inagaki, M. Tsukamoto, H. Mori, S.
Nakajima, T. Sasaki and S. Kawasaki: A
centrifugal model test of microbial carbonate
precipitation as liquefaction countermeasure.
Japan Geotechnical Journal 6 (2011) 157–167(in Japanese with English abstract).
[8]. J. T. De Jong, B. M. Mortensen, B. C. Martinez
and D. C. Nelson: Bio-mediated soil

Fig. 9 Needle penetration test results of the reinjection test case in syringe.

136

GEOMATE- Osaka, Nov. 16-18, 2015

improvement. Ecological Engineering 36 (2010)197-210.
[9]. W. De Muynck, N. De Belie and W. Verstraete:
Microbial
carbonate
precipitation
inconstruction materials: A review. EcologicalEngineering 36 (2010) 118-136.

137

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

NUMERICAL MODELING OF ROCKING OF SHALLOW
FOUNDATIONS SUBJECTED TO SLOW CYCLIC LOADINGWITH CONSIDERATION OF SOIL-STRUCTURE INTERACTION
S. Mohsen Haeri* and Aria Fathi
Civil Engineering Department, Sharif University of Technology, Tehran, Iran.
*Corresponding author, smhaeri@sharif.edu

ABSTRACT
Strong Vibration of buildings during seismic or wind loading may result in an uplift or partial separation of thefoundation from the underneath soil. To date, various researches have indicated that Soil-Structure Interaction(SSI) has many favorable features including a decrease in shear base demand in structures and a probable increasein natural period of soil-structure system. Furthermore, Rocking is one of the most important factors in describing
the rotational behavior of structures built on shallow foundations especially on soft soils which can affect the
dynamic behavior of the structures noticeably. To study the effects of rocking of shallow foundations subjected toslow cyclic loading with consideration of soil-structure interaction, a Finite Element Method (FEM) using
ABAQUS software has been deployed to simulate the rocking motion of shallow foundations. For a more efficientsimulation of the soil, both linear and non-linear elastic-perfect plastic behavior of the soil have been taken into
account in the analysis using the sub-routine coded in FORTRAN. The results notably show that allowing thefoundation to rock, may result in stiffness degradation of the soil-structure system and increase in energy
dissipation of soil-structure, especially in high rise structures. Additionally, results describe that deploying thelinear elastic-perfect plastic approach may result in higher uplift of the foundation in comparison to that using a
non-linear elastic-perfect plastic approach, particularly in structures with lower heights.
Keywords: Soil-Structure Interaction, Rocking Behavior, Uplift, Energy Dissipation, Linear Analysis, Non-Linear
Analysis, Elastic-Perfect Plastic Approaches.
strong earthquakes, however, recent studies indicatethat allowing the structures to a controlled rocking
may result in energy dissipation by the soil-structure
system allowing some amount of uplift without
jeopardizing the structure to overturn [2], [3].
In general, the non-linear behavior of the soil has
many beneficial effects during seismic loading suchas a probable increase in natural period of the soilstructure system and a decrease in shear base demandin structures. Furthermore it can also play a key rolein energy dissipation during strong vibrations.
However, permanent displacements of foundations
are the most noticeable disadvantage of nonlinearity
which may lead to inevitable damages [2], [3].
Importance of the rocking mode and nonlinearity ofthe soil encourages researchers to apply thesecharacteristics into engineering design. Hence,
geotechnical design codes are being shifted from theclassical-limit analysis to the performance-based
design approach. A recent study shows that, allowing
foundation to yield, instead of the superstructure, canimprove the overall integrity of the structure during
earthquake [4]. Similarly, another study has shownthat the input energy to the soil-structure system
could be dissipated between superstructure andfoundation by a controlled rocking [5].
In this paper, rotational behavior of 10, 20 and 30
story structures subjected to slow lateral cyclicloading, deploying SSI, have been taken into

INTRODUCTION
In recent years, many attempts have been carriedout to predict the behavior of soil during dynamic
loading such as earthquake. These predictions
generally have led to safer and more efficient designof structures especially those which located on soils.
One of the most important aspects of seismic design
which attracts attentions among structural andgeotechnical engineers is the recognition of SoilStructure Interaction (SSI) in their design, and thesignificant effects of these two systems on each other
under dynamic loadings. The rocking mode can beconsidered as one of the most important aspects ofdynamic SSI to scrutinize the dynamic behavior ofstructures with shallow foundations located on
flexible soils. Therefore, several researches havetaken this aspect of the analysis into account. Primaryresearches on rocking have started from early 1960s
when Housner [1] -with considering his observations
over severe earthquakes- investigated on rigid blocks
subjected to horizontal loading so as to study theirseparation from the underneath surface. The results of
his study showed that structures which were able torock had been less damaged in comparison toapparently more stable and state-of-the-art ones [1].
Conventional engineering design methods
emphasize on preventing uplift of the foundation forpreventing the overturning of the structures during
138

GEOMATE- Osaka, Nov. 16-18, 2015

consideration. To study the rocking effects of shallow
foundations on these structures, Finite Element
Method (FEM) has been used. In this respect, twolinear and non-linear elastic-perfect plastic soil
behaviors have been taken into account for a more
efficient simulation of SSI by adding two subroutines coded in FORTRAN to the main code. Twoseries of analyses including six numerical models
have been carried out using ABAQUS program. The
purpose of these analyses is
to assess energy
dissipation of the soil-structure system the criticalcontact area, the amount of uplift and also thepermanent settlement of the shallow foundations of
the studied structures. The results are studied andpresented in the following sections.

Mohr-Coulomb model has been employed in allelasto plastic cases for the soil underneath of thestructural foundation. Although, there is a generalconsensus among researchers that shear modulus of
the soils, particularly the granular soils, vary withboth shear strain levels and confining pressure [6],however, in majority of numerical dynamic SSI
analyses, this intrinsic feature of the soil is ignored
and is considered only at static shear strain levels.
Therefore, for a more realistic result, the nonlinearity,
inhomogeneity and plasticity of the soil have beentaken into consideration in this work by using a subroutine coded in FORTRAN. Maximum shear

MODEL CHARACTERISTICS AND
PARAMETERS

Gmax( KN / m2 )  218.82K 2(max)  ( ' ) 0.5

To study the rocking behavior of shallow
foundations, six numerical models including soil,
structure and foundation systems were simulated.
Three different heights for the structure representing
10, 20 and 30 story buildings are considered for the
analysis. Two types of behaviors, namely linear andnonlinear elastic-perfect plastic, are assumed for thesoil underneath the structure. The aim of this study is
to study the effect of soil nonlinearity and plasticity
on the rocking behavior of these soil-structure
systems under cyclic loadings. Also to study theeffect of height of the structure underlain by linear or
nonlinear elasto plastic soils subjected to cyclicloadings with consideration of SSI is another aim of
this research.
Therefore, three structures with different heightsof 30m, 60m and 90m representing respectively 10,
20 and 30 story buildings are considered for the
analysis. The analyzed structures have somecharacteristics in common, such as length and width,
elements type, and material properties; yet, theirheights are different. The model of the building as anintegrated rigid system consists of a foundation andthe structure. The mass of the structures in
conjunction with their foundations were estimatedbased on the mass of conventional buildings. The
parameters associated with the analyzed structures
are given in Table 1.

' 

modulus of the soil ( Gmax ), is obtained from Eq. (1)
[7].

10
Story
30
16620
6850
25
0.35

 '1   2'   ' 3'

obtained from Eq. (2);  1 ,  2 and  3 are the'''maximum, intermediate and minimum effective
principal stresses, respectively; and the magnitudes of
K 2(max) vary from 35 to 70 for sandy soils (taken to
be 52 for all cases which refers to fine granular soils
with medium relative density).
The model proposed by Reference [8] for variation ofshear modulus with shear strain was implemented,
considering the variation of maximum shear modulus
with confining pressure. Taking into account above
points, in the first series of the analyzed cases thenonlinear elastic-perfect plastic behavior isconsidered for the soil.In the second series of analyses,
the soil behavior is assumed to be elasto-plastic in a
way that the soil behaves linear-elastic before theMohr-Coulomb Failure Envelope, and behavesperfectly plastic when the soil yields by reaching to
the shear strength or Mohr-Coulomb Failure criteria.
In dynamic analysis, damping plays a key role in
the response of the structure to the motion by
dissipating a part of elastic energy. In this study, theformulation proposed by Reference [9] has been used.

C  [M ]   [ K ]
20
Story
60
39240
11800
25
0.35

(2)

3

where  is the mean effective confining stress,

Table 1 Structure parameters
Structural
Properties
Height (m)Weight (KN)
No. of Elements
E (GPa)Poison Ratio

(1)

(3)

In above relationship, [C] is the damping matrix ofphysical system; [M] is the mass matrix of physicalsystem; [K] is the stiffness matrix of the system, and and  are Rayleigh damping coefficients which
can be computed by two significant natural modes byimplementing Eq. (4).

30
Story
90
58860
13800
25
0.35

2ii    i2
139

(4)

GEOMATE- Osaka, Nov. 16-18, 2015

where  is damping ratio (taken to be 8% for all thesoil systems and 5% for the structure systems).
One of the most delicate measure for obtaining
Rayleigh damping coefficient, is the selection of themost efficient natural frequency modes to be
implemented in formulations. Park and Hashash [10]suggested to use the first mode as well as the first odd
mode which its frequency is higher than the loading
frequency for solving the Eq. (4)M  Ph .Pcg  M s .g.hcg . sin( )(5b)where Pact is reaction force which is obtained during
the applied lateral cyclic displacement; M s is massof the structure; M is moment acts at the base center
point; hs is monolithic height of structure andfoundation; hcg is center of gravity height of thestructure-foundation system; g is gravitational
acceleration; and  is rotation of the structure.

Lateral Cyclic Displacements on Structures
Figure 1 shows a schematic view of soil-structure
system in conjunction with the external loads applied
to the structure and the forces which are translated tothe base center point of the soil-structure interface.

Figure 2 shows the sinusoidal slow lateral cyclic
displacements were taken into consideration in
analyzed structures. To assess the stiffness variationof the soil-structure systems at their interface, all thetime history-displacements apply constant rotation toeach of the structures during loading. The slow cyclic
time history-displacements include three clusters
with different amplitudes, that each of the clusters
have three cycles as shown in Fig. 2.

Lateral cyclicloading

Displacement
(m)

10 S

Soil system with considering two linear andnonlinear elasto-plastic behaviors(m)

Displacement

20 S

(m)

Displacement

30 S

1
0.5
0
-0.5 0-1
10.5
0-0.5 0-1
10.5
0-0.5 0
-1

100

200

300

400

500

Time (s)200400Time (s)200400Time (s)Fig.2 Slow lateral time history-displacements for 10,
20, and 30 story analyzed structures.
**Notice: S stands for story
Verification

Fig. 1

The finite element model of rocking behavior ofshallow foundations, deploying SSI effects has beenverified using S21 test result which is related tosecond series of centrifuge tests (Krr02), provided by
K. R. Rosebrook & B. L. Kutter [11]. In S21 test, a
double-wall structure configuration which includes
two Aluminum shear walls with parallel aluminum
strip footings, has been seated on Nevada sand with arelative density of 60 percent located in a container of
20 centimeter height. The soil characteristics as wellas mechanical properties of the shear walled structure

Schematic model of soil-structure system aswell as the external loads applied on the
structure.

Based upon the Fig.1, the external loads are
obtained by:Ph  Pact

(5a)

140

GEOMATE- Osaka, Nov. 16-18, 2015

are described in Rosebrook and Kutter [11]. The
centrifuge was spinning with an acceleration of 20 g,
to convert model data to the prototype scale. The soilstructure system in prototype scale for the S21 test is
shown in Fig. 3. For verification of the numericalmodeling in this study, the soil behavior is considered
to be nonlinear elastic-perfect plastic.

RESULTS OF THE ANALYSIS
Static Analysis Results
Immediate static settlements of the soil underneaththe base centeral points of the foundations of 10, 20,
and 30 story structures are computed, consideringboth linear and nonlinear elasto-perfect plastic
behaviors for the granular soil; and are compared withthe approximate initial settlement method proposedby Mayne and Poulos (1999) [12] as shown in Table
2. The results show that the immediate static
settlement in linear elasto-plastic approach is closer
to that resulted from approximate method. The reason
is that the approximate approach is based on linear
elasticity behavior of the soil. However, the values ofimmediate static settlement using nonlinear elastoplastic approach are much higher due to yielding of
the soil when shear modulus decrease atcorresponding high shear strain levels.

Fig. 3 The prototype scale of the soil-structure
system for the S21 test.
The applied sinusoidal slow cyclic time historydisplacement for the centrifuge test consisted of threecycles as shown in Fig. 4.

Table 2 Immediate static settlements of thefoundation computed by three different approaches.

Displacement
(mm)

500

Structure
Properties
Approximate method(mm)
Linear elasto plastic
soil (mm)Nonlinear elasto
plastic soil (mm)

0
-500

0

200

400

600

Time (s)Fig. 4
Applied time history-displacement for the
S21 centrifuge test [11].
Comparison of numerical model and centrifuge test
Figure 5 shows the rotation-moment relationshipof soil-structure system at the center point of the baseof the footing computed by proposed numericalmodeling, and is that for the centrifuge test results
reported by K. R. Rosebrook & B. L. Kutter [11]. The
agreement between result of the numerical andexperimental models is very promising, as could beseen from Fig. 5.
Numerical

Rotation (rad)Rotation (rad)

20
Story
18.4

30
Story
27.6

7.8

15.0

23.4

57.5

72.0

82.0

Dynamic Analysis Results and Rocking Behavior
Energy dissipation
degradation

androtational

stiffness

It is clear that a part of the input energy is
dissipated by the rocking motion of the structureswith shallow foundations subjected to dynamic
loading, in the real world and in all kinds of analysis,
and is observed in rotation-moment diagrams asshown in Fig. 6. In this regard, the hysteresis loops
are wider with a larger area of dissipated energy when
the nonlinear elasto-plastic behavior is considered forthe soil beneath the foundation. Indeed, yielding of
the soil at sides of the foundation during the rocking
mode owing to increasing the shear stress anddecreasing the shear modulus in these areas,
eventuate in more energy dissipation through thenonlinear approach. The rotational stiffness
degradation in nonlinear approach is slightly more,compared to that for the linear one, showing that the
stiffness degradation as well as softening of the soil
system are associated with increasing of the rotational

Moment (KN.m)

S21 centrifuge test

10
Story
9.2

Fig. 5 Comparison of rotation-moment relationshipof the proposed numerical modeling, and that for thecentrifuge test results reported by K. R. Rosebrook &B. L. Kutter [11] for the aluminum shear walled
structure on dense sand.
141

GEOMATE- Osaka, Nov. 16-18, 2015

amplitudes (Fig. 6).

especially for the linear behavior of the underneathsoil.

Non-linear approach

Linear approach

-0.02
0
-500

0.02

0.02

-0.02
0
-500
Rotation (rad)

0.02

0.02

500

-0.02
0
-500
10 S

500

500

-0.02
0
-500
20 S

500

-0.02
0
-500
10 S

30 S

500

Settlement (mm) Settlement (mm) Settlement (mm)Moment (MN.m)

20 S

Moment (MN.m)

Moment (MN.m)

Non-linear approach
30 S

0.02

500

-0.02
0
-500

100
0
-100 0-200100
0-100 0-200100
0-100 0
-200

250

250

Linear approach

30 S 1000500 -100 0-200
20 S 1000500 -100 0

30 S

250

500
20 S

250

500

-200
10 S 100250

0500 -100 0
-200

0.02
Rotation (rad)

10 S

250

500

Rotation (rad)Soil settlement
Foundation displacement

Rotation (rad)First cluster of the loading
Second cluster of the loading
Third cluster of the loading

Fig. 7 Comparison of settlement and displacementtime history plots for the center point of soilfoundation interface through the nonlinear and linear
approaches.

Fig. 6 Rotation-moment diagrams for the 10, 20,
and 30 story structures with consideration of two
linear and non-linear elasto-plastic approaches for thesoil underneath the foundation.

Mobilized Moment and effective contact area of thefoundation

Additionally, Fig. 6 shows that energy dissipationof soil-structure system during rocking could beenhanced not only by increasing the rotational
amplitudes but also by increasing the height of thestructures.
The soil settlement-time history for the center
point of the soil-foundation interface has been plotted
over the displacement time history of the similar pointon the base of the foundation, and is shown in Fig. 7.
It is evident from these two plots that a great amountof input energy is dissipated through the nonlinear
elaso-plastic behavior for the soil especially in the tall,
slender structure (30 story structure). In fact, thecompatibility of the soil settlement and the foundation
displacement plots show that the input energy can be
dissipated by yielding of the soil, which result in morepermanent settlement and lower reflection offoundation in nonlinear soil. The linear behavior of
the soil results in much higher separation of thefoundation of structure from the underneath soil
showing less compatibility between the soil and thestructure and more foundation separation and upliftduring rocking. Moreover, during the first two
clusters of the loading, in both cases the movementsof soil and foundation are more compatible showing
little uplift. However, in the third cluster of loadingwith higher load amplitude, the amplitudes of soilfoundation separation and foundation uplift is greater

In each cycle of loading, the foundation contact
area is varied by the applied rotation, therefore,
mobilized moment, energy dissipation, and uplift offoundation during rocking could have a close relation
with ratio of foundation contact area to foundation
area which is defined by following equation.
𝐶𝑜𝑛𝑡𝑎𝑐𝑡 𝐴𝑟𝑒𝑎 𝑑𝑢𝑟𝑖𝑛𝑔 𝑅𝑜𝑐𝑘𝑖𝑛𝑔  AC  𝐹𝑜𝑢𝑛𝑑𝑎𝑡𝑖𝑜𝑛
𝐴𝑐𝑡𝑢𝑎𝑙 𝐹𝑜𝑢𝑛𝑑𝑎𝑡𝑖𝑜𝑛 𝐴𝑟𝑒𝑎
A

(6)

Critical amount of  as well as its corresponding
mobilized moment of soil-structure system arecomputed at maximum rotational amplitudes of eachcluster of loading and are shown in Table 3. In thisregard, maximum mobilized moment in each clusterof loading is obtained, where the foundation has theleast contact area with the underneath soil at a shortperiod of time. Based on obtained results shown in
Table 3, the nonlinearity of the soil decreases the
maximum mobilized moment in the structure due tocyclic loading and increases the effective contact areaof the foundation during rocking which both enhances
the safety of the structure against overturning andreduces the internal loads and decreases theassociated material consumption when the results arecompared with those of linear soil behavior.

142

GEOMATE- Osaka, Nov. 16-18, 2015

Table 3 The ratio of foundation contact area during rocking to actual foundation area as well as the maximum
mobilized moment.
Maximum Mobilized
Moment (MN.m)

Nonlinear elasto
plastic soil

Rotation(rad)0.00150.005
0.015
End of Loading

10 S
0.83
0.45
0.33
0.63

Linear elasto
plastic soil

Rotation(rad)0.00150.005
0.015
End of Loading

0.71
0.37
0.22
0.47

20 S
0.87
0.48
0.42
0.87

30 S
0.96
0.66
0.45
0.92

10 S

20 S

30 S

115.76152.12212.000178.08265.64301.170229.09331.80411.41
0

0.80
0.39
0.23
0.53

0.84
0.47
0.26
0.58

123.78160.11236.060188.24289.39336.070240.88396.65483.110

for Seismic Protection of Structures", Bulletin ofEarthquake Engineering, Vol. 8, 2009, 309-326.
[5] Weian L, Hutchinson TC, Kutter BL,
Hakhamaneshi M and Gavras AG, "Balancing
the Beneficial Contributions of Foundation
Rocking and Structural Yielding to Improve
Structural Seismic Resilience", 4th ECCOMAS
Thematic Conference on Computational
Methods in Structural Dynamics and Earthquake
Engineering, 2013.
[6] Kramer
SL,“Geotechnical
Earthquake
Engineering”, Prentice Hall, 1996.
[7] Seed HB and Idriss IM, "Soil Moduli anddamping factors for dynamic response analysis",
1970, Report No. EERC 70-10, University ofCalifornia,,Berkeley.
[8] Seed HB, Wong RT, Idriss M and Tokimatsu K,“Moduli and Damping Factor for Dynamic
Analysis of Cohesionless Soil”, Journal of
Geotechnical Engineering Division, ASCE, Vol.
112, 1986, No.11.
[9] Rayleigh JWS & Lindsay RB, “The Theory ofSound” Dover Publications, New York, 1945.
[10] Park D and Hashash YMA, "Soil Damping
Formulation in Nonlinear Time Domain site
Response Analysis", Journal of Earthquake
Engineering, Vol. 8, 2004, No. 2, 249-274.
[11] Rosebrook KR. and Kutter BL, "SoilFoundation-StructureInteraction:
Shallow
Foundations Centrifuge Data Report For
KRR02", Earthquake Engineering ResearchCenter, University of California, Davis, 2001,
EEC-971568, Peer 2061999 & 2142000.
[12] Mayne PW and Poulos HG, "Approximate
Displacement Influence Factors for Elastic
Shallow Foundations" Journal of Geotechnicaland Geoenvironmental Engineering, Vol. 125,
1999, No. 6.

CONCLUSION
The obtained results from the slender, high rise
structures, which are allowed to rock show that the
stiffness degradation of the soil-structure systemdecreases as well as increasing the energy dissipation.
Although, the nonlinear elastic-perfect plastic
approach result in dissipating the major amount of the
input energy in comparison to the linear elasticperfect plastic behavior considered for the soil, yet, it
causes the noticeable permanent settlement whichmight have destructive effects on the structures.
Additionally, deploying the linear approachthrough the analysis eventuate in the less contact area
during rocking, and it can make structures less stableagainst overturning. To sum up, with all this taken
into account, rocking and nonlinearity of the soil have
many beneficial effects for the soil-structure system
during strong vibrations that should be more noticed
in future engineered designs.
REFERENCES[1] Housner GW, "The Behavior of Inverted
Pendulum Structures During Earthquakes",
Bulletin of the Seismological Society of America,
Vol. 53, 1963, No. 2, pp. 403-417.
[2] Gajan S, Kutter BL, Phalen JD, Hutchinson TC
and Martin GR, "Centrifuge modeling of loaddeformation behavior of rocking shallow
foundations", Soil Dynamics and Earthquake
Engineering, Vol. 25, 2005, 773-783.
[3] Deng L and Kutter BL, "Characterization ofRocking Shallow Foundations Using Centrifuge
Model Tests", Earthquake Engineering andStructural
Dynamics,
2011,
DOI:
10.1002/eqe.1181.
[4] Anastasopoulos I, Gazetas G, Loli M, Apostolou
M and Gerolymos N, "Soil Failure Can be used143

FifthInternational Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan,Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

FUZZY LOGIC APPLICATION TO COAXIAL AND NON-COAXIALCOMPONENTS OF SHEAR STRENGTH AND CONSOLIDATION OF
SOILSJ.Rajaraman 1 K.Thiruvenkatasamy 2 and S.Narasimha Rao 3
1, 2 Professor, Department of Harbour and Ocean Engineering,
AMET University, 135 East Coast road, Kanathur -603 112
Chennai, India
3, Director,Dredging Corporation of India ( Govt. of India)

ABSTRACT
Fuzziness is explored as an alternative to randomness for describing uncertainty. The new sets –as – points
geometric view of fuzzy sets is developed. This view identifies a fuzzy set with a point in a unit hyper cube and a
non-fuzzy set with a vertex of the cube. In Fuzzy logic a crisp set is a set in which all members match the classconcept, and the class boundaries are sharp. The degree to which an individual observation z is a member of theset is expressed by the membership function F, which can take the value of 0 or 1for Boolean sets. In fuzzy logic
the membership function is a number in the range of 0.1 with 0 representing non-membership of the set and 1
representing full membership of the set. “Kosko cube” is the Starting point. A two dimensional version of Kosko
cube is considered. The sediments usually consist of clay and sand. If p is percentage of clay, then (1-p) is % of
sand. Clay properties relate to pure shear. Similarly the sand % represents simple shear. Fuzzy logic suits thisproblem when % are expressed in the range 0, 0.1, 0.2 etc. up to 1.0. The membership functions in the
combination of coaxial and non-coaxial shear component (pure and simple shear) is represented in Kosko cube.
Taking Skempton’s experimental data (clay + Sand) points distributed in quadrants I, II, III, IV inside the cube
as points are interpreted through simple Geological processes. Fuzzy logic also suits soil consolidation.
Keywords: Fuzzy Logic, Coaxial and Non-coaxial Shear, Kosko Cube, Membership function.

(1 0), and (0 1). This 1s and 0s indicate the presenceor absence of the ith element xi in the sub-set. Moreabstractly, each sub-set A is uniquely defined by oneof the two – valued membership functions mA: X →{0, 1}.
Now consider the fuzzy sub- sets of X. The fuzzy

INTRODUCTION
Zadeh (1965) writes, “ The notion of a fuzzy set
provides a convenient point of departure for theconstruction of a conceptual framework which
parallels in many a respects the framework used in
the case of ordinary sets, but it is more general thanthe latter and, potentially, may prove to have a muchwider scope of applicability, particularly in the fields
of pattern classification and information processing.
Essentially, such a framework provides a natural
way of dealing with problems in which the source ofimprecision is the absence of sharply defined criteriaof class membership rather than the presence ofrandom variables.”sub-set A=

can be viewed as one of thecontinuum – many continuous – valued membershipfunctions mA: X →{0, 1}. Indeed this is the classical
Zadeh sets – as - functions definition of fuzzy sets.
In this example element x1 belongs to , or fits in,
sub-set A a little bit- to degree. Element x2 hasmore membership than not at . Analogous to the bit
vector representation of finite (countable) sets , wesay that A is represented by the fit vector

I.
THE GEOMETRY OF FUZZY SETS:
SETS- AS- POINTS (Kosko Cube)
[1].

. The

element mA(xi) is the ith fit or fuzzy unit value. The
set -as –points view then geometrically represents
the fuzzy sub-set A as a point in 12, the unit square
as in figure1.The mid-point of 1n maximally fuzzy.

In Figure 1 consider the set of two elements X={x1x2}. The non- fuzzy power set 2X contains four
sets :
2X ={ Ø,X,{x1}, {x2}}.These four sets correspondrespectively to four bit vectors (0 0),(1 1),

All its membership values are . The mid point is
unique in two respects.

144

GEOMATE-Osaka, Nov. 16-18, 2015

Fig. .3 Result of a simulated Dissolution in a textural
triangle [2].
Dissolution at first affects mainly the particles ofclay size, especially the fine clays, leading to an
evolution towards the base of the triangle. Then thesilts are attacked in their turn, whereby an inflexionis seen in the trajectory towards the sand corner.
If we start from a collection of already sandy
particles (point y), there is no trajectory observable
by simulation. The point representing the collection
remains almost in place in the triangle. In otherwords, it is impossible to create a clayey or evensilty material starting from a sand composed ofparticles of size 2000��. When the diameter will be
reduced to 1000��, this sand will continue to be
counted in the coarse sands (particle size class2000 �� - 200 �� ). The size class will not change
in the system of measurement. However it would
have lost 87.5% of its mass. The chemical reductionof sands does not change the particle size class.

Fig. 1 Kosko cube
II.
A
CONCEPTUALMODELOFDISSOLUTION OF THE SKELETON GRAINS
Here dissolution is the ensemble of phenomenon
that chemically attack the particles of various sizes,
whether through solubilization or by hydrolysis. In
this model the particles are sorted by size in a table.
The particles of diameter 200�� will be in box no.
200. Since dissolution being gradual, it consists oftranslocating the particles from box to box without
ever skipping a box. This process comes to a haltwhen the thickness taken off is dl. The following
figure 2 explains such a model.

III.
ISOTROPICDEVIATORIC STRESSSTRESSAND

The amount of water existing in the soil mass will
significantly influence the engineering behavior ofsoil. Karl Terzaghi has said in effect, that there
would be no need for soil mechanics if not for water.
This is because the presence of water affects thestate of stress within a soil mass. The water content
also has bearing on potential volume change,
progressive failure, densification, shear strength, andsettlement. The mechanism of soil –water interaction
is complex and its behavior is not only dependent on
soil types, but is also related to the current and pastenvironmental conditions and stress histories,
Isotropic stress and deviatoric stressFig.. 2 Model of Dissolution of the skeleton grains[2].
In this a collection of particles containing sands, silts
and clays at the same time, the point representing
which is located at the centre of the textural triangle
as shown in the figure as point X. In reality, thesmallest particles have a greater surface area/ mass
ratio. They are therefore more susceptible todissolution and disappear in greater proportion byweight. The result is a trajectory towards the sandcorner, at the cost of a very pronounced loss in total
mass as shown in the figure 3.

Isotropic stress acts equally in all directions, it
results in a volume change of the body. Deviatoricstress, on the other hand, changes the shape of abody as shown in figure 4. [3].

145GEOMATE-Osaka, Nov. 16-18, 2015

In Figure 5 The rigid spheres slide past one anotherto accommodate the shape change without distortionof the individual marbles . In figure 5b the shapechange is achieved by changes in the shape ofindividual clay balls to ellipsoids, are quite different.

Fig.4 The mean (hydrostatic) and deviatoriccomponents of the stress. (a) mean stress causes
volume change and (b) Deviatoric stress causes
shape change.
Fig.6 Homogeneous Strain
IV.
THE CONCEPT OF COAXIAL AND
NON- COAXIAL COMPONENTS OF SHEAR
STRENGTHIn Figure 6 Homogeneous strain describes thetransformation of a square to a rectangle or a circle
to an ellipse. Two material lines that remain
perpendicular before and after strain are theprincipal axes of the strain ellipse [solid lines]. The
dashed lines are material lines that do not remain
perpendicular after strain; they rotate toward thelong axis of the strain ellipse.

In a homogeneously strained, two-dimensional
body there will be at least two material lines that do
not rotate relative to each other, meaning that theirangle remains the same before and after strain. A
material line connects features , such as an array ofgrains, that are recognizable throughout a body’s
strain history. The circle deforms and changes into
an ellipse. In homogeneous strain, two orientationsof material lines remain perpendicular before andafter strain. These two material lines form the axesof an ellipse that is called strain ellipse. The
principal incremental strain axes rotate to the finite
strain axes, a scenario that is called non- coaxial
strain accumulation. The case in which the samematerial lines remain the principal strain axes at
each increment is called coaxial strain accumulation.
The coaxial component of shear strength is called
pure shear and the non-coaxial component of shear
strength is called simple shear. The combination of
simple shear (a special case of non-coaxial strain)
and pure shear (coaxial strain ) is called general
shear or general non-coaxial strain. Two types ofgeneral shear are possible. It is explained in figure 6.

Fig.7 A combination of Simple shear and Pure shear.
In figure 7 a combination of simple shear [a special
case of non-coaxial strain] and pure shear [coaxial
strain]is called general shear or general non-coaxial
strain. Two types of general shear are transtension[a] and transpression[b], reflecting extension andshortening components.

The following figure 5 explains simple shear andpure shear[3].

V.
THE PROPERTIES OF SEDIMENTS
DERIVED FROM SECONDARY ROCKS AND
MANIFESTATION OF COAXIAL AND NONCOAXIALCOMPONENTS
OF
SHEAR
STRENGTH
The properties of sediments derived fromsecondary rocks are worth mentioning in thiscontext:(1) Rock is aggregate of minerals. Chemicalcomposition is a direct function of mineralogy,
and mineral composition varies with grain size.
The major- element chemical composition ofFig. 5 Simple Shear and Pure Shear Explained146

GEOMATE-Osaka, Nov. 16-18, 2015

shales and mudstones is related also to grain
size.

does not mean that they were necessarily absent
or scarce in the source rocks. Feldspars would
have been converted chemically to clays.
The ultimate products of weathering following theabove properties of sediments ends up in sand andclay. The coaxial and non-coaxial components ofshear strength are the hidden signature to sedimentsin the presence of water.

(2) Grain size and shape, control coaxial and noncoaxial strains of the sediments. Angular grains
increase the angle of internal -friction of thesoil.
(3) Because the chemical composition ofsiliciclastic sedimentary rocks is closely related
to the mineral composition of these rocks, thechemical composition varies as a function ofgrain size along with variations in mineralogy.
For example that SiO2 abundance decreases
progressively from fine sands to fine clays,
whereas the Al2O3 content systematicallyincreases.
(4) Quartz arenites composed of 90 to 95%
siliceous grains quartz, chert, quartzose rockfragments).
(5) Fine grained siliciclastic sedimentary rocks,
composed mainly of particles smaller thanapproximately 62
microns,
makeup
approximately 50% of all sedimentary rocks instratigraphic record.
(6) Quartz tends to be more abundant in coarse
grained mudstones and shales, whereas clay
minerals are more abundant in fine grain
mudstones and shales.
(7) Quartz arenites are more poorly sorted and may
contain high percentages of sub-angular toangular grains. Some quartz arenites exhibit
textural inversions such as a combination ofpoor sorting and high rounding, a lack ofcorrelation between roundness and size, such as
small round grains and larger angular grains, or
mixtures of rounded and angular grains within
the same size fraction.
These textural
inversions probably result from mixing ofgrains from different sources, erosion of older
sandstones, or environmental variables suchas wind transport of rounded grains into aquiet- water environment.
(8) Angular grains may result also fromdevelopment of secondary overgrowths.
(9) Now the problem has to do with the inherent
relationship of parent rock grain size and size ofrock fragments. Only fine size parent rocks
yield substantial quantities of rock fragments ofsand size. Therefore, coarse grained parent
rocks are poorly represented by rock fragments
in sandstones.
(10) Collectively, the changes brought about in thecomposition of sediment by weathering anderosion, transport, reworking at the depositionalsite can be significant. Provenance analysis
requires that we cannot use the absence ofparticular constituents as a guide to provenanceinterpretation; we can use only the presence.
The fact that feldspars and heavy minerals may
be absent or scarce in sandstone, for example,

VI.
THE
COMPLEXPERMEABILITY[4].

FUNCTION–

Permeability is a complex function of particle size,
sorting, shape, packing ,and orientation of sediments.
These variable factors can be expressed in terms ofheterogeneity factor. For a formation with a mixtureof clay and sand the following equations with thisand���
heterogeneity
factors
This variable factor CV is believed to decrease withdecreasing particle size and decreasing sorting .This
factor CV is affected by particle orientation. It is also
affected by the orientation parallel to bedding planeor perpendicular to the orientation. To make it asimple factors for the purpose of calculation theheterogeneity of clay is taken as
and for sandas.
The general eqn for CV total isof variation or Heterogeneity)

cvto ta l (CV = Coefficient

pcv21  (1  p) pcv2 2

P = 1 (Taking element No: 1 as clay)
Element 2 sand (1 - p) = o

cvtotal  1cv21  (1  1)cv2 2
cvtotal  cv21  cv2 (for clay)
Similarly for p = 0 for clay

cvtotal  0cv21  (1  0)cv22

cvtotal  cv2 2  cv2 (for sand)= [C + tan
The common shear strength eqn isф] where is shear strength, C is cohesion and isnormal stress and φis the angle of internal friction of
the soil.
= [C + tan ф] cos α, andcos  pcv21  (1  p)cv22

  (c   tan  ) pcv2  (1  p)cv2
1

2

When α = 900, cos α = 0 for pure clay p = 1, Sand (1- p) = 0,
φ = 0.

  (c   tan  ) cv2  0cv2 , for cos(90)=0
1

2

  c(cv ) for pure clay. cv ,  c
For pure sand p = 1.  = (C + tan ф)
1

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GEOMATE-Osaka, Nov. 16-18, 2015

( in this case, Wk = ∞ fig. 8d).When α = 00, Cos α=
1, represents simple shear. When α = 900, Cos α = 0,
represents pure shear.
[5].
The component describing the rotation of thematerial lines with respect to the principal strain axis
is called the internal vorticity, which is a measure of
the degree of non-coaxiality.
If there is zero internal vorticity, the strain history is
coaxial, which is sometimes called pure shear. The
non-coaxial strain history describes the case inwhich the distance perpendicular to the shear planeremains constant; this is also known as simple shear.

  (c   tan  ) pCv2  (1  p)Cv2 for cos
1

2

0=1
For pure sand p = 0.

  (c   tan  ) 0Cv2  (1  0)Cv2
12

  (c   tan  )( 0  C )2v2

  (c   tan  )Cv

2

For clay C = 0,  (Cv ) tan  , and Cv  1
2

2

For interpretation the data (after Skempton,1964 )indicating the variations of angle of internal friction( φ) with percentage of clay content is shown in a
family of nine points, distributed over the first three
quadrants as shown in fig.9

Heterogeneity
Cv1 or Cv2  1

 =tan ф.

VII.
THE
RELATION
BETWEENCOAXIAL AND NON-COAXIAL STRAIN ANDSKEMPTON POINTSThis figure.8, shows the sharing of coaxial andnon-coaxial strain or strength by different soil
samples. No point lies in quadrant IV which is highcohesion and high friction zone but in nature highcohesion and high friction cannot exist together in asoil sediment system, when sharing the same volume
or space between clay and sand (0.0,1.0 or 1.0,0.0).

Fig.8 Particle paths or flow lines during progressive
strain accumulation. [5].

Fig..9 variation of φult with percentage of clay
content. (After Skempton 1964) All Skempton
points lie in quadrants I, II,III.
[6].

These flow lines represent pure shear [a], general
shear [b], simple shear [c], and rigid –body rotation[d]. The cosine of the angle α is the kinematic
vorticity number,Wk for these strain histories; Wk=0,0 < Wk <1, Wk=1, and Wk = ∞ respectively.
Avoiding the math, a convenientgraphical way to understand this parameter isshown in fig.8 When tracking the movement of
individual points within a deforming body relative toa reference line, we obtain a displacement field (orflow lines) that enables us to quantify the internalvorticity. The angular relationship between theasymptotes and the reference line defines Wk. Wk=cos α.
For pure shear Wk= 0 fig. 8a, for general shear 0<
Wk <1 fig. 8b and for simple shear Wk = 1 fig 8c .
Rigid- body rotation or spin can also be described by
the kinematic vorticity number

In the above fig.9.,if ∝ = 0, the slope line coincides
with x axis , GFE, cos ∝= cos0 = 1.0. If ∝ = 900, theslope line becomes vertical and coincides with yaxis, GHA, cos∝ = cos 900 = 0.0.
In Table I the activity increase from quartz to clay
minerals or from frictional soil to cohesive soil or
simple shear to pure shear combinations as
Skempton Points is shown.

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GEOMATE-Osaka, Nov. 16-18, 2015

The small movements of sand are indicated from Y1,
Y2, Y3 and Y4. In this figure 10 the importance isgiven to the maximum fuzzy point M and all other
Boolean crisp corner points of the cube. The
sedimentation processes sometime undergo thefollowing processes : a) sand deposit gaining clay
particles in the voids b) already existing sandy clay
deposits may undergo loss of clay particles c) clay
deposits may gain sand in the voids d) in somecases clayey sand may undergo loss of sand. In allthese cases the membership function related to
Geological process will be different. For clarity only
dissolution is discussed.
Table II Connecting Fuzzy logic, Fuzzy Paths,
Cohesion and Frictional Components of Shear
Strength of Soils

TABLE I ACTIVITY NUMBER, CLAYS ANDSKEMPTON POINTS [6].

Quadrant

I

No. of
Skemptonpoints
02

II

03

III

04

IV

Nil

Type of
Quadrant
Low cohesion,
High friction
Low cohesion,
Low frictionHighcohesion,
Low frictionHighcohesion,High friction

Quadrant

Fuzzy

Fuzzy path

Fuzzy path

Geo-Tech

Area

Away fromtowards

Classification

BCDM

Mid point

Cohesion (0.0)

Low Cohesion

Max.Fuzzy

Friction (1.0)

High Friction

destinationI

VIII. FUZZY LOGIC (MEMBERSHIPFUNCTIONS) APPLICATION TO COAXIAL
AND NON-COAXIAL COMPONENTS OFSHEAR STRENGTH OF SOILS

Crisp pointII

In marine environment the sea beats the waves on
the sandy shores whose particles are subjected tointense dissolution, which does not, however,prevent them from remaining sandy. Now
transferring the triangle in correct orientation, andignoring gravity, to each of the quadrants (I II III
IV) into the Kosco’s cube the following integrated
figure is obtained.

III

ABMH

HMFG

Mid-point

Cohesion (0.0)

Low Cohesion

Max.Fuzzy

Friction (0.5)

Low Friction

Crisp

Mid point

High cohesion

cornerCohesion(0.5)

Low Friction

Cohesion

Friction (0.5)

(1.0)
IV

MDEF

Friction

Cohesion (0.5)(0.0)

Friction (0.5)

High
CohesionHigh Friction

Cohesion

Cannot exist(1.0)

in NaturalFriction

soils

(1.0)

IX.
THE SUITABILITY OF FUZZYLOGIC (MEMBERSHIP FUCTIONS) AND
INTERPRETATION OF GEO-TECHNICALPARAMETERS
Fig.10 Fuzzy logic (Membership Functions) and
Shear Strength of soils

The Maximum Fuzzy, Mid-Point M:
The mid-point represents maximum fuzzy with(membership function 0.5, 0.5) and also thepercentage of clay 50% and sand 50% intrinsicallyrelated to coaxial shear (pure shear) of clay and noncoaxial shear (simple shear) in a unique logic. This
mid-point is a reality in fuzzy logic.

In above integrated figure 10 the Boolean cornercrisp points and the fuzzy paths and the mid –Point
M with maximum fuzzy are shown. Since marine
conditions are considered only dissolution isconsidered for the fuzzy logic. The movement from
X1 in the direction of the arrow indicates the
movement of clay. It is applicable to X2 , X3 and X4.
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GEOMATE-Osaka, Nov. 16-18, 2015

is significant, since the extent of dissipation ofexcess pore water pressure is different for different
locations except at the beginning and the end of
consolidation process. The third dimensionlessparameter, relating to time, and called “TimeBut this mid-point is forbidden to classical logic(Boolean logic) and set theory. The arbitrary
insistence on bivalence or sharp crisp sets is themain reason for the restriction. At the middle pointof the cube the classical theory breaks down
completely because both sand and clay exist in
reality with membership functions (0.5, 0.5).
Hence conclusion number 1. is, if laws ofmathematics applied to reality they are not certain.
This is the reason for avoiding mid-point. Fuzzy
logic is for reality (in this case Geology) andtherefore it admits mid-point under all conditions. In
other words, when crisp sets in the four corners of
the cube contracts to a mid-point, the classical(Boolean Logic fails).

2

factor”, T is defined as follows: T = Cv t/H where
Cv is the coefficient of consolidation, H is the length
drainage path, and t is the elapsed timed from thestart of the consolidation process. The figures 11 1nd
12 are valid for double drainage conditions.

The Minimum Fuzzy or Boolean or Crisp
Sets:
When the mid-point expands and propagates toinclude all the four corners of crisp sets, then also
the classical logic fails. For classical logic at the
mid-point nothing is distinguishable. At the vertices
everything is distinguishable. At the corners of the
cube (vertices) point E representing high friction andhigh cohesion (1.0, 1.0) cannot exist simultaneously
in nature in soils in a given sample.
There is residual friction even for very pure clay.
Similarly a feeble cohesion for fine sands. The
corner points (0.0) (0,1), (1,0) can exist in soil
processes and samples in ideal cases. But the corner
point (1, 1) can not exist in any conditions.Therefore
conclusion number 2 is, if the laws of mathematics
are certain (sharp or crisp sets), they do not refer toreality.

Fig.11. Graphical Solution for consolidation
equation[8].

Fig. 12 Average consolidation at time factor 0.848[8].

X.
FUZZY LOGIC ANDCONSOLIDATION[7].

Fuzzy logic and membership function
In fuzzy logic two distinct rotations are mostcommonly employed in the literature to denotemembership functions. In one of them themembership function of a fuzzy set A is denoted by
μA : X→[0,1] where X denotes the universal set
under consideration and A is a label of the fuzzy set
defined by this function. The universal set is always
assumed to be a crisp set . For each x ϵ X, the value
μA(x) expresses the degree (or grade) of membershipof element x of X in fuzzy set A. Some examples ofmembership functions are included inside theconsolidation figure 13 (the combination of
consolidation and membership functions).
Separating the time factor T = 0.20 thecorresponding membership function meets the time

Figures 11 and 12 presents an excellent pictorial
idea of the process of the consolidation in anespecially instructive manner. At the start of theprocess t = 0 and T = 0, Uz=0 for all depths. In thisprocess three dimensionless parameters are
introduced for convenience in presenting the results
in a form usable in practice. The first is Z/H, relating
to the location of the point at which consolidation isconsidered, H being the maximum length of thedrainage path. The second is the consolidation ratio,
Uz defined to indicate the extent of dissipation of thehydrostatic excess pressure in relation to the initialvalue Uz = (ui – u) / ui = [1-u/ui]. The subscript z
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GEOMATE-Osaka, Nov. 16-18, 2015

1. If the laws of mathematics applied to reality they
are not certain. This is the reason for avoiding midpoint. Fuzzy logic is for reality ( in this caseGeology) and therefore it admits mid-point under
all conditions. In other words, when crisp sets in thefour corners of the cube contracts to a mid-point, theclassical Boolean logic fails.
2.If the laws of mathematics are certain (sharp or
crisp sets). They do not refer to reality.
3.The Boolean logic says that the shape of the Earthis a perfect sphere but as the reality demands,
Geologist admit the reality and say that the the shapeof the Earth is a spheroid which is ellipsoid of
revolution. Seeing is always believing.

factor curve at the point P. This point p is common
for time factor T = 0.2 and the membership function.
Similarly different membership functions A,B,C,D,E
and F meet the time factor curve at P the common
point for all. This is because of the fact that Terzaghi
assumed soil to be a homogeneous and isotropic
medium in which consolidation process takes place.
Each membership function deviates from Terzaghi’s
curve which the author calls as Terzaghi shift. But
when the membership function assumes the shape asshown in figure F inside figure 13 almost the two
curves ( Terzaghi’s curve and membership function
curve) almost merge and become one curve only. So
fuzzy logic is indirectly captures the anisotropic
characteristics of the soil. So the figure F represents
Terzaghi’s assumptions in tune with membershipfunctions. But the figure A to E in figure 13
represents anisotropic behavior of soil with different
mineral mixture as parent material. In these isotropic
conditions in figure 13 A to E, the coefficient ofconsolidation, permeability, coefficient of volume
compressibility are different because basically thesoil is heterogeneous in nature.

(for Consolidation of soil and fuzzy logic)
4.If we consider membership functions the fuzzy
logic suits even anisotropic soils.
5.Out of six membership functions the membershipfunctions in figure 13 F alone suits Terzaghi’s
assumptions for consolidation Theory.
6. The membership functions in figure 13 A to E
presents anisotropic soils from different origins
though they share a common point P .

REFERENCES[1] Bart Kosko, “Fuzziness versus Probability”,
International Journal General Systems, vol.17,
1990,pp. 211-240[2] Jean – Paul Legros, V.A.K. Sarma, Major soil
groups of the World, CRC Press Taylor andFrancis group, Chap 3, 2013,pp.100-105[3] Van der pluijm,ben a, Earth StructureW.W.Norton & Company inc, New York,
10110, 2003,pp.65-68[4] Jensen Jerry, Statistics for Petroleum Engineers
and Geoscientists, Prentice Hall, Cuevas,pp.3236,chapter 6, 1993,pp. 143-150[5] Van der pluijm,ben a, Earth StructureW.W.Norton & Company inc, New York,
10110, 2003,pp. 68[6] Skempton,a.w, long term stability of clay
slopes, Geotechnique, vol.14, 1964,pp.77[7] Demicco Robert V, Klir George J, Fuzzy logic
in Geology, Elsevier Academic Press, 2004, pp
12 – 15.
[8] Venkatramaiah C, Geo-Technical Engineering,
Wiley Eastern Ltd.1993, pp268-273

Fig. 13 A to F Consolidation of Soil andMembership Function
XI.

CONCLUSIONS(for Shear Strength of soils and fuzzy logic)

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Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

DEVELOPMENT OF A PROBABILISTIC LIQUEFACTIONPOTENTIAL MAP FOR METRO MANILA
Chua, Renz Anderson D.1 and Dungca, Jonathan R.2
Civil Engineering Department, De La Salle University Manila, Philippines;

1

ABSTRACTThe study aimed to create a probabilistic liquefaction potential map for Metro Manila. A
liquefaction opportunity map was produced by computing the probability of an earthquake with a moment
magnitude of at least 5.2 from occurring. Historical data were gathered from 7 surrounding active faults within a
150 km radius of Metro Manila to determine their recurrence. A liquefaction susceptibility map was also
developed by taking into consideration the conditions of the soil. Approximately 1000+ borehole logs scatteredacross Metro Manila was used in developing this map. These two maps were then combined to create theprobabilistic liquefaction potential map. The results show that there is only a 2% chance of an earthquake
capable of triggering a liquefaction occurring in a given year but in 50 years, there is a 10% chance of
exceedance coming from an earthquake with an acceleration of 0.7g. The earthquake is most likely to comefrom the Valley Fault System which runs straight through Metro Manila.
Keywords: PSHA, Liquefaction Potential, Liquefaction Opportunity, Liquefaction Susceptibility

INTRODUCTION
Geotechnical hazards by definition are events
that are directly caused by the action of the groundthat would cause adverse effects on humanity. This
includes but is not limited to earthquakes,
liquefaction, soil settlement, soil heaving, collapse,
land-slide and scouring [1].
Among these,
earthquakes and its corresponding after effects arewhat cause not only the loss of millions of lives but
also the halt of economic growth.
The two geotechnical hazards, earthquake andliquefaction must continuously be looked out for.
Since Manila is the capital of the Philippines, itsvulnerability is that much greater than any otherlocation in the country. This is why a study on the
different geotechnical hazards must be done toprepare not just the professionals for their design but
also for the normal individuals to protect their lives.

METHODOLOGY
Data Gathering
Determination of Source – In order to determine theactive faults situated around the area of interest, theresearcher first consulted the National Structural
Code of the Philippines (NSCP) to see which among
the known faults are within the 150km radius of
NCR. This 150km radius is in accordance to [2].
Earthquake Records – Records of earthquakes fromthe faults determined above having values not lessthan 5.2 in magnitude in the Richter’s scale weregathered as can be seen in Fig. 1.

Fig. 1 Spread of EQ events in the area of study
Source Characteristics – This included its geometry,source type, and direction of movement that canaffect the modelling of each earthquake generator.
Borehole Logs – These contain vital soil informationthat show the susceptibility of the soil at hand.
Data Analysis

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Magnitude Limits – Magnitude 5.2 served as thelower limit in this study because according to [3]this surface magnitude was the smallest earthquakethat was able to trigger liquefaction. This happenedin Laoag city in the Northern parts of Luzon. The
upper limit used was a magnitude of 8.2 since it is
the largest earthquake ever recorded in the country[4]. It should be noted that both these limits are interms of surface magnitude, the most common typeof magnitude recorded in the country. It is to be
expected that the data will not have a significant
spread throughout all the magnitude between thesaid ranges above so to cover this problem, aninterval of 0.6 magnitudes will be used as a range toensure that each group will have enough data within
it. This interval range was adopted from the work of[5] where he had found out that this range is themost effective way of distributing the data points
without hindering the study’s results.
Modelling of the Earthquake Events –First
assumption is that surface magnitudes below orequal to 5.7 were considered to come from a point
source since the magnitude is relatively small incomparison [6].
On the other hand, those thatexceed this value were considered as either a linear
fault rupture or an area rupture depending on thegenerator’s geometry and source type. The second
assumption was that both linear fault and area fault
ruptures will follow the finite fault rupture model by[7] which states that the earthquake generated at thefocus will propagate and felt equally along the
rupture length or area. The relationship between
length and the moment magnitude of a strike slip
fault is expressed by Eq. (1) [8].

where:
𝑳𝑳 = 𝟐𝟐𝟐𝟐

Probability Distribution of distance R – Todetermine P(R), the assumption made was that theradius used in solving for the probability is thenearest distance between the point of interest and asplice or part of the rupture area or length. This wasmade in accordance to the finite fault rupture modelby [7] where it states that an earthquake has an equal
probability of occurrence in the whole length or areaof the fault rupture area or length. For an areasource type, the region of permissible foci needs tobe determined first. This region was then divided
into 1 square kilometer, placing the focus on thecenter of each grid. This origin was then extendedin accordance to the aspect ratio of the magnitudeinterval used. The shortest distance from that area tothe site will be taken as R. To compute for the
probability distribution of R, the number of rupturesthat falls on each 10 km distance interval must be
normalized by the total number of ruptures possible
for the source zone. The same concept was used forlinear source types except that instead of dividing
into 1 square km intervals, the length of rupture wasdivided into 1km interval. The length was thenextended from the origin of each interval. This will
give the value R. The computation for P(R) was thesame as above [6].

0𝐥𝐥𝐥𝐥𝐥𝐥 𝑳𝑳 = 𝟎𝟎. 𝟕𝟕𝟕𝟕𝑴𝑴𝒘𝒘 − 𝟑𝟑. 𝟓𝟓𝟓𝟓 𝝈𝝈𝐥𝐥𝐥𝐥𝐥𝐥 𝑳𝑳 = 𝟎𝟎. 𝟐𝟐𝟐𝟐
(1)

-0.5

PFZ: ragay gulf

valley fault system

5

6

7

8

LOG (N)

-1.5
-2
-2.5
-3

(2)

Fig. 2Equation (4) on the other hand uses a model that wasderived from a database containing earthquakes
from subduction zones. This equation was then used
to determine the area fault rupture of the event byapplying the determined length value in the aspectratio prepared by [10].
𝑴𝑴 = 𝟒𝟒. 𝟓𝟓𝟓𝟓𝟓𝟓 + 𝟎𝟎. 𝟖𝟖𝟖𝟖𝟖𝟖 𝐥𝐥𝐥𝐥𝐥𝐥 𝑳𝑳(4)manila trench-1

Despite having the equation in terms of moment
magnitude, the models Eq. (2) and Eq. (3) developedby [9] successfully converted the surface and bodywave magnitude available in the country into
moment magnitude so that it can be used in thisequation.
𝑴𝑴𝒘𝒘 = 𝒆𝒆−𝟎𝟎.𝟐𝟐𝟐𝟐𝟐𝟐+𝟎𝟎.𝟐𝟐𝟐𝟐𝟐𝟐𝑴𝑴𝒔𝒔 + 𝟐𝟐. 𝟖𝟖𝟖𝟖𝟖𝟖
𝑴𝑴𝒘𝒘 = 𝒆𝒆−𝟒𝟒.𝟔𝟔𝟔𝟔𝟔𝟔+𝟎𝟎.𝟖𝟖𝟖𝟖𝟖𝟖𝑴𝑴𝒃𝒃 + 𝟒𝟒. 𝟓𝟓𝟓𝟓𝟓𝟓(3)

4lubang fault

MOMENT MAGNITUDE

Annual rate of occurrence

Annual Rates of Earthquake Activity – The annual
rate of activity in Fig. 2 for each seismic generator
was made by distributing each recorded earthquake
event to the source nearest to its epicenter. The
number of events per magnitude range per generator
divided by the total number of years that hasavailable record resulted to the annual rates ofearthquake activity.
This was repeated every
magnitude range to every available faults. A Semi-

𝝈𝝈𝑴𝑴 = 𝟎𝟎. 𝟑𝟑𝟑𝟑𝟑𝟑
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𝑵𝑵𝟔𝟔𝟔𝟔𝒄𝒄𝒄𝒄 = 𝜶𝜶 + 𝜷𝜷𝑵𝑵𝟔𝟔𝟔𝟔

logarithmic regression was also made from thevalues determined and was used for determining thenext variable needed.
Probability Distribution of Earthquake Magnitude M– This can be done by using Eq. (6) by [11]. The 𝜷𝜷used in the equation was the absolute value of theslope determined from the regression mentioned
above. Multiplying the results to the probability
values of R for each interval for each source andthen combining all of them will result to theprobability of occurrence of earthquakes that cancause liquefaction to occur at a specific location.
𝒇𝒇𝑴𝑴 (𝒎𝒎) =𝜷𝜷 𝐞𝐞𝐞𝐞𝐞𝐞[−𝜷𝜷(𝒎𝒎−𝒎𝒎𝒍𝒍 )]𝟏𝟏−𝐞𝐞𝐞𝐞𝐞𝐞[−𝜷𝜷(𝒎𝒎𝒖𝒖 −𝒎𝒎𝒍𝒍 )]𝑷𝑷[𝒎𝒎𝒍𝒍 ≤ 𝒎𝒎 ≤ 𝒎𝒎𝒖𝒖 ] =𝒎𝒎𝒖𝒖
𝒎𝒎∫𝒎𝒎 𝒇𝒇𝑴𝑴 (𝒎𝒎) ≈ 𝒇𝒇𝑴𝑴 �𝒎𝒎𝒍𝒍 + 𝒖𝒖 � (𝒎𝒎𝒖𝒖 − 𝒎𝒎𝒍𝒍 )
𝟐𝟐

𝒍𝒍

(10)

where:
𝜶𝜶 = 𝟎𝟎 𝒇𝒇𝒇𝒇𝒇𝒇 𝑭𝑭𝑭𝑭 ≤ 𝟓𝟓%
𝟏𝟏𝟏𝟏𝟏𝟏
𝟏𝟏.𝟕𝟕𝟕𝟕−��

𝑭𝑭𝑪𝑪𝟐𝟐 𝒇𝒇𝒇𝒇𝒇𝒇 𝟓𝟓% < 𝑭𝑭𝑭𝑭 < 𝟑𝟑𝟑𝟑%
𝜶𝜶 = 𝒆𝒆𝜶𝜶 = 𝟓𝟓 𝒇𝒇𝒇𝒇𝒇𝒇 𝑭𝑭𝑭𝑭 ≥ 𝟑𝟑𝟑𝟑%
𝜷𝜷 = 𝟏𝟏 𝒇𝒇𝒇𝒇𝒇𝒇 𝑭𝑭𝑭𝑭 ≤ 𝟓𝟓%
𝑭𝑭𝑪𝑪𝟏𝟏.𝟓𝟓
𝒇𝒇𝒇𝒇𝒇𝒇 𝟓𝟓% < 𝑭𝑭𝑭𝑭 < 𝟑𝟑𝟑𝟑%
𝜷𝜷 = 𝟎𝟎. 𝟗𝟗𝟗𝟗 +𝟏𝟏𝟏𝟏𝟏𝟏𝟏𝟏
𝜷𝜷 = 𝟏𝟏. 𝟐𝟐 𝒇𝒇𝒇𝒇𝒇𝒇 𝑭𝑭𝑭𝑭 ≥ 𝟑𝟑𝟑𝟑%

(5)
(6)

Ground Motion Intensity – Because of the lack ofstrong ground motion data in the country, theattenuation relationship, Eq. 7, developed by [12]with a similar tectonic setting was used instead.

𝐥𝐥𝐥𝐥𝐥𝐥 𝑨𝑨 = 𝟎𝟎. 𝟒𝟒𝟒𝟒𝟒𝟒 − 𝐥𝐥𝐥𝐥𝐥𝐥(𝑹𝑹 + 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎 × 𝟏𝟏𝟏𝟏𝟎𝟎.𝟒𝟒𝟒𝟒𝟒𝟒 )− 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎 + 𝟏𝟏𝟏𝟏𝟏𝟏(7)
𝝈𝝈𝐥𝐥𝐥𝐥𝐥𝐥 𝑨𝑨 = 𝟎𝟎. 𝟐𝟐𝟐𝟐

Given the value of A, the probability that a target
PHA (a*) was then exceeded should an earthquakeof the given magnitude interval occur at the given
distance range, P(A), can be estimated by computing
the standard normal deviation z Eq. (8) and thenobtaining the cumulative distribution function (CDF)
value [6].
𝒛𝒛 ∗=𝒍𝒍𝒍𝒍𝒍𝒍 𝒂𝒂∗−𝒍𝒍𝒍𝒍𝒍𝒍 𝑨𝑨
𝝈𝝈𝒍𝒍𝒍𝒍𝒍𝒍 𝑨𝑨(8)Correction Factors on N value – The N value shownin the borehole logs cannot be used directly in the
succeeding equations needed in determining theliquefaction susceptibility. The N value needed tobe adjusted first by several correction factors before
it can be used. This was done by using Eq. (9) andEq. (10) by [13]. The summary of the results can beseen in Fig. 3.
𝑵𝑵𝟔𝟔𝟔𝟔 = 𝑵𝑵𝑪𝑪𝑵𝑵 𝑪𝑪𝑬𝑬 𝑪𝑪𝑩𝑩 𝑪𝑪𝑹𝑹 𝑪𝑪𝑺𝑺

(9)
Fig. 3where:
𝑵𝑵𝟔𝟔𝟔𝟔 - corrected N value;
N - standard N value;
𝑪𝑪𝑵𝑵 - factor to normalize N to a common reference
effective over burden stress;
𝑪𝑪𝑬𝑬 - correction for hammer energy ratio;
𝑪𝑪𝑩𝑩 - correction for borehole diameter;
𝑪𝑪𝑹𝑹 - correction for rod length;
𝑪𝑪𝑺𝑺 - correction for samplers;

SPT value per meter depth

𝑪𝑪𝑪𝑪𝑪𝑪𝟕𝟕.𝟓𝟓 – With the corrected value of 𝑵𝑵𝟔𝟔𝟔𝟔𝟔𝟔𝟔𝟔 ,𝑪𝑪𝑪𝑪𝑪𝑪𝟕𝟕.𝟓𝟓 was then determined by Eq. (11) developedby [13]. Equation (12) and Eq. (13) were then used
to adjust this value to the right magnitude andoverburden pressure.
𝑪𝑪𝑪𝑪𝑪𝑪𝟕𝟕.𝟓𝟓 =
(11)

154

𝟏𝟏

𝟑𝟑𝟑𝟑−𝑵𝑵𝟔𝟔𝟔𝟔+

𝑵𝑵𝟔𝟔𝟔𝟔𝟏𝟏𝟏𝟏𝟏𝟏+ (𝟏𝟏𝟏𝟏𝑵𝑵

𝟓𝟓𝟓𝟓

𝟐𝟐𝟔𝟔𝟔𝟔 +𝟒𝟒𝟒𝟒)−

𝟏𝟏𝟐𝟐𝟐𝟐𝟐𝟐

GEOMATE- Osaka, Nov. 16-18, 2015

𝑲𝑲𝑴𝑴 =

𝟏𝟏𝟏𝟏𝟐𝟐.𝟐𝟐𝟐𝟐

(12)

𝝈𝝈′

(13)

𝑴𝑴𝟐𝟐.𝟓𝟓𝟓𝟓
𝒘𝒘

𝑲𝑲𝝈𝝈 = ( 𝒗𝒗)𝒇𝒇−𝟏𝟏
𝑷𝑷𝒂𝒂

Fig. 4

Liquefaction Opportunity – Now that the values ofP(R), P(A), P(M) and 𝒗𝒗𝒊𝒊 are known for eachmagnitude interval. Substituting all these values
into
Eq. (15) resulted to the annual exceedance rate forvarious target PHAs in each source.

𝐶𝐶𝐶𝐶𝐶𝐶 and the ‘a’ to trigger – From the value
determined above, given a factor of safety of 1.3, thevalue for CSR was determined. This value was thenequated to Eq. (14) developed by [14] to determine
the acceleration needed to possibly trigger aliquefaction. This acceleration can then be used todetermine the susceptibility of the area andsubsequently its liquefaction potential also.
𝑪𝑪𝑪𝑪𝑪𝑪 =where:

𝟎𝟎.𝟔𝟔𝟔𝟔𝒂𝒂𝒎𝒎𝒎𝒎𝒎𝒎 𝝈𝝈𝒗𝒗𝒗𝒗 𝒓𝒓𝒅𝒅
𝒈𝒈𝝈𝝈′𝒗𝒗𝒗𝒗

Liquefaction Opportunity

𝝀𝝀[𝑿𝑿 ≥ 𝒙𝒙] ≈𝑴𝑴𝑴𝑴𝑴𝑴𝑴𝑴∑𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔𝒔 𝒊𝒊 𝝂𝝂𝒊𝒊 ∫𝑴𝑴𝑴𝑴 ∫𝑹𝑹|𝑴𝑴 𝑷𝑷[𝑿𝑿 ≥ 𝒙𝒙|𝑴𝑴, 𝑹𝑹]𝒇𝒇𝒎𝒎 (𝒎𝒎)𝒇𝒇𝑹𝑹|𝑴𝑴 (𝒓𝒓|𝒎𝒎)𝒅𝒅𝒅𝒅𝒅𝒅𝒎𝒎(15)

Combining them together will produce the annual
rate of exceeding a target PHA when an earthquake
capable of triggering liquefaction occurred at any of
the sources nearby as can be seen in Fig. 4.

(14)

𝒂𝒂𝒎𝒎𝒎𝒎𝒎𝒎 - peak horizontal acceleration

𝒓𝒓𝒅𝒅 = 𝟏𝟏 − 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎 𝒇𝒇𝒇𝒇𝒇𝒇 𝒛𝒛 ≤ 𝟗𝟗. 𝟏𝟏𝟏𝟏𝟏𝟏

𝒓𝒓𝒅𝒅 = 𝟏𝟏. 𝟏𝟏𝟏𝟏𝟏𝟏 − 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎 𝒇𝒇𝒇𝒇𝒇𝒇 𝟗𝟗. 𝟏𝟏𝟏𝟏𝟏𝟏 < 𝒛𝒛 ≤ 𝟐𝟐𝟐𝟐𝟐𝟐
Results

Fig. 5

Liquefaction Susceptibility

Liquefaction Susceptibility – Given that an
earthquake with a specific magnitude range from afixed distance of 15km to the target site, theprobability of exceeding the target acceleration thatis needed to trigger the event by the acceleration by
the given magnitude range and fixed distance wasthen computed. Example of results can be seen in
Fig. 5.
Liquefaction Potential – Assimilating both theliquefaction opportunity and susceptibility produced
a liquefaction potential map for the site. Figure 7

155GEOMATE- Osaka, Nov. 16-18, 2015

showed the probability of exceeding the accelerationneeded to trigger a liquefaction by combining every
combination of magnitude and distance for every
seismic generator around the area.

used in this study was a 10% probability ofexceedance in a 50 year time frame Eq. (16) [6].
𝝀𝝀𝒚𝒚∗ =

𝐥𝐥𝐥𝐥(𝟏𝟏−𝑷𝑷[𝒀𝒀𝑻𝑻 >𝒚𝒚∗])𝑻𝑻=𝐥𝐥𝐥𝐥(𝟏𝟏−𝟎𝟎.𝟏𝟏)𝟓𝟓𝟓𝟓= 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎(16)
Fig. 6

Hazard Curve for all seismic generators

Hazard Curve – The resulting values was then used
to generate hazard curves for the liquefaction
opportunity as can be seen in Fig. 6.

Fig. 8 Deaggregation of results
Deaggregation – The results was then be
deaggregated, as shown in Fig. 8, to show whichamong the magnitude intervals will be responsible in
the occurrence of the most probable earthquake thatwill cause liquefaction in the area of interest. It also
showed which among the seismic generators
contribute the most to the probabilities. Thisknowledge can then be used to make to prepare forthe worst and properly mitigate and lessen thedamages that will be incurred by the phenomenon.

Fig. 7 Liquefaction Potential for Metro Manila
Design Ground Motion – Aside from the annual
probability that the target acceleration (a*) will be
exceeded should an earthquake capable of triggeringliquefaction occurred in any of the source zones,
𝝀𝝀[𝑿𝑿 ≥ 𝒙𝒙], it is also necessary to compute the PHA
corresponding to a given probability of exceedance
in a given time frame. The design ground motion

CONCLUSION

156

GEOMATE- Osaka, Nov. 16-18, 2015

It can be seen that there are 8 active faults
surrounding Metro Manila that are capable oftriggering a liquefaction in the said target area
namely: Central Mindoro Fault, Manila Trench,
Valley Fault System, Lubang Fault, PFZ: Digdig
Segment, PFZ: Ragay Gulf Segment, PFZ Infanta
Segment, and East Zambales Fault. Among theeight seismic generators, only one is an area fault
rupture which is the Manila trench. Valley fault
system on the other hand is the closest among themand yet the only one that has a few recorded
movements in the past 368 years [15]. This gives us
the idea that it could possibly move anytime now.
Since it runs straight through the region, having it
move would surely cause a massive damage to thecity proper’s.
From the earthquake opportunity analysis, thegreatest probability of an earthquake with amagnitude of at least 5.2 has only 1% chance ofoccurring within a given year but if we look at theseismic hazard map, one could see that in the next
50 years, there is a 10% chance of having anearthquake generate an acceleration of 0.7g in thearea which is a bit higher to the 0.6g value
determined by [5]. The increase in value could be
attributed to the increase in data within 20 years.
This value, according to the deaggregation, would
likely come from the valley fault system with amagnitude range of 5.8 – 6.3 from a distance rangeof 0 – 10km away from the site.
The vertical mid-section of the Metro Manila has
rocky or very hard soil in it though they start
showing at around 3m deep. Both the western andeastern part of the region has sandy top soil for the
first 6 meters, beyond that it is mostly clays and silts.
On the other hand, SPT values show a much moreconstant range of values throughout the entire 20m
depth. Seeing that the top soil for all seem to be redand yellow in color even for the vertical mid-sectionof the region. This is because this layer of soil havenot been consolidated yet. After that the red andyellow section continue to cluster in the same area
where the sandy soils are found, both in the westernand eastern part of the region. Since the liquefaction
susceptibility of the area is largely influenced by thesoil type map and SPT map, it was no surprise thatthe patterns shown in the susceptibility maps are
pretty much the same in nature.
Finally, the liquefaction potential developed fromthis study shows the same pattern as that of thesusceptibility except the values are very much lower,
the greatest probability only having a 2% chance of
exceedance in a given year. But just like with theearthquake opportunity map, this should not be
underestimated since this value increases as time
passes by.

[1] Orense R P, Geotechnical Hazard: Nature,
Assessment and Mitigation, Quezon City: The
University of the Philippines Press, 2003.
[2] Yould T, Perkins D, “Mapping liquefaction
induces ground failure potential” Journal of the
Geotechnical Engineering Division, ASCE 104(4), 1978, pp. 443-446.
[3] SEASEE (Southeast Asia Association ofSeismology and Earthquake Engineering),
Series on Seismology vol IV, 1985.
[4] Phivolcs (Philippines Institute of Volcanology
and Seismology), Catalogue of Mentro Manila
Earthquakes, magnitude 5.2 and above (1907 –
2013), 2014.
[5] Thenhaus P, Hanson S, Algermissen S, Bautista
B, Bautista M B J P, et al., “Estimates of theregional ground motion hazard in the Philipiines.
National Disaster Mitigation in the Philipines.”
DOST-PHIVOLCS, 1994, pp. 45-60.
[6] Beroya M & Aydin A, “Seismic hazard analysis
of Laoag City, Northern Philippines forliquefaction potential assessment”, Engineering
Geology 96, 2008, pp. 28-42.
[7] Kiureghian D & Ang A, “A fault rupture model
for seismic risk analysis”, Bulletin of the
Seismological Society of America 67, 1977, pp.
1173-1194.
[8] Wells D, Coppersmith K, “New empirical
relationships among magnitude, rupture length,
rupture width, rupture area and surface
displacement” Bulletin of the SeismologicalSociety of America 84 (4), 1994, pp. 974-1002.
[9] Giacomo D, Bondar I, Storchak D A, Engdhal E
R, Bormann P, Harris J, “ISC-BEM: Global
Instrumental Earthquake Catalogue (1900-2009),
III. Re-computed Ms and mb, proxy Mw, final
magnitude composition an completenessassessment”, Physics of the Earth and Planetary
Interiors, 2014.
[10] Geller R, “Scaling relations for earthquake
source parameters and magnitudes”, Bulletin of
the Seismological Society of America 80 (5),1976, pp. 757-776.
[11] Kramer
S,
Geotechnical
Earthquake
Engineering, New Jersey: Prentice Hall, 1996.
[12] Fukushima Y & Tanaka T, “A new attenuation
relationship for peak horizontal acceleration ofstrong earthquake ground motion in Japan”,
Bulletin of the Seismological Society ofAmerica 80, 1990, pp. 1501-1523.
[13] Youd T, Idriss I, Andrus R, Seed, “Liquefaction
resistance of soils summary report from 1996
NCEER and 1998 NCEER/NSF workshops on
Evaluation of Liquefaction Resistance of Soil”
Journal of Geotechnical and Geoenvironmental
Engineering, ASCE 127. 2001, pp. 817-833.
[14] Seed H B, Idriss I M, “Simplified procedure for
evaluating soil liquefaction potential”, J.
Geotech Engrg. Div., 1971, pp. 1249-1273.

REFERENCES

157

GEOMATE- Osaka, Nov. 16-18, 2015[15] Bautista M L P & Oike K, “Estimation of themagnitudes and epicenters of Philippine
historical earthquakes”, Techtonophysics 317,
2000, pp. 137-169.

158

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-990.3958-4-5 C3051

EFFECT OF RICE STRAW LENGTH ON MECHANICALPROPERTIES OF QUICK LIME AND RICE HUSK ASH
STABILIZED SOIL
Ayaka Oya1, Satoru Iida 1, Masamitsu Fujimoto2, Ryoichi Fukagawa2and Der-Her Lee 3
Graduate School of Science and Engineering, Ritsumeikan University, Japan, 2Department of Civil
Engineering, Ritsumeikan University, Japan, 3Department of Civil Engineering, National Cheng Kung
University, Taiwan
1

ABSTRACTRiverbank collapse often occurs along the Saigon River which flows through Ho Chi Minh City (HCMC). One of
the influencing factors is the soft ground which is the riverbank composed of. Our research about the ground
improvement method using quicklime, rice husks ash so far. Rice straw is newly added to the improved soil inthis paper. The Rice straw is expected to improve the failure strain property, that is to restrain brittle failure.
These mixing materials can be obtained easily and cheaply in Vietnam. A series of unconfined compression tests
are carried out to improve the mechanical properties of improved soil. Moreover pH tests are conducted tovalidate the chemical action of the improved soil and evaluate the effect of improved soil on surrounding
environment.
Keywords: Ground improvement,

series of unconfined compression tests [3]. As theresults of this study, we could experimentally makeclear that appropriate quantities of quick lime andrice hulk ash improve unconfined compression
strength of the soft clay soil. However, someproblems remains, that is, the failure strain of the
improved soil is decreased as the curing days
increase, and the brittle fracture occurs in somecases.
In this paper, to overcome the problem above,rice straw is more applied to improved soil in
addition to quicklime and rice hulk ash. The ricestraw is assumed that it improves failure strain andrestrains the occurrence of brittle failure. A series of
unconfined compression tests are carried out in order
to investigate the strength characteristics of the
improved soil. Moreover pH tests are performed forvalidation of the chemical action of the improved
soil and considering the effect on surrounding
environment.

INTRODUCTION
Major cities in South East Asia have beendeveloped on river plain. Its soft ground often causes
problems such as settlement. Particularly, Riverbank
failure occurs along Saigon River, which flows in
the center of Ho Chi Minh City. Fig.1 shows theriverbank failure along Saigon River. Saigon Riveris main water resource of HCMC, and has Saigon
Port, which plays an important role of trading. Oneof the main factors of riverbank failure is assumed tobe soft ground composing riverbank [1]. It causes
serious damage to buildings and agricultural land in
HCMC. Fig.2 shows that the building is leaned by
the riverbank failure. Such damages can be seen on
many points on Saigon riverbank. However, enoughcountermeasures to deal with the riverbank
destruction have not been fully employed because of
the restraint of budget and enormous length of target
riverbank. Therefore it is necessary to propose
effective and economical countermeasure forreinforcing riverbank in order to contribute further
development of HCMC.
To solve this problem, our research group pays
attention to improved soil with quicklime and ricehusks ash to stabilize the Saingon Riverbank. Thesematerials can be obtained easily and cheaply in
Vietnam. The improved soil with quicklime and ricehulk ash has been proposed and its shear strengthand durability to erosion by rainfall are estimated [2],but the relationships between material mix
proportion and strength properties etc. are not fully
investigated. Then, we have been investigated themechanical properties of improved soil based on a

Fig. 1. The riverbank failure along Saigon River
159

GEOMATE- Osaka, Nov. 16-18, 2015

GEOTECHNICAL CHARACITERISTICS OFSAIGON RIVERBANK
Fig.3 show the cross sectional profile of the
Saigon riverbank. SPT (Standard Penetration Test) s
were carried out at two points at this observation site
and record fill soil above either three or four
compositionally distinct layers [1]. The surface ofthe ground is covered with a 0.5 m thickness of fill
soil, which is carried from outside of the sample site.
Layer 1 is organic clay with a SPT-N value of 1–2
and a thickness of 13.8–13.9 m. Layer 2 is clay sandwith a SPT-N value of 12–13 and a thickness of2.6–
3.0 m. Layer 3 is clay and has a SPT-N value of 7–
11. Layer 3a, as shown in Fig.4, is clayey sand andhas a SPT-N value of 8 and a maximum thickness of
2.7 m. In the presence, and considerable thickness of
this very soft soil, is of particular importance forslope stability assessment of the Saigon riverbank.
This research focuses on the soft clay soil on layer 1.

Fig. 2. The Building affected by riverbank failure
0.5 m

0.5 m

Fill soil

Layer 1: Organic clay

Layer 3a: Clayey sand14.3 m17.3 m

INPROVED SOIL USING THE QUICKLIME,RICE HUSKS ASH AND RICE STRAW

14.4 mLayer 2: Clay sand17.0 mQuicklime, rice husks ash and rice straws areapplied to stabilize the soft clay soil. The
characteristics of the materials and hardening
mechanism are explained in this chapter.

Layer 3: Clay
19.0 mFig. 3. The cross sectional profile of the riverbank

Characteristic of the materials
Quicklime (Fig.4) is used for soil stabilization inpractical work for long time, and shows dehydration
action and pozzolanic reaction. Rice hulk ash (Fig.5)
includes silicon dioxide about 90 present of its
weight, and this value is higher than that of fly ash,which used as pozzolanic material. Rice straw is dry
stem of gramineous plant. The rice straw (Fig.6) is
expected to increase the failure strain and to preventthe brittle fracture of improved material.

Fig. 4. Quicklime

Hardening mechanism of the improved soil
The improved soil is hardened by dehydration
action of quicklime, which is short term reaction,and pozzolanic reaction of silica, which is long term
reaction.
Firstly, water in the soil vaporized by calciumoxide ( CaO ) which is the main component of quick
lime. Then, the water content of the soil approaches
optimum water content and compaction strength
increases. In this process, water ( H 2 O ) reacts with
CaO and hydrated lime ( Ca(OH) 2 ) is produced(Formula (1)) .

Fig. 5. Rice hulk ash

H 2 O + CaO → Ca(OH) 2(1)

Next, Ca(OH) 2 gently reacts with silicon dioxideFig. 6. Rice straw
160

GEOMATE- Osaka, Nov. 16-18, 2015

( SiO 2 ) which is included in both rice hulk ash andsoil and calcium silicate hydrate ( nCaO ･ SiO 2 ･mH 2 O ) is produced (Formula (2)).
Ca(OH) 2 + [SiO 2 ] →nCaO･SiO 2 ･mH 2 O

Table 1. Mix proportion of the materials(The weight ratio to the weight of Fujinomori clay)
Fuzinomori clay
100 %
Water
About 50 %Quicklime
5%
Rice husks ash
10 %

(2)

where n and m are integer. Improved soil ishardened in this stage. This reaction is called“pozzolanic reaction”. Silicate hydrate ( nCaO ･
SiO 2 ･mH 2 O ) is similar to cement and enhances
durability and water-tightness and strength of thesoil increases.
In addition, it is assumed that rice straws mixed
into improved soil increase the failure strain of thematerial because tensile force and superficial
cohesion of the soil are increased by fibers of therice straw.
UNCONFINED COMPRESSION
IMPROVED SOIL

TEST

in a standard compression rate that a compression
strain produces of 1% per a minute continually.
Test condition
We used 3 types of wet rice straw to validate theeffect of its length, that is, the length of the ricestraw for the tests are 0.3-1.0 cm, 1.0-3.0 cm and3.0-7.0 cm respectively. The curing periods of test
specimens are 1, 3, 7, 14, 28 and 56 days. The
specimens are cured in the air until 3 day, after thatthey are put under the water, because when
improved soil is practically used in the fields, mostof them are assumed to be put under the water.

OF

In order to validate the effect of the length of ricestraws and understand mechanical properties of the
improved soil, unconfined compression is carriedout. In this study, unconfined compression strength,
failure strain and modulus of deformation are
estimated from the unconfined compression test toevaluate the mechanical properties. In this research,
the target values of unconfined compression test areunconfined compression strength: 200 kN/m2 andfailure strain: 5 % [4][5].

Test results and discussion
The stress-strain curves of the improved soil,
using 0.3-1.0 cm of wet rice straw are shown in Fig.
7. Unconfined compression strength increases as thecuring days. The stress-strain relationships arestrongly influenced by the curing period. After
curing period of 14 days, the value of failure strain isdecreased. The Compression stress is dropped
rapidly after yield. And, although the materials are
mixed sufficiently, testing results vary more widelyas the curing days. This is because the testing results
highly depend on chemical reaction of the materials
used in the improved soil. Validation of the strengthof the improved soil should be estimated to expect
deterioration of strength, which should be left for
future works.
The stress-strain curves of the improved soil,
using 3.0-7.0 cm of wet rice straw are shown in Fig.
8. Unconfined compression strength increases as thecuring days as well as 0.3-1.0 cm rice straw
specimens. The unconfined strength increases more
early than that of 0.3-1.0 cm rice straw improved
soil. The unconfined strength is dropped at thecuring period 56 days. It is assumed that rice straws
disturb the specimen, because the length of ricestraws is large toward the size of specimen (5.0 cm
×10.0 cm). The tests with other size of specimens
should be done to consider the effect of the length ofrice straws.
The relationships between unconfined compression
strength and curing period is shown in Fig. 9. The
results of the improved soil with no rice straw [3] isshown in the graph for the comparison. The
unconfined strength exceeds the target value (200Test method
First of all, specimens for the test are prepared.
Fujinomori clay which is similar to soft groundmaterial of the field is applied to the tests.
Fujinomori clay and water are mixed and then thequicklime, rice husks ash and rice straw is mixed
with soil in the mixer sufficiently. After that,
improved soil is packed in a mold, which is 10
centimeter in height and 5 centimeter in diameter.
Improved soil is packed dividing into 3 parts. The
weight of packed soil in the mold is 300 g in eachmold. Mix proportion of the materials for specimens
is indicated in Table 1. 3 testing specimens areprepared for each testing condition.
Then, the specimens are cured. The specimens
are cured in the air until 3 day, after that under thewater, because when improved soil is practicallyused in the fields, most of them are assumed to be
put under the water.
After curing, unconfined compression test is
conducted. Test method in this study is based on thestandard of The Japanese Geotechnical Society [JGS0511 Method for unconfined compression test ofsoils]. During a test, the test specimen is compressed161Compression stress (kN/㎡)

Compression stress (kN/㎡)

GEOMATE- Osaka, Nov. 16-18, 2015

1200
1
2
3

900
600
300
0

0

2

4
8
6
Compression strain (%)

10

1200
1
2
3

900
600
300
0

0

1200
1
2
3

900
600
300
0

0

2

4
6
8
Compression strain (%)

10

1
2
3

900
600
300
0

0

Compression stress (kN/㎡)

Compression stress (kN/㎡)

600
300
00

2

8
4
6
Compression strain (%)

10

Compression stress (kN/㎡)(c) Curing period 14day

1
2
3

8
4
6
Compression strain (%)10(d) Curing period 56day

Compression stress (kN/㎡)

Fig. 7. The stress-strain curves of the improved soil
with 0.3-1.0 cm of wet rice straw

1200
1
2
3

900
600
300
00

2

4
6
8
Compression strain (%)

1200
1
3

900
600
300
0

0

2

4
6
8
Compression strain (%)

10

kN/m2) in all case. In terms of the strength at curing
period 56 days, 0.3 cm-1.0 cm rice straw is mostsuitable among the condition in this paper.
Fig.9 shows that the failure strains of the specimens
mixing rice straw are higher than specimens without
rice straw, so that we suppose that improved soil
added rice straw can improve the failure strain
property to some extent. Moreover among low
failure strain, specimens corresponding to 0.3-1.0
cm rice straw are the most high failure strain
comparing with other specimens although the failure
strain does not reach the target value (5 %). This
reason is assumed that rice straw is not long, but ismuch number because rice straws are broken up into
small pieces, and they are mixed in entire soil. The
improved soil with 0.3-1.0 cm rice straw is the best
suited in the conditions which are validated in thistime in terms of both the strength and the failure
strain.

300
210Fig. 8. The stress-strain curves of the improved soil
with 3.0-7.0 cm of wet rice straw

600

00

4
6
8
Compression strain (%)(d) Curing period 56day

1200
900

2

(c) Curing period 14day

1
2
3

900

10

1200

(b) Curing period 7 days

1200

4
6
8
Compression strain (%)(b) Curing period 7 days
Compression stress (kN/㎡)

Compression stress (kN/㎡)(a) Curing period 1 day

210(a) Curing period 1 day

162

GEOMATE- Osaka, Nov. 16-18, 2015

UNCONFINED COMPRESSION
IMPROVED SOIL

Failure strain (%)

10

No rice straw

8

0.5-1cm

4

3-7cm

1-3cm

Test method

2
0

10

20
30
40
Curing period (day)50

The method for PH test is based on thestandard of the Japanese Geotechnical Society [JGS211 Test method for pH of suspended soils]. A pH
meter was calibrated at first by Neutral phosphate,
Phthalate and Borate standard solution. Then, soil
sample used for unconfined compression tests ismixed with water, and the suspension for the test is
prepared. After the suspension is left for 30 minutesat least, pH is measured by pH meter (HORIBA D51).

60

Fig. 9. The relationships between unconfinedcompression strength and curing period
1200

Unconfined compression
strength (kN/m2)

OF

pH tests are performed for validation of the soil andconsidering the effect on surrounding environment.

6

0

TEST

No rice straw

1000

3-7cm

800

1-3cm

600
400

Test condition

0.5-1cm

200
0

0

10

20
30
40
Curing period (day)50

The difference of test conditions is the same as
the cases of unconfined compression tests. A
specimen was used for unconfined compression test
at first, and then the specimen was broken byhammer and next used for pH test as testing samples.

60

Fig. 10. The relationships between failure strain andcuring period
13

pH

Test results and discussion

No.1
No.2
No.3

12

The results of pH tests corresponding to every
curing period are shown in Fig. 11. The pH values
distribute from about 12 to about 10 during 56
curing period at all condition. These shows that
improved soil is changing reaction of alkali fromneutrality. Moreover, the pH is decreasing largely
not later than 7 days of curing period. It is due todehydration action which is short term reaction. The
amount of strong alkali which is in CaO reacts withwater (H 2 O), and weak alkali (Ca(OH) 2 ) is
produced. As a result of this reaction, the pH is
rapidly decreased until the period of 7 days. From
the results of the pH tests, it is assumed that thedehydration action end by 7 days, and Pozzolanic
reaction occurs after curing period of 7 days. Since
there is not big difference between the testing
conditions of rice straw length, the difference of ricestraw length does not much effect on overalltendency.

11
10
9
0

10

20
30
40
Curing period (day)5060(a) Rice straw 0.3 cm-1.0 cm
13
No.1
No.2
No.3

pH

12
11
10
9

0

10

20
30
40
Curing period (day)5060(b) Rice straw 1.0-3.0 cm
13
No.1
No.2
No.3

pH

12

CONCLUSION
This paper presented research results on thehardening mechanism of improved soil usingmaterials of quicklime, rice husks ash and rice straw.
The research was carried out based on theunconfined compression tests and the pH tests. In
this paper, the improved soil with rice straw can
improve failure strain property, but the failure
strains are less than the target value. Among low

11
10
9

0

10

20
30
40
Curing period (day)5060(c) Rice straw 3.0-7.0 cm
Fig. 11 The relationships between pH and curing
period (Rice straw 0.3-1.0 cm)
163

GEOMATE- Osaka, Nov. 16-18, 2015[2] Der Her Lee, “Strength and erosion property ofthe stability soils”, Institute for National Cheng
Kung University engineering works process
master’s thesis, April 1976.
[3] Oya et al., “Study on mechanical characteristics
of improved soil using quick lime and rice hulk
ash for riverbank erosion around cultural
heritage”, ，Journal of Disaster Mitigation for
Historical Cities, Vol. 8（2014, July）pp.139144.
[4] Anh et al., “Study on Strength and Durability ofFiber-Cement-Stabilized Soils by using Rice
Straw”, Proc. of the 1st Vietnam/Japan Joint
Symposium on Saigon River Bank Erosion,
pp.100-110.
[5] Mori et al,. “An experimental study on strengthof fiber-cement-stabilized mud by use of paper
sludge and durability for drying and wetting
tests”, Shigen-to-sozai, Vlo. 122, pp. 353-361,
2006.

failure strain, specimen with 0.3-1.0 cm rice straw is
the most high failure strain of other specimen. This
reason is assumed that rice straw is not long, but ismuch number and is mixed in entire soil.
Unconfined compression strength is higher than thetarget value. pH value of the soil are 12 at curing
period 1 day and about 10 at curing period 56 day.
pH test can make sure reaction of alkali fromneutrality.
For future works, improved soil with larger
amount of straw should be tested, because there is apossibility that failure strain by increasing thecompound ratios of rice straw. The tests with othermix proportions should be conducted to clarify thesuitable mix proportions. In addition, it is importantto keep highly alkali to maintain the strength of the
improved soil [], so we should investigate validationof pH values of the improved soil.
REFERENCES[1] Oya et al., “Seepage flow-stability analysis of
the riverbank of saigon river due to river waterlevel fluctuation”, Int. J. of GEOMATE, March.,
2014, Vol. 8, No. 1 (Sl. No. 15), pp.1212-121

164

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

GIS BASED PROBABILISTIC HAZARD RISK ASSESSMENT OF
ROAD ACCIDENTS
Dr. Ather Ashraf1 and Hania Arif2
Punjab University, Pakistan; 2 Punjab University, Pakistan

1

ABSTRACT
Accident is any unintentional and unplanned event happed due to lack of intention. Road Accidents are oneof most common type of accidents which usually happen due to crashing of automobiles. Road traffic injury has
become a serious ill health globally, inflicting over one-million deaths annually. Ninety percent of those deaths
occur in the third world countries wherever they're the second leading reason behind death in older, kids andadults. Road traffic injuries square measure coupled to the atmosphere factors like road style, density of
population around it and handiness of alternatives for pedestrians and two wheelers. It is very important to findthe root cause analysis of road accident to avoid them in future. But before finding root cause analysis, it isrequired to identify most vulnerable road based on risk assessment and to explore sensitivity of results aboutroad uncertainty. The aim of the study is to analyze and discuss possible reasons of road accidents and its
impacts. The main goal was to identify vulnerable roads, risk assessment and to explore sensitivity of results
about road uncertainty. We applied our study in Lahore, a major city of Pakistan. Pakistan is a developing
country. Roads are not managed as they could be managed. Some roads are more vulnerable where accidents
occur daily, particularly in Lahore, this ratio is very high. Identifying such vulnerable road is very important. For
this we applied a GIS, geographic data systems, enabled risk assessment of road in our study. The study started
with the spatial data compilation from the primary and secondary sources. The accident data is analyzed andtabulated for probability calculation which used in the development of hazard layer. The element at risk is
identified based on hazard map. Their cooping capacity and with stand capacity are measured and used in thedevelopment of vulnerability and exposure map. Finally risk map is developed by overlaying hazard map,
vulnerability map, exposure map and capacity map through weighted overlay technique.

Keywords: GIS, Analytic Hierarchy Process (AHP), Hazards, Vulnerability
further into poverty and deprivation as their savings
and income can be spent on treatment. Traffic
injuries and traffic accidents increased in spite of thegrowing burden of road traffic safety. Reasons mayvary in each region, but general awareness andgeneral health scale and serious effect of traffic
accidents on the issue of the lack of specificinformation, including social and economic costs offalls, and interventions or measures are road traffic
accidents that can prevent or reduce the damage.
In developing countries including Pakistan,there is no attention to reduce traffic accidents by thehigher authorities. Traffic accidents in Lahore
increased tremendously over the years. Therefore itis necessary to overcome road accidents to save thelives of the people. This study will be helpful toidentify the root causes of accidents and alsosignificant for the reduction of accidents ratio. It will
also be helpful for making the transport system more
efficient.
Transportation is the backbone for thedevelopment of a country. But due to extensive flowof traffic, accidents occur. In case of Pakistan, traffic
accidents management is neglected. Some factors
like road geometry, icebergs and surface of roads,

INTRODUCTION
In big cities the road accidents are very
common and a big issue. A large number of people
injured and died every year. Science and technology
in the modern world, devised many ways to avoid
the accidents. But the fact is that number ofaccidents has been increased with the rapid increasein population. According to world statistics, low andmiddle income countries are expecting that theirroad accidents ratio will be increased about 80% in
next 15 years. These countries have no plan to
avoid/reduce this ratio. Traffic accidents areincluded among the top 10 causes of deaths anddisabilities in the South-East Asian Region. It is also
estimated that by the end of year 2020, death andinjuries are mostly caused by traffic accidents in theworld [7]. Road accidents have also a great negative
impact on economy. Mostly male population is diedand injured during accidents as compared to female
population. About 70 percent of the population age
group is 15-44 years that is active and take part in
the development of country [25].The death or
disability due to such accidents affects the educationand living conditions of children, culminating165

unskilled drivers, carelessness and lack ofknowledge regarding traffic rules are contributed inroad accidents. These factors can be minimized andtraffic can be managed which may prevent
thousands of causalities and economic losses.
Mustafa Karasahin & Serdal Terzi tried to
identify the sections of road highways as hazardous
locations. GIS is used for the analysis of locations
that were hazardous. Different layers were
established and after this combined all layers. SQL(Structured Query Language) were applied toacquire the results by using GIS. Hazardous
locations were marked if more accidents occurred onthat place. For this purpose GPS was used to getreference points [15]. This study was conducted in
Delaware. They had developed GIS based traffic
accident information. Occurrence of accidents,
number of accidents, accident sites and such sort ofother information were recorded. These points wereanalyzed using GIS for the management andreduction of traffic accidents [10].
Ravin Hasseea had studied traffic accidents
by using GIS for processing and analysis. For data
storing, processing, displaying and querying GIS
was used. Spatial analysis, proximity analysis,
network analysis etc. were effective in traffic
accidents management. GIS provided facility for
spatial query and displaying results in the form ofmaps like chloropleth maps, thematic maps, dotmaps etc. to display different results of accidents[13].
Department of Civil Engineering in
University of Putra Malaysia (UPM) developed asystem using GIS applications. Their observation
concluded that thousands of people and vehicle werethe victim of dangerous accidents each day. Theydeveloped a prototype Geographic Information
System and Road Accident View System (GISRAVS) for the reduction of traffic accidents.
According to researchers this system is very
effective to identify the accident location, ranking ofaccidents, store and retrieve accidents database,show the roads where most of the accidents occurredand different statistical analysis were performed in ashort time. Different queries were also applied on
the above data for analysis [19].
Driving is essential part of daily life foralmost all people. Many people driving on the roaddo not have same physical & psychological senseand driving any vehicle is a risky activity. US police
generated a report on road accidents, 6.4 million
accidents happened in US. To reduce traffic
accidents, efficient traffic accident handling is very
necessary for reducing impact of traffic accidents &to save millions of lives. GIS is used to store, edits,
display and analyze location information. GIS havebeen used for many years for research,transportation and management purposes because it
has powerful tools for visualization and to manage

data (features) into databases. Visualization cangreatly facilitate the analysis of traffic accidents.
This research conducted to make use of aanalytical hierarchal process (AHP) method foranalyzing accidents. This method made it possible toutilize all relevant information on traffic accidents
including accurate location for earlier response,route numbers, route name, mile post, accurate event
locations. These indicators help in better analyzing
traffic accidents and visualization hot spot ofincidents. The proposed method able to generate
new datasets for accidents and it displays theaccidents on the top of all existing road networks.
Finally a risk map is developed based on hazard,
vulnerability and capacity calculated using AHP andspatial techniques.

METHODOLOGY AND ANALYSIS
Accident data and ancillary data was
acquired and was maintained appropriately in thetabular form. This data was collected for studying
and identifying the accidental locations and theirpattern in the Lahore district. This data wasprocessed to frame in spatial form. Topological error
were removed and thematic maps were formed.
Figure 1 is showing the bar graph generated fromthis dataset at different landmarks.

Figure 1: Road Accidents Bar Graph
Vulnerability Assessment:
In Lahore, the recorded causes ofaccident are mostly Carelessness (speed, Don’t
Obeying Law, Unskilled Drivers), Awareness(Awareness, Uneducated Drivers, and Lack ofInformation) and Fitness (Vehicle Fitness andDrivers Conditions). These causes are taken as
vulnerability criteria in case of road accidents. This
research uses AHP results to identify vulnerable
roads and crossings. A risk map was developed withthe help of Hazard, Capacity and Vulnerability value.
A spatial risk surface was developed using IDW
analysis and define safety measure on this.
In Pakistan, most of the people are hit due
to carelessness. Carelessness is the fault of people.
Violation of traffic rules, driving without traffic
license, driving during or after drinking or having
166

drugs, driving while using mobile phone, continuouschanging of traffic lanes, over speed driving andother violation is a usual trend that is being observedon the daily basis. Most of the accidents occurred bypeople whose ages have less than 18. Second mostimportant cause is awareness. In Pakistan, mostpeople are illiterate and they have no sense tounderstand the signs of traffic symbols. Most of thedrivers are uneducated. One of the most basic
reasons of road accident is that they have no
sufficient information about traffic lane and its
distribution. Fitness is the last but not the least
criteria of road accidents. In the whole world, after
10 years vehicles are expired and there are notallowed to run on main roads specially. Most of thevehicles are expired in Pakistan. But they are stillrunning on roads without any check and balance.
Vehicles fitness is one of the most important criteria
for a safe journey. Beside the vehicle fitness, drivercondition is also important. Some of accidents
occurred due to the drivers who take drugs. And
they drive vehicle in such condition which cause
serious accidents. These accidents are in small
numbers but they caused serious damage because ofdriving on main road.
These criteria are used in AHP for
assigning weight to different situations. The
processed data is used to develop a vulnerability
map showing in figure 2 below.

Capacity of road is the maximum potential of traffic
on it. It is defined as traffic count per hour or per day.
Every country has its own standards to fix the valueof capacity, usually depends upon road width. Rural
areas have low capacity of roads as compared tourban areas. In road accidents scenario, traffic
wardens and signals are also used as capacity to
reduce accident ratio.
We collected all the information related toroad capacity and assign it to each crossing androads as attribute data.
Risk Assessment
Risk assessment is done to examine thecauses of accidents. The risk is calculated byfollowing formula:

Where,𝑅𝑅 =𝐻𝐻∗𝑉𝑉∗𝑇𝑇
𝐶𝐶--------------- (eq: 1)

R = Risk,
H = Hazard
V = Vulnerability
T = Time andC = Capacity

Figure 2: Vulnerability Assessment Map
Capacity of Road
Figure 3: Risk Assessment

In case of road, Capacity is basically used
to reduce the vulnerability and road accidents.
167

Figure 3 is showing the risk assessment
mapping using equation 1. This map is helpful in
identifying the risk pattern and can help finding
solution and taking necessary precautions to preventor lower these risks.
CROSSING ACCIDENTS
There are three types of crossing e.g. junction, crossintersection and round crossing. Junction is a place
where three roads cross each other. Cross section isa place where four roads cross each other and roundcrossing is a place where five roads cross each other.
Most of the accidents occurred on crossing. Signals
are erected on crossings but some of them are notfunctional partially or completely and also most ofpeople do not follow the signals that are evenfunctional. That’s why constables / wardens performtheir duties on crossing to reduce the accident’s ratio.
But unfortunately accident’s ratio is not reduced
significantly yet. Figure 4 below is showing the bar
graph of accident at different crossings. Most of theRoad Accidents occurred on Ferozpur road andCanal road as the graph is representing it. Most ofroad accidents occurred on Kalma chowk (Ferozpur
Road), Ichra chowk (Ferozpur Road) and Johar
Town chowk (Canal Road) as shown in Figure 4.
Figure 5: IDW analysis with vulnerable crossings
Capacity of Crossing
Crossing capacity is defined as traffic
count per hour or per day. Every country has its own
standards to fix the value of capacity. It is usuallydepends upon road arms. During peak hours, acrossing bears the maximum potential of traffic.
Crossing capacity also included signals andconstables. Accidents ratio reduced by using
functional signals and constables.

Figure 4: Road Accidents for Crossings
Vulnerability Assessment
The vulnerability criteria for crossing
are same as of road, i.e., Carelessness (speed, Don’t
Obeying Law, Unskilled Drivers), Awareness(Awareness, Uneducated Drivers, and Lack ofInformation) and Fitness (Vehicle Fitness andDrivers Conditions). These criteria are used in AHP
for analyzing vulnerable crossing. The AHP
quantifies these crossing and a surface is developed
using IDW to identify vulnerable areas and crossing.
Figure 5 is showing the result of IDW withvulnerable crossings.

Risk Assessment for Crossings
Risk at crossings is also calculated using
equation 1. The hazard, vulnerability and capacity
are measured as mentioned above. The result of risk
assessment is shown in figure 6 below.

168

Traffic Engineering & Transport
Planning Agency (TEPA) is one of the major
stakeholders which can play a better role in thisregard. Therefore, the agency must give attention
towards higher accident rates and take preventive
measures to save lives of thousands of people.
The strategy generates a brand newform for managing traffic accident and shows theminimum and maximum value of risk of road
network in spatial form. If this search is effectively
applied then it may be very helpful to reduce roadaccidents. This research identifies pattern andscenario applied to reduce accidents and other traffic
management matters. Some strategies must be
applied to design Road geometry, to fix traffic
speeds on roads, manage traffic flow and density,
define lanes for different vehicles and control speed
variation of vehicles.

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Watershed. Remote Sensing Anna University ofChennai, India.

Figure 6: IDW analysis with chowks
CONCLUSION
From the analysis performed, the results showsthat road accident is one of the important and serious
issues which must be dealt effectively and efficiently.
This research study proposed measures for the
reduction of road accidents using GIS based analysis.
It identifies vulnerable roads and chowks to
calculate the risk and on the basis of analysis anddefine safety measure. According to result of roadanalysis, Ferozpur Road and Canal Road are mostrisky roads of Lahore on the basis of vulnerability
and road accidents. Their road accidents value isabove 8000 per year. This road accidents value isvery high because this value is based on only those
accidents that are rescued by Rescue 1122. Ringroad, Grand Trunk Road and Multan Road have roadaccidents are above 4000 per year. These roads areless risky than above roads (shown in Figure 3).
.
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have most risk because Road Accidents value isabove one thousand in one year. Johar Town
Chowk (Canal Road), Batti Chowk (Grand TrunkRoad) and Chungi Amer Shudu Chowk (Ferozpurroad) are also very dangerous but less risky than
Kalma chowk and Ichra chowk (shown in Figure 6).
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170

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

PREDICTIONS FROM STANDARD PENETRATING TEST USINGARTIFICIAL NEURAL NETWORK
A.G.Mahgoub1, Dahlia H. Hafez2 and Mostafa A. Abu Kiefa3
DAR AL-Handasah, Egypt; 2,3Faculty of engineering, Cairo University, Egypt

1

ABSTRACTThe Standard Penetrating Test (SPT) can be considered as one of the most common in-situ popular andeconomic tests for subsurface investigation. Therefore, many empirical correlations have been developed
between the SPT N-value, and other properties of soils. The principle objective of the current study is todemonstrate the feasibility and efficiency of using artificial neural networks (ANNs) to predict the soil angle ofinternal friction (Φ), the soil modulus of elasticity (E) and tip resistance (q c ) of cone penetration test (CPT)
results from SPT results considering the uncertainties and non-linearities of the soil. In addition, ANNs are used
to study the influence of different input parameters that can be used to improve the prediction. A large amount offield and experimental data including SPT/CPT results, plate load tests, direct shear box, grain size distribution
was obtained from a project in the United Arab Emirates to be used in the training and the validation of theANNs. The ANN results are compared with some common traditional correlations. The results show that thedeveloped ANNs can efficiently predict the aimed parameters from the SPT results. The predicted parametersfrom ANN were in very good agreement with measured results compared to the predicted values from available
traditional correlations.
Keywords: Angle of Internal Friction; Cone Penetrating Test; General Regression Neural Network; Soil
Modulus of Elasticity, Standard Penetrating Test.

offer viable solutions. It has been shown that ANNsare capable of mapping nonlinear and complex
relationships in nature and are very beneficial whena problem is difficult to formulate analytically.
To train and test a neural network a large amountof data is needed. In the current study, field andexperimental data including SPT/ CPT results, plateload tests, direct shear box, grain size distribution
were obtained then filtered and processed from alarge-scale project that covers the United ArabEmirates (UAE). The soil in UAE is mostly
cohessionless soil. UAE is witnessing a lot ofdevelopment and many construction projects. It isbelieved that using data from such active areas inconstruction for prediction of soil properties would
be of benefit to engineers in this area specificallyand to geotechnical engineers in general.
The available data used for estimating Φ (Direct
shear box), E (plate loading test) and q c (CPT
results) from N (SPT results), is first presented. The
different ANN models are then developed. Different
input parameters were considered to study the
influence of the input parameters on the ANN
models. The predictions from ANN are compared to
predictions from other correlations available in theliterature. Conclusions highlighting the efficiency of
the ANNs are finally presented.

INTRODUCTION
The Standard Penetration Test (SPT) is one of themost commonly used in-situ tests for site
investigation and foundation design. Many empiricalcorrelations have been developed between the SPT
N-value, and other engineering properties of soils.
Despite the fact that this test has disadvantages suchas discrete strength measurement and dependence on
operator and apparatus, it is still the most popularand economic mean for subsurface investigation.
The current paper studies the feasibility andefficiency of using artificial neural networks(ANNs) to estimate the soil properties Φ, E and CPT
results (q c ) [1]-[5] from SPT result and toinvestigate which parameters should be included in
the soil property estimation to improve theprediction models.
Artificial neural networks have been intensively
studied and applied to many geotechnicalengineering problems [6], [7]. It has been applied toestimate many soil and material properties [8], [9]and it proved to be a powerful tool that can havesuperiority over other correlation techniques. The
idea of neural network technology is similar to thebrain’s own problem-solving process. An ANN iscomposed of a large number of connected neurons
which act like simple processors. Generally, when alarge volume of data is available for training, ANNs
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GEOMATE- Osaka, Nov. 16-18, 2015

The neural networks used in the current study
were developed using the neural network program
Neuroshell 2. This program implements several
different neural network algorithms. The general
regression neural network (GRNN) is used in thecurrent study. GRNNs are known for their ability to
train quickly on sparse data sets [10].
The GRNN models developed were three-layer
networks (input layer, output layer and one hidden
layer). The number of neurons in the input layer isequal to the number of inputs and the number ofneurons in the output layer is equal to the number of
outputs, while the number of neurons in the hidden
layer is usually equal to the number of correct
patterns given to the model to learn from.
The inputs were scaled using a linear scale
function [0,1]. The GRNN used was genetic
adaptive; i.e. it uses a genetic algorithm to find an
input smoothing factor adjustment. The genetic
breeding pool size of 100 was used in the developed
GRNN. An initial smoothing factor was taken as 0.3.
The smoothing factor is an important parameter in
the GRNN which determines how tightly the
network matches its predictions to the data in the
training patterns.
For each of the data sets prepared to estimate Φ, E
and q c from N, 20% of the data was randomly
extracted. This 20% was used as a testing set while
the rest of the data was used as a training set.

AVAILABLE DATA
The data for this study was collected from theresults of geotechnical investigation work that has
been done for a large-scale project. The projectextends all over United Arab Emirates UAE where
the soil is mainly cohessionless soil.
The project has about 820 boreholes with variable
depths and with SPT for each borehole along the
project alignment. About 400 CPT were executed
beside the boreholes. Moreover, there are about 630
test pits with maximum depth 3.0m along the projectalignment with 260 plate loading test to determine
the modulus of soil elasticity and 606 CBR tests. In
addition, lab tests were performed on the soil sample
for classifications such as, Grain size distribution
tests (Sieve analysis and Hydrometer), and lab tests
for determining the shear strength parameters suchas direct shear box.
NEURAL NETWORK MODELINGThe current study uses a supervised ANN. In a
supervised network, a large number of correct
predictions are given to the model from which it canlearn. Examples of supervised networks are backpropagation networks (BPN), general regression
networks (GRNN) and probabilistic neural networks(PNN).
The architecture of a supervised ANN, generally,
consists of an input layer, an output layer and one ormore hidden layers. The input layer contains the
input variables. The output layer contains the target
output vector. At least one hidden layer that contains
the artificial neurons (processing units) is used
between the input and output to assist in the learning
process. The neurons in the different layers areinterconnected. Each connection has a 'weight'
associated with it. Input values in the first layer are
weighted and passed on to the hidden layer. Neurons
in the hidden layer produce outputs by applying anactivation function to the sum of the weighted input
values [10], [11]. These outputs are then weighted
by the connections between the hidden and output
layer. The output layer produces the desired results.
Two main phases are included in neural network
operation. The first is the training phase and thesecond is the testing phase. In the first phase the data
is repeatedly presented to the network while theweights of the data are updated to obtain the desired
output. In the second phase the trained network withthe frozen weights is applied to data it has neverseen. A properly trained network can model theunknown function that relates the input variables tothe output variables. It can then be used to makepredictions for a given set of previously unseen
input patterns where the output values are unknown.

ESTIMATING Φ FROM SPT RESULTS
Output/Input Variables of ANN Analysis
For estimating Φ from SPT results, the SPT
results (N values), direct shear test and grain size
analysis were used from the available date. The
readings of the SPT test were filtered to be at thesame elevation of the lab tests. A total of 84 data
points were prepared. The parameters that were
investigated as input parameters to be included in
the GRNN models developed were N (obtained from
SPT results), Fc (fines content) and D 50 (defined asgrain diameter corresponding to 50% of the material
being smaller) obtained from grain size analysis andδ eff (the effective overburden pressure) calculated at
the same level of the SPT test. The calculation ofeffective overburden pressure was based on a unit
weight of soil of 18 KN/m3 and the unit weight ofwater of 10 KN/m3 taking into consideration theeffect of ground water level.
The output of the GRNN models considered istan Φ which was both measured (obtained fromdirect shear box) and estimated by the GRNN
models developed. Five different GRNN models
were developed with different input parameters to
study the influence of the input parameters on the172

GEOMATE- Osaka, Nov. 16-18, 2015

obtained tan Φ. To evaluate the efficiency of the
GRNN models developed, the coefficient ofcorrelation (r2) was used. r2 is a statistical measure of
the strength of the relationship between the actual
versus predicted outputs. r2 value of 1 indicates aperfect fit, while that of 0 indicates no relationship.

(δeff) is the Second input variable that influences the
network and N- value (SPT result) is the last one.
0.9

tanΦ measured

0.85

Results of Neural Networks
Figure 1 shows 5 different GRNN models
developed (GRNN1 to GRNN5) with 5 different
input combinations and the corresponding r2 (for alldata points) obtained for each Network. GRNN2
with inputs (N, δ eff ) is the best model to represent
the correlation predicting (tanΦ) from SPT results
and effective overburden with high value of r2 of
97.55%.
Table 1 presents the data used in GRNN2 as input
and the measured tan Φ. The comparison between
the predicted tan Φ from GRNN and the actual
measured values is presented in Fig. 2.

0.8
0.75
0.7
0.65

r2 = 0.9758

0.6
0.55
0.5

0.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

tanΦ Predicted

Fig. 2 Comparison between predicted and measuredtan (Φ) from SPT results with r2% coefficient
3

2.905

weight factor

2.9
2.8
2.7
2.6
2.6
2.5
2.4

δeff

N

Fig. 3 The weight factors for the correlation between
angle of internal friction and SPT results (GRNN2)

Comparison between Neural Networks and a Set
of Traditional Methods
Table 2 shows some of the correlations used for theestimation of Φ from SPT results available in theliterature. The available data is applied to theavailable correlations in the literature and are plottedin Fig. 4 along with the results from the ANN model
developed.
From Fig. 4, it is clear that the ANN model
predicted values of angle of internal friction that arevery close to the measured values compared to theother available correlations. Therefore it is a good
indication for the applicability of using this model incomparison with other correlations

Fig. 1 Trials used to predict tan (Φ) from SPT results
considering different input parameters withr2 coefficient (%)
Table 1 The used data for estimation of φ (GRNN2)
Index

Effective stress(Kpa)

N

TAN (Φ)

Min

14.85

1

0.46604

Max

89

68

0.86865

Mean

43.21941

12.6670.60258

Table 2 Different correlations for predicting (tanφ)from SPT results
Researcher
Kulhawy and Mayne,(1990)
Wolff, (1989).
Shioi and Fukui (1982)
Hatanaka and Uchida,(1996)For GRNN2, the weight (influence) of each input
parameter on the relation is reflected by theindividual smoothing factor of each input parameter.
The individual smoothing factors for each input are
shown in Fig. 3. It is concluded from Fig. 3 that

Correlation
𝝓𝝓 = 𝒕𝒕𝒕𝒕𝒕𝒕−𝟏𝟏 (𝑵𝑵/(𝟏𝟏𝟏𝟏. 𝟐𝟐𝟐𝟐 + 𝟐𝟐𝟐𝟐. 𝟑𝟑𝟑𝟑𝜹𝜹𝒗𝒗𝒗𝒗
/𝒑𝒑𝒑𝒑)𝟎𝟎.𝟑𝟑𝟑𝟑
𝝓𝝓 = 𝟐𝟐𝟐𝟐. 𝟏𝟏 + 𝟎𝟎. 𝟑𝟑𝟑𝟑𝟑𝟑−. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝑵𝑵𝟐𝟐
𝛟𝛟 = 𝟒𝟒. 𝟓𝟓 𝐍𝐍 + 𝟐𝟐𝟐𝟐
𝝓𝝓 = �𝟏𝟏𝟏𝟏 𝑵𝑵𝟏𝟏 + 𝟐𝟐𝟐𝟐

Where, 𝑵𝑵𝟏𝟏 = �

173

𝟗𝟗𝟗𝟗

𝜹𝜹𝒗𝒗𝒗𝒗𝑵𝑵 & 𝜹𝜹𝒗𝒗𝒗𝒗 𝒊𝒊𝒊𝒊 𝑲𝑲𝑲𝑲/𝒎𝒎𝟐𝟐

GEOMATE- Osaka, Nov. 16-18, 20151.8

tanΦ predicted

1.6

GRNN2

1.4

Kulhawy and Mayne, (1990)

1.2

Wolff, (1989)

1
0.8

Shioi and Fukui (1982)

0.6

Hatanaka and Uchida, (1996)

0.4

0.4

0.6

0.8

1
1.2
tanΦ measured

1.4

1.6

1.8

Fig. 4 Comparison between actual (measured) andpredicting (tanΦ) from SPT.
ESTIMATING E FROM SPT RESULTS
Fig. 5 Trials used to predict E from SPT results
considering different input parameters withr2 coefficient (%).

Output/Input Variables of ANN Analysis
For estimating E from SPT results, the SPT-(Nvalues) results, plate loading test and grain size
analysis were used from the available date. The
readings of the SPT test were filtered to be at thesame elevation of the lab tests and at the zone of 120
cm below Plate loading test (influence zone while
the plate width is 60cm). A total of 34 data points
were prepared. The parameters that were
investigated as input parameters to be included in
the GRNN models developed were N (obtained from
SPT results), Fc (fines content), D 50 and depth ofwater below the plate loading test .

2.752943
weight factor

2.5
2

1.71765

1.5
1

0.5
0
D50

N

Fig. 6 The weight factors for the correlation between
angle of internal friction and SPT results (GRNN2)

Results of Neural Networks

80
E measured (Mpa)

70

Figure 5 shows 7 different GRNN models
developed (GRNN1 to GRNN7) with 7 different
input combinations and the corresponding r2 (for alldata points) obtained for each Network. GRNN2
with inputs (N, D 50 ) is the best model to represent
the correlation predicting (E) from SPT results andeffective overburden with high value of r2 of 95.46%.
Fig 6 shows the influence factors for every input.
Table 3 presents the data used in GRNN2 as
input and the measured E. Fig 7 shows thecomparison between the predicted E from GRNN2
and the actual measured values.

D50

N

0.121

2

9.9

Max

0.2159

53

73.05

Mean

0.1557235

12.41177

29.75

St. Deviation

2.58E-02

11.80169

14.7353840
30
20

r2 = 0.954

0
0

10

20

30
40
50
60
E Predicted (Mpa)7080Fig. 7 Comparison between predicted and measured E
Comparison between Neural Networks and a Set
of Traditional Methods
Table 4 and Fig. 8 show some of the correlations
used for estimation of E from SPT results available
in the literature. From Fig. 8 it is shown that the
ANN model (GRNN2) was in very good agreementwith measured values of E compared to the otheravailable correlations in the literature.

E (Mpa)

Min

50

10

Table 3 The used data for estimation of E (GRNN2)
Index

60

174

GEOMATE- Osaka, Nov. 16-18, 2015

other correlations (GRNN3, GRNN4, GRNN8). In
addition, D50 is the last factor that has no effect on
output results for GRNN7.
Table 5 presents the data used in GRNN7 as
input and the measured q c . Fig 11 shows thecomparison between the predicted q c from GRNN7
and the actual measured values.

90
80
GRNN2
Kulhawy and Mayne,(1990)
Denver(1982)

60
50

Schmertman (1970)

40

Webb (1969)30

Ohsaki and Iwasaki (1973)

20

D’Appolonia -1970

10
0
0

10

20

30 40 50 60
E measured (Mpa)

70

80

90

Fig. 8 Comparison between actual (measured) andpredicting (E) from SPT.
Table 4 Different correlations for predicting (E)from SPT results
Researcher
Kulhawy and Mayne, (1990)

Correlation
𝟓𝟓𝟓𝟓 = 𝑬𝑬�𝐏𝐏𝐚𝐚

𝐄𝐄(𝑲𝑲𝑲𝑲𝑲𝑲) = 𝟕𝟕𝟕𝟕𝟕𝟕𝑵𝑵

Schmertman(1970)
Denver(1982)
Webb (1969)
Ohsaki and Iwasaki (1973)

𝑬𝑬(𝑴𝑴𝑴𝑴𝑴𝑴) = 𝟕𝟕√𝑵𝑵

Fig. 9 Trials used to predict q c from SPT resultswith r2 coefficient (%).

𝐄𝐄 = 𝟕𝟕. 𝟏𝟏𝟏𝟏 + 𝟎𝟎. 𝟒𝟒𝟒𝟒𝟒𝟒𝟒𝟒 𝐄𝐄 = 𝐤𝐤𝐤𝐤/𝐜𝐜𝐜𝐜𝐜𝐜
𝑬𝑬 = 𝟑𝟑. 𝟓𝟓 ∗ 𝑵𝑵 𝟎𝟎. 𝟖𝟖

Schultze & Menzenbach (1960)

𝑬𝑬 = 𝟏𝟏𝟏𝟏. 𝟕𝟕𝟕𝟕 + 𝟎𝟎. 𝟕𝟕𝟕𝟕𝟕𝟕𝟕𝟕

Trofimenkov (1974)

E= 500 log10(N)D’Appolonia (1970)

E= 7.46+0.517N

GRNN3 (r2=90.7%)

3

GRNN4(r2=91.0%)

2.5

GRNN8(r2=90.0%)
GRNN7(r2=90.0%)

2
1.5
1

ESTIMATING qc FROM SPT RESULTS

0.50

Output/Input Variables of ANN Analysis
For estimating q c from SPT results, the SPT-(Nvalues), results of grain size analysis (Fc, D 50 , D 30 ,D 10 ) and effective overburden pressure (δ eff) wereused from the available date. The readings of the
CPT test were filtered to be at the same elevation of
the lab tests and N-value of SPT. A total of 94 data
points were prepared.

N

δeff

D50

D30

D10

Fc

Fig. 10 The weight factors for the correlation
between q c (CPT result) and N (SPT result).
Table 5 The used data for estimation of q c (GRNN7)

Results of Neural Networks
Figure 9 shows 10 different GRNN models
developed (GRNN1 to GRNN10) with 10 different
input combinations and the corresponding r2 (for alldata points) obtained for each Network. GRNN7
with inputs (N, Fc, D 50 , D 30 , D 10 , δ eff ) is the bestmodel to represent the correlation predicting (q c )from SPT results and effective overburden with highvalue of r2 of 90%.
Fig 10 shows the influence factors for every input
for GRNN3, GRNN4, GRNN7, GRNN8 that have r2>90%, however, GRNN7 is considered the strong
correlation between qc (CPT result) and N-value of
SPT result because N value has a high influence
factor of 2.77 and is considered the last factor in the

Index

D50

D30

D10

Fc %

N

MIN. Value
MAX. Value
Mean
Std.
Deviation

0.03
0.3
0.15

0.013
0.14080.10

0.00150.0707
0.06

7.1
65.1
11.83

1
47
12.42

EFFECTIVE
PRESSURE(KN/m2)
21
87
42.58

0.03

0.02

0.01

9.85

8.48

18.25

10

20
30
40
qc Predicted (Mpa)60
qc measured (Mpa)

E predicted (Mpa)

70

50
40
30
20
10
0
0

50

60

Fig. 11 Comparison between predicted and measured q c
for GRNN7.

175

qC
(Mpa)
3.8
53
18.53
12.04

GEOMATE- Osaka, Nov. 16-18, 2015

the correlation for predicting (qc) from SPT results
and results of grain size distribution tests.

Comparison between Neural Networks and a Set
of Traditional Methods

REFERENCES
Table 6 and Fig. 12 show some of thecorrelations used for estimation of q c from SPT
results available in the literature. It is clear from thefigure that the ANN model yields better predictionof qc.
60[1] Robertson, P.K., Guide to Cone Penetration
Testing. Signal Hill: Gregg Drilling & Testing,
2010.
[2] Zervogiannis, C.S. and Kalteziotis, N.A. (1988),“Experience and Relationships from Penetration
Testing in Greece”, International Symposium on
Penetration Testing, ISOPT-1, Orlando, 2,1063-71, Balkema Pub., Rotterdam.
[3] Burt Look, "Handbook of Geotechnical
Investigation and Design Tables." (2007)[4] Cem Ozan July 2003, "Estimation of Grain
Characteristics of Soils by Using Cone
Penetration Test (Cpt) Data." MSc. Thesis, The
Graduate School of Natural and Applied
Sciences of The Middle East Technical
University, Turkey.
[5] Salehzadeh H., Hozouri A., Golestani A.R.,"Correlation between Cone and Standard
Penetration tests." 5th SASTech 2011,
Khavaran Higher-education Institute, Mashhad,
Iran. May 12-14.
[6] Abu Kiefa M.A (1997), “Prediciting The Driven
Pile Capacity on Cohessionless Soil by Neural
Networks. Pro. Of 3rd Int. Geotechnical Eng.
Cof., Cairo University, 5-8 January, Cairo
Egypt, pp.227-240.
[7] Abu Kiefa M.A (1998a), “General Regression
Neural Networks for Driven Piles in
Cohessionless Soil.” Journal of Geotechnicaland Geoenvironmental Engineering, ASCE, Vol.
124, No. 12, Dec., pp. 1177-1185.
[8] D. Singh, M.Zaman and s. Commuri, "Artificial
Neural Network Modeling for Dynamic
Modulus of Hot Mix Asphalt Using Aggregate
Shape Properties", Journal of Geotechnical andGeoenvironmental Engineering, ASCE, Civ.
Eng. 2013.25, Jan. 2013, pp 54-62.
[9] Hasanadehshooiili, H. Lakirouhani, A. andMedzvieckas, J. (2012) "Superiority of
Artificial Neural Networks over Statisticalmethods in prediction of the optimal length ofRock Bolts", V18(5):655-661.
[10] Ceryan, N, Okkan, U, and Kesimal, A. (2012)"Application of Generalized Regression Neural
Networks in Predicting the UnconfinedCompressive Strength of Carbonate Rocks"
Rock Mech Rock Eng, V45: 1055-1072.
[11] Specht, D.F. 1991. "A general regression neuralnetwork." IEEE Trans. Neural Networks, 2(6),568-576.
[12] Goh, A.T.C. (1995a), “Back propagation Neural
Networks for Modeling Complex System,”
Artificial Intelligence in Engineering, Vol.9,
no.3, PP.143-151.

GRNN7

50

qc Predicted (Mpa)

Kulhawy and Mayne(D50), (1990)

40

Kulhawy and Mayne(Fc), (1990)

30
20
10
0

0

10

20
30
40
qc Measured(Mpa)5060Fig. 12 Comparison between actual (measured) andpredicting (q c ) from SPT.
Table 6 The variables correlations for predicting(q c ) from SPT results
Researcher

Kulhawy and Mayne, (1990)

Correlation
𝐪𝐪𝐪𝐪( �𝐩𝐩𝐩𝐩)/𝐍𝐍= 𝟒𝟒. 𝟐𝟐𝟐𝟐 −𝑭𝑭𝒄𝒄
𝟒𝟒𝟒𝟒. 𝟑𝟑

𝐪𝐪𝐪𝐪
�𝐩𝐩𝐩𝐩)/𝐍𝐍 = 𝟓𝟓. 𝟒𝟒𝟒𝟒 𝐃𝐃𝟎𝟎.𝟐𝟐𝟐𝟐
𝟓𝟓𝟓𝟓

CONCLUSION
The paper studied the feasibility and efficiency ofapplying artificial neural networks (ANN) to
predict ϕ (angle of internal friction), E (modulus ofelasticity) and qc (CPT result) from SPT results (Nvalues) which is one of the most commonly usedin-situ tests. A large amount of data forcohesionless soil was used that was collected froma project covering most of UAE. The effect ofdifferent input parameters was investigated andANN results were compared with other available
correlations. The following can be concluded:- The results of the ANN models developed forpredicting ϕ, E and qc from N gave a very good
agreement with actual results compared to some of
the traditional methods available in the literature.
- ANN model (GRNN2) with coefficient ofcorrelation (r2= 97.6%) and inputs (N, δeff) was
the best model to represent the correlation
predicting (tanΦ) from SPT results (N) andeffective overburden stresses (δeff).
- ANN model (GRNN2) with r2= 95.6% and inputs(N, D50) was the best model to represent thecorrelation of predicting (E) from SPT results andmean grain size distribution.
- ANN model (GRNN7) with coefficient ofcorrelation (r2= 90%) and inputs (D10, N, δeff,
D30, FC and D50) was the best model to represent
176

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

COMPRESSIBILITY BEHAVIOR OF SIDOARJO MUD VOLCANO(LUSI)
Luky Handoko1, Noriyuki Yasufuku2, Ryohei Ishikura3 and Ahmad Rifa’i4
Department of Civil Engineering, Universitas Atma Jaya Yogyakarta, Indonesia; 2&3Department of Civil
Engineering, Kyushu University, Japan; 4Department of Civil and Environmental Engineering, Universitas
Gadjah Mada, Indonesia
1

ABSTRACT
Sidoarjo mud volcano (Lusi) is one of the most challenging problems regarding to the compressibility of finegrained soils in Indonesia. Understanding about compressibility behavior is essential to construct appropriate
remediation program for this problems. Consolidation test has been conducted to investigate the compressibility
behavior of Lusi mud volcano. In this study, the laboratory test result is compared with estimated value derived
from some empirical equations. A simple equation can be used to predict e-log σ’ curve very well by using thecorrelation between initial void ratio (e0) and void ratio corresponding to unit pressure (e1); and correlation between
compression index (Cc) and physical properties of soils, such as liquid limit (wL) or natural water content (wn).
This equation predict better than other available equations in literature when compared with laboratory test of Lusi
mud volcano.
Keywords: High Water Content Material, Consolidation, Compression Index, Hydraulic Conductivity

apparatus may increase the disturbance degree of soil
sample. Accuracy of laboratory equipment also plays
important role in order to obtain reliable results. On
the other hand, recently many equations are available
in literature to calculate compression index based on
physical properties. Physical properties of soils canbe determined directly from laboratory test easily andrequire only disturbed samples. This method is
considered to be easier to use compare with the direct
measurement method. Furthermore, this method
requires less cost and time compare with the other one.
This paper discuses compressibility behavior of
Lusi mud volcano. One-dimensional consolidation
test using Oedometer apparatus was conducted inlaboratory. The results of the test were plotted in void
ratio (e) and vertical effective stress (σ’) (in log scale).
Estimation methods from available empiricalequations were conducted as well and the results wereplotted together with the experimental results.
Performance of some empirical equations are
discussed.

INTRODUCTION
One of the most challenging problems regarding
to the compressibility of fine grained soils inIndonesia is the eruption of mud volcano in Sidoarjo
district, East of Java. This mud is known as Lusi. It
has been erupted since May 2006 and still continues.
The fresh erupted material is considered as high watercontent material which collected inside of 12 metersdike. Subsequently, this materials undergo
sedimentation process and self-consolidation process
and settled in about 640 ha area. Large amount of mud
was produced and it becomes serious environmental
issue in the future if not used appropriately.
Appropriate remediation program need to beconstructed to solve this problems and mostly
required the knowledge of the compressibility
behavior of this materials.
Compressibility behavior requires appropriate
consideration when dealing with fine grained soils.
Compression index (Cc) is one of the most importantparameters that used to describe compressibility offine grained soils. Compression index can be derived
from two different ways: direct measurement byconducting laboratory test and calculation fromavailable empirical equations. Oedometer and Rowecell are two famous apparatuses generally used todetermine compression index in laboratory. Since
laboratory test requires undisturbed samples, theaccuracy of this method relies on the quality of thespecimens. Good quality of undisturbed specimens
are difficult to be obtained. Sampling process,transportation process, and adjustment process in the

AVAILABLE COMPRESSIBILITYEQUATIONS IN LITERATURE
Numerous empirical equations to estimate Cc has
been proposed. The compression index of soils has
been tried to be correlated with single variable ofliquid limit (wL) [1,2,3,4,5,6,7,8], plasticity index (IP)[7,9,10,11], shrinkage index (IS) [11], natural watercontent (wn) [5,7,8,12,13,14,15], initial void ratio (e0)[2,7,14,15,16,17,18], initial porosity (n0) [19], dry
density (γd) [15] and multivariable equations
177

GEOMATE- Osaka, Nov. 16-18, 2015

Table 1 Selected empirical equations to determine compression index (Cc)
Empirical equations
Applicability
References
Intrinsic variables (liquid limit (wL))
0.007(wL-10)
Remolded clays
Skempton [1]0.0046(wL-9)
Brazilian clays
Cozzolino [2]0.017(wL-20)
All clays
Shouka [3]0.009(wL-10)
Normally consolidated clays
Terzaghi and Peck [4]0.006(wL-9)
All clays with wL < 100%
Azzouz et al. [5](wL-13)/109
All clays
Mayne [6]0.011(wL-6.36)
Busan clays, Korea
Yoon et al. [7]0.0118(wL-20.7)
Silts and clays, Ireland
McCabe et al. [8]State variables (natural water content (wn))
0.01(wn-5)
0.01 wn
0.01(wn-7.549)
0.0115wn
0.01(wn+2.83)
0.008wn-0.044
0.014(wn-22.7)

All clays
All clays
All clays
Organic silts and clays
Busan clay, Korea
Remolded Iranian soils
Silts and clays, Ireland

combining those previously mentioned indices withother physical properties such as specific gravity(Gs)[5,7,12,15]. Physical properties of soils areclassified into two groups, namely intrinsic properties
and structural properties [15]. The intrinsic properties
are then referred as intrinsic variables and thestructural properties are the state variables [20]. The
intrinsic variables consist of consistency limit (wL, IP
and IS). Those variables are influenced by mineralogyof soil particle and are independent from disturbanceof specimen[15,20]. The state variables consist of wn,
e0, n0 and γd. Those variables are sensitive to
specimen environment disturbance[15,20]. Liquid
limit-based and natural water content-based empiricalequation are reported to give better results comparedthan other indices. Table 1 presents some selected
empirical equations to determine Cc available inliterature.
Previously mentioned literature that propose
estimation of Cc do not predict the void ratio-effective
stress relationship (e-log σ’). They only predict theslope of the e-log σ’ curve. The e-log σ’ curve
generally can be written linearly as in Eq. (1) where
e1 is the void ratio corresponding to effective verticalstress at unit pressure (e when σ’=1 kPa) and Cc is theslope of the curve. Once the e1 can be determined,
many different e-log σ’ equations can be derived fromdifferent Cc equations.

Azzouz et al. [5]Koppula [12]Herrero [13]Bowles [14]Yoon et al. [7]Abbasi et al. [15]McCabe et al. [8]Unit pressure void ratio, e1

432
e1=1.1073e0+0.0696
r2=0.922
1

0
0

1

2

3

Initial void ratio, e0

Fig. 1 Relationship between initial void ratio (e0)and unit pressure void ratio (e1) (data from
Reference [15])obtained. Fig. 1 shows the plotted data of e0 and e1.
The correlation between e0 and e1 is presented in Eq.
(2) and it shows good correlation between e0 and e1,
indicated from high coefficient of determination(r2=0.922). Eq. (2) and one of the equation presented
in Table 1 can be used to draw e-log σ’ based on Eq.
(1).

e1  1.1073e0  0.0696 ...................................... (2)Abbasi et al. [15] proposed Eq. (3) and (4) basedon experimental results to obtain e-log σ’ equation in
Eq. (5).

e  e1  Cc log ' .............................................. (1)Cc  0.461 d  0.883 ................................... (3)Abbasi et al. [15] conducted test on 26 different
Iranian remolded soils. Each specimen was preparedin three different density and subjected to onedimensional consolidation test to obtain Cc. Based on
the results, relationship between e0 and e1 can bee1  1.78 d  3.70 ......................................... (4)e   1.78 d  3.7    0.461 d  0.883 log  ' .
........... (5)178

GEOMATE- Osaka, Nov. 16-18, 2015

Berilgen et al. [21] proposed an empiricalequation to satisfy the A, Z and B constants for e-log
σ’ equation proposed by Liu and Znidarcic [22] that
presented in Eq. (6). A, Z and B are constants that
depended on e0, IP and IS. The equations to determine
those constants are presented in Eq. (7),(8) and (9).

METHODS
Lusi mud volcano was selected to assess thecompressibility equations. Physical properties of Lusi
mud volcano has been published [24]. Lusi mud
volcano contains mostly of fine grained soils(84.47%) and classified as high plasticity silt (MH).
It has Gs = 2.71, wL =58.44%, wP = 30.77%,
wS=22.27% and IP 27.66%. One-dimensional
consolidation test using oedometer apparatus was
conducted to obtain compressibility behavior of lusi
mud volcano. The initial water content is about 82%
and corresponding to initial void ratio 2.22. The e-log
σ’ curve obtained from laboratory test is comparedwith estimated curve from several approaches.

e  A ' Z  ................................................... (6)BA  2.69exp0.008I P ............................... (7)B  1  e0 0.008 lnI P   0.054 ............... (8)Z  1  e0  exp1.97  3.9 lnI L  ............... (9)RESULTS AND DISCUSSIONS

Tripathy and Mishra [23] proposed an e-log σ’equation based on Skempton’s compression index
equation as shown in Eq. (10); where Iv is intrinsic

Eq. (1) is used as basis equation to derive
estimated e-log σ’ curves. The e1 can be obtained
from Eq. (2) and Cc is estimated from wL and wn.
These two indices have been selected to represent
intrinsic variables group and state variables group.
Both indices has been reported to have highcorrelation with Cc compare to other indices within
their group [8,15,20]. Fig. 2 shows the estimated elog σ’ curves in which Cc is derived from liquid limit
and Fig. 3 shows the similar thing but Cc is derived
from natural water content. Based on Eq. (2), unitpressure void ratio, e1, for Lusi mud volcano is
estimated to be 2.53. Both Fig. 2 and Fig. 3 indicatethat most of the estimated e-log σ’ curves might have
similar tangent value but some of them quite far fromlaboratory test results plotted data. It is also indicatedthat e-log σ’ curve from Yoon et al. [7] (Reffered as
Eq. (1)-[7]) gives best prediction among group basedon wL to predict Cc and Abbasi et al. [15] (Reffered as
Eq. (1)-[15]) gives best prediction among groupbased on wn to predict Cc.
Performance of Eq. (1)-[7] and Eq. (1)-[15]equations are then compared with other method togenerate e-log σ’ curves from Abbasi approach (Eq.
approach
(Eq.
(6)),
and
(5)),
Berilgenvoid index, C c* is intrinsic compression index and*is the void ratio corresponding to a verticale100

effective stress of 100 kPa. The equation to calculate*
are presented in Eq. (10) and (11). BothC c* and e100

constants are depended on void ratio at liquid limit(eL). The Iv is depended on vertical effective stress(σ’) as presented on Eq. (13).
*e  I vCc*  e100................................................. (10)Cc*  0.256eL  0.04 ..................................... (11)*e100
 0.016eL3  0.089eL2  0.679eL  0.109......... (12)I v  2.45  1.25 log '0.015log ' ...... (13)

2.5

2.5

2.0

2.0

Void ratio

Void ratio, e

3

1.5
1.0
Lab test
Eq. (1)-[2]
Eq. (1)-[4]
Eq. (1)-[6]Eq. (1)-[8]

0.5
0.0
10

Eq. (1)-[1]
Eq. (1)-[3]
Eq. (1)-[5]Eq. (1)-[7]Lab test
Eq. (1)-[12]Eq. (1)-[14]Eq. (1)-[15]Eq. (1)-[5]Eq. (1)-[13]Eq. (1)-[7]
Eq. (1)-[8]

1.5
1.0
0.5
0.0

100

1000

10

Efective vertical stress, σ' (kPa)

100

1000

Efective vertical stress, σ' (kPa)

Fig. 2 Comparison of estimated e-log σ’ curve
based on Eq. (1) (e1 from Eq. (2) and Cc fromliquid limit-based equation) and oedometer test
results

Fig. 3 Comparison of estimated e-log σ’ curve
based on Eq. (1) (e1 from Eq. (2) and Cc fromnatural water content-based equation) andoedometer test results
179

GEOMATE- Osaka, Nov. 16-18, 2015[4] K. Terzaghi, R.B. Peck, Soil mechanics inengineering practice, 2 ed., John Wiley and Sons,
Inc., New York, 1967.
[5] A.S. Azzouz, R.J. Krizek, R.B. Corotis,"Regression analysis of soil compressibility",
Soils and Foundations, 16 (1976) 19-29.
[6] P.W. Mayne, "Cam-clay prediction of undrained
strength", Journal of Geotechnical Engineering,106 (1980) 1219-1242.
[7] G.L. Yoon, B.T. Kim, S.S. Jeon, "Empiricalcorrelations of compression index for marine
clay from regression analysis", Canadian
Geotechnical Journal, 41 (2004) 1213-1221.
[8] B.A. McCabe, B.B. Sheil, M.M. Long, F.J.
Buggy, E.R. Farrell, "Empirical correlations forthe compression index of irish soft soils",
Proceedings of the Institution of Civil Engineers,
167 (2014) 510-517.
[9] V.A. Nacci, M.C. Wang, K.R. Demars,
Engineering behavior of calcareous soils, Civil
Engineering in the Oceans III, ASCE, Newark,
1975.
[10] A. Nakase, T. Kamei, O. Kusakabe, "Constituteparameters estimated by plasticity index",
Journal of Geotechnical Engineering, 114 (1988)
844-858.
[11] A. Sridharan, H.B. Ngaraj, "Compressibility
behaviour of remoulded, fine-grained soils andcorrelation with index properties", Canandian
Geotechnical Journal, 37 (2000) 712-722.
[12] S.D. Koppula, "Statistical estimation ofcompression index", Geotechnical Testing
Journal, 4 (1981) 68-73.
[13] O.R. Herrero, "Closure of universal compression
index equation", Journal of GeotechnicalEngineering Division, 109 (1983) 755-761.
[14] J.E. Bowles, Physical and geotechnicalproperties of soils, McGraw-Hill Book Company
Inc., New York, 1989.
[15] N. Abbasi, A.A. Javadi, R. Bahramloo,"Prediction of compression behaviour of
normally consolidated fine-grained soils", World
Applied Sciences Journal, 18 (2012) 6-14.
[16] Y. Nishida, "A brief note on compression index
of soil", Journal of Soil Mechanics andFoundation Engineering, 82 (1956) 1-14.
[17] B.K. Hough, Basic soils engineering, 1 ed., The
Ronald Press Company, New York, 1957.
[18] G.B. Sowers, Introductory soil mechanics andfoundation, 3 ed., The Macmillan Company,
London, 1970.
[19] J.H. Park, T. Koumoto, "New compression index
equation", Journal of geotechnical andGeoenvironmental Engineering, 130 (2004) 223226.
[20] C. Lee, S.-J. Hong, D. Kim, W. Lee,
"Assessment of compression index of busan andincheon clays with sedimentation state", Marine

2.5

Void ratio

2.0
1.5
1.0
0.5

Lab test

Eq. (6)

Eq. (10)Eq. (5)Eq. (1)-[15]Eq. (1)-[7]

0.0
10

100

1000

Efective vertical stress, σ' (kPa)

Fig. 4 Comparison of oedometer test results andestimated e-log σ’ curve from different methods
Tripathy&Mesra approach (Eq. (10)). The
comparison results are shown in Fig. 4. This figure
indicates that e-log σ’ curves derived from Eq. (1)shows the best prediction.
CONCLUSIONS
Compressibility behavior of Lusi mud volcano
has been investigated from two different methods.
Direct measurement from laboratory test using
oedometer apparatus has been conducted. The resultof test was compared with estimated e-log σ’ curves.
A simple equation (Eq. (1)) can predict e-log σ’ very
well by using the correlation between initial void ratio(e0) and void ratio corresponding to unit pressure(σ’=1 kPa) (e1) as presented in Eq. (2); and correlation
between compression index (Cc) and physicalproperties of soils, such as liquid limit (wL) or natural
water content (wn). This equation predict better thanother available equations in literature when comparedwith laboratory test of Lusi mud volcano.
ACKNOWLEDGEMENTS
The first author would like to express his gratitude
to Department of Civil Engineering of Universitas
Atma Jaya Yogyakarta for funding support of thispaper. The authors also gratefully for the supports
from colleagues and students to conduct laboratory
tests and fruitful discussions.
REFERENCES[1] A.W. Skempton, "Notes on compressibility ofclays", Quarterly Journal of the GeologicalSociety, 100 (1944) 119-135.
[2] V.M. Cozzolino, Statistical forecasting ofcompression index, The 5th International
Conference on Soil Mechanics and Foundation
Engineering, Paris, 1961.
[3] H. Shouka, Relationship of compression index
and liquid limit of alluvial clay, 19th Japan Civil
Engineering Conference, Tohoku, 1964, pp.
40.41-40.42.
180GEOMATE- Osaka, Nov. 16-18, 2015[23] S. Tripathy, A.K. Mishra, "On the use of
skempton's compression index equation",
Geotechnical and Geological Engineering, 29(2011) 129-135.
[24] L. Handoko, A. Rifa'i, N. Yasufuku, R. Ishikura,Physical properties and mineral content of
sidoarjo mud volcano, The 5th International
Conference of Euro Asia Civil Engineering
Forum (EACEF-5), Surabaya, 2015.

Georesources and Geotechnology, 33 (2012) 2332.
[21] S.A. Berilgen, M.M. Berilgen, I.K. Ozaydin,"Compression and permeability relationships inhigh water content clays", Applied Clay Science,
31 (2006) 249-261.
[22] J.C. Liu, D. Znidarcic, "Modeling onedimensional compression characteristics ofsoils", Journal of Geotechnical Engineering, 117(1991) 162-169.

181

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

AN EXACT FINITE ELEMENT MODEL FOR AXIALLY LOADEDPILE IN ELASTO-PLASTIC SOIL
C. Buachart1, C. Hansapinyo2, J. Tanun3, N. Tanakhum4, and W. Sommanawat5
1,2,3,4
Faculty of Engineering, Chiang-Mai University, Thailand,
5
Ubon Ratchathani Rajabhat University, Thailand

ABSTRACTA displacement based finite element method for analyzing axially loaded pile embedded in finite depth ofelasto-plastic soil is presented. The investigation herein is conducted on the condition of shape function by which
exact value may be reproduced at the nodal point regarding to a few number of element. The examined shapefunctions which satisfy the homogeneous governing equations in elastic and plastic soil are introduced to obtainthe so-celled exact element stiffness matrix via total potential energy principle. Numerical examples of elastostatic pile embedded in elasto-plastic Winkler foundation illustrates the accuracy of proposed element comparewith conventional finite element shape functions. Axial force and displacement solutions show very good
agreement with data from the available literature. Then the proposed shape functions are also used to conduct
free vibration analysis of axially loaded pile embedded in elastic soil. The results from finite element modal
analysis show fairly accurate compare with analytical solutions.
Keywords: Axially loaded pile, Displacement method, Finite element, Shape function, Soil-pile interaction

force vector are expressed based on total potential
energyprinciple. Example of elasto-static pile embedded inelasto-plastic soil is solved to verify the accuracy
and efficiency of proposed bar element. Freevibration of a pile embedded in elastic soil
foundation using the consistent mass matrix for thispile element is also investigated.

INTRODUCTION
Accuracy of a finite element solution depends
significantly on the extent to which the assumed
displacement pattern is able to reproduce the actual
deformation of the structure. For a one-dimensional
problem with single variable, Tong [1] proved thatthe finite element nodal solution can be obtained
exactly if the assumed shape functions satisfy thehomogeneous differential equation of the problem.
Kanok-Nukulchai et al. [2] extended Tong’s concept
to one-dimensional problem with several dependent
variables, i.e., authors developed an exact two-node
deep beam element. Ma [3] also applied concept of
nodal exact shape function to solved axial vibration
problems of elastic bars, exact solution was obtained
for undamped harmonic response analysis. Forcebased formulation of pile-embedded in elastic soil
was proposed in Reference [4] and obtained theexact solution for problem taken from Li et al. [5].
The displacement-based formulation was also
proposed in Reference [6], the exact nodaldisplacements and forces was obtained for elastic
soil.
In this study, the nodal exact shape function
concept suggested by Tong [1] and Buchart [6] isemployed to solve the elastic bar embedded inelasto-plastic soil problem. Two sets of shapefunctions which satisfies the homogeneous
differential equation will be derived for developing apresent bar element. The stiffness matrix and nodal

MATHEMATICAL FORMULATION
Problem Definition
The analysis considers a single circular pile, withdiameter d (cross section A = 0.25πd2 and perimeter
U = πd), embedded into soil deposit (Fig. 1). The
pile has a total length L and is subjected to an axial
force P 0 at the pile head which is flush with theground surface. The soil medium is assumed to beelastio-plastic, isotropic and homogeneous, withelastic properties described by equivalent springcoefficient k s . Once the soil displacement go beyond
the yielding displacement w * , the shear resistancewill keeps constant as long as the displacement
increases. The soil bearing capacity at pile’s end ispresented by coefficient k b . The pile is assumed tobehave as an elastic column with Young’s modulus
E. The Poisson’s ratio of the pile material isneglected. Figure 1(a) shows the elastic and plastic
zone occurred in soil due to the axial displacement
w 0 at top pile head is greater than yielding
displacement w * occurred at depth z 0 from pile head.
R

R

R

R

R

R

R

R

R

R

182

R

R

R

R

GEOMATE- Osaka, Nov. 16-18, 2015

Hence, the length of an elastic portion in Fig. 1 isdenoted by ℓ, the magnitude of axial displacement inelastic portion is less than or equal to yielding
displacement.
Governing Differential Equations

the sum of internal potential energy (strain energy)
and the external potential energy due to externalload as follow:=
ΠConsider one-dimensional element in Fig. 2, thetotal potential energy of this soil-pile element
subjected to the axial forces P 1 and P 2 is defined as

P0

1

2
L
 dw  dz + k U w2 dz s∫  dz 
∫ 
0
0

L

 EA 

2

(1)

− Pw
− P2 w2
1 1

where w(z) is the vertical pile displacement at depthz. The first and second terms in Eq. (1) represent thestrain energy in pile and surrounding soil,
respectively.

W0
plasticzone

Z0
●

ℓ

elasticzonekb(a)Fig. 2

Axially loaded pile embedded in soil andcorresponded two-node finite element
model.

stress

The first variation of Eq. (1) leads to
L

δΠ EA
=

fs

∫
0

( )( )
dδ w

dw

dz

dz

dz + k sUL∫ ( δ w )wdz
0(2)

− P1δ w1 − P2δ w2

ks

Applying the appropriate Gauss-Green theorem
to Eq. (2) and setting δП = 0, gives the differential
equation for equilibrium

1

2

W*

− EA

displacement

dz

2

+ k=
Uw 0 for 0 < z < Lsand a set of natural boundary conditions as follows(b)Fig. 1d w

Axially loaded pile and soil model
183

(3)

GEOMATE- Osaka, Nov. 16-18, 2015

dw
dw
P1 =
− EA
and P2 =EA
dz z 0=dz z L=(4)
Shape Functions for An Exact Pile Element

N1 ( z ) =N2 ( z ) =Following the concept presented by [1] and [2], apile element is developed with the shape functions
that satisfy the homogeneous differential equation,
Eq. (3). Two groups of shape functions are derived
following elastic and plastic soil conditions.

(z − z)
0

(10)

z0
z

(11)

z0

Derivation of Element Stiffness Matrices
Refer to first variation of strain energy terms in
Eq. (2), due to the arbitrariness of δw (see also [7]),the element stiffness matrix for pile and soil can be
expressed as follows:

Elastic soil conditions

 dN i   dN j dzdz   dz 0 LK ij = EA∫ 
pile

The field variable w, which satisfy Eq. (3), canbe represented by the following hyperbolic function

(12)

L

=
w ( z ) c1 cosh (α z ) + c2 sinh (α z )∫(5) K ij = k sU N i N j dz
soil

(13)

0

where α2 = k s U /EA is the characteristic parameter ofpile. By applying the nodal displacement boundary
conditions to elastic soil portion (Fig. 1), Eq. (5) can
now be expressed in terms of nodal variables, w 1
and w 2 , as follows
w ( z ) w1 N 1 ( z ) + w2 N 2 ( z )=where the indices i and j are ranged from 1 to 2.
Element stiffness for an elastic soil portion
Substituting shape functions from Eq. (7) and (8)into element stiffness formulation in Eq. (12) and(13), the component of element stiffness matrices forelastic soil portion can be explicitly expressed as(6)

where the shape functions can be expressed as

N1 ( z ) =N2 ( z ) =sinh [α (  − z )]sinh βsinh (α z )sinh β

K=
K=
11
22
pile

(7)
pile

K12

2

( β csch β + coth β )
2

(14)

α EA
=
−
K 21 =
csch β (1 + β coth β )
2
pile

and

( coth β − β csch β )
2α
kU
soil
soil
− s csch β (1 − β coth β )K12 =K 21 =
2α
K=
K=
11
22
soil

Plastic soil conditions
Consider the plastic soil portion, upper portion in
Fig. 1, displacement w in second term of equilibrium
equation (3) have to be replaced by yielding
displacement w * as follow

soil

k sU

2

(15)

Element stiffness for plastic soil portion
Substituting shape functions from Eq. (10) and (11)into element stiffness formulation in Eq. (12) and(13), the component of element stiffness matrices forplastic soil portion can be explicitly expressed as

2

d w
dz

α EA

(8)

with the non-dimensional parameter β = α  .

− EA

pile

Uw* 0 for 0 < z < z0
+ k=
s
2

(9)
The field variable w, which satisfy homogeneous
differential equation part of Eq. (9), can be
represented by linear function of soil depth. Hence,
displacement shape function of plastic portion canbe expressed as a ramp function used in typicallinear FEM, i.e.

EA
pile
pile
pile
pile
K11 =
K 22 =− K12 =
− K 21 =
z0

(16)
and

184

GEOMATE- Osaka, Nov. 16-18, 2015

=
K11 =
K 22
2=
K12
2=
K 21
soil

soil

soil

soil

k sUz0element are identical to the exact analytical solutions
in reference [8]. The conventional linear element
behaves stiffer than proposed element and exact
solutions, greater pile head load is required to obtainyield displacement at pile head.

(17)

3

Consistent Mass Matrix
In free vibration analysis, the consistent mass
matrix has to be constructed. Formula of consistentmass matrix is similar with soil stiffness matrix in
Eq. (15) or (17), except the factor k s U replaced by
ρA, i.e.

Table 1 Calculated loads and settlement of the pileat any values of plastic depth.

L∫

M ij = ρ A N i N j dz

z0
(m)
0
9
18
27
36
45

(18)

0

where ρ = mass density of pile material. An explicit
expression of this consistent mass matrix will beomitted for the sake of simplicity and clarity of thepresentation.

Present (Exact)
P 0 (kN)
w0
(mm)
2086
2.60
2951
3.91
3796
5.64
4593
7.71
5291
9.95
5807
11.98

Linear FEM
P 0 (kN)
w0
(mm)
2451
2.60
3192
4.03
3931
5.78
4648
7.80
5302
9.97
5807
11.98

NUMERICAL EXAMPLESFree Vibration Analysis
In this section, two numerical examples arepresented to illustrate the effectiveness of the finite
element proposed in the previous section.
Application of this pile element to the free vibration
analysis is also demonstrated by the second example.
Analytical solutions of all problem tests areavailable in the literatures [6, 8, 9, and 10].

Natural frequencies and mode shapes of a fixedended pile is computed with ten pile elements. A
bored pile was installed in the medium clay of length
50 m and diameter d = 1 m. Equivalent soil elastic
coefficient k s = 34200 kPa/m. The end bearing
stiffness is very large (fixed at the bottom). The unit
mass is of pile shaft is taken as ρ = 2400 kg/m3. To
perform modal analysis, element stiffness andconsistent mass matrices of proposed element andlinear conventional element are constructed withassumed elastic soil condition. Result of the natural
frequencies, compared with the exact theory [10] isshown in Table 2. Percent error of natural
frequencies, which are obtained from conventional
linear element are slightly better than present
element. The first three mode shapes of proposed
element are also plotted in Fig. 3 and appear to bealmost indifferent from the exact ones.

Static Analysis
A bored pile was installed in the medium silt
clay and the end bearing layer is sandstone. The pilelength is 45 m, and the diameter d = 1 m. The elastic
modulus of pile shaft E = 2.2 × 107 kPa. From soil
tests, the values of equivalent soil elastic coefficientk s = 12000 kPa/m, the yielding displacement of soilw * = 2.6 mm, and end bearing stiffness k b = 684000
kPa/m [8]. The value k b A is added into the last
diagonal member of stiffness matrix.
The numerical test was performed using twoelement assembly to construct three algebraic
equations. Assuming the value of plastic depth z 0 ,the displacement at bottom end was solved fromthird row of algebraic equation. Then, thedisplacement at pile head w 0 was obtained fromsecond row of algebraic equation, and the value ofload P 0 at pile head was computed. Two cases were
run to compare the results: (i) proposed stiffness,
Eqs. (14) and (15) were used to constructed element
stiffness of bottom part, and Eqs. (16) and (17) for
upper part, and (ii) two conventional linear elements[7] were used to constructed the global stiffness
matrix.
The results from two cases are shown in Table 1.
As expect, the present pile element model prove to
be flawless: finite element solutions of proposed

Table 2 Circular frequency of fixed-ended pile
Circular Frequency (rad/sec)

185

Mode

Ten piles(Present)

Ten piles(Linear)

Exact
Theory

1
2
3
4
5
6
7
8

153
313
504
712
938
1184
1446
1711

153
312
502
710
935
1181
1443
1708

153
309
490
676
864
1053
1242
1432

GEOMATE- Osaka, Nov. 16-18, 2015

910

Fig. 3

1943
2084

1940
2082

behavior of axially loaded piles”, KSCE J. Civ.
Eng., Vol. 16(3), 2012, pp. 325-333.
[6] Buachart C, Hansapinyo C, and Sommanawat
W, “An exact finite element model for axiallyloaded pile”, in Proc. 1st Int. Conf. on ACESD,
2014, pp. 483-486.
[7] Zienkiewicz OC and Taylor RL, The Finite
Element Method. Vol. I. Oxford: ButterworthHeinemann, 2000.
[8] Chou YC and Hsiung YM, “A normalized
equation of axially loaded piles in elasto-plasticsoil”, J. Geo Eng., Vol. 4(1), 2009, pp. 1-7.
[9] Timoshenko SP, Young Zina D H, and Weaver
WL, Vibration Problems in Engineering, USA:John Wiley & Sons, 1974.
[10] Wu WB, Wang KH, Ma SJ, and Chin JL,“Longitudinal dynamic response of pile inlayered soil based on virtual soil pile model”, J.
Cent. South Univ. T., Vol. 19, 2012. pp. 19992007.

1621
1811

Fixed tip pile problem: the first three modeshapes from the free vibration analysis of aten-element model.

APPENDIX
Exact Solution of Pile Static Load Test

CONCLUSIONS

Prescribe the boundary conditions (4) into the
The new finite element model for pile subjected
solution of governing equations (3) and (9). The load
to axial load is proposed. A necessary condition for
applied to the pile top could be expressed as
the present finite element model to reproduce theexact values at the nodal points is that it has =to(19)
P0 w* [α EA tanh (α  + γ ) + z0 k sU ]satisfy the homogeneous differential equation of the
problem. Numerical example for static load pileand the displacement at the pile top is therefore
embedded in elasto-plastic soil indicates that anexact finite element solution can be obtained even2with minimum number of element used. In addition,
z0 k sU 
1 
w
=
w
+
w
tanh
α

+
γ
+
(
)
the same shape functions can produce fairy0**
α EA 
2 satisfactory results in free vibration problem on three(20)
fundamental modes.
12− w* tanh (α  + γ )2
REFERENCES
The variable γ is the characteristic value of the end
bearing capacity of soil at pile tip.

[1] Tong P, “Exact solutions of certain problems byfinite element method”, AIAA J., Vol. 7, 1969,
pp. 178-180.
[2] Kanok-Nukulchai W, Dayawansa PH, andKarasudhi P, “An exact finite element model fordeep beams”, Int. J. of Struc, Vol. 1, Jan. 1981,
pp. 1-7.
[3] Ma H, “Exact solutions of axial vibration
problems of elastic bars”, Int. J. Numer. Meth.
Eng., Vol. 75, 2008, pp. 241-252.
[4] Sae-Long W, Limkatanyu S, Prechasaree W,
Damrongwiriyanupap N, and Kuntiyawichai K,“Natural stiffness matrix for bar with lateralinterfaces: exact force-based derivation”, in
Proc. 18th Conf. on NCCE, 2013, pp. 110-116.
[5] Li JP, Tan YW and Liang FY, “A modified
analysis method for the nonlinear load transfer

tanh ( γ ) =

kb

αE

(21)

In derivation of Eqs. (19) and (20), the soil of end
bearing capacity is assumed in the elastic condition.
Detail derivation of Eqs. (19)–(21) are described inreference [8].
Free Vibration of Fixed-Ended Pile
The governing equation for free vibration of pile
is expressed as

186

GEOMATE- Osaka, Nov. 16-18, 2015

∂ w
∂ w
=
+ k sUw ρ A 2 , 0 < z < L
2
∂z
∂t
2

− EA

2

(22)

Assume that pile subjected to fixed boundary
condition at the pile tip w = 0 at z = L. Natural
circular frequency ω and mode shape ѱ of pileembedded in soil can be expressed as=
ψ n cos=( λn z ) ; n 1, 2, 3,...

(23)

and

( λ + α ) E ; n 1, 2, 3,...
=
=
ω
2

2

n

n

ρ

(24)

where the wave number parameters λ n are defined asn− 1 π ; n =1, 2, 3,...
λn = 2 L(25)

Note that Eq. (23) always satisfies boundary
condition at pile tip, i.e. ѱ n (L) always equal to zero.

187

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

LIQUEFACTION ASSESSMENT BY MICROTREMOR
MEASUREMENTS IN BABOL CITY

1,2

Asskar Janalizadeh Choobbasti1, Sadegh Rezaei2 and Mehran Naghizadeh3
Civil Engineering, Babol university of technology, Iran; 3 Civil Engineering, IAU Zanjan branch, Iran

ABSTRACTThe recent researchers have discovered microtremor applications for evaluation of the liquefaction potential.
Microtremor measurement is a fast, applicable and cost-effective method with extensive applications. In thepresent research the liquefaction potential has been reviewed by utilization of microtremor measurement resultsin Babol city. By using the fundamental frequency and amplification factor, the value of vulnerability index (K g )was calculated and the liquefaction potential has been evaluated. For controlling the accuracy of this method, its
output has compared with the results of Seed and Idriss method in 30 excavated boreholes within the study area.
These two methods show an acceptable conformity with each other. Also, the artificial neural network (ANN)with different inputs was trained. Regarding the results of the three methods, we can conclude the thresholdvalue of liquefaction potential is K g =5. By reviewing and comparing these three methods, it will be observedthat microtremors have the capability of assessing the liquefaction potential with desirable accuracy.
Keywords: Liquefaction, Microtremor, Vulnerability index, Artificial neural networks, Microzonation

critical buildings and infrastructures and it is built
upon the recent, loose, and therefore, generally
liquefaction-prone sediments of the Babolrood River.
With rwgards Nakamura’s method, H/V spectral
ratios, fundamental frequency and amplification
factor and finally vulnerability index (K g ) were
calculated for all microtremor stations.
There are geotechnical boreholes near 30
microtremor recording stations with suitable data.
The Seed and Idriss method is used to assess theliquefaction potential [5].
The goal of the aim activities includes theassessment of liquefaction by a new, fast andapplicable method in other stations and preparationof Babol city’s liquefaction map.

INTRODUCTION
Earthquake is one of the most devastating natural
disasters which has always threatens human lives
and properties. The most important effects caused byan earthquake are the liquefaction phenomena.
Investigations of failures of soil masses are subjects
touching both geology and engineering.
On the basis of both the field and the laboratory
types of observations of soil behavior attempts weremade to propose methods to evaluate theliquefaction potential of a particular soil. In theliterature, several simplified methods can be foundto assess nonlinear liquefaction potential of soil.
Derived from several field and laboratory tests,
various procedures, also named as conventional
methods, have been developed by utilizing casestudies and undisturbed soil samples [1].
Recently, the H/V technique [2] is becoming
more popular with its data collection facilities andapplication allowance in all areas. By employing the
H/V ratio, we were able to determine thepredominant frequency (F p ) and the amplification
factor (A p ) of the site. Nakamura [3] also proposed
the vulnerability index “K g value” as a means todetermine the extent of liquefaction. The present
study mainly aims to show how the microtremor
measurements can play a signiﬁcant role in aliquefaction assessment [4].
Microtremor measurement was undertaken at 60
stations in the Babol, north of Iran, during 2011 and2012. The study was conducted in Babol city for one
important reason: the city is densely populated with

METHODOLGY
Ambient seismic noise or microtremors arefeeble ground motions with displacement amplitudes
of about 0.1–1 μm and that can be detected byseismograph with high magnification [6]. Recently,
to assess potential hazards associated with groundfailure or liquefaction, the vulnerability index (or K gvalue) that is derived from microtremor data may beevaluated. Among the various approaches tomicrotremor study, the H/V spectral ratio techniqueintroduced by Nakamura (1989) was chosen for thisinvestigation due to ease of application. This
technique has been described in a number of papers.
By employing the H/V ratio, we were able todetermine the predominant frequency (F p ) and theamplification factor (A p ) of the site. Nakamura also
188

GEOMATE- Brisbane, Nov. 16-18, 2014

proposed the vulnerability index “K g value” as ameans to determine the extent of liquefaction [3].
For this purpose, he compared the results ofvulnerability index and the damage points in the
Kobe earthquake and concluded that K g is a suitableindex for determining the site vulnerable points,
such that each point with higher K g value is moredamage potential. The K g value is simply derived
from strain of ground structures. It can be defined
as:

Kg =

A p2
Fp

(1)

After Nakamura some scientist have usedmicrotremor to liquefaction evaluation [7]-[9].

Fig. 2Fundamental frequency

Fig. 3

Amplification factor

By using the results of figures 2 and 3, the K gvalues are calculated for different stations. These
results are indicated in fig. 4. By reviewing thescientific literature, it has been concluded that thehigher K g value, the more liquefaction potential willbe. In fact K g value has a qualitative meaning, forinstance the possibility of liquefaction occurrence ismore in recording station 35 than recording station
25.
In the next sections, firstly, the possibility ofliquefaction occurrence will be evaluated by using
the data of 30 geotechnical boreholes and the Seedand Idriss (1985) method. Secondly, the ANN withdifferent inputs including type of soil, total stress,
effective stress and corrected SPT blow count wastrained. Subsequently, by comparing the stations inwhich liquefaction has occurred (using Seed andIdriss (1985) and ANN) and the values obtained
from microtermor measurement, it was possible toobtain a precise value for K g which is the thresholdvalue for liquefaction occurrence.

DATA COLLECTION AND RESULTS
Microtemor measurements were carried out at 60
stations in Babol during the period 2011 and 2012.
Fig. 1 shows the H/V spectral ratio for somestations. According to the analysis, the fundamental
frequency (F p ) varies from 0.69 to 12.60 Hz within
the study area. Also, the amplification factor (A p )varies from 1.45 to 4 within the study area. Fig. 2and fig. 3 show the fundamental frequency andamplification factor in the study area, respectively.
As it can be observed in the fig. 2 and fig. 3 for ten
recording stations, fundamental frequency andamplification factor have not presented. Since thesestations have been surrounded by the factories andhighways in the city, accurate microtremor recording
was impossible all day long [10], [11].

ASSESSMENT AND COMPARISON OF
LIQUEFACTION POTENTIAL BY USINGCONVENTIONAL METHOD

Fig. 1

Simpliﬁed procedures, originally proposed by
Seed and Idriss [5], using the standard penetration
test (SPT), are frequently used to evaluate theliquefaction potential of soils. This procedure has
been revised and updated since its original
development [12]. The method was developed fromﬁeld liquefaction performance cases at sites that had
been characterized with in situ standard penetration
tests. Using a deterministic method, liquefaction ofsoil is predicted to occur if the factor of safety (FS)which is the ratio of the cyclic resistance ratio(CRR) over cyclic stress ratio (CSR), is less than orequal to one. No soil liquefaction is predicted if FS >1 [12].

H/V spectral ratio

By using the 30 geotechnical boreholes in theregion, we can assess the liquefaction potential in
Babol city. The results of this assessment in 4
boreholes have been shown in fig. 5. In these 30
boreholes we compare the two methods of189

GEOMATE- Brisbane, Nov. 16-18, 2014

conventional and microtremor and by analyzing theresults it is possible to obtain a precise value for K g
which is the threshold value for liquefaction
occurrence. Table 1 shows the depths of evaluatedliquefaction using conventional method against theK g value. By reviewing table 1, it is observed that in
all the stations with K g values higher than 5,liquefaction phenomena occurred. Consequently thevalue of K g =5 can be considered as a thresholdvalue for these 30 stations, in that for all the stationsof this region with K g value higher than 5, theoccurrence of liquefaction phenomena is possible
and for stations with lesser values, this possibility
does not exist.

Fig. 4

conventional and ANN method
In the next section, we assess the potential ofliquefaction using ANN method and the results will
be compared with the results of microtremor
measurement to be able to prepare the liquefaction
microzonation map of Babol city.
Table 1 The liquefaction depths versus K g value
Station
B01
B02
B04
B05
B07
B09
B11
B12
B15
B18
B19
B22
B23
B25
B27
B28
B30
B32
B35
B37
B39
B42
B43
B44
B45
B50
B52
B56
B57
B58

map of K g distribution in Babol city

Liquefaction depths by using
Seed & Idriss method (m)2-4.5 & 5.5-8 & 8.5-10.5
5-10
5.5-7
5-6.5 & 8-9.5
6-7 & 9-10
8.5-10.5
3-5 & 5.5-8 & 8.5-10.5
5.5-8
3-6 & 6.5-10.5
6.5-9.5
5-12
4.5-7 & 8-10
7-8
4-6.5
4-6 & 7-8 & 8.5-9.5
3.5-5 & 6.5-10.5
6.5-11-

K g Value
7.51
7.01
2.53
5.21
4.80
4.63
1.53
6.15
4.63
1.10
5.93
3.65
5.42
10
4.12
5.51
1.25
4.74
14.6
6.61
4.7
8.14
7.19
5.2
5.17
6.79
11.57
8.34
1.92
1.16

ASSESSMENT AND COMPARISON OF
LIQUEFACTION POTENTIAL BY USING
ANN

Fig. 5Today, the application of artificial neuralnetworks in the engineering world is well known toengineering sciences [13]. Considering that ANN
can assess liquefaction potential in this precise
manner, we will assess the liquefaction phenomenaby this method in the 20 remaining stations and theresults will be compared with the microtremor

Evaluation of factor of safety using
190

GEOMATE- Brisbane, Nov. 16-18, 2014

measurement result.
The input–output data pairs used in the present
work consist of four input variables, including soil
type, total stress, effective stress, corrected SPT
blow count and one output factor of safety. Before
using the training procedure the data werenormalized to their mean value and standard
deviation 1. A training set of 23 out of 30 input–
output data pairs is used to train the MLP-type
neural network with only one hidden layer based on
BP algorithm. At the end of the training process, it isnecessary to evaluate the capability of ANN modelin prediction of liquefaction potential. The
remaining 7 data pairs are used to test the network
performance. Since there are four input variables the
network has four neurons in the input layer and one
neuron in the output layer. Hence there is no specificmethod to determine the number of neurons in thehidden layer, Trial and error was used. Fig. 6 depicts
the effect of different neural network architecture(different number of neurons in the hidden layer) on
the RMS error of the network obtained from thenormalized data. As can be seen a network with 15
neurons in the hidden layer has an acceptableperformance.

conformity with each other. Only in 4 stations of
B32, B36, B44 and B45, the results are different.
The K g value in these stations is close to 5 which isconsidered the threshold value for liquefaction
phenomena. The value of K g at these stations
changes with a minor change in the fundamental
frequency and amplification factor. Also, we used
the ANN for comparing the two methods whichhave a small error in prediction. For this reason, thementioned minor difference is not taken into
consideration and the value of K g =5 is introduced asa threshold value, such that soils with higher K gvalues will have liquefaction potential.
From these results, it is reasonable to conclude
that K g is clearly a value which corresponds to thesite and can be considered as vulnerability index ofthat site, an indicator which might be useful inselecting weak points of ground especially inliquefied areas.

Fig. 6 Effects of the number of hidden neurons on
the network performance
The excellent behavior of the MLP-type neuralnetwork is also shown in Fig. 5. The figure reveals
that the trained network is able to model and predict
the outputs successfully. It is evident from test data
sets that the experimental ANN can be applied
successfully to predict liquefaction potential.
Fig.7 shows the liquefaction microzonation byusing microtremor measurements and fig.8 indicatesthe liquefaction microzonation by the ANN. In these
two figures, the areas shown in red include the zones
with liquefaction potential. By comparing the two
methods, it is concluded that they have very goodFig. 7
Liquefaction microzonation of Babol bymicrotremor measurement
CONCLUSION
In the present research, the microtremor
measurements were used in 60 stations in order toevaluate the liquefaction potential.
191

GEOMATE- Brisbane, Nov. 16-18, 2014

REFERENCES

Fig. 8ANN[1] Saygili G, “Liquefaction potential assessment insoil deposits using artificial neural network”, M.Sc.
Thesis, Concordia University, 2005.
[2] Nakamura Y, “A method for dynamic
characteristics estimation of subsurface using
microtremor on the ground surface”. Quarterly
Report of RTRI, Vol. 30, 1989, pp. 25–33.
[3] Nakamura Y, “Real-time information systems forhazard mitigation”, 10th World Conference inEarthquake Engineering, Spain, Madrid, 1996.
[4] Huang HC, Tseng YS, “Characteristics of soilliquefaction using H/V of microtremors in Yuan-Linarea, Taiwan”, TAO, Vol. 13(3), 2002, pp. 325–338.
[5] Seed HB, Idriss IM, “Simpliﬁed procedure for
evaluating soil liquefaction potential”, J Soil MechFound Div SM, Vol. 9, 1971, pp.1249–1273.
[6] Dikmen U, Mizaoglu M, “The sesimic
microzonation map of Yenisehir-Bursa, NW of
Turket by means of ambient noise measurements”, JBalkan Geoph Soc, Vol. 8, 2005, pp.53–62.
[7] Rezaei S, Choobbasti AJ, “Liquefaction
assessment using microtremor measurement,
conventional method and artificial neural network(Case study: Babol, Iran)”, Frontiers of structuraland civil engineering, 2014, DOI 10.1007/s11709014-0256-8[8] Uehan F, Nakamura Y, “Ground motion
characteristics around Kobe City detected by
microtremor measurement”,11th World Conferenceon Earthquake Engineering, Acapulco, Mexico,1996.
[9] Saita J, Nakamura Y, Sato T, “Liquefaction
caused by the 2011 off the Pacific coast of Tohoku
earthquake and the result of the prior microtremor
measurement”, 9th International Conference on
urban earthquake engineering, Tokyo, Japan, 2012.
[10] Choobbasti AJ, Rezaei S, Farrokhzad F,“Evaluation of site response characteristics using
microtremors”, Gradevinar, 65, 2013, pp.731-741.
[11] Choobbasti AJ, Rezaei S, Farrokhzad F,
Heidarzadeh Azar P, “Evaluation of site response
characteristics using nonlinear method (Case study:
Babol, Iran)”, Frontiers of structural and civilengineering, 2014, DOI 10.1007/s11709-014-02314[12] Seed HB, Tokimatsu K, Harder LF, Chung RM,“Inﬂuence of SPT procedures in soil liquefaction
resistance evaluations”, J Geotech Eng ,Vol. (12),1985, 1425–1445.
[13] Rezaei S, Choobbasti AJ, Soleimani Kutanaei S,“Site effect assessment using microtremor
measurement, equivalent linear method and artificial
neural network (Case study: Babol, Iran)”, Arabian
Journalof
Geosciences,
2013,
DOI:
10.1007/s12517-013-1201-1

Liquefaction microzonation of Babol by

By using the fundamental frequency andamplification factor, the value of vulnerability index(K g ) was calculated. To obtain a precise value forK g which is the threshold value such that at higher
values, the liquefaction potential exists, themicrotremor results have been compared with theconventional method results. By comparing theseresults, it is concluded that in Babol city, the stations
with K g values higher than 5, have the liquefaction
potential. Subsequently, in order to assess theliquefaction potential, the ANN was utilized in theremaining stations. By comparing the ANN andmicrotremor results, it was observed that the results
are in good conformity with each other and except
for 4 stations, the liquefaction phenomena has
occurred in all those other stations where their K gvalues were higher than 5. With regards the resultsof the three methods, we can conclude the thresholdvalue of liquefaction potential is K g =5.
Furtheremore, we prepared two maps forliquefaction microzonation by using the microtremorand ANN results. Finally, it can be stated thatmicrotremor measurements are considered suitable
and complementary method among conventional
methods for reviewing the liquefaction potential
which is fast, applicable and cost-effective.

192

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

STUDIES ON CYCLIC LOADING OF RECYCLED CONCRETE
AGGREGATE WITH ADDITION OF RUBBER CHIPS

1

Wojciech Sas, Andrzej Głuchowski Alojzy Szymański
Facultyof Civil and Environmental Engineering, Warsaw University of Life Sciences, Poland

ABSTRACTIn this paper, tests on the reclaimed waste, Recycled Concrete Aggregate (RCA) was presented. Reclaimed
concrete is still a problem which were solved usually by storing concrete debris on landfills. One of ideas tosolve this issue in another manner was the introduction of recycled materials to pavement engineering.
Geotechnical study was undertaken to obtain physical and mechanical properties of RCA improved with rubber
grains. Such mix, could behave more elastic strain development during load cycles, typical for traffic.
For better understanding of its exceptional behavior under repeated loading, cyclic CBR test was conductedin various stages of loading. For the purpose of bearing capacity analysis, uniaxial test was also conducted.
While the cyclic loading and a mechanical stabilization are a cause of aggregates crushing, created anotherparticle size distribution, could not fulfill quality requirements. Determination of resilient modulus M r fromrepeated loading test and development of strains during the tests was also presented. The paper also includes anproposition of possible guideline of RCA mix as a material in pavement subbase construction.
Keywords: Cyclic loading, cCBR, rubber chips, recycled concrete aggregate

anthropogenic material which is a product fromdemolition of exploited constructions. Conducted
researches on RCA recycling has lead to form many
possible ways of application in bound and unboundform. Important fact is that for sustainable
development of resources, non-renewable resources
as sand, gravel etc. should be replaced by renewable
resources as RCA [4]. Application of renewablematerials can reduced green-house gas emission
simply by avoiding of mining process [5].
Cyclic loading is phenomena which can beobserved in many constructions. Characterization of
the material subjected to repeated loading is desired
mostly by road engineers and foundation engineerswhen industrial settlement is constructed. New areaof utilization of this problem is wind plants
foundation engineering where wind caused
movement of the center of gravity and creates
cyclically changed stresses over subgrade soils [6]On order to understand cyclic loading
phenomena for each material proper tests must be
conducted. Most common method to obtain suchdata is to perform cyclic triaxial tests. this tests
unfortunately are expensive and not widespread
especially in road laboratories.
In previous article [7] authors presents a new
method of dealing with cyclic loading phenomena.
The cCBR method was presented as a equivalent ofstandard cyclic triaxial tests. The cCBR method
have many advantages, it is low cost due tocommon standard of CBR tests, no new equipment
is needed. Procedure of the cCBR test is based on
the CBR method. Experienced laboratory crew can

INTRODUCTION
Developing of car industry results in increase ofcars. One of wastes which are produced by the timeof car exploitation and after vehicle utilization isrubber tires. Among many geo-engineering methodsof recycling including rubber retaining walls, socalled rubber chips and tire shreds are one ofpossible ways of renew this source [1]. Intensive
studies to find possible application when rubber
chips (RC) is mixed with natural aggregates has
been conducted lately. Takano et al. [1] tested shear
behavior of RC and silica sand mix using micro
focus X-ray CT scanner. Results of this tests wasthat under direct shear, mix of sand and RC, shear
stress rises monotonically and no peak stress isobserved. Also dilatancy effect was smaller whencomparing to pure sand. Interesting is fact that RC
additions can decrease shear strain propagation. This
fact is important for road engineering where shear
stress is cause of many construction damages. Also
mix of gravel, fly ash and waste fibers was studied
by [2]. Series of CBR tests and direct shear tests
have shown increase of CBR characteristics, optimal
addition of waste tyre rubber is equal from 0.2 to
2.0% of dry unit weight of soil. [3], used shredded
rubber from waste to reinforcement and cement as abinding medium to soft clay with different
percentages of rubber content. Series of CBR andunconfined compressive strength tests have shown
improve of bearing capacity, strength and highcompressibility of the material.
Recycled Concrete Aggregate (RCA) is an
193GEOMATE- Osaka, Nov. 16-18, 2015

easily perform this tests.
The purpose of performing cCBR test is to findkey factor which characterizes cyclic loading resilient modulus M r .
Resilient modulus is characteristic value ofcyclic loading where resilient strain is taken to
consideration as design factor. The M r value iscalculated as follows:
𝚫𝚫𝝈𝝈𝑴𝑴𝒓𝒓 = 𝒅𝒅(1)
𝚫𝚫𝜺𝜺𝒓𝒓where, 𝚫𝚫𝝈𝝈𝒅𝒅 is deviator stress pulse ∆𝝈𝝈𝒅𝒅 = ∆𝝈𝝈𝟏𝟏 −∆𝝈𝝈𝟑𝟑 , 𝝈𝝈𝟏𝟏 is major principal stress, 𝝈𝝈𝟑𝟑 = 𝝈𝝈𝟐𝟐 is minor
principal stress, and 𝚫𝚫𝜺𝜺𝒓𝒓 is resilient strain overdeviator pulse 𝚫𝚫𝝈𝝈𝒅𝒅 [8].
2.

MATERIAL AND METHODS

2.1 Material
Material for tests was obtained from demolishedconcrete from building demolition site. Concrete
aggregates were an element of construction, whosestrength class was estimated from C16/20 to C30/35.
Aggregates were in 100% composed from broken
cement concrete. Grain gradation curve was adopted
according to EN ISO 14688: 2a [9] and placed
between upper and lower grain gradation limits.
For estimation of physical properties, a series oftests was conducted. The sieve analysis led toclassifying this material as sandy gravel (saGr), in
reference to [9, 10]. Test results are shown in Figure
1. This distribution of particles from 12.0mm to
0.063mm is typical for soils used for sub-base andsupport another structures.
The Proctor test results are presented in Figure 2.
The test procedure involved compaction in theProctor mold, whose volume equaled 2.2dm3, byusing standard energy of compaction, equal
0.59J/cm3. Optimum moisture content for sandy
gravel was m opt = 9.54% and maximum dry densityof optimum moisture content was 1.97g/cm3. For
RCA with 0.5% RC addition, m opt = 8.47% andρ max = 1.94 g/cm3. for RCA with 1% RC addition,
m opt = 8.02% and ρ max = 1.91 g/cm3. Addition of RC
caused decrease of ρ max and corresponded m opt . Thisphenomena is caused, by smaller than RCA, RC
density. Decease of the moisture content seems toprove that much of water fill inner pores due tograter capillary forces than in open pores [11].

Fig. 1

Particle size distribution of tested soil.

Fig. 2The Proctor test results for sandy gravel.

The Proctor tests observations concerning thehigh water absorption of the RCA results in proper
sample curing before compaction. RCA wascompacted after leaving moisture material inhermetic box for 24h to prevent moisture loss.
Material conditioned in such way give morereasonable data concerning physical properties
repetition.
When curing of material and compaction in
CBR mold in respect to Proctor's energy ofcompaction were done, cCBR tests were performed.
The cCBR method was based on standard CBR test.
The cCBR test bases on CBR method. Procedure of
performing the cCBR test is simple and bases on
standard CBR test procedure. Previous studies on
cyclic loading performed on porous material was
conducted by performing cyclic triaxial apparatus.

2.2 Methods
After the optimum moisture content has been
estimated, the cCBR tests on samples with 0, 0.5 and
1% RC content were performed.

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GEOMATE- Osaka, Nov. 16-18, 2015

In recent years mechanistic-empirical pavement
design has become more popular and traditional
empirical methods of design are replaced. CBRvalue method of design the road construction was
replaced by mechanistic approach where resilient
modulus and Poisson ratio is employed for
calculations of layer thickness. cCBR method is a
way to compromise reliable CBR equipment withnew standards of designing.
The cCBR test method bases on two parts oftesting. First one is standard CBR test where loading
is conducted to 2.54mm plunger penetration withvelocity equal 1.27mm/min. Next step is beginningof second phase. After loading maximal stress at
2.54mm is noted. Unloading is conducted to 10% of
maximal stress, called minimal stress. Loading andunloading constitutes first cycle of cCBR test. Next
step is repetition of loading to maximal andunloading to minimal stress. cCBR test is terminatedwhen plastic strains which occurs in one cycle areless than 1% of total strain in one cycle. Commonly
the number of repetitions for un-cohesive soils isaround to 50 cycles. The number of the cycles toobtain this condition usually oscillates around 50 [12,13].
After cCBR test, last hysteresis loop is taken to
calculations of resilient modulus M r . To calculate
M r value. Resilient strain Δ𝜀𝜀𝑟𝑟 is amount of strain
during unloading where elastic response to
decreasing stress is observed. Deviator stress isdifference between maximal stress where elasticstrains occurs and minimal stress.
Because of CBR test standards, not all area ofspecimen top is loaded. Therefore correct strain
cannot be measured. For calculate the M r value
another equation was proposed [8] (2):where plastic displacements occur but constitutes
small amount of total displacements.

Fig. 3Stress – displacement curve from cCBR test
for RCA with 0% RC.

1.513�1−𝜈𝜈1.104 �Δ𝜎𝜎 ∙𝑟𝑟

𝑝𝑝
𝑀𝑀𝑟𝑟 =(2)∆𝑢𝑢1.012
where: ν –Poisson’s ratio [-] (in this study 0.35 for
granular materials), Δσ p – change between
maximum and minimum axial stress in one cycle[MPa], r – radius of plunger [mm], Δu – recoverable
displacement in one cycle.

3. RESULTS
Tests conducted on RCA have shown good
performance of this material. cCBR test results for
RCA with 0% RC in details is presented in figures 3,
4 and 5, Plots present results of cCBR test in
function of axial stress and displacement on fig. 3.
Figures 4 and 5 present the changing in time ofdisplacement and axial stress respectively. RCA inthis test presents resilient response. After 50
repetitions resilient modulus value was M r =495.6MPa. Elastic (resilient) displacement was
0.275mm which is 99.5% of total displacement incycle. In terms of shakedown concept [14], material
have reach steady point called plastic shakedown

Fig. 4
Displacement – time curve from cCBR
test for RCA with 0% RC.

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GEOMATE- Osaka, Nov. 16-18, 2015

7 and 9 present the changing in time of displacementfor 0.5 and 1% of RC content respectively.

Fig. 5
Axial stress – time curve from cCBR
test for RCA with 0% RC.
Fig. 7
Displacement – time curve from cCBR
test for RCA with 0.5% RC.

Fig. 6 Stress – displacement curve from cCBR
test for RCA with 0.5% RC.
Fig. 8 Stress – displacement curve from cCBR
test for RCA with 1% RC.

cCBR test results for RCA with 0.5 and 1% of
RC content is presented in figures 6, 7, 8 and 9,
Plots present results of cCBR test in function of
axial stress and displacement on fig. 6 and 8. Figures

Addition of 0.5% RC increased CBR bearing
capacity from 47% (RCA with 0% RC) to 56%.
196

GEOMATE- Osaka, Nov. 16-18, 2015

Calculations of resilient modulus have shown
increase of M r . For 0.5%RC content RCA M r value
was M r = 632.4MPa and was 137MPa greater thanfor RCA without RC addition.

bases. Eurocode 7 EN 13286-7:2004 [15] classifies
RCA by mechanical performance parameter M r andthis material reached the highest C1 class.

Fig. 10
CBR value and M r value for different
addition of RC from cCBR test.

Fig. 9Displacement – time curve from cCBR
test for RCA with 1% RC.
Resilient modulus describes not only bearing
capacity therefore rise in CBR value was equal 19%
but rise in M r value was 27%. This mean that grater
improvement of plastic strain dissipation wasobserved.
For sample with addition of 1% RC CBR bearing
capacity was 66% and rise to 19% in compare to 0%
RC. The resilient modulus have shown increase of
M r . For 1% RC content RCA was M r = 698.0MPa
and was 202MPa greater than for RCA without RC
addition. On figure 10 detailed view of results waspresented. Resilient modulus value rises more whencompared to CBR value. This phenomena isbounded with plastic strain increase. No RC addition,
results in grater plastic strains and in one cycleamount of plastic strain is grater for 0% RC.
Addition of RC seem to improve resilient
properties of material. RCA with RC addition canfaster dissipate plastic strains. This may be caused
by elastic properties of rubber. Stress between grain
contacts may be better arranged by rubber chips.
Abovementioned statement presents in details figure
11.
Properties of RCA as a anthropogenic soilmaterial need further studies to understoodmechanical properties but overall mechanicalcharacterization proved that this material isappropriate for construction of the unbound sub-Fig. 11
Plastic displacement change during
cCBR test for RCA with various RC content.
4. CONCLUSION
Results of cCBR test on stabilized and nonstabilized RCA presented in this paper are as
follows:
1. Optimal moisture content for RCA with RC
addition is lower. Also maximal dry density
decrease with increasing of RC addition.
2. The CBR value for tested samples rises withincrease of RC addition. For pure RCA CBR value
was equal 47% for 0.5% and 1% RC content CBRvalue was 56% and 66% respectively.
3. The resilient modulus M r values growth wasobserved with increase of RC content. For pure RCA
the M r value was 495.6MPa for for 0.5% and 1%
RC content the M r value was equal 632.4 and698.0MPa.
4. Plastic displacements are decreasing when RC
was added. For 0.5 and 1% RC content plastic
displacements are smaller but between this contents,
differences are not so great.
197

GEOMATE- Osaka, Nov. 16-18, 2015

5. Resilient modulus values in compare toEuropean standards classified this material tohighest class C! and RCA with RC addition can bepart of road construction as sub-base.

International Conference on GEOMATE, 2014,
pp. 196-201.
[8] Araya A.A., “Characterization of UnboundGranular Materials for Pavements” PhD Delft,
2011.
[9] EN 1997-2:2007 Geotechnical design – Part 2:
Ground investigation and testing.
[10] WT – 4: Mieszanki niezwiązane do dróg
krajowych. GDDKiA (Unbound mix fornational pavements).
[11] Deshpande
YS,
HillerJE,"Porecharacterization of manufactured aggregates:
recycled concrete aggregates and lightweightaggregates", Materials and Structures Vol. 45(1),2011, pp.67-79[12] Sas W, Głuchowski A, Szymański A,“Determination of the resilient modulus M R for
lime stabilized Clay obtained from repeatedloading CBR tests”, Annals of Warsaw
University of Life Sciences-Land Reclamation,
Vol. 44, 2012, pp. 143-153.
[13] Sas W, Głuchowski A, „Metodyka wyznaczania
modułów sprężystości (E i Mr) na podstawie
badania CBR pod obciążeniem cyklicznym”,
Scientific Review– Engineering andEnvironmental Sciences, Vol. 57, 2012,
pp. 171- 181.
[14] Werkmeister S, Dawson AR, Wellner F,"Permanent deformation behaviour of granularmaterials and the shakedown theory", Journal ofTransportation Research Board, 2001, Vol.1757,
2001, pp. 75-81.
[15] BS EN 13286-7:2004, Unbound andhydraulically bound mixtures. Cyclic load
triaxial test for unbound mixtures.

REFERENCES[1] Takano D, Chevalier BJ, Otani J, "Experimental
and numerical simulation of shear behavior on
sand and tire chips" in Proc. 10th International
Conference on Computer Methods and Recent
Advances in Geomechanics, 2015, pp. 15451550.
[2] Srinivas A, Prasad DSV, AnjanKumar M,
Prasada Raju GVR, "Utilisation of wastematerials in the construction of roads",
International Journal of Engineering Researchand Development, Vol. 11(1) Jan. 2015, pp. 5562.
[3] Ghatge SH, Rakaraddi PG, “Soil stabilization
using waste shredded rubber tire chips”, Journalof Mechanical and Civil Engineering (JMCE),
Vol. 11, 2014, pp. 20-27.
[4] Tosic N, Marinkovic S, Dasic T, Stanic M,"Multicriteria optimization of natural andrecycled aggregate concrete for structural use",
Journal of Cleaner Production Vol.87, 2015, pp.
766-776.
[5] Soleimanbeigi A, Edil T, Benson C,"Engineering Properties of Recycled Materials
for Use as Embankment Fill", In Proc. GeoCongress, 2014, pp. 3645-3657.
[6] Randolph M, Gouvernec S, "Soil response"
Offshore Geotechnical Enigineering, Spon Press,
1st ed., 2011. pp. 113-144[7] Sas W, Głuchowski A, Szymański A, "Cyclic
behavior of recycled concrete aggregate
imptoved with lime addition" In Proc. 4th198

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

ULTIMATE BEARING CAPACITY OF FOOTING ON SANDY SOIL
AGAINST COMBINED LOAD OF VERTICAL, HORIZONTAL AND
MOMENT LOADS
Du L. Nguyen1, S. Ohtsuka2, and K. Kaneda3
Department of Energy and Environment Engineering, Nagaoka University of Technology, Japan2
Department of Civil and Environmental Engineering, Nagaoka University of Technology, Japan
3
Takenaka corporation, Japan

1

ABSTRACT
Rigid plastic finite element method employing the rigid plastic constitutive equation, which considers non-linear
shear strength properties against confining pressure, is used for the assessment of ultimate bearing capacity offooting on sandy soils against the combined load of vertical, horizontal and moment loads. The numerical results
were compared with the results predicted by semi-empirical bearing capacity formulate of Architectural Instituteof Japan and others. The comparison is conducted in terms of vertical and horizontal loads plane and vertical andmoment loads plane. The limit load is expressed in normalization form by the limit vertical load, Vo. Results
show that the normalized vertical load decreases with the increase in the normalized horizontal load and/or
moment load. Effect of non-linear shear strength on the normalized limit load space in vertical, horizontal andmoment loads is clearly indicated. The normalized horizontal load is obtained greater than that of linear shear
strength property.
Keywords: Ultimate Bearing capacity, Size effect, Finite element method, Inclined load

INTRODUCTION(1)
where c: cohesion, γ: unit weight of soil, Df: depth ofembedment, B: footing width; Nc, Nq, Nγ: bearing
capacity factors; ic, iq, iγ: inclination factors, α andβ express the shape coefficient and α = 1 and
β = 0.5 are recommended by De Beer (1970),
respectively; qu is ultimate vertical bearing capacity
per unit area of footing (kN/m2). η : the size effectfactor is defined as:

The ultimate bearing capacity of footing related toinclined loads is an important aspect in geotechnicalengineering. Because the number of superstructurebuildings has increased and great earthquakes occur
regularly, estimating the ultimate bearing capacity offooting with considering the effect of footing widthis necessary. The strip footings are often subjected tothe inclined loads and the combined loads. The
ultimate bearing capacity for combined vertical andhorizontal loads (with no moments) is resolved by
Green (1954). The general case of vertical,
horizontal and moment loads has received lessattention. Several authors (notably Meyerhof (1953),
Hansen (1970) and Vesic (1975)) provide
procedures for a general case; however they only
conduct empirical generalizations of the simpler
cases without examining in detail.
In previous geotechnical research, the combined
vertical and horizontal load is referred as theinclined loads. Their results showed that the verticalbearing capacity significantly decreased when the
inclined angle θ = atan(H/V) increased.
By using the ultimate bearing capacity factors Νc , Νq(Prandtl), and Νγ (Meyerhof), the Architectural
Institute of Japan (AIJ, 1988, 2001) developed anultimate bearing capacity formula which considers
the size effect factor and now is widely used inJapan. It was developed by semi-experiments. The
ultimate bearing capacity formula is expressed as follows:

m

 B 
(2)
η =   Bo 
where, B0: reference value in footing width
m: coefficient determined from theexperiment (m = -1/3 is recommended in practice).
Meyerhof (1956) introduced ‘inclination factor iγ’that is defined as follows:V  atan (H V ) (3)= 1 −
V0 
φwhere φ : internal friction angle, H and V: horizontal
and vertical of the load applied on the footing, Vo:vertical ultimate load.
Inclination factor has been estimated by FE analysis.
But, there are few analyses for sandy soils except
Loukidis et al. (2008). However, the effect offooting width on ultimate bearing capacity is notconsidered directly. As shown in Eqs. (1) and (2),the size effect of footing is large in case of sandy
soil. It can be seen in the combined load space of
2

iγ =

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GEOMATE - Osaka, Nov. 16-18, 2015

stress tensors. The strain rate ε , which is purely
plastic component, should satisfy the volumetricconstraint condition which is derived by Eq. (5) as
follows:

vertical, horizontal and moment loads. This is amajor topic of this study.
Recently, the numerical methods are efficienttechniques for solving problems related togeotechnical engineering. The rigid-plastic finite
element method (RPFEM) was applied in
geotechnical engineering by Tamura (1991). In thisprocess, the limit load is calculated without theassumption about the potential failure mode. The
method is effective in calculating the ultimate
bearing capacity of footing against the threedimensional boundary value problems. Although
RPFEM was originally developed based on theupper bound theorem in plasticity, Tamura provedthat it could be derived directly by using the rigid
plastic constitutive equation.
This paper investigated the ultimate bearing capacityof footing on sandy soils against the combined loadof vertical, horizontal and moment loads. This
research applied rigid plastic finite element method
which employs the rigid plastic constitutive equationin which non-linear shear strength properties against
confining pressure is inlcuded. The vertical load V,
horizontal load H and moment M, which wereapplied at the center of the footing, were subjects inthis study. The analytical method provides thereliable computational results. The relation innormalization form of H/V0 vs V/V0 and V/V0 vs
M/BV0 were acquired and then were compared withthe relationship by Meyerhof (1956), Architectural
Institute of Japan (1988, 2001) and Loukidis et al.
(2008).
RIGID
PLASTICMETHODFINITEh (ε) = ε v −3a + 1e = ε v − ηˆ e = 0

2

(6)

2

Each strain rate, which is compatible with DruckerPrager’s yield criterion, must satisfy the kinematicalconstraint conditions of Eq. (6). η̂ is a coefficient
determined by Eq. (6) which is on the dilation
characteristics. The rigid plastic constitutive
equation is expressed by Lagragian method after
Tamura (1991) as follows:






b
ε ˆ
3a
ε
(7)
σ=
+β I



e
e
1
1


3a 2 +
3a 2 +
22 
The first term expresses the stress component
uniquely determined for the yield function, and thesecond term expresses the indeterminate stresscomponent, defined to be parallel to one of thegenerators of the cylindrical cone of the yieldsurface. The indeterminate stress parameter β̂ still
remains unknown until the boundary value problem
with Eq. (6) is solved.
In this study, the constrain condition on strain rate is
introduced into the constitutive equation directlywith the use of penalty method:





ε
ε
3a
b
 (8)

+ κ(ε v − ηˆ e ) I σ=




e
e
1
1
3a 2 +

3a 2 +2 
2where, κ is a penalty constant. This technique makes
the computation more stable and faster. In rigid
plastic finite element method (RPFEM), theoccurrence of zero energy modes has been pointed
out and some numerical techniques to avoid it havebeen introduced into FEM. However, zero energy
modes have not been observed in computation withthe rigid plastic constitutive equation using thePenalty method.

ELEMENT

Stress – Strain rate relationship
Tamura (1987, 1991) developed the rigid plastic
constitutive equation for the frictional material
whose strength sastifies the Drucker-Prager yield
criterion:f (σ) = aI 1 + J 2 − b = 0

3a

(4)

where I 1 : first stress invariant
J 2 : second invariant of deviator stress s ij

Rigid plastic constitutive equation for non-linear
shear strength property

The coefficients a and b express the soil
constants corresponding to the internal friction angle
and cohesion, respectively.
The volumetric strain rate is expressed as follows: ∂f (σ ) ε v = tr (ε ) = tr λ ∂σ 
(5)
 

3a
s
 =
e
= tr λ αI +

 
2
J
3a 2 + 1
2  2
where λ: the plastic multiplier, and e : the norm ofstrain rate. I and s express the unit and the deviatoric

Tatsuoka (1986), and other researchers (Hettler, A.
and Gudehus, G. (1988)) reported the effects ofconfining pressure on the internal friction angle forsandy soils by experiments. The property of thenormalization between internal friction angle andfirst stress invariant always holds irrespective of thereference value of the confining pressure in thestandardization of internal friction angle (Du N. L. et
al. (2013)). From Fig. 1, the obtained results fromexperiment on Toyoura sand, Degebo sand, Eastern
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GEOMATE - Osaka, Nov. 16-18, 2015

Scheldt sand, and Darmstadt sand indicated that
although internal friction angles are different
between soils, the normalized internal friction angle
shows the same trend for all case studies although
any reference values of φ0 and I10 are employed in

The authors successfully proposed a rigid plastic
equation using non-linear shear strength property
against confining stress to RPFEM to assess theultimate bearing capacity for the vertical load casesof rigid flat footing (Du N. L. et al. (2013)). The
results of RPFEM were obtained similarly to theultimate bearing capacity formula by Architectural
Institute of Japan (AIJ, 1988, 2001), which take into
account the size effect of footing.

normalization form. Thus, non-linear shear strength
property against confining pressure is included in
RPFEM in order to assess the ultimate bearing
capacity of footing on sandy soils by taking accountof the size effect of footing. The internal friction
angle φ = 300 at I1 as 150 kPa is employed as
references through the following case studies.
1.05

OF

Ultimate bearing capacity for combined verticaland horizontal loads

Eastern Scheldt sandDarmstadt Sand
Degebo Sand
Toyoura sand

1.00

ϕ/ϕ₀

ULTIMATE
BEARINGCAPACITY
FOOTING UNDER COMBINED LOADSThe rigid plastic finite element method was used toassess the ultimate bearing capacity of strip footingsof which the width varied from 1m to 100m,
subjected to the inclined load at an inclination angle
θ with respect to the vertical. The boundary
conditions and typical mesh for analysis are shownin Fig. 2.

0.95

0.90

0.85
0

2

4

σ2/σ20

6

8

10

Fig. 1 Relationship between internal friction angle
and first stress invariant for various kinds of sands
The high order hyperbolic function is introduced to
the yield function of sandy soils as follows:

Fig. 2 Typical finite element mesh and boundary
conditions

f (σ) = aI 1 + (J 2 ) n − b = 0(9)where a and b are the soil constants. The index n
expresses the degree in non-linearity in shear
strength against the first stress invariant. Eq. (9) is
identical with Drucker-Prager yield function in caseof n=1/2. The non-linear parameters a, b and n are
identified by the testing data. In this case studies, a =0.24, b = 2.4 kPa and n = 0.56 was set based on theexperiment data from Fig. 1.
The non-linear rigid plastic constitutive equation for
confining pressure is obtained as follows:

Because of the absence of loads symmetry, the entire
soil domain of dimensions will be considered. The
numerical simulation procedure was used for thecomputation of the (H, V) failure envelope (where H
and V are the horizontal and vertical ultimate
footing loads, respectively).
For inclined load, the application of RPFEM islimited to the case where the contact pressure
between footing and ground is positive. In otherwords, the ratio H/V is set comparatively in small
range. Further detailed discussion will not conductedin this study.
Fig. 3 provides the RPFEM result on the relationshipbetween normalized horizontal and vertical loads on
H-V space. Two cases considered include (i) linear
shear strength property and (ii) non-linear shear
strength property. The results by AIJ and Meyerhof
formulae are also shown. Since AIJ formula
employs the same coefficient with Meyerhof method,
the results in normalization form from AIJ andMeyerhof show unique and coincident line. The
inclination coefficient proposed by Loukidis et al.
(2008) is also shown in this figure. They proposed
the inclination factor iγ based on the FE analysis for
B=10m as follows:

1− n2
  2n−1 ε
3a  1  e 2 σ=+ 2  3a  − 3a  εvn  2n  εv 






n


(10)
2
2n −1  b
1 1  e 2 −  2  3a  − 3a 
 3a 3a  2n  εv 

I
+
1− n


2
 2n−1 a  1  e 2 − n  2n 2  3a ε  − 3a 

v 




Stress is uniquely determined for plastic strain rateand it is different from Eq. (8) for Drucker-Prager
yield function in this equation.

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GEOMATE - Osaka, Nov. 16-18, 2015

(1.5 tan φ +0.4 )2

0.15

V  (H V ) (11)= 1 −
iγ =tan φ V0 This coefficient is developed for linear shear

0.12

strength, but it differs from the lines of Meyerhof
and AIJ as shown in Fig. 3a. In the figure, thenormalized horizontal load is indicated greater than

H/Vo

0.09

those of Meyerhof and AIJ. The obtained results by

0.06

RPFEM are plotted for various footing widths. It isapparent that the results match with the model of Eq.

Meyerhof
AIJ
Loukidis et al.
RPFEM (linear strength)
RPFEM (non-linear strength)

0.03(11) by Loukidis et al. though they are varied for
footing width.

0.00
0.0

0.2

0.4

0.6

0.8

1.0

V/Vo

Fig. 4 Comparison inclination coefficients among
the various methods at footing width B = 10m
Fig. 3b indicates the inclination coefficient in case of
non-linear shear strength. AIJ formula is developedby taking account of the size effect of footing.
However, since the inclination coefficient ofMeyerhof is introduced into the formula, theapplicability of AIJ formula for inclined load has notbeen examined. The results by RPFEM taking
account of non-linear shear strength are plotted in
the figure. Fig.4 indicates the ultimate load in H/V0
and V/V0 space to compare the inclination
coefficient among the various methods at B=10m. It
is readily seen that RPFEM affords the identicalresults by Loukidis et al. in case of linear shear
strength, but the greater results than that by Loukidis
et al. in case of non-linear shear strength. Although
φ is constant in case of linear shear strength, φdecreases by confining pressure in case of non-linear
shear strength. Since the decrease in φ mostly
depends on the magnitude of vertical load, thedecrease in ultimate bearing capacity is largest forvertical loading. For the inclined load, the decreasein φ becomes moderate with the increase in
inclination angle of inclined load. It derives thenormalized horizontal load in case of non-linear
shear strength greater than that of linear shear
strength.
Fig. 5 showed the failure modes of ground for nonlinear and linear shear strength. They are similar, but
the deformation area in the case of linear shear
strength is larger than that in case of the non-linear
shear strength. The mechanism is found composedof three different zones and similar to themechanism assumed by Meyerhof and Hansen.

a) RPFEM with linear shear strength propertyb) RPFEM with non-linear shear strength property
Fig.3 The relation between normalized horizontal
and vertical loads

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GEOMATE - Osaka, Nov. 16-18, 2015

At each height of superstructure value, the ultimate
bearing capacity of footings subjected to combined
loading was computed under the condition ofseismic load applied to superstructure. By changing
superstructure height and the seismic coefficient
H/V, the forces H, and V the moment load wascomputed.

a) RPFEM with linear shear strength property

1.0

0.8

V/Vo

b) RPFEM with non-linear shear strength property

0.6

Fig. 5 Deformation mechanism analysis at footing
width B = 10m

0.4

Ultimate bearing capacity for vertical, horizontal
and moment loads

0.2

h = 10m
h = 15m
h = 20m
0.00

0.03

0.06

0.09

0.12

0.15

M/BVo

The type of loads, which is often known as
combined loads, is important to the stability ofsuperstructure where footings are subjected tovertical, horizontal and moment loads combination.
Typically, the vertical force is stemmed from theweight (W) of superstructure, while the horizontal
load comes from the seismic coefficient H/V, andthe overturning moment load is caused by thehorizontal load. In case study, vertical loads range
from about 150 kN to 300 kN and the overturning
moment varies from 200 kN.m to 500 kN.m. A
series of finite element analysis were conducted forsandy soil with unit weight γ = 18 kN/m3, cohesion c= 5 kN/m2, internal friction angle φ = 300 , the heightof superstructure h (10-20m), and at the footing
width B = 3m. The moment load is given to thefooting by the external force where the summations
in vertical and horizontal loads are zero and theresultant moment at the center of footing is samewith the prescribed moment load. The results
demonstrated the interaction between the vertical,
horizontal and moment loads. Fig. 6 shows therepresentative finite element meshes of analysis.

a) Linear shear strength property
1.0

V/Vo

0.8

0.6
h = 10m
h = 15m
h = 20m

0.4

0.2
0.00

0.03

0.06

0.09

0.12

0.15

M/BVo

b) Non-linear shear strength property
Fig. 7 The relation between normalized vertical andmoment loads
Fig. 7 shows ultimate bearing capacity of footing
failure in the normalized V-M form by changing
superstructure height (10m, 15m and 20m). The
results showed that the normalized load V/V0
decreases with an increase in M/BV0. In the case of
linear strength, the values that represent therelationship between the normalized V/V0 and
M/BV0 are similar. It is not affected by height ofsuperstructure; while in case of non-linear strength
those values are discrepancy. It is explained that thiscase influences the internal friction angle responding
to the confining stress. It means that the effect ofmoment load in non-linear case is clearer than that inlinear shear strength property.

Fig. 6 Representative finite element meshes under
superstructure on the strip footings condition at 3mof footing width203

GEOMATE - Osaka, Nov. 16-18, 2015

Fig. 8 shows examples of the deformation
mechanism from evaluates at the collapse. The
larger the combination loads, the smaller the limit
load. The results from analysis computation also
show that the failure mechanism is asymmetrical andconfined to one side of the footing.

On the contrary, it is obtained greater than that by
Loukidis et al. in case of non-linear shear strength. It
is understood by the following reasons:(i) The internal friction angle decreases by confiningpressure and the decrease is the most for the case ofvertical loading.
(ii) In inclined loading, the decrease in internal
friction angle becomes smaller since the ultimate
vertical load decreases with the increase in
inclination angle. Thus, the computed results can beobtained.
(3) The combination of vertical, horizontal andmoment loads is considered to evaluate the stabilityof buildings during earthquake. The effect ofmoment load on ultimate bearing capacity isinvestigated through case studies.

H/V = 0.1

H/V = 0.4

References[1] Architectural Institute of Japan, AIJ (1988,
2001), “Recommendations for design ofbuilding foundations”, 430p.
[2] Du, N. L., Ohtsuka, S., Hoshina, T., Isobe, K.
and Kaneda, K. (2013) “Ultimate bearing
capacity analysis of ground against inclined
load by taking account of non-linear propertiesof shear strength”, Int. Journal of GEOMATE,
Vol. 5, No. 2, pp. 678–684.
[3] De Beer, E. E. (1970), “Experimental
determination of the shape factors and theultimate bearing capacity factors of sand”,
Geotechnique, Vol. 20, No. 4, pp. 387 – 411.
[4] Hettler, A. and Gudehus, G. (1988), “Influenceof the foundation width on the ultimate bearing
capacity factor”, Soils and Foundations, Vol. 28,
No. 4, pp. 81-92.
[5] Loukidis, D., Chakraborty T., and Salgado R.,(2008), “Bearing capacity of strip footings on
purely frictional soil under eccentric andinclined loads”. Can. Geotech. J., Vol. 45, pp.
768-787.
[6] Meyerhof, G. G. (1956), “Rupture surfaces insand under oblique loads (discussion)”, Journalof soil mechanics and foundations division,
ASCE, Vol. 82, No. SM3.
[7] Tamura, T., Kobayashi, S. and Sumi, T. (1987),“Rigid Plastic finite element method for
frictional materials”, Soils and Foundations, Vol.
27, No. 3, pp. 1-12.
[8] Tamura, T. (1991), “Rigid plastic finite element
method in Geotechnical engineering”, Current
Japanese Materials Research, Vol. 7, Elsevier
Applied Science, London, pp. 135-164[9] Tatsuoka, F., Sakamoto, M., Kawamura, T. andFukushima, S. (1986), “Strength anddeformation characteristics of sand in planestrain compression at extremely low pressures”,
Soils and Foundations, Vol. 26, No. 1, pp. 65–
84.

a) Linear shear strength property
H/V = 0.1

H/V = 0.4b) Non-linear shear strength property
Fig. 8 Deformation mechanism analysis subjected to
combined loads
CONCLUSION
In this study, the limit load in ultimate bearing
capacity is expressed in normalization form. The
ultimate bearing capacity of footing that is subjectedto the inclined loads and the combined loads of strip
footing has been investigated in this study. The
obtained conclusions can be summarized as follows:(1) The non-linear shear strength model for sandy
soil is employed in RPFEM to evaluate the size
effect of footing on ultimate bearing capacity.
Through the case studies the applicability of themethod was clearly exhibited.
(2) Ultimate load space in normalized vertical andhorizontal loads was shown to match with that by
Loukidis et al. (2008) and be greater than those by
Meyerhof (1956) and AIJ (1988, 2001) in case of
linear shear strength.
204

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

THE PROPAGATION BEHAVIOR OF PILE-DRIVING-INDUCEDVIBRATION DONE ON SOIL AT VARYING DISTANCES AND ITSEFFECTS ON EXISTING STRUCTURES
D.Y. Acosta, M.B. Juego, H.M. Sanchez and I.S. Sanchez
College of Engineering, De La Salle University Manila, Philippines

ABSTRACT
Pile driving operations are becoming a prevalent practice in Metro Manila due to the presence of soft soil
conditions. However, ground vibrations are induced in the process, which could cause possible structural
damages to the nearby existing structures. Ground vibration measurements were recorded at three designatedsites at 3, 15, and 30 meters away from the pile-driving source through the use of three of Guralp’s
accelerometers. A defined attenuation behavior of the emitted vibrations was observed when the pile was incontact with the predominantly sand and silt layers, vibration prediction models for sand and silt layers were
formulated and then validated with the measurements obtained at the other two sites, predicted results werecorrelated to existing vibration limits, a zone of influence was then generated and is capable of identifying thetypes of structures that are susceptible to structural damage within a radius of the source of pile driving.
Keywords: Attenuation Behavior, Zone Of Influence, Vibration Prediction Model, Vibration Limits, Structural
DamageRayleigh (R) –waves, as seen in Fig. 1, which
attenuate in amplitude in proportion to the squareroot of distance [2],[3].

INTRODUCTION
Pile driving operations are becoming a prevalent
practice in Metro Manila due to the presence of soft
soil conditions. However, ground vibrations are
induced in the process, which could then cause
possible structural damages to the nearby existing
structures. Several vibration limits are set forth byinternational codes to minimize the negative effect,
however, there is still a lack of proper translation ofthese limits for comprehensive on-site usage. Sincemodern construction work is usually done in urban
areas, it is essential to establish vibration limits upon
consideration of the structures that are vulnerable tostructural damage within a certain radius of thesource of pile driving.

Fig. 1BACKGROUND

Waves generated
operations [5]

from

pile

drivingVibration Attenuation and Frequency

The transmission of energy from the hammer
down to the pile and then to the surrounding soil
governs the entire vibration transmission process
and forms a crucial part in analyzing the behavior ofground vibrations. A simple concept of the energy
transmission is then presented.
When the hammer hits the pile head, energy is
being transferred down to the body of the pile in theform of a compressional body wave [1]. S-waves are
generated in the Pile shaft and propagate conicallyfrom the pile. Compressional (P) – waves and Shear(S) -waves are also generated in the pile toe and they
propagate in spherical waveforms in all directions.
As these S and P – waves reach the ground surface,a part of the waves are being converted into

Ground vibrations, depending on its type andsource of emission, attenuate due to geometricdamping. Its attenuation is also influenced bymaterial damping, arising due to the presence ofdifferent ground properties [5].
The Bornitz Equation considers the combined
effect of both material and geometric damping,allowing the characterization of the propagation ofground vibrations as it moves away from thevibration source, it is then expressed in Eq. (1):𝑟𝑟

𝑛𝑛

𝐴𝐴2 = 𝐴𝐴1 � 1� 𝑒𝑒 −𝛼𝛼(𝑟𝑟2−𝑟𝑟1 )
𝑟𝑟2

205

(1)

GEOMATE- Osaka, Nov. 16-18, 2015

𝐴𝐴1 and 𝐴𝐴2 are vibration amplitudes at distances
𝑟𝑟1 and 𝑟𝑟2 , 𝑛𝑛 is the geometric damping coefficient = 1(surface waves), and 𝛼𝛼 is the material damping
coefficient. According to [1] and [2], the material
damping coefficient ( 𝛼𝛼) can be estimated as afunction of vibration frequency (𝑓𝑓), and is expressedin Eq. (2):𝛼𝛼 =

2𝜋𝜋𝜋𝜋𝜋𝜋𝒗𝒗𝒔𝒔𝒔𝒔 = 𝒌𝒌𝒔𝒔 𝑭𝑭𝒗𝒗 𝑬𝑬𝑻𝑻(𝑐𝑐)𝜀𝜀2𝜋𝜋𝜋𝜋)Where, 𝜀𝜀 is the adjustment factor which isdependent on the pile to ram weight ratio denoted by𝜂𝜂 , 𝐿𝐿 is the pile length in meters, and 𝑘𝑘 = 0.5(Concrete Piles at restrike).

𝜼𝜼
𝜼𝜼

0.01

0.10

0.30

0.50

0.70

0.90

1.00

1.50

𝝃𝝃

0.10

0.32

0.52

0.65

0.75

0.85

0.86

0.98

2.00

3.00

4.00

5.00

10.0

20.0

100

𝝃𝝃

1.08

1.20

1.27

1.32

1.42

1.52

1.57

∞

(5)

Several limits were set forth by various
government agencies to complement the empiricalprediction models to aid in mitigating the negative
effects of vibrations, specifically by providing
threshold values for structural damage in relation to
the magnitude of the peak particle velocity and thefrequency of the vibration.
There are 4 frequently used standards onvibration limits and they were incorporated into asingle graph, refer to Fig. 2, as presented by [5]. The
graph served as the basis for vibration assessment in
the study.

(3)

Table I Adjustment factors (𝜀𝜀) [11]𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄

Vibration Limits

Where, 𝑐𝑐 is the propagation velocity of R-waves
and 𝐷𝐷 is the damping ratio.
The dominant frequency of vibration can then be
approximated by knowing the pile and hammer
properties, specifications utilized in the pile driving
operation as suggested by [11], and is given in Eq.
(3):𝑓𝑓𝑤𝑤 = 𝑘𝑘 (𝒓𝒓𝒓𝒓

Where, 𝑣𝑣𝑠𝑠𝑠𝑠 is the vertical PPV measurement in
mm/s, 𝑘𝑘𝑠𝑠 , 𝐹𝐹𝑣𝑣 and 𝐹𝐹 𝐻𝐻 are the vibration, amplificationand efficiency factors associated with the model. 𝐸𝐸𝑇𝑇
is the toe vibration efficacy, 𝜃𝜃 is the angle ofincidence of P-waves and 𝑟𝑟𝑟𝑟 is the radial distancefrom the pile toe to a point on the ground surface.

(2)

𝑐𝑐

(𝑭𝑭𝑯𝑯 𝑾𝑾𝟎𝟎 )𝟎𝟎.𝟓𝟓

𝝅𝝅Empirical Prediction Models
Two empirical vibration prediction models were
utilized in the study as a basis for validation against
the actual vibration measurements obtained on site.
These were the empirical models presented by [6]and [7] respectively.
The model suggested by [6] only takes into
account the hammer energy (𝑊𝑊𝑜𝑜 ) and the distanceaway from the vibration source (𝑟𝑟) , where no
consideration is made on the geological conditionsof the site; it is then presented in Eq. (4):𝑊𝑊

𝑣𝑣 = 𝑘𝑘( 𝑜𝑜 )𝑥𝑥
𝑟𝑟

Fig. 2Vibration Limits suggested by [1]METHODOLOGY
Data Acquisition
Vibration measurements in terms of vertical peak
particle velocity units were taken in AMAIA Steps
Pasig condominium in Eusebio, Pasig City (Site A);
Three E-com Center in Bayshore Avenue, Pasay
City (Site B); and Meridian Park along Diosdado
Macapagal Avenue, Pasay City (Site C) through theuse of three of Guralp’s Digital Tri-axial
Accelerometers positioned at distances 3, 15 and 30
meters away from the vibration source, as seen in
Fig 3.
Vibration measurements were then recorded
throughout the entire duration of the pile-driving
operation, they were digitally logged in a computer(4)

Values of k = 1.5 and x = 1 were adopted forconservative measurements of vibrations [4].
The said model was then utilized in contrast to amore sophisticated vibration prediction model
presented by [7], wherein imperative aspects ofenergy transfer at the hammer – pile and pile-soil
interface, and the influence of the soil properties on
the vibration magnitude are considered. The modelis then expressed in Eq. (5):206

GEOMATE- Osaka, Nov. 16-18, 2015

and were extracted through the use of the SCREAM
Software, as provided by Guralp Systems.

Fig. 3

The retrieval of the time histories of the vibration
measurements recorded at the ground surface led tothe acquisition of vertical peak particle velocities(PPV) at each soil type transition at three distancesaway from the vibration source, they were thenplotted against the pile penetration depth as shownin Fig. 4.

Placement of accelerometers

The raw data obtained on the three sites werethen exported through a Strong motion Analysis
Tool, named ART, in which the vibration
measurements were analyzed and documented incorresponding measurements of peak particle
acceleration, velocity and displacement taken in the
North, East and Vertical directions.
Fig. 4A

Site Information
Geotechnical conditions, determined through theacquisition of the borehole data, revealed that Site A
was underlain with predominantly sand and silt
deposits having varying soil densities across several
layers. Site B was underlain with a top sand layer
followed by multiple predominantly silt layers and asandstone layer at the bottom most soil profile.
While, Site C was underlain with a combination ofsand, clay and silt layers, weathered rock layers
were then located at the bottom most layer of thesoil profile.
The number of blows needed to drive the piles
down to a certain depth below the ground surface
was also properly documented together with thecorresponding hammer and pile specifications
utilized on the three sites, refer to Table II, to aid in
the analysis of the ground vibration measurements.
Table II

During the entire driving process, the highest
vertical PPV measurement was obtained upon
driving into the medium dense silty sand layer,
situated at 0 - 1.75 m below the ground surface.
It is known that in the case of pile driving in sanddeposits, the surrounding soil is being compacted
through static pressure [8]. The acquisition of thehighest vibration magnitude can be attributed to thecompaction of the surrounding soil during pilepenetration and as well as the increasing shaft-soil
contact area, leading to an increase in the shaft
friction of the pile [9].
A sharp decline of the vertical PPV measurement
followed upon the transition of the driving from thepredominantly sand layer to the stiff clayey silt layer
at a depth of 1.75 - 4.75 m below the ground surface.
Pile driving in Clayey Silt soil layers remoulds thesurrounding soil and excess pore water pressure ss
are generated, which could have resulted to thedecrease in the shear resistance of the surrounding
soil [3],[10], thereby leading to the acquisition of asmaller magnitude of vertical PPV measurement.
The influence of the dynamic soil properties on
the vibration magnitude can be evidently seen at a
distance near the vibration source, but at a farther
distance, it becomes more difficult to determine theinfluence as the vibration magnitudes cluster at a
closer range and become quite similar. On the other
hand, it is then crucial to analyze the attenuation
behavior of the vibrations emitted on different typesof soils possessing different dynamic properties at
distances farther away from the vibration source.

Hammer and Pile Specifications
Hammer

Pile

Site

Type

WeightSquareLength

A

Bruce

7100

450

23

B,C

NH70

7000

450

21

Vertical PPV readings of the 3rd pile in Site

ANALYSIS AND RESULTS
Ground Vibrations Measurements
207

GEOMATE- Osaka, Nov. 16-18, 2015

satisfactorily depict the influence of the soil
properties on the vibration magnitude.
However, it can be seen in Fig. 2, thatoverestimation of the vertical PPV measurements
are made on the upper layers while there is anunderestimation on the lower layers correspondingly,
the overestimation of the measurements on the upper
layers can be attributed to the low associated P-wave
velocity in calculating the vibration magnitude,
while the underestimation can be attributed to theuse of the radial distance in determining the vertical
PPV measurement at the lower layers.

Frequency Content
The corresponding frequency content of thevibration measurements were then obtained throughthe Fourier Transform of the time histories and wereanalyzed in the Fourier Spectra (Frequency Domain).
In Site A, the frequencies of the vertical PPV
measurements ranged from 6 to 15Hz, having an
average frequency of 9.33Hz, see Table III. The
dominant frequency of the propagating waves due toimpact sources has a span of values within 3 to 60
Hz [11] and the acquired values were found to fallwithin the said range.
Table III Vertical Vibration Frequency in Site A

Fig. 5
Actual and Calculated Vertical PPV
readings from the model suggested by [6]It was interesting to see that the calculated
dominant frequency of vibration in Site A upon
using the model suggested by [11], having a value of
9.778Hz, yielded percentage errors ranging from 2
to 8% when compared to the measured averagefrequency of the vertical component of vibrations
obtained on the three driven piles.
This would entail to the rationale that dominantfrequencies of ground vibrations can be estimatedwith considerable accuracy prior to pile driving if
pile-hammer properties and specifications are known.
Leading the parameter to serve as reliable referencein the study of the behavior of the propagation andattenuation of ground vibrations during the predriving phase.
Predicted and Actual Vibration Measurements

Fig. 6
Actual and Calculated Vertical PPV
readings from the model suggested by [7]The vertical PPV measurements obtained at thethree distances were plotted against the calculated
values obtained through the empirical models
suggested by [6] and [7] as shown in Fig. 5 and 6
respectively.
Conservative measurements of vibration
magnitudes were obtained upon using the model
suggested by [6], however no consideration wasmade on the influence of the different soil properties
on the vibration readings. On the other hand,vibration measurements obtained through the use of
the model suggested by [7] have shown toUpon comparing the predicted and actual values ofthe vertical PPV readings, it was observed that it isstill difficult to predict vibration measurements withhigh accuracy, as many factors such as the soil,
hammer, and pile properties, and events of wave
interference can greatly contribute to the change inthe vibration magnitude. Despite the complex natureof predicting ground vibrations, the attenuation
behavior was found to hold substantial informationon the extent to which the ground vibrations would
be of significance in the study.
Attenuation of Ground Vibrations
208

GEOMATE- Osaka, Nov. 16-18, 2015

analysis of the field data, involving two main
parameters, the dominant frequency content of thevibrations and the distance from the vibration source.
Vibration prediction models for sand and silts werethen generated and are expressed in Eq. (6) and (7):The attenuation behavior of the vertical PPV
measurements in Site A were generated andanalyzed by plotting the vibration readings at threedifferent distances away from the vibration source.
They were subdivided into two main groups,
vibration readings emitted on the predominantly
sand and predominantly silt layers respectively, thiswas made in order to analyze the difference between
the attenuation behavior of the two different types ofsoils, as seen in Fig. 7 and 8.

𝑦𝑦 = 𝑒𝑒 0.3649 ln(𝑥𝑥1 )−1.1780 ln(𝑥𝑥2 )+4.8072
𝑦𝑦 = 𝑒𝑒 −1.144 ln(𝑥𝑥1)−0.0902 ln(𝑥𝑥2 )+5.0949

(6)
(7)

Where, 𝑦𝑦 is the vertical PPV in mm/s at a
distance 𝑥𝑥2 from the vibration source having adominant frequency of 𝑥𝑥1 .
The vibration velocities obtained through theuse of Eq. (6) and (7) showed to have correlated
well with the actual vibration measurements of SiteA, yielding an R square value of 0.94 and 0.86. The
predicted values were then validated with themeasurements of Sites B and C and good
agreement was found between the predicted values
and the actual vibration measurements obtained in
the other two sites.
The predicted measurements obtained throughthe use of Eq. (6) and (7) can then be correlated withthe vibration limits, referred to in Fig. 2 as suggested
by [5], and a corresponding zone of influence canthen be generated, refer to Fig. 10, to identify the
nearby existing structures that are vulnerable tostructural damage mainly due to pile-driving
induced ground vibrations.

Fig. 7
Attenuation of vertical PPV in Sand
Layers at Site A (3rd Pile)

Fig. 8
Attenuation of vertical PPV in Silt
Layers at Site A (3rd Pile)
The vertical PPV measurements at a distance of
three meters away from the vibration source were
found to be quite dispersive; this was seen on boththe attenuation graphs of the predominantly sand andsilt layers. The said behavior can be attributed to the
influence of the dynamic soil properties on thevibration magnitude, thereby explaining theacquisition of a dispersive set of readings.
However, the measurements obtained on the
predominantly sand layers damped considerably to anarrow range at a distance of 15 and 30 meters awayfrom the vibration source. While measurements
obtained on the predominantly silt layers did notcome to a closer range up until a distance of 30
meters away from the vibration source.
Having observed a defined attenuation behavior
on both the predominantly sand and silt layers, abest fit line was then developed through regression

Fig. 10

Zone of Influence

Several limitations bind the use of the proposed
prediction models and they are stated as follows,
only vibration measurements generated throughimpact pile-driving operations obtained at the
ground surface, at a distance of at least 15 meters
away from the pile-driving source can be predictedwith higher accuracy, as measurements near thevibration source were found to be dispersive. Theenergy imparted by the hammer unto the pile should
be within the range of 41,000 – 41,130 Joules andthat there should be a minimum individual layer
thickness of 0.75 meters as indicated in the site
borehole data for both sand and silt layers.
CONCLUSION
209

GEOMATE- Osaka, Nov. 16-18, 2015[2] Selby, A. (1991). Ground Vibrations Caused by Pile
Installation. Proceedings Of The 4Th International
Conference On Piling And Deep Foundations.
[3] Deckner, F. (2013). Ground vibrations due to pileand sheet pile driving - influencing factors,
predictions and measurements (Ph.D). Royal Instituteof Technology.
[4] Deckner, F., Viking, K., & Hintze, S. (2012). Groundvibrations due to pile and sheet pile driving –prediction models of today. Proceedings Of The
22Nd European Young Geotechnical Engineers
Conference. ECGD 4122-Foundation Engineering.
(2008).
[5] Athanasopoulos, G., & Pelekis, P. (2000). Groundvibrations from sheetpile driving in urban
environment: measurements, analysis and effects onbuildings and occupants. Soil Dynamics And
Earthquake
Engineering,19(5),
371-387.
doi:10.1016/s0267-7261(00)00008-7[6] Attewell, P.B. & Farmer, I.W. (1973). Attenuation ofground vibrations from pile driving. GroundEngineering, Vol. 3, No. 7, pp. 26-29.
[7] Massarsch, K.R., Fellenius, B.H. (2008). Ground
Vibrations Induced By Impact Pile Driving. The 6thInternational Conference on Case Histories inGeotechnical Engineering. Arlington, Virginia.
[8] Terzaghi, K. (1996). Soil Mechanics In Engineering
Practice (3rd ed., pp. 322 - 323).
[9] Hope, V.S. & Hiller, D.M. (2000). The prediction ofgroundborne vibration from percussive
piling.
Canadian Geotechnical Journal, Vol. 37, No 3, pp.
700-711.
[10] Rajapakse, R. (2008). Pile design and construction
rulesof
thumb.
Burlington,
MA:
Elsevier/Butterworth-Heinemann.
[11] Svinkin, M. R. (1992). “Pile driving induced
vibrations as a source of industrial seismology.”
Proc., 4th Int. Conf. on the Application of StressWave Theory to Piles, The Hague, the Netherlands, F.
B. J.
[12] Dowding, C.H. (1996). Construction Vibrations.
Prentice Hall, Upper Saddle River, USA.
[13] Woods, R.D.(1997).Dynamic Effects of Pile
Installations on Adjacent Structures. NCHRP
Synthesis 253, National Cooperative Highway
Research Program, Transportation Research Board,
National Academy Press, Washington D.C., USA.
[14] Svinkin, M. (2004). Minimizing construction
vibration effects. Practice Periodical On Structural
Design And Construction, 9(2), 108—115.

There has long been a concern for the damages that
impact pile driving may bring to nearby existing structures.
Therefore, it is necessary to establish reliable references toensure the safety of the structures within a certain radiusfrom the source of pile driving.
Based on the findings of the study, different types ofsoils, possessing varying dynamic properties, greatly
influence the vibration magnitude, which makes thegeological conditions of the site a highly importantconsideration in analyzing ground vibrations, however, theinfluence of the soil dynamic properties can only beevidently seen at a distance near the vibration source, inthis particular case at a distance of three meters where
dispersive sets of readings are obtained.
At farther distances, vibration measurements are moresuited to be analyzed based on their attenuation behavior.
Measurements obtained on the predominantly sand and silt
layers on Site A damped considerably and clustered at a
closer range at a distance of 15 and 30 meters away fromthe vibration source regardless of the type of soil layers
the vibrations were emitted from.
Upon having observed a defined attenuation behavior,
two vibration prediction models were developed throughregression analysis and are capable of predicting vibration
magnitudes at any distance away from the vibration source
having a certain dominant frequency. The prediction
models were validated on Sites B and C and goodcorrelation was found to exist between the obtained
measurements.
The prediction models can be then be correlated withthe vibration limits presented in the study to map a zone ofinfluence, where structures that are vulnerable to structural
damage within a radius or source of pile driving can be
identified.

ACKNOWLEDGEMENTS
The support of GEOMATE is gratefully acknowledged.
The authors would also like to express their gratitude to
the thesis adviser, Dr. Jonathan Rivera Dungca for his
constructive comments and thorough review on this paper.
Much thanks is also given to the pile driving company for
granting permission to use the sites, and to Condoza
Software Solutions for providing the accelerometers,
which made the acquisition of data possible for thisresearch.

REFERENCES[1] Woods, R.D. (1997). Dynamic Effects of Pile
Installations on Adjacent Structures. NCHRP
Synthesis 253, National Cooperative Highway
Research Program, Transportation Research Board,
National Academy Press, Washington D.C., USA.

210

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

SOCIAL MEDIA GIS TO SUPPORT THE UTILIZATIONOF DISASTER INFORMATION
Kayoko Yamamoto and Shun Fujita
Graduate School of Information Systems, University of Electro-Communications, Japan

ABSTRACTThis study aims to design, develop, operate and evaluate a social media GIS (Geographical Information
Systems) specially tailored to mash-up the information that local residents and governments provide to support
information utilization from normal times to disaster outbreak times in order to promote disaster reduction.
Social media GIS, an information system which integrates Web-GIS and SNS in addition to an information
classification function into a single system, was operated in Mitaka City, Tokyo. The social media GIS used adesign which showed information as semitransparent circles depending on its present location data and contents.
This made it propose an information utilization system based on the assumption of disaster outbreak times when
information overload happens as well as normal times.
Keywords: Social Media GIS, Disaster Information, Disaster Reduction, Regional Knowledge, Support for
Information Utilization

accumulate the knowledge together. Moreover,Committee for Policy Planning on Disaster
Management – Final Report by the Central Disaster
Management Council (2012) [3] particularly
specified the importance of the roles of GIS(Geographic Information Systems) and social media
in the collection and transmission of disaster
information.
In Japan, as a disaster countermeasure, local
governments provide information to local residents
in the form of disaster prevention maps and hazard
maps which show local hazardous places, evacuationsites and so on. However, this information is mainly
published as maps that are in paper form or in PDF
format on the website. Therefore, it is difficult to
update the information on disaster prevention maps
and hazard maps in real time, and these forms of
information are not very suited to being shared
during a disaster outbreak. Further, so that
information can be efficiently accumulated andshared during a disaster outbreak, it is desirable that
information systems which people are accustomed tousing in normal times can be used as is during
disasters. However, when a disaster occurs, asituation where the amount of contributed
information increases, and there is an excessive
amount of information can be expected; therefore, itis necessary for systems to automatically classify
contributed information.
Against the above-mentioned background, thepresent study aims to design and develop a social
media GIS for reducing the effects of natural
disasters in normal times through to disasteroutbreak times. This is achieved by designing anddeveloping a social media GIS that integrates a
Web-GIS with an SNS and includes a function for

1. INTRODUCTION
According to the White Paper on Disaster
Management (2012) [1], measures for disasterprevention and reduction of the effects of natural
disasters in Japan include “self-help”, “mutual help
(cooperation)”, and “public help (rescue andassistance by public bodies)”.“Self-help” refers to
local residents, businesses, and other entities
protecting themselves from disaster; “mutual help
(cooperation)” refers to local communities helping
each other; and “public help” refers to measures bygovernment bodies such as national and local
governments. Further, the most fundamental form ofhelp was said to be self-help, which involves
measures taken by individuals. Nowadays, anybody,
anywhere, anytime can use an information system toeasily send, receive, and share information, andthrough the effective use of information systems,
disaster information possessed by local residents canbe accumulated and shared.
Additionally, the Science Council of Japan(2008) [2] divided “local knowledge” into “expert
knowledge” based on scientific knowledge, and“experience-based knowledge” produced by theexperiences of local residents, and indicated its
importance. Concerning “local knowledge” that isthe “experience-based knowledge” of local residents,and exists as “tacit knowledge” that is not visualized
if it is not communicated to others, as a measure fordisaster prevention and reduction, it is essential forthe “experience-based knowledge” to be transformed
into “explicit knowledge” which is of a form thatcan be accumulated, organized, utilized, and madepublicly available through the use of information
systems, and to have local related entities
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classifying contributed information. The social
media GIS enables disaster information provided by
local residents and governments to be mashed up on
a GIS base map, and for the information to beclassified and provided to support the utilization of
the information by local residents. Further, thepresent study also aims to operate the social media
GIS and evaluate the operation. During normal times
when there is no disaster, disaster information iscollected via the SNS, and local disaster informationis accumulated. Through this, the system aims to
improve the disaster prevention awareness of local
residents. Further, during disaster outbreaks when
there is an excess of information, if a
communications environment (electricity, internet,
information terminals, etc.) can be secured, thesystem aims to support evacuation activities byautomatically classifying disaster information,
promptly displaying it on the digital map of the
Web-GIS, and ensuring its noticeability. Throughhaving people use the system routinely and get used
to it in normal times in this manner, the possibility
that the system can be effectively used with noproblem as a means for reducing the effects ofnatural disasters even in tense situations during
disaster outbreaks can be anticipated.

times. Yamanaka et al. (2010) [7] proposed a
support system for situation assessment which
utilized text data with spatio-temporal information
and was designed for use by managers of facilities.
They conducted an experiment in a large-scale park
which involved a hypothetical disaster outbreak,summarized contributed text data information, anddisplayed the congested local situation on a Web
map using icons as messages. Further, Moriya et al.
(2009) [8] proposed a system in which text data
relating to local information in blogs was used todisplay the local situation (such as image,
impression, and atmosphere of a place) on a Web
map by using circular depictions.
In the above-mentioned prior research in fields
related to the present study, measures for reducing
the effects of natural disasters during normal times
include development of a system which uses GIS tosupport disaster prevention workshops, anddevelopment of a social media GIS whichaccumulates disaster information. Government
measures include the Japanese Ministry of Land,
Infrastructure, Transport and Tourism’s Hazard Map
Portal Site1), and disaster prevention maps andhazard maps provided by local governments. Further,
there is research which proposed a system which
utilizes text data to display the local situation on a
Web map using icons and circles which wasdemonstrated as being useful. However, except forin research by Murakoshi et al. (2014) [6], up to now,there has not been development of a systemdesigned for use from normal times through todisaster outbreak times which, in order to reduce theeffects of natural disasters on a sustained andcontinuous basis throughout such periods, supports
utilization of disaster information possessed by local
residents by automatically classifying disaster
information provided by local residents andpromptly displaying it, and providing it on a GIS
base map together with disaster information
provided by governments with which it is mashed up.
Therefore, the present study is based on the research
results of Murakoshi et al. (2014) [6] in particular,
and is unique in that it uniquely designs anddevelops a social media GIS aimed at realizing thefeatures outlined above. Further, the present study is
unique in that it operates the social media GIS andcarries out an evaluation of the operation.

2. RELATED WORKThe aim of the present study is to develop asocial media GIS that supports the utilization ofdisaster information and acts as a measure for
reducing the effects of natural disasters from normal
times through to disaster outbreak times. Therefore,prior research in fields related to the present study[4]-[21] can be classified into the following threeareas: (1) Research relating to system development
which utilizes GIS; (2) Research relating to thedevelopment of social media GIS; and (3) Research
relating to proposals for systems that classify
contributed information.
As representative examples of research in recent
years, in the research area of (1), as a measure to
reduce the effects of natural disasters during normal
times, Murakami et al. (2009) [4] developed asystem that supports disaster prevention workshops
by using a GIS. In the research area of (2), Okuma etal. (2013) [5] developed a social media GIS that
integrated a Web-GIS, an SNS, and Twitter, anddemonstrated improvement of local residents’disaster prevention awareness through theaccumulation of disaster information, as a measure
for reducing the effects of natural disasters during
normal times. Further, Murakoshi et al. (2014) [6]integrated a function for classifying contributed
information into the social media GIS of Okuma etal. (2013) [5], and developed a system effective as ameasure for reducing the effects of natural disasters
from normal times through to disaster outbreak

3. SYSTEM DESIGN
3.1. System Features
In the present study, a social media GIS effective
in supporting the utilization of disaster information
has a design in which a function capable ofclassifying and displaying contributed informationaccording to location information and content isincluded in a system which combines two web212

GEOMATE- Osaka, Nov. 16-18, 2015

applications – an SNS and a Web-GIS, as shown in
Fig. 1. The system has a structure in which the WebGIS and the contributed information classification
function are installed in the SNS. Use of the SNS
enables provision of information from local
residents, and enables local information concerning
disasters possessed by local residents as tacitknowledge to be converted to explicit knowledge
and accumulated and shared among users. During
normal times, through the SNS, local residents who
are the users of the social media GIS will contributeinformation such as information concerning
dangerous and safe places in disaster outbreak times
which is not noted in detail on disaster prevention
maps or hazard maps produced by the government.
In disaster outbreak times, through the SNS, thelocal residents will contribute similar information,
after ensuring their own safety. Further, disaster
information provided by the government in normal
times, such as that concerning overall degree of
danger and facilities that provide support in disasteroutbreak times, will be accumulated in advance, anddisaster information provided by the government
during disasters will also be collected andaccumulated.
The above-mentioned disaster information
contributed by local residents and disaster
information provided by the government will besaved in a database, and the former information will
be classified according to location information andcontent. Together with the disaster information fromthe government, the classified information will be
integrated into the Web-GIS, mashed up on a digital

map, and visualized. Therefore, on the Web-GIS
digital map, even in situations where a disaster has
occurred and there is an excess of information, highnoticeability of all contributed information can be
maintained, and on the digital map, users canefficiently accumulate and share disaster information
which includes location information among
themselves.
Based on the above, the usefulness of the present
system lies in the fact that it can ease the restrictions
mentioned in the following three points:
1) Bidirectionality of information transmission:
In this system, information contribution and viewing
functions are installed in the SNS. This allows allusers to contribute and view information anytime
from anywhere regardless of whether it is during
normal times or disaster outbreak times.
2) Mitigation of burden of obtaining information:
In this system, the system automatically classifies
contributed information and displays it on the digital
map of the Web-GIS. Therefore information can beprocessed in real time and users can easily determine
the riskiness and safety of places that is conveyed by
the information. Further, the situation where a user
does not know their own present location outside has
been anticipated, and the system obtains location
information and displays facilities that provide
support during disasters in the neighborhood where
the user is located, and also displays information
contributed concerning the neighborhood where theuser is located. Therefore the system can reduce theburden experienced when obtaining disaster
information.

Fig. 1 Design of Social Media GIS

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3) Mitigation of spatial and temporal restrictions:
The information terminals that this system isdesigned for use with are PCs and portable
information terminals. Concerning the latter, thesystem is designed for use with smartphones andtablet terminals, whose use has rapidly spread inrecent years. Both these types of portable
information terminals have touch panels with large
screens, making them easy to use when dealing withdigital maps, and both types allow connection to theinternet from anywhere via cellular phone data
communication networks. Anticipating the situationof users using the system outdoors as well during
both normal times and disaster outbreak times, asystem compatible with both PCs and portable
information terminals has been designed anddeveloped. Thanks to this, the system can be usedanytime, regardless of whether the user is indoors or
outdoors.

MAPPLE10000 conforms to the former geodetic
system coordinates; therefore, ArcTky2Jgd, which isprovided by Esri as product support, was used toconvert the MAPPLE10000 geodetic systemcoordinates to the new coordinates. Furthermore,
editing was performed using ArcMap 10.0 such thatdisaster information provided by Mitaka City andthe Metropolis of Tokyo, the regions for operation,and information peculiar to each place could be
input. Since the object of the present study is
disaster information, users can use the Web-GIS torefer to a detailed road system that includes small
streets that is output from MAPPLE10000, and by
doing so they can accurately check locations related
to contributed information, and in particular,
precisely display disaster information related to therisk and safety of evacuation routes in disasteroutbreak times. Further, the system has been set
such that when the PC interface for users is used, thedigital map can be viewed using full screen display.
Thanks to this, users can get a general view ofcontributed information over a wide range, andenlarge the digital map to check information indetail.

3.2. System Design
3.2.1. System configuration
The social media GIS of the present study isformed using three servers - a web server, a databaseserver, and a GIS server. The web server mainly
performs processing related to the SNS, andaccesses the GIS server and the database server tointegrate each of the functions. The SNS is operatedusing JavaScript and PHP, and the database server ismanaged using MySQL, and stores disaster
information contributed by local residents which iscollected through the SNS and disaster informationfrom the government. For the web server and the
database server, the rental server of the information
infrastructure center of the organization to which theauthor of the present study belongs was used. For
the GIS server, as the OS, Microsoft Corporation’s
Windows Server 2008 was used, and as the GIS
server software, Esri’s ArcGIS Server 10.0 was used.

3.2.3. SNS
In the present study, an SNS was selected as thesocial media for integration with the Web-GIS. The
SNS was uniquely designed and developed to suitthe objectives of the present study. An SNS waschosen because if an SNS is used, the system can be
uniquely designed and developed to suit theobjectives of use, and detailed system configuration
can be performed to suit regional characteristics ofthe regions in which the system is to be operated. In
this system, community functions limiting topics andusers were not included, and functions related touser personal data registration and profile
publication, information and image contribution andviewing, and commenting were uniquely designed tosuit to the objectives of the present study. Since theobject of this system is disaster information, forwhich reliability is regarded as important, the system
has been designed such that it requires users toregister, and each individual user can be identified
by either their real name or an assumed user name.
Therefore, the system is designed such that anenvironment in which it is difficult to makeinappropriate statements or behave inappropriately
has been prepared in advance, and the reliability ofcontributed information and comments can be
improved.
Further, a button function and ranking function
have been included in the SNS. Concerning the
button function, it is used when users view disaster
information which has already been contributed, andallows users to easily express that they have thesame information. Further, a ranking function was

3.2.2. Web-GIS
In the present study, for the Web-GIS, Esri’s
ArcGIS Server 10.0 was used, and for the GIS basemap of the Web-GIS, the SHAPE version (Rel.8) of
Shobunsha Publications, Inc.’s MAPPLE10000,
which is part of their MAPPLE digital map data andincludes detailed road system data, was used. As themap that was superimposed with this digital map
data, the user interface of Google Maps was used.
Among the options provided by Esri that are ArcGIS
Server 10.0 API targets, the Google Maps userinterface is the one that has been used the most in
earlier studies in fields related to the present study.
Concerning the superimposition of MAPPLE10000(SHAPE version) and Google Maps, Google Maps
employs the new geodetic system coordinates, while214

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added to the button function, and on the disaster
information ranking page of the user screen, the ten
information items for which the most amount ofusers have the same information are displayed indescending order.

examples are presented in advance on the initial
page of the system and in the instructions for use of
the system which are distributed to all users, so thatusers do not use vague expressions. Through this,due to having the users contribute informationaccording to the above-mentioned rules during
normal times, even in disaster outbreak times it canbe anticipated that users will continue to contributeinformation in a similar manner; therefore, theprobability that the system can appropriately classify
contributed information can be increased.
As outlined above, when contributed informationthat has been classified is registered in the database,flags are added to it; therefore, the system isdesigned such that fields are created in advance inthe database and flags can be stored in the database.
When illustration is made using the Web-GIS,
illustration is made using semitransparent circles
that are color-coded based on the flags (red for
danger, green for safety). Thanks to this, even incases where many pieces of information areconcentrated in certain areas, different types ofcontributed information can easily be distinguished.
Further, taking into account the accuracy ofacquisition of GPS location information of portable
information terminals, the radius of the circles was
set at 50 m.

3.2.4. Twitter
The present study employs a system design
which allows realization of long-term operation by
preventing reduction in the number of active users
and allows users to contribute information anytime
using portable information terminals regardless ofwhether they are indoors or outdoors. Therefore,
Twitter was selected from among the various formsof social media, and was mashed up with the SNS
which was uniquely developed, as mentioned in theprevious section. Of the various forms of social
media, Twitter has the easiest information
contribution method, and many tweets per day canbe expected, so it is essential for realizing long-term
operation of the system. The system is designed suchthat when users contribute information from Twitter,they tag the information with the hashtag #mtkgis
and with location information which employs a GPS.
3.2.5. Function
informationforclassifying

contributed

3.2.6. Function for checking facilities that provide
support during disasters

Anticipating situations where a lot of informationis contributed when a disaster has occurred and there
is an excess of information, a function which
automatically classifies and organizes contributed
information was set up and installed in the SNS.
Similarly to what is described in Moriya et al.
(2009) [8] in Section 2, a method in which words
related to local information are obtained fromcontributed information, and the local situation andthat location are displayed on the digital map via acircular illustration is employed. In this study,
contributed information is classified into two
categories according to whether it relates to either
danger or safety. The system automatically classifies
contributed information by searching contributed
content using multiple character string searches
which employ regular expressions. Specifically,
when the words “danger” or “dangerous” areincluded in contributed information it is classified as
information relating to danger, and when the word“safe” is included in contributed information it isclassified as information relating to safety.
Contributed information is classified right after
being contributed and can be displayed on the WebGIS digital map immediately.
Concerning this point, so that the system canaccurately automatically classify and display
contributed information as information related toeither danger or safety, rules for making
contributions which include specific illustrative

In this study, seven types of facilities included in
the Tokyo Metropolitan disaster prevention maps2)
are designated as facilities that provide support
during disasters. They are temporary stay facilities,
shelters, evacuation areas, water supply points,
medical institutions, stations for aiding return tohome (shops, etc. that provide support when people
for whom it is difficult to return home are returninghome on foot), and gas stands. Further, a system has
been designed which enables users to check facilities
that provide support during disasters in an optional
range based on present location or any location, andbased on any facility category. The facility names,
categories, and addresses of the facilities thatprovide support during disasters are published, but
the distance between two points cannot be calculated
based on addresses. Therefore geocoding is
conducted,
addresses
areconvertedtolatitude/longitude, and then that data is stored in the
database in advance.
Therefore, the system has a design which allows
the search range to be selected from the range of 50
to 500 m, and facility categories to be selected fromthe seven categories mentioned above. When arequest for a check of facilities that provide support
during disasters is sent from an information terminal,
in order to measure the distance between thedesignated point and disaster support facilities in the215

GEOMATE- Osaka, Nov. 16-18, 2015

area of that point, the difference between thelatitude/longitude that is sent from the information
terminal and the latitude/longitude of each disastersupport facility is used to calculate the distance
between the designated point and each disastersupport facility. Thus, facilities that are within arange designated by the user are confirmed, andfurther, facilities in a designated category aredisplayed on the digital map of the Web-GIS.

automatically classified by the system and illustrated
on the digital map in semitransparent circles divided
into red for danger and green for safety, so users caneasily distinguish between different types of
information.
4.1.2. Function for viewing disaster information
provided by governments
The overall degrees of danger (the degrees ofdanger calculated by combining the risk of building
collapse and the risk of fire) published on thedisaster prevention maps provided by Mitaka Cityand the Metropolis of Tokyo (the regions for
operation of the system), and the seven types ofdisaster support facility described in Section 3.2.6
were input into the GIS base map using Esri’s
ArcMap, and a disaster prevention map unique to thesystem of the present study was created. Throughthis, disaster information contributed by users who
are local residents and disaster information provided
by governments is all mashed up on the GIS basemap, and users can check all this information on the
one screen using the Web-GIS.

4. SYSTEM DEVELOPMENT
4.1. System Front End
In the present study, as is described in detailbelow, unique functions for users are operated, andsupport for utilization of disaster information isconducted.
4.1.1. Disaster information contribution and viewing
functions
When a user contributes disaster information,
they go to the contribution page using “Post disaster
information”. On the contribution page, they input“Title”, “Image”, “Main Text”, “Location”, andcontribute their information. Concerning input oflocation, when the PC interface is used, the user
clicks the location on the map and location
information is added; however, when the interface
for portable information terminals is used, it ispossible to add location information using a GPS.
Concerning display of the time, as described indetail in Section 4.2.2, for this system, thedevelopment of an operation system run by multiple
administrators that is operated starting from normal
times of no disaster is anticipated. If users movefrom a dangerous place to a safe one and thencontribute information, concerning contributed
information that describes circumstances at a time in
the past, administrators change the time display tomatch that time. Further, as described in Section 3.1,
in both normal times and disaster outbreak times,
users can contribute information about dangerous orsafe places and so on, communicate by commenting
about information contributed by other users, andupdate contributed information in real time byadding to or amending the contents of all contributed
information using comments.
When users log into the system, they can checkthe latest ten items of contributed information on theinitial page, and check contributed content andimages on the disaster information list page. Further,as outlined in Section 3.2.2, users can view allcontributed information on the digital map of the
Web-GIS, and when using the PC interface for users,
users can also view contributed information in detailusing full-screen display. Further, as described in
Section 3.2.5, all contributed information is

4.1.3. Function for checking facilities which provide
support in disaster outbreak times
Users can search for seven types of disastersupport facility by setting their choice of range andfacility category. Concerning search range, a radiusof 50 to 500 m from a designated point can beselected from a pull-down menu. Facility category
can be selected from among seven types, anddisaster support facilities in the area of a present
location or a location of the user’s choice can bechecked.
4.1.4. Disaster information ranking function
By viewing disaster information that has alreadybeen contributed and using the button function, users
can easily indicate that they have the same
information if that is the case. In addition, a ranking
function has been added to the button function, so
disaster information items for which the mostamount of users have the same information aredisplayed in descending order on the disaster
information ranking page of the user screen.
4.2. System Back End
4.2.1. System for classifying contributed information
When users contribute disaster information, thesystem classifies the contributed information. Before
contributed information is stored in the database,matching using regular expressions with respect tocharacter strings stored in PHP variables is216

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performed. In the case when a character string which
is a matching target matches when a preg_matchfunction is used, a value returned by the function is
obtained. In this case, 1 or 2 is assigned to thevariable for a flag, and is stored in the database
together with the contributed content. The character
strings which are targets of matching are the words“danger” and “dangerous” for information relating
to danger, and the word “safe” for information
relating to safety. Concerning this point,
misclassification due to fluctuation in expression isprevented by displaying contribution examples on
the initial page of the system in advance.
Information classified as outlined above is updated
when the web page is loaded, and is depicted on thedigital map of the Web-GIS using color-coded
semitransparent circles.

pieces of contributed information contradict eachother, it is necessary to check the information byreferring to information provided by other media andinformation systems, and then delete inappropriate
information. Further, it is necessary to delete andamend information provided by local residents andgovernments in the case that it has become
inappropriate in the circumstances of the disaster,which change by the minute.

4.2.2 System for management of contributed
information by administrators

5. TEST OPERATION AND OPERATION IN
THE REGIONAL COMMUNITY

Every user’s contributions of information andimage files are all stored as data in the database of
the system. Administrators manage users and check
contributed information using a list screen designed
especially for the purpose. Administrators can takethe measure of suspending accounts of users who
have made inappropriate transmissions or behaved
inappropriately, and if by any chance aninappropriate contribution is made, administrators
can delete the contribution with just one click.
Thanks to these features, there is no need foradministrators to search to see whether or not
inappropriate contributions of information have been
made within the system; therefore, their burden canbe lessened.
Further, for this system, it is desirable to develop
a system of operation in which starting from normal
times of no disaster, there are multiple
administrators, consisting of government employees,
people in firefighting and police organizations, andlocal residents. In addition, it is desirable that in thesystem of operation, information contributed by
local residents is routinely checked on a highfrequency basis, and the reliability and consistencyof information can always be guaranteed by taking
the measure of deleting inappropriate contributionsof information should any be discovered, andchecking that contributed information is being
appropriately classified by the system for classifying
contributed information mentioned in the previous
section. When a disaster has broken out, it isnecessary to take not only the measures outlined
above, but also to update published contributed
information in real time and always guarantee thereliability and consistency of the information bytaking the following measures. When multiple

In accordance with the operation process inTable 1, test operation of the social media GIS
designed and developed in the present study andevaluation of the test operation were conducted, andthen full-scale operation was conducted.

4.3. System Interfaces
The system has three kinds of interface – a PC
screen for users (Fig. 2), a portable information
terminal
screenespecially
optimizedforsmartphones and tablet terminals, and a PC screen
for administrators.

5.1. Comparison with Existing Services in Region
for Operation of System
In Mitaka City, the region for operation of thesystem, a plan for supporting the evacuation ofpeople requiring special help during disasters wasformulated in 2011, and a disaster prevention map3)
has also been created and provided. Table 2
compares disaster prevention maps relating to
Mitaka City with the features of the system of thepresent study. The Mitaka City disaster prevention
map can be used to check locations of large-area
evacuation sites designated by the city and theoverall degree of danger of locations within the city(the degree of danger in Mitaka City is ranked using
three levels only – 1 to 3). However, since it ispublished in PDF format, information cannot beviewed all at once. The Tokyo Metropolitan disasterprevention maps are published as digital maps, so it
is easy for users to view information they need, andthe maps include the facilities that provide support
during disasters that were mentioned in Section 3.2.6
and other information. However, the maps do nothave detailed disaster information concerning eachregion. Further, these government disasterprevention maps do not include any disaster
information that is experience-based information
possessed by local residents at all.

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Fig.2 PC screen for users

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Table 1 Operation process of the system
Process
1. Survey ofpresent
conditions
2. System
configuration

Aim
To understand efforts related
to tourism in the region for
operation (Yokohama City)
Configure the system in detailto suit the region for operation

PeriodJuly 2014

Specific details- Survey of government measures and internet services- Interviews with government departments responsible,
tourist associations, etc.
- Define system requirements- System configuration- Create operation system

3. Operation test

Conduct the system operation
test

September
2014- Create and distribute pamphlets and operating
instructions- System operation test

4. Evaluation ofoperation test

Reconfigure the system basedon results of interviews with
operation test participants

October
2014- Evaluation using interviews- System reconfiguration- Amendment of pamphlets and operating instructions

5. Operation

Carry out actual operation of
the system

OctoberDecember
2014- Appeal for use of the system- Distribution of pamphlets and operating instructions- System operation management

6. Evaluation

Evaluate the system based on
the results of questionnaires,the results of access analysis
which used log data during
the period of actual operation,and the results of analysis ofcontributed information

January
2015- Evaluation using web questionnaires , access analysis
which used log data, and analysis of contributed
information- Identification of measures for using the system evenmore effectively

August2014

Table 2 Comparison of features with disaster prevention map related to region for operation
Tokyo Metropolitan
disaster prevention
maps
Mitaka disasterprevention maps
The system of thepresent study

Use of digitalmap

Viewing means

Contributed information fromlocal residents

Automatic classificationof contributed information

Yes

Digital map

No

No

No

PDF

No

No

Yes

Digital map

Yes

Yes

Therefore, the system of the present study isuseful in that it can effectively provide detailed
disaster information specific to the region where it isoperated, by gathering and accumulating disaster
information contributed using an SNS by users who
are local residents and displaying the disaster
information on the digital map of a Web-GIS.
Further, it is useful in that disaster information
provided by users who are local residents and bygovernments can all be checked visually on thedigital map, useful in that the system automaticallyclassifies contributed information and immediately
displays it on the digital map, and can also handle
situations during disasters when there is an
information glut, and useful in that users can use thesystem to check facilities that provide support during
disasters. Even in disaster outbreak times, if anenvironment in which communication can be
conducted can be secured, disaster information canbe gathered, accumulated, and provided by thesystem, in the same way it is during normal times.
Therefore, the system can also supplement efforts
made by governments concerning disasters.
5.2. Operation Test and Evaluation of Operation
Test
Before full-scale operation was conducted, three
students in their twenties who were residing or
attending school in Mitaka City were selected and aone-week operation test was conducted. After theoperation test, the students who participated in theoperation test were interviewed. In the interviews,
the students expressed the opinion that a large-sized
map was desirable when viewing disaster
information on the digital map. Therefore, thesystem was redeveloped concerning this point only –the full-screen display for the PC interface for users,
mentioned in Section 3.2.2.

219

GEOMATE- Osaka, Nov. 16-18, 2015

Table 3 Outline of users and respondents to the questionnaire

Number of users(people)Number ofquestionnaire
respondents(people)Valid response
rate (%)

Aged 10
to 19

Twenties

Thirties

Forties

1

7

6

16

15

3

2

50

1

7

6

14

11

3

2

44

100.0

100.0

100.0

87.5

73.3

100.0

100.0

88.0

Fifties

SixtiesSeventies

Total

supporting utilization of disaster information inorder to reduce the effects of natural disasters fromnormal times through to disaster outbreak times –was conducted. Further, based on the results of theseevaluations, measures of using the system even more
effectively were identified.

5.3. Operation
When full-scale operation was conducted, local
residents aged eighteen years or over residing in,commuting to, or attending school in Mitaka City
were targeted as users. Pamphlets and operating
instructions were placed in the city hall and bases ofcitizen activities in order to appeal for use of thesystem. When first using the system, users registered
the personal information items of “User name”,“Password”, “Age group”, “Gender”, “Region”, and“Email address”. Concerning “Age group”,“Gender”, and “Region”, which were displayed on
the screen, users could set whether or not to makethese items public at their discretion. After
registering, users could use the system when theylogged in, and after logging in, users could reset theabove-mentioned items of personal information thatthey had registered.
Table 3 shows details of the users during thetwo-month period of operation. There were 50 users
in total, with 28 being male and 22 being female.
Users in their forties and fifties made up the greatest
proportion of users, at 32% and 30%, respectively,
while the proportion of users in their twenties,
thirties, and sixties and over was 14%, 12%, and10%, respectively, so it can be said that the system
was used by a wide range of age groups. After
having each user use the system for about one month,
web questionnaires were given to users, and use of
the system was evaluated. The number of valid
respondents to the questionnaire was 44 (a valid
response rate of 88%), with 26 valid respondents
being male and 18 being female.

6.1. Evaluation Concerning Use of the System
6.1.1. Evaluation of usability of system
As shown in Fig. 3, an evaluation of the usabilityof the system was conducted focusing on thesystem’suniquefunctions.
Concerning
distinguishing between different types of
information through the function for classifying
contributed information, 89% of responses were
“Easy” or “Fairly easy”, so it can be said that thesystem, automatically classifying contributed
information and displaying the information on thedigital map of the Web-GIS after color-coding it was
useful. Concerning checking information using thefunction for checking facilities that provide support
during disasters, 91% of responses were “Easy” or“Fairly easy”. It is considered that improving themethod of displaying present location and facilities
that provide support during disasters on the digital
map led to this high rating. Concerning use of theinterface for portable information terminals, 77% of
responses were “Easy” or “Fairly easy”, so it can besaid that developing a user interface optimized for
portable information terminals was effective.
6.1.2 Evaluation concerning
contributed information

6. EVALUATION

noticeabilityof

As shown in Fig. 4, an evaluation concerning thenoticeability of contributed information was
conducted. The evaluation asked users two questions– that is, whether it was easy to identify each pieceof contributed information on the digital map of the
Web-GIS even in places where contributed
information was concentrated in certain parts of themap, and whether it was easy to check informationin the vicinity of the user’s present location whenIn this section, according to the operation process
shown in Table 1, firstly, based on results of thequestionnaire, an evaluation of operation concerninguse of the system is conducted. Next, based on
results of access analysis utilizing log data andanalysis of contributed information from during fullscale operation, an evaluation of operation
concerning the aim of the system – that is, the aim of220

GEOMATE- Osaka, Nov. 16-18, 2015

viewing the digital map using the screen for portable
information terminals. The former question relates tothe ease of identifying each piece of contributed
information during disaster outbreaks in situations
where there is a glut of information, and 86% ofresponses were “Easy” or “Fairly easy”. Therefore,
it can be said that displaying contributed informationon the digital map in semitransparent circles colored
red or green according to the content of theinformation was useful. Further, the latter question
relates to the ease of checking information(information contributed by local residents,
information relating to facilities which provide
support during disasters, etc.) using portable
information terminals during disaster outbreak times
when users are mainly outside, and 84% of
responses to this question were “Easy” or “Fairlyeasy”. Therefore, it can be said that along with theuser interface optimized for portable information
terminals, the above-mentioned color-coded displayof contributed information on the digital map andthe display of facilities that provide support during
disasters using different icons for each category offacility (which was an improvement made to thesystem) were effective. Consequently, based on theabove evaluation results, it is possible that thesystem can be used as a means for reducing theeffects of natural disasters in disaster outbreak times
as well if a communications environment can be
secured, by using a digital map to provide disaster
information for portable information terminals ofusers who are mainly outside, and supporting
evacuation activities.

6.2. Evaluation Concerning Support of Utilizationof Disaster Information
6.2.1. Access count and means of access(1) Outline of access analysis
In order to analyze user trends, analysis was
performed using access logs collected during theperiod of operation. In the present study, Google
Analytics was used for collecting log data. Google
Analytics is an access analysis service provided freeof charge by Google, and is widely used. It can beused by setting up a tracking code in the source of a
web page.
(2) Results of access analysis
The total access count for the period of operation
was 2,537. Although there were differences in theaccess counts for each week, the average weekly
access count was approximately 24, and it can besaid that users used the system on a continual basis.
Concerning the access counts for each page, theaccess count for the contribution page was thehighest, being 34% of the total. Next was the accesscount for the page for checking facilities which
provide support during disasters (10%), followed by
the access count for the viewing page (9%). It can beseen that even if users did not contribute information,
many users checked disaster support facilities andviewed disaster information. Concerning means ofaccessing the pages, 95% of access was from PCs
and 5% was from portable information terminals;
therefore, it can be presumed that use was mainly
from PCs, and portable information terminals wereused as a supplementary means of access. However,the fact that portable information terminals were also
used shows that it is possible that the system canmitigate spatial and temporal restrictions during bothnormal times and disaster outbreak times.

6.1.3. Evaluation concerning effects of use of thesystemAs shown in Fig. 5, an evaluation of the effects
of using the system was conducted. The evaluation
asked users two questions – that is, whether they had
become interested in danger and safety in the region
through using the system, and whether they wanted
to use the system in the future as well. 91% of
responses to the former question were “Became
interested” or “Became a little interested”, so it canbe said that the disaster prevention awareness (whichleads to reduction of the effects of natural disasters)of more than half the users improved. Further, in
response to the latter question, 94% of responses
were “Want to use it” or “Lean toward wanting touse it”. Therefore, it can be anticipated that thesystem will continue to be used in line with its
objective of supporting the utilization of disaster
information in order to reduce the effects of natural
disasters.

6.2.2. Features of contributed information
Fig. 6 shows changes in the total number of users
and the total number of contributions of informationfrom week to week during the period of operation.
Both the number of users and the number ofcontributions of information gradually increased,
although there are differences in the rate of increase
from week to week. The total number ofcontributions of information was 260 (the weekly
average is approximately 26). Contributions fromTwitter were all made using portable information
terminals, but there were only six contributions fromTwitter. Of the total number of contributions, 81%
included images, and 4% of contributions werecommented on. The fact that the comment function
was also used and users were able to communicate
with each other indicates that it can be anticipatedthat this function will be used not only in normal
times but also in disaster outbreak times.
221

GEOMATE- Osaka, Nov. 16-18, 2015

Information concerning danger made up 20% of thetotal, information concerning safety made up 68%,
and other types of information made up 12%, andconcerned disaster prevention storehouses, watersupply points, and water wells for earthquake
disasters.
Further, as shown by Fig. 7, contributions of
information are dispersed over the whole area of
Mitaka City, so it can be assumed that the locationsof users’ places of residence, commutation

destinations, and schools were not concentrated inlimited areas. However, there are regions where theamount of contributions of information is somewhat
concentrated. In particular, there were many
contributions of information concerning the area ofthe train station. This is considered to be due to thefact that many people come and go through this kindof area daily, and there are many places where
narrow streets become even narrower due to bicycles,
luggage, and the like.
0%

20%

40%

60%

80%

100%

80%

100%

80%

100%

Distinguishing between different types of information throughthe function for classifying contributed information

Checking information using the function for checking facilities
that provide support during disasters

Use of the interface for portable information terminals

Easy

Fairly easy

Fairly difficult

Can’t say either way

DifficultFig. 3 Evaluation of usability of system
0%

20%

40%

60%

Identifying each piece of contributed information during
disaster outbreaks in situations where there is a glut of
information

Checking information using portable information terminals
during disaster outbreak times

Easy

Fairly easy

Fairly difficult

Can’t say either way

DifficultFig. 4 Evaluation concerning noticeability of contributed information
0%

20%

40%

60%

Whether to become interested in danger and safety in theregion through using the system

Whether to want to use the system in the future as well

Became interested / Want to use it
Became a little interested / Lean toward wanting to use it
Can’t say either way
Not became very interested / Not lean toward wanting to use it
Not become interrested / Not want to use it

Fig. 5 Evaluation concerning effects of use of the system

222

GEOMATE- Osaka, Nov. 16-18, 2015

(User)(Contributed Information)

60

300

50

250

40

200

30

150

20

100

10

50

0

0
1st
2nd 3rdweek week week

4th
5th
6th
7th
8th
9th
week week week week week week

Users

Contributed information

10th
week

Fig. 6 Changes in the total number of users and the total number of contributions of information during theoperation period

Fig. 7 Distribution of contributed information in the region for operation of the system (Mitaka City)
vicinity of the user and push-notifies the user of
information within a set range in the vicinity of theuser. Through this, passive users are notified of
information.
(2) Operation of the system using cloud computing
In order to be sure the system operates evenduring disaster outbreaks, the system is distributedin earthquake-proof data centers. Doing this allows
more reliable operation of the system than server
operation by individuals. Further, combining
ArcGIS Online (which is provided by Esri, Inc.)

6.3. Identification of Measures for Using the
SystemBased on the results of the evaluation of theoperation in this section, measures for using thesystem even more effectively were summarized into
the two points below.
(1) Notification of information to passive users
GPS information of users’ information terminals
is continually obtained at a set interval, and thesystem searches for disaster information in the223

GEOMATE- Osaka, Nov. 16-18, 2015

with the system when it is used allows all servers to
be operated using cloud computing.

like to use the system again in the future. Therefore,
it can be anticipated that the system will continue tobe used in the future in accordance with its
objectives. Access log analysis showed that thesystem was continually accessed throughout theperiod of operation and that 260 pieces of disaster
information were contributed, dispersed throughout
Mitaka City.
Future topics for research include expanding thestages of use of the system to times of post-disaster
restoration and redevelopment, cooperating with
firefighters, police, and so on and operating thesystem with the participation of a wider user base,and increasing the track record of use of the systemby operating it in other regions as well, and further
increasing the significance of using the social media
GIS developed in the present study.

7. CONCLUSION AND FUTURE RESEARCHTOPICS
The conclusion of the present study can besummarized into the following three points:(1) A social media GIS which integrated a WebGIS, an SNS, and Twitter and which included afunction for classifying contributed information was
designed and developed. A system which supports
utilization of information in order to reduce theeffects of natural disasters which anticipates use not
only in normal times but also use during disasters
when there is an information glut was proposed. The
system supports utilization of information by
depicting contributed information based on location
information and content using color-coded
semitransparent circles, and by displaying
information based on information about present
location. Mitaka City in the Metropolis of Tokyo
was selected as the region for operation of thesystem. After a survey of existing conditions was
conducted, the system was developed in detail.
(2) Since full-scale operation was to be
conducted for ten weeks, a one-week operation test
was conducted in advance and an area for
improvement of the system was identified. After that,the system was reconfigured. People targeted as
users of the system were those residing in,commuting to, or attending school in Mitaka City
aged eighteen years of age or over. The number ofusers was fifty in total. Users in their forties andfifties made up the greatest proportion of users, at
32% and 30%, respectively, while the proportion ofusers in their twenties, thirties, and sixties and above
was 14%, 12%, and 10%, respectively, so the system
was used by a wide range of age groups. During theperiod of operation, users accessed the system from
PCs and portable information terminals, andcontributed and viewed information.
(3) An evaluation of the operation was conductedbased on questionnaires to users and access log
analysis. Results from the questionnaires showedthat due to the function for classifying contributed
information and the function for checking facilities
that provide support during disasters, it was easy forusers to distinguish between different types of
information and check information. Further, it ispossible that the system can provide disaster
information to portable information terminals ofusers who are mainly outside during disasteroutbreak times as well, and can be used as means for
reducing the effects of natural disasters. In addition,
an improvement in disaster prevention awareness(which leads to reduction of the effects of natural
disasters) was observed as a result of use of thesystem, and many users responded that they would

ACKNOWLEDGEMENTS
In the operation of the social media GIS and the
web questionnaire of the present study, enormouscooperation was received from those in Mitaka Cityof Tokyo metropolis and the neighboring area inJapan. We would like to take this opportunity to
gratefully acknowledge them.
NOTES
1)

2)

3)

Japanese Ministry of Land, Infrastructure,
Transport and Tourism’s Hazard Map Portal
Site
http://disapotal.gsi.go.jp/，Accessed 2014,
August 20．
Tokyo Metropolitan disaster prevention maps
http://map.bousai.metro.tokyo.jp/，Accessed
2014, August 20．
Mitaka City disaster prevention map
http://www.city.mitaka.tokyo.jp/c_service/003/
003310.html，Accessed 2014, August 20．

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225

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

CLAY-CEMENT STABILIZED CRUSHED ROCK BASE FORWESTERN AUSTRALIAN ROADS: STRENGTH PROPERTYINVESTIGATION

1,2

Peerapong Jitsangiam 1, and Matthew Merrin 2
Dept of Civil Engineering, Curtin University, Australia

ABSTRACTWith the current base course material in Western Australia, namely hydrated cement treated crushedrock (HCTCRB) base, roads using HCTCRB require excessive maintenance causing from its uncertainties.
This study aims to determine specific strength properties of a potential replacement material of a clay-cement
stabilized crushed rock. The findings showed that a crushed rock material with a newly developed 3% claycement binder, possessed unconfined compressive strengths and resilient moduli significantly greater than thatof HCTCRB. The developed stress dependent equation also purports that this material admixture is still
exhibiting unbound performance characteristics. A material’s ability to acquire the accompanying strength
advantages of a 3% clay-cement binder, whilst still potentially resisting common failure methods such as
shrinkage cracking, suggests that b a s e d o n its potential performance as a base course layer in a pavement
structure, clay-cement stabilized crushed rock base is considerable to be a viable base course material for Western
Australia.
Keywords: Base course materials, Clay-cement stabilization, Crushed rock base, Pavements, Base course layers

INTRODUCTION

developed clay-cement binder.

Flexible highway pavements in Western Australia
are constructed in a layered idealistic design, withthe pavement layers preferably constructed withlocally sourced materials due to the associatedeconomic benefits. When local materials do notpossess sufficient strength for this purpose
however, an alternative solution is to implement
artificial (e.g., cement and lime) stabilization
practices, providing a solution that is more
economically viable, and one which allows theengineering properties of the material to becontrolled [1]. Whilst this is a common practice
adopted globally in pavement engineering practices,
Main Roads Western Australia have been
fundamentally unsuccessful when trying to
implement this procedure for Western Australianpavements. The most recent artificially stabilizedbase course material implemented, hydrated cement
treated crushed rock base (HCTCRB), has someuncertainties (i.e., drying shrinkage cracking, proper
curing process and manufacturing issues), leading toroads using HCTCRB required excessive
maintenance and rehabilitation. This has requireda larger amount of capital expenditure than that
expected. Therefore the investigation into a betterbase course material for Western Australianpavement structures is required. This paper
therefore presents specific strength parametersdeveloped in the laboratory investigation of acrushed rock base material stabilized with a newly

HISTORY OF BASECOURSESTABILISATION IN WESTERN AUSTRALIA
From the first attempted stabilization of Western
Australian base course’s in the 1970’s, ambiguities
in the resulting pavement performances have led toMain Roads Western Australia
essentiallyprohibiting the use of stabilized pavements, andplacing a number of limitations on the use ofmodified pavements [2].
In 1975, extensive research was commissioned
into the development of a new cement binder toreplace the inefficient bitumen stabilization
practices that were being implemented. Initiallaboratory investigations produced promising results,
with limestone stabilized with 2% cement possessing
strength greater than that of limestone stabilized with
3% bitumen. When sections of Leach Highway were
utilized as trial pavements however, it was establishedthat fatigue cracking was a major issue for thematerial due to the stiffness of the cement [3].
As a result, Main Roads Western Australia
reverted back to using local materials in their virginstate, predominantly crushed rock base. After the
failure of segments of the Kwinana Freeway in
1992 however, attributed to the moisture sensitivityof the crushed rock base, Main Roads once again
commissioned extensive research into thedevelopment of a new base course material. Afterdetailed laboratory testing, HCTCRB was developed,
226

GEOMATE- Osaka, Nov. 16-18, 2015

and introduced in a trial section of Reid Highway in1997. In 2003, field testing carried out indicated thatthematerial
exhibitedstrongpavement
performance characteristics. In 2009 however,similar testing showed that the material was
exhibiting characteristics of an unbound material,
due to a retardation of the cement content over time[4]. This not only reduced the strength, but alsomeant the crushed rock base course was far more
permeable and susceptible to moisture ingress,
which was the original problem faced in 1992.
Whilst periodic changes have since been made to
the material specification of HCTCRB, it isspeculated that the material itself may be erraticwith regard to fundamental elements such as quality
control, and material uniformity [5]. With nosolution developed to mitigate the sporadic
performance of Western Australian pavements,
the investigation into a new material was carriedout, with the laboratory testing regime presented in
the subsequent section.

strength, compressive modulus, resilient modulus,
and the stress dependent parameters of the claycement stabilized crushed rock base.
Selection of Optimal Binder Ratio
A clay-cement stabilized crushed rock basematerial has never been previously developed, so
the ratio of clay to cement that will optimize theperformance of the crushed rock base wasinitially determined. This involved compaction
testing and unconfined compressive strength
testing on the fines of a crushed rock base material,stabilized with 2% kaolin clay, 1% kaolin clay and
1% cement, and 2% cement respectively. The finesof the crushed rock base material were utilized
due to soil-cement matrices developing easieraround smaller sized particles [ 1 0 ] , therefore
the effect that each binder would have would bemagnified.
The unconfined compressive strength of eachmaterial admixture is presented in Fig.1. As thisfigure details, there is a direct increase in thestrength of the material with an increase in
cement content. Whilst the 2% cement specimens
exhibited additional strength, the 1% clay – 1%
cement samples still possessed a strength greaterthan that of HCTCRB at the same age, ofapproximately 0.75 MPa. This was also sufficientfor the material to be classed as a modified
pavement material.
Compaction testing also detailed that the 1%
clay – 1% cement samples possessed a larger
maximum dry density than the 2% cement
samples, with past research detailing that soilmaterials with a larger dry density will have an
increase in strength occurring over a longer
period of time [11]. With past field investigations
by Main Roads also concluding that 2% cement
samples are subject to fatigue failures [3], it
was established that a binder of one part kaolin
clay to one part cement was optimal. Therefore, forthe next stage of testing, all specimens wereprepared with a clay-cement binder at a 1:1 ratio.

LABORATORY TESTING METHODOLOGYWith the fundamental parameter of a basecourse being strength [ 6 ] , an investigation into
specific strength properties of a clay-cement
stabilized crushed rock base has been undertaken.
Whilst the major faults with HCTCRB werestabilizer permanency and the subsequent moisturesusceptibility, if the clay-cement stabilized crushedrock did not possess sufficient strength, then an
improvement in these faults would be an
insignificant
development.
Therefore,
theunconfined compressive strength and resilient
modulus of the material were determined, withthese two parameters principle factors indetermining the strength and performance of acement treated pavement material [7]. To
determinethesematerial
properties,
twostandardized tests were undertaken, namely an
unconfined compressive strength test in accordancewith [ 8 ] , and a repeated load triaxial test inaccordance with [ 9 ] .
These tests were carried out on specimens ofa naturally occurring crushed rock material,stabilized with a composite artificial binder. Thisconsisted of a synthetic kaolin clay, and general
purpose cement, ranging in contents of 1%, 2%, and
3% by dry mass respectively.
EXPERIMENTAL RESULTS & ANALYSISThe initial stages of testing developed the
optimal binder ratio of clay to cement required forthe desired performance as a base course material.
Subsequent testing was then carried out to obtain
the data previously detailed in the laboratory
regime, namely the unconfined compressive227

GEOMATE- Osaka, Nov. 16-18, 2015

Fig. 1

Unconfined Compressive
Varying Artificial Binders.

Strength

Unconfined Compressive Strength of
Cement Stabilized Crushed Rock Baseunconfined compressive strengths greater than 1
MPa. Clause 1.1.12 in Main Roads Engineering
Road Note 9 however, specifies that pavements
must not incorporate modified granular materials
with a 7 day unconfined compressive strength
exceeding 1 MPa [ 2 ] . This alone would
immediately suggest that that this material would
not be a viable option to be utilized in pavement
structures in Western Australia. With this material
having never been previously developed or tested,
and with Main Roads specifications primarily
developed off empirical methods, it is near
impossible to categorize its overall performance offof this alone however.
The 28 day unconfined compressive strength
results presented in Fig.2, also enables theclassification of the material based off of Austroads’
established criteria. This method suggests thatsamples with 1% binder are modified, samples with
2% binder are lightly bound, and that samples with
3% binder are a heavily bound pavement material[12]. This criteria proposes that the 2% and 3%
material admixtures exhibiting bound properties,
will be susceptible to unfavorable failure modes
such as transverse dry shrinkage cracking. Onceagain however, with Austroads’ classification
criteria also based off an empirical approach, it isarduous to classify a material solely based off of its
28 day unconfined compressive strength.
In comparison to the base course materialpresently adopted in Western Australian pavements,
HCTCRB, all clay-cement stabilized crushed rockbase specimens tested exhibited larger unconfinedcompressive strengths. With HCTCRB possessing
a 28 day unconfined compressive strength ofapproximately 0.93 MPa, even specimens with aslittle as 1% clay-cement binder produced a higher
compressive strength.
The unconfined compressive strength was not
the only performance parameter obtained from theseresults, with the compressive modulus of the material
also determined. Determined as the gradient of thelinear sections of the stress-strain curve, thecompressive modulus of crushed rock basespecimens with varying percent clay-cement binder
contents is presented in Fig.3. As this figure
displays, there is a linear relationship between thecompressive modulus and percent binder additive
for clay-cement stabilized crushed rock base, forthe binder contents tested in this thesis. That is, forevery percent increase in clay-cement binder content,
there is also a direct increase in the compressive
modulus of the material of approximately 63 MPa.
This suggests that whilst additional binder content
is increasing the ultimate strength of the material,
it is also increasing its ability to undergo smaller
deformation rates when compressed, as well as its
ability to resist failure phenomena such as
shrinkage cracking. This is very beneficial,

of

Clay-

The unconfined compressive strengths for crushedrock base specimens stabilized with 1%, 2%, and 3%
clay-cement binder contents, at 7 and 28 days
respectively, is presented in Fig.2. As this figure
details, there is a strong linear correlation between an
increase in binder content and an increase in thematerial’s unconfined compressive strength for bothcuring time durations. There is also a distinct
increase in strength over time, with the 2% and 3%
specimens tested at 28 days significantly stronger
than those tested at 7 days.

Fig. 2

Unconfined Compressive Strength with
Respect to Varying Curing Time andPercent Binder Content.

All samples tested, even those stabilized with aslittle as 1% of a clay-cement binder, possessed
228

GEOMATE- Osaka, Nov. 16-18, 2015

especially for a pavement base course material.

Fig. 3Compressive Modulus with Varying Percent
Binder Content.

Fig. 4

Figure 3 also displays how specimens modified
with a 1% clay-cement binder, possess a very low
compressive modulus. This advocates that thismaterial mixture is susceptible to excessive
deformation under loading, which can lead to thepremature failure of not only the base course layer,
but the entire pavement structure. This suggests thatwhilst the 1% binder content samples developed
sufficient unconfined compressive strength, thematerial mixture would not be a feasible basecourse material to be utilized in pavement
engineering
practicesdue
tounfavorable
serviceable characteristics.

Resilient Modulus of Varying Percent
Binder Content.

In comparison to the peak resilient modulus of
HCTCRB of approximately 1130 MPa, the 1%
clay-cementbindersamples
exhibitedasignificantly lower resilient modulus of only 757
MPa, suggesting that it will be susceptible to greater
deflections under the same loading. This
reinforces the compressive modulus data obtained
for the 1% binder samples, further reiterating thatthe serviceable characteristics of the material are
not sufficient. This further advocates that theperformance of a material cannot be characterized offof its unconfined compressive strength alone. Thisis supported with HCTCRB possessing a smaller
unconfined compressive strength than the 1%
clay- cement binder specimens, however providing
a better elastic response to traffic loading.
The 2% and 3% clay-cement binder samples
however exhibited larger peak resilient moduli
than the HCTCRB, of approximately 1189 MPa and1277 MPa respectively, purporting that the elastic
response of these material admixtures under thesame repeated loading is greater. With the 3% claycement binder specimens possessing the largest
resilient modulus, previous testing data isreinforced, suggesting that this material admixture
will exhibit the strongest performance characteristics
as a pavement base course layer.
The resilient modulus data also enabled thedevelopment of the stress dependent equation of
the material, with this parameter further aiding in theidentification of a material’s performance, denoting
whether or not a material will potentially exhibit
bound or unbound characteristics. Increasing theapplied bulk stress increases the hardening or stiffnessof materials, which results in a higher resilient
modulus. It is established that bound materials that
already exhibit high stiffness’, present erratic
resilient moduli responses with increased bulk

Resilient Modulus of Clay-Cement Stabilized
Crushed Rock Base.
The resilient moduli for crushed rock basespecimens at 28 days, stabilized with 1%, 2%, and
3% clay-cement binder contents respectively, ispresented in Fig.4. As this figure details, for every
increase in percent binder content tested in thisthesis, there is a corresponding increase in theresilient modulus of the material. That is, the materials
elastic response, or ability to withstand excessive
deflections under repeated traffic loading, directly
increases with an increase in binder content.
This reinforces the information obtained fromtheunconfinedcompressive strength andcompressive modulus data. Unlike these two
previous sets of data however, the relationshipbetween the resilient modulus and the percent
binder content of the material is not linear in nature.

229

GEOMATE- Osaka, Nov. 16-18, 2015

stresses. Therefore with the bulk stresses applied tothe specimen plotted against the corresponding
resilient modulus, it is established that materials
displaying uniform resilient moduli responses withthe applied bulk stress state, are potentiallyexhibiting unbound characteristics [13].
The stress dependent equation for crushed rockbase specimens at 28 days, stabilized with 1%, 2%,
and 3% clay-cement binder contents respectively, ispresented in Fig.5. With uniform resilient moduli
data with applied bulk stresses enabling a strong
trend line to be developed for all material
admixtures, it is suggested that specimens
stabilized with 1%, 2% and 3% clay-cement binder
contents respectively, are potentially exhibiting
unbound characteristics.

Fig. 5

CONCLUSIONS AND RECOMMENDATIONSWith the current base course material inWestern Australian pavement structures requiringexcessive capital expenditure due to premature
pavement failure phenomenon, this paper aimed topresent the strength properties of a potential
replacement material. A naturally occurringcrushed rock base material, stabilized with 3% by dry
mass of a clay-cement composite binder, exhibited
unconfined compressive strength and resilient
modulus characteristics significantly greater thanthat of HCTCRB. The material also potentiallyexhibits
unboundcharacteristics, suggesting
common pavement failure phenomena such as
shrinkage cracking will be mitigated.
However, empirical evidence from Main Roads
Western Australia, as well as Austroads, will
suggest that this material will be subjected tounfavorable pavement failure mechanisms such as
tensile fatigue cracking. Main Roads Western
Australia also specify that this material cannot be
incorporated into any pavement structural systems
due to these reasons.
Whilst specific strength parameters are sufficient,
it is arduous to classify the feasibility of theimplementation of a material as a base course layer
off of this alone. Evidence of this fact is presented
with the performance of HCTCRB. Whilst thematerial possessed adequate strength, it failed due
to other mechanisms of stabilizer permanency andmoisture
susceptibility.
Therefore
further
investigation is required, to determine other key
base course performance parameters, to establish
the viability of a clay-cement stabilized crushedrock material as a base course layer in Western
Australian pavements.

Stress Dependent Equation of Varying
Percent Binder Content.

Whilst, in comparison to HCTCRB, the increase
in resilient modulus for the 2% and 3% clay-cementbinder specimens is not substantial, the mostimportant observation is that both admixtures arepotentially exhibiting unbound properties. Whilst
HCTCRB also exhibits these properties, anadditional hydration phase was required for this,leading to differential performance results due toarduousmaterial mixing and construction
procedures.
The most auspicious realization from thisresearch is the fact that the 3% clay-cement binder
samples are exhibiting strength characteristics
significantly better than HCTCRB, whilst still
potentially displaying unbound characteristics. A
material’s ability to acquire the accompanying
strength advantages of a 3% artificial binder, whilst
still potentially resisting common failure methods
such as shrinkage cracking, suggests that its
performance as a base course layer in a pavement
structure is very promising.

REFERENCES[1] Horpibulsuk S, Chinkulkijniwat A, Suddeepong
A, Neramitkornburee A and Suksiripattanapong
C, “Cement Stabilization for Pavement Material
in Thailand”, Geotechnical Engineering Journal,
Vol.45, No.1, 2014, pp.95-102.
[2] MAIN
ROADS
Western
Australia,
Engineering Road Note 9. Perth: MAIN
ROADS Western Australia, 2012.
[3] Nikraz H, and Yeo Y S, "Cement Stabilisation ofRoad Base Course: A Chronological
Development in Western Australia.", Australian
Geomechanics, Vol.44, No.3, 2009, pp.63-72.
[4] Harris D, and Lockwood N, Reid Highway
Basecourse Test Sections Performance toDecember 2008: Revision of Report 2004/17m.
WA: Main Roads Western Australia, 2009.
[5] Jitsangiam P, Chummuneerat S, Phenrat T,
andNikrazH,"Characteristics
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Performance of Cement Modified-Base Course
Material in Western Australia.", Journal of
Materials in Civil Engineering, Vol.26, No.9,
2013.
[6] MAIN ROADS Western Australia, A Guide
to the Selection of Naturally Occurring
Materials as Base and Subbase in Roads inWestern Australia. Perth: MAIN ROADS
Western Australia, 2003.
[7] Jiang Y J, and Fan L F, "An Investigation of
Mechanical Behavior of Cement-Stabilized
Crushed Rock Material Using Different
Compaction Methods.", Construction and
Building
Materials, Vol.48, No.0, 2013,
pp.508-515.
[8] Standards Australia, Methods for Preparation
and Testing of Stabilized Materials UnconfinedCompressive
StrengthofCompacted Materials, 2008.
[9] Austroads, DeterminationofPermanent
Deformation
andResilient
ModulusCharacteristics of Unbound Granular Materials
under Drained Conditions. Sydney: Austroads
Inc., 2007.
[10] Pakbaz M S, and Alipour R, 2012. "Influenceof Cement Addition on the Geotechnical
Properties of an Iranian Clay." Applied Clay
Science, Vol.67–68, No.0, 2012, pp.1-4.
[11] Li H, Zhu Y, Zhang J, and Lin C, "Effects ofTemperature, Strain Rate and Dry Density on
Compressive Strength of Saturated Frozen
Clay." Cold Regions Science and Technology,
Vol.39, No.1, 2004, pp.39-45.
[12] Austroads, Guide to Pavement Technology Part
4d: Stabilised Materials. Sydney: Austroads Inc.,
2006.
[13] Buchanan S, Resilient Modulus: What, Why,and How?, 2007.

231

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

APPLICATION OF FUZZY SET THEORY TO SEISMIC
VULNERABILITY ASSESSMENT
Kshama S. Roy1, Md. Jakiul Hassan2, Kamrul Islam3, Naveel Islam4
Faculty of Engineering and Applied Science, Memorial University of Newfoundland, Canada
2
Mechanical Engineer, Lower Churchill Project, Nalcor Energy, Newfoundland and Labrador, Canada
3
Department of Civil Engineering, Military Institute of Science and Technology (MIST), Bangladesh
1, 4

ABSTRACT
Seismic vulnerability assessment of the existing buildings has become a prime concern now-a-days due to themajor threats that have been imposed by the several earthquakes all over the world. Recent studies showed that
the Turkish Method may be applicable for the vulnerability assessment of the existing buildings in thedeveloping countries. This article addresses the investigation of the significant factors that influence theperformance score of the Turkish Method with particular attention to the application of the Fuzzy set theory. The
results of the detailed survey that was done in the old town of the Dhaka city are extensively used for theapplication of the Fuzzy set theory. A comparative analysis is done with the results of a statistical analysis basedon Design of Experiment (DOE) methodology. Finally the comparative evaluation shows that pounding effect,
topographic effect, plan irregularity, area of the ground floor and the number of frames in the X and Y directions
are the most significant factors that influence the performance score of the Turkish Method.
Keywords: Seismic Vulnerability, Turkish Method, Fuzzy Set Theory, DOE, Performance Score
earthquake can cause unbearable devastation andpose risk to lives of people and their assets as well as
the basic infrastructure of the country. Seismic
vulnerability assessment of existing buildings ofDhaka city, therefore, has gained a significant
attention to the researchers and engineers. According
to the opinion of a German earthquake
expert [4] from the University of Kassel, Germany,
Turkishmethodofseismic
risk
assessment may be applicable for a developing
country like Bangladesh. In this method, thediscriminant scores obtained from several
discriminant functions are combined in an optimal
way to classify existing buildings as “safe”, “unsafe”
and “requires detailed evaluation” [1].
The Turkish method, based on the statisticalanalysis of observed earthquake damages of RCF
buildings in Turkey, and related building attributes
is expected to provide reasonable results forassessment of buildings in Bangladesh according to[5]-[6]. Several researchers in the past have used thismethodology for vulnerability assessment ofdifferent cities of Bangladesh [7]-[13]. Turkishmethod is a two level seismic risk assessment
procedure considering a number of factors
described by Ozcebe et al. [5]. Considering thefactors it gives a performance score for eachbuilding being assessed. But as the method uses anumber of empirical equations and tables fordetermining the score, it is difficult to find the mostsignificant factors that affect the performance score.
The present study focuses on developing a fuzzy
based technique to qualify and quantify thesignificant factors of Turkish method described by

INTRODUCTION
Natural disaster like earthquake causes damage
to civil engineering structures resulting indeaths, economic loses, and damage ofinfrastructures. Most recent earthquakes in Nepal(2015), Japan (2011), New Zealand (2010 and2011), Mexico (2011), Indonesia (2010 and 2011),
China (2010) and Haiti (2010) have brought
attention to researchers to predict the severity ofdamage of infrastructures. Many cities of thedeveloping countries of the world contain asignificant number of vulnerable buildings underconsiderable seismic risk. Seismic vulnerability
assessments of these buildings are gaining more
attention to assess correctly the degree of risk towhich such critical structures may be subjected to in
the event of an earthquake. Vulnerability assessment
procedures found in the literature [1]-[3] are mainly
based on building plan, foundation, structural
system, structural and non-structural components,and structural performance. Such vulnerability
assessment procedures show accurate and realistic
performance of a building only if the building wasbuilt according to proper building design code andarchitectural features. Recent years, Bangladesh
has seen the construction boom of multi-storied(mainly 6 stories and above) reinforced concrete
frame (RCF) buildings in the capital city of Dhaka
where standard building codes are not followed
properly. Most of these buildings have open parking
space in the ground floors resulting in softstory construction while some of them havestructural irregularities. Moderate to severe232GEOMATE- Osaka, Nov. 16-18, 2015

Ozcebe et al. [5] and also on comparing the fuzzy set
theory with Design of Experiment (DOE)
methodology for seismic vulnerability assessment of
RCF buildings in Dhaka city. Details of the DOE
methodology can be found in authors’ previous
works [14] and is not repeated here.

Strategy of Experimentation by DOE
The levels for the factors of the analysis arechosen on the basis of the survey that was done in
the ward 75, 76 and 77 of the old town of Dhaka city.
The details of the survey results can be found in
Ahmed et al. [15]. A Microsoft Excel software
program is developed for the analysis of theTurkish Method [16], and the data are analyzed by
the non-commercial software, Design Expert 7.1.3.
Detailed descriptions of DOE methodology can befound in Roy et al. [14]. Ten (10) parameters of theLevel 2 survey of Turkish method are used as factors
for the DOE analysis. The factors considered are: A)
Pounding effect, B) Topographic effect, C) Planirregularity, D) Area of the ground floor, E) Area of
the above floor, F) No of frames in X direction, G)
No of frames in Y direction, H) Column dimension x, I) Column dimension -y and J) No of columns.
Performance score is used as response in the DOE
analysis. Among the ten factors A) Pounding effect,B) Topographic effect and C) Plan irregularity are
considered as categorical factors and others are
considered as numeric factors. For the categoricalfactors, the high and low levels are “yes” and “no”
respectively. The levels of other numeric factor are
given in Table 1. As ten (10) factors and two (2)levels are considered, 210 = 1024 runs were required
if full factorial design was used. So to minimize
the runs, fractional factorial design is used which
reduces the required runs from 1024 to 128.

TURKISH METHODThe Turkish seismic vulnerability assessment
method for buildings consists of two-levelevaluation: (i) level one - walk down evaluation and(ii) level two - measurements at ground leveland basement [5]-[6]. The walk down evaluation
procedure mainly consists of taking notes ofdifferent external features of the building. Buildings
are then described as belonging to high risk,
moderate risk or low risk. After level 1 survey,
buildings falling into the moderate and high risk
levels can be subjected to more detailed level 2
surveys to determine their performance scores.
Details of Level 1 and Level 2 Survey procedures
can be found in Ahmed et al. [15] and Roy et al. [16]and is not repeated here. Once the vulnerability
parameters of a building are obtained from two-level
surveys and its location is determined, the seismic
performance scores for survey levels 1 and 2 are
calculated according to several empirical equations
and tables [17]. A general equation (Eq.1) for
calculating the seismic performance score (PS) can
be formulated as follows,
PS = (Initial Score) – Σ (Vulnerability parameter) x(Vulnerability Score)(1)

Results from DOE Analysis
The results show that pounding effect, topographic
effect, plan irregularity, area of the ground floor,
number of frames in X and Y direction and theinteraction between the number of frames in
X and Y direction are the most significant factors. A
linear regression model is developed from the DOE
analysis for computing the performance score tocompare with the results obtained from the Turkish
Method. Regression Equation for Performance Score
in terms of coded factors is shown in Eq. 2.

The detailed procedures for vulnerability scoring canbe found in Sucuoglu and Yazgan [17].
DOE METHODOLOGY
DOE Methodology involves the study of any
given system by a set of independent variables(factors) over a specific region of interest (levels)and provides a straightforward technique withlinear graphs to determine the relationship(interactions) between the considered factors, which
can be used for practical experimentation [18]. By
using DOE, it is possible to investigate theexperimental process, to screen the importantvariables (or factors), to build a mathematicalmodel with prediction, and even to optimize theresponses where necessary. Among the different
techniques in DOE method, Fractional factorial
design is one of the common techniques forexperimentation which is used in this study. The
detailed procedures for DOE methodology appliedin this study can be found in Roy et al. [14].

ln(PS) = +4.12 -0.026*A – 0.042*C-0.038*D+0.084*F +0.084*G -0.045*F*G(2)Details of the DOE analysis can be found in authors’previous studies [14].
FUZZY SET THEORYA major contribution of fuzzy set theory is its
capability of representing vague data. A fuzzy set isa class of objects with a membership function
ranging between zero and one [19]. Fuzzy set theory
resembles human reasoning in its use of
approximate information and uncertainty to generate233

GEOMATE- Osaka, Nov. 16-18, 2015

The membership function A of triangular fuzzy
number on R = (−∞, +∞) is A: R  [0, 1]. The
triangular membership function, A is defined as,

decisions. It was specifically designed tomathematically represent uncertainty and vagueness.
Fuzzy set theory implements groupings of data withboundaries that are not sharply defined (i.e.
fuzzy) [20].
A triangular fuzzy number (TFN) is the specialclass of fuzzy number whose membership isdefined by three real numbers, expressed as (l, m, u)[21]. Fig. 1 depicts the structure of a Triangular
Fuzzy Number (TFN). According to Tae-heon Moon
the triangular fuzzy numbers is represented asfollows where A is triangular membership functions.


ߤ஺

௫ି௟

, ݈≤ ‫݉ ≤ ݔ‬௠ ି௟= ൞ ௨ି௫௨ି௠, ݉ ≤ ‫ݑ ≤ ݔ‬ൢ(3)0, ‫ݐ݋‬ℎ݁‫݁ݏ݅ݓݎ‬Where, l ≤ m ≤ u, and l and u stand for the
lower and upper values of the support of the fuzzy
number respectively, and m for the modal value [21].

Table 1 Levels for Numeric Factors, [14]FactorName

Low ActualHigh Actual

Low CodedHigh Coded

D

Area of the ground floor

46.45 m2

278.71 m2

-1.0

+1.0

E

Area of the above floor

37.16 m2

464.52 m2

-1.0

+1.0

F

No of frames in X
direction
No of frames in Y
direction
Column dimension in X
direction
Column dimension in Y
direction
No of columns

2

20

-1.0

+1.0

2

20

-1.0

+1.0

0.076m1.016m

-1.0+1.0

0.076m1.016m

-1.0

+1.0

4

50

-1.0

+1.0

G
H
I
J

peoples are supposed to prefer the best among themany choices, thus, an analytical way tomake a successful decision is needed. Decision
makers might have difficulties in accurately
determining the various factor weights andevaluations in certain situations. In such cases, theAHP can be used. In AHP the decision maker starts
by laying out the overall hierarchy of the decision.
This hierarchy reveals the factors to be considered aswell as the various alternatives in the decision. Here,both qualitative and quantitative criteria can becompared using a number of pair wise comparisons,which result in the determination of factor weights
and factor evaluations. Finally, the alternative withthe highest total weighted score is selected as thebest alternative [21].

Fig. 1 Triangular membership Function
The operational laws between two triangularfuzzy numbers M1 and M2 are as follows in Eq. (4)and (5) [21],M1+M2 = l1+l2, m1+m2, u1+u2(4)

M1*M2 = l1l2, m1m2, u1u2

(5)

Analytical Hierarchy Process (AHP)

Fuzzy- Analytic Hierarchy Process (Fuzzy AHP)

AHP is a method for ranking decision
alternatives and selecting the best one when thedecision maker has multiple criteria. With AHP, thedecision maker selects the alternative that best meets
his or her decision criteria developing a numericalscore to rank each decision alternative [21]. An
analytical way to reach the best decision is more
preferable in many platforms. When variables
are quantitative and number of criteria is not high,then one can use several analysis tools (forexample: Multi factor evaluation process) and makehis/her decision and solve the problem.
However, many times beside the measurable
variables, there exist qualitative variables, or

The conventional AHP method is incapable ofhandling the uncertainty and vagueness involved in
the mapping of one's preference to an exact number
or ratio .The major difficulty with conventional AHP
is its consistency. It is due to the transitivity property
involved in the pair wise comparisons [21].
In Fuzzy-AHP, pair wise comparisons are doneusing fuzzy linguistic scale ranging from 0 to 10.
Table 2 displays the fuzzy linguistic scale within 0
to 1. For consistency, the reciprocal fuzzy
numbers are removed from the pair wise comparison
matrix by using TFN corresponding to eachlinguistic variables used in the scale. The triangular234

GEOMATE- Osaka, Nov. 16-18, 2015

fuzzy numbers corresponding to different verbal

judgment is demonstrated using Table 2 [21].

Table 2 Linguistic levels and preference scale
Linguistic
Representation
Extremely low (EL)Very low (VL)
Slightly low (SL)Explanation of the linguistic values

Triangular Fuzzy number(TFN)

Response is extremely unimportant
Response is quite unimportance
Response is slightly unimportant(0, 0, 1/6)
(0, 1/6, 1/3)
(1/6, 1/3, 1/2)

Medium (M)Response is neutral(1/3, 1/2, 2/3)

Slightly high (SH)Response is mildly important(1/2, 2/3, 5/6)

Very high (VH)

Response is quite important(2/3, 5/6, 1)

Extremely high (EH)

Response is extremely important(5/6, 1, 1)

Table 2 and Table 3 have been sent to ten different
experts of seismic vulnerability assessment
to rank each of the factors of Table 3 according toTable 2 that has been made on the basis of
Eq. 6.

Fuzzy Set model for Turkish method
The membership function and triangular fuzzy
function are determined by the following Eq. 6,
shown by Hipeng Li [22].
ଵ

1 − 6‫ݔ‬, 0 ≤ ‫≤ ݔ‬଺ߤ(‫ = )ݔ‬ቐቑ
ଵ0, ≤ ‫ ≤ ݔ‬1ߤ(‫= )ݔ‬

⎧
⎪
⎨
⎪
⎩

଺

௞ିଶ

6‫ ݔ‬− (݇ − 2), ଺ହAggregation of Expert’s Judgment
0, 0 ≤ ‫≤ ݔ‬

௞ିଶ

⎫
⎪
≤ ‫≤ݔ‬(݇ = 2,3,4,5,6)
଺
⎬
௞ିଵ
⎪
0,
≤ ‫ ≤ݔ‬1
⎭
଺
௞ିଵ

଺

According to Lin and Wang [23], aggregation ofexpert’s judgment should be performed to avoid
conflict among expert’s judgment. An operator has
to be used to convert several experts’
judgment fuzzy sets into a single fuzzy number.
Average mean method is most common and for
aggregating n experts' judgment in triangular fuzzy
number ࡱࡶ࢔ = (࢒࢔, ࢓ ࢔, ࢛࢔ ) can be defined as(6)0, 0 ≤ ‫≤ ݔ‬଺ߤ(‫ = )ݔ‬ቐቑ
ହ
6‫ ݔ‬− 5, ≤ ‫ ≤ ݔ‬1଺

Where, µ(x) denotes the membership function
and x denotes normalized values.

෪ = (૚ ∑࢔࢏ୀ૚ ࢒࢏, ૚ ∑࢔࢏ୀ૚ ࢓ ࢏, ૚ ∑࢔࢏ୀ૚ ࢛࢏)ࡱࡶ

ANALYSIS FOR THE FUZZY SET MODEL࢔

For the analysis by Fuzzy Set Model, the ten
factors that were used in the DOE Methodology[14] has been listed as eight factors as column
dimension in X and Y direction and no of frames in
X and Y direction do not need to be separated for X
and Y direction due to the reason that the expert
cannot distinguish the difference between the two
directions. The new factors that have been selected
for the analysis are given in Table 3.

Name
Pounding effect

B

Topographic effectC

Plan Irregularity

D

Area of the ground floor

E

Area of the above floor

F

No of frames in the X and Y direction

G

Column dimension

H

No of columns(7)

Defuzzification
Defuzzification is a method that converts the fuzzy
numbers
into
a
crisp
value
[20].
The crisp value will be triggered into the Fig. 2 to
calculate the importance level of the eachparameter. Several techniques are available and in
this paper the centroid method [24] is used fordefuzzification of experts’ judgment.

Table 3 Factors for Fuzzy AnalysisA࢔෪ is the average linguistic judgment of n
Where ࡱࡶexpert and l, m, u are the lower, intermediate
and upper values for the triangular fuzzy number.

Expert’s judgments

Factor࢔

Fig. 2 Mapping linguistic importance levels values

235

GEOMATE- Osaka, Nov. 16-18, 2015

Table 4 Aggregation of Experts’ judgment anddefuzzified values

FactorName

A

Pounding effectAggregatedvalue ofthe Experts’
judgment
0.6, 0.77, 0.93

Topographic effect0.73, 0.9, 0.98

0.8815

Plan Irregularity

0.47, 0.62, 0.8

0.6379Area of the ground

0.58, 0.77,
0.95

0.7726

0.05, 0.22, 0.4

0.2317

0.82, 0.98,
0.25

0.92

B
C
D
E

floor
Area of the abovefloor
No of frames in

Defuzzified
value
0.7748F

the X and Y

G

Column dimension

0.23, 0.4, 0.57

0.4086

No of columns

0.1, 0.27, 0.43

0.2748

direction
H

Table 5 Comparison between results from Fuzzy
Set Theory and DOE Methodology

FactorRanking value
from Fuzzy Set
theoryCoded Regression
Co-efficientfrom the DOE
methodology

Pounding effect0.8235-0.0260.898-0.0220.636-0.0420.766-0.038Topographic
effectPlan Irregularity
Area of theground floor

calculated. The ranking shows that the pounding
effect, topographic effect, plan irregularity, area of
the ground floor and the number of frames in X andY direction are the most significant factors for the
performance Score in Turkish Method. The
calculated ranking values are shown in Table 5.
COMPARISON BETWEEN FUZZYTHEORY AND DOE METHODOLOGYSETThe most significant factors that have been foundby the Fuzzy Set Theory exactly match with theresults obtained from DOE methodology. For the
ANOVA analysis in the DOE methodology, ten
factors were taken in the model and the pounding
effect, topographic effect, plan irregularity, area of
the ground floor and the number of frames in X andY direction were found as the most importantfactors for Turkish Method. The same ranking has
been found from the Fuzzy Set Theory except the
ranking values which are not same as the regression
co-efficient from the DOE methodology. This isbecause the Fuzzy set theory is dependent on theexperts’ judgment which is not numericallyaccurate. The numerical values are not same fromboth of the analysis. However, the ranking fromboth analyses is comparable which indicates thatthe pounding effect, topographic effect, plan
irregularity, area of the ground floor and thenumber of frames in X and Y direction are the mostsignificant factors for the performance score of theTurkish Method.
CONCLUSION

The defuzzified values have been calculated byusing MATLAB 7.8.0. The results of theaggregation of the experts’ judgment and thedefuzzified values are given in the Table 4.

This paper presents a new approach to validate theapplicability of Turkish method for seismic
vulnerability assessment of existing RCF buildings
in developing countries like Bangladesh.
Fuzzy Set Theory is a well-known approach forranking in the AHP. This research proves theapplicability of DOE methodology in the Turkish
Method. The results represent that the pounding
effect, topographic effect, plan irregularity, area of
the ground floor and the number of frames in X andY direction are the most significant factors for theseismic vulnerability assessment by Turkish
Method which exactly matches with the results
obtained from the DOE methodology. Therefore,
this study primarily focused on the use ofqualitative judgments as well as quantitative
parameters of the various factors of Turkish method
in order to rank the factors according to theirsignificance.

Ranking

REFERENCES

The defuzzified values have been plotted to Fig. 2
and then the ranking for each of the factor has been

[1] Hassan, A.F. and Sozen, M.A. 1997. Seismic
vulnerability assessment of low-rise buildings

Area of the above
floor

0.234

No of frames in
the X and Y

0.915+0.084

0.415

N/A

0.25

N/A

direction
Columndimension
No of columns

Defuzzified expert’s judgment,࢛෪ ᇱ = ∫࢒࢛࢞.ࣆ(࢞)ࢊ࢞
ࡱࡶ∫࢒ ࣆ(࢞)ࢊ࢞

(8)

236

GEOMATE- Osaka, Nov. 16-18, 2015

Earthquake Engineering Research Center(EERC), Dept. of Civil Engineering,
Chittagong University of Engineering &Technology (CUET), Bangladesh.
[14] Roy, K. S., Islam, K. and Arafat, M. Shams.
2011. A Novel Statistical Approach for
Investigating the Significant Factors that
Influence the Performance Score of the Turkish
Method,ProceedingsofInternational
Conference on Geotechnique, Construction
Materials and Environment (GEOMAT-2011),
Tsu City, Mie, Japan.
[15] Ahmed, M.Z., Islam, K., Roy, K. S., Arafat, M.
S. and Al-Hussaini, T.M. 2010. Seismic
Vulnerability Assessment of RCF Buildings in
Dhaka City, Proceedings of the 3rdInternational Earthquake Symposium (IESB-3),
Dhaka, Bangladesh.
[16] Roy, K. S., Islam, K. and Arafat, M. S. 2010.
Seismic Risk Assessment of Existing Low-rise
Buildings in Unplanned Urban Regions of
Dhaka City, Proceedings of the 2nd
International Conference on Construction in
Developing Countries (ICCIDC-2), Cairo,
Egypt.
[17] Sucuoglu, H. and Yazgan, U. 2003. Simple
Survey Procedures for Seismic Risk
Assessment In Urban Building Stocks, Seismic
Assessment and Rehabilitation of Existing
Buildings, 97-118,NATO Science Series,
IV/29, Editors: S.T. Wasti and G. Ozcebe,
Kluwer.
[18] Lye, L. M., 2003. Some Applications of
Statistical Design of Experiment Methodology
in Civil Engineering. Annual Conference of theCanadian Society for Civil Engineering,
Moncton, Nouveau-Brunswick, Canada[19] Zadeh, L. A. 1965. Fuzzy Sets. Information
and Control 8, 338-353.
[20] Klir, J.G. and Yuan, B. 2001. Fuzzy Sets andFuzzy logic Theory and Application. Prentice,Hall of India Private Ltd.
[21] Chatterjee, D., Chowdhury, S. and Mukherjee,
B. 2010. A Study of the Application of Fuzzy
Analytical Hierarchical Process (FAHP) In the
Ranking of Indian Banks. International
Journal of Engineering Science andTechnology: 2(7), 2511-2520[22] Li, H. 2007. Hierarchical Risk Assessment of
Water Supply Systems. Doctor of Philosophy
Thesis. Department of Civil and Building
Engineering
Loughborough
University,
Leicestershire, UK[23] Lin, C. and Wang, M. J. 1997. Hybrid fault tree
analysis using fuzzy sets. Reliability
Engineering and System Safety, 58: 205-213.
[24] Yager, R. R. 1980. A general class of Fuzzyconnectives. Fuzzy Sets and Systems, 4: 235242.

in region of infrequent earthquake. ACIStructural Journal, 94(1): 31 -39[2] Gulkan, P.and Sozen, M.A. 1997. Procedurefor determining seismic vulnerability ofbuilding
structures. ACI Structural Journal, 96(3): 336342[3] Kircher, C. Reitherman, R.K., Whitman, R.V.,
and Arnold, C. 1997. Estimation of Earthquake
losses to buildings. Earthquake Spectra, EERI,
13(4): 703-720[4] Dorka, U. 2005. Personal Communication.
[5] Ozcebe, G., Yucemen, M. S., Aydogan, V., andYakut, A. 2003. Preliminary Seismic
Vulnerability
Assessment
of
Existing
Reinforced Concrete Buildings in Turkey- Part
I: Statistical Model Based on Structural
Characteristics, Seismic Assessment andRehabilitation of Existing Buildings, NATO
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[6] Ahmed, M. Z., Islam, M. K., Ahsan, R. andOzcebe, G. 2007. Seismic Vulnerability
Assessment Methods: A Possible Application
to Bangladesh, First Bangladesh Regional
Science Association (BRSA) Conference,
Dhaka.
[7] Ahmed, M. M., Jahan I., Alam M. J. 2014.
Earthquake Vulnerability Assessment of
Existing
Buildings
inCox’s-Bazar using Field Survey & GIS.
International Journal of Engineering Research& Technology (IJERT) ,Vol. 3 Issue 8[8] Sarraz, A., Ali, M. K.and Das, D. C. 2015.
Seismic Vulnerability Assessment of Existing
Building Stocks at Chandgaon in Chittagong
city, Bangladesh. American Journal of Civil
Engineering, 3(1): 1-8[9] Sadat, M. R., Huq, M. S., Ansary, M. A. 2010
Seismic vulnerability assessment of buildingsof Dhaka city. Journal of Civil Engineering,
Inst. of Engineers, Bangladesh. 38 (2) 159-172[10] Ishita, R. P. and Khandaker, S. 2010.
Application of Analytical Hierarchical Process
and GIS in Earthquake Vulnerability
Assessment: Case Study of Ward 37 and 69 in
Dhaka City. Journal of Bangladesh Institute ofPlanners , 3:103-112[11] Rahman, M. A., Ullah M. S., 2013. Seismic
Vulnerability Assessment of RC Structures:
A Review. Asian Transactions on Eng., 02(06)[12] Ahmed, M., Khaleduzzaman, K. M., SiddiqueN. A.
and Islam S., 2012. Earthquake
Vulnerability Assessment of Schools andColleges of Sylhet, a North-eastern City of
Bangladesh. SUST Journal of Science andTech., 19(5): 27-34.
[13] Alam, M. J., Khan, M. A. R., and Paul, A.
2009. “Seismic Vulnerability Assessment of
Existing RC Buildings in GIS Environment”,
237

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

GNOMON AS SOURCE OF INFORMATION ON PLANET RHYTHMS
1, 2

Alina Paranina1, Roman Paranin2
Faculty of geography, Herzen State Pedagogical University, Russia

ABSTRACT
For millennia, humans have been using the gnomon whose shadow passes the trajectory of the light source(sun or moon) as a measurement tool. With the gnomon authors prove that the ancient stone labyrinths inNorthern Europe are solar calendars, sundials and compasses. Studies of cultural heritage around the world showthat the majority of them might serve as orientation in space-time. According to the authors, the collection ofancient navigational facilities can be considered as ancient astronomical and geodetic network and as a record ofastronomical history of the planet. For systemic analysis of this information it is necessary to create a base of
paleo-astronomical data. The acquired information may be useful for studies of the history of the Earth and thehistory of science: the dynamics of changes of inclination of the Earth's axis, offset of the polar circles and thetropics, the characteristics of long-period rhythms of the planet, the evolution of climatic environments and thedevelopment of navigation technologies of the past. In addition, the creation of a network of solar observatories,
with the technique of automatic registration shadow or a ray of light, will provide objective information on thesolar-terrestrial relations and rhythms of nature
Keywords: Gnomon, a Stone Labyrinth, Paleoastronomical research, Navigation, Rhythmic of natural processes
The proposed concept of the labyrinth-gnomon –a tool of back sight of the sun – from the shadow set
in the center of the object, opens the possibility of its
use as a sundial compass and calendar [4] - [7]. The
shadow of the object is easy to observe, record,measure, and its movement reflects and a form encodes all the movements of the sun and isconsistent with the position of the elements of thestructure of the labyrinth; landscape orientation on
the horizon (mountains, valleys) are not necessary,on the contrary, water environment is the optimal
without creating distortions of azimuths ofsunrise/sunset (their normal location is on the islandor cape).
Author's concept of a labyrinth-gnomon
technology solves the problem of the calendar use,
and is consistent with all elements of a wide range ofsymbolic interpretation of the signs of the labyrinth
and Labrys.
Paleo-astronomical studies of natural andcultural heritage indicate the possibility of their use
for orientation in space-time. The ubiquity of ancientastronomical instruments deserve systematic study
to clarify the concepts of modern science and thehistory of rhythmic changes in the nature of the
Earth.

INTRODUCTION
Northern labyrinths can be found in England,
Iceland, Norway, Denmark, Sweden, Finland,
Estonia and Russia. They are located on isles,
peninsulas, near harbors and in river mouths. Their
picture is complicated but organized. In terms ofstructure, there are unispiral, bispiral, concentric andradial types. In terms of outer shape: circles, ovals,
rarely squares [1].
Hypotheses about the designation of stonelabyrinths can be divided into two groups: calendar
and non-calendar. It should be noted that despite all
the diversity of facts of non-calendar use, most partof them is often associated with time and stage oflife.
Hypothesis of calendar designation of labyrinths
are mainly based on the assumption of a direct
projection of the trajectory of space objects on the
Earth's surface (Herman Wirth, Daniel Svyatskiy,
Sergei Yershov) or consider pattern of labyrinth as arecord of the results of direct sight of the annual
variation of the Sun (Yuri Chekmenev). However,direct sight cannot explain the technology for using
the labyrinth: 1) it is impossible to explain thequantitative ratios of the trajectory of a celestial
object and its reflection in the stone pattern; 2) it iseven more difficult to imagine the use of the pattern- with a diameter of 20-30 meters, it is impossible"to read" from the human height; 3) the problem ofmonitoring the trajectory of the sun is that bright
light dazzles eyes, just after sunrise its movementtakes off from landmarks.

LABYRINTH-GNOMONNAVIGATION NETWORKIN

ANCIENTThe objects of study were the monuments ofancient material culture of European Russia (siedis,
menhirs, stone labyrinths). From 2009 to 2013 theobjects located on coast of the White Sea are
238

GEOMATE- Osaka, Nov. 16-18, 2015

investigated: in the archipelago of Kuzova, in thearchipelago Solovki, in the gulf Kandalaksha, in themouth of the river Vyg (Fig. 1).

movement of the Sun on a firmament. In 2009 theauthors proved that drawing of a labyrinth fixes
astronomically significant points: 1) the provision ofa midday shadow in days of winter and summer
solstice corresponds to extreme arches of spirals, 2)
the ends of spirals correspond to azimuths ofrisings/calling, 3) the entrance to a labyrinth notes
the beginning of an annual cycle (in an equinox or asolstice) [4].
The sketch of a shadow of a gnomon in days
gives the schedule similar to a pitchfork, horns,
wings, a fish tail. The shadow schedule in a year fills
the space whose shape form represents labris - abilateral two-horned axe of god of light [5] - [7].

Fig. 1 Labyrinth with a stele in the archipelago of
Kuzova.

А
B

Fig. 2Labyrinth No. 1 Big Zayatsky Island of
Solovetsky archipelago.

Fig. 3

White Sea petroglyphs (6000 y.o).

СFig. 4Labyrinth No. 1:
topographical plan (A); gnomon (B) and geometryof its shadows per year (C).

The applied field research methods (survey,
description, observation, work with maps) and Earthremote sensing, as well as methods of mathematical,
conceptual modeling and mapping. Theoreticalanalysis is based on the theory of reflection andsystemic and chorological approach, methodologicalstatements of historical geography by V.I. Paranin[2], [3].

Labyrinths of Nordic countries as elements oflocal and regional astronomical and geodetic
networks
Stone labyrinths are located, as a rule, on a plotof sea coast estuaries (at the source of fresh water) it's convenient for rest and orientation, waiting forthe desired date of astronomical calendar, in whichmarks of important phenological events of the area(cycles of fishing animals, climate and hydrologicalmode, lighting) can be made [5] - [7].
Key elements of the picture calendar are
diameters of arcs and azimuths of entrance and end
spirals - reflect the effect of two factors: the latitude

Labyrinths of Big Zayatsky Island of Solovetskyarchipelago as the astronomical instruments
For interpretation of a northern labyrinth thegnomon – the elementary astronomical tool wasused. The shadow of a gnomon codes a trajectory of239

GEOMATE- Osaka, Nov. 16-18, 2015

and discrepancies of physical horizon (surface relief)
with the astronomical horizon.
Polar regions differ from moderate latitudes interms of azimuths of sunrise/sunset in the solsticethat vary considerably in adjacent parallel (Table 1).
If latitudes 40 - 50º rise at the summer solstice andshift by only 6.92º, and at latitudes of 50-60º only
twice – 13.42º, then advancing further at only 5º (60- 65º) to the north – rise shifts at 17.37º, andlatitudinal range of 1.5º (65 - 66.5º, i.e. B. Zayatsky
Island to the Arctic Circle ) – to 20.03º. It is obviousthat planetary space conditions of astronomical
observations in the polar latitudes become the main
reason for specific features of drawings.
The distorting influence of the physical horizon
line on measurement of astronomical azimuths canbe levelled by locating the instrument on the beach,
whose calm surface coincides with the astronomical
horizon, this explains the location of the labyrinth
near water. This fact partly explains the abundanceof labyrinths in a small area of the Big Zayatsky
Island (more than 30 items on 1.25 km²): firstly, thelabyrinths are located on parts of the shore, open todifferent sectors of the horizon, which provides
accurate measurements for different astronomical
dates and various astronomical objects (objects inthe light of the moon cast a shadow as well);secondly, the construction of new labyrinths is
associated with the retreat of the shoreline; thirdly,
arranged compactly enough, they form a local
network.
An equally important reason for the constructionof new labyrinths is variability of subpolar latitudes
of astronomical targets not only in space but also intime - here the change in slope of the Earth's axis ismost visible; being observed according todisplacement of the position of the Arctic Circle atother latitudes, these changes are not as dramatic(Table 1).

significantly less than modern, therefore, the line of
the Arctic Circle was located closer.
Most labyrinths are located in the most dynamic
area approximately from latitude 57° to 66.5°, whichprimarily determines the differences in their pattern.
At the latitude of the Arctic Circle azimuths ofsolstices coincide with the meridian, and theboundaries of the astronomical seasons are in the
shape of direct cross. In some cases the center
corresponding to the polar day, is marked by aclosed circle or spiral, as in a labyrinth in Iceland.
North of the Arctic Circle, only equinoxes can bereliably determined by azimuth of sunrise/sunset. To
divide the year into periods between the polar night
and polar day, you can use the azimuths ofsunrise/sunset, which, depending on latitude, moreor less rapidly move in the range of 0° +/- 180°.
When the sun does not set over the horizon, lengthof midday shade – diameters of arcs – become theonly way to divide time into days.
Labyrinths in global navigation system
Stone labyrinths and their images are known on
every continent except Antarctica. In Russia, a highconcentration of images is noticed in the North
Caucasus (Dagestan). Single units of stonelabyrinths are marked by archaeologists on the
Russian plain, for example during excavations in
Mostishchensky labyrinth 1 – Bronze Agesettlement at the Cape of high original bank of the
Don River. Mostishchensky labyrinth is located atlatitude of famous astronomical observatories like
Stonehenge and Archaim. On the same latitude 5152° – one of the borders of the seven ancient
climates there are a number of major European cities
and sacred centers of Asia: Big Salbyksky mound in
Khakassia, large burial Argens - in Tuva. Kereksur
can be considered as analogue of labyrinths concentric structures in Asia oriented to the horizon.
At the same time, in the tropics there are almost no
such structures.

Table 1 Dynamics of astronomically significant
directions in space and time.

REGIONALFEATURESDYNAMICS OF SHADOWS

N (º)

2010
3000 BC
WS* 22.12 SS**22.06 WS 07.01 SS 02.08__________________________________________________
65
160.00 20.03
165.31 15.46
60
142.86 37.40
144.82 35.48
50
128.41 51.82
129.55 50.71
40
121.29 58.74
122.25 57.81
30
117.39 62.74
118.20 61.97
20
115.05 64.97
115.83 64.29
10113.85 66.19
114.50 65.56
0113.44 66.56
114.09 65.91*WS - winter solstice;**SS - summer solstice.
The table shows that 5,000 years ago, theazimuth of the summer solstice (SS) was

OF

ANNUAL

According to the terms of lighting since Aristotle
five zones have been identified: tropical – hot, twomoderate and two Polar – cold. They are limited by
the tropics and the polar circles and different
standing height of the midday sun above the horizon,
the duration of the day and thermal conditions. The
observation conditions of shadows in them differ
substantially.
About 40% of the Earth's surface is in the hot
area lying between the tropics. Day and night areslightly different in duration, and the sun is at its
zenith is twice a year. 52% of the Earth falls on
located between the tropics and the temperate zones
240

GEOMATE- Osaka, Nov. 16-18, 2015

which provided an opportunity to compare them.
According to the ratio of the gnomon height to thediameter of the site for monitoring annual dynamics
of the shadows, the following three basic types ofsolar observatories can be found: "labyrinth", "staff"
and "rod".

of the Arctic Circle, where the sun is never at its
zenith, the duration of day and night depending on
latitude and time of year. Near the Arctic Circle (60°
to 66.5°) for a short time in the summer sun andshallow out to the horizon, evening and morningdawns merge, and there are so-called white nights.
The cold (polar) zone occupies only 8% of theearth's surface from the poles to the Arctic Circle. In
winter there are polar nights and summer – polardays, which last a day or more.

АLatitudinal differences in the dynamics of the
shadows

12
11

Reflection of the lighting conditions in theannual dynamics of different regions of the shadowshave been studied by means of building andcomparison of graphs (Fig. 5) and charts of theannual dynamics of midday shade critical latitudesof the northern hemisphere parallels and all other atintervals of 10º. Models concentric labyrinths fordifferent latitudes are shown in Figs. 6-7.

20

1
2

15
5
0

10

Row1

3

10

Row2

4

9

Row4
Row5

5
8

Row3

Row6

6
7

B

7
Ряд1

6

Ряд2

5

Ряд3

4

Ряд4

3

Ряд5

12

Ряд6

2

11

Ряд8
2

3

4

5

6

7

8

9

10

11

12

10

Fig. 5Modern annual dynamics of length of
midday shadow at different latitudes:
Horizontal - months of the year from January (1);vertical - length of shadow (7 m).

2

Row1

3

5

0
1

1

10

Ряд7

1

15

Row2

4

0
9

5
8

Row3
Row4
Row5
Row6

6
7

Fig. 5 rows 1-8 – shadows at latitudes from theequator to 70º N at intervals of 10º. In the shadow of
the polar latitudes shadows of months 1, 11 and 12
are not indicated for the enlargement zoom, on 50º N
the length of the shadows in December is decreased
for the same purpose. The graph shows a decrease in
the length of the shadow when moving to theequator, the greatest latitudinal differences for the
winter, the change in the shortest shadow of the year(6) of a value equal to the height of the gnomon at
70º N (Row 1), up to half of 50º N (Row 3), andclose to zero 30º N. No shade is observed on one of
the tropics in the solstice at the equator – In the daysof the equinoxes, at other times at different latitudes
between the tropics. Coding latitude diameter of theinner circle using a standard-sized portable line or
anthropometric equivalent (foot length) could be thebasis of the latitudinal orientation.
Modeling of concentric labyrinth (Figs. 6, 7) was
carried out for the "standard" gnomon height of 1 m,

Fig. 6
Models concentric labyrinths:
A - for the Arctic Circle 66.5º N;
B - for Zayatsky island 65º N;
Row 1 - 1 and 11 months; Row 2 - 2 and 10;
Row 3 - 3 and 9; Row 4 - 4 and 8; Row 5 - 5 and 7,
number 6 - June.
Winter - Rows 1 and 2; Equinox - Rows 3;
Summer - Rows 4-6.
In the subpolar regions of > 60° radius of themodel exceeds the height of the gnomon manytimes, forcing to abandon the arc of large diameter,and summer shadows are grouped in the center –type "labyrinth 11".
At the latitude of 50 - 60° shadows of all monthsof the year are placed in a circle of relatively small
diameter, it is convenient to use 7-arc "classic"
labyrinth.
241

GEOMATE- Osaka, Nov. 16-18, 2015

А

E

1

10

12

2

8

12

6

11

10

0,8

Row1

3

4

Row2

2

Row3

4

0

1
2

0,6

11

Row1

3

0,4

Row2

0,2
10

Row4

Row3

4

0

Row4

Row5
Row6

9

5

8

9

Row7

Row5

5
8

6

Row6

6

Row7

7

7

B

F

4
3
2
1
0

12
11
10

1

Row1

2

12

Row2

3
4
5

8

6

Row1

2

Row2

3

0,2

Row4

10

Row5

9

1

0,4

11

Row3

0,6

Row3

0

4

Row4
Row5

Row6

9

Row7

7

5
8

6
7

Row6
Row7

C

12 2

Row1

1

11

1

3

Row3

10

0

4

Row4

5

Row5

9
8

Fig. 7 Models of concentric calendars for different
latitudes:
Row 1 - December;Row 2 - January and November;Row 3 - February and October;Row 4 - March and September;Row 5 - April and August;Row 6 - May and July;
Row 7 - June;A - 60º N; B - 50º N; C - 40º N; D - 30º N; E - 10ºN;
F - 0º.

Row2

2

Row6

6
7

Row7

D

121,5
1
11
0,5
10
0

Row1

1

Row2

2
3

Row3

4
5

9
8

6
7

In temperate and subtropical latitudes of 40-30°
radius of the outer circle of the gnomon 2-fold – atype of "staff", resorting to associations with highenough staffs Zeus, Egyptian or Babylonian rulers or
deities (staves which can be of up to 5 - 7 feet) .
In the tropics, the calendar size of the shadow isplaced on the gnomon – type "rod." The position of
the sun at its zenith twice a year between the Tropic
and the Equator makes it necessary for the formationof gnomon faces to ensure a consistent record of theprovisions of the shadows without imposing
seasonal sites for which it passes several times.

Row4
Row5
Row6
Row7

242

GEOMATE- Osaka, Nov. 16-18, 2015

Thus, analysis of the graphs reveals latitude
where the record of calendar positions of the shadow
can be in the form of an ornament on the gnomon, oron the floor and walls of the church, in all cases, thegnomon can serve as a ruler. Marking may also
include an indication of the height of the gnomon,
which correspond to the length of the marked
shadows (especially for staff). The calendar can be
ornamental figurative sign, reproducing the stages ofthe year economic cycle or more abstract forms ofspecific forms of geometry of shadows; encoding ofcalendar months is possible in the animal style,which corresponds to the sequence of the zodiac
animals. Calendar hexagonal rods in India and cultobjects in Ancient Egypt are noteworthy as well.

dependent on solar energy and information. On the
one hand, it creates a complexity of development, on
the other it explains the appeal of the Arctic region
for researchers in the past – rich material is left byancient authors, there are numerous megalithic sitesof navigation, and today – only here you can get
accurate information about the current status of theplanet and trends development of the global
environment.
CONCLUSION
Our studies have shown that the gnomon – thesimplest astronomical instrument – opens thepossibility of using the primary stone labyrinths as
solar calendars. The ubiquity of ancient astronomical
instruments can be regarded as a source of
information to clarify changes (rhythm, dynamics
and evolution) of planetary parameters in the past.
The modeling of gnomon shadow during a year,
presented in the article, shows the sensitivity of thestructure of the labyrinth to the characteristics oflighting modes at different latitudes. It follows thatfor the purposes of long-term forecast rhythms of theplanet it is advisable to create a network of solar
observatories.

Areas of space indication
Obviously, the greatest possibilities of rhythmicchanges in the definition of the position of the sun
belong to polar circles and the tropics. It is known
that changes in the tilt of Earth's axis about 21.9º to
24.3º (according to some sources up 25.3º) causes
displacement of the polar circles and the tropics forabout 41,000 years. The result is a periodic
expansion of the area of the temperate zone and the
reduction of the polar and tropical regions.
Nutation of the Earth's axis shifts the line of the
Arctic Circle to 3 meters per day and 100 per year.
For example, until 2015 the Arctic Circle will moveto the north, and then for 9 years, move up to 400
meters south.
The northern regions of the Earth provide otherunique features of space observation targets: annual
passage of sunrise / sunset 360º circle at the latitudeof the Arctic Circle, the full daily observations of thezodiacal circle in 18 hours sidereal time and theposition of the North Star. It is no accident ancientscience allocated zone of the Arctic at a latitude ofabout 55° N.
It is worth mentioning that at noticeable annual
dynamics of the Sun, in the North simple facilities
are accurate, sensitive, accessible to a wide range of
observers. In southern latitudes, the increasing theaccuracy of the gnomon increase in summer isachieved by increase of its height, and in winter - theerection of screening surfaces, such projects requirea significant effort (the legend of the Tower ofBabel).
With regard to the influx of solar radiation of
low latitudes are more stable, which is proved, inparticular, by saturation of endemic and relict formsof life. At high latitudes, the smallest global climatechange lead to a fundamental restructuring of thelandscape and natural disasters.
Thus, a regional analysis of information
resources geographic space shows that the polar
regions of the planet are the most dynamic and

REFERENCES[1] Kern G, Through the Labyrinth. MunichLondon-New York: Prestel Verlag, 2000.
[2] Paranin VI, Historical Geography of theChronicle of Russia. Petrozavodsk: Karelia,
1990.
[3] Paranin VI, The history of the barbarians.
Ed..St. Petersburg: RGO, 1998.
[4] Paranin RV, Paranina GN, “The Labyrinth:orientation in geographic space and theevolution of the mark”. in Proc. 2nd Int. Conf.
Space geocultural European North: the genesis,
structure and semantics. Arkhangelsk: State
Pomor University of Russia. 2009. pp. 516-518.
[5] Paranina AN, “Northern Labyrinth – A Key toTime, Space, Information”. Scientific Research
Publishing (Eastern Connecticut State Univ.,
USA) Natural Resources (NR). Vol. 4. Aug.
2013. pp. 349-356.
[6] Paranina AN, “Navigation in Space-Time as theBasis for Information Modeling”. Scientific
Research Publishing (Eastern Connecticut State
Univ., USA), Archaeological Discovery (AD),Vol. 2, N3, July 2014. pp. 83-89.
[7] Paranina AN, Paranin RV, “Interaction of thenature and ancient persons on the coast of theWhite Sea”. J. Wetlands Biodiversity (JWB).
Vol. 4. Dec. 2014. pp. 131-140.

243

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

LONG TERM PERFORMANCE PREDICTION OF ROADEMBANKMENT ON ESTUARINE DEPOSITS: A CASE STUDY IN
SOUTHEAST QUEENSLAND

1

Jia Xie1, Barry Wai-Choo Kok2
Geotechnical Engineer, Principal Geotechnical Engineer, Geoinventions Consulting Services, Australia
2

ABSTRACTThe estuarine deposits in the coastal areas of Southeast Queensland, Australia impose great challenge inassessing long term performance of highway and motorway embankments. A major transportation project wascompleted in Brisbane, Southeast Queensland in 2010 with a great part of road embankments constructed oversoft Holocene Clay. Areas within the motorway embankments have been experiencing large after-construction
settlements. A case study was carried out to back-analyze the long term settlement of the road embankment
based on the most updated field monitoring data. Following the back-calculation of the key consolidation
parameters by curve fitting method, the Post Construction Settlements were re-assessed by numerical analysis.
This paper describes the methodology adopted to back-analyze the long term performance of road embankment.
The results of the analytical and numerical analysis are presented and discussed.
Keywords: Road Embankment, Soft Holocene Clay, Post Construction Settlements, Back-Analysis, Curve Fitting

Queensland are classified as originating from
Holocene to Pleistocene age [2]. They arecompressible clays of high plasticity and low
strength. Soft clays with Holocene deposits being
the primary source are commonly found in the
coastal areas of Southeast Queensland with depth up
to 30m [3]. Figure 1 shows the distribution of
Holocene deposits in Brisbane [4].

INTRODUCTION
Significant depth of soft estuarine clays with
substantially varying thickness and very low
strength were found along the alignments ofhighways and motorways in Southeast Queensland[1]. Under this circumstances, to achieve tight longterm performance criteria in terms of Post
Construction Settlements (PCS) is a great challenge
for engineers. Areas within a major motorway
project in Brisbane have been experiencing
excessive PCS since construction completion in2010. A case study was carried out to back-analyze
the long term settlement of the critical embankment
section with higher level of accuracy, and in turn toinvestigate the possible causes of under-estimationof PCS during design and construction.
This paper presents the analytical methodology
and results of the long term settlement back-analysis.
The establishment of sub-soil profiles is describedwith respect to the regional geotechnical condition.
Key consolidation parameters of soft clays are backcalculated by curve fitting method. The PCS till theend of the 10-year Defect Liability Period and theend of the 40-year pavement design life are then reassessed by using commercial software Settle3D.
The back-analyzed PCS are compared to the original
prediction made during construction. Possible
mitigation solutions and lessons learned are
discussed along with the analysis results.

Fig. 1

Distribution of Holocene
Brisbane.

Deposits in

As illustrated in Figure 1, the project site is
generally part of a deltaic plain formed by theBrisbane River. Embankments are situated on
Holocene Clays ranging from 5m to 23m which

REGIONAL AND LOCAL GEOLOGY
The estuarine deposits exist in Southeast
244

GEOMATE- Osaka, Nov. 16-18, 2015

have been sub-divided into two distinct layers:
Upper and Lower Holocene Clays. The Upper
Holocene Clay is very soft to soft with numerous
sand lenses, while the Lower Holocene Clay is soft
to firm.

and embankment height, worst case embankmentcross-sections were identified to represent each subzone, and site specific soil models were establishedaccordingly. Figure 3 shows the geology longsection of Zone 1 and Zone 2.

METHODOLOGY
Critical Section Review
Settlement profile and criteria
An extensive field monitoring program has been
implemented and settlement data have beencollected periodically. Based on detailed review on
the settlement profiles summarized from themonitoring data, the embankment section presented
in Figure 2 was identified as the most critical withmeasured PCS up to 282mm till 2015 which is morethan 4 times of the original prediction made during
design and construction stage.
To limit the post-construction settlement andminimize the loss of serviceability the road, strict
settlement criteria have been imposed on many
major highway and motorway projects in Australia[1]. The project of the Authors’ study was required
to achieve a maximum PCS of 200mm till 2020 - theend of the 10-year Defect Liability Period. The
measured settlement of the critical embankment
selected has well exceeded the PCS criteria, andtherefore, was adopted for long term settlement
back-analysis.

Fig. 2

Fig. 3

Geology long section under the criticalembankment section.

Ground improvement scheme
Excessive settlement caused by soft clays leads
to the need of ground improvement [3]. Preloadingwith vertical drains is one of the most economical
ground improvement methods and was adopted forthe critical embankment section. Preloading on its
own can decrease the total settlement facilitating the
choice of foundation. The settlement process can be
accelerated significantly when vertical drains areused with preloading [5]. Table 1 outlines theembankment geometry and ground improvement
scheme adopted.
Table

1 Embankment geometry
improvement scheme

Embankment Height(m)
Preload Surcharge
Height(m)
Preload Duration(months)
Wick Drain Length(m)Wick Drain Pattern
Wick Drain Spacing(m)

Settlement profile of critical embankment
section.

andgroundZone 1

Zone 2

4.1

3.7

4.5

2.3

4.9

9.4

20

23

Triangular

Triangular

1.4 c/c

1.4 c/c

Sub-surface profile
Embankment loading
The subsurface profile was anticipated based on
in-depth study of the geotechnical investigation
information of the project. Due to the variableground condition encountered and different ground
improvement scheme adopted, the critical section
was sub-divided into two zones namely Zone 1 andZone 2. Depends on the Holocene Clay thickness

Embankment loading is never a single-step load.
The actual magnitude and rate of settlement is likely
to differ significantly from the estimation due toinaccurate approximation of embankment loading[6]. Analyses in this study were based on the as-built
embankment construction sequences of Zone 1 andZone 2 recorded in the Preload Removal Documents
245

GEOMATE- Osaka, Nov. 16-18, 2015

issued during construction.

settlements were adopted to plot the actual time
settlement curve. The baseline time settlement curve
computed by adopting original consolidation
parameters were compared to the actual settlement
time curve as demonstrated in Figure 5.

Modelling and Analysis
Curve fitting technique is commonly adopted in
the industry to provide PCS prediction with higher
degree of accuracy by comparing and matching thenumerically computed time settlement curve to theactual time settlement curve plotted from
instrumentation readings. The key element of thistechnique is to carefully review the settlement modeland back-calculate the key consolidation parameters.
Numerical modelling
Commercial software Settle3D was employed toassess the settlement of the critical embankment
section numerically. Settle3D is a 3-dimensional
program for the analysis of vertical consolidationand settlement [7]. In this study, numerical models
were established for the representative embankmentcross-sections of Zone 1 and Zone 2. As shown in
Figure 4, staged construction process were modelledaccording to the actual construction sequences, andtime-dependent consolidation analyses were
performed including primary and secondary
consolidation. The baseline time settlement curves
were computed by inputting the original soil
parameters which were adopted during the designand construction stage by the principal designer ofthe project.

Fig. 5

Settlement time curves: baseline versus
actualThe Settle3D computed curve was then adjusted
in order to match the actual time settlement curve.
As the focus of this study was the long term
performance of the embankment, the target sectionof the curve fitting practice was the secondary
settlement section of the curve. The new set ofparameters resulted in the best fitted curves are theback-calculated parameters.
The primary consolidation parameters that were
back-calculated during the curving fitting procedure
are the secondary compression index (Cαɛ) and overconsolidation ratio (OCR) of the Holocene Clay.
Cαɛ and OCR are the key parameters that could
impact significantly on the long term postconstruction performance of road embankments.
The original design parameters were derived
from conventional laboratory oedometer tests. In thisstudy, values of Cαɛ and OCR were back-calculated
for Zone 1 and Zone 2 through curve fitting. The
calculation results were calibrated by referring tolaboratory test outcomes of the project and publisheddata of local experience in Queensland.
Long term PCS prediction

Fig. 4The consolidation parameters back-calculated bycurve fitting were input into the Settle3D models to
predict the PCS from 2015 to the end of 10-year
Defect Liability Period (2020) and end of pavement
design life (2050). The back-analyzed PCS werecompared to the original prediction assessed during
construction in order to identify the difference
between the original and the current assessments,
and in turn to explore potential mitigation
approaches.

Settle3D model for Zone 1 with original
soil parameters.

Curve fitting and parameter back-calculation
It is important to monitor the performance of anembankment supported by soft ground during andafter construction [8]. Periodic field surveys werecarried out during and after the construction
completion. Annual monitoring data from settlement
plates till 2015 were collected. The measured

RESULTS AND DISCUSSION
246

GEOMATE- Osaka, Nov. 16-18, 2015

Backcalculation

Original

2.0

1.0

2.5

1.8

2.5

2.5

Rankine [9] in 2007 suggested that Cαɛ values vary
between 0.7% and 3.8% for Brisbane clays andbetween 0.5% and 8.1% for Sunshine Coast clays.
The laboratory test results of the project of authors’
study indicated Cαɛ values range fromapproximately 0.7% to 3.7% which compare wellwith the data from the literature.
The originally adopted Cαɛ values for Holocene
Clays were around the lower bound of the historical
data and likely to have under-estimated thesecondary compressibility of the soft clays under theembankment. Although the parameter backcalculation was essentially based on the statisticalanalysis by curve fitting, the back-calculated Cαɛ
values between 2.0% and 3.0% was well within therange indicated by literature and hence were adopted
for PCS back-analysis.
OCR is another key consolidation parameter
which is defined as the ratio of pre-consolidationpressure to the current effective overburden pressure.
It is stress history dependent and not an intrinsic
property of a soil. The magnitude of secondary
compression in a given time was found to begenerally greater in less over-consolidated clays [13].
As revealed by Figure 5, by comparing to themeasured settlement plate readings, the baseline
settlement time curve of Zone 1 considerably underestimated the PCS, and hence justified the decreaseon OCR values. On the other hand, the baseline
settlement time curve of Zone 2 only underestimated the PCS slightly. Therefore, a reasonably
fitted curve could be achieved by moderately
increasing the Cαɛ value without major modificationon the OCR values. Empirical relations are relied on
for the determination of OCR values when there are
doubts on the sampling and testing quality [1]. Thus,a realistic estimation of OCR value is always
difficult and literature of local experience are few.

1.6

1.5

PCS Back-Analysis Results

Parameter Back-Calculation Results
During the back-calculation process, particularemphasis was on the consolidation parameters of
the soft Holocene Clay. Based on the resultssummarized in Table 2 and Table 3, the Cαɛ valuesof the Upper and Lower Holocene Clay layers under
the critical embankment section generally increased
from 1.0% – 1.8% to 2.0% – 3.0%. The OCR values
decreased from 2.0 – 2.5 to 1.5 – 2.0 for Zone 1
while they almost remained unchanged for Zone 2.
Table 2 Consolidation parameters of Zone 1: backcalculated versus originalCαɛ (%)

OCR

Upper
Holocene
Lower
Holocene
Upper
Holocene
Lower
Holocene

Backcalculation

Original

2.5

1.0

3.0

1.8

2.0

2.5

1.5

2.0

Table 3 Consolidation parameters of Zone 2: backcalculated versus originalCαɛ (%)

OCR

Upper
Holocene
Lower
Holocene
Upper
Holocene
Lower
HoloceneLong term PCS of the critical embankment
section was re-assessed by adopting the backcalculated consolidation parameters in the Settle3Dmodels of Zone 1 and Zone 2. The analysis results
are listed in Table 4 and Table 5, together with themeasured settlements and original predictions madeduring construction for comparison purposes.

Secondary compression is the compression thatfine grained soils experience following thedissipation of excess pore pressure resulting fromloading [9]. In general, the coefficient of secondary
compression Cα remains constant, decreases, orincreases with time [10]. In this study, the secondary
compression was assessed as ratio of Cα to thecompression ratio (Cc) and denoted as secondary
compression index Cαɛ. The Cα/Cc concept wasdeveloped by Mesri and Godlewski [10] and has
been recommended as a practical tool for evaluating
the secondary settlement in field situations [11].
The Cαɛ is approximately constant [9] and Mesri
and Godlewski [10] gave values ranging from 2.5%
to 8.5%. Peter and David [12] examined thegeotechnical compressibility of a wide variety of
Queensland sediments in 2002 and found Cαɛ values
range between 1.7% and 5.0%. Study conducted by

Table 4 Summary of PCS back-analysis of Zone 1
Settlement since project completion(mm)
Settlement
BackOriginal
Platesanalysis
Prediction
2010

247

2012

195

Baseline
183

88

2014

265

223

120

GEOMATE- Osaka, Nov. 16-18, 2015

2015

282

260

130

2020

N/A

328

179

2050

N/A

594

314

The predicted settlement between 2015 and 2020
indicates the severity of excess PCS and the
difficulty on controlling the long term performancein accordance with the tight PCS criteria. The
predicted settlement between 2020 and 2050
suggests the performance of the motorway
embankments over a longer period of time till theend of the pavement design life.

Table 5 Summary of PCS back-analysis of Zone 2
Settlement since project completion(mm)
Settlement
BackOriginal
Platesanalysis
Prediction
2010
2012

168

Baseline
168

2014

225

239

152

2015

245

257

170

2020

N/A

346

222

2050

N/A

586

377

122

Lessons Learned
Elements affecting the accuracy of long term
performance prediction for road embankments
constructed over soft soils are always complex. In
this case study, the subsoil condition were reviewed
based on information of geotechnical investigation
and as-built wick drain installation. The original
design process and soft soil parameter adoption were
investigated based on back-calculation and in-depth
literature study. Under-estimation of PCS was
possibly caused by the following factors:• Under-estimation of the secondary consolidation
parameters due to the lack of understanding of
the soft soil characteristics.
• Adoption of identical consolidation parametersacross a large zone over deep multi-layer soft
clays due to lack of awareness of possible longterm problems.

Based on the above results, the original assessment
under-predicted the PCS of Zone 1 by around 55%
and PCS of Zone 2 by around 30%. The backanalyzed PCS from project completion in 2010 till
2015 fit well with the field measurement aspresented in Figure 6. Therefore, the expected
settlements from 2015 to the end of the 10-year
Defect Liability Period (2020) and the end of
pavement design life (2050) were predictedaccordingly as summarized in Table 6.

Fig. 6

CONCLUSION
This paper has focused on the long term
performance back-analysis of the critical section of amajor motorway project. The numerical analysis
based on consolidation parameters back-calculated
through curve-fitting method indicates that the PSC
was under-estimated by at least 30% in the original
design. Regular asphalt rehabilitation is required tomitigate the excess long term settlement in order toconform to the project PCS criteria.
Some simple guidance notes are summarizedfrom this study in order to predict the long term
performance of road embankments on soft estuarine
deposits in Southeast Queensland with a higher
degree of accuracy:• To acquire sufficient knowledge of the in-situ
subsurface condition through geotechnicalinvestigations and comprehensive review onliteratures based on local experiences.
• To prevent the ignorance of localized weakzones or paleo-channels where greater long term
settlements will be developed.
• To adopt a systematic review process conductedby experienced soft soil engineers during andafter construction with the assistance of
instrumentation monitoring.

Settlement time curves: back-calculation
versus actual

Table 6 Expected settlements from 2015 to 2020 and
2050
Expected Settlement (mm)Zone 1

Zone 22015 - 2020

68

89

2020 - 2050

266

240
ACKNOWLEDGEMENTS
248

GEOMATE- Osaka, Nov. 16-18, 2015[6] Olson RE, “Settlement of embankments on soft
clays”,
J.
of
GeotechnicalandGeoenvironmental Engineering, Vol. 124, Aug.
1998, pp. 659-669.
[7] Rocscience Inc., “Settle3D – Settlement andConsolidation Analysis Theory Manual”,
Toronto: Rocscience Inc., 2009.
[8] Tan YC and Gue SS, “Embankment Over SoftClay – Design and Construction Control”,
Penang: Seminar on Geotechnical Engineering,
Sep. 2000.
[9] Rankine BR, “Assessment and Analysis ofQueensland Clay Behavior”, Brisbane: Schoolof Engineering, James Cook University, 2007,ch 4.
[10] Mesri G and Godlewski PM, “Time and stresscompressibility inter-relationship”, J. of
Geotechnical Engineering Division, ASCE, Vol.
103, No. 5, May. 1977, pp. 417-430.
[11] Mesri G and Choi YK, “Settlement analysis ofembankments on soft clays”, J. of GeotechnicalEngineering Division, ASCE, Vol. 111, No. 4,
Apr. 1985, pp. 441-464.
[12] Peter M and David L, “GeotechnicalCompressibility and Consolidation Parameters
and Correlations for Remoulded Fine-grained
Marine and River Sediments”, Brisbane:
Sustainable Tourism, 2002, ch. 4.
[13] Craig RF, “Consolidation theory”, Craig’s Soil
Mechanics, 7th ed. London: Taylor & Francis
Group, 2004, pp. 257-258.

The authors of this paper would like to express
appreciation to construction consortium and theinfrastructure owner for their support in providing
the necessary project information for the preparationof this paper.
REFERENCES[1] Balasubramaniam AS, Cai H, Zhu D, Surarak C
and Oh EYH, “Settlements of embankment insoft soils”, J. of SEAGS & AGSSEA, Vol. 41,
Jun. 2010, pp. 1-19.
[2] Transport
Technology,“Geotechnical
Investigation: Brisbane Port Road – Stage 2
Investigation”,
Brisbane:
Queensland
Department of Main Roads, 2000.
[3] Oh EYN, “Geotechnical and Ground
Improvement
AspectsofMotorway
Embankments in Soft Clay, Southeast
Queensland”, Gold Coast: Griffith School ofEngineering, Griffith University, 2006, ch. 2.
[4] Whitaker WG and Green PM, “Moreton
geology” [Online], Queensland: Department ofMines,
1980,
Available:
http//www.ga.gov.au/map/ [16 June 2015].
[5] Indraratna B, Sathananthan I, Bamunawita C
and Balasubramaniam AS, “Chapter 2:
Theoretical and numerical perspectives andfield observations for the design andperformance evaluation of embankments
constructed on soft marine clay”, Ground
Improvement – Case Histories, 1st ed. Elsevier,
2005, pp. 199-230.

249

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

EVALUATION OF INFILTRATION BEHAVIORS USING ADVECTIVE
DIFFUSION ANALYSIS ON RADIOACTIVE SUBSTANCES IN SOILS
Yoji Ueyama1 and Shinya Inazumi2
1

Student, Advanced Course of Architecture and Civil Engineering, Akashi College, National Institute ofTechnology, Japan; 2Associate Professor, Department of Civil Engineering, Akashi College, National
Institute of Technology, Japan

ABSTRACT
Fukushima Daiichi nuclear disaster occurred by Great East Japan Earthquake in 2011. As a result, a lot ofradioactive substances was discharged to atmosphere and deposited in surface of ground by rain. In damaged
area, decontamination has been conducting. Usually decontamination for the ground is digging out for 5 cm fromthe surface of the ground, but its safety and validity is apprehended. In this study, per-meation behavior analysisof radioactive substance in the ground was carried out in order to prepare new decontaminations. Specifically,
behavior and advection-dispersion analysis was conducted by advection-dispersion equation was embedded aterm that considered half-life. As a result, we gained validity for this analysis method by comparing analysis
result with result on the spot. Furthermore analytic evaluation for the current decontamination is performed.
Keywords: Radioactive substances, Osmosis, Advection-Diffusion analysis, Radioactive half-life

examining various the soils have effects on osmosis
behavior in the soils.

INTRODUCTIONTokyo An electric power Daiichi Fukushima
nuclear power plant accident has happened by theeastern Japan great earthquake in 2011. Therefore
the radioactive substances released were carried bywind and fallen to the surface of the ground or thesea. Iodine-131, cesium-134 and cesium-137 havebeen detecting in the widespread land area such as
the farmland and the ground of city area in Tohoku
and Kanto [1].
Geo-Environmental pollution range by this
accident was so huge that decontamination iscontinued now. Generally decontamination for the
ground is digging and removing the ground layer
about 5cm, like Fig. 1 [2]. However analysis forbehavior of radioactive substances has been everexamined. So the safety and validity for it isapprehended. And the construction site of theradioactive material storage container becomes aproblem. It is necessary to comprehend behavior ofradioactive substances in soils in order to prepare
new decontamination for the ground.
In
experimental
investigation
handling
radioactive substances is danger in experimental
investigation. Therefore in this study, we embedded
a term that considered radioactive half-life toadvection-dispersion equation in the order toevaluate osmosis behaviors of radioactive substancesin the soils. The aim of this study is confirming
validity for this analysis method by comparing
analysis result with result on the spot. Furthermore

BEHAVIORS OF RADIOACTIVE
SUBSTANCES IN THE SOILSRadioactive substances fallen to the surface ofthe ground have four actions. They are concentration
decrement with radioactive decay, moving in thesoils, scattering and flowing out by weather andabsorption by the plants. However scattering andflowing out by weather and absorption by the plants
are extremely small values and in order to evaluate
osmosis behavior in the soils, in this study, density
decrement with radioactive decay and moving in theFig. 1 The decontamination for the ground250

GEOMATE- Brisbane, Nov. 19-21, 2014

soils are focused on.
Concentration decrement with radioactive decay
depends on radioactive half-life. Radioactive halflife of Iodine-131 is about 8 days, its survival rate
becomes 0% instantly. But radioactive half-life ofcesium-137 is about 30 years so that long-term
examination for it is necessary.
The existence form of radioactive cesium andsoil property and importance point moving in thesoils. Radioactive cesium in the soils exists asmonovalent cation, Cs+. There are clay minerals andorganic matter which have a lot of negative electriccharge on their surface. As with other heavy metals
and positive ion, radioactive cesium adsorbs them.

Where: T1/2= the radioactive half-life.

INSPECTION OF THE VALIDITY FOR THE
OSMOSIS AND ADVECTION-DISPERSIONANALYSIS
We compare the analysis result withconcentration distribution measurement results of
Cs-137 in the area struck by Tokyo An electric
power Daiichi Fukushima nuclear power plant
accident in order to validate appropriateness of thisanalysis method. The measurement results wasmeasured by Pref. Saito in the spot approximately
70km away from the Daiichi Fukushima nuclear
power plant three times, 9 months progress, 18months progress and 20 months progress [3].
According to the result, as depth becomes big,concentration becomes the tendency to be small
exponentially. Besides Cs-137 had stayed by about 5
cm a long period of time. And by soils size
measurement at the same time, the soils of the spot
was proved to be the silt layer.
Then the analysis soils is set to the silt layer. The
representative figures of each parameter of the silt isshown to Table 1 [4], [5]. On the other hand, about
the radioactive substances, the coefficient ofmolecular diffusion and the initial concentration of
Cs-137 in this point was reported in the other report
are used, as shown to Table 2 [6]. The analysis isentered these values and carried out.
Figure 2 shows the measurement result andanalysis result of Cs-137 concentration in the depth
direction. According to it, their values are very

THE OSMOSIS AND ADVECTIONDISPERSION ANALYSIS FOR RADIOACTIVE
SUBSTANCES
Generally, substances moving in the soils
analysis is modeled by the advection-dispersion
equation. By this equation is added a term which
considered radioactive half-life, it is applicable tomoving analysis for radioactive substances.
The general differential equation for twodimensional osmosis can be expressed as  H 
Q 
k
t
x i  x i 

(1)

Where: H = the total head, k = the hydraulicconductivity, Q = the applied boundary flux, θ = thevolumetric water content, and t = time.
The advection-dispersion equation which
including radioactive half-life can be written asTable 1 Parameters of silt
the hydraulic conductivityk(m/sec)the volumetric watercontent θ
the dry mass density of thesoilsρd(g/m3)
the distribution coefficient
Kd(g/m3)C
S C C 
C 
 d

Dij

t
C t
xi 
x j 

 Ui

C  C  Sd 
xi

(2)

Where: C = the concentration of radioactive
substance, ρd = the dry mass density of the soils, Dij= the dispersion tensor, Ui = the D’arcian velocity, λ= the decay coefficient and S = the adsorption.
The advection-dispersion equation is added aterm with the decay coefficient λ that expresses theconcentration decrement by radioactive half-life. It
is leads toln( 2.0)
T1 / 2

1.0×10-6
0.4
1.2×106
8.0×10-3

Table 2 Parameters of Cs-137
the radioactive half-life
T1/2(years)
the coefficient ofmolecular diffusion
D*(m2/day)the initial concentration
C0(Bq/m2)
the dispersivity of porous
medium α(m)

(3)

251

30.2
7.2×10-6
3.0×108
0.01

GEOMATE- Brisbane, Nov. 19-21, 2014

has comfortable validity for the evaluation of
osmosis behavior of radioactive substances in thesoils.

Concentration（Bq/kg）
0

2000

4000

6000

8000

10000

0
1

THE ANALYTIC EVALUATION OF THE
CURRENT DECONTAMINATION

Depth（cm）

2
3

The osmosis and advection-dispersion analysis
including radioactive half-life is carried out in twocases, sand layer and clay layer, on the analysis
section with water head difference which isdescended Cs-137 which is necessary for long-term
examination, like Fig. 3. Analysis section is set tothe saturation state. And the boundary colored blueshows the drainage condition. The parameters ofsand and clay which necessary for the analysis areused their central values showed Table 3 [4], [5].
About parameters of Cs-137, they are entered thesame as the above.
Figure 4 and 5 show the transition of Cs-137
concentration in the sand and clay layers. In the sandlayer, there is a large osmosis around the top of
slope so that Cs-137 reaches by the foot of slope,
placed depth 20cm, at five years progress.
Consequently the current decontamination, digging
and removing the ground layer about 5cm, has noeffective in this case at five years progress. On the4

Analysedvalue
Measured
value

5
6
7

(a) 9 months progress
Concentration（Bq/kg）
0

2000

4000

6000

8000

10000

0
1

Depth（cm）

2
3
4

Analysedvalue
Measured
value

5
6

Cs-13770.2m(b) 18 months progress
0

2000

4000

6000

8000

0.5m

10000

0

0.3m

0.5m

Concentration（Bq/kg）

1

Depth（cm）

2

1.5m

3

4
5
6

Fig. 3 The analysis section

Analysedvalue
Measuredvalue

Table 3 Parameters of sand and clay

7

parameter
the hydraulicconductivityk(m/sec)the volumetricwater content θ
the dry massdensity of thesoilsρd(g/m3)
the distribution
coefficient
Kd(g/m3)(c) 20 months progressFig. 2 The measurement result and analysis resultof Cs-137 concentration in the depth
direction

closely in every depth and the tendency to decreaseof analysis result is exponentially, like measurement
result. Furthermore they are the same even if time
passes. Thereby the osmosis and advectiondispersion analysis including radioactive half-life
252

sand

clay

1.0×10-5

1.0×10-7

0.4

0.4

1.5×106

9.5×105

4.0×10-3

1.0×10-2

GEOMATE- Brisbane, Nov. 19-21, 2014

other hand, there is, on the whole, little osmosis in
the clay layer, Cs-137 stays by outer layer 5cm.
Therefore the current decontamination has effectivein clay layer.
From the results, the differences of the osmosis
behavior of radioactive substances occurs by thesoils property. For that reason, we have to select thedecontamination by soils.

Elevation (m)Concentration
Cs-137 Concentration（g/m3）
0 -1e-005 - -4e-006 g/m³
Small
-4e-006 - 2e-006 g/m³
0.5
2e-006 - 8e-006 g/m³
8e-006 - 1.4e-005 g/m³
1.4e-005 - 2e-005 g/m³
0.4
2e-005 - 2.6e-005 g/m³
2.6e-005 - 3.2e-005 g/m³
3.2e-005 - 3.8e-005 g/m³
0.3
3.8e-005 - 4.4e-005 g/m³
4.4e-005 - 5e-005 g/m³
5e-005 - 5.6e-005 g/m³
0.2
5.6e-005 - 6.2e-005 g/m³
6.2e-005 - 6.8e-005 g/m³
6.8e-005 - 7.4e-005 g/m³
0.1
7.4e-005 - 8e-005 g/m³
8e-005 - 8.6e-005 g/m³
8.6e-005 - 9.2e-005 g/m³
0.0
Large
-5
9.2×100.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5

CONCLUSION

Distance (m)In this study, we embedded a term thatconsidered radioactive half-life to advectiondispersion equation in the order to evaluate osmosis
behaviors of radioactive substances in the soils. Thismethod was confirmed the validity for it bycompering measured results. Furthermore, Osmosis
and advection-dispersion analysis was carried out intwo cases, sand layer and clay layer. We canevaluate analytical for the current decontamination
with osmosis and advection-dispersion analysis
considered radioactive half-life. As a result, thedecontamination should be selected by soils.
Henceforth, it is necessary to evaluate the current
decontamination from the analysis results that is
almost the true condition, for example,
implementing the osmosis and advection-dispersion
analysis considered uncertainties and setting theanalysis section modeled the real topography.

(a) 1 year progress
0.5

Elevation (m)

0.4

0.3

0.2

0.1

0.0
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5

Distance (m)(b) 5 years progressFig. 4 The transition of Cs-137 in the sand layer
0.5

REFERENCES

Elevatioin (m)

0.4

0.3

[1] Ministry of Education, Culture, Sports, Science,
and Technology “About making on a
distribution map of radioactivity level by
Ministry of Education, Culture, Sports, Science,
and Technology”, Ministry of Education,
Culture, Sports, Science, and Technology, 2015[2] Ministry of the Environment “Decontamination
guidelines revised edition”, Ministry of theEnvironment, 2015[3] Saito K, Aoki K, Tanihata I “Confirming depth
distribution of radioactive substances in thesoils”, Japan Atomic Energy Agency, 2015[4] Japanese Geotechnical Society “Soil tests, bases
and guidance”, Japanese Geotechnical Society,2010, p. 91[5] Inoue Y, Morisawa S ” The distribution coefficientof radioactive substances between soil andwater”, Journal of Nuclear Science andTechnology, Vol. 18, 1976, pp. 524-534[6] Suzuki K, Komiya Y, Minami, H, Mizuta M“Analysis for depth distribution of radioactive
cesium in soils”, Journal of Japanese
Association for Digital Humanities, Vol. 27,
2013, pp. 11-14

0.2

0.1

0.0
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5

Distance (m)(a) 1 year progress
0.5

Elevatioin (m)

0.4

0.3

0.2

0.1

0.0
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5

Distance (m)(b) 5 years progressFig.5 The transition of Cs-137 in the clay layer

253

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

FLOWABILITY EVALUATION OF FLUIDIZED TREATINGSOILS BASED ON MOVING PARTICLE SIMULATION
Masaki Kaneko1, Shinya Inazumi1, Yuji Shigematsu2 and Hiroshi Kawamura3
1

Student, Advanced Course of Architecture and Civil Engineering, Akashi College, National Institute ofTechnology, Japan; 2Associate Professor, Department of Civil Engineering, Akashi College, National
Institute of Technology, Japan; 3Sanwa Co. Ltd., Japan; 4Sumitomo Densetsu Co. Ltd., Japan
ABSTRACT

Recently, fluidized soils have come to be used as landfill material or filler in such landfill of undergroundspace in Japan. However, the design and construction of the fluidized soil at present is based only on the rule ofthumb. Therefore, taking into account the findings based on the theory is useful. This study has focused on
moving particle simulation. Good results have been obtained in the numerical flow analysis by using the
Bingham model to moving particle simulation. In addition, the author has developed fluidized soil with increased
fluidity (super-fluidized soil). It has evaluated from two aspects of mechanical properties and flow properties of
the high flow of treated soil. In the mechanical properties has showed that it meets the quality that is required forsuper-fluidized soils. In flow properties has showed that it has a high fluidity than the fluidized soil.
Keywords: Fluidized soil, MPS method, Bi-viscosity model, Flow simulation
INTRODUCTION

required in Euler description (Table 1). The large
deformation of the free boundary surface can beeasily expressed. Flow analysis by the MPS methodin the fresh concrete and high fluidity of concrete on
the basis of the merit of particle method has been
tried. Therefore, it is considered that it is possible tocarry out the same way flow analysis also in thefluidized soil cement is used as the solidifying
material. In this study, author focused on the MPS
method. It is a kind of particle method as a

Fluidized soil is the wet stabilization soil which inprinciple be composed of muddy soil, the material
contains the fine fraction of clay and silt enough,and solidification material to stabilize themechanical characteristic. This fluidized soil tends
to be used many as filler in a sewage pipe or the
backfill of the underground (Figs. 1 and 2). However,the design and the construction are based on theheuristics of the past in the field at present.
Therefore, the following points can be realized bytaking into account the theoretical knowledge in
addition to heuristics of the past.
(1) The most suitable combination design of thefluidized soil will enabled.
(2) It is possible to quantitatively grasp about thepumping distance and the filling distance in thefield.
(3) Proper selection of equipment (apparatus) canbe performed.
From these, it may be said that it is useful to taketheoretical knowledge into account.
For knowledge-based theory, particle method as amethod of dealing with large deformation in recent
years is attracting attention. Particle method is using
the particles as a method of discretization ofcontinuum unlike the grid method. So there is noneed to perform the production of the latticerequiring complicated working time. The particle
method is a Lagrangian description. Therefore there
is no need to calculate the convection section isFig. 1 Pumping of underground space

Fig. 2 The backfilling of the retaining wall
254

GEOMATE- Brisbane, Nov. 19-21, 2014

theoretical knowledge. In addition, it is conducted
flow simulation of fluidized soil using the MPS
method.

Table 1 Grid method and Particle method
Particle method

Use Grid

Not use Particle

Discretization

OVERVIEW OF THE FLUIDIZEDPROCESSING SOIL

Grid method

Fluidized soil is a material used at the time of
backfilling and backfilling underground structure ofvarious structures thing. In addition, it is a material
that has been developed in order to make effective
use of the construction waste soil. The basic concept
is to expect a hardening effect by mixing asolidifying material in an appropriate amount to the
muddy soil (It has a compaction unnecessary andappropriate flowability). In recent years, sociallyrecycling has become important. Therefore thefluidized soil is widely used in urban areas [1]. In
other words, to understand in relation to thecharacteristics of the fluidized soil also can be said
that also lead to the promotion of reuse of
construction waste soil.
Anamnestic research of the fluidized soil, arebeing carried out experimental study regarding themechanical properties and flowability. Mechanicalproperties and flowability have also been studied
when mixed with the fibers or changing the type ofthe muddy soil. However, analytical understanding
with respect to the fluidity of the fluidized treatedsoil is not performed.
In this study, author conducted an experimentand analysis by using two fluidized Soil. As thedifference between the two types of fluidized soil,
the difference the stock solution (muddy soil).
Namely include the difference between the
maximum particle sizes. Undiluted solution of thematerial that is to B in this study is the particle size
is composed of 74µm or less. Undiluted solution ofmaterials that are treated as A has allowed themixing of particle size 74µm or more fine sand.

Fig. 3 Bi-viscosity modelThis method uses a particle in the advection terms.
The other terms it is a method of calculating a grid.
Therefore, a large numerical diffusion occurs.
In the sense of using the particle only, pureparticle method is SPH method has been proposed in
1977. SPH method is a method of calculating thecompressible flow. So far it has been mainly handled
in the area of astrophysics. In recent years, it has
spread also be applied to the free liquid surface flow
and solid mechanics.
Whereas SPH method was proposed as a methodof calculating the compression flow, MPS method
has been proposed in 1995 as a method for
calculating the non-compressible flow. MPS method
is calculated the incompressibility condition as aparticle number density constant. This method has
introduced a pressure Poisson equation. Free surface
is determined by a decrease in particle number
density. Therefore there is no need to draw the liquidsurface shape [2][3][4].

THE PARTICLE METHOD
The particle method is a method of analyzing the
motion of the continuum as the motion of a finite
number of particles. It has not used mesh. Particle
method is also easy to apply to three-dimensional. In
recent years it has attracted attention as an analysis
method for three-dimensional continuum.
Particle method is a relatively new technique.
The first particle method is PAF (Particle-andForce) method it has been proposed in 1965 in the
United States Rosuaromosu National Laboratory.
Then, MAC (Marker-and- Cell) method has been
proposed. But the particles in this method are used
as a tracer of the liquid surface. Furthermore PCI(Particle-in-Cell) method has also been proposed.

MPS METHOD APPLYING THE BIVISCOSITY MODEL
In this study, we have used the fluidization
treated soil as the analysis material. This material
can be regarded as a Bingham fluid. Therefore, it is255

GEOMATE- Brisbane, Nov. 19-21, 2014

necessary to apply the MPS method to Bingham
model. However, it is very difficult to handle the
Bingham model in numerical analysis. This isbecause the stress becomes unstable in the following
yield value. Therefore, the authors are using the biviscosity model as an approximation model (Fig. 3).
The constitutive equations employed in this modelare expressed in Eqs. (1) And (2).
𝜏!" = −𝑃𝛿!" + 2 𝜂 +𝜏!" = −𝑃𝛿!" + 2 𝜂 +

!!
!

!!
!

𝜀!"

  Π ≥ Π! (1)𝜀!"  Π ≥ Π! (2)using the particle number density. Calculation ofparticle number density has used the weighting
function. Particle number density is determined from
Eq. (6).
𝑛!=

!!! 𝑤

𝑟! − 𝑟!

(6)

ni represents the particle i-th particle number
density. 𝑟! is the i-th particle position vector, 𝑟! is theparticle j-th position vector. Incompressible fluid isthe density and particle number density is constant.
Therefore, it is represented by n0 the particle number
density in the initial placement of the particle as areference value of the particle number density.
The MPS method discretization is performed infirst and second terms of the right-hand side of Eq.
(7).

𝜏!"   𝜀!" represent stress components and strain ratecomponent of the viscous fluid. P representspressure. 𝜂 represent plastic viscosity. 𝜏! represent
yield value. Π = 𝜀!" 𝜀!" . The governing equation
obtained from these constitutive equations isexpressed by Eq. (3) and Eq. (4).

!!
!"

𝜏! !
𝜕 𝜏!
𝜕𝑢 1
=
−∇𝑃 + 𝜂 +∇ 𝑢 + 2𝜀!"
𝜕𝑥! Π
𝜕𝑡 𝜌
Π

!

= − ∇𝑃 + 𝜈∇! 𝑢 + 𝐹(7)!

In MPS method Gradient model and Laplacianmodel, as these discretization, are represented by Eq.
(8) and Eq. (9).

+𝐹
(3)

∇𝜙 ! =

𝜏! !
𝜕 𝜏!
𝜕𝑢 1
=
−∇𝑃 + 𝜂 +∇ 𝑢 + 2𝜀!"𝜕𝑥! Π!
𝜕𝑡 𝜌
Π!

!
!!

∇! 𝜙 ! =

+𝐹

!!
!"!

!!!

!! !!!
!! !!!

!

𝑟! − 𝑟! 𝑤 𝑟! − 𝑟!

!!! 𝜙! − 𝜙!

(8)

𝑤 𝑟! − 𝑟!

(9)(4)
Where    ! represents that it has the approximatedusing a particle model in the i-th particle. d isrepresents the number of dimensions. 𝜆 is acoefficient for matching the analytical solution anddistributed in the variable distribution. This isrepresented by Eq. (10).

Eq. (3) is an expression of the state in which theflowing. Eq. (4) is an expression of the state inwhich the immobile. 𝑢 represent flow velocity
vector. 𝑡 represent time.   𝜌 represent density.   𝐹
represent external force vector.   Π! is using thefluidized limit strain rate(π! ) Π! = (2π! )! .
In the MPS method, continuous body puts asparticles of finite number. Particle interaction modelutilizes the weighting function w represented by theEq. (4). This force acting on arbitrary particle
assumed to interact with the particles present fromthe particles within a certain range. In this analysis it
is used a logarithmic weighting to improve accuracy
than the standard weighting function [5]. It shows
the weighting function in Eq. (5)
𝑤 𝑟 =

𝑙𝑜𝑔
0

!!
!

𝑒
   𝑟≤𝑟
𝑟𝑒 >𝑟

𝜆=

𝑗≠𝑖 𝑤

𝑟𝑗 −𝑟𝑖𝑟𝑗 −𝑟𝑖𝑗≠𝑖 𝑤 𝑟𝑗 −𝑟𝑖

2

(10)

The discretization of differential operators that
gradient model Laplacian model represented is afeature of the MPS method.
UNDERSTANDING AND EVALUATION OF
LIQUIDITY BY THE FLOW TESTThe flow test based on "Test Method for Air
mortar and air milk" (JIS A 313-1992, using an air
cylinder of φ 80mm h 80mm) in order to understandthe flow of the fluidized soil were performed [6].
Furthermore, author went shooting a movie at thesame time. In this experiment is using a flow ability
different fluidized soil in order to evaluate the flow

(5)

Where r is the distance between particles, re is
the radius of influence ranging interaction between
the particles. Also, evaluate the density of the fluid
256

GEOMATE- Brisbane, Nov. 19-21, 2014

ability. Formulation of the fluidized soil that wasused in the experiment is shown in Table 2.
In this study has conducted an image analysis tomake a comparison with the analysis. The image
analysis is intended to perform grasp flow behavior.
It is based on flow test video (Fig. 4). Measurementof the flow value is measured in the x and y(measured value a, measured value b) direction
orthogonal. The image analysis results of thefluidized soil are shown in Figs. 5 and 6.
Looking at Figs. 5 and 6, it can be seen that A
has a short flow stop time compared to B.
Furthermore, A can be read that has a high fluidity.
The liquidity of the difference is thought to be due todifferences in particle size. In addition each
fluidized soil from these figures is read that spread
evenly, because the measured value ‘a’ andmeasured value ‘b’ are almost identical.

Table 2 The recipe of fluidized soil andParameters of analysis

Specific gravity
Plastic viscosity(Pa･s)
Yield value(Pa)
Distance between
particles of first
(m)
𝛑𝒄

A
1.302

B
1.224

2.349

1.209

2

3
0.0002
0.5

UNDERSTANDING AND EVALUATION OF
LIQUIDITY BY PARTICLE METHOD
To understand the flow of the fluidized soil froma theoretical point of view, author has conducted
flow analysis by particle method using bi-viscosity
model as a response to Bingham fluid [7]. Analysis
is being carried out in two-dimensional, because theflow of the fluidized soil has been carried out
uniformly. Parameters of analysis used are shown in
Table 2. The analysis cross section are using a flow
test cross-section (Fig.7). In addition, Fig.8 andFig.9 show the visualization of the analysis results.
It shows a diagram of a combination of 'flow
behavior by analysis based on the particle method'and 'flow behavior by flow test' in each fluidized
Soil. (A is shown in Fig. 10. B is shown in Fig. 11).
From Figs. 10 and 11, it can be read that the flow
behavior of each fluidized soil is well suited. At firstglance, these figures do not seem to match. However,
the range has become very small compared to thescale of the construction. Therefore, it has become acomparison at the micro range. Thus it can be said
that there is validity.
Then evaluate the fluidity. From Figs. 10 and 11,

Fig. 4 Overview of image analysis

Fig. 5 Fluid behavior of A by image analysis
resultFig. 6 Fluid behavior of B by image analysis
resultFig. 7 Flow test cross-section

257

GEOMATE- Brisbane, Nov. 19-21, 2014

Fig. 8 The results of visualization of A

Fig. 9 The results of visualization of B

it is read that the A from the results of analysis byparticle method have a high flowability. Therefore, it
is expected to high filling properties and workability
in B. Finally, the difference in the fluidity from theresult of analysis based on the particle method it was
revealed that the plastic viscosity and the specificgravity is affecting.
CONCLUSION
In this study, it has evaluated the flowability of
the fluidized soil based on the experimental
evaluation and particle method on the liquidity of
the fluidized soil. The results and findings of thisstudy are shown below.
(1) With regard to liquidity, high-flow fluidized soil(B) higher than the flow of treated soil (A).
Comparison of the flowability of the fluidized
soil with a maximum particle size of different
undiluted solution, the particle size distributionof the undiluted solution showed that influence
the flowability.
(2) Bi-viscosity model was apzplied particles
method (MPS method) the results of flow
analysis of fluidized soil is consistent with thereal problem, validity has been confirmed.
(3) High-flow fluidized soil (B) from the results offlow analysis using a particle method tocorrespond to the bingham model was confirmed
to have a high fluidity.
(4) The main factors that affect the flowability of
fluidized processing soil showed that it is the
specific gravity and plastic viscosity from flow
analysis and test results.

Fig. 10 Fluid behavior of A

Fig. 11 Fluid behavior of B

258

GEOMATE- Brisbane, Nov. 19-21, 2014

REFERENCES[1] Yukihiro K, “Future challenges and mechanicalproperties of the fluidized soil”, Japan Societyof Civil Engineers Papers F, vol.62, No.4,
pp.618-627, 2006.
[2] Seiichi K, “Numerical analysis of the flow by
the particle method”, Nagare 21, pp.230-239,
2002.
[3] Seiichi K, Kazuya S, Kouhei M, “Particle
method Introduction (From the basics of fluid
simulation to parallel computation andvisualization)”, Maruzen Publishing Co., Ltd.,
2014.
[4] Seiichi K, “Particle method Computational
Mechanics Lecture Series 5”, Maruzen
Publishing Co., Ltd., 2006.

[5] Hayashi Y, Toyotani Z, Sumida K, “The watercolumn collapse simulation by pressurestabilization particle method”, Nippondaigaku
Seisankougakubu 46th Academic lecture,
pp.307-308, 2013.
[6] Dokuritugyouseihouzin Dobokuzimusyo,“Fluidized soil use Technical Manual (Second
edition)”, Dokuritugyouseihouzin
Dobokuzimusyo, 2007.
[7] Urano S, Nemoto K, “A study on theworkability of fresh concrete using flow
simulation
method”,
Simizukensetu
Kenkyuhoukokusyo, No.90, 2013.

259

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

PREDICTION OF SPACE DISTRIBUTION FOR
SOIL SURVEY VALUES BASED ON GEOSTASTICS
1

Kohei Urakami1, Shinya Inazumi2, Kozo Okita3 and Tsuyoshi Okita3Student, Advanced Course of Architecture and Civil Engineering, Akashi College, National Institute ofTechnology, Japan; 2Associate Professor, Department of Civil Engineering, Akashi College, National
Institute of Technology, Japan; 3Okita-Ko Co. Ltd , Japan
ABSTRACT

Recently, land subsidence and liquefaction are becoming evident. But neither the countermeasure nor
research technique have not been established. In order to determine the cause, more detailed comprehension ofsoil properties is essential; therefore, in this research Improved Swedish Sounding Testing machine, or NSWS *1,
capable of measuring more detailed physical properties of in-ground was utilized and conducted a subsurface
investigation at a narrow detailed house at which land subsurface have occurred. Based on the result of theinvestigation presented physical properties of the in-ground in the plane manner using Kriging, one ofgeostatistics methods. Also, the comparison of converted N-value measured by the NSWS and converted Nvalue estimated by Kriging is presented to examine the composite capability and benefit of Kriging and NSWS
for simplified on-site verification and re-measurement for reaffirmation (diagonal measurement) in the confined
detached house ground.
Keywords: Geostatistics, Kriging for Swedish weight sounding test, disaster prevention
INTRODUCTIONNSWS for simplified on-site verification and remeasurement for reaffirmation ( diagonalmeasurement) in the confined detached houseground.

The vast area of Urayasu city, Chiba prefecture
was liquefied when 2011 Tohoku earthquake struck
and followed by the permanent displacement
causing heavy damage to the buildings. Although
JIS standard for a research method orcountermeasure construction method for liquefaction
have been around for long time, they are still being
developed. A technology that correspond to fastpaced changes in the natural disaster has not been
established. Especially, the detached house grounds
are having difficulties because they are private
properties and very small in Japan. Also, due to thereasons of time, finance, vibration, a mechanicalreason for installation area, and the conventional JIS
method enable a limited number of data collection
when conducting in-situ soil analysis. For that, ingeneral an estimation is carried out using somemethods based on a limited number of soil analysis
for un-investigated area.
In this research, the NSWS was utilized at anembankment of a sunk detached house measuring
converted N-value in details at each survey point.
Furthermore, based on depth distribution of
measured converted N-value, Kriging is used to
predict space distribution of the unknown domain
with unclear N-values within the survey ground.
Also, the comparison of converted N-value
measured by the NSWS and converted N-value
estimated by Kriging is presented to examine thecomposite capability and benefit of Kriging and

INVESTIGATION TESTING EQUIPMENT
AND ON-SITE SUMMARY
The research conducted a ground survey at land
subsidence occurred site. On this survey, the NSWS,
was utilized as the survey testing machine. The
machine has refined the conventional Swedish
Sounding Testing machine; it evaluates soil strength
by loading weight on a penetrating rod andcollecting data by rotation.
The followings are the characteristics of the NSWS:
1. Maximum loading amount is 2500N possessing
high penetration capability
2. It can penetrate soil layers with gravels mixed,
and soft rock layers.
3. A measurement resolution is 10.8mm, very
fine, enabling to collect detailed data.
4. Not only a vertical measurement but diagonalmeasurement are possible enabling to collectmany data at the confined research area.
The next is about the venue. The venue is a
detached house embankment in Nishinomiya city,
Hyogo prefecture. It is a slope area with a staircasing residential area; two two-story general
detached houses are line up on the site. A part of thesite caused land subsidence causing distortion on the260

GEOMATE- Brisbane, Nov. 19-21, 2014

Krigings under the assumption of fulfillment ofstationary in random variables and calculates
weighted mean value of the variables. In this study,
random variables are converted N-values. Also,
stationary means that expected values of randomvariables are fixed throughout the whole targetedarea, and a covariance depends solely on distanceamong data and is independent of the locations ofdata.
A general formula of Ordinary Kriging is asfollows (1):
𝒘𝒘 𝒙𝒙 =

𝒏𝒏𝒊𝒊'𝟏𝟏 𝒃𝒃𝒊𝒊 𝒘𝒘 𝒙𝒙𝒊𝒊

(1)

W(x): Estimated value, W(x): Measured value, b1:
Weight at each survey location
Also, formula (2) is necessary to fulfill theaforementioned stationary requirement:
Fig 1. NSWS𝒏𝒏
𝒊𝒊'𝟏𝟏 𝒃𝒃𝒊𝒊 = 𝟏𝟏(2)

The formula requires the sum of weights to beequal to one; under this condition, Kriging can beapplied.
In this study, Ordinary Kriging was carried out
based on converted N-value measured by the NSWS.
The NSWS can collect data with 1.0cm pitch. So,with this measurement resolution, or by changing
the number of data analyze the change of results ofinterpolation and also analyze the accuracy of theestimations by changing the number of dataassuming the measured values as true values.
Kriging was carried out with 1.0cm pitch and 25cm
pitch. And, besides comparing these two, theapplicability of Kriging to the detached housegrounds was examined as well.
Also, in order to make estimations on different
cross sections, Kriging was utilized likewise atsurvey locations 3 and 7 with 1.0cm pitch. This is tojudge the applicability of Kriging at different crosssections. On the cross sections created between
survey locations 3 and 7 exist survey location 4 thatwas measured vertically, and survey location 10 thatwas diagonally measured from survey point 3.
Therefore, by estimating this cross section andcomparing with vertically and diagonally measured
data, the applicability of Kriging can be judged aswell as judging whether it is possible for Kriging tointerpolate point data in a plane manner.

Fig 2. One-site summarypart of the building and concrete-block wall thatsurround the house. These phenomenon mainly
happened at the South part of the site. As depicted in
Fig.2 survey locations are the vertical measurement
points at the North of the site (1, 2, 5, and 6) and thevertical measurement points at the South of the site
(3, 4, 7, and 8), the side of the retaining wall. Also,
diagonal measurement of survey locations (10, 11,
12, and 13) are meant to comprehend the bounds of
weak area and insufficiency of compaction in thevicinity of the retaining wall.
.
SPATIAL DISTRIBUTION PREDICTION BY
ORDINARY KRIGING
In this study interpolated a cross section among
survey locations using Ordinary Kriging andestimated the distribution of converted N-value
along the cross section. Ordinary Kriging is one of261

GEOMATE- Brisbane, Nov. 19-21, 2014

Fig 3.Result of Kriging on 1.0cm pitch
Fig 7.The result of Kriging on other cross section

Fig 4.Result of Kriging on 25.0cm pitch

Fig 8.Actual value on Point 3, 4, 7, 10

Fig 5. Actual values on Point1,5 and 6
Fig 6.

Fig 9. Compare to actual and prediction on Point4
Fig 7. Compare to actual and prediction on point 2
262

GEOMATE- Brisbane, Nov. 19-21, 2014

ranged hard grounds appear. Fig.9 is the comparisonof the measured value to estimated value at survey
point 4, and Fig.10 is the comparison of thecomparison of the measured value to estimated value
at survey point 10. Fig.9 has overall small deviations.
Next, one can confirm that down until 180 cm depth
Fig.10 has small deviations. But the deviation
become bigger from there. Table 1 shows relative
errors of at both survey points. According to Table 1
both points have the relative errors less than one.
This means both have relatively small errors.
Because of these results, it is fair to say that theestimations for vertical and diagonal measurements
are highly precise. Therefore, Kriging is capable ofinterpolating in a plane.
CONCLUSION
In this study, the followings were examined:
Applicability of Kriging to the detached housegrounds with a small area.
2. It is possible for Kriging to interpolate point
data in a plane.
3. The change in the estimation accuracy bychanging the number of data.
Kriging was carried out by changing
measurement data pitch. One could confirm that theincrease in number of data leads to higher accuracy.
Also, Kriging was applied at two cross sections, andrelative errors with estimated values were small; one
could support the applicability of Kriging to thevertical measurement. Lastly, the applicability of
Kriging was tested against the diagonalmeasurement; Kriging was applied at the crosssection of survey point 3 and 7. The result supports
Kriging for the diagonal measurement. Therefore, it
is fair to say Kriging is capable of interpolating in aplane. Therefore, it sees Kriging has applicability on
detached house ground with a small area. Also eachsection have soft grounds to depth 100cm, it seesvery likely to be the soft ground to depth 100cm onthis detached house grounds by Kriging.
Kriging is used on interpolation in grounds witha small area. Thereby, on this study it thinks
interpolation in grounds with a small area become
precision.
This means if estimated value by Kriging has
small deviation to measured value of additional
search for verification, the soil survey can befinished; this helps financially as well, but if thedeviation was big, additional survey needs to be
conducted to find the cause.
1.

Fig 10. Compare to actual and prediction on Point10
Table 1. Relative error with the real value
Items
Relative error
Point 40.284210892
Point 100.595928736
APPLICABLE EXAMINATION BY
ORDINARY KRIGING
Fig. 3 shows the results of Kriging with 1.0cm
pitch at survey points 1, 5, and 6. Fig. 4 is the resultsof Kriging with 25cm pitch at survey points 1, 5, and6. By comparing these two one can confirm thatfrom about depth 130 cm to 150 cm Fig. 3 has afiner and more various distribution. Therefore, finer
measurement resolution enables more detailed
estimations. Fig.3 and Fig4 show the soft ground on
depth 100cm. Therefore, it sees very likely to be thesoft ground to depth 100cm by Kriging. Fig. 6 is acomparison of two estimations with measured
converted N-value at survey point 2. One can tellthat at the deep ground the estimated value with
1.0cm pitch deviates less from the measured value.
From this confirmation, one can see the need tocollect many data by measuring with narrowinterval. Also, since both estimations have small
deviations up till 140cm depth, it is safe to say theestimations have high-precision when treating
measured values as true values and it thinks Kriging
has applicability on vertical.
Fig. 7 shows the result of Kriging from survey
point 3 to 7 with 1.0cm pitch. With Fig. 7 one canconfirm the existence of soft grounds down until 120
cm deep. From there, at the vicinity of survey point
7 hard grounds appear at the vicinity of 120 cm
depth, and at the vicinity of 180 cm depth a wide-

REFERENCES[1]   Goushi N, Shinya I, Kozo O, Takumi K:
Suggestion of ground investigation example anda reinforcing structure, the reinforcement
method of construction for the residential land263

GEOMATE- Brisbane, Nov. 19-21, 2014

laying earth on the ground on the slant bedrock,
Ground improvement symposium memoirs Vol
11, The society of Materials Science, Japan.,
pp.431-438，2014[2]   Tetsuya S, Katsuaki K: Interpolation of thespace value in consideration of an error,
Geothermal research society of Japan No.4,
pp.183-194，2007[3]   Yuzo O, Makoto T, Hideaki O: Basic study on
estimationofphysicalparametersinheterogeneous ground, Journal of JSCE, No.457,
Vol.21, pp.51-58, 1992
[4]   Makoto H, Makoto S, Minoru U: Optimality
criteria for borehole location design in theestimation of pile tip levels, Journal of JSCE,
No.610, Vol.45, pp.43-45, 1998
[5]   Yusuke H, Yu O, Hideaki K: A simplified
scheme to evaluate spatial variability andstatistical estimation error of local average of
geotechnical parameters in reliability analysis,
Journal of JSCE C, No.1, Vol.68, pp.41-55,
2012[6]   Kenji M, Yoshito M, Kiyoshi I, Makoto S:
Probabilistic estimation of spatially distributed
N-Values and its application to pile design,
Journal of JSCE, No.436, Vol.16, pp.57-64,
1991[7]   Takatoshi N, Masanori T, Yoichi W, Saiichi S,
Tomofumi S: Verification of 3-D visualizationof ground at Tokyo International Airport,
Japanese Geotechnical Journal, No.1, Vol.6,
pp.69-79, 2010264

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

ALTERNATIVE MATERIALS OF SUB BASE (RESERVOIR BASE) FOR
POROUS PAVEMENT

1,2

Yulvi Zaika1 and Ludfi Djakfar2
Engineering Faculty, Brawijaya University, Indonesia

ABSTRACTThis study assessed characteristic and gradation some types of stone materials as stone crush, limestone and steel slag forporous pavement and evaluate this performance based on California Bearing Ratio (CBR) and porosity. Naturally thesecharacteristics are contrary, in which for high porosity materials will have low bearing capacity. As many as 7 gradationsin 3 types soil material were prepared to test to obtain bearing capacity and porosity of material. The result showed that
CBR of stone crush is high in uniform course material gradation but limestone and steel slag are in well graded material.
In the term of porosity, the result completely the same for all materials in which the uniform gradation give the highporosity. It makes sign the influence of hardness and gradation of material will give contribution for bearing capacity andonly gradation influence to porosity. The material are suitable for reservoir layer of porous pavement for light traffic load.
Keyword: Porous, Pavement, Reservoir, Traffic, Stone

are limestone and steel slag. The objective of study todetermine the effect of soil type and soil distribution toporosity and CBR. At the end it can defined gradation
band of every type of soil will provide maximum
porosity at allowable CBR for porous pavement.

INTRODUCTION
Climate change in various parts of the world such as
rainfall is high enough to give direct effect tothe pavement structure. In the beginning, roaddrainage system able to drain runoff to the sewer, but theincreased rainfall causing puddles on the roadsurface. The surface flow becomes greater than
the infiltration which is causes a negative effect on the
structure, especially the structure of the pavement. Thiscondition also occurred in Indonesia, where highrainfall became one of the causes of damage tothe pavement structure.
One
of
the
solutionsofferedinthe pavement technology is the use of porous
pavement that anticipate puddles of water on the roadsurface, in addition to increasing soil water infiltration.
The pavement layer consists of base and surface
layer that is able to flow the rainfall. For this purposes,
materialoftheporouspavement
must have two important properties which are able
to support the weight of the vehicle and also porous. To
find these characteristics in the material, CBR(California Bearing Ratio) test will conducted todetermine resistance to load and porosity test to
determine its ability to drain water.
California Bearing Ratio is the characteristic soil
usually used as load capacity for highway
construction. CBR is determined based on the rocktype and gradation. There are several typesof soil that has a high CBR due to rock hardness origin,but it must be supported with proper gradation such asstone crush, limestone.
In general, the use of crushed stone pavement on thefoundation layer because it has a large bearing
capacity. In this study used two other rock types

THE STRUCTURE OF POROUS PAVEMENT
Porouspavementisapavementthatis constructed or created using materials that allow
the flow of water infiltration into the subsoil. Mechanism
the porous pavement can be shown in Fig. 1. Structure ofporous pavement has to resist the vehicle as live load.
The characteristic layer of pavement usually represent in

CBR.
Fig. 1 Mechanism flow of rainfall in porous pavement
The Components of porous pavement has
few as well as the flexible pavements consisting ofasurfacelayer,
thebase
layer and subgrade. Material and gradation should choose according to the vehicle load. The material
used on porous pavement are similar to waterresistant
pavementmaterial.
It
is
just
that
the
material
has

265GEOMATE- Osaka, Nov. 16-19, 2015

been designed or specially engineered so that it has theability to absorb water.
Porous pavement layer components are:(1) The surface course such as asphalt porous, porous
concrete, soil filled plastics and open cell concrete
block.
(2) Sub base/ reservoir course
In the structure of porous pavement, sub-base canalso be called reservoir. So called because it primaryfunction
other
than
to
resist the load, but also used for the flow of surface
water over the road to drainage channel. In
general, hydrological and structural function of pave
ment materials are combined into a layer called thebase reservoir.
In addition, the reservoir also serves to retain
water before it flows into the ground or into a drainage
pipe. Water storage volume is the same with the volume
of void between the particles aggregate. The greater thevolume of air voids, the greater the volume of water thatcan be accommodated

limited so it is necessary to find alternative materials as a
substitute.
The limestone is a sedimentary rock composed of themineral calcite and aragonite are also used as building
materials and pavement. It has high CBR is around
80% but the mineral calcite in limestone dissolves easily
in water depends on the temperature and acidity (pH).
Limestone reserves is very large and almost spread
across the region in Indonesia. East Java is one of
the major producer of limestone. The Los Angeles
Abrasion of limestone is 26.6 %.
In nature, iron, copper, steel, nickel and other metals
are found in impure states called ores, often oxidized andmixed in with silicates of other metals. During smelting,when the ore is exposed to high temperatures, these
impurities are separated from the molten metal and canbe removed. Slag is the collection of compounds that areremoved. In many smelting processes, oxides are
introduced to control the slag chemistry, assisting in theremoval of impurities and protecting the furnace
refractory lining from excessive wear. Steel slag, a byproduct of steel making, is produced during theseparation of the molten steel from impurities in steelmaking furnaces. The slag occurs as a molten liquid melt
and is a complex solution of silicates and oxides thatsolidifies upon cooling.
The use of steel slag as an aggregate is considered astandard practice in many jurisdictions, with applications
that include its use in granular base, embankments,
engineered fill and hot mix asphalt pavement. Prior to its
use as a construction aggregate material, steel slag must
be crushed and screened to meet the specified gradation
for the particular application. Stone dust from crushedstone are used as filler in this study. Some advantages ofusing slag are highly angular in shape and have roughsurface texture. Typical physical and mechanicalproperties of steel slag are shown in Table 1 and 2.

o a layer called the base reservoir.

Fig. 2 Various forms of pavement base reservoir
MATERIALS
The material used as the porous pavement must thefollowing criteria:
1. The material must have appropriate water storage
capacity and is able to flow the water in a certain
period without erosion.
2 The material must have sufficient rigidity to support
traffic loading.
3.Materials should be able to get passed the impurities
pavement.
4.Materials must be able to filtering and prevent shearing
between the base, sub base and the subgrade.
Crushed stone or angular rock is, typically producedby mining a suitable rock deposit and breaking theremoved rock down to the desired size using crushers. It
is distinct from gravel which is produced by naturalprocesses of weathering. Crushed stone is a rock that has
been frequently used as a pavement because of
the angle that produces high friction (interlocking),
the large carrying capacity (CBR) of more than 80%,
frost resistance and high bulk density .Because of
crushed stone provided by nature so availability is

Table 1 The Physical Properties of Steel Slag
Physical properties
Specific gravity
Unit Weight kg/m3
Absorption

Value
3.2 – 3.6
1600 - 1920
< 3%

Table 2 The Mechanical Properties of Steel Slag
Mechanical properties
Los Angeles Abrasion (ASTM C131), %Angle of Internal Friction
Hardness (measured by Mohr’s scale ofmineral hardness)
California Bearing Ratio (CBR), % top size
19 mm (3/4 inch)

Value
20-25
40-50
6–7
>300%

In this study steel slag has 24.3 % Los Angeles Abrasion
and impact is 9.49%.

266

GEOMATE- Osaka, Nov. 16-19, 2015

Due to their high heat capacity, steel slag aggregateshave been observed to retain heat considerably longer
than conventional natural aggregates. The heat retention
characteristics of steel slag aggregates can beadvantageous in hot mix asphalt repair work in cold
weather.

overall volume of the mold. To find the volume of empty
space filled with air can be done by filling out a certain
amount of water, the amount of water taken into thevoid is equal to the volume of empty space filled with air
cavities.
𝑛𝑛 =METHODOLOGY

California Baring Ration is comparison between
stress of soil and California Stress Standard that can bewrote as Eq.(1).
𝑝𝑝

13

13

(1)

𝑝𝑝𝑝𝑝

Where:
P : stress of soil
Ps : California Stress Standard

5

5

mL

13,8

Value of CBR represent quality of soil compare to
stress of standard in stone crush who’s CBR 100%.
Standard procedure for CBR test conduct to ASTM D1883-73 and AASHTO T-193-81. The harder material
will reflect the high CBR. CBR for some type of soilmaterial can be shown in Table 3 and for layer pavement
are shown in Table 4.

Material

Some conditions will influence porosity as follows:
1. Distribution soil particle, uniformly graded soil has
high porosity because volume of void bigger
compare if the well graded soil.
2. Cementation of soil grand, if the soil particles havehigh degree cementation, the void become small andporosity is law.
3. Compaction
increasethestrength,lower
compressibility and reduce permeability of soil byrearrange soil particle.
4. Degree of angularity, the shape individual particle
is important as grain size distribution in affecting
engineering response. The rounded particles havehigh porosity than angular.

Value of CBR
(%)
100
80
80

Stone crush, subgrade layer
Natural soil aggregate as subgrade
Limestone
Table 4 Relative CBRCBR in Base
Course
100
80

13,8

Fig. 3 Porosity Test

Table 3 Value of CBR for Some Types of Soil Material

Excellent
Good
Fair
Poor

(2)

𝑉𝑉

Where:
V v : volume of void
V t : total volume

California Bearing Ratio (CBR)

𝐶𝐶𝐶𝐶𝐶𝐶 =𝑉𝑉𝑉𝑉

CBR in Sub Base
Course50
40
30Types and distribution soil particles
There are 4 types (stone crush, limestone, steel slag)of soil and 6 gradation variation were prepare in thisstudy as shown in Fig. 4.

50

Porosity of Soil
Soil aggregate is consist of soil particle and void
between particles where water and air take place. Ratio
between volume of void and total volume is called
porosity as shown in Eq. (2). The porosity is usually
express as a percentage and influence of density of soil,
higher porosity is lower density. It means easier forwater to flow in soil aggregate.
The concept of porosity testing to determine
the percentage of the volume of empty space with the

267

100

100.0

80

80.0

60

60.0

CBR (%)

passing (%)

GEOMATE- Osaka, Nov. 16-19, 2015

40
20

40.0
20.0

0
0.01
LL
X3

0.1

1
particle size (mm)
UL
X1
X4
X5

10

0.0

100

LL

UL

X1

X2

X3

X4

X5

X2

Fig. 6 California Bearing Ratio of Stone Crush
The results are porosity test and California
Bearing Ratio test shown in Fig.5 and 6, where
the biggest results of the two experiments wereobtained from the same graded X4.

Fig. 4 Design Gradation Variation in Experiment
From that figure, X5 is control gradation because it was
designed inside the gradation band as specified byIndonesia Highway Specification (Binamarga), which
was represented by UL (Upper Limit) and LL (Lower
Limit).

35.00
30.00

Porosity (%)

RESULT AND DISCUSTION
Several tests conducted to determine thecharacteristics of all the soil types with variationsof gradation.

25.00
20.00
15.00
10.00
5.00
0.00

LL

UL

X1

X2

X3

X4

X5

X4

X5

CBR(%)

Fig. 7 Porosity of LimestoneFig. 5. Porosity of Gradation Variation of Stone Crush

70
60
50
40
30
20
10
0

LL

UL

X1

X2

X3

Fig. 8 California Bearing Ratio of Limestone
The limestone have different characteristic, which is thehighest porosity value is derived from X4 gradation asshown in Fig. 7, but the highest CBR on gradation X5(Fig.8). The gradation of X5 is gradation in specificationof Binamarga (Indonesian Highway Specification). Due
to limestone is easily soluble in water especially with highacidity, so in this study conducted CBR test in

268

GEOMATE- Osaka, Nov. 16-19, 2015

soaked conditions. The soaked CBR is lower value
compare un-soaked as shown in Figure 9.

From
three
types
of
soil
are
obtained that gradation X4 delivers maximum porosity.
The number size gradation has shown that nearly
uniform
grain
size.
Based
on
CBR
test
for
each
trial
gradation
variationareobtaineddifferent
results where the largest CBR value for crushedstone gradation is X4 but the limestone and steel slag
is gradation X5 showed the more dominant of thegradation. It is a bit different with crushedstone,
whichchangesthegradation
eliminated
even
small
will
influence CBR value. This indicates that the strengthof the rock is more dominant in bearing capacity.

50

CBR (%)

40
30
20
10
0

LL

UL

X1

X2

X3

X4

X5

Fig. 9 Soaked California Bearing Ration of Limestone
CONCLUTION
1. Compared with limestone and steel slag, crushedstone has a greater bearing capacity.
2. The more uniform gradation with a larger
size will give high porosity. In which porosity only
determined by the gradation of the mixture.
3. California Bearing Ratio of the soil depends on thehardness of the soil and gradation.
4. Despite the porous nature of the mixture as
desired but the carrying capacity is only
suitable for light traffic loads that require further studyof material.

Based on result of stone crush and limestone, 3variation gradations steel slag were taken to analyses asthat is X2, X4 and X5. The porosity and CBR ofsteel slag can be seen in Fig.10 and 11. Based on the
figures are seen that highest porosity is in X4 and highest
CBR is X5.

50

REFERENCES

POROSITY(%)

40[1] ASTM INTERNATIONAL, “Standard Test Method
for California Bearing Ratio of Laboratory
Compacted Soils “,2007 (D1883-07E2).
[2] ASTM INTERNATIONAL,” Standard Test Method
for Permeability of Granular Soil Constant Head“,2007,(D1883-07E2)[3] Asphalt Institute,”Soils Manuals”, Maryland. Asphalt
Institute. 1978.
[4] Binamarga,”General Specification for Aggregate
Type Base Course”, (Indonesian).
[5] Chopra.M, Wanielista.M.” Performance Assessmentsof Portland Cement Pervious Pavement, Compressive
Strength of Pervious Concrete Pavement”, Strom
Water Management Academy University Florida,
Central Florida, Orlando, 2007.
[6] Djakfar.L, Zaika.Y and Bowoputro.H,” Effect ofMore Uniform Gradation on Permeability andStrength of Base Course for Porous Pavement”,
Proceedings of the Eastern Asia Society forTransportation Studies, Vol.9, 2013.
[7] Yusof .N.I.M at al, ” Utilization of Steel Slag as
Aggregate Replacement in Porous Asphalt Mixtures”,
Journal Teknology UTM, Vol. 69, 2014,No.1 ,67-73,
eISSN 2180-3722.
[8] Kuosa.H, Niemeläinen.E, Loimula.K, “Pervious
Pavement System and Material”, Research Report
VVT- R- 08222- 13,2013, Finland,

30
20
10
0

LL

LU

X2

X4

X5

CBR(%)

Fig.10 Porosity of Gradation Variation of Steel Slag

70
60
50
40
30
20
10
0

LL

LU

X2

X4

X5

Fig.11 California Bearing Ratio of Steel Slag

269

GEOMATE- Osaka, Nov. 16-19, 2015

[9] Roseen.R.M. Ballestero.TP, Houle.J.J ,Briggs. J,Sand Houle.K.M,” Water quality and Hydrologic
Performance of Porous Asphalt Pavement as a
Strorm-Water Treatment Strategy in a Cold Climate”,
Journal Environmental Engineering, Vol. 138, No.1,
2013, IO.10.1061/(ASCE)EE.1943-7870.0000459.
[10] State New Jersey Department of Environmental
Protection,” New Jersey Strom water BestManagement Practices Manual”, 2009, New Jersey.
[11] PA Department of Environmental Protection(PADEP) (2005),” Pennsylvania Storm water BestManagement Practices Manual, Pennsylvania Storm
Water Management Manual,[12] Richardson, D.,” Drain Ability Characteristics ofGranular Pavement Base Material”, Journal ofTransportation Engineering, Vol.123, 1997, Issue 5,385-302. DIO.10.1061/(ASCE)0733-947X.
[13] University New Hampshire Storm Water
Center, ”Design Specifications for Porous Asphalt
Pavement and Infiltration”,2009, Beds, Durham.
[14] Wang.Y and Wang.G ,” Improvement of Porous
Pavement” , 2011,US Green Building Councils.
[15] Zhang, L., Ong, G.P., Fwa, T.F. ” A Review On
The Use Of Porous Pavements To Reduce TirePavement Noise”, Proceedings. APTE VII, 2012,
Songkhla, Thailand.

270

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

CONSOLIDATION ANALYSIS OF VIETNAM SOFT MARINE CLAY
BY FINITE DIFFERENCE METHOD WITH APPLICATION OF
CONSTANT RATE OF STRAIN CONSOLIDATION TEST

1

Cong-Oanh Nguyen1, and Thanh Thi Tran2
Division of Soil Mechanics and Foundation Engineering, Southern Institute of Water Resource Research,and Posco Engineering Co. Ltd, Viet Nam; 2 Southern Institute of Water Resource Research, Viet Nam
ABSTRACT

There have been many reclamation projects built on the soft ground in recent years in Vietnam especially in
the Southern region. Geotechnical engineers always face challenges in dealing with soft ground treatment
methods. As such, ground improvement technique using preloading in combination with prefabricated verticaldrains (PVD) is regularly employed to accelerate the consolidation of the thick soft clay deposits and to ensuresatisfactory post-construction settlement specification. This paper presents the results from a series of numericalback-analyses using Finite Difference Method which are conducted for a case study from a typical ground
improvement project in Southern Vietnam. In this study, the input consolidation parameters are established fromconstant rate of strain (CRS) consolidation tests on high quality soil samples obtained by hydraulic piston
sampler. The approach aims at numerical solution of multi-subsoil layer model with the use of an FDM
procedure coded by the authors. In comparison with field monitoring data, the back-analyses show goodcorrelations to both magnitudes and changes with time on ground settlement, the dissipation of excess pore waterpressures and interpreted gain of un-drained shear strength.
Keywords: Consolidation, CRS test, FDM, PVD, Settlement, Un-drained shear strength

INTRODUCTION
In recent years, construction on soft soil has been
widely carried out over the Southern part of Vietnam.
Mostly, the soft soils found in these construction
projects have been improved by vertical drains andsurcharge preloading method. Therefore, it is still achallenge for geotechnical engineers to estimate thecorrect settlement of the soft clays under surchargeload. This paper examines the results of a series of
consolidation analyses using a Finite Difference
Method procedure coded by the authors. The subsoil
is divided into sub-layers to incorporate the change
in compressibility and consolidation characteristics
over the clay thickness. Input parameters of the soft
Holocene soil deposit which is in Mekong Delta are
determined with application of CRS test on
undisturbed samples collected by piston sampler
technique.
The chosen case study is Saigon PremierContainer Terminal project, which is around 25 kmfrom South West of Ho Chi Minh City, Vietnam. In
general, for the container yard area the requirement
is under a design net operating load of 40 kPa, thetarget end of construction (residual) settlement ofless than 5% of estimated total settlement. Due tothe presence of thick and highly compressible soft
marine clay deposit at the site (between 30 and 35
meters in thickness), the widely popular construction
technique using preloading method in combination
with prefabricated vertical drain was employed forground improvement. Because the area is close to
the shore line of Soai Rap River estuary, surcharge
sand is filled by both hydraulic method and dump
trucks.
Fig. 1 shows the location map, layout plan of thewhole site and arrangement of monitoring
instruments including 2 settlement plates, 1
extensometer lot, 1 piezometer lot, 3 CPTU test
points in tender stage, and 1 group of CPTU andFVT before surcharge removal for the investigatedarea, Phase II (2-2).
UNDISTURBED SAMPLING AND TESTING
Reliability of consolidation yield stress σ’ y ,compression indices C c1 , C c2 , C r and coefficient ofconsolidation c v , c h values is dependent on thequality of undisturbed samples. Some authors [4],[3] investigated the quality of soil samples retrievedby various types of samplers and concluded thatstationary piston sampler provide the good quality
for samples.
It was revealed that in the Mekong delta region,
soft clay samples retrieved by Shelby thin wall tube
shows un-acceptable quality for determine
consolidation characteristics. On the review of morethan thirty soil investigation reports for Mekong
delta region, Bui, T. M. found that the yield stress(or pre-consolidation pressure) is smaller than over271

GEOMATE- Osaka, Nov. 16-18, 2015burdened pressure and is not increasing with depth,
thus OCRs were less than unity [1]. From the changeof void ratio by recompression to in-situ overburdened stress, he attributed the above phenomenon
to sample disturbance. The disturbance might bycaused by sampling technique and other processes.
A comparison of samples taken by both piston
sampler and Shelby sampler, and a comprehensive
study on the soil characteristics of soft clay in
Mekong Delta has been previously conducted [2].
That study reported that mechanical properties of
Mekong Delta clay has not been well studied due tosample disturbance.
In this study, all the samples were collected byhydraulic piston sampler to unsure the quality ofsamples for laboratory tests. For the determinationof consolidation characteristics of the soft soil, CRS
tests are employed. This is because CRS tests canshorten the testing time and testing data are ofcontinuous strain-time relation. Therefore, it iseasier to determine required consolidation
parameters for soft clays.

Fig. 1 Location map, layout plant and investigated zone phase II (2-2)
LL, PL & wc (%)
25 50 75 100 0σy'(kPa)
200

Void ratio e0
400 1

2

cv (cm2/d)

Cc1, Cc2 & Cr
3

0

1

2

3 10

100

P remilinary fill

1

0

3

9
10

-20

14
wc
LL
PL

-30
(a)

17
19
Cc1
Cc2
Cr

σv0'
w'o filled sand
(b)

(c)

(d)

Fig. 2 Profile of soft clay properties at various depth
Table 1 Compressibility and consolidation properties of sub-layers
272

(e)

Piezometer P10-10

Extensometer E09

Elevation (m)5GEOMATE- Osaka, Nov. 16-18, 2015

Sample
Ho
e0
Cr
C c1
C c2
c v(NC)
σ' v0
σ' y
σ' b
(depth)
(cm)
(kPa)
(kPa)
(kPa)
(cm2/d)1(3.95)300
13.22 1.676
65 (50)400
0.120.75 (1.40)
0.75
153(6.95)300
28.28 2.195
99 (70)
250
0.15
1.50
1.00
35
5(9.90)500
43.18 1.968
81
140
0.30
1.70
0.90
38
9(15.8)400
73.78 1.966
132
230
0.25
1.90
1.00
30
10(17.45)
600
82.36 2.020
165
320
0.25
2.70
1.00
17
14(23.45)
400
114.36 1.854
226
370
0.30
1.90
0.90
19
17(27.95)
300
139.16 1.585
218
450
0.30
1.40
0.75
20
19(30.95)
200
156.52 1.449
251
500
0.25
1.20
0.70
40
Note: Numbers in brackets are used for Model-2 in consolidation analysis at settlement plate SP18
GROUND PROFILE AND
PARAMETERSDESIGN

third C c2 is used for normally consolidated stage
with stress level larger than σ’ p as found in Fig. 5.

SOIL

Fig. 2(a) shows natural water content w c , liquid
limit LL and plastic limit PL for the site. It is
obviously that the clay is very soft with natural
water content is almost close or larger than liquid
limit. Furthermore, the void ratio ranges from 1 to 2
for the first 7 sub-layers and close to 1 for the last 2
sub-layers from the top of soft marine clay aspresented in Fig. 2(c). Unloading-reloading index
Cr ranges from 0.10 to 0.30, Cc1 from 0.50 to 1.00
and Cc2 larger than 1.00. The figure also shows such
soil parameters determined from CRS test results as
the consolidation yield stress, the verticalcoefficients of consolidation of sub-layers of the softsoil.
The effective over-burdened stress of intact soil
is estimated without considering the load ofreclaimed fill because this layer of had notconsolidated the clay deposit thoroughly. The
Holocene clay is still in lightly over-consolidated
state with OCR ranging from 1.50 to 4.90. This
proves that the undisturbed samples are in goodquality enough for the engineering purposes.
Consolidation yield stresses determined by both
CRS and CPTU in form of eq. 1 as indicated in Fig.
2(b) are found to be consistent to one another.

1
3

σ ' y = ( qT − σ v 0 )C v(OC)(cm2/d)
150
350
380
300
170
190
200
400

2.4

101

102

103

Void ratio, e

2
1.6
1.2
0.8
0.4

σ'v(kPa)

Fig. 3 e-logσ’ v by CRS tests on 22 samples atvarious depths of the soft clay deposit
101

102

103

cv(cm2/d)

103

102

(1)
101

The CRS test results on 22 samples at various
depths for the soft Holocene clay are showed in Fig.
4 and Fig. 5. It is clear that the soil compressibility
varies with stress level with the S-shape of e-logσ’ v
curves, so it can be an error for the consolidation
analysis if a constant compressibility index is used.
Therefore, the authors will apply threecompressibility values at three different stages ofloading. The first value C r is used for unloading andreloading or over consolidated stage; the second C c1
is used for normally consolidated between σ’ y to σ’ pwhere there is a reduction in compressibility; theσ'v(kPa)

Fig. 4 c v -logσ’ v by CRS tests on 22 samples atvarious depths of the soft clay deposit
As indicated in Fig. 5 for a typical CRS result,the vertical coefficient of consolidation in normallyconsolidated state is fluctuated around a constantvalue which is defined as c v(NC) . Furthermore, thevertical coefficient of consolidation in overconsolidated stage is almost 10 times as much as that273

GEOMATE- Osaka, Nov. 16-18, 2015

in normally consolidated stage. So, c v(OC) =10c v(NC) .
The horizontal coefficient of consolidation for thissoft marine clay is chosen to be as much as 3-5 timesof the vertical coefficient of consolidation which isdetermined by CRS test results. Detailed FDM
analyses are performed in the next part of this paper.
101

102

103
slope Cr

2
1.6
1.2

Slope Cc2

σy '
σp '

0.8

σv0'

Void ratio, e

2.4

surface settlement plates SP18, SP19, one
extensometer lot E09, and one piezometer lot P10 asindicated in the figure to evaluate the consolidationof soft ground. SP18 and SP19 were installed at theelevation of +4.650m Chart Datum Level (mCD),
E09 includes one magnetic plate at +5.347m; threemagnetic spiders at 0.057m, -9.872m and -19.887m;
three pore water pressure transducers at +0.000m, 10.000m and -20.000m. Layout of settlement plates,
piezometers and extensometers for phase II (2-2) ispresented in Fig. 1, elevation arrangement of 3 porewater pressure transducers and 3 magnetic spiders aswell as a magnetic plate can be found in Fig. 4.
Observation well Ob.04 was installed out of thesurcharge area so it was not affected by thesurcharge stress increase; then water level resulted
in it is used to evaluate the dissipation of excess porewater pressure.

Slope Cc1

cv(cm2/d)

0.4
103cv(OC)~10cv(NC)

10

2

CONSOLIDATION ANALYSIS
cv(NC)

Calculation method and software
101

This method is used for reclamation area where
the loading is uniformly loaded in a wide spreadzone. A unit cell around a vertical drain isconsidered as a drainage boundary in the coupledconsolidation calculation as soon as it is installed in
the ground.
Finite Difference procedure called CALPRO isdeveloped and coded by the authors based on thegeneral consolidation equation, dealing with the axisymmetric meshing. The procedure allows up to 20
subsoil layers and 50 loading stages in thecalculation.
The results include settlement for subsoil layers,
effective stresses for each layer as well as verticalstrain for each layer over each time step.

σ'v(kPa)

Fig. 5 Conceptual determinations of parameters
With the variation of soft soil properties overdepth, average values of those parameters should not
be used for the whole deposit. In order to consider
the changes in over-burdened stresses, yield stresses
and other consolidation characteristics as well as
nonlinearity, the soft clay stratum is divided into 8
sub-layers as indicated in Fig. 2. Representativevalues of compressibility and consolidation
parameters for consolidation analyses are chosen
and tabulated in Table 1. The total thickness of soft
clay in this area is ranging from 29 to 35 meters
underplayed by sandy soil which works as bottom
drainage boundary for prefabricated vertical drain.

Settlement Behavior
Original ground elevation is at +3.00m (mCD).
A reclamation fill of 1.6m was carried out during thefirst land preparation. PVDs were installedhydraulically with 150cm pitch down to the bottomof soft marine clay layer at time of 221 days and 261
days for SP18 and SP19 respectively. Drainage layerof 50cm by coarse sand was filled after thereclamation work was done; then twelve layers of
50cm of surcharge sand were filled accordingly.
Besides that, PVC perforated pipe wrapped aroundby geo-textile fabric and water collection wells were
installed to assist the water squeezing process during
surcharge preloading.
As indicated in Fig. 6 the settlement at plate
SP19 and E09 is almost the same in margin as long
as E09 data are shifted by 45cm as correction. Thisis because E09 is close to, but installed 245 days
after SP19.

MONITORING INSTRUMENTS
The monitoring instrumentation consists of 4
observation wells (Ob.01 to Ob.04) used formeasuring ground steady state ground water level,
40 surface settlement plates, 19 monitoring groups
with one pore water pressure transducer(piezometer) lot and one magnetic multi-level
settlement lot (extensometer) for each group. They
were used for the assessment of behavior of soft
marine clay under preload fill for the whole project.
In this study, only one zone called phase II (2-2)
is considered for the consolidation analysis by afinite difference code developed by the authors. The
investigated zone of reclamation and ground
improvement has one water observation well, two
274

10
8

Elevation (m)640Settlement (cm)bottom of soft marine clay are presented in the
figure as dotted lines. The total settlement at themagnetic plate is the continuous line. The calculated
and measured data are well consistent and plotted in
the same graphs.

t=0 on 04/Nov/2006
Phase II (2-2), SP19 Model-1

Measured(SP19)
Measured(E09+45cm)
Calculated (c h=3c v)
Calculated (c h=4c v)
Calculated (c h=5c v)

100
200
300
400
120

SF =337cm

240

360 480 600
Elapsed time (days)

720

840

Fig. 6 Settlement by FDM calculation at SP19

6
Measured(SP18)
Calculated Model-2

100
200
300

SF =379cm

240

360 480 600
Elapsed time (days)

720

840

Fig. 8 Settlement by FDM calculation at SP1840+5m to +0m

Fig. 6 and Fig. 10 show the monitored data ofplate SP18 is larger than those at SP19 and E09. At
t=600d, the settlement at SP18 is 350cm while it isaround 300cm at SP19 and E09. Based comparisonof CPTu-27 at SP18 and CPTu-28 at SP19 and E09
in Fig. 9, it can be concluded that the top two sublayers at SP18 are softer than those at SP19 and E09.
Therefore, modification of yield stress for the two
sub-layers is done accordingly for SP18. The new
soil model for SP18 is Model-2 with a reduction inyield stress by 15kPa for the first sub-layer and
29kPa for the second sub-layer. With the newparameters as indicated in bracket in Table 1, FDM
analysis shows well consistent results with themeasured data. The final settlement for SP18 is379cm.

-20m to bottom

Dissipation of Excess Pore Water Pressure

100-10m to -20m

10
Elevation (m)

8

400
120

A series of FDM analyses are performed with thedetermined parameters and various values of c h . The
calculation results show that the case with c h =3c v
gives best fit result compared to the monitored data.
Therefore, soil parameters for these two instruments
are the same. The soil model for SP19 and E09 isnamed as Model-1. The FDM analysis result shows
good agreement between measured and calculated
settlement vs. time for the two above mentioned
instruments. The final settlement under surcharge
load for this case is 337cm.

Settlement (cm)1004/Nov/2006
Phase II (2-2), SP18 Model-2, , c h=3c v

40Settlement (cm)Elevation (m)GEOMATE- Osaka, Nov. 16-18, 2015

E09 (Model-1, c h=3c v)

8
6

Measured (E09)
Calculated by FDM (E09)

Piezometer P10 is installed close to E09 andSP19, so Model-1 is used in the FDM analysis ofpore water pressure dissipation at P10. Fig. 9 shows
good agreement between measured and calculated
excess pore water pressure dissipated vs. time.
Dissipation rate of excess pore water pressure iswell consistent to one another for both measured andcalculated data at P10. Measured data from time of570 days are a bit smaller than calculated values due
to recent reduction in water level; therefore it causes
the decrease in excess pore water pressure.

+0m to -10m

200

300
240 300 360 420 480 540 600 660 720
Elapsed time (days)

Fig. 7 Measured and calculated data at E09
Fig. 8 plots detailed calculated results by FDM at
E09 for sub-layers. The settlement from +5m to+0m; +0m to -10m; -10m to -20m; and -20m to the275∆u (kPa) ∆u (kPa) ∆u (kPa)Elevation (m)GEOMATE- Osaka, Nov. 16-18, 2015shows the consistency between measured andcalculated un-drained strength gain.

P10 (Model-1, c h=3c v)

108

CONCLUSION

6

Measured
Calculated by FDM

Soil parameters determined from CRS test results
give good consistency for the settlement analysis inboth settlement and pore water pressure dissipation.
Direct application of CRS test results into theconsolidation analysis of Holocene soft clay gives
well agreement resulted between measured andcalculated data for surface settlement plates,
extensometer, piezometer as well as un-drained
strength gain.
For this Holocene deposit, horizontal coefficientof consolidation is as much as three times of verticalvalue determined by CRS test results c h =3c v . The
vertical coefficient of consolidation in overconsolidated stage is almost 10 times as much as thatin normally consolidated stage c v(OC) =10c v(NC) .
Consolidation analyses by FDM withconsideration of multi-subsoil layers to incorporate
the changes in soil parameters in the soft clay
stratum can give good results in comparison between
calculated and field monitored data.

4
60
P10-1

30
0
60

P10-2

30
0
60

P10-3

30

0
240 300 360 420 480 540 600 660 720
Elapsed time (day)Fig. 9 Measured and calculated data at P10
Strength Gain

5

0

20

su (kPa)40 60

80 100REFERENCE

CPTu-27 in Tender

[1].

0

Elevation (m)

-5
-10

[2].
CPTu-28 in Tender

-15
-20

[3].

-25
-30
-35

(qT - σv0)/18 (CPTu, in Tender)(qT - σv0)/12 (E09, 20 June 2008)by FDM analysis
FVT (12 June 2008)
VST-11 (05 May 2008)[4].

Fig. 10 un-drained strength gain by field tests
and calculated by FDM
Fig. 9 presents the un-drained shear strength gain
measured by field tests and calculated by FDM.
Cone factors N kt are 18 and 12 respectively for for
CPTu done in Tender stage and before surcharge
removal. These N kt are determined based on undrained shear strength tested by FVT. The differencein cone factors is due to the different cone types andmanufacturers. As indicated in the figure, thestrength gain is 19kPa as average value for the entire
Holocene clay deposit. Furthermore, the plot also
276

Bui, T. M. (2003). Initial Evaluation ofConsolidation Characteristics of Mekong SoftClay and Their Use in Engineering
Hanoi
Engineering
Geology
Practice.
Workshop. Ha Noi: 1-13.
Takemura, J., Y. Watabe, Tanaka, M. (2007).
Characterization of alluvial deposits in
Characterisation
andMekongDelta.
Engineering Properties of Natural Soils,
Taylor & Francis Group, London. 1: 18051829.
Tanaka, H. (2000). "Sample quality ofcohesive soils: Lessons from three sites,
Ariake, Bothkennar and Drammen." Soils andFoundations 40(4): 57-74.
Tanaka, H., P. Sharma, et al. (1996).
"Comparative study on sample quality using
several types of samplers." Soils andFoundations 35(2): 57-68.

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

SIMULATION OF DAMPING RATIO AND ELASTIC MODULUS
CHANGES DURING THE EARTHQUAKE OCCURRENCE IN A
SANDY SLOPE BY NUMERICAL MODELING
Fardin Jafarzadeh1, Sina Heidari2 and Hadi Farahi Jahromi 3
1
Sharif University of Technology, Tehran, Iran;Geotechnical Engineering, Sharif University of Technology, Tehran, Iran;3
Geotechnical Engineering, Sharif University of Technology, Tehran, Iran2

ABSTRACT
Earthquake induced landslides endanger many structures and human life every year. Thus, many researchersand scientists have performed numerous physical and numerical modeling with various aspects of thephenomenon. This paper as a part of a thorough research project, defined and supported by Tehran Gas
Company numerically evaluated a sandy slope dynamic responses under a 7.5-8.5 earthquake. The ABAQUS
F.E. program was used for numerical modeling and the results were verified with the laboratory records andobservations. The paper focuses on soil damping and elastic modulus changes during earthquake occurrence.
Although, numerical simulations can model the soil behavior non-linearity, the varying nature of damping andmodulus of elasticity during dynamic excitation as a cause of densification, cannot easily be modeled. As acompensation, the slope section was divided to three parts with different ζ and E. Finally 3 numerical analysis
were performed and the best model were selected. The numerical outcomes were confirmed by comparing thedisplacements and acceleration responses of slope toe and crest. The process through which the models was
improved showed that, "if the geotechnical parameters of materials were correctly selected and somemodifications were done", the numerical calculation would be validated and could lead acceptable results. The
comparisons show the effectiveness of the modifications which was verified by 1g shaking table test results.
Keywords: Land Slide, Numerical Modeling, Damping Ratio, elastic modulus, shaking table test.

But the problem is that, finite element analysis
has many limitations in predicting seismicallyinduced soil deformations as it cannot model themesh separations through a dynamic loading.
Besides that, numerical instability and otherdifficulties, has made the FEM results almost
unreliable. To compensate the fact, a practitioner
should use engineering experience and judgment tointerpret FEM results.
Consequently, different approaches have beendeveloped to investigate the stability of slopes underseismic loading conditions, including: limit
equilibrium (LE) techniques, analytical sliding blocktechniques, and numerical modeling procedures(such as finite element (FE) or finite difference (FD)techniques) [1].
However, the first two approaches often neglect
important elements such as the three-dimensional
scale and the stress-strain relationship of soil, thesoil deformability and nonlinear soil behavior.
As an illustration, Nian et al. (2011) used thestrength reduction elasto-plastic FE method andmainly focused on the slope stability under inclined
seismic loading using pseudo-static approach forthree dimensional slopes [2].
It is important to understand the conditions ofslopes after slope failure. Abe et al. (2013) carried

INTRODUCTION
Destructive effect of dynamic slope instability
endangers human life and engineering facilities
worldwide. So, any method, predicting permanent
ground deformations (PGD) almost accurate andacceptable, is undoubtedly applicable for both
university researchers and engineering officepractitioners to design any retrofitting scenario.
There are three attitudes for slope deformations
prediction. The first method is laboratory testing.
This method not only is an expensive one, but alsoneed many preparations and arrangements. Besidethese, preparing the test materials and dynamic
testing are time consuming activities. As analternative, anyone can use empirical formulas for
slope deformation calculations. Although, thismethod have been improved in the last decade andmany modifications have been introduced forgaining an accurate assessment, it is not a sufficient
scheme which can be relied on in engineering
projects. Thus, this requirement, has led theresearches to examine other approaches. Throughmany methods, stress-deformation approaches arewidely used in university theses and has been
experienced many advances recent years.

277

GEOMATE- Osaka, Nov. 16-18, 2015

out a series of shaking table tests with large-scaleslope models. The models consist of a slope withweak layer which has high response acceleration at
top of the slope. Results from the tests indicated that
it is important to consider large deformation alongslip lines in the weak layer and amplification andphase lag of response acceleration at the top. The
one layered slope model was analyzed by FEM witha nonlinear model focusing on the amplification andphase lag of response acceleration at top of model[3].
Non-linear dynamic effective stress analysis is an
essential tool in seismic design in geotechnicalearthquake engineering. But requires analyticalskills and a very thorough knowledge of soil
behavior. The selection of an appropriate
constitutive model for the job at hand requires
knowledge of the past history of the model. The
calibration of the model for the job at hand needs tobe conducted on tests specimens that are
representative of field conditions.
The designer needs to be aware of what the main
factors controlling the results of the analysis are andmake judgments as to whether any of them [4].
This paper attempts to calibrate a FEM
numerical modeling with a laboratory shaking table
test results, which models a dynamic slope
instability in Tehran, Iran's capital. Through thisapproaches, a basic numerical model will be
introduced and validated by a laboratory testing
records.

[6].
Dynamic slope responses were recorded byaccelerometers and LVDTs installed in the slope.
The maximum horizontal displacement of the sliding
mass, vertical displacement of slope crest andacceleration time histories of slope’s toe (ACC4)and crest (ACC9) were used for numerical model
verification.
3000
368

LVDT
Accelerometer

1000

1118

1500

100

618
1850

400

400

1732

650

618

Figure 1. Soil box and sensors arrangements
0.4

Acc (g)

0.2
0

-0.2
-0.4

0

1

2

3
4
Time(s)5

6

7

Figure 2.Induced acceleration time history

MODEL TEST CHARACTERISTICS
The artificial slope, scaled to 1/10 of a prototype
one, [5], simulates a typical slope in Tehran. The
research group designed a series of dynamic
sinusoidal excitations [6]. Tehran cemented soil wasmodeled by Babolsar coastal sand having nocohesive ingredients.
The soil rigid box frame composed from steel
profiles and plates. Also, a Plexiglas plate in one
side enabled the engineers to monitor slope
deformations during excitations. In addition to visualobservations, many accelerometers and LVDTs
recorded model's dynamic responses. Figure 1
illustrates the model and sensors configuration in therigid box (dimensions are in mm).
Using the method suggested by Prof. Seed [1], aharmonic sinusoidal record having 0.33 g amplitude,
5 HZ frequency and 25 cycles (Figure 2) was
selected to model a 7.5-8.5 magnitude earthquake.
Figure 3 and 4 show pre and after test situations. In
order to monitor the slope displacement during thetests, 3 cm plaster strips were poured adjacent to thePlexiglas plate in each 10 cm soil layer. Throughdefining and analyzing the induced displacement bymeasuring strips horizontal movement, comparisons
made between the pre and posttest slope conditions

Figure 3. Constructed slope withplaster strips before test

Figure 4. Plaster strips showing deformed slope
278

GEOMATE- Osaka, Nov. 16-18, 2015

NUMERICAL MODEL SPECIFICATIONSThe numerical model was simulated andanalyzed by the FE based ABAQUS program. The
slope material properties were selected as thoseassumed for the target slope (Table 1). In order tonumerically have similar conditions to prototype
situation, a discrete rigid box has been simulated.
Soil-box interaction was modeled by assuming hardcontact and frictional behavior in normal andtangential directions respectively. The frictionalcoefficient between soil-steel plates and soilPlexiglas were 0.5 and 0.3 respectively.
Modulus reduction curves were used to define
the nonlinear behavior of the soil. EPRI guidelines[7] for sandy soils were used to determine the cyclic
shear strain dependency of elastic moduli at various
depths.
The calculated displacements in horizontal andvertical directions are depicted in figures 5 and 6.
The observed maximum horizontal displacement inlaboratory scale slope (U1) were 33-35 cm at mid
slope section. Also, the mid-crest settlement (U2)
was 3.5 cm. Besides these two basic factors, therecorded mid crest acceleration (ACC9) was also
used for numerical model verification. Regarding tofigures 5 and 6, the U1 and U2 parameters were 60.6
and 17.4 cm. Figure 7 compares the recoded andcalculated ACC9 parameters for these two models.
Concerning the deviations between the
numerically analyzed and actual laboratory results,
some modifications are required. In order tocompensate these shortcomings, two basic changes
in key parameters (elastic moduli and damping ratio)
were introduced to the models and their effects were
investigated.

Figure 5. Horizontal displacement of model 1

Figure 6. Vertical displacement of model 1

Acceleration (g)ACC 9 - Shaking Table Data

Table 1 Model 1 characteristics and results
Model
Name

E
(Mpa)

1

10

Damping
Ratio
(%)
5%

U1
(cm)
60.6

U2
crest
(cm)

Acc

17.4

0.5

ACC 9 - Model 1

0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
0

1

2

3
4
Time (Sec)

5

6

7

max

(g)

Figure 7- Crest acceleration comparison for model 1
and laboratory results

INTRODUCING ADVANCED NUMERICAL
MODELS AND DISCUSSIONS
Regarding to previous discussions, althoughmodel 1 attempted to simulate a 1.3 height sandy
layer, but the calculated responses were notacceptable. So a series of 3 models which gradually
improved the situations are introduced hereafter. The
basic concept of newly developed models is todivide the soil to a few layers having different
modules of elasticity and damping ratios. Based on
figure 4, 3 basic layers were recognized. The top
layer, in which the first slip surface was formed, was279

GEOMATE- Osaka, Nov. 16-18, 2015

affected in the first stage of dynamic loading (10cycle) and had almost 20 cm depth. However, thesecond and the third sliding surfaces were formed in
the last part of dynamic loading and occurred in the
mid layer sands. Similar to the top layer, the second
layers had 20 cm depth letting bottom layer (86 cm
height) be almost unchanged. These almost separate
layers had dissimilar geotechnical behaviors. As, thesoil pouring height during model construction, wasarranged to be 70, cm, it has relative density of 4555%. However, the relative density is a verticalstress dependent parameter which can change theelasticity of layers from their initial values.
Obviously low-strain stiffness (E 0 ) in non-cohesive
soils is known to be directly proportional to thevertical effective stress by the following equation [8].

Figure 9. Damping ratio of dry sandy soil versus
shear strain [11]Figures 10, 11, 12 and table 2 summarize thenumerical calculation results for the second stage
simulations (model 2). The outcome were more
acceptable than model 1. It is interpreted that the soil
layering technique even for a laboratory scale slope,can improve the numerical result simulations.
However there were some deviations between thecalculated and observed records. So, the damping
ratios of second and third layers were separated in
the second stage of modifications. Regarding tovisual observations and video films, captured during
dynamic tests, different values of shear strains in thefirst and second layers were recognized. The thirdlayer had no more than 1% shear strain. However
the first and the second layers had γ>10% and
10%>γ>1% respectively. So, the damping of top
layer was increased to 15 %. Although this division
is considered helpful, but it increased the difficultyof model analysis and needed more time.
Figures 13, 14 and 15, illustrate the numericalresults of model 3. The slope horizontal movement
which is a key factor for model verification, was 35
cm. However, the mid-crest settlement were
extremely higher than actual records (9.4 cm).
Figure 15 compares the recorded and calculated
slope crest acceleration. Although it experienced aclear advancement comparing to model 1 and 2, it
needs more modifications. So it is decided to change
the position of maximum elastic modulus
assignments. In the 2nd and 3rd model, the E 0 wereassigned to layers mid height. But in the 4th model,
it is assigned to each layer's bottom.
Figures 16, 17 and 18 represent the horizontal
and vertical displacements and response accelerationof model 4. Although the soil displacements had notchanged (comparing to model 3), the acceleration
response of slope crest is acceptably improved.
Besides the crest acceleration, figure 19 compares
the recorded and calculated toe accelerations.
Although the crest settlement deviated from actual
value, these comparisons confirm the validity ofnumerical modeling.

(1)
Where E1 and E2 are the stiffness moduli at two
different points with vertical stresses of 𝜎𝜎 1 and 𝜎𝜎 2 ,respectively. As a result, the low-strain elasticity
modulus increases with depth. Regarding to thiseffect, the elastic modulus of the layers at mid height
were considered to be 10, 14 and 20 MPa.
Ishibashi and Zhang (1993) proved the cyclic
shear strain-dependency of material damping.
Figures 8 and 9 summarize various graphs of thisbehavior [9]. However, the ABAQUS program
cannot modify the material damping values due toshear strain changes during excitations. Clearly, thethree layers experienced different horizontal
displacements causing the soil to have different
shear strains (figure 4). As compensation, the overall
damping ratios of these layers were considered 5, 10
and 10 %. It is expected that “a reasonably selected
overall damping ratio could model the energy
dissipation during dynamic loading”.
R

R

Table 2 Model 2, 3 & 4 characteristics and results

Figure 8. Damping ratio of different soil versus t
cyclic shear strain [10]

280

Model
Name

Yong
Modulus
(Mpa)

2
3
4

10-14-20

Damping
Ratio (%)

U1(cm)

U2crest(cm)

5,10,10
5,10,15
5,10,15

40.6
35.0
34.0

10.4
9.4
8.8

GEOMATE- Osaka, Nov. 16-18, 2015

Figure 14. Vertical displacement of model 3
Figure 10. Horizontal displacement of model 2
ACC 9 -Shaking Table Data

Model 3 - ACC 9

0.8
0.6

Acc (g)

0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
0

Figure 11. Vertical displacement of model 2

Acc (g)

ACC 9-Shaking Table Data

1

2

3
4
Time (Sec)

5

6

7

Figure 15. Crest acceleration comparison formodel 3 and laboratory results

Model 2 - ACC 9

0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
0

1

2

3
4
Time (Sec)

5

6

7

Figure 12. Crest acceleration comparison formodel 2 and laboratory results

Figure 16. Horizontal displacement of model 4

Figure 17. Vertical displacement of model 4
Figure 13. Horizontal displacement of model 3281

GEOMATE- Osaka, Nov. 16-18, 2015

ACC 9 - Shaking Table Dataand shear strain observations. The three layers have5, 10 and 15% overall material damping.
The calculated results for acceleration response
which is a key factor in revealing the wave
propagation algorithm verified the modellingcorrectness. In addition, the Horizontal displacementof 4th model coincided the laboratory observed one.
However, the crest settlement was higher than actual
records and need modification.
So, it is concluded that the modifications(layering) proved to be helpful and the final model isverified. Also, the authors consider the change ofdilation angle in future numerical modeling to adjust
the settlements.

ACC 9 - Model 4

Acc (g)

0.8
0.6
0.4
0.2
0
-0.2
-0.4
-0.6
-0.8
0

1

2

3
4
Time (Sec)

5

6

7

Figure 18. Crest acceleration comparison for model
4 and laboratory results
ACC 4 - Shaking Table Data

ACKNOWLEDGEMENTS

ACC 4 - Model 4

The authors greatly acknowledge the Tehran
Gas Company, which defined and financially
supported the research project. The project which
consists of two laboratory (shaking table testing) andnumerical modeling (ABAQUS program) aspects, isreally enriched by helpful scientific suggestions of
IIEES (international institute of earthquake
engineering and seismology) as a supervisors of theresearch project

Acc (g)

0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
0

1

2

Time
3 (Sec)45

6

7REFERENCES

Figure 19. Toe acceleration comparison formodel 4 and laboratory results[1] Steven L. Kramer, "Geotechnical Erathquake
Engineering", University of Washington, (1996).
[2] Nian T.K, Huang R.Q, Zhang K.L, Chen G.Q,
“ Seismic stability analysis of three-dimensionalslopes using elasto-plastic FE method”
International Journal of Advancements in
Computing Technology Volume3, Number9,367-376. 2011[3] Abe K., Izawa J, Nakamura H, Kawai T,
Nakamura S, “Analytical study of seismic slope
behavior in a large-scale shaking table model
test using FEM and MPM”, Proceedings of the
18th International Conference on Soil
Mechanics and Geotechnical Engineering, Paris,
1407-1410. 2013
[4] Liam Finn W.D, “Seismic Slope Stability”,
Geo-Congress, 2269-2295. 2013[5] Iai S., (1989). Similitude for shaking table tests
on soil-structure-field model in 1g gravitational
field. Soils and Foundations 29, 105-118[6] H. Farahi Jahromi, M. Joshaghani, M. Sehi
zadeh & S. Yousefi," comparison of
dynamically induced experimental slope
deformations with newmark sliding blocktheory“,Fifth International Young GeotechnicalEngineering Conference 5iYGEC’13”,paris,
France.
[7] Electric Power Research Institute (EPRI), 1996.
Guidelines for determining design basis groundmotions, Final Rep. No. TR-102293, Palo Alto,
California.

CONCLUSION
A laboratory scale sandy slope washarmonically excited to model a 7.5-8.5 magnitude
by shaking table device in Sharif University oftechnology. Although the dynamic test, was a part ofthorough research investigations, but the various
numerical analysis with different aspects, were
designed to compensate the shortcomings oflaboratory testing. But the numerical models, needverification, and this stage was performed bycomparing the recorded and calculated results.
ABAQUS finite element program were selected andmany models were constructed.
This paper summarizes a numerical model
calibration of one dynamic test. The geometry ofmodel and prototype was similar and model
parameters were reasonably selected. Then, the firststage results were compared and some deviations
were recognized.
In order to improve the numerical results, athree phase modification was performed on
numerical models. The model slope was divided tothree separate layers based on displacement
observations. Each layer have different elastic
modulus and damping ratios. The forth model which
passed the verification process, had 10, 14 and 20
MPa elasticity which is assigned to each layer
bottom. The damping ratios of these layers werechosen regarding to proposed graphs in the literature
282

GEOMATE- Osaka, Nov. 16-18, 2015[8] Das B.M. 1985. Advanced Soil Mechanics,
McGraw-Hill[9] Ishibashi I., Zhang X., 1993. Unified dynamic
shear moduli and damping ratios of sand andclay. Soils and Foundations 33, 182-191[10] Vucetic, m. and dobry, r, "Effect of soil
plasticity on cyclic response', Journal of
Geotechnical Engineering, ASCE, vol. 117, 89107. 1991.
[11] M. H.T, Rayhani and M.H. El Naggar”, Seismic
response of sands in centrifuge tests, Canadian
Geotechnical gournal, 45: 470-483 (2008).

283

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

FURTHER DEVELOPMENTS IN THE AASHTOLIV EQUATIONS TOFORWARD-CALCULATE SUBGRADE AND PAVEMENT MODULI

1

Moshe Livneh1
Civil and Environmental Engineering Faculty, Technion-Israel Institute of Technology, Haifa, Israel

ABSTRACT
Various studies have demonstrated the usefulness of the forward-calculation technique as a relevant approachto determine layered elastic moduli from in-situ load-deflection data. Recently, the well-known forwardcalculation method from the 1993 AASHTO design guide was modified by this author previously to take into
account the influence of (a) errors induced in the original forward-calculations as a result of correlative equations
developed for the two-layer model, and (b) depth to bedrock measured from the subgrade surface. The present
paper develops modifying equations that take into account the influence of a three-layer model, as opposed to theaforementioned two-layer model, on the forward-calculated subgrade and pavement moduli. Using a 3-layer
model with the existing equations yields an increase in the subgrade modulus outputs by a multiplier value ranging
from 1.0 to over 1.5; however, for the majority of flexible pavements with small to moderate total thicknesses, theaforementioned multiplier value is equal or very close to 1.0. In addition, using a 3-layer model with the existing
equations yields an increase in the outputs of the ratio of pavement modulus to subgrade modulus, raised to thepower of 1/3, by a multiplier value ranging from 1.15 down to under 0.85. Finally, practical examples of in-situ
FWD measurements conducted on four flexible pavements generally support the two conclusions above, where
the multiplier value for (a) the subgrade modulus values ranged from 1.0 to 1.07, and (b) the pavement modulus
values ranged from 0.65 to 1.45.
Keywords: Deflection, Forward-calculation, Modulus, Subgrade, Pavement.

seed-layer moduli or other stiffness properties areneeded for this closed-form method. In addition,
several programs exist for this forward-calculation
procedure, such as the Wiseman-Greenstein program[2], ROADHOG [3], DEFOD [4], the old 1993
AASHTO design guide equations [5, 6], ROHDE [7],MDOT [8], Romanoschi-Metcalf [9], FDOT [10],YONAPAVE [11], FHWA [12], and the new
EVALIV program [13]. All these programs offer
quick direct-calculation procedures that utilize a
two-layer model for any given pavement structure.
Among the aforementioned forward-calculation
techniques, the technique from the old 1993
AASHTO design guide is still used by several
agencies around the world, including the American
FHWA (Federal Highway Administration) and theIsraeli National Roads Company (formerly PWD).
For this matter, also see the comparison studies of
the 1993 AASHTO design guide’s original
equations, given in [14, 15]. As these original
equations do not calculate the subgrade andpavement resilient moduli in a fully direct manner, it
became necessary to replace the existing forwardcalculating equations with modified ones, termed
AASHTOLIV equations. The development of theseequations is shown by this author in two previous
papers [16, 17]. These modified equations, however,are based on the deflection characteristic of a 2-layer
model; therefore, further modifications are needed to

INTRODUCTION
Non-destructive testing (NDT) on pavements ishaving a significant impact on the design andevaluation of pavement systems. To determine thelayer moduli of a pavement system, deflection
measurements from NDT are taken. Commonly,
these measurements utilize the Falling WeightDeflectometer (FWD) device, which applies animpact load to the surface of the pavement. Sensors
at the load location and at fixed radii from the load
center measure surface deflections. The resulting set
of deflections is known as the deflection bowl orbasin. Considerable background information about
this method is provided in several existing studies,such as [1].
Special computer programs are available to backcalculate a modulus profile for the pavement systemfrom the peak values of the measured input FWD
force and the resulting deflection basin. These
programs include (a) iterative elastic-layer backcalculation algorithms and (b) closed-form backcalculation algorithms, which are also known as
direct-calculation or forward-calculation algorithms.
The closed-form back-calculation process differsfrom iterative back-calculation in that moduli
estimates from the former are calculated directlyfrom the load and the deflection data by means ofclosed-form equations, rather than iterative ones. No
284

GEOMATE- Osaka, Japan, Nov. 16-18, 2015

include the deflection characteristics of a 3-layer
model.
Given this background, the objectives of thepresent paper were formulated as follows:• to present the existing modified AASHTOLIVAASHTO equations for forward-calculating
the pavement and subgrade resilient moduli;• to develop new corrective-factor equations
that take into account the deflection
characteristics of a 3-layer model;• to elaborate on the existing corrective-factor
equations that take into account any given
depth to bedrock measured from thepavement-subgrade interface;• to demonstrate the use of the current
developed AASHTOLIV equations bypresenting four forward-calculation examples.
Finally, the following sections will detail theprocess of accomplishing this paper's four objectives
and the consequent conclusions.

various given data from a 3-layer model with infinite
depth to bedrock. Surface deflection-basins can becalculated theoretically with the aid of either the
Burmister equations or the Odemark-Ullidtz
equations as formulated by Wiseman & Greensteinin [1]. Such theoretical calculations were performed
with the latter equations for the following given
data: (a) subgrade and pavement Poisson ratios of
0.5, (b) subgrade modulus of 50 MPa, (c) asphaltic
modulus of either 2,500, 5,000 or 10,000 MPa, (d)granular layer modulus of 200 and 400 MPa, leading
to ratio of pavement modulus to subgrade modulus
that ranged from 6.2 to 25.2, (e) pavement thickness
from 350 to 1,750 mm, (f) thickness of asphaltic
layer of 150 mm, (g) radius of loading plate of 150
mm, and (h) reference load of 9.5889 kN (i.e.
reference pressure of 135.7 kPa). The outputs ofthese calculations were then used to calculate, forthe pavement structures characterized by the data
mentioned above, the ratio of the true subgrade
resilient modulus to that forward-calculated by Eq. 1(which, along with Fig. 1, the present paper terms
the AASHTOLIV subgrade modulus).

EXISTING AASHTOLIV EQUATIONS
Following the findings of [16], the suggested
modified Es uc equation of AASHTOLIV is given by
the following expression, where Es uc denotes theforwardcalculated AASHTOLIV subgrade modulus;
i.e. the basic uncorrected equation:(1)
Es uc =min[0.24×P/(d r ×r)]
where r is equal to either 900, 1,200, 1,500, or 1,800mm, and d r is the recorded deflections for eachlateral distance, r. Note that the associated corrective
expression for Eq. 1 is developed in the next section.
As for the Ep uc values (i.e., the AASHTOLIV
pavement modulus), numerical calculations from the
AASHTO original expression lead to the following
relationships to calculate these values from thecalculated Es uc values (i.e. those calculated from
Eq. 1), the given Hp (pavement thickness), a (radiusof loading plate), and d 0 (central deflection) data.
(2)(Ep/Es) uc =A×[(Es×d 0 )/(1.5×p×a)]-B
3
2
A=-0.3447×[log(Hp/a)] +0.8528×[log(Hp/a)] -0.5174×log(Hp/a)+0.8766(3)3
2
B=-0.8883×[log(Hp/a)] +2.8376×[log(Hp/a)] -3.6121×log(Hp/a)+2.9094(4)
where A and B denote curve-fitting coefficients forcalculating Ep/Es values.
Finally, it should be pointed out that all of thefour equations above relate to the case of (a)deflection characteristics of a 2-layer model, and (b)infinity depth to bedrock (i.e., D/a=∞).

1.4

Ratio of true to AASHTOLIV Es

1.2

1.0

Equality line
R²=0.9561

0.8

0.6

0.4

0.2
Simulated data of a 3-layer model with an infinite depth to bedrock
0.0
0

1

2

3

4

5

6

Pavement thickness [meter]¢(AASHTOLIV Ep/Es)^(1/3)

Fig. 1 Ratio of true to AASHTOLIV subgrade
modulus versus apparent AASHTOLIV
structural numbers for all simulated threelayer structures with infinite depth to bedrock
when a=150 mm.
For the current case (i.e., the case of D/a=∞where D is the depth to bedrock from pavement andsubgrade interface, and a is the radius of the loadingplate), Fig. 1 shows the variation of the subgrade
modulus ratio over the range of values of
Hp×(AASHTOLIV Ep/Es)1/3, as calculated by
Eqs. 2, 3 and 4, and referred to as “the apparent
AASHTOLIV structural number” within the present
paper. This variation possesses a high R2 value aswell as ratio values higher than 1.0 (maximum 1.08)
for apparent structural numbers lower than 3.10.
The variation of the true apparent structural
number in Fig. 1 clearly demonstrates theapproximate nature of Eq. 1. Thus, this equationneeds to be multiplied by a correction factor (Fs)equal to the variation of the ratio of true-toAASHTOLIV subgrade modulus with apparent
P

3-LAYER CORRECTIVE EQUATIONSIn order to check the accuracy of the modified
equations given in the previous section, simulated
data for a surface deflection-basin was generated for285

P

GEOMATE- Osaka, Japan, Nov. 16-18, 2015

respectively) are as follows:(7)
Es m =Fs×Es uc(8)
Ep m =(Ep/Es) m ×Es m
where Es uc is calculated according to Eq. 1, Fs iscalculated according to Eq. 5, and (Ep/Es) c is
calculated according to Eq. 6.

AASHTOLIV structural number, as given in Fig. 1
for the D/a=∞ case. Note again that the apparent
AASHTOLIV structural number was obtained fromthe AASHTOLIV forward-calculation equations (i.e.,
Eqs. 2, 3 and 4). According to Fig. 1, the formulationof Fs is as follows:
Fs=-0.0020×[Hp×(Ep/Es) uc 1/3]3-0.0056×[Hp×(Ep/Es) uc 1/3]2+(5)+0.0138×[Hp×(Ep/Es) uc 1/3]+1.0707
where Fs denotes the multiplier factor for Eq. 1 toobtain corrected (true) Es values for the case of a 3layer model with an infinite depth to bedrock (i.e.,
Es m ); Hp denotes the total pavement thickness inmeters; and (Ep/Es) uc denotes the forward-calculated
Ep to Es ratio by Eqs. 2, 3 and 4 (i.e. theAASHTOLIV Ep/Es).
As for the Ep values, Fig. 2 depicts the variationof the corrected (true) apparent structural numbers
over the range of AASHTOLIV (uncorrected)
apparent structural numbers for the same simulated
3-layer model structures that possess an infinite
depth to bedrock when a=150, (i.e., again, for the
D/a=∞ case, where D is the depth to bedrock and ais the radius of the loading plate). Similar to Fig. 1,
Fig. 2 indicates that the R2 value for this variation is
also very high. To sum up, the formulation of thisvariation is as follows:
Hp×(Ep/Es) m 1/3=0.0426×[Hp×(Ep/Es) uc 1/3]3-0.2929×[Hp×(Ep/Es) uc 1/3]2+(6)+1.3965×[Hp×(Ep/Es) uc 1/3]-0.0308
where Hp denotes the total pavement thickness, inmeters; (Ep/Es) m denotes the corrected (true) Ep to
Es ratio; and (Ep/Es) uc denotes the forwardcalculated Ep to Es ratio by Eqs. 2, 3 and 4 (i.e. theAASHTOLIV Ep/Es value).

3-LAYER VERSUS 2-LAYER MODEL
As mentioned, the previous Esm equations referto the case of a 3-layer model structure with infinite
depth to bedrock. The outputs of these equations
differ from those given in [16] by the following Fsm
ratio. To recall, the equations given in [16] refer tothe case of a 2-layer model with an infinite depth to
bedrock (also see Fig. 3):(9)
Es m =Fs m ×Es c
1/3 3
Fs m =0.0130×[Hp×(Ep/Es) uc ] -0.0497×[Hp×(Ep/Es) uc 1/3]2+(10)+0.0750×[Hp×(Ep/Es) uc 1/3]+0.9666
where Fs m denotes the multiplier factor of Es c i.e.
the corrected Es (subgrade modulus) for cases of a2-layer model with an infinite depth to bedrock, ascalculated by Eq. 10 given in [16] values) to obtaincorrected (true) Es values for the case of a 3-layer
model with an infinite depth to bedrock (i.e., Es m );Hp denotes the total pavement thickness in meters;and (Ep/Es) uc denotes the forward-calculated Ep to
Es ratio by Eqs. 2, 3 and 4 (i.e., the AASHTOLIV
Ep/Es).
Ratio of corected 3-layer to 2-layer AASHTOLIV Es

2.0

6

Pavement thickness [meter]×(True Ep/Es)^(1/3

Equality line
5

4
R² = 0.9786

3

1.6

R²=0.9950
1.2
Equality line
0.8

0.4

Simulated data of a 2 and 3-layer model with an infinite depth to bedrock

0.0
0

1

2

3

4

5

Pavement thickness [meter]¢(AASHTOLIV Ep/Es)^(1/3)

2

Fig. 3 Ratio of corrected 3-layer to 2-layer
AASHTOLIV subgrade modulus versus
apparent AASHTOLIV structural numbers
for all simulated structures with infinite depth
to bedrock when a=150 mm.
Fig. 3 indicates that the Fs m values, derived from
Eq. 10, vary from 1.0 to over 1.5. In addition, for the
majority of conventional apparent AASHTOLIV
structural numbers (i.e., up to 2.0 meters for flexible
pavement with small to moderate total pavement
thickness; see Fig. 4), Fs m is equal to 1.0. At thisjuncture it is worthwhile noting that Fig. 4 depicts
the cumulative distribution of the apparent

1

Simulated data of a 3-layer model with an infinite depth to bedrock
0
0

1

2

3

4

5

6

Pavement thickness [meter]¢(AASHTOLIV Ep/Es)^(1/3)

Fig. 2 True to apparent AASHTOLIV structural
number versus apparent AASHTOLIV
structural numbers for all simulated 3-layer
structures with infinite depth to bedrock
when a=150 mm.
To sum up, the corrected (true) Es and thecorrected (true) Ep (termed Es m and Ep m ,286

GEOMATE- Osaka, Japan, Nov. 16-18, 2015

by Eq. 9, given in [16] to obtain corrected (true)
Ep/Es values for the case of a 3-layer model with an
infinite depth to bedrock (i.e., (Ep/Es) m in order tocalculate Ep m using Eq. 8); Hp denotes the total
pavement thickness in meters; (Ep/Es) uc denotes theforward-calculated Ep to Es ratio according toEqs. 2, 3 and 4 (i.e., the AASHTOLIV Ep/Es).
Fig. 5 indicates that the Fp m values of Eq. 11
vary above and below 1.0. In addition, for the
majority of conventional apparent AASHTOLIV
structural numbers (again, up to 2.0 meters forflexible pavement with small to moderate total
pavement thickness; see Fig. 4) Fp m varies between
0.85 and 1.15.

AASHTOLIV structural numbers forward-calculated
for four given structures which are described in thefollowing section of the present paper.
100

85 Percentile

Cumulative Percentage [%]

80

60

40

Conventional; Hp=730 mm
Conventional; Hp=1,560 mm
Conventional; Hp=1,560 mm
Conventional; Hp=800 mm

20
15 Percentile

0
0

1

2

3

4

5

FINAL CALCULATING EQUATIONS

Pavement thickness [meter]×(AASHTOLIV Ep/Es)^(1/3)

Fig. 4 Cumulative distribution of AASHTOLIV
apparent structural numbers for four given
flexible-pavement structures with infinite
depth to bedrock.

In order to forward-calculate the subgrade andpavement moduli for cases where the depth to
bedrock is not infinite, it is suggested to apply thedepth to bedrock effect corrective equations from theexisting 2-layer model AASHTOLIV equations
given in [16]. This suggestion is based on the
postulation that the aforementioned corrective
equations also apply to the present 3-layer model
AASHTOLIV equations developed in the previous
section. Thus, the final forward-calculation
equations for the subgrade and pavement moduli are:(13)
Es md =Fs m ×Es d
3(14)
Ep md =Fp m ×(Ep/Es) d ×Es md
where Es md is the final forward-calculated subgrade
modulus for the case of a 3-layer model and a given
depth to bedrock; Es d is the calculated subgrade
modulus according to Eqs. 12, 13, 14, 15 and 16,
given in [15] for the case of a 2-layer model with agiven depth to bedrock; Fs m denotes a multiplierfactor as defined by Eq. 10 in the previous section;
Ep md is the final forward-calculated pavement
modulus for the case of a 3-layer model with a given
depth to bedrock; Ep d is the pavement modulus
calculated according to Eqs. 17, 18, 19, 20 and 21,
given in [16] for the case of a 2-layer model with agiven depth to bedrock; Fp m is the multiplier factor as
defined by Eq. 12 in the previous section.

Ratio of 3-layer to 2-layer corrected (Ep/Es)1/3

1.6

1.2

Equality line
R²=0.9970
0.8

0.4

Simulated data of a 2 and 3-layer model with an infinite depth to bedrock

0.0
0

1

2

3

4

5

Pavement thickness [meter]¢(AASHTOLIV Ep/Es)^(1/3)

Fig. 5 Ratio of corrected 3-layer to 2-layer
AASHTOLIV Ep/Es values versus apparent
AASHTOLIV structural numbers for all
simulated structures with infinite depth to
bedrock when a=150 mm.
Similarly to Esm, the previous (Ep/Es)m equations
relate to the case of a 3-layer model structure with
infinite depth to bedrock. The outputs of theseequations differ from those given in [16] by thefollowing Fpm ratio. To recall again, the equations
given in [16] refer to the case of a 2-layer modelwith an infinite depth to bedrock (see also Fig. 5):(11)(Ep/Es) m 1/3=Fp m ×(Ep/Es) c 1/3
1/3 6
Fp m =-0.0034×[Hp×(Ep/Es) uc ] ++0.0615×[Hp×(Ep/Es) uc 1/3]5+-0.4429×[Hp×(Ep/Es) uc 1/3]4+1.6273×[Hp×(Ep/Es) uc 1/3]3-3.1745×[Hp×(Ep/Es) uc 1/3]2+(12)+2.8956×[Hp×(Ep/Es) uc 1/3]+0.1940
where Fp m denotes the multiplier factor of (Ep/Es )c ,the corrected Ep/Es values for the case of a 2-layer
model with an infinite depth to bedrock as calculated

FORWARD-CALCULATED EXAMPLES
In order to demonstrate the use of the new
AASHTOLIV equations developed in the present
paper, forward-calculations were performed on
deflection bowls measured at four given flexiblepavement structures. Three of these structures (i.e.
excluding the conventional structure which had atotal thickness of 800 mm) were constructed at thesame site, Ben Gurion International Airport, Israel,and possessed a heavy clay subgrade. The remaining
structure, which was constructed at a different site
(Ovda Airport, Israel), possessed a hard silty
subgrade. Here it is worthy to note that all four287

GEOMATE- Osaka, Japan, Nov. 16-18, 2015

aforementioned structures served as the -forwardcalculation examples given in [16].
These forward-calculations include finding thesubgrade and pavement resilient moduli for the two
following cases: (a) when the depth to bedrock fromthe subgrade surface is infinite, and (b) when thedepth to bedrock from the subgrade surface has afixed value of 40×a=6.0 meters, where a=0.150
meters, representing the radius of the loading plate.
This fixed depth was chosen according to therecommendations outlined in [18]. Figs. 6 and 7
depict the AASHTOLIV subgrade resilient moduli
outputs from the 3-layer model compared to outputs
from the 2-layer model for the four aforementioned
structures. Fig. 6 presents the case of infinite depth
to bedrock while Fig. 7 presents the case of 6.0
meters to bedrock, measured from the subgradesurface.

pavement thickness value. This finding is consistent
with that from Fig. 4 in the previous section.
In the same manner, Figs. 8 and 9 depict the
AASHTOLIV pavement resilient moduli outputs
from the 3-layer model compared to the 2-layer modelfor the four aforementioned structures. Again, Fig. 8
presents the case of infinite depth to bedrock while
Fig. 9 presents the case of 6.0 meters to bedrock,measured from the subgrade surface.
Corrected 3-layer model AASHTOLIV EP [MPa]

4,000

800

Corrected 3-layer model AASHTOLIV ES [MPa]y=1.0014x
R²=0.9990

700

y=1.0654xR²=0.9725

Equality line
2,500

1,500

500
y=0.8091x
R²=0.6091

100
Equality line
200

300

400

500

600

700

3,500

y=0.6947xR²=0.9766y=1.5306xR²=0.9861

1,000

Equality line

y=1.0632xR²=0.9913

Equality line

1,500

y=1.0013x
R²=0.9992

6.0 meter depth to bedrock
Conventional; Hp=730 mm
Conventional; Hp=1,560 mm
Conventional; Hp=1,560 mm
Conventional; Hp=800 mm

500
y=0.8116xR²=0.5709
0
0

500

1,000

1,500

2,000

2,500

3,000

3,500

Corrected 2-layer model AASHTOLIV EP [MPa]Fig. 9 Corrected 3-layer model AASHTOLIV Ep
versus corrected 2-layer model AASHTOLIV
Ep for (a) a 6.0 meter depth to bedrock, and(b) the four given flexible structures.
Figs. 8 and 9 indicate that the characterizing ratio(as previously defined) of the corrected 3-layer
model AASHTOLIV Ep value to the corrected 2layer model varies for the four given flexible
structures from 0.70 to 1.55, where the lower
average ratio value relates to the higher total
pavements thickness value. This finding is consistent
with that from Fig. 5 in the previous section, where
its y-axis values were raised to the power of 3.

400
300
200

6.0 meter depth to bedrock

100

Conventional; Hp=730 mm
Conventional; Hp=1,560 mm
Conventional; Hp=1,560 mm
Conventional; Hp=800 mm

0
100

3,000

y=1.3406xR²=0.2845

2,000

800

0

2,500

2,500

Fig. 6 Corrected 3-layer model AASHTOLIV Es
versus corrected 2-layer model AASHTOLIV
Es for (a) infinite depth to bedrock, and (b)the four given flexible structures.

500

2,000

3,000

800

Corrected 2-layer model AASHTOLIV ES [MPa]y=1.0079x
R²=1.0000

1,500

3,500

0

y=1.0297xR²=0.9986

1,000

4,000

Corrected 3-layer model AASHTOLIV EP [MPa]Infinite depth to bedrock
Conventional; Hp=730 mm
Conventional; Hp=1,560 mm
Conventional; Hp=1,560 mm
Conventional; Hp=800 mm

600

500

Fig. 8 Corrected 3-layer model AASHTOLIV Ep
versus corrected 2-layer model AASHTOLIV
Ep for (a) infinite depth to bedrock, and (b)the four given flexible structures.

200

700

Infinite depth to bedrock
Conventional; Hp=730 mm
Conventional; Hp=1,560 mm
Conventional; Hp=1,560 mm
Conventional; Hp=800 mm

Corrected 2-layer model AASHTOLIV EP [MPa]

300

100

y=1.5321xR²=0.9882

1,000

0

400

0

y=0.6952xR²=0.9888

2,000

0

y=1.0079x
R²=0.9999

500

Corrected 3-layer model AASHTOLIV Ep [MPa]3,000y=1.0306xR²=0.9975

600

y=1.3358xR²=0.2742

3,500

200

300

400

500

600

700

800

Corrected 2-layer model AASHTOLIV Ep [MPa]Fig. 7 Corrected 3-layer model AASHTOLIV Es
versus corrected 2-layer model AASHTOLIV
Es for (a) a 6.0 meter depth to bedrock, and(b) the four given flexible structures.
Figs. 6 and 7 indicate that the characterizing ratioof the corrected 3-layer model AASHTOLIV Es
value versus that of the corrected 2-layer model (i.e.,the slope of the linear equation obtained from thezero intercept regression) vary for the four given
flexible structures from 1.00 to 1.07, where thehigher average ratio value relates to the higher total

CONCLUSIONS
Findings from the current study on forwardcalculation of FWD deflection-basin measurements
serve as an important means to upgrade the existing
AASHTOLIV equations given in [15] and [16]. The
288

GEOMATE- Osaka, Japan, Nov. 16-18, 2015[4] Sidess, A., Bonjack, H. & Zoltan, G., “Overlay

results yield the following conclusions:• The current AASHTLOLIV equations, basedon the 2-layer model for any given depth to
bedrock from [15] and [16], require correction
factors in order to take into account the 3-layer
model effect.
• Using a 3-layer model with the existing
equations yields an increase in the subgrade
modulus outputs by a multiplier value ranging
from 1.0 to over 1.5; however, for the majorityof flexible pavements with small to moderate
total thicknesses, the aforementioned multipliervalue is equal or very close to 1.0• In addition, using a 3-layer model with theexisting equations yields an increase in the
outputs of the ratio of pavement modulus tosubgrade modulus, raised to the power of 1/3,
by a multiplier value ranging from 1.15 down
to under 0.85.
• The four given foreword-calculation examples
generally support the two conclusions above,
where the multiplier value for (a) the subgrade
modulus values ranged from 1.0 to 1.07, and(b) the pavement modulus values ranged from0.65 to 1.45.
Finally, it is important to add that despite theadvantages of utilizing the current modified
AASHTOLIV equations, a general statement should bemade: possible results from other in-situ deflection
bowl measurements may not necessarily mean thatforward-calculation is always superior to backcalculation.

design procedure for pavement maintenancemanagement systems”, Transportation Research
Record 1374, 1992, pp. 63-70.
[5] AASHTO, “AASHTO Guide for design ofpavement structures 1993”, Washington, DC:
American Association of State Highway andTransportation Officials, 1993.
[6] Baus, R. L. & Jonson, A.M. 1992. “AASHTO
direct structural capacity method error analysis”,
Journal of Transportation Engineering 118 (1),
1992, pp. 20-32.
[7] Rohde, G. T., “Determining pavement structural
number from FWD testing”, Transportation
Research Record 1448, 1994, pp. 61-68.
[8] Rada, G.R., Shepher, K.L., Zeigler, T.D. &Smith, T.E., “Montana's automated deflection
analysis procedure”, Transportation Research
Record 1570, 1997, pp. 151-162.
[9] Romanoschi, S. & Metcalf, J. B., “Simple
approach to estimation of pavement structural
capacity”. Transportation Research Record 1652,
1999, pp. 198-205.
[10] Badu-Tweneboah, K., Ruth, B.E. & Miley, W.G..
“Dynaflect Evaluation of layer moduli in
Florida's flexible pavement systems”, Transportation Research Record 1196, 2003, pp. 96107.
[11] Hoffman, M.S., “Direct method for evaluating
structural needs of flexible pavements with
Falling-WeightDeflectometer
deflections”,
Transportation Research Record 1860, 2003. pp.
41–47.
[12] Stubstad, R.N., Jiang, Y.J. & Lukanen, E.O.,“Forwardcalculation of pavement moduli withload-deflection data”, Transportation Research
Record 2005, 2007, pp. 104–110.
[13] Livneh, M., “On the reliability of excluding
thickness in forward-calculating pavement
parameters”, Road Materials and Pavements
Design 11, 2010, pp. 171-195.
[14] Hassam, H.F., Mousa, R.M., & Gadallah, A.A.,"Comparative analysis of using AASHTO andWESDEF approaches in back-Calculation of
pavement
layer
moduli",
JournalofTransportation Engineering, Vol. 129, 2003, pp.
322-329.
[15] Lee, Y.-H., Ker, H.-W., Lin, C.-J., & Wu, P.-H.,"Study of backcalculated pavement layer moduli
from the LTPP database", Tamkang Journal of
Science and Engineering, Vol. 13, No. 2, 2010,
pp. 145-156.
[16] Livneh, M., "Recent modifications to the 1993
AASHTO equations for forward-calculating
subgrade and pavement moduli", in Proc. 6th Int.
Conf. on Bituminous Mixtures and Pavements,
Thessaloniki, Greece, 10-12 June 2015.
[17] Livneh, M., "Additional issues concerning recent
modified
1993
AASHTOequations(AASHTOLIV) for analyzing measured
deflection bowls", in Proc. 6th Int. Conf. on

ACKNOWLEDGEMENTS
The data of the measured values presented in thispaper were obtained in the course of engineeringstudies performed by the author for the Israeli
Airport Authority. Thanks are therefore due to thisauthority.
REFERENCES[1] Alavi, S., Lecates, J.F., Tavares M.P. & Sierra
Transportation Engineers, Inc., “Falling weightdeflectometer usage”, NCHRP Synthesis 381,
National Cooperative Highway Research Program,
Synthesis of Highway Practice, Transportation
Research Board, Washington, DC, 2008.
[2] Wiseman, G., Uzan, J., Hoffman, M.S., Ishai, I.
& Livneh, M., “Simple elastic models forpavement evaluation using measured deflection
bowls”. In Proc. 4th Int. Conf. on Structural
Design of Asphalt Pavements, Vol. II, Ann Arbor,
Michigan, 1997, pp 416-426.
[3] Hall, K. D. & Elliot R. P., 1992. “ROADHOG-aflexible pavement overlay design procedure”,
Transportation Research Record 1374, 1992,
pp. 9-15.
289

GEOMATE- Osaka, Japan, Nov. 16-18, 2015

Bituminous
MixturesandPavements,
Thessaloniki, Greece, 10-12 June 2015.
[18] Rohde, G.T. & Scullion, T., “MODULUS 4.0:
expansion and validation of the modulus backcalculation system”, Report No. FHWA/TX91/1123-3, Texas Department of Transportation,
Austin, TX, 1990.

290

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

LIQUEFACTION POTENTIAL ASSESSMENT
BASED ON LABORATORY TEST
Abdul Hakam1, Febrin Anas Ismail1 and Fauzan3
Civil Dept. of Engineering Faculty, Andalas University, Indonesia

1

ABSTRACTThe physical properties of sand soil which give effect to the resistance of liquefaction include grain size anddensity. Those physical properties of sand soil associated to liquefaction resistance have been studied inlaboratory. Based on that study, the method to assess the liquefaction potential then is proposed. In laboratory
tests, the vibration source is given by using the shaking table. During the tests, the acceleration and settlement
are recorded. It then concluded that there is a relationship between density and gain size particles associated withliquefaction resistance for certain acceleration of vibration. The cone penetration and relative density
relationship has been developed based on experiments in laboratory. Based on the results of those laboratory
tests, the liquefaction potential of a certain site then assessed. It is found that the relative density and mean gain
size relationship can be used to assess liquefaction potential in sand deposits.
Keywords: Liquefaction, Earthquake, Soil particle size, Relative density, Laboratory test

density and effective stress at the time it is subjected
to shaking. It has been summarized number offactors that affect soil liquefaction resistance in [9],that are:- Relative density, D r- Initial stress of the soil, σ i- Mean grain size of the soil, D 50
- Applied peak acceleration, a max- Duration of the motion, t- Over consolidation ratio, OCR- Initial pore pressure, u i
Even though historically, sands were considered
to be the only type of soil susceptible to liquefaction,but observation showed that fine-grained soil also
been suffered from liquefaction. The fine-grainedsoils may have a tendency to liquefy under avibration load if they satisfy the Chinese criteria [10]that are:- Fraction finer than 0.005 mm less than 15%- Liquid Limit, LL less than 35%- Natural water content more than 0.9 LL- Liquidity Index less than 0.75

INTRODUCTION
The liquefaction potential assessment in a soil
deposit is an important aspect of geotechnicalearthquake engineering practice since Niigata
earthquake in 1964. Based on the occurrence ofliquefaction and field test data a method named"simplified method" was proposed [1]. Theliquefaction potential assessment at the coast of
Padang using 'simplified method' has been presented[2]. The application of this method actually is not as
simple as its name. It involves many factors thatrarely used in civil engineering such as earthquake
magnitude and depth factor. This procedure has been
continuously improved based on a number ofliquefaction histories around the world [3]. Based onthese methods, further Shibata and Teparaksa [4]developed a method for evaluating the liquefaction
potential based Cone Penetration Test results.
Based on the Cone Penetration method, theanalysis of liquefaction susceptible at several
locations in the city of Padang due to the 2009
earthquake has been presented [5]. Although thesepenetration-based methods (SPT and CPT) andcyclic stress ratio are well developed, but in the use
still require advanced knowledge in choosing theparameters. The guidance for using penetrationbased methods has been discussed [6].
In the past it has been summarized that the newly
deposited loose sands under the shallow groundwater are susceptible to liquefaction [7]. Kramer in1996 [8] has summarized a number of methods toevaluate the liquefaction potential of a soil deposit.
Those are the liquefaction history, the geologicalprocess, the soil type and fine size particles, soil

Cyclic stress ratio

rasio tegangan siklis

0.5
0.4
0.3

4 8
10
12

0.2
0.1
0.0
0

Fig. 1

291

10

20

30

N1

40

50

60

70

Liquefaction using ‘simplified method’ [2].

GEOMATE- Osaka, Nov. 16-18, 2015

Based on the grain size analysis test from several
location due to Kocaeli earthquake in Turkey in1999 [11] and due to Padang earthquake 2009, it
have been reported the results of sieve analysis testsof liquefied soil samples as shown in Figure 2(shadowed). The soil gradation of Padang is just in
the middle of liquefaction boundaries from Aydan.
The distribution of liquefied soil particle in Padang
generally composed fine sand more than 60%. The
fine content of liquefies soil of Padang is less than
20%. The mean grain size D 50 is about 0.15mm to
0.35mm.

of liquefaction potential assessment based onlaboratory experiments is presented. The factors
have been considered in the laboratory experimentsare:- Relative density, D r- Cone resistance of the soil, q c- Mean grain size of the soil, D 50
- Applied peak acceleration, a max- Duration of the motion, t
LABORATORY TEST RESULTS
A series of laboratory testing has been done byplacing indicator bar on soil samples in the roundcontainer. In these tests the relative density D r andthe mean grain size D 50 are varied. The samples areplaced on the shaking table and then vibrated for0.3g and 0.6g accelerations. During the testing theacceleration and the settlement of the indicator bar
are recorded.

10
Liquefied soil

80

40

Potentially liq. soi% passing60

In field liquefaction, a seismic shaking can cause
sand deposit to loose its contract and increase thewater pore pressure. It happens because the seismic
shaking occurs relatively fast and the soil performs
an undrained loading. If soil has reachedliquefaction condition then the effective stress in soil
mass is decreased hence its shear strength can drop.
In the liquefaction the individual soil particles are
released from any confinement [13]. The same
phenomenon in these experiments when liquefaction
occurs in the sample, the shear strength of the soil
dropped thus the indicator bar will settle down
during the shaking.

20
0
0.001
Fig. 20.01
0.1
1Grain size (mm)10Grain size limit for liquefaction [10].

Based on the field case histories on evaluation ofliquefaction potential for 50-year around the world[12], mean grain size of liquefied soils are presented
in Figure 3. It can be seen that from those 155
occurrences of liquefaction, 78% of liquefaction
happened on the soil with mean grain size between
0.113 to 0.338 mm.

The rate of settlement during shaking isapproximately 0.1 cm/sec is taken as the separation
criterion of settlement rate values. The rate
settlement more than 0.1 cm/sec indicated thatliquefaction has happened in this saturated soil
samples. The general results of the tests are shownin Figure 4. The linear boundary line is made up foreach acceleration 0.3g and 0.6g.

60
50
40
30
20
10
0

Fig. 30.1

1

grain size (mm)

100
90

10

D 50 for liquefied soils, based on [12].

Dr (%)

0.01

Occurrence

D50 Aydan

Therefore, this study is conducted to find out arelationship between density and gain size particles
associated with liquefaction resistance for certain
acceleration of vibration which is simply can beused to assess liquefaction potential of soil deposits.

80

amax = 0.3 g> 0.1 cm/sec.

70< 0.1 cm/sec

60
50

amax = 0.6 g> 0.1 cm/sec.

40< 0.1 cm/sec.

30
20

0.3 g

0.6 g

10
0
0,01

The simple liquefaction potential assessment isimportant to have good estimation of theliquefaction problem. In this paper, the application

0,1

1

Grain size (mm)Fig. 4292

D r – D 50 for liquefaction test.

10

GEOMATE- Osaka, Nov. 16-18, 2015

qc (kg/cm2)

Since Cone Penetration Test is very famous inpractice, the q c – D r relationship become essential
for liquefaction potential analysis based on Cone
Penetration Test (CPT). The calibration studies of
the q c is effected by sand density, in-situ effective
stressandsandcompressibility.
Sand
compressibility is controlled by grain characteristics,
such as grain size, shape and mineralogy. The q c –D r relationships for sand then is written as follows[14]:
Dr = C 2 (-1) ln Q/C 0

0

20

40

60

100

0,50

depth (m)

1,00

1,50

2,00

(1)

Where C 0 =15.7, C 2 =2.41 and Q=(q c /p a )/(σ’/p a )-0.5.
Here p a is reference pressure taken as 100kPa, in thesame unit as q c and σ’.

2,50

3,00

LIQUEFACTION ASSESSMENT

Fig. 7

During Padang earthquake 2009, there are many
locations along the shore suffered from liquefaction.
One of those location is Pasir Jambak district where
has sand deposit and very shallow water table. The
field soil testing has been conducted to investigate
soil distribution and cone resistance as presented in
Figures 6 and 7.

CPT of Pasir Jambak.

Based on those parameters the point each depth
is plotted in the Figure 8. The same value for
maximum acceleration of Padang earthquake isabout 0.3g. It shows that point D 0.5 just in line 0.3g
which indicated that the liquefaction happed during
the earthquake.
amax: 0.3 g
0.6amaxg = 0.3 g

100
80% passing

80

0,00

60

0.5 m

> 0.1 cm/sec.

1.0 m< 0.1 cm/sec

1.5 m

amax = 0.6 g> 0.1 cm/sec.

2.0 m

40
D2.0

20

< 0.1 cm/sec.

D1.5

0
0,01

Fig. 6

1
0,1
Grain size (mm)

D0.5

10

D1.0

Particle distribution of Pasir Jambak sand.

Table 1 shows the mean grain size of sand which
is determined from the grain distribution chart andrelative density is calculated using equation (1) withthe unit volume of 12 kN/m3.

Fig. 8CONCLUSION

Table 1 Parameter for liquefaction assessment
Name

Dept
(m)

D 50
(mm)

qc
(kg/cm2)

Dr
(%)

D 0.5

0.5

0.2

17

3

D 1.0

1.0

0.25

25

5

D 1.5

1.5

0.25

50

25

D 2.0

2.0

0.25

65

30

D r – D 50 for Pasir Jambak assessment.

This paper presented that relative density andmean particle size can be associated with theliquefaction susceptibility of soil deposits.
Liquefaction resistance of the sands increases withthe relative density and mean particle size. Bothrelative density and mean particle size give uniquerelationship for resistance of sand soil against
shaking.
Here, the liquefaction assessment of Pasir
Jambak sand deposit due to Padang earthquake 2009is presented. This analysis is practically simple to293

GEOMATE- Osaka, Nov. 16-18, 2015

estimate the liquefaction potential. Thus, therelationship further can be used to assessliquefaction potential in certain sand deposits.

and Geoenvironmental Engg., ASCE, April
2001, pp. 297-313[7] Day R. W., , Geotechnical Earthquake
Engineering Handbook, The McGraw-Hill
Comp., 2002
[8] Kramer,S.L.,
Geotechnical
Earthquake
Engineering. Prentice Hall, Englewood Cliffs,
N.J., 1996
[9] Das, BM., , Fundamental of Soil Dynamics,
Elsevier Pub., New York, 1983
[10] Wang, W. S., “Some Findings in Soil
Liquefaction”, Report Water Conservancy andHydroelectric Power Scientific ResearchInstitute, Beijing, China, August 1979, pp. 1-17
[11] Aydan, O., Ulusay, R. and Atak, VO.,
“Evaluation of ground deformations induced by
the 1999 Kocaeli earthquake (Turkey) atselected sites on shorelines”, Environ Geol(2008) 54. , 2008, pp. 165–182[12] Cetin K. O., Seed R. B., Moss R. E. S., Der
Kiureghian A., Tokimatsu K., Harder, Jr. L. F.,
and Kayen R. E., , Field Case Histories for SPTBased In Situ Liquefaction Potential Evaluation,
Geotechnical Engineering Research Report No.
UCB/GT-2000109, Department of Civil andEnvironmental
Engineering,
California
University, Berkeley., 2000
[13] Ishihara. K., ”Stability of Natural Deposits
during Earthquakes”, Proc. The 11th
International Conf. on Soil Mechanics andFoundation Engineering, San Francisco, 1985,
Vol. 1. pp. 321-376[14] Robertson P. K. and Cabal K.L., Guide to Cone
Penetration Testing, GREGG inc., 2010, pp. 42

REFERENCES[1] Seed, H. B., and Idriss, I. M., ‘‘Simplified
procedure for evaluating soil liquefaction
potential.’’ J. Geotech. Engg. Div. , ASCE,
97(9) , 1971, pp 1249–1273[2] Hakam, A. and Darjanto, “Liquefaction
potential assessment of Padang Beach Based on
grain size and standard penetration resistance”(in Indonesia: Penelusuran Potensi Likuifaksi
Pantai Padang Berdasarkan Gradasi Butiran dan
Tahanan Penetrasi Standar), Jurnal Teknik Sipil– ITB, Vol. 20 No. 1, April 2013, pp. 33-38[3] Seed, H. Bolton dan Idriss I.M., Ground Motion
and Soil Liquefaction During Earthquake,
Earthquake Engineering Research Institute,
Berkeley, 1982
[4] Shibata, T. and Teparaksa, W., “Evaluation ofliquefaction potential of soils using cone
penetration tests”, J. Soils and Foundations, Vol.
28., N0. 2, 1998, pp. 49-60[5] Hakam, A., “Soil Liquefaction of Padang due toPadang Earthquake 30s’09”, The Journal of
Civil Engineering Dimension, Volume 14, no 2,
2012, pp. 64-68[6] Youd, T. L. and Idriss, I. M. (2001).
“Liquefaction resistance of soils: Summary
report from the 1996 NCEER and 1998
NCEER/NSF workshop on evaluation ofliquefaction resistance of soils”, J. Geotechnical294

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

STRENGTH PROPERTIES OF ROAD BASE MATERIALS BLENDED
WITH WASTE LIMESTONES
Jonathan R. Dungca1 and Lydia Francisca T. Dychangco2
Gokongwei College of Engineering, De La Salle University, Philippines1, 2

ABSTRACT
Most areas in the Philippines experience high road construction cost due to the limited supply of suitablematerials for road embankment within economic haul distances. Considering that the country is currently having
extensive infrastructure development, potential use of naturally-occuring materials such as limestones was tested
resulting not only as an economic alternative to the conventional materials, but leading to an improved strengthof roads. Geotechnical and morphological properties of pure and blended materials were determinedcharacterizing the behavior of the strength of the materials as limestone blend varies. A 50% blend of limestone
to conventional road base materials provided optimum strength increasing the values up to 30%, 100% and 40%
for unsoaked CBR, soaked CBR and UCS, respectively.
Keywords: Limestone, Road Embankment, CBR, Road Base Material
unconfined compressive strength (UCS) andundrained shear strength. Regression analysis was
also performed correlating the values of different
strength tests with different blend proportions oflimestone.
Other than geotechnical properties, physicalproperties were also considered since the strength isfound to be greatly influenced by its mineralogicalcomposition. The mineralogy of the materials was
defined through analyzing the mineral peaks fromthe X-ray Diffraction (XRD) graphs. Furthermore,
Scanning Electron Microscope (SEM) was also
conducted to determine some of the morphologicalcharacteristics of the materials.

INTRODUCTION
In the Philippines, road construction is one of theprevailing infrastructure developments by thegovernment because of the many inland transportsystems used to deliver goods in different regions of
the country. High haulage cost due to inflation is oneof the constraints in the availability of providing
high quality underlying soil layers in most areas of
the country. These underlying soil layers known as
subgrade, subbase and base act as the foundation of
the pavement and its performance is strongly
influenced by its stability, bearing strength,
consolidation over time and moisture susceptibility.
Hence, taking into account the materials andstrength requirements of these layers are importantin achieving the expected performance of the roads.
The possibility of using naturally-occuring materials
for road embankments was given emphasis in thisstudy to provide economic solution to this problem.
An abundant resource recognized in this study islimestone. Limestone is a calcium carbonate type ofrock that is widely available all over the world, as it
constitutes more than 4% of the Earth’s crust. In thePhilippines, the Mines and Geosciences Bureau(MGB) projected an estimate of 4 billion metric tonsof limestone deposit in 1992. Limestones arepotentially known to exhibit characteristics needed
for road embankments.
The objective of the study is to determine thesuitability of blending waste limestones toconventional materials as a road embankment
material and to establish strength properties ofdifferent limestone blends. Strength properties
considered are the California bearing ratio (CBR),

MATERIAL SOURCES
Limestone
Limestones coming from Guimaras Island are
accredited by the Department of Public Works andHighways (DPWH) of the Philippines for use as
material for road construction. Specifically,
limestones tested were from Sta. Fe Lime
Enterprises, located in Buenavista, Guimaras Island,
Philippines. The quarry covers a total area of 350m2
with an annual extraction rate of 70,000 mertric tonsof limestone. Currently, Sta Fe. Limestone quarry
have already extracted more than 60% of the area.
The company is planning to expand, having an
additional four (4) hectares of land to be extracted.
The company is in partnership with Dorilag Cement
Corp. where they supply lime as the main ingredientfor cement.
Conventional Materials
295

GEOMATE- Osaka, Nov. 16-18, 2015

In characterizing the commonly used aggregatesin the Philippines, conventional materials for roadbases from Batong Angono Aggregates Corporation(BAAC) were examined. Aggregates from BAAC
were good representatives of the aggregates in thePhilippines considering that BAAC supplied most of
the DPWH road projects.

Strength Tests
CBR strength test (ASTM D1883) was
performed under soaked and unsoaked conditions.
Under each result, corrections were made due tosurface irregularities, depicted by a concave upward
behavior at the start of the Stress vs Penetration
curve. The strength property is the basis fordetermination of the materials applicability as roadembankment.
The ASTM D2166 was referenced for procedure
for the determination of the unconfined compressive
strength, a height-to-diameter ratio of 1.65 wasconsidered and was remoulded following therequired specimen conditions. The test was also able
to estimate the undrained shear strength of thespecimens in the wet state.

METHODOLOGY
Soil Specimen Preparation
The materials used in the study were oven dried
to make sure that materials acquire 0% water content
as required for testing. In addition, sieving was done
for both conventional materials and limestones tomeet the grading requirements provided in the study.
Index Properties

TEST RESULTS
The index properties (i.e. specific gravity,
Atterberg limits, particle size distribution andrelative density) of both the control mix andlimestone-blended conventional road base materials
were identified following the ASTM procedures [1].
Index properties defined the material’s basic
properties providing basis for further strength
behavior analyses.

Geotechnical
Properties
ofConventional
Materials Blended with Different Percentages ofLimestone
Specific Gravity
The control and blended specimens showed adecreasing trend of specific gravity as the percentage
blend of limestone is increased due to the differenceof the weights of minerals in material and thepresence of depressed structure within particle
surfaces. The behavior of values resulted to an
empirical formula showed in Eq. (1).

Blending of Materials
The blending of materials was arithmeticallycomputed following a specific gradation. Specificweights of materials for each range of size wereidentified and measured satisfying the material
requirements for subgrade, subbase and base courses
as stipulated in the DPWH standards andspecifications for Highways, Bridges and Airports[2]. The study required blending of limestone having
different gradation to suitable aggregates to come up
with a blend that will meet the gradation
specifications. The materials passing No. 4 sieve(4.75mm opening) were mixed at blend proportionsof 0, 20, 40, 60, 80 and 100% of limestone byweight, equivalent to 0, 7, 14, 21, 28 and 35% oflimestone by total weight, respectively.

Gs= 2.7775exp0.0009L(1)

Where:
Gs = specific gravity;L= limestone content in percent.

Atterberg Limits - Liquid Limit
The results of soil-limestone mixture at different
percentages are shown in Fig. 1. Plotting the values
provided an empirical formula shown in Eq. (2).

Compaction Characteristics
Following the ASTM procedure for MoistureDensity Relationship, compaction characteristics ofthe materials were determined. Standard Proctor
Tests were obtained for each blend wherein nine (9)points were identified in order to acquire precise
values of maximum dry densities and optimum
moisture contents. Such property enabled thedetermination of the water content at which thecompaction is best.

LL = 15.33exp0.0031L(2)

Where:
LL = Liquid Limit.
Evidently, increasing limestone content increases
the liquid limit of the blend since limestones havegreater tendency to attract water to its particle

296

GEOMATE- Osaka, Nov. 16-18, 2015

Liquid Limitsurfaces. Changes in engineering properties of theaggregates blended with limestone may be due to the
cationic exchange, flocculation of the clay,
agglomeration and pozzolanic reactions as explainedby Thompson [3] and Bell [4].
23
22
21
20
19
18
17
16
15
14

R² = 0.8724

17.24 17.62

線形(LowerBound)

15.40
0

50

The results confirmed that the plasticity index
increases with increasing limestone content at allpercentages. Because both materials exhibit low
plasticity characteristics, the combination of the twomaterials still satisfies the general specification forroad embankment which requires that plasticity
index not to exceed a value of six (6).

線形(UpperBound)

19.28

100
Plasticity Index

Limestone Content, %Fig. 1. Liquid Limit vs Limestone Content

Atterberg Limits -Plastic Limit
Soil-limestone mixture makes variations ofplastic limit at different percentages as shown in Fig.
2. Similar to the liquid limit, changes in theengineering properties may be due to the reactions
mentioned as limestone is blended to crushed
aggregates. In addition, the values of plastic limit
followed similar behavior as the liquid limit whereinvalues behaved exponentially generating acorrelation shown in Eq. (3).
PL = 12.876exp0.0025L

Plastic Limit

16.17 16.12

15
15.02

14

14.46

13

13.02
13.07

12
0

5.76

2.43

2.79

2.60

20

40

60

3.15

80

100

Maximum and Minimum Index Densities
In performing tests for identification of
maximum and minimum index densities, it was
found that in both loosest and densest condition of
the materials, values of index densities are
decreasing as limestone content is increased. Even
though the range of particle size is standardized forthis test, it can still vary by volume considering thatlimestone is larger in volume due to its light weight.
As limestone content is increased, moreconventional materials are substituted by lighter
particle providing drop in unit weight of thelimestoneblended
materials.
Empirical
relationships developed are as follows:(3)

Plastic
Limit

16

R² = 0.7919

Fig. 3. Plasticity Index vs Limestone Content

18
R² = 0.8011

8.00
7.00
6.00
5.00
4.00
3.00
2.00
1.001.93
0.00
0Limestone Blend, %Where :PL
= Plastic Limit.

17

(4)

Where:PI= Plasticity Index.

Liquid
Limit

21.88

16.85

PI = 1.8771exp0.0088L

Upper
Bound
Lower
Bound

50
100
Limestone Content, %Max. Index Density = 22.562 exp0.002L
Min. Index Density = 18.101 exp0.002L

(5)
(6)

Fig. 2. Plastic Limit vs Limestone Content
The exponential behavior of the data provided
correlation of values wherein index densities can beobtained at any given limestone blend.

Atterberg Limits –Plasticity Index
At different percentages of limestone, deviation invalues of plasticity indices is measured as shown in
Fig. 3. Similarly, values also formed a correlation
wherein values of plasticity indices can be estimatedat any limestone content as shown in Eq. (4).

Compaction Characteristics
The Standard Proctor Test was performed
following the ASTM D698 procedures in order to297

GEOMATE- Osaka, Nov. 16-18, 2015

obtain the exact amount of moisture needed insample preparation for strength tests. The results inthe determination of optimum moisture content(OMC) and maximum dry density (MDD) aresummarized in Table 1:

Where:
MDD = maximum dry density;
OMC =optimum moisture content.
The empirical formulas obtained provided
exponential behavior for both property in which
values of MDD and OMC can be determined at any
limestone blend.

Table 1 Summary of OMC and MDD Values for
Each Blend
Limestone
Content, %
0
20
40
60
80
100

OMC, %MDD, kN/m3

5.00
5.56
5.98
6.63
7.98
9.54

24.23
23.05
22.18
22.09
21.68
21.08

Strength
Characteristics
ofConventional
Materials Blended with Different Percentages ofLimestone
California Bearing Ratio- Unsoaked condition
Remarkable values of CBR under unsoaked
condition
were
obtained
providinggood
performance for road subgrade, sub-base and base.
On the average, different CBR values were obtainedfor each blend providing an empirical relationshipshown in Eq. (9).

As shown in Table 1, the MDD decreases while the
OMC increases with increasing percentage of
limestone. Considerable agglomerations of smaller
particles of limestone occur resulted to larger ones,
which prohibit the specimens to be properly
compacted. This happens when there is cationicexchange wherein higher valence cations replacethose with lower valence and larger cations replacethose that are smaller having same valence. With thevoids not filled up as much, the MDD then decreases.
It can easily be seen that the limestone has lighter
weight as compared to the conventional materials
because of its mineralogical composition. On theother hand, the OMC increases mainly due to theadditional water held within the flocculated soil
structure. The increase in fines content as well as thehigh affinity of limestone to water caused theincrease in OMC as percentage blend of it isincreased. According to Akoto and Singh [5],increase in water is needed for providing more Ca2+
ions for the cation exchange reaction. As observed,
large drop of MDD is experienced with increasing
limestone content up to 40%. Okagbue and Yakubu[6] explained that the initial drop is due to theflocculation and agglomeration of the clay particles
due to the cation exchange reaction. Smaller
decrease in MDD as more limestone is added is dueto the replacement of particles of limestone, whichhave comparatively low specific gravity with that of
the conventional road base materials.

𝑪𝑪𝑪𝑪𝑪𝑪𝑼𝑼𝑼𝑼𝑼𝑼𝑼𝑼𝑼𝑼𝑼𝑼𝑼𝑼𝑼𝑼 , % = −𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝑳𝑳𝟐𝟐 + 𝟎𝟎. 𝟗𝟗𝟗𝟗𝟗𝟗𝟗𝟗𝟗𝟗 + 𝟓𝟓𝟓𝟓. 𝟑𝟑𝟑𝟑𝟑𝟑(9)

Where:CBR unsoaked = unsoaked CBR of the specimen.
Given this model that will produce themaximum CBR value, it can be computed that theblend accounts to blend L42.96-C57.04. This is
likely to give an approximate CBR value of 70%.
Comparing the unsoaked CBR strength value of
53.75% provided by the controlled specimens to the
optimum blend with unsoaked CBR value of 70%, it
can be concluded that optimum blend can increase
the strength by 30%. The behavior of the CBR value
is increasing until it reached the 42.96% blend oflimestone and eventually decreases as it approaches
100%. The increase of strength of the blended
materials with increasing limestone content ismainly due to the considerable plasticity oflimestone. This provides binding property among
particles that allows the material to gain strength.
The cohesive strength limestone provided increased
the over-all strength of the specimen. Other authors
working on the soil-limestone mixture noted similar
behavior. Newbauer and Thompson [7] explainedthat the increase in strength may be due to theimmediate reaction endowed by the cation exchange
and the flocculation and agglomeration reactions,while Van Ganse [8] postulated that this is due to theformation of crumbs of soils maintaining theirindividuality when the mixture is compacted.
On the other hand, increasing the limestone
blend to more than 42.96% decreased the over-allstrength of the specimen. Although the cohesivestrength of the blend increases, there is a point inPlotting the behavior of MDD and OMC at
different limestone content gave an empiricalformulas shown in Eq. (7) and (8), respectively.
MDD,kN/m3 = 23.881exp0.001L(7)

OMC,%= 4.9213exp0.0061L

(8)

298

GEOMATE- Osaka, Nov. 16-18, 2015

With this equation, L50-C50 is found to
generate the optimum strength value of 70kPa.
Comparing the UCS of controlled specimen to thatof the optimum blend, an increase of 40% in
strength is observed. The behavior of the results
followed a parabolic curve concaving downward
which entails that a 50% limestone substitution to
the fine aggregates would produce maximum
strength of the specimen since it adds cohesion to
the strength of material owing to its binding
characteristics. Since undrained shear strength canbe estimated with results obtained in the UCS, it canbe concluded that this property exhibited similar
behavior with that of the UCS. According to Lees etal. [9] and Bell [10], the increase in strength may be
due to the formation of larger particles, making it
behave as coarse-grained, strongly bonded,particulate material. Nonetheless, further increase of
limestone would tend to decrease its over-allstrength due to the drop of frictional strength as aneffect of the domination of fine particles in volume.
This permits less contact of coarse particles with one
another leading to loss of friction.

which the frictional strength is affected by the
reduction of conventional materials due to theincrease in volume of the limestone. Frictional
strength is contributed by large particles and highcohesive strength would mean lesser contact oflarger particles with each other thus, a drop offrictional strength.
California Bearing Ratio- Soaked condition
To test the strength of the limestone-blended
materials at its worst condition, CBR values forsoaked condition were also identified. The values
demonstrated good performance resulting to CBRvalues greater than 20. The blend that is anticipated
to attain the peak strength is L50-C50, which will
give a CBR value of 60% based on the empirical
relationship as shown in Eq. (10).
𝑪𝑪𝑪𝑪𝑪𝑪𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺𝑺 , % = −𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝑳𝑳𝟐𝟐 + 𝟎𝟎. 𝟗𝟗𝟗𝟗𝟗𝟗𝟗𝟗 𝑳𝑳 + 𝟓𝟓𝟓𝟓. 𝟑𝟑𝟑𝟑

(10)

Where:CBR soaked = the soaked CBR of the specimen.

CONCLUSION
Being able to determine the optimum strength
enabled the estimation of percentage increase instrength of the optimum blend with respect to thecontrolled specimen. With a soaked CBR value of
30% and 60% for controlled and optimum blend
respectively, a 100% increase in strength is realized.
It can be observed that CBR values from soaked
condition is lesser compared to that of the CBRvalues under unsoaked condition. This is due to thepermeation of water in soaked condition decreasing
its strength. The behavior of CBR values undersoaked condition is similar to that of the unsoaked
condition. Reporting the swell of the materials after
four (4) days of soaking period, it was observed thaton the average, swell of materials on all blends haveno significant value accumulating less than 1% swell.

Having analyzed the different properties andstrength characteristics of road base materials
blended with limestones at different percentages, thefollowing conclusions were made:Waste limestones from Guimaras Island,
Philippines can be utilized in road construction
considering that replacing 100% of the fineaggregates with limestone yielded to acceptablevalues of CBR for both soaked and unsoaked
conditions;
From the empirical relationships given by thestrength parameters considered, it can be concluded
that a range of 40% to 50% blend of limestone
would optimally give the most favorable strength forboth unsoaked and soaked CBR and UCS.
Comparing the strength values of optimum blend topurely conventional materials, a remarkable increaseof up to 30% for unsoaked CBR, up to 100% forsoaked CBR and upto 40% to unconfinedcompressive strength values were realized;

Unconfined Compressive Strength
To further verify the results provided by CBRtests, the Unconfined Compressive Strength (UCS)
test was conducted. This quickly estimates theundrained shear strength of the material. The
empirical relationship was determined to describe
the strength behavior as limestone content varies
forming an empirical formula shown in Eq. (11).

ACKNOWLEDGEMENTS
We would like to extend our deepest gratitude
to DOST-ERDT for providing financial assistancefor this research. We also thank LaFarge for theirsamples of road base materials. Also, the study
would not be possible without the help of the DLSU
Civil Engineering Faculty.

𝑼𝑼𝑼𝑼𝑼𝑼, 𝒌𝒌𝒌𝒌𝒌𝒌 = −𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝑳𝑳𝟐𝟐 + 𝟎𝟎. 𝟖𝟖𝟖𝟖𝟖𝟖𝟖𝟖𝟖𝟖 + 𝟒𝟒𝟒𝟒. 𝟑𝟑𝟑𝟑𝟑𝟑(11)

Where:UCS = unconfined compressive strength299

GEOMATE- Osaka, Nov. 16-18, 2015[6] Okagbue, C.O. and Yakubu J.A. (2000),“Limestone Ash waste as a Substitute for Limein Soil Improvement for Engineering
construction”, Bulletin of engineering Geology
and the Environment. Vol 5 No. 2, pp 107-133.
[7] Newbauer H. and Thompson M.R. (1972),“Stability Properties of Uncured Lime-Treated
Fine-Grained Soils”, Highw Res Rec 381: 20-26.
[8] Van Ganse R. F. (1974), “Immediate
Amelioration of Wet Cohesive soils by quickLime”, Transp Res Rec 501: 42-53.
[9] Lees G., Abdelkader M.O., Hamdani S.K.
(1982) “Effect of Clay Friction on Some
Mechanical Properties of Lime-Soil Miztures”,
J. Inst Highw Wng 29: 209-220.
[10] Bell F. G. (1989) “Lime Stabilization of clay
Soils”, Bull IAEG 39:67-74.

REFERENCES[1] American Society for Testing and Materials(2004). “ASTM Standards on Transportation
Applications”.
[2] Department of Public Works and Highways(2004) “Standard specification for Highways,
Bridges and Airports”.
[3] Thompson M.R. (1965), “Influence of Soil
Properties on Lime-Soil Reactions”, Public
Works 96: 120-123.
[4] Bell F.G. (1993), “Engineering treatment ofSoils”, Chapman and Hall, London.
[5] Akoto B.K.A. and Singh G. (1981), “Somegeotechnical properties of Lime-Stabilizedlaterite containing a high proportion ofaluminum Oxide”, Eng Geol: 85-100

300

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

BEARING CAPACITY OF STRIP FOOTINGS ON CLAYS WITH
INCREASING SHEAR STRENGTH

1, 2

Seyednima Salimi Eshkevari1, Andrew J. Abbo2
Australian Research Council Centre for Geotechnical Science and Engineering, School of Engineering,
University of Newcastle, Australia

ABSTRACTThis paper considers the bearing capacity of surface strip footings sitting on a deep layer of clay soil withshear strength profile that increases linearly with depth. While an exact solution for the bearing capacity has
previously been obtained using the method of characteristics, it is not in a form that is convenient for accurate
evaluation. Significant research has been done previously in this area. In those works however, the focus has
been on the techniques rather than practical application and the results are either presented for limited cases orsummarised in graphs that are not easy to interpret in same range of parameters. In the present study, Finite
Element Limit Analysis (FELA) is used to compute rigorous upper and lower limits on the ultimate bearing
capacity of strip footings resting on the non-homogeneous clay layer. The results of the numerical limit analysis,
which bracket the true bearing capacity within a range of typically less than 2%, compare well with the exact
solutions available in the literature. New expressions are provided for the bearing capacity in which the bearing
capacity factor is expressed as a simple function of the dimensionless rate of increase of soil strength.
Keywords: Bearing capacity, Non-homogenous clay, Increasing shear strength
bearing capacity of footings. The earliest
recommendations on the bearing capacity of thefootings over non-homogeneous clays (qu ) are given
by Terzaghi [3] and Skempton [4] who suggested anaverage bearing capacity factor to be calculated overa shallow depth underneath the footing. Limit
Equilibrium was utilised by Nakase [5], using acircular slip surface. This method was later repeatedby Raymond [6] that in general, overestimates thetrue ultimate bearing capacity of the footing. Limit
analysis techniques have been a popular approach
amongst the researchers with many upper boundestimates of bearing capacity [7]-[14].
One of the best approaches to calculate thebearing capacity of footings over non-homogeneous
soils is provided by Davis and Booker [1] who
employed the method of characteristics to obtain the
bearing capacity of both smooth and rough footings
resting on clay of increasing shear strength withdepth. They derived an exact expression for the
bearing capacity of the form

INTRODUCTION
A nonhomogeneous shear strength profile is acommon situation encountered in geotechnicalengineering when considering normally consolidated
or lightly over-consolidated clays. In such soils theundrained shear strength ( cu ) tends to increase
almost linearly with depth. As show in Fig. 1, theshear strength profile of such soils is usuallydescribed in terms of the rate of increase in shear
strength with depth (ρ). The rate of increase may be
expressed as dimensionless quantity ρB⁄c0 where B
is the footing width and c 0 is the undrained shear
strength of clay at the foundation level [1]. The rateof increase in shear strength (ρ) is known to be an
inherent soil property, varying in the range of(0.6~3.0 kPa/m) which may be estimated as a
product of soil effective unit weight (γ )́ and∆cu ⁄∆σ ́v that is the rate of increase in cu due to theincrease in the soil effective vertical consolidationstress [2]. The soil effective unit weight and∆cu ⁄∆σ ́v vary in the range of (4~10 kN/m3) and(0.15~0.3) respectively [2]. The influence of clay
non-homogeneity on the bearing capacity of footings
is particularly significant in the offshore engineering,where large diameter shallow foundations are
installed over normally consolidated sea bed
producing very high degrees of non-homogeneity;
However the benefits of considering increasing
shear strength profile of soil is not limited tooffshore practice.
Many researchers have previously considered theinfluence of soil increasing shear strength on thequ =F[(2+π)+ 𝜌𝜌B⁄4](1)

where the factor F is a function of ρB⁄c0 . No
analytic expression was provided for F with values
provided in a simple graph for ρB⁄c0 ranging fromzero to 30. Values of F are not easy to readaccurately from the graph, particularly in the lower
range of ρB⁄c0 . This method was later used by
Houlsby and Wroth [15] who used the same
techniques to obtain solutions for circular footings.
The bearing capacity software of Martin [16] which
301

GEOMATE- Osaka, Nov. 16-18, 2015

tight upper and lower bounds of the exact solutionsfor ρB⁄c0 ranging from zero to 30. This is done toderive accurate expressions to predict modified
bearing capacity factor for a practical range of ρB⁄c0 .
Although the exact solution of Davis and Booker [1]is already available, it is not easy to accurately
obtain equivalent modified bearing capacity factor
from their presented graph of F. The average of limit
loads were used to calculate Nc and two expressions
were found using curve fitting technique to calculate
Nc very accurately for rough strip footings over thewhole practical range of ρB⁄c0 .

B
c0

cu
Cu=C0+r Z
1

Z

r

Fig. 1 The shear strength profile of the soil.
is based on the method of characteristics can also be
used to find the upper and lower bounds of qu forfootings on non-homogenous clay. Tani and Craig[2] used this method to solve the problem of skirted
foundations over non-homogenous clay for bothplane strain and axisymmetric conditions. Other
numerical methods such as finite elements and finite
difference have also been utilised to investigate
bearing capacity of these soils; for example Yun andBransby [11] and others [17]-[21].
In comparison to the number of theoreticalstudies, there are only a few experimental results
reported in the literature due to difficulties regarding
the scale rule for physical modelling [2]. The
centrifuge tests performed on surface strip footings
by Nakase [22] and Kimura and Takemura [23] areexamples of experimental studies undertaken on
footings over non-homogeneous clay. In addition,some centrifuge tests performed on skirted
foundations are reported by Tani and Craig [2],Prevost et al. [24] and more recently by Gourvenec
and O'loughlin [25].
Modified bearing capacity factor defined asq(Nc = u ) is used by most researchers to present the

METHODOLOGY

A parametric study was performed over a rangeof material properties and footing widths that are
typically encountered in the design of strip footings.
The study considers values of ρ between 0.1 to 3.0
kPa/m and B and c 0 varied between 1.0 to 150 m
and 10 to 60 kPa respectively. FELA is a finite
element method formulated from the limit theoremsof plasticity. The FELA formulation used in thisresearch is based on the method originally developedby Sloan [27] and [28] using linear programing
methods. The formulations have been further
developed later, applying non-linear programing as
was described by Lyamin et al. [29] and [30]. The
current formulation also benefits from adaptive remeshing technique of Lyamin et al. [31] and [32].
Soil was modelled as an associated MohrCoulomb material. A gradient and a reference pointin the geometry was introduced to the software in
addition to the absolute value of the undrained shear
strength of the clay to model the increasing shear
strength of the clay with depth. Adaptive re-meshingtechnique allowed tighter upper and lower bounds ofthe true solutions. The target solution accuracy,
number of adaptive re-meshing and the starting
number of elements were set so to obtain bounds
around 1% of the exact solution by Davis andbooker [1]. The results of maximum power
dissipation and direction of velocity vectors wereused to capture the failure mechanism of the
problem. The average of upper and lower limit loads
was used to calculate the ultimate bearing capacityof the footing following Salgado et al. [33] and thebearing capacity factor Nc was calculated using the
average qu . Finally expressions were found for Nc as
a function of ρB⁄c0 using curve fitting, which wellpresents the results of the analysis for all practicalvalues of ρB⁄c0 .

c0results of the analysis which is only a function of
ρB⁄c0 . The variation of Nc is either plotted versus
ρB⁄c0 in a graph or is given in a table for limited
values of ρB⁄c0 . It is difficult to accurately interpret
Nc from these graphs, especially in the lower rangeof ρB⁄c0 and the values given in the tables are forlimited number of ρB⁄c0 . Presenting expressions
that can well approximate Nc can be helpful forengineers. Bransby [26] proposed the following
expression for rough strip footings using the resultsof Davis and Booker [1], Tani and Craig [2] andHoulsby and Wroth [15].
ρB 0.662

Nc = (2+π)+1.646 � �
c0(2)

However this expression is not a safe estimate ofbearing capacity as it over-estimates the resultspresented by abovementioned researchers [11] .
Finite Element limit Analysis (FELA) is used in
the present study to investigate the bearing capacityof a rough strip footing sitting on the surface of aclay soil with increasing undrained shear strength
profile. The results of the analysis provided very

RESULTS AND DISCUSSIONS

The results of this study are presented in terms of
the bearing capacity factor Nc that can be used302

GEOMATE- Osaka, Nov. 16-18, 2015

14
Salencon (1974)
Alshamrani (2005)

13

Reddy (1981)
Yun (2007)12

Yuang (2010)
This study (Upper Bound)

Bearing capacity factor

11

This study (Lower Bound)
10

9

8

7

6

5
0

1

2

3
ρB∕c0

4

5

6

Fig. 2 Comparison of FELA results to upper bounds of others.

calculations provide very close results to these upperbounds (maximum 2% apart).
The average of the computed upper and lower
bounds of Nc are plotted in Fig.3 and Fig. 4 together
with the best fits for each case. The following
expressions shown by Eq. (2) and Eq. (3) wereobtained using curve fitting technique which is very
accurate and simple to be used in design.

together with conventional bearing capacity theory.
Nc is presented as a function of ρB⁄c0 in the graphsof Fig. 3 and Fig. 4. The range of ρB⁄c0 less than 2
presented in Fig. 3 is more appropriate for strip
footings while the range of ρB⁄c0 between 2 and 30
shown in Fig. 4 may be used for large rectangularfootings in offshore practice. Figure 2 indicates theupper and lower bounds of Nc obtained in this study
together with the upper bound solutions of [7], [8],[10], [11] and [14]. The range of ρB⁄c0 shown in Fig.
2 is based on available data in the literature.
It can be seen that the upper bound of Nc is thelowest of all other available upper bound solutionsin the literature and are close to those obtained frommethod of characteristics provided by [1], [2], [15]and [16] that are presented in Table 1. The bearing
capacity factors calculated by Reddy [8] andAlshamrani [10] are over predicted significantly as(ρB⁄c0 ) increases. The accuracy of the upper boundsolutions depends on the assumed failure mechanism
which will be discussed later. The lower boundNc = -4.29 e�-0.41ρB⁄c0� +9.46

Nc = 1.36( ρB⁄c0 )

0.73

+5.37

ρB⁄c0 <2ρB⁄c0 ≥2

(3)
(4)

These expressions provide the almost exact value ofthe modified bearing capacity factors with excellent
accuracy that can conveniently be used by engineers.
Using separate expressions of Nc for cases with
ρB⁄c0 greater than and less than two increased theaccuracy of curve fitting. More over for widths ofstrip footings more usually encountered inengineering practice, ρB⁄c0 will be typically lessthan two. In the graphs of Fig. 3 and Fig. 4 the303

GEOMATE- Osaka, Nov. 16-18, 2015

8

22.0

FELA (Average)
Bransby (2001)
Equation 3

7.5
7FELA (Average)
Bransby (2001)
Equation 4

19.0
16.0

Nc 6.5

Nc

13.0

6
10.0

5.5

7.0

5
0

0.5

1

1.5

𝜌𝜌𝜌𝜌⁄𝑐𝑐0

2

2

FELA
Upper
bound

FELA
Average

0
0.5
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
10.0
15.0
20.0
30.0

5.12
5.92
6.53
7.55
9.07
10.34
11.51
12.59
15.05
17.31
21.46

5.16
6.02
6.66
7.64
9.18
10.44
11.61
12.69
15.21
17.44
21.63

5.14
5.97
6.60
7.60
9.13
10.39
11.56
12.64
15.13
17.38
21.55

14

18

22

26

30

𝜌𝜌𝜌𝜌⁄𝑐𝑐0

Table 1 Bearing capacity factors (N c ).
FELA
Lower
bound

10

Fig. 4 Bearing capacity factor (N c ) for ρB⁄c0 ≥2.

Fig. 3 Bearing Capacity Factor (N c ) for ρB⁄c0 <2.
Nc

6

Houlsbyand
Wroth(1984)
5.14
7.57
9.08
10.37
11.52
12.67
-

bearing capacity factor calculated from Eq. (1) by
Bransby [26] is also plotted for comparison. It isclear that Eq. (1) overestimates Nc in the range ofρB⁄c0 less than 2 and underestimates N c for ρB⁄c0
greater than 20.
The best solutions for bearing capacity of strip
footings over non-homogenous clays have beenobtained using method of characteristic and recently
by some finite element models. Some of the mostaccurate results published so far are presented inTable 1, together with lower bound, upper boundand the average values of Nc calculate in this study.
The average values compare very well with theexact solution provided by Davis and Booker [1] andare in excellent agreement with the results presented
by Houlsby and Wroth [15] and those obtained using
the bearing capacity software written by Martin [16].
The limited cases presented by Gourvenec et.al [21]are very close to the average of FELA and the

Tani
et.al.
(1995)
5.14
6.84
7.75
9.23
10.49
12.73
21.69

ABC
SoftwareMartin(2003)
5.14
5.97
6.61
7.60
8.41
9.13
9.79
10.42
11.01
11.58
12.67
15.14
17.40
21.58

Gourvenec
andRandolph(2003)
5.20
6.70
7.71
8.55
10.62
12.95
-

Gourvenec
et.al(2011)
5.16
9.82
17.46
-

results by the above mentioned researchers, while
calculated Nc by Tani et al. [2] and Gourvenec et.al18] slightly over estimates the true bearing capacity
factors.
Failure mechanism
The results of maximum power dissipation fromthe upper bound analysis indicate the failure
mechanism of the footing, providing the lowest
upper bound to the ultimate load on the footing. The
influence of the degree of non-homogeneity on themode of failure has been studied here and acomparison is made between the obtained failure
mechanisms from this study and the ones proposedby Yun et.al. [11]. Figure 5a indicates the failure
mechanism for ρB⁄c0 = 2. It can be seen that the
failure mechanism is limited to much shallower
depth compared to Prandtl failure mechanism [34].

304GEOMATE- Osaka, Nov. 16-18, 2015

estimate with high accuracy the ultimate bearing
capacity of strip footings over clays with increasing
shear strength in depth. A comparison between the
failure mechanisms observed in this study with theassumed failure mechanisms by Yun et al. [11]indicated that better agreement exists for lower
degrees of non-homogeneity ( ρB⁄c0 =2) and thegeneral shape of the failure mode remains the sameas ρB⁄c0 increases but smaller rigid block is seen forhigher values of ρB⁄c0 .

The optimum failure mechanism presented by Yun
et al. [11] is also plotted in this figure and these arerepresentative of the collapse mechanisms that canbe interpreted from the bands of high power
dissipation intensity. Figure 5b shows observed
failure mechanism for a higher degree of nonhomogeneity. It can be seen from both the results of
Yun et al. [11] and from the FELA analysis that asρB⁄c0 , increases the failure mechanism becomesshallower to the regions of the soil profile where thesoil is weaker. However, the proposed failure
mechanism by Yun et al. [11] does not quite capture
the failure mechanism for the case of ρB⁄c0 equal to
5 in the area directly beneath the footing as it omits
the wedge directly beneath the footing. The resultsof upper bound analysis indicated that the failure
mechanism for very high degrees of nonhomogeneity (ρB⁄c0 of 20) follows the same pattern
as shown in Fig. 5b, however the triangular rigid
block under the footing becomes much smaller.

ACKNOWLEDGMENT

The authors wish to express their gratitude toProfessor Andrei Lyamin for providing andmaintaining the Finite Element Limit Analysis
software and his guidance and support in its use.
REFERENCES[1] Davis, E. H. & Booker, J. R. The effect ofincreasing strength with depth on the bearing
capacity of clays, Géotechnique, 23, 1973, pp.
551-563.
[2] Tani, K. & Craig, W. H. Bearing capacity ofcircular foundations on soft clay of strength
increasing with depth, Soils and Foundations, 35,
1995, pp. 21-35.
[3] Terzaghi, K. & Peck, R. B. Soil mechanics inengineering practice, New York, John Wiley andSons, 1948.
[4] Skempton, A. W. The Bearing Capacity of Clays,
Building Research Congress, 1951, pp. 180-189.
[5] Nakase, A. Bearing Capacity of Cohesive Soil
Stratum, port and Harbor Research, 1966.
[6] Raymond, G. P. The Bearing Capacity of large
footings
andEmbankmentsonclays,
Géotechnique, 17, 1967, pp. 1-10.
[7] Salençon, J. Discussion: The effect of increasing
strength with depth on the bearing capacity ofclays, Géotechnique, 24, 1974, pp. 449-451.
[8] Reddy, A. S. & Rao, K. N. V. Bearing capacityof stripfooting on anisotropic andnonhomogeneous clays, Soils and Foundations,21, 1981, pp. 1-6.
[9] Kusakabe, O., Suzuki, H. & Nakase, A. An
upper bound calculation on bearing capacity of acircular footing on a non-homogeneous clay,
Soils and foundations, 26, 1986, pp.143-148.
[10] Al-shamrani, M. A. Upper-bound solutions forbearing capacity of strip footings overanisotropic nonhomogeneous clays, Soils andFoundations, 45, 2005, pp. 109-124.
[11] Yun, G. & Bransby, M. F. The undrained
vertical bearing capacity of skirted foundations,
Soils and Foundations, 47, 2007, pp. 493-505.
[12] Huang, M., Qin, H. & Guo, Y. Upper boundsolutionforbearing
capacityofnonhomogeneousandanisotropic
clay

Fig. 4 Power dissipation intensity from upper boundanalysis showing failure mechanism of
Yun et al. [11] for (a) ρB⁄c0 =2 (b)ρB⁄c0 =5.
CONCLUSION

Rigorous upper and lower bounds on the ultimate
bearing capacity of strip footing over nonhomogenous clays were calculated using FELA
technique. The average of the limit loads were invery close agreement with the exact solutions ofDavis and Booker [1] and were used to derive
expression for the modified bearing capacity factor(N c ). These expressions can conveniently be used to305

GEOMATE- Osaka, Nov. 16-18, 2015

foundation,
Yanshilixue
Yu
Gongcheng
Xuebao/Chinese J. of Rock Mechanics andEngineering, 27, 2008, pp. 511-518.
[13] Qin, H. L., Huang, M. S., Wang, Y. J. & MA, S.
K. Calculations of Nc for anisotropic andnonhomogeneous soils, Yantu Gongcheng
Xuebao/Chinese J. of Geotechnical Engineering,32, 2010, pp. 1194-1199.
[14] Yang, F., Zhao, L. H. & Yang, J. S. Upper
bound ultimate bearing capacity of roughfootings on anisotropic and nonhomogeneous
clays, Yantu Lixue/Rock and Soil Mechanics, 31,
2010, pp. 2958-2966.
[15] Houlsby, G. T. & Wroth, C. P. Calculation ofStresses on Shallow Penetrometers and Footings,
Seabed Mechanics, Denness, B. ED. Springer
Netherlands, 1984, pp. 107-112.
[16] Martin, C. M. New software for rigorous
bearing capacity calculations, BGA Int. Conf. on
Foundations, Innovations, Observations, Design
and Practice, 2003, pp. 581-592.
[17] Griffiths, D. V. Computation of bearing
capacity on layered soils, Numerical methods ingeomechanics, Proc. 4th Int. conference,
Edmonton, Volume 1, 1982, pp. 163-170.
[18] Gourvenec, S. & Randolph, M. Effect ofstrength non-homogeneity on the shape of failureenvelopes for combined loading of strip andcircular foundations on clay, Geotechnique, 53,
2003, pp. 575-586.
[19] Zhao, S. F., Luan, M. T., Fan, Q. L., LU, A. Z.
& Yuan, F. F. FLAC-based numerical analyses
of bearing capacity and failure mechanism of
strip and circular footings on nonhomogeneous
foundations, Yantu Lixue/Rock and Soil
Mechanics, 27, 2006, pp. 1909-1914+1921.
[20] Bransby, M. F. & Yun, G. J. The undrained
capacity of skirted strip foundations under
combined loading, Geotechnique, 59, 2009,
pp.115-125.
[21] Gourvenec, S. M. & Mana, D. S. K. Undrained
vertical bearing capacity factors for shallow
foundations, Geotechnique Letters, 1, 2011,
pp.101-108.
[22] Nakase, A., Kusakabe, O. & Wong, S.-F.
Centrifuge model tests on bearing capacity ofclay, J. of Geotechnical Engineering, 110, 1984,
pp. 1749-1765.
[23] Kimura, T. & Takemura, J. A study on the
deformations and bearing capacities of normallyconsolidated clay layers under strip loading,
Doboku Gakkai Ronbunshu, 1987, pp. 157-166.
[24] Prevost, J. H., Cuny, B. & Scott, R. F. Offshore
gravity structures: centrifugal modelling, J. of thegeotechnical engineering division, 107, 1981, pp.
125-141.
[25] Gourvenec, S. & O'loughlin, C. D. Drum
centrifuge tests of shallow skirted foundations on

soft clay, Proc. of the 6th Int. Conf. on PhysicalModelling in Geotechnics, 2006, pp. 645-650.
[26] Fraser Bransby, M. Failure envelopes andplastic potentials for eccentrically loaded surface
footings on undrained soil, Int. J. for Numericaland Analytical Methods in Geomechanics, 25,2001, pp. 329-346.
[27] Sloan, S. W. Lower bound limit analysis using
finite elements and linear programming, Int. J.
for Numerical and Analytical Methods inGeomechanics, 12, 1988, pp. 61-77.
[28] Sloan, S. W. Upper bound limit analysis using
finite elements and linear programming, Int. J.
for Numerical & Analytical Methods inGeomechanics, 13, 1989, pp. 263-282.
[29] Lyamin, A. V. & Sloan, S. W. Lower boundlimit analysis using non-linear programming, Int.
J. for Numerical Methods in Engineering, 55,
2002, pp. 573-611.
[30] Lyamin, A. V. & Sloan, S. W. Upper boundlimit analysis using linear finite elements andnon-linear programming, Int. J. for Numericaland Analytical Methods in Geomechanics, 26,
2002, pp. 181-216.
[31] Lyamin, A. V., Krabbenhøft, K., Sloan, S. W.
& HJIAJ, M. An adaptive algorithm for upperbound limit analysis using discontinuous velocity
fields, ECCOMAS 2004 - European Congress on
Computational Methods in Applied Sciences andEngineering, 2004.
[32] Lyamin, A. V., Krabbenhoft, K. & Sloan, S.
W. Adaptive limit analysis using deviatoricfields, Proc. of the 6th Int. Conf. on Adaptive
Modeling and Simulation, ADMOS 2013, pp.
448-455. 2013.
[33] Salgado, R., Lyamin, A. V., Sloan, S. W. & Yu,
H. S. Two- and three-dimensional bearing
capacity of foundations in clay, Geotechnique,54, 2004, pp. 297-306.
[34] Prandtl, L. Uber die Harte plasticher KBrper,
Nachrichten von der KBniglichen Gesellschaft
der Wissenschaften, GBttingen, 1920.

306

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

DRAINAGE SYSTEM OF PRAMBANAN TEMPLE YARD USING NOFINE CONCRETE OF VOLCANIC ASH AND BANTAK MERAPI
Ahmad Rifa’i1, Nor Puji Lestari2 and Noriyuki Yasufuku3
Dept of Civil and Environmental Engineering, Faculty of Engineering, Universitas Gadjah Mada,
Indonesia; 3Dept of Civil and Structural Engineering, Kyushu University, Japan

1,2

ABSTRACTThis paper focuses on the potential utilization of volcanic ash and coarse volcanic slag for no-fine concretefor drainage structure of Prambanan temple yard. This material produced from volcanic eruption also becomes
environmental important issues as waste material if it is not effectively reduced or reused. Coarse volcanic slag
is commonly known by local people as Bantak. The experimental study was conducted to determine engineering
properties of no-fines concrete materials and discharge capacity of drainage system was calculated to identify the
optimal dimensions of the drainage channel. In addition, a numerical analysis was carried out using SAP2000
and Plaxis to control structure stability. Based on numerical and experimental study, the utilization of no-fineconcrete from volcanic ash and bantak by using certain mix design can be used as a porous drainage structure.
By utilizing the eco-friendly material as structural material, the originality of the building will not be disturbed.
Keywords: Volcanic Ash, Coarse Volcanic Slag, No-Fines Concrete, Drainage System, Prambanan Temple Yard

INTRODUCTION
The water flow in the soils has a significant
influence in any civil engineering project, especiallyrelated to drainage system. Reference [1] shows thepore space in the soils or rock is a very influential
factor in hydrogeological problems, because thewater or its movement occured in the pore space.
Water flow appearing in the soil is normally occured
because of the interconnection of pore spaces orcracks and this is the potential ability of soils/ rockto drain water.
Seepage control on geotechnical structures isneeded to lower the ground water level base surface
that is aimed to be protected or strengthened
regarding to prevent ponding water on the groundsurface. Ditch or channel is an effort to collect andcontrol water run off in the saturated soils condition.
The functions of porous media are both as a drainage
medium and a filter to separate suspension particles
from water, so the particles won’t be carried awaywith the water flow.
Refrence [2] shows that the use of clay andconcrete as impermeable media is a misconception
because the water still flows through the exposed
surface of the material, but the water does not seep
significantly due to the low permeability of materials.
No-fine concrete is one of concrete type that use alittle or even zero fine aggregate material, so it has
more pores than normal concrete. No-fine concrete
aggregate increase permeability and reduce theproportion of cement, but the compressive strength
value obtained is lower than normal concrete.
One of the alternatives to reduce post rain-puddle
in Prambanan temple yard is by application of nofine concrete as drainage structure regarding to the

As one of UNESCO World Heritage, Prambanan
Temple is the largest Hindu temple in Indonesia thatneeds to be preserved, maintained, and conserved.
Due to heavy equipment activities during renovation
period after Yogyakarta earthquake in May 2006,
soil in the temple yard has become dense, andponding often occurred during rainfall as ilustrated
in Figure 1. This condition shows that existingdrainage system is not sufficient and needs more
effort to organize and improve the drainage systems
in the First Yard of Prambanan Temple.

Fig. 1

Ponding area in Prambanan Temple Yard

307

GEOMATE- Osaka, Nov. 16-18, 2015

ability to drain of water, so the application of theconcrete will not lead reduction of the soil shear
strength due to long time inundation of land surface.
The aggregate developed in this study was the
Merapi eruption material. Merapi eruption material
is used because of the easiness to find thesematerials in large quantities along the river and theineffectiveness of utilization of this material. This
material is low quality of porous gravel (bantak) andvolcanic ash.
Coarse volcanic slag is commonly known by the
local people around Merapi volcano, Yogyakarta,
Indonesia as bantak. Bantak are the materials
product from the eruption of a volcano together withsand, which represent lightweight gravel. Bantak is agravel sized as a waste material of sand mining.
Based on the research conducted at the Department
of Civil and Environmental Engineering UGM,
structurally, bantak has low structural strength, so it
is not too familiar for building materials.
Indonesia is one of the most countries that has anumber of active volcanoes. Eruption often occurs
with high intensity. Spilled lava and volcanic
material out from core of the earth makes the areaaround the volcano has a thick layer of volcanic ash.
A lot of volcanic material will become importantenvironmental issues as a waste if it is noteffectively reduced or reused. Literatures relatedwith characterization and utilization of volcanic ash
and coarse volcanic slag in geotechnical field arelimited. It becomes a new approach in a view ofenvironmental aspect.
This study focuses on replacement of cement andcoarse aggregate with volcanic ash and bantak to
produce more economical and eco-friendly concrete
product. The results of this research are expected to
be able to contribute on engineering practice as wellas give advantages in geo-environment view. Study
and analysis on no-fines concrete as a drainage
structure are described and discussed.

in Figure 3. The used material size of volcanic ash is
the material which passed by sieve number 270.
Volcanic ash is a natural pozzolan which is requiredin chemical reaction in concrete mixture. The
mineral compositions of volcanic ash that used inthis research are given in Table 1. Volcanic ash
from Merapi has dissolved heavy metal content
below the threshold by KEP 02/MENKLH/I/1988
for a list of criteria for water quality class D (safewater for agricultural purposes and can be used forurban businesses, industry and hydropower).
Therefore, the volcanic ash was safely applied .

Fig. 2Site plan of Prambanan Temple Yard (1st,
2nd, and 3rd yard)

Table 1 Physical properties of volcanic ash
Mineral composition
Silica (SiO 2 )Alumina (Al 2 O 3 )Calcium Oxide (CaO)
Magnesium Oxide (MgO)
Iron Oxide (Fe 2 O 3 )Potash (K 2 O)
Natrium Oxide (Na 2 O)
LOI (Loss of Ignition)

MATERIAL AND RESEARCH METHODThis research is located in the first yard of
Prambanan Temple, which is the highest point of theseries of Prambanan Temple Yard as shown in
Figure 2. The land area is about 110 meters by 110
meters and a spacious courtyard of the temple of3747 m2. According to [3]. the stratification of thesoil in Prambanan temple yard is silty sand. Some
improvements have conducted in the first yard byaddition of a puddle layer on top of the original soils
and drainage system improvements. The drainage
system applied in the temple courtyard washorizontal and vertical recharge.
Research materials were disposed volcanic ash
and bantak from Mount Merapi. Volcanic ash
resulted from Merapi eruption in 2010, was takenfrom Cangkringan, Yogyakarta, Indonesia as shown

Fig. 3 Volcanic ash material

308

Value (%)
57.59
21.36
4.24
2.26
5.89
3.32
4.10
0.96

GEOMATE- Osaka, Nov. 16-18, 2015

𝑄𝑄𝑖𝑖 = 𝑓𝑓𝑓𝑓𝜓𝜓Δ𝜃𝜃
𝑓𝑓𝑖𝑖 (𝑡𝑡) = 𝑘𝑘 �
+ 1�𝐹𝐹𝑖𝑖 (𝑡𝑡)Table 2 Physical properties of coarse volcanic slag(bantak)
Value
1360.221507.61
2.49
2.56
4.61
4.40
54.75

Unit
kg/m3
kg/m3
%
%
%

𝐵𝐵′ =

−𝑓𝑓𝑓𝑓𝑓𝑓
𝑓𝑓𝑓𝑓𝑓𝑓𝑛𝑛𝑛𝑛 �𝑙𝑙𝑙𝑙 �1 −
��
𝑄𝑄

(4)

f is geometric factor which calculated by Eq. (5).
𝑓𝑓 =Coarse volcanic slag is used as an aggregate inthis experimental research. Figure 4 shows thecoarse volcanic slag that was supplied from thequarry in Boyong River, Yogyakarta, Indonesia. The
aggregates obtained from the quarry were screened
into 10-20 mm size fractions. Table 2 shows thephysical properties of coarse volcanic slag. In thisstudy percentages of volcanic ash which added into
the concrete mix design were 0% and 30%. Concrete
parameters would be used as a reference standard for
concrete such as unit weight, porosity, permeability
coefficient of no-fines concrete, and the uniaxial
compressive strength.
Rainfall analysis data was determined by using
HAVARA program. The analysis obtained thecharacteristics of rainfall that occurs at the location
including the duration and the intensity of rainfall
for a certain period. The hourly rainfall data fromfour rain gauges around the study area for a time
period from 1998 to 2012 were used. The peakdischarge of rainfall calculated by using Eq. (1).
𝑄𝑄𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 = 0,278𝐶𝐶𝐶𝐶𝐶𝐶(3)

Where Q i is infiltration capacity, f is infiltration
velocity, A is area, k is hydraulic conductivity, F i iscumulative amount of infiltrated water, ψ is suction,and θD is difference between porosity with initialsoil moisture.
Then, the result of rainfall analysis is used as
input to calculate the drainage capacity. At this stage
the analysis of the suitability of the rain discharge(Q l ) and discharge channel (Q f ) was done. If
obtained Q l ≥ Q f , then the redesign of trench or
channel should be done. However, if Q l ≤ Q f , it only
needs to take some improvement, one of the
improvement methods with proposed drainage
channel from bantak and volcanic ash. Reference [4]Dimension of the channel was determined by trial
and error using Eq. (4).

Fig. 4 Coarse volcanic slag (bantak)

Parameters
Loose unit weight
Compacted Unit weight
Specific gravity
Specific gravity in SSD
Water absorption
Water content
Abrasion(2)

4𝐻𝐻 + 4√𝑏𝑏𝑏𝑏𝑙𝑙𝑙𝑙2

𝐻𝐻 + 4√𝑏𝑏𝑏𝑏
𝐻𝐻 2𝑙𝑙𝑙𝑙 �+ ��
� + 1�
6√𝑏𝑏𝑏𝑏6√𝑏𝑏𝑏𝑏(5)

Where B is length of the channel, k ispermeability coefficient, T is rainfall duration time,n is porosity of material, b is channel width, H is
depth of the channel, and Q is discharge capacity.
According to [5] the seep discharge into the channel(Q f ) calculated by using Eq. (6).
𝑄𝑄𝑓𝑓 = 𝐴𝐴. 𝑉𝑉𝑥𝑥

(6)

If the concrete channel wall were added by nofine concrete, with Q 1 = Q f, can be obtained by theminimum hydraulic conductivity value of no-fineconcrete material that must be provided to the wallsof the channel can be calculated using Eq. (7).
𝑘𝑘𝑓𝑓 =

𝑄𝑄𝑓𝑓
sin 𝛼𝛼 . 𝑡𝑡𝑓𝑓

(7)

Analysis result obtained the optimum distance
between adjacent channels, water discharge that wasable to enter into the channel and the minimum
thickness of the channel. Based on [5] the optimum
distance between two channels calculated by using
Eq. (8). The representation of the subsurface flow
can be seen in Figure 5.

(1)

Where Q is peak rainfall discharge, C is rational
method coefficient, I is rainfall intensity and A isdrainage area. Meanwhile the natural infiltration
capacity calculated using Eq. (2) and Eq. (3).

309

GEOMATE- Osaka, Nov. 16-18, 2015

𝑆𝑆 = �

1
2

4𝑘𝑘ℎ𝑚𝑚𝑚𝑚𝑚𝑚
�ℎ𝑚𝑚𝑚𝑚𝑚𝑚 2 − ℎ𝑤𝑤 2 + 2𝑑𝑑ℎ𝑚𝑚𝑚𝑚𝑚𝑚 − 2𝑑𝑑ℎ𝑤𝑤 ��𝑖𝑖

Fig. 5

Table 3 Recapitulation of drainage capacity analysis(8)

Parameter
Vertical infiltration
Horizontal infiltration
Total runoff
Peak rainfall discharge
Natural infiltration capacity
Residual discharge
Total amount of discharge

Value
7.67×10-4
2.70×10-3
3.47×10-3
4.70×10-2
4.16×10-2
1.93×10-3
4.63×10-3

Unit
m3/s
m3/s
m3/s
m3/s
m3/s
m3/s
m3/s

Effect of Merapi eruption material to concrete
parameters such as porosity, compressive strength,
and modulus of elasticity of no-fine concrete can beseen in Table 4. Based on laboratory tests, the unit
weight of no-fine concrete (Bj) obtained at 2065
kg/m3, or in other words, no-fines concrete has
successfully produced according to SNI 03-28472002, the obtained value from the test can be
categorized as lightweight concrete, because of thevalue of Bj < 2200 kg/m3.
The relationship of concrete curing period for
concrete compressive strength is shown in Figure 6.
It shows that with the same mixture, the trend ofincreasing no-fine concrete strength will continue toincrease with increasing curing time, as well as to
the relationship of no-fine concrete strength againstcompaction energy applied. Based on Figure 7, thecompaction energy derived from the comminution
process greatly affects the strength of the concrete.
However with longer curing time, no-fine concrete
strength is relatively the same. The permeability
coefficient of no-fine concrete generated in thisstudy amounted to 1.33×10-3 m/sec.
From the Eq. (6), the seep discharge into thechannel is Q f = 1.49×10-5 m3/sec. If the channel wallof concrete walls were added with no-fine concrete,
the analogy, with Q 1 = Q f, can be obtained by theminimum hydraulic conductivity value of no-fineconcrete material that must be provided to the wallsof the channel at 1.49×10-4 m/s. The permeability
coefficient value of no-fines concrete applied tochannel wall is 1.33×10-3 m/s then the media will
fulfill the qualification as a drainage layer. However,this condition does not make this qualified as afiltration function, so it is still necessary to carriedsoil particles into the channel.

The representation of the subsurface flow
into the channel

The numerical analysis were carried out using
SAP2000 and Plaxis assuming the drainage structure
as a plain strain model in Plaxis and frame model in
SAP2000. Calculation analysis about the strength of
the structure channel is based on the result of thesimulation using Plaxis and SAP2000 programs.
RESULT AND DISCUSSION
Table 3 shows the drainage system applied to thefirst yard of Prambanan temple has not beeneffective enough to accommodate the flood
discharge that occurs on this area. There is residual
discharge resulted the ponding area in Prambanan
Temple yard. Therefore an improvement of drainage
system, either in the form of new canals or drainage
management arrangement on the drainage system, isneeded to solve this problem. Based on fieldobservations, horizontal recharges system is not
functioning properly. The number of puddle
distribution locations above the horizontal recharges
system indicates this. The total amount of discharge(Q) used in a cross-sectional dimension of planning
control in the channel is 4.63×10-3 m3/sec. Thisvalue represents the difference discharge of rain thatfell in the area of the temple courtyard to natural
infiltration capacity land and total discharge verticalinfiltration.
The value of total run off represents thedifference between the natural discharge of rain thatfell in the temple courtyard and the total discharge ofvertical recharge. Based on these considerations, thelength of channel is determined to be planned inaccordance to the existing total length of recharge
channels. By trial and error using Eq. (4), theacquired new channel dimension is 0.40 meters by
0.40 meters.

Table 4 Engineering properties test result of no-fineconcrete
Parameter
Volcanic ash percentage
PorosityCompressive strength (28 days)
Elastic modulus
Density
Permeability

310

Value
30
10.03
9.61
2480
2065
1.33×10-3

Unit
%
%
MPa
MPa
kg/m3
m/s

GEOMATE- Osaka, Nov. 16-18, 2015

loading of Condition 2 was applied (see Figure 9),then it was used as a parameter value to determine
the minimum thickness of channel structure. The
minimum thickness required of the channel is 0.05
m. Results of numerical analysis can be seen in
Table 5.

25

f'c (MPa)

20
15
10
5
0

0

10

20

30

40

50

60

Curing time (days)
No-fines concrete (30% VA)
No-fines concrete (0% VA)

Fig. 6

Correlationofcuringcompressive strength

time

with

16

Strength (MPa)

14
12
10
8
6

with compaction [6]4without compaction

2

Fig. 8The proposed drainage system

Fig. 9

Scheme of loading models on the groundsurface

0
0

10

20

30

40

50

60

Time (days)

Fig. 7

Influence of the concrete compaction toincreased strength

Further evaluation of reviews, these calculations
were added to determine the channel spacing of
subsurface drain. The optimum distance between
adjacent channels is 7.50 m. To anticipate thedrainage system damage due to structural failure, itwould require some evaluation of loading models.
Numerical analysis were performed to determine theeffectiveness of Merapi eruption material fordrainage channel. The proposed typical channel
model proposed can be seen in Figure 8.
Representation of working load location on the
ground surface illustrated in Figure 9. In thisanalysis, simulations were assumed on the locationof the ground level water table (the most criticalcondition).
Figure 10 shows a comparison of the bending
moments value occured in the channel structure dueto the influence of forklift movement andgroundwater pressure from SAP2000 and Plaxis
simulation. The calculation results for the thicknessof channel structure are shown in Table 5. It presents
that the maximum channel structure moments when

(a)

(b)

Fig. 10 Moment of channel structure simulate in (a)Plaxis and (b) SAP2000
311

GEOMATE- Osaka, Nov. 16-18, 2015

Table 5 Results of numerical analysis

Load

Displacement
(m)

1
2
3

1.63×10-3
6.79×10-4
6.63×10-3

M max(kN.m)
SAP2000 Plaxis
0.28
0.56
0.54

0.30
0.56
0.44

Min.
thickness
(m)
0.03
0.05
0.04

From the hydrological analysis and evaluation of
the channel structure calculations, the typicalchannel model that can be applied to the first yard of
Prambanan Temple is subsurface channels with theaddition of filter media in the form of non-woven
geotextile to avoid clogging by fine soil
particles. Theoritically, the silty sand soil in
Prambanan Temple yard has a low-capability ofdraining water (small permeability value). To avoid
the impairment of soil hydraulic conductivity (k)caused by the visitor activities and the heavyequipment movements, layers formed of gravel andsand should be added on the top of slab channel tomake the water completely seep into the channel.

Based on numerical analysis and experimental
study, the utilization of no-fine concrete fromvolcanic ash and bantak by using certain mix design
can be used as a porous drainage structure. This canbe seen from the strength of no-fine concrete basedon numerical modelling and the ability to drain thewater. The proposed drainage structure is very ecofriendly because the use of waste material is surely
compatible to be used as structural improvement forhistorical site such as Prambanan Temple. Byutilizing the eco-friendly material as structural
material, the originality of the building will not be
disturbed.
ACKNOWLEDGEMENTS
The authors express their gratitude to Directorate
General Higher Education (DGHE) Ministry of
Education and Culture, Indonesia and Universitas
Gadjah Mada for financial supporting byCompetitive Grant of International Collaboration
Research and Publication with contract no. LPPMUGM: 304/LPPM/2015, dated March 2, 2015. The
authors also would like to thanks to colleagues ofDepartment of Civil and Environmental Engineering,
UGM and Preservation Center of Cultural Heritage,
Prambanan Unit for their helpful and fruitful
discussion and spirit in conducting field andlaboratory tests.

CONCLUSION
Drainage system of Prambanan Temple yard
using no-fine concrete as a porus media withvolcanic ash and coarse volcanic slag are presented.
The utilization of volcanic material is purposed toreduce cement need on drainage construction, whichleads to global environmental problem. The
experimental study was conducted to determine theoptimum mix design and the engineering propertiesof the material. The discharge capacity of drainage
system also was calculated to identify the optimal
distance of two ditches. In addition, numericalanalysis were carried out using SAP2000 and Plaxis
to control structure stability.
The value of natural soil infiltration capacity at
first yard of Prambanan Temple is of 4.16×10-2
m3/sec, whereas maximum rainfalls discharge of
4.7×10-2 m3/sec. This condition shows that existingdrainage system was not sufficient and still need
improvement using porous media of no-fines
concrete. The optimum distance between adjacentchannels is 7.50 m with channel dimensions of 0.4 m
by 0.4 m at depth of 1.00 m as typical proposed
channel model. The value of permeability of no-fineconcrete using volcanic ash and bantak is 1.33×10-3
cm/sec. Meanwhile, the seep discharge into thechannel has smaller value than permeability ofconcrete that is 1.49×10-4 cm/sec. According to thisstatement, the proposed drainage system of volcanic
ash and bantak shows that no-fines concrete material
has fulfilled the qualification as a material fordrainage layer.

REFFERENCES[1] Bawono, Y.Y., “Analisis dua dimensi rembesan
air pada penggalian terowongan dengan
menggunakan metode elemen hingga”, Tesis,
Yogyakarta: Program Pascasarjana Fakultas
Teknik Universitas Gadjah Mada, 2007.
[2] Terzaghi, K. & Peck, R.B., Soil Mechanics inEngineering Practice. 3rd ed, New York, John
Wiley & Sons, 1996.
[3] Rifa'i, A., “Sifat Teknis Tanah dan Struktur
Fondasi Candi Wisnu Dalam Tinjauan
Geoteknik”, PIT-XIII HATTI Developement of
Geotechnical Engineering in Civil Construction
2009.
[4] Sunjoto, S., “The Recharge Trench as A
Sustainable”, Journal of Environmental
Hydrology, The Electronic Journal of theInternational Association for Environmental
Hydrology On the World Wide Web at
http://www.hydroweb.com, Vol. 16, 2008, pp.111.
[5] Reddi, L.N., “Seepage in soil: principles andapplications”, John Wiley & Sons, 2003.
[6] Abbas, I., “Pemanfaatan abu vulkanik sebagai
bahan geostruktur pada perlindungan tebing”,
Tesis, Yogyakarta: Program Pascasarjana
Fakultas Teknik Universitas Gadjah Mada, 2014.

312Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

MODELING OF EVAPORATION PROCESSES UNDER THE GROUND

1

B. Liu1, A. Kobayashi1, Y. Tsukada and M. Chijimatsu2
Department of Civil and Environmental Engineering, Kasai University, Japan; 2Hazama Ando corp., Japan

ABSTRACTThe vaporization process is modeled by incorporating the relation between pressure and enthalpy into thenumerical simulation code for the coupled thermal, hydraulic and mechanical phenomena under the ground. The
boundary between superheated steam and two-phase of steam and water was modeled by the previous study. On
the other hand, the boundary between compressed water and two-phase of steam and liquid is modeled by the
deduced equation with pressure and enthalpy in this study. Temperature is selected to be the unknown variable inthe model. The enthalpy is estimated from the pressure and temperature, and then the situation of phase is examined.
As an example, the situation about the disposal of the waste generating heat to 300˚C is simulated by the simple
2-D and detail 1-D models.
Keywords: Groundwater, Evaporation, Coupled analysis, Nuclear Waste Disposal

INTRODUCTION
MODEL OF PHASE CHANGE
In Japanese nuclear energy policy, the high level
radioactive waste from the nuclear fuel cycle isplanned to be disposed in deep geology. The vitrified
waste is stored for fifty years for cooling and thenthose are stored in overpack and disposed. This isbecause the stable and safe disposal is necessary. The
plan, therefore, needs long time for disposal.
However, since the accident at the nuclear plant
Fukusima Daiichi, the disposal of nuclear waste
requires urgent attention. If the nuclear fuel rod is
directly disposed in the deep geology, the volume ofwaste may become very large and the heat generationfrom the waste is also very high. In this case, theeffect of the high heat generation from the waste has
to be examined and then repository has to be designed
as most safe and stable as possible.
In this study, the vaporization process is modeledby incorporating the relation between pressure andenthalpy into the numerical simulation code for the
coupled thermal, hydraulic and mechanicalphenomena under the ground. The boundary between
superheated steam and two-phase of steam and liquid
was modeled by the previous study. On the other hand,the boundary between compressed water and twophase of steam and water is modeled by the deduced
equation with pressure and enthalpy in this study. In
general, temperature is the unknown variable for the
coupled problem and the boundary condition is given
by temperature. Therefore, temperature is selected tobe the unknown variable in the model. The enthalpy
is estimated from the pressure and temperature, andthen the situation of phase is examined. As an
example, the situation about the disposal of the waste
generating heat to 300˚C is simulated by the simple
2-D and detail 1-D model.

Pressure-enthalpy Diagram
Mercer and Faust [1] and White at al. [2] gave thediagram of phase change presented by pressure andenthalpy as shown in Fig.1. By using this diagram,the phase of fluid can be recognized. The bold covex
line in the graph indicates the boundary of phases, in
1 and ○
2 is thewhich the boundary between area ○phase change between liquid and steam-liquid two
2 and ○
3 is the one between
phase, and that between ○steam-liquid two phase and superheated steam.
Although the diagram is the plane coordinate withthe vertical axis of pressure and horizontal one ofenthalpy, the isothermal line can be seen in the
diagram. The phase change is originally explained bythree dimensional diagram adding temperature asshown in Fig.2.

Fig.1 Pressure-enthalpy diagram for water and steamwith thermodynamic regions [1], [2]313GEOMATE- Osaka, Nov. 16-18, 2015

𝝍𝟐 (𝒑, 𝒉) = −𝟑𝟕𝟒. 𝟔𝟕 + 𝟒𝟕. 𝟗𝟗𝟐𝒑 − 𝟎. 𝟔𝟑𝟑𝟔𝟏𝐩𝟐+ 𝟕. 𝟑𝟗𝟑𝟗 × 𝟏𝟎−𝟓 𝒉𝟐
𝟑. 𝟑𝟑𝟕𝟐 × 𝟏𝟎𝟔 𝟐 𝟎. 𝟎𝟑𝟓𝟕𝟏𝟓−
𝒉 +𝒑𝟐 𝒉 𝟐𝐩𝟑−𝟗 𝟑− 𝟏. 𝟏𝟕𝟐𝟓 × 𝟏𝟎 𝒉 𝒑
𝟐. 𝟐𝟔𝟖𝟔 × 𝟏𝟎𝟏𝟓−𝒉𝟒
(7)
As the boundary between liquid and two phase region
was not given by Huyakorn and Pinder, the following
equation is reduced in this study;
𝒍𝟓𝒍𝟒

𝒑𝒍 = −𝟓 × 𝟏𝟎−𝟏𝟏 𝒉∗ + 𝟒 × 𝟏𝟎−𝟖 𝒉∗ − 𝟓 ×
𝒍𝟑

𝒍𝟐

𝒍

⁡𝟏𝟎−𝟔 𝒉∗ + 𝟎. 𝟎𝟎𝟎𝟑𝒉∗ + 𝟎. 𝟎𝟎𝟐𝟖𝒉∗ −𝟎. 𝟏𝟒𝟐𝟐
Fig.2 Pressure-enthalpy-temperature diagram forwater and steam phases(8)Fig. 3 shows the fitting of Eqs. (1) and (8) to the steam
diagram, from which it is understood that Eqs. (1) and(7) give the relatively good approximation.
In this study, the phase of fluid is examined byusing the above relations.

Equation of Boundaries
2 and ○
3 in
The equation of boundary between ○Fig.1 is given by Huyakorn and Pinder [3] as

10000

𝑔

1000

(1)
Pressure (kg/cm2)ℎ∗ = 2822.82 − 39.952⁄𝑝 + 2.5434⁄𝑝2 −0.93888𝑝2

where hg* is the enthalpy (kJ/kg) at the boundary
between two phase region and superheated steam, andp is the pressure (dyne/cm2). Temperature in the
1 at which enthalpy h is smaller than theregion ○enthalpy at the boundary between water and twophase region hl* is given by
𝑇 = 𝜓1 (𝑝, ℎ) + [𝜓0 (𝑝) − 𝜓1 (𝑝, ℎ∗𝑙 )]

100

10

1

Eq. (1)
Eq. (8)

0.1

(2)
0.01
0

where

100

200

300

400

500

600

700

Enthalpy (cal/g)

Fig. 3 Approximation relations and those fitting result

𝜓0 (𝑝) = 4667.075/(12.599 − ln⁡(10𝑝)⁡) − 273.15(3)

GOVERNING EQUATIONS

𝝍𝟏 (𝒑, 𝒉) = −𝟐𝟖. 𝟏𝟓𝟐 − 𝟎. 𝟏𝟑𝟕𝟒𝟔𝒑 + 𝟎. 𝟑𝟎𝟏𝟏𝟐𝒉
𝟑𝟓𝟑𝟔. 𝟒+− 𝟒. 𝟑𝟏𝟗𝟐 × 𝟏𝟎−𝟓 𝒉𝟐𝒉(4)2 where enthalpy h isTemperature in the region ○between hl* and hg* is given as
𝑻 = 𝝍𝟎 (𝒑)Mass Conservation Law
The governing equation of seepage is given as

 w

(5)

𝒈

(9)

where  is the volumetric moisture content, is thematric potential, P is the total head, w is the densityof water, kij is the hydraulic conductivity, and t is time.
The density of water l (gm/cm3) is given as afunction of pressure p and enthalpy h by Marcer and
Faust,

3 where enthalpy h isTemperature in the region ○larger than hg* is obtained as

𝑻 = 𝝍𝟐 (𝒑, 𝒉) + [𝝍𝟎 (𝒑) − 𝝍𝟐 (𝒑, 𝒉∗ )] P  w k ij P, j , j  0
 t

(6)

in which
314

GEOMATE- Osaka, Nov. 16-18, 2015

0.98988  4.0089  10 4 p  4.0049  10 5 h 

7
7 2
 2.6661 / h  5.4628  10 ph  1.2996  10 hl  
for h  2001
for h  200 kll kgg 
k ij  k ijl  k ijg   r l  r g  gK ij  

where Kij is the intrinsic permeability of the material.
By applying the van Genuchten relation to themoisture capacity of the relation between  and , Eq.
(9) can be solved by considering the situation of phasechange of fluid.

(10)
The density of superheated steam g is given as g  2.2616  10 5  0.043844 p  1.7909  10 5 ph 3.6928  10  8 p 4  5.1764  10 13 ph3

Energy Conservation Law(11)
The saturation degree of water Sl can be obtained

The equation describing energy transport is written
as

by
for

C  T  S  c v T    T   L dS   Q  0h  hlgmfor h  h

g


t

l

l

(14)

On the other hand, the viscosity of water l andthat of steam g are obtained as a function oftemperature T by

Thermal conductivity (W/mK)krl  S l 

2

2

(16)

By using above parameters, the hydraulicconductivity tensor of water klij and that of steam kgij
is obtained bykijl 

g

g
m

g

g

g

s

s

l
m

(20)

s
m

(21)

0.8

7000

0.7

6000

0.6

5000

0.5
4000

0.4
3000
0.3
2000

0.2

1000

0.1

0

0
0

k  gKijlrl

E is the energy of the element and the second term isthe pressure per unit weight (J/g or kJ/kg). The
enthalpy and pressure is used to judge the phasesituation.

(15)

in which the unit of the viscosity is g/(cm·s).
The relative permeability of water klr and that ofsteam kgr are given as a function of saturation degreeof water and steam as followings;

2lh  E  p / w

 l  10 6  239 .4  10 248.37/ T 113

krg  S g   1  S l ,iin which Cl is the specific heat of water and Cg isthat of steam. λlm is the thermal conductivity of waterand λgm is that of steam. Those are the function oftemperature as shown in Fig. 4. Moreover, λsm is thethermal conductivity of solid and n is the porosity.
L is the latent heat and dSg is the change insaturation degree of steam. By solving Eq. (9) and(19), total head P and temperature T are obtained.
Then, the enthalpy is calculated by

The density of waterw at the two phase of liquidand steam is given by g  0.407 T  80 .4  10 6

h

w

m

(13)

 w   g S g   l S l   g 1  S l    l S l, j ,iC   nS  C  S  C   1  n  C
  nS   S    1  n m

S  1 S

m ij

where

(12)
The saturation degree of steam S is given by
l

w w i

(19)

for hl  h  hg

g

g

r

l


k  g gKij
(17)
kijg 
g
The hydraulic conductivity in Eq. (9) is given bySpecific heat (J/kg℃）


1


l
S  0 g h*g  h 

l
g
l
l
g
g

 h     h*   h*  

(18)

50

100

150

200

250

300

350

Temperature ℃

Thermal Conductivity of water

Thermal conductivity of steam

Specific heat of waterSpecific heat of steam

Fig.4 Nonlinear relation of thermal conductivity andspecific heat to temperature
SIMULATION EXAMPLESlgr

2-D Model
315

GEOMATE- Osaka, Nov. 16-18, 2015neighboring waste and rock and that in the rockneighboring buffer material when temperature at thewaste reached 300 ˚ C. The enthalpy in the buffermaterial neighboring waste is a little smaller than hl*
and still evaporation does not occur. It is found thatthe enthalpy becomes small with the distance fromthe waste. Fig. 9 shows the temporal change ofrelation between pressure and enthalpy in the buffermaterial neighboring waste. It is found that thepressure and enthalpy becomes gradually large andparticularly the pressure raises to about 100 kg/cm 2by the thermal expansion of water. As the pressure
becomes high, evaporation does not occur even if
temperature is about 300˚C. It is, however, difficult
to estimate the situation in the buffer material because
the coarse mesh as shown in Fig. 7. Thus, the 1-D
model is considered to estimate the detail situation in
the buffer material in the following section.

Model explanation
Fig. 5 shows the 2-D model of the situation
around the waste in a repository. The horizontal
length of the region is 36 m, the height is 29 m, waste
is 1x1 m, tunnel with buffer material is 5x5 m and the
distance between wastes is 14 m.
The thermal initial condition is 15˚C and hydraulicone is 300 m for all region. As a boundary condition,
normal deformation is fixed, total head is 300 m andtemperature is 15˚C for all boundaries.
Fig. 6 shows the heat generation history at thewaste. Temperature increases until 300˚C by 1˚C /10days. Table 1 shows the parameters used in theanalysis.
Analysis results
Fig.7 shows the temperature distribution whentemperature at waste becomes 300˚C. The rock

Table 1 Parameters used in simulation
Parameter
E (MPa)
ν
K (m)
VG
θs
θr
α
n
λf(J/(kg˚C))
s(J/(kg˚C))
Cf(W/(m˚C))
Cs(W/(m˚C))
e

P=300 m, T=15℃, normal fixed for all
boundaries

Rock

Waste

Buffer material
8

2 12

10

5

Waste
2.00×105
0.30
1.0x10-30
0.0001
0.0
8.0x10-3
1.60
53.00
460
0.0001

Buffer
3.44×101
0.30
4.0x10-20
0.403
0.0
8.0x10-3
1.60
0.60
1.95
4180
341
0.675

Rock
1.82×1030.21
1.3x10-15
0.311
0.0
3.7x10-1
1.13
0.60
108
0.451

8

36

Fig. 5 Schematic view of 2-D model (unit: m)

Fig. 7 Temperature distribution in the rock whentemperature at waste becomes 300˚C.
Fig.6 History of heat generation at the waste
between tunnels has temperature of over 120˚C. Fig.
8 shows the situation in the buffer material
316

GEOMATE- Osaka, Nov. 16-18, 2015

10000

Pressure (kg/cm2)

1000
100
10
●waste side in buffer▲rock side in buffer
●rock

1

0.1
0.01
0

100 200 300 400 500 600 700
Enthalpy (cal/gm)

Fig. 8 Situation of phase at the different locations
when temperature at waste becomes 300˚C
Fig. 10 Schematic view of the detail 1-D model
The prescribed temperature of 15˚C, no deformation
for mechanical condition and prescribed total head of
300 m. The other boundaries have no flow for thermaland hydraulic conditions and normal deformation
fixed condition.
Analysis results
Figs. 11-13 shows the histories of relation between
pressure and enthalpy at the element neighboring thewaste in the buffer material, the one at the center ofthe buffer material and the one neighboring rock inthe buffer material. It is found that the buffer material
neighboring the waste and rock enter into the twophase region as shown in Figs. 11 and 13. On theother hand, the part at the center of the buffer material
is not at two phase region as shown in Fig. 12. Thismeans that evaporation occurs at the both outer sides
in the buffer material and evaporation does not occur
at the inner parts in the buffer material.
Fig. 14 shows the distribution of pressure in thebuffer material. It is found that the pressure has thelargest value at the inner parts of the buffer material.
The reason why evaporation does not occur at the
inner parts is because the pressure of fluid at the innerparts becomes higher than that at the outer parts.
Since temperature distributes mostly homogeneously
in the buffer material, the high pressure may becaused from the result of fluid movement in the buffermaterial. This may be temporal situation and thesituation of phase may change with time bydepending on the boundary conditions and material
properties. The verification of temporal situation
change and validation of model are the remained
tasks.
Moreover, the pressure becomes high at theboundary of liquid and two phase situation as shown

Fig.9 History of relation between pressure andenthalpy at the buffer material neighboring waste
1-D Detail Model
Model description
As mentioned above, the buffer material was
made with two elements from waste side to rock massside in 2-D model, the detail behavior in the buffermaterial is difficult to discuss. Fig. 10 shows theschematic view of the 1-D model, which considers theprevious concept of disposal with canister containing
the waste package. The analysis region extends fromthe center of waste to the rock mass with the constantsection of 1 m2 and length of 25 m. The buffermaterial of which length is 0.72 m consists of 36
elements. The boundary conditions at the edge face ofwaste are the prescribed temperature as same as Fig.
6, no deformation for mechanical condition and noflow for hydraulic one. Temperature at the nodes in
the waste region raises as shown in Fig. 6. The
boundary conditions at the edge face of rock mass are317

GEOMATE- Osaka, Nov. 16-18, 2015

14

10000

12
10

1000

8

Pressure (MPa)

Pressure (kg/cm2)

6

100

4

2
0

10

-2
-4

1

Buffer Material

-6
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

Horizontal distance from center of waste (m)Fig. 14 Pressure distribution in the buffer material
when temperature of the waste is 300˚C

0.1

0.01
0

100

200

300

400

500

600

700

in Figs. 12 and 13. The isothermal lines exit verticallyat liquid region and become horizontally in two phaseregion as shown in Fig. 2. Therefore, the increase intemperature causes the increase in pressure close to
the boundary between liquid and two phase region.

Enthalpy (cal/gm)

Fig. 11 History of relation between pressure andenthalpy at the element neighboring the waste in thebuffer material
10000

CONCLUSIONS

Pressure (kg/cm2)

1000

The disposal project of high level radioactive waste
is very important for the countries having the nuclear
plants. If the waste generates high heat, the effect of
evaporation in the ground has to be considered forsafe and stable disposal. The model to assess theeffect of the evaporation of groundwater is developedin this study. The obtained conclusions arefollowings:
1) Evaporation process is dependent on temperatureand pressure of fluid. It is found that the developedmodel has an ability to predict the complicatedchange in phase in the buffer material around thewaste.
2) When temperature becomes high and the hydraulicconductivity is low, the fluid pressure may become
very higher because of the thermal expansion of fluidand difficulty of movement. Such a situation may
prevent evaporation according to the circumstances.
3) While the results obtained in this study is very
important, the verification of the phenomena andvalidation of the model are the remained subject.

100

10

1

0.1

0.01
0

100

200

300

400

500

600

700

Enthalpy (cal/gm)

Fig. 12 History of relation between pressure andenthalpy at the element existing at the center of thebuffer material
10000

Pressure (kg/cm2)

1000

100

REFERENCES
10[1] Mercer, J. M. and Faust, C. R. : "Simulation ofwater- and vapour-dominated hypothermal
reservoirs", Paper SPE 5520, presented at the50th Annual Fall Meeting of the Soc. Pet. Eng.
AIME, Dallas, Texas, 1975.
[2] White, D. E., Muffler, L. J. P., and Trusdell, A.
H. : "Vapor-dominated hydrothermal systems
compared with hot-water systems", ECON.
Geol. 66, 1971, pp.75-97.
[3] Huyakorn, P. S. and Pinder, G. F.:“Computational method in subsurface flow”,
ACADEMIC PRESS, 1983.

1

0.1

0.01
0

100

200

300

400

500

600

700

Enthalpy (cal/gm)

Fig. 13 History of relation between pressure andenthalpy at the element neighboring rock in the buffermaterial

318

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

A COMPARISON OF THE BASE RESISTANCE OF OPEN-ENDEDPILES EVALUATED BY DIFFERENT DESIGN METHODS
Janaka J. Kumara, Yoshiaki Kikuchi and Takashi Kurashina
Department of Civil Engineering, Tokyo University of Science, Japan

ABSTRACT
Bearing capacity of an open-ended pile depends largely on the accuracy of the design method. Although theknowledge of shaft resistance is understood well, the base resistance has not yet fully understood due to theeffects of soil plugging. This paper compares the base resistance of two open-ended field piles constructed in theTokyo Bay project evaluated by CPT- and SPT-based design methods. The CPT-based design methods, which
are not popular in Japan were also included to evaluate their effectiveness. The CPT-design methods discussed
on this paper classify open-ended piles into plugged or unplugged modes. The results reveal that the Fugro (i.e.,
CPT-based) and API design methods overestimate the base resistance. On contrast, the ICP (i.e., CPT-based) andPARI (i.e., SPT-based) design methods underestimate the base resistance. On a rational approach, we
recommend the UWA design method (i.e., CPT-based) as it produces the nearest results to the actual baseresistance.
Keywords: Base Resistance, Cone Penetration Test, Design Methods, Open-Ended Piles, Soil Plugging

profiles (Kikuchi et al., 2005; Saimura et al., 2005).
The base resistance of the two field piles wasevaluated using the design methods. Each of thedesign methods has their own definitions for many
parameters as explained in the following sections.

INTRODUCTION
The baring capacity of an open-ended pileconsists of three parts as given in Eq. 1 (see Fig. 1
too). The base resistance is produced by the
combination of plug resistance and annulus
resistance (see Eq. 2). The plug resistance is theminimum of inner frictional resistance, Q in or soilbase resistance, Q b,soil (see Fig. 1).
(1)
𝑄𝑄u = 𝑄𝑄an + 𝑄𝑄out + 𝑄𝑄plug
Where Q u is bearing capacity, Q an is annulus
resistance, Q out is outer frictional resistance andQ plug is plug resistance.

Qin

Qout

(2)
𝑄𝑄b = 𝑄𝑄an + 𝑄𝑄plug
Where Q b is base resistance, Q an is annulus
resistance and Q plug is plug resistance.
The evaluation of the base resistance is notstraightforward compared to shaft resistance, due tothe effects of soil plugging. In this paper, we
discussed evaluation methods of the base resistanceby several design methods. Nowadays, CPT-based
design methods are quite popular in many parts of
the world although Japanese companies still do not
encourage CPT-based design methods. Three CPTbased design methods (i.e., ICP-05, UWA-05 andFugro-05) and two non-CPT-based methods (i.e.,
PARI and API) were included in this study.
Data base of the field piles with both CPT q c and
SPT-N profiles are hard to be found, particularly inJapan. Two field piles constructed in the Tokyo Bay
construction project provide both SPT-N and CPT q c

Qan

Qb,soilFig. 1 The bearing capacity of an open-ended pile
ICP-05 Method
ICP-05 design method is based on the cone
penetration test (CPT) results (Jardine et al., 2005).
The discussion here is limited to the base resistance.
The ICP-05 design methods produce design
formulae for both sandy and clayey grounds. Inner
frictional resistance is not evaluated separately. The
319

GEOMATE- Osaka, Nov. 16-18, 2015

Where q b is base resistance, q c,avg is average CPT
resistance and A ef is effective area ratio (see Eq. 7).

base resistance of an unplugged open-ended piledriven in sandy soil is given in Eq. 3. The q c,avg isdetermined taking the average of CPT q c within ±1.5D (D is pile outer diameter) from the pile tip assuggested by Bustamante and Gianeselli (1982)(also known as the Dutch method).

𝑑𝑑 2

𝐴𝐴ef = 1 − 𝐹𝐹𝐹𝐹𝐹𝐹 � �(7)𝐷𝐷Where A ef is effective area ratio, FFR is final filling
ratio (see Eq. 8), d and D are inner and outer pile
diameter respectively.

(3)
𝑞𝑞b = 𝐴𝐴r 𝑞𝑞c,avg
Where q b is base resistance, A r is area ratio (see Eq.
4) and q c,avg is average CPT resistance.

𝑑𝑑

𝐹𝐹𝐹𝐹𝐹𝐹 = Min �1, � �
1.5

𝑑𝑑

(8)

The FFR is the incremental filling ratio (IFR)
defined for the final 20 diameters of penetration.
When the information of IFR is not available, Eq. 8
is used to determine the FFR. The q b /q c,avg ratio in
Eq. 6 depends on the annular area and the degree ofsoil plugging over the final stages of pile driving.
This ratio varies from 0.15 for a thin-walled openended pile (i.e., 1 of FFR) to 0.60 for a fullyplugged pile (i.e., 0 of FFR) similar to a closedended pile.
The q c,avg is determined according to the Dutch
method as simplified in Eq. 9 (Schmertmann, 1978).
The CPT resistance in the zone A, q c,A (see Eq. 9
and Fig. 2) is the average of the envelope ofminimum cone resistance recorded above the pile tip
over 8D (D is pile outer diameter) distance. The
CPT resistance in the zone B, q c,B is evaluated using
Eq. 10. 𝑞𝑞c,avg = 0.5�𝑞𝑞c,A + 𝑞𝑞c,B �(9)Where q c,avg is average CPT resistance, q c,A and q c,B
are CPT resistance of zone A and B respectively.

An open-ended pile is considered plugged if thefollowing conditions are satisfied. Otherwise it isconsidered an unplugged pile.
𝐷𝐷CPT

�

Where FFR is final filling ratio and d is inner pile
diameter (in meters).

𝑑𝑑 2

𝐴𝐴r = 1 − � �(4)𝐷𝐷Where A r is area ratio, d and D are inner and outerpile diameter respectively.

𝑑𝑑 < 0.02(𝐷𝐷r − 30) and

0.2

𝑞𝑞

< 0.083 c

𝑝𝑝a

Where d is inner pile diameter (in meters), D r is
relative density (in percentage), D CPT is diameter of
CPT probe (in meters), q c is CPT resistance and p a
is reference pressure (i.e., 100 kPa).
The base resistance of a plugged open-ended pile
is evaluated using Eq. 5. Equations 3 and 5 simply
imply that a plugged open-ended pile produces alarger base resistance than a similar unplugged openended pile. In Eq. 5, there are two lower limits
provided for a fully-plugged open-ended pile (i.e.,the capacity of the unplugged pile by A r q c,avg and thecapacity predicted for the piles of D > 0.9 m by
0.15q c,avg (Jardine et al., 2005).

(10)
𝑞𝑞c,B = 0.5�𝑞𝑞c,avg,B + 𝑞𝑞c,min,B �
Where q c,B is CPT resistance of zone B, q c,avg,B is theaverage cone resistance recorded below the pile tip
over 0.7 – 4D distance and q c,min,B is the minimum
cone resistance recorded below the pile tip over thesame distance of 0.7 – 4D.

𝑞𝑞b = 𝑞𝑞c,avg Max �0.5 − 0.25log �𝐷𝐷�𝐷𝐷

� , 0.15, 𝐴𝐴r � (5)CPT
Where q b is base resistance, q c,avg is average CPT
resistance, D is outer pile diameter, D CPT is diameterof CPT probe and A r is area ratio (see Eq. 4).

UWA-05 Method
UWA-05 design method was developed largely
from the ICP method by incorporating several
modifications (Lehane et al., 2005). Hence, it
considered a larger data base than the ICP and Fugro
methods. This method, unlike other CPT-based
design methods, introduces an effective area ratio,
which combines the incremental filling ratio andannular area ratio of the pile (Gavin and Lehane,
2003; White et al., 2005). This method proposes asingle equation for both closed- and open-ended
piles as given in Eq. 6. The effective area ratio (seeEq. 6) becomes unity for closed-ended piles (i.e., A ef
= 1).
𝑞𝑞b = 𝑞𝑞c,avg �0.15 + 0.45𝐴𝐴𝑒𝑒𝑒𝑒 �Overlyingweak layer

End bearing
layer
Influence
zone

qc,A
qc,B

Fig.2 Schematic
diagram of influence zone of CPT q c resistance

(6)
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GEOMATE- Osaka, Nov. 16-18, 2015

value at the pile tip (≤ 50) and N 2 is the average
SPT-N value recorded above the pile tip over 4D (Dis pile outer diameter) distance.

However, in the UWA-05 design method, thepile diameter, D is used only for the closed-ended
piles. It is replaced by the effective pile diameter
given in Eq. 11 for open-ended piles.

Table 1 Design parameters for cohesionless siliceous
soil (modified from API, 2005)(11)
𝐷𝐷ef = 𝐷𝐷𝐴𝐴0.5
ef
Where D ef is effective pile diameter, D is pile
diameter and A ef is effective area ratio (see Eq. 7).

DensitySoildescription

Soil-pile
frictionangle(degree)

Limiting
unit end
bearing
resistance
(MPa)

Nq

Fugro-05 Method
Very
Loose
Loose
MediumLoose
MediumDense
Medium
Dense
Dense
Very
Dense
Dense
Very
Dense

The Fugro design method also proposes a single
equation for both closed- and open-ended piles asgiven in Eq. 12 (Kolk et al., 2005). The area ratio (inEq. 12) becomes unity for closed-ended piles (i.e.,A r = 1).
𝒒𝒒𝐛𝐛 = 𝟖𝟖. 𝟓𝟓𝒒𝒒𝐜𝐜,𝐚𝐚𝐚𝐚𝐚𝐚 �𝒑𝒑𝐚𝐚

𝒒𝒒𝐜𝐜,𝐚𝐚𝐚𝐚𝐚𝐚

𝟎𝟎.𝟓𝟓

�

𝑨𝑨𝟎𝟎.𝟐𝟐𝟐𝟐
𝐫𝐫(12)

Where q b is base resistance, q c,avg is average CPT
resistance, p a is reference pressure (i.e., 100 kPa)
and A r is area ratio (see Eq. 4).
The q c,avg is determined taking the average of
CPT q c within ± 1.5D (D is pile outer diameter)from the pile tip same as in the ICP method.

Sand
Sand-Silt
Silt
Sand
Sand-Silt
Silt
Sand
Sand-Silt
Sand
Sand-Silt
Gravel
Sand

15

8

1.9

20

12

2.9

25

20

4.8

30

40

9.6

35

50

12.0

Unlike the CPT-based design methods, the PARI
method does not take account the ground condition
below the pile tip when evaluating the design SPT-N
value. Table 2 briefly summaries the influence zones
applied for CPT- and SPT-based design methods. It
clearly indicates that the influence zone defined ineach design method is different to each other except
between the ICP and Fugro methods, which used thesame method.

API Method
The base resistance is evaluated using Eq. 13(API, 2005). The dimensionless bearing capacity
factor (see Eq. 13) depends on the soil type and soil
density. The value of N q can be read from a tablegiven in API (2005). The table also gives thelimiting base resistances for particular groundconditions (see Table 1).

Table 2 The influence zone for the average CPT-q c
or SPT-N values
DesignAbove pile tip
method
ICP
1.5D
UWA
8D
Fugro
1.5D
PARI
4D
Note: D is pile outer diameter(13)
𝑞𝑞b = 𝜎𝜎v′ 𝑁𝑁q
Where q b is base resistance, σ v ’ is effective
overburden pressure at the pile tip and N q isdimensionless bearing capacity factor (see Table 1).
PARI Method
PARI design method is based on the SPT-N
value (PARI, 2009). Since Japanese companies do
not encourage CPT-based design methods, this isquite popular in offshore foundation designs inJapan. The base resistance is evaluated using Eq. 14.
The degree of soil plugging is incorporated into thebase resistance by a non-dimensional parameter, α.

Below pile tip
1.5D
0.7-4D
1.5D-

RESULTS AND DISCUSSIONS
The details of the two field piles can be found in
Kikuchi et al. (2005). Fig. 3 shows the groundprofile at the T4 and T5 piles. T4 and T5 piles areembedded into sandy gravel and solid sand layers
respectively. The dry density, ρ t of clayey and sandy
soils were taken as 1400 – 1550 and 1700 -1750
kg/m3 respectively. The ρ t of sandy gravel and solid
sand were taken as 2208 and 1850 kg/m3
respectively (Kikuchi et al., 2005). The water levelis at 7 m from the pile head. The base resistance ofthe two piles were evaluated at 73.5 and 86 m depths(14)
𝑞𝑞b = 300𝛼𝛼𝑁𝑁 ∗Where q b is base resistance, α is a dimensionlessparameter and N* is evaluated as given in Eq. 15.
(15)
𝑁𝑁 ∗ = 0.5(𝑁𝑁1 + 𝑁𝑁2 )Where N is the design SPT-N value, N 1 is SPT-N*

321

GEOMATE- Osaka, Nov. 16-18, 2015

for T4 and T5 piles respectively. CPT q c and SPT-N
profiles at the site is shown in Figs. 4a and 4b
respectively.

methods are 50.14 and 89.57 MPa respectively forT4 pile. The design CPT q c value (i.e., q c,avg ) for
ICP-05 and Fugro-05 design methods then becomes
69.86 MPa for T4 pile. q c,A and q c,B for UWA-05
method are 27.66 and 80.42 MPa respectively for T4
pile. The design q c,avg then becomes 54.04 MPa.
Therefore, we can see that the design CPT q c value
is different among the design methods (except
between the ICP and Fugro methods). The q c,avg forT5 pile is summarised in Table 3 along with T4 pile.
q c,B for UWA-05 (see Eq. 10) was evaluated for 4D(D is pile outer diameter) distance below the pile tip
as the variation of CPT q c was non-uniform.

Sea levelsurface

7

Clay
Sand
Sandy gravel
44

Solid sand

46

50
55

53
57

60

Table 3 The results of q c,avg for the CPT-based
design methods

63

70

72

T4 pile tip

78

DesignmethodT5 pile tip

90

Unit is in meters

ICP
UWA
Fugro

Fig. 3 The ground profile at the site (modified from
Kikuchi et al., 2005)
0

10

20

30

40

50

10
20

40
50
60

1.6

70

1.4

80

1.2

0

20

40

60

Measured
Evaluated, linear
Evaluated, non-linear
y = -0.631x+1.343 (R2 = 0.44)
y = 0.557x-1.477 (R2 = 0.56)

1.0

(a)

CPT-qc (MPa)
65

0.8

80

100

120

0.6

0.396

0.4

70

0.302

0.2

Depth, H (m)q c,avg (MPa)
T4
T5
pile
pile
69.86 40.95
54.04 27.84
69.86 40.95

qm/ 300NAp

Depth, H (m)3090q c,B (MPa)
T4
T5
pile
pile
89.57 50.75
80.42 34.78
89.57 50.75

The value of α (see Eq. 14) was taken as 0.302
using a non-linear relationship proposed for the data
available in PARI (2009) as shown in Fig. 5. Fig. 5
also indicates that a linear relationship would
produce a larger value of α (i.e., 0.396) which thenwill produce a larger base resistance (see Eq. 14). N 1
and N 2 were found to be 50 and 45 respectively (seeEq. 15). Therefore, the design SPT-N value (i.e., N*)
for the PARI method becomes 47.

SPT-N value0

q c,A (MPa)
T4
T5
pile
pile
50.14 31.16
27.66 20.90
50.14 31.16

q = 300NαAp

0.0

75

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Pile diameter, D (m)80

Fig. 5 The results of α parameter (modified from
PARI, 2009)85

90

Figures 6a and 6b show the base resistance of T4
and T5 piles respectively. The results clearly suggestthat API and Fugro methods overestimates the baseresistance in both piles. In fact, the API method
gives 12.84 and 14.97 MPa for the base resistance ofT4 and T5 piles respectively by Eq. 13, which aremuch higher than the corresponding measured
values (i.e., 8.88 and 6.37 MPa respectively).
However, since the API method assigns a limiting
value (see Table 1), the limiting values were used in(b)Fig. 4 (a) SPT-N profile (modified from Kikuchi,2008) and (b) an enlarged CPT q c profile atthe pile tip (modified from Yu and Yang,
2012)
CPT resistance in the zone A and B, q c,A and qc,Brespectively (see Fig. 2) for ICP-05 and Fugro-05
322

GEOMATE- Osaka, Nov. 16-18, 2015

Figs. 5a and 5b. In both T4 and T5 piles, N q wasassumed to be 40. However, given the soil condition,
the use of even 50 of N q is possible, which thenwould have overestimated the measured values byeven a larger margin. This design method is quite
tricky compared to the other methods since it does
not take variations within a soil type. All otherdesign methods discussed on this paper incorporate
average behaviour of the ground at the pile tip,either as an average CPT q c or SPT-N value.
On contrast, ICP and PARI methods
underestimate the base resistance in both piles (seeFigs. 6a and 6b). Therefore, based on a conservative
approach, we can recommend the ICP and PARI
methods for the evaluation of base resistance of
unplugged open-ended piles. However, they
underestimate the base resistance by a big margin.
The PARI method considers the SPT-N values lessthan 50 (see Eq. 15). Therefore, it is worthwhile tostudy how a corrected N value might be influenced
the base resistance. Also, it was understood that the
PARI method does not incorporate the groundconditions below the pile tip like all the CPT-based
methods do. Given the ground conditions below thepile tip is stiffer than the above it (in both piles, seeFig. 4b), a design formula incorporating it willproduce a higher base resistance, which then would
reduce the gap between the measured and designed
values.
Among the design methods discussed here, theUWA method produces the nearest values to themeasured base resistance although it overestimates
the base resistance for one pile by a small margin(see Fig. 6a). Therefore, based on a rational
approach, we can recommend the UWA design
method. Only the UWA method incorporates theincremental filling ratio (as a function of final filling
ratio) to the design base resistance. The degree ofsoil plugging is described by the incremental filling
ratio to a greater degree. Therefore, the UWA
method incorporates the degree of soil plugging in
the base resistance.
As aforementioned, both the ICP and Fugro
methods assign the same criterial for the design CPT
q c value (see Table 2). However, they underestimate
and overestimate the base resistance respectively inboth piles. Therefore, we can understand that not
only the evaluation method of the design CPT q c but
also the other parameters contribute to the difference
among the design methods.
The results on this paper were produced only
from two field piles. Therefore, we recommend thatthe modifications to the existing design methods
should be proposed after a number of field piles arestudied. Given the field piles with both SPT-N and
CPT q c profiles cannot be found in Japan, theoverseas projects with both SPT-N and CPT q c canbe studied. This study though provides the basic of acomparison of the SPT- and CPT-based design

methods. We plan to extend this study further byincreasing the number of field piles analysed in near
future. Then, we will propose modifications to theexisting design methods.
12

Base resistance, qb (MPa)

10
8
6
4
2
0

Measured value

ICP

UWA

PARI

API

Fugro

API

Fugro

Design method(a)10Base resistance, qb (MPa)

8

6

4

2

0

Measured value

ICP

UWA

PARI

Design method(b)Fig. 6 Base resistance of (a) T4 and (b) T5 pile
respectively
CONCLUSSIONSThe base resistance of two field piles constructedin Tokyo bay project was determined by fivedifferent design methods. The study includes both
CPT- and SPT-based design methods. The following
conclusions are drawn from the study.
The closest results to the measured baseresistance was achieved by the UWA design method,which is the only method incorporates theincremental filling ratio (or degree of soil plugging)
explicitly.
The ICP and PARI (a method practiced
popularly in Japan) underestimate the baseresistance, approximately by similar margins. A useof the corrected SPT-N value in the PARI method
would have reduced the gap between the designed
and measured values. Also, incorporating the groundconditions below the pile tip like in the CPT-based
323

GEOMATE- Osaka, Nov. 16-18, 2015

Symposium in Penetration and Testing,Amsterdam, 493-500.
[5] Lehane, B.M., Schneider, J.A. and Xu, X.
(2005), The UWA-05 method for prediction of
axial capacity of driven piles in sand, Proc. ofInternational Symposium on Frontiers in
offshore Geomechanics, Taylor and Francis,
London, 683-689.
[6] Gavin, K.G. and Lehane, B.M. (2003), The
shaft capacity of pipe piles in sand, Canadian
Geotechnical Journal, 40(1), 36-45.
[7] White, D.J., Schneider, J.A. and Lehane, B.M.
(2005), The influence of effective area ratio on
shaft friction of displacement piles in sand, Proc.
of International Symposium on Frontier in
Offshore Geotechnics, Perth, Australia.
[8] Schmertmann, J.H. (1978), Guidelines for conetest, Performance and Design (No. FHWATS78209), US Federal Highway Administration,
Washington, D.C.
[9] Kolk, H.J., Baaijens, A.E. and Senders, M.
(2005), Design criteria for pipe piles in silica
sands, Proc. of International Symposium on
Frontiers in offshore Geomechanics, Taylor andFrancis, London, 711-716.
[10] API (2005), Recommended Practice forPlanning, Designing and Constructing Fixed
Offshore Platforms - Working Stress Design,
AmericanPetroleum
Institute(API),
Washington, USA.
[11] PARI (2009), Technical Standards andCommentaries for Port and Harbour Facilities inJapan, The Overseas Costal Area Developmentof Institute of Japan, Port and Airport ResearchInstitute (PARI), Yokohama, Japan.
[12] Kikuchi, Y. (2008), bearing capacity evaluationof long, large-diameter, open-ended piles, In
Proc. of 6th International Conference on Case
Histories in Geotechnical Engineering, Arlinton,
USA, 11-16 Aug, pp. 1-21.

design methods may reduce the variation. Therefore,
further study on the PARI method is recommended.
API design method overestimates the baseresistance. The dimensionless parameter (i.e., N q )suggested for the soil type and density needs afurther study as it could result a base resistancedeviated much from the actual value.
Fugro method gives the least matched results tothe measured base resistance. Given other CPTbased design methods give better results, we do not
encourage the use of Fugro method for open-ended
piles.
ACKNOWLEDGEMENTS
The financial assistance to the first author
through a President Postdoctoral Fellowship by
Tokyo University of Science, Japan is highly
appreciated. The financial supports for the research
works
providedby
the Japan Iron
and Steel Federation (JISF) and International Pressin Association (IPA) are also acknowledged.
REFERENCES[1] Kikuchi, Y., Kusakabe, O., Fukui, J., Sasaki, H.,
Geshi, H., Saimura, Y., Nishimura, N. and Sakai,
K. (2005), Proc. of 40th JGS Annual Conference,
Hakodate, Japan, pp. 1671–1672 (in Japanese).
[2] Saimura, Y., Kusakabe, O., Kikuchi, Y., Fukui,J., Sasaki, H. and Geshi, H. (2005), Verticalbearing capacity of large diameter steel pipe
piles in Tokyo port seaside road project, Proc.
of 40th JGS Annual Conference, Hakodate,Japan, pp. 1673–1674 (in Japanese).
[3] Jardine, R.J., Chow, F.C., Overy, R.F. andStanding, J.R. (2005), ICP design methods fordriven piles in sands and clays, Telford, London.
[4] Bustamante, M. and Gianeselli, L. (1982), Pile
bearing capacity prediction by means of static
penetrometer CPT, Proc. of 2nd European

324

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

MEASUREMENT OF ARITHMETICAL MEAN ROUGHNESS OF
CONCRETE SURFACE BY TRANSCEIVER TYPE AERIALULTRASONIC SENSORNAGAOKA Seiya*, OKAJIMA Kenji*, ISHIGURO Satoru*, ITO Ryouei*, WATANABE Ken** and ITO
Tesu****

Graduate school of Bioresources, Mie University, 1577 Machiyacho, Kurima, Tsu, Mie 514-8507, JAPAN**
Maruei concrete industry Co., Ltd., 1518 Majima, Fukujyu-machi, Gifu 501-6293, JAPAN
***X-ability Co., Ltd., 4-15 Hongo, Bunkyo-ku, Tokyo 113-0033, JAPANABSTRACTIn the hydraulic performance malfunction of the concrete agriculture irrigation canal the roughness coefficientis assumed one of the evaluation criteria. The arithmetical mean roughness of the concrete surface is applied tothe estimate of the roughness coefficient. In this study, we suggested the measurement method of the roughnessof the concrete surface using the aerial ultrasonic wave of the transceiver type. We examined the influence of thedispersion of measured values, relations between the arithmetical mean roughness and the peak to peak value ofthe reflection wave, influence of wet and dry of concrete surfaces and the verification about the measurement
rang. As a result, following became clear. The peak to peak value of reflection wave could estimate arithmeticalmean roughness well. In addition, the arithmetic average of 15 times measurement values was sufficientaccuracy. The peak to peak value of the reflection wave was little affected under wet condition. The aerial
ultrasonic wave measured the diameter in the approximately range 600(mm) from distance of 1000(mm). The
aerial ultrasonic wave measured the diameter in the approximately range 300(mm) from distance of 550(mm).
The results of this study reveal that measurement of the arithmetical mean roughness of the concrete surface canusing the aerial ultrasonic sensor of transceiver type.
Keywords: aerial ultrasonic, peak to peak value, Arithmetical mean roughness, Hydraulic performance,
Functional diagnostic
1.

economical measurement of the arithmetical meanroughness is difficult. Over the years, considerable
attention has been paid to the study of measurementof the arithmetical mean roughness. The first attempt
to assess the roughness of the concrete surface of thecanals was made by reference [7]. Reference [7]suggested using the moulage gauge in themeasurement of the arithmetical mean roughness.
This method is the simple measurement, sincemanagers only pushes the moulage gauge to theconcrete surface. However, managers needcomplication analysis that read displacement fromone by one the moulage gauge. Reference [5]suggested using the laser displacement sensor in themeasurement of the arithmetical mean roughness.
Measurement range of this method is line
information of the concrete surface. Reference [4]suggested using the three dimensional image
processing in the measurement of the arithmeticalmean roughness. Measurement range of this method
is line information without three dimensional imageof the concrete surface. However, these method is
not used in the general maintenance of the roughnessof the canal. Since the agriculture irrigation canals is

INTRODUCTION

Japan has possessed main concrete agriculture
irrigation canals that have total length of 49,239km.
These were constructed since 1954-1973 of the higheconomy growth. When canals became too old forwork, they were necessary to be repaired. Especially,
the hydraulic performance malfunction is used it as
the repair factor. However, visual inspection is often
used in the general maintenance of the roughness ofthe canal. Some canals managers want to assess
quantitative of the roughness of the concrete surfaceof the canal quantitative. Since main concrete
agriculture irrigation canals have enormous length,
the method is required of quantitative assessmentthat is simple, short time and economical.
Flow velocity decreasing and water level raising
are caused by the roughness of the concreate surface.
These hydraulic performance malfunction of theconcrete agriculture irrigation canal the roughness
coefficient is assumed one of the evaluation criteria.
The arithmetical mean roughness of the concretesurface is applied to the estimate of the roughness
coefficient. However, the simple, short time and325

GEOMATE- Osaka, Nov. 16-18, 2015

long total extension distance, managers request
measurement of wide range.
In this study, we attempted using the aerial
ultrasonic wave of the transceiver type in themeasurement of the arithmetical mean roughness.
The wave of the aerial ultrasonic wave has
characteristic of spreading wide. Experiment date
was acquired by the oscilloscope. In addition,measurement interval is 25 times per second. Thismethod can have potential of the simple, short timeand economical quantitative assessment.
In this study, first, we examined of the dispersionof the measurement value. Next, we examined
relation between the peak to peak value and thearithmetic mean roughness. Next, we examined theinfluence of wet and dry of concrete surfaces. Last,
we verified the measurement range.
2.

A

3.

We used LZ-EZ1 (MaxBotic, Inc) as the aerial
ultrasonic sensor of transceiver type. The sensor
used designed for the ultrasonic measuring sensor.
We selected a frequency from relationship ofattenuation in the air. Since ultrasonic waves higher
80kHz in frequency attenuates from distance of2000(mm) and ultrasonic wave of the lower 20kHz
in frequency may become the audible range, we
selected about the 40kHz. The ultrasonic wave of
40kHz can measure from distance of 500~2000(mm)without attenuation. Next, we compared the open
type (the sensitivity: min. -80.5dB) and thewaterproof type (the sensitivity: min. -58.2dB) in
sensitivity. The open type has 13 times higher
sensitivity than the waterproof type at each output
voltage. So, we selected the open type. But
measurement of the aerial ultrasonic wave must limit
not to get wet. Other specification shows table.1.
Experiment date of the peak to peak value was
acquired by the digital oscilloscope TBS1152(Tektronix, Inc).

weightA
B
C
D

THE MEASUREMENT PRINCIPLE

Peak

to

peak

Figure 1 The measurement principle of theultrasonic sensor
4.

Table 1 Specification the aerial ultrasonic transducerof transceiver type
42
16.4
15.5
19.9
22.1
4.3

C

Figure1 shows the measurement principle ofmeasurement of the arithmetic mean roughness ofthe concrete surface by the aerial ultrasonic wave oftransceiver type. The wave of the aerial ultrasonicwave is reflected by the concrete surface. The
reflection wave was diffusely reflected by theroughness of the concrete surface. We evaluatedamount of the reflection wave with the peak to peakvalue. The peak to peak value is difference between
the maximum value and the minimum value of thereflection wave value.

THE MEASUREMENT EQUIPMENT

frequency
dimension

D

B

ARITHMETIC MEAN ROUGHNESS

4.1 The Moulage Gauge and Determination
In this study the arithmetic mean roughness wasmeasured by the moulage gauge. The length of themoulage gauge is 147(mm). The number of steel
sticks is 183. Measurement interval is 0.8(mm).
Figure2 shows the measurement surface of thearithmetic mean roughness 0.30(mm).
Figure3 shows the image of the arithmetic meanroughness. The formula 𝒇𝒇(𝒙𝒙) is the roughness curveof the concrete surface. The formula 𝒀𝒀(𝒙𝒙) is liner
approximation that is calculated from the roughness
curve.

KHz
mm

grams

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GEOMATE- Osaka, Nov. 16-18, 2015

𝑹𝑹𝒂𝒂 is calculated by ration between the integral
value of �𝒇𝒇(𝒙𝒙) − 𝒀𝒀(𝒙𝒙) � and the length of the moulage
gauge.
𝒍𝒍
𝑹𝑹𝒂𝒂 = ∫𝟎𝟎 �𝒇𝒇(𝒙𝒙) − 𝒀𝒀(𝒙𝒙) � 𝒅𝒅(𝒙𝒙)𝒍𝒍𝟏𝟏𝑹𝑹𝒂𝒂 :
𝒇𝒇(𝒙𝒙) :𝒀𝒀(𝒙𝒙) :l:

Table 2 examination items and the arithmetical meanroughness
Ra (mm)
0.00
0.30
0.37
0.45
0.67

(1)

The arithmetic mean roughness
The roughness curve
The Formula of liner approximation
The length of the moulage gauge

5.

Dispersion○○

Application
○
○
○
○
○

Wet
○
○

EXAMINATION ITEM

5.1 The Dispersion of The Measurement Value
Measurement of the aerial ultrasonic wave has thedispersion of the measurement value. Therefore, thedispersion of the measurement value needs to be
decreased by the average of times. This experimentof purpose that decides the average of times of themeasurement value.
We measured the peak to peak value. We
measured 119 times. We got 100 sample of themoving average of 1~20 times. We evaluated ration
between the standard deviation and the mean valueof the moving average. The aerial ultrasonicmeasured from distance of 1000(mm) and 550(mm).
Figure4 indicated that the dispersion of themeasurement value decreased so that the arithmetic
average increased.
The dispersion of themeasurement value in 550(mm) tended to be larger
than in 1000(mm). However, the arithmetic averageof 15 times restrained the dispersion of themeasurement value in 550(mm) and 1000(mm). The
15 times of average of measurement value issufficient accuracy. In this study measurement value
defines it as the average of 15 times.

Figure 2 The measurement surface of the arithmetic
mean roughness in 0.30(mm)y𝒇𝒇(𝒙𝒙)

l

𝑹𝑹𝒂𝒂

ul

0

𝑌𝑌(𝑥𝑥)
x

The length of the moulage gauge (mm)0.12
the standard deviation(mv)/the meanvalue(mv)Figure 3 The image of the arithmetic meanroughness
4.2 Selected Measurement Surface
Reference [7] indicated the relationship between
the arithmetical mean roughness and the number ofyears of the agriculture irrigation canals. The
agriculture irrigation canal used 40 years, thearithmeticalmeanroughnessbecomes
approximately 0.7(mm). We were selected inconsideration of soundness of the canal.Table2
shows examination items and the arithmetical meanroughness.

Ra=0.30(1000mm)
0.1
Ra=0.45(1000mm)
0.08
Ra=0.30(550mm)

0.06

Ra=0.45(550mm)

0.04
0.02
0
0

5

10

15

the average of times(s)Figure 4 The dispersion of the measurement value
327

20

GEOMATE- Osaka, Nov. 16-18, 2015

the concrete surface. Therefore, the arithmetic meanroughness was decreased. However, figure3
indicates that the standard deviation of thearithmetic average of 15 times from distance1000(mm) was ±15.7(mv). Since the difference ofthe measurement value was within the range of thestandard deviation, we concluded that whether wetor dry of concrete surfaces do not have influence themeasurement value. Fig6 shows reflected
waveforms from wet and dry of concrete surfaces.
Waveforms were not different from wet and dry.
Here those can be seen that the peak to peak value ofthe reflection wave was little affected under wet
condition.

5.2 The Application of Measurement of The
Arithmetical Mean Roughness

Table 3 The Measurement value of wet and dry

Ra=0.30 (mm)Ra=0.45900
y = -490.31x + 840.13
R² = 0.9191

800
700

the refection wave value (V)the peak to peak value(mv)

We measured the peak to peak value. The aerial
ultrasonic measured from distance of 1000(mm). We
measured five concrete surfaces that is 0.00~0.67(mm) in the arithmetical mean roughness.
It is clear from figure5 that the peak to peak value
was decreased with increasing the arithmetical meanroughness. In addition the formula of liner
approximation had high correlational relationship.
The formula of liner approximation is used toestimate the arithmetic mean roughness from thepeak to peak. Measurement of the transceiver typeaerial ultrasonic wave can estimate the arithmeticalmean roughness well.

600
500
400
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

the arithmetical mean roughness(mm)Figure 5 The relationship between the peak to peakvalue and the arithmetical mean roughness

wet (mv)
717.6
601.5

dry
704.4
590.1

2.6
2.4
2.2
2
1.8

wet

1.6
1.4
0.0055dry
0.006
0.0065
measurement time (s)5.3 Influence of Wet and Dry of Concrete
Surfaces

Figure 6 Reflected waveforms from wet and dry ofconcrete surfaces in Ra=0.30(mm)When managers measure agriculture irrigation
canals, the canals don’t have the water level during
non-irrigation period. But, it may rain on themeasurement day before. We expected that many
canals can be wet condition of the concrete surface.
We evaluated the effect of the measurement value
by the concrete surface condition. We compared andexamined the measurement value of wet and dry ofconcrete surfaces.
When we reproduced wet condition of the concretesurface, we noted to be water pocket in theroughness depression. The aerial ultrasonicmeasured from distance of 1000(mm).
Table3 shows the measurement value of wet anddry of concrete surfaces. The measurement value ofwet was a little larger than dry. We were able toguess that water filled the roughness depression of

5.4 The Verification of The Measurement Range
Measurement of the aerial ultrasonic can measure
the wide range. However, the measurement rangethat has inference on the measurement value is
uncertain. To calculate the correct measurement
range is almost impossible. The purpose is to clarify
the measurement range that has inference on themeasurement value.
Figure7 shows the experiment principle. We
examined measurement range by widening diameterof the grave on the flat board. The flat board is0.00(mm) in the arithmetic mean roughness. The
grave of particle size is 2mm. Figure8 shows theused grave. The aerial ultrasonic measured fromdistance of 1000(mm) and 550(mm).
Figre9 and 10 indicate that the peak to peak value
was decreased with widening diameter of the grave.
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GEOMATE- Osaka, Nov. 16-18, 2015

Fig9 shows the relationship between peak to peakvalue and diameter of grave from distance of1000(mm). When the grave diameter is 200(mm),the peak to peak was largely decreased. When thegrave diameter is 200~400(mm), the peak to peak
was few decreased. After 600(mm) in diameter ofthe grave, the peak to peak was not changed. Since
600(mm) in diameter of the grave presumably
exceed measurement range, the upper limit of themeasurement range is approximately 600(mm) indiameter.
Figure10 The relationship between peak to peakvalue and diameter of grave from distance of
550(mm). When the grave diameter is 200(mm), thepeak to peak was largely decreased. When the grave
diameter is 200~300(mm), the peak to peak was few
decreased. After 300(mm) in diameter of the grave,the peak to peak was not changed. Since 300(mm) indiameter of the grave presumably exceed
measurement range, the upper limit of themeasurement range is approximately 300(mm) indiameter.
The dominant range of measurement isapproximately 200(m) in diameter from distance of
550~1000(mm). The measurement range was large
in proportion to the measurement range and inproportion to square of the measurement distancepeak to peak value(mv)Figure 8 The used grave
900
800
700
600
500
400
300
0

200

400 600 800 1000
diameter of grave(mm)Figure 9 The relationship between peak to peakvalue and diameter of grave from distance of1000(mm)peak to peak value(mv)

1600
1400
1200
1000
800
0

100

200

300

400

500

diameter of grave(cm)Figure 10 The relationship between peak to peakvalue and diameter of grave from distance of
550(mm)Figure 7 The diagram of experiments for searching
the measuring range

6 CONCLUSION
We examined influence of the dispersion of themeasurement values, relation between thearithmetical mean roughness and the peak to peakvalue of the reflection wave, influence of wet anddry concrete surfaces and the verification of themeasurement rang. As a result, following became
clear. The peak to peak value of the reflection wave
could estimate the arithmetical mean roughness well.
The average of 15 times of measurement values
were sufficient accuracy. The peak to peak value of329

GEOMATE- Osaka, Nov. 16-18, 2015

the reflection wave was little affected under dry
condition. The aerial ultrasonic wave measured thediameter in the approximately range 600(mm) fromdistance of 1000(mm). The aerial ultrasonic wave
measured the diameter in the approximately range
300(mm) from distance of 550(mm). The dominant
range of measurement was 200(m) in diameter fromdistance of 550~1000(mm). Measurement range waslarge in proportion to measurement range and inproportion to square of the measurement distance.
When manager estimate the arithmetical meanroughness, only evaluate the peak to peak of thereflection wave. Manager can measure wet conditionof the concreate surface. We think of measurement
range is wide. The results of this study reveal thatmeasurement of the arithmetical mean roughness ofthe concrete surface can using the aerial ultrasonicsensor of transceiver type.
It remains a challenge for future research toverification of the measurement in various weather
such as temperature and wind velocity, andmeasurement of the canal on field.

[3]

[4]

[5]

[6]

[7]
ACKNOWLEDGEMENTS
This study is funded by the Ministry of Agriculture,
Forestry and Fisheries.
[8]

REFERENCES[1] Horiguchi K, “ Wear of concrete-The state of
the Art-“, Concrete Journal, Vol. 31, No.10, pp.
17~22, Oct. 1993.
[2] Kitamura K, Honma S and Kato T,“Quantitative Evaluation for Forecast of330

Irrigation Canal Abrasion”, J of Water, Landand Environmental Engineering , Vol 76, No.
9, pp. 823~828, Sep. 2008
Uchida K, Ishida M and Ogawa S, “Estimationof the Coefficient of Roughness for Irrigation
Canals by Transcription Method for Concrete
Surface”, presented at JSIDRE, pp. 432~433,
2008.
Otagaki K, Hasegawa Y, Suzuki T, Matsumoto
S and Sato S, “Study on Measurement Methodof Surface Roughness of Concrete Canal by
Three Dimensional Image Processing “, J of
JSIDRE, NO. 280, pp. 7~13, Aug 2013.
Nakaya T, Tokashiki M, Mori M and Mori T,“Estimation of the Coefficient of Roughness
from Surface Characteristics og Abraded
Concrete Channel”, Trance of JSIDRE, No.
258, pp. 23~28, Dec 2008.
Tokashiki M, “ Studies on Erosion Mechanism
and Accelerated Erosion Test of Concrete
Irrigation Canal” Bull. Natl. Inst. Rural Eng.
Japan, Vol. 52, pp. 1~57, 2013.
Kato T, Honma S, Kitamura K and Imaizumi
M, “Deterioration of Irrigation Canals andChange in Roughness Coefficient”, Tech. Rep.
Natl. List. Rural Eng. Japan, Vol. 207, pp.
183~193. 2008.
Nagaoka S, Tago Y, Okajima, K, Ishiguro S,“Measurement of roughness of concrete surface
using airborne ultrasound”, presented at
JSIDRE, pp 7~13, 2014.

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

OVERTURNING AND SLIDING STABILITY OF THE GEOTEXTILE
REINFORCED TIDE WALL FOR OVERFLOW
Naoki OYAMA1, Yutaka HASHIZUME2 and Kenji KANEKO3
1,2,3
Civil Engineering, Hachinohe Institute of Techology, Japan
ABSTRACTThe authors aiming at development of new tide wall reinforced by geotextile technology. We examined themost suitable tide wall shape for tsunami energy reduction. As the results, we understood that the steep slope
was more effective than the traditional tide wall for the tsunami energy reduction. In this study, we examinedmechanical stability of the steep slope tide wall reinforced by geotextiles for overflow tsunami. At first, we
installed pressure gauges in the rigid model and measured horizontal tsunami pressures. We considered themeasured forces to the external forces and designed the tide walls which changed some parameters.
We carried out tsunami model experiments to examine for overturning and sliding stability. As the results, itwere found of the following things. Tide wall were not destroyed by the first wave impulsively, it weredestroyed lately by the water buoyancy after the overflow Tsunami. When we designing an advanced toughness
tide wall for overflow tsunami, It must substitute the submerged unit weight instead for wet unit weight in theequation of stability calculations.
Keywords: Geotextile Reingorced Tide Wall, Overflow, Tsunami Force, Stability
INTRODUCTION

slope both in the case of a steep tide wall is greatly
reduced tsunami energy [4]. As a technique forbuilding a steep slope tide wall, it is also
conceivable to use concrete. However, because it is
exposed to the combined degradation environmentof the freeze-thaw and salt damage, the durability
becomes a problem especially in cold climates. So,
for the tide wall by reinforced embankment by
geotextile, to consider the stability against tsunami,
stable results were obtained. However, the width of
the tide wall in these experiments is set very large.
In this study, we examined mechanical stability ofthe steep slope tide wall reinforced by geotextile foroverflow tsunami.
At first, we installed pressure gauges in the rigid
model and measured horizontal tsunami pressures.
We considered the measured forces to the external
forces and designed the tide walls which changedsome parameters. We carried out tsunami modelexperiments to examine the stability of designed tidewall model. As the experimental results, we got thesome important knowledge to design a tenacious tide
wall.

Tide wall is most important structure in thetsunami disaster prevention. it is built to defend
from the tsunami a living space and life behindslightly land side from the coastline as the main
purpose. In response to the 2011 earthquake,tsunami measures in soft surfaces, such as thoserelated to refuge are bathed in the leg light, and earn
the time of refuge, in order to reduce the damage ofthe hinterland to reduce the energy of tsunami as
much as possible the embankment is still important.
However, since it is a structure that functions only
once tsunami decades or hundreds of years has notbeen performed so many studies. In 2011 thegenerated tsunami by Tohoku region Pacific Ocean
earthquake, many seawalls have been destroyed.
Therefore, Enormous human and material damage
has occurred in the hinterland. From the 2011 off the
Pacific coast of Tohoku Earthquake, study on thetide wall have been promoted. Many of study areattention to the erosion of scouring and tide wall of
toe of slope ground by overflow tsunami [1]–[3].
Many of the civil engineering structures, its
own strength and stability are primarily studied.
Tide wall reducing the damage behind the main
purpose is different from the general civilengineering structures. The authors, first, by tsunami
Hydraulics test by rigid-body model that changing
the shape of the tide wall, was examined gradient
optimal seawall in reduction in the tsunami energy.
As a result, compared to the gradient used in theconventional tide wall, the sea side and land side

MEASUREMENT OF THE TSUNAMI WAVEFORCE BY THE RIGID BODY MODEL
At first, in order to measure the wave force foroverflow tsunami, an experiment was conducted byusing a rigid model and open channel equipment. In
this study, we measured tsunami external force fordesign external force. For the tide wall design,331

GEOMATE- Osaka, Nov. 16-18, 2015

Fig.1 Open channel equipment

Fig.2	
  Results of wave height meterFig.3	
  Results of water velocity meter

knowledge about the wave force to act on a tide wallby a tsunami is important. There is intended to
discuss it and erforms the experiment here to set
external as existing intellect.
Experiment Outline
It is shown in Fig.1 experiment device of themimetic diagram. The experiment, fixed a rigid
model after having created rigid basic ground model
made by stainless steel in these open channel
equipment. Rigid-body model is modeled to scale
1/100 assuming a seawall height 8.0m, it was
reproduced in 80mm height of the tide wall model.
In this study, is adopt the bore wave which is theform that is the nearest to a real tsunami as an action
wave in all experiment cases. The result of the wave
height is shown in Fig.2 and the flow velocity isshown in Fig.3. Installation position of the wave
height meter or the flow velocity meter is as shownin Fig.1. Horizontal direction of the flow rate of justbefore the seabed gradient maximum is about
60cm/s, the flow rate of the vertical direction isFig.4 	  Installation position of the rigid model
dimensions and wave pressure gaugesmall. From the results, wave velocity is about 1.6
m/s. it will become 50 km/h if it calculation by the
Froude similarity rule. Speed of tsunami observed in
the vicinity of land at the time of the Tohoku-Pacific
Ocean earthquake in 2011 is several hundred km/h
or several decades km/h, it can be said that areasonable flow velocity. From the figure, thereproducibility of the wave action is very good.
In order to measure the horizontal Tsunamipressure acting on the seawall, a wave pressure332

GEOMATE- Osaka, Nov. 16-18, 2015

Fig.7 The horizontal external force by the assumed
tsunamiFig.5 	  Measurement results of the horizontalpressure

Table.1 	
  Position of the resultant force and theaction point

In Fig.6, maximum shock pressure generated inshock, it shows the relationship between the heightfrom the horizontal pressure. Here, the horizontalpressure acting on the upper part of the tide wall, as
shown in Fig.7 p1, the horizontal pressure acting on
the lower p2, was calculated by using theseapproximate straight line. Furthermore, we assumed
trapezoid distribution and demanded horizontal
resultant force F and action height x. I show thesecalculation results in table.1. In addition, in the table,similarity law in consideration of a 1/100, and isexpressed in full-scale units. Three times experimentresult average is 854.6 kN/m. In addition, the action
height becomes 3.58m. We decided to use horizontal
load 860kN/m, action height 3.6m in as design
external force to perform in a following chapter.

Fig.6	
  Vertical distribution of horizontal pressuregauge was placed in different sea side three-point
height on the rigid body model shown in Fig.4. We
assume it ch-1, ch-2, ch-3 from the vertical direction
top. The wave pressure meter examines it in resting
hydraulic pressure and confirms that I show accurate
numerical value. In addition, I decided to perform
three times of experiments. In addition, as a result of
having confirmed it about the horizontal distributionof the tsunami wave force preliminarily, we
understand that hardly change.

DESIGNOF
THE
REINFORCED TIDE WALL

GEOTEXTILE

Examination of the stability for the tsunami is amain purpose in this study. It was with the design
external force the tsunami wave force (Horizontal
action force) shown in the previous chapter. It
examined the external stability for the tsunami of thetide wall mainly. For review of these overall stability,
since even scouring measures such as the peripheral
ground must be examined and taking into account,
this is not performed. In addition, of the externalstability, it is thought that there is not muchinfluence by the tsunami either about the support
power. From that, it was considered not covered inthis study.

Experimental Result
It is shown in Fig.5 measured change of horizontalpressure of the three waves pressure meter. From theFig.5, The distributed over the depth direction andnear to the foundation ground, it understood that theeffect is greater pressure. If bore waves of such long
period as a tsunami, it is considered that constantpressure is exerted over a little more time. In thisstudy, we decided to carry out the design of tidewalls based on maximum shock load. We decide touse a similar tsunami in the following experiment.

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GEOMATE- Osaka, Nov. 16-18, 2015

Table.2	
  Fundamental soil propetys

Table.3	
  The result of the design

Fig.8	
  Grain size distrubution

Fig.9	
  Figure of summary of the tide wall model

Design Condition

Fs =Tide wall height (8m), material (silica sand No. 5,
ferro-nickel slag), geotextile material species and,
position and spacing of the height direction was
Table.2	
  Fundamental soil propetys unified. Fig.8
shows the grain size distribution curve of silica sandNo.5 and ferro-nickel slag. Table.3 shows the basic
nature. The geotextiles materials assumed standard
SR-80.

cL + µΣV
≥ 1.5
ΣH

(1)

Where Σ V is vertical load in the embankment
bottom（kN/m）. Σ H is horizontal load in theembankment bottom （ kN/m ） . L is theembankment bottom width. μ is the coefficient offrictionor. c is cohesion. In this case, the calculationof the vertical load by the embankment deadweightis determined by multiplying the embankment
sectional area in the unit volume weight (Volume
per unit length). In this study, tide wall is assumed
tsunami on overflow, it becomes a situation where
embankment is immersed in water. From these, wetunit weight and submerged unit weight design itwith both cases, we decided to weigh it.
Consideration of overturning is all overturning
moment around the embankment toe and where e isaction point eccentric distance of the resultant forceof resistance moment. To judge stability whether not
become the middle third of the bottom width. It
make a decision that we are safe if satisfied without
tensile stress occurring on all parts of the bottom. As
indicated by the following equation. In addition, unit
volume weight is included the resistance moment
calculation formula, we designed it using two kindsof unit volumes weight like a sliding calculation.

Internal Stability
In this study, slope gradient of tide wall is 1:0.3.
It is classified in reinforced soil wall. Here, we
examine the external stability by the tsunami. In thisinternal stability, tide wall examined whether it
could stand in the steep slope. In addition, we madea model on a scale of 1/100. The laying distanceassumed it 1.2m. (The model is 1.2cm)
External Stability
The examination of the sliding stability is
resistance divided by sliding-energy and it comparewith the safety factor. It is shown in following
equation.

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GEOMATE- Osaka, Nov. 16-18, 2015

Fig.10	  State of overflow of the geotextiles seawalld=ΣM R − ΣM 0
ΣV

(2)

e=

L
L
−d ≤
2
6

(3)

It produced the tsunami like two chapters．It isconsiderd the stability. We examine the design
condition. It is shown in Fig.9.
TSUNAMI HYDRAULIC EXPERIMENT OFGEOTEXTILES REINFORCEMENT TIDE
WALL

Where d is distance from the embankment toe
to the action point of the resultant force R. Σ MR isresistance moment around the toe. Σ M0 isoverturning moment around the toe. e is resultant
force. R is deviation from the embankment bottom
center of the action point. The result of the design,
the tide wall embankment of 6 cases in the above
manner is shown in table.3. In the next chapter, we
made a model of 1/100.

In this chapter, it was modeled on the basis of
the geotextile reinforced soil tide wall of design inthe previous section. We examined the experimental
study of the sliding and the overturning in theexternal stability. Also, because the foundation
ground was made of stainless, in order to ensure theboundary friction between the model and foundation,
the same sand was sandwiched between them.
335

GEOMATE- Osaka, Nov. 16-18, 2015

Outline of The Experiment

CONCLUSION

The embankment material is at same size andusing readily available silica sand No.5. And, it uses
a large slow cooling ferro-nickel slag of density thansilica sand. That adjusted it to optimum moisture
content and made soil compaction reinforcement soilmodel to become degree of compaction, 90% and95%. The first layer from on the wall height and
8cm is 0.8cm, from the second layer to the sixth
layer created the tide wall embankment is divided
into seven layers of each 1.2cm. Geotextile
reinforcement material and wall material was using
polypropylene (mesh 1mm × 1.03mm). As a modelof retaining sheet, used a wiping cloth made of pulp.
In addition, cover the whole laying embankment inwall material to prevent the scour caused by thetsunami. Fine consistency of real thing and a modelby the similarity law cry caught, also has not been
carried out measurement of strain such as reinforced
material.
Tsunami action waves used in the experiment is
the same as Section 2. （Fig.2，3）The tide wallmodel assumed it case 1-6 shown in the foregoing
chapter. We photographed the state of the tsunami
which did overflow by video camera, and it isobserving the fracture morphology.

In this study, to examine the stability against
overturning, sliding of geotextile reinforced tide wall,
first, to measure the horizontal pressure of thetsunami in rigid model and determine the horizontal
load using these. We designed of geotextile
reinforced tide wall using the horizontal load andaction point position as external force. The model
examined performs the tsunami hydraulicexperiment. As a result, we understood the following
things,・	  If it assume the horizontal external force
caused by the tsunami and design it using
submerged unit weight, the way of thinking of
the stable calculation of basic overturning andslide is proper.
・	  Geotextile reinforced tide wall is possible toensure stability by appropriate. Tsunami
energy reduction effect is also large.
・	  From the above, it can be that reduce thewhole embankment by using large on theembankment material of the density as
ferronickel slag.
For the development of tenacious using thegeotextile reinforced tide wall, it includes the futureissues of the following,・	  Examination of scouring performance of thetide wall for long time overflow.
・	  Detailed examination of the internal stability
and whole stability.
・	  Examination in consideration of buoyancy isnecessary.

Experimental Result
Show a state of overflow of the tide wall modelin Fig.11. In any case, integral structure of thereinforced soil wall is not lost even if can be
overflow, shape by geotextile reinforcement ismaintained. In Case 1, 2, 5, it is withstand the
maximum pressure wave, but sliding occurred after
overflow.
From a photograph, the timing to sliding by thesize of the model are different but it is seen how themodel is moving horizontally. In addision, in case 4,
it has begun to slide in a state receiving the shock
wave force after 0.5s from tsunami arrival. Even if
not destroyed it by the impact load during the shock,buoyancy occurs, it is thought that vertical load
becomes small very much. Case 3 and 6, it is themost top wide case in each of the embankment
material. Stability was high, and the destruction suchas slide, and the overturning was not seen in after
overflow.
From the above, it can be that reduce the wholeembankment by using large on the embankment
material of the density as ferronickel slag. In
addision, it is thought that it is effective to reduce
costs while ensuring stability.

REFERENCES[1] Yanagisawa M, “A New Type Tsunami Barrier
Seawall of Sand Dune using Reinfoecing
Technology”, Journal of Japan Society of Civil
Engineers, Ser. B2 (Coastal Engineering), Vol.
68, No.2, 2012, I_886-I_890.
[2] Kawabe S, “Model Test on GRS-Integral
Bridge Affected by TSUNAMI”, Geosynthetics
Engineering Journal, Vol. 28, 2013, pp. 245-250.
[3] Tokita K, “Hardware Measures to AdvanceToughness ofEarth Bank Against TSUNAMI
Flood”, Geosynthetics Engineering Journal, Vol.
28, 2013, pp. 259-264.
[4] Hashizume Y, “Fundamental Hydraulics Model
Tests of Tide Embankment Reinforced by
Geotextiles For TSUNAMI”, Geosynthetics
Engineering Journal, Vol. 28, 2013, pp. 237-244.

336

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

DEVELOPMENT OF GEOCELL REINFORCEMENT METHOT TOPREVENT THE SCOURING OF THE FOUNDATION OF TIDE WALL
Yuataka HASHIZUME 1, Naoki OYAMA 2 and Kenji KANEKO 3
1,2,3
Civil Engineering, Hachinohe Institute of Techology, Japan

ABSTRACTThe tsunami caused by the 2011 earthquake off the Pacific coast of Tohoku inflicted a huge damage on thetide wall of the coastal areas of Tohoku region, due to the large scale scouring of the bottom side of the landsideslope. Than those reasons we need know some valid knowledge about constructing the tide wall embankment.
But unfortunately we only have few research papers about the tide wall embankment against big Tsunami. In
order to prevent the damage of the tide wall by tsunami, it is necessary to protect its foundation by preventing thescouring of the foundation of the landside slope. We conduct several types of comparative experiment changing
number and type of reinforcement by geocell (geocell is the one of the geosynthetics material). In an attempt to
examine the preventive effect of the geocell reinforcement against the scouring of the foundation of the landsideslope of tide wall, in this research, we conducted an experiment using an open channel recirculating water tank
and a model ground. As a result, it was found out that it is possible to prevent the scouring and the collapse of
the tide wall by combining the geocell and the geogrid reinforcement without using the solidification structure
like a concrete structure.
Keywords: Scouring, Tsunami, Geocell

INTRODUCTION
In 2011 the generated tsunami by Tohoku
region Pacific Ocean earthquake, many tide walls
have been destroyed. As one of the fracture
morphology of tide wall, include the scouring of thefoundation of the landside slope of tide wall. Tide
wall is most important structure in the tsunami
disaster, it is built to defend from the tsunami aliving space and life behind slightly land-side fromthe coastline as the main purpose. However, I did
not attract attention before the earthquake disasterother than tsunami outbreak because it was
unnecessary. In addition, foundation groundscouring has not been performed many studies,because it was assumed not doing overflow. In thisstudy, geosynthetics material examine the preventive
effect of the geocell reinforcement against thescouring of the foundation of the landside slope oftide wall, we conducted hydraulic experiment.

Fig.1

Open channel device

side. Therefore, the height of the tide wall is a
150mm [1]. The foundation ground assumed a rigid
ground. We set up the scouring ground between
800mm of tide wall behind. In Fig.1, the part of thescouring ground carried out a preliminary
experiment as a rigid ground. It shows the flow ofsteady overflow state in outline. Tide wall of the sea
side is about depth 200mm. Water velocity is about
10cm/s. It was 45mm, and Overflow water depth of
the tide wall top was around 30% of the tide wallheight. In addition, after the overflow of the tidewall is the distance from the tide wall of the waterflow hits the foundation ground was about 150mm.
Test case that was carried out in this study is shownin Fig.2. Test case 0 is the case of unreinforced case,and only scouring ground. Test case 1 is laying apanel made by thickness 2mm, 50mm squarealuminum from a tide wall to 300mm as a concrete
block model. Test case 2 is laying one layer of
geocell from a tide wall to 300mm.

EXPERIMENTAL OUTLINE
It is shown in Fig.1 test device of the outline
figure. The size of the open channel is channel
length L=10m, channel width W=0.6m, channel
depth H=0.8m. It is possible to long time maintain asteady overflow for the tide wall model bycirculating water using a pump. We made the tidewall model of rectangular thickness 30mm, height
350mm, and width 600mm. It was fixed by footprotection 250mm to the position of 4m from sea
337

GEOMATE- Osaka, Nov. 16-18, 2015

Table.1

Fig.2

Test case

Fig.3

Geocell and geocell model

Test case 3 is pile up a geocell to three layer withcover soil of 1cm and sets it up. Test Test case 4 islaying a geogrid between cover soil and geocell andbinds a geocell and a geo-grid in unity wire. Geocell
is a three layers, it laying the only crushed stone in
the bottom. In addition, we laid a geogrid between
the geocell and crushed stone of the bedrock andlaying a nonwoven between a scouring ground andthe crushed stone layer. Test case 5 is laying only
crushed stone to a geocell reinforcement part of Test
case 4.
Geocell was created using the polypropylene OHP
sheet. we assumed a geocell of 300mm in height tobe 1/25 scale model and made a geocell of 12mm inheight （ Fig.3 ） . We paste silica sand No.8 ofparticle size 0.05-0.15mm to reproduce the materialof geocell. We assumed broken stone of around100~250mm for fill material in a geocell and usedthe crushed stone of 4.75mm~9.5mm. We filled
crushed stone in geocell before soil compaction. The
silica sand No. 8 in the void of crushed stone wasfilled with water compaction. In addition, I made themodel of the geo-grid with the net of a screen door
made by PP. And, we used wiping cloth as
nonwoven. Table.1 shows the fundamental soil
propatys of the nature sand used in scouring ground.
Fig.4 shows the grain size distribution curve. Fig.4
shows the grain size distribution curve.

Fig.4

Fundamental soil propetys

Grain size distrubution

The scouring ground was prepared by compaction
divided into 10cm by two layer to be a degree ofcompaction 90%[2].
EXPERIMENTAL RESULT
It is shown in Fig.5 photos of the experiment. And
shown in Fig.6 a the ground surface from thecaptured image. Scouring begins immediately after
the overflow Looking at the case 0 is subjected tothe measures, after 180s (Fig.5 (a)) scour has
reached the basic bottom panel on the back of
expansion Tsutsumi. Tide walls because it is rigidly
connected to Shi put roots seawall to the bottom
panel in this experiment are self-supporting.
However, it is consider that the scouring lacks tidewalls stability progressed to support ground of tidewalls in the real structure. Test Case 1 is a case thatwas constructed aluminum panel on the surface ofthe scouring ground. Immediately after the overflow
started (Fig.5 (b)) to begin the panel flows out
scouring, the panel that has been involved in waterflow faster scouring than the case 0 is observed
phenomenon that scrape the sand ground wasprogress. Case 2 is a case constructed one layer
which is reinforced soil by geocell. Immediately
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GEOMATE- Osaka, Nov. 16-18, 2015

Fig.5

The conditions of scouring experiment

Fig.6

The changes due to scouring of the foundation ground surface
339

GEOMATE- Osaka, Nov. 16-18, 2015

after the overflow stated, the stuffing materials had
been flow out. And the geocell its self was also flowout. After the geocell layer has been shed, thescouring is progress in the same manner as the test 0,
reinforcing effect of geocell cannot be confirmation
almost. Test Case 3 is a case of enforcement threelayers of geocell reinforcement. That case was wereable to a delay ground scouring beginning over
several tens of seconds compared to the three cases
described above. But it is not possible to completely
reduce the energy of the fluid even in the a threelayers of geocell reinforcement soil part collapse by
the stuffing material was leaked in the top and thatthe sand beneath the geocell layer is flowing out
gradually It was leaked. After the reinforcements
outflow, scouring has been progress in a short period
as well as the experimental cases. Case 4 is thefollowing experiment cases.two sheets of geogrid
were laid to The upper and lower surfaces of thethree layers of reinforced soil by geocell so as tosandwich, further provided a gravel layer and thenonwoven fabric to the base layer. Tide walls has
not occurred even eight minutes after the overflow
start is scouring of enough to become unstable.
Stuffed material in the top of the geocell the slightly
flow out, but reinforced portion is the not collapse. It
can be said that some geocell prevent flow out of
stuffing materials and it can be reduce the fluidenergy that reaches sandy ground. Because of thehardness of reinforced soil, the hydraulic jump has
occurred. Shown by Fig.5 (e). If scour does notoccur, it can be considered that the overflow tsunami
energy may be reduce. For the porpoise of
considering effect of scour measures by geocell, wewere prepared experimental case 5 that has gravelcompaction layers instead of geocell reinforced
layers. Based on the result of test case 4. In the caseof test 5, because of not using geocell, it cur notexpected the confining effect by geocell.
CONCLUSION
In this study, we examined scouring preventionof the tide wall behind using geocell. As a result, I
found out that it is possible to prevent the scouring
and the collapse of the tide wall, by combining the
geocell and the geogrid reinforcement.
As the future issues, the reinforcement patternof the ground of the tide wall behind is thought
about elsewhere. We want to examine rational
scouring prevention method by increasing
experiment case.
REFERENCES[1] Minemura R, “A Study on Scouring by
Overflow from Seawall and Applcation of an
Artificial Trench”, Journal of Japan Society of
Civil Engineers, Ser. B2 (Coastal Engineering),
Vol. 69, No.2, 2013, I_791-I_795.
[2] Kazama
M,“Experimentof
The
Countermeasure Using Reinforced Soil Method
Sgainst to The Scouring at Back Toe of Tide
Embankment”, Annual Report of Society for
The Promotion of Construction Engineering,
No.48, 2013, pp. 11-14.
[3] Hatogai S, “Hydraulic Model Experiments on
Scour Landward of The Coastal Dike Induced
by Tsunami Overflow”, Journal of Japan
Society of Civil Engineers, Ser. B2 (Coastal
Engineering), Vol. 68, No.2, 2012, I_406-I_410.

340

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

EFFECT OF SOIL LIQUEFACTION ON THE NATURAL FREQUENCYAND DAMPING OF PILE-SUPPORTED STRUCTURES

1

Domenico Lombardi1 and Subhamoy Bhattacharya2
School of Mechanical, Aerospace and Civil Engineering, The University of Manchester, UK; 2Department
of Civil and Environmental Engineering, University of Surrey, UK

ABSTRACTThe effect induced by soil liquefaction on the natural frequency damping ratio of pile supported structures isinvestigated experimentally by means of a series of tests carried in a shaking table at normal gravity. The
supported structures (two single piles and two pile groups) with and without superstructure mass were tested.
The experimental investigation aimed to monitor the variation in natural frequency and damping of the fourphysical models at different degrees of excess pore water pressure generation and in full-liquefaction condition.
The experimental results showed that the natural frequency of pile-supported structures may decrease
considerably owing to the loss of lateral support offered by the soil to the pile. On the other hand, the damping
ratio of structure may increase to values in excess of 20%. These findings have important design consequences.
In fact for low-period structures, substantial reduction of spectral acceleration is expected as a consequence ofsoil liquefaction.
Keywords: Liquefaction, Piles, Frequency, Damping, Seismic Demand

Engineering (BLADE) at the University of Bristol(UK). The soil container, shown in Fig. 1, consistedof a rigid box with external dimensions of 2.4 m
length, 2.4 m height and 1.2 m width. The soil
container was secured to the shaking table by bolting
the bottom plate of the container to the aluminum
platform by using a set of steel wedges. The main
limitations of using rigid containers wererepresented by the generation and reflection of Pwaves from the end walls. In a typical soil layer,
which may be idealized as semi-infinite extended
deposit, the energy associated with the wave
propagation diminishes gradually with distance. Thisdissipation may be related to the combined presenceof hysteretic and radiation damping as well as thefact that energy is spreading to a larger volume ofsoil, the so-called geometric attenuation [2]. To
minimize the reflection of P waves from the endwalls as well as to aid the dissipation of energy,
sheets of absorbing materials were placed on bothend-walls of the container [3].
The four structures consisted of two single piles
and two pile groups (see Fig. 2). Model “Single Pile– 1” and “Single Pile – 2” corresponded to the single
pile models with outer diameter of 25.4 mm and41.27 mm respectively. Model “Pile Group – 1” and“Pile Group – 2”corresponded to the pile groups,
which were composed of four piles having outer
diameter of 25.4 mm and 41.27 mm respectively.
Aluminium alloy (type L114-T4 6082-T4) waschosen as the material for all piles. The dimensions
and mechanical properties of the models are listed inTable 1.

INTRODUCTION
The seismic design of piles in liquefiable soils is
often carried out by considering simplified pseudostatic analyses. Consideration of the dynamic
characteristics of the structure is employed only forthe assessment of the seismic demand. This isnormally determined from acceleration response
spectra. In fact, according to conventional seismic
methods [1], the seismic demand imposed by anearthquake on a structure can be computed fromnatural frequency and equivalent viscous damping
ratio of the structure. In the routine practice, theseare often computed neglecting soil-structureinteraction effects and potential effects induced bysoil liquefaction phenomena.
This
paper
presents
anexperimental
investigation on the effect induced by soilliquefaction on the natural frequency damping ratioof pile supported structures. Specifically, a series oftests are carried in a shaking table at normal gravity
on four structures comprising of two single piles andtwo pile groups. The main aim of the tests is tomonitor both variation in natural frequency anddamping of the four structures at different degrees ofexcess pore water pressure generation and in fullliquefaction condition.
SHAKING TABLE TESTSThe experimental investigation presented in thispaper was performed on the shaking table facility of
the Bristol Laboratory for Advanced Dynamics
341

GEOMATE- Osaka, Nov. 16-18, 2015transition to liquefaction; (iii) at full liquefaction.

Pile Group - 1

Single Pile - 2

Single Pile - 1
Pile Group - 2

Fig. 2

Fig. 1

Experimental rig mounted on the shaking
table .

Table 1 Dimensions and mechanical properties ofthe physical models
Model ID
Outer
EI
Pile-cap2
diameter[Nm ]weight[mm][kg]
Single pile 1
25.4
294
1.9
Single pile 2
41.2751305
8.44Group pile 1
25.4
294
13.08
Group pile 2
41.2751305
22.72

Figure 2 shows a schematic of the
instrumentation layout. To estimate the modalparameters of the four structures, namely frequency
and damping, each pile-cap was instrumented withan accelerometer. The input motion applied throughthe shaking table was also recorded by anaccelerometer located on the table. Finally theground response was measured by means
accelerometers embedded in the soil deposit as
illustrated in Fig. 3. Excess pore water pressure weremonitored by means of pore water pressuretransducers (PPTs), which were placed at four
depths as shown in Fig. 3
The soil container was subjected to broadband
random white noise with 0-100Hz bandwidth
frequency. This was applied by the shaking table fora total duration of 300s. Specifically, three different
acceleration amplitudes, i.e.: 0.02g, 0.04g and 0.15g
were applied without stopping the shaking, for a
total duration of 100s each. This allowed to monitor
the four structures in three different conditions,
namely: (i) before liquefaction; (ii) during the342

Experimental rig mounted on the shaking
table.

The assessment of frequencies and damping
ratios were performed dividing each phase into 15“blocks” having a duration of 20s each. Consideringthat the longer the record the higher the frequency
resolution is, the length of each block was so
determined to obtain an acceptable frequency
resolution of the FRF and to ensure that all thefrequencies of interest were sufficiently excited.
These two considerations may be achieved bychoosing a length of the block much larger than the
period of the lowest mode of interest. On the other
hand, the length of the block cannot be very long,
due to the fact that the system under investigation ishighly non-linear and therefore its modal parameters
are changing with time. In particular, the effect of
having blocks of longer duration may cause
widening of the spectrum, which may lead to an
overestimation of the damping and erroneousassessment of the natural frequency. Subsequently
the FRF was evaluated for each block considering
the acceleration response recorded by theaccelerometer mounted on the shaking table as input,
and the response measured by the accelerometer
placed on the pile cap as output. To estimate thereliability of all measurements, the associatedcoherence function was estimated for each FRF.
More details about the techniques used for theassessment of the modal parameters can be found in[4], [5].
The damping ratio of the four models wasestimated from the width of the resonant peak in the
FRF using the “Half-Power Bandwidth Method”.
However, in order to achieve accuracy before theassessment of damping, the measured FRF wasmatched with a theoretical FRF of a single degree of
freedom system using a curve fitting technique. The
matching was carried out in the vicinity of theresonance peak by minimizing the squared error
between the real response and the fitting function.
The value of damping was estimated using the HalfPower Bandwidth Method, which was applied on thefitted FRF. Figure 4 shows a flow chart summarizing

GEOMATE- Osaka, Nov. 16-18, 2015

small amplitude shaking (i.e. acceleration level~0.02g). In the second phase (i.e. 0-200s), as r u
increased (a maximum of r u =0.35 was measured at
a depth of 1m), the natural frequencies reduced by
20%-25% for models Single Pile – 2 and Pile
Groups – 1 and 2 (i.e. f/f inital ~0.80-0.75), although
for the Single Pile – 1 this change was considerably
lower, probably due to the fact this model tilted
slightly during the saturation of the soil.

the different modal analysis procedures employed
for the assessment of natural frequencies anddamping ratios of the four physical models

INPUT MOTIONWhite noise

frequency domain analysis

Frequency Response
Function (FRF)

Matching measure
and theoreticalFRFs

Peak- Pick Method

Half- Power Bandwidth
MethodNATURAL FREQUENCYDAMPING RATIO

Fig. 4Modal analysis procedures used for theassessment of the modal parameters.
Variation in natural frequency

f/fintial

1.2
1

Schematic of instrumentation layout
Damping [%]Fig. 3

EXPERIMENTAL RESULTS (1): VARIATION
IN NATURAL FREQUENCY AND DAMPING
DURING SOIL LIQUEFACTION

0.75
0.5
0.25

Single Pile -1

0Single Pile -2

25

Pile Group -1

20

Pile Group -2

Variation in damping ratio

15
10
5
0

Excess pore water pressure ratioru

Figure 5 plots the variation of natural frequencies
and damping ratios of the structures computed fromthe acceleration response histories at different
degrees of liquefaction. The latter is commonly
expressed in terms of excess pore water ratio r u
defined as the ratio between the excess pore waterpressure Δu to effective overburden stress σ' v0 . Thechange in frequency is depicted in Fig. 5 in terms ofratio f/f inital , where f is the average value of natural
frequency estimated considering a block of 20s, andf inital is the frequency of the system measured in thefirst 20s of the input. The latter was assumed as thevalue of frequency in the initial condition.
From Fig. 5 it may be observed that, in the firstphase (i.e. 0-100s), the natural frequencies of themodels slightly increased with the shaking, and no
significant pore water pressure built up in the soil(r u <0.1). The small increase in frequency wascaused by the densification of the soil due to the

1
0.8
0.6
0.4
0.2
0

PPT4
PPT3

PPT1
PPT2

Input ground motion

0.3

a [g]

0.15
0
-0.15
-0.3
0

50

100

150

200

250

300

Time [s]Fig. 5

Variation of natural frequency and damping
ratios of the four physical models

During the last phase (200-300s), the amplitude of
the input motion was incremented to 0.15g, and thesoil started to liquefy from top to bottom, this can beobserved from excess pore water pressure ratios
plotted in Fig. 5. Full liquefaction was reached atabout 250s up to a maximum depth of 1.6m. When
this length was compared with the pile’s length (2m),a ratio 1.6/2.0 = 0.8 was obtained (i.e. 80% of the343

GEOMATE- Osaka, Nov. 16-18, 2015

pile was fully unsupported). At this stage, thefrequency reduced to 20% for Single Pile – 1 (i.e.
f/f inital ~0.80), 50% for Pile Group – 1 (i.e. f/f inital~0.50) and 60% for Single Pile – 2 and Pile Group –2 (i.e. f/f inital ~0.40).
The variation of the damping ratios of the fourphysical models is plotted in Fig. 5. It can berecognized that the initial damping ratios of thesystems were different, specifically, 5% for Single
Pile – 1, around 8% for Single Pile – 1 and Pile
Group – 1 and just above 10% for Pile Group – 2. In
the first phase of loading (0-100 s), the damping
ratios remained approximately constant for allmodels. In Phase 2 (100-200s), as the acceleration
amplitude increased, the damping slightly increased
and reached a value of about 10%. At fullliquefaction (i.e. after 250s), the models experienced
a significant increase in damping ratio up to values
above 20%.

acceleration and velocity by the angular frequency
and square angular frequency respectively.
1.4
Before liquefaction
Soil response (-0.6m)

Response acceleration [g]

1.2

Table
After liquefaction

1

Soil response (-0.6m)
Table

0.8

0.6

0.4

0.2

0
0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.40

0.45

0.5

Period [s]Fig. 6EXPERIMENTAL RESULTS (2): EFFECT OF
SOIL LIQUEFACTION ON ACCELERATIORESPONSE SPECTRA

Computed acceleration response spectra
estimated before and at full liquefaction
obtained considering an average damping
ratio of the models of 3%

The results showed that before liquefaction thehighest spectral peak determined from theaccelerometer located at 1 m depth, i.e. 1.34g,
occurred at 0.07s. It can also be observed that the
response spectra of the free-field was very similar inmagnitude and shapes with the one obtained fromthe shaking table, which considered the response at
the bedrock. After soil liquefaction, the free-field
response spectra exhibited a significant attenuationof the spectral acceleration in the 0.1s to 0.9s period
ranges. The de-amplification factor calculated withrespect to the response spectra computed on thetable after liquefaction (depicted in black dottedline) was by a factor of 1.4. Finally, it can beobserved that the response spectra shifted towards
long periods, which in turn slightly amplified
spectral acceleration beyond 0.9s.
To confirm the de-amplification of the spectral
accelerations, the acceleration time histories
measured by the accelerometers located on the pileheads of the four structures are plotted in Fig. 7. The
measurements clearly demonstrated that with theonset of liquefaction the acceleration response of themodels was considerably reduced

The response spectrum of an earthquake
represents a practical tool for determining theseismic input to be considered for designingstructures in earthquake-prone areas. However inthis research, response spectra are computed in order
to better understand the influence of soil-structureinteraction on the seismic response of the models
prior to and after soil liquefaction. This wasachieved by evaluating the response spectra from thetime histories recorded in the soil which can beconsidered representative of the free-field response.
Furthermore, to better identify the effects induced by
the soil softening, the response spectra from data
recorded on the table have been also computed andcompared with the spectra associated with the freefield. The structural damping ratio used in thecalculation was calculated from the damping ratiosof the structures estimated during the free vibration
tests before the pluviation of the soil [4], [5].
Therefore, it is important to highlight that thisdamping reflected only the response of the structureand neglected the additional damping introduced by
the flexibility of the foundation and SSI effects.
Based on this consideration a damping ratio of 3%
was employed in all analyses. Figure 6 shows thecomputed acceleration response spectra estimatedfrom the accelerometer at 1m depth. The spectra
determined from the acceleration time histories
recorded on the table have also been plotted indotted lines for comparison.
It should be noted that the spectral valuesplotted in Fig. 6 are not pseudo quantities, however,these can be obtained by multiplying the spectral

CONCLUSIONS
The results presented in this paper focused on thechange in natural frequency and damping ratios offour structures due to generation of excess porewater pressure. It is important to note that the fullliquefaction condition (i.e. r u =1.0) was reached at a
maximum depth of 1.6m, thus, the results must be
interpreted bearing in mind that the ratio between
the total depth of liquefaction (1.6m) and the lengthof the pile (2m) was 0.8.

344

GEOMATE- Osaka, Nov. 16-18, 2015

The experimental results show that the natural
frequency of four structures decreases considerably
owing to the loss of lateral support offered by thesoil to the pile whilst the damping ratio increases tovalues in excess of 20%.

effects and consequently
overturning moment.

The authors would like to acknowledge thefinancial support from EPSRC/H015345/1 forcarrying out the test.

0

a [g]

of

ACKNOWLEDGEMENTS

Single Pile - 1

0.5

amplification-0.5

Single Pile - 2

0.5

a [g]

REFERENCES

0[1] EN 1998-1:2004 Eurocode 8: Design ofstructures for earthquake resistance — Part 1:
General rules, seismic actions and rules forbuildings.
EuropeanCommittedeNormalisation, 2004.
[2] Bhattacharya S, Lombardi D, Dihoru L, Dietz
M, Crewe AJ, Taylor CA. “Model container
design for soil-structure interaction studies”,
Role of seismic testing facilities in
performance-based earthquake engineering.
Spring Series- Geotechnical, Geological andEarthquake Engineering ISBN, 2011.
[3] Lombardi, D., Bhattacharya, S., Scarpa, F., &Bianchi, M. “Dynamic response of ageotechnical rigid model container withabsorbing boundaries”. Soil Dynamics andEarthquake Engineering, 69, 2015, pp. 46-56.
[4] Lombardi D, “Dynamics of pile-supported
structures in seismically liquefiable soils”. PhD
Thesis, University of Bristol, Bristol, UK, 2013
[5] Lombardi D and Bhattacharya S. “Modal
analysis of pile‐supported structures during
seismic liquefaction”, Earthquake Engineeringand Structural Dynamics, 43(1), 2014, pp. 119138.

-0.5

Pile Group - 1

0.5

a [g]

0
-0.5

Pile Group - 2

0.5

a [g]

0
-0.5

Excess Pore Pressure Ratio [%]1
PPT4

ru 0.5

PPT3

PPT1
PPT2

0

Input Table Shaking

0.5

a [g]

0
-0.5

0

50

100

150

200

250

300

Time [s]Fig. 7
Acceleration time histories recorded on
the pile caps of the four models
These findings have important designconsequences (a) for low-period structures,substantial reduction of spectral acceleration is
expected; (b) during and after liquefaction, theresponse of the system may be dictated by theinteractions of multiple loadings, that is, horizontal,
axial and overturning moment, which were
negligible prior to liquefaction; and (c) with theonset of liquefaction due to increased flexibility ofpile-supported structure, larger spectral displacement
may be expected, which in turn may enhance P-delta

345

ESTIMATION OF DISTRIBUTION OF CONE PENETRATIONRESISTANCE INSIDE EARTH-FILLS WITH USE OFGEOSTATISTICAL METHOD
Kazunari Imaide, Shin-ichi Nishimura and Takayuki Shuku
Graduate School of Environmental and Life Science, Okayama University, Japan

ABSTRACTThe purpose of this study is to estimate the distribution of cone penetration resistance inside earth-fills using
a geostatistical method. The spatial distribution of the strength is identified by sounding tests. In this research,
the cone penetration test (CPT) is employed, and the spatial variability of the tip resistance (Q t ) is obtained.
Firstly, statistical models for Q t are determined from the CPT results. For this task, the minimization of Akaike’s
information criterion (MAIC) method is done to evaluate the statistical model of an embankment, which
involves the mean function and the covariance function. Secondly, the Q t distribution derived from the CPT
results is spatially interpolated with the sequential Gaussian simulation method (SGSIM), which is one of thegeostatistical methods. Finally, the spatial distribution of the probability that Q t is lower than the threshold value
is calculated with a sequential Gaussian simulation. In conclusion, it is found that the spatial variability of thestrength inside earth-fills can be visualized in detail by applying the SGSIM to CPT results.
Keywords: Earth-fill, Static cone penetration test, Spatial variability, Geostatistical method, AIC

results. Nishimura and Shimizu [4] determined thecorrelation parameters of the N-value at a coastal
dyke with the maximum likelihood method.
Although point information values are obtained
from sounding tests, the information between themeasuring points should be interpolated. Therefore,
the sequential Gaussian simulation method (SGSIM)[5], one of the geostatistical methods, is employed
here. Sounding test results are interpolated spatiallywith the SGSIM. Furthermore, the spatiallyinterpolated values comply with the statistical modelfor embankment strength.
Firstly, statistical models for tip resistance Q t are
determined from the CPT results. For this task, theminimization of Akaike’s information criterion [6](MAIC) method is done to evaluate the statisticalmodel of an embankment, which involves the meanfunction and the covariance function. Variograms [7]are also estimated to verify the statistical model
determined by the MAIC. Secondly, the Q t derived
from the CPT results is spatially interpolated with
the SGSIM. Finally, the spatial distribution of thestrength inside an earth-fill is visualized in detail,and then the weak areas inside the embankment are
identified.

INTRODUCTION
There are many earth-fill dams in Japan. Some ofthem are getting old and decrepit, and therefore,
have weakened. Making a diagnosis of the dams is
an important step to stopping them from collapsing
and to increasing their lifetime. An investigation of
the strength inside the dam embankments is required
for this task.
In the present research, the spatial distribution of
the strength of earth-fills is discussed, and an
identification method for this distribution is
proposed. Although the strength of earth-fills is
generally predicted from standard penetration test
(SPT) N-values, the electric cone penetration test
(CPT) is employed in this research to obtain thespatial distribution of the tip resistance, Q t . The CPT
is advantageous in that it is easier to make shortinterval exams with this test than with the SPT, andthe CPT can measure the strength with highaccuracy even in soft grounds, for which the Nvalues are almost equal to zero.
In general, the identification of the spatial
correlation of soil parameters is difficult, since theusual sampling intervals are greater than the spatial
correlation. Therefore, sounding tests are convenientfor determining the correlation lengths. Tang [1]determined the spatial correlation of a ground bycone penetration tests (CPTs). Cafaro and Cherubini[2] also evaluated the spatial correlation with CPT
results. Uzielli, Vannucchi and Phoon [3] consideredseveral types of correlation functions for the CPT

SPT-N Compared with CPT-Q t
In this research, an embankment at Site A is
analyzed, for which CPT tests were conducted at ten
points at 5-m intervals along the embankment axis,
as shown in Fig. 1. The soil profile for the346

GEOMATE- Osaka, Nov. 16-18, 2015

A representative variable for the soil properties, s,
is defined by Eq. (1) as a function of location X=(x,y, z). Variable s is assumed to be expressed as thesum of mean value m and random variable U, which
is a normal random variable in this study.

s ( X ) = m( X ) + U ( X )Fig. 1

Plan view of embankment and testing
interval.
ÅQt

The random variable function, s(X), is
discretized spatially into a random vector st=(s 1 ,s 2 ,...,s M ), in which s k is a point estimation value
at location X=(x k , y k , z k ). Symbol M signifies thenumber of test points. The soil parameters, which
are obtained from the tests, namely, Q t in the case ofthis study, are defined here as St= (S 1 ,S 2 ,..., S M ).
Vector St is considered as a realization of therandom vector st= (s 1 ,s 2 ,...,s M ). If the variables s 1 ,s 2 ,...,s M constitute the M - variate normal
distribution, the probability density function of s canthen be given by the following equation:

N-Value

J

N-SPT
0

2

3

4

5

6

7

G
G G
G
G
G
G G
GG
GG G G G G
G G GGG G GGGGJ
G G GGG
GG
G
G
G
G
G
G
G
GGG G
G
G
G
J
G GG
G
GG
GG
G
G
G
G
G G
GGG
GG
G
J
G G
GGG
G
GGG
GG
G
G
GG GGG
J
G
G
G
G
G G G
GG
G
G
G
GGG
G
G
GGG
J
G
G
G
GG
G
G
G GG G
GGG
G
J GG
G
G
G
G
G GG
G
G
GGG G
G
G
G
G GG J G
GG
GG
G
G
G
GG G
G
G
GG G
GJ GG G
G

2
4
6
8

(2)

 1

× exp − (s − m) t C −1 (s − m) 
2



800
1000

200
400
600

0

in which mt=(m 1 ,m 2 ,...,m M ) is the mean vector of
random function st=(s 1 ,s 2 ,...,s M ); it is assumed to be
the following regression function. In this research, a
2-D statistical model is introduced; it consists ofhorizontal coordinate x, which is parallel to theembankment axis, and vertical coordinate z. The
other horizontal coordinate, y, which isperpendicular to the embankment axis, isdisregarded.

Q t (kPa)
Fig. 2−1 / 2J

1012f s (s) = ( 2π ) − M / 2 C

1600
1800

Depth (m)

1

1200
1400

0

(1)

Comparison between SPT N-Value and
CPT Qt.

embankment is categorized as intermediate soil; it
consists of a sandy material with a fine content.
Standard penetration tests were conducted at the
nearby location of No. 6, and the SPT N-value andtip resistance Q t of the CPT are compared at the
points in Fig. 2. In this figure, Q t and the N-value
show a similar tendency in the vertical direction, if
correspondence between N=2 and Q t = 500 kPa is
assumed. Kasamatsu and Suzuki [8] derived
different relationships between the N-value and Q t ,so that N=2 corresponds to Q t =1200 kPa. The reason
for this difference in the two studies is that
Kasamatsu and Suzuki derived the relationshipbetween the N-value and Q t from a wide range of Nvalues, namely, values up to 40, and the accuracy
within this range of N-values is not high. In thisstudy, however, the relationship is discussed for a
low range of N-values, namely, N-values smaller
than 10.

mk = a 0 + a1 x k + a 2 z k + a 3 x k2 + a 4 z k2 + a 5 x k z k(3)

in which (x k , z k ) means the coordinate corresponding
to the position of parameter s k , and a 0 , a 1 , a 2 , a 3 , a 4 ,and a 5 are the regression coefficients.
C is the M×M covariance matrix, which is
selected from the following five types in this study:
C = [Cij ] =({)

σ 2 exp − xi − x j / l x − zi − z j / l zσ 2 exp − (xi − x j )2 / l x2 − (zi − z j )2 / l z2

 2
2
2
2
2
σ exp − (xi − x j ) / l x − (zi − z j ) / l z2 N eσ exp − xi − x j / l x − zi − z j / l z22 N eσ 2 exp − (xi − x j ) / l x2 − (zi − z j ) / l z2
i, j = 1,2,, M

{

(

{

 N e = 1 (i = j ) N e ≠ 1 (i ≠ j )STATISTICAL MODEL OF SOIL STRENGTH
Modeling Method
347

}

)

}

(a)
(b)
(c)
(d)

} (e)

(4)

GEOMATE- Osaka, Nov. 16-18, 2015

Depth z (m)Depth z (m)No.1
0Fig. 3

0
1
2
3
4
5
6
7
8
9
10

No.2
5

0

0
1
2
3
4
5
6
7
8
9
10
0

J
G
G
G
JJ
G
G
G
G
G
G
JJ
G
G
G
JJG
G
G
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JJG
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GG
G
G
GJJJJ
G
G
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JJ
G
G
G
JJ
G
GG
G

No.4
15

No.5
20

Testing point
x(m)

0
1
2
3
4
5
6
7
8
9
10
5000 10000 0
Qt (kPa)

0
1
2
3
4
5
6
7
8
9
10
5000 10000 0
Qt (kPa)

0
1
2
3
4
5
6
7
8
9
10
5000 10000 0
Qt (kPa)

0
1
2
3
4
5
6
7
8
9
10
5000 10000 0
Qt (kPa)

5000 10000
Qt (kPa)

No.6
25

No.7
30

No.8
35

No.9
40

No.10 Testing point
x(m)
45

G
JJG GG
GGGJJJJG
G
G
J
G
GG
JJ
G
G
G
G
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G
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GG
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JJ GG G
G

GG G
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JJ G
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G
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G
G
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G JJJJ
G
G
J
G
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G
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J
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G
G
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JJJ G
GG
JJ G
GG
G
JJ
G GG
G
G
JJ
G
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JJ
GG
G
J GG G
J

No.3
10
G
JJG
GGGG
GG
G
G JJJJ
G
G
G
G
JJG
GG
GJJJJG
G
JG G
GG
G
G
JJ
G
G
J
G
J
G JJ
G
G
G
G
G
G
GJJJJJG
GG
G
G
JJJ G
G
G
G
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G
G
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GJJJJ
G
G
G
JJ
G
G
G
G
GG JJJJ
GG
G JJJ
G
GJJJ
G
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G
G
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G
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G
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J
G
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GGJJJJ
G
G
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GG
G
G
G
G
G GJJJJG
JJG
G
GGG

0
1
2
3
4
5
6
7
8
9
10
5000 10000 0
Qt (kPa)

0
1
2
3
4
5
6
7
8
9
10
5000 10000 0
Qt (kPa)

Mean

Test data

G
JJG
GG
G
JJ
G
G
G
JJ
G
G
GGJJ GGG
JG
G
G
G
JJG
GG
G
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G
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JJ G
G G
JJ GG
G
G
JJJG
G
JJ G
G
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G
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G
JJJG
G
G
G
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G
G
JJJG
G
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JJG
JJJG
G
G
G
G
JJGG
G
G
GG
JJJG
G
JJ G G
G
JJG
GG
JJG
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G
JJ G
G G
G
GJJJJG
G
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GJJJJ
G
G
JJGG
G
JJ
GG
G
J
GG
G
JJ GG
GG
JJ
G
G
J
GG
G
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G
G
G
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J
G
GG JJ

G
GG
JJG
G
G
G
JJG
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JJ
G
G
G
JJ G
G
G
G
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GGG
G
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J
G
G
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JJ G
G G
JJG
G
GG
G
JJ G
GG
JJ G
GGGG
JJ G
G
G
JJG
G
G G
G
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G
GG
JJJ G
G
G
J GG
G
G
G
G
JJJ GG
G
JG
G G
G
JJJG
G
G
G
JJG
G
G
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GG
JJ G
G
GG
JJGG
JJG
GG
GG
G
JJG
G G
JJGG
G
JJG
G
G
JJG
G
G
JJG
G
G
G
JJ
G
G

JG
G
GJJG
GG
G
JJ
G
G
G
G
GJJJJJGG
GG
JG
G
JJJG
G
G
GG
G
JJG
G
J
G
G
GJJJG G
G
G
G
G
J
G
JJGG
G
G
G
G
JJJG
G
G
JG
G
JJJG
GG
G
JJGG
G
G
G
GG
JJJG
J
GG
G
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G
J
GG
GJJJJ
G
G
JJ
G
G
G
JJ
G
G
G
G
GJJJ
G
G
GGJJJJ
G
G
JJ G
G
G
JJ
G
G
JJ
G
G
G
G
GG JJJJJ
G
G
G
GGJJG

0
1
2
3
4
5
6
7
8
9
10
5000 10000 0
Qt (kPa)

JJG
GGG
G
G
JJG
G
G
G
G
JJ
G
G
GJJJJG
G
G
G
G
JG
G
JG
G
G
G
JJG
G
J
G
G
GJJJJGG G
JJG
GG
JG
G
G
JJ G
G
GG
JJ G
G GG
G
G
GG
JJ G
JJGG
GGGG
JJJ G
JG
G
GGG
G
JJJG
G
GG
G
G
JJJG
JJG
GG
G
GG
JJG
G
JJG
G
G
JJ G
G
G
JJG
G
G
G
JJG
G
JJG G
JJGG
GG

G
G
JJ
GGG
G
G
JJ
G
G
G
G
JJ
GG
G
G
JJ
G
G
JJG
GG
JJG
G
G
G
G
JJG
G
G
JG
G
J
G
G
G
JG
G
G
G
JJJGGG
G
JJG
G
G
G
JJG
G
G
G
G
JJG
G
G
GG
JJJG
JG
G
G
JJJ
G
G
J
G
JG
G
GG
JJJG
G
JJ G G
G
G
G
JG
G
JJ G
G
G
JJ
G
G
G
JG
JJ
G
GG
G
G
G
GJJJJ
G
G
G
G JJJJ
G
G
G
GJJJJ
G
GG
GJJG
JJG
G
G
JJ G
G
G
G
JJ
G
G
G
G
G JJJJJGGGGG
GJJJ GG
GG
JG G

0
1
2
3
4
5
6
7
8
9
10
5000 10000 0
Qt (kPa)

GGG
JJG
G
GGG
JJ
G
JJ
G
G
G GJJG
G
G
JJG
GG
GG
JJG
G
J
G
G
J
G
G
G
JG
G
G
GG
JJ
G
G
GG
JJ
JJG
G
JJG
GGGG
G
JJG
G
G
G
JJG
G
JG
G
G
JJJG
G
G
G
GJJG
G
G
G
JJJG
G
G
GG G
JJJG
GGG

5000 10000
Qt (kPa)

Testing data, mean (trend) of Qt in each bore hole.

in which the symbol [C ij ] signifies an i-j componentof the covariance matrix, σ is the standard deviation,and l x and l z are the correlation lengths for the x andz directions, respectively. Parameter N e is related to
the nugget effect. Akaike’s Information Criterion,
AIC, is defined by Eq. (5) considering thelogarithmic likelihood.

two-step approach is proposed as a strategy forcalculating the variograms. Firstly, the mean (trend)function and the standard deviation are determinedby the MAIC. Subsequently, the variograms areevaluated in the horizontal and vertical directions as
individual functions of the sampling intervals.

AIC = −2 ⋅ max{ln f s (S )}+ 2 L

γ x (q ⋅ ∆x ) ={}= M ln 2π + min ln C + (S − m ) C −1 (S − m ) + 2 Lt(5)

γ z (q ⋅ ∆z ) =in which L is the number of unknown parameters
included in Eq. (2). By minimizing AIC (MAIC), theregression coefficients of the mean function, thenumber of regression coefficients, the standard
deviation, σ, a type of the covariance function, thenugget effect parameter, N e , and the correlation
lengths, l x and l z , are determined.
As the correlation lengths of soil parameters areoften short compared to the sampling or the testing
intervals, sometimes the correlation lengths cannot
be determined using the aforementioned method. For
such cases, variograms are employed to identify thespatial correlation structure.
In this research, the statistical model determinedfrom the MAIC was verified by comparing thestatistical model for the variograms. The following∑ ∑ {U (x z ) − U (x + q ⋅ ∆x, z )}
Nz

N x−q

j =1

i =1

2

i,

j

i

j

2 N z (N x − q )∑ ∑ {U (x z ) − U (x , z + q ⋅ ∆z )} (6)
Nx

N z−q

j =1

i =1

2

j, i

j

i

2 N x (N z − q )q = 1,2,

where γ x and γ z are the variograms for the x and zaxes, respectively, U(x,z) is a measured parameter atpoint (x,z) from which the mean value is removed,
namely, the value of (s(x,z)−m(x,z))/σ, ∆x and ∆z are
sampling intervals, and N x and N z are the number ofsampling points for the x and z axes, respectively.
Next,
the
calculated
variograms
areapproximated by the following theoretical variogram
functions, and the correlation lengths are identified.
An exponential type of function, Eq. (4d), isemployed here.

348

GEOMATE- Osaka, Nov. 16-18, 2015

1
0.8

Variogram

Table 1 Two-dimensional spatial distribution ofQt from CPT.

Measured
Regression Func.

Mean function

0.4µ =1945.26-28.87x -359.92z +0.46x 2+60.89z 2-5.14xz

0.2

C.L.(m)
Type
S.D.
Ne
0.98
4e
722.56l x =10.00,l z =0.51µ :Mean function.
x :Horizontal coordination(m). z :Vertical coordination(m).
S.D.:Standard deviation. Type:Type of covariance matrix.
N e :Nugget effect parameter. C.L.:Correlation length≧0.

0

0

10

20

30

40

50

∆x (m)(a)

Horizontal direction

1

Variogram

C0

C1

C 0x =0.20,C 0z =0.09

C 1x =0.80,C 1z =0.91

C.L.(m)l x =77.52,l z =0.97

0.6
horizontal and vertical directions, respectively, since
the accuracy of the variogram values is high within
the range of the small values for ∆x and ∆z.
Table 1 shows the statistical models determinedby the MAIC and the variograms. Table (1a)corresponds to the MAIC results, while Table (1b)corresponds to the variogram results. The covariancefunctions are selected by the MAIC, as seen in Eq.
(4e).
The lateral correlation length obtained by the
MAIC is 20 times that of the vertical one. This
relationship agrees with the published values [9].
However, the lateral correlation length obtained by
the variograms is 50~80 times that of the verticalvalue. Comparing the results of the MAIC and thevariograms, the lateral correlation length of thevariograms is identified as being almost 5~8 times
that of the MAIC. And the vertical correlation lengthof the variograms is identified as being almost 2
times that of the MAIC. There is a general tendency
for variograms to exhibit relatively longer
correlation distances in comparison to the MAIC,
since the correlation length is identified along thesingle coordinate in the case of the variograms.

0.4
0.2
0 0

0.2

0.4

0.6

0.8

1

∆z (m)(b)

Vertical direction

Fig. 4

Variograms and regression functions.

()({)} i ≠ j(7)
γ ( z − z ) = C + C {1 − exp(− z − z / l )} i ≠ j
γ x xi − x j = C0 x + C1 x 1 − exp − xi − x j / l xiResults of variograms(b)

Measured
Regression Func.

0.8z

Results of MAIC

(a)

0.6

j

0z

1z

i

j

z

γ x (0) = γ z (0) = 0
In Eq. (7), C 0x and C 0z are the parameters used forthe nugget effect in the x and z directions,
respectively, while C 1x and C 1z are the parameters
used to express the shape of the variogram functions.
Statistical Model of Embankments
Figure 3 shows the distribution of Q t and theaverage functions at Site A. The average functions
are determined by the MAIC as nonlinear functionsof the embankment axis and the vertical direction,
respectively.
Figure 4 shows the variograms for the horizontal
and vertical directions at Site A. In these variograms,
Q t is standardized as (Q t -m)/σ to remove the trend
in which m is the mean value and σ is the standard
deviation; they are obtained by the MAIC. In thisfigure, the regression curves given by Eq. (7)approximately correspond to the observed values.
These regression curves are obtained by the leastsquares method, and finally, parameters C 0x , C 1x ,C 0z , C 1z , l x , and l z are determined. Variogram valuesof less than 15 m in the horizontal direction and lessthan 0.5 m in the vertical direction are employed to
identify the approximate functions of Eq. (7) for the

APPLICATIONMETHODOF

GEOSTATISTICAL

Sequential Gaussian Simulation Method
To spatially interpolate the soil parameters
obtained as point information, kriging is often
employed. Kriging is one of the geostatisticalmethods. A 2-D statistical model is also introducedin this method with horizontal coordinate x andvertical coordinate z. The other horizontal coordinate,y, is disregarded. Estimated parameter ξ* is definedat any location as Eq. (8) in kriging.
nξ ∗ = ∑ λα ξ α
α =1

349

(8)

GEOMATE- Osaka, Nov. 16-18, 2015

study, the sequential Gaussian simulation method is
conducted; the analysis code SGSIM [5] isemployed for the task. Realization ξ c (l), by SGSIM,
is given by Eq. (12); it is conditioned at the sample
points so that the realized values coincide with thesample values.

in which λ α is an interpolation coefficient, which isa function of the coordinates, x and z at the point α,and ξ α is a sample value of a parameter at the point
α. Symbol n signifies the number of sample points
that are used for an interpolation. The kriging
method is characterized by parameter λ αdetermined from the spatial correlation of parametervalues. In order to use Eq. (8), it is necessary to
determine an appropriate λ α . The way to do this isindicated simply as seen below.
Although the expansion of the middle of theformula is omitted here [7], the expected value forthe square residual of a true random process ξ(x,z)
and the interpolated value ξ*(x,z) are given in thefollowing equation.
2
The squared residual σ K ( x, z ) between the true
parameter value ξ and estimated value ξ* is definedas:{

σ K2 ( x, z ) = E ξ ( x, z ) − ξ ∗ (x, z )2ξ c(l ) ( x, z ) = ξ ∗ ( x, z ) + ξ (l ) ( x, z ) − ξ ∗(l ) ( x, z )(12)in which ξ c( l ) ( x, z ) means the l th realization, ξ*(x,z)
is an estimated value by kriging, and ξ(l)(x,z) is the lth realization without conditioning. ξ*(l)(x,z) is thekriged estimation using the values of ξ(l)(x,z) at the
sampling points. On the right side of Eq. (12), thefirst item is an estimated value of kriging as anaverage. The remainder of the second and third
items shows an estimation error of kriging.
Realization ξ c( l ) ( x, z ) is used as a Q t value in thefollowing chapter.

}

Applying SGSIM to CPT Resultsn= σ ξ2 − 2∑ λα Cξ ( x, z; xα , zα )(9)

The results of CPT are actual penetration
resistance values; and thus, the accuracy of themeasured values at the measuring points is good.
However, the CPT results are point estimation
values, and the interpolation of the values is needed
to visualize the spatial distribution. In this research,
the values for Q t are spatially interpolated using the
SGSIM. Furthermore, a Monte Carlo simulation isrepeated 900 times to evaluate the probabilistic
distribution of Q t .
Figure 5 presents the analysis results at Site A.
Figures (5a) and (5b) correspond to the mean and theprobability that the Q t values are smaller than 500
kPa, respectively. Due to the comparison of SPT-N
and CPT-Q t in Fig. 2, the threshold value isdetermined to be Q t =500 kPa.
According to Fig. (5a), around depth z=4-8 m
and x=10-30 m, the lowest value is detected.
Corresponding to Fig. (5a), the highest value for the
probability is obtained at the same location in Fig.
(5b).
According to Figs. (5a) and (5b), the spatial
distribution of Q t and the weak areas are identified
in detail; and thus, this study is effective for making
a diagnosis of deteriorated earth-fill dams.

α =1+ ∑∑ λα λβ Cξ (xα , zα ; x β , z β )nnα =1 β =1

Variable λ α , is determined by minimizing σ K2 (x, z ) ina restrained condition under which ∑α =1 λα = 1 . Thenparameter, σ ξ means the variance of the covariancefunctions defined in Eq. (4e).
2

 
2
 n
∂  
∗ E ξ ( x, z ) − ξ ( x, z )  − 2κ  ∑ λβ − 1  = 0∂λα   
 β =1(10)

From Eq. (10), the following simultaneous linear
equations are given, and coefficient λ α is obtainedby solving Eq. (11).
λβ Cξ (xα , zα ; x β , z β ) − κ
∑
β
n

=1

= Cξ ( xα , zα ; x, z )∀α = 1,2,, n

(11)

n

λβ = 1∑β=1CONCLUSION

in which, κ is a Lagrange multiplier. The coefficientλ α is determined as a function of the spatial
coordinate (x,z).
Although the average value is estimated by Eq.
(8) in kriging, the variance is not zero, except at the
sampling point α, and then the interpolated value forthe parameter has variability. Therefore, to consider
the variability, many realizations are required andthe simulation is repeated many times. In the present(1) Comparing the test results of the SPT-N and the
CPT-Q t values near the test points at Site A, thecorrespondence of N=2 and Q t =500 kPa has beenfound. The relationship is available within the rangeof N-values smaller than 10.
(2) The correlation lengths are determined by the
MAIC and the variograms. When comparing thecorrelation lengths obtained by the MAIC and the350

GEOMATE- Osaka, Nov. 16-18, 2015

Horizontal coordination x (m)Depth z (m)05

10 15 20 25 30 35 40 45

0
1
2
3
4
5
6
7
8

REFERENCES

Qt(kPa)3000[1] Tang WH, “Probabilistic evaluation of
geotechnical property variability”, Journal of
Geotechnical Engineering, ASCE, 105(GT10),
1979, pp. 1173–1191.
[2] Cafaro F and Cherubini C, “Large sample
spacing in evaluation of vertical strengthvariability of clayey soil”, Journal of
GeotechnicalandGeoenvironmental
Engineering, 128(7), 2002, pp. 558-568.
[3] Uzielli M, Vannucchi G and Phoon KK,“Random field characterization of stressnormalized cone penetration testing parameters”,
Geotechnique, 55(1), 2005, pp. 3-20.
[4] Nishimura S and Shimizu H, “Reliability-based
design of ground improvement for liquefaction
mitigation, Structural Safety, 30, 2010, pp. 200216.
[5] Deutsch CV and Journel AG, Geostatistical
Software Library and User’s Guide: Oxford
University Press, 1992, ch. 5.
[6] Akaike H, “A new look at the statistical model
identification”, IEEE Trans. on AutomaticControl, AC-19(6), 1974, pp. 716-723.
[7] Jourel AG and Huijbregts ChJ, Mining
geostatistics: Academic Press, 1978, ch. 1 and 7.
[8] Suzuki Y and Tokimatsu K, “Correlations
between CPT data and soil characteristics
obtained from CPT”, J. Struct. Constr. Eng., AIJ,
No. 566, 2003, pp. 73-80.
[9] Nishimura S, “The investigation of theestablishment method of soil parameters”,
Symposium of performance design anddeformation prediction of geotechnical structureduring earthquake in proceedings: The Japanese
Geotechnical Society, 2007, pp.121-126.

2500
2000
1500
1000
500
0

9

Mean (Qt)(a)

Qt<500kPa
10 15 20 25 30 35 40 45 Probability
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

Depth z (m)Horizontal coordination x (m)
0
1
2
3
4
5
6
7
8

0

9

5

(b)

Probability (Qt<500kPa)

Fig. 5

Statistical values of Qt by SGSIM at Site A.

variograms, the latter are longer than the former.
The cause of this difference seems to be thedifference in the statistic models for each method.
(3) The probability of the interpolated spatial
distribution of Q t , which is smaller than 500 kPa, isobtained by applying the sequential Gaussian
simulation method to the CPT results; the vulnerable
areas inside the earth-fill have been identified.
(4) Since the CPT is more sensitive to sounding
tests than the SPT, and short interval tests can be
conducted with it, the distribution of Q t from the
CPT is found to be more accurate for presenting the
spatial distribution of the strength inside
embankments than the N-values.
Consequently, the CPT can contribute to theappropriate maintenance of deteriorated earth-filldams.
ACKNOWLEDGEMENTS
This research was partly supported by JSPS
KAKENHI Grant Number 25292143.

351

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

BEARING CAPACITY OF STRIP FOOTINGS ON DESICCATED CLAY
CRUSTS
Qiang Xie1, Andrew J. Abbo2, Daniel W. Wilson2 and Andrei V. Lyamin2
School of Civil, Environmental and Mining Engineering, University of Western Australia, Australia
2Australian Research Council Centre of Excellence for Geotechnical Science and Engineering, University of
Newcastle, Australia
1

ABSTRACT
Numerical limit analysis was used to investigate the ultimate bearing capacity of a rigid strip footing sitting
on a soft clay with a stiff nonhomogeneous desiccated crust. Rigorous bounds on the ultimate bearing capacity
are obtained using Finite Element Limit Analysis (FELA) in which the upper and lower bound limit theorems ofclassical plasticity are used as the basis of the finite element formulations. Both upper and lower bound FELA
methods assume a perfectly plastic soil model, require the solutions of a large optimization problem, andproduce a rigorous bound which combine to accurately bracket the true collapse load. A parametric analysis was
conducted that considered varying shear strength distributions in the crust, for a range of crust thicknesses andfooting widths. Results from the limit theorems accurately bracketed the true collapse load to withinapproximately 6%. Results show that the modelling of even a relatively thin crust can provide a significantly
increase in bearing capacity over that of the underlying soft clay.
Keywords: Bearing capacity, Desiccated, Layered, Nonhomogeneous, Finite Element Limit Analysis

potential failure of the stiff crust due toconstruction equipment’s traffic load.
Park et al. [19] described two approaches for
estimating the bearing capacity of a layered clay
soils with a stiff crust. The first approached is toevaluate the bearing capacity of the footing byconsidering the nonhomogeneous shear strength
profile directly. The alternative method determines
a representative shear strength of the stiff crust andthen evaluates the bearing capacity using existing
solutions for two-layered clays with uniform shear
strengths. Park et al. [19] investigated the bearing
capacity of surface footings on the clay underlying
stiff crust of decreasing shear strength with depth
by finite element analysis and provided an insight
into the failure mechanisms and design charts forsome typical ratios of crust thickness to the footing
width.
In this research Finite Element Limit Analysis
is used to investigate the bearing capacity of strip
footings sitting on a desiccated clay crust with ashear strength that decreases linearly with depth.
For this study, the desiccated crust sit atop a very
soft layer of clay with an undrained shear strengthof 10 kPa. A parametric study was conducted for
typical ranges of shear strengths and the footing
geometries encountered in the design temporary
working platforms. The collapse mechanisms
associated with the ultimate bearing capacity of the
strip footing are illustrated using plots of upperbound velocities and power dissipation intensity.

INTRODUCTION
The bearing capacity of shallow foundations on
single-layered [1]-[3] and multi-layered [4]-[7] clay
soils has attracted the interest of researchers formany decades. Most published research consider
clay soils where the shear strength is constantwithin a soil layer. This is despite the fact that there
are many situations in which soil the shear strengthof clays may vary with the depth.
An undrained shear strength profile that
increases linearly with depth is encountered innormally consolidated clays [8]. Despite thepublication by Davis and Booker [9] of ananalytical solution for bearing capacity of a strip
footings on clay soils with a shear strength that
increase linearly with depth, researchers havecontinued to consider the bearing capacity of thesesoils [10]-[14].
The change in shear strength with depth is also
encountered in under-consolidated soils in which asurface crust may develop through the action of
weathering desiccation or/ and chemical processes[15]. Such crusts are typically much stiffer than theunderlying clays and exhibit a shear strength
profile that decreases with depth. The underlying
clay exhibits relatively constant shear strength [16][17] or one that gradually increases with the depth[18]. These conditions are frequently encounteredin newly reclaimed clay or slurry tailings [19],which makes it important to reasonably estimate
the bearing capacity in such cases because of the352

GEOMATE- Osaka, Nov. 16-18, 2015

0.5, 1.0 and 2.0 were selected to allow accuratecomparison with previous studies [7] [19].

METHODOLOGYThe upper and lower bound theorems ofplasticity are powerful tools when applied togeotechnical stability problems. The solution to thelower bound optimization problem defines astatically admissible stress field and provides arigorous lower bound on the ultimate bearing
capacity. A statically admissible stress field is one
that satisfies equilibrium, the yield criterion and thestress boundary conditions. The upper boundoptimization problem considers a kinematicallyadmissible field and gives a rigorous upper boundon the ultimate bearing capacity. A kinematicallyadmissible field satisfies compatibility, the flow
rule and the velocity boundary conditions.
In this study Finite Element Limit Analysis
methods are applied to bracket the true collapse
load by computing both upper and lower bounds on
the bearing capacity of a footing resting on the twolayered clays. The finite element formulations usedin this study are based upon the methods originallydeveloped by Sloan [20] and Sloan & Kleeman[21] that used finite elements with linear
programming. The current FELA implementation
includes the major improvements described by
Lyamin and Sloan [22] [23] and Krabbenhoft et al.
[24] [25] and importantly, the adaptive re-meshingtechniques of Sloan [26]. Important features ofthese methods include linear elements to model thestress/velocity fields and collapsed elements tosimulate the discontinuities. In addition, the global
optimisation problem is solved using a bespoke
conic programming scheme.

Fig.1 Footing model and shear strength profile.
Verification of the Model
The adequacy of the finite element limit
analysis model was verified by comparing with the
FE analysis results from Park et al. [19]. Figure 2
shows the bearing capacities for cases with St=100kPa, Sb=10 kPa and varying H/B from both finite
element limit analysis and FE analysis. The upperand lower solutions from finite element limit
analysis closely bracket the FE analysis solutions.
It can be inferred that the finite element limit
analysis model used in this project is reasonable forthe estimation of bearing capacity for
aforementioned condition.

ANALYSIS AND RESULTS

30

Problem Definition
The footing geometry and soil profile are shownin Figure 1. The size of the soil domain is 25B wideby 15B deep, which has been checked of large
enough to minimize the boundary effects to thesolution. As shown in Fig. 1, the crust is modelledwith a linear variation of shear strength across its
depth (H), with the undrained shear strength
decreasing from the strength at the soil surface (St)to that of the underlying layer (Sb).
The parametric study considered undrained
shear strengths at the surface of the crust (St)between 10 kPa and 100 kPa. In this study, theundrained shear strength the bottom layer was
limited to an extreme case of a very soft clay with
Sb=10 kPa. The undrained shear strengths St wasvaried in increments of 10 kPa. The parametricstudy also considered a range of geometries;
footing width (B) of 1.0m was modelled while thecrust depth (H) was varied to obtain dimensionlesscrust depths (H/B) of 0.25, 0.33, 0.5, 0.667, 1.0,
1.667, 2.0, 2.5, 3.0, 4.0 and 5.0. The ratios of 0.25,

Bearing Capacity qu (kPa)

25

20

15
Upper Solution
Park et al. (2010)10
Lower Solution

5
0.25

0.75 H/B

1.25

1.75

Fig. 2 Model verification with St=100 kPa and
Sb=10 kPa
353

GEOMATE- Osaka, Nov. 16-18, 2015

velocity of the ground surface to the side of thefooting is moving downwards with the rigid
footing. These are similar to the mechanisms
reported by Merifield et al. [7] for layered soil
profiles in which a strong clay overlays a relatively
weak clay. For a softer crust with St=50 kPa, Figure
4 shows that the failure mechanism contracts as thestrength of the crust is reduced, with the plastic
hinges becoming less prominent. As the shear
strength of the crust approaches that of theunderlying clay, Figure 5 shows that the failure
mechanism reflects the general shear failure
associated with a footing resting on an isotropic
clay soil.
The influence of the crust depth on the collapse
mechanism is shown Figures 4, 6 and 7 for cases inwhich St=50 kPa and Sb=10 kPa. For H/B=1, Figure
4 shows that the mechanism penetrates deep into
the underlying clay layer and extends widely due tothe stiff upper layer. For H/B=2 (Figure 6), asimilar mechanism can still be observed and it
extends quite deeply as well. For a very thick crust(H/B=5), Figure 7 shows the failure mechanism
contracts to the top of the crust only reflecting ageneral shear failure of a footing.

Results and Discussion
Upper and lower bound results from the Finite
Element Limit Analysis accurately bracketed thebearing capacity to within 6% of the true collapse
loads.
Plots of power dissipation intensity andvelocities for selected cases within the parametricstudy are shown in Figures 3 to 7. Note that thevelocity plots are not generated using results fromthe analyses to accurately compute the ultimate
bearing capacity. The very fine meshes require for
accurately have a very high density of nodes in partof the mesh and velocity vectors obscure one
another. Instead, velocity plots are generate froman upper bound analyses performed with a coarse
mesh of approximately 1000 elements. Figures 3, 4
and 5 illustrate the failure mechanisms for the caseof a unit parametric thickness (H/B=1) with theshears strength of the crust ranging from stiff(St=100 kPa) to very soft (St=20 kPa). For the caseof a stiff crust (Figure 3), the failure mechanism
extends very wide and is characterized by plastic
hinges forming in the upper layer locatedapproximately 1.5B and 5B from the footing
centerline. Unlike the usual failure mechanism
associated with the bearing capacity failure, theFig.3. Velocity and power dissipation plot for case with St=100 kPa, Sb=10 kPa, H=1 m and B=1 m.

Fig.4. Velocity and power dissipation lot for case of St=50 kPa, Sb=10 kPa, H=1 m and B=1 m.
354

GEOMATE- Osaka, Nov. 16-18, 2015

Fig.5. Velocity and power dissipation plot for case with St=20 kPa, Sb=10 kPa, H=1 m and B=1 m.

Fig.6. Velocity and power dissipation plot for case with H=2 m, B=1 m, St=50 kPa and Sb=10 kPa.

Fig.7. Velocity and power dissipation plot for case with H=5 m, B=1 m, St=50 kPa and Sb=10 kPa.
Results of the entire parametric study arepresented in Figure 8 which plots the ultimate
bearing capacity, calculated as the average of theupper and lower values computed by FELA, verse
the dimensionless crust thickness. The figure showsthat for a very thin dimensionless crust thickness,with a very stiff upper layer (St=100kPa), that thebearing capacity is approximately 40% greater thanof the underlying clay. A crust thickness of
H/B=1.5 is representative of the design of tracked
355

GEOMATE- Osaka, Nov. 16-18, 2015

contained with the upper portions of the crust andthe bearing capacity is greater than 90% of a strip
footing on an isotropic soil with an undrained shear
strength of St.

plant working above a 1m thick desiccated crust. In
this case the crust provides an increase in bearing
capacity of up to over four times greater than theunderlying soft clay for the range of parametersconsidered in this study. For the thickest of crusts
considered (H/B=5), the failure mechanism is
500

St=100 kPa
450
St=90 kPa
400
Bearing Capacity qu (kPa)

St=80 kPa
350
St=70 kPa
300
St=60 kPa
250

St=50 kPa200
St=40 kPa
150

St=30 kPa

100

St=20 kPa

50
0

1

2

H/B

3

4

5

Fig.8 Ultimate bearing capacity of strip footing.
These mechanisms are consistent with thosedescribed by Merifield et al. [7] for the bearing
capacity of layered clay soils with isotropic
strengths.

CONCLUSION
Finite Element Limit Analysis was used toinvestigate the bearing capacity of footing sitting on
desiccated soil profile. The modelling was
performed using a layered soil model that consistedof a stiff clay crust, with an undrained shear
strength that decreases with depth, overlaying avery soft clay soil.
The results of the parametric study showed thata very thin crust (H/B=0.25) with a peak shear
strength of 100kPa increased the ultimate bearing
capacity by approximately 75% over and above thatof the underlying very soft clay. For a thick crust(H/B), a very stiff crust increased the bearing
capacity by nearly an entire order of magnitude.
The failure mechanisms governing the ultimate
bearing capacity were also investigated. For crusts
with large relative thicknesses or when the crust has
a similar strength to the underlying soft clay, thefailure of the footing was a general shear failure
which the classical mechanism associated with theundrained bearing capacity of footings on clay. Fora thin crust and/or a strong crust the mechanism
extended much wider and deeper, with the crust
acting as a stiff beam and developing plastic hinges.

KNOWLEDGEMENTS
This research was supported by the Australian
Research Council Centre of Excellence for
Geotechnical Science and Engineering in
Newcastle University via an undergraduate
vacation scholarship to support collaborative
research between the University of Newcastle andthe University of Western Australia.
REFERENCES[1] Meyerhof. GG, “Some Recent Research on
Bearing Capacity of Foundations”, Canada
Geotech J., Vol. 1, 1964, pp 16-26.
[2] Terzaghi K, “Theoretical Soil Mechanics, New
York: Wiley, 1943.
[3] Vesic AS, “Analysis of Ultimate Loads of
Shallow Foundations”, J. of Soil Mechanics
and Foundations Division, Vol. 99, 1973, pp
45-73.
356

GEOMATE- Osaka, Nov. 16-18, 2015[4] Brown JD, and Meyerhof GG, “Experimental
Study of Bearing Capacity in Layered Clays”,
in 7th ICSFME, 1969, pp 45-51.
[5] Button, SJ, “The Bearing Capacity of Footings
on Two-Layer Cohesive Subsoil”, in 3rd
ICSMFE, 1953, pp 332-335.
[6] Chen WF, “Limit Analysis and Soil Plasticity”,
Elsevier, 2013.
[7] Merifield RS, Sloan SW, and Yu HS,“Rigorous Plasticity Solutions for the Bearing
Capacityof
Two-Layered
Clays”,
Géotechnique J., Vol. 49, No. 4, 1999, pp 471490.
[8] Atkinson, J. (ed.) 2007. The Mechanics ofSoils and Foundations, New York: Taylor &Francis.
[9] Davis EH, and Booker JR, “The Effect ofIncreasing Strength with Depth on the Bearing
Capacity of Clays”, Géotechnique J., Vol. 23,
No. 4, 1973, pp 551-563.
[10] Tani K and Craig W, “Bearing Capacity of
Circular Foundations on Soft Clay of Strength
Increasing with Depth”, Soils and Found, Vol.
35, No.4, 1995, pp 21-35.
[11] Lutenegger AJ, “Geotechnical Behavior of
Overconsolidated Surficial Clay Crusts”,
Transportation Res Record, No. 1479, 1995, pp
61-74.
[12] Konrad JM and Ayad R, “Desiccation of a
Sensitive
Clay:
Field
Experimental
Observation”, Canada Geotech J., Vol. 34,
1997, pp 929-942.
[13] Lee SR, Byeon WY, Park HG, and Jee SH,"Trafficability Evaluation of PTM TreatedDredged Soil Deposit", in 4th ICSSE, 2006, pp
657-666.
[14] Lefebvre G, Lefebvre LM, and Rosenberg P,“Behavior of a Cemented Plastic Clay as an
Embankment Foundation”, Canada Geotech J.,
Vol. 11, 1974, pp 46-58.
[15] Raymond GP, “The Bearing Capacity of large
footings and Embankments on clays”,
Géotechnique, Vol. 17, 1967, pp. 1-10.
[16] Salençon J, Discussion: “The effect ofincreasing strength with depth on the bearing
capacity of clays”, Géotechnique, Vol. 24,
1974, pp. 449-451.
[17] Reddy AS, & Rao KNV, “Bearing capacity of
strip
footingonanisotropic
andnonhomogeneous
clays”,
Soils
andFoundations, Vol. 21, 1981, pp. 1-6.
[18] Kusakabe O, Suzuki H and Nakase A, “Anupper bound calculation on bearing capacity ofa circular footing on a non-homogeneous clay”,
Soils and foundations, Vol. 26, 1986, pp.143148.
[19] Park H, Lee SR and Jee SH, “Bearing Capacityof Surface Footing on Soft Clay Underlying
Stiff Nonhomogeneous Desiccated Crust”, Int.

J. of Offshore and Polar Engineering, Vol. 20,
No. 3, Sep. 2010, pp 224-232.
[20] Sloan SW, “Lower Bound Limit Analysis
using Finite Element and Linear Programing”,
Int. J. of Numerical Analysis Methods
Geomech., Vol. 12, 1988, pp 61-67.
[21] Sloan SW and Kleeman PW, “Upper Bound
Limit Analysis using Discontinuous Velocity
Fields”, Computer Methods in Applied
Mechanics and Engineering, Vol. 127, 1995,
pp 293-314.
[22] Lyamin AV, Sloan SW, “Lower bound limit
analysis using nonlinear programming”, Int. J.
for Num. Meth. in Eng., Vol 55, 2002, pp. 573611.
[23] Lyamin AV, Sloan SW, “Upper bound limit
analysis using linear finite elements andnonlinear programming”, Int. J. for Num.
Meth. in Geo., Vol 26(2), 2002, pp. 181-216.
[24] Krabbenhoft K, Lyamin AV, Hjiaj M, Sloan
SW, “A new discontinuous upper bound limit
analysis formulation”, Int. J. for Num. Meth. inEng., Vol. 63, 2005, pp. 1069-1088.
[25] Krabbenhoft K, Lyamin AV, Sloan SW,“Formulation and solution of some plasticity
problems as conic programs”, Int. J. of Solids
and Structures, Vol. 44, 2007, pp. 1533-1549.
[26] Sloan SW, “Geotechnical stability analysis”,
Géotechnique, Vol. 63(7), 2013, pp. 531–572.
51st Rankine Lecture.

357

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

NUMERICAL EXPERIMENT FOR VIRTUAL PLASTER MODELTESTS SIMULATING BLOCK SHEAR TESTS
Tatsuro Nishiyama1 and Takashi Hasegawa2
Faculty of Agriculture, Ehime University, Japan; 2Professor Emeritus, Kyoto University, Japan

1

ABSTRACTThe strength of in-situ rock masses has been estimated by in-situ rock shear tests for a long time. However,
the mechanisms for the appearance of strength in such tests have not been clarified sufficiently. This paper
presents the results of a numerical analysis of virtual plaster model tests used to simulate block shear tests, which
are of a kind of in-situ test. In the authors’ former study, results were obtained for rock shear tests, another kindof in-situ test, along with real plaster model tests and finite element analyses. In the present study, some cases
simulating block shear tests were analyzed. The appearance and propagation of cracks in the testing process
were simulated with enhanced elements, which represented the displacement discontinuity in each element, as in
the former analysis. The results were compared with the former results to investigate the differences between
the two conditions. The shear strength in the two sets of results was found to be generally similar; however,there were some small differences. The patterns for the appearance and propagation of cracks differed from eachother, while some common features also appeared. The concentration of stress in the two testing processes
occurred in different parts of the materials under the two conditions, and this led to differences in both the failure
mechanism and the shear strength.
Keywords: Block Shear Test, Rock Mass, Shear Strength, Crack, Finite Element Analysis

presents the results of a numerical analysis of virtual
plaster model tests used to simulate the block shear
test. Virtual plaster model tests were adopted
because it would be tough to conduct real plastermodel tests which resemble the authors’ former ones
for simulating block shear tests due to the difficultyin the bonding between the other materials. The
results were compared with the former results toinvestigate the differences between the two kinds ofin-situ tests.

INTRODUCTION
The strength of in-situ rock masses, such as therock foundations of large dams, has been estimatedby in-situ rock shear tests for a long time. The
method for such tests is constructed as a direct shear
test, and the results have been treated as the shear
strength with Coulomb’s criteria in many practicalapplications. However, the mechanisms for theappearance of strength in such tests have not beenclarified sufficiently.
Two kinds of methods for such tests haverecently been prescribed by, for example, theJapanese Geotechnical Society [1]. One is the rockshear test, where the rock to be tested is shaped in a
block form and loads are applied to the rock block.
The other is the block shear test, where a concrete
block is set on the rock to be tested and loads areapplied to the concrete block.
The authors [2]-[3] have investigated some basic
features of the rock shear test with some plastermodel tests and a numerical analysis. Although theshear strength and the displacement obtained fromthe numerical analysis were lower than thoseobtained from the real plaster model tests, themechanical features, including the failure
mechanisms, generally resembled those of the realplaster model tests.
The block shear test, which is another kind of insitu test, is different from the rock shear test in termsof the stiffness of the block to be loaded. This paper

METHODS
Object of Analysis
The loading methods used in the in-situ tests areschematically shown in Fig. 1. In this figure, (a) and(b) represent the sections of the blocks to be loaded.
In each testing process, firstly, the normal force, F N ,is given and kept constant, and secondly, theinclined force, F I , is increasingly applied. Suchloading causes direct shear on the anticipated shear
plane.
Figure 2(a) shows the shape of the plaster models
which were used in the authors’ former study. The
conditions were set to be similar to those in the rockshear test, except that both surfaces were confinedwith clear plates to create the plane strain condition.
Figure 2(b) presents the virtual model for simulating
the block shear test, resembling the plaster model inFig. 2(a), except that the block part is almost rigid.
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GEOMATE- Osaka, Nov. 16-18, 2015

(a)

(a)

(b)
In both tests, normal stress σ and shear stress τ
are given as
σ = (F N + F I sin θ) / A, τ = F I cos θ / A
on the anticipated shear plane. The maximum
value for τ and the corresponding value for σ
are used to determine the strength. Initialnormal stress σ n is given as
σn = FN / A .
(b)Fig. 1

Two types of in-situ rock shear tests: (a)Rock shear test, (b) Block shear test.

Fig. 2

Plaster models: (a) Model for simulatingrock shear tests (Nishiyama & Hasegawa[2]), (b) Virtual model for simulating block
shear tests.

Fig. 3

Numerical model for simulating the virtual
plaster model for simulating block shear
tests, which are shown in Fig. 2(b).

In order to compare only the rigidity of theblocks, the steel cap was kept separated from theblock part even in the block shear test type (Fig.
2(b)), as was done in the rock shear test type (Fig.
2(a)).
The former plaster model tests were conducted
for dozens of cases under different values for the
initial normal stress, σ n , on both intact and layered
models, and all the cases were numerically analyzed(Nishiyama & Hasegawa [3]). In the new analysis,shown in this paper, tests were conducted on the
intact model considering 7 different levels of initialstress, namely, σ n = 0.375, 0.625, 1.25, 2.5, 3.75, 5,
7.5 (MPa).
Given Conditions and Procedure
The numerical model for the virtual model in Fig.
2(b) was composed of finite elements, as shown in
Fig. 3. All the elements were simple constant strain
triangles. The material parameters are given in
Table 1; they are the same as those in the authors’former analysis.
At the boundary between the cap and the block,359

GEOMATE- Osaka, Nov. 16-18, 2015

Table 1 Material properties.
Material
PlasterItem
Density (kg/m3) *Elastic modulus (MPa)Poisson’s ratio
Uniaxial compressive
strength, σ ci (MPa)
Uniaxial tensile strength(MPa)
Hoek-Brown’s constant, mHoek-Brown’s constant, sSteelElastic modulus (GPa)Poisson’s ratio* Density was ignored in the analysis.

Value
1,121
3,697
0.35
16.56
−2.844
5.65
1
200
0.3

Fig. 4The strength of the plaster material was described as Hoek-Brown’s failure criteria (Hoek[4]), namely,

Comparison of shear resistance values between the two types of tests.

this figure, the values for each type differ from one
another. The shear strength for the block shear test
type is smaller than that for the rock shear test typein the lower normal stress range, and such atendency seems to make the row of data points
become straighter. In the higher normal stress range,
the results of the two types of tests are not the same;
however, they commonly appear to be lower thanthe material strength.
Figure 6 shows the cracking sequence in one of
the cases of the block shear test type. Referring to
Fig. 5, the first small failure which localized on theside opposite to the loading side of the inclined force
corresponds to the peak shear stress. Then, in Fig. 5,
block heaving has already appeared. After that,
small failures occurred gradually along the base of
the block, and finally, the block was completely
separated from the foundation. Referring to theformer results for the rock shear test type, shown in
Fig. 7, the width of the broken zone is narrower inthe block shear test type.
The difference in the features of the cracking
sequences, according to the initial normal stress, ismuch less noticeable than the difference in the rockshear test type. The curved propagation of a tensile
crack just under the loading side happened only inthe cases of the lower normal stress with the rockshear test type, whereas it occurred only in the casesof the higher normal stress with the block shear test
type. Figure 8 shows the crack distribution at theresidual state in one of the cases under higher
normal stress.

σ1 = σ 3 + m σ ciσ 3 + sσ ci 2
where σ 1 and σ 3 are the major and the minor
principal stress values, respectively.

double nodes were adopted for the contact analysis.
A simple contact analysis was also conducted on theouter boundary; however, the boundary between theblock and the foundation was treated as an ordinary
element boundary with single nodes because theblock and the foundation should be bonded throughthe loading process.
An incremental analysis was conducted in thesame way as in the former analysis, as follows. In
every case, the boundary condition corresponding to
the normal load, F N , was given as the nodal force inthe first step. Then, from the second step, theboundary condition corresponding to the inclined
load, F I , was given incrementally as the nodal
displacement.
To simulate crack propagation, the CST elements
whose stress had reached the material strength were
replaced at every step with enhanced elements, eachof which included an interface within itself (Bolzon[5]).
When the stress of the enhanced elements
reached the material strength, the elastic modulus of
the concerned elements was reduced to 10% of theoriginal value to express the crushed material.
Failure was basically not considered for the steel
elements. However, only the steel elements whichwere adjacent to the plaster elements where failure
occurred were replaced with enhanced elements on
account of the activation on the interface nodes ofthe enhanced plaster elements.

DISCUSSION
The block is stiffer than the foundation to bebroken in the block shear test type, while thestiffness is the same in the rock shear test type.
Therefore, the stress distribution in the foundation
under the block would need to be made to be nearly
uniform in the block shear test type. On the other
hand, the non-uniform stress distribution seemed tocause a reductio n in str ength in the model

RESULTS
Figure 4 shows the macroscopic shear strength
values which were obtained from the analysis. In
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GEOMATE- Osaka, Nov. 16-18, 2015

1

Fig. 5

Relation between the displacement and theshear stress in the case of σ n = 0.625 MPa.
The numbers correspond to those in Fig. 6.
2

tests of the rock shear test type because the peak
shear stress appeared with a small fatal failure in
every case. Before the analysis, therefore, thestrength in the block shear test type was expected tobe higher than that in the rock shear test type.
In the analysis of the block shear test type,
uniform stress distributions certainly appeared, as
shown in Fig. 9(b). However, the stress concentration caused on the side opposite to the loadingside was remarkable. The reason is that it is easierto transmit the compressional stress to the opposite
side with a stiffer block. Such a stress concentration
caused a decrease in strength in the analysis. This
reduction in strength corresponds to the knowledge
reported by the Japanese Geotechnical Society [6].
In the rock shear test type, the regularity of therow of data points for the shear strength varied
through the normal stress ranges according to thevariation in failure mechanisms. The variation in
failure mechanisms, according to the normal stress,
was very little in the block shear test type; however,
the obvious propagation of a tensile crack under theloading side appeared only in each case under thehigher normal stress. Once again, referring to thedetails in Fig. 4, the row of data points appears to be
divided into two straight rows which consist of threepoints each, and the data point in the center seems torepresent the transitional state. Such a variation in
failure mechanisms, including the variation in the
directions of the cracks, should also be related tothat in the rock shear test type.

3

Fig. 6

CONCLUSION
A finite element analysis was carried out for
virtual plaster model tests to investigate the basic
mechanisms of block shear tests. From the analysis,
some differences between block shear tests and rockshear tests were found.

361

Cracking sequence in the case of lower
normal stress level, σ n = 0.625 MPa, of theblock shear test type. Displacement isemphasized as 30 times. The blue areas
represent the regions where the elastic
modulus has been reduced. The numbers
correspond to those in Fig. 5.

GEOMATE- Osaka, Nov. 16-18, 2015

Fig. 7(a)Crack distribution at the residual state in thecase of σ n = 0.625 MPa of the rock shear
test type (Nishiyama & Hasegawa [3]).

(b)Fig. 8Crack distribution at the residual state in thecase of higher normal stress level, σ n = 3.75
MPa, of the block shear test type.

Fig. 9The difference between block shear tests androck shear tests is the degree of stiffness of theblocks. This in turn causes a difference in the
distribution of stress in the rock to be broken andbrings about changes in the failure mechanisms.
Such a difference also leads to a difference in themacroscopic shear strength.
The macroscopic shear strength is lower in theblock shear tests than in the rock shear tests underlower normal stress. Such a tendency makes the row
of data points become straighter.
The above conclusions are of course only certain
for the conditions given in limited cases. Further
investigations in the future, for example, a detailed
stress analysis or additional analyses for manydifferent cases, are required to obtain more general
conclusions.

Comparison of the stress distributions at thepeaks of the shear stress in the cases of σ n =0.625 MPa: (a) Rock shear test type, (b)Block shear test type. The segments
represent the principal stress axes, shown inblack for compression and in magenta fortension, and σ cm =�𝑠𝑠𝜎𝜎ci2 represents the compressional strength of mass of the material.
In the foundation part, just under eachblock, the compressional stress is distributed widely in (a), while there is little loadin (b).

REFERENCES[1] The Japanese Geotechnical Society, “Method
for In-situ Direct Shear Test on Rocks”, JGS
3511-2004, 2004.

362

GEOMATE- Osaka, Nov. 16-18, 2015[2] Nishiyama T & Hasegawa T, “A fundamental
study on the strength measured by in-situ rockshear tests”, in Proc. 3rd Int. Symp. on Deformation Characteristics of Geomaterials, 2003,
pp. 97-104.
[3] Nishiyama T & Hasegawa T, “A practical useof the finite element with an embedded interface
for simulating the direct shear on brittle
materials”, in Proc. 14th IACMAG, 2014, pp.
323-327.
[4] Hoek E, “Strength of Jointed Rock Masses”,
Géotechnique, Vol. 3, 1983, pp. 185-223.
[5] Bolzon G, “Formulation of a triangular finite
element with an embedded interface via isoparametric mapping”, Computational Mechanics, Vol. 27, 2001, pp. 463-473.
[6] The Japanese Geotechnical Society, Methods ofDetermination of Geotechnical Constants for
Design –Rock Mass Edition-. Tokyo: The
Japanese Geotechnical Society, 2007, p. 12 (inJapanese).

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Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

CREEP AND RELAXATION BEHAVIOR OF HIGHLY ORGANIC SOIL
Shoji KAMAO1
Nihon University, Japan

1

ABSTRACT

Because of the limited land area in Japan, many structures are built on peat and/or highly organic soft
ground. . These soils are well known for exhibiting large settlement and secondary compression during and after
construction. Preloading is suitable for ground improvement of peat and highly organic soil, as well as clay.
The author performed a series of laboratory loading/unloading creep tests to simulate the preloading method
for peat and silty clay which samples collected in Japan. Previously, the author reported that the characteristics
of long-term creep settlement after unloading are affected by the overconsolidation ratio, preloading time andplasticity index. In this paper, the experimental data from creep and stress relaxation tests are used to further
explore the behavior of highly organic soil.
Keywords: Peat, Highly organic soil, Preloading method, Creep, Relaxation

INTRODUCTION

USED SOIL

Recently, urban development has been brisk
around large cities in Japan. Because of the limited
land area, many structures are built on peat and/or
highly organic soft ground, which used to be amarsh. Highly organic soil is known for its large
compressibility during and after construction.
In addition, many areas along Pacific Ocean
were damaged by tsunami at the time of the Great
East Japan Earthquake in 2011. Therefore,
developments of new revival residential land havebeen constructed inland and at higher places fromthe coastal line to prevent damage caused bytsunami. Many developments have been made inlandand on higher ground that was formerly used for ricepaddies or marsh. Due to the construction on highlyorganic soil, a geotechnical problem of large longterm settlement often occurs after construction. This
paper focuses on improving the long-term settlementof soft soil by using the conventional preloading
method. The effect of preloading was considered on
the basis of the results of two types of experiments,
i.e. the creep test with loading and unloading and thestress relaxation test that tolerates no settlement
during loading. The two tests are used to clarify the
reduction of long–term settlement generated after
unloading the preload.
The determination of OCR (overconsolidation
ratio) is often a difficult problem in the design of the
preloading method, because there are only a few
studies related to design and construction preloadingthat consider large long-term settlement for highlyorganic soils (e.g. Kamao et al. 1995, and Fukazawa
et al. 1998).

Table 1 shows the typical properties of the soil
used in this study. The M soil is highly organic soil
sampled in Chiba Prefecture near Tokyo. The S soil
is silty clay sampled in Tokyo Bay area. The K soil
is organic clay that is mixture of the M and S soils.
All specimens were used in remolded condition
under reconsolidation pressure of 20 kPa. They wereprepared as follows: the water content of the
disturbed sample was adjusted to be twice the liquid
limit (LL) and then consolidated under thereconsolidation pressure of 20 kPa for
approximately two weeks.

Table 1 Soil properties

TEST PROGRAM
This study considers the long-term settlement of
the soft ground as a one-dimensional consolidation
phenomena like embankment of a widely spreadarea without lateral movement. Creep and relaxation
tests were conducted in the laboratory using a
conventional consolidation apparatus (Φ=60mm,
h=20mm, JIS A 1217).
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GEOMATE- Osaka, Nov. 16-18, 2015

The Creep Test

The Relaxation Test

Fig. 1 and Table 2 show the loading scheme andtest conditions for the creep test.
In the first step, reconsolidation pressure (p 0 ) of
the remolded soil specimen was loaded for 24 h. In
the second step, the preload (pp) was loaded for
preloading time tp . In the last step, Δp load wasremoved.
The laboratory experiments correspond to thepreloading method in the field, OCR, the rate ofsecondary compression (εα) and the rebound ratio(εR) were chosen as parameters for evaluating
preloading effects defined in Equations (1), (2) and(3). Other details are shown in Kamao et al. 1995.

The relaxation test was performed by reducing aload such that settlement generated in a specimen
was eliminated completely when the load was
reduced after loading the specimen with a preload ofp p for tp. The relaxation tests were conducted asshown in Fig. 2 and Table 3. Each case had two
relaxation phases (relaxation1 of t1 and relaxation2
of t2) at consolidation pressures of pp1 (80 kPa) andpp2 (160 kPa), respectively. In some cases, residual
consolidation pressure was retained for a few weeks
after the relaxation period (t2) to measure the longterm resettlement.
By comparing the creep test and the relaxation
test results, the author implemented to make basic
data for preloading method considering the longterm settlement of highly organic soil.

OCR = (p 0 + pp) / (pp − Δp) = (p 0 + pp) / pf(1)
εα = Δε / Δlog t × 100 (%)(2)

εR = SR / S × 100 (%)

(3)

where
p0: preconsolidation pressure (kPa)pp: preloading pressure (kPa)Δp: unloading pressure (kPa)Δε: strain during long-term settlement
S: settlement before unloading (mm)SR: rebound due to unloading (mm)t: elapsed time (min)Fig. 2 Loading scheme of the relaxation test

Table 3 Test conditions for the relaxation test

Fig. 1 Loading scheme of the creep test

Table 2 Test conditions for the creep test

EXPERIMENTAL RESULTS
Results of the Creep Test
Fig. 3 shows typical experimental results (S –log t curves) of the creep test. With removal of the
preload at time tp, rebound due to swelling occurred
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GEOMATE- Osaka, Nov. 16-18, 2015

immediately for all OCRs except OCR of 1.0. Then,
resettlement was observed after the rebound phase
for all OCRs except the OCR of 16.0.
Relationships between the rebound ratio (εR) andOCR are shown in Figs. 4 and 5. Fig. 5 shows thedetails of Fig. 4.
From these figures, linear relationships between
rebound ratio (εR) and OCR are obtained for eachsoil. The amount of rebound for soil M is 5 to 6
times larger than that for soil S and 1.6 to 1.8 times
larger than that for soil K. The value of εR depends
on the plasticity index (PI) of the soil.

settlement

OCR=16.0

Fig. 5 Relationship between εR and OCR(1<OCR<3)
The rate of secondary compression (εα) is
defined by Equation (2). Fig. 6 shows therelationship between εα and OCR. The rate ofsecondary compression (εα) decreases with increaseof OCR. The reduction ratio (R) of the rate ofsecondary compression, which is seen in Fig. 6, isdefined as the ratio of ε α and ε α, OCR=1.0 , as indicatedby Equation (4). This value can help evaluate thepreloading effect.
The relationship between R and OCR is shownin Fig. 7. The optimum OCR (i.e., which minimizes
resettlement) was approximately 2.0 to 2.5 for all
types of soil. Fukazawa et-al. (1984) reported the
optimum OCR as approximately 1.3 to 1.5 in fieldmeasurement data. Laboratory data from the present
study tended to a larger OCR number than the fielddata of Fukazawa et al.. The reason for thisdifference is considered to be the difference in thecondition of the specimen. The disturbed specimen
for this study has weaker soil structure thanundisturbed field deposits (e. g., see Kamao, 1995).

OCR =2.0
OCR =1.6=1.3

unloading(t p =60 min)

10

-1

10

1

OCR =1.0
10

3

10

5

elapsed time
Fig. 3 Experimental results of the creep test

Fig. 4 Relationship between εR and OCR(1<OCR<16)

Fig. 6 Relationship between εα and OCR
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GEOMATE- Osaka, Nov. 16-18, 2015

R = {1 − (ε α / ε α, OCR = 1.0 )} × 100 (%)
(4)
where
ε α : rate of secondary compression
ε α,OCR = 1.0 : rate of secondary compression at
OCR = 1.0

PI of the soil. The decreased consolidation pressure

Δp1 (Δp 2 ) in Fig.2, which was defined as theconsolidation pressure at t=104 min by extending thestraight line of the latter part of the test, is shown in
Table 4. Consolidation theory indicates that t=104
min is enough consolidation time for thin specimens
to undergo the laboratory oedometer test, and toundergo comparison with the creep test. The final
decreased consolidation pressure is similar to theunloading of the creep test shown in Fig. 1. Using
residual consolidation pressure p f (= p 0 + p p1 − Δp 1 ),the “absolute OCR” (i.e., which has no long-term
settlement (εα = 0) during the relaxation phase) was
calculated for each soil using Equation (1). The
calculated absolute OCRs are shown in Table 4. The
relationships between absolute OCR and PI for
different soils are shown in Fig. 12. Regardless ofthe consolidation pressure p p , OCR is wellcorrelated with PI of soil.

Fig. 7 Relationship between reduction ratio (R)and OCR

Results of the Relaxation Test
Figs. 8, and 9 show typical experimental resultsof the relaxation test. Fig. 8 shows the relationshipbetween settlement and elapsed time before and after
the relaxation phase (relaxation time is 120 min). Fig.
9 shows the relationship between consolidationpressure and relaxation time. From these figures, it
can be seen that no settlement occurs after the startof relaxation time (Fig. 8), and significant reductionof the consolidation pressure was observed for all
three types of soil, immediately after shifting to therelaxation phase. Then consolidation pressure
gradually decreased with relaxation time to keep
settlement zero (Fig. 9). Reductions were achievedby stemming rapid settlement just before shifting to
the relaxation phase, proving preferable andcontrolling operation.
It is difficult to estimate the final decreased
consolidation pressure from Fig. 9, because theconsolidation pressure decreases gradually withrelaxation time. The relationship between
consolidation pressure p and the logarithm of therelaxation time t1 is linear at the latter part of the test,
as shown in Fig. 10. The slopes of the latter p-log t 1
curves (p/t) in Fig.10 are similar to the secondary
compression settlement in Fig. 3. The magnitude ofthe linear slope, seems to be proportional to theamount of secondary consolidation settlement. Fig.
11 shows the linear relationship between p/t and theFig. 8 Experimental result of the relaxation test(s – log t curves)

Fig. 9 Experimental result of the relaxation test(p - t curves)367

GEOMATE- Osaka, Nov. 16-18, 2015

Fig. 12 Relationship between calculated
absolute OCR and PI from relaxation test

Fig. 10 Experimental result of the relaxation test(p – log t curves)

These absolute OCRs were plotted on the creep
test result (Fig. 7), as shown in Fig. 13. For εα=0
(absolute OCR), the reduction ratio (R) become
100(%) from Equation (4). Green stars on the figure
indicate the absolute OCR from the relaxation test.
The green lines are tendency lines.
Fig. 13 shows the tendency lines of therelaxation test are steeper than the creep test data.
The differences between the relaxation test and thecreep test are caused by the speed of unloading(rapid or slow unloading) and duration of loading.
The estimation method for the final reduced pressure(Δp1) also seems to introduce differences. Rapid
unloading (as in the creep test) allowed the specimen
to swell and rebound; however, slow unloading (likerelaxation test) did not allow either. Slow unloading
took considerable time.
With rapid unloading, the soil structure wasdisturbed and changed due to the disruption of bonds
between particles and aggregates (e. g., Den Haan etal., 2003). Therefore rapid unloading made thereduction ratio (R) smaller than slow unloading.

Fig. 11 Relationship between p/t and PI fromthe relaxation test
Table 4 Estimated residual consolidation pressure
and calculated absolute OCR at the relaxation timeof 104 min

DISCUSSIONThe OCR of the preloading method is discussed
by comparing the creep test with relaxation test. The
absolute OCR (which has no long-term settlement(εα=0)) is 2.1 to 2.2 for highly organic soil (M). For
silty clay (S) and organic clay (K), absolute OCRs
are 11.3 to 1.4 and 1.8, respectively.
368

Fig. 13 Relationship between the reduction ratio(R) and OCRGEOMATE- Osaka, Nov. 16-18, 2015

CONCLUSIONREFERENCES

Two series of laboratory model loading/unloading
tests were performed. The following conclusions
were reached:

Kamao, S., Yamada, K., Sato, F. and Aita, K. (1995)
Characteristics of long-term resettlement of soft
ground after removal of the preload. International
Symposium on Compression and Consolidation ofClayey Soils. (I.S. Hiroshima): 75-78(1) The creep test showed that the rebound ratio ε Rincreases in accordance with increased OCR
and the rate of secondary compression ε αdecreases in accordance with increased OCR.
(2) The optimum OCR (i.e., that which minimize
long-term settlement) is approximately 2.0 to
2.5 for all soils.
(3) The relaxation test showed that reduction of
consolidation pressure was well controlled,
without settlement and rebound.
(4) The absolute OCR was obtained using
estimated residual consolidation pressure.
(5) The absolute OCR was approximately 2.1 to
2.2 for highly organic soil (M). The tendencyline is steeper than the creep test data for thissoil.

Fukazawa, E. and Monoi, Y. (1998)
Measurement of long-term settlement of highlyorganic soil improved by preloading. International
Symposium on Problematic Soils. (I. S. Tohoku):
75-78
Fukazawa, E., Yamada, K. and Kurihara, H. (1994)
Predicting long term settlement of highly organic
soil ground improved by preloading. Journal ofJapanese society civil engineering, No.493, III-27,
pp59-68 (in Japanese)
E. J. den Haan, Kamao S. (2003)
Obtaining Isotache parameters from a C. R. S. K 0 oedometer. Soils and foundations, vol. 43, No. 4,
pp203- 214

ACKNOWLEDGEMENTS
All laboratory experiments were performed by
student members of Environmental geotechnics
laboratory of Nihon University.

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Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

INVESTIGATION OF SEISMIC BEHAVIOR OF EARTH DAMEMBANKMENTS BY USING FEM AND CENTRIFUGAL LOADING TESTS
Shohei Tsurui 1, Takahiro Sugano1, Akira Kobayashi 3 and Akira Murakami 1
1
Graduate School of Agriculture, Kyoto University, Japan
3
Faculty of Environmental and Urban Engineering, Kansai University, Japan

ABSTRACT
There are many earth dams in Japan. In particular, the number of irrigation tanks is estimated to be over
210,000. Many irrigation tanks in Japan were constructed on the basis of experience gained over one hundredyears ago. Many of these tanks have suffered earthquake damage over the years. The types of damage reported
include cracks at the crest in the dam-axis direction. In order to clarify this mechanism, model experiments usinga small shaking table in a 1-G gravity field, which can visualize the behavior of the cross-sections, are conducted.
It is found that vertical tension and compression zones appear alternatively, and that tensile stress is observed at
the crest of the model from the volumetric strain distributions. Cracks at the crest are estimated to be caused bytensile stress, which is not considered in the design process. In this research, a finite element analysis andcentrifugal loading tests in a 50-G gravity field are applied to examine the mechanism. As a result, it is foundthat the distribution where the vertical tension and compression zones exist alternatively is attributed to theheterogeneity of the dam body.
Keywords: Seismic Behavior, Earth Dam, Centrifugal Loading Test

simulate the behaviour of a full-scale prototype. On
the other hand, 1-G shaking table tests are carriedout to examine the quantitative aseismic behavior. In
order to clarify the mechanism, the modelexperiments were conducted using a 1-G small
shaking table, which could visualize the detailed
behavior of the cross-section (Miyanaga et al., 2013).
Figures 1 and 2 show the results of the modelexperiments. In the shear strain distributions, theshear strain becomes large at the foot of the slope
when the specimen passes the shaking center. In thevolumetric strain distributions, the tension andcompression zones exist vertically and alternatively,
and tension stress is observed at the top of the model.
It is inferred that the seismic crack at the crest of thedam is caused by tensile stress.
In this study, the tensile stress at the crest,
observed in the 1-G shaking tests, is confirmed bycentrifugal loading tests with a gravity field of 50 G.
Moreover, the mechanism of the tensile stress at thedam crest is examined by a numerical analysis.

INTRODUCTION
In Japan, there are approximately 210,000 small
earth dams, which have been very important sourcesof water supply for irrigation. The height of thedams is shorter than 15 m, and such dams are called
irrigation tanks in Japan. Many irrigation tanks wereconstructed on the basis of experience gained over
100 years ago. It is reported that about 20,000
irrigation tanks suffered earthquake damage in thepast and are in need of repair.
As for the damage to many irrigation tanks,
including those which are not considered to have an
earthquake-resistant design, it is reported that cracks
at the crest in the direction of the dam axis were
induced by earthquakes. That mechanism isconsidered to be different from slide failure, which
is taken into consideration in the design manual. It isdifficult to repair irrigation tanks effectively because
the mechanism of such cracks on the crest has notbeen clarified.
The most important factor in the seismic stabilityof irrigation tanks is the earthquake response. The
material properties, including the water content of
the soils and the shape of the irrigation tanks,
influence the aseismic behaviour. Investigations of
the seismic behavior of irrigation tanks have been
conducted based on numerical analyses and modelexperiments.
Two types of dynamic tests have been used forthe dynamic model tests, namely, dynamic
centrifuge tests and 1-G shaking table tests.
Dynamic centrifuge tests utilize the gravity force to370

GEOMATE- Osaka, Nov. 16-18, 2015

CENTRIFUGAL LOADING TESTS

0.01

150
Height (mm)Test conditions
00

The centrifugal loading tests were conductedunder 50-G with a 1/50 scaled model. In thisexperiment, the model had a height of 100 mm andupstream and downstream gradients of 1:1. The
model was made from No. 7 silica sand and kaolin
clay with a mixture ratio of 8:2 by dry weight. The
water content of the mixture was 13%. In order toevaluate the seismic behavior of the model, thegauge points were placed on the surface.
Accelerometers were installed at the top of themodel, the bottom of the model, and on the soilchamber. Figure 3 presents an experimental view ofthe model.
The model was shaken with a horizontal sine
wave of 50 Hz whose amplitude was 1 mm. The
input seismic wave is shown in Figure 4. The input
seismic wave corresponds to the earthquake groundmotion with a peak acceleration of about 300 gal.

-0.01
0

200

150

0.02

Height (mm)Breadth (mm)a) Small model

0

0

0.02
0

520
Breadth (mm)b) Large model

1

Fig. 1 Shear strain distributions of modelexperiments

1

0.015

Height (mm)

100 mm150

0Fig. 3 Experimental view of the model

0
200

0

-0.015

Breadth (mm)a) Small model

150
Height (mm)

0.02

0

0

0

Breadth (mm)

520

-0.02

Fig. 4 Input seismic waveb) Large modelFig. 2 Volumetric strain distributions of model

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GEOMATE- Osaka, Nov. 16-18, 2015

Digital image analysis
In order to evaluate the displacement of thegauge points, the digital image analysis method,used in the 1-G gravity field (Miyanaga et al., 2013),
is applied.
Firstly, a static image is taken before the modelis shaken. While the model is shaken, continuous
images are taken. The images are transformed into
black and white binary images in order to make theimage process quick and easy, as shown in Figure 5.
Then, the coordinates of the gauge points aremeasured in the unit of pixels by calculating thecenter position of each white element representing
the gauge point. Finally, the distance between two
reference points on the surface of the soil chamber,which was exactly 150 mm, is measured in the unitof pixels. The scale calibration is performed using
the distance of the reference points, and thecoordinate of the gauge points is estimated as therelative location of the reference point in the unit ofmm. By repeating this procedure for all the dynamic
images, the displacement variation for each gauge
point can be obtained.

Left end

CenterRight end

CenterFig. 6 Shear strain distributions of centrifugal
loading tests
Fig. 5 Black and white binary image of modelTwo points at the bottom of figure are the reference
pointsExperimental results
Aseismic behavior
The shear strain distributions are shown in
Figure 6. The shear strains become large at the topof the model. The left parts of the models havenegative values, while the right parts have positive
values. Figure 7 shows the displacement between
two gauge points at the center of the model, which isshown by red lines in Figure 8. It is found that thedisplacement at the top becomes large with time.
However, no change is seen for the displacements inthe middle or at the bottom. Therefore, it is foundthat the upper part of the model expands in ahorizontal direction.

Fig. 7 Displacement between two gauge points
during shaking

372

GEOMATE- Osaka, Nov. 16-18, 2015

Figure 10 shows the cracks at the crest of thespecimen. The cracks developed gradually andfinally the depth became 5 mm. The settlement wasobserved at 5 mm.

cracksFig. 8 Positions at which the displacement ismeasured
Figure 9 shows the volumetric strain
distributions of the centrifugal loading tests. The
volumetric strain is large at the top of the model
during shaking. Moreover, the compression zones
are small and are seen only in the right part at thebottom. The extension zones are observed along theslope. It is inferred that the tensile stress in the upper
part causes the cracks at the crest.

Fig. 10 Cracks observed in centrifugal loading tests
SIMULATION ANALYSIS
Model Shapes
In this study, two types of dam models withdifferent cross-sections, examined in 1-G tests, aresimulated. The first type, with a height of 150 mm
and upstream and downstream gradients of 1:0.545,
is called the small model. The second type, with thesame height and gradients of 1:1.5, is called thelarge model. These model shapes are the same as
those in the experiments shown in Figures 11 and 12.

Left end

CenterRight end

CenterFig. 11 Model types of numerical analyses

Fig. 9 Volumetric strain distributions of centrifugal
loading tests
373

GEOMATE- Osaka, Nov. 16-18, 2015

Case 136.5 (mm)Figure 14 shows the shear strain distributions of
the small and large models when the model shakes
from downstream to upstream and passes the centerof shaking. It is found that the maximum shear strain
is given at both ends of the bottom for the small
model. On the other hand, the maximum shear strain
is obtained at the central part of the bottom for thelarge model.
Figure 15 shows the volumetric strain
distributions of the small and large models at thesame time as in Figures 14 and 15. The maximum
volumetric strain is shown at the top and at bothends of the bottom for the small model. On the other
hand, the maximum volumetric strain is shown at theslopes for the large model. The volumetric strain
observed in the small model is larger than that in thelarge model. The volumetric strain for both cases is
distributed in a vertically striped pattern.

150
(mm)

5 (mm)
200(mm)Fig. 12 Small model with base (Case 2)
Methods
To examine the strain distributions, the elastoplastic FEM, using Mohr-Coulomb’s criteria as theyield function, is applied in this study. The bottomsof the models are subjected to 2.4 Hz of horizontal
sine wave. The amplitude is 280 gal. The wave is thesame as that applied in the 1-G tests.

Table 1 Material parameters

Analytical Conditions
The shear strain distributions and the volumetric
distributions are examined for the following threecases;
Case 1 The basic case, with no hydrodynamic
pressure
Case 2 The small model has a base whose
thickness is 5 mm. Homogeneous accelerations are
not input into the bottom of the dam body.
Case 3 Hydrodynamic pressure is exerted on the
upstream slope of the large model.
The model and the base in Case 2 are set asshown in Figure 12. Case 2 is the case for which the
input wave is not homogeneous. When the bottom of
the base is subjected to a given wave, the fluctuationof the bottom of the dam body becomes a little
inhomogeneous, while the amplitude is not so
different.
The analytical parameters are set by reference tothe measured parameters of the 1-G tests, as shownin Table 1.

Parameters

Dam Body

Cohesion (kPa)
Unit weight (kN/m3)
Elastic modulus(kPa)
Internal friction
angle (°)
Poisson’s ratio

15
18

Base
(Case2)
15
18

3,000

2,850

35

35

0.3

0.3

Fig. 13 Hydrodynamic pressure when the model islocated at center of shaking table

Hydrodynamic Pressure
Figure 13 shows the measured hydrodynamic
pressure when the model is located at the center ofthe shaking. The hydrodynamic pressure becomeslarge when the model shakes from upstream todownstream. The hydrodynamic pressure becomes
small when the model shakes from downstream to
upstream.
Results of Analyses

374

GEOMATE- Osaka, Nov. 16-18, 2015

3×10

Downstream

Height (mm)150 Upstream

the homogeneous accelerations would not be input
into the bottom of the model. The shear strain at the
top is very small. As for the volumetric strain, theextension andcompression zones
appearalternatively at the lower part of the model.

-5

0

0

-3
0

Breadth (mm)a) small model

200

×10

150

Upstream

Downstream

Upstream

-5

3 ×10

-5

0

0

3

0

Height (mm)Downstream

Height (mm)150

Breadth (mm)

200

-3

a) Shear strain distribution
Upstream0

Height (mm)

150

0
0

b) large model

520

3

Fig. 14 Shear strain distributions of Case 1

Height (mm)Upstream0

Height (mm)-3

Extension
0Compression
0
Breadth (mm) 200 -4b) Volumetric strain distribution

×10
Downstream 1
Extension

Fig.16 Strain distributions of Case 2
Case 30Compression
Breadth (mm)200 -1

-3

15

×10
4

0
-3

150

Downstream

Upstream

Downstrea

×10
1
Extension
0

BreadthCompression

0
Breadth (mm)

520

-1

Fig. 15 Volumetric strain distributions of Case 1

Case 2
Figures 16 show the shear strain distribution ofCase 2 when the models are at the center of shaking
from downstream to upstream. By setting the bases,
375

In order to examine the effects of hydrodynamic
pressure on embankments, the hydrodynamic
pressure is applied to the upstream slope of the large
model. The results are shown in Figures 20 and 21.
The figures show the strain distributions when they
shake from downstream to upstream and pass thecenter of shaking. The shear strain becomes large at
the central part of the model. The parts at the slope
have small strain in comparison to that shown in
Figure 17. This is probably because the body isconstrained by the hydrodynamic pressure. As forthe volumetric strain distributions, the extension
strain develops along the upstream slope and thecompression strain extends to the downstream parts.
The distributions are in contrast to the results
without hydraulic pressure, shown in Figure 17.

GEOMATE- Osaka, Nov. 16-18, 2015

1) Large extension strain was observed in theupper parts of the dam in the results of both 1-G andcentrifugal loading tests. In the centrifugal loading
tests, a crack was observed similarly to the actual
damage. Therefore, it can be concluded that thecrack at the crest was caused by extension stress.
2) An ordinary elasto-plastic analysis was notable to realize the large extensional strain in theupper part. The shear strain distributions were alsoquite different from the results observed in theexperiments. As a present conclusion, it can be said
that an ordinary elasto-plastic model is not suitable
for analyzing the dynamic behavior of earth dams.
3) The alternative distributions of extensionaland compressional volumetric strain were realized
by the inhomogeneous input of waves. However,large extensional strain was not realized by thesimulation. As a result, not only the selection of thenumerical model, but also the heterogeneous
distribution of properties has to be examined for thetheoretical consideration of cracks at the crest.

Downstream

Height (mm)150 Upstream

0

520
0
Breadth (mm)a) Shear strain distribution
1 ×10

Height (mm)15
Upstream-3

Downstrea
Extension
0Compression

520
Breadth (mm)-1b) Volumetric strain distribution
0

Fig. 17 Strain distribution of Case 3

REFERENCES

Summary of Analyses[1] Miyanaga, Y., Kobayashi, A. and Murakami, A.
(2013) : 1-G model test with digital image
analysis for seismic behavior of earth dam,Geotechnical Engineering Journal of the
SEAGS & AGSSEA, Vol. 44, No. 2, pp. 26-34.
[2] Uwabe, T., Kudo, K. and Osada, M. (1989) :Experimental Study on Hydrodynamic Pressure
to Act on Composite Breakwater, Technical
Note of the Port and Harbour Research Institute
Ministry of Transport, JAPAN, No. 641, pp. 328..
[3] Suzuki, H., Nakahashi, S. and Kishino, T.
(2001) : An Introduction of the Dynamic
Centrifugal Loading Experimented Facility andthe Shaking Experiment for Investigation ofModel Embankment, Nippon Koei Technical
Forum 2001 No. 9, pp. 125-137.
[4] Masukawa, S., Yasunaka, M. and Hatashida, Y.
(2008): Shaking table tests by silicone rubber
dam model with different ratio of crest length todamheight,GeotechnicalEngineering
Magazine, JGS, Vol. 56, No. 10, pp. 16-19 (inJapanese).

The analysis results are different from theexperimental results. In particular, large extensional
volumetric strain cannot be realized at the upper partby the analyses. The distributions of shear strain are
also quite different from the observed ones. As apresent conclusion, the ordinary elasto-plastic model
is found to unsuitable for the dynamic analysis ofearth dams.
CONCLUSION
Many earth dams developed cracks in the
direction of the dam axis at the crest when a large
earthquake occurred. Up to now, however, design
guides have not mentioned extension failure; only
shear failure has been considered.
In this study, to examine the mechanism of
cracks along the dam axis, 1-G shaking tests andcentrifugal loading tests have been conducted.
Moreover, numerical analyses have been used to
examine the mechanism theoretically. The obtained
results are as follows:376

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

A CYCLIC ELASTOPLASTICITY MODEL OF UNSATURATED SOILS
Tomotaka SATO, Hirotaka SAITO and Yuji KOHGO
United Graduate School of Agricultural Science, Tokyo University of Agriculture and Technology, Japan

ABSTRACT
We have been proposing a cyclic elastoplastic model for unsaturated soils. In order to obtain real cyclic
stress-strain relationships, we introduced the subloading concept with a hardening rule named jumped kinematic
hardening rule. In the elastoplastic model, two suction effects are also taken into account in order to express
unsaturated soil properties. Some simulations of experimental tests for an unsaturated silt were conducted toverify the elastoplastic model. They were isotropic consolidation and triaxial compression tests with cyclicloadings. Finite element method was used for these simulations. It was found from the simulations that the model
could well simulate hysteresis of loading and unloading, and different stress-strain curves with different suction
values. However it was hard to estimate exactly volume changes due to cyclic shear stresses, especially, thevolume reductions due to unloadings.
Keywords: Cyclic loads, Elastoplastic model, Unsaturated soils, Suction

elasticity to plasticity can be established. The
subloading surfaces are defined to be similar to thenormal yield surfaces. The center of similarity isfixed at the origin of stress spaces in original
subloading surface model, while it can move within
the yield surfaces in the extended subloading surface
model. In this model, a hardening rule for the centerof similarity is introduced. We will name thehardening rule jumped kinematic hardening rule. In
the rule, the following assumptions are used: 1) the
centers of similarity can move whenever reverses ofloadings occur, and 2) the moved points are
consistent with the stress points at which reverses ofloadings occur. Other assumptions: 3) the current
stress point is always on the current subloading
surfaces, and 4) the subloading surfaces keep thesimilar shape to the normal yield surfaces, are thesame as those of the original extended subloading
surface model.
Figure 1 shows the schematic explanation of thisconcept. For simplicity, subloading and normal yieldsurfaces are assumed to be elliptical. It is also
postulated to ignore the hardening and softening of
the normal yield surface with loadings. The verticaland horizontal axes in Fig. 1 are the square root of
the second stress invariant of deviator stresses (J 2 )and the first stress invariant (I 1 ), respectively.
Supposing that the loading starts from the origin(α ij 0: coordinate of origin) that is also the center ofsimilarity. The initial subloading surface (SL(0))
may expand with the loading. Here the tensor α ij 0 is
the coordinate of the center of similarity and thesuperscript of the tensor denotes number of reversesof loadings. If the loading reversed at the point withthe effective stress tensor σ’ ij 1, the new center ofsimilarity should be consistent with the stress tensor:
α ij 1 ≡ σ’ ij 1 (see Fig. 1(a)). This operation may be

INTRODUCTION
It is important to consider the cyclic externalloads of level 2 earthquakes for embankment dams
and foundations and to estimate permanent
deformations simultaneously generated in order toassess long-term stabilities of the dams. The idea oflevel 2 earthquake, which is a motion with a large
magnitude but a low frequency to occur in service,
should be incorporated in the performance-based
design. Those soil structures are generally
constructed by compacting geo-materials. The
compacted geo-materials usually exist underunsaturated conditions. Then the elastoplastic model
used in the design should be capable to predict
unsaturated soil behaviors. Kohgo et al. [1, 2] andKohgo [3] have proposed elastoplastic models basedon the cyclic plasticity concept in which the originalor extended subloading surface models (Hashiguchi,[4]) are simplified. In the model proposed by Kohgo[3], a hardening concept named jumped kinematic
hardening rule is adopted. The concept isincorporated into the generalized elastoplastic model
for unsaturated soils proposed by Kohgo et al. [5]. In
this paper, the model was verified by simulating
isotropic consolidation tests and triaxial compression
tests for an unsaturated silt (DL clay).
SUBLOADING SURFACE MODEL WITHJUMPED KINEMATIC HARDENING RULE
In the following formulation, compression
stresses are assumed to be negative. In the
subloading surface model, loading surfaces named
subloading surface are introduced within yieldsurfaces (normal yield surfaces) defined in classic
plasticity theory. Then smooth transition from377

GEOMATE- Osaka, Nov. 16-18, 2015

J 21/2(a)
The 0th loading path

σ′ =α
1
ij

J 21/2 ( β ) m
ij nNormal yield surface(σ ij′ ) nm

Normal yield surface(α ij ) nm

1
ij

Stress point

α

0
ij

Subloading
surfaces (SL0)J 21/2(b)
The 1st loading path(unloading)− I1(σˆ ′ )

m
ij n

J

1/2
2

− I1

Conjugate pointis negative, the reverse of loading will occur. Then,
Reverse of loading,(2)d ( R ) mn < 0
Subloading
surfaces (SL1)(c)
The 2nd loading path
(reloading)d (=R ) mn ( R ) mn − ( R ) mn-1 ,− I1(α ij ) nm+1 =Subloading
surfaces (SL2)− I1′ m (σ ij′ ) nm − ( β ij ) nm ,(σ=ij ) ncontinuously done whenever the reverses of loadings
occur. This operation means that once a reverse ofloading occurs, the existing subloading surface
disappears at the reverse point and the new
subloading surface (SL(1) or SL(2) shown in Figs.
1(b) and (c)) may expand from the reverse pointwith the reversed direction loading. When the stresspoint encounters the normal yield surface, thecalculation of plastic stresses will be conductedunder the manner of the classic plasticity theory. The
generalized return mapping algorithm (Ortiz andSimo, [6]) is used in the calculation process.
m

(4)

(6)

where σˆ ij′ = conjugate effective stress; (σ ij′ ) mn =current effective stress tensor; and (σ ij′ ) mn = current
effective stress tensor with respect to the local
coordinate system.
The hardening modulus H under isotropic
hardening is assumed to be:

H = Hˆ - α h ln( R ) mn ,(7)where Ĥ = hardening modulus at the conjugate
point, which lies on the normal yield surfaces andhas the same outward normal as the current stresspoint does, shown in Fig. 2; and α h = material
parameter. The value of Ĥ is given by( 0 ≤ ( R ) mn ≤ 1 ) isdefined geometrically as (see Fig. 2)(σ ij′ ) nm − (α ij ) nm
(α ij ) nm − ( β ij ) nm,=(σˆ ij ) nm − (α ij ) nm
(α ij ) nm

(I c ) mn +1(α ij ) nm ,(I c ) nm

where I c = yield stress (see Figs. 2).
The relationship between the conjugate point andthe back stress is given by the following equations(see Fig. 2):(σ ′ ) m(σˆ ij′ ) nm = ij mn ,(5)( R )nFig. 1 Explanation of jumped kinematic hardening
subloading surface model. (a) along the 0th loading
path, (b) along the 1st loading path, and (c) along the
2nd loading path.

The similarity ratio ( R ) n(3)

where d ( R ) mn = increment of similarity ratio.
As the center of similarity should be always
present within the normal yield surface, it may be
adjusted using the following equation:

Normal yield surface

α ij2

=
( R ) mn

Subloading surface

Fig. 2 Back stresses and conjugate stresses.

Normal yield surface

α ij1
σ ij′2 = α ij2(σ ij′ ) nm

( I c ) nm

(1)

∂f ∂ψ
,
Hˆ = − p∂ε ij ∂σˆ ij′

where tensor ( β ) = coordinate tensor of the originmij nof local coordinate system for subloading surface(back stress), and superscript m and subscript n
express number of reverses of loading and numberof load increments, respectively. The judgement
whether the reverse of loading occurred is conductedby the increment of similarity ratio. If the increment(8)

where ε ijp = plastic strain tensor; f = normal yieldfunction; and ψ = plastic potential function.
The elastoplastic stress can be calculated on thebasis of the generalized plastic theory (Dafalias andHermann, [7]) using the stress gradients at conjugate
stress point. The process is as follows:
378

GEOMATE- Osaka, Nov. 16-18, 2015

ep=
dσ ij′ Dijkl
 d ε kl − d ε kl  ,pd ε=d λ ⋅ nˆijp ,
ij

dλ =

nˆ ijf =nˆ ijp =where σ' ij = effective stress tensor; σ ij = total stresstensor; δ ij = Kronecker delta; u eq = equivalent porepressure; u a = pore air pressure; u w = pore waterpressure; s = suction; s* = effective suction; s e = airentry suction; a e = material parameter; and brackets< > denote the operation <z> = 0 at z<0 and <z> = z
at z ≥ 0.
The second suction effect can be evaluated byformulating the state surface that expresses
elastoplastic volume change behavior (Kohgo et al.,[8]). Here the following equations are used toestimate the state surfaces (Kohgo et al., [2]).
(19)e=−λ *log ( − p ′ ) + Γ * ,

(9)
(10)

e
nˆklf Dklqr
d ε qr,e
H + Dabcd nˆabf nˆcdp

(11)

∂f
∂σˆ ij′12

 ∂f   ∂f   ∂σˆ kl′   ∂σˆ kl′  

,

(12)

∂ψ
∂σˆ ij′λ*= λ +,
12 ∂ψ  ∂ψ   ∂σˆ kl′  ∂σˆ kl′  (13)

λ *f 1 s *

,

(20)

(Γ − e ) λ * ,(21)

s * + a *1

0
* e01
+
Γ=

0
01

λ

where λ* = slope of e-log (-p') curves; Γ* = void
ratio of e-log (-p') curves at p' = unity; e = void
ratio; p' = mean effective stress; λ and Γ are valuesof λ* and Γ* at saturation, respectively; e0 01 , λ* f1 ,and a* 1 = material parameters.

where dσ ij′ = increment of effective stress tensor;
De ijkl = elastic modulus tensor; dε ij = increment ofstrain tensor; dεp ij = increment of plastic strain
tensor; n̂ijf and n̂ijp = unit normal vector to yieldfunction and plastic potential function at conjugate
point.

GENERALIZED ELASTOPLASTIC MODELFOR UNSATURATED SOILSMODELING OF SUCTION EFFECTS
The model described here is modified based on
Kohgo’s model (Kohgo et al., [2]).
At first we have to define normal yield andloading surfaces. This model has two normal yieldsurfaces as shown in Fig. 3 (a). One is the MohrCoulomb type failure surface (f 1 ) and the other is theelliptical cap model with corners (f 2 ). Both areconnected on the critical state line (f). They are:

The mechanical properties of unsaturated geomaterials may be formulated by considering two
suction effects: (1) an increase in suction increases
effective stresses, and (2) an increase in suction
enhances yield stresses and affects the resistance tointer-particle slides (plastic deformations). The firstsuction effect controls changes of the shear strengthforlightlyoverconsolidatedor
normally
consolidated samples due to suction, volume
reductions due to an increase in suction andswellings due to a decrease in suction. The second
suction effect controls both changes of the shear
strength for heavily overconsolidated samples due tosuction and volume reductions due to a decrease insuction, namely saturation collapse (Kohgo et al.,[2, 8]).
The first suction effect postulates to be estimatedby the following effective stress equations.
′ σ ij − δ ijueq ,σ=ij

ueq= ua − s

s − se ,f1= α * I1 +( s ≤ se ) ,( s > se ) ,

J2

= 0,

(22)

J2

− K *= 0 ,(23)g (θ )g (θ )

f 2 = b 2 ( I1 − I 0 ) + a 2
2

g (θ ) =(14)
a s* 
ueq =ua −  se + es * + ae =s ua − u w ,s*=

f = α *cs I1 +J2

g (θ )

3 − sin φ ′2

2− a 2b2 = 0 ,

(24)

,

(25)

( 3 cosθ − sin θ sin φ ′)where θ = Lode angle; φ' = internal friction angle atfailure with respect to effective stress; and I 0 , a, b,
K*, P 2 , α* and α* cs are defined in Fig. 3 (a). The
values of I 0 , a, b, K*, P 2 are function of I c . They are
given by,

(15)

(16)
(17)

=
I 0 I c /(1 + R) ,

(18)
379

(26)

GEOMATE- Osaka, Nov. 16-18, 2015a=
I0 − Ic =− RI 0 ,(27)

b = −α *cs I 0 ,

(28)

K* =
− (α *cs −α *) I 0 ,(29)(α *cs −α *) I 0 ,(30)

P2 = −

α*2sin φcs′

α *cs =α* =3(3 − sin φcs′ )2sin φ ′,J21/2
Critical stateline f

α∗cs
1

(a)

f1
1

α∗

K*
P2

(31)

f2

b

Ic

I0

a =-RI0

J21/2

(32)

Phase transformationline ψ

where R = material parameter and φ' cs = internal
friction angle at critical state with respect toeffective stress. Kohgo et al. [1] presented thedetails.
The following two elliptical plastic potentialfunctions are adopted as shown in Fig. 3 (b). They
are connected on the phase transformation line.

13(3 − sin φ ′),ψ=b * ( I1 − P1 ) + a J 2 − a b *= 0 ,
1

(33)

2
b *2 ( I1 − P1 ) + a22 J 2 − a22 b *=
0,
ψ=
2

(34)

2

2

2
1

-I1

2
1

2

2

(b)

αpt

ψ2

ψ1
b*
P1
P2

a2=-R1P1

a1

Ic

-I1

Fig. 3 Generalized elastoplastic model. (a)Normal yield functions, (b) and plastic potentialfunctions.
SIMULATION OF LABORATORY TESTS

where a 1 , a 2 , b* and P 1 are defined in Fig. 4(b).
They areSome laboratory tests are simulated using theelastoplastic model described above. Finite element
saturated unsaturated consolidation analysis method(35)
was used for the simulations. Tangential model(36)(Kohgo, [9]) was used to model the soil water
retention curve (SWRC). Here elastic properties are(37)
postulated to be evaluated as follows:−2.3 (1 + e0 )(38)=
K
+ Ki ,(42)=
P1 I c /(1 + R1 ) ,a=
P2 − P1 ,1a=
P1 − I c ,2
b* = −α pt P1 ,α pt =
α pt =

α pt =

2sin φpt′3(3 − sin φpt′ )2sin φpt′
3(3 + sin φpt′ )tan φpt′
9 + 12 tan 2 φpt′κat (σ=
1 σ2 > σ3 ) ,(39)

at (σ1 > σ 2 =σ3 ) ,(40)

G=
Gi + γ j J 2 − γ p p′ ,(43)

where K = bulk modulus; G = shear modulus; κ =slope of e-log (-p') curves at unloading; e 0 = initial
void ratio; and K i , G i , γ j and γ p = material
parameters.
The identified material parameters are shown in
Table 1. The parameters concerned with unsaturated
properties can be directly obtained from the triaxial
compression tests and isotropic consolidation tests.

at plane strain condition, (41)where φ' pt = phase transformation angle; and R 1 =material parameter.
Supposing this is an isotropic hardening modelwith plastic volumetric strain ε v p as a hardening
parameter, the yield stress I c may be evaluated bymeans of the state surface concept (Kohgo et al.,[8]). Obtaining the value of I c , we can easily
calculate I 0 , a, b, K*, a 1 , a 2 , P 1 , P 2 and b* by using
the relationships shown in Fig. 3.

Table 1 Material parameters used for DL clay
Elasticity
κ
0.02
Subloading
αh380

K i (kPa)
0.1
φ'

G i (kPa)
0.1
Plasticity
φ 'cs =φ 'pt

Effective stress
γp
70.0

γj
-97.0

R =R 1

e0

1×106

26.2
30.0
State Surface

0.7

1.04

λ
0.152

Γ
1.306

a 1*
17.4

λ * f10.044

e 0 010.508

a e (kPa)s e (kPa)
33.3
10.0
Initial Condition
S r0
s 0 (kPa)
0.44

20.0

GEOMATE- Osaka, Nov. 16-18, 201500
s = 0 kPa
Experimentals = 0 kPa
Predicted

1234Deviatoric stress, q
[kPa]

0

10
100
Mean effective stress, p' (kPa)5
10
Axial strain, εa [%]15

20

Fig. 5 Simulation results of triaxial compression
tests for DL clay (s = 0 kPa).

s = 0 kPa Experimentals = 0 kPa Predicted
1000

300

1.02

200

1.00

(b)

0.98

s = 25 kPa
Experimentals = 25 kPa
Predicted

100

0.96
s = 50 kPa Experimentals = 50 kPa Predicted

0.94

0
0

0.92
1000

Volumetric strain, εv
[%]

10
100
Mean effective stress, p' [kPa]1
1.04
Void ratio, e

s =0 kPa
Experimantal
s =0 kPa
Predicted

100

(a)

1

Void ratio, e

200Volumetric strain, εv
[%]

1.02
1.00
0.98
0.96
0.94
0.92
0.90
0.88

300

Deviatoric stress, q[kPa]

Void ratio, eSimulation results of isotropic consolidation tests
for an unsaturated silt are presented in Fig. 4. The
test results for three specimens with different
constant suction values (s = 0, 50 and 80 kPa) were
simulated. In these figures, symbols with brokenlines and thick solid lines denote experimental andsimulation results, respectively. The each simulation
result well expressed the whole void ratio- confining
effective stress relationship of experimental one. The
predicted lines draw the hysteresis of unloading andreloading as well as, a smooth transient stress-strain
relationship from elasticity to plasticity.

1.02

(c)

1.00
0.98
0.94
1

10
100
Mean effective stress, p' [kPa]2
s = 25 kPa
Experimentals = 25 kPa
Predicted

34

s = 80 kPa Experimentals = 80 kPa Predicted

0.96

1

0

1000

5

10
Axial strain, εa [%]15

20

Fig. 6 Simulation results of triaxial compression
tests for DL clay (s = 25 kPa).

Fig. 4 Simulation results of isotropic consolidation
tests for DL clay.

Deviatoric stress, q
[kPa]

300

Volumetric strain, εv[%]

Figures 5-8 show the simulation results ofdrained triaxial compression tests of specimens withs = 0, 25, 50, 100 kPa. The specimens had the same
confining pressures σ 3 = 100 kPa. These figures
show the deviatoric stress q vs. axial strain ε a andvolumetric strain ε v vs. ε a relationships, respectively.
Figure 5 shows the simulation result in saturated
case. The stress-strain relationship without cyclicloading was expressed well, and the volumetricstrain – axial strain relationship was also simulated
well but for the cyclic process the properties weredifferent. Especially the compression behavior due
to unloading could not be expressed well.
The simulation results for unsaturated specimens
are shown in Fig. 6 – 8. Both the experimental andpredicted results show 1) increasing of peak strength200
s =50 kPa
Experimentals = 50 kPa
Predicted

100
0
0
1
2

s = 50 kPa
Experimentals = 50 kPa
Predicted

3
4
0

5

10
Axial strain, εa [%]15

20

Fig. 7 Simulation results of triaxial compression
tests for DL clay (s = 50 kPa).
381

GEOMATE- Osaka, Nov. 16-18, 2015

ACKNOWLEDGEMENT

Deviatoric stress, q
[kPa]

300

The research herein was supported by the Japan
Society for the Promotion of Science (JSPS) (thegrant No. was 26292125).

200
s = 100 kPa
Experimentals = 100 kPa
Predicted

Volumetric strain, εv
[%]

100
0
-1

REFERENCES[1] Kohgo, Y., Nakano, M. and Miyazaki, T.
1993b. Verification of the generalized
elastoplastic model for unsaturated soils, Soils
and Foundations 33(4), 64-73.
[2] Kohgo, Y., Asano, I. and Hayashida, Y. 2007a.
An elastoplastic model for unsaturated rockfills
and its simulations of laboratory tests, Soils andFoundations 47(5), 919-929.
[3] Kohgo, Y. 2010. An introduction of Jumped
Kinematic Hardening Rule to Elastoplastic
Model for Unsaturated Geo-materials, Proc. of
the 5th Int. Conf. on Unsaturated Soils, Vol.2,
857-862.
[4] Hashiguchi, K. 1989. Subloading surface modelin unconventional plasticity, Int. J. Solids
Struct. 25, 917-945.
[5] Kohgo, Y., Hayashida, Y. and Asano, I. 2007b.
A cyclic plasticity model for unsaturated soils，Proc. of the 3rd Asian Conf. on Unsaturated
Soils, UNSAT-ASIA 2007, Nanjing China,
Science Press, 365-370.
[6] Ortiz, M. and Simo, J. C. 1986. An analysis of a
new class of integration algorithms forelastoplastic constitutive relations, Int. J.
Numer. Meth. Eng. 23, 353-366.
[7] DafaliasY. F. and Herrmann, L. R. 1982.
Bounding surface formulation of soil plasticity,
Chapter 10 in Soil Mechanics – Transient andCyclic Loads, edited by Pande, G. N. andZienkiewicz, O. C., John Wiley & Sons.
[8] Kohgo, Y., Nakano, M. and Miyazaki, T.
1993a. Theoretical aspects of constitutive
modeling for unsaturated soils, Soils andFoundations 33(4), 49-63.
[9] Kohgo, Y. 2008. A hysteresis model of soil
water retention curves based on bounding
surface concept, Soils and Foundations 48(5),633-640.

01

s = 100 kPa
Experimentals = 100 kPa
Predicted

2
3
0

5

10
Axial strain, εa [%]15

20

Fig. 8 Simulation results of triaxial compression
tests for DL clay (s = 100 kPa).
and 2) smaller volumetric strain reduction as suction
increases, and 3) strain softening behavior. As we
mentioned above for saturated case, the stress –strain curves of the experimental tests were almost
consistent with those of the simulation. Meanwhilein the ε v - ε a relationships, simulated amounts ofvolume change were less than those of theexperimental tests. The reason was that predictedcurves cannot express the compression due tounloadings. This point should be more investigated.
CONCLUSION
An elastoplastic model, which can express
unsaturated soil behavior during cyclic loadings, wasdescribed in detail. In the model, we adopted ahardening concept named jumped kinematic
hardening rule. The rule was incorporated into a
generalized elastoplastic model with subloading
surface proposed by the authors. In the concept,
centers of similarity of subloading surfaces to thenormal yield surfaces may jump to the reversed
stress points whenever reverses of loadings occur.
So the model has a potential to express anisotropic
behavior due to cyclic loadings. The cyclic
properties can be modeled by only one parameter α hthat was adopted in the previous model (Kohgo et al.
[1, 2]). Simulations of isotropic consolidation andtriaxial compression tests for an unsaturated silt
named DL clay were conducted to verify the model.
The simulation results in isotropic consolidation
tests could successfully express cyclic properties.
However in the triaxial compression test, some moreinvestigations concerned with plastic potentialfunctions and hardening procedures will benecessary. The verifications were only restricted fora few laboratory tests. We will try to apply themodel to other cyclic laboratory tests.

382

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

GEOTECHNICAL HAZARD ANALYSIS OF RIVER EMBANKMENT
OF BANGLADESH AND ITS PROTECTABILITY

1

Md. Wasif Zaman1, Md. Yousuf Rumi1, Md Salequzzaman2 and H. M. Shahin1
Dept. of Civil and Environmental Engineering, Islamic University of Technology, Gazipur, Bangladesh
2
Environmental Science Discipline, Khulna University, Khulna, Bangladesh
ABSTRACT

Bangladesh has a long artificial river embankment, developed mainly for the protection of its inhabitants andresources from disastrous flash floods, tidal water, cyclone surges, river currents and others. This processincreases erosion of embankments and water turbidity which bring the concerned sediments from alluvial land to
the inland sides and river bed that further increase vertical soil accommodation and decrease the water depth. In
this research geotechnical hazard analysis has been conducted based on the statistical riverine data and flood
occurrence information, obtained from various recognized institutions and secondary literatures and scientificinvestigation of soil structural failures from field survey. The soil characteristics and existing conditions of theembankments of the three big rivers of Bangladesh - the Ganges, the Meghna, and the Brahmaputra-Jamuna,have been investigated. The research also includes the reasons behind the failure of different embankments thatwere happened in last decades. A design methodology has been proposed to make these embankments more
durable and to improve the strength using a pre-designed methodological case study.
Keywords: Embankment, Soil detachment, River discharge, Vetiver, Reinforcing material

BWDB. Bangladesh has steady economic growthand is self-sufficient in food. So, protection isneeded against the recurring flooding to make thisgrowth sustainable.
Due to the socio-economic condition of
Bangladesh most of the embankments are simply
constructed with earthen materials. These earthen
embankments are vulnerable to rain splash and theflow of flood water; hence they cannot solve theflood problem effectively and permanently [2].
Recent studies and news outlets has pointed out
many instances of embankment failures andbreaches in the past several decades. These earthen
embankments are prone to breach due to their faulty
design and wrong construction. For the repair andreconstruction of these existing embankments, thegovernment has been spending a lot of money
annually. So, embankments along the sea, estuaries
and rivers and their associated drainage channels andsystems need to be upgraded to offer greater
protection from tidal activity, storm surges andwater level rise. In this study the embankment
failure and riverbank erosion have been investigatedwith respect to rainfall and flood occurrence and soil
properties. Thus the study is mainly aimed to: (i) toinvestigate the present condition of embankments ofthe major rivers of Bangladesh by field observationand discussion with local community, (ii) to
determine the effect of flood and rainfall onembankment and their subsequent structural collapse(iii) to provide suggestions for designing sustainableembankment (i.e. slope, factor of safety, alternate
construction materials justified by present socio-

INTRODUCTION
Despite being a small country, Bangladesh faces
the highest number of natural calamities due to its
unique geological feature which presents the country
has its largest delta in the world, formed by theGanges, the Brahmaputra and the Meghna (GBM)
river system. The Bengal delta is characterized byflat terrain interlaced with an intricate network of
about 700 rivers, canals and streams with a total
length of approximately 22,115 km which create
estuaries, tidal inlets and tidal creeks [13]. The
combined catchment basin of the GBM river systemmeasures to 1,758,000 square km, which is morethan 12 times the size of Bangladesh. The amount of
sediment carried annually by the rivers of the Bengal
delta is about 2 billion tons. Over 92 percent of theannual runoff generated in the GBM area whichflows through Bangladesh, which is only about 7
percent of the total catchment area [13]. During therainy seasons (June to September), more than 85%
rainfall occurs that brings huge amount of silts fromthe origin of rivers that started from the slope of
Himalaya. Therefore, within a period of 4 months,
nearly a trillion cubic meter of water carrying about
two billion tons of silt passes through the Bengal
delta. Riverine floods occur when the amount of
runoff originating in a watershed exceeds the
carrying capacity of natural and constructeddrainage system. A total of 5695 km ofembankments including 3433 km in coastal areas,
1695 flood control/regulating structures and 4310
km of drainage canals have been constructed by383

GEOMATE- Osaka, Nov. 16-18, 2015

economy) using pre-designed methodological casestudy.
STUDY AREAPRESENT SCENARIOS OF EROSION AND
EMBANKMENTS IN BANGLADESH

For the Ganges basin we have selected Lohajang
Upazila (23.46670N to 90.34170E, with an area of
130.12 km2) of Munshiganj district, for the
Brahmaputra-Jamuna basin the selection was
Kazipur Upazila (24.64170N to 89.65000E, with anarea of 368.63 km2) of Sirajganj district respectively.
For Lohajang Upazilla, the average annual rainfall is2102 mm, and for Kazipur Upazila, the averageannual rainfall is 1649 mm [12]. In this study, acomparison of physical and geotechnical properties
is carried out by field observation with the help ofavailable previous research paper, data fromgovernment institutions, and national newspapers.

An average 256.1 ha and 622.2 ha of total landarea of Gaibandha and Sirajganj respectively wereeroded including embankments per year during theperiod of 1973-2009 [4]. During this period, thestudy areas i.e. Lohajang and Kazipur observed 3.82
ha and 6.89 ha erosion per year respectively [6].
Throughout June 2015, major bank erosions were
estimated to take place at 48 places along a 210 km
stretch of the Brahmaputra-Jamuna, Ganges andMeghna riverbank [6]. The government has built
1,209 km of embankments and has repaired 15,358
km of embankments in the 2014-15 fiscal year
(April to March). Recent breaching of embankments
at different location with their subsequent damages
and date of breaching are shown in table 2.

Table 1 Embankment soil characteristics at study
Lohajang,
Munshiganj

Soil sample

Ganges sandy
soil

TextureOM (%)
Soil characters

LocationType

Sand (%)
Alluvium(%)
Clay (%)
Percolation ratelocation [12], [13].

Kazipur,Sirajganj

CAUSES OF EMBANKMENT FAILURE

Sandy loam

Brahmaput
ra’s alluvial
soil
Loam

1.37
Noncalcareous
grey
0

0.8
Noncalcareous
alluvium27

Since most of the embankments of Bangladesh are
made of earth only without any surface protection,
so the top soil easily washed away with the rainsplash and current or flow of water. Also scouring at
the toe of embankments decreases the length ofslope and subsequently weakens the slope [1].

17

72

83

1

Poor

MediumNatural Forces
1) Wave action (daily/periodic and created byconstant wind):• The impact of tidal waves are much greater on
the embankments located at the vicinity of thesea.
• Cyclonic storms in the coastal zone (occurring
repeatedly) act upon the water surface, which inturn moves it towards the shore with enormous
hydraulicloads.

Table 2 Recent embankments breach in Bangladesh
Region/District

River

Name of embankments

Faridpur

Padma

Bogra, Dhonut

JamunaFaridpur city protection
embankment
Dhonut embankment

Sirajganj,
Dhekuria
Bogra;
Jamalpur;Sirajganj;
Rajshahi

JamunaDhekuria embankment

JamunaFlood protection
embankments

Padma

Feni, Fulgaji

Muhuri

Pabna, Sathiya

Ichamoti

Rajshahi cityprotection
Muhuri riverembankment
Ichamoti river

Date ofbreachMay 18, 2015

20 meter bolder collapsed

May 05, 2015

35 meter embankment damaged

March 29,2015
August 30,
2014

10 houses destroyed

August 07,
2014
June 22, 2014
May 20, 2014384

Damages

150 villages flooded in Bogura; 20
villages flooded in jamalpur; 600
families shelter less in sirajganj
Soil moved and damaged
30 villages flooded
Crops and fisheries worth 2 croreGEOMATE- Osaka, Nov. 16-18, 2015

embankment
2) Rainfall impact (from both the regular monsoon
rains and torrential rains): The heaviest rainfall
occurs in July and ranges from 350 mm to over 875
mm accordingly (Bangladesh Meteorological
Department, 2015). The main features of rainfall
impact are:
• Surface runoff caused by rainfall results in sheet
erosion.
• Flooding (monsoon/periodic floods and thosecreated by storms/cyclones).
• Monsoon rainfall causes flooding which gives
rise to serious washing of embankment top soil.
3) Turbulent water currents (mainly in rivers and atcoastlines):• The high flow rate of water makes the water
current turbulent which is accompanied by
vortex motion in rivers and estuaries often cause
erosion of the banks.
• At the originating point of a branch river or
canal, especially in the surroundings ofhydraulic structures, the turbulent water current
erodes the banks and subsequently theembankments.
4) Wind action: The slow and steady action of windin the relatively sparse fields and coastlines blows
away the topsoil of the embankments where it issandy or a mixture of silt and sand.

damaged5) Improper design and construction technique: Inmany cases the embankments are designed withinsufficient setback, resulting in increased
exposure to waves and current action. This may
be due to the high costs involved in landacquisition.
6) Deforestation in upstream region: Deforestationof steep slopes is assumed to lead to accelerated
soil erosion and landslides during monsoon
rainfall, which contributes to floods of biblicalproportion in the downstream regions like thatof Bangladesh.
7) Other natural and man-made reasons behind
embankments collapse are- soil erosion, seepage
and sliding, insufficient supervision during
construction, insufficient or no clod breaking,inadequate compaction and or no insufficientlaying of topsoil layers, use of inferior materials,
inadequate , river migration maintenance etc.

Human Interference

Concrete Embankments

1) Homesteadandagricultural
practices:
Embankments often become the privileged sites
for the construction of villages and isolated
homesteads. Also agricultural practices on
embankments are encouraged by a highdemographic pressure on the available land andaccordingly a shortage of land for the rural
population.
2) Cattle pasture: Cattle, mainly belonging topeople living on the embankment, cause erosion
by uncontrolled pasture. When the embankmentis over pastured, plant species and thevegetative cover, especially the grasses, exhibits
reduced growth, weaken and cannot provide
adequate protection of the embankment.
3) Public cuts: Public cuts and tubes linking a riveror seaside with the country side of its
embankment are seen at most establishments.
These have a negative effect on the strength ofembankments, which makes them vulnerable toslow but continual erosive forces. During floodor cyclonic surges, collapse or major erosion
occurs at those locations.
4) Unplanned afforestation of embankment slope:
Afforestation without appropriate planning andmanagement
techniques
destroys
theundergrowth grass cover and becomesineffective for erosion protection.

A very few embankments are constructed using
concrete or reinforced earth methods. They have acomparatively higher cost of construction. The DND
embankment which protect Dhaka, Narayanganj,
and Demra from the adjoining Buriganga andShitalkhya rivers, Brahmaputra right handembankment which protect from BrahmaputraJamuna river channel are some of the examples [2].

TYPES OF EXISTING EMBANKMENTS IN
BANGLADESH
Earthen Embankments
Most of the embankments are constructed by fillembankments or earthen embankments due to it are
relatively cheaper and have a low initial cost.

City Protection Embankments
Almost all of the cities of Bangladesh are situated on
the banks of either one or more rivers. To protect thecities from flooding, city protection embankments
are constructed. The slope of the city protection
embankments are often covered with concrete blocks(sometimes sand bags are also used). The MeghnaDhonagoda embankment and others that have been
constructed to protect cities and towns like Rajshahi,
Shirajganj, Chandpur, Khulna and Barisal arebelonging to this category [2]. These embankments
let the river water remain confined only to theirchannels and pass directly to the sea.
DESIGN APPROACH OF EMBANKMENT
Design Methodology Followed By BWDB
According to design manual, return period is to betaken 20 years for full flood control embankment
where agricultural damage is predominant. The
385

Soil type
Factor of
1.4
Clay safety
1.45
Clayey1.6
Deviationloam(%)
Recommeded
F.S.(without
EQ)GEOMATE- Osaka, Nov. 16-18, 2015

S = slope gradient (%),
R = Correlation factor
The effect of slope on detachment rate increases asslope gradient becomes greater [7]. R2 was taken as
0.97 for different soil characters of major rivers of
Bangladesh [7].

While they’re en route to course change, hydraulicstructures are merely adequate to restrict the rivers.
There is a strong geologic possibility based on
historic river course changes like that ofBrahmaputra from its old Brahmaputra to present
Jamuna or that of old Ganges from Bhagirathi topresent Padma [8]. Although such epochal shifts arevery rare instances of occurring. But, they can’t beignored also. River experts consider the
Brahmaputra as one of the earth’s most turbulent
and dynamic rivers. They fear that the position ofBrahmaputra on a fan of its own silt on the northernpart of Bangladesh is tell of another possible naturalshift of river change. So, it is prudent to constructand design the embankment by geographicallymodeling the river first based on its possibility ofcourse change, shifting and alignment with nearby
river channels.
ALTERNATIVE EMBANKMENTREINFORCING MATERIALS
Based on the socio-economic conditions ofBangladesh, a very cheaper yet effective method toprotect the surface of existing earthen embankments
would be the plantation of Vetiver (Chrysopogonzizanioides) grass along with the application of Jute
Geo Textiles (JGT) [3]. Vetiver not only serves the
purpose of slope protection but also adds
environment reducing pollution. The in-situ shear
test reveals that, the shear strength and effective soil
cohesion of vetiver rooted soil matrix are
respectively 2.0 times and 2.1 times higher than that

Figure 1 Predicted soil detachment rate in 2014.
Brahmaputra-Jamuna is the most erosion prone river
basin in Bangladesh. Soil erosion rate is higher in
Brahmaputra river basin than in Ganges and Meghnabasin (Fig. 1). Greater incoming wave energy causes
toe scouring, which tends to weaken theembankment and fastens the structural collapse insloping dikes (fig.2). So, embankments in
Brahmaputra basin need to be stronger than the othertwo as there severity of detachment of soil is larger.
Naturally the steeper the slope of a field, the greater
the amount of soil loss from erosion by water.

of bared soil [5].
Figure 3(a) Just after planting the vetiver grass alongwith Jute Geo-Textile (JGT);

Figure 2. Cross section of an embankment showing
toe scouring.
Change of river course
The major rivers of Bangladesh such as the Ganges,
the Brahmaputra-Jamuna and the Meghna have atendency to change their courses over alluvial plain.
387

GEOMATE- Osaka, Nov. 16-18, 2015

geotechnical characteristics and stability analysis,
American. J. of Environ. Sci. Vol.7, No.2, 2011, pp.
102-107.

Figure 3(b) Cross section of vetiver grass on embankment
showing large root depth.
During the time of vetiver root growth, the soil protection
can be attained by using geo-jute. While the geo-jutedegrades with time, simultaneously vetiver roots grow andtake over to support the soil instead of the geo-jutes. Also
to increase the solidarity of the embankment, other steps
can be taken such as use of additives or reinforcing
materials like soil-cement, natural or geo-synthetic fibers,
geo-tubes, pressure berms etc. Pressure berms have beenfound quite effective in stabilizing earth retaining
structures. A research project lead by the Norwegian
Public Road Administration (NPRA) is presently
investigating the possible use of Granulated Foamed Glass(cellular glass) as a lightweight material for road
construction applications [9]. Granulated foamed glass(cellular glass) is produced by recirculating waste glass. In
Bangladesh, every year huge quantity of wastes isgenerated which contains a significant amount of waste
glass. They can be put to a good use by recycling theminto granulated foamed glass.

[2] Hossain M.Z. and Sakai T, (2008) Severity of floodembankments in Bangladesh and its remedialapproach, Agricultural Engineering International:
the CIGR E-journal, Manuscript LW 08 004, Vol.10,
2008.
[3] Islam M.S, Shahriar B.A.M. and Shahin H. M. (2013)
Study on grow of vetiver grass in tropical region forslope protection, Int. J. of GEOMATE, Vol.5, No. 2,
2013, pp. 729-734.
[4] Rahman M.R. (2010) Impact of riverbank erosion
hazard in the Jamuna floodplain areas in Bangladesh,J. Sci. Foundation, Vol.8 (1 &2), 2010, pp. 55-65.
[5] Islam M.S., Nasrin S., Islam M.S. and Moury F.R.
(2013) Use of vegetation and geo-jute in erosion
control of slopes in a sub-tropical climate. World
Academy of Science, Engineering and Technology,
Vol. 73, 2013, pp. 1162-1170.
[6] CEGIS (2005) Monitoring and Prediction of Bank
Erosion along the Right Bank of the Jamuna River,Dkaka, 2005.
[7] Zhang G.H, Liu B.Y, Nearing M.A, Huang C.H. andZhang K.L. (2002) Soil detachment by shallow flow,
American Society of Agricultural Engineers. Vol.
45(1), 2002, pp. 1-7.
[8] Hoque M.M, Siddique M.A. (1995) Flood control
projects in Bangladesh: Reasons for failure andrecommendations for improvement. Disasters,
Vol.19, No.3, 1995, pp. 260-263.
[9] Erik F.T. and Roald A. (2002) Use of waste materials
for lightweight fills. Int. Workshop on Lightweight
Geo-materials, Tokyo Japan, Mar. 2002.
[10] Cernica, J.N. (1994) Geotechnical Engineering: Soil
Mechanics, New York, John Wiley and Sons Inc. 1st
Edition, P.453.
[11] Anisuzzaman M, Arifuzzaman and Ridha (2013)
Sub-soil characteristics of selected coastal regions ofBanglades. Int. J. of Chemical, Environmental &Biological Sciences, Vol. 1, Issue 5, 2013, pp. 710714.
[12] SRDI (Soil Resources Development Institute) (2000)
Land and soil resource user guide, Dhaka, May 2000,
June 1998.
[13] BBS (Bangladesh Bureau of Statistics (2009)
Compendium of Environment Statistics ofBangladesh,
Dhaka,
2009.

CONCLUSIONS
The following points can be concluded from this research.
1. The soil of Ganges tidal floodplain (Lohajang,
Munshiganj) has poor percolation rate indicating lesswater intrusion in pore spaces of soil which
substantiates with lesser occurrences of soil erosionand embankment breach in the subsequent area. The
soil of active Brahmaputra-Jamuna floodplain(Kazipur, Sirajganj) has medium percolation rate
indicating moderate water intrusion on pore spaces ofsoil which substantiates with higher occurrences ofsoil erosion and embankment breach in thesubsequent area.
2. The failure of soil mostly depends on the factor ofsafety. The more the safety factor the more would be
the assurance of soil strength along with the lesschance of failure possibility. From table 4, we canrecommend Factor of Safety of 1.5.
3. While searching for alternative material to increase
the solidarity of embankment, it is found that plantingof Vetiver grass along with the application of Jute
Geo-Textile (JGT) in the slopes of embankments as
the most suitable practice in light of Bangladesh’s
present socio-economic aspects. Also adding pressure
berms in the design, the durability of embankment
can be improved.
4. Designing of embankments, effect of river water
discharge is very much required to obtain morereliability in designing stable embankment.
REFERENCES[1] Hossain M.B, Sakai T, and Hossain M.Z, (2011)
River embankment and bank failure: A study on

388

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

AXIAL RESISTANCES OF OPEN-ENDED PILES DRIVEN INTOGRAVEL LAYERS: DYNAMIC LOADING TESTS OF PILES AT ROAD
BRIDGE CONSTRUCTION SITE IN MIZUSHIMA PORT
Takaaki Mizutani
Head of Foundations Group, Port and Airport Research Institute, Japan
ABSTRACTThe axial resistance of a pile, in many cases, is determined using an appropriate estimating equation
depending on the type of its bearing layer. However, the existing equations are not applicable to piles driven into
gravel layers. A series of dynamic load tests of steel-pipe piles driven into gravel layers was conducted at a roadbridge construction site in the Mizushima port for obtaining a basis for the estimation of their axial resistances.
The test results indicated that both the base and shaft resistances of the piles were relatively smaller although thepiles were open-ended. This paper provides the test results in detail and suggests estimating equations fordetermining the base and shaft resistances of piles driven into gravel layers. The safety factor necessary for
utilizing the test results in the design of structures is also discussed.
Keywords: Pile, Axial Resistance, Gravel Layer, Dynamic Load Test
INTRODUCTION

In the Mizushima port, the steel-pipe sheet pile
structure has been employed for foundations of aroad bridge with gravel layers as the bearing layer.
A series of dynamic load tests of steel-pipe piles
driven into gravel layers was conducted to obtain abasis for estimating their axial resistances at theconstruction site. This paper will provide the test
results in detail and suggest a method for estimating
the axial capacity of piles driven into gravel layers.

The axial resistance of a pile is determined using
an estimating equation selected based on the site
condition. Most of these estimating equations are
based on field test data whereas some of them havetheoretical backgrounds. Each estimating equation
has its range of applicability in terms of theconfiguration of piles, type of soil, etc. In theabsence of an equation appropriate for the site
condition, it is necessary to conduct pile loading
tests at the site for designing pile foundations.
Researchers and design codes have suggested
and described a large number of estimating
equations for determining the axial resistance of apile [1]-[3]. However, those for piles driven into
gravel layers have not yet been established in Japan.
Shin-Kurashiki
StationOUTLINE OF DYNAMIC LOADING TEST
Site Condition
The construction site is near the mouth of
Takahashi River in Kurashiki City, in the westernpart of Japan. Figure 1 shows the location of the site.
The width of the river is approximately 1400 m.
Nineteen foundations, designated as P1–P19, areproposed to be constructed at the site at regularintervals.
Figure 2 displays the soil profile at theconstruction site. The profile is almost flat, with acondition similar to those at the locations of thefoundations. Ag and Dg2 constitute the gravel layers,and Dg2 was selected as the bearing layer of thefoundations. The issues faced during this study were
the pile’s base resistance in the Dg2 and shaft
resistance in the Ag and Dg2 layers.
Boring surveys were conducted at the location ofeach foundation. The Ag layer was a medium-todense gravel layer comprising subrounded andsubangular particles of 2–30 mm diameter. The Dg2
layer was a dense-to-very-dense gravel layer, theparticles of which ranged from subrounded toKurashiki City

Tamashima
area

TakahashiRiver

Mizushima area

construction
site

Mizushima Port

Fig. 1

Location of construction site389

GEOMATE- Osaka, Nov. 16-18, 2015

Fig. 2

Soil profile at construction sitesubangular, of diameters 2–40 mm. Gravels larger
than 50 mm in diameter were occasionally observedin the Dg2 layer.
Figures 3 and 4 show the depth distribution of
the SPT-N values in the Ag and Dg2 layers,respectively. The values varied widely in both layers
and no clear tendency was found in the obtained
depth distributions. Therefore, it was decided to treat
their simple mean values as the representing valuesof each layer: N = 31 for the Ag layer and N = 59 forthe Dg2 layer, as indicated in Fig. 5, where N is thesimple mean value of the SPT-N values.

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Test Method���

Dynamic load tests of piles were conductedaccording to the Japanese standard for dynamic load
test of single piles [4] at the location of eachfoundation. The Pile Driving Analyzer (Pile
Dynamics Inc.) and CAPWAP (Case Pile Wave
Analysis Program) tools were used for obtaining thestatic axial resistances of the test piles.

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Fig. 4layer

Depth distribution of SPT-N values in Dg2

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layer

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Depth distribution of SPT-N values in Ag

Fig. 5390

N values for each layer

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GEOMATE- Osaka, Nov. 16-18, 2015

The test conditions are listed in Table 1. Fourtypes of piles were tested. The label “Cross” inTable 1 denotes a pile with a rib plate at its tip. The
rib plate was 2 m long from the tip and cross-shaped
in sectional view. The label “Cross (3 m)” is a pilewith a rib plate of 3 m length. The label “Arch”
indicates a pile with three arched rib plates, each of
2 m length [5]. The plan was to collect data using acommonly-used data collection technique fordynamic load tests in Japan. The test method, in
itself, was not the issue of the present study.

At P3, four test piles with different tip
geometries were tested after one day and nine days
since the piles were driven. The test results areshown in Fig. 7. It can be seen that the Rp of the
open-ended pile is smaller than that of the others.
The Rp values of the non-open-ended piles were notvery different. The value of Rp was observed toincrease very slightly with progress in the curing
period (i.e., setup capacity was observed).
The test result statistics are listed in Table 2.
These statistics were calculated for open-ended andCross piles separately. The Cross (3 m) and Arch
piles were omitted because of the limited number oftests. The test results for the piles whose curing
period did not exceed one day were eliminated to
avoid the effect of setup, albeit it being very small.
The mean value of the test results for open-ended
piles was roughly in accordance with that from aprevious study, which described the results ofseveral static load tests at a nearby area [5]. The
average Rp of Cross piles was only 1.08 times larger
than that of open-ended piles. It could be concluded
that the rib plates at the tip of the piles had verysmall effect at this site. The coefficient of variation(CV) for both open-ended and Cross piles wasapproximately 20%.

BASE RESISTANCE
Figure 6 shows the base resistance (Rp) obtained
from the tests conducted along the bridge axis. Rp
appears to depend on the geometry of the test pile.
Table. 1 Test conditions
pile type

P1
P2

6
test
4
1
2
3
4
test
test
test
1
test
test1 – test4
test
1–4
test
2
test
test
test
2
K-1 – K-4
6, 9
test
test
1
2–4
1–4
test
test
1–4
1–3
4
test
20
29

Cross
open-ended
Cross
open-ended
Arch
Cross
Cross (3 m)open-ended
Cross
open-ended
Cross
open-ended
Cross
open-ended
Cross
open-ended
Cross
open-ended
Cross
open-ended
Cross
open-ended
Cross
open-ended
Cross
open-ended
open-ended
open-ended
open-ended
Cross
open-ended
open-ended
open-ended
open-ended
Cross
Cross

P3

P4
P5
P6
P7
P8
P9
P10
P11
P12
P13
P14
P15
P16
P17
P18
P19

curing period(days)
9
7
6
1, 9
1, 9
1, 9
1, 9
7
6
7
6
7
6
7
6
7
5
12
7
11
6
0, 6
0
9
7
13
14
7
10
6
14
6
6, 28
7
7
7

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Rp obtained from tests

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Rp of different types of piles at P3

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Table. 2 Rp statistics
pile typeopen-ended
Cross

sample
size
33
20

mean
(kN)
2570
2790

CV
(%)
18.4
19.6

As shown in Fig. 9, lines A and B intersect at thecentroid of the data. Consequently, both the lines
conduce to the same value of total shaft friction (Rf)in the Ag layer. Therefore, even if rf has anincreasing tendency with depth, there is no needconsider it for calculating the overall shaft resistanceof the pile. However, it must be considered for thelimit state design. The increasing tendency of rf wasneglected and the lower limit of the 95% confidence
interval of the mean value (line A') was employed as
the characteristic value of rf (rfk) of the Ag layer, in
this study.
The mean value of rf was 31 kN/m2 and thelower limit of its one-sided, 95% confidence interval
was 28.6 kN/m 2. This value was approximately

standard
error
82.2
122

The Rp of a pile driven by a hammer is mostoften related to its cross-sectional area and the SPTN value of the bearing layer. The cross-sectional
area (Ap) of the test pile and N of the Dg2 bearing
layer were π/4 m2 and 59, respectively. From adesign reliability perspective, it is important toconsider the accuracy of the test results and thecharacteristic value of Rp (Rpk) must be determined
carefully. The lower limit of the one-sided, 95%
confidence interval is frequently used as Rpk. It waseasy to derive the confidence interval for Rp using
the standard error values listed in Table 2.
Consequently, Rpk was determined as 2430 kN for
open-ended piles, yielding Eq. (1), as follows:������������
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Rpk = 52NAp

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Because the piles were open-ended, Eq. (1) islikely to include the effect of plugging. Hence, thecoefficient in Eq. (1) is likely to change uponaltering the pile diameter, thus limiting theapplicability of the equation to the site of this study.

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SHAFT RESISTANCE

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Strength of Shaft Resistance per Unit Area in Ag
Layer

Fig. 8Figure 8 shows the depth distribution of thestrength of the shaft resistance per unit area of thepile surface (rf) in the Ag layer, obtained from all the
tests. The data displayed a wide variety but a clear
increasing trend in terms of depth. The rf values ofpiles with curing period less than two days wererelatively smaller. The effect of the type of pile on
the rf was not observed.
Because of the wide variety in the rf values, it
was natural to use their simple mean value as thecharacteristic value of the full thickness of the Aglayer. In contrast, obtaining a regression line fromthe data could be regarded to be better as the rf has atendency to increase with depth. Both results areshown in Fig. 9. Lines A and B represent the meanvalue and regression line, respectively whereas lines
A' and B' indicate the lower limits of their one-sided,
95% confidence intervals, respectively. Here, theCross (3 m) and Arch piles are omitted and the test
results of piles whose curing period did not exceed
one day are eliminated as they are the same as thestatistic calculation for Rp. The regression line wasobtained using the least-square method.

Depth distribution of rf in Ag layer
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Fig. 9 Mean value (line A) and regression line
(line B) of rf in Ag layer; and lower limits of their
one-sided, 95% confidence intervals (lines A' and B',
respectively)

392

GEOMATE- Osaka, Nov. 16-18, 2015

���The mean value of rf in the Dg2 layer was 53
kN/m2 and the lower limit of its one-sided, 95%
confidence interval was 49 kN/m2, whichcorresponds to 0.83N. The simplest idea of theestimation equation for rfk in the Dg2 layer is asfollows:������������
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The CV of rf was 53%, worse than that for the
Ag layer.

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CONSIDERATION FOR SAFETY FACTOR

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A discussion of the safety factor is necessary for
utilizing the estimating equations obtained from theabove-mentioned analyses. In the Japanese
specifications for highway bridges [1], the safety
factor for the overall axial resistance (Rt), obtainedby adding Rp and Rf, is 3.0 for end-bearing piles, andcan be reduced to 2.5 if pile loading tests areappropriately conducted at the site.
Safety factors depend on the variability of theestimation equations outlined in the specifications. A
past study presumed the CV of the estimation
equations to be approximately 30% [6]. In this study,
however, a large amount of loading test data wasavailable, owing to which the CV of Rp and Rf could
be estimated. Therefore, the safety factor could bedetermined more clearly.
The distribution of the characteristic value of Rt(Rtk) was assumed to be a normal distribution, andthe load acting on the piles could be fixed withoutstochastic variation. As a result, the safety factor for
obtaining the predefined fracture probability could
be calculated easily. For example, consider the casein which the fracture probability required is <0.001.
The value of the 0.001 fractile of the standard
normal distribution is approximately −3.1. The
design value of Rt (Rtd) for achieving a fracture
probability of 0.001 can be calculated using thefollowing equation:����������������

Fig. 10 Depth distribution of rf in Dg2 layer
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Fig. 11 Mean value (line A) and regression line
(line B) of rf in Dg2 layer; and lower limits of their
one-sided, 95% confidence intervals (lines A' and B',
respectively)

Rtd = Rtk − 3.1σt
0.92N. Thus, the following equation can be derived:
rfk = 0.92N(4)Here, σt is the standard deviation of Rtk. The safety
factor, which equals Rtk / Rtd, is obtained as follows:(2)

Rtk / Rtd = 1 / ( 1 − 3.1σt / Rtk )The CV of rf was approximately 39%. It wasobserved that the rfk derived from the tests had
somewhat poor accuracy.

(5)

According to Eq. (5), the safety factor can becalculated if the value of σt / Rtk, which is the CV of
Rtk, is known.
Because Rtk is the sum of Rpk and Rfk, thevariance of Rtk, which is σt2, can be obtained using
Eq. (6).

Strength of rf in Dg2 Layer
The data processing was similar to that
performed for the Ag layer. Figure 10 shows thedepth distribution of the strength of rf in the Dg2
layer and Fig. 11 shows its mean value andregression line.

σ t2 = σ p 2 + σ f 2
393

(6)

GEOMATE- Osaka, Nov. 16-18, 2015

Here σp2 and σf2 are the variances of Rpk and Rfk,
respectively. The unknown parameters forcalculating the CV of Rtk are Rpk, Rfk, and their CVs.
As the values of Rpk and its CV are already known, it
is necessary to obtain Rfk and its CV.
Rfk is the sum of the shaft resistances in eachlayer. Once the values of rfk in each layer are known,
Rfk can be obtained automatically. The values of rfk
in each layer are listed in Table 3. The values for
Asc1, Ac, As, and Asc2 were derived from the test
result using a method similar to that used for the Ag
and Dg2 layers. Comparing the values of Rfk and Rf,
which is the total shaft resistance of each pileobtained from the test results, the CV of Rfk could be
estimated. Table 4 lists the statistics of Rf / Rfk. The
CV of Rf / Rfk obtained was 33.6%. Therefore, theCV of Rfk was assumed to be approximately 35% for
the study.
Based on these values and Eqs. (5)–(6), the CVof Rtk could be calculated. As this value depends on
the pile length in each layer, the CV of Rtk at thelocation of each foundation must be calculated. The
result of the calculation indicated that the CV of Rtk
was less than 20% at any foundation. As a result, thesafety factor was approximately 2.63. Owing tospace constraints, detailed calculations are notshown in this paper. In this case, the calculated
safety factor was comparable with that outlined in
the Japanese specifications for road bridges for thecase with appropriate in-situ load testing.

Table. 3 Values of rfk in each layer
Asc1
Ac
As
Asc2
Ag
Dg2

rfk (kN/m2)
4.8
10
22
33
29
49

Table. 4 Rf / Rfk statistics
pile typesample
size

mean

CV
(%)

standard
error

open-ended
and Cross

53

1.09

33.6

0.0503

ACKNOWLEDGEMENTS
The test data discussed in this paper wasprovided by the Chugoku Regional Development
Bureau, Ministry of Land, Infrastructure, Transport
and Tourism of Japan. A part of this work was
supported by a research grant from the Japan Iron
and Steel Federation. We deeply appreciate thesignificant assistance provided.
REFERENCES[1] Japan Road Association, Specifications for roadbridges. Maruzen Co. Ltd., 2012, Part IV, ch. 12(In Japanese).
[2] Ports and Harbours Bureau, Ministry of Land,
Infrastructure, Transport and Tourism; National
Institute for Land and Infrastructure Management; and Port and Airport Research Institute,
Technical standards and commentaries for port
and harbour facilities in Japan. Overseas Coastal
Area Development Institute of Japan, 2009, Part
III, ch. 2.
[3] Jardine, R., Chow, F., Overy, R. and Standing,J., ICP design methods for driven piles in sandsand clays. Thomas Telford, 2005.
[4] Japanese Geotechnical Society, Method for
dynamic load test of single piles. JGS 18162002, 2002.
[5] Tominaga, M., Nei, M., Echigo, Y., Kenmochi,
S., Kage, S. and Hashimoto, S., “Kawasaki
Steel Corporation”, in “Foundation work oflarge-scale seaside plant” (feature article),Foundation Engineering & Equipment, Vol. 4,
No. 5, 1976, pp. 55-90. (In Japanese)[6] Matsui, K. and Ochiai, H., “Bearing capacity offriction piles with consideration of uncertainty
of soil properties”, Journal of JSCE, No. 445 /III-18, 1992, pp. 83-92.

CONCLUSION
A series of dynamic load tests of open-ended
piles driven into gravel layers was conducted at aroad bridge construction site in the Mizushima port
for obtaining a basis for the estimation of their axial
resistances. The test results indicated that both the Rp
and rf of the piles were relatively smaller although
the piles were open-ended. The average strength of
the Rp per unit area of the pile tip was 52N kN/m2.
The rf in the gravel layer is given by 0.8–0.9N
kN/m2.
The safety factor for utilizing the test results inthe foundation design was also discussed. The
derived safety factor was approximately 2.63 for afracture probability of <0.001. This value wascomparable with that outlined in the Japanese
specifications for road bridges for the case withappropriate in-situ load testing. With the target
fracture probability being controvertible, thisprocedure for determining the value of Rtk and thesafety factor for design is regarded to be applicable
extensively.

394

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

LABORATORY INVESTIGATION ON THE EFFECTS OFOVERBURDEN PRESSURE, WATER, AND TIME ON SLAKINGINDUCED MATERIAL PROPERTY DEGRADATION OF COAL MINE
SPOIL
Chaminda Gallage1, Golam Mostofa2, David Vosolo3 and Jayasiri Rajapakse4
Science and Engineering Faculty, Queensland University of Technology (QUT), Australia

1,2,3,4

ABSTRACTIn open-cut strip mining, waste material is placed in-pit to minimise operational mine costs. Slope failures inthese spoil piles pose a significant safety risk to personnel, along with a financial risk from loss of equipment
and scheduling delays. It has been observed that most spoil pile failures occur when the pit has been previously
filled with water and then subsequently dewatered. The failures are often initiated at the base of spoil piles where
the material can undergo significant slaking (disintegration) over time due to overburden pressure and water
saturation. It is important to understand how the mechanical properties of base spoil material are affected byslaking when designing safe spoil pile slope angles, heights, and dewatering rates. In this study, fresh spoil
material collected from a coal mine in Brown Basin Coalfield of Queensland, Australia was subjected to highoverburden pressure (0 – 900 kPa) under saturated condition and maintained over a period of time (0 – 6 months)
allowing the material to slake. To create the above conditions, laboratory designed pressure chambers were used.
Once a spoil sample was slaked under certain overburden pressure over a period of time, it was tested forclassification, permeability, and strength properties. Results of this testing program suggested that the slaking ofsaturated coal mine spoil increase with overburden pressure and the time duration over which the overburdenpressure was maintained. Further, it was observed that shear strength and permeability of spoil decreased withincrease in spoil slaking.
Keywords: Coal Mine Spoil, Slaking, Overburden Pressure, Shear Strength, Permeability

Coal mine spoil is waste materials removed to
expose coal seams during the mining process. This
material is placed in spoil piles behind the active
strip, creating what is commonly referred to as alow-wall. It is also called boney pile, gob pile, or pit
heap. The spoil materials are typically composed ofblasted fragments of mudstone, siltstone andsandstone [1]. The mean particle size of spoil ishighly, ranging from fines to particles of size 0.5 m
to 1.0 m depending on in-situ properties ofoverburden, blasting technique and equipment used.
Within the coal mine industry, spoil materials are
categorised into four groups; Category 1 (Cat -1),
Category 2 (Cat – 2), Category 3 (Cat-3),andCategory 4 (Cat -4) [2].

form the final spoil profile. To accommodate
increasingly high volumes of spoil within a mine pit,
spoil pile heights and slope angles are increased. In
the Brown Basin Coalfield of Queensland inAustralian, the typical height of spoil pile is ranging
from 30 m – 70 m and the slope of the spoil pilevaries between 260 to 400 [3]. As spoil pile heights
are increased, the foundation material is subjected toa higher stress regime that may increase the rate ofdisintegration (slaking) of base spoil material due
mechanical breakdown. The slaking of the basematerial is further increased with time when thishigh stress is maintained in saturated conditions. In
open cut mining, it is common to observe a 10 – 15m height of water in the pit when mining andpumping is stopped temporarily. Therefore, it isassumed that the base spoil material is saturated.

In open-cut strip mining, waste materials are placed
in-pit to minimise mine operational costs. It is
common to observe blasted rock (Cat-3 or Cat -4)
being placed by draglines to form the base for spoil
piles and the weaker spoil materials such as soil
(Cat-1) and highly weathered rocks (Cat-2) areplaced by dump trucks on the top of base material to

Slaking has been found to transform fresh or slightly
weathered Permian (Cat -4 or Cat -3) at the base of
spoil pile to weaker or/and weathered Permian [2].
This mechanical breakdown leads to a decrease inparticle sizes, consequently reducing the shear
strength and permeability of slake affected materials.
This reduction in material properties of spoil base

INTRODUCTION

395

GEOMATE- Osaka, Nov. 16-18, 2015

materials have been a root cause for the failures in
spoil piles during dumping, and flooding/dewatering
events. The failures of spoil piles can cause humancasualties, damage to equipment, delay in miming,and an increase in mine operational costs.

Minerals
QuartzAlbiteKaolinMixed layer illite
Mica, 2M1
AnataseIn order to determine safe height and slope angles
for spoil piles and their appropriate dewatering rates,
it is important to understand how the shear strengthand permeability of saturated spoil foundationmaterials decrease with overburden pressure andtime. Research has been conducted [4] to determine
the shear strength and other properties of relatively
fresh coal mine spoil under high overburden stress.
However, no research has been conducted toinvestigate the degradation of properties of saturated
spoil material due to slaking under overburdenpressure and time. This paper presents the results oflaboratory testing carried out to investigate theeffects of slaking on classification, strength, andpermeability properties of coal mine spoil (Category-3).

Slaking Chamber
The saturated spoil material was subjected toslaking under different overburden pressures, which
represent the height of the spoil pile, and time overwhich the overburden pressured were maintained.
The pressures were achieved in the laboratory using
specially designed pressure chambers. Figure 2
shows a schematic diagram and a photo of a pressurechambers used. Four such chambers were employedin this experimental program.
The chamber consists of an acrylic cylinder withinternal diameter and height of 360 mm and 400 mm,
respectively, and a wall thickness of 20 mm. The top
and the bottom stainless steel plates are fastened byeight bolts to seal the chamber. The chamber canaccommodate approximately 60 kg of spoil (assume
initial unit weight of 18 kN/m3) allowing space forthe piston plate. This mass is expected to be
sufficient to yield results for different tests such asclassification, shear strength, and permeability ofslaked spoil materials. Simulated overburdenpressure is achieved by means of applying air
pressure to compartment above the piston whichthen compresses the spoil in the lower compartment
with the applied pressure. A rubber seal is attachedto the piston to prevent air leak through the space
between the piston and the inner wall of the acrylic
cylinder. A pressure of 20 kPa is needed toovercome the friction between the piston and theinner wall of the cylinder.
Two porous stones (Bronze) are embedded in thebase plate and in the piston where the material is incontact. The sample can be saturated by sending
water from the bottom and allowing water to freely
flow through the sample and to exit from the top.
Water pressure in the sample is maintained at
atmospheric conditions. It is important to make surethat the water in the chamber is under atmosphericconditions so that the applied pressure on the spoil
from the piston is equal to the effective stress. The
consolidation of the spoil over the period of time
under the applied constant overburden pressure canbe measured directly from the linear variable
differential transformer (LVDT) attached to the

TEST MATERIAL AND APPRATUS
This section describes the basic properties of
spoil material and testing apparatus used in thisexperimental program
Test Material
Fresh coal mine spoil (Category -3) wascollected from an open-cut mine in Brown basin
coalfield in Queensland, Australia. The particle size
distribution of the collected spoil material is shownin Figure 1. According to Australian standard AS
1289.3.9, Liquid Limit, Plastic Limit, and Linear
Shrinkage were determined as 34.1%, 22.0% and4.9%, respectively. Further, the material was
analysed for its mineral composition using X- raydiffraction with the results are shown in Table 1.
10080% Finner

60
40
20
0
0.00010.01 Particle Size1 (mm)Amount (%)
34.00
28.80
2.30
9.20
16.40
8.90

100

Fig. 1 Particle size distribution of test material
Table 1: Mineral composition of test material
396

GEOMATE- Osaka, Nov. 16-18, 2015

To obtain each spoil sample given in Table 2, thefollowing methodology was followed:

piston shaft.
••
The third level headings
These headings should be in 10 pt, italics, andsentence case. Insert one blank line before and after
the headings. The further lower level headings
should be avoided.

•

TABLES, FIGURES AND EQUATIONS

•

•

•

320 mm height of the pressure chamber was
divided into 10 equal layers as shown in Figure
3a
The water content of the spoil was measured.
Bulk mass of spoil required for each layer toachieve dry unit weight of 18 kN/m3 wascalculated. Dry unit weight of 18 kN/m3 waschosen as it was the average unit weight of spoil
pile measured in the field.
Bulk mass of spoil required for each layer toachieve the dry unit weight of 18 kN/m3 waspoured into the chamber and tamped using theweight shown in Figure 3a until the required
height was achieved. This procedure wasrepeated for all 10 layers to achieve the spoil
height of 320 mm.
The piston and the top plate were mounted and
LVDT was set to measure the verticaldisplacement of the piston. The vacuum wasconnected to the top port coming through thepiston and air in the compacted spoil was
sucked for about 2 hours.
As shown in Figure 3b, the water tank was thenconnected to the bottom of the chamber and thesystem was left for 1 day (24 hours) for
saturation.
After saturation, air pressure equivalent to
overburden pressure of (e.g: 300, 600,800(900)kPa) was applied in to the upper chamber andmaintained for a period of 2 days, 3 months, 6months, and 12 months.

Figure 2. (a) Schematic diagram, (b) Photo of apressure chamber used in the experimental program
TESTING PROGRAM AND METHODOLOGY
To achieve the objectives of this research,classification, shear strength, and permeability
properties of spoil that would be subjected to slaking
under the conditions given in Table 2 will bemeasured in the laboratory using standard laboratory
test methods. Sample 1 is the reference material
which is fresh spoil and not subjected to overburdenpressure in the chamber.

Figure 3. (a) Compacting spoil into the pressurechamber, (b) Saturation of spoil compacted into thepressure chamber

Table 2: Slaking condition of spoil samples•
Duration ofslakingZero ( 2 days)
3 Months
6 Months

Overburden pressure (kPa)
300
600
900
Sample 2 Sample 3 Sample 4
Sample 5 Sample 6 Sample 7
Sample 8 Sample 9 Sample 10•397

After the saturated spoil has been subjected toslaking over a predetermined period of time
under a certain overburden pressure, the spoil
was taken out from the chamber after releasingpressure and removing the top plate and thepiston.
Each sample slaked under different pressuresover different time periods was tested forGEOMATE- Osaka, Nov. 16-18, 2015

•

•

•

particle size distribution following Australianstandards
To obtain particle size distribution down to 75
microns, wet sieving was conducted. Particle
size distribution for particles smaller than 75
microns was obtained using a Malvern particle
size analyser.
To measure the permeability, the spoil wascompacted into the mould measuring 150 mm
diameter (see Figure 4) to achieve the dry unit
weight of 18 kN/m3 and the constant head
permeability was performed.
To measure the shear strength parameters, thespecimen with 100 mm diameter and 200 mm inheight compacted to achieve the dry unit
weight of 18 kN/m3 was tested in triaxial
apparatus under undrained consolidation test
conditions(withpore-waterpressuremeasurement) following multi-stage test
procedures. Figure 5 depicts the triaxial systemused for this experimental program.

RESULTS AND DISCUSSION
Results of soil classification tests (particle size
distribution), permeability, and shear strength testsof sample 2 - 10 are compared with those of Sample
1. Sample 1 is considered as the reference material
as it is unslaked (fresh) spoil which was directlycollected from an open-cut coal mine in Brown
basin coalfield in Queensland, Australia. These
results can be used to discuss overburden pressure
and time effects on spoil slaking.
Grain Size Distribution
The effect of slaking on particles size
distribution of spoil samples was determined fromcombined results of wet sieve analysis and laser
diffraction analysis (Malvern apparatus). The Figure
6 represents particles size distribution of nine slaked
and one fresh spoil sample.

Figure 4. Permeability cell used for measuring
permeability using constant head method
Figure 6. Particle size distribution of slaked spoil
samples
As seen in Figure 6, the amount of fine particles
increased with the increment of slaking time andoverburden pressure. For spoil Sample 1, the amountof material finer than 75µm was approximately 8%
which grew with increasing time and pressure toabout 25% for spoil Sample 10 after 180 days under
900kPa overburden pressure. However, there was no
significant increment of clay particles (particle size
less than 0.002mm) due to slaking but had asignificant increment of silt particles. The increasein the amount of fine particles in spoil samples withsaturation time and pressure is an indication of morematerial disintegration or slaking
Hydraulic Conductivity
The influence of slaking on hydraulicconductivity was determined by the constant head
permeability test. Figure 7 portrays the influence ofoverburden pressure and slaking time on
permeability. It can be seen that the permeability
decreases with an increase in overburden pressure aswell as an increase in slaking time. Overburden

Figure 5. Triaxial system used for measuring shear
strength properties of spoil

398

GEOMATE- Osaka, Nov. 16-18, 2015

pressure has a significant impact on permeability
when it is slaked over a longer duration (e.g. 180days). When the spoil is subjected to slaking over along period for example, after 180 days of slaking,the permeability decreased from 0.884×10-06 m/sec
to 0.017×10-06 m/sec (about 1/44), when the
overburden pressure was increased from 300 to 900
kPa. For shorter slaking periods (e.g. two days), thepermeability decreases from 2.1 x 10-06 m/sec to0.96 x 10-06 m/sec (about ½) when the overburdenpressure increases from 300kPa to 900kPa.

After consolidating the specimen for about twohours under drained conditions and stage one stressconditions (cell pressure 500kPa and backpressure
300kPa), it was sheared by applying monotonic
vertical stress with a vertical strain rate of 0.05% perminute. The failure for each stage was assumed
when maximum deviator stress (σ d ) was reachedpeak or 2% vertical strain (from the beginning ofeach loading stage) was achieved. Once the failure
was reached for stage one, the applied vertical stress(deviator stress) was released and stage two stressconditions were applied (cell pressure, σ3 = 700 kPa
and backpressure = 300 kPa). The specimen wasthen consolidated under drained conditions for about
two hours before shearing it under undrained
conditions until above mentioned failure criteria wasachieved. Then, following the same steps, stage
three of the test was completed. Figure 8 illustrates
the observed axial strain (ε a ) versus deviator stressbehavior of a multi-stage triaxial testing of the spoil
specimen prepared using Sample 9. Using thesevalues and confining pressures for each stage, total
and effective major principal stresses (σ 1 and σ 1 ’)and total and effective minor principal stresses (σ 3and σ 3 ’) were computed. Using effective major (σ 1 ’)and minor (σ 3 ’) principal stresses at failures theMohr-Coulomb failures envelop was then drawn toobtain effective shear strength parameters: effective
cohesion (c’) and effective friction angle (ϕ’). Table
3 summarizes the shear strength properties of spoil
samples obtained from multi-stage triaxial testing.

Figure 7. Effect of slaking time and overburdenpressure on hydraulic conductivity
When the overburden pressure is increased, particles
are crushed and compacted to decrease the volume
of voids. The overburden pressure was maintained
over a period of time. Spoil particles are further
degraded and broken down in smaller fragments. As
a result of the degradation of particle size theporosity decreased and the unit weight increased
with time and overburden pressure. Permeability or
flow through any sample is a function of unit weight,
porosity and effective particle size and shape.
Therefore, permeability decreases with increasing
overburden pressure and slaking time. In brief, due
to slaking over 180 days under different overburdenpressures, spoil materials become low permeablegravel type materials from medium permeable.

Figure 8. Deviator stress vs axial strain obtained
from multi-stage triaxial test on Sample 9

Shear Strength
The multistage consolidated undrained triaxial
tests were performed on cylindrical spoil samples.
Each test specimen of 100mm diameter and 200mm
height was prepared to achieve a dry density of
18kN/m3 by wet compaction with initial moisture
content of 10%. Once a specimen was enclosed in a
0.8 mm thick latex membrane, it was saturated toachieve B-value of above 0.95. Multistage triaxial
tests were conducted in three stages with threedifferent cell confining pressures of 500kPa, 600kPa
and 700kPa and at the beginning of each stage backpressure was maintained at 300kPa. The effective
confining pressures at the beginning of each stage
were 200, 300 and 400kPa.

The effective shear strength parameters cohesion
and friction, varied from 1kPa to 40kPa and 38.62°
to 33.22° due to slaking under different conditions(Table 2.4). As can be seen from Table 3, effective
friction angle of spoil materials dropped due toslaking approximately 5.4° from the initial fresh
condition. Furthermore, effective cohesions were
exhibited by gradual increment up to 90 days andafter 180 days; it dramatically increased to 40kPa.
For reduction of friction angle, both slaking time andoverburden pressure played similar roles whereas for
effective cohesion the increment slaking time played
a dominate role. The undrained friction angle andcohesion presented in table 3 indicated a similar
399

GEOMATE- Osaka, Nov. 16-18, 2015conclusion. Due to slaking, the undrained friction
angle for sample 2 was dropped to 18.47° from26.98° (Sample 1). The friction angle gradually
decreased with minor exemptions, with slaking timeand overburden pressure which finally reached to15.78° for spoil Sample 10. The undrained cohesion
gradually increased from 10kPa, Sample 1 to 30kPa
for Sample 7. Then, it increased dramatically to
190kPa. In fact, undrained cohesion was influenced
dominantly by slaking duration

tests revealed that the material strength of spoil
materials, category type 3 was medium to low withsmall cohesion. Triaxial tests on fresh spoil gave
effective friction angle of 38.60, undrained friction
angle of 270, effective cohesion of 1 kPa, andundrained cohesion of 12 kPa. Due to slaking of
spoil material over six months under 900 kPa
overburden pressure, the effective friction andundrained friction angles decreased by 5.4° and
11.2°, respectively. The effective and total cohesion
increased by 40 kPa and 170 kPa, respectively.

Table 3: Shear strength properties of spoil samples

The constant head permeability test results indicateda reduction in hydraulic conductivity due to slaking.
The permeability property for fresh spoil was 6.99 x
10-6 m/sec and dropped rapidly for short termedslaked spoil samples with pressure. After six monthsof slaking, the effect of overburden pressure became
insignificant and the permeability coefficient
dropped to approximately 1.70 x 10-8 m/sec

Samples

Effective
Cohesion,C' (kPa)

Effective
frictionangel, φ'
(°)

Total
Cohesion,
C (kPa)Totalfrictionangel,
φ (°)

Sample 1

1.00

38.62

12.00

26.98

Sample 2

2.00

34.75

12.00

18.47

Sample 3

2.00

33.29

10.00

15.83

Sample 4

2.00

34.59

15.00

18.75

Sample 5

2.00

34.74

20.00

17.05

Sample 6

3.20

33.30

19.00

16.30

The authors gratefully acknowledge themanagement of a coal mine in Brown Basin in
Queensland, Australia who provides financial andin-kind support for this research project. Further,authors wish to express their gratitude to Mr. Glen
Barnes, Senior technician at QUT for his continuous
support when conducting laboratory experiments.

Sample 7

4.00

33.83

30.00

16.90

REFERENCES

Sample 8

10.00

33.75

40.00

17.37

Sample 9

20.00

33.52

50.00

15.68

Sample
10

40.00

33.22

190.0015.78

ACKNOWLEDGEMENTS[1] Kalinski ME, Karem WA and Little LM,“Estimating hydrocompression potential ofmine spoils from a site in eastern Kentucky
using dry unit weight and moisture content”,
International Journal of Mining, Reclamationand Environment, Vol.24(4), 2010, pp. 350–
362.
[2] Simmons J, and McManus D, (2004). “Shearstrength framework for design of dumped spoil
slopes for open pit coal mines”, Proceedings ofAdvances in Geotechnical Engineering, Vol.2,
2004, pp. 981-991.
[3] Williams DJ, Zou JS and Graham J, “Reliability
index versus safety factor for coal mine slope
pipe stability”, Proc. of the International
Conference on Slope Stability, Isle of Wight,
England, 1991, pp. 71-77,[4] Hendrychova M, Salek M, Tajovsky K and
Rechor M, “Soil Properties and Species
Richness of Invertebratesons on Afforested
Sites after Brown Coal Mining”, Restoration
Ecology, Vol. 20(5), 2012, pp. 561–567.

CONCLUSION
The grain size distributions of spoil samples (slaked
samples) suggest that this category 3 soil material is
not a highly slakable material. The amount of fines(particle smaller than 0.075 mm) in the fresh soil
sample (Sample 1) was about 8%. It increased to the
maximum of 25% in sample 10 which was slaked
over 180 days under 900 kPa overburden pressure.
No significant increase in clay particles (particles
smaller than 0.002 mm) were observed in sample 10compared to sample 1 (fresh spoil).
The shear strength properties of spoil samples wereobtained from consolidated undrained triaxial tests
with pore-water pressure measurement. The
undrained shear strength parameters from triaxial
400

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

LIQUEFACTION ANALYSIS OF RECLAIMED GROUND IN
URAYASU CITY CONSIDERING MAIN SHOCK AND AFTERSHOCKOF THE 2011 OFF THE COAST OF TOHOKU EARTHQUAKE
Sayuri Kimoto1, Hiroshi Matsuoka1, Hirokazu Yui1, Fusao Oka2
Kyoto University, Japan; 2Professor Emeritus, Kyoto University

1

ABSTRACT
A wide area of the reclaimed ground in Urayasu city liquefied due to the 2011 off the Pacific Coast of
Tohoku Earthquake. One of the characteristics of the earthquake is a long duration of the ground motion, whichis considered to increase the damage by liquefaction. In the present study, numerical analyses have been
performed for the ground in Urayasu city with an earthquake record of the 2011 off the Pacific Coast of Tohoku
Earthquake including aftershock by using a liquefaction analysis program, ‘‘LIQCA2D14’’(LIQCA Researchand Development Group) [1]. In the analyses, the soil is modeled as an elasto-plastic material for sandy layers
including reclaimed ground layer, and an elasto-viscoplastic material for clay layers. In order to quantify the
degree of liquefaction, we defined an index named LRI (Liquefaction Risk Index), which is given by theweighted integration of the effective stress decreasing ratio with respect to the depth. Moreover, theconsolidation process after the earthquake motion has been analyzed in order to obtain the ground settlementwith the dissipation of the pore water pressure. The simulation results of the degree of liquefaction and thesettlement, are compared with the measured value.
Keywords: Liquefaction, FEM, Numerical analysis, Risk index

INTRODUCTION

NUMERICAL METHOD

Due to the 2011 off the Pacific Coast of Tohoku
Earthquake, liquefaction occurred over a wide areafrom the Tohoku district to the Kanto district inJapan. In particular, a wide area of the reclaimed
ground in Urayasu city liquefied. One of thecharacteristics of the earthquake is a long duration of
the ground motion, which is considered to increase
the damage by liquefaction. In practice, a simplified
evaluation method, such as, the liquefaction
potential index, PL, is widely used in Japan toevaluate the possibility of liquefaction. There are,however, difficulties to evaluate the duration of theground motion as well as the amplitude history
which are known to affect the cyclic behavior of soil.
In order to quantify the degree of liquefaction,
we introduce an index named LRI (Liquefaction
Risk Index), which is calculated from the results ofnumerical analysis and defined as the weighted
integration of the effective stress decreasing ratiowith respect to the depth. Numerical analyses havebeen performed for the ground in Urayasu city withan earthquake record of the 2011 off the Pacific
Coast of Tohoku Earthquake including aftershock,and the value of LRI is validated by comparing withthe liquefaction damage in Urayasu city. Moreover,settlement during the consolidation process
following the earthquake motion will be discussed.

We have numerically analyzed the deformation ofa horizontally layered ground during an earthquake
using a soil-water coupled method. In the numericalsimulation of the dynamic behavior of the ground,
we used a liquefaction analysis program,“LIQCA2D014”(LIQCA
ResearchandDevelopment Group, 2014) [1]. The method is a twoor three-dimensional water-soil fully coupledanalysis methods based on the two-phase porous
theory which adopts a u-p formulation in theinfinitesimal strain field [2], [3]. The equation of
motion is discretized by FEM and the continuity
equation is discretized by FDM. As for the time
discretization in the time domain, Newmark’s beta
method is used. In addition, Rayleigh’s damping is
employed in the analysis, which is proportional tothe initial stiffness matrix and the mass matrix.
For the constitutive model for soils, we applied acyclic elasto-plastic model considering both thekinematical hardening rule [4] and the straininduced degradation for sandy soils [5], and anelasto-viscoplastic model for clayey soils [6].
The model used in the present analysis is a model
within a framework of continuum mechanics. Hence,
the model represents the liquefaction state by thevery soft continuous material with small meaneffective stress. The model can describe thedegradation of the material with the shrinking the401

GEOMATE- Osaka, Nov. 16-18, 2015

overconsolidation boundary surface and thevolumetric softening which is a feature ofliquefaction.
SIMULATION MODEL AND BOUNDARYCONDITIONS
Fig.1 shows a map of Urayasu city. A wide areaof the reclaimed ground which covers three fourthsof the city liquefied due to the 2011 off the Pacific
Coast of Tohoku Earthquake. We have chosen twosites of the reclaimed ground for the analyses, that is,
Point Y and Point Z as indicated in Fig. 1. Point Y is
located in Shinmachi area in which the reclamation
began in 1972, while Point Z is located in
Nakamachi area in which the reclamation began in
1965. Thus, the reclamation period is different
among each site. The ground profile for the two sites
are shown in Fig. 2. The thickness of the reclaimed
soil layer is around 9 m and 5 m at Point Y and Point
Z, respectively.
Material parameters for the elasto-plastic
constitutive model are determined based on thecyclic strength curve obtained from cyclic undrained
shear tests of samples taken at each site [7][8]. The
parameters of the reclaimed sandy layer for eachpoint are shown in Table 1. The cyclic shear strength
curves obtained from experiments and model
simulations are shown in Fig. 3.
The time discretization is based on Newmark's
beta method, with β and γ set at 0.3025 and 0.6,
respectively. The time increment in the calculation
was set to be 0.001 s which is small enough toguarantee the accuracy of the results.
Fig.4 shows the input earthquake motions whichis the recorded earthquake obtained during the 2011
off the Pacific Coast of Tohoku Earthquake at a
depth of 36 m at Shinagawa seismologicalobservatory [9]. The maximum acceleration isaround 65.9 gal and the duration of the groundmotion is greater than 400 sec. for the main shock.
In the earthquake record, aftershock occurred after
29 minutes of the main shock. In the present analysis,
however, the duration between main shock andaftershock is disregarded for saving time since weconfirmed that the pore pressure
dissipation
occurred mainly after 29 minutes, hence it did notstrongly affect the calculations. Calculations are
conducted until 800000 sec. (i.e. 9.3 days) in order
to investigate the dissipation process.

Fig. 1 Map of Urayasu city.

Fig. 2Ground profile.

(a) Point Y，reclaimed soil layer (B, F)
(b)

402

GEOMATE- Osaka, Nov. 16-18, 2015

Fig. 3(b) Point Z，reclaimed soil layer (B, F)
Cyclic strength curve of the reclaimed soil.

Fig. 4

Input seismic motion (Shinagawa, at a
depth of 36 m).

SIMULATION RESULTS
Acceleration-time profiles of the ground surface
are shown in Fig. 5. The maximum acceleration of
the ground surface is 148 gal at Point Y, and 103 gal
at Point Z. The input motion is amplified at bothsites, and the amplification is greater at Point Y.

Table 1 Material parameters
Parameter

Point Y
(B, F)

Point Z
(B, F)

Density ρ (t/m3)
Coefficient of
permeability k (m/s)
Shear modulus
G0/σ’m0
Initial void ratio e0
Compression index λSwelling index κStress ratio at
MaximumCompression M*mStress ratio at failure
M*fQuasi-OCR*(=σ’mai/σ’m0)
Hardening parameter
B*0,B*1,Cf
Fading memory CdParameter ofdilatancy D*0, nReference of plasticstrain γrP*
Reference of elasticstrain γrE*

1.68

1.68

Sandy
layer
(As)
1.93

1.0×10-4

1.0×10-4

1.0×10-3

1254

2257

1.46
0.013
0.008

1.46
0.013
0.008

527 (Y)706 (Z)
0.93
0.08750.01420.84170.8417

1.083

1.083

1.083

1.319

1.0

1.0

1.25

6000,80,
10
2000

1500,15,
10
2000

6000,150,152000

0.9,6.0

1.0,1.0

3.75,10

0.005

0.004

0.0015

0.1

0.08

0.05

(a) Point Y

Fig. 5403(b) Point Z
Acceleration-time profile of the groundsurface.

GEOMATE- Osaka, Nov. 16-18, 2015

We estimate the liquefaction degree at each
depth by the effective stress decreasing ratio, ESDR,as:

ESDR = 1 − σ m′ σ m′ 0

settlement after consolidation slightly overestimatesthe measured value.
Effective stress paths of the top element of thereclaimed layer (B, F) and the sandy layer (As)during earthquake are shown in Fig. 9 and Fig. 10,
respectively. The mean effective stress of the top
element approaches to zero in both two cases asshown in Fig. 9, while it decrease but does not reachzero in sandy layer as shown in Fig. 10. Effective
stress paths of the top element of clay layer (Ac) are
shown in Fig. 11. The mean effective stressdecreases even in clay layer, and the ESDR in clay
layer reaches around 0.23 in both sites.

(1)

in which σ m′ 0 is the initial mean effective stress andσ m′ is the current mean effective stress. When the
ground liquefies completely, the mean effective
stress reaches 0 and ESDR reaches 1.0. In order toquantify the degree of overall liquefaction at the
point, we defined a liquefaction risk index [10], LRI,as:

LRI = ESDR ⋅ W (z , D )dz∫

Table 2 Maximum acceleration, settlement and LRI

D

0

(2)

where z is the depth from the ground surface andW(z, D) is the weighting function of z . LRI is
obtained by the weighted integration of the ESDR
with respect to the depth from the ground surface toa depth of D m. We adopt D=20 m in the present
study and obtained LRI as:

LRI = ESDR ⋅ (1 − 0.05 z )dz

∫

Max.
acc.
(gal)

Settlement after
consolidation (m)Point Y

148

0.533

Point Z

103

0.581

LRI
(Max.)
7.38(781s)
6.35(712s)

20

0(3)Distributions of ESDR obtained from the analyses
are shown in Fig. 6. The values of ESDR reaches 1.0
in the reclaimed layer both at Point Y and Z after themain shock, i.e., 420 sec. Time profiles of LRI
calculated using Eq. (3) for Point Y and Z are shownin Fig. 7. LRI increases during input motion in bothcases and the increasing rate during the main shockis larger in the case of Point Z. This is because thecyclic shear strength of reclaimed soil layer issmaller in the case of Point Z than that of Point Y asshown in Fig. 3. Whereas the maximum value of
LRI is 7.38 in Point Y and 6.35 in Point Z. It is
probably because the thickness of reclaimed soil
layer is larger in Point Y. In the previous research[10], we have evaluated the LRI values by data fromthe 1995 Hyogoken-Nanbu Earthquake. It was foundthat the obtained LRI values were greater than 6.0
for the points where heavy liquefaction damage had
occurred. The value obtained in this research isgreater than 6.0 in both sites, which is acceptableconsidering the heavy damage in Urayasu city. The
maximum acceleration, the settlement and LRI for
two cases are summarized in Table 2.
Vertical displacement-time profiles are shown in
Fig. 8. The final settlement after consolidation is0.533 m in Point Y and 0.581 m in Point Z. From asurvey report, observed settlement differs among thearea and the maximum settlement in the reclaimed
land was around 0.4 – 0.5 m [11]. The calculated(a) Point Y

Fig. 6404(b) Point Z
Distributions of the effective stressdecreasing ratio, ESDR.

GEOMATE- Osaka, Nov. 16-18, 2015(a) Point Y

Fig. 7(a) Point Y(b) Point Z
Time profiles of Liquefaction Risk Index,
LRI.

Fig. 9(a) Point Y(b) Point Z
Effective stress paths of the top element ofreclaimed layer.

(a) Point Y(b) Point Z
Fig. 10 Effective stress paths of the top element ofsandy layer.

(b) Point Z
Fig. 8 Vertical displacement-time profile.
405

GEOMATE- Osaka, Nov. 16-18, 2015

REFERENCES[1] LIQCA Research and Development Group(Representative: F. Oka), “Materials for
LIQCA2D14 ・ LIQCA3D14 (2014 released
print)”, 2014.
[2] Zienkiewicz OC, Chan AHC, Pastor M,
Schrefler BA and Shiomi T “Computational
Geomechanics with Special Reference toEarthquake Engineering”, John Wiley & Sons,
1999.
[3] Oka F, Yashima A, Shibata T, Kato M and
Uzuoka R, “FEM-FDM coupled liquefaction
analysis of a porous soil using an elasto-plastic
model”, Applied Scientific Research, Vol. 52,
1994, pp. 209-245.
[4] Armstrong PJ and Frederick CO, “A
mathematical representation of the multiaxial
Bauschinger effect”, Report RD/B/N 731, 1966.
[5] Oka F, Yashima A, Tateishi A, Taguchi Y andYamashita S, “A cyclic elasto-plastic
constitutive model for sand considering aplastic-strain dependence of the shear modulus”,
Geotechnique, Vol. 49 (5), 1999, pp. 661-680.
[6] Kimoto S, Shahbodagh Khan B, Mirjalili M and
Oka
F,
”A
cyclicelasto-viscoplastic
Constitutive model for clay considering thenonlinear kinematic hardening rules and thestructuraldegradation”,
2013,
doi:10.1061/(ASCE)GM. 1943-5622.0000327.
[7] Japanese Geotechnical Society, Workshop on
constitutive model for soil, May 27-28, Tokyo,
2013 (in Japanese).
[8] Ishikawa K, Yasuda S, Aoyagi T, “Studies on
the reasonable liquefaction-prediction method”,
Japanese Geotechnical Journal, Vol. 9, No. 2,
2014, pp. 169-183 (in Japanese).
[9] Bureau of Port and Harbor, Tokyo Metropolitan
Government, http://www.kouwan.metro.tokyo.
jp/business/keiyaku/kisojoho/jishindou.html.
[10] Oka F, Yui H, Kimoto S, Kinugawa T, “Newevaluation method for liquefaction of groundusing dynamic liquefaction analysis method andits application”, Int. J. Geomechanics, 2015, toappear.
[11] Fukutake K, Mano H, Hotta H, Taji Y,
Ishikawa A, Sakamoto T, “Studies on soilliquefaction and settlement in the urayasu
district using effective stress analyses for the2011 East Japan Great Earthquake”, Research
paper, Institute of Technology, Shimizu
Corporation, Vol. 89, 2012, pp.39-50 (inJapanese).

(a) Point Y(b) Point Z
Fig. 11 Effective stress paths of the top element ofclay layer.
CONCLUSIONSIn the present study, liquefaction analyses were
performed for two sites in Urayasu city with anearthquake record of the 2011 off the Pacific Coastof Tohoku Earthquake including aftershock by using
a liquefaction analysis program LIQCA. In order toquantify the degree of liquefaction, we introduced an
index named LRI (Liquefaction Risk Index), which
is given by the weighted integration of the effective
stress decreasing ratio with respect to the depth. The
obtained LRI values are 6.35 are 7.38 for each site,
and those seem to be acceptable compared to theprevious research [10]. In order to evaluate the index,
we need to obtain more data indicating relationships
between the liquefaction damage and the value ofthe index. In addition, settlement due to dissipationof excess pore pressure is evaluated. The calculated
total settlement after consolidation slightly
overestimates the measured settlement.

406

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

THE PERFORMANCES OF THREE SOIL RETAINING STRUCTURES:GABION, MODULAR BLOCK AND GEOCELLSTATHAS Dionysios1, WANG Jui Pin1
Civil & Environmental Engineering, HKUST, Hong Kong (SAR)

1

ABSTRACTOne of the major tasks of a geotechnical engineer is the design of earth retaining walls. Apart from the rigid
reinforced concrete wall, several types of alternative facings, such as Gabion, Geocell and Modular Block, withor without soil reinforcement, are used to offer a more environmental friendly and low cost solution at the earthretaining problem. Although several studies have been conducted investigating the performance of each of thethree structures, there is no clear comparison between the mentioned earth retaining walls. We have conductedsmall-scale tests (1:10 scale) comparing the three types of structures in terms of load capacity, deformation
behavior and failure mode. The results of this study are shown in this paper. With a total of six tests conducted,
the structures were loaded until failure with and without soil reinforcement. Gabion and Modular Blockminiatures were made with a 3D printer providing a more precise configuration as real ones. The Geocell isproved to perform better in terms of absolute load capacity. However, the interlock of the modular blocks
provides a significant resistance parameter increasing the efficiency of the particular facing type.

Keywords: Retaining structures, Reinforced soil, Modular Block, Gabion, Geocell

displacement of segmental geosynthetic reinforced
soil walls through the study of a wall data base [2].
Various studies investigating separately theperformance of Gabion, Modular Block or Geocell
have also been conducted recently. Geocell was
proved to perform quite well under seismic loading[5], [8]. Chen and Chiu (2007) carried out small
scale physical models to describe the influence ofvarious parameters such as position ofreinforcement, wall batter and load position, on thewall performance and make some design
suggestions [3]. Laboratory tests to describe thebehavior of Gabion wall facing under cyclic loading
have also been conducted in the past years [6].
Numerical implementations are another common
effort to understand and describe the performance ofsegmental geosynthetic reinforced walls. Most of thetime numerical results are validated with laboratory
tests. The numerical studies included static
surcharge loading [4] as well as earthquake loading[7].
A lot of effort is being carried out to understanda structure which has many parameters influencing
its behavior. This study is adding some
supplementary data to the existing ones. Moreover
new trends in technology are implemented to assist
the physical modeling of small scale models.
Finally, three common realistic structures are chosen
and compared under totally same conditions,providing assistance to practical engineers in orderto gain a better knowledge concerning the behavior

INTRODUCTION
Earth retaining structures are one of the mostcommon and important civil engineering tasks.
Geotechnical engineers need to find the mostsuitable solution for soil retaining problems andsatisfy safety, environmental and economic criteria.
During the years, various types of earth retaining
walls have been developed and proposed. Apartfrom the typical rigid reinforced concrete cantileveror gravity wall, alternative types of earth retaining
walls such as gabion, modular block or geocell canalso provide a reliable solution.
Gabion and Geocell are two types of elements,
which can build whether a gravity wall or they canjust be used as the facing of a reinforced soil
structure. On the other hand, modular block ismainly used as the facing of reinforced soil systems.
In the past years various studies have been
conducted in order to investigate the performance of
these structures. The importance of facing stiffness
to the performance (Load Capacity, wall
deformation, reinforcement tensile force) ofreinforced soil retaining structures has been an
important research topic during the last decades [1],[10].
The deformational behavior of geosynthetic
reinforced soil walls with continuous facing panel
has been also a discussion topic in the past [9]. Also
morerecently
researchers
proposedrecommendations concerning the maximum wall407

GEOMATE- Osaka, Nov. 16-18, 2015of these structures and make decisions.
1.85

Dry density (gr/cm3)

TEST SETUP
The foundation was chosen deep enough toreduce any boundary effects. The wall height waschosen 200mm and the backfill length 300mm. The
model width was 210mm. Figure 1 shows the test
setup. The block offset is 2mm for every modular
block and geocell layer. For gabion the block offset
was 6mm. The wall batter was 6º for all types offacing. Following table is summarizing the basic
geometrical characteristics of every facing type. The
sides of the box were covered with low friction
plastic sheets to reduce side resistance.

1.80

1.75
0%

2%

4%

6%

8%

10% 12% 14%

Water content
Fig. 2Compaction curve of China Standard ISO
sand.

Infill Material
The infill soil, which was used for Geocell andModular Block was the same with the wall backfill.
The Gabion baskets were filled with gravel having agrain size larger than 10mm. The specific gravity of
the gravel is 2.6g/cm3 and the porosity of the gabionbaskets 50%, which resulted in an average density of
1.30g/cm3.
Experimental setup.
Passing percentage

Fig. 1

Table 1 Facing geometrical characteristics
FacingGabionMod.
BlockGeocellNo. ofLayers
4
10

Width
(mm)
50
30

Offset
(mm)
6
2

10

60 - 90

2

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
0.01

0.1

1

10

Sieve diameter (mm)
Soil Properties

Fig. 3

Backfill

Grain size distribution of China Standard
ISO sand.

Miniatures

The sandy soil used in the model tests is China
Standard ISO sand. The specific gravity of this soil
is 2.65g/cm3, with the maximum and minimum dry
density equal to 1.91g/cm3 and 1.62g/cm3,
respectively. The angle of internal friction is around
38° at a relative density of 70%. This sand can beclassified as poor graded sand (SP) according to the
UnifiedClassification
system(Uniformity
coefficient Cu = 6.0 and coefficient of gradation Ck =0.41).
The wetted sand used in tests had 3% watercontent and it was compacted to a dry density of
1.79g/cm3, which equals 98% compaction degree.
Figures 2 and 3 show the compaction test according
to BS and sieve analysis results.

The geometrical characteristics of the miniatures
were chosen according to products used in praxis.
Furthermore, their dimensions, apart from being
realistic, were chosen in such a way to make thethree facings comparable with each other from ageometric point of view.
GabionGabion sides were manufactured with a 3D
printer. The material used is acrylonitrile butadienestyrene (ABS). The gabion sides were madequadratic with 50mm width and 1mm thickness. The
408

GEOMATE- Osaka, Nov. 16-18, 2015

mesh has regular hexagonal with 10mm diameter.
The sides were bound together with plastic ties toform the gabion box. The average weight of eachgabion box is 162g.

TEST RESULTS
Test without reinforcement
Gabion and Geocell failed due to sliding,whereas for Modular block the governing failure
mechanism observed, was overturning. The rotation
point was at 4.5cm from the foundation level(0.25H). The slip surface on the back of the wall for
Gabion and Geocell has been a plane surface
according to Rankine theory (Fig. 5 and 7). In caseof Modular block, on the other hand, a curved
surface was formed similar to a log-spiral slipping
surface (Fig. 6).
Gabion and Modular block showed slightdifference in terms of load capacity, 29.0kPa and26.0kPa respectively. The Geocell, proved to havealmost twice larger load capacity (48.0kPa)
compared to the other two facings.
The magnitude of maximum wall deformation
has been quite similar for Gabion and Modular block.
On the contrary, the deformed shape observed, wasquite different. The maximum deformation for themodular block occurred at the top layer, whereas for
Geocell and Gabion at the height of 0.75H (Fig. 7).
In case of the gabion facing type, it is necessary tomention, that LVTD 2 at 9.5cm penetrated into thegabion basket and this particular measurement
should not be taken into account.
Figure 8 shows the maximum wall displacement
in relation to surcharge loading on the wall backfill
for each wall facing.

Modular Block
The modular blocks were also manufactured
using a 3D printer with the same material as gabionsides. The size of each block is 48x30x20mm. Eachblock weights 34g and 7.2g, with and without sandinfill respectively.
Geocell
The Geocell was made with transparent printing
paper and the seams were made with staples. The
height of the Geocell is 20mm and the size of anopen cell is 32 x 30mm. The seams were positioned
every 450mm. The total Geocell length layer is
210mm and the average width is 75mm. Each layer
infilled with soil had a weight of 576g.
Reinforcement
For the soil reinforcement was used nylon sheets.
The reinforcement length was chosen 30% of thewall height, which is equal to 60mm.

Table 2 Summary of
reinforcement
FacingFig. 4Load
Capacity
(kPa)
29.0
26.0

test

resultMAX
Displacement(mm)
2.2 at 0.75H
1.9 at H

GabionMod.
BlockGeocell
48.0
0.6 at 0.75H
Note: H is 200mm the wall height

Miniatures.

Testing procedure

WITHOUT

Failuremode
Sliding
Overturning
at 0.25H
Sliding

The three types of structures can be designed likea gravity wall. The weight of the structure is themain parameter, which determines their load
capacity. The structure weights are 2600g, 1360g,
and 5760g for Gabion, Modular Block and Geocell
respectively. Gabion and modular block behaved
similarly in terms of load capacity and load
deformation behaviour, although modular block is amuch lighter structure. This fact can be justified
from the high axial rigidity of the modular block
facing [10]. As reported from Tatsuoka in his
Keynote lecture [10], Roles of facing rigidity in soil
reinforcing, facings with a high axial rigidity can

In total there were conducted 6 tests. The first
three tests were carried out without soil
reinforcement. The second test set included the three
types of facing with reinforcement.
The loading was applied uniformly 20mm fromthe back of the facing. A rigid wooden plate 100 x
200mm was used in order to distribute the load
uniformly on the backfill.
A pneumatic actuator was used to generate theload, which was increased manually at steps of
2.5kPa every minute. All structures were brought tofailure.

409

GEOMATE- Osaka, Nov. 16-18, 2015transfer through friction on the back face, part of thebackfill weight effectively. Consequently, thevertical force acting as resisting force for gravity
structures will be increased.
Finally, it is worth mentioning that Geocell
showed no deformation until the surcharge reached
the value of 44kPa. The increased load capacity canbe justified from the larger weight of the structure
towards the other types of facings.

20

Wall height (cm)15

10

GB 13.5kPa
GB 29.0kPa
5

MB 13.5kPa
MB 26.0kpa
GC 13.5kPa
GC 48.0kPa

0
0.0

Fig. 6Modular blockslipping surface

without

1.0

1.5

2.0

2.5

Wall displacement (mm)Gabion without reinforcement: slipping
surfaceFig. 7

Surcharge load (kPa)

Fig. 5

0.5

reinforcement:

Wall deformation of retaining structures
without soil reinforcement
80
75
70
65
60
55
50
45
40
35
30
25
20
15
10
5
0

GB
MB
GC
0.0

0.5

1.0

1.5

2.0

2.5

3.0

MAX Wall displacement (mm)Fig. 8Load – max displacement relation ofretaining
structures
without
soil
reinforcement

Test with reinforcement

Fig. 7The soil reinforcement increased significantly
the load capacity of the facings as expected. The
failure observed for Gabion and Geocell was internalsliding. The sliding surface was at a height ofapproximately 0.25H from foundation level for both
structures. The reinforced soil structure with amodular block facing failed due to sliding at the

Geocell without reinforcement: slipping
surface410

GEOMATE- Osaka, Nov. 16-18, 2015

foundation level. The slip surface on the back of thewall formed a two wedge shape (Fig. 9 – 11).
The load capacity was quite similar for Gabion
and modular block (Table 3). Geocell was proved tohave the best load capacity (Table 3) from the three
types of structures as well as the slightest
deformation (Fig. 12) in comparison with the othertwo facing types.
Soil reinforcement had an influence on the wall
deformed shape in case of Geocell, compared to theunreinforced case. The maximum deformation wasrecorded on the top of the structure (Fig. 12). For theother two cases, the position of the maximum
deformation was similar to the unreinforced tests.
Finally, note that the soil reinforcement reduced
significantly the deformation of the wall apart fromreducing increasing the load capacity.
Table 3 Summaryofreinforcement
FacingGabionMod.
BlockGeocell

test

resultLoad
Capacity
(kPa)
67.5

MAX
Displacement(mm)
1.5 at 0.75H

69.5

1.8 at H

79.0

1.3 at H

Fig. 10 Modular block with soil reinforcement:slipping surface

WITH

Failuremode
Internal
Sliding
Sliding
Internal
Sliding

Fig. 11 Geocell with soil reinforcement: slipping
surface

Note: H is 200mm the wall height

20

Sliding, whether internal or not, was thegoverning failure mechanism for all three types ofstructures. The Geocell had the best performance interms of load capacity and maximum deformation.
However, modular block although is the lightest
structure seems to perform quite satisfactory. Theinterlock of the modular block provides a significant
advantage to this type of structure since a sliding
between facing layers is impossible.

Wall height (cm)15

10

GBR 26.5kPa
GBR 67.5kPa
5

MBR 26.5kPa
MBR 69.5kPa
GCR 26.5kPa
GCR 79.0kPa

0
0.00

0.50

1.00

1.50

2.00

2.50

Wall displacement (mm)Fig. 9Gabion with soil reinforcement: slipping
surfaceFig. 12 Wall deformation of retaining structureswith soil reinforcement411

GEOMATE- Osaka, Nov. 16-18, 2015

Surcharge load (kPa)REFERENCES
80
75
70
65
60
55
50
45
40
35
30
25
20
15
10
5
0

[1] Bathurst, R.J., Vlachopoulos, N., Walters, D.L.,
Burgess, P.G., Allen, T.M., “The influence of
facing stiffness on the performance of two
geosynthetic reinforced soil retaining walls.”,
Canadian Geotechnical J. 43, 2006, pp. 12251237.
[2] Bathurst R.J., Miyata Y, Allen T.M., 2010,“Facing Displacements in Geosynthetic
Reinforced Soil Walls”, Keynote paper, Earth
Retention Conference 3(ER2010), ASCE GeoInstitute, Bellevue, Washington 1-4 August, 18
p., pp. 442- 459[3] Chen, R.H., and Chiu, Y.M., “Model tests ofgeocell retaining structures”, Geotextiles andGeomembranes, 26,2008, 56-70[4] Hatami, K., and Bathurst, R.J., “A numericalmodel for reinforced soil segmental walls undersurcharge loading”, J.Geotech. Geoen. Eng.,
ASCE, 132(6), 2006, 673–684.
[5] Leshchinsky, D., Ling, H.I., Wang, J.P., Rosen,A., and Mohri, Y., “Equivalent seismic
coefficient on geocell retention systems”,
Geotextiles and Geomembranes, 27, 2009, pp.
9-18.
[6] Lin, Y. L., Yang, G. L., Zhao, L. H.,“Engineering behaviors of reinforced gabion
retaining wall based on laboratory test”, Journalof Central South University of Technology,
17(6), 2010, 1351-1356.
[7] Ling, H. I., Yang, S., Leshchinsky, D., Liu, H.,
and Burke, C., “Finite-element simulations offull-scale modular-block reinforced soil
retaining walls under earthquake loading”, J.
Eng. Mech. ASCE, 136(5), 2010, 653-661.
[8] Ling, H.I., Leshchinsky, D., and Wang, J.P.,“Seismic response of geocell retaining walls:
Experimental studies”, J. Geotech. Geoenviron.
Eng. ASCE, 135(4), 2009, pp. 515-524.
[9] Rowe R.K., Ho S.K., “Horizontal deformationin reinforced soil walls.”, Canadian
Geotechnical Journal 35, 1998, pp.312 – 327[10] Tatsuoka, F., “Roles of facing rigidity in soil
reinforcing.” , Keynote Lecture. In Proceedingsof the International Symposium on EarthReinforcement Practice, IS Kyushu 1993.
Fukuoka, Japan, 14 November 1993. Edited by
H. Ochiai, S. Hayashi, and J. Otani. A.A.
Balkema, Rotterdam, The Netherlands, vol. 2,
pp. 831 - 870.
[11] Wang, J.P. ,“Full-scale shaking table test on
reinforced soil structures with geocell facing”,
Ph.D thesis, Columbia University,2007.

GBR
MBR
GCR
0

0.5

1

1.5

2

2.5

3

MAX Wall displacement (mm)Fig. 13 Load – max displacement relation ofretaining structures with soil reinforcement

CONCLUSIONSAll three types of structures are easy and quick toconstruct. In engineering problems, where a fast andlow cost construction method is required, these typesof structures can be a reliable and environmental
friendly solution.
Geocell due to its larger weight was proved tohave the highest load capacity. Moreover, its
deformational behaviour was much better than theother two types of facing. However, in terms of
relative to the structure’s weight performance,
modular block because of its high axial and shear
rigidity can be suggested as an efficient solution
especially in problems of limited space.
However, the aesthetic effect of the facing, thematerial availability on site and the erosion
resistance of the facing should also be taken into
account in the decision making process.
Finally, more and more advanced measuring andinstrumentation techniques are being implemented inphysical modelling in geotechnics. The use of 3D
printing, which is becoming more popular day byday, can be a valuable additional tool to assist small
scale physical modelling, for manufacturing
complex miniatures with higher precision.

412

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

DEVELOPMENT OF CAPACITANCE DISPLACEMENTMONITORING SYSTEM AND ITS PERFORMANCE TESTS
LIN Wenli1,2, Katsutoshi UENO 1, SHEN Mingrong2, Ryosuke UZUOKA1, Hisashi SUZUKI1
1
The University of Tokushima, Japan;
2
School of Civil Engineering, Tongji University, Shanghai 200092, China
ABSTRACT
Geological disasters caused by slope instability frequently result in great loss to human life and property, andreal-time and on-line slope monitoring is a most effective method for mastering the deformation dynamics andearly warning of landslide disasters. Considering the numerous landslides and high cost of slope monitoring
system, three types of capacitance displacement transducer based on the principle of electrostatic capacitancewere designed. Basic performance and long-term stability performance of these capacitors were examined, andthe impact of temperature behavior on the capacitance measurement circuit was evaluated. The results showed
that, comparing with conventional displacement transducers used for slope monitoring, capacitance typetransducers can provide sufficient precision, resolution and measurement range and applying them in slope
monitoring could bring obvious advantages, for their remarkable economic benefit and effectiveness.
Keywords: Linear Capacitance Displacement Transducer, Landslide, Slope Monitoring, Performance
INTRODUCTION

plates is given by Eq. (1).

Landslide is a serious geological disaster andwidely distributed in the global scope, which
frequently causes great loss to human life andproperty. It is difficult to invest immense economic
and technology to manage all the potential landslides.
Real-time and on-line monitoring of slope istherefore, a most effective method for mastering thedeformation dynamics and early warning oflandslide in time [1]. However, current monitoring
technology has the defects of time-consumption,
costly and highly skilled, which result in their poor
universal applicability in landslide monitoring. In
view of this, it is of great significance to develop a
simple, low-cost and easily embedded displacementmonitoring instrument.
Starting with this premise, the development of
low-cost slope monitoring system for consumers
was presented in this paper. Based on the principleof electrostatic capacitances, three types ofcapacitance displacement transducers, Area-alterable
Differential Type Parallel-plate Capacitor (ATPC),
Area-alterable Type Cylinder Capacitor (ATCC) andPermittivity-alterable Type Cylinder Capacitor(PTCC), were designed, and performance of thesetransducers was examined.

C=ε 0ε r S
d(1)

Where ε 0 is the permittivity of free space; ε r is
the relative permittivity of the dielectric material, in
the air, ε r =1 ; S is the overlap area of the plates(m2); d is the distance between two plates (m).
As can be seen from Eq. (1), there are threevariables, ε r , S, d, and it is advisable to maintainany two of them as constant and adjust the othervariable by changing the displacement of a movablepart of the capacitance transducer, thereby
establishing a linear relationship between thecapacitance and the adjustable variable. A measureof the capacitance is therefore, a measure of thedisplacement of the movable part of the capacitancetransducer, and the change in capacitance can beconverted into digital signal indicating the linear
displacement of the transducer by a matchingmeasuring circuit.
1.1 Area-alterable Type Parallel-Plate Capacitor(ATPC)The ATPC capacitor is formed by having thefirst and second parallel electrodes fastened on a
stationary assembly to constitute the measured
capacitance Cm, and the third parallel electrode 3
fastened on a movable assembly constitutes theelectrostatic capacitances C13 and C32 respectively
with the first and second electrodes. The electrode 3,

1. PRINCIPLE OF THE CAPACITANCEDISPLACEMENT TRANSDUCER
Capacitance displacement transducer is used forconverting a displacement caused by a change of aphysical variable into an electrical signal using
electrostatic capacitances [2], [3]. Ignoring edge
effects, the capacitance between a pair of parallel

413

GEOMATE- Osaka, Nov. 16-18, 2015is shown in Fig.2.

moving with the measured object, is provided as ashield and connected to GND. The structure of
ATPC capacitor is shown in Fig.1.
In this case, electrode 1 and 2 are composed ofpolypropylene material, the lower surface ofelectrode 1 and the upper surface of electrode 2 arecovered with a piece of conductive paper(aluminizing paper) and electrode 3 is composed of
conductive paper. The thickness of these threeelectrodes are 1 mm, lengths are 330 mm, whichmeans that the measurement range of this transducer
is 330 mm. Since this transducer used relatively
simple material, therefore, it could be manufactured
at significantly reduced cost.

ΔL

Dielectric material
R2

Elecrode 1

In this case, both cylinders are composed ofmetal material. The solid inner cylinder 2 has adielectric material affixed to its surface. Surrounding
the inner cylinder 2 is the hollow cylinder 1 which
has an isolated material affixed to its outer surface.
The total measurement range of this capacitor is 900
mm.
Capacitance variation formula of ATCC
transducer is similar to the ATPC displacementtransducer and given by Eq. (4).

Shutter Elecrode 3

Baseplate

L=330mm-Elecrode 2

2πε 0ε r a ( L − ∆L)
2πε 0ε r
=
∆L
C=
C0 −
ln( R1 R3 )ln( R1 R3 )Fig.1 The structure of ATPC capacitor

remains constant and adjust the overlap area S by the
movable electrode 3 of the transducer, the initial
overlap area is S0 =a×L, here a is the width and L is
the length of the parallel electrodes, so the initial
capacitance is given by Eq.(2)ε 0ε r Sε 0ε r aL=d

1.3 Permittivity-alterable Type Cylinder Capacitor(PTCC)The structure of PTCC capacitor is shown in
Fig.3. The two relatively fixed inner and outercapacitor cylinders 1 and 2 are spaced from the innerand outer surfaces of the middle insulating cylinder
3 by a dielectric air gap. The isolating cylinder 3 isfastened to a movable assembly and slidably
engaged within the inner and outer cylinders so that,as the insulating cylinder 3 is displaced, thepermittivity between the two relatively fixed innerand outer cylinders changes.

(2)d

The change in the overlap area of the relatively
fixed electrodes 1 and 2 caused by the linear
displacement ∆L of electrode 3 relates to its
capacitance directly, and the changed capacitance isgiven by Eq.(3)ε ε a( L − ∆L)

Cm =0 rd=C0 −ε 0ε r a
d

∆L

(4)

Where, R1 is the inner radius of cylinder 1, R3 isthe inner radius of cylinder 2, L is the measurement
length and ∆L is the displacement of movablecylinder.

We are setting permittivity ε r and distance d=
C0

Isolated materialFig.2 The structure of ATCC capacitor

X

d

R3

Outer electrode 1

Aluminum Paper

Isolated Paper

R1

Inner electrode

＋

ΔL

L-ΔL

(3)
L-ΔL

ΔL
Dielectric material

1.2 Area-alterable Type Cylinder Capacitor (ATCC)

R 2 R1

R4

R3

Inner electrode 2This ATCC capacitor is formed by having one
hollow cylindrical electrode 1 fastened on a
stationary assembly and another solid cylindricalelectrode 2 fastened to a moveable assembly. The
inner cylinder slides within the outer cylinder so that,as the movable electrode is displaced, the overlap
area of the measured capacitance changes as afunction of linear displacement. Its structure

Outer electrode 1

Isolating cylinder 3
Isolated material

Fig. 3 The structure of PTCC capacitor

In this case, both cylinders 1 and 2 are composedof metal material. The solid inner cylinder 2 has a414

GEOMATE- Osaka, Nov. 16-18, 2015

dielectric material affixed to its surface. Surrounding
the inner cylinder 2 is the hollow insulating cylinder
3 which is composed of polyethylene material. The
outer cylinder 1 has an isolated material affixed toits outer surface. In addition, the total measured
range of this capacitor is 900 mm.
Here, the overlap area S0 and distance d remains
constant and the permittivity between cylinder 1 and
2 is adjusted by the displacement of the movableisolating cylinder 3, the initial capacitance is given
by Eq.(5).

C0 =2πε 0ε r Lln( R1 R3 )Capacitancedisplacementtransducer

MeasuredobjectMeasurement
unit

DisplayPC
Feedback

Fig.4 Measurement system of the capacitancedisplacement monitoring system

3. BASIC PERFORMANECE TEST AND ITSEVALUATION(5)

The basic performance, including linearity,
resolution, measurement range and the relative errorof slope of transducers, were examined. The changeof capacitance caused by the displacement of
movable part was directly measured by themeasurement circuit and recorded in value of count.
Here 1 count is roughly equivalent to 3 fF
capacitance. The relationship between displacement
and count was drawn (see Fig.6~8) and the results
were showed in Table 1.

If the isolating cylinder 3 moves through thefixed inner and outer cylinders 1 and 2, acapacitance Cm will be formed by two sections. The
first contribution on the total capacitance Cm will be
the capacitance between the inner and outer cylinder
for the area in which both cylinders are notoverlapped by the movable isolating cylinder 3. The
length corresponding to this area is designated as ∆L .
The second contribution to the total capacitance Cm
will be the capacitance between the inner and outercylinders for the area in which both cylinders areoverlapped by the movable isolating cylinder 3 andthe length corresponding to this area is designated as
L − ∆L . The total capacitance Cm is given by Eq.
(6).

90000
Test 1
Test 280000

Fitting curve 1

Count value, C

70000

2πε 0 (ε r − 1)
Cm =
C0 −
∆L
ln( R1 R3 )Fitting curve 2

60000
50000
40000
30000

(6)

20000
0

We can see from the above equation that as themovable isolating cylinder is displaced, themeasured capacitance changes as a linear function of
the displacement ∆L .

5

10

15

20

25

30

35

Displacement, d, (cm)Fig.6 Basic performance test results of ATPC
2400002. MEASUREMENT CIRCUIT OF THE
CAPACITANCEDISPLACEMENTMONITORING SYSTEM

Test 1
Test 2Fitting curve 1
Fitting curve 2Count value,C

200000Figure 4 showed the measurement system. The
variation of capacitance is induced by thedisplacement of the measured object. Measurement
unit composed of embedded microcomputer andsensing amplifier [4]-[6] is utilized to realize realtime data transmission, processing and conversion,and finally display the result to PC to accomplish
real-time monitoring the displacement of measured
object.

160000

Formula of Fitting Curves
C1 = 228404-1560.9d
R² = 0.9993
C2=226298-1539.3d
R² = 0.9994

120000

80000
0

20

40

60

80

Displacement, d, (cm)Fig.7 Basic performance test results of ATCC

415

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GEOMATE- Osaka, Nov. 16-18, 2015

4. STABILITY PERFORMANECE TEST AND
ITS EVALUATION

250000
Test 1
Test 2
Test 3Fitting curve 1
Fitting curve 2
Fitting curve 3

Count value,C

2300004.1 Stable Performance of Capacitance Transducers

210000190000The long-term stability of acquisition is neededin slope monitoring. Count value of parallel-plate
type and coaxial type capacitors were recorded
under indoor environment for 15 hours. According
to Eq. (7), the variation rate of count value can becalculated, and the relationship between variation
rate of count value and time was drawn in Fig.9.

Formular of Fitting Curves
C1=244874 -908.16d
R² = 0.9998
C2= 244497-895.08d
R² = 0.9998
C3= 244651-905.31d
R² = 0.9999

170000150000
0

20

40

60

80

100

Displacement, d, (cm)

CROC =

Fig.8 Basic performance test results of PTCC

Table 1 Basic performance test results of three typesof capacitance transducers(7)

Where, C0 is the initial capacitance; Ct is thecapacitance measured in different time.

Evaluation
Item

ATPC

Type

Parallelplate

cylindrical

2

2

ATCC

PTCC
0.003

Measurement
Range
（mm）

330

R

0.9989900
0.9997

Vatiation rate of Count, CROC

Test Times

Ct -C0C0cylindrical
3
900

0.002
0.001
Coaxial type0
-0.001-0.002
Parallel-plate type-0.003-0.0040:00

0.9999

2:24

4:48

7:12

9:36

12:00

14:24

16:48

Time, t, (h:m)

Sensitivity
Resolution（mm）
Relative
errors of theslope
Structure

166

153

80

0.00600.00640.01250.899%

0.697%

0.620%

Simplemanufacture

Complexwiring

SimplewiringFig.9 Long-term stability performance of parallel-plate
type and coaxial type transducers

Figure 9 shows that, in the first 5 hours, thecount variation rate of the parallel-plate type isbasically stable, and gradually decreases with time.
Its variation rate is -3.03×10-03~1.00×10-03 within
15 hours, which is equivalent to about 0.33mm
displacement. In contrast, in the first 5 hours, thecount variation rate of coaxial type capacitor isincreased with time, and then gradually turns to bestable. Its variation rate is 10-3, which is equivalent
to about 0.2 mm displacement and the error issmaller.
Based on the test result, we can see that thestability performance of the cylindrical type
capacitor is more satisfied than the parallel-plate
type one, while a minor change to the electrodestructure of this type of displacement transducer isstill necessary. Furthermore, there still remains toimprove the shielding system for both types ofcapacitors since we did not take it into account in thetests.

From Figure 6~8 & Table 1, we can see that thecorrelation coefficient (R) are all higher than 0.99.
The measurement ranges of cylindrical capacitors(ATCC& PTCC) are 900mm, which is larger thanparallel-plate capacitor (ATPC). The resolution of
ATPC and ATCC are better than PTCC. The
structure of ATPC and PTCC are simpler than
ATCC. The results show that capacitance typetransducers can provide sufficient precision,
resolution and measurement range, comparing withconventional displace transducers used for slope
monitoring. Therefore, it is feasible to develop amonitoring system based on the principle ofcapacitance.

4.2 Stable Performance of Measurement Circuit

416

GEOMATE- Osaka, Nov. 16-18, 2015

count value CROC increases by 0.032 from -0.014 to
0.018. Its calculated temperature coefficient is about
2×10-4 /℃, which is small and can be neglected if
the requirement of measurement accuracy is not high.
(2) The variation law of N2 and N4 sensing
amplifiers are very similar. With the increase of
temperature, both of their variation rates of countvalue are changed slightly when the temperature is
below 50 ℃ , while they all fell sharply withtemperature when the temperature is higher than
60℃.
(3) Both of the N2 and N4 sensing amplifier are
installed with a silicon diode. Although silicon
diodes have good stability at elevated temperatures,a phenomenon of reverse leakage current which
increased with temperature, still exist. When thetemperature is higher than 50℃ or 60℃, reverse
current will increase multiply for every 10℃increase,and decreasing its capacitance accordingly. This is
why the measured count value of N2 and N4 sensing
amplifiers drop dramatically as the temperature rises
up to 50 ℃.
(4) In reality, when the sensing amplifier isembedded in the slope, the environment temperature
would not be higher than 50 ℃. As the temperature
ranges from 0℃ to 40℃, the variation rates of countvalue of N2 and N4 sensing amplifiers are quite
smaller. When the sensing amplifier is embedded ina measurement box placed on the ground, its
environment temperature would be higher insummer, and N5 sensing amplifier is moresatisfactory. However, proper amount of temperaturecompensation is necessary for improving themeasurement accuracy.

It is also necessary to evaluate the stability of themeasurement system under the condition of longtime at different temperatures. Measurement circuitof this capacitance displacement monitoring system
is composed of sensing amplifier, microcomputerand coaxial cables.
4.2.1 Temperature characteristic test of the sensing
amplifierThe sensing amplifier selected in this paper isused for amplifying the measured signals obtained
from the capacitor. 5 types of sensing amplifier wereselected for temperature characteristic test, theirdifference depends on their constituent elements
including IC model, silicon diodes, supply voltage
and resistance (see Table 2).
Table 2 List of constituent elements of the sensing
amplifierType

IC Model

N2
N4
N5
N6
N15

74HC4538
MC14538
MC14538
MC14538
MC14538

SupplyVoltage
5V
3.3V
3.3V
5V
3.3V

Silicon
Diodes
Have
Have
None
None
None

Resistance
(Ω)
1M
1M
1M
1M
100k

1) Impact of silicon diodes and different IC models
Three types of sensing amplifier, N2, N4, N5,
were selected for conducting the temperature
characteristic test to evaluate the impact of silicon
diodes and different IC models. The relationshipbetween their variation rate of count value andtemperature were drawn in Fig.10.

2) Impact of different resistances of RC integrating
circuitTwo types of sensing amplifiers with different
resistance, N5 (1MΩ) and N15 (100kΩ), wereselected to evaluate the impact of different
resistances. The relationship between the countvariation rate and temperature was drawn in
Figure.11.

N5 MC14538 without Di

0.02

N2 HC4538 without Di
0.01

N4 MC14538 with Di

N2

0

N4
-0.01

N5
-0.02
0.10

Variation rate of count value, (c-c0)/c0

Variation rate of count value,(c-c0)/c0

0.03

-0.03
-0.04
-40

-20

0

20

40

60

80

100

Temperature of the sensing amplifier board, T, (℃)

Fig.10 Temperature characteristics test result of different
IC models

Figure 10 shows that:(1) The count variation rate of N5 shows a linear
growth with temperature. As the temperature
increases from -30 ℃ to 90 ℃, its variation rate of

0.08

N5 1MΩ

0.06

N15 100kΩ

0.04
0.02
N5 1MΩ

0.00
-0.02
-0.04

N15 100kΩ

-0.06
-0.08
-40

-20

0

20

40

60

80

100

Temperature of the sensing amplifier, t, (℃)

417

GEOMATE- Osaka, Nov. 16-18, 2015

Fig.11 Test results of different resistances R

rates are quite small, which means that temperature
has very litter impact on different supply voltages.
Based on the comprehensive consideration ofenergy consumption, noise interference, andtemperature characteristic, the sensing amplifier
with 3.3V supply voltage can meet the requirementsof this measuring system.

As can be seen in Figure11, the variation rates ofN5 and N15 both linear increase with the increase of
temperature, and the variation rate of N15 is nearlyfive times larger than N5. Their calculated
temperature coefficients are 14 × 10-4/ ℃ and
2× 10-4/℃ respectively. We can see that sensing
amplifiers with a 1MΩ resistance would be moresuitable for improving the measurement accuracy.
The reason is that: In the RC integrating circuit,the charging time of a capacitor is proportional to its
resistance. The smaller the resistance is, the smaller
the output pulse amplitude of the sensing amplifier
will be. During the IC operation process, a certain
time for rising and falling the pulse signal of thesensing amplifier is needed and it depends on the IC
speed correlated with temperature significantly. Thatis, the pulse rise and fall time will be affected by thetemperature. As for smaller resistance, whentemperature rises up, the output pulse signal of thesensing amplifier will be smaller correspondingly,
resulting in the increase of rise and fall time of pulse
signal, thereby increasing the variation rate of countvalue.

4.2.2 Temperature
microcomputercharacteristic

test

of

the

Microcomputer is utilized for transforming themeasured capacitance from the sensing amplifier
into count value by the on-chip clock pulse
generator which is composed of an external crystal
oscillator and a built-in PLL loop. Since the changeof temperature on the crystal oscillator and the PLL
loop will change the frequency of the clock pulse
generator, the measurement accuracy will also be
influenced as well. Therefore, it is necessary toevaluate the temperature characteristic of the
microcomputer.
Sensing amplifier has three output channels
recorded, ch1, ch2 and ch4, measured data fromthese three channels were transformed to themicrocomputer. Keeping the microprocessor in thedryer and adjusting the temperature from 30 ℃ to
100 ℃ and measuring the count value from thesethree channels meanwhile. The relationship between
the temperature and variation rate of count value canbe drawn (see Figure 13).

3) Impact of different supply voltage
Two types of sensing amplifiers with different
supply voltages, N5 (3.3V) and N6 (5V), wereselected to evaluate the impact of different supply
voltages. The relationship between the countvariation rate and temperature was drawn in Fig.12.

Variation rate of count value,(c-c0)/c00.0006

Variation rate of count value,(c-c0)/c00.02
N5 3.3V without Di

N6 5V

N6 5V without Di

0.015

Linear fitting N5 3.3V
Linear fitting N6 5V
0.01

N5 3.3V

0.005

0.0004

ch1
ch2
ch4

ch1

0.00020-0.0002-0.0004

ch4

ch2-0.0006-0.0008

0

0

20

40

60

80

100

120

Tempareture of the microcomputer board, T, (℃)-0.005
0

20

40

60

80

100

Fig.13 Temperature characteristic test results of the
microcomputer

Temperature of the sensing amplifier, t, (℃)

From Figure 13, we can see that the variation
amount of count value is about 1×10-4 and its
calculated temperature coefficient is about 1 × 10-6/℃
with the temperature increasing from 30 ℃ to
100 ℃ , which is very small. We can draw theconclusion that temperature has very little impact on
the measurement accuracy of the microcomputer andcan be neglected.

Fig.12 Temperature characteristics test results of different
supply voltage

Seen from Figure12, the variation rate of countvalue of N5 increases from 0 to 1.3×10-2 as thetemperature increases from 30 ℃ to 80 ℃, and its
calculated temperature coefficient is 2.5×10-4 / ℃ ;the variation rate of N6 increases from 0 to 1.8×10-2,
and its calculated temperature coefficient is 4.3×10-4/ ℃, which is larger than N6. Both of their variation

4.2.3 Stable performance test of coaxial cable418

GEOMATE- Osaka, Nov. 16-18, 2015

Fig.15 Temperature characteristic of coaxial cable

In order to be better close to the realistic
situation, the temperature characteristics test of the
5D-2V coaxial cable, which is an importantcomponent for data transmission to sensing
amplifier, was conducted at indoor temperature for24 hours. Temperature was measured by the built-in
temperature sensor both in sensing amplifier andmicrocomputer, and capacitance was measured andrecorded by Teraterm software. Test results wereorganized in Fig.15. Curve 1 and curve 2 are themeasured temperatures of sensing amplifier andmicrocomputer respectively, curve 3 is the variation
amount of count value of coaxial cable whose initialvalue is 33,992.

Figure 15 shows the variation rate of count value
at different environmental temperatures. As a whole,the variation rate of count value presents a linear
negative relationship with the temperature. Its
calculated temperature coefficient is about 0.1deg/℃/m=2.8×10-4/℃/m per electrical length, its order ofmagnitude is reasonable. Therefore, conclusions canbe drawn that temperature has little impact on themeasurement accuracy of coaxial cables, a properamount of temperature compensation is necessary
for improving the measurement accuracy though.
CONCLUSION

Variation of count value, （Ct-C0）
Temperature,t,(℃)

40

30

1

20

2

(1) Based on the principle of capacitance, three
types of capacitance displacement transducers were
designed, they are Area-alterable Type Parallel-plate
Capacitor (ATPC), Area-alterable Type Cylinder
Capacitor (ATCC) and Permittivity-alterable Type
Cylinder Capacitor (PTCC).
(2) Basic performance of these three transducers
was examined. The result shows that capacitancetype transducers can provide sufficient precision,
resolution, measurement range and stableperformance, comparing with conventional displacetransducers used for slope monitoring.
(3) Stability test of capacitance displacementtransducers was conducted. The result shows that thestability performance of the cylindrical type
capacitor is more satisfied than the parallel-plate
type one, while a minor change to the electrodestructure of this type of displacement transducer isstill necessary.
(4) Temperature characteristics of capacitancemeasurement circuit were investigated, and results
show that:a) As for the sensing amplifier, it tends to besignificantly different depending on its internal
constitute elements, thus the measurement accuracy
can be increased by choosing suitable internal
elements of the sensing amplifier;b) As for the microcomputer, temperature has
little impact on it and the error can be basicallyignored;c) As for the coaxial cables used to connecttransducers to sensing amplifiers, the count variation
rate shows a linear negative relationship withtemperature. Therefore, a proper amount of
temperature compensation is necessary for
improving the measurement accuracy.

10

3

0

-10

-20
18:00

0:00

6:00

12:00

18:00

0:00

6:00

12:00

18:00

Time, t, (h:m)

Fig.14 Temperature characteristics test result of 5D-2V
coaxial cable

It can be seen from Figure14, at the beginning(from 22:30 to 6:00), as the temperature drops by
3.14℃, the variation of count value shows a trend ofincrease and increases by 15. Then as thetemperature rise up, the count value appeares adecreasing phenomenon. It can be seen that thevariation of count value is small and appears anegative increase with temperature. In order tofurther understand the relationship between variation
rate of count value and temperature, the test data
was further processed and the test curve was shownin Fig.15.

Variation Rate of Count, CROC

0.00080.00060.00040.00020-0.0002

REFERENCES-0.0004-0.0006
23.5

24.0

24.5

25.0

25.5

26.0

26.5

27.0

[1] Josep A Gili, Jordi Corominas, Joan Rius. Using
Global Positioning System Techniques In

27.5

Temperature of Micro, T, (℃)

419

GEOMATE- Osaka, Nov. 16-18, 2015

Landslide Monitoring [J]. Engineering Geology,
2000, 55(3):167-192.
[2] Lu Yunfeng, Zhao Jianting, He Qing, Zhong
Qing. Research on Non-magnetic Displacement
Measurement System Based on Capacitive
Sensor [J].Chinese Journal of Scientific
Instrument, 2011, 32(6):213-217, in Chinese.
[3] Joseph Habib, Jackson Mich, et al. Linear
Capacitance Displacement Transducer: UnitedStates, 4961055[P].Oct.2,1990.
[4] Katsutoshi
Ueno,
Toshiyaki
Takahara.
Application of precise capacitance meter for

geotechnical monitoring [J]. Proc, 45th JGS
annual meeting, 2010:137-138, in Japanese.
[5] Toshiyaki. Takahara, Katsutoshi Ueno andToshihide Sugimoto. In Situ Measurements
using Developed High Resolution CapacitanceType Sensor and Its Performance Evaluation,
Proc. of the Int. Workshop on ICT in GeoEngineering, pp. 69-75, 2012, Research
publishing.
[6] Katsutoshi Ueno. Capacitance type Moisture
Meter and Water Gage. Patente Gazette,
JP,5688731,B, 2015, in Japanese.

420

FifthInternational Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan,Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

THE CHARACTERISTIC OF SAND-KAOLIN CLAY MIXTURE AS
ARTIFICIAL MATERIAL FOR LABORATORY SOIL TESTING

1

Purwana Y.M.,1, Nikraz H2
Civil Engineering Dept., Sebelas Maret University, Indonesia;2,Curtin University, Australia

ABSTRACTLaboratory soil testing is a routine activity in geotechnical engineering. For the tests, either the characteristic,
behaviour of soil or the performance of the laboratory apparatus is studied. One of the most important
requirements for laboratory study is that the material should have repeatability property. However, due to theheterogeneity many soil samples do not comply with this requirement. To overcome this situation, articial
material might be able to be utilised. The series of laboratory tests using a new material of sand-kaolin clay
mixture have been conducted. The low cohesion, no plasticity, and high internal friction possessed by sandmixed with the high cohesion, high plasticity, and low internal friction possessed by kaolin clay produce a newmaterial. The desired new properties are obtained by changing the proportion of sand-kaolin clay mixture. The
index properties, compaction characteristic, shear strength properties, and CBR results are presented. It can beconcluded that sand-kaolin clay mixture is suitable material for geotechnical laboratory test.
Keywords: Artificial Material, Laboratory Testing, Repeatability Property, Sand-kaolin Clay Mixture

INTRODUCTION

THE PROPERTY OF SAND AND KAOLIN CLAY

Laboratory soil testing is a routine activity ingeotechnical engineering. For the tests, either thecharacteristic or behaviour of soil or the performance ofthe laboratory apparatus is studied. However, due to theheterogeneity many soil samples do not comply withthis requirement. To overcome this situation, articial
material might be able to be utilised. Artificial material
is made to resemble the characteristic and the propertyof original material. One of the geotechnical materials
that can be used for producing artificial material iskaolin clay.
Kaolin clay is a versatile material that has a wide
range of uses. It has been utilesed in wide range various
industries such as : ceramic, cement, fiberglass, paint,
paper filling/coating, rubber, plastic, pharmacy, textiles,
detergent, and even cosmetics [1]. In geotechnicalengineering, kaolin clay has been used for slope
stabilization and erosion protection [2] and as a barrier
for waste disposal facility. The extensive use of kaolin
clay is due to its unique properties.
This paper presents the use of artificial material forlaboratory soil testing made from sand-kaolin clay
mixture in different proportion. The characteristic of thematerial is presented in term of physical and engineering
properties of the material resulted from the test. A briefof each material characteristic; sand and kaolin clay is
firstly described, followed by the description of new
artificial material of sand-kaolin clay mixture.

The Properties of Sand
The most important physical characteristic of sand isparticle size distribution and particle shape. The
distribution alse affects the engineering properties.
Other influencing factors are density, particle
arrangement and isotrophy [3]. Poorly graded sandtends to have a narrower range of density compared towell-graded sand. Particle size distribution can also
affect the properties of sand. The increase in the rangeof particle size (increase in the coefficient of
uniformity,Cu) causes a decrease in void ratio.
In terms of strength and compressibilty, sand has
relatively beneficial engineering properties compared toclay. It has high internal friction, low compressibility,
non-plasticity, low swell-shrinkage potential, and ingeneral, the behaviour of sand is rarely affected by watercontent. A small amount of cohesion may be generated
through the capillary tension among the particles, due tothe presence of water. The increase in moisture content,
to some extent, can cause this increase in cohesion. With
the increment of moisture, cohesion also increases
significantly. However, when the moisture content
reaches a certain value, cohesion will decrease and evendisappear when a saturated condition is reached [4].
The shape of the particles also strongly influence theengineering properties of sand. The shape can be
angular, sub angular, rounded or semi-rounded. The
compaction result of sand containing angular particles
tends to be less dense when compared to sand withrounded particles. For ideal uniform-size spherical421

GEOMATE-Osaka, Nov. 16-18, 2015

particles, five different possible packing arrangements
are proposed; (a) simple cubic, (b) cubic tetrahedral, (c)tetragonal sphenoidal, (d) pyramidal, and (e) tetrahedral.
Simple cubic packing has the loosest stability andhighest void ratio of 0.91, whereas tetrahedral packing
has the densest arrangement and the lowest void ratio of
0.34.

it has little cohesion. The strength of inactive soil ismainly caused by internal friction.
PREVIOUS STUDIES ON SAND-KAOLIN CLAYMIXTUREThe effects of kaolin clay content of sand-kaolin
clay mixture plus water on the void ratio with thedifferent proportion of the mixtures has been studied [7].
The mixtures were also compacted using a static load.
The results indicated that for narrowly graded fine sand,the increase in kaolin clay content led to the decreasedin void ratio. However, for coarsed-finesand, theincrease in kaolin clay to some extent led to a decreasein void ratio.
In addition to the study, the investigatios has also
been conducted to understan the effect of kaolin clay
content with different water content on the unconfinedcompression strength. The results indicated that with acertain water content, the increase in kaolin clay content
led to an increase in strength. The results also showedthat an increase in water content in the mixture caused adecrease in strength [8].
Sand-kaolin clay mixtures have also been studied
by other researchers [9]. They used three different
compacted sand-kaolin clay mixtures of 0%, 5%, 10%
and 30% to investigate the effects of kaolin clay content
on the hydraulic conductivity of the mixtures. They
conculed that the increase in kaolin clay content causes
the increase in hydraulic conductivity.
Some other studies concerning the engineering
properties of sand-kaolin clay mixture have been
conducted by several investigators [10]-[15]. However,
the information about this mixture is still relatively rare
and more studies are required.

The Properties of Kaolin ClayThe shape of kaolin clay particle is platy crystal withthe length of particle being 0.2 m to 2 m with athickness of 0.05 m to 0.2 m. Amongst other clay
minerals, kaolin is a mineral with the largest particle
size. Kaolin clay is categorized as a silicate mineral.
Kaolin clay is one of the minerals in the kaolin group
including haloysite, dickite, and nacrite. Theoretically,
the main components of kaolin are alumina (Al2O3),
silica (SiO2), and water. Kaolin clay consists ofalternating layers of one silica tetrahedral sheet and one
alumina octahedral (or gibbsite) sheet tied with oxygens
and hydroxyls. The common molecule formula forkaolin is Al2Si2O5(OH4). Table 1 shows the typicalresults from chemical analyses of different sources ofkaolin clay from Georgia, England, and with from
Unimin Pty Ltd, Australia.
Table 1 Typical chemical analysis of kaolin clay [1] and[5]
Component(%)

Georgia

England

UniminAustralia

SiO2
Al2O3
Fe2O3
TiO2
MgO
CaO
Na2O
K2O

45.3
38.38
0.3
1.44
0.25
0.05
0.27
0.04

46.77
37.79
0.56
0.02
0.24
0.13
0.05
1.49

49.2
39.4
1.01
0.935
0.358
0.51
< DL
0.18

TESTING PROGRAM
This study was emphasized on laboratory test for
obtaining the physical and engineering properties ofsand-kaolin clay mixture in different proportions,starting from index properties (gradation, atterberg
limits, classification), continued by compaction
characteristic, shear strength, and California Bearing
Ration (CBR). To understand the effect of unsaturated
condition on the mixture, the measurement of suction
was also studied. Note that chemical properties test was
not conducted in this study.

Compared to other clay (montmorillonite andattapulgite), kaolin clay has relatively better properties.
It has lowest liquid limit, plastic limit, and activity.
Active clay has a high water holding capacity, highcompaction under load, high cation exchange capacity,
low permeability, and low shear resistance. Therefore,
very active soils can be problematic for engineers [6].
The presence of certain types of clay mineral in the soil
affects its engineering properties. Soil with a highactivity has high swelling when wetted and highshrinkage when dried. Activity can also be used forpredicting the amount of clay fraction in the soil and is avery
usefulindicator
forpredicting
theswelling/shrinkage potential of soil. Conversely,inactive soils may not cause problems for engineers, as

RESULTSThe Effect of Kaolin Clay on Index Properties
The mixture of two or more materials with different
index properties may produce a material with newproperties. The index property analysis on the newmaterial was required to determine its properties. Fig 1
shows the gradation curve of sand, kaolin clay, andsand-kaolin cay mixtures.
422

GEOMATE-Osaka, Nov. 16-18, 2015

Atterberg limits tests were also performed. The
results indicated that the increase in kaolin content
caused a decrease in the liquid limit and plasticity index
respectively. The classification system was theperformed on the new materials according to ASTM D
2487. Due to the addition of a proportion of kaolin clay
to the sand, the group of specimens altered from poorlygraded sand (SP) for sand and 95:5, poorly graded sandwith clay (SC-SM) for 90:10, clayey sand (SC) for
80:20 and 60:40 mixtures, and High plasticity clay (CH)for kaolin clay. Based on plasticity, the sand and 95:5
mixtures were categorized as non-plastic specimens,
whereas the rest were low-plastic specimens. The
summary of index properties of the mixtures ispresented in Table 1.

the specimens may be close to one of packing
arrangements proposed in [3].
It can be assumed that to some extent (up to 20%),
the increase in kaolin clay content would cause theincrease in dry density and the decrease in void ratio.
However, the void ratio starts to increase when the
kaolin clay content of the mixture was > 20 %. This
result is in accordance with the results in [7]. It can beconcluded that the addition of kaolin clay to sand tosome extent could produce a new material with better
properties.
1.0
0.8

100
90

95:5
mixture90:10
mixture80:20
mixture60:40
mixtureKaolinclay

70% finer

Void ratio

Sand

80
60
50
40
30
20
10
0.01

0.1

0.4
0.2
0.0
0

20

40

60

80

100

Kaolin clay content (%)

0
0.001

0.6

1

Grain size (mm)Fig. 2 Curve showing the effect of kaolin clay on void
ratio

Fig. 1 Gradation curves of different mixtures
Effect of Kaolin Clay on Shear Strength
Table 2 Index properties of mixtures
SpecimenSand
95:5 mix
90:10 mix
80:20 mix
60:40 mix
Kaolin

Gs
2.63
2.63
2.63
2.62
2.60
2.58

LL
N.A
N.A
21.3
26.6
33
58

PL
N.A
N.A
15.4
16.7
20
31

PI
N.P
N.P
5.9
11.3
13
27

A series of direct shear test on saturated specimens
was performed to obtain the effective shear strengthparameters (c’ and ’) for all mixtures. Three different
normal forces of 4 kg, 14 kg, and 24 kg were given toeach mixture to impose an initial normal stress of 11.2
kPa, 39.2 kPa, and 67.1 kPa. During shear, this normal
stress changes due to the change in the shearing area ofthe sample.
The effect of kaolin clay on shear strength ofsaturated direct shear test of compacted samples of sandand sand-kaolin clay mixture is shown in Fig 3. A brief
description on this figure has been published by authorsin [15]. It can be observed that the presence of kaolin
clay in the mixture contributes to the increase ofcohesion. Note that the test on 60:40 mixture and kaolin
clay specimens was not conducted due to the specimen
condition. The higher content of kaolin clay would
cause to the consistence of the mixture to be softer.

Class.
SP
SP
SC-SM
SC
SC
CH

Effect of Kaolin Clay on Compaction Result
Standard Proctor test has been conducted on the
aforementioned specimens. The result was presented asshown in Fig 2. For the poorly graded sand, the density
due to compaction was mainly caused by rearrangementof the particles. During compaction, the particles of sandmove to find the best position. The voids become
narrower and the specimen becomes denser and morecompact. However, due to the lack of smaller particles,
the pore spaces in poorly graded sand remain relatively
unfilled. Assuming that the particles of poorly gradedsand are uniform and round, the packing arrangement of423

GEOMATE-Osaka, Nov. 16-18, 2015

The Effect of Kaolin Clay on the CBR

80
100% sandShear strength (kPa)70

A series of CBR tests were performed on the sandkaolin mixture with the same proportions as the
specimens in the direct shear test. The tests were carriedout on soaked and unsoaked specimens. The procedure
for test was conducted in reference to the ASTM D
1883-07. According to this standard, the CBR test canbe performed on the specimen either in soaked or
unsoaked conditions. The result of CBR test is presented
in Table 4.

95:5 mixture

60

90:10 mixture

50

80:20:
mixture

40
30
20
10

Table 4 Soaked and unsoaed CBR value of mixtures

0
0

10

20 30 40 50 60
Normal stress (kPa)70

80

SpecimenSand
95:5 mix
90:10 mix
80:20 mix
60:40 mix
KaolinFig 3 Failure envelopes of sand-kaolin clay mixtures aspreviously presented by authors in [15]During direct shear test, sand exhibits hardening
behaviour until peak value, followed by softening. The
behaviour of cohesionless soil under direct shear testingis dependent on the compactness of the soil. For dense
and medium sand, the shear stress increases with shear
displacement to a peak value, and then decreases to an
approximate constant value. For loose sand, shear stressincreases with shear displacement and then remains
relatively constant up until ultimate value is reached.
For 95:5 mixture, the strain hardening behaviour
was taking place, starting from zero displacement todisplacement at peak stress, followed by strain softening
behaviour until a residual value was reached. The lowest
dilation was experienced by the specimen when thenormal stress of 67.1 kPa was applied, whereas thehighest was when the normal stress of 11.2 was
imposed. In general, the higher the normal stress, thelower the dilation. The similar (strain hardening anddilation) behaviour was also taking place in 90:10 and
80:20 mixture.
The behaviour of the specimen during the test
determines the shear strength parameters. Due to limited
space in this paper, the stress and strain behaviour
during direct shear test is not presented. The parameters
resulted from the test are presented in Table 3.

c’ (kPa)
0
2.86
3.21
7.54
-

’ (o)
39.3
39.2
37.1
35.7
-

CBR
Unsoaked
13
12
25
40
-

Note

Not conducted
Not conducted

It can be observed that the values of CBR are
typically similar to that the result of compaction. The
increase in kaolin clay to some extent (up to 20 % kaolin
clay content) contributes to the increase in soaked andunsoaked CBR. Note that due to the same reason as
direct shear test, the CBR test for 60:40 mixtures andkaolin clay was not conducted. However it can be easily
predicted that their value might be relatively low.
CONCLUSION
The use of sand-kaolin clay mixture as artificial
material for laboratory soil testing has been described.
The behaviour of material was determined by their
proportion. The low cohesion, no plasticity, and highinternal friction possessed by sand mixed with the highcohesion, high plasticity, and low internal friction
possessed by kaolin clay produce a new material. The
desired new properties are obtained by changing the
proportion of sand-kaolin clay mixture. The index
properties, compaction characteristic, shear strength
properties, and CBR results were presented. It can beconcluded that sand-kaolin clay mixture is suitablematerial for geotechnical laboratory test.

Table 3 Cohesion and internal friction angle of mixtures
SpecimenSand
95:5 mix
90:10 mix
80:20 mix
60:40 mix
KaolinSoaked
13
11
19
20
-

Note
Saturated
Saturated
Saturated
Saturated
Not conducted
Not conducted

ACKNOWLEDGEMENTS
The writers gratefully acknowledge the Indonesian
Ministry of Education for providing funds through the
DIKTI Scholarship Project.

REFERENCES[1] Murray HH, “Overview-clay mineral application”,
Applied Clay Science, 5, 1991, pp. 379-395.
424

GEOMATE-Osaka, Nov. 16-18, 2015[2] Jotisankasa
A,
SawangsurriyaA,
Booncharoenpanich P and Soralump S, “Influenceof kaolin mixture on unsaturated shear strength ofdecomposed granitic silty sand”, Unsaturated Soils:
Research and Applications. Springer, 2012, pp.
385-390.
[3] Mitchell JK and Soga K, “Fundamentals of Soil
Behavior”, New Jersey, John Wiley and Sons, 2005.
[4] Wu A and Sun Y, “Granular Dynamic Theory and
Its Applications”, Beijing, Metallurgycal Industry
Press, 2008.
[5] Shankar P, Teo J, Leong YK, Fourie A. and Fahey,
M, 2010. “Adsorbed phosphate additives forinterrogating the nature of interparticles forces inkaolin clay slurries via rheological yield stress”,
Advanced Powder Technology, 21(4), 2010, pp.
380-385.
[6] Grim RE, “Applied Clay Mineralogy”, New York,Mc Graw-Hill Company Inc., 1962.
[7] Mullins CE and Panayiotopoulos KP, “Compaction
and shrinkage of sands and sand-kaolin mixtures”,
Soil & Tillage Research, 4, 1984, pp. 191-198.
[8] Mullins CE, and Panayiotopoulos KP, “Thestrength of unsaturated mixtures of sand and kaolin
and concept of effective stress”, Journal of Soil
Science, 35, 1984, pp. 459-468.
[9] Chiu TF and Shackelford CD, “Unsaturated
hydraulic conductivity of compacted sand-kaolin
mixtures”,
Journalof
GeotechnicalandGeoenvironmental Engineering, 124(2), 1998,
pp.160-170.
[10] Frenkel H and Levy GJ, “Clay dispersion on
hydraulic conductivity of clay-sand mixtures asaffected by addition of various anions”, Clay andClay Minerals, 40(5), 1992, pp. 515-521.
[11] Boutin C, Kacprzak G and Thiep D,“Compressibility and permeability of sand-kaolin
mixtures.
Experiments
versusnon-linear
homogenization schemes”, International Journal for
Numeric and Analytical Methods in Geomechanics,
2011, pp. 35: 21-52.
[12] Jigheh HS and Zare C, “Mechanical behaviour ofover consolidated clay-sand mixtures”, Journal of
Civil Engineering and Urbanism, 2(1), 2012, pp.
40-44.
[13] Purwana YM, Nikraz H, “Experimental Study ofSuction-Monitored CBR test on Sand-kaolin Clay
Mixture”, Int’l Journal of Geomate Vol 3 No.2,
2012, pp. 419-422.
[14] Purwana YM, Nikraz H, “The Correlation between
the CBR and Shear Strength in Unsaturated Soil
Conditions”, Int’l Journal of Transportation
Engineering, 2013, p 211-222.
[15] Purwana YM, Chegenizadeh A, Nikraz H,“Suction-monitored direct shear apparatus: a simple
device for unsaturated soil testing”, Australian
Geomechanics 48 (3), 2013, pp. 129-139

425

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

ESTIMATING UREASE ACTIVITY IN MICROORGANISM OF PEATBASED ON ELECTRIC CONDUCTIVITY

1

Atsuko Sato1, Satoru Kawasaki2, Toshiro Hata3 and Takahiro Yamanashi1
Civil Engineering Research Institute for Cold Region, Japan; 2 Hokkaido University, Japan;
3
Toyama Prefectural University, Japan

ABSTRACT
Peaty soil is widely distributed in Hokkaido. Untreated soil is too soft for use as a civil engineering material.
Soil solidification improvement technologies that harness the metabolism of microbes in soil has recently been
gaining attention. Such R&D has proceeded within and beyond Japan. Focus was put on a technology to achievepeat solidification by capitalizing on the ability of microbes that inhabit soil at construction sites to precipitate
calcium carbonate, and consideration was given to solidifying peaty soil by harnessing the ability of thosemicrobes. The peaty soil in Hokkaido investigated so far was confirmed to be inhabited by microbes that haveurease activity. The degree of that activity, however, was unknown. Therefore, the degree of urease activity wasdetermined based on electric conductivity. The results showed that the degree of urease activity varies dependingon the kind of peat and that it is possible to solidify peaty soil without any additional treatment.
Keywords: Urease activity, Peat, Electric Conductivity, Microorganism

precipitation of calcium carbonate. The authors
decided to estimate the degree of urease activity of
microorganisms in the target soil samples, in orderto understand the degree of solidification reaction of
the soil.

INTRODUCTION
Peaty soil, which is widely distributed in
Hokkaido, is a highly organic and soft soil thatcannot be used as an embankment material without
improvement1) 2). Toward developing methods forutilizing peaty soil as a material for civil engineering
works, the authors have been studying a soil
foundation solidification technology that relies on
the urease activity of microorganisms. The authors
were able to give extremely soft peaty soil
unconfined compressive strength sufficient for easytransport by enhancing its urease activity3). The
authors thought it might be possible to predict thesolidification potential of peaty soil by measuring
the degree of urease activity of such soil. We
conducted an experiment that addressed theelectrical conductivity of peaty soil. This paper
summarizes the results.

METHOD
The authors focused attention on a method forevaluating the rate of urea hydrolysis8) as atechnique to estimate the degree of urease activity ofthe subject peaty soil. In this method, the urease
activity is estimated by measuring the electricalconductivity of the peaty soil. The microorganisms
were liquid cultured on Christensen citrate agar, andurea solution was mixed in the medium. The rate ofurea hydrolysis by the microorganisms wasestimated by measuring the electrical conductivity ofthe solution. However, isolating the microorganismsthat enhance solidification of peaty soil requires
special knowledge and techniques that have not been
considered practical for improving civil engineering
materials. Therefore, the authors decided not toisolate the microorganisms but to do experiments forsolidifying the peaty soil by using the
microorganisms naturally living in the peaty soil.
Hata et al.8) used a mixture of 10ml of microbe
culture solution and 40ml of urea solution tomeasure electrical conductivity. The authors used asuspension of 10g of peaty soil in 40ml of urea
solution for measuring the electrical conductivity ofthe soil, because the water content of peaty soil isvery high. Fig. 1 shows the procedure for measuring
the increase in electrical conductivity of the solution.

SOLIDIFICATION
USING
THE
METABOLISM OF MICROORGANISMS
There are two common methods of soft soil
solidification
using
the
metabolismofmicroorganisms. One utilizes calcium carbonate4) 5);
the other utilizes silica6) 7). The calcium carbonate
method utilizes the calcium carbonate thatprecipitates in voids in the soil from the action of
microorganisms with urease activity. The soil
foundation is solidified by the action of precipitatedcalcium carbonate, which causes soil particles toadhere to each other. The greater is the urease
activity of the microorganisms, the greater is the426

GEOMATE- Osaka, Nov. 16-18, 2015

Preparing a urea solution of 1.5mol/L in concentration

Mixing 10g of peaty soil and 40ml ofurea solution (test solution)

Mixing 10ml of pure water and 40ml ofurea solution (control solution)

Measuring the electrical conductivity every minute for 20 minutes
immediately after the solutions are mixed.

The increase in electrical conductivity that is afforded by the action of the
microorganisms is obtained by subtracting the change for the control
solution from the change for the test solution.
Fig. 1 Procedure for measuring the rate of urea hydrolysis
The figure in Reference #2 was partly modified and simplified.

and ignition loss, and have very small soil particle
density. Peaty soil is characterized by low pH. The
presence of microorganisms that have urease activity
was determined by using Christensen citrate agar11).
As shown in Fig. 2, all three media turned red. The
culture results showed that microorganisms withurease activity live in the tested peaty soil.

The peaty soil samples used in the experimentwere collected from the Shiribeshi, Ishikari, andHidaka areas from among the numerous areas in
Hokkaido where peaty soil is widely distributed9).
Table 1 shows the basic properties of the peaty soil
samples. Compared with common soil10), the peaty

Table 1 Basic physical properties of the peaty soil samples
Sample (sampled area)
Area for sample collection
Water content wn (%)
Soil particle density ρs(g/cm3)
Volatile solids Li(%)
pH

Iwanai
Shiribeshi
1028.64
1.557
93.813
4.5

Ebetsubuto
Ishikari
545.921.895
56.653
4.1

Tomikawa
Hidaka119.552.206
39.007
2.5

soil samples are very high in natural water content

IwanaiEbetsubuto

Fig. 2 Test for urease activity

427

Tomikawa

GEOMATE- Osaka, Nov. 16-18, 2015of time indicates high urease activity. The increasein electrical conductivity with time is small in the
Iwanai and Ebetsubuto samples, which means thatthe urease activity of these two samples is low. The
increase in electrical conductivity with time is greatin the Tomikawa sample, which means that theurease activity of this sample is high. The urease
activity of the samples varied.
Fig. 4 also shows the data for increase in theelectrical conductivity of the samples with Bacillus
pasteurii and Sporosarcina aquaimarina2). Bacillus
pasteurii is a land bacterium, and its effectiveness inground solidification has been clarified and is usedwidely in Japan and abroad. Sporosarcinaaquaimarina is a seawater bacterium with urease
activity, whose use in soil solidification has been
anticipated. The rate of increase in the electricalconductivity of the soil from Tomikawa was about
one-tenth (1/10) that of Bacillus pasteurii and about
one-fourth (1/4) that of Sporosarcina aquaimarina.
The microorganisms in the peaty soil of Hokkaido
have lower urea activity than that of the above two
types of bacteria; however, the soil from Tomikawa
has comparatively high urease activity. It is thoughtthat solidification of peaty soil using the metabolismof the indigenous microorganisms is possible.
The Iwanai and Ebetsubuto samples showed
relatively small increases in electrical conductivity
in a short test. Therefore, their electricalconductivity was measured at 1 hour and 3 days
after mixing, which were longer periods after mixing
than that of the experiment by Hata et al. The
measurement results are shown in Table 2. A slightincrease in electrical conductivity was observed in
the Ebetsubuto sample. In the Iwanai sample, theelectrical conductivity increased by 340µs/cm in

RESULTS
Conditions of the solutions
A suspension was produced as shown in Fig. 3
when the peaty soil and urea solution were mixed.
All the samples from Iwanai, Ebetsubuto, andTomikawa have a similar appearance. It was foundto be possible to measure the electrical conductivityof the samples in this suspension condition.
Estimating urease activity based on electricalconductivity
Fig. 4 shows the relationship between the timeafter the soil and urea solution were mixed and theincrease in electrical conductivity. The large
increase in electrical conductivity with the passagea. Peaty soilb. Peaty soil after
mixing with urea solution

Increase in electrical conductivity (µs/cm)

Fig. 3 The appearances of the samples

300

Tomikawa
Iwanaiy= 12.63x
Tomikawa
Ebetsubuto R² = 0.9623

pasteuriy = 137x
R² = 0.972

250

aquimarina

200

y = 42xR² = 0.990

150
100
50

IwanaiEbetsubuto

y = 0.7862x
R² = 0.7983

y = 0.6517x
R² = 0.8322

0
0

5
10
15
Time after mixing (min)Fig. 4 Time after mixing, and the increase in electrical conductivity
428

20

GEOMATE- Osaka, Nov. 16-18, 2015

three days. It was thought that by setting a longer
time, the solidification of peaty soil by using themetabolism of microorganisms is possible.

Journal of Japan Society of Civil Engineers, Ser.
G, Vol.64, No.1, pp.168-176, 2008.
[5] Yukiko Inagaki, Masayoshi Tsukamoto,
Hirotoshi Mori, Susumu Nakajima, Tetsuya
Sasaki, Satoru Kawasaki, “A centrifugal model
test of microbial carbonate precipitation asliquefaction countermeasure”, GeotechnicalEngineering Journal, Vol.6, No.2, pp.157-167,
2011.
[6] Satoru Kawasaki, Akira Murao, Masami
Tsunekawa, Katsuhiko Kaneko, ”Fundamental
study on novel grout cementing due tomicrobial metabolism”， Journal Japan Societyof Engineering Geology, Vol.47, No.1, pp.2-12,
2006.
[7] Rei Terajima, Shunsuke Shimada, Tadao
Oyama, Satoru Kawasaki,”Fundamental studyof siliceous biogrout for eco-friendly soil
improvement”, Journal of Japan Society of Civil
Engineers, Ser. C, Vol.65, No.1, pp.120-130,
2009.
[8] Toshiro Hata, Tamami Yokoyama, Hiroshi Abe,
“Investigation of a soil improvement techniquefor coastal regions on the basis of calcium
carbonate precipitation by using the ureahydrolysis rate”, Japanese Geotechnical Journal,
Vol. 8, No. 4, Aug. 2013, pp. 505–515.
[9] Civil Engineering Research Institute for Cold
Region, “Manual for Construction on Soft Peaty
Ground”, Mar. 2011, pp. 4.
[10] The Japanese Geotechnical Society, “Ground
Material Testing Methods”, Nov. 2009.
[11] Toshikazu Sakazaki, Etsuro Yoshizaki, andKanji Miki, ”In Bacterial Culture Media”, BookI, Kindai Shuppan, 1978.

CONCLUSION
The following were found as a result of themeasurements.
(1) It is possible to measure the electricalconductivity of a sample in suspension without
isolating the microorganisms.
(2) Microorganisms with different urease activity
live in different types of peaty soil. The urease
activity in the peat of Hokkaido was about one-tenth(1/10) that of Bacillus pasteurii and about one-fourth(1/4) that of Sporosarcina aquaimarina.
(3) There are some peaty soils whose urease activity
is low but will increase over a longer period.
REFERENCES[1] Public Works Research Institute, “The technicalmanual for recycling construction generated soil
(4 th Edition)”, Dec. 2013.
[2] Ministry of the Environment , “Waste
Management and Public Cleansing Act”, 1970.
[3] Atsuko Sato, Satoru Kawasaki, Toshiro Hata,
and Takahiro Yamanashi, “Experiment for
Solidifying Peat using Microorganisms”,
Proceedings of the 69th Annual Conference of
the Japan Society of Civil Engineers, Jul. 2014.
[4] Toshiro Hata, Reiko Kuwano, Hirofumi Abe,“Fundamental study of in-situ permeability
control technique using microbial function”,

429

STUDY OF SPH SIMULATION ON FACE COLLAPSE BEHAVIOUR
AND EFFECT OF STABILIZATION METHOD AROUND TUNNELPORTAL ZONE
T.Matsuo1, K.Mori2, N.Hiraoka2, M.Sun2, Ha H.Bui3 and R.Fukagawa2
1

2

Department of Technical Division, KFC, Ltd., Japan
Department of Civil Engineering, Ritsumeikan University, Japan3
Department of Civil Engineering, Monash University, Australia

ABSTRACT
Maintenance measures for tunnel portal zones have attracted increasing interest owing to the increased
collapse risk of slopes by severe torrential rain and inland earthquakes in Japan in recent years. However, theselarge-scale collapse behaviours cannot be simulated by finite difference method (FDM) analysis. In this study,
we applied the smoothed particle hydrodynamics (SPH) method to analyse the collapse behaviour of the tunnel
face under construction and the effect of the stabilization method. First, we simulated the collapse behaviour at
the tunnel portal zone by two-dimensional SPH simulation and compared it with an aluminium-bar experiment,
and we showed that the SPH method is effective to model the overall tendency of the large deformation and thecollapse behaviour in the experiments. Second, we simulated the collapse behaviour using the SPH method in
the real tunnel portal zone collapse and provided a restraint result for collapse using facebolts. Therefore, we
demonstrated that the SPH method is an effective technique to evaluate reinforcement measures.
Keywords: SPH, Aluminium-bar experiment, Collapse behaviour, Tunnel face, Tunnel portal zone

INTRODUCTION

and verified its effectiveness.

Owing to the increased risk of disasters such as
torrential rains or inland earthquakes, measures toensure long-term stability during a tunnel’s service
life have assumed increasing importance in recent
years. However, simulations of large-scale
deformation of tunnel portals using the finite
element method (FEM) and finite difference method(FDM) have yielded divergent results. Furthermore,an analytical technique to treat deformation from asmall to a large scale to evaluate the effect ofreinforcement measures remains lacking.
This study clarified the efficiency of the smoothed
particle hydrodynamics (SPH) method in modelling
the large-scale deformation and collapse behaviourof the tunnel portal zone.
First, we analysed the behaviour of collapse
phenomena at the tunnel face using a twodimensional SPH simulation and compared theseresults with those of an aluminium bar experiment.
These efforts clarified that the SPH method iseffective to simulate deformation from a small to alarge scale to as well as the collapse behaviour of thetunnel face.
Second, we simulated the collapse behaviour
using the SPH method in an example of real portalzone collapse. Furthermore, we proposed a method
for analysing the effect of reinforcement measures

SPH OUTLINE AND SPH APPROXIMATIONS[1-3]
The SPH method is a Lagrangian meshfree
method in which particles carry field variables suchas mass, density, and stress tensor and move with thematerial velocity. By using the SPH method, thepartial differential equations for the continuum areconverted into the equations of motion of theseparticles. Other grid-based numerical methods suchas FEM and FDM suffer from mesh distortion owing
to large deformations. In contrast, SPH can handle
large deformations, post-failure, and complex
geometries very well, and it can model complex freesurfaces without any special treatments.
The SPH method has recently been applied tosolving large deformations and post-failure
behaviour of geomaterials [4-7]. However, it has yet
to be applied to the tunnel face collapse behaviour or
evaluation of effectiveness of reinforcement
measures on face stability.
In the SPH method, the computational domain ismodelled using a set of discrete particles. The
particles have a kernel function to define theirinteraction range, which is called the influence
domain. The field variables are calculated using aninterpolation process over its neighbouring particles

430

𝑁

located within the influence domain. Theinterpolation process is based on the integral
representation of a field function 𝑓(𝑥) as follows:〈𝑓(𝑥)〉 = ∫ 𝑓 (𝑥′)𝑊(𝑥 − 𝑥 ′ , ℎ)𝑑𝑥 ′〈∇𝑓(𝑥)〉 = ∑
𝑗=1

𝛺

′

∫ 𝑓 (𝑥′)𝑊(𝑥 − 𝑥 , ℎ)𝑑𝑥 = 1

f(x)Wij=W(x-x’,h)Influence domain

Neighbouring particle
i

j

(2)

Interpolation point𝛺The second condition is called the delta function
property; it should be satisfied when the smoothinglength approaches zero:𝑙𝑖𝑚 𝑊(𝑥 − 𝑥 ′ , ℎ) = 𝛿(𝑥 − 𝑥′)ℎ→0Fig. 1(3)

𝑤ℎ𝑒𝑛 |𝑥 − 𝑥′| > 𝑘ℎModel of aluminium bar-experiment
We tested the behaviour at the time of the collapseof the tunnel face by the two-dimensional modelexperiment using an aluminium bar and compared itwith simulation by SPH analysis. Table 1 shows theexperimental conditions.
The tunnel height (D) is 8 cm with aluminium
bars of two diameters, 1.6 mm and 3.0 mm, and 50mm length, and four patterns of overburden, H/D =1.0, and 2.0, are used.
Figure 2 shows the initial condition of thealuminium bar experiment.

(4)

where k is a constant which specifies the nonzero
region of the kernel function for an interpolation
point.
The choice of the kernel function in SPH directly
affects the accuracy, efficiency, and stability of thenumerical algorithm. Many kernel functions havebeen proposed for SPH thus far. In this study, weapply the most popular cubic spline function
proposed by Monaghan and Lattanzio [8], which has
the following form:
2⁄3 − 𝑅2 + 𝑅3 ⁄2, 0 ≤ 𝑅 < 1
𝑊𝑖𝑗 = 𝛼𝑑 × {(2 − 𝑅)3 / 6, 1 ≤ 𝑅 < 2 (5)0,𝑅≥2where d is the normalization factor, which is15/(7𝜋ℎ2 ) in two-dimensional space, and R is thenormalized distance between particles i and j definedas 𝑅 = 𝑟/ℎ . The continuous integral representation(1) can now be discretized as a summation over theparticles in the influence domain as follows:𝑁〈𝑓(𝑥)〉 = ∑𝑗=1

𝑚𝑗
𝑓(𝑥𝑗 )𝑊(𝑥 − 𝑥𝑗 , ℎ)𝜌𝑗Image of the SPH interpolation

SPH SIMULATION

where 𝛿(𝑥 − 𝑥′) is a delta function
The third condition is called the compact supportcondition:𝑊(𝑥 − 𝑥 ′ , ℎ) = 0(7)where 𝛻𝑊(𝑥 − 𝑥𝑗 , ℎ) is a spatial derivative of𝑊(𝑥 − 𝑥𝑗 , ℎ).
Further details of the SPH integration scheme canbe found in [3] and [5].

(1)

where x represents the particle location;  theinfluence domain of the integral; W, the basis
function of the approximation, which is called thekernel function; and h, the smoothing length, which
defines the influence domain of W. The
approximation 〈 〉 is called a kernel approximation.
The kernel function W must be chosen to satisfy
the following three conditions:
The first one is called the normalization condition:′𝑚𝑗𝑓(𝑥𝑗 )∇𝑊(𝑥 − 𝑥𝑗 , ℎ)𝜌𝑗Table 1 Specifications of aluminium bar-experiment
Items
Variable
ValuesTotal lengthL
42 cm
Tunnel length
L1
12 cm
Tunnel heightD
8 cm
OverburdenH8, 16 cm
(Ratio of H/D
H/D)(1.0), (2.0)(6)

Aluminium-bar

Hwhere j = 1, 2, …, N indicate particles within theinfluence domain of the particle at x, the so-calledneighbour; mj and j are respectively the mass anddensity of particle j.
Figure 1 shows an approximation of this equation.
The approximation for the spatial derivative
𝛻𝑓(𝑥) can be obtained simply by substituting
𝑓(𝑥) with 𝛻𝑓(𝑥) in equation (1). Integrating by partsand using the divergence theorem gives

D

Tunnel coverFace stopper

L

L1

Fig. 2 Initial condition of aluminium bar-experiment(H/D=1.0)

431

are similar to a straight line for two H/D values. The
failure lines in the experiment are nearly straight for
H/D = 1.0 and curved for H/D = 2.0.
These results clearly show that SPH analysis iseffective to simulate the collapse of the tunnel facein the aluminium bar-experiment.

Analysis condition
We used the two-dimensional SPH method withthe elastoplastic Drucker-Prager constitutive modeland the nonassociated plastic flow rule to reproduce
an aluminium bar-experiment. Table 2 shows the
specifications of the simulation. The soil parameters,
except for the unit weight, were similar to thosemeasured by Umezaki et al. [9]. The unit weight ofthe soil model is = 21.7 kN/m3. Figure 3 shows thesimulation model.
2280 SPH particles were used to create the modelwith H/D = 1.0 shown in Fig.3, with an initialsmoothing length of 6 mm. Table 2 shows the total
number of particles in other cases. Free-roller
boundary conditions with ghost particles [10] areused at the left and right ends, and full-fixity using
virtual particles [5] are used at the base and tunnel
top. The initial stress condition is obtained byapplying gravity loading to soil particles [5].
Then, the simulations are started by removing theface wall (A) in the side restriction shown in Fig.3.
3.75D

(m)
0.16

Experiment (H/D = 1.0)

SPH simulation (H/D = 1.0)
Total displacement00.35(m)

1.5D

Experiment (H/D = 2.0)

H

0.12

(m)

12
0.04
12

Ａ

D

0.08

0
0

0.1

0.2

0.3
: Soil particles

Fig. 3 SPH simulation model

0.4(m): Boundary particles(H/D = 1.0)

Table 2

Specifications of SPH simulation
Items
ValuesUnit weight = 21.7 kN/m3
Young’s modulus
E = 5.84 MN/m2
Poisson’s ratio
Cohesionc = 0 kN/m2
Internal friction angle
Note: Number of SPH particles is 2280 (H/D = 1.0),
and 3624 (H/D = 2.0)

SPH simulation (H/D = 2.0)

Total displacement00.35(m)(m)Comparison between experiment and SPH
simulation

Fig.4 Comparison between SPH simulation andexperiment for post-failure behavior

Figure 4 shows a comparison of the final
configuration after collapse between the aluminium
bar-experiment and simulation for H/D = 1.0 and 2.0.
The surface configurations in both the experimentand the SPH simulation shown in Fig.4 are straightfor H/D = 1.0 and H/D = 2.0.
The white dotted lines in Fig.4 indicate theboundary between the region of moved soils andnonmoved soils. These lines are equivalent to failurelines. The lines in the experiment and the simulation

STUDY ON TUNNEL COLLAPSE BY SPH
Collapse process of tunnel
We tried to simulate a tunnel collapse, as shownin Photo 1 for a case of construction during torrential
rains in Japan in 1995 [11], and provided a method
for examining the efficacy of reinforcement by SPH.
In this case, the tunnel and tunnel portal zone slope

432

Stage 1: The tunnel face squeezed out first and then,cracks were formed. A slip surface occurred at the
ground.
Stage 2: Flaking and falling off from the tunnel face
and the crown of the tunnel occurred, and large
deformation or beginning of failure occurred at thetunnel face. Concurrently, the ground surface
subsided vertically.
Stage 3: The tunnel collapsed, and landslide began at
the ground surface.
Stage 4: The slip surface extended and the tunnel
portal zone was crushed.

have collapsed. The geology consists of layers ofsandstone, tuff, and mudstone from the Neogene
period. The water seeping into the ground owing toheavy rainfall caused the collapse of the tunnel face
and the entire portal zone.
Figure 5 shows the collapse mechanism. The
collapse process was as follows.

SPH Simulation
Figure 6 shows the analytical model andconditions of the SPH simulation. Figure 6 shows
the size of the model and the soil parameters. The
soil parameters are decided as follows: E, , , and 
values were set as those of soft rock that is common
to Japan in reference to the parameters for numericalanalysis [12]. Cohesion is decided based on theoccurrence of failure through a parametric study by
SPH, and this value is equivalent to the case of 10-m
thickness of the landslide block [13]. A total of 7477
SPH particles were used to create the model with an
initial smoothing length of 0.6 m.

：Slip surface：Loosening zone
：Tunnel

33m

Stage 2 : Tunnel face collapsed andground surface subsided

γ = 20 kN/m3
E = 50 MN/m2  
c = 10 kN/m2
φ = 30° = 15°face

portaltunnel5.5m 5.5m

Stage 1 : Tunnel face squeezed out
and slip surface occurred=11m

Photo 1 Overview of collapse of tunnel and tunnel
portal zone [11]

26m
80m

Fig.6 SPH model
Figure 7 shows a contour plot of the accumulated
strain at 0.4 s and 0.8 s, which is the time just after
the face collapse has started in the SPH simulation.
Figure 7 shows that the slip surface extends forward
and upward from the bottom of the tunnel face, andthe shear band extends from the top of the tunnel at
the face to the ground surface vertically.
Figure 8 shows the post-collapse configuration by
SPH simulation. Figure 8 shows that the maximum
displacement is 6.8 m. It is also obvious that the slip
surface extending upward from the top of the tunnel
reaches the ground surface, and subsidence occurs.
Therefore, a second stage is simulated in the SPH, asshown in Fig.5.
In actual collapse, a landslide is caused by thecrushing of the tunnel. In this simulation, the cave-inof the crown is not considered. If we incorporate theconditions under which the cave-in of the crownoccurs, it should be possible to simulate the actualStage 3 : Tunnel collapsed andlandslide began

Stage 4 : Slip surface extended andtunnel portal zone crushed.

Fig.5 Process of tunnel collapse433

Table 3 shows the analysis case.
Figure 10 shows a contour plot of the relative
strain as determined by SPH. Here, the relative
strain is one divided by the maximum value of eachcase. The black particles denote those of the
improved particles.
For one bolt, the displacement and strain are large
and the tunnel face collapses. On the other hand, thedisplacements for two and three bolts arecomparatively smaller. The time until thedeformation converges is relatively short, being lessthan 1 s, and we could confirm that the face is stable.
However, particles attempt to inflow from theupper part of the tunnel face area, as indicated by acircle, in the case of two bolts. Therefore, three bolts
are effective as a reinforcement.

phenomena absolutely. However, it suffices to
examine the efficacy of a reinforcement forverifying the reproducibility of early collapse
behaviour by SPH.
0

Accumulated strain

Times = 0.4s

0.5Times = 0.8s

Fig.7 Contour plot of accumulated strain just after
the face collapse has started
Total displacement
0

2.0

4.0

6.0m

Steel pipe for reinforcement boltof tunnel faceaTunnel radiusa : distance between the bolts in the width direction

Original groundE0 , C0 , A0d

Reinforcement
ES , τS , AS
Improved groundE1 , C1 , A1

d : SPH particle diameter

Fig.8

Final configuration after collapse

E1=ΣE･A/A1 = (Es･As /a+E0･A0)/A1
c1=Σc･d/d = (τs･As /a +c0･ d) / d

Analysis of face reinforcement
We modelled the face reinforcement using
facebolts of 5-m length from the face.
In this study, we describe the reinforcing works as
the equivalent of Young’s modulus and the shear
strength with the cross-sectional performance of thematerial and ground. Figure 9 shows a facebolt
model and the procedure to evaluate the propertiesof facebolts as parameters for analysis for the
improved ground.
The cross-sectional area A1 is assumed to be equal
to A0 of 0.5 m2, which is multiplied by the SPH
particle diameter d of 0.5 m and unit width of 1.0 m.
E0 and c0 are shown in Fig.6. We applied steel pipes
with an outer/inner diameter of 76 mm/68 mm, as
shown in Fig.9, for facebolts, and their properties are
as follows: cross-sectional area As is 95
mm2,Young’s modulus Es is 210 GN/m2, and shear
strength s = 135 MN/m2. In addition, the distance
between the bolts in the width direction a is set to
1.5 m.
According to the equations shown in Fig.9, theequivalent value E1 is 320 MN/m2 and c1 is 180
kN/m2 for the improved region.

Fig.9 Model of facebolts
Table 3 Locations of facebolts
Number of bolts
Height from bottom (m)13.8
22.3, 4.3
3
1.8, 3.3, 4.80
One bolt

Relative strain

1.0

Two bolts

Fig.10 SPH results with facebolts

434

Three bolts

Engineering, JSCE, Division A, Vol.23, No.2,
2006, pp.307-319.
[5] Bui HH, “Lagrangian mesh-free particle method(SPH) for large deformation and post-failure ofgeomaterials
usingelasto-plastic
soil
constitutive models”, Ph.D. Dissertation ，Ritsumeikan University，Japan，2007．
[6] Nonoyama H, Sawada K, Moriguchi S,
Yashima A and Itoh K, “A 2D SPH simulation
for real-scale slope excavation experiment”,
Journal of JGS, Vol.7No.4,2012, pp.543-555(inJapanese).
[7] Bui HH, Fukagawa R, Sako K, Okamura Y,“Earthquake induced slope failure simulation by
SPH”, Fifth Intrenational Conference on Recent
AdvancesinGeotechnical
Earthquake
Engineering and Soil Dynamics, 2010.
[8] Monaghan JJ and Lattanzio JC,”“A refinedparticle method for astrophysical problems”,
Astronomic and Astrophysics, 149, 1985, p.135.
[9] Umezaki T, Kawamura T and Ochiai H,“Increment of confining pressure due to pulloutof reinforcement and modelling of reinforcing
mechanics”,Geosynthetics Engineering Journal,
Vol.120,2005,pp.241-248(in Japanese).
[10] Liversky LD, Petschek AG, Carney TC,Hipp
JR and Allahdadi FA, “High strain Lagrangianhydrodynamics: a three dimensional SPH codefor dynamic material response” , Journal ofComputational Physics, 109, 1993, pp.67-75.
[11] Japan Tunneling Association: “Physiognomy
guide for tunneling engineers No.6”, Tunnels
and undergrounds，Vol.43, No.10, 2012, p.50
(in Japanese).
[12] Japan Railway Construction, Transport, andTechnology Agency, “Standard design andconstruction of mountain tunnel”, 2008, p.310(in Japanese).
[13] Japan Road Association, “Earth works on
Road: Guiding principle on slope works andslope stability works”, 2013, p.349 (inJapanese).

CONCLUSION
In this study, we applied the SPH method to
examine tunnel collapse behaviour.
The results of our study are presented below.
・As an outline of the SPH method, we presented
the basic formula of the SPH method and listed
some details when this method is applied for
simulating the ground deformation.
・Through a model experiment conducted using an
aluminium bar and SPH analysis, we simulated thecollapse behaviour of the tunnel face andconfirmed that the SPH simulation results are
consistent with the experimental results. These
results clearly demonstrate that SPH can be used
to model the two-dimensional tunnel face collapse
behaviour.
・We analysed an actual case of collapse in a tunnel
portal zone using the SPH method. We clearly
demonstrated that the SPH method can sufficiently
explain the actual collapse phenomena.
・ We modelled a case in which reinforcement
works are installed in the tunnel face, and we
confirmed the positive effect of this model for
improving the stability of the face.
The SPH method used in this study can model
large-scale deformations and separation behaviour
that are difficult to handle using FEM or FDM andother grid-based continuum models. In addition, thismethod is easier to apply compared with discreteelement model (DEM) and other discontinuum
models.
As a future research agenda, we plan to advancefurther studies on the formulation of threedimensional models for the detailed assessment andsetting of external-force conditions when caused byearthquakes, rainfall, etc..
REFERENCES[1] Lucy L, “A numerical approach to testing thefission hypothesis”, Astonom. J. 82, 1977,
pp.1031-1024.
[2] Gingold RA and Monaghan JJ, “Smoothed
particle hydro-dynamics theory and application
to non spherical stars”, Monthly Notices R.Astronom. Soc.181, 1977, pp.375-389.
[3] Bui HH, Fukagawa R, Sako K and Ohno S,“ Lagrangian meshfree particles method (SPH)
for large deformation and failure flows ofgeomaterial
using
elastic-plastic
soil
constitutive model” , International Journal for
Numerical and Analytical Methods inGeomechanics, 32, 2008, pp.1537-1570.
[4] Maeda K, Sakai H and Sakai M, “Developmentof seepage failure analysis method of groundwith Smoothed Particle Hydrodynamics”,
Journalof
Structual
andEarthquake

435

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

3D PRINTING OF GRANULAR MATERIAL AND ITS APPLICATION
IN SOIL MECHANICS
Satoshi Matsumura1, Takaaki Kobayashi1 and Takaaki Mizutani1
1
Port and Airport Research Institute, Japan

ABSTRACTThe objective of this study was to quantify the effect of particle arrangement on the macroscopic response of
granular materials prepared using 3D printing. We first conducted X-ray computed tomography scanning on a
specimen of packed natural gravel. Using these data as a blueprint, replicas of the gravel were created using a 3D
printer and a resin-based material. Although the mechanical properties of the printing material differed from thoseof the natural gravel, the particle arrangement was strictly reproduced. Tri-axial compression tests were performed
on both the gravel and its replicas to compare their mechanical responses. In this paper, we first describe theexperimental procedures and geometrical reproducibility of 3D printing for granular materials. Then, based on thetri-axial test results, the stress–strain behaviors of the natural gravel and its replicas are compared. Finally, as achallenging application, the effect of the particle arrangement on the macroscopic response is evaluated.
Keywords: 3D printing, X-ray CT, Granular material, Particle arrangement

application, the effect of the particle arrangement on
the macroscopic response is evaluated. The results
indicate that not only the packing void ratio but also
the particle arrangement significantly affects themacroscopic response of the granular material.

INTRODUCTION
Soil is composed of granular aggregation, in
which the individual particles vary widely in terms of
their size, shape, and physical properties.
Furthermore, the structural effect of the arrangementof the particles largely affects the macroscopic
response of the soil. Thus, any conventional soil tests
naturally exhibit scatter because of the variation in theparticle arrangement. In this context, soil “element”
tests do not exist.
The objective of this study was to quantify theeffect of the particle arrangement on the macroscopic
response of granular materials prepared using 3D
printing. Some prior works have treated the above
issue of particle arrangement [1] and applied the 3D
printing technique to reproduce individual particles
for mechanical tests [2]. In this study, we challenged
reconstructing the packing arrangement withindividual particles. We first conducted X-ray
computed tomography (CT) scanning on a specimenof packed natural gravel. Using these data as a
blueprint, replicas of the gravel were created by a 3D
printer using a resin-based material. Although themechanical properties of the printing material
differed from those of the natural gravel, the particle
arrangement was strictly reproduced. Tri-axial
compression tests were performed on both the natural
gravel and its replicas to compare their mechanicalresponses. In this paper, we first describe theexperimental
procedures
andgeometricalreproducibility of the 3D printing for granularmaterials. Then, based on the tri-axial test results, thestress–strain behaviors of the natural gravel and its
replicas are compared. Finally, as a challenging

TESTING MATERIALS AND PROCEDURE
Figure 1 presents a flow chart of the experimental
procedures used in this study. The main testingprocesses included X-ray CT scanning and tri-axial
compression testing of the original specimen, image
processing of the 3D printed tri-axial specimen, andtri-axial compression tests of the 3D printed specimen.
The following sections describe each procedure indetail.

Fig. 1 General scheme of experimental procedure.
436

GEOMATE- Osaka, Nov. 16-18, 2015a360°; thus, 3D images can be produced using multiple
X-ray scans from different angles.
The pressure and water in the tri-axial test can becontrolled outside the X-ray scanner using the control
panel, which enables the entire tri-axial compression
testing with scanning to be conducted after set-up.
Note that processes such as loading must be
interrupted during scanning. In this study, we used thetri-axial test apparatus modified for usage in the Xray scanner. Accordingly, by not equipping themetallic columns, only the acrylic cell bears thereaction to compression. In addition, because thecompressive force range was less than approximately
2 kN, we regarded the compressive behavior of thecell as being negligible compared with thecompression of the specimen.
The tri-axial compression test using the gravel
was conducted as follows. The cylindrical specimenof 50 mm in diameter and 100 mm in height wasreconstituted with the oven-dried gravel in the triaxial mold by tapping. Afterwards, the specimen was
consolidated under a total confining pressure of 50
kPa (with no back pressure). Moving the tri-axial
apparatus into the X-ray CT scanner, as shown in Fig.
3, the specimen preparation was successfully
performed before the X-ray CT scanning andcompression.
To obtain the X-ray image of the gravel from which
the 3D-printed specimen originated, the specimen
was scanned once before loading. In the test, thespatial resolution was approximately 83.8 m pervoxel. After scanning the initial state, the specimen
was compressed at a loading rate of 0.1%/min, andthe exhausting condition until the axial strain reachedapproximately 15%.

bFig. 2

Materials used: a. gravel and b. 3D printed
material.

Fig. 3

Overview of X-ray CT scanner with tri-axial
test apparatus.

Materials
We used natural gravel with particle sizes ranging
from 4.76 mm to 9.5 mm for the prototype specimenof the tri-axial compression tests (50 mm × 100 mm).
An image of the gravel is presented in Figure 2a. This
relatively large particle size is due to the limitation of
the resolution of the X-ray CT and the 3D printer.
Figure 2b also shows the 3D printed material
created based on the X-ray CT scanning of the
prototype specimen. Note that the printed gravel
shown in Figure 2b was loosened after the tri-axial
compression tests. The solid densities were 2765
kg/m3 for the natural gravel and 1105 kg/m3 for the
3D printed material at 20 °C.

Image Processing and 3D Printing
The newly installed Agilista-3100 developed byKeyence Corp. at the Port and Airport ResearchInstitute was used as the high-resolution 3D printer inthis study. The 3D printer uses UV-cured printing, theprinciple of which is demonstrated in Figure 4. The
3D printed material is formed by alternating between
spraying a UV-curable resin and applying UVradiation. On the outward path from the initial
position, the UV-curable resin is sprayed on the stage
using the exhaust nozzles from the inkjet head withroller flattening. On the return path, UV radiation isused to harden the material. Using this instrument, themaximum printing area is 297 mm in the x-direction,
210 mm in the y-direction, and 200 mm in the zdirection. The spatial resolutions in the x- and ydirections are 600 and 400 dpi, respectively.
Furthermore, in the z-direction, the model material
layer can be accumulated at 15 m per layer at aminimum.

X-ray CT Scanning and Tri-axial Compression
Test
Figure 3 presents an overview of the X-ray CT
scanner at the Port and Airport Research Institute.
The voltage and current of the X-ray tube primarily
ranged up to 225 kV and 600 A. The flat panel for
detecting the transmitted X-ray had dimensions of
418 mm × 418 mm, which produces X-ray CT imagesof 3008 × 3008 pixels at a maximum. Accordingly,
the highest spatial resolution is approximately 10 m.
In the figure, the tri-axial test apparatus used is placedon a table. The round table under the apparatus rotates437

GEOMATE- Osaka, Nov. 16-18, 2015

To duplicate the inherent particle arrangement of
the soil specimen, the most important feature requiredof the 3D printer is that the support forming the porepart in the 3D printed specimen can be removed
without any external action, e.g., heating or water
flow. For the 3D printer, the model and pore parts canbe simultaneously and individually reconstructedusing two types of resins, i.e., the model material andwater-soluble support described as the white and theblack areas in the figure, respectively. The watersoluble support can thus melt in the water(specifically, the water in which the same resin as the
support material melts to a certain degree) simply bysubmerging the printed object, as schematicallyillustrated in Figure 5. In this study, to simplify the
post-processing of the 3D printed specimen, we
prepared a specimen for which the neighboring
particles mutually bond slightly. Thus, the 3D printed
specimen can stand alone after the water-soluble
support is removed (see Figs. 5 and 8b).

solid and pore parts were separated. In this situation,
however, the neighboring particles could still bondstrongly. Therefore, we employed “watershed”
processing to segment the individual particles. Thismethod enabled separation of each particle withdifferent appearance by adding some spatial blanks
between particles (see Fig. 6c). This figure
demonstrates that the particles obviously failed to besegmented; i.e., the particle indicated by the arrow in
Fig. 6c was separated into two particles. This error
tends to occur for particles that are narrow in the
middle. We did not address this error in this study.
Finally, Fig. 6d presents an image processed by“erode” after “watershed” processing to shrink theobject in the image. The above series of image
processing is the result of trial and error between
processing the image and actual 3D printing. Because
the objective of this study was to duplicate the soil
structure, we selected the X-ray CT image processing
method that resulted in a 3D printed specimen that
was as geometrically and mechanically close to theoriginal gravel specimen as possible.

x
a

b

c

d

Fig. 4 UV-cured 3D printing using Agilista-3100.

Fig. 6
Fig. 5 Post-processing of 3D printed material.
(Left) With the water-soluble support shortly
after submerging the 3D printed specimen.
(Right) The 3D printed specimens without
the support after remaining in water foralmost five days.

Image processing for 3D printing: a. original
image of gravel specimen, b. threshold, c.
watershed, and d. erode.

Table 1 Specimen conditions
Gravel3D printed
material

Soil particle density,
2765
1105
kg/m3
Dry density, kg/m3
1640
553
Void ratio
0.686
1.008
Note: the soil particle densities were investigated at
20 °C.

Figure 6 shows the image processing scheme used
to make the 3D printing input file. We used “ImageJ”[3] as the image-processing tool. As shown in Fig. 6a,the X-ray scanned and reconstituted image wasobtained in grayscale (of 16 bit). To distinguish theparticles in the image, first, “threshold” processing
was performed (see Fig. 6b). Using this process, the438GEOMATE- Osaka, Nov. 16-18, 2015

In this study, we generated the 3D image forprinting by accumulating 2D images processed using
the aforementioned method, as illustrated in Figure 7.
Figure 8 shows the 3D surface image for printing andthe actual 3D printed specimen. Table 1 lists the
specimen conditions using the gravel and 3D printed
material. The void ratio of the 3D printed specimen
was 1.008, which is larger than that of the original
gravel specimen. This discrepancy results from theimage processing for printing explained above.
Subsequently,
weperformed
tri-axial
compression tests using the 3D printed specimen.
Unlike the gravel specimen, the 3D printed specimen
was set up without tapping in the tri-axial apparatus.
The confining pressure and loading rate testing
conditions were precisely the same as those used forthe gravel specimen. The 3D printed specimen iscalled “D-specimen,” and the gravel specimen iscalled “G-specimen”.
Furthermore, to evaluate the effect of the soil
structure, i.e., particle arrangement, on themechanical response, we prepared an additional
specimen reconstituted by filling the discrete 3D
printed material by tapping. The material was
prepared by deforming the D-specimen to make eachparticle discrete. Then, the void ratio of the specimen,
called the “DR-specimen,” was controlled in the samemanner as the D-specimen. The tri-axial compression
test was conducted in the same manner as for the Gand D-specimens.

Fig. 7 3D image prepared by accumulating processed
2D images.

aFig. 8ba. 3D printing input file and b. 3D printed
specimen. The sample size is 50 mm indiameter and 100 mm in height, the same as
the dimensions of the original gravel
specimen.

RESULTS
We now discuss the geometrical and mechanicalproperties of the 3D printed specimen based on the Xray CT scanning and tri-axial test results. Table 2 lists
the testing conditions.

Table 2 Testing condition of tri-axial specimens. The
G-specimens comprised the natural gravel
soil. The D-specimens were printed with thesoil structure. The DR-specimens werereconstituted by the 3D printed material.
Specimen name,No.
G-specimen, No.1
G-specimen, No.2
G-specimen, No.3
D-specimen, No.1
D-specimen, No.2
D-specimen, No.3
DR-specimen, No.1
DR-specimen, No.2
DR-specimen, No.3
DR-specimen, No.4

Material
used
Gravel3D printed
material
w/ structure
3D printed
material
w/o structure

Geometrical Property of 3D Printed Specimens
To demonstrate how accurately the soil structure
can be reconstructed by 3D printing, Figure 9 presents
a comparison of the particle arrangements of Gspecimen No. 1 and D-specimens No. 1 to 3 (seeTable 2). Here, we calculated the angular distribution
every 20° in the longitudinal direction of each particle
from the x-axis, as shown in Fig. 6a. For the
calculation, we selected certain horizontal crosssections at similar locations, i.e., 2D images, of theabove four specimens. The overall tendency of thethree D-specimens agrees well with that of the Gspecimen. A subtle difference, however, can also be
observed, which results from the insufficientaccuracy of the image processing explained above
rather than that of the 3D printing.
Figure 10 reveals that the content rate of theparticles is smaller than the individual particle area,

X-ray CTscanning
Yes
No
No
Yes
Yes
Yes
No
No
No
No

439

GEOMATE- Osaka, Nov. 16-18, 2015

which is plotted along the x-axis. Although theparticle areas of the duplicated specimens are
consistent with one another, overall, they tend to besmaller than that of the original specimen. This result
occurs because the erode processing for the 3D
printing shrunk the particles. Thus, from the above
simple particle analysis, it can be roughly concluded
that 3D printing can duplicate granular material suchas gravel.

dilatancy behavior at larger strain levels. The figuresindicate that the strength and stiffness at lower strainlevels of the G-specimens were both slightly higher
than those of the D- and DR-specimens. In addition,
the axial stress begins to fluctuate with the evolutionof the axial stress. These results imply that thedifference in the material—particularly, themicroscopic surface friction—can be significant inexerting shear resistance even though the particle size
and arrangement are duplicated similarly by the 3D
printing.
In addition, the effect of the particle arrangementon the stress–strain curves is apparent. Comparedwith the D-specimens, for which the soil structure
was duplicated for three tests, the results of the DRspecimens vary more widely. This tendency is likely
to be more conspicuous for the behavior at lower
strain, less than approximately 4%. In terms of these
two types of specimens, because the difference issupposed to indicate only whether the duplicatedstructure was maintained before the axial
compression, the results varying widely can be
attributed to the effect of the particle arrangement.
To quantify the variation of the stress–strain
curves, Figure 12 shows the relation between the
resulting axial stress and the mean values of three (orfour) specimens for every 0.1% of axial strain. In the
figure, dashed lines representing 0.8–1.2 times themean value are also included. From the figures, theaxial stress of the G- and DR-specimens, which werereconstituted in the same manner for each uniform
void ratio but with different particle arrangements,
widely change from 10%–20% versus the mean value.
Furthermore, this trend appears to be significant at
lower stress–strain levels. In addition, little variationof the D-specimens was observed. Thus, although
only three (or four) specimens were tested, the Dspecimens repeated the particle arrangement result inalmost the same macroscopic response against thecompression. This result implies that the 3D printing
technique accompanied by X-ray CT scanningenables the net effect of the soil structure on themechanical properties of the soil to be quantified.

G-specimen, No.1
D-specimen, No.1
D-specimen, No.2
D-specimen, No.3

Fig. 9 Particle arrangement distribution. The particle
angle was calculated based on the angle ofthe longitudinal direction from the x-axis
shown in Fig. 6a.

Content rate, %G-specimen, No.1
D-specimen, No.1
D-specimen, No.2
D-specimen, No.3

Fig. 10 Particle area distribution. The content rate of
the particles is smaller than the individual
particle area, which is plotted along the xaxis.
Tri-axial Compression Test Results
Figure 11 presents axial stress–strain curves of theG-, D-, and DR-specimens. The axial stress iscalculated as the axial load divided by the initial
cross-section area of the specimen because thevolume change of the specimen during loading was
not measured. Therefore, note that the stress value
could contain more variation resulting from thea440GEOMATE- Osaka, Nov. 16-18, 2015bcFig. 12 Variation of stress due to soil structure:a. G-specimens, b. D-specimens, and c DRspecimens.

CONCLUSION
To quantify the effect of the soil structure, i.e., theparticle arrangement, on the macroscopic response of
the soil, we attempted to produce replicas of the
granular material using X-ray CT scanning and 3D
printing followed by tri-axial compression tests.
Based on the image analysis and resulting stress–
strain relations, we conclude that this approach canduplicate the granular material as a cylindrical mass
with the same soil structure as that of the original.
Furthermore, we emphasize that even though the
granular specimen can be reconstituted at the same
packing void ratio in the same manner that is usually
been performed, the difference in particle
arrangement causes the mechanical response to vary
more widely.

cFig. 11 Axial stress–strain curves: a. G-specimens,b. D-specimens, and c. DR-specimens.

ACKNOWLEDGEMENTS
This work was supported by JSPS KAKENHI
Grant Number 15K14034.
REFERENCES

a[1] Oda, M.: Initial fabrics and their relations tomechanical properties of granular material, Soils
and Foundations, Vol.12, No.1, pp.17-36, 1972.
[2] Marc Z. Miskin and Heinrich M. Jaeger:
Adapting granular materials through artificial
evolution, NATURE MATERIALS, Vol. 12,
pp.326-331, 2013.
[3] Rasband, W.S.: ImageJ, U. S. National Institutesof Health, Bethesda, Maryland, USA,
http://imagej.nih.gov/ij/, 1997-2015.

b441

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

EFFECT OF SOIL MOISTURE CONDITIONS ON SEISMICSTABILITY OF EMBANKMENT SLOPE
Koichi Sumi1, Nobutaka Hiraoka2, Masamitsu Fujimoto3 and Ryoichi Fukagawa3
1
Graduate School of Science and Engineering, Ritsumeikan University, Japan
2
Research Organization of Science and Technology, Ritsumeikan University, Japan
3
College of Science and Engineering, Ritsumeikan University, Japan

ABSTRACT
Recent studies on sediment disasters have pointed out that slope failure due to earthquakes was affected byantecedent precipitation before the earthquake. Here, we focus on the variation in soil moisture conditions due toantecedent rainfall for slope failure due to earthquake. To clarify the relationship between the difference involumetric water content of soil conditions due to elapsed time after rainfall and the scale of slope failure, we
conducted a series of vibration loading experiments under different soil moisture conditions. In the experiments,‘rainfall’ was applied to the model slope using an artificial rain simulator, and the model slope was subjected toseismic wave loading using a shaking table. Soil moisture sensors and laser displacement sensors were used tomeasure the volumetric water content of soil and displacement of the slope, respectively. The results indicatedthat the top of slope collapsed and, eventually, sliding failure occurred when the volumetric water content of soil
was low. The crest of the model slope settled down considerably without eroding the sliding surface when thevolumetric water content of soil was high. Our study indicated that the soil moisture distribution has a significant
influence on the scale of slope failure.
Keywords: Slope Failure, Antecedent Rainfall, Vibration Loading Experiments, Sliding Failure
water content of soil conditions and seismic slope
failures.

INTRODUCTION
Many slope failures happen in Japan every year
and inflict enormous damage. Rainfall and seismic
movements are major factors in slope failures.
Previous studies examined the topographic effects
on landslides triggered by earthquakes [1]-[3] andproposed a prediction method for risk assessment ofseismic slope failure in mountainous area based on
local topographical and mechanical conditions [4].
Although these studies pointed out the potential
effects of topography and geology on seismic slope
failure, they did not focus on the influences of soil
moisture on seismic responses and the damage toembankment slopes due to the earthquake. The
Niigata Chuetsu earthquake in 2004 was an example
where soil moisture and groundwater conditions
affected the slope failure. Reference [5] reported thatthe soil moisture and groundwater conditions weresignificant factors in controlling the strength of theslope in seismic slope failures and the movement ofcolluvial soils after the collapse. However, few
studies have investigated the effects of soil moisture(groundwater) conditions on seismicity by in situ
measurements [6] or laboratory experiments [7], [8].
In this study, we focused on variation in soil
moisture conditions due to antecedent rainfall. We
also conducted a series of loading experiments under
different soil moisture conditions to clarify the
relationship between the variation in volumetric

EXPERIMENTS
Experimental Methods
We focused on the relationship between soil
moisture conditions and the displacement of a modelslope. Experiments were conducted using an
artificial rain simulator (Daiki Rika Kogyo Co., Ltd.,
DIK-6000S) and a shaking table (Shinken Co., Ltd.,
G-9210). We made a model slope with granite soil
(Masa soil) in a tank. Table 1 shows the propertiesof the soil. We provided vibration loading to theslope model via the shaking table after artificial
rainfall and examined the failure configuration. In
order to clarify the collapse processes, we took
videos during the vibration loading experiments. A
stainless steel tank, 100 cm long, 60 cm wide, and70 cm high, was mounted on top of the shaking table.
Figure 1 shows an image of the model slope andmeasurement points of soil moisture anddisplacement. The size of model slope was 65.6cm
long, 60cm wide, and 40cm high. Ten soil moisturesensors (Decagon Devices, S-SMx-M005) were used
to measure the variation in volumetric water content
of soil and six laser displacement sensors (Micro
Epsilon, ILD1300-200) were used to measure thedisplacement of a model slope. The vertical and442

GEOMATE- Osaka, Nov. 16-18, 2015

horizontal displacements were measured by the laser
displacement sensors of SV1-3 and SH1-3,
respectively.

800

Acceleration (gal)

600
400
200
0
-200
-400
-600
-800

0

5

10

15

20

25

30

Elasped time (s)Fig. 2

Waveform of acceleration

RESULTS AND DISCUSSION
Fig. 1
Schematic figure of the embankment andthe measurement points

A Small Rainfall before the Loading

Table 1 Properties of the Masa soil
Relative density, Dr
Initial moisture content, w
Dry density, ρd
Wet density, ρt
Coefficient of permeability, k
Void ratio, ePorosity, nFigure 3 shows final longitudinal profile of slope
failure and Fig. 4 shows photograph of final slope
shape in Case 1. During rainfall, the increase involumetric water content of soil from the initialvalue was about 8% in the lower layer and about 4%
in other layers. After the rainfall ended, thedecreases in volumetric water content of soil in theupper and middle layers were larger than those in thelower layer. At point D in the lower layer, thedecrease in soil moisture showed little change after
the rainfall ended. This result was caused by lessinfiltration of water from the lower layer to deeper
area of slope due to the undrained condition ofbottom of slope (Fig. 5). The amounts ofdisplacement in Case 1 were larger than those inCases 2 and 3 (Fig. 6). The time until collapse after
loading in Case 3 was longer than the other cases.
Although a collapse with a sliding surface in themodel slope was confirmed in Cases 2 and 3, it was
not in Case 1. Following settlement of the crest after
providing the loading in Cases 2 and 3, collapse of
the top of the slope occurred, forming the sliding
surface. In Case 1, not only the surface but also theback of the model slope collapsed.

78%
10%
1.60g/cm3
1.76g/cm3
2.09×10-5m/s
0.631
38.7%

Experimental Conditions
In this study, six case experiments wereconducted. Table 2 shows the rainfall conditions.
We set a different elapsed time from the end of theartificial rainfall to provide the loading for creating
differences in the volumetric water content of soil.
In all experiments, the rainfall intensity wasmaintained at 30 mm/h. For the rainfall duration, it
was 1 h in Cases 1-3 and 3 h in Cases 4-6. The
bottom, rear, and the sides of the tank were
undrained and the slope angle was set at 50°. Figure
2 shows waveform of acceleration. Maximum
acceleration was set for about 600 gal and thefrequency was set at 5 Hz.
Table 2 Experimental conditions
Amount of
rainfall per
hour
Case1
Case2
Case3
Case4
Case5
Case6

Rainfallduration
1hour

30mm/h3hour

Elapsed timeafter rainfall
no time1hour
1day
no time1hour
1day

Fig. 3 The longitudinal profile of the slope failure(Cases 1-3)443

GEOMATE- Osaka, Nov. 16-18, 201520(a) SH1
15

(a)

Case1
Case2
Case3

10

5

Displacement (cm)00(b) SV2
-5

Case1
Case2
Case3

-10

-15
0

(b)

-2

(c) SV3

-4

Case1
Case2
Case3

-6
-8
-10

0

Fig. 4 A photograph of the slope failure from (a)side and (b) front view (Case 1)

10

15

20

25

30

35

Elasped time (s)Fig. 6 Displacement after the loading at (a) SH1,(b) SV2 and (c) SV3 (Cases 1-3)

26
24

I
J

(a) Upper layer

22

A Large Rainfall before the Loading→stop raining

20

Figure 7 shows final longitudinal profile of slope
failure and Fig. 8 shows photograph of final slope
shape in Case 4. In the lower layer, the decrease in
the value of soil moisture (C and D points) showed
little change (Fig. 9). The decreases in volumetricwater content of soil in the lower layer were similar
to Cases 1-3. Although the volumetric water content
of soil decreased approximately equally for both
points A and B, the time to reach the minimum value
was different. In Cases 4-6, settlement of the crest
and collapse of the top of the slope with a sliding
surface occurred, as in Cases 2 and 3. The amount of
displacement in Cases 4-6 showed a large difference(Fig. 10). The time to collapse in Case 6 was longer
than in Cases 4 and 5.

18
16
14

Volumetric water content of soil (%)

5

12
26

24

E
F
G
H

(b) Middle layer

22
20
18

1day

16
14
12
26
(c) Lower layer

24
22
20
18

A
B
C
D

16
14
12

0

500

1000

1500

Elasped time (min)Fig. 5 The volumetric water content of soil in the(a) upper, (b) middle and (c) lower layer (Case 3)
Fig. 7 The longitudinal profile of the slope failure(Cases 4-6)444

GEOMATE- Osaka, Nov. 16-18, 201520(a) SH1

(a)

15

10

Case4
Case5
Case6

5

0
0

Displacement (cm)(b) SV2

(b)

-5

Case4
Case5
Case6

-10

-15
0
(c) SV3

-1
-2

Case4
Case5
Case6

-3
-4
-5

0

5

10

15

20

25

30

35

Elasped time (s)Fig. 8 A photograph of the slope failure from (a)side and (b) front view (Case 4)

Fig. 10 Displacement after the loading at (a) SH1,(b) SV2, and (c) SV3 (Cases 4-6)

26

I
J

(a) Upper layer

24
22

Effect of The Soil Moisture Conditions on Seismic
Slope Failure→stop raining

20

Figure 11 shows the position of the sliding
surface and Fig. 12 shows a spatial distribution chartof the volumetric water content of soil before
loading. Table 3 and Table 4 show time required forcollapse and scale. We categorized the collapse into
three stages based on visual observation: thesettlement of crest, deformation of top of slope, andthe formation of sliding surface during collapse. We
measured the time required for the formation of
three stages. The position of sliding surface became
deep when the volumetric water content of soil was
low. The differences in the spatial distribution of thesoil moisture affected the depth of the sliding
surface. Full-scale collapse occurred with a uniform
soil moisture distribution and partial collapse
occurred with a non-uniform soil moisture
distribution. Although the volumetric water content
of soil at near the top of slope was high in Case 1
and Case 4, the volumetric water content of soil in
Case 1 was higher than that in Case 4 (Fig. 12). The
distance of sediment movement in Case 4 was about
25cm larger than that in Case 1. The large collapse
at top of slope in Case 1 with a uniform soil

18
16

Volumetric water content of soil (%)

14
12
26

E
F
G
H

(b) Middle layer

24
22
20
18

1day

16
14
12
26
24
22
20
18
16

A
B
C
D

(c) Lower layer

14
12

0

500

1000

1620

Elasped time (min)Fig. 9 The volumetric water content of soil in the(a) upper, (b) middle and (c) lower layer (Case 6)445

GEOMATE- Osaka, Nov. 16-18, 2015

Table 4 Time required for collapse and scale forcases of large rainfall amounts

moisture distribution made the longer distance of
sediment movement relative to Case 4. Thus, the soil
moisture distribution had an effect on the failure
configuration. As a result, the amount of
displacement and distance of sediment movement
increased, causing collapse in a shorter time after theloading when the volumetric water content of soil
was high.

Rainfall amounts
Elapsed time after
rainfallTime required forsettlement of crest
Time required forcollapse of top of
slope
Time required forsliding failure
Distance of
sediment
movement

Fig. 11 Shape of sliding surface

Case4
Case5
Case6
30mm (30mm/hr×3hr)
no time

1hour

1day

10s

12s

16s

14s

14s

12s

16s

19s

23s

71.4cm37.4cm43.4cmCONCLUSION
We conducted a series of vibration loading
experiments under different soil moisture conditions
to examine the relationship between differences involumetric water content of soil with seismic
stability. We confirmed that seismic stability was
impaired with increased volumetric water content ofsoil from results of the amount of displacement andthe distance of sediment movement. This study
demonstrated that the difference of soil moisture
distribution affected the scale of slope failure. We
found the soil moisture distribution is significant
factor of prediction of slope failure. However, it isimportant to consider drainage conditions, rainfall
conditions, and earthquake scales to predict theoccurrence of slope failure. A problem is toreproduce the collapse mechanism accurately
through analysis. We must compare the result ofanalysis with failure configurations on a model slope
and the amount of displacement to the examine
reproducibility of any analytical model.

Fig. 12 Spatial distribution of volumetric watercontent of soil before the loading
Table 3 Time required for collapse and scale forcases of small rainfall amounts

Rainfall amounts
Elapsed time after
rainfallTime required forsettlement of crest
Time required forcollapse of top of
slope

REFERENCES[1] Nishida K, Kobashi S and Mizuyama T,“DTM-based
topographicalanalysisoflandslides caused by an earthquake”, Journal of
the Japan Society of Erosion Control
Engineering, Vol. 49, No. 6, 1997, pp. 9-16 (inJapanease).
[2] Asano S, Ochiai H, Kurohara U and Okada Y,“Topographic effects on earthquake motion thattrigger landslides”, Journal of the Japan
Landslide Society, Vol.42, No.6, 2006, pp. 457466 (in Japanease).
[3] Torii N and Okimura T, “Effect of torographic
conditions and depth of surface soil layer onseismic response characteristics on mountainslope”, Journal of the Japan Society of Civil
Engineers, Vol.64, No.2, 2008, pp. 369-382 (in

Case1
Case2
Case3
30mm (30mm/hr×1hr)
no time

1hour

1day

10s

13s

14s

12s

15s

16s

Time required forsliding failurenosliding
failure17s

18s

Distance of
sediment
movement

46.9cm42.4cm32.8cm446

GEOMATE- Osaka, Nov. 16-18, 2015

Japanease).
[4] Wakai A, Tanaka N, Abe S, Yoshimatsu H,
Yamabe K and Watanabe T., “Large–areadamage prediction system based on finite
element method for risk assessment of seismic
slope failure in mountains area”, Journal of theJapan Landslide Society, Vol.45, No.3, 2008, pp.
207-218 (in Japanease).
[5] Chigira M, “Geological and geomorphologicalcharacteristics of landslides generated by the
mid niigata prefecture earthquake in 2004”,
Journal of the Japan Society of Engineering
Geology, Vol.46, No.3, 2005, pp. 115-124 (inJapanease).
[6] Mori T, Chiba T, Uzuoka R and Kazama M,“The relationship between change of soil
moisture resulted from rainfall sand seismic
response characteristics in the valley fillground”, Journal of the Japan Association for
Earthquake Engineering, Vol.10, No.4, 2010, pp.
1-13 (in Japanease).
[7] Matsumura K and Yokota Y, “Experimental
study on seismic reactions of gentle slope withground water”, Journal of the Japan Society of
Erosion Control Engineering, Vol.66, No.2,
2013, pp. 48-56 (in Japanease).
[8] Tanabe T, Hiraoka N, Sumi K, Fujimoto M andFukagawa R, “Experimental study on dynamic
stability of slope failure due to earthquake after
antecedent rainfall”, Journal of the Kansai GeoSymposium 2014, pp. 223-226 (in Japanease).

447

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

A METHOD FOR JUDGING THE RISK OF SLOPE FAILURE BASEDON PRECIPITATION AND SOIL MOISTURE CHARACTERISTICS
Tetsuya Iida1, Masamitsu Fujimoto2, Nobuyuki Kumakura3, Daisuke Takasaki3,
Nobutaka Hiraoka4 and Ryoichi Fukagawa2
Graduate School of Science and Engineering, Ritsumeikan University, Japan,
2
College of Science and Engineering, Ritsumeikan University, Japan
3
Community Solutions Division, Toshiba, Japan
4
Research Organization of Science and Technology, Ritsumeikan University, Japan
1

ABSTRACT
Slope failures often occur in Japan, and they frequently result from an increase in the moisture content of thesoil. Thus, it is important to consider soil water content. In this study, we carried out a series of laboratory
experiments to evaluate changes in soil moisture during rainfall. In the experiments, we constructed a modelslope in which we installed 10 soil moisture sensors. The results indicated that the volumetric water content in
the slope increased with rainfall, and the increments in volumetric water content were affected by the intensity of
the rainfall. Then, using the experimental results, we developed a technique for judging slope failure risk basedon absolute values of volumetric water content and rainfall characteristics. Our proposed method will be usefulin judging the risk of slope failure because the criteria for the method are based not only on the precipitation but
also on the soil moisture in-situ.
Keywords: Slope Failures, Laboratory Experiments, Soil Moisture Sensors, Volume Water Contents, Rainfall

operating costs of measurements on a hazardous
slope, a measurement system for soil water content
would have a good return as a warning system for
impending slope failure. In this study, we evaluatedchanges in soil moisture during rainfall using
laboratory experiments, and developed a techniquefor judging the risk of slope failure based on theexperimental results.

INTRODUCTION
In Japan, many slope failures have occurred withheavy rainfall. Several models for predicting slope
failure in forested areas have been proposed sincethe 1980s. Most studies used an approach thatcombined a physical model to simulate rainfall
infiltration processes with slope stability analysis[1]-[9]. With regard to the physical model, one ofmain factors in slope failure is an increase in the
moisture content of the soil. Most of these studies
analyzed slope failure based on pore water pressuresin the soil. The soil water content may be a good
indicator to gain predictive information regarding
slope failure because the soil water content responds
to rainfall events as well as to pore water pressure.
Although measurements of soil water content havebeen conducted in situ [10], [11], the role of soil
water content in slope failure processes is still not
well understood.
Generally, soil water conditions are measured bya tensiometer for pore water pressure and soil
moisture sensors for soil moisture. A measurement
system using a tensiometer and soil moisture sensors
normally receives electricity by cables, leading tohigh costs for installing a measuring system. Thus,
there is a need to develop a wireless measurement
system for determining the water content in soil.
Although tensiometers require regular maintenance,with degassed water in the equipment, soil moisturesensors have no such requirement. Considering the

METHODSWe carried out two laboratory experiments toassess changes in soil water content during rainfall.
We conducted one using an artificial rain simulator(Daiki Rika Co., DIK-6000S) at Ritsumeikan
University. Granite soil (Masa soil) was used tomake a model slope. Measurement devices for porewater pressure and soil moisture content were atensiometer (with hydraulic gauge; Nidec Copal
Electronics Corp., PA-850-102V-NGF) and soil
moisture sensors (Decagon Devices, S-SMx-M005),
respectively.
We performed two experiments for different
purposes. The experiment in Case 1 was designed toevaluate the relationship between pore waterpressure and soil moisture content and the responses
in slope failure. Case 2 was conducted to determinechanges in soil moisture from the start of rainfall toafter the rainfall ended.
In Case 1, slope failure did not occur due to thedevelopment of gully erosion related to rainfall
448

GEOMATE- Osaka, Nov. 16-18, 2015

intensity (Photo 1). Based on these results, we do notdiscuss the dynamics of soil water contents for slope
failure processes directly. Details of the model slopeand experimental conditions for Case 1 are shown in
Table 1 and Fig. 1. Details of the model slope andexperimental conditions for Case 2 are shown in
Table 2 and Fig. 2. We provided artificial rainfall tothe model slope in the experiments to recreate
natural conditions of soil moisture. The amount of
rainfall per hour was changed twice in Case 1 in an
attempt to provoke slope failure. The drainage
condition was changed in Case 1 to raise thegroundwater level in the model slope.

Tensiometer (A～L)
A

C

25cm

26cm

E

D

G

15cm

15cm

K
L

F
H

10cm

25cm

20cm 11cm 10cm

B

15cm 10cm

Soil moisture sensor(A～J)

30cm

90cm

20cm 10cm

30cm

I

40deg
43cm

25deg

94cm

Fig. 1J

30cm

Details of model slope (Case 1)

Table 2 Experimental conditions (Case 2)
Moisture content

10%

Dry density

1.60 g/cm3

Wet density
Preliminary rainfall
condition
Rainfall condition

1.76 g/cm3

15 mm/hr (14 hrs)

Measuring interval

10 mins

25 mm/hr (2 hrs)

10%1.85 g/cm (basement layer)
1.60 g/cm3 (soil layer)
2.035 g/cm3 (basement layer)
1.760 g/cm3 (soil layer)
discharged water (10 hrs)

Wet density
Drainagecondition
Preliminary
rainfallcondition

Fig. 2

H

G

12cm

12cm

I

12cm

J 40deg

Details of model slope (Case 2)

RESULTS AND DISCUSSION

not discharged water (11 hrs)

Relationships between Pore Water Pressures andVolumetric Water Contents in Soil

25 mm/hr (2 hrs)

The results of the Case 1 experiment are shownin Fig. 3, which indicates the variation inaccumulated rainfall, amount of rainfall per hour,pore water pressure, and volumetric water content.
The result of the Case 2 experiment are shown in Fig.
4, which indicates the time series variation inaccumulated rainfall, amount of rainfall per hour,and volumetric water content.

50 mm/hr (15.5 hrs)
120 mm/hr (1 hr)

Measuringinterval

E

12cm

67.5cm

25 mm/hr (4.5 hrs)
Rainfallcondition

12cm

D

5cm

8cm

3

Dry density

12cm

B

10cm

8cm
F

Table 1 Experimental conditions (Case 1)
Moisture content

C

10cm

Photo 1 Model slope after experiment (Case 1)

12cm

10cm

40cm

8cm

Soil moisture sensor

10cm

A

10cm

10cm

20cm

1 min

449

1200

400

40
20

200

0

0

Rainfall (mm/hr)

600
60

0

-20
C(10)
F(21)

-40
10
5
0
-5
-10
-15
-20
H(10)
K(75)

G(51)
J(10)

-25

200

15

150

10

100

accumulated rainfall (mm)rainfall (mm/hr)

0

20

B(30)
E(25)

250

20

5

40

A(10)
D(60)

300

25

I(41)
L(54)

-30
35

30

25

Volumetric water content (%)

800

80

30

25

Volumetric water content (%)

Pore water pressure (cmH2O)

Rainfall (mm/hr)

rainfall (mm/hr)

100

Pore water pressure (cmH2O)
Volumetric water content (%)

1000

24

Volumetric water content (%)

accumurated rainfall (mm)120

Accumulated rainfall (mm)

140

32
30
28
26
24
22
20
18
16
14
12

0

A(10)
F(21)

B(30)
G(51)

C(10)
H(10)

15

0

3

6

9

12

15

D(60)
I (41)

18

23
22
21
20
19
18

22
20
18
16

C

E

D

14

1

2

3

4

5

6

H

G

F

E(25)
J (10)

78IJ

9 10 11 12

Elapsed time (day)21

Fig. 4

Elapsed time (hr)Fig. 3

B

A

24

0

20

50

Accumulated rainfall (mm)GEOMATE- Osaka, Nov. 16-18, 2015

Time series variation in pore water pressureand volumetric water content

Time series variation in volumetric watercontent

The relationship between pore water pressureand volumetric water content at D, G, and I is shownin Fig 5. When the pore water pressure exceeded 0,
the value of volumetric water content had a large
range, especially at point D. The increase in thevolumetric water content after saturation was due toa decrease in pore air in the soil under saturated
conditions. This result also indicated that soil
saturation cannot be detected by a specific value ofthe volumetric water content of the soil. Thus, therisk of slope failure with saturation should be judged
using the range of the volumetric water content.

In Case 1, slope failure did not occur despitechanges in rainfall intensity (Photo 1), as the surface
soil of the model slope in Case 1 was eroded. Mostof the pore water pressure measurements in the slope
increased with the rainfall. Increased pore waterpressure occurred at a shallow measurement depth.
Following the initial increase in pore water pressure,the values of pore water pressure became constant.
Moreover, the pore water pressure of points atgreater depths (D, G, and I) exceeded 0, indicatingthat saturation occurred at the bottom of the soil
layer. The soil moisture in the slope increased due tothe rainfall. Additionally, the soil water contents
became constant after the increase during the rainfall,
as did the pore water pressure. Moreover, thevolumetric water content rose again before the endof the rainfall. The second increase in the volumetricwater content may have been caused by rainfall
directly on the soil moisture sensor because of thedenudation of the model slope.

Responses of the Volumetric Water Content at
Different Measurement Depths
In Case 2, the volumetric water content in theslope rose with the initial rainfall, after which, thevolumetric water content at shallow depth points (A,B) decreased due to 4 dry days between the
preliminary rainfall and the subsequent rainfall. The
volumetric water content of other points did notdecrease, largely because of drainage conditions of450

GEOMATE- Osaka, Nov. 16-18, 2015

the model slope. The volumetric water content
measurements, which declined after the preliminary
rainfall ended, then rose again with further rainfall.
The range of increase in the volumetric watercontent differed between the preliminary andsubsequent rainfall. One cause of these differences
was the differing rainfall intensity between the
preliminary and subsequent rainfall.

determined the boundary values of volumetric watercontent with different degrees of saturation. Two
proposed values of S s and S a of the degree ofsaturation were used in our judgment method. The
volumetric water contents corresponding to thesaturation degree were calculated using theequations above. The meanings of S s and S a are as
follows: When the degree of saturation was under S s
percent (%), we assumed that the soil moisturesensor at the measurement point was in a safe state.
We assumed that soil moisture was in alert statuswhen the measured value of the degree of saturation
was >S s (%). When the value of the degree ofsaturation is >S a (%), we assumed that the soil
moisture condition was in evacuation status.
To judge the risk over whole of slope, we scoredindividual soil moisture sensors. We gave 1 point
where the soil moisture sensor was in a safe state; 2points if the soil moisture sensor was in alert status;and 3 points if the soil moisture sensor was inevacuation status. We judged the state (safe, alert, or
evacuation) based on thresholds for the summed
totals of the individual measurement points.

40
D (60)
G (51)I (41)

Pore water pressure (cmH2O)

30
20
10
0
-10
-20
-30
15

20

25

30

Application of the Method for Judging the Riskof Slope Failure

35

Volumetric water content (%)

Fig. 5Here, we show examples of the judgment
method. We determined that the values of S s and S afor the degree of saturation were 50% and 70%,
respectively. The volumetric water content
corresponding to those saturation degrees was 19.2%
and 26.9%, respectively. We judged a safe state
when the sum of the 10 soil moisture sensor scores
was less than 15 points, alert status was 16-20
points, and evacuation status was considered to be a
total score of 21 or more points. The judgment
results for Cases 1 and 2 are shown in Figs. 6 and 7,
respectively. In this study, we judged the potential
for slope failure based on two indices of soil
moisture and the characteristics of the precipitation,
which were used as an index of the effective rainfall.
Effective rainfall is an indicator to evaluate theresidual effect of previous precipitation on soil
moisture in the ground. We calculated the effective
rainfall using Eqs. (3) and (4):Relationship between pore water pressureand volumetric water content

Method for Judging the Risk of Slope Failure
We developed a technique for judging the risk ofslope failure based on the experimental results. In
this study, we judged the risk for the whole slope
after judging the risk based on individual soil
moisture sensors. First, the risk based on individual
soil moisture sensors was judged, and we thenjudged the risk for the whole slope using a summaryof the risk values of individual soil moisture sensors.
We first tried to judge the risk of slope failure ateach measurement point using an absolute value ofvolumetric water content. A reference value needed
to be determined for an absolute value of volumetricwater content. The judgment values of volumetricwater contents were calculated using Eqs. (1) and(2):ρρ
w = ( w－ w) 𝑆𝑆𝑟𝑟
(1)
ρd

ρs

θ = wρ d

𝑅𝑅𝐺𝐺 = 𝑅𝑅0 +𝑎𝑎1 𝑅𝑅1 +𝑎𝑎2 𝑅𝑅2 +・・・・・𝑎𝑎𝑛𝑛 𝑅𝑅𝑛𝑛
𝑎𝑎 = (0.5)1/𝑇𝑇

(2)

(3)
(4)

where R G is the effective rainfall (mm), R n is therainfall before n minutes (mm/10min), 𝑎𝑎 is areduction factor (0 < 𝑎𝑎 < 1), and T is the half-period.
The x-axes of Figs. 6 and 7 are the effective rainfallthat had a half-period of 72 h, and the y-axes are theeffective rainfall where the half-period was 1.5 h.
In Case 1, the slope was judged as safe before
the preliminary rainfall, and that judgment remained
after the preliminary rainfall began. At 1 h and 10

where w is the moisture content (%), ρ w is the water
density (g/cm3), ρ d is the dry density (g/cm3), ρ s is
the soil particle density (g/cm3), S r is the degree ofsaturation (%), and θ is the volumetric water content(%).
The dry density of the soil and the soil particle
density were measured in laboratory experiments
using in-situ soil samples. From the results, we
451

GEOMATE- Osaka, Nov. 16-18, 2015180

min after the beginning of the preliminary rainfall,
the judgment changed to alert status, which
continued after the end of the preliminary rainfall.
Then, at 12 h after the end of the preliminary
rainfall, the judgment returned to a safe state. With
regard to the later rainfall, after the preliminary
rainfall, at 2 h and 50 min from the beginning of
rainfall, the judgment changed to alert. Due to thecontinuing heavy rainfall, at 5 h and 30 min from the
beginning of the rainfall, the judgment was shifted toevacuation status.
In Case 2, the judgment before the preliminary
rainfall was that the slope was safe, as in Case 1, andthis judgment of risk was maintained after the
preliminary rainfall began. However, at 1 h and 30
min from the beginning of the preliminary rainfall,
the judgment shifted to alert status. Due to theincreased total amount of rainfall, at 2 h from thestart of the second rainfall, the judgment enteredevacuation status. At 3 h and 30 min from the end of
the rainfall, the judgment of risk returned to alert
status.
Although we judged the risk of slope failure, ourmethod did not consider the measurement depth.
However, it is vital to consider depth to accurately
judge the risk of slope failure. When themeasurement point at a deep depth was judged to bein evacuation status, the risk of slope failure was
supposed to be very high. Thus, we weighted thescore for evacuation status according tomeasurement depth. When the measuring point in
the middle layer entered evacuation status, we gave
it 4.5 points, and when the measurement point in the
deep layer went to evacuation status, we gave it 6
points. Based on these changes in the scores ofindividual soil moisture sensors, we also revised thescores for the whole slope. We judged a safe state
when the sum of the 10 soil moisture sensors wasless than 15 points. Alert status was 16-24 points,
and evacuation status was a score of 25 or morepoints. The results of judgments for Cases 1 and 2
are shown in Figs. 8 and 9.
In Case 1, the times in safe and alert states were
the same as with no weighting, but the time ofevacuation status was delayed by 40 min. Also, inCase 2, the times in safe and alert states were thesame, but the time of evacuation status was delayedby 1 h and 30 min. Moreover, the time when thestatus reverted from evacuation to alert moved 2 h
and 20 min earlier.

Safe
Alert

160

Evacuation

Effective rainfall (mm)(T=1.5hr)

140
120
100
80
60
40
20
0
0

Fig. 6

200

400
600
800
Effective rainfall (mm)(T=72hr)

1000

Result of judgment (Case 1)

40
Alert

Effective rainfall (mm)(T=1.5hr)

Safe

Evacuation

30

20

10

0
0

Fig. 750100150
Effective rainfall (mm)(T=72hr)

200

250

800

1000

Result of judgment (Case 2)

180
Safe
Alert
Evacuation

160

Effective rainfall (mm)(T=1.5hr)

140
120
100
80
60
40

CONCLUSION

20
0

In this study, we carried out laboratory
experiments to evaluate the relationship between
pore water pressures and soil moisture conditions
and assessed changes in soil moisture during andafter simulated rainfall. The volumetric watercontent increased with the rainfall and showed a0

Fig. 8

452

200

400
600
Effective rainfall (mm)(T=72hr)Result of weighted judgment (Case 1)

GEOMATE- Osaka, Nov. 16-18, 2015

40
Alert

Safe

slope stability using GIS modeling”,
Geographic Information Sciences, Vol. 6, 2000,
pp.121-128.
[7] Kosugi K, Mizuyama T, Fujita M, “Accuracyof a shallow-landslide prediction model toestimate groundwater table”, Journal of theJapan Society of Erosion Control Engineering,Vol. 55, 2002, pp. 21-32. (in Japanese withEnglish abstract)[8] Uchida T, Mori N, Tamura K, Terada H,
Takiguchi S, Kamee K, “The role of datapreparation on shallow landslide prediction”,
Journal of the Japan Society of Erosion Control
Engineering, Vol. 62, 2009, pp. 23-31. (inJapanese with English abstract)[9] Fujimoto M, Kosugi K, Hayashi Y, Tani M,
Mizuyama T, “The effect of resolution ofelevation model data on shallow landslide
analysis”, Journal of the Japan Society of
Erosion Control Engineering”, Vol. 64, 2011,
pp. 3-10. (in Japanese with English abstract)[10] Kubota J, Fukusima Y, Suzuki M,“Observation and modeling of the runoff
process on a hillslope”, Journal of Japanese
Forest Society, Vol. 69, 1987, pp. 258-269. (inJapanese with English abstract)[11] Kubota J, Fukushima Y, Suzuki M,“Observation and modeling of the runoff
process on a hillslope (II)- Water budget andlocation of the groundwater table and its rising”,
Journal of Japanese Forest Society, Vol. 70,
1988, pp. 381-389. (in Japanese with English
abstract)

Evacuation

Effective rainfall (mm)(T=1.5hr)

30

20

10

0
0

Fig. 950100150
Effective rainfall (mm)(T=72hr)200

250

Result of weighted judgment (Case 2)

large range of values after reaching saturated
conditions. Then, we developed a judgment method
for slope failure risk based on absolute values ofvolumetric water content. Soil moisture andcharacteristics of the precipitation were used tojudge the risk of slope failure. Although the result of
the judgment differed with consideration of depth,
our proposed method can judge the risk of slope
failure in a comprehensive manner. We must
examine its applicability to an actual slope in futurestudies.
REFERENCES[1] Okimura T, Ichikawa R, “A prediction method
for surface failures by movements ofinfiltrated water in surface soil layer”, Natural
Disaster Science, Vol. 7, 1985, pp. 41-51.
[2] Hiramatsu S, Mizuyama T, Ishikawa M, “Studyof a method for predicting hillside landslides
by analysis of transient flow of water insaturated and unsaturated soils”, Journal of theJapan Society of Erosion Control Engineering ,Vol. 43, 1990, pp. 5-15. (in Japanese withEnglish abstract)[3] Montgomery DR, Dietrich WE, “A physically
based model for the topographic control onshallow landsliding”, Water Resources
Research, Vol. 30, 1994, pp. 1153-1171.
[4] Wu W, Sidle RC, “A distributed slope stability
model for steep forested basins”, Water
Resources Research, Vol. 31, 1995, pp. 20972110.
[5] Pack RT, Tarboton DG, Goodwin CN, “TheSINMAP approach to terrain stability
mapping”,
ProceedingofInternational
Congress of the International Association forEngineering Geology and the Environment, 8,
1998, pp. 1157-1165.
[6] Khazai B, Sitar N, “Assessment of seismic453

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

EFFECT OF ANALYSIS FOR THE INCLINATION ANGLE OF THE
BACK SUPPORT OF INCLINED EARTH RETAINING STRUCTURE
Minsu Seo1, Jongchul Im2, Changyoung Kim3, Wooje Lee4 and Jaewon Yoo5
1

Graduate Research Assistant, Department of Civil Engineering, Pusan National University, South Korea,
2
Ph.D., Professor, Department of Civil Engineering, Pusan National University, South Korea*,
3,4,5
Pusan National University, South Korea* Address correspondence to Jongchul Im, Professor, Department of Civil Engineering, Pusan National
University, 2 Busandaehak-ro 63beon-gil (40 Jangjeon-dong), Geumjeong-gu, Busan, South Korea.

ABSTRACTAn inclined earth retaining structure (briefly called ‘IER’) is a structure using the earth retaining pile (front
support) and a stabilizing pile (back support). The back supports are fixed to front supports in order to reduce theearth pressure acting on the front wall. In this study, in order to the effects of the installation angle of the backsupport, model tests were performed. As a result, excavation depth increased largely by the reduction effect ofearth pressure acting on the front support by the back support. Especially, excavation depth at failure increased
about 2.7 to 3.1 times by the back support.
Keywords: IER method, Inclined earth retaining structure, Front support, Back support, Installation angle

In the IER method, inclined two piles are used
and fixed each other tightly. Figure 1 shows an IER
model

INTRODUCTION
The modern buildings have been Manhattanized
by industrialization and urbanization in metropolitan
cities, hence auxiliary techniques of undergroundexcavation have been developed and used in order toincrease the excavation depth and guarantee thestability during the excavation. This auxiliary
method of ground anchors and soil nailing method,
can cause problems interfere with the adjacent
ground and the structure. And in the case of otherauxiliary method of bracing method, an earthretaining wall using struts needs a number of struts
that are costly and the numerous struts that cross a
construction site make excavating ground difficult.
That can be self-supporting to secure these
problems Inclined Earth Retaining Structure Method(hereafter denoted as “IER method”) has beendeveloped. An IER method is a method to incline anearth retaining pile (front support) and a stabilizing
pile (back support). The back supports connected tofront supports reduce the earth pressure acting on thefront wall and front supports by distributing it toback supports in order to increase the structural
stability.
In this study, it is intended to analyze the effectaccording to the inclination of the front support. So,
using earth pressure theory and model tests, theeffect of inclination angle are analyzed. And also,
the installation effect of back support was estimatedby the laboratory model tests.

Fig. 1 IER model
Characteristics of IER method
The earth retaining wall using back support has
five characteristics to increase the stability of thetemporary structure as shown in Figure 2.
Firstly, the earth pressure acting on the front walldecreases by eliminating the weight of soil above theinclined front support (See ‘A’). Secondly, the headsof both front support and back support are fixed witha beam connecting them in order to make the

IER METHOD

454

GEOMATE- Osaka, Nov. 16-18, 2015

structure an integrated system transferring the force
acting on the front support to back support (See ‘D’).
Thirdly, the frictional resistance between the backsupport and soil causes the effect of ground anchor(See ‘E’). Fourthly, the back support plays a role asa stabilizing pile by resisting the activity of sliding
mass (See ‘C’). Lastly, if required, an anchor can be
attached to the batter pile (See ‘F’)

THEORY
TheoryCoulomb’s theory [1] of the active earth pressure
is expressed by Eq. (1).
1P= γH 2aiR
2cos( θ − β ) cos( θ − α ) sin( β − φ )2cos θ sin( β − α ) sin( 90  + δ + θ − β + φ )(1)

Where, P aiR (=P ai ): active earth pressure, γ: unit
weight of soil (kN/m3), H: height, θ: Inclination
angle of front support (°), case of inclined earth
retaining wall : -(negative) value, φ: internal
friction angle of soil (°), δ: wall friction angle (°), α:slope angle of back of front support (°), β: failure
angle of slope (°)
The slope of front support was analyzed byincreasing the slope from 0° to 20° by increments of
1°. As changing β on the slope of front support ofeach, maximum value of the calculated earthpressure was determined by the earth pressure ofslope, and β on the value was determined by failure
angle(In this case, β is increasing from 0.5° to 90°
by increments of 0.5°. Table 1 presents analysis
conditions.
Table 1 Analysis conditions
Initialvalue(°)

Increm-ent (°)

Maximum
value (°)

Inclination
angle of front
support (θ)

0

1

20

Failure angleof slope(β)

0.5

0.1

90

Fig. 2 Basic structure of IER Method
Field Application
Figure 3 shows the IER method applied in thefield. It is located in the soft clayey ground. The
front support is made by SCW (Soil Cement Wall),and installation angle of the back support wasapplied by 10°.

Soil conditions
Condition of analysis ground was conducted as
single layer by conditions of Coulomb earth pressure
theory, was applied soil properties of general sandground. Table 2 shows the properties of model
ground (sand soil), and wall friction angle was set to
20° by applying 2/3φ. Analysis height (H) is set to
6m.
Table 2 Soil Properties
Parameter

γ t (kN/m3)

φ(°)

c (kN/m2)

Fill material

18

30

0

Results of calculations
Fig. 3 Field example applied to soft clay
ANALYSIS

USING

EARTH

Figure 4 shows the relationship between the
inclination angle of front support and the earth

PRESSURE
455

GEOMATE- Osaka, Nov. 16-18, 2015

pressure acting on front support. As the inclination
angle is increasing, it can be seen that the earthpressure and failure angle is reduced. Figure 5 shows𝑃𝑃
earth pressure ratio ( 𝜃𝜃 ) on the slope of front

considering soil-tank and height of sand rainer. The
dry density at this time is 1.632g/cm3, and therelative density is 93.8%.

𝑃𝑃𝜃𝜃=0support (θ). As a result, earth pressure at θ=10° is
smaller as a 77.93%.

Fig. 6 Plane strain model soil-tank
Table 3 Properties of Jumunjin standard sandFig. 4 Relationship between the inclination angle offront support and the earth pressure.

Fig. 5 Earth pressure ratio (𝑃𝑃𝜃𝜃𝑃𝑃𝜃𝜃=0Description

Index

PropertyMax. void ratio

e max

0.923

Min. void ratio

e min

0.604

Max. dry density3

γ dmax (g/cm )1.652

Min. dry density

γ dmin (g/cm )1.378

Specific gravity

Gs

2.65

Water content

w (%)

0.30

Average particle size

D50 (mm)0.595

Effective particle size

D10 (mm)

0.443

Uniformity coefficient

Cu

1.402

Coefficient ofcurvature

Cg

0.912

3

Table 4 Variation of φ DS with relative density for
Jumunjin standard sand) on the inclination

angle of front support (θ)
ANALYSIS OF MODEL TEST
Test equipment and soil materials
Figure 6 shows a plane strain model soil-tank
applied in this study. Dimension of soil-tank islength 1,700mm, Height 760mm, and width 410mm,
soil was used Jumunjin sand. Table 3 and Table 4
presents physical and mechanical properties ofJumunjin sand. Because soil change mechanicalproperties according to the density, soil must
construct by keeping the density constant. Figure 6
shows a change of dry density according to drop
height of Jumunjin sand. Figure 7 presents thatdensity tended to converge near 1.625g/cm3 fromdrop height of 0.9m. In this study, density wasconstructed uniformly to construct ground by soilrainer method [2] on the drop height of 1.2m
456

Test No.

1

2

3

4

γ d (g/cm3)

1.378

1.468

1.522

1.561

Dr (%)

0

36.9

57.0

70.7

φ DS (˚)

31.2

33.0

35.0

37.7

Test No.

5

6

7

8

3

γ d (g/cm )

1.572

1.602

1.632

1.646

Dr (%)

74.4

84.3

93.8

98.2

φ DS (˚)

38.0

39.2

41.1

42.0

GEOMATE- Osaka, Nov. 16-18, 2015

Table 5 The types and conditions of model tests
No.

S00 S05 S10 D00 D05 D10
DoubleForm of support Single support
supportAngle (°) 0
5 10 0
5 10
Front
Thickness
support15(mm)
Angle (°)
10
Thickness
Back
15
(mm)
supportInterval
102(mm)

Fig. 7 Variation of Density with the Drop height
Conditions of model tests

Methods of model tests

Front and back support of IER method made touse plywood as shown in figure 8. The front
retaining wall usually consists of front supports andboards at a construction site. However the front
supports and boards of the model were instrumented
as a unified wall since this study. The front and backsupport of the model had the same vertical length as
the height of the model ground which was 70 cm. As
shown in the Table 5, tests were conducted threekinds angles of front support inclination.

After the model structure was installed in theplane strain soil tank, sand sprinkled the earthpressure difference don't occur between the front andback walls. And measuring a Lateral displacementof the wall was used a dial gauge capable ofmeasuring a 1/100mm from the top of the front wall.
The lateral displacements measured on the upper
left- and right-side of the wall were averaged at eachexcavation stage. The each excavation consisted ofintervals of 5 cm, the lateral displacement wasmeasured by recording the time to convergence after
the excavation.
Results of model tests
Lateral displacement of the top of the front wallby excavation depth according to the types of tests
as shown in figure 9. The excavation depths at
failure were estimated by using the failure criteria
suggested by [3], which is the rotational
displacement of retaining wall according to soil
types at failure as shown in Table 6. The value of
0.00075 was used for the analysis of test results
because the value is suggested in the table for range
from 0.0005 to 0.001 of the dense sandy soil which
was filled in the soil tank. A method of determining
the rotational displacement from Eq. (2). And the
excavation depths corresponding to the failure
criteria of 0.00075 are tabulated in Table 7.

Fig. 8 Experimental model

Rotational displaceme nts =457

Lateral wall displaceme nt(2)
Wall heightGEOMATE- Osaka, Nov. 16-18, 2015

Table 6 Rotational displacements of retaining wall at
failure [3]Effect of back support

Soil Type

Wall rotation(Lateral wall displacement/Wall height)

Soil Type

Active state

Passive state

Loose sandy soil

0.001-0.002

0.01

Lateral displacement before failure decreased up
to 88%~98% compared with single support system,
regardless of inclination angle of the front support.
Excavation depth at failure increased to 3.1 times
in the case that the installation angle of front support
is 0° by back support. And 2.8 times in the case of
10°.

Dense sandy soil 0.0005-0.001

0.005

CONCLUSIONSSoft clay soil

0.02

0.04

Stiff clay soil

0.01

0.02

The results of model tests are concluded asfollows.

Table 7 Excavation depth and Excavation depth at
failure by caseNo.

Excavation depth(cm)

Excavation depth at
failure (cm)

S00

45

12.8

S05

45

15.5

S10

45

17.8

D00

50

40.2

D05

70

42.1

D10

70

50.1

1) Earth pressure reduces according to theinclination angle of front support by Coulomb's earthpressure theory.
2) The back support has two roles. One is to
reduce the earth pressure acting on the front support,
the other is to increase the stability and reduce thelateral displacement of front support wall.
REFERRENCE[1] Coulomb, C.A. (1776). “Essai sur une
Application des Règles de Maximis et Minimisàquelques Problèmes de Statique, relatifs al’Architecture,”Mem. Roy. des Sciences, Paris,
Vol.3,38.
[2] Park, L. K. (2003), “A Study on the
Characteristic of Ground Behaviour andSettlement Restraining effect of Micropile
during Shallow Tunnel Construction in SoftGround”, Unpublished doctoral dissertation,
Pusan National University, pp.134~145.
[3] Rankine, W.M.J. (1857). “On Stability on
Loose Earth,”Philosophic Transactions of Royal
Society, London, Part I, 9-27.

Fig. 9 Variation of tip lateral displacement withexcavation depth according to the case ofexperiments
Analysis of model test results
Effect of the inclination of the front support
In the case of single support, the lateraldisplacements of S05 and S10 using the inclined
front support occurred by about 54% and 45 %respectively of S00 at the 10cm of excavation depth.
And in the case of a double support, D05 andD10 compared with D00 occurred 88% and 40%
respectively at the 40cm of excavation depth.

458

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

CHARACTERISTICS OF SUBSURFACE WATER MOVEMENTS IN
SOIL LAYERS ON THE HILLSLOPE BEHIND KIYOMIZU TEMPLE
Yuuki Arimitsu1, Masamitsu Fujimoto2, Nobutaka Hiraoka3,Toru Danjo4, Yuko Ishida5
and Ryoichi Fukagawa2
1
Graduate School of Science and Engineering, Ritsumeikan University, Japan,
2
College of Science and Engineering, Ritsumeikan University, Japan,
3
Research Organization of Science and Technology, Ritsumeikan University, Japan,
4
Department of Storm Flood and landslide Research, National Research Institute for Earth Science andDisaster Prevention, Japan,
5Institute of Disaster Mitigation for Urban Cultural Heritage, Ritsumeikan University, Japan

ABSTRACT
Recently, slope failures have occurred due an increase of heavy rainfall and typhoon events. As typicalexamples, sediment disasters have occurred frequently at Kiyomizu-dera Temple, including debris flows in 1972and shallow slope failures in 1999 and 2013. Kiyomizu-dera temple is a UNESCO World Heritage Site, and oneof the Historic Monuments of Ancient Kyoto, Japan. An increase in shear stress and decrease in shear strength
due to infiltration of rainwater led to slope failures. In this study, subsurface water movements were investigatedby measuring pore water pressure changes and hydraulic gradients during rainfall events to prevent damage toimportant cultural assets by natural disasters. Saturation was found to occur after very small rainfall events,
regardless of soil moisture conditions. Although subsurface water normally flows from upper to lower areas ofthe slope, water movements from the bedrock to the soil layer were observed during rainfall events, suggesting
the existence of potentially hazardous slope failure conditions.
Keywords: Slope Failure, Pore Water Pressure, Hydraulic Gradient, Infiltration of Rain Water

granitic geology [2], [3] and sedimentary rockgeology [4]. These studies showed that subsurface
water movements are more complex in sedimentaryrock compared to granitic sites, due to theheterogeneity of the soil structure. The complex
responses of pore water pressures in soil on a slope
based on sedimentary rock in Kiyomizu-dera
Temple have been studied [5]–[7]. However, thesubsurface water movements in the soil layer are notfully understood.

INTRODUCTION
Recently, slope failures have occurred due to anincrease in heavy rainfall and typhoon events inJapan, which cause significant damage. Kiyomizudera Temple is located on the slope of a mountain in
Higashiyama, Kyoto Prefecture, Japan and was
designated a cultural asset of national importance in
1994. Five million tourists from all over the world
visit Kiyomizu-dera Temple each year [1]. Sedimentdisasters have occurred on a number of occasions at
Kiyomizu-dera Temple, including a debris-flow
event in 1972 and shallow slope failures in 1999 and2013 [1]. Figure 1 shows a slope failure at
Kiyomizu-dera Temple in 2013. It is necessary toforecast landslide and debris flow hazards, toprevent and alleviate damage to an important
cultural asset and protect human life.
Previous research has noted that slope failure due
to rainfall is triggered by two main factors following
rainwater infiltration into the soil. Firstly, an
increase in shear stress occurs due to an increase insoil weight. Secondly, a decrease in shear strength
occurs due to decreasing soil cohesion. It is therefore
necessary to evaluate soil moisture content, soil
water movement and groundwater in the soil layer
during rainfall events. In-situ measurements of porewater pressures were conducted in a catchment with

Fig. 1

459

Slope failure that occurred in 2013.

GEOMATE- Osaka, Nov. 16-18, 2015

In this study, subsurface water movements areevaluated by investigating pore water pressurechanges and hydraulic gradients during rainfall
events.

Table 1 shows the observation depths of thetensiometers.Tensiometer depths were decided basedon the results of portable dynamic cone penetration
tests. The observation period was from 13 August
2014 to 1 January 2015.

METHODRESURTS AND DISCUSSION
The study area is located on the hillslope behind
Kiyomizu-dera Temple (Fig. 2). The soil-bedrockand measured were composed of sandstone, shaleand chert. Surface layer composed of the colluvial
deposit ranges 0.9 from to 4.0 m thick. A network
system with data loggers was installed (Campbell,
CR-1000) to record pore water pressures in real-time.
Pore water pressures and rainfall were measured at
10-min intervals, using a tensiometer and tippingbucket rain gage, respectively. Tensiometers were
installed at 14 locations (Fig. 2).

Characteristic of Pore Water Pressures

700
600
500
400
300
200
100
0

Rainfall (mm/hr)

50
40
30
20
10
0

Accumulative rainfall (mm)Pore water pressures at points B, M, P2 and C(yellow points on Fig. 1) were evaluated toinvestigate subsurface water movements along thelongitudinal direction of the slope. Figure 3 shows
the variation of pore water pressure during theobservation period (13 August 2014 to 1 January2015). The cumulative rainfall during the period was
634 mm, with a peak rainfall of 38 mm/h at 13:00 on
16 August 2014. Pore water pressures reachedpositive values at each observation depth, indicating
saturation at these depths. Decreases in pore waterpressures after rainfall at deep observation depths(B-200 cm, B-260 cm M-190 cm and C-230) were
smaller than those at shallow observation depths,
indicating that deep soils retained wet conditions
over a long period of time. However, at pore water

200
0
-200
-400

Fig. 2-600

Locations of observation points.

-800
-1000

Table 1 Observation depths of tensiometers

B-40cm
B-80cmB-100cm

B-200cm

B-260cm

M-20cmM-40cmM-60cmM-80cmM-100cm

M-190cm

P2-30cm

P2-60cm

P2-100cm

P2-200cm

C-40cmC-80cm

C-100cm

C-230cm

200
0

Observation depths (cm)40, 80, 100
40, 80, 100, 200, 260
40, 80, 100, 230
40, 80, 100
20, 40, 60, 80, 100, 190
30, 6530, 60, 100, 200
30, 8030, 60, 100
30, 60, 100, 200
30, 60, 100, 200, 280
30, 60, 100
30, 60, 100, 200
30, 60, 110

Pore-water pressure(cmH O)
2Points
A
B
C
D
M
P1
P2
P3
P4
P5
P6
P7
P8
P9

-200
-400
-600
-800
-1000
200
0
-200
-400
-600
-800
-1000
200
0
-200
-400
-600
-800
-1000
8/13

9/29

11/15

1/1

Fig. 3 Variation in the pore water pressure during
rainfall events
460

GEOMATE- Osaka, Nov. 16-18, 2015

pressures varied widely compared to other points atthe soil-bedrock interface (B-260 cm, M-190 cm andC-230 cm). These results indicate that changes inpore water pressure showed different trends
according to observation point and depth.

values during rainfall events (Fig. 4 b, c and d). This
result indicates that the direction of soil water
movement during dry conditions was from deep toshallow soil layers by evapotranspration and that
infiltration occurred after rainfall events. The
hydraulic gradients were positive before the start of
rainfall and attained negative values during rainfall(Fig.4e). The result at point B indicated water
movement from deep to shallow layers during
rainfall events. This suggests that exfiltration fromthe bedrock layer to the soil layer occurred at thispoint, leading to a high probability of slope failure.
Figure 5 shows (a) hyetograph and hydraulicgradient between (b) B-100 cm and M-100 cm, (c)M-100 cm and P2-100 cm, (d) P2-100 cm and C-100
cm (between each depth of 100 cm). Hydraulicgradients showed positive values during theobservation period, indicating that subsurface waterflows from the upper to lower levels of the slope atall times (Fig. 5b and d). Figure 5 c indicates that thehydraulic gradient reached negative values during
the period 26 November 2014 to 1 January 2015. In
addition, hydraulic gradients at deep observation
points were positive during the entire observation
period, indicating that subsurface water flows fromthe upper to lower areas of the slope (Fig. 6).

Direction of Subsurface Flow

700
600
500
400
300
200
100
0

Rainfall (mm/hr)

50
40

(a)

30
20
10
0
10
5

(b)

Accumulative rainfall (mm)The hydraulic gradient across the study area wasinvestigated to evaluate groundwater flow in the soil
layer. Figure 4 shows (a) hyetograph and hydraulicgradient between (b) B-40 cm and B-80 cm, (c) B80 cm and B-100 cm, (d) B-100 cm and B-200 cm,(e) B-200 cm and B-260 cm. The hydraulic gradient
was calculated from the hydraulic head and the
distance between two observation depths. Hydraulicgradients with positive values indicate downward
vertical flow between two observation depths at thesame measurement point, while hydraulic gradientswith negative values indicate upward vertical flow
between two observation depths at the same point.
The hydraulic gradients had negative values before
the start of a rainfall event, and reached positive

Effect of Soil Moisture Conditions on Subsurface
Flow

B-40cm_80cm

To evaluate the relationships between theantecedent rainfall and the pore water pressure
response, the effective rainfall was calculated using
the method proposed by Suzuki and Kobashi [8].

0
-5
-10
-15

Hydraulic gradient

-20
15
10
5
0
-5
-10
-15
-20
-25
4

(c)

B-80cm_100cm

M  (ln 0.5) / (1)DM (T )  DM (T  1)e  R(T ) 2(d)B-100cm_200cm(e)

B-200cm_260cm(2)

where M is the half-life,  is a reduction factor,
DM is the effective rainfall (mm), T is time (hour)
and R(T ) is the rainfall (mm/h).

2
0
-2
-4
-6
5
4
3
2
1
0
-1
-2
-3

8/13

9/29

11/15

Values of effective rainfall for six half-life cases (2 h,
12 h, 24 h, 48 h, and 72 h) were calculated (Fig. 7).
In the calculation, we considered the rainfall until 13
August 2014. The peaks of effective rainfall withhalf-lives of 2 h, 12 h, 24 h, 48 h and 72 h were 49mm, 69 mm, 81 mm, 110 mm and 142 mm,
respectively (Fig. 7). Figures 8 and 9 show the
relationship between the pore water pressures atsampling points M-100 cm and M-190 cm,
respectively, and the effective rainfall. The negativevalues of pore water pressures at 100 cm were larger
than those at the soil-bedrock interface. At points M100 cm and M-190 cm, the positive values of porewater pressure were constant throughout the

1/1

Fig. 4 (a) Hyetograph and hydraulic gradient
between (b) B-40 cm and B-80 cm, (c) B-80 cm andB-100 cm, (d) B-100 cm and B-200 cm, and (e) B200 cm and B-260 cm.

461

(a)

40
30
20
10
0

(b)

B-100cm_M-100cm

10M-100cm_P2-100cm

-2
1.0
0.8
0.6
0.4
0.2
0.0

(d)

-0.2

8/13

P2-100cm_C-100cm
9/29

11/15

1/1

30
20
10
0
2.0
(b)

Pore-water pressure (cmH2O)

Rainfall (mm/hr)(a)Accumulative rainfall (mm)

700
600
500
400
300
200
100
0

50

1.5

10

Effective rainfall (2-hr half-life)

Effective rainfall (12-hr half-life)

160
140
120
100
80
60
40
20
0

Effective rainfall (24-hr half-life)

160
140
120
100
80
60
40
20
0

Fig. 5 (a) Hyetograph and hydraulic gradient
between (b) B-100 cm and M-100 cm, (c) M-100 cm
and P2-100 cm, and (d) P2-100 cm and C-100 cm(between each depth of 100 cm ).

40

20

160
140
120
100
80
60
40
20
0

2

(c)

30

0

3

-1

40

160
140
120
100
80
60
40
20
0

Effective rainfall(mm)Hydraulic gradient

1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2

50

Rainfall (mm/hr)

700
600
500
400
300
200
100
0

50

Accumulative rainfall (mm)Rainfall (mm/hr)

GEOMATE- Osaka, Nov. 16-18, 2015

B-260cm_M-190cm

1.0
0.5

Effective rainfall (48-hr half-life)

160
140
120
100
80
60
40
20
0

Effective rainfall (72-hr half-life)

200
0
-200
-400
-600
-800

M-190 cm

M-100 cm

8/13

9/29

11/15

1/1

Hydraulic gradient

0.0

Fig. 7 Hyetograph, effective rainfall, and porewater pressure at M-100 cm and M-190 cm during
the observation period.

2.0
1.5
1.0
0.5
0.0
-0.5

(c)

M-190cm_P2-200cm(d)

P2-200cm_C-230cm

observation period regardless of the difference in
effective rainfall. In addition, the results indicate thatsaturation occurred at small values of effective
rainfall. For dry periods, although values of porewater pressure were strongly influenced by theantecedent rainfall (i.e., long half-life), the effects
were limited to a short time period after rainfall
events.

-1.0
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
8/13

9/29

11/15

1/1

CONCLUSION

Fig. 6 (a) Hyetograph and hydraulic gradient
between (b) B-260 cm and M-190 cm, (c) M-190 cm
and P2-200 cm, and (d) P2-200 cm and C-230 cm.

In this study, subsurface water movements were
evaluated by investigating pore water pressure462

GEOMATE- Osaka, Nov. 16-18, 2015

400

400
2-hr half-life

2-hr half-life

200

200

0

0

-200

-200

-400

-400

-600

-600

-800

-800

400

400
12-hr half-life
200

0

0

-200

-200

-400

-400

-600

-600

Pore-water pressure at M-190 cm(cmH O)
2

Pore-water pressure at M-100 cm(cmH O)
212-hr half-life
200

-800
400
24-hr half-life
200
0
-200
-400
-600
-800
400
48-hr half-life
200

-800
400
24-hr half-life
200
0
-200
-400
-600
-800
400
48-hr half-life
200

0

0

-200

-200

-400

-400

-600

-600

-800

-800

400

400

72-hr half-life

72-hr half-life

200

200

0

0

-200

-200

-400

-400

-600

-600

-800

-800
0

20

40

60

80

100

120

140

160

0

Effective rainfall (mm)

20

40

60

80

100

120

140

160

Effective rainfall (mm)Fig. 8 Relationship between the pore waterpressure at the M-100-cm point and the effective
rainfall.

Fig. 9 Relationship between the pore waterpressure at the M-190-cm point and the effective
rainfall.

changes and hydraulic gradients during rainfall
events. Decreases in pore water pressures after
rainfall at deep observation depths were smaller thanthose at shallow depths, indicating that deep soil
layers retained wet conditions over a long time
period. The hydraulic gradient at point B indicateddifferent trends between shallow layers and the soilbedrock interface. Hydraulic gradients between 100-cm depth and the soil-bedrock interface werepositive during the observation period, indicatingthat subsurface water flowed from the upper area tothe lower end of the slope at all times. These results
indicate that the saturation occurred after very small
amounts of rainfall, regardless of soil moisture
conditions.

463

GEOMATE- Osaka, Nov. 16-18, 2015

1991, pp. 549-568 (in Japanese with English
abstract).
[5] Sako K, Fukagawa R and Satomi T, “Slope
monitoring system at a slope behind an
important cultural asset”, Journal of Disaster
Research, Vol.6, No.1, 2011, pp.70-79.
[6] Fujimoto M, Danjo T, Tsuchiyama T, Kimura T
and Fukagawa R, “Detection of ground water
movement using measuring method of sound ofgroundwater flow on the hillslope behind
Kiyomizu Temple”, Journal of Disaster
Mitigation for Historical Cities, Vol.8, 2014,
pp.145-150 (in Japanese with English abstract).
[7] Danjo T, Fujimoto M, Kimura T, Hiraoka N and
Fukagawa R, “Adamage of the slope around theKiyomizu temple by the heavy rain from the
Typhoon in 2013, and a study of the changing
moisture in the ground of the slope behind an
important cultural asset”, Journal of Disaster
Mitigation for Historical Cities, Vol.8, 2014, pp.
115-122 (in Japanese with English abstract).
[8] Suzuki M and Kobashi S, “The critical rainfall
for the disasters caused by slope failures”,
Journal of Erosion Control Engineering, Vol.34,
1981, pp.16-26 (in Japanese with English
abstract).

ACKNOWLEDGEMENTS
This study was supported by grants from the Japan
Society of the Promotion for Science (23221009 and
15K18714).
REFERENCES[1] Ishida Y, Fujimoto M, Hiraoka N, Oya A, Sako
K and Fukagawa R, “Study on criteria for slope
failure based on rainfall intensity at Kiyomizudera”, Journal of Disaster Mitigation for
Historical Cities, Vol.8, 2014, pp. 151-158 (inJapanese with English abstract).
[2] Uchida T, Asano Y, Mizuyama T and
McDonnell J, “Role of upslope soil porepressure on lateral subsurface storm flow
dynamics”, Water Resources Research, Vol.40,
2004, W12401.
[3] Fujimoto M, Ohte N and Tani M, “Effects ofhillslope topography on hydrological responses
in a weathered granite mountain, Japan:
comparison of the runoff response between thevalley-head and the side slope”, Hydrological
Processes, Vol.22, 2008, pp. 2581-2594.
[4] Onodera S, “Subsurface water flow in theMulti-Layered
hillslope”,
JournalofGeographical Review of Japan, Vol.64A-8,

464

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

SEDIMENT DISASTER RISK EVALUATION WITH THE USE OFSLOPE STABILITY ANALYSIS AT YOKOGAKI-TOGE PASS
Tsuyoshi Kibayashi1, Yuko Ishida2 Tatsuo Konegawa3, Masamitsu Fujimoto4, and Ryoichi Fukagawa5
1
Graduate school of Science and Engineering, Ritsumeikan University, Shiga, Japan
2
Research Organization of Science and Engineering, Ritsumeikan University, Shiga, Japan
3
Mie Prefecture Mihama Town Office, Mie, Japan4, 5
Department of Science and Engineering, Ritsumeikan University, Shiga, Japan
ABSTRACTThe ancient road of the Kumano Pilgrimage route is one of the few world heritage sites registered by UNESCO.
Yokogaki-toge, which is part of the Kumano Pilgrimage route, is located in the south of Mie Prefecture, near thecenter of Japan. In Yokogaki-toge, one landslide occurred in the rainy season in 2007, and three slope failuresoccurred due to typhoon Talas in 2011. It is important both to conserve the precious cultural heritage and to protect
domestic and international visitors. Recognizing dangerous or safe sites will facilitate disaster mitigation related
to slope disaster in the future. In this paper, slope failure at Yokogaki-toge was reproduced theoretically by a slope
stability analysis using 2D FEM. Then, some slopes that were considered dangerous were analyzed using slopemodels developed in the same way, reproducing the analysis in Yokogaki-toge. Results of the analysis showedthat the relative safety of these slopes could be estimated based on the slope angle. In conclusion, the correlation
between safety factor and average slope angle of each slopes in Yokogaki-toge was calculated and the possibilityof sediment disaster risk evaluation by only the correlation was shown.
Keywords: Kumano Pilgrimage route, groundwater level, slope stability analysis, slope risk assessment
reproduction analysis. In this study, we firstly focusedon the relationship between rainfall and groundwaterfluctuation. The groundwater fluctuation caused by
the two kinds of geological conditions was considered,and groundwater levels at the time of slope failure
were estimated using 4 month observational data on
groundwater level and rainfall at the No. 2 landslide
at Yokogaki-toge. However, the result obtained waswrong. Therefore, another groundwater level was
estimated by sensitivity analysis of groundwaterlevels. Then, the slope failure at Yokogaki-toge was
reproduced theoretically by a slope stability analysis.
Finally, some dangerous slopes that had not collapsed
were analyzed using the model developed for
Yokogaki-toge, and a slope failure risk assessment
for Yokogaki-toge was conducted. It is consideredthat the surface geometry and the angle of the slope
have major impacts on the safety factor. We studied
how the safety factor could be estimated based on thesurface geometry and the slope angle.

1. INTRODUCTION
The Kumano Pilgrimage route, which runs throughthe Kii peninsula, was registered as a UNESCOWorld Heritage site in 2004. The Kumano Pilgrimage
route has been familiar to many people through theages as a sacred route to the Kii mountains. In 2011,
typhoon Talas hit the Kii peninsula and inflictedmuch damage on the Kumano Pilgrimage route.
On the route, Yokogaki-toge is located on
Nishinomine mountain, in the south of Mie Prefecture,
as shown in Fig. 1. The Kounogi Rhyolite, a Kumano
acidic igneous rock, is found at the crest, and aweathered mudstone of the Kumano Group is foundin the lower half of Nishinomine mountain. In
Yokogaki-toge, heavy rain in the rainy season of 2007
caused a landslide, and typhoon Talas in 2011 caused
three slope failures, as shown in Fig. 2 [1]. It is
important to identify dangerous places to ensure thesafety of both this precious cultural heritage andsightseers from around the world.

3. RELATIONSHIP BETWEEN RAINFALL
AND GROUNDWATER FLUCTUATION

2. METHOD
We tried to evaluate the risk of slope failure using
numerical analysis. The evaluation model was madeby reproduction analysis of slope failure at Yokogakitoge in 2011. Understanding the groundwater level
conditions at the time of slope failure is essential for

The relationship between rainfall andgroundwater fluctuation was considered using
rainfall and groundwater level observational datafrom boreholes at the site of the No. 2 landslide. The
soil water index was used for considering antecedent
465

GEOMATE- Osaka, Nov. 16-18, 2015

correlation equation is shortage of heavy rainfall data.
The maximum soil water index in observed 4 months
was 143. There is room for reconsidering thisestimate method if more heavy rainfall data obtain.

rainfall. Figure 3 shows the site of the borehole
survey at the landslide location. To understand thevariability of the groundwater levels caused bydifferences in geological conditions, spots B3 and B7
were chosen as representative points of Kounogi
Rhyolite, and spot B4 was chosen as a representative
point of weathered mudstone from the boring log.
Figure 4 shows the relationship between the soil
water index and the groundwater fluctuation. The
characteristics of the groundwater fluctuation differ
depending on the geological conditions. The
groundwater level response at B4 (weatheredmudstone) due to rainfall is sharper than that at B3
and B7 (Kounogi Rhyolite). The geological structuresat B3 and B7 are the same, namely Kounogi Rhyolite
on top of shale, and the behavior of the groundwaterfluctuation is similar, although the altitude differed.
There is a correlation between the soil water index
and the groundwater level. Furthermore, the section
with a high soil water index shows a strong
correlation with the groundwater level. The typicalpeak point of the soil water index is indicated by a red
sphere in Fig. 4, and the correlation coefficients
between groundwater level (B3, B4) of the E survey
line and the soil water index were determined, as
shown in Fig. 5. The correlation coefficient for B3 is0.328, and that for B4 is 0.754. The correlation
coefficient at B4 is high.

Fig. 1 Study area (add Google Maps)

4. ESTIMATED GROUNDWATER LEVEL AT
THE
TIME
OF
SLOPE
FAILURE
OCCURRENCE

Fig. 2 Collapses and geological condition

The groundwater levels of the slope failure
occurrence in 2011 were estimated based on therelationship between the soil water index and thegroundwater fluctuation. The three slope failures
were caused by typhoon Talas from September 3 to 4,
2011. The graph of Fig. 6 illustrates the presumed
groundwater level based on the soil water index from
July 1 to September 30, 2011, at B3 (Kounogi
Rhyolite) and B4 (weathered mudstone) in thelandslide area. Over 20 mm/h rainfall continued morethan 10 hours, and the maximum recorded rainfall
was 66 mm/h during September 3–4. This rainfall was
considered to have caused the slope failure. The soil
water index increased to a maximum of 376. It is
physically impossible because the presumed
groundwater level at B4 in this period is over groundlevel.
Therefore, the result suggested that estimation ofgroundwater level at the time of slope failure
occurrence using this correlation equation is
impossible. The possible cause of this wrong

Fig. 3 Implementation site of borehole survey

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GEOMATE- Osaka, Nov. 16-18, 2015

5. DEVELOPMENT OF THE STANDARDSLOPE STABILITY ANALYSIS METHOD ATYOKOGAKI-TOGE
The standard analysis method is developed in orderto evaluate slope stability of various slopes at
Yokogaki-toge. Sensitivity analysis was conducted
to estimate the groundwater level at the time of theNo. 3 slope failure. Finite element method (FEM)
analysis was performed using the commerciallyavailable PLAXIS 2D software. A constitutive
model of a geological situation is an elastic–
perfectly plastic model. The yield criterion in thisanalysis uses the Mohr–Coulomb model. A shear
strength reduction method is used in slope stability
analysis. The conditions for making the model slope
were as follows:Surface geometry: 1/500 ground plan.
Surface thickness: handy dynamic cone
penetration test
Geological boundary: the research report oflandslide 2007.

Groundwater level: changes in parallel with thegeological boundary of the substratum layer andthe weathered layer.
The analysis parameters were based mainly on a site
investigation. Figure 7 and Table 1 show the modelslope analysis and analysis parameters. The safety
factor of the slope was calculated based on increases
in groundwater level, calculated in 1-m increments.
The relationship between groundwater depth andsafety factor is shown in Table 2. The rise in
groundwater level led to a commensurate decrease in
the safety factor. The results of the analysis suggestthat the groundwater level at the time of slope failure
occurrence had increased to -8.0 m. Figure 8 shows
the total displacement of pattern 4. The dotted line
represents the current state line at the time of slope
failure occurrence. The displacement analysis largely
matched the displacement of the actual slope failure.

Fig. 4 Soil water index and groundwater level

Fig. 5 Correlation between soil water index
and groundwater level

6. RELATIVE RISK“YOKOGAKI-TOGE

ASSESSMENT

IN

The 6 sites such as deep valleys and steep areas
having slope failure risk at Yokogaki-toge werechosen. The analysis of these slopes was performed
using the standard method developed in Chapter 4.
The 6 lines in Fig. 9 shows the transversal lines ofhazardous slopes from No.1 to No.6. Slope models ofthese sites were prepared, and slope stability analyses
were conducted. The conditions for the model slope
are as follows.
Surface geometry: 1/500 ground plan.

Fig. 6 Estimated groundwater level467

GEOMATE- Osaka, Nov. 16-18, 2015

Table 1 Parameters of analysis

Slope model range: from the forest road to 15 m
above the pilgrimage route.
Surface thickness: 2.9 m (average of handy
dynamic cone penetration test).

Geological boundary: site reconnaissanceresults.

Groundwater level: -9.0 m below geologicalboundary.
From No.1 to No.6 slope models and total
displacement are shown in Fig. 10 and 11. From a
result, the correlation between the model slope andthe safety factor was confirmed. First, the correlation
between the surface geometry and the analysis result
was investigated. The displacement of each slope
focused on areas with steep slopes and boundaries
between gentle and steep slopes. The displacement offlat surface, such as the No. 2 slope case, covered thewhole of the slope. The direct connection between
surface geometry and the safety factor was notconfirmed, although there is an incline in thedisplacement. Second, the correlation between the
average slope angle and the analysis result wasstudied. Table 3 shows the safety factor and theaverage slope angle for each analysis. The safety
factors of steep slopes, such as the No. 1 slope case,indicated low values compared with the others.
Figure 12 shows the relationship between the averageslope angle and the safety factor. The correlation
coefficient for the relationship between the averageslope angle and the safety factor had a high value,with increases in the average slope angle associatedwith decreases in the safety factor. The relationshipbetween average slope angle and safety factor has
merit as a simple evaluation method for slope stability
in Yokogaki-toge.

Table 2 Relationship between groundwater depth
from the geological boundary and safety factor

Fig. 8 Pattern 4: total displacement
and sliding surface

Fig. 7 Model slope

Fig. 9 Inquest cross- section surface
468

GEOMATE- Osaka, Nov. 16-18, 2015

Fig. 10 Slope models

Fig. 11 Total displacement469

GEOMATE- Osaka, Nov. 16-18, 2015

occurrence based on the relationship between
groundwater fluctuation and soil water index.
The variable characteristics of groundwater levels
caused by differences in geological conditions were
examined. However, we could not estimate thegroundwater level at the time of slope failure
occurrence, because there is no heavy rainfall datathat might have caused a slope failure in theobservation data. Second, a relative risk assessment
using the groundwater level developed in thesensitivity analysis and standard method developed in
the same way of reproduce analysis was conducted at
the 6 slopes that were assumed to be potentiallyhazardous. The risks of each slopes were evaluatedbased on the safety factor. We confirmed that the
average slope angle greatly affected the safety factor.
The results suggested the possibility of estimating thesafety factor simply based on the average slope angle.

Table 3 Safety factor and average slope angle

ACKNOWLEDGEMENTS
This work was supported by Grants-in-Aid for
Scientific Research (C) No. 26350380 (2014-2016)
from the Japan Society for the Promotion of Science.
In addition, we would like to sincerely thank Kumano
agriculture and forestry office for providing
groundwater level observation data.
Fig. 12 Relationship between average slope angle
and safety factor

REFERENCES[1] Geological Survey of Japan, AIST (ed.). 2014.
Seamless digital geological map of Japan 1: 200,000.
Jan 14, 2014 version. Geological Survey of Japan,
National Institute of Advanced Industrial Science andTechnology.

7. CONCLUSION
In this study, we firstly tried to estimate thegroundwater level at the time of slope failure

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FifthInternational Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan,Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

DYNAMIC RESPONSE OF SINGLE PILE LOCATED IN SOFT CLAYUNDERLAY BY SAND
Deendayal Rathod1, K. Muthukkumaran2 and T.G. Sitharam 3
Assistant Professor, Department of Civil Engineering, NIT, Tiruchirappalli, India.
2
Associate Professor, Department of Civil Engineering, NIT, Tiruchirappalli, India.
3
T.G. Sitharam, Professor, Department of Civil Engineering, IISc Bangalore, India.
1

ABSTRACT
Piles have been extensively used for supporting axial loads and lateral loads for variety of structures
including high rise buildings, transmission towers, power stations, offshore structures and highway and railway
structures. Pile foundations are subjected to lateral loading due to wind action, wave action, earthquake andimpact of ships in dock and harbour structures. In such cases, studying the behavior of pile foundation due
dynamic loading is essential. Compare to field tests, the numerical modeling is an economical way to analyze theresponse of piles. This paper presents the dynamic response of a single concrete pile of 0.4m diameter, 11.5m
length located in soft clay underlay by sand subjected to static vertical load as well as ground acceleration
simultaneously. Finite element software PLAXIS 2D is used to analyze the pile. From the analysis, thedeformation and acceleration behavior of pile with respect to time has been studied.
Keywords: Single pile, Dynamic response, Plaxis 2D, Deformation, Acceleration

carried out tests on model groups of piles to support
dolphin-type structures. Static load tests have been
conducted on instrumented model pile groups
embedded in a marine clayey bed. The spacing
between piles, number of piles in a group, andarrangement of pile group with respect to thedirection of lateral loading have been varied. The
results indicated that the capacity of pile group not
only depends on the spacing between the piles, butalso on the arrangement of piles in the group.
Mezazigh and Levacher [11] conducted thecentrifuge test on model single aluminum pile placedon horizontal ground and sloping ground surface incohesionless soil by varying the distance from thecrest of slope. The effect of distance to the slope,slope angle and soil properties were studied. The
load versus deflection curves; bending-moment andp-y curves were derived for piles close to slopes andcompared to the horizontal ground response. Rao et
al. [14] conducted experiments on pile groups andstudied the influence of parameters like flexural
rigidity of pile material, embedment length of pileand arrangement of piles on the behavior of laterally
loaded pile groups. The results indicated that thelateral load capacity of the pile group depends
mainly on the rigidity of pile soil system fordifferent arrangements of piles within a group.
Rollins et al. [15] performed static lateral load test
on a full-scale pile group to determine the resulting
soil-pile interaction effects. A 3 × 3 pile group at
three-diameter spacing was driven into a soil profile
consisting of soft to medium-stiff clays and silts
underlain by sand. It is observed that the pile groupdeflected over two times more than the single pile

INTRODUCTION
Piles are columnar elements in a foundation
which have the function of transferring load fromthe superstructure through weak compressible strata
or less compressible soils onto rock. Piles used inmarine structures are subjected to lateral loads fromthe impact of berthing ships and from waves. Piles
used to support retaining walls, bridge piers andabutments, and machinery foundations carries
combinations of vertical and horizontal loads. It isfound that at various sites in the coastal region, thetop layer soil is soft clay with varying thickness of
5.0 to 25m and most of the structures which areconstructed on soft clay. Matlock and Reese [9] gave
a generalized solution in non-dimensional form forthe laterally loaded pile for both elastic and rigid
behavior assuming soil modulus variation linearlywith depth. Broms [2] developed solutions for theultimate lateral resistance of a pile assuming the
distribution of lateral soil pressure and considering
static of the problem. Singh and Verma [16]conducted large-scale model tests in sand undercontrolled density. Lateral load tests were conductedon single pile and pile groups. The load deflection
curves were found to be non-linear and were flatterat higher load levels showing loss of soil resistance.
Poulos and Davis [12] gave comprehensive
collections of solutions for the design of pilefoundations to resist static lateral load. Ernestnaesgaard [5] carried out full-scale field tests toassess the ability of the piles to withstand large
lateral deformations that may be caused byearthquake-induced soil liquefaction. Rao et al. [13]471GEOMATE-Osaka, Nov. 16-18, 2015

under the same average load and trailing rows
carried lesser load than that of leading row, andmiddle row piles carried the lowest loads. Ilyas et al.
[7] conducted a series of centrifuge model tests to
examine the behavior of laterally loaded pile groups
of 2, 2×2, 2×3, 3×3, and 4×4 piles with a center-tocenter spacing of three or five times the pile width innormally consolidated and over consolidated kaolin
clay. It is established that the pile group efficiency
reduces significantly with increasing number of piles
in a group due shadowing effect. And also, it isfound that the front piles experience larger load andbending moment than that of the trailing piles.
Boominathan and Ayothiraman [3] carried out thestatic and dynamic lateral load tests on model
aluminium single piles embedded in soft clay to
study their bending behavior. The results indicatedthat the maximum dynamic bending moment of pilein soft clay is about 1.5 times higher than the
maximum static bending moment. Cubrinovski et al.
[4] studied the pile response to lateral spreading by
3-D soil–water coupled dynamic analysis by shaking
in the direction of ground flow. Liyanapathirana andPoulos [8] studied the analysis of pile behavior inliquefying sloping ground. Hasan Ghasemzadeh andMehrnaz Alibeikloo [6] carried out the pile–soil–pileinteraction in pile groups with batter piles underdynamic loads. Amin rahmani and Ali pak [1]studied dynamic behavior of pile foundations undercyclic loading in liquefiable soils. Therefore, theliterature on the behavior of piles under static load isabundant. Very few researchers studied thefundamental characteristics of pile in soft clay
under dynamic conditions. This paper aims to fillthis gap through a comprehensive numerical analysis
using PLAXIS 2D carried out on single piles
embedded in soft clay underlay by sand subjected tostatic vertical load as well as ground acceleration
simultaneously.

FINITE ELEMENT MODEL
For dynamic analysis, the 1990 earthquake dataof California having local magnitude (ML) 5.40 on
Richter scale with peak horizontal acceleration 239.90cm/sec2 is used. Fig.1 shows real
accelerogram ground acceleration versus time graphused for analysis. It contained in standard SMC
format (Strong motion CD-ROM) which can be readand interpreted by PLAXIS. The earth quake ismodeled by imposing a prescribed displacement at
the bottom boundary. PLAXIS has convenientdefault setting to generate standard boundary
conditions for earth quake loading using SMC files.

Fig.1 Ground acceleration vs. TimeThe geometry is simulated by means ofaxisymmetric model in which the pile is positioned
along the axis of symmetry. Both pile and soil aremodeled with 15-noded elements. Interface elements
are placed around the pile to model the interaction
between pile and soil. The boundaries of model aretaken sufficiently far away to avoid the influence ofboundary conditions. Standard absorbent boundaries
are used to avoid the spurious reflections. The
presence of ground water table is neglected. Fig.2
shows the geometry model of pile. The pile isconsidered to be linear elastic.

MATERIAL PROPERTIES
Single concrete pile with 0.4m diameter and11.5m length is located in soft clay underlay by sandsubjected to static vertical load as well as groundacceleration simultaneously.The pile parameters aretaken as Young's modulus of concrete (E) =3x107 kN/m2, Poisson's ratios of concrete (µ) = 0.1,
Unit weight of concrete (γ c ) = 25 kN/m3 for dynamic
analysis. The properties of soft clay are taken as
γ unsat =16kN/m3, γ sat =18kN/m3, E ref =15000kN/m2,
R inter =0.5, C=2kN/m2, ϕ=24°, µ=0.3 and theproperties of sand were taken as γ unsat =17kN/m3,
γ sat =20kN/m3, E ref =50000kN/m2, E oed =50000kN/m2
E ur =150000kN/m2, P ref =100kN/m2, R inter =0.67,
C=1kN/m2, ϕ=31°, µ=0.2.

Fig.2 Geometry model of pile472

GEOMATE-Osaka, Nov. 16-18, 2015

The top layer of soil (clay) is modeled withsimple Mohr-Coulomb model with undrain
condition and the bottom layer of soil (sand) is
modeled by means of hardened soil model in orderto model the nonlinear deformations below the tip ofpile. The pile is subjected to vertical load of 700 kN
and earthquake ground excitation simultaneously.
Fig.3 shows the finite element mesh generated. The
mesh near the pile is observed with highconcentration of stresses.

Fig.5 Deformation Vs Time at bottom surface of
pile
Figs.6 and 7 shows the acceleration versus timeplots at top and bottom surface of pile when it issubjected to both static and dynamic loading. The
acceleration of 1.34X10-4 m/sec2 is observed at t=0.3
sec. at top surface of pile and the acceleration of
7.7X10-4 m/sec2 observed at t=0.5sec at the bottom
surface of pile.

Fig.3 Finite element mesh

RESULTS AND DISCUSSION
The fig.4 shows the deformation versus time plot
at top of pile surface and fig.5 shows thedeformation versus time at bottom of pile surface
when it is subjected to vertical load of 700 kN andearthquake ground excitation (both static anddynamic loading simultaneously).The maximum
displacement of pile at top and bottom observed tobe 10x10-7m at t=0.2 Seconds and 18.6X10-7 m at
t=1 Seconds.

Fig.6 Acceleration Vs Time at top surface of pileFig.4 Deformation Vs Time at top of the pile473

GEOMATE-Osaka, Nov. 16-18, 2015

dynamic analysis: Shaking in the direction ofground flow. Soil Dynamics and Earthquake
Engineering 28 (2008) 421–435[5] Ernestnaesgaard(1991) “ Lateral load tests to
examine large-strain (seismic) behavior of piles”
Can. Geotech. J. 29, 245-252 .
[6] HasanGhasemzadeh, MehrnazAlibeikloo(2011)“Pile–soil–pile interaction in pile groups withbatter piles under dynamic loads”.Soil
Dynamics and Earthquake Engineering 31(2011) 1159–1170.
[7] Ilyas T., C. F. Leung, Y. K. Chow and S. S.
Budi (2004). “Centrifuge Model Study of
Laterally Loaded Pile Groups in Clay”.Journalof Geotechnical and Geo-environmental
Engineering,Vol. 130, No. 3, March 1, 2004.
[8] Liyanapathirana D.S. and H.G. Poulos(2010)“Analysis of pile behavior in liquefying sloping
ground”. Computers and Geotechnics 37 (2010)
115–124.
[9] Matlock,H.,and Reese,L.C. (1960).“Generalized
solutions for laterally loaded piles". J. of Soil
Mechanics and Foundation Div., ASCE, 86(5),63-91.
[10] Muthukkumaran, K., R. Sundaravadivelu and
S.R. Gandhi (2008) “Effect of slope on P-Y
curves due to surcharge load”, Soils andFoundations 48(3), 353 -361.
[11] Mezazigh, S. and Levacher, D(1998)“ Laterally
loaded piles in sand: slope effect on p-y reaction
curves”, Can. Geotech. J., 35, (3), 433–441.
[12] Poulos, H. G. and Davis, E. H. (1980) Pile
foundation analysis and design, Wiley, NewYork.
[13] NarasimhaRao, S., Ramakrishna, V. G. S. T.,
and Raju, G. B. (1996) “Behavior of pilesupported dolphins in marine clay under lateralloading.” J. Geotech. Geoenviron. Eng., 122(8),607–612.
[14] NarasimhaRao, S., Ramakrishna, V. G. S. T.,
and Rao, M. B. (1998) “Influence of rigidity onlaterally loaded pile groups in marine clay.” J.
Geotech. Geoenviron. Eng., 124(6), 542–549.
[15] Rollins, K. M., Peterson, K. T. and Weaver, T.
J. (1998) “Lateral load behavior of full-scale
pile group in clay”, J. Geotech. Geoenviron.
Eng., 124, (6), 468–478.
[16] Singh, A. and R.K.Verma(1973) “Lateralresistance of a field model of pile group in sandand its comparison with laboratory model”.
Indian Geotech. Journal, 3, 113-117.

Fig.7 Acceleration Vs Time at bottom surface ofpile
CONCLUSIONS
Finite element software PLAXIS 2D is used toanalyze the pile. The dynamic response of a single
concrete pile located in soft clay underlay by sandsubjected to static vertical load as well as groundacceleration simultaneously were analysed. From theanalysis, the deformation and acceleration behaviorof pile with respect to time has been studied.The
maximum displacement of pile at top and bottom is
10x10-7m at t=0.2 Seconds and 18.6X10-7 m at t=1
Seconds respectively. The acceleration of 1.34X10-4
m/sec2 is observed at t=0.3 sec. at top surface of pilewith less number of cycles and the acceleration of
7.7X10-4 m/sec2 observed at t=0.5sec at the bottom
surface of pile with more number of cycles. The top
tip of the pile is more susceptible for damage
compare to bottom of pile.
REFERENCES[1] Amin Rahmaniand Ali Pak(2012) “Dynamic
behavior of pile foundations under cyclicloading in liquefiable soils” Computers andGeotechnics 40 (2012) 114–126.
[2] Broms, B. B. (1964)."Lateral resistance of piles
in cohesive soils."J. Soils Mech. and Found.
Div., ASCE, 90(2), 27-63.
[3] Boominathan A. and Ayothiraman R. (2007).
“An experimental study on static and dynamic
bendingbehavior of piles in soft clay”.
Geotech.Geologic. Eng., 25, 177–189.
[4] Cubrinovski M, R. Uzuoka, H. Sugita, K.
Tokimatsu, M. Sato, K. Ishihara,Y. Tsukamoto,T. Kamata(2008) “Prediction of pile response tolateral spreading by 3-D soil–watercoupled474

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

INFLUENCE OF MEASURED AND ANALYSIS RAINFALLS ON
SEEPAGE: NUMERICAL SIMULATION ON SLOPE AT THE
YOKOGAKI-TOGE PASS
Yuko Ishida1, Tsuyoshi Kibayashi2 , Tatsuo Konegawa3, Masamitsu Fujimoto2 and Ryoichi Fukagawa2
1
Institute of Disaster Mitigation for Urban Cultural Heritage, Ritsumeikan University, JAPAN，
2
Department of Science and Engineering, Ritsumeikan University, JAPAN; 3 Mihama Town, JAPANABSTRACT
Many world cultural heritages are consisted of buildings, including shrines, temples, and ruins; only two such
heritages are include pilgrimage routes have a figuration as roads. One of these is the Kumano Pilgrimage Route,which is a 307.6 km route in the Kii Peninsula in central Japan, on which there is a risk of sediment disasters. In
2011, Typhoon Talas caused a slope failure at Yokogaki-toge, which is part of the Kumano pilgrimage route.
Risk assessment is needed to protect cultural heritage sites from sediment disasters. Seepage and slope stability
analysis using a two-dimensional finite element method can be applied to slopes for risk evaluation. We haveobserved rainfall at multiple locations within Yokogaki-Toge, with regional variations in amount of rainfall also
noted. We conducted a saturated-unsaturated seepage flow analysis of the collapsed slope using two types ofrainfall, and also considered suitability of these rainfalls for risk assessment in this paper.
Keywords: Slope failure, Seepage flow analysis, Slope stability, Disaster Mitigation of Cultural heritage site.

characteristics of this mountainous area.

1. INTRODUCTION“Yokogaki-toge” is part of the world heritagesite “Sacred Sites and Pilgrimage Routes in the Kii
Mountain Range”. Since ancient times, the Kii
Mountain Range has been considered a special place
where gods dwell in Japanese mythology. This
world heritage site, which is located on the Kii
Peninsula in central Japan, consists of three sacredsites and pilgrimage routes that are inseparable fromthe nature of the Kii Mountain Range. The roads thatcomprise this world heritage site are approximately
307.6 km long; Yokogaki-toge is approximately 1.8
km long. Many people experience a feeling of
spirituality, known as “Wabi-Sabi”, when travelling
this beautiful route as shown in Fig.1, which isapproximately 1,200 years old.
The majority of the pilgrimage route isvulnerable geologically and slope failures often
occur along its course. In Yokogaki-toge, a landslide
occurred in the rainy season in 2007, and a deepseated landslide, slope failure, and debris flow
occurred in 2011 during Typhoon Talas, as shown in
Figs. 2 and 3.
To protect this precious ancient route against
sediment disasters, we need to evaluate thevulnerability of the slopes. Mass movements such as
landslides, slope failures, surface failures, and debris
flows result from a combination of influencing
factors. This study considered the occurrence ofvarious mass movements at Yokogaki-toge, focusing
on rainfall as a trigger for slope failures. Rainfall has
been measured at nine points at Yokogaki-toge sinceAugust, 2014 (Fig.3) to determine the rainfall

Fig. 1 The “Yokogaki-toge” Pilgrimage RouteFig. 2 The deep-seated landslide at Yokogaki-toge
475

GEOMATE- Osaka, Nov. 16-18, 2015

gauge No.1 had a loose connection. Therefore, theresults were analyzed without including the datafrom this gauge.
Figure 4 compares the rainfall data for thecollapsed and non-collapsed sites. The data farthest
to the left (green), called Mihama, were obtained by
the Japan Meteorological Agency, and used as thestandard rainfall. The three right-most bars (blue)
are for the non-collapsed site, and the other data(red) are the rainfall at the collapsed site. The upper
figure compares the total amount of rainfall; themiddle figure compares the maximum hourly
rainfall; and the lower figure compares the
maximum 10-minute rainfall.
The shielding effect of trees is seen in the total
amount of rainfall and maximum hourly rainfall.
Excluding sites Nos.2 and 3, which were notshielded by trees, the total and maximum hourly
rainfall at the collapsed site did not differ markedly
from that at the non-collapsed sites. However theamount of rainfall at the collapsed site was slightly
greater than that at the non-collapsed site. The
maximum 10-minute rainfall differed by location
irrespective of collapse or non-collapse.

Mass movements
Measurement points

Deep-seatedlandslide
No.1
No.2
No.4

No.9
No.8
Debris flow
No.7
No.6

Landslide

Slope failure
No.5

No.3

200m

Fig. 3 Mass movements and measurement spots [1]2. ANALYSIS OF FACTORS CAUSING MASS
MOVEMENTS
To predict collapse, it is important to identify thecauses of the different types of mass movement that
occurred at Yokogaki-toge during the rainfall eventof Typhoon Talas in 2011. Using a geographic
information system and field and document surveys,
we evaluated geological factors such as slope angle,
aspect, and curvature, the mechanism of collapse,
and rainfall. Geological structure was identified as
the main cause of the collapse.
Generally, large, deep landslides result from a
big total rainfall event with or without high-intensity
rainfall that occurs at the end of an event, while
surface failure results from high-intensity rainfall
following a medium rainfall event. Since thecharacteristics of a rainfall event might predict thecharacteristics and scale of slope failures, we
focused on rainfall to answer the following
questions:
1. Is the rainfall at a collapse site greater than therainfall at a non-collapsed site?
2. Would a north-facing slope collapse because theamount of rainfall on it is greater compared to asouth-facing slope?
Nine rain gauges were set up on Yokogaki-toge, as
shown in Fig. 3: Nos. 1 and 2 were on the southfacing slope; Nos. 3 and 4 were on the north-facing
slope; and the north and south slope areapproximately 100 m apart. Nos. 5‒9 were placed
100–300 meters apart on the east- or southeastfacing slopes. Nos. 1–4 were on the deep-seatedlandslide site; No. 6 was on the slope failure site;
No.7 was on the debris flow site; and Nos. 5, 8, and9 were on non-collapse slopes. Ideally, rain gauges
should be set in unobstructed open space; however,there are few such places in Yokogaki-toge becauseof its forest. Consequently, gauge No. 1, and Nos. 4–9, were affected by shielding from trees. Nos. 2 and3 were set in the open space caused by the collapse.

Total amount of rainfall (mm)MihamaMaximum hourly rainfall (mm/h)

MihamaMaximum 10 minutes rainfall (mm/10min)

MihamaFig. 4 Rainfall data for each rain gauge
3. CHARACTERISTICS OF MEASURED
RAINFALL

Figure 5 shows the directions of the slopesaround Yokogaki-toge. The deep-seated landslide
started from the tops of the north- and west-facing

During the 4-month measurement period, rain
476

GEOMATE- Osaka, Nov. 16-18, 2015

slopes. Thirty rainfall events during the observation
period were extracted using the following procedure:
if the rain stopped for > 24 hours, a rainfall event
was counted as a string of data. Rainfall events
involving < 20 mm of cumulative rainfall were
excluded. To investigate the influence of rainfall on
collapse, the data for No. 3 (north-facing slope) and
No. 2 (south-facing slope) were compared, as shownin Fig 6. The red bars in Fig 6 indicate the differencein cumulative rainfall, calculated by subtracting thedata for the south-facing slope from that of thenorth-facing slope. The blue points in Fig 6 show
cumulative rainfall of each rainfall event.
Generally, the cumulative rainfall for each
event was greater on the north-facing slope than thesouth-facing slope. At first glance, this suggests thatthe collapsed slope and the slope receiving a large
amount of rainfall are the same, and that there is acorrelation between the cumulative rainfall during
each event and the difference in rainfall between thenorth- and south-facing slopes. However, the trend
differed for heavy rainfall events, which might
trigger a collapse, as seen for events #13 and #15.
The case of over 100mm cumulative rainfall as #13
and #15, their difference are very few or more
rainfall is on south-facing slope than north-facing
slope. Therefore, it is possible that the influence rateof rainfall for collapse is lower than that of otherfactors, such as geological structure. The total
amount of rainfall on the north-facing slope wasgreater than on the south-facing slope,it means
promoting weathering as an indirectly work.
North
West

In summary:
1. Rainfall at the collapsed and non-collapsed sites
did not differ so much.
2. The difference in the amount of high-intensity
rainfall during a short time at each site was larger
than the differences in the total and maximum
hourly rainfall.
3. Rainfall has less influence on the occurrence ofdeep-seated landslides than other factors.
4. The greater total rainfall on the north-facing
slope might promote weathering compared withthe south-facing slope.
4. SATURATED-UNSATURATED SEEPAGE
FLOW ANALYSIS OF THE COLLAPSEDSLOPE
To evaluate slope stability, it is important tomeasure the rainfall at the site correctly. However,
the rainfall measurement at Yokogaki-toge indicatedthat the observed values lacked precision due to theshielding effect of trees. Therefore, we used two
types of rainfall data to evaluate slope stability:
rainfall measured directly in the field, called
automated meteorological data-acquisition system“AMeDAS”, and analysis rainfall obtained fromrader observation. The nearest AMeDAS point isapproximately 3.5 km from Yokogaki-toge, andanalysis rainfall on each 1km mesh is offered.
Figure 7 shows the two types of rainfall data
during the slope failure of 2011. Cases 1 to 12 in Fig.
7 indicate the time of evaluation. The difference inthe two types of rainfall data is considered based onsaturated-unsaturated
seepageflow analysis
performed using PLAXIS 2D PlaxFlow (Plaxis;
Delft, The Netherlands), which is a finite element
software package intended for the analysis ofseepage flow. This software calculates seepage flowusing Darcy’s equation. The slope model shown in
Fig. 8 was determined based on a field survey andstandard penetration test conducted in the landslidearea. The parameters were determined based on
experiments of sampling soil and values taken fromthe literature. Table 1 shows the hydraulicconductivity of each soil.

No.9
East
No.8

South

No.7
No.6

No.1
No.2

No.5

No.3
No.4

Fig. 5 Directions of the slopes at Yokogaki-toge [2]30

雨量(mm)
DifferenceN-S (mm)№3 連続雨量(mm)No.3cumulative
rainfall (mm)

大規模崩壊地での雨量比較

150

20

100

10

50

0

0

-10

-50

-20

1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930

Case4～10200

北向き斜面連続雨量(mm)

北向き斜面-南向き斜面雨量(mm)40Case11
Case1

Case2

Case3

Case12-100

Time (day)30 rainfall
events
降雨イベントFig. 6 Differences in rainfal(subtracting south- facing from north-facing)Fig. 7 Two kinds of rainfall data for the period
from 17 August to 4 September, 2011
477

GEOMATE- Osaka, Nov. 16-18, 2015

Weathered rhyolite
Colluvial soil
Yokogaki-touge route

Weatheredmudstone

Talus sedliment
Embankment
under the forest
road

Underground water

Case 1: At the end of the antecedent precipitation- Flow velocity vectors are seen at the toe of theslope. The analysis rainfall caused a large
amount of seepage.
- Phreatic lines developed in the weatheredmudstone layer in the right figure (analysis
rainfall).

mudstone
78.1m

Fig. 8Slope model

Table.1 Hydraulic conductivity of each soil
① colluvial soil
② weathered rhyolite③ talus sediment④ weathered mudstone⑤ mudstone⑥ embankment

K: hydraulic conductivity (m/day)
23.50113.04639.57139.571
8.64E+03
0.432

5. RESULTS AND DISCUSSION

Case 2: At the end of the small rainfall- Flow velocity vectors are seen at the toes ofboth slopes.
- Some phreatic lines developed in the weatheredmudstone layer in the left figure (AMeDAS).

The analysis was started on 17th August 2011, 2
weeks before the rainfall event that caused thecollapse. The collapse was estimated to haveoccurred on either September 3rd or 4th. Althoughhourly rainfall differed, as shown in Fig. 7, the times
at which the rain started and stopped were very
similar. The flow velocity conditions were evaluatedfor the following 12 cases:
Case 1: at the end of the antecedent precipitation
Case 2: at the end of a small rainfall event
Case 3: at the beginning of the large rainfall event
Cases 4‒10: near the peak rainfall, including the
peakCase 11: 17 hours after the peakCase 12: 24 hours after the peak
Table 2 summarizes the time of analysis andcumulative rainfall in each case. The flow velocity
vector diagrams for each case are shown in Fig. 9.
The diagram on the left is based on the measured“AMeDAS” rainfall, and that on the right side uses
the analysis rainfall.

Case 3: At the beginning of the large rainfall- Flow velocity vectors are seen at the toes ofboth slopes.
- Phreatic lines developed on the upper side of
the weathered mudstone layer toward thegeological boundary in the left figure(AMeDAS).
- Phreatic lines developed on the lower side of
the weathered mudstone layer in the rightfigure (analysis rainfall).

Table.2 Cumulative time and rainfall in each case
Case

Cumulateve rainfall
Cumulateve rainfall
Time
Time
Amedas Analysis Case
Amedas Analysis
(day)
(day)
(mm) rainfall (mm)(mm) rainfall (mm)

1

14.42

201

151

7

18.04

985

1115

2

16.71

355

284

8

18.08

1009

1136

3

17.42

527

444

9

18.13

1033

1156

4

17.83

755

787

10

18.17

1042

1167

5

17.96

878

978

11

18.71

1105

1226

6

18.00

944

1063

12

20.00

1111

1235

Case 4: Near the peak rainfall
478

GEOMATE- Osaka, Nov. 16-18, 2015

- Very slight flow velocity vectors are seen at thetoes of both slopes.
- Phreatic lines grew increasingly toward thegeology boundary through the upper side of
the weathered mudstone layer in the left figure(AMeDAS).
- Phreatic lines developed into not only the lower
side, but also the upper side of the weatheredmudstone layer in the right figure (analysis
rainfall).
- A phreatic line arose in the surface layer ofcolluvial soil near the tops of both slopes.

weathered mudstone layer at the middle of theslope in the right figure (analysis rainfall).
- Connected phreatic lines extend to the surface
layer and weathered mudstone layer under thegeological boundary in the left figure(AMeDAS).
- The bottom phreatic line moved upwards
slightly, near the toe of the slope in the rightfigure (analysis rainfall).
estimated slip line
sliding surface

Case 7: 1 hour after the peak
- Flow velocity vectors are concentrated in thetoe of the slope in the left figure (AMeDAS).
- Phreatic lines are found along the estimatedslip line in the right figure (analysis rainfall).

Case 5: Peak 1 (AMeDAS 2.16 m, analysis 1.32 m/3h)- Many slight flow velocity vectors arose in thesurface layer of talus sediment from the toe ofthe slope to the geological boundary in the left
figure (AMeDAS).
- Very slight flow velocity vectors are found at
the toe of the slope only in the right figure(analysis rainfall).
- The phreatic line in the mudstone layer isconnected with the phreatic lines thatdeveloped on the upper side of the weatheredmudstone layer toward the geologicalboundary in the left figure (AMeDAS).
- Marked phreatic lines developed at the toe ofthe slope and geological boundary in theweathered mudstone layer in the right figure(analysis rainfall).
- Phreatic lines arose at the surface layer ofcolluvial soil on both slopes.

Case 8: 2 hours after the peak
- Flow velocity vectors decreased on both slopes(AMeDAS and analysis rainfall).
- Phreatic lines weakened on both slopes.

Springwater
Springwater

Case 9: 3 hours after the peak
- Flow velocity vectors decreased on both slopes.
- Phreatic lines weakened on both slopes.
Case 6: Peak 2 (AMeDAS 2.04 m, analysis 1.58 m/3h)- Many slight flow velocity vectors are found in
the surface layer of the talus sediment near the
lower slope, and some flow velocity vectors
surround the springwater caused piping after
the slope failure in the left figure (AMeDAS).
- Some velocity vectors are found at theboundary of the talus sediment layer andCase 10: 4 hours after the peak479

GEOMATE- Osaka, Nov. 16-18, 2015

- Slight flow velocity vectors are found at the toeof the slope in the left figure (AMeDAS).
- Phreatic lines weakened on both slopes.

layer. During the most advanced stage, there weremany phreatic lines around the estimated slip line.

Case 11: 17 hours after the peak
- Very slight flow velocity vectors are found on
both slopes.
- Phreatic lines decreased on both slopes.

Fig.10 Transition of the maximum flow velocity

Time (day)6. CONCLUSION
This study discussed the results of multi-point
rainfall measurements. The amount of rainfall ateach point differed slightly, while the rainfall
intensity over a short period of time varied markedly.
North-facing slopes catch more rainfall than southfacing slopes, which might promote the weatheringof these north-facing slopes.
Saturated-unsaturated seepage flow analysis was
carried out based on two types of rainfall using thebefore-collapse slope model. The seepage processes
based on the two types of rainfall were similar,
although the positions of the phreatic lines differed.
With AMeDAS rainfall, seepage occurred over abroad area, while analysis rainfall resulted inphreatic lines around the estimated slip line. It might
be considered that closer rainfall to actual
phenomenon at Yokogaki-toge is AMeDAS by thereasons as followings:
1. More Seepage flows arise in the weatheredmudstone layer over a broad area.
2. Seepage flows arise surround the springwater
position at the peak.
It should verify this assumption by a coupledanalysis, saturated-unsaturated seepage flow analysis
as an issue in the future.

Case 12: 24 hours after the peak
- Flow velocity vectors are not found on either
slope.
- The bottoms of the phreatic lines fall to a low
level, and many phreatic lines that arose in theweathered mudstone layer disappeared forboth slopes.
Fig.9 Flow velocity vector and phreatic lines
Flow velocity vectors based on both AMeDAS
and analysis rainfall first appeared at the toe of theslope and developed toward the geological boundaryon the upper side. The time lag of the maximum
flow velocity based on both kinds of rainfall was 1
hour, as shown in Fig. 10. According to this result,the evaluations using either type of rainfall weresimilar in terms of the flow velocity arising in theslopes.
The phreatic lines based on AMeDAS rainfall
were concentrated in the surface layer at the earlystage, and progressed into the lower weatheredmudstone layer in the middle stage. The bottom
phreatic line, and many other small phreatic lines
within the layers, were connected during the
maximum rainfall.
The phreatic lines based on the analysis rainfall
appeared at deeper positions than those based on the
AMeDAS in the early stage, and appeared graduallyaround the toe of the slope and geological boundaryof the surface. In addition, the bottom line and shortlines did not connect in the weathered mudstone

ACKNOWLEDGEMENTS
This work was supported by a Grant-in-Aid for
Scientific Research (C) No. 26350380 (2014‒2016)
from the Japan Society for the Promotion of Science.
We would also like to thank Kumano Agriculture
and Forestry Office for providing the geologicalinvestigation data.
REFERENCES[1] Mie Prefecture: aerial photo data of south part ofMie prefecture, 2012.
[2] Geospecial Information Authority of Japan:
Basement map information,
http://fgd.gsi.go.jp/download/, (2012.2.12 access).
480Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

EXPERIMETAL MEASUREMENT OF SOIL-PRESTRESSEDFOUNDATION INTERACTION
Radim Cajka1, Petr Mynarcik2 and Jana Labudkova3
Department of Structures, faculty of Civil Engineering, VSB-Technical University Ostrava, Ludvika
Podeste 1875/17, 708 33, Ostrava – Poruba, Czech Republic

ABSTRACT
Several experimental measurements of reinforced concrete slab – subsoil interaction are compared withnumerical analysis of shallow foundation by means of FEM. At the Faculty of Civil Engineering VSB –Technical University of Ostrava testing device was constructed so that the phenomena of soil – foundationinteraction could be experimentally investigated and compared with numerical models. The experimental model
was designed as a cutout of prestressed foundation slab-on-ground and the static load test was conceived as asimulation of local loading of a square shape column. During the static load test, measurements focused on
observation of horizontal deformations, tension inside the slab and on the contact surface between foundation
structure and subsoil. The described static load test was part of a series of experiments realized at the Faculty of
Civil Engineering, VŠB –Technical university of Ostrava.
Keywords: Foundation structure, experimental measurement, soil – structure interaction, interaction models,contact stress, FEM calculation.

INTRODUCTION
This article presents experimental static load testof post-tensioned concrete foundation model. This
test was part of a series of experiments focused on
problematic of interaction between concrete
structures and subsoil and was realized at theFaculty of Civil Engineering, VSB – Technicaluniversity of Ostrava.
Subsoil-structure interaction problematic isinternational research theme and many researchteams around the world produce research activity on
this scope [1]-[6]. Interaction between concrete
structure and subsoil is one of the main research
directions at the Faculty of Civil Engineering, VSB– Technical university of Ostrava [7]-[12].
Actually, research is focused on post-tensioned
concrete foundation slabs and industrial floors. For
experimental static load test was concreted model ofpost-tensioned foundation slab and static load test
was conceived as a simulation of loading by squareshape column. The experiment served to a better
understanding and possible improvement of thesetechnologies from the perspective of subsoil
interaction.
DESCRIPTION OF THE PRESTRESSEDCONCRETE FOOR MODEL
The experimental model was designed as a
cutout of prestressed concrete foundation slab.
Schematic plan of experimental model is displayedon Fig. 1.

Fig. 1481

The schematic plan of experimental model
dimensions and prestressing threadbars
positions

GEOMATE- Osaka, Nov. 16-18, 2015

Thredbars were anchored by domed nuts andrecessed anchor plates. Positions of threadbars were
designed and realized as a centric post-tensioning
system. The model was laid on homogenous clay
subsoil with gravel bed layer. The sliding joint wasplaced between contact surface of concrete
foundation slab model and gravel bed layer. For thisexperiment was used developed sliding joint
construction from parallel research direction at theFaculty of Civil Engineering, VSB – Technicaluniversity of Ostrava [13],[14].

The basic dimensions of experimental model
were 2000 x 2000 x 150 mm. The concrete typeC35/45 XF1 was used for concreting. The
experimental model was post tensioned by six fully
threaded prestressing threadbars – displayed on
Fig. 2. The materials of threadbars were from low
relaxation steel with designation Y 1050 anddiameters of these threadbars were 18 mm. The
prestress force for each threadbar was 100 kN. The
presstresing system with hollow hydraulic cylinder
is displayed on Fig. 3.

DESCRIPTION OF SUBSOIL ATRIBUTESThe experimental model was implemented oncompacted gravel bed. Gravel bed layer thickness
was 300 mm and was compacted on prime clay
subsoil without greensward [15],[16]. The subsoil
characteristics were determined by standard
geotechnical measurements. The sliding joint wasmade from combination of PVC foil and geotextile.
The experimental model was situated in the testing
device for experimental measurements of foundation
slabs on the subsoil “STAND” [17].

Subsoil attributes:
Fig. 2The steel formwork
prestressing bars

with

ducts

for







Subsoil consists of loess loam – soil
classification F6 (according CSN 73 1001).
Thickness of subsoil layer is about 5 meters.
Volumetric weight of soil γ = 18,5 kN.m-3.
Poisson coefficient ν = 0,35.
Static Young’s modulus
EDEF = 33,86 MPa.
Oedometric modulus of elasticity
EOED= 4,27 MPa.

DESCRIPTION OF TESTING DEVICES AND
MEASUREMENTS

Fig. 3The presstresing
hydraulic cylinder

system

with

Fig. 4The complete assembly for static load test

The outdoor testing device for experimental
measurements of foundation slabs on the subsoil“STAND” consists of two frames. Crossbeams
enable variability of the press machine location. The
frames are anchored with screws into the steel grate
based in the reinforced concrete strip foundations.
The construction is anchored with 4 m long
micropiles. The highest possible vertical load is 1
MN [17]. The experimental static loading test on a
prestressed concrete foundation slab model was theassembly of a set of measurements. Measurementgauges completed the experimental measurement
line [7], [8]. Described experiment was short-term
static load test without influence of concrete andsubsoil creep [18], [19]. Measured data will servefor comparison with results obtained by numerical
FEM modeling of soil – prestressed foundation slab
interaction by [20]-[23]. The complete assembly forstatic load test is displayed on Fig. 4.

hollow

482

GEOMATE- Osaka, Nov. 16-18, 2015

The experimental measurement line:










Fig. 5

3 geotechnicalpressurecells
formeasurement of the stress on the interfaceof the slab and subsoil. Displayed on Fig. 5.
Built-in pressure sensor for measurement of
the vertical load.
4 strain gauges for measurement on thesurface of the slab – tension of concrete.
4 strain gauges for measurement inside theexperimental slab – tension of concrete.
Displayed on Fig. 6.
14 potentiometric position sensors formeasurement of vertical deformations(subsidence). Displayed on Fig. 7.
8 temperature sensors for measurement of
temperature inside and on the surface.
Recording station for potentiometricsensors
Recording station for geotechnical pressure
cells
Leveling device for stiffness control of
outdoor testing device “STAND”.

Fig. 7

Potentiometricposition
sensorsformeasurement of vertical deformations andbuilt-in pressure sensor for measurement of
the vertical load

DESCRIPTION OF EXPERIMENTAL
MEASUREMENT PROCESSThe vertical load was caused by the high tonnage
hydraulic cylinder. The loaded equipment wasplaced between the experimental model and the steel
extension fixed on the testing device forexperimental measurements of foundation slabs on
the subsoil “STAND” The hydraulic system wasequipped with the pressure sensor. Potentiometricposition sensors were installed on the surface ofconcrete foundation model. These gauges wereconnected to the same sensor station with automatic
scanning and recording. Shape and size of load area
simulated square shape column. Dimensions of load
area were 200 x 200 mm. Fixed interval of loading
process - 75kN / 30 min was chosen for thisexperimental testing. After each loading process a
30 minutes long relaxation step continue. Loading
process and relaxation step make together one
loading step.

Installation of geotechnical pressure cells
for measurement of the stress on theinterface of the slab and subsoil.

MEASUREMENT OF VERTICAL
DEFORMATION – SUBSIDENCE

Fig. 6

Vertical deformations were measured by the setof 16 potentiometers. Potentiometers wereconnected to the recording sensor station. The
station was programmed to automatic scanning andrecording measured values. Time interval for recordof deformation measurement was 10 seconds.
Schematic plan of sensors is displayed on Fig. 8.
Subsidence results of potentiometric position sensors– deformations at particular points are displayed on
Fig.9 and subsidence of experimental post-tensioned
foundation model in line 1-1´ is displayed on Fig.10.

Strain measurement gauges inside theexperimental slab

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GEOMATE- Osaka, Nov. 16-18, 2015

STAND EXPERIMENTAL DEVICE –STIFFNESS CONTROL
During the course of the experiment, it wasnecessary to check the experimental device stiffness.
The test stand was equipped with a leveling target
and, the leveling device was targeted at this markduring the test. Thanks to this measure, the beam
deflection, to which was connected a hydraulic press,
could be continuously controlled.

COMPRESSIVE STRENGTH
CHARACTERISTICS OF TESTEDCONCRETE

Fig.8

Compressive strength characteristics of the tested
concrete were verified in according European
Standard EN 206:2013. Three pieces of test cubes
were taken during the concreting. These cubes, withdimensions 150 x 150 x 150 mm, were allowed tothe mature for 28 days. Cube samples were used forthestrengthcharacteristic
verificationinCompressive strength characteristics of the tested
concretewereverifiedinaccording
CSN EN 12390 - 3. Tested concrete was classified
as C35/45. Tests of compressive strengthcharacteristics were realized in the laboratory ofFaculty of Civil Engineering in Ostrava.

Schematic plan of potentiometricposition sensors

EXPERIMENTAL EVALUATION OF THE
POST-TENSIONED CONCRETE SLABCONTACT SURFACE
Fig. 9The location of the test pattern inside the testing
device for experimental device “STAND” and theuse of available handling techniques enabled to lift
the test slab. Threadbars were used for anchorage forstretching hanging straps. A pair of chain hoists washung on the “STAND” beams. The experimental
pattern was lifted and allowed the course of cracks
evaluation right on concrete slab surface that was incontact with the subsoil. The crack cross ishighlighted with red color on Fig.11. Punching shear
damage are displayed on Fig. 12 and Fig. 13. Next
cracks were localized near anchors of middle
prestressing threadbars – displayed on Fig.14.

Results of potentiometric position sensors –deformations at particular points

Fig. 10 Subsidence of experimental prestressed
concrete foundation model(line 1-1´ on Fig.8)484

GEOMATE- Osaka, Nov. 16-18, 2015

CONCLUSION
Experimental prestressed concrete foundationslab model resist the loads exerted after seven load
cycles and induced maximal load level 525 kN. First
significant cracks were detected after fourth loading
step Cracks were located near anchors of middle
threadbars. After sixth loading step were detected
first signs of punching shear. Experiment was ended
in moment, when the model was strongly damagedby punching shear. Experiment bring many
information about influence of prestressing topunching shear resistance. Measured data will servefor comparison with results obtained by numerical
FEM modeling of soil – prestressed foundation slab
interaction.

Fig. 11 The course of cracks on the contact surface
of experimental post-tensioned slab

ACKNOWLEDGEMENTS
The works were supported from sources for theconceptual development of research, development
and innovations for 2015 at the VSB-Technical
University of Ostrava, which were granted by the
Ministry of Education, Youths and Sports of the
Czech Republic.
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Fig. 12 Punching shear damage on the uppersurface of experimental post-tensioned slab

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Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

A NEW RHEOMETRICAL EQUIPEMENT FOR THE
CHARACTERIZATION OF LARGE GRANULAR MATERIALSINVOLVED IN FAST LANDSLIDES
Anna Maria Pellegrino1, Anna Scotto di Santolo2, Aldo Evangelista3 and Leonardo Schippa1
Department of Engineering, University of Ferrara, Italy; 2Pegaso University, Italy; 3University of Napoli
Federico II, Italy.

1

ABSTRACTThe present study focused on novel rheometrical tool for the determination of the rheological behaviour oflarge particle mixtures with particular interest in the application to granular materials involving in fast landslides
as mud and debris flows. The main goal is the development of the Sphere Drag Rheometer (SDR), a home-madelarge-scale rheometer that allow to estimate the rheological properties of these mixtures considering the wholegrain size distribution. The SDR is composed of a sphere rigidly joined to a support that moves at constantvelocity into the mixture. Compared to standard rheometer, the SDR uses much higher volume of mixtures ofwider grain size distribution next to that in situ. Several pyroclastic soils collected from the source area of debris
flows occurred in Campania region (Southern Italy) were analyzed. The paper reports the experimental results onfine particles mixtures, large particle mixtures, and their comparison.
Keywords: Four or Five Keywords (First Characters of Each Word are in Capital/Uppercase Letters), Italic

a continuous viscous fluid and refers to rheologicalmodels derived from the resistance formula of
Newtonian and Non-Newtonian fluids [6]. When arheological model is considered, only the material
flow curve, e.g. expressing the shear rate-shear
stress relation, is required. Nowadays, efficientrheometrical apparatuses for the determination of therheological parameters of mixtures and fluids
containing fine particles exist and the experimental
methods related to them are widely consolidated [5][7]-[8]-[9]-[10]. For mixtures and fluid containing
large particle, the study of the influence of the grain
size is more complicated. Experimental devises
equipped with a large geometrical configurations are
required and such apparatuses are few and notcommercially developed [11]-[12]-[13]-[14]-[15].
The debris flow materials hereby consideredpresents a wide grain size distribution and a small
content of fine particles. Therefore, in order toestimate the rheological properties of these mixtures(i.e., considering the whole grain size distribution), ahome-made experimental large-scale rheometer was
design and built, the Sphere Drag Rheometer (SDR).
The system could be an efficient rheometrical tool
for the investigation of fluids containing particles up
to 10 mm grain size.

INTRODUCTION
The paper focuses on an innovative rheometricalsystem for the determination of the rheologicalproperties of fluids containing large particles, i.e.
maximum grain size larger than 1 mm. The
knowledge about the rheological properties of large
particle fluids is of great and increasing importancebecause of their relevance in several fields likenatural hazards, building materials, gas and oil, andfood industry [1]. In the present study fast landslides
as debris flows and the application of rheometry fordebris flows are of primary interest. Debris flows putin danger people and animals, and cause damage inhilly and mountainous areas. More than 55000
people died in the last hundred years worldwide due
to debris flows or combined debris flow and flood
events. In Italy more than 4000 people die from1963 to 2012 due to debris flows and flooding
phenomena. In order to protect people, animals andinfrastructures from damage, hazard zone mapping
and warning systems as well as structural protective
measures must be considered depending on thespecific situation. During the definition process ofsuch protective measures, the debris flow modelingis a fundamental task [2]-[3]. Numerical andphysical modeling help in the hazard zones
delineating and in the structural design and testing[4]. In order to understand and simulate the flow andthe deposition process, an appropriate rheologicalmodel is a very useful physical concept for mud,debris and hyper-concentrated flows [5]. The
simplest method is to assimilate the flowing mass to

THE SHERE DRAG RHEOMETER SDR
Characteristics and procedures
Referring to the works of [14]-[16], wedeveloped an innovative system for measuring the
motion’s resistance for large particle mixtures called
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GEOMATE- Osaka, Nov. 16-18, 2015

Ω, are showed. For each imposed velocity the valueof the required drag force F D (value at which thesphere starts moving through the material sample) is
evaluated as the average between the measured
points in a full rotation, of the sphere, as illustrated
in Fig. 2b. The apparent flow curve was obtained byapplying an increasing and decreasing rotational
speed ramp. In order to ensure the stability of themeasurements, each value of velocity was imposedfor 30 seconds. This time was in agreement with theexperimental results carried out with conventionalrheometer on the same pyroclastic debris flow
materials as described in [5].

SDR. The SDR is composed of a sphere, withvariable diameters D, rigidly joined to a support thatmoves at constant velocity into the mixture. The
sphere is connect to a load cell that measures thedrag force necessary to move the ball through thefluid at the imposed speed. The system, illustrated inFig. 1, is composed by a cylindrical container (i.e.
having radius d c equal to 130 mm, height h c equal to
60 mm and sample volume equal to 0.5 l) in which
the material sample are located and of an eccentric
sphere (i.e. with a variable eccentricity r, relative tothe motor, varying from 20 to 46 mm) fixed to a thin
vertical shaft. Differently from conventional
apparatus, the SDR rheometer allows to consider
larger sample volume (0.5 l in SDR versus 30 ml instandard rheometers) and larger grain size
distribution (up to 10 mm grain size in SDR versus
0.5 mm grain size in standard rheometer). The
device can be equipped with different spheres
having diameters ranging from 8 to 18 mm. The
main components of the SDR apparatus are (see Fig.
1): a base station with two motors (i.e., a “slowmotor” goes from 0.4 to 9.5 rpm and a “fast motor”
goes to 10 to 260 rpm); a telemeter as broadcasting
system; load cells having different full scale FS (i.e.,
one equal to 250 g and the other equal to 1000 g); a
box for the electronica controlling; a reed (i.e. amagnetic position sensor); a software called
SMV_V1 developed ad hoc for the SDR rheometer
control.
The experiment consists in measuring the dragforce F D at a specified rotational speed Ω, while thesphere makes one full rotation within the material
sample.

Fig. 2

Silicon oil with viscosity equal to 60 Pa·s.
a) Measurements of FD during a fullrotation of the sphere (D = 18 mm) at
different values of the imposed rotational
speed Ω (diamonds 1.24 rad/s, squares 2.27
rad/s, triangles 3.28 rad/s, circles 4.19 rad/s,stars 5.15 rad/s, crosses 6.93 rad/s, little
dashes 7.83 rad/s, big dashes 8.63 rad/s); b)
Resulting flow curve F D -Ω.

Calibration of the SDR rheometer

Fig. 1The SDR rheometer was calibrated using several
reference materials for which the rheologicalbehaviour was already analyzed and well-known.
Tests were performed on different Newtonianviscous materials (i.e. glycerin and silicone oils at
different peculiar viscosity) and on Non-Newtonianviscous materials with yield stress (i.e. hair gel andtomato sauce). After, the calibration was carried out
using a representative soil-water mixture, composedby water and kaolin, in order to approach thefeasibility of the experimental apparatus withparticles-fluid suspensions close to debris flow
material mixtures tested in traditional rheometer

Upside: geometrical scheme of the SDR
rheometer.
Downside:completeconfiguration of the SDR rheometer.

In Fig. 2a the measured points, obtained during
one full rotation of the sphere, for different values of488

GEOMATE- Osaka, Nov. 16-18, 2015

In equation (2), τ is the shear stress (Pa), τ c is theyield stress (Pa), γ is the shear rate (s-1), k is theconsistent coefficient (Pa·sn) and n is thedimensionlesspseudo-plasticity
index.
The
considered conversion theory is applicable only inthe treatment of Yield Stress Fluid. As observed in[5] the soil-mixtures considered in the present study
are Yield stress Fluid and their behaviour is welldescribed using the Herschel & Bulkley model(equation (2)). In this condition the drag force F D
can be computed as following:

before [5]. The kaolin-water mixture wasreconstituted according to the total solid volumetricconcentration Φ T , defined as the ratio of the volume
of solids to the total volume (water plus solids), as
follow:

ΦT =

VS
VS
=
= Φ f + Φg
VTOT VS + VW

(1)

where V TOT , V W , V S are, respectively, the total
volume, the volume of water and the volume of solidin the sample, and Φ f , Φ g are, respectively the solid
volumetric concentration of fine particle and thesolid volumetric concentration of coarse particle ofthe mixture. The complete description of thecalibration is reported in [17].

FDk n=1 + ⋅ γapp
FC
τc

(3)

Where k is an increasing, positive, dimensionlessfunction of the surface of the object and decrease
towards a critical, minimum value k c and F C is thecritical drag force FC equal to:
FC = 4π ⋅ R ⋅τ c ⋅ kc
(4)

Conversion of measured data into rheologicalparameters
Unlike traditional rheometers, the measuringsystem adopted for the SDR is not based on theclassic shear flow between two parallel surfaces buton the flow regime around the object, a sphere thatmoving as in Fig. 3. For this reason it was defined an
appropriate theory of conversion that allow to relate
the two schemes of flow.

Where R is the sphere radius. Considering thenumerical work of [23], the relationship between themeasured sphere velocity and the apparent shear rateis the following:γapp =vl(5)

Where v is the linear velocity of the sphere (m/s)
and l is equal to 1.35 multiplied the radius of thesphere R. The complete description of the applied
conversion theory is reported in [17].
MATERIALS AND METHODS
Fig. 3

Schematic principal task for the sphere dragmeasuring system on the left. Scheme of
the classic shear flow on the right.

The SDR experimental results reported are
related to pyroclastic soils which are representativeof real pyroclastic debris flows occurred in Southern
Italy. The soil mixture analyzed with SDR were
before tested with standard rheometer as reported in[5]. The soil A and B derives from the most recent
deposits produced by the volcanic activity of mount
Somma/Vesuvius and they are sandy silt with asmall clay fraction. The geotechnical andmechanical properties of such materials are well
documented in literature [24]. All the experiments
involved mixtures of dry soils with different
amounts of water. The materials were mixed withappropriate amount of distilled water in order toobtained mixtures having different solid volumetricconcentrations Φ according to equation (1). The
range of solid concentration here considered was
defined according to natural porosity of the soils andtaking into consideration the previous study of [5]. A
total amount of about 500 ml of mixture (distilledwater and soils) was pre-pared and each sample wascontinuously mixed for the time needed to obtain afairly homogeneous mixture. The whole sample wasused performing SDR test. The entire experimental
program was carried out at constant temperature

The analytical solution of the free motion of an
object into a Newtonian fluid goes back to the workof [18]-[19] reported in [20]. The problem of the
motion of an object of spherical shape through a
Yield Stress Fluid is more complex than the case of
Newtonian Fluid and requires a different treatment.
No simple solutions were found yet and various
analytical expressions were also been proposed [21][22]-[23]. All the theoretical works, which assume asimple yielding behavior, predict a particle velocity
progressively decreasing to zero as the applied force
tends to the critical value for incipient motion. The
experimental results presented in the literature are inagreement with this basic expectation, namely, thatthe object can move through the fluid only when theforce applied to it becomes greater than a critical one.
In the case of Yield Stress Fluid we assume that thefluid remains perfectly rigid in its solid regime andfollows a constitutive equation such as Herschel &Bulkley in its liquid regime as:τ = τ c + k ⋅ γ n ⇒ τ > τ c

(2)
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GEOMATE- Osaka, Nov. 16-18, 2015

equal to 23°. Fine-grained mixtures (i.e., mixtures
composed by soil fraction with a particle diameter
less than 0.5 mm) were tested in order to compare
the data to those obtained using standard rheometer[5]. Also coarse-grained mixtures (i.e., mixtures
composed by soil fraction with a particle diameter
up to 0.5 mm) were tested. The complete
experimental program is illustrated in table 1. A total
amount of about 500 ml of mixture (distilled waterand soils) was pre-pared and each sample wascontinuously mixed for the time needed to obtain afairly homogeneous mixture. The whole sample wasused performing SDR test. The entire experimental
program was carried out at constant temperatureequal to 23°.

the equations (3) and (4). First, we note that there isa good match between the measured data and thoseobtained through the application of the conversion
theory. The water-soil mixtures behave like NonNewtonian fluid and flow resistance increases with
the increasing of shear rate regardless the value ofsolid concentration. The solid fraction clearly
influences the rheological behavior of the materials.
Although it is not possible to precisely identify thevalue of the critical drag force F D associated to each
mixtures, especially for more dilute mixtures (e.g.,
because of the lack of measurement points at very
low rotational speed), even though the data show atrend equal to that obtained with tests in standard
rheometer [5]. The resistance force is strictly
influences by the solid volumetric concentration.
The intrinsic strength of the mixtures increases as
the particle fraction increases and there is a criticalvalue of drag force below which the flow is possible.
The coarse-grained water-soil A and B mixtures
were tested with SDR rheometer according toexperimental program described in Table 1. The
experimental data obtained are reported in Figures 6
and 7 for soil A and soil B, respectively, at different
solid volumetric concentrations Φ and different
grain size distribution. The graphs report theevolution of the drag force F D as a function of theapparent shear rate. For each mixtures considered,
the apparent shear rate was derived from the value ofthe imposed rotational speed Ω according toequation (5). In this way we defined theexperimental flow curve in which the measured dragforce F D is related to the calculated apparent shear
rate. Also in this case, the coarse-grained water-soil
mixtures behave like Non-Newtonian fluid and theflow resistance increases with imposed shear rate
regardless the value of solid concentration.

Table 1 Experimental program.
Test
(#)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

Soil
(-)
A
A
A
A
A
A
A
A
A
B
B
B
B
B
B
B

ΦT
(%)
35
38
40
42
35
38
40
35
38
32
35
38
30
32
35
38

Φf
(%)
35
38
40
42
28
31
32
21
24
32
35
38
21
22
25
29

Φg
(%)
7
7
8
14
16
9
10
10
9

d MAX
(mm)
0.5
0.5
0.5
0.5
5.0
5.0
5.0
10.0
10.0
0.5
0.5
0.5
5.0
5.0
5.0
5.0

RESULTS AND DISCUSSION
The fine-grained water-soil A and B mixtures
were tested with SDR rheometer according toexperimental program described in Table 1. The
experimental data obtained are reported in Figures 4
and 5 for soil A and soil B, respectively, at different
solid volumetric concentrations Φ. The graphs report
the evolution of the drag force F D as a function of
the apparent shear rate. For each mixturesconsidered, the apparent shear rate was derived fromthe value of the imposed rotational speed Ωaccording to equation (5). In this way we defined theexperimental flow curve in which the measured dragforce F D is related to the calculated apparent shear
rate. Then the experimental flow curves (e.g.,
symbols in Figures 4 and 5) were compared with thetheoretical curve (e.g., lines in Figures 4 and 5)
obtained starting to the conversion theory and using

Fig. 4Fine-grained water-soil A mixtures atseveralvaluessolid
volumetricconcentration Φ: comparison between SDR
measured data (symbols) and theoreticalcurve (lines).

The solid fraction also influences the rheologicalbehavior of the materials. The data show a trend
equal to that obtained with tests on fine-grained
mixtures.

490

GEOMATE- Osaka, Nov. 16-18, 2015

Fig. 5Fine-grained water-soil A mixtures atseveralvaluessolid
volumetricconcentration Φ: comparison between SDR
measured data (symbols) and theoreticalcurve (lines).

We note that the resistance force is strictly
influences by the grain size distribution andincreases as the size particles increases (Fig. 6)
according to [12] and [13]. The experimental results
obtained on coarse-grained water-soil A and B
mixtures were compared with those obtained onfine-grained water-soil A and B mixtures according
to experimental program described in Table 1. The
comparisons obtained are reported in Figures 8 and
9 for soil A and soil B, respectively, at different
solid volumetric concentrations Φ and different
grain size distribution. We observe that, at equal
total solid volumetric concentration, the adding of
the solid fraction with a diameter less than 5 mmbrings to a reduction of the characteristic resistanceof the mixture regardless the material tested.
Moreover, when you consider that the addition of
the fraction of particles with a diameter less than 5
mm corresponds to a solid volumetric concentrationof coarse particles Φ g of about 8-10% of the entire
total solid concentration, this result becomesconsistent with several previous experimental
observations ([12]-[13]) and with the experimental
results obtained through tests on the same coarsegrained mixtures with conventional measurement
systems reported in [15]. Several studies havealready demonstrated that the addition of coarse
particles brings to an increase of the rheologicalcharacteristics of the mixture but, for relatively low
solid volumetric concentration, a minimum
depletion in fine particles fraction within the mixture
does not seem to be sufficient to induce a increase ofrheological parameters ([12]-[13]-[14]- [15]).
Different considerations can be done when the solid
fraction with a diameter less than 10 mm is added.
We observe that, at equal total solid volumetricconcentration, there is an increase of thecharacteristic resistance of the mixture. Indeed, it isnoted that the addition of the fraction of particles
with a diameter less than 10 mm corresponds to asolid volumetric concentration of coarse particles Φ gof about 14-16% of the entire total solid
concentration.
491

Fig. 6

Coarse-grained water-soil A mixtures atseveral solid volumetric concentration andgrain size distribution according to Tab. 1.

Fig. 7

Coarse-grained water-soil B mixtures atseveralvaluessolid
volumetricconcentration Φ and grain size distribution
according to Tab. 1.

Fig. 8Water-soil A mixtures: comparison
between fine-grained and coarse-grained
mixtures according to Tab. 1.

Fig. 9Water-soil B mixtures: comparison between
fine-grained and coarse-grained mixtures
according to Tab. 1.

GEOMATE- Osaka, Nov. 16-18, 2015[12] Coussot P & Piau JM, A large-scaled field
concentric cylinder rheometer for the study of
the rheology of natural coarse suspensions,
Journal of Rheology 39, 1995, 105-124.
[13] Ancey C & Jorrot H, Yield stress for particle
suspension within a clay dispersion. Journal of
Rheology 45, 2001, 297-319.
[14] Schatzmann M, Bezzola GR & Minor HE,
Rheological Behaviour of Fine and Large
Particle Suspensions, Journal of HydraulicEngineering 129, 2009, 796-803.
[15] Scotto di Santolo A, Pellegrino AM &Evangelista A, 2010. Experimental study on therheologic behaviour of pyroclastic debris flow,
Journal of Natural Hazards and Earth Systems
Science 10, 2010, 1-8.
[16] Müller, M., Tyrach, J. & Brunn, P.O. 1999.
Rheological char-acterization of machine
applied plasters. ZKG International 52: 252-258.
[17] Pellegrino AM, Scotto di Santolo A &Evangelista A, A new rheometer for mud anddebris flow, Accepted in Workshop on Volcanic
Rocks & Soil, 2015.
[18] STOKES GG, On the effect of the internal
friction of fluids on the motion of pendulums.
Trans. Camb. Phil. SOC. 9 (11), 1851, 8-106.
[19] Einstein A, Investigation of the Brownian
Movement, Ann. Physik 19, 1906, 289-306.
[20] Lapple CE & Shepherd CB, Calculation ofparticles trajectories, Industrial and Engineering
Chemistry 32, 1940, 605.
[21] Ansley RW & Smith TN, Motion of sphericalparticles in a Bingham plastic, AIChE Journal,
13, 1967, 1193-1196.
[22] Beris AN, Tsamopoulos JA, Armstrong RC &Brown RA, Creeping motion of a spherethrough a Bingham plastic, Journal of Fluid
Mechanics 158, 1985, 219-244.
[23] Beaulne M & Mitsoulis E, Creeping motion of asphere in tubes filled with Herschel-Bulkley
fluids, Journal of Non-Newtonian Fluid
Mechanics 72, 1997, 55-71.
[24] Picarelli L, Evangelista A, Rolandi G, Paone A
Nicotera MV, Olivares L, Scotto di Santolo A,
Lampitiello S, Rolandi M, Mechanicalproperties of pyroclastic soils in Campania
Region. In Proc. of 2nd Int. Workshop on
Characterisation and Engineering Properties of
Natural Soils, 2007.

CONCLUSIONSThe obtained experimental results, conveniently
converted into rheological parameters, show that the
SDR rheometer is able to analyze flowing materials
similar to those involved into pyroclastic debris flow.
REFERENCES[1] Forterre Y & Evangelista A, Some observations
on the prediction Pouliquen O, Flows of dense
granular media, An. Rev. of Fluid Mech., 40,
2008, 1-24.
[2] Scotto di Santolo A & Evangelista A, Someobservations on the prediction of the dynamic
parameters of debris flows in deposits in theCampania region of Italy, International Journalof Natural Hazards, 50, 2009, 605–622.
[3] Pudasaini SP, A General Two-Phase Debris
Flow Model, Journal of Geophysical Research,
117, 2012, F0310.
[4] Pellegrino AM, Scotto di Santolo A & Schippa
L, Numerical modeling of a debris flow event
occurred in Campania region, southern Italy:
consideration on the rheological model
parameters on the run-out, in Proc. Latest
Trends in Engineering Mechanics, Structures,
Engineering Geology, 2014.
[5] Scotto di Santolo A, Pellegrino AM,
Evangelista A, Coussot P, Rheologicalbehaviour of reconstituted pyroclastic debris
flow, Geotechnique Journal, 62 (1), 2012, 19-27.
[6] Coussot P, Mudflow Rheology and Dynamics,
IAHR Monograph series, Balkema.
[7] O’Brien JS & Julien PY, Laboratory analysis of
mud-flow properties, Journal of HydraulicEngineering 114, 1988, 877-887.
[8] Kaitna R, Rickenmann D & Schatzmann M,
Experimental study on the rheologic behaviourof debris flow material, Acta Geotechnica 2,
2007, 71-85.
[9] Bisantino T, Fischer P & Gentile F, Rheologicalcharacteristics of debris-flow material in SouthGargano watersheds, Nat. Hazards Earth Syst.
Sci. 54, 2010, 209-223.
[10] Jeong SW, The viscosity of fine-grained
sediments: A comparison of low-to mediumactivity and high-activity clays, Eng. Geol. 154,
2013, 1-5.
[11] Bagnold RA, Experiments on gravity-freedispersion of large solid spheres in a Newtonian
fluid under shear, Royal Society A 225, 1954,
49-63.

492

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

UNDRAINED BEHAVIOUR OF JOHOR SAND IN CYCLIC TRIAXIAL
TEST
Aminaton Marto, Choy Soon Tan, Ahmad Mahir Makhtar and Siti Norafida Jusoh
Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

ABSTRACTAs liquefaction due to earthquake hazards may occur on both clean sand and soil comprising of sand withfines, the current research trend is more focusing on determining the boundary limits of liquefaction
susceptibility of sand matrix soils. Series of laboratory testing had been carried out by researchers around theworld but many of the results are contradictory. Thus, it is important to first determine the condition in which theclean sand is most susceptible to liquefaction, then only the liquefaction susceptibility of sand matrix soils could
be compared and discussed under this specific condition. This paper presents the undrained behaviour of Johorsand obtained from cyclic triaxial tests. Stress controlled triaxial test apparatus was used to shear theisotropically consolidated sand samples under undrained two-way cyclic loading until the initiation ofliquefaction. The liquefaction was defined based on: (i) excess pore pressure was equal to effective confiningpressure or (ii) double amplitude strain of 5 % was reached, whichever was achieved first. The results of twoway cyclic triaxial tests on clean sand showed that besides the cyclic stress ratio, the liquefaction resistance ofthe sand under undrained loading was proportional to effective consolidation pressure and density index. The
Johor sand was more liquefiable at its loose state and under low effective consolidation pressure, when subjectedto earthquake loading.
Keywords: Liquefaction, density index, consolidation pressure, excess pore pressure.

is fixed the finer the sand, the lower the resistancetowards liquefaction. Thus, it is important to firstcompare at which testing environment that the clean
sand is potentially liquefiable, and then only theliquefaction susceptibility of sand matrix soils could
be compared under these specific conditions. This
paper presents the undrained behaviour of Johorsand obtained from cyclic triaxial tests. It compares
various important factors in the triaxial testing
environment and determines the most susceptible
environment of the Johor clean sand towardsliquefaction hazards.

INTRODUCTION
Soil liquefaction, the secondary effect of anearthquake; is a transformation of granular material
from a solid state into a liquefied state with asignificant increase of pore water pressure until theeffective stress reaches zero [1]. For many years,
research on liquefaction has been a mandatory focus
on uniform clean sands, containing little or no fines;
with a common assumption that the cyclic behaviourof clean sand is remarkably representing all types of
in-situ sand deposits. Unfortunately, empiricalevidences including the 2010 Canterbury Earthquake[2], the 2011 Christchurch Earthquake [3] and the2011 Tohoku Earthquake [4] have shown that thesand matrix soils (sand dominant soil with limiting
percentage of fine particles existed within) does
actually liquefy.
Lots of the laboratory testing had been carriedout by researcher worldwide [5, 6] using the cyclic
triaxial testing system. Nonetheless, the researchbreakthroughs are somewhat conflicting. There is
not enough evidence at this point to obtain a global
agreement in describing how the fines particles
influence the soil liquefaction resistance underdynamic loading.
Head [7] stressed that sand is largely influenced
by its composition characteristics such as soil grains,
packing density and void ratio. Research by
Choobbasti et al. [5] found that when density index

LABORATORY TESTING
The two-way cyclic triaxial test, in accordance to
the ASTM D5311-13M Load Controlled Cyclic
Triaxial Strength of Soil [8] was carried out tosimulate the cyclic loading on cylindrical sizereconstituted clean sand soil specimen of
approximately 76 mm height and 38 mm diameter.
Material
The clean sand used in this study was the natural
sand obtained from a river in Johor, Malaysia. The‘Johor’ sand has a sub-angular shape with lightbrown colour. Based on the particle size distribution
curve shown in Figure 1, the sand was poorly graded(SP) containing no fines with medium fine particle
493

GEOMATE- Osaka, Nov. 16-18, 2015

size in a narrow range. As pointed out by
Choobbasti et al. [5], poorly graded sand is very
susceptible to liquefaction than well graded sand.
Hence, this soil is a suitable type for studying therole of fines in liquefaction susceptibility of sandmatrix soils. The minimum and maximum densitiesof the sand were 1.37 Mg/m3 and 1.59 Mg/m3,
respectively. Based on these values, the density ofsand for the required density index (I D ) was
calculated and prepared for the cyclic loading tests.

Fig. 1various testing conditions; (i) effective consolidationpressure (100 and 200 kPa); (ii) density index of
20% to represent the loose state and 60% torepresent the dense state and (iii) cyclic amplitude interms of CSR at 0.1, 0.2, 0.3, 0.4 and 0.5. The natureof cyclic loading applied to the soil deposit is highly
dependent on the loading source, which could be
relatively uniform with single frequency or
randomly with a range of frequencies. Althoughvarious researchers carried out the test with varying
frequency ranging from 0.1 Hz [9] to 2 Hz [6], thetesting standard of ASTM D5311-M13 [8] states thatthe frequency of
1 Hz is more preferable. Since
the simulation of earthquake loading in a triaxial
testing system is always represented by 1 Hz [10],all sand matrix soils in this study were tested with 1
Hz cyclic frequency.
The cyclic loading process was terminated either
when the soil reached 10 % of axial strain or when
100 cycles of cyclic loading had been subjected tothe soil specimen, whichever encountered first. In
this study, the initiation of liquefaction (failure) was
defined as either when excess pore pressure is equal
to effective consolidation pressure [11] resulting in a
temporary condition of zero effective stress in thespecimen (r u=1 ) or when the double amplitude strainof 5 % (ε DA =5%) was reached [12, 13]. Marto [14]pointed out that the two-way cyclic loading typicallygenerates two components of the pore pressure
responses, which are known as the resilient response
and the permanent response. The pore pressure forthe resilient response were observed at the peak of
the compression and extension zones, and known as
the u peak(c) and u peak(e) , respectively. In this study, thepore pressure for the permanent response (u permanent )was used to characterise the pore pressure response
due to cyclic loading.

Particle size distribution of Johor sand

Cyclic Triaxial Test
Prior to the saturation process, carbon dioxidegases and de-aired water was flushed through thespecimens from the bottom drainage line to the top
until an amount equal to the specimen’s void volume
was collected. Saturation process was carried out
with a linear increase of cell and back pressure,keeping a constant difference of 10 kPa effective
stress. This process was continued until theminimum B-value of 0.96 was achieved. Thenisotropic consolidation was performed andcompleted in a very short period. Upon thecompletion of isotropic consolidation, the stresscontrolled undrained cyclic triaxial tests werecarried on the specimen using the back pressure of
200 kPa. In order to apply the two-way loading, thetop cap was locked to the loading ram to enable theapplication of extension forces using an extension
top cap and vylastic sleeve. The cyclic loading interms of cyclic stress ratio (CSR) was applied to thespecimen accordingly to simulate different loading
amplitude. The CSR was calculated as the ratio of
the applied shearing stress (one-half of the applied
deviator stress amplitude) to the initial effective
consolidation stress.
The sand specimens were loaded cyclically under

RESULTS AND DISCUSSION
Cyclic triaxial test under undrained condition
was conducted on 12 clean sand specimens
subjected to different testing condition, at varying
density index, effective confining pressure andcyclic stress ratio. Table 1 shows the number ofcycles at the initiation of liquefaction for clean sandobtained from two-way cyclic triaxial tests. The
respective testing condition and failure criteria foreach test were also shown. It is found that loose
specimen exhibited large strain on compression side
(positive value of deviator stress) whereas dense
specimen exhibited dominant deformation on
extension side (negative value of deviator stress).
This result was also observed by Lombardi et al [9].
Figure 2(a) shows the critical state line obtained
from monotonic triaxial tests [15] together with theeffective stress paths (ESP) of the loose sandspecimen subjected to cyclic loading. It could beseen from the plot that the ESP moved to the left
494

GEOMATE- Osaka, Nov. 16-18, 2015

during the cyclic loading. This is because as thevolume change of soil was not allowed in undrained
test, the positive pore pressure was developed andreduced the stresses accumulated in the soil due toshearing. For the loose sand specimen, it approached
the critical state line in a sudden manner when theinitiation of liquefaction occurred. Figure 2(b) shows
the ESP of the dense sand specimen subjected tocyclic loading. It could be seen from the plot that the
ESP also moved to the left during the cyclic loading.
However, the dense sand generated the characteristicof butterfly shape ESP before failure. Both theresults are comparable to the stress paths of Red Hillsand subjected to cyclic loading, modelled by
Lombardi et al [9]. The stress path was seen to have
moved to the left in a sudden manner, which wascaused by large deformation as a result ofliquefaction. The dense sand specimen on the other
hand, developed a progressive decrement in theshear stress with the addition of the number of cycle.
Thus, the stress path was observed in a butterfly
shape loop when the condition of zero effective
stress was ascertained.

Table 1 Results of cyclic triaxial test on Johor sandEffective confiningpressure (kPa)
100
100
100
100
100
100
100
100
200
200
200
200

Specimen
SAND1
SAND2
SAND3
SAND4
SAND5
SAND6
SAND7
SAND8
SAND9
SAND10
SAND11
SAND12Note:

Densityindex (%)
20
20
20
20
20
60
60
60
20
20
20
20

ε DA =5% : 5 % axial strain at double amplitude

r u=1
Nc
CSR

Fig. 2

CSR
0.1
0.2
0.3
0.4
0.5
0.1
0.3
0.5
0.1
0.2
0.4
0.5

FailureCriteria
ε DA =5%ε DA =5%ε DA =5%
r u=1
r u=1
ε DA =5%ε DA =5%r u=1ε DA =5%ε DA =5%ε DA =5%
r u=1

Nc
40
27
19
14
10
75
54
36
67
52
34
26

: ratio of excess pore pressure to consolidation pressure = 1: number of cycle: cyclic stress ratio

Effective stress paths of Johor clean sand subjected to two-way cyclic loading
initial effective confining pressure (σ’ 3C ) of 100 and
200 kPa, respectively. Figure 3 shows the effect of
initial effective confining pressure towardsliquefaction susceptibility of Johor clean sand under

Effect of Confining Pressure
The effect of confining pressure was investigatedby performing the two-way cyclic triaxial tests with495

GEOMATE- Osaka, Nov. 16-18, 2015

60 % was 54. The results showed that additional
number of cycles was required to initiate theliquefaction at high I D with the same CSR. Theliquefaction curve shifted to the right when the I D
was increased from 20 to 60 %. The findings ofKrim et al [17] and Lombardi et al [9] also showedsimilar findings. From their research, they concluded
that the liquefaction resistance of sand is directly
proportional to the density index. Head [7] indicatedthat the sand is considered loose for 20 % I D and
dense for 60 % I D . Thus, the results obtained in thisstudy are similar to other types of sand [9, 17] inwhich the loose state of Johor sand is also more
susceptible to liquefaction than the dense state.

triaxial testing system. The results show that
additional number of cycles (N c ) was required toinitiate the liquefaction at a high initial effective
confining pressure with the same CSR. Theliquefaction curve shifted to the right when theinitial effective confining pressure was increased
from 100 to 200 kPa. For an example, it can be seenfrom the figure that for a constant CSR value of 0.2,
the N c for σ’ 3C of 100 kPa was 27 while it was 52
for σ’ 3C of 200 kPa. The findings of Della et al [16]and Krim et al [17] also showed that liquefaction
resistance of sand is proportional to the effective
confining pressure. The liquefaction could occur at
low confining pressure but as the confining pressure
increased to a higher value, the sand becomes denser
thus having better resistance towards liquefaction. In
addition, the results also agreed on the common
ground with the general concept of liquefaction
susceptibility of sand. Generally, the liquefaction
only occurred to the soil at shallow depths. Youd etal [18] indicated in the summary of NCEER
workshop that loosely deposited sand with depth ofless than 15 m is susceptible to liquefaction. At
shallow depths, the effective confining pressure islow.

Fig. 4

Fig. 3

Effect of density index on cyclic behaviourof Johor clean sand at 100 kPa initialeffective confining pressure and subjected
to 1 Hz cyclic frequency

In general, the results obtained in this study arein agreement with the concept of liquefaction
susceptibility of sand as stated by Kramer [19]whereby the liquefaction is more prone to occur inloose sand. When there is an external loading
exerted on saturated sand under the undrained
condition, the response of cyclic softening on sanddiffers in accordance to density state. For loose sandof low density and with contractive tendency, theloading was too quick for the pore water to escape.
In response to the compressibility of soil, the porepressure would build up to counter the external load.
At a point where the pore pressure is exceeding thecontact stresses between sand grains, the soil wasobserved to flow like a liquid form. On the other
hand, liquefaction hardly occurs to the sand in a highdensity state. Dense sand is not susceptible to flowliquefaction because its strength is greater inundrained condition than in drained condition [20].
Besides that, due to its dilative behaviour in nature;

Effect of confining pressure on cyclic
behaviour of Johor clean sand at initial
density index of 20 % and subjected to 1Hz
cyclic frequency

Effect of Density Index
The effect of density index (I D ) was investigatedby performing the two-way cyclic triaxial tests withinitial density index of 20 and 60 %. Figure 4 showsthe effect of density index towards liquefaction
susceptibility of Johor clean sand under two-way
cyclic triaxial tests. From the figure, for CSR of 0.3,
the N c at I D of 20 % was 19 while the N c at I D of496

GEOMATE- Osaka, Nov. 16-18, 2015

the pore pressure would easily escape from thematrix of dense sand grain during cyclic loading.
Hence, dense sand has more resistance toliquefaction as compared to loose sand.

clean sand at initial density index of 20 %, initialeffective confining pressure of 100 kPa and cyclic
frequency of 1Hz
Through the regression analysis, the CSR was
correlated with the N c to form the liquefaction
susceptibility curve in natural logarithmic relation.
The coefficient of determination (R2) of 0.99
indicated that it is a very good relation [21].
Statistically it means that 99% of the total variationof N c , as a dependent variable, is explained by the
regression line using the log e CSR, as theindependent variable. Hence, the liquefaction
susceptibility curve obtained could be used to divide
the liquefaction and non-liquefaction zones withgreat accuracy. The equation of the liquefaction
susceptibility curve is as follows:

Liquefaction Susceptibility
Laboratory test results showed that besides CSR,
the liquefaction resistance of the sand under
undrained cyclic loading was also proportional to theinitial effective confining pressure and the density
index. In this study, the cyclic resistance of soils had
been expressed in terms of the N c required for theliquefaction to occur. It has been found that thehigher the CSR, the lower the N c required for theliquefaction to initiate. For the effect of initialeffective confining pressure, the N c obtained was
lower for 100 kPa than for 200 kPa at the same valueof CSR. In term of the effect of initial density index,
the N c was lower for 20 % I D than for 60 % I D at thesame value of CSR. Thus, from this study the mostliquefiable condition for Johor clean sand under 1Hz
cyclic frequency of two-way triaxial tests was beingat its loose state condition (I D = 20 %) and confinedwith low effective confining pressure (100 kPa).
Figure 5 shows the liquefaction susceptibility
curve of sand obtained from this study under thetesting condition of I D = 20 % and confined withinitial effective confining pressure of 100 kPa. Theliquefaction susceptibility curve is the plot of CSR
versus N c which divided the sand into two zones;liquefaction and non-liquefaction. The shape of thecurve was in general agreement with the curve
obtained by previous researchers [10, 11 and 13].
However, due to different testing conditions, thecurves obtained in previous studies showed different
magnitude and location compared to the results
obtained in this study.

Fig. 5N c = -18.71 ln (CSR) -3.17
In which,(1)

N c = number of cycle
CSR = cyclic stress ratio

CONCLUSION
The undrained behaviour of Johor clean sandunder the two-way cyclic triaxial tests had been
determined successfully and the following
conclusion had been drawn:
1. The response of cyclic softening on Johor clean
sand differs in accordance to density state;liquefaction was more prone to be occurred inloose sand. The loose sand approached thecritical state line in a sudden manner when theinitiation of liquefaction occurred while thedense sand generated the characteristic ofbutterfly shape effective stress path and took alonger period before eventually failed.
2. The higher the cyclic amplitude or cyclic stressratio during cyclic loading, the smaller thenumber of cycles required for the initiation ofliquefaction.
3. The higher the initial confining pressure, thedenser the sand thus the better resistancetowards liquefaction.
4. The Johor sand was more liquefiable at loose
state (I D = 20 %) and under low effective
consolidation pressure (100 kPa), whensubjected to earthquake loading. At thisspecific
condition
theliquefaction
susceptibility curve, used as a boundary forliquefaction and non-liquefaction, had been
established and could be used as a guideline forengineers in preliminary design.

Liquefaction susceptibility curve of Johor
497

GEOMATE- Osaka, Nov. 16-18, 2015

SSI in liquefiable soils”, Soil Dynamics andEarthquake Engineering, Vol. 66, 2014, pp.
293-304.
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[11] Geremew AM and Yanful EK, “Laboratory
investigation of the resistance of tailings andnatural sediments to cyclic loading”,
Geotechnical and Geological Engineering, Vol.
30(2), 2012, pp. 431- 447.
[12] Salem M, Elmamlouk H and Agaiby S, “Staticand cyclic behavior of North Coast calcareous
sand in Egypt”, Soil Dynamics and Earthquake
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[13] Benghalia Y, Bouafia A, Canou J and Dupla JC,“Liquefaction susceptibility study of sandy soils
effect of low plastic fines”, Arabian Journal of
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[14] Marto A, “Pore pressure response of undrained
two-way cyclic loading of silt”, Malaysian
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[15] Marto A, Tan CS, Makhtar AM and Tiong KL,“Critical state of sand matrix soils”, The
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Geosciences, Vol. 4(5), 2011, pp. 983-991.
[17] Krim A, el Abidine Zitouni Z, Arab A and
Mostéfa B, “Identification of the behavior of
sandy soil to static liquefaction andmicrotomography”,
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Geosciences, Vol. 6(7), 2013, pp. 2211-2224.
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NCEER/NSF workshops on evaluation ofliquefaction resistance of soils”, Journal of
GeotechnicalandGeoenvironmental
Engineering, Vol. 127(10), 2001, pp.-833 817.
[19] Kramer
SL,
Geotechnical
Earthquake
Engineering. New Jersey: Prentice Hall, 1996.
[20] Das B, Advanced Soil Mechanics. Florida: CRC
Press, 2013.
[21] Marto A, Volumetric compression of silt underperiodic loading. University of Bradford, 1996.

ACKNOWLEDGEMENTS
The authors gratefully acknowledged thefinancial supports by the Ministry of Higher
Education(MOHE)
Malaysia,
undertheFundamental Research Grant Scheme (UTM vot
No.: 4F316) and also the Universiti Teknologi
Malaysia (UTM) through the Zamalah program, inundertaking the research for the publication of thispaper.
REFERENCES[1] Day R, Geotechnical Earthquake Engineering.
New York: McGraw Hill Professional, 2012.
[2] Wotherspoon L, Pender M and Orense RP,“Relationship between observed liquefaction at
Kaiapoi following the 2010 Darfield earthquake
and former channels of the Waimakariri River”,
Engineering Geology, Vol. 125, 2012, pp. 45-55.
[3] Orense RP, Kiyota T, Yamada S, Cubrinovski
M, Hosono Y, Okamura M and Yasuda S,“Comparison of liquefaction features observed
during the 2010 and 2011 Canterbury
earthquakes”, Seismological Research Letters,Vol. 82(6), 2011, pp. 905-918.
[4] Kokusho T, Nagao Y, Ito F, and Fukuyama T,“Aging effect on sand liquefaction observed
during the 2011 earthquake and basic laboratory
studies”, in International Symposium on
Engineering Lessons Learned from the 2011
Great East Japan Earthquake, 2012, pp. 759-770.
[5] Choobbasti AJ, Ghalandarzadeh A and Esmaeili
M, “Experimental study of the grading
characteristic effect on the liquefaction
resistance of various graded sands and gravelly
sands”, Arabian Journal of Geosciences, Vol.
7(7), 2013, pp. 2739-2748.
[6] Taylor ML, Cubrinovski M, Bradley BA andHorikoshi K, “The undrained cyclic strength ofundisturbed and reconstituted Christchurch
sands” in NZGS Geotechnical Symposium,
2013.
[7] Head KH Manual of soil laboratory testing.
Florida: CRC Press, 2006.
[8] American Society for Testing and Materials
2013 D5311M − 13: Standard Test Method for
Load Controlled Cyclic Triaxial Strength of Soil
[9] Lombardi D, Bhattacharya S, Hyodo M and
Kaneko T, “Undrained behaviour of two silica
sands and practical implications for modelling498

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

INVESTIGATING THE EFFECT OF CHANGING GEOMETRIC ANDRESISTANCE PROPERTIES OF MULTI-TIERED REINFORCED SOIL
WALL, ON IT’S BEHAVIOR
Mohammad Reza Atrchian 1 , Ahmad Armani 2
Faculty of Engineering, Islamic Azad University, Zanjan Branch, Iran
2
Faculty of Engineering, Islamic Azad University, Central Tehran Branch, Iran1

ABSTRACTIn reinforced soil walls, whenever it is about constructing a wall with a high height, because of its increasing
cost, it is necessary to consider constructing multi-tiered wall so that the cost of materials can be reduced to alarge extent. In this research, performance of multi-tiered geogrid reinforced soil walls has been considered andanalyzed under static loads. The research was conducted numerically and by using Plaxis software. The obtained
results indicate that: By increase of the length of reinforcement and width of the step, the maximum horizontal
and vertical displacement decreases. The value of horizontal and vertical displacement of wall in clay soil ismore than that of sand soil. By increase of the width of the step and length of the reinforcement, the maximum
deformations are transferred towards toe of the wall and the mode of deformation changes from bulging mode
into slide mode in the toe of the wall. The value of safety factor in sand soils is higher than that of clay soils, andalso by increase of the length of the reinforcement, the safety factor increases intensely. By increase of the lengthof the reinforcement and width of the wall step, the maximum effective stress decreases. The value of effective
and total stress of wall in clay soils is more than that in the sand soils. Finally, it can be concluded that increasing
the length of the reinforcement, width of the wall step, and using sand soil, improve the soil behavior.
Keywords: Reinforced soil, step width, multi-tiered wall, geogrid

numerical methods in this field can be considered asa strong tool [3]. Therefore, the main objectives ofthis research is to investigate the effect of length ofreinforcements, width of wall step, and changing soil
properties on behavior of stepped-reinforced soil
wall using numerical method.

INTRODUCTION
Geosynthetics have been used for three decades
to reinforce soil masses and now their application iswell accepted in soil constructions. Geosynthetics
have been used in soil structures including retaining
walls, slopes, embankments on soft soils, verticalembankment, and soil foundations [1]. Applicationof reinforcements increase resistance in soil mass
through providing tensile force, decreases lateraldisplacements, and consequently increases generalstability of soil structures [1]. The reinforced soil
system has been established as a new method toimprove performance of soil structures in various
points and has caused advances of this method inconstruction projects [1]. Stepped wall, compared tosimple wall with the same width, reduces the costs[2].
Multistep walls transfer a portion of their weighton the slope and naturally the volume of excavationfrom the natural land of the region and also thevolume of backfill would be decreased [2]. Behaviorof reinforced soils is subject to various factors.
Particle size, soil type, reinforcement method, andstructure height are considered as effective factors insoil behavior. Investigating the effect of each ofthese factors by using empirical or analytic methods
is very difficult and costly [3]. However, using

REINFORCED SOIL SYSTEM
Generally, reinforced embankment systems havethree main components [4]:- The reinforcement- Embankment- Facade components
As reinforcement, both metal and non-metalmaterials have been used [4]. In order to meetrequirements such as drainage, necessary durability,
and transmitting friction stress from thereinforcement to the soil, generally, granularmaterials are selected for embankment. Façade
components are used for maintaining embankment
materials along with the wall, preventing fromcorrosion of reinforcement, and also providing
beauty for the constructed wall (figure 1) [4]. Façade
components are only designed for small horizontalpressures and they are generally made ofprefabricated concrete panels, prefabricated metal
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GEOMATE- Osaka, Nov. 16-18, 2015

sheets, wire meshes, polymer networks, and avariety of other materials [4].

wrap-around face, prefabricated panels, integrated
concrete panels, wooden façade, gabion, etc [3].
The face used in this research is of the type of
wrap-around face, so that it is constructed based oneach reinforcing layer and its nature is in accordancewith reinforcement and geogrid with stiffness of
1400 kN/m.

Backfill
Concrete panel

Loading
To study the effect of overburden on deformationof the retaining wall, static loading was selected.
The value of the static overburden considered isequal to 6 kN/m with distance of 2m from the facadeof the last wall step.

Embankment with highquality material

Steel belt

Foundation Soil
Fig.1

Components of reinforced soil wall [4]The model used in this analysis is MohrCoulomb or the elasto-plastic model. The MohrCoulomb model includes 5 input parameters:
Elasticity modulus (E), Poisson ratio (ʋ), internal
friction angle (φ), cohesion (C), and dilation angle(ψ) [6].
Parameters related to soil foundation which areconsidered as constant are as the following table:

CONSTANT AND VARIABLE PARAMETERS
IN MODELINGIn this research, to study the effect of properties andgeometry of the wall, 50 numerical models weremade so that their geometric properties and constantand variable parameters are as follows:
Reinforcement Strength

Table 1 Parameters of soils used in thefoundation

Axial elastic normal strength (EA) which is also
known as stiffness range, is determined by the
manufacturer. Stiffness range of geosynthetic
reinforcements usually vary from 500 ( ) to 10000

Parameter
Foundationsoil (GC)γ( ) [5]. The type of geogrid used in this research isthe single-axis 80/20 R6. The value of its tensile
stiffness in 2% strain is equal to 28 , and byconsidering the strain and stiffness of interest, thevalue of its axial elastic normal strength is obtained
as 1400 .

Parameter
Foundationsoil (GC)γsat

E(KN/m3)(KN/m3)(KN/m2)

ν

21

22

60000

0.25

Φ

(KN/m2)

ψ

n

37

10

0

10%

C

Variable Parameters in Analysis
Variable parameters in the analysis are as
follows:- Widths of wall steps which are: 1m, 1.5m, 2m,
2.5m, 3m.
- Length of the reinforcements which areconsidered in this research based on total height of
the wallwhich is 15m, as follows:
L=0.5 H=7.5 m
L=0.6 H=9 m
L=0.7 H=10.5 m
L=0.8 H=12 m
L=0.9 H=13.5 m
The soils used in the body of the wall are the two
types of clay and sand soils as follows:

Distance between Reinforcement Layers
In this research, face of wall is reinforced bywrapping geogrid, for this reason, there is no needfor observing the vertical distance according to
prefabricated concrete face. In this research, thevertical distance between reinforcing layers in allheight of the wall is considered as constant andequal to 50 cm.
Wall Face
Of various types of faces used in reinforced soil
walls, we can mention regular block faces of MBW,
500

GEOMATE- Osaka, Nov. 16-18, 2015

Table 2 Parameters of soils used in the body of
the wallγγsatEνParameter (KN/m3) (KN/m3) (KN/m2)
Clay (CL)19.5
22.2
10000 0.35
Sand (SC)
19
22
20000
0.3

Parameter
Clay (CL)
Sand (SC)C

φ(KN/m2)

ψ

n

15
30

30
15

0
0

27%
30%

decrease of soil volume causes decrease of pressure
on face part of the wall and horizontal displacement
decreases as a result. If we want to define amathematical
relation
betweenhorizontal
displacement changes and step width, we can have:
W+0.5= 0.9 ~ 0.95 ΔH
Where,
W= Width of wall step
ΔH= Horizontal displacement

OUTPUTS AND ANALYSES OF RESULTS
In this paper, according to advantages andcapabilities of Plaxis software, numerical modelingwas conducted by using this software. The models(50 model) were created according to various
geometric models or different resistance properties
explained. In the created models, the effect ofchanging three parameters of horizontal distance,
geogrid length, and changes of soil type on behaviorof reinforced soil walls, have been studied.
In order to study the effect of these three parameters,
maximum stresses, maximum deformations, andsafety factors have been used.
In the following figure (Fig.2), geometricproperties of the reinforced soil wall related to thisresearch are indicated schematically:

Fig.3 Output of horizontal displacement in thereinforced soil wall (Reinforcement Length
0.5H=7.5m, step width=1m, and sand soil type)Fig.4 Output of horizontal displacement in thereinforced soil wall (Reinforcement Length
0.9H=13.5m, step width=3m, and clay soil type)Fig. 2 Geometric properties of models
Effect of Geogrid Length, Step width, and Soil
Type, on Horizontal Displacement

As it can be observed in figure 5 and 6, byincrease of the length of the reinforcements,
horizontal displacement decreases, and by increaseof the length of geogrids, the surface between thesoil and the reinforcement increases, and this way,
increase of interaction surface causes morestabilization of the wall and decrease of extent ofhorizontal displacement. It should be noted that
increase of step width compared to increase of

As it can be observed in figure 3 and 4, byincrease of step width, the value of maximum
horizontal displacement decreases. This issue is dueto this fact that the more step width increases, thevolume of soil existing in the face in which the mosthorizontal displacement occurs decreases, and501

GEOMATE- Osaka, Nov. 16-18, 2015

geogrid length, has a higher effect on decreasing
horizontal displacement. The mathematical relation
which exists between changes of horizontal
displacement and length of the reinforcement is asfollows:
L+0.1h= 0.95 ~ 0.99 ΔH
Where,
W= Width of wall step
ΔH= Horizontal displacement
h= Total height of wall
Also the other important point which can beobserved in the diagrams is that the extent ofhorizontal displacement of wall in sand soils is morethan that of clay soils. The reason is that sand soils
have a higher internal friction angle and also havemore sharp corners, so such properties of soil
particles and meshed shape of geogrid layers, cause
a better interlock between geogrid layers and soil
particles, and to the extent this interaction increases,
the displacements decrease.
L= 7.5
L= 12

L= 9
L= 13.5the bottom of the wall. The reason of this fact is thatby increase of width of wall step, volume of soil
decreases and as a result of that the pressure in upperparts of the wall decreases and is transmitted to thelower layers and result in more deformations inlower parts of the wall.
Besides increase of step width, increase of thelength of the reinforcement is also effective in shiftof the situation of maximum horizontal displacement
to lower parts of the wall face. The reason of thisfact is that by increase of the length of thereinforcement and establishment of more interaction
between the soil and geogrid, and also since lessforce is exerted to the upper layers, maximum
deformations are created in lower layers of the wall.
In other words, by increase of step width andlength of the reinforcement, deformation modechanges from bulging mode to sliding mode.
Meaning that if horizontal displacement occurs in
the highest point of the wall (peak), deformation
mode changes to overturning mode, if it occurs incentral parts of the façade, the deformation modechanges to bulging mode, and if it occurs in lower
parts of the wall, the deformation mode changes tosliding failure [7].

L= 10.5

maximum horizontal displacement
(m)

0.6
0.55
0.5

0.45

Effect of Geogrid Length, Step Width, and Soil
Type on Vertical Displacement

0.4

0.35

According to figure 7 and 8, by increase of step
width, the extent of maximum vertical displacement
decreases. In order to define a mathematical relation
between step width and maximum verticaldisplacement we have:
W+0.5= 0.87 ~ 0.97 ΔV
Where,
W= Wall step width
ΔV= Vertical displacement

0.3

1

1.5

2

2.5

3

stair width (m)maximum horizontal displacement (m)Fig.5 Maximum horizontal displacement against
step width in clay soil for different lengths ofgeogrid
L= 7.5
L= 12

0.2

L= 9
L= 13.5

L= 10.5

0.19
0.18
0.17
0.16
0.15
0.14
0.13

1

1.5

2

2.5

3

stair width(m)Fig.6 Maximum horizontal displacement against
step width in sand soil for different lengths ofgeogrid
By increase of step width, the situation of the
maximum horizontal displacement moves farther
from the middle of wall height and moves to nearFig.7 Output of vertical displacement in reinforced
soil wall (Reinforcement Length 0.5H=7.5m, step
width=1m, and sand soil type)502

GEOMATE- Osaka, Nov. 16-18, 2015

L= 7.5
L= 12

0.2

L= 9
L= 13.5

L= 10.5

0.19
0.18
0.17
0.16
0.15
0.14
0.13
0.12

1

1.5

2

2.5

3

stair width (m)Fig.10 Maximum vertical displacement against step
width in sand soil for different lengths of geogrid
Fig.8 Output of vertical displacement in reinforced
soil wall (Reinforcement Length 0.9H=13.5m, step
width=3m, and clay soil type)

By increase of wall step width and length of thereinforcement, the situation of X which is the
maximum vertical displacement, moves farther fromthe wall face and moves towards internal part of thewall. The issue which is completely clear in the
outputs is that the maximum vertical displacement
occurs under the overburden and its situation interms of its coordinate (X) is in the middle part ofthe overburden. The reason of this fact is that the
overburden is the main factor of creating verticaldisplacement.
The maximum vertical displacement occurs on
the last wall or under the overburden. In fact,
coordinate of the height of the point of maximum
vertical displacement is not subject to length of thereinforcement, step width, and soil type, and thereason is that the more we take distance from theplace overburden applies, less pressure applies to thesoil mass and the highest pressures occur under the
overburden [8].

According to figure 9 and 10, it can be observedthat by increase of length of reinforcements, theextent of vertical displacement also decreases likehorizontal displacement. The reason of this fact canbeattributed to increase of interaction surface
between the soil and the reinforcement. The
mathematical relation between changes of verticaldisplacement and length of reinforcement is asfollows:
L+0.1h= 0.92 ~ 0.99 ΔV
W= Wall step width
ΔV= Vertical displacement
h= Total wall height
Also the other important point which can beobserved is that the extent of vertical displacementof the wall in clay soils is more than that of sandsoils.
L= 7.5
L= 12

maximum vertical displacement (m)

0.55

L= 9
L= 13.5

Effect of Geogrid Length, Step Width, and Soil
Type on Effective Stress

L= 10.5

As it can be observed in figure 11 and 12, bychanging the step width, some changes would occur
in extent of effective stress and by its increase, theextent of the maximum effective stress decreases.
Since the effective stress is the stress between solid
particles of the soil, by increase of the width of thestep, volume of the solid particles decreases, andbecause of that the extent of maximum effective
stress decreases [8].
Also by increase of the length of geogrids,
because of increase of capacity of reinforcements
and transmission of more force to reinforcements,
the extent of effective stresses decreases [8]. In
addition to step width and length of reinforcements,
soil is also effective in extent of maximum effective

0.5
0.45
0.4
0.35
0.3

1

1.5

2

2.5

3

stair width (m)Fig.9 Maximum vertical displacement against step
width in clay soil for different lengths of geogrid
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GEOMATE- Osaka, Nov. 16-18, 2015

reinforcement of a larger volume of soil, safety
factors and as a result of that, stability of reinforced
soil wall increase.
Besides step width and length of thereinforcement, the type of soil is also effective invalues of the safety factors. In sand soils, because ofhigh interaction of soil and the reinforcement andalso friction between soil grains, safety factor ishigher [8].

L= 7.5

L= 9

L= 12

L= 13.5

L= 10.5

On the whole, it can be moded that increase oflength of reinforcement and using sand soil, have a
noticeable effect on the reinforced soil wall stability.

865
855
845
835

2

825

1.8

1

1.5

2

2.5

3

safety factor

maximum effective stress (KN/m2)

stress. In clay soils, because the soil is fine-grained,
the sum of supporting surface of grains is higher, so
the effective stress in this soil type is also higher.
Maximum effective stresses occur in clay soils inlower layers of the wall or on the foundation, and insand soils, because of high friction between thegeorgrid and the soil, it occurs at the end of
reinforcement of the first step.

stair width (m)Fig.11 Maximum effective stress against step width
in clay soil for different lengths of geogrid

L= 7.5

L= 9

L= 12

L= 13.5

L= 10.5

1.6
1.4
1.2
1

1

1.5

2

2.5

3

stair width (m)860

L= 7.5

L= 9

L= 12

L= 13.5

L= 10.5

Fig.13 Safety factor against step width in clay soil
for different lengths of geogrids.

855
850

L= 7.5
L= 12

845
2
1.9
1.8
1.7
1.6
1.5
1.4
1.3
1.2
1.1
1

840
835
830

1

1.5

2

2.5

3

stair width (m)Fig.12 Maximum effective stress against step width
in clay soil for different lengths of geogrids.

1

L= 9
L= 13.5

1.5

2

L= 10.5

2.5

3

stair width (m)Effect of Geogrid Length, Step Width, and Soil
Type on Safety Factor

Fig.14 Safety factor against step width in sand soil
for different lengths of geogrids

Safety factor is the most important factor in
examining stability of reinforced soil walls [1]. As it
can be observed in figure 13 and 14, the extent of
increase of width of wall step does not have a
noticeable effect on changes of the safety factor, andanyway, increase of width of wall step causes atrivial decrease in the safety factor. Also by increaseof the length of geogrids, because of increase of
capacity of reinforcements and transmission of ahigher force to the reinforcement and also

CONCLUSION
According to the conducted numerical models
with Plaxis software and drawn diagrams based onmodeling results, the following conclusions can be
made:
By increase of the length of the reinforcement
and increase of the step width, the maximum
horizontal and vertical displacements decrease. The
504

GEOMATE- Osaka, Nov. 16-18, 2015

extent of vertical and horizontal displacements inclay soil is higher than sand soils. Under the sameloading conditions, the extent of effect of
displacement decrease, by increase of the length ofreinforcements in clay soils, is more than sand soils.
This extent of displacement decrease is noticeable inboth vertical and horizontal displacements.
Increase of step width and length of thereinforcement cause transmission of situation of the
maximum horizontal displacement from the central
part of the wall face to the bottom part of the wall
face. In other words, by increase of step width andlength of the reinforcement, the deformation modechanges from bulging mode to slide mode in the toeof the wall. Maximum deformations in walls withclay soil occur at the bottom of the wall and in sandwalls occur in the center of the face. Meaning thatbulging mode occurs in reinforced sand soil walls
and the mode of slide at the toe occurs in reinforced
clay soil walls.
By increase of the length of the reinforcement
and step width, the maximum effective stressdecreases. The extent of effective and total stressesof wall in clay soils is more than sand soils. The
effect of increase of step width in decrease ofdisplacements is more than the effect of increase oflength of geogrids in decrease of displacements.
Value of safety factor in sand soils, because of
having a better interlock between grains, is morethan clay soils, and also by increase of the length of
the reinforcement, safety factor intensely increases.

REFERENCES[1] Atrchian M R, Mohagheghi M and Niazi Gh,“Soil Nail Walls design and Construction
Guidline FHWA” . Zanjan, Iran: Islamic Azad
University of Zanjan, 2014, ch.1.
[2] Kargarian Marvasti S, komakpanah A, Dynamic
analysis of soil reinforced with steel straps,
Master's thesis, 2008, University of Tarbiat
Modarres.
[3] Ghorbanbeigi S, Kharaghani S, Yazdan AA,
Analysis and study on stability of slopes
reinforced With geosynthetic, 4th International
Conference on Geotechnical Engineering andSoil Mechanics (ICGESM), 2010 .Tehran, Iran.
[4] Shafabakhsh Gh and Haddad A, “Final Reportof Research Project of Guides and Design and
Implementation of Geosynthetic Reinforced
Soil Walls”, 2005, Semnan University.
[5] Barani A and Mosallanejhad M, “ Design
Optimization of Reinforced Soil Wall and Studyon effect of Designing Parameters on Its
Behavior and stability” 9th Congress of Civil
Engineering, 2012, University of Isfahan, Iran.
[6] Niroumand H, Niroumand B, Advanced training
Plaxis, publications Naghoose Andishe, 2008.
[7] Sabermahani M, Ghalandarzadeh A, Fakher A,
Experimental study on seismic deformation
modes of reinforced-soil walls, Geotextiles andGeomembranes, 2008.
[8] Tahouni S, “Principles of Foundation
Engineering “, 2013, ch 11.
[9] Publication No. FHWA-NHI-00-043, Federal
Highway Administration (FHWA), Washington(DC), 2001.

505

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan,Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

THE MONITORING OF ELECTROKINETIC STABILISATIONTECHNIQUE ON BATU PAHAT MARINE CLAY
Saiful Azhar Ahmad Tajudin1,6, Nurul Syakeera Nordin2, Aminaton Marto3, Ian Jefferson4, Aziman Madun5
and Mohd Hazreek Zainal Abidin6
1
Research Center For Soft Soil, Universiti Tun Hussein Onn Malaysia
Faculty of Civil and Environmental Engineering, Universiti Tun Hussein Onn Malaysia
3
Faculty of Civil Engineering, Universiti Teknologi Malaysia
4
School of Engineering, University of Birmingham, United Kingdom

2,5,6

ABSTRACTThis paper outlines the monitoring results from an experimental study of electrokinetic stabilisation (EKS)
technique, as those results were important in assessing the efficiency of the technique towards Batu Pahat marine
clay. Two reactors were set up; 1.0 mol/liter of calcium chloride (CaCl 2 ) and sodium silicate (Na 2 SiO 3 ) as theelectrolyte and stainless steel plates as the electrodes. EKS technique was continued for 21 days with a constantvoltage gradient (50 V/m). This technique was performed in two phases where the alteration of EKS was the
combination of stabilizers used. The combinations of stabilizers in phase 1 and 2 were CaCl 2 – distilled water(DW) and CaCl 2 – Na 2 SiO 3 , respectively. The technique was monitored using electric current, total inflow andoutflow of electrolytes and pH of electrolytes. It showed fluctuated profiles of electric current for both phases,
attributed by the introduction of calcium ions from the anode compartment. The inflow and outflow ofelectrolytes results showed that electromigration and electroosmosis occurred during EKS and it indicated the
movement of anion and cation to the opposite direction. The pH of electrolytes kept constant value and balancedby electrolysis process at the cathode.
Keywords: Electrokinetic stabilisation, marine clay, calcium chloride, sodium silicate.

ground improvement, either by physical or chemical
stabilisation technique [4].
EKS is the one of the principles of chemical
stabilization and has been chosen as a potential
technique to enhance the characteristics of soil [5]. It
is suitable for soft clayey soils which have low
hydraulic conductivity and require strengthening to
improve the soil condition. Furthermore, it can also
be used to stabilize the over consolidated clayey soil.
The advantage of EKS instead of using traditional
mix-in-place chemical stabilization is that thetechnique allows for remote treatment through soil
without any excavation work [6]. The technique canbe enhanced by use of non-toxic stabilizing agents
such as lime or CaCl 2 solutions [7-9] that can be fed
either at the anode or cathode depending on the ions
to be transferred into the soil. A few soil parameterssuch texture, plasticity, compressibility andpermeability will be altered by addition of thesestabilizing agents, hence it can be very effective in
improving soil characteristics by reducing theamount of clay size particles and increasing theshear strength [10].

INTRODUCTION
Marine clay is an uncommon type of clay andnormally possesses a soft consistency. Batu Pahat
marine clay is classified as clayey silt with loworganic content [1] and usually found in the ocean
bed [2]. It can be found onshore as well. Theirproperties rely on their initial conditions, where thesaturated marine clay soil differs significantly frommoist and dry soil. Marine clay is microcrystalline innature. Clay minerals and non-clay minerals are alsopresent in the soil. The clay minerals contained inmarine clay are chlorite, kaolinite and illite while
non-clay minerals found in marine clay are quartz
and feldspar. Furthermore, the higher proportion oforganic matter in the soil acts as a cementing agent[2]. The location of marine clay deposits in
Peninsular Malaysia are shown in Figure 1 [3].
Enhancing the substructure of soil is the major
aspect concerned in construction work before any
superstructure can be applied on it. It is important tostrengthen the soil so that it can surpass the
maximum imposed load to evade any failure thatmight occur afterwards. There are two options in

506

GEOMATE-Osaka, Nov. 16-18, 2

Fig. 1 Location of marine clay deposits in Peninsular Malaysia.
MATERIALS AND METHODS
Experimental apparatus

Soil sample preparation

The EKS test rig was designed for this research.
A schematic diagram of EKS test rig is shown in
Figure 2. It was made using transparent acrylic platewith 420 mm depth, 170 mm width and 358 mm
length. The thickness of the acrylic plate was 15 mm.
The transparent acrylic plate for the EKS test rig wasused to prevent short circuiting and to monitor thesoil level during consolidation at the main
compartment and level of water and/or chemicalsolution at the small compartments. The EKS test rig
comprised of three compartments, which wereseparated with perforated walls. The soil sample wasplaced into the main compartment and the two small
compartments were used to supply the chemical
stabilizers into the soil.
The electrode, electrolyte and stainless steel plate
were placed as shown in Figure 2. For the first phase,
1.0 mol/liter of CaCl 2 and distilled water (DW) were
fed at the anode and cathode compartment,
respectively. While in the second phase, 1.0 mol/literof calcium chloride (CaCl 2 ) and 1.0 mol/liter of
sodium silicate (Na 2 SiO 3 ) were fed at the anode andcathode compartment, respectively. A constantvoltage gradient (50 V/m) was applied to the soil
sample and the experiment was performed for 21
days for both phases.

Soil sample for EKS technique was preparedconsistently throughout this research for all treatment
periods. Batu Pahat marine clay was dried in the ovenfor 24 hours. The dried sample was ground using agrinder machine to get very fine material that would
pass a 425 µm sieve. The slurry sample was preparedby mixing the soil samples with distilled water toachieve 90 % of water content. The water content of
the slurry sample was chosen based on 1.5 times ofliquid limit (LL). Then, the slurry sample was placed
inside the main compartment (278 x 165 x 413 mm)
and a uniformly distributed load was applied to it byusing large strain consolidation to reduce the watercontent resulting in a stiffer soil condition [7-9].
EKS monitoring
Throughout the EKS experiment the applied
electric current, the pH of anode and cathodesolutions were observed during the 21 days of EKS
experiment for both phases. The total amount ofstabilizers added and amount of effluent watercollected from the measuring cylinder weremonitored over the same period of time. On the firstday of the EKS experiment, the reading of current
value was taken at 1, 2, 4, 6 and 8 hours. From thesecond day onwards, reading of current value wastaken every 24 hours. Volume of stabilizers added tothe experiment was taken every 24 hours together
with effluent water which was collected using
measuring cylinders. The pH testing was performedon the extracted electrolytes taken from theelectrolyte compartment. In addition, calcium
chloride was constantly supplied at anodecompartment for CaCl 2 – DW and CaCl 2 – Na 2 SiO 3
systems, whereas sodium silicates were continuously
supplied during the experiment in the CaCl 2 –Fig. 2 Schematic diagram of EKS test rig.
507

GEOMATE-Osaka, Nov. 16-18, 2

Na 2 SiO 3 system to assure the effectiveness of EKS
experiment towards the clay soil.

3.082 A. After 8 hours the electric current values
recorded were between 3.084 A to 3.094 A. In the
CaCl 2 – Na 2 SiO 3 system, a different trend wasobserved compared to the first phase. The profiles ofelectric current showed that the values increased fromthe beginning until the end of the experiment. At the224th hour the electric current values started toremain constant until the end of the experiment andrecorded as the highest value at 2.33 A. There was afluctuating trend of current values that occurred at the
4th hour of experiment. The fluctuating trend that
occurred in both phases was possibly caused by the
introduction of additional ions by calcium chloride at
the anode compartment.

RESULTS AND DISCUSSION
Soil classifications
Table 1 shows the soil classification for
untreated Batu Pahat marine clay where it was usedas the control value and as the comparisons with EKS
treated values. Current results and results from
Abdurahman [11] were presented and compared.
Those aspects measured were plastic limit (PL),liquid limit (LL), plastic index (PI), specific gravity(G s ) and pH. They showed that the current results
were in range and closed as reported by Abdurahman[11]. If the plastic and liquid limit of soil were in therange of 25-40 % and 30-110 %, respectively, hence
the soils are considered as kaolinite soil [4]. Kaolinite
soil which has a low cation exchange capacity will
exhibit high electroosmotic water transport when thesoil is saturated with dilute electrolyte solution due tothe high water counter ion ratio in the internal phase.
Furthermore the soil is generally used in EK
application. The EK technique is particularly well
suited for soils of low permeability such as kaolinite
in which the application of stabilizing agents byhydraulic means is impracticable [5].
Table 1 Classification of Batu Pahat marine clay.
Soilclassification
PL
LL
PI
Gs
pH

Current
results
36 %61 %24.77 %
2.60
2.73

Fig.3 Electric current for CaCl 2 – DW system withtime.

Results fromprevious study[11]20 – 35 %37 – 65 %13 – 31 %2.18 – 2.65-(-) Not stated
Monitoring data
Figure 3 shows the profile of electric current for
CaCl 2 – DW system during EKS experiment. On theother hand, profiles of electric current for CaCl 2 –Na 2 SiO 3 system are shown in Figure 4. The electric
current values were recorded at hour 0, 1, 2, 4, 6 and
8 on the first day of the experiment and every 24 hour
afterwards for 21 days period of time.
For the CaCl 2 – DW system, the values on thefirst day of the experiment show a fluctuating trend
and values remained constant afterwards until the endof the experiment. The highest value occurred at the
6th hour of experiment at 3.107 A, while the lowest
value was recorded at the beginning of experiment at

Fig.4 Electric current for CaCl 2 – Na 2 SiO 3 systemwith time.
The total volume of inflow and outflow of CaCl 2
at anode compartment for CaCl 2 – DW system ispresented in Figure 5. It showed that there was nooutflow value of CaCl 2 collected by the measuring
508

GEOMATE-Osaka, Nov. 16-18, 2cylinder from the anode compartment where it
remained zero from the beginning until the end of 21days. At hour 224, the inflow values remained
constant until the end of experiment. The total inflow
and outflow values of DW at the cathodecompartment for the CaCl 2 – DW system wereshown in Figure 6. It showed that inflow of DW only
occurred at 0 hour at 1705 ml, while the outflow
volume increased at hour 32 of the EKS experiment.
The volume of outflow was taken at the flushingchamber (cathode compartment) that was collectedfrom the measuring cylinder. Thus, it indicated that
there was no fluid discharge from the anodecompartment throughout the experiment and it
proved that the stabilisers have fully migrated to thesoil sample.

Figure 7 presents the volumes of inflow andoutflow of CaCl 2 for CaCl 2 – Na 2 SiO 3 system. It
showed that there was no outflow volume from theanode compartment from the beginning until the endof experiment where it was similar to the first phase.
The inflow volume of CaCl 2 showed that it decreased
rapidly compared to amount at the beginning of theexperiment. The total inflow and outflow of Na 2 SiO 3
is presented in Figure 8 and it showed that the inflowof Na 2 SiO 3 occurred at 0, 152 and 320 hours. This isbecause the cathode compartment was cleaned every
7 days during the experiment and the empty
compartment was replaced with the same amount of
Na 2 SiO 3 to maintain and ensure the effectiveness ofthe experiment. The outflow volume from thecathode compartment occurred after 32 hours ofexperiment and the volume was in the range of 110 –390 ml. The results explained that the process ofelectromigration and electroosmosis occurred during
the experiment and that the anions and cations weremoving in opposite directions.

Fig.5 Inflow and outflow of CaCl 2 for CaCl 2 – DW
system with time.

Fig.7 Inflow and outflow of CaCl 2 for CaCl 2 –Na 2 SiO 3 system with time.

Fig.6 Inflow and outflow of DW for CaCl 2 – DW
system with time.

509

GEOMATE-Osaka, Nov. 16-18, 2

Fig.9 pH of electrolytes for CaCl 2 – DW systemwith time.

Fig.8 Inflow and outflow of Na 2 SiO 3 for CaCl 2 –Na 2 SiO 3 system with time.
The variation of pH of electrolytes for the CaCl 2 –DW system for the 21 day experiment is shown in
Figure 9. The fluids from the anode and cathodecompartment were extracted to obtain the pH valuesof the electrolytes. It showed that the pH of CaCl 2
inside the anode compartment remained constant
starting from the 80th hour of the experiment.
Meanwhile, the pH for DW inside the cathodecompartment or the flushing chamber shows afluctuating trend from hour 0 until hour 104. It
showed that the pH value increased at hour 32 anddropped at hour 56 and hour 80 of the experiment.
The pH values for the CaCl 2 for 21 days of theexperiment ranged between pH 3.9 to pH 5.5, while
for DW the value were between pH 7 to pH 13.
Figure 10 presented the variations of pH ofelectrolytes for CaCl 2 – Na 2 SiO 3 system. The pH
value for CaCl 2 dropped considerably starting fromhour 56 and stabilized until the end of the experiment.
The highest value recorded was pH 6.06 at hour 56
and the lowest value was pH 3.09 at hour 464. The
pH value of Na 2 SiO 3 showed that almost the entire
specimen was subjected to alkaline conditions
showing a constant trend with a pH of around 13
during the experiment. The constant value ofNa 2 SiO 3 showed its continuous supply at theflushing chamber where it was cleaned and replaced
every 7 days during the experiment to stabilise thepH value and balance the pH changes via electrolysis
process at the cathode.

Fig.10 pH of electrolytes for CaCl 2 – Na 2 SiO 3
system with time.
CONCLUSIONS
A 50 V/m of voltage gradient or equal to 14 V
was applied towards the soil in the EKS experiment.
The electric current values were strongly affected by
the chemical composition of soil and influenced by
the ionic concentration of the pore fluid within thesoil matrices [12]. The electric current generated in
the experiment was due to the existence of inorganic
soil elements in high concentration for example iron,calcium and magnesium. The inflow and outflow
volumes of the stabilizing agents towards the soil
were affected by the physicochemical characteristics510

GEOMATE-Osaka, Nov. 16-18, 2

of soil, applied current and the pH. Those values
were important in assessing the efficiency ofelectromigration and electroosmosis process inmigrating stabilizing agents into the system hence
improving the soil properties after EKS. The
monitored pH values relied on the pH of electrolytes
that was continuously supplied to the electrolytecompartment in the experiment since there were
small changes of pH value shown during EKS.
Monitoring data provided information on theefficiency of the EKS experiment as it proved that
there were electric current and stabilizing agents
being continuously supplied until the end of theexperiment. It also acts as a precaution if any failure
or error occurs during the experiment so that it could
be detected immediately.

[5] Asavadorneja P and Glawe U, “Electrokinetic
strengthening of soft clay using the anode
depolarization method”, Bulletin Engineering
Geology and Environment, Vol. 64, 2005, pp.
237-245.
[6] Barker JE, Rogers CDF, Bordman DI andPeterson J, “Electrokinetic stabilisation: an
overview and case study” Ground Improvement,
Vol. 8(2), 2004, pp. 47-58.
[7] Nordin NS, Ahmad Tajudin SA and Abdul Kadir
A, “Physical properties of treated soft clay byusing electrokinetic stabilisation method” Soft
Soil Engineering International Conference, 2013.
[8] Nordin, NS, Ahmad Tajudin SA and Abdul
Kadir A, “Stabilisation of soft soil using
electrokinetic
stabilisation
method”,
International Journal of Zero Waste Generation,
Vol. 1(1), 2013.

ACKNOWLEDGEMENT
The authors would like to thank the Ministry ofHigher Education and Universiti Tun Hussein Onn
Malaysia for supporting this research under
Fundamental Research Grant Scheme (FRGS) Vot.
1455.

[9] Nordin NS, Ahmad Tajudin SA, Abdul Kadir A,
Yunus R, Mohammad Azmi MA and Mohamed
Hanif MNS, “Effect of calcium and sodium ionsin soft clay using electrokinetic stabilisation
technique”, Applied Mechanics and Materials,
773-774, 2015, pp. 1433-1437.

REFERENCE[1] Rahman ZA, Yaacob WZW, Rahim SA, Lihan T,
Idris WMR and Mohd Sani WNF, “Geotechnicalcharacterization of marine clay as potential liner
material”, Sains Malaysiana, Vol. 42(8), 2013,
pp. 1081–1089.

[10] Nasim, M, Erwin O and Gary C, “A review ofelectrokinetic treatment technique for improving
the engineering characteristic of low permeable
problematic soil”, Int. J. of GEOMATE, Vol.
2(2), 2011, pp. 266-272.

[2] Basack S and Purkayastha RD, “Engineering
properties of marine clays from the eastern coastof India”, Journal of Engineering andTechnology Research, Vol. 1(6), 2009, pp. 109–
114.

[11] Abdurahman MN, “Short term performance
evaluation of a rural road embankment withdifferent reinforcement constructed on soft soil
pertaining to vertical settlement”, Master of civilengineering, KUiTTHO, 2005.

[3] Indraratna B, Balasubramaniam AS andBalachandran S, “Performance of test
embankment constructed to failure on soft
marine clay”. Journal of GeotechnicalEngineering, Vol. 118(1), 1992, pp. 12–43.

[12] Ahmad Tajudin SA, Jefferson I, Madun A,
Zainal Abidin MH, Baharuddin MFT and
Mohammad Razi MA, “Monitoring of electric
current during electrokinetic stabilisation test forsoft clay using EKG electrode”,
AppliedMechanics and Materials, 773-774, 2015, pp.
1560-1564.

[4] Mitchell JK and Soga K, “Fundamentals of Soil
Behavior. 3rd ed.” Berkeley: University ofCalifornia, 2005.

511

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

USING 3DEC TO SIMULATE FRACTURES AND CANISTERS INTERSECTIONSAND THEIR APPLICATIONS FOR REPOSITORY LAYOUT
Chi-Jen Chen1, Yuan-Chieh Wu1, Meng-Hsiu Hsieh1
1Institute of Nuclear Energy Research

ABSTRACT
Taiwan is in the Pacific earthquake ring, and seismicity is high. It’s important to evaluate effects of large
earthquakes on high level waste disposal. Many studies show that canisters could failure due to fracture shear
displacement induced by earthquakes. The most crucial step for long term safety assessment of repository isevaluating the fractures and canisters intersections. This paper follows the methodology of Swedish SKB reportand calculates number of canisters intersected by fractures by using 3DEC, and discusses the implications ofintersection for repository layout. The results show our 3DEC simulation method is available in comparison with
SKB’s result and their applications are also illustrated for repository layout by using visualization tools of 3DEC.
Keywords: Earthquake, 3DEC, Repository layout, DFN

calculate number of canisters intersected byfractures. There is a commercially software develop
by SKB using Microsoft Excel.
In order to localize technique and optimize thevisualization, this study developed the methodologyof number of canisters intersected by fractures
simulation by using 3DEC code.

INTRODUCTION
There are many factors may influence repository,e.g. shaking, rock failure, pore pressure change oraftershock etc., see figure 1. But “The major damage
mechanism is induced slip along pre-existing joints
and secondary faults due to an earthquake. [4]” forassessing KBS-3V (engineer barrier systemdeveloped by Sweden) post-closure canister damage.

METHODOLOGY
Creating DFN can be simplified to 4 parts,
fracture orientation, fracture size distribution,
fracture density and fracture spatial pattern.
The most common fracture orientation equationis Fisher distribution [6] in geologic literature:𝑓𝑓(𝜃𝜃) =𝐾𝐾 𝑠𝑠𝑠𝑠𝑠𝑠 𝜃𝜃𝑒𝑒 𝐾𝐾𝐾𝐾𝐾𝐾𝐾𝐾 𝜃𝜃
𝑒𝑒 𝐾𝐾 −𝑒𝑒 −𝐾𝐾(1)where 𝜃𝜃 is angular deviation from the mean vector,
and 𝐾𝐾 is the Fisher constant or dispersion factor.
The fracture size distribution is determined fromprobability function, including power-law, Gauss,lognormal and exponential distributions etc. It’s
generally accepted that fractures follow a power-law
distribution in nature. Fracture power-law
probability density function is formulated as [2]:Fig. 1 Schematic diagram illustrates effects of large
earthquakes on deposition holes.

𝑘𝑘𝑟𝑟 𝑘𝑘

Description of fractures (dip, dip direction, size,
position etc.) is using DFN (Discrete Fracture
Network) model, based on geological investigation.
Fractures are defined as any discontinuous in rockmass, e.g. joint, fault, bed plane etc. and scale is tens
to hundreds meters. “The DFN models required forearthquake studies differ from the DFN models used
for fluid flow modeling [5].”One of the most important steps for evaluationof effects of large earthquakes on repository is to0
𝑓𝑓(𝑟𝑟) = 𝑘𝑘+1

𝑟𝑟

𝑟𝑟0 ≤ 𝑟𝑟 < ∞(2)where 𝑟𝑟0 is the smallest fracture radius, r is thefracture radius and k is a shape parameter.
Fracture density is using P 32 (m2/m3), which isexpressed as total fracture area per volume unit for
specific shape and size range of fracture. Fracture
spatial pattern is simply assumed that locations offracture centroids are uniform distribution.
512

GEOMATE- Osaka, Nov. 16-18, 2015

Fracture shear displacement of earthquakes
effects is function of driving stress, shear modules,
Poisson’s ratio and radius [1]. Canisters are designed
to afford 10 cm shear displacement in SR-CAN
report. It means there is a minimum fracture size,
𝑟𝑟𝑀𝑀𝑀𝑀𝑀𝑀 that has to be avoided:
𝑟𝑟𝑀𝑀𝑀𝑀𝑀𝑀 =𝑑𝑑𝐶𝐶𝐶𝐶𝑖𝑖𝑡𝑡

For comparison purpose, we refer to parametersof Swedish SKB report [3], see Table 1 and Table 2.
There are 5 clusters of fractures, represented byparameters, pole (trend and plunge) and kappa fororientation and by fracture intensity, shapeparameter and r 0 for size distribution. Minimum
fracture radius is 100 m, calculated by Eq. (3).

(3)

𝑏𝑏

Table 1 DFN parameters, from R-05-29.

where 𝑑𝑑𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 is 0.1 m and b is typically 0.001 [3].
When fracture radius is larger than 𝑟𝑟𝑀𝑀𝑀𝑀𝑀𝑀 , thefracture is available to shear 10 cm from a centroid
point to a circle. The radius of circle is defined as
critical radius:𝒓𝒓′𝑪𝑪𝑪𝑪𝑪𝑪𝑪𝑪 = �𝒓𝒓𝟐𝟐 − 𝒓𝒓𝟐𝟐𝑴𝑴𝑴𝑴𝑴𝑴

(4)

Trend
PlungeKappak
r0
P32(r 0 ,r ∞ )𝒓𝒓 > 𝒓𝒓𝑴𝑴𝑴𝑴𝑴𝑴

NS
87.2
1.7
21.66
2.88
0.318
0.602

NE
135.2
2.7
21.54
3.02
0.318
2.07

NW
40.6
2.2
23.9
2.81
0.318
0.448

EW
190.4
0.7
30.63
2.95
0.318
0.226

SubH
342.9
90
8.18
2.92
0.318
0.605

Table 2 Additional parameters, from R-05-29.

SIMULATION STEPS

Maximum fracture radius
Canister diameter
Canister height
Constant, b
Critical shear distance at deposition
holeFirst of all we create a hypothetical full model ofrepository geometry at 500 m depth in 3DEC, see
figure 2, and repository can accommodate 2728
canisters. Using 3DEC to generate DFN andgenerating domain is extended maximum fractureradius from each edge of repository to make sureevery effective fracture was simulated.
Finally, we identify the canisters intersected byfractures and eliminate the canisters outside criticalradius of fractures.

500 m
1.05 m
4.833 m
0.001
0.1 m

RESULTS
The average number of intersected canisters is60.2 canisters by 500 times simulations. The
probability of canister intersected by fracture is60.2/2728 ≒ 2.21 %, each probability of single
cluster is shown in table 3. Apparently, results issimilar and little larger compared to Swedish SKB
report [3]. Furthermore, the distribution of 500 times
simulations, figure 3, has the similar trend, but the
distribution is more concentration in this study.
Table 3 3DEC simulations result in comparison with
R-05-29

Average(canisters)
Probability(%)
Probabilityof [3] (%)
Fig. 2 Full model of repository geometry.
PARAMETERS
There are assumptions need to be declare. The
fracture spatial pattern is assumed centroid of
fractures to be uniform position. Fracture shape isassumed infinite thin circular discs. Fractureorientation distribution and fracture size distribution
are assumed Fisher and power-law respectively.
513

All
60.2

NS
10.1

NE
16.4

NW
10.9

EW
2.3

SubH
19.9

2.21

0.37

0.60

0.40

0.09

0.73

1.91

0.32

0.51

0.34

0.08

0.66

GEOMATE- Osaka, Nov. 16-18, 2015

Fig. 3 Distribution of 500 times simulations.

Fig. 4 Full perimeter criteria, from TR-10-21 .

APPLICATIONS FOR LAYOUT
Main tunnel

When we can estimate the number of number ofintersectedcanisters
beforeundergroundconstruction, it’s great help for feasibility
assessment stage of chosen candidate repository
sites. Repository layout could be design more
accommodation to make sure all canisters could be
loaded with relevant margin.
However, we can simulate fractures by DFN
model; it’s hardly to measure the size of a fracture
essentially. Where we can see fractures is only onborehole and tunnel wall. Therefore, SKB develop
the rejection criteria of deposition holes, FPC (FullPerimeter Criteria), to conservatively identify thedeposition holes that potentially be intersected bylarge fracture [7].
If tunnel is full intersected by fracture, it’s
defined as Full Perimeter Intersection (FPI) [7]. FPC
means that any deposition holes will be considered
for rejection regardless of true fracture size, if
canisters were intersected by the extrapolation of
FPI, see figure 4. Consequently, deposition holelocation is adjusted to avoid intersection.
There is a case from 3DEC simulation, see figure
5. Canisters along deposition tunnel are intersectedby large fracture. In this case, FPI could be
identified in main tunnel before excavation ofdeposition tunnel. FPI become a rick-informed anddecision-making technique preventing large amountrejection deposition holes. FPI also provide
information that we should do more investigation or
adjust layout design.

Deposition
tunnelFracture

Fig. 5 Large fracture intersects canisters along whole
deposition tunnel, example from 3DEC simulation.
CONCLUSIONSThe simulation results are similar to Swedish
SKB report. 3DEC can simulate fracture/canisterintersection and present results with powerfulvisualization function. The distribution of number ofintersected canisters has thick tail, which increases
the uncertainty. This technique is useful not only forearthquake effects but also for undergroundconstruction and repository layout configuration. It’s
necessary to combine deposition holes rejection
criteria in simulation to reduce the uncertainty infuture work.
REFERENCES[1] Atkinson BK, Fracture Mechanics of Rock. Asn
Diego: Academic Press Inc. Ltd, 1987, ch. 8.
[2] Evans M, Hastings N and Peacock B, Statistical
distributions, New York: John Wiley & Sons,
Inc.
[3] Hedin A, An Analytic Method for EstimatingtheProbabilityofCanister/Fracture514

GEOMATE- Osaka, Nov. 16-18, 2015

Intersections in a KBS-3 Repository, Stockholm:
Svensk Kärnbränslehantering AB, 2005.
[4] La Pointe P, Wallmann P, Thomas A and Follin
S, A Methodology to Estimate Earthquake
Effects on Fractures Intersecting Canister Holes,
Stockholm: Svensk Kärnbränslehantering AB,
1997.
[5] La Pointe P and Cladouhos T, Calculation of
Displacements on Fractures Intersecting
Canisters Induced by Earthquakes: Aberg,

Beberg and Ceberg Examples, Stockholm:
Svensk Kärnbränslehantering AB, 1999.
[6] R. Fisher, “Dispersion on a Sphere”, Royal
Society of London Proceedings, Vol. 217, 1953,
pp. 295-305.
[7] Munier R, Full Perimeter Intersection Criteria,
Definitions and Implementations in SR-Site,
Stockholm: Svensk Kärnbränslehantering AB,
2010.

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Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

EVALUATION OF GEOTECHNICAL CORRELATIONS USING ATICMETHOD
Waled A. Daoud1, Kiyonobu Kasama2, Naser M. Saleh3, Abdelazim M. Negm4
1,4
Egypt-Japan University of Science and Technology, Egypt
1,2
Faculty of Engineering, Kyushu University, Japan
1,3
Faculty of Engineering at Shoubra, Benha University, Egypt

ABSTRACT
Geotechnical correlations are widely used because it can provide fast and cost-effective means of parameter
estimation using simple tests. Most of the current correlations were derived from data fitting of measurements
for specific site conditions and need to be evaluated before used in other sites. During the correlation
development, the deviation of the correlation from the measured values was evaluated using simple statisticalmeasures. A better method of correlation evaluation may reduce the overall deviation of the Geotechnicalparameter. Amended Theil Inequality Coefficient (ATIC) is proposed as an evaluation tool because it has theadvantage that it considers both position and trend conformities between observed and correlated values. To
address the efficiency and rationality of ATIC, evaluation of, 92 compression index correlations by ATIC anddifferent statistical measures was carried out. Comparison between the results showed that ATIC is efficient inassessing the best and worst correlations and it can be considered a good tool for correlations evaluation.
Keywords: Compression Index Correlations, Statistical Evaluation, Position and Trend Conformities[14] used experimental results to evaluate theapplicability of 14 correlations using analyticalexamination. Giasi et al. [8] evaluated 32
correlations using experimental results for 46 soil
samples from Italy. The authors used both ranking
distance (RD) and Ranking Index (RI) to evaluate
these correlations. The authors highlighted theaccuracy problems of using RD and RI in
correlations evaluation.
Yoon et al. [15] evaluated the results of using 15
empirical correlations to predict Cc of Korean coast
marine clay using experimental results for more than
1200 consolidation tests. The authors used R toevaluate the newly, site-specific correlations andcorrelated/observed ratio (K-factor) for literaturecorrelations. Rani and Rao [16] evaluated 12
correlations using one way and two way ANOVA
along with mean absolute difference (MAD) to rankthese correlations. Onyejekwe et al. [1] assessed theapplicability of using 18 correlations for theestimation of Cc for Missouri region. The authors
used the root mean square of deviation (RMSD), Kfactor, RD, and RI to evaluate the correlations. Lee
et al. [17] used R2 and MAD to evaluate 29
correlations based on experimental results in South
Korea.
Most of the above used statistical measures
shortcoming that it considers position conformity or
trend conformity separately. This shortcoming may
cause misjudgment and wrong selection of best
correlation. This paper introduced Amended Theil
Inequality Coefficient (ATIC) method as a tool forcorrelation evaluation. ATIC method has theadvantage that it considers both position and trend

INTRODUCTION
The estimation of soil parameters using empiricalcorrelations is widely used in geotechnicalengineering [1]. Most important reasons are; direct
measurements contains uncertainty, not always
applicable, costly, and time-consuming [2], [3].
Thus, empirical correlations can provide fast andcost-effective means of parameter estimation using
simple tests [4]. Most of these correlations were
derived from data fitting of measurements at specificsite conditions that may cause large deviation if used
for other sites [5]–[7].
During the correlation development, thedeviation of the correlation from the measured
values was evaluated using simple statisticalmeasures such as correlation coefficient (R) anddetermination coefficient (R2) [5]–[11]. Better
statistical evaluation may reduce the overall
deviation of the estimated geotechnical parameter.
Evaluation of different geotechnical correlations
using statistical measures grabs the attention ofmany researchers for decades.
For shear strength parameters correlations;
Hatanaka and Ushida [12] evaluated internal friction
angle correlations with standard penetration test
(SPT) results and proposed a rectified correlationthat consider the effect of in-situ confining pressure.
Nassaji and Kalantari [13] evaluated different
undrained shear strength correlations with SPT NValue. The authors used the standard deviation andvisual inspection to evaluate different correlations.
Compression index (Cc) correlations were
evaluated by many researchers. Nagaraj and Murthy516

GEOMATE- Osaka, Nov. 16-18, 2015

conformities in the overall ranking process. The
viability of using ATIC method to evaluate
geotechnical correlations is addressed and comparedwith other statistical evaluation measures.
The Cc correlations shall be considered in thispaper as test-case. The reasons are: Cc determinationis complex and time consuming that made empiricalcorrelations more important; many correlations fordifferent soil conditions; previous attempts ofcorrelation evaluation were done; and its
development starts as early as 1944 and newcorrelations still being developed [17].

Table 2 Studied Cc correlations
Cor.
Formula [Ref.]ID
C01 𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟐𝟐𝟐𝟐(𝐞𝐞 − 𝟎𝟎. 𝟐𝟐𝟐𝟐)
C02
C03

[18]

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟏𝟏𝟏𝟏𝟏𝟏𝟏𝟏 + 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎
[1]

C48

𝐂𝐂𝐂𝐂 = 𝟏𝟏. 𝟐𝟐𝟐𝟐 + 𝟏𝟏. 𝟎𝟎𝟎𝟎𝟎𝟎(𝐞𝐞 − 𝟏𝟏. 𝟖𝟖𝟖𝟖)

C49

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟒𝟒(𝐞𝐞 − 𝟎𝟎. 𝟐𝟐𝟐𝟐) [20]
[21]

C53

[19]

C04 𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟑𝟑𝟑𝟑 (𝐞𝐞 − 𝟎𝟎. 𝟓𝟓)
C05 𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟒𝟒𝟒𝟒(𝐞𝐞 − 𝟎𝟎. 𝟐𝟐𝟐𝟐)[18]
C06 𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟓𝟓𝟓𝟓(𝐞𝐞 − 𝟎𝟎. 𝟐𝟐𝟐𝟐)[19]C07
C08

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟐𝟐𝟐𝟐𝟐𝟐 + 𝟎𝟎. 𝟒𝟒𝟒𝟒(𝐞𝐞 − 𝟎𝟎. 𝟖𝟖𝟖𝟖)[19]
𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟒𝟒𝟒𝟒𝟒𝟒(𝐞𝐞 − 𝟎𝟎. 𝟑𝟑𝟑𝟑𝟑𝟑)[23]
𝐂𝐂 = 𝟎𝟎. 𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝟐 + 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎
C10 𝐂𝐂[24][21]
C11 𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟕𝟕𝟕𝟕(𝐞𝐞 − 𝟎𝟎. 𝟓𝟓)
𝟏𝟏.𝟔𝟔
[26]
C12 𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟐𝟐𝐞𝐞
𝐂𝐂 = 𝟏𝟏. 𝟏𝟏𝟏𝟏(𝐞𝐞 − 𝟎𝟎. 𝟑𝟑𝟑𝟑)
C13 𝐂𝐂[27]
𝐂𝐂 = 𝟏𝟏. 𝟏𝟏𝟏𝟏(𝐞𝐞 − 𝟎𝟎. 𝟗𝟗𝟗𝟗)
C14 𝐂𝐂[27]
C15 𝐂𝐂𝐂𝐂 = 𝟏𝟏. 𝟏𝟏𝟏𝟏𝟏𝟏
𝐂𝐂 = 𝟎𝟎. 𝟓𝟓𝟓𝟓(𝐞𝐞 − 𝟎𝟎. 𝟑𝟑𝟑𝟑)[27]
C16 𝐂𝐂[15]
𝐂𝐂 = 𝟎𝟎. 𝟑𝟑𝟑𝟑(𝐞𝐞 − 𝟎𝟎. 𝟏𝟏𝟏𝟏)
C17 𝐂𝐂[15]
𝐂𝐂 = 𝟎𝟎. 𝟑𝟑𝟑𝟑(𝐞𝐞 − 𝟎𝟎. 𝟐𝟐𝟐𝟐)
C18 𝐂𝐂[15]
𝐂𝐂 = 𝟎𝟎. 𝟒𝟒𝟒𝟒(𝐞𝐞 − 𝟎𝟎. 𝟐𝟐𝟐𝟐)
C19 𝐂𝐂[6]
C20 𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟑𝟑𝟑𝟑𝟑𝟑
𝐂𝐂 = 𝟎𝟎. 𝟒𝟒𝟒𝟒(𝐞𝐞 − 𝟎𝟎. 𝟓𝟓) [14]C09

USED DATA
Subsurface investigation reports were collectedfrom reputable geotechnical firms in Egypt, UAE,
Iraq, and Indonesia. The collected reports contained
field and laboratory tests results for more than
35,000 boreholes collected during the last three
years. The most reliable boreholes were entered withconsistent and unified units into customized
geotechnical database. The data were checked withthe source data to ensure the quality and consistencyof information. To serve the research needs, only 27
tables were filled with 5087 boreholes
Data for this study was collected from thedatabase with the condition that the sample has the
all needed independent parameters to maintain samelevel of consistency and accuracy. Data wasvalidated and only true outliers were excluded
considering the very different characteristics of soils
in the above countries. A total of 82 records wasfound to be eligible for the research needs. Table 1
shows descriptive statistic for the used soil
properties.

C21
C22

𝐂𝐂

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝟐

C23 𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟑𝟑𝟑𝟑𝟑𝟑𝟑𝟑𝟑𝟑
C24 𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝐞𝐞𝐋𝐋
C25 𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝐞𝐞𝐋𝐋
C26 𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟐𝟐𝟐𝟐𝟐𝟐𝐞𝐞𝐋𝐋
𝟎𝟎.𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎

Range

Mean

[8]

Initial Voids Ratio
0.32 - 4.35 1.58
Bulk Density (t/m3) 1.04 - 2.29 1.61
Water Content (%) 11.9 - 168.1 57.15
Liquid Limit (%)
17.1 - 166.2 62.68
Plasticity Index (%) 2.48 - 113.9 30.71
Compression Index
0.07 - 1.66 0.57
Std. Dev. is the standard deviation

C36 𝐂𝐂𝐂𝐂 =(𝐋𝐋𝐋𝐋 −𝟏𝟏𝟏𝟏)
𝟏𝟏𝟏𝟏𝟏𝟏

𝐂𝐂

C46

𝐂𝐂

C65
C66

C75
C76
C77
C78
C79
C80
C81

[28]

C82

𝐋𝐋

𝐋𝐋

C64

[21]

[7]
𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎(𝐋𝐋𝐋𝐋 − 𝟏𝟏𝟏𝟏. 𝟓𝟓)[7]
𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎(𝐋𝐋𝐋𝐋 − 𝟖𝟖)
C42 𝐂𝐂[1]
𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝐋𝐋𝐋𝐋
C43 𝐂𝐂[1]
𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎(𝐋𝐋𝐋𝐋 − 𝟏𝟏𝟏𝟏)
C44 𝐂𝐂[10]
C45 𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎(𝐋𝐋𝐋𝐋 + 𝟏𝟏𝟏𝟏. 𝟒𝟒)
𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎(𝐋𝐋 − 𝟔𝟔. [15]𝟑𝟑𝟑𝟑)

Compression index of soil depends on both fieldstate and intrinsic properties. Main field state
properties are field density, water content, and voids
ratio. Main intrinsic property is soil plasticity.
Several correlations were developed to relate Cc
with field state properties and intrinsic properties. A
total of 92 correlations were considered in this study
as shown in Table 2.

C63

[23]

C41

STUDIED CORRELATIONS

C62

C74

𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎(𝐋𝐋𝐋𝐋 − 𝟑𝟑𝟑𝟑)
C37 𝐂𝐂[19]
𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎(𝐋𝐋𝐋𝐋 − 𝟏𝟏𝟏𝟏)
C38 𝐂𝐂[1]
C39 𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎(𝐋𝐋𝐋𝐋 − 𝟕𝟕)
𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎(𝐋𝐋 − 𝟐𝟐𝟐𝟐)[10]

C40

C61

C73

𝐂𝐂𝐂𝐂 = 𝟏𝟏. 𝟏𝟏𝟏𝟏 − 𝟎𝟎. 𝟔𝟔𝟔𝟔 𝛄𝛄𝐝𝐝[15][15]𝐂𝐂 = 𝟏𝟏. 𝟏𝟏𝟏𝟏𝟏𝟏𝟏𝟏 − 𝟎𝟎. 𝟓𝟓𝟓𝟓𝟓𝟓𝛄𝛄𝐝𝐝
C33 𝐂𝐂[6]
𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎(𝐋𝐋𝐋𝐋 − 𝟗𝟗)
C34 𝐂𝐂[19]

C35 𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎(𝐋𝐋𝐋𝐋 − 𝟗𝟗)

C60

[9]

[8]

C31 𝐂𝐂𝐂𝐂 = 𝟐𝟐. 𝟒𝟒 − 𝟏𝟏. 𝟔𝟔𝟔𝟔 𝛄𝛄𝐝𝐝

Std.
Dev.
0.84
0.31
31.09
25.22
17.84
0.3

C59

[8]

[11]

C32

Property

C57
C58

C69
C70
C71
C72

C29 𝐂𝐂𝐂𝐂 = 𝟏𝟏. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝐧𝐧 + 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎 [11]Table 1 Used soil properties descriptive statistics

C56

C68

[9]

𝛄𝛄𝐰𝐰 𝟐𝟐.𝟒𝟒

C55

[11]

𝟐𝟐

C30 𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟓𝟓 � 𝛄𝛄𝐝𝐝 �

C54

[5]

𝟎𝟎.𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎

𝐂𝐂𝐂𝐂 = 𝟏𝟏−𝟎𝟎.𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎

C50
C51
C52

C67

C27 𝐂𝐂𝐂𝐂 = 𝟏𝟏−𝟎𝟎.𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎
C28

Cor.
ID
C47

[15]

C83
C84
C85
C86
C87
C88
C89
C90
C91
C92

Formula [Ref.]
𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎(𝐋𝐋𝐋𝐋 − 𝟏𝟏𝟏𝟏. 𝟗𝟗)[15]

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎(𝐋𝐋𝐋𝐋 − 𝟏𝟏𝟏𝟏)[5]𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎(𝐋𝐋𝐋𝐋 + 𝟐𝟐𝟐𝟐. 𝟓𝟓)[6]𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝐋𝐋𝐋𝐋
𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝐋𝐋𝐋𝐋
𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎 ( 𝐋𝐋𝐋𝐋 − 𝟐𝟐𝟐𝟐. 𝟕𝟕)

[11]
[11]
[7]

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝐈𝐈𝐏𝐏 + 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎[22]𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝐈𝐈𝐏𝐏 + 𝟎𝟎. 𝟏𝟏𝟏𝟏𝟏𝟏[6]𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎 ( 𝐖𝐖𝐜𝐜 − 𝟕𝟕. 𝟓𝟓𝟓𝟓𝟓𝟓)[28]

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎 ( 𝐖𝐖𝐜𝐜 − 𝟖𝟖)
𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎 𝐖𝐖𝐜𝐜 𝟐𝟐 +𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎 𝐖𝐖𝐜𝐜 − 𝟎𝟎. 𝟏𝟏𝟏𝟏𝟏𝟏[20]

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝐈𝐈𝐏𝐏 + 𝟎𝟎. 𝟎𝟎𝟎𝟎

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝐈𝐈𝐏𝐏 + 𝟎𝟎. 𝟏𝟏𝟏𝟏𝟏𝟏
𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝐖𝐖𝐜𝐜
𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎 ( 𝐖𝐖𝐜𝐜 − 𝟓𝟓)

[15]

[15]

[25]
[21]

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎 𝐖𝐖𝐜𝐜

[7]

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎 ( 𝐖𝐖𝐜𝐜 − 𝟑𝟑. 𝟖𝟖𝟖𝟖)[15]

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎 (𝐖𝐖𝐜𝐜 − 𝟏𝟏𝟏𝟏. 𝟐𝟐𝟐𝟐)[15]

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎 𝐖𝐖𝐜𝐜

[21]

[24]

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎 (𝐖𝐖𝐜𝐜 + 𝟐𝟐. 𝟖𝟖𝟖𝟖)

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝐖𝐖𝐜𝐜 − 𝟎𝟎. 𝟏𝟏𝟏𝟏𝟏𝟏𝟏𝟏
𝐖𝐖

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟖𝟖𝟖𝟖 �𝟏𝟏𝟏𝟏𝟏𝟏𝐜𝐜 �

𝟏𝟏.𝟓𝟓

[15]

[6]

[29]

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝐖𝐖𝐜𝐜[5]

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎 ( 𝐖𝐖𝐜𝐜 − 𝟐𝟐𝟐𝟐. 𝟕𝟕)[7]

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎 𝐆𝐆𝐒𝐒 𝐈𝐈𝐏𝐏
[1]
𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟒𝟒(𝐞𝐞 + 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝐖𝐖𝐂𝐂 − 𝟎𝟎. 𝟐𝟐𝟐𝟐)[21]
𝟏𝟏+𝐞𝐞 𝟐𝟐
𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟒𝟒𝟒𝟒𝟒𝟒 �𝐥𝐥𝐥𝐥(𝐆𝐆𝐒𝐒 ) � 𝐆𝐆 � + 𝟎𝟎. 𝟐𝟐𝟐𝟐𝟐𝟐�
𝐒𝐒

𝟏𝟏+𝐞𝐞 𝟐𝟐

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟏𝟏𝟏𝟏𝟏𝟏𝟏𝟏𝐆𝐆𝐒𝐒 � 𝐆𝐆 �

[23]
[23]

𝐒𝐒

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟒𝟒𝟒𝟒𝟒𝟒 − 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝐆𝐆𝐒𝐒 + 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎[6]𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟒𝟒𝟒𝟒𝟒𝟒𝟒𝟒 + 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝐋𝐋𝐋𝐋 − 𝟎𝟎. 𝟏𝟏𝟏𝟏𝟏𝟏 [28]
𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟑𝟑𝟑𝟑(𝐞𝐞 + 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝐋𝐋𝐋𝐋 − 𝟎𝟎. 𝟑𝟑𝟑𝟑)[21]
𝐂𝐂 = 𝟎𝟎. 𝟑𝟑𝟑𝟑(𝐞𝐞 + 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝐋𝐋 +𝐂𝐂

𝐋𝐋

𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝐖𝐖𝐂𝐂 − 𝟎𝟎. 𝟑𝟑𝟑𝟑)

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟒𝟒𝟒𝟒𝟒𝟒𝟒𝟒𝟒𝟒 − 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝐖𝐖𝐂𝐂 − 𝟎𝟎. 𝟏𝟏𝟏𝟏𝟏𝟏

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝟐 + 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝐋𝐋𝐋𝐋 +𝟎𝟎. 𝟎𝟎𝟎𝟎𝐖𝐖𝐂𝐂 + 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎
𝛄𝛄

𝟐𝟐

[21]
[6]
[6]

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟏𝟏𝟏𝟏𝟏𝟏 �𝐆𝐆𝐒𝐒 � 𝛄𝛄𝐰𝐰 � − 𝟎𝟎. 𝟏𝟏𝟏𝟏𝟏𝟏�
[23]

𝐝𝐝

𝟏𝟏+𝐞𝐞 𝟐𝟐.𝟑𝟑𝟑𝟑𝟑𝟑

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟏𝟏𝟏𝟏𝟏𝟏 𝐆𝐆𝐒𝐒 � 𝐆𝐆 �𝐒𝐒𝛄𝛄𝐬𝐬𝐬𝐬𝐬𝐬 𝟐𝟐.𝟒𝟒

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟏𝟏𝟏𝟏𝟏𝟏 𝐆𝐆𝐒𝐒 � 𝛄𝛄 �
𝐝𝐝

[23]
[23]

𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟏𝟏𝟏𝟏𝟏𝟏 (𝟏𝟏 + 𝐞𝐞) ( 𝐥𝐥𝐥𝐥(𝐋𝐋𝐋𝐋 ) −𝟑𝟑. 𝟎𝟎𝟎𝟎)[7]𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎 𝐋𝐋𝐋𝐋 𝐆𝐆𝐒𝐒
[14]𝐂𝐂𝐂𝐂 = (𝟏𝟏 + 𝐞𝐞) (𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎 +𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝐂𝐂 )[5]𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟏𝟏𝟏𝟏𝟏𝟏𝟏𝟏 (𝟏𝟏 + 𝐞𝐞) ( 𝐥𝐥𝐥𝐥(𝐖𝐖𝐂𝐂 ) − 𝟑𝟑. 𝟎𝟎𝟎𝟎)[7]𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟏𝟏𝟏𝟏𝟏𝟏𝟏𝟏 − 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝐈𝐈𝐏𝐏 +𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝐖𝐖𝐂𝐂 − 𝟎𝟎. 𝟐𝟐𝟐𝟐𝟐𝟐[15]
𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟓𝟓𝟓𝟓𝟓𝟓𝟓𝟓 + 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝐋𝐋𝐋𝐋 −𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝐖𝐖𝐂𝐂 − 𝟎𝟎. 𝟑𝟑[15]𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟏𝟏𝟏𝟏𝟏𝟏 + 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝐋𝐋𝐋𝐋 +𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝐖𝐖𝐂𝐂 − 𝟎𝟎. 𝟐𝟐𝟐𝟐𝟐𝟐[15]
𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝐖𝐖𝐂𝐂 + 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝐋𝐋𝐋𝐋[7]
𝐂𝐂𝐂𝐂 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝐖𝐖𝐂𝐂 + 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝐋𝐋𝐋𝐋 − 𝟎𝟎. 𝟏𝟏[21]

Where e: voids ratio, n: porosity, 𝛾𝛾𝑑𝑑 : dry density(g/cm3), L L : liquid limit, I P : plasticity index, W C :natural water content, and G S : specific gravity
QUANTITATIVE STATISTICAL ANALYSIS
517

GEOMATE- Osaka, Nov. 16-18, 2015

equations for correlations ranking based on ATIC
method are given in Fig. 2.
Both position conformity coefficient,D(Xo , Xc ),and trend conformity coefficient, T(Xo , Xc ), ranges
between 0 and 1. When the value is close to 1, it
indicates better consistency between the observedand correlated values.

The maximum, minimum, mean, and standard
deviation of the correlated values were compared tothose of the observed values as shown in Fig. 1. No
general conclusion can be made based on the abovemeasures except the scatter of the correlated values
around the observed values.

Fig. 2 Correlations ranking based on ATIC method.
ATIC method was used to rank the 92 Cc
correlations. Summary of the results for the 5 topmost and bottom-most ranked correlations
considering both position and trend is given in Table
3. If position conformity was considered separately;
C60 and C67 will be considered the best ranked, andC13 and C15 are the worst ranked correlations.
When considering the trend conformity; C26 andC22 are the best ranked, and C62 and C15 are theworst ranked correlations.

Fig. 1 Correlated values (a) Maximum, (b)Minimum, (c) Mean, and (d) Standard deviation
with those of the observed values (solid line).
CORRELATIONS EVALUATION
Till now, there are no formal standards forevaluating the goodness-of-fit between the observedand correlated values, either visually or numerically[30]. In this section, ATIC method as a tool forcorrelation evaluation is introduced and comparedwith different statistical measures.

Table 3 ATIC results summary for the 10 top-mostand 10 bottom-most ranked correlations

ATIC Method

Rank

5 Bot.
Cor

5 Top Cor.

Theil inequality coefficient (TIC) is widely usedsince 1977 because it is simple and easy tounderstand. However, it suffers of many flaws thathave been discussed in detail by Song et al. [31] who
proposed ATIC method. The ATIC covers many of
the TIC method flaws; especially it considers both
position and trend differences between the observedand correlated values using principle component
analysis approach. The standard procedures and

518

1
2
3
4
5
88
89
90
91

Cor.
ID
C22
C80
C43
C10
C50
C03
C14
C13
C62

𝐃𝐃(𝐗𝐗 𝐨𝐨 , 𝐗𝐗 𝐜𝐜 𝐓𝐓(𝐗𝐗 𝐨𝐨 , 𝐗𝐗 𝐜𝐜
0.64740.64750.64050.6484
0.6346
0.29690.27100.1607
0.19800.46070.45660.46020.45570.45860.14190.10250.10250.0565yj
1.62591.6182
1.6175
1.6173
1.60770.60890.50250.38030.3310GEOMATE- Osaka, Nov. 16-18, 2015

92

C15

0.09800.1025values and they only evaluate the trend of the values
without considering their relative positions.

0.3108Correlation and Determination Coefficients
Mean Squared Deviation, Root Mean Squared
Deviation, and Mean Absolute Difference

Correlation (R) and determination coefficients(R2) give an indication about the strength of linear
association between the correlated and observed data[30]. Both R and R2 were calculated between theobserved and correlated values using Eq. (2) and Eq.
(3) [32].
𝑅𝑅 =

∑𝑛𝑛
����
���
𝑜𝑜 )(𝑥𝑥𝑐𝑐𝑐𝑐 −𝑥𝑥
𝑐𝑐 )𝑖𝑖=1(𝑥𝑥𝑜𝑜𝑜𝑜 −𝑥𝑥

2 𝑛𝑛
����
���𝑐𝑐 )2�∑𝑛𝑛𝑜𝑜 ) ∑𝑖𝑖=1(𝑥𝑥𝑐𝑐𝑐𝑐 −𝑥𝑥
𝑖𝑖=1(𝑥𝑥𝑜𝑜𝑜𝑜 −𝑥𝑥

𝑅𝑅2 = �∑𝑛𝑛
���𝑜𝑜� )(𝑥𝑥𝑐𝑐𝑐𝑐 −𝑥𝑥���𝑐𝑐 )
𝑖𝑖=1(𝑥𝑥𝑜𝑜𝑜𝑜 −𝑥𝑥���𝑜𝑜� )2 ∑𝑛𝑛
���𝑐𝑐 )2�∑𝑛𝑛𝑖𝑖=1(𝑥𝑥𝑜𝑜𝑜𝑜 −𝑥𝑥
𝑖𝑖=1(𝑥𝑥𝑐𝑐𝑐𝑐 −𝑥𝑥�2

The Mean Squared Deviation (MSD), its squareroot (RMSD), and Mean Absolute Difference(MAD) are indicators of the difference between thepositions of correlated values from the observed
values. MSD, RMSD, and MAD were calculated
between the observed and correlated values based on
Eq. (4), Eq. (5), and Eq. (6), respectively [30].

(2)

𝑀𝑀𝑀𝑀𝑀𝑀 =

(3)

2
∑𝑛𝑛
𝑖𝑖=1(𝑥𝑥𝑜𝑜𝑜𝑜 −𝑥𝑥𝑐𝑐𝑐𝑐 )𝑛𝑛∑𝑛𝑛 (𝑥𝑥𝑜𝑜𝑜𝑜 −𝑥𝑥𝑐𝑐𝑐𝑐 )2

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 = � 𝑖𝑖=1

Where 𝑥𝑥𝑜𝑜𝑜𝑜 , 𝑥𝑥𝑐𝑐𝑐𝑐 observed and correlated value for���,each point i, n: number of points, 𝑥𝑥
𝑜𝑜 𝑥𝑥�𝑐𝑐 : average of
observed and correlated values, respectively.

𝑛𝑛

(4)
(5)

∑𝑛𝑛
𝑖𝑖=1|𝑥𝑥𝑜𝑜𝑜𝑜 −𝑥𝑥𝑐𝑐𝑐𝑐 |(6)
𝑀𝑀𝑀𝑀𝑀𝑀 =𝑛𝑛
Where 𝑥𝑥𝑜𝑜𝑜𝑜 observed value for each point i, 𝑥𝑥𝑐𝑐𝑐𝑐 :correlated value for each point i, n: number of points

The Cc correlations were ranked based on bothcoefficients and the best correlation is C29 that was
ranked the 12th best based on ATIC method, theworst correlations are C53, C54, C55, and C56. Fig.
4.a and 4.b shows the values for the best and worst
ranked correlations based on ATIC and R&R2 values,
respectively with the observed values.

The Cc correlations were ranked based on thosecoefficients. The best correlation is C60 same resultsof ATIC method when considering only the position
conformity. Fig. 5 shows the correlated values for
the C22 and C60 in-line with the observed Cc values.
It can be seen that both correlations are nearly
accurate nevertheless C60 is not accurately follow
the observed values trend. The worst correlation is
C15 same as ATIC method rank.

Fig. 4 Observed C C values and correlated values forbest correlations based on ATIC and MSD-RMSDMAD values.
Ranking Index
Fig. 3 Best (a) and worst (b) correlations based on
ATIC method and R&R2 values.

The ranking index (RI) is proposed by Briaud
and Tucker [33] to rank different methods of pile
capacity determination as given in Eq. (9). The
authors suggested that the lower the value of RI the
better rank of the correlation.

Figure 4.a shows that both correlations are nearly
accurate to predict the observed values nevertheless;
C22 is more accurate especially at the extreme
values. From Fig. 4.b it can be seen that the ATIC
worst correlation (C15) is too far from the observed
values in contrast to the worst correlations based on
R&R2. The main reason for that the R and R2
coefficients are sensitive for abrupt changes of the

𝑥𝑥𝑐𝑐
𝑥𝑥𝑐𝑐
𝑅𝑅𝑅𝑅 = 𝜇𝜇 �ln � �� + 𝜎𝜎 �ln � ��
𝑥𝑥𝑜𝑜
𝑥𝑥𝑜𝑜

519

(9)

Where 𝜇𝜇, 𝜎𝜎: represents mean and standard deviation;and 𝑥𝑥𝑜𝑜, 𝑥𝑥𝑐𝑐 : observed and correlated values

GEOMATE- Osaka, Nov. 16-18, 2015

Fig. 6 Observed C C values and correlated values forbest correlations based on ATIC and RD.

Some correlation resulted illogical negativevalues that can’t be used in the normal log function
in Eq. (9) that prevents successful ranking of all
correlations. When ranking the other correlations,the best correlation was C67 that was ranked the 2ndbest in ATIC method when considering only the
position conformity. The worst correlation is C15,
same as ATIC rank. Fig. 6 shows Correlations C67
and C22 in line with the observed C C values.

CONCLUSION
This paper focused on the study of the validity andefficiency of Amended Theil Inequality Coefficient(ATIC) method for geotechnical correlations
evaluation. ATIC has the advantage that it considers
both position and trend conformities between theobserved and correlated values. Total of 92
compression index correlations were used as testcase. Different statistical measures results that wereused in the literature were compared with the resultsof the ATIC method.
The best correlation based on ATIC method was
C22 and worst correlation was C15, bothcorrelations relate compression index value with theinitial voids ratio of the soil.
Based on R and R2 coefficients, the best
correlation is C29 and the worst correlations are C53,
C54, C55, and C56. When visually inspect these
correlations with the worst correlation based on
ATIC, it was concluded that ATIC is more accurate.
Both coefficients have shortcoming that they
consider only the trend of the values without
considering their relative position.
The best and worst correlations based on MSD,RMSD, and MAD is C60 and C15 respectively.
When comparing these results with ATIC results,
both methods give the same worst correlation, butdifferent results for the best correlation. The MSD,RMSD, and MAD values have the shortcoming thatthey evaluate only the values’ position around theaverage without considering its trend.
The RI best correlation was C67 that was ranked
the 2nd best when considering only the position
conformities in the ATIC method. The worst
correlation was C15, same as ATIC rank. The RI
considers only the position of the data that limits its
ability for efficient correlation evaluation.
The RD best correlation was C02 that was ranked
the 58th best based on ATIC method. The worst
correlation was C15, same as ATIC rank. It wasnoted that RD is biased for the odd ratios between
the correlated and observed values and it considers
only values’ position that may be considered thereason for the large difference between ATIC
method results and RD results.
Based on the results of this paper, ATIC can beconsidered a good method of geotechnicalcorrelations evaluation. Future research willconcentrate on how to integrate ATIC method withexpert knowledge to validate geotechnicalcorrelations.

Fig. 5. Observed C C values and correlated values forbest correlations based on ATIC and RI.
Ranking Distance
The ranking distance (RD) was proposed by
Cherubini and Orr [34] as a rational approach tocompare between observed and correlated data asgiven in (10). The authors suggested that the lower
the value of RD the better the accuracy and precisionof the correlation.
𝑥𝑥

2

𝑥𝑥

2

𝑅𝑅𝑅𝑅 = ��1 − 𝜇𝜇 � 𝑐𝑐 �� + �𝜎𝜎 � 𝑐𝑐 ��
𝑥𝑥𝑜𝑜

𝑥𝑥𝑜𝑜

(10)

All variables as defined in Eq. (9)The best correlation based on RD values iscorrelation C02 that was ranked 58th best based on
ATIC method. The worst correlation is C15, same as
ATIC rank. During the data analysis, it was notedthat the RD is biased for the odd ratios between
correlated and observed values. Also, it considers
only the position of the values without considering
its trend. This may be the reasons for the large
difference between the results of the ATIC method
and RD. Correlations C02 and C22 are shown in Fig.
7 in-line with the observed C C values.

ACKNOWLEDGMENTS

520

GEOMATE- Osaka, Nov. 16-18, 2015

Waled A. Daoud is thankful to the Egyptian
Ministry of Higher Education and Egypt-japan
University of Science and Technology for funding
his PhD studies. Also, he would like to thank Dr.
Hazem Barakat for his technical and financial
support during the data collection period and special
thanks to Kyushu University for offering the tools
and equipment needed for the research.

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compressibility of egyptian clays.,” Soils Found.,vol. 30, no. 2, pp. 128–132, 1990.
[6] N. Dayal, “Consolidation analyses of greater
Cincinnati soils,” University of Cincinnati, 2006.
[7] B. A. McCabe, B. B. Sheil, M. M. Long, F. J.
Buggy, and E. R. Farrell, “Empirical correlations
for the compression index of Irish soft soils,”
Proc. ICE-Geotechnical Eng., vol. 167, no. 6, pp.
510–517, 2014.
[8] C. I. Giasi, C. Cherubini, and F. Paccapelo,“Evaluation of compression index of remoulded
clays by means of Atterberg limits,” Bull. Eng.
Geol. Environ., vol. 62, no. 4, pp. 333–340, 2003.
[9] J. H. Park and T. Koumoto, “New compression
index equation,” J. Geotech. Geoenvironmental
Eng., vol. 130, no. 2, pp. 223–226, 2004.
[10] A. W. Skempton and O. T. Jones, “Notes on thecompressibility of clays,” Q. J. Geol. Soc., vol.
100, no. 1–4, pp. 119–135, 1944.
[11] B. Tiwari and B. Ajmera, “New correlation
equations for compression index of remolded
clays,” J. Geotech. Geoenvironmental Eng., vol.
138, no. 6, pp. 757–762, 2011.
[12] M. Hatanaka and A. Uchida, “Empiricalcorrelation between penetration resistance andinternal friction angle of sandy soils.,” Soils
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[13] F. Nassaji and B. Kalantari, “SPT capability toestimate undrained shear strength of fine-grainedsoils of Tehran, Iran,” Electron. J. Geotech. Eng.,
vol. 16 N, pp. 1229–1238, 2011.
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[34] C. Cherubini and T. L. L. Orr, “A rational
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Construction Materials

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Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

STRUCTURE OF SUPERCONDUCTING CABLE COMPONENTS
Svetlana Barannikova1,2,3, Anatoly Malinovsky3 and Dmitrii Pestsov3
1

Institute of Strength Physics & Materials Science, SB RAS, Russia; 2National Research Tomsk State
University, Russia; 3Tomsk State University of Architecture and Building, Russia

ABSTRACT
Using atomic force, optical and electron microscopy methods, the changes in the microstructure andphase composition were investigated for the alloy Nb47%Ti used for the manufacture of superconducting cable
employed as current-carrying elements in the magnetic system for International Thermonuclear Experimental
Reactor. The test samples were prepared from the superconducting wire at an intermediate step of the drawing
process for the area reduction Ø1.3→Ø1.2 mm. The effect of cold drawing and intermediate annealing on theproperties of as-worked Nb-Ti alloy was assessed. Local strain zones were found to occur in the rupture area.
The shape and chemical composition of Nb-Ti wire was examined for both a defect-free area and the rupturearea. A Nb diffusion barrier was found to occur in the copper matrix of Nb-Ti wire.
Keywords: Superconductors, microstructure, plastic strain localization

The structural investigations were performed
using test samples of the Nb-Ti alloy containing
47.5% Ti (by mass) or 63.7% Ti (at. percent). The
test
sampleswere
preparedfromthesuperconducting cable wire at the intermediate stageof cold drawing with the area reduction of theoriginal bar Ø1.33→Ø1.2 mm. To provide for highspace resolution level, several methods were used
for investigation of the composition and structure of
the alloy: optical microscopy (Olympus GX 71
units); raster electron microscopy (Philips SEM 515
unit); atomic force microscopy (Solver PH47-PROunit) and contact method. The element distribution
was determined for material on the border of copper
matrix and Nb-Ti cable core, using the method ofraster electron microscopy (Quanta 200 3D ionelectron unit equipped with detectors of secondaryand back-scattered ions).

INTRODUCTION
Materials traditionally used in electricalengineering have been to some extent supplanted bysuperconductive materials, which are cost-effective
and suitable for a wide range of applications, inparticular, nuclear energetics [1]. Thus Nb-Ti alloys
are worthy of special attention [2]-[4]. These areused for the manufacture of superconducting cable
employed as current-carrying elements in themagnetic system for International Thermonuclear
Experimental Reactor. The cable houses several
thousand strands of superconducting wire in acopper matrix, each strand being 2…5 mμ in
diameter [2]. Such cables must be capable ofmeeting a number of stringent requirements, i.e.
stability of criticality parameters and of currentvoltage characteristics, strength-resistance tobreaking and structural uniformity of thesuperconducting wire, uniform cross-section
geometry of wire [5]-[7].
By the manufacture of superconducting Nb-Ti
wire, the most important stage is cold drawing withthe area reduction of the original bar Ø60 …70
mm→Ø0.1…1.0mm[8].
As-produced
superconducting wire is expected to have highstrength resistance to breaking as well as structural
uniformity, i.e. pre-set density of micro-defects forcontrolling formation of pinning centers. The goal of
the given work is examination of the influence of thesevere plastic deformation (SPD) treatment bydrawing on the structure of superconducting Nb-Ti
wire.

RESULTS AND DISCUSSION
The coated cable construction houses several
thousand strands of superconducting Nb-Ti wire incomposite copper matrix (Fig. 1). The composite
might contain resistive or diffusion barriers,
stabilizing coating or armoring [2]. Metallography
investigation was performed using cross-sections ofsuperconducting Nb-Ti wire strands, which had around form with the average diameter ~10 μm.
However, the intermediate layer adjacent to thecopper matrix all the Nb-Ti wire strands haverhomb-like shape with13-μm and 11-μm diagonals,
which might be attributed to the SPD treatment bycold drawing.

MATERIALS AND METHODS524

GEOMATE- Osaka, Nov. 16-18, 2015

respectively; the wire strands in the point of rupture
have the same chemical composition. In the former
case, intermediate layer wire strands have a roundform and in the latter, an elongated one.

Fig. 1 The cross-section of superconducting Nb-Ti
cable wire strands at the intermediate stage of cold
drawing with the area reduction Ø1.3→Ø1.2 μm: 1 –copper matrix; 2 – Nb-Ti wire strands and 3 –copper core.
Using atomic force microscopy in conjunction
with contact method in constant force mode, crosssection relief was examined for different cable zones[9], [10]. The scanning revealed a diffusion Nb
barrier, which is clearly defined by the relief hills
observed in the area of Nb-Ti strands and copper
matrix (Figs. 2a and 3a). A profilogram plotted by
the linear-intersept method shows a smooth relief of
low-amplitude lines of Ni-Ti strands and copper
matrix (Fig. 2b and 3b), which separate highamplitude maximums 250…260 nm wide
corresponding to the Nb barrier (Fig. 2c and 3c).
The SPD treatment would cause grain refinementin the copper matrix. As a result, a
submicrocrystalline structure is formed with averagegrain size ~800 nm, which also contains individual
grains and conglomerates composed of up to eightgrains. The most heavily deformed area is locatedalong the copper core-intermediate Ni-Ti layer
border with the grain sizes varying from ~2120 nm
to ~310 nm. The intermediate copper matrix layers
separating Nb-Ti wire strands contain equiaxed
grains having average size ~800 nm, while the outer
copper layers have average grain size ~1050 nm.
Using electron microscopy methods, material
structure was examined in the intermediate layer on
the Ni-Ti strands-copper core border; a specificdefect was found to occur in the material (Fig. 4),
which is due to the rupture of superconducting
strands. The investigations were carried on using araster electron microscope Carl Zeiss EVO 50,
which was equipped with an attachment designed for
X-ray
dispersion
microanalysis(Oxford
Instruments). It was found that the outer layer andthe core are comprised of copper; however, elementsfrom the intermediate Ni-Ti layer would penetrate
into the cable’s copper core as well as intermediateouter layer interphase. The chemical composition ofintermediate layer wire strands on the core border is
35.66% (at) and 63.07% (at) for Nb and Ti,

Fig. 2 The relief of longitudinal cross-section
showing diffusion Nb barrier within unpolisheddefect-free area of Nb-Ti strands and copper matrix(a); a profilogram of the same area (b); 3D image (c).
Using scanning microscopy in secondary
electrons mode in conjunction with characteristic Xray radiation on a unit Quanta 200 3D, thecomposition of Nb-Ti strands was examined andfound to be uniform.
Energy dispersive X-ray composition microanalysis
was used to identify the elemental composition ofmaterial. In the area examined, i.e. intermediate NbTi strands layer-copper matrix-intermediate Nb-Ti
strands layer, the following elements were found tooccur: Nb, Ti and Cu.

525

GEOMATE- Osaka, Nov. 16-18, 2015

partially in the point of rupture. The material within
the matrix was in a deformed and relaxed state.
Individual dislocations would occur within thegrains; however, no dislocation clusters areobservable.

(a)

(b)

(c)
Fig. 3 The relief of transverse cross-section showing
Nb barrier around the wires in a conductor matrix:(a), (c) 2D and 3D images, respectively; (b)profilogram of a 30 × 30 μm region. (1) Copper
matrix, (2) Nb–Ti wires, and (3) niobium barrier.
The non-homogeneous space distribution ofcharacteristic X-ray photons of Ti, Nb and Cu in the
Nb-Ti strands and the copper matrix suggests that adiffusion layer occurs in the direction across the NbTi strands-copper matrix border. The maximal andthe minimal number of characteristic X-ray photonsof Nb and Ti are observed, respectively for the NbTi strands and the copper matrix. Exactly theconverse situation is observed for the copper matrix,
i.e. the maximal number of characteristic X-ray
photons is observed for Cu and the minimal numberof the same photons, for Ti and Nb. In the case ofintermediate diffusion layer, the number ofcharacteristic X-ray photons of Nb would firstremain constant, which supports the evidence for theoccurrence of Nb barrier, which was obtained by the
AFM method. Then the number of characteristic Xray photons of Nb and Ti would either grow ordecrease depending on whether the number of thesame photons of Cu grows or decreases.
Using ion beam, the fine structure of cable
components was examined by the thin-foil method.
The Nb-Ti strands are found to have a heavily
deformed structure; grain boundaries were indistinct,
evidence of relaxation unavailable. Individual
dislocations within the grains of Nb-Ti strands could
not be resolved. The Nb barrier on the Nb-Ti
strands-copper matrix border could be defined only

Fig. 4 Longitudinal section showing rupture ofunpolished Nb-Ti strands in as-received defect-freestate (a); 3D image (b); 1– copper matrix; 2 –Nb-Ti
strands.
The Nb barrier would become noticeable on goingalong the Nb-Ti strands-copper matrix border; it has
submicrocrystalline structure, small-size grains havea low degree of non-equiaxiality (Fig. 5).
The morphology of material within the localized
plastic deformation zone was investigated. Several
layers of material 0.5-mm each were removed withthe aid of abrasive from the latter zone to be
examined by metallography technique. It was foundthat all the Nb-Ti strands of the inner layer adjacentto the copper core have a round shape. The SPDtreatment by drawing has left traces in theintermediate layer of the region adjacent to thecopper core. The Nb-Ti strands in the outer layer are
rhomb-shaped.

526

GEOMATE- Osaka, Nov. 16-18, 2015

vicinity of boundary between the intermediate layerof Nb-Ti strands within the copper matrix and thecopper core, i.e. two Nb-Ti strands of irregular shape,
which had smaller size relative to the near-lying
ones.

Fig. 5 Fine structure of superconducting cable: (a)boundary between Nb–Ti fiber and matrix; (b)boundary between copper matrix and niobium
barrier.
The cross-section topography was examinedby the methods of optical and atomic-force
microscopy. After the region adjacent to the copper
core had been polished with abrasive to a depth of
0.5 mm, Nb-Ti strands on its border would assumeirregular shape and would form a localized
deformation zone similar to that observed for asreceived Nb-Ti strands which have not beenpolished (Fig. 6a, b and Fig. 7a, b). In the point ofrupture the intermediate copper matrix layer
separating Nb-Ti strands had average grain size
~850 nm.
The statistic treatment data suggests that the
average grain size of the defect-free zone of thecopper matrix has average grain size ~800 nm,
which is comparable to that of copper in the point ofrupture, i.e. ~850 nm. The profilogram obtained forthe cross-section polished to a depth of 0.5 mmshows a Nb barrier in the point of rupture, whichsurrounds Nb-Ti strands within the copper matrix.
This presents a series of high-amplitude sharp
maxima having width ~250 nm, which is similar tothat observed for as-received material without
polishing. A similar Nb barrier surrounding Nb-Ti
strands in the location of breakage was observed formaterial that had been polished to a depth of 1 mm.
Metallography investigation was performed for
Nb-Ti strands cross-sections, which were subjected
to etching and polishing to a depth of 1 mm. It was
found that Nb-Ti strands appear to be unchanged bycomparison with as-received unpolished material.
However, the localization zone has a different aspect(Fig. 6c and Fig. 7c). The lobe-like strands coalesce
to form one strand, which suggests that the strands
are of different thickness. The Nb-Ti strands within
the localization zone assume a round shape.
The structure of section surface, which had beenpolished to a depth of 2mm, was examined in an
optical microscope. The general aspect of
localization zone is found to have changed
significantly relative to as-received defect-free state.
The Nb-Ti strands in the point of rupture graduallyform a conglomerate (Fig. 6d and Fig. 7d); the nearlying Nb-Ti strands have a round shape, which ischaracteristic for defect-free zones. It should be
noted that another defect was found to occur in theFig. 6 The AFM image of the zone of localized
plastic deformation at the point of breakage of Nb-Ti
strands (designated by arrows): 1–copper matrix; 2–
Nb-Ti strands; depth of polishing: (a) 0 mm; (b) 0.5mm; (c) 1 mm; (d) 2 mm.
Using the contact method of AFM technique, we
examined the cross-section surface of Nb-Ti strands
which had been subjected to etching an polishing toa depth of 2 mm.
In this case, a Nb barrier is found to occur on boththe inner and the outer surface of the Nb-Ti strands
in copper matrix. As noted above, a similar Nbbarrier had been observed previously in a defect-freearea of as-received material; this shows in
profilograms as a series of high-amplitude narrow
maxima having width ≤ 250 nm.
The evolution of localized plasticity zone is
illustrated in Figs. 6 and 7. Analysis of the curve
was made in order to define in what way a change inthe cross-section area of Nb-Ti strands in the pointof rupture is affected by the depth of polishing (Fig.
8). The sections of Nb-Ti strands polished to a depthof 2mm were examined; the results obtained suggestthat the SPD treatment by drawing caused neckformation within the localization zone observed for
the Nb-Ti strands.

527

GEOMATE- Osaka, Nov. 16-18, 2015(a)

deformation has been studied in sufficient detail. On
the base of experimental evidence a one-to-one
correspondence has been established between thework hardening law, which is acting at the given
flow stage of the stress-strain curve, on the one hand
and the space-time distribution plastic distortion
tensor components observed for the deforming
material on the other hand [12].

(b)CONCLUSION(c)

Analysis was made of the effect of the SPDtreatment by drawing on material structure of themulti-strand superconducting cable on the base of
Nb-Ti alloy. The following results have beenobtained.
- Deformation localization zones are found to occur
in the point of rupture of the cable. Sections of NbTi strands were polished to a depth of ≤ 2 mm toreveal that the adjacent Nb-Ti strands have irregularshape.
- A variation in the shape and size of Nb-Ti strands
is found to occur in a defect-free area of theintermediate layer; on the copper core border Nb-Ti
strands have a round shape and diameter ~ 10 μm
and those on the copper matrix border are rhombshaped with diagonals ~ 13 and 11 μm.
- The SPD treatment by drawing causes grain
refinement; a submicrocrystalline structure will formin cable components: the copper core and theintermediate layer of material between the strands
within the matrix has grain size ~800 nm; material in
the point of rupture and in the intermediate layer
between the strands within the matrix has grain size
~850 nm and material of the upper layer has grain
size ~1050 nm.
- A diffusion Nb barrier about 250 nm wide is foundto occur around the Nb-Ti strands in the copper
matrix in a defect-free area; using step-by-steppolishing to the depth of 2000 μm, a similar barrier
was discovered within a localized deformation zone.
- A change in the chemical composition and shape of
Nb-Ti strands in the intermediate layer wasobserved; in a defect-free area all the Nb-Ti strands
have a round shape and the following chemicalcomposition: 35.66 at.% Nb and 63.07 at.% Ti; inthe area of rupture the Nb-Ti strands have a regularshape and the following chemical composition:
35.57 at.% Nb and 63.33 at.% Ti.

(d)Fig. 7 The optical microscopy image of the localized
plastic deformation zone in the site of breakage of
Nb-Ti strands; depth of polishing: (a) 0 mm; (b) 0.5mm; (c) 1 mm; (d) 2 mm.
Thus, the area next to the cable copper core isfound to contain localized deformation zone in the
point of rupture of Nb-Ti strands. Moreover, the Nbbarrier is present only partially in the same area,
which is indicative of a varying extent of
deformation in the sections, which were polished todepths varying from 0.5, 1 and 2 mm. A major
drawback to the conventional approach employed in
the analysis of the ultimate tensile strength ofsuperconductors on the base of Nb-Ti alloy is theconceptual representation of plastic deformation as asteady-state and homogeneous process [11].

Fig. 8 A change in Nb-Ti strands cross-area (S) in
the point of rupture (within the localization zone) vsdepth of polishing: h = 0 mm; 0.5 mm; 1 mm; 2 mm.

ACKNOWLEDGEMENTS
The work was performed in the frame of the
Tomsk State University Academic D.I. Mendeleev
Fund Program (No. 8.1.18.2015) and the Program of
Fundamental Research of State Academies of
Sciences (No. III.23.1.2) for the period 2013-2016
yrs.

The experimental evidence for the nature ofplastic deformation strongly suggests that at theearly stages of straining the plastic deformation
would exhibit an inhomogeneous behavior, which is
liable to cause formation of localized plasticity
nuclei [12]; in one of these nuclei rupture of cablestrands would occur. The macro-scale localization of528

GEOMATE- Osaka, Nov. 16-18, 2015

[7] Cherny OV, Andrievskaya NF, Ilicheva VO,“The microstructure and critical current densityof Nb-48 wt.%Ti superconductor with very highalpha-Ti precipitate volume and very highcritical current”, A Advances in Cryogenic
Engineering, Vol. 48B, 2002, pp. 883-890.
[8] Cherny OV, Storozhilov GE, Andrievskaya NF,
Ilichova VO, Starodubov YD, Volchok OI,
Chirkina LA, Lazareva MB, Okovit VS,“Structure and properties of differently directed
deformed niobium-titanium alloy”, IEEE
Transactions on Applied Superconductivity, Vol.
15, 2005, pp. 3502-3505.
[9] Zuev LB, Shlyakhova GV, Barannikova SA,
Kolosov SV, “Microstructure of elements of asuperconducting alloy Nb-Ti cable”, Russian
Metallurgy (Metally), Vol. 2013, 2013, pp. 229234.
[10] Shlyakhova GV, Barannikova SA, Zuev LB,“On structure of localization zones of plastic
deformation in superconductive cable based on
Nb-Ti alloy”, Metallofizizika i Noveishie
Tekhnologii, Vol. 35, 2013, pp. 453 – 465.
[11] Zuev LB, Barannikova SA, Zavodchikov SYu,
“Localization of tensile deformation in apolycrystalline Zr-based alloy”, Physics ofMetals and Metallography, Vol. 87, 1999, pp.
244-246.
[12] Zuev LB, Danilov VI, Barannikova SA,
Gorbatenko VV, “Autowave model of localized
plastic flow of solids”, Physics of Wave
Phenomena, Vol. 17, 2009, pp. 1–10.

REFERENCES[1] Wilson MN, “NbTi conductors with very low
loss: a review”, Cryogenics, Vol. 48, 2008, pp.
381-395.
[2] Kozlenkova N, Vedernikov G, Shikov A,
Potanina LV, Filatov AV, Vorobieva AE,
Pantsyrnyi VI, Gubkin I.N, ”Study on Ic (T, B)
for the Nb-Ti strand intended for ITER PF insert
coil”, IEEE Transactions on Applied
Superconductivity, Vol. 14, 2004, pp. 10281030.
[3] Lu B, Luongo CA, “Parametric estimation studyof interstrand conductance in multi-strand
superconducting cables”, Cryogenics, Vol. 47,
2007, pp. 546-552.
[4] Ochiai S, Oki Y, Sekino F, Hojo M, Tanaka M,
Okuda H, Moriai H, Sakai S, Watanabe K,“Fracture of filaments and its influence on
critical current and residual strength of fatigued
Nb–Ti/Cusuperconducting
composite”,
Cryogenics, Vol. 43, 2003, pp. 45-51.
[5] Beloshenko VA, Chishko VV, Matrosov NI,
Dyakonov VP, Szymczak R, Pietoza J, Puzniak
R, Szymczak H, Gaida D, Zaleski A, “Effect of
deformation and heat treatment with equalchannel, multiple-angle pressing on the
superconducting properties of NbTi alloy”, Low
Temperature Physics, Vol. 36, 2010, pp. 10451048.
[6] Lee PJ, Larbalestier DC, McKinnel JC, “Hightitanium Nb-Ti alloys initial microstructural
studies”, Advances in Cryogenic Engineering,Vol. 34, 1988, pp. 967-974.

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Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

A FRESH APPROACH TO CHARACTERISATION OF HOT MIX
ASPHALTS
Yeaman J1, Yeaman S.D 1Queensland Pavement Centre, University of the Sunshine Coast, Australia; 2,3
Jacobs Engineering, Australia

ABSTRACT
Pavement Management embraces the full range of engineering activities (Design, Construction, Operation,
Maintenance and Renewal), throughout the life cycle.
Over the past 4 years, The Queensland Pavement Centre at the University of the Sunshine Coast, Australia, hasbeen studying the various parameters that influence pavement behaviour for the whole of life of a road pavement,
under real operational conditions.
Pavement Design requires an in depth understanding of the properties of the component materials. Hot Mix
Asphalt (HMA) is the most important component of a pavement since it provides the strength and flexibility tosupport the loads it is intended to carry; waterproofing of the unbound components and a smooth, safe riding
surface for all users.
Hot Mix Asphalt is one of the most complex materials that an engineer can engage, it is brittle at low
temperatures and will flow at high temperatures, The performance of HMA depends of the pavement
temperature and the rate and magnitude of loading, all of which will vary many times in any 24 hours, yet we
assume one value for “stiffness” through the resilient modulus for perhaps the next 20 years.
This paper examines how the Dynamic Modulus (|E*| can be developed in the Laboratory as a Master Curve,
coupled with temperature gradient parameters to provide characterization for Dynamic Modulus that can beused in a simulation method to determine the Number of Cycles to fatigue failure for a pavement lifetime
spectrum of environment and traffic
Keywords: Asphalt, Master Curves, Dynamic Modulus, Emissivity, Conductivity

INTRODUCTION
The Engineering Science of Pavement Management
has stagnated for the past 20 years, with practioners
reducing the Science to becoming an inventory of
the stock of road assets and a means of justifying
decisions that are often based on very little
knowledge of the performance of the asset under an
extensive range of conditions and manufacturingmethods.
The design of a pavement to meet the conditions of
its use must be paramount, since if the design is
unsuitable or simply incorrect then the remainder ofthe management process is uselessMathematics and computing power mean that we nolonger have the need to design pavements for one
season (The Mean Monthly Average Pavement
Temperature), One Load (An equivalent Standard
Axle) and one material (defined by Stiffness at one
temperature and one time of loading).
This paper will examine the use of mathematicalsimulation process, using statistical data from datacollected from operational pavements and materials
to determine the probability of a pavement meeting
its whole of life expectancy, for the conditions
pertinent to its use.

TRANSFER EQUATIONIn Australia, we use one transfer function, to link
Asphalt properties to the Number of fatigue cycles(Nf) Reference 1
N= �

𝟔𝟔𝟔𝟔𝟔𝟔𝟔𝟔(𝟎𝟎.𝟖𝟖𝟖𝟖𝟖𝟖𝟖𝟖𝟖𝟖+𝟏𝟏.𝟎𝟎𝟎𝟎 𝟓𝟓
𝑺𝑺 𝑴𝑴𝑴𝑴𝑴𝑴𝟎𝟎.𝟑𝟑𝟑𝟑 𝝁𝝁𝝁𝝁

� Equation 1

We could, as easily, use (Reference 2
𝑵𝑵𝑵𝑵 = 𝟏𝟏𝟏𝟏. 𝟒𝟒 (𝟏𝟏𝟏𝟏. 𝟒𝟒(𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎−𝟑𝟑.𝟐𝟐𝟐𝟐𝟐𝟐 )𝑬𝑬−𝟎𝟎.𝟔𝟔𝟔𝟔𝟔𝟔
Equation 2
Where.
S mix, E are the HMA Stiffness, (Modulus)
Ε , μ ε ar e t h e st r a i n r ele v a n t to t h e s t ud y
Vb is the Volume of Bitumen in the HMA.
From which it can be seen that there is no reference
to traffic , with the only material constant being theModulus, Stiffness of the Mix which is strongly
correlated to temperature.
DEVELOPMENT OF MASTER CURVES FOR
530

GEOMATE- Osaka Nov. 16-18, 2015

HOT MIX ASPHALT (HMA)
The development of Master Curves for HMA isfully described in three University of Sunshine Coast,
final year projects (2014) by McPherson, JamesJohnson and Jopson these publications are available
through the University Library. Papers from thisresearch will be submitted for publication during
20
1
5
The Equipment
In the above studies, students prepared 100mm
diameter by 150mm high asphalt samples An IPC
Global Shear Box Compactor was used to compact
A thirty kg billet 450mm x 200mmx200mm
compacted to 95% solids. Three test cylinders werecreated from the one billet and trimmed with an IPC
Global diamond saw
The Materials
Fo r t h is s t ud y t wo ma t er i al s we r e c h o se n
A standard Brisbane City Council Mix B771 18mm
Maximum Size with 15% FRAP (Fine Recycled
Asphalt Product) and a standard 35mm, Brisbane
City Council Mix D771 with a substitution of 20%
FRAP and 10% fine recycled Glass
The Testing Regime
Each Core was fitted with 3 Linear Variable
Distance Transducers (LVDT) spaced at 120° andwith gauge points at 70mm. In the case of the
Marshall specimens a set of gauging points were
manufactured so that the adjusted evenly across theglued joint. Each set of test specimens were placedin the Environmental Chamber .Each sample wastested under repeated loading cycles at Temperaturesof 10°, 20°C, 30°C, and 40°C ± 1 °C andFrequencies (in Hertz) of 0.1, 0.5, 1, 3, 5.10,15,20
with a Contact Stress of 45 KPa
Computation of the Master Curve
The Modulus of each specimen is calculated directlyfrom the data obtained from the Asphalt Mixture
Performance Tester (IPC Global) as effective stress /strain (within the Elastic limit as given by Hooke’slaw) for each o f the test temp eratures andf r e q
u e
n c
i e
s .
Complex Modulus:
𝝈𝝈𝟎𝟎 𝒔𝒔𝒔𝒔𝒔𝒔(𝝎𝝎𝝎𝝎)𝝈𝝈|E*| = =Equation 3𝜺𝜺𝟎𝟎 𝒔𝒔𝒔𝒔𝒔𝒔 (𝝎𝝎𝝎𝝎−𝝓𝝓)𝜺𝜺Where:
σ 0 = peak (maximum) stress= peak (maximum) strainε0Φ= phase angle, degrees
ω= angular velocityt= time, seconds

Figure 1 Example Master Curve Construction
after Yanqing Shao, Jimin Tang and Liu( Reference 1)
The format of the Master Curve
The Asphalt Master Curve is of the form:
𝜶𝜶
𝑳𝑳𝑳𝑳𝑳𝑳|(𝑬𝑬*|) = 𝜹𝜹 + 𝜷𝜷+𝜸𝜸 𝒍𝒍𝒍𝒍𝒍𝒍 𝒕𝒕 Equation 4
𝟏𝟏+𝒆𝒆
Where
|E*| is the Dynamic Modulus of the Asphalt
t is the reduced time (the inverse of frequency)
α, γ, δ are Material constants
β is a temperature dependent variable
T is the required Temperature in degrees Celsius
For Example from Experimentation (McPherson) wehave for Mix 2 (above – the Glass substitute mix and
20% FRAP)
Table 1 Value of Master Curve Variables byExperimentation
Temperature
Alpha
Beta
Gamma

10°C

20°C

30°

40°C

MEAN

2.331
-1.521

2.597-1.0962.342-0.4912.774-0.305

2.473

0.315

0.299

0.315

0.290

0.307

2.278

2.026

2.267

1.857

2.142

20

30

40

Delta

1
0.5
0

-0.5 5

10

-1
-1.5

The Dynamic Modulus |E*| for each temperature is
shifCobber_03
ted to the one curve Figure 1 This is “The Master
Curve.

-2-2.5

Figure 2 Beta (β) as a function of temperature
531

GEOMATE- Osaka Nov. 16-18, 2015

Clearly Equation 4 (above) can be used to calculate
the Dynamic Modulus E=|E*| with α, β, γ, and δ
being obtained from testing and t being calculated as
the time of loading the function

𝒕𝒕 =𝒍𝒍+𝟐𝟐𝟐𝟐

Fortunately Solar Radiation is an unique commodity
in that it can be calculated for any point on theearth’s surface for any time of the day and day of theyear, for example at the Sippy Downs site at
Latitude 26° 43’23” South and Longitude 153°05’02”East – Solar Radiation is at its peak at 12:03
p m
e v e r y
d a y .
Moisture content also has a serious impact onpavement performance. In Queensland we are notsubjected to Freeze Thaw cycles but the State is
subject to “drought and flooding rains”. Pavements
can be dried by the environment to well under
optimum and can be totally inundated for weeks.
The test runs for 24 hours including both heating
and cooling cycles data is downloaded for analysis.

Equation 5

𝑺𝑺

Where
t = loading time (secs)
l= length of tyre contact area (mm)h= thickness of asphalt layer (mm)S = speed of vehicle (mm/sec)
The frequency time relation is given by:
𝟏𝟏
𝒇𝒇 = Equation 6

Where
F= frequency (Hz)T= time (secs)

𝒕𝒕

Table 1 Elements of Pavement Monitoring Site at
Sippy Downs DriveAir
temperature

To give a typical Performance Characteristics

Relative
Humidity

Table 2 Performance Characteristics for D771G HMA
Temperature
(°C)

45

30

25

20

Frequency
( Hz)

0.015
0.04
0.01
0.015
0.04
0.01
0.015
0.04
0.01
0.015
0.04
0.01

High
Med
Slow
High
Med
Slow
High
Med
Slow
High
Med
Slow

Velocity(km /hr.)

120
45
18
120
45
18
120
45
18
120
45
18

S Mix
(MPa)
Modulus
|E*|
3,800
3,250
2,750
9,500
8,250
7,150
10,6009,250
8,100
16,30014,70013,250Weather
StationAtmosphericPressure
RainfallIn
Pavement

Wind Speed

Horizontal
Strain Gauge@ 75mm
Vertical Strain
Gauge @
650mm
Thermocouple
on Surface
Thermocouple@ 75mmMoisture
Gauge @
Moisture
Gauge @
CameraWind
Direction
Solar
Radiation
Solar panels provide electrical energy
Data are collected every minute 24/7 and transferred
dally at 6:am to the researcher’s computer
The camera records a photograph of the road surface
every day at 10:00 am and a photograph of any vehicle
that exceeds a nominated strain level

Relationship between Surface Temperature anddepth into pavement
Equations were developed for each of the HMA
mixes ,as above with the following relationships
HMA BCC B771 ( 18mm Max Size with 15%
F
R
A
P
)
Winter T = 0.025z2- 0.069z +DMPT Equation 7

RELATIONSHIP BETWEEN SOLAR
RADIATION AND ROAD SURFACETEMPERATUREThe University of the Sunshine Coast has had a fully
instrumented section of road on Sippy Downs Drive,
adjacent to the Campus since December 2012 (750days). This site essentially consists of the following
elements Table 3:
Solar Radiation is a critical parameter impacting on
the pavement’s environment. The quantum of solar
radiation drives the pavement surface temperature,oxidation and polymerisation of the bitumen binders
in HMA mixtures and surface treatments, thepredominate pavements adopted in Queensland.

Summer T = 0.033z2 – 0.094z +DMPT Equation 8
Where T =Asphalt Temperature at depth z (°K)
DMPT is Daily Maximum Pavement Surface
Temperature
CYCLES TO FAILURE NF

532

GEOMATE- Osaka Nov. 16-18, 2015

The development of Hot Mix Asphalt Master
Curves through the Superpave studies has provided a
powerful tool, enabling the determination of Mix
Stiffness (Dynamic Modulus) to be determinedmathematically at temperature and time of loading.
Pavement temperature at depth as a function of
actual diurnal solar radiation, under laboratory
conditions, enables prediction of pavement
temperature for a nominated HMA
Simulation techniques enables the prediction of the
Number of Cycles to failure using Asphalt Master
Curves and temperature prediction at depth.

Miners hypothesis and the linear summation of
Cycle Ratios
Miners Hypothesis is one of the most frequently
used tools for understanding the mechanism for
failure in the fatigue mode. Developed in 1945, themodel was originally used to understand pressure
v
e
s
s
e
l
s
.
Simply stated if there are k different levels of Strain
then the average number of cycles to failure at the ith,εi

is Ni. The damage factor D is given by
𝒏𝒏𝒏𝒏

𝑫𝑫 = ∑𝒎𝒎
𝟏𝟏 𝑵𝑵𝑵𝑵 Equation 10

Where n i is the number of cycles accumulated at
Strain ε i
D is the fraction of life consumed by cycles at thedifferent strain levels. In general if the damage
fraction reaches 1 failure occurs.
T h i s l i n e a r s u m m a t i o n o f c yc l e r a t i o s i sfundamental to understanding pavement analysis

FUTURE DIRECTION
The Queensland Pavement Centre at the Universityof The Sunshine Coast, Australia continues toprogress this approach with final year students
building a catalogue of both HMA Master Curves
and Temperature at depth as a function of Solar
R
a
d
i
a
t
i
o
n
.
Samples compacted in the Shear Box Compactor to
95% solids (5% air Voids) provide analysis of both
Master Curves and Thermal Conductivity
Structure through the use of micro CT (x-ray based)imaging system, (CAT scans) will soon be added tothe suite of techniques.
This data will ultimately find its way into USC’s
Engineering Learning Hub through Simulation and
Visualization technology to become a new method
for pavement design and analysis

PAVEMENT DESIGN THROUGHSIMULATION
Monte Carlo Simulation is a computational methodthat is based on taking random readings from aphysical law or mathematical model numerous times
in order to obtain a numerical integration, an
optimum value or a probability distribution.
The forensic examination of a premature failure of
an Urban Collector Road is fully described in the
Final Year Project thesis of Kristopher Jopson –available through USC library. This thesis will be
published as a technical paper during 2015

REFERENCES[1] Austroads “Guide to Pavement TechnologyPart2 : Pavement Structural Design , 2008 pp 76[2] Tanquist, BA August 1, 2001, “Development ofa Quick Reliability Method for MechanisticEmpirical Asphalt Pavement. , Minnesota
Department of Transportation

In this application Monte Carlo Simulation wasused to determine the values for Smix from the
HMA Master Curves and the temperature at depth
obtained calorimeter box tests
These values together with volumetric properties ofthe HMA (obtained from the HMA catalogue –Example Appendix A), such as Volume of Bitumenin the HMA (Vb) were used within the Transfer
function (Equation 3) to determine the number ofcycles to failure (Nf). The damage factor (Nf)calculated by this method divided but the design life
The probability density function was determined todetermine the probability of failure within the design
This analysis returned an expected time to failure
under actual traffic of 6 years compared ith an actual
result of 7 years; at an 80% level of confidence.

[3] James-Johnson, 2014 “Master curve productionof a typical Queensland HNA compacted using
varying for Mechanistic- Empirical Analysis.
Final Year Thesis – University of the Sunshine
C o a s t ,A u s t r a l i a[4] Gray C.J. , 2014 “the Development of aprocedure to determine temperature of Asphalt
at Depth” Final Year Thesis – University of theSunshine Coast, Australia[5] Jopson K “A forensic analysis of premature
pavement failure of a road pavement using
Asphalt Master Curves and Computer
simulation-“Final Year Thesis , University of
the Sunshine Coast, Australia

CONCLUSION

533

GEOMATE- Osaka Nov. 16-18, 2015

Mix Description: BCC Type 3: 18mm Dense Graded Asphalt
Mix Name: B771 Maximum Size: 18mm
Features: Mix B771 has incorporated the addition of 15% Fine Reclaimed Asphalt Product (FRAP) as anaggregate substitute within the mix
Compaction Method: Shear Box Compaction
Tested by – Cody James-Johnson
Master Curve:
Time-Temperature Shift Method
Sigmoidal Model:
Williams-Landel- Ferry

Sample Density:
2.397
Maximum Theoretical Density 2.503
Solids95.8
Percent Air Voids
4.2

Volumetric Properties
Bitumen Content (by Weight)
4.3%
Volume of Bitumen
10.1%
Voids in Mineral Aggregate
14.4%
Voids filled by Bitumen
70.4 %

Aggregate Gradation
Sieve Size
37.5mm
26.5mm19.0mm
13.2mm
9.5mm
4.75mm
2.36mm
1.18mm0.600mm0.300mm
0.150mm0.075mm

Per Cent Passing
100
100
100
95
76
46
34
26
20
14
7.2
5.5

Mix Design
100
100
100
90 – 100
68 – 82
42 -5430 – 40
22 -30
16 -2410 -16
6.0 – 10.0
4.0 – 6.0

DYNAMIC MODULUS @ 25°C
25,00020,00015,00010,0005,000
00.0001 0.001

0.01

0.015

0.04

0.1

1

10

100

1000

Properties
Shift Factor Type WLF
Temperature

5

10

20

30

40

alpha

2.324

2.331

2.597

2.342

2.317

beta

-1.814-1.521-1.096-0.491-0.026

gamma

0.316

0.315

0.299

0.315

0.316

delta

2.285

2.278

2.026

2.267

2.290

534

GEOMATE- Osaka Nov. 16-18, 2015

Performance

Temperature (°C)
45

30

25
20

Frequency ( Hz)
0.015
0.04
0.01
0.015
0.04
0.01
0.015
0.04
0.01
0.015
0.04
0.01

Velocity (km /hr)

High
Med
Slow
High
Med
Slow
High
Med
Slow
High
Med
Slow

120
45
18
120
45
18
120
45
18
120
45
18

535

S Mix (MPa)Modulus |E*|
4,645
3,892
3,290
9,975
8,640
7,500
12,80011,325
9,980
17,09015,43010,350Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

A STUDY RELATING SOLAR RADIATION, ROAD SURFACETEMPERATURE AND TEMPERATURE AT DEPTH IN HOT MIX
ASPHALTJohn Yeaman1,
Queensland Pavement Centre, University of the Sunshine Coast, Australia;

1

ABSTRACT
Solar Radiation is a computable commodity, it is a function of a point on the earth’s distance from the sun.
For the past two years the Queensland Pavement Centre has been measuring Solar Radiation at a permanentmonitoring station and comparing that with Road Surface temperatures every minute of every day. Some
surprising results have been observed. It is postulated that Pavement Surface Temperatures increase by 5% for
every Kilowatt per square metre of impact radiation, heat at twice the rate at which they cool during the hours ofsunlight and emit heat beyond an equilibrium at the sun’s zenith. Surface temperature will fall even though solar
radiation is still impacting on the site. This data has been programmed into an asphalt calorimeter andtemperature at depth in the pavement determined as a function of road surface temperature. These are allimportant factors when deciding on pavement design temperatures.
Keywords: Solar Radiation, Emissivity, Thermal Gradient in Asphalt
INTRODUCTION
Queensland, Australia is an area of diverse climaticconditions stretching from Latitude 28°South toLatitude 11°South ( approximately 2000 km long )and Longitude 153° East to Longitude 138° East
( approximately 1600 km wide) and covering
1,852,642 square kilometers . Queensland is larger
than many countries of Europe. Climaticconditions, therefore can vary from moderate
climate of South East Queensland to that of desert
and tropical rain forest.
Hot Mix Asphalts (HMA) are designed principallywith respect to the Number of Cycles to fatigue
failure computed via a transfer function usedthroughout Australia: Reference [1]N= �since that date.Of particular interest has been theeffect of critical strains, with respect to temperatureand moisture content, where it has been shown
Reference [2] that there are both diurnal andseasonal variations impacting on the magnitude andnature of strain.
This study examines the impact of Solar Radiation
on Road Surface Temperature and the subsequent
impact on temperatures throughout the mix
thickness. An example of Modulus’ dependency on
temperature follows (figure 1)

𝟔𝟔𝟔𝟔𝟔𝟔𝟔𝟔(𝟎𝟎.𝟖𝟖𝟖𝟖𝟖𝟖𝟖𝟖𝟖𝟖+𝟏𝟏.𝟎𝟎𝟎𝟎 𝟓𝟓
𝑺𝑺 𝑴𝑴𝑴𝑴𝑴𝑴𝟎𝟎.𝟑𝟑𝟑𝟑 𝝁𝝁𝝁𝝁

� (1)Where N= the number of cycles of EquivalentStandard Axles
Vb = the volume of binder in the HMA
S Mix= the Stiffness of the Mix at a given
temperature and time of loading
με = the horizontal tensile strain at the base of thebound layer. It is important for pavement managers
and design engineers to understand the temperatureof the HMA at the critical depth in the pavement(the depth where strain is being measured) yet we
adopt one value (the Weighted Mean Annual
Pavement Temperature- WMAPT). The
Queensland Pavement Centre at the University of
the Sunshine Coast, Australia, installed a fullpavement monitoring site and weather station on acollector road adjacent to the University Campus in
May 2013. This site has been monitoring 14
pavement associated parameters minute by minute

Figure 1 Modulus of Hot Mix asphalt as afunction of temperature
Source: Supplement to the Austroads Pavement
Design Guide, for Long Life Asphalt Pavement
Design, AAPA, 2015)

536

GEOMATE- OSAKA, Nov 16-18 2015

Figure 2 Radiation & Temperatures for 13 June
2013

THE SITE
Sippy Downs Pavement Monitoring Site, situatedat Latitude 26° 43’23” South and Longitude 153°05’02”East was initiated by Sunshine Coast Council.
Sippy Downs Drive is a Sub-arterial connecting theSunshine Coast Motorway to the Bruce Highway
and was being upgraded to a 4 lane separated
carriageway. The installation was to be part of that
upgrade. The site is described elsewhere andconsists of the following elements
Air
temperature
Relative
Humidity

Weather
StationAtmosphericPressure

RainfallIn
Pavement

From which the data can be summarized as follows:
Table 1 Comparison of Radiation & Road
Surface Parameters for Mid-WinterRadiation
Maximum
Value@
11:58
QuantumofRadiation

Horizontal
Strain Gauge @75mm
Vertical Strain
Gauge @
650mm
Thermocouple
on Surface

MEAN
Standard
Deviation

Thermocouple@ 75mmMoisture
Gauge @
Moisture
Gauge @
Camera

Wind
Speed
Wind
Direction
Solar
Radiation
Solar panels provide electrical energy
Data are collected every minute, 24/7, andtransferred dally at 6:am to the researcher’s
computer
The camera records a photograph of the roadsurface every day at 10:00 am and a photographof any vehicle that exceeds a nominated strain
level

760
Watts
per Sq.m262,036390
Watts
per Sq.m
299
Watts
per Sq.m

Road Surface
Temperature
Maximum
Value @
24.8°C
15:06
Area underCurve
11,577MEAN

18.7°CStandard
Deviation

4.2°C

Similarly for 26th December 2013 Mid-Summer - a
hot cloudy day we have For the 14 hours 23 Minutes(04:36 to 18:59) of Sunlight to give:

THE IMPACT OF SOLAR RADIATION ON
ROAD SURFACE TEMPERATURE
Let us consider two different months
June 2013 Early Winter, clear Sky. During the 11hours 11 Minutes of sunlight (06:15 to 17:26) wehave

Figure 3 Radiation & Temperature for 26 December
2013 - Mid-Summer
From which the data can be summarized as
Table 2 Comparison of Radiation & Road
Surface parameters for Mid-Summer
Radiation
Maximum
Value@ 10:51
Area under
Curve
MEAN
Standard
Deviation

537

1,122
Watts perSq.m
409,014524 Watts
per Sq.m
330 Watts
per Sq.m

Road Surface
Temperature
Maximum
51.5
Value @
14:21
Area under
35,090Curve
MEAN
45.0°CStandard
Deviation

6.8°C

GEOMATE- OSAKA, Nov 16-18 2015

every Kilowatt per Square Metre of Solar Radiation
at longitude 153° E.

Table 3 Side by Side Comparison of Solar
Radiation for a full year
Sunlight
hours(h:min)
11:22
11:11
11:20
11:49
12:15
12:20
12:37
12:59
13:06
12:44
12:17
11:43
11:14

May 13June 13
July 13
August 13
September 13
October 13
November 13
December 13
January 14
February 14
March 14
April 14
May 14

Maximum(Watts per
m2 )
902
760
845
892
1,203
1,050
1,139
1,121
1,571
1,033
1,345
1,125
992

Radiation@time(h:min)
12:35
11:58
12:09
12:02
11:13
12:04
12:33
10:51
12:41
12:03
12:25
12:08
10:59

Table 4 Side by Side
Temperatures for a full year
Month

Total forsunlight
hours
268,837
262,036
175,672
350,594
356,406
128,400
463,505
409,104
307,571375,266
321,820
284,315
184,607

Mean

Standard
Deviation

390
390
258
493
492
173
612
524
393
491
437
404
273

304
299
197
323
347
138
377
330
291
379
356
318
243

Comparison

HEATING & COOLING

An analysis of the road surface, during each
diurnal cycle brought to light an apparent hysteresis
loop between heating and cooling. From Table 3(above) it can be observed that the hours of sunlightin winter are approximately 11 hours 11 minutecompare with summer (December) where the hours
of sunlight are close to 13 Hours. Over the same
period the quantum of Impact Solar Radiation varies
from 260 K Watts in June to over 400 K Watts in
December.
Peak Radiation occurs around 12 to 12:30 each day
whilst peak road surface temperature occurs
anywhere between 11:00 am and 3:30 pm dependingon the effect of cloud cover
Clearly there must be some period where the roadsurface is getting cooler even though solar radiationis still impacting on that surface.
Let us consider the Heating and Cooling effect for
June and December 2013.

ofRoad Surface Temperature

May 2013
June 2013
July
AugustSeptember
OctoberNovember
DecemberJanuary
February
March
April
May

Max.
27.1
24.8
29.1
30.1
38.3
31.5
49.2
51.5
45.1
51.0
40.7
36.0
28.4

Min
15.1
12.2
19.1
10.9
19.1
25.5
22.1
29.3
27.5
30.2
24.8
20.6
18.3

Difference
12.0
12.6
10.0
19.2
19.2
6.0
27.1
17.6
17.6
20.8
16.0
15.4
10.1

60
50
40
30
20

An analysis of the relationship between the impact
of solar radiation on the road surface temperature isshown in figure 3 following

10
0

ROAD SURFACE TEMPERATURE ( deg
C)

Month

0

y = -1E-05x2 + 0.0127x + 18.344
R² = 0.859
y = 2E-05x2 - 0.0012x + 9.8926
R² = 0.9967
SOLAR RADIATION ( Watts per Sq.m)
200

400

600

800

INCREASE in TEMPERATURE (deg C)

30
25

Figure 5 Effect of Solar radiation on Road
Surface Temperature for June 2013

y = 0.0526x
R² = 0.9053

20

Here we can see that the road surface heats fromaround 10°C to 20°C, but then starts to cool eventhough there is some solar radiation input
If we look at the same conditions for December
2013 we see the same pattern, albeit a larger rangeof temperature and quanta of solar radiation

15
10
5

0

100
200
300
400
500
QUANTUM SOLAR RADIATION ( KW per Sq.m)

60
Surface Layer Temperature ( deg C)

0

Figure 4 The impact of Solar Radiation on road
Surface Temperature
From which we can derive the following hypothesis.
It is important to note that this study is limited to a
study of a site at Latitude 26° 45 S, Longitude 150
East, and this relationship may not hold forelsewhere in the world. It is however a good starting
point and one from which it is expected otherresearchers will add to the knowledge.

y = -2E-05x2 + 0.0276x + 41.249
R² = 0.9554

5040

y = 0.0172x + 31.801
R² = 0.5707

30
20
10
0

0

200

400

600

800

Solar Radiation ( Watts per Sq m)

Hypothesis
Road Surface Temperature increases by 5% for
538

1000

1200

GEOMATE- OSAKA, Nov 16-18 2015

radiation. The ratio of reflected thermal radiation isdependent on the material, its effective colour andtexture. The coarse texture and dark colour of hot
mix asphalt is typically low, however an asphalt
surface changes to grey with time, which will impact
on the Albedo value with time. Albedo is a factor of
the theory of energy balance where Radiation in =Radiation out for an asphalt surface.

Figure 6 Effect of Solar Radiation on Road
Surface Temperature for December 2013
Here we can see on the same scale that themagnitude of the results is much greater but that theshape of the curves are similar
Ratio of Heating & Cooling Results
The best way to consider the ratio of heating to
Cooling in terms of solar radiation is to examine therate of change by taking the first derivative of thetrend equation. From above it will be observed thatthe trend equation is in fact a quadratic equation andthe first derivative is in fact a function of the solar
radiation. |If however we plot the relationship as alinear equation, which is close to the quadratic, thenthe slope of the equation of the form: Y = mx+b
Where m = the slope (or first derivative)
The following table lists the results for month bymonth for both the heating and cooling cycles

Emissivity
Emissivity is a factor for diffusion heat transfer
within the pavement model and represents theeffective radiation (out) emission properties.
Understanding the emissive properties in a Hot Mix
Asphalt is essential to understanding the incoming
heat flow throughout a pavement and is required tomodel the temperature variation of conductivity.
Conductivity
Conductivity is an associated factor of
diffused heat transfer within the pavement model,
representing the heat flow through the material.
Fourier’s Law (1878) relates the change intemperature as a function of distance from the heatsource, when heating is in a steady state condition.
Here the heat flow in the reference direction is
proportional to the thermal coefficient ofconductivity. The conductivity coefficient of asphalt
material will depend on the constitutive material,
which in most cases will be the coarse aggregate.
Therefore for Hot Mix Asphalt the pavement- depth
relationship will vary according to the material
composition.

Table 5 Ratio of Heating to Cooling Cycles forMonthly Radiation & Temperature data
MonthHeating

Cooling

RATIO
Heating to
Cooling

May-130.01310.005

2.6

Jun-130.01420.0046

3.1

Jul-130.01230.0102

1.2

Aug-130.019

0.0085

2.2

Sep-130.01090.00721.5

Oct-13

0.003

0.004

0.8

Nov-130.01110.0122

0.9

Dec-13

0.017

0.01

1.7

The
value ofalbedoFeb-14

0.008

0.005

1.6

0.01

0.006

1.7

Mar-14

0.01

0.006

1.7

Apr-14

0.013

0.007

1.9

May-14

0.009

0.012

0.8

MEAN

1.7

Road Surface temperatures were charted against
cumulative radiation during sunlight hours for themonths of June (winter), September, April and
December (summer) 2013 (Figure 6) from which it
can be seen that there is a defined point of maximum
road surface temperature. This maximum occurs
between 11:30 am and 1:00 pm on each occasion i.e.
the sun’s zenith ± 1 hour. This time at a maximum
is related to the amount of cloud cover at that time,
given that at longitude 153°E the sun is at its zenith
at 12:03.
Examination of this data indicates that at aroundmidday, during any season, the HMA pavement isemitting heat at the same rate as it is being radiatedby the sun. Road Surface temperature is now fallingeven though solar radiation is impacting on thesurface. This is an important consideration whenchoosing a design temperature for the design ofasphalt pavement thickness

Hypothesis
During hours of Sunlight at Longitude 153°E
road surface temperatures heat at twice the rate at
which they cool.
DEFINITIONS OF APPLICABLETERMINOLOGY
AlbedoThe value for Albedo represents a reflected
percentage of a surface when exposed to thermal

Hypothesis
Road Surface temperature will reach a maximum
value beyond which further irradiation by the sun will not
produce higher temperatures

539

GEOMATE- OSAKA, Nov 16-18 2015

ROAD SURFACE TEMPERATURE ( deg C)

60

December
40

20

June
0

Cumulative Radiation ( watts per sq.m)
0100000200000300000400000500

Figure 7 Comparison of Cumulative Radiation
and Road Surface Temperatures June, April,
September and December 2013

Researchers should add to this knowledge by
evaluatingthese
conclusionsatdifferent
geographical locations
From this study it is concluded:
1. Road surface temperatures increase by 5%
for every Kilowatt per square metre of
impact radiation
2. A Hot Mix asphalt pavement will heat at
twice the rate at which it cools
3. A heat transfer relation can be establishedin the laboratory using actual solar
radiation data to determine temperature at
depth for a specific Hot Mix Asphalt
composition

REFERENCES[1] Austroads “Guide to Pavement TechnologyPart2 : Pavement Structural Design , 2008 pp 76

MEASUREMENT OF ASPHALTTEMPERATURE AT DEPTH
This information, compounded over 1 year,
provided an impetus for the development of amethod to measure the temperature at depth within
an asphalt pavement, permitting the calculation of
stiffness (modulus) and therefore the number ofcycles to failure in the fatigue mode (equation 1) andfigure 1. Gray (Reference [3] designed and built anasphalt calorimeter which enabled the determinationof temperature at depth for any day in the year. He
was able to establish a relation for temperature as afunction of depth on a seasonally adjusted basis.
This data was then used Reference [5] to compare
the thickness requirement for any asphalt (ascharacterised by the so called “Master Curve”
Reference [4] for any specific location.
Gray has demonstrated (reference [4] savings inasphalt thicknesses, for the same criteria of 7% of
thickness.
An extension of this work is recommended.

[2] Yeaman J, Yeaman S & Yeaman JW 2015 A
New Look at Pavement Management a paper
submitted to Fifth International Conference on
Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015[3] Gray C.J. , 2014 “the Development of aprocedure to determine temperature of Asphalt
at Depth” Final Year Thesis – University of theSunshine Coast, Australia[4] James- Johnson C, 2015 “Asphalt Laboratorycompaction methods – Do they really impact
the Master Curve?” – Proceedings 16th AAPA
International Pavements Conference – Gold
Coast , Australia[5] Gray C.J 2015 “The Development of aProcedure to Determine Asphalt Temperature at
Depth and Three Season Pavement Modelling AAPA
International
Proceedings
16thPavements Conference – Gold Coast , Australia

CONCLUSION
It must be understood that these conclusions
refer to data from a specific site at Latitude 23°S,
Longitude 153° E.
Further studies at other locations are being
undertken to test the following hypotheses .

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EXPERIMENTAL STUDY ON THE EPS-BASED SHOCK ABSORBING
MATERIAL FOR THE ROCK-SHED
Shen-Hsiang Hsu1, Koji Maegawa2, Li-Hsien Chen3
Graduate student, Graduate School of Natural Science and Technology, Kanazawa University, Japan
2
Faculty of Environmental Design, Institute of Science and Engineering, Kanazawa University, Japan
3
Faculty of Department of Civil Engineering, National Taipei University of Technology, Taiwan
1

ABSTRACT
Shock absorbing material made of piled up expanded polystyrene (EPS) placing above the roof of the rockshed was commonly used to advance the impact capacity against rockfall, but the required thickness increased
with the rockfall energy and resulted in high construction cost and gradually led to the decrease of building
amount. Though rock-net and rock-fence are widely applied to rockfall mitigation constructions in recent years,
the rock-shed, which has characteristic of the highest rockfall energy resistance, is still irreplaceable. For thisreason, a new type of EPS-based shock absorbing material that composed of two EPS-blocks, surrounded bycage wire netting, then a steel grid placed onto was tested under a series of static load and impact load tests inthis study to make a rock-shed more cost effective and have better performance. Test results of static load test
showed that the equipping of cage wire netting and the size of specimen contribute to 20% strength increase at
the later stage, and force diffused more uniform due to the stiffness of the steel grid. And the results of impact
load test indicated that the limit capacity of energy absorbing amount in the EPS-based shock absorbing material
was around 490.0-627.2 kJ, which was 50% less than the empirical formula suggested by a rockfall mitigation
code.

Keywords: Rockfall, Rock-shed, Shock absorber, EPS

installation, maintenance, and superior energy
absorbing function is expected to be developed tolower the impact load from rock-fall, and to further
reduce the cross section of the rock-shed to cut down
the total construction cost.
To distribute and absorb an impact load of rockfall, and consequently transmit a less impact load tothe main structure of the rock-shed, a new type ofshock absorbing material dominated by expanded
polystyrene (also known as EPS) with combination ofsteel material is introduced in this study. The designof this EPS-based shock absorbing material is eager
to promote the distribution effect by the stiffness ofthe steel material, to simplify the reconstruction workby the modular design, and to cut down the lifecycle
cost by the reduction of material. Thus, a series ofstatic load and impact load tests had been conductedto the EPS-based shock absorbing material to confirm
the basic properties in this study.

INTRODUCTION
Rockfall is one of natural disasters that often
occurs and strikes to the transportation routes and theunderlying infrastructures in mountainous terrain.
Despite usually impacting to small region, the rapid
and occasional characteristic of rockfall often results
in severe consequents of the same level as loss of
lives induced by the other slope disasters. For thisreason, various rockfall prevention and protection
methods had been developed and increased rapidly inrecent years, but the rock-shed, however, is stillreferred to as the most secure, reliable method due toits shelter-shaped structure and the highest rockfall
energy capacity among all [1].
The rock-shed, a structure that is made up of
reinforced concrete or shaped steel, is usually
installed at steep slope area where the space alongside
the road is insufficient to protect from direct rockfall,
and is especially efficient against a rockfall of large
energy. However, as the rock volume or falling heightincreases, enlarging the cross section of the rock-shed
structure itself or gaining the thickness of the shock
absorbing material on the roof above are some
general ways to withstand higher rockfall impact, but
the bulk volume also incurs high construction cost.
On this condition, a more effective shock absorbing
material of which having characteristics of easy

COMPOSITION OF THE EPS-BASED SHOCKABSORBING MATERIAL
EPS was generally used as a shock absorbing
material on the roof of the rock-shed due to its lightweight, easily deformed, and durable characteristics.
However, despite having great advantage of shock
absorption, the EPS material was still limited in load541

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

distribution effect. Furthermore, because of the joints
implanted between EPS-blocks offered insufficientconnection, EPS-blocks were easy to spread around
while being impacted by rockfall. Thus, to improvethose disadvantages, an EPS-based shock absorbing
material has been developed. Figure 1 shows a basic
unit of the EPS-based shock absorbing material, it iscomposed of two EPS-blocks, surrounded by cage
wire netting (hereinafter referred to as framed EPS),
piling up from 1 to 3 layers in a crisscross way, then asteel grid is placed onto the framed EPS. The
adoption of steel grid here is used to widen the
distribute range of impact load due to its great
stiffness, whereas the cage wire netting is expected toconnect several framed EPS together, and to offer
better coverage to prevent EPS-blocks from spreading
around while being impacted.
Configurations of the main components in the
EPS-based shock absorbing material are listed below.
EPS of which unit weight is 16 kg/m3 is used and thesize of an EPS-block is 2.0 m long, 1.0 m wide, and
0.5 m thick. A framed EPS approximately becomes
the size of length 2.05 m, width 2.05 m, and thickness
0.52 m. The cage wire netting is made of 300
g/m2 aluminum-zinc coating steel wires to withstandthe natural corrosion and with size of 4.0φ-75×75.
The steel grid is composed of several sheets of SS400
and steel bars and the size is 2.05 m long, 2.05 mwide, and 0.1 m high.

A steel grid is placed onto a framed EPS in series
SE2 and a difference in this series is the number oflayers of framed EPS. And in series SE3, an extra
framed EPS is set in each layer, and a longer steel
grid with size of 4.0 m long, 2.0 m wide, and 0.1 m
thick is applied. The static load was applied throughthe loading plate and was gradually increased until
EPS-block approached 80-90% strain, which
regarded to be as the limit state in accordance withthe former research [2].

Fig. 2

Fig. 1

Test method of static load test on the EPSbased shock absorbing material

Table 1 Static load test cases of the EPS-based shock
absorbing material

A basic unit of the EPS-based shock
absorbing material

Case
No.
SE1-1
SE1-2
SE1-3
SE2-1
SE2-2
SE2-3
SE3-1
SE3-2

OUTLINE OF THE EXPERIMENTS ON THE
EPS-BASED SHOCK ABSORBING MATERIAL
Static Load Tests
Static load tests were carried out on the EPSbased shock absorbing material by using a testingmachine loading at the center position of test
specimens as shown in Fig. 2. Since a rockfall having
weight of 1.0 ton has an equivalent diameter of about
0.9 m, a loading plate of 1.0 m in diameter wasconservatively adopted.
Eight specimens of different composition werecarried out and were categorized into three test series
as shown in Table 1. Test specimens applied in series
SE1 are framed EPS, but are different in piling layers.

Compositionof specimen
Framed EPS
Framed EPS& Steel grid
Framed EPS& Steel grid

Layer of
Remarkframed EPS
1
1 framed EPS
2
in each layer
3
1
1 framed EPS
2
in each layer
3
1
2 framed EPSs
2
in each layer

Impact Load Tests
The shock absorbing material of pure EPS had
been investigated under impact load tests so far [2].
In this study, the impact load test was carried out by
dropping a block vertically onto a test specimen as542

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Fig. 3. On the other hand, Fig. 4 shows the specimen
composed of framed EPSs which are assembled in a 3
by 3 way and piled up crisscross with two layers.
Then six pieces of bolt-connected steel grids witheach size of 2 m × 3 m are placed on the framed EPSs
and a sand cushion of 0.5 m thick is set on the top of
those steel grids to cut off the ultraviolet fromsunlight which might do harm to the EPS material.
Three impact load tests have been carried out on
specimens of the same spec, but the applied impact
energies are different as shown in Table 2, because
the masses and falling heights of blocks are 2.5 ton20 m, 3.2 ton-20 m, and 4.2 ton-25 m in cases No.
DE1, DE2, and DE3, respectively. The block ofwhich shape is specified in the guideline ETAG 027[3] is used in the tests and made of reinforced
concrete wrapped with steel plates.

Fig. 4

Configuration of the specimen under impact
load test
Table 2 Impact load test cases of shock absorbing
material

Fig. 3

Case
No.
DE1
DE2
DE3

Test method of impact load test on the EPSbased shock absorbing material

Block mass Falling height
(ton)
(m)
2.5
20.0
3.2
20.0
4.2
25.0

Impact energy
(kJ)
490.0
627.2
1029.0Measurement
The main measuring item and measuring methodin the static load test on shock absorbing material are
listed below:
1. Load: measured by the load cell built inside thetesting machine with hydraulic servomechanism.
2. Displacement: measured by the stretch of thestroke of the testing machine.
3. Deformation of steel grid: measured by ameasuring tape.
Additionally, in the impact load test on shock
absorbing material:
1. Acceleration of a block: a transceiver, a tri-axial
accelerometer and an amplifier-recording device
were set into the center of the block to receive a
trigger signal and record the acceleration data at
a sampling rate of 2 kHz.
2. Transmitted impact load to floor concrete: 13
earth pressure cells were set on the base concreteat 900 mm interval as shown in Fig. 4 andcovered with sand layer of 50 mm thick.
3. Moreover, several normal and high speed video543

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cameras were set up in the front and the lateralsides to record the test condition.

example uniform deformation substituted forpunching shear failure on each top surface of framed
EPS and Photo 1b shows a continuous deformation in
the entire EPS-based shock absorbing material, and isdifferent from the discontinuous deformation result inseries of SE1 (Photo 1a).
In test series SE3, the stress-strain curves in Fig. 7
show almost the same results with SE2. However,during the test, both longitudinal ends tilted and madediscontinuous deformation (Photo 1c). The
deformation area on the surface of framed EPS was
about 2000 mm diameter, which indicate that steel
grid extended about 1000 mm wider than the loadingplate.

RESULTSThe EPS-based Shock Absorbing Material under
Static Load Test
Figures 5, 6, and 7 show the stress-strain
relationships of test series SE1, SE2, and SE3,
respectively, where the stress is obtained from
dividing the applied load by a loading plate area of
1.0 meter in diameter. In addition, the stress-strain
curve of a pure EPS-block with size of 80 mm long,
80 mm wide, and 160 mm high, which has beenmeasured under a compression test, is also plotted asa dotted line for comparison.
Firstly, comparing the results of Pure EPS and the
1-layered framed EPS (case SE1-1) in Fig. 5, thetendencies of those two curves are similar, but theframed EPS has relatively weak stiffness at the earlystage of loading due to the gap between the cage wire
netting and EPS-block. However, after the cage wire
netting is embedded into EPS-block in the test
process, the stress of framed EPS slightly expands inelastic domain, and eventually is approximately 20%
higher than that of Pure EPS at 70% strain. Hence wespeculated that a cage wire netting might cause thedifference of stresses between SE1-1 and Pure EPS,
but the difference must be caused by the force
diffused inside SE1-1 due to the specimen
dimensions of SE1-1 being considerably larger thanthat of a loading plate. The reason is evident asfollows.
After the tests of cases SE1-1 to SE1-3, a
L 1 =1100 mm diameter punching shear failure area
was found on the top surface of the 1st framed EPS
layer (counted from the top) in all cases as shown in
Fig. 8. On the surface of the 2nd layers in cases SE1-2
and SE1-3, L 2 =1600-1700 mm diameter deformation
area were observed. And on the 3rd surface layer offramed EPS in case SE1-3, an entire smooth
deformation was observed (L 3 =2000 mm diameter).
According to these results, the area changing between
the layers were restricted by the boundary of thespecimen among the 2nd and the 3rd layers, but anapproximately 30 degree stress distribution angle
which was the same as in the Pure EPS [2] could still
be found in the framed EPS.
From Fig. 6 of test series SE2 for which a steel
grid was placed onto framed EPS, the stresses inelastic domain increases about 4-5 times due to thehigh stiffness of the steel grid, and the strain in elastic
domain becomes 10-20 %. On the other hand, theeffect of piling amount of framed EPS is not obvious
in SE2-2 and SE2-3 of Fig. 6 compared with test
series SE1 of Fig. 5 because of the force spreadsufficiently on the framed EPS owing to a steel grid.
These facts are also clear from visual recognition. For
544

Fig. 5Stress-strain curves of framed EPS and Pure
EPS

Fig. 6

Stress-strain curves of framed EPS with
2000mm×2000mm steel grid and Pure EPS

Fig. 7

Stress-strain curves of framed EPS with

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

2000mm×4000mm steel grid and Pure EPS(a) Case SE1-2

Fig. 8Stress distribution inside framed EPS(c) Case SE3-2(b) Case SE2-2

Photo 1 Loading condition of the EPS-based shock
absorbing material
Table 3 Deformation range in each component of the EPS-based shock absorbing material
Case
No.

Projected
block
area
(m2)

W
(m)
2.1
2.3

A
(m2)
4.2
5.1

Deformation range (m2)
Framed EPS
Steel grid
(top layer)
L
W
A
L
W
A
(m)
(m) (m2) (m)(m) (m2)
2.3
2.3
5.3
2.0
2.0
4.0
2.2
2.9
6.4
2.0
2.0
4.0

2.9

8.4

3.5

Sand cushion

DE1
DE2

1.16
1.37

L
(m)
2.0
2.2

DE3

1.64

2.9

Framed EPS(bottom layer)
L
W
A
(m)
(m)
(m2)
2.0
2.0
4.0
2.0
2.0
4.0
2.0
2.0
4.0
3.4 11.9 2.0
2.0
4.0(0.8)* (1.0)* (0.8)*
L=length, W=width, A=area, ( )*=range of local failuresurface of the sand cushion.
For the impact load and time curves in Fig. 9,
about two waves are found in each case. The firstwave begins at t=0, reaches to the peak at t=6 ms, andends at t=10-20 ms. The second wave begins at t=2030 ms, reaches to the peak at t=70-90 ms, and ends
approximately at t=150 ms. Thus the duration of thesecond wave is much longer than the first wave,
which means the shock absorption function mainly
operate in this period. Moreover, the entire
waveforms of cases DE1 and DE2 are similar, boththe maximum impact loads occur at the first wave
which valued 985.2 and 1052.9 kN respectively, as
shown in Table 5. But in case DE3, the maximum
impact load 1644.5 kN appears at the moment of
maximum penetration depth in the second wave. It is
supposed that the local failure has occurred on thesurface of the 2nd layer of framed EPS due to the limit
state of the material. However, comparing the EPSbased shock absorbing material with a 90 cm-thicksand cushion which commonly used on the roof ofthe rock-shed in the past [4], a theoretical impact load
equation is adopted [1]:The EPS-based Shock Absorbing Material under
Impact Load Test
Table 3 shows the deformation range of eachcomponent in shock absorbing material. After a block
falls to the specimen, the impact load is transmitted tothe base concrete through a sand cushion, a steel grid,and two framed EPS layers orderly. The data showthat the expansion of deformation range becomeswider in order of a block, a sand cushion, and a steel
grid, but then reduces to 4 m2 at both two layers offramed EPS. This indicates that most of the loadconcentrated in a limited range around impact
location, then caused plastic deformation to theframed EPS. Additionally, a local failure of 0.8 m2
deformation range was found at the surface of the 2nd
layer of framed EPS in case DE3.
For the data measured by the accelerometer andearth pressure cells, two quantities are defined asfollow. (1) Impact load: acquired from multiplying
block mass by block acceleration. (2) Transmitted
impact load: acquired from the integration of earthpressure. A combination of load and time curves ofcases DE1 to DE3 are shown in Fig. 9, where thesolid curves represent impact load and the dottedcurves represent transmitted impact load. Here, t=0 is
the moment that a block comes in contact with the

2

2

3

𝑃𝑃𝑚𝑚𝑚𝑚𝑚𝑚 = 2.108 ∙ (𝑚𝑚 ∙ 𝑔𝑔)3 ∙ 𝜆𝜆5 ∙ 𝐻𝐻 5 ∙ 𝛼𝛼(1)
α = (T/D)−0.5(2)where P max , m, 𝑔𝑔, λ, H, T and D represent impact
load [kN], mass of a block or a falling rock [t],545

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gravitational acceleration [m/s2], Lame’s constant[kN/m2], falling height [m], thickness of sand cushion[m], and diameter of a block [m], respectively.
Lame’s constant equals to 1000 kN/m2 while using asoft material such as sand cushion. Besides, theoverdesign factor α should be considered in thecircumstances that the equivalent diameter of a blockis greater than the thickness of the sand cushion.
Applying the block mass and falling height
conditions of 2.5 ton-20 m, 3.2 ton-20 m, and 4.2 ton25 m from cases DE1 to DE3, the results of thetheoretical impact load are 1977, 2429, and 3483kN
in case of the thickness of 90 cm of the sand cushion
which is doubly greater than that on the EPS-based
shock absorbing material.
And for the transmitted impact load and time
relationships in Fig. 9, the curves start early with theincrease in block mass: t=18, 15, and 6.5 ms in cases
DE1, DE2, and DE3, respectively. Furthermore, themaximum transmitted impact load 718.7, 1263.0, and2959.1 kN listed in Table 4 are observed at aboutt=90 ms in all cases. As a result, the falling energy
around 490.0-627.2 kJ is speculated to be the limit
capacity that the shock absorbing material could still
remain its function.

Fig. 10 Earth pressure in case DE1

Fig. 11 Earth pressure in case DE2

Fig. 12 Earth pressure in case DE3
The arrangement of earth pressure cells are shownin Fig. 4, and the measured earth pressure in eachcase are shown in Fig. 10, 11, and 12. Since the block
impacted at the center of the specimen, the maximum
earth pressure is supposed to be detected by thecenter earth pressure cell #1. But in fact, the locationof earth pressure cell #1 was exactly beneath theinterface of two EPS-blocks inside a framed EPS,
which may have received insufficient earth pressure,
such as the results appear in cases DE1 and DE2 (Fig.
10 and Fig. 11). But in case DE3, the earth pressure issignificantly large in earth pressure cell #1. It is
speculated that a 0.8m2 local failure area on thesurface of the 2nd layer of framed EPS could induce
stress concentration, and the range of stressconcentration contains only earth pressure cell #1.

Fig. 9 Load-time curve of block impact
Table 4 Results of impact load test
Case
No.
DE1
DE2
DE3

Maximum
impact load (kN)
985.2
1052.91644.5

Maximum transmitted
impact load (kN)
718.7
1263.02959.1

CONCLUSION
The experiment results of static and impact load
tests on the EPS-based shock absorbing material are
listed below.
1. The stress measured in the framed EPS wasapproximately 20 % greater at 70 % strain thanin Pure EPS in this study. The adoption of wire
netting might made influence on that result, but
we speculated that it was mainly cause by thedimensions of the framed EPS being546

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2.

3.

4.

considerably larger than that of a loading plate.
The force spread sufficiently on the framed EPS
owing to the placing of a steel grid, and the stressincreased about 4-5 times in elastic domain of
the EPS-based shock absorbing material.
The limit capacity of energy absorbing amount in
the EPS-based shock absorbing material wasaround 490.0-627.2 kJ.
The impact load on the EPS-based shock
absorbing material was 50% less than theempirical formula suggested by a rockfall
mitigation code.

ACKNOWLEDGEMENTS
Special thanks to Raiteku Company and therelevant partners for the cooperation and all kinds ofsupport during these tests.
REFERENCES[1] Japan Road Association, “Rockfall Mitigation
Handbook (in Japanese)”, Maruzen, 2000.
[2] Yoshida H., Fujii T., Minami K. and Maegawa
K., “Experimental Study on Shock Absorbing
CharacteristicofReinforced
Expanded
Polystyrol against Falling Rocks (in Japanese)”,Proceedings of the Symposium on Impact
Problems in Civil Engineering, Vol. 4, pp.6-11,
1998.
[3] ETAG 027, “Guideline for European TechnicalApproval of Falling Rock Protection Kits”,
European Organisation for Technical Approvals(EOTA), 2000.
[4] Kishi N., Nakano O., Matsuoka K. and Nishi H.,“Field Test on Absorbing Capacity of Sand
Cushion (in Japanese)”, Journal of StructuralEngineering, Vol. 39 A, pp.1587-1597, 1993.

547

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A STUDY FOR SEISMIC IMPROVEMENT OF CONCRETE FRAME
WITH PERFORATED WALLSA CASE OF A STAIRCASE WITH GIRDERS AND SLABS AT MID-STORY
Ryota Ishii1, Tetsuya Ohmura1
Graduate School of Engineering, Tokyo City University, Japan

1

ABSTRACTThe Japanese building standard act was revised in 1981 considered non-linearity. However, a number ofbuildings built before 1981 are existing, seismic evaluations for those building have actively been assessed basedon the seismic evaluation standard published by The Japan Building Disaster Prevention Association after
especially 1995 Kobe Earthquake. In practical seismic evaluation works, it is relatively simple and easy to makemodels of general buildings, but unconventional frames are complex such as a staircase or a frame with different
level girders. A perforated concrete wall is modeled as a shear wall, if the opening ratio is 0.4 and less. On theother hand, one with more than the opening ratio of 0.4 is modeled as a column with a wing wall. The modelingconcept is simplified to smoothly assess the seismic performance of a building even though it is not confirmedthat the models is suited to their actual behavior. In this paper, FE analyses were performed, and their results
were examined. Finally, partially closing the openings down to an opening ratio of at least 0.4 is recommended
because seismic slits could fail to work well.
Keywords: Wall with openings, Lateral force resist mechanism, Ratio of openings, Compressive strut

INTRODUCTION

𝑟𝑟0 = �The Japanese building standard act was revisedin 1981 considered non-linearity. However, anumber of buildings built before 1981 are existing,seismic evaluations for those buildings have actively
been assessed based on the Seismic Evaluation
Standard [1] published by The Japan Building
Disaster Prevention Association after especially1995 Kobe Earthquake. This standard takes thestrength and ductility into account and defines threegrades of seismic assessments to evaluate theseismic index. The modeling for columns and walls
is extremely important in particularly the second
grade of the seismic evaluation often taken, because
the girders are assumed as rigid, and the strength andductility of columns and walls directly influence on
the seismic index.
This standard also corresponds to diversified
buildings. However, for a frame of such as astaircase with beams located mid-story, themodeling concept depends on a judgment by anengineer.
In practical seismic evaluation works, it isrelatively simple and easy to make models ofgeneral buildings, but when dealing withunconventional frames such as staircases or a framewith different level girders as mentioned above, it ismuch more complex.
First of all, the ratio of opening for a perforated
wall should be calculated based on the standard. The
ration of openings is defined as the square root of
the openings area divided by the wall area as follows∑ ℎ𝑖𝑖 ∙𝑙𝑙𝑖𝑖ℎ∙𝑙𝑙𝑤𝑤(1)

where r o is the ratio of opening; h i and l i are theheight and width of the opening; h is the height ofthe story; l w is the bay.
A perforated concrete wall is modeled as a shear
wall, if the opening ratio is 0.4 and less. On the other
hand, one with more than the opening ratio of 0.4 ismodeled as a column with a wing wall. The
modeling concept is simplified to smoothly assess
the seismic performance of a building even though it
is not confirmed to be suited to their actual behavior.
However, in order to secure the flexible length forseismic improvement, a seismic slit is often locatedat the edge of a column or a window in a practicalseismic improvement work.
In this paper, an actual frame with a perforated
wall was modeled and FE analyses were performed.
To be compared with the original model, also threemodels with seismic slits mentioned above wereanalyzed.
FRAME PROPERTIES
Fig.1, 2 and 3 show the focused frame in a threestory concrete school building built in 1973. The
seismic index of 1st , 2nd and 3rd story wasrespectively 0.53 , 0.40 , 0.84 in the longitudinal
direction, and more than 1.0 in every story in the
span direction. Some mullion walls and spandrel
walls located between the columns 1 to 2 in Fig.1
548

GEOMATE- Brisbane, Nov. 19-21, 2014

were ignored when the frame was modeled, because
only the behavior of the perforated wall locatedbetween columns 3 to 4 in Fig.1 should be focused.
The building was damaged by the 3.11
earthquake. The cracking condition is shown in
Fig.1. The crack width without any note was 0.3mm.
Diagonal cracks were observed at around the edge of
openings. 20.7 N/mm2 of the concrete strength was
assumed in the models, because the effect of theconcrete strength difference in each story should be
eliminated at first in this paper.
The wall had 120mm of thickness and 9mm oflongitudinal and transverse rebar with 200mm space.
The orthogonal frame were ignored.

0.2

0.2

Crack width without notes is 0.3mm.

MATERIAL PROPERTIES IN THE ANALYSIS
1

2G1

2G3

700

2G2

700

900

SectionSection

300

300

300

Upper

4-D19

5-D19

4-D22

4-D19

3-D19

2-D22

500

Rebar

10-D22

Lower

Hoop

-9φ @100STP-9φ @200

Fig.2 Column and girder

Focused frame

Entrance

Broadcasting4

Fig.1 Elevation and crack patern damaged by 3.11 EQ

ANALYSIS
The model is shown in Fig.5. A four nodes andquadrilateral isoparametric plane stress element wasapplied to concrete meshed about 100mm square.
Longitudinal rebar in columns and girders wasembedded and only axial stress was considered. All
rebar in wall and the transverse rebar in columns andgirders was smeared and layered.
Table 1 shows analysis models and Fig 6 showsslits location. Model #1 has no slit. Model #2 has *1of the slits at the edges of the windows as shown
Fig.6. Model #3 has *2 of slits at the edges of the

3

2

500

Material properties in the analysis is shown in
Fig.4. Parabolic model for a compressive concrete,
the Hordiji’s model for a tensile concrete and Von
Mises criterion were adopted. Rebars were modeled
as a bi-liner model with perfect bond.

0.5

Science room
InfirmaryHomeeconomics
room

Music
room

Fig.3 Plan
columns as shown Fig 6. Model #4 has *2 and*3 of slits. The lateral force from the left to theright was applied for Model #1 to #4 and thatfrom the right to left for Model #1( － ) to#4(－).
(a) Compression(b) Tension [3](c) Criterion

Fig.4 Concrete model [2]549GEOMATE- Brisbane, Nov. 19-21, 2014

Table 1 Models
No.

Slit

3W

RF

2W

3F

1W

2F

Loading direction

#1

N/A

#2

At windows (*1)
Left to right#3

At left columns (*2)#4

At both columns (*2 and 3)#1 (－)

N/A#2 (－)

At windows (*1)

#3 (－)At left columns (*2)

#4 (－)

At both columns (*2 and 3)

Right to left

1F

*1 , *2 and *3 are indicated in Fig.7
Fig.5 Mesh and lateral load
The lateral forces was applied to the each slab.
The amount of the lateral force depend on their rule
floor area and the lateral force distribution factor
was 3, 2 and 1 respectively. In addition, 12kN/m2 of
the gravity load was applied at first. The foundation
girders were supported by pin supports and theuplifting of the foundation girders wasn’t taken into
account.

*2
*2
Notes

BASE SHEAR FORCE AND WHOLE DRIFT*2*1 Slit at windows*2 Slit at the left column*3 Slit at the right column

Fig.7 shows the base shear force and whole driftof Model #1 to 4. The whole drift was calculated by
the lateral displacement divided by the height of thecenter of the roof girder. The maximum base shear
force of Model #1 which had no slit was the largest
and the others showed approximately the same base

4,000

#1
#3

2,000

#2

#4

1,000

0
0.0

0.2

0.4

0.6

*2
*1 *1

Displacement (mm)
100

50

3,000

*3

Fig.6 Slit plan

Lateral force (kN)

Lateral force (kN)

4,000

*1 *1

All slits width are 30mm.

Displacement (mm)
0

*3

0.8

0

50

100

3,000

#1(－)
#3(－)

2,000

#2(－)
1,000

#4(－)

0

1.0

0.0

Drift (%)

0.2

0.4

0.6

0.8

1.0

Drift (%)

Fig.7 Lateral force and drift ( L to R )Fig.8 Lateral force and drift ( R to L )shear force. About 20% of the maximum base shear
force reduction was shown because of the effect of
their slits. On the other hand, the difference of the
maximum base shear force between #2 to 4 which
had slits was extremely small value. Therefore, it
could be considered there is only a slight effect of
the slit pattern difference.
Fig.8 shows the base shear force and whole driftof Model #1(－) to #4(－) which from the right tothe left of lateral force were applied. The maximum
base shear force of #1(－), #3(－) and #4(－) wereapproximately the same. In other words, the effect of
the slits at the edges of the columns is extremely
small. In addition, about 13% of the maximum baseshear force reduction was shown because of theeffect of the slits at the edges of the windows.
Fig.9 shows the comparison of the difference ofthe lateral force direction. The maximum base shear550

2,000

#1(－)

1,000
0

0.0

1.0
Drift (%)(a) #1 and #1(－)

4,000
3,000

#2

2,000

#2(－)

1,000
0

0.0

Drift (%)

1.0(b) #2 and #2(－)

4,000

Displacement (mm)
0
100

#3

3,000
2,000

#3(－)

1,000
0

0.0

Drift (%)

1.0(c) #3 and #3(－)

Lateral force (kN)

#1

3,000

Displacement (mm)0100

Lateral force (kN)

4,000

Displacement (mm)0100

Lateral force (kN)

Lateral force (kN)

GEOMATE- Brisbane, Nov. 19-21, 2014

Displacement (mm)
0
100

4,000

#4

3,000
2,000

#4(－)

1,000
0

0.0

Drift (%)

1.0(d) #4 and #4(－)

Fig.9 Lateral force and drift ( L to R )force in the model which from the left to the rightlateral force was applied is larger than that in themodel which the backward lateral load was appliedin all comparisons.
The maximum difference of the maximum baseshear force was 26% between Model #1 and #1(－)
as shown Fig.9 (a).
When slits were put into a perforated wall, thestiffness and strength should be appropriately
evaluated, because the seismic evaluation of thewhole building depends on the stiffness and thelateral strength of the frame with perforated walls.
And the maximum lateral strength could be not thesame as one under the backward lateral load.

At －0.04%
Fig.10 Crack strain vector at 0.04% drift

CRACK STRAIN VECTOR
Fig.10 shows the crack strain vector at 0.04% of
drift of Model #1 and #1(－). The range of dashlines shows the result of Model #1(－). The crackstrain vector in our analysis was observed at theedges of the windows and similar to the crack
observation damaged by 3.11 as shown Fig. 1. Our
analysis result of this showed approximate good
agreement with the crack observation.
Generally, in a seismic evaluation based on the
Seismic Evaluation Standard, a perforated wall withover 0.4 of opening ratio is modeled as a column
with wing wall having a flexible length as shown
Fig.11.
The modeling concept is simplified to smoothly
assess the seismic performance of a building eventhough it is not confirmed that the models is suited
to their actual behavior, since the ground for the
research and test data is insufficient.
Therefore, the flexible length as shown Fig.12
should be improved in a seismic evaluation. We
suggest that a perforated wall with over 0.4 of
opening ratio should be modeled as not a column
with wing walls but a perforated wall with low
stiffness and strength.
On the other hand, girders are ignored in thesecond grade of the seismic evaluation based on the
Seismic Evaluation Standard. If seismic slits would
be located at the edges of columns, the flexible

*
*

*

*
*

*

Fig.11 Flexible and rigid length(a)#1 (0.4%)(b)#2 (0.4%)

Fig.12 Deformation
length would be long in the same as Model #2. In
addition, also a girder damage evaluation could be
ignored even though the girder would besignificantly damaged at the edge of seismic slits. It
is caused by spandrel and hanging walls with theslits at the edges of openings.

551

GEOMATE- Brisbane, Nov. 19-21, 2014

N/mm2(a) #1 at 0.2%(b) #1 at 0.4%(c) #1(－) at 0.2%(d) #1(－) at 0.4%

Fig.13 Principal stress (σ2) #1 and #1(－)
PRINCIPAL STRESS

N/mm2

Fig.13 shows the compressive principal stress
distribution of Model #1 with no slit. The flexible
length based on the compressive principal stress
distribution shown in Fig.13 is similar to a general
perforated wall than the assumed flexible length
shown in Fig.11.Although the opening ratio is morethan 0.4, the concrete compressive struts wereformed. It showed lateral force was transmitted in
the diagonal direction.
As mentioned above, the flexible length asshown Fig.11 should be improved in a seismic
evaluation, and a perforated wall with over 0.4 of
opening ratio should be modeled as not a column
with wing walls but a perforated wall with low
stiffness and strength in view of the results in thischapter.
In case of a concrete frame with different level
girders such as the models in this paper, the stiffness,
strength and damage should be properly evaluated,because the compressive principal stress distribution
diagram in the positive was different from one in thenegative.
Fig.14 shows the compressive principal stress
distribution of Model #2, 3 and 4. As shown
Fig.13(b), the concrete compressive struts wereformed, however, slits prevent from forming a
diagonal compressive strut such as Fig.14(b) in
Model #1 with no slit, because the slits were locatedto interrupted the diagonal compressive strut.
In addition, even if slits were located to lengthen
the flexible length of the column, only two columns
enclosed by the dot line expectedly deformed asshown Fig.14(b) and (c). Therefore, the slits in thismodels didn’t work not effectively and unexpectedly.

(a) #2(b) #3(c) #4

Fig.14 Principal stress (σ2) at 0.4%
distribution diagram in the positive was different
from one in the negative.
In a seismic improvement, not only seismic sits
location planning but also the damage of girders at
the edges of the slits should be examined when slits
would be located at windows, because the damage
would be concentrated to the both end of the seismic
slits.
Closing the openings down to 0.4 of opening
ratio is recommended based on the improving ways
in the seismic evaluation standard, because theseismic slits would work unexpectedly and the strut
forming was obstructed in the models with seismic
slits.
REFERENCES[1] Japan Building Disaster Prevention Association(2001), Seismic Evaluation Standard andCommentary for Existing Concrete buildings[2] Diana User’s Manual (2002), Material Library[3] Hordiji, D.A. (1991), Local approach to fatigueof concrete, PhD thesis, Delft University ofTechnology

CONCLUSION
In case of a concrete frame with different level
girders and symmetric openings such as the models
in this paper, the stiffness, strength and damage
should be properly evaluated, because the hysteresis
curve and the compressive principal stress552

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

EVALUATING CONCRETE PROPERTIES USING RECYCLED GLASSSAND FOR RIGID PAVEMENT APPLICATIONS
Hadi Khabbaz1, Abraham Khoury2
School of Civil and Environmental Engineering, University of Technology Sydney (UTS), NSW, Australia

1,2

ABSTRACTThis study investigates the influence of using recycled glass as part of fine aggregates on the performance ofpavement concrete. The properties of fresh and hardened concrete included recycled glass are compared to acontrol mix design not containing any glass sand. Hence, trends in concrete performance associated withdifferent fine aggregate replacement by incorporating recycled glass are studied to verify the optimal glass
replacement content in rigid pavement base mixes. The findings of this project indicate that using recycled glass
sand in rigid pavement applications can yield not only more sustainable concrete at a 40% replacement content,
but also induce improvements in drying shrinkage behaviour and concrete workability. The neutral effects ofglass sand on air entrainment and marginal reductions in hardened concrete properties yielded from this study
further supports the use of glass sand in this type of application.

Keywords: Recycled Glass Sand, Rigid Pavement, Drying Shrinkage[4] have investigated the fine aggregate replacementwith waste materials such as recycled glass or crumbrubber.
The utilization of post-consumer glass inconcrete manufacture is another industry initiative topromote concrete sustainability. There is an
estimated 75,000 tonnes of crushed glass fines in
New South Wales (NSW) [5] that is available foruse in concrete production. Initially, such glass
waste was intended to be disposed of in landfills.
However, the potential to incorporate such byproduct glass waste in concrete production has
depicted the potential alternative application ofwaste glass products [6]. Waste reduction andrecycling are very important elements in a waste
management framework because they help toconserve natural resources and reduce demand forvaluable landfill space [7].
Boral Australia, the Australia's largest
construction materials and building products group,
has recognised the importance of development ofenvironmentally
sustainableconcrete.
The
organisation has introduced a recycled glass sandproduct into its standard 32 MPa concrete mixes.
In addressing the sustainability concern
associated with concrete production, this study
examines the viability to introduce recycled glass
sand as a sustainable alternative to natural fineaggregate used in concrete mixes. In thisexperimental study the fresh and hardened concrete
properties are examined in relation to performance
tolerances depicted in the RMS-R83 specification[8] (the Road and Maritime Services of New SouthINTRODUCTION
Concrete can be viewed as a non-sustainablebuilding product due to its reliance on incorporating
non-renewable raw material components in its
production process. The reality is that, “concrete is
the world’s most used construction material: over 25
billion tons of concrete are used worldwide eachyear, and this number continues to increase”[1]. The
major components of basic concrete are sand,aggregates, water and cement. Aggregates areusually quarried materials from the earth’s surface.
The fact that the earth contains a finite amount ofsuitable aggregate sources with desirable properties
for concrete production highlights its limited
capacity to serve infinite concrete production for
future generations. Cement production also heavily
rely on the use of non-renewable resources in its
production process. According to CCAA [2],limestone, clay, shale and sand represent the main
raw materials required for cement production. Suchraw feed is available in limited quantities, possibly
restricting the production of cement in the future.
The rising difficulties associated with escalating
carbon dioxide emissions and the increasing demand
for more sustainable building solutions are concurrently placing pressures on the concrete industry
to amend traditional concrete mix design practices.
Concrete manufacture is not only profoundly reliant
on the use of earth’s scarce resources, but also an
intensive energy process leading to the generation ofundesirable greenhouse gases. Such characteristics
define the unsustainable nature associated withtraditional concrete manufacture. Researchers [3],553GEOMATE- Osaka, Nov. 16-18, 2015

Wales, Australia specification for paving mix
design).
Incorporation of recycled glass at replacement
contents of 10%, 20% and 40% of the fine aggregate
contents in a paving base concrete mix is enabled thedetermination of how recycled glass interacts within
the concrete matrix at various intensity volumes.
The key aim of this study is to assess the effect of
introducing 30% glass replacement content in the
new revision of the RMS-R83 specification [8].

admixture in trialed concrete mixes allowed for thefacilitating a more workable concrete without adding
excessive quantities of water and compromisingstrength development. (2) Retarder N, a retarding
admixture, was applied in this experimental
investigation. The incorporation of this admixture intrialed concrete mixes allowed the retardation oftime to initial set as well as prolonging theconsistency of fresh plastic concrete. (3) Sika Air, an
air entraining admixture, was also employed in thisexperimental investigation.

EXPERIMENTAL INVESTIGATION
Developing Mix Designs
The experimental procedure has been conducted
to examine the influence of using a recycled materialreferred to as glass sand produced from a recycling
firm called Benedict’s in a concrete production
application. It is a 50/50 blend of recycled glass withexcavation sand. The main objective of this project
has been to assess the effect of using this recycled
glass on various quantities on fresh and hardened
concrete properties. The assessment of suitability
was examined on its viability to be used in standard
32-MPa grade concrete as well as the Roads andMaritime Services (RMS) pavement base concrete.
However, only the results for the latter presented inthis paper due to the paper length limitation.

In examining the performance of the recycled
glass sand on concrete performance, it was essential
to develop a reference mix that did not contain glass
sand. Hence its performance provided thebenchmark to allow us to assess the viability ofmixes incorporating recycled glass sand as areplacement of the fine aggregate component in
concrete mixes. The reference mix design was
adopted from Boral’s current supply of RMS R83
concrete. The adopted mix design was then utilized
to introduce the variable component of glass sand to
be introduced into the mix. Three additional mix
designs were developed at the different replacement
rates of 10%, 20% and 40% of the fine aggregate
component in a concrete mix. The proposed testing
regimes are illustrated in Table 1 for fresh andhardened concrete. In determining the hardened
concrete properties of mixes, it was necessary to
prepare and cast the required moulds to yield test
specimens. This involved preparing and curing test
specimens in accordance to AS1012.8.1 [9] (forcompressive and indirect tensile test specimens) andAS1012.8.2 [10] (for flexural specimens).

Raw Materials
The properties of materials were determined inaccordance to the relevant Australian Standards.
General purpose cement was sourced from Boral
Cement in Berrima NSW, Australia for use in thisexperimental investigation. Fly ash was utilized as asupplementary cementatious material in trialed
concrete mixes. The fly ash utilized in thisexperimental investigation was purchased from Fly
Ash Australia, sourced this by-product from Eraring
Power Station located in Morisset, NSW, Australia.
Eraring fly ash is classified as class F fly ash. 20 mm
and 10mm crushed river gravel sourced from Boral’s
Emu Plains Quarry in NSW, Australia constituted as
the coarse aggregate fraction blend utilized in thetrialed paving mixes. A triple blend of sands was
utilised in all conducted trial mixes. The RMS
paving base mixes were characterised with a blendof Emu Coarse Sand, Kurnell Fine Sand andBenedict’s 50/50 blend of recycled glass sand. The
standard 32 grade trialed concrete mixes werecharacterised with a blend of Emu Coarse Sand,
Dunmore Fine Sand and Benedict’s 50/50 blend ofrecycled glass sand. Clean Sydney tap water was
utilized in batching of the conducted trialed concrete
mixes.
Three admixtures, products of Sika Australia,
were used, including (1) Plastiment 10 is a water
reducing admixture utilized in this experimental
investigation. The use of such water reducing

Table 1 Testing regime agenda
Test Descriptions
Compressive Strength(1, 3, 7, 28, 56 days)
Indirect Tensile Test
(28 days)Flexural Strength(7, 28, 56 days)
Drying Shrinkage(7, 14, 21, 28, 56 days)
Chlorides (%)
Sulphates (%)
Air Content (%)
Plastic Density
Bleeding Rate (%)
Slump
Setting Time of Fresh ConcreteStandard [11]AS1012.9
AS1012.10
AS1012.11
AS1012. 13
AS1012.20
AS1012.20
AS 1012.4.2
AS 1012.5
AS 1012.6
AS 1012.3.1
AS 1012.18

One day prior to the mixing being conducted,
batching of concrete constituents was undertaken.
This involved measuring required concrete
constituents as defined in proposed mix designs
554

GEOMATE- Osaka, Nov. 16-18, 2015

The results, generated from each conducted mix
were obtained as per specifications detailed in therelevant Australian standards. Results obtained fromconducting RMS concrete trial mixes to assess theinfluence of incorporating glass sand (10%, 20% and40%) as a partial replacement of the fine aggregate
component are presented in Tables 2, 3 and 4,
respectively.

from stored stockpiles. Obtained samples fromstockpiles were well mixed to ensure homogeneity
in the retrieved samples. The measured quantities
were stored in sealed containers ready to be placedin the mixing drum once ready to conduct theoperation. In preparing the aggregates to be batched
for use in the proposed trial mixes, the aggregateswere moistened to visually prepare all aggregate
sources to be in a moisture condition just oversaturated surface dry (SSD) condition. Samples werethen obtained from the prepared batches todetermine the actual moisture conditions of the fineand coarse aggregates. The measuring laboratory
conditions were set at 23°±1° degree with a relative
humidity of 50±5%. The air flow in the drying roomis also controlled by the degree of water evaporationfrom beakers filled with 375ml of water. The rate ofwater evaporation from such beakers shall be within
the tolerance of 12±5 mL per 24-hour period.

Table 3 Test results of reference trial mix
incorporating 10% glass sand
Test Descriptions
Compressive Strength (MPa)(1, 3, 7, 28, 56 days)
Indirect Tensile Test (MPa)(28 days)Flexural Strength (MPa)(7, 28, 56 days)
Drying Shrinkage (µs)(7, 14, 21, 28, 56 days)
Chlorides (%)
Sulphates (%)
Air Content (%)
Plastic Density (kg/m3)
Slump

Results
16.5, 28.5, 37, 50.3, 57.5
4.3
4.6, 6, 7.1
280, 370, 420, 460, 550
0.005
0.75
3.6
2340
60

Table 4 Test results of reference trial mix
incorporating 20% glass sand
Test Descriptions
Compressive Strength (MPa)(1, 3, 7, 28, 56 days)
Indirect Tensile Test (MPa)(28 days)Flexural Strength (MPa)(7, 28, 56 days)
Drying Shrinkage (µs)(7, 14, 21, 28, 56 days)
Chlorides (%)
Sulphates (%)
Air Content (%)
Plastic Density (kg/m3)
Slump

Fig. 1 Positioning of marked points on flexural
machine depicting equal distances between supports.
EXPERIMENTAL RESULTS
This section highlights key findings, obtained
from conducting the proposed trial mixes. The main
results are summarised in Tables 2-4.

Results

Compressive Strength (MPa)(1, 3, 7, 28, 56 days)
Indirect Tensile Test (MPa)(28 days)Flexural Strength (MPa)(7, 28, 56 days)
Drying Shrinkage (µs)(7, 14, 21, 28, 56 days)
Chlorides (%)
Sulphates (%)
Air Content (%)
Plastic Density (kg/m3)
Slump

14.5, 30.5, 39, 52, 60.5

4.47
4.7, 6.1, 6.4
290, 370, 430, 470, 550
0.007
0.75
3.6
2360
55

Table 5 Test results of reference trial mix
incorporating 40% glass sandTable 2 Test results of reference trial mix
incorporating no glass sand
Test Descriptions

Results
17.5, 28, 38, 50, 54

Test Descriptions
Compressive Strength (MPa)(1, 3, 7, 28, 56 days)
Indirect Tensile Test (MPa)(28 days)Flexural Strength (MPa)(7, 28, 56 days)
Drying Shrinkage (µs)(7, 14, 21, 28, 56 days)
Chlorides (%)
Sulphates (%)
Air Content (%)
Plastic Density (kg/m3)
Slump

5.1
4.8, 6.1, 6.6
270, 350, 410, 450, 540
0.004
0.77
3.6
2340
60

555

Results
16.5, 28.5, 36.5, 49.7, 55
4.3
4.85, 5.43, 6.5
260, 350, 400, 440, 530
0.005
0.75
3.6
2340
65

GEOMATE- Osaka, Nov. 16-18, 201528 days. From the conducted experimental
investigation, it was determined that incorporating
glass at a rate of 40% of the fine aggregate
component in a paving mix yielded a compressive
strength value of 49.7 MPa, which surpasses RMS
requirements.

DISCUSSION
Paving Base Concrete Trial Mixes
The results of fresh and hardened concrete samples
are separately discussed in this section.
Fresh concrete properties

Table 6. Compressive strength (MPa) of RMS R83
mixes with different sand glass replacement
percentages at the various maturity days

Air content remained constant at a value of 3.6 %across all trialed paving mixes, which werecharacterised with various glass replacement
contents. This indicates that the quantity of
introduced glass in concrete had no influence on the
air entrainment of the inherent concrete and yielded
air content results that were within the R38 toleranceof 4.5 ± 1.5%.
An increase in free water, associated withrecycled glass aggregate as the replacement rate,
was noticed. This resulted in a reduced water
demand of the introduced mix water to ensure a
constant water to cement ratio was maintained. The
consideration of such aggregate moisture andintroduction of a constant water reducing admixture
yielded the development of concrete slumps in alltrialed paving mixes that were within the R83
tolerance values of 55-65mm. This indicates theintroduction of glass had minimal influence on
workability of trialed concrete mixes due to being afine aggregate replacement, thus influences due tophysical characteristics of glass particles wereminimal.
Bleed water increased relatively with a rise in the
introduced glass content in a concrete mix. The
impermeable nature of glass mainly contributed tothis phenomenon occurring. The highest recorded
bleed rate of 0.7% at a 20% glass replacement rate
was well-below the 3% bleed limit specification in
the R83 paving concrete specification.
The setting time of trialed paving mixes remained
relatively constant. The consistency indicated theamount of utilized glass in a concrete mix had
insignificant influence on varying concrete set times.
The experienced trend can mainly be attributed tothe constant amount of free water maintained in alltrialed paving mixes.

Rep.
(%)
0%
10%
20%
40%

1d

3d

7d

28 d

56 d

14.5
16.5
17.5
16.5

30.5
28.5
28
28.5

39
37
38
36.5

52
50.3
50
49.7

60.5
57.5
54
55

The compressive strength the of control samples
and samples with 40% replacement of fine aggregatewith glass sand are shown in Fig. 2. This high glass
replacement content (40%) has been the highest
trialed value and still complies with the RMS
specifications.

Fig. 2 Comparison of the compressive strength theof control samples with samples with 40%
replacement of fine aggregate with glass sand.
The one day compressive strength of trialed
paving mixes incorporating 10%, 20% and 40%
glass replacement contents, where all higher than thecontrol mix containing no glass sand. A 20%
increase in one day compressive strength wasachieved by the mix containing a 20% glass
replacement rate. The observed behaviour can be
attributed to pozzolanic reactivity of the glass
promoting early strength development.
The compressive strength of trialed paving mixes
incorporating 10%, 20% and 40% glass replacement
contents from 3-day testing all experienced strength
results that were slightly lower than the control mix.
Additionally, compressive strength of concrete

Hardened concrete properties
Table 6 highlights the average compressive
strength of samples as paving mixes incorporating
glass sand at the various maturity dates. Even though,the introduction of glass sand reduced theexperienced compressive strength exhibited by theinherent concrete, all trialed mixes wherecharacterised with a compressive strength value that
surpassed the RMS-R83 requirement of compressive
strength at 28 days. The R83 specification calls for aminimum compression strength value of 40 MPa at
556

GEOMATE- Osaka, Nov. 16-18, 2015

decreased with an increase in the glass content
introduced in the mix at each tested curing period.
The phenomena of changing compressive strength
behaviour from 3-day testing suggested that therelative weaknesses characterising glass aggregate
due to its inherent recycling process dictatedconcrete strength. In essence, micro-cracks in theglass aggregate facilitated the early failure ofconcrete. Such failure mechanism becomes theprime mode of failure at relatively later ages oftesting, when cement strength exceeds the strength
capacity of glass aggregates. Hence, it has beenconcluded that the relative weakness of glass
aggregates in the concrete matrix facilitates early
concrete failure.
All trialed paving mixes where characterisedwith compressive strength values that surpassed the
R83 requirement of a 40 MPa compressive strength
value at 28 days. The paving mix containing 40%
glass replacement content yielded an averagecompressive strength value of 49.7 MPa.
The average 28 day indirect tensile strength
reduction was approximately 15% when a glass
replacement rate of 10% and 40% was utilized incomparison to the control mix. A 20% glass
replacement rate yielded an approximate strength
reduction of 12%. This experienced indirect tensile
strength reduction at 28 days may again be attributed
to inherent cracks in glass aggregate facilitating
early concrete failure.
The 28 day flexural strength results of trialed
paving mixes indicated that the flexural strength
decreased with a relative increase in the glasscontent introduced in the mix. This trend indicatedthat the inherent cracks in glass aggregate become
more prevalent to causing early concrete failure
when the dosage rate is increased.
All trialed paving mixes where characterisedwith a flexural strength at 28 days that surpassed the
4.7MPa tolerance depicted by the R83 specification.
The mix containing a 38.5% glass replacement rateachieved a flexural strength of 5.43MPa.
Negligible drying shrinkage variation was
examined in mixes containing a 10% and 20% glass
replacement rate at all dates of testing. The mix
containing 40% glass replacement content achieved
reduced drying shrinkage results at all dates oftesting in comparison to the control mix. This
reduction in drying shrinkage could be attributed tothe ability of glass in a mix to increase the effective
water content in the matrix due to its impermeability.
Glass aggregate contributed to increasing thechloride content characterising a concrete mix. A
relative rise in chloride content was examined withincrease in the glass replacement content.
Meanwhile, it is found that the glass aggregate had
negligible influences on the inherent sulphate
content of the concrete samples.

The presented discussion above suggests that a
40% incorporation of recycled fine glass in R38RMS paving mix designs can yield concrete thatexhibits fresh and hardened properties, whichsurpasses the tolerances depicted in the R83
specifications. Considering the high variability inglass product quality, due to varying original sourcesof glass to be recycled and the inherent recycling
process that crushes glass, a factor of safety should
be applied to the determined 40% glass replacement
rate that deemed concrete performances surpassing
R83 requirements. As such, the proposed 30% glass
replacement rate to be introduced in the newrevision of the R83 specification is regarded to be
acceptable from findings of this experimental
investigation. The 30% replacement rate provides asense of security in the mix design to accommodate
for any inconsistencies associated with the recycled
glass products.
CONCLUSIONSThe proposed experimental investigation was
conducted to examine the feasibility of incorporatinga recycled glass sand product as a partial
replacement of the fine aggregate component inpaving mixes in promoting the development ofsustainable concrete pavement. This assessment
included to analyse fresh and hardened concrete
properties of many trialed RMS paving mixes
incorporating glass sand at various replacement
contents. The findings of this experimental
investigation clearly indicated that even up to 40%
incorporation of recycled fine glass in the paving
mix designs can produce concrete with fresh andhardened properties, which exceeds the minimum
requirements specified in the Road and Maritime
Services (RMS) of New South Wales, Australia
specifications, which is called RMS-R38 [8] forpaving mix design.
For further research, it is recommended to
conduct an experimental investigation quantifying
the durability of glass aggregates, when incorporated
in concrete. Although the influence of glass sand on
the chloride and sulphate contents of concrete wasstudied in this experimental study, a major area of
durability, concerned with the introduction of glass
aggregate in concrete, pertains to the facilitation of
the alkali-silica reaction. This is due to the relatively
high silica content of glass aggregates, whichfacilitates the occurrence of this reaction in concrete.
The occurrence of this reaction in concrete may
severely degrade concrete durability.
It can be noted that, this study investigated theinfluence of incorporating recycled glass as a fineaggregate in paving concrete applications. Future
study should investigate the influence ofincorporating waste glass as a replacement for thecoarse aggregate component
557

GEOMATE- Osaka, Nov. 16-18, 2015

[5] Wood, M., Jeffreys, C. and Richardson, P.
“National Waste Policy”, Department of
Sustainability, Environment, Water, Population
and Communities, Waverly, 2011.
[6] Ling, TC, Poon CS and Wong, HW,“Management and recycling of waste glass inconcrete products: Current situations in HongKong”. J. of Resources, Conservation andRecycling, Vol. 70, 2013, pp. 25-31.
[7] Ling, TC, Poon CS and Kou SC Kou,
“Feasibility of using recycled glass inarchitectural cement mortars”. J. of Cement
and Concrete Composites, Vol. 33, 2011, pp.
848-854.
[8] RMS-R83 specification, “Concrete Pavement
Base”, Roads and Maritime Services, IC-QASpecification R83, 120 p., 2013.
[9] AS 1012.8.1, Standards Australia, “Methods oftesting concrete - Method of making and curingconcrete: Compression and indirect tensile test
specimens”, Publisher: Standards Australia,
Sydney, 2000.
[10] AS 1012.8.2, Standards Australia, “Methods oftesting concrete - Method of making and curingconcrete: Flexure test specimens”, Publisher:Standards Australia, Sydney, 2000.
[11] Standards Australia 1012, “Methods of testing
concrete”, Publisher: Standards Australia,
Sydney, 2000.

ACKNOWLEDGEMENTS
The Authors would like to express theirappreciation to David Hocking from Boral Australia,
for his technical support and provision of Boral
Australia’s Concrete Laboratory Testing Facility forcompleting this research project. Furthermore, theauthors extend their appreciation to Boral Concrete
Laboratory technicians for their assistance incompleting the experimental test procedures.
REFERENCES[1] Schokker, AJ, 2010, “The Sustainable Concrete
Guide”, The Green Concrete Council of theUnited States of America, 2010.
[2] Cement Concrete and Aggregates Australia(CCAA), “Sustainable Concrete Materials”
Sydney, 2010.
[3] Madandoust, R, Ghavidel, R “Mechanicalproperties of concrete containing waste glass
powder and rice husk ash”, J. of Biosystems
Engineering, Vol. 116, 2013, pp. 113-119.
[4] Mohammadi, I, Khabbaz, H and Vessalas, K,“In-depth Assessment of Crumb Rubber
Concrete (CRC) Prepared by Water-Soaked
Treatment Method for Rigid Pavement Use” issubmitted to the journal of “Construction &Building Materials”, Vol. 71, 2014, pp.456-471.

558

FifthInternational Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan,Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

AN EXPERIMENTAL INVESTIGATION OF PHYSICAL, CHEMICALAND RHEOLOGICAL PROPERTIES OF RAP MODIFIED ASPHALT
BINDER

1

Ashwini Shanbhag1, Dinesh S V2, H S Lalithamba3 and Anand Tadas4
Post Graduate Student, 2Professor, 3Associate Professor, 1,2,3Siddaganga Institute of Technology, India;
4
Product Specialist Nanometrics, Malvern Aimil Instruments Pvt Ltd, India.

ABSTRACTDue to high price of crude oil, and asphalt binder, the application of reclaimed asphalt binder is widely
considered. Very few investigations are related to Rheological and Chemical properties of RAP modified asphalt
binders with Waste Engine Oil (WEO) as a rejuvenator. The present paper reports the results of a laboratory
research, carried out to investigate the effects of addition of a rejuvenating agent to HMA with RAP. Test
samples comprised of Virgin bitumen, Aged Asphalt Binder that were extracted from RAP, together with WEO
were added in different ratios. Optimal amount of oil was determined by conducting basic tests. Rheologicalproperties were then determined for samples containing optimal rejuvenator content. The physio chemical
analysis of RAP modified asphalt binder was carried out using Fourier Transform Infrared Spectroscopy. The
main concern of using WEO is stripping of the binder from the aggregate for which all the RAP modified
samples containing optimal WEO were tested.
Keywords: Waste Engine Oil; Hot Mix Asphalt; Reclaimed Asphalt Pavement; Fourier Transform Infrared
Spectroscopy.

percentage affects greatly the performance based
properties of the blended aged asphalt binder and the
rejuvenator [3,4,5,6,]. The performance evaluationof asphalt mixtures with high rap content using awarm mix asphalt (WMA) additive, Sasobit H8,
HMA with 75% RAP recycled at lower temperatureshow performance nearly equivalent to that of aconventional mixture [7,8]. The physical andrheological properties of bitumen change with time.
It may become harder or less elastic. The RAP
modified binder chemistry before and after aging
process is very important. The process of aging
chemically alters the structure of the RAP modified
binders [9].
It is observed that, majority of the investigators
have focused on physical properties of binder andmechanical properties of mixes but the rheologicaland chemical characteristics are the main pointers to
the material performance in actual practice. The
objective of the present study is to investigate thechemical and rheological changes occurring invirgin binders blended with RAP binders extracted
from cold milled RAP. And also, to study the effectof rejuvenators on the performance based propertiesof the rejuvenated aged binder, i.e., the blends of theaged binder (RAP) containing the rejuvenator, using
a series of dynamic shear rheometer tests. The
characteristics of RAP modified binders areinvestigated in terms of chemical properties, its
composition and binding parameter of modified
binders for rutting.

INTRODUCTION
Asphalt binder is chemically composed ofasphaltenes and maltenes. The maltenes fraction
dissipates during oxidation causing a change inasphaltene –maltene ratio, which affects the stiffness
properties of the asphalt [2]. This is called aging andit leads to an increase in stiffness and brittleness atintermediate and low temperatures, resulting in
reduced resistance to fatigue and low temperaturecracking. The process that restores aged asphalt’s
properties to a point considered to be comparable toa virgin material is called as asphalt rejuvenation.
This process is done by adding rejuvenating agentsto RAP mixtures. Some of the rejuvenating agentsare industrial process oil, “softer” Performance
Grade binders (PG binders), asphalt flux oil, engine
oil and waste cooking oil. Increasing the RAP
content in a mixture increases its stiffness andreduces its shear strain, indicating the increased
resistance to rutting. Adding rejuvenators canbalance the asphaltene- maltene ratio of aged asphalt
in RAP mixtures.
Using RAP in HMA applications could possiblypresent a lower performance due to the behavior of
the aged binder, which loses its lighter fractions withtime. A binder rejuvenator is used in order toimprove the mixture properties. This allows themodification of the aged binder, restoring some ofits original properties and promoting an adequate
performance of the mixture. The rejuvenator
559

GEOMATE-Osaka, Nov. 16-18, 2015

low stress (0.1 kPa) for 10 creep/recovery cycles
then the stress is increased to 3.2 kPa and repeated
for an additional 50 cycles. Fourier transform
spectrometry measures the infrared (IR) lightabsorbed by a material. This absorption depends onthe chemical functional groups present in thematerial. Technically an infrared light beam ischarged through the dissolved material. This energy
causes bonds in molecules to vibrate and rotate at
distinct frequencies. The energies absorbed at given
frequencies are identified with a detector, and aspectrum of absorbance versus wavenumber isobtained.
The RAP is mixed with virgin bitumen indifferent percentages varying from 0 to 100%. WEO
is also added in percentage varying from 0 to 25%.
Basic tests are carried out on all these samples andoptimal WEO for all the samples is determined.
Rheological and chemical analysis is further carriedout for the samples containing optimal WEO. Figure
1 shows the details of experimental investigations.

EXPERIMENTAL INVESTIGATION
Materials
Materials used in this study include 60/70 grade
bitumen, recovered rap binder and waste engine oil(WEO) as rejuvenator. WEO is the engine oil usedin the vehicle during a routine oil change. Large
quantities of engine oil on pavements impose
damage on the asphalt pavement. However, small
amounts of oil well blended in the mix may prove
beneficial when combined with rap by offsetting theincreased stiffness in order to produce a pavementwith performance similar to one made of virginmaterials.
.
Methods
Bitumen extraction
In this study, the extraction of bitumen from theused HMA was done based on the ASTM D2172
procedure, using centrifuge extractor and benzene as
the solvent. The used HMA was obtained from
Bangalore-Mysore NICE road which was paved 5
years ago and had deteriorated to a considerable
extent. It was made using 60/70 grade of bitumen
and it served heavy traffic volume.
Test methods
Binders are tested as per the procedure laid in the
IS: 73:2006 for their physical properties.
Rheological properties are determined using DSR
according to AASHTO T315-08 [1] and are widelyused to characterize the viscous and elastic behaviorof asphalt binders at high and intermediate service
temperatures. The DSR measures the complex
modulus (G*) and phase angle (δ) of the asphalt
binders at the desired temperature and frequency ofloading. The sample is prepared by heating thebinder until it becomes fluid to pour. The heatedbinder is poured into a rubber mould and allowed tocool until it attains solid consistency. Then thesample is removed and placed between the fixedplate and oscillating spindle of the DSR. The excess
binder beyond the edge of the spindle is trimmed.
Frequency sweep tests are performed on RAP
modified binders containing optimal WEO. The
samples are poured and trimmed at around 90°C.
The frequency sweep tests are performed from 1 to
10 Hz under a stress of 120 Pa for temperaturevarying from 50 to 80oC. For MSCR test,
one‐second creep load is applied to the above
mentioned samples. After one‐second load isremoved, the sample is allowed to recover for 9
seconds. The test is started with the application of a

Fig. 1 Details of Experimental Investigation
RESULTS AND DISCUSSION
Basic Test Results
Basic tests were conducted on RAP modified
samples containing WEO varying from 0 to 25%
and optimal WEO was determined. The results ofbasic tests are shown in Table 1.
The penetration decreases as the percentage of
RAP binder increases due to the loss of volatiles in
the aged RAP binder. The penetration value ofvirgin bitumen is 64 mm. With the increase of
RAP % penetration decreases and for 100% RAP
bitumen after the long term ageing penetration value
is 23mm, the lowest. But with the addition of
optimum oil content, the RAP modified binders
showed penetration values in the permissible limit of
60/70 grade bitumen.
The softening point of all RAP binder
proportions increase as they undergo ageing becauseof hardening. 100% RAP bitumen exhibits thehighest softening point at 64°C after being subjected560

GEOMATE-Osaka, Nov. 16-18, 2015

Stripping test is carried out on the optimally
rejuvenated samples and there was no evidence ofstripping.

to long term ageing, whereas virgin bitumen has asoftening point of 45°C. But with the addition of
optimum oil content, the values of all the RAP
modified binders reduced to the permissible limit of
60/70 grade bitumen.
The ductility decreases as the percentage of RAP
binder increases due to the loss of volatiles in theaged RAP binder. The ductility value of virgin
bitumen is 80 cm. With the increase of RAP %ductility decreases and for 100% RAP bitumen after
the long term ageing ductility value is 5 cm, thelowest. But with the addition of optimum oil content,
the ductility values of the binders with 10 to 40%
RAP increased to the permissible limit of 60/70
grade bitumen. But there was not much
improvement in the 100% RAP.

Rheological Test ResultsFrequency sweep test
Tests are conducted on samples containing
optimal WEO. It is observed from Fig. 2, the generaltrend is that the complex modulus (G*) decreases
with the increase in temperature due to visco elastic
nature of bitumen. The RAP modified binders havehigh complex modulus than virgin bitumen due toincorporation of aged binder in increasing
percentages of 10 to 40, because the bitumen
becomes more viscous. Also, RAP modified binders
have less phase angle than virgin bitumen which
directly affects the elastic recovery properties. In
conclusion, the addition of RAP binder to the basebitumen produces stiff and tough bitumen.

Table 1 Basic test results
Softening
Point test

60-70 mm

45-550C

64 mm

0

45 C

80 cm

10% RAP

58 mm

480C

62 cm

10% RAP +
4% EO

65 mm

450C

82 cm

20% RAP

52 mm

500C

58 cm

20% RAP +
4% EO

64 mm

460C

81 cm

30% RAP

42 mm

530C

54 cm

COMPLEX MODULUS V/S TEMPERATURE
4.00E+07
3.20E+07
2.40E+07
1.60E+07
8.00E+06
0.00E+00

30% RAP +
6% EO

68 mm

460C

88 cm

40% RAP

32 mm

580C

25 cm

40% RAP +
8% EO

66 mm

0

45 C

84 cm

100% RAP

23 mm

640C

5 cm

100% RAP+ 25% EO

64 mm

470C

10 cm

30%
RAP

40%
RAP

100%
RAP

Optimal
WEO

4%

4%

6%

8%

25%

65

70

75

80

Virgin Bitumen

10% RAP

20% RAP

30% RAP

40% RAP

100% RAP

80.00
60.00
40.00
20.00

50

55

60

65

70

75

80

TEMPERATURE

Table 2 Optimal WEO for varying RAP content
20%
RAP

60

100.00Thus, from the above basic tests, the optimal
content of WEO for all the RAP modified binders
was determined. Table 2 shows the optimal WEO
for varying percentages of RAP.

10%
RAP

55

PHASE ANGLE V/S TEMPERATURE

0.00

Bitumen50

TEMPERATURE

PHASE ANGLE

IS Limit( 60/70 )VirginBitumenDuctility
test
Min of 75
cm

COMPLEX MODULUS

Penetration
test

Bitumen

Virgin Bitumen

10% RAP

20% RAP

30% RAP

40% RAP

100% RAP

Fig. 2 Variation of complex modulus and phaseangle with respect to temperature
In order to replace the lost lighter fractions in theaged RAP binder WEO is used as a rejuvenator.

561

GEOMATE-Osaka, Nov. 16-18, 2015

Optimal content determined from the basic tests isadded and test is conducted. From Fig. 3,4,5,6 and 7,
it can be observed that there is a significant
improvement in the complex modulus of all the RAP
modified binders, but not much in case of phaseangle of all the RAP modified binders. Hence, it canbe concluded that addition of optimal WEO canrestore the lost fractions, thus improving its
rheological properties.

PHASE ANGLE v/s TEMPERATURE
100
PHASE ANGLE

80
60
40
20
0

50

55

60

65

70

75

80

TEMPERATURECOMPLEX MODULUS v/s TEMPERATUREVBCOMPLEX MODULUS

4.00E+07

RAP-20-4-WEO

3.20E+07

Fig. 4 Variation of G* and δ with respect totemperature for 20% RAP with 4% oil

2.40E+07
1.60E+07
8.00E+06
0.00E+00

COMPLEX MODULUS v/s TEMPERATURE
50

55

60

65

70

75

4.00E+07

80

VB

RAP-10COMPLEX MODULUS

TEMPERATURE
RAP-10-4-WEO

PHASE ANGLE v/s TEMPERATURE
100.003.20E+07
2.40E+07
1.60E+07
8.00E+06
0.00E+00

80.00
PHASE ANGLE

RAP-20

50

55

60

65

70

75

80

TEMPERATURE

60.00
VB

40.00
20.00
0.00

RAP-30

RAP-30-6-WEO

PHASE ANGLE v/s TEMPERATURE
50

55

60

65

70

75

100.00

80

80.00

VB

RAP-10PHASE ANGLETEMPERATURE
RAP-10-4-WEO

60.00
40.00
20.00

Fig. 3 Variation of G* and δ with respect totemperature for 10% RAP with 4% oil

0.00

50

55

60

65

70

75

80

TEMPERATURECOMPLEX MODULUS v/s TEMPERATURE

VB

RAP-30

RAP-30-6-WEO

COMPLEX MODULUS

40000000
32000000

Fig. 5 Variation of G* and δ with respect totemperature for 30% RAP with 6% oil

24000000
16000000
8000000
0

50

55

60

65

70

75

80

TEMPERATURE
VB

RAP-20

RAP-20-4-WEO

562

GEOMATE-Osaka, Nov. 16-18, 2015

Multiple Stress Creep Recovery Test

4.00E+07
3.20E+07250%

2.40E+07
1.60E+07

Percentage Recovery

COMPLEX MODULUS

COMPLEX MODULUS V/S TEMPERATURE

8.00E+06
0.00E+00

50

55

60

65

70

75

80

TEMPERATURE
VB

RAP-40

RAP-40-8-WEO

200%
150%
100%
50%
0%

119%

100%

8%

2%
VirginPHASE ANGLE V/S TEMPERATURE

98%

10%RAP
100Pa

3%
10%RAP+ 4% WEO

3200Pa

80.00

250%

60.00
Percentage Recovery

PHASE ANGLE

100.00

40.00
20.00
0.00

50

55

60

65

70

75

80

TEMPERATURE
VB

RAP-40

200%
128%

150%
100%

100%

88%

50%

9%

2%

RAP-40-8-WEO

0%

Virgin

20%RAP
100Pa

Fig. 6 Variation of G* and δ with respect totemperature for 40% RAP with 8% oil
Percentage Recovery

COMPLEX MODULUS

4.00E+07
3.20E+07
2.40E+07

155%
150%
100%

100%

97%

50%

13%

2%

8.00E+06

Virgin30%RAP
100Pa

55

60

65

70

75

RAP-100

30%RAP+6% WEO

3200Pa230%

250%

RAP-100-25-WEO

Percentage Recovery

VB

2%

80

TEMPERATURE

PHASE ANGLE V/S TEMPERATURE
100.0080.00
PHASE ANGLE

3200Pa200%

0%1.60E+07

50

20%RAP+4% WEO

250%

COMPLEX MODULUS V/S TEMPERATURE

0.00E+00

4%

200%
150%
100%

112%

100%

50%

25%
2%

0%

60.00
40.00

Virgin

40%RAP
100pa

5%40%RAP+8% WEO

3200Pa

20.00
0.00

50

55

60

65

70

75

80

Fig. 8 Variation of percentage recovery with appliedstressTEMPERATURE
VB

RAP-100

RAP-100-25-WEO

MSCR could not be performed on 100%RAP
binder due to its stiffness.
It is clear from Fig. 8 that, RAP infused binders
show higher percentage recovery both at high andFig. 7 Variation of G* and δ with respect totemperature for 100% RAP with 8% oil
563

GEOMATE-Osaka, Nov. 16-18, 2015

low stress levels. At lower stress levels virgin
bitumen showed fair percentage recovery, at higher
stress levels the percentage recovery of virgin
bitumen was found to reduce. The addition of RAP
to the bitumen results in the higher percentage ofrecovery, higher the percentage recovery better isthe material in terms of rutting. And, with theaddition of optimum WEO, the percentage ofrecovery reduced nearly to that of virgin bitumen
indicating reduced resistance to rutting.
0.080%

0.076%

Fig. 11 IR Spectrum of 20%RAP modified bitumen
with 4%WEO content

0.067%

0.060%

0.048%

0.047%

Jnr

0.040%
0.016%

0.020%
0.000%-0.020%VB

0.013%

0.010%
0.004%0.004%

10% 4% 20% 4% 30% 6% 40% 8%
RAP WEO RAP WEO RAP WEO RAP WEO

3200PaFig. 9 Variation of Non Recoverable Creep
Compliance with applied stressFig. 12 IR Spectrum of 30%RAP modified bitumen
with 6%WEO content

From Fig. 9 it can be observed that the virgin
bitumen has higher non recoverable compliance (J nr )when compared to modified bitumen. Higher nonrecoverable compliance can possibly increase themagnitude of rutting in the actual service life.
However, addition of RAP reduces the J nr thus
reducing the possibility of rutting. Inclusion of
optimal WEO increases the non recoverable creep
compliance, but when compared to the virgin
bitumen, it is less. Hence, the possibility of rutting isless when compared to that of virgin bitumen.
Fourier Transform Infrared Spectroscopy

Fig. 13 IR Spectrum of 40%RAP modified bitumen
with 8%WEO content

Fig. 10 IR Spectrum of 10%RAP modified bitumen
with 4%WEO content
Fig. 14 IR Spectrum of 100%RAP modified bitumen
with 25%WEO content
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GEOMATE-Osaka, Nov. 16-18, 2015

CONCLUSION
A comprehensive experimental investigation was
carried out to evaluate the physical, rheological andchemical properties of RAP modified asphalt binder.
From the analysis of the results, the following
conclusions are drawn.
1) With the inclusion of RAP binder into the virgin
the penetration and ductility consistently
decrease whereas softening point consistently
increase.
2) The complex modulus increases with increase inpercentage of RAP binder. However, phase angle
decreases with increase in percentage of RAP
binder.
3) With the addition of optimum content of WEO tothe RAP modified binder, the characteristics ofvirgin bitumen were obtained (60/70 grade).
4) Complex modulus of RAP binder with WEO
decreased when compared to that without WEO.
But, there was not much improvement in thephase angle with the addition of WEO.
5) The addition of RAP results in higher percentageof recovery, higher the percentage recovery
better is the material in terms of rutting.
6) The virgin bitumen has higher non recoverable
compliance (J nr ) when compared to modified
bitumen. Higher J nr can possibly increase themagnitude of rutting in the actual service life.
However, addition of RAP reduces the J nr thus
reducing the possibility of rutting. Inclusion of
optimal WEO increases the J nr , but whencompared to the virgin bitumen, it is less. Hence,the possibility of rutting is less when comparedto that of virgin bitumen.
7) The infrared spectra illustrated that both asphalt
binder and waste engine oil consist of similarfunctional groups and molecular structures. The
inclusion of WEO into asphalt binder did notshow much improvement in the chemical
structure of the binder.

Fig. 15 IR Spectrum of WEO
FTIR is a tool to identify chemical evolution inbitumen and indicate severity of oxidation
experienced by bitumen after ageing. It measures
Infrared light transmitted by sample materials. FTIR
is useful in identifying the presence of certain
functional groups which transmit at definite
frequencies. The FTIR spectra of asphalt binders andwaste engine oil are shown in Fig. 10, 11, 12, 13, 14
and 15. Since both asphalt and WEO arehydrocarbons, the samples are generally comprisedof saturated and aromatic hydrocarbons. The
absorption peaks at 2923 cm-1 and 2852 cm-1 areassociated with C-H stretching of alkane and O-Hstretching of carboxylic acids respectively. A
stretching vibration of C-C in the aromatic ring is
detected at 1469 cm-1 and it also represents thebending of C-H indicating the group of -CH 2 - and –C-CH 3 . The peaks at 1271 represent C-N stretch in
the aromatic ring and C-O stretch containing
alcohols, carboxylic acids, esters and ethers. A
stretching vibration of S=O and C-N is detected at1030 cm-1 representing sulphoxides and amines. Two
compounds of most interest are the sulphoxides andcarbonyls showing peak at 1030 cm-1 and 1700 cm-1
respectively. These two compounds are commonlyused as an indication of the aging, asphalt binder has
undergone. Generally, both WEO and asphalt binderconsist of similar functional groups and molecular
structures. Oxidised components are introduced into
virgin asphalt binder by both WEO and aged asphalt.
This inclusion increases the relative oxidation ofasphalt blend. Hence, it is clear from the figure
above, that there is not much significant
improvement in the performance of RAP modified
samples even with optimal amount of WEO as theyoxidise at a faster rate. However, an aging
performance study on these RAP modified samples
containing optimal WEO would show the rate ofoxidation.

REFERENCE[1] AASHTO T 315: Determining the Rheological
Properties of Asphalt Binder Using a Dynamic
Shear Rheometer (DSR).
[2] O’Sullivan, K. A. (2011) “Rejuvenation ofreclaimed asphalt pavement (RAP) in hot mix
asphalt recycling with high RAP content”.
[3] Junan Shen, Serji Amirkhanian and Boming
Tang (2006) “Effects of Rejuvenator on
Performance based properties of RejuvenatedAsphalt Binder and Mixtures”, Construction and
Building Materials 21 (2007) 958–964.
[4] J.R.M. Oliveira, H.M.R.D. Silva and C.M.G.
Jesus (2011) “Asphalt Mixtures Produced With
100%
Reclaimed
Materials”,
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[5] Ehsan Yaghoubi, Mohammad Reza Ahadi,
Mohsen Alijanpour Sheshpoli and Hamid
Jahanian Pahlevanloo (2013) “Evaluating the
Performance of Hot Mix Asphalt with Reclaimed
Asphalt Pavement and Heavy Vacuum Slops as
Rejuvenator”,
International
JournalofTransportation Engineering, Vol.1/ No.2/ Spring
2013.
[6] Xiaoyang Jia, Baoshan Huang , Benjamin F.
Bowers and Sheng Zhao (2014) “Infrared spectra
and rheological properties of asphalt cement
containing waste engine oil residues”,
Construction and Building Materials 50 (2014);
683-691.
[7] Rajib B. Mallick, Prithvi S. Kandhal and Richard
L. Bradbury (2008) “Using Warm-Mix Asphalt
Technology to Incorporate High Percentage ofReclaimed Asphalt Pavement Material in
Asphalt Mixtures”, Transportation Research
Board, Transportation Research Record 2051,
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[8] Luis Loria, Elie Y. Hajj, Peter E. Sebaaly, Matt
Barton, Said Kass, and Tara Liske (2011)“Performance Evaluation of Asphalt Mixtureswith High Recycled Asphalt Pavement Content”,
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566

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

EXPERIMENTAL STUDY ON LOAD-DEFORMATIONTEMPERATURE BEHAVIOUR OF POLYMER GEOGRID
Thitapan Chantachot 1, Warat Kongkitkul 2 and Fumio Tatsuoka 3
1,2
Department of Civil Engineering, Faculty of Engineering,
King Mongkut’s University of Technology Thonburi, Thailand;
3
Department of Civil Engineering, Tokyo University of Science, Japan

ABSTRACTTo evaluate the effects of ambient temperature on the load - deformation behaviour of polymer geosynthetic
reinforcement, a series of tensile loading tests was performed on two types of geogrid using a wide variety ofload and temperature histories. The loading schemes included monotonic loading and sustained loading under
different controlled ambient temperature conditions. With an increase in the ambient temperature, the rupture
strength and stiffness decreased. The creep strain increased with an increase in temperature, associated with adecrease in the stiffness. The creep strain by sustained loading during which the temperature was elevated from30o C to 50o C was significantly larger than the one by sustained loading during otherwise monotonic loading at
the constant temperature equal to either 30o C or 50o C. The residual tensile strength observed at the sameambient temperature was rather independent of pre-rupture loading histories.
Keywords: Geosynthetic reinforcement, Temperature, Tensile loading test, Creep, Residual strength

performed with a series of elevated constanttemperatures on a single geosynthetic specimen.
To understand the load-deformation behaviour
during creep of geosynthetics affected bytemperature, a number of temperature-accelerated
creep tests on geosynthetic reinforcements havebeen performed [5]-[8]. However, in these tests,
only load-deformation responses during sustained
loading are evaluated, while the behaviour after
monotonic loading is restarted following sustained
loading are not. Moreover, experiments to evaluate
the effects of temperature on the tensile strength are
limited. The study to evaluate the combined effectsof sustained loading and temperature changes cannot
be found in the literature.
In view of the above, in this study, a series oftensile loading tests were performed on two types ofgeosynthetic reinforcement. Various loading
schemes and temperature changes were applied toevaluate the effects of load - temperature histories
on the load-deformation behaviour and rupturetensile strength.

INTRODUCTION
The effects of ambient temperature on thestrength and stiffness of geosynthetic reinforcement
is one of the important factors to be taken into
account in the design of geosynthetic-reinforced soil
(GRS) structures. Moreover, to prevent long-term
creep rupture of geosynthetic reinforcement,
allowable tensile strength of geosynthetic
reinforcement is used in design, as suggested by
Koerner and GRI Standard Practice [1]-[3]. The
allowable tensile strength is obtained by reducing
the tensile rupture strength by fast loading tests
using a creep reduction factor, which is typicallybetween 1.5 to 4.0, depending on the type ofgeosynthetic and application [1]-[3].
The creep reduction factor is evaluated from acreep rupture curve formulated by analysing results
originally from a set of conventional creep tests.
However, this type of tests is extremely timeconsumption, therefore extremely costly. To shorten
the time required for the entire test program, thecreep tests are often conducted at elevatedtemperatures in order to accelerate the creep process.
In the method called the time-temperature
superposition (TTS), a set of creep tests are
performed on geosynthetics specimens until rupture
at a set of constant but different ambient
temperatures. The TTS can expedite the creep, but it
requires multiple replicated specimens to obtain areliable result. Then, the stepped isothermal method(SIM) was developed in order to avoid the effect ofmultiple specimens [4], in which the creep test is

APPARATUSES AND MATERIALS
Loading and Heating Apparatuses
The tensile loading tests on polymer geosynthetic
reinforcements were performed by using a loadcontrolled loading system [9]. The tensile load wasapplied by controlling the air pressure in the air
cylinder. The loading direction (load/unload) and

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GEOMATE- Osaka, Nov. 16-18, 2015

load rate were controlled by means of an electro
pneumatic transducer (EP).
The temperature surrounding the specimen was
controlled by using a heating unit connected to atemperature-controlled chamber containing a test
specimen (Fig. 1). The heating unit consists of aheater, a network of air-pipes and a temperature
controller. The heater provided hot air to thechamber via the air-pipes. By using this apparatus,
the temperature inside the chamber can be controlled
as planned [10].

Air cylinder
Electro pneumatictransducer (EP)

Load cell
Connect toheating unit

Thermo-couple
Griping device(Roller-clamps)

Measuring Devices
Temperaturecontrolledchamber

A load cell was connected to the loading piston
to measure the tensile load (Fig. 1). A displacement
sensor was arranged on a tiny frame that was
attached on the central part of the specimen tosensitively measure the local tensile deformation of
the specimen. The ambient temperature surrounding
the specimen was measured by using a thermocouple that was installed inside the temperaturecontrolled chamber.

Fig. 1
Tensilemeasuring devices

loading

apparatusandTest Materials
Two different polymer geogrids were used (Fig.
2): i) high-density polyethylene (HDPE); and ii)
polypropylene (PP). HDPE geogrid is a uniaxial
type designed for use as reinforcement in one
direction. The aperture shape is long-elliptical shape.
On the other hand, PP geogrid is a biaxial type. The
size of aperture is 35 mm (centre-to-centre of themembers) in both longitudinal and transverse
directions. The mechanical properties of these two
geogrids according to their manufacturers are listed
in Table 1.
TEST METHOD
Specimen Preparation

Fig. 2

Geosynthetic reinforcements

The top and bottom ends of respective specimens
were clamped by using a pair of gripping device
connected to the loading piston. The roller-clamps ofeach gripping device consists of a steel cylinder witha groove made to grip the specimen with a steel rod.
A sheet of sand paper was glued on the surface ofthe roller-clamp to avoid slippage of the specimen
around the roller clamp during testing (Fig. 1). Prior
to the start of each tensile loading test, a very small
preload of 20 N was applied to minimize the settlingerror of specimen.

Table 1

Mechanical properties of geogrids

Type
HDPE
PP

Ultimate tensile
strength (kN/m)
90≥ 40

Yield point
elongation (%)
13≤8

Test Program
The following three different loading andtemperature histories shown in Fig. 3 were
employed:(1) ML-CT: Monotonic loading was performed at aconstant load rate of 0.6 kN/m/min towards the
specimen’s rupture. The ambient temperature
was kept constant between 30o C and 50o C
throughout each test.
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GEOMATE- Osaka, Nov. 16-18, 2015

Tensile load, V (kN/m)

80

HDPE geogrid
70 Loading Type: ML-CT: End of test
: Yield (Maximum curvature)
60
40 oC

50
o

35 C
40

T = 30 oC

50 oC

30

45 oC

20
10
0

a)

0

5

10

15

20

25

30

35

Tensile strain, ε (%)a)
60

Tensile load, V (kN/m)

50b)

PP geogrid
Loading Type: ML-CT
x : Rupture
30oC

40
o

35 C

50oC

30
o

45 C
40oC

20
10
0
0

Fig. 3
Loading and temperature histories:(1) ML-CT tests were performed at constanttemperature equal to 30o, 35o, 40o, 45o or 50o C.
(2) SL-CT tests were performed at constanttemperature equal to either 30o or 50o C.
(3) SL-VT tests were performed with thetemperature during sustained loading that wasincreased from 30o C to 50o C
In this figure, for ML-CT and SL-CT, only tests at
T=30o C are depicted.

b)

2

4

6

8

10

12

14

Tensile strain, ε (%)

Fig. 4
Tensile load and strain relations from
(1) ML-CT tests: (a) HDPE; and (b) PP
reach the peak tensile load states at the largest strain
applied, about 30 %. Hence, the rupture strength was
defined by the yield load at the point of maximum
curvature along the V-ɛ relation.
As the effects of strain rate on the tensile rupture
strength (Vmax) of geosynthetic reinforcement are
significant [11], the measured values of Vmax werecorrected to the value at a strain rate of 0.1 %/min(selected as a reference strain rate). As the loading
apparatus used is of load-controlled type, the strain
rate at rupture along different V-ɛ relations weredifferent, controlled by the tangential stiffness at the
moment of tensile rupture. The values of Vmax at astrain rate of 0.1 %/min, Vmax,cor, were evaluatedfrom the measured values by numerical simulations
based on the non-linear three-component model
assuming that the viscous behaviour of the tested
geogrids is of isotach type. The details of thecorrection are described in Kongkitkul et al. (2012)[10].
The values of Vmax,cor and rupture strain (ɛrup) aresummarised in Table 2. The secant modulus at
rupture (E) of V-ε relation, defined as E = Vmax,cor/ɛrup, is also listed in Table 2. Obviously, the valuesof Vmax and E decrease significantly with an increasein the ambient temperature from 30o C.

(2) SL-CT: Monotonic loading was continuously
applied at a constant load rate of 0.6 kN/m/min
until the tensile load was reached a certain value
where sustained loading was performed for aperiod of three hours, followed by the restart ofmonotonic loading at the original load rate. The
ambient temperature was controlled to beconstant, either 30o C or 50o C, throughout theentire test.
(3) SL-VT: This loading type is similar to (2) SL-CT
except that the temperature during sustained
loading was increased from 30o C to 50o C in 10
steps at a rate of 2o C/step.
TEST RESULTS AND DISCUSSIONS
Temperature Effects on Rupture Strength
Figs. 4(a) and 4(b) show the relationships
between tensile load (V) and tensile strain (ɛ) from
(1) ML-CT tests on HDPE and PP geogrids,
respectively. In these tests, HDPE geogrid did not
569

GEOMATE- Osaka, Nov. 16-18, 2015

70

Test results from ML-CT loading type

HDPE

PP

ɛrup
(%)
19.98
20.01
19.75
19.96
20.00
10.27
10.52
10.74
11.20
11.66

Vmax,cor(kN/m)
53.14
48.09
44.88
41.82
39.05
44.00
43.20
41.88
40.47
38.42

E
(kN/m)
266.01240.36227.20209.57
195.19428.29410.57390.12361.49329.47

Tensile load, V (kN/m)

T
(o C)
30
35
40
45
50
30
35
40
45
50

Types

60

Tensile load, V (kN/m)

60
50

0

5

10

15

40

20

25

30

30
20

b)

70 %
60 %
50 %
40 %

10

60
50

Load level = 30 % of V@end of test

2

6

8

10

HDPE geogrid
Loading Type: SL-VT
Temperature: 30-50oC+ : End of testx : Yield

12

14

16

Load level = 30 % of V@end of test

50 %
40 %

20

30 %
Load level = 20 % of V@end of test
ML-CT (50oC)

0
0

10

12

14

5

10

15

20

25

30

35

Tensile strain, ε (%)a)

Tensile strain, ε (%)

ML-CT (30oC)

60 %

30

10

8

4

40

0
6

35

Tensile strain, ε (%)

40 %

4

30

80

70 %
60 %
50 %

2

25

ML-CT

20

70

0

20

Fig. 6
Tensile load and strain relations at T =50o C from (2) SL-CT tests: (a) HDPE; and (b) PP

35

PP geogrid
Loading Type: SL-CT
Temperature: 30oC
x : Rupture
ML-CT

10

15

30

0

Tensile load, V (kN/m)

Tensile load, V (kN/m)

40

10

PP geogrid
Loading Type: SL-CT
Temperature: 50oC
x : Rupture

b)

60
50

5

0

Tensile strain, ε (%)

a)

30%

10

50

Load level = 20 % of V@end of test

0

40%

20

60

30 %

10

50%

Tensile strain, ε (%)

60 %
50 %
40 %

20

60%
30

a)

HDPE geogrid
Loading Type: SL-CT
Temperature: 30oC
+ : End of testx : Yield

ML-CT

40

0

40
30

ML-CT

load level = 20% of V@end of test

80
70

50

HDPE geogrid
Loading Type: SL-CT
Temperature: 50oC
+ : End of testx : Yield

0

Tensile load, V (kN/m)

Table 260
50

Tensile load, V (kN/m)

Fig. 5Tensile load and strain relations at T =30o C from (2) SL-CT tests: (a) HDPE; and (b) PP
Creep Deformation Characteristics
The V-ɛ relations obtained from (2) SL-CT tests
at T = 30o C and 50o C are shown in Figs. 5 and 6,
respectively. The V-ɛ relations from (3) SL-VT tests
are shown in Fig. 7. It may be seen from thesefigures that the V-ɛ curves before the start ofsustained loading in SL-CT and SL-VT tests arenearly the same as those from ML-CT tests at thesame temperature. This result indicates a highrepeatability of test in this study.

40

PP geogrid
Loading Type: SL-VT
Temperature: 30-50oC
x : Rupture
ML-CT (30oC)

30

70 %
60 %

20

50 %
40 %
Load level = 30 % of V@end of test

10

ML-CT (50oC)

0
0

b)

2

4

6

8

10

12

14

16

Tensile strain, ε (%)

Fig. 7
Tensile load and strain relations from
(3) SL-VT tests: (a) HDPE; and (b) PP
570

GEOMATE- Osaka, Nov. 16-18, 2015

Fig. 10strain rate

Fig. 8
Schematic diagram showing the trend of
behaviour in SC-VT tests
16

3.
HDPE geogrid

12SL-CT (30 C) :SL-CT (50oC) :SL-VT (30-50oC):

4.

Creep strain,

CR

ε (%)

o

8

4

0

20

30

40

50

6
SL-CT (30oC) :SL-CT (50oC) :SL-VT (30-50oC):

Creep strain,

CR

ε (%)

PP geogrid

4

2

0

b)
Fig. 9

30

40

50

60

The subsequent V-ɛ curves approaching theultimate failure at T = 50o C in SL-CT and SLVT tests tend to re-joins those in ML-CT tests at
T = 50o C.
Reflecting the trends described above, the creep
strain by sustained loading during which thetemperature increased from 30o C to 50o C in
SL-VT tests is significantly larger than the one
in SL-CT at T = 30o C or 50o C.

Fig. 9 compares the creep strains (ɛCR) defined as
the strain increment by sustained loading for aperiod of 3 hours observed at different load levels
from SL-CT and SL-VT tests. These ɛCR values havebeen corrected to those when the initial creep strain
is 0.1 %/min (Fig. 10). That is, in this study, theactual initial creep strain rate at the start ofrespective sustained loading stages was different due
to different load levels and temperatures. On theother hand, the ɛCR for a given period decreases witha decrease in the initial creep strain rate and viceversa [11]. Therefore, to remove the effects of initial
creep strain rate, the start of sustained loading was
redefined at the moment when the strain rate was
0.1 %/min and the total creep strain for a period of
three hours were estimated by slightly extrapolating
the measured time history of creep strain.

60

Load level (% of V@end of test)a)Defining creep strain for the same initial

70

Load level (% of V@end of test)

Comparison of creep strains for: (a)Effects of sustained load level on creep strain

The following trends of behaviour, as
schematically illustrated in Fig. 8, are also seen:
1. In Fig. 7, the V-ɛ curves immediately after therestart of monotonic loading at T = 50o C,
following sustained loading, in SL-VT tests are
stiffer, while exhibiting larger strains, than thosein ML-CT tests at T = 50o C.
2. As seen by comparing Figs. 6 and 7, the V-ɛ
curves immediately after the restart ofmonotonic loading at T = 50o C in SL-VT tests
are as stiff as those in SL-CT tests at T = 50o C,
although the strains are different.

It may be seen from Fig. 9 that the creep strain
increased with an increase in the sustained load level.
This trend is obvious particularly with HDPE
geogrid.
Effects of temperature on creep strain
It may be seen from Fig. 9 that, in SL-CT tests at
different constant temperatures, with an increase in
the temperature, the creep strain increases associatedwith a decrease in the stiffness (Table 2). Kongkitkul
and Tatsuoka (2007) [13] has shown that this trendof behaviour can be simulated based on the non571

GEOMATE- Osaka, Nov. 16-18, 2015

Creep strain rate, dεCR/dt (%/min)linear three-component model taking into accountthe temperature effects.
It may also be seen from Fig. 9 that the creep
strain increment by sustained loading during which
the temperature increased from 30o C to 50o C in SLVT tests is significantly larger than both of those at
T = 30o C and 50o C in SL-CT tests. Besides, thecreep strain increment in SL-VT tests is not a simple
summation of those at T = 30o and 50o C in SL-CT
tests, but it is the creep strain that developed due tothe viscous property coupled with temperatureeffects in a non-linear manner affected by loadtemperature history. In this respect, Fig. 11 shows
the time histories of creep strain rates (dɛCR/dt)
during sustained loading at the same load level in
SL-CT and SL-VT tests of HDPE geogrid. In thisfigure, the moments when the temperature waschanged in the SL-VT case are marked “x”. It may
be seen that, as far as the load and temperature are
kept constant in SL-CT tests, the creep strain rateconsistently and smoothly decreased with time
towards a value much lower than the initial value.
On the other hand, in SL-VT tests, the relationshipbetween the creep strain rate and the elapsed time
exhibits a discontinuous decrease in the decreasingrate of creep strain rate at the moments when thetemperature changed. Importantly, the creep strain
rate during the last stage at 50o C in SL-VT test issignificantly higher than that at the same
temperature (50o C) for the same period in SL-CT
test, associated with a much higher strain rate at thestart of that stage. This trend indicates that the
instantaneous creep strain rate is not a uniquefunction of current load, strain and temperature, but
it is controlled by loading and temperature history. It
will be reported in the near future that these trendsof behaviour can be simulated by the non-linear
three-component model taking into account thetemperature effects.

HDPE geogrid
Loading Type: SL-CT and SL-VT
Load level = 50 % of V@end of test

30oC

0.10

32

0.08
34

0.06

Remark: x were the point thatchange temperature

36
38

40

0.04

42 44
46 48 50

30-50oC

50oC

30oC
0.02
0

2000 4000 6000 8000 10000 12000 14000

Elapsed time, ∆t (sec)Fig. 11
Creep strain rate vs. time for different
test conditions
65
ML-CT:
ML
SL-CT and SL-VT:
Load level (% of V@end of test)
20 %:
30 %:
50 %:
40 %:
60 %:

HDPE geogrid

Tensile load, V (kN/m)

60

90%

55
50
110%
45
40
35

Remark: V were corrected for dε/dt = 0.1 %/min

30
25

30

35

40

45

50

55

Temperature, T (oC)a)
55

ML-CT:
ML
SL-CT and SL-VT:
Load level (% of V@end of test)
30 %:
40 %:
60 %:
50 %:
70 %:

Tensile load, V (kN/m)

PP geogrid
50
90%
45

40

110%

35
Remark: V were corrected for dε/dt = 0.1 %/min

Effects of Loading and Temperature History on
Residual Rupture Strength

30
25

b)
Fig. 12(a) and 12(b) show the residual tensile
rupture strengths plotted against the temperature at
the end of the respective tests (i.e., the temperaturewhen the residual strength was measured) of HDPE
and PP geogrids, respectively. The data from MLCT tests at different constant temperatures aredenoted by solid squares. A line is depictedconnecting these data points. Two other lines aboveand below this central line denote the rupture
strengths that are 90 % and 110 % of the values from
ML-CT tests. The other data points denote theresidual strengths obtained by restarting monotonic
loading following sustained loading in SL-CT and
SL-VT tests. It may be seen that, under the same
temperature at rupture, the residual tensile strengths

Fig. 12

30

35

40

45

50

55

Temperature, T (oC)Residual rupture strengthfor different pre-rupture loading and temperature
histories are generally similar. With PP geogrid, thescatter of data is relatively large. However, there isno systematic trend due to any specific pre-ruptureloading/temperature history. Therefore, it can beconcluded that the residual strength of the geogrids
tested is a rather unique function of temperature andstrain rate at rupture, while independent of prerupture loading/temperature history. That is, therising of ambient temperature exhibits negative
effects on the load-deformation behaviour of geogrid.
On the other hand, creep by sustained loading is not

572

GEOMATE- Osaka, Nov. 16-18, 2015

a degrading phenomenon for the rupture strength ofgeogrid.

polyester geogrids”, in Proc. 6th Int. Conf. on
Geosynthetics, 1998, pp. 691-698.
[5] Jeon HY, Kim SH, and Yoo HK, “Assessmentof long-term performances of polymer geogrids
by accelerated creep test”, J. of Polymer Testing,Vol. 21, Aug. 2002, pp. 489-495.
[6] Bueno BS, Costanzi MA, and Zornberg JG,“Conventional and accelerated creep tests on
nonwoven needle-punched geotextiles”, J. of
Geosynthetics International, Vol. 12, Nov. 2005,
pp. 276-287.
[7] Jones CJFP and Clarke D, “The residual
strengthofgeosynthetic
reinforcement
subjected to accelerated creep testing andsimulated seismic events”, J. of Geotextiles andGeomembranes, Vol. 25, Feb. 2007, pp. 155169.
[8] Franҫa FAN and Bueno BS, “Creep behaviourof geosynthetics using confined accelerated
tests”, J. of Geosynthetics International, Vol. 18,
Oct. 2011, pp. 242-254.
[9] Kongkitkul W, Hirakawa D, Tatsuoka F, andUchimura T, “Viscous deformation ofgeosynthetic reinforcement under cyclic loading
conditions and its model simulation”, J. of
Geosynthetics International, Vol. 11, Apr. 2004,
pp. 73-99.
[10] Kongkitkul W, Tabsombut W, Jaturapitakkul C,
and Tatsuoka F, “Effects of temperature on therupture strength and elastic stiffness ofgeogrids”, J. of Geosynthetics International, Vol.
19, Apr. 2012, pp. 106-123.
[11] Hirakawa D, Kongkitkul W, Tatsuoka F, andUchimura T, “Time-dependent stress-strain
behaviour due to viscous properties of geogrid
reinforcement”,J. of Geosynthetics International,Vol. 10, Dec. 2003, pp. 176-199.
[12] Tatsuoka F, Benedetto D, Kongkitkul W,
Kongsukpraserat L, Nishi T and Sano Y,“Modelling of ageing on the elasto-viscoplastic
behaviour of geomaterial”, J. of Soils andFoundations, Vol. 48, Apr. 2008, pp. 155-174.
[13] Kongkitkul W and Tatsuoka F, “A theoreticalframework to analyse the behaviour of polymer
geosynthetic reinforcement in temperatureaccelerated creep tests”, J. of Geosynthetics
International, Vol. 14, Feb. 2007, pp. 23-38.
[14] Zornberg JG, Byler BB, and Knudsen JW,“Creep of geotextiles using Time-Temperature
Superposition methods”, J. of ASCE, Vol. 130,
Nov. 2004, pp. 1158-1168.

CONCLUSIONS
The following conclusions with respect to thetemperature effects on the load-deformation
behaviour of polymer geosynthetics can be derived
from the test results presented in this paper:
1. The rupture tensile strength and stiffness of thetested two geogrids decreased while the creep
strain increased significantly with an increase in
the ambient temperature.
2. The creep strain increment by sustained loading
during which the temperature was elevated from30o C to 50o C was significantly larger than the
one by sustained loading during otherwise
monotonic loading at the constant temperatureequal to either 30o C or 50o C.
3. The residual strengths that were obtained withand without pre-rupture sustained loading were
essentially the same, showing that creep is not adegrading phenomenon.
ACKNOWLEDGEMENTS
The authors are grateful to the National ResearchCouncil of Thailand (NRCT), the Commission on
Higher Education, the Thailand Research Fund
(TRF) and the National Research University (NRU)
Project for financial support. Thanks are also
extended to Vigor Merger Co., Ltd and Tencate
Geosynthetics (Thailand) Co., Ltd who provided
geogrids for this study.
REFERENCES[1] Koerner RM, Designing with Geosynthetics.
New Jersey: Pearson Education, Inc., 2005, ch.
3.
[2] Geosynthetic Institute, "Determination of theLong-Term Design Strength of Stiff Geogrids",
GRI Standard Practice GG4(a), 2012.
[3] Geosynthetic Institute, "Determination of theLong-Term Design Strength of Flexible
Geogrids ", GRI Standard Practice GG4(b),2012.
[4] Thornton JS, Paulson JN, and Sandri D,“Conventional and Stepped Isothermal Methods
for characterizing long term creep strength of573

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

MANAGING VALORIZATION OF DREDGED SEDIMENTS VIA AN
INNOVATIVE MATHEMATICAL MODEL USED FOR CIVIL
ENGINEERING APPLICATION
M.Benzerzour1, N.Abriak1, I.Khezami1, R.Mansi1,
1

Civil & Environmental Engineering department, Ecole Mines Douai,

59500 Douai, France

ABSTRACT
Several years ago, global consumption of materials used in the ﬁeld of Civil Engineering andespecially materials for public works, has strongly growth. The use of marine and/or river dredged sediments
presents a promising alternative to deal with the problematic of sustainable development. In this paper, westudy a management problem of existing treatments for these sediments. Looking to the economical andlegislative obligations associated with the treatment process, these operations present one of the main
encountered problems during the valorization process of sediments. The solution for this problem is to ﬁnd an
optimal set of treatments respecting these economical and legislative constraints. This optimal solution is
obtained by solving a non-linear mathematical model with binaries variables. The proposed resolution
algorithm for this model is based on a linearization of the nonlinear constraints in order to solve a simple
problem using a solver.

Keywords: dredged sediments, valorization, mathematical model, civil engineering

Introduction
1
The exploration and the exploitation ofexisting and future ﬁelds are increasingly
regulated looking to the economic, social andenvironmental reasons. The important need toconserve environment and to manage thevalorisation of wastes, presents an unavoidable
industrial stakes looking to its ﬁnancial andenvironment impact.
Each year, huge quantity (600 million
m3/year [4]) of sediments and sands resulting
from the dredged operations executed tomaintaining and to ensure the accessibility ofports and waterways. Port authorities have to ﬁnd
solutionsforintegrated
andenduring
managements of these sediments. Several ways ofvalorization in Civil Engineering applications are
possible, such as the foundation of roads, dikes
and landscaped mounds, concrete and artiﬁcial
aggregates [6,8]. However, this management
remains complex and mainly depends on several
principals parameters environmental, mechanicaland economic parameters.

Scientific approach

Several experiments of valorization are
realized in the context of European research
projects such as: experimental roads based on
marine sediments (ex: experimental road of 200 m
in Caen, France (project SETARMS 1 );implementation of dikes with river sediments(Project PRISMA 2).
These scientific researches currently
instrumented give very good results, but thesustainability of the valorization processes is notacquired and meets strong mechanical, economicand environmental challenges. Indeed, thesevalorizations in road construction require many
specific treatments such as dehydration,
de-pollution mineralogical and organic treatments,
and reinforcement treatment by adding hydraulicbinders and/or granules. Moreover, thesevalorizations process should deal with the
optimizations problems of deposits sediments574

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

available locally and costs of treatment.
In the diagram below, we present the
global scientiﬁc approach for the development ofdredged sediments in road applications.
The valorisation of dredged sediment is composedon six main steps:
1. Zoning: localization of the potential deposit
to be dredged
2. Dredging: which can be mechanical or
hydraulic
3. Storage: according to the environmental
nature of the sediment
4. Treatment / formulation
5. Laboratory validation
6. In-situ validation

are mainly done by granular additions [6] and also
by additions of hydraulic binder [12, 13]. The
optimization of these treatment methods andformulation is mainly based on the economic,
mechanical and environmental criteria.
2

The objective of our research is to ﬁnd asolution that provides an acceptable material for
valuation in road construction. This solution is in
the form of a mixture of sediment with one ormore noble materials (ex: sand) which is
subjected to a set of physical and chemicaltreatments in order to respond to requirements andnormative constraints. We are interested only by
the chemical part in order to solve a choicetreatments problem.
Before presenting the diﬀerent constraintsof our model, we introduce a set of variables X
associated with each sediment. x i ∈ X is a binary
variable which equal to 1 if the sediment i is usedand equal to 0 else, for i = 1, . . . , n where n is thenumber of sediments. The ﬁrst constraint should
ensure the choice of one and only one sediment.
The mixture of several sediments can activate achemical interaction between elements witch
disturbs strongly the eﬃciency of any
programmed treatment. This constraint can bepresented by:

One of the most important and delicate steps inthe valorisation process is the treatment and theformulation of new materials based on sediments.
It requires a process of optimization and reflection,
often difficult. It also requires different treatments
to improve sediment characteristics respecting
mechanical, environmental and economic
constraints of feasibility. Several treatment
techniques exist such as reducing the pollution
using electrokinetic methods [2]; reducing theorganic matter by bioremediation technical or by
calcinations [11]; reducing the water content byfilter press or natural settling [7].

ZoningDredging𝑛𝑛� 𝑥𝑥𝑖𝑖 = 1

Storage

𝑖𝑖=1

Characterization:• Physico-mechanical parameters,• Environmental parameters: chemical, mineralogical, biological.

• Natural drying• Lagoons• Filters
G t b

Granularadditions• Sand• Natural soil
• Aggregates• Other

Decontamination

Binder additions• Organic
matter• Heavy metals• PAHs• Cement• Lime• Silica fumes• Fly ash

2.1

Chemical constraints

The environmental aspect in theconstruction of roads based on a sub product or
waste is one of the most important constraints
according to the regulation (Guide SETRA 3 ).
Each country requires some limit values forpolluting elements in the raw materials used forconstruction. The acceptability limits to usematerials in road application are mainly
concerning the concentration of contaminants
elements in these materials. For example, theLaboratory Validation: Mechanical, Environmental
Experimental in-situ validation: Mechanical, Environmental

Figure(1)

The set of treatments applied on sediment depends
principally on its pollution and on the field ofvalorisation. In this paper we consider the case ofroad application.

Treatment / Formulation

Dehydration

Mathematical Model

1: Valorization process

Formulation’s methods look to increase
the resistance of the material. These formulations

575

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

limits required in France are given in the nexttable.

Elements
TOC: total organic carbon
BTEX : Benzene, toluene,
ethylbenzene and xylenes
PCB : Polyvinyl biphenyls
HCT : total hydrocarbons
PAH : Polycyclic aromatichydrocarbons
As
Ba
Cd
Cr : total
Cu
Hg
Mo
Ni
Pb
Sb
Se
Zn
FluoridechlorideSulfatefraction soluble

the two previous treatments can reduce theconcentration of Zn until (32% × 10% = 3.2%) of
its initial concentration. We note that in practicerarely are the cases where the sediment requires
multiple treatments.
To model this constraint, we introduce
the binary variables 𝑇𝑇𝑇𝑇 for 𝑡𝑡 = 1, … , |𝑇𝑇| where|𝑇𝑇| is the number of treatments:
𝑇𝑇𝑡𝑡 = �𝑗𝑗

1 for treatment t
0 else𝑞𝑞𝑖𝑖 ∈ [0,1] is a continuous value thatindicates the quantity of the contaminant element
𝑗𝑗 in the used sediment i and 𝑄𝑄𝑗𝑗 presents the limit
associated with the element j. Denote by |𝑄𝑄| the𝑗𝑗number of elements. The parameter 𝛿𝛿𝑡𝑡𝑡𝑡
represents the reduction percentage associatedwith the element j and resulting by the applicationof treatment 𝑡𝑡 on the sediment𝑖𝑖. Environmental
constraints can then be written as follows:

Limit (mg/kl)
60000
6
1
500
50
2
100
1
10
50
0.2
10
10
10
0.7
0.5
50
150
15000
20000
60000

|𝑇𝑇|

𝑗𝑗
𝑗𝑗
𝑞𝑞𝑖𝑖 � �1 − 𝛿𝛿𝑡𝑡𝑡𝑡 𝑇𝑇𝑡𝑡 � ≤ 𝑄𝑄𝑗𝑗 + (1 − 𝑥𝑥𝑖𝑖 )𝑀𝑀
𝑡𝑡=1

𝑖𝑖 = 1, … , 𝑛𝑛, 𝑗𝑗 = 1, … , |𝑄𝑄|

(2)

With 𝑀𝑀 is a large positive constant which helps
to relax the constraint when the sediment 𝑖𝑖 is not|𝑇𝑇|𝑗𝑗used ( 𝑥𝑥𝑖𝑖 = 0 ). The product ∏𝑡𝑡=1 �1 − 𝛿𝛿𝑡𝑡𝑡𝑡 𝑇𝑇𝑡𝑡 �
models the reduction of the percentage of the𝑗𝑗
concentration𝑞𝑞𝑖𝑖 .
Other constraints should be respectedconcerning the incompatibility of using two or
more treatments together.
� 𝑇𝑇𝑡𝑡 ≤ 1

Table 1: Legislative limits for chemicalelements in France

𝑡𝑡∈𝐶𝐶𝑇𝑇𝑘𝑘

These limits can change from a country
to another, but the constraints are always present.
To make an acceptable material (sediment in ourcase) for road construction, it must be cleaned. In
other world, it must be exposed to a set of known
techniques of pre-treatment and treatment to
reduce the concentration of contaminants
elements if necessary. We know that eachtreatment has an impact on all contaminantelements. For example, the electrokinetic
treatment can reduce the concentration percentage
of some heavy metals such as Zn up to 32% [2]and the treatment with remediation can reduce it
further to 10% [3]. Therefore, the application of

𝑘𝑘 = 1, … , |𝐶𝐶𝑇𝑇 |(3)

For the constraint (3), suppose that there are two
possible treatments, the first one is based on theacid chemical treatment and the second uses thebacteria to reduce the organic matter.
The mixing of these two treatments cancels therole of the bacteria treatment. In order toefficiently use every treatment and then reduce thecost we should use at most one of these twotreatments.
2.2

576

Objective function

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

The objective of our mathematical model is
to find an acceptable solution for road application
with a minimum costs. These costs are divided
into two parts. The first one is concerning theacquisition and/or the dredging operations,presented by the vector 𝐶𝐶 1 = (𝑐𝑐11 , … , 𝑐𝑐𝑛𝑛1 ) and thesecond one is the cost of treatments presented by
2) . The objective
the vector 𝐶𝐶 2 = (𝑐𝑐12 , … , 𝑐𝑐|𝑇𝑇|function is presented then by:

number of possible treatments (at most |𝑇𝑇| = 20).
|𝑇𝑇|
requires the
The linearization of Φ𝑖𝑖
introduction of new variable 𝑦𝑦ℎ associated withthe combination of variables 𝐶𝐶ℎ𝑇𝑇 (h is the index
of the combination):

𝑛𝑛

|𝑇𝑇|

𝑖𝑖=1

𝑡𝑡=1

The associated cost with 𝑦𝑦ℎ is
calculated then by 𝑐𝑐ℎ2 = (∑𝑡𝑡∈𝐶𝐶 𝑇𝑇 𝑐𝑐𝑡𝑡2 ) and the� 𝑐𝑐𝑖𝑖1 𝑥𝑥𝑖𝑖 + � 𝑐𝑐𝑡𝑡2 𝑇𝑇𝑡𝑡

𝑀𝑀𝑀𝑀𝑀𝑀

𝑦𝑦ℎ

(4)

𝑗𝑗

𝑀𝑀𝑀𝑀𝑀𝑀

|𝑇𝑇|

𝑖𝑖=1

s. t.

|𝑇𝑇|

𝑛𝑛

𝑡𝑡=1

� 𝑥𝑥𝑖𝑖 = 1
𝑖𝑖=1

(1)

𝑖𝑖 = 1, … , 𝑛𝑛, 𝑗𝑗 = 1, … , |𝑄𝑄|

� 𝑇𝑇𝑡𝑡 ≤ 1

𝑡𝑡∈𝐶𝐶𝑇𝑇𝑘𝑘

2|𝑇𝑇|

(4)

𝑗𝑗
𝑗𝑗
𝑞𝑞𝑖𝑖 � �1 − 𝛿𝛿𝑡𝑡𝑡𝑡 𝑇𝑇𝑡𝑡 � ≤ 𝑄𝑄𝑗𝑗 + (1 − 𝑥𝑥𝑖𝑖 )𝑀𝑀
𝑡𝑡=1

� 𝑦𝑦ℎ

3.1

(5)

In similar cases in the literature, analternative approach based on the column
generation algorithm [1] is adopted i.e. only
promising variables (combinations) will be
generated. This is difficult in our case because of|𝑇𝑇|
the non-linearity of Φ𝑖𝑖 . So to solve our problem,
we will use the opposite approach of the column
generation algorithm. More specifically, we will
generate all the variables (combinations). Notethat the variables 𝑦𝑦 will not be generated at thesame time, but they will be added to the model
step by step during the resolution. In practice, thenumber of treatments for sediment is very limited,which leads us to give priority to the small
combinations of treatment. Then, we will consider
in the beginning a combination of two treatments
and after we generate a combinations of three,four and five treatments if not more. This proposal
linearization for the constraint (2) allows us toeliminate the constraint (3). This constraint will be
respected during the generation of different
combinations. The new linear model is presented
below.

(2)(3)

Heuristic
Linearization of nonlinear constraints (2)Constraints (2) are nonlinear. They posea particular difficult to solve our problem. In order
to simplify our model, it is necessary to linearize
these constraints, i.e. the quadratic expression
|𝑇𝑇|
|𝑇𝑇|
Φ𝑖𝑖 = ∏𝑡𝑡=1 (1 − 𝛿𝛿𝑡𝑡𝑡𝑡 𝑇𝑇𝑡𝑡 ) . So we list all the
combinations of variables 𝑇𝑇𝑡𝑡 . This expensive
process (2|𝑇𝑇| cases) is justified by the limited

≤1

ℎ=1

𝑘𝑘 = 1, … , |𝐶𝐶𝑇𝑇 |

3

𝑗𝑗

ℎ

For example: the combination 𝐶𝐶ℎ𝑇𝑇 = {2,3,4} ofvariables is presented by the new binary variable
with
cost
𝑐𝑐ℎ2 = (𝑐𝑐22 + 𝑐𝑐32 + 𝑐𝑐42 )
and
𝑦𝑦ℎ
𝑗𝑗
𝑗𝑗
𝑗𝑗
coefficients 𝑎𝑎𝑖𝑖ℎ = (1 − 𝛿𝛿2𝑖𝑖 ) × (1 − 𝛿𝛿3𝑖𝑖 ) × (1 −𝑗𝑗𝛿𝛿4𝑖𝑖 ).
To finalize this linearization, we add theconstraint (5) to impose the choice of a single
treatment combination as possible.

The mathematical model for the problemof valorization of sediments is presented by thefollowing Quadratic MIP 0-1 problem. Thismodel is non-linear because of constraints (2).
� 𝑐𝑐𝑖𝑖1 𝑥𝑥𝑖𝑖 + � 𝑐𝑐𝑡𝑡2 𝑇𝑇𝑡𝑡ℎcoefficients of 𝑦𝑦ℎ are 𝑎𝑎𝑖𝑖ℎ = ∏𝑡𝑡∈𝐶𝐶 𝑇𝑇 (1 − 𝛿𝛿𝑡𝑡𝑡𝑡 ) .

2.3 General Presentation of mathematical
model

𝑛𝑛

1 if ∀𝑡𝑡 ∈ 𝐶𝐶ℎ𝑇𝑇 , 𝑇𝑇𝑡𝑡 = 1; ∀𝑡𝑡 ∉ 𝐶𝐶ℎ𝑇𝑇 , 𝑇𝑇𝑡𝑡 = 0
0 else=�

𝑀𝑀𝑀𝑀𝑀𝑀

577

𝑛𝑛

|𝑇𝑇|

𝑖𝑖=1

𝑡𝑡=1

� 𝑐𝑐𝑖𝑖1 𝑥𝑥𝑖𝑖 + � 𝑐𝑐𝑡𝑡2 𝑇𝑇𝑡𝑡

(4)

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

𝑛𝑛� 𝑥𝑥𝑖𝑖 = 1(1)𝑞𝑞𝑖𝑖 � 𝑎𝑎𝑖𝑖ℎ 𝑦𝑦ℎ ≤ 𝑄𝑄𝑗𝑗 + (1 − 𝑥𝑥𝑖𝑖 )𝑀𝑀

(2)

𝑗𝑗

s. t.

𝑖𝑖=1

2|𝑇𝑇|

ℎ=1

2|𝑇𝑇|

𝑗𝑗

𝑖𝑖 = 1, … , 𝑛𝑛, 𝑗𝑗 = 1, … , |𝑄𝑄|

� 𝑦𝑦ℎ ≤ 1ℎ=1

𝑥𝑥 ∈ {0,1}𝑛𝑛 , 𝑦𝑦ℎ ∈ {0,1} ,ℎ = 1, … , 2|𝑇𝑇|

[4]

(5)

[5]

The resolution of this mathematical model can bedone using an approximate method (heuristic for
MIP 0-1 problems) or using an exact algorithms.
The latters can spend a large execution time. To
solve our model, we used a free solver Lpsolve.
Other more efficient commercial solvers can also
be used; for example Cplex. These solvers are
principally based on exact algorithms.
4

[6]

[7]

Conclusions[8]

In this paper, we proposed amathematical model that optimizes the
management and the choice of used treatments fora decontamination of marine and/or river
sediments. This problem has been modelled as anonlinear mathematical model with mixed
variables. Our solving approach is mainly basedon a linearization of this model. This linearization
is based on the enumeration of all non-linear
combinations.
Finally,thesimplified
mathematical model is solved using a numericalsolver. This mathematical model can be widened
to other parameters, in particular, physical andmechanical parameters, in order to use sediments
as alternative materials.

[9]

[10]

[11]

References
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[2]

[3]

George, B, Dantzig and Philip, Wolfe
Decomposition Principle for Linear
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Electrokinetic extraction of heavy metals
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Lors, C., Tiffreau, C., Laboudigue, A.

[12]

[13]

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Effects of bacterial activities on thereleaseofheavy
metalsfromcontaminateddredgedsediments.
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Jones-Lee, A., Lee, G.F. Water Quality
AspectsofDredgedSediment
Management,
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Encyclopedia:
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QualityandResource
Development.
Wiley, Hoboken, NJ:
122-127, 2005.
Chen, H.J., Yang, M.D., Tang, C.W.,
Wang, S.Y.
Producing synthetic
lightweight aggregates from reservoir
sediments. Construction and Building
Materials, 28: 387-394, 2012.
Zentar, R., Dubois, V., Abriak, N
Mechanical behavior and environmental
impacts of a test road built with marine
dredgedsediments.
Resources,
Conservation and Recycling, 52:
947–954, 2008.
Dubois, V., Abriak, N., Zentar, R.,
Ballivy, G The use of marine sediments
as a pavement base material. Waste
Management, 29: 774-782, 2009.
Miraoui, M., Zentar, R., Abriak, N
Road material basis in dredged sedimentand basic oxygen furnace steel slag.
Construction and Building Materials, 30:
309-319, 2012.
Tang, C.W. Chen, H.J., Wang, S.Y.,
Spaulding, J. Production of synthetic
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Troalen, J.P., Duron, C., Hagnere, C.
Traitement de vases de l’estuaire de laSeine.
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FrançaisedeGéotechnique, 77: 31-36, 1996.
Lafhaj, Z., Samara, M., Agostini, F.,
Boucard, L., Skoczylas, F., Depelsenaire,
G Polluted river sediments from theNorth region of France: Treatment with
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bricks.
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Materials, 22: 755-762, 2008.
Tremblay H., Leroueil S., Locat J.
Influence of the nature of organic matteron the soil stabilization with cement.
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Satone, H., Mamiya, T., Harunari, A.,
Mori, T., Tsubaki, T. Solidification
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Particle Settling — Analysis of the Final
State of the Sediment.
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Research Article Advanced Powder
Technology, 19(3): 293-306, 2008.
Levacher,D.,
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Characterization of marine sediments fora reuse inland disposal land embankment.
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Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

AN INVESTIGATION OF POLYURETHANE FOAM WITH A VAPOURPHASE CORROSION INHIBITOR AS CORROSION PROTECTIONSYSTEM
Steve Hecker1 and Vanissorn Vimonsatit2
Department of Civil Engineering, Curtin University, Western Australia

ABSTRACT
Polyurethane (PU) foam is currently being explored by local corrosion consultants as a corrosion protection
method. Vapour phase Corrosion Inhibitors (VpCI) are successfully being used for internal airtight spaces likeelectrical cabinets and PU foam has been successfully used as vehicle for certain additives such as flame
retardants. The purpose of this study is to provide scientific data to develop an initial understanding of thecharacteristics of using VpCI as an additive to PU foam. This research is divided into two parts: Part I
investigates the effects of adding a VpCI additive to PU foam and compares it to natural PU foam, and Part II is
to conduct a life cycle cost analysis of PU foam to compare against existing method of replacement. The findings
indicate that VpCI additive has a dramatic effect on the foams properties. This study enables corrosion experts
and future researchers a foundation to explore other areas to develop this proposed corrosion protection method.
Keywords: Corrosion Protection, Polyutrethane Foam, Corrosion Inhibitor, VpCIadequacy as per the required objectives of the PU
foam for this application and in accordance with
AS1366:1992 [5].
Adhesion of PU foam is used extensively
throughout the construction and transport industries[6]. Researcher [7] and many other PU foam
institutes such as Evonik [8] have researched andtested not only the adhesion of PU foam naturally
but also with adhesion additives. The adhesion
strength of unmodified PU foam was found to be
0.144 N/mm2 to 0.232 N/mm2 depending on
formulation used. However, in addition, it was foundthat adding a primer to the surface of the steel
dramatically increased the adhesion strength by up
to 300%. What effects will another additive such as
polyurethane foam have on the adhesion strength of
PU foam is another un-researched area of interest of
this study.
Vapour Phase Corrosion inhibitors (VpCI) excel
in closed situations, as such are perfect for thisapplication [9]. Past researches [10], [11], [12] thathave looked at the effect of pre-corrosion on VpCI
performance were concluded that whilst it had anegative effect, it could be controlled, and as such it
was shown [13] that even some inhibitors performed
better with pre-corrosion. PU foam can be very
resilient to moisture absorption, this is critical to thesuccess of this study proposed application. Moistureis bad for VpCI effectiveness and is the cause of
corrosion. Literature showed how to model moistureabsorption [14] and it was found that PU foam
samples could absorb 8 to 10% moisture [15].
Water absorption of the PU foam has to meet
AS2498.8 [16].

INTRODUCTION
The nature and extent of any corrosion is linked
to the amount of time and severity that the steel has
been exposed to moisture and air pollution [1].
These environments have been identified andclassified in AS2312:2002 [2], which cover theguide to the protection of steel against atmosphericcorrosion for Australia and New Zealand. These
classifications aid in assisting designers in selecting
an appropriate coating system as a hostileenvironment such as acidic environments (Category
F in clause of 2.3 AS2312:2002) will require a muchhigher standard of protection than one in a placid
environment (Category A in clause 2.3 of
AS2312:2002).
Internal corrosion of steel hollow section ispreventable if the section is sealed [3]. However
evidence shows corrosion still occurred overtime
due to fatigue cracks.
Polyurethane (PU) foam consists of its two main
ingredients, isocyanate and polyol. However many
additives have been used in a variety of applications.
These additives that may be required can be used tomodify reaction conditions, change the finish of the
final product or control the foam. These additives
include, but are not limited to catalysts, chain
extenders, cross linkers, fillers, moisture scavengers,
colorants and others [4].
Different additives are well tried and tested in
PU foam however they can potentially have varying
effects on the desired performance. Using corrosion
inhibitors in PU foam as an additive in principal is
not new however data is required to assess its

580

GEOMATE- Osaka, Nov. 16-18, 2015

The use of higher compressive strength type PU
foams
are
highlyrecommended,
whilstunderstanding the effects of temperature on thecompressive strength is critical [17]. The effect oftemperature on PU foam can result in change involume and mass and subsequent density [18].
In Part I of this paper the objective is toinvestigate the quality of PU foam with a VpCI
additive to existing steel hollow sections, inaccordance with AS1366.1:1992 [19] andAS2498.0:1993 [20], through analysing the physicalappearance (i.e. cell density, cracks, splits, voids)
and water absorption of PU foam with and without a
VpCI additive. Part II presents a life cycle cost
analysis of PU foam. Life-Cycle Cost in accordancewith AS4536:1999 [21] which outlines a detailed
estimation into the cost of this proposed corrosion
protection method was conducted. This wasachieved using engineering economic models suchas Net Present Values (NPV) and Annualized Net
Present Value (ANPV) to determine direct costs ofthe cash flow. Values have been defined in order tocompare against current and future corrosion
protection systems to the internals of hollow sections.

VpCI additive
Natural PU foam
線形 (VpCI additive)
線形 (Natural PU foam)

80

Density (kg/m3)

60
40
20
0

0

0.001

0.002

0.003

0.004

Volume (m3)

Fig. 1

Density of PU foam with and without VpCI

Physical Appearance
The physical appearance of the polyurethane
foam specimens with and without a vapour phasecorrosion inhibitor has been conducted inaccordance with AS1366.1:1992 guidelines.
CracksPU FOAM WITH VPCI ADDITIVE
The quality of PU foam with and without VpCI
additive as corrosion protection of steel hollow
section is tested in accordance with AS1366.1:1992
and AS2498.0:1993. Steel sections used in the testare 100x100x2.0mm SHS, 40x40x2.0mm SHS,
88.9x3.0mm CHS, and 42.4x2.0mm CHS Section.
Each section has been orientated at both 90ᵒ and 45ᵒ
to simulate differing positions typically seen on
conveyor systems.

Cracks were less prominent with natural
polyurethane foam with an average rating of low tovery low (0.88). With Corrosion inhibitor the cracks
were more present with an appearance rating of 1.06
(20.5% difference). However with the additive,
crack score was still varying from low to very low
and was not of significant interest.

Density

Internal voids between the two types of PU foampresented in Fig. 2 show a 19% difference (1.56
VpCI to 1.31 Natural) in score values with range
being for both of low to medium. Internal voids are
not detrimental to the success of the VpCI additive
as internal voids are sealed off to the atmosphereresulting in no moisture penetration andsubsequently no corrosion.

Internal Voids

The density of PU foam depends on section sizeand subsequent volume. The density of natural PU
foam varies from 24.5 kg/m3 to 53.2 kg/m3; thedensity of PU foam with the VpCI additive varies
from 24.8 kg/m3 to 60.1 kg/m3. The overall results
are shown in Fig. 1. The scatter of the test data wasdue to inconsistency during the application of thefoam therefore the test on the same section was
conducted twic for accuracy. The linear trends havebeen added to show an average of the densities for
sections for both types of foam from all the tests.
VpCI additive represented by linear blue line shows
an increase in foam density over natural PU foam.
The average density of natural PU foam is 34.1
kg/m3 and of VpCI additive is 42.3 kg/m3, giving
an increase in the density of 24%. Physicalobservation confirms the notion that the vapour
phase corrosion inhibitor has repressed thepolyurethane’s expansion chemical reaction andcreated a more dense and heavier polyurethane foam.

External Voids
External voids are critical to the efficiency of a
VpCI additive. If the foam cannot be fully adhered
to the steel and leaves external voids then corrosion
inhibitor will not easily create a protective barrier
against moisture corrosion. In addition, external
voids will easily allow the “pooling” of water andmoisture penetration further exacerbating thecorrosion problem. Natural PU foam scored anaverage of 1.43 varying from low to medium with
the VpCI additive actually scoring lower with 1.23
(difference of 16.5%). Again both scores were from581

GEOMATE- Osaka, Nov. 16-18, 2015

low to medium and as such no significantly
difference is present. The extra denseness can
explain the slight decrease in external voids as more
space is being inhabited by foam.
Elongated voids
Elongated voids were present in both types ofpolyurethane foam. It was apparent that whenever
there were voids in the VpCI specimens that they
were elongated style voids. This was also further
reinforced with a 45% more elongated cells with anaverage score of 1.18 scoring low to medium
compared to natural foam which had an average of
0.81 (very low to low). Again however elongatedvoids were only present with internally voids, and as
a result, elongated voids are not detrimental to thesuccess of the VpCI additive as internal voids aresealed off to the atmosphere resulting in no moisturepenetration and subsequently no corrosion.
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0

Fig. 2Natural PU Internal Voids

Pu foam with VpCI Colour

Natural PU foam Colour

Fig. 3

Voids and Colours

Table 1 Water absorption

CracksInternal Voids
External Voids
Elongated Voids

VpCI

VpCI Internal Voids

NaturalPhysical appearances index

ColourItems

Surfacearea
m2

InitialWeightg

Weight
96hr
g

VpCI
1
VpCI
2
PU 1

0.05

41.0

45.0

0.04

28.0

31.0

0.04

23.0

26.0

PU 2

0.05

27.0

31.0

Water
Absorbed%[g/mm2]
9.76[79.26]
10.71[69.48]13.04[70.11]
14.81[83.62]

From these values, it is apparent that with theaddition of a VpCI additive the PU foam samples
prevented the absorption of water by 3.70%. As
described previously, an objective of the PU foam inthis study is to prevent water moisture to theinternals of hollow sections, these results are
beneficial to this study. When considering water
absorbed per unit of surface area (g/m2), specimens
with the VpCI additive averaged a water
absorbed/unit of surface area (g/m2) of 74.37 g/m2
whilst the natural PU foam specimens averaged
76.86 g/m2. These results show the natural PU foam
specimens absorbed slightly more water per unit ofsurface area (3.35% more water absorption), which
does not present an abnormal or major differingresult.

The colour although not critical to thisapplication, however when considering further uses
in insulation and construction the colour has an off
white colour compared to the natural yellowish
colour known to polyurethane foam. Figure 3 shows
the difference in colour; colour is something thatshould be agreed upon by both client and supplierand as such does not have a required standard toadhere to.
Water Absorption
Table 1 presents the water absorbed as a percent(%) and water absorbed per unit of surface area(g/mm2) as per ASTM D 2842 [22]. Test on eachmaterial was conducted twice.
Water absorbed in PU specimens 1 and 2 with a
VpCI additive are 9.76% and 10.71% water moisture,
respectively, i.e., an average of 10.23%. In Natural
PU specimens 1 and 2 with no additive, water
absorbed are 13.04% and 14.81% water moisturerespectively, i.e., an average of 13.92%.

LIFE CYCLE COST
Maintenance to steel structures includes all
activities throughout the design life of the structurethat are performed to mitigate corrosion, to repair
corrosion-induced damage, and to reinstate thestructure, which has become unusable as a
582

GEOMATE- Osaka, Nov. 16-18, 2015

consequence of corrosion. These activities cometogether to form Corrosion Management. Whilst
corrosion management and its activities differ
depending on the type of structure and environment
it is a recognised and practiced way of planning andfacilitating for corrosion [23], [24].
When dealing with corrosion on existing
structures or for the design of new structures someform of corrosion management and corrosion design
is required. These ideas are used to forecast costs tocorrosion and determine the best corrosion practicefor the specific steel structure.
In the present study, the following 8 costelements, in no particular order (cf. Section 4.3 (a)of AS4536:1999), are considered.
1. Polyurethane Material Supply: The cost andsupply of PU foam for site use.
2. Vapour Phase Corrosion Inhibitor Powder
Supply: The cost and supply of VpCI for site use.
3. Surface preparation and external paintcoatings: Abrasive blast cleaning, Protective primer
and paint coating system to externals of the sections
being maintained.
4. Metalwork to Members: This task includes
the welding, cutting, grinding and any additional
work to repair extremely degraded members to raise
the health to a level of quality required for PU foam
injection.
5. Preparation of mixing the PU foam and
VpCI: On-site mixing and preparation required preinstallation.
6. Installation of PU foam with Injection
Machine: Injection of Polyurethane foam into thesteel hollow sections. This task mainly includes the
man and machine labour required to fulfil task.
Machine injection is considered the most efficientand practical way to install polyurethane foam inlarge quantities.
7. Setting Period: This task includes creamtime, Gel time and Tack-free setting time.
8. Cleaning: Spillage, off-cutting required andgeneral cleaning required for external paint andcoating systems.
The cost element assumptions and exclusions areaccording to Section 4.3(b) of AS4536:1999. The
following points identify cost elements that will notvary between comparing alternatives, therefore will
be excluded.
• Any structure consisting of hollow sections
could be used, however in this study, thestructure considered in the cost analysis is atruss system as typically seen on processing
plant conveyor systems.
• The size and number of members do not varyfrom method to method and therefore any
sections’ and subsequent area properties andlengths can be used to estimate absolute cost.
• Working at heights or other conditions that
require extra costs have been excluded; thesecosts would need to be added on if required.
This can be done as no matter the protection
system it will be same condition• The protective coating system VpCI has a lifeof 2 years as per Cortec recommendations for
VpCI 609/609S used for this study• Using Boddington gold mine as an example theexpected mine life is approximately 20 years[25]. Mines life vary from mine to mine and as
it is a constant variable regardless of thecorrosion protection method, 20 years will be
used.
• All prices exclude Goods and Services Tax.
• Overhead costs such as administration work,
Occupational Health & safety and design work
such as drawings have been excluded.
The obtained cost in this study is from using
500m length of 110 circular hollow sections todetermine a $/m of the protection system. All costs
are in Australian dollars.
Direct Cost of PU Foam
Direct cost includes material and construction
cost. This section estimates the direct cost of using
PU foam as the corrosion protection material. The
unit costs of PU and VpCI are from local suppliersin Western Australia; other cost items such as thesurface preparation and external paint coatings,
metalwork, materials preparation and installation,
setting and cleaning, are taken from SPON:2012[26].
Material
1. PU foam ($/per m3): $465.00/m3
2. VpCI ($/per m3): $48.91/m3
Construction
1. Surface preparation and external paintcoatings: $50.03/m2
2. Metalwork to Members: $25.60/m
3. Preparation of mixing the PU foam and
VpCI: $0.66/m3
4. Injection machine hire costs: $20.00/m3
5. Setting Period: $13.80/m3
6. Cleaning: $39.90/m3
Net Present Values of this corrosion method cash
flow has considered a 1 metre length of 114.3 x 4.50
CHS as discussed in section 5.1.3 of this report.
Using the area units, a cost per metre of
114.3x4.5CHS is $35.56/m.
Using 114.3 x 4.5 CHS, with the internal volume
per metre of 0.00871 m3/m, the total cost of thisprotection method is $35.56/m.
583

GEOMATE- Osaka, Nov. 16-18, 2015

and life of protection method as the two methods
have different protection periods, annualized Value(AV) was estimated at $14,096.00.

Direct Cost of Replacement
Without application of corrosion protection tosteel members, it might be inevitable to replace thecorroded steel with a new member. The cost of
replacement is estimated in this section.

Uncertainties associated with the cost estimation
are beyond the control of the estimation. The
effectiveness of the Vapour phase inhibitor toprevent degradation is uncertain and may affect the
lifetime of product but is neglected in this study.
Weather constraints placed on construction may
affect the time and subsequently the cost of theconstruction.

Material
For a new member, fabrication and supply costs
obtained from a supplier’s quotes are used todetermine an accurate estimate (in year 2014) ofsteel sections; cost per tonne is $2145/tonne.

CONCLUSION
Construction
The findings of this study show that vapour
phase corrosion inhibitors have a high potential ofbeing a successful corrosion protection method inindustry for steel hollow sections. This study
presents, for corrosion consultants, the effects of the
VpCI on polyurethanes behaviour and properties
with no stand out concerning results which is apromising outcome. These results enable corrosion
experts and future researchers a foundation to
explore other areas, which are recommended asfollows:(1) The effectiveness of the VpCI to prevent further
corrosion when using a polyurethane based foam
as a vehicle requires further research, not only tothe protection of bare steel, but also to slightly
corroded steel.
(2) Vapour phase corrosion inhibiters typically havea design life of 6 to 24 months depending on thetype as recommended by inhibitor suppliers.
Further research needs to be conducted to ensurethat this design life can be achieved as it directly
effects the annualized value of this method.
(3) If maintenance or re-installation of the foam isrequired, a methodology with guidelines should
be investigated.
(4) Due to constraints on this research, only part ofthe properties required to comply with AS
1366.1:1992 (Rigid cellular plastics sheets forthermal insulation, Part 1: rigid cellularpolyurethane) have been investigated andsubsequently tested. It is recommended that all
properties required by AS1366:1992 to be tested
in accordance with AS2498.0:1993 should betested.
(5) Currently, no formal standard exists for theapplication of polyurethane foam to enclosed
spaces. As a result this study referred toAustralian Standards for polyurethane insulation.
It is recommended further research with the helpof this study should be conducted to develop a
set of guidelines to ensure this application meets
installation quality as regarded by industry
experts and research.

1. Surface preparation and external paintcoatings: $50.03/m2 Abrasive blast clean,Primer and Paint coating materials andLabour.
2. Metalwork to remove Members: $10.25m.
3. Metal work to install new members:$25.63/m
For the net Present Values of this replacement
method, the cost is considered per a 1 metre lengthof 114.3 x 4.50 CHS as to keep it consistent with
PU/VpCI corrosion protection method. Using theabove steel areas, a cost per metre is: $61.91/m.
Net Present Values and Annualised Values
Net present value (NPV) is used to determine if a
proposed project is of cost benefit to thestakeholders. It uses a cash flow model based onwhat future monies are worth today. The annualised
value (AV) is the economic figure which will be
used to fairly and accurately compare PU with VpCI
method against replacement of corroded members.
Subsequently NPV and annualised value were
estimated for the replacement option and were used
to compare the results.
PU Method
With the guidelines and parameters considered, acost of coating was estimated as $39.94/m of
110x4.5 CHS section. Subsequently Present Day
Value (PDV) of this method was calculated at$154,179.00. When considering lifetime of structure
and life of protection method as the two methods
have different protection periods, annualised Value(AV) was estimated at $12,372.00
Replacement Method
A cost of coating was established of $39.94/m of
110x4.5 CHS section. Subsequently Present Day
Value (PDV) of this method was calculated at$175,675.00. When considering lifetime of structure584

GEOMATE- Osaka, Nov. 16-18, 2015

Corrosion Conference and Exposition: Water andWaste Water Industries, Denver, CO, 24-29 Mar
1996, NACE International, Houston, TX, 1996.
[11] Gulbrandsen E, Nesic S, Stangeland A, Burcbardt T,
Sundfaer B, Hesjevik SM, Skjerve S, Effect ofPrecorrosion on the Performance of Inhibitors for
CO 2 Corrosion of Carbon Steel, Corrosion 98,
Norway, NACE International, 1998, Paper 13.
[12] Paolinelli LD, Perez T, Simison SN, The Effect of
Pre-Corrosion and Steel Microstructure on Inhibitor
Performance in Co2 Corrosion." Corrosion Science,
50 (9), 2008, pp. 2456-2464.
[13] Dougherty JA, Stegman, DW, “The Effects of Flowon Corrosion Inhibitor Performance”, CORROSION
95, paper no. 113, (Houston, TX: NACE
International, 1995) and Materials Performance 35,4(1996): p. 47.
[14] Hearle JWS, Polymers and Their Properties, Vol. 1:
Fundamental of Structure and Mechanics, Ellis
Horwood Ltd., 1982, 437p.
[15] Saha MC, Mohan S, Hickman D, Balakrishnan A,
Moisture Absorption Behavior and Its Effect on
Flexure Properties of Polyurethane Foams, inProceedings of the XIth International Congress andExposition June 2-5, 2008 Orlando, Florida USA,
Society for Experimental Mechanics Inc.
[16] AS2498.8[17] Batallas M, Yih H, Singh P, Determining the
Performance of Polyurethane Foam Pipe Insulationfor High Temperature Service, NACE International,
Northern Area Western Conference Calgary Alberta,
Feb 6-9, 2006.
[18] Zarr RR, Nguyen T, "Effects of Humidity andElevated Temperature on the Density and Thermal
Conductivity of a Rigid Polyisocyanurate Foam."
Journal of cellular plastics 30: 9, 1994, pp. 422-430.
[19] AS1366.1:1992 Rigid cellular plastics sheets forthermal insulation Part 1: Rigid cellular polyurethane(RC/PUR), Standards Australia.
[20] AS2498.0:1993 Methods of testing rigid cellular
plastics - General introduction and list of methods,
Standards Australia.
[21] AS4536:1999, As/Nzs 4536 - Life Cycle Costing-an
Application Guide. Standards Australia International,
1999.
[22] ASTM D 2842 [21].
[23] Balvanyos T, Lave L, “Appendix B - Economic
Analysis Methods." Carnegie Mellon University,
Pittsburgh,
Pennsylvania,
2004, //www.corrosionda.com/pdf/economicanalysis.
pdf.
[24] Kogler R, Corrosion Protection of Steel Bridges.
Pittsburgh, PA, FHWA-IF-12-052, Vol. 19, 2012,21pp.
[25] Mining Technology, Boddington Gold Mine (BGM),Western Australia, available in http://www.miningtechnology.com/projects/boddington/, 2014[26] SPON, Price Books Update, Langdon D, editor,
Taylor & Francis, 2012.

(6) Investigations on the structural effects ofpolyurethane expandable foam with and without
a VpCI additive on steel hollow sections should
be conducted to include the structural dynamicchanges (effects) induced by structure as a resultof filling the steel hollow sections withexpandable polyurethane foam.
If future investigations include, but not limited to,these recommendations are achieved, constraints
such as financial ones can be eased off allowing
polyurethane foam and VpCI to be applied as acorrosion protection method. More specifically inregards to practicality, this method is highly
desirable to process plant operators as no downtime
to structure is necessary when retro-fitting the foamincreasing effectiveness of resources which can beused in a more sustainable manner.
REFERENCES[1] Mandeno WL, Sarraf RI, Protective Coatings for
Steel Bridges - a Guide for Bridge and MaintenanceEngineers, NZ Transport Agency, Wellington, 2014,53pp.
[2] AS2312:2002 As/Nzs 2312: Guide to the Protectionof Structural Steel against Atsmopheric Corrosion by
the Use of Protective Coatings. Standards Australia.
[3] Tournay M, Internal Resistance to Corrosion in Steel
Hollow Sections, France, CIDECT Final Report 10B78/3, 1978.
[4] Sharmin E, Zafar F, Polyurethane: An Introduction,
INTECH,
New
Delhi,
India.
http://dx.doi.org/10.5772/51663, 2012, pp. 3-16.
[5] AS1366:1992 Rigid cellular plastics sheets forthermal insulation Part 3: Rigid cellularpolystyrene—Moulded (RC/PS—M), Standards
Australia.
[6] Siversten
K,"PolymerFoams",
2007, //ocw.mit.edu/courses/materials-science-andengineering/3-063-polymer-physics-spring2007/assignments/polymer_foams.pdf[7] Schiller C, Glos M, Gahrens S, Eilbracht C,
Aesthetics and Adhesion Behavior of Pu Foam BasedComposites with Rigid Facer Materials. Germany,
available on line at//www.pu-additives.com/product/puadditives/en/about/downloads/technical-paperspresentations/Documents/Aesthetics_and_Adhesion_
Behavior_of_PU_Foam_based_Composites_with_rig
id_Facer_Materials.pdf.
[8] Evonik, Creating high performance polyurethane
foams,
http://www.pu-additives.com/product/puadditives/en/Pages/default.aspx[9] Prenosil M, Volatile Corrosion Inhibitor Coatings,
Cortec Corp, Supplement to Materials Performance,
January 2001, pp. 14-17.
[10] Kowata K and Takahashi K, Interaction of Corrosion
Inhibitors with Corroded Steel Surface, Annual585

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

USE OF DESIGN OF EXPERIMENT IN SEISMIC VULNERABILITYASSESMENT FOR EXISTING RC BUILDINGS BY JAPANESEMETHOD
Naveel Islam1, Kshama S. Roy2 and Kamrul Islam3
Faculty of Engineering and Applied Science, Memorial University of Newfoundland, Canada
3
Department of Civil Engineering, Military Institute of Science and Technology (MIST), Bangladesh
1,2

ABSTRACTThe paper provides an overview on the Japanese Method of seismic vulnerability assessment where Designof Experiment (DOE) methodology was applied to investigate the most significant parameters that affect theseismic performance indices of an existing reinforced concrete (RC) building. The assessment emphasized on aregion of recently constructed but densely populated RC buildings having uniformity in building height andsurface area. Minimum Run Characteristic V Factorial Design was used to study the controlling factors whichwere largely dependent on the structural dimensions of a building. The results show that, lengths of the wall in X
and Y directions, length of wall effective to the story concerned, total floor area supported by the story, total
cross sectional area of columns in the story, time index and grade index are the most influencing parameters forJapanese method. A linear regression equation including the significant factors is developed by Design Expert
software version 9.0.3.1 for computing the seismic indices. The results are also compared with the seismic
indices obtained by the Japanese Method in the surveyed area.
Keywords: Japanese Method, Seismic Vulnerability, DOE, Reinforced Concrete building, Factorial Designmodern reinforced concrete buildings [2]. In regions
where earthquake occur in intervals measured in
centuries, there is a need for a simple evaluation
method that focuses on selection of buildings withhigh vulnerability rather than those with a highprobability of survival [3]. Over the last decade,significant efforts have been devoted in devisingreliable estimates incorporating the various
uncertainties and randomness involved in seismic
demand and capacity. Thus assessment of potential
damage should be carried out based on statisticaland probabilistic techniques [4].
The “Standard for Seismic Evaluation and
Guideline for seismic retrofit of existing RC
buildings” developed by the Japan Building Disaster
Prevention Association is popularly known as theJapanese Method of Seismic Vulnerability
Assessment. The underlined target of the standard
was to assist other earthquakeprone countries thoseface similar problems (e.g. earthquake disasterprevention efforts) like Japan. [5]. Japanese method
evaluates seismic performance indices for eachbuilding comparing seismic demand index of thelocalized region. As the evaluation process involves
a number of empirical equations and standardized
tables for determining the indices, finding the mostsignificant factors that affect the seismic
performance index is a major challenge. The present
study aims to describe a novel statistical approach

INTRODUCTION
The rush of urbanization has produced some of
the most dangerous built environments: multi-story
buildings, over-reliance on concrete and a loss ofknowledge that protected previous generations. The
pressure to meet the needs of growing populations
leads to improperly implemented building
regulations. Around three-quarters of all deaths inearthquakes are due to building collapse(www.theguardian.com). Low-cost and informalbuildings are most likely to fail, meaning thatearthquakes disproportionately affect the developing
countries. In recent years, damage in buildings withlow seismic capacities has occurred in a number ofearthquake prone countries like Nepal (2015), Japan(2011), New Zealand (2010 & 2011), Mexico(2011), Indonesia (2010 & 2011), China (2010),
Haiti (2010). Most building codes in the world
explicitly or implicitly accept structural damage to
be occurred in a building during strong earthquakes
as long as hazard to life is prevented [1]. But dealing
with these structural damages, under the effect ofnatural hazards like earthquake to predict thepotential damage before its occurrence is still
considered as a major scientific challenge. Almost
all of the developing countries have underlined theneed for seismic evaluation of existing buildings due
to massive damage caused by recent earthquakes to586

GEOMATE- Osaka, Nov. 16-18, 2015

Umemura [6] and by Japan Building Disaster
Prevention Association in 1990 [5].
A detailed spreadsheet using Microsoft Excel
software program has been developed for calculating
the seismic index considering all the tables andempirical equations. Authors were successful insimilar analysis for other seismic vulnerability
assessment methods and details of the analyses canbe found at Roy et al. [7]-[8] and Ahmed et al. [9]using Design of Experiment (DOE) for investigating
the significant factors in Japanese Method. The
paper also focuses on developing a linear regression
equation for determining the seismic indices from
DOE method and compares the results with actual
empirical equations.
JAPANESE METHODThe Japanese seismic performance evaluation isbased on both site inspection and structural
calculation to represent the seismic performance ofexisting medium and low–rise reinforced concretebuildings in terms of seismic performance index ofstructure, Is. Three levels of screening procedures,
namely the first, the second, and the third levelscreening, have been prepared for the seismic
evaluation. Any level of the screening procedures
may be used in accordance with the purpose ofevaluation and the structural characteristics of thebuilding [5]. Building inspections are conducted tocheck the structural characteristics of the building
which are necessary to calculate the seismic index ofstructure, Is. Appropriate methods for inspection are
selected in accordance with the screening level, suchas site inspection, collection of design drawings, andmaterial test. In case, design drawings of thebuilding are not available, inspections on the
structural dimensions, diameters and arrangementsof reinforcing bars need to be examined on site,which are necessary for seismic evaluation of thebuilding in accordance with the screening level [5].
The seismic performance index of structure (Is)is evaluated by the following equation at each story
and to each direction,

DESIGN OF EXPERIMENT METHODOLOGY

Seismic safety of structure is judged by thefollowing Equation,

Design of Experiment (DOE) methodology [10][12] is used for finding the most significant factorsthat affect the seismic index of the structure in theJapanese Method. DOE provides a quick and costeffective method to understand and optimize
products and processes [13].
DOE lets
experimenters develop a mathematical model thatpredicts how input variables interact to create output
variables or responses in a process or system [14].
DOE method was used previously for other seismic
vulnerability assessment methods i.e. Turkishmethod [8]. The present study is based on a survey(detailed site inspection) conducted in DOHS
Mirpur area of Dhaka, the capital city of
Bangladesh. The particular location was chosenbecause of its uniformity in the height and surface
area of the buildings. Most of the buildings wereseven (7) storied and from the seismic evaluation, it
was found that, the top story has the lowest seismicindex. For the aforementioned reasons, theexperiment is based on the data that was collectedfrom the top story of each building. Factorial design
is conducted in this experiment by Design Expert
9.0.3.1[15] to find the most significant factors that
affect the Japanese method of vulnerability
assessment. A linear regression equation is obtained
from the design of experiment methodology and theregression equation is validated using a typicalsurvey data of a building different from the initiallyselected buildings. Similar analyses for other seismic
vulnerability assessment methods can be found inauthors’ previous studies [7]-[8].

Is ≥ Iso

Factors and Responses

Is = E0 * SD* T(1)

Where,
E0 = Basic seismic index of structure,
SD = Irregularity index and T = Time index(2)

Where, Is = Seismic index of structure and
Iso = Seismic demand index of structure

Based on the empirical equations and tables, nine(9) parameters are identified as the controllingparameters and are used as factors in the experiment.
The factors and its levels are shown in Table 1. The
levels of the first seven factors (A, B, C, D, E, F andG) are based on the structural dimension of thebuilding. The levels of the last two factors (H and I)
are set based on the tables given in the standard forseismic evaluation. The seismic demand index along
the two directions, namely IsX and IsY are set as the
responses of the experiment.

If Eq. (2) is satisfied, the building is assessed as‘Safe - the building possess the seismic capacity
required against the expected earthquake motions’.
Otherwise, the building is assessed as ‘Uncertain’ inseismic safety. The thorough procedure can be foundin the Standard for seismic evaluation and Guideline
for Seismic Retrofit of existing reinforced concretebuilding revised from the general outline given by587

GEOMATE- Osaka, Nov. 16-18, 2015

Experimental Design and Procedure

RESULTSA regular two level factorial design with nine(9) factors would require 29 = 512 runs for acomplete replicate. To minimize the runs, a
Minimum Run Characteristic V Design which isa Resolution V design where the two factors
interactions (2FI) are aliased with three factor(3FI) and single factor is aliased with four
factors interaction was used. One (1) center point
was considered for checking the existence of
curvature in the model. The final design includes
47 runs. The low, center and high coded values
for the factors are shown in Table 1.

The responses obtained for the 47 runs wereanalyzed by Design Expert 9.0.3.1 [15]. The termswith the significant effects were determined using
half normal plot. It is found that for IsX, the factors
A, C, F, G, H and I are significant while for IsY, thefactors B, C, F, G, H and I are the significant asshown in Fig. 1.
The analysis of variance (ANOVA) was
conducted with the significant terms found from
Figs. 1&2. Table 2 & 3 show the ANOVA tables for
IsX and IsY respectively. The analyses show that the
responses need a power transformation after
checking the assumption of ANOVA, the R2 andadjusted R2. The model with natural log

Table 1 Factors and their levels
Factors

Name

H

Length of wall in the X
Direction
Length of wall in the Y
Direction
Length of wall effective
to the story concerned
Thickness of peripheral
wall
Thickness of interior
wall
Total floor area
supported by the story
Total cross sectional areaof columns in the story
Time Index

I

Grade Index

A
B
C
D
E
F
G

Symbols

Units

Low
ActualCenterActualHigh
ActualLow
Coded

Center
Coded

High
Coded

lw1 (X)

mm

5000

52500

100000-1

0+1

lw1 (Y)

mm

6000

78000

150000

-1

0

+1

lw3

mm

20000

110000200000

-1

0

+1

tp

mm

127

190.5

254

-1

0

+1

ti

mm

127

190.5

254

-1

0

+1

ΣAf

mm2

100

400

700

-1

0

+1

1.00

3.05

6.00

-1

0

+1

6

T

*10
mm²
_

G

_

Ac

0.70

0.90

1.00

-1

0

+1

0.80

0.90

1.00

-1

0

+1

(b)

(a)

Fig. 1

Half Normal Plots for (a) IsX and (b) IsY

transformation gave the higher R2. The results given
in Tables 2 & 3 are obtained after the natural log
transformation. Tables 2 & 3 also show that, there iscurvature effect on both the responses but it is notmuch significant compared to the other significant
factors. MS (treatment) is also larger than MS(residuals) which may have resulted in larger F
values. As the pvalues are << 0.05 for the factors in
Tables 2 & 3, all the selected effects are highly

significant. The values of R2 for both of the
responses (0.9035 for IsX and 0.9065 for IsY) arealmost near to 1 which means the model is fairly
good enough and about more than 90% of variabilityof data can be explained by the model.
Statistical Testing
The following four assumptions must be satisfied
588

GEOMATE- Osaka, Nov. 16-18, 2015

3. Departures from group mean are normally
distributed for all groups.
4. All groups have equal variance.

for ANOVA:
1. All samples are random samples from theirrespective populations.
2. All samples are independent of one another.

Table 2: ANOVA Table for Seismic Index, IsX
ANOVA for selected factorial model : IsX
Analysis of variance table [Partial sum of squares - Type III]
SourceModel
A-lw1X
C-lw3
F-ΣAf
G-Ac
H-T
I-G
ResidualCor. Total
Std. Dev.

Sum ofSquares
72.28
10.87
5.05
38.51
6.01
2.99
14.13
7.72
80.00

df

Mean Square

F Valuepvalue, Prob > F

6
1
1
1
1
1
1
40
46
0.44

12.05
10.87
5.05
38.51
6.01
2.99
14.13
0.19

62.41
56.32
26.16
199.50
31.15
15.48
73.19

< 0.0001< 0.0001< 0.0001< 0.0001< 0.0001
0.0003< 0.0001

Significant

RSquared

0.9035Mean

0.40

Adj RSquared

0.8890C.V. %

108.70Pred RSquared

0.8643

PRESS

10.85

Adeq Precision

34.807

Table 3: ANOVA Table for Seismic Index, IsY
ANOVA for selected factorial model : IsY
Analysis of variance table [Partial sum of squares - Type III]
SourceSum of Squares

df

Mean Square

F Valuepvalue, Prob > F

Model
B-lw1Y
C-lw3
F-ΣAf
G-Ac
H-T
I-G
ResidualCor. Total
Std. Dev.

89.57
18.06
6.79
41.30
7.73
2.28
13.93
9.23
98.80

6
1
1
1
1
1
1
40
46
0.48

14.93
18.06
6.79
41.30
7.73
2.28
13.93
0.23

64.66
78.21
29.39
178.91
33.48
9.90
60.34

< 0.0001< 0.0001< 0.0001< 0.0001< 0.0001
0.0031< 0.0001

Mean
C.V. %PRESS

RSquared
Adj RSquared
Pred RSquared
Adeq Precision

0.49
98.16
12.85

Significant

0.90650.89250.870032.001

Checking Model Adequacy
45˚ line, which also satisfies the assumption of
ANOVA.

Variation for Normality and Constant Variance
The normal plot of residuals in Fig. 2 shows that,most of the points follow the normal distribution line
for both of the responses and thus satisfy the first
assumption of ANOVA: residuals are normally
distributed. The data points in Fig. 3 are wellscattered which concludes that the data points have aconstant variance.

ANOVA RESULTS

Variation for Randomness and Goodness of Fit

ln (IsX) = -6.28284 +1.02832E-05* lw1X
+3.72996E-06* lw3 -3.13609E-03* ƩAf +1.48625E07* Ac +1.71844* T +5.56512* G(3)All the assumptions of ANOVA are satisfied and themodel is used for further analysis to determine thepredicted model in the form of equations.
The predicted equations from the model in terms
actual factors are as follows:

Most of the data in the Residual vs. Run plots (Fig4) are randomly scattered which indicates that thesequence of runs is random. In the Predicted vs.
Actual plots (Fig. 5), most of the points lie on the589

GEOMATE- Osaka, Nov. 16-18, 2015

ln (IsY) = -6.21238+8.74274E-06* lw1Y+4.32366E06*lw3-3.24721E-03* ƩAf+1.68552E-07*Ac+1.50194*T+5.52780*G(4)

VALIDATION OF PREDICTED MODEL
The validity of the proposed equations in termsof actual factors for predicting seismic index is doneby detailed inspection on random buildings(different from the 47 building data) on the same
area. Equations (3)-(4) are used for determining thepredicted responses. Comparisons between thepredicted and actual responses are presented in

Equations (3)-(4) in terms of actual factors will
be used to make predictions of the Seismic Index of
the structure based on the significant factors in theequations.

(a)

(b)

Fig. 2

Normal Plot of Residuals for (a) IsX and (b) IsY

(a)

(b)

Fig. 3

Residuals vs. Predicted for (a) IsX and (b) IsY

(a)

(b)

Fig. 4

Residual vs. Run for (a) IsX and (b) IsY

(a)

(b)

Fig. 5Predicted vs. Actual for (a) IsX and (b) IsY

590

GEOMATE- Osaka, Nov. 16-18, 2015[3] Sozen, M. A., and Hassan, A. F. 1997, “Seismic
Vulnerability Assessment of Low-Rise Buildings inRegions with Infrequent Earthquakes”, ACI
Structural Journal, 94 (1), 31 -39.
[4] Ozcebe, G., Yucemen, M. S., Aydogan, V., andYakut, A. 2004. “Statistical Seismic Vulnerability
Assessment of Existing Reinforced Concrete
Buildings in Turkey on a regional scale”, Journal of
Earthquake Eng., 8(5): 749-773[5] Standard for Seismic Evaluation and Guidelines
for Seismic Retrofit of Existing Reinforced
Concrete Buildings, 2001 (English Edition),
Japan Building Disaster Prevention Association.
[6] Umemura,H., 1980 “A Guideline To Evaluate
Seismic Performance of Existing Medium and
Low Rise RC Buildings And Its Application”,
Proc. of The 7th World Conf. on Earthquake
Eng., Istanbul, Turkey, 4: 505-512[7] Roy, K. S., Islam, K. and Arafat, M. S. 2010.
Seismic Risk Assessment of Existing Low-rise
Buildings in Unplanned Urban Regions of Dhaka
City, Proceedings of the 2nd International
Conference on Construction in Developing
Countries (ICCIDC-2), Cairo, Egypt.
[8] Roy, K. S., Islam, K. and Arafat, M. Shams.
2011. A Novel Statistical Approach for
Investigating the Significant Factors that
Influence the Performance Score of the Turkish
Method,ProceedingsofInternational
Conference on Geotechnique, Construction
Materials and Environment (GEOMAT-2011),
Tsu City, Mie, Japan.
[9] Ahmed, M.Z., Islam, K., Roy, K. S., Arafat, M.
S. and Al-Hussaini, T.M. 2010. Seismic
Vulnerability Assessment of RCF Buildings in
Dhaka City, Proceedings of the 3rdInternational Earthquake Symposium (IESB-3),
Dhaka, Bangladesh.
[10] Montgomery, D.C. 2013. Design and analysisof experiments 8th ed., John Wiley & Sons
Incorporated, New York, NY, USA[11] Box, G.P., Hunter, J., Hunter W. 2005 Statistics
for experimenters design innovation anddiscovery. 2nd ed. Wiley and Sons
[12] Lye, L.
2014. ENGI 9516: Similitude,
Modeling and Data Analysis –Course Note,
Memorial University of Newfoundland, Canada[13] Antony, J. 2002. Training For Design ofExperiment Using a Catapult. Journal of Quality
and Reliability Eng. International, 18: 29-35[14] Lye, L. M., 2003. Some Applications of
Statistical Design of Experiment Methodology
in Civil Engineering. Annual Conference of theCanadian Society for Civil Engineering,
Moncton, Nouveau-Brunswick, Canada[15] Stat. Ease, 2014 Design Expert 9.0.3.1 Designof Experiment software developed by Stat-Ease,
Inc.

Table 5. The results for both of the responses, IsX
and IsY for the predicted model are in good
agreement with the actual model. The predictedequations from the model are found to give almost
similar results as found by the actual Japanesemethod.
Table 5 Comparison of Predicted & Actual Responses
No. of Buildings
Surveyed
1
2
3
4
5
6
7
8
9
10
11
12

Building
Number
165
188
310
450A
519
545-547548
581-582652-654
698
781
1047

Predicted Response
IsX
IsY
1.43
1.18
1.15
1.10
2.48
2.06
1.37
1.08
1.77
1.63
1.66
1.70
1.98
1.86
0.88
1.16
1.37
1.04
1.38
1.32
0.96
0.83
1.11
1.04

Actual Response
IsX
IsY
1.25
0.86
1.46
1.89
2.25
2.13
1.95
1.55
1.26
1.66
1.12
1.49
1.45
1.85
0.70
1.39
1.57
1.24
1.35
1.69
0.86
0.79
1.16
1.27

CONCLUSIONSThe present study is conducted to find the significant
factors that affect the Japanese method and thereby
forming linear regression models including only thesignificant factors in determining the seismic
performance indices of Japanese method. The
experiment was started with nine (9) factors relatingwith each other with a group of empirical equations
and tables (Japanese method) and finally ended up
with six (6) significant factors for each responses
along with two (2) prediction equations (DOE)
which can predict identical responses to that of theactual responses. The paper also verifies theadequacy of the regression equations by several
diagnostic plots which are in good agreement withthe model equations. The validation runs of theexperiment show that the anticipated model has highaccuracy with mere variation on the results. Further
validation runs can be conducted by making detailed
inspection from a number of buildings to foresee thetrue validity of the predicted equations.
ACKNOWLEDGEMENT
The authors are very grateful to Dr. Leonard Lye
who is a pioneer in using the Design of Experiment
method [14].
REFERENCES[1] Otani, S. 2000. “Seismic vulnerability Assessment
Methods for Buildings in Japan”, Earthquake
Engineering and Eng. Seismology, 2 (2): 47-56[2] Nanda, R.P and Majhi, D. R. 2013. “Review on
Rapid Seismic Vulnerability Assessment for Bulk of
Buildings”, Journal of Inst. of Engineers (India),
94(3): 187-197

591

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

THE ROLE OF POLYPROPYLENE FIBERS IN IMPROVING HIGHLY
PLASTIC CLAY INTEGRITY
1

Muawia A Dafalla 1, Arif Ali Baig Moghal1 and A. Obaid1
Department of Civil Engineering, College of Engineering, King Saud University, Riyadh.

ABSTRACTThe geotextile industry research and development is growing at a fast rate and new products of variable
specifications are now available for earthworks and design engineers. The polypropylene fiber material is nonbiodegradable which can stand aggressive chemical exposure when placed along with soil media. This researchis conducted on a polypropylene fiber reinforced soil treated with chemical admixture in order to study theinterlocking mechanism and to study the extent of improvement on their tensile behaviour. Locally available
plastic clay was selected for this investigation. The extent of improvement by the polypropylene fibres inclusionon the tensile behaviour was evaluated by carrying out a series of brazilian tensile strength tests. The dominantmechanism responsible for the increase in tensile strength properties has been proposed. The effect of various
geotechnical parameters like grain size distribution, density and moisture content on the tensile strength
improvement has been discussed. The study shows that the addition of polypropylene fibers alone to highly
plastic clay will not suffice the tensile strength requirements. The polypropylene fibers when added with lime of
6% can improve the tensile strength by further by 13 to 28% depending on the dose or fiber content.
Keywords: Polypropylene fiber, Clay, Expansive soil, Bonding mechanism

expected to hold strong for extended periods.
Adding polypropylene fibers is thought to be ofadvantage due to uniformity and better control.
Works of reference [7] provided evidence that clays
reinforced with fibers can have reduced crack sizes
and reduced crack depths but it did show that the
number of surficial crack is increased. The
polypropylene fibers produced as an additive toconcrete is too smooth to be bonded to clay particles.
Adding extra cementing agent like lime or cement isbelieved to overcome this problem.

INTRODUCTION
The clay or intact soil material remains intact
when a sufficient cohesive or attraction force isholding the grains together. The material isdisintegrated when this force is not sufficient to holdthe grains particularly when the material is subjected
to pull, bending or even temperature gradient. This
study is performed to measure the tensile strength ofpolypropylene fiber reinforced highly plastic clay
treated or untreated with lime additive.
The idea of computing
tensile test strength
indirectly goes back to 1943 as quoted by reference[1]. Tensile strength of compacted clays wasinvestigated in the literature since the seventies of
the twentieth century. Reference [2] presented amethod for measuring tensile strength of soils. This
test became popular and many versions wereintroduced to measure the tensile strength indirectly.
[2] and [3]. The procedure is standardized by the
ASTM (ASTM Standard Test Method D3967-81)[4] and suggested by the International Society forRock Mechanics in 1981[5]. It is either carried out
as direct measurement of tension or indirectly by
calculating bending stresses. This study considered
flattened Brazilian test similar to the approach ofreference [6]. Introduction of fibers to soil mixturesand clay is an ancient technique used to enhance theadhesion and erosion resistance properties. Natural
fibers or organic straw material derived from driedplants or grasses can provide more tensile strength
by bonding clay lumps or clay particles withadditional link. The natural organic material is not

MATERIAL AND METHODSClay material from Al-Qatif town located in the
Eastern province of Saudi Arabia was used in thisstudy. Al Qatif clay as classified in accordance withASTM D2487 is highly plastic clay (CH). This clay
is known of its high expansion and shrinkage
properties. Several research studies were performed
to characterise and evaluate this type of clay [8]-[10].
The standard proctor maximum dry density ismeasured at 11.5 to 12 kN/m3[11]. The optimum
moisture content is reported as 30 to 32%. Al Qatif
clay indicated liquid limit of
130 to 150 withplasticity index is in the order of 60 to 70 [12],[13].
The lime material used in this investigation is ananalytical grade Calcium Hydroxide, supplied byWinlab Chemicals, UK.
The polypropylene material used in this study is
12mm in length produced by a United Kingdom
manufacturer (Propex concrete systems). A finemonofilament with a melting point of 162 º C (324 º
592GEOMATE- Osaka, Nov. 16-18, 2015

the high dose of 1200 gm/m3. This can beunderstood due to the fact that fibers can be of nonstraight stretch and not cemented to the clay particle.
On adding lime the tensile strength jumped to morethan double. The addition of fibers with lime
improved the tensile strength by 13 to 28%. This
study suggests that the use of fibers without acementing agent is of little or no benefit to highly
plastic clay and clays in general. Observations onmodes of failures indicate a ductile nature which isfacilitated by the fibers. Failure plains started close
to the edges before the major splitting failure plain isdeveloped at the central zone of the sample. The
load application angle determines the location of
crack initiation [15]. Edge cracks are sometimes
referred to as wing cracks in the literature andclaimed that they normally stop when the central
cracks are initiated. The bond between fibers and theclay is dependent on the geometry of clay particles,
grain size distribution, void ratio and state ofcompaction. The influence of these parameters canbe judged theoretically based on contact areas
between fibers and clay particles. The moisture
content can also have a role as it is related to thelevel of compaction.

F) and ignition point of 593º C (1100º F). The
material is of low electrical and thermal conductivity.
The specific gravity is in the order of 0.91. The
guidelines as specified in ASTM D 3967 were
followed. Each and every sample was carefully
prepared in a mould of inner dimension of 50 mm
and length of 100mm. In order to have a uniform
density static compaction was applied using two
piston rods; at top and at the bottom. The samples
prepared at dry densities of 12 kN/m3 were kept for
7 days curing at room temperature. The testing
frame used is a Wykhamm Farrance system shownin Figure 7. Samples were loaded through a spacer
applying load through a flat surface. The machine isequipped with a calibrated proving ring anddisplacement dial gauge. Failure tensile strength T is
calculated using the following formula:T = 2P/ π DL
Where
P = applied load
D = diameter of the specimen
L = length of the specimen(1)

3 EXPERIMENTAL PROGRAM
The investigation program included two sets ofsample prepared in cylindrical moulds in order toassess the influence of fibers in reinforcing the clay.
One set was Al-Qatif clay reinforced with fibers
using three different dosage ; 600 gm, 900 gm and1200 gm in addition to plain clay. The other set was
similar to the first set but included addition of 6%
lime. The tensile strength of clay is extremely low
and hardly taken into account in practice. It is
widely variable and dependant on the structure, stateof packing and soil suction [14]. The material was
proportioned and prepared as a homogeneous mix
and then statically compacted in a cylindrical mouldof 5 cm diameter and 10 cm length. The two sets ofsamples were left to cure for 7 days period. Eachmix was prepared in two samples and the results
were reported as an average value. In order to view
the profile of stress strain relationship of fiber
reinforced clay the tensile strength was recorded atfixed intervals. The strain rate used was 0.3 mm/min.

Table 1 Splitting tensile strength (kPa) for fiber
reinforced clay
Fiber amount
gm/m3
0
600
900
1200

0%lime
54.43
54.06
53.37
56.59

6% Lime133.43150.46165.87171.16

4 RESULTS AND DISCUSSIONSThe test results indicated that adding fibers without acementing agent is unlikely to add any tensile
strength to the clay material. The tensile strength
reported for clays without reinforcement with fibers
was 54.4 kPa. This value did not increase when
adding 600 gm/m3 fiber dose and even went lower
for 900 gm/m3. A very slight increase is shown with

Fig 1. Tensile strength of fiber reinforced and nonfiber reinforced clays.
Table 2.
593

Splitting tensile strength profile for fiber

GEOMATE- Osaka, Nov. 16-18, 2015

reinforced clay with 900 gm per cubic meter.

Fig. 4 Main central splitting area starts showing.

Fig. 5 Splitting progress retarded by fibers
Strain rate = 0.3 mm/min. Deformation Dial: 1 unit =0.01 mm; Proving Ring. Load Dial: 1 unit = 0.737 Kg
0%Lime and 900g/m3 Fiber

Spilitting tensile
strength (kPa)

60
40
20
0

0

1

2

3

4

Fig. 6 Failure state reached.

Displacement (mm)Fig. 2

Profile of splitting tensile strength of fiber
reinforced clay.

Fig. 3 Failure planes starting at the edges of thesample.

Fig. 7 Equipment used594

GEOMATE- Osaka, Nov. 16-18, 2015

unsaturated soils UNSAT95 edited by . Alonsoand P. Delage, eds, Balkema, Rotterdam: 51-56.
[4] ASTM D3967 - 08. Standard Test Method forSplitting Tensile Strength of Intact Rock Core
Specimens.Book of Standards Volume: 04.08[5] International Society for Rock Mechanics(1981).Suggested
Methods"Rock
Characterisation, Testing and Monitoring"
Editor E.T. Brown, Pergamon Press 1981, pages
119 to 121.
[6] Agaiby, Shehab Sherif Wissa (2013).Fracture
characterization of clays and clay-like materials
using flattened Brazilian Test. Massachusetts
InstituteofTechnology.
http://hdl.handle.net/1721.1/85819.
MasterThesis.
[7] Freilich, B.J.,Kuhn,J.A.and Jorge, G. Zornberg( 2008 ). Desiccation of Fiber-Reinforced
Highly Plastic Clays. The First Pan American
Geosynthetics Conference & Exhibition,
Cancun, Mexico[8] El-Junaid, M., Shamrani, M. A., Puppala, A. J.,& Dafalla, M. A. (2011). Evaluation ofchemical stabilization of a highly expansive
clayey soil. Transportation Research Record:
Journal of the Transportation Research
Board, 2204(1), 148-157.
[9] Elkady T. Y., Dafalla, M. A., Al-Mahbashi A.
M. and Al-Shamrani M. A. (2013) “Evaluationof Soil Water Characteristic Curves of SandClay Mixtures.” International Journal of
GEOMATE, June, 2013, Vol. 4, No. 2 (Sl. No.
8), pp. 528-532. Geotec., Const. Mat. & Env. ,ISSN: 2186-2982(P), 2186-2990(O), Japan.
[10] Dafalla, M. A., Elkady, T. Y., Al-Shamrani, M.
A. and Shaker, A. A. (2013). “An Approach to
Rank Local Clays for Use in Sand Clay Liners”,
Third International Conference on GeotechnicalEngineering, Tunisia Feb 2013.
[11] ASTM D 698 (2000), “Standard Test Methods
for Laboratory Compaction Characteristics ofSoil Using Standard Effort (12 400 ft-lbf/ft3(600 kN-m/m3))”, Vol. 4.08. D-18 Committee
on Soils and Rocks. West Conshohocken, PA.
[12] ASTM, D 4318-00 (2003), Standard test
methods for liquid limit, plastic limit, andplasticity index of soils. Annual Book of ASTM
standards, 582-595.
[13] ASTM D 422 (2002) Standard Test Method forParticle-Size Analysis of Soils, Vol. 4.08.D-18
Committee on Soils and Rocks. WestConshohocken, PA.
[14] Rainer M. Zeh and Karl Josef Witt (2007). The
tensile strength of compacted clays as affectedby suction and soil structure. Experimental
Unsaturated
Soil
Mechanics
SpringerFig 8. View of failed samples

CONCLUSION
The study shows that the addition ofpolypropylene fibers alone to highly plastic clay will
not suffice the tensile strength requirements. The
bond between the fibers and clay cannot beestablished without considering a cementing agent.
Lime addition of 6% to Al-Qatif clay indicatedimprovement in the tension capacity of the clay bymore than 100%. The polypropylene fibers when
added with lime of 6% can improve the tensile
strength by further by 13 to 28% depending on thedose or fiber content. The fiber addition is also
found to give the clay a ductile state where large
deformation can be reported beyond the elastic orsemi-elastic zone. Failure was found typical to
Brazilian tests of various materials. It is noted thatcracking starts at the rim of the sample until a major
splitting failure plain is developed at the central zone.
ACKNOWLEDGEMENTS
This project was funded by the National Plan for
Science, Technology and Innovation (MAARIFAH),
King Abdulaziz City for Science and Technology,
Kingdom of Saudi Arabia, Award Number(11BUI1489-02).

REFERENCES[1] Li, Diyuan, and Louis Ngai Yuen Wong(2013).
"The Brazilian Disc Test for Rock Mechanics
Applications: Review and New Insights." RockMechanics and Rock Engineering 46 (2013):
269-87.
[2] Fang, H. Y. and Fernandez, J. (1981).
Determination of tensile strength of soils byunconfined penetration test, ASTM STP 740, pp.
130-144.
[3] Favaretti, M. (1996). Tensile strength ofcompacted clays. State of the art in Unsaturated
Soils.
First international conference on

595

GEOMATE- Osaka, Nov. 16-18, 2015

Proceedings in Physics Volume 112, 2007, pp
219-226 .
[15] Wang QZ, Xing L (1999) Determination of
fracture toughness Kjc by using the flattened
Brazilian disk specimen for rocks. Eng Fract
Mech 64:193-201

596

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

PARAMETRIC STUDY OF REINFORCED CONCRETE SLABS UNDERBLAST LOADS
1

Mujtaba M. N. Shuaib1, Osama M. A. Daoud2
Faculty of Engineering, University of Khartoum, Sudan; 2Building and Roads Research Institute,
University of Khartoum Sudan

ABSTRACTThis paper includes an investigation for the deformations, including deflections and damage modes, which occur
in reinforced concrete (RC) slabs when subjected to blast loads of explosions. The slab considered was subjected
to close-in detonations of three different charge weights for a constant standoff distance. For the study, the slab
was analysed using the numerical method by means of nonlinear finite element analysis. The slab was modelled
as 3-D structural continuum using LS-DYNA software. For concrete modelling, two constitutive models wereselected, namely the KCC and Winfrith concrete models. Blast loads were applied to the slab through the
Lagrangian approach, and the blast command available in the software, namely LOAD_BLAST_ENHANCED,
was selected for the application. Also, slab was analysed using the single-degree-of-freedom method in UFC 03340-02 manual. The deflections and damage modes results obtained were compared to those from a previously
published experiment. In addition, a parametric study was made which investigate changing reinforcement ratio
and explosive standoff distance.
Keywords: blast, reinforced concrete, slab, finite element, deformation.
The effect of blast loads on reinforced concrete
structures had been investigated in many works andstudies. Deformations which occur in a structuralmember due to blast can be assessed by applying
either the experimental or the analytical approach.
The later approach includes computationaldifficulties normally encountered with material
nonlinearities in dynamic analysis. Several empiricalsimplified methods were introduced, and the mostwidely used one is the single-degree-of-freedom(SDOF). This method is known to be simple,
inexpensive and reliable tool to evaluate thedynamic structural response [2].

INTRODUCTION
Nowadays, the protection against the effects of
blast loads has become an essential need. Explosions
can be seriously harmful towards humans as well as
structures. Currently, blast applications are not
restricted to military use only; the destructive use of
blast has become a main technique that used in
terrorist attacks against civilians. In other hand, blast
application has spread to include the beneficial use
as in some industry applications. In this field, many
blast accidents have occurred during handling andstoring of explosive materials. In general, explosion
accidents may cause a serious impact on societies
and economies. This has led to develop studies andmethods that evaluate blast effects on structures
which will be beneficial for their mitigation.

Finite element (FE) method of analysis is anadvanced method which is commonly applied
recently to solve complex structural analysis
problems including blast problems. Many works
such as those carried out by Hao and Zhongxian [3],Wang et al [4], Zhao and Chen [5], Tai et al [6] andothers have proved that by using the FE method ofanalysis, the local deformation occurs in concretedue to blast loads can be successfully andsatisfactory assessed. The blast on structures in FE
method can be applied and simulated using several
approaches such as the Lagrangian and MultiMaterial Arbitrary Lagrangian Eulerian (MM-ALE)
approaches [7].

Reinforced concrete (RC) is one of principal
material used for blast mitigation because it
encompasses good characteristics in blast resistance.
Among the RC structural elements, slabs are usedwidely for both structural and non-structural
purposes. Blast wave generated in an explosion
imposes a short duration dynamic loads on
structures. In general, materials including concreteand steel behave differently under dynamic loads.
They experience an increase in strength under
rapidly applied loads [1]. RC elements possess
different general behaviours depending on theintensity and standoff distance of the explosive.

This paper provides comparative and parametricstudies for slabs under blast loads. First, deformation
results for one-way RC slab subjected to blast in
previously conducted experiment, done by Wang et597

GEOMATE- Osaka, Nov. 16-18, 2015

al [4], were compared to those obtained by analysis.
Methods used for this purpose were FE and SDOF
methods. Afterwards, a parametric study was made.
This investigated the effect of changing
reinforcement ratio and standoff distance of charge
on slab deflection. Model description and analysis
results are addressed in the following sections.

FINITE ELMENT MODELINGThe software used for modelling was LS-DYNA
software, and the program library was used for theselection of element types and constitutive models
for concrete and steel. The simulation of blast
loading was carried out using the blast commandavailable in the software. Slab was assumed to be
fixed at the two supporting sides, although it waspartially fixed in the experiment. A schematic finite
element detail for RC slab is shown in figure 3.

MODEL DESCRIPTION
The model was similar to the slab in Ref. [4],one-way RC slab supported at two opposite sides
and subjected to close-in detonation of TNT
explosives. The charge weights considered for thedetonations were 0.2, 0.31 and 0.46kg, and theirconfiguration is as shown in Figure 1. The RC used
for slabs in the experiment had the properties of 39.5
MPa compressive strength (fc), 4.2 MPa tensile
strength (ft'), and 28.3 GPa Young's modulus (Ec).
The slab dimensions were 1000 mm x 1000 mm x
40 mm, and it was reinforced at its both directions
by 6 mm steel bar reinforcement mesh with 75 mm
spacing and 20 mm concrete cover from slab top.
These are as shown in figure 2.

Fig. 1

Fig. 3

Schematic finite element detail for RC slab

Slab Modeling and Meshing
The concrete of the RC slab was modelled as asolid box of dimensions 1000mm x 1000mm x
40mm which was composed of 8-node hexahedronsolid elements with constant stress solid element
formulation. The size of each element is 5mm x
5mm x 5mm which results in a number of 8elements through the slab thickness. In the model,
the total number of solid elements is 320,000
consisting of 363,639 nodes.
The steel reinforcement mesh was modelled as
one layer of 6 mm diameter reinforcement bars andspaced at 75 mm. The type of element assigned tobars was Hughes-Liu beam element with mesh sizeof 5 mm, knowing that either truss or beam element
can be used to model reinforcement bars. The total
number of beam elements is 6,000 consisting of
5,835 nodes.

Configuration of the explosive charge.

Material Constitutive Models
For concrete modelling, two constitutive models
were selected. These models are namely Karagozian& Case Concrete (KCC) (MAT072R3) and theWinfrith concrete (MAT084) models. Both KCC
and the Winfrith concrete models are invariant
isotropic plasticity models, and they are suitable forblast problems, as they can capture the keys ofconcrete behaviours such as strain rate enhancement
effect; however, the former proved to have better
prediction for the localized shear. Both models takerelatively simple input [8][9].
The KCC Concrete Model
Fig. 2

RC slab geometry anddetails.

The KCC (MAT072R3), is one of the material
models that commonly used for concrete modelling.
The main advantage offered by the KCC model thatit allows the automatic generation of all thereinforcement598

GEOMATE- Osaka, Nov. 16-18, 2015

parameters by inputting only the unconfinedcompressive strength and the density of concrete [8].
The KCC model has three independent strengthsurfaces which are generated based on parameters
calibrated from test data. The failure surface isinterpolated between maximum strength surface andeither the yield strength surface or residual strengthsurface.

data which is a collection of conventional weapons
effects calculation from the equations and curves of
TM 5-855-1 manual (similar to UFC 03-340-02[11]) [12] [13]. To estimate the pressure time
history, ConWep uses the Friedlander curve
represented by the following equation:(1)

For modelling, the default model parameter
generation feature was used. This required thespecification of the unconfined compression strengthand density of concrete. For 39.5MPa concrete, theinputs in the model cards were as follows: concretedensity (R0) 2400 kg/m3, negative of the unconfinedcompressive strength (A0) -39.5 MPa, conversion
factors for length factors for length (inches-tometers) and pressure (psi-to-MPa) (RSIZE & UCF)
39.72 and 145 for respectively. The remaining
parameters were generated and determined
automatically by the software.

This equation represents an idealization for
actual pressure time history. Curve shape resulting
from applying this equation and its parameters are asshown in figure 4.

The Winfrith Concrete Model
Fig. 4

The Winfrith concrete model (MAT084) is asmeared crack model implemented in the 8-node
single integration point continuum element. The
shear surface in the Winfrith model is generated
based upon four-parameter model developed by
Ottosen. Similar to MAT072R3, it also allows theautomatic generation of all the parameters by
inputting certain parameters [9]. Those imported tothe software were as follows: mass density ofconcrete (R0) 2400 kg/m3, initial tangent modulus
of concrete (TM) 28.3 GPa, Possion’s ratio (PR)0.29, uniaxial compressive strength (UCS) 39.5 MPa,
uniaxial tensile strength (UTS) 4.2 MPa andaggregate size (ASIZE) 16 mm (assumed). The
remaining parameters were generated anddetermined automatically by the software.

Idealized pressure-time variation

SINGLE-DEGREE-OF-FREEDOMMODELING
To calculate deflection, RC slab was also
analysed using the single-degree-of-freedom(SDOF) simplified method. Deflection results
obtained using this method are known to beconservative compared to actual deflections. The
procedure in UFC 03-340-02 manual was used forthis purpose. This method is similar to the one
developed by Biggs [14] in which the principles of
SDOF system (shown in figure 5(a)) were applied.
Since the analysed slab is one-way, the failure
pattern anticipated and SDOF developed areassumed to be at the middle of slab as shown infigure 5(b).

Steel Constitutive Model
The steel material properties in the slab
reinforcement used in the experiment were as
follows: 600 MPa yield stress (fy), 200 GPa Young's
modulus (Es) and 0.29 Poisson’s ratio (υ). MAT003,
which suited to model isotropic and kinematic
hardening, was selected for steel modelling, and thepreviously mentioned variable values were used as
input.

(a)(b)

Fig. 5 (a) SDOF system, (b) Failure pattern in
one-way slab

Blast Modeling

For simplification, factors required forcomputing slab deflection (including elastic stiffness
Ke, maximum resistance Ru, natural period Tn) were
calculated by applying formulas for simply
supported one-way structural elements. Furthermore,
dynamic strength properties were considered for

For blast simulation, the Lagrangian approach
was used for applying the blast loads on the RC slab
by means of LOAD_BLAST_ENHANCED (LBE)
keyword provided by LS-DYNA. The air blastpressure is computed empirically with ConWep [10]599GEOMATE- Osaka, Nov. 16-18, 2015

concrete and steel. This was through multiplying
strengths by the dynamic increase factors (DIF),
which are 1.25 and 1.23 for concrete and reinforcing
steel bar respectively for the case of close-indetonations.
For simplification, the average overpressure wasassumed to be uniformly distributed in order tocalculate peak load Po. In UFC, the maximum
deflection Xm, can be obtained from curves having
td/tn and Ru/Po values. When these values were
calculated for the case investigated, small values
were obtained for these ratios which were out ofcurves range. Therefore, the following equation in
Ref [1] was used to estimate Xm
2
1  I * 2 * f   1 d  X m / X e   0
2 
Ru




Fig. 7 Pressure contours for applied reflectedpressure on slab top, for pressure for WTNT= 0.31 kg
and R = 0.4 m (Z = 0.59m/kg1/3)
Deflection Results
The FE analysis indicated the pattern ofdeflection for the one-way square slab under blast
loading through time. Before RC slab reached theone-way slab failure pattern, the square slab went
through different stages before reaching the one-way
failure pattern. Both the KCC and Winfrith Concretematerial models exhibited a similar behaviour.
Graphical presentations for the slab deflection
contours at different time instants for 0.2 kg TNT
charge weight for the KCC material model is shownin figure 8, and cross section of the slab at maximum
deflection for KCC and Winfrith concrete models
are shown in figure 9(a) and (b) respectively for
WTNT = 031 kg.

(2)

were µd is ductility demand, Xe maximum elastic
deflection, Io loading impulse and f frequency.
RESULTS AND DISCUSSIONSBlast Results
The general blast wave characteristics provided
by LBE were verified by observing the pressuretime histories which proved to follow theFriedlander curve as shown in figure 6. The scaled
distance Z for the 0.2, 0.31 and 0.46 kg TNT charge
weights and 0.4 m standoff distance was 0.68, 0.59
and 0.52 m/kg1/3 respectively. Since Z values areless than 0.71 m/kg1/3, the detonation type for all
charges is close-in [11]. The pressure contours on
slab for WTNT=0.2 kg at different time steps areshown in figure 7.

(a) 0.4 msec(b) 1.0 msec(c) 3.0 msec(d) 10.0 msec

Fig. 8 Contours of Z-displacement in the RC slab
model using the KCC Concrete for WTNT= 0.20 kg at
different time instants

35,000Pr, 0.46 kg
Pr, 0.31 kg
Pr, 0.2 kg
Pso, 0.46 kg
Pso, 0.31 kg
Pso, 0.2 kg

30,000Pressure (kPa)

25,00020,000(a) The KCC Concrete material model

15,00010,000
5,000
0
0.00

0.10

0.20

0.30

0.40

(b) The Winfrith Concrete material modelTime (msec)

Fig. 9 Cross sections at the middle of slab model
along supports direction for maximum deflection.
WTNT= 0.31 kg

Fig. 6 Pressure-time histories of reflected andincident pressures(a) 0.16 msec(b) 0.20 msec

Deflections were measured at the slab surface
mid-point, and the displacement-time histories atthis point for slabs with different concrete models
are shown in figure 10. In general, the maximum
deflection, shown in this figure was noticed to be
increased when the TNT charge weight was(c) 0.40 msec

600

GEOMATE- Osaka, Nov. 16-18, 2015

increased. Besides the FE method, slab was also
analysed by means of SDOF method. The maximum
slab deflection was calculated for different charge
weights.
0.68 kg/m1/3(MAT072R3)
0.59 kg/m1/3(MAT072R3)
0.52 kg/m1/3(MAT072R3)
0.68 kg/m1/3(MAT084)
0.59 kg/m1/3(MAT084)
0.52 kg/m1/3(MAT084)
0.68 kg/m1/3(Exp.)
0.59 kg/m1/3(Exp.)
0.52 kg/m1/3(Exp.)

5.00

Displacement (mm)whenever the material is actively yielding. The slab
damage modes at maximum deflection for thedifferent scaled distances obtained from LS-DYNA
models were compared to the actual modes in theexperiment. The damage modes for 0.2, 0.31, 0.46
kg TNT charge weight and 0.4 m standoff distancefor the KCC and Winfrith concrete material models
are shown in figure 12. The damage modes can beeffectively estimated by using the KCC modelcompared to Winfrith. In the actual slab, spallation
occurred at top of slab which caused from nonuniform with high intensity pressure of the close-indetonation [11]. This could not be effectively
assessed in KCC model because the LBE command
applies blast loading using the Lagrangian approachin which the interaction between the ambient air andsolid slabs is not considered. ALE approachconsiders this interaction.

-5.00-15.00-25.00-35.00-45.000.00 2.50 5.00 7.50 10.00 12.50 15.00 17.50 20.00
Time (msec)

Fig. 10 Displacement-time histories at middle ofslab for the KCC (MAT072R3), the Winfrith
concrete models (Exp. Max: maximum displacementin the experiment)
Comparing results obtained from KCC and theWinfrith models with those form the experiment (asshown in Table 1, it was noticed that both materials
estimated the maximum slab deflection effectively,
and the former appeared to estimate it more
effectively. In other hand, the SDOF appeared to beconservative as it overestimated the maximum slab
deflection by almost twice the actual one. The
variation in deflection can be observed in figure 11.
Slab
No.

WTNT
(kg)

R
(m)

1
2
3

0.2
0.31
0.46

0.40
0.40
0.40

(a) Top of Slab(b) Bottom of SlabMaximum Deflection (mm)
Z
FEM
1/3
(m/kg ) Experiment
KCC
Winfrith SDOF
(MAT072R3) (MAT084)
0.68
10.00
10.36
7.41
20.94
0.59
15.00
26.92
11.87
36.09
0.52
35.00
38.84
18.99
62.27

Fig. 12 Damage mode of the slab for 0.31 kg, and R= 0.4 m (Z = 0.59 m/kg1/3). KCC (left), theWinfrith (middle) the experiment [4] (right).
PARAMETRIC STUDY

Table 1 Summary of slab maximum deflection forcharge weights

Two parameters were chosen to investigate theireffect on slab deflection namely the reinforcement
bar spacing (or ratio) and standoff distance. The
effect of these two parameters was investigated andby means of FE analysis using the KCC and
Winfrith concrete models.

Maximum slab central deflection
(mm)

70
60

Experiment50

KCC(MAT072R3)

40
30

Winfrith(MAT084)

20

SDOF

Effect of Reinforcement Ratio (Bar Spacing)
The effect of reinforcement spacing was studied
by means of FE analysis, in which the LS-DYNA
concrete models namely the KCC and Winfrithmodels were used. The reinforcement usedpreviously was 6 mm bar mesh spaced 75mm (1.3%
reinforcement ratio). For investigation, additional
three reinforcement bar spacing were chosen: 50mm,
100mm and 125mm responding to reinforcement
ratios of 1.95, 0.975 and 078% respectively. These
were investigated for the 0.2, 0.31 and 0.46 TNT
charge weight and 0.4m standoff distance. The
variations in maximum displacement for different

10
0
0.2

0.31

0.46

TNT charge weight (kg)Fig. 11 Variation
deflectioninmaximumslab

centralDamage Modes Results
FE analysis using LS-DYNA, damage modes
were captured by observing the effective plasticstrain contours of the material which grows
601

GEOMATE- Osaka, Nov. 16-18, 2015

charge weights and bar spacing for the KCC and
Winfrith concrete models are shown in figure 13 and
14 respectively. Generally, it was observed that
increasing the bar spacing has its effect as the
maximum displacement was increased by an amountthat varies depending on the charge weight and thebar spacing.

Pressure (kPa)

16000
14000

Pr (0.40m)

12000

Pr (0.75m)

10000

Pr (1.00m)

8000

Pso (0.40m)

6000

Pso (0.75m)
Pso (1.00m)

4000
2000
0

40.00
35.00

0.2kg

30.00

0.25

0.5

0.75

1
1.25
Time (msec)

1.5

1.75

2

Fig. 15 Pressure-time histories of reflected andincident pressures for WTNT = 0.2kg at different
standoff distances

45.00

1.8

25.00

0.31kg20.00

1.6

Scaled Distance (m/kg1/3)

Maximum Central Displacement
(mm)

0
50.00

15.00

0.46kg

10.00
5.00
0.00
50 mm
(1.95%)

75 mm(1.30%)

100 mm(0.975%)125 mm
(0.780%)Bar spacing(reinforcement ratio)

1.4
1.2

0.2 kg10.31 kg

0.8

0.46 kg

0.6

0.4
0.2

Fig. 13 Variation of slab maximum central
displacement for different reinforcement ratios using
the KCC concrete model (MAT072R3).

00.40 m0.75 m1.00 mStandoff DistanceFig. 16 Variation of scaled off distance for different
TNT charge weights and standoff distances
30000

20.00

Reflected pressure (kPa)Maximum Central Displacement
(mm)

25.00

15.00
0.2kg
10.00

0.31kg0.46kg

5.00
0.00
50 mm
(1.95%)

75 mm(1.30%)

100 mm(0.975%)

25000
20000
0.2 kg15000

0.31 kg
10000
0.46 kg
5000

125 mm
(0.780%)00.40 mBar spacing(reinforcement ratio)

0.75 m1.00 mStandoff DistanceFig. 17 Variation of reflected pressure for different
TNT charge weights and standoff distancesFig. 14 Variation of slab maximum central
displacement for different reinforcement ratios using
the Winfrith concrete model (MAT084)

The displacement time histories for the KCC
concrete slab model were obtained for 0.40, 0.75 and1.00 standoff distances. For 0.2 kg TNT charge
weight, displacement time history is as shown infigure 18).

Effect of Standoff Distance
The effect of standoff distance was investigatedby studying its effect in the applied pressure and thedisplacement behaviour of slab. Three standoff
distances were considered: 0.40, 0.75 and 1.00 m for
0.2, 0.31 and 0.46 kg TNT charge weights. Varying
the standoff had effects by changing the type of
detonation from close-in to far range detonation.
This had its impact on the blast characteristics andits parameters. For the 0.2 kg TNT charge weight,
reflected and incident pressure time histories
obtained for 0.40, 0.75 and 1.00 standoff distancesfrom centre of slab are shown in figure 15. The
variation in scaled distance and reflected pressuresfor charge weights and standoff distances are shownin figure 16 and figure 17.

Displacement (mm)

4.00
2.00
0.00

0.40 m-2.00
0.75 m

-4.00
-6.00

1.00 m

-8.00-10.00-12.00

0.00

2.50

5.00

7.50

10.00

12.50

15.00

17.50

20.00

Time (msec)

Fig. 18 Displacement-time histories at middle ofslab for the KCC (MAT072R3) for different standoff
distances, WTNT = 0.2 kg
In the case of 0.40 m standoff distance, theloading took the form of relatively high reflectedpressure and small impulse, while in the 0.75 and1.00m the loading were having lower reflected602

GEOMATE- Osaka, Nov. 16-18, 2015

pressures with greater impulses. This difference had
its effect on the maximum deflection attained byslab in addition to the pattern of the displacement
time histories. For the 0.2 kg charge weight and 0.40m standoff distance, the slab centre displacement
time history indicated that it responded plastically(displacement did not spring back after reaching the
maximum deflection). When the standoff distancewas changed to 0.75 m and 1.00 m, the slab
responded differently as the displacement time
history took the form of damped harmonic oscillator(figure 18). Also, it can be observed that bydecreasing standoff distance, the displacement was
decreased greatly compared to the one in 0.40 m
standoff distance (figure 19). Similarly, the slab
behaved in models for the 0.31 and 0.46 kg charge
weight for the considered standoff distances.

Severe Impulsive Loads," Journal of StructuralEngineering, vol. 116, no. 4, pp. 1061-1079,
April 1990.
[3] D. Hao and L. Zhongxian, “Numerical analysis
of dynamic behavior of RC slabs under blast
loading,” Transactions of Tianjin University,
vol. 15, no. 1, pp. 61-64, February 2009.
[4] W. Wang, D. Zhang, L. Fangyun, S.-C. Wang
and F. Tang, “Experimental study andnumerical simulation of the damage mode of asquare reinforced concrete slab under close-in
explosion,” Engineering Failure Analysis, vol.
27, p. 41–51, 2013 .
[5] C. F. Zhao and J. Y. Chen, “Damage
mechanism and mode of square reinforced
concrete slab subjected to blast loading,”
Theoretical and Applied Fracture Mechanics ,vol. 63–64, p. 54–62, 2013.
[6] Y. S. Tai, T. L. Chu, H. T. Hu and J. Y. Wu,“Dynamic response of a reinforced concreteslab subjected to air blast load,” Theoretical andApplied Fracture Mechanics, vol. 56, p. 140–
147, 2011.
[7] Z. S. Tabatabaei and J. S. Volz, "A Comparison
between Three Different Blast Methods in LSDYNA: LBE, MM-ALE, Coupling of LBE and
MM-ALE," in 12th International LS-DYNA
Users Conference, Dearborn, Michigan, USA,2007.
[8] L. Schwer and L. Malvar, “Simplified Concrete
Modeling
with*MAT_CONCRETE_DAMAGE_REL3,”in
JRI LS-DYNA User Week 2005, Nagoya, Japan,
2005.
[9] L. Schwer, “The Winfrith Concrete Model :Beauty or Beast ? Insights into the Winfrith
Concrete Model,” in 8th European LS-DYNA
Users Conference, Strasbourg, 2011.
[10] D. W. Hyde, "User's Guide for Computer
Programs
CONWEPandFUNPRO.
Applications of TM 5- 855-1, "Fundamentals ofProtective Design for Conventional Weapons","
Washington, DC, 1988.
[11] USA Department of Defence, “Structures toResist The Effect of Accidental Explosions Unified Facilities Criteria (UFC) 3-340-02”,
Department of Defence, 2008.
[12] G. Randers-Pehrson and K. A. Bannister,“Airblast Loading Model for DYNA2D and
DYNA3D,” Adelphi, Maryland, 997.
[13] L.S.T.C., LS-DYNA Keyword User's Manual,
Livermore: Livermore Software Technology
Corporation (LSTC), 2007.
[14] J. M. Biggs, Introduction to Structural
Dynamics, New York: McGraw-Hill, 1964.
[15] M. M. N. Shuaib, "Numerical Modeling ofReinforced Concrete Slab under Blast Loads",
M.Sc. Thesis, University of Khartoum,
Khartoum, Sudan, 2014.

Maximum Central Displacement (mm)

45.00
40.00
35.00
30.00
25.00

0.2 kg20.00

0.31 kg

15.00
0.46 kg
10.00
5.00
0.00
0.40 m0.75 m1.00 mStandoff DistanceFig. 19 Variation of slab maximum deflection fordifferent TNT charge weights and standoff distances
CONCLUSION
The numerical models of the one-way RC slab
subjected to blast loads, which are applied using
Lagrangian approach, for close-in detonations
indicated that the slab deflection can be estimatedsatisfactorily. Both KCC and the Winfrith concretemodel can indicate the slab deflection effectively,
while the SDOF appeared to obtain conservative
results. Damage modes obtained by applying the
Lagrangian approach may not indicate the local
shear which occurs in the tension zone. From theparametric studies, it can be inferred that slab
deflection can be controlled by adjusting slab
reinforcement ratio or explosive standoff distance.
REFERENCES[1] ASCE Task Commitee on Blast Resistant
Design of the Petrochemical Committee of theEnergy Division of the American Society of
Civil Engineers, Design of Blast Resistance
Buildings in Petrochemical Facilities, New
York: American Society of Civil Engineers(ASCE), 1997.
[2] T. Krauthammer, S. Shahriar and H. M. Shanaa,"Response of Reinforced Concrete Elements to603

FifthInternational Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan,Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

SOME ASPECTS OF PHYSICAL AND MECHANICAL PROPERTIESOF SAWDUST CONCRETE
1,2

A.S.M. Abdul Awal1, A.A.K. Mariyana2 and M.Z. Hossain3
Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor,
Malaysia 3Faculty of Bioresources, Mie University, Japan
ABSTRACTThis paper presents experimental results on some physical and mechanical properties of concrete containing
sawdust. Concrete specimens having various cement to sawdust ratios of 1:1, 1:2 and 1:3 by volume were madeand tested for workability, density, water absorption, strength and modulus of elasticity at different curing
periods of 7, 14 and 28 days. It has been found that with the increase in the amount of sawdust, the workability
and density of concrete decreased; the water absorption capacity of concrete, however, increased with theincrease in volume. Although, the strength of sawdust concrete increased with curing period, the strength and thecorresponding modulus of elasticity decreased with the increasing amount of sawdust in the mix. The results
obtained and the observations made in the short-term investigation suggest that sawdust concrete can suitably be
used as a building material in construction.
Keywords: Timber waste, Sawdust concrete, Physical properties, Strength, Deformation.

INTRODUCTION

Vast quantities of waste materials andby-products from various sources are
generated from the manufacturing process,service industries and municipal solid waste.
As a result, solid waste management has
become one of the major environmental
concerns in the world. With the increasingawareness of the environment protection,
significant research has been going on
globally on the utilization of wastematerials and by-products as construction
materials [1]. Recycling of such wastes into
new building materials could be a viable
solution not only to the pollution problem,
but also to the challenge of high cost ofbuilding materials currently faced by both
the developed and developing nations [2][3].
One of such potential waste material issawdust which is relatively abundant andinexpensive.
Sawdust or wood dust is an industrial
waste obtained as by-products from cutting,sawing or grinding of timber in the form offine particle (Fig. 1). Sawdust bonded withcement is also known as ‘sawdust cement’[4]. Although sawdust consists largely ofcellulose (Fig.2), it also contains soluble
sugar, acids, resins, oils and waxes, andFig. 1 Sample of sawdust used in the study

Fig.2 Scanning electron micrograph (SEM)of sawdust particles [5].
other organic substances which have an
inhibiting effect on the setting andhardening of the cement. Despite setting
604

GEOMATE-Osaka, Nov. 16-18, 2015and hardening problems, most of thesoftwood sawdust is rendered compatible
with the cement if a mixture of lime or
cement is used as the binder [6]. Eventually,
the strength of concrete reduces with theincrease in sawdust volume. Paramasivam
and Loke [7] found that sawdust concretewith a cement to sawdust ratio of 1:1 has
good bond strength and comparable to thenormal concrete. The drying shrinkage,
however, is very high; almost 10 times as
great as in most other lightweight concretes,
and thus greatly limits the usefulness of thismaterial. In spite of the limitations, sawdust
concrete has a good insulation value,
resiliency, low thermal conductivity andcan be sawed and nailed [8].
With the advancement in concrete
technology, the utilization of sawdust in the
manufacture of building materials hasreceived some attention over the past years
in many places of the world. Other than in
concrete, recent studies on the use of wood
sawdust wastes as a new brick material
supplement appears to be viable solution
not only to the environmental problem but
also to the problem of economic design ofbuildings [9]. Considering the availability
and the inherent quality of this wastematerial, this study aims at exploring thesuitability of sawdust as building material
through investigation of physical andmechanical properties of concrete.

ratio of 1:1, 1:2 and 1:3 by volume were
utilized. Ordinary Portland cement (ASTM
Type I) was used throughout the researchwork.
By nature, sawdust particles are porous
and absorb most of the water leaving
insufficient water for the setting of cement.
The water cement ratio of sawdust concrete
usually varies from 0.4-1.2 depending on
the mix proportions [7]. In thisinvestigation the sawdust particles weretreated to saturated surface dry state to
reduce the effect of water absorption by theparticles. Following several trials, thewater-cement ratio of 0.6 was adopted forthe study. Throughout the study, supplied
tap water was used for mixing of concrete.
Casting andSpecimens

TestingofConcrete

Concrete specimens comprising of cube(100 mm), cylinder (100x200 mm) andprism (100x100x500 mm) were cast for
determining compressive, tensile andflexuralstrengthrespectively.
The
specimens were cast in metal moulds andwere demoulded after 24 hours, and cured
by polythene sheeting until testing time.
Casting and testing of concrete specimens
were done in the Structure and Materials
laboratory of the Faculty of Civil
Engineering, Universiti Teknologi Malaysia.
The ambient temperature and relative
humidity in the laboratory were 27 ± 3˚C
and 85 ± 5% respectively.
The strength tests for compression,
tension and flexure were conductedaccording to BS 1881: Part 116 [10],ASTMC496-05 [11] and BS1881: Part118[12] standards respectively. While the tests
on modulus of elasticity of concrete wereconducted in accordance with the standard
stipulated in ASTM 469-05 [13]. Alongwith strength measurement, ultrasonic pulse
velocity (UPV) was also measured on thetest specimens.

MATERIALS AND TEST METHODS
Sawdust and Concrete Mix Proportions
Sawdust used in this study was collectedfrom a local plank and furniture market ofJohor Bahru, Malaysia. The sawdust
consisted mostly of fine chippings fromrubber tree (Fig. 1). In the case of lightweight aggregate, it is generally
recommended that the proportions are
specified in terms of volume rather than byweight because the bulk specific gravities
are not in the same order due to different
sizes of grains [7]. In this study, three mix
proportions of cement to sawdust in the

RESULTS AND DISCUSSION
605

GEOMATE-Osaka, Nov. 16-18, 2015

Physical Properties

The compressive strength test was
conducted on concrete cube specimens (Fig.
3) and the results obtained for the threemixes are illustrated in Fig. 4. The resultspresented in the figure showed an average
strength development of 14.45, 13.60 and
8.40 MPa obtained at the age of 7 days forthe mix ratios of 1:1, 1:2 and 1:3
respectively. A slight increase in thestrength was found to occur after 14 days.

The physical properties of sawdust
concrete are presented in Table 1. The
consistency of the fresh mix, tested in termsof slump has been found to vary dependingon the amount of sawdust in the mix. In
general, higher the amount of sawdust
lower was the slump. Slump values of 40,
15 and 5 mm were obtained for mix ratiosof 1:1, 1:2 and 1:3 respectively, and were
found to fall within the medium, low andno-slump ranges according to Euro code
Standard classifications (BS EN 206-1) [14].
Similar observation has been made by
Oyedepo et al. [15]The density of sawdust concretemeasured at 28 days for the mix ratios of
1:1, 1:2 and 1:3 are 1450, 1280 and 1065
kg/m3 respectively. The test results showthat the density values are inversely
proportional to the volume of sawdust
content. By assuming the average density of
OPC concrete to be 2400 kg/m3, the mix
proportion of 1:1 provides about 40%
reduction in density. This reduction
highlights the potentials of sawdust
concrete to be used as lightweight building
material in construction.
The water absorption, expressed aspercentage, was obtained by measuring theamount absorbed against the dry mass.
Unlike slump and density, the water
absorption of sawdust concrete was foundto increase with the increasing amount ofsawdust in the mix. This is obvious,
because sawdust is relatively porous than
the aggregates like sand, stone etc. used innormal concrete [16].

Fig. 3 Testing of concrete cube specimen
for compressive strength.
At the age of 28 days there was asignificant increase in compressive strength
in all the mixes. Strength values of 18.65,
17.20 and 12.80 MPa, for instance, wereobtained for the mix ratios of 1:1, 1:2 and1:3 respectively. From the results obtained
it is clear that the strength of sawdust
concrete decreases with an increase in thevolume of sawdust in the mix proportions.

Table 1 Physical properties of sawdust concrete
Mix
ratio

Slump
(mm)

Density(kg/m3)

1:1

40

1450

Water
absorption
(%)
13

1:2

15

1280

15

1:3

5

1065

19

Fig. 4 Development of compressive
strength of concrete.
Figure 5 illustrates a liner relationship

Compressive Strength606

GEOMATE-Osaka, Nov. 16-18, 2015

between the compressive strength andultrasonic pulse velocity (UPV) of sawdust
concrete at the age of 28 days. Even though
the UPV values increased with the increasein compressive strength, the highest valueof 2620 m/s obtained for 1:1 ratio falls
within the low quality range [8].

Fig. 5 Relationship between compressive
strength and ultrasonic pulse
velocity (UPV) of concrete.

Fig. 6 Determination of tensile strength.

Tensile Strength
The splitting tensile strength of sawdust
concrete was also determined (Fig. 6) at theage of 7, 14 and 28 days, and the results arepresented in Fig. 7. The development ofsplitting tensile strength was somewhat
similar to that observed in the case ofcompressive strength i.e. splitting tensile
strength decreased with the increase in theamount of sawdust. For example, at 28 days
the strength values of 2.05, 1.95 and 1.30
MPa were obtained for mixes of 1:1, 1:2
and 1:3 respectively.
The relationship between the 28-daycompressive and tensile strength of sawdust
concrete is shown in Fig. 8. It can beobserved that the relationship is somewhat
liner i.e. compressive strength is
proportional to the tensile strength;
however, the value of the relationshipdiminishes with the increase in sawdust mix
ratio.

Fig. 7 Development of tensile strength of
concrete.

Fig. 8 Relationship between compressive
and tensile strength of concrete.
in Fig. 10. It has been found that like that ofcompressive and tensile strength, theflexural strength of sawdust concrete also
followed the same trend. For example,
when the mix proportions changed from 1:1
to 1:2, the flexural strength decreased from2.75 to 2.20 N/mm2, which represents a

Flexural Strength
The flexural strength test was conducted
on concrete prism specimen (Fig. 9) at theage of 28 days and the results are presented

607

GEOMATE-Osaka, Nov. 16-18, 2015

decrease of about 35%, while a loss ofabout 30% occurred with the lean mixes
from a ratio of 1:2 to 1:3.

Modulus of Elasticity
The modulus of elasticity of sawdust
concrete was experimentally determined(Fig. 12) at the age of 28 days. Along withthe experimental investigation, theoreticalmodels were also studied in predicting themodulus of elasticity following the British
Standard for the structural use of concrete
BS 8110-2:1985 [17, 18]. Data presented inFig. 13 reveal that the elastic modulus of
17.10, 16.40 and 11.95 GPa were obtained
for the mixes of 1:1, 1:2 and 1:3
respectively while 20.30, 19.50 and 16.0
GPa were calculated using theoreticalmodel for the same mix ratios. Although
the predicted values were somewhat higher,
data obtained from the limited experimental
work are quite satisfactory.

Fig. 9 Determination of flexural strength of concreteFig. 10 28-day flexural strength of sawdust
concrete.
The relationship between 28-daycompressive strength and flexural strengthof sawdust concrete is demonstrated in Fig.
11. Although there was a progressive
increase in the two parameters, as can beseen in the figure, the relationship is not
liner as observed in case of compressive
and tensile strength.

Fig. 12 Determination of modulus ofelasticity of concrete.

Fig. 11 Relationship between compressive
strength and flexural strength.

Fig. 13 Modulus of elasticity of sawdust
concrete.
608

GEOMATE-Osaka, Nov. 16-18, 2015

CONCLUSION

Int Tribology Conference Malaysia 2013,
Vol. 68, pp. 365-371.
[6] Aigbomian EP and Fan M, “Developmentof wood-concrete building materials fromsawdust and waste paper”, Construction
and Building Materials, Vol. 40, 2013, pp.
361-366.
[7] Paramasivam P and Loke Y O, “Study ofsawdust concrete”. The International
Journal of Lightweight Concrete, Vol.1, No.
2, 1980. pp. 57-61.
[8] Nevillie A M, Properties of Concrete. 4th ed.
ELBS Pitman, 2000.
[9] Halil MA and Turgut P, “Limestone dust
and wood sawdust as brick material”,
Construction and Building Materials, Vol.
22, 2007, pp. 3399-3403.
[10] BS1881-116, Testing concrete. Method for
determination of compressive strength of
concrete cubes, 1983.
[11]ASTMC496, Test for splitting tensile
strength of cylindrical concrete specimens,
2005.
[12] BS1881-118, Testing concrete. Method for
determination of flexural strength, 1983.
[13] ASTM C469, Test for static modulus ofelasticity and Poisson’s ratio of concrete incompression, 2002.
[14] BS EN 206-1, Concrete. Specification,
performance, production and conformity,
2000.
[15] Oyedepo OJ, Oluwajana SD and Akande
SP, “Investigation of properties ofconcrete using sawdust as partial
replacement of sand”, Civil andEnvironmental Research, Vol. 6, No.2,
2014, pp.35-42.
[16] Mageswari M and Vidivelli B, “The useof sawdust ash as fine aggregate
replacement in concrete”. J. of Environmental Research and Development, Vol.
3, No.3, 2009, pp.720-726.
[17] BS 8110-2, Structural use of concrete.
Code of practice for special circumstances, 1985.
[18] Neville AM and Brooks JJ, Concrete
Technology, 2nd ed. Longman Group UK
limited, 1987.

In this study sawdust concrete withvarious cement to sawdust ratios, byvolume was made and the physical andmechanical properties were investigated.
This paper highlights the potential benefitsof sawdust concrete in terms of physicaland mechanical properties. The results
obtained and the observations made in thisstudy conclude that sawdust concrete can beused as light weight concrete with asatisfactory strength performance. Longterm strength development including
durability aspects has been put forward asrecommendation for future study in order toexplore better understanding of sawdust asa sustainable building material inconstruction.
ACKNOWLEDGEMENTS
The financial support received from the
Universiti Teknologi through Research
Management Centre (Cost Center No.
Q.J130000.2522.10H06)
isgratefully
acknowledged.
REFERENCES[1] Siddique R, Waste Materials and ByProducts in Concrete. Springer-Verlag
Berlin Heidelberg, 2008.
[2] Chandra S, Waste Materials Used inConcrete Manufacturing. Noyes Publications, New Jersey, 1997.
[3] Udoeyo F F and Dashibil PU, “Sawdust ash
as concrete material’, J. of Materials in
Civil Engineering, Vol.14, 2002, pp.173176.
[4] Short A and Kinniburgh W, Lightweight
Concrete. CR books Ltd, London, 1968.
[5] Shaaban A, Se-Meng M, and Dimin MF,
Characterization of biochar derived fromrubber wood sawdust through slow
pyrolysis on surface porosities andfunctional groups, Procedia Engineering,609

FifthInternational Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan,Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

DAMAGE INVESTIGATION AND RETROFITTING OF WEST
SUMATRA GOVERNOR’S OFFICE BUILDING
Fauzan1, Febrin Anas Ismail2, Zaidir3, Nugrafindo Yanto4
Civil Engineering Department, Andalas University, Indonesia

1,2,3,4

ABSTRACTWest Sumatra Governor's office building is one of the buildings that damaged by September 30, 2009 Sumatra
Earthquake. Damage of the building structure was identified after demolition of all the partitions and column
cover panels. In order to know the cause of the damage, an investigation has been done. This paper discusses theresult of a building assessment to find the causes of the damage and proposes the recommendation method for
retrofitting the building structure. Based on field investigation, structural damages occurred on almost allcolumns in first floor due to lack of shear reinforcement. A numerical simulation was carried out to evaluate thecapability of building structures to resist loads, especially earthquake load based on Indonesian Standard Code,
SNI 03-1726-2012. The result of simulation indicates that the damage of the columns was shear failure due tothe big distance of shear reinforcement. Retrofitting of the structure was proposed using concrete jacketing for
all columns in first floor and installed shear wall in the corners of the building until third floor.
Keywords: Column, Shear failure, Earthquake Load, Jacketing, Shear wallstrengthening the structural elements, such ascolumn and beam jacketing, while Global Retrofit
was conducted by installing shear wall and bracing
on the structure. Based on the results of research
conducted by Fauzan et.al [4], the capacity ofcircular columns after jacketing increased
significantly.

INTRODUCTION
Indonesia is in the confluence of four major
tectonic plates, i.e. the Eurasian, the Indo-Pacific,
Australia, and the Philippines plates so it is known
as the Ring of Fire that caused many of theearthquakes occurred due to the movement of theplate. On September 30, 2009, the 7.6 SR tectonic
earthquake have hit the West Sumatra Province [1].
The earthquake was centered at coordinates 0 ° 50 '24 "South latitude and 99 ° 39 ' 0" East longitude.
The damage was very severe in the area along thecoast of West Sumatra Province. Padang as thecapital city of West Sumatera with a distance of 100
km from the epicenter could not escape from severe
damage. The quake with a depth of 70 km wasrecorded as the strongest earthquake ever to hit thecity of Padang since this city stand [2].
One of the buildings that could not escape thedamage is West Sumatra Governor's Office Building(Figure 1), located in the center of Padang City. Thisbuilding was reinforced concrete structure, built in
1961. Damage to the structure of the building was
identified after a demolition of all the partitions andcolumn cover panel when the building repair will bestarted. The columns were covered by wood panels,
which are not monoliths with columns so the crack
occurred in the columns was not visible from theoutside. The crack visually can be observed after the
demolition of the wooden panels that covering thecolumn, as can be seen in Figure 2.
There are two kinds of retrofitting can be done todamaged buildings, i.e. local retrofit and global
retrofit [3]. Local retrofit was conducted by

Figure 1. West Sumatra Governor's office building
In this study, retrofitting using combined
concrete jacketing and shear wall was proposed toretrofit the West Sumatra Governor's office building.

a. Wooden panels covering the columns
610

GEOMATE-Osaka, Nov. 16-18, 2015b.

Shear cracks of columns after demolition of
the wooden panels
Figure 2. Damage of the columns on first floor(a)(b)
Figure 3. Damage of the column on first floor

EVALUATION OF EXISTING BUILDING

Based on visual investigation, it is found that thelack of shear reinforcement contribute to the shear
failure of the columns.

A building assessment was carried out on West
Sumatra Governor's office building to investigate thecause of damage. In addition, detail of dimensions ofstructural elements (column, beam etc) and otherdata for structural analysis was collected in the field
because the building was old and no data available
from the government.

Concrete and steel reinforcement
Hammer test was carried out to know the quality ofconcrete on the building. The result indicates thatcompression stress of the concrete (fc’) was 22,2
MPa (225 kg/cm2). Meanwhile, Yield stress ofreinforcement (fy) was around 240 MPa from thetensile test of steel reinforcement.
The damage of several beams was also observed
on 2nd floor, as shown in Figure 5.

Damage to the columns and Beams
The damage of columns mostly shear crack that
occurred on the middle of the columns. As seen in
Figure 3a, the concrete cover of the column was
spalling and the steel reinforcement can be seenvisually. In addition, the distance of shear
reinforcement was bigger which are not following
the current standard code. The shear cracks occurredat almost all columns in first floor, as shown in
Figure 3b. The location of damaged columns in the
1st floor can be seen in Figure 4.

Figure 4. Location of damaged columns

611

GEOMATE-Osaka, Nov. 16-18, 2015

Figure 5. Damaged of beam on second floor
Structural Analysis of Existing Building
Structural analysis of existing building was
conducted using computer software ETABS 9.7.4[5]. The earthquake load applied based on current
Indonesia standard code (SNI 03-1726-2012) [6].
Result of analysis shows that the capacity of allcolumns to resist the axial load was strong enough(Figure 6) however the shear capacity of thecolumns was not sufficient, as shown in Table 1.

Figure 6. Interaction Diagram P-M (30/80 Column)

Table 1. Shear Capacity of Column 30/80
NOMINAL SHEAR CAPACITY OF COLUMN

Nominal Shear Capacity:

Vn= Vc + Vs(kN)

229.51:Vr= φs Vn(kN)

172.13

:

Vu

(kg)

20630.10(kN)

202.38SHEAR CAPACITY DESIGN OF COLUMN

Shear Capacity Design

SHEAR CAPACITY ULTIMATE OF COLUMN

Shear Capacity Design(from structural analysis)

SHEAR CAPACITY CONTROL


Vr

≥

Vu

172.13 kN<

202.38 kN… NOT OK !!

RETROFITTING STRUCTURE
JacketingJacketing of columns was proposed to retrofit
West Sumatra Governor Office building. The crosssectional dimension of columns was enlarged from30/80 to 40/90 and the number of reinforcement wasincreased by installing a wire mesh type M5-150, as
seen in Figure 7.

Figure 7. Jacketing methods of column
Re-analysis of structure after jacketing was
conducted in order to know the capacity of jacketedcolumns. The result shows that the shear capacity ofcolumns increased by around 50 - 60 %, as shown in612

GEOMATE-Osaka, Nov. 16-18, 2015

Table 2. The use of wire mesh contributes to theincrease of shear capacity of the column.
Table 2. Comparison of column capacity
Type
Force
Ex
Jc
K5

M (kNm)P (kN)
V (kN)

550.652974.55
165.62630.823999.05
248.668

Ratio
(%)
14.56
34.44
50.14

K4

M (kNm)P (kN)
V (kN)

346.372312.35
121.45370.473122.10190.15

6.96
35.02
56.57

K3

M (kNm)P (kN)
V (kN)

276.052069.42
99.37

300.6
2798.20160.90

8.73
35.22
61.92

building, reinforced concrete shear wall (Figure 8)
were proposed for additional retrofitting on thebuilding. The thickness of shear wall was 25 cm
using two layers reinforcement bar D16-150. The
location of the shear wall allocation can be seen in
Figure 9.
Results of analysis of the structure with the shear
walls shows that the displacement of the building on
the top floor reduced significantly as shown in
Figures 10 and 11.

Installing Shear WallWest Sumatra Governor's office building
consists of three blocks separated by dilatation.
Dilatation between the building blocks currently is
about 5 cm which is very small. In order to reducethe displacement as well as the shear force of the

Figure 8. Shear wall

Location of Shear wall

Figure 9. Location of shear wall on left/right building and center building

Figure 10. Displacement of Left/right Building

Figure 11.Displacement of Centre Building
613

GEOMATE-Osaka, Nov. 16-18, 2015

In the SNI 1726-2012 explained that the building
is separated structurally should have reasonably
sufficient distance to avoid a damaging collision.
The separation of the structure must be able toaccommodate the maximum displacement withequations that are already set out in the regulations.
From Figure 10, it can be seen that the maximum
displacement after installing shear wall at theleft/right building is 0.81 cm and the maximum
displacement of the central building is 0.40 cm(Figure 11).

requirement of dilation space based on SNI
1726-2012
4. Retrofitting of West Sumatra Governor's officebuilding can be done after a restoration of eachstructural elements.
ACKNOWLEDGEMENTS
Thanks to the Building Assesment Team of Andalas
Disaster Study, Material Structure Laboratory of
Civil Engineering Andalas University and Public
Works Department, West Sumatra over the supportand help of the required data.

Minimum dilatation according SNI 1726-2012 is:Left/Right building := 0,81 cm
S1= 4,06 cm
Sm 1
Center Building := 0,40 cm
S2= 2,00 cm
Sm 2
Minimum dilatation space :δm= √𝑆𝑆𝑆𝑆11 + 𝑆𝑆𝑆𝑆22
δm= 4,54 cm < 5,00 cm (ok)

REFERENCES[1] Meteorology and Geophysics Department ofIndonesia. 2009.
[2] Ismail, Febrin Anas et.al. “The Bumiminang
Hotel’s building damage due to the Padang
Earthquake September 30, 2009". Journal Of
Civil Engineering, Bandung Institute Of
Technology. Vol. 10 No. 2, pp. 119-125,
August 2011, ISSN 0853-2982.
[3] Public Works Department dan Indian Building
Congress. Handbook on Seismic Retrofit of
Buildings.
Association
Indian InstituteTechnology.2007.
[4] Fauzan, et al. "Structure Analysis ofRetrofitting the Andalas Mosque after
earthquake 30 Sept 2009". Journal Of Civil
Engineering, Andalas University. Volume 7 No.
1 February 2011, ISSN 1558-2133.
[5] Computers and Structures, Inc. ManualETABS(IntegratedBuildingDesignSoftware).
California, USA. 2005.
[6] State Standardization Department. Earthquake
Resistance Planning for Building and NonBuilding Structures. SNI 1726:2012. Jakarta.
[7] Boen, Teddy. “How to repairing simple
buildings damaged by the earthquake”. Jakarta.
2009.

Retrofitting Implementation Methods
The first step that needs to be done to the West
Sumatra Governor's office building are doing therestoration actions on the elements of structure.
Restoration can be implemented as follows [7]:1. Small cracks on the beams and columns injectedwith cement or epoxy materials. Small cracks arecracks that have wide gaps less than 0.6 cm.
2. The elements of the structure that had large
cracks should be dismantled and replaced with a
new concrete.
After the restoration, the retrofitting of structureusing jacketing and shear wall was conducted.
CONCLUSION
1. The damage on the Office of the Governor ofWest Sumatra was shear failure due to the lackof shear reinforcement.
2. Retrofit of the West Sumatra Governor's officebuilding can be carried out using a combinationof jacketing and installation of shear wall.
3. Installing shear wall on the building reduced thedisplacement of the buildings so it can meet the614

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

HORIZONTAL PERMEABILITY OF CLAY MIXTURESUNDER LARGE SHEAR DEFORMATIONS
Po- Hao Hsu1, Yohei Katayama1, Takashi Tsuchida1 and Athapaththu A.M.R.G.1
1
Hiroshima University, Japan

ABSTRACT
After Tohoku Earthquake, a huge amount of disaster waste including radioactive substances was released tothe environment due to the accident of Fukushima No.1 nuclear power plant. This study is related to offshore
waste disposal facility for storing the waste materials. A sealing layer is one of the major component in adisposal facility. Previous studies by some of the co-authors in this paper revealed that the coefficient of
permeability of 5 x 10-10 m/s was achieved on a predesigned clay mixture. This research was focused to examine
the change of permeability characteristics under shear deformations due to earthquakes, and Tsunami. Hollow
Cylinder Torsional Permeability Test (HCTPT) was conducted to examine the effect of shear deformation on
permeability characteristics of the various clay mixtures. The coefficients of permeability in the horizontal
direction were in the range of 1.5 - 2.0 times that of coefficient of permeability in the vertical direction. It was
found that there is no considerable increase of coefficient of permeability due to the change of shear
deformations.
Keywords: Permeability, Hollow Cylinder Torsional Permeability Test, Clay Mixture, Shear deformation
treatment outside Fukushima Prefecture within thenext 30 years (Ministry of Environments, 2013).
However, no technical surveys have been carried out
officially on the sites and the structures of the final
disposal facility. This study is related to thepossibility of constructing offshore disposal facility
for these wastes.
Sealing layer is the major component in adisposal facility and its function is to make sure thatthe radioactive substances to become an innocuous
level after passing of it. Previous studies showed thatthe coefficient of permeability should be less than
5.0 × 10-10 m/s to achieve the targets of the sealing
layer and this condition can be achieved by nixing
marine clays with bentonite (Murakami et al.,2015)
The permeability may be changed by deforming
effect in natural hazard, such as earthquakes,

INTRODUCTION
After Tohoku Earthquake in Japan, a hugeamount of disaster waste including radioactive
substances was released to the environment due toFukushima No.1 nuclear power plant at the east
coast of Honshu Island, Japan. These wastes possessa great concern to the local people though thegovernment
carriedoutmeasureson
decontamination. A huge amount of waste was
generated and remained in Fukushima prefecture.
The Ministry of Environments, Government ofJapan started to build an interim storage facility near
Fukushima No.1 nuclear power plant. The volume isabout 15 million to 28 million m3. Area of the
facility is estimated to be about 3-5 km2. It is
expected that the waste will go through the final

22.3(16.0)

600

正弦波200gal

Acc(gal)

400

1.1
(0.7)

200
0
-200
-400
-600
0

10

12.6
(8.1)

20

30
t(s)

40

50

5%< γmax <10%10%< γmax

Maximum and mean shear strain of each element, γmax (γmean)
14.7(10.7)

5.2
(3.3)

2.7
(1.5)

2.3
(1.2)

2.1
(1.1)

2.5
(1.3)

11.8

4.2

2.7

(8.4)

(2.3)

(1.4)

2.2
(1.1)

1.9
(1.0)

2.4
(1.3)

20.3
(13.2)

24.8(10.2)

26.3
(8.9)

29.7
(9.9)

31.2
(11.0)

29.4(10.8)

Fig. 1 Deformation Analysis based on FLIP (Tsuchida et al., 2013)

615

60

GEOMATE- Brisbane, Nov. 16-18, 2015

tsunami etc. When an earthquake occurs, huge force
may act on the sealing layer. There is a possibility toform cracks and hence the permeability may
increase by this phenomenon.
Japan located near plate boundaries and therefore
earthquakes
occur
frequently.
These
earthquakes would may affect the safety of waste
disposal facility. Some analyses were done based on
FLIP (Finite element analysis of Liquefaction
Program). FLIP is a well-known program and has
been extensively used for the design of port andharbor structures in Japan (Iai et al., 1998, Ichii et al.,2002). A result of a deformation analysis of an
offshore waste disposal facility is shown in Fig. 1(Tsuchida et al., 2013), in which 4 m thick sealing
layer was considered on the seabed. In the analysis,
multiple shearing spring model was used as stressstrain model of soils (Iai et al.,1992). It was assumed
that sealing layer is completely resting on the sea
bed and liquefaction would not occur because of thedense sandy seabed. 20 sine waves of 200 gal wereapplied to the structure. The results shown inside theframe are the maximum and mean shear strains(mean value is in parentheses). It can be found thatabout 20-30% shear stain generated in the sealing
layer of the disposal facility. Between the sheet piles
which was constructed for lateral sealing, over 10%
shear strains acted. The result showed that, when astrong earthquake occurs, considerable shear
deformation may be generated in the sealing layers.
Therefore, it is necessary to study the permeability
characteristics of sealing layer under large shear
strains.

Fig. 2

Hollow cylinder torsional permeability device

Fig. 3 Concept of permeability test

METHOD OF STUDY

Table 1 Properties of raw materials

Experiment setup
A series of experiments was conducted on a
newly developed hollow cylinder torsional
permeability test (HCTPT). As the triaxial test
cannot simulate the real condition of shear
deformation of horizontal ground under dynamic
loadings, this test becomes a very common test for
simulating the real condition when disasters such as
earthquake occurs. However, this time, we add somefunction on the device so that it can test thepermeability when applying torsion. The details will
be furnished later in this paper.
Fig. 2 shows that the schematic view of a
HCTPT. In order to carry out permeability tests, twoporous stone and the pipes were instrumented to theapparatus. The detail of this mechanism is shown in
Fig. 3. In the permeability test, arrows are thedirection of water. Water comes from one of buretteto another burette. By recording the time and flow ofwater , coefficient of permeability can be determinedand shearing displacement on the specimen can begiven through a torsion by a motor equipped in theapparatus.

Material

Density(g/cm3)Liquidlimit (%)

Plastic
limit (%)

Tokuyama clay

2.616

110.6

40.0

Bentonite

2.898

510.6

44.0

Table 2 Mixing conditions of experiments
Case
T0
T25
T50

Bentonite1)(kg/m3)25
50

Water content2)
(%)
165.9
192.6
205.6

Preparation of the specimens
The specimens of this experiment were madefrom marine clay mixed with bentonite and Portland
cement. Marine clay was dredged from Tokuyama
Port in Yamaguchi Prefecture, Japan. Bentonite wasimported from Wyoming (America). Table 1 shows
the properties of clay and bentonite.

616

GEOMATE- Brisbane, Nov. 16-18, 2015

Clay and bentonite Mixture
Sealing material is made from a clay mixture.
This time, the experiment adopt clay and bentonite
as raw materials. The procedure of preparing
samples are as following.
1) Mix clay and bentonite according to JGS 0821.
Table 2 shows the mixing conditions used in theexperiment.
2) Remold the specimens by pre-consolidation.
There are three step in this procedure as mentioned
in the text below.
3) After consolidation, trim the specimen as in Fig. 4.

Fig. 4 Dimensions of the specimen

Cement treated soil
Cement treated clay is easier to handle than claybentonite mixture. To study the possibility of using
cement treated clay as sealing material, thecomparison was made with clay mixture. The clay is
Tokuyama port clay and the cement is normal
Portland cement. The specimen of cement treatedsoil is called C180. The unconfined compressive
strength at 7 days of C180 was 180kPa.
EXPERIMENTAL PROCEDURE
Hollowed cylinder torsional permeability test
was conducted to measure the coefficient of
permeability of clay mixtures under different stagesof shear deformation. The following is theprocedures of the experiment.
1) Preparation of the specimen
Clay and bentonite were mixed with artificial sea
water, which was made by adding sodium chloride
and other components. The sample was remolded
and reconsolidated in the laboratory. The
consolidation pressure was given in stages as
12.3kPa, 24.5kPa and 49kPa. Finally, the specimen
was trimmed to a hollow shape as shown in Fig.4.
2) Setting up the specimen
The rubber membrane was placed to protect
specimen from water invasion. The photo is shownin Fig. 5. Fig. 6 shows the final setting of thespecimen in the triaxial cell.
3) K 0 consolidation
In the experiment, it was hoping to simulate realcondition when build the offshore waste disposal
facility. Considering the typical structure of offshore
disposal facility as shown Fig.1, the representative
effective consolidation pressure of sealing layer was
calculated to be 150 kPa. The K 0 consolidation was
adopted to the specimen until the σ v ’ =150 kPa. The
σ h was given by increasing the cell pressure, and thedeviator stress was given to make the verticaldisplacement of specimen accompanied with the K 0
condition. The relationship between volume change
and vertical displacement is shown in Fig.7. The cellpressure was applied in steps as shown in Fig. 8.
After the K 0 consolidation was finished, backpressure was given to make the specimen saturated.

Fig. 5

Setting the sample in membrane

Fig. 6

Sample in a Tri-axial Cell

Volume Change, ⊿V (cm3)

30
25
20
15
T0
T25
T50
C180

10

ΔV=3.14Δh

5
0

0

1

2

3

4

5

6

7

8

9

10

Axial Displacement⊿h (mm)Fig. 7 Relationship between volume change andvertical displacement

617

11

GEOMATE- Brisbane, Nov. 16-18, 20154) Permeability test before shearing
The main purpose of this research is to measurecoefficient of permeability. The following is a newequation, Eq. (1), derived by Darcy’s law.
R ln 2 R 
1× 10 4  1 
2πHt∆PB ×
9.81∆q

Consolidation Pres. (kPa)

k=

160

(1)

where Δq is the total flow, H is height of thespecimen(cm), ∆t is the period of the test (sec),
ΔP B is the difference of air pressure (kPa), R 1 andR 2 are the inner and outer radius of the specimen(cm), respectively. After the accurate values wereconfirmed, we calculated average value of them.
5) Application of shear deformation andpermeability test
The purpose of this research should know thephenomenon of shearing deformation effect on
the permeability. Some shearing deformations were
given on specimen by torsion. Apply the shearing
deformation from 0% to 20%, and examine thepermeability every 5% of deformation. The speed is
was about 0.1% per minute. When the shear strain
was determined, the calculation was based by outerradius. Therefore, in this case, the shear strain is the
maximum shear strain. The stress-strain curve isshown in Fig. 9. In the figure, it can be found thatevery step have a peak value and the strength
decreases later. It means specimen had undergone a
failure.

T0

140

Axial Pres.
Lateral Pres.

120
100

σ'v=148.6kPa
σ'h=55.0kPa

80
60
40
20
0

0

100

200

300

400

500

600

Time (min)Fig. 8 Consolidation pressure with time
60

Shear Stress (kPa)

50
40
30
20

T0
T25
T50

10
0

0

5

10

15

20

25

Shear Strain (%)
Fig. 9 Stress-strain curve (T0)Permeability (m/s)

1.E-07

RESULTS AND DISCUSSION
Results of Permeability Test
The results are shown in Fig. 10. It was observedthat the coefficient of permeability does not increase
when the shear strain increases. Permeably was
shown to be slightly decreased when strain increases.
This is assumed to be due to restructuring of thevoid or assembly change. The change ofpermeability ratio is shown in Fig. 11. According to
the results above, it shows that there is a small
possibility that permeability of sealing material
increase by shearing deformation due to a natural
disasters.
The value of coefficient of permeability is higher
than about 100 times for cement treated clays. As no
pre-consolidation was given in making specimen of
cement treated soil, the void ratio e of cement
treated soil was larger than that of clay bentonite
mixture. This is the reason for higher permeability
value of cement treated soil.
Ueno et al. (2008) carried out laboratory model
test on sealing layer using Nagoya port clay mixture
and cement treated clay on shear deformation.
Fig.12 illustrated the model used by Ueno et al.
(2008) to examine the permeability of sealingmaterials. Fig.13 shows the result of model

T0
T25
T50
C180

1.E-08

1.E-09

1.E-100

510Shear Strain (%)

15

20

Fig. 10 Relationship of horizontal permeability andshear strain

Normalized Permeability

1
0.9
0.8
T0
T25
T50
C180

0.7
0.6

0

5

10

15

20

Shear Strain (%)
Fig. 11 Normalized horizontal permeability withshear strain

618

GEOMATE- Brisbane, Nov. 16-18, 2015

experiment (Ueno et al., 2008). The permeability of
cement treated soil increased rapidly with shear
strain, however, Nagoya port clay mixture did notshow the increase with shear strain of 4%. The
present study showed no considerable change ofpermeability to shear strain within 20%.
Feasibility of New Testing Method
The coefficients of vertical permeability, k v of
Tokuyama clay-bentonite mixtures, which wereobtained by conventional consolidation tests areshown in Fig.14 for comparison (Murakami et al.,2014). As shown in Fig.14, k v decreases with theconsolidation pressure. The value of k v when theeffective consolidation pressure was 150 kPa wascompared with the horizontal coefficient of
permeability, k h measured in this study as shown in
Fig. 15. The result showed that the k h values ranged
from 1.5 to 2.0 times the k h values. Mizukami et al.
(1996) carried out a series of laboratory
consolidation test of Osaka Bay Clay to compare thecoefficients of consolidation c v for vertical flow andc h for horizontal flow. The results are shown in Fig.
16. In the figure, the values of c h is from 1.5 to 2.0
times to the values of c v . The coefficient of
permeability k is given as Eq. (2).
k = c v × mv × γ w

Coefficient of Permeability, k (m/s)

Fig.12 Model test adapted by Ueno et al.(2008)

Average Shear Strain, γ (%)

Fig. 13(2)

where c v is coefficient of consolidation, m v iscoefficient of volume compressibility, and γ w is unit
weight of water. m v can be calculated by Eq. (3) as
follows.

Permeability, kv(m/s)

V∆σ'Change of permeability with average shear strain
for Nagoya clay mixture measured by laboratorymodel test (Ueno et al., 2008)
1.E-08∆V
mv = −

Cement treatedsoil
Nagoya port
clay mixture(3)

where ∆V is changing of volume, V is original
volume, and ∆σ ' is changing of stress. No matterhorizontal or vertical direction, m v would not change
as the different direction. According to above
equations, the relationship of coefficients ofconsolidation c h and c v is are similar with therelationship of coefficient of permeability k h and k v .

T0
T25
T50

1.E-09

1.E-101.E-111

10

100

Horizontal Coefficient ofPermeability, kh (m/S)

5E-10

T0

kh =2kv

4E-10

T25

3E-10

T50

2E-10

kh =kv

1E-100σ'v=150kPa
0

1E-10

2E-10

3E-10

4E-10

1000

Mean consolidation pressure, p (kPa)
Fig. 14 Vertical permeability of Tokuyama port clay
mixture (Murakami et al, 2014)

5E-10

Vertical Coefficient of Permeability, kv (m/s)

Fig. 15 Relation of horizontal and vertical permeability

619

Fig. 16 Horizontal and vertical coefficient ofconsolidation (Mizukami et al.,1996)

GEOMATE- Brisbane, Nov. 16-18, 2015

From the research of Mizukami et al.(1996), it wassuggested that the k h is about 1.5-2.0 times to thevalues of k vv . According to the above, this new
testing method for measuring horizontal coefficientof permeability seems to be appropriate.

Civil Engineers, Vol. 19, No. 1, pp. 21s-31s,
2002.
[3] Iai,S., Matsunaga, Y. and Kameoka, T.,”Strain
space plasticity model for cyclic mobility, soils
and Foundations, Vo,No.2, pp.1-15, 1992.
[4] Katayama, H., Murakami, H, and Tsuchida, T.,“Study of Permeability for Geo-material in
Giving Shearing Deformation”, GroundEngineering; Joural of Chugoku Branch ofJapanese Geotechnical Society, Vol. 32, No. 1,
2014, pp. 111-116. (in Japanese)[5] Mizukami, J., Kobayashi, M., and Tsuchida, T.,“The Horizontal Coefficient of Consolidationc h ”, Journal of Japan Society of Civil Engineers,Vol. 34, No. 535, 1996, pp. 1-12. (in Japanese)[6] Murakami, H., Tsuchida, T., and Katayama, Y.,“Study of Geo-material Used for Coastal
Disposal Facilities for Waste Including Cesium”,
Journal of Japan Society of Civil Engineers B3,Vol. 70, No. 2, 2014, I_846-I_851. (in Japanese)[7] Ministry of Environment, 2013. On theinvestigation of interim storage facility, 1stversion, Jan. 2013. http://josen.env.go.jp/
material/pdf/dojyou_cyuukan.pdf.
[8] Tsuchida, T., Watabe. Y., Shizuma, K., Endo, S.,
and Ueno, K., Development of Offshore or
Onshore Disposal Facility for Disaster Wasterand Decontamination Waster Polluted by
Radiation, Integrated Research Report ofEnvironment Research and Technology
Development Fund, Japan, 2013, pp. 1-69. (inJapanese)[9] Ueno, K., Yamada, K., and Watabe Y.,“Proposal of Sealing Geomaterial for Waste
Disposal Sites in Coastal Area”, Journal ofJapan Society of Civil Engineers, Vol. 64, No. 2,
2008, pp. 177-186. (in Japanese)[10] Wakioka, H., Tsuchida, T., and Kang, G.,“Study of Strength Properties for Cement
Treated Soil”, 50th Symposium on GeotechnicalEngineering, 2014, pp. 646-650. (in Japanese)

CONCLUSIONS
1. It was found that mixing of bentonite considerably
decrease the permeability of Tokuyama port clay.
2. A new testing method to measure the horizontal
coefficient of permeability was developed and it iscalled as hollow cylinder torsional shear test. The
measured horizontal coefficient of permeability k hranged from 1.5 to 2.0 times to the verticalcoefficient of permeability k v .
3. After applying the K 0 consolidation, torsional
shear was given to the specimen and k h wasmeasured. As the measured value of k h did notincrease, there is no considerable change inpermeability of the sealing material whenmaximum shear deformation of 20% is applied.

ACKNOWLEDGEMENTS
This research was supported by Environment
Research and Technology Development Fund (No.
3K122109) by Ministry of Environments, Japan.

REFERENCES[1] Iai,S. ,Ichii,K., Liu,H. and Morita, T.,”Effective
stress analyses of port structures”, Special issue
of
Soils
and
Foundations,Japanese
Geotechnical Society, pp.97-114, 1998.
[2] Ichii, K., Iai, S., Sato., Y., and Liu, H., “Seismic
Performance Evaluation Charts for Gravity
Type Quay Walls,” Journal of Japan Society of620

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

EXPERIMENTAL STUDY OF INFLUENCE OF SEAWATER ON
STRENGTH OF CONCRETE STRUCTURES
Moshood N.TIJANI1, James I. INIM2 and Saheed O. ADETU1
Department of Geology, University of Ibadan, Ibadan - Nigeria
2
Department of Geology, Akwa Ibom State University, Uyo - Nigeria
1

ABSTRACTAgainst the background of direct and indirect action of physical and chemical deterioration of coastal andoffshore infrastructures, the potential influence of seawater on the strength and durability of concrete structures
were investigated. Cement concrete cubes of 150mm x 150mm x 150mm with cement, sand and aggregate mix
ratio of 1:2:4 (mix-1) and 1:1.5:3 (mix-2) were prepared. A total number of 96 concrete cubes of different water
cement ratio (w/c) of 0.4, 0.45 and 0.5 by weight were moulded. For the two mix ratios, casts in triplicate were
cured in both freshwater (as control) and seawater for different periods of times (i.e. 14, 21, 28, and 90 days)
followed by crushing-compressive strength tests. The study shows a proportionate increase in strength in thecontrol casts from 17,286 to 23,673KN/m2 and from 21,599 to 29,555KN/m2 for mix-1 and mix-2 respectively
as the curing time increased from 14 to 90 days. High compressive strengths observed for mix-2 casts comparedto those of mix-1 casts was attributed to higher proportion of cement employed. In addition, the strength
development for all cubes cured in seawater were relatively lower compared to those cured in freshwater at theend of the different testing periods until after 90 days; hence indications of possible negative impacts of salinity
on the coastal concrete infrastructures and the need for protection measures.
KEYWORDS: Seawater, Freshwater, Cement concrete, Curing; Compressive strength

piles, decks, break-water, or retaining walls) areusually exposed to impacts of seawater either
directly or indirectly. Furthermore, in many
developed countries, in order to relieve land frompressures of urban congestion and pollution, floating
offshore platforms made of concrete are being
considered for location of new airports, power plants,
and waste disposal facilities etc. while the use of
concrete offshore drilling platforms and oil storage
tanks is already on the increase.
Consequently, the long-term durability ofconcrete-based infrastructures and installations incoastal environments (in the face of impacts ofseawater) are of concerns to geoscientists, civil /structural engineers especially with respect to thephysical and geotechnical properties of thecomponent earth materials, choice of reinforcement
as well as mixing ratio in order to avoid failure(geotechnical and structural wise). The pioneering
work of J. Smeaton and L. J. Vicat on the effects ofseawater on concretes as reported in [4] revealed
that a large number of coastal concrete structures in
the United States, Canada, Cuba and Parama areexposed to chemical deterioration.
However, the effects of seawater on concretes
are usually related to factors characteristics ofseawater exposure impacts on concretes and theelements of specific concrete to be affected as wellas the consequences of interactions of seawater withthe concretes [5]. Hence factors inherent in seawater

INTRODUCTION
Concretes, a combination of cement, sand andaggregates and water mixed in different proportion
have received wide applicability in the constructionindustry. Concrete is one of the major building
materials in modern day constructions both for inland and coastal/offshore projects such as buildings,
dams, foundations, columns, beam and slab in shellstructures, bridges, highways, sewage- treatment
works, railway sleepers, cooling towers, dams,
chimneys, harbours, off-shore structures, coastal
protection work among others [1]–[3]. For several
reasons, effect of seawater on concrete deserves
special attention due to apparent impacts on strengthand durability of concrete structures. This is more
obvious in coastal and offshore structures which areusually exposed to the simultaneous action of anumber of physical and chemical actions of seawater.
Concrete structures and walls (either with or without
reinforcements) are critical components ofinfrastructural developments in coastal areas. Thus,
there is the need to understand the complexity ofconcrete durability problems in coastal environment.
Coastal environments are characterized by urban,
industrial and economic activities, with major
metropolitan cities located along coastal areas
world-wide. Consequently, in such coastal cities alarge number of infrastructures like jetties, harbours
and dockyards that are concrete-based (e.g. concrete621

GEOMATE- Osaka, Nov. 16-18, 2015

exposure impacts on concrete are wetting and drying
as well as chemical reactions of chlorides, sulphates
and alkalis while the elements of concrete that canbe affected these are cement, aggregates andreinforced steel components [5].
Consequently, a number of studies had reported
the vulnerability of the section of concrete above
high-tide to cracking and spalling [6], while
deterioration due to stresses caused by
crystallization pressure of salts within concrete
under wetting and drying conditions were also
observed [7]–[9]. In addition, a number of studies
had also reported deterioration of concrete in marine
environment due to the effects of chemical reactionof seawater constituents with cement hydration
products, reactive alkali aggregate expansion as wellas corrosion of embedded steel in reinforced or prestressed members [1]; [10]–[11]. In the light of theforegoing, the properties of concrete structures, suchas strength, durability, stability, resistance tochemical attacks of seawater under wetting anddrying conditions among others requires thoroughinvestigations.
Hence, over the years, it has become imperatives
to ascertain the qualities of properties of coastal
structures in contact with seawater in course of theirdesign life-span. Based on the above background,
this experimental study is aimed at assessing theinfluence of seawater on the concrete/construction
structures for purposes of highlighting thecontrolling factors in respect of the dynamics ofimpacts on strength and durability of concrete
structures on one hand. On the other hand to giveinsight into the possible modifications to themoulding parameters in order to mitigate thenegative influence of seawater and enhance
durability of concrete structures under seawaterattacks.

rodding and hard compactions. Two mixing
proportions of cement, sand and aggregate at ratios
1:2:4 and 1:1.5:3 (hereinafter referred to as mix-1
and mix-2) were employed in this study. Both mixes
were prepared separately with water/cement ratio of
0.4, 0.45, and 0.5. The concrete casts were thencured in seawater while additionally mix-1 and mix2 of 0.4 water/cement ratio (w/c) were cured in
freshwater as control casts.
Batching of concrete materials by weight was
adopted as the standard mode of quantifying
materials, using weighing balance. The mixing wasdone such that the already batched fine aggregate(sand) were first spread on a clean levelled hardsurface. The measured cement was then spread on
the fine aggregate and dry mixed uniformly. The
mixed sand and cement was again spread and thealready batched coarse aggregate (granite) was
spread on top. The correct quantity of water, basedon the water/cement ratio, was then gradually added
at interval during mixing until all the components
are properly and uniformly blended. The details of
the mixing ratio with respect to the weights of thedifferent components of the concrete are presented
in Table 1.
Table 1: Mixing ratios and weight of different
components employed in casting of concrete cubes
W/C Water Cement Sand Aggr.
ratio
(kg)
(kg)
(kg)
(kg)
1
0.40
12.5
30
60
120
2
0.40
12.5
30
60
120
3
0.45
6.75
15
30
60
4
0.50
7.5
15
30
60
5
0.40
12.0
30
66
132
6
0.40
12.0
12
66
132
7
0.45
6.75
15
33
66
8
0.50
7.5
15
33
66
W/C = Water-cement ratio; Aggr = Aggregates

Mix

MATERIALS AND METHODSFor this study, the fresh concrete was placed inalready lubricated and carefully prepared standard
moulds which were filled in three successive layers.
Each layer was compacted with 35 strokes with a
25mm high yield iron rod. The cast cubes (96 in all)were labeled and appropriately stored for 24 hours to
avoid vibration or any form of impact.

The materials used in this study include the socalled Portland cement type, fine and coarse
aggregates as well as fresh and seawater used for
mixing and curing. The cement was stored under dry
condition and free of lumps in conformity with BS
12. The fine aggregate consist of stream-bed washedsand deposit (free of organic matters), while thecoarse aggregate consist of crushed granite rock of
3/4 inch size. Fresh tap-water in the laboratory wasused for mixing while seawater from Atlantic Ocean
at Bar-Beach in Lagos, Nigeria was used for curing.

Curing and Compressive Strength Tests
Subsequent to the casting, the specimen cubes
were carefully stripped and placed in fresh and sea
waters for curing at different time durations of 14,
21, 28 and 90 days. At the end of the respective time
durations, triplicate cubes of mix-1 and mix-2 were
removed from the different solutions and kept inopen dry environment to allow for draining ofexcess water after which the weights of the cubes

Preparation and Casting of Concrete Cubes
The test specimen cubes for the determination ofcompressive strength of concrete were preparedusing the standard metallic moulds with dimensionsof 150×150×150mm adopting the procedure of622

GEOMATE- Osaka, Nov. 16-18, 2015

were recorded. Then, load was applied, with therespective cube placed with the cast face in contact
with the platens of the crushing machine for thedifferent cube after 14, 21, 28 and 90 days
respectively. The crushing was done at theGeotechnical laboratory of the Ministry of Works
andTransport,
Secretariat,
Ibadan.
Thecharacteristics compressive strengths of the concrete
cubes were later determined through calculation
using the already determined crushing loads and thecross-sectional area of the cube. Three specimens
were used in each test, and the averages were used
for analyses.

water or wave actions despite a cementing medium[12].
Chemical Characters of Fresh- and Seawaters
In addition, the results of the physico-chemical
analysis of the curing (seawater) and mixing (fresh)water are presented in Table 4. Expectedly, thevalues of pH, EC and TDS are 6.7, 1,352µS/cm and1,014ppm for freshwater and 9.2, 57,733µS/cm,
47,330ppm for seawater indicating relatively highdissolved solids and more free mobility of the ionsin the seawater vis-a vis the freshwater. This is also
consistent with the relatively higher concentrationsof the major cations (Na, Mg and K) and anions (Cland SO 4 ) in the seawater compare to the lower
concentration in the freshwater (Table 4). Hence, it
is quite possible to infer the possible types ofseawater attacks to include sulphate attacks(magnesium and potasium sulphates) of theconstituents of the hardened concrete cement;
chloride corrosion of reinforced steel metals andalkalis-aggregate reaction: all of which will lead todeterioration or damage to concrete structures as
pointed out by a number of previous studies such as[5]; [13]–[15].

RESULTS AND DISCUSSIONSGrain-size Distribution of Materials
The results of the grain-size distribution analyses forthe gravel and sand aggregate used in this study arepresented in Table 2 and 3. With coarse sand andfines size fraction of 87% and 8.1% respectively.
Table 2: Sieve analyses of the gravel aggregate
employed in casting of concrete cubes
Diameter
(mm)
25
19
12.5
9.5
4.75
Pan

Mass of Soil
Retained (g)
0.0
144
424
272
95
65

%
Retained
0.0
14.4
42.2
27.2
9.5
6.5

%
Passing
100
85.6
43.4
16
6.5
0.0

Table 4: Physiochemical and chemical analyses ofthe fresh water and seawater
Parameters
EC (µS/cm)
TDS (mg/l)
pH
Ca2+ ( mg/l)
Mg2+ (mg/l)
Na (mg/l)
K+ (mg/l)
Cl- ( mg/l)
SO 4 (mg/l)

Table 3: Sieve analysis of sand aggregate employedin casting of concrete cubes
Diameter
(mm)
4.75
2.36
0.85
0.43
0.30
0.212
0.150
0.106
0.075
Pan

Mass of Soil
Retained (g)
0.0
21.0
54.0
73.0
84.0
73.5
61.5
47.5
38.0
39.6

%
Retained
0.0
4.3
11.1
14.8
17.0
14.9
12.5
9.6
7.7
8.0

%
Passing
100
95.7
84.6
69.8
52.8
37.9
25.4
15.8
8.0
0.0

Mixingfreshwater
1,352
1,014
6.7
8.3
1.4
3.2
1.8
220
110

Curingseawater
57,73347,330
9.2
415
1,350
11,041406
22,500
1,475

The attack of magnesium sulphate on concrete
can be attributed to two principal reactions [5]; [16]–[18]: the first involves reaction with calcium
hydroxide Ca(OH) 2 or with calcium silicate hydrate(as component of Portland cement) to form soluble
magnesium hydroxide (Mg(OH) 2 ) leading to theformation of hydrated calcium sulphate (gypsum CaSO 4 .2H 2 O) and silica gel (SiO 2 ∙nH 2 O). The
second reaction involves reaction of magnesiumhydroxide and silica gel to form magnesium silicate
hydrate (MgSiO 2 ∙nH 2 O). In both cases, whitemushy substance usually appears on the surface ofconcrete [16], a situation that was also observed in
the course of this experimental study.
Furthermore, it had been reported that, in mostcases, the loss of adhesion and strength is the

The aggregate fall under the zone two of thecalibration graph considered suitable for concrete
setting for underwater construction. The low
percentage of the fine fraction (about 8%) is also
considered advantageous, as excessive fine particles
would easily be affected by the effect of flowing
623

GEOMATE- Osaka, Nov. 16-18, 2015

primary manifestation of sulphate as MgSO 4 attack
leading to decalcification of calcium-silicate-hydrate(C-S-H) to form magnesium-silicate hydrate (M-SH) followed by the formation of expansive salt
crystals causing concrete deterioration throughexpansion and cracking [5]–[6]. For chloride attack,the common impact is the penetration of the chloride
ions into the concrete and attendant acceleration ofreinforcement corrosion [18]. However, since theplain concretes were used in this study, further
discussion in the subsequent section with respect tothe impacts of seawater chemistry are limited tosulphate attacks.

strength or equilibrium leading to 90 days curing
time.

Compressive strength (MPa)35

Results of Compressive Strength Tests

mix1

mix2

30
25
20
15
0

20

40

60

80

100

Curing time (days)
Table 5 and 6 show the average compressive
strength of mix-1 and mix-2 cubes cured in
freshwater and seawater for 14, 21, 28 and 90 days
and at different water-cement (w/c) ratios.

Fig. 1: Plot of compressive strength against curing
time for w/c of 0.4
However, in comparison to the strength
development revealed by the freshwater cured
concretes as presented in Fig.1, the compressive
strength for concrete cubes cured in seawater at w/cratio of 0.40 reduced from 13.51MPa at 14 days to12.31MPa at 21 days and later increased to
31.85MPa at 90 days for mix-1 (Fig. 2).

Table 5: Compressive strength (in MPa) of mix1
concrete cubes cured in fresh water and seawater
FW
@0.4
w/c
17.29
19.11
20.76
23.67

SW
@0.4
w/c
13.51
12.31
14.74
31.85

SW
@0.45
w/c
13.10
11.66
13.39
30.67

SW
@0.5
w/c
12.92
11.91
12.86
30.22

35
Compressive strength (MPa)Curingtime(days)
1421
28
90

FW= Freshwater; SW= Seawater
Table 6: Compressive strength (in MPa) of mix2
concrete cubes cured in fresh water and seawater
Curing
FW
SW
SW
time
@0.4
@0.4
@0.45
(days)
w/c
w/c
w/c
14
21.60
25.24
21.18
21
24.44
20.28
15.85
28
25.10
21.67
18.15
90
29.56
38.96
36.37
FW= Freshwater; SW= Seawater

SW
@0.5
w/c
20.62
15.85
17.41
33.33

0.4w/c30

0.45w/c25

0.5w/c

20
15
10
5
0

20

40

60

80

100

Curing time (days)
Fig. 2: Plot of compressive strength against curing
time for mix-1

As presented, the compressive strength for theconcrete cured in freshwater at 0.4w/c expectedly
increases with increase in curing time (Fig. 1) from
17.2MPa at 14 days to 23.7MPa at 90 days for mix-1
while with mix-2 showed a relatively higher valuesof 22MPa to 30MPa which is clearly an indicationof higher cement content. The observed increase instrength of concrete cubes cured in freshwater is
expected as strength of concretes increase withincrease in curing time and cement setting until
equilibrium is reached [19]–[20]. This situation is
also clearly reflected by the curves in Fig. 1 as there
seems to apparent tendency towards stable constantSimilar trend with decrease in strength from25.24 MPa at 14 days to 20.28MPa at 21 days andlater increase to 38.96MPa at 90 days were observed
for mix-2 as shown in Fig. 3. The decrease instrength in the first 21 days can be attributed topossible chemical reaction or sulphate attack by
seawater (in form of cation exchange process) whichcaused the formation of soluble magnesiumhydroxide (Mg(OH) 2 ) and subsequently hydrated
calcium sulphate (gypsum - CaSO 4 .2H 2 O) among
others.

624

GEOMATE- Osaka, Nov. 16-18, 2015

Compressive strenght (MPa)40sulphate [21]–[22]. In addition, the presence ofchlorides, as in seawater, is said to also inhibit the
expansion and swelling of concrete due to sulphateattack while the greater solubility of hydrated
CaSO 4 in chloride solution also prevented swellingin deteriorated concretes [5]; [21]. Hence, these andapparently the limited duration of this study explain
why no swellings or expansions were observed in
the cured concrete cubes, despite the apparent
sulphate attacks.
The strength development for all cubes cured in
sea water were lower than those of the cubes cured
in fresh water until after 50 to 60 days of curingwhen the strength of the cubes cured in seawater atvarious w/c ratios increased more than that of cubeswith freshwater water (Table 5 and 6). Furthermore,it can be observed that for the seawater cured cubesat different w/c ratios the strength development formix-2 revealed higher strengths compared to thoseof mix-1 (Figs. 2 and 3). This is a clear reflection of
the positive impacts of higher cement ratio in mix-2.
In other words, appropriate cement to aggregate
ratio of concrete structures can enhance strength andensure resistance against deterioration in the face of
seawater attacks.

0.4w/c
0.45w/c
0.5w/c

35
30
25
20
15
10
0

20

40
60
80
Curing time (days)

100

Fig. 3: Plot of compressive strength against curing
time for mix-2
However, it had been reported that magnesium
sulphate rather than sodium sulphate is more often
responsible for sulphate attack of the several
constituents of Portland cement [5]. According to
the ACI Building Code 318-83, sulphate attack isclassified as severe, when the sulphate ion
concentration is higher than 1,500 mg/l [18]. Hencewith sulphate concentration of 1,475 mg/l for the
curing seawater used in this study and alongsidewith magnesium concentration of 1,350 mg/l, it
possible to infer the dominance of MgSO 4 attack as
the major causes of concrete deterioration in thisstudy.
The evidence of such sulphate attack was clearlyobserved on the surfaces of the moulded concrete
cubes after few weeks (Day 21) of this experimental
study as traces of white mushy substances /crystallized salts were observed till the end of theexperiment. Chemical equations of this deterioration
process can be outlined as follows:
Ca(OH) 2 + MgSO 4 ∙7H 2 O →+CaSO 4 ∙2H 2 O
5H 2 O .........(1)Mg(OH) 2+

3CaO∙2SiO 2 ∙nH 2 O + MgSO 4 ∙7H 2 O →+Mg(OH) 2
CaSO 4 ∙2H 2 O
SiO 2 ∙nH 2 O ...(2)+4MgSO 4 + SiO 2 ∙nH 2 O →4MgO∙SiO 2 ∙8∙5H 2 O
4∙5H 2 O ............(3)n-+SUMMARY AND CONCLUSION
Experimental studies of the influence of seawateron the strength of concrete structures involved
various laboratory and experimental works in whichconcrete cubes was cast and subjected to various
laboratory work and analysis in other to assess thecontrolling factors in respect of dynamics onstrength of concrete.
Strength development of cubes cured in fresh
water was significantly different from concrete
cubes cured in seawater throughout the 90-day
period of curing while the strength development in
concretes cured in seawater was not significantly
different from each other at 90 days of curing. At 90
days of curing, all the cubes cured in seawater had
increased far more than cubes cured in freshwater.
The increase in strength was highest in cubes withmix 2 at 0.4w/c. On the other hand, a decrease incompressive strength as the amount of water tocement ratio increased through the 90-day curing
period, was observed.
All the concretes cured in sea water showed
lower compressive strength with decrease in cement
mixture on one hand and on the other hand, adecrease in compressive strength were also observed
as the amount of water to cement ratio increased
through the 90-day curing period. This signifies that
increase in the water to cement ratio reduces thestrength of concrete, and could greatly affect theresistance to the aggressive elements in the seawater.
Nonetheless, based on the chemistry of thecuring seawater, it can be inferred that, at the early

For this study, it is possible to assume that due
reactions represented by Eq. (1) and Eq. (2) are themost likely processes observed due to limited
duration of the study that may not allow forcompletion of reaction process in Eq. (3). Moreover,it had been reported that the reduction in the amountof free Ca(OH) 2 due to pozzalanic reaction usuallylimits the degree of reaction of Ca(OH) 2 and625

GEOMATE- Osaka, Nov. 16-18, 2015stage of curing, the concrete deterioration isassociated with the reaction of cement matrix andcalcium hydroxide forming magnesium hydroxide(Mg(OH) 2 ) and later hydrated calcium sulphate(gypsum) with an appearance of white, mushy
substance as noticed on the concrete cubes.
In addition, the lost in strength of concrete cubes
cured in seawater can be attributed to impacts ofhigh Na+ and K+ ions (11,041 mg/l and 406 mg/l
respectively) causing aggressive alkali-aggregate
reaction on one hand and the weakening of thecement paste by Mg and SO 4 ions (sulphate attack)on the other hand. However, this study revealed thathigher cement ratio can reduce impacts of theseawater attacks and enhance durability of coastal
concrete structures. Further studies in respect ofimpacts of chloride on corrosion potential ofreinforced concretes are recommended.

strength of concrete. Journal of Engineering
Applied Science, Vol. 2(2), 2007; pp. 412–415.
[10] Mohammed, TU, Hamada, H, and Yamaji, T,
Performance of seawater-mixed concrete in the
tidal environment. Cement and Concrete
Research, Vol. 34 (4), 2004; pp.593–601.
[11] Aburawi, MA, and Swarmy, RN, Influence ofSalt Weathering on the properties of Concrete.
Arabian Journal for Science and Engineering,Vol. 33 (No. 1B); 2008; pp. 105-115.
[12] Zaher, K. and Shihada, S, Effect of Gazaseawater on concrete strength for different
exposures. Journal of the Islamic University ofGaza, 11 (20), 2003; pp. 156–172.
[13] Benstend J, A discussing if the paper“Magnesium sulphate attack on Portland
cement: Part II. Chemical and mineralogicalanalysis,” by Bonen D and Cohen MD; Cement
and Concrete Res. Vol. 23 (30) 1993; pp. 743744.
[14] Bonen D, A microstructure study of the effectproduced by magnesium sulphate on plain andsilica fumes-bearing Portland cement mortars,
Cement and Concrete Res. Vol. 23 (30) 1993.
pp. 541-553.
[15] Wakeley, D, Poole, TS. Ernzen, JJ, Neeley, BD,
Salt saturated mass concrete under chemical
attack, in: P. Zia (Ed.), High Performance
Concrete in Severe Environments, Am. Concr.
Inst. SP-140, 1993, pp. 239– 267.
[16] Skainy J, Marchand J, Odier I, Sulphate attack
on Concrete. London: 2002. Spon Press.
[17] Hill J., Byars, EA, Sharp, JH, Lynsdale, CJ,
Cripps, JC, Zhou, Q,. An experimental study of
combined acid and sulphate attack of concrete.
Cement & Concrete Composites 25; 2003; pp.
997–1003, Elsevier Science Ltd.
[18] Wegian, FM, Effect of seawater for mixing andcuring on structural concrete. The IES Jour.
Part A: Civil and Structural Engineering; Vol.
3, (4): 2010; pp. 235-243.
[19] Kaushik, SK, and Islam S, Suitability ofseawater for mixing structural concrete
exposed to a marine environment. Cement &Concrete Composites, Vol. 17(3); 1995;
pp.177-185.
[20] Al-Amoudi OSB, Perfomance of 15 reinforced
concrete mixtures in magnesium –sodium
sulphate environments, Construction and
Building Materials, 9 (3); 1999; pp 149-158.
[21] Lea, FM, The Chemistry of Cement andConcrete. St. Martin's Press, New York, N. Y.,
1956.
[22] Massazza, F, Pozzolanic Cements. Cement &Concrete Composites, Vol. 15(4); 1993; pp.
185– 214.

REFERENCES[1] Kaushik, SK, and Islam, S, Suitability ofseawater for mixing structural concrete exposed
to a marine environment. Cement and Concrete
Composites, 17 (3), 1995; pp. 177–185.
[2] Neville, A, Water, Cinderella ingredient of
concrete. Concrete International Vol. 22(9);2000; pp. 66-71.
[3] Olutoge, FA, and Amusan, GM, The Effect of
Seawater on Compressive Strength of Concrete.
International Journal of Engineering Science
Invention, Volume 3(7); (2014); pp. 23-31.
[4] Tibbetts, DC, Performance of Concrete in
Seawater. Canadian Building Series, No.2,
1968; pp. 159 – 180.
[5] Mather, B. Effects of Seawater on Concrete.
Symposium on Effects of Aggressive Fluids on
Concrete at the 44th Annual meeting ofHighway Research Board (Committee MC-B2
on Performance of Concretes: Chemical
Aspects). Publ. US Army Engineers Waterways
Experiment Station, 1964: 19p.
[6] Mehta PK., Sulfate attack on concrete: A
critical review. In: R.R. Villarreal (Ed.),
Concrete Durability, Univ. Autonoma de Nuevo
Leon, 1993; pp. 107– 132.
[7] Kumar, S, Influence of water quality on thestrength of plain and blended cement in marine
environments, Cement and Concrete Research,
Vol. 30; 2000; pp 345-350[8] Mehta, PK and Monteiro, P, Concrete:
microstructure, properties, and materials. 3rd ed.
New York: McGraw-Hill Professional, 2005.
135–136.
[9] Akinkurolere, OO, Jiang, C, and Shobola, OM,
The influence of salt water on the compressive

626

Environment

627

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

POTENTIAL USE OF SILT AND SLUDGE AS LANDFILL CAPPINGMATERIAL
Hamidi Abdul Aziz1,2, Wong Chin Yik1, Muhd. Harris Ramli1, Salem S. Abu Amr1
1
School of Civil Engineering,Engineering Campus, Universiti Sains Malaysia,
14300 Nibong Tebal, Penang, MALAYSIA
2Solid Waste Management Cluster, Engineering Campus, Universiti Sains Malaysia

Abstract
Landfills have been the most common methods in municipal solid waste disposal in developing countries. Landfill
capping is a containment technology that forms a barrier between the contaminated media and the surface. Presently,
the commonest capping materials for landfills are sandy material and laterite soil. The objective of this research was
to study the suitability of silt from Ulu Kinta dam in Malaysia and sludge (domestic wastewater sludge and water
treatment sludge) as landfill capping material. The 2 materials were mixed with varying proportions of pure silt, 20,
40, 60, 80 and 100 percent of sludge. The results showed that silt material has moderate hydraulic conductivity withk of value 4.81x10-04 cm/s, poor cohesion strength, 7.64kN/m2 and good friction angle, 36.22°. Sewage sludge has
the best properties among the 3 materials, good hydraulic conductivity with k of 2.07 x10-06 cm/s, moderatecohesion strength, 8.95kN/m2 and very high friction angle of 45.73°. Water treatment sludge’s hydraulicconductivity, k was 2.9 x10-06 cm/s which is good but having poor cohesion strength and friction angle of
6.16kN/m2 and 6.13°, respectively. Silt and sludge were also mixed to test whether the method improves theproperties of the final product. The result was negative. The use of 100% sewage sludge exhibited better results thanothers.
Keywords: silt, sewage sludge, hydraulic conductivity landfill capping

628

The main objective of covering systems is to
reduce or eliminate the transport of fluids through thewaste. Covering systems must function withminimum maintenance, promote drainage, minimize
erosion of the cover, accommodate settling, and havehydraulic conductivity less than or equal to that ofany bottom liner system or natural soil present. In
humid climates, cover and/or re-vegetation areusually required for erosion protection and infiltration
control. The regulations do, however, permit
alternative designs if they can achieve erosion andinfiltration protection equivalent to an acceptable
conventional cover system. This indicates thesignificance of searching different alternatives tocompacted clay-based barriers in arid areas andevaluates their performance under various
environmental conditions [8]. In the present day, theavailable capping materials consist of sand material
and laterite soil. Most wastewater treatments process
produce sludge, which has to be disposed.
Conventional secondary sewage treatment plants
typically generate a primary sludge in the primary
sedimentation stage of treatment and a secondary,biological, sludge in final sedimentation after the
biological process. The characteristics of thesecondary sludge vary with the type of biologicalprocess and, often, it is mixed with primary sludge
before treatment and disposal. Approximately onehalf of the costs of operating secondary sewage
treatment plants can be associated with sludge
treatment and disposal.
Many laboratory tests are needed to ensure thatthe materials being considered for each of the landfill
cap components are suitable. Landfill instability canbe solved by understanding interface friction
properties between all material layers, natural orsynthetic. The major engineering soil properties thatmust be defined are the shear strength and hydraulicconductivity. Shear strength may be determined withthe unconfined compression test, direct shear test, or
triaxial compression test. The study aimed to find analternative capping material which has little
commercial value thus saving the cost of landfill
capping material without neglecting the strength
properties of the landfill capping for safety purposes.
In this regard, the performance of silt and sludge as
capping material for landfill in comparison with sandand laterite soil is evaluated. Both are considered aswaste materials with no commercial values. Theirusefulness as capping material either as an individual
or in a mixed, form has not been well investigated,
The performance in terms of the hydraulicconductivity and shear strength of silt, sludge and a
mixture of sludge and silt are determined.

INTRODUCTION
A landfill site is a site for the disposal of wastematerials by burial and is the oldest form of waste
treatment. Historically, landfills have been the mostcommon methods of organized waste disposal andremain so in many places around the world. Many of
the old landfills that are problems today wereconstructed in this fashion. When hazardous waste
was disposed, it was placed in metal drums thatrusted through in a few years, leaving the waste toseep through the landfill. Water was allowed to seepthrough the cover of the landfill, saturate the wastes,and come out the bottom or sides as leachate (liquidthat leaches through the landfill and collects inpockets in the landfill material or below the landfill).
Landfill must have a capping system. Landfill
capping is a containment technology that forms abarrier between the contaminated media and thesurface, thereby shielding humans and theenvironment from the harmful effects of its contents
and perhaps limiting the migration of the contents.
A cap must restrict surface water infiltration into
the contaminated subsurface to reduce the potential
for contaminants to leach from the site. Presently, thecommonest capping materials for landfills are sandy
material and laterite soil. Most wastewater treatments
process produce sludge, which has to be disposed.
Conventional secondary sewage treatment plants
typically generate a primary sludge in the primary
sedimentation stage of treatment and a secondary,biological, sludge in final sedimentation after the
biological process. The characteristics of thesecondary sludge vary with the type of biologicalprocess and, often, it is mixed with primary sludge
before treatment and disposal. Sanitary landfill is oneof the secure and safe facilities for the disposal of
MSW; it needs high standard of environment
protection in the operation of landfill [2, 3].
To investigate the performance of sanitary landfill
the behavioral patterns namely; leachate generation,
landfill gas (LFG) emissions, etc. are required [4, 5].
Sanitary landfill plays a significant role in thedisposal of MSW in most of the developing countries
like Bangladesh. Most of the landfill in developing
countries does not have any liners at the base orproper top covers, which results in the potential
problemsofgroundwater/surface
watercontamination due to the leachate generated fromsolid waste landfill. The assessment of environmental
risks associated with leachate contamination of water
resources requires the understanding of the transportof contaminants along the river networks and into theaquifer [6, 7].

629

failure, such as slope failure, ground depression,deferential settlement and tension crack, can beprevented. Therefore, the main characteristics;
permeability and strength of the soil mix material
were studied in order to assess the suitability of thelandfill capping material.
As a standard practice in soil mechanics, before
any soil characteristic test is conducted, the sample
must be classified. The classification of the sample isrequired in order to choose the suitable soil
characteristic test. The classification test starts withsieve analysis, as grain particle-sorting was perform
on sand, silt and laterite soil to determine the grain
size distribution curve. The sieve analysis test was
performed according to the ASTM C136-06 standard
test method for sieve analysis of fine and coarse
aggregates. Based on the standard sieve analysis, theparticle size analysis for finer material that isavailable in silt and laterite soil was assessed by using
a hydrometer test in accord with the ASTM D422-63
standard.
The changes in water content can also alter themechanical properties of a clay material. Therefore,
the liquid limit test based on the BS 1377 standard
and the plastic limit test based on the ASTM D4318
standard were conducted on silt and laterite soil tounderstand this change in mechanical properties.
Based on the information obtained from sieve
analysis, liquid limit and plastic limit tests, the soil
classification group was determined by using theunified soil classification system (USCS) based on
the ASTM D2487 standard practice for theclassification of soils for engineering purposes. The
specific gravity of sand, silt and laterite were
determined in accord with the ASTM D854-00
standard test for specific gravity of solids by water
pycnometer.
Once the soil had been classified and its
characteristics were identified, the permeability andshear tests were conducted. Prior to these assessments,
the maximum dry density of the sample wasdetermined. The maximum dry density values of thesample are required in order to ensure that the sample
prepared for the permeability and shear tests wereable to simulate the actual compacted soil at the site.
The sand, silt and laterite maximum dry densities
were determined by using the modified proctor test
based on the ASTM 1557 standard.
Since the landfill capping material requirement
required low permeability of the material, the fallinghead permeability tests were conducted. The
permeability tests were performed on silt, laterite anda silt-sludge mixture of soil. The ratios of the silt andsludge mixture are 0, 20, 40, 60, 80 and 100%. Twotypes of sludge, as mentioned in the materials and

MATERIALS AND METHODS
MATERIALS AND SAMPLING
The main focus of this study is to find analternative landfill capping material that uses various
soil mixtures; specifically a mixture of silt and sludge.
The geotechnical characteristics of the mixture werealso compared with other different types of soil.
Therefore, silt, sludge, sand and laterite soil wereused in this study.
The silt used in this study was obtained from thePerak Water Board (LAP) dam in Ulu Kinta, Perak.
The silt is basically sediment transported by the flowof river water into the dam. Approximately 15 kg ofsilt was taken from piles of excavated silt with ashovel. The sludge was obtained from the Bayan Baru
wastewater treatment plant and the LAP Jalan Baru
water treatment plant. Approximately 15 kg of fresh
sludge were taken from sludge piles under the
dewatering machine.
The sand used in this study had a fine texture andit was purchased from a nearby sand mining quarry.
The purpose of the sand in this study is to compare
the strength properties of sand with a silt and sludge
mixture. The laterite soil used in this study wasmaterial that was available locally. It is commonly
found in hot and wet tropical regions such as
Malaysia. Laterite soil, which is rich in iron andaluminum, develops through an intense and lengthy
weathering process of the parent rock. This process
influences its soil characteristics and makes it areddish, clayey soil due to iron oxides. The purposeof the laterite soil is to compare the strength andhydraulic conductivity of laterite with the silt andsludge mixture.
EXPERIMENTAL PROCEDURES
In selecting a suitable landfill capping material,
there
are
severalimportantgeotechnicalcharacteristics that merit consideration. Since a
landfill should be able to contain waste material, it isvery important to ensure that the soil’s permeability isable to perform according to the landfill’s designed
concept. Besides the permeability of the capping
material, the geotechnical strength characteristics ofthe material is also important in order to ensure thatthe landfill is structurally stable and able to perform
according to its design specifications for many years.
If this can be achieved, any geotechnical structure630

sampling section, were use in this study. In order toassess the strength of the silt and sludge mixture, adirect shear test was conducted based on the ASTM
D3080 standard test method for the direct shear testof soils under consolidated drained conditions. The
same tests were also conducted on sand, silt andlaterite soil for comparison. Based on the tests thatwere conducted, the alternative landfill capping
material capability of various soil mixtures wasassessed.
RESULTS AND DISCUSSIONSBoth table1 and figure1 shows the results ofmodified proctor test. Table 1 shows the maximum
dry unit weight (g/cm3) ratios for silt, sand andlaterite soils. Referring to the graph above, the resultof hydraulic conductivity is as plotted. The hydraulicconductivity of sludge regarding whether its sewerageor water treatment sludge, it is better than thehydraulic conductivity of silt. The mixing ratio of silt
to sludge is 0, 20, 40, 60, 80 and 100 percent ofsludge. As the sludge percentage increases thehydraulic conductivity drops which represent a dropin permeability. It is good since the landfill capping
requires low permeability material.
Table 1: Summary result for the modified
proctor test

Maximum dry unit
3
weight(g/cm )

Silt

Sand

Laterite

1.8

1.98

1.85

Hydraulic conductivity,k(cm/sec)

Graph of Hydraulic conductivity,k(cm/sec) versus Sludge
percentage,%water treatment sludge

sewerage sludge

1.00E-03
1.00E-04
1.00E-05
1.00E-06

0

20

40

60

80

100

Sludge percentage,%

Figure 1:Graph of Hydraulic conductivity,k(cm/sec) versus Sludge percentage,%Comparing both water treatment and seweragesludge, the k value of sewerage sludge has a better
advantage. The permeability of sewerage sludge is631

lower than water treatment sludge, which makes it amore ideal material for landfill capping. Laterite soil
which is the currently used capping material has ak value of 8.24 e-08 cm/sec, which none of the
combined sludge/silt samples nor did pure sludge
could achieve such low hydraulic conductivity. The
lowest achievable is 2.07 e-06 cm/sec by 100%
sewerage sludge, which is still quite a significant
difference, compared to laterite soil.
The result of the direct shear test can bereferred in tables 2 - 7, which shows the c-value(cohesion strength) and phi value (soil friction
angle) of each soil sample, including the combined
sample of silt/sludge of 0, 20, 40, 60, 80 and 100%
sludge. There is a significant difference between
sewerage sludge and water treatment sludge. By
mixing silt with sewerage sludge, it tends to weaken
the cohesive strength and friction angle. Whereas
the silt strengthens the cohesive strength and friction
angle of the water treatment sludge. The highest
strength of all the mixed samples is the pure seweragesludge sample, with a c value of 8.95 kN/m2 and afriction angle of 45.73°. The friction angle of pure
sewerage sludge exceeds both laterite and sandmaterial, which means the failure plane is more stablecompared to both laterite and sand material; however,the c value (cohesion) is lower than both sand andlaterite. Laterite has the highest c value, 13.72 kN/m2,
whereas sewerage sludge’s c value is 8.95 kN/m2.
The experiment suggests that the best material toreplace the current landfill capping material would be
100% pure sewerage sludge with no mixed silt in it.
Laterite soil with a k value of 8.24 e-08 cm/sec
tends to resist water infiltration into the sample, thus
enhancing the surface water runoff. The k value of silt
is 4.81 e-04 cm/sec, the significant difference of
hydraulic conductivity between both samples
concludes that silt will not provide surface waterrunoff as effectively as laterite soil. From the lab tests’
results, the hydraulic conductivity of sewerage sludgeis 2.07 e-06 cm/sec. Although the hydraulicconductivity of sewerage sludge is not as low as thelaterite sample, it is sufficient to support surface
water runoff as the hydraulic conductivity value ofsewerage sludge is considered as low permeability.
The hydraulic conductivity of water treatment sludge,
k is 2.9 e-06 cm/sec, which is just slightly higher thansewerage sludge. Nevertheless, it is considered as low
permeability. The permeability level of water
treatment sludge would be sufficient to support
surface water runoff. In terms of shear strength
resistance, referring to table 2, silt has a c value of7.64 kN/m2, whereas laterite soil and sand sample has
a c value of 13.72 kN/m2 and 11.17 kN/m2
respectively. This proves that the bond strength of silt

is not good at all. Silt is very weak in bond strength,
when held in hand it tends to only stick to the hand alittle. Whereas the friction angle value, Ø of silt is
36.22°. The friction angle value is in between lateriteand sand sample. 41.91°< 36.22°< 34.12°. Laterite <silt < sand is the sequence of friction angle value.
This means that the failure plane of silt is on theacceptable region, and the results are satisfactory.

times compared water treatment sludge with lateriteand sand sample, proving that the bond strength inwater treatment sludge is poor. The friction angle
value, Ø of water treatment sludge is 6.13°. The
friction angle value is very much lower than lateriteand sand sample, 41.91°and 34.12°. Pure water
treatment sludge would not be a good option for
capping material since both the cohesion strength andfriction angle value is too low compared to lateriteand sand sample.

Table 2: C and Phi value of laterite, sand and silt
sample in direct shear test

Table 5: Hydraulic conductivity, k(cm/s) of silt,
laterite and sewerage sludge sample

laterite

sand

silt

c

13.72

11.17

7.64

phi

41.91

34.12

36.22

Silt
Hydraulic
4.81E-04
conductivity (cm/s)

Table 3: C and Phi value of mix sample ofsewerage sludge and silt in direct sheartest
Seweragesludge(%)

100

80

60

40

20

silt

c

8.95

8.9

8.77

7.88

7.74

7.64

phi

45.73

43.83

40.03

39.56

37.77

36.22

Laterite

Failed Sewerage
Sludge8.24E-08

2.66E-02

Table 6: Hydraulic conductivity, k(cm/s) of mix
sample of sewerage sludge and silt
100

In terms of shear strength resistance, referring totable 3, sewerage sludge has a c value of 8.95 kN/m2.
The results proved the fact that sewerage sludge does
not have good bonding properties. Whereas thefriction angle value, Ø of sewerage sludge is 45.73°.
The friction angle value is higher than laterite andsand sample, 45.73° < 41.91° < 34.12°. Seweragesludge < laterite < sand is the sequence of friction
angle value. This means that the failure plane ofsewerage sludge is better than both laterite and sandsample which makes sewerage sludge a very goodmaterial to replace the current landfill capping.

80

60

40

20

Hydraulicconductivity 2.07E-06 3.78E-06 9.50E-06 1.57E-05 7.79E-05 4.81E-04(cm/s)

Table 7: Hydraulic conductivity, k(cm/s) of mix
sample of water treatment sludge and silt
Watertreatment
sludge(%)

100

80

60

40

20

Water
treatment
sludge(%)

CONCLUSION
80

60

40

20

silt

c

6.16

5.21

5.82

6.44

7.02

7.64

phi

6.13

27.7

30.07

31.5

33.36

36.2
2

silt

Hydraulicconductivity 2.90E-06 5.05E-06 1.14E-05 1.93E-05 1.04E-04 4.81E-04(cm/s)

Table 4: C and Phi value of mix sample of water
treatment sludge and silt in direct shear test

100

silt

Based on this study, silt is considered not suitable
for landfill capping due to its moderate permeability
and low bond strength. Although the bond strength ofsewerage sludge is not desirable, the hydraulicconductivity and friction angle value shows satisfying
results. Judging from the strength and permeability
properties of sewerage sludge, it can be accepted as
the replacement for landfill capping material. The
ratios of mixed samples are the same in both types ofsludge, which are 0%, 20%, 40%, 60%, 80% andReferring to table 4, the water treatment sludge
has a low c value of 6.16 kN/m2. The difference
between the cohesion strength is approximately two632

100% sludge. In the sewerage sludge, the hydraulicconductivity, cohesion strength and friction angle insludge itself is better compared to silt. In the water
treatment sludge sample, the hydraulic conductivity
shows low permeability, which is good. But its
cohesion strength and friction angle has very weakvalue. By mixing the silt in the water treatment
sludge, the cohesion strength increased slightly
whereas the friction angle improved a lot. Hydraulicconductivity of the water treatment sludge increases
as silt is added in it. The case is the same withsewerage sludge. Among all the samples of mixture,
100% sewerage sludge poses the best and suitableproperties for landfill capping usage.

[8] Blight G, (2008). "Slope failures in municipal
solid waste dumps and landfills." Waste Management
and research, 26, 2008, 448 - 463.

REFERENCES[1] Tubtimthai O, Landfill lysimeter studies forleachate characterization and top cover methane
oxidation. Resources and Development, Master thesis,
Asian Institute of Technology, School ofEnvironment, Resources and Development, Thailand,
2003,[2] Davis ML, Cornwell DA, Introduction toEnvironmental Engineering: Third Edition. New
York, McFraw-Hill International, ISBN 0-07, 1998,
115237-4.
[3] Weand B L"Landfill Covers for Use at Air ForceInstallations." Report for the Air Force Center ofEnvironmental Excellence. Mitretek Systems , 2010.
[4] Visvanathan C, Trankler J, Kuruparan P,
Xiaoning Q, "Influence of landfill operation andwaste composition on leachate control: lysimeter
experiments under tropical conditions, Urban
Environmental Engineering and Management
Program,Asian Institute of Technology, Thailand,
2002.
[5] Fatta AP, Loizidou MA, study on the landfill
leachate and its impact on the groundwater quality of
the greater area, 2011, 175–190.
[6] Bodzek J SG, Hung YT, "Treatment of landfill
leachate." Handbook of Industrial and Hazardous
Wastes Treatment, 2004, 1155–1208.
[7] Barker JEB, Labonte M, "Groundwatercontamination at a landfill sited on fractured
carbonate and shale, 1988, 1–25.

633

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

OPTIMAL DESIGN OF RAIN GAUGE NETWORK IN JOHOR BY
USING GEOSTATISTICS AND PARTICLE SWARM OPTIMIZATION
Mohd Khairul Bazli Mohd Aziz1, Fadhilah Yusof2, Zalina Mohd Daud3, Zulkifli Yusop4, Mohammad Afif
Kasno5
1
Centre of Preparatory and General Studies, TATI University College, Malaysia, 1,2Department ofMathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia, Malaysia, 3UTM Razak Schoolof Enggineering and Advanced Technology, Universiti Teknologi Malaysia, Malaysia, 4Institute ofEnvironmental and Water Resource Management (IPASA), Faculty of Civil Enggineering, Universiti
Teknologi Malaysia, Malaysia, 5Malaysia – Japan International Institute of Technology (MJIIT),Universiti
Teknologi Malaysia, Malaysia.

ABSTRACTThis study proposes particle swarm optimization (PSO) approach to determine the optimal number andlocations for the optimal rain gauge network in Johor state. The existing network of 84 rain gauges in Johor isalso restructured into new locations by using daily rainfall, humidity, solar radiation, temperature and windspeed data collected during the monsoon season (November – February) of 1975 until 2008. This study used the
combination of geostatistics method (variance-reduction method) and particle swarm optimization as thealgorithm of optimization during the restructured proses. The numerical result shows that the new rain gauge
location provides minimum value of estimated variance. This shows that the proposed method can serve as ananalysis tool for a decision making to assist hydrologist in the selection of prime sites for the installation of rain
gauge stations.
Keywords: Rainfall Network, Geostatistics, Particle Swarm Optimization

In hydrological studies, the rain gauge network
used is often sparsed and thus incapable of providing
adequate rainfall estimation necessary for effectivehydrological analysis and design of water resources
projects. Significant design errors in the water
resources projects will eventually cause in theimmense loss of lives and property damages resulted
from use of inaccurate rainfall data.
Thus, identification and selection of the optimalrain gauge network configuration with optimal
number and locations of rain gauge stations is themain objective of the network design. Hence, theoptimal rain gauge network should contain thenumber and locations of rain gauge stations in such a
way that it can produce optimum rainfall
information and data with minimum uncertainty andcost [2,4]. One can approach the problem either byremoving redundant stations from the network tominimize the cost or by expanding the network withinstallation of additional stations to reduce theestimation uncertainty [5]. Some studies applied the
kriging technique in combination with othertechniques such as entropy [3] and multivariate
factor analysis [7] for the network design. A few
studies also combined optimization method based on
simulation tools (e.g. simulated annealing) with the
kriging technique [2, 8, 9] to obtain the optimal rain
gauge network.

INTRODUCTION
Collectively, rainfall data deliver important input
for effective planning, designing, operating andmanaging of water resources projects. Rainfall data
are employed in numerous water resourcesmanagement tasks such as water budget analysis andassessment, flood analysis and forecasting,streamflow estimation, and design of hydraulicstructures. A reliable and optimal rain gauge
network can provide accurate and precise rainfall
data that is crucial for effective and economic designof hydraulic structures for flood control. This will
help the researchers to minimize the hydrologicaland economic risk and errors involved in different
water resources projects. Rain gauge network is
usually installed to facilitate the direct measurementof rainfall data that characterize the spatial andtemporal variations of local rainfall patterns in acatchment [14]. A rain gauge network should be
denser than networks used to measure othermeteorological elements (e.g. temperature), because
the highly variable rainfall patterns and its spatial
distribution cannot be represented effectively
without having a network of enough spatial density[2]. A well-designed rain gauge network thus should
contain a sufficient number of rain gauges, whichreflect the spatial and temporal variability of rainfall
in a catchment [3].
634GEOMATE- Osaka, Nov. 16-18, 2015

A network design methodology was developed inthis study to determine optimal number andlocations of the existing stations in the current rain
gauge network located in the Johor state, Malaysia.
The procedure involves a methodical search for the
optimal number and locations of rain gauge stationsin the network that minimize estimated variance.
The methodology presented in this study is in linewith that of [2] who used the kriging-based
geostatistical and simulated annealing as an
optimization method to determine the optimal
number and location of rain gauge stations in theexisting The major contribution here is that unlike
the work of [2], the developed methodology
considered the particle swarm optimization
technique as an optimization method to provide fast
convergence optimization procedure. The rest of thepaper has been organized as follows. First, details of
the study area and datasets used are presented, which
is followed by the methodology. The results are
summarized next and finally, the conclusions are
drawn.

LegendH!

Rain Gauge Station
River
Mountain

Fig.1

Rain gauge locations

METHODOLOGY
Geostatistical Method
The network design problem consists in
obtaining the number N and the location of rain
gauges stations that give the best estimate areal

STUDY AREA AND DATA DESCRIPTION

mean rainfall. The estimation variance σ is a basic
tool of variance reduction techniques for optimal
selection of sampling locations. For the applicationof the variance reduction method to optimal locationof sampling sites, a semivariogram must be
modelled.
A semivariogram, γ h is one of the significant
functions to indicate spatial correlation in
observations measured at sample locations.
Semivariogram is represented as a graph that shows
the difference in measure with distance between all
pairs of sampled locations. The estimated variancedepends on the semivariogram model, the number N
of rain gauges and its spatial location. Therefore,choosing an appropriate semivariogram model isvital in determined the optimal estimation variance.
Let h be the lag or distance, and Z be an intrinsic
random function and let Z ( xi ) , for i = 1,2,  , N
2Johor is the second largest state in the Malaysia
Peninsular, with an area of 18,941 km2. The rivers inJohor and its streams are important sources of watersupply for the people of Johor. The catchment area
contains a dense rain gauge network, 84 rain gauges
covering 19,210 km2 in Johor (see Figure 1). The
current overall network density for the whole ofJohor state is about 194 km2 per gauge, whichsurpasses even the ideal WMO recommendation of
one gauge per 600 – 900 km2 for flat areas. In fact, italso fulfills the ideal density for mountainous regionof 100 – 200 km2 per gauge. The data used toperform the analysis was from the daily rainfall,elevation, humidity, temperature, solar radiation andwind speed measurement from November until
February of 1975 through 2008 from 84 rain gauge
stations that are located all over Johor. Figure 4
shows the mapping of the humidity, temperature,solar radiation, wind speed and elevation in Johor.
From Figure 4, the highest humidity area is thecentre of Johor, west side of Johor receives moreheat based on the solar radiation and the temperature,and the wind speed is highest along the coast. The
data was obtained from Department of Irrigation andDrainage(DID)
Malaysia
andMalaysia
Meteorological Department (MMD).

( )be a sampling of size N. Then the following
expression is an unbiased estimator for thesemivariogram of the random function:

γ (h ) =1 n (h )[Z (xi + h ) − Z (xi )]2∑2n(h ) i =1

(1)

Equation 1 is used to compute experimental
semivariogram from the data under study. Bychanging h, both in distance and direction, a set ofthe sample (or experimental) semivariograms for thedata is obtained [10].

635GEOMATE- Osaka, Nov. 16-18, 2015[

The exponential semivariogram models are
selected to fit the data. The exponential
semivariogram model equation is as follow:γ (h ) = C 1 − e


3h
−
a

Where W = λ1
4. Calculate
variance






(2)

i =1

i =1 j =1

Where
(4)

i =1

k

∑ λ = 1.
i =1position of all particles in the population (swarm).
The personal best position represents the best
position found by a particle so far and is denoted aspi = ( pi1 , pi 2 ,  , pin ) .

iThis is an algorithm for the ordinary kriging
estimation to calculate the estimation variance (Olea,
2003):
1. Calculate each term in matrix G.
Let xi ’s be the sampling sites of a sample subset(

The velocity vi = (vi1 , vi 2 ,  , vin ) gives the

position change of a particle. In the original
proposed PSO of Kennedy and Eberhart, the newvelocity for each particle is calculated according to
Eq. (1). To update the new position of each particle,
Eq. (2) is used:)of size k , i = 1,2,  , k and let γ xi , x j ’s be
the experimental variogram. Then the G is thematrix γ (x1 , x1 ) γ (x 2 , x1 )γ (x , x ) γ (x , x )
2
2
 1 2

G=



γ (x1 , x k ) γ ( x 2 , x k )
 1
1

(7)

solution. Each particle is aware of its current
position, its own personal best position, its current
velocity and the single global (or local) best position.
The global best position is represented as
g i = ( g i1 , g i 2 ,  , g in ) and symbolizes the bestkSubject toestimation

PSO was developed by Kennedy and Eberhart,[12] in 1995, inspired by the social behavior of bird
flocking. The movement of each swarming particle
is determined by a combination of a stochastic
element and a deterministic element, [1]. A
population (swarm) of particles is initialized in an ndimensional search space in which each particle
xi = ( xi1 , xi 2 ,  , xin ) represents a possiblekZˆ ( x0 ) = ∑ λi Z ( xi )kriging

Partical Swarm Optimization

σ 2 (x0 ) = 2∑ λi γ (xi , x0 ) − ∑∑ λi λ j γ (xi , x j ) (3)kordinary′− µ] .

σ 2 (x0 ) = g ′W = g ′G −1 g.

where C is the sill and a is the range.
Once the model of the semivariogram is fixed,
the estimation variance only depends on the number
N and the location of the rain gauges. To calculate
the estimation variance using ordinary kriging,ktheλ2  λk γ (x k , x1 ) 1  γ (x k , x 2 ) 1  (5)
 γ (xk , xk ) 1 

1
0 

vi = vi + c1 .r1 .( pi − xi ) + c 2 .r2 .( g − xi )(8)

xi = xi + vi

(9)where i = 1,2,  , N , with N as population size. c1
and c 2 are two positive constants; r1 and r2 are two
random numbers with range (0,1).
In PSO, Eq. (8) is used to calculate the newvelocity according to its previous velocity and to the
distance of its current position from both its own
best historical position and the best position of theentire population or its neighbourhood. Generally,
the value of each component of v can be constrictedby setting the maximum velocity v max to the upper

2. Calculate each term in matrix g.
Let x 0 be the estimation location, then the g is
the matrix′
g = [γ ( x0 , x1 ) γ ( x0 , x 2 )  γ ( x0 , x k ) 1] (6)and lower bounds of the decision variable range.
Then the particle flies toward a new position
according to Eq. (9). This process is repeated until auser-defined stopping criterion is reached.
The minimisation of objective function given in
Eq. (3) is a problem of combinatorial optimization.

3. Solve the system of equations

GW = g ,W = gG −1 ,636

GEOMATE- Osaka, Nov. 16-18, 2015

Estimatedvariance

A typical procedure of rain gauge network design
has to look for a combination among all rain gauge
stations in such a way that minimizes the estimation
variance and/or maximizes the information content
for the observed data. This can be achieved either by
optimal positioning of additional and redundantstations or simply removing redundant stations that
forms the scope of this paper.
The steps of PSO algorithms applied for
determined optimal rain gauge network as follow:
1. Randomly initialize the x particles each
representing different combinations of N rain
gauge placements.
2. Evaluate each particle in the population using thefitness function, Eq. (3) and minimum fitness
function is set as pbest.
3. Compare particle’s fitness evaluation withparticle’s pbest. If current value is < than pbest,then set pbest as gbest.
4. Generate a new set of x particles by Eq. (8).
5. Stop the algorithm if the stopping criterion issatisfied or maximum number of iterations isreached.

0.95
0.9
0.85

0

50

100

Number of stations
Fig. 2 Estimation variance versus number ofstations
Table 2 Estimated variance value with optimal
number of stations
Estimatedvariance
0.8618

Stations
selected
67

Stations
removed
17

This new optimal rain gauge network
demonstrates that the high density of existing rain
gauge in Johor has been reduced by removing
several stations. Fig. 2 depicts that the estimatedvariance value decreased as the number of rain
gauges increased. This value approach optimal value
when the optimization process gives the number of
optimal rain gauge stations. In Table 2, it is shownthat the optimal number of rain gauge stations is 67
stations with estimated variance value 0.8618. The
locations of the 67 selected locations are as shown in
Fig. 3 below. From Fig. 3, most of the removed
stations are redundant stations that are unnecessary
needed.

RESULTS AND DISCUSSION
The variance reduction technique requires anappropriate semivariogram model that fitted theobserved
data.
Initially,anexperimental
semivariogram from the experimental data is derived.
Then, a functional semivariogram model is fitted tothe experimental variogram. The obtained
semivariogram model has essential information to be
used in kriging interpolation of observed data.
Fitting and selection of suitable semivariogram
model can be accomplished through the variogram
modelling technique. Once a proper semivariogram
model is selected for the observed dataset, kriging isapplied for the generation of interpolated surfaces
and the estimation of the corresponding kriging error.
The semivariogram model fitted to the experimental
data is shown as in table below:
Table 1

1

Exponential semivariogram model
Nugget0.93917Sill
1.5568Range
1.05449

LegendRetained stations

When the semivariogram is successfully fitted tothe empirical rainfall data, the particle swarm
optimization method is applied to find the minimum
objective function (equation 3) in order to get the
optimum number and location of rain gauge stations.
The optimization technique done based on the steps
mentioned earlier. The results of the optimization
process were shown in Figure 1 and Table 2.

E

Removed stations

Fig. 3 Optimal locations for 67 selected rain
gauge stations.
Table 3 shows the comparison of observed andestimated areal average rainfall for the existing andnew optimal rain gauge stations. It shows the new
optimal rain gauge stations manage to increase the637

GEOMATE- Osaka, Nov. 16-18, 2015

mean areal rainfall despite the increase in the error
due to the lack number of stations.
Table 3 Comparison between
estimated areal average rainfall
Estimated Mean Areal
Rainfall (mm) for therain gauge network
Existing
Optimal5.562404

5.5721

observed

andError (%) obtained forthe rain gauge network
Existing
network
2.83

Optimal
Network
3.35

Humidity (g/m3)
0.609 - 0.7735
0.7736 - 0.8269
0.827 - 0.856

After the execution of the optimization process,two scenarios were considered for the 17 removed
stations in Table 2.
• Scenario-1: Optimal positioning of 17 removed
stations fitted into Figure 3 to find their optimal
locations.
• Scenario-2: All 84 existing stations were reset
into new optimal locations
For the redesignation purpose of the network, thewhole study area is discretized into 250 square gridswith each unit equivalent to 100km² (Fig. 4f). This is
in line with the criterion set by the World
Meteorological Centre [11] which stated that everyrain gauge in the mountain region of temperate,
Mediterranean and tropical zone need to be in thearea of 100-250km² each. In order to redesign the
network, the following criteria were also considered
and the design is based on these criteria:
1. Each selected grid cannot have more than one
station.
2. The new rain gauge must be put in an area where
it is not more than 100 meters above sea level.
This is because most of the existing rain gauge
stations in Johor are located not more than 100
meters above the sea level as can be seen in Fig.
4(e).
3. The new rain gauge must be put in an area withhigh humidity reading value as this shows thatthe area receives more rainfall.
4. The new rain gauge must be put in an area with
low temperature as lower temperature shows thatthe area is humid and receives more rainfall.
5. The new rain gauge must be put in an area wherethe value solar radiation is low as this shows thatthe area is humid and has low temperature.
6. The new rain gauge must be put in an area that is
protected from the wind in all direction. Area
with low wind speed ensures that the collected
data is not influenced by the wind speed.
The PSO algorithm is applied once again to
determine the optimal rain gauge location for the
existing 84 stations from the 250 possible sites. The
humidity, temperature, solar radiation, wind speed
and elevation data in Fig. 4 will help the algorithm
locates suitable locations for the rain gauges.

0.8561 - 0.8808
0.8809 - 0.9065

a. humidity

Temperature (°C)
21.21 - 26.8
26.81 - 28.53
28.54 - 29.2
29.21 - 29.59
29.6 - 30.32

b. temperature

Solar Radiation (kW/m2)
13.9 - 17.57
17.58 - 18.39
18.4 - 19.03
19.04 - 19.74
19.75 - 21.1

c. solar radiation

638

GEOMATE- Osaka, Nov. 16-18, 2015

accuracy of the network by placing it in an optimum
location. For scenario 2, the estimated variancedecreases lower than scenario 1 and previous
estimated variance. This shows that in order toachieve an optimal network with the existingstations, all stations must be reorganized andrelocated into new locations. Elevation, temperature,
wind speed, solar radiation and humidity data has
help in identifying the optimal locations in thediscretized Johor.
The new locations of the restructured rain gauge
networks for both scenarios are as shown in Fig.5. In
Fig. 5a, the 17 removed stations were relocated into
new optimal locations. The placements of the 17
removed stations allow the stations to fill the gap in
empty potential grid in Fig. 4f. These 17 stations
will act like an additional stations to already optimal
67 stations obtained in the first process. The
placements of these additional stations will improve
estimation accuracy that leads to an optimal rain
gauge network.
In Fig. 5b, the 84 existing rain gauge stations
were restructured into a whole new optimal rain
gauge network. This process considered the numberof existing stations but need to be located into a new
possible optimal location. Figure 5b also depicts
locations of all rain gauge stations in Johor that are
no longer with redundant stations located into new
locations which are complied with the criterion to
not have stations located in one same grid. The PSO
algorithm managed to determine a new site for eachrain gauge stations and it is well placed into a flatand terrain area with elevation not more than 100m
above sea level. It is because, when determining thetheoretical placement of any rain gauge, themanufacturer often assumes the absence of any
physical obstacles that could reduce or obstruct therainfall. Placing a rain gauge close or on a hilly area
will severely alter the rainfall data.

Wind Speed (m/s)
0.8175 - 1.128
1.129 - 1.38
1.381 - 1.688
1.689 - 2.095
2.096 - 2.935

d. wind speed

Elevation (m)0 - 100
103 - 709

e. elevation

Table 4 Estimated variance

Estimated Variancef. Johor with grids
Fig. 4 Humidity, Temperature, Solar Radiation,
Wind Speed, Elevation and Grids
Table 4 shows the resulted estimated variance forboth scenarios. For scenario 1, the estimatedvariance value reduces when the removed stations
were located into new optimal locations. This showsthat the removed stations were able to improve the639

Scenario 1
0.8473

Scenario 2
0.7163

GEOMATE- Osaka, Nov. 16-18, 2015

existing stations. Scenario 1 was most likely to be
applied when the rain gauge network need an
additional stations while scenario 2 was morepractical in the early planning and designing of an
optimal rain gauge network. Redesignation of all 84
stations depicts the minimum optimum estimatedvariance compare to only 17 relocated stations in
scenario 1. This approach is more expensive because
it requires a completely new network to beestablished but the accuracy and the reliability of therain gauge network is surely improved.
Overall, this study has illustrated that thegeostatistics
methodwith particle swarm
optimization can be used as the optimization method
to provide the solution in designing an optimal rain
gauges network system. This optimal network also isessential in providing better rainfall data. This modeland methodology can provide information that will
help decision makers to understand the relationshipbetween numbers and locations of rain gauge
stations in order to provide a better and moreaccurate rainfall data. Further researches on the
optimization technique are required for better results.

Legend!.

Optimal stations

E

Relocated stations(a) Scenario 1

ACKNOWLEDGEMENTS
The authors would like to thank the Malaysian
Ministry of Higher Education, TATI University
College and Universiti Teknologi Malaysia for theirfinancial funding and support.
Legend!.

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(b) Scenario 2
Fig. 5 New optimal locations for 84 rain gauge
stations
CONCLUSIONS
Combination of geostatistics methods andparticle swarm optimization as an algorithm of
optimization can be used as a framework for rain
gauge network design models as it improves theexisting rainfall network by minimizing the varianceof estimation value. Through this study, it was foundthat the optimal number of stations for Johor is 67
stations with the estimated variance of 0.8618.
Particle swarm optimization as an algorithm ofnumerical optimisation also improves the optimal
network of rain gauge stations in Johor by variancereduction method with the help of rainfall, elevation,
humidity, solar radiation, temperature and windspeed data. From the data analysis, it was also foundthat the 17 stations were either removed or relocatedinto new locations. This study considered two
scenarios. Scenario 1 consideration was to optimallyrelocate the 17 removed stations to new locations
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[11]Guide to Meteorological Instruments andMethods of Observation, (WMO-No.8, Seventh
edition), WMO, Geneva, 2008.
[12]Eberhart, R. and Kennedy, J. A new optimizer
using particle swarm theory. In Proceedings of
the Sixth International Symposium on Micro
Machine and Human Science (MHS ’95), pages
39–43. IEEE Press, 1995.
[13]Olea, R. A. Geostatistics for Engineers andEarth Scientists, Kluwer Academic Publishers,
ISBN 0-7923-8523-3, Massachusetts, 2003.
[14]Sajal, K. A., Abdullah, G. Y. and Nitin, M.
Optimal design of rain gauge network in the
Middle Yarra River catchment, Australia.
Hydrol.Process.2014.

641

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

EFFECTIVENESS OF JACKFRUIT SEED STARCH AS COAGULANTAID IN LANDFILL LEACHATE TREATMENT PROCESS
Mohd SuffianYusoff 1, Noor Aina Mohamad Zuki 1 and Mohd Faiz Muaz Ahmad Zamri1
1
School of Civil Engineering, Universiti Sains Malaysia, Malaysia.

ABSTRACT
Aluminium sulphate (alum), ferric chloride and polyaluminium chloride (PAC) are the most common
coagulants being used for leachate coagulation-flocculation treatment. However, the impact of these residual’s
coagulants have sparked huge concern ceaselessly. Therefore, development of natural coagulant as an alternative
coagulant for treatment process has been given full attentions. In this attempt jackfruit seed starch (JSS) which
produce by extraction method was used as coagulant aid with PAC. The removal efficiency was determinedusing jar test method. The removal of leachate pollutants was compared between PAC coagulant with andwithout JSS. Interestingly the JSS coagulant aid has succeed to reduce the dosage of PAC from 900 mg/L to 600
mg/L by increasing the removal of COD, colour and ammonical nitrogen (NH 3 -N) up to 33.6%, 93.6%, and13.1 % respectively. While the removal for turbidity and suspended solid (SS) were maintained at 94% and 92 %respectively. The addition of JSS has succeeded to reduce 33.3% the usage amount of PAC in treatment processof landfill leachate. The result proved that JSS was effective to be used as coagulant aid landfill leachate
treatment.
Keywords: Landfill Leachate, Natural Coagulant, Jackfruit Seed and Starch.

and dilution of JSS using jar test.

INTRODUCTION
Leachate is highly contaminated liquid that definedas the aqueous effluent generated throughprecipitation, degradation and percolating process oflandfill storage (waste) within period time [1].
Leachates usually contain large amounts of organicand inorganic matter [2], which are toxic to livingorganisms and ecosystems. Generally the physicalchemical treatment is effective method for old andstabilized leachate. One of common physicalchemical method that used was coagulationflocculation.
Coagulation-flocculation is a relatively simple
physical–chemical technique that may be employedsuccessfully for the treatment of stabilized and oldlandfill leachate [3]. Nevertheless the performancesof the treatment are varies and depending on the typeof process parameter implemented.
Jackfruit (Artocarpus heterophyllus Lam.) is an
important naturalized plant of Southeast Asia which
rich with starch sources. There are various natural
based coagulants or flocculants has been
explored in water and wastewater treatment
applications. Jackfruit seed starch has not been
considered and exploited as a potent source of starch.
However, only a few published articles are available
on this material. Thus this paper is focused onlandfill leachate treatment using JSS as coagulant in
Kuala Sepetang Landfill Sites. The efficiency ofremoval of organic matter and ammonia in leachate
were investigated by comparing between powder

METHODOLOGY
Leachate Sampling and Characterization
The leachate samples were collected from a
Kuala Sepetang Landfill Sites,Taiping, Perak. The
characteristics of raw leachate collected are shownin Table 1.
Preparation of Jackfruit Seed Starch (JSS)
Preparation of JSS is used from a modified
method of Tulyathan, Mukprasit and Sajjaanantakul[4,5]. Method of isolation of JSS is based on
modified method of Bobbio, Dash, and Rodrigues[6]. Slurries of JSS was prepared in 0.05 M sodiumhydroxide (NaOH) solution and constantly stirredfor 6 h. The slurries were centrifuged for 20 min at
4°C. The supernatant was drained and the upper
brown sediment was scraped and followed by asecond extraction with a 0.05 M NaOH solution.
The remaining sediment was mixed with distilledwater and filtered by a sieve (0.15 mm mesh size) toeliminate fibers. The filtrate was neutralized with 0.1
M hydrochloric acid (HCl) to pH 7.0 and the slurries
were centrifuged for 20 min at 4°C. The supernatant
was drained and the upper brown sediment was
scraped and the remaining was washed with distilledwater and centrifuged for 20 min at 4°C. The starchcake was dried at 50°C for 12 h.
642

GEOMATE- OSAKA, Nov. 16-18, 2015

optimum percentage removal of pollutants. Based on
the result obtained, the optimum dosage forpollutants removal was 900 mg/L. This optimum
dosage was successfully removing 13.9% of COD,
93.5% of colour, 90.3% of turbidity, 95.6% of SS
and 9.7% of NH 3 -N.
This result displayed high removal of efficiency
of contaminants which conforming that stabilizedleachates are rich with organic matters [9] such as
humic substance (measured as COD intensity) andfulviclike fraction [7, 10]. The removal of
substance can be explained by the charge
neutralization
mechanism
incoagulationflocculation process. Basically, the amount of
coagulant added depends on the magnitude ofelectrical charge surrounding the colloidal particles(zeta potential) in samples. Since the charge of PAC(Al3+) is positive, the negative charges of particles inleachate are neutralized by the addition of PAC
during coagulation flocculation process [11].
Furthermore, as the landfill getting older, moreorganic matter with negatively charged particles
exists in the leachate.

Jar Test
Coagulation test was performed by using jar test
equipment (SW6 Stuart Bibby Scientific Limited,
UK). 3M NaOH and 3M HCl were used to adjust
the pH sample. 500 ml of leachate samples werefilled into six beakers and agitated simultaneouslywith combination of rapid mixing speed of 200 rpm
in 3 min and 40 rpm in 30 minutes for slow mixing,
and settling time was 60 minutes.
RESULTS AND DISCUSSION
Characteristics of Raw Leachate
Characteristics of leachate at Kuala Sepetang landfill
was summarized in Table 1. The obtained pH (8.16)
agrees with the other studies conducted for stabilizedleachate characteristics in Malaysia [7, 8]. However,according to BOD5/COD ratio below, the leachateshowed higher biodegradability than expected..
Although the age of the leachate is already morethan 10 years. The value obtained indicates that theleachate are partially stabilized leachate (0.1<
BOD5/COD<0.3) This probably happened because
the landfill is still under operating and producing
young leachate which is mixed together with oldleachate and this may effects the biodegradability of
the leachate itself. Therefore, biological degradationis still occurring in the leachate [8].

Table 2 PAC-leachate coagulation performance atselective coagulation

Table 1 Characteristics of Kuala Sepetang raw
leachate.

No

Parameters

Value

1

pH

8.16

2

Turbidity(NTU)106

3

Colour (Pt Co)

4588

4

Suspended Solid (mg/L)

187

5Ammonia-N (mg/L)

264

The6 third level headings
BOD5 (mg/L)

100

7

COD (mg/L)

898

8
9

BOD5/COD
pH

0.11
8.16

Parameter

Removal(%)

Standard*

13.9

Supernatantconcentration(mg/L)
720.0

Chemical
oxygendemand(COD)
Colour (PtCo)
Turbidity(NTU)
Suspended
solids (SS)Ammoniacalnitrogen(NH3-N)

93.5

323.0

90.3

9.0

100
ADMI
-

95.6

8.2

50.0

9.7

2170.05.0

400* Malaysian Environment Quality Regulations 2009

Figure 1 shows the effect of JSS dosage as coagulant
aid on the removal of COD, colour, turbidity, SS and
NH 3 -N by using PAC (900 mg/L, pH 5) ascoagulant in leachate treatment. The percentage
removal of COD, colour, turbidity, SS and NH3-N
the dosage range were tested from 500mg/L until
3000 mg/L. Table 3 shows the optimum percentage
removal of JSS dosage as coagulant aid at optimum
dosage of PAC (900 mg/L, pH 5). Based on the
result obtained, the optimum dosage for pollutants
removal was 500 mg/L for colour, turbidity andsuspended solid removal. While the optimum dosage

Removal Rate
In this study, the optimum dosage of PAC wasinvestigated at the optimum pH (pH5). The
percentage removal of COD, colour, turbidity, SS
and NH 3 -N the dosage range were tested from400mg/L until 1100 mg/L. Table 2 shows the643

GEOMATE- OSAKA, Nov. 16-18, 2015

COD and NH 3 -N removal were 1000 mg/L and1500
mg/L respectively. This optimum dosage was
successfully removing 31.6% of COD, 93.3% of
colour, 92.3% of turbidity, 94.5% of SS and 13.1%
of NH 3 -N.

was successfully removing 33.5% of COD, 93.6% of
colour, 92.3% of turbidity, 94.5% SS and 14.1%
NH 3 -N.
Table 3 PAC- JSS leachate coagulation performanceat selective coagulation.
Parameter
Removal Supernatant Standard*(%)
concentration(mg/L)
Chemical
31.6
636.0
400
oxygendemand(COD)
Colour (Pt
93.3
335.0
100
Co)
ADMI
Turbidity
92.3
10.3
(NTU)
Suspended
94.5
12
50.0
solids (SS)Ammonical13.1
2780.05.0
nitrogen(NH3-N)

100
90
80
70
COD
TURBIDITY
NH3-N

60
Removal (%)

50

COLOUR
SS

40
30
20
10
0
500

1000

1500

2000

2500

3000

Dosage of JSS (mg/L)* Malaysian Environment Quality Regulations 2009

Fig. 1 Effect of JSS dosage as coagulant aid on theremoval of COD, colour, turbidity, SS and NH 3 -N
by using PAC (900 mg/L, pH 5) as coagulant inleachate treatment.

The effect of optimal dosage of JSS as coagulant aid
at optimal dosage PAC displayed good improvement
for the pollutants removal. The removal of COD,
colour and ammonical nitrogen has increased to33.6%, 93.6%, and 13.1 % respectively. While theoptimum dosage for turbidity and suspended solid
removal were remain the same. This proved that theaddition of JSS as coagulant as coagulant aid has
succeeded to reduce 33.3% the usage amount of
PAC in treatment process of landfill leachate.

The implementation of JSS as coagulant aid with
PAC displayed improvement for the pollutants
removal compared to PAC without JSS. The
removal of COD turbidity, SS and NH 3 -N has
increased to 31.6%, 92.3%, 94.5% and 13.1 %respectively. The removal of substance can be
explained by the bridging mechanism incoagulation-flocculation process. Basically, theflocculation process promoted the agglomeration of
flocs with the polluted particles. The addition of JSS
has increased the agglomeration within the flocs andthe size of floc formation. The agglomerations of theflocs provide strong attraction to the suspended
particles and increased the floc size for settlement.
Thus the addition of JSS with PAC (900mg/L at pH5) has improved removal of the pollutants.

100
90
80

Removal(%)

70

Figure 2 shows the effect of PAC dosage as
coagulant on the removal of COD, colour, turbidity,
SS and NH 3 -N at optimal dosage of JSS (500 mg/L)
as coagulant aid leachate treatment. The percentage
removal of COD, colour, turbidity, suspended solidand NH 3- N the dosage range were tested from300mg/L until 1000 mg/L. Table 4 shows the
optimum percentage removal of PAC dosage as at
optimum dosage of JSS. Based on the result
obtained, the optimum dosage for PAC was 600
mg/L for COD, color and NH 3 -N removal. While
the optimum dosage for turbidity and suspended
solid removal was 500 mg/L. This optimum dosage

COD
TURBIDITY
NH3-N

60
50

COLOUR
SS

40
30
20
10
0

300

400

500 600 700 800
Dosage of PAC (mg/L)

900

1000

Fig. 2 Effect of PAC dosage as coagulant on the removalof COD, colour, turbidity, SS and NH 3 -N by using JSS(500 mg/L) as coagulant aid in leachate treatment.

644

GEOMATE- OSAKA, Nov. 16-18, 2015

Table 4 The optimal dosage of PAC- JSS removal
performance.
Parameter

Removal(%)

Chemical
oxygendemand(COD)
Colour (PtCo)
Turbidity(NTU)
Suspended
solids (SS)Ammoniacalnitrogen(NH3-N)[3] Amor, C., Torres, S.E.D., José, A.P., Maldonado,
M.I., Oller, I., Malato, S., Lucas, M.S. “Mature
landfillleachate
treatmentbycoagulation/flocculation combined with Fentonand solar photo-Fenton processes”. Journal ofHazardous Material. Vol 286, April 2015,
pp.261-268.

Standard*

33.5

Supernatantconcentration(mg/L)
580.0

93.6

318.0

100 ADMI

92.3

10.3

-

94.5

12

50.0

14.1

2430.05.0

400[4] Tulyathan, V., Tananuwong, K., Songjinda, P.,& Jaiboon, N. “Some physicochemical
properties of jackfruit (Artocarpus heterophyllusLam) seed flour and starch”. Science Asia, Vol
28, 2002, pp. 37-41[5] Mukprasit, A., Sajjaanantakul, K. “Physicochemical properties of flour and starch fromjackfruit seeds (Artarpus heterophyllus Lam.)compared with modified starches”. International
Journal of Food Science & Technology, Vol. 39,
2004, pp. 271-276.

*Malaysian Environment Quality Regulations 2009

CONCLUSION
The performance of JSS with PAC coagulant was
further optimized. The results shows the dosage
optimization of PAC at optimal dosage of JSS wasachieved 600mg/L. The addition of JSS has
succeeded to reduce 33.3% the usage amount of
PAC in treatment process of landfill leachate.
Therefore, this study suggests that starch from the
jackfruit seed has a potential to be used as coagulant
aid in removing organic matter and ammonia present
in the landfill leachate.

[6] Bobbio, F. O., El-Dash, A. A., Bobbio, P. A.,
Rodrigues, L. R. (1978). Isolation an
characterization of the physiochemical propertiesof the starch of jackfruit seeds (Artocarpus
heterophyllus). Cereal Chemistry, Vol. 55, 1978,
pp.505–511.
[7] Zainol N. A., Aziz H. A., and Yusoff M. S.
“Characterization of Leachate from Kuala
Sepetang and Kulim Landfills, A comparative
Study” . Energy and Environment Research, Vol.
2, 2012, pp. 45.

ACKNOWLEDGEMENTS
The writers would like to express their heartiest
thanks to Universiti Sains Malaysia for the financial
support funded under the RUI USM grant(1001/PAWAM/814166).

[8] Aziz H. A., Alias S., Assari F., Adlan M. N.
“The use of alum, ferric chloride and ferrous
sulphate as coagulants in removing suspended
solids, colour and COD from semi-aerobic
landfill leachate at controlled pH”. Waste
Management & Research, Vol. 25, 2007, pp.
556-565

REFERENCES[1] Clarke, B.O., Anumol, T., Barlaz, M., Snyder, S.
A, “Investigating landfill leachate as a source oftrace organic pollutants”. Chemosphere. Vol. 127,
2015, pp. 269–275.

[9] Tats, A. A., Zouboulis A. I., MAtis K. A., andSamaras P.“Coagulation-flocculatio pretreatmentof sanitary landfill leachates”. Chemosphere, Vol.
53, 2003, pp. 737-744.

[2] Liu, Z.P., Wu, W.H., Shi, P., Guo, J.S., Cheng, J.
“Characterization of dissolved organic matter inlandfill leachate during the combined treatment
process of air stripping, Fenton, SBR andcoagulation”. Waste Management. In Press.
April 2015.

[10] Kulikowska D., and Klimiuk E. “The effect oflandfill age on municipal leachate composition,
Bioresource Technology”. Vol 99, 2008, pp.
5981-5985.

645

GEOMATE- OSAKA, Nov. 16-18, 2015[11] Al-Hamadani Y. A. J., Yusoff M. S., and Umar
M., Bashir M. J. K., and Adlan M.
N.“Application of psyllium husk as coagulant
and coagulant aid in semi-aerobic landfill
leachate treatment”. Journal of Hazardous
Materials,
Vol.190,
2011,pp.
582-587.

646

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

WATER QUALITY AND COMPOSITIONS OF THE
PHYTOPLANKTON AND ZOOPLANKTON BEFORE AND AFTERBUILDING A BULKHEAD MAINTENANCE CONSTRUCTION IN
LAKE FUKAMI-IKE, JAPAN
1

Hiromi Suda 1, Masaaki Tanaka 2 , Maki Oyagi 2, Megumi Nobori 1 and Akihiko Yagi 1
Faculty of Civil Engineering Department of Urban Environment, Aichi Institute of Technology, Japan;
2
Faculty of Environmental and Information Sciences, Yokkaichi University, Japan
ABSTRACT

Lake Fukami-ike is a small monomictic and eutrophic lake, located in southern Nagano Prefecture in central
Japan. Water quality improvement was expected from maintenance of farm village drainage and waterfront
function was carried out for town activation in 1992. However, no blue-green algal bloom outbreak had occurred
before. We studied the water quality and compositions of the zooplankton and phytoplankton before and after
building a bulkhead maintenance construction.
Large decreased values for NO3-N, NH4-N and NO2-N were 1.87→0.50 gNm-2 (26.7%), 1.49→0.78 gNm-2(52.3%) and 0.085→0.030 gNm-2 (35.3%), respectively. Aphanizomenon flos-aquae (Cyanophyceae) were
dominant in the summer; some species of the genus Synedra were dominant among phytoplankton during study
periods. The dominant species of zooplankton during study periods were Keratella cochlearis (Rotatoria);
smaller than Cyclops vicinus (Crustaceae) were dominant species of zooplankton before the construction.
Keywords: Building a Bulkhead maintenance construction, Water quality, Phytoplankton, Zooplankton
35°32′55″77, east longitude 137°81′93″56. The
surface area of the lake is about 2.0 ha, with amaximum depth of 7.75 m, and volume 1.0 × 105 m3
and with a small diameter: 150 m, 300 m. (Fig. 1
and Fig. 2) [3].
The lake is eutrophic lake because of having 12
units considering inflow nutrients from surrounding
paddy fields. The maximum depth was reported 9.3
m in 1951 [4], 8.1 m in 1993 [5], and 7.75 m inrecently [3].
The lake has five inflow rivers and the lake water
exits from an outflow river. These rivers receive therunoff sewage water from around houses.The lakewater is not dry up because there is a lot of springwater which become a source of inflow water having
less than 30 cm wide.
Circulation periods were in November to March,and stagnation periods were in April to October; thedissolved oxygen concentration was zero in about
the 4 m to 5 m deeper layer in mid-summer [6].
Water plants (such as Phragmites australis;
Poales, Typha latifolia; Typhales) live around thelakeshore. Local residents mow these weeds on aregular basis.
Lake Fukami-ike has been described in previous
studies about manganese, purple nonsulphur bacteria,
microbial sulfur cycling, dial vertical migration of
Chaoborus flavicans (Diptera) [3],[7]-[9]. It isinteresting to note that the chlorophyll-a amount in
the winter was higher than that of summer caused
the interruption of upward nutrient transportationfrom the tropholytic layer due to distinct stagnation

INTRODUCTION
Water pollution is worldwide problem, andcontrol of river inflow that is the source of nitrogenand phosphorus is important for the water quality
improvement [1]-[2].
Lake Fukami-ike is a small monomictic andeutrophic lake, located in southern Nagano
Prefecture in central Japan. The studies in the lakewere begun from 1978, and it is continuing now
mainly about water quality roughly once a month.
Maintenance of farm village drainage andwaterfront function and water quality expectations
became better. However, although water color stays
a dark green or brown, the transparency variation for
35 cm - 470 cm in the 1992 - 2010s was larger thanthat of 50 cm - 150 cm in the 1980s - 1992.
Moreover, no blue-green algal bloom (Microcystis
aeruginosa) outbreak had occurred before, and highchlorophyll-a concentration (357 μgL-1) and lowtransparency (35 cm) was observed in June 2000.
We studied the water quality in the water column
and compositions of zooplankton and phytoplankton
before and after building a bulkhead maintenanceconstruction.
METHODS
Study Area
Lake Fukami-ike is located in southern Nagano
Prefecture in central Japan; north latitude
647

GEOMATE- Osaka, Nov. 16-18, 2015of water and the consumption of nutrients byphotosynthetic
sulfurbacteria(asbacteriochlorophyll-c) growing near the top of theanoxic layer in summer [10].
Chlorophyceae (classes of green algae) reached arich “Vegetationstrübung” condition, and no water
bloom occurred in the lake [11].

were measured by ion chromatography analysis(DKK-TOA CORPORATION, PCI-311S).
Total phosphorus and total dissolved phosphorus
were measured by the molybdenum blue
colorimetric method [12].
Chlorophyll-a was measured by the fluorometricmethod [13].
Water temperature was measured with athermistor thermometer, and dissolved oxygen wasdetermined with a DO meter (HORIBA DO Meter
OM-12).
Plankton samples were taken with a Van Dorn
water sampler (10L, Rigo Co., Ltd., Tokyo Japan)
every 1 m from the upper to bottom layer. All
samples were preserved in 1% formalin in the field
immediately, then counted and identified using an
optical microscope (BX51, OLYMPUS Optical Co.,
Ltd., Tokyo, Japan) in the laboratory.
RESULTS AND DISCUSSION
Inflow and Outflow rivers

Fig. 1

The interannual variability of the inflow rate data
were shown in Table 1. Before building thebulkhead maintenance, inflow data were 4 LS-1 in
1973, 9.9 LS-1 in 1979, 5.7 LS-1 in 1980. However,
0.52 LS-1 and 0.62 LS-1 in 2008 were measured after,
the mean values were dramatically decreased from
6.5 LS-1 to 0.57 LS-1.

Bathymetrical map of Lake Fukami-ike.
0

5,000

10,0000.0

15,00020,00025,000ｍ3

Depth（ｍ）

1.0

Table 1 Mean abundance of flow rates in inflow andoutflow rivers

2.0
3.0

maintenanceconstruction

4.0
5.0
6.0

Before7.0

Fig. 2Stratified volume amount of Lake Fukamiike.
After

Observationday
Nov 2, 1973
Sep 15, 1979
Feb 15, 1980
Means (LS-1)
Feb 15, 2008
Oct 22, 2008
Means (LS-1)

Inflow(LS-1)
4
9.9
5.7
6.5
0.52
0.62
0.57

Outflow(LS-1)
14.4
19.5
7.7
13.8
0.68
0.46
0.57

Sampling and analysis
Outflow data 14.4 LS-1 in 1973, 19.5 LS-1 in
1979 and 7.7 LS-1 in 1980, 0.68 LS-1 and 0.46 LS-1
in 2008. Mean values were 13.8 LS-1 before
construction, and 0.57 LS-1 after. Because the inflow
river course had a bypass to avoid the lake for thebulkhead construction in 1992, inflow and outflow
dramatically decreased one-twentieth and one
twenty-fourth respectively.

Lake water samples were collected at the deepest
point with a hand-operation water pump connected
to a polyvinylchloride tube from every 0.25 m depth
during the period of water stratification from April
to October or from every 50 cm - 1 m depth in othermonths.
Part of the water samples was filtered through a
glass fiber filter (Whatman, GF/F, 47 mm)
immediately after the sampling. The samples werestored at -20 ℃ until chemical analysis in thelaboratory.
The filtrate was used for the determination of
inorganic nitrogen (NO3-N, NH4-N and NO2-N)

Nutrients
Nitrogen
Seasonal depth-time diagram distribution of648

GEOMATE- Osaka, Nov. 16-18, 2015

inorganic nitrogen from 1978 to 1980 and 1999 to2000 was shown in Fig. 3 (NO3-N), Fig. 4 (NO2-N)
and Fig. 5 (NH4-N).
All inorganic nitrogen (NO3-N, NH4-N and NO2N) concentrations in the lake were decreased after
maintenance construction than before it; in particular,
NO3-N concentrations from 0 m to 3 m in theepilimnion were much decreased.
The interannual mean values of NO3-N, NH4-N
and NO2-N in the water column before (1979-1992)
and after (1992-2013) for NO3-N, NH4-N and NO2N were 1.87 → 0.50 gNm-2 (26.7 %), 1.49 → 0.78
gNm-2 (52.3 %) and 0.085 → 0.030 gNm-2 (35.3 %),respectively. In particular, NO3-N value decreased
one fourth before the maintenance construction.
Because inflows contain from paddy fields andorchards (allochthonous inorganic matter) stop to
significantly reduce, NO3-N value decrease largely
after the construction.

Fig. 5Depth-time diagram distribution of NH4-N(µg at. N l-1) concentrations in 1978-1980(above) and 1999-2000 (below). (Modified
from [14])Phosphorus

Fig. 3The mean total phosphorus (TP) amounts in thelake before the construction (1979-1992) and after(1992-2013) were shown in Fig. 6.
TP in the oxic layer increased about 1.2 times
from 0.95 gPm-2 (before) to 1.12 gPm-2 (after), andparticulate organic phosphorus amounts weredecreased from 0.28 gPm-2 (before) to 0.19 gPm-2(after), respectively.
On the other hand, the mean TP amounts in theanoxic layer were almost equal to 1.84 gPm-2(before) and 1.80 gPm-2 (after), and particulateorganic phosphorus (POP) amounts were increased
from 0.09 gPm-2 (before) to 0.45 gPm-2 (after). The
POP amounts in the anoxic layer increased 5-foldbecause it was associated with the increase ofphotosynthesis sulphur bacteria.

Depth-time diagram distribution of NO2-N(µg at. N l-1) concentrations in 1978-1980(above) and 1999-2000 (below). (Modified
from [14].)

Fig. 6

Fig. 4

Depth-time diagram distribution of NO3-N(µg at. N l-1) concentrations in 1978-1980(above) and 1999-2000 (below). (Modified
from [14])Mean amount of total phosphorus in theoxic and anoxic layer of the water column
before and after maintenance construction.

Chlorophyll-aMonthly mean chlorophyll-a amounts in the oxic649

GEOMATE- Osaka, Nov. 16-18, 2015

layer in the lake before (1979-1992) and after (19922013). The values from April to November(stagnation periods) were 167.6 mg m-3 (maximum
321.5 mgm-3, minimum 50.1 mgm-3) before
construction and 188.5 mg m-3 (max 378.0 mgm-3,
minimum 50.1 mgm-3) after it.
The values from December to March (circulation
periods) were 353.3 mg m-3 (maximum 642.9 mgm-3,
minimum 179.6 mgm-3) before construction and557.1 mgm-3 (maximum 730.1 mgm-3, minimum
246.0 mgm-3) after it. The values in the circulation
periods after the construction were slightly higher
before. The tendency the for values to be low in thestagnation period and high in the circulation period
was the same before and after the maintenanceconstruction.

Fig. 7 Seasonal changes in the abundance ofphytoplankton group found.

Plankton
Phytoplankton

Zooplankton

Seasonal changes in the abundance ofphytoplankton found from April 2013 to March2014 were shown in Fig. 7.
Bacillariophyceae were dominant from April toMay, and from the beginning November to March.
Chlorophyceae were dominant from spring tosummer, and Cyanophyceae were dominant in July.
Aphanizomenon flos-aquae (Cyanophyceae)
were dominant in July. Bacillariophyceae were
included Fragilaria construens and F. crotonensis,
Synedra rumpens and some species of the Synedra.
Chlorophyceae were included Gleocystis sp.,
Oocystis sp. and Crucigenia tetrapedia.
The phytoplankton found in Lake Fukami-ike
was previously reported [4],[15]-[16]. The dominant
species of seasonal changes was reportedly, Synedraacus from middle of March to the middle ofSeptember,
andAulacoseira
ambigua(Bacillariophyceae) from the end of September tothe beginning of March, and others (Chlorophyceae
and Bacillariophyceae) were found in 1978-1979[16].
Chlorophyll-a amounts in the lake before andafter the building construction did not change greatly
previously; chlorophyll-a contents (phytoplankton)
showed little change.

Seasonal changes in the abundance ofzooplankton found from April 2013 to March 2014
were shown in Fig. 8.
Rotatoria were dominant from April to May,
from the beginning of July to the beginning ofNovember, in January and March. Protozoa were
dominant in June, and from the end of November toDecember, and February. As for the dominantProtozoa the end of November, the upwelling waterfrom the bottom layer included Protozoa carried up
from all layers, because observations wereconducted and zooplankton were collected just at the
beginning of circulation periods.
About Keratella cochlearis (Rotatoria) became
dominant from April to May, followed by
Trichocerca similis and Keratella cochlearis var.
tecta (Rotatoria) in summer, Tintinnopsis lacustris(Protozoa) in autumn, and Filinia longiseta(Rotatoria) in winter. Crustaceae were foundNauplius of Cyclops in the beginning of July,
Bosmina longirostris were found in January.
In 1978-1979, the dominant species were
Cyclops vicinus in the beginning October to the
middle of February. Bosmina longirostris(Cladocera), Mesocyclops Leuckarti (Cyclopoida),
and Filinia longiseta (Rotatoria) were found [16].
However, in 2013-2014, a few Crustaceae, larger
than Rotatoria, were found.

650

GEOMATE- Osaka, Nov. 16-18, 2015

REFERENCES[1] Schwoerbel J., “Materials budget of natural
waters”, I, Handbook of Limnology, John Wiley& Songs, 7, 1978, pp.67-94.
[2] Wetzel, R. G., “Lake and River Ecology”,
Limnology, 3th ed., Academic Press, Tokyo.,
2001, pp.298-301.
[3] Yagi A, “Biological manganese cycle in theoxic and anoxic layers of Lake Fukami-ike”,
Limnology in Tokai Region of Japan, Vol.43,
2010, pp.51-60.
[4] Ueno M, “Lake Fukami-ike in Shimoinagun,
Nagano Prefecture”, Iida, An educational
association of Shimoinagun (in Japanese), 1952,
pp.120.
[5] Yagi A, Miyachi S and Shimodaira I, “Change
in depth by sedimentation of Autochonousorganic substances in eutrophic lake”, Journalof Nagoya Women’s university, Home
Economics ･ Natural Science, No.39, 1993,
pp.77-82.
[6] Yagi A, Funahashi J, Nishio N, and Umemura
M, “Seasonal changes of methane and organicacid in Lake Fukami-ike”, Verh. Internat.
Verein. Limnol., Vol.30, 2009, pp.878-883.
[7] Yagi A, Miyachi S and Terai H, “Vertical
distribution of purple nonsulphur bacteria andtheir utilization of dissolved organic carbon in
Lake Fukami-ike” Archiv für Hydrobiologie,
1991, 121: pp.307-317.
[8] Nakagawa M, Ueno Y, Hattori S, Umemura M,
Yagi A, Terai K, Koba K, Sasaki Y, Makabe A
and Yoshida N, “Seasonal change in microbial
sulfur cycling in monomictic Lake Fukami-ike,
Japan”, Limnology and Oceanography. Vol.57,
No.4, 2012, pp.974-988.
[9] Nagano M, Yagi A, Yoshida T, “Seasonalpattern and induction cues of dial verticalmigration of Chaoborus flavicans in Lake
Fukami-ike,
Nagano,Japan”
Springer.,
Ecological Research, 2014, pp.145-152.
[10] Yagi A, Shimodaira I, Terai H and Saijo Y,“Seasonal Change of Chlorophyll-a andBacteriochlorophyll-c in Lake Fukami-ike” Jpn.
J. Limnol., Vol. 44, 1983, pp.283-292.
[11] Tanaka M, “The current state of theeutrophication of Japan lakes and marshes
judged from plankton lakes (in Japanese)[Translated from Japanese]”, Water, Vol.23,
No.9, 1981, pp.90-95.
[12] Murphy J, J.P. Riley, “A modified single
solution method for the determination ofphosphate in natural waters”, Analytica
Chimica Acta, Vol.27, 1962, p.36.
[13] Holm-Hansen O., et al., “Fluorometricdetermination of Chlorophyll”, J. of Cons.
perm. int. Explor. Mer, Vol. 30, pp.3-15.

Fig. 8 Seasonal changes in the abundance ofzooplankton group found.

CONCLUSION
NO3-N amounts were largely reduced after theconstruction, this results suggested that the inflow
water from paddy fields and orchards has stopped by
the construction. However, Chlorophyll-a amounts
not changed before and after the construction.
Phytoplankton it believed to using nitrogen(ammonium and phosphorus) which haveaccumulated in the bottom. Water quality is not
improved even stop the inflow of nutrients because
the nutrients have already accumulated in the bottom[17].
Recently, Cyanophyceae has dominant in earlymidsummer, it seems that chlorophyll-a contents(phytoplankton species) only changed before andafter the construction. One should consider the
possibly that Cyanophyceae can make advantage thecompetition for nutrients (NO3-N) to be used for
growth [18].
Rotatoria were dominant recently because thatthey were released from predation pressure of large
size (Cyclopoida). Fish predation has emphasized asa factor to determine the species composition of thezooplankton community [19]. These problem needmore investigation and is still open to discuss. We
found that there is significant difference for longterm changes of plankton in the Lake Fukami-ike.

ACKNOWLEDGEMENTS
We are grateful to the Anan-cho authorities in
Shimoina-gun, Nagano Prefecture, who generously
provided research facilities. We especially thankSohey Hane and Seiya Kato for their help in thefield.

651

GEOMATE- Osaka, Nov. 16-18, 2015[14] Limnology of committee of Shimoina kyoikukai, “Shimoina-shi”, Shimoina Editorial
Committee of Shimoina-shi edited, (inJapanese) [Translated from Japanese], 2009,
pp.338-339.
[15] Tanaka M, “11-18. Lake Fukami-ike”, The
lakes in Japan (in Japanese), 1th ed. Nagoya,
The University of Nagoya Press., 1992,
pp.417-421.
[16] Sakurai Y, Watanabe Y, “Lake Fukami-ike”,
Limnology of Shinshu, 1, 1974, pp.117-128.
[17] Japan Society On Water Pollution Res,. edited,marsh environment survey guideline (inJapanese),
pollution
control
techniqueassociation of corporate executives, 1982,
pp.153-166[18] Horne Alexander J. and Goldman Charles R.,“Phytoplankton and periphyton”, “Limnology”,
Kyoto, Kyoto University Press., 2th ed., 1999,
pp.243-275.
[19] Benndorf, J., “Possibility and limits forcontrolling eutrophication by biomanipulation”,
International Revue gesamten, Hydrobiologie,
1995, Vol.80, pp.519-534.

652

Fifth In
nternational Co
onference on Geeotechnique, C
Construction Materials
MandEnvironmeent, Osaka, Jappan, Nov. 16-18
8, 2015, ISBN: 978-4-9905958
8-4-5 C3051

NEW MICROBBIAL-FU
UNCTION
N-BASEDD REINFO
ORCEMEENT METTHODFO
OR SLOPPES
Toshiro HA
ATA1, Kazuhirro KANEDA2 and Tomohirro TANIKAW
WA3
1Faculty ofo Engineeringg, Toyama Preefectural Univ
versity, JAPAN
N;
2,3
Tak
kenaka Reseaarch & Develo
opment Institute, JAPANABSTRACTT
In reccent years, eroosion control has been inccreasingly imp
plemented forr the land sloppes of public roadways(e.g., exppress highwayys). The development of ann easy-to-use maintenance method for ssuch slopes basedb
on anatural prrocess is an immportant subject in geo-envvironmental engineering.
eThe
T aim of thiss research is tot develop
an eco-frriendly mainteenance metho
od for land-sloope managem
ment that is low cost and prrotects the naatural ecosystem. In this paper, thet general co
oncept of the newly propossed method tested in the labboratory and thet results
merical simulattion are presen
nted. The mo st important advantage
aof thist techniquee is the enhan
ncement ofof a numurease acctivity in the inn-situ microorrganism commmunity. Norm
mally, this techhnique requirees a two- to three-monthcultivatioon term prior to the mainteenance work. During the co
onstruction phhase, only sprraying of the microbial
culture m
medium and solidificationss
substance miixture is carriied out. The results of thee numerical simulation
demonstrrate the slope controlcachievved because o f the reinforceement effect ofo the proposeed method.
Keywords
ds: Erosion conntrol, Microbiial function, U
Urease activityy
soiil strength based on a microbial function(hyydrolysis of urea).
u
The meechanism of hydrolysishof urea and the precipittation of caalcite arepresented by equationse(1), (2), and (3).
rep

DUCTIONINTRODIn Jappan, the mainntenance cost of infrastructture
facilities has been increasing.
iIn
I addition, theefrrom
amount of carbon dioxide emissions
constructtion and maiintenance activities has bbeen
increasingg in recent yeears. Thereforre, low-cost eecofriendly maintenancee techniquess need to be
i a very popuulardevelopedd in the near future. Urea isagriculturral material used for suppplying nutrieentsfrom soill to plants. Inn this study, we developeed aslope maaintenance meethod that usees in-situ ureaaseproducingg bacteria. TheseT
bacteriaa live in norm
rmalsoil (pH = around 7 annd electrical co
onductivity = 0.6
mS/cm). The advantagge of the propo
osed techniquue isa low cost.
that it is eeco-friendly andSoil whose strenngth is enhaanced by ussing
i called “Biio-Soil.” In ppast
microbiall functions is
research, specific baacteria isolateed from Yaggaji
wfound to enhance soil
island inn Okinawa were
strength to up to 2 MPa after cultivationcfoor 2o extract soluttion
months [1]. In anotherr study, potatos column too control the soil
was injeccted into the soil
permeabiility; that is, an in-situ leakage reppair
method thhat uses in-sittu microorgannism activity w
was
employedd [2]. The present papeer describes theexperimeental results ofo the analyssis of Bio-Sooil’s
physical pproperties andd the effect off the soil on sllope
stability bbased on num
merical simulattions.

(Hydrolysis of urea)uO(NH2)2 + 2H2O → 2NH+4 + CO2-3 pH (↑)(
CO(Prrecipitation off calcite (calciu
ium carbonatee))COO2 + H2O → HCO
H -3 + H +2+
HCCO 3 + Ca + OH- → CaCOO3 (↓) + H2O

(1)
(2)
(3)

Soil samppling

Treatabilityy tests

Evaluation of ureaase activity

×

Other maintenace mathodm○
Large scale cuultivation

Spraying culture medium andsolidification substance

Fig
g. 1

Process of the mainteenance method
d

Fig
g. 2

Schemaatic diagram of the mainttenancework

OVERVIIEW OF THE
E PROPOSE
ED METHODD
In thhis research, we developed a new eecofriendly mmaintenance methodmto inccrease the surfface
653

GEOMATE- Osaka, Nov. 16-18, 2015

These reactions require urea, a calcium source(calcium chloride, calcium nitrate, etc.), and ureaseproducing bacteria [2], [3]. In this study, we selecteda specific microorganism named Bacillus pasteurii,
which has high urease activity and salt tolerance [4].
The process of the proposed eco-friendly
maintenance method is shown in Fig. 1, and aschematic diagram of the remediation system isshown in Fig. 2. Before initiating this type of
maintenance work, treatability tests were performed
using in-situ soil microorganisms. Then, anothertreatability test was performed in a laboratory toevaluate the urease activity with various
combinations of nutrients and calcium sources. A
combination of nutrients and a calcium source wasselected according to the treatability test, and thiscombination was sprayed on the surface soil toaccelerate the reinforcement activities of
microorganisms. During the maintenance work,
microbial monitoring was also carried out to ensurethat the conditions were optimal for the entire
microbial community in general and for the
microorganisms in particular.

sand increased the dry density of Toyoura sand. Thatis, the precipitation of calcite in the sand pore
increased the soil density. The same results wereobtained when the cohesion (C) value of the MCPtreated sand was used. However, the internal friction
angle (φ) remained constant after the MCP process.
These results suggested that the increase in the sandstrength increased the C value, similar to what
happens in the case of cement-treated soil. The
results of the CD triaxial compression tests(confining pressure of 100 kPa) are shown in Fig. 3.
The use of the MCP-treated sand increased the peakstrength by approximately 20%.
Table 1 Physical properties of sand sample
C
φ
Item
ρd
(g/cm3)(kN/m2)(°)
Toyoura sand
1.364
3.3
36.5
(control)
MCP sand
1.472
11.9
36.9
The cyclic axial load of a sinusoidal wave with afrequency of 0.1 Hz was applied to the soil. The cellpressure and water pore pressure were measured.
Table 2 shows the comparison of the results for
MCP-treated soil and untreated soil. The results
showed the MCP-treated soil had higher E0 and G0
values than the untreated (control) sand.

LABORATORY TESTS
Material and methods
The soil used in the experiments was Toyoura
sand. Aerobic urease-producing alkaliphilic bacteria
named Bacillus pasteurii (ATCC11859) were usedas the microorganisms in this study. Calcite
precipitation was carried out using the following
procedure. The bacterial culture medium was
infiltrated with a solidification medium. The soilcolumns were immersed in a solution with 1 L of thesolidification medium containing 0.30 M CaCl2 andurea thrice per 24 h. The soil columns were thenremoved from the solution for one day before being
used for strength testing. Cyclic triaxial tests andconsolidated-drained (CD) triaxial compression tests
were performed using a compression machine to test
the physical properties of microbial carbonate
precipitation (MCP) sand and the liquefaction
strength. After the strength tests, the CaCO3 weight
was measured as follows: (1) After oven drying(110°C, 24 h), the weight of the soil samples wasmeasured. (2) The soil samples were washed with 1
N HCl twice or thrice to dissolve precipitatedcarbonates. (3) The soil samples were rinsed with
ion exchange water, drained, and oven dried (110°C,
24 h). (4) The weight of the rinsed soil samples wasmeasured. Three samples each were prepared for
MCP sand and control sand.

NUMERICAL SIMULATIONS
Outline of numerical simulations
Figure 4 shows the numerical mesh and boundary
conditions. The side and bottom of the ground wereadopted as the repeated boundary and viscosity
boundary, respectively. The embankment was 3.0 m
in height, 16 m in width, and 4.0 m in width at the
crest. First, self-gravity analysis was performed forboth the ground and embankment. Then, dynamic
Deviator stress(kPa)

500
Conctol
MCP treated sand

400
300
200
100

Confinig pressure of 100kPa

00Fig. 35
10
15
Axial strain εa(%)

20

Process of the maintenance method

Table 2 Results of cyclic triaxial cell tests

Results
The physical properties of the sand samples arelisted in Table 1. The dry density of the two sandtypes was measured to be 1.364 and 1.472 g/cm3.
The experimental results showed that MCP-treated

MCP
sand
Untreated sand

654

r

r

E0
(MN/m2)

G0(MN/m2)



435.5

1.84E-02

145.4

2.76E-02

106.0

5.43E-02

35.4

8.14E-02

GEOMATEGO
Osaka, Nov. 16
6-18, 2015

analysis was performeed. Table 3 listslthe mateerial
constantss. Figure 5 shows the initial Vs, shhear
stiffness, yield stress, and referencee strain after theh no embankm
ment.
self-graviity analysis att the area with
Assumingg Vs = 150 m/s
m at the groound surface, theshear stifffness G is calculated from equatione(1). T
The
yield streess and reference strain aree calculated frrom
equationss (2) and (3). The strength of the groundd isgiven by the cohesion c = 50 kPa anda friction anngle
φ = 40° (TTable 3).

Table 3 Materi
rial constants
Self-gravityy analysis

Analysis mesh
m
n

 
G0  Gref  m 0  (1) 
 ref wheree Gref : initiall shear stiffnesss for the testG0  
Gref  m   ref nDepth (m) m sin   c cos 

-4

-6

-6

-8
-10

-8
-10

-12

-12

-14

-14

-16

-16

-18

-18
-20
200

400

0

Vs (m/s) y   m sin   c cos  (2)


-2

-4

0

n: m
material constaant (n = 0.5)

r 

-2

-20

 ref : initial constrraint stress forr the test m 0 : mean effective stress at eaach element

y

0

(3)

wheree c: cohesion
φ: fricction angle

0

0

-2

-2

-4

-4

-6

-6

-8
-10

200000300000

-8
-10

-12

-12

-14

-14

-16

-16

-18

-18

-20

-20
0

100

200

300

0

Yield stress (kPa)

The ddynamic defoormation charracteristic (G//G0)
and dampping ratio h area calculated
d from equatiions(4) and ((5). For the ground,ghmax isi assumed too be
20.0%.

00
10000

Shear stiffness (kPa)

Depth (m)Fig. 40Depth (m)Depth (m)Dynamic aanalysis

Fig. 5

05
0.000

0.001

0.0015

Referrence strain

Iniitial states afteer self-gravity
y analysis

1.0

G
1

(4)
G0 1  

G/G0

0.8

r

0.6

0.4


G 
 (5)h  hmax 1 
 G0 

Treated
Un-treatedHyperbaric curvee(un-treated)
Hyperbaric curvee(treated)

0.2

0.0
1.E-4

Figuree 6 shows thet
G/G0,h~γ curve obtaiinedusing expperimental datta at the embaankment for bbothtreated annd untreated soil
s at a consttraint pressuree of
50 kPa.
ment area w
with
Figuree 7 shows the embankm
treated aand untreatedd soil. The surface of theembankmment is treatedd with urease.

1.E-3

Fig.
F 6

1.E-2

 (%)

1.E
E-1

1.E+0

G/G00,h~γ curves

The change ini input acceleeration is show
wn in Fig.
m acceleratioon is 7.95 m/s, and the8. The maximum
me duration is
i 50 s. Thee earthquake wave is
tim
app
plied to the bo
ottom as 2E.

655

GEOMATEGO
Osaka, Nov. 16
6-18, 2015

COONCLUSION
N

Acc.(m/s2)

Fig. 7In this stu
udy, laboratorry tests and numericalsimmulations weere performeed to study
y a new
microbial-function-based reinnforcement methodmfor
opes. The main conclusionns of this stu
udy are as
slofolllows:
1) The MCP method can improve thee physicalproperties ofo Toyoura sannd.
2) MCP-treated Toyora sanand increases the peakstrength byy approximateely 20% baseed on the
production ofo calcite in thhe sand pore.
3) MCP can increase thehe cohesion value ofnd.
Toyoura san4) The MCP treatment oof the surfacce of thent can increease the surrface soil
embankmen
strength andd change the nno-failure conndition foran earthquake.
In this research, we perfformed laboraatory tests
andd two types of numerical siimulation. So
ome of our
ressults show th
hat the surfaace soil prop
perties are
imp
proved. How
wever, the ap
application off specificmicroorganisms (such as B
Bacillus pasteurii) to
he in-situ
acttual sites is difficult. Fuurthermore, thureease-producing activity of the bacteria should beenh
hanced by coontrolling the physical andd chemical
pro
operties of thee soil.

Treateed and untreatted soil at theemmbankment

8
6
4
2
0
-2
-4
-6
-8
-10
10

15

20

Fig.. 825

30
Time (s)

35

40

4
45

50

Channges in input accelerationa

Numericcal results
Figure 9 shows the distribution ofo the equivallent
t untreated case, failuree isstrain at 50.0 s. In thement. On the ottherobserved at the top of the embankm
hand, in tthe treated casse, no failure is observed eeven
when tthe input maximum acceleration isapproxim
mately 8.0 m/s.

Unttreated caseCKNOWLED
DGEMENTSACThis researchh was partiallyly supported byb a Japan
Society for the PromotionP
off Science (JSPPS) GrantCfor
in Aid for Scieentific Researrch (B) and CenterPof the Japan Sccience andReevitalization Promotion
Technology Ageency for proviiding funds

Trreated caseFig. 9 Distributionn of equivalentt strain (50 s)

RE
EFERENCES
S

Figure 100 shows the tim
me history off the settlemennt at
the top oof the embannkment. One can see that thesettlemennt of treated soil
s is smallerr than that off theuntreatedd soil. Figure 11 shows thee time historyy ofthe shear strain of the elementecircleed in red in Figg. 8.
In the treeated case, forr high shear sttiffness, the shheartin the unttreated case. O
Onestrain is ssmaller than thatcan seee that the surface treatment of thement by ureasse is effectivee for suppresssing
embankmshear straains caused byy an earthquakke.
Settlement (m)0.002
0-0.002-0.004-0.006-0.008-0.01-0.012-0.014[1] T.Danjo andd S.Kawasaki, ”Formation mechanisms
mof beachrocks in Okinaw
wa and Ishikaw
wa, Japan,
with a focus on cem
ments”< Mateerials andTransactionss, Vol.55, No.33, 2014, pp.493
3-500.
[2] Guo HX,Cheng XH annd Li.M,”Experimental
analysis off bio-stimulateed sealing processping”, Scienceenvironmenttal getotechniccal engineering
China Technnological Scieences,Vol.56, No.3,2013.
N
pp.732-738.
[3] Fujita Y. et al, “Stimulaation of microbial urea
nce calcitehydrolysis in groundwatter to enhann”, Environmenntal Science Technology
Tprecipitation
Journal, Vol.42, No.8, 20008, pp. 3025-30
032.
W, Leon A.V.PP and Marien P.H,”[4] Victoria S.W
Microbial Carbonate Preciipitation as a Soil
nt Technique”, Geomicrobiollogy
Improvemen
Journal,No.2
24,2007, pp.4117-423.
Urease acttivity in[5] Keri L.B et al,”inducedd calcite
microbiologiccallyprecipitation, Journal of Biotechnology
y, Vol.93,
2002, pp.171-181.

Un-treated case
Treated case
10

15

20

2
25

30
Time (s)

35

40

45

50

Fig. 10 Tim
me history of settlements
0.01

Shear strain

0.005
0

-0.005Un-treated case

-0.01

Treated case-0.015
-0.02
10

15

Figg. 11

20

25
5

30

35

Time (s)

40

45

50

Tim
me history of shear strain
656

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

ANALYSIS OF EVACUATION BEHAVIORS IN DIFFERENT AREAS
BEFORE AND AFTER THE GREAT EAST JAPAN EARTHQUAKE
1

Tetsuo Morita1, Shinya Tsukada2 and Akira Yuzawa3
Graduate School of Engineering, Tohoku Institute of Technology, Japan; 2 Maebashi City, Japan,
3
Graduate School of Engineering, Maebashi Institute of Technology, Japan
ABSTRACTThis study aims to obtain the insights necessary for future disaster prevention planning and management inJapan. To this end, a questionnaire survey on evacuation behaviors was administered to the residents of Yamadamachi in Iwate Prefecture and Ishinomaki-shi in Miyagi Prefecture, which had been stricken by the Great East
Japan Earthquake. The obtained data were analyzed on the basis of the regional differences in said behaviors.
The analytical results are summarized as follows. (1) Prevention measures, such as earthquake drills andoccasional discussions regarding earthquakes, tended to be implemented in areas facing the Rias coast before theGreat Earthquake occurred. (2) The interval between the occurrence of the earthquake and the initiation ofevacuation was shorter in the regions fronting the Rias coast and in those located along a coastline than in areas
situated on a plain and along a river. (3) Residents of the regions facing the Rias coast and the areas locatedalong a river tended to evacuate by foot, whereas people living in other areas favored evacuation by automobile.
Keywords: Great East Japan Earthquake, evacuation behavior, Yamada-machi, Ishinomaki-shi
INTRODUCTION

accordingly analyzing the dissimilarities in
evacuation behaviors across the different areas. In
this study, we use basic analysis technique without
simulating the evacuation behaviors.

Purpose of the study
The Great East Japan Earthquake (hereinafterreferred to as the Great Earthquake) that occurred on
March 11, 2011 severely damaged East Japan. The
epicenter of seismic activity was located in Tohoku
district and resulted in a casualty of approximately
20,000 people. Given the necessity for safety andprevention arising from the disaster, this study aims
to acquire the information essential for futuredisaster prevention planning and management in the
country. To achieve this goal, a questionnaire survey
on evacuation behaviors was administered to theresidents of Yamada-machi in Iwate Prefecture andIshinomaki-shi in Miyagi Prefecture, which had
been seriously stricken by the Great Earthquake. The
obtained data were analyzed on the basis of theregional differences in evacuation behavior.

200km
Yamada-machi

Ishinomaki-shi
Seismic center
M9.0
500km

Overview of the study
Focal region

Research on the Great Earthquake has only
recently been initiated, with several researchers
comprehensively
examiningtheevacuation
behaviors of residents in disaster-stricken areas [1][5]. In addition, the study on simulation of theevacuation behaviors is pushed forward [6]-[7].
Previous studies focused on specific areas, but thecurrent research deviates from the typical approachby dividing the regions of interest into four areas
according to distance from a coast or a river andTokyo

Source: Geospatial Information Authority of Japan
Fig. 1657

Study regions

GEOMATE- Osaka, Nov. 16-18, 2015

STUDY REGIONS AND SURVEY METHODS

Table 1 and Fig. 3 show the situation of thedamage of the study regions.

Study regions

Table 1 Damage of the study regions

As previously stated, the study sites chosen forthis research are Yamada-machi located in Iwate
Prefecture and Ishinomaki-shi situated in Miyagi
Prefecture (Fig. 1). These two regions were chosenbecause (1) they differ in terms of geographicalcharacteristics but (2) are similar with regard totsunami height witnessed in the regions and building
damage ratio. The locations were therefore deemedsuitable for the analysis of variances in evacuation
behaviors across different areas.
The study regions experienced several times ofgreat earthquakes in the past: 1896(Meiji Sanriku
earthquake), 1933(Showa Sanriku earthquake) et al.
In these earthquakes, the East Japan greatearthquake disaster brought the heaviest damage.
Fig. 2 shows the tsunami inundation ranges of
the two regions. The ratios of tsunami inundation
area to inhabitable land in Yamada-machi and
Ishinomaki-shi were 19.2% and 30.1%, respectively.
The number of casualties in the former was 825(4.3% of the total population) and that in the latterwas 3,957 (2.5% of the total population).

Population
Household
Area

Yamadamachi
19,2707,192
263 km2

GeographicalRias coastfeatures

Height oftsunamiInundation
area
Death
personMissing
personIshinomakishi
160,826
57,871556 km2

2001,
Beforeearthquake

Plains

6-19 m5.8-10.4 m

5 km2

73 km2

676

3,512

149

445

Rail

SensekiLine,
Isinomaki
Line

YamadaLine

Road

Route 45

Route 45,
Route 108km

Yamada-machi

Yamada-machi

km
km

Ishinomaki-shi
Ishinomaki-shi: Inundation range
Source: Geospatial Information Authority of Japan

Source: Geospatial Information Authority of Japan

Fig. 2

Fig. 3

Tsunami inundation ranges
658

Damaged areas in study regions

GEOMATE- Osaka, Nov. 16-18, 2015

Survey methods

Table 3 Details of classification

Table 2 presents core information on thequestionnaire
surveyadministeredintheinvestigated localities.

Zone A(along acoast)

Table 2 Details regarding the questionnaire survey

Zone B (on aplain)

Yamada-machi

Ishinomaki-shi

JuneSeptember,
2011
184(approximately
1.6% of the
population in
the tsunami
inundation
range)

OctoberDecember,
2011
279(approximately
0.2% of the
population in
the tsunami
inundation
range)

Zone C(along ariver)
Zone D(facing theRias coast)

PeriodNumber of
respondents

Datacollection
methodLocation ofdata
collection

Items

Investigator

Ishinomaki-shi: Direct distancefrom a coast, less than 300 m (lessthan 3 m above sea level)
Ishinomaki-shi: Direct distancefrom a coast, 300 m or higher (3 mor higher above sea level)
Ishinomaki-shi: Direct distancefrom the Old Kitakami River, lessthan 300 m
Yamada-machi

Table 4 Details of survey responses
Population

Zone A
Zone B
Zone C
Zone D

Hearing survey
Places of refuge and temporary
dwellings

Number of Response Mortality
responses rate (%) rate (%)

10,83330,333
7,085
15,084

105
113
61
184

0.97
0.37
0.86
1.22

5.83
2.33
8.07
4.44

Table 5 Pre-earthquake behaviors in different areas(1) Personal attributes (sex, age,and occupation)(2) Situation immediately after theoccurrence of the Great
Earthquake(3) Tsunami evacuation behaviors
The Great East Japan Earthquake
Disaster Tsunami Evacuation Joint
Survey Group (number ofmembers: 30)

Zone Zone Zone Zone
A
B
C
D

Survey items(1) I haveparticipatedin disasterprevention
drills every
year.
(2) I canrecognize ahazard map.
(3) Myfamily has
occasional
discussions
regardingearthquakes.
(4) I
evacuatedfrom myhomeduring/after
the 2010
Chileantsunami.
(5)
Estimatedtsunamiarrival time

EVACUATION BEHAVIORS IN DIFFERENT
AREAS
Area classification
To understand the evacuation behavior patterns
across the regions of interest, these were separated
into different zones. Ishinomaki-shi was classified
into zones A (along a coast), B (on a plain), and C(along a river), as determined according to distancefrom a coast or a river, and Yamada-machi was set
as zone D, in accordance with its position fronting
the Rias coast (Table 3). Table 4 lists the populationin the localities, the number of responses received
from the participants, the response rates of thequestionnaire, and the disaster mortality rates in theregions (the number of deaths in the area divided by
the total population of the area). The disastermortality rate was highest in zone C, followed byzones A, D, and B.
659

Expected
frequency

28

30

17

54

Actualfrequency

11

30

11

77

15

16

9

30

7

9

8

46

Expected
frequency

43

46

27

85

Actualfrequency

41

35

16

109

Expected
frequency

41

44

25

75

Actualfrequency

41

30

19

95

[Min]

18.6

19.7

56.1

23.8

Expected
frequency
Actualfrequency

Items in which the actual frequency was
smaller than the expected frequency
Items in which the actual frequency waslarger than the expected frequency

GEOMATE- Osaka, Nov. 16-18, 2015

Pre-earthquake behaviors

after

the

Zone A

Fig. 4

Zone B

Zone C

Zone DTime taken to initiate evacuation

Returning-hometi (%)

Behaviors immediately
Earthquake

Time required to initiate

To examine the differences in behaviors acrossvarious areas before the Great Earthquake, a chisquare test was performed to determine theplausibility of the null hypothesis that no differences
exist among the regions. Table 5 lists the survey
items, for which the chi-square test revealed asignificant difference at the 5% level.
The number of responses was compared with the
expected frequency, which was obtained on theassumption that no differences in evacuation
behaviors exist among the studied areas. With regard
to item #1, the actual frequency of participation indisaster prevention drills every year in zone D(fronting the Rias coast) was larger than the
expected frequency; the actual frequency was
smaller than the anticipated frequency in zones A(along a coast) and C (along a river). In terms ofitem #2, the actual number of people who could
recognize a hazard map was larger than the expected
number only in zone D; the values obtained for the
rest of the areas were smaller than the expectedvalues. Concerning item #3, the number of families
who had occasionally discussed earthquakes waslarger than the expected number only in zone D; therest of the areas exhibited values smaller than thoseanticipated. With respect to item #4, the actual
number of people who had evacuated from the 2010
Chilean tsunami was larger than the predicted
number in zone D, whereas the expected number
was smaller than the anticipated number in zones B
and C. The estimated tsunami arrival time wasaround 20 min before the Great Earthquake in zones
A, B, and D, but this estimate was more than twice
as long (56 min) in zone C.

Zone A

Fig. 5

Zone B

Zone C

Zone D

Returnee ratio to Houses

Table 6 Analytical results on evacuation methods
Survey items
Means ofevacuation

Great

With respect to immediate behaviors after theGreat Earthquake, Fig. 4 shows the interval between
disaster occurrence and evacuation initiation in eacharea, and Fig. 5 indicates the ratio of residents who
returned home to the total population (returnee ratio
hereafter) in each area after the occurrence of theGreat Earthquake. The interval between theoccurrence of the Great Earthquake and the initiationof evacuation was shortest in zone D, followed byzones A, B, and C. The returnee ratio was lowest inzone B and highest in zone D.
To examine the differences in evacuation
methods among the areas, a chi-square test was
carried out to determine the plausibility of the null
hypothesis that no differences exist across thestudied regions. Table 6 lists the survey items, forwhich the chi-square test indicated a significant
difference at the 5% level. With regard to “means ofevacuation,” the people in zones A and B tended to

Evacuation
sites

Zone Zone Zone Zone
A
B
C
D
○

By foot
By carBridgesand places
located at
higherelevations
Upper
floors ofhouses andofficebuildings
Upwardslopes or
hills

○

○

○

○

○

○

○

○

○ = items that exceeded the expected values
evacuate by automobile, those in zone C favoredboth evacuation by car and by foot, and those inzone D tended to evacuate by foot. In terms of“evacuation sites,” the people in zone A tended toevacuate to bridges and places that were located at
660

GEOMATE- Osaka, Nov. 16-18, 2015higher elevations to avoid the erosion caused by theearthquake; the residents of zones B and C preferred
evacuating to the upper floors of their houses andoffice buildings; and the residents of zone D tended
to evacuate to upward slopes or hills.

measures, in addition to constructing tsunami
evacuation buildings.
The strongest tsunami waves caused by the Great
Earthquake arrived approximately 30 min after theoccurrence of the earthquake. In the case of Tokai,
Tonankai, and Nankai consolidated earthquakes, thestrongest tsunami wave is predicted to arrive lessthan 30 min after earthquake occurrence. Therefore,
people living in areas susceptible to such
earthquakes must be evacuated within a short period.
Achieving this goal necessitates prevention andmanagement measures that are specific to theevacuation behaviors of residents in different
regions.

CONCLUSION
Analytical results
The analytical results are summarized as follows.
(1) With regard to pre-earthquake behaviors,
prevention measures, such as earthquake drills
and occasional discussion on earthquakes,
tended to be implemented in areas facing theRias coast. No prevention measures were taken
in Ishinomaki-shi.
(2) The interval between the occurrence of theGreat Earthquake and the initiation ofevacuation was shortest in zone D, followed byzones A, B, and C. The people living in thesefour areas tended to return home, even though
they had evacuated early after the occurrence of
the Great Earthquake.
(3) The people living in the areas fronting the Rias
coast and those located along a river favoredevacuation by foot, whereas the residents ofother regions tended to evacuate by car. Withreference to evacuation sites, the people livingin the four studied areas tended to evacuate toeasily accessible sites.

REFERENCES[1] Kanai M and Katada T, “Decision-Making of
Evacuation from Tohoku Tsunami in 2011”,
Japan Society for Disaster Information Studies,No. 10, 2012, pp. 91-102.
[2] Urata J and Hato E, “An Analysis of Evacuation
Timing with Pre-Activities in Tsunami
Disasters: With a focus on the evacuation
behaviors in the city of Rikuzentakata during
the 2011 Great East Japan Earthquake”, Journalof the City Planning Institute of Japan, Vol. 48,
No. 3, 2013, pp. 807-812.
[3] Kikuchi Y and Minami M, “Study on the
Tsunami Evacuation Behavior of the School in
the Great East Japan Earthquake: A Case of the
Elementary and Junior High Schools in the
Iwate Coast”, Journal of the City Planning
Institute of Japan, Vol. 49, No. 3, 2014, pp. 333338.
[4] Fujiu M, Ishikawa T and Takada K, Language
Analysis of Tsunami Evacuation Behavior in
Kesennuma City During the 2011 off the Pacific
Coast of Tohoku Earthquake, Journal of JSCE,
A1, Vol. 69, No. 4, 2013, pp. 798-806, 2013.
[5] Yanagihara S and Murakami H, A Study on
Travel Means and Distances of Tsunami
Evacuation in Ishinomaki Urban Area After the2011 Great East Japan Earthquake Disaster,
Journal of JSCE, A1, Vol. 69, No. 4, 2013, pp.
1013-1020, 2013.
[6] Takada K, Fujiu M and Onomura K, Research
on Reproduction Method of the Situation of
Tsunami Evacuation with Car – Case Study ofKesennuma City -, Journal of JSCE, A1, Vol.
69, No. 4, 2013, pp. 973-979, 2013.
[7] Morita T, Hasegawa H, Tsukada S, Hashimoto
T and Yuzawa A, Analysis on Effects ofDisaster Prevention Measures Based on Refuge
Behavior Data - A Case Study of Ishinomaki
City -, Socio Technology Research, Vol. 12,
2015, pp. 51-60.

Future tsunami disaster prevention andmanagement measures
On the basis of the analytical findings, thefollowing recommendations for future tsunami
disaster prevention and management were
formulated.
The analytical results on the evacuation
behaviors indicate that participation in earthquake
drills, the recognition of a hazard map, and in-family
discussions of earthquakes are important preventive
measures that increase survival rates. In relation tomeans of evacuation, the facilities to be occupied by
evacuees should be examined to determine whether
these are accessible by automobile or other means.
An important requirement for facilitating auto-based
evacuation is the construction of roads that are easily
cleared of traffic jams even after a given area is
struck by an earthquake. With reference toevacuation by other means, safe evacuation sites
must be established within walking distance given
the travel constraints imposed by disasters. For
houses and office buildings located on plains, anessential measure is to design effective anti-tsunami
661

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

AN APPROACH TO ASSESS PERFORMANCE OF SEWER PIPESSUBJECTED TO BOULDER IMPACT
Thayalan Nall, and TaeWoo Eom
Coffey International Pty Ltd, Brisbane, Australia;

ABSTRACT
Design studies are now underway to rehabilitate concrete sewer pipes in the city of Brisbane which are over
100 year old. Some sections of the sewer have been laid in an unlined, hand dug rock tunnel excavated byuncontrolled blasting. It is believed that due to the construction technique used, the rock in the tunnel roof
region may have been highly fractured, and the loosely wedged boulders may fall in the event they are disturbed.
Parallel to this sewer alignment, a major road widening is now being proposed involving construction of deep
foundations. Consequently, there are concerns that potential vibration from such activities may trigger rockfalls
and potentially damage the dilapidated sewer pipes.
For health and safety reasons it was not possible to inspect the conditions of the ailing tunnel roof conditions.
Hence, a desk study was undertaken to assess the performance of the sewer pipe if it were subjected to rockfall.
For this study, the impact load due to free fall of rock boulders was computed using simple but adequate
methods. They were then modelled using finite element techniques to assess the behaviour of the sewer pipe.
This paper describes the assessment techniques adopted in the desk study and how the outcomes helped to planfurther investigations for conclusive results.
Keywords: Boulder impact, Sewer pipe, Tunnel, Sewer rehabilitation
potential size of boulders that are susceptible to fall
and their height of drop. Hence a scenario study withboulder weighing up to 250kg and them falling overa range of heights from 0.25m to 0.75m have been
considered.
In stage 2, structural behaviour of the existing
sewer pipe was assessed under rockfall impact
loading. For this assessment, the geometry of theexisting dilapidated concrete pipe as described in Fig.
1 has been considered.

INTRODUCTION
Concrete sewer pipes in the city of Brisbane areover 100 years old and will be rehabilitated with aflexible internal liner and by grouting the annularspace between the liner and the pipe. Some sectionsof them lay in an unlined, hand dug rock tunnel then
excavated by uncontrolled blasting. It is believedthat due to the construction technique used, the rockin the tunnel roof region may have been highly
fractured and the loosely wedged boulders may fallin the event they are disturbed.
Prior to the commencement of sewer
rehabilitation, a potential road upgrade project was
proposed near the sewer alignment. The project was
to involve activities of some deep foundation
construction, and there were concerns that potential
vibration from such activities may trigger rockfalls
and may damage the dilapidated sewer pipes. In thiscontext a desk study was undertaken to assess the
behaviour of the sewer under rock impact loading
and on the integrity of the proposed rehabilitation.

Dilapidated sewer
thickness reduced
to 150mm

Fig. 1: Existing Dilapidated Sewer Pipe
25mm thick liner
50mm thick grout

METHOD OF ASSESSMENT
This study was performed in four stages: In stage
one, the impact load due to free fall of rock boulders
on the sewer pipe was computed using establishedexpressions. In the absence of detailed information
on degree and extent of blast induced fractures on
the tunnel roof, tunnel geometry and size of loosely
wedged boulders; it was difficult to predict theFig. 2: Proposed Rehabilitation
662

GEOMATE- Osaka, Nov. 16-18, 2015

In stage 3, the structural behaviour of the
dilapidated sewer pipe was evaluated with theproposed rehabilitation (with internal flexible liner)
as described in Fig. 2. The existing dilapidated
concrete pipe has been considered as the 1st line ofdefence against rockfall in this evaluation (i.e. themaximum allowable stress of the dilapidated pipe as
the limiting value).
The stage 4 assessment is very similar to stage 3,
but the flexural strength of the liner has been
considered as the limiting stress. This is to reflect
the scenario of potential subsequent hits by boulders
after breaking of the outer pipe.

The Japanese design guide for design of rockfall
impact structure by the Japan Road Association [1][2] – this approach is referred to as Method 1. The
impact force is computed using the expression:
 = 2.108⁄ ⁄  ⁄ ⁄ .

(1)

Design guide from Switzerland Federal Road
Office (ASTRA) for the design of rockfall protection
galleries [3]-[7] – this approach is referred to as
Method 2. The impact force is computed using theexpression:
 = 2.8 .  . . tan  DYNAMIC BEHAVIOUR OF STRUCTURESUNDER IMPACT LOADING

× ! ⁄2".#(2)

Where; P - Impact force (kN), m - Mass offalling rock (t), g – gravitational acceleration (m/s2),v - Impact velocity (m/s), M - Modulus of thecushion layer (kPa), φ - Internal friction angle of thecushion layer (°), T - Thickness of the cushion layer(m),  - Lame’s constant proposed as 1000kPa, r or
D - Radius or Diameter, respectively, of anequivalent sphere representing the boulder (m), andH - Height of free fall.
Although there are a number of other guides thatprovide expressions to compute P, design guides
from Japan and Switzerland have been adopted,
primarily because both countries have madesignificant advances in designing rockfall protection
galleries, and also share similar levels of risk
acceptance as Australia.
For this assessment, where the formulae adopted
to compute the impact force involve a cushioning
layer with its stiffness, then the dilapidated pipe wall
has been considered as a hard cushion with amodulus of 11GPa and a friction angle of 30
degrees.
Published studies indicate that the material
behaviour under dynamic loading is different to that
under static loading, and plays an important role inthe structural performance. In this study however,the dynamic material behaviour has not been
considered since the expected impact velocity isrelatively low.

The kinematic or potential energy of a moving
object can be computed using well establishedexpressions. However, the resulting impact force ona structure, is not straightforward as it involves
aspects of geotechnical, structural and material
engineering. This subject has been widely studied by
two scientific communities in general. One by the
military for their applications and the other for thedevelopment of protective structures to withstand
impacts either due to civil activities or those ofnatural origin such as rockfalls, wave action, etc.
Findings from the latter are more freely available in
the public domain, and therefore used in this study.
Further, the developed procedures for design of
rockfall galleries being closely resemble this study;
approaches from those studies are incorporated in
the assessment.
In general, impact problems are classified by the
impact velocity, either as low (up to 50m/s) or highvelocity impact and by the way the kinetic energy is
dissipated, either as a soft or hard impact. The
assessment of impact load on a sewer pipe would beconsidered to be in the low velocity regime, and it isreasonable to consider that the material behavior willremain largely in the elastic stage and yielding may
only occur locally.
By this definition, rockfall on concrete pipeshould be treated as a hard impact, and the sewer
pipe has to dissipate the impact energy by means ofdeformations. It is also possible that deposited
sediments around the sewer pipe over flood events
since construction may have formed a cushioning
layer, and may contribute towards a softer impact.
However for a conservative estimate of impact load,
this option had been ignored.

Estimation of Static Equivalent Force
Both design guides considered prescribe amethod to estimate a static equivalent force torepresent the impact force. This is expressed inequation 4.
%&' = (Computation of Impact Load

(4)

For the constant ‘C’, the Japanese guiderecommends values between 1.2 and 2.0, while the
Swiss guide suggests values of 0.4 and 1.2 forductile and brittle failures respectively. For thisassessment a value of 1.2 has been adopted for ‘C’.

The impact load from the free fall of boulders
was assessed using some of the existing design
guides developed for rockfall galleries. They aredescribed below;
663

GEOMATE- Osaka, Nov. 16-18, 2015

The performance of the pipe was evaluated byassessing bending moments, shear forces and major
principal stresses in the pipe. Typical plots of shear
force, bending moment distributions and stresscontours under a static point load are shown in Fig. 4
to Fig. 7.

The computed static equivalent forces (SEF)
using these methods are shown in Fig. 3. It should
be noted that although these approaches are simple
to use, they do not fully incorporate the dynamic
behaviour of the structure in arriving at SEF.
1400

Method 1 : H=0.25m
Method 2 : H=0.25m
Method 3 : H=0.25m
Method 1 : H=0.5m
Method 2 : H=0.5m
Method 3 : H=0.5m
Method 1 : H=0.75m
Method 2 : H=0.75m
Method 3 : H=0.75m

1200

Static Force (kN)

1000
800
600

Fig. 4: Typical Shear
Force Distribution

Fig. 5: Typical Bending
Distribution

Fig. 6: Typical
Distribution of
Principal Stress (σ1)

Fig. 7: Typical
Distribution of Principal
Stress (σ3)

400
200
0
0

100

200
Rock Mass (kg)

300

400

Fig. 3: Computed static equivalent forces

STRUCTURAL PERFORMANCE OF THE
EXISTING SEWER PIPE WITHOUT THE
LINER

Computed Shear Force

By undertaking finite element modelling using
the commercially available software 2D-PLAXIS,
the structural performance of the sewer pipe wasassessed, subjecting the sewer pipes to the static
equivalent force computed.
The sewer pipe section as shown in Fig. 1 wasmodelled in PLAXIS within a tunnel section for agiven ground model, where the overlying rock layer
is not in contact with the pipe. The static equivalent
force calculated by these methods was applied as apoint load on the crown of the sewer pipe, and theresulting shear force, bending moment and stress inthe sewer pipe have been computed. For modelling:• The rock layer beneath the sewer pipe wasmodelled as a homogeneous Mohr-Coulomb
material with a Young’s modulus of 600MPa,
cohesion of 75kPa and friction angle of 35
degrees.
• The sewer pipe was modelled as a tunnel element.
It was considered that the pipe thickness has
been reduced to 150mm from its initial thicknessof 225mm, after being subjected to internal
erosion and gas attack over time. Although it wasconstructed with a 25MPa concrete then, a
reduced modulus of 11GPa, corresponding to
5MPa concrete has been adopted.
• Concrete supporting the sewer pipe wasmodelled as a linear elastic material, also with aYoung’s modulus of 11GPa.

The shear force distribution on the composite
sewer pipe due to static equivalent force fromboulders dropping from a height of 0.25m, 0.5m and
0.75m was evaluated. For the case of 0.5m free fall,
the computed maximum shear forces are presented
in Fig. 8.

Shear Force (kN)

3000

2500
2000
1500
1000
Method 1

500

Method 2

0
0

50

100
150
Rock Mass (kg)

200

250

Fig. 8: Maximum Shear Force for 0.5m Free Fall
Computed Bending Moments
The computed maximum bending moments on
the sewer pipe due to static equivalent force fromboulders dropping from a height between 0.25m and
0.75m were evaluated. For the case of 0.5m free fall,computed maximum bending moments are presented
in Fig. 9.

664

Bending Moment (kNm)

GEOMATE- Osaka, Nov. 16-18, 2015

above, but with the additional components to it as
described below:• Flexible liner was modelled as a 25mm thicktunnel element having stiffness values of 1.8 and6.5GPa.
• The grout infill within the annular between thepipe and the liner was considered 50mm thickand modelled as a liner elastic material with aYoung’s modulus of 16GPa, corresponding to
10MPa concrete.

1200
1000
800
600
400
Method 1

200

Method 2

0
0

50

100
150
Rock Mass (kg)

200

250

Fig. 9: Max. Bending Moment for 0.5m Free Fall
Computed Shear ForceStress on Pipe
The shear force distribution on the composite
sewer pipe due to static equivalent force fromboulders dropping from a height between 0.25m and
0.75m was evaluated for the two proposed liners.
The order of difference between the computed
maximum shear force with the liner having a
modulus of 6.5GPa and 1.8GPa was found to be lessthan 10kN. Hence, the computed average maximum
shear forces are presented in Fig. 11 for a 0.5m free
fall height. This indicates that the introduction ofliner has reduced the shear force by around 10%6

H=0.25m

H=0.5m3000

H=0.75m
Shear Force (kN)Maximum Stress (MPa)The maximum stresses on the sewer pipe wall
due to static equivalent force from boulders
dropping from a height between 0.25m and 0.75m
were evaluated without the proposed flexible liners.
The ultimate compressive strength of the dilapidated
concrete pipe was considered as 5MPa (limitingstress) to arrive at the maximum size of boulders thatwould impart that order of stress. They are tabulated
in Table 1 and are also shown in Fig. 10, for Method
2 for the case without the liner.

5
4
3
2
1

2500
2000
1500
1000

Method 1

500

Method 2

0

0

0
0

25
50
Rock Mass (kg)50

75

Height ofFall = 0.75m

1

93

57

43

2

49

38

33

Average

71

48

38

Bending Moment (kNm)

Limiting Size of Boulder in Mass (kg)Height ofFall = 0.5m

250

The maximum bending moments on thecomposite sewer pipe due to static equivalent force
from boulders dropping from a height between
0.25m and 0.75m were evaluated with both flexible
liners proposed. The maximum bending moment fora free fall height of 0.5m are shown in Fig. 12. This
indicates that the introduction of liner has reduced
the bending moment by around 40%

Table 1: Limiting Boulder Size without Liner

Height ofFall = 0.25m

200

Fig. 11: Computed Max. Shear Force under Static

Fig. 10: Maximum Compressive Stress and Limiting
Rock mass – Computed by Method 2

Method100
150
Rock Mass (kg)

STRUCTURAL PERFORMANCE OF THE
SEWER PIPE WITH THE LINER
To assess the structure performance of the
dilapidated concrete sewer under a static equivalentpoint load, similar numerical analyses were
undertaken through PLAXIS 2D modelling of atunnel cross-section with the composite pipe. The
composite pipe was modelled in a similar manner as

700
600
500
400
300
200
100
0

Method 1: 6.5GPa
Method 2: 6.5GPa
Method 1: 1.8GPa
Method 2: 1.8GPa
0

50

100
150
Rock Mass (kg)

200

250

Fig. 12: Computed Bending Moment under Static

665

GEOMATE- Osaka, Nov. 16-18, 2015

analysis similar to those described before has been
considered.
In stage 1, for larger size boulders with rockmass between 1000kg and 1750kg, the potential
impact load was computed. Since the above study
indicated that among the two methods considered,
the impact loads estimated using the Swiss
Guideline resulted in the highest value, the Swiss
approach has been adopted in the assessment toarrive at reasonably conservative outcomes. Further,among the three free fall heights considered, the midvalue of 0.5m which is likely to be the clearance
between the pipe and tunnel crown was selected tocompute the impact loads on the pipe.
In Stage 2, finite element analyses using 2D
PLAXIS, similar to models described previously,
have been undertaken to assess the stresses on theliner with the following changes:• Recent CCTV investigations undertaken within
the sewer pipes, suggest that in some sections of
the pipe the crown regions of the sewer pipes
have undergone more damage compared to thesides and would be conservative to adopt a wall
thickness of 100mm in place of 150mm. Taking
this into account the concrete sewer pipe wasmodelled as a tunnel element of 100mm thickwith a Young’s modulus of 11GPa,corresponding to 5MPa concrete.
• Flexible liner was also modelled as a tunnel
element. For this study liner thickness of 25mm
and 35mm has been considered and a range ofstiffness values varying from 1.8GPa to 20GPa
have been examined. The flexural strength wasconsidered to be linearly proportional to its
stiffness as shown in Fig. 14.
• The grout infill within the annulus between thesewer pipe and the liner was considered 25mm
thick and modelled as a liner elastic material
with a Young’s modulus of 11GPa,corresponding to 5MPa concrete.

Stress on Pipe

Maximum Stress (MPa)The maximum stresses on the sewer pipe wall
due to static equivalent force from boulders
dropping from a height between 0.25m and 0.75m
were evaluated with the proposed flexible liners.
The computed maximum stresses with the 6.5GPaliner by Method 2 are shown in Fig. 13.
Since the dilapidated concrete pipe will act as the
1st line of defence, the ultimate compressive strength(5MPa) of the dilapidated concrete pipe as thelimiting stress to arrive at maximum size of boulderthat would impart that order of stress. They aretabulated in Tables 2 and 3. This indicates that the
introduction of liner has increased the tolerable
boulder size by around three fold.
6

H=0.25m

H=0.5m

H=0.75m

50

100
Rock Mass (kg)

150

5
4
3
2
1
0
0

200

Fig. 13: Maximum Compressive Stress and Limiting
Rock mass – Computed by Method 2

Table 2: Limiting Boulder Size with 6.5GPa liner

Height ofFall = 0.25m

Height ofFall = 0.5m

Height ofFall = 0.75m

1

270

166

125

2

148

115

100

Average

209

141

113
Flexural Strength (MPa)

Limiting Size of Boulder in Mass (kg)MethodTable 3: Limiting Boulder Size with 1.8GPa liner
Limiting Size of Boulder in Mass (kg)MethodHeight ofFall = 0.5m

Height ofFall = 1.0m

Height ofFall = 1.5m

1

246

152

114

2

135

105

91

Average

191

129

103

160
140
120
100
80
60
40
20
0

0

2

4

6
8 10 12 14
Liner Modulus (GPa)

16

18

20

Fig. 14: Young’s Modulus and Flexural Strength of
Liner

STRUCTURAL PERFORMANCE OF THE
FLEXIBLE LINER

The static equivalent force calculated as per the
Swiss guideline for dislodged boulders falling froma height of 0.5m was applied as a point load at thecrown of the sewer pipe and the resulting maximum

For the assessments of required minimum
stiffness and thickness of the liner, a two stage
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GEOMATE- Osaka, Nov. 16-18, 2015

presented above are based on an equivalent static
loading corresponding to an impact load, andignoring the time-dependent dynamic behaviour ofthe pipe following an impact.
The conclusions are only based on tolerable
compressive stress on the sewer pipe and the liner.
At the time of preparation of this paper noinformation available on the bending and shear
capacities of the sewer pipe.
The methodology discussed in this paper should
be complemented with laboratory load testing on thecomposite sewer pipes, in consultation withstructural engineers, to assess shear and bending
capacities, and to further interpret their ability towithstand the estimated shear forces and bending
moments.
Introduction of liner with grout filled in theannular space would improve the capacity towithstand 3 times larger size boulders compared tothe unlined sewer pipe.
To withstand boulders of size larger than 1000kg
falling from a height of 0.75m, the internal liners
have to be stronger and at least have a Young’s
Modulus of 10.6GPa.

stresses in the flexible liner instead of in the sewer
have been computed through PLAXIS modelling.
The computed maximum stress was compared withthe flexural strength of the liner to arrive at suitable
stiffness values for the proposed liner. They arepresented in Figs 15 and 16 for 25mm and 35mm
thick liners respectively and summarised in Table 4.
Table 4: Boulder Size and Liner Stiffness

Boulder
Mass (kg)

Required Minimum Liner Stiffness(GPa)

25mm Liner

35mm Liner

750

8.6

7.9

1000

10.6

9.7

Maximum Stress (MPa)

160
140
120
100
80
60
40
20
0

REFERENCES
750kg rock
0

5

10
15
Liner Modulus (GPa)1000kg rock20

[1] Ishikawa N. Recent progress on rock-shed
structures studies in Japan. In: Joint JapanSwiss Scientific Seminar on Impact Load by
Rockfalls and Design of Protection Structures,
1999, Kanazawa, Japan.
[2] Jacquemoud J. Swiss guideline for the design of
rockfall protection galleries: Background, safetyconcept and case histories. In: Joint Japan-Swiss
Scientific Seminar on Impact Load by Rockfalls
and Design of Protection Structures, 1999,
Kanazawa, Japan.
[3] Chikatamarla R, Laue J, Springman S M.
Rockfall impact on protection galleries.
Zingoni, A. (edition.): Progress in StructuralEngineering, Mechanics and Computation,
Taylor & Francis, London; 2004.
[4] Schellenberg K, Vogel T. Swiss Rockfall
Galleries - Impact Load. In: Proceedings IABSE
Symposium Lisbon 2005, 'Structures andExtreme Events', Zurich, 2005.
[5] Chikatamarla R, Laue J, Springman SM.
Rockfall on protection galleries. SEMC, 2004,
Cape Town, South Africa.
[6] Chikatamarla R. Rockfalls on slopes andstructures, PhD Thesis, Swiss Federal Instituteof Technology, Zurich, 2005.
[7] Schellenberg, K. On the Design of Rockfall
Protection Galleries. Swiss Federal Institute ofTechnology, Zurich, Switzerland, 2009.

25

Maximum Stress (MPa)Fig. 15: Maximum Stress on 25mm thick Liner
160
140
120
100
80
60
40
20
0

750kg rock
0

5

10
15
Liner Modulus (GPa)1000kg rock20

25

Fig. 16: Maximum Stress on 35mm thick Liner
The above results indicate that, for the free fallheight considered (0.5m), if the liner stiffness is lessthan 10.6GPa and 9.7GPa respectively for 25mm
and 35mm thickness, any boulder of mass greaterthan 1000kg (0.4m3) would potentially cause
damage to both the pipe and the liner. It should be
noted that this assessment does not cover localised
punching failures.
CONCLUDING COMMENTS
It should be noted that the analyses and outcomes667

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

STUDY ON ASSESSING THE VALUE OF THE TENGUIWA
IRRIGATION CANAL
1

Shinya Tsukada1, Tetsuo Morita2, and Akira Yuzawa3
Maebashi City Office, Japan, 2 Graduate School of Engineering, Tohoku Institute of Technology, Japan;
3
Graduate School of Engineering, Maebashi Institute of Technology, Japan

ABSTRACTIn Japan, following changes in socioeconomic conditions, such as financial deterioration, an increase insocial capital, and a decrease in population, ideas about infrastructure improvement have greatly changed.
Recently, irrigation canals possessing historic value have attracted the attention of the public as assets forcommunity development. The present study focused on the Soja district, in which many historical and cultural
resources exist but where these resources have not been developed. A questionnaire was administered to the
local residents and revealed the value of the Tenguiwa Irrigation Canal as an environmental asset. The present
study also examined the relationship between the value of the Tenguiwa Irrigation Canal as an environmental
asset and the direction of community development in the Soja district.
Keywords: Irrigation canal, historic value, environmental asset, Maebashi-shi,

INTRODUCTIONSTUDY REGIONS AND SURVEY METHODS

Purpose of the study

Study regions

In Japan, following changes in socioeconomic
conditions, such as financial deterioration, an
increase in social capital stock, and a decrease inpopulation, ideas about infrastructure improvement
have greatly changed. Recently, irrigation canals
possessing historic value have attracted the attentionof the public as assets for community development.
The aim of the present study is to understand theresidents’ awareness of the value of the Tenguiwa
Irrigation Canal as an environmental asset and the
direction of community development in the Soja
district.

The present study investigated the Tenguiwa
Irrigation Canal in the Soja district, Maebashi-shi,
Gunma Prefecture, in which urban development had
not been carried out. The Tenguiwa Irrigation Canalis an agricultural water channel passing through
Maebashi-shi, Takasaki-shi, and Tamamura-machi
from the Tone River (Fig. 1).
In 1604, Nagatomo Akimoto, the lord of Soja
Castle, dammed up the Tone River to create theTenguiwa Irrigation Canal. Therefore, the canal has
a history of more than 400 years. Flower beds andpromenades have been prepared along the canal, andthe local residents enjoy walking along thepromenades. A volunteer organization wasestablished to protect the environment around theOverview of the study
There have been previous studies on irrigation
canals. First, Marumo et al. assessed the value of the
Yanagawa canal [1]. Second, Maki et al. assessedthe value of the Old Mita Channel [2]. Third,
Yamashita et al. assessed the value of the Tamagawa
Channel [3]. However, only a few studies haveassessed the value of an irrigation canal as anenvironmental asset, and comprehensively andquantitatively examined the residents’ awareness ofthe direction of community development. The
present study focused on the Tenguiwa Irrigation
Canal in the Soja district, Maebashi City, assessedthe value of the Tenguiwa Irrigation Canal, andquantitatively examined the direction of community
development in the Soja district.

Fig. 1
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Tenguiwa Irrigation CanalGEOMATE- Osaka, Nov. 16-18, 2015

Tenguiwa Irrigation Canal, and the organization has
been inspecting facilities and maintaining the flower
beds and promenades by weeding, planting, andseeding.

Table 2 Assessment items
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
C19
C20

Survey methods
Table 1 presents core information about thesurvey administered in the investigated localities.
Table 1 Details regarding the questionnaire survey
Date of survey
Location ofdata collection
Datacollection
methodItems

Distribution
and collection- Distribution: July 24-27, 2010
- Collection: August 15, 2010(latest posting date)
Soja district, Maebashi-shi,
Gunma Prefecture- Distribution: Putting in mailbox- Collection: Mailing(1) Satisfaction levels about the
Tenguiwa Irrigation Canal andthe surrounding environment(2) Direction of community
development- Number of questionnaire cards
distributed: 1,000- Number of questionnaire cards
collected: 253- Recovery: 25.3%

Management of fences
Management of trees and plants
Garbage scattering
Crime-free
Sidewalk construction
Walking safety
Nighttime illumination
Traffic safety
Safe playgroundRest facility
Waterside organisms
Rich in nature
Quality of landscape
Bird-watching
Water sanitation
Rich in trees
Peace of mind
Region exchange
Local history
Community symbol

Table 3 Results of the factor analysis
Facility
improvement

NaturallandscapeRegional
characteristics

Safetyandsecurity

C5

0.79010.17070.18050.0994

VALUE ASSESSMENT OF THE TENGUIWA
IRRIGATION CANAL

C6

0.7195

0.09340.15680.3377C3

0.68110.13490.1691-0.0656

Assessment of the environment around the
Tenguiwa Irrigation Canal

C1

0.61890.20470.04660.0878C2

0.61540.20700.17640.1140Regarding the environment around the Tenguiwa
Irrigation Canal, the residents were asked to evaluate
the items shown in Table 2 using a five-point scalefrom “very good” to “very bad”.

C8

0.5976

0.03330.06290.1798C13

0.23400.72640.19770.0023C12

0.17520.72270.1715-0.0341C14

0.10450.69930.00360.1294C16

0.27650.59870.16180.0921C17

0.31160.59200.3706

0.2734C15

0.21450.51000.31890.2162C11-0.11190.42390.03780.2374C20

0.20150.20170.82830.1793C19

0.17210.2436

0.79270.1138C18

0.26340.39080.45560.3177C9

0.13300.06490.12960.8251C7

0.24450.03830.13720.4249C4

0.35350.1817-0.1078

0.3936C10

0.01960.12820.10330.3837Extraction of environmental factors regarding
the Tenguiwa Irrigation Canal
To summarize the assessment results of theabove 20 items, a factor analysis was performed.
Table 3 shows the results of the factor analysis. Fourenvironmental factors, i.e., “facility improvement,”“natural landscape,” “regional characteristics,” and“safety and security” were extracted from the factor
analysis.
Model for assessing the Tenguiwa Irrigation
Canal as environmental assets
Fig. 2 shows a model for assessing the Tenguiwa
Irrigation Canal as an environmental asset using
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GEOMATE- Osaka, Nov. 16-18, 2015

C4

C6

C5

C10

0.787

0.422

0.644

0.642
0.682

Facility
施設整備
improvement

Safety and治安安全security

C7

0.673

0.846

0.564

C1

C3

C2

0.600
C8

C9

0.652

Overall

0.766
0.664
C18

C19

0.697

0.602
用水総合
canal

0.813

C13

0.706

C12

Natural
自然景観
landscapeRegional
地域特性
characteristic
s

0.640

0.853
0.871
0.676

0.365

C20
C11

Fig. 2

0.747

C15

0.803
C17

0.697

C14

C16

Model for assessing the Tenguiwa Irrigation Canal as an environmental asset

satisfaction levels. In this model, “overall canal”
represents a comprehensive assessment of theenvironment of the Tenguiwa Irrigation Canal.
Under “overall canal,” four environmental factors,i.e., “facility improvement,” “natural landscape,”“regional characteristics,” and “safety and security,”that were extracted in the factor analysis wereallocated as potential factors (level 1). Under thesefour potential factors, assessment items shown in
Table 2 were allocated as observation variables(level 2). The adjusted goodness of fit index of themodel was 0.765. The t-values of all pathcoefficients satisfied a level of significance at 0.1%.
A covariance structure analysis was performed
for this model. The values of the path coefficients
indicated that the effect of “natural landscape”(0.813) on “overall canal” was the greatest. Of theitems under “natural landscape,” the effect of “C13:
Quality of landscape” (0.747) was the greatest.
Of the items under “facility improvement,” theeffect of “C5: Sidewalk construction” (0.846) was
the greatest. Of the items under “regional
characteristics,” the effect of “C20: Community
symbol” (0.871) was the greatest. Of the items under“safety and security,” the effect of “C9: Safe
playground” (0.652) was the greatest.

VALUE ASSESSMENT OF THE TENGUIWA
IRRIGATION CANAL AND THE DIRECTION
OF COMMUNITY DEVELOPMENT
Assessment of the Tenguiwa Irrigation Canal
The results of the covariance structure analysis
revealed that the effect of “natural landscape” on thevalue of the Tenguiwa Irrigation Canal as anenvironmental asset was larger than that of otherenvironmental factors. Of the items under “naturallandscape,” the effect of “quality of landscape” was
the greatest.
The direction of community development
Table 4 shows the results of the regression
analysis, in which the comprehensive assessment of
the environment of the Tenguiwa Irrigation Canal
was used as an objective variable and “community
development using regional history and culture” andfive other items were used as explanatory variables.
These explanatory variables were expressed in termsof a five-level assessment of the residents’ intensity
in considering the direction of community
development.

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GEOMATE- Osaka, Nov. 16-18, 2015

Table 4 Results of the regression analysis
Variable

Partialregression
coefficient

Community development using regional
history and culture
Community
development
performed
mainly by local residents
Assignment of traditional buildings as tobe preserved
Community development using ancient
tombs and castle sites
Development of streets and parks byperforming land readjustment
Conservation of landscapes, stores, andhouses although it may be inconvenient
Constant termStandard
regression
coefficient

T-value

Judgment

0.13580.17702.40930.00610.00930.12860.02500.02970.32700.10340.1302

1.5080-0.0760-0.0901-1.0098

0.00390.00530.06402.44997.7161

* 5%

** 1%

Soja district.
Previously, land for roads had been prepared bylaying irrigation canals underground in Japanbecause social infrastructure, such as urban roads,
had not been sufficiently provided. Recently,
irrigation canals have been reassessed asenvironmental assets.
Consequently, the present study created a model
for assessing the Tenguiwa Irrigation Canal as anenvironmental asset, and quantitatively analyzed theeffects of assessment items on environmental factors.
The present study also quantitatively analyzed
the relationship between the value of the Tenguiwa
Irrigation Canal as an environmental asset and the
direction of community development using regional
history and culture.

The result of the regression analysis, the item of
the comprehensive assessment of the environment of
the Tenguiwa Irrigation Canal, the effect of“Community development using regional history
and culture” (0.1770) was the greatest. And, t-values
satisfied a level of significance at 0.5%.
Consequently, the value of the Tenguiwa
Irrigation Canal as an environmental asset wasclosely related to the direction of community
development in the Soja district.
CONCLUSION
This study focused on the Soja district, in which
many historical and cultural resources exist but
where these resources have not been developed. A
questionnaire was administered to the local residents
and revealed the value of the Tenguiwa Irrigation
Canal as an environmental asset. The present study
also examined the relationship between the value of
the Tenguiwa Irrigation Canal as an environmental
asset and the direction of community development in
the Soja district.
The results of the covariance structure analysis
revealed that the effect of “natural landscape” on thevalue of the Tenguiwa Irrigation Canal as anenvironmental asset was larger than that of otherenvironmental factors. And, the result of theregression analysis revealed that the effect of“Community development using regional history
and culture” on the value of the Tenguiwa Irrigation
Canal as of the residents’ was larger than that ofother directions of community development in the

REFERENCES[1] Marumo Y, Kikuchi S, “The situations of the
Present Waterway in Canaltown ‘Yanagawa’and the influence of Measures Adopted Improve
Waterways”, J. Archit. Plann., AIJ, No.603,
2006, pp.1-7.
[2] Maki H, Ishikawa M, “A Study on the CulturalLandscape of Mita Waterway and its HistoricalTransformation”, Journal of the City Planning
Institute of Japan, Vol. 45, No. 3, 2010, pp. 397402.
[3] Yamashita Y, Ito H, Ono R, Shimomura A,“Changes of the Relationship between GreenSpaces and Residents on Tamagawa Josui
Green Way”, LRJ, 75(5), 2012, pp.677-682.

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Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

THE EFFECT OF FLOODING ON THE INTEGRITY OF ROADSUBGRADE
Abdul Naser Abdul Ghani1, Nur Izyan Roslan2 and Ahmad Hilmy Abdul Hamid3
School of Housing Building and Planning, Universiti Sains Malaysia, Malaysia

1,2,3

ABSTRACT
Deterioration of road structural integrity because of flooding may cause huge expenditure for rehabilitationand maintenance of roadway. In principle, the design of pavement structure is based on the strength ofcompacted soil known as the subgrade or road foundation. Therefore, subgrade is a significant part of the roadstructural system. When roads are inundated for a long time or repeatedly, the materials in each layer of roadstructure become saturated, and the original condition of subgrade soils will be compromised. This study
investigated the effect on sub-grade strength and properties due to road submergence period and repeatedsubmergence of the road structural systems. Two types of soil that are normally used as the embankment
material in road construction, which can be categorized as cohesive and cohesionless materials, were used in thisstudy. California Bearing Ratio (CBR) test and consolidation settlement test were carried out on various
categories of inundation and loading conditions including repeated inundation. The findings indicated that thestrength of subgrade materials were affected differently depending on the inundation period and number of
repeated inundation. These findings are useful for road design and maintenance strategies of flood affected roadlinks.
Keywords: Flooding, Road Sub-grade, Pavement

collapsed in federal roads damaged by floods in thestate of Kelantan. Previously, Bernama [3] reported
that the state of Terengganu spent more than RM 74
million for flood damaged roads of only one flood
event.
Similarly in town of Sibu in the state of Sarawak
it was reported that the city requires RM 500 million
to repair road related infrastructure damaged byflooding [4]. Malaysian federal government has also
allocated RM 106 million [5] to repair federal roads
damaged by flash floods in between October 2012
and January 2013 (monsoon season). Figure 1 is atypical road inundated during flash flood.

INTRODUCTION
Floods have resulted in many undesirable
outcomes on human and the environment. This ismainly due to their adverse impacts on humans,
properties, environmental surrounding, roadstructures and many forth. Additionally, humanactivities can also contribute to the flooding event
which include: (i) farming and deforestation that
exposes the soil to erosion and increases surface
runoff, (ii) urbanization by unplanned building
construction in vulnerable areas without following
the regulations of town planning, poor watershedmanagement and failure to control the flooding
promptly, and (iii) obstruction of natural flow ofwater through drainage modification [1]. The impact
of flood disaster can be significant after the event
since it may impact the whole infrastructure
involved as well as can have long term effect interms of the maintenance work. In the long run,flooding can bring the deterioration to roadpavement foundation when the phenomenon keeps
repeating. Continuous flood submersion of roads
could bring damage on large part of the roadinfrastructure, thus affecting the stability of asphaltic
concrete pavement layer [2].
Damages of roads structure due to flood eventare commonly causing a huge expenditure for the
rehabilitation and maintenance works of roads.
Recently, Malaysian federal government has
allocated RM42 million to repair the embankment

Fig.1: Typical road inundation during flash flood
Monsoon season will always come every year
and it is only logical to expect the same or more

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GEOMATE- Osaka, Nov. 16-18, 2015

amount of money will be required to reinstate thedamaged roads annually. On the other hand, when
the roads are inundated for a long time, the materials
in each layer for road pavement become saturated,
and then as floodwaters drained, the sub-grade soils
began to shrink and subside. The excessive water
can drain into the foundation reducing its load
bearing efficiency. Thus, this situation can cause thestrength of road pavement systems to becompromised.

Table 1 Properties of Soil
Soil
Soil 1(Well-graded sandwith clay andgravel)Soil 2(Silty clay andgravel)The Sub-grade
The sub-grade is a foundation for the pavement
structure to support the load from upper layer to thebeneath soil. Basically, sub-grade must be stable inperformance to carried load in any weather
conditions. Generally in road engineering, CBR test
is performed to determine the strength of sub-grade
soil and these CBR values will be used to design the
thickness of flexible pavement [6]. As soil is ahighly variable engineering material due to its
composition and the dependence of its properties onenvironmental conditions, it is logical to evaluate theeffect of the variability associated with sub-grade
strength on pavement design and performance [7],[8]. Generally, no systematic study has been
performed to evaluate the effects of weak, variable
sub-grade conditions on pavement design,
construction, and performance prediction [9], [10].
Change of the properties of sub-grade soil isdefinitely affecting the performance of pavement
structure.
Fairweather and Yeaman [11] recently studied
the influence of flooding on road pavement
deterioration and recommended further research tobetter predict pavement failure.

Properties

ValuesLiquid limit
Plastic limit
Plasticity
index

78.5
34.2
44.3

Liquid limit
Plastic limit
Plasticity
index

88.8
38
50.8

Sample Preparation
Initially, the experiments such as grain size
distribution and index properties were conducted tofind out the different properties of soil. After that,
the CBR test was performed on unsoaked andsoaked specimens in different days of submergenceas well as the soil samples were kept submerged inwater for certain period in the case of repeatedflooding in order to determine the strength ofsubgrade soil. Finally, the Odometer consolidation
tests were carried out to determine the settlement ofsoil.
CBR Test
CBR test was carried out according to theBS1377. 4300g of soil was compressed in the mould
and assign to unsoaked, soaked and repeatedsubmerged condition. The unsoaked soil sample istested immediately after the soil being compressed
into mould, while soaked soil sample is tested for its
strength after being soaked for 1, 3 and 7 days.
Furthermore, to simulate the effect of repeatedinundation the samples were kept in water for 1 houron Day 1, Day 3 and Day 7. The penetration wasmeasured using a dial gage which has accuracy
0.01mm.

EXPERIMENTAL SETUP
The samples of sub-grade soil were taken fromtwo different quarries that supply soil which isusually used as the embankment soil in road works.
The soil samples were categorized as cohesivematerial and cohesionless material according toStandard Specification for Road Works by PublicWork Department (JKR), Malaysia. The properties
of soil are shown in Table 1.

Oedometer Consolidation Test
The Odoemeter test was carried out according to
the ASTM D2435. In this research, the Oedometer
was modified by using data logger for data recording.
The standard Oedometer test is carried out on acylindrical specimen of saturated soil with thedimension of 75 mm diameter and 20 mm thick. The
soil sample is enclosed in a metal ring and is placedon a porous stone. The soil samples were preparedfor 1-day submerged and 3-days submerged before
tested. The test involves applying increments of 1kg,
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GEOMATE- Osaka, Nov. 16-18, 2015

reduced by 76% from the condition of unsoaked
sample to the 7-day soaking sample. Obviously, thepresence of water when the soil had been soaked for
1, 3 and 7 days contributes to the decreasing of soil
strength. Soil had been loss strength starting on 1day soaking when it compared to the unsoaked
condition. Soil sample in unsoaked condition show
its capability to sustain the higher load since it isevident that there have no subsequent loss ofstrength. The unsoaked sample basically showed
better performance on their strength and the CBR
strength probably can be increased with wellcompacted on soil tested.
However, the CBR value for soaked condition
decreased with the strength accordingly due to thenumber of inundation days for each soaked soil
samples. It was found that further increase in thenumber of days of soaking decreases the CBR value
gradually and it is also observed that the loss of
CBR value between conditions of 1 day until 7 days
soaking. Significant loss of strength was observed
caused by inundation and subgrade soil becomessaturated within the soaking period. From the results,
it is concluded that the value of CBR for the given
soil sample decreases rapidly from unsoaked
condition to 1 day of soaking. Additionally, it is also
observed that the variation between 1-day and 7-day
soaking values are quite different. Soil had been loss
more strength on 7-day soaking compared to 3-daysoaking since the percentage of CBR value decrease
from 12.2% to 18.6% respectively. The volume ofsoil has been changed effect from the soaking
condition, thus the strength of soil become less due
to number of inundation days.

2kg, 4kg, 8kg, 16kg and 32kg of vertical static loadto the sample and recording the corresponding
settlement. The time intervals were 6s, 15s, 30s, 60s,
120s, 240s, 480s, 900s, 1800s, 3600s, 7200s, 14400,
28800s and 86400s.
RESULTS AND DISCUSSION
The mechanical and physical properties for thetwo types of the tested soils were determined and theparticle size distribution for the tested soils,
according to American Association of State
Highway and Transportation Officials (AASHTO),
is shown in Fig. 2 indicating that the percentage
passing no. 200 sieve for Soil 1 and Soil 2 are 0.6%
and 1% respectively. Based on the AASHTO soil
classification system, both soils belongs to soil
group which usual types of significant constituent
materials are silty or clayey gravel and sand.
Moreover, according to the Unified Soil
Classification System (ASTM D2487), the tested
soils were found and can be classified in group nameof “well-graded sand with clay and gravel” for the
Soil 1, whereas for the Soil 2 is “poorly graded silty
clay and gravel”.

Fig. 2 Particle Size Distribution Curve

CBR PerformanceFig. 3 Comparison of CBR values of inundated
samples (Soil 1)

Soil 1
The result of CBR strength presented in Fig. 3
illustrated the comparison of soil strength forunsoaked and soaked condition. The soil samples
were inundated for 1, 3 and 7 days for soaked
condition. From the bar chart, it shows the CBRvalue for unsoaked condition relatively higher than
CBR value for soaked condition due to the saturated
period for soaked soil samples. It shows that the
CBR value for unsoaked condition was 35.7% andon submerging the soil samples for 1, 3 and 7 days,
the CBR values were 15%, 12.2% and 8.6%
respectively. Generally, the soil strength has been

Meanwhile, the bar chart in the Fig. 4, for the
repeated submerged condition has shown different
result of unsoaked and soaked condition. The soil
samples were submerged for 1 hour only on day-1,
day-3 and day-7. It shows that the CBR value forunsoaked condition was 35.7% and on repeatedsubmerging for 1 hour on day-1, day-3 and day-7,
the CBR values were 25%, 15.9% and 18.5%
respectively. Basically, the result shows that
unsoaked condition still have the higher CBR
strength value when it compared to the repeatedsubmerged condition of soil samples. In the repeated674

GEOMATE- Osaka, Nov. 16-18, 2015

submerged case, the CBR strength was reduced onday-1 after submerged for 1 hour and subsequently
the CBR value also reduced on day-3 compared tothe unsoaked sample. However, on the day-7, thesoil sample was gaining its strength again wheninundated for 1 hour after on the day 3. The CBR
strength was increased by 16% after submerged for
1 hour on day-7.
Soil sample in unsoaked condition show its
capability to sustain the higher load since it isevident that there were no subsequent loss ofstrength. Moreover, the CBR values for repeatedsubmerged condition is higher than soaked condition
since the soil was only inundated for short period
when it compared to the soaked condition which thesoil has been inundated for a longer period. CBRvalues were strongly affected by the long-term
inundation compared to the case of repeatedsubmerged condition. Since the soil 1 wascategorized as well-graded sand with clay and gravel,
the possibility of soil to gain the strength on day-7
after submerged on 1 hour is easier because of thesoil particle and lower pore water pressure itself.

samples since it is evident that there has no
subsequent loss of strength. However, the CBR
strength of soil samples for soaked condition was
decreased due to submerging time.
The unsoaked sample basically showed better
performance on their strength and the CBR strength
probably can be increased with better compaction
before the soil will be tested. Meanwhile, soil thatsoaked for 7 days show the deterioration of its
strength performance compared to the 1 and 3 daysof soaking condition. Significant loss of strength
was observed caused by inundation and subgrade
soil becomes saturated within the soaking period.
From the results, it is concluded that the value of
CBR for the given soil sample decreases rapidly
from unsoaked condition to 1 day of soaking. Soil
had been loss more strength on 7-day soaking
compared to 3-day soaking.

Fig. 5: Comparison of CBR values of inundated
samples (soil 2)

Fig.

4:

Comparison of CBR of
submergence samples (Soil 1)

On the other hand, the bar chart in the Fig. 6
shows the repeated submerged condition seendifferently compared to the unsoaked and soaked
condition. The soil samples were submerged similar
with soil 1 condition which is the soil samples were
submerged for 1 hour only for 1, 3 and 7 days. It
shows that the CBR value for unsoaked condition
was 22.9% and on repeated submerging for 1 houron day-1, day-3 and day-7, the CBR values were
13.5%, 8.05% and 5.25% respectively. Basically, theresult shows that unsoaked condition still have thehigher CBR strength value when it compared to therepeated submerged condition of soil samples. In therepeated submerged case, the CBR strength was
reduced on day-1 after submerged for 1 hour andsubsequently the CBR value also reduced on day-3
and day-7 compared to the unsoaked sample. The
reduction of strength was 61.1% from day-1
submerged for 1 hour to day-7 submerged for 1 hour.
This condition occurs probably due to clay condition
which its properties consist of small particle size
which tends to be very dense. The density of claythat thicker and heavier than other soil types willtakes longer time to clay particles absorb this water,
and further slowing the flow of water through thesoil.

repeated

Soil 2
Figure 5 presented the result of CBR value forthe second soil samples which comparing between
unsoaked and soaked condition of soil samples. The
test and soil conditions were conducted similar withthe first soil samples. From the bar chart, it shows
the CBR value for unsoaked condition relatively
higher than CBR value for soaked condition due tothe saturated period for soaked soil samples. It
shows that the CBR value for unsoaked condition
was 22.9% and on submerging the soil samples for 1,
3 and 7 days, the CBR values were 10.7%, 6.84%
and 3.42% respectively. Generally, the CBR value
for both conditions on second soil quite different
from soil 1 since the soil 2 was categorized as silty
clay and gravel. CBR strength has been reduced by
85% from the condition of unsoaked sample to the7-day soaking sample. Basically, the second soil
sample in unsoaked condition shows its capability tosustain the higher load similar with the first soil
675

GEOMATE- Osaka, Nov. 16-18, 2015

Figure 8 shows the settlement of soil against time
for the 3-days submerged condition. The pattern lineof graph can be seen quite different when comparedto soil samples of 1-day submerged condition since
the initial settlement occurs for each load is higher
than 1-day submerged condition. This is because thelonger period of inundation cause the quick andhigher initial settlement. The higher settlement takes
places when 32kg load was applied and reach theconstant settlement at 1.70mm which higher than the
1-day submerged condition but the consolidation
occur in a relatively short time to reach the constantsettlement.
The test provides a reasonable estimate of theamount of settlement on soil samples However, therate of settlement is often underestimated, that is, thetotal settlement is reached in a shorter time than thatpredicted from the test data. This is largely due tothe size of sample which does not represent soil
fabric and its profound effect on exact conditions.
Besides the natural condition of the sample,
sampling disturbance will have a more pronounced
effect on the results of the test done on small
samples. Furthermore, the boundary effect from thering enhances the friction of the sample. The friction
reduces the stress acted on the soil during loading
and reduces swelling during unloading.

Fig. 6 Comparison of CBR values of repeatedsubmergence samples (Soil 2)

Consolidation Performance
Consolidation is a process by which soils
decrease in volume. In this study, the focus is toobserve the settlement effect from various loading
capacity. Figure 7 and Figure 8 show the graph ofsettlement against time for soil condition of 1-day
submerged and 3-days submerged respectively. For
1 day submerged condition, as shown in Fig. 7, thesettlement increases due to the increment of loading.
Generally, the load of 1kg, 2kg and 4 kg obtainquick initial settlement at 60s before the soil reachedat the optimum settlement. In addition, the load for
8kg, 16kg and 32kg take longer time to reach theconstant settlement. The average time to soil reached
the constant settlement was about 2 hours. The
higher settlement takes places when 32kg load wasapplied and reach the constant settlement at 1.52mm.

CONCLUSION
Experimental study has been carried out todetermine the strength of soil samples when tested indifferent inundation conditions. The CBR strengthfor both soils samples indicated the decreases of its
strength due to higher increment number ofinundation days. It can be concluded that thestrength of soil further decrease when they are
inundated for a longer period. Similarly,
consolidation test also prove that higher settlement
could occur when the soil is inundated for a longer
period. A more extensive testing will provide thebasis for the inclusion of inundation effect in theroad design procedures.

Fig. 7 Graph of settlement against time for 1-day
submerged sample

ACKNOWLEDGEMENTS
The authors gratefully acknowledge financial
support from FRGS research grant no.
USM/203/PPBGN/6711257 and NAPREC grant no.
USM/401/PPBGN/510059/I-124.
REFERENCES[1] Adedeji, A. A. and Salami A. W., (2008):
Environmental hazard: Flooding and Its Effects
on Residential Buildings in Ilorin, Nigeria.
http://www.scribd.com/doc/9268022/ . Date
Accessed Jan 29 2015.

Fig. 8 Graph of settlement against time for 3-days
submerged sample

676

GEOMATE- Osaka, Nov. 16-18, 2015[2] Bowoputro, H. et al. (2009): Temperature
Influence and Lapindo Mud Submersion
towards the Stability Value of Asphaltic
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[4] mySarawak (2009): RM 500 juta bina
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mySarawak, February 8 , 2009[5] The Star (2013): RM 106 Juta Diperuntuk
Untuk Baik Pulih Jalan Persekutuan Rosak
Akibat Banjir. The Star, Jauary 11, 2013.
[6] Naagesh, S., Sathyamurthy, R., Sudhanva, S.
(2013): Laboratory Studies On Strength andBearing Capacity Of GSB-Soil Sub-Grade
Composites.
[7] Deshpande, V. P., Damnjanovic, I., and Gardoni,
P. (2008): Modeling The Effects ofRehabilitation Actions on The Reliability of
Flexible Pavements, Proc., Transportation
Research Record 87th Annual Meeting,Transportation Research Board, Washington,
D.C.

[8] Tarefder, R. A., Saha, N., Hall, J. W., and Ng, P.
T. T. (2008): Evaluating weak subgrade forpavement design and performance prediction: A
case study of US 550, Journal of
GeoEngineering, 3(1), 13–24.
[9] Khogeli, W. E. I. and Mohamed, E. H. H.
(2004): Novel Approach for Characterization
Of Unbound Materials. Journal of theTransportation Research Board, No. 1874,
Washington, D.C., 38-46.
[10] Theyse, H. L., Hoover, T. P., Harvey, J. T.,
Monismith, C. L. and Coetzee, N. F. (2006): A
Mechanistic-Empirical Subgrade Design Model
Based On Heavy Vehicle Simulator Test Results,
Geotechnical Special Publication No. 154,
Pavement Mechanics and Performance, B.,
Huang, R., Meier, J., Prozzi, E., Tutumluer, Geo
Institute, ASCE, 195-202[11] Fairweather, H. and Yeaman, J. (2014). “A
study of the parameters affecting theperformance of roads under extreme rainfall
event”. Int. J. of GEOMATE, Sept., 2014, Vol.
7, No. 1 (Sl. No. 13), pp.955-960

677

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

ARSENIC RELEASE PROCESSES INTO CONFINED AQUIFERS OF
THE SEINO BASINS, NOBI PLAIN, JAPAN1

Yusuke Kawai1, Masahiko Katoh2, 3, Ryuki Mori3, Yu Otake4, and Takeshi Sato3
Graduate School of Engineering, Gifu University, Japan; 2 School of Agriculture, Meiji University, Japan; 3
Faculty of Engineering, Gifu University, Japan; Faculty of Engineering, Niigata University, Japan

ABSTRACTTo understand the mechanisms of arsenic release from the sediment of the Seino Basins, Nobi Plain, Japan, thepresent study investigated the characteristics of arsenic dissolution and arsenic phases in the sediments of the G 1and G2 aquifers and their upper layers. No significant, direct positive relationship between the total and watersoluble arsenic was found. The results of sequential extraction showed that the main sources of water-solublearsenic were the non-specifically and weakly bound arsenic phases. In addition, a small portion of the arsenic
specifically sorbed on the surface of the sediment was dissolved by water extraction. Multiple regression analyses
demonstrated that high pH, total arsenic content, and water-soluble organic carbon (WSOC) content strongly
contributed to the enhancement of arsenic release from the specifically bound arsenic fraction. These results
suggest that high levels of total and non-specifically bound arsenic are responsible for higher levels of arsenic
release. In addition, high pH and WSOC content facilitate arsenic release.
Keywords: Alluvial Plain, Arsenic Release, Confined Aquifer, Groundwater

arsenic has not been detected in the G2 aquifer despite
the upper layer of both aquifers (a sedimentary layer
accumulated during the Pleistocene age) containing
arsenic-contaminated sediment.
The observation of high arsenic concentrations insuch sedimentary aquifers provides impetus forresearch focused on the sources of arsenic and themechanisms of arsenic release. In many cases,
including the Seino Basins, the minerals that compose
the sedimentary layer are considered to be the mostprobable sources of dissolved arsenic in groundwater[8]. The mechanisms of arsenic release into
groundwater have been studied by many researchers,e.g., the oxidation of arsenic-containing sulfide [2],[9] and the dissolution of sorbed iron hydroxidearsenic [10], [11]. In addition, the chemical
characteristics of groundwater such as pH [12],phosphate ion concentration [12], bicarbonate ion
concentration [13] affect arsenic dissolution.
Arsenic release greatly depends on the sitecharacteristics; therefore, the mechanisms of arsenic
release at one site may not be applicable to another.
For some alluvial plains in Japan, arsenic releasemechanisms have been proposed [7], [14]; however,
little information is available on arsenic release at the
Seino Basins [8], [15]. In particular, it is notunderstood why high arsenic concentrations aredetected in groundwater from the G1 aquifer, but notfrom the G2 aquifer.
The present study investigated the characteristics
of arsenic dissolution and arsenic phases in thesediments of the G1 and G2 aquifers and their upper
layers, using samples from boring cores. The aim of

INTRODUCTION
Arsenic is hazardous to human and animal health.
Because of its potential migration into groundwaterand subsequent plant uptake, it may also be
detrimental to plant growth. High groundwater
arsenic concentrations, above the World HealthOrganization (WHO) standard of 0.01 mg/L, havebeen observed in many areas of the world,
particularly in Asian countries. The Bengal Delta
Plain in Bangladesh and West Bengal, India, has beenrecognized as the most serious case of groundwater
arsenic contamination. In this region, serious
problems caused by arsenic contamination have risen.
It is estimated that in Bangladesh, 20 to 50 million
people are at risk from drinking water with a higharsenic concentration [1], and in West Bengal, 180
thousand people were found to have suffered fromarsenic poisoning [2]. Therefore, much international
and local research has been undertaken to elucidate
the mechanisms of arsenic release into groundwater[3]–[5].
Similar to the areas mentioned above, arsenic
concentrations exceeding the WHO standard havebeen found in groundwater from alluvial plains inJapan [6], [7]. One such plain is the Nobi Plain, which
is located in central Japan. The Seino Basins that arelocated in the south-west region of the Nobi Plain
have two Pleistocene aquifers: the upper, G1, aquifer
exists at a depth of 50–80 m and the lower, G2, aquifer
exists at a depth of 170–200 m. Arsenic
concentrations above the WHO standard have beenfrequently detected in the G1 aquifer. However,678

GEOMATE- Osaka, Nov. 16-18, 2015

extracted with 25 ml of 0.05 M (NH4)H2PO4 solution(specific phase). The sediment remaining after thesecond extraction was further extracted with 25 ml of
0.2 M NH4/oxalate buffer at pH 3.25 by shaking for 4
h in the dark (amorphous Fe/Al oxide bound phase).
The sediment remaining after the third extraction was
extracted with 0.2 M NH4/oxalate buffer including
0.1 M ascorbic acid at pH 3.25 in a 95 °C water bathwith occasional agitation (crystalline Fe/Al oxidebound phase). The remaining sediment was thendigested with 5 ml HNO3 and 2 ml 30% H2O2 using a
microwave oven, and diluted in a 50 ml measuring
flask (residual phase). All extracted or digested
solutions were passed through a 0.45-µm filter. The
arsenic concentrations of the water soluble fraction
were measured by inductively coupled plasma mass
spectrometry(ICP-MS;
7500cx,
Agilent
Technologies Inc., USA), and those of the remaining
fractions by ICP atomic emission spectrometry (ICPAES; ULTIMA2, HORIBA Ltd., Japan) equippedwith a hydride generator.

the study was to elucidate the reasons why arsenic isreleased into the G1 aquifer but not the G2 aquifer.
MATERIALS AND METHODS
Study Area and Sample Preparation
This study was conducted using sediment samples
from boring cores, which were collected from the
Seino Basins located at 35° 9ʹ 37ʺ N and 136° 39ʹ 56ʺ
E in Kaizu, Gifu prefecture, Japan. The following
eight sediment samples were selected for chemical
analysis: the Nobi layer at depths of 46 and 52 m, theG1 aquifer at depths of 60 and 64 m, the Atsuta layer
at depths of 167 and 173 m, and the G2 aquifer at
depths of 180 and 184 m. The sediment samples of
the Nobi and Atsuta layers were mainly composed ofsilt and clay; in contrast, those of the G1 and G2
aquifers were sand and gravel. The samples were airdried and passed through a 2- or 0.45-mm sieve prior
to analysis.
Water-Soluble and Total Arsenic Contents andArsenic Phases by Sequential Extraction of
Sediment

Chemical Properties of the Sediment
The pH and water-soluble organic carbon(WSOC) content were determined in the same
solution used for water-soluble arsenic, using a pH
meter (MM-60R, DKK-TOA Co., Japan) and a totalorganic carbon (TOC) analyzer (TOC-VWS Shimadzu
Co. Japan), respectively. The amorphous andcrystalline forms of metals in the sediment samples
were extracted according to the method of Shuman[17], and the iron, manganese, and aluminum
concentrations were measured by ICP-AES.

Total water-soluble arsenic in the sediment
samples was extracted using ultra-pure water (1:10
solid/liquid ratio). The inorganic and organic arsenic
fractions in the water extraction were determinedbecause arsenic behavior was considered likely todiffer markedly between these fractions. The
fractions were separated by a permeable membranewith a molecular weight cut off of 1,000 (Spectra/Por
7, SPECTRUM). Arsenic concentrations in the total
and organic fractions of the water extraction were
measured, and the inorganic arsenic concentration
was calculated by difference. The total arsenic
content of the sediment sample was determined byacid microwave digestion with 14.5 M HNO3 and 12
M HCl. Sequential extraction was performed on thesediment sample following the procedure describedin Wenzel et al. [16]. A 1.0 g, < 0.425-mm sample
was extracted with 25 ml of 0.05 M (NH4)2SO4
solution (non-specific phase). The sedimentremaining after the first extraction procedure was

G1
aquifer

60 m

Atsuta
layer

167 m

G2
aquifer

180 m

Atsutalayer
G2 (lower G1
Nobi
aquifer part) aquifer layer
Nobi
layer

46 m

Water-soluble arsenic (mg/kg)
0.2
0.4
0.6

Water-Soluble, and Total Arsenic Contents, andArsenic Phases by Sequential Extraction of
Sediment
The highest water-soluble arsenic concentration(0.64 mg/kg) was found in the sediment sample of the
Nobi layer at a depth of 52 m. The second highest was
found in the sediment sample of the G1 aquifer at a
depth of 64 m (Fig. 1a). In the sediment samples of
0

0.8

52 m
64 m
173 m
184 m

Nobi
layer

46 m

G1
aquifer

60 m

Atsuta
layer

167 m

G2
aquifer

180 m

Atsutalayer
G2 (lower G1
Nobi
aquifer part) aquifer layer

0

RESULTS AND DISCUSSION

(a)

30

52 m
64 m
173 m
184 m

Fig. 1 Water-soluble (a) and total (b) arsenic contents in sediment sample.
679

Total arsenic (mg/kg)
10
20

(b)

GEOMATE- Osaka, Nov. 16-18, 2015

arsenic release. The percentage of organic andinorganic arsenic in the water extractions is shown in
Fig. 3. The percentage of organic arsenic differed
greatly among the sediment samples, ranging from 1
to 68%. In addition, it was not significantly related tothe total amount of water-soluble arsenic (r2 = 0.001,
data not shown). The water-soluble organic arsenic
would be easily desorbed from the surface of thesediment because the complexation of arsenic withorganic matter enhances its mobility [3], [18], [19].
However, the poor relationship between the total
amount of water-soluble arsenic and the percentage
of water-soluble organic arsenic suggests a low
contribution of arsenic-organic matter complexation
to arsenic release in this study.
Figure 4 shows the arsenic contents of the various
fractions in the sequential extraction. The average
recovery ratio, which is defined as the ratio of the sum
total of each fraction after sequential extraction to thetotal arsenic content in the sediment sample, was 88± 13%. In all samples, except those of the Atsuta layer,
the dominant fraction in the sequential extraction was
the amorphous Fe/Al oxide bound fraction, in whicharsenic concentrations ranged from 1.44 to 8.86
mg/kg. The non-specifically bound fraction, which is
the most soluble fraction, showed relatively low
arsenic content, 1% or less of the total arsenic content.
Figure 5 shows the relationship between the amountof non-specifically bound arsenic and the amount ofwater-soluble arsenic in the sediment samples. In
some samples, which are shown under the broken line,
the amount of water-soluble arsenic was lower than
the amount of non-specifically bound arsenic,
demonstrating that the main sources of water-solublearsenic were the non-specifically and weakly boundarsenic phases on the surface of the sediment.
However, in other sediment samples, shown above
the broken line, the amount of water-soluble arsenic
was higher than that of the non-specifically boundfraction, indicating that a small amount of arsenic thatwas specifically sorbed on the surface of the sediment
was dissolved during the extraction at the same time

the G2 aquifer at depths of 180 and 184 m, a relatively
high level of water-soluble arsenic was observed;
however, these values were less than 0.1 mg/kg, theequivalent value of the Japanese environmental
standard (converted using the standard value of 0.01
mg/L and the solid/liquid ratio of the water
extraction). The total arsenic contents in the sediment
samples of the Nobi layer, particularly at a depth of
52 m, were higher than those in the other sediment
samples (Fig. 1b). The total arsenic contents in thesediment samples of the G1 aquifer, the Atsuta layer,
and the G2 aquifer ranged from 1.9 to 5.8 mg/kg. The
relationship between the total and water-solublearsenic contents was investigated, and the result isshown in Fig. 2. When all the samples were included,
a significant relationship was found (r2 = 0.716**).
However, if the highest concentration sample (opencircle in Fig. 2) was excluded, no relationship was
found. These results indicate that arsenic release fromthe sediment was not fully explained by total arsenic
content, and that physicochemical characteristics andarsenic phases in the sediment likely controlled

Water-soluble arsenic (mg/kg)

1

0.8(1) r2 = 0.716**(2) r2 = 0.055

0.6

0.4

0.2

0
0

5

10

15

20

25

Total arsenic (mg/kg)

Fig. 2 Relationship between total and watersoluble arsenic contents in sediment sample.
A correlation coefficient (1) was evaluatedincluding all the samples. A correlation coefficient(2) was evaluated in the case of removing an opencircled plot.

% of arsenic in water extraction
0%

46 m

G1
aquifer

60 m

Atsuta
layer

167 m

G2
aquifer

180 m

Atsutalayer
G2 (lower G1
Nobi
aquifer part) aquifer layer

Nobi
layer

20%

40%

60%

80%

100%

52 m
64 m
173 m
184 m
Inorganic arsenic

Organic arsenic

Fig. 3 Percentage of inorganic and organic arsenic fraction in water extraction of sediment sample.
680

GEOMATE- Osaka, Nov. 16-18, 2015

Nobi layer

46 m

G1
aquifer

0

60 m

Nobi
layer

Atsuta layer

G1aquiferG2
aquifer

Atsutalayer

G2
aquifer

Arsenic content (mg/kg)
4
6

2

8

10

52 m

64 m
167 m
173 m
180 m
184 m
Non-specifically boundSpecifically boundAmorphous Fe/Al oxide bound

Crystalline Fe/Al oxide boundResidual

Fig. 4 Sequential extraction of arsenic from sediment sample.
Chemical Characteristics of Sediment Sample
Water-soluble arsenic (mg/kg)

1

Values of pH in the sediment samples are shownin Table 1. The pH values of the G2 aquifer at depthsof 180 and 184 m were higher than those of the G1
aquifer at depths of 60 and 64 m, respectively.
Similarly, the pH values of the Atsuta layer at depthsof 167 and 173 m were higher than those of the Nobi
layer at depths of 46 and 52 m, respectively. These
results suggest that arsenic would be released more
readily from the sediment of the Atsuta layer and theG2 aquifer compared with the sediment of the Nobi
layer and the G1 aquifer because higher pH induces
an increase in the surface net negative charge of
sediment, which leads to desorption of oxyanions
such as arsenate and arsenite [20]. Figure 6 shows the
WSOC contents of the sediment samples. The highest
WSOC content of 735 mg/kg was observed in the
Nobi layer at a depth of 52 m, and the second highest

0.8

0.6

0.4

y=x

0.2

0
0

0.1

0.2

0.3

Non-specifically bound arsenic (mg/kg)

Fig. 5 Relationship between non-specificallybound and water-soluble arsenic contents insediment sample.
A broken line indicates y = x.

Table 1. pH of sediment sample.
as non-specifically sorbed arsenic was released.
Specifically sorbed arsenic can be desorbed bychanges in chemical properties of the sediment suchas pH, redox condition, and dissolved organic carbon(DOC) concentration [3], [12], [19]. Therefore, thechemical properties of such sediments would lead toan enhancement in the release of arsenic from
specifically sorbed phases. In the following section,
we investigated the relationship between the amountof water-soluble arsenic and the chemical propertiesof the sediment samples.

Nobi layer
G1 aquifer
Atsuta layer
G2 aquifer

681

Depth (m)
46
52
60
64
167
173
180
184

pH
4.4±0.0
6.1±0.2
6.3±0.1
7.2±0.1
5.9±0.0
6.4±0.1
7.2±0.1
8.6±0.7

GEOMATE- Osaka, Nov. 16-18, 2015

(calculated by dividing coefficient by average valueof variable), where a higher sensitivity indicates agreater contribution to the model, was not high for all
variables. The sediment pH showed the highest
sensitivity; second was total arsenic content, and third
was WSOC. These results demonstrate that thesethree variables mainly induced the enhancement inrelease of water-soluble arsenic from the sediment.
As mentioned in the previous section, a portion of
the specifically-bound arsenic phase was waterextractable, although the main source of watersoluble arsenic was the non-specifically boundfraction. Samples having a higher level of watersoluble arsenic than non-specifically bound arsenic
were found at depths of 52, 64, 180, and 184 m (Figs.
1a and 5). At 52 m, the high total arsenic and WSOC
contents would be responsible for the high level ofwater-soluble arsenic. However, the level of watersoluble arsenic in the sediment sample at 46 m waslow, even though the levels of total arsenic and
WSOC were high. This can be explained by the low
pH of the sediment sample at a depth of 46 m (Table1), because arsenic sorption on sediment surfaces
increases at low pH [20]. The second highest level ofwater-soluble arsenic was found in the sediment
sample at a depth of 64 m, whereas total arsenic and
WSOC contents here were comparable to other lowlevel samples. The pH of the sediment sample at 64
m was 7.2 (Table 1), and this relatively high pH likely
led to an enhancement in the level of water-solublearsenic. The levels of water-soluble arsenic in thesediment samples of the G2 aquifer at depths of 180
and 184 m were also relatively high, but were lower
than the Japanese environmental standard. Despite
the high pH and WSOC content potentially enhancing
the level of water-soluble arsenic, the low total andnon-specifically bound arsenic contents would lead toits suppression, resulting in the preservation of a
relatively low level of water-soluble arsenic.
In the Seino Basins, arsenic is detected at levels
higher than the Japanese environmental standard ingroundwater from the G1 aquifer, but is not detectedvalues were in the Nobi layer at a depth of 46 m andthe G2 aquifer at depths of 180 and 184 m. In the othersamples, the WSOC content ranged from 33 to 49
mg/kg. The presence of DOC can enhance arsenic
release due to the competition for sorption sites [18],[20]; thus, high WSOC concentrations likely induce
arsenic release. The amorphous and crystalline metal
contents of the sediments are summarized in Table 2.
These greatly depended on the metal species and thedepth of the sample, particularly for amorphous ironand crystalline aluminum. However, no significant,positive correlations between the amount of watersoluble arsenic and these chemical properties were
found (data not shown). Therefore, the multiple
regression analysis between water-soluble arsenic
content and the chemical characteristics and totalarsenic content of the sediment samples was
conducted using forward and backward selections forthe choice of explanatory variables to identify themost influential chemical properties on theenhancement of arsenic release. The model fitting
was evaluated by determining the t value of eachvariable, the coefficient of determination, the F value,and Akaike’s information criterion (AIC); theparameters obtained from these analyses are listed inTable 3. According to the results of the analyses,
sediment pH, total arsenic content, WSOC,
amorphous iron, and crystalline aluminum wereextracted as the variables. However, the sensitivity

0

46 m

G1
aquifer

60 m

Atsuta
layer

167 m

G2
aquifer

180 m

Atsutalayer
G2 (lower G1
Nobi
aquifer part) aquifer layer

Nobi
layer

Water soluble organic carbon (mg/kg)
200
400
600
800
1000

52 m
64 m
173 m
184 m

Fig. 6 Water soluble organic carbon in sediment
sample.

Table 2. Amorphous and crystalline metal contents of sediment sample (mg/kg).

Depth (m)Nobi layer

G1 aquifer

Atsuta layer

G2 aquifer

Amorphous

Crystalline

Fe

Mn

Al

Fe

Mn

Al

46

7875±740

378±371488±7210552±1335

165±194758±626

52

4648±424100±71380±38

13419±811

226±168576±560

60

2638±16363±8

383±408305±398101±4

1907±16364

1431±12066±23153±1246905±21496±2

1046±47167

4089±569

256±97685±4410345±967

148±8

4160±327173

8392±556

467±311060±757945±287161±7

2564±171180

4449±1098

374±130443±245414±285109±6

1437±93

184

10099±1955

299±971706±1205473±230103±51736±140682

GEOMATE- Osaka, Nov. 16-18, 2015

Table 3 Multiple regression analysis between water-soluble arsenic and chemical
characteristics in sediment.
Coefficientt value

Sensitivity1)

Intercept-0.2845-2.017-pH

0.04922.771

7.56×10−3

Total arsenic

0.0323

3.384

4.81×10−3

WSOC

0.0005

2.541

3.23×10−6

Amorphous iron

-0.0001-2.488-1.80×10−8

Crystalline aluminum-0.0001-2.205-3.10×10−8

Coefficient of determination

0.9522Adjusted coefficient of determination

0.9389F value

71.66

AIC

-135.96

1) Coefficient/averagein the G2 aquifer. As mentioned above, this study
demonstrates that non-specifically bound arsenic was
the main source of water-soluble arsenic, andchemical characteristics such as pH, WSOC, and totalarsenic content in the sediment caused specificallybound arsenic to become water-soluble. Therefore,
the results suggest that in the sediment of the Nobi
layer and the G1 aquifer, the high levels of total andnon-specifically bound arsenic (as compared withthose of the Atsuta layer and the G2 aquifer,respectively), are responsible for a higher level ofarsenic release. In addition, high pH and WSOC
content would facilitate the release of arsenic fromthe specifically bound fraction in the Nobi layer and
the G1 aquifer. This study used only a small numberof sediment samples; therefore, in future work,
numerous samples should be used to gain a more
complete understanding of arsenic release fromsediment in the Seino Basins.

specifically and weakly bound arsenic phases.
However, in other sediments, the amount of watersoluble arsenic was higher than that of the nonspecifically bound fraction, indicating that a small
portion of the arsenic specifically sorbed on thesurface of the sediment was dissolved during theextraction. The results of chemical analyses andmultiple regression showed that high pH, total arsenic
content, and WSOC content strongly contributed tothe enhancement of water-soluble arsenic. These
results suggest that in the sediment of the Nobi layer
and the G1 aquifer, the high levels of total and nonspecifically bound arsenic would be responsible forthe higher level of arsenic release. In addition, highpH and WSOC content would facilitate the release ofarsenic from the specifically bound fraction.
ACKNOWLEDGEMENTS
The ICP-AES used for the chemical analysis in
this study was made available by the Division ofInstrumental Analysis at Gifu University. The authors
are grateful to Prof F. Li and Prof T. Yamada (Gifu
University) for allowing the use of the TOC analyzer
and ICP-MS. This study was supported by Japan
Society for the Promotion of Science (JSPS)
KAKENHI (grant number 26420481).

CONCLUSION
The present study investigated the characteristics
of arsenic dissolution and phases in the sediment of
the G1 and G2 aquifers and their upper layers, theNobi and Atsuta layers, respectively, in order tounderstand the mechanism of arsenic release from thesediment in the Seino Basins, Nobi Plain, Japan. Ahigh level of water-soluble arsenic (over 0.1 mg/kg)
was found in the sediment samples of the Nobi layer
and the G1 aquifer, but not in those of the Atsuta layer
and the G2 aquifer. No significant, direct positive
relationship between total and water-soluble arsenic
contents was found. In some sediments, the amountof water-soluble arsenic was lower than that of thenon-specifically bound fraction, demonstrating thatthe main source of water-soluble arsenic was the non-

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McLaughlin MJ, Bundschuh J, Panullah G,“Arsenic in the environment: Biology andchemicsty”, Sci. Total Environ., Vol. 379, 2007,
pp. 109–120.
[2] Das D, Samanta G, Mandal BK, Chowdhury TR,
Chanda CR, Chowdhury PP, Basu GK,
683

GEOMATE- Osaka, Nov. 16-18, 2015

Chakraborti D, “Arsenic in groundwater in six
districts of West Bengal, India”, Environ.
Geochem. Health, Vol. 18, 1996, pp. 5–15.
[3] Anawar HM, Akai J, Komaki K, Terao H,
Yoshioka T, Ishizuka T, Safiullah S, Kato K,“Geochemical occurrence of arsenic ingroundwater of Bangladesh: sources andmobilization processes”, J. Geochem. Explor.,
Vol. 77, 2003, pp. 109–131.
[4] Seddique AA, Masuda H, Mitamura M, Shinoda
K, Yamanaka T, Itai T, Maruoka T, Uesugi K,
Ahmad KM, Biswas DK, “Arsenic release frombiotite into a Holocene groundwater aquifer in
Bangladesh”, Appl. Geochem., Vol. 23, 2008, pp.
2236–2248.
[5] Polizzotto ML, Harvey CF, Li G, Badruzzman B,
Ali A, Newville M, Sutton S, Fendorf S, “Solidphases and desorption processes of arsenic
within Bangladesh sediments”, Chem. Geol., Vol.
228, 2006, pp. 97–111.
[6] Shimada N, “Geochemical conditions enhancing
the solubilization of arsenic into groundwater inJapan”, Appl. Organomet. Chem., Vol. 10, 1996,
pp. 667–674.
[7] Kaneko E, “Arsenic concentration in
groundwaters in Sendai City”, Chikyukagaku(Geochemistry), Vol. 13, 1979, pp. 1–6. (inJapanese with English abstract)[8] Nishizawa T, Katoh M, Kitazawa H, Sato T,“Chemical speciation and release of arsenic togroundwater in Seino Basins, Nobi Plain”, J.
Japan Society Civil Engineering, Ser. G, Vol. 68,
2012, pp. 670–679. (in Japanese with English
abstract)[9] Mandal BK, Chowdhury TR, Samanta G,
Mukherjee DP, Chanda CR, Saha KC,
Chakraborti D, “Impact of safe water for
drinking and cooking on five arsenic-affectedfamilies for 2 years in West Bengal, India”, Sci.
Total Environ., Vol. 218, 1998, pp. 185–201.
[10] Nickson RT, McArthur JM, Ravenscroft P,
Burgess WG, Ahmad KM, “Mechanism ofarsenic release to groundwater, Bangladesh andWest Bengal”, Appl. Geochem., Vol. 15, 2000,
pp. 403–413.

[11] McArthur JM, Ravenscroft P, Safiullah S,
Thirlwall MF, “Arsenic in groundwater: Testing
pollution mechanisms for sedimentary aquifers
in Bangladesh”, Water Res., Vol. 37, 2001, pp.
109–117.
[12] Smedley PL, Kinniburgh DG, “A review of thesource, behavior and distribution of arsenic innatural waters”, Appl. Geochem., Vol. 17, 2002,
pp. 517–568.
[13] Anawar HM, Akai J, Sakugawa H, “Mobilizationof arsenic from subsurface sediments y effect ofbicarbonate ions in groundwater”, Chemosphere,
Vol. 54, 2004, pp. 753–762.
[14] Masuda H, Ibuki Y, Tonokai K, “Mechanism ofnatural arsenic pollution of shallow groundwaterin the northern part of Osaka prefecture, Japan”,
Journal Groundwater Hydrology, Vol. 41, 1999,
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release into
groundwater in Seino Basins, Nobi Plain”, J.
Japan Society Civil Engineering, Ser. G, Vol. 68,
2012, pp. III_507–III_515. (in Japanese withEnglish abstract)[16] Wenzel WW, Kirchbaumer N, Prohaska T,
Stingeder G, Lombi E, Adriano DC, “Arsenic
fractionation in soils using an improved
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microelements”, Soil Sci., Vol. 140, 1985, pp.
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[19] Bauer M, Blodau C, “Mobilization of arsenic by
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ferrihydrite in the presence and absence ofdissolved organic carbon”, J. Environ. Qual., Vol.
31, 2002, pp. 1115–1123.

684

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

ARSENIC FIXATION STUDIES ON
LIME STABILIZED SEMI ARID SOILS
Arif Ali Baig Moghal1, Krishna R Reddy2, Syed Abu Sayeed Mohammed3,
Mosleh Ali Al-Shamrani1 and Waleed M Zahid1
1
Department of Civil Engineering, College of Engineering, King Saud University,
Riyadh – 11421, Saudi Arabia
2
Department of Civil and Materials Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA.
3
Department of Civil Engineering, HKBK College of Engineering, Bangalore 560045, India.
ABSTRACT
Fine-grained geologic materials are potential barriers for preventing the migration of contaminants from waste
disposal sites into local hydrogeological regimes. They have excellent potential to retard the movement ofcontaminants, they come into contact with. The release of arsenic from dam tailings, spent heap, leach operations
and other mining wastes constitutes a major problem owing to its acute toxicity. In this study, locally available
semi-arid soils (Al-Ghat and Al-Qatif) having different chemical and mineralogical characteristics are
considered as barrier materials, and their response to arsenic adsorption at varying initial concentrations, pH
conditions, temperature and dilution ratios is studied. Empirical models (Langmuir and Freundlich) are applied
to ascertain monolayer or heterogeneous adsorption. Lime is added to these soils in order to enhance theirgeotechnical properties. As such, the arsenic adsorption studies are also performed on these soils in the presenceof lime. Kinetic models are employed to validate the type and nature of arsenic sorption onto these soils (whether
pseudo first-order or second-order). It is found that both Al- Ghat and Al- Qatif soils when amended with lime
can attenuate arsenic, and the experimental results correlate well with selected empirical models.

Key Words: Sorption, Arsenic, Langmuir isotherm, Elovich model, Intraparticle diffusion model, Pseudo second
order model, Pseudo first order model.

Introduction
Arsenic is one of the most commonly found metal
contaminants in wastes and at waste disposal sites.
Arsenic can occur naturally in rocks and soil, water,
air, plants and animals, or it can be released into theenvironment through natural activities such as
volcanic action, erosion of rocks, and forest fires.
Moreover, anthropogenic contributions of arsenic in
the environment can occur through its use as woodpreservatives in paints, dyes, metals, drugs, soaps
and semi-conductors. High arsenic levels can also
be released from certain fertilizers and animal
feeding operations. Industry practices such ascopper smelting, mining and coal burning also
contribute arsenic to our environment. Higher
levels of arsenic tend to be found in ground water
sources than in surface water sources (i.e., lakes andrivers) of drinking water. The demand on groundwater from municipal systems and private drinking
water wells may cause water levels to drop andrelease arsenic from rock formations and exacerbate
the arsenic levels in ground water [3]. Arsenic
exposure can result in carcinogenic and noncarcinogenic responses in humans. Inorganic
arsenic compounds are known human carcinogens;
USEPA ascertained this designation based onincreased lung cancer mortality in multiple human

Soil is a basic building block for the most terrestrial
ecosystems and a complex heterogeneous medium
consisting of both solid and fluid phases. The
ability of these soils to adsorb and desorb metal
ions from the aqueous phase is of great importancewhen dealing with both the environmental andagricultural issues. The clay fraction predominantly
affects the metal ion concentrations inenvironmental systems owing to their ability toadsorb these ions and any release in part or whole
when subjected to varying natural conditions.
Heavy metal and metalloid removal from aqueous
solutions can be achieved through processes such as
chemical precipitation, solvent extraction, ionexchange, reverse osmosis, and adsorption.
However, adsorption process, with the selection ofa suitable adsorbent, is commonly used as it is a
simple and effective technique for the removal ofheavy metal ions. Soils and soils amended withadditives have also been used as an adsorbent toremove many heavy metals. The efficiency ofretention is found to vary considerably with thenature of soil, type of heavy metal (including
oxidation state and concentration), pH of thesolution, and other environmental factors [1-2].

685

GEOMATE-Osaka, Nov. 16-18, 2015

populations exposed primarily through inhalation,
increased mortality from multiple internal organcancers (liver, kidney, lung and bladder), andincreased incidence of human cancer in
populations consuming drinking water high ininorganic arsenic. The critical effects are hyper
pigmentation, keratosis, and possible vascularcomplications based on human chronic exposure
The maximum limit for human consumption ofarsenic is limited to 10 ppb by USEPA.

(clay with high plasticity) with specific gravity of
2.84 and 2.71, respectively. It was found that AlGhat soil to be a kaolinitic soil and Al-Qatif soil tobe a montmorillonitic soil. Lime was used as soil
amendment (6% by dry weight of the soil), and thelime used was obtained as AR grade calcium
hydroxide from Qualigens Company. Arsenic was
obtained as arsenic trioxide (AR- grade) suppliedby Qualigens Company, which was used as source
chemical for arsenic in this study.

Arsenic speciation can be quite complex in soils.
As+3 or As(III) is the dominant form under reducing
conditions, while As+5 or As(V) is generally thestable form in oxidizing environments. Arsenic
compounds adsorb to soils, clays, organic matterand metal oxides/ hydroxides [4]. Arsenic ischallenging to treat, and some of the common
remediation methods may include containment
using barriers, soil washing/flushing, electrokinetics,
solidification and stabilization [5]. Solidificationand stabilization (S/S) is considered to be the costeffective method for treating high levels of arsenic
in soils [6]. Additives and binders used in S/Soxidize As+3 and As+5 and form insoluble complexes
or become immobilized due to formation ofstrongly adsorbed species or form co-precipitateswith calcium or iron [7]. Calcium has been found toretain arsenic by replacing itself for arsenic in aratio of 1: 1 mole [8]. However, a sufficient amountof Ca(OH) 2 is also required for the hydration toproceed in the system [9,10,11].

Batch Equilibrium Test Procedure
Batch adsorption experiments were conducted byshaking mechanically a series of bottles containing
the soil sample and heavy metal ion solutions
maintained at different pH values. Soil sample of
5g was mixed with 100 ml of the solution
maintained at a particular pH in 500 ml
polyethylene bottles to obtain the soil slurry. In all
the tests, the liquid to solid dilution ratio wasmaintained at 20. This slurry was agitated with amechanical shaker at room temperature (25±2°C)
for 2 h until the pH was stabilized. The pH of theslurry was adjusted to the desired value in the rangeof 2 to 10 with 0.1M HNO 3 and NaOH [12].
Predetermined amounts of source chemical wereadded to the bottles to result in the desired arsenic
concentrations (10, 15, 20, 25 and 30 mg/l), and thebottles were further agitated until equilibrium
concentrations were attained. The slurry wasfiltered using Whatman 42 ashless filter paper andthe residual concentration of arsenic in the filteredsolution was measured using atomic absorption
spectrophotometry (AAS). The amount of arsenic
adsorbed by the clay fraction was taken as thedifference between the initial and residual
concentrations of the arsenic in the solution. In
order to determine the removal by hydroxide
precipitation at various pH values, a set of blanktests were also conducted using solutions
maintained at different pH values without soil.
Tests were conducted in triplicates and the average
concentrations are calculated.
In order toinvestigate the effect of temperature on adsorptionof arsenic, a series of tests was conducted incontrolled constant water bath conditions following
the same adsorption testing procedure [13].

In this study, the retention of As in two semi-aridsoils, originating from Al-Ghatt and Al-Qatif, isinvestigated to evaluate their suitability as barriermaterials. Further, the effects of amending thesesoils with lime on enhanced arsenic retention are
explored. Several series of batch equilibrium tests
are conducted to systematically investigate theeffects of initial concentration, solution pH,
temperature, and time dependent kinetics on arsenic
retention in the soils with and without lime
amendment.
MATERIALS AND METHODS
Materials
Soil samples collected from Al-Ghatt and Al-Qatif
are selected for testing in this study. Al-Qatif is ahistoric coastal oasis region located on the western
shore of the Persian Gulf in the Eastern Province ofSaudi Arabia (26° 56′ 0″ N, 50° 1′ 0″ E). Al-Ghatt
is a town located 270 km to the Northwest of
Riyadh at latitude 26° 32' 42'' N and longitude 43°
45' 42'' E. The physicochemical properties of soils
were determined and the USCS (Unified Soil
Classification System) classified both soils as CH

The effects of solids to liquid ratio on adsorption
results was assessed by preparing the samples withsolid to liquid (S/L) ratios of 1:20, 1:30, 1:40, 1:100,
and 1:200 and shaking them for 24 hours. Then,
100 mgL-1 equivalent arsenic mass was added to all
samples and again shaken for 24 hours. The
samples were then removed, filtered and the filteredliquid was analyzed for arsenic concentration.
Another series of experiments was conducted to686

GEOMATE-Osaka, Nov. 16-18, 2015

investigate pH effects by employing the similar
procedure except the pH of the contaminantsolution was first adjusted to the required level andthen the adsorbent was added while maintaining a
constant S/L ratio of 1:20 throughout the procedure.

Al- Ghatt with As+3
Al- Ghatt + 6% Lime with As+3
Al - Qatiff with As+3
Al - Qatiff + 6% Lime with As+3

0.8

Sorption Coefficient in mg/g

0.7Adsorption Kinetics Test Procedure
A series of kinetic adsorption tests was conductedto study the time dependent arsenic adsorption andhelp determine the time necessary to achieve themaximum removal capacity. For this test series,
batch experiments were conducted for different
predetermined time intervals in different
polyethylene bottles prepared and maintained undersimilar conditions. At the end of each fixed timeinterval, the soil slurry was filtered and theconcentration of arsenic was determined as above.
This procedure was repeated till the concentrationof arsenic in the filtrate remained unchanged withtime. The experimental results were validated byapplying them to isotherm and kinetic models.
Table 1 gives details of all the models used.

0.6
0.5
0.4
0.3
0.2
0.1
10

15

20

25

30

35

40

Initial concentration in mg/L

Fig. 1 Sorption of As in soils with and without lime
amendment under different initial concentration
The pH plays a major role in sorption of arsenic,
and the results showed a gradual increase in theamount of sorbed arsenic with the increase in pH.
This increase in sorption can be explained by thechanges that occur to the sorbate (soils and theiroxide surfaces) and the formation of hydroxides ofmetal complexes under different pH conditions.
The pH of the solution affects the protonation of thefunctional group on the adsorbent surface of soils aswell as metal complexation. At low pH values,
adsorbed protons that are exchanged on surface canform proton bonds between surface and metal
complexes. The sorbed protons, also, generate
positive charges at the surface repelling or
attracting positively or negatively charged metal
complexes respectively. It was found that thepresence of calcium in lime played a major role insorption as it increased the pH of the solution thereby precipitating the As ions. It was found that theeffective sorption took place at a pH range of 4.5 to
8.5 which can be attributed to the formation of AsCa complexes at this pH range [15].

RESULTS AND DISCUSIONS
The results obtained from batch equilibrium andkinetic adsorption experiments are assessed to study
the adsorption behavior of arsenic in the tested twosoils.
Effects of Initial Concentration, Dilution Ratio
and pH on Arsenic Adsorption
Results in Fig.1 show that sorption coefficientincreases with initial concentration, with theincrease in concentration the competition fromother ions reduced resulting in an efficient sorptionof arsenic. A decrease in soil amount decreases thenumber of active sites that are available for sorption
and the amount of sorption decreases. This isevident from Fig.2 in that as dilution ratio increases,
more amount of metal ion become available forsorption. Only stable metal ions get sorbed leading
to an effective and permanent sorption. Heike [14]also reported similar results in that an increase in
the solids results in an increase in retention. This is
attributed to an increase in the available surface
area and thus an increase in active sites. However,
the increase in contaminant retention is notproportional to the amount of the solids; which is
attributed to the decrease in the mass transfer
gradient as the remaining concentration ofcontaminant diminishes.

180

Al- Ghatt -As+3
Al- Ghatt+ 6% lime - As+3
Al- Qatiff -As+3
Al- Qatiff + 6% Lime - As+3

Sorption Coefficient in mg/g

160
140
120
100
80
60
40
20
0
0.00

0.01

0.02

0.03

0.04

0.05

Dilution Ratios

Fig. 2 Sorption of As in soils with and without lime
amendment under different solids to liquid ratios
687

GEOMATE-Osaka, Nov. 16-18, 2015

Effect of Temperature on Arsenic Adsorption

The sorption versus time data was used to test
different kinetic models such as pseudo first order,
pseudo second order, Elovich, and intraparticle
diffusion. The results obtained are summarized inTable 3. It was observed from regression analysis
that pseudo second order kinetic model was better
than the first order kinetic model as its regression
coefficient was closer to one. Also for aheterogeneous material like soil, monolayer
sorption alone does not occur. Instead, a number ofprocesses such as chemi-sorption, ion exchange andprecipitation occur simultaneously. Pseudo second
order being a better model reinforces this assertionand this conclusion is also in general agreementwith previous studies [15]. Elovich kinetic model
tries to model the processes of desorption andchemisorption. It can be seen from the regression
analysis, as given in Table 3, that the regression
coefficient is closer to 1 which indicates thatchemisorption is also possibly occurring, where theά and β values represent sorption and desorption.
Further, sorption increases with concentration,
while desorption decreases with concentration [16].

Temperature is known to affect the complexation of
metal ions. The influence of the temperature on
retention of arsenic was investigated in the range
from 25° to 55°C. The selected temperature range
encompasses the typical temperature expectedduring the course of the year in Saudi Arabia. The
average temperature of 25°C corresponds to winter,
while 55°C is the maximum possible temperatureduring the summer. All the tests were conducted atneutral pH value (6.8 to 7.2). The effect oftemperature under different pH conditions was
beyond the scope of this study. For a given initialconcentration, the amount of arsenic retained
increased considerably with increase in temperature.
This effect was more pronounced at higher initialconcentration values. The increase in retention
levels for arsenic could be attributed to conditions
favoring enthalpy and entropy changes in both AlGhat and Al-Qatif soils. The increased retention
capacity at elevated temperatures is primarily due toentropy increase arising due to increased disorder ofwater molecules and respective cations in the soil
slurry. The results obtained indicate that for both
Al-Ghat and Al-Qatif, the increase in temperature
aids in higher arsenic removal capacity.

1.0

Al- Ghatt+ 6% Lime-As(10mg/L)
Al- Ghatt+ 6% Lime-As(20mg/L)
Al- Ghatt+ 6% Lime-As(30mg/L)
Al- Ghatt+ 6% Lime-As(40mg/L)

Sorption coefficient (mg/L)

0.9

Sorption Isotherms
Batch equilibrium sorption data was modeled using
two common sorption isotherm models, namely
Langmuir model and Freundlich model. The
isotherm parameters were calculated and aresummarized in Table 2. The values of monolayer
sorption capacity calculated using Langmuir
isotherm are in the similar range for all the soil lime
mixtures. For Freundlich isotherm, the R2 is close toone, but its adsorption values are unrealistic. Hence,
Freundlich isotherm does not hold good for thesesamples. The Langmuir isotherm may be expressedin terms of equilibrium parameter R L which is adimensionless constant referred as separation factor.
R L value indicates the sorption nature to be
unfavorable if R L > 1; linear if R L =1; favorable if 0
< R L < 1; and irreversible if R L =0. A separation
factor of less than one is obtained for all thesamples, which indicates that Langmuir isotherm isfavours this sorption.

Al- Qatiff + 6% Lime - As (10mg/L)
Al- Qatiff + 6% Lime - As(20mg/L)
Al- Qatiff + 6% Lime - As(30mg/L)
Al- Qatiff + 6% Lime - As(40mg/L)

0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

20 40 60 80 100 120 140 160 180 200 220 240

1500Time (minutes)

Fig. 3 Time dependent adsorption of As soils withand without lime amendment
At a higher concentration, sorption takes place
predominantly on sorption sites whereas at lower
concentration desorption is dominant as competing
ions predominate the sorption sites. Similarly, intra
particle diffusion model tries to predict thephenomenon of diffusion in the sorption process. If
the results follow a linear fit passing through theorigin, then it implies that only diffusion acts as the
predominant phenomenon. It can be observed from
Table 3 that, the regression value is closer to 1, but
the regression line does not coincide with the origin
which shows that along with sorption, diffusion is
also active, but it is not the dominant process in
sorption.

Adsorption Kinetics of Arsenic in Soils
It can be observed from Fig. 3 that significant
sorption occurs immediately and it increases withtime with maximum sorption levels in 30 minutes,
and thereafter it remains constant till 1440 minutes(24hrs). Sorption increased with time as well aswith initial arsenic concentration.
688

GEOMATE-Osaka, Nov. 16-18, 2015

Table 1 Linear forms of Models used

Table 2 Langmuir and Freundlich Isotherm Parameters for Arsenic Sorption in Soils with and Without Lime
Amendment

Table 3 Kinetic model parameters for Arsenic Sorption in Soils with and Without Lime Amendment

their retention capacity of arsenic. The results
obtained indicate that both initial concentration andrise in temperature favor the retention
characteristics for both soils with lime quite
significantly. Surface adsorption is known to be
dominant at lower pH levels, while precipitation
favored at higher pH levels. The presence of
calcium in lime played a major role and formed
stable precipitates of calcium arsenate at highly

CONCLUSIONS
The retention of metal ions by soils is a complex
process involving complex mechanisms, and iscontrolled by different variables that can interact. In
this study, two field soils (Al-Ghat and Al-Qatifsoils) having variable chemical composition wereselected and amended with 6% lime to enhance
689

GEOMATE-Osaka, Nov. 16-18, 2015

REFERENCES

oxidizing conditions and moderate pH conditions of
4.5 to 8.5. At elevated temperatures the hydrationof calcium occurs efficiently which contributed toefficient sorption of arsenic. The addition of lime insoil to retain arsenic creates a solidified monolith
where the mobility is diffusion controlled [17]. This
reduces the leaching of arsenic significantly andthis soil lime mixture can be considered to suitablebarrier material (e.g. soil liner for a landfill)
Relatively higher retention of arsenic was recordedin case of Al-Qatiff (montmorillonitic soil) withlime than Al-Ghatt (kaolinitic soil) with lime due todifference in chemical composition, entropy andenthalpy changes. This is also consistent with the
published studies that montmorillonitic soils retain
arsenic better than kaolinitic soil.

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Battaglia-Brunet F, C Crouzet, et al. "Decreased
leachability of arsenic linked to biological oxidation of
As(III) in solid wastes frombioleachingliquors"
Hydrometallurgy, Vol. 107(1–2) , 2011, pp. 34-39.
[5]Shih, CJ, and CF Lin, "Arsenic contaminated site
at an abandoned copper smelter plant: waste
characterizationandsolidification/stabilizationtreatment"Chemosphere,Vol.53(
7),2003,pp.691-697.
[6]
Lenoble V O, Bouras, etal. "Arsenic Adsorption
onto Pillared Clays and Iron Oxides." Journal of Colloid
and Interface Science, Vol. 255(1), 2002, pp. 52-58.
[7]
Jing C, S. Liu, et al. "Arsenic leachability andspeciation in cement immobilized water treatment sludge."
Chemosphere Vol.59(9), 2005, pp. 1241-1247.
[8]
Dermatas D, DH Moon, et al. "An evaluation ofarsenic release from monolithic solids using a modified
semi-dynamic leaching test" Journal of Hazardous
Materials Vol. 116(1–2), 2004, pp.25-38.
[9]
Dusing D C, P L Bishop, et al. "Effect of Redox
Potential on Leaching from Stabilized/Solidified Waste
Materials" Journal of the Air & Waste Management
Association,Vol. 42(1), 1992, pp. 56-62.
[10]
Randall PM, “Arsenic encapsulation using
Portland cement with ferrous sulfate/ lime and terra bondtechnologies- Microcharacterization and leaching studies”,
Science of total environment, Vol. 420, 2012, pp.300-312.
[11]
Mohammed S, Moghal A, “Soils Amended with
Admixtures as Stabilizing Agent to Retain Heavy Metals”,
ASCE Geotechnical Special Pub No. 234, 2014, pp.22162226.
[12]
Moghal B, Sivapullaiah P, “Retention
Characteristics of Cu2+, Pb2+ and Zn2+ from Aqueous
Solutions by Two Types of Low Lime Fly Ashes”, J. Toxic.
Environ. Chem. Vol. 94-10, 2012, pp.1941-1953.
[13]
DutréV,andCVandecasteele,"Solidification/stabil
isation of arsenic-containing waste:[14]
Leach tests and behaviour of arsenic in theleachate" Waste Management. Vol. 15(1), 1995,pp. 55-62.

The following specific conclusions can be drawn
from this study:
1. Al- Ghatt soil and Al- Qatiff soil amended
with 6% lime were found to be goodsorbents for arsenic. Al-Qatiff is a better
adsorbent than Al- Ghatt soil.
2. Sorption is pH dependent and the
maximum sorption occurs over a pH rangeof 4.5-8.5 for arsenic in both the soils.
Retention of arsenic increased withincrease in initial concentration, dilution
ratio, and pH.
3. Langmuir monolayer sorption capacity isshown to decrease with concentration,
whereas Elovich’s model sorption is foundto increase with concentration anddesorption to decrease with concentration.
4. The adsorption kinetic experiments show
for both soils that the maximum sorption
occurs within an hour and thereafter
reaches equilibrium.
Arsenic retention in soils us attributed toseveral processes such as sorption,
chemisorptions,
ion
exchange,precipitation and diffusion occurring
simultaneously, with no one process isbeing dominant.

[15]

Heike B, “Adsorption of Heavy metal ions on
soils and soils constituents”. J. Colloid and Interface Sci,
Vol. 277, 2004, pp. 1 -18.
[16]
Ho Y, Mc Kay G, “Pseudo –second order model
for sorption processes”. J. Process Biochem,Vol. 34, 1999,
pp.451 – 465.
[17]
Kim, MJ, J Nriagu, et al., "Carbonate Ions andArsenic Dissolution by Groundwater." Environmental
Science & Technology,Vol. 34(15), 2000, pp 3094-3099.
[18]
Daniels J L, and GP Das, "Leaching Behavior ofLime–Fly Ash Mixtures" Environmental Engineering
Science, Vol. 23(1), 2006, pp. 42-52.

ACKNOWLEDGEMENTS
This project was funded by the National Plan for
Science,Technology
andInnovation(MAARIFAH), King Abdul Aziz City for Scienceand Technology, Kingdom of Saudi Arabia, Award
Number (12ENV2583-02).

690

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

INVESTIGATION ON THE ARSENIC POLLUTION AROUND THE
DOMESTIC WASTE DISPOSAL SITE IN SUZUKA CITY
Masaaki Takahashi1, Yukimasa Takemoto1
1

Faculty of Environmental and Information Sciences, Yokkaichi University, Japan

Abstract
Recently, arsenic pollution in the ground water has been widely found around the domestic waste disposal site of
Suzuka city which is situated in the central part of Japan. In order to identify the source of this pollution, we haveinvestigated the ground water and the leachate at eleven observation sites around this landfill site. Concentrations of thefollowing parameters such pH, EC, arsenic, Cl ion and ionic balance were analyzed. A slight relationship between thearsenic, Cl ion and EC was found. However, the source of the arsenic pollution was not apparent from the analysis on
ionic balance, and further investigations are considered to be necessary.
Keyword: Arsenic pollution, Ground water, Disposal site, Suzuka City
The Fukaya disposal site was constructed in 1972 on
the valley of a hill (altitude about 30m) in Hachino town,
Suzuka city , and was used till 1997. The disposal site isdivided into two sections (No.1 and No.2). The size of the
dumping area is 53480 m2 (in area) and 728355 m3 (involume) respectively. The total amount of the waste in
the disposal site is estimated to be 710,000 tons.
This waste site was filled on a natural clay bed in
Neogene period [4], and enclosed by a clay levee (inNo.1) and concrete levee (in No.2) at the each lower
reach. The feature of the dumping site is shown in Fig.1.
Almost all rain fall moves into the layer of the waste, anddeposited inner part of the levee, and evacuated outsideusing the electric pumping. The small amount of water ,however, is expected to pass through the clay bed.
In order to check the ground water pollution, somemonitoring stations (monitoring wells or leaching water)
were prepared around the dumping site, and monitoringof this site has been carried out for a long time. .

INTRODUCTION
Arsenic is known as one of the harmful elements, andin recent years, as for the arsenic, an environmental
standard became severer in Japan..
A significant
amount of arsenic has been found in natural soils, andmuch pollution of arsenic has been reported [1],especially in the ground water [2, 3] in Japan. Five years
ago, arsenic pollution was found in the Fukaya waste
disposal site in Suzuka situated in the central part ofJapan, 50km south west from Nagoya. Almost all of theincinerated ash discharged from Suzuka city has been
disposed. The pollution which corresponds two or threetimes higher than the Japanese water environmental
standard was found at two monitoring stations, and it hascontinued today. The arsenic pollution was found in alimited area of this landfill site and also was foundabout 30 years later after the construction of this site.
In order to solve the cause of this pollution, someinvestigations were carried out from the request of the
Suzuka municipal government.
.

Monitoring stations

INVESTIGATION METHOD

The monitoring was carried out at seven stations asshown in Table 1 and Fig.1.

The feature of this dumping site

691

St.1
Dumping area

St.5
St.6
St.4

St.7

St.2

St.3

Fig. 1 The dumping site and monitoring stations

Table 1 Monitoring stations

Table 2 Parameters and analytical methods

No.

Type

Situation

Analysis kinds and analytical methods :

St.1

Well

West part of the disposal site

St.2

Well

Well near of reservoir(old one)

St.3

Well

Well at the down stream

St.4

Well

Well in the dumping site

St.5

Well

Well in the dumping site

St.6

Reservoir

The water from theParameter
As
Cl
SO4
NO3
HCO3
Na
Mg
K
Ca

reservoir(old one)
St.7

Reservoir

The water from thereservoir(new one)

692

analytical method
ICP-MS
Ion chromatographIon chromatographIon chromatographIon chromatographIon chromatographIon chromatographIon chromatographIon chromatographfour years. The relation between arsenic pollution and Cl
concentration is not evident.

Analysis parameters and analytical methods
Parameters and analytical methods are shown in table 2

Ion balance

RESULTS AND DISCUSSIONSAs mentioned above, almost of all rain fall is removed
from the dumping site. However, a small amount of water
passes through the clay bed, and flows out of the
dumping site. In order to find a leaching of the waterfrom dumping site, ion balances were investigated. The
same patterns are found into two groups (group A: St.1
and St.2, group B: St.4, St.5, St.6). A different pattern
was found in St.3. This result indicates that the water of
St.3 has little relation with the dumping site. A lot ofarsenic pollution is found around these areas, and thearsenic pollution is thought to be come from the othernatural sources. In the future ,widely survey will beneeded.

The trend of the arsenic pollution
At first, the arsenic pollution was found at St.2 and St.3
in Jun. 2010, and it has been continuing today. At St.3, ahigher concentration of arsenic pollution was found thanat St.2 as shown in Fig. 2. Arsenic pollution was notfound in other monitoring stations where all domestic
disposal waste have been buried.
From the monitoring data of EC and Cl ion, the slightincreases of EC and Cl ion were found at St.3 in the last

As (mg/L)
0.070

0.060
0.050

As（St1）
As（St2）
As（St3）

0.040
0.030
0.020
0.010
0.000
Date

Fig. 2 The trend of the As(arsenic) pollution of the three sites693

Fig. 3 Radar chart of the ion composition.
Society on Water Environment Vol. 24, No.11, 739-744(2001) (in Japanese)[4] The survey report on the ground water ofSuzukacity-Hukaya Syorizyou: Nakanihon Engineering
Consultants Co., LTD. Mar., 2013 (in Japanese)[5] Masaaki Takahashi, Yukimasa Takemoto, Kayoko
Awaya, Mikihiro Ioka and Peng Guo: Ground waterpollution around the northern part of Yokkaichi City,
Journal of Geological Resource and Engineering Vol.2,
No.3 (2014) 174-179

CONCLUSION
In order to find a cause of arsenic pollution, the ionic
distribution of the arsenic concentration, and the ionicbalance analysis were carried out. Results of theseinvestigations indicate that the arsenic polluted water did
not come from the dumping site, but from other sources.
The arsenic pollution of ground water is found in thenorthern area of Mie prefecture [5], almost of all areconsidered from natural origin. Therefore, widely
expanded surveys will be needed in the near future.

ACKNOWLEGEMENTS
We express much thanks to the Suzuka city municipal
government who have offered the data for
thisresearch. .

REFERENCE[1] Environmental Geochemical Science: Chikyu
Kagaku Kouza 7, Japan Geochemical Society Edit.,
Nagoya University Publish, Baihukan Co. , LTD,
135-136 (2007) (in Japanese)[2] Senichi Ebise and Kazuyoshi Miki: Evaluation on
characteristic of heavy metals in the R. Yodo and its
tributary stream by high frequency observations, Journalof Japan Society on Water Environment Vol. 24, No.11,
715-723 (2001) (in Japanese)[3] Yukiko Hamatani, Satoko Tomimori, Kiyoshi
Hashizume, Takako Aizawa and Mari Asami: Groundwater features relation to arsenic – the relation to thestrata in northern part of Mie prefecture, Journal of Japan694

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

CHANGES OF CHLOROPHYLL-A, BACTERIOCHLOROPHYLL-C
AND DOC BEFORE AND AFTER REVETMENT WORK IN LAKE
FUKAMI-IKE, JAPAN
Megumi NOBORI1, Hiromi SUDA1, Maki OYAGI2, Akiko YOKOYAMA3, Akihiko YAGI 1
1

Aichi Institute of Technology (AIT), Faculty of Civil Engineering, Department of Urban Environment,
1247 Yachigusa, Yakusa-cho, Toyota 470-0392, Japan.
2
Yokkaichi University
3
Water Agency Inc.
ABSTRACT

Lake Fukami-ike is a small eutrophic lake of 2.1 ha with a maximum depth of 7.70 m in central Japan andwater stratified from March to October. Anoxic conditions prevailed below 4-5 m depth from April to Octoberand a photosynthetic green sulfur bacteria (BChl.c) accumulated in the hypolimnion. In Lake Fukami-ike, therevetment work was environment maintenance for activation of the town in 1992, and the water quality of thislake was considered to be clean. However, fluctuation in the transparency became larger than before. Data before
and after revetment work (1992) was studied.
Keywords: Chlorophyll-a, bacteriochlorophyll-c, DOC, eutrophic lake

INTRODUCTION
Lake Fukami-ike is a small eutrophic lake of 2.1
ha with a maximum depth of 7.70 m in central Japan(Fig. 1). The lake is well protected from the windand lake water stratified from March to October.
Anoxic conditions prevailed below 4-5 m depth
from April to October and a photosynthetic green
sulfur bacteria (BChl.c) accumulated in thehypolimnion [1], [2]. Transparency, dissolved
oxygen,hydrogensulphydeandvertical
distributions of nutrients have been observed indetail since 1978 in Lake Fukami-ike [1]-[9]. In
Lake Fukami-ike, the revetment work wasenvironment maintenance for activation of the town
in 1992, and the water quality of this lake was
expected to be clean. However, fluctuation in thetransparency became larger than before.

JAPAN

NAGANO Pref.

PURPOSEThe chlorophyll-a amounts in winter were higher
than in summer. The reason for such a difference
was considered because of the long stagnation
period [1]-[4]. Transparency was clearly changed
before and after revetment work (1992), 70-280 cm
in 1950s, 50-150 cm in 1970-’80s, and 35-470 cm in1990s-2000s (Fig. 2). Thus recent transparencychanges have remarkably high values. Stable state of
the lake water was obtained so that the inflow ofnutrient amount was stopped and had been disturbed
in water column after the revetment work.
Differences before and after the data were studied toclarify the influence of revetment work.

LAKE FUKAMI-IKE

Fig. 1 Investigation spot

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GEOMATE- Osaka, Nov. 16-18, 2015

Revetment work (1992)

Fig. 2 Transition of transparency
Chl.a and BChl.c were measured by colorimetricmethod; Chl.a by using 663, 645, 630 nm
wavelength, and BChl.c by using 662 nm [10].
BChl.c was photosynthetic sulfur bacteria and it
grew extensively at the boundary layer of the oxic
and anoxic layers.

METHODS
Investigation Spot
Lake Fukami-ike is located in Nagano Prefecture,
Japan (North latitude 35 ° 32 ′ 55 ″ 77, East
longitude 137°81′93″56) and has an area of 2.1 ha(maximum depth of 7.7) [7]-[8]. There are 6
inflowing streams and one outflow. The lake
receives the runoff water from orchards anddomestic sewage.

Total organic carbon (TOC) & dissolved organiccarbon (DOC)
Water samples were placed in 10 mL plastic
bottle and mixed after adding in 0.1 mL of 1N
H 2 SO 4 . Five minutes after, the inorganic carbon wascompletely expelled from water samples. The DOC
was filtered. The TOC and DOC were quantified bytotal organic carbon meter (TOC-V (SHIMAZU))[6], [7].

Water Sampling Method
Watersamples
were
taken
with
a
polyvinylchloride tube and a hand pump. In
principle, the water was taken at 25-cm intervals inredox interface and 50~100 cm intervals in the otherlayer. Water samples were filtered with glass filter(GF/F, 47 mm). Water sample which was notfiltered was total and the filtrated sample wasdissolved,
respectively.
Chlorophyll-aandbacteriochlorophyll-c were determined by thespectrophotometric method [1].
Measuring Method
Water temperature (WT), Dissolved oxygen (DO)WT and DO were measured by a DO meter(input electrode type, fluorescence method, HACL
Inc., turned electrode type).

Picture 1 Lake Fukami-ike (20 Jul 2013)

Chlorophyll-a (Chl.a) and bacteriochlorophyll-c(BChl.c)696

GEOMATE- Osaka, Nov. 16-18, 2015

Amount of DO in Lake Fukami-ike was measured
by the depth and each volume to obtain the
difference before and after revetment work.
Maximum value of 1978-79 and 2013-14 were 1150
kg in January and 1550 kg in February, respectively.
Minimum value of 1978-79 and 2013-14 were also
490 kg in June and 70 kg in November, respectively.
1978-79 and 2013-14 for maximum value minus
minimum value were 660 kg and 1480 kg,
respectively. Mean value was not different, butstandard deviation was larger.

RESULT & DISCUSSION
Water Temperature (WT), Dissolved Oxygen(DO)
Water temperature in 1979 of 4.5℃ in wintercirculation period, gradually became 8℃ up in water
stratification. 28℃ in the epiliminion of the summer
stratification period and 10 ℃ under the watertemperature stratification reached the stable state in
the hypolimnion. In 2014, 4℃ in winter circulation
period besides water temperature in the hypolimnion
increased to 12℃. Low DO 1 mgL-1 concentration
was obtained below 6 m depth in April and below
3.5 m because of stable water temperature
stratification in August of 1979, respectively. An
anoxic layer started previously below 5.75 m depth
at the end of March and the uppermost anoxic layer
appeared at 4 m depth in July, 2014. Decreases in
the oxic and anoxic layers were measured.

Chlorophyll-a (Chl.a) and Bacteriochlorophyll-c(BChl.c)Figure 5 shows the aging of Chl.a amount andtransparency in the oxide layer. Compared before
and after revetment work, the mean of Chl.a amount
before revetment work was 109 mgm-2, and 174
mgm-2 after that work. It was 1.6 times. And themean before that in the circulation period was 243
mgm-2, 427 mgm-2 after the work. It was 1.8-fold.
The Chl.a amount in the circulation period waslarger than that of the stratification period, and thistrend did not change before and after work.
Figure 6 shows the monthly variation of Chl.a in
the water column in the oxidized layer. The amountin winter was more than in summer. It was the trend
from ancient times of Lake Fukami-ike. The change
to the circulation period from the stagnation period
was calm before that work. In contrast, thefluctuation increased after that work.
Figure 7 shows monthly variation of Chl.a andBChl.c. The Chl.a and BChl.c value showed nodifference in 1979 (before revetment work), but
BChl.c increased in 2008 & 2013 (after revetmentwork). Maximum Chl.a amounts were 0.5 gm-2 and0.7 gm-2 in 1979 and 2008, respectively. It had
increased 1.4 times, but decreased 0.4 gm-2 in 2013.
Maximum BChl.c amounts were 0.18 gm-2 in 1979,
2.6 gm-2 in 2008, and 2.9 gm-2 in 2013. The average
Bacteriochlorophyll-c amounts in the water column
in 1979 (before revetment work), 2008 & 2013 (after
revetment work) were 74.4 mgm-2 in 1979, 1576
mgm-2 in 2008 and 1081 mgm-2 in 2013,
respectively. That data in 2008 and 2013 increased
21 times and 15 times that in 1979, respectively.
As a feature of the internal production,
phytoplankton and photosynthetic green sulfur
bacteria amounts in the water column were 209
mgm-2 and 1081 mgm-2. Therefore, thephotosynthetic green sulfur bacteria amount was 5
times more than that of phytoplankton in Lake
Fukami-ike in 2013.
Chl.a concentration decreased a little after workin the stagnation period, but increased after work in

Fig. 3 Isotherm (℃) and DO isosmotic line (mgL-1)

Fig. 4 Monthly changes of dissolved oxygen amount
(kg)

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GEOMATE- Osaka, Nov. 16-18, 2015

the circulation period. Bchl.c maximum value before
work in August was 100 μgL-1, after work it was 702µgL-1, growing 7 times. Similarly, in September, it
increased 100 µgL-1 to 664 µgL-1 (Fig. 8).

1979

2008

Fig. 5 Aging of the Chl.a amount and transparency
in the oxide layer (1959-2013)

2013

Fig. 7 Monthly variation of Chl.a and BChl.c inwater column.

Fig. 6 Monthly variation of Chl.a in water column inoxidized layer

Dissolved Organic Carbon (DOC)

Table 1 Chl.a (mgm-2) maximum, minimum andaverage value before and after revetment
work.
BeforeFigure 9 shows aging of the DOC in the watercolumn in the stagnation period. Figure 10 showsmonthly changes of the DOC in the water column.
Fluctuations of DOC amounts were observed before
work, but not after it. The mean DOC amount before
work was 4.24 gm-2, against 9.05 gm-2 after work. It
became 2-fold. The difference between maximum
and minimum value was up to 1.9 gm-2 before work,and 5.5 gm-2 after work. Therefore, Lake Fukami-ike
was stable throughout the year before work. There
was the rice field around the lake before revetment
work and the runoff inflowed directly. Lake Fukamiike is stable because the purifying effect of rice field
was acting on influent water such as household
waste water (low C/N ratio) and precipitation. But
after the work it was not stable. It seems possiblethat some domestic wastewater (low C/N ratio) was
still flowing into this lake.
Figure 11 shows vertical distribution of the DOC.
Change of the DOC was not observed before
revetment work, but a high concentration was notedin the hypolimnion from June to September. A

After

month

max

min

ave

max

min

ave

4

171

171

171

294

50

144

5

92.9 92.9 92.9

100 26.8 81.9

6

85.6

3.4 50.1

250 56.7

7

212

124

128

153

8

312

104

104

159 88.2

133

9

233

139

186

350 24.4

201

10

512

131

321

416

340

378

11

288

288

288

420

112

311

12

643

643

643

1101

150

626

1

406

406

406

960

500

730

2

180

180

180

703

550

627

3

237

133

185

319

150

246

180

50 79.9698

GEOMATE- Osaka, Nov. 16-18, 2015

uniform concentration was observed from
epilimnion to hypolimnion before work, butdifferent values were seen at each depth after work.
Chl.a, BChl.c (µgL-1)

Fig. 10 Monthly changes of DOC in the watercolumn (before and after revetment work).

Fig. 8 Monthly vertical distribution of Chl.a andBChl.c (µgL-1) (before and after revetment
work)

Fig. 11 Vertical distribution of DOC (before andafter revetment work)

Fig. 9 Aging of the DOC in the water column instagnation period
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GEOMATE- Osaka, Nov. 16-18, 2015

Table 2 DOC amounts (gm-2) in the water column inthe stratification period (maximum,
minimum and average)

ave

max

min

1978

4.6

5.2

3.8

1979

4.8

6.7

3.6

1980

1.3

1.5

1.2

1981

7.2

7.2

7.2

1982

7.4

7.4

7.4

1983

2

2

2

1986

2.3

2.5

2.2

1999

9.9

9.9

9.9

2000

10.7

13.1

8.6

2007

10.6

17.1

4.3

2008

11

16.2

1.2

2010

8.8

11

7.1

2013

3.4

5.1

2

year

REFERENCES[1]

Yagi A, Shimodaira I, Terai H and Saijo Y,“Seosonal Changes of Chlorophyll-a andBacteriochlorophyll in Lake Fukami-ike”, The
Japanese Journal of Limnology, 1983, Vol. 44,
No. 4, pp. 283-292[2] Takahashi K, Wada E and Sakamoto M,“Carbonisotopediscriminationbyphytoplankton and photosynthetic bacteria inmonomictic
Lake
Fukami-ike”,
Arch
Hydrobiol., 1990, Vol. 120, No. 2, pp. 197210[3]
Yagi A, Miyachi S and Nagoya university,“Vertical distribution of purple nonsulphur
bacteria and their utilization of dissolved
organic carbon in Lake Fukami-ike”, Arch
Hydrobiol., 1991, Vol. 121, No. 3, pp. 307317[4] Yagi A, “Manganese cycle in Lake Fukamiike”, Verh. Internat. Verein. Limnol., Sep.
1993, No. 25, pp. 193-199[5] Yagi A, “Manganese flux associated with
dissolved and suspended manganese forms in
Lake Fukami-ike”, 1996, Vol. 30, No. 8, pp.
1823-1832[6] Yagi A, Funahashi J, Nishio N and Umemura
M, “Seasonal changes of methane and organicacid in Lake Fukami-ike”, Jan. 2009, Verh.
Internat. Verein. Limnol., Vol. 30, Part 6, pp.
878-883[7] Yagi A, “Biological manganese cycle in theoxic and anoxic layers of Lake Fukami-ike”,
Limnology in Tokai Region of Japan, 2010,
Vol. 43, in Japanese[8] Nakagawa M, Ueno Y, Hattori S, Umemura M,
Yagi A, Takai K, Koba K, Sasaki Y, Makabe
A and Yoshida N, “Seasonal change inmicrobial sulfur cycling in monomictic Lake
Fukami-ike, Japan”, Limnol. Oceanogr., 2012,
Vol. 57, No. 4, pp. 974-988[9] Yokoyama A, Oyagi M, Nakagawa M andYagi A, “Existent state of iron and manganese
in inflow, outflow and inside of Lake Fukamiike”, Limnology in Tokai Region of Japan”,
2013, Vol. 60, pp.1-6[10] Takahashi M, and Ichimura S, “Vertical
distribution and organic matter production ofphotosynthetic surfur bacteria in Japaneselakes”, Limnol. Oceanogr., 1968, 13: pp. 644655.

CONCLUSION
Chl.a was increased in both water concentrationand the water column. The DOC amount of
stagnation period was increased after revetment
work more than before it, and the difference between
maximum and minimum values also increased.
BChl.c had been confirmed to increase concentration
and biomass. The revetment work was environment
maintenance for activation of the town in 1992, andthe water quality of this lake was expected to beclean. However, the inflow of domestic wastewater
did not stop; nutrients increased and Chl.a, BChl.cand the DOC also increased. Such a phenomenon isconsidered to be one of the factors in the variation in
the transparency increase.
ACKNOWLEDGEMENTS
Assistance of the Town authorities of Anan-cho,
Shimoinagun, Nagano Prefecture is also gratefully
acknowledged in kindly providing the facilities forour research.

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Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

GENETIC DIVERSITY OF RESTORED ENDANGERED SPECIES,PENTHORUM CHINENSE IN THE RIVERBED

1Michiko Masuda1 and Fumitake Nishimura2
Department of Civil Engineering, Nagoya Institute of Technology, Japan; 2 Department of Environment
Engineering, Kyoto University, Japan

ABSTRACTThe Ministry of Land, Infrastructure and Transport has done the business of digging on the river road toincrease the flowing quantity of Ibi River every year since 2000. It is reported to the dug region the Penthorum
chinense populations which is the threatened species the restored in 2001. Digging is done from 2000 to 2010
every year. Therefore, we investigated P. chinesne population of which extent to which region grew. As a result,it was shown some the growth of the plant of 200 individuals in the 2002 digging region, 4000 individuals in the2001 digging region and 1000 individuals in the 2000 digging region, and so on. Though it is thought that the
population formed in 2000 is seed bank origin, whether the populations formed in 2001 and later the burial seedorigin or formed with the seed of an existing population, it is uncertain. Then we sampled 8 populations of thespecies form these areas, and studied them for allelic variation at 16 enzyme loci. There was no significant
correlation between the actual population size and genetic diversity parameters, suggesting that the effective
population size was very small even for the large populations. However, population that restored at the riverbed
and population that approved on the embankment were able to distinguish obviously in the populations that had
been approved in 2002. As for the population approved to the embankment, it was shown that genetic variation
was very high in the population approved to the riverbed thought it was hardly admitted.
Keywords: Population size, Burial seeds, Restorelation, Endangered species, River management

chinense populations approved by digging in theriver in this research. Then we investigated thegenetic diversity of the species with three questions,
1) Is there a relationship between the location andgenetic distance? 2) Are the new populations
appeared from burial seeds? 3) Is there a relationshipbetween population size and genetic diversity? It
was paid attention to these three points and analyzed.

INTRODUCTION
The Ministry of Land, Infrastructure andTransport has dug up the river in Ibi River (Centerof Japan) for the flood prevention every year since2000 to 2010. This area has been disturbed by flood
damage, so the digging can make the flow rate of theriver increased and stop a flood, and provide the low
marsh area in the river. In the new marsh area by the2000 digging it was observed that a population ofPenthorum chinense recovered in 2001. From 2000
to 2010 the digging was done by The Ministry ofLand, Infrastructure and Transport every year, andnew populations of P. chinense were appeared in
new marsh area. And sometime new populations
were appeared on the bared embankment. P.
chinense has been described to the red list as
vulnerable level (VU). A few studies reported thatthe populations of P. chinense have restoration by
buried seeds [1]–[3]. In general, the populations
restored it from the buried seeds are thought thatgenetic diversity is high. However, the character ofthe genetic diversities was hardly reported.
On the other hand, the natural population of P.
chinense was observed in the river. The seeds of thespecies is so small that dispersed very long distanceeasily. It was suggested the new population wasconstructed by the seeds from natural population.
Then, it paid attention to genetic diversities of the P.

MATERIALS AND METHODSThe Study Site and field census
The study was carried out on the Ibi River at Gifu
Pref. (from 35o 20’ 25’’N, 136o 38’ 92’’E, altitude
9.3 m to 35o 17’ 50’’N, 136o 37’ 7’’E, altitude 0.2m). The Ibi River is very steep and wide range river
system, water system of the extension 121km and1840 km2 in the valley area. Because this river
region had frequently received the flood damage,bank repair and a river dredge are continuing. Our
study was dome the area 36.2km to 32.3km from themouth of the river (Table 1).
We counted the number of P. chinense on 20th,
Oct. in 2004, when the leaves of the species turned
red and yellow, so we can find the species easily tofind out.

701

GEOMATE- Osaka, Nov. 16-18, 2015

resolve the following 16 putative loci: mdh-1, mdh-2,
mdh-3, pgi-1, pgi-2, pgm, ald, tpi-1, tpi-2, adh, acp1, acp-2, acp-3, idh, me and mr. Samples wereground in a cold extraction buffer described by
Odrzykoski and Gottlieb [8]. The enzymes wereresolved on 10.8% starch gel. System 5 of Soltis etal. [9] were used. Staining procedures followed
previous works [9]-[11].

The Study Plant
Penthorum chinense Pursh (Fig. 1) is a polycarpic
herb plant distributed in eastern Asia [4], but its
population has been decreasing in Japan and Red
Data Book of Japanese vascular plants listed thespecies a ‘vulnerable’ level species [5][6]. This
species is often distributed in marsh river flood plain,salt marsh area, wetland around paddy fields, andabandoned paddy fields in Japan. The species isreproduces both sexually by seeds and vegetatively
by rhizomes [7].

The Statistical analysis
For each populations we calculated the number ofalleles per locus (A), percentage of polymorphic loci(P), and gee diversity (h). We used all loci data inthe calculation of A, and regarded a locus aspolymorphic if the frequency of its most frequent
alleles is under 0.95. In addition, total gene diversity[12] was calculated for species level. The population
genetic structure was analyzed by initiallycalculating Nei’s G ST value [12]. Values for genetic
indentities (I) and standard genetic distance (D) werecomputed for each pairwise comparison of all
populations. The neighbor joining method [13]based on D was used for constructing a phenogramfor P. chiense.

Table 1 The characters of study site, establishedyear, habitat condition, the distance fromthe mouth of the river
Study
site
A
B
C
D
E
F
G
H

Established
year
2001
2002
2002
2003
2003
2002
2002
Naturalhabitatdistance (km)

Marsh
Bank
Marsh
Bank
Marsh
Marsh
Marsh
marsh

32.2-32.6
32.6-33.0
32.6-33.0
34.6-35.8
34.6-35.8
32.8-33.8
36.2-37.0
18.8

RESULTS
Field Census
The number of individuals was shown in Table 2.
The numer of individuals ranged from 85 to 11000.
Table 2 The number of P. chinense in 2004.
Population

A

B

C

D

E

No. of individuals

412

45

950

120

1780 850

F

G

H

312

52

Genetic Diversity

Fig. 1

Sixteen loci were scored: mdh-1, mdh-2, mdh-3,
pgi-1, pgi-2, pgm, ald, tpi-1, tpi-2, adh, acp-1, acp-2,
acp-3, idh, me and mr. In all populations, mdh-1,
pgi-1, pgi-2, adh, acp-1 and acp-2, six loci werepolymorphic. In all population mdh-3, tpi-2 and acp3 were monomorphic. Allele frequencies at thepolymorphic loci are listed in Appendix 1.
Table 3 summarizes the resultant values of A, P
and h for each population. And total gene diversity(H T ) of P. chinense was 0.487. The levels of genetic
diversity in P. chinense was higher than that of otherendangered species, for example, Aster kantoensis
growing in the river bed were 1.53 (A), 0.36(P) and
0.142 (h) [14]. And other endangered species
showed A (1.44 to 2.01), P (0.199 to 0.65) and h(0.037 to 0.43) [15]-[18].

The flowers of P. chinense.

Electrophoresis
Fresh leaves were collected from 30 individuals
per population at August in 2003. Leaves were kept
on ice during 4 hours transportation to the laboratory,
after which they were refrigerated 2 days until
electrophoresis. The following enzyme systems were
examined: malate dehydrogenase (MDH), phosphoglucose isomerase (PGI), phospho-glucomutase(PGM), aldolase (ALD), triose-phosphate isomerase(TPI), alcohol dehydrogenase (ADH), acidphosphate (ACP), iso-citrate dehydrogenase (IDH)
and malic enzyme (ME). Leaves were used to702

GEOMATE- Osaka, Nov. 16-18, 2015

Table 3 Mean number of polymorphic loci (A),proportion of polymorphic loci (P), andgene diversity within a population (h) at 16loci for examined populations of P.
chinense.
Population
A
B
C
D
E
F
G
H

P

A

0.538
0.769
0.923
0.769
0.692
0.692
0.846
0.769

2.385
2.385
2.615
2.846
2.538
2.154
2.385
2.077

G ST was 0.339. The value of G ST suggested the highlevel differentiation was occurred. It is always
observed
long
distance
populations.
(nodifferentiation: 0-0.05, low level differentiation:
0.05-0.15, middle level: 0.15-0.25, high level: 0.25-).

h
0.210
0.286
0.286
0.343
0.334
0.383
0.413
0.208

DISCUSSION

Population structure
The value of I and D was shown in Table 4.
There is no relationship between real distance andgenetic distance. It is suggested that new constructed
populations result from burial seeds previous
population. Fig. 3 showed the genetic distanceamong established populations and natural
population. The genetic distance between the nearest
populations were small, but the longest distantpopulation H was not out of the phenogram.
Table 4 Estimated values of genetic identity (I)(upper) and standard genetic distance (D)(under triangle).
Poplation

A

B

C

D

E

F

G

H

A

-

0.976 0.982 0.943 0.959 0.924 0.892 0.930

B

0.024 -

0.990 0.954 0.965 0.942 0.919 0.953

C

0.018 0.010 -D

0.057 0.046 0.038 -0.962 0.974 0.940 0.918 0.952

E

0.041 0.035 0.026 0.020 -F

0.076 0.058 0.060 0.064 0.044 -GH

0.108 0.081 0.082 0.083 0.063 0.036 0.965
0.070 0.047 0.048 0.064 0.042 0.032 0.035 -0.980 0.936 0.917 0.936

F

H 0.008
0.008
0.044
0.026 0.042 0.016
0.028
0.002
0.018
E

G

0.956 0.937 0.958

B

0.964 0.968

0.006
0.036

C
0.00070.017

AD
Fig. 2 Phenogram using the neighbor joining method
based on Nei’s (1987) standard genetic
distance.
The result of total population genetic structure703

In this study, genetic diversity of the populationsof P. chinense that restored by the excavating workof the river for the flood prevention was clarified.
Because the flowing quantity of the river has been
made constant by the construction of dams for
decades, the disturbance in the river has been
suppressed. Therefore, the transition of thevegetation of the river proceeds, and the growth of apeculiar plant in the river that depends on the
disturbance has been suppressed. However, if the
excavating work of the river was done at moderatefrequency, the effect similar to the disturbance wasbrought, and the restoration of the endangered
species can be done. Moreover, it was shown that it
was thought that the approval of the populations by
digging was a burial seed origin, and genetic
diversity was also not low than that of endangered
species.
Some highly suggestive research [3][19][20]showed that the soil seed bank could restore thegenetic diversity. But the cost of the restoration ishigh. In our research, the adaptive moderate
excavation can be restored the endangered species
population with high genetic diversity. The Ministryof Land, Infrastructure and Transport conducted the
excavation at winter season when the seedlings were
not occurred. And the depth of excavation was 0 to
3m, with loose slop to the low embankment. So, thegradient with water containing was made. The
condition could serve the adaptive condition of P.
chinense. This is suggested that the design of
excavation can support the habitat of disturbancedependent species.
In addition, restored populations surprisingly
tended to be different according to dug depth andplace. It is thought that the populations is adjacentand approved can admit, and each population has
been approved from a slightly different seeds at theage by the dug depth region.
Three genetic diversity indexes were very high inall restored populations. Sometimes, endangered
species has low genetic diversity and the cause of
low genetic diversity lead to extinction vortex. But
there is no relationship between population size andgenetic diversity, so the condition of the genetic

GEOMATE- Osaka, Nov. 16-18, 2015

1) The excavation for the flood management
should be done with loose slope provide thedifferent water level habitat.
2) The excavation should be done in winter whenthe endangered species is dormant.
3) The excavation area should not be large to avoid
the uniformity is occurred, that can be provide
the genetic diversity.

diversity is curious condition. The genetic diversity
from the C population that was made at 2002
digging is higher than that of B in this study. The
populations of B and C were at the position placed
between two spur dikes. Spur dikes were made forthe protection of the embankment. It lowers thecurrent velocity of the river. Therefore, the piling up
action works, the condition of being easy to retire
from a lot of seeds arises. And there is a possibility
that the populations with high genetic diversity
restored it from the burial seed in population C.
Because it is the population B approved to a position
that is about 2m higher than C, the piling up of theseed is thought to be only an increase of the flowing
quantity of the river though population B is locatedin similar, too. And the same condition was occurred
between population D and E.
The real distance of the population C and F was
shorter than that other population. But the genetic
distance is very large, 0.058-0.060. So the real
distance cannot affect the genetic structure. It
suggested that the condition of excavation depth andtime of seeds accumulation. And the natural
population H is very close to population G, where is
the farthest from natural population. Moreover all
populations are high differentiation. This sort ofdifferentiation is usually observed among islands[21].
It was shown that the P. chinense populations
restored it by the river construction as a result of thisresearch. Moreover, it was shown that the genetic
diversity of restoration populations was different
from that of the natural populations. It will besuggested that attention be necessary for the
populations that appears when constructing it likethis managing in the future.

ACKNOWLEDGEMENTS
We wish to thank the member of Ministry ofLand, Infrastructure and Transport Kiso River Office
for research support and permission to work in theriver. We also thank the members of our laboratory
for their assistance in the field works.
REFERENCES[1] Imahashi M & Washitani I, “Evaluation of soil
seed banks as plant materials for the restorationof flood plain vegetation”, Japanese J.
Conservation Ecology, 1996, Vol. 1, pp. 131147.
[2] Kimura Y et al., “Possibility of restoration ofPenthorum chinense Pursh by using soil seedbank”, Japanese J. Conservation Ecology, 2000,
Vol. 5, pp. 197-204.
[3] Honnay O et al., “Can a seed bank maintain thegenetic variation in the above ground plant
population?”, Oikos, 2008, Vol. 117, Jan. pp. 15.
[4] Ohwi J, Kitagawa M, New Flora of Japan,
Tokyo: Shibundo, 1983. (in Japanese)[5] Endangered Plant Survey Group, The Red Data
Book of Japanese Vascular Plants. Tokyo:
Nature Conservation Society of Japan, 1989. (inJapanese)[6] Kato T & Ota H, Endangered Wild life of Japan.
Osaka: Hoikusha Pubulishing Co., 1993. (inJapanese)[7] Kimura Y, et al., “The life history of Penthorum
chinense Pursh and its growth traits inexperimentally different water condition”,
Bulletin of Water Plant Society, Japan, Vol. 66,
1999, pp. 15-18.
[8] Odrzykoski IJ & Gottlieb LD, “Duplications ofgene coding 6-phosphofluconate dehydrogenase
in Carkia (Onagraceae) and their phylogenetic
implications” Systematic Botany, 1984, Vol. 9,
pp. 479-486.
[9] Soltis DE et al., “Starch gel electrophoresis offerns: a compilation of grinding buffers, gel andelectrode buffer and staining schedules”,
American J. of Botany, 1983, Vol. 73. Jan. pp.
9-27.
[10] Wendel JF & Weeden NF, “ The effect ofserpentine on the population structure of Silene

CONCLUSION
We get three main conclution.
1) There is no relationship between the real
distance and genetic distance. The populations
at the same position with different water level
showed small genetic distances.
2) The genetic construction of natural population
was very different from restored populations. So
the restored populations appeared from burial
seed banks.
3) There is no relationship between population sizeand genetic diversity.
Based on the conclusion, two proposals about the
management method of the excavation.

704

GEOMATE- Osaka, Nov. 16-18, 2015

dioica (Caryophyllaceae)”, Evolution, 1989,
Vol. 46, pp. 1537-1548.
[11] Chepliak WM & Pitel JA, Techniques for starchelectrophoresis of enzymes from forest tree
species.
Information
reportPI-X-42.
Penthawana: Penthawana National Forestry
Institute, Canadian Forestry Service, 1984.
[12] Nei M, Molecular evolutionary genetics. New
York: Columbia University Press, 1987.
[13] Saitou N & Nei M, “The neighbor joining
method: a new method for reconstructing
phylogenetic trees”, Molecular Biology and
Evolution, 1987, vol. 4, pp. 106-425.
[14] Maki M et al., “Genetic diversity andhierarchical population structure of a rare
autotetraploid
plant,
Aster
kantoensis(Asteraceae)”, American J. of Botany, Vol. 83,
Mar. 1996, pp. 296-303.
[15] Soltis DE & Soltis PS, “Genetic consequencesof autotetraploidy in Tolmiea (Saxifragaceae),
Evolution, Vol. 43, 1989, pp.586-589.
[16] Ness BD et al., “Autotetraploidy in Heuchera
micrantha (Sazifragaceae)”, American J. of
Botany, Vol. 76, 1989, pp. 614-626.
[17] Wolf PG et al. “Chloroplast-DNA andelectrophoretic variation in diploid anautotetraploid
Heucheragrossularifolia”,
American J. of Botany, Vol. 77, 1990, pp. 232244.
[18] Shore JS, “ Tetrasomic inheritance and isozyme
variation in Turnera ulmifolia vars. elegance
Urb. and intermedia Urb. (Turnraceae)”,
Heredity, 1991, Vol. 66, pp.305-312.
[19] Macgraw JB et al., “Ecological genetic variationin seed banks I. Establihsment of a timetransect”, J. of Ecology, 1991, Vol. 79, pp. 617625.
[20] Baskin JM & Baskin CC, “The seed bank in a
population of an endemic plant species its
ecologicalsignificance”,
BiologicalConservation, Vol. 14, 1978, Sep. pp. 125-130.
[21] Tomaru N, “Genetic diversity and population
structure in widespread deciduous broad-leaved
trees in Japan”, Earth Environment, 2013, vol18. Pp.119-126.

705

GEOMATE- Osaka, Nov. 16-18, 2015

Appendix 1. Allele frequencies at 13 polymorphic loci of 8 examinied
populations of P. chinense
Locul Allele A
B
C
D
E
F
G
H
mdh - 1 N
48
45
33
45
40
40
38
40
a
0.323 0.167 0.439 0.044 0.075 0.100 0.197 0.300b0.500 0.644 0.561 0.256 0.825 0.813 0.645 0.375
c0.135 0.189 0.000 0.500 0.100 0.088 0.158 0.325d0.042 0.000 0.000 0.200 0.000 0.000 0.000 0.000
mdh - 2 N
a
b
c
d

16
0.563
0.313
0.125
0.000

30
0.000
1.000
0.000
0.000

32
0.531
0.469
0.000
0.000

39
0.180
0.423
0.308
0.090

30
0.117
0.417
0.367
0.100

34
0.706
0.235
0.059
0.000

34
0.529
0.471
0.000
0.000

31
0.903
0.097
0.000
0.000

pgi - 1 N
a
b
c
d

48
0.177
0.698
0.115
0.010

44
0.295
0.636
0.068
0.000

30
0.050
0.850
0.100
0.000

45
0.056
0.411
0.444
0.089

43
0.047
0.640
0.256
0.058

32
0.469
0.406
0.125
0.000

35
0.443
0.514
0.043
0.000

40
0.288
0.663
0.050
0.000

pgi - 2 N
a
b
c
d

48
0.063
0.771
0.146
0.021

45
0.222
0.567
0.211
0.000

30
0.117
0.750
0.133
0.000

44
0.216
0.682
0.080
0.023

42
0.357
0.536
0.095
0.012

38
0.145
0.684
0.118
0.000

35
0.386
0.529
0.086
0.000

40
0.200
0.788
0.013
0.000

pgm

N
a
b
c

30
1.000
0.000
0.000

38
0.237
0.658
0.105

27
0.463
0.500
0.037

40
0.738
0.163
0.100

66
0.530
0.462
0.008

32
0.453
0.484
0.063

34
0.088
0.897
0.015

40
0.150
0.850
0.000

ald

N
a
b
c

30
0.047
0.938
0.016

30
0.033
0.783
0.183

25
0.200
0.500
0.300

20
1.000
0.000
0.000

21
1.000
0.000
0.000

32
1.000
0.000
0.000

30
1.000
0.000
0.000

40
1.000
0.000
0.000

tpi - 1

N
a
b
c

23
1.000
0.000
0.000

34
0.559
0.441
0.000

27
0.593
0.407
0.000

28
0.071
0.893
0.036

38
0.711
0.289
0.000

25
1.000
0.000
0.000

35
1.000
0.000
0.000

40
1.000
0.000
0.000

706

GEOMATE- Osaka, Nov. 16-18, 2015

Appendix continued.
Locus
adh

Allele
N
a
b
ｃ

A
31
1.000
0.000
0.000

B
32
1.000
0.000
0.000

C
30
0.883
0.117
0.000

D
35
0.543
0.457
0.000

E
40
1.000
0.000
0.000

F
40
0.075
0.925
0.000

G
35
0.114
0.657
0.229

H
40
0.600
0.400
0.000

acp - 1 N
a
b
c
d

47
0.085
0.809
0.106
0.000

45
0.178
0.789
0.033
0.000

30
0.067
0.750
0.167
0.017

45
0.133
0.600
0.133
0.133

44
0.216
0.511
0.273
0.000

32
0.594
0.406
0.000
0.000

35
0.414
0.586
0.000
0.000

39
0.295
0.705
0.000
0.000

acp - 2 N
a
b
c
d

47
0.185
0.554
0.250
0.011

44
0.125
0.795
0.080
0.000

30
0.133
0.783
0.067
0.017

44
0.091
0.443
0.432
0.034

45
0.144
0.356
0.200
0.300

40
0.188
0.750
0.063
0.000

39
0.410
0.090
0.500
0.000

40
0.125
0.875
0.000
0.000

idh

N
a
b

23
1.000
0.000

35
1.000
0.000

30
0.883
0.117

25
1.000
0.000

32
1.000
0.000

40
1.000
0.000

34
0.546
0.544

40
1.000
0.000

mr

N
a
b

29
1.000
0.000

33
1.000
0.000

25
1.000
0.000

36
1.000
0.000

27
1.000
0.000

32
1.000
0.000

35
0.543
0.457

40
0.425
0.575

me

N
a
b
ｃ
d

24
0.000
1.000
0.000
0.000

22
0.318
0.682
0.000
0.000

32
0.313
0.500
0.188
0.000

27
0.333
0.667
0.000
0.000

32
0.234
0.766
0.000
0.000

32
0.781
0.219
0.000
0.000

32
0.875
0.094
0.031
0.000

40
0.688
0.125
0.000
0.188

707

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

EARTH QUAKE AND SPATIAL TSUNAMI MODELLING FOR THE
SOUTH JAVA COASTAL AREA – INDONESIA
Agus Hartoko1*, Muhamad Helmi2, Mujahid Sukarno3, Hariyadi MT2Dept of Fisheries. Fac of Fisheries and Marine Science & Postgraduate Progamme-Coastal Resources
Management. Diponegoro University. Semarang - Indonesia
2
Dept of Marine Science. Fac of Fisheries and Marine Science. Diponegoro University. Semarang.Indonesia
3
Waindo SpecTerra. Jakarta. Indonesia
1

ABSTRACTLong history of Indonesia with many event in experiencing tsunami, with the fact the country position
surrounded by plate tectonic subduction zone (ring of fire) with very high frequency recorded tectonic earth
quake and risk of tsunami. The need of reliable tetonic plate earth quake and risk of tsunami spatial data basemodel was inavitable.
The paper built neccesary numeric and spatial data base, analysis and develop tsunami modelling forthree sites of south Java (Serang – west of south Java, Bantul - central and Banyuwangi - east of south Java)
which were regarded as the most dense populated island. Phase one steps were built spatial database of thetectonic plate subduction zone with variable of depth, distance and ocean floor topography using ETOPO-2
USGS bathymetric data. Phase two steps including the development of tectonic plate earth quake history database with above 6 Richter scale for the past five years. Development of numeric tsunami spatial model using
MIKE-21 software (Evaluate License) with variable of tectonic plate earth quake coordinate, magnitude of 8
Richter scale and depth. Followed with detail analysis of the tsunami waves characters such as the height andtravel time and direction towards coastal area, tsunami wave oscilation, time and run-up height and distance inreaching the coastal area. Phase three steps including development of spatial data base of contur Digital
Elevation Model (SRTM-data), run-up model and wave height in reaching the dense population area and detailvillage and sub-district boundary, population, number of house and buildings, infrastructure and landuse of theaffected area and finally followed by escape route scenario and site-selection for possible community gatheringbuilding.
Key words : earth quake, tsunami, south-Java, Indonesia*Corresponding author : Postgraduate Building 3rd Floor. Coastal Resources Management Study Programme.
Diponegoro University. Jl. Imam Barjo SH. Semarang. Indonesia. agushartoko@gmail.com

hold enough energy to release 9.0 magnitude tremor
that could produce deadly tsunami echoing the Aceh2004 tsunami that klled more than 230,000 people insoutheast Asia [3]Great extensional faulting events in the shallow
outer trench-slope region seaward of subduction
zones are relatively rare, with the three largestknown events being the 1933 Sanriku, Japan,
earthquake (moment magnitude Mw58.4), the 1977
Sumbawa, Indonesia,earthquake (Mw58.3). These
huge intraplate earthquakes all had relatively highstress drop and strong tsunami genesis, withruptures extending through the oceanic crust and at
least 15–20 km into the uppermost mantle. These
extensive fractures of the oceanic lithosphere are a
result of plate bending and slab-pull forces. The
deep faulting probably facilitates hydration of thelithosphere
beforesubduction,
enabling
intermediate-depth and deep earthquakes once theplate subducts. The Sanriku and Sumbawa events

INTRODUCTION
Indonesia as the most vulnerable country
for tectonic erathquake surounded with ‘ring of fire’of the Eurasian, Indo-Australian and Pacific plate,espescially at the west coas of Sumatra, south coastof Java, along the south coast of West and East
Nusa Tenggara, north Papua, Sulawesi/ Celebes
and Molucas. The discrepancies in the past view
year in spatial modelling for marine risk and hazards
including risk of tsunami waves is especially due tothe limitation of spatial data-base [1], analysis
method and lack of a holistic point of view that an
ecological spatial distribution state and analysis
should be based on the whole ecosystem parametersitself as a unity, and should not be analyzed
partially [2]. After three earthquakes with 8.4, 7.9
and 7.0 magnitude occurred along the coast of
Mentawai islands West Sumatra since the last quakewith 8.9 magnitude in 1833 the region might still708

GEOMATE- Osaka, Nov. 16-18, 2015

occurred seaward of subduction zones for which thelargest interplate thrust events are small, suggesting
weak frictional coupling of the megathrusts. The
2009 Samoa event, with a seismic moment thatplaces it among the four largest known trench-slope
events, thus appears similar to the 1933 Sanriku and1977 Sumbawa earthquakes, with the Tonga
subduction zone megathrust apparently having weakfrictional coupling [4].
The paper aimed to built
spatial database, tsunami wave numeric modellingat along the south coast of Java. As well as to plannof escape zone for the worst tetonic earthquake
scenario above 8 magnitude, for dense populatedarea of south Java coast at Serang (sout of westJava) , Bantul (south of central Java)and
Banyuwangi regency (south of east Java).

The spatial affection of the possible
tsunami waves generation after plate tectonic
earthquake on the coastal for coastal area zonation
and risk and hazards aspects antisipation in order tominimise human lost, coastal infrstuctural damages
and coastal management purposes were the main
focus of the paper. Analysis of ocean floor
morphology was cansidered as one of important
point, where as also in the case of Aceh Tsunami
2004 that there is actually present a deep bottom
channel structure infront of Banda Aceh region [2]and in this case is the presence of steep slope ofbottom morphology reach to Sunda strait andapproaching near to Lampung Bay (Fig. 1). Thatboth we can use to anticipate and take into accountthe strength of tsunami wave striking energy since
there was no ocean floor barrier structure in
reducing the tsunami wave energy striking to thecoastal area.
Based on bathymetric and ocean
morphology analysis we can identify the depth at
subduction zone at Sunda strait is 6,453 m with
distance to the most dense population district ofSerang regency is 291.7 Km (Fig. 1). Scenario fortsunami wave model for Serang was set with 8.4
Richter scale at coordinate for tectonic earthquake atlatitude : 06° 32’09.60” S, longitude : 103°
34’19.20” E located at the outer part of Sunda strait,
inline with subduction zone (Fig. 2). Lesson learned
based on the west Sumatra earthquake that theregion might still hold enough energy to release amagnitude-9.0 tremor that could produce a deadly
tsunami, echoing the 2004 tsunami that killed morethan 230,000 people in southeast Asia. The
earthquakes released pressure from a 700-kilometrelong region, known as the Mentawai section, which
had been building since the last quake, an event ofmagnitude 8.6–8.9 in 1833 [5]METHOD
Ocean floor morphology analysis using
ETOPO2. v2 2006, USGS USA with 2-minute
Gridded Sea Floor Topography to generate 2Docean floor morphology. Data base of tectonic
earthquake history was compilled for ten years
periode, consisting of coordinates, magnitude andtectonic earthquake depth (Km). Spatial tsunami
wave modelling was built using MIKE-21 software(evaluaton license, Waindo SpecTerra Company)
with variable of tectonic earthquake such as Initial
rupture time [hh:mm:ss - UTC], Dip-angle ( δ ) : 35,
Slip-angle ( α ) : 90, Strike angle ( θ ) : 315, , Fault
mechanism : subduction dip-slip, Mw [-] : 8.4,
Shear Modulus [Gpa] : 29.4 , Lambda [Gpa] : 25.9,
with vertical deformation oftsunami wave
generations.
Three area of south Java coast
tsunami model were madewith scenario ofearthquake magnitude of 8.4 Richter scale, depth(Km) was 23 Km for Serang coast latitude : 06°
32’09.60” S, longitude : 103° 34’19.20” E (south ofwest Java), with 315 degree of earthquake slope
angle. Magnitude of 7.9 Richter scale, depth of 20
km for Bantul coast (south of central Java) with 300
degree of earthquake slope angle and magnitude of
7.0 Richter scale, depth of 15 km for Banyuwangi(south of east Java) with 270 degree earthquake
slope angle.
All the three model with geodetic
datum of WGS1984. Magnitude of earthquake wasmade based on range of ten years data 6.1 – 8.7
Richter scale) based on ten years earthquake
recorded data along the west Sumatra and south ofJava.
Analysis on predicted impact to coastal
region using DEM-SRTM and Geo-Eye satellitedata, tsunami run-up model, demographic or district
population density data, coastal infrastructure suchas roads and buildings.

Fig. 1. Ocean morphology and the subduction zoneof Serang south of West Java.

RESULT and DISCUSSION
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GEOMATE- Osaka, Nov. 16-18, 2015

the existance of many important and large scaleindustrial zones, industrial and passenger harbour,
power plants, luxurius resetlements, resorts andhotels. Tsunami escape point scenario was madewith radius from 200 up to 800 meter radius zone
(Fig.5) characterised with hill and flat coastal area
and alluvial soil type. Spatial model of run-up
height and zone was used to set based on maximum
tsunami wave elevation plus 30% and plus 3m
height for safe escape building height plann.

Fig. 2. Tectonic database and tectonic earthquake
scenario of 8.4 Richter scale and depth 23 km
After running the numerical spatial tsunami wave
model, when the tsunami waves reach the coastal
area then most dense population coastal district as
the most vulnerable coastal zone was identified at 1hour and six minute with two big tsunami wave up
to 10 m height (Fig. 3) with 30 minute lag-time
followed with a 3 m low-seawater between the two
big tsunami waves.
Fig. 4. High population density at most vulnerable
coastal districts of Serang regency – Banten
Province

T

iW

Fig. 5. Distance and analysis for the escape zone
and building at Serang coastal zone

Fig. 3. Tsunami wave run-up (m) to reach the dense
coastal population zone of Serang coastal area.

Bathymetry analysis made at south Bantul
coast (south central Java) indicates the presence ofrelatively
more gentle slope of ocean floor
comparred to Banyuwangi (south east Java) with amore steep ocean floor (Fig.6) as assumed relatively
will have a beter bottom resistance for tsunamy
wave energy. Distance from subduction zone to thecoast of Bantul is 247.2 Km, while distance at
Banyuwangi is 257.8 Km. After running in thetsunami wave model for these two coast, about

Based on the tsunami wave model that reach to the
Serang west Java, about 231.63 Km long of coastal
area was affected by tsunami waves (Fig 3). Twomost dense populated district at Serang weredetected at the most vulnerable coastal zone withpopulation density between 6,413 - 8,437 perhectare (Fig.4). The area was highly packed with
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GEOMATE- Osaka, Nov. 16-18, 2015

79.42 Km long at Bantul coast and about 111.16
Km long coastal area at Banyuwangi which mainly
would be affected by high tsunami run-up model(Fig 7 and Fig 9) up to 8m high with 2m low-water
cycle (Fig 8 and Fig 10). Time lag in between highand low water run-up model at Bantul coast was
about 30 minutes, while at Banyuwangi coast was
only 10 minutes delay. The difference of high-low
water time lag at Bantul and Banyuwangi wasconsidered due to the difference of ocean floor
morphology, where more gentle slope identified at
Banyuwangi. The two coastal area were mostly
flat with sandy soil type (Fig 11). Population
density at these coastal districts were less than 100
people per hectare and much lesscoastalinfrastructure such as harbour, electricity plant,
highway and big roads, comparred to Serang coast.

Fig. 8: Run-up numerical model at Bantul south ofcentral Java coastal area

Fig. 9. Tsunami wave model at Banyuwangi southof east Java coastal area
Fig. 6. Bathymetry at south Bantul (south central
Java) and Banyuwangi (south east Java)

Fig. 10 : Run-up numerical model at Banyuwangi
south of east Java coastal area

Fig. 7. Tsunami wave model at Bantul south ofcentral Java coastal area

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GEOMATE- Osaka, Nov. 16-18, 2015

second phase of spreading started in the N–S
direction and during this period, India drifted in N–S
direction from Antarctica with a rapid speed of 11 to
7 cma21. The Indian and Australian plates merged
and formed as a single Indo-Australian plate during
the middle Eocene. The third phase of spreading
initiated in northeast (NE)–southeast (SE) direction
and appears to continue since then where Javaisland characterised with mainly North-South plate
tectonic movement and fault zones [6]-[9].
Rigidity, extent of rupture of any mega tectonic
fault, coastal deformation [10] combined withanalysis on spatial in the future would be next
importantfor coastal area risk and hazard
zonation and planning.

Fig. 11. Escape zone at the south of Banyuwangi
coastal area
Refer to the world's largest earthquakes occur along
the contact between subducting and overriding
tectonic plates in subduction zones. Rock andsediment properties near this plate interface exert
important controls on the frictional behaviour offaults and earthquake rupture dynamics. An
important material property to define along the plateinterface is the rigidity (the resistance to shear
deformation). Rigidity affects the degree of
earthquake shaking generated by a given fault
displacement through its influences on seismic wave
speed and earthquake rupture velocity. Hundreds ofsubduction zone events had been analysed from
northern Japan, the Alaska and Aleutian islands
region, Mexico, Central America, Peru and Chilesatisfying the following criteria: (1) close proximity
to the interplate contact, (2) faulting geometry
consistent with interplate thrusting, (3) moment
magnitude (Mw) between 5.0 and 7.5, and (4)availability of at least four high-quality teleseismic
digital P-wave recordings with good azimuthal
coverage and high signal to noise ratio. If stress dropis assumed constant, rigidity appears to increase
with depth in each seismogenic zone by a factor of
5 between depths of 5 and 20 km. This fact isconsistent with the hypothesis that `tsunami'
earthquakes (characterized by large slip for a given
seismic moment and slow rupture velocity) occur inregions of low rigidity at shallow depths 3 - 5m. The
rigidity trends should provide an important
constraint for future fault-zone and earthquakemodelling efforts [4]-[5].
The Indian Ocean has experienced, along with threemain phases of seafloor spreading, two major plate
reorganizations from the late Jurassic to the present.
With the first phase of spreading started in northwest(NW)–southeast (SE) direction and resulted inIndia’s movement away from Antarctic-Australia
during the early Cretaceous. During the middle
Cretaceous, it appears that the Indian plate rotatedfrom its early NW–SE to north (N)–south (S)direction and moved at a slow spreading rate. The

CONCLUSION
Based on the running of tsunami wave model
gererated, the three coastal area at the south of Java
(Serang, Bantul and Banyuwangi coastal area)
would be characterised with two up to 8 m hightsunami waves coupling with a 2m of low-water
striking to the coastal area after 60 minutes travel
time, with average of 30 minutes time-lag in
between.
ACKNOWLEDGEMENTS
The authors would like to tank to Waindo Specterra
Company who had provide some Geo-Eye satellite
data. Also special thanks to the grant from
Directorate General of Higher Education, Ministryof Research and Technology and Higher Education– Indonesia for presentation at Osaka Geomate
2015.
REFERENCES[1] Hartoko,A. Supply of Thematics Data Sets of
Coastal Resources of West Sumatra Provinces.
Report. Ministry of Marine and Fisheries,
Asian Development Bank.& Bappeda of West
Sumatra Province. 2004. Chapter IV. Pp 150 160[2] Hartoko,A and M Helmi. Tsunami Aceh : The
Role of Marine Geomatics for Science andSociety. Proceeding. Joint Working Committe
for Scientific Cooperation. Indonesia and The
Netrherlands. Open Science Meeting III.
Science and Society : New Challenges andOpportunities. 2005 pp : 225 - 230[3] Lay.Thorne, Charles J Ammon, Hiroo
Kanamori, Luis Rivera, Keith D Koper,
Alexander R Hutko. The 2009 Samoa-Tonga
Great Earthquake Triggered Doublet. Letters.
Nature. 2010. Vol. 466 : 964-968

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[4] Cyranoski,David. Worse Quake to Come,
Indonesia Warned. NATURE. 2007. Vol. 449:
270-278[5] Susan L. Bilek & Thorne Lay. Rigidity
variations with depth along interplate
megathrust faults in subduction zones. Instituteof Tectonics and Earth Sciences Department,
University of California, Santa Cruz, California
95064, USA. NATURE. 1999. Vol 400 : 443 446[6] Bemmelen, R.W., The Geology ofIndonesia. The Hague, Martinus Nijhoff,
Vol.IA. 1970. Pp : 565 - 570[7] Bhattachary, G.C and Chaubey,A.K. Western
Indian Ocean-a glimpse of the Tectonic
Scenario. The Indian Ocean – A Perspective. R
Sen Gupta and E Desa (Eds). New Dlhi India :Oxford and IBH Publishers. 2001. pp : 691-729.
[8]

[9]

Chatterjee.S, R. Bhattacharyya and T.J.
Majumdar. Generation and study of highresolution satellite gravity over the Sumatran
earthquake region. Earth Sciences andHydrology Division, Marine and Earth
Sciences Group, Remote Sensing Applications
and
Image
Processing
Area,
Space
Applications Centre (ISRO), Ahmedabad –380 015, India. International Journal of
Remote Sensing. 2007. Vol. 28 : 2915–2925[10] Subarya.Cecep, Mohamed Chlieh, Linette
Prawirodirdjo, Jean-Philippe Avouac, Yehudja
Bock, Kerry Sieh, Aron J Meltzer, Danny H
Natawidjaja, Robert McCaffrey. Plateboundary deformation associated with the great
Sumatra-Andaman earthquake. Nature. 2006.
Vol. 440 : 46-51

Ramana, M.V Khrisnha, K.S. Ramprasad,T,Desa, M. Subrahmanyam, V and Sarma,
K.V.L.N.S,. Structure and Tectonic Evolutionof the Norteastern Indian Ocean. in The Indian
Ocean - A Perspective. R Sen Gupta and E
Desa (Eds). New Dlhi India : Oxford and IBH
Publishers. 2001. pp:731-816

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Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

MODELLING OF PERMEABILITY CHARACTERISTICS OFSOIL-FLY
ASH-BENTONITE CUT-OFF WALL USING RESPONSE SURFACEMETHOD
Joenel G. Galupino and Jonathan R. Dungca2
Faculty, De La Salle University, Philippines

1,2

ABSTRACT
In order to prevent the contamination of the groundwater by restricting the horizontal movement of water, cutoff walls were recommended. Currently, one of the factors in designing cut-off walls is to provide efficient andrelatively inexpensive means of containing contaminants. In order to obtain acceptable permeability, it wassuggested to provide a mix of 96% soil and 4% bentonite in the design of cut-off walls, but bentonite is relatively
expensive, thus the viability of fly ash as a replacement for bentonite was considered. Soil mixtures were
proposed and rigorous laboratory tests was performed to determine the individual properties. Tests for specificgravity, soil index property, relative density, microscopic characterizations, elemental composition andpermeability were performed to garner data that were utilized for the model. Permeability models were generatedusing Response Surface Method. The minimum permeability requirement for the cut-off wall was achieved byproviding various mixtures for soil-bentonite-fly ash.
Keywords: Permeability, Fly Ash, Bentonite, Waste Utilization, Environment
consist of silt-sized particles and consequently
possess high permeability [7]. Tests were performed
to determine the viable mixture, and data garnered
were utilized for the permeability model.

INTRODUCTION
Nearly a decade ago, the World Bank found that
the San Mateo Landfill located in Rizal Province
and Carmona Landfill in Cavite Province of thePhilippines containing over 23 million cubic metersof corrupting waste were contaminating the groundwater of their nearby Barangays [2].
The
Philippines was given financial incentives to manage
their landfills [9] but still, these landfills still posethreat to the groundwater supply their nearby
barangays. In order to prevent the contamination of
the groundwater by restricting the horizontal
movement of water, cut-off walls wererecommended. Currently, one of the factors indesigning cut-off walls is to provide efficient andrelatively inexpensive means of containing
contaminants.
In order to obtain acceptable permeability, it wassuggested to provide a mix of 96% soil and 4%
bentonite in the design of cut-off walls [1], but
bentonite is relatively expensive, thus the viability of
fly ash as a replacement for bentonite wasconsidered. Permeability refers to the susceptibilityof a material to allow fluid to move through its pores.
In the context of soil, permeability generally relates
to the propensity of a soil to allow fluid to movethrough its void spaces [5].
It was proposed that fly ash is mixed with silty
sand since power plants discharge large amounts of
fly ash as waste but only half of them are used andthe remaining half is trashed to land and sea, its
disposal became an environmental concern. The
utilization of fly ash may be a viable alternative forporous backfill material because fly ashes generally

METHODOLOGYFly ashes generally consist of silt-sized particles
and consequently possess high permeability [7]. To
check the effect of the different soil, varying
mixtures were tested, shown on Table 1.
Table 1. Soil Mixtures
Soil
Fly AshMixture
(%)
100FA
100
75FA25S
75
50FA50S
50
25FA75S
25
100S
0
100B
0
96S4B
0
96S4FA
4
96S2B2FA
2

Soil
(%)
0
25
50
75
100
0
96
96
96

Bentonite
(%)
0
0
0
0
0
100
4
0
2

Each soil mix underwent rigorous laboratory
tests: specific gravity test based on ASTM D854 wasutilized, it is the standard of determining the densityof the soil. Atterberg limit tests based on ASTM
D4318 for determining the Liquid Limit, Plastic
Limit and the Plasticity Index of the Soil. For emax
and emin Tests and Relative Density, ASTM D4253
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GEOMATE- Osaka, Nov. 16-18, 2015

and ASTM D4254 were used to determine themaximum and minimum index densities for soils.
Particle size analysis uses ASTM D422 to determine
the percentage of different grain sizes in a soil.
The scanning electron microscopy (SEM) wasused to evaluate the microscopic characterization ofeach soil mixture. Scanning electron microscopy(SEM) with energy dispersive X-ray spectroscopy(SEM/EDX) is the best known of the surface
analytical techniques. High resolution images of
surface topography are produced using these tests.
Using the Energy Dispersive X-ray Spectroscopy(EDX), chemical composition of soil is determinedto give information on the elements present in the
soil.
Permeability of the different soil mixes were
determined by the constant head test method andfalling head test method. The direction of flow ofwater is also important, thus, vertical and horizontal
orientations of permeameter were used. A proposed
set-up for permeameter was used and modified todetermine the horizontal permeability [8] of the soil
mixtures, shown on Fig. 1. The equation utilized forthe permeability set-up is Eq. 1.
A proposed Response Surface Methodology(RSM) model based on the data garnered wasformulated. Response Surface Methodology (RSM)
modelling is a statistical process for estimating therelationships among variables. It includes manytechniques for modeling and analyzing several
variables, when the focus is on the relationshipbetween a dependent variable and one or more
independent variables.

k=Ql/Aht

where:
k = coefficient of permeability, cm/s; Q= quantity(volume) of water discharged during test, cm3; l=length between manometer outlets, cm; A = crosssectional area of specimen, cm2; h = head (differencein manometer levels) during test, cm; t = timerequired for quantity Q to be discharged during test,s.
RESULTS AND DISCUSSIONS
Physical and Chemical Properties
Using ASTM D854 the specific gravity of eachsoil blend was determined. The summary of thespecific gravity of various soil mixtures are shown
in
Table
2:
Table 2. Summary of Specific Gravity
Soil Mixture
Gs
100FA
75FA25S
50FA50S
25FA75S
100S
100B
96S4B
96S2B2FA
96S4FAThe said models were validated using Equality
Manometer

AirWater
CylinderPermeameter

Pump

(1)

Outflow
Collector

Fig. 1: Horizontal Permeability Set-up
Line and other Models. Validation using equalityline usually involves a 45-degree line as a guidelinethat provides insight into the measured variables andas a critical part of the analysis. When the data arenear the 45-degree line, this means that the residualis small and the predicted coefficient of permeability
is near the measured coefficient of permeability.

The specific gravity of the soil mixes was reducedby the addition of fly ash [6] since the usual of thespecific gravity of fly ash is low. With the results
shown on Table 1, the addition of fly ash reduces the
specific gravity of a soil mixture, this is due to thelight weight property of fly ash.
ASTM D4253 and ASTM D4254 were used todetermine the maximum and minimum void ratios ofthe different mixes.
It can be noticed from Table 3, the Maximum
Void Ratio (e max ) ranges from 1.78 to 1.99 because
the fine contents of the fly ash contributed to thepercentage of voids. 100S has the lowest value while
100FA has the highest, also from Table 3, 100S has
the lowest fines content, while 100FA garners thehighest. Their fines content and microfabric may
have contributed to the minimum and maximum
void ratio.
Table 3. Summary of e min and e max
Soil Mixture
e min
100S
0.84
100FA
0.27
100B
0.36
96S4B
0.8
50FA50S
0.47
75FA25S
0.37

715

2.02
2.11
2.31
2.49
2.58
2.75
2.61
2.6
2.52

e max
1.78
1.99
1.98
1.80
1.94
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GEOMATE- Osaka, Nov. 16-18, 2015

smaller particles tend to fill the voids created by thelarger particles shown in the figure, thus creating asmaller inter-particle void. Looking closer tomagnification of 1000x and 5000x, strand-likeparticles are present, his indicates that theseelongated particles also fill the voids, giving small
passageways for water to permeate.

Soil Mixture
e min
e max
25FA75S
0.72
1.93
96S4FA
0.76
1.80
96S2B2FA
0.78
1.81
These minimum and maximum void ratios
together with the target relative density of 90% wereused to determine the void ratio to be utilized for thepermeability specimens.
Table 4. Summary of Particle Size Analysis Results%
Soil
Passing
D 10
D 30
D 60
Mixture
#200
100S
21.84
0.01
0.4
1.2
100F
61.83
0.029
0.03
0.04
100B
58.36
0.0022 0.0055 0.032
96S4B
29.33
0.018
0.043
0.125
50FA50S
29.79
0.032 0.0375
0.12
75FA25S
50.78
0.019
0.032
0.06
25FA75S
25.79
0.015
0.042
0.15
96S4FA
22.27
0.035
0.09
0.13
96S2B2FA 23.82
0.03
0.08
0.25

Fig. 2. Microfabric of 100S (5000x, 1000x and 500x
Magnification)
As shown in Fig. 3, with 500x magnification for
fly ash, it is a combination of larger silt grains andsmaller silt grains to form the micro fabric. Fly ash
is a silt thus normally 0.002-0.05 mm in size. As
seen on the 500x magnification, particles havealmost similar size, forming larger inter-particle void,
compared with silty sand and bentonite, to allow
water to pass through. On the 1000x and 5000x
magnification, the surface of the particle is not
smooth, this create passageway/voids for water topass through.

Summary of results from the particle size analyses
are shown on Table 4. 100FA has the greatest
percentage of fines compared with other blends. Fly
ash and soil are considered fines but theclassification differ, fly ash is silt and soil is plastic.
It can also be noticed that mixing fly ash with othersoils increases the fines content.
In the Energy Dispersive X-ray Spectroscopy(EDX), chemical composition of soil is determinedto give information on the element present in the soil.
Oxygen (O) is very abundant, followed by Silicon(for Silty Sand) and Calcium (for Fly Ash). Silicon
and Calcium are predominant in the soil elemental
composition. Due to the presence of Oxygen andother dominant elements: Silica (from Silicon), Lime(from Calcium) and Alumina (from Aluminum) are
the dominant minerals in the soil sample.
Most of the soil properties and characteristics
like strength, compressibility and permeability are
ascribed by its microfabric or microstructure. The
scanning electron microscopy (SEM) was used toevaluate the microfabric of soil, fly ash andbentonite. Scanning electron microscopy (SEM)with energy dispersive X-ray spectroscopy(SEM/EDX) is the best known of the surface
analytical techniques. High resolution images of
surface topography, are produced using these tests.
Pure soils were initially tested to check theirmicroscopic characteristics, mixed soils were also
tested thereafter.
As shown in Fig. 2, with 500x magnification for
100S, it is a combination of extremely strandy grains,large angular grains and abundant silt grains formed
the micro fabric. The silt grains have a rough surface.
The particles are well-graded microscopically. The

Fig. 3. Microfabric of 100FA (5000x, 1000x and
500x Magnification)

As shown in Fig. 4, with 500x magnification for

Fig. 4. Microfabric of 50FA50S (5000x, 1000x and
500x Magnification)
50FA50S, it is a combination of extremely strandy
grains, large angular grains and abundant larger silt
grains and smaller silt grains formed the micro
fabric. The silt grains have a rough surface. Looking
closer to magnification of 1000x and 5000x, strandlike particles are present but noy prevalent comparedwith the pure soil, the soil particles may contributeto the reduction of permeability but the silt grains of
fly ash will counteract to allow water to drain faster.
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GEOMATE- Osaka, Nov. 16-18, 2015

As shown in Figure 5, with 500x magnification for
96S2F2B, it is a combination of extremely strandy
grains, large angular grains, silt grains and elongatedsmooth grains formed the micro fabric. The particles
are still well-graded microscopically. Looking closer
to magnification of 1000x and 5000x, strand-likeparticles are present, this indicates that theseelongated particles also fill the voids, giving small
passageways for water to permeate. Also the smoothsurface of bentonite particles gave a smaller interparticle-void which the permeability is reduced but
counter-acted by the presence of fly ash’s silt grains
which contributed to additional drainage.

Soil Mixture
50FA50S
25FA75S
100S
100B
96S4B
96S2B2FA
96S4FAMin K, cm/s
3.70E-05
3.39E-05
2.25E-05
1.30E-08
1.65E-07
8.04E-07
2.52E-05

Max K, cm/s
4.34E-05
3.49E-05
2.66E-05
3.53E-08
2.72E-07
9.87 E-07
2.70E-05

The horizontal permeability of the various soil
mixtures is important because it can discerned how
long the contaminated water penetrated in thehorizontal direction. Shown in Table 6, are the rangeof permeability values gathered for the horizontal
oriented constant head permeability test.
It can also be noticed that the horizontal
permeability values are larger than the verticalpermeability values. This agrees with the collected
data of Das (2008), where he stated that thehorizontal permeability is always larger than thevertical permeability. This is due to the pressure
head induced during the permeability test. The
specimen is laid in a horizontal position, which
experiences no pressure drop within its body, unlike
the vertical specimen, which experiences pressure
drop, resulting to a slower flow of water.
The permeability of silty sand ranges: (1) verticaloriented 1.47x10-05 cm/s to 2.09x10-05 cm/s and (2)horizontal oriented 2.21x10-05 cm/s to 2.70x10-05
cm/s. 100S’ microfabric having a combination of
extremely strandy grains, large angular grains andabundant rough-surfaced silt grains contributed tothe drainage.
Fly ash is the recommended addition to the soil
mixtures since waste materials are aimed to be
utilized and the addition of fly ash to soils changes
the inter-particle void ratio [6], which is prevalent to
the microscopic characterization test for 100F.
It is a combination of larger silt grains and smaller
silt grains to form the micro fabric. Silt particles
have almost similar size, forming larger interparticle void, contributing to a much larger interparticle voids. Due to a larger inter-particle voids,
the permeability of pure fly-ash ranges: (1) verticaloriented 4.51x10-05 cm/s to 5.35x10-05 cm/s and (2)horizontal oriented 1.93x10-05 cm/s to 7.29x10-05
cm/s.
Bentonite has a hydraulic conductivity of 1x106
cm/s to 1x10-12 cm/s (IBECO, 1998). Its
microfabric usually composed of a combination ofsmooth elongated grains and smaller grains, thus,
smaller inter-particle voids are present. In the study
permeability of pure bentonite ranges: (1) verticaloriented 6.13x10-09 cm/s to 1.97x10-08 cm/s and (2)horizontal oriented 1.30x10-08 cm/s to 3.30x1008
cm/s. Bentonite is a viable candidate for the cutoff wall since it attained the minimum permeability
requirement of 9.9x10-7 cm/s for a cut-off wall. The
price of 100B is relatively high that is why pureFig. 5. Microfabric of 96S2F2B (5000x, 1000x and
500x Magnification)
Permeability Characteristics
A proposed approach in determining the verticalpermeability of the various soil mixtures was
utilized, it was referred on the study of Smith (2010)
and was modified. Shown in Table 5, are the rangeof permeability values gathered for the verticaloriented constant head permeability test.
Table 5. Range of permeability values for verticaloriented permeability test
Soil Mixture
Min K, cm/s
Max K, cm/s
100FA
4.51E-05
5.35E-05
75FA25S
2.93E-05
3.97E-05
50FA50S
2.81E-05
2.98E-05
25FA75S
2.05E-05
2.50E-05
100S
1.66E-05
1.90E-05
100B
6.13E-09
2.48E-08
96S4B
1.16E-07
2.98E-07
96S2B2FA
6.90E-07
7.79E-07
96S4FA
1.93E-05
2.40E-05
It is prevalent that the permeability is increased
when the amount of fly ash is increased. It now
agrees with the study of Prashanth (2001) that fly
ashes generally consist of silt-sized particles andconsequently possess high permeability. Thus, theamount of fly ash increase the permeability of thesoil mixes.
Table 6. Range of permeability values for horizontal
oriented permeability test
Soil Mixture
Min K, cm/s
Max K, cm/s
100FA
6.15E-05
7.29E-05
75FA25S
4.19E-05
5.46E-05
717

GEOMATE- Osaka, Nov. 16-18, 2015

bentonite is not recommended for a cut-off wall.
96S2B2FA was the soil mix proposed in the study,
its permeability ranges: (1) vertical oriented
6.29x10-07 cm/s to 9.60x10-07 cm/s and (2) horizontal
oriented 8.04x10-07 cm/s to 9.87x10-07 cm/s. The
main objective of the study is to determine the mostviable permeability characteristic of the various soil
mixes of soil, fly ash and bentonite for cut-off wall.
Thus, this mixture of soil, fly ash and bentonite is a
viable candidate for the cut-off wall. The addition of
fly ash may reduce the permeability of the 96S4B
mixture, but still, incorporating the 2% fly ash as thereplacement of bentonite, the minimum requirementof 9.9x10-7 cm/s for the permeability of cut-off wallis still attained.

Fig. 7. RSM Model for Kh
The models utilize the percentage of fly ash,bentonite and soil as the independent variables,
while the vertical and horizontal permeabilities, kv
and kh, are the dependent variables, respectively.
These models can predict the permeability (verticalor horizontal oriented) of any soil-fly ash-bentonite
mix, once the percentages of fly ash, soil andbentonite are available.

Response Surface Methodology (RSM) Model
To check the effect of fly ash and bentonite when
added to soil, the mixtures were tested. Theirpermeability values were used to generate RSM
models. The said models were able to establish arelationship between the percentage of fly ash,bentonite, soil and permeability. The delineated
RSM models are shown on Figs. 6 and 7.
It can be noticed that the RSM models follow thetrend that was observed with the experimental valuesof the soil-fly ash-bentonite mixtures - because of
the silty property of fly ash, once it is increased, thedrainage is also increased. The increase in drainage
is due to its microfabric, which is a combination of
extremely strandy grains, large angular grains andabundant larger rough-surfaced silt grains andsmaller rough-surfaced silt grains that contributes toa much larger inter-particle void.

𝒌𝒌𝒗𝒗 = 𝒆𝒆𝒆𝒆𝒆𝒆−𝟏𝟏𝟏𝟏.𝟗𝟗𝟗𝟗𝟗𝟗∗%𝑺𝑺 ∗ 𝒆𝒆𝒆𝒆𝒆𝒆−𝟏𝟏𝟏𝟏.𝟕𝟕𝟕𝟕𝟕𝟕∗%𝑭𝑭𝑭𝑭 ∗𝒆𝒆𝒆𝒆𝒆𝒆−𝟏𝟏𝟏𝟏𝟏𝟏.𝟔𝟔𝟔𝟔𝟔𝟔∗%𝑩𝑩

(2)
(3)

𝒌𝒌𝒉𝒉 = 𝒆𝒆𝒆𝒆𝒆𝒆−𝟏𝟏𝟏𝟏.𝟔𝟔𝟔𝟔𝟔𝟔∗%𝑺𝑺 ∗ 𝒆𝒆𝒆𝒆𝒆𝒆−𝟏𝟏𝟏𝟏.𝟏𝟏𝟏𝟏𝟏𝟏∗%𝑭𝑭𝑭𝑭 ∗𝒆𝒆𝒆𝒆𝒆𝒆−𝟏𝟏𝟏𝟏𝟏𝟏.𝟖𝟖𝟖𝟖𝟖𝟖∗%𝑩𝑩

where:
kv = vertical permeability, cm/sec; kh = horizontal
permeability, cm/sec; %S = percentage ofsoil;%FA = Percentage of fly ash; %B =Percentage of bentonite.
Validation
To check the Experimental Data vs. RSM Model,
the measured Coefficients of Permeability for eachsoil mix were compared with the predicted
Coefficient of Permeability of RSM Model.
×10 ]
-5

7

Predicted Kv, cm/s

6

Fig. 6. RSM Model for Kv

5
4
3
2
2

3
4
5
Experimental Kv, cm/s

Legend:Kv, cm/s
Equality Line

6

7
[×10-5]

Fig. 8. Regression Model Equality Line for Kv

718

GEOMATE- Osaka, Nov. 16-18, 2015

×10 ]
-5

8.04x10-07 cm/s to 9.87x10-07 cm/s which isrelatively above the minimum requirement. Thus,
96S2B2FA is a viable mixture for the cut-off wall.

7

Predicted Kh, cm/s

65

ACKNOWLEDGEMENTS

4

The researchers would like to express their
gratitude to those individuals who helped themaccomplish this research: Dr. Mary Ann Q. Adajar,
Dr. Lessandro Estelito O. Garciano, Dr. Bernardo A.
Lejano, Engr. Irene Ubay-Anongphouth, Engr. King
Anongphouth, Dr. Renan T. Tanhueco, Engr. Erica
Elice S. Uy, Engr. Patrick B. Taclibon, Engr. Edrick
P. Lim, Ms. Ma. Dianne J. Buagas, Ms. Ioulany
Nayre, Engr. Jamiel L. Jayme, Engr. Daniel Valerio,
Engr. Jason Maximino C. Ongpeng, Engr. Raymund
P. Abad, Engr. Melody S. Doliente, Mr. Antonio
Kalaw, and Mr. Miller Cutora for their guidance,
support and generosity.

3
2
2

3
4
5
Experimental Kh cm/s

Legend:Kh, cm/s
Equality Line

6

7
[×10-5]

Fig. 9. Regression Model Equality Line for Kh
A line that shows equality between the variablemeasured (Experimental Data) on the horizontal axisof a diagram and the variable predicted (RSM Model
Data) on the vertical axis. The equality line graph isshown on Fig. 8 and 9.Furthermore, the capability ofour proposed Regression model of permeability may
be validated by various references.

REFERENCES[1] Baxter, D. Y. (2000, April 14). Mechanical behaviorof soil-bentonite cut-off wall. Virginia Polytechnic
Institute and Stat University, Department of Civil
Engineering. Blacksburg, Va.
[2] Caplano, J. (2001, December 30). Closed landfills
contaminating groundwater, World Bank warns.
(Philippine Star) Retrieved November 2013, from
Philippine
Star:
http://www.philstar.com/nation/145333/closedlandfills-contaminating-groundwater-world-bankwarnsAnnnn B, “Unpublished work but accepted”,
accepted, Year.
[3] Das, B. M. (2008). Advanced soil mechanics. New
York: Taylor & Francis.
[4] IBECO. (1998). Bentonit-Technologie. Bentonit im
Tiefbau GmbH, 1.
[5] Liu, C., & Evett, J. B. (2000). Soil Properties: Testing,
Measurement and Evaluation. (E. Francis, Ed.)
Charlotte, North Carolina, USA: Prentice Hall.
[6] Prabakar, J., Dendorkar, N., & Morchhale, R. (2004).
Influence of fly ash on strength behavior of typicalsoils. Construction and Building Materials, 18, 263267.
[7] Prashanth, J., Sivapullaiah, P., & Sridharan, A.
(2001). Pozzolanic fly ash as a hydraulic barrier inlandfills. Engineering Geology, 60, 245-252.
[8] Smith, C. B. (2010). Horizontal Permeameter. Magna
Cum Laude Honors Research Project, University of
Florida, Department of Civil and Coastal Engineering,
Gainesville, Florida.
[9] World Bank. (2012, October 24). Philippines:
Toward Greener Waste Management. Retrieved
November
2013,
from
World
Bank:
http://www.worldbank.org/en/news/pressrelease/2012/10/24/philippines-toward-greenerwaste-management

CONCLUSION
Each soil mixture underwent rigorous
laboratory tests such as specific gravity tests,
particle size analysis, Atterberg limit tests (liquid
limit and plastic limit), relative density tests (e max
and e min tests) and permeability tests to determine
the most viable permeability characteristic of thevarious soil mixes of soil, fly ash and bentonite forcut-off wall.
As a criterion in selecting the viable mixture forthe cut-off wall, it was recommended that theminimum permeability requirement of x10-7 cm/s fora cut-off wall. Baxter (2004) suggested to provide amix of 96% soil and 4% bentonite (96S4B) in thedesign of cut-off walls, but bentonite is relatively
expensive, thus the viability of fly ash as areplacement for bentonite was considered. Fly ash is
the recommended addition to the soil mixtures, sincewaste materials are aimed to be utilized. But theaddition of fly ash to soils changes the inter-particle
void ratio [6], it increases the permeability, thus, themicroscopic characteristics of the soil mixtures may
contribute to the increase in permeability.
Since, 96S4B’s attained permeability is above
the minimum required value, fly ash wasincorporated in the mix. Fly ash may increase thedrainage but a certain amount of fly ash can be
added and still attaining the minimum required
permeability [1]. 96S2B2FA was tested, it uses 2%
fly ash, which replaces half of the recommended
bentonite percentage of Baxter (2004). 96S2B2FA’s
permeability ranges: (1) vertical oriented 6.29x10-07
cm/s to 9.60x10-07 cm/s and (2) horizontal oriented719

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

VEGETATION RECOVERY PROCESS ON LANDSLIDE STEEP SLOPE
AFTER Alnus sieboldiana AND Miscanthus condensatus PLANTING
WITH SIMPLE TERRACING WORK IN MIKURA-JIMA ISLAND,
JAPAN
Tetsuo Okano and Teruo Arase
Faculty of Agriculture, Shinshu University, Japan
ABSTRACT
Typhoon 9512 hit the island of Mikura-jima in 1995, causing many landslides and destruction of forest
vegetation. For long-term observation of vegetation change, a research plot was established on a landslide slope
in 2003. A simple terrace was built on this slope, which was planted with native species (Alnus sieboldiana andMiscanthus condensatus) in 2000 to accelerate vegetation recovery. From 2003 to 2012, a vegetation survey ofthe plot was conducted to elucidate the vegetation recovery process at an early stage. A. sieboldiana grew steadily
and its density of individuals decreased. The number of species gradually increased due to invasion of new
seedlings from adjacent forests, i.e. Castanopsis cuspidata, Persea thunbergii. We found some differences invegetation recovery from another research site, which is a landslide slope seeded with exotic pasture grasses byhelicopter in 2002. Seeding of exotic pasture gasses prevented the establishment of trees and reduces successional
velocity.
Keywords: Vegetation recovery, Species composition, Species diversity, Exotic pasture grasses
transplant locality-certified seedlings of native
species (A. sieboldiana and M. condensatus) [6]. On
some landslide slopes, exotic pasture grasses wereseeded by helicopter after the transplanting of these
two species.
Seeding of herbaceous species, especially exotic
pasture grasses, forms a dense, carpet-likecommunity in the short term, and this community
prevents the germination and the growth of tree
species [7-9]. However, there are few studies of theinfluence of seeding of herbaceous species on
ecological succession in isolated ecosystems. In thispaper, we show clearly the effect of seeding of exotic
pasture grasses on ecological succession in a mature
natural forest area with data from 10 years ofcontinuous monitoring.

INTRODUCTION
Revegetation in isolated ecosystems should be
carried out in ways that restore and do not disrupt thenative ecosystem. In an insular ecosystem, habitat for
wildlife can easily be destroyed due to disaster ordevelopment. Island ecosystems are generally rich inendemic species but tend to be homogeneous in
habitat, making them vulnerable to invading species[1-3].
Mikura-jima island is 20.58 km2 in area, locatedca. 180 km south of Tokyo, and is one of the isolated
volcanic islands of the Izu islands in the Pacific
Ocean off the main island of Japan. The island has sea
cliffs more than 100 m in height and steep slopes lessthan 700 m above sea level. The highest peak, Mt.
Oyama, is 850 m. The annual mean temperature is
17.9 ﾟ C and the annual precipitation is 2910 mm [3].
It is covered with natural or secondary vegetation.
The main vegetation of the island is an evergreen
broad-leaved forest, Carci-Castanopsietum sieboldii(Arachniodo-Castanopietum sieboldii [4]) below 500
m and Daphniphyllo-Trichodendretum aralioideae
above 500 m [5].
In September 1995, Typhoon 9512 hit Mikurajima island (Fig. 1) and produced many landslide
slopes in mature forests. The bedrock and solid soil
were exposed on the landslide slopes from the surface
soil being washed away. After the typhoon,
vegetation recovery efforts were begun with projects
to put in simple terracing to retain the soil and to

Fig. 1 Path of Typhoon 9512 [3] and location of
Mikura-jima island.
720

GEOMATE- Osaka, Nov. 16-18, 2015

METHODS
Sites and monitoring plots
Two study sites were selected to examine theinfluence of exotic pasture grass seeding on
ecological succession on landslide slopes. One was
Ofunato No. 2 slope and the other was Torino-o No.
3 slope. Both slopes are located at an elevation
ranging from 500 to 550 m above sea level. Slope
direction and average angle are respectively NW and
37ﾟ for Ofunato No. 2, and N and 27 ﾟ for Torino-o
No. 3. The distance between the two sites is about 1
km.
On both slopes, landslides were caused by
Typhoon 9512 (precipitation: 648.5 mm, maximum
wind velocity: 67.8 m/s) in 1995. From 2000 to 2002,
simple terracing was placed and A. sieboldiana andM. condensatus seedlings were transplanted to eachslope. Planting densities of these species were 1.0
seedling per square meter for A. sieboldiana and 1.5
stumps per square meter for M. condensatus. In 2002,
exotic pasture grasses (including Agrostis stolonifera,
Festuca sp. and Lespedeza cuneata) were seeded on
the Torino-o No. 3 slope.
In August 2003, two monitoring plots were
established in Ofunato No. 2. One was on the upper
part of the slope, the other one was on the lower partof the slope, with a plot size of 62.3 m2 and 84.0 m2,
respectively. In August 2004, one monitoring plot
was established in Torino-o No. 3. Plot size was 47.15
m2. In subsequent years, growth and species
composition were investigated in summer (earlyAugust).

Fig. 2 Location of landslide slopes formed by
Typhoon 9512 on Mikura-jima island.
emergence pattern was investigated thoroughly foreach species, and all species were divided into several
groups. Tree species diversity was indicated by the
Shannon diversity index (H’) [11] as follows:H’= - SUM[(pi)･ln (pi)](2)

where SUM is a summation and pi is the proportionof the total number of individuals that is represented
by species i. Based on the characterized species
groups and the diversity index, differences in thechange in species composition over ten years wasclearly observed between Ofunato No. 2 and Torinoo No. 3, indicating an influence of exotic pasturegrasses on ecological succession.
RESULTS

Growth of A. sieboldiana

Growth of A. sieboldiana

Transplanted A. sieboldiana was measured forplant height (H) and diameter at the base (D0), and theparameter D02H was calculated as an estimate ofaboveground biomass. Since self-thinning wasobserved, we estimated the regression as follows:

Figure 3 shows the growth of A. sieboldiana overa period of 9 years. In each of the Ofunato and Torinoo plots, a phenomenon resembling self-thinning was
confirmed for A. sieboldiana: the mean plant density
decreased yearly, while individual D02H values
increased. Although the mean plant density seemed to
be greater and individual D02H values smaller in 2004
in the Torino-o plot than the Ofunato plots, theprogress of values tended to converge after several
years. Analysis of co-variance detected no significant
differences in the regression coefficients (F-test, p =0.113 and 0.867 for a and b, respectively) among thethree plots.

1/x = a +by(1)

where x is the tree density, y is the mean individual
D02H, and a and b are constants [10].
Species composition and diversity
To assess species composition, invading andregenerated trees more than 5 cm in height were
investigated for each species. For ten years (Ofunato
No. 2: 2003–2012, Torino-o No.3: 2004–2013), theDensity and number of species of invading trees
Figure 4 shows the density per 100 m2 of the721

GEOMATE- Osaka, Nov. 16-18, 2015

1400

Tree density (individuals per 100 m2)

100000

Stem volume (cm3)

10000

1000

100

10

1200
1000
800
600
400
200

1
0.1

1

0

10

2003

Tree density (individuals per m2)

2005

2007

2009

2011

2013

Year
Fig. 4 Changes in the density of invading trees in eachplot. Solid and open circles indicate upper andlower plot of Ofunato. Solid triangles indicate
Torino-o plot.

Fig. 3 Changes in the density and stem volume perindividual of transplanted Alnus sieboldiana.
Solid and open circles indicate upper and lower
plot of Ofunato. Solid triangle indicates Torinoo plot.

ing on species. Group D (4 species) did not show any
obvious tendencies.
At the Ofunato site, 13 species were included inGroup A. Nine of these species were also growing at
the Torino-o site, but only two species (E. japonica
and Trachelospermum asiaticum var. intermedium)
were included in the same group at both sites; otherspecies, Hydrangea involucrata f. idzuensis, Rubus
trifidus and Stachyurus praecox var. matsuzakii
belonged to Group C, and P. thunbergii, Rubus

invading trees over a period of 10 years in each plot.
Until 2010, the density increased in all plots. In thelower plot of the Ofunato site, the density was higher
than in the upper plot. In 2012, the density of bothplots was similar, ca. 1200/100 m2. On the other hand,
until 2012, the density in the Torino-o plot was about
half that in the upper plot of the Ofunato site, although
the density increased. After that, the density
decreased.
Figure 5 shows the number of species per plot of
invading trees over a period of 10 years in each plot.
The number of species indicates the species richness.
These numbers increased with small fluctuations. The
species richness in the Torino-o plot were about halfthat of the lower plot of Ofunato site. The species
richness of the upper plot of the Ofunato site was in
between that of the lower plot and Torino-o plot.

Number of species ( no. per plot)

30

Species composition and diversity
Table 1 shows the species composition, thenumber of individuals in the study period from 2003
to 2013 and the species groups of the invading trees
at each site. Thirty-nine invading tree species wereconfirmed and divided into four groups. Species ofGroup A (13 species) were present from the first year
and had a tendency to increase in number yearly.
Species of Group B (18 species) appeared aftermonitoring began. Group C (4 species) varied intendency, increasing or decreasing in number depend-

25
20
15
10
5
0
2003

2005

2007

2009

2011

2013

Year
Fig. 5 Changes in the number of tree species in eachplot. Solid and open circles indicate upper andlower plot of Ofunato. Solid triangles indicate
Torino-o plot.
722

GEOMATE- Osaka, Nov. 16-18, 2015

Table 1 Individual density of invading trees and species groups for each species from 2003 to 2013.
Species
Castanopsis cuspidata var. sieboldii
Morus kagayamai
Persea thunbergii

Species group
Ofunato Torino-oAADA

Hydrangea involucrata f. idzuensis

A

C

Rubus trifidus

A

C

Rubus ribesioideus

A

D

Prunus lannesiana var. speciosa

A

D

Mallotus japonicus
Eurya japonica

A
A

A

Stachyurus praecox var. matsuzakii

A

C

Styrax japonica var. jippei-kawamurae
Trachelospermum asiaticum var. intermedium

A
A

A

Callicarapa japonica var. luxurians

A

D

Podocarpus macrophullus
Ficus erecta
Akebia trifoliata
Rubus buergeri

B
B
B
B

B

Ilex crenata var. hachijoensis

B

B

Ilex integra Thunb.
Microtropis japonica
Euonymus japonicus
Elaeocarpus sylvestris var. ellipticus
Camellia japonica

B

B

B
B

Cleyera japonica
Dendropanax trifidus
Cornus controversa
Ardisia japonica

B
B
B
B

B

Ardisia crenata

B

B

Symplocos prunifolia
Osmanthus insularis
Clerodendrum izuinsulae
Alnus sieboldiana

B
B
C

B
C

Hydrangea macrophylla f. normalis

C

C

Hydrangea involucrata
Weigela coraeensis var. fragransC
C

Illicium religiosum
Buxus microphylla f. major
Elaeagnus macrophylla
Damnacanthus major

D
D
D

B
B

C

D

Site
O
O
O
T
O
T
O
T
O
T
O
T
O
O
T
O
T
O
O
T
O
T
O
O
O
O
T
O
T
O
T
T
O
O
T
O
O
O
O
T
O
T
O
O
T
O
T
O
T
O
O
T
O
O
O
T

2003
2
1
2
4
7
25
3
7
19
1
1
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
11
11
0
13
0
1
1

04
2
0
6
0
3
0
11
6
27
6
4
2
6
26
21
2
0
1
3
0
3
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
17
0
17
0
2
24
0
0
2
1
4

05
2
0
4
0
2
0
35
23
41
6
10
2
12
53
55
1
0
1
7
8
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
188
2
30
0
3
49
0
0
1
1
11

06
1
1
4
0
5
0
50
30
64
15
12
0
12
73
66
8
0
1
10
11
5
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
266
4
83
13
2
91
0
0
2
1
6

07
1
0
13
0
14
0
31
28
151
2
16
0
11
72
72
7
0
2
11
4
2
0
1
0
0
0
21
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
195
8
100
53
1
84
0
0
2
2
2

Year
08
2
0
12
0
24
2
65
59
217
4
26
0
14
107
91
8
2
3
23
23
3
2
1
0
0
1
21
0
0
0
0
2
1
0
0
1
0
0
0
0
0
0
0
1
0
160
11
141
123
2
110
0
1
1
1
0

09
2
1
11
0
37
15
50
59
244
4
37
0
14
154
151
7
4
2
23
8
4
2
0
0
0
0
34
0
0
0
0
2
1
0
0
0
0
0
0
0
0
0
0
1
0
147
8
127
125
0
83
4
0
1
1
0

10
1
1
12
0
47
15
60
49
305
8
35
0
20
190
210
9
6
3
27
36
5
2
1
0
0
1
19
0
0
0
0
2
1
1
0
0
0
0
0
0
0
0
0
1
0
82
0
159
168
0
76
0
0
1
2
4

11
2
4
13
0
59
25
65
51
297
6
41
0
26
198
288
11
4
5
79
93
10
2
1
0
0
4
51
2
0
1
0
2
1
1
0
0
0
1
0
0
1
2
1
1
0
51
0
155
131
0
64
0
0
1
2
6

12
8
4
16
2
86
15
69
38
290
21
38
0
28
238
299
18
4
4
93
68
8
2
3
1
0
3
108
2
2
0
2
2
1
2
2
1
3
0
1
2
6
2
1
19
0
32
0
138
112
0
58
0
0
1
1
6

13

0
15
32
13
2
246
4
91
2

153
4
2
2
0

0
0
2
0
68
0

6

Site O: Ofunato, T: Torino-o.
ribesioideus, Prunus lannesiana var. speciosa andCallicarpa japonica var. luxurians belonged toGroup D. Group B included 18 species (Ofunato: 15
species, Torino-o: 8 species). Most species of thisgroup germinated in 2011 or 2012; only 5 species
were found at both sites, Rubus buergeri, Ilex crenata
var. hachijoensis, C. japonica, Ardisia japonica andArdisia crenata. Group C included 4 species(Ofunato: 4 species, Torino-o: 3 species). A clear
peak in the number of A. sieboldiana was noted onlyat the Ofunato site in 2006. A peak in the individual
density of Hydrangea macrophylla f. normalis wasnoted in 2010 at both sites. A clear peak of Weigela
coraeensis var. fragrans density was noted only at the
Ofunato site in 2008. Group D included 4 species(Ofunato: 3 species, Torino-o: 1 species). Fluctuationin the density of Buxus microphylla f. major was
small and constant after 2008, whereas the density of
Elaeagnus macrophylla and Damnacanthus major
continued to fluctuate.
723

GEOMATE- Osaka, Nov. 16-18, 2015

was lower than in other plots (Figure 4). The coverage
percentage of exotic pasture grasses was more than
80% in 2004. The percentage decreased rapidly toless than 40% by 2008 and to less than 5% in 2011[3]. This phenomenon indicated that interference by
exotic pasture grasses didn’t affect the germination of
invading seeds or growth of seedlings after 2008. On
the other hand, mean plant height of M. condensatus
increased and tended toward a plateau in 2008 (ca.
170–200 cm) [3]. Therefore, the establishment ofinvading trees might be prevented by existing exotic
pasture grass communities in the initial stage, and by
M. condensatus communities in the next stage. The
extent of changes in number of species was similar tothe changes in invading tree density in the survey
plots. The increasing number of species might beaffected by the community dynamics of exotic
pasture grasses and M. condensatus.
Species composition could be characterized bydefining A, B and C species groups (Table 1). C.
cuspidata var. sieboldii, P. thunbergii, E. japonica,
Styrax japonica var. jippei-kawamurae and T.
asiaticum var. intermedium, assigned to Group A in
the Ofunato plot, were more frequent tree species inmature natural forests on Mikura-jima island [5], [13],[14]. The former three species in particular wereabundant in mature forest stands adjacent to the
Ofunato and Torino-o landslide slope sites. Other
species in this group were mid-successional
deciduous trees or shrubs with a wide distribution on
Mikura-jima island. H. involucrata f. idzuensis, R.
trifidus and S. praecox var. matsuzakii increased andoften later decreased in individual density at theTorino-o site. The density of P. thunbergii, R.
ribesioideus, P. lannesiana var. speciosa, and C.
japonica var. luxurians were lower at the Torino-osite than the Ofunato site. The characteristic behaviorof these seven species suggests that the expansion of
exotic pasture grasses or the M. condensatuscommunity prevented germination or growth of theinvading tree species. Most of the species in Group B
were evergreen broad-leaved and late-successional
species occurring in mature natural forests on
Mikura-jima island. The number of species at theTorino-o site (8 species) was smaller than at the
Ofunato site (15 species). The difference of species
composition between plots may be relate to the speedof ecological succession. At the present time, thesuccessional change to the mature stage at the Torinoo site will be slower than at the Ofunato site. It is
expected that the number of species represented inGroup C, including A. sieboldiana, will decrease

2.6
2.4

H' value

2.2
2.0
1.8
1.6
1.4
1.2
1.0
2003

2005

2007

2009

2011

2013

Year
Fig. 6 Changes in tree species diversity (H’) in eachplot. Solid and open circles indicate upper andlower plot of Ofunato. Solid triangles indicate
Torino-o plot.
Tree species diversity
Figure 6 shows the tree species diversity (H’) overa period of 10 years in each plot. H’ values of all plots
decreased in 2005. This phenomenon was remarkablein the upper plot of the Ofunato site. After that, H’values of all plots increased with small fluctuations.
In 2012, the Ofunato plots reached H’ values greaterthan 2.2, but the value for Torino-o was less than 1.8.
Therefore, tree species diversity of recovered
vegetation at the Ofunato site was higher than at theTorino-o site.
DISCUSSION
For A. sieboldiana, the tree density decreased
yearly, while the individual D02H values increased(Figure 3). Although the mean plant density seemed
to be greater and the individual D02H value smaller in2004 in the Torino-o plot than the Ofunato plots,these values tended to converge after several years.
There are allometric relationships between stem sizeand biomass of each organ (stem, branch and leaf) in
A. sieboldiana [12]. The dry weight of leaves per unit
area of A. sieboldiana in each plot would be nearly
equal because no significant differences were noticed
among plots in tree density and individual D02H
values. Therefore, we concluded that the lightenvironment under A. sieboldiana canopies did not
differ remarkably among the three plots.
The density of invading trees in the three plots
increased yearly, but the density in the Torino-o plot
724

GEOMATE- Osaka, Nov. 16-18, 2015

because these are pioneer species.
The trend in tree species diversity on bothlandslide slopes was similar to changes in the tree
density and the number of species. However, the H’values of all plots decreased remarkably in the
Ofunato plots in 2005 (Figure 6), although thenumber of species increased. This decrease wascaused by a change in evenness of the species
composition. Due to a rapid increase in the number ofindividual A. sieboldiana seedlings, evenness
declined.

arboreous plant community by deeding method”,
J. of the Japanese Society of Revegetation
Technology, Vol. 24, Nov. 1998, 90-98.
[8] Arase T, Okano T, Kimura T and Inoue S,“Influence of seedling exotic pasture grasses on
the vegetation recovery of landslide slopes
formed by typhoon 9512 in Mikura-jima Island,
Izu Islands, Japan”, J. of the Japanese Society ofRevegetation Technology, Vol. 35, Feb., 2010, pp.
448-461.
[9] Holl KD, Loik ME, Lin EH and Samuels IA,“Tropical montane forest restoration in Costa
Rica: overcoming barriers to dispersal andestablishment”, Restoration Ecology, Vol. 8, 2000,
pp.339-349.
[10] Yoda K, Forest ecology. Tokyo: Tsukiji Shokan,
1971, 331 pp.
[11] Shannon CE, “A mathematical theory ofcommunication”, Bell System Technical Journal,
Vol. 27, 1948, pp. 379-423.
[12] Saito H, “Some features of dry-matterproduction in a young Alnus siebodiana
community on dumped detritus with specialreference to reproductive parts”, Japanese J. ofForest Society, Vol. 72, 1990, pp. 208-215.
[13] Kamijo T, Isogai T, Hoshino Y and Hakamada
H, “Altitudinal zonation and structure of warmtemperate forests on Mikura-jima Island, Izu
Islands, Japan”, Vegetation Science, Vol. 18,
2001, pp.13-22.
[14] Hoshino Y, Nozaki R, Isogai T, Maesako Y,
Kamijo T, Kobayashi H and Watabe Y, “Studies
for ecology Island Mikura-jima Area”, Report of
Pro Natura Fund, http://www.nacsj.or.jp/pn/
houkoku/h01-08/h05-no03.html.

CONCLUSION
The seeding of exotic pasture grasses prevents theestablishment of trees that invade landslide slopes
from adjacent mature forests. Moreover, ecologicalsuccessional velocity will be reduced due to thedominance of exotic pasture grasses and M.
condensatus. When soil erosion is controlled bysimple terracing, the seeding of exotic pasture grasses
is unnecessary for recovery of the native vegetation.
ACKNOWLEDGEMENTS
The authors would like to thank the staff ofMikura-jima Village office and Mr. Masayuki Hino
for their friendly assistance, and Mr. Tadao Hiraoka
for his unfailing support.
REFERENCES[1] Loop LL, Hamann O and Stone CP, “Comparative
conservation
biologyofoceanic
archipelagoes – Hawaii and the Galapagos”, Bio
Science, Vol. 38, 1988, pp. 272-282.
[2] Ono M, Wildlife in solitary island. Tokyo: Iwanami Shoten, 1994, 239 pp.
[3] Arase T and Okano T, “Influence of fertilization
on native plants and exotic pasture grasses on the
fascined landslide slopes in Mikura-jima Island,
Japan”, GEOMATE, Vol. 8, June. 2015, pp. 13161322.
[4] Miyawaki A, Vegetation of Japan Kanto. Tokyo:
Shibundo, 1986, 641 pp.
[5] Ohba T, “Die Vegatation von Mikura-Insel leight
200km südlich von Tokyo”, Bull. of Kanagawa
Prefectural Museum, Vol. 1(4), Feb. 1971, pp.2553.
[6] Kyoju no Kai ed., Saving earth (1) Practices torestore nature in Mikura-jima Island. Tokyo:
Kyoju no Kai, 2004, 48 pp.
[7] Yoshida H, “Stratificational revegetation of725

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

QUANTIFICATION OF PATHOGENIC BACTERIA IN FLOOD
WATER IN MALAYSIA
Zummy Dahria Mohamed Basri1, Zulhafizal Othman2 and Marfiah Ab. Wahid3
1,2,3
Faculty of Civil Engineering, Universiti Teknologi Mara, Malaysia

ABSTRACTThe monsoon season occurs every end of year in the east coast states of Peninsular Malaysia’s brings morerain than usual and eventually suspected annual floods on low altitude areas. However flood in late 2014 in
Malaysia bring severe destruction as compared to its normal yearly floods. Besides unusual rainfall recorded,
uncontrolled deforestation was said as major factors of this flood. Problem arises when floods with high waterflow carried with it large leavings including logs. It washed away homes and destroyed other amenities including
septic tanks. As we know septic tanks impacted by flood were containing wastewater contaminated with harmful
bacteria. Water samples taken during this flood indicated the presence of up tp 800 cfu/ml of Pseudomonas
Aeruginosa, 650 cfu/ml of Salmonella typhi and 1700 cfu/ml of E.coli. In soil samples also showed higher
presence of bacteria levels where as many as 6750 cfu/ml of E.coli and 1500 cfu/ml of Salmonella typhi weredetected. Post-flood showed a decreasing trend and lower concentration of bacteria present in water samples
which 25 cfu/ml of Pseudomonas aeruginosa, 75 cfu/ml of E.coli, 625 cfu/ml of Salmonella typhi and 475
cfu/ml of Shigella were detected. The presence of pathogenic bacteria will affect the health of the population live
where the flood and post-flood seasons showed an increase in waterborne diseases as reported by the press.
Keywords: Flood, Pathogenic bacteriaof irrigation and drainage, rain of 850mm for 10
days in Kelantan is more than the normal rate of 100
mm. Coupled with a static clouds situation thatshould move to Johor brought heavier rain thanusual [2]. Besides heavier rain factor, there is also
deforestation factor. Some deforestation evenoccurred in the catchment area or waterway.
Logging activities from the deforestation explained
why a lot of facilities damaged [3].
Some private septic tank and sewage systems
were found in a central residential area, schools andgovernment and private buildings were shattered(see Fig. 2). It makes all the dirt out of septic tanks
swept. Faeces usually bring impurities containing
pathogenic bacteria. When the tank burst, theseharmful bacteria are spread through the air. In theevent of direct contact to human, it will cause
disease.
Great dangers of flood not only the physicaldamage from floodwaters but also the health hazardsof large-scale contamination of water for humanconsumption [4]. Among the diseases that arecommonly encountered when flooding is foodpoisoning or diarrhea due to the use of uncookedwater and direct contact will cause skin disease.

INTRODUCTION
The devastating floods in Kelantan at the end of2014 brought great destruction to property.
Monsoons that hit the east coast every end of theyear also led Pahang flooded. The dirty flood water
caused many victims suffered from infectious
diseases. The most health risks related to the flood
are water and vector borne diseases [1].
2014 flood in Kelantan is the worst since 1967.
Kuala Krai is the worst area hit by the floods.
Crossing the river from upstream systems that meetat the confluence Kuala Krai quickly cause overflow(see Fig. 1).

Fig. 1 Bank of the river after flood
According to a report released by the department
726

GEOMATE- Osaka, Nov. 16-18, 2015

Fig. 2 Facilities damaged in flood

Fig. 3 Sungai Nal

Table 1 shows several microorganisms found infloodwaters in previous study. During flood, general
health condition become worst and the mostprevalent condition was fever, continued by diarrhea
and skin diseases [5].
Table 1 Types of microorganisms in floodwaters
Microb.
Shigella sp., Lestospira sp.,
Vibrio cholerae
Shigella flexneri, E.coli,
Salmonella typhimurium
E.coli, Intestinal enterococci
Campylobacter,Legionella
pneumophila

References
[4]
[6]
[7]
[8]

Fig.4 Sungai Lebir
Sungai Lebir and Sungai Nal are streams which
located 53 meters above the sea level. The
coordinates of Sungai Lebir are 5°31'0" N,
102°12'0" E and Sungai Nal are 5° 35' 0" N, 102°
11' 0" E. Both Sungai Lebir and Sungai Nal are
populated place.
For Pahang case, Kampung Teluk Sentang(Fig.5) was also affected by flood in the end of 2014.
The sampling area is near Pahang River. This
residential area chosen for floodwater sampling
suffered major flooding.

This study was conducted to investigate theoccurrence of harmful bacteria in floodwater andpost-flood water in Pahang and Kelantan, Malaysia.
MATERIAL AND METHODS
Study Area
Kuala Krai is the place most affected by theflood end of 2014 in Kelantan. Floodwater sampling
had been done in two places namely Sungai Nal andSungai Lebir (Fig.3 and Fig. 4).

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GEOMATE- Osaka, Nov. 16-18, 2015

Fig. 5 Kampung Teluk Sentang, Sungai Pahang
Fig. 6 Pathogenic bacteria detected in Kelantan andPahang floodwater

Collection of Water Sample and Analysis
Water samples were collected during and after
flooding in December 2014 and January 2015.
Samples were collected from two areas affected byflooding Kelantan and one area in Pahang. In
Pahang, there were two points of sample collectedalong Pahang River. All water samples had beencollected in sterile bottle, stored in 4oC ice box andbrought to laboratory for analysis within 24 hours.
All tests were done according to Standard
Method using MacConkey agar for e.coli and
Salmonella concentration, and Cetrimide agar for
detection of Pseudomonas Aeruginosa. For eachsample, one series of dilution had been made andstreak on the specific agar. The plate had been
incubated for 24 hours at ±35oC. The pathogenic
bacteria had been counted after removing the platefrom incubator.
E.coli had been known as familiar pathogenic
bacteria that exists in floodwater. On MacConkey
agar, E.coli will showed 1-3mm pink colonies. On
the same agar, 1-2mm colourless colonies indicatedthe presence of Salmonella typhimurium. The
occurrence of Pseudomonas aeruginosa showed thecolourless appearance on Cetrimide agar.

The data indicates there are more harmful
bacteria present in the sludge compared tofloodwater. This probable the bacteria that found in
the flood water, trapped in the crevices of solid
particles in water flooding. With the presence ofmore harmful bacteria in the mud, it can be said that
mud brought by flood can be more dangerous than
the flood water itself.
In addition to E.coli, Salmonella typhi and
Pseudomonas A. also detected in floodwater and in
the mud. The pathogenic Salmonella can cause
diseases like fever to those who are infected. There
were 400 – 1500 cfu/ml of Salmonella detectedduring the water sampling in the infected area. It
shows that the occurrence of the pathogenic bacteria
higher than detected in floodwater in Pahang during
2013 flooding [6]. These pathogenic bacteria cancause infectious diseases to human. Not only during
flooding, but the harmful bacteria can be detected
after-flood water In Kelantan (Fig. 7).

RESULTS AND DISCUSSION
Floodwater both in Kelantan and Pahang were
found contaminated with pathogenic bacteria (Fig.
6). The figure showed that the mud in Sungai
Pahang and Sungai Lebir was fecally contaminatedup to 6750 cfu/ml of E.coli. High concentrations ofE.coli were detected in Kelantan and were caused byruptured sewage tank located at the upstream. The
concentration of E.coli in the floodwater exceeded
the limit (1 cfu/ml) for criteria of recreational water(full body contact), according to [9].
Fig. 7 Pathogenic bacteria detected in Kelantan river
post-flood water
728

GEOMATE- Osaka, Nov. 16-18, 2015[2] Malaysian Meteorological Department (MET),
Monthly Weather Bulletin, retrieved at
http://www.met.gov.my.
[3] Wardah T, “Banjir kuning bagai tsunami ancam
Kelantan”,
retrieved
on
Jan
17th
at http://www.bharian.com.my/node/29743.
[4] Yuphakhun C, Punnida T, Hajime T, Bon K and
Suwimon K, “Microbiological evaluation ofwater during the 2011 flood crisis in Thailand”,
J. of Science of the Total Environment, 463-464,
2013, pp. 959-967.
[5] O Kunni, Nakamura S, Abdur R, Wakai S, “The
impact on health and risk factors of the diarrhea
epidemics in the 1998 Bangladesh floods”,
Public Health, 116, 2002, pp. 68-74.
[6] Zummy Dahria, MB, Zulhafizal, O, Marfiah,
AW, “Detection of pathogenic bacteria in flood
water”, Proceeding of International Symposiumon Flood Research and Management, 2015.
[7] Helen DM, Imke L, Frans VK, Ana MDRH,“Potential health risk associated with exposure
to microorganisms in urban floods”, Novatech,2013, Pathogens, 1-4.
[8] Helena S, Gertjan M, “Microbial health risks
associated with exposure to stormwater in awater plaza”, J. of Water research, 74, 2015, pp.
34-46.
[9] EPA, “Bacterial Water Quality Standards For
Recreational Waters (Freshwaters And Marine
Waters) Status Report”, 2003.
[10] Kamarul AB, Shaik FAB, Nik Hisamuddin
NAR, Nik Arif NM, Tuan Hairulnizam, TK,
Abu Yazid MN, Mohd Roslani AM, “Therecord-setting flood of 2014 in Kelantan:
Challenges and Recommendations from anemergency medicine perspective and why the
medical campus stood dry”, Malays J. Med Sci.,
Mar-Apr 2015, 22(2), pp. 1-7.

25 cfu/ml of Pseudomonas, 75 cfu/ml of E.coli,
625 of Salmonella typhi. and 475 cfu/ml of Shigellawere detected in post-flood water in Kelantan.
Occurrence of the pathogenic bacteria detected
showed the degradation trend, but it still harmful tothose directly exposed to the contaminated water.
In Kelantan, Kuala Krai Hospital (the mostaffected place) had the largest number of patients
being medevac during flood [10]. It shows that morediseases occurred and some of them associated to
the floodwater. Infectious diseases during flood
should be taken seriously by the relevant parties
because damaged sanitation systems lead to more
infections occurred.
According to the Pahang Health Department, 28
from 1,220 cases of infectious diseases detected in 6days of flooding are listed under the Communicable
Disease Control and Prevention, 1988. There were
27 cases of food poisoning while the rest involve
mild infectious diseases such as skin and eye
diseases.
CONCLUSION
From the study indicated that
1. Pseudomonas Aeruginosa, E.coli and
Salmonella typhi were found in floodwater.
2. Pathogenic bacteria mostly found in the
mud compared in floodwater itself.
3. Pathogenic bacteria were higher during
flood compared to post-flood event.
REFERENCES[1] Haitham BA, Jayant KR, “Assessing factors
affecting flood-induced public health risks in
Kassala State of Sudan”, J. of Operations
Research for Health Care 3, 2014, pp. 215-225.

729

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

CONVERSION OF A DECOMMISSIONED OXIDATION LAGOONINTO A FUNCTIONAL WETLAND
Jeram Mallis1, Rhiannon Hamilton2, Scott Rayburg3, Melissa Neave4
City West Water, 2Phoenix Mecano Australia, 3Faculty of Engineering, Science and Technology,
Swinburne University of Technology, 4School of Global, Urban and Social Studies, RMIT University
1

ABSTRACT
Wetlands in Australia have immense ecological value and are known for both the diversity of their habitats andthe biota they support. Wetlands in the Altona region, 15 km southwest of Melbourne, are of internationalsignificance due to the threatened and endangered biota that inhabits (and or migrates through) them. This study
focuses on the conversion of a decommissioned oxidation lagoon at the Altona Treatment Plant in Victoria,
Australia, into a wetland ecosystem. Throughout Victoria and most of Australia, there is an ongoing andprogressive tightening of the regulation for sewage treatment. These regulatory changes are placing pressure on
the water industry to improve the quality of treated effluent discharges to local waterways, bays and oceans. Thisis resulting in the gradual decommissioning of oxidation lagoons in favor of more technically advanced
alternatives. This study proposes an ecologically valuable reuse for a decommissioned oxidation lagoon, which
could be replicated elsewhere. Previous design attempts for this project had failed due to the potential risk they
posed to both the surrounding environment and the Altona Treatment Plant itself. Therefore one of the objectivesof this project was to undertake multiple assessments to mitigate these risks. The most important of these, andthe focus of this paper, was the determination of the optimal source and quantity of water needed to sustain thewetland. Potential water sources included: water from a nearby estuarine swamp; treated class C or class A
effluent from the treatment plant; and rainfall-fed runoff from the treatment plant site. Through an analysis ofcost and quality of the available water sources, it was determined that locally captured rainfall-fed runoff withClass-A recycled water as a backup supply was the most feasible. In addition, hydrologic modelling revealed
that this source could maintain flow in the wetland year round, even in drought years. These findings mitigate
the concerns raised in association with previous designs and demonstrate that the proposed wetland is viable.
Keywords: Constructed Wetland, Recycled Water, Asset Recycling, Water Reuse

INTRODUCTION
ecosystems in these nations being halved in the 20th
Century [2]. For example, in North America in theearly 1900’s, wetlands were drained by the federal
government in the interests of urban and agricultural
expansion, and for the eradication of mosquitoes [3].
Meanwhile, in Iraq in the late 20th century, thenation’s leader at the time committed what was
described by [4] as ‘ecogenocide’ by draining themarshlands of Iraq to destabilise the humaninhabitants of an area which had revolted against theregime. In both examples, one deliberately (Iraq)
and the other inadvertently (USA), the vitalfunctions of wetlands and their associated benefits tohumanity were lost, a pattern that has recurred all
too frequently around the world. Throughout
Australia, extensive wetland removal occurred overmuch of the 19th and 20th centuries.
It is only in the last 30-40 years that theecological value of wetlands has been recognised at
the global level [5]. This became formalised, for the
first time in international law, in 1971 with theintroduction of the Ramsar Convention. This
convention was introduced as an instrument to

Wetlands in Australia have immense ecologicalvalue and are known for both the diversity of theirhabitats and the biota they support. Moreover, thehigh biological productivity of wetlands and thestrong selection pressures of an aquatic existence
produce a rich biota associated only with wetlands[1]. This distinct and unique wetland biota includes
communities of birds, reptiles, amphibians, fish,mammals and invertebrates which both live andbreed in wetlands as a consequence of the shelter
they provide from predators and the availability ofabundant food sources. In addition to the ecologicalbenefits, wetlands also serve a hydrological function
by retaining flood waters, improving water quality
and in the case of tidal wetlands, protecting coasts
from erosion.
Despite their inherent importance, wetlands
throughout the world have been extensively
degraded and actively removed for various reasons,ranging from urbanisation to war. Wetland
ecosystems in Europe, North America, Australia andChina have faced, or are currently facing, substantial
threats to their existence with the number of wetland
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GEOMATE- Osaka, Nov. 16-18, 2015

encourage conservation of habitat with a focus on
the conservation of wetlands from a legal
perspective. The convention aims at protecting
habitats rather than a particular species [6], which
was a new concept at the time of its implementation.
However, as many wetlands had already been lost by
the time the Ramsar Convention was enacted (andcontinued to be), mitigation strategies for wetland
replacement are still needed in many parts of theworld. In an effort to redress some of the damage
caused by wetland destruction, the use of artificial orconstructed wetlands has become increasingly
popular, especially in urban areas where nearly
100% of natural wetlands have been lost. In 1996
there was an estimated 1,000 constructed wetlands
world-wide ranging in size from 500 m2 to 4000 ha[7]. Since this time, the number of constructedwetlands has vastly increased as awareness of theneed for wetlands habitats has become more
apparent.
Throughout Victoria and most of Australia, there
is an ongoing and progressive tightening of theregulation for sewage treatment. The Victorian EPA
expects the water industry to work towards a future
way of operating that has little to no impact, or a net
benefit to the environment [8]. These regulatorychanges are placing pressure on the water industry to
improve the quality of treated effluent discharges tolocal waterways, bays and oceans. The pressure on
the industry is resulting in the gradualdecommissioning of oxidation lagoons in favour ofmore technically advanced alternatives. This study
proposes an ecologically valuable reuse for adecommissioned oxidation lagoon, which could be
replicated elsewhere.
The purpose of this project is to investigate thedesign of a constructed wetland to replace adecommissioned oxidation lagoon and to assist withthe development of a project that meets predetermined design criteria. Specific objectives
associated with this project are to:1) ensure that the wetland would complement theother wetland habitats in the area; and2) select a preferred water source for the newwetland that ensures the wetland has good water
quality and that the wetlands hydrology is
consistent with surrounding natural wetlands.

Consequently, the existing treatment system,
including the oxidation lagoon was decommissioned.

Fig. 1 Map, showing the Altona Treatment
Plant relative to the Melbourne CBD.
On the eastern side of the treatment plant site is
the 90,000 square meter decommissioned oxidation
lagoon which is surrounded by a concrete covered
retaining bank. The base of the lagoon in its current
state is located at an elevation of approximately 1 m
on the Australian Height Datum (AHD) while thesurrounding natural surface sits at between 2.5 and2.6 m (AHD). The lagoon has a thick clay liner toprevent groundwater contamination. This liner dates
back to the treatment plant’s construction in 1968,
therefore its current condition is unknown. Beneaththe liner are substantial quantities of volcanic basalt
and dense clay. The water table in the area is at
approximately 1 m AHD and is highly saline due tothe close proximity of the site to Port Phillip Bay.
Currently an estimated 10,000 cubic meters ofbiosolids is located within the lagoon and this has
been moved into ‘windrows’ in anticipation of its
removal at a later date. Historically (since 2005) the
lagoon fills from rainfall-fed runoff in late winterand early spring (July-September) to a depth of 400600 mm and evaporates in the summer months untildry. The lagoon also acts as an emergency overflow
storage unit for the sewerage treatment facility. The
location of the proposed wetland is show in Fig. 2.

SITE DESCRIPTION
The City West Water’s Altona Treatment Plant islocated 15 km southwest of Melbourne’s city centre(Fig. 1). This plant was built in 1968 and iscomprised of a series of trickling filters and a large
oxidation lagoon. In 2005 the plant received asignificant upgrade to a more modernized treatment
facility, built around a central IDEA rector thatcurrently treats up to 20 ML of sewage per day.

Fig. 2 The Altona Treatment Plant site showing
the large decommissioned oxidation lagoon which isthe proposed site for the new constructed wetland.
731

GEOMATE- Osaka, Nov. 16-18, 2015

wetland mosaic alterations and metapopulationinteractions in constructed wetland projects. Hence,
although it is assumed that certain species will visit
the proposed wetland at different times, it is
impossible to know, a priori, which species thesemight be and what uses they will make of thewetland habitat.

METHODS
To begin the process of determining how best toconvert a decommissioned oxidation lagoon into afully functioning wetland, it was required to identify
which type of wetland would be most suitable forthe site and its surrounds. Specifically, this involveda series of site visits to both the decommissioned
lagoon and nearby natural and artificial wetland
ecosystems. At each site a visual survey was
conducted to determine the species of flora andfauna present. An assessment was also carried out toclassify the types of wetlands present in the local
area and how the proposed wetland might interact
with these as part of a localised wetland matrix.
Another important consideration was theavailably of a suitable water source for the proposed
wetland and the associated quality of that water.
There are four potential water sources in the vicinityof the proposed wetland, including: 1) Truganina
swamp, a saline estuary that forms part of Laverton
Creek and is located outside of the boundary to theeast of the treatment plant; 2) treated effluent fromthe waste water treatment plant that can supply large
volumes of class C water to the wetland; 3) rainfallfed runoff from the large treatment plant site whichis estimated to have 80,000 square meters ofcatchment area; and 4) a supply of class A recycledwater from the Altona Recycled Water Plant. The
approximate locations of these water sources arepresented in Fig. 3.

Fig. 3 Potential water sources for the newwetland.

RESULTS AND DISCUSSION
The proposed wetland, as part of the Altona
wetlands region, is expected to primarily interact
with the two closest freshwater wetlands: 1)
Truganina Wetland, which is a small storm water
wetland 1 km south of the treatment plant; and 2)
Cherry Lake, a large storm water retaining basin 3
km north of the treatment plant (Fig. 4). These twowetlands are inhabited by existing metapopulations
and migrating flocks of birds that are expected toutilise the Altona Treatment Plant wetland to someextent. However, as part of a greater wetland mosaic,
the proposed wetland is also expected to interact
with both the freshwater and saline wetland
environments within close proximity to the site,
including the region’s many saline wetlands,
estuaries and beaches. Depending on the behavior ofindividual species, some birdlife may prefer to feedor nest in one particular wetland type over another;for example, the purple swamp-hen (Porphyrio
porphyrio) has been shown to prefer wetlands thatare densely planted with the Common Reed
(Phragmites australis) and bulrush [9]. Althoughsimilar observations have been made for otherwetland species in natural systems there is currently
no information available to aid in the prediction ofFig. 4 Natural wetlands in the vicinity of the
proposed constructed wetland: 1a) Westona
Wetlands as part of Laverton Creek; 1b) Truganina
Swamp as part of Laverton Creek; 2) Tuganina
Swamp; 3) Cheetham Wetlands; 4) Cherry Lake;and 5) Altona Coastal Park.
Bird surveys in the existing oxidation lagoon
however, have shown that as many as 16 different
bird species regularly visit the existing wetland withseveral of these being threatened and/or endangered.
In addition, other biota types in the Altona region
that are of high importance might use this site. For
example, [10] suggests that due to the halving of
habitat area in Altona over the past 40 years, thethreatened Altona skipper butterfly (Hesperilla
flavescens) is facing extinction, because the specificsedgelands (Gahnia filum) that it inhabits havebecome isolated. This prevents metapopulations of
the butterfly from developing due individualcolonies of the species being unable to interact. Oneof the aims of the current project is to provide
habitat for the Altona skipper butterfly by planting
732

GEOMATE- Osaka, Nov. 16-18, 2015

clusters of the Chaffy Saw-sedge (Gahnia filum). It
is hoped that these new sedgelands would assist in
closing the gaps between the existing sedgelands at
the treatment plant site and nearby wetlands. This
will allow for interactions between colonies of thebutterfly, thereby creating a new metapopulation.
This information was used to design vegetationplans for the proposed wetland that aim to meet
three objectives: 1) to be consistent with the native
species that occur in and around the site; 2) toinclude species that provide favourable habitat forthe 16 species that visit the current decommissioned
oxidation lagoon; and, 3) to provide significant
habitat for the Altona skipper butterfly.
Once the vegetation plans were developed, thenext step in the design process was to determine thebest water source(s) for the proposed wetland. The
potential water sources included: water from anearby estuarine swamp (Laverton Creek); treatedclass C or class A effluent from the treatment plant;
and rainfall-fed runoff from the treatment plant site.
To determine which one of these would be the bestwater supply source, locally available water quality
samples (from Laverton Creek and the recycledwater supplies) were laboratory tested and thesewere then assessed to determine if they were suitable
for the proposed wetland.
The results of these analyses showed that thewater quality of Laverton Creek was poor for
multiple parameters when compared to ANZECC
trigger values, Melbourne Water mean values andother known water standards. In particular, 1) themean measured turbidity was 460% higher than thegiven ANZECC trigger value; 2) the peak measured
salinity was of a similar salinity to sea water; 3) themean metal content including Aluminium, Boron,
Chromium and Zinc where significantly higher thanthe ANZECC trigger values; and 4) the mean
measured E.coli was 1137 organisms/100 ml which
is more than seven times the mean value asmeasured by Melbourne Water for E.coli across
Melbourne’s waterways. In addition, the nutrient
levels were very high, although this observation isbased on anecdotal evidence as there are no existing
trigger values or averages for nutrient levels that are
applicable. Hence, although Laverton Creek is able
to supply the volume of water required to meet thewetland’s demands, this water source is not of a
sufficient quality and is therefore excluded as apotential source.
The second considered water source was treatedeffluent from the Altona Treatment Plant (eitherclass C or class A). The effluent quality wasavailable from regular sampling and testing
programs that already occur at the treatment plant(Table 1).
There was limited data available to compare thetreated effluent quality with, because the ANZECC
trigger values are not applicable to treated effluent.

Consequently, the quality of the effluent wasassessed against a consultancy report by [11],commissioned by City West Water to investigate thepotential for using this water in artificial wetlands. A
risk assessment within this report concluded that thewater quality of the treated effluent was poor due toelevated salinity, nutrients, dissolved solids andsulphates. The risks were identified as moderate andhigh and included; 1) odour, which posed a risk to
the treatment plant’s EPA licence agreement; and 2)
eutrophication, which could potentially be harmful
to the treatment facility if treated effluent containing
toxic algae was returned to the plant for re-treatment.
It was therefore concluded that although the treatedeffluent was of a higher water quality than the waterfrom Laverton Creek and could provide a sufficientquantity of water to meet the proposed wetland’s
needs, it was unsuitable as a water source and wastherefore excluded as a potential source for thewetland.
Table 1 Altona Treatment Plant effluent discharge
quality.
Parameter
TN
TP
TDS
EC
SS
BOD
Sulphate

Average value
6 mg/L
3 mg/L
4600 mg/L
7500μS/cm
5 mg/L
3 mg/L430 mg/L

This left rainfall-fed runoff as the only remaining
potentially viable water source for the proposed
wetland. To determine if this source would be able
to meet the wetland’s requirements, a flow balance
model was created to determine the frequency andduration of wetting that could be achieved bycapturing this water and routing it to the wetland. As
the water source in this case would be rainfall runoff,
it was assumed that the runoff would be of sufficientquality.
The inputs into the flow-balance model included
rainfall and evaporation rates that were sourced from
Bureau of Meteorology records (1976-1999), rainfall
catchment area, estimated runoff coefficients,
wetland volume and wetland surface area. The
model simulated a 24 year period (the period forwhich input data existed) in 24 hour iterations to
identify the seasonal variation in wetland volume
and retention times. This long term dataset is ofsufficient duration that it included both wet and dry
periods that are representative of local rainfall
variability and as such should be a valid surrogate
for future conditions.
The modelling scenario involved the supply of
rainfall-fed runoff from a catchment area of 85,000
733

GEOMATE- Osaka, Nov. 16-18, 2015

square m (the area available on the grounds of the
Altona Treatment Plant from which water could
easily be routed to the proposed wetland) into thewetland as the only water source. The model
assessed the viability of rainfall-fed runoff as the
only water source and allowed for the following
conclusions to be drawn: 1) the optimum volume of
the wetland for the utilisation of rainfall-fed runoff
was found to be 6600 kL with a surface area of
13,700 sq m, these were the largest dimensions
possible without extending the longest dry period
beyond 90 days; 2) the wetland displayed
characteristics similar to that of an ephemeral
wetland, with the water body being full for the
majority of the year and having healthy retention
times but drawing down below 50% capacity during
an average summer period; 3) in a hot, dry summer
the wetland had the potential to completely dry off;and 4) during periods of prolonged drought thewetland would remain empty or near empty (<1 ML)
for extended periods of up to 90 days, completely
removing the wetland as an aquatic ecosystem andaesthetic amenity for this period as well as
increasing the risk of odour through the exposure of
the wetland’s base to the atmosphere. The results ofthe simulation are presented in Fig. 5.
To minimise the potential for the wetland to drycompletely, a Class A recycled water supply was
introduced to the model to supplement the rainfallfed runoff. In the model, recycled water was madeavailable when the wetland water level dropped
below two-thirds capacity and was then delivered in
100 kL allocations for each day the wetland
remained below this capacity.

supply; 3) the full wetland permanently retains its
ecosystem benefits and aesthetic amenity; and 4) the
retention time was estimated to be 18 days on
average over the 24 year period. The results of thesimulation are presented in Fig. 6. Given thepotential risks associated with the wetland dryingout and the ecological benefits of a permanent
wetland amenity, it was determined that the recycledwater supplementation scenario was most viable.

Fig. 6 Simulation of wetland storage levels overtime using the rainfall-runoff water supply option
with a recycled water top-up.
Once both the vegetation plan and the mostfavourable water source were identified, the next
step was to complete a risk assessment to determine
how well the proposed wetland would perform
across a broad spectrum of potential threats to itself
and to the surrounding local community. The resultsof this assessment are provided in Table 2. Table 2
also summarises proposed control measures to help
minimize potential risks associated with the wetland.
These control measures include: using rainwater as
the primary source of inflows to the wetland;
maintaining perennial flow in the wetland using
recycled water top-ups as needed; optimizing
wetland layout to minimize stagnant areas; using
mechanical aeration to further prevent stagnant
water; and optimizing plant selection to suit theneeds of locally important animal species and tolimit other threats. It is anticipated that these control
measures will reduce all potential threats to be ofmoderate consequence or better, an unlikely
occurrence or better and a medium risk or better.

Fig. 5 Simulation of wetland storage levels overtime using the rainfall-runoff water supply option.
All other parameters for the model remained thesame as in the first scenario. From the model outputs,
the following conclusions can be drawn: 1) the
wetland displayed characteristics of a permanent
wetland amenity, where the water body remained at
greater than 60% capacity for the full duration of thesimulation; 2) the average volume of recycled waterrequired per year for the wetland was 3.9 ML whichis estimated to cost approximately $10,000 pa to

CONCLUSION
A design is proposed to replace adecommissioned oxidation lagoon with aconstructed wetland at the Altona Treatment Plant in
Altona, Victoria. The design accomplished all of its
key objectives. Thus, the proposed wetland will
provide meaningful habitat for locally relevant
species, including rare and endangered populations734

GEOMATE- Osaka, Nov. 16-18, 2015

Table 2 Risk assessment for the proposed wetland [11].
Risk category Impact on
What can happen & how
can it happen
Water source
WetlandDrought causes thehealthwetland to dry harming
the ecosystem
Odour
Local
Development of odour inresident
the wetland.
amenity
Algae
Wetland
Toxic algal blooms harm
healththe ecosystem

Mosquitos

Weedmanagement

Dust

Local
resident
amenity
Wetlandhealth andaesthetic
amenity
Local
resident
amenity
WetlandhealthIncreased mosquito
population
Weed proliferation
harms the ecosystem

Control measures

Cons.

Like.

Top up rainfall withrecycled water

MO

UN

Ris
k
M

1. Wetland layout; 2.
Plant selection; 3.
Mechanical aeration
1. Wetland layout; 2.
Plant selection; 3.
Mechanical aeration; 4.
Action plan
1. Wetland layout; 2.
Plant selection; 3.
Mechanical aeration
Weed management
plan

MI

UN

L

MI

UN

L

I

LI

L

MO

UN

M

Dust production from a
Top up rainfall withMIR
L
dry wetland disrupts therecycled water
local community
Heavy metals
Bioaccumulation of
Use rainfall as primary
I
R
L
metals and otherwater source; limit topcompounds harms theups with recycled water
ecosystemto times of need
Note: Cons. = consequence: I = insignificant; MI = minor; MO = moderate. Like. = likelihood: UN =unlikely; L = likely; R = rare. Risk: L = low; M = medium.
of birds and other wildlife; the wetland will containhigh quality water (supplied by local rainfall-fed
runoff) and will stay wet perennially with needsbased recycled water top-ups; and a risk assessment
identified potential threats and elucidated a series ofcontrol measures that minimised potential risks to
the wetland habitat itself and the surrounding
community. This proposal demonstrates how
artificial wetlands can help to reverse the worldwide
decline in wetland habitats and that decommissioned
oxidation lagoons present opportunities forredevelopment into artificial wetlands.

[4] Dellapenna, JW 2007, Ecocide and genocide in
the Iraqi Marshlands, Ramsar Convention, 104,
401-409.
[5] Dale, PER & Knight, JM 2008, Wetlands andmosquitoes: A review, Wetl. Ecol. and Mgmt,
16, 255-276.
[6] Ansari, AHI, Irini, A, Jariah, I & Ismail, I 2011,
Ramsar convention and convention on
biological diversity as mechanism forpromotingbiodiversity
conservationof
Malaysian Wetlands, J. of App. Sci. Res., 7,2385-2393.
[7] Kadlec, R.H., Knight, R.L., 1996, Treatment
Wetlands, Lewis Publishers, Boca Raton, FL.
[8] EPA Victoria 2011, EPA Water Plan 3
Guidance, Publication 1406.1, pp1[9] Grussu M 1999, Status and breeding ecology of
the Purple Swamphen in Italy. British Birds 92,
183–192.
[10] Relf, M & New, T 2009, Conservation needs of
the Altona skipper butterfly, Hesperilla
flavescens flavescens Waterhouse (Lepidoptera:Hesperiidae), near Melbourne, Victoria, J.
Insect Cons., 13, 143-149.
[11]SKM 2011, Altona Wetland Aesthetic Amenity
Study Risk Assessment. Sinclair Knight Merz,
Melbourne Victoria pp 28.

ACKNOWLEDGEMENTS
The authors gratefully thank City West Water for
their financial backing of this study and theirprovision of site access and data.
REFERENCES[1] Gibbs, JP 2000, Wetland Loss and Biodiversity
Conservation, Cons. Bio., 14, 314-317.
[2] Moreno-Mateos, D, Power, ME, Comin, FA &Yockteng, R 2012, Structural and functionalloss in restored wetland ecosystems. Plos Bio.,10, e1001247.
[3] Carlson, AE 2010, The other kind ofreclamation: Wetlands drainage and national
water policy, 1902-1912, Ag. Hist., 84, 451-478.
735

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

DESIGNING URBAN RIVERS TO MAXIMISE THEIR GEOMORPHIC
AND ECOLOGIC DIVERSITY
1

Melissa Neave1, Scott Rayburg2
School of Global, Urban and Social Studies, RMIT University, Australia; 2Faculty of Engineering, Scienceand Technology, Swinburne University of Technology, Australia

ABSTRACTRiver geomorphic complexity is vital to support abundant and diverse ecological assemblages in riverenvironments. With the ever increasing population of global cities, and the consequent spread of urbanized land,pressures on engineers and land planners to modify and control urban rivers channels could be detrimental totheir ecological diversity. This research project provides an analysis of the geomorphic complexity andheterogeneity of an urban stream. The study compares different sections of Orphan School Creek in western
Sydney, Australia to investigate how channelization and/or alternations in riparian vegetation impact on
geomorphic heterogeneity. The sections of Orphan School Creek examined range from freely meandering tofully concrete channelized reaches. The results of this research project clearly show that urbanization has
detrimental effects on the geomorphic complexity of urban streams, due to both catchment urbanization andchannelization. Through the analysis of Orphan School Creek it was concluded that channelization reduces rivergeomorphic complexity, with concrete channels providing little or no geomorphic complexity or diversity.
However, if managed and/or designed with a view towards optimising geomorphic complexity, urban rivers canattain meaningful ecological benefits while still being controlled to prevent damage to the urban environment
from flooding and/or erosion.
Keywords: Pools and riffles; geodiversity; channelization; asymmetry[5] and it is generally expected that geomorphicallyvariable and complex landscapes will have a higher
chance of supporting diverse ecologic assemblages
by providing a greater selection of niche spaces [6].
Conversely, geomorphically simple rivers, such as
those created through channelization, will havefewer available habitats and an associated reduction
in aquatic ecologic diversity.
There are several ways to compare the physicaldiversities of channelized and non-channelized river
reaches, perhaps the most obvious of which is toconsider channel planform, or the view of a channel
from above. Natural river planforms vary fromstraight to highly sinuous or meandering, althoughnatural straight systems are not particularly common
and occur mostly when a river is constrained insome way (e.g., by geologic structure). In contrast,
channelized rivers are often straight or only mildly
sinuous, which is designed to increase flow
velocities and thereby reduce flooding [7], [8].
Straightening a channel however, also reduces its
geomorphic and hydraulic complexity, which means
such a channel typically exhibits limited physicaldiversity. Planform is relatively easily investigated,using parameters that quantify the degree ofmeandering [9], such as sinuosity (SI) which is theratio of the channel length to the long valley length[10], [11]. Meandering reaches are generally defined
as having a sinuosity ratio of at least 1.05 whereas
heavily channelized reaches have a sinuosity of less

INTRODUCTION
Urban rivers are often extremely compromised,having dramatically altered flow regimes,
geomorphologies, and ecologies in comparison totheir non-urban counter-parts [1]-[3]. This occurs
because urbanization within river catchments istypically associated with increases in impervious
surface area that cause the adjacent rivers to be‘flashy’ (i.e., have relatively high peak discharges
and short flow periods), while most urban rivers are
physically transformed (i.e., channelized) in some
way to reduce both flooding and erosion. This
channelization
typically
involves
channelstraightening and smoothing, and the reinforcementof the bed and banks with a non-erodible material
that both enhances smoothness and prevents ongoingchannel migration. In addition to these impacts,
stormwater runoff from the catchment transports
high pollutant loads that degrade water quality.
One of the most obvious outcomes of stream
channelization is a loss of physical diversity thatfurther manifests itself in the river through reduced
hydraulic and habitat diversities. Morphologic
adjustments to rivers can greatly affect theavailability of physical habitats, particularly inassociation with the simplification of fluvial
structures (e.g., bed features) in the down- and crossstream directions [4]. Indeed, there is a strong link
between biotic diversity and spatial heterogeneity
736

GEOMATE- Osaka, Nov. 16-18, 2015

than 1.05 and often close to 1.0.
The physical diversity of a river channel can also
be considered in terms of longitudinal variations in
its bed. Many natural rivers have alternating deeps
and shallows (or undulations) on their bed. Thesefeatures, typically identified as pools and riffles(especially in gravel-bed systems), are thought toreduce the rate of energy expenditure by increasing
flow length [12], [13], but are often removed during
channelization. Thus, most channelized rivers have asmooth bed with a constant slope in the downstream
direction [8], [9]. Pools and riffles create nonuniform flow conditions [14] that offer a range of
habitats for aquatic organisms. Thus, the removal ofpools and riffles (or bed undulations) from streams
results in a concomitant loss of habitat variability.
The geomorphic complexity and diversity of rivers
in the longitudinal direction can be assessed using
several bedform parameters, such as the extent towhich a bed varies from a straight condition in thedownstream direction and undulation asymmetry[15]-[17].
A final characteristic of river channels that canbe used to assess physical diversity is cross-sectionalvariation in channel form. Indeed, [18] argues thatcross-sectional asymmetry and variability imply atendency towards irregularity and complexity.
Natural streams typically have highly asymmetriccross-sections along much of their length, whereas
urban or channelized systems are often highly
symmetrical to ensure the rapid throughflow ofwater [19]. Cross-sectional asymmetry can bequantified using a number of indices, most of which
represent some form of comparison between left andright channel areas [19].
This research investigates relationships between
the physical diversity (i.e., geomorphic complexityand heterogeneity) of an urban stream, its degree ofchannelization and the general condition of its
riparian vegetation. These findings are further
considered in terms of the stream’s habitat potential.
Thus, the specific aim of the research is to establish
a link between urbanization, channelization andgeomorphic complexity in an urban river system.

has been extensively modified over the last 50 years.
Thus, the creek is ‘naturally’ vegetated at both its
upstream and downstream ends but has an
approximately 1.5 km long section in the middle
where it runs through firstly a pipe and then aconcrete lined trapezoidal channel.

Fig. 1

Position of Orphan School Creek in western
Sydney, Australia (a) and location of thefive study sites used in this research [21].

The Orphan School Creek system is managed by
the City of Fairfield, who balances a need for flood
protection with a desire to improve the overall healthof its river systems in general. The mean annual
maximum temperature of the region is 23.1 oC while
the mean minimum temperature is 12.2 oC [22]. The
average annual rainfall is approximately 870 mm,
the majority of which falls in January through March,
although all months average at least 45 mm [22].
Major floods in 1986, 1988 and 2001 caused
significant damage in the catchment and increased
community pressure to develop and implement flood
protection measures. At the same time, the Council
has instigated a Creek Care Program that aims to
produce river environments that support biodiversity
and provide for community engagement [23].
Activities under this program include cleaning (e.g.,
direct litter removal and stormwater quality
improvement), weed control, bush regeneration andstream channel rehabilitation.
METHODS
Orphan School Creek exhibits a variety ofconditions along its length, ranging from naturally
vegetated to fully channelized. Five sections within
the first 7 km of the creek, which differed both interms of channel form and condition and the typeand extent of surrounding vegetation, were
investigated for this study (Fig. 1b, Fig. 2). Site 1 isin the most upstream section of the creek and isextensively vegetated, with native trees and shrubs(dominated by Eucalyptus and Casuarina species)
on both banks. This site is positioned about 900 m
downstream of the creek headwaters. Site 2 isapproximately 800 m downstream of Site 1. The
creek in this region is grassed on both sides,
although there are trees relatively close to the

STUDY SITE
To assess the relationship between urban rivercomplexity and/or heterogeneity and urbanization
and/or channelization, survey data were collectedfrom Orphan School Creek in western Sydney,
Australia (Fig. 1a). Orphan School Creek isapproximately 12.5 km long and is a tributary ofProspect Creek, which flows into the Georges Riverand Botany Bay. Orphan School Creek has two
small tributaries, Clear Paddock Creek (~5 km in
length) and Green Valley Creek (~5.6 km in length),
and a total catchment area of 34.3 km2 [20]. The
catchment is highly urbanised and the creek itself
737

GEOMATE- Osaka, Nov. 16-18, 2015

asymmetry (At from [16]) was calculated using Eq.
(1)southern bank. This site is immediately downstreamof a culvert and, subsequent to the present study, hasbeen partially rehabilitated by the Council who
reinforced the banks for erosion control. Site 3 isapproximately 200 m downstream of Site 2 and ispositioned within a narrow (approximately 50 m) but
dense patch of native trees. The channel in thisregion has not been extensively modified. Site 4 is afurther 2.5 km downstream and is a trapezoidal
concrete channel running through a grassed parkland.
Site 5 is another 2.3 km downstream and sitsadjacent to a sports reserve. A ranking of these sites
from best to worst, in terms of vegetation cover anddegree of channelisation, would be as follows: Site 1(‘best’), Site 3, Site 2, Site 5 and Site 4 (‘worst’).

At = (Art-Alt)/A(1)

Cross-sectional centerline asymmetry (A* from[18],[19]) was calculated using Eq. (2)A* = (Arc-Alc)/A(2)

Two longitudinal asymmetry parameters (Aa and AL2from [17] were calculated using Eq. (3) and Eq. (4)Aa = (Ap-Ar)/Apr(3)

AL2 = (Lp-Lr)/L(4)

Where: A is the cross-sectional area; Art and Alt are
the cross-sectional areas to the right and left of thethalweg, respectively; Arc and Alc are the crosssectional areas to the right and left of the middle ofthe channel, respectively; Ap and Ar are the areas
below and above a longitudinal trendline,
respectively; Apr is the total area around alongitudinal trendline; Lp and Lr are the pool andriffle lengths, respectively; and L is the total
longitudinal length. Both A* and At have limits of -1
to 1, with 0 being symmetrical and 1 or -1 being
extremely asymmetrical.
Thus, ten variables were calculated for eachsurvey site, six of which were cross-sectional, threeof which were longitudinal and one of which wasplanform. These variables were subsequently
compared between the five survey sites using MannWhitney U and Levene’s W tests to examine means
and standard deviations, respectively.
RESULTS

Fig. 2

Representative cross-sectional surveys for eachof the five sites on Orphan School Creek arepresented in Fig. 3. Averages and standard
deviations for the six cross-sectional variables thatwere calculated at each site are presented in Table 1
and statistics comparing results between sites arepresented in Table 2. These data indicate that thesites that have been more extensively modified (4 &5) are deeper, have larger channel areas and aresmoother. In addition, for most parameter values
there is a statistical difference between the more
modified sites and the less modified sites.
Longitudinal surveys for each of the five sites on
Orphan School Creek are presented in Fig. 4.
Averages for the longitudinal asymmetry variables
and ranges of depths are provided in Table 3. These
data suggest that the bed is less regular in thedownstream direction in the unmodified reaches
than the modified reaches.

Aerial views of Sites 1 (a), 2 (b), 3 (c), 4 (d)and 5 (e) on Orphan School Creek.

Within each of these five sites both crosssectional and downstream surveys were performed
using an automatic level. The cross-sectional
surveys were collected along an approximately 80 mchannel length and positioned roughly 10 m apart,
dependingupon
channelconditions.
The
downstream surveys were taken along the thalweg,
with data points collected every 1-2 m.
The survey data were used collectively tocompute a series of cross-sectional, planform andlongitudinal variables. These variables include
bankfull width (W), depth (d), area (A) andhydraulic radius (R) and sinuosity (SI) and bed
elevation range. In addition, cross-sectional thalweg
738

GEOMATE- Osaka, Nov. 16-18, 2015

Table 1 Averages (Avg) and standard deviations(SD) for six cross-sectional variables at fivesites on Orphan School Creek, Australia.
Site
1
2
3
4
5
Site
1
2
3
4
5

W (m)
Avg
SD
6.05
1.66
4.21
1.68
4.55
1.19
6.13
0.42
6.30
2.29
R (m)
Avg
SD
0.34
0.07
0.27
0.07
0.36
0.06
0.69
0.08
0.31
0.06

d (m)
Avg
SD
0.40
0.09
0.41
0.25
0.49
0.12
0.82
0.10
0.37
0.09
A*
Avg
SD
0.28
0.23
0.18
0.10
0.19
0.19
0.00
0.00
0.16
0.15

A (m)
Avg
SD
2.45
0.95
1.53
0.61
2.16
0.51
4.99
0.49
2.34
1.12
At
Avg
SD
0.35
0.22
0.23
0.23
0.38
0.23
0.00
0.00
0.22
0.26

Table 2 Statisticallydifferent
cross-sectionalparameter comparisons between sites (e.g.,Sites 1 v 2 were statistically different only
for W) based on the Man Whitney U
analyses.

Site

1

1
2
3
4
5

W
W
D,A,R,At,A*
A*

Site

1

1
2
3
4
5

*

A
D,A,At,A*

Mann Whitney U
2
3
A
All 6
W,A

4

#

D,At,A*

8
8
6
20
4

4

#

All 6

3
2
7
17
6

All 6

Levene’s W
2
3*

A
D,At,A*

D,R,At,A*
W,A

Note: # is the number of statistically significant
differences observed for each site.
Table 3 Averages for two longitudinal asymmetry
variables and ranges for flow depth at fivesites on Orphan School Creek, Australia.
Site
No.
Aa
AL2
Range (m)
1
2
0.05
0.00
0.73
2
1
0.00
0.26
0.91
3
4
0.18
0.28
1.38
4
0
0.00
0.00
0.14
5
1
0.29
0.19
0.54
Note: No. = the total number of deviations aboveand below the bed observed at each site.
Fig. 3 Representative cross-sections for Sites 1 (a),2 (b), 3 (c), 4 (d) and 5 (e) on Orphan
School Creek, Australia.

Finally, in terms of planform Site 1 had asinuosity ratio (SI) of 1.21. Sites 2 and 3 each had an

739

GEOMATE- Osaka, Nov. 16-18, 2015

SI of 1.14. Site 4 had an SI of 1.00 and Site 5 had a
SI of 1.10. This indicates that the more modified
reaches (e.g., Sites 4 and 5) were straighter than thewell vegetated and unconfined reaches.

Fig. 4

between samples, reinforced that Site 4 wassignificantly different to the other sites for virtually
all cross-sectional parameters. The low standard
deviations for this site for most parameters indicatethat this reflects a lack of variability of form.
An assessment of the cross-sectional asymmetry
parameters (A* and At) for the five sites on Orphan
School Creek shows a clear trend of declining
asymmetry with channelization. The highest crosssectional asymmetry values were recorded at Sites 1
and 3, which had the densest vegetation covers andwere the least confined. In contrast, Sites 4 and 5,
which were the most altered reaches, had the mostsymmetrical cross-sections. This finding is relevant
to discussions of fluvial ecologic diversity, withasymmetrical systems offering greater diversity interms of flow and habitat than symmetrical ones.
Thus, these findings support previous work thatshows that an increase in urbanisation can lead tomore uniform channels and [26] and that this has
implications for aquatic biodiversity.
In addition to channel cross-sectional form,
urbanisation and channelization have the potential toinfluence a river’s planform and longitudinal
structure. The sinuosity of Orphan School Creek
decreased with increasing channelization, with Sites
1-3 having the highest sinuosity ratios. Thisconfirms the trend evidenced in the cross-sectional
asymmetry data of decreasing channel diversity withincreasing intervention.
The longitudinal asymmetry data (Table 3) are alittle more difficult to interpret. Sites 3 and 1 show
the greatest variability in bed structures in thedownstream direction (Fig. 3), although this isn’t
necessarily reflected in the longitudinal asymmetry
results (Table 5). Indeed, Site 5, which had limited
riparian vegetation and a relatively low SI, returned
the largest Aa value, which suggests that it has thegreatest longitudinal variability. However, the bed
survey data (Fig. 3) indicate that this isn’t
necessarily the case and points to a need to examine
the data in concert with a visual assessment.
Likewise, the total variation in bankfull depth along
these channels (as indicated by Range in Table 3)
was considerably lower for the more modified sites(4 & 5) than the less modified sites (1, 3 & 2).

Longitudinal streambed profiles for Sites 1(a), 2 (b), 3 (c), 4 (d) and 5 (e) on Orphan
School Creek, Australia. All profiles wereon the same axes (f).

DISCUSSION
This research analysed and contrasted data fromfive sites along an urban stream in Sydney’s western
suburbs, ranging from a ‘natural’ (unconfined) reachwith relatively dense native riparian vegetation (i.e.,trees) on both banks (Site 1) to a straight concrete
channel flanked by grass covered surfaces (Site 4).
The objective was to identify differences in thegeomorphic complexities of these channel types andto try and explain these differences in terms ofchannelization and bank vegetation. Statisticalcomparisons between the average cross-sectionalparameters for the five sites (Table 2 Mann-Whitney
U) indicate that Site 4 (channelized) was the mostdifferent in terms of physical character. It was
typically larger (both deeper and wider) than theother sites, especially Sites 1, 2 and 3 which were
better vegetated and had not been channelized. Site 4
differed the least from Site 5, which also lacked adense vegetation cover and was relatively straight.
Indeed, Sites 4 and 5 were statistically different onlyin terms of depth. This confirms previous researchthat indicates that urbanisation and channelization
lead to both channel widening and deepening as aresult both of human intervention (i.e.,channelization and devegetation) and changes inrunoff from urbanising catchments [24], [25].
The Levene’s tests, which compare the variances

CONCLUSIONSThis research identifies some of the impacts ofurbanization and channelization on urban streams,
particularly in terms of geomorphic complexity andheterogeneity. The study examined five sites along
Orphan School Creek in western Sydney, Australia.
Overall, unconfined sites with relatively dense
native vegetation covers exhibited higher diversities
in their in-channel physical form (in both the crosssectional and downstream dimensions). In contrast,
as the impacts of channelization became more
740

GEOMATE- Osaka, Nov. 16-18, 2015

apparent, through direct interventions in the channel(e.g., straightening and enforcement) and alterationsin the type and density of riparian vegetation (e.g.,transformations to grass covered banks), there was a
reduction in the physical diversity in the channel.
These findings have important implications for
fluvial ecosystems, with increased geomorphic
complexity and heterogeneity known to support
increased biodiversity through the provision ofdiverse niche spaces for organisms to occupy.
Recognizing the importance of geomorphic diversity
for biota should help those designing urban rivers tomake more informed decisions about their systems.

[12] Yang CT, “Formation of riffles and pools,
Water Resources Research, Vol. 7, 1971, pp.
1567-1574.
[13] Wohl EE, Vincent KR, Merrits D, “Pool andriffle characteristics in relation to channel
gradient”, Geomorphology, Vol. 6, 1993, pp.
99-110.
[14] Julien PY, “River Mechanics”, Cambridge
University Press, 2002, 434 pp.
[15] Richards KS, “The morphology of riffle-poolsequences”, Earth Surface Processes, Vol. 1,
1976, pp. 71-78.
[16] Rayburg SC, Neave M, “Morphologic
complexity and diversity in natural andchannelized streams”, Engineers Australia, Vol.
4, 2008, pp. 1902-1911.
[17] Rayburg SC, Neave M, “Assessing morphologic
complexity and diversity in river systems using
three-dimensional asymmetry indices for bed
elements, bedforms and bar units”, River
Research and Applications, Vol. 24, 2006, pp.
1343-1361.
[18] Leopold LB, Wolman MG, Miller JP, “Fluvial
processes in geomorphology”, Freeman, 1964,
522 pp[19] Knighton AD, “Asymmetry of river channel
cross-sections: Part I. Quantitative indices”,
Earth Surface Processes and Landforms, Vol. 6,
1981, pp. 581–588.
[20] Sinclair Knight Merz, “Flood study for Orphan
School Creek, Green Valley Creek and Clear
Paddock Creek. Final Report”, 2008,
http://www.fairfieldcity.nsw.gov.au/upload/dkry
h96595/FinalReport3TribsOct08Part1.pdf.
[21] Fairfield City Council State of the Environment
Report 2005-2006:
Chapter 2: Creeks,
http://www.fairfieldcity.nsw.gov.au/upload/tilhz
85984/soe0506_Creeks_ch2.pdf.
[22] Australian Government Bureau of Meteorology,
http://www.bom.gov.au.
[23] City of Fairfield, Catchments Creeks andWetlands,
http://www.fairfieldcity.nsw.gov.au/default.asp?
iNavCatId=2181&iSubCatId=2194.
[24] Douglas I, “The impact of urbanization on river
systems”, Proceedings of the International
Geography Union Regional Conference, 1974,
pp. 307-317.
[25] Roberts CR, “Flood frequency and urbaninduced change: some British examples”,
Floods: Hydrological, sedimentological, andgeomorphological implications. Beven K,
Carling P, Eds. John Wiley & Sons, 1989, pp.
57-82.
[26] Hammer TR, “Stream channel enlargement due
to urbanization”, Water Resource Research, Vol.
8, 1972, pp. 1530-1540.

REFERENCES[1] Baer KE, Pringle CM, “Special problems ofurban river conservation: The encroaching
megalopolis”, Global perspectives on riverconservation: Science, Policy and Practice.
Boon PJ, Davies, BR, Petts, GE. Eds. John
Wiley & Sons, 2000, pp. 385-402.
[2] Roesner LA, Bledsoe BP, “Physical effects ofwet weather flows on aquatic habitats”, Final
Report to Water Environment Research
Foundation, 2002, WERF Project Number 00WSM-4. 250 pp.
[3] Vaughan IP, Diamond M, Gurnell AM, Hall KA,
Jenkins A, Milner NJ, “Integrating ecology withhydromorphology; a priority for river scienceand management”, Aquatic Conservation, Vol.
19, pp. 113-125.
[4] Gregory KJ, Davis RJ, Downs PW,“Identification of river channel change due tourbanization”, Applied Geography, 1994, Vol.
12, pp. 229-318.
[5] Huston M, “Biological diversity: thecoexistence of species on changing landscapes”,
Cambridge University Press, 1994, 681 pp.
[6] Hunter ML, Jacobson GL, Webb T,“Paleoecology and the coarse-filter approach tomaintaining biological diversity”, Conservation
Biology, Vol. 4, 1988, pp. 375-385.
[7] Emerson JW, “Channelization: a case study”,
Science, Vol. 173, 1971, pp. 325-326.
[8] Brooker MP, “The ecological effects ofchannelization”, The Geographical Journal, Vol.
151, 1985, pp. 63-69.
[9] Lofthouse C., Robert A, “Riffle-pool sequences
and meander morphology”, Geomorphology,
Vol. 99, 2008, pp. 214-223[10] Mueller JE, “An introduction to the hydraulicand topographic sinuosity indexes", Annals of
the AAG, Vol. 58, 1967, pp. 371-378.
[11] Boano F, Camporeale C, Revelli R, Ridolfi, L,“Sinuosity driven hyporheic exchange inmeandering rivers”, Geophys. Res. Lett., Vol.
33, 2006, L18406.

741

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

CHANGES IN DEPTH AND THE SEDIMENT RATE BEFORE AND
AFTER THE LAKESHORE DEVELOPMENT IN LAKE FUKAMI-IKE,
JAPAN

1

Maki Oyagi1, Megumi Nobori2, Hiromi Ssuda2, Akiko Yokoyama3 , Akihiko Yagi 2
Yokkaichi University, JAPAN, 2Aichi Institute of Technology(AIT), JAPAN, 3Water Agency Inc, JAPANABSTRACT
Changes in depth and decrease of the sediment rate before and after a building breakhead maintenanceconstruction were studied in small monomictic and eutrophic Lake Fukami-ike in central Japan. The
maintenance of farm village drainage and the waterfront function was carried out for the activation of the town
in 1992, and the water quality improvement was expected. However, variations of transparency were observed
and no blue-green algal bloom outbreak had occurred before recently observed. Maximum depth changed from
9.3 m (1951), 8.5 m (1979), 8.1 cm (1992) to 7.8 m (2012), respectively. Changes in the autochthonous andallochthonous matters in the lake were thus considered. Sediment rates of 19.5 ± 10.19 gm-2d-1 (1978 to 1979)
and 4.40 ± 2.27 gm-2d-1 (2007 to 2008) were observed, and decreased 22.6%. These deposition rate data
corresponded to 3.1 cm year-1 (1979) and 1.2 cmyear-1 (2009), respectively. The decreased percentage of organic
matter and the reduced deposition rate were because rice fields and forest around the lake give way to takeconcrete roads. It was considered to be because the inflow of sediment stopped when it rained, and allochthonous
inorganic matter was significantly reduced.

Keywords: Eutrophic lake, Sediment rate, Ttransparency and Lakeshore development

INTRODUCTION
With Fukami-ike as the central point, Mount
Hanso, the highest point in the surrounding area andlocated on the north side, and Senkizawa river on thesouth side were connected with a north-south line
[2].
Fukami-ike is surrounded by mountains, and islocated in a basin. It is affected little from the wind,
and its depth is significant compared to the surface
area. Therefore, temperature stratification of water
occurs in the lake from April to November.
Especially in midsummer, there is absence ofoxygen under 4 m [3].
In this paper, we present several scientificcharacteristics of Fukami-ike. First, it is a natural
eutrophic lake, rich in nutrient salt. Therefore, thelake water is cloudy, and dark or faded green incolor. The values of pH and dissolved oxygen arehigh during the summer season. Furthermore, thislake has a high primary production which is
produced by a large number of phytoplankton.
Especially, during the autumn circulation period,
chlorophyll a concentrations of 80 to 100 mg m-3
was measured throughout all the layers. Even during
the winter season, phytoplankton bloom occurs, andchlorophyll a concentrations become higher than thesummer [3].
At Fukami-ike, as a water environment
improvement project (Led by Nagano prefecture),

Fukami-ike is a natural eutrophic lake located in
Oshimojo, Anan-cho, Shomoina-gun, Nagano
prefecture, specifically, N35°19’ and E137°49’, anda sea level of 484 m. The minor axis of the lake is
150 m, and the major axis of the lake is 300 m. The
area of the lake is 2.1 ha (Figure 1). The catchment
area is 0.247 km2 [1], and there were 12 residences
within the catchment in 2008. As for land use prior
to lakeshore development involving fertilization andnutrient salt influx, the land was used as an orchard.
However, presently the land is only used as ricepaddies (4% of the catchment area: 0.010 km2) andfor farm use (65%: 0.16 km2). There are 10 inlets (7locations have continuous influx) and one outlet.

Fig.1 Lake basin of lake Fukami-ike

742

GEOMATE- Osaka, Nov. 16-18, 2015

lakeshore construction began in November 1992.
The two main goals of this water environment
improvement project were (1) maintenance of farm
village drainage and (2) landscaping as a hydrophilic
function. As a result, due to maintenance of farm
village drainage, gray water influx to the lake fromthe surrounding area stopped, and due tolandscaping, boardwalks were built around the lake,
and influx of surface water from water catchment
area was almost eliminated. Afterwards, experts’opinions were taken into account, and theconstruction was carried out with the water depth
lowered to 5 m instead of the usual 7.75 m, andinstead of draining the whole lake water. Also,
before and after the lakeshore development, there
were no changes to the position and the height of theoutlet. In this report, this water environment
improvement project is defined as the lakeshoredevelopment of Fukami-ike and issues are discussed.
To determine whether internal production volume
changed due to the lakeshore development of
Fukami-ike, transparency and deposition rate werediscussed based on the values reported in theliterature in the past 50 years and the newobservation results.

Transparency of Fukami-ike was 0.7 m - 2.8 m(average value ± standard deviation，1.38 ± 0.67 m)
during 1950’s. Later on, (1970’s), prior to thelakeshore development (April 1992), values ranged

Fig.2 Changes of transparency

Fig.3 Seasonal changes of transparency (before
lakeshore development)
METHODS
Use at most three levels of headings that
correspond to chapters, sections and subsections.
The first level headings for chapter titles should
be in 10pt, bold, justified, and upper case font.
Leave one-blank line before and after the firstlevel headings, respectively. Transparency wasobserved by secchi disk from 1978 to 2009.
Precipitated matter were collected in sedimentbottles each depth (3m, 5m and 7m) and recovery
one month. Bottom sediment were collected by
Ekman-Birge grab from 2007 to 2009. Water
content ratio and density of bottom sediment were
measured to find sediment rate. The formula [4]of the sediment rate was indicated.
h = ( b / a c ) × ( 100 / (100－w))×365
h:sediment rate (cm year-1), w:average watercontents of surface sediment (0-10cm depth),a:mouth area of sediment trap bottle (cm2), b:dryweight of precipitated matter (g), c:density of
sediment, t:total days

Fig.4 Seasonal changes of transparency (afterlakeshore development)from 0.50 m to 2.30 m (1.04 ± 0.30 m). Compared toduring 1950s, both average value and seasonalchanges are lower. After the lakeshore development(data from July 1999 to December 2009), averagevalues increased significantly (0.35 m - 4.70 m :1.93 ± 0.88 m).
Generally, primary production is high in lakes
during the summer season when the watertemperature is high, and low during the winterseason when the water temperature is low. However,Fukami-ike has unusual characteristics as reportedby [3]. There is little regression of nutrient salt fromdeep to surface water layers occur during thesummer stratification period. During the autumn
circulation period, nutrient salt is carried to thesurface layer, and primary production is high even

RESULTS AND DISCUSSIONS
Transparency
Transparency of Fukami-ike has been reported
since July, 1950 [5]. Though there are many gaps,
there are accumulated data of over half a century
until December, 2009 [5],[6]. In this report,
characteristics of transparency over each generation
will be discussed based on these data (Figure 2).
743

GEOMATE- Osaka, Nov. 16-18, 2015

during the winter. Seasonal changes in transparency
prior to the lakeshore development are shown in
Figure 3, and after the development are shown in
Figure 4. Average values and seasonal changes show
some variations. But in both before and after thedevelopment, transparency is high during thestratification period when regression of nutrient salt
from deep-water layer is low, and transparency is
low during the circulation period when primary
production is high. These results confirmed theunusual characteristics of Fukami-ike.
The highest value of transparency during 1950 to2009 was 4.70 m in April 2004, and the lowest value
was 0.35 m in June 2000. At that point, the presenceof blue-green algae was visually confirmed. Until
then, the presence of blue-green algae has not been
confirmed. But since the latter half of 1990s, it was
confirmed several times, and even local residents
reported the presence of blue-green algae. All theobservations were made after the lakeshoredevelopment, thus the presence of these algae isassumed to be related to the lakeshore development,
and will be discussed in detail later. The lowest
value of chlorophyll a concentration, 357 μg L-1, wasobserved in the surface layer, and was determined torepresent the decreased depth due to phytoplankton
bloom. Though it is a eutrophic lake, Fukami-ike did
not have notable occurrence of blue-green algae inthe past, and was considered an unusual lake. But
after the lakeshore development in 2000, blue-green
algae were noted often.
The distribution of nitrate nitrogen concentrationis shown in Figures 5 and 6 as water quality data ofnutrient salt before and after the lakeshoredevelopment. During 1978 to 1979 before thelakeshore development, high NO 3 -N values of 10 to50 μgat L-1 (0.14 ～ 0.70mg L-1) were observed
during October to May. From August to early
October, values remained low at around 1 μgat L-1(0.014 mg L-1). From 1999 to 2000, a period after
the lakeshore development, values ranged from 0.4
to 7 μgat L-1 (5.6～98μg L-1). Before and after thelakeshore development, seasonal changes have asimilar trend, but the concentration of nitrate
nitrogen dropped significantly to about 10%. At
Fukami-ike, it was shown by [7] that nitric acidflows in from the water catchment area after
precipitation. This may indicate an influx of surface
water from orchards and rice paddies around thelake prior to the lakeshore development, but after thelakeshore development, concrete block boardwalks
were built around the lake shore and thus direct
influx of surface water ceased. Furthermore, after
the lakeshore development, influx of sewage ceased
as well. In Washington Lake, USA, since sewage
influx to the lake ceased, a significant increase intransparency was observed. This is due to the change
in composition of zooplankton community from thedecreased nutrient salt [8]. Based on theseunderstandings, we believe that the decreased
concentration of nitric acid after the lakeshoredevelopment contributed to the increasedtransparency of Fukami-ike.
Next, seasonal changes in chlorophyll a per unit
area before and after the lakeshore development are
shown (Figures 7 and 8). During 1978 to 1979, aperiod before the lakeshore development, the values
ranged from 45 to 260 mg m-2, and during 1999 to
2000, a period after the lakeshore development,
values ranged from 25 to 965 mg m-2. Blue-green
algae were noted in July 1999 (844 mg m-2) and June
2000 (233 mg m-2). The highest value was noted in
January 2000, but no blue-green algae were noted atthat time. Seasonal changes of chlorophyll a before
and after the lakeshore development had low values
during the stratification period, and a trend of highvalues during the circulation period. These results
agreed well with the seasonal changes oftransparency. However, chlorophyll a concentrations
roughly increased after the lakeshore development,
and do not agree with the increasing trend oftransparency. It’s been suggested that the changes intransparency may be caused by changes in size
compositions (zooplanktons changing from small tolarge ones) [9]. These results indicate that at
Fukami-ike, the cause is the size composition of
zooplanktons
insteadofphytoplankton
concentrations.
The cause of the increased averagetransparency value at Fukami-ike is as follows: First,
before the lakeshore development, sewage influx
and surface water influx from rice paddies andorchards of water catchment area during
precipitation were occurring. But all of that ceased
after the development, leading to a decrease inconcentration of nitric acid, a nutrient salt. Secondly,
a chrolyphyll concentration increase after thelakeshore development was confirmed, and thepossibility of the size composition of zooplankton
was pointed out. However, the lowest value wasobserved after the lakeshore development, andincreased chlorophyll a concentration and presenceof blue-green algae were observed; thus, significant
decrease in nutrient influx led to a decreasing trend
in internal production volume. Yet it is believed thatthe material circulation in the lake has changed.
Depth variation
Bathymetric maps were drawn three times for
Fukami-ike in 1951, 1978 and 2004. The maximum
depth was of the pond reported to be 9.3 m in 1951
by Ueno (1952), and was reported to be 8.5 m in1979 (1993) and 7.85 m in 2004 (Figure 9) by Yagi,
et al. Although bathymeric maps were not prepared,
the maximum depth was determined to be 7.85 m in2009. Decrease in depth from 2004 to 2009 cannot
be confirmed, but the survey determined there had

744

GEOMATE- Osaka, Nov. 16-18, 2015

been a significant decrease of 1.45 m in depth over a
period of 58 years.

1

The rate at which sedimentation built up in thepond prior to lakeshore development was shown tobe 3.1 cm y-1 by [10] based on 1978-1979 data. Also,

Fig.5 Seasonal changes of nitrate (before lakeshoredevelopment) unit:μgat L-1

Fig.6 Seasonal changes of nitrate (after lakeshoredevelopment) unit:μgat L-1
745

GEOMATE- Osaka, Nov. 16-18, 2015

compared to the sedimentation rate obtained by [10]by trap experiments, the results were consistent withthe actual measurement of 80 cm (3.1 cm x 28 years= 86.8 cm over 1951 to 1979). Depth decreasemeasured over 30 years (1979 to 2009) was 65 cm.
From 1979, until the lakeshore development

Fig.7 Seasonal changes of chloropfll-a (before
lakeshore development)

Fig.8 Seasonal changes of chloropfll-a (afterlakeshore development)
sedimentation rate after improvement of lakeshore
drainage facilities was calculated based on 20072008 data. Figure 10 shows sedimentation rate foreach month from January 2007 to December 2008. It
was 0.69 cm/yr-1 during the stratification period (lateMarch to early October) and 1.24 cm yr-1 forcirculation period (mid October to mid March),presenting a higher value during the circulation
period. This corresponded well with the decreasing
trend of transparency during the circulation period.
Newly obtained average sedimentation rate was 1.17
cm/yr-1about 37% of the sedimentation rate from1978 to 1979 prior to the lakeshore development.
This clearly shows that sedimentation rate
significantly decreased after improvement oflakeshore wastewater treatment facilities.
The annual average of ignition loss for new
sediments (IL) was 37.9 ± 9.94%. To understand the
movement of load to Fukami-ike, total organiccarbon (TOC), total nitrogen (TN), and total
phosphorus (TP) load for the total inflow andoutflow were obtained (Table 1). For each item, theoutflow load was lower than the inflow load. TOC(113.8 g C day-1), TN (100.3 g N day-1), and TP(10.4 g P day-1) accumulated in the lake due tointernal production. These results conclude that the
new sediments derived from internal production of
the lake.
Firstly, depth decrease measured during 1951 to
1979 was 80 cm over 28 years. When this value is

Fig.9 Changes of maximum depth at lake Fukamiike

Fig.10 Seasonal changes of sediment rate ( from2007 to 2008)
in 1992, sedimentation rate of 3.1 cm yr-1 [10] wasused. Up to 2009, after improvement of lakeshore
wastewater drainage facilities, depth decrease wascalculated using the newly obtained sedimentation
rate of 1.17 cm/yr-1. Results were 40.3 cm (3.1 cm x
13 years) for 1979 to 1992, and 19.9 cm (1.17 cm x
17 years) for 1992 to 2009. A total depth decrease of
60.2 cm was estimated by calculation. The
746

GEOMATE- Osaka, Nov. 16-18, 2015

calculated value is approximately the actual 65 cm
depth decrease. And these results show that before
and after improvement of lakeshore facilities,
Although eutrophication did progress, the rate ofdepth decrease became more gradual.
This report focuses on transparency andsedimentation rate of Fukami-ike, an eutrophic lake.
Using long-term data over half a century, changes ininternal production before and after the lakeshoredevelopment were investigated. Although long-term
variation has been monitored in extensive lake andmarsh areas, there has currently not been muchresearch data from long-term monitoring of smalllakes. This lake, however, has been affected bylakeshore development; through transparency andsedimentation rate changes, we were able to showthat the resulting cessation of sewage introduction
by improvement of farm village drainage facilities
had a large effect on internal production of the lake.
Although eutrophication has advanced despite
increased annual average transparency anddecreased sedimentation rate, it has become more
gradual following lakeshore drainage facilities
improvement. Since blue-green algae was stillobserved following lakeshore facilities improvement,
it would not be accurate to assume that man-induced
eutrophication problems have been solved. By
continuing the observation, we hope to more clearly
understand the effect of the improving lakeshore
wastewater facilities on lakes and marshes.

sedimentation rate decreased considerably in thepond,decreasing
theinternal
production
significantly.
[2] Limnology Comittee of Shimoina Kyoiku-kai``Shimoina-shi”. Editorial Committee of
Shimoina-shi 2009 (in Japanese).
[3] Yagi, A., I. Shimodaira, H. Terai and Y. Saijo,`` Seasonal change of chlorophyll-a andBacteriochlorophyll in Lake Fukami-ike”. Jpn. J.
Limnol, 1983 44:283-292.
[4] Koyama T., Handa N. and Sugimura Y.
``Analysis of lake and sea water”, Kodansya
scientific 1983 (in Japanese).
[5] Ueno, M. ``Lake Fukami-ike in Shimoinagun,
Nagano Prefecture”, An educational associationof Shimoninagun, IIda. 1952 (in Japanese).
[6] Sakurai Y. and Watanabe Y. ``Limnoligy ofShinsyu area”, No.1, Society for scientific studyof environmental chemistry 1974 (in Japanese).
[7] Terai, H. ``Influence of precipitation on
development of the denitrification process in
Lake Fukami-ike”, Jpn. J. Limnol., 1988,
49:87-92.
[8] Edward A. Laws ``Basic sience of water
environment”, Gihodo shuppan Co., Ltd, 1996
(in Japanese).
[9] Haga H. and Otsuka T. ``Changes intransparency (Secchi disk reading) of thepelagic zone of the main basin of Lake Biwa
over a span of 73 years”, The Japanese Societyof Limnology, 2003, 64:133-139. (Translated
from Japanese with English abstract)[10] Yagi A., Miyachi S. and Shimodaira I ``Change
in depth by sedimentation of autochtonousorganic sunstances in eutrophic lake”, Journalof Nagoya Women’s University, 1993,
No.39:77-82 (in Japanese).

REFERENCES[1] Nagano pref. ``Improvement project of water
environment report”, 1991 (in japanese).

747

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

MODELING OF FLUID INTRUSION INTO POROUS MEDIA
WITH MIXED WETTABILITIES USING PORE NETWORK
Junichiro Takeuchi, Wataru Sumii and Masayuki Fujihara
Graduate School of Agriculture, Kyoto University, Japan

ABSTRACT
Water or air intrusion into a porous medium filled with the other fluid is affected by many factors such assize, configuration, and connectivity of pores as well as the contact angle of grain. To deal with these pore-scale
factors, a pore network model is utilized. According to the modified Delaunay method, pore networks are
extracted from randomly packed spherical grains computed by the distinct element method. Displacement
processes of two immiscible fluid in a porous medium with mixed wettabilities, are modeled in an invasion
percolation manner, in which simultaneous invasion is allowed, and water retention curves in drainage andimbibition processes are computed. The results obtained show that hydrophobic grains do not have just a strong
effect on the water retention properties, but also the amount of water/air residue after a drainage/imbibition
process depends on the invasion rate.
Keywords: Water Retention Curve, Residual Water, Trapped Air, Invasion Percolation

network comprised of relatively large pore bodies
connected by narrower pore throats are generally
utilized up to the present. The topology of a network
can also be represented by a regular two- or threedimensional lattice, in which sites (pore bodies)
have a common coordinate number, which is thenumber of bonds (pore throats) connected to the site.
But real porous media have more irregular structures
regarding the coordinate number, pore size, andbond length. And so, several network extraction
methods from pixelated images of pores and grains
by X-ray tomography or a randomly packed spheresthat are simulated by numerical models such as thediscrete element method (DEM) have beendeveloped [11]-[12].
Percolation theory, which is a branch ofprobability theory dealing with properties of a
network media [7], is used to analyze theconnectivity of a network when sites or bonds have
two different states: ‘open’ or ‘close’, with a certain
probability. For two-phase immiscible displacements
governed by capillary effects in a pore network, a
special version of the percolation theory known as
the invasion percolation is often applied, e.g., [6]-[9].
The invasion percolation originally proposed by
Wilkinson and Willemsen (1983) [3] is a dynamic
percolation process, and accessibility rule is imposed
for movement of interfaces between invading anddefending fluids. Accessibility to an outside fluid
reservoir through sites or bonds filled by the samekind fluid is required to both invading and defending
fluids, or to either fluid, depending on the materials
and situations of interest.
Water-repellent soils, which have resistance tospontaneous water intrusion, have been reported

INTRODUCTION
Soil is an essential medium for plants andanimals, because it does not only supports primary
producers physically, but is also a supply source ofwater and nutrients through its pores. So, a large
amount of works have involved figuring out themechanism of soil properties such as thepermeability and the water retention properties.
While such hydraulic properties of porous media aremacroscopic, they originate from microscopic
structures such as pore size, configuration,connectivity, and wettability as well as fluid
physical properties. Further, the fact that theconstitutive relation depends on the history of fluid
distribution and the direction of fluid movement
complicates matters. One of the methodologies that
estimate soil’s properties is based on the pore
network model, which treats a pore space in a
porous medium as an assemblage of pore segments
and incorporates pore-level mechanisms like
capillary phenomena. Since the network model forporous media was firstly proposed by Fatt (1956) [1],the pore network approach has been applied in many
disciplines, such as the petroleum engineering, e.g.,[2]-[4], the hydrology and soil physics [5]-[9].
Through a series of works, the structure andgeneration methods of pore networks, and thealgorithms for transport of fluids have evolved by
new findings and the development of high-spec
apparatuses [10]-[12].
Fatt (1956) [1] was the first to utilize a twodimensional regular cubic network of cylindricaltubes joining at volumeless junctions. After
Mohanty and Salter (1982) [2] introduced, a pore748

GEOMATE- Osaka, Nov. 16-18, 2015

from the early 1900’s in various countries such as
New Zealand, Australia, and Netherlands [13].
Many works revealed that hydrophobic grains mixed
into normal (hydrophilic) grains have potential tochange the hydraulic properties drastically [14], [15].
Ustohal et al. (1998) proposed a functional model topredict the hydraulic properties of porous media
variously mixed with hydrophobic grains bystochastically integrating clusters that containmultiple grains, based on the Brooks-Corey model[15]. Takeuchi et al. (2014) extended the model byUstohal et al. (1998) and developed a distributedmodel to estimate the water retention properties [16].
However, in the cluster-based models the influencesby hydrophobic grains are blunted when larger
clusters are used. Moreover, the distribution of poresizes, which is essential for estimating the hydraulicproperties, must be identified with observed data
indirectly or from literature.
The objective of this study is to develop a pore
network model for predicting the hydraulicproperties of porous media mixed with hydrophobic
grains at various fractions, incorporating pore-level
capillary actions. In this paper, changes of water
retention properties by mixed hydrophobic grains
are investigated, focusing on the algorithm used in
the invasion percolation. While invading fluid enters
only the most invadable pore in one step in theordinary invasion percolation, simultaneous
invasions are allowed in this study, and the effects
are examined through numerical experiments.

tessellation is a widely used spatial segmentation
method for generation of computational meshes usedin numerical simulations. After a 3-D domain isdiscretized into tetrahedral subdomains, based on thespherical grain’s centers as nodes, a network is
generated by connecting inner points of neighboring
subdomains. But the network has two significant
problems. First, the network has a fixed coordination
number, which equals to four since a tetrahedron has
four faces. Second, the location and size of porebodies are not correct. To overcome these problems,a modified approach in which multiple subdomains
are merged for identification of pore body’s locationand size is proposed. A realistic irregular pore
network of randomly packed spherical grains could
be obtained by this modification.
Here, virtual porous media randomly packed
with equally-sized spherical grains are produced by
DEM. To obtain relatively dense porous media, asmall value was given to the friction coefficient inthis study. About 58 hundreds grains are packed in a
cuboidal container as shown in Fig. 1 (a). The lengthof horizontal edge corresponds to the length of 16
straightly lined grains, and the size of a network is
derived from the required minimum network size [6].
The extracted pore network is also shown in Fig. 1(b), and the pore network consists of 14,522 porebodies and 36,761 pore throats. The spheres in Fig. 1(b) represent pore bodies, and the pipes connecting
pore bodies are pore throats. The shape of a porethroat cross-section is given as the void surroundedby grains as shown in Fig. 2, in which cases of threeand four grains are illustrated.
Figure 3 shows the distributions of thecoordinate number, the radii of pore body and throat,and the length of pore throat in the extracted pore
network. The radius of a pore throat is represented
as that of the maximum inscribed circle locating

MODEL DESCRIPTION
Generation of Pore Network ModelIn this study, the modified Delaunay-tessellation
approach proposed by Al-Raoush et al. (2003) [11]is employed to generate pore networks. Delaunay

Pore throat

Inscribed
circleGrain(a) Three grains(b) Four grains(a) Porous medium model(b) Pore network modelFig. 1 Extraction of pore network

749

Fig. 2 Cross-sectionof pore throat

0.4Frequency (-)

0.6

0.2

0.3

0.0
2

4

6

8 10 12 14 16 18 20

0.0
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Neighbor mean
coordinate number (-)

GEOMATE- Osaka, Nov. 16-18, 2015

10

0

0

20

10

(a) Coordinate number (-)(b) Pore body radius (0.1 mm)(a) Coordinate number (-)

20

0.4

0.1

0.2

Neighbor meanpore body radius(0.1 mm)

Frequency (-)

2.0
0.2

0.0
0.0
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0(c) Pore throat length (0.1 mm)(d) Pore throat radius (0.1 mm)

Fig. 3 Histograms of coordinate number and pore sizes

inside the void surrounded by grains as illustrated inFig. 2. The radius of a pore body is also represented
as that of the maximum inscribed sphere locating
inside the void in the same way. The length of a porethroat represents the length between the centers ofpore bodies connected by the pore throat.
Figure 4 shows the correlations of the coordinate
number and pore body size between pore bodies
connected by a throat. The graphs in both figures arealmost flat, and this means that there is no spatial
correlation in the coordinate number and the size
between pore bodies.

1.0

0.0
0.0

1.0

2.0

(b) Pore body radius(0.1 mm)Fig. 4 Correlation ofneighbor pore bodythroats here, have two states: ‘Water’ and ‘Air’. The
Water/Air state represents the pore is filled withwater/air. Therefore, the meniscus is formed on aboundary of a pore body in one state and a neighborpore throat in the other state. Whether one fluid canintrude into the other fluid or not depends on themagnitude relationship between the capillarypressure PC on the interface and the capillary entry
pressure PE assigned to all pores. These are givenby the following equations.
P=Pa − Pw(1)CPE =σ cos ϕ e(2)R
where Pa and Pw are the air and water pressures at
In this study, it is assumed that one fluid
the vicinity of the interface, respectively, σ is thedisplaces the other fluid at a sufficiently low
interfacial tension, ϕ e is the equivalent contact
capillary number. So, the movement of an interface
angle of the pore, and R ( = A / L ) is the hydraulicbetween two immiscible fluids occurs quasiradius, A is the cross-section area of the pore, andstatically by capillary forces. In addition, only
L is the perimeter of the pore cross-section. In thispiston-like displacements are assumed in bothstudy, a pore body are represented as a sphere,
drainage and imbibition processes. According towhich is the maximum inscribed sphere in the void
Dullien et al. (1989) [17], water residue in a porous
surrounded by multiple grains as described in themedium packed with glass beads with smooth
subsection of ‘Generation of Pore Network Model’.
surface was approximately 10% after a drainage
A pore throat is represented by a tube with constantprocess, while that in a medium with roughened
cross-section, and the shape of the cross-section is
glass beads by etching was approximately 1%. This
represented as a grain boundary void, which has
result indicates that the effects of wetting layers andmore than three cusps, as illustrated in Fig. 2.
films are negligible when smooth glass beads are
The equivalent contact angle ϕ e of a pore isused.
assumed to be given by the Cassie-Baxter equation.
Furthermore, neither residue in a drainage
When the pore is composed of multiple grains withprocess nor pre-invasion in an imbibition process
two types of wettability, the equivalent contact angle
along the corners of hydrophilic pore throats andis described as follows.
bodies by wetting fluid are considered in this study.
N1
N2
Accordingly, pendular rings of wetting fluid around=cos ϕ ecos ϕ1 +cos ϕ 2 (3)N1 + N 2
N1 + N 2
the two grains contact are not considered in thismodel.
where N1 and N 2 are the numbers of hydrophilic
Pores, which represents both of pore bodies andPore-level Description

750

GEOMATE- Osaka, Nov. 16-18, 2015and hydrophobic grains surrounding the pore,
respectively; and ϕ1 and ϕ 2 are the contact angles
of hydrophilic and hydrophobic grains, respectively.
In addition to these equations, invadability I ,which is an index that represents the degree of
easiness when one fluid invades a pore filled withthe other fluid, is defined as follows.
=
I PC − PE(4)which are defending against air intrusion andsatisfy the accessibility rule, and sort these indescending order based on the invadability value.
3. Among the listed pore bodies and throats, theIPinterfaces of the top N in the list are moved
simultaneously (the state in the pores is changed
from Water to Air) if the invadability value ispositive.
4. Iterate the processes 2 and 3 until no state change
occurs, and saturation of the pore network is
calculated when a steady state is achieved.
If the next sample is computed continuously, theobtained result is used as an initial condition and thewater pressure imposed on the bottom is decreased
in a stepwise manner, and go back to 2. This process
corresponds to the suction method and pressure plate
method, in which one sample is used continuously
under a gradually changing pressure condition. In
contrast, in the soil column method, different
samples are used for each pressure condition. So, if
the soil column method is supposed, go back to 1
and start from the dry condition for the next pressure.
In an ordinary invasion percolation process,invading fluid is allowed to intrude into only one
IPpore that have the largest invadability, i.e., N = 1 .
In this study, simultaneous intrusions into multiple
pores are allowed, and this type of invasion
percolation is termed as a generalized invasion
percolation here. The simultaneous intrusions are
considered to correspond to a drainage process withrelatively large capillary number.
In a case of an imbibition process, the algorithm
is described in the same way with the drainage
process. The main difference is in the list of
candidates. The list in an imbibition process is sorted
in an ascending order of the invadability, while it issorted in a descending order in a drainage process.
IP
Then the top N of pores would be invaded bywater if the invadability value is negative.

In a drainage process, invading fluid (air) can
intrude into a pore, only when the invadability is
positive. On the other hand, invading fluid (water)
can intrude in an imbibition process, only when theinvadability is negative.
Generalized Invasion Percolation
Two types of boundary are given to the pore
network: one is a no-flow boundary, and the other isan open-flow boundary. A sufficiently large
reservoir which is a supply source or sink isconnected to the open-flow boundary. In this study,
the no-flow boundary is given to the four lateralfaces, and the open-flow boundary to the top andbottom faces. The top face contacts with an air
reservoir, and the bottom with a water reservoir. The
air pressure inside and outside of the pore network is
set as a constant, and the water pressure inside of thepore network is determined by the pressure imposedon the bottom, assuming the hydrostatic pressure.
Drainage and imbibition processes are describedin an invasion-percolation manner. In the invasion
percolation, fluids are required to satisfy anaccessibility rule in addition to the invadability
condition. In this study, the accessibility rule is
imposed to both invading and defending fluids toconnect to each other’s reservoir through pores filledwith the same fluid, when the interface moves. The
algorithm of the invasion percolation forcomputation of one sample in a drainage process is
described as follows:
1. All the pore bodies and throats are filled withwater, and a certain water pressure is imposed on
the bottom of the pore network.
2. Produce a list of all the pore bodies and throats

WATER RETENTION PROPERTIES
Experimental Results
Toyoura standard sand and glass beads are usedas materials. The mean diameter of Toyoura sand is

-40

0%

75 %

25 %

100 %

Matric potential [cmH2O]

Matric potential [cmH2O]

-40
-60

50 %

-20

0
0.0

0.1
0.2
0.3
0.4
Volumetric water content (-)

0.0

0%

50 %

25 %

75 %

100 %

-20

0

20

40

0.1
0.2
0.3
0.4
Volumetric water content (-)0.0(a) Toyoura sands(b) Glass beads
5-1 Drainage process

0.1
0.2
0.3
0.4
Volumetric water content (-)

0.0

0.1
0.2
0.3
0.4
Volumetric water content (-)(a) Toyoura sands(b) Glass beads
5-2 Imbibition process

Fig. 5 Water retention curves751

GEOMATE- Osaka, Nov. 16-18, 2015

IP
number of simultaneous intrusions N and theinitial condition of each sample. Here two options
are given in each setting. Regarding the intrusion
number, N IP is limited to ten in one case, and nolimitation is given in the other case. Regarding theinitial condition, the previous computed result isgiven as an initial condition in one case, and thesaturated/dry
condition
is
given
in
a
drainage/imbibition process in the other. Therefore,
four cases are considered by the combination ofthese options. Among these four cases, two cases areshown below: one is the combination of ‘limited toten’ and ‘previous result’, and the other is the
combination of ‘no limitation’ and ‘saturated/dry
condition’. The former is referred to as Case 1, andthe latter as Case 2 in this study. The results of therest two cases were pretty much the same with Case
1. It is considered that these three cases correspondto a relatively small capillary number, and that Case
2 to a relatively large capillary number.
The computed water retention curves of Case 1
and Case 2 are shown in Fig. 6, and the computed
states of pores in the pore network are shown in Fig.
7, where the black pores represent ones occupied bywater and the white pores by air. Figure 6 shows thatthe water retention curves are altered byabout 0.25 mm, and that of glass beads is 0.2 mm.
Toyoura sand and glass beads are hydrophobized byperfluorooctylethltrichlorosilane (CF 3 (CF 2 ) 7 (CH 2 ) 2
SiCl 3 ) and octyltrichlorosilane (CH 3 (CH 2 ) 7 SiCl 3 ),respectively. Figure 5 shows the measured water
retention curves of each material mixed withhydrophobic grains at various mixture fractions by
the soil column method [16]. From these figures, it
is found that water retention curves are largely
altered by the mixed hydrophobic grains, and thatthe degree of the alteration depends on the mixture
fraction.
Computational Results
Water retention curves are computed using theabove mentioned pore network model in thegeneralized invasion percolation manner. The
contact angles of the hydrophilic and hydrophobic
grains are given as 58 degrees and 102 degrees,
based on the measurement of the apparent contact
angles of glass beads with the sessile drop method[18]. And common values are used for drainage andimbibition processes without considering receding
and advancing contact angles. Several options canbe considered in the computational settings about the
-100
0%

75 %

25 %

100 %

-40

Matric Potential (cmH2O)

Matric Potential (cmH2O)

-80

50 %

-60
-40
-20
0

0%

75 %

25 %

100 %

50 %
-20

0

20

20
40

40
0.0

0.5
Saturation (-)

1.0

0.0

0.5
Saturation (-)

0.0

1.0

(a) Case 1(b) Case 2
6-1 Drainage process

0.5
Saturation (-)

1.0

0.0

0.5
Saturation (-)(a) Case 1(b) Case 2
6-2 Imbibition process

Fig. 6 Computed Water Retention Curves: Water-filled pore
: Air-filled pore(a) Case 1(b) Case 2
7-1 Drainage process(Pressure on bottom = -100 cmH2O)(a) Case 1(b) Case 2
7-2 Imbibition process(Pressure on bottom = 0 cmH2O)

Fig. 7 Trapped Water/Air in Pore Network (Mixture Fraction = 0%)

752

1.0

GEOMATE- Osaka, Nov. 16-18, 2015[3] Wilkinson D and Willemsen JF, “Invasion
percolation: a new form of percolation”, J. Phys.
A: Math. Gen., Vol. 16, 1983, pp.3365-3376.
[4] Piri M and Blunt MJ, “Pore-scale modeling of
three-phase flow in mixed-wet systems”, SPE
77726 the SPE Annual Tech. Conf. and Ex., San
Antonio, Texas, Sep., 2002.
[5] Jerauld GR and Salter SJ, “The effect of porestructure on hysteresis in relative permeability
and capillary pressure: pore-level modeling”,
Transport in Porous Media, Vol. 5, 1990, pp.
103-151.
[6] Soll WE and Celia MA, “A modified
percolation approach to simulating three-fluid
capillary pressure-saturation relationships”, Adv.
Water Resour., Vol. 16, 1993, pp. 107-126.
[7] Berkowitz B and Ewing R, “Percolation theory
and network modeling applications in soil
physics”, Surveys in Geophysics, Vol. 19, 1998,
pp. 23-72.
[8] Raoof A and Hassanizadeh SM, “A newformulation for pore-network modeling of twophase flow”, Water Resour. Res., Vol. 48, 2012,
W01514, doi: 10.1029/2010WR010180.
[9] Yang Z, Niemi A, Fagerlund F, andIllangasekare T, “Two-phase flow in roughwalled fractures: comparison of continuum andinvasion-percolation models”, Water Resour.
Res., Vol. 49, 2013, doi: 10.1002/wrcr.20111.
[10] Blunt MJ, “Flow in porous media – porenetwork models and multiphase flow”, Current
Opinion in Colloid & Interface Science, Vol. 6,
2001, pp. 197-207.
[11] Al-Raoush R, Thompson K, and Willson CS,“Comparison of network generation techniquefor unconsolidated porous media”, Soil Sci. Soc.
Am. J., Vol. 67, 2003, pp. 1687-1700.
[12] Ngom NF, Garnier P, Monga O, and Peth
Stephan, “Extraction of three-dimensional soil
pore space from microtomography images using
a geometrical approach”, Geoderma, Vol. 163,
2011, 127-134.
[13] DeBano LF, “Water repellency in soils: ahistorical overview”, J. Hydrol., Vol. 231-232,
2000, pp. 4-32.
[14] Annaka T, “Wettability indices and water
characteristics for sands of mixed wettability”, J.
Jpn. Soc. Soil Phys., Vol. 102, 2006, pp. 79-86.
[15] Ustohal P, Dtauffer F, and Dracos T.
“Measurement and modeling of hydrauliccharacteristics of unsaturated porous media with
mixed wettabilities”, J. Contam. Hydrol., Vol.
33. 1998, pp. 5-37.
[16] Takeuchi J, Takahashi T, and Fujihara M, “SubDarcy scale modeling of non-uniform flow
through porous media with mixed wettabilities”,
Int. J. GEOMATE, Vol. 6, 2014, pp. 840-847.
[17] Dullien FAL, Francis Lai FSY, and Macdonald
IF, “Hydraulic continuity of residual wettinghydrophobic grains in common with theexperimental results. Some discrepancy is seen
especially in the drainage process between theexperimental and computational results. Whilerelatively small differences among the various
mixture fractions are seen in the experimental results(Fig. 5-1), relatively large differences are seen in thecomputational results (Fig. 6-1). This suggests thatdifferent contact angles, or receding and advancing
contact angles, should be used for evaluating thedrainage and imbibition processes.
The main difference between Case 1 and Case 2
is seen in residual water in the drainage process andin trapped air in the imbibition process. While 10%of water or air remains after each process in Case 2,
30% to 40% is left behind in Case 1. From Fig. 7,
residual water is seen in clusters of relatively small
pore bodies, and trapped air are seen in clusters ofrelatively large pore bodies in Case 1. In a drainage
process, such clusters of smaller pore bodies areformed because water is drained firstly from asequence of larger pore bodies ahead of smaller ones
and the connectivity between the clusters of smaller
pore bodies and the outside reservoir is cut off. In
the case of the imbibition process, clusters ofrelatively large pore bodies are formed in the sameway.
CONCLUSION
Numerical experiments for water retention
properties of porous media with mixed wettabilities
were conducted, using an extracted pore networkfrom randomly packed spherical grains. The
air/water intrusion process are modeled by thegeneralized invasion percolation, in which
simultaneous intrusions are allowed. Single or asmall number of simultaneous intrusions correspondto a relatively small capillary number, and all or alarge number of simultaneous intrusions corresponds
to a relatively large capillary number. The
computational results showed a similarity to theexperimental ones in regard to the dependence of the
mixture fraction, and that the amount of residual
water and trapped air depends on the invasion rate.
ACKNOWLEDGEMENTS
This work was supported by JSPS KAKENHI
Grant Number 24780232.
REFERENCES[1] Fatt I, “The network model of porous media”,
Trans. AIME, Vol. 207, 1956, pp. 144-181.
[2] Mohanty KK and Salter SJ, “Multiphase flow inporous media: II. Pore-level modeling”, SPE
11018 the 57th Tech. Conf. at SPE-AIME, New
Orleans, Sep., 1982, pp. 26-29.
753

GEOMATE- Osaka, Nov. 16-18, 2015

phase in porous media”, J. Colloid Interface Sci.,
Vol. 109, 1986, pp. 201-2018.
[18] Bachmann J, Ellies A, and Hartge KH,“Development and application of a new sessile
drop contact angle method to assess soil water
repellency”, J. Hydrol., Vol. 231-232, 2000, pp.
66-75.

754

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

DEVELOPMENT OF TERRESTRIAL AND HYDROPHYTIC FLORA
IN RELATION TO WATER MANAGEMENT IN OBASUTE-OIKE
IRRIGATION POND IN JAPANTeruo Arase1, Tetsuo Okano1, Yoshiyuki Uchikawa1 and Taizo Uchida2
Faculty of Agriculture, Shinshu University, Japan; 2Faculty of Engineering,
Kyushu Sangyo University, Japan

1

ABSTRACT
In order to clarify the factors affecting the species diversity of plant communities around an irrigation pond,
we surveyed Obaste-oike Irrigation Pond in Nagano Prefecture, Japan. The pond is comprised of threesubdivided ponds, Kami-ike, Naka-ike, and Shimo-ike. Despite being adjacent to each other and connected bystreams, the water levels in the three ponds differ. Five plant communities were studied, and one or two survey
plots were selected in each community along each subdivided pond. A 32-m2 quadrat was established in eachplot of grassland, and a 400-m2 quadrat was established in each plot of forest. The results of the distribution of
species revealed that approximately 40% of the species were endemic to each subdivided pond in the terrestrial
communities, whereas over 60% were endemic in the hydrophytic communities. To increase species diversity inirrigation ponds, especially in hyrdophytic communities, we suggest that dividing the ponds into several zones
with distinct water levels and managing them in a sustainable manner is important.
Keywords: Obaste-oike Irrigation Pond, Water Level, Hydrophytic Plant Community, Species Diversity
irrigation pond functions, we should pay close
attention to the impact that water use for agriculture
or other human uses has on the ecosystem aroundirrigation ponds. Therefore, we decided to focus on
the fluctuation of water levels rather than water
quality since water levels are directly linked to the
distinctiveness of irrigation ponds. A better
understanding of the influence of water levels in
irrigation ponds will facilitate comprehension of
how plant communities develop, and also provide
information on how to maintain and control
irrigation ponds from the viewpoint of plantcommunities with high diversity.
We studied Obaste-oike Irrigation Pond (Fig. 1
and Table 1), which experiences large fluctuations inwater level. The pond is comprised of threesubdivided adjacent ponds (Kami-ike, Naka-ike, andShimo-ike). Since both the spilled water (through asluice gate) and the deep water (through a bottom
sluice) are led into the adjacent subdivided ponds(Fig. 1), the three subdivided ponds are estimated tohave little differences in water quality. On thecontrary, large fluctuations in the water levels of thesubdivided ponds occur because water is used fromeach pond in a pre-defined order (firstly from
Shimo-ike, next from Naka-ike, and last from Kamiike) owing to water shortage (Table 1) [5], [6].
Consequently, it is appropriate to assume that thefluctuation in water level would be larger in Shimoike than Naka-ike, and Naka-ike than Kami-ike.
As for hydrophytic plant communities in Obasteoike Irrigation Pond, a previous study found that

INTRODUCTION
Recently, irrigation ponds are expected to havevarious functions, such as ecological conservation,
disaster damage prevention, and scenic health resorts[1]. In ecological conservation, it is desirable that
ecosystems include numerous plants and animals
with high species diversity. However, according to
the species-area curve theory, the number of species
generally increases and reaches a maximum as thearea increases [2], [3]; it follows as a consequencethat a small area may include a low number of
species. Is there anything that can be done toincrease species diversity around an irrigation pond
besides increasing its area?
Irrigation ponds are artificial reservoirs that holdwater, and in Japan, they are mainly connected withrice paddy fields. Water demands in downstream
areas regulate the water level of irrigation ponds. For
example, in rice paddy fields, fine adjustments are
made to control the water level to suit the growth ofrice. Thus, the water level of irrigation ponds
fluctuates largely within a year compared withnatural water bodies that maintain an almost
constant water level [1].
To date, many reports on species diversity inirrigation ponds have focused on hydrophytic
vegetation, but most underrate the influence of waterlevel. For example, a study by Shimoda and Kagawa
concentrated on conventional water quality analyses[4]. However, since the function of supplying water
for agriculture is more important than all other755

GEOMATE- Osaka, Nov. 16-18, 2015

Table 1 Comparison of the three subdivided pondsof Obasute-oike Irrigation Pond.
Items

Kami-ike

Nakaike
28,900

Shimoike
40,000

Water reserves 191,500(m3)
Maximum
61,00015,00012,000filling area (m2)
Maximum
5.1
2.8
8.1
water depth (m)Order of water
last
first
use
Frequency oflowhighsluice control
Fluctuation of(small)(large)water level
Note: This information was previously tabulated by
the authors [5].
but water shortages were still so severe that
irrigation disputes occasionally took place among
villages. In 1946, Shimo-ike was added to help
alleviate water shortages.

Fig. 1
The water system of Obaste-oike
Irrigation Pond.
approximately 66% of the species surveyed wereendemic to each subdivided pond [5]. The low
species similarity among the three subdivided ponds
implies that the hydrophytic communities in eachsubdivided pond appear to have developed
independently [5]. However, since the flora of theterrestrial plant communities surrounding the pond
was not studied in the previous report [5], it cannot
be confirmed whether water level fluctuations
caused endemic hydrophytic flora to develop
independently in each subdivided pond.
Thus, in the present study, we investigated bothhydrophytic and terrestrial plant communities
surrounding the subdivided ponds of Obaste-oike
Irrigation Pond. By analyzing the species
distribution among the three subdivided ponds, weaimed to gain information on the development offlora and the influence water level fluctuations. We
also aimed to offer technical recommendations formaintaining and controlling irrigation ponds withhigh-diversity plant communities.

Vegetation Survey
The vegetation of the investigation site wasclassified into five plant communities (Fig. 2) as
follows: A) terrestrial plant communities composedof A1) short grasslands on the banks, and A2)
mountainside forests; and B) hydrophytic plantcommunities composed of B1) marshes at the foot ofa mountain, B2) grasslands along the shores, andB3) the water area.

METHODS
Investigation Site
Obasute-oike is located at an elevation of 820 m
in Chikuma Heights, Chikuma City, Nagano
Prefecture, Japan. It irrigates the Obaste-tanada area,
which contains famous terraced paddy fields with anarea of about 820,000 m2.
This pond was originally constructed in the Edo
period (1600-1868), encouraging the use of watercollected in a hollow formed by a landslide. The
banks were raised in the Taisho period (1912-1926),

Fig. 2 Distribution of plant communities.
756

GEOMATE- Osaka, Nov. 16-18, 2015

Table 2 Number and distribution of plant species in the three subdivided ponds of Obaste-oike Irrigation Pond.
Plant
community
A: Terrestrial
A1%
subtotal %A2%subtotal %B: Hydrophytic
B1%
subtotal %B2%
subtotal %B3%
subtotal %Endemic species
Kami- Naka- ShimoIke
ike
ike
5
12.2

6

22
13.8

21
13.2

5
14.6

31
33.7

27
29.3

7

10
17.2

12.1
1

2
16.7

Kami-ikeNaka-ike

33.3

3

12.2
39.0
23
14.5
41.5

17

6

16

6.5
69.6
20
34.5
63.8
1
16.7
66.7

Common species
Naka-ike
Kami-ike
Shimo-ike Shimo-ike
7

7.3

3
17.1

17
10.7

14
10.7

4
17.4

8

8.8
2

4.3
3

13.8
1

7.3

5.2

16.7

100.0
159

6

92
6.5
30.4

7
5.2

0
0.0

12
29.3
61.0
45
28.3
58.5

2.2
3

0

Total
All the3 ponds

12.1
36.2
1

0.0

16.7
33.3

41

100.0

100.0
58
100.0
6
100.0

tested using the χ2-test performed with a spreadsheet
program (Microsoft Excel 2007).

The sizes of the investigation plots were 32 m2(4 m × 8 m) for A1, B1, and B2, and 400 m2 (20 m ×20 m) for A2. As for B3, we observed the wholewater area from the shores and used a boat to createa list of the plant species present because of its
sparse vegetation. Two investigation plots were
established in each of A2, B1, and B2 in Kami-ike
and Naka-ike. Only one investigation plot wasestablished in each of the other plant communities
because of their limited area.
Vegetation surveys of hydrophytic plantcommunities (B1, B2, and B3) were conducted in
2010, and those of terrestrial plant communities (A1and A2) in 2014 and 2015. The plant coverage ofeach plant species was measured in the plots of A1,
A2, B1, and B2. We employed the following classesof plant coverage in each plot for the field survey: +,the plant species covered less than 1% of the plot
area; 1, the plant species covered from 1 to 9%; 2,
the plant species covered from 10 to 24%; 3, theplant species covered from 25 to 49%; 4, the plant
species covered from 50 to 74%; and 5, the plant
species covered from 75 to 100%.
To determine the number and distribution ofplant species, the plant coverage data wereconverted into presence or absence data (1 or 0,
respectively), and categorized by subdivided pond.
Species found in only one of the subdivided ponds
were considered “endemic species”, while thosefound in two or three of the subdivided ponds wereconsidered “common species”. The difference in the
categorized totals among plant communities was

RESULTS
Vegetation
Terrestrial plant communities
Short grasslands on the banks (A1) along eachsubdivided pond were dominated by orchard grass(Dactylis glomerata) and tall fescue (Festuca
arundinacea). This plant community seemed to be
cut frequently.
In mountainside forests (A2) along eachsubdivided pond, the tree stratum was dominated by
Japanese red cedar (Cryptomeria japonica) anddeciduous broad-leaved trees such as Japanesechestnut (Castanea crenata), a species of willow(Salix sp.), and konara oak (Quercus serrata). The
herbaceous stratum was dominated by ferns such as
Dryopteris crassirhizoma, a bamboo grass(Sasamorpha borealis), and sedges such as Carex
stenostachys.
Hydrophytic plant communities
Marshes at the foot of a mountain (B1) werecolonized by the common reed (Phragmites
australis), but its coverage class ranged from + to 3
among plots. Leersia japonica and Impatiens textori
were also found in each subdivided pond.
757

GEOMATE- Osaka, Nov. 16-18, 2015

response to the microenvironment.
Based on the results of the present study, sometechnical recommendations can be made to maintainand control irrigation ponds with high-diversity
plant communities. If the environment of a pond ishomogeneous, the number of plant species will belimited by its area, as a consequence of the speciesarea curve theory [2], [3]. It seems difficult toincrease species diversity in terrestrial plantcommunities because of the lack of regulating
factors as substantial as water. However, inhydrophytic plant communities, dividing the area
into several zones with different microenvironments(i.e. different water depths or flooding frequencies)
is expected to increase species diversity beyond thecapacity of its area, as demonstrated in thesubdivided ponds of Obasute-oike Irrigation Pond. It
is important to create a heterogeneous environment
throughout the area, and to establish and maintainseveral zones of distinct microenvironments.
However, the water level in an irrigation pond
can be maintained on the assumption that someone
regulates it. In Obaste-oike Irrigation Pond, the mostfundamental method of adjusting the water level isthe indispensable water demand of continuing
traditional rice cultivation in the downstream areas.

Grasslands along the shores (B2) were colonizedby some emergent plants such as Leersia japonica,
Scirpus wichurae, and Scirpus triqueter, but theircoverage class ranged from + to 4 among plots.
In the water area (B3), only water chestnut(Trapa japonica) was observed to be a common
species. Some submerged plants such as
Potamogeton crispus were present, but existed very
sparsely in the plant community.
Number and Distribution of Plant Species
Table 2 shows the number and distribution ofplant species in the three subdivided ponds of
Obaste-oike Irrigation Pond. The number of species
wasdifferent
among
plant
communities;
mountainside forests (A2) had the highest number of
species at 159, whereas the water area (B3) had theleast at 6. The grasslands of A1, B1, and B2 had 41,
92, and 58 species, respectively (Table 2).
In terrestrial plant communities (A1 and A2), thepercentages of endemic and common species were
nearly 40% and 60%, respectively. On the contrary,these percentages were reversed in hydrophytic
communities (B1, B2, and B3) where thepercentages of endemic and common species wereless than 40% and over 60%, respectively (Table 2).
A significant difference in the categorized totals was
detected between terrestrial and hydrophytic
communities (χ2-test, p<0.0001).

REFERENCES[1] Uchida, K., Irrigation ponds: the multifaceted
functions and application. Tokyo: Norin Tokei
Kyokai, 2008, 171pp.
[2] Arrhenius, O., “Species and area”, Journal of
Ecology, Vol. 9, 1921, pp. 95-99[3] Preston, F.W., “The canonical distribution ofcommonness and rarity: part I”, Ecology, Vol.
43, 1962, pp. 185-215[4] Shimoda, M. and Kagawa, H., ”Aquatic plant
distribution of irrigation ponds in relation toland use and water quality in an agriculturallandscape dominated by citrus orchards in Hojo
area, Shikoku Island, southwestern Japan”,
Vegetation Science, Vol. 26, 2009, pp. 65-78.
[5] Arase, T., Kumagai, M., Uchikawa, Y., Okano,T., Maruyama, K. and Uchida, T., “Growth ofhydrophytic communities in relation to water
management in Lake Obasute-oike, Nagano
Prefecture, central Japan”, Bulletin Shinshu
University Alpine Field Center, No. 10, 2012,
pp. 91-100.
[6] Takeshita, H., “The true state of water use andlandscape conservation in Obasute-tanada
terrestrial paddy fields”, Master Thesis of
Shinshu University, 2010, pp. 27-34.

DISCUSSION
From the results of the percentages of endemic
and common species, a reversal and a similarity
were detected among the five plant communities in
Obasute-oike Irrigation Pond. It is important to notethat the majority of species in terrestrial plantcommunities (A1 and A2) were common species
whereas endemic species were more prominent inhydrophytic plant communities (B1, B2, and B3).
The percentages were relatively close between A1
and A2, and also among B1, B2, and B3, although
the landscape and the total number of species weredistinct in each plant community (Table 2).
The most intrinsic difference between theterrestrial and hydrophytic environment is whether
ordinary water flooding occurs or not. Since thesubdivided ponds are estimated to have little
differences in water quality, it can be assumed thatwater level would be a significant factor in theregulation of plant communities. In terrestrial plantcommunities, relatively similar soil environments
would allow many common species throughout thearea. However, in hydrophytic plant communities,
fluctuations in water level strongly increase themortality of plants and influence their growth, which
results in a flora with many endemic species in758

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

EFFECTS OF WATER FLOW RATE ON RIVER WATERPURIFICATION BY ATTACHED GROWTH MICROORGANISM ON
RIVER BED
- AN EXPERIMENTAL STUDY ABOUT NITRIFICATION -1Fumitake Nishimura1 and Michiko Masuda2
Department of Environmental Engineering, Kyoto University, Japan; 2 Department of Civil Engineering,
Nagoya Institute of Technology, Japan

ABSTRACT
Activity or growth of microorganisms is the key factor for river water purification. Especially the activity of
attached growth microorganism on river bed plays important roles for the purification. The activity can beaffected by several factors such as water temperature, dissolved oxygen concentration, pH, and some otherphysical conditions. Understanding these effects can lead to the effective design and operation to enhance selfpurification ability of rivers.
In this study water flow rate is focused as one of the factors affecting activity or growth of microorganisms
on river bed. Water flow is necessary for growth of microorganisms because material exchange such as
supplying substrate and removal of metabolite can be caused by water flow. However large flow rate can bepromote detachment of microorganisms from river bed and it can result in deterioration of Water Purification
activities.
Experimental study was conducted and it was shown that bacterial growth(nitrifying bacteria) was affectedby water flow rate and the growth rate increased with increase of water flow rate under the condition that theflow rate(water velocity) was within 219(cm/min).
Keywords: Nitrification, Attached growth media, Water flow rate, Water velocity, Bacterial growth

reactor size such as pH, DO, HRT or loading rate,
but also factors which can be affected by the reactor
scale.
In this study, water flow rate in a reactor was
focused as a factor which can be affected by thereactor size, and experimental investigation was
conducted. Polyurethane foam was used as an
attached growth medium. Nitrifying bacteria weretargeted as attached growth bacteria on the media.
The media were fixed in the reactor and the waterflow rate on the media was changed by controllingthe water recirculation rate. It can be expected thatmaterial penetration into the biofilm and its
exchange between biofilm and bulk-water arepromoted with the increase of water flow rate, which
can enhance bacterial activity. On the other hand,shearing stress on the biofilm will also increase as
water flow rate becomes larger, that can promotedetachment of bacteria from the media. Therefore, it
is considered that water flow rate can have both
positive and negative effects on the bacterial activityof the biofilm, and there would be some range which
enhance the activity the most. These effects were
investigated with chemostats.

INTRODUCTION
The key concepts for operating bioreactor areboth concentration of bacteria working on the
biological reaction and enhancement of its biologicalactivity. For this purpose, attached growth media
such as polyurethane foam, activated carbon andimmobilized PVA were applied for accomplishinglong term solid retention time(SRT), and suitableenvironmental conditions about water temperature,
pH, dissolved oxygen(DO) and so on were
investigated.
Usually, these information wereobtained through experiments of which scale were
smaller than that of actual plants, and as results ofthe experiment, operational parameters such as
hydraulic retention time(HRT), loading rates andbiological activities were presented which could beconsidered applicable regardless the scale ofbioreactor. However, some physical parameters
don’t seem to be constant regardless the reactor size,for example, water flow rate in a reactor andshearing stress on bacterial attached growth media
caused by water flow can be different even if HRTs
are the same. Therefore, in order to indicate thesuitable design and operational parameters, not only
factors which seem to be constant regardless of

MATERIALS AND METHODS759

GEOMATE- Brisbane, Nov. 19-21, 2014

with a designed flow rate(HRT 6hr; reactor base) into the bottom of the DO supply tank. The liquid wasrecirculated between two tanks and discharged fromthe top of the DO supply tank. The recirculation rate
was regulated by controlling the valve settled besidethe pump. Three cases of different recirculation
flow rate were set for the experiment. Before
starting the experiment, the attached growth media
were submerged into mixed liquor with activatedsludge from a domestic wastewater treatment plant.
for one day in order to add bacteria into the reactor.
Water samples are periodically collected frominfluent and effluent, and water quality indexes suchas NH 4 +-N, NO 2 --N, NO 3 --N, T-N, S-N, pH, andalkalinity were confirmed to the methods describedin Standard Methods(APHA, AWWA, WEF, 1998)
or Methods for the Examination of Sewage(Japan
Sewage Works Association, 1997). DO and watertemperature at the bulk water in both tanks were alsomeasured and influent water flow rates of all
experimental cases were checked at the same time.
After the day when sufficient amount of bacteria
seem to attach on the media, SS on the surface of
attached growth media and some of the attachedgrowth media were sampled from reactors, and SS
amount and bacterial activity were measured bybatch tests. Bacterial activity was evaluated byoxygen utilization rate(OUR), OUR withallylthiourea(ATU-OUR) which can relate to the
biological activity without nitrifying bacteria.
Bacteria in the reactors were roughly categorized
into two groups based on the location of the attachedgrowth media. One was the group of bacteria
locating on the surface of the media(surface
bacteria), and the other was these locating in the
inner part of the polyurethane foams(inner bacteria).
At 65 days after the start up, there were someamount of bacterial floc around the polyurethane
foam. At that time, bacteria in the reactors were

The schematic diagram of the reactor used in thisstudy is shown in Fig. 1. The reactor was composedof two tanks; one was used for DO supplier where
air diffuser was incorporated at the bottom, the othertank was used as bioreactor(nitrifying tank) whereattached growth media were fixed. Both tanks weremade of acrylic material. The effective volume of
DO supply tank was 2.8L with height of 280mm andsurface area of 100*100mm, whereas that of
nitrifying tank was 2.0L with height of 320mm andsurface area of 80*80mm. The two tanks wereconnected with a pipe between the bottoms, andliquid was recirculated from the DO supply tank tonitrifying tank by a water pump connected on thepipe. An acrylic plate with small pores(φ=5mm)
were placed at the bottom of the nitrifying tank ascurrent plate in order to moderate stream current
from the water pump, 92 pierces of cubic
polyurethane foam with dimensions of 15*12*12mm
was connected by the stainless needles and fixed in
the nitrifying tank. The apparent total volume of the
cubic polyurethane foam was corresponded to 10%
of nitrifying tank.
Artificial wastewater was shown in table 1,
which was synthesized from the typical data of
NH 4 +-N concentration in Japan. It was supplied
artificial
wastewaterP

cubic polyurethane foam
(attached growth media)

tube pumptreated water

Table 1 composition of artificial wastewaterchemicals
concentration
NH 4 Cl
30(mgN/L)
KH 2 PO 4
5(mgP/L)
MgSO 45(mgMg/L)
NaHCO 3257(mgCaCO 3 /L)
Tap water-

diffuser

2.8L

2L
nitrifying tank

Table 2 experimental condition
recirculationwater velocity
water flow rate
Case No.
(cm/min)(L/min)
1
3.0
46.9
2
6.0
93.8
3
14.0
219

DO supply tankP
valve

Fig.1 Schematic diagram of experimental
apparatus
760

GEOMATE- Brisbane, Nov. 19-21, 2014+

NH4 -N （ mgN/L）

sampled for check of the bacterial activity. The
surface bacteria which locate more than about
0.5mm away from the media surface was collectedwith a pipette carefully, and remaining bacteria withthe polyurethane foam was also sampled separately.
The sampled polyurethane foam was put into an
Erlenmeyer flask of 200mL and agitated slowly.
Separated bacteria by this agitation were also usedas surface bacteria, and other bacteria was used as
inner bacteria.
The reactors were operated almost successfully
under the designed conditions.
The water
temperature in each reactor was controlled between
22°C and 26°C during the experimental period, andthe difference among the reactors was less than 1°C.
DO concentration at the bulk water in all of threereactors were kept more than 6.3 mg/L, and thedifference among the reactors was less than 0.5
mg/L. pH was also kept between 7.5 and 8.2, which
doesn’t seem to affect nitrifying activities(U. S. EPA,
1975), because of the sufficient amount of NaCO 3 asa pH buffer in the wastewater. Therefore, the effectof these factors were considered to be enough small
to give any deference in bacterial activities among
the experimental cases. Time course of NH 4 +-N,

NO 2 --N, and NO 3 --N are shown in Fig. 2. There
were clear differences among three experimental
cases. Ammonium oxidation was accomplishedwithin 5 days after start-up in the case 3 where waterflow rate was the highest, whereas the day whencomplete oxidation of ammonia attained was 10days and 12 days in the case 2, and the case 3,
respectively. Similar phenomena were observed in
the nitrite oxidation, and nitrite accumulation was
the most in the case 2 as a result. Biologicaloxidation of nitrogenous compounds increases with
the increase of water flow rate under theexperimental conditions of water velocity between
46.9 and 219 cm/min. Average SS concentrations in
the effluent were 0.54, 0.29, and 1.22 mgSS/L, in thecase 1, 2, and 3, respectively. Although SS
concentration in the effluent itself was enough small,that of the case 3 was the highest. It is suggested
that shearing force by water flow caused thedifference.
With these results, it was shown that water flowrate can give several affects on bacterial growth.
Therefore, the effect will be considered from theviewpoints of bacterial growth. Biological reactionin the reactor can be modelled in the way describedin Fig. 3. According from the diagram, materialbalance equations of NH 4 +-N, NO 3 --N, andnitrifying bacteria can be described as follows;

30
20

Case 1Case 2
Case 3
influent

10

30

Case1
Case2
Case3

20
10

-

(1)

V

dN 3
= QN 30 − QN 3 + VR2
dt(2)

NH4+-N; N1

0

NO 3 -N （ mgN/L）

dN1= QN10 − QN1 − VR1 − VY2 R2
dt

If there are enough amount of substrates andnutrients, bacteria are in log growth phase. The
growth pattern can be described with exponentialfunctions;-

NO 2 -N （ mgN/L）

0

V

R1

30

(1-Y1)

20

Case 1Case 2
Case 3

10

NO2--N; N2
R2

0
0

10

20
30
Time (day)Fig.2 Time course of nitrogenous
compounds

NH3 oxidizing R3
bacteria; X1

(Y1)

40

Y2R2

NO2 oxidizing R4
bacteria; X2

NO3--N; N3

Fig.3 Schematic diagram of biologicalreaction in the reactor
761

GEOMATE- Brisbane, Nov. 19-21, 2014

dX 1
= VY1 R1 − VR3 = Vµ1 X 1 = VX 1,t =0 µ1e µ1t
Vdt

N1 = e(3)

N3 = edX 2
V= VY2 R2 − VR4 = Vµ 2 X 2 = VX 2,t =0 µ 2 e µ2t
dt

where
R 1 : NH 4 +-N oxidation rate by ammonium
oxidizing bacteria(mg/L/hr)
R 2 : NO 2 --N oxidation rate by nitrite oxidizing
bacteria(mg/L/hr)
R 3 : detachment rate ofammonium oxidizing
bacteria(mg/L/hr)R4:detachment rate ofnitrite oxidizing
bacteria(mg/L/hr)µ 1 ，µ 2 :net specific growth rate ofnitrifying bacteria (1/hr)
X 1 ,t=0，X 2 ,t=0: initial concentration of nitrifying
bacteria(mg/L)
X 1 , X 2 : concentration of nitrifying bacteria (mg/L)
Y 1 ，Y 2 : Yield (mg-assimilated N/mg-oxidized N)
Suffix 1,2 means ammonium oxidizing bacteria,
nitrite oxidizing bacteria, respectively
N 10 , N 30 : concentration of NH 4 +-N, NO 3 --N in
influent, respectively (mgN/L)
N 1 , N 3 : concentration of NH 4 +-N, NO 3 --N in
effluent, respectively(mgN/L)
t:
time
(hr)
V, Q: reactor volume(L), influent and effluent
flow rate(L/hr)

α1 + N10 −QtV

α 2 + N 30 +Vµ1 X 2,t = 0
QY1 + Vµ1Y1

e µ1t (5)Vµ 2 X 2,t = 0
QY2 + Vµ 2Y2

e µ 2 t (6): constants determined by initial

The first terms of right side in the equations (5)and (6) can be neglect according to the passage oftreatment time. Therefore, net specific growth rateof nitrifying bacteria(µ 1 , µ 2 ) can be expressed as aslope in single logarithmic chart of t-(N 10 -N 1 ) or tNet specific growth rate of(N 3 -N 30 ) curve.
nitrifying bacteria calculated with this methods areshown in Fig. 4. It is shown that net specific growthrate becomes larger with the increase of watervelocity. The values were between 0.0075 and0.0152(1/hr) in ammonium oxidizing bacteria,
whereas these of nitrite oxidizing bacteria were in asurface SS
inner SS
Total SSSS concentration(mgSS/one medium)

20
15
10
5

Bacterial activity (mgO2 /SS/hr)

0

If the value of Y 2 R 2 is so smaller than R 1 thatY 2 R 2 can be neglect in the equation (1), N 1 and N 3
can be expressed as follows;

50

100 surface
150 bacteria(OUR)200
250
surface bacteria(ATU-OUR)
water flow rate
(cm/min)inner bacteria(OUR)
inner bacteria(ATU-OUR)

0

50

100
200
250
nitrifying 150
activity (surface)nitrifying
activity(inner)water flow rate (cm/min)
nitrifying activity (Total)

0

50

0.2 0
0.15
0.1
0.05
0
200

nitrifying activity(mgN/L-medium/hr)

net specific growth rate (1/hr)

−

Q
t
V

α1, α2conditions

(4)

0.016
0.014
0.012
0.01
0.008
0.006
0.004
0.002
0

−

150
100

ammonium oxidizing bacteria
nitrite oxidizing bacteria
0

50

100

150

200

50
0

250

water verosity (cm/min)

100

150

200

water velocity (cm/min)Fig.4 Net specific growth rate of nitrifying
bacteria

Fig.5 Results of batch test

762

250

GEOMATE- Brisbane, Nov. 19-21, 2014

range from 0.0051 to 0.0088(1/hr). Bacterial
activity and SS concentration both inside and aroundthe attached growth media are shown in Fig. 5.
Bacterial activity of unit SS weight did not increase
with the increase of water velocity. However, inner
SS concentration increased as water velocity
increased. It is considered that material penetration
into the inside of the media was promoted as thewater velocity increased, therefore, it could be easierfor bacteria to exist at the inside. The results ofbacterial activities shown in Fig. 5 were the potential
activities, therefore the actual bacterial activities in
the case of low water velocity might be lower. It isalso suggested that penetration of materials such as
substances and DO affect the treatment efficiencies.
Operational and design parameters from these pointof views also should be investigated quantitatively.

grants from the Maeda Engineering Foundation(2015).
REFERENCES[1] APHA, AWWA, WEF, Standard Methods for
the Examination of Water and Wastewater 20th
Edition, Washington D. C.: American publish
health association, (1998), ISBN 0-87553-235-7.
[2] Hisashi, S., Satoshi, O., Yoshimasa, W.,
Ammonium Oxidation in Mixed-population
Biofilms Determined with the Use of
Microelectrodes and in situ Hybridization, J. of
Japan Society on Water Environment, Vol.22,
No.3, 206-214(1999). (in Japanese)[3] Japan Sewage Works Association, Methods for
the Examination of Sewage(1997). (in Japanese)[4] Satoshi, O., Hatsuka, N., Yoshimasa, W.,
Spatial Distributions of Ammonia-Oxidizing
Bacteria in wastewater Biofilms Analyzed by
Fluorescent in situ Hybridization, J. of Japan
Society on Water Environment, Vol.22, No.3,
191-198(1999). (in Japanese)[5] U.S.EPA, Process design manual for nitrogen
control, (1975).
[6] Chen Y, Wang L, Ma F, Yang JX, Qiu S.,
Tracking composition of microbial communities
for simultaneous nitrification and denitrificationin polyurethane foam, Water Science andTechnology, Vol. 69, No.9, 1788-1797(2014).
[7] Aure´ lie Ce´bron1, Josette Garnier, Nitrobacterand Nitrospira genera as representatives ofnitrite-oxidizing
bacteria:
Detection,
quantification and growth along the lower Seine
River (France), Water Research, Vol. 39, 49794992(2005).
[8] Mélanie Raimonet, Lauriane Vilmin, Nicolas
Flipob, Vincent Rocher, Anniet M. Laverman,
Modelling the fate of nitrite in an urbanizedriver using experimentally obtained nitrifier
growth parameters, Water Research, Vol. 73,
373–387(2015).
[9] Deepshikha Sharma, Arun Kansal, Assessmentof river quality models: a review, Reviews inEnvironmental Science and Bio/Technology,
Vol. 12, Issue 3, 285-311(2013).

CONCLUSION
In this study, effects of water flow rate on
bacterial growth at attached growth media were
investigated. The results obtained in this study weresummarized as follows;
1) In each case, complete nitrification was obtained
under the conditions that HRT, influent NH 4 +-N
concentration, volumetric incorporative ratio
were 6hr, 30mgN/L, and 10%, respectively,
where NH 4 +-N loading rate was 50 (mgN/Lmedia/hr).
2) Specific growth rate of ammonium oxidizing
bacteria was in the range of 0.0075 to
0.0152(1/hr), whereas that of nitrite oxidizing
bacteria was in the range of 0.0051 to
0.0088(1/hr). Specific growth rate of nitrifying
bacteria increased with the increase of watervelocity of which value was between 46.9 and219 (cm/min).
3) SS concentration inside the media increased with
the increase of water velocity.
4) Potential nitrification rates were in the range of
95.2 to 148 (mgN/L-media/hr)
In this study, it was certainly shown that waterflow rate can have effects on bacteria, therefore,
these effects should be taken into account whenexperimental data would be applied to the different
scale reactor and system.
ACKNOWLEDGEMENTS
This research was partially supported by the763

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

BIODIESEL INDUSTRY WASTE RECOVERY IN AGRICULTURE
Mariana Ferreira Rabelo Fernandes1, Essaid Bilal1, 2, Regynaldo Arruda Sampaio1, Jatnel Alonso Lazo3 Luiz
Arnaldo Fernandes1, Adolf Heinrich Horn4 and Najla Lassoued5
1Instituto de CiênciasAgrárias, Universidade Federal de Minas Gerais, Caixa Postal 135, CEP 39404-006,
Montes Claros, MG, Brasil; 2 Ecole Nationale Supérieure des Mines de Saint Etienne, CNRS UMR 5600,
F42023 Saint Etienne, France; 3 Instituto de Ciência Animal, Apartado Postal 24, San José de las Lajas,
Mayabeque, Cuba; 4 Instituto do Geociência, Universidade Fédérale de Minas Gerais, Brasil; 5 National
Agronomic Institute of Tunisia, Tunis, Tunisia.

ABSTRACT
We examine in this study the possibility of using biodiesel industry waste mixed with urban sewage sludge asa source of nutrients for the production and seedling development in agriculture. Biodiesel industry waste is
diatomaceous earth (DE) compounds and paraffin. We added DE with urban sewage sludge. The advantage ofthis operation is to eliminate the two wastes, residues of the biodiesel industry and sludge from wastewater
treatment plants. We studied the behavior of Camaldulensis Eucalyptus and white mulberry (Morus Alba Yu vc62).
The Dickson Quality Index (DQI) of White Mulberry and Eucalyptus Camaladulensis plants tested indifferent substrates (urban sewage sludge and DE) are all above the minimum recommended value of 0.2. The
Camaladulensis eucalyptus and white mulberry (Morus Alba) plants have good growth in substrates containing
urban sewage sludge and diatomaceous earth compared to commercial substrate used as control. Biodiesel
industry waste can used up to 50% by volume of the substrate without compromising the quality of the plants
with a reduction in the cost of production.
Keywords: Biodiesel, Diatomaceous earth, Sewage sludge, Eucalyptus, Morus Albalightweight material with low-density porous
structure with low thermal conductivity and nontoxic [2], [3]. At the end of the production, processof biodiesel, it is as a residue impregnated of organic
material.
We added the DE residues of Biodiesel withsludge from urban wastewater treatment or manure
to stimulate the activity of soil bacterial [4]. The
advantage of this operation is to eliminate bothwastes, residues Biodiesel industry and sludge
treatment plants.
Given the economic and environmental
importance of reusing these resources considered aswaste, this study aims to evaluate substrate test (DEand sludge sewage) as an alternative for improving
the composition of soils for plant production.

INTRODUCTION
Recently, with the growing demand for biodiesel
in the world, several plants for biofuel production,
mainly from vegetable oils, are being installed in
Brazil. For the elimination of various impurities,
both the first and itself biodiesel material, theseindustries use filters of various types, particularly
those using diatomaceous earth (D.E.) as a filter
element.
The organic compounds formed from industrial
waste can be used as sources of organic matter andnutrients in a substrate [1]. They increase the water
retention capacity by improving aeration of the rootsof plants and they increase the availability ofnutrients from the growth of beneficial
microorganisms. Can interfere with the increase of
pH and levels of exchangeable cations of substrates.
To avoid environmental problems associatedwith deposits of residues biodiesel industry, we canuse as a source of nutrients for the production anddevelopment of plants seems like a very good option.
The residue D.E. has specific physical properties
that can improve the soil properties.
The D.E. is a naturally occurring, soft, siliceous
sedimentary rock, consisting of shells or frustules
seaweed, which has many properties. It is a

MATERIAL AND METHODS
The study was conducted in the period MayAugust 2014 at the Institute of Agricultural Sciences- ICA, of the Federal University of Minas Gerais UFMG, Montes Claros, located in northern Minas
Gerais state. The climate of the region is semi-arid,
hot and dry tropical. The rainy season isconcentrated between Octobers to March. The
764

GEOMATE- Osaka, Nov. 16-18, 2015

average annual rainfall is 1060 mm and uneven [5].
The experimental substrate was prepared fromdifferent proportions of commercial substrate control(Bioplant), DE, sewage sludge (SS) or manure (M)(table 1). Manure from breeding ICA-UFMG.
Sewage sludge from the treatment plant wastewaterMontes Claros - MG. Diatomaceous earth from thedisposition of Petrobras biodiesel plant in Montes
Claros, after use as a filtering agent of vegetable oils
(soybean). Diatomaceous earth, before use, wassubmitted to the combustion process for the removalof residual oil.
The pots had a volume of 55 cm3 and the species
studied were Camaladulensis Eucalyptus and whitemulberry (Morus Alba Yu vc-62). The seeds of
Eucalyptus Camaladulensis foram obtained from the
ICA / UFMG and seeds of white mulberry Morus
Alba vcYu-62 were CIA / Cuba. At the time ofsowing three seeds were placed in each pot and 21days after sowing took place clarified leaving only
one plant per pot. The experiment consisted of two
tests, one for each species. The experimental design
was a randomized complete block design with three
replications and four pots per experimental unit.
The composition of the pots were made on April
30 and May 1, 2014. The plants were sown on May
2. A shade sail was used with a rate 30% and 50%
shade and was placed 1.5 m above the ground,oriented east-west to provide shade from theexperimental unit. This shade sail was removed 30
days after the lifting of plant. Both treatments were
irrigated three times daily.
Scanning electron microscopy (SEM) coupledwith energy dispersive spectroscopy (EDS).
Observations and elemental analyzes were
performed on a scanning electron microscope Jeol
JSM 6400 coupled to an Oxford EDS analyzer(ENSM-SE). Observations were carried out on
secondary electrons mode in the case of granular

raw unpolished samples and backscattered electron
mode.
Table 1 The materials used and their proportions byvolume for each substrate tests
Substrate Tests (ST)ST1 100% SC substrate control (Bioplant)
ST2 75% SC + 12.5% SS + 12.5% DE
ST3 75% SC + 12.5% SS + 12.5% DE
ST4 50% SC + 25% SS + 25% DE
ST5 50% SC + 16.7% SS + 33.3% DE
ST6 75% SC + 12.5% M + 12.5% DE
ST7 75% SC + 8.3% M + 16.7% DE
ST8 50% SC + 25% M + 25% DE
ST9 50% SC + 16.7% M + 33.3% DE
Physical and chemical analyzes of different
substrates tests and control substrate (commercial
substrate Bioplant) were performed at theLaboratory of Solid Waste UFMG following themethodology of Embrapa [6]. The diameter andheight of the stem of the plants were measured fromthe 49th day to the 118th day. Starting at thirty days,
we calculated the rate of emergence of two cultures.
Where the species reached a height greater than 10
cm and 42 days after planting, we add theammonium sulfate fertilizer.
The number of leaves per plant was calculated.
Fresh weight of shoots, leaves and roots were
measured by weighing. After drying in an oven at
65 ° C for 72 hours to constant weight, the dryweight matter was weighed.
We evaluated the relationship between shoot dry
weight and root dry weight. All data were subjected
to analysis of variance and means were compared by
the Scott-Knott test at 5% probability.

Table 2 The chemical characteristics of substrate tests (ST2 to ST9) and substrate control (ST1)
pH
CE
ST
-1
P
K
Ca Mg H + Al
SB
t
T
V
dS m
ST1
ST2
ST3
ST4
ST5
ST6
ST7
ST8
ST9

6.4
6.4
6.4
6.4
6.7
6.9
6.8
7.6
7.5

1.54
1.72
1.68
1.64
1.71
1.82
1.78
1.65
1.35

- mg dm-3 -900 249
1.020 497
980 298
960 497
880 895
510 970
960 990
960 846------------------------ cmol c dm-3 ------------7.3 5.4
0.91
13.34 13.34 14.25
8.2 3.4
2.32
12.88 12.88 15.2
9.3 3.0
2.32
13.07 13.07 15.38
9.6 3.5
1.38
14.38 14.38 15.75
8.0 3.0
1.39
13.30 13.3 14.69
7.0 3.6
1.55
13.09 13.09 14.64
6.0 4.0
1.55
12.54 12.54 14.09
4.0 4.8
0.85
10.97 10.97 11.82
765

%
94
85
85
91
91
89
89
93

MO

C

--- dag kg-1 -18.57 10.79
38.44 22.34
18.19 10.57
13.6
7.9
14.6
8.48
16.34
9.5
16.7
9.7
16.34
9.5

GEOMATE- Osaka, Nov. 16-18, 2015

T5 (0.55) and very low in treatment with manure T7(0.13), T8 (0.18) and T9 (0.11).
RESULTSTable
The pH in the control substrate ST1 chosen as
substrates tests mixtures of ST2 to ST4 is the same
6.4; it passes to 6.7 in the mixture T5 (33% of DE).
The pH was higher in mixtures with manure
between 6.9 and 7.6. The electric conductivity (1.64
to 1.82 dS / m) in the mixtures ST2 to ST8 is higher,
compared with control substrate (1.54 dS/m). Except
for the case ST9, manure mixture, is very low 1.35
dS/m. K is high 880 mg/dm3 in the mixture T5. K
remains high in the mixtures Manure (647-990
mg/dm3) compared with the control substrate (249mg/dm3). The organic matter (OM), total porosity,
water holding capacity (WHC) and density decreasesignificantly in mixtures ST4, ST5 and ST9 (table 2
and 3).

3 Water retention capacity (WHC),
density (ρ) and total porosity (TP) of
substrate tests (ST2 to ST9) and substrate
control (ST1).
Substrate
WHC
ρ
TP
tests
mL g-1
gcm-3
%
ST1
0.56
0.35
64.7
ST2
0.49
0.46
73.4
ST3
0.42
0.47
62.2
ST4
0.38
0.55
58.4
ST5
0.43
0.55
56.3
ST6
0.60
0.43
63.5
ST7
0.53
0.43
64.3
ST8
0.57
0.46
64.4
ST9
0.44
0.50
62.6

6
ST9
Control
ST1

4

ST4

ST3

ST

SEI

ST2

ST5

2

DE+M

ST8

DE+SS

ST6

Fig. 1

Image scanning electron microscopy of
biodiesel industry waste from Petrobras
plant in Montes Claros, MG, Brazil.

0

The root volume of Eucalyptus Camaldulensis isvery low in the ST5 (2.5 cm3) compared to thecontrol substrate ST1 (3.69 cm3) or ST2 (5.42 cm3),ST3 (4.92 cm3) e ST4 (4.53 cm3). It is similar to theroot volume of Eucalyptus Camaldulensis growing
in substrates with manure (1.69 to 3 cm3). The
heights of rods, the diameter, number of leaves andthe root volume are determines the seedling quality
higher in all the substrates tests than in the control
substrate. However, all the morphologicalcharacteristics and weight biomass of the seedling insubstrates with manure are less than those seedlings
growing in substrates tests with sewage sludge anddiatomaceous earth. Seedlings treatments ST4 andST5 have the largest diameter 4.46 mm and 4.76 mm
respectively. All other values are higher than theminimum required (2.0 to 2.5 mm). DQI [7]seedlings Morus Alba is high in the treatment withsewage sludge and diatomaceous earth T4 (0.42) and0
Fig. 2

0.1

0.2

0.3

DQI
Dickson Quality Index (DQI) versus Speed
Emergency Index (SEI) of Eucalyptus
Camaldulensis seedlings from different
substrates tests and control substrate.

DISCUSSION AND CONCLUSION
Eucalyptus Camaladulensis
The observed values of the plant height, diameterof the stem and the fresh and dry weight of roots andshoots in the treatment with a manure anddiatomaceous earth were statistically lower,
compared with the control substrate (fig. 1).
Whereas the physical and chemical properties of thesubstrates were similar in all treatments (Table 2 and3), with the exception of pH, the effects observed
may be attributed to the higher pH (higher than 6.5)of these substrates.
766

GEOMATE- Osaka, Nov. 16-18, 2015

In more alkaline pH conditions, there may benutrients and micronutrients are not available which
will cause a physiological disequilibrium plants. The
pH should be in the range of pH 5.5 to 6.5 to obtain
the recommended quality of seedlings. According to[8] and [9], the precipitation of the solution ofaluminum ground causes, among other effects, theincrease of pH. Moreover, the application of manure
may cause complexing Al by organic acids releasedby these materials. The high acidity of the soil
caused by the sludge can be associated with thenitrification of ammonium nitrogen and theoxidation of sulfites and feasible production oforganic acids during the degradation by
microorganisms of the residue [10]. If we consider
that seedlings good quality must have a minimum
stem diameter of 2.0 to 2.5 mm [11], [12], thetreatment with the substrate containing sludge
sewage and diatomaceous earth corresponds to thedesired quality (fig. 2).

plants more vulnerable to adverse environmental
conditions [14]. The faster root development isimportant, the better the vegetative development andstability of the plant [15].
6ST2

4Control
ST1

2

2
1

ST6

SEI

ST9

RV

ST5

ST2

ST

0

DE+M

0

0.2

0.4

0.6

DQ
Fig. 3

DE+M

0.1

0.2
0.3
DQI
Dickson Quality Index (DQI) versus root
volume (RV cm3) of Eucalyptus
Camaldulensis from different substrates
tests and control substrate.

The GPDM/RDM ratio showed no statisticaldifference between the different substrates (Table 5).
However, DQI is more important in the substrate test
mixture of sludge sewage and DE (0.17-0.23),
whereas it is very low in substrates based on manure
and DE (0.05-0.09) and in the order of 0.12 in thecontrol substrate (fig. 2, 3 and 4); we have a goodcorrelation with total dray matter TDM and DQI.
This links up the observations [16] and [17] foundthat on the field the best results in the survival of
eucalyptus seedling are those substrates containing
sewage sludge. These authors also noted that 2/1 of
GPDM/RDM is a good balance for the growth ofplants. In our case, the values are slightly higher but
still reasonable for the different substrates tests. We
find that the ratio between the stem height anddiameter of the plant did not differ significantly
between the substrates tests studied (Table 4), but
the root volume of Eucalyptus Camaldulensis ishigher (more 4 cm3) in blended substrate with DE
and SS comparatively to control substrate andamended substrate with manure (fig. 4). Greater thevalue of this ratio is, the better the ability to survivaland establishment of seedlings in the field [18]. The
best treatments are those with sewage sludge anddiatomaceous earth (fig. 4). The DQI is above therecommended minimum [19]. These authors argue
that DQI should be greater than 0.2. DQI than, thehigher the quality standards seedlings. The study of

DE+SS

ST7

ST8

ST9

0

ST4

ST3

DE+SS

ST5

0

Control
ST

ST

ST7
ST6

Fig. 4
3

ST3

Dickson Quality Index (DQI) versus Speed
Emergency Index (SEI) of Morus Alba
seedlings from different substrates tests andcontrol substrate.

The seedlings may be placed on the field after
120 days of growth, with an average diameter stemfrom 2.65 to 2.95 mm. The emergency speed index
of T2 and T9 is statistically equivalent to treatment
with substrate control (Table 4). This result differsfrom that observed by [13]; these authors studied thevalue of speed emergency index based on mixturesof the substrate with rice husk, cow manure,powdered coconut and fine vermiculite.
Different mixtures had no effect on the speed
emergency index of Eucalyptus urophylla. It should
be noted that the decline in the rate of seedlingemergence is an undesirable feature, because mostof the time in the early stages of growth, it makes
767

GEOMATE- Osaka, Nov. 16-18, 2015

help determine the doses of fertilizer must be
applied in the production of seedlings.
The speed emergence index of Morus Alba show
(fig. 3) no statistical difference between treatments.
At the stage of the germination and emergence ofseeds, nutrients are not necessary for the reactions
leading to the formation stem and root (fig. 5). Only
hydration and aeration of the substrate and goodporosity of the substrate allows a movement of the
air and water favoring the quickest germination [25].

seedlings of Eucalyptus urophylla by [19] showed
higher values for the DQI treatments with manurethan what we got.
Morus Alba Yu vc-62
Tests substrates with sewage sludge and diatoms
favor the growth of white mulberry (Morus Alba)
seedlings (fig. 5). They are usually better than thecontrol substrate (Table 5). For all treatments thechemical and physical properties were similar except
for pH (Tables 2 and 3), we can consider thatsewage sludge played an important role in nutrient
level and diatoms have improved physical properties
including porosity, water capacity and density ofretention substrates tested.
These observations are consistent with the Maia[20] found that sewage sludge should not be usedpure, despite its relative fertility [21], sewage sludge
cause compression of the substrate. Diatomaceous
earth is an important addition to the growth ofseedlings.

3

ST5

ST2

2
1

The GPDM/RDM ratios of Morus Alba seedlingare not statistically different for all treatments. They
indicate that the seedlings have the same probabilityof survival. The GPDM/RDM ratio and TDM isdirectly related to the composition of substrate tests
strongly influenced by the availability of water,which favors the flow of nutrients and seedling
growth.
The HI ratio (H/D stem height /stem basediameter) of seedlings Morus Alba shows nostatistically significant difference 2.35 to 3.12. Thisratio is less than that observed for the Eucalyptus(7.47 to 9.63) for the same experimental conditions.
The HI ratio and root volume are likely to depend on
the species of seedlings because in spite of the low
values, seedlings showed good conditions foradaptation and resistance on the ground. The DQI
values Morus Alba seedlings growing in test
substrates with sewage sludge and diatomaceous
earth, are all higher than the recommended
minimum value of 0.2, with high value for ST4 andST5 rich substrate diatomaceous earth (fig. 5).
Treatments with manure does not meet the desired
quality standard.
The DQI is obvious that the morphologicalparameters’ used to evaluate the quality of seedlings
and morphological parameters should not beanalyzed separately to determine the quality level.
The Eucalyptus and Morus Alba seedlings
produced with test substrates (sewage sludge,
diatomaceous earth and commercial substrate)
showed similar results to those obtained with the use
only commercial substrate.
The Eucalyptus and Morus Alba seedlings havegood growth in substrates containing sewage sludge
and diatomaceous earth. They show better results
than using only the commercial substrate. They canbe used up to 50% by volume of the substrate
without compromising the seedlings quality with areduction of cost of seedlings production.

ST4
DE+SS
DE+M
ST6

0
RV

ST8
ST3

ST7
ST1
Control
ST9

0

0.2

0.4

0.6

DQI
Fig. 5

Dickson Quality Index (DQI) versus root
volume (RV cm3) of Morus Alba fromdifferent substrates tests and control
substrate.

All treatments showed levels of potassium (Table2) very good according to the classification of [22],which explains the high values of stem diameter of
seedlings. Valeri and Corradi [23] showed that thepotassium regulates the opening of the stomata andpromotes the thickening of the stem of the seedling.
According to Daniel [24], the diameter of the stem isthe most appropriate to evaluate the ability of
seedling survival. It is also the most widely used to768

GEOMATE- Osaka, Nov. 16-18, 2015

Table 4 Means of the variables total height (H), collar diameter (D), number of branches (NB), number ofleaves (NL), root volume (RV), fresh biomass of the above ground part, (GPFM), root fresh matter(RFM), dry biomass of the above ground part (GPDM) and root dry matter (RDM) seedlings of
Eucalyptus Camaldulensis from different substrates tests and control substrate.
Substrates tests
T1
T2
T3
T4
T5
T6
T7
T8
T9

H
cm
22.03
24.51
23.25
20.34
21.98
16.55
16.97
13.49
15.36

D
mm
2.43
2.95
2.65
2.75
2.89
1.78
2.02
1.85
1.59

NB
NL
o
-- -- n /seedling -1.62
5.12
1.60
5.85
1.75
5.49
1.85
6.08
1.79
5.97
1.65
4.78
1.55
4.57
1.62
5.05
1.66
5.03

RV
Cm3
3.69
5.42
4.92
4.53
2.72
2.83
3.00
2.44
1.69

GPFM
RFM
GPDM
RDM
------------------ g ---------------------------2.59
1.14
0.86
0.45
3.96
1.57
1.50
0.54
3.83
1.39
1.41
0.54
4.24
1.58
1.52
0.65
4.15
1.43
1.36
0.61
1.89
0.65
0.55
0.27
1.86
0.80
0.55
0.26
1.93
0.69
0.61
0.27
1.57
0.58
0.39
0.20

Table 5 Means of the variables total height (H), collar diameter (D), number of leaves (NL), root volume(RV), fresh biomass of the above ground part, (GPFM), root fresh matter (RFM), dry biomass of theabove ground part (GPDM) and root dry matter (RDM) seedlings of Morus Alba from different
substrates tests and control substrate.
Substrates tests
T1
T2
T3
T4
T5
T6
T7
T8
T9

H
cm
7.49 C
7.96 A
10.10 A
12.38 A
11.11 A
9.06 B
7.96 C
8.98 B
9.24 BDmm
3.04 C
3.83 B
3.78 B
4.46 A
4.76 A
3.53 B
2.90 C
3.49 B
2.99 CNL
RVGPFM-- no /seedling --Cm3
2.75 A
1.00 B 1.00 C
2.47 B
3.28 A 1.61 B
2.67 B
1.50 B 1.47 B
2.48 B
2.92 A 2.27 A
2.54 B
3.11 A 2.19 A
2.84 A
1.67 B 1.28 B
2.86 A
1.25 B 1.03 C
2.91 A
1.67 B 1.31 B
3.06 A
0.64 B 1.12 C

RFM
GPDM
RDM
------------------g---------------------1.45 B
0.35 B
0.43 C
2.01 A
0.57 B
0.63 B
1.75 B
0.48 B
0.56 B
2.51 A
0.83 A 0.78 A
2.90 A
0.82 A 0.94 A
1.55 B
0.37 B
0.43 C
0.99 C
0.29 B 0.23 D
1.35 B
0.38 B 0.30 D
0.92 C
0.28 B 0.20 D

Note: Means without letters or followed by the same letter are not statistically different according to the Scott-Knott test at 5%
probability

ACKNOWLEDGEMENTS
This work supported by FAPEMIG (Fundação de
Amparo a Pesquisa de Minas Gerais) and theInstitute of Agricultural Sciences of the Federal
University of Minas Gerais (UFMG), Regional
Campus Montes Claros. We also thank the French
Embassy and the Rectorat of UFMG for the grant ofa visiting professor Essaid Bilal (ENSMSE, France)in the framework of the France-UFMG Academic
Chair; and Ecole Nationale Supérieure des Mines forchemicals analyses support. The authors are gratefulto Prof. Monica Butnariu for their assistance and for
useful discussions.
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Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

LESSONS FROM THE INTERNATIONAL COMPARISON OF
CONTAMINATED LAND POLICIES WITH RISK GOVERNANCE IN
JAPAN, THE NETHERLANDS, AND THE UK
Tomoko Miyagawa1, Noriko Otsuka2 and Hirokazu Abe3
Faculty of Systems Engineering, Wakayama University, Japan; 2ILS Institut für Landes- und
Stadtentwicklungsforschung gGmbH, Germany; 3Cyber Media Center, Osaka University, Japan
1

ABSTRACT
After the Great East Japan Earthquake and Fukushima Daiichi nuclear disaster, decontamination has been
undertaken primarily to remediate residential areas. Therefore, it is necessary to consider risk governance indecontamination process. This paper aims to examine contaminated land policies by comparing Japan, theNetherlands, and the UK by literature reviews from the aspects of risk governance towards sustainabledecontamination process. The results clarified that policies in Japan is disintegrated and sectionalised byseparate acts. Contrastingly, integrated contaminated land regimes are practiced in the Netherlands and the UK
on contaminated risks from current land uses. In terms of risk governance in contaminated land policies,
although the Netherlands has a limited application, Japan and the UK have a certain degree of community
participation in recent policies. Thus, recent contaminated land policy frameworks are adapting to promote risk
governance in decontamination process by introducing statutory requirements. However, there is a limitation toensure risk governance by statutory actions and more support to voluntary actions is needed.
Keywords: Contaminated Land, Brownfields, International Comparison Of Policies, Risk Governanceprocess, which includes ‘the totality of actors, rules,
conventions, processes, and mechanisms’ with its
relevance in its collection, analysis, communication,and management decisions, due to ‘the nature of therisk requires the collaboration of, and co-ordination
between, a range of different stakeholders’ [2], [3].
It is a dynamic continuous process with
understanding gradually and adjusting to manage
carefully of ‘complexity, scientific uncertainty,
and/or socio-political ambiguity’ which needs to be‘flexible’, ‘interactive and inclusionary’ [4]. It also
aims to consider ‘institutional agreements (theregulatory and legal framework that determines therelationship, roles and responsibilities of the actors
and co-ordination mechanisms)’ and ‘politicalculture’, to overcome differences in risk perception[2].

INTRODUCTION
Four years had passed from the Great East Japan
Earthquake and Fukushima Daiichi nuclear disaster.
At present, decontamination is in process with the
Act on Special Measures concerning the Handling ofEnvironment Pollution by Radioactive Materials
Discharged by the Nuclear Power Station Accident
Associated with the Tohoku District –Off the Pacific
Ocean Earthquake that Occurred on March 11,
2011(After that, Act on Special Measuresconcerning the Handling of Environment Pollution
by Radioactive Materials; 2011) released from the
Fukushima Daiichi nuclear power plant which is
caused by the Great East Japan Earthquake.
However, contamination is widely spread into vast
areas and it is not possible to decontaminate to its
original condition in a short time [1].
Moreover, in the decontamination process,communication and community participation is a
key aspect in the case of remediation sites to belocated nearby residential areas. Additionally, in thecase of severe contamination, it may take longer
period for remediation, therefore, communicationand community participation needs to be integrated
in long term remediation. Hence, decontamination
process in Fukushima applies to the above situation
with primarily targeted to remediate residential areas,
thus, risk governance is necessary.
The definition of risk governance is a ‘collective
decisions’ through an interactive decision making

The Public Concerns Of Risk Governance In
Fukushima City, JapanOn the other hand, according to the report on
consciousness of citizens for radioactivity by postal
questionnaire survey to a random selection of people
who are living in Fukushima city and living
elsewhere to escape from the city in May, 2014,
there was a tendency to have more concerns onanxiety on health risks from radiation, i.e., families
than themselves [5]. From the opinions of citizens
on what should be prioritized in actions in the future
by local authorities, prefecture, and central
771

GEOMATE-Osaka, Nov. 16-18, 2015

government had highlighted the highest concerns on
the disclosure of accurate information at 68.8 per
cent, and the health management of citizens at 64.6
per cent to follow [5]. Another investigation on
perceptions of Japanese parents by online survey atfour groups of Tohoku, Kanto, and Kansai regions
and Fukushima prefecture in March, 2012
summarized that reasons for feelings of anxiety to bemainly from distrust of the outlook and actions by
the central government, and secondly from‘uncertainty about scientific data disseminated in thepast about low dose radiation’ as well as ‘invisible
risks’ , i.e., spots with high dose of radiation or foodproduced without monitoring from radiation [6].
Furthermore, improvement of the quality of
information and disclosing information completely
was strongly requested for information providers [6].
Above situations may illustrate the publicconcern on communication and community
participation, therefore, it is necessary to consider
risk governance in decontamination process. Until
present, international comparison of contaminatedland policies have been explored fromenvironmental and spatial planning perspectives, i.e.,
US and Europe [7], North America and Europe [8],the UK and China [9], and England and Japan [10],however, discussions on incorporating social aspectsof community participation is rather limited. Thus,
this paper aims to examine contaminated land
policies by literature reviews from the aspects of risk
governance towards sustainable decontamination
process by comparing Japan with separate set of acts,
the Netherlands with the risk assessment system, andthe UK with the planning system.

estimation’, ‘risk characterization’, and ‘risk
evaluation and risk management’; pre-estimation
stage is a screening of actions and problems which
are related to risks; interdisciplinary risk estimation
is to undertake both scientifically based risk
assessment and concern assessment to include socioeconomic issues; risk characterization is an element
based on evidence, while risk evaluation is anelement based on the value to make decisions on the‘tolerability and /or acceptability of a risk’; and risk
management is to re-examine ‘all relevant data andinformation generated in the previous steps’ todecide adequate actions in consideration of ‘societal
acceptability and tolerability’ [4].
In risk
governance, stakeholder and public involvement has
been stated as a core feature [3], [4], and constantcompanions to all phases [2], [4] for transparent
process, supervision by the public, andunderstandings among each other about the risks andtheir governance [2] should be ensured.
In the decontamination process, both the direct
toxicological impacts and indirect affects to health,i.e., stress and anxiety, should be considered forresidents living on or near in case of higher risks ofcontamination [11], [12].
Public concerns on
contaminated land and its risks are identified as ascientific issue of ‘health of self and family’, as well
as a range of socio-economic issues, ‘property
values’, ‘amenity’, ‘liability’, ‘level of confidence ingovernment’s ability to protect’, and ‘damage toenvironment’ [15]. It is also discussed further thatthe indirect affects to residents in ways ofphysiological, economic and psychological, in ‘aless certain and less transient way than observed
pollution incidents’ [14]. Furthermore, it has beenreported that ‘more open and proactive style of risk
communication and consultation’ had led to lessdissatisfaction in the community on brownfield landwith contamination [16].
For radioactive
contaminated land, ‘a systematic consideration of
ethical and social issues’ is going to make aselection of countermeasures ‘more transparent andless controversial for society’ and assist in
sustainable restoration and long-term management[17]. Therefore, social and economic factors need tobe discussed not only to examine thedecontamination approaches from the aspects of its
technological effectiveness [1]. To ensure the longterm sustainability, public acceptance and social
sustainability need to be undertaken in thedecontamination process [18].

THE
SIGNIFICANCEOF
RISK
GOVERNANCE IN DECONTAMINATIONPROCESS
Environmental risk communication is recognized
its importance by legislator, environmental groups
and citizens that extra effort is needed to changeplans in order ‘to provide citizens with moremeaningful input’ [7], i.e., participation of thecommunity in the early stages of the process [7],[11]-[13].
Moreover, communication should beundertaken continuously in the whole process ofactivities [13]. It is also suggested that ‘meaningfulcommunity engagement’ takes a highly influential
part of actions for the public health in a case ofcontaminated land [11]. Therefore, the impact ofeffective communication on stakeholders needs to beconsidered, since it is ‘critical to the successful
delivery of remediation projects’ [14]. From theabove, communication and community participation
can be said to be one of the most important issues
for sustainable decontamination process.
The framework of risk governance comprises offour phases; ‘pre-estimation’, ‘interdisciplinary risk

CONTAMINATED LAND POLICIES IN
JAPAN, THE NETHERLANDS AND THE UK
Japan has similarities with the Netherlands andthe UK that central governments are having amajority of role in setting and enforcing
contaminated land policies [8], i.e., severely772

GEOMATE-Osaka, Nov. 16-18, 2015

Table 1 Comparison of contaminated land policies
Level ofJapan
contamination

the Netherlands the UK

Agricultural Land Soil Pollution Prevention Law(1970) [19] Soil Protection Special Site [25]-[27]; Part
Severely
Law Concerning Special Measures against Dioxins(1999) [20] Act (1987) with IIA to include radioactively
contaminated Act on Special Measures concerning the Handling of
Risk assessment contaminated land (2006)
Environment Pollution by Radioactive Materials (2011) [21] [23], [24][26], [27]Contaminated Soil Contamination Countermeasures Act(2002) [22]contaminated sites are dealt by the central
governments or governmental organizations ofEnvironment Agency as such in the UK. However,contaminated land regime in Japan had developed
differently. In Japan, contaminated land regime isdealt by separate acts for specific land uses andsubstances which are introduced after the severe
contaminated land cases, i.e., acts for agriculturallands, dioxins, and radioactive materials (Table 1).
For example, for agricultural land, Agricultural Land
Soil Pollution Prevention Law (1970) was
introduced to deal with the ‘Itai-Itai disease’ in the
Jinzu River Basin area in 1968 to cause soil
pollution on agricultural land by chronic cadmium[19], and for dioxins, Law Concerning Special
Measures against Dioxins (1999) has been
introduced to deal with dioxins which is emitted
from waste incinerators which became to cause
pollution problems in recent years [28]. Thus, it canbe said to be disintegrated and sectionalized from
Soil Contamination Countermeasures Act (2002)
that covers contaminated land in general.
Although EU has a number of directives tobridge the gaps between environmental laws among
member states, individual member states are having
a key role in policy-making [8]. The Netherlands is
one of the countries to introduce contaminated land
policies in early periods in 1983 with Interim Soil
Remediation Act, owing to its dense population andbeing as an industrial country [24]. In case of severe
contamination, it is dealt by Soil Protection Act(1987) with risk assessment supported by web based
decision making support systems ‘to combinescientific aspects of risk assessment with policy
choices’ [23], [24].
In the UK, contaminated land regime came into
force in 2000 by part IIA of the Environmental
Protection Act (1990) and the Environment Act(1995) in a case of causing or having a potential tocause significant harm, or causing or likely to cause
pollution of controlled waters [25]-[27].
For severe contamination, Part IIA has been
extended to include Special Site which Environment
Agency to be responsible for the remediation [25],[27] under the conditions stated in circular 01/2006

Part IIA (2000) in
Soil Protection
Environmental Protection
Act (1987) [23],Act (1990) and Environment[24]
Act (1995) [25][26], as well as radioactively contaminated land in2006 [26], [27]. Contamination risks from its
current use is dealt by the above system, by contrast,
such risks for proposed use is controlled by Town
and Country Planning Act (1990) [25]-[27].
Contamination is a ‘material consideration’ which
requires planning authorities to consider themthrough development planning and development
control [25], [26], i.e., remediation can be thecondition before granting planning permission by
the local authorities for new proposed land uses.
Therefore, the UK approach is regarding
contamination under the condition of ‘only in
relation to particular sites and particular end-uses’[29].
Japan developed separate acts concerning eachenvironmental issue, contrastingly, the Netherlands
and the UK has an integrated contaminated land
regime to extending and enhancing the current acts
to deal with soil contamination by Soil Protection
Act (1987), and Part IIA of Environmental
Protection Act (1990) and Environment Act (1995)
to include severely contaminated sites (Table 1)[23]-[27]. Furthermore, the Netherlands has anational inventory of contaminated sites based on
the risk of ‘health and the environment or potential
impact on key resources’ including groundwater [8],while Japan and the UK have not been establishing
the equivalent inventories. However, the UK had
developed contaminated land policies from the
aspects of ‘recycling brownfields sites ’ due topressures on efficient land uses, rather than focusing
on the public health and environmental quality
which can be seen in other countries [30]. For
instance, concerning risks of contamination fromproposed land uses, the UK has a separate system byTown and Country Planning Act (1990) [29], [30],[31] to bring ‘operational tensions’ by thedifferences of each system professionally andtechnically [30].
COMPARISON OF RISK GOVERNANCE IN
CONTAMINATED LAND POLICIESSteps And Opportunities Of Risk Governance773

GEOMATE-Osaka, Nov. 16-18, 2015

decontamination process [19], [20]. However, there
is some advancement in contaminated land policies
on this issue in recent years. For example, Soil
Contamination Countermeasures Act (2002) statesthat the public are available to browse the registry ofdesignated areas [22]. Furthermore, Act on Special
Measures concerning the Handling of Environment
Pollution by Radioactive Materials (2011) includes
several steps as follows; disclosure of theinformation which is available to browse the registryof sites under management for soil removal of areasin action for decontamination; consultations withresidents associations and interest groups to create
opportunities to deliver opinions from land owners;
and admitted parties (stakeholder but was notincluded by the previous system/law, i.e., residents)
by the governor (leader/chairman of the state/county
council) are able to participate if necessary [21].
In the Netherlands, Soil Protection Act (1987)
states various entitled parties should becommunicated before making decisions in case ofseriously contaminated cases [33].
In the UK, disclosure of the information is
statutory that local authority should make additions
to public register of contaminated land which isavailable to browse by the public [26].
It is also stated that consultations with residents
associations
andinterest
groupsandcorrespondences during the decontamination process
to be statutory [26]. Therefore, local authority has toarrange and plan procedures of remediation
strategies by communicating, and catering for the
information of parties involved including owners,
occupiers of land, and other relevant interested
parties; as well as information and complaints fromthe public, businesses and voluntary organizations[26].

There are a number of steps and opportunities
which enables better communication andparticipation as followings; disclosure of theinformation;
consultations
withresidents
associations and interest groups; community
participation to consultative, decision making
meetings; and correspondences during thedecontamination process [32]. First and foremost,disclosure of the information is necessary for
citizens to have a chance/opportunity to participate
in the process, because it is considered as a highly
important factor in risk communication [5], [6]. It
does not directly engage with ‘affected parties or thewider public’, and becomes ‘a critical first step’ incase of a need of communication with complexity[13]. Then, consultations and participations ofresidents and interest groups are important for
improving communication between stakeholders andto activate participation in decision making process[32]. It is a direct involvement with stakeholders,and by ensuring to listen and take into account of
their opinions from these opportunities, it can be ahelp to smooth the process [13]. Moreover, thereflective involvement enables to ‘pursue thepurpose of finding a consensus on the extra marginof safety that potential victims would be willing totolerate and potential beneficiaries of the risk would
be willing to invest in order to avoid potentiallycritical and catastrophic consequences’ [4]. During
the decontamination process, involvement of all
stakeholders needs to be considered in order toprovide access to advice at most [14], which alsoenables ‘to build confidence and trust between allparties’ by working together to maintain consistency
and approachability [12]. In risk governance,
mutual actions between actors from scientists, public
and private sectors, and citizens are undertaken inaccordance with ‘public participation, stakeholder
involvement and governance structures’ horizontallyand vertically [2]. Furthermore, correspondences forresidents during the process should also be
considered in terms of the health and safety and after
the process to meet with the local needs [32]. In
addition, correspondences after the decontamination
process may be also necessary since some of themrequire management on sites or have limitations onland uses due to the remediation methods [32],particularly for the long term cases i.e. in Fukushima.

Comparison And Applicability Of ContaminatedLand Policies With Risk Governance
From the comparison of contaminated land
policies among the three countries regarding on risk
governance, it became clear that aspects of risk
communication are rather limited in the Netherlands.
However, recent policies in Japan and the UK are
shifting to include some perspectives of community
participation by incorporating disclosure of theinformation for the public to access the registry ofcontaminated sites and consultations of stakeholders.
In addition, there are advancements to include
community participation to consultative, decision
making meetings in Japan, and correspondences
during the decontamination process in the UK.
However, in practice, application of policies has alimitation that became evident from a case in
Fukushima to have a problem of disclosure of
accurate information.
According to the framework of risk governanceStatutory Requirements Of Contaminated Land
Policies With Risk Governance
In Japan, Agricultural Land Soil Pollution
Prevention Law (1970) and Law Concerning Special
Measures against Dioxins (1999) both have not been
defined in terms of the aspects of communicationand community participation, therefore, it is notmandatory to include such action in the774

GEOMATE-Osaka, Nov. 16-18, 2015

comprising of the four main phases from preestimation to management has been proposed [4],however, contaminated land policies have statednone of the actions dedicated to risk governance inthe statutory process. This may be due to the factthat contaminated land policies are based on
scientific data and information, and lacking toincorporate socio-economic aspects in the process.
However, communication has been partially statedas a part of statutory actions, which is undertaken
throughout the process in parallel to the four phases.
Thus, there is a limitation to ensure risk governanceby statutory actions, and many set of actions needs
to be dealt by voluntary. This may be because theconcept of risk governance is a continuous set ofinclusive and consolidative actions, while
contaminated land policies are scientifically based
and has a tendency to follow administrative
procedures and not to be interactive. Therefore, tofill the gap, guidelines have been provided todemonstrate best practices as a way ofrecommendation to support voluntary actions, i.e.,
SNIFFER [13], [15]. In addition, promoting further
integration between the four phases of risk
governance and communication may also lead toenhancement of actions on risk governance incontaminated land policies.

which has a limitation to extend or enhance.
However, risk assessment/management also require
to set clear goals and to be measurable for achieving
the effectiveness [34], therefore, clarification ofgoals may be one of the key issue for smooth
implementation of contaminated land policies andsustainable decontamination process.
In terms of the comparison of risk governance incontaminated land policies, the Netherlands has asystem with limited application, while the aspects ofcommunity participation has been incorporated tosome extent in recent policies in the UK and Japan,i.e., disclosure of the information for the public tobrowse the registry of contaminated sites andconsultations of stakeholders.
Furthermore,community participation to consultative and decision
making meeting in Japan as well as correspondences
during the decontamination process in the UK
should be also considered as good practices.
Therefore, recent contaminated land policy
frameworks are adapting to promote risk governancein decontamination process by introducing statutory
requirements. However, there is a limitation toensure risk governance by statutory actions, andmany set of actions needs to be dealt by voluntary.
This may be because of the concept of risk
governance is a continuous set of inclusive andconsolidative actions, while contaminated land
policies have a tendency to follow administrative
procedures and not to be interactive. Thus, to fill thegap, guidelines have been provided to demonstrate
best practices as a way of recommendation to
support voluntary actions.

CONCLUSION
In this paper, it had examined contaminated land
policies in Japan, the Netherlands, and the UK fromthe aspects of risk governance towards sustainabledecontamination process to incorporate social
aspects. From the comparison, Japan had developed
contaminated land regime in a way of disintegrated
and sectionalized, owing to separate sets of acts had
been introduced to cover each specific and severely
contaminated land cases.
By contrast, theNetherlands and the UK has an integrated
contaminated land regime by extending andenhancing the current acts to cover both severely
contaminatedandcontaminatedsites
oncontamination risks from current land uses.
Although the above regime of the Netherlands
covers both current and future proposed land uses,
the UK has a separate system for future proposed
land uses by town and country planning system.
Thus, the differences of contaminated land
policies in the three countries may be illustrating thedifferences of having measures of generic numericalvalues of environmental quality which requires a setof soil values to have disintegrated and sectionalizedinJapan,
whereas
the
idea
of
risk
assessment/management can be applied to various
cases to allow the enhancement and integration of
the current system. In order to consider the
integration in contaminated land policies in Japan, it
may be necessary to examine the current system

ACKNOWLEDGEMENTS
The authors acknowledge the financial support
provided by the Japan Society for the Promotion of
Science (no. 25289207), and grants from the
Obayashi Foundation (research grant, no.10, 2012).
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Vol. 27, No.3, 2005, pp. 275-280.
[12] Atenstaedt RL et al., “Good practice in risk
communication: A case study related to people
residing on an infilled clay pit”, Vol. 10, Issue 1,
J. of Environmental Health Research, 2010, pp.
57-63.
[13] SNIFFER, “Risk Communication Booklet:
Communicating understanding of contaminatedland risks”, 2010, pp. 1-24.
[14] Cross M, “Effective communication incontaminated risk management”, Proc. of theInst. of Civil Engineers, Municipal Engineer,Vol. 164, Issue ME4, 2011, pp. 229-239.
[15] SNIFFER, “Communicating understanding ofcontaminated land risks”, Project UKLQ13,
Final Report, 2010, pp. 3-51.
[16] Eiser JR, et al., “Risk perception and trust in thecontext of urban brownfields”, Environmental
Hazards, 7, 2007, pp.150-156.
[17] Oughton D, et al., “An ethical dimension tosustainable
restoration
andlong-term
management of contaminated areas”, J. ofEnvironmental Radioactivity, Vol. 74, No.1-3,
Special Issue, 2004, pp.171-183.
[18] Yasutaka T, Naito W and Nakanishi J, “Costand effectiveness of decontamination strategies
in radiation contaminated areas in Fukushima inregard to external radiation dose”, PLOS ONE,Vol. 8, Issue 9, Sept. 2013, pp. 1-11.
[19] Ministry of the Env., “Conservation of Soil
Environment”,
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at:www.env.go.jp/en/water/wq/wemj/soil.html(accessed 3 Mar. 2015).
[20] Ministry of the Env., “Law Concerning Special
Measures against Dioxins”, 1999, pp. 1-20.
[21] Ministry of the Env., “The Act on Special
Measures concerning the Handling ofEnvironment Pollution by Radioactive Materials”(in Japanese), 2011, pp. 1-59.
[22] Ministry of the Env., “Soil Contamination
Countermeasures Act” (in Japanese), 2005, pp.
1-25.
[23] Ministry of Infrastructure and the Env., “SoilRemediation Circular 2009”, 2009, pp. 3-78.
[24] Swartjes FA, et al., “State of the art ofcontaminated site management in The
Netherlands: Policy framework and risk
assessment tools”, Science of The Total
Environment, Vol. 427-428, Jun. 2012, pp. 1-10.
[25] Env. Agency, “Dealing with contaminated land
in England”, 2002, pp. 1-39.
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Protection Act 1990: Part 2A, Contaminatedland, 2006, pp. 5-190.
[27] Env. Agency, “Dealing with contaminated land
in England”, 2009, pp. 1-38.
[28] Ministry of the Env., “Countermeasure for
Dioxins” (in Japanese), undated, Available
at: www.env.go.jp/chemi/dioxin/(accessed 3
March 2015).
[29] Cullingworth B and Nadin V, “Town andCountry Planning in the UK”, 13th ed.,Routledge, 2002, pp. 186-188.
[30] Catney P, et al., “Dealing with ContaminatedLand in the UK through ‘Development
Managerialism’ ”, J. of Environmental Policy
and Planning, Vol. 8, Issue 4, 2006, pp. 331-356.
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pp. 290-291.
[32] Miyagawa T, and Nakayama T, “Study on carefor the public in land contamination treatment
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Reports of the City Planning Institute of Japan,
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Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

OCEAN DECONTAMINATION: REMOVAL EFFICIEMCY OF
RADIOACTIVE CESIUM FROM OCEAN SLUDGE BY USINGMICRO BUBBLES AND ACTIVATING MICROORGANISMS
Kyoichi OKAMOTO1 and Takeshi TOYAMA1
1
CST, Nihon University, JAPANABSTRACTThe Fukushima nuclear accident of March 11, 2011, soil and water had been contaminated by radioactive
cesium. Moreover, radioactive cesium was found in the ocean sludge in Tokyo Bay by flowing from rivers. Here,it cannot be easily removed the cesium which is adsorbed to the sludge. On the other hand, one of the authors
had developed the decomposition system for ocean sludge with circulation type by micro-bubbles, which
decompose and purification sludge by activating the aerobic bacteria, after creating an aerobic state by microbubbles. Here, based on the hypothesis that radioactive cesium is adsorbed on the surface of the sludge
deposition. It is considered that radioactive cesium can be eluted, after decomposing the deposited sludge byusing the decomposition system for ocean sludge with circulation type. If the cesium will be eluted in the water,
we can fix the cesium to existing technology such as “Zeolite”. In this study, our objects is to check the removal
performance of cesium after the decomposition and elution of the sludge and then fix of cesium by setting zeolitein the decomposition system with circulation type by micro-bubbles and activating microorganisms. As theresults, we obtained the removal efficiency of cesium was 62.7%.
Keywords: Decontamination, Radioactive Cesium, Ocean Sludge, Micro-bubble, Microorganism, Zeolite

was very sensitive so that we had to carry out theseveral experiments.
In this study, our objects is to check the removal
performance of cesium after the decomposition andelution of the sludge and then fix of cesium by
setting “Zeolite” in the decomposition system forocean sludge with circulation type by micro bubbles
and activating microorganisms.

INTRODUCTION
The Fukushima nuclear accident on March 11,
2011, soil and water had been contaminated byradioactive cesium. Moreover, radioactive cesium
was found in the ocean sludge in Tokyo Bay byflowing from rivers. A report says it becomes 13
times of the cesium for 7 months from August, 2011
in [1]. Here, it cannot be easy to remove the cesium
which is adsorbed to the sludge.
On the other hand, one of the authors had
developed the decomposition system for ocean
sludge with circulation type by micro-bubbles,
which decompose and purification sludge byactivating the aerobic bacteria, after creating an
aerobic state by micro-bubbles.
Here, based on the hypothesis that radioactive
cesium is adsorbed on the surface of the sludge
deposition in [2], it is considered that radioactive
cesium can be eluted, after decomposing thedeposited sludge by using the decomposition systemfor ocean sludge with circulation type.
If the cesium will be eluted in the water, we canfix the cesium by the existing technology such as“Zeolite”.
In our past research in [3], removal performanceof cesium was 30.9% by using the decomposition
system for ocean sludge with circulation type by
micro-bubbles and activating microorganisms. But
Measurement of cesium by the iron chromatography

Fig. 1 Purification System of Circulation Type.

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Here, we have a way by using of thedecomposition system for ocean sludge withcirculation type. After decomposition of the sludge
adsorbed cesium by our system, cesium is eluted
into water, shown in Fig 3. That is our hypothesis.

DECOMPOSITION SYSTEM WITH
CIRCULATION TYPE
It is very important to reduce sedimentary sludge
in the ocean. Plans to reduce the sludge are usually
dreading or sand covering. Dredging is a simple way
and aims to cut off the sludge. But after cutting off,
treating the dredged sludge takes much more timeand, of course, cost. Sand covering, in general, gives
a big load to living organisms and the ecologicalsystem.
So that, a more efficient way is needed to reduce
the sludge while not imparting environmental load in
the local sea area. Here, attention was paid to microbubble technology for application to the purificationof the sludge. The important point in this technique
is to activate the bacteria existing in the area by
micro-bubbles. Micro-bubbles (that is MB) canchange conditions into an aerobic state. If the
bubbling stops, the situation changes into anaerobic
state, according to recent research. So, we selected amethod for decomposing the sludge by
microorganisms.
One of the authors had developed thedecomposition system for ocean sludge withcirculation type by micro-bubbles, shown in Fig.1,
which decompose and purification sludge byactivating the aerobic bacteria, after creating an
aerobic state by micro-bubbles.

ELUTION EXPERIMENTS FOR CESIUM
Procedure of Experiment
The experimental devices consist of two parts,
shown in Fig. 4. The water circulates through twotanks. In one tank (Width40xLength28x Hight28cm),
micro-bubbles are generated. The micro-bubbles
have micro-size diameter and high solubility. Thismeans the water with high concentration ofdissolved oxygen circulates through these tanks. The
other
part
is
the
experimentaltank(W60xL29xH35cm). We used sea-water 30(litter)and sludge 1(kg). Here, a micro-bubble generator isbased on [4], [5] and the flow rate is 900 (litter/hour).
The flow rate of water pumps connected each tanks
are 300 (litter/hour).

MECHANISM ON FIXING OF CESIUM FROMELUTION
Microbubble Generator

In general, ocean sludge has a negative charge.
When cesium with a positive charge flows fromriver, sludge was adsorbed cesium, shown in Fig 2.
So that, sludge adsorbed cesium cannot eliminate byusual way.

Circulation Pump

ZeoliteFig.4 Experimental System for Elution of Cesium.
We had caught the sludge and the sea water at
Funabashi Port in Chiba Prefecture in JAPAN, as
shown in Fig.4 and 5. Here, we had removed under
10cm of the sludge from seabed before sampling as
experimental procedure, because we have to remove
the initial value of cesium in the sludge, from [3].
We used the cesium chloride before 24 hours ofstarting time and the concentration of cesium ion is
100 (ppm). A cooler for water tank was set at side of
the tank for generating microbubbles, for the
purpose of setting water temperature 30 degree
centigrade.
After setting the decomposition system withcirculation type by micro-bubbles, experiment starts
at the same time of generating micro-bubble device
and also the zeolites were set in the tank.
After 6 hour, the microorganism activator was
put in the experimental tank. Main staff of theactivator is Kelp and including nutrients and someenzyme. Our used activator is reported to show
effective results in purification for grease trap.

Fig.2 Mechanism on Adsorption of Cesium

Fig.3 Mechanism on Fixing of Cesium from Elution.
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GEOMATE- Osaka, Nov. 16-18, 2015

Results and Discussions
Results of water temperature, pH and DO as
environmental condition
Fig.7-9 show the water temperature, pH and DO(Dissolved Oxygen) as the results of environmental
condition of this experiment.
Water temperature is almost constant about 30
degree centigrade after 6 hours, by setting the cooler
for water tank in the experimental system. pH is also
constant about 7.5 to 8.7. The difference between
Case 1,2 and Case 3,4 is caused by zeolite adsorb
some ion when sludge is decomposed. DO in allcases is saturation state after 24 hours.
40
Water temperature[℃]

Fig.5 Catching Point of Sludge and Sea Water at
Funabashi Port in Tokyo Bay.

30
20
10
0

24

48
72
Time[hour]96

Fig.7 Changes in Water
Environmental Conditions.

Fig.6 Scene of Catching Sludge.
Dissolved oxygen (DO), water temperature andpH are measured by using of multi-parameter water
quality meter. Ammonium nitrogen (NH4-N), total
nitrogen (T-N) and total phosphorus (T-P) aremeasured by using of digital-water-analyzer bydigital “Packtest”, by water filtered after sampling inexperimental tank.

120

Temperature

as

12
10
pH

8
6

Case1(3A)
Case2(4A)
Case3(5A)
Case4(Z13)4Experimental Conditions

2
0

Basically, zeolite have small and so many halls.
As the results, it can fixed the cesium. We used thezeolite by composed type for experiment. Since thediameter of cesium is in general about 0.338(nm),we selected 4A type which has nearly diameter, andalso 3A and 5A type of zeolite. And more selected
Z13 type which has not uniform diameter, 0.2 to
1.0(nm). From the above, these are experimental
conditions, shown in Table.1.

24

48
72
Time[hour]

96

120

Fig.8 Changes in pH as Environmental Conditions.
10

DO[mg/L]

8

Table 1 Experimental Conditions
Case 1Case 2
Case 3
Case 4

Case1(3A)
Case2(4A)
Case3(5A)
Case4(Z13)6
Case1(3A)
Case2(4A)
Case3(5A)
Case4(Z13)42

Kinds of Zeolite
3A（≒0.3nm)
4A（≒0.4nm)
5A（≒0.5nm)
Z13（≒0.2-1.0nm)

0

24

48
72
Time[hour]

96

120

Fig.9 Changes in DO as Environmental Conditions

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GEOMATE- Osaka, Nov. 16-18, 2015

Results of H2S, NH4-N, DIN and T-N

T-N[mg/L]

Fig.10-13 are shown in results of H2S (Hydrogensulfide), NH4-N(Ammonium nitrogen), DIN (Total
inorganic nitrogen) and T-N(Total nitrogen).
H2S in all cases decrease rapidly. It seemed by
the supply of oxygen, and also another reason iszeolite can adsorb the H2S. NH4-N in all cases has
tendency of decrease by 12 hours and then decrease
slowly. DIN (= NH4-N+NO2-N+NO3-N) shows 60%
decrease. It seems it happen to denitrification by
microorganisms from [7]. T-N in case 3 at 72 hours
only increase. This reason is not clear but including
measurement error. But, T-N in all cases decreaseover 50% or maximum 75.0% by 120 hours. This
purification efficiency is very good.

0

H 2 S[mg/L]

Case1(3A)
Case2(4A)
Case3(5A)
Case4(Z13)

24

48
72
Time[hour]96

液相Cs [mg/L]

60

NH 4-N[mg/L]

Case1(3A)
Case2(4A)
Case3(5A)
Case4(Z13)

50
30
20

0

24

48
72
Time[hour]96

Case1(3A)
Case2(4A)
Case3(5A)
Case4( Z13)

40

10

1
0

120

70

4

2

96

120

Fig.10 Changes in H2S.

3

48
72
Time[hour]

The results cesium were obtained by the ironchromatography shown in Fig 14.
The result of cesium is zero at 48 hours in caseof 3A, and also is zero at 24 hours over in the othercases. It is especially very good the result of cesiumin case 4A becomes zero until 12 hours. As theresults of the above, fixed values of cesium byzeolite in all cases are 100 %.

0.05
0

24

Results of Cesium (liquid)

0.2

0.1

Case1(3A)
Case2(4A)
Case3(5A)
Case4(Z13)

Fig.13 Changes in T-N.

0.25

0.15

8
7
6
5
4
3
2
1

24

48
72
Time[hour]

96

120

Fig.14 Changes in Cesium in Water.

120

Fig.11 Changes in NH4-N.
Results of Cesium (solid)
10

We paid attention to cesium and silica (Si), as
there are many chemical element in sludge. We usedthe energy dispersion type X-ray analysis device(EDX), because we can measure by the solid state.
Weight ratio of cesium and silica (CS/Si) in solid ofdry sludge are shown in Fig.15.
Ratio of decontamination of cesium wascalculated from ratio of content of beginning andfinal value measured by EDX, as the standard values
including silica in dried sludge after measuring theweight of dried sludge.

DIN

8
Case1(3A)
Case2(4A)
Case3(5A)
Case4(Z13)

6
4
2
0

24

48
72
Time[hour]

96

120

Fig.12 Changes in DIN.
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GEOMATE- Osaka, Nov. 16-18, 2015

REFERENCES

Ratio of decontamination is obtained over 50%
in all cases. Especially in case of 4A, the efficiency
of decontamination is obtained 62.7%.
It seemed this experimental system including
zeolite was obtained very good results and themechanism on fixing of cesium from elution bydecomposition of the sludge.

[1] Yomiuri News Paper, 13 times by 7 months
about cesium in ocean soil in Tokyo Bay, May,
13th, 2012.
[2] Okamoto Kyoichi, Hotta Kenji, Toyama
Takeshi and Kohno Hideki, Purification System
of Ocean Sludge by Activating Microorganisms,
International Journal of GEOMATE (Geotec,
Const. Mat. & Env.), Vol.6, No.1, pp.791-795,
March, 2014.
[3] Okamoto Kyoichi and Toyama Takaeshi: Ocean
Decontamination: Removal of Radioactive
Cesium from Ocean Sludge by Using Microbubbles and Activating Microorganisms, The
Japan Society of Naval Architects and Ocean
Engineers, May, 2014.
[4] Imai, T, Shioshige, K, Ukita, M, Sekine, M,
Higuchi, T, Fukagawa, K and Fujisato, T.
Development of Device for Dissolving High
Concentration Gas for Purification at bottom
layer around enclosed water area like Dam Lake,
Proc. 39th Forum on
Environmental
Engineering of Civil Engineering Society, 10-12,
2002.
[5] Matsuo, K, Maeda, K, Ohnari, H, Tsunami, Y,
and Ohnari, H., “Water Purification of a Dam
Lake Using Micro Bubble Technology”,
Progress in Multiphase Flow Research I,
pp.279-286, 2006.
[6] Okamoto Kyoichi and Hotta Kenji, Purification
System of Ocean Sludge by Using Coagulants
and Activating Microorganisms, International
Journal of GEOMATE (Geotec, Const. Mat. &Env.), Vol.4, No.2, pp.574-579, June, 2013.
[7] Sone Takaaki, Yamashita kazuhiro andOkamotoKyoichi,
IdentificationofMicroorganism in Purification Process for
Ocean Sludge by using Purification System with
Circulation Type, Japan Association for Coastal
Zone Studies, July 20143
固相 Cs/Si [％]

2.5
2

1.5
1

0.5

Case1(3A)
Case2(4A)
Case3(5A)
Case4(Z13)0Time[hour]

120

Fig.15 Changes in (Cesium)/(Silica).

CONCLUSION
We had carried out the elution and fixing
cesium by setting zeolite, after decomposing thedeposited sludge, by using the decomposition systemfor ocean sludge with circulation type by microbubble and activating microorganisms.
From the results by measurements for water
qualification,(1) T-N decreases maximum 75.0% in case of
3A. Purification efficiency is very good.
From the results by iron chromatography,(2) Fixed values of cesium by zeolite in allcases are 100 % and cesium in case 4A becomeszero until 12 hours.
From the results of the energy dispersion type
X-ray analysis device, the weight ratio of cesium
and silica (CS/Si) in solid of dry sludge is denoted,(3) The efficiency of decontamination is
obtained over 62.7% in cases of 4A.
ACKNOWLEDGEMENTS
The authors would like to express sincere thanks
for executing the experiments to Ayato INAMURA,
Takayuki HORIUCHI and Kazuhiro NAKAMURA,
Students of Nihon University, and for chemical
analysis to Hirosuke HIRANO, Graduate student of
Nihon University, in Japan.
This work was supported by KAKENHI (Grant-inAid for Scientific Research (C-26420831)).

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Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

THE CHOICE FACTOR OF THE SLOPE PROTECTION METHOD OFRICE TERRACE
1

Shoko Nishiwaki1, Kenji Okaima1
Graduate school of Bio resources, Mie University, 1577 Machiyacho, Kurima, Tsu, Mie 514-8507, JAPANABSTRACT
Rice terrace which are one of the farmland using the slant ground has slopes between each terrace field. In
slope protection methods of the rice terrace, there are fundamental two types of “earth slope” and “stone wall”.
Some research suggested that the type of the slope protection method of rice terrace depended on the presence orabsence of unearthed rocks at the time of the paddy field reclamation.
In this paper, we analyzed the type of the slope protection method of rice terrace in the point of one
hundred selection of Japanese rice terraces using a subsurface geological map drawn on a scale of 1 to 200000.
The geology in each point of one hundred selection of Japanese rice terrace was classified into three main groups
as volcanic rock, sedimentary rock and metamorphic rock. Three main groups were further classified by rocktypes or geological timescale.
We formed clear that rock types or geological timescale of the subsurface geology in the point was related tothe choice of the slope protection method of rice terrace. Stone walls were chosen at most points in the area of
volcanic rock, sedimentary rock before Mesozoic era and metamorphic rock. Earth slope was chosen at majority
points in the area of sedimentary rock in Cenozoic era. In addition, we showed the possibility that thegeographical location relation to surrounding geology and the mason culture affected choice of the stone wall.
Keywords: Rice terrace, Slope protection method, Subsurface geological structure, Stone wall

INTRODUCTION

EXAMINATION OF THE SLOPEPROTECTION METHOD

Rice terrace is one of the farmland in the slant
ground that has slopes between each terrace field.
Slope protection methods of rice terraces are one of
the landscapes to create a regional identity. The
slope protection methods have fundamental two
types of “earth slope” and “stone wall”. [1] Reported
that the two types slope protection methods
depended on presence or absence of unearthed rocks
at the time of the paddy field reclamation. However,
the report was not a quantitative results based on thedata.
The purpose of this study was to indicate theselective factor of slope protection methods
quantitatively using geological data in major riceterrace regions in Japan. The geological data as thepresence or absence of unearthed rocks was
described on geologic maps. In addition, major riceterrace regions are “one hundred selection ofJapanese rice terraces.”
The slope protection methods were investigatedby “one hundred selection of Japanese rice terraces”
designated by [2]. The rice terrace regions wereclassified according to geological types. The
relationship between the slope protection methods
and geological types of rice terrace regions wasanalyzed. We conducted field investigations to somerice terrace regions to verify the results of theseanalyses.

The slope protection methods were investigatedby data of “one hundred selections of Japanese riceterraces”. [2] designated 134 major rice terrace
regions as “one hundred selections of Japanese riceterraces” in 1999. These rice terraces wererecommended by local governments. It has been
desirable to maintain beautiful landscapes at theserice terraces in future. Figure1 shows the distributed
condition of rice terrace. It can be seen that manyrice terraces are distributed over West Japan. This
distribution applies to the entire rice terraces inJapan. [1] pointing out that the cause of this
distribution is caused by the difference in thelandform. There are many small alluvial plains inWest Japan. On the other hand, there are manylarge-scale alluvial plains in the East Japan. Data of“one hundred selections of Japanese rice terraces”
are consisted of mean gradient of the rice terrace, thearea of the region, the number of rice terrace, size offarm per household, kind of water resources, kind ofslope protection method, the origin of paddy fieldreclamations, and harvested quantity.
In this paper, kind of slope protection method of
the data was used to analysis. Slope protection
methods are classified into “earth slope”, “stonewall”, and combination of “earth slope” and “stonewall”. Figure 2 shows the composition of slope782

GEOMATE- Osaka, Nov. 16-18, 2015

protection methods in “one hundred selections ofJapanese rice terraces”. It is clear from Figure 2 that“earth slope”, “stone wall” were almost the same
number. From this fact, there was no bias in theselection of the slope protection method.
Stones were used in the slope protection at therice terraces which have combination of “earth slope”
and “stone wall”. Therefore, we regarded that theserice terraces chose “stone wall” in the slope
protection.

GEOLOGIC CLASSIFICATION OF RICETERRACES
The geologic map indicates what type of stoneand stratum are distributed under the surface soil. In
this paper, we used a one-200,000th Japaneseseamless geologic map which National Institute ofAdvanced Industrial Science and Technology
published.
In this geologic map, color legend series is
uniform in Japan. And this geologic map is updated
based on the latest results of research at any time.
In this paper, the rocks which of the subsurface
geological structures on the geological map areclassified into three general classes of rock: igneous,sedimentary (sediment) and metamorphic.

Fig. 1・Igneous rockIgneous rock is formed through the cooling andsolidification of magma or lava. Igneous rock may
form with or without crystallization, either below thesurface as intrusive (plutonic) rocks or on thesurface as extrusive (volcanic) rocks. The volcanic
rock includes andesite, basalt, rhyolite, and so on.
Plutonic rock includes granite, gabbro, serpentine,and so on. These rocks are used as stone wall andconstruction materials. However, there is differencein the importance of the construction.
In the fresh igneous rock, change of physicalproperties is little over time. However, igneous rocks
change to gravel or sand by weathering.

The distributed condition of “one hundred

selection of Japanese rice terraces”
Fig. 2 The composition of slope protection methods
in “one hundred selections of Japanese riceterraces”
The sedimentary rock (or sediment) which
deposited in Pliocene epoch and Miocene does somecompaction and is called “soft rock”. The
sedimentary rock (or sediment) which deposited
before the Mesozoic era does compaction strongly
and called “hard rock”. In this paper, we hypothesizethat the rice terraces whom there were in thesubsurface geological structures deposited before
Pliocene epoch making ends meet as a rock havechosen “stone wall” in a slope protection method.
Therefore, we hypothesize that “earth slopes” werechosen in a slope protection method in rice terraces
that have the geological feature that had deposited
after Pleistocene which had not yet become hardrock.

・Sedimentary rock
Sedimentary rock is formed of compressed
sediment by time progress. The sediment consists ofmud, sand, organic remains and volcanic ashes.
Sedimentary rock’s strength is further increasedby cementation and consolidation. Therefore,
sedimentary rock is become harder and denser withthe passage of time. Table 1 shows the relationshipabout geological epoch and sedimentary rock’s
strength. [3] reported that semimetal rock (orsediment) deposited in the Cenozoic of Holocene
and Pleistocene has not yet been consolidated.

・Metamorphic rock
Metamorphic rock was formed recrystallizes
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GEOMATE- Osaka, Nov. 16-18, 2015

material in the sedimentary rock and igneous rock.
The formation of the metamorphic rock has athing by contact metamorphism and a thing byregional metamorphism. Contact metamorphism isthe name given to the changes that take place whenmagma is injected into the surrounding solid rock
(Contact
metamorphicrock).
Regional
metamorphism is the name given to changes in great
masses of rock over a wide area. Rocks
metamorphosed simply by being at great depths
below the Earth's surface, subjected to hightemperatures and the pressure caused by theimmense weight of the rock layers above (Regional
metamorphic rock).
In this paper the classification, only regional
metamorphic
rock
had
existence
among
metamorphic rocks. Furthermore, only schist wasseen among wide area metamorphic rocks.
Regional metamorphic rocks have not been
applied to important constructions for stone wall.
For that reason many of these rocks split readily in
one direction along mica-bearing zones (schists).
But they are used for stone wall use and flat stones
in private houses and farmlands. Therefore in thispaper, regional metamorphic rock was regarded as
rock suitable for stone wall. Furthermore, schist was
only confirmed in metamorphic rocks of “onehundred selections of Japanese rice terraces”.

Table.1 Relations of the geological epoch andthe hardness of sedimental rockmetamorphic
rock, 6cases,
5%

sedimentaryrock(sediment),
86cases,64%

Fig.3 shows a ratio of igneous rock,sedimentary rock (sediment) and metamorphic rockin the subsurface geological structures of “onehundred selection of Japanese rice terraces”. In
Figure 3, 64% (86/134 cases) of the “one hundred
selection of Japanese rice terraces” of the riceterraces was present in the subsurface geologicalstructures on sedimentary rocks. [4] says that, riceterraces were reclamation in landslide topography.
The subsurface geological structures that a landslide
happened are classified as a sedimentary rock ofCenozoic era. Therefore, it was thought that there
was much existing rice terrace on the subsurface
geological structures of the sedimentary rock.

igneous rock,42cases,31%

igneous rocksedimentary rock(sediment)
metamorphic rockFig. 3 the ratio of the subsurface geologicalstructures of “one hundred selection ofJapanese rice terraces”

Similarly, the results of organized data about
the subsurface geological structures in the cases ofsedimentary rock are indicated in Figure 5.
Figure 5 indicates that most of the rice terraces
to select “earth slope” were present in Cenozoic thesubsurface geological structures. The sedimentaryrock formed before the Mesozoic becomes the hardrock. Most of rice terraces whom there were in areathat subsurface geological structure is “hard rock”
chose a “stone wall” in a slope protection method of
construction.
Furthermore, the subsurface geologicalstructures of most of the rice terraces that haveselected the “earth slope”(98% 55/56 cases) were
Cenozoic sedimentary rocks (sediments). The
probability is that the Cenozoic sedimentary rocks(sediments) are too soft to use as the material ofstone wall. Therefore, the rice terraces in the
Cenozoic sedimentary rocks (sediments) area chose“earth slope”.
The results of organized data about thesubsurface geological structures in the cases ofmetamorphic rock are presented in Figure 6.

RELATIONS OF THE SLOPE PROTECTIONMETHODS AND THE SUBSURFACEGEOLOGICAL STRUCTURES
The results of organized data about thesubsurface geological structures in the cases of
igneous rocks are indicated in Figure 4.
As the Figure 4 indicates the subsurface
geological structures in the cases of igneous rock,tend to choose a stone wall (98% 41/42 cases). The
igneous rocks are used as stone wall andconstruction materials. For that reason, the results of
Figure 4 are proper. For example the rice terrace thatchosen the “earth slope” despite the subsurface
geological structure is granite, we will discuss in thenext chapter.
784

GEOMATE- Osaka, Nov. 16-18, 2015

26
24
22
20
18
16
14
12
10
8
6
4
2
0

number of rice terraces

Figure 6 indicates that most of the regions of
the metamorphic rock in the subsurface geologicalstructures have selected the stone wall. Metamorphic
rocks are used for not only “stone wall” of slope
protection method of rice terraces but stone-wall ofprivate house.
CONSIDERATION OF EXCEPTION REGIONIn Figures 4-8, we formed four kinds of riceterrace the exception area.
・ Choose “earth slope” and the subsurface
geological structures is granite“Yamabuki Rice terrace” in Iwate Prefecture
has been classified this case. Granite is distributed
widely around “Yamabuki rice terrace”. However,“Yamabuki rice terrace” have selected the earth
slope for the slope protection method. [5] reported
that, this area can’t produce rocks because granite
weathers deeply in this area. That is to said, there
was no stone that can be building materials for“stone wall” in this region. It was considered that
“Yamabuki Rice terrace” was not able to choose the“stone wall”.

slope tamping

stone wall

number of rice terraces

Fig. 4 The subsurface geological structures of riceterrace in the cases of igneous rock・Choose a stone wall with Cenozoic sedimentaryrock (23 cases)
Rice terraces that were selected the "stone wall"
to the slope protection method in spite of thesubsurface geological structures of Cenozoic
sedimentary rock are classified two cases.
The first case was that the subsurface
geological structures were consolidated to the extent
available in “stone wall” material. (8/23 cases)
The other case was that stone that can be used
as a stone wall material (hard sedimentary rock,igneous rock, or metamorphic rock) at the positionof higher altitudes than the rice terrace. (15/23 cases)
Cobbles or boulders of higher altitudes are used as
farmland stone wall material has been reported by[6]. For field survey of the rice terrace that applies tothis case will be introduced in a later chapter.

26
24
22
20
18
16
14
12
10
8
6
4
2
0

slope tamping
stone wall

number of rice terrace

Fig. 5 The subsurface geological structures of riceterrace in the cases of sedimentary rocks

・Choose “earth slope” with metamorphic rock (3cases)
Rice terraces classified in this case were existed
in the area that was difficult to produce stone wallmaterials. [7] reported that metamorphic rocks ofthese areas are easy to peel. For this reason,
metamorphic rocks of these areas cannot be used to“stone wall” material.

26
24
22
20
18
16
14
12
10
8
6
4
2
0

slope tamping

stone wall

Greenschist

Other schist

Fig. 6 The subsurface geological structures of riceterrace in the cases of metamorphic rocks

785

GEOMATE- Osaka, Nov. 16-18, 2015

・Choose “earth slope” with Mesozoic sedimentaryrock. (2 cases)
Sedimentary rocks that formed in the Mesozoic
are “hard rock”. Therefore, we considered that therice terraces exist in such surface geologicalstructures have chosen “stone wall” to the slope
protection method. Against all expectations, theserice terraces chose “earth protection” to the slope
protection method. According to the investigation
with the telephone to the management group, onecase of the fourth exception area use not only “earth
slope” but also “stone wall”. This result was in close
agreement with our consideration previously
described. Field investigation is necessary to theother case of rice terrace.
VERIFICATION BY FIELD INVESTIGATION
Fig. 7 The subsurface geological structures of“Maruyama riceterrace” and thatneighboring area

Based on the above-mentioned consideration,
we did some field investigations. As an example ofthe field investigation, we show “Maruyama riceterrace.” The field investigation was conducted on
April 1, 2014. The field investigation aims were two.
First, the data of slope protection method of“Maruyama rice terrace” registered in data of “onehundred selection of Japanese rice terraces” waswhether correct.
The second was to know the kind of the stones
that were used to “stone wall” material.
Maruyama rice terrace” is in the Mie Prefecture, andhas consisted of “stone wall” to slope protection
methods.
By way of example, the subsurface geologicalstructures of “Maruyama rice terrace” and thatneighboring area are given in Figure 7. Location of
the survey point of Figure 8 and location of the slope
failures point of Figure 11 are given triangle andcircle symbols in Figure 7. According to Figure 7,
the subsurface geological structure of “Maruyama
rice terrace” was the soft sedimentary rock of the
Miocene.
However, the data of “one hundred selectionof Japanese rice terraces ” registered as “stonewall”. In other words ”Maruyama rice terrace”
was classified to the case of ・Choose a stone wallwith Cenozoic sedimentary rock (32 cases).
In fact, the subsurface geological structure of higher
altitudes mountain adjacent to the “Maruyama riceterrace” was the rhyolite. Rhyolite is a kind of
igneous rock, and that is a rock which may be used
as a building stone.
According to the field investigation, stone wall
had been selected certainly in “Maruyama riceterrace” to slope protection method.
Therefore, the rhyolite that produced at a higher
altitude was used in stone wall material. This fact
conforms to our consideration.
Rhyolite had been used as material of the stone
wall in the “Maruyama rice terrace”.

Fig. 8 The subsurface geological structures of riceterrace in the cases of sedimentary rocks

Fig. 9 Slope failures point appearance in the“Maruyama rice terraces”

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GEOMATE- Osaka, Nov. 16-18, 2015

metamorphic rock.
In case of the subsurface geological structures
enough to use as building materials or rock exists
around, tend to select “stone wall”.
To further verify the results, future work should
perform more field investigation of various riceterrace area.
We can safely say that there were close relation
at the choice factor of the slope protection method ofrice terraces and the subsurface geological structures.

Figure 8 shows the stone wall of “Maruyama
rice terraces.” From Figure 8, the stone wall wasformed of a uniform rock. These rocks are rhyolitethat had become the stone wall. Figure 11 shows theappearance of the slope failure in the “Maruyama
rice terrace”. According to Figure 11, the subsoil
layer in “Maruyama rice terrace” has many boulder
stones of rhyolite. It is probable that the boulder
stones of rhyolite were fell from the adjacent
mountain, and were used as material for stone wallin “Maruyama rice terrace”. The field investigation
supports the consideration of “choose a stone wallwith Cenozoic sedimentary rock”.

REFERENCES[1] Nakashima M, “Distribution and Characteristicof Rice Terraces”, J. of, JSIDRE, Vol. 70 (3),Mar. 2002, pp.195-198[2] Ministry of Agriculture, Forestry and Fisheries,“one hundred selection of Japanese riceterraces”1999[3] Arai T, “Origins of Japan's geotechnical”, Thegeology to learn in case, 2005, The Japanese
Geotechnical Society[4] Shida M, “What is the rice terraces”, Riceterrace Introduction, 2014, pp.3-14[5] I Mekata Y, Sasaki N, Land classification basic
research of Kamikitayama development area
Rikutyuohhara 1/50000, 1973, pp.18-22,
pp.23-27[6] Nishiwaki S, “Tendency of distribution of stone
in the farmland stone wall when limestone isused as an index”, presented at JSIDRE, 2014,522-523[7] Inazumi H, Hidaka M, Momoi H, Moriyama Y,“ⅡThe subsurface geological structures”, Landclassification basic research of Kusumi:Iida
Wide-area agricultural development area
Beppu 1/50000, 1972, pp14-24

CONCLUSION
The purpose of this paper is to clear the
relationship of the subsurface geological structures
and the choice factor of the slope protection methodof rice terrace.
As a result, 96% (54areas/56 areas) of regionsof selected “earth slope” existed in the area of thesubsurface geological structures in the cases ofsedimentary rocks of the Cenozoic.
In addition, the subsurface geological structures
whether igneous rock, or hard sedimentary rock thatformed previously Mesozoic, or metamorphic rock,“stone wall” had been chosen to slope protection
method in 96% (54areas/56 areas) of the riceterraces.
However, in the following cases on thesubsurface geological structures had weather ormetamorphic, it was not a “stone wall”, even if thesubsurface geological structures of the area with riceterraces were igneous rock or hard sedimentary rockthat was completed in Mesozoic previous or787

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

OXYGEN AND HYDROGEN ISOTOPE STUDY FOR PRECIPITATIONSAMPLED AT OSAKA AND MATSUE IN JAPANTetsuya Fukano1, Hiroyuki Ii2
Graduate School of Systems Engineering, Wakayama University, Japan
2
Faculty of Systems Engineering, Wakayama University, Japan

1

ABSTRACT
Oxygen and hydrogen isotopic ratios in precipitations at Matsue and Osaka are high around March. Isotopic
ratio in each precipitation was clarified to depend on altitude at the dew point calculated from meteorological
data. When the calculated altitude at the dew point is high, isotopic ratio of the precipitations is high. Rainparticle evaporates from the altitude at a dew point during falling because air was saturated with water above thealtitude at the dew point and then isotopic ratio changes with evaporation. Then, altitude at the dew point was
important to determine the isotopic ratio of precipitation.
Keywords: Isotope ratio, Precipitation, Altitude at a dew point
δ18O＝(Ｒsample /Ｒstd − 1) × 1000［‰］＊Ｒstd= 18 O/ 16 O of SMOW
δD＝(Ｒsample /Ｒstd − 1) × 1000［‰］＊Ｒstd=D/ 1 H of SMOWINTRODUCTION
To study about oxygen and hydrogen isotope
ratio of rain is important for the discussion about
global environment. It is known isotopic ratios ofprecipitation change with temperature, amount ofprecipitation, sea level, distance from sea, andlatitude. [1]
But it is not reported comparing isotopic ratios ofprecipitation at the Japan Sea side and inland of theJapan Island for long term.
From March 2012 to June 2013, we measured
oxygen and hydrogen isotopic ratios at Matsue andsouth aria of Osaka to study about the characteristic
difference, and found it was important to think about
the altitude at a dew point.

Fig.1 shows isotopic ratios gradually change.
We sampled only first 9 mm of precipitation foreach event. Diameter of sampler’s funnel is 18cm.
So sampler can get about 50 ml of precipitation.

Photo: Rain fall sampler

A METHOD OF MEASUREMENT

After the equilibrium method, the oxygen andhydrogen isotopic ratios of the sampled water were
measured with mass spectrometer (Sercon). The
measurement
errorof
isotoperatio
is±0.100［‰］.
SEASONAL CHANGE

Fig. 1 Change of isotopic ratios
Isotopic ratios gradually change.
1, 2, 3 are sampled in a session, each 50ml.
Almost precipitations show the same result.

Fig. 2
788

Seasonal change of δ18O at Osaka
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GEOMATE- Osaka, Nov. 16-18, 2015

ISOTOPE VALUES DEPEND ON ALTITUDE
AT DEW POINT

Fig. 3Fig. 4

Seasonal change of δ18O at Matsue
0T

Fig. 6

Altitude at dew point and δ18O

Fig. 7

Altitudes at dew points and δD

Seasonal change of δD at Osaka
0T

From Fig.6-7, when the altitude at the dew points
are high, isotope ratios are high.

Fig. 5

Seasonal change of δD at Matsue
0T

Fig.2-5 show both oxygen and hydrogen isotopic
ratios at Matsue and Osaka are high around March.
And rough appearance on both oxygen andhydrogen isotope ratios at Matsue and Osaka are low
in summer and high in winter.
Isotopic ratios of precipitations were usually low
in summer and high in winter [2].
To study about these results, we checked altitude
at the dew points. In this case, “Altitude at the dew
point” means the lowest altitude reached to 80-90%
of humidity.
Upper air observation is announced by theMeteorological Agency. We refer to the upper air
datas of Shionomisaki and Matsue at 9:00 [3].
Shionomisaki, the most south aria of Wakayama,
is the nearest to Osaka of the announced points.

Fig. 8
Seasonal change of Altitude at a dew
point at Osaka

Fig. 9point

Seasonal change of Altitude at a dew
at Matsue

Fig.8-9 show altitude at the dew points are higharound March at Matsue and Osaka.

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GEOMATE- Osaka, Nov. 16-18, 2015

CONCLUSIONREFERENCES[1] Isao MACHIDA, Akihiko KONDOH ”Stable
Isotope Ratios of National Water in Japan-The
Analysis by Using Environmental Isotope
Database” J. Japan Soc. Hydrol. & Water
Resour. Vol.16. No.5. (2003). pp. 556-569.
[2] Hiroyuki Ii, Tatemasa HIRATA, Naomi
SAHARA, Kunihiko Fukushi “ChemicalComposition, Hydrogen and Oxygen Isotopic
ratios of Precipitation in Wakayama and Osaka
Prefectures” Annual Journal of HydraulicEngineering. Vol.45. (2001). pp.289-294.
[3] Hitoshi SAKAI, Yukihiko MATSUHISA“Stable Isotope Geochemistry” University ofTokyo Press. (1996). ISBN4-13—60713-8.
PP.93-100.
[4] www.data.jma.go.jp/obd/stats/etrn/upper/index.
php

Oxygen and hydrogen isotopic ratios at Matsue
and Osaka showed high values around March.
And rough appearance on both oxygen andhydrogen isotope ratios at Matsue and Osaka are low
in summer and high in winter.
To study about these results, we checked
altitudes at the dew points. And we found isotope
values at each precipitations depend on altitude ateach dew point.
When the altitude at the dew point is high,isotope value is high.
As rain water starts to evaporate from the altitude
at a dew point and light isotope, 16O and 1H, starts toevaporate selectively. This is why fall distance ofrain from the altitude at a dew point is thought toincrease with isotope values.
Around March, the season of “Kosa”, altitudes at
dew points are high. It is well known yellow sandfrom China often blows to Japan in the spring by theupper stream.

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Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

IMPORTANCE OF ROOM VENTILATION FOR MAINTAININGHEALTHY CO2 CONCENTRATIONS
Hiroyuki Ii1, Kazuki Taguchi2, and Masahiro Yamashita3
Faculty of Systems Engineering, Wakayama University, Japan; 2Wakayama Prefecture, Japan; 3Graduate
School of Systems Engineering, Wakayama University, Japan

1

ABSTRACT
Using a portable CO 2 sensor, the CO 2 concentration in a classroom at Wakayama University was measured
and “natural ventilation”, the open door or window effect was evaluated based upon the number of persons,room size, door or window condition and CO 2 concentration. Under a closed door and window condition, CO 2
production per person per hour in the room due to respiration was 0.01 to 0.02 for a private house under relaxed
conditions and 0.005 to 0.015 m3/hour/person for a classroom during a lecture. Maximum CO 2 concentration
reached 1,300 ppm after 100 minutes in the private house and over 4,000 ppm in the classroom after 90 minutes.
The amount of CO 2 gas exchanged outside through doors or windows was in agreement with the amount of CO 2
gas produced by respiration in the room with open doors or windows whose sizes were 1.6 to 8 m2 or whose totalroom size per open door or window size was 32 to 180.
Keywords: Carbon dioxide concentration, classroom, ventilation, respiration

INTRODUCTION

December 2013 to January 2015. The room size ofthe private house was 23.5 m2 with 6 tatami mats.
The size of the classrooms were 1,297 m2 for A101,
430 m2 for A103, 413m2 for A104, 522 m2 for A202,
416 m2 for A203, 286 m2 for A204, 551 m2 for B101,
and 260 m2 for B202 as shown in Table 1. For allclassrooms both air conditioners and ventilation
worked together. The size of the door and window inthe classroom were 1.9 m2 and 1.5 m2.

CO 2 concentrations in classrooms using air
conditioners are based on the Japanese School
Environmental Standard. The Ministry of Education,
Culture, Sport, Science and Technology propose that
the CO 2 concentration in classrooms be less than
1,000ppm [1]. Classrooms are likely to have a highnumber of person present and regularly ventilatingclassrooms is not commonly done at Universities. In
addition, buildings in recent years often have highair tightness because of improvements in building
technologies [2].
Recently, reducing the use of air conditioners has
been practiced to save electricity. Howeverventilation fans work in conjunction with air
conditioners in recently constructed buildings. In
most classrooms at Wakayama University, aventilation fan does not work separately with an air
conditioner. In particular, air conditioners are notused during spring and autumn time because ofmoderate temperatures, thus CO 2 concentration was
thought to be high. The purpose of this study was toevaluate the “natural ventilation” effect of opening adoor or window.

Table 1 Room size of house and classroom
Room nameSize m3
Private house
23.5
A101 classroom
1,297
A103 classroom
430
A104 classroom
413
A202 classroom
522
A203 classroom
416
A204 classroom
286
B101 classroom
551
B202 classroom
260
B203 classroom
260

METHOD AND CONDITION

A portable sensor, GCH-2018, was used formeasuring CO 2 concentrations in the air as shown in
Fig1. The measuring place was in the center of theroom for the private house and at the back side at 80
cm in height of the each classroom.

CO 2 gas measurement in the air was performedin the room at a private house in August and October
2014, and in a classroom at the faculty of Systems
Engineering at Wakayama University from791

GEOMATE- Osaka, Nov. 16-18, 2015

From the results, CO 2 production per hour perperson by respiration was calculated in the houseroom as shown in Fig.3. The CO 2 production perhour per person decreased 0.02 to 0.01
m3/hour/person with time. The calculated values
with 56 kg weight and 175cm in height were averagevalues per hour per person under normal conditions[2]. The decrease with time was thought to dependon the respiration activity of humans. High CO 2
production per hour per person from the start ofmeasurements till the 40 minute mark was thought
to be due to the still high respiration activity for
preparation of experiment.
Fig. 1

Portable sensor

5000

CO2 concentration ppm

1200
1000
800

7th Aug
24th Aug
9th Oct
10th ct

600
400

3000
2000
1000
0
80
40
60
20
elapsed time (min)

0

200
0

20

40

60

80

100

CO2 production perhour per people

0120

Elapsed time (min)0.03
CO2 (m3)/hour/people

4000

CO2 concentration ppm

1400

A203 1
A203 2
A203 3
B203 1
B203 2

7th Aug
24th Aug
9th Oct
10th ct

0.025
0.02
0.015
0.01

0.025
0.02
0.015
0.01
0.005
A203 1
A203 2
A203 3
B203 1
B203 20-0.005
-0.01
0

0.005
0

0

20

40

60

80

100

100

20
40
60
80
elapsed time (min)100Fig. 3 CO 2 production per hour per person inclassroom (A203, B203) with no ventilation andclosed doors and windows from autumn 2013 tospring 2014.

120

Elapsed time (min)Fig.2 CO 2 concentration in room and production ofprivate house.

Classroom on University with No Ventilation

RESULTS

Fig. 3 shows that CO 2 concentration and CO 2
production in the classroom during a lecture with noventilation after the windows and doors were closed
from 2013 to June 2014 for the A203 and B203
classrooms. The CO 2 concentrations increased withtime and were over 2,000 ppm after one lecture timeof 90 minutes. Maximum values reached over 4,000
ppm with 80 persons and a 416 m3 volume. CO 2
production values in the classroom decreased withtime as well as the private room and they were lower
than those in the private room.

House Room per Person with No Ventilation
Fig.2 shows CO 2 concentrations in the room
under quiet conditions after the windows and door
were closed. CO 2 concentrations in the air increased
from 300 to 1,300 ppm at 100 minutes each time.
The results showed that CO 2 concentration in the air
under a no ventilation condition reached over 1,000
ppm and ventilation was necessary for normal life.
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GEOMATE- Osaka, Nov. 16-18, 2015

CO2 concentration ppm

4000

A101 4
A103 2
A104 4
A202 1
A203 1
A204 2
B101

3500
3000
2500

University Classroom with One Open Door
Fig.5 shows CO 2 concentration and CO 2
production per hour per person in classroom (A203,
B203) with an air conditioner and one open door
from autumn 2013 to spring 2014. CO 2
concentrations increased with time and were over
1,000 to 3,000 ppm after one lecture time of 90
minutes. Maximum values reached over 3,000 ppm
with 76 persons and a 416 m3 volume. CO 2
concentration depended on room size and number ofpeople however the CO 2 production per hour perperson in classroom was calculating from person
number and room size and it was directly evaluatedeach time.
CO 2 production values in the classroom under
one open door decreased with time as well as theprivate room and they were lower than those values
in the classroom under the condition of no open
doors nor windows. Even though some CO 2
production values were less than 0 after 60 minutes,
CO 2 concentration in the classroom exceeded 1000
ppm after 90 minutes. Therefore, one open door
ventilation was not enough to reduce CO 2
concentration.

2000
1500
1000
500
0

CO2 production m3/hour/people

0

20

40
60
80
100
Elapsed time (min)

120

0.04
A101 4
A103 2
A104 4
A202 1

0.03
0.02

A203 1
A204 2
B101

0.01
0
-0.01
-0.02
0

20

40
60
80
100
Elapsed time (min)

120

3500

Fig. 4 CO 2 concentration and CO 2 production perhour per person in classroom (A101, A103, A104,
A202, A203, A204, B101) with no ventilation andclosed doors and windows from spring 2014 toautumn 2014.

A203
B203 1
B203 2
B203 3
B203 43000
2500
CO2 ppm

2000

Fig.4 shows CO 2 concentration and CO 2
production per hour per person in classroom (A101,
A103, A104, A202, A203, A204, B101) with noventilation and closed doors and windows fromspring 2014 to autumn 2014. CO 2 concentrations
increased with time and were over 1500 ppm after
one lecture time of 90 minutes. Maximum values
reached over 3,500 ppm with 110 persons and a 522
m3 volume. Therefore, the CO 2 concentration of theclassroom with no ventilation reached over 1,000
ppm which was the recommended maximum value.
During spring and autumn seasons, air conditioners
are not employed because temperatures in theclassroom are moderate. Therefore, the ventilation
fan did not work because it was on the same switchof the air conditioner. As a result, in spring andautumn high CO 2 conditions were sometimes
observed.
CO 2 production values in the classroom were
uniform with time but not as uniform as the private
house
room
and
were
0.005
to
0.015 m3/hour/person, lower than those in theprivate room. Both CO 2 production values werelower than those in the private room. One possibility
was because of exchanging air when some students
entered into the room through the door.

1500
1000
500
0
0

20

40
60
80
elapsed time (min)100

CO2 production m3/hour/people

0.02
0.015
0.01
0.005
0

-0.005

A203
B203 1
B203 2
B203 3
B203 4-0.01-0.015
-0.02
0

20

40
60
80
elapsed time (min)100Fig. 5 CO 2 concentration and CO 2 production perhour per person in classroom (A203, B203) with noair conditioner and one open door from autumn 2013
to spring 2014.
793

GEOMATE- Osaka, Nov. 16-18, 2015

0.004

A103 1
A103 2
A103 3
A103 4

5000

A103 5
A103 6
A103 7
A202 1CO2 production m3 /hour/people

CO2 concentration ppm

6000
A104 1
A104 2
A104 3
B101 1

4000
3000
2000
1000

B101
B203

0.003
0.002
0.001
0
-0.001-0.002-0.003
0

0
20

40
60
80
elapsed time (min)CO2 production(m3)/hour/people

A103 1
A103 2
A103 3
A103 4
A103 5
A103 6

0.02

40
60
80
elapsed time (min)

100

0.015

0.04
0.03

20

100
CO2 production(m3)/hour/people

0

A103 7
A202 1
A104 1
A104 2
A104 3
B101 1

0.01

0.01
0.005
0

-0.0050-0.01

A202 2
A204 2-0.01

A204 3-0.015
0

20

-0.02
0

20

40
60
80
elapsed time (min)

40
60
80
elapsed time (min)

100

100

Fig. 7 CO 2 production per hour per person inclassroom (B101, B203, A202, A204) with no air
conditioner and one open door and one open
window.

Fig. 6 CO 2 concentration and CO 2 production perhour per person in the classroom (A103, A202,
A104, B101) with no air conditioner and one open
door from spring 2014 to autumn 2014.

CO 2 concentration decrease and plus showing a CO 2
concentration increase. Therefore, open one door
and one window was not enough to provide
ventilation for reducing CO 2 concentration in theclassroom.

Fig.6 shows CO 2 concentration and CO 2
production per hour per person in classroom (A103,
A202, A104, B101) with air conditioner and one
opened door from spring 2014 to autumn 2014. The
CO 2 concentrations increased with time andincreases were over 1,000 ppm after one lecture timeof 90 minutes. Maximum values exceeded 4000 ppm
with 43 persons and 430 m3 volume. Therefore, CO 2
concentration in a classroom with one open door for
ventilation exceeded 1,000 ppm which was therecommended maximum value.
CO 2 production values under one open door
varied from –0.01 to 0.015 m3/hour/person. The
difference of CO 2 production values between one
door being open and no door being open was small
therefore one door being open had little effect inreducing CO 2 concentration.

CO2 production(m3)/hour/people

0.01
0.005
0
-0.005
A204 1
B202 2-0.01

A103 9
B202 1-0.015
0

20

40
60
80
elapsed time (min)100Fig. 8 CO 2 production per hour per person inclassroom (A103, A204, B202) with no air
conditioner and one open door and two open
windows.

University Classroom with One Open Door and
one Open Window
Fig.7 shows CO 2 production per hour per person
in classroom (B101, B203, A202, A204) with no air
conditioner and one open door and one open
window. The CO 2 production values were variablewith half of values were minus values showing a

University Classroom with One Open Door andTwo Open Windows
Fig. 8 shows CO 2 production per hour per person794

GEOMATE- Osaka, Nov. 16-18, 2015

production values were minus. Therefore, opening
two doors was enough ventilation for reducing CO 2
concentration in the classroom.

in classroom (A103, A204, B202) with no air
conditioner and one open door and two open
windows. CO 2 production values varied with mostof the values being minus values. Therefore, open
one door and one window was enough for providing
ventilation for reducing CO 2 concentration in theclassroom.

DISCUSSION AND CONCLUSION
Table 1 shows CO 2 production 1/1,000 m3/hour/person at the 15 minute mark (beginning of
lecture) and the 75 minute mark (end of lecture).
The size of the door and window were 1.9 and 1.5
m2. The window was facing outside and the door
was facing the floor. Comparing each CO 2
production value under the condition of no open
door and window, most CO 2 production values fromthe small size, private house room with 23.5 m2 tothe big classroom, A101 with 1,297 m2 were 0.010
to 0.015 m3/hour/person not depending on time androom size. These were a little lower than the average
production values of respiration under relaxed
condition.
Next, the effect of doors and windows wasestimated. For A103, the difference (D) of one door,
one door and two windows, and one window
compared with no door and window were 1 to -11×
1/1,000 m3/hour/person and the difference per unit
size of door and window (D/a) were -5.8 to 0.5×
1/1,000 m/hour/person/size. D/a shows the effect toreduce CO 2 concentration by the door or window.
As a result, the effect of door and window per size,
D/a, was variable however for A103, original CO 2
production with no ventilation was 2 to 11×1/1,000
m3/hour/person. Then to keep uniform CO 2
concentration, the open door or window size was
6.5(average)×1/1,000/ 2.7(average)×1/1,000, 2.4 m2.
As well as for A104, the difference (D) of one
door compared with no door and window were -8.5
to -6 ×1/1,000 m3/hour/person and the difference perunit size of door and window (D/a) were -4.5 to 3.5×1/1,000 m/hour/person/size. Similarly, as for
A104, original CO 2 production with no ventilation
was 9×1/1,000 m3/hour/person. Then to keep
uniform CO 2 concentration, the open door size was
9×1/1,000/ 3.8(average)×1/1,000, 2.4 m2. For A202,
the difference (D) of one door, one door and one
window, and one window compared with no door
and window were -5 to 2×1/1,000 m3/hour/person
and the difference per unit size of door and window(D/a) were -1.6 to 0.6×1/1,000 m/hour/person/size.
As for A203, original CO 2 production with noventilation was 9×1/1,000 m3/hour/person. Then to
keep uniform CO 2 concentration, open door orwindow size was 10×1/1,000/ 1.3(average)×1/1,000,
7.7 m2.
As for A203, the difference (D) of one door
compared with no door and window were 3 to 8
×1/1,000 m3/hour/person and the difference per unit
size of door and window (D/a) were -4.2 to
1.6×1/1,000 m/hour/person/size. As for A203,
original CO 2 production with no ventilation was

CO2 production(m3)/hour/people

0.01
0.005
0
-0.005

A202 1.5
A202 1
A204 1
A103 1
A202 2
A203 2-0.01-0.015
0

50
100
150
elapsed time (min)200

Fig.9 CO 2 production per hour per person inclassroom (A103, A202, A203, A204) with no air
conditioner and no open door and one or two open
windows.
CO２production m3/hour/people

0.006
0.004

B202
B203

0.002
0
-0.002-0.004-0.006
0

20

60
80
40
elapsed time (min)100

Fig.10 CO 2 production per hour per person inclassroom (B202, B203) with no air conditioner andtwo open doors.
University Classroom with One or Two OpenWindows
Fig. 9 shows CO 2 production per hour per person
in classroom (A103, A202, A203, A204) with no air
conditioner, no open door and one or two open
windows. Most of the CO 2 production values wereplus. Therefore, open one door and one window was
not enough ventilation for reducing CO 2
concentration in the classroom.
University Classroom with One or Two OpenWindows
Fig. 10 shows CO 2 production per hour perperson in classroom (B202, B203) with no air
conditioner and two open doors. Most of the CO 2795

GEOMATE- Osaka, Nov. 16-18, 2015

9×1/1,000 m3/hour/person. Then to keep uniform
CO 2 concentration, open door or window size was
10×1/1,000/ 1.3(average)×1/1,000, 7.7 m2. For A204,
the difference (D) of one door and one window, one
door and two windows, and one window comparedwith no door and window were -4 to -18×1/1,000
m3/hour/person and the difference per unit size ofdoor and window (D/a) were -0.8 to -8.0×1/1,000
m/hour/person/size. As for A204, original CO 2
production with no ventilation was 11×1/1,000
m3/hour/person. Then to keep uniform CO 2
concentration, open door or window size was
11×1/1,000/ 4.4(average)×1/1,000, 1.6 m2. For B101,
the difference (D) of one door, one door and one
windows compared with no door and window were 31 to 9×1/1,000 m3/hour/person and the difference
per unit size of door and window (D/a) were -0.9 to
4.7 × 1/1,000 m/hour/person/size. As for B101,
original CO 2 production with no ventilation was
4×1/1,000 m3/hour/person. Then to keep uniform
CO 2 concentration, open door or window size was
4×1/1,000/ 0.5(average)×1/1,000, 8 m2.
For B202 or B203, the difference (D) of one door,
one door and one window, one door and two
windows, and two doors compared with no door andwindow were 3 to -16×1/1,000 m3/hour/per/person
and the difference per unit size of door and window(D/a) were -4.2 to 1.5×1/1,000 m/hour/person/size.
As for B202 or B203, original CO 2 production withno ventilation was 11×1/1,000 m3/hour/person. Then
to keep uniform CO 2 concentration, open door orwindow size was 11×1/1,000/ 1.4(average)×1/1,000,
7.9 m2. As a result, open door or window size foreach room was estimated and total volume of room
per the open door or window size were 430/2.4 =180
for A103, 413/2.4=173 for A104, 522/7.7= 68 for
A202, 416/7.7=54 for A203, 286/1.6=180 for A204,
551/8=69 for B101, and 260/7.9=32 for B202.

2.7
A10
4
413

A20
2
522

A20
3
416

A20
4
286

P.H.
23.5

Time
(min
)

15
75

A10
1
129
7
A10
3
430

15
75

B10
1
551

B20
2
B20
3
260

CO 2 Production 1/1,000 m3 /hour/person
No
1D
1D1
1D 1,2
2D
W
2
W
1.9
W
3.8m
3.4
1.5~
2
m2
m2
4.9 3m2
m2
15~
25
13~1
6
34
8

15

2

D
D/a
75

11

D

-

D/a

-9
~15
1
0.5
-2
~2
-11
-5.8

-2
13
-

9

D
D/a
75

-

D
D/a
15
D
D/a
75
D
D/a
15
D
D/a
75
D
D/a
15
D

9
14
6
4~
17
4~
12
-

-5
~8
-6
-3.2
-1
~0
-8.5
-4.5
12
-2
-1.0
3
-3
-1.5
19

2
0.6

4~8
-9
-2.6

7

D
D/a

-

15
D
D/a
75
D
D/a
15

6
2

5~
21
-

D/a
75

7

D
D/a

-

4
-5
-1.6
-2
~7
-1
-0.3

3
1.6

15
-

75

D

9
-3
-0.9
8

8
4.2
11

D/a

Table 1 CO 2 Production (1/1,000 m3 /hour/person)
Roo
m
Size
m3

15

-11
~1
-12
-3.5
15
9
4.7
1
-1
-0.5
3~
13
-8

-25
-31
-0.9
1
-1
-0.3
-1

-4.2

-4.1

-2
~12
3
1.5

-2

-14

-9
-2.6

-3
18
3.7
3

3
-12

-4
0.8

-7
-4.7

-8
0

-3

1~2

16
3.3
0

-12

-7
1.4

-3.2
-2
-9
-2.4

REFERENCES[1] Noriko Ochiai, Kazunari Yamashita, “Indoor
carbon dioxide of Shimane University lectureroom and control of air quality”, Shimane
Prefecture University Bulletin, Vol.4, 2010, pp.
39-45.
[2] Homepage of Ministry of Education, Culture,
Sport, Science and Technology[3] Masahiro Yamashita1 and Hiroyuki Ii,“Unexpected high CO 2 concentration inclassrooms of Wakayama University”, in Proc.
forth Int. Conf. on GEOMAT, 2014, pp. 572577.

2
1.3
-0.5
0.3
3
-8
-5.3

796

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

OPTIMUM CONDITIONS OF OYSTER SHELLS AS MATERIAL
COVERING SEDIMENT AND INHABITATION SIMULATION OF
INTERNAL LOAD USING ECOSYSTEM MODEL
Masaaki KONDO1, Yuji KATO2 and Takamitsu KAJISA3
Graduate School of Bioresources, Mie University, Japan

1,2,3

ABSTRACT
A remediation program for a canal with water quality problem will be carried out in the future, and internalload from the bottom sediment can be new problem. Remediation measure against internal load was investigatedby using oyster shells as covering material. So that shell-covered sediment experiments and numericalexperiments were carried out. Pre-treatment such as graining and heating might be in vain, and non-heated shellsof chip or powder size will be optimum. Properties of nutrient elusion were also obtained at the experiments.
Numerical experiment was investigated on reducing patterns against external load and internal load to evaluate
the various remediation measures by using ecosystem model added shell-covered sediment model. Only shellcovering might have the small effect of water quality improvement. Good water quality is expected to bearchived in the long period by a combination of reducing external load and applying bioresources.
Keywords: Oyster shell, Shell-covering effect, Internal load, Ecosystem model, Simulation

shell still remains a large problem.
Uses for oyster shell have been studied to
improve various bad environments. First, improving
bottom sediment was investigated. It was found thatoyster shells adsorb the hazardous sulfuratedhydrogen in river-mouth mud [3]. Oyster shells
decrease the sulfide in brackish-lake mud as in [4].
Second, using oyster shells as material for purifying
water was investigated. Oyster shells were immersed
in water for agriculture, decreasing the total amountof nitrogen, especially ammonia nitrogen, in water[5]. Oyster shells were used in the process ofchanging ammonia nitrogen into nitrate nitrogen anddenitrifying [6]. Last, oyster shells were used asmaterial covering sediment. Sediment areas covered
with oyster shells and without shells were set up in
Lake Hamanako, and the concentrations over theseareas were measured as in [7]. They showed that thesulfide concentration over the test area was a tenthof the concentration over the control area. Reference[8] measured the concentrations of PO 4 -P and NH 4 N over sediment covered with both shells andwithout shells in the laboratory. The sediments were
sampled from the bottom of Lake Kojimako. The
concentrations of the covered sediment were lower
than those of the control. Reference [9] indicatedthat another kind of shells led to similar results.
Almost all previous research was done in areas withbrackish water and salt water. Thus, it will benecessary to investigate applications for freshwater
areas.
It is important to evaluate the impact of sedimenton water qualities in order to calculate theseconcentrations in water area. The nutrient exchange

INTRODUCTION
Areas lower than sea level tend to have the water
quality problems due to water retention because
water drainage can barely proceed by gravity in lowlying areas. Water quality has deteriorated in themain drainage canal that runs through K. Town, Mie
Prefecture, Japan. Due to topography constraints
drainage does not flow like a river in low-lying areas
such as K. Town, so the town must depend on
pumping machines. Aquaculture effluent containing
feed residue from fish has been dumped into thecanal in addition to agricultural effluent containing
soil particles from paddy fields. A remediation
program has been discussed and planned for a longtime to improve the water quality. While thisprogram will be carried out in the future, by then,the internal load from the canal sediment could begreater than the improvement to the water quality inthe canal.
Changes in the domestic production of oyster
shells in the last decade are shown in Fig. 1 [1]. The
weight ratio of oyster shells divided by oysters is 0.8
according to [2]. Shell weight was estimated bymultiplying the oyster weight by the ratio of 0.8. The
average annual production of oyster shells amounts
to approximately 160,000 tons within Japan. Thereis a yield of 4,000 tons of oyster shells per year inMie. Disposing the shells, which are industrial waste,is a major matter in any other producing areas aswell as Mie. The aftermath of the 2011 Tohoku
earthquake and tsunami led to a considerable declineof oyster shells containing Miyagi's production asshown in Fig.1. However, the disposal of oyster797

GEOMATE- Osaka, Nov. 16-18, 2015

200,000

Weight of oystershell, t

180,000
160,000

Japan
Hiroshima
Miyagi
Okayama
Iwate
HyougoMie

140,000
120,000
100,000
80,00060,00040,00020,00002003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Fig. 2

Time, yrFig. 1

Changes of annual production of oystershellwith time [1].

Water qualities and ecosystem model withoyster shell coveringfrom wastewater treatment plants, and food factory
effluent. The concentrations had a similar trend witheach other in the main canal. Therefore, the entire
main drainage canal was simplified as a single layer
model.
The water quality and ecosystem model based on
algal shown in Fig. 2 was used for analysis. It wasadded to this model that oyster shells covering thesediment inhibited nutrient elution from sediment.
The model equations Eq. (1)-(4) are ordinary
differential equations. The variables and constant are
indicated together at the end of this paper.

processes between sediment and its above water,
especially sedimentation process and decomposition
process of phosphorus, were focused andinvestigated. Elution of phosphorus was calculated
by using the phosphorus elution model fromsediment, then various remediation measures against
phosphorus release from sediment were estimated[10]. However, it is not easy to fractionate
phosphorus components in sediment for themodeling, and a simple elution model appears to beneeded.
In this study, experiments on internal load fromshell-covered sediment were carried out undervarious shell conditions in order to apply the oyster
shells as material covering the sediment infreshwater areas, and the optimum condition for the
pre-treatment of oyster shells was investigated.
Furthermore, this was done on the assumption thatoyster shells would be used as covering material in
the main drainage canal, and how shell-covered
sediment could impact water quality in the field wasinvestigated. A shell-covered sediment model wasadded to a known ecosystem model, and numericalsimulations were carried out. The essential results ofshell-covered sediment experiments were used toconsider the model parameters and simulation
patterns.

𝐝𝐝𝑨𝑨
𝝊𝝊𝑨𝑨
= 𝝁𝝁 ⋅ 𝑨𝑨 − 𝒌𝒌𝒅𝒅 ⋅ 𝑨𝑨 − 𝑨𝑨𝐝𝐝𝒕𝒕𝒛𝒛+{∑(𝑨𝑨𝒊𝒊𝒊𝒊 ⋅ 𝒒𝒒) − 𝑨𝑨 ⋅ 𝑸𝑸}/𝑽𝑽

𝐝𝐝𝑫𝑫
𝝊𝝊𝑫𝑫
= 𝒓𝒓𝑫𝑫 ⋅ 𝒌𝒌𝒅𝒅 ⋅ 𝑨𝑨 −𝑫𝑫 − 𝒌𝒌𝑴𝑴 ⋅ 𝑫𝑫
𝐝𝐝𝒕𝒕
𝒛𝒛+{∑(𝑫𝑫𝒊𝒊𝒊𝒊 ⋅ 𝒒𝒒) − 𝑫𝑫 ⋅ 𝑸𝑸}/𝑽𝑽
𝐝𝐝𝑵𝑵= −𝒓𝒓𝑵𝑵 ⋅ 𝝁𝝁 ⋅ 𝑨𝑨 + 𝒓𝒓𝑵𝑵𝑵𝑵 ⋅ 𝒌𝒌𝑴𝑴 ⋅ 𝑫𝑫
𝐝𝐝𝒕𝒕
𝑰𝑰𝑰𝑰𝑵𝑵 𝑻𝑻−𝑻𝑻𝒄𝒄+(𝟏𝟏 − 𝜱𝜱𝒊𝒊𝒊𝒊𝒊𝒊𝒊𝒊 )𝜽𝜽𝒛𝒛 𝑵𝑵+{∑(𝑵𝑵𝒊𝒊𝒊𝒊 ⋅ 𝒒𝒒) − 𝑵𝑵 ⋅ 𝑸𝑸}/𝑽𝑽
d𝑃𝑃= −𝑟𝑟𝑃𝑃 ⋅ 𝜇𝜇 ⋅ 𝐴𝐴 + 𝑟𝑟𝑃𝑃𝑃𝑃 ⋅ 𝑘𝑘𝑀𝑀 ⋅ 𝐷𝐷
d𝑡𝑡
𝐼𝐼𝐼𝐼𝑃𝑃 𝑇𝑇−𝑇𝑇𝑐𝑐+(1 − 𝛷𝛷𝑖𝑖𝑖𝑖ℎ𝑃𝑃 )𝜃𝜃𝑧𝑧 𝑃𝑃+{∑(𝑃𝑃𝑖𝑖𝑖𝑖 ⋅ 𝑞𝑞) − 𝑃𝑃 ⋅ 𝑄𝑄}/𝑉𝑉

METHOD
Method of numerical experiment

(1)

(2)

(3)

(4)

The time integration method of Eq. (1)-(4) was
the fourth order Runge-Kutta method. The time step
was set to be one day. It was supposed that the water
volume V of the main drainage canal was constant inthese equations. In low-lying areas, water such as
rainfall runoff gathers through a hydraulicallycontinuous lateral drainage canals. Drainage pumped
at each downstream edge daily causes only a small
flow in the main drainage canal, and consequently, asmall fall in the water level rapidly returns to normal.

Calculation model
The main drainage canal in K. Town is T-shaped
and is about five kilometers long water area. Water,
especially rainfall runoff, flows into the main
drainage canal though not only the upstream edge ofthe canal but also many branch drainage canals. Twodrainage pump station are located at the otherdownstream edge of the canal. Besides rainfall
runoff, water in the main drainage canal consists ofagricultural effluent from paddies, treated water
798

GEOMATE- Osaka, Nov. 16-18, 2015

Input data for calculation model
Monthly input data used for the calculation are
shown in Figs. 3 and 4. Shown are the discharges ofvarious influents, chlorophyll-a concentrations A in ,suspended solids D in , total nitrogen concentrations
N in , total phosphorus concentrations P in , watertemperature T in the main drainage canal, and theilluminance L around the field. These input data
were determined by a committee [11] between April
2002 and March 2003, except for the illuminance.
Illuminance data were quoted from a data book [12].
Waters influent into the main drainage canalconsist of rainfall runoff from a basin, agricultural
effluent from paddies and upland farms, treatedwater from the wastewater treatment plants, andfood factory effluent. Each influent discharge q wasmonthly calculated on the basis of the water balancein the basin. Suspended solids SS were substituted
for detritus concentrations D in of each of the influent
waters.
Rainfall runoff, calculated on the basis of rainfall,
appears to flow into the main canal through thehydrological continuum of branch drainage canals.
Since the daily drainage done by two pump stations
pulls only a small volume of water into the main
canal, the pollutant runoff coefficient could be great
because the pollutant settles under slow water
movement, and inflow loads associated with rainfall
runoff were supposed to be zero for all water quality
items.
Agricultural water supplied into the basin caused
the agricultural effluent to discharge. The irrigation
period was between April and August in K. Town.
Agricultural effluent corresponds to almost all of the
agricultural water supply and period. Inflow load
associated with agricultural effluent was calculatedon the basis of observed concentrations at paddies.
Since the wastewater treatment plants deal withhousehold effluent, the plants have monthly constantdischarges and regular concentrations. The inflow
load was calculated by using constant discharges andobserved concentrations.
Discharge of food factory effluent wasincommensurably small value, as shown in Fig.3 (A).
Furthermore, since the waste outlet of the foodfactory is very close to the pump station at thedownstream edge in the main canal, the effluent has
no effect on the whole of the main canal.
Consequently, all inflow loads for factory effluent
were supposed to be zero.
Chlorophyll-a, total nitrogen, total phosphorus,
and water temperature were determined in the main
drainage canal. The observed date of the watertemperature was fit as a five order polynomial
equation, shown in Fig.4 (A), to be used for model
calculation.
Instead of illuminance, the amount of global
solar radiation was used to calculate influenceFig. 3

Changes of influent discharges into themain drainage canal and their water
qualities

Fig. 4

Changes of (A) water temperature in themain drainage canal, and (B) amount of
global solar radiation.

function Φ s . The monthly amount of global solar
radiation shown in Fig.4 (B) was measured near K.
Town [12].

799GEOMATE- Osaka, Nov. 16-18, 2015

Table 1

List of reduction ways of external load and internal load.

External load
Reduction
Symbolratio
000% off
E-1025% off
E-2050% off
E-3-

Internal load
Reduction ratio /inhibition ratio, Φ inh

SymbolNo remedy (000% off constant)*
Perfect dredging (100% off constant)*
Covering type 1 (stepwise decrease)**Covering type 2 (linear decrease)**

I-1
I-2
I-3
I-4

*) Φinhs of N, P are the same. **) Φinhs are different as shown in Fig.7.
Fig. 5

Table 2 Numerical experiment patterns.
External
load
E-1
E-2
E-3

I-1
Run01
Run05
Run09

Internal load
I-2
I-3
Run02
Run03
Run06
Run07
Run10
Run11

Experimental equations for nutrient elution fromsediment are often expressed as a function of watertemperature T [13]. The author modifies andproposes new experimental equation Eq. (5) and (6)with inhibition ratio Φ inh , which shows that shellcovering inhibits nutrient elution from sediment. The
variables IR N , IR P were calculated with the data of
the shell-covered sediment experiment in the next
chapter. The time series of Φ inh were set up as
imaginary patterns.

(1 − 𝛷𝛷𝑖𝑖𝑖𝑖ℎ𝑃𝑃 )𝐼𝐼𝐼𝐼𝑁𝑁𝑧𝑧
𝐼𝐼𝐼𝐼𝑃𝑃𝑧𝑧

𝜃𝜃𝑁𝑁 𝑇𝑇−𝑇𝑇𝑐𝑐

𝜃𝜃𝑃𝑃

𝑇𝑇−𝑇𝑇𝑐𝑐forshell-

shown as remediation measures against internal loadin Table 1. Inhibition ratio Φ inh here means how
much oyster-shell covering inhibits nutrient elution
from sediment compared with non-covered sediment.
No remedy, shown by I-1, means that Φ inh is zero.
Perfect dredging of I-2 was supposed that all
sediment was clear off in canal bottom, then Φ inh
was 100%. The patterns of covering types 1 and 2,
shown by I-3 and I-4, both mean that the sediment
was covered with oyster shells. Both patterns weredifferent in the variation of Φ inh . The inhabitation
ratio Φ inh s of covering type 1 decreased in a
stepwise fashion, and those of covering type 2
decreased linearly. These patterns reflected theelution properties of the shell-covered sedimentexperiment.

I-4
Run04
Run08
Run12

Simple model equation for covering elution fromsediment(1 − 𝛷𝛷𝑖𝑖𝑖𝑖ℎ𝑁𝑁 )Experimental arrangements
covered sediment experiment.

Material and methods for shell-covered sedimentexperiment(5)

Oyster shells for experimental material(6)

Barnacles and muddy blobs were adequately
washed out of oyster shells by using a lot of fresh
water. Long immersion in fresh water eliminated thesalt content in the shells, and the shells were thendried in the sun. Three types of shell sample sizes
were arranged for the experiment. The samples werecalled “original size”, “chip size”, and “powder size”.
Chip size samples were kept on a sieve with a meshsize of 4.5 mm after granulating the dried original
shells with a hammer. The powder size samples
were fine shells passed through the sieve. The shellsamples were heated at various high temperatures
for six hours in an electric furnace. From a study on
the removal of various ions using heated oyster
shells, reference [14] confirmed the optimum heatedtemperature to be 600°C. Therefore, the three typesof shells were heated at four calcination
temperatures, 100°C, 200°C, 400°C, and 600°C,
except for non-heated oyster shells kept at room
temperature. Shell-covered sediment experiments
were carried out. The water used for measurement
was sampled at the upper part of a pipe used in theexperiments. Water samples were subject to an

Design for numerical experiment patterns
Table 1 indicates the various ways to reduce
external load and internal load in the main drainage
canal. The experimental patterns listed in Table 2
were generated by combining these ways, andnumerical experiments were carried out.
Three ways of reducing external load are shownin Table 1, and the contents are as follows. The
symbols are E-1 for reducing external load by 0%,
E-2 for reduction by 25%, and E-3 for reduction by
50%. Reduction by 0% means no remediation
measure. Preliminary numerical experiments
resulted in deciding reduction values such as 25%
and 50%. While the reduction values are not basedon any theory, the author will ensure that theexternal load is reduced by 25% or 50%. It was
expressed in the calculation for the reduction of
external load that the concentrations of all effluentslike rainfall runoff were equally decreased.
Four kinds of ways to inhibit nutrient elution are800

GEOMATE- Osaka, Nov. 16-18, 2015

8

Chip
Powder

6
5
4
3
2
1
0

phosphorus.
Thus, total nitrogen TN 10 was 3 mg/L,
1.2
1I-1(N, P)
I-2(N, P)
I-3(N)
I-3(P)
I-4(N)
I-4(P)

0.8
0.6

R.Temp 100℃ 200℃ 400℃ 600℃
Non-coveredΦinh

TN, mg/L

7
absorptiometerto determine
Non-covered(3.0)the concentrations of

0.4

Shell-covered

0.2
0.5

Chip

PowderNon-covered(0.4)

TP, mg/L

-0.2

0.3

0

0.2
0.1
0

0.0

Fig. 70

R.Temp100℃ 200℃ 400℃ 600℃
Non-covered

Fig. 6

All patterns
are zero
except I-2.

00.4

Shell-covered

5

Time, yr

10

Changes of inhibition patterns on internalload from sediment.

and total phosphorus TP 10 was 0.4 mg/L after 10
days.

Resultofshell-covered
sediment
experiment after 10 days. Dashed lines
show each concentration under the noncovered.

Results under shell-covered condition
As shown in Fig. 6, both the chip and powder
samples had a similar tendency of nutrient elution
from the sediments except for the chip sample
heated at a temperature of 600°C. While theinhabitation ability of the shell covering diminishedin the range from room temperature to 200°C, shells
heated over 200°C inhibited the elution from thesediment. The concentrations of nitrogen andphosphorus with no heated shells was as low as
those with shells heated in a high temperature rangeof 400 - 600°C, except for TN at 600°C in theexperiments. Therefore, pre-treatment such as
graining and heating might be in vain, and nonheated shells at both sizes will be optimum.
The elution of nutrients such as nitrogen andphosphorus was different depending on inhibition
ratio. First, the nitrogen concentrations with chip andpowder samples at room temperature were
determined to be 1.63 mg/L and 1.25 mg/L,
respectively. Therefore, shell covering inhibited
elution by 50% compared with the non-shellcovered condition. Second, the phosphorus
concentrations of the chip and powder were 0.05
mg/L and 0.00 mg/L, respectively. These differencesmean that the shell covering inhibited elution by
90% compared with the non-shell-covered condition.
The experiments were carried out at the longest
for 10 days, and not long term such as for months or
years. According to elution tests worked out using
many sediment samples from different irrigation
ponds in Japan [15], elution concentrations such asof nitrogen and phosphorus indicated thatconcentration peak within 10 days or so. If longterm experiments were carried out, it would benecessary to discuss that the nutrients eluted fromsediment may change or settle in water in addition to
the elution. Ten days is enough for discussing

total nitrogen (TN) and total phosphorus (TP).
Experimental procedure
For the experiments, lumps of sediment 300 mm
high were placed into the bottom of a clear PVC
pipe, and the air in the sediment was let out (Fig.5).
Sediment sampled at the main drainage canal in K.
Town was used. Three hundred grams of shells wereput over the sediment, and 4 L of tap water was
funnelled with as little erosivity as possible. The
upper water in the pipe was periodically sampled,
non-filtered, and measured for a period of up to ten
days. The shells used were the chip and powder
types and were heated at all calcination temperatures.
The following experimental processes were notcarried out. We did not beat the water in a pipe tomake it homogeneous, and we did not control thequality of the water associated with the elution ofnutrients such as dissolved oxygen. Elution ofnutrients from sediment was supposed to be
maximum with tap water containing no nutrients. To
make sure there was no phytoplankton growth fromthe sediment, the experiments were conducted in adarkened room.
RESULTS AND DISCUSSION
Results of shell-covered sediment experiments
Results under non-shell-covered condition
Figure 6 shows the water quality of the watercolumn after ten days for the shell-covered sedimentexperiments. Under non-shell-covered condition,
elution amounts were different in nitrogen and801

GEOMATE- Osaka, Nov. 16-18, 2015

elution from sediment. Consequently, the inhibition
ratio for the long period was set as an imaginarytime, as shown in Fig.7.

Run11, and Run12 under the reduction of internalParameters for model nutrient elution fromsediment
The variables of IR N and IR P used in Eq. (5) and(6) were calculated like Eq. (7) and (8) by usingparameters under the non-shell-covered condition.
According to the specifications of apparatus used in
the experiment and experimental conditions, watercolumn h Exp was 0.5 m long, as shown in Fig.5, andexperiment time D u was 10 days as mentioned
above. For the non-shell-covered condition, theconcentrations of TN 10 and TP 10 were 3 mg/L and
0.4 mg/L, respectively. The values of IR N and IR P ,assessed at 0.15 and 0.02 g/m2/d, respectively, werein good agreement with those of Lake Suwa [16].
𝐼𝐼𝐼𝐼𝑁𝑁 =𝐼𝐼𝐼𝐼𝑃𝑃 =𝑇𝑇𝑇𝑇10 ⋅𝐴𝐴Exp ⋅ℎExp
𝐴𝐴Exp ⋅𝐷𝐷𝐷𝐷

𝑇𝑇𝑇𝑇10 ⋅𝐴𝐴Exp ⋅ℎExp
𝐴𝐴Exp ⋅𝐷𝐷𝐷𝐷

=

=

3×0.5
10

= 0.15 (g/m2 /d)

0.4×0.5
10= 0.02 (g/m2 /d)

(7)
(8)

Two things were simplified in the paper. First,
elution test as non-shell covered sedimentexperiment under various water temperatures was
not carried out for elution model equation. Though
the IR N and IR P were obtained under one
temperature condition, these values were in good
agreement with those of a Japanese lake. Thus, thethird terms of Eq. (3) and (4) were corrected byusing each one experimental data of nitrogen andphosphorus, respectively. Second, not theundisturbed sediment samples but disturbed samples
were applied to elution tests. Oyster-shell covering
condition might be severe as elution condition in therespect of using disturbed samples in addition to use
tap water not field water. It was again noticed thatthe values of IR N and IR P obtained under thisexperimental condition were in the proper range of aJapanese lake.

Fig. 8Several results of numerical experiments.

Results of numerical experimentFig. 9 Comparison of integrated concentration ofchlorophyll a during 10 years shown in Fig.8.

Figure 8 introduced several simulation results forthe concentration of chlorophyll-a. For Run01, there
was no remediation measure against both external
and internal loads. While Run09, Run10, Run11, andRun12 were in common reduction series of externalload by 50%, these series were different in terms ofremediation measure against internal load. For
Run09, there was no remedy, I-1, against internalload. For Run10, there was perfect dredging againstinternal load, I-2. Run11 and Run12 for covered
sediment mean covering type 1, I-3, and covering
type 2, I-4.
The concentrations of chlorophyll-a for Run10,

load demonstrated the same concentrations as Run09
for 10 years except for Run10. If the bottom
sediment is ideally dredged and sustained to bedredged, then a concentration of chlorophyll-a likethat of Run10 might be kept much lower than theothers. While the concentration of chlorophyll-a forcovering type 1 like Run11 was inhibited the sameas that by the dredging of Run10 for the first half often years, the latter concentration of chlorophyll-a
was the same level as that of Run09, which had no802

GEOMATE- Osaka, Nov. 16-18, 2015

remedy.
The concentration of chlorophyll-a for covering
type 2 such as Run12 gradually became the same asthat of Run09 with no remedy over 10 years. Figure
8 presented a 50% reduction of the external load
mentioned above, except for Run01. The results fora reduction of 25% had similar tendency. As you cansee, only a simple interaction was shown, andsimulation is different from the actual world. For
example, it is very difficult to keep the perfectdredging condition.

To evaluate various remediation measures, theconcentration A(t) of chlorophyll-a was integrated
over the 10 years. The integration result is shown in
Fig. 9, in which each integration results isrecalculated with a value of 100 representing theconcentration load of Run01.
It was predictable that perfect dredging would
have the best effect for the various remediation
measures against internal load. Comparing Run02
with Run05, it was better to dredge than to reduce
external load somewhat. The problem with actual
dredging is that careful work is needed not to return.
In addition, the reduction of external load was alsoneeded to sustain the inhabitation effect of dredging.
Another problem remains of how to deal withdredged sediment.
Only shell covering might have a small effect onwater quality improvement. Good water quality is
expected to be achieved through a combination of
reducing external load and this shell covering. The
water quality problem cannot be corrected in a shorttime, so it is a problem that should be continued tobe tackled over a long period of time. One approach
would be to use various kinds of bioresources, andprolonged activity is needed to do. In the author’s
experience, application to the field is often quite
tough. It was tried that aquatic plants were grown instyle of floating island, and nutrient in water was
imbibed by the plants in the pursuit of clear water inthe main drainage canal. Many turtles eat up aquaticplant rootage growing out of floating island, which
results in aquatic plants not growing.
If concentrations at various times instead ofconcentration integration were used for judgement,
then it would be convenient to compare theconcentrations with water quality standards. In thispaper, the concentration above was not selected,
because a complex simulation model or enoughinput data was not required for calculation.
Integration of concentration over 10 years would
cover the various shortages of the simulation.

carried out, and the properties of nutrient elution
from sediment and experimental parameters wereobtained. The shell-covered sediment model, whichreflects the experimental parameters of elution, wasadded to the ecosystem model. A numericalexperiment was investigated on reducing patterns
against external load and internal load to evaluate
the various remediation measures.
Sample graining such as chip and powder andvarious heated temperatures were investigated in theexperiment. Pre-treatment such as graining andheating might be in vain, and non-heated shells atchip or powder size are optimum.
Under non-shell covered condition, TN and TP
concentrations for 10 days were determined, and IR N
and IR P were 0.15 g/m2/d and 0.02 g/m2/d,
respectively. These values of IR N and IR P were usedas parameters of the numerical model.
Experiments under the non-shell covered
condition resulted in elution nutrients such as
nitrogen and phosphorus being different in inhibition
ratio. First, for the nitrogen concentration of the chipand powder samples, shell covering inhibited elution
by 50% compared with the non-covering condition.
Second, the covering on phosphorus inhibited
elution by 90% compared with non-covering.
Twelve reducing patterns in the combine ofexternal load and internal load were investigated in anumerical experiment. The ways of reducing
external load consisted of no remedy, reduction by
25%, and reduction by 50%. The remediation
measures of internal load had four kinds: no remedy,
dredging, covering type 1, and covering type 2. The
inhibition ratio of covering type 1 followed a
stepwise decrease. The ratio of covering type 2
decreased
linearly.
The
concentrationofchlorophyll-a was integrated over 10 years toevaluate various remediation measures.
It was predictable that perfect dredging would
have the best effect on various remediation measuresagainst internal load. If dredging work is properly
accomplished, then its performance will have a
profound effect on water quality improvement. Only
shell covering might have a small effect on water
quality improvement. Good water quality is
expected to be archived through a combination of
reducing external load and this shell covering. Whilebioresources such as water plant is planned to bepromoted, other living things and conditions could
disturb the system.
A remediation program for external load is
ongoing in the field. While oyster shell covering isonly proposed to be one of remediation measures inthis paper, the investigation results are expected to
be useful of remediation.

CONCLUSIONACKNOWLEDGEMENTS

Estimation of remediation measure

The author is indebted to the local public

An oystershell-covered sediment experiment was
803

GEOMATE- Osaka, Nov. 16-18, 2015

remediate organically enriched coastal marine
sediments", Bioresource Technology, 100, 2009,
pp.4127-4132.
[4] Fujisawa K and Miyake Y, "Effects of shell ofoyster scattered at the bottom mud in Kojima
Lake -1", Bull. of Fisheries Experiment Station
Okayama Prefecture 1979, 1980, pp.30-32.
[5] Sasaki C, Enari K, Koseki K, Ito T and
Nakayama M, "The water purification test using
oyster shells as industrial waste", Trans. of theJapanese Society of Irrigation, Drainage andReclamation Engineering, Vol.67, No.2, 1999,
pp.233-241.
[6] Konishi H, Makino M and Miyama T,"Research on small-scale wastewater treatment
for utilizing the waste （ 3rd report ） －Advanced wastewater treatment utilizing
nitrification - denitrogenation and phosphorus
removal", Research Report from Ishikawa
Prefectural Institute of Public Health andEnvironmental Science, 46, 2009, pp.9-14.
[7] Fukuro S and Shimada Y, "Development oftechnology which improves water quality byusing oyster shell", Fujita Technical Research
Report, 41, 2005, pp.63-68.
[8] Fujisawa K and Miyake Y, "Effects of crushed
shells of oyster scattered at the bottom mud in
Kojima Lake -2", Bull. of Fisheries Experiment
Station Okayama Pref. 1980, 1981, pp.18-20.
[9] Agatsuma S, Murakami K, Gomyo M and
Amano Y, "Influence analysis of sediment
treatment using shell fragment as regional
unused resources on sedimental environment",
The 48th Annual Conf. of Japan Society on
Water Environment 2014 in Sendai, 2014, p.132.
[10] Hosomi M, "Release and control on nitrogenand phosphorus from sediment material",
Journal of Japan Society on Water Environment,
Vol.16, No.2, 1993, 91-95.
[11] Mie Pref., "Handout of the second exploratorycommittee for K. district", Local Water
Environment Development Program, 2006.
[12] National Astronomical Observatory of Japan,"Chronological scientific tables", Maruzen
Publishing, 1993, p.263.
[13] Shiratani E, "Water quality analysis (4) -water
quality analysis by ecosystem model-", Journalof the Japanese Society of Irrigation Drainage
and Reclamation Engineering, Vol. 62, No. 1,
1994, pp.47-53.
[14] Nishioka H, Uchida T, Muramatsu Y, Yazawa T
and Amako T, "Optimization of heat treatment
conditions for oyster shells to increase theirability to remove ions with negativeenvironmental impacts", Journal of the Japan
Society of Material Cycles and Waste
Management, Vol.22, No.4, 2011, pp.276-283.
[15] Hiramatsu K and Suzuki H, "Experiments insediment-desiccation effect on phosphorus andnitrogen dynamics within irrigation ponds",
Irrigation, drainage and rural engineering
journal, Vol.75, No.4, 2007, 411-418.
[16] Tanaka T and Okino T, "Modeling thecirculation of substances in Lake Suwa",
Studies on the ecosystem of the watershed areaof Lake Suwa, No.1, 1978, pp.73-90.

officials and the committee for providing theircooperation and the valuable data for the study.
List of variables
Variable
ExplanationA
conc. of chl.-a in main drainage canal
Dconc. of detritus in main drainage canal
Nconc. of nitrogen in main drainage canal
Pconc. of phosphorus in main drainage canaltTimeQtotal influent discharge, Q=Σqqindividual influent discharge
Vtotal water volume of main drainage canalµ=µ max ･Φ I ･Φ A ･Φ S ･Φ Tµ
maximum specific growth rate
µ max
𝑵𝑵
𝑷𝑷
𝜱𝜱𝑰𝑰 =⋅
ΦI
𝒌𝒌𝑵𝑵 + 𝑵𝑵 𝒌𝒌𝑷𝑷 + 𝑷𝑷
kN, kP
half saturation constant of N and P
𝜱𝜱𝑨𝑨 = 𝟏𝟏
ΦA

ΦS
L
Lc

ΦT
T
Tc
kd
υA, υDzrD
kM
rN, rPr ND , r PDΦ inhN,Φ inhP

IR N , IR P
A in , D in ,N in , P in𝜱𝜱𝑺𝑺 = (𝑳𝑳⁄𝑳𝑳𝒄𝒄 )𝐞𝐞𝐞𝐞𝐞𝐞(𝟏𝟏 − 𝑳𝑳⁄𝑳𝑳𝒄𝒄 )Illuminancecritical illuminance𝜱𝜱𝑻𝑻 = 𝑻𝑻⁄𝑻𝑻𝒄𝒄
water temperature
T c =20°C
death factor of phytoplankton

settling velocity of phytoplankton and detritus

mean depth of main drainage canalconversion factors between phytoplankton
and detritus
decomposition
andmineralization
constant for detritus
conversion factors between phytoplankton and N, P

conversion factors between detritus and N, P

inhibition ratio of N and P from sedimentelution rate of N, P from sedimentconc. of A, D, N and P in influent waters

TN 10 ,
TP 10

conc. of total-N and total-P after 10 days

A Exp ,h Exp
Dusectional area and water column long ofexperimental cylinder
experimental time

REFERENCES[1] Ministry of Agriculture, Forestry and Fisheries,"Statistics report on production of fisheries andaquaculture 2003-2012", Association ofAgriculture and Forestry Statistics, 2005-2014.
[2] Noguchi H, "Guideline of shell recycle forfishinggrounddevelopment",
Monthly
Construction, 51(10), 2007, pp.27-29.
[3] Asaoka S, Yamamoto T, Kondo S and
Hayakawa S, "Removal of hydrogen sulfide
using crushed oyster shell from pore water to804

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

ESTIMATION OF USEFUL SPECIES OF RIVER INSECTS AND
WATER PLANTS AS AN INDEX OF METAL CONTAMINATION IN
THE KINOKAWA RIVER CATCHMENT
Takuma Kubohara1, Hiroyuki Ii2
Graduate School of Systems Engineering, Wakayama University, Japan;
2
Faculty of Systems Engineering, Wakayama University, Japan

1

Abstract

Heavy metals in river insects, Japanese fresh water crabs, caddice-worms, dobsonfly larvae, dragonfly larvae,
stonefly larvae, crane fly larvae, mayfly larvae and corixidae and water plants, reeds, fern, moss, Japaneseknotweed and coix in the contaminated area and non-contaminated area were studied in order to evaluate usefulspecies that can serve as good indicators of metal contamination. Moss was useful as an indicator for Cu, Co and
Ni contamination because moss had the highest concentration factor among river insects and water plants and awide range of concentration depending on contamination level for Cu, Co and Ni. Fern leaf was useful as anindicator for Cd contamination because fern leaf had the highest concentration factor among river insects andwater plants and a wide range of concentration depending on contamination level for Cd.
Keywords: Useful Species, Moss, Fern Leaf, Heavy Metals

INTRODUCTION

Kinokawa River is classified into A river based on
the Ministry of Land, Infrastructure, Transport andTourism of Japan. Length and total area of the
Kinokawa River are 136 km and 1,750 km2 [9].
Izumi Group composed of sedimentary rocks,
sandstone, mudstone and conglomerate is distributedin the northwest part of the catchment. Sanbagawa
Belt composed of metamorphic rocks, serpentinite
and crystalline schist is distributed in the southwest
part and the northeast part of the catchment.
Hidakagawa Group composed of sedimentary rocks,
sandstone and shale is distributed from the southernpart to the northeast part of the catchment. Chichibu
Belt composed of sedimentary rocks, sandstone,
mudstone, limestone and chert is distributed in theeastern part of the catchment [10]. Ryoke Complex
composed of plutonic rocks, granite and gneiss is
distributed in the northeast part of the catchment.
The Kinokawa River catchment has serpentinite
and the closed Cu mines. The chemistry ofserpentinite is quite different from another rocks, inparticular, serpentinite has high concentration of Mg,
and Ni. The closed Cu mine produced a lot of Cu
and Fe sulfide ore and waste water was low pH andhigh concentration of sulfate with metal. Then, in
this study, catchments were divided into threegroups, serpentinite area, Cu mine area and normal
area based on the geological aspect.

Even if there is a metal contamination sourcefrom metal mines in a catchment, metal
concentrations of river water in the catchment is notalways high because of dilution of rain water [1].
Therefore, metal concentration of river water is notalways useful for evaluating metal contamination ina catchment. On the other hand, metal
concentrations of insects and plants are often higher
than those of river water because of highbioconcentration factor.
Metal concentrations for many river insects andwater plants have been studied in metal
contamination areas and non-contamination areas.
At past, concentrations of river insects, caddiceworm [2]-[3], dobsonfly [4], stonefly [5], mayfly [5],water plants, fern [6], moss [7], and reed [8] havebeen studied. However, useful species for metal
contamination index using river insects and water
plants have not been determined because nocomparison of contaminated and non-contaminatedareas, although some caddice-worm were shownpossibility as index of metal contamination.
Therefore, comparing metal concentrations ofriver insects and water plants sampled at thecontaminated area and non-contaminated area,
useful species as a good indicator of metal
contamination was determined.
STUDY AREA
Fig.1 shows the study area. The Kinokawa Riveris located in the center of Kinki district and flows
into the Kii Channel through the Kii plain. The
804

GEOMATE- Osaka, Nov. 16-18, 2015

Osaka Pref.

Wakayama
Pref.

Cu mineSerpentinite
Cu mineFlowbelt: border between geology: sampling point of the serpentinite area
: sampling point of the Cu mine area

Fig.1 Study area
mg/kg-dry, respectively. Cu concentrations ofJapanese freshwater crab, dobsonfly larva, dragonfly
larva, crane fly larva and corixidae in another area
were 45 to 73 mg/kg-dry, 16 to 81 mg/kg-dry, 18 to
59 mg/kg-dry, 11 to 130 mg/kg-dry and 77 to 190
mg/kg-dry, respectively. Cu concentrations of reed,
fern, moss, Japanese knotweed and coix in the Cu
mine area were 8 to 700 mg/kg-dry, 10 to 10,000
mg/kg-dry, 31 to 21,000 mg/kg-dry, 18 to 2,900
mg/kg-dry and under detection limit to 100 mg/kgdry, respectively. Cu concentrations of reed, fern,moss, Japanese knotweed and coix in another area
were under detection limit to 24 mg/kg-dry, underdetection limit to 150 mg/kg-dry, under detection
limit to 87 mg/kg-dry, under detection limit to 10
mg/kg-dry and under detection limit to 23 mg/kg-dry,
respectively. Then, their Cu concentrations in the Cu
mine area were higher than another area. In the past
study, it was clarified that Cu of river insects andwater plants in the Cu mine area in the KinokawaRiver catchment were affected by Cu mine [1].
Therefore, their Cu concentrations in the Cu minearea were affected by Cu mines.
Cu concentrations of river insect species, moss
and water plant species excluding moss influenced
by the Cu mine were 11 to 1,400 mg/kg-dry, underdetection limit to 21,000 mg/kg-dry and underdetection limit to 10,000 mg/kg-dry, respectively.
Then, moss had the highest and a wide range of Cu
concentration. Figs.2 and 3 show Cu concentration
factors of river insects and water plants. The
concentration factor was regarded as 1 when theconcentrations were under detection limit.
Maximum Cu concentration factors of river insect
species, moss and water plant species excluding
moss influenced by the Cu mine were 66,300,
160,000 and 70,000, respectively. Then, Cu
concentration factor of moss was the highest among

STUDY METHODRiver water, river insects and water plants were
sampled in the Kinokawa River catchment.
Sampling points were shown in fig.1. Cu, Co, Ni and
Cd concentrations of river water, river insects andwater plants were measured. River water samples
were filtered with the membrane filter with 0.45
micrometer of pore size in site and concentrated
nitric acid was added in laboratory. They were kept
cool until analyzing. The sampled river insects andwater plants were desiccated by dryer at first. Afterdrying, they were dissolved with concentrated nitricacid and it was filtered with the membrane filter
with 0.45 micrometer of pore size before analysis.
Their Cu, Co, Ni and Cd concentration were
measured by ICP-AES. The actual detection limit of
ICP-AES is 0.01ppm for Cu, Co, Ni and Cd.
The species of sampled river insects wereJapanese fresh water crab, caddice-worm, dobsonfly
larva, dragonfly larva, stonefly larva, crane fly larva,
mayfly larva and corixidae. The species of sampled
water plants were reed, fern, moss, Japaneseknotweed and coix. Water plants excluding moss
were divided into leaf, upper part stem, middle part
stem, lower part stem and root and each part isseparately analyzed.
RESULTS AND DISCUSSION
Evaluation for Cu
Figs.2 and 3 show Cu concentrations of river
insects and water plants. “N.D.” means underdetection limit in the figure. Cu concentrations ofJapanese freshwater crab, dobsonfly larva, dragonfly
larva, crane fly larva and corixidae in the Cu minearea were 114 to 241 mg/kg-dry, 590 mg/kg-dry, 70
to 440 mg/kg-dry, 50 to 1,400 mg/kg-dry and 520

805

GEOMATE- Osaka, Nov. 16-18, 2015

100000them. Therefore, moss was a useful species forindicator of Cu contamination.
serpentinite area

Cu mine area

Cu(mg/kg-dry)

100000

10000
1000

normal area

10000

100

10

1

N.D.
0.1
1000000

0.1
N.D.

1000001000000
100000Cu concentration factor

100

1

10

Cu concentration factor

Cu(mg/kg-dry)

1000

10000
1000

10

serpentinite area
Cu mine area
normal area

corixidae

mayfly larva

stonefly larva

crane fly larva

dragonfly larva

dobsonfly larva

caddice-worm

Cu mine area

normal area

10000
1000
100
10
1

1

Japanese
freshwater crab

serpentinite area

stem(upper part)stem(middle part)stem(lower part)
root
leaf
stem(middle part)stem(lower part)
root
moss
leaf
stem(upper part)stem(middle part)stem(lower part)root
stem(upper part)stem(middle part)stem(lower part)
root

100

serpentinite area
Cu mine area
normal area

reed

Fig.2 Cu concentrations and Cu concentration
factors of river insects.

fernJapanese knotweed

coix

Fig.3 Cu concentrations and Cu concentration
factors of water plants.
Therefore, their Co concentrations in the Cu minearea were affected by Cu mines.
Co concentrations of river insect species, moss
and water plant species excluding moss influenced
by the Cu mine were under detection limit to 82
mg/kg-dry, under detection limit to 200 mg/kg-dry
and under detection limit to 38 mg/kg-dry,
respectively. Then, moss had the highest and a wide
range of Co concentration. Figs.4 and 5 show Co
concentration factors of river insects and water
plants. The concentration factor was regarded as 1
when the concentrations were under detection limit.
Maximum Co concentration factors of river insect
species, moss and water plant species excluding
moss influenced by the Cu mine were 2,733, 3,666
and 3,000, respectively. Then, Co concentration
factor of moss was the highest among them.
Therefore, moss was a useful species for indicator of
Co contamination.

Evaluation for Co
Figs.4 and 5 show Co concentrations of river
insects and water plants. “N.D.” means underdetection limit in the figure. Co concentrations ofJapanese freshwater crab, dobsonfly larva, dragonfly
larva, crane fly larva and corixidae in the Cu minearea were under detection limit to 2 mg/kg-dry, 26
mg/kg-dry, 7 to 30 mg/kg-dry, under detection limit
to 82 mg/kg-dry and 20 mg/kg-dry, respectively. Co
concentrations of Japanese freshwater crab,
dobsonfly larva, dragonfly larva, crane fly larva andcorixidae in another area were under detection limit,
under detection limit to 6 mg/kg-dry, underdetection limit to 23 mg/kg-dry, under detection
limit to 10 mg/kg-dry and under detection limit,
respectively. Co concentrations of reed root, fernroot, moss, Japanese knotweed root and coix root in
the Cu mine area were under detection limit to 30
mg/kg-dry, under detection limit to 21 mg/kg-dry,under detection limit to 200 mg/kg-dry, 36 to 38
mg/kg-dry and under detection limit to 5 mg/kg-dry,
respectively. Co concentrations of reed root, fernroot, moss, Japanese knotweed root and coix root in
another area were under detection limit to 6 mg/kgdry, under detection limit to 10 mg/kg-dry, underdetection limit to 33 mg/kg-dry, under detection
limit to 25 mg/kg-dry and under detection limit,
respectively. Then, their Co concentration in the Cu
mine area were higher than another area. In the past
study, it was clarified that Co of river insects andwater plants in the Cu mine area in the KinokawaRiver catchment were affected by Cu mine [1].

Evaluation for Ni
Figs.6 and 7 show Ni concentrations of river
insects and water plants. “N.D.” means underdetection limit in the figure. Crane fly larva, mayfly
larva, corixidae, reed and coix cannot be sampled in
the serpentinite area. Ni concentrations of Japanese
freshwater crab, caddice-worm, dobsonfly larva,
dragonfly larva and stonefly larva in the serpentinite
area were under detection limit to 39 mg/kg-dry, 150
to 220 mg/kg-dry, under detection limit to 83 mg/kgdry, under detection limit to 60 mg/kg-dry and underdetection limit to 20 mg/kg-dry, respectively. Ni

806

GEOMATE- Osaka, Nov. 16-18, 2015

10
1

N.D.
0.1
10000

Co concentration factor

100serpentinite area
Cu mine area
normal area

10
1

N.D.
0.1
10000

serpentinite area
Cu mine area
normal area

Co concentration factor

Co(mg/kg-dry)

100

1000

serpentinite area
Cu mine area
normal area

Co(mg/kg-dry)

1000

1000
100
10

1000
100
serpentinite area
Cu mine area
normal area

10
1stem(upper part)stem(middle part)stem(lower part)
root
leaf
stem(middle part)stem(lower part)
root
moss
leaf
stem(upper part)stem(middle part)stem(lower part)root
stem(upper part)stem(middle part)stem(lower part)root

corixidae

mayfly larva

crane fly larva

stonefly larva

dragonfly larva

dobsonfly larva

caddice-worm

Japanese
freshwater crab

1

Fig.4 Co concentrations and Co concentration
factors of river insects.

reed

fern

Japanese knotweed

coix

Fig.5 Co concentrations and Co concentration
factors of water plants.

concentrations of Japanese freshwater crab, caddiceworm, dobsonfly larva, dragonfly larva and stonefly
larva in another area were under detection limit to 1
mg/kg-dry, under detection limit to 20 mg/kg-dry,under detection limit, under detection limit to 10
mg/kg-dry and under detection limit, respectively.
Ni concentrations of fern, moss and Japaneseknotweed in the serpentinite area were underdetection limit to 270 mg/kg-dry, 9 to 590 mg/kgdry and 7 to 320 mg/kg-dry, respectively. Ni
concentrations of fern, moss and Japanese knotweed
in another area were under detection limit to 30
mg/kg-dry, under detection limit to 50 mg/kg-dry
and under detection limit to 23 mg/kg-dry,
respectively. Then, their Ni concentrations in theserpentinite area were higher than another area. In
the past study, it was clarified that Ni of river insects
and water plants in the serpentinite area in the
Kinokawa River catchment were affected byserpentinite [1]. Therefore, their Ni concentrations inthe serpentinite area were affected by serpentinite.
Ni concentrations of river insect species, moss
and water plant species excluding moss influenced
by the serpentinite were under detection limit to 220
mg/kg-dry, under detection limit to 590 mg/kg-dry
and under detection limit to 320 mg/kg-dry,
respectively. Then, moss had the highest and a wide
range of Ni concentration. Figs.6 and 7 show Ni
concentration factors of river insects and water
plants. The concentration factor was regarded as 1
when the concentrations were under detection limit.
Maximum Ni concentration factors of river insect
species, moss and water plant species excluding
moss influenced by the serpentinite were 22,000,

59,000 and 32,000, respectively. Then, Ni
concentration factor of moss was the highest among
them. Therefore, moss was useful species forindicator of Ni contamination.
Evaluation for Cd
Figs.8 and 9 show Cd concentrations of river
insects and water plants. “N.D.” means underdetection limit in the figure. Cd concentrations ofJapanese freshwater crab, dobsonfly larva and crane
fly larva in the Cu mine area were 0.2 to 7.4 mg/kgdry, 10 mg/kg-dry and under detection limit to 7
mg/kg-dry, respectively. Cd concentrations ofJapanese freshwater crab, dobsonfly larva and crane
fly larva in another area were under detection limit,
under detection limit to 2 mg/kg-dry and underdetection limit, respectively. Cd concentrations oflower part stem and root of reed, fern excluding
middle part stem, moss, Japanese knotweed root andupper part stem and root of coix in the Cu mine area
were under detection limit to 5 mg/kg-dry, underdetection limit to 96 mg/kg-dry, under detection
limit to 10 mg/kg-dry, under detection limit to 7
mg/kg-dry and under detection limit to 9 mg/kg-dry,
respectively. Cd concentrations of lower part stem
and root of reed, fern excluding middle part stem,
moss, Japanese knotweed root and upper part ofstem and root of coix in another area were underdetection limit, under detection limit to 2 mg/kg-dry,under detection limit to 2 mg/kg-dry, underdetection limit and under detection limit,
respectively. Then, their Cd concentrations in the Cu
807

GEOMATE- Osaka, Nov. 16-18, 2015

mine area were higher than another area. It is known
that Cd was drained from mines [11]. Therefore,
their Cd concentrations in the Cu mine area wereaffected by Cu mines.
1000

100

Cd(mg/kg-dry)

serpentinite area
Cu mine area
normal area

100
10
1

1

N.D.
0.01
10000

Cd concentration factor

serpentinite area
Cu mine area
normal area

1000

serpentinite area
Cu mine area
normal area

Cd(mg/kg-dry)Ni(mg/kg-dry)

100

10

10

corixidae

1

N.D.
0.01
10000

0.1

Cd concentration factor

N.D.
0.01
100000

Ni concentration factor

serpentinite area
Cu mine area
normal area

0.1

1

10000
1000

10

mayfly larva

corixidae

mayfly larva

crane fly larva

stonefly larva

dragonfly larva

caddice-worm

dobsonfly larva

Japanese
freshwater crab

Fig.8 Cd concentrations and Cd concentration
factors of river insects.

Fig.6 Ni concentrations and Ni concentration
factors of river insects.

100

crane fly larva

1

1

100

serpentinite area
Cu mine area
normal area

10

stonefly larva

10

100

dragonfly larva

100

caddice-worm

1000

dobsonfly larva

10000

Japanese
freshwater crab

N.D.
0.01
100000

Ni concentration factor

10

0.1

0.1

1000

serpentinite area
Cu mine area
normal area

serpentinite area
Cu mine area
normal area

stem(upper part)stem(middle part)stem(lower part)
root
leaf
stem(middle part)stem(lower part)
root
moss
leaf
stem(upper part)stem(middle part)stem(lower part)root
stem(upper part)stem(middle part)stem(lower part)
root
reed

fern

1000
100
10
1

1

Japanese knotweed

serpentinite area
Cu mine area
normal area

stem(upper part)stem(middle part)stem(lower part)
root
leaf
stem(middle part)stem(lower part)
root
moss
leaf
stem(upper part)stem(middle part)stem(lower part)root
stem(upper part)stem(middle part)stem(lower part)root

Ni(mg/kg-dry)concentration of another part [12]. Figs.8 and 9
show Cd concentration factors of river insects andwater plants. The concentration factor was regarded
as 1 when the concentrations were under detection

reed

fern

Japanese knotweed

coix

Fig.9 Cd concentrations and Cd concentration
factors of water plants.

coix

Fig.7 Ni concentrations and Ni concentration
factors of water plants.

limit. Maximum Cd concentration factors of river
insect species, fern leaf and water plant species
excluding fern leaf influenced by the Cu mine were1,000, 9,600 and 6,100, respectively. Then, Cd
concentration factor of fern leaf was highest among
them. Therefore, fern leaf was a useful species forindicator of Cd contamination.

Cd concentrations of river insect species, fern
leaf and water plant species excluding fern leaf
influenced by the Cu mine were under detection
limit to 10 mg/kg-dry, under detection limit to 96
mg/kg-dry and under detection limit to 61 mg/kg-dry,
respectively. Then, fern leaf had the highest and awide range of Cd concentration. It is reported that
Cd concentration of fern leaf is higher than Cd

CONCLUSION
808

GEOMATE- Osaka, Nov. 16-18, 2015

RIVER CATCHMENT”, Int. J. of GEOMATE,
Vol.10, No.1, Feb.2016, pp.1600-1606.
[2] Tochimoto H, “Study of Aquatic Insect,Caddisfly, Stenopsyche marmorata as a Biomonitor of Trace Element Contamination inRivers and Streams: Cadmium and copper”,
Journal of Japan Society on Water Environment,
Vol. 17, No.11, 1994, pp. 737–743.
[3] Aizawa S, Tsunoda K and et al., “Evaluation of
Trichoptera as an indicator organism forenvironmental pollution by heavy metals.”
BUNSEKI KAGAKU, Vol.43, 1994, pp.865871.
[4] Fujino A and Ii H, “IMPORTANCE OF
CORYDALIDAE AS AN INDEX OF METALCONTAMINATION OF RIVER”, Int. J. of
GEOMATE, Vol.9, No.2, Dec. 2015, pp.14831490.
[5] Nehring R.B., “Aquatic insects as biologicalmonitors of heavy metal pollution.”, Bull.
Environ. Contam. Toxicol. , 15 (2), Feb. 1976,
pp. 147-154.
[6] Nishioka H and Kodera H, “Heavy metal
content in fern plants”, Journal ofEnvironmental
Conservation
Engineering,
Vol.34, No.4, 2005, pp. 301-305.
[7] Satake K, Shibata K and et al., “Copper
accumulation and location in the moss
Scopelophila cataractae”, Journal of bryology,
Vol.15, No.2, Jan. 1988, pp.353-376.
[8] Sasaki S, “Environmental Characteristics
around River Area Based on Analysis ofChemical Contaminants in Reed”, Soilmechanics and foundation engineering, 55 (8),Aug. 2007, pp.28-30.
[9] River Bureau Kinki Regional Development
Bureau Ministry of Land, Infrastructure,
Transport and Tourism, Kinokawa Basin,
http://www.kkr.mlit.go.jp/river/kasen/kinokawa.
html[10] Wakayama City Children’s Science Museum,
http://www.city.wakayama.wakayama.jp/kodomo/sassi/ganseki/chizu/wakayama.htm[11] Ministry of Land, Infrastructure, Transport andTourism of Japan, http://www.mlit.go.jp/
sogoseisaku/region/recycle/pdf/recyclehou/man
ual/sizenyuraimanyu_zantei_honbun.pdf,
Mar.2010, pp.11-12.
[12] Hikichi H, “Plant and Metals”, EQUAL, Vol.2,
May 1988, pp.34-37.

In this study, Cu, Co, Ni and Cd concentrationsof river water, river insects and water plants werestudied in the serpentinite area, the Cu mine area andnormal area of the Kinokawa River catchment inorder to evaluate useful species that can serve as
good indicators of metal contamination.
Cu, Co and Cd concentrations of Japanese
freshwater crab, dobsonfly larva, crane fly larva andmoss in the Cu mine area were affected by Cu mines
because their concentrations were high in the Cu
mine area. Cu concentrations of dragonfly larva,
corixidae, reed, fern, Japanese knotweed and coix
were affected by Cu mines because theirconcentrations were high in the Cu mine area. Co
concentrations of dragonfly larva, corixidae, reedroot, fern root, Japanese knotweed root and coix root
were affected by Cu mines because theirconcentrations were high in the Cu mine area. Ni
concentrations of Japanese freshwater crab, caddiceworm, dobsonfly larva, dragonfly larva, stonefly
larva, fern, moss and Japanese knotweed in theserpentinite area were affected by serpentinite
because their concentrations were high in theserpentinite area. Cd concentrations of lower partstem and root of reed, fern excluding middle part
stem, root of Japanese knotweed and upper part stem
and root of coix in the Cu mine area were affectedby Cu mine because their concentrations were highin the Cu mine area.
Moss had the highest concentration factor among
Japanese fresh water crab, caddice-worm, dobsonfly
larva, dragonfly larva, stonefly larva, crane fly larva,
mayfly larva, corixidae, reed, fern, moss, Japaneseknotweed and coix and a wide range ofconcentration for Cu, Co and Ni. Therefore, moss
was useful as an indicator for Cu, Co and Ni
contamination.
Fern leaf had the highest concentration factor
among them and a wide range of concentration for
Cd. Therefore, fern leaf was useful as an indicatorfor Cd contamination.
REFERENCES[1] Kubohara T and Ii H, “CU, CO, CR AND NIOF RIVER WATER, RIVER INSECT AND
WATER PLANT IN THE KINOKAWA

809

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

ESTIMATION OF EVAPORATIONRATE OF SURFACE WATER TO USING HYDROGEN
AND OXYGEN ISOTOPIC RATIO
Masahiro Yamashita1, Hiroyuki Ii2
Graduate School of Systems Engineering, Wakayama University, Japan;
2
Faculty of Systems Engineering, Wakayama University, Japan

1

ABSTRACT
Hydrogen and oxygen isotopic ratios increased with evaporation rate and the slope, δD/δ18O was found to
decrease with temperature. The δD/δ18O was estimated by the experiment. Changes of both δD and δ18O values
per evaporation rate were uniform values under the condition of variable humidity and temperature. δD value perevaporation rate was determined to be 0.6 to 2.6 per mile /% andδ18O value per evaporation rate was determined
to be 0.09 to 0.26 per mile /% experimentally. Then, evaporation rate can be calculated from change of δD or
δ18O value per evaporation rate and difference of isotopic ratios between before evaporation and after
evaporation.
Keywords: Evaporation rate, Hydrogen isotopic ratio, Oxygen isotopic ratio, Evaporation experiment

INTRODUCTION
It is concern that depletion of water resource will
occur as one of the biggest environmental problems
in the 21st century. It is considered that the amountof water that we can use will decrease because theamount of evaporation of water from the groundincreases further by global warming. So, it is very
important to grasp the evaporation rate for managing
of water resource in about dam, reservoir andirrigation water, etc. There are various methods tocalculate evaporation rate such as Thornthwaite
method [1] and the Penman method [2], etc. The
relation between evaporation and isotope isdiscussed [3], [4]. The estimation method of
evaporation rate using isotopic ratio was made byAllison et al. [5] or Gibson et al. [6]. However theapplication of their method is not straightforward
because they need complex parameters as forexample expression (1) [7], [8].
dδLhA (δL −δA ) / (1 +δL ) − ε *=(a − hA )(a ⋅ α vap− liq ⋅ ei , L / e + ei / e )d ln f(1)

δ L : isotopic ratio of waterf : ratio of water that remains for the first waterδ A : isotopic ratio of vaporh A : humidity
a : water activity
ε* : aε +⊿ε (ε：equilibrium vapor pressure ratio，⊿ ε ： amount of change equilibrium vapor
pressure ratio)
α vap – liq : partition coefficiente i ,L : diffusion resistance of heavy water in watere : diffusion resistance of steam in atmospheric(Constant)e i : diffusion resistance of heavy water inatmosphericPast, using oxygen isotopic ratio, simple
estimation of evaporation was carried out [9] but
both oxygen and hydrogen isotopic ratios have notbeen clarified.
The purpose of this study was to clarify
relationship between evaporation rate, isotopic ratios,
humidity and temperature and to propose a simple
estimation method.
METHOD
Experiment was performed at thermostaticchamber (SANYO MIRI153: Fig.1). In thisexperiment, temperature of the thermostatic chamber
was changed from 20 degree C to 50 degree C.
Temperature and humidity during the experimental
period were recorded every 30 minutes by thestorage meter (SATO METER WORKS SKL200TH2: Fig.2). Tap water amount of about 2000g
put in polyethylene bottle of capacity 2L evaporated.
Detail of the polyethylene bottle is 126mm ofdiameter, 245mm of height and 75mm of diameterof the mouth. Weight of residual tap water wasmeasured and tap water was sampled once a few
days until dried up. Experimental process is asshown in Fig.3.

810

GEOMATE- Osaka, Nov. 16-18, 2015

δD ＝ [(D/H) Sample / (D/H) SMOW -1] ×1000: (2)δ18O ＝ [(18O/16O) Sample / (18O/16O) SMOW -1]
×1000: (3)Fig. 1

Thermostatic chamber.

Fig. 4
Isotopic ratio(Sercon Geo Wet System).

measurement

systemRESULTS
The temperature in the thermostatic chamber
was varied in 5 degree C interval from 20 degree C
to 50 degree C. Tap water amount of about 2000g
was evaporated and taken until dried up at eachtemperature condition. Water sampling period ateach temperature are as follows.
Fig. 2

Storage meter of temperature and humidity.








20 degree C: 16/04/2014~01/07/2014
25 degree C: 17/12/2014~03/03/2015
30 degree C: 04/03/2014~15/04/2014
35 degree C: 28/07/2014~29/08/2014
40 degree C: 16/01/2014~04/02/2014
45 degree C: 13/05/2015~04/06/2015
50 degree C: 20/10/2014~09/12/2014

Table1 shows number of collected samples,
average measured temperature and average humidity
at each temperature. Total number of evaporation
experiments was 122.
Table1 Summary of each temperature.
Fig. 3

Process of evaporation experiment.

Measurement of hydrogen and oxygen isotopic
ratios was carried out in isotopic ratio measurement
system (Sercon Geo Wet System: Fig.4). Hydrogenand oxygen isotopic ratio is presented in per mil (‰)
of the standard average seawater (SMOW: Standard
Mean Ocean Water). The formulas are shown inequation (2) and (3). Hydrogen and oxygen isotopic
ratio of SMOW are denoted as (D/H) SMOW ,(18O/16O) SMOW and hydrogen and oxygen isotopic
ratio of sample are denoted as (D/H) Sample , (18O/16O)
Sample . Measurement error of hydrogen isotopic ratio
δD is ±1.0‰ and oxygen isotopic ratio is ±0.1‰.

Fig. 5-10 show relationship between evaporation
rate and the hydrogen isotopic ratios excluding 45
degree C. And Fig. 11-16 show relationship between

811

GEOMATE- Osaka, Nov. 16-18, 2015

evaporation rate and the oxygen isotopic ratios
excluding 45 degree C. Samples under 45 degree C
condition have not been analyzed.

Fig.8
Relationship between evaporation rate andδD at 35 degree C.
Fig.5
Relationship between evaporation rate andδD at 20 degree C.

Fig.9
Relationship between evaporation rate andδD at 40 degree C.
Fig.6
Relationship between evaporation rate andδD at 25 degree C.

Fig.10 Relationship between evaporation rate andδD at 50 degree C.
Fig.7
Relationship between evaporation rate andδD at 30 degree C.

Fig. 5-10 show relationship between evaporation
rate and the hydrogen isotopic ratios at 20, 25, 30,
35, 40 and 50 degree C. δD value of the tap water
before evaporation was -52.3 to -50.3‰. In four
experiments excluding 20 and 50 degree C, δD
values increased and in particular after 60 to 80%
evaporation rate, they remarkably increased.
However, at 20 degree C, although δD values
increased in the same way until 60% evaporation
rate, they decreased with evaporation after 70%
evaporation rate. δD values at 30 degree C in Fig.7

812

GEOMATE- Osaka, Nov. 16-18, 2015rose to 172.1‰ finally, it was the largest increasing
among all the 113 samples.

Fig.14
Relationship between evaporation rateand δ18O at 35 degree C.
Fig.11 Relationship between evaporation rate andδ18O at 20 degree C.

Fig.15 Relationship between evaporation rate andδ18O at 40 degree C.
Fig.12 Relationship between evaporation rate andδ18O at 25 degree C.

Fig.16 Relationship between evaporation rate andδ18O at 50 degree C.
Fig.13 Relationship between evaporation rate andδ18O at 30 degree C.

Fig. 11-16 show relationship between
evaporation rate and the oxygen isotopic ratios at 20,
25, 30, 35, 40 and 50 degree C. δ18O value of the tap
water before evaporation was -7.66 to -7.39‰. δ18O
at 30 degree C in Fig.13 rose to 33.44‰ finally, it
was the largest increasing among all the 113 samples.
δ18O values at 25, 30, 35, 40 and 50 degree C
increased with evaporation rate and in particularafter 50% evaporation rate remarkably increased aswell as δD values. At 20 degree C, δ18O values
decreased after 70% evaporation rate. Typically,hydrogen and oxygen isotopic ratio increased when813

GEOMATE- Osaka, Nov. 16-18, 2015

evaporation proceeds. However, decreasing with
evaporation rate of both δD and δ18O values wasobserved at 20 degree C. Comparing 20 degree C
and other temperature condition, humidity of theexperiment of 20 degree C was 64.2%, higher thanthose under other temperature condition. Therefore,
humidity was also thought to be critical parameter
controlling δD and δ18O values. Then, it is necessary
to consider the influence under high humidity
condition.

Fig.17

Fig.18

Fig.19Fig.20Relationship δD and δ18O at 35 degree C.

Fig.21

Relationship δD and δ18O at 40 degree C.

Fig.22

Relationship δD and δ18O at 50 degree C.

Relationship δD and δ18O at 20 degree C.

Relationship δD and δ18O at 25 degree C.

Relationship δD and δ18O at 30 degree C.

Fig. 17-22 show relationship between δD andδ18O at each temperature. Most of δD/δ18O values
were distributed on one straight line. However, eachslope, δD/δ18O at each temperature was different.
The slope value depended on temperature and it
varied from 4.40 at 50 degree C to 6.26 at 20 degree
C. The slope of the meteoric water line
(δD=8×δ18O+10) is 8. It was found that δD/δ18O
values decreased from 8 for meteoric line with thetemperature rising. δD/ δ18O values were 6.26 at 20
degree C, 5.96 at 25 degree C, 5.47 at 30 degree C,
4.95 at 35 degree C, 4.85 at 40 degree C and 4.40 at
50 degree C.
As a result of regression analysis, equation {y=0.064x+7.45 (x: temperature, y: δD/ δ18O)} was814

GEOMATE- Osaka, Nov. 16-18, 2015

determined. Moreover, from Fig.23, it was foundthat all lines intersected at -52.3 to -50.3‰ for δD
and -7.66 to -7.39‰ for δ18O which were isotopic
ratios of original water before evaporation.
Table2 The changes of δD values per evaporation
rate at each humidity.

volume at high evaporation rate and its isotopic
ratios were apt to be influenced by air isotopic ratios.
Therefore, from this result, both the changes of δD
and δ18O values per evaporation rate at low
evaporation rate were able to use as an evaporation
parameter. From the changes of δD or δ18O values
per evaporation rate, difference of isotopic ratios
between before evaporation and after evaporation,
evaporation rate can be calculated.
REFERENCES

Table3 The changes of δ18O values per evaporation
rate at each humidity.

DISCUSSION AND CONCLUSION
Tables 2 and 3 show the changes of δD and δ18O
values per evaporation rate at 0 to 30%, 31 to 50%
and 51 to 100% humidity. From tables 2 and 3
indicate that the changes of δD and δ18O values perevaporation rate were not uniform however both thechanges of δD and δ18O values per evaporation rate
were uniform less than 60% evaporation rate. After
60% evaporation rate, at 0 to 50% humidity, both thechanges of δD and δ18O values per evaporation rateincreased however at over 50% humidity, thechanges of δD and δ18O values per evaporation rate
decreased. Therefore, the changes of δD and δ18O
values per evaporation rate were uniform, at the lessthan 60% evaporation rate. The change of δD value
per evaporation rate was 0.6 to 2.6 per mile /%. Thechange δ18O value per evaporation rate was 0.09 to
0.26 per mile /%. However over 60% evaporation
rate, they were variable and depended on humidity.
The variation was thought to be caused by air
isotopic ratios because residual liquid water is small[1] Thornthwaite, C.W.: An approach toward a
rational classification of climate. Geographical
Review, 38, pp.55-94, 1948
[2] Penman, H.L.: National evaporation from open
water, bare soil and grass. Proceedings of theRoyal Society of London. Ser. A.,
Mathematical and Physical Science, 193,
pp.120-145, 1948.
[3] Machida I, Kondoh A: Stable Isotope Ratios of
Natural Water in Japan –The Analysis byUsing Environmental Isotopes Database, J.
Japan Soc. Hydrol. & Water Resour. Vol.16，
No.15, pp556-569, 2003.
[4] Yoshimura K, Fujita K, Kurita N, Abe O:
International Workshop on Isotopic Effects in
Evaporation, -Revisiting the Craig-Gordon
Model Four Decades after its Formulation Participation report, Journal of Japan Society ofHydrology & Water Resources.19, pp.420-423,
2006.
[5] Allison, G.B., Brown, R.M. and Fritz, P:
Estimation of the isotopic composition of lake
evaporate., Jour, of Hydrol. , 42, pp.109-127,
1979.
[6] Gibson, J.J., Edwards, T.W.D., Bursey, G.G.
and Prowse, T.D. : Estimating evaporation
using stable isotopes: quantitative results andsensitivity analysis for two catchments innorthern Canada, 24, pp.79-94, 1993.
[7] Fontes, J. –ch. and Gonfiantini, R.:
Comportment isotopique an ocours delevaporation de deux basins Sahariens, Earth
Plan.Sci. Letters, 3, pp.277-374, 1967.
[8] Pupezin, J., Jancso, G. and Van Hook, W.A.:
Vapor pressure isotope effect in aqueous
systems. I, Jour. Phys. hem. , 76, pp.743-762.
[9] Miyahara S and Ii H,“Proposal of simple
measurement method for evaporation rate byusing oxygen isotopic ratio” Int. J. of
GEOMATE, Vol. 9, No.1, 2012, pp. 318-324.

815

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

EXAMINING THE IMPACT OF THE 120-DAY WINDS ON
EVAPOTRANSPIRATION CONSIDERING PAN EVAPORATION
COEFFECIENT IN WEST REGION OF AFGHANISTAN
1

Homayoon Ganji1 Takamitsu Kajisa1 Masaaki Kondo1 Behroze Rostami1
Graduate School of Bioresources, Mie University, 514-8507 Kurima-machiya-cho 1577, Tsu, Japan

ABSTRACT
Evapotranspiration (ET) as important applications in irrigation planning plays a significant role in regionaland global climate, through the hydrological cycle. ET adversely affected by the “120-day winds” in west regionof Afghanistan. In this study, by examining the impact of metrological factors on evaporation process, therelationship of the wind speed (u 2 ) and pan coefficient (K p ) with reference evapotranspiration (ET 0 ) is examined.
Penman-Monteith Method is used to estimate (ET 0 -PM), whereas pan evaporation (E pan ) data, which ismeasured directly at the site, with experimental coefficient K p is used to estimate (ET pan ). K p is presented as K p S, which is obtained from Snyder (1992) equation and K p -D, which is derived from ET pan and ET 0 -PM equations.
As results, by comparing ET 0 -PM with ET pan and E pan , it has been found that, in windy season from June until
September, the ET 0 -PM is lower than the E pan . Whereas, the ET 0 -PM is smaller when compare with ET pan .
Contrary to what is expected, determination coefficient (R2) of E pan shows higher value with ET 0 -PM than the
ET pan . Furthermore, standard error estimation (SEE) of E pan with ET 0 -PM is obtained larger than that of theET pan . On the other hand, by considering R2, effect of temperature is fond more significant on ET pan than thewind speed (u 2 ) and relative humidity (RH). K p -S and K p -D show similarity only in windy season from June
until September. On the other hand, by increasing u 2 , K p -S decreases. Hence, the effect of strong wind on K p -S
is more than the RH in windy area like Herat.

Keywords: Reference Evapotranspiration, Pan Evaporation, pan Evaporation Coefficient
Many various factors influence water availability
at the region as one of the main factor is strong
winds locally known as “120-day winds”.
The “120 day winds” usually begin in early Julyand go on until late September with a great force
7m/s averagely [6]. This period is cover entire of thesummer season as this time is the main season ofcrop growing. According to the data measured in
2009, the precipitation is almost zero at the windy
season and daily average temperature is high as
17.5 °C. The ET 0 in Herat has the highest rate incompare to the other cities in Afghanistan, as thedaily average value is more than 10 mm [5]. One of
the factors among the all other factors which
adversely affect the irrigation is the “120-day winds”.
The great impact of wind velocity is increasing ET
which can have profound implications forhydrologic processes and agricultural crop
performance [3].
ET estimation has important applications in
irrigation planning as it plays a significant role inregional and global climate, through thehydrological cycle [9] [10] [11].
Many different concepts are used for defending
the ET term, as one of them is reference
evapotranspiration (ET 0 ). The evaporative demand

INTRODUCTION
Afghanistan, as a dry country, is characterized byextremes of climate and weather that can be counted
as continental climate [2].
The climate variation is observed arid in thesouth and southwest and to semi-arid in the mostother parts of the county.
Agriculture as a main source of income in
Afghanistan has been facing challenges sincedecades. Water scarcity is the extreme challenge
which threatening the agricultural production.
The main consumer of fresh water in
Afghanistan is Agricultural irrigation. There in
Afghanistan, both rain fed and irrigated cropping
agriculture is practiced. A land use survey was
conducted on 1990s which estimated 3.2 million ha
was irrigated of which 48 percent was intensively
irrigated and 52 percent was intermittently irrigatedwith one or more crops [12].
Spatial distribution of water availability is notequal among the regions in Afghanistan.
Western region consists of four provinces suchas Herat, Farah, Badghis and Ghour province, ischaracterized with semi-arid climate that has low
precipitation, as the total precipitation was 345.6mm
in 2009.
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of atmosphere apart from the crop type, cropdevelopment and management practices is
introduced by ET 0 [1]For estimating the ET 0 , many different methods
have been developed based on their daily
performance under the given climatic condition
worldwide, of which six well known methods has
been examined by H. Ganji et al. (2015) to estimate
ET 0 value for the west region. By considering pan
evaporation (E pan ) as indicator, the PenmanMonteith method is confirmed the most accurate
model for estimating the ET 0 among the six wellknown models in the west region of Afghanistan.
For estimating the ET 0 using E pan value,considering appropriate coefficient (K P ) is importantin calculation.
The empirically derived coefficient K p is acorrection factor which depends on the prevailing
upwind fetch distance, average daily wind speed,
and relative humidity conditions associated with thesitting of the evaporation pan [4].
The K P is ranged from 0.35 to 0.85 depends ondeferent conditions [1]. Many various equations
have been presented for calculating the K P in theworld, but dose equations cannot cover the effective
environmental factors on K p compatibly, as the local
estimation is necessary for estimating the accurate
value of ET 0 .
Among the all K P estimated equations that
presented till now, Snyder (1992) equation Eq. 3 isused for estimating the ET 0 in this study Table 1.
The proposed equation by Snyder (1992) is a
simpler equation for calculation daily K P as afunction of daily mean wind speed, measured at 2
meter height (Km/day), daily mean relative humidity(RH) (%) and upwind distance fetch of low growing
vegetation (m).
In this paper, emphasize is focused on effect of
K P in estimation of the ET pan using E pan measured
data, especially in windy season. Estimating theET pan using E pan measured data is related to the Eq.
2 which requires the coefficient K P [1].
It’s hypothesized that, K P is effected more thanrelative humidity as well as, ET pan is effected by thewind speed more than the other required factors.

the east. In 1968 approximately 15 hectares of thetotal area were cultivated for the first time andextended until now as a research center [11].
According to the measured data of climatic
factors in 2009, shown in Fig. 1 to 4, the maximum
mean annual temperature is around 37.5°C andminimum mean temperature is 0.5°C, the total
precipitation is 345.6 mm, the daily average relative
humidity is 41.3 % and the daily average wind speed
is 271.7 km.
Metrological Data
Many different sources are used for datacollection Table 2 [5]. The metrological data iscollected for year 2009, which includes the daily
E pan , sunshine hours, daily values of precipitation,
maximum and minimum temperatures daily averagerelative humidity and daily average wind speed.
Mostly the climatic data is provided by Urdu
Khan Research center where has not been equippedwith standard and modern measurement devices yet.
(I.e. the E pan is measured through a very simple panapart from standard A- class pan or etc.).
Furthermore, data missing is occurred sometimes as
there were some missing data for some days in 2009.
Thus, interpolation method is sued to cover missing
data.
Table 1 Accessible online database for irrigation
planning [5].
Name

Features

CROPWAT (FAO)

Data of mean ET 0

CLIPWAT (FAO)

Component data of ET 0
Air temperature, dew
point, and wind velocity
Basic daily data. Cloud
cover, wind velocity, airtemperature and humidity
at the airport. Hourly data
may be available.
Data of E pan , temperature,sun shine andprecipitation

NCDC (NOAA)

Weatherspark.com

Urdu khan Research
centerMETHODS AND DATA
Calculation equations

Site
The only official center where is appropriate forresearching is Urdu khan Regional Agricultural
Research Station. This station is the largest station in
the west region which was considered as study areain this research. Urdu khan research center with a
total area of 225 hectares, located in a latitude of 39°
11' N and a longitude of 68° 13' E with an elevationof 964 meters in Urdu khan village, at 5.8 kilometers
southeast of Herat city. The station is bounded by
Urdu khan right canal on the north and east canal on

The Equations that have been used in calculation
are listed in Table 2 as:
1. ET 0 and ET pan have been estimated by using
Penman-Monteith methods and E pan data Eq.
(1and 2 respectively).
2. Kp-S and Kp-D were calculated by using Eq. (3and 4 respectively)
3. SEE is calculated by sing Eq. (5).

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GEOMATE- Osaka, Nov. 16-18, 2015

Table 2 Calculation equations
Model
Penman-Monteith (ET 0 -PM)ET panDerived Pan Coefficient
Snyder Pan CoefficientStandard Error Estimation (SEE)

Equation
𝟗𝟗𝟗𝟗𝟗𝟗𝟎𝟎. 𝟒𝟒𝟒𝟒𝟒𝟒∆(𝑹𝑹𝒏𝒏 − 𝑮𝑮) + γ
𝒖𝒖 (𝒆𝒆 − 𝒆𝒆𝒂𝒂 )𝑻𝑻+
𝟐𝟐𝟐𝟐𝟐𝟐 𝟐𝟐 𝒔𝒔
𝑬𝑬𝑬𝑬𝟎𝟎 − 𝐏𝐏𝐏𝐏 =∆ + γ(𝟏𝟏 + 𝟎𝟎. 𝟑𝟑𝟑𝟑𝒖𝒖𝟐𝟐 )No
1

𝑬𝑬𝑬𝑬𝑷𝑷𝑷𝑷𝑷𝑷 = 𝑲𝑲𝒑𝒑 × 𝑬𝑬𝒑𝒑𝒑𝒑𝒑𝒑

2

𝑲𝑲𝒑𝒑 − 𝐒𝐒 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎 + [𝟎𝟎. 𝟐𝟐𝟐𝟐𝟐𝟐𝟐𝟐(𝑭𝑭)] − 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝒖𝒖𝟐𝟐 +0.0045 RH

4

K p -D= 𝑬𝑬𝑬𝑬𝟎𝟎 − 𝐏𝐏𝐏𝐏 ÷ 𝑬𝑬𝒑𝒑𝒑𝒑𝒑𝒑

3∑(𝒀𝒀 − 𝒀𝒀′ )𝟐𝟐
𝛔𝛔𝒆𝒆𝒆𝒆𝒆𝒆 = �𝑵𝑵

5Where:ET 0 is grass reference evapotranspiration (mm day-1),
R n is net radiation (MJ m-2 day-1), G is soil heat flux
(MJ m-2 day-1), γ is the psychometric constant(kPa °C-1), es is the saturation vapor pressure (kPa),ea is the actual vapor pressure (kPa), ∆ is the slopeof the saturation vapor pressure-temperature curve(kPa °C-1), T is the average daily air temperature(°C), u 2 is the mean daily wind speed at 2 m (m s-1
except in Eq. 4 is Km/day) [2] [14]. T is the mean
monthly temperature (°C), K P -S and K p -D is the pancoefficient, E pan is the pan evaporation (mm day-1), F
is the fetch (m), RH is the daily average humidity(%), σ est is the standard error of the estimate, Y is anactual score (ET pan ), Y' is a predicted score (ET 0 PM), and N is the number of days.

Km/dayWind Speed (u2)
800
700
600
500
400
300
200
100
0

Windy Season

Fig. 1 Daily average wind speed, 2009
Temperature (T)
40
35
30
25
20
15
10
5
0

RESULT AND DESCUSSION°C1) daily variation of metrological variables foryear 2009 is shown in (Fig. 1 to 4).
Wind speed is seen high from around June until
end of September, as the peak occurred in August
more than 500 km/day Fig. 1. This period is known
as the “120-day winds” in the west region of
Afghanistan, especially in Herat province.
Temperature increases from January on, until
medal of August, as the peak is seen over 30 °C.
From August on, the temperature decreases as thelowest rate is seen in December and January lessthan 5 °C Fig 2.
Relative humidity is one of the requirements
factor for K p calculation. Humidity decreases from
April on, as the lowest daily average rate is seen in
July less than 20 %. The peak occurred in December
more than 80 % Fig. 3. Furthermore, Precipitation(PRCP) occurs mostly from December until May as
the peak is seen in March more than 8 mm/day, butfrom May on, there is no any PRCP Fig. 4. This
period coincides with windy season “120-day
winds”.

Windy Season

Fig. 2 Daily average Temperature, 2009
Relative Humididty (RH)

Windy Season

%

100
90
80
70
60
50
40
30
20
10
0

Fig. 3 Daily average relative humidity, 2009

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GEOMATE- Osaka, Nov. 16-18, 2015

Precipitation (PRCP)

15

10

10

20
30

Epan

0

mm/dayWindy Season20

y = 1.2667x - 0.1278
R² = 0.8822

5
0

40

0

5

10

Fig. 4 Daily average precipitation, 2009

20

20

ETpan

15

(mm/day)10

y = 0.9152x + 0.8768
R² = 0.8091

5
0

0

5

10
ET0-PM

15

20

Fig. 8 Regression between ET pan and ET 0 -PM, 2009

ET0-PM
Penman-Montieth

Table 3 Correlated coefficient of E pan and ET pan withET 0 -PM including standard error estimation value

15
10
5

20

Fig. 7 Regression between E pan and ET 0 -PM, 20092) Compression of E pan and ET pan withET 0 -PM.
By comparing them, it has been found that, in thewindy season, form June until September, the ET 0 PM is lower than the E pan , which was measured
directly at the site Fig. 5, whereas the difference issmaller when ET 0 -PM compared with ET pan Fig. 6.
The reason refers to the K p application. ET pan isestimated by using the Eq. 2. K p as a correction
factor is recommended for accurate estimation of
ET pan value.
E
Epan
pan

15

ET0-PM50

coefficients
Windy Season

0

SEE mm/day

ModelsR2a

ET pan0.8091

0.915

2.0

E pan

0.8822

1.267

2.7

Fig. 5 Daily average E pan and ET 0 PM, 2009
ET0-PMET
0-PM

3) In this study, K p is presented as K p -S,
obtained by using Eq.3, which is proposed by
Snyder (1992) and K p -D, which is calculated byusing Eq. 3. Both Kp are compared between eachother, as they show similarity only in windy seasonfrom June until September Fig. 9. By using
correlation method, it has been found that, thecoefficient R2 is 0.0108 Fig. 9.

ETpan
ETpan

20
(mm/day)15
105Windy Season0

KpKp-D
-D

Kp-S
Kp-S

8
6

Fig. 6 Daily average ET pan and ET 0 PM, 20094

It was expected that, the ET pan has higher
correlation with ET 0 -PM than the E pan , but contrary
to what is expected, determination coefficient (R2)of E pan shows higher value with ET 0 -pM (Fig 7 and
8). Furthermore, by using the Eq. 5, SEE of E pan is
obtained less than the SEE of ET 0 -PM Table 3.

Windy Season

20

Fig. 9 Daily K p -D and K p -S, 2009
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GEOMATE- Osaka, Nov. 16-18, 2015

2

According to the Allen et al. (1998), K p is ranged
from 0.35 to 0.85 depends on deferent conditions,but the K p -S has been found ranged from 0.66 to
1.25. On the other hand, as shown in Fig. 11, byincreasing u 2 K p -S and RH decrease. Therefore, therelationship between K p -S and u 2 is shown in Fig.
12, as the R2 value is 0.5986.
Effect of strong wind on K p -S is more than the
RH in windy area like Herat. As it is seen in Fig. 13,
at the windy season, impact of wind factor is larger
than RH.
It was expected that, the effect of u 2 on ET pan ismore than the effect of T and RH, but contrary towhat is expected, by correlating them with ET pan , it
was found that, T has highest R2 which is 0.7981 and
RH has the second R2 value which is 0.6137,
Whereas the u 2 has the lowest R2 value which is0.3631(Fig. 14 to 16).

Kp-S

2

y = -0.0008x + 1.1578
R² = 0.5986

Kp-S

1

0

0

600

800

Fig. 12 Correlation between Kp-S and u2, 2009
0.000376u2-0.0045RH
0.0045/0.000376

60
50
40
30
20
10
0
-10

y = 0.022x + 0.9297
R² = 0.0108

Fig. 13 Comparing of u 2 and RH, 2009
20
2

4
Kp- D

6

8

ETpan (mm/day)

015

Fig. 10 Correlation between K p -D and K p -S
u2
u2 (km/day)(km/d)

RH (%)
RH(%)

Kp-S
KP-S

1000

100

100

10

10
5
0

0

20
ETpan (mm/day)

1-Dec

1-Nov

1-Oct

1-Sep

1-Jul

1-Aug

1-Jun

1-Apr

0

1-May

1

1-Feb

1

1-Mar

10

y = 0.0179x + 1.3975
R² = 0.3631

200

400
u2 (Km/day)

600

800

Fig. 14 Correlation between ETpan and u2, 2009

Windy Season

1-Jan

Km/day400
u2 (Km/day)

1

0

200

Fig. 11Daily average of u 2 , RH and K p -S in 2009

y = 0.4424x - 1.4951
R² = 0.7981

15
10
5
0

0

10

20
T (°C)

30

Fig. 15 Correlation between ETpan and T, 2009

820

40

GEOMATE- Osaka, Nov. 16-18, 2015[3] Bandyopadhyay, A., et al. "Temporal trends inestimates of reference evapotranspiration overIndia." Journal of Hydrologic Engineering 14.5
(2009): 508-515.
[4] Doorenbos, Jan. "Guidelines for predicting cropwater requirements." FAO irrigation anddrainage paper 24, Revised 1977, pp. 15-29.
[5] Ganji H, Rahmany Ab. S, Kajisa T, Kondo M,
Hajime N, “Effect of the 120-day winds” on
Agriculture and Environmental Condition in
Herat, Afghanistan”. Brisbane, Australia. 4th Int.
Conf. on International Conference on
Geotechnique, Construction Materials andEnvironment, 2014, pp. 500.
[6] Ganji H, Rahmany Ab. S, Kajisa T, Kondo M,
Hajime N, “Comparison of the Crop Water
Need between Actual Wind Condition and ZeroWind Simulation; Wind Velocity within 24Hour Interval in Herat, Afghanistan”. In Tokyo
University of Agriculture. 20th Int. Conf. on
ISSAAS, 2014, pp. 91.
[7] Ganji H, Kajisa T, Kondo M, Rostami B,“Examining the six well known equations for
estimating reference evapotranspiration in Herat
Afghanistan”. In Tsu City, Japan. 1th Int. Conf.
on Science & Environment, 2014.
[8] J. M. Norman, 1996: Use of remote sensing forevapotranspiration monitoring over land
surfaces. Hydrol. Sci.—J. Sci. Hydrol., 41, 495–
516.
[9] Kalma, J. D., and I. R. Calder, “Land surface
processes in large scale hydrology”, World
MeteorologicalOrganization
Operational
Hydrology Rep, 1994, pp. 40, 60.
[10] Kustas, W. P., and C. S. T. Daughtry, 1990:
Estimation of the soil heat flux/net radiation
ratio from spectral data. Agric. For. Meteor., 49,
205–223.
[11] Osmanzai, M. 2009. Annual Report. CIMMYT,
Afghanistan.
[12] Rout, Bob, and Cynthia Lee. How the waterflows: a typology of irrigation systems in
Afghanistan. Kabul: Afghanistan Research and
Evaluation Unit, 2008, pp 13.
[13] Snyder, Richard L. "Equation for evaporation
pan to evapotranspiration conversions", J. of
Irrigation and Drainage Engineering, Vol. 118.6.
1992, pp. 977-980.

ETpan (mm/day)

20
15

y = -0.1437x + 12.191
R² = 0.6137

10
5
0

0

20

40

60

80

RH (%)
Fig. 16 Correlation between ETpan and T, 2009100

CONCLUSION
ET adversely affected by strong wind “120-day
winds” as the daily average ET 0 is more than 15 mm.
the reason refers to the climate condition as in thesummer season, the wind speed is strong high, thehumidity is low as less than 20%, Temperature ishigh more than 30 °C and the precipitation is almostzero.
ET pan is found closer to the ET 0 -PM than that of
E pan . Hence, SEE of ET pan with ET 0 -PM is smaller
than that of the E pan .
For daily accurate estimating of ET pan in the west
region of Afghanistan, appropriate K p as acorrection factor is needed. In this study, K p -S,
which is calculated through Snyder proposed
equation, is confirmed appropriate coefficient for
ET pan calculation as it ranges from 0.66 to 1.25. ithas been found that, effect of u 2 on K p -S is morethan RH in windy condition like Herat. Furthermore,
K p -S decreases in windy season when the windspeed is strong.
ACKNOWLEDGEMENTS
Access to the accurate data is still a challenge in
Afghanistan due to the luck of modern stations, but
recently, the obtained data is more accurate than thelast decades.
REFERENCES[1] Allen,
Richard
G.,
et
al.
"Crop
evapotranspiration-Guidelines for computing
crop water requirements-FAO Irrigation anddrainage paper 56" FAO, Rome , Vol. 300(9),year 1998.
[2] A Qureshi, Asad Sarwar. Water resourcesmanagement in Afghanistan: The issues andoptions. Vol. 49. IWMI, 2002.

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Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

HEAVY METAL CONCENTRATIONS OF SEAWEED FROM OSAKABAY TO SOUTH OF KII PENINSULA, JAPAN
Hiroyuki Ii1 and Miho Kohara2
Faculty of Systems Engineering, Wakayama University, Japan; 2Nishimatsu Construction, Japan

1

ABSTRACT
Seaweed from locations ranging from Osaka Bay to the southern top of the Kii Peninsula facing the Kurosio
Current at the Kii Peninsula were measured for concentrations of arsenic (As), strontium (Sr), zinc (Zn), iron(Fe), manganese (Mn), and lead (Pb). Except for one location at Kanayama neighboring a closed Pb mine,
concentrations in seaweed were variable but their average values agreed with world data. Fe concentration inbrown algae and Sr concentrations in green algae were high at the northern Kii Peninsula while Asconcentrations in seaweed and Zn concentrations in red and green algae were also high. Other seaweed metal
concentrations at both the south and north sections of Kii Peninsula had almost the same values. On the whole,
As and Sr concentrations in brown algae and Fe concentrations in green algae were high. Zn, Mn, and Pb
concentrations in brown, red and green algae had almost the same values. Zn and Fe concentration in seaweed at
Kanayama was remarkably high being 10 to 100 times that of the world value. Mn and Pb concentrations inseaweed at Kanayama were also 10 times higher than world values.
Keywords: Seaweed, metal concentration, mine waste, Kii Penisula and Kanayama

INTRODUCTION
Many papers detailing metal concentrations invarious kinds of seaweeds have been published [1]–[2]. The relationship between time or sea metal
concentration and the bio-concentration factor was
clarified. Metal concentrations in seaweed at heavily
contaminated areas were published and metal
concentrations in water and contaminated soil werealso described [3]–[5]. The Kii Peninsula in central
Japan, neighbors the metropolises of Osaka andKobe at the north and contacts the Kuroshiro
Current in the south. Two different water resources,
metropolitan sewage water and the Kuroshio Current,
flow past the Kii Peninsula. Waste water from the
now closed Pb Kanayama mine, located to the south,
flows directly into Kanayama Bay [6]. Metal
concentrations in seaweed were measured under
several different sea conditions to clarify theirrelationship with that particular sea condition.

Fig. 1 Sampling points at the Kii Peninsula
Saikasaki May 2014, Tagui, February 2015, Senjyo
and Senjyo N April 2013, Kanayama May 2013,
Hachijyuu June 2014, and Susami, Tako andKiitahara April 2015. Sampled seaweed was driedthen dissolved with a concentrated nitric acidsolution. Metal concentrations in the solution after
filtration were analyzed by ICP-AS.

METHOD AND CONDITION
Fig.1 shows 14 sampling points, Ozaki, Misaki,
Tomogasima Island north, Tomogasima Island west,
Tomogasima Island south, Kada, Saikasaki, Tagui,
Senjyo north, Senjyo, Kanayama, Hachijyuu,
Susami, Tako and Kiitahara at the Kii Peninsula. All
sampling points, except for Kanayama, were on the
coast facing the outside sea.
Sampling dates were Ozaki March 2015, Misaki
and Tomogasima January 2015, Kada March 2014,

Kanayama Sampling Point
Kanayama sampling point was inside Kanayama
Bay. Drainage water from two veins of Kanayama
mine flows into Kanayama Bay. One drainage water
pathway flows into the bay through the river.
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GEOMATE- Osaka, Nov. 16-18, 2015

3 and 4 show the Fe, Pb and Mn loads of river andspring. Fe load from the spring increased, from
2,000 to 4,000 kg per year and Fe load from the rivervaried at about 2,000 kg per year. Then total Fe load
was about 4,000 to 6,000 kg per year. Pb load fromthe spring was stable, 10 kg per year and Pb loadfrom the river varied around 10 kg per year. Then
total Pb load was about 20 kg per year. Mn loadfrom the spring was stable at 80 kg per year and Mn
load from the river varied around 100 kg per year.
Then total Mn load was about 180 kg per year.

20000
River Fe(kg/year)
spring Fe(kg/year)
Fe(kg/year)

15000

10000

5000

Order and Species of Sampled Seaweed
4/1/2014

4/1/2013

4/1/2012

4/1/2011

4/1/2010

4/1/2009

0

Many kinds of seaweed were sampled and theorder of representative seaweed was listed as shownin Fig, 5, 6, 7, 8, 9, and 10. Order (species) of
sampled brown algae were Laminariale 1 (Undaria
pinnatifida), Laminariale 2 (Ecklonia cava, Undaria
undarioides, Agarum clathratum), Fucales 1(Sargassum muticum, Myagropsis myagroides,
Sargassum ringgoldianum), Fucales 2 (Sargassum
fusiforme), Fucales 3 (Sargassum thunbergii),
Fucales 4 (Sargassum piluliferum), Dictyotales 1(Dictyota dichotoma), Dictyotales 2 (Dictyopteris
undulata, Dictyopteris prolifera), Ishigeales (Ishige
foliracea, Ishige okamurae), Dictyotales 3 (Padina
arborescens), Scytosiphonales 1 (Myelophycus
simplex),
Scytosiphonales
2(Scytosiphon
lomentaria), Scytosiphonales 3 (Colpomenia sinuosa,Colpomenia peregrine), and Scytosiphonales 4(Petalonia binghamiae).
Order (species) of red algae was Gigartinales 1(Chondracanthus
tenellus,
Chondracanthusintermedius), Gigartinales 2 (Grateloupia asiatica,
grateloupia livida), Gigartinales 3 (Grateloupia
lanceolata, Polyopes affinis, Polyopes prolifer,
Polyopes lancifolius), Gigartinales 4 (Grateloupia
elliptica, Chondrus ocellatus, Chondrus verrucosus),
Gigartinales 5 (Gloiopeltis complanata), Gigartinales
6 (Ahnfeltiopsis flabelliformis), Gigartinales 7(Gloiopeltis furcata), Gigartinales 8 (Caulacanthus
ustulatus), Gigartinales 9 (Bonnemaisonia hamifera),
Gelidiales (Gelidium crinale, Gelidium japonicum,
Pterocladiella tenuis), and Bangiales (Pyropia
yezoensis, Pyropia suborbiculata).
Order (species) of green algae were
Cladophorales
1(Cladophorawrightana),
Cladophorales 2 (Chaetomorpha gracilis), Ulvales 1(Ulva intestinalis, Ulva prolifera), and Ulvales 2( Ulva pertusa, Monstroma nitidum).

Fig. 2 Fe load of Kanayama mine.
250
River Pb(kg/year)
spring Pb(kg/year)

Pb (kg/year)

200
150
100
50

4/1/2014

4/1/2013

4/1/2012

4/1/2011

4/1/2010

4/1/2009

0

Fig. 3 Pb load of Kanayama mine.
600

Mn (kg/year)

500

River Mn(kg/year)
spring Mn(kg/year)

400
300
200
100

4/1/2014

4/1/2013

4/1/2012

4/1/2011

4/1/2010

4/1/2009

0

Fig. 4 Mn load of Kanayama mine.
The other drainage pathway is a spring on the coast
facing the bay. Total Zn and Cu loads were 3,000 to
5,000 kg per year and several kg per year [6]. Fig 2,

RESULTS
823

GEOMATE- Osaka, Nov. 16-18, 2015a kind of Bangiales, Pyropia yezoensis, called “Nori”
in Japanese were several10 ppm. As concentration in
the very popular green algae food, Ulvales 1, Ulva
intestinalis, Ulva prolifera and Ulvales 2, Ulva
pertusa, Monstroma nitidum, called “Aonori” inJapanese was 0.4 to 100 ppm. In particular, Asconcentrations in seaweed at Kanayama were nothigh compared to other areas.

As Concentration in Seaweed

Laminariale
1, 2
Fucales
1, 2, 3, 4
Dictyotales 1,2
Ishigeales
Dictyotales 3
Scytosiphonales
1, 2, 3, 4
Gigartinales
1,2,3,4,5,6,
7,8,9
Gelidiales
Bangiales
Cladophorales1,2
Ulvales 1,2

0.1

Tag
Se N
Se
Hac
Susa
Tako
Kita
Kan

Sr Concentration in Seaweed

1

10

100

1000

100

1000

Sr concentrations in brown algae at the north andsouth Kii Peninsula were 50 to 4,000 ppm and 70 to
4,000 ppm as shown in Fig.6. However, they werevery high, 500 to 4,000 ppm and 400 to 4,000 ppm
excluding Scytosiphonales 3 and Scytosiphonales 4.
Sr concentrations in red algae at the north and south
Kii Peninsula were 30 to 1,000 ppm and 30 to 1,000
ppm however most Sr concentrations in red algae
were 30 to 200 ppm and 30 to 300 ppm. As
concentrations in green algae at the north and south
Kii Peninsula were 40 to 2,000 ppm and 70 to 400
ppm.
Sr concentration in brown algae was much high.
Therefore, analysis of brown algae forconcentrations of Sr was found to be an effective
method of determining levels of radioactive Srcontamination in seawater surrounding atomic
power stations.

As (ppm)plant
Laminariale
1, 2
Fucales
1, 2, 3, 4
Dictyotales 1,2
Ishigeales
Dictyotales 3
Scytosiphonales
1, 2, 3, 4
Gigartinales
1,2,3,4,5,6,
7,8,9
Gelidiales
Bangiales
Cladophorales1,2
Ulvales 1,2

0.1

Oza
Mis
TI S
TI W
TI N
Kad
Sai

1

10
As (ppm)plant

Laminariale
1, 2
Fucales
1, 2, 3, 4
Dictyotales 1,2
Ishigeales
Dictyotales 3
Scytosiphonales
1, 2, 3, 4
Gigartinales
1,2,3,4,5,6,
7,8,9
Gelidiales
Bangiales
Cladophorales1,2
Ulvales 1,2

Fig.5 As concentrations in seaweed at north andsouth Kii Peninsula.
As concentrations in brown algae at the northand south Kii Peninsula were 10 to 300 ppm and 4 to
1,000 ppm as shown in Fig.5. As concentrations in
red algae at the north and south Kii Peninsula were
0.3 to 40 ppm and 0.4 to 100 ppm. As concentrations
in green algae at the north and south Kii Peninsula
were 0.5 to 20 ppm and 1 to 100 ppm. As
concentration in seaweed at the South Kii Peninsula
was higher than that at the north of Kii Peninsula
and As concentrations in brown algae was higher
than those in green and red algae.
As concentration in brown algae, a kind of
Fucales 1, Sargassum muticum, Myagropsis
myagroides, and Sargassum ringgoldianum and akind of Fucales 2, Sargassum fusiforme and a kindof Fucales 3, Sargassum thunbergii were much high,several 100 ppm. Sargassum fusiforme is a very
popular brown algae food known as “Hijiki” inJapanese. Furthermore, As concentration in a kind of
Laminariale 1, Undaria pinnatifida or “Wakame”, avery popular seaweed food, in Japanese was 10 toless than 100 ppm. As concentrations in anotherbrown
algae,
Dictyotales,
Ishigeales
andScytosiphonales were several to several 10s ppm andnot very different from those of red and green algae.
As concentration in a very popular red algae food,

Oza
Mis
TI S
TI W
TI N
Kad
Sai

10

100

1000

104

Sr (ppm)plant
Laminariale
1, 2
Fucales
1, 2, 3, 4
Dictyotales 1,2
Ishigeales
Dictyotales 3
Scytosiphonales
1, 2, 3, 4
Gigartinales
1,2,3,4,5,6,
7,8,9
Gelidiales
Bangiales
Cladophorales1,2
Ulvales 1,2

10

Tag
Se N
Se
Hac
Susa
Tako
Kita
Kan

100

1000

104

Sr (ppm)plantFig.6 Sr concentrations in seaweed at north andsouth Kii Peninsula.
824

GEOMATE- Osaka, Nov. 16-18, 2015

On the whole, Sr concentrations at the south Kii
Peninsula were as the same as those at the north Kii
Peninsula excluding Ulvales 1,2 at Kada. In
particular, Sr concentration in seaweed at Kanayama
was not high compared to other areas. Srconcentrations of green algae at Kada were moderate
to high.

at the north and south Kii Peninsula were 10 to 200
ppm and 4 to 1,000 ppm excluding seaweed at
Kanayama. Zn concentration of green algae at thenorth and south Kii Peninsula were 7 to 70 ppm and
5 to 200 ppm excluding seaweed at Kanayama.
Therefore Zn concentrations at the south Kii
Peninsula were as the same as those at the north Kii
Peninsula and the Zn concentration difference
between brown, red and green algae were found tobe small excluding seaweed at Kanayama.

Zn Concentration in Seaweed
Laminariale
1, 2
Fucales
1, 2, 3, 4
Dictyotales 1,2
Ishigeales
Dictyotales 3
Scytosiphonales
1, 2, 3, 4
Gigartinales
1,2,3,4,5,6,
7,8,9
Gelidiales
Bangiales
Cladophorales1,2
Ulvales 1,2

Oza
Mis
TI S
TI W
TI N
Kad
Sai

100

10

1

Laminariale
1, 2
Fucales
1, 2, 3, 4
Dictyotales 1,2
Ishigeales
Dictyotales 3
Scytosiphonales
1, 2, 3, 4
Gigartinales
1,2,3,4,5,6,
7,8,9
Gelidiales
Bangiales
Cladophorales1,2
Ulvales 1,2

104

1000

10

Oza
Mis
TI S
TI W
TI N
Kad
Sai

100

Zn (ppm)plant

10

100

1000

104

105

Fe (ppm)plant

Laminariale
1, 2
Fucales
1, 2, 3, 4
Dictyotales 1,2
Ishigeales
Dictyotales 3
Scytosiphonales
1, 2, 3, 4
Gigartinales
1,2,3,4,5,6,
7,8,9
Gelidiales
Bangiales
Cladophorales1,2
Ulvales 1,2

1

104

1000

105

Tag
Se N
Se
Hac
Susa
Tako
Kita
Kan

Laminariale
1, 2
Fucales
1, 2, 3, 4
Dictyotales 1,2
Ishigeales
Dictyotales 3
Scytosiphonales
1, 2, 3, 4
Gigartinales
1,2,3,4,5,6,
7,8,9
Gelidiales
Bangiales
Cladophorales1,2
Ulvales 1,2

106

10

Tag
Se N
Se
Hac
Susa
Tako
Kita
Kan

100

1000

104

105

106

Fe (ppm)plant

Zn (ppm)plantFig. 8 Fe concentrations in seaweed at north andsouth Kii Peninsula.

Fig. 7 Zn concentrations in seaweed at north andsouth Kii Peninsula.

Fe Concentration in Seaweed

As shown in Fig.7, Zn concentrations in brown,
red, and green algae at Kanayama were several
1,000s to 30,000, 2,000 to 30,000, and 500 to 30,000
ppm. These values were much higher than those atother areas. On the whole, Zn concentrations inseaweed at the Senjyo and Senjyo N were 10s to
several 1,000s ppm and higher than those at theother area. The Senjyo and Senjyo N points were 1.2
km north of Kanayama sampling point and close to alarge hot spring. The Hachijyu point was 1.6 km
south of Kanayama sampling point. Zn
concentration at Hachijyu was not as high comparedto other areas.
Total Zn load into Kanayama Bay was 3,000 to
5,000 kg per year, influence of mine waste water on
Zn concentration in seaweed in Kanayama Bay was
found. Zn concentrations in seaweed at the Senjyo
and Senjyo N were thought to be influenced by mine
waste water or hot spring water. Zn concentrationsin brown algae at the north and south Kii Peninsula
were 5 to 200 ppm and 3 to 200 ppm excluding
seaweed at Kanayama. Zn concentration in red algae

Fe concentrations in brown, red, and green algae
at Kanayama were several 400 to 10,000, 2,000 to
50,000, and 1,000 to 200,000 ppm and much higher
than those at other areas as shown in Fig.8.
Total Fe load into Kanayama Bay was 4,000 to
6,000 kg per year and large as was the Zn load.
However, Fe concentrations in seaweed at the
Senjyo, Senjyo N and Hachijyu were not always
high relative to other areas. Therefore, high Fe
concentration in seaweed was found to be just at
Kanayama Bay in spite of large amount of Fe load.
Fe concentrations in brown algae at the north andsouth Kii Peninsula were 30 to 3,000 ppm and 20 to
800 ppm excluding seaweed at Kanayama. Fe
concentration in red algae at the north and south Kii
Peninsula were 20 to 3,000 ppm and 10 to 3,000
ppm excluding seaweed at Kanayama. Fe
concentration in green algae at the north and south
Kii Peninsula were 200 to 7,000 ppm and 80 to
4,000 ppm excluding seaweed at Kanayama.
825

GEOMATE- Osaka, Nov. 16-18, 2015

Therefore, Fe concentrations at the south Kii
Peninsula were the same as those at the north Kii
Peninsula excluding seaweed at Kanayama and ingeneral, Fe concentrations in green algae was higher
than those in brown and red algae at the both
Kanayama and other areas. “Aonori”, Ulvales 1,
Ulva intestinalis, Ulva prolifera and Ulvales 2, Ulva
pertusa, Monstroma nitidum, was relatively high,
100 to several 1,000s ppm.

Pb Concentration in Seaweed
Total Pb load into Kanayama Bay was 20 kg per
year. Pb concentrations in brown, red, and green
algae at Kanayama were 4 to 400, 30 to 1,000, and
4 to 1,000 ppm and much higher than those at otherareas as shown in Fig.10. Pb concentrations inbrown algae at the north and south Kii Peninsula
were 0.2 to 20 ppm and 0.2 to 10 ppm excluding
seaweed at the Kanayama. Pb concentration in red
algae at the north and south Kii Peninsula were 0.3
to 40 ppm and 0.1 to 10 ppm excluding seaweed at
Kanayama. Pb concentrations in green algae at thenorth and south Kii Peninsula were 2 to 40 ppm and
0.5 to 20 ppm excluding seaweed at Kanayama.
Therefore, Pb concentrations at the south Kii
Peninsula were lower than those at the north Kii
Peninsula excluding seaweed at Kanayama in spiteof Pb load, 20kg at Kanayama, south Kii Peninsulaand Pb load at Kanayama was found to be small at
the Kii Peninsula. On the whole Pb concentrations inbrown algae were lower those in green and red algae
at Kanayama however no clear differences between
brown, red and green algae were found in the otherareas.

Mn Concentration in Seaweed
Total Mn load into Kanayama Bay was 180 kg
per year. Mn concentrations in brown, red, and green
algae at Kanayama were 7 to 500, 40 to 8,000, and 4
to 8,000 ppm and much higher than those at otherareas as shown in Fig.9. Mn concentrations in brownalgae at the north and south Kii Peninsula were 4 to
300 ppm and 2 to 400 ppm excluding seaweed at
Kanayama.
Mn concentrations in red algae at the north andsouth Kii Peninsula were 5 to 400 ppm and 1 to 300
ppm excluding seaweed at Kanayama. Mn
concentrations in green algae at the north and south
Kii Peninsula were 20 to 700 ppm and 6 to 200 ppm
excluding seaweed at Kanayama. Therefore, Mnconcentrations at the south Kii Peninsula were thesame as those at the north Kii Peninsula excluding
seaweed at Kanayama and generally Mn
concentrations in green, brown and red algae at theboth Kanayama and other areas were the same.
Laminariale
1, 2
Fucales
1, 2, 3, 4
Dictyotales 1,2
Ishigeales
Dictyotales 3
Scytosiphonales
1, 2, 3, 4
Gigartinales
1,2,3,4,5,6,
7,8,9
Gelidiales
Bangiales
Cladophorales1,2
Ulvales 1,2Laminariale
1, 2
Fucales
1, 2, 3, 4
Dictyotales 1,2
Ishigeales
Dictyotales 3
Scytosiphonales
1, 2, 3, 4
Gigartinales
1,2,3,4,5,6,
7,8,9
Gelidiales
Bangiales
Cladophorales1,2
Ulvales 1,2

0.1

Oza
Mis
TI S
TI W
TI N
Kad
Sai

1

1

10

100

Pb (ppm)plant

10

100

Laminariale
1, 2
Fucales
1, 2, 3, 4
Dictyotales 1,2
Ishigeales
Dictyotales 3
Scytosiphonales
1, 2, 3, 4
Gigartinales
1,2,3,4,5,6,
7,8,9
Gelidiales
Bangiales
Cladophorales1,2
Ulvales 1,21000

Mn (ppm)plant
Laminariale
1, 2
Fucales
1, 2, 3, 4
Dictyotales 1,2
Ishigeales
Dictyotales 3
Scytosiphonales
1, 2, 3, 4
Gigartinales
1,2,3,4,5,6,
7,8,9
Gelidiales
Bangiales
Cladophorales1,2
Ulvales 1,2

1

Oza
Mis
TI S
TI W
TI N
Kad
Sai

Tag
Se N
Se
Hac
Susa
Tako
Kita
Kan

0.1

Tag
Se N
Se
Hac
Susa
Tako
Kita
Kan

1

10

100

1000

104

Pb (ppm)plantFig.10 Pb concentrations in seaweed at north andsouth Kii Peninsula
10

100

1000

104

Mn (ppm)plantFig. 9 Mn concentrations in seaweed at north andsouth Kii Peninsula.
826

GEOMATE- Osaka, Nov. 16-18, 2015

Table 1 Metal concentration (ppm) of seaweed at
north and south Kii Peninsula
site
brown
red
green
As
north
10~ 300
0.3~400.5~ 20
south 4 ~ 1000 0.4 ~ 100
1 ~ 100
Ref
62
6
1.2~6
Sr
north 50~4,000 30~1,000 40~2,000
south 70~4,000 30~1,000
70~ 400
Ref
800~
95~45060~220
1,600
Zn
north
5 ~ 200
10 ~ 200
7~ 70
south
3 ~ 200
4 ~ 1,000
5 ~ 200
Kan
1,000~
2,000~
500~
30,00030,00030,000Ref
20~14021~1506~260
Fenorth 30~3,000 20~3,000
200~
7,000
south
20~80010~3,000
80~
4,000
Kan
400~
2,000~
1,000 ~
10,00050,000200,000
Ref
13~1,900
200~
160~750
1,800
Mn north
4 ~ 300
5 ~ 400
20 ~ 700south
2 ~ 400
1 ~ 300
6 ~ 200
Kan
7~500
40~8,000
4~ 8,000
Ref
1~400
16~50016~300Pb
north
0.2 ~ 20
0.3 ~ 40
2 ~ 40south
0.2 ~ 100.1 ~ 100.5 ~ 20Kan
4 ~ 400, 30~1,000
4~ 1,000
Ref
2~38
2.9
40

than at the south Kii Peninsula and As
concentrations in seaweed and Zn concentrations in
red and green algae at the south Kii Peninsula werehigher than at the north Kii Peninsula. Other
seaweed metal concentrations at both the south andnorth Kii Peninsula had almost the same values.
Although As, Sr, Zn, Fe, Mn, and Pb
concentrations in seaweed from Osaka By to south
top of the Kii Peninsula facing the Kurosio Current
at the Kii Peninsula were very variable, their averagevalues were in good agreement with the world data
except for Kanayama. Therefore, As, Sr, Zn, Fe, Mn,
and Pb concentrations in seaweed at the Kii
Peninsula were normal values. In general, metal
concentration distribution was uniform therefore theinfluence of the Kuroshiro Current and Osaka Bay
facing the metropolis on metal concentration inseaweed was found to be small. As and Srconcentrations in brown algae were higher those in
red and green algae and Fe concentrations in green
algae were higher than those in brown and red algae.
Zn, Mn, and Pb concentrations in brown, red andgreen algae were all the same.
REFERENCES[1] Lunde G, “Analysis of trace elements inseaweed”, J. of Sci. Fd. Argic., Vol. 21, Aug.
1970, pp. 416-418.
[2] Yamamoto T, and Ishibasi M, “The Content ofTrace Elements in Seaweeds”, in Proc. 7th Int.
Seaweed Symposium, 1971, pp. 511-514[3] Ghada F El-Said and Amany El-Sikaily,“Chemical composition of some seaweed from
MediterraneanSeacoast,
Egypt”,
Environmental Monitoring and assessment, July
2013, Vol. 185, Issue7, pp 6089-6099[4] Alahverdi M and Savabieasfahani M, “Metal
Pollution in Seaweed and related Sediment of
the Persian Gulf, Iran”, Bull Environ. Contam.
Toxicol, Vol. 88, 2012, pp. 939-945.
[5] Griselda MRF, Evgueni S and Ignacio SR,“Heavy metal Pollution monitoring using thebrown seaweed Padina durvillaei in the coastalzone of the Santa Rosalia mining region, Bajacalfornia Peninsula, Mexico”, J. Appl. Phycol.,Vol. 21, 2009, pp. 19-26.
[6] Ii H, “Cu, Zn and As contamination of seaweed
beside Shizuki and Kanayama metal mines inJapan”, Int. J. of GEOMATE, Vol. 9, No.1, Sep.
2015, pp. 1411-1417.
[7] Asami T. and Chino M., “Elements inorganism”, Environmental Chemistry of the
Elements, 1st ed. Hakuyusha, 1983, pp. 101–
105. In Japanese.

DISCUSSION AND CONCLUSION
Table 1 shows As, Sr, Zn, Fe, Mn, and Pb andconcentrations in brown, red and green algae
between the north Kii Peninsula (north), the SouthKii Peninsula (south) excluding Kanayama,
Kanayama (Kan) and the world values (Ref) [7].
Metal concentrations in seaweed at Kanayama
showed abnormally high values. The Kanayama
sampling point was inside the bay and total Fe, Zn,
Mn, and Pb loads of mine waste water from theclosed mine into the bay were 4,000 to 6,000, 3,000
to 5,000, 180, and 20 kg per year. Zn and Fe
concentrations in seaweed at Kanayama were 10 to
100 times relative to the world value. Mn and Pb
concentrations in seaweed at Kanayama were 10
times higher than the world values. Fe
concentrations in brown algae and Sr concentrations
in green algae at the north Kii Peninsula were higher
827

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

DISTRIBUTION CHARACTERISTICS OF THE LOAD AMOUNT OFNUTRIENTS, INCLUDING AT THE FLOODS TIME IN YAMATO
RIVER BASIN
Masanobu Taniguchi1 ,Hiroyuki Ii1, Tatemasa Hirata1
Facult of Systems engineering,Wakayam University, Japan;

1

ABSTRACT
It is important that outflow to the Osaka Bay at the time of the flood of nutrients in the Yamato River. In orderto the form of nitrogen and phosphorus nutrition is changed by Purification treatment of sewage treatment plantand purifying effect of rivers, it is necessary to clarify the distribution of nutrients amount in Yamato River.
Until now load analysis using LQ formula and intensity method has been carried out, the error is large. This
study clarify the runoff characteristics of nutrients at the time of the flood using the data of automatic stations ofgovernment, and suggested new method of calculation of flow rate. As a result, the nitrogen load of the YamatoRiver basin was calculated. As a result, the flood condition of nitrogen load accounts for 45% of annual. The
nitrogen load of treated water of sewage system is 23%.
Keywords: the Yamato River, nitrogen load, flow rate, distribution, flood

INTRODUCTIONNThe Yamato River had problem of water quality
in 1970s by High-growth period. In the resent years,
the water quality is improved by installation of
sewage system in the whole of the Nara Basin.
However, the total of nitrogen load is not reduced in
the Yamato River, the river has been eutrophication.
The riverbed of part region of the river basin is
discolored black, so it is estimated that oxygen in thewater is insufficient. It is important to understand the
distribution of amount of nitrogen load.
The nitrogen load in flood condition is unknown.
The organic component is collected by low flow infine day at spot. The riverbed of high nitrogenconcentration flows by the flood, and the amount ofwater is much, so the nitrogen load in the flood
condition is estimated high. The survey in the flood
condition is danger, and automatic observing station
is not measured the total nitrogen concentration.
Therefore the total nitrogen concentration isestimated by another data, and analyzed relation
between the nitrogen concentration and the otherwater quality data.
The research object is the distribution of total
nitrogen load including the flood condition.

Tatsuta River Nara City

Osaka City

Saho River

Yamato River
Asaka
Kunitoyo Brdge

Kasaoki Mountainkamenose

Osaka Ba

Nukatabe Bridge

Kawachi Brdge
Ishikawa
Brdge
Nishioki River
Ishi River
Osaka Pref.

Fujii

Hase River(Source of the Yamato River）

Nara Pref.
Soga River

0

Fig. 1

5

10 km

Research area information of the YamatoRiver Basin

The area of the Yamato River basin is 1070km2. The
population of the basin is 200 million people. The
basin has big cities in upper stream, and the
population of the Nara basin is 109 million people.
Population has increased significantly in highgrowth period. Therefore the sewage system isestablished from 1970s. The sewage systemdiffusion rate is 7.4% in 1975, and is 33% in 1990,
and is 58.7% in 2000, and is 85.3% in 2013. The
river purification facilities are established as thewater quality of improvement from 1994.
The analysis of nitrogen load uses the water
information system of Ministry of Land,
Infrastructure, Transport and Tourism, Japanese
Government. To calculate the correct flow rates,
Wakayama University surveyed the river crosssection and flow velocity. The flow rate of null data
are calculated by the new manning method that issuggested in this research. Some observing stations

THE SERARTH AREA AND THE METHOD
Figure 1 shows research area information of the
Yamato River Basin. The land use map uses
Ministry of Land, Infrastructure and Transport
Ministry of Land numerical information
Land use mesh (Heisei 18, 2006)

828

GEOMATE- Osaka, Nov. 16-18, 2015

have both of water level and flow rate, and therecent data are not updated in near 2 or 3 years. It isnecessary to interpolate the flow rate, to calculate
the nitrogen load in whole a year. The calculated
flow rate is compared with the upper stream and thelower stream, it confirmed the consistency.

1975 to 2010．The usual ammonia nitrogen load infine day is average 16g/sec, and is tend to decrease
mostly from 1975 to 2010 ． The sewage systemdiffusion rate is increased from 7% to 85% in thisterm, however the total nitrogen load is not littlechanged constantly. The cause is estimated thatammonia nitrogen is changed to nitrate nitrogen,total nitrogen is not changed.

THE TIME SERIES OF FLOW RATE ANDNITROGEN LOAD IN REAL DATATHE ANALYSIS OF T-N LOAD AT FUJII AND
ESTIMATION OF FLOW RATE

2000

The Fujii observing station is important for calculate
the runoff of the whole Nara Basin , as the lowest
point in the Nara Basin. However the Fujii has noflow rate data in 2011. It is necessary to estimate theflow rate.
2013-01

2010-01

2004-01

2007-01

2001-01

1998-01

1995-01

1992-01

1989-010

The time series of flow rate at Fujii(lower
stream in Nara Basin)

Distance

Fig. 2

1986-010

1983-01

1000

1980-01

Flow rate(m3/sec)

Flow rate(m3 /sec)

Figure 2 shows the time series of flow rate in
Fujii. The observing station is lower stream in the
Nara Basin, and this point is all collected water in
the Nara Basin. The observing station have both ofwater level and flow rate, and have automatic water
quality measurement, and many water quality items.
The max of flow rate is about 1200m3/sec in 1982.
The usual flow in fine day is about 13m3/sec, and themonthly data is measured by around the value offlow rate. The value is calculated by 50% of the rankof the flow rate, as the value is less than at 185 daysin 365 days. The most new data is the data in 2010,is no data in after 2011.
400

Average Depth 19.4 cm
Breadth 29.2 m
Area 5.4 m3
Average velocity 1.37 m/sec
Flow rate 7.32 m3/sec
Gradient 0.175
a 0.034
Water level correction -0.260

10

20
0

Fig. 3100
Distance from the base point200

The river cross section at Fujii

The actuary cross section is possible to calculate
the flow rate from water level. The velocity is
calculated from follow equation.
1

1

v = 1n a 2 H 6(1)
v:velocity, n:Roughness coefficient, a:Eigenvalue ofobservatory, H:water depth

NO3 - -N load(g/sec)
NH4 + -N load(g/sec)
T-N load(g/sec)

The equation is derived by the Manning equation.
The Manning method uses Hydraulic radius(R), it ispossible to R replace H in generally in big rivers.
The Manning equation can be modified thefollowing equation. ,200

Fig. 32013-01

2010-01

2007-01

2004-01

2001-01

1998-01

1995-01

1992-01

1989-01

1986-010

1983-01

100

1980-01

T-N load(g/sec)

300

1

3

2

v 2 = n12 H 3 H 3 I 2The time series of nitrogen load at Fujii(2)
Figure 3 shows the time series of the nitrogenload at Fujii. The nitrogen load is calculated by thereal measured data, and not include the nitrogen loadin the flood condition. So, it is estimated that
nitrogen load is more big value in annual. The usual
total nitrogen load in fine day is average 63g/sec,
and is tend to decrease mostly from 1975 to 2010．
The usual nitorete nitrogen load in fine day isaverage 29g/sec, and is tend to increase mostly froma = HI(3)
I: Hydraulic gradientEquation (1) is made by substituting the equation(3) into equation (2).As the bot of flow rate andwater level is measured in Fujii, the area of the riveracross section is calculated by the water level. The

829

GEOMATE- Osaka, Nov. 16-18, 2015

average velocity of the river cross section in a day isdetermined by dividing the flow rate in the area.
Unknowns become one of parameter I. The
parameter I is calculated by the Manning method.
The parameter a is calculated by equation (3), andfigure 4 shows the relation between average water
depth and Hydraulic gradient (I).
1

Hydraulic gradientHydraulic gradient(a=0.034)
Hydraulic gradientFig. 6 The relation between water level and EC bymonthly data from 1989 to 2011 at Fujii
0

0

100

400

300

200

Figure 6 shows the relation between water level
and EC by monthly data from 1989 to 2011 at Fujii.
T-N concentration is not measured every day,moreover there are little data in annual. However EC
value is measured every day by automatic observing
station. EC value is measured at flood time. EC
value is decrease with water level and the relation
between water level and EC showed the same tendfrom 1989 to 2011.

Average depth(cm)Fig. 4

The relation between average water depth
and Hydraulic gradient (I) at Fujii in 2010

The average parameter a can be calculated byannual data or some major depth. Now is annual
data, parameter a is determined 0.034 at Fujii. The
water level is fitting to the survey depth, the current
value is -0.26m, and the roughness coefficient (n) is
0.100 in general apply for the natural river.
400

300

200

2011-122011-11

2011-10

2011-09

2011-08

2011-072011-06

2011-05

2011-04

2011-030

2011-02100

2011-01

Flow rate(m3/sec)

Fjii(m3 /sec)Ouji(m3 /sec)

Fig. 5The time series of the flow rate at Fujii andat OjiFigure 5 shows the time series of the flow rate at
Fujii and at Oji. Oji is upper stream of Fujii, and asmall river flows into between Oji and Fujii. It was
confirmed the calculated flow at Fujii is almost
exactly.

Fig. 7 The relation between water level and T-Nconcentration by monthly data from 1989 to 2011
at Fujii
Figure 7 shows the relation between water level
and T-N concentration by monthly data from 1989
to 2011 at Fujii. T-N concentration is decrease withwater level and the relation between water level andEC showed the same tend from 1989 to 2011
Therefore T-N concentration and EC value is same
Tend, T-N concentration and EC value is are
dependent on the water level.
830

GEOMATE- Osaka, Nov. 16-18, 2015(5)
(6)
Equation (6) is made by substituting the equation(4) into equation (5).
400T-N量(g/sec)WL
T-N量(g/sec)EC

T-N量(g/sec)WL

300

200

2011-122011-10

2011-11

2011-09

2011-08

2011-072011-06

2011-04

2011-05

2011-02

2011-030

2011-01

100

Fig. 10 The time series of T-N load (g/sec) at Fujii

Fig. 8 The relation between water level and EC byevery day data in 2011 at Fujii

Figure 10 shows the time series of T-N load at
Fujii calculated from EC value. The annual T-N load
is calculated by the sum of the flow rate times the TN concentration. The result of annual T-N load at
Fujii is calculated as 1501t/year.

Figure 8 shows the relation between water level
and EC value by every day data in 2011 at Fujii. The
EC value is decrease with Water level. The
approximate line between Water level and EC value
shows by next equation.

T-N LOAD AT KASHIHARA
500

(4)

T-N(g/sec)EC

T-N(g/sec)EC

400

7
T-N(mg/l) = 3.369 / Water level(m)6

T-N(mg/l)

300
200
100

Fig. 9

3
2

0

1

2

3

4

Water level(m)Fig. 92011-122011-11

2011-10

2011-09

2011-08

2011-072011-06

2011-05

2011-04

2011-03

The time series of T-N load (g/sec) at
Kashihara

The Kashihara observing station is located atlowest stream in the Yamato River as non-influenceof sea. The Kashihara station has both of theflowrate and the water level. The approximate line
between T-N concentration and EC value shows by
next equation.

1
0

2011-020

4

2011-01

T-N(mg/l)

5

The relation between water level and T-Nconcentration by every day data in 2011 at
Fujii

(7)
The result of annual T-N load at Kashihara iscalculated as 1836t/year. The flow rate in fine daymeans the flow rate (75% flow) not less than this 95
days through 1 year. The nitrogen load in flood
condition is calculated by divided by 75% flow. As aresult, the nitrogen load in flood condition is835t/year, and average load is 108g/sec. The

Figure 9 shows the relation between water level
and T-N concentration by every day data in 2011 at
Fujii. The T-N concentration is decrease with Water
level. The approximate line between Water level andEC value shows by next equation.

831

GEOMATE- Osaka, Nov. 16-18, 2015

nitrogen load in fine day is 1021/year, and averageload is 43g/sec. The nitrogen load in flood condition
is more 2 times of nitrogen load in fine day, andflood condition is 89 days in 2011. The flood
condition of nitrogen load accounts for 45% ofannual.

concentration in one point, Type of water quality is
divided in Alkaline earth non-carbonated salt. So,
the type of water quality is same mostly in the wholebasin, the nitrogen concentration is possible toestimate by EC value.
THE ESTIMATION OF FLOW RATE UPPERSTREAM FROM FUJII180

THE EC VALUE INFLUENCE RATE FOR
THE DISSOLVED ION

160

In generally, the 80% of water of Yamato Riveris sewage. So it is estimated that water of same
origin is mostly same water quality. The EC value isincreased for constant rate by ion concentration of
1mg/L. Table 1 shows EC value for ion
concentration of 1mg/L.

Itahigashi

120
100

EC value per
1mg / L(mS/m)

Ion
concentration

EC value per
1mg / L(mS/m)

Na+

0.213

K+

0.184

NH4+-N

0.524

Ca2+

0.260

Mg2+

0.382

Cl-

0.214

NO3--N

0.510

HCO3-

0.0715

CO32-

0.282

SO42-

0.154

0

%
100

NO3 →

←

600

Hota = 0.278 Hujii+

SO4 2+

2+

Cl +

2+

Ca

Hota

0%

0%
0%

100

%

100

%

Hota

30 Ya-28
31 Ya-29
39Ya-34
Ya-35
37
32
Ya-29-1
Ya-30
3633Ya-33
67
Ya-61
50
Ya-45
2 Ya-2
6566Ya-59
Ya-60
23
Ya-23
3525Ya-34-1
Ya-32
38
Ya-23-3
42
Ya-38
56
Ya-51
3 Ya-3
43
24
Ya-23-2
548
Ya-5
45Ya-39
Ya-40
1 Ya-1
Ya-43
72
Ya-66
44
Ya-39-2
29
Ya-27
53
Ya-48
4 Ya-4
27Ya-44
Ya-25
649
Ya-6
40Ya-31
Ya-36
4734
Ya-42
28
46Ya-26
Ya-41
Ya-37
52
Ya-47
64 41
Ya-58
59
Ya-54
57 Ya-52
55 Ya-50
2021Ya-20
Ya-21
13Ya-64
Ya-13
1769Ya-17
62 Ya-57
15
Ya-15
Ya-14
814
Ya-8
63 Ya-57-2
18 Ya-18
60 Ya-55
16
Ya-16
54Ya-12
Ya-49
68
Ya-62
712
Ya-7
11
Ya-11
58
Ya-53
Ya-10
61 10
Ya-56
26 Ya-249 Ya-9

Mg

100

400

Itahigashi observing station has a little water
quality data, and the data is not enough to estimate
nitrogen load. Figure 11 shows The Relation
between the flow rate of Fujii and Itahigashi in 2011.
The nitrogen load is calculated by ratio of the flowrate of Fujii and Itahigashi. The flow Rate of
Itahigashi is 38% of Hujii, and some rivers flow into
between Fujii and Itahigashi. Assuming flowing at
the same rate, the nitrogen load is estimated
570t/year.

%
100

←

51
30Ya-46
Ya-28

31
33Ya-29
Ya-30
32
Ya-29-1
37
Ya-34
39Ya-33
Ya-35
36
50
Ya-45
Ya-61
267
35Ya-2
Ya-32
38
Ya-34-1
65 Ya-59
42
53Ya-38
Ya-48
66
Ya-60
27
Ya-25
3 Ya-43
Ya-3
548
Ya-5
29
Ya-27
Ya-54
7259
Ya-66
34
Ya-31
Ya-6
Ya-44
Ya-50
4 6Ya-4
14955
Ya-1
64Ya-42
Ya-58
47
56
Ya-51
28
52 Ya-26
Ya-47
21 Ya-21
69Ya-52
Ya-64
57
43
Ya-39
45
60
Ya-55
8 Ya-17Ya-8
44 Ya-40
Ya-39-2
1820Ya-18
Ya-20
17
13
Ya-13
7 Ya-7
40
Ya-36
54Ya-15
Ya-49
1015
Ya-10
11
Ya-11
12
Ya-12
14
Ya-14
58
Ya-53
41 Ya-37
9 Ya-919 Ya-19
16 Ya-16

2-

4 +
SO

Mg 2+
→

Fujii

200

0%
3

←

22 Ya-22

00

68 Ya-62
24 Ya-23-2

25 Ya-23-3

0%

100
%

3

%

100

0%

61 Ya-56Ｙａ-65-2
Ya-57
466263Ya-41
26 Ya-24 71
Ya-57-2
70 Ya-65

-

+

K →
0%

NO

HCO -

+

+
Na

0%

200

Fig. 11 The Relation between the flow rate of Fujii
and Itahigashi in 2011

51 Ya-46

22 Ya-22

30 Ya-28

2+

0

19 Ya-1971 Ｙａ-65-2
70 Ya-65

31 Ya-29
18 Ya-18
39Ya-34
Ya-35
3736
Ya-33
50
Ya-45
3233
Ya-29-1
2Ya-30
Ya-2
3Ya-60Ya-3
19 Ya-19
65
Ya-59
6642
Ya-38
51 Ya-46
48
Ya-43
425
Ya-4
45
Ya-40
35
Ya-32
49
Ya-44
29
Ya-27
43
Ya-39
6Ya-66
Ya-6
44
Ya-39-2
38
Ya-34-1
28
Ya-26
Ya-23-3
556
Ya-5
23
Ya-23
47
Ya-42
27
Ya-25
24
Ya-23-2
20
Ya-20
34
Ya-31
46
Ya-41
57
Ya-52
172
Ya-1
Ya-51
17
Ya-17
69
Ya-64
59
Ya-54
40
Ya-36
53
Ya-48
41
Ya-37
55
Ya-50
14
6263
Ya-57
61Ya-14
Ya-56
15
Ya-15
13
Ya-13
Ya-57-2
52
8Ya-47
Ya-8
Ya-58
60
Ya-55
Ya-12
Ya-10
58
Ya-53
11
Ya-11
54
Ya-49
16
Ya-16
712
Ya-7
910
Ya-9
26
Ya-24
21 64
Ya-21
68
Ya-62
71
70Ｙａ-65-2
Ya-65
67 Ya-61
22 Ya-22

← Ca

60
20

The sewage water includes salt and nitrogen
origin of organic compound, so the water is rich in
Na+, Cl-, NO 3 -, NH 4 +. Na+ of EC value per 1mg/l is
0.213mS/m, Cl- is 0.214mS/m, NH 4 +-N is0.524mS/m, NO 3 --N is 0.510mS/m. The dissolved
ions of EC value is high response in the all of ions in
the figure.

100%

80
40

Table 1 EC value for ion concentration of 1mg/L
Ion
concentrationy = 0.3883x
R² = 0.9398

140

23 Ya-23

-

100%

Cl →

100 200 300 400 500 600 700
Hujii

Fig. 12 The Relation between the flow rate of Fujii
and Hota from 1997 to1999

Fig. 10 The triennia diagram of the Yamato River
Basin in 1999

Figure 12 shows The Relation between the flowrate of Fujii and Hota from 1997 to 1999. The data ismost new in Hota. To account for errors, it is shown

The Yamato River Basin of water is rich in
Na+K, Cl by more than 70%. The distribution is832

GEOMATE- Osaka, Nov. 16-18, 2015

three-year relationship of the flow rate. The flow
Rate of Hota is about 28% of Hujii, and some rivers
flow into between Fujii and Hota. Assuming flowing
at the same rate, the nitrogen load is estimated
417t/year.

River in 2011
Figure 15 shows the schematic diagram of the
Yamato River in 2011. The flow rate of upperstream was estimated by compare with the flow rateof Fujii observing station. The flow rate of Itahigashi
is 38% of Fujii. Nukatabetaka Bridge andKamihanda Bridge flow into Itahigashi, and treatedwater from sewage system flows into Itahigashi. The
ratio of flow rate is calculated by monthly few data,
because data is little. As a result, the nitrogen load is
calculated to dividing the nitrogen load of Fujii by
the ratio of flow rate. The flow rate of Nukatabetaka
Bridge is 10% of Fujii, and Kamihanda Bridge is 5%
of Fujii, and the treated water from the sewage
system is 23%. Therefore the nitrogen load at
Nukatabetaka Bridge is estimated as 150t/year,
Kamihanda Bridge is 75t/yea and the treated waterfrom sewage system is 345t/year.

30
Shintatsuta = 0.145 Fujii + 0.

Shintatsuta

Shintatsuta

20

10

0
0

300

200

100
Fujii

CONCLUSION

Fig. 13 The Relation between the flow rate of Fujii
and Shintatsuta from 1986 to1989
Figure 13 shows The Relation between the flow rateof Fujii and Shintatsuta from 1986 to 1989. The data
is most new in Shintatsuta. To account for errors, it
is shown three-year relationship of the flow rate. The
flow Rate of Shintatsuta is about 15% of Hujii, andsome rivers flow into between Fujii and Shintatsuta.
Assuming flowing at the same rate, the nitrogen load
is estimated 218/year.

Since there is not enough data in the upstream, it
was difficult to estimate the nitrogen load of the
upstream. Moreover new data is not aligned, it isnecessary to predict by using other data. This study
improve the accuracy of load amount calculated byinterpolation of the data of the up and down stream
observation stations and apply flow rate calculationfrom the water level using by the new Manningequation. In other less data observing station, thenitrogen load is calculated by ratio of flow rate for
Fujii of flow rate. The nitrogen load of the YamatoRiver basin was calculated. As a result, the flood
condition of nitrogen load accounts for 45% ofannual. The nitrogen load of treated water of sewage
system is 23%.

Kamihanda

Nukatabetaka Bridge

2011/6/23

2011/5/17

2011/4/12

2011/3/10

2011/2/1

2011/1/11

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

REFERENCES

Sewage System[1] Masanobu Taniguchi, Hiroyuki Ii, Tatemasa
Hirata, Analysis of organic nitrogen compoundin domestic sewage in Yamato River using asocial experimental test, 2010, vol. 30, Part 10,
p. 1587–1590, Stuttgart, April 2010[2] Ministry of Land, Infrastructure and Transport,
Japan, Water information system,
http://www1.river.go.jp/[3] Yamato River construction
office, http://www.yamato.kkr.mlit.go.jp/YKNE
T/index.html[4] Masanobu Taniguchi, Hiroyuki Ii, Tatemasa
Hirata, The development of water resource
system – The Water balance of Yamato River
Baisin, Environmental Engineering Research
Papers Vol. 48,2011.

Fig. 14 The ratio of flow rate in Itahigashi upper
The two rivers and treated water of sewage system
flow into the Itahigashi observing station. Figure 14
shows The ratio of flow rate in upper stream of
Itahigashi. The ratio of flow rate of the two rivers
are less than 50% of all flow rate, and the amount oftreated water of sewage system is more than 50%
constantly. It is estimated that the influence oftreated water of the sewage system is big at whole.
Shintatsuta

Nukatagetaka
Bridge218t/year
Kashihara
1836t/year

Fujii

Kawai

Taishi100%
1501t/year

Doumyouji
335t/year

10%
150t/year
Sewagesystem23%
345t/year

Itahigashi
38%
570t/yearHota
417t/year

Kamihanda Brdge
5%75t/year

Other rivers 296t/year

Fig. 15 The schematic diagram of the Yamato833

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

SEDIMENT IMPROVEMENT MATERIALS COMPARISONAPPLIED TO EUTROPHICATED SEASIDE PARK POND
Kazuhito Murakami1, Saki Agatsuma 1,2 and Akiko Inoue-Kohama3
Department of life and Environmental Sciences, Chiba Institute of Technology, Japan; 2Mikuniya
Corporation, Japan; 3Department of Environment and Energy, Tohoku Institute of Technology, Japan
1

ABSTRACTThis study was aimed to analysis the influence on bottom sediment environment by sprinkling shell as
regional unused resources. In addition, adsorption isotherm was calculated by culture experiments of bottom
sediment material improvement. And adsorption effect of the nutrient in each processing was assessed. Water
quality of pore water was as follows; T-N in pore water showed the low concentration with sprinkling shells as
regional unused resources. As reason for this, NH4-N eluted from sediment into water by the influence ofsprinkled CaO. T-P in pore water also showed the low concentration with sprinkling shells, and moreover it was
remarkable that in burning-treated system more than in non-treated system. By sprinkled, Ca3(PO4)2 was formed
with Ca2+ and PO43- bonding on the shell surface, and PO4-P in the pore water decreased. As assessment byadsorption isotherm; NH4-N and T-N showed the negative adsorption isotherm. As reason for this, NH4-N eluted
in water by sprinkled bottom sediment improvement materials, and nitrogen in the pore water decreased. For T-P
and PO4-P, all bottom sediment improvement materials showed positive adsorption effects. CRM treatmentusing CaO showed the highest effect
Keywords: Bottom Sediment Improvement, Langmuir’s Adsorption Isotherm, Pore Water, Nutrient,
Eutrophicated Brackish Pond

INTRODUCTION
With the population growth, eutrophication in
enclosed water bodies is progressing by inflows ofdomestic wastewater into rivers. Therefore, lakes are
the environment susceptible to nutrient load andorganic matter. Some eutrophication measures havebeen implemented. However, problems such as thecollapse of the ecosystem issues and maintenanceand the cost has occurred. Shells were used as thesprinkled material in this study is a principal
component of calcium carbonate, it is consideredthat the influence on the ecosystem is less. Then, theCa of the components of the shell, also has theability of adsorbing the nutrients.
Hasunuma Seaside Park pond in Sanmu city of
Chiba prefecture locates at 350m inland fromKujyukurihama coast line. This pond is strong
enclosed, and its surface area is about 10,000m²,
water volume is about 7,400m3, the water depth is
0.74m in average (Fig.1) [1]. In past, a rental boat
shop opened in this pond, but the problem of stink
and deterioration of landscape occurred because of
Aoko in summer. Improvement trials of drying inthe sun and drainage work conducted, but Aoko
occurred again in summer of next year (Fig.2) [2].
Therefore, the eutrophic cause in these ponds is theelution of the nutrients from bottom sediment.
Dominant species of Aoko in this pond isAnabeana spiroides, and it has “air nitrogen fixation

5m
Fig.1 Hasunuma seaside park pond

1m
Fig.2 Aoko occurred in Hasunuma

834

GEOMATE- Osaka, Nov. 16-18, 2015

ability”, therefore, they can multiply explosively ifthere is even phosphorus in water. The other side,
many shells are scattering in Kujyukurihama coaston nearly of Hasunuma Seaside Park pond.
This study was aimed to analysis the influence
on bottom sediment environment of sprinkling shell
as regional unused resources. In addition, adsorption
isotherm was calculated by microcosm culture
experiments of bottom sediment material
improvement. And adsorption effect of the nutrientin each processing was assessed

Fig.3 Shell fragment as regional unused resources

MATERIALS AND METHODS
Regional Unused Resources
In this study, Anadara broughtonii bivalves was
supplied as a regional unused resources. This shell
was broken into 1-3mm size fragment (Fig.3). Morethan 90% of shell body component is made from
CaCO3. It sprinkled on the bottom mud of the pond
where is nutrient resource. Shells are scattered muchin Kujyukurihama coast of neighboring of
Hasunuma Seaside Park pond. And when the
fragment sprayed on bottom mud, the surface of
sediment looks bright by a reflection of the sun light.

Fig.4 Magnesium oxide (MgO)

Culture Method
Fig.5 Calcium oxide (CaO)

Water volume in the microcosm test is generally
300-1,000 ml [3]. From this reason, clear glass
container (volume: 470ml, height: 14cm, diameter:7cm,) was supplied to this study. 100g of bottom
sediment collected from the pond was put in thebottom of the container to be flat, and 380mL of
pond water was poured without disturbing thesediment. Crashed shells were sprinkled on thesurface of bottom sediment.
The microcosm systems were cultured inincubator. Culture period was set to 20 days, andculture system were no-treated system as control andsprinkled systems of 50, 100g/m² of shell fragment.
Culture condition was 25 degrees (Celsius) intemperature and 2,400lux in illuminance (L/D ＝
12/12hr.). Measuring parameters were T-P, PO4-P,
T-N, NH4-N, NO2-N, NO3-N, COD, pH and T-S.

treated and burning-treated) (Fig.3). MgO can besuppressed occurrence of Chl.a and elution ofphosphorus. Therefore, it is expected to be viable inthe bottom mud treatment of enclosed lakes [4]. CaO
is possible to suppress the elution of phosphoric acidfrom the bottom mud. However, NH4 washout fromthe bottom sediment and rapid rise in pH occurs.
That impact on the ecosystem is considered large ina concern. DAF process is a method for making the
aerobic condition sediments.
Adsorption isotherm was calculated using
Langmuir’s adsorption isotherm as formula (1) as
below [5].
W=a・Ws・C(1)

Extraction of pore water

RESULTS AND DISCUSSION

50-100g of bottom sediment was sampled into
precipitation tube, and centrifuged under 2,0003,000rpm for 10min.

Water quality of pore water
Concentration ratio of nitrogen
NH4-N (Fig.6a): In 10g/m2 and 20g/m2 sprinkled
system, no change was observed in the concentration
ratio either of the burning system and a non-burning
system. In 50g/m2 and 100g/m2 sprinkled system,
reduction of 40-50% concentration was observed in
the non-burning system, and 60-70% concentration
ratio recreased in burning-treated system. Thus, due

Evaluation by the adsorption isotherm
This study evaluated CRM (Chemical
Remediation Materials) treatment (MgO and/or
CaO) (Fig.4, 5), hybrid treatment (DAF treatment
plus CRM treatment) and the shell sprinkled asregional unused resources treatment (non-burning
835

GEOMATE- Osaka, Nov. 16-18, 2015

to the sprinkling of calcium, by increasing the pH of
the sediment, NH4-N elution occurred by washout
effect. In a system that has been subjected to aburning treatment in particular, shows a high valueof pH11, pore water of the bottom mud is inclined toalkaline. It is thought that the elution amount of
NH4-N was greater than in the non-burning system.
NO2-N (Fig.6b): As with NH4-N, there was nochange in 10g/m2 sprinkled systems and the nonburning system of 20g/m2 sprinkled system. In
burning-treated system of 20g/m2 sprinkled system,
50g/m2 and100g/m2 sprinkled system reducing theconcentration of 20-30% has occurred in a nonburning system. And concentration was decreased
by 20-40% of the burning-treated system.
NO3-N (Fig.6c): In 100g/m2 sprinkled system, it
has become a high concentration of 2.5 times of the
NO3-N concentration in the overlying water incomparison with a non-dusting system. Nitrification
reaction of just above water has been promoted in
the elution of NH4-N from the sediment. Therefore,
it is eluted from the bottom mud NH4-N of a large
amount compared to the non-dusting system,
nitrification reaction amount of sediment has been
suppressed. Therefore, it thought that change was
not observed in the NO3-N concentration in the porewater.
In this way, the promotion of a series ofprocesses of nitrification denitrification has beensuggested by sprinkling shells and burning process.
For a porous material is widely used for
denitrification and nitrification promote, further
study is necessary in the future.
T-N (Fig.6d): Pore water showed the low
concentration of T-N by sprinkled shells as regional
unused resources. As reason for this, NH4-N eluted
from sediment into water with the influence ofsprinkled CaO.
Concentration ratio of phosphorus
PO4-P (Fig.6e): In PO4-P, adsorption effect has
not been seen in 10g/m2 sprinkling system and20g/m2 sprinkling system. However, graduallyconcentration decreased from 50g/m2 sprinklingsystem and high adsorption effect has been shown inthe burning system than non-burning system. By
sprinkled shells, Ca3(PO4)2 was formed with Ca2+and PO43- bonding on the shell surface, and PO4-P in
the pore water decreased..
T-P (Fig.6f): As with T-P, as the application rateis large, the concentration of the pore water isreduced in a system subjected to a burning treatment
was shown.
Concentration ratio of total sulfide
T-S (Fig.7): The decrease of sulfide amount wasobserved by sprinkled the shells, and moreover it

Fig.6 Nutrient concentration ratio in the pore waterwith shell sprinkling
836

GEOMATE- Osaka, Nov. 16-18, 2015

was remarkable in burning-treated system more thanin non-treated system, as same as T-P. In 100 g/m2
sprinkled system, sulfide concentrations weredecreased by about 1.3 times more in burning
system than non-burning system. As reason for this,CaSO4 was formed with Ca2+ and SO42- bonding. As
reason adsorption force was higher in the systemthat has been subjected to a burning treatment,
porosity of the shell is increased by burning [6][7].
Thus, the surface area of the suction surface
increases than non-burning system.
Fig.7 Total Sulfide concentration ratio in the porewater in the shell sprinkled

Assessment by adsorption isotherm
Comparison of CRM treatment and hybrid treatment

equation in a system that was sprinkled with CaO.
T-P (Fig.11): The adsorption equation of T-N
showed below.
MgO:
W=9.7×10-3C
CaO:
W=5.6×10-2C
DAF+MgO: W=4.0×10-2C
DAF+CaO: W=5.0×10-2C
From these equations, adsorption effect was
shown to be higher in the following order.

Fig.8-11 shows the adsorption of various
nutrients of CRM treatment and hybrid treatment.
NO3-N (Fig.8): The adsorption equation of NO3N showed below.
MgO:
W=1.6×10-2C
CaO:
W=3.2×10-3C
DAF+Mg:
W=1.4×10-2C
DAF+CaO: W=1.3×10-3C
From these equations, adsorption effect was
shown to be higher in the following order.

CaO＞DAF+CaO＞DAF+MgO＞MgO
CaO showed higher adsorption effect 5.81 times
higher than MgO. As reason for this, binding force
of CaO is higher than that of the MgO [3][8]. CaO
showed higher adsorption effect 1.14 times higher
than DAF+CaO. As reason for this, organic matter isreduced by applying the DAF process.
From the results above, high adsorption effect innitrogen was obtained in MgO sprinkled system thatwas sprinkled. And that adsorption is highly
effective in a system that was sprinkled with CaO
has been shown in phosphorus.

MgO＞DAF+MgO＞CaO＞DAF+CaO
The highest adsorption effect was showed in
MgO. MgO showed higher adsorption effect than
12.1 times of that of DAF+CaO.
NH4-N (Fig.9): The adsorption equation of NH4N showed below.
MgO:
W= 1.1×10-4C
CaO:
W=-2.2×10-4C
DAF+MgO: W= 2.4×10-5C
DAF+CaO: W=-1.5×10-4C
From these equations, adsorption effect was
shown to be higher in the following order.

Comparison with Regional unused resources
Fig.12 shows the adsorption of various nutrientsby regional unused resources.
It compared the adsorption isotherm in the caseof using the shells and adsorption isotherm ofbottom sediment improvement process adsorption
effect was highest.
In NO3-N, non-burning system showedadsorption effect of 16.5 times compared to MgO
but showed adsorption effect of 2.8 times comparedwith MgO at the burning treated system.
In NH4-N, non-burning system showedadsorption effect of 0.18 times compared higher
with MgO but showed adsorption effect of 40 times
lower compared with MgO at the burning treatedsystem.
In T-N, non-burning system showed adsorption
effect of 43.8 times higher compared withDAF+MgO it showed adsorption effect of 54.8
times compared to DAF+MgO at the burning treated

MgO＞DAF+MgO＞DAF+CaO＞CaO
Adsorption isotherm of CaO and DAF+CaO
showed negative adsorption. As reason for this,wash out effect occurred that weakly basic of NH4-N
is eluted into water by sprinkled Ca.
T-N (Fig.10): The adsorption equation of T-N
showed below.
MgO:
W= 1.5×10-5C
CaO:
W=-2.6×10-4C
DAF+MgO: W= 7.3×10-5C
DAF+CaO: W=-1.7×10-3C
From these equations, adsorption effect was
shown to be higher in the following order.
DAF+MgO＞MgO＞DAF+CaO＞CaO
PO4-P: Similarly, it became negative adsorption
837

GEOMATE- Osaka, Nov. 16-18, 2015

Fig.8

Adsorption characteristics of NO3-N invarious sediment material improvement

Fig.10 Adsorption characteristics of T-N in various
sediment material improvement

Fig.9

Adsorption characteristics of NH4-N invarious sediment material improvement

Fig.11 Adsorption characteristics of T-P in various
sediment material improvement

system.
In T-P, non-burning system showed adsorption
effect of 13.8 times lower compared with CaO but
showed adsorption effect of 11.3 times compared to
CaO at the burning treated system. Adsorption effectof regional unused resources sprinkled in T-P isabout 1/10 of CaO treatmemt has been shown.
By CaO sprinkling, elution of NH4-N was
observed from the bottom mud due to the rise in pH.
It has resulted that affect possibility is suggested toecosystem
In this study, culture period of microcosm system
was set to 20 days, and this was too short for
saturated adsorption. The values of adsorption
capacity are important, but it was not detected in thisstudy. Furthermore, studies followings are required
to practice in action the shells spraying as regional
unused resources. Some growth suppression effecton the benthos which play an important role ofconsumer of sediment ecosystem directly affectedby sediment material improvement [9], need tonon-burning system

Fig.12

838

burning system

Adsorption characteristics of various
nutrients in the shell

GEOMATE- Osaka, Nov. 16-18, 2015

continue considering about what would affect theecosystem structure consisting of complex foodchain. To investigate these problems, on-site scale
mesocosm system experoment should be conducted,
and from the comparison between laboratory scale
microcosm system and on-site scale mesocosm
system, the results obtained from this study can be
implemented in the field.

REFERENCES[1] Murakami K, Ishii T, Taki K and Matsushima H,“Internal Production Control by Sediment
Treatment in Eutrophicated Lakes using
Experimental Mesocosm System”, Papers on
Environmental Information Science, Vol.22,
2008, pp.487-492.
[2] Murakami K, Ishii T and Taki K, “Reeutrophication caused by bottom sediment sundrying in shallow and strongly enclosed parkpond”, Papers on Environmental Information
Science, Vol.23, 2009, pp.435-440.
[3] Amano Y, Taki K, Murakami K, Ishii T and
Matsushima H, “Sediment remediation forecosystem in eutrophicated lakes”, The
Scientific World, Vol.2, 2002, pp.885-891.
[4] Kanda R, Nakazawa Y, Inamori R, Suzuki R,
Murakami K, Xu K Q and Inamori Y, “Effects ofmagnesium hydroxide as an environmental
improvement agent on the survival and viabilityof aquatic organisms”, Journal of Bioindustrial
Science, in press, 2015.
[5] Kondo S, Ishikawa T and Abe I, “Science ofAdsorption”, Maruzen, 2001, 236pp.
[6] Yavitt J B and Fahey T J, “Peat and solution
chemistry responses to CaCO3 application inwetland next to woods lake”, Experimental
Watershed Liming Study, Kluwer Academic
Publishers Biochemistry, 1996, pp.103-121.
[7] Crimo P C and Driscoll C T, “The impacts of awatershed CaCO3 treatment on stream andwetland biogeochemistry in the adirondach
mountains”, Experimental Watershed Liming
Study, Kluwer Academic Publishers, 1996,
pp.123-156.
[8] Murakami K, Agatsuma S, Gomyo M and InoueKohama
A,“Eutrophicatedsediment
remediation using shell fragment as regional
unused resources for nutrients elution control”,
International Journal of GEOMATE, Vol.8,
2015, pp.1239-1244.
[9] Murakami K, Gomyo M, Agatsuma S, Amano Y
and Inoue-Kohama A, “Environmental conditionindex for estimation on eutrophic state of
enclosed aquifer ecosystem”, International
Journal of GEOMATE, Vol. 5, 2013, pp.706-711.

CONCLUSIONS
Pore water quality analysis of bottom sediment
where the shell sprinked treatment as regional
unused resources and the comparison of theadsorption
characteristics using Langmuir’s
adsorption isotherm with a variety of sediment
improvement materials were conducted. Results canbe concluded as follows.
1) In sediment pore water, NH4-N is eluted into
upper water by wash-out effect especially in theburning treatment system, and the concentration of
nitrogen in the pore water was reduced. T-P and
PO4-P also reduced the concentration in pore water,
with combine of phosphate and Ca as the main
component of the shell. T-S also reduced in porewater as same as phosphate.
2) In adsorption characteristics calculated using
Langmuir’s adsorption isotherm as various sediment
improvement materials, nitrogen was reduced in
MgO sprinkled treatment (MgO and DAF+MgO),
and phosphate was reduced in CaO sprinkled
treatment (CaO and DAF+CaO). In shell fragment
sprinkled treatment, higher adsorption effect wasdemonstrated in nitrogen, but only 1/10 adsorption
effect in phosphorus.
From these outcomes, shell fragment as regional
unused resources can be considered as one of theeffective tools for sediment eutrophication
remediation.
ACKNOWLEDGEMENT
Special thanks for the cooperation of Sanmu
Local Maintenance Center of Chiba prefecture, andthis study was partly supported by grant-in Aid for
Scientific Research of JSPS (KAKENHI)(24651029).

839

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

RISK-BASED ENVIRONMENTAL PREPAREDNESS FOR DEALINGWITH DEBRIS AND WASTE FROM GREAT TSUNAMI

1

Keiichi KITAZUME1, Taira OZAKI2 and Toru MORIOKA3
Faculty of Environmental and Urban Engineering, Kansai University, Japan; 2,3 Kansai University, Japan

ABSTRACTIn Japan, the government predicts that a large earthquake and tsunami will occur in high probability. Therefore,
people have to prepare for the disaster and well treat debris and waste generated by this. This paper develops aframework of the risk-based environmental preparedness. First, areas along the coast of the Japan’s island are
categorized from the geographical, demographic and social point of view. Second, the key factors whichsignificantly affect the length of dealing time and amount of emissions are revealed and structured respectively by
the case of the Great East Japan Earthquake and Great Hanshin-Awaji Earthquake for reference. Third, pass ways
for considering the resilient management of areas with large amount of debris and waste are developed. As resultsof this research, the method of risk-based environmental preparedness which enables to have merits both before
and after a great earthquake is proposed.
Keywords: Preparedness, Earthquake, Waste Management, Risk Management

between designated ones considering integration. The
area is 27,186 km2 and consists of 14 prefecture (or139 cities, towns and villages) and population is10,346 thousand in 2010. There is a diversity in thisarea and meticulous policy measures are required.

INTRODUCTION
The Earthquake Research Promotion predicts thata great earthquake will occur in approximately 70%of the probability within 30 years along the Nankai
Trough located south of the Japan’s island of Honshu.
Great Tsunami will also attack the areas and generatelarge amount of debris and waste. On the other hand,the areas include not only metropolitan areas but also
depopulated towns and villages with fishing ports,sightseeing areas and so on. In addition the compact
city policy will continue to change the distribution of
population, facilities and land use in next 30 years andtherefore the volume and distribution of debris andwaste will be different in timing of earthquake, land
condition, city policy and so on. The difference will
effect on the methods of treating them and we
recognize these differences as risks. Despite these
difficulties, these should be cleared soon fordeveloping national resilience policy. Therefore, aconcept of risk-based environmental preparedness isrequired as an effective approach to this issue.
The purpose of this paper is to develop aframework of the risk-based environmental
preparedness for dealing with debris and wasteconsidering the diversity of timing of earthquake,land condition and city policy.

- Inundated more than 30 cm
within 30 minutes- A municipality located between
designated ones
- Included

Municipalities: 14 Pref. and 139 cities (2014）
Population: 10,346 thousand (2010)
Area: 27,186 km2 (2015)
The area includes metropolitan areas
as well as depopulated towns and villages.

Source: Cabinet Office

Fig. 1

Designated areas of policy measures

Average -20.7%
100%
90%

70%
60%
50%
40%

Average 31.8%

EARTHQUAKE AND TSUNAMI

30%
20%

Figure 1 shows the areas where the Nankai
Trough earthquake and following Tsunami will attack
reported by the Cabinet Office of Japan. These areas
are inundated more than 30 cm within 30 minutes
after the earthquake or municipalities located

10%
0%
-70%

-60%

-50%

-40%

-30%

-20%

-10%

Ratio of Depopulation ( Pop (in 2040) / Pop. (in 2010))

Fig. 2840

Data of municipalities in the area

0%

Ratio of City Planning Area

80%

GEOMATE- Brisbane, Nov. 19-21, 2014

the Great East Japan case, stakeholders were notmainly municipalities but prefectures, the upper levelof local government, because the disaster areas werevery wide and included many types of cities, towns
and villages. Small municipalities could not treat thelarge amount of debris and wastes by themselves,
therefore prefectures arranged the co-relations among
them and advised them methods. Accordingly, theratio of cross border treatment of debris and waste
was lower in the Great Hanshin-Awaji case than in
the Great East Japan case. In Japan, the usual basic
concept is that a municipality should treat the wastefrom the own area there. However, in the emergency
case many towns and villages could not treat almostof all debris and waste and some other municipalities
helped them. Recycle ratios between the two cases arealso different. The ratio in the Great East Japan casewas higher than that in the Great Hanshin-Awaji casesince the capacity of disposal plants which treat
mainly municipal solid waste was very low. Finally,as shown in figure 3, the term of clearance of waste
and debris were both two years, but the timing of startin the Great East Japan case was 1 year later for
arrangement of co-operation among municipalities.
The Nankai Trough case will be relatively similar
to the Great East Japan case. The disaster area will
widely spread over and consist of many types ofmunicipalities which includes small towns andvillages. For fast start treatment, prefecture or
national government must arrange co-operation oftreatment of waste and debris smoothly.

Figure 2 shows data of target municipalities.
Horizontal axis shows ratio of depopulation in 2040
to that in 2010 and vertical axis shows ratio of city
planning area [1]. The average of ratio ofdepopulation is -20.7% while less than -50.0% isobserved in some municipalities. In these
depopulated municipalities, ratios of city planningarea are very low and the volume and distribution ofhouses and buildings will be expected to radicallychange. On the other hand, the average of ratio of city
planning area is 31.8% and 100 % is observed in somemunicipalities. Even though the city planning area
consists of urbanization control area as well as
promotion area, high ratio represents high density of
population and facilities. In addition, population insome cities with 100 % of city planning area also will
decrease from -20 % to -50 %. We can divide thesemunicipalities into three categories, one is high ratioof city planning area and low ratio of depopulation,
second is high ratio of city planning area and highratio of depopulation and the last one is low ratio ofcity planning area and high ratio of depopulation. The
amount of population which may be related tohierarchy of city such as designated, core, or special
city and metropolitan area or not and so on, also effecton the difference. However, both of the ratio of city
planning area and depopulation represents well theestimated volume and distribution of debris and waste.
Experiences of former earthquakes

100
Ratio(%)

Table 1 shows experiences of former twoearthquakes in Japan, one is the Great Hanshin-Awaji
earthquake in 1995 and the other is the Great East
Japan in 2011.
In Great East Japan earthquake, Tsunami attacked
sea coast areas and generated not only waste but alsodebris from cars, ships and others and the amount ofdebris and waste was larger than the Great HanshinAwaji earthquake. Thus the cost was also higher. The
main stakeholder of the Great Hanshin-Awaji casewas cities and towns, mainly Kobe city, because
Kobe was the main disaster area and had much powerand capability of governance. On the other hand, inGreat Hanshin-Awaji

Volume of Waste( million t )Cost(thousand Yen/t)
Main Stakeholder
Term(plan)
Approximately
Term(real)

Recycle Ratio(%)
Cross border
Treatment

debris
14.30(Hyogo)

1937
Prefecture and City

2 years
98% finished in 2 years
38
Within the Prefecture
Out of Prefecture (10%)

40

3 years
the Great East Japan

After 1 month
After 1 year0

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55
month
阪神・淡路
東日本
Pref. 4.29 million t
Hyogo Pref.：14.3
million t Iwate
Miyagi Pref. 11.71 million t

Fig. 3

Ratio of treatment of disaster waste

SCENARIO
Concept of compact city
The national government and many researchers
propose the concept “Compact City”. Although theconcept has many meanings, commonly narrowerarea for residents and urban activities should be
required for a depopulated city. Most of urban areas
will be controlled to be narrow, in other wordcompact, since the number of population will be
reduced 20% on average in the next 30 years.
In Sendai, for example, which is the largest city inthe disaster area of the Great East Japan earthquake,the center of city is located relatively far from the sea
coast and main public transportation system is also.
Therefore, main urban activities were safe while other

Great East Japan
Debris, Cars,
Ships, others
Waste 20.19(13Pref.)Deposit 11.02

City and Town

60

20

Table 1 Experiences of former two earthquakes
Contents

the Great Hanshin
2 years80

within 3 years
3 years(Iwate, Miyagi)
Continue(Fukushima)
Waste 82,
Deposit 99 (13pref.)
Requested
to other Prefecture841

GEOMATE- Brisbane, Nov. 19-21, 2014

cities which are located just on the sea coast received
much damage. In a compact city of the future, someresidences and urban facilities will get concentrate
into the center of the city along the next 30 years, thedebris and waste by Tsunami will decrease in volume.
This means that distance between the city center andsea coast is one of the important factor to evaluate thevolume of debris and waste.

over the city area while the population will decreasein number. In the scenario 1, the center of city or themain urban facilities will remain in low lying area juston sea coast despite of depopulation. The city centeror main urban facilities are originally located there
and no relocation policy measures will be introduced.
In this case, tsunami will directly attack thesefacilities and generate large amount of debris andwaste.
In the scenario 2, some people will move toupland area and will be followed by some daily
facilities. Since the relocation to upland area will be
carried out along 30 years passing, the timing ofoccurrence of earthquake will be key factor toevaluate the volume and distribution of debris andwaste. If an earthquake will occur in the near future,
the volume will be larger and required sites forstorage sites and depots will be wider. Furthermore,
the candidates of them will not easier be preparedbecause many inhabitants still live in the low lying
area and daily facilities will also be located there.
In the scenario 3, co-relational measures will be
introduced between municipalities in an upland area
and a low lying area. In this case, demands or usageof some public facilities will decrease because ofdepopulation. Relocation of these facilities should beplanned. If the new facility will be selected to belocated in upland area or the existed facility in the
upland area will be selected as an integrated one,people will also be expected to move there and thevolume of debris and waste will decrease. However,
the relocation will be also made along the time
passing, therefore the timing of earthquake will be
key factor for the evaluation.

Locations of sites for treatment
Figure 4 shows typical step of treatment of waste.
Debris and waste are generated everywhere in urban
areas and their distribution may have much relation to
the distribution of buildings, houses, urban facilities
and infrastructures. After the lifesaving activities andother emergency works, smoothly these aretransported to primarily temporarily storage sites.
Because of required smoothness, the location of thesites are near the built areas. After sorting work, these
are carried to the secondarily temporarily storage sites.
These sites should be located far from residential
areas, because wastes and garbage may stink bad
smell or become any reasons of troubles. The sites
also require wide area. Therefore, these sites arelocated mainly by the sea coast or other low urban
activity areas. After more sorting works, debris andwastes are carried to depots which are located inavailable areas. After that, they are treated into
fracture, incineration, reuse or recycle or final
disposal.
Debris
Waste

Transport

EverywherePrimarily
Temporarily
Storage Site

Secondarily
Temporarily
Storage Site

Near the Built Area

Far form Residential Area

Process of depopulation and compact cities
10years

Fracture
大船渡駅周辺Depot

30years ・・・

Incineration
Scenario 1
Remain in
low lying area

Reuse / Recycle
Final Disposal

Land Conditions

Available Area

20years

BaU

Fig. 4 Steps of treatment of waste
Since it was very difficult to get the sites forprimarily/secondarily temporarily storage and depot,
the local government should prepare candidates of thesites. However, the locations have relation to
distribution of urban areas in the next 30 years.
Depopulation and compactness will effect on the
distribution of population and urban facilities.
Therefore, the candidates for sites will also change
along time passing. This matter will be risk for a
prepared plan for disaster.

Scenario 2
Moved inUpland area
Scenario 3
Co-relation
between bothareas

Fig. 5

Scenarios by timing of earthquake

CONCLUDING REMARKS
Pass way management
Depopulation and relocation of residents andurban facilities which will effect on the volume anddistribution of debris and waste, will graduallyproceed. Therefore, the timing of earthquake isimportant not only for the volume and distribution ofdebris and waste but also the treatment after the

Scenarios
Figure 5 shows some scenarios along the next 30
years according to the factors this paper discussed. In
the BaU case, urban facilities and houses will spread
842

GEOMATE- Brisbane, Nov. 19-21, 2014

more analytical approach to pass way management
will be required as described in figure 7.

Categorization

Area
A

Area
C

Area
B

Index:Cost
Time
Recycle Ratio

Existence

UplandIndividual

Cooperated

Sea Area

Sea Coast

Low Recycle

High Recycle

A CityPublicPublicCitiesPublicPrivate- Terrain (Height, Area, and so on)- Distribution of Population Density- Distribution of Facilities for Waste- Depopulation and Compact City Policy

BaU

Scenario 1

Scenario 2

Scenario 3

BaU

Scenario 1

Scenario 2

Scenario 3(1)(2)(3)
Timing(1)(2)(3)(1)(2)(3)(1)(2)(3)tTime * Policy Measure

Time

Generalization

Fig. 6

BeforeHigh Rise

After

Earthquake

After
Separated

Resilient

Area
C

Area
B

Social Scenario Recycle Scenario

Time Passing

Area
A

Volume of Waste

occurrence of earthquake. These problems depend on
pass way to relocation because of timing in progress.

Fig. 7

Concept of pass way management

Index
Resilience- Decrease of Waste Volume- Shortening of Days for Restoration

framework of pass way management

This research presents the only framework of thepass way management policy. The analysis ofconcrete policy measures and their benefits and costs
are required as future researches.

Figure 6 shows a concept of pass way
management for policy measures. As a result of thisresearch, the author proposes requirement of the pass
way management to make a plan for the disaster. The
dominant factors will be terrain, for example height,
distance from the sea coast and so on, distribution of
population density which are affected by thegeographical matters of each city, distribution offacilities and infrastructures which is effected by thatof residential areas and land use policy, and progressof depopulation and compact city policy. Furthermore,
more important factor is the timing of occurrence ofan earthquake. Especially occurrence in the near
future, the volume of debris and waste will be large
and the treatment will be more difficult because ofdifficulty to obtain the sites for temporary storage anddepot. The local government in the designated areas
reported by the Cabinet Office of Japan alreadyprepare plans for treatment for disaster waste anddebris. However, most of them do not consider thesedifferences depend on the timing and the concept and

ACKNOWLEDGEMENTS
This paper is a part of research (3K153008)
funded by the Environment Research and Technology
Fund of Ministry of the Environment of Japan. The
authors acknowledge this grant with deep gratitude.

REFERENCES[1] National Institute of Population and Social
Security Research, Population Projections forJapan -2011 to 2060 -, 2012.
[2] City Planning Association of Japan, Annual
Report of City Planning, 2012.

843

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

MATHEMATICAL MODELING OF CONTAMINANT FLOW IN
CLOSED RESERVOIRValeriy Perminov1, Alexander Goudov2
Department of ecology and basic safety, Tomsk Polytechnic University, Russia
2
Department of UNESCO, Kemerovo State University, Russia

1

ABSTRACT
A mathematical model of the impurity distribution in a flooded mine due to allocation of groundwater isunder consideration. The boundary-value problem is solved numerically by using the finite volume method.
Distribution of the main functions of the process (the velocity field, the concentration of the pollutant
components, etc.) over time was obtained with the help of numerical solution. The ecological situation in
Kuzbass and many other mining regions can be characterized by the problem of water body pollution withharmful substances. Light and heavy materials (with density that is less or greater than the density of water) suchas oil products, coal particles and stones eventually either float or collect at the bottom of a coal mine.
Mathematical modeling is used to solve practical problems associated with the distribution of such impurities ina closed reservoir and water treatment.
Keywords: Water Pollution, Coal Mine, Mathematical Modeling, Finite Volume

Influenced by flow some impurities partially leave
the mine while the remaining part gravitates to thebottom. To describe this transfer process differential
equation system is used. They express the laws ofconservation of mass, momentum and elements
concentration in the domain. Mathematically thefollowing differential equation system for turbulent
flow should be solved. Many practical problems are
devoted to such impurity distribution in an enclosed
water body.

INTRODUCTION
Water body pollution by mining and quarrywaters is a typical problem for Kuzbass and manyother mining regions [1]. Mining waters usuallycontain particles of coal dust, clay, calcium
compounds, magnesium, oil products, etc. Light
substances (which density is less than water density)
such as oil products accumulate on water surface
while other particles remain suspended or sedimentgradually. The problem of mining water treatment
by pumping into abandoned mines and further use of
the water after precipitation of impurities (for heavymaterials) or impurity floating up (for light particles)
is of great interest.

∂ρ∂+(ρu j ) =0∂t ∂x j∂∂∂p
( ρu i ) +( ρu i u j ) = −
+
∂t
∂x j
∂xi

∂
(− ρu i′u ′j ) − ρSC d u i u − ρg i ,+∂x j

PHYSICAL AND MATHEMATICALSETTING
The paper considers the process of fluid flow
containing impurity particles in a flooded mineshown in Fig.1.

ρ(

∂Yk
∂Y
∂Y
+ u1 k + (u 3 − u 3k ) k ) =
∂t
∂x1
∂x3

∂
=
(− ρYk′u ′j ),
∂x j
Yk
k Mk

p = ρR0T ∑

(1)

(2)

(3)

(4)

gd k ρ k(5)g = (0, g ), u 3k =
− 1).
(
18ν ρ
The following symbols are used in the equation
system above: t, x i – time and spatial coordinates(i=1, 3); u i – velocity vector projection on thecorresponding axis of cartesian reference system, p2

Fig.1. Computational domain.
Water with impurities inflows into a minethorough the boundary АВ (KD, CI, GN). Water
leaves the domain under consideration though theboundary EF and ground water inflow though HG.
844

GEOMATE- Brisbane, Nov. 19-21, 2014

– pressure; g – gravitational acceleration, R 0 –absolute gas constant, M k – molecular weight k –components, ρ – density of the mixture of fluid withparticles, ν – kinematic viscosity coefficient, D t –diffusion coefficient, d k , ρ k, u 3k - diameter, densityand velocity ofparticle settling, Y k – massconcentrations k – components (k=1 – water, 2 –solid particles). Equation system (1)-(4) contains
elements related to turbulent convection and needs aclosing equation. Tensor components of turbulent

impurity distribution at different time moments wereobtained as the result of numerical integration.
Taking into account three dimensional domain sidewalls are considered not to influence the impurity
distribution process and fluid flow. Thus the
problem is solved in the two-dimensional domain. A
flooded mine (length -100 meters horizontally,
depth- 10 meters) is under consideration. Water with
impurity concentration 0.5 and particle sizes d k =5·10-5 - 10-4 м enters this flooded domain throughthe left boundary. Impurity particles density is 2000
kgs/m3 that is twice as much as water density. The
rate of groundwater inflow from the upper layer HG
is 0.1 m/s. Fig.2-3 show distribution of impurity
concentration at different time moments.

stresses - ρ vi′v ′j are described with the help of mean
floatation gradients according to the formulas:∂ u ∂uj  2(6)
 − kδ i j− ρ u i′u ′j = µ t  i +
 3
∂ x
∂
x
j
i


Taking into account that the flow is turbulent, thecoefficient of turbulent viscosity is used

µ t = ρC µ k 2 / ε , where: k = ui′ui′ /2 – turbulent

kinetic energy; ε – its dissipation, С μ - constant. The
flow -ρ ui′Yk′ is modeled with the help of assumption
concerning gradient diffusion − ρu i′Yk′ = Γk

∂ Yk
,
∂ xi

Fig.2
Impurity distribution in a flooded mine (t=115
sec.)

where Г k – coefficient of turbulent transport,
corresponding to scalar function Y k . At this point theassumption concerning the all-around isotropic
turbulence is implicitly mentioned. Transfer
coefficient Г k for scalar functions is considered to beequal to ratio of turbulent viscosity to turbulent
Prandtl number Γk = vt / Prt . The equation for
turbulent kinetic energy k is as follows [2]: ∂k 
∂∂∂  µ t+ µ 
( ρk ) +(u i ρk ) =

−
∂t
∂x i
∂x i  σ k ∂x i  (7)Fig.3
Impurity distribution in a flooded mine (t=900
sec.) ∂u ∂u j  ∂u iµ ∂T− µt  i +− βρg i t− ρε ,
 ∂x
 ∂x
∂
u
Pr ∂x ij j
 jwhere β = − 1  ∂ρ  .
ρ  ∂T  p
The equation for dissipation of turbulent kinetic
energy ε is as follows: ∂ε 
∂∂∂  µ t( ρε ) +(u i ρε ) =+ µ 
+

∂t
∂x i
∂x i  σ ε ∂x i  (8)

ε

Fig.3
Impurity distribution in a flooded mine (t=900
sec.)

ε2

(G k + G B ) − C 2 ρ,kkwhere Ϭ k , Ϭ ε , C 1 , C 2 – empirical constants, and G k ,G B – turbulence generation because of forced andnatural convection.
+ C1

NUMERICAL SOLUTION AND RESULTS
Based on mathematical statement of the
problems (1)–(7) numerical calculations were madeto determine the pattern of impurity distribution
process in a flooded mine with the help of
PHOENICS [2-3]. Vector fields of velocity and

Fig.4
Impurity distribution in a flooded mine (t=4000
sec.)
845

GEOMATE- Brisbane, Nov. 19-21, 2014

According to the distributions of impurity
concentration displayed above the impurities
gradually distribute in the mine by gravitating to thebottom. While carrying out numerical calculation
with t=6000 sec. (Fig.5) two last figures show thatsteady flow and impurity distribution at the bottom
were identified. Fig.5 shows the impurity
distribution.

The results of calculation showed if the sizes ofparticles are decreased then the deposition rates are
decreased too. So, water with strong impurity
concentration leaves the domain under consideration(Fig.8).

Fig.8
Impurity distribution in a flooded mine (t=6000
sec.).
Fig.5
Impurity distribution in a flooded mine (t=6000
sec.)

In other words, in order to decrease impurity
concentration on the outflow boundary it’s necessary
to prolong impurity residence time in the domain orchange the pumping rate of polluted fluid at theentrance of the domain under consideration.

Fig.6 shows the field distribution of flow
velocity in a flooded mine at t=6000 sec.

CONCLUSION
The general-purpose CFD software PHOENICS
has been customized and validated for modelingcontaminant transfer in a flooded mine due toground waters allocation. The influence of flow
velocity and size of particles at the entrance of thedomain on the distribution impurity concentration ina reservoir as well as on the outflow boundary was
under consideration by using this software.

Fig.6
Field distribution of velocity and impurity
concentration (t=6000 sec.).
If the fluid velocity at the entrance of the domain
is doubled then the impurity distributes more evenly
at the bottom compared to the previous case.

REFERENCES[1] Y.N.Zakharov, Potapov V.P., Schastlivtsev E.L.,
Chiryukina A.V. Simulation of impurity distribution
in flooded mine working \\ Vestnik NGU.
Information technologies . 2009. Volume 7,4, P.6672[1] PHOENICS On-Line Information System:
www.cham.co.uk/ChmSupport/polis.php.
[2] Patankar S.V., Numerical Heat Transfer and Fluid
Flow. New York: Hemisphere Publishing
Corporation, 1981.

Fig.7
Impurity distribution in a flooded mine (t=6000
sec.)

846

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

FUZZY RANKING FOR LANDFILL SITE SELECTION
IN INDIAN CONTEXT
J.E.M. Macwan and Hecson Christian
CED, S V National Institute of Technology, Surat-395007 Gujarat, India
ABSTRACT
Landfill site selection in an urban area is critical issue in the urban planning process. It creates major
impact on economy, ecology, and the environmental health of the region. With the growth of urbanization as well as
the desire to live in cities, larger amount of wastes are produced. Therefore, unfortunately the problem gets bigger
every day. A selection of proper waste disposal site is a function of many parameters pertaining to urban planningand Environmental aspects. The normal practice is to select a site where open space is available. Parameters
pertaining to above aspects are hardly attended to. The right method of selection should have been based uponcorrect assessment of parametric evaluation. All parameters have various scales and values too. In order to integratethese values, fuzzy approach seems to be the reasonable selection method. Thus, the objectives of this paper are tohighlight solid waste scenario in Indian context, to present the assessed parameters and to present a model of fuzzyranking approach to solve the problem. The fuzzy multi criteria decision making (FMCDM) is used to rank different
land fill sites. Site II is best, if we look for environmentally safe site criteria. Site I is having most favorable
accessibility criterion. Overall output suggest that site I is the best site for landfill with 0.6705 index and site III isleast preferable site with 0.4243 indexes. Site II and site IV are having 0.5900 and 0.5183 index respectively. The
solution suggested shall be a useful tool for urban environmental applications.
Keywords: Urbanization, Municipal Solid Waste, Fuzzy logic, analytical hierarchy process, landfill,

disposal of municipal solid waste, whereenvironmentally safe landfill site selection issignificant. Four sites are proposed, out of which thebest site is to be identified. Fuzzy logic approach has
given ranks of proposed four sites, to make decision.
Analytic Hierarchy Process (AHP) is used to evaluate
parametric weights. The city under study is the fastest
growing urban centre in India.

INTRODUCTION
Municipal Solid Waste (MSW) is key
environmental issues for soaring urban centres. During
the early period, MSW was conveniently disposed in
low lying areas with large open land space. The
population growth leads to increase in Solid Waste
generation. The problem of waste disposal and its
adverse impact on the environment is matter of great
concern. Municipal Solid Waste in India has createdmany environmental imbalances. Unfortunately
environmental planning as well as socio-economic
factors are hardly quantified and considered to decidedisposal locations. An approach based on fuzzy
analysis is presented considering Indian situation.

STUDY AREA
Surat city is selected for this research work, which is amajor industrial up and fastest growth centre.
For the study area, it is proposed to include 4 sites
located at various zones of the city for the purpose of
Solid Waste Landfill disposal.
The locations of the sites (as shown in the map) are as
follows:
Site I- South-Zone
Site II East-Zone
Site III-North-Zone
Site IV-West-Zone

In India, the growth of cities is taking place from two
major perspectives. Firstly,in a natural anduncontrolled way, without following scientificprinciples of urban development. Secondly, few cities
are growing by following certain scientific principles
adopted by urban planners based on urban theories anddevelopment experiences.

OBJECTIVES
Objectives of this research are as follows•

India’s urban population is growing at the fastest rate
globally. Therefore, it is difficult to combat the urban
challenges and scientific growth pattern. A scientificdecision making approach needs to be adopted for847

To evaluate urban planning parameters,
influencing solid waste landfill site selection.

••Figure: 2 shows the composite structure of the basic
indicators, selected for site selection from urban
planning point of view.
The membership function for each of the basic criteria
can be constructed, where Zi,h(x) is an interval valueof the basic criteria at the confidence level(membership degree) h, [i.e., a≤ Zi,,h (x) ≤b].

To determine weights of each parameters by
AHP.
To identify ranks of each landfill site byusing fuzzy approach.

Level-I

Level II

Level IIIFig 1 Proposed Location of Landfill Site
Review of Literature
S.M Issa et.al.(2) used eight parameters to identify
landfill site in Abudhabi using similar approach,while
Afzali et al(4) carried scientific studies in selecting
suitable sites for landfill.V.Akbari et.al.(6) advocatedforranking
basedondecisionmakingmethod ,zeinhom EL Alfy(3) used weighed linear
combination (WLC)and AHP to rank the landfill sites,
GH.R.Dini (1) used the Boolean process and followed
by fuzzy approach to identify fuzzy land fill site,
A.Karkazi(5) took 6 input parameters, used fuzzy logic
for analysis of data and the evaluation of the final
results. Juan M.SAchez(7) used MCDM method forlocations while Mehnaz Eskandari and Mehndi
Homaee(8)used integrated approach for environmental
and socio cultural data.

Fig 2 Fuzzy Composite Structure
The best and worst value for the basic criterion isdetermined by expert’s perceptions. Using the bestvalue of Zi(BESZi) and the worst value ofZi(WORZi)for the ith basic indicator, the actual value
Zi,h(x) is transformed into an ith normalized basic
criterion value. The actual value Zi, h(x) is transformed
into an index value denoted by Si, h(x). In the model
Zimin is minimum value and Zimax is maximum value in
observed data. The normalized values for field data are
determined by the following equation. (Bogardi,1992).

𝑆𝑆𝑆𝑆 =

Fuzzy Composite Programming Approach

𝑆𝑆𝑆𝑆 =

This multi-objective analysis of site selection includes
uncertainties in terms of fuzzy membership function.
The membership in the sets cannot be defined on ascale of yes/no in fuzziness as the boundaries of thesets are unclear. The membership degree for a vague
value can be found by expert’s judgment based on
knowledge and practical experience (Stan bury et al.,1991). Uncertainty analysis or fuzziness in site
selection was included to take into account thevagueness in the data range.

(𝑍𝑍𝑍𝑍−𝑍𝑍𝑍𝑍𝑍𝑍𝑍𝑍𝑍𝑍)(𝑍𝑍𝑍𝑍𝑍𝑍𝑍𝑍𝑍𝑍−𝑍𝑍𝑍𝑍𝑍𝑍𝑍𝑍𝑍𝑍)(𝑍𝑍𝑍𝑍𝑍𝑍𝑍𝑍𝑍𝑍−𝑍𝑍𝑍𝑍)(𝑍𝑍𝑍𝑍𝑍𝑍𝑍𝑍𝑍𝑍−𝑍𝑍𝑍𝑍𝑍𝑍𝑍𝑍𝑍𝑍)(When Zimax is best)(When Zimin is best)

Where Si is normalized ith fuzzy indicator; Zi is valueof ithfuzzy indicator; Zimax is maximum possible valueof ith indicator and Zimin is minimum possible value of
ith indicator. The composite distance was computedby the following equation (Bogardi, 1992)

848

𝑛𝑛

𝑏𝑏𝑏𝑏

1/𝑏𝑏𝑏𝑏

𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿(𝑥𝑥) = ��(𝑊𝑊𝑖𝑖𝑖𝑖 �𝑆𝑆𝑖𝑖𝑖𝑖 (𝑥𝑥)� �
𝑗𝑗

Where Lj, h(x) is fuzzy composite distance in group j,
Nj is the number of elements in the first level group j;Si, hj(x) is the index value for the ith indicator in thefirst level group j of basic indicators; wij is theweight reflecting the importance of each basic
indicator in the first level group. Pj= the balancing
factor for the first level group j. The index values, Lj,
h(x) of the second and third level indicators
respectively can be calculated.

2

0.581
0.374
0.44250.26270.2948

Table 2: Best and Worst Indicator Values for
Criterion
Criteria
Population density (ppha)
Transfer station
distance(km.)
Site topography
Residential land (%)
Size (ha.)

AXW=ƒmaxXW
Moreover, the unit eigenvector, (W) corresponding toƒmaxyields the preference weights for the criteria
compared. The maximal deviation is presented bybalancing factor p between the indicators of samegroup. The normal values used for balancing factors inequation are one and two. In this study balancing
factor considered is 1.

Access to services/utilities(Km.)
Vacant land(Ha.)
Future expansion(Ha.)

Best
Value
0
5

Worst
Value
100
15

1/10
0
250

1/1
35
100

0

5

50
25

25
0

Table 3 is showing existing site parameters values for
all four sites under consideration.

RESULTS AND DISCUSSIONS

Table 3: Values of Parameters for sites
Criteria
Site- SiteSiteI
II
III
Population density
48
1260(ppha)
Transferstation12
10
13
distance (km.)
Site topography
1:5
1:4
1:6
Residential land (%)
20
1032
Size (ha.)
175
200
175
Access
to
2
3.5
1.6

On the basis of survey collected from 20 experts
weights of each criteria is determined using AHP. A
weight of each criterion is shown in Table 1.
Population criteria are having maximum weights
among second level parameters.
Table 1: Weights for each criteria
Indicators and Criteria
Population Density
Transferstation0.24950.72480.2751
0.0444

Determination of best and worst values for first-levelindicators is based on design standards suggested by
Ministry of Environment and Forest, Government ofIndia. The Best/Worst value of each of the indicator isshown in Table 2.

To calculate the weight for different indicators,(parameters) weights are selected as per Saaty’s scale.
Pair wise comparisons are used to determine therelative importance of each alternative. To compare
indicator i with indicator j, the decision makerassigns values aij suggested in AHP method. If thedegree of importance of the aji= (1/r). If i = j, thenaij= aji=1. Saaty (1988) has shown that the eigen
vector corresponding to the maximum Eigen value ofmatrix A is a cardinal ratio scale for the indicators
compared.

Level
1distancesite topography
residential land
size
access toservices/utilities
vacant land
future expansion
Population Criteria
Site Criteria
Accessibility Criteria

Weights
0.67460.0757849

SiteIV
55
8
1:8
30
150
0.8

services/utilities(Km.)
Vacant land (Ha.)
Futureexpansion
(Ha.)

50
25

30
20

35
15

40
20

Output of the model is tabulated in Table 4. Resultshows that on the basis of population criteria, site III isleast suitable; all other sites are having almost
favorable conditions. Site II is best, if we look forenvironmentally safe site criteria. Site I is having mostfavorable accessibility criterion. Overall output
suggest that site I is the best site for landfill with
0.6705 index and site III is least preferable site with
0.4243 indexes. Site II and site IV are having 0.5900
and 0.5183 index respectively.
Table 4: Li, j, k, Second and Third Stage parameters for
all Sites
Site
Population
Criteria
Site Criteria
Accessibility
Criteria
Selection
index

I

II

III

IV

0.26340.27940.22820.26510.11770.1841

0.05240.51300.28930.12640.14350.20200.67050.59000.42430.5183

2.

3.

4.

5.

6.

7.

8.
CONCLUSION:
1. Population criteria plays major role for decision
makers for urban planning process.
2. Site I is environmentally safe, as it is least populated
and other factors are favorable for solid waste disposal.
3. Once site I is filled than other sites may beconsidered as per rank.
ACKNOWLEDGEMENT
Authors are grateful to local municipal authorities forhelp in supplying necessary data. Experts are also
acknowledged for their kind suggestions. Instituteauthorities were also kind in co-operating this research.
REFERENCES
1.

GH. R. Dini, M. Amini, P. Zieaean Firooz Abadi,
Seid Ramezan Mousavi. “GIS–Based Site
Selection for Solid Waste Landfill Using Fuzzy

850

Logic Case Study: Sari City, Iran,” Queensland
Spatial Conference, (2008).
Zeinhom El Alfy & Rasha Elhadary,” Integrating
GIS with MCDM to Deal with Landfill Site
Selection”, International Journal of Engineering &Technology IJET-IJENS Vol:10 No:06, (2010).
Afzali, J. M. V. Samani, M. Rashid,” Municipal
Landfill Site Selection For Isfahan City By Use Of
Fuzzy Logic And Analytic Hierarchy Process”,
Iran. J. Environ. Health. Sci. Eng., 2011, Vol. 8,
No. 3, pp. 273-284, (2011).
A.Karkazi, T. Hatzichristos, A. Mavropoulos, B.
Emmanouilidou,
Ahmed Elseoud, “ Landfill
Siting using GIS & Fuzzy Logic”, Springer
Publications, (2011).
V. Akbari, M.A. Rajabi, Chavoshi, Shams,“Landfill Site Selection by Combining GIS andFuzzy Multi Criteria Decision Analysis, Case
Study: Bandar Abbas, Iran”, World Applied
Sciences Journal 3 (Supple 1):pp 39-47, (2008).
Juan M.Sánchez-Lozano & JerónimoTeruel-Solano,“Geographical Information Systems (GIS) andMulti-Criteria Decision Making (MCDM) methods
for the evaluation of solar farms locations: Casestudy in south-eastern Spain”, Elsevier Journal,(2013).
Mahnaz Eskandari & Mehdi Homaee, “Anintegrated multi criteria approach for landfill siting
in a conflicting environmental, economical andsocio-cultural area”, Elsevier Journal, (2012).
André L. O. Melo, Maria Lúcia Calijuri, Shahla
Mahmodi, “Strategic Decision Analysis for
Selection of Landfill Sites”, Journal of Survey
Engg, ASCE,(2006).

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

TOURISM PLANNING PROPOSAL FOR VISAKHAPATNAM CITY,
ANDHRA PRADESH, INDIA
Dr. Krupesh A. Chauhan 1, Y. Pavan Teja 2
1 Asso. Professor, PG in-charge, M. Tech (urban planning), Department of Civil Engineering, Sardar Vallabhbhai
National Institute of Technology, Surat, Gujarat, India
2 Research Student, Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology,
Surat, Gujarat, India

ABSTRACT
Tourism industry plays an important role in developing economy, especially in some cities where there aredifferent historical, landscape and other natural attractions. Visakhapatnam City, Andhra Pradesh, India is oneof the major city in south east coast of India has outstanding natural landscapes, moderate weather especially inspring and summer. The main aim of this study is to evaluate the strengths, weaknesses, opportunities, andthreats (SWOT) in tourism Development in the city of Visakhapatnam and giving suitable proposals to enhance
tourism. It is supported with various case studies on global & national level. The analysis of tourist profile iscarried out in different occasion in different months to know about the present situation and various Issues
performed. A SWOT and analytical hierarchical process (AHP) method analysis is formulated to analyze thedata.This Proposals will benefit local area in which various tourist Infrastructures attract more tourist in
Visakhapatnam, Andhra Pradesh, India
Keywords: Tourism Development, SWOT, AHP, Proposal
city respectively, and based on that SWOT analysis
is formulated and different proposals are prepared
for the up gradation of tourism in the
Visakhapatnam city

1.0 INTRODUCTION
The World Tourism Organization defines Tourism isan activity done by an individual or a group ofindividuals, which leads to a motion from a place toanother. From a country to another for performing a
specific task, or it is a visit to a place or several
places in the purpose of entertaining, which leads toan awareness of other civilizations and cultures, also
increasing the knowledge of countries, cultures, andhistory. Tourism as a whole is a booming industry
and particularly offers a lot of varieties in terms ofactivities. Offshore and onshore activities haveattracted tourist to coastal areas all around the world.
In India tourism activities, policies are boosting
economic growth and economic activities in many
regions and many local governments are using
tourism policy to boost their economic growth andraise income level for local people and local
business. Visakhapatnam is one of the major city inthe state of Andhra Pradesh located at south east
coast of India attracts many travelers from tourism
perspective who visit and sightsee. The city has apotential to serve as a center of tourism in the region
as it has beautiful beaches, pilgrimage centers,
recreational parks, heritage sites, etc. There is a lackof facilities at some places and need for up
gradation.Therefore study is very much required forproper development and to attract more number oftourists in city data collection is made byinterviewing experts and tourists in Visakhapatnam

2.0 OBJECTIVES AND SCOPE
The objective of this study is to assess the present
situation of Tourism development in Visakhapatnam
City, analyze the present situation based on SWOT
analysis and to provide future proposals.
The Scope of this study is limited to tourist places in
Visakhapatnam City area which is under Greater
Visakhapatnam Municipal Corporation limits.
3.0 STUDY AREA PROFILE
Visakhapatnam is the largest city in Andhra Pradesh,a sprawling industrial city and one of the emerging
metropolis. Visakhapatnam city lies between
17.6883° N latitude, and 83.2186° E longitude. Its
periphery consists of plains along the coastline while
the interior boast of the beautiful hills of the Eastern
Ghats, which surround it on the North and the West.
The City is located on the sea shore of Bay of
Bengal in the East, with a population of 20.91 lakhs
and occupying 530 sq.km

851

GEOMATE- Osaka, Nov. 16-18, 2015

Visakhapatnam
District

India

AndhraPradeshVisakhapatnam city

Fig 1: Location of Visakhapatnam city
Visakhapatnam city is divided into 6 zones and 72
Wards

Fig.3 showing the methodology adopted
Fig.2 Zone map of Visakhapatnam City

5.0 DATA COLLECTION AND ANALYSISThe Inventory data is collected from different
sources like Tourism department, Visakhapatnam
Urban Development authority and Municipal
Corporation and the field data are collected fromtourists, Experts and Stakeholders

4.0 METHODOLOGY
The methodology adopted for the study and thesequence of various steps undertaken is shown in the
fig 3. The first stage is selection of the study areawith aim, objectives. In second stage literature
review has been done. Third stage followed bycollection of inventory and field data. Fourth stage
consists of analysis and calculation for future
requirements. In fifth stage proposals has been given
in various locations to enhance tourism

Thotlakonda

Rushikonda
Kailasgiri

Kambalakonda
Mudasalova
Submarine Museum
Shimhachalam
R.K BeachLine of 3 Temples

Fig.4 Map showing tourism location in
Visakhapatnam City

852

Light house

GEOMATE- Osaka, Nov. 16-18, 2015

SWOT analysis is developed following an
integrative procedural model that includes thetourists, stakeholders, and experts

PURPOSE OF VISIT

Tourists

Vacation,fun
13%

Fig. 6 Chart showing purpose of visit

SWOT

Experts

Rest andrelaxation29%

Historica
l culture
8%
Recreati Bussinesons reason
11%
8%

Needs &
Experience

Strategic
VisionOther
6%

Religious
25%

5.1.2 Comparative analysis:
In the fig 7 the comparison between different
tourism resources like facilities, connectivity andaccommodation is shown and it clearly shows
tourists are mostly satisfied with the accommodation
but when comes to facilities there are somehow
dissatisfied in some tourist spots .

Target market,
employment

stakeholders

40%
30%

Fig. 5 Data collection procedure20%

5.1 Analysis
The analysis gives a complete understanding to
identify behaviors of tourists, stakeholders andexperts for existing situation in tourism of
Visakhapatnam city which is followed bycomparative analysis, sentimental analysis, gap
analysis and SWOT analysis.

10%0%Facilities

Connectivity

Accomodation

very poorsome how disatisfied

satisfiedvery good

excellent

5.1.1 Purpose of Visit:
From the fig 6, it is shown that 29 % tourists comeVisakhapatnam for Rest and relaxation While 25%
come for Religious. And 11 % tourists coming for
Recreation .Therefore tourists willing to comeVisakhapatnam for mostly relaxation and enjoy over
ere and religious because of important Pilgrimage
also study includes general characteristics like
purpose of visit, Duration of Stay, Mode oftransportation and other related information like
expenditure pattern which will affect the Local
Economy in Visakhapatnam city

Fig. 7 Comparison chart
The comparison of other tourism resources likeinfrastructure, management and maintenance is also
included in the study
5.1.3 Sentiment Analysis:
In the Fig 8 shows the perception of the tourists istaken into consideration how there sentimental feel
about the religious & pilgrimage places in
Visakhapatnam city. Nearly 34% of the people weresatisfied and 26% people are somehow dissatisfied
and 17 % feels the condition is very good and 15 %says excellent and 8 % people says the condition is
poor

853

GEOMATE- Osaka, Nov. 16-18, 2015

improved and 10 % people feel that there must be a
cycling track enabled a beach

RELIGIOUS/PILGRIMAGE
very
poor
8%

some howdisastisfied
26%% Tourist

excellent
15%
very
good
17%

satisfied
34%
Fig .8 Sentimental analysis of Pilgrimage
The Sentimental analysis also done for knowing the
tourists perception of beach and heritage spots in
Visakhapatnam city.

2011

12.54

2.54

15

35%

15%

13% 8.30%

5.1.3 SWOT Analysis
Based on the Survey with 358 stakeholders and 8
experts SWOT was formulated
Strengths:•

Good rail and road connectivity to
Visakhapatnam city
• Well road networks to connect almost all themain tourist destinations.
• The Visakhapatnam is one among very few
which has both lined up hills on one side andstretches of sandy beaches• Mix of destinations and attractions of all
types- Beach tourism, hill stations, religious,
Heritage with immense natural beauty. The
majority of the tourists visit for its breath
taking and inspiring natural beauty• Tourists have choice to see different beaches,different places like Simhachalam, Sri
Kanaka Mahalaxmi temple, kailasigiri
recreational park on hill etc.
Weaknesses:• Lack of proper Skill man power in the
tourism department.
• Lack of facilities and wayside amenities fortourist in Visakhapatnam city
• Lake of Maintenance at some tourist spots• Lack of adequately trained guides andinformation
Opportunities:• Government of India has declared
Visakhapatnam for developing as smart city
Expand current offerings• Ecotourism sites in and around the city
• Many Archaeological Buddhists sitesaround the city
• Increasing the employment opportunity for
local people.

Table 1 Gap analysis of recreation

Gap(NM)

10%

Fig .9 Facilities to be upgraded

The gap analysis is done on the basis of standard
norms considering current scenario for up-gradationof future tourism in Visakhapatnam city

Existing Recreational
Percentage
MinimumofRequirement
Recreational of Percentage

sanit
Drink
Parks
ation
ing wate shopi
/Amu
and
wate
r
ng
cycle smen
toiletr
sport Facili
t/gar
block
facilit
s
ty
dens
s
y
18%

5.1.4 Gap Analysis:

Year

40%
35%
30%
25%
20%
15%
10%
5%
0%

Where,
P = Recreational facilities gap.
M = Existing Recreational
N =Required Recreational facilities
Based on Urban and Regional development plans
Formulation and Implementation URDPFI Norms
India 2015 for a port city the recreation area can be
minimum 15 to 20 percent of the total land use of
the city
5.1.5 Facilities to be upgraded:
From the fig 9 it is shown that parks and also therequirement of amusement is suggested by the
maximum number of Tourists with 35% of themgiving various opinions the improvement ofsanitation and toilet Facilities is second with 18% of
the tourists. Also,15 % of the tourists felt that the
Availability of water facility must be improved and
13 % of tourist feel that water sports at beach mustbe854

GEOMATE- Osaka, Nov. 16-18, 2015

•

•
•
•

many Government vacant lands .This zone is also
known as an educational hub of Visakhapatnam

Tourism has been declared as an industry
by the Government of Andhra Pradesh.
This makes the sector eligible for a host of
incentives and concessions
Accommodations are sufficient to support
tourists.
The highway passes through the city andconnects to Kolkata on the north east andChennai on the south of the city
Visakhapatnam
is on track for thedevelopment of the Metro rail and BRTS

Threats• Sometime the environmental conditions,
Nature calamities also giving threat to thetourism actives• Lack of proper vision and mission• Increasing lack of user friendliness• Competing wild life sanctuaries in thevicinity• There are no activity programs that support
Tourism

Fig. 12 Vacant land site in North zone
Below fig 14 showing the proposed amusementpark .The park is mainly having the features ofbeautiful green infrastructure and water rides

6.0 PLANNING PROPOSALS

Garden

Proposals are made for vision of future developmentof tourism in Visakhapatnam city. The proposals are
given based on the existing situation in the city

Water Rides
Childrens play

6.1 Proposal -I
For the proposal of amusement park the vacant lands
are chosen based on the growth patterns in the city
and availability of urban space. Their vacant lands
are chosen in different zones of Visakhapatnam city
All the sites which are chosen are having goodstrength for setting up the amusement park but for
better approach of assessment seeking the view ofexperts is taken into consideration and giving
ranking based on AHP and choosing the best site for
proposal

Total Area 9 ha

Fig. 14 Proposed Amusement Park
6.2 Proposal -II
Kailasigiri Recreational park is one of the most
attracting tourist spot in Visakhapatnam wasrecently badly affected by Hud Hud Cyclone has lostthe green infrastructure. The park has a potential toattract many tourists

Table 2 Ranking analysis based on AHP

The selected site is located towards north directionof the city in the zone 1. The site is having a land of
approximately 15 hectors and this zone there are

Fig. 15 Existing situations at Kailasgiri Park855

GEOMATE- Osaka, Nov. 16-18, 2015

Which gives a positive approach for encouraging
future tourism development and also the proposed
study of this paper is included with proposals likeamusement park, Redevelopment of Kailsigiri
recreational park and proposal for development ofaqua sports are given for vision of future which led
to the growth of economy and also helpful for
employment generation

According to the report of ministry of tourism
Andhra Pradesh 2012, where there have carried out
potential carrying capacity and estimated that by theyear 2021 the dwell unit will be 12591persons per
day which is presently 2850 per day. So giving
proposal further hopes for the betterment ofrecreation in the park.

8.0 ACKNOWLEDGEMENT
We earnestly wish to express our heartfelt thanks
and a sense of gratitude to all the officials of
Visakhapatnam Urban Development authority,
Municipal Corporation Tourism Department andstakeholders for providing the information.
9.0 REFERENCES[1] Archana Bhatia, “SWOT Analysis of Indian
tourism Industry”, IJAIEM , Volume 2, 2012
[2] Asia pacific economic cooperation,
theeconomic impact of travel & tourism 2013,
travel & tourism economic impact[3] K.Mythili , “Tourism-impact on Environmentalobservation for north costal Districts of Andhra
Pradesh.”International journal of Social
management
Economics
andBusinessEngineering, 2012, Vol. 7[4] Iis P. Tussyadiah ,“ The City Of Cape May
Tourism Plan National Laboratory for Tourism& ecommerce (NLTeC)”, 2012
[5] Kamran Noori1, Farhad Zand (2013)“Rural
Tourism Development Based on SWOT Model”(Case Study: Paveh Township, Kermanshah)[6] KomainKantawateera,
AreeNaipinit,
Thongphon Promsaka Na Sakolnakorn, “ASWOT Analysis of Tourism Development inkhonkean, Thailand”. Asian Social Science; Vol.
9, 2013,No. 17.
[7] Mohammad Ali Firoozia, Majid goodarzi,
Abdolmotalleb Akbaric, “ Analyzing the role ofBavan Valley in Mamasani as tourists attraction”
Management Science Letters Management
Science Letters 2 (2012) 2449–2456.
[8] Salma Begum, “Revitalizing Public OpenSpace-The Case of Dhakeshwari ” Proceedingsof the 1st National conference of Sustainable
and Smart cities,2015, Surat.
[9] TCPO, Government of India Ministry of Urban
Development.
“Urbanandregional
development
plans
formulation
andimplementation”(URDPFI)
guidelines

Total Area 160 ha

Fig. 16 Proposed Amusement Park
6.3 Proposal-III
Towards North east of the selected area, there aremany private resorts and recreation clubs. Between
the hills and with a sea coast , the area has a lot of
potential to develop as a recreational area for new
aqua sports can be launched like beach volley balland speed boating.

Fig. 17 Proposed Cycle track and water sports region
7.0 CONCLUSION
In this paper, it has been presented the main
objective of the study to analyze the strengths,
weaknesses, opportunities, and threats of tourism in
Visakhapatnam City, India. On the basis of analysis.
It is concluded that strengths and more opportunities
are more comparable to threats and weakness.

volume1”,january 2015.

856

Fifth International Conference on Geotechnique, Construction Materials andEnvironment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

SOLID WASTE MANAGEMENT IN DHAKA CITY CORPORATION
Md. Aminul Islam and Zakaria Hossain

Faculty of Bioresources, Mie University, Japan

ABSTRACT
Unplanned urbanization and inadequate collection of municipal waste are the main problems for solid waste
management in Dhaka City Corporation. Due to rapid urbanization, expanding economic activity andconsumption of consumer items; quantities of municipal solid waste is increasing rapidly. Domestic orhousehold waste is the largest amount of municipal solid wastes. In this paper, a model has been developed todetermine if the population is known and the increasing rate of waste generation remain same. As the results,
average generation rate of domestic waste has been determined 0.36 kg per capita per day and in general average
waste generation rate has been determined 0.56 kg per capita per day. It is observed that most of the domestic
wastes can be recycled or reused. In data analysis from the field survey, it is found that most of the recyclable
materials were collected by the waste pickers. It is concluded that the scenario of waste disposal facility in
Bangladesh is not satisfactory. A typical suggestion for a well-established waste management system has been
recommended at the end of this paper.
Keywords: Population, Urbanization, Dhaka City, Municipal Waste Generation, Waste management.
Existing infrastructure for waste management
showed that waste collection efficiency in different
urban areas varies from 37% to 77% with an averageof 55% [2]. The overall waste collection situation is
not satisfactory. Huge amount of uncollected waste,a high proportion of which is organic, makes
nuisance and pollutes the local environment rapidly.
Solid waste disposal poses a greater problem
because it leads to land pollution if openly dumped,
water pollution if dumped in low lands and air
pollution if burnt [3]. Dhaka, the capital city of
Bangladesh is facing serious environmental
degradation and public-health risk due touncollected disposal of waste on streets and otherpublic areas, clogged drainage system byindiscriminately
dumpedwastesandbycontamination of water resources near uncontrolled
dumping sites. The study concentrated on municipal
solid waste management of Dhaka City Corporation.
It has identified the existing recycling condition ofdomestic waste and the scope of further recycling orreuse of domestic waste, present condition ofmunicipal solid waste management and survey on
waste pickers. The study also dealt with future
prediction of municipal solid waste generation witha view to proposing a better municipal waste
management system.

INTRODUCTION
The population of Dhaka is now nearly 10
million within its city area of 360 sq. km. In addition
to this, about one million commuters visit the city
every day in search of their livelihood. The
population of Dhaka city has increased rapidly, witha growth rate of 3.72% per year. In Dhaka City
Corporation (DCC) area, 60% houses are of low
income, 37% middle income and the rest 3%
constitutes high-income houses. The old part of
Dhaka is more densely populated than the new partof Dhaka. There are over 1,000 small and large
industries including 149 tanneries, about 500 clinics
and hospitals in Dhaka Metropolitan area [1]. Solid
wastes make an incredible environmental hazard andsocial problem in city lives. A massive volume ofsolid waste is generated every day in the city areas.
Now, solid waste management has becomeaserious problem due to the limited resources andlack of proper management system for handling theincreasing rate of generated waste.
Rapid urbanization has made solid waste
management (SWM) a serious problem today [5].
The urban area of Bangladesh generates
approximately 16,015 tons of waste per day, whichadds up to over 5.84 million tons annually. It isprojected that this amount will grow up to
47,000tons/day and close to 17.16 million tons per
year by 2025, due to growth both in population andthe increase in per capita waste generation. Based on
the present total urban population, per capita waste
generation rate is found at 0.41 kg/capita/day inurban area of Bangladesh.

METHODOLOGY
To pursue this study, a few questionnaire surveys
were conducted on the type and rate of generation ofdomestic solid waste, collection and recycling
condition of municipal solid waste and field visits
857

GEOMATE- Osaka, Nov. 16-18, 2015

were paid to some waste treatment centers anddisposal sites. Another questionnaire survey was
also conducted on the waste pickers in Dhaka City
Corporation. The secondary data were collectedfrom the different published sources like newspapers,
books, journals, research reports etc.
Site Selection of Study Area
The study site was selected with a view todetermine the type and rate of generation ofdomestic wastes, collection disposal and recycling.
The purpose of such selection was to obtain ascenario of existing waste management andrecycling situation of Dhaka City Corporation forproposing a better municipal waste management
system.

Fig-1 Composition of Municipal solid waste
CLASSIFICATION OF MUNICIPAL SOLID WASTE

Present Scenario of SWM in Dhaka City

The classification of municipal solid waste isgiven in Table 1 showing component and its
description and rate of waste generation is given in
Table 2.

The present scenario of Municipal Solid- Waste
Management in Dhaka City Corporation is notsatisfactory at all. With the increasing population,
expansion of the city- without proper planning thesituation aggravated further. It has been found thatthe household solid constitute the largest volume ofmunicipal solid wastes. But they are not properly
segregated at sources and it makes the task of waste
management more difficult. With the scanty
resources in terms of money, skilled manpower andlogistics support, it is very difficult to handle thebulk volumes of solid wastes in Dhaka City where
about 4,500 tons of waste per day generated by morethan 10 million people. As a result more than half ofthe city's daily generated wastes remain uncollected
and more disposed locally and informally making
the environment and health of the metropolis quite
gloomy and dismal. The city-dwellers seem to haveaccepted to live with garbage scattered around alongwith strong stench and severe health andenvironmental hazards; not only basic civic sense ofthrowing of wastes are lacked in city but also thereis tremendous lack- in part of both citizen and thegovernment to come up with alternative plans tomanage the huge amount of solid wastes produced in
the city.

Table-1 Classification of municipal solid waste
Items
Food
wastesRubbishAshes
andresidues

Demolition andconstructionwastesCOMPOSITION OF MUNICIPAL SOLID WASTE

The following Figure 1 shows the composition ofmunicipal solid waste in Dhaka city Corporation.
Food waste constitute 78.87%, Glass/metal/
Construction 8.17%, paper and cardboard 4.29%,
and plastic 4.1%, textiles 4.57%. Here, food waste
is the largest amount of solid waste generated in the
study area.

858

Description
The animal, fruit, or vegetable residues (also called
garbage) resulting from the handling, preparation,cooking, and eating of foods, Because food wastes
are putrescible, they will decompose rapidly,
especially in warm weather.
Combustible and noncombustible solid wastes,
excluding food wastes or other putrescible
materials. Typically, combustible rubbish consistsof materials such as paper, cardboard, plastics,
textiles, rubber, leather, wood, furniture, and garden
trimmings. Noncombustible rubbish consists ofitems such as glass, crockery. Tin cans. Aluminumcans. Ferrous and nonferrous metals, dirt, andconstruction wastes.
Materials remaining from the burning of oil, coal,coke, and oilier combustible wastes. Residues frompower plants normally are oil included in thiscategory. Ashes and residues are normallycomposed of fine. Powdery materials, cinders,clinkers, and small amounts of burned and partiallyburned materials.
Wastes from razed buildings and other structures
are classified as demolition wastes. Wastes from theconstruction, remodeling, and repairing ofresidential, commercial field industrial buildings
and similar structures are classified as constructionwastes. These wastes may include dirt, stones,
concrete, bricks, plaster, lumber, shingles, andplumbing, heating, and electrical parts.

SpecialwastesWastes such as street sweepings, roadside litter,
catch-basin debris, dead animals, and abandoned
vehicles are classified as special wastes.

Treatment
plant
wastes

The solid and semisolid wastes 'from water,
wastewater, and industrial waste treatment facilities
are included in this classification.

GEOMATE- Osaka, Nov. 16-18, 2015

Table: 2 Waste Generation Rate (WGR) and Total Waste generation (TWG) in Dhaka City and other cities, 2004
City/Town

WGR(kg/No.

ofcap/day)

City/Town

Total

Population

TWG(ton/day)Average(TWG)

Population

-2005

Dry

wet

seasonseason

Dhaka

0.56

1

6,116,731

6728404

3,767.91

5,501.14

4,634.52

Chittagong

0.48

1

2,383,752

2,622,098

1,258,.61

1,837.57

1,548.09

Rajshahi

0.3

1425,798

468,378140.51205.15127.83

Khulna0.27

1879,281

967,365

261.19381.34321.26

Barisal0.25

1397,724

437,009109.25

159.51134.38Sylhet0.3

1351,724

386,896116.07169.46142.76

Pourasavas

0.25

298

13,831,187

15,214,306

3,803.58

5,553.22

4,678.40

Other

0.15

281

8,379,647

9,217,612

1,382.64

2,018.68

1,700.65

urban

CentresWaste generation per capita for residential, commercial and public areas are given in Table 3 showing different
places of Dhaka city.
Table: 3 Waste generation per capita (residential, commercial and public areas)

CURRENT STATEMANAGEMENT

OF

SOLID

and Uttara; 'house to house' waste collection service
has been organized by some private initiative [4].
Rickshaw vans are used for collection of waste fromhouses to municipal containers. 50% of population
are using waste enclosure or bins, 20% of population
using roads to dispose of waste, 20% of population
using drains to dispose of waste and 10% of
population using open ground to dispose of waste.
The total amount of solid waste generated every
day in DCC area is about 4500 to 5000 tons.
According to Mott Macdonald International Ltd
report of 1991, waste stream fraction of Dhaka cityis 46.8% domestic, 21.8% street sweeping, 19.2%
commercial, 12.9% industrial and 0.5% clinical.
Solid waste flow of a residential area is 58.7%
domestic, 33.08% street sweeping, 7.9% commercial

WASTE

Dhaka City Corporation collects municipal
wastes which are accumulated in DCC's bins orcontainers.
About 7,146 cleaners are employed for street
sweeping and collection of waste found in places
other than dustbin, road side, open spaces, ditches
etc. by hand trolley. It has 2,080 hand trolleys forprimary collection of waste. DCC has 128
demountable container carrier trucks for collectionof accumulated waste in 414 container and 242 open
trucks to collect waste from municipal bins at
different locations. In some residential areas like
Kalabagan, Dhanmondi, Banani, Gulshan, Baridhara859

GEOMATE- Osaka, Nov. 16-18, 2015

and 0.32% clinical (Salam,2001).Analysis ofphysical composition of domestic waste show thatthe primary component is food waste covering
78.87%, Glass/metal/ Construction 8.17%), paperand cardboard 4.29%, and plastic 4.1%, textiles
4.57%. There is a variation of waste composition
between down town and residential area of newparts of Dhaka. Domestic waste generation rate forresidential area is 0.60 kg per person a day (Salam,
2001)

Estimating the generation rate of daily produced
municipal solid wastes is very significant for a better
waste management system. In this paper, average
generation rate of domestic waste in Dhaka City
Corporation has been determined 0.36 kg per capita
per day and in general average solid waste
generation rate has been determined 0.56 kg per
capita per day. There is a very close connection
between the increase of population and thegeneration rate of solid wastes. The waste generation
rate increases with increase of
population .The
projection of future population is very essential toestimate the exact generation rate of solid wastes fora better waste management system. With the help of
the following equation, the future population can beprojected and the generation rate of solid waste canbe predicted.
P=P 0 (1+r)nWhere, P= Expected Population
P 0 =Present Population
R= Growth Rate
N= YearFig 2 :Existing waste collection and management
systemThe figure 2 shows the general waste collection
process and management system in Dhaka City
Corporation.The DCC is responsible for collecting
the solid wastes from municipal collection points
and for disposal of the wastes in the disposalsites.But the DCC openly states that the collection
system cannot cope with the task of handling thelarge volume municipal wastes produced by theever-growing number of city dwellers.It has beenfound that only 40-50% daily generated solid
wastes is being collected (DCC 2011) [6].On theother hand ,the uncollected wastes are badly
contributing to environment pollution anddeteriorating the public health and sanitation.

CONCLUSION
The Dhaka City Corporation with a large numberof population is facing a serious problem to manage
the huge amount of daily generated solid wastes.
Due to the lack of manpower; absence of modern
tecknology; most importantantly, the lack of proper
estimation of the generation rate of solid waste. It isunable to collect and properly manage the solid
waste.The categorization and exact estimation of thetotal amount of solid waste generation is verysignificant for a better waste management system.
Average generation rate of domestic waste has been
determined 0.36 kg per capita per day and in general
average waste generation rate has been determined
0.56 kg per capita per day.

RESUL TAND DISCUSSION
Categorozation and Composition of Solid waste

REFERENCES
Categorization and composition of solid wastes
are very important for the proper management ofmunicipal solid wastes. From the study it has beenfound that food waste constitutes the largest amountof solid wastes in Dhaka City Corporation. It isestimated that around 78% of the total solid is foodwastes. On the contrary, plastic waste is the lowest
amount of daily produced solid waste. The DCC
collects only 40 -50% solid wastes. As a result, theuncollected wastes are badly polluting theenvironment and thereby creating a risk for publichealth and sanitation.

[1] JICA, Clean Dhaka Master Plan, The Study On
The Solid Waste Management In Dhaka City.
Final Report, Volume-i Summary Japan
International Cooperation Agency, March 2005.
[2] Ahmed, M.F., "Municipal Waste Management in
Bangladesh with Emphasis on Recycling"
Aspects of Solid Waste Management:
Bangladesh Context, German Culture Centre,
1991.
[3]. Hasan, S. Problems of Municipal Waste
Management in Bangladesh: An Inquiry into its
Nature, in Habitat International, 22(2): 191-202,
1998.

Generation Rate of Solid Wastes
860

GEOMATE- Osaka, Nov. 16-18, 2015

[4] Ashfaq, K. N. Analysis of Heavy Metals in Bed
Sediments of Dhanmondi Lake in Dhaka City, M
Engineering Thesis, Dcpl. of Environmental
Engineering, BUET, Dhaka.
[5] Enayetullah, I., Sinha, A. H. M. M. and Khan, S.
S. A. ‘Urban Solid Waste Management Scenario of

Bangladesh: Problems and Prospects’, Waste
Concern Technical Documentation, Dhaka,Bangladesh, pp.3-18, 2000.
[6] DCC, Solid Waste Management by Dhaka City
Corporation, Dhaka City Corporation, Dhaka, 2011.

861

Fifth International Conference on Geotechnique, Construction Materials and
GEOMATE- Osaka Nov. 16-18, 2015
Environment, Osaka, Japan, Nov. 16-18, 2015, ISBN: 978-4-9905958-4-5 C3051

INFLUENCE OF SOIL REPLACEMENT ON BEARING CAPACITY
AND DEFORMATION OF SOFT CLAY IN ARABIAN GULF

(1)

Nuri al- Mohamadi (1) and Vahik K. Bedrosian (2)Civil Engineering, Applied Science University, Amman, Jordan(2)
Vahik K. Bedrosian
ABSTRACTThe major part of the present research was directed to the evaluation of ultimate bearing capacity (qu) andsoil displacement (ρ) by performing loading tests on rigid circular model footings founded on the surface oflaboratory prepared soft clay layer overlained by a dense sand layer. A special device is developed to measurepore water pressure in the clay layer during the loading tests. The prepared soil properties were measured,
including; Atterberg limits and indices, undrained shear strength Cu and modulus of deformation Es. The
influence of model footing diameter B, the replacement ratio R= H/B, (where H= thickness of sand layer) andload increment (L.I) on the bearing capacity and displacement was studied. The main findings of this study are;qu increased by a ratio of 21.5% to 82% and the displacement decreased by 19.4% to 97% depending on thereplacement ratio (R). This improvement was most beneficial at the range of: 1 ≤ 𝑅𝑅 ≤ 2 , which give an
increase in qu of (59% to 82%) and decrease in displacement of (68% to 97%). qu and displacement of larger
footings were mostly affected by soil replacement. The comparison between the measured pore water pressure
values and the theoretical values were in good agreement. The experimental results were compared with thetheoretical values proposed by Meyerhof (1974) good agreement was achieved. Experimental results of soil
displacement were compared with those obtained from linear elastic finite element analysis, very good
agreement was observed. The difference between the two analyses was only 4.4%.
Keywords: soft clay, soil replacement, sand layer, bearing capacity, displacement, foundations.
INTRODUCTION
Compression index;
Cc
Activity; A
Clay fraction; %The formation of the triangular Fao
peninsula in southern Iraq was a result ofchanging the course of Shattal-Arab Riverduring the past 6000 years. This area iscomposed mainly of: normally consolidated(N.C) young clay along the present river course,
N.C aged clay along the old river trace near Um– Qasir and surrounding area. These clayeysoils are considered as the most soft andcompressible in Iraq. Soil properties are
summarized in table [1],

0.86
28-30

The reconstruction of Fao town has imposedcomplicated problems on the main author
studying the appropriate foundations fordifferent types of structures. It was decided to
improve the top soil beneath foundations byreplacing it with 1.0 m thick of compacted sandlayers stabilized with cement. Therefore, it wasessential to study this proposal theoretically andexperimentally and to compare the results withavailable theories to reach to a satisfactory
method for design and construction offoundations in soft clay.

Table 1 Average properties of soft clay from
Fao region
Property
Specific gravity; G.s
Water content, w%
Liquid limit; L.L%
Plastic limit; P.L
Plasticity index;Ip%
Total unit weight; ɣt
Dry unit weight; ɣdInitial void ratio; e0.24-0.27

Range
2.72
46-48
48-50
24
24-26
16.3-17.78 KN/m3
11.2-12.2 KN/m3

THEORETICAL CONCEPTS
Meyerhof [2], proposed a semi empiricaltheory for the estimation of ultimate bearing
capacity qu for a soft clay layer. The
assumption evolved from punching shear theoryis that; at the ultimate load,

1.13-1.39

862

GEOMATE- Osaka Nov. 16-18, 2015TESTING EQUIPMENTS
A soil mass in the upper sand layer (friction angle
ɸ1) of roughly truncated pyramidal shape is pushed
in the lower soft clay layer (cohesion C 2 ) in the
direction of applied load.

The testing equipment shown on Fig. (1)consist of four main parts;(i) Containers for sample preparation made ofgalvanized perforated steel plate cylinders
450mm in diameter, 500mm high with collar
400 mm high to prepare samples from slurry.
(ii) Aluminum model footings 50, 75,100 mm
in diameter, 25 mm thick.
(iii) Specially built loading apparatus to allow
consolidation of sample before testing using
lever arm system, during load test a dead weightsystem is used.
(iv) Pore water pressure device, speciallydeveloped for this work.
TESTING PROGRAM
A total of 14 loading tests on model footings
50mm and 75mm in diameter, including 2 tests
subjected to cyclic loading as shown on table(2).
Table 2 Model footing test results

Fig.1 Details of loading apparatus for themodel footing tests

Test
No.
1
2
3
4
5
6
7
8
9
10
11
12
13c
14c

According to Myerhof [2], qu of a rough strip
footing of width B and depth Df is given by Eq.
(1).
𝑞𝑞𝑢𝑢= 𝑞𝑞𝑏𝑏+ 2𝑃𝑃𝑝𝑝 sin 𝛿𝛿⁄𝐵𝐵−𝛾𝛾1𝐻𝐻
Where:(1)≤ 𝑞𝑞𝑡𝑡2𝐷𝐷

𝑃𝑃𝑝𝑝= 0.5𝛾𝛾1 𝐻𝐻2 �(1 + 𝑓𝑓) ∗ 𝐾𝐾𝑝𝑝 / cos 𝛿𝛿�(2)
𝐻𝐻
For practical purpose δ= 0.5ɸ1 and Ks tanɸ1=
Kp tan δ.
Where: Ks is coefficient representing punching
shear resistance on the vertical plane throughfooting edge. Eq. (1) can be rewritten as follows

B
mm
75
75
50
50
75
75
75
50
50
75
50
75
50
75

R
0
0
0
0.5
0.5
1
1
1
2
2
2
2
0.5
0.5

L.I
KPa
4
4
4
4
4
4
8
8
8
8
16
16
8
8

qe
KPa
24
36
52
52
52
52
96
96
104
104
160
160
64
56

ρe
mm
9.6
12.5
6.05
5.85
6.0
1.75
7.5
7.5
2.52
2.10
7.05
6.4
8.43
7.25

qy
KPa
13.5
22.4
30.3
38.6
36.7
-75.4
74.5
--116.0
127.4
48.0
47.6

ρy
mm
0.64
2.27
1.39
1.95
1.99
-2.08
2.19
--1.99
2.30
3.17
2.60

P.S
1
2
2
2
2
2
3
3
3
3
4
4
4
4

SAMPLE PREPERATION AND TESTINGPROCEDURE∅

𝑞𝑞𝑢𝑢= 𝑞𝑞𝑏𝑏 + 𝛾𝛾1 𝐻𝐻2 �1 + 2𝐷𝐷𝑓𝑓 /𝐻𝐻� ∗ �𝑆𝑆𝑆𝑆𝐾𝐾𝑠𝑠 tan 1 � −𝐵𝐵(3)𝛾𝛾1 𝐻𝐻 ≤ 𝑞𝑞𝑡𝑡
So is a shape factor = 1.10-1.27, forConservative design So= 1, [3].

The soil from Fao town area was mixed withwater to form slurry. Sample container warped
with filter paper and 25mm sand drainage layer
was placed at bottom of container. The
container is then filled with slurry to top ofcollar to allow for consolidation. The porepressure initial value is recorded as uo.

In the above equation qt is the ultimate bearing
capacity of a homogeneous thick bed of theupper sand layer, and qb is the ultimate b.c of
the footing on a very thick bed of clay layer.

863

GEOMATE- Osaka Nov. 16-18, 2015

Consolidation process is carried out under astress of 38.4 KPa to obtain a void ratio e = 1.2
to 1.33 and a moisture content ω = 46% to 48%
which represent properties of the natural Fao
clay. The consolidation process lasted for 20 to
22 days. Loading stage started by applying loadincrements scheduled for each test, which was
4, 8, or 16 KPa, until failure occurred when
excessive settlement was observed. Afterloading tests were completed, samples wereobtained for engineering and physical properties
tests.

Results obtained are: qu= 22.4KPa and verticalsettlement ρy= 2.27mm. To study theeffectiveness of soil replacement on qu and ρy,the same soil is used and the test is repeatedwith sand layer thickness H= 0.5B, i.e areplacement ratio R= H/B = 0.5. Results of thistest are shown on fig. (3).

ANALYSIS OF TEST RESULTS
Ultimate Load Criterion
Soil failure under a loaded footing is clearly
defined in the case of general shear failuremode, in the case of other failure mode; local
and punching shear, failure point is difficult toestablish [6]. A very consistent ultimate load
criterion that is adopted in this paper defines theultimate load at the point of break of the loaddisplacement curve in a log-log plot Fig.(3b).
Although some researchers refer this point as“Yield point load qy”, and consider that the total
failure is still beyond this point.
Model Footing Test Results
Fig. (2) shows typical results of loading testof 75mm diameter footing resting on soft clay,(R=0).

Fig.3 load –displacement relationships for test
No.7
Values obtained are: qu = 36.7 KPa and ρy =1.99mm. This result indicates an improvement
in qu of 39% and ρy of 12.3%. Similar results
were observed for 50mm diameter footing, butwith different improvement values due to scaleeffect. Sand replacement ratio R was increasedin other tests to R=1 and R=2. Values obtained
are: qu= 75.4 KPa ρy = 2.08mm and qu= 127.4
KPa, ρy = 2.32mm for the two ratios
respectively. These results impliy that anextremely large improvement in ultimate
bearing capacity qu is obtained as the thicknessof sand replacement layer is increased. Thiseffect is clearly shown on fig. (4).

Fig.2 load-displacement relationships fortest No. 2 on natural clay864

GEOMATE- Osaka Nov. 16-18, 2015

Results of cyclic loading tests, pore waterpressure measurement could not be presented inthis paper due to space limitation.

oTheoretical
Estimation of Ultimate Bearing Capacity
According to Meyerhof [2] proposed theory qu ofcircular model footing founded on surface of dense sandlayer overlying soft clay layer, can be estimated from
Eq.(6), which is a modification of Eq.(3) to suit acircular footing:
𝑞𝑞𝑞𝑞 = 1.2𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 +2𝛾𝛾1 𝐻𝐻 2 (𝐾𝐾𝑠𝑠 tan ∅1)
𝐵𝐵≤ 𝑞𝑞𝑡𝑡(6)

Where Nc=5.14 for Φ=0, ɣ1=18 KN/m3, φ1=45o Ks =𝐾𝐾𝐾𝐾 tan 𝛿𝛿�, δ= 0.5 φ1, Kp = 21.38 (columb eq.).
tan ∅′In the present study; Eq. (6) to analyze the ultimate
bearing capacity qu and to compare it with observed
ultimate load intensities qy from load tests for various
values of replacement ratio R. this comparison ispresented in table (4) and on Fig.(5).

Fig.4 load vs displacement relationships
for model footing tests; 2, 5, 7 &12

Table (4) Analyses of ultimate bearing capacity

Modulus of subgrade reaction
An attempt has been made to estimate themodulus of subgrade reaction Ks from modelfooting load tests. Terzaghi [7], defined Ks as:
𝑞𝑞
𝐾𝐾𝐾𝐾 = �𝜌𝜌(4)
Where q and ρ are obtained from load
displacement relationship at 0.5 qy. Values of
Ks can be used to calculate modulus of soil
deformation Es from Eq. (5),[8]𝐾𝐾𝐾𝐾 =𝐸𝐸𝐸𝐸

𝐵𝐵(1−𝜇𝜇 2 )∗ 𝐼𝐼𝑤𝑤(5)

Where Iw is a factor depending on shape andrigidity of the footing. For circular rigid footing
Iw=0.88. Adopting values for Poisson’s ratio μ= 0.45 for clay and μ =0.30for sand, Es can becalculated from Eq. (5), and presented in table(3). Values of Es for soft clay ranged from 1185
KPa to 1474 KPa, while for sand Es ranged
from 4645 KPa to 5465 KPa. These values arerather low if compared with typical values.
Modulus of subgrade reaction Ks ranged from28000 KN/m3 to 33778 KN/m3 for soft clay andfrom 91000 KN/m3 to 116000KN/m3 for sand.
The calculated values of Ks showed goodcomparison with those proposed by [7]especially for dense sand. While for soft clay
they were slightly overestimated.

Test
No.

B
mm

R

H
mm

Su
KPa

qu
KPa

qb KPaqt KPaqy KPa

∆
%

2
3
4
5
7
8
11
12
13c
14c

75
50
50
75
75
50
50
75
50
75

0
0
0.5
0.5
1
1
2
2
0.5
0.5

0
0
25
37.5
75
50
100
150
25
37.5

5.33
=
=
=
9.66
=
8.75
=
=
=

32.9
32.9
36.9
38.9
83.5
75.5
117.7
149.6
58.0
59.9

32.9
32.9
33.35
33.58
60.9
60.5
55.8
56.7
54.5
54.7

----118.2
177.4
177.4
118.2
118.2
177.4
118.2
177.4

22.4
30.3
38.6
36.7
75.4
74.5
116.0
127.4
48.0
47.6

31.9
7.9
-4.6
5.6
9.7
1.3
1.4
14.8
17.2
20.5

Values of qu from Eq.(6) and qy from land tests showed
good agreement, with qu values are a bit higher than qy.
This is expected since qy does not represent the actual
failure values as mentioned before.

Fig.(5) Theoretical and experimental
results of ultimate bearing capacity

865

GEOMATE- Osaka Nov. 16-18, 2015

CONCLUSIONSREFERENCES

Based on the results obtained from thisresearch, the following conclusions can be
drawn:
1. The partial replacement process of soft clay
by a dense layer improves qu and decrease Po.
For replacement ratios R= 0.5, 1.0 and 2.0 theincrease in qu was = 39.0%, 70.3% and 82.4%
respectively, while footing settlement werereduced by 19.4%, 68.7% and 85.0%. Thesesettlement values were compared for a stresslevel of 32.0KPa, to obtain valid comparison for
all tests. Similar conclusion was reported by arecent study [7].
2. The relative thickness of replaced soil in theorder of (1.0 B≤ 𝐻𝐻 ≤ 2.0𝐵𝐵) was found to givethe most beneficial effect on qu.
3. Comparison between qu obtained from modelfooting tests and those theoretically calculated
from Meyerhof (Eq.3) showed excellent
agreement with a difference in the order of
1.3% to 14.8%.
4. Apparently the most beneficial results for quand ρy in construction of foundations of Fao city
structures, were for footings width B= 1.0m.
For larger footings the improvement will be lesseffective, since R < 1.

[1] Al – Mufty, A. (1990); "Analysis ofstress deformation behavior of Fao soil"
M.Sc. thesis, Univ. of Baghdad.
[2] Meyerhof, G.G. (1974) "Ultimate
Bearing Capacity of Footings on Sand
Layer Overlying Clay" Geotechnical J.
Vol.11, Ohawa, Canada.
[3] Hanna, A.M. and Meyerhof, G.G.
(1980), "Design Charts for Ultimate
Bearing Capacity of Foundations on
Sand overlying Soft Clay" CanadianGeot.J., Vol.15.
[4] Meyerhof, G.G. and Hanna, A.M.
(1978), "Ultimate Bearing Capacity ofFoundations on Layered Soil Under
inclined Load" Canadian Geot.J.,
Vol.15.
[5] Brown, J.D. and MEYERHOF, g.g.
(1969) "Experimental Study of Bearing
Capacity in Layered Soil", Proc. 7th
ICMFE, Vol.2.
[6] Vesic, A.S. (1975): "Bearing Capacityof Shallow Foundations" ch.3 in:Foundation Engineering Hand Book, by
Winterkorn, H.F. and Fang, H., Van
Nostrand Reinhold Ltd.co.
[7] Murat, A., Ahmet, D., Mustafa, L., an
Abdulazim,Y.
(2012)"Numerical
Analysis of circular Footings on
Natural Clay Stabilized with a GranularFill" Acta Geotechnical, Slovenia.

ACKNOWLEDGEMENT
Main author would like to acknowledge thesupport of Applied SCIENCE University,
Amman, Jordan to participate in thisconference.

866

Authors IndexA
Abdelazim M. Negm
516
Abdul Awal A.S.M.
604
Abdul Hakam
291
Abdul Naser Abdul Ghani
672
Abdullah A. Shaker
78
Abraham Khoury
553
Abriak N.
574
Acosta D.Y.
205
Adolf Heinrich Horn
764
Agus Hartoko
708
Ahmad Armani
499
Ahmad Hilmy Abdul Hamid
672
Ahmad Mahir Makhtar
493
Ahmad Rifa
177
Ahmad Rifa’I
307
Aiassa Martinez Gonzalo
114
Akihiko Yagi
647, 695, 742
Akiko Inoue-Kohama
834
Akiko Yokoyama
695, 742
Akira Kobayashi
370
Akira Murakami
370
Akira Yuzawa
657, 668
Akira Yuzawa
668
Aldo Evangelista
487
Alercia Biga Carolina
114
Alexander Goudov
844
Alina Paranina
238
Alojzy Szymański
193
Aminaton Marto493, 506
Anand Tadas
559
Anatoly Malinovsky
524
Andrei V. Lyamin
352
Andrew J. Abbo
301, 352
Andrzej Głuchowski
193
Anna Maria Pellegrino
487
Anna Scotto di Santolo
487
Aria Fathi
138
Arif Ali Baig Moghal
592, 685
Arnaldo Fernandes
764
Arrúa Pedro
114
Arul Arulrajah
54
Ashwini Shanbhag
559
Asskar Janalizadeh Choobbasti 188
Athapaththu A.M.R.G
615

Ather Ashraf
Atsuko Sato
Ayaka Oya
Aziman Madun

165
426
159
506

B
Barry Wai-Choo Kok
Behroze Rostami
Behzad Fatahi
Benzerzour M.
Brian Lamb
Buachart C.

244
816
90
574
48
182

C
Chaminda Gallage
Changyoung Kim
Cherdsak Suksiripattanapong
Chi-Jen Chen
Chijimatsu M.
Choy Soon Tan
Chua Renz Anderson D.
Cong-Oanh Nguyen

395
454
54
512
313
493
152
271

D
Dahlia H. Hafez
Daisuke Takasaki
Daniel W. Wilson
Danny Useche InfanteDavid Vosolo
Deendayal Rathod
Der-Her Lee
Dilrukshi R.A.N.
Dinesh S V
Dmitrii Pestsov
Domenico Lombardi
Du L. Nguye
Dungca Jonathan R.
Dychangco T.

171
448
352
108
395
471
159
84
559
524
341
199
152
295

E
Eberhardt Marcelo
Erica Elice Saloma Uy
Essaid Bilal

114
125
764

Authors Index

F
Fadhilah Yusof
634
Fardin Jafarzadeh
277
Fauzan291
Fauzan Febrin Anas Ismail
610
Febrin Anas Ismail
291
Fukagawa R.
430
Fumio Tatsuoka
567
Fumitake Nishimura
701, 759
Fusao Oka
401
G
Golam Mostofa
Gonzalo Aiassa Martinez

395
108

H
Ha H. Bui
430
Hadi Farahi Jahromi
277
Hadi Khabbaz
90, 553
Hamidi Abdul Aziz
628
Hania Arif
165
Hansapinyo C.
182
Hariyadi MT
708
Hayder Hasan
90
Hecson Christian847
Hiraoka N.
430
Hirokazu Abe
771
Hirokazu Yui
401
Hiromi Ssuda
742
Hiromi Suda
647, 695
Hiroshi Kawamura
254
Hiroshi Matsuoka
401
Hirotaka Saito
377
Hiroyuki Ii
Hiroyuki Ii 788, 791, 804, 810, 822, 828
Hisashi Suzuki
413
Homayoon Ganji
816
Hossain M.Z.
604
I
Ian Jefferson
Ishiguro Satoru
Ito Ryouei
Ito Tesu

506
325
325
325

J
Jaewon Yoo
454
James I. Inim
621
Jana Labudkova
481
Janaka J. Kumara
319
Jatnel Alonso Lazo Luiz764
Jay Lobwein
66
Jayasiri Rajapakse
395
Jean-Frank Wagner
96
Jeram Mallis
730
Jia Xi
244
Jim Shiau
14, 48, 66, 72
Joenel G. Galupino
714
John W Yeaman
35
John Yeaman
35, 536
Jonathan R. Dungca
295, 714
Jongchul Im
454
Juego M.B.
205
Jun Watanabe
84
Junichiro Takeuchi
748
K
Kamrul Islam
Kaneda K.
Katsuhiko Koizumi
Katsutoshi ueno
Kayoko Yamamoto
Kazuhiro Kaneda
Kazuhito Murakami
Kazuki Taguchi
Kazunari ImaideKazuya Inoue
Kenji Kaneko
Kenji Okaima
KhezamiKitazume K.
Kiyonobu Kasama
Kobayashi A.
Kohei Urakami
Koichi Sumi
Koji Maegawa
Kozo Okita
Krishna R Reddy
Krupesh A. Chauhan
Kshama S. Roy

232, 586
199
120
413
211
653
834
791
346
102
331, 337
782
574
840
516
313
260
442
541
260
685
851
232

Authors Index

Kshama S. Roy
Kyoichi Okamoto

586
777

L
Lalithamba H S
Leonardo Schippa
Li-Hsien Chen
Liet Chi Dang
Lin Wenli
Liu B.
Ludfi Djakfar
Luky Handoko
Lydia Francisca

559
487
541
90
413
313
265
177
295

M
Macwan J.E.M.
847
Mahgoub A.G.
171
Maki Oyagi
647, 695, 742
Mansi R.
574
Marcelo Eberhardt
108
Marfiah Ab. Wahid
726
Mariana Ferreira
764
Mariyana A.A.K.
604
Mark Albert H. Zarco
60
Mary Ann Q. Adajar
60
Masaaki Kondo
797, 816
Masaaki Takahashi
691
Masaaki Tanaka
647
Masahiko Katoh
678
Masahiro Yamashita
791, 810
Masaki Kaneko
254
Masamitsu Fujimoto
159,442,448,459,465,475
Masanobu Taniguchi
828
Masayuki Fujihara
748
Mathew Sams
66, 72
Matsuo T.
430
Matthew E. Nton
96
Matthew Merrin
226
Md. Aminul Islam
857
Md. Jakiul Hassan
232
Md. Nakibul Hasan Khan131
Md. Salequzzaman
383
Md. Wasif Zaman
383
Md. Yousuf Rumi
383

Megumi Nobori
647, 695, 742
Mehran Naghizadeh
188
Melissa Neave
730, 736
Meng-Hsiu Hsieh
512
Michiko Masuda
701, 759
Miho Kohara
822
Minoru Yamanaka
120
Minsu Seo
454
Mohammad Afif Kasno
634
Mohammad Ali M.
41
Mohammad Reza Atrchian
499
Mohd Faiz Muaz Ahmad Zamri 642
Mohd Hazreek Zainal Abidin
506
Mohd Khairul Bazli Mohd Aziz 634
Mohd SuffianYusoff
642
Mohsen Haeri S.
138
Mori K.
430
Morioka T.
840
Moshe Livneh
284
Moshood N.Tijani
96, 621
Mosleh Ali Al-Shamrani
685
Mostafa A. Abu Kiefa
171
Muawia A Dafalla
592
Muhamad Helmi
708
Muhd. Harris Ramli
628
Mujahid Sukarno
708
Mujtaba M. N. Shuaib
597
Muthukkumaran K.
25, 471
N
Nagaoka Seiya
325
Najla Lassoued764
Naoki Oyama
331, 337
Narasimha Rao S.
144
Naser M. Saleh
516
Naveel Islam
232, 586
Nikraz H.
421
Nobutaka Hiraoka
442
Nobutaka Hiraoka
459
Nobuyuki Kumakura
448
Noor Aina Mohamad Zuki
642
Nor Puji Lestari
307
Noriko Otsuka
771
Noriyuki Yasufuku
177, 307
Nur Izyan Roslan
672

Authors Index

Nuri al- Mohamadi

Nurul Syakeera Nordin
O
Obaid A.
Ohtsuka S.
Okajima Kenji
Osama M. A. Daoud
Ozaki T.
P
Pavan Teja Y.
Pedro Arrúa
Peerapong JitsangiamPetr Mynarcik
Po- Hao Hsu
Purwana Y.M.
Q
Qiang Xie

862

506
592
199
325
597
840
851
108
226
481
615
421
352

R
Rabelo Fernandes
764
Radim Cajka
481
Rajaraman J.
144
Regynaldo Arruda Sampaio
764
Rhiannon Hamilton
730
Robert Jones
90
Roman Paranin
238
Ryohei Ishikura
177
Ryoichi Fukagawa 159,442,465,475
Ryosuke Uzuoka
413
Ryota Ishii
548
Ryuki Mori
678
S
Sadegh Rezaei
188
Saheed O. Adetu
621
Saiful Azhar Ahmad Tajudin
506
Saki Agatsuma
834
Salem S. Abu Amr
628
Sanchez H.M.
205
Sanchez I.S.
205
Satoru Iida
159
Satoru Kawasaki
84, 131,426
Satoshi Matsumura
436

Sayuri Kimoto
401
Scott Rayburg
730, 736
Seyednima Salimi Eshkevari
301
Shahin H. M.
383
Shen Mingrong
413
Shen-Hsiang Hsu
541
Shin-ichi Nishimura
346
Shinya Inazumi
250,254,260
Shinya Tsukada
657, 668
Shohei Tsurui
370
Shoji Kamao
364
Shoko Nishiwaki
782
Shuichi Hasegawa
120
Shun Fujita
211
Sina Heidari
277
Sitharam T.G.
471
Siti Norafida Jusoh
493
Sommanawat W.
182
Stathas Dionysios
407
Steve Hecker
580
Stuart Yeaman
35
Subhamoy Bhattacharya
341
Suguru Shimazaki
131
Suksun Horpibulsuk
54
Sun M.
430
Svetlana Barannikova
524
Syed Abu Sayeed Mohammed 685
T
TaeWoo Eom
Taizo Uchida
Takaaki Kobayashi
Takaaki Mizutani
Takaaki Mizutani
Takahiro Sugano
Takahiro Yamanashi
Takamitsu Kajisa
Takashi Hasegawa
Takashi Kurashina
Takashi Tsuchida
Takayuki Shuku
Takeshi Sato
Takeshi Toyama
Takuma Kubohara
Takuya Sanei

662
755
436
389
436
370
426
797, 816
358
319
615
346
678
777
804
120

Authors Index

Tamer Y. Elkady
Tanakhum N.
Tanun J.
Tatemasa Hirata
Tatsuo Konegawa
Tatsuro Nishiyama
Teruo Arase
Tetsuo Morita
Tetsuo Okano
Tetsuya Fukano
Tetsuya Iida
Tetsuya Ohmura
Thanh Thi Tran
Thayalan Nall
Thiruvenkatasamy K.
Thitapan Chantachot
Tirawat Boonyatee
Tomohiro Tanikawa
Tomoki Kurasawa
Tomoko Miyagawa
Tomotaka Sato
Toru Danjo
Toshiro Hata
Tsukada Y.
Tsutomu Tanaka
Tsuyoshi Kibayashi
Tsuyoshi Okita
V

78
182
182
828
465, 475
358
720, 755
657, 668
720, 755
788
448
548
271
662
144
567
125
653
102
771
377
459
426, 653
313
102465, 475
260

Vahik K. Bedrosian

862

W
Waled A. Daoud
Waleed M Zahid
Wang Jui Pin
Warat Kongkitkul
Watanabe Ken
Wataru Sumii
Wojciech Sas
Wong Chin Yik
Wooje Lee

516
685
407
567
325
748
193
628
454

Valeriy Perminov
Vanissorn Vimonsatit

844
580

Y
Yeaman J.
Yeaman S.D
Yohei Katayama
Yoji Ueyama
Yoshiaki Kikuchi
Yoshiyuki Uchikawa
Yu Otake
Yuan-Chieh Wu
Yuataka Hashizume
Yuji Kato
Yuji Kohgo
Yuji Shigematsu
Yukimasa Takemoto
Yuko Ishida
Yulvi Zaika
Yusuke Kawai
Yutaka hashizume
Yuuki Arimitsu

530
530
615
250
2, 319
755
678
512
337
797
377
254
691
459, 465,475
265
678
331
459

Z
Zaidir Nugrafindo Yanto
610
Zakaria Hossain
857
Zalina Mohd Daud
634
Zulhafizal Othman
726
Zulkifli Yusop
634
Zummy Dahria Mohamed Basri 726

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