GEOMATE 2021 KYOTO, JAPAN 03-05 NOVEMBER, 2021
GEOTECHNIQUE, CONSTRUCTION MATERIALS AND ENVIRONMENTiPROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE – GEOMATE 2021
GEOTECHNIQUE, CONSTRUCTION MATERIALS AND ENVIRONMENT, KYOTO, JAPAN
03-05 NOVEMBER, 2021

Geotechnique, Construction
Materials and Environment
Edited byProf. Zakaria Hossain

Department of Environmental Science and Technology
Graduate School of Bioresources
Mie University, JapanTHE GEOMATE INTERNATIONAL SOCIETYii

Copyright @ 2021 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/

iii

ISBN Number: 978-4-909106063 C3051

Eleventh International Conference on Geotechnique, Construction Materials and Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

Table of Contents

Prefacexii
xiii

Organization

ID
k1

Keynote Papers

1

DEVELOPMENTS IN SLOPE STABILITY IN ENGINEERING PRACTICE: A REVIEW OF RESEARCHPUBLISHED BY GEOMATEJohn Victor Smith

2

Technical Papers

8

ID

Geotechnique9

gxi211EVALUATING THE EFFECTS OF POLYMERS ON MICROBIALLY INDUCED CARBONATEPERCIPITATION
Zhen Yan, Sivakumar Gowthaman, Kazunori Nakashima and Satoru Kawasaki

10

gxi216

HIGH WATER CONTENT PEAT SOIL IMPROVED BY MICP TECHNIQUE WITH FIBER
REINFORCEMENT
Meiqi Chen, Sivakumar Gowthaman, Kazunori Nakashima, Shin Komatsu and Satoru Kawasaki

16

gxi224

OFF-CENTER TETRAHEDRAL WEDGESJohn Victor Smith22

gxi226IMPACTS OF SUFFUSION FACTOR ON HETEROGENIZATION OF SOILS
Koji Nakashima, Katsuyuki Kawai and Takayuki Fumoto28

gxi230MEASUREMENT OF THE FROST HEAVE ACTING ON THE FULL SIZE GROUND ANCHOR AND
SOIL NAILING
Atsuko Sato, Osamu Hatakeyama, Hirochika Hayashi, Masahiko Yamaki, Jun Ikeda, Takayuki
Iizuka, Noboru Mikami, Tuyoshi Nakamura, Fumitada Mikami and Yuji Kyouden

34

gxi232

THE RECOVERY CHARACTERISTICS OF SHEAR STRENGTH OF CEMENT-STABILIZED CLAY
Tomoro Ueda, Kiyonobu Kasama and Masaki Kitazume

40

gxi241

VISUALIZATION OF LIQUEFACTION PHENOMENON BY COUPLED CAE ANALYSIS OF MPS AND
DEM
Koki Nakao, Katsumi Ookori, Tsuyoshi Takahashi and Shinya Inazumi

46iv

gxi242BEARING CAPACITY BEHAVIOR OF SINGLE PILE DURING GROUNDWATER LEVEL CHANGE BY
CENTRIFUGE MODEL
Sutasinee Intui, Suttisak Soralump and Shinya Inazumi

52

gxi244SURFACE TREATMENT ON ANODE ELECTRODES FOR THE HIGHER BIOELECTRICITY
GENERATION IN PLANT MICROBIAL FUEL CELLS
Shohei Uramoto and Md.Azizul Moqsud

58

gxi250PLANT ENZYME INDUCED CARBONATE PRECIPITATION FOR SAND CEMENTATION
Linyu Wu, Linchang Miao and Satoru Kawasaki

63

gxi251STRENGTH AND PERMEABILITY CHARACTERISTICS OF EXPANSIVE SOIL WITH GYPSUM AND
RICE HUSK ASH USED FOR ROADWAY CONSTRUCTION
Angelo B. Edora and Mary Ann Q. Adajar

69

gxi253

OPTIMIZATION OF THE STRENGTH PROPERTIES OF EXPANSIVE SOIL STABILIZED WITH
AGRICULTURAL WASTES
Mary Ann Q. Adajar and Kigia R. Valbuena

75

gxi262

DEVELOPMENT OF SITE FACTOR FOR SURFACE SPECTRAL ACCELERATION CALCULATION AT
THE ALLUVIAL SOILS
Windu Partono, Masyhur Irsyam, Ramli Nazir, Frida Kistiani and Undayani Cita Sari

81

gxi263

METROPOLITAN MANILA SEISMIC HAZARD MAP USING MIDORIKAWA & HORI SITEAMPLIFICATION MODEL
Jonathan R. Dungca and Mariamae Francia G. Montejo

87

gxi264

DEVELOPMENT OF SOIL TYPE REFERENCE OF METROPOLITAN MANILA USING GENETIC
ALGORITHM
Jonathan R. Dungca and Izzhar Christian P. Sumagaysay

93

gxi265

PREVENTION OF SLOPE FAILURE USING NATIVE MICROORGANISMS
Ryosuke Kawano and Md. Azizul Moqsud

99

gxi278NONLINEAR OPTIMIZATION FOR A 16 METERS HIGH TRAPEZOIDAL MODULAR BLOCK WALL
Vuttichai Chatpattananan, Somchai Prayongphan, Thanadol Kongsomboon and
Vatanavongs Ratanavaraha

105

gxi279ESTIMATION OF SUBSURFACE STRUCTURE OF LANDSLIDE AREA BASED ON MICROTREMOROBSERVATION IN THE HOJOJIMA, NAWASHIRO AND AMEDAKI AREA, TOTTORI, JAPANTatsuya Noguchi, Isamu Nishimura and Takao Kagawa

111

gxi287SUBSURFACE STRUCTURES BASED ON MICROTREMOR OBSERVATIONS IN LANDSLIDE AREA
OF TANDIKAT, WEST SUMATRA, INDONESIA
Isamu Nishimura, Tatsuya Noguchi, Yusuke Ono and Masanori Kohno

117

gxi288INVESTIGATION OF TUNNEL RESPONSE DUE TO THE EFFECT OF ADJACENT LOADED PILE ROW
BY 3D SIMULATION ANALYSIS
Puckjira srikaewsai, Narunat Heama, Prateep Lueprasert and Suchatvee Suwansawat

122

gxi290VERTICAL LOADING TESTS ON LOCAL SCOURED SPREAD FOUNDATION ON ALUMINUM RODS
MODEL GROUND
Yuna Sasaki and Hidetoshi Nishioka

128

gxi293

THREE-DIMENSIONAL ANALYSIS OF SOIL-PILE-STRUCTURE INTERACTION PROBLEM ON HIGHRISE BUILDING ON IMPROVED SOFT SOIL
Thanapon Tipsunavee and Goran Arangjelovski

134

gxi295

RELATIONSHIP BETWEEN LIQUEFACTION STRENGTH OF SAND WITH FINE FRACTION AND
VARIOUS VOID RATIOS
Kentaro Ishii, Wen LIU and Shoji KAMAO

140

gxi297

MONITORING AND PREDICTION OF SETTLEMENT USING ASOKA METHOD AND HYPERBOLAMETHOD WITH PREFABRICATED VERTICAL DRAIN AS GROUND IMPROVEMENT METHOD
Muhammad Haziq Taqiuddin bin Rezuwan and Saiful Azhar bin Ahmad Tajudin

146v

gxi306

AN EXPERIMENTAL STUDY ON TEMPERATURE EFFECTS ON ELASTIC AND CREEPDEFORMATION BEHAVIOURS OF A SAND
Yodphao Punya-in and Warat Kongkitkul

156

gxi321ANALYSIS OF SOIL-PIPE INTERACTION SUBJECTED TO NORMAL AND REVERSE FAULTING
USING THE DISCRETE ELEMENT METHOD
Yusuke Ono164

gxi325

THE APPLICATION OF STRUCTURE FROM MOTION METHOD FOR ROCKFALL ANALYSIS, A CASESTUDY ON ENDE-MAUMERE ROAD KM 18+600, EAST NUSA TENGGARA
Cahya Ahmad Gumilar and Muhammad Rizaldi Nuraulia

173

gxi326

EFFECT OF COCONUT SHELL ASH ON PORE WATER PRESSURE CHANGES IN SAND
Gabriel Jose G. Lim, Mary Ann Q. Adajar and Erica Elice S. Uy

180

gxi336EXPERIMENTAL STUDY ON THE EFFECT OF ANISOTROPIC PRESSURIZING AND
UNPRESSURIZING THE TBM-SEGMENTS
Haruka Mashiko, Naotaka Kikkawa, Nobutaka Hiraoka and Kazuya Itoh188

gxi342

MONITORING OF ROCKFALL NET FENCE TECHNOLOGY BY AERIAL PHOTOGRAPHYTECHNOLOGY IN ENDE, NUSA TENGGARA TIMUR, INDONESIA
Ihwan Fauzi, Cahya Ahmad Gumilar and Muhammad Rizaldi Nuraulia

194

gxi347PERMEABILITY OF THE DIKE 1’S MATERAILS OF KAENG KRACHAN DAM, THAILAND
Uba Sirikaew, Witthawin Hopeancharoen and Uma Seeboonruang

200

gxi361PERFORMANCE OF REINFORCED BASE COURSES OF FLEXIBLE PAVEMENTS OVERLYING SOFT
SUBGRADES: INSIGHTS FROM LARGE-SCALE MODEL EXPERIMENTS
Ramu Baadiga, Umashankar Balunaini and Sireesh Saride

206

gxi370EFFECT OF BENDING RIGIDITY OF PRECAST L-TYPE RETAINING WALL ON LATERAL PRESSURE
CAUSED BY REPEATED WHEEL LOADING
Yukimine Otomo and Osamu Maruyama

212

gxi371INVESTIGATION AND STABILITY ANALYSIS OF SLOPE FAILURE AT EXTREMELY SHALLOW LAYER
AFTER SNOW MELT IN SNOWY COLD REGIONS
Naoki Miura, Dai Nakamura, Takayuki Kawaguchi and Shunzo Kawajiri

218

gxi377ANALYTIC CONSIDERATIONS ON TWO-DIMENSIONAL MODELING OF PARTIAL FLOATINGSHEET PILE METHOD
Nanase OGAWA, Kakuta FUJIWARA and Kentaro NAKAI

224

gxi380USAGE LIMIT OF LUNAR SOIL SIMULANT
Keito Endo, Ryoichi Fukagawa and Taizo Kobayashi

230

gxi387

MONITORING DEFORMATION WITH WIRELESS SENSOR NETWORK AND EVALUATION OF
MECHANICAL STABILITY BY FDM SIMULATION FOR SLOPE
Satoshi Sugimoto and Yoichi Ishizuka

236

gxi388UNDERSTANDING THE EFFECT OF DEGREE OF SATURATION ON COMPRESSIVE
STRENGTH BEHAVIORS FOR RECONSTITUTED CLAYEY SOIL OF DHAKA
Sultan Al Shafian, Sk. Abu Talha, Musaddik Hossain and Hossain Md. Shahin

242

gxi400SAFETY FACTORS VALUE OF ROCK MASS SLOPE AFTER DIFFERENT APPROACH OF ROCKFAILURE CRITERIA BY LIMIT EQUILIBRIUM METHOD
Arief Rachmansyah, Eko A Suryo, Hasyim Alhadar and Retno P Rini247

gxi403IMPACT OF UNDERGROUND DISPLACEMENT BEHAVIOR ON SLOPE FAILURE INITIATION DUETO RAINFALL BASED ON EXPERIMENTAL STUDY
Xin Qin, Masamitsu Fujimoto and Yoshifumi Masamitsu

255

gxi425

THREE-DIMENSIONAL ANALYSIS OF GEOGRID REINFORCED FLEXIBLE PAVEMENT USINGFINITE DIFFERENCE PROGRAM FLAC3D
Rojimol J. and Umashankar B.

261

vi

gxi426

gxi426 NUMERICAL STUDY ON THE AXIAL AND RADIAL SWELLING BEHAVIORS OFUNSATURATED COMPACTED BENTONITE
Shinya Tachibana, Daisuke Hayashi, Tomoko Ishii, Tomohide Takeyama and Atsushi Iizuka

267

gxi437

INFLUENCE OF RICE HUSK ASH WITH MINIMAL DOSAGE OF CEMENT ON SHEAR STRENGTH
AND MICROSTRUCTURE OF SOIL
Najmun Nahar, Sayful Kabir Khan, Alex Otieno Owino, Zakaria Hossain and Noma Tamaki

273

gxi438

EFFECTS OF BASALT FIBER ON STRENGTH AND PERMEABILITY OF RHA TREATED EXPANSIVE
SOILSAlex Otieno Owino, Najmun Nahar, Saiful Kabir Khan, Zakaria Hossain and Noma Tamaki

279

gxi381SEISMIC PERFORMANCE EVALUATION OF PFS METHOD BY SOIL-WATER COUPLED FINITE
DEFORMATION ANALYSIS
Kentaro Nakai, Kakuta Fujiwara and Nanase Ogawa

288

gxi429STRENGTH EVALUATION OF CEMENT-TREATED SAND-EXPANDED POLYSTYRENE BEAD
LIGHTWEIGHT FILL
Khola Iqbal, Hammad Siddique, Waleed Iqbal, Taro Uchimura and Zain Maqsood

294

ID Construction Materials

298

gxi219ADHESIVE FRACTURE MODES BETWEEN STEEL ANCHOR AND INJECTABLE MATERIAL AND
CONCRETE IN CONSIDERATION OF WEDGE-SHAPED EFFECT
Tanaka Hidenori

299

gxi255

MODELING OF THE FLEXURAL BEHAVIOR OF CORRODED RC BEAMS STRENGTHENED BY CFRPSHEETHoai Anh Tran, Ngoc Tan Nguyen, Trung Kien Nguyen, Hoang Giang Nguyen

305

gxi261SUCCESSIVE OVER RELAXATION METHOD FOR SOLVING HEAT EQUATION OF THERMALCONDUCTIVITY OF CONCRETE
Dalal Adnan Maturi

311

gxi283RESPONSE SURFACE MODELLING OF PERFORMANCE OF CONCRETE WITH BAUXITE LATERITE
SOIL AND FLY ASH
Joenel Galupino, Mary Ann Adajar, John Jacob Abejuela, Ernst Kenneth Alcantara and Philippe
Daniel Ebreo
NUMERICAL SIMULATION OF PERVIOUS CONCRETE PILE IN LOOSE AND SILTY SAND AFTERTREATING WITH MICROBIALLY INDUCED CALCITE PRECIPITATION
Hayder A. Hasan, Sabah H. Lafta, Muhannd W. Majeed, Hadi Khabbaz and Mehdi
Aghayarzadeh
AN EXPERIMENTAL STUDY ON THE CREEP OF CONCRETE MADE WITH RECYCLED COARSEAGGREAGES: CASE STUDY IN HANOI
Thanh Quang Nguyen, Hoang Giang Nguyen, Ngoc Tan Nguyen, Viet Cuong Tran and Ha Tan
NghiemINFLUENCE OF EACH MIXTURE PARAMETER ON FRESH PROPERTIES AND COMPRESSIVE
STRENGTH OF GEOPOLYMER MORTAR
Takumi Osedo, Yoichi Mimura and Itaru Horiguchi

gxi284TENSILE CREEP BEHAVIOR CONSIDERING STIFFNESS DEVELOPMENT OF HIGH STRENGTHCONCRETE AT EARLY AGE
Takuya Hara, Yoichi Mimura and Itaru Horiguchi

gxi268gxi272gxi280

316

322

328

334

340

gxi298

EFFECTS OF POLYETHYLENE TEREPHTHALATE (PET) PLASTICS ON THE MECHANICALPROPERTIES OF FLY ASH CONCRETE
Mary Ann Adajar and Irene Olivia Ubay-Anongphouth

346

gxi304

GRINDING PROCESS INFLUENCES TO THE PHYSICAL AND MORPHOLOGICAL CHARACTERISTICSOF ULTRAFINE TREATED RICE HUSK ASH (UFTRHA) AS REACTIVE MINERAL ADDITIVECONSTRUCTION MATERIAL
Siti Asmahani Saad, Aida Nabila Jamaluddin, Siti Aliyyah Masjuki, Salmia Beddu and Nasir
Shafq

352

gxi316

REVIEW ON CURRENT SITUATION OF GENERATION AND MANAGEMENT OF COAL ASH IN
VIETNAM
Tran Viet Cuong, Ken Kawamoto and Nguyen Hoang Giang

357

vii

gxi344

POROSITY AND PERMEABILITY OF PERVIOUS CONCRETE USING CONSTRUCTION AND
DEMOLITION WASTE
Kim Tuan NGO, Quang Minh PHAN, Hoang Giang NGUYEN, Tien Dung NGUYEN and Ken
Kawamoto365

gxi383ADSORPTION PROPERTIES OF MATERIALS ADSORBING SELECTIVELY ARSENIC FROMAQUEOUS SOLUTIONS CONTAINING MULTIPLE CHEMICAL SPECIES
Ayane Yanaka, Yuri Takeshita, Keiichiro Shibata, Yoshihiro Suenaga and Hidenori Yoshida372

gxi398

EFFECT OF SCREW ON THE AXIAL PERFORMANCE OF COLD-FORMED STEEL
Desy Setyowulan, Eva Arifi, Devi Nuralinah and Edwardo Pradana A.N.

378

gxi413PERFORMANCE OF INTERLOCKING BRICK WALLS AGAINST OUT-OF-PLANE EXCITATION
Aiko Furukawa, Johanes Jefry Prasetyo and Junji Kiyono

383

gxi440

INFLUENCE OF FREEZING AND THAWING CONDITIONS AT INITIAL AGE ON THE STRENGTH
DEVELOPMENT OF MORTARSPeter Kaba, Shushi Sato, Masahiro Yamamoto and Keikichi Takahashi

389

ID Environment

394

gxi222

TEMPORAL CHANGES IN THE INDOOR RADON CONCENTRATION AS AN EARTHQUAKEPRECURSOR
Jae Wook Kim, Han Young Joo, Young Seo Kim, So Yun Jeong and Joo Hyun Moon

395

gxi209PARAMETRIC MODEL OF LINE TRANSECT SAMPLING FOR GROUPED DATA SET
Noryanti Muhammad and Gamil. A. A. Saeed

401

gxi212gxi229SEDIMENT ACCUMULATION ASSESSMENT DURING WET AND DRY SEASONS IN FISH CAGE
CULTURE AREA: A CASE STUDY OF THE RANGSIT CANAL 13 IN THAILAND’S PATHUM THANIPROVINCE
Boontarika Thongdonphum, Kittima Vanichkul, Sangob Srimueang and Adun Bunchaleamchai
WATER POLLUTION OF THE JYUSHI-GAWA RIVER IN YOKKAICHI-CITY
Yukimasa Takemoto, Masaaki Takahashi, Maki Ooyagi, Seiji Iwasaki, Shigeaki Inagaki, Reigirou
Hashimoto, Kazuo Hirata, Tadamichi Hattori, Katsuo Okuda, Atsushi Suzuki and Takanori
Terazawa

405

410

gxi236EXPERIMENTAL STUDY ON GROUND STRENGTHENING AND ELECTRIC POWER GENERATIONWITH USING RECYCLED ASPHALT PAVEMENT MATERIAL
Shoji Yokohama

416

gxi240STUDY ON CHANGES OF PUBLIC TRANSPORT USE AND CONSCIOUSNESS DURING THE COVID19 INFECTION PERIOD
Ryotaro Kinoshita, Tetsuo Morita, Tomohiro Miyazaki and Shinya Tsukada

422

gxi247

THE ISO DESIGN GUIDE FOR GEOSYNTHETIC BARRIERS AND THE USE FOR SAFE AND
ECONOMIC BARRIER SOLUTIONS
Kent von Maubeuge and Jake Stoyle428

gxi248STUDY ON THE RELATIONSHIP BETWEEN THE CHANGE OF RIVER MOUTH SANDBAR AND
RIVER DISCHARGE AND OCEAN WAVE AT THE OMONO RIVER
Junya Taniguchi, Kazuya Watanabe and Noritoshi Saito

436

gxi254

PREDICTION ACCURACY OF RETRAINED SELF-ORGANIZING MAP BASED SURROGATE MODEL
FOR A REAL COASTAL AQUIFER
Dushyantha Wagale and Bithin Datta

442

gxi269EFFECT OF PACLOBUTRAZOL CONCENTRATIONS OF FOLIAR APPLICATION ON FLOWERING OF‘MAHACHANOK’ MANGO
Yaowarat Wongsrisakulkaew, Unaroj Boonprakob and Lop Phavaphutanon

450

gxi281

THE RELATIONSHIP BETWEEN AQUATIC ORGANISMS AND ENVIRONMENTAL PARAMETERS
IN URBAN RIVER WITH IMPROVEMENT
Michiko Masuda, Koichi Takada and Fumitake Nishimura

455

viii

gxi282

EVALUATION OF THE VISIBILITY OF STAIRCASES IN UMEDA UNDERGROUND STREET BY USING
DEPTHMAP
Yuanhang SONG, Akira TAKAHASHI, Kensuke YASUFUKU and Hirokazu ABE

463

gxi289

THE OCCURRENCE OF MICROPLASTICS ON THE START-UP PROCESS OF AN ANOXIC BIOFILMBATCH REACTOR
Ansiha Nur, Mhd Fauzi, Prayatni Soewondo, Ahmad Soleh Setiyawan and Katharina Oginawati

471

gxi312CHANGES IN RIVER ENVIRONMENT AT THE DOWNSTREAM AREA DUE TO MAINTENANCE
FLOW DISCHARGE FROM THE HIRAIDE DAM LOCATED ON KATASHINA RIVER
Naoki Miyazato, Yuko Kakegawa, Keiji Nakajima, Hiroyuki Aizawa, Yuya Saito,Yuishi Yagi,
Akihiro Horio, Toru Aoi and Yoshikatsu Yamamoto477

gxi317

DEVELOPMENT OF ANFIS MODEL FOR AIR QUALITY FORECASTING IN DHAKA CITY
A. A. Abeer, J. Farzana and M. T. Rahman

483

gxi346

THE RECYCLING WASTE FROM SUGAR INDUSTRY: THE OPTIMALIZATION OF CO2 ADSORPTIONCAPACITY ON SUGARCANE BAGASSE-BASED BIOCHAR AND ZEOLITE
Ambar Pertiwiningrum, Margaretha Arnita Wuri, Widyawati Luhur Pambudi, Andang Widi
Harto, Alva Edy Tontowi and Rachmad Nur Besari489

gxi349REPRODUCTIVE ECOLOGY OF THE ENDANGERED PYRUS CALLERYANA DECNE IN URBAN
PARKS
Naoya Motoori and Michiko Masuda

493

gxi350CONSIDERATION ON THE DYNAMICS OF PARK USERS UNDER COVID-19 CRISIS; TAKINGSHIKISHIMA PARK IN MAEBASHI CITY AS AN EXAMPLE
Shinya Tsukada, Xingyu Tao, Tetsuo Morita and Toshikazu Nishio499

gxi353

CONVERSION OF BLAST FURNACE SLAG INTO HYDROGARNET FOR HUMIC ACID REMOVAL
Takaaki Wajima

504

gxi355INTEGRATED APPROCH BASED ON FLUID PHENOMENA FOR BENTONITE SUBJECTED TO
THERMAL ACTION
Tomoyoshi Nishimura

510

gxi364STUDY OF SOIL ERODIBILITY UNDER THE POTATO FARMING SYSTEM IN THE UPSTREAM
LEMBANG WATERSHED
Aprisal, Bambang Istijono, Irwan Darpis, Mimin Harianti and Teguh Huria Aditia

516

gxi368A STUDY ON APPLICATION OF DEEP LEARNING TO WATER ENVIRONMENT PLANNING
Shengping Zhang and Jie Qi

522

gxi372ESTIMATION OF USEFUL SPECIES OF BENTHIC ANIMALS AND WATER PLANTS FOR AN INDEXOF AL AND CR CONTAMINATION FOR RIVER IN THE KINOKAWA RIVER CATCHMENT
Takuma Kubohara and Hiroyuki Ii

528

gxi373

FLOATING AUTOMATED FACTORY SYSTEM FOR EFFECTIVE UTILIZATION OF SEABEDRESOURCES AND REDUCTION OF CO2 EMISSIONS
Shinji Sato and Ryo Deyama534

gxi384

THE EFFECT OF POROUS CONCRETE HANDLING ON EROSION REDUCTION IN SLOPED SOIL
AND SANDY CLAY CONDITIONS
Bimo Brata Aditya, Putranto, Dinar DA, Anis Saggaff and Hanafiah

540

gxi385PROPOSAL OF ISOLATION MEASURES FOR MOUNTAIN VILLAGES BASED ON ANALYSIS OFDISASTER PREVENTION DRILLS
Zhijun Tang and Takeyasu Suzuki

546

gxi386SPATIAL STATISTICAL ANALYSIS OF THE POROUS MEDIUM EXTRACTED FROM VIRTUAL
PACKED GRAINS WITH RANDOM SIZESYu Song, Junichiro Takeuchi, Yuto Takeuchi and Masayuki Fujihara

552

gxi419GROUNDWATER FLUORIDE HAZARD ZONATION IN THE VOLCANOGENIC POLLUTED AREA,
EAST JAVA, INDONESIA
Dhandhun Wacano, Lutfia Isna Ardhayanti, Suphia Rahmawati, Ihsan
Diyahulhaq Fadhlurahman, Mawarti Sekar Ratri Nastiti, Auniatul Aulia and Minoru Yoneda

558

ix

gxi423

TRANSITION TOWARDS SUSTAINABLE ENERGY IN THAILAND: DRIVERS, BARRIERS AND
CHALLENGES OF SOLAR ENERGY INITIATIVE AT KRABI PROVINCE
Chariya Senpong and Dawan Wiwattanadate564

gxi427

CLIMATE VARIABILITY’S IMPACT ON RAINFED-SUGARCANE PRODUCTION IN THAILAND’SNORTHEAST
Piyapong Wongkhunkaew, Supasit Konyai and Vichai Sriboonlue

573

gxi433IMPLEMENTATION OF GREEN CAMPUS AT UNIVERSITIES IN INDONESIA
Any Eliza, Dinda Fali Rifan, Lindrianasari and Nur Wahyu Ningsih

579

gxi213METALAXYL RESISTANCE, PATHOGENICITY, AND CROSS-INFECTIVITY OF PHYTOPHTHORA
PALMIVORA FROM DURIAN AND PARA-RUBBER DISEASES IN SOUTHERN THAILAND
Pornprapa Kongtragoul and Thammanoon Phelawan

586

gxi227

3D NUMERICAL ANALYSIS OF TSUNAMI–INDUCED FLOW AND TOPOGRAPHY CHANGE IN AN
ABRUPT EXPANSION AREA WITH DIFFERENT SHORELINE CONDITIONS
Yuki Kajikawa and Masamitsu Kuroiwa

591

gxi245EVALUATION OF UNDERGROUND SPACE FOR TSUNAMI EVACUATION SAFETY BY AGENTBASEDSIMULATION
Akira Takahashi, Kensuke Yasufuku and Hirokazu Abe

597

gxi246

ON GEOGRAPHIC INFORMATION MAP BY THE SPATIAL ATTRACTIVENESS
Ami Hasegawa and Kazunari Tanaka

605

gxi257

ON EVALUATION OF ENVIRONMENTAL SOUND BY DEGREE OF HUMAN CONCENTRATION
Haru Kanda and Kazunari Tanaka

610

gxi266

ON CHANGES IN THE PSYCHOLOGICAL “AFFORDANCE” IN URBAN SPACE DUE TO THE
INFLUENCE OF COVID-19
Kazuki Miyake and Kazuknari Tanaka

616

gxi270DATA MODELING WITH TRANSFORMATION IN LAPLACE AUTOREGRESSIVE MODEL USINGREVERSIBLE JUMP MCMC
Suparman and A.M. Diponegoro

620

gxi296

A COMPARATIVE STUDY ON THE CHANGE OF TOWNSCAPE IN HISTORICAL BLOCKS BETWEEN
XI’AN AND KYOTO
Kun YUAN, Hirokazu ABE, Kensuke YASUFUKU and Akira TAKAHASHI

625

gxi305MID-INFRARED SPECTRA OF CHLORIDE ION IMMOBILIZED ON CEMENT
Yu Hashimoto, Shinichiro Okazaki, Kenji Wada, Ichiro Ishimaru and Koji Kanasaki

632

gxi308

TILLER AND RHIZOME GROWTH ON EXCAVATED SLOPES IN TWO Carex SPECIESDERIVED FROM COLONIES ON LANDSLIDES
Teruo Arase, Tetsuo Okano, Tetsuoh Shirota, Masaaki Furuno and Taizo Uchida638

gxi310gxi314gxi319gxi323

NUMERICAL STUDY OF PARTICLE FLOW(PM10) IN LOW-VOLUME IMPACT SEPARATOR:PARTICLE’S PATH AND COLLECTION PERFORMANCE
Pannita Phirommark, Watcharapong Chookaew, Chakrit Suvanjumrat, Sakchai Uapipatanakul
and Machimontorn Promtong
IMPACT OF THE GREAT EAST JAPAN EARTHQUAKE AND NUCLEAR ACCIDENT ON LAND PRICE
FLUCTUATIONSTakato Azegami, Akira Takahashi, Kensuke Yasufuku, Noriko Otsuka, Tomoko Miyagawa andHirokazu Abe
NUMERICAL INVESTIGATION INTO MOLTEN METAL FLOW AND ITS SOLIDIFICATION UNDERGRAVITY SAND MOULDING IN PLUMBING COMPONENTS: POURING TEMPERATURE AND
RUNNER STRUCTURE
Sirawit Namchanthra, Watcharapong Chookaew, Chakrit Suvanjumrat, Wichuphan
Wijitdamkerng and Machimontorn Promtong
MOTORCYCLE ACCIDENT MODEL BASED ON MOTORCYCLIST CHARACTERISTICS FOR DESIGNTHE DEDICATED LANE
Sobri Abusini and Lasmini Ambarwati

x

645

654

662

671

gxi330

STUDY OF DISTANCE AND NUMBER OF REBARS ON VELOCITY MEASUREMENT USINGNONDESTRUCTIVE TEST
Christin Remayanti Nainggolan, Indradi Wijatmiko, Hendro Suseno and Ananda Insan Firdausy

677

gxi331NON-DESTRUCTIVE EVALUATION OF VARIOUS FLOOR COATING USING ULTRASONIC PULSEVELOCITY AND ULTRASONIC PULSE ECHO: A COMPARATIVE STUDY
Indradi Wijatmiko, Christin Remayanti Nainggolan, Sugeng Prayitno Budio, Ming Narto Wijaya
and Indra Waluyohadi

683

gxi334ANALYTICAL OF MULTI CRITERIA APPROACH FOR IDENTIFYING THE WEIGHT AND FACTOR OF
RURAL ROAD MAINTENANCE PRIORITIZATION.
Pawarotorn Chaipetch, Chisanu Amprayn Paijit Pawan and Vatanavongs Ratanavaraha

689

gxi343

TRANSPORT MODE CHOICE SPLIT FOR COMMUTER LINES IN PANDEMIC COVID-19 SITUATION
Lasmini Ambarwati and Achmad Wicaksono

695

gxi357

PREDICTION OF THE COMPRESSIVE STRENGTH OF FOAM CONCRETE USING THE ARTIFICIALNEURAL NETWORK
Husnah, Rahmat Tisnawan, Harnedi Maizir and Reni Suryanita

701

gxi363STUDY ON EVALUATION OF HUMAN DAMAGE FROM TSUNAMI CONSIDERED CONGESTIONOF EVACUEE
Ken-ichi Fujita and Harumi Yashiro

706

gxi369EXPERIMENTAL STUDIES ON THE TAYLOR-COUETTE FLOW OF SHEAR-THINNING FLUIDS
Yuki TANO, Tatsuya KAWAGUCHI and Takushi SAITO

712

gxi392

NUMERICAL BEACH EVOLUTION MODEL CONSIDERING SEDIMENT TRANSPORT IN SWASH
ZONE
Shuhei Miki, Masamitsu Kuroiwa and Kajikawa Yuki

718

gxi393

ANTIBACTERIAL ACTIVITY OF HELIOTROPIUM INDICUM CRUDE EXTRACTS AGAINSTSTAPHYLOCOCCUS AUREUS FROM CLINICAL AND SUBCLINICAL MASTITIS IN DAIRY COW
Duangkamol Taemchuay, Nareerat Phangsub, Kanokpon Bunya-atichart and Wimonmat
Boonmee724

gxi431ANALYSIS OF LOCAL RESIDENTS' MOVEMENTS FOR DAILY LIFE IN URBAN AREAS IN JAPAN
Hibiki MOTOI, Yukihiro YAMASHITA, Daisuke KOBAYASHI and Yukikazu YAMAGUCHI

729

gxi432

THE DEDICATED LANE FOR MOTORCYCLE BASED ON MOTORCYCLIST CHARACTERISTICS ANDSPACE DEMAND
Sobri Abusini and Lasmini Ambarwati

735

gxi439FLOOD DAMAGE ASSESSMENT: A REVIEW OF MULTIVARIATE FLOOD DAMAGE MODELS
Sumiliana Sulong and Noor Suraya Romali

741

xi

Eleventh International Conference on Geotechnique, Construction Materials andEnvironment, Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

PrefaceOn behalf of the GEOMATE 2021 Organizing Committee, we would like to welcome you in attending theInternational Conference on Geotechnique, Construction Materials and Environment held at Kyoto Research
Park, Kyoto, Japan in conjunction with The GEOMATE International Society, Useful Plant Spread Society,
Glorious International, AOI Engineering, HOJUN, JCK, CosmoWinds and Beppu Construction, Japan.
On Friday 11 March 2011, at 14:46 Japan Standard Time, the northeast of Japan was struck and severely
damaged by a series of powerful earthquakes which also caused a major tsunami. This conference was first
dedicated to the tragic victims of the Tohoku-Kanto earthquake and tsunami disasters. The Geomate 2021
conference covers three major themes with 17 specific themes including:
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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

Due to COVID-19, this year we have received less number of paper submissions from different countries.
The participated countries are Australia, Bangladesh, China, Germany, India, Indonesia, Iraq, Israel, Japan,
Philippines, Saudi Arabia, Singapore, South Korea, Thailand, and Vietnam. The technical papers wereselected 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 thesolid cooperation of numerous people to organize a conference of this size. Hence, we appreciate everyone
who supports as well as participate in this 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 2020. 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. Zakaria Hossain,
Mie University, Japan
Chairman (General)

Dr. Dr. John V. Smith
RMIT University,
Australia Chairman(Vice-chairman)xii

Eleventh International Conference on Geotechnique, Construction Materials andEnvironment, Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 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
Prof. Dr. Bajang B.K. Huat, UPM, Malaysia
Scientific Committees:
Conference Chairmen:
Prof. Dr. Zakaria Hossain, Mie University, Japan (Chairman)
A/Prof. Dr. John V. Smith, RMIT Univ. Australia (Vice-chairman)
Conference Organizing Committee:
Dr. Zakaria Hossain, Prof. Mie University, Japan (Chair)Dr. Satoshi Kaneco, Prof., Mie University, Japan (Co-Chair)Dr. Sohji Inoue, E/Prof. Mie University, Japan (Co-Chair)
Dr. Toshinori Sakai, Prof. Mie University, Japan (Co-Chair)
Dr. Takamitsu Kajisa, Prof. Mie University, Japan (Co-Chair)Dr. Masaaki Kondo, A/Prof. Mie University, Japan (Co-Chair)
National & International Advisory Committee:
Prof. Dr. Fumio Tatsuoka, Tokyo University of Science, Japan
Prof. Dr. Junichiro Takeuchi, Kyoto University, Japan
Prof. Dr. Kingshuk Roy, Nihon University, Japan
Prof. Dr. Sai Vanapalli, University of Ottawa, Canada
Prof. Dr. Musharraf Zaman, Univ. of Oklahama, USA
Prof. Dr. Rafiqul Tarefder, University of New Mexico, USA
Prof. Dr. M. Bouassida, National Sch. of Engg. of Tunis
Prof. Dr. L.R. Austriaco, Angles Univ. Found., Philippines
Prof. Dr. M. Ibn Ibrahimy, Int. Islamic Univ., Malaysia
Prof. Dr. Bujang B.K. Huat, Univ. Putra Malaysia
Prof. Dr. Nemy Banthia, Univ. of British Columbia, Canada
Prof. Dr. Ian Jefferson, Univ. of Birmingham, UK
Prof. Dr. John Bolander, Univ. of California, USA
Prof. Dr. Shamsul Chowdhury, Roosevelt Univ., USA
Prof. Dr. Isabel Pinto, University of Coimbra, Portugal
Prof. Dr. Mark Jaksa, University of Adelaide, Australia
Prof. Dr. Hj. Ramli Bin Hj. Nazir, UTM, Malaysia
Prof. Dr. M.S. Hossain, International Islamic Univ. Bangladesh
Prof. Dr. Suksun Horpibulsuk, Suranaree Uni of Tech, Thailand
Dr. Muzamir Hasan, Director, CERRM, Univ. Malaysia Pahang
Dr. Afshin Asadi, S/Lecturer, Int. College of Auckland, New Zealandxiii

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. Nazar Oukaili, University of Baghdad, Iraq
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
Prof. Quanmei Gong, Tongji University, China
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, Shibaura Institute of Technology, Toyosu, Koto, Tokyo, 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
A/Prof. Teodor Lucian Grigorie, University of Craiova, Romania
A/Prof. Paresh Dalal, Shri Vitthalrao Shankarrao Naik Arts, Commerce and Science College, India
A/Prof. Muhammad Qasim, Abdul Wali Khan University Mardan, Pakistan
A/Prof. R. S. Ajin, GeoVin Solutions Pvt. Ltd., 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 Bin A Rashid, Univeristi Teknologi Malaysia, Malaysia
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, Malaysiaxiv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. Choy Soon Tan, Univeristi Teknologi Malaysia, Malaysia
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. Mohd Hairy Ibrahim, Sultan Idris Education University, Malaysia
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. Teresa Lopez-Lara, Universidad Autonoma De Queretaro, Mexico
Dr. Usama Juniansyah Fauzi, University of Tokyo, Japan
Dr. Yusep Muslih Purwana, Sebelas Maret University, Indonesia
Dr. Ana Almerich-Chulia, Universitat Politecnica de Valencia, Spain
Dr. Khor Shing Fhan, Universiti Malaysia Perlis, Malaysia
Dr. Afshin Asadi, International College of Auckland, New ZealandConference Correspondence:Prof. Dr. Zakaria Hossain, Conference Chairman,
Dept. of Env. Sci. & Tech., Mie University, Japan,
E-mail: conference@geomate.org
Tel & Fax: +81-59-231-9578
Editorial Committee and Executive Committee:
Prof. Zakaria Hossain, Prof. Mie University, Japanxv

Keynote Paper1

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

DEVELOPMENTS IN SLOPE STABILITY IN ENGINEERING
PRACTICE: A REVIEW OF RESEARCH PUBLISHED BY GEOMATEJohn Victor Smith
RMIT University, Australia and CMW Geosciences, Australia
ABSTRACT
Many papers have been published in the International Journal of GEOMATE making some contribution to the
topic of slope stability. A selection of these papers have been reviewed with the aim of classifying the types ofcontributions made. Three main areas of application focus have been identified: 1) Context, 2) Mechanics and 3)
Techniques. Context refers to papers which address the risk posed to people or infrastructure in the location andalso refers to papers which are identified as case studies of a particular slope failure event or slope region.
Mechanics refers to papers which focus on the mechanism of failure or on the properties of the materials in which
failure has occurred. Techniques refers to papers which focus on presenting methods of data collection, modellingand methods of stabilization and protection for slopes. Each of these different focus areas is shown to be a
distinctive way that papers have contributed to the integrated knowledge of slope stability.
Keywords: Slope Stability, Risk Assessment, Case Study, Mechanism, Stabilization

INTRODUCTION

contributes to the broad field of slope stability
research and practice.
As categories of studies emerged from the review
these categories were arranged into a hierarchy. A
three-level hierarchy has been developed. It is
intended that this hierarchy may be a useful tool for
future studies to recognize their main area ofcontribution to the field.

The International Journal of GEOMATE “aims tobecome an efficient mean of publishing anddistributing high quality information fromresearchers, scientists and engineers.” The journal
scope covers the main areas of Geotechnique,
Construction Materials and the Environment.
This contribution has been prepared as a Keynote
presentation to the 11th annual conference held in
Kyoto, Japan in 2021. The paper presents a review of
selected papers from the journal which have a focus
on slope stability. A framework of studies will bepresented to assist future researchers and practitioners
to recognize the diverse types of investigations and toassist in identifying opportunities for future researchand improvements to engineering practice.

RESULTSA summary of the categories of studies identified
in the review is provided in Fig.1.

METHODOLOGY
The review covers papers in the journal from
Issues 1 to 85. Papers were initially selected for thereview based on the title containing some reference toslopes or slope stability. It is possible some papers
with relevance to slope stability have been omitted.
As the list of relevant references was compiled,each paper was assigned to a grouping based on themain aspects of the paper. The nature of thegroupings emerged as the examples were compiled.
This process was subjective according to this author’sinterpretation. It is possible that other authors andauthors of the original papers may choose a different
categorization for individual papers.
It is not suggested that any paper is restricted to aparticular category. The categories are intended torecognize the main way in which each paper

Fig. 1 Framework of the review (from papers
published in J. GEOMATE Issues 1-85)
The outer boundary of Fig.1 acknowledges thatslope stability is not an isolated field. Many other2

GEOMATE – Kyoto, Japan, 3-5 November 2021

fields of study are relevant to slope stability or benefit
from the outcomes of slope stability research.
The first level of categories identified as the mainareas of application focus are: 1) Context, 2)
Mechanics and 3) Techniques (Fig.1). Context refers
to papers which address the risk posed to people orinfrastructure in the location and also refers to papers
which are identified as case studies of a particularslope failure event or slope region. The context
includes the application of susceptibility mapping
methods. Mechanics refers to papers which focus on
the mechanism of failure or on the properties of thematerials in which failure has occurred. Techniques
refers to papers which focus on presenting methodsof data collection, modelling and methods ofstabilization and protection for slopes.
A third level of hierarchy was identified undereach category. These sub-categories are summarized
in Fig.2.
DISCUSSION
The research on slope stability published in thejournal is based on work in the field and laboratories
of many countries. The brief comments in this
discussion highlight only a small number of research
examples and opportunities.
Development Areas
Case Studies
The importance of case studies is a feature ofslope stability research. It is only through the ongoing study of field examples that new problems andissues can be identified.
Where there is a significant historic or current
slope instability event it is essential that the context
and contributing factors are collated and published.
Each study not only makes a contribution to theunderstanding of the location investigated but also
contributes the database of factual information which
is fundamental to future research.
A case study, as a secondary function, can also
address other categories of investigation including
those listed in Fig.2. However, these secondary issues
emerge in a case study as needed to understand theconditions found in the location.

Fig. 2 Categories of study focus in the review
framework, with the paper references listed
noted that probabilistic methods of analysis are
becoming more common in this field of study [22].

Rainfall and Water Infiltration
Integration of Soil and Rock Mechanics
A significant number of papers were observed toinvestigate the role of rainfall and water infiltration.
This field is constantly developing as theunderstanding of unsaturated soil mechanics has
progressed in recent decades [23].
Rainfall is commonly observed to be a trigger forslope instability so these studies also make significant
contributions to risk assessment [9-10]. It is also

Throughout much of the history of scientific andengineering progress soil mechanics and rockmechanics have remained separate disciplines.
However, these fields need to become integrated asboth material types contribute to slope instability.
Landslide scars often show that structures in the
bedrock have contributed to slope failure by planar
3

GEOMATE – Kyoto, Japan, 3-5 November 2021

sliding, wedge sliding or toppling [18], [27].
Use of material strength parameters from the fieldof rock mechanics, such as the Hoek-Brown criteria
can be incorporated into the range of material
property models used in slope stability analysis [42].
The field of rockfall is an areas where rockmechanics plays an important role. It is also a fieldof investigation where numerical and physical models
have made a significant contribution e.g. [32].

assist researchers to contribute to existing threads ofresearch or to identify gaps needing new researchcontributions. The theoretical, laboratory and fielddata in these papers and others provides a basis for
future developments in engineering practice with theaim of improving the living conditions and safety ofcommunities in many parts of the world.

Vegetation

The many authors of papers in the International
Journal of GEOMATE are thanked for theircontributions.

ACKNOWLEDGMENTS

Vegetation has long been recognized as astabilizing influence on slopes. Methods forquantifying the strength improvement by field andnumerical studies of root strength and other aspectsof vegetation have the potential to influenceengineering practice e.g. [38].

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Future Developments
This review framework can provide impetus toguide future research in the field. For example,
researchers may recognize items in Fig.2 where they
could make a contribution based on their own
investigations. Alternatively researchers may observe
gaps in the items in Fig.2 and may seek out
opportunities to expand the range of topics.
Unstable and potentially unstable slopes are acommon feature of rural and urban settings (Fig.3).
Future research into the context, mechanics andtechniques for investigations are needed to informfuture developments in engineering practice.

Fig. 3 Photograph of an unstable slope in an urban
setting
CONCLUSIONS
A review of papers on slope stability published in
the International Journal of GEOMATE has been
conducted to develop a framework of investigation
categrories. The framework provides a scheme to4

GEOMATE – Kyoto, Japan, 3-5 November 2021

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7

Technical Papers

8Geotechnique911th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

EVALUATING THE EFFECTS OF POLYMERS ON MICROBIALLY
INDUCED CARBONATE PERCIPITATION
Zhen Yan1, Sivakumar Gowthaman2, Kazunori Nakashima2, and Satoru Kawasaki2
Graduate School of Engineering, Hokkaido University, Sapporo, Japan
2Faculty of Engineering, Hokkaido University, Sapporo, Japan

1

ABSTRACTMicrobially induced carbonate precipitation (MICP) is relatively a new environmentally friendly soil
improvement method. In MICP process, the formation of calcium carbonate occurs within the soil matrix as theconsequence of biochemical process mediated by ureolytic bacteria. The objective of this preliminary work is toevaluate the effects of adding polymers on MICP process. Basically, the polymers are organic macromolecules
and have potential to offer several benefits such as increasing solution viscosity, extending supply time,
localization of cementation reactions, reduction of harmful byproduct ammonium and enhancement of strength.
For the above-mentioned objective, two different types of polymers are studied: (i) Polyvinyl alcohol (PVA) and(ii) Polyacrylic acid (PAA). Lysinibacillus xylanilyticus are cultivated in the standard medium with varying
contents of polymers. The growth and urease activity of the bacteria are determined with the incubation time. In
addition, set of test tube tests is performed to assess the precipitation characteristics of calcium carbonate at thepresence of polymers. The outcomes indicated that the polymers do not have any adverse effects on the growthof bacteria. However, the PAA is found significantly enhance the urease activity compared with typical bacteria
culture medium, whereas it is not evidenced in PVA. Moreover, compared to the typical MICP process, theaddition of polymers produce more spherical-shaped calcium carbonate crystals, which highly differ from typicalrhombohedral-shaped crystals. With the above findings, future works are recommended on soil to investigate thecementation efficiency and adsorption of ammonium by-products.
Keywords: Microbially induced carbonate precipitation (MICP), soil improvement, polymer, urease activity,
crystal morphology
INTRODUCTION

xylanilyticus is one of such reliable species, andwhich was previously isolated from Hokkaido
expressway slope, Japan [5]. Following thedemonstration by Japan MICP researchers, thespecies were repeatedly used in many MICP related
works [6]–[7]. Due to their consistence performance,
the Lysinibacillus xylanilyticus is used in this study
to evaluate the effects of polymers in MICP process.
Use of additives such as polymers to improve theweak soil is an alternative biological approach to the
MICP process [8]. Number of researches has done
previously to investigate the applicability ofhydrophilic polymers. Hydrophilic polymer solution
can restrain the desiccation of the cementation
solution and extend the time window for MICPrelated reactions following one-shot injection, which
can be critical for certain applications where thetreated surficial soils are exposed to air and sunlight.
Owing to these favorable characteristics, hydrophilicpolymers have increasingly been utilized forassisting mineral precipitation-based improvement
and remediation approaches in the field of civilengineering [9].
Previously, the effect of polyvinyl alcohol (PVA)on strengthening was investigated. Using PVA to
increase the surface erosion resistance of MICP
treated soil also studied. Their outcomes indicats

In recent years, the biological method has
attracted attention as an environmentally friendly
method of ground improvement. Microbial Induced
Carbonate Precipitation (MICP) is one of such kindof novel geotechniques. In MICP process,precipitation of CaCO3 is induced by nonpathogenic bacteria that mediate the hydrolysis of
the urea [1]. The sequential bio-chemical reactions
that occur during the MICP process are listed in Eqs.
(1-3).
CONH2 2  H2O Urease H2NCOO -  NH 4-
3

H2NCOO  H2O  HCO  NH 3
Ca 2  HCO 3  NH 3  CaCO3  NH 4

1
2
3

Most of the MICP related research works attempt
to involve Sporosarcina pasteurii which is a popular
gram positive bacteria with high urease activity [2]–[4]. In due course, few researchers worked on
identifying some other potential ureolytic bacteriafor MICP purposes. Their outcomes indicate that theuse of indigenous bacteria, instead of popularexogenous bacteria, offers numerous benefits interms of reliability and bio-safety. Lysinibacillus

10GEOMATE – Kyoto, Japan, 3-5 November 2021

case C, neither PVA nor PAA was added. It wasused as a control group to compare the effects ofadditional polymers on microorganisms.

that the polymers provide additional cohesive forces[10]. In another instance, polyacrylic acid (PAA)
was applied to not only improving the mechanicalstrength of the loose soil but also mitigatingexcessive ammonium contamination [11]. It is worth
mentioning, however, only a very few studies
focused on the effect of polymer on biological andbio-chemical responses, providing insufficient
information in the scientific literature.
Thus, the objectives of this original work are toevaluate the (i) effects of polymers on the biologicalresponses and (ii) efficacy of incorporating polymers
in MICP process. For the assessment, two popularpolymers: PVA and PAA, are chosen in this study.
The experimental program mainly involves urease
activity measurements and assessments ofprecipitation characteristics.

Table 1. The compositons of main culture
Case
No.
V1
V2
V3
V4
V5
V6
C
A1
A2
A3
A4
A5
A6

MATERIALS AND METHOD
2.1 Polymers used
The PVA used herein was obtained from Wako
Pure Chemical Industries Ltd., Tokyo, Japan. The
average degree of polymerization is between 400 to
600. The average molecular mass of PAA (Wako
Pure Chemical Industries Ltd., Tokyo, Japan) used is
25000. Chemical structures of the PVA and PAA are
shown in Fig. 1(a) and Fig. 1(b), respectively.

PVA
(g/L)
5
10
20
40
60
80

0

PAA
(g/L)

0

2.5
5
7.5
10
12.5
15

2.3 Urease activity test
Urease activity of the bacteria culture wasmeasured using indophenol spectrophotometry
method. 0.5 mL of bacterial culture was taken frommain culture and added in 50 mL phosphate buffer
solution with 0.1 mol/L urea. At the presence of
hypochlorite, the ammonium ions produced from theurea hydrolyses react with phenol and produce theblue colour indophenol dye in the alkaline medium.
The intensity of indophenol dye was measured via
the wave length of 630 nm (OD630) at every 5 mininterval of catalyzation. Using a calibration curve
established between ammonium ion concentrationand intensity (OD630), the rate of urea hydrolysis andurease activity can be obtained [12].

Fig. 1 Structures of the polymers (a) PVA (b) PAA
2.2 Preparation of bacteria cultureThe
gram-positive
ureolytic
bacteria
Lysinibacillus xylanilyticus, previously identified
from the Hokkaido slope were used in this researchstudy. Firstly, to prepare the preculture, 5 mL of
NH4 -YE medium was inoculated by the stock
bacteria (preserved at -80 ℃) and kept in the
shaking incubator at 25 ℃ and 160 rpm for 24 hours.
Secondly, 1 mL of the preculture was added into 100
mL of NH4-YE medium with varying quantities of
PVA or PAA (hereinafter referred to as main
culture). The main culture was then kept in the
shaking incubator under the same condition as
preculture.
The culture mediums tested herein are all have
1.57 g of Tris buffer, 1 g of Ammonium sulfate and2 g of Yeast extract per 100 mL. The additional
adding polymers clearly presented in Table 1.
From case V1 to V6, the content of PVA

2.4 Precipitation test
After 1 ml bacteria samples separately took fromeach case in Day 3, centrifugated and removed
supernatant and each sample tube was added 10 mLsame concentration (0.3 mol/L) of CaCl2 and Urea
solution. Samples were kept in the shaking incubator
under 25 ℃ and 160 rpm speed for 24 hours. Thenthe reaction mixture was centrifuged to collect the
precipitate, and supernatant of tubes were removed
separately by using filter paper (11 μm).
Both of the filter papers and the tubes with theprecipitate were oven dried at 60 ℃ for 48 hours anddry weights were measured. Weight of the
precipitate was calculated by subtracting the empty
weight of the tube and the empty weight of the filter
paper from the dry weight of the tube and the dryweight of the filter paper.

increases. Similarly, from case A1 to A6, thecontent of PAA gradually increases. But for the11

GEOMATE – Kyoto, Japan, 3-5 November 2021

2.5 Scanning
analysisElectron

Microscopy(SEM)

The shape of calcium carbonate crystals can beaffected by many factors. In order to minimize
interference from organic components in the culture
medium and different urease activities from different
cases, using 1 ml of bacteria sample in the same
control case C in day 3. After centrifugation andsupernatant removement, 3 groups both added withthe 10 mL of same concentration (0.3 mol/L) of
Urea and CaCl2 solution. One group was added with
1 g/L of PVA, and the other group was added with 1
g/L of PAA, one group left as control group.
Samples were kept in the shaking incubator
under 25 ℃ and 160 rpm speed for 24 hours. Thenthe reaction mixture was centrifuged to collect the
precipitate. After 48 hours of 60 ℃ oven dry,scanning electron microscopy (SEM) was performed
using Miniscope TM3000, Hitachi (Tokyo, Japan) to
identify the morphology of the precipitated calcium
carbonate crystals.

Fig. 2 Variation of the bacteria population withadding polymer (a) PVA (b) PAA

RESULTS
3.1 Growth of the bacteria
Variation of the population of samples under
different polymers concentrations are given in theFig. 2. It can be seen from the Fig. 2(a) that the
PVA has no obvious inhibitory effect on the growthof bacteria, bacteria can still grow, even at a highconcentration up to 80 g/L. In contrast, PAA
strongly inhibits bacterial growth when theconcentration is higher than 10 g/L (Fig. 2(b)).

Fig. 3 Variation of the urease activity with adding
polymer (a) PVA (b) PAA

12

GEOMATE – Kyoto, Japan, 3-5 November 2021

3.2 Urease activity

3.4 Morphology of the precipitated crystals

Figure 3 shows that different concentrations of
PVA have no obvious effect on the activity ofbacteria. The activities were almost similar to thatobserved of the control. On the other hand, the low
concentrations of PAA have a very obvious
promotion effect on urease activity. For instance, atthe concentration of 10 g/L, the urease activity isfound to be relatively optimum, that exceeds of avalue over 11 U/mL. However, when the PAA
concentration further increases, the urease activity
dramatically diminishes.

According to Fig. 5, scanning electron
microscope (SEM) images of various morphologies
are shown at the same scale.

3.3 Amount of CaCO3 precipitation
Figure 4 shows the variation of the amount of
CaCO3 for the different considered cases. In thecases of adding PVA do not show any evident trend(no significant changes). But, for the PAA cases,
when the concentration increases, the amount ofprecipitation shows an increasing tendency.
However, when the concentration increases above
10 g/L, it suddenly drops very close to 0 g.

Fig. 5 Variation of the CaCO3 morphology under
different condition (a) no polymer adding (b) PVA(c) PAA.
The calcium carbonate crystal morphology
without any polymers is shown in Fig. 5(a) (control),indicating the formation of typical rhombohedralshape crystals. When the PVA is added to themedium, relatively spherical-shaped crystals are
precipitated. At the same time, an accumulation ofFig. 4
Variation of the CaCO3 amount withdifferent polymer cases (a) PVA (b) PAA.

13

GEOMATE – Kyoto, Japan, 3-5 November 2021

pallets-like calcium carbonate crystals could be seenin the PAA added cases. It should be noted that themorphology of the crystals seen in Fig. 5(a)significantly different from that observed in Fig.
5(b). This suggests that the crystallization effects
highly rely on the intrinsic characteristics ofpolymers.

crystals with the attendance of polymer. The
relationship between the crystal morphology andground improvement is still not clear. However, thisis a quite important point in MICP. It thus should be
revealed in the future work by comparing the crystal
polymorph and the strength of cured soil.
REFERENCESDISCUSSION[1] Dhami N. K., Reddy M. S., and Mukherjee M. S.,
Biomineralization of calcium carbonates andtheir engineered applications: A review,
Frontiers in Microbiology, Vol. 4, Issue OCT,
2013, pp. 1–13.
[2] Whiffin V. S., van Paassen L. A., and Harkes M.
P., Microbial carbonate precipitation as a soil
improvement
technique,
Geomicrobiology
Journal, Vol. 24, Issue 5, 2007, pp. 417–423.
[3] van Paassen L. A., Ghose R., van der Linden T. J.
M., van der Star W. R. L., and van Loosdrecht M.
C. M., Quantifying Biomediated Ground
Improvement
byUreolysis:
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Biogrout Experiment, Journal of Geotechnicaland Geoenvironmental Engineering, Vol. 136,
Issue 12, 2010, pp. 1721–1728.
[4] Gomez M. G., Anderson C. M., Graddy C. M. R.,
DeJong J. T., Nelson D. C., and Ginn T. R.,
Large-Scale Comparison of Bioaugmentation
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[5] Gowthaman S., Mitsuyama S., Nakashima K.,
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expressway soil, Japan, Soils and Foundations,Vol. 59, Issue 2, 2019, pp. 484–499.
[6] Gowthaman S., Iki T., Nakashima K., Ebina K.,
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From the outcomes, it is found that the PVA
does not inhibit both the growth and urease activityof bacteria, regardless of its concentration in thereaction medium. In contrast, the addition of PAA
was found to favor the urease activity of the bacteria,
which showed an increase over more than two-folds.
However, the concentration over 10 g/L strongly
inhibited the bacteria growth, urease activity andprecipitation efficiency. This could probably be
attributed to the characteristics of the polymer. The
PVA is a neutral polymer, while the PAA is anacidic one; therefore, the low pH conditions causedby high concentration of PAA (above 10 g/L) were
extremely unfavorable and harsh for bacterial
growth.
In consideration of both Fig. 3 and Fig. 4, there
were no considerable positive or adverse influences
observed from the PVA on the MICP process.
Interestingly, the lower concentrations of the PAA(below 10 g/L) promoted the ureolytic performanceof the bacteria, hence the mineralization efficiency.
One possible explanation could be that thepreferential consumption of PAA by the bacteria that
aided to produce more amino acids. Nevertheless,the clear mechanism for the incredible performanceis still unclear, needing further investigation (left for
the future work).
It was evident (from the SEM analysis) that withthe addition of polymer, the morphology of thecrystals transitioned from rhombohedral to round/spherical-shaped crystals with more aggregation.
The morphology changes could possibly be due tothe adsorption of the polymers on the crystal surface,which led to the formation of new crystal faces.
CONCLUSIONS
According to the obtained results, a moderateamount of PAA has a significant positive effect onurease activity, which has great significance to thepractical application of engineering and can be used
to achieve high strength cementation in the future
research. On the contrary, PVA does not promote or
inhibit MICP, even at high concentrations. It also
has potential application value which can be used toadjust the viscosity of the cementation solution tocontrol the occurrence position of the MICP.
Morphology of the crystals changes from thepolyhedral crystals to agglomeration of round14

GEOMATE – Kyoto, Japan, 3-5 November 2021

[9] Wang X., and Tao J., Polymer-modified
microbially induced carbonate precipitation for
one-shot targeted and localized soil improvement,
Acta Geotechnica, Vol. 14, Issue 3, 2019, pp.
657–671.
[10] Wang X., and Tao J., Polymer-modified
Microbially-Induced Carbonate Precipitation
Treatment Method for Surface Erosion
Prevention. 2018.

[11] Zhao Z., Hamdan N., Shen L., Nan H., AlmajedA., Kavazanjian E.,and He X., Biomimetic
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1511th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

HIGH WATER CONTENT PEAT SOIL IMPROVED BY MICPTECHNIQUE WITH FIBER-REINFORCEMENT
Meiqi Chen1, Sivakumar Gowthaman2, Kazunori Nakashima2, Shin Komatsu3 and Satoru Kawasaki2
1

Graduate School of Engineering, Hokkaido University, Sapporo, Japan
2
Faculty of Engineering, Hokkaido University, Sapporo, Japan
3
Meiwa Seishi Genryo Co., Ltd., Osaka, Japan
ABSTRACTPeat soil is referred to as problematic soil that uniquely forms under anaerobic and water-saturated conditions.
It is usually characterized as organic soil of high compressibility, high water content, and low shear strength. To
reduce the environmental impact, instead of using cement products to stabilize it, a relatively novel techniquebased on the bio-mineralization principle, microbially induced carbonate precipitation (MICP), was adopted to
improve the soil matrix. Besides, this study introduced wastepaper fibers from the paper recycling process onto
MICP treatment. The objectives were to investigate the effects of fiber reinforcement (0-50% addition) on the
undrained shear strength (USS) improvement and contributing factors to the MICP effectiveness. Furthermore, thefiber-reinforced and MICP treated samples were subjected to dispersive crumb tests to examine their stability
under a water-logged environment. The results showed a significant improvement in USS up to 360 folds observed
when 50% of fibers were introduced onto MICP treatment. The dispersion tests revealed a stable state of MICP
treated sample with fiber reinforcement, while the fiber reinforcement alone showed a swelling behavior.
Keywords: Peat soil, Microbially induced carbonate precipitation (MICP), Wastepaper fiber, Fall cone test,
Dispersive crumb test

INTRODUCTION
This technique has broad application prospects,
including soil stabilization, liquefaction prevention,
coastal erosion control, fugitive control, etc. [7, 8]. In
this study, the focus is concentrated on soil
improvement, aiming to enhance one type of highwater content peat soil. The use of wastepaper fibers
recycled from a paper mill differs this research frommost other reports. Due to its excellent water holding
capacity, the wastepaper fiber was expected tostabilize the excessive water in the peat. Globally, therecycling rate of wastepaper fibers keeps at a low
level in developing countries. If these wastes could
become useful materials in the construction industry,
the increasing recovery rate would contribute tosustainable development.
The objectives of this study are i) to investigate
the factors that determine the effectiveness of MICP
on peat soil; ii) to examine the feasibility ofstabilizing high water content peat soil using
wastepaper fiber-reinforced MICP technique; iii) toevaluate the performance of the treated soil samples
in a water-clogging condition. Strength improvement
data were obtained using fall cone test. The
effectiveness of MICP was evaluated by themeasurement of calcium carbonate content anddispersive crumb test. These experimental works
provide a fresh insight into the reuse of wastematerials and generate sustainable ideas that combine
wastes with novel techniques to achieve a win-win
situation.

According to the latest report on the global
distribution of peatland, peat soil covers
approximately 2.84% of the world land area [1].
Formed under extreme conditions of high acidity andwaterlogging, this type of soil usually exists at highorganic and water levels [2]. Peat deposits possess
undesirable engineering properties, so improving thismarginal land has been challenging for engineers [3].
For the past several decades, most conventional
methods using many cement products have beenaccused of contributing significantly to CO2emissions [4]. Nowadays, since we are facing an
unprecedented crisis due to global warming,
engineers now tend to consider more undesirable
consequences that may come with the improvement
than before, generating more and more novel
technologies of sustainability.
Microbial induced carbonate precipitation(MICP) is one of these techniques, and now it isincreasingly described as a green and reliable
alternative to conventional methods in many research
fields. Unlike conventional techniques, in MICP
process, urease produced by living organisms plays avital role in controlling the reaction rate flexibly. The
following equations explain how the biomineralization occurs [5, 6]:
UreaseCO(NH2 )2 ＋ 2H2 O �⎯⎯� 2NH+4 + CO23

CO23 +

Ca

2+�⎯⎯� CaCO3 ↓

(1)
(2)
16

GEOMATE – Kyoto, Japan, 3-5 November 2021

MATERIALS AND METHODOLOGY8) after 72 hours of cultivation at 25℃ [9]. Since theprevious report has corroborated its reliability, thebacterial preparation was done following the same
method, using NH4-YE (ATCC 1376) as the culture
medium and 25 ℃ as the cultivation temperature.

Peat Soil
Peat soil samples were collected from Iwamizawa,
Hokkaido, Japan. Table 1 presents the basic
characteristics of Iwamizawa peat. The distinguishing
characteristics between Iwamizawa peat and normal
soils are its high water content (around 800%) and pH
lower than 5.

Table 2 Experimental Cases

Category

Conc. of
CaCl2 & urea

Fiberaddition

non

0%
10%
30%
50%

pH 4.5
pH 7
1M
2M
3M

non

1M

0%
10%
30%
50%

Table 1 Basic characteristics of Iwamizawa peat
Parameters

ValuesWater content

711 - 824 %Density

1.821 g/cm3

Ignition loss

65.815 %pH

4.6 - 4.8

k

-4

Set 1

Set 2

10 - 10-5 cm/s

Note: Samples were examined from the lower layer to the upper
layer; k is the permeability coefficient.

Set 3

Effect of fiber
addition
Effect of initialpH
Effect of conc.
of CaCl2 &
urea
MICP
treatment
reinforced byfibersWastepaper Fiber
Experimental Cases
Wastepaper fibers (WPF) are a mixture of wastematerials produced in a paper mill, with cellulose
fibers, the basic structural constituent of all plantfibers, and ash content as two dominant components.
Meiwa Seishi Genryo Company, Japan, provided thefibers used in this study. The image captured byscanning electron microscopy (SEM) illustrates theappearance of WPF (see Fig. 1 below). Basically, awide range of fiber lengths from 20 to 500 µm wasobserved.

Cases set in this experiment were depicted inTable 2. In this study, considering the characteristics
of peat soil, MICP treatment was conducted bymixing all the materials added in the following order:
0.5% of CaO2, for pH adjustment; WPF at different
ratios; CaCl2 & urea; and bacterial culture solutions,with an OD600 around 10. It should be mentioned that
all the ratios presented in this study refer to proportionby the weight of soil. For each 182 g (100 cm3) of peatsoil, 15 mL of bacteria were added. The concentrationof cementation materials was calculated based on thevolume of soil. Molded samples were sealed and thenput into an incubator with a constant temperature of
25℃ for curing. Strength examinations were carriedout three times: right after the sample preparation, 24
hours, and day 7.

Long fibers

Ash content

Short fibers

Evaluations
×400
20 µm

Fall cone test
The fall cone apparatus (release-stop type DH22NM, cone angle=60°) is provided by Seikensha,
Tokyo, Japan. The tests are conducted according to
the JGS 0142-2009 [10]. It has been reported that thistest could be a feasible and reliable method toestimate the shear strength for soft clay materials. For
Iwamizawa peat, this test is a quick way to examine
the strength improvement over time. The estimation
is based on an approximate relation between the
undrained shear strength (su) and the depth ofpenetration (h), as presented in Eq. 3.

Fig. 1 SEM image of WPF
Bacteria
The ureolytic bacterium (as a source of urease)used in this study is Lysinibacillus xylanilyticus, agram-positive species with a rod shape (8-10 µmlong) which was previously isolated from Hokkaido
by Gowthaman et al. [9]. Its urease activity tends topeak at around 3.5 U/mL at a neutral condition (pH 717

GEOMATE – Kyoto, Japan, 3-5 November 2021

120

(3)

100

USS (kPa)

Where fall cone factor k=0.29, cone mass m=60 g,
and earth gravity acceleration g=9.81 N/kg.

900

USS
Water content

750

Water content, %𝒔𝒔𝐮𝐮 =k(mg/h2 )

80

600

60

450

40

300

20

150

Calcium carbonate measurement
The measurement of calcium carbonate isachieved by using a sealed container equipped with apressure gauge, which was developed by Fukue et al.
[11]. It is based on a simple principle: the pressure
inside the container increases due to the carbon
dioxide produced by the reaction between calcium
carbonate and hydrochloric acid. This change inpressure could be transformed into the mass ofcalcium carbonate using an established calibration
curve. For each test, 2 g of dry samples were takenfrom the sample and reacted with 20 mL of HCl
solution (2 mol/L).

Fig. 2 Effect of fiber addition on undrained shear
strength (USS) improvement and the watercontent

RESULTS

Contributing Factors of MICP Treatment

Fiber Reinforcement

Effect of initial pH
Having an acidic condition (pH 4.5) makes it quite
challenging to improve the peat by MICP treatment
because it inhibits the urease activity of bacteria andthe precipitation of carbonate. Therefore, to tackle
this problem, the acidic peat was adjusted to a neutral
condition (around pH 7) by adding 0.5% of CaO2
(agricultural fertilizer). A comparison of the adjusted
and unadjusted strength is illustrated in Fig. 3(a),revealing a significant difference in strength
improvement. What is striking here is that a doubled
improvement ratio when the acidic condition wasaltered into a neutral one, which indicates asignificant enhancement in the effectiveness of MICP.

The first test was carried out to measure thevariation in strength with different fiber contents, as
shown in Fig. 2. It can be found that the addition of
WPF enhanced the USS significantly. When the fiber
content was up to 50%, an improvement ratio of 200
folds and a significant reduction of water content
were observed. It is considered that the water
reduction is responsible for the improvement since
the high water content controls the shear strength of
Iwamizawa peat.

(a) 30
25
20
15
10
5
0

Day-0

Day-1
Curing time/day

30
25

USS (kPa)

USS (kPa)(b)

pH 4.5
pH 7

Day-7

20

0

10

30

Fiber content, %

50

day 0
day 1
day 7
CaCO3,%

0

30
25
20

15

15

10

10

5

5

0

0
1M
2M
3M
Con. of cementation materials (mol/L)

Calcium carbonate content, %0Fig. 3 Contributing factors of MICP: (a) effect of initial pH; (b) effect of concentration of cementation materials

18

300

day 0

USS (kPa)

250

day 1

day 7

CaCO3,%

30
25

200

20

150

15

100

10

50

5

0

0

10
30
Fiber content, %50

0Calcium carbonate content, %GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 4 MICP treatment reinforced by different fiber content (10%, 30%, 50%), with carbonate content shown by
the dash-dotted line
0.3

Concentration of CaCl2 & urea
To investigate the effect of different
concentrations of cementation materials, a set of
MICP treated samples with 1 mol/L, 2 mol/L and 3
mol/L of CaCl2 & urea were prepared. From the bar
chart in Fig. 3(b), it can be seen that samples with ahigher concentration appeared to be less improved by
the treatment. Strong evidence was found in the
results of calcium carbonate content. The 1 M caseyielded a calcium carbonate precipitation of 18%,
while only 1% of precipitation was obtained in the 3
M case. The concentration of 1 mol/L was the
optimum among three cases, with an improvement
ratio of more than 20 times.

MICP-0% fiber
MICP-10% fiber
MICP-30% fiber
MICP-50% fiber
Peat
10% fiber
30% fiber
50% fiber

Turbidity (OD600)

0.25
0.2
0.15
0.1
0.05
0

0

120

Time/ min

240

360

Fiber Reinforced MICP Treatment
Fig. 5 Turbidity-time curve in crumb tests conducted
on untreated peat, fiber-reinforced samples,and fiber-MICP treated samples

Figure 4 illustrates the enhanced strength by
WPF (10%, 30%, 50%) reinforced MICP treatment
during 7-day curing. It is apparent from this figurethat a small amount of fiber addition did not enhancethe effectiveness of MICP, which can be explained by
the similar calcium carbonate precipitation obtained
from 0% case and 10% case. However, when the
addition was up to 30%, one-week strength increased
by six folds compared to the 0% case. What stands
out in this figure is that 50% of fiber addition
contributed an improvement ratio of 15 times.
Meanwhile, the calcium carbonate content wasincreased by 25% compared to the 0% case.
The enhancement observed in this figure can becompared with the results shown in Fig. 2. As
mentioned before, fibers enhanced the strength
significantly by holding the water. Incorporated with
MICP treatment, a nearly doubled enhancement was
obtained in cases with high fiber content.

Dispersive Crumb Test
Since the peat was formed in conditions withexcessive water, investigating how the stabilized peat
performs under water-clogging is essential. Therefore,both untreated and treated peat samples were
subjected to crumb tests. The results were presented
both in pictures and statistics. As shown in Fig. 5, thequantified turbidity of treated samples verified theremarkable improvement in dispersive properties.
Pictures in Fig. 6 illustrate the samples kept in awater-logging condition after 6 hours. High watercontent peat collapsed immediately when samples
were subjected to water immersion (Fig. 6(a)). It isevident from Fig. 6(b-h) that samples became morestable with the increasing fiber addition, with lesscrumbled material observed at the bottom of the
beaker. Remarkably, almost no crumb was found in
the MICP case with 50% of fiber addition. It is worth
noting here that the fiber-reinforced samples
exhibited a swelling behavior during the 7-day curing
19

GEOMATE – Kyoto, Japan, 3-5 November 2021

period, as shown in Fig. 6(c), which accelerated thedamaging process when soaking into water. Closer
observation on the crumb settled at the bottom of the
beaker suggests that fiber-reinforced MICP treatedsamples are more stable in water for the reason thatsuspended particles in peat were connected by the
bio-cementation.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

Fig. 6 Demolded samples in a water-clogging state with a 6-hour duration: (a) untreated peat soil; (b) 10%, (c)30%, and (d) 50% of fiber-reinforced peat; (e) MICP treated sample (1 mol/L); (f) MICP treatment
reinforced by 10%, (g) 30% and (h) 50% of fibers

DISCUSSION

activity and fibers (finer) with a larger specificsurface area were used to enhance the strength
further. What stands out in this study is that the
improvement ratio by wastepaper fibers was morethan 5-fold as that by bamboo fibers when the
addition was up to 50%. It can be explained by theirdifference in the capacity of moisture reduction.
Compared to bamboo fibers, wastepaper fibers
possess a larger specific surface area, thus holding
more water, which leads to a significant enhancementin the shear strength.
The results of the dispersive crumb test
illuminate a significant improvement in peat soil's
stability. As generally known, cellulose fibers holdwater and expand, which explains why a relatively
poor performance was observed in fiber-reinforced
samples. However, it seems that fiber-reinforced
MICP treatment could overcome this obstacle since
the precipitation functions as a cementitious material
that binds the loose fibers.
Considering the organic matter, mainly humic
substances in the peat soil, the addition of CaO2 might
have affected the solubility of some substances since
the pH condition was greatly altered. Furthermore,when mixing the calcium carbonate and urea, there isa severe fluctuation in temperature. Both thesechemical and physical changes disturbed the original
structure of the soil, which might inhibit the MICP to

A previous experimental study on improving
Iwamizawa peat combined bamboo fibers and MICP
treatment [12]. Due to the limited urease activity of
the native bacteria, the treated soil remained weak.
Overall, it has been confirmed that fiber materials
could stabilize high water content soil, which further
aids the MICP treatment.

Improvement ratio

500

WPF
WPF-MICP
Bamboo fiber
Bamboo fiber-MICP

400
300
200
100
0

0

10

20
30
40
Fiber content, %50

Fig. 7 Improvement ratio by fibers and fiber-MICP[12]
Similarly, in this study, bacteria of higher urease
20

GEOMATE – Kyoto, Japan, 3-5 November 2021

some extent. As reported in the previous study, thehumic substances are of great cation exchange
capacity (particularly humic acids), which could be
the most challenging part of MICP treatment
considering the indispensable role of calcium ions[12]. In soil humus science, these substances,
unfortunately, are remained elusive to scientists.
Therefore, to obtain a deeper understanding of thesesubstances' role in the MICP process, more efforts
need to be made.

[2] Huat B.B.K., Kazemian S., Prasad A., Barghchi
M., State of an art review of peat: Generalperspective. International Journal of the PhysicalSciences, Vol. 6, Issue 8, 2011, pp.1988-1996.
[3] Kazemian S., Huat B.B.K., Prasad A., Barghchi
M. A state of art review of peat: Geotechnicalengineering perspective. International Journal of
the Physical Sciences, Vol. 6, Issue 8, 2011,
pp.1974-1981.
[4] United Nations Environment Programme,
Emissions Gap Report 2020.
[5] DeJong J.T., Mortensen B.M., Martinez B.C.,
Nelson D.C., Bio-mediated soil improvement.
Ecological Engineering, Vol. 36, 2010, pp. 197210.
[6] Mujah D., Shahin M.A., Cheng L., State-of-theArt Review of Biocementation by Microbially
Induced Calcite Precipitation (MICP) for Soil
Stabilization. Geomicrobiology Journal, Vol. 34,
2017, pp.524-537.
[7] Dejong J.T., Soga K., Kavazanjian E., Burns S.,
Van Paassen L.A., Al Qabany A., Aydilek A.,
Bang S.S., Burbank M., Caslake L.F., et al.,Biogeochemical processes and geotechnicalapplications: Progress, opportunities andchallenges. Géotechnique, Vol. 63, Issue 4, 2013,
pp. 287-301.
[8] Jiang N.J., Tang C.S., Hata T., Courcelles B.,
Dawoud O., Singh D.N., Bio-mediated soil
improvement: The way forward. Soil Use
Management. Vol. 36, Issue 2, 2020, pp.185-188.
[9] Gowthaman S., Mitsuyama S., Nakashima K.,
Komatsu M., Kawasaki S., Biogeotechnicalapproach for slope soil stabilization using locallyisolated bacteria and inexpensive low-grade
chemicals: A feasibility study on Hokkaido
expressway soil, Japan. Soils and Foundations,Vol. 59, Issue 2, 2019, pp. 484-499.
[10] JGS Testing Methods and Their Interpretation
for Geotechnical Materials (in Japanese). 2009.
[11] Fukue M., Nakamura T., Kato Y., Cementationof Soils Due to Calcium Carbonate. Soils andFoundations, Vol. 39, Issue 6, 1999, pp. 55-64.
[12] Chen M., Gowthaman S., Nakashima K.,
Kawasaki S., Evaluating Mechanical Strength ofPeat Soil Treated by Fiber Incorporated BioCementation.
International
Journalof
GEOMATE, Vol. 20, Issue 78, pp.121-127.

CONCLUSIONSThe present study was designed to investigate theeffect of fiber-reinforcement on high water content
peat improvement, two contributing factors of MICP
treatment on peat, and evaluate the effectiveness offiber-reinforced MICP treatment. The first set ofexperiments conducted in this research has identified
a remarkable enhancement in soil strength whenreinforced by wastepaper fibers. The second set
confirmed a favorable condition for effective MICP
treatment. The results of the third set revealed adoubled improvement in shear strength when fiber
reinforcement was combined with MICP treatment,
followed by dispersive crumb test, which indicates anessential finding of the treated samples' stability. A
discussion of samples' durability lies beyond thescope of this study, which is recommended for future
work.
In general, this study suggests an essential role forwastepaper fibers in amending high water content
peat and highlights the feasibility of MICP treatment
on soil improvement.
ACKNOWLEDGMENTS
The authors express their gratitude to Civil
Engineering Research Institute for Cold Region,
Sapporo, Japan, for their contribution and technicalsupport.
REFERENCES[1] Xu J., Morris P.J., Liu J., Holden J., PEATMAP:
Refining estimates of global peatland distribution
based on a meta-analysis. Catena, Vol. 160, 2018,
pp.134-140.

21

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

OFF-CENTER TETRAHEDRAL WEDGESJohn Victor Smith 1
1

RMIT University, Australia
ABSTRACT

Tetrahedral wedges in rock slopes can have symmetric and asymmetric shapes. Asymmetry is commonly
recognized as differences in dip angles of the sliding planes. This form of asymmetry is known to influence thekinematic feasibility and stability of a wedge. Asymmetry also influences the appearance of wedges and therefore
the ability to recognize wedges in rock faces and in data from rock masses. The relative orientation of the sliding
planes in relation to the lower and upper slope faces is also part of the asymmetry system. In particular, thedirection of the line of intersection which determines the sliding direction of two planes, relative to the directionof the slope of the rock face introduces an additional type of asymmetry. This form of asymmetry is defined here
as off-center wedges. A detailed terminology for asymmetric tetrahedral wedges is presented and the implicationsfor kinematic feasibility, sliding mechanism and stability analysis are discussed. A field example from Auckland
New Zealand is presented.
Keywords: Wedge, Kinematic stability, Rock slope, Sliding

INTRODUCTIONof the shape and symmetry of a tetrahedral wedge.
The typical shapes of feasible wedges are illustrated
on a stereograph. Examples of applications of theapproach are given for data from jointed and faulted
sedimentary rock in Auckland, New Zealand. The
feasibility and factor of safety are shown to be related
to the shapes of the wedges, including the off-center
asymmetry of some wedges.

The stability of a tetrahedral wedge exposed in arock slope has been shown to be influenced by its
shape in both static [1-3] and dynamic [4-6]conditions. Wang et al. [7] demonstrated limitequilibrium and numerical solutions to wedge
stability including comparison of symmetrical andasymmetrical examples. In a probabilistic analysis ofwedge stability it was found that the reliability results
were very sensitive to variations in the shape andtherefore symmetry of the wedge [8].
Large amounts of discontinuity orientation dataneed to be investigated in field and synthetic rockmass characterization projects [9]. Stereographic
methods form the initial step in wedge stability
assessment in many rock engineering investigations[10-17]. In a summary of conventional stereographic
methods, Wyllie and Mah [18] indicated that wedgeforming poles are restricted to inside the daylighting
envelope of the slope face plane. However, the limits
to wedge-forming poles has been shown to be more
complex and defined by limits to the orientations ofgreat circles on which wedge-forming pole pairs canlie [19].
Stereographic methods have also been used to
distinguish between mechanisms of single-plane andtwo-plane sliding mode of wedges using the line ofintersection [20] or the poles and daylighting
envelope [21].
This paper is focussed on tetrahedral wedges
defined by two sliding planes and an upper and lower
slope face. The presence of tension cracks or basal
sliding planes is not considered. Asymmetry isdeveloped by the relationship between all four faces.
The typical distributions of planes which can form
kinematically feasible wedges are considered in terms

TERMINOLOGY
Angle Between Sliding Planes
The angle between sliding planes is an important
geometric parameter of wedges. The term dihedral
angle can be used to specifically identify the angle
measured in a plane perpendicular to the line ofintersection of the planes. The terms thin and thickwere applied by Hudson and Harrison [3] up to adihedral angle of 90o. They did not extend theiranalysis beyond that value. When higher dihedral
angles are considered the wedges become thinnerwith respect to the slope face. Therefore, the terms
narrow (for thin) and open (for thick) are adopted.
The term very open is applied to wedges where thetwo sliding planes are approaching parallelism witheach other (Table 1). As wedges become increasingly
open, they can approach the dip direction of the slope
face and therefore approach the planar sliding casewhich represents a minimum stability for that dip
angle. On a stereograph, that appears as poles tosliding planes approaching the opposite direction to
the dip direction of the slope face. Lateral limits ofplanar sliding are typically accepted as +/-20o [18].
These limits are a general reference only and do not
define a change in failure mechanism. As will beshown below, the change in mechanism from single
22

GEOMATE – Kyoto, Japan, 3-5 November 2021

plane to two-plane sliding varies according to thedegree to which wedges are off-center.

Table 1 Terminology used to describe features oftetrahedral wedges observable on a stereographic
representation

Wedge Symmetry
Application

The symmetry of a tetrahedral wedge must be
considered in two ways. First, the symmetry of thetwo sliding planes relative to each other. Second, thesymmetry of the sliding planes relative to the slope
face also needs to be considered. These aspects ofsymmetry can be considered to be related to thesliding plane dip and dip directions, respectively. If
the two sliding planes have the same dip then a plane
halfway between them would be vertical. If the two
sliding planes have the different dips then a plane
halfway between them would be dipping. Hudson andHarrison [3] refer to this as upright and inclined,
respectively. For simplicity, the dip angle of eachplane will be compared and will be termed equal or
unequal. An arbitrary 10o difference in dip between
the planes is applied here to separate the two terms.
Regarding the influence of dip direction, if awedge is oriented such that its sliding direction(intersection line) is the same as the dip direction of
the slope face that will be described here as a centeredwedge. Other sliding directions will be referred to as
off-center. Therefore, the symmetry of the tetrahedral
wedge is a combination of its centered or off-centercharacteristics and its equal versus unequalcharacteristics (Table 1). A symmetrical wedge must
be both centered and equal. Note that the sliding
direction can be directly observed as the pole to thegreat circle on which the wedge-forming poles lie
[19].

Term

Describes the
Narrow(dihedral) angle
between two
sliding planes
forming a
wedge
Open

Very open

Describes theSymmetricoverall shapeof a tetrahedral
wedge (defined Asymmetricby four planes) or Nonsymmetric

Off-center Wedges
Describes the
direction of
two-plane
sliding relative
to the slope
direction

Poles lying in other locations other than on acentered great circle are capable of forming a wedge.
Such poles will typically lie on a great circle which
has a different strike to the strike of the slope face.
An off-center example has a pole (equivalent to lineof intersection of wedges) trending northeast (Fig. 1).
For simplicity, a southeast trending example is notshown, as the principle can be applied in a mirror
image for comparison. On this basis, a wide range ofwedge-forming possibilities can be considered forexample, the eight general locations of poles shownas numbered stars on Fig. 1.
Location 1 represents a pole close to the laterallimit of planar sliding near where the centered andoff-center great circles intersect. Location 2 andlocation 3 occur on the same side of the stereograph,between the lateral limit and the primitive circle(outer edge of the stereograph) on the off-center great
circle. Locations 4 and 5 occur on the same side of thestereograph as location 1, between the lateral limit
and the primitive circle on the off-center great circle.
Location 5 lies close to the primitive circle on theCentered

Off-center

Highly offcenter

Describes thedifference in
dip of thewedge sliding
planes23

Equal
UnequalComment/
Quantification<60o (>120o pitch
on stereographbetween poles on
opposite sides ofthe slope face)
60o-120o (60o120o pitch angle
on stereograph)>120o (<60o pitch
on stereographbetween poles on
opposite sides ofthe slope face)
Only possible forcentered, equal
wedgesAsymmetry of atetrahedral wedge
can be due to offcenteredorientation of thegreat circle
joining thesliding planepoles and/or
unequal dip ofsliding planes
Great circle
joining sliding
plane poles dips
180o from slope
face direction
Great circle
joining sliding
plane poles dips
180o +/- <30ofrom slope face
direction
Great circle
joining sliding
plane poles dips
180o +/- >30ofrom slope face
direction
Difference of dip<10oDifference of dip>10oGEOMATE – Kyoto, Japan, 3-5 November 2021

off-center great circle and is distinguished by dippingback into the slope face. The numbering of theselocations is entirely arbitrary and they have been
selected for the purpose of illustrating generalexamples of shapes of kinematically feasible wedges
according to the position of poles on a stereograph.
Any combination of poles from great circle
distribution (e.g. any pair of poles form locations 1-5
forms a wedge with the same intersection plunge (redstar on Fig. 1). The shape will differ greatly for eachpair of planes. For example, the wedge formed bypoles at locations 1 and 2 are very open whereas awedge formed by poles at locations 3 and 5 would benarrow.

FIELD EXAMPLE: AUCKLAND, NEWZEALAND
The rocks in the field area are interbedded
sandstone and siltstone of the Miocene age
Waitemata Group. Weathered rock and soil
approximately 2 m thick is present at the top of thecliff. Coastal retreat in sedimentary rocks north of
Auckland, New Zealand is controlled by bedding,joints and faults [22]. A report on a fatal rockfall at
Rothesay Bay, in the northern suburbs of Auckland
New Zealand by Hancox [23] concluded that the
failure occurred by joint-controlled sandstone block
failure related to erosional undercutting of asandstone bed with heavy rain two weeks prior and a
low to moderate earthquake 16 hours prior was likely
to have influenced the timing.
Bedding dips very gently to the west (into the cliffface). Joints are close-spaced and clustered in two
main sets with vertical to steep easterly dipping andnortheasterly dipping orientations (Fig. 2, Table 2).
Faults are also present with moderate to steep dips.
Kinematic analysis shows that wedges can form withcombinations of joints with faults and faults withother faults (Fig.3, Table 3).
Table 2 Structural readings from Rothesay Bay,
Auckland, New Zealand
Dip Dip Dir.

Structure

Notes

78
013
Joint set 1
N=41, SD=12.4
Joint
set
2
86
100
N=40, SD=9.5
81310
Fault 1a54
140
Fault 1b79
320
Fault 2a69
140
Fault 2b68
110
Fault 349
290
Fault 4a55
076
Fault 4b
Slope face orientation 70-063, height 30 m.
N=number of points, SD=Angular standard
deviation based on Fischer dispersion

Fig.1 Stereograph of potential wedge-forming poles(black stars) illustrating off-center wedges
Wedge Sliding Mode
The lateral limits of planar sliding are recognized
as an arbitrary range for practical purposes [18]. The
occurrence of single plane sliding wedges and twoplane sliding wedges depends on other geometric
relationships [20,21]. For centered wedges, the single
plane sliding mode cannot occur. Even two planes
one degree either side of the plane perpendicular tothe slope face will form a two-plane sliding pair, inthe centered case. Where a pair of planes (poles) are
joined by an off-center great circle the zone of single
plane sliding is defined by the difference between the
strikes of the slope face and the off-center great circle.
The example in Fig. 1 has a 30o difference andtherefore the range of the single plane sliding zones is
30o as shown on Fig. 1. A wedge formed by a pair ofpoles with one of those poles being in that zone will
have the mechanism of single plane sliding on thatplane. A wedge formed by a pair of poles both ofwhich are outside that zone will have the mechanismof two plane sliding.

DISCUSSION
The common practice of assessing wedge
kinematic stability according to the line ofintersection of the sliding planes fails to provide
information on the shape of the wedges.
The method of assessing kinematic stability fromthe poles of sliding planes allows the shape of thewedges to be observed concurrently. This approachallows symmetrical and non-symmetrical wedges tobe distinguished on the stereograph. Symmetry of
wedges is observable according to the two factors: (1)the dip directions of sliding planes relative to the24GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 2 Location maps (a-b) and satellite image (c) of the rocky cliffs south of Rothesay Bay, Auckland, New
Zealand (Google Earth). Star shows approximate location of rockfall reported by Hancox [29]. Dashed line shows
the strike of bedding on the rock platform. (d) Cliffs approximately 30 m high (person circled for scale) and slopeof approximately 70o (dashed line). (e) Typical jointing pattern on the surface of a bed (backpack is approximately
30 cm wide. (f) Location near top of cliff where a joint-block in a bed has fallen from the cliff (arrow). (g) Joint
surface dipping out of the slope face. (h) Conjugate faults (dashed lines). (i & j) A sea cave formed in the
hangingwall of a fault

Fig. 3 Stereographic representations (equal angle, lower hemisphere) of structural data from the coastal cliff southof Rothesay Bay, Auckland. (a) Stereograph with joint sets and faults labelled F1-4 with conjugate pairs labelled
a and b. The northeast facing slope is shown as a great circle and its daylight window (pink lines). The great circle(black line) between joints does not pass through the daylight window indicating stability. (b) Wedges between
joint set 1 and east-southeast-dipping faults. (c,d) Single plane sliding on F4b forming a wedge with joint set 1 andfaults. (e-g) Wedges formed by F4b, joint set 2 and other faults
25

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 3 Potential wedges, Rothesay Bay, Auckland,
New Zealand
Wedge

Wedge

FoS

Mech

CONCLUSIONS
The relationship between wedge sliding compared
to a rock slope face dip direction is described here as
centered or off-center. This feature can be observedby the position of the poles of the sliding planes along
their shared great circle. Only off-center wedges canslide with a single plane sliding mechanism.
The shapes of wedges are defined in terms ofnarrow, open and very open. This property can also
be readily observed on a stereograph using the circle
method of wedge analysis. The further apart are thepoles along the great circle, the more narrow is thewedge. The closer the poles are located to each otherthe more open the wedge is. The more open a wedge
is, the lower the factor of safety tends to be.

Fig.

angle
(o)
W(J1-J2)

95

NW

5a

W(J1-F1b)

68

1.05241&2

5b

W(J1-f2b)61

0.75151&2

5bW(J1-f3)

88

0.386

1&2

5b

W(J1-f4b)59

0.4043slide

5c

on f4bW(F1b-F4b) 53

0.4043slide

5d
ACKNOWLEDGMENTS

on f4bW(J2-f1a)

32

NW

5e

W(J2-f2a)

43

NW

5e

W(J2-F4b)

38

2.53181&2

5e

The author acknowledges the discussions with Dr
Christian Arnhardt, British Geological Survey onstereographic analysis of wedges.

W(F2a-F4b)75

0.78251&2

5g

REFERENCES

W(F3-F4b)

32

0.93161&2

5f

W(F2b-F4b) 57

0.4043slide

5c[1] Hoek E., Bray J. W., and Boyd, J. M., The stabilityof a rock slope containing a wedge resting on two
intersecting discontinuities. Quarterly Journal ofEngineering Geology and Hydrogeology 6 (1)1973, pp. 1-55.
[2] Chan H.C. and Einstein H.H., Approach tocomplete limit equilibrium analysis for rockwedges—the method of “artificial supports”.
Rock Mechanics 14 (2) 1981, pp.59-86.
[3] Hudson J.A. and Harrison J.P., Engineering RockMechanics: An Introduction to the Principles.
Elsevier. 1997, pp.1-444.
[4] Ghosh A. and Haupt W., Computation of theseismic stability of rock wedges. Rock Mechanics
and Rock Engineering 22 (2) 1989, pp. 109-125.
[5] Aydan Ö., Kumsar H., An experimental andtheoretical approach on the modeling of sliding
response of rock wedges under dynamic loading.
Rock Mechanics and Rock Engineering 43 (6),2010, pp. 821-830.
[6] Kumsar H., Aydan Ö., and Ulusay R., Dynamic
and static stability assessment of rock slopes
against wedge failures. Rock Mechanics and RockEngineering 33 (1) 2000, pp.31-51.
[7] Wang Y.J., Yin J.H., Chen Z., and Lee, C.F.,
Analysis of wedge stability using different
methods. Rock Mechanics and Rock Engineering
37 (2) 2004, pp.127-150.
[8] Jimenez-Rodriguez R., and Sitar N., Rock wedge
stability analysis using system reliability methods.

on f4bW(F1a-F3)36

2.95261&2

5g

W(F2a-f3)

44

1.766

1&2

5g

HEADINGS
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 blankline before and after the first level headings,
respectively.
Fig.4 Stability and shape of wedges in Table 3.
Anomalous low FoS cases are labelledslope face and (2) the similarity dip of the sliding
planes.
The factor of safety is closely related to the wedge
shape and symmetry (Fig. 4). The factor of safety has
been calculated by the limit-equilibrium method in
the software ‘Roscience SWedge’ which is based on
methods developed by Evert Hoek and colleagues [1].
Wedges with one or more of the planes close to theplanar sliding direction (e.g. F4b) have lower factorsof safety than the general trend (Fig. 4). This situation
occurs in both single plane and two-plane sliding
cases.

26

GEOMATE – Kyoto, Japan, 3-5 November 2021

Rock Mechanics and Rock Engineering 40 (4)2007, pp.419-427.
[9] Grenon M. and Hadjigeorgiou J., A design
methodology for rock slopes susceptible to wedge
failure using fracture system modelling.
Engineering Geology 96(1-2), 2008, pp.78-93.
[10] Paronuzzi P., Bolla A., and Rigo E., 3D Stress–
Strain Analysis of a Failed Limestone Wedge
Influenced by an Intact Rock Bridge. RockMechanics and Rock Engineering 49, 2016, pp.
3223-3242.
[11] Mandalawi M.A., You G., Dowling K,. andDahlhaus, P., Kinematic assessment of slopes athandlebar hill open cut mine, Mt. Isa, Queensland,
Australia. International Journal of GEOMATE,
10(1), 2016, pp.1575-1583[12] Alzo’ubi A.K., Rock slopes processes andrecommended methods for analysis. International
Journal of GEOMATE, 5(25), 2016, pp.2520-27.
[13] Barla G., Wedge Instability on the Abutment of
a Gravity Dam in Italy. Geotechnical andGeological Engineering 35, 2017, pp.3025-3033.
[14] Basahel H., and Mitri H., Application of rockmass classification systems to rock slope stability
assessment: A case study. Journal of RockMechanics and Geotechnical Engineering 9, 2017,
pp.993-1009.
[15] Cerri R.I., Reis F.A.G.V., Gramani M.F.,
Rosolen V., Luvizotto G.L., do Carmo GiordanoL., and Gabelini B.M., Assessment of landslide
occurrences in Serra do Mar mountain range using
kinematic analyses. Environmental Earth

Sciences 77, 2018, pp.325.
[16] Sonnekus M. and Smith J.V., A review of
selected unexpected large slope failures.
International Journal of GEOMATE, 15(48),2018, pp.66-73.
[17] Iyaruk A., Phien-wej N., and Giao P.H.,
Landslides and debris flows at Khao Phanom
Benja, Krabi, southern Thailand. International
Journal of GEOMATE, 16(53), 2019. pp.127-134.
[18] Wyllie D.C. and Mah, C., Rock SlopeEngineering. Taylor & Francis. 2004, pp.1-456.
[19] Smith J.V., A method for assessing discontinuity
poles for potential wedge sliding. Engineering
Geology 202, 2016, pp.55-61.
[20] Öcal A. and Özgenoğlu A., Determination ofsliding mode of tetrahedral wedges in jointed rockslopes. Rock Mechanics and Rock Engineering 30(3) 1997, pp.161-165.
[21] Smith J.V. and Arnhardt C., Distinguishing
between single and double plane sliding oftetrahedral wedges using the circle method.
Engineering Geology 25, 2016, pp.98-101.
[22] Moon V.G. and Healy T.R., Mechanisms ofcoastal cliff retreat and hazard zone delineation insoft flysch deposits. Journal of Coastal Research
10, 1994, pp.663–680.
[23] Hancox G., Report on the fatal rock fall on the
coastal cliff south of Rothesay Bay, Auckland, on
2 July 205, GeoNet Response Report No.
460W6000, 205, pp.1-7.

27

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

IMPACTS OF SUFFUSION FACTOR ON HETEROGENIZATION OF
SOILS
Koji Nakashima1, Katsuyuki Kawai1 and Takayuki Fumoto1
1

Department of Civil and Environmental Engineering, Kindai University, Japan
ABSTRACTIn recent years, embankment structures have often failed due to extreme weather events, such as typhoons andtorrential rains. Suffusion, in which finer particles within the soils are transported by seepage flow, causes
heterogenization of the soils and may lead to deterioration of the embankment. Various suffusion factors, such as
material properties and hydraulic conditions, have been confirmed. However, the impact of these factors on the
degree of heterogenization is uncertain. In this study, repeated seepage tests on cylindrical specimens with different
relative densities were conducted. X-ray CT scans of the specimens during the test were also performed. A greater
erosion rate was observed in the first seepage flow experience, and a decreasing trend during seepage was observed
to differ depending on the hydraulic gradient. It was confirmed that the relative density had a larger impact on theoccurrence of suffusion compared to the hydraulic gradient or the number of seepage events. Investigation of CT
images demonstrated that heterogenization of soil had occurred by the formation of lower density zones within thesamples.
Keywords: Seepage, Erosion rate, Hydraulic gradient, Relative density

INTRODUCTION

the resilience of the embankment structure against
extreme rain and flooding events.
On the other hand, suffusion is a microscopic
phenomenon; hence, it is quite difficult to visually
understand the deterioration of the structures derived
from it. Internal visualization using X-ray CT images
is useful for investigating the mechanical behavior ofengineering materials, and several laboratory
experiments have previously utilized this method in
the geotechnical engineering field as well. Nguyen etal. [3] captured CT images during a suffusion test andinvestigated the microstructural changes during thesuffusion process, such as strain fields, spatial
distribution and fines content.
In this study, repeated seepage experiments on
cylindrical specimens with different relative densities
were conducted. X-ray CT scans of the specimens
during the test were also performed.

In recent years in Japan, embankment structures,such as levees and earth dams, have often failed dueto the torrential rain and devastating flood events that
accompany climate change. Causes of levee failure,
such as overtopping, increasing pore water pressure
and piping, have mainly been mentioned.
Furthermore, the existence of deteriorated areas
within the structures also proves critical in terms ofoperation and maintenance and should be carefully
considered.
Natural soil materials are used for construction of
the embankment structures; hence, it is thought thatthey have irregular homogeneity in terms of material(e.g. particle size distribution, density) andmechanical (e.g. permeability, strength) properties.
Internal erosion, which is the migration of soil within
the embankment, also has a large impact on theheterogenization of the soil materials and may lead todeterioration of structures and, in the worst case,catastrophic failure during a flood event. Internal
erosion can be classified into four types: suffusion,
backward erosion, contact erosion and concentrated
leak erosion [1]. This study focuses on suffusion, in
which finer soil particles are transported among thevoids of the coarse fraction following seepage flow
(Figure 1). Void change due to migration of finesinduces various geotechnical concerns. Several
laboratory experiments have previously revealed
suffusion factors, such as material properties andhydraulic conditions [2]. However, the impact ofthese factors on the degree of heterogenization is
uncertain. Understanding them is vital to improving

Fig. 1 Schematic illustration of suffusion

28

GEOMATE – Kyoto, Japan, 3-5 November 2021

EXPERIMENTAL STUDY

size distribution curves (Figure 3). Soils which haveminimum H/F of more than 1.3 are classified as stable.
From the particle size distribution curve of the test
material, minimum H/F was calculated as(H/F)min=0.05. According to both of these criteria, thetest material can be interpreted as having the potential
for instability to internal erosion. The material
properties are presented in Table 1.
A constant head permeability test of the test
material was conducted for 24 hours. Figure 4 shows
the fluctuation of the coefficient of permeability kduring the permeability test. The results indicate thatpermeability slightly decreased with time.

Test material

Percent finer by weight (%)

100

80

Coefficient of permeability k (cm/s)

The test material was prepared by mixing No.3
and No.7 silica sands, in equal portions by weight.
The particle size distribution curve of the test materialis shown in Figure 2. The test material is generallyclassified as gap-graded soils, which are often
internally unstable. Chang and Zhang [4] proposed
the criterion of internal instability by applying gap
ratio Gr, as illustrated in Figure 3. This is defined as
the ratio of the maximum particle size dmax and theminimum particle size dmin. Soils which have fines
content of less than 10% are classified as stable when
the gap ratio is smaller than 3.0. The gap ratio of thetest material was Gr=4.72 (dmax=1.18mm,
dmin=0.25mm). Alternatively, the criterion proposedby Kenny and Lau [5] is well known for classifying
internal stability of soils and can be defined by two
parameters, H and F, which are obtained from particle
Silica No.7
Silica No.3
Mixture

60

10 -2

10 -3

0

300

600

900

1200

1500

Elapsed time t (min)Fig. 4 Results of constant head permeability test

40

Test procedures

20

0
0.01

0.1

1

The prepared unsaturated test material was
installed into a cylindrical acrylic column, and thespecimen was constructed by compaction of fivelayers at a target relative density. Figure 5 illustrates
an overview of the test equipment. A 0.23mm meshfilter was set to allow the washing away of finerparticles. Water from the upper tank was supplied tothe bottom of the specimen. A hydraulic gradient wasconstantly maintained by the water level difference
between the upper tank and top of the specimen.
The repeated seepage tests were conducted toinvestigate heterogenization of the soils due to cyclic
hydraulic power, as shown in Figure 6. The
cylindrical column was installed into the X-ray
testing apparatus (Figure 7), and CT scans were also
performed at points (a)~(f) shown in Figure 6. CT
images were captured under the acquisition
conditions explained in Table 2. After specimen
preparation, the specimen was saturated with waterfor 24 hours. The upper tank was then lifted to thetarget position where the hydraulic gradient would be
the critical hydraulic gradient ic. This position wasmaintained for 0.5 hours. Drainage water from the topof the specimen was collected every 6 minutes, for 5
collections in total; flow rates and turbidity were
measured. After seepage, the water supply was
stopped, and the CT images of the specimen were
captured. Next, the upper tank was lifted so that the10Grain size (mm)Fig. 2 Particle size distribution curve
Percent finer by weight (%)

100

d min

d max
H

F
0

D

4D
Grain size

Fig. 3 Definition of parameters for internal
instability
Table 1 Material properties
Specific
gravityGs
2.60

Uniformity
coefficientUc
8.81

Maximumvoid ratio
emax
0.83

Minimumvoid ratio
emin
0.48

29

GEOMATE – Kyoto, Japan, 3-5 November 2021

water, and the seepage test was performed in the samemanner as in the first cycle described above.
In this study, the seepage tests were performed on
specimens with relative densities of Dr=60% and 80%.
The theoretical critical hydraulic gradient ic is 0.98 forthe specimen of Dr=60%, and 1.03 for Dr=80%.
Hereinafter, in the test results section below, thegraph legend is displayed as “relative density”“hydraulic gradient”-“cycle”. For example, d60-i2-c2
means the result of the test at 2ic of the second cycle
for the specimen compacted at the relative density of
60%.
TEST RESULTS

Hydraulic gradientFig. 5 Overview of test equipment

The following sections explain the test results interms of erosion response, fluctuation of permeability,
and CT images. For the soil specimen with relative
density of 60%, boiling failure was immediately
observed at 2ic of the first seepage cycle, as displayedin Figure 8. Because of this, the seepage test was thenfinished at this point.

0.5h

2i c

(f)

(c)

(e)

(b)

ic
0.5h
24h

(d)

(a)

Crack

Time (h)Fig. 6 Test condition

Flat panel

Fig. 8 Failure state of the specimen
Erosion and permeability response
Figure 9 shows the results of turbidity during theseepage tests. The turbidity tended to gradually
decrease in each case. Greater turbidity wasconfirmed in the results for Dr=60% compared tothose for Dr=80%. As shown by the results for
Dr=80%, the greatest turbidity was observed at the
beginning point of ic in the first seepage cycle. It is

Scanning area
X-ray source

Fig. 7 X-ray testing apparatus
100

Voltage(kV)

Current
(A)

180

100

Frame
rate
(fps)
3.0

Projection
views

Resolution(mm/pixel)

2000

0.0615

Turbidity (degree)

Table 2 Acquisition conditions of CT images

hydraulic gradient would be 2ic, and measurements
and X-ray CT scans were conducted. After the CT
scan, the water within the specimen was temporary
drained by gravity. The soil was again saturated with

d60-i1-c1
d80-i1-c1
d80-i2-c1
d80-i1-c2
d80-i2-c2

10

1

0.1
0

10

20

Elapsed time (min)Fig. 9 Turbidity during seepage
30

30

40

GEOMATE – Kyoto, Japan, 3-5 November 2021

presumed this was because the sands had neverexperienced an erosion history. Hence, less turbidity
was observed at the beginning point of the second
seepage cycle compared to the first seepage cycle. It
was also confirmed that the turbidity of the beginningpoint of the second cycle was greater than that of theterminate point of the first cycle. This is probably
because soil structure changed due to the downward
drainage by gravity after the first cycle.
Turbidity of the artificial suspension wasmeasured in advance. The relationship between theturbidity and the density of the soil in the suspension
was then investigated, and can be approximated by alinear function, as shown in Figure 10. Using this, thevalues of the turbidity measured from each test can beconverted into density. As an example, the result of
d60-i1-c1 is shown in Figure 11. The eroded soil masscan be obtained by applying a non-linear
approximation, as follows, and integrating it.

Density (g/cm 3 )0.08

M e (t )M (t )

0.04

0.00

0

60

80

100

0.5
ExperimentCalculation

(1)

0.4
-0.30[1-exp(-0.12t)]+0.35
0.3

0.2

0.1

0.0

0

5

10

15

20

25

30

Elapsed time t (min)Fig. 11 Eroded soil mass

(2)

Erosion rate R e (t)10 -1
d60-i1-c1

10 -2
10 -3

d80-i2-c2

10 -4

d80-i2-c1

10 -5
10 -6

d80-i1-c2

d80-i1-c1
0

10

20

30

40

Elapsed time t (min)Fig. 12 Instantaneous erosion rate
1

Erosion rate R e

d60-i1-c1
10 -1

10 -2
d80-i1-c1

d80-i2-c1

10 -3

t

d80-i2-c2

d80-i1-c2

(3)
10 -4

0

where, Q (m /s) is the flow rate of water; w (kN/m )is the unit weight of water; h is the difference ofwater level between the upstream section and the340

Fig. 10 Relationship between turbidity anddensity

Where, Me(t) is the eroded soil mass obtained from
Eq. (1). M(t) is the total soil mass at a certain time.
The fluctuations of the instantaneous erosion rate areshown in Figure 12. For the results of seepage with ic,
the instantaneous erosion rate decreased linearly withtime; whereas, for the results of 2ic, the trend showeda smooth curvature. The cause of this is uncertain, yet,it implies that the seepage condition has some sort of
influence on erosion progression. Figure 13 shows therelationship between the erosion rate and hydraulicgradient. The erosion rate is plotted as the rate of totaleroded soil mass to initial total soil mass. In the figure,the hydraulic gradient is shown as the value obtained
from the test condition. For Dr=60%, greater erosion
rate was confirmed compared to results for Dr=80%.
Therefore, the relative density of soils have larger
impacts on the degree of suffusion compared to thehydraulic gradient or the number of seepage events.
Marot et al. [7] proposed the expanded energy
equation, which is the time integration of theinstantaneous power dissipated by water seepage forthe experimental duration, as follows.
E flow ( t ) = Q wht

20

Turbidity (degree)

Where, a, b and c are the constant determined byfitting, and t denotes the elapsed time. Subsequently,
an instantaneous erosion rate was calculated from thefollowing formula [6]Re ( t ) =

0.06

0.02

Eroded mass M e (g)M e ( t ) = −a 1 − exp ( −bt ) + c

0.10

2

0

1

2

Hydraulic gradient i

Fig. 13 Relationship between erosion rate andhydraulic gradient31

3

GEOMATE – Kyoto, Japan, 3-5 November 2021

10 1

Heterogenization under seepage history

Eroded mass M e (g)d60-i1-c1

For investigating the heterogenization of soils dueto the seepage force, the gray values of the CT images
were analyzed. Figure 16 shows the results obtainedby subtracting the gray values of CT images at eachpoint in Figure 6. The white parts of the images
indicate the area where the density increases due toseepage. The results for Dr=80% explicitly show theremarkable heterogenization of soil in the layer where
the density had potentially become loose. Lessheterogenization can be observed around the layerboundary where the soils were densely compacted
during specimen preparation. Greatest variation was
confirmed at ic of the second seepage cycle, yet an
obvious variation related to the magnitude ofhydraulic gradient cannot be specifically observed.
Results for Dr=60%, as opposed to the results of
Dr=80%, seem to indicate that the heterogenizationof the entire soil dominates, rather than local
heterogenization; however, further investigation isneeded on this point.

1d80-i1-c1

d80-i2-c1
d80-i2-c2

d80-i1-c2
10

-1

0.1

1

10

Expanded energy E flow (kJ)Fig. 14 Relationship between eroded mass andexpanded energy

Coefficient of permeability k (cm/s)

downstream section; and t is the time for a certain
duration. Figure 14 represents the relationshipbetween the total eroded soil mass and the expanded
energy. According to the previo column us results [7],the eroded mass increased in agreement with anincrease in the expanded energy. The results obtained
from this study show approximately similar trends.
Figure 15 shows the fluctuation of the coefficientof permeability during the seepage tests. The
coefficient of permeability tended to gradually
decrease with time in each case. The trend appeared
significant with the first seepage event, and themagnitude decreased about a tenth. A similar trend
was also confirmed from the constant head
permeability test, as represented above. Moreover,this trend roughly corresponded to the turbidity
fluctuation. It could be interpreted that the decreasing
turbidity implies a clogging of fine particles, thus
forming a poorly permeable area. It can be observedthat permeability at the beginning of the second
seepage cycle recovered compared to that of theterminate point of the first cycle. This is because thesoil structure changed due to the downward drainageof gravity between the cycles, as mentioned before,
and the permeable area could change.

Dr = 60% (a)→(b)Dr = 80% (a)→(b)Dr = 80% (b)→(c)Dr = 80% (d)→(e)Dr = 80% (e)→(f)

10 1

1

10

-1

d80-i1-c1
d80-i2-c1
d80-i1-c2
d80-i2-c2
d60-i1-c1

10 -2
10 -3
10 -4

0

10

20

30

40

Elapsed time t (min)Fig. 15 Fluctuation of the coefficient of
permeability

Fig. 16 Heterogenization due to seepage

32

GEOMATE – Kyoto, Japan, 3-5 November 2021

CONCLUSIONSREFERENCES

In this paper, repeated seepage tests on specimens
with different relative densities were conducted toinvestigate the impact of suffusion factors on the
degree of heterogenization of soils. Visualization of
the progressive changes within the soils using CT
images of the specimen under seepage histories was
also performed. The following conclusions weredrawn;
1) Turbidity under seepage tended to gradually
decrease with time regardless of the test
condition. Greater turbidity was confirmed for
Dr=60% compared to Dr=80%. The decreasing
trend of instantaneous erosion rate during
seepage differed depending on the hydraulicgradient. In general, the relative density of soils
have larger impacts on the degree of suffusioncompared to the hydraulic gradient or thenumber of seepage events.
2) The coefficient of permeability tended togradually decrease with time in each case. The
trend appeared significantly in the first seepage
event. Additionally, it was observed that thistrend roughly corresponded to turbidity
fluctuation.
3) Investigation of CT images demonstrated thatheterogenization of soil had occurred by theformation of lower density zones within thesamples. An obvious variation related to themagnitude of hydraulic gradient could not be
observed.

[1] Fell, R., Fry, J.J., State of the art on the likelihoodof internal erosion of dams and levees by meansof testing. In: Bonelli, S. (Ed.), Erosion inGeomechanics Applied to Dams and Levees.
ISTE-Wiley, London, UK, 2013, pp.1-99,
Chapter 1.
[2] Horikoshi, K., Takahashi, A., Suffusion-inducedchange in spatial distribution of fine fractions inembankment subjected to seepage flow, Soils andFoundations, Vol. 55(5), 2015, pp. 1293-1304.
[3] Nguyen, C.D., Benahmed, N., Andò, E., Sibille,
L., Philippe, P., Experimental investigation of
microstructural changes in soils eroded by
suffusion using X-ray tomography, Acta
Geotechnica, Vol.14, 2019, pp. 749-765.
[4] Chang, D.S., Zhang, L.M., Extended internalstability criteria for soils under seepage, Soils
and Foundations, Vol. 53(4), 2013, pp.569-583.
[5] Kenny, T.C., Lau, D., Internal instability of
granular filters. Canadian Geotechnical Journal,
22(2), 1985, pp.215-225.
[6] Sato, M., Kuwano, R., Suffusion and clogging
by one-dimensional seepage tests on cohesive
soil, Soils and Foundations, Vol. 55(6), 2015,
pp.1427-1440.
[7] Marot, D., Rochim, A., Nguyen, H.H.,
Bendahmane, F., Sibille, L., Assessing thesusceptibility of gap-graded soils to internal
erosion: proposition of a new experimental
methodology, Natural Hazards, 83, 2016,
pp.365-388.

33

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

MEASUREMENT OF THE FROST HEAVE ACTING ON THE FULLSIZE GROUND ANCHOR AND SOIL NAILING
Atsuko Sato1, Osamu Hatakeyama1, Hirochika Hayashi1, Masahiko Yamaki1, Jun Ikeda2, Takayuki Iizuka2,
Noboru Mikami2, Tuyoshi Nakamura3, Fumitada Mikami3 and Yuji Kyouden3
Civil Engineering Research Institute for Cold Region, Japan; 2Nittoc Construction Co., Ltd., Japan;
3
Japan Foundation Engineering Co., Ltd., Japan

1

ABSTRACTIn snowy and cold regions such as Hokkaido, ground anchor and soil nailing are damaged by frost heaves, andtheir functions are considered to be degraded, which is a problem in maintenance. However, the relationshipbetween the degree of frost heaving power and the time of action and damage has not been clarified. For thisreason, a design method and a maintenance management method that take into account frost heave prevention
technology have not been established for slope structures. In order to understand the mechanism of damage causedby frost heave and to study the technology for countermeasures against frost heave, a full-scale test andconstruction was performed on the ground anchors and the soil nailings, and the applied load was measured. For
ground anchors, we focus on the free length, introduced tension load, and pressure receiving area, and for soil
nailings, we focus on the length of the reinforcing material and the area of the pressure receiving plate. As a resultof the measurement, the frost heaving force exceeding the design load was applied to the ground anchors and theground reinforcement soil works in the winter period. It was also found that the freezing depth could be reducedby covering the ground with continuous fiber reinforced soil and rubber mat.
Keywords: Frost heaving force, ground anchors, soil nailings, frost depth, full size

INTRODUCTION

countermeasure [2], [3]. This paper summarizes theresults.

In cold, snowy regions such as Hokkaido, frost
heave damages ground anchors and soil nailngs,
reducing the functionality of these structures.
Administrators have considered this damage as an
issue to be addressed in order to improve maintenanceand management techniques for these structures [1].
However, the relationship between the degree and theperiod of frost heave force acting on these structures
and the resulting damage has not been clarified.
Therefore, design methods and maintenance andmanagement methods that consider frost heave
countermeasure technologies for slope retaining
structures have not been established. To clarify themechanism behind frost heave damage to groundanchors and soil nailngs, we constructed full-scale
experiment models for these two types of soil nailngs
and measured the loads acting on the structures. As
investigation items for ground anchors, we focusedon the free length, the initial tension load, and the areaof the pressure plate. For soil nailngs, we focused on
the length of reinforcing materials and the area of thepressure plates. We investigated a frost heave
countermeasure for ground anchoring and earth
reinforcement work. The pressure plates of theground anchors and the soil nailngs were covered
with continuous fiber-reinforced soil or rubber mats,which were the insulating materials, and we measured
and compared the temperatures of the ground undereach insulation material to those without a

TEST METHODS
Basic data for the ground anchor systems and thesoil nailngs
To measure the frost heave force that acts on
ground anchor and earth reinforcement work using
soil nailings, we constructed these two types ofstructures at an experiment site in Tomakomai,
Hokkaido, the northernmost prefecture of Japan. The
basic data for the constructed ground anchors and thesoil nailings are shown in Tables 1 and 2.
The ground anchor and the soil nailing were
installed at the locations shown in Fig. 1.
Investigations were done comparing the target
parameters set for the 6 ground anchors. The
difference based on the free length was tested using
GA-1 and GA-2. The difference based on thetensioning strength was tested using GA-3, GA-4, andGA-5. The difference based on the area of thepressure plate was tested using GA-5 and GA-6.
Comparison investigations were also done for the soil
nailings. RB-1 and RB-3 were for comparison ofdifferent lengths of reinforcement materials. The
difference in the areas of the pressure plates was
examined by using the pair of RB-1 and RB-3 and thatof RB-2 and RB-4. The design area of the pressure
plate is determined depending on the bearing capacity
34

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1 The basic data for the constructed ground anchors

Anchoringload (kN)

Type
GA-1

PC steel
wire
φ15.2

GA-2
GA-3
GA-4
GA-5
GA-6

PC steelwires
φ9.5×7

Allowable load (kN)
0.6Tus(JIS) 0.9Tys(JIS) 0.9Tys
Ordinary
DuringTest
condition
earthquakes
table

70
235

156.6

199.8

227

428.4

547.2

648

Apparent
free
length(m)
7.00

Anchoring
length
(m)
10.0

14.00

4.0

18.85

400

6.0

18.71
Tus: Tensile load

Area ofpressure
plate
(m2)
3.13

5.62
4.14

Tys: Yield load

Table 2 The basic data for the constructed soil nailings.
Type
RB-1
RB-2
RB-3
RB-4

Anchoringload (kN)

SD345D25

20

Allowableload (kN)Anchoring
length(m)
7.0
5.0
7.0
5.0

137

Area of pressure
plate(m2)
0.16
0.10

N value
0 10 20 30 40 50

0Depth (m)5

Tomakomai

10

15

Fig. 1

Installation location of ground anchor andsoil nailSurface soil

Coal ashvolcanic ash Volcanic
ash mixed with pumice
Soil mixed withpeat and humus
Volcamic ash

Sand gravel

Fine sand

20
Sand mixed with gravelof the ground (the bearing power of soil), on which
the pressure plate is installed, and on the design load.
In the experimental installation in this study, pressure
plates with different areas were used to investigate thepossibility of reducing the frost heave force acting on
the pressure plates according to the differences in thearea. The pressure plates used with the groundanchors were steel, and those used with the soil
nailing were glass fiber or carbon fiber composite
reinforced plastic grids

25

Fig. 2 Columnar section of construction site.
force for the ground anchors and soil nailings, we
replaced the soil layer at the installation site. A 90cmdeep soil layer was replaced with frost susceptible
material that has the basic physical properties shownin Table 3. When ground anchors and soil nailings areconstructed on the slope, water supply from the backof the structures affects frost heaving, depending on
the height of the structure on the slope. In this study,
we constructed the ground anchors and soil nailings
on flat ground to equalize the frost heaving conditions
for all structures. The replaced material had a highConstruction method
We installed the ground anchors and soil nailingsin ground whose geologic columnar section is shownin Fig. 2. To equalize the conditions for frost heaving
35

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 3 Basic physical properties of the frost
susceptible material
Physical characteristics
Soil particle density ρs (g/cm3)
Natural water content wn (%)
2mm-(%)
Grain size
75μm-2mm (%)
characteristics-75m (%)
Ground material classification
Liquid limit wL(%)
Plastic limit wP (%)
Cone index qc (kN/m2)
FrostFrost heave rate (mm/hr)
susceptible Degree of compaction (%)
Max. dry density ρdmax (g/cm3)
Optimum moisture content wopt (%)

1
2
1

Value
2.678
50.0
25.6
30.9
43.5
SFG
63.3
42.4
667
0.78
94.6
1.125
42.9

2

3

Ground anchors

1

2

1
2
3

3

Groundfrost1 susceptibility, with a freezing rate of 0.78mm/h[4]. The cone index of this material with natural water
2 was 667kN/m2. Construction machines suchcontent
as bulldozers were able to travel on this material
safely; however, roller compaction machines such astier rollers were not [5]. Because of the strength
properties of the material, the test ground was
prepared by using a bulldozer. We considered that
installing these structures on the slope would not
result in equal the water supply conditions because of
the different installation heights of the structures.
Therefore, we constructed the ground anchors and thesoil nailings on flat ground. The covering materials
were a layer of 20cm-thick continuous-fiber
reinforced soil and a 2cm-thick rubber mat. Eachcovering material is 2m long and 2m wide, and theconstruction area is 4m2.

Soil nailings

Fiber reinforced soil

1 2 3
Rubber mat
Fig. 3

Measurement position.

soil and the ground without a reinforcing structure,and marks were painted on the rubber mat.
TEST RESULTS
Deformation of the ground anchors and the acting
load
Fig. 4 shows the air temperature, snow depth, andfreezing conditions of the ground. Fig. 5 shows thedisplacements of the ground anchors, and Fig. 6
shows the loads acting on the ground anchors. Whenthe ground started to freeze and the cumulative daily
average air temperatures were negative, the load
acting on the pressure plate started to increase. Which
indicated that the frost heaving force was starting to
act on the structure. After reaching the maximum, thefrost heaving force gradually decreased. When thedaily average air temperatures became constantly
above the freezing point, the frost heaving force of theground anchors became smaller than the fixing load.
The displacement of GA-2, whose free length isgreater than that of GA-1, was greater than thedisplacement of GA-1. The load acting on GA-2 wasslightly lower than that acting on GA-1. The frost
heave force was thought to be controlled in GA-2,
whose free length was great. The greater
displacement of GA-2 is thought to have absorbed thefrost heave force. In the comparison among the
ground anchors with different tension strengths (GA-

Measurement method
The load acting on the pressure plate was assumed
as the frost heaving force acting on the groundanchors or soil nailing. This load was measured byinstalling a hollowed load cell between the pressure
plate and the bearing plate of each structure. To
understand the freezing conditions of the ground, soil
temperatures were measured by using temperature
sensors installed at an interval of 10cm in the depth
direction from the ground surface. The airtemperature at the height of 1.5m was also measured.
The load and temperatures were automaticallymeasured every hour. The frost heave-induced
displacements for the ground anchors and soil
nailings were surveyed every two weeks at thelocations shown in Fig. 3. The snow depth wasmeasured at the same time as these measurements. To
measure the changes in height, concrete blocks wereplaced as markers on the continuous fiber-reinforced
36

GEOMATE – Kyoto, Japan, 3-5 November 2021

10

-100
-200

-5
-300

-10

-15

-400
12/1

400
300

204.8

200
GA-1

0
12/1

1/1
2/1 3/1
4/1
5/1
Observation date (Month /Day)

700

Load (kN/m2)

0.10

1/1
2/1
3/1
4/1
Observation date (month / Date)

5/1

Load
Allowable load (test table)
Anchoring load
Allowable load (ordinary condition)

600

0.15

Snow depth (m)

500

100

Average temperature and Cumulative temperature

500

400
300
200

174.6
GA-2

100
0
12/1

0.05
700

0.00
Load (kN/m2)

Snow depth
0.00
0.05
0.10
0.15
0.20
0.25
0.30

500

5/1

400
300

342.6

200

GA-3

100
0
12/1

Load (kN/m2)

1/1
2/1 3/1
4/1
5/1
Observation date (Month /Day)

600

500
400

5/1

455.7

300

200

Freezing depth of the ground

1/1
2/1
3/1
4/1
Observation date (month / Date)

Load
Allowable load (test table)
Anchoring load
Allowable load (ordinary condition)

700

12/1

GA-4

100

Fig. 4

1/1
2/1
3/1
4/1
Observation date (month / Date)

Load
Allowable load (test table)
Anchoring load
Allowable load (ordinary condition)

600

12/1 1/1
2/1 3/1
4/1 5/1
Observation date (month / Day)

Freezing depth of theground(cm)LoadAllowable load (test table)
Anchoring load
Allowable load (ordinary condition)

600

5
0

700

0
Load (kN/m2)

Average temperature
Cumulative temperature

Cumulative temperature(℃･days)

Average temperature (℃)

150Status of measurement points.

12/1

1/1
2/1
3/1
4/1
Observation date (month / Date)

5/1

700

Load (kN/m2)

Displacement amount (m)

0.10
0.05

0.00

582.1

600
400
300

Load
Allowable load (test table)
Anchoring load
Allowable load (ordinary condition)

200

GA-1
GA-4
Ground

-0.05

GA-2
GA-5

100

GA-3
GA-6

0
12/1

-0.10

Fig. 5

1/1
2/1
3/1
4/1
5/1
Observation date (Month / Day)

1/1
2/1
3/1
4/1
Observation date (month / Date)

5/1

700
Load (kN/m2)

12/1

GA-5

500

Displacement amount of ground anchors.

3, GA-4 and GA-5), the load was greater for GA-5,
whose tension strength was the greatest. From this,
we found that it is necessary to be careful about
setting the tension strength of the ground anchorswhen installing them in ground where frost heave
force may act on them. The frost heave force acting

600

537.0

GA-6

500
400
300

Load
Allowable load (test table)
Anchoring load
Allowable load (ordinary condition)

200
100
0
12/1

Fig. 6
37

1/1
2/1
3/1
4/1
Observation date (month / Date)

5/1

Loads acting on the ground anchors.

GEOMATE – Kyoto, Japan, 3-5 November 2021

on the unit area of the pressure plate under the same
soil and freezing conditions is equal. For GA-5 andGA-6, in which the areas receiving pressure weredifferent, the frost heave force acting on the groundanchors, whose pressure-receiving area was small,
was considered to be small. In the measurement, wefound that the frost heave force at GA-6, whosepressure-receiving area was small, was slightly
smaller than that of GA-5. The displacement in GA-6
was also smaller than that of GA-5. In constructing
ground anchors, if we only consider the frost heave
force, it is possible to control that force by designing
the pressure plate such that its area is small. We also
found that loads which were far greater than thenormal-time allowable load acted on five of thepressure plates, excluding that for GA-3. When the airtemperature rose and the ground started to melt at
each ground anchor, the frost heave force became
small, even smaller than the original fixing load. The
displacement at this time indicated that the heights ofthe markers were lower than the original heights. It isconsidered that the real fixing load for the groundanchor was smaller than the design fixing load
because the ground around the ground anchors at theapplication of the initial tension was slightly frozen.
This initial state of ground is also considered to be
why the final heights of the markers were lower thanthose at the start of the observation. The construction
and tensioning of ground anchors should be done
before the ground freezes.

Load (kN/m2)

200

150
100

83.4

RB-1

50
0
12/1

Load (kN/m2)

200

1/1
2/1
3/1
4/1
5/1
Observation date (month / Date)

Load
Allowable load (test table)
Anchoring load

150
100

69.5

RB-2

50
0

12/1

Load (kN/m2)

200

1/1
2/1
3/1
4/1
5/1
Observation date (month / Date)
Load
Allowable load (test table)
Anchoring load

150
100

65.9

RB-3

50
0
12/1
200

Load (kN/m2)

Deformation of the soil nailings and the acting
load
Fig. 7 shows the displacement of the soil nailings,
and Fig. 8 shows the loads acting on the soil nailings.
There was almost no displacement due to frost
heaving in soil nailings. Similar to the load conditionof the ground anchors, the load acting on the pressure
plates of the soil nailings started to increase when the
ground around them started to freeze, and it became
small when the frozen ground melted. It is understoodthat frost heave force acted on the pressure plates.
The loads acting on RB-1 and RB-2 (soil nailings
with greater areas) were compared with those acting

1/1
2/1
3/1
4/1
5/1
Observation date (month / Date)
Load
Allowable load (test table)
Anchoring load

150
100

63.1

RB-4

50
0
12/1

Fig. 8

1/1
2/1
3/1
4/1
5/1
Observation date (month / Date)

Loads acting on the soil nailings.

on RB-3 and RB-4 (soil nailings with a smaller areas).
It was found that the loads acting on the pressure
plates with greater areas were slightly smaller thanthose acting on the pressure plates with smaller areas.
When the areas of two pressure plates were equal but
the lengths of the reinforcement material weredifferent, the loads acting on them were nearly equal.

0.10

Displacement amount (m)LoadAllowable load (test table)
Anchoring load

0.05
0.00

-0.05

RB-1
RB-4

RB-2
GroundDeformation from frost heaving of the covered
structures, and control of the loads acting on the
structures

RB-3

-0.10
12/1

Fig. 7

1/1
2/1
3/1
4/1
5/1
Observation date (Month / Day)

The displacement of the ground depending on the
difference in covering are shown in Fig. 9. The frost
penetration depth of the ground depending on the

Displacement amount of soil nailings.
38

GEOMATE – Kyoto, Japan, 3-5 November 2021

0Freezing depth (cm)Displacement amount (m)

0.10
0.05

0.00
-0.05

Rabber mat
Fiber reinforced soil

-0.10

12/1

Fig. 9

1/1
2/1
3/1
4/1
Observation date (Month / Day)

5

10
15
Rubber mat
Fiber-reinforced soil
Ground

20

25

5/1

12/1 1/1
2/1 3/1
4/1 5/1
Observation date (month / Day)

Displacement of the covering part.
Fig. 10 Freezing depth of the ground at the covered
area.

difference in covering are shown in Fig. 10. The
heights of the continuous fiber-reinforced soil and therubber mat started to increase at the same time thatthe displacement of the ground without covering
started to increase from frost heaving, and theseheights started to decrease when the heaving fromfreezing in the ground without covering started todecrease. The two test structures with covering had
lower heights at the end of the measurements than
before the start of measurements. This is thought tobe because the ground was already frozen on the day
when the measurement was started. The
displacements of the soil nailings with covering wereabout 2cm smaller than those of the soil nailingwithout covering. The soil nailing covered with arubber mat returned to its original height about two
weeks earlier than the ground without covering did.
The continuous fiber-reinforced soil took longer thanthe other two to return to its original height. The frost
penetration depth of the ground without covering was
the greatest, that of the rubber mat covering was next,
and that of the continuous fiber-reinforced soil was
the shallowest. In controlling the frost penetration
depth and the deformation from frost heaving, itconsidered effective to cover the structures withrubber mats or continuous fiber-reinforced soil.

strengths, and greater areas of the pressure plate.
[2] The frost heave force of the ground acted on thesoil nailing; however, no considerable
deformation from frost heaving was observed. For
the pressure plate of the soil nailing with a greaterarea, the frost heave force was slightly greater.
[3] Covering the structures with rubber mats orcontinuous fiber-reinforced soil was found to beeffective in controlling the frost penetration depth
and the deformation from frost heaving.
REFERENCES[1] Study Group Concerning an Investigation and
Design
Methodof
Frost-Heaving
Countermeasure, Investigation and design
manual for frost protection of slopes (Draft),Public Interest Incorporated Association, The
Japanese Geotechnical Society, Hokkaido
Branch, 2016 (in Japanese).
[2] Masahiko Yamaki, Atsuko Sato, Osamu
Hatakeyama, Jun Ikeda, Takayuki Iizuka andTsuyoshi Nakamura, A Consideration on Heat
Conduction Analysis for Frost HeaveCountermeasures, Japan Society of Civil
Engineers Hokkaido Branch Paper Report, No.
76, 2020, C-02 (in Japanese).
[3] Atsuko Sato, Osamu Hatakeyama, Jun Ikeda,
Takayuki Iizuka, Noboru Mikami,Tsuyoshi
Nakamura, Fimitada Miura and Yuji Kyouden, ,Measurement of the frost heave acting on the fullsize ground anchor and soil nailing, 54rd Japan
National
conferenceon
GeotechnicalEngineering, No.54, 2019, pp.789-790 (inJapanese).
[4] Japanese Geotechnical Society, “Test method for
frost heave measurement” and “Test method for
frost susceptibility” in the Japanese Standards
and Explanations of Laboratory Tests ofGeomaterials, 2020, pp. 249-281 (in Japanese).
[5] Japan Road Association, Road Construction
Summary, 2009, p.287 (in Japanese).

SUMMARY
Ground anchors and soil nailings wereconstructed on frost-susceptible ground, anddeformation of the structures from frost heaving wasinvestigated. The effects of covering on the structures
were also investigated. The findings are as follows.
[1] Frost heave force that exceeded the design load
(normal time) acted on the ground anchors. The
pressure plates of the ground anchors were liftedwith the increase in the frost heaving. The frost
heave force acting on the pressure plate wasgreater for greater free lengths, greater tension

39

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

THE RECOVERY CHARACTERISTICS OF SHEAR STRENGTH OFCEMENT-STABILIZED CLAY
Tomoro Ueda1, Kiyonobu Kasama2 and Masaki Kitazume3
1,3

Civil and Environmental Department, Tokyo Institute of Technology, Japan;
2
Civil Engineering Department, Kyushu University, Japan
ABSTRACT

Many cement stabilization techniques have been frequently applied for various types of infrastructures andfor various improvement purposes. The target strength of stabilized soil depends on the type of technique andimprovement purpose, but is usually of the order of a couple MPa. The cement stabilized soil is subjected tolarge earthquakes, which may induce local failure in the stabilized soil even for stabilized soil with large strength.
As it is practically quite difficult to repair such a damaged soil after earthquake, it is desirable for stabilized soil
to have a sort of self-curing phenomenon where the damaged soil strength is recovered to same extent during thefurther curing. And the cement stabilized soil is expected to have the self-curing phenomenon due to the cement
hydration. The self-curing phenomenon of the cement stabilized soil by the bio-chemical additives has been investigated. However, they are very limited. In this research, a series of laboratory direct shear tests was carriedout on the cement stabilized soil to investigate the effects of the initial shear and additives on the strength recovery.
Keywords: Cement stabilization, Strength recovery, Direct shear strength test, ClayINTRODUCTION

investigate the effects of the initial shear and additives on the strength recovery.
MATERIALS AND TEST PROCEDURE

Many cement stabilization techniques have beenfrequently applied for various types of infrastructures and for various improvement purposes. This
technique is one of the essential techniques for developing infrastructures worldwide. The strength ofcement stabilized soil is usually controlled by thetype and amount of cement. The target strength depends on the type of technique and the improvement
purpose, but is usually of the order of a couple MPa.
The cement stabilized soil is subjected to large
earthquakes, which may induce local failure in thestabilized soil even with large strength. As it is practically quite difficult to repair such a damaged soil
after earthquake, it is desirable for stabilized soil tohave a sort of self-curing phenomenon where thedamaged soil strength is recovered to same extent
during the further curing. And the cement stabilized
soil is expected to have the self-curing phenomenon
due to the cement hydration. In the concrete engineering, many research efforts have been conducted
to investigate the self-curing phenomenon of theconcrete mixed with fly ash and/or silica fume as anadditive1,2). In the geotechnical engineering, the selfcuring phenomenon of the cement stabilized soil by
the bio-chemical additives have been investigated3,4).
However, they are limited that focus on the strength
recovery phenomenon of the cement stabilized soil
subjected the initial shearing in young curing stage.
In this research, a series of laboratory direct shear
tests was carried out on the cement stabilized soils to

Clay and binder
Two type clays were used in the tests, Kawasaki
clay and Kaolin clay, the physical properties ofwhich are summarized in Table 1. Ordinal Portland
cement was used as a binder and blast furnace slag
fine powder was also used as an additive.
Table 1. Physical properties of clay samples.
Specific gravity, Gs
Plastic limit, wP (%)
Liquid limit, wL (%)
Liquid index
Ignition loss (%)

Kawasaki
clay
2.66
51.0
25.6
25.4
6.0

Kaolin clay
2.63
82.1
34.7
47.4
13.0

Test procedure
The soils with the initial water content of 100%
were stabilized with ordinal Portland cement of thebinder content, aw of 10% to achieve the target
shear strength of about 150 kPa. The chemical additives of blast furnace slag fine powder were also
mixed with the stabilized soil changing the cw of 0,
5 and 10% of the dry weight of the clay. The stabilized soil and cement mixture were poured into amold of 60 mm in diameter and 20 mm in height forthe direct shear test and cured in a laboratory.

40

GEOMATE – Kyoto, Japan, 3-5 November 2021

In the case of the initial shearing sample, the sample was subjected to the initial shear to the shear
stress of about 90 % of the peak stress at either 3, 7
or 14 days curing. This corresponds the stress condition after peak strength at the residual stage (called
as post sample) and before the peak strength (called
as pre sample).
After subjecting the initial shearing, the sample
was cured without overburden pressure. The sample
was subjected to the direct shear test at 28 days curing where the sample was subjected to the horizontal
displacement of about 3 mm, the total of about 7
mm, again to measure the strength. The test procedure followed the Japanese Geotechnical Society
standard (JGS-0561)6. In the direct shearing, theoverburden pressure was 100 kPa for Kawasaki clay
and it was changed 50, 100 and 150 kPa for Kaolin
clay to investigate its effect on the shear behavior.

strength of the intact sample at 28 days. The Young's
modulus in the second shearing stage at 28 days islarger than that in the initial shearing stage at 7 days.
In the case of stabilized Kaolin clay, Fig. 2(b), a
similar phenomenon can be seen. The intact sample
at 28 days shows a clear peak strength followed by
the gradually decreased residual strength with theincrease of the horizontal displacement. The initialsheared sample at 7 days shows the rapid increase in

Shear stress (kPa)

250

200
150
100
7 days28 days

50
0
0

1

2

3

4

5

Horizontal displacement (mm)TEST RESULTS AND DISCUSSION(a) Kawasaki clay
Shear stress and horizontal displacement curve
Shear stress (kPa)

250

Figure 1(a) shows examples of the shear stressand horizontal displacement curves of stabilized
Kawasaki clays with the aw of 10% and the cw of
0% at various curing days. The shear stress is increased very rapidly with the increase of the horizontal displacement to show the clear peak stress,
and followed by the gradual decrease in the shear
stress to the residual stage. Similar phenomenon canbe seen in the other sample at the different curingdays, while the peak stress is decreased with theshort curing days.
Similar phenomenon can be seen in the case ofstabilized Kaolin clay, Figure 1(b), where the shear
stress is increased rapidly with the horizontal displacement to show the clear peak stress followed by
the gradual strength decrease to the residual stage.
Figure 2 shows examples of the stress and horizontal displacement curves for the intact and theinitial sheared samples at 28 days for the cement
content, aw of 10% and cw of 0% under the verticalpressure of 100 kPa. In the case of stabilized Kawasaki clay, Fig. 2(a), the shear stress is increased rapidly with the horizontal displacement to show thepeak strength of about 187 kPa, after then the stress
was decreased to the residual stage of about 110
kPa. In the case of the initial shearing sample, theshear stress at 7 days is also increased rapidly withthe horizontal displacement to show the maximum
strength of about 136 kPa but the shear stress wasalmost constant with further increase of the horizontal displacement to about 3 mm. The sample wassheared again at 28 days curing. In the second shearing at 28 days, the shear stress is increased very rapidly again with the increase of the horizontal displacement to show the peak strength of about 171
kPa, which is almost the same order of the peak

3 days7 days14 days
28 days

200
150

100
50
0
0

1

2

3

4

5

Horizonral displacement (mm)(b) Kaolin clay
Fig. 1. Shear stress and horizontal displacement
curves of intact sample at various curing days.
250

Shear strength (kPa)

200
150
100
initial shearing
intact

50
0

0

1

2

3

4

5

Horizontal displacement (mm)(a) Kawasaki clay
Shear stress (kPa)250
initial shearing

200

intact

150
100
50
0
0

1

2

3

4

5

Horizontal displacement (mm)(b) Kaolin clay
Fig. 2. Shear stress and horizontal displacement
curves of intact and initial sheared samples.
41

GEOMATE – Kyoto, Japan, 3-5 November 2021

the strength to show a peak strength at the horizontal
displacement of about 1 mm, and followed by thegradually decreased to the residual stage. In the second shearing stage at 28 days, the shear stress isincreased very rapidly again with the increase of thehorizontal displacement to show a clear peakstrength followed by the gradually decrease to theresidual stage. The peak strength at the second
shearing stage is smaller than that in the first shearing stage, which is the different from the case of
Kawasaki clay.

Shear strength, t (kPa)

250

200

intact150

sheared at 3days
sheared at 7days

100

50

Kaolin clay (intact sample)
Kaolin clay (sheared sample)

0
0

5

10

15

20

25

30

Curing period (day)Effect of initial shearing on 28 days' strength

Fig. 3. Relationship between the shear strength andcuring period for stabilized Kaolin clay with the aw
of 10% and the cw of 0%.

Figure 3 shows the relationship between the shear
strength at 28 days and the curing period for the stabilized Kaolin clay with the aw of 10% and the cw
of 0%, which were measured at the vertical pressureof 100 kPa in the direct shear test. The shear strengthof the intact sample is rapidly increased at the beginning with the curing period and gradually increase further curing period. The test data of thesample subjected to the initial shear at 3 and 7 days
(post sample) are also plotted in the figure. The
strength at 28 days becomes small for the sample
subjected to the initial shearing: 114 kPa for the initial shearing at 3 days and 108.4 kPa for the initialshearing at 7 days. It is found that the strength decrease is the almost same irrespective of the day at
the initial shearing.
Figure 3 is replotted in Fig. 4 in which thestrength ratio of the shear strength to the shear
strength of the intact sample at 28 days is plotted on
the vertical axis. The shear strength ratio at 3 days is
about 0.4 but is increased with the curing period.
The strength ratio of the sample subjected to theinitial shearing at 3 and 7 days are about 0.55 and0.76 respectively. By comparing the shear strength
at the initial shearing at 3 and 7 days, the shear
strength is slightly increased at 28 days.
Figure 5 shows the strength ratio, which is theshear strength of the initial sheared sample at 28
days to the initial shear stress. In the figure, twotypes of sample are plotted; the sample sheared beyond the peak strength, "post sample", and thesheared before the peak strength, "pre sample". In
the case of "post sample", the strength ratio is about
1.2 to 1.5, which means that the shear stress is increased to 1.2 to 1.5 at 28 days of the magnitude of
the initial shear. In the case of "pre sample", thestrength ratio is about 1.5 to 2.0, which is larger thanthat of the "post sample". It can be seen that thestrength ratio is large at the 3 days rather than the 14days, which indicates that the shear strength recovery is dominant in the early stage shearing.
Figure 6 shows the effect of the strength ratio ofshear stress of the sheared sample at 3, 7 and 14
days to the shear strength of intact sample at 28 days.
In the figure, two types of sample are plotted; theShear strength ratio, t/t, intact

1.5

intact1.0

sheared at 3days
sheared at 7days
0.5
Kaokin clay (intact sample)
Kaolin clay (sheared sample)
0.0
0

5

10

15

20

25

30

Curing period (day)Shear strength ratio, t,s8 shear/t, shear

Fig. 4. Relationship between the shear strength ratio
and curing period for stabilized Kaolin clay with theaw of 10% and the cw of 0%.
2.5

2.0

1.5

1.0

0.5

Kaolin clay (shear sample (post))
Kaolin clay (shear sample (pre))

0.0
0

5

10

15

20

25

30

Day at initial shear (day)Fig. 5. Relationship between the strength ratio andthe day at initial shearing.
sample sheared beyond the peak strength, "postsample", and the sheared before the peak strength,"pre sample". In the case of the "post sample", thestrength ratio at 28 days is ranging about 0.5 to 0.87,
which is slightly larger than the shear stress level.
This indicates that the shear stress of sample shows
the strength gain even after the initial shearing, but
not to that of the intact sample. In the case of the"pre sample", the strength ratio is almost 1 irrespective of the time of the initial shearing, which meansthat the shear stress of sample subjected to the initial

42

GEOMATE – Kyoto, Japan, 3-5 November 2021

1000

800

Shear strength, t28 (kPa)Shear strength ratio, t, shear/t, 28

1.5

1.0

0.5

Kaolin clay (sheared sample (post))

600

400

200

Kaolin clay (sheared sample (pre))Kawasaki clay,100kPa

0.0

0
0

5

10

15

20

25

30

0

Day at initial shear (day)

5

10

15

20

Content of blast furnace slag fine powder (%)

Fig. 6. Effect of initial shear on shear strength at 28
days.

(a) Kawasaki clay
500

Shear strength, t28 (kPa)shearing shows the large strength gain to that of theintact sample at 28 days.
Effect of blast furnace slag fine powder on 28
days' strength
Figure 7(a) shows the effect of content of blast furnace slag fine powder on the strength of stabilized
soil of the aw of 10% at 28 days, which were measured at the vertical stress of 100 kPa in the direct
shear test. It is found that the shear strength is almost
linearly increase of the increase of the blast furnace
slag fine powder in the stabilized Kawasaki clay.
Similar phenomenon can be seen in the stabilized
Kaolin clay as shown in Fig. 7(b), but the effect ofslag on the strength increment is dominant in stabilized Kawasaki clay.

400

300

200

Kaolin clay, 50 kPa
Kaolin clay, 100 kPa
Kaolin clay, 150 kPa

100

0
0

5

10

15

20

Content of blast furnace slag fine powder (%)(b) Kaolin clay
Fig. 7. Effect of amount of slag on shear strength at
28 days.

Shear strength t28 (kPa)250

Evaluation of Mohr-Coulomb failure criteria
Figure 8 show that the Mohr-Coulomb failure criteria for the Kaolin clay stabilized with ordinal Portland cement with the aw of 10 % and the slag content, cw of 0% for the peak and residual conditions
respectively.
Figure 8(a), the shear strength at the peak is increased with the increase of the vertical stress for the
intact sample, "post shear sample" and "pre shear
sample". Though there is a lot of scatter in the test
data, the shear strength is increased almost linearlywith the vertical stress irrespective of the type ofsample. The friction angle, defined as the shear
strength increment to the vertical stress increment, isabout 28.8 degree, almost the same irrespective ofthe type of sample, while the cohesion, the intersect
to the vertical axis, is largest for the intact sample
and the lowest for the "sheared sample (post)".
In the residual stage, Fig. 8(b), the shear strengthis also increased with the increase of the verticalstress irrespective of the type of sample. The friction
angle and the cohesion are almost constant of about
21 degree and 20 kPa respectively irrespective of thetype of sample. The friction angle is slightly smaller

200

150

100

(iii) C sample
Intact sample

50

Sheared sample (post)

Sheared sample (pre)
0
0

50

100

150

200

Vertical stress, sv' (kPa)(a) Peak strength

Residual shear strength, t28 (kPa)250
Intact sample
Sheared sample (post)

200

Sheared sample (pre)

150

100

50

0
0

50

100

150

200

Vertical stress, sv' (kPa)(b) Residual strength
Fig. 8. Vertical stress and shear strength of Kaolin
clay with the aw of 10 % and the cw of 0%.

43

GEOMATE – Kyoto, Japan, 3-5 November 2021

than that of the peak strength, but the cohesion isquite smaller than that of the peak. This indicatesthat the cohesion component is loosed in the stabilized soil when subjecting to the shear deformation.

largest for the intact sample. In the residual stage,
the friction angle and the cohesion are almost
constant irrespective of the type of sample.
The amount of Ettringite and Calcium aluminate
hydrate is increased with the curing period, whichindicates that the cement hydration is still proceeded after the initial shearing. This hydration
enhances the strength increase with the curing period.

Chemical composition at shear plane by Powder
X-ray diffraction test
Table 2 summarized the chemical formula of theclay mineral and chemical compounds derived fromchemical reaction between cement and water. The
clay mineral and chemical compounds were targeted
for XRD test. Kaolin clay used in our study mainly
consists of Kaolinite, Al4Si4O10(OH)8, and silica, SiO2.
Tricalcium silicate, Ca3SiO5, Dicalcium silicate,
Ca2SiO4, Tetracalcium aluminoferrite, Ca4Al2Fe2O10,
and Calcium hydroxide,Ca(OH)2, are main chemicalcompounds of Portland cement. Calcium carbonate,
CaCO3, Calcium silicate hydrate, nCaO-SiO2-mH2O,
and Ettringite, 3CaO-Al2O3-3CaSO4-32H2O arechemical compounds resulted from hydration
chemical reaction between cement and water.
Figure 9 and Table 3 show the X-ray diffraction(XRD) test results on the three samples of stabilized
Kawasaki clay with the aw of 10% and the cw of
10%, which was subjected to the shear at 7 days, theshear at 28 days and the initial shear at 7 days andthe second shear at 28 days. And they were sheared
under the vertical stress of 100 kPa. The figure
shows the weight ratio of compounds, which showsthat the amount of Ettringite and Calcium aluminate
hydrate are increased with the curing period, whichindicates that the cement hydration is still proceeded
after the initial shearing. This hydration enhances
the strength increase with the curing period.

Table 2. Chemical formula of clay mineral andchemical compounds for XRD test
Name

Chemical Formula

Mineral (Kaolinite)
MineralMineral

Al4Si4O10(OH)8,SiO2

Tricalcium silicate

C3S

Ca3SiO5

Dicalcium silicate

C2S

Ca2SiO4

Tetracalcium aluminoferrite

C4AF

Ca4Al2Fe2O10

Calcium hydroxide

CalciumhydrateCa(OH)2

Calcium carbonate

CalsiteCaCO3

Calcium silicate
hydrateC-A-H

nCaO-SiO2-mH2O

Ettringite

Ettringite

3CaO-Al2O3-3CaSO432H2O

20%

60%

0%

40%

80%

100%

Intact sample
at 7 days
Intact sample
at 28 days

CONCLUSIONS

Initial shear
sampleat 28 days

In this study, a series of direct shear tests was carried out on Kawasaki clay and Kaolin clay stabilizedwith ordinary Portland cement with/without blast
furnace slag fine powder, in which the vertical stress
was also changed in the shear test. The major conclusions derived in the test are as follow:

Initial composition
shear
Fig. 9. Chemical
at shear plane by
Intact sample Intact sample
samplePowder X-ray diffraction
test.at 28 days
at 7 days
Mineralat 28 days
83.90

Table 3. The result of XRD test.83.90

The shear strength of the stabilized soil is increased with the increase of the curing period irrespective of the type of soil.
The stabilized soil with the initial shearing shows
the strength recovery during the curing, the magnitude of the strength recovery is depend on thesoil type and amount of additives content and also
by the vertical pressure at shearing.
According to the Mohr-Coulomb failure criteria,
the friction angle at the peak strength stage is almost constant irrespective of the initial shearing,
while the cohesion depends on the sample type,
44

83.90

C3S

0.45

0.66

0.95

C2S

0.55

2.31

1.80

C4AF

0.05

0.16

2.80

Calcium hydroxide

1.00

1.65

0.10

Calcite

3.45

4.40

7.10

Ettringite

4.15

3.57

2.40

C-A-H

5.90

3.02

0.90

Others

0.30

0.33

0.39

GEOMATE – Kyoto, Japan, 3-5 November 2021

ACKNOWLEDGEMENT
This research was supported by JSPS KAKENHI
Grant Number 19H02233.

[4] Hata, T., Tateno, N. and Abe, H., Evaluation ofcoastal region microbial carbonate precipitation
using urease produced microorganisms, Japanese
Geotechnical Journal, Vol. 6, No. 2, pp. 305315, 2011 (in Japanese).
[5] Japanese Geotechnical Society, Practice for making curing stabilized soil specimens without
compaction (JGS 0821-2000), Laboratory Testing Standards of Geomaterials, pp. 426-434,
2009 (in Japanese)．
[6] Japanese Geotechnical Society, Method for consolidated constant volume direct box shear test
on soils (JGS 0560-2000), Laboratory TestingStandards of Geomaterials, pp. 666-670, 2009
(in Japanese).
[7] Nakamura, S. and Gibo, S., The recovered
strength of landslide soils and its relation to theeffective normal stress, Journal of the Japan
Landslide Society, Vol. 37, No. 3, pp. 18-24,
2000 (in Japanese).

REFERENCES[1] Seung-hyun, N., Hama, Y., Taniguchi, M. and
Sagawa, T. The self-healing performance ofconcrete using ground granulated blast-furnace
slag and fly ash, Proceedings of the Japan Concrete Institute, Vol. 34, No. 1, pp. 1402-1407,
2012 (in Japanese).
[2] Ueda, R., Kawano, J., Iiboshi, T. and Eriguchi,A., Influence of dosage of amino acid on selfhealing performance of concrete, Cement Science and Concrete Technology, Vol. 70, No. 1,
pp. 321-327, 2012 (in Japanese).
[3] Kawasaki, S., Present status of ground improvement technologies using microbial functions,
Journal of MMIJ, Vol. 131, No. 5, pp. 155-163,
2015 (in Japanese).

45

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

VISUALIZATION OF LIQUEFACTION PHENOMENON BY
COUPLED CAE ANALYSIS OF MPS AND DEM
Koki Nakao1, Katsumi Ookori2, Tsuyoshi Takahashi2 and Shinya Inazumi3
1

Graduate School of Engineering and Science, Shibaura Institute of Technology, Japan;
2
Aomi Construction Co. Ltd., Japan;
3
College of Engineering, Shibaura Institute of Technology, Japan
ABSTRACTThe mechanism of liquefaction and the factors that cause liquefaction have already been examined andevaluated both analytically and experimentally, and liquefaction countermeasure construction is being
implemented based on these findings. However, there are no cases in which the mechanism of liquefaction
generation in the original ground and the effects of liquefaction countermeasures were visually examined andevaluated. Therefore, this study theoretically visualizes the mechanism of liquefaction generation and evaluate thebehavior of particles in the ground. Specifically, MPS-DEM coupled CAE analysis is used to visualize the insideof the ground when an external force acceleration is applied, simulating seismic waves, and reveal the behavior
inside the ground. The MPS-DEM coupled CAE analysis succeeded in visualizing the liquefaction phenomenon
by applying an external force acceleration that simulates a seismic wave to the ground modeled threedimensionally. This study is expected to clearly show the mechanism of liquefaction generation, and to contributeto the design and accountability of more efficient and economical liquefaction countermeasures, regardless of the
field of specialization.
Keywords: CAE, DEM, Liquefaction, MPS, Visualization
Based on the above background, in this study,
the mechanism of liquefaction generation istheoretically visualized using the coupled computer
aided engineering analysis (CAE) of the moving
particle semi-implicit (MPS) and the discrete element
method (DEM) [4], [5]. The CAE is a general term
for technology that simulates and analyzes prototypes
on a computer created by a computer-aided design(CAD) and so on, considering the site conditions. The
CAE in this study is executed by an algorithm inwhich both MPS and DEM methods are combined.
In this study, external force acceleration thatsimulates seismic waves is applied to the groundmodeled three-dimensionally by the coupled CAE
analysis of the MPS and the DEM, and the purpose isto reveal the behavior. Evaluate the behavior ofparticles in the ground and examine whetherliquefaction occurs. By visualizing the liquefaction
phenomenon, it is expected that the mechanism ofliquefaction generation will be clearly shown and that
it will contribute to the design and accountability ofmore efficient and economical liquefaction
countermeasures. By utilizing CAE analysis in thefield of liquefaction, it is possible to visualize thegenerating mechanism of liquefaction, which cannot
be seen originally, and solve complicated problems
associated with the liquefaction [4], [5]. In addition,
since design and evaluation can be performed while
comparing and examining various condition patterns,
it is considered that the design quality can be

INTRODUCTION
In Japan, the history of study on liquefaction isshort. Since it was recognized that the sandy groundbecame liquid during the 1964 Niigata earthquake,study began to be actively conducted, and only aquarter of a century has passed since then [1], [2].
With the 1964 Niigata earthquake, liquefaction study
has been energetically conducted, and nowadays, theground conditions that are easily liquefied havebecome clear. However, the damage caused byliquefaction caused by the 2011 off the Pacific coastof East Japan Earthquake extended to the Kanto
region, and the metropolitan area also suffered greatdamage [3]. The mechanism of liquefaction and thefactors that cause liquefaction have already been
examined and evaluated both analytically andexperimentally, and despite the fact that liquefaction
countermeasure construction is being implemented
based on these findings, The liquefaction damage
caused by the 2011 off the Pacific coast of East Japan
Earthquake was enormous. One of the reasons for thisis that there are no cases in which the effects of theconstruction of liquefaction countermeasures werevisually examined and evaluated. At present, Japan
has a large number of soft ground composed of fineparticles such as sand, and earthquakes are likely tooccur due to the positional relationship of rocks on
the earth. Therefore, in order to prevent damage
caused by liquefaction, efficient and economicalliquefaction measures are indispensable.
46

GEOMATE – Kyoto, Japan, 3-5 November 2021

improved at an early stage and a more effective
construction method can be designed.

time, a region having a solid property and a region
having a fluid property are mixed. In order to deal
with such problems, it is necessary to develop ananalysis method that can express the transition region
between solid and fluid.
In this study, the authors propose a CAE analysis
that combines a MPS as one of the typical particle
based methods (PBMs) and the DEM as an analysis
model that can express the phase change processduring liquefaction. This is a combination of sandparticles in the target ground analyzed by theindividual element method and interstitial water by
the PBM. An analysis will be performed using thismethod to examine the effectiveness of the analysis
model.

GENERALAS OF LIQUEFACTION
With the 1964 Niigata earthquake and the 1995
Great Hanshin-Awaji earthquake, study onliquefaction has been actively conducted [1], [2] [6].
And
theunderstanding,prediction,andcountermeasure technology for liquefaction havebeen almost established. Liquefaction during anearthquake is a phenomenon in which when saturated
sand ground is repeatedly subjected to shear stress,
the shear rigidity decreases as the effective stressdecreases, and finally the shear resistance is lost likea viscous fluid. Therefore, in order to analyze themechanical behavior of soil during the liquefaction
process, the process of change from solid to fluid
must be considered.
Effective stress analysis is one of the typicalmethods for analyzing ground deformation duringliquefaction [7], [8]. The effective stress analysis
incorporates the constitutive law of sand into theequation of the field in which the soil skeleton and theinterstitial water are coupled, which is obtained basedon solid dynamics. The effective stress analysis
covers the initial state of the ground, the dynamic
behavior of the ground during an earthquake, and theoccurrence of liquefaction. So far, many constitutive
equations of sand based on solid-state mechanics
have been proposed based on the results of laboratory
tests on the repeated shear behavior of sand [9], [10].
These sand constitutive equations can reproduce
stress-strain relationships and dilatancy behavior
under various stress conditions. As the constitutive
equation of sand, a repetitive elasto-plastic model, anelasto-plastic underload surface model, adensification model and so on have been proposed.
On the other hand, analysis methods targeting only
the flow process after liquefaction has occurred havealso been proposed, and these can be divided into
methods based on solid-state mechanics and methods
based on fluid mechanics. As a method based onsolid-state mechanics, for example, Yasuda et al. [11],[12] have proposed a residual deformation analysis
method that can analyze the flow associated withliquefaction with simple and practical accuracy. As amethod based on fluid mechanics, for example,
Uzuoka et al. [13] proposed a method to analyze
liquefied sand as a Bingham fluid, and quantitatively
show the flow force and large deformation after flow,
although under relatively simple boundary conditions.
The above-mentioned analysis method has been
proposed by paying attention to each of the solid
property and the fluid property of the liquefied sand,and is not a fusion of the two properties. In the actualliquefaction phenomenon, the phase change process
between solid and fluid is considered to occur nonuniformly both spatially and temporally. At a certain

MPS-DEM COUPLED CAE ANALYSIS
A computer aided engineering (CAE) is analternative technology for large-scale experiments,
conducted in a room or in-situ, using prototypes thathave been prepared in a study as part of thedevelopment process of “manufacturing”. In otherwords, CAE is a general term for technology thatsimulates and analyzes prototypes on a computer
created by a computer-aided design (CAD) and so on,
considering the site conditions [14], [15], [16], [17].
At the same time, CAE may refer to computer-aided
engineering work or its tools for the priorexamination, design, manufacturing, and process
design of construction methods and products. In thefield of geotechnical engineering, CAE can be used
not only to visualize the inside of the ground and thestress loading on the inside of the ground, but also toestimate the results of experiments that would requirehuge costs and/or phenomena that would be difficult
to reproduce. In addition, by performing appropriate
post-processing, it is possible to communicate withother people in a visually easy-to-understand manner.
In this study, external force acceleration thatsimulates seismic waves is applied to the groundmodeled three-dimensionally by the coupled CAE
analysis of the moving particle semi-implicit (MPS)
as one of the typical particle based methods (PBMs)
and the discrete element method (DEM), and theparticles in the ground are subjected to the loading.
The purpose is to clear the behavior.
ANALYSIS MODEL AND CONDITIONS
The analytical model is shown in Fig. 1. Sand
particles and water particles are arranged according to
the void ratio on a 3000 mm square cube. The sandparticles are expressed in yellow and the waterparticles are expressed in blue to visually express theground. In addition, the ground simulatingliquefaction countermeasures will be reproduced. The
pile model is shown in Fig. 2. The piles are 400 mm
in diameter and 3000 mm in length, and 6 piles are47

GEOMATE – Kyoto, Japan, 3-5 November 2021

arranged on each side. The piles are set as a distancefunction. Place the pile shown in Fig. 2 on the groundshown in Fig. 1 and performs the analysis.
In this study, liquefaction is expressed byapplying an external force acceleration that simulatesa seismic wave to the ground. Assumed parameters of
the external force acceleration are shown in Fig. 3.
Acceleration is given in the X-axis direction every
0.01 seconds for a total of 59.99 seconds. Figure 4
shows the relationship between acceleration andseismic intensity [18]. The maximum value ofacceleration is adopted as a parameter representing
seismic intensity. The maximum value of the external
force acceleration parameter is 207.33 (cm/s2), whichcorresponds to an earthquake with a seismic intensityof 5 or higher, as shown in Fig. 4. The reason for
setting this seismic intensity is that the liquefaction
phenomenon often occurs with an earthquake with aseismic intensity of 5 or higher.
In this study, water is set as fluid by the MPS andsand is set as powder particles by the DEM. Table 1
shows the physical characteristics of water particles
modeled by the MPS, and Table 2 shows the physicalcharacteristics of sand particles modeled by the DEM.
All of these physical property parameters refer to andcite previous experimental values [4], [5], [16], [19],[20], [21]. Attention should be paid to the DEM

parameters for sand particles shown in Table 2,
especially the setting of sand particle diameter anddensity. The typical sand particle diameter anddensity are 0.2 mm and 2.634 g/cm3 respectively. In
this study, the diameter and density of the sandparticles modeled by DEM are set so that the particle
sedimentation velocity of Stokes fluid [22], [23] is
250
200
Acceralation (cm/s^2)

150
100
50
0
-50
-100
-150
-200
-250
0

10

20

30
Duration (s)

40

50

60

Fig. 3 Assumed external force acceleration
104

Seismic intensity:

Acceleration (cm/s2)

103

Sand particle
Water particle

7
6 upper
6 lower
5 upper102

5 lower
4

101

3
2

100

Cross section to
be evaluated

10-1
10-2

1

10-1

100

Cycle (seconds)

101

102

Fig. 4 Relationship between period and acceleration ineach seismic intensity scale 4)

Fig. 1 Cross-section model assumed for analysis (Case 1
and Case 2)

Table 1 MPS parameters for water particles
Particle density (kg/m3)
Particle size (m)Rheology model

Water
1000
0.03
Newtonian fluid

Table 2 DEM parameters for sand particles

Cross section to
be evaluatedParticle density (kg/m3)
Particle size (m)Young's modulus (GPa)Poisson's ratio

Sand
1000.118
0.03
1
0.485

Table 3 Assumed gap ratio of sand ground (typical valueof sand ground is adopted)
Fig. 2 Cross-section model assumed for analysis (Case 3;
lattice-shaped ground improvement pile)

Maximum void ratio
Minimum void ratio

48

Sand ground
1.19
0.71

GEOMATE – Kyoto, Japan, 3-5 November 2021

equal to that of typical sand particle and sand particle
modeled by DEM. The authors focus on the void ratio
and express the sand ground by void ratio using the
maximum and minimum void ratios of typical sandground. The void ratio is shown in Table 3. The void
ratio of 1.19 is Case 1, the void ratio of 0.71 is Case2, and the void ratio of 1.19 with piles is Case 3.

of Case 3 is shown as a cross-sectional view. In allcases, it is confirmed that sand particles are depositedat the bottom of the analysis model over time andwater particles are floating. In other words, thisanalysis was able to roughly visualize the liquefactionof the ground when an external force acceleration
simulating a seismic wave was applied.
The mechanism of the liquefaction phenomenon
is that when a sand soil saturated with water receivesa large shaking, the skeletal structure of the sandparticles that make up the ground collapses and thesand particles try to fall into the void spaces. As aresult, excessive pore water pressure is generated andaccumulated inside the ground. Furthermore, whenthe excess pore water pressure becomes equal to theeffective loading pressure of the ground before it
receives a large shaking, the effective stress loading
between the soil particles becomes zero, and as aresult of the above, a liquefaction phenomenon occurs.
The analysis results shown in Figs. 5, 6 and 7 also
show the same phenomenon as the above-mentioned
liquefaction generation mechanism. In addition,
when comparing the central part and the four cornersof the analysis model in all cases, it is confirmed thatthe amount of sedimentation of water particles in thefour corners is smaller than that in the central part.
This is because the rearrangement of water particles
and sand particles occurs due to external force, andthe sand particles try to settle and deposit at thebottom of the model. While sand particles settle anddeposit, water particles try to rise to the top of themodel. As a result, the sand particles at the four
corners are sedimented at the bottom of the model andthen flow out to the center of the model, resulting ina rearrangement of the particles.
Comparing Case 1 and Case 2 with different
void ratios, in Case 1, water particles are floating in

RESULTS AND DISCUSSIONS
Figures 5, 6 and 7 display the analysis results on
Case 1, Case 2 and Case 3 respectively at theavaluating cross section witn Figs. 1 and 2. Thesefigures show from 0 seconds to 59.99 seconds every
10 seconds, respectively. Because Case 3 surrounds
the ground with piles, Fig. 7 that is the analysis result(a) 0 seconds later(b) 20 seconds later(c) 40 seconds later(d) 60 seconds later

Fig. 5 Liquefaction occurrence in Case 1 with external
acceleration (void ratio: 1.16)(a) 0 seconds later(b) 20 seconds later(c) 40 seconds later(d) 60 seconds later

Fig. 6 Liquefaction occurrence in Case 2 with external
acceleration (void ratio: 0.71)(a) 0 seconds later(b) 20 seconds later(c) 40 seconds later(d) 60 seconds later

Fig. 7 Liquefaction occurrence in Case 3 with external
acceleration (void ratio: 1.16, with piles)

49

GEOMATE – Kyoto, Japan, 3-5 November 2021

Kiku, H., Yamada, S., Hosaka, Y., Tsukamoto, Y.,
Towhata, I., Wakai, A., Ugai, K. (2011): Review of
topographic and soil conditions for liquefactionrelated damage induced by the 2007 off Mid-Niigata
earthquake, Soils and Foundations, Vol. 51, Issue 3,
pp. 533-548, doi: 10.3208/sandf.51.533.
[3] Yasuda, S., Harada, K., Ishikawa, K. and Kanemaru,
Y. (2012): Characteristics of liquefaction in Tokyo
Bay area by the 2011 Great East Japan Earthquake,
Soils and Foundations, Vol. 52, Issue 5, pp. 793-810,
doi: 10.1016/j.sandf.2012.11.004.
[4] Inazumi, S., Jotisankasa, A., Nakao, K. andChaiprakaikeow, S. (2020a): Performance of
mechanical agitation type of ground-improvement by
CAE system using 3-D DEM, Results in Engineering,Vol. 6, doi: 10.1016/j.rineng.2020.100108.
[5] Inazumi, S., Kuwahara, S., Jotisankasa, A. andChaiprakaikeow, S. (2020b): MPS-CAE simulation
on dynamic interaction between steel casing andexisting pile when pulling out existing piles,
International Journal of GEOMATE: Geotechnique,
Construction Materials and Environment, Vol. 18,
Issue 70, pp. 68-73, doi: 10.21660/2020.70.9166.
[6] Kazama, M., Kawai, T., Mori, T., Kim, J. andYamazaki, T. (2018): Subjects of the liquefaction
research seen to the liquefaction damage of the great
East Japan earthquake disaster, Journal of Japan
Association for Earthquake Engineering, Vol. 18, No.
3, pp. 26-39, doi: 10.5610/jaee.18.3_26.
[7] Rapti, I., Caballero, F.L., Razavi, A.M.F., Foucault,
A. and Voldoire, F. (2018): Liquefaction analysis anddamage evaluation of embankment-type structures.
Acta Geotechnica, Vol. 13, pp. 1041–1059, doi:
10.1007/s11440-018-0631-z.
[8] Finn, W.D.L. (2000): State-of-the-art of geotechnicalearthquake engineering practice, Soil Dynamics andEarthquake Engineering, Vol. 20, pp. 1-15, doi:
10.1016/S0267-7261(00)00033-6.
[9] Wu, W., Lin, J. and Wang, X. (2017): A basic
hypoplastic constitutive model for sand, Acta
Geotechnica, Vol. 12, pp. 1373-1382, doi:
10.1002/nag.1610181203.
[10] Tasiopoulou, P. and Gerolymos, N. (2016):
Constitutive modeling of sand: Formulation of a newplasticity approach, Soil Dynamics and Earthquake
Engineering, Vol. 82, pp. 205-221, doi:
10.1016/j.soildyn.2015.12.014.
[11] Yasuda, S., Yoshida, N., Adachi, K., Kiku, H. andIshikawa, K. (2017): Simplified evaluation method ofliquefaction-induced residual displacement, Journalof Japan Association for Earthquake Engineering,Vol. 17, No. 6, pp. 1-20, doi: 10.5610/jaee.17.6_1.
[12] Yasuda, S., Yoshida, N., Adachi, K., Kiku, H., Gose,S. and Masuda, T. (1999): A simplified practicalmethod for evaluating liquefaction-induced flow,
Journal of Geotechnical Engineering, Proceedings of
JSCE,
No.
638/III-49,
pp.
71-89,
doi:
10.2208/jscej.1999.638_71.
[13] Uzuoka, R., Yashima, A., Kawakami, T. and Konrad,
J.M. (1998): Fluid dynamics based prediction ofliquefaction induced lateral spreading, Computers
and Geotechnics, Vol. 22, No. 3, pp. 243-282, doi:
10.1016/S0266-352X(98)00006-8.
[14] Chang, K.H. (2014): Product Design Modeling using
CAD/CAE: The Computer Aided Engineering
Design Series, Elsevier.

the upper part of the model. It can be considered that
this is the mechanism of the liquefaction phenomenon,
that the loosely deposited sandy ground and thehigher groundwater level are more likely to cause theliquefaction phenomenon. Comparing Case 1 andCase 3 with respect to the presence or absence ofliquefaction countermeasure, sand particles are
stopped on the model at the inner part of the pile inCase 3. From this, it is considered that liquefaction
measures that surround the ground with piles are
effective. The physical principle of liquefaction
countermeasures by piles is to keep the shear
deformation caused by seismic motion small, block
the propagation of pore water pressure from thesurroundings, and prevent the outflow of pore waterand sand into the supported layer [24], [25]. This
analysis was able to reproduce the same phenomenon
as the physical principle of liquefaction
countermeasures using piles.
CONCLUSIONSThe behavior of water particles and sandparticles inside the ground, in which an external force
acceleration simulating a seismic wave loaded on the
ground, was evaluated using MPS-DEM coupled
CAE analysis, and an attempt was made to visualize
the liquefaction phenomenon in this study.
The results and findings obtained in this study
are shown below.
(1) MPS-DEM coupled CAE analysis succeeded invisualizing the liquefaction phenomenon by
applying an external force acceleration thatsimulates a seismic wave to the ground modeled
three-dimensionally.
(2) The effect of ground conditions such as void ratioon the behavior of particles in the ground during
an earthquake was visualized. By using MPSDEM coupled CAE analysis, it is possible toevaluate the behavior of particles in the ground
during an earthquake and examine whetherliquefaction occurs.
(3) The liquefaction phenomenon on the ground withpiles simulating the liquefaction countermeasure
work was visualized. By visualizing theliquefaction phenomenon, it can be expected tocontribute to the design and accountability ofefficientandeconomicalliquefaction
countermeasures.
In order to visually evaluate the need forliquefaction countermeasures, it is necessary tosimulate and analyze more actual liquefaction
countermeasures.
REFERENCES[1][2]

Kuribyashi, E. and Tatsuoka, F. (1975) : Brief review
of liquefaction during earthquakes in Japan, Soils andFoundations, Vol. 15, No. 4, pp. 81-92.
Onoue, A., Yasuda, S., Toyota, H., Inotsume, T.,

50

GEOMATE – Kyoto, Japan, 3-5 November 2021

on fluidization treated soils based on flow analysis by
MPS method, International Journal of GEOMATE:
Geotechnique,
Construction
Materials
andEnvironment, Vol. 12, Issue 29, pp. 53-58.
[21] Inazumi, S., Kaneko, M., Shigematsu, Y. andShishido, K. (2018): Fluidity evaluation of
fluidisation treated soils based on the moving particle
semi-implicit method, International Journal of
Geotechnical Engineering, Vol. 12, Issue 4, pp. 325336, doi: 10.1080/19386362.2016.1277618.
[22] Makino, M. (2020): Lateral migration of particles
dispersed in Stokes fluid, Japanese Journal ofMultiphase Flow, Vol. 34, No. 1, pp. 3-10, doi:
10.3811/jjmf.2020.T001.
[23] Doi, M. and Makino, M. (2005): Sedimentation ofparticles of generl shape, Physics of Fluids, Vol. 17,43601, doi: 10.1063/1.1884625.
[24] Saha, P., Horikoshi, K. and Takahashi, A. (2020):
Performance of sheet pile to mitigate liquefactioninduced lateral spreading of loose soil layer under theembankment, Soil Dynamics and Earthquake
Engineering,
Vol.
139,
106410,doi:
10.1016/j.soildyn.2020.106410.
[25] Liu, W.M.B., Rasouli, R., Aoyama, S. and Towhata,
I. (2019): Performance of piles with different
configurations subjected to slope deformationinduced by seismic liquefaction, Engineering
Geology,Vol.
263,
105355,doi:
10.1016/j.enggeo.2019.105355.

[15] Pan, Z., Wang, X., Teng, R. and Cao, X. (2016):
Computer-aided
design-while-engineering
technology in top-down modeling of mechanicalproduct, Computers in Industry, Vol. 75, pp. 151-161,
doi: 10.1016/j.compind.2015.05.004.
[16] Inazumi, S., Tanaka, S., Komaki, T. and Kuwahara, S.
(2021): Effect of insertion of casing by rotation on
existing piles in removal of existing pile,
Geotechnical Research, Vol. 8, Issue 1, pp. 25-37,
doi: 10.1680/jgere.20.00042.
[17] Adeli, B.H. and Kumar, S. (2020): Distributed
Computer-Aided Engineering for Analysis, Design,
andVisualization,
CRC
Press,
doi:
10.1201/9781003067795.
[18] Sakai, A. (2019): Relationship between acceleration
waveforms determined in addordance withacceleration response spectra and various seismic
intensity levels and long-period seismic intensity,
Division A1 (Structural Engineering & Earthquake
Engineering), Journal of Japan Society of Civil
Engineers, Vol. 75, No. 3, pp. 266-279, doi:
10.2208/jscejseee.75.266.
[19] Inazumi, S., Kaneko, M., Shigematsu, Y. andShishido, K. (2018): Fluidity evaluation of
fluidisation treated soils based on the moving particle
semi-implicit method, International Journal of
Geotechnical Engineering, Vol. 12, Issue 4, pp. 325336, doi: 10.1080/19386362.2016.1277618.
[20] Inazumi, S., Kaneko, M., Tomoda, Y., Shigematsu, Y.
and Shishido, K. (2017): Evaluation of flow-ability

5111th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

BEARING CAPACITY BEHAVIOR OF SINGLE PILE DURINGGROUNDWATER LEVEL CHANGE BY CENTRIFUGE MODEL
Sutasinee Intui1, Suttisak Soralump2 and Shinya Inazumi3
1

Graduate School of Engineering and Science, Shibaura Institute of Technology, Japan;
2
Faculty of Engineering, Kasetsart University, Thailand;
3
Department of Civil Engineering, Shibaura Institute of Technology, Japan
ABSTRACTThe recent decrease in the groundwater level in the Bangkok area has mainly occurred due to inappropriate
water pumping for industrial and agricultural activities. This inappropriate pumping is not only causing land
subsidence, but it is also affecting the load capacity of the soil around the piles. At present, the groundwater levelin the Bangkok area is showing a tendency to increase to the ground surface thanks to the reduction in groundwater
pumping. This is an important issue of research into the soil load capacity behavior around piles with changes in
the groundwater level. This study focuses on the behavior of a single pile by comparing the results of centrifuge
tests and the results of previous studies. The test results are compared for each groundwater level. Finally, theresults of the tests are summarized into two parts. The first part is that the bearing capacity of the sand around thepile is related to the changing groundwater level, but in an inverse manner. The soil strength is seen to decrease as
the groundwater level increases or rises up to the ground surface. The bearing capacity in clay mainly depends onthe consolidation process. The second part is that the results of the pile load behavior from the centrifuge tests tend
to be like those of the previous studies. Therefore, centrifuge test results can be used to evaluate and design pilefoundations in the Bangkok area with changing groundwater levels.
Keywords: Bangkok clay, Centrifuge test, Bearing capacity, Settlement, Groundwater

INTRODUCTION

level by installing piezometers at many stations [1].
The results showed that the groundwater level was
draw down to the minimum level around 1997 due togroundwater pumping. Then, a decrease in the
groundwater pumping rate led to the recovery of thegroundwater level to the ground surface. The
groundwater level is presently recovering to theground surface thanks to monitoring at piezometerstations in Bangkok and its vicinity.
Much research has been done on the strength of
the Bangkok area soil. The strength and stiffness of
Bangkok clay has been monitored at Sukhumvit MRT
Station using the PLAXIS 3D model to compare thehardening soil model and the Mohr-Coulomb model[2]. The results revealed that the Bangkok clay
characteristics of the hardening soil model were better
than those of the Mohr-Coulomb model for
excavation work. The numerical model proved thatthe results of the hardening soil model can be
employed to analyze and predict the ground surface
settlement in both 2D and 3D analyses. A comparisonof the load capacity between barrette piles and boredpiles at similar depths found that barrette piles, withdimensions of 1.5x3.0 meters and a depth of 57
meters, have a higher bearing capacity than boredpiles in Bangkok, Thailand [3]. Additionally, thebearing capacity of the two types of piles is not

At present, most constructions in soft soil areasuse pile or deep pile foundations. The type of pile ischosen by the characteristics of each construction, thetype of building, and the load of the structure. Thus,
the bearing capacity is the most important factor to
analyze in terms of the pile performance. The
significant parameters affecting the pile capacity are
the groundwater level and the soil characteristics.
Bangkok and its vicinity are located in a soft soil
area that used to be part of the sea. The soil profileshows alternating layers of sand and clay from soil
deposits. The first soil layer is a top clay crust with athickness of 1 to 2 meters from the ground surface.
The second soil layer is a very soft to soft clay layer
with a thickness of about 14 meters. The third soil
layer is a medium stiff to very stiff clay layer,
extending to a depth of about 25 meters, followed bya medium dense sand layer, referred to as the firstsand layer, extending to a depth of 40 meters. The
deeper layers comprise alternating layers of stiff clay
and dense sand, reaching great depths that have very
high soil strength.
In terms of the groundwater situation in Bangkok,
the groundwater level changes in relation to thequantity of groundwater pumping. The Department of
Groundwater Resource evaluated the groundwater
52

GEOMATE – Kyoto, Japan, 3-5 November 2021

different. Thus, that research recommends barrette
piles for high pile capacity designs. On the contrary,
the soil strength estimated through a numericalmethod showed that the positive skin friction value ofbarrette piles is more than that of bored piles [4]. The
strength of soft Bangkok clay was measured using therecompression and SHANSEP techniques [5]. The
results of their comparison of the undrained shear
stress values showed that recompression tests yieldhigher values than those estimated from the
SHANSHEP technique by about 28%. Finally, theresearch proved that the corrected field vane shear
tests match the SHANSHEP triaxial data well.
Additionally, the undrained shear strength of the
MRT blue line project and the MRT blue line
extension project, Bangkok, Thailand was examined[6]. Their research used a single pile with a diameterof 1.5 meters and a length of 55 meters. The results
revealed the undrained shear strength of stiff clay of
about 40 to 100 kN/m2 by triaxial tests and theundrained shear strength of soft clay of about 10 to 50
kN/m2 by vane shear tests. The development ofnegative skin friction was found on piles driven into
soft Bangkok clay [7]. This research showed that theundrained shear strength values of medium stiff tostiff clay and sand are 3 to 8 t/m2 and 18 to 25 t/m2,
respectively, by direct shear tests (DSs), field vaneshear tests (FVs), Dutch Cone tests (DCs), and Ko
consolidated undrained triaxial tests (CKoUs). The
results of the pile load tests conducted in the Bangkok
area by driven piles and bored piles have beensummarized. For the stiff clay layer, the undrained
shear strength is 200 to 250 kN/m2 by unconfinedcompression tests and standard penetration tests, andthe average skin friction is about 100 kN/m2 [8].
It is important to give consideration to thegroundwater level in constructions since the bearing
capacity of the soil affects the safety of the structures.
This is because the water level, in terms of the porewater pressure, will reduce the soil strength. The
present research shows the results of centrifuge tests
performed during the recovery or rising up of thegroundwater level. The results were verified with thedata from the previous research by the authors [9].
Thus, this research will show the bearing capacity ofa single pile while the groundwater level changes
from the surface and then experiences groundwaterlevel drawdown due to water pumping until therecovery of the groundwater level to the groundsurface.

different intervals. The centrifuge tests were divided
into three stages for one model to represent a boredpile under the impact of engineering structures during
the changes in groundwater level by means ofgeotechnical centrifuge tests done at Hong Kong
University of Science and Technology [10].
Model preparation
Load distribution
The soil strength theory depends on many
parameters, such as the soil layers, soil properties, andgroundwater level. Following the previous study [3],this research chooses two soil layers to represent thesoil condition in the Bangkok area for the model
because they have a very high load transfer.
Following Fig. 1, the load distribution refers to thepercentage of load transfer to each soil layer of thebarrette and bored piles. Most of the load istransferred to the medium dense sand layer, followedby the hard clay layer and then the stiff clay layer.
%Load transfer
5,290 Ton 2,700 Ton
0.0

17%

20.0 Medium 41%
Dense sand30.0 Hard clay 23%

40.0

Dense
sand

6%

Bored pile

Stiff clay

5%

Depth
(m)

10. 0

5%

Barrette pile

Soft clay

12%
33%

24%

11%
9%
9%
Base grouting

Fig. 1 Percentage of load transfer to each soil layer inpile load tests.
Soil model
Following the capacity of the soil from theprevious study, medium dense sand and stiff clay
were chosen for this research to simplify the model
preparation and to represent the Bangkok soil profilethat has alternating layers of clay and sand. Even
though the hard clay layer has the 2nd highest
percentage of load transfer for both types of piles, it
is almost impossible to prepare this layer to attain thedensity of the hard clay layer in the centrifuge model.

METHODOLOGYThis research focuses on the bearing capacity
behavior of a single pile, and attempts to model asingle pile while changing the groundwater level at
53

GEOMATE – Kyoto, Japan, 3-5 November 2021

Thus, Kaolin clay and Toyoura sand were used as thestiff clay and medium dense sand, respectively. The
Speswhite Kaolin clay was prepared at a watercontent of 27%, a density of 1.65 t/m3, and undrained
shear strength of 35 kPa. The Toyoura sand was
prepared with a controlled density of 1.53 t/m3.

Bored piles
Three bored piles were installed in the model inorder to perform a pile load test at each water level.
The diameter of the pile was 2.4 meters and the length
was 50.4 meters. The three single piles were installedin the strongbox, as shown in Fig. 4.

Water flow system
The water flow system in the centrifuge tests wasmodeled by installing a standpipe at the bottom of astrongbox. A standpipe is a PVC tube covered with ageotextile. The end of the standpipe was connected toa hole in the strongbox and a reservoir or water tanknext to the strongbox. This system was installed tocontrol the water level at each stage of the tests, asmentioned previously
Single pile model
The model for the single pile was an aluminium
tube equipped with strain gauges. The dimensions of
the model pile are a diameter of 2.4 meters (30 mm
for the pile model) and a length of 50.4 meters (630
mm for the pile model). Wheatstone bridge strain
gauges were installed at seven levels in the pile tomeasure the values of the pile movement during
testing. The aluminium tube was divided into six
segments because doing so would make it easy toinstall the strain gauges inside the pile segments. In
order to prevent the electric circuit installed in eachsegment from water, tape was wrapped around thecircuit to protect it, as shown in Figs. 2 and 3. After
connecting the pile segments together, the pile model
was coated with a thin layer of liquid epoxy to form awatertight layer.

Fig. 4 Testing procedure of centrifuge tests.
Instruments
The focus of the tests is the results of the pile
bearing capacity. Three types of instruments were
installed in the soil model. Firstly, linearly variable
differential transformers (LVDTs) were installed at
the pile caps and the ground surface to measure thesurface soil displacements. Secondly, pore waterpressure transducers (PPTs) were installed in both soil
layers. Thirdly, strain gauges were installed inside themodel pile. After all centrifuge tests had been
performed, the undrained shear strength of the soil
was measured by pocket vane shear tests.
Additionally, cameras were installed around thestrongbox to monitor the external movement andgroundwater level in the model during the testing.
Testing proceduresThis study uses a 2D model box. The model had
the dimensions of 350x750x1245 mm. A water
system was installed at the bottom of the model box
with two holes to control the groundwater level anddistribute the water. Then, the two soil layers wereprepared. Toyoura sand was released from a rate
hopper and spread to a fixed height to control thedensity of the sand (1.53 t/m3). The sand was filled in,layer by layer, with a maximum thickness of 50 mm
per layer, until reaching a total thickness of 300 mm.
After that, the groundwater level was increased by thestandpipe system to saturate the sand for about 24
hours. Then, water was released from the model box
by the standpipe system to make the uniform densityof the sand layer. Next, the Kaolin clay wascompacted on top of the sand layer by a density

Fig. 2 Strain gauges by full Wheatstone bridge circuit.

Fig. 3 Single pile model.
54

GEOMATE – Kyoto, Japan, 3-5 November 2021

control method to a thickness of 250 mm. Then, thesoil model was drilled to install the three-pile model,
as shown in Fig. 4. The piles were installed in both thestiff clay layer and the medium dense sand layer. The
pile tips were driven into the medium dense sandlayer about 140 mm from the interface of the sandlayer. After installing all the instruments in the modelbox, a balance weight was equipped on the opposite
side of the model box to balance the centrifuge
machine during testing. A summary of the centrifuge
test is given in Table 1. The testing model consistedof the model box and one water tank to control thewater level.

results shows the settlement of the pile and the groundsurface during the pile load tests. The second set ofresults shows the bearing capacity and the unit skinfriction during the pile load tests. The last set of resultsshows the undrained shear strength values after
testing at 0 g.
Settlement
Based on Figs. 5 to 7, the settlement behavior
between a single pile and the ground surface in eachstage condition of the groundwater level during thepile load tests is given. The circular marker in eachtest, representing the settlement of the ground surface,shows that small settlement occurred in all the tests
compared with pile settlement. On the other hand, thetriangular marker shows that very large settlement
occurred in the first pile load test due to the soil model
just starting to consolidate and the voids between thesoil particles filling with water due to groundwaterlevel reaching the ground surface. In the same way, it
was found that the second test had the smallest
settlement because of the decrease in water level tothe soil interface between the clay and the sand as theclay layer was consolidated. The last test showed that
more settlement occurred than in the second test, but
still less than in the first test. These results confirm
that the groundwater level directly affects thesettlement behavior of the soil around the piles inevery stage.

Table 1 Summary of centrifuge test
Testing
No.

BP1
BP2
BP3

Water
level inprototype
(m)
0
20
0

Water
level in
model
(mm)
0
250
0

Initialwater
content
(%)
27
27
27

Note: BP refers to the type of pile, namely, a bored pile, and thenumber after BP is the number of single piles.

The centrifuge test consisted of three stages thatwere carried out at an acceleration equal to 80 times
the force of gravity (80 g). Each stage is related to thegroundwater level at a different period of time. The
three stages are called BP1, BP2, and BP3. The firststage, BP1, refers to the stage in which the bearing
capacity behavior of the soil around a single pile isinvestigated by performing a pile load test after the
groundwater level reaches the ground surface. This
stage represents the original groundwater in the past
before the occurrence of the pumping issue. The
second stage, BP2, refers to the stage in which thegroundwater level decreases to the interface of thesand layer and clay layer after which a pile load test
is performed. This stage represents the decrease in
groundwater due to the high pumping rate during theperiod of increasing industrial development. The third
stage, BP3, is similar to the BP1 stage. The
groundwater level reaches the ground surface again
after the BP2 stage and a pile load test is performedon the pile head. Every test was performed at anacceleration of 80 g with the pile load test rate of 2
mm/min by ASTM, as in [11].

Bearing Capacity
The pile load capacity is discussed in this section.
The strain gauge results represent the strength of thesoil around the piles. Based on Fig. 8, the pile load is
directly affected by the changing water level. The firststage has the smallest load capacity due to the highgroundwater level and the short-term consolidation.
After consolidation, the bearing capacity issignificantly increased. Especially in the last stage,
BP3, the condition is represented whereby thegroundwater level rises up and the Kaolin clay layer
has already consolidated for the longest period. Fig. 9
confirms that the period of consolidation has a huge
effect on the strength of the soil around a single pile.
However, the unit skin friction results for the sandlayer are not related to the consolidation condition ineach test. The third stage has a lower unit skin friction
value than the second stage because the groundwaterrises up to the ground surface leading to high porewater pressure. The soil strength of each test is
explained in terms of the effective stress in Fig. 10.
The effective stress is equal to the total stress minus
the pore water pressure. The groundwater level at
each stage refers to the pore water pressure that

RESULTS AND DISCUSSION
The results of the pile load tests in the centrifuge
test will be detailed here. The results of the threestages were separated into three parts. The first set of55

GEOMATE – Kyoto, Japan, 3-5 November 2021

directly affects the effective stress. Thus, the second
test has the highest effective stress due to the decreasein water to the interface of the soil.
Soil strength

Depth (m)0

The undrained shear strength, after the centrifuge
tests done at 0 g, is discussed here. Fig. 11 shows acomparison of the undrained shear strength between
the centrifuge test results and the results obtained byother researchers. The results revealed that many
researchers obtained almost the same undrained shear
strength values at every depth [12]–[15]. Even the
results of the pocket vane shear tests showed higher
strength than in the tests done by others researchers,
but still with the same trend. Additionally, the authors
confirm the values when using the Modified CamClay theory to recheck the values of the pocket vaneshear tests, as seen in Fig. 12. The results reveal thatthe values of the undrained shear strength from thevane shear tests are lower than those of the Modified
Cam-Clay theory at each soil depth.

0
10
20
30
40

Fig. 8 Comparison of axial load of each test.

0

7

7.2

0

Unit skin friction (kN/m2)
20
40
60
80 100Depth (m)

0

120

BP1
BP2
BP3

10
20
30

Fig. 9 Unit skin friction of each test.

7.4

0

Effective stress (kPa)
100
200
300
400

0

On surface
On capDepth (m)Settlement (cm)

6.8

3000
BP1
BP2
BP3

Time (Years)
6.6

Axial load (kN)
1000
2000

50
100

500

BP1
BP2
BP3

10
20
30

150

Fig. 10 Effective stress of each test.
Fig. 5 Settlement during first pile load test.
0Time (Years)
580

600

620

640

0.00

Depth (m)Settlement (cm)560

0On surface
0.03

10
20

0.06
0.09

Su (kPa)
100

150

200

Hand vane shear test
Stewart (1989)
Springman (1989)
Phillip (1987)
Garnier (2002)

Fig. 11 Comparison of undrained shear strength.

Fig. 6 Settlement during second pile load test.

10

Time (Years)

20

30

Su (kPa)
20 40 60 80 100 120 140 160 180
0

40

0.00

Depth (m)Settlement (cm)50

On surface

10

0.10

20

0.20
0.30

30

MCC_Before test
MCC_At BP1
MCC_At BP2
MCC_At BP3
MCC_After spin down
Vane shear test_Point1
Vane shear test_Point2

Fig. 12 Comparison of undrained shear strength.

Fig. 7 Settlement during third pile load test.
56

GEOMATE – Kyoto, Japan, 3-5 November 2021

CONCLUSIONS[4] Hosoi T., Yagi N. and Enoki M., Consideration to
the skin friction of diaphragm wall foundation,
Interconference on deep foundation practiceincorporating PILETALK, Singapore, 1994.
[5] Seah T.H. and Lai K.C., Strength and deformation
behavior of soft Bangkok clay, Geotechnical
Testing Journal, Vol. 26, No. 4, 2003, pp. 1-11.
[6] Likitlersuang S., Surarak C., Wanatowski D., Oh
E. and Balasubramaniam A., Geotechnicalparameters from pressuremeter tests for MRTBlue Line Extension in Bangkok. Geomechanics
and Engineering: An International Journal, Vol. 5,
No. 2, 2013, pp. 99-118.
[7] Indraratna B. and Arumugam B., Development ofnegative skin friction on driven pile in soft
Bangkok clay, Canadian Geotechnical Journal,
Vol. 29, 1992, pp. 393-404.
[8] Balasubramaniam A.S., Phienwej N. and Oh Y.N.,
Back-analysis and interpretations of driven andbored pile tests data in Bangkok sub-soils,
Conference proceedings, International Society for
Soil Mechanics and Geotechnical Engineering,
2004, pp. 81-87.
[9] Intui S. and Soralump S., The Vertical Bearing
Capacity Behavior of Single Pile by GeotechnicalCentrifuge, Kasetsart Engineering Journal, Vol.
31, No. 104, 2018, pp. 55-68.
[10] Ng C.W.W., Zhang L.M. and Wang Y.H.,
Proceedings of 6th Int. Conf. on PhysicalModeling in Geotechnics (TC2), 2006.
[11] ASTM, Standard test method for deep
foundations under static axial compressive load,
ASTM D1143-81 (Reapproved 1994), In Annual
Book of ASTM Standards. New York, 1994, pp.
1-15.
[12] Stewart D. I., Groundwater effect on in-situ walls
in stiff clay, Ph.D. Thesis, University ofCambridge, 1989.
[13] Springman, Lateral loading in piles due tosimulated embankment construction, Ph.D.
Thesis, Cambridge University Engineering
Department, UK, 1989.
[14] Phillip, R., Ground deformation in the vicinity ofa trench heading, Ph.D. dissertation, Cambridge
University, 1987.
[15] Garnier S.M., Properties of soil samples used incentrifuge models, Physical Modelling in
Geotechnics: ICPMG’02, 2002.

This paper focused on a comparison of the
behavior of a single pile during changing
groundwater levels by considering the soil strengthparameters, ground settlement, and pile headsettlement. All the results of this research werecompared with those of a previous study. It wasproven by hand calculation that the trends of theresults are the same. For the axial load behavior of asingle pile, the bearing capacity of the soil around thepile was found to be related to the water level and theconsolidation of the soil. The bearing capacity of theclay layer increased because of an increase in theeffective stress due to a decrease in the groundwaterlevel. On the other hand, the bearing capacity in thelast of the three stages was seen to increase becauseof the consolidation process in the clay layer when the
groundwater rose up again. However, the bearing
capacity in the sand layer was found to depend on the
groundwater level or the pore water pressure, as in theeffective stress theory. Therefore, this research can beused to estimate and design the bearing capacity ofsoil with changing groundwater levels.
ACKNOWLEDGMENTS
The research was supported in every aspect by
the Geotechnical Research and Development Center(GERD). The writers would like to thank eachresearcher of the Geotechnical Engineering Researchand Development Center (GERD) and eachtechnician of the Geotechnical Centrifuge Facility,
the Hong Kong University of Science andTechnology (HKUST), for providing the machines
and other necessary equipment.
REFERENCES[1] Department of Groundwater Resource (DGR),
Groundwater situation in Bangkok and urban area,
2012.
[2] Likitlersuang S., Cheng C., Soraral C. andBalasubramaniam A., Strength and stiffness
parameters of Bangkok clays for finite element
analysis, Geotechnical Engineering Journal of the
SEAGS & AGSSEA, Vol. 49, No. 2, 2018, pp. 150156.
[3] Singtokaew K., Submaneewong C. and
TANSENG P., The highest static load which everbeen tested on piles in Bangkok subsoils, 40th
anniversary the foundation of success, 2001, pp.
103-108.

57

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

SURFACE TREATMENT ON ANODE ELECTRODES FOR THE
HIGHER BIOELECTRICITY GENERATION IN PLANT MICROBIAL
FUEL CELLS
Shohei Uramoto¹, and Md.Azizul Moqsud²
1,2

Department of Civil and Environmental Engineering, 1,2Yamaguchi University, Japan
ABSTRACTIn this study, plant microbial fuel cells (PMFCs) were constructed to enhance the power generation by usingchemical treatment of carbon-based anode materials. To increase the power output to utilize it for the real-lifeusage is the biggest challenge in the microbial fuel cell research. Iron (Fe) , Manganese (Mn) and both of thechemicals coated anode materials have been prepared in the laboratory for the efficient electron transfer and usedin PMFCs with Monstera plants. It was observed that the anode which was treated with both Iron and Manganesegenerated the highest power density, which was 46%～69 higher than Cells whose anode was coated by either
Iron or Manganese. Voltage generation and stability of voltage variation were also improved with Iron andManganese. So, the surface treatment of the carbon-based electrode materials by this environment-friendly
chemical can be used for the plant microbial fuel cell application in the future.
Keywords: Plant Microbial Fuel Cells, Anode surface treatment, Bio electricity, Iron, Manganese

INTRODUCTION

MATERIALS AND METHODS

Recently, massive energy revolution has been
occurred, especially in the developed nations over theworld. Although solar power generation, wind power
generation, and water power generation are thedominant clean ・ sustainable energy source, somedrawbacks are also observed such as instability of its
power output due to weather conditions, huge area of
construction, and damage to the environment.
Therefore, noble energy source called Microbial Fuel
Cell (MFC) is gathering an attention.[1,2]Microbial Fuel Cells introduced plants in its
system called as PMFCs are promising technology[3,4] due to the fact of that organic matters which arenecessary for bacteria’s activity are theoreticallysupplied by plants permanently in the soil. This is
expected to become new sustainable energy source,
however; the biggest drawback in PMFC has been
low power output. Some researchers have been
attempting to overcome the problem, [5,6]Recently, it was discovered that anode electrodes
treated with Fe and Mn shows higher power output in
Microbial Fuel Cells. [7]Since there is few research which tries toimproved PMFCs’ power generation based onconventional MFCs’ technologies, this report willattempt to construct PMFCs by utilizing previous
MFCs’ researches.
The main objective in this paper is to construct
PMFCs with chemically treated anode. In addition, tocompare power characteristics between treated anduntreated anode was also set as a research purpose.

Experimental set-up of PMFCs
Three PMFCs were prepared with organic soil,
Monstera plants, bacteria, and chemically treatedelectrodes in laboratory. The properties of organic
soil are shown in table 1, and Fig 1.

Table 1. Soil properties

Sample soil
Water content (%)
pHElectrical conductivity (mS/cm)
Loss On Ignition (%)

Organic soil
190
6.36
0.24
51.7

Percent Passing（％）

100
90
80
70
60
50
40
30
20
10
0
0.01

0.1

1
Gain Size10（mm）

Fig 1. Grain size distribution curve of organic soil
58

100

GEOMATE – Kyoto, Japan, 3-5 November 2021

Regarding plant species, Monstera deliciosa,which is one typical house plant was chosen due tothe fact of that this has shade tolerance, as well as
tolerance to high temperature, humidity. These
properties are ideal for the indoor experiment. Herein,
Monstera Deliciosa is abbreviated to Monstera.
Figure 2 shows diagram of Monstera’s PMFC.

submerged to absorb the solution while mixing at
100rpm for 2 hours, and rinsed with distilled water,
and then left to air dry for 12 hours in the laboratory.
The ratio of mol concentration between iron andmanganese was decided to be at 1:1 because of theprevious work which had discovered this ratio is an
optimum. [7]A base growth iron (Ⅱ) solution was preparedconsisting of iron (Ⅱ) sulfate heptahydrate (FeSO₄・
7H₂O)
at
1mmol
in
distilledwater.
Sodecylbenzenesulfonate (SDBS) surfactant wasadded to this iron growth solution. Given the amountof iron (Ⅱ) particles, the SDBS surfactant’s molconcentration was adjusted to be 0.02M in the beaker.
The dried PEI-Fe(Ⅲ)-Bamboo charcoals were placedin this solution while stirring at 100rpm for an hour.
To activate the growth reaction of the Fe
nanoparticles, an aqueous solution of 5.0M NaOH
was added and quickly mixed to produce thecharacteristic green suspension.
Before moving to the next step, one electrode which
was supposed to have only Iron was removed and leftin solution for 48 hours.
After 5 min, 1 mM aqueous solution of potassium
permanganate (KMnO4) was added and mixed tocreate a murky brownish precipitate. The PEIIron(Ⅱ) Bamboo charcoals were left in this solution
to promote the Fe(Ⅲ)-Mn(Ⅶ) growth process for 48
h. The bamboo charcoals were then removed from thesolution and rinsed thoroughly with distilled water toensure the termination of the growth process. Finally,
the completed samples were left to air-dry overnight
before use within PMFC testing. Green suspension
and a murky brownish precipitate which wereappeared in the experimental process are shown in
Fig 3.

Fig 2. Plant Microbial Fuel Cell using Monstera
Shewanella Oneidensis (Japan Collection of
Microoganisms) was chosen as the electroactive
bacteria. Shewanella Oneidensis was cultured in the
petri dish at Incubator at 28℃. The medium consistedof Trypto-soya agar (Nissui Pharmaceutical Co., Ltd.）at 40g, and distilled water at 1000mL.
Three types of anode electrodes were prepared inorder to evaluate the effect of Iron and Manganese.
Procedure of Anode Surface Treatment
First of all, bamboo charcoals curled with iron
wire were combined with Polyethylenimine (PEI)
solution. Bamboo charcoal curled with iron wire had
been discovered to have higher performance due tothe lower internal resistance in previous study. [8]To form the combination of bamboo charcoal and PEI,
bamboo charcoals with iron wire was submerged in
PEI (10g/L) for 3 hours while stirring with magnetic
stirrer at 100rpm to be mixed thoroughly, and rinsed
with distilled water, and then left to air dry for
12hours in the laboratory in which air temperature
was set at 25℃. This process was conducted in allelectrodes.
Secondly iron (Ⅲ) particles seeding process was
conducted with the two PEI-Bamboo charcoals out of
three because one electrode was supposed to contain
only manganese. PEI-Bamboo charcoals were firstplaced in a Fe( Ⅲ ) solution (Fe₃O₄ ; 1g/L) andab

Fig 3. a) Green suspension, b) Murky brownish
precipitate
When it comes to electrode which was supposed
to be treated by manganese, SDBS treatment and 1
mM aqueous solution of potassium permanganate(KMnO4) was conducted, then left to the solution for59

GEOMATE – Kyoto, Japan, 3-5 November 2021

48 hours.
These anode electrodes were submerged in
Shewanella Oneidensis’ bacteria solution for hour,then used in PMFCs. Electrode distance was fixed at
4 cm in all cells.
Figure 4 illustrates anode electrode treated with Iron,
Manganese, and both of them respectively.

ba

at approximately 320 mV. Turning to Fe-PMFC,
voltage begun to increase from the initial stage, andthis trend seems to the same as Fe-Mn PMFC’s
voltage increase. However, Fe-PMFC suddenly
experienced voltage decrease at the middle of June 5th,then its gradual voltage decline continued by June 7th.
The blank seen between June 7th and 8th was occurredby mechanical trouble of data logger. The highest
voltage of Fe-PMFC was obtained at June 8th’safternoon whose value was 453.3 mV. An order of thehighest voltage in whole duration is Fe・Mn-PMFC,
Fe-PMFC, then Mn-PMFC, and its voltage was 578.9
mV, 453.3 mV, and 352.9 mV respectively.
It was no voltage drop that Iron and Manganesecoated anode’s cell was discovered to have extremely
high performance. This indicates that both metal ion,
Iron and Manganese are essential factors for the
stable, and higher voltage generation.

c

Fig 4. Chemically treated anodes a) Fe-anode,b) Mn-anode, c) Fe・Mn-anodePower Generation

RESULTS AND DISCUSSION

Figure 6 explains power characteristics of Fe・
Mn-PMFC, Fe-PMFC, and Mn-PMFC respectively.
These power characteristics were measured after June
9th shown in voltage variation section. This graph was
obtained with data logger (Crenova digital multimeter,
Crenova Ink, Japan) as well as external resistances.

Voltage Variation

Voltage (mV)

Figure 5 illustrates voltage variation for 5 days
with PMFCs whose anode electrodes were coated by
Iron, Manganese, and both Iron and Manganese.
700
600
500
400
300
200
100
0

Iron
Manganese
Iron and Manganese

6/4

6/5

6/6
6/7
Duration (day)

6/8

6/9

Fig 5. Voltage variation with timeFig 6. Power characteristics in three PMFCs

Herein, each cell will be denoted as Fe-PMFC,
Mn-PMFC, and Fe ・ Mn-PMFC respectively forsimplify. In the initial stage, it is observed that all
voltages are almost the same, value. Fe・Mn-PMFC
generated 293.9 mV. When it comes to Fe-PMFC and
Mn-PMFC, its initial voltages were 242. 5 mV and
272.0 mV respectively.
After that, Fe ・ Mn-PMFC’s voltage increased
gradually without voltage drops, and the highest
voltage was produced at the end of duration, which
was 578.9 mV. On the other hand, Fe-PMFC and MnPMFC experienced some fluctuation during 5 days.
Regarding Mn-PMFC, voltage generation changes
slightly. After the initial voltage, its value went up
marginally until June 5th. However, voltage started to
decline from the time, and it reached first bottom at
15:26 on June 5th. This kind of slight changes wereseen four times, but in average, its voltage was fixedFirst of all, peak power density of Fe・Mn-PMFC
was 46.0 mW/m²which is the highest value out of
three PMFCs. This highest power was generated
when 993 ohm’s external resistance was connected.
That is to say, this cell’s internal resistance iscalculated to 993 ohm.
Secondly, Fe-PMFC’s highest power density wascalculated to 32.1 mW/m², which is around 15mW
lesser than Fe ・ Mn-PMFC. Internal resistancedetermined with the same method described above
was 668 ohm. Although the peak power is smaller in
Fe-PMFC, the internal resistance was better in Fe
coating anode than Fe・Mn coating anode’s PMFC.
Thirdly, Mn-PMFC’s maximum power was 21.6
60

GEOMATE – Kyoto, Japan, 3-5 November 2021

First, it was discovered that Iron and Manganesecoated anode exhibited highest voltage generation inwhich no voltage decline occurred in PMFCs.
Secondly, PMFC whose anode was treated by Fe
and Mn showed the highest power, which implies thatboth Iron and Manganese are essential for the
improvement in PMFC.
Thirdly, although future research will be required
to identify the cause of gaps in Fe-PMFC, and MnPMFC, this is considered that there may be optimum
metal ion for each bacterium.

mW/m², which was the lowest in all cells. Its internal
resistance was 993 ohm, and this is the same value as
Fe・Mn-PMFC, however; due to the lower highest
open circuit voltage, Mn-PMFC’s power was lesser
than Fe・Mn-PMFC by around 25 mW.
Although the maximum power density was highest in
Fe・Mn-PMFC, internal resistance was lower in FePMFC by 325 ohm. Further research will benecessary in order to identify the reason of this,however; difference between this experiment andprevious experiment [7] was bacteria’s species. Thismight be the cause of higher power performance in Fe
coated anode since some researchers [9] reported thepower generation can be improved with the
supplement of Fe(Ⅲ).

ACKNOWLEDGMENTS
The authors acknowledged the financial supportof the Chugoku Denryoku for conducting thisresearch.

Image Analysis by Scanning Electron Microscope

REFERENCES

Figure 7 illustrates the comparison of untreatedanode, and Iron-Manganese treated anode using SEM.

a[1] P.M.D. Serra, Espirito Santo A., Sourcing powerwith microbial fuel cells: A timeline. Journal ofPower Souces, Vol 482, 2021
[2] Mostafa E. Elshobary, Hossain M.Zabed, Junhua
Yun, Guoyan Zhang, Xianghui Qi., Recent
insights into microalgae-assisted microbial fuelcells for generating sustainable bioelectricity.
Internal Journal of Hydrogen Ennergy, Vol 46,
Issue 4, 2021, pp 3135-3159[3] Rukhsar Shaikh, Afshan Rizvi, Marzuqa Quraish,
Soumya Pandit, Abhilasha Singh Mathuriya,
Piyush Kumar Gupta, Joginder singh, Ram
Prasad., Bioelectricity production using plantmicrobial fuel cell: Present state of art. South
African Journal of Botany. 2020
[4] Chiranjeevi.P,
Yeruva,D.Y,
Kumar,A.K,
Mohan,V, Varjani,S., Plant- Microbial Fuel Cell
Technology.
Microbial
Electrochemical
Technology, 2019, pp.549-564[5] Moqsud,M.A J. Yoshitake, Q.S. Bushra, M.
Hyodo, K. Omine, David Strik., Microbial fuelcell (MFC) for bioelectricity generation fromorganic wastes. Waste management, Vol 36,
2015, pp 63-69[6] Helder,M, Buisman,C.J.N, Hamelers,H.V.M,
Strik,D.P.B.T.B., Design criteria for the PlantMicrobial Fuel Cell Electricity generation withliving plants from lab to application. 2012, pp 45.
[7] Duarte,K.D.Z, Kwon,Y., Enhanced extracellularelectron transfer of yeast-based microbial fuelcells via one pot substrate-bound growth ironmanganese oxide nanoflowers. Journal of Power
Source, Volume 474, 2019.
[8] Moqsud.M.A, Omine,K, Yasufuku,N, Hyodo,M,
Yukio,N., Microbial fuel cell (MFC) forbioelectricity
generationfromorganic
wastes.Waste management, Vol 33, Issue 11,2013, pp.2465-2469b

Fig 7. Comparison of anodes, a) Untreated anode,b) Fe and Mn treated anode
In both figures, some unique porous structure ofbamboo material can be observed in the background.
A number of small particles are also shown in thesection a) in Fig 7’s Fe・Mn treated anode surface.
They are iron and manganese nano particles. Thesenano particles are still captured on the surface ofelectrode even though electrodes were rinsed with
distilled water. Thus, a function of polyethienine as
an adhesive is thought to be working well. As
previous research [7] explains, the reason of higher
power generation in Enhanced anode PMFC is thatcountless particles can play an essential role as anedge which enables bacteria to stay on the surface
easier. By contrast, these nano chips are not observedin section b) in Fig.7
CONCLUSIONSThis research was conducted in order to construct
Plant Microbial Fuel Cells based on recent
developments which had reported in Microbial Fuel
Cells’ study. Moreover, to compare power
characteristics between treated and untreated anode
was also purpose in order to determine which metal
plays more essential role.

61

GEOMATE – Kyoto, Japan, 3-5 November 2021

[9] Di Wu, Defeng Xing, Lu Lu, Ma Wei, Bingfeng
Liu, Nanqi Ren., Ferric iron enhances electricity
generation by Shewanella oneidensis MR-1 in

MFCs. Bioresource Technology, Vol 135, 2013,
pp 630-634

6211th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

PLANT ENZYME INDUCED CARBONATE PRECIPITATION FOR
SAND CEMENTATION
Linyu Wu1, Linchang Miao2 and Satoru Kawasaki3
1,2

Transport School, Southeast University, China; 3Faculty of Engineering, Hokkaido University, Japan
ABSTRACT

Enzyme-induced carbonate precipitation (EICP) is an emerging biogeotechnology for enhancing theengineering properties of granular soil. In this study, to lower the cost of urease used in EICP, urease was extracted
from soybeans. The effects of temperature and pH on soybean urease activity were studied. Then the test-tube
experiments of urease-induced calcium carbonate precipitation were carried out by controlling cementation
solution concentration. Based on this, sand was cemented by injecting urease solution and cementation solution
circularly. The ultrasonic test and unconfined compressive strength test were performed to evaluate thecementation effect of sand solidified with EICP. The results show that the optimum pH of soybean urease is 8, andthe maximum urease activity is observed at 75°C. With the increase of cementation solution concentration, theprecipitation ratio of calcium carbonate increases first and then decreases, and when the cementation solution
concentration is 0.75 M, the precipitation ratio of calcium carbonate arrives at the highest during tests. The calcium
carbonate induced by soybean urease cements the loose sand into a strong sand column. The longer the cementing
time is, the more compact the treated sand is and the higher the unconfined compressive strength is. Overall,soybean urease has a great potential to replace commercial urease for sand treated via EICP.
Keywords: Sand cementation; Soybean urease; Enzyme induced carbonate precipitation (EICP); Unconfinedcompressive strength

INTRODUCTION

PVC cylinders containing sand to obtain cemented
sand samples, confirming the effectiveness of EICP
technique. Neupane et al. [8] reported that the
maximum unconfined compressive strength ofenhanced sand specimens using EICP was 380 kPa.
Carmona et al. [9] studied the application of EICP
technology in sand cementation, and found that it caneffectively enhance the mechanical properties of sand.
However, the cost of urease greatly restricts thepromotion of EICP technology. According to thecurrent price of urease, the cost of urease accounts forthe main part of the total cost of EICP technology.
Commercial urease is produced in high-purity form
for medical, food and other industries, so the price ishigh [10]. Therefore, the potential of using plantderived urease-induced carbonate precipitation has
been demonstrated. Nam et al. [11] extracted urease
from jack bean with phosphate buffer, indicating thatthe crude urease extract of the plant had the potential
to replace the purified urease for improving thestrength of the material. Dilrukshi et al. [12] used
crude urease extracted from crushed watermelon
seeds to cement sand and obtained estimatedunconfined compressive strength of several kPa to
MPa. Javadi et al. [13] applied a method of blending,filtration, and acetone fractionation to extract andpurify urease from watermelon seeds for EICP
treatment of soil. They reported the urease activity of
approximately 611 U/mL and demonstrated theformation of CaCO3 precipitation.
Soybean is rich in urease and has a wide range ofMicrobially induced carbonate precipitation(MICP) is a promising method of improving soil [1,2]. MICP is mostly based on a high-yield urease
microorganism that uses its own metabolic activity to
produce urease, which hydrolyzes urea into NH4+ andCO32-, and then CO32- combines with Ca2+ in thesurrounding environment to form carbonate, so as to
cement sand particles into a whole, causing an
increase in strength of the soil [3]. Due to its mild
reaction conditions and weak negative effects, thistechnology has been widely used in geotechnicalengineering and environmental engineering and otherfields in recent years.
Although MICP technology has been successfully
applied in both laboratory and field scales, there arestill some challenges in improving soil. The size andaerobic nature of the urease-producing bacteria limit
the application of MICP, including the type and depthof the treated soil [4]. Plant-derived enzyme induced
carbonate precipitation (EICP) is an alternative
method to MICP for the enhancement of soil. EICP
technology has two significant advantages over
MICP. First, small size free urease induces carbonate
precipitation to be less prone to biological clogging
and can penetrate finer grained soil. Second, freeurease is degradable, avoiding long-term impact on
the environment [5, 6].
In recent years, Neupane et al. [7] injected the
mixture of urea, calcium chloride and urease into
63

GEOMATE – Kyoto, Japan, 3-5 November 2021

solution was uniformly mixed with 10 mL ofcementation solution, and then the tube was placed in
an incubator at 30°C for 24 h.
The theoretical amount of calcium carbonate is
calculated by C×V×M, where C is the cementation
solution concentration in moles per liter, V is thevolume of the cementation solution in liters, and M is
the molar mass of calcium carbonate of 100.09 g/mol[7]. The actual amount of calcium carbonate wasobtained by the acid digestion method: After thereaction, the reactant was filtered through filter paper.
Then the precipitate and filter paper were oven-driedat 60°C, and the total mass of them was weighted(M1). Subsequently, hydrochloric acid with aconcentration of 4 M was used to rinse until nobubbles were produced, followed by rinsing with
deionized water. The insoluble matter and filter paper
were oven-dried to obtain the total mass (M2). The
difference in mass before and after the acid digestion
was the actual amount of calcium carbonate (M1–
M2). The ratio of the actual amount of calcium
carbonate to the theoretical amount was represented
as the precipitation ratio of calcium carbonate.

sources [14, 15]. In this study, in order to reduce thecost of urease used in EICP, a simple method wasinvestigated to crudely extract soybean urease. The
effects of various factors on soybean urease activity
were studied. Then, urease was used in calcium
carbonate precipitation test to obtain the optimum
cementation solution concentration. Furthermore,soybean urease and cementation solution with
optimum concentration were used to cement sand,and the cementation effect was evaluated byultrasonic test and unconfined compressive strength.
Finally, the X-ray diffraction (XRD) test andscanning electron microscope (SEM) test were
performed to explore the cementation mechanism of
the treated sand.
MATERIALS AND METHODS
Soybean Urease Extraction
The soybean flour was obtained by pulverizing
dried commercial soybeans and passing through a 100
mesh sieve. 100 g of soybean flour was placed in a
conical flask, and then 1 L of distilled water wasadded, which was thoroughly shaken for 30 min.
After being stored in a refrigerator at 4°C for 24 h, thesoybean flour solution was centrifuged at 3000 r/min
for 15 min. The supernatant was collected as urease
solution for subsequent tests.

Sand Cementation Test
Sand column treatment
In this study, the commercial sand was dried at
105°C before being placed in PVC cylinders with an
inner diameter of 4.7 cm and a height of 15 cm. The
particle size distribution curve was shown in Fig. 1.
The median diameter of the sand grains was 0.65 mm,
and according to ASTM D2487 [17], the sand wasclassified SP. 300 g of sand was packed into the PVC
cylinder by layered compaction method. All samples
had a same initial dry density of 1.73 g/cm3 and aheight of 10 cm.

Urease Activity Test
The urease activity can be obtained by measuring
the change in conductivity of the solution [16]. In thisstudy, 3 mL urease solution was mixed with 27 mL
urea solution with a concentration of 1.5 M, and the
conductivity change in 10 min was monitored. The
temperature of the whole process remained constant.
The value of conductivity change per minute wascalculated, and then it can be converted to the ureahydrolysis rate representing urease activity (mM ureahydrolysedꞏmin-1).
In order to determine the effective temperature
range where cementation is possible, urease solution
and urea solution were mixed at different
temperatures ranging from 15-85°C. To investigate
the changes of urease activity with different acid-baseconditions, the temperature was 25°C and the pH
varied from 6 to 10 to obtain the influence of pH on
urease activity.

120

Percent passing(%)

100
80
60
40
20
0
10

Calcium Carbonate Precipitation Test
The cementation solution was the mixture of urea
and an equal molar concentration of calcium acetate.
In order to investigate the optimum cementation
solution concentration, cementation solution withdifferent concentrations (0.25, 0.5, 0.75, 1, 1.25 and1.5 M) was prepared. In the test tube, 10 mL of urease1
0.1
Grain size(mm)0.01

Fig. 1 The grading curve of sandAll samples were placed vertically, and the urease
solution was injected from the bottom of samples at a
speed (2 mL/min) until it reached saturation, and64

GEOMATE – Kyoto, Japan, 3-5 November 2021

Urease activity(mM urea hydrolysed min-1)

approximately 50 mL of urease solution was used.
After two hours, 50 mL of cementation solution (0.75
M urea and 0.75 M calcium acetate) was injected into
samples at 2 mL/min as well. After 1 day, new urease
solution and cementation solution with the same
concentration and volume were added. In this way,
new urease solution and cementation solution wereadded daily until required cementing time wasattained. The required cementing time of samples was
2, 4, 6, 8 days.
Ultrasonic test
After the sand was cemented, deionized water was
injected to flush out the byproducts of ammonium
ions, and then samples were placed in an oven at 60°C
for 48 h. Then every soil specimen with a height of 10
cm was divided into three sections along the verticallength, and the measuring points were selected at the
middle points of each section, denoted as A, B and C
successively, which was 17, 50 and 83 mm from thebottom of the sample respectively. Ultrasonic wave
propagated along the diameter of samples at eachmeasuring point, and the compactness of the samples
was determined by comparing the sound time valuesof different samples.

28
24
20
16
12
8
4
0
15

25

35

45
55
Temperature(℃)

65

75

85

Fig. 2 The urease activity at different temperatures
Effect of pH
Each enzyme has an optimal pH at which it has
the greatest activity [19]. The urease activity at
different pH is shown in Fig. 3. The urease activity
increased first and then decreased with an increase inpH from 6 to 10. The urea hydrolysis rate reachedmaximum at the pH of 8, i.e., 7.55 mM per minute.
Therefore, the optimal pH of urease is 8. For the pHof 6 and 10, the urea hydrolysis rate was 7.32 and 7.17mM per minute, respectively, and it was noticed that
there was no significant` decrease in the ureahydrolysis rate compared with the pH of 8, whichshowed that urease had strong tolerance tomoderately acid and alkali environment. During the
EICP process, urease hydrolyses urea into NH4+ andCO32-, resulting in the increase of pH, which creates a
conducive environment to carbonate precipitation[20], hence, the resistance of urease to acid and alkali
is beneficial to its application in practical engineering.

Unconfined compressive strength test
The sand columns were trimmed to make themflat at the top and bottom before unconfinedcompressive strength (UCS) test. Under the condition
that the sample had no lateral pressure, the loading
rate of 1mm/min was controlled, the axial pressure
was applied until the sample failed, and the maximum
axial stress was taken as the UCS.
RESULTS AND DISCUSSION

Urease activity(mM urea hydrolysed min-1)

Urease Activity Test
Effect of temperature
At high temperatures, urease will be inactivated
due to degeneration [18]. In this study, urease activity
was investigated between 15 and 85°C. The urease
activity at different temperatures is shown in Fig. 2.
Temperature had a significant effect on urease activity.
The urea hydrolysis rate of urease was 5.36 mM per
minute at 15°C, at the same temperature, this soybean
urease activity was about 13 times higher than
microbial urease activity previously studied by
Whiffin [16], which demonstrated that the extracted
soybean urease activity was higher. Afterward, urease
activity increased and approached the maximum at
75°C. Then, it dropped sharply at 85°C, this might bedue to the inactivation of urease at high temperatures,
which meant that excessive temperature was notconducive to urease development.

7.7
7.6
7.5
7.4
7.3
7.2
7.1

6

7

8
pH

9

10

Fig. 3 The urease activity at different pH values
Calcium Carbonate Precipitation Test
The comparison between the actual andtheoretical amount of calcium carbonate at different
cementation solution concentrations is shown in Fig.
65

GEOMATE – Kyoto, Japan, 3-5 November 2021

4. As can be seen from the figure, when the
cementation solution concentration increases from0.25 to 0.75 M, the actual amount of calcium
carbonate gradually increased and the precipitation
ratio also increased. Afterward, the actual amount ofcalcium carbonate gradually deviated fromtheoretical amount when the concentration of
cementation solution continued to increase, that is,
the precipitation ratio of calcium carbonate decreased.
This might be due to that the concentration of urease
was too high relative to the cementation solution
concentration as the cementation solution
concentration was low, which might suppress thecalcium carbonate precipitation reaction [7, 21].
Therefore, if the cementation solution concentration
is appropriately increased at this stage, theprecipitation ratio of calcium carbonate will increase.
However, when the concentration of cementation
solution exceeds the optimum concentration, thefunction of urease will be limited, leading to the
reduction of the catalytic hydrolysis capacity ofurease and the reduction of calcium carbonate
precipitation ratio. Considering that the precipitation
ratio of calcium carbonate is up to 84% at thecementation solution concentration of 0.75 M, hence,
0.75 M is taken as the optimum cementation solution
concentration, and the cementation solution with thisconcentration is adopted in subsequent sandcementation test.

shown in Fig. 5. With the increase of cementing time,
the sound time value of each test position of the sandcolumn decreased. Because more urease solution andcementation solution were injected as the cementing
time was increased, and more precipitation wasformed, the sample was more compact.
50
48

Calcium carbonate amount(g)Sound time value(μs)

44
42
40
38
36
34
32
30
28

A

B

C

Test position

Fig. 5 The sound time value of cemented samples
Unconfined compressive strength test
The UCS of the sample after cementing is shownin Fig. 6. It could be seen from the figure that thecementing time had a significant influence on the
UCS of the sample. The UCS of the sample increased
with the increase of the cementing time. The UCS of
the sample was only 0.07 MPa at the cementing timeof 2 days, and when the cementing time was 8 days,
the UCS of the sample could reach 2.18 MPa, whichwas 31 times greater than the cementing for 2 days.
In general, the more compact the material is, thestronger it is. The ultrasonic test results showed thatthe samples were more compact with the increase of
cementing time, hence, the UCS of the sample
increased.

1.6
1.4

2d
4d
6d
8d

46

Theoretical
Actual

1.2
1.0
0.8
0.6
0.4
0.2
0.25

0.50

0.75

1.00

1.25

Unconfined compressive strength(MPa)

2.4
0.0
0.00

1.50

Cementation solution concentration(mol/L)

Fig. 4 The calcium carbonate production at different
cementation solution concentrations
Sand Cementation Test
Ultrasonic test
The propagation velocity of ultrasonic wave
varies with the degree of medium compactness.
Therefore, sound time value is taken as an index toevaluate the compactness of cemented samples. The
difference of sound time value of each sample
indicates that the degree of compaction is different,
that is, the degree of cementation is different. The
sound time value of the sample after cementing is

2.0
1.6
1.2
0.8
0.4
0.0

2

3

4

5
6
Cementing time(d)78Fig. 6 The unconfined compressive strength ofcemented samples
66

GEOMATE – Kyoto, Japan, 3-5 November 2021

XRD Test and SEM Test
The XRD test was conducted with the driedprecipitate. Figure 7 presents the X-ray diffraction
pattern of precipitate products. According to thediffraction peak, the mineral composition of thesoybean urease induced precipitation is calcite.
8000¨ Calcite¨

7000

Intensity(CPS)

6000
5000
4000

Fig. 8 The SEM images of the cemented sample: (a)magnification of 100 times and (b)magnification of 10000 times

3000
2000
¨

1000
0

10

20

¨

¨

¨

¨
¨
¨
¨ ¨ ¨ ¨¨¨

3040
2 theta(degree)

50

60

CONCLUSIONS
70

Urease was extracted from soybeans at low cost.
On the basis of studying influencing factors of urease
activity and calcification, the soybean urease
cemented sand test was carried out. The following
conclusions can be drawn:(1) Plant enzyme is simple and economical. The
extracted soybean urease has higher urease activity,and its optimal pH is 8. Compared with pH,
temperature has a more significant effect on urease
activity, and the urease has a temperature optimum at
75°C.
(2) The cementation solution concentration has
great influence on calcification. When the
cementation solution concentration is low (<0.75 M),
increasing cementation solution concentration will
increase the precipitation ratio of calcium carbonate,while excessively high concentration of cementation
solution will reduce the precipitation ratio of calcium
carbonate.
(3) EICP technology can successfully cement
loose sand particles into a whole. As for cementing
samples, with the increase of cementing time, morecalcium carbonate is generated to cement and fill thesand, which improves the compactness and the UCSof the sample. The UCS of the sample can reach 2.18
MPa after cementing for 8 days. It demonstrates that
EICP technology can appreciably improve themechanical properties of soil and has a broad
application prospect.
It is recommended to optimize the soil treatment
parameters and conduct some large-scale field tests
before EICP technology can be used in practicalengineering.

Fig. 7 The X-ray diffraction pattern of precipitate
product
Figure 8 shows the SEM image of the cemented
sand sample. It could be seen that there were calcium
carbonate crystals on the surface of sand grains andbetween adjacent sand grains (Fig. 8(a)). With thedevelopment of EICP reaction, the size of generated
calcium carbonate crystals kept growing. Some
calcium carbonate crystals cemented between sandparticles, while others filled the pores among sandparticles, thus making loose sand particles a whole.
However, there were still some pores in the sand,which indicated that calcium carbonate crystals
generated by EICP reaction were not enough to fill all
the pores in the sand. This explained why the longer
the cementing time was, the more compact the sample
was. Figure 8(b) shows the SEM image withmagnification of 10000 times. As shown in the figure,the morphology of calcium carbonate crystals
induced by soybean urease was spherical or sphericalaggregates with different sizes, which might berelated to the sedimentary environment.

ACKNOWLEDGMENTS
This work was supported by the National Natural
Science Foundation of China (No. 51578147). The
authors thank the valuable comments from the67

GEOMATE – Kyoto, Japan, 3-5 November 2021

reviewers.

[11] Nam I. H., Chon C. M., Jung K. Y., Choi S. G.,
Choi H. and Park S. S., Calcite precipitation byureolytic plant (Canavalia ensiformis) extracts
as effective biomaterials, KSCE Journal of Civil
Engineering, Vol. 19, Issue 6, 2015, pp. 16201625.
[12] Dilrukshi R. A. N., Nakashima K. and KawasakiS., Soil improvement using plant-derived
urease-induced calcium carbonate precipitation,
Soils and foundations, Vol. 58, Issue 4, 2018, pp.
894-910.
[13] Javadi N., Khodadadi H., Hamdan N. andKavazanjian Jr E., EICP treatment of soil byusing urease enzyme extracted from watermelon
seeds, in Proc. Int. Foundation Congress andEquipment Exposition, 2018, pp. 115-124.
[14] Kerr P. S., Blevins D. G., Rapp B. J. and Randall
D. D., Soybean leaf urease: comparison withseed urease, Physiologia plantarum, Vol. 57,
Issue 3, 1983, pp. 339-345.
[15] Kumar S. and Kayastha A. M., Soybean (Glycinemax) urease: Significance of sulfhydryl groups
in urea catalysis, Plant Physiology andBiochemistry, Vol. 48, Issue 9, 2010, pp. 746750.
[16] Whiffin, V. S., Microbial CaCO3 precipitation
for the production of biocement (Doctoral
dissertation, Murdoch University), 2004.
[17] ASTM Committee D-18 on Soil and Rock,Standard Practice for Classification of Soils forEngineering
Purposes(Unified
Soil
Classification System) 1, ASTM international,
2017.
[18] Kumar S., Dwevedi A. and Kayastha A. M.,
Immobilization of soybean (Glycine max)urease on alginate and chitosan beads showing
improved stability: Analytical applications,
Journal of Molecular Catalysis B: Enzymatic,Vol. 58, Issue 1-4, 2009, pp. 138-145.
[19] Van der Schoot B. H. and Bergveld P., The pHstatic enzyme sensor: An ISFET-based enzyme
sensor, insensitive to the buffer capacity of thesample, Analytica chimica acta, Vol. 199, 1987,
pp. 157-160.
[20] Hamdan N. and Kavazanjian Jr E., Enzymeinduced carbonate mineral precipitation for
fugitive dust control, Géotechnique, Vol. 66,
Issue 7, 2016, pp. 546-555.
[21] Chandra A. and Ravi K., Application of EnzymeInduced Carbonate Precipitation (EICP) to
Improve the Shear Strength of Different Type ofSoils,
In
Problematic
Soils
and
Geoenvironmental Concerns, Vol. 88, 2021, pp.
617-632.

REFERENCES[1] Mitchell J. K. and Santamarina J. C., Biologicalconsiderations in geotechnical engineering,
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[2] Van Paassen L. A., Daza C. M., Staal M., Sorokin
D. Y., van der Zon W. and van Loosdrecht M.
C., Potential soil reinforcement by biologicaldenitrification, Ecological Engineering, Vol. 36,
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[3] DeJong J. T., Mortensen B. M., Martinez B. C.
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[4] Hamed Khodadadi T., Kavazanjian E., vanPaassen L. and DeJong J., Bio-grout materials:
A review, in Proc. Grouting, 2017, pp. 1-12.
[5] Hamdan N., Kavazanjian Jr E. and O’Donnell S.,
Carbonate cementation via plant derived urease,in Proceedings of the 18th International
Conference on Soil Mechanics and GeotechnicalEngineering, 2013, pp. 2-6.
[6] Kavazanjian E. and Hamdan N., Enzyme induced
carbonate precipitation (EICP) columns forground improvement, in Proc. Int. Foundation
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[7] Neupane D., Yasuhara H., Kinoshita N. and Unno
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and Pedro A. M., Improvement of a sandy soil
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68

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

STRENGTH AND PERMEABILITY CHARACTERISTICS OFEXPANSIVE SOIL WITH GYPSUM AND RICE HUSK ASH USED FOR
ROADWAY CONSTRUCTION
Angelo B. Edora1 and Mary Ann Q. Adajar1
1

Gokongwei College of Engineering, De La Salle University, Philippines
ABSTRACTThe presence of expansive soils in the site poses threats not only for built residential houses and superstructures
but also for roadway construction. The shrink-swell behavior of expansive soils creates a continuous strain on the
pavements which can result in cracking and settlements. Therefore, there is a need to stabilize such problematicsoils. This study aims to establish a more economic and environmental-friendly way of stabilizing clay loam from
Kauswagan, Lanao del Norte. Varying combinations of gypsum and rice husk ash (RHA) were used in soil
stabilization. Untreated and treated soil specimens were tested for their California Bearing Ratio (CBR) andcoefficient of permeability per ASTM Standard D1883 and D2434 respectively. The CBR of the soil slightly
increased with the addition of 15% gypsum. While the addition of 10% RHA with the same amount of gypsum
dramatically increased the soaked CBR index of the soil. A peak average value of 21.11% was recorded for 15%
gypsum + 10% RHA soil specimen. Falling head permeability tests showed that as the amount of gypsum in
gypsum + 10% RHA soil specimen increases, the coefficient of permeability of the soil also increases. A direct
relationship was then formed between strength and permeability. This can be attributed to the formation of
micropores in the soil specimen with the addition of 10% RHA, allowing an easier flow of water through it. The
stabilized soil passed the DPWH Standard for the subgrade layer but slightly fell short of the requirement for thesubbase layer.
Keywords: Expansive soil, Roadway construction, CBR, Permeability, Soil stability

INTRODUCTION

soil stabilization. This is done by altering several
properties of the soil by incorporating additives, toimprove the necessary parameters for engineering
purposes. Cementitious materials such as cement andlime are the typical characteristics of additives used.
However, the use of cement in soil stabilization
contributes to the rising carbon footprint ofconstruction. Therefore, the use of waste materials
exhibiting the same properties is the focus of research
nowadays to promote sustainable construction.
Gypsum is known for its application in the flash
setting of cement however, the larger percentage ofuse of gypsum is in the production of plasterboards.
In Japan, the sudden rise in construction demand
increases the consumption and disposal of excess andunderqualityplasterboards
which
poses
environmental problems in their country [3]. With
this, an opportunity to use it as a soil stabilization
additive was established by past studies [4],[5]. It was
found that gypsum combined with rice husk ash(RHA) was able to improve the strength properties
and control the expansion of expansive soils [5].
Agricultural wastes such as RHA were also usedin soil stabilization techniques because of theirabundance. In the Philippines, the top ten riceproducing provinces produced nearly about 9 million
metric tons of rice per year which can also be
associated with the huge amount of rice husk waste.
The viability of RHA in the field of soil stabilization

Expansive soils are characterized for their drastic
volume change depending on the water present inthem [1]. This behavior is attributed to the mostdetrimental clay mineral in expansive soils which is
the montmorillonite. Expansive soils exhibit threecharacteristics that are considered problematic inconstruction – expansiveness, crack, and overconsolidation. These characteristics are highly
dependent on the amount of water or moisture in thesoil and may manifest during dry-wet cycles [2].
Excessive expansion and shrinking of these soils
produce continuous ground movement which causes
multiple damages to the overlying structure. Also, thepropagation of cracks in the soil reduces its overall
integrity and strength and increases its sliding ability.
Another thing to consider is the over-consolidation
characteristics of these soils which hinder thedrainage of tightly packed particles of soil. In solving
these, soil stabilization techniques are implemented to
improve the properties of the soil and make it moresuitable for its served purpose. The use of heavyequipment and mechanically stabilizing the soil onsite are the usual ways of improving the strength
properties of soil however, these practices havevarious negative impacts on the environment and are
not economically possible for all instances. One of themore economical ways of stabilizing soils is chemical
69

GEOMATE – Kyoto, Japan, 3-5 November 2021

was proven by past studies [6],[7]. It was found that
RHA has pozzolanic properties due to its silica andaluminum content. This characteristic is important in
the development of the strength property of soils.
The main objective of this study is to determine
the optimum mix proportions of waste gypsum andRHA that will yield the maximum values of thestrength property and permeability acceptable forroadway embankment application. In determining theimprovement on the strength property of the soil,
CBR tests are done using untreated and treated soil
specimens. On the other hand, falling head
permeability tests are done in determining the mix
proportion of gypsum and RHA that yields the peakvalue for the coefficient of permeability. Empiricalmodels are then formulated to describe therelationship of soaked CBR index and coefficient of
permeability with the amount gypsum in a mixed
proportion.
With the implementation of the ‘Build, build,build’ program of the government, the demand forconstruction materials and the waste generated
including gypsum increases. And considering theeffect of gypsum on the environment, this increase ina waste generation may cause environmental
problems in the future [8]. Considering the country’s
rice production, using RHA in stabilizing soils is
ideal. This promotes a more sustainable, economical,
and eco-friendly way of improving the geotechnicalproperties of soils.

Fig. 1 Soil sample formed in big clumps.

Fig. 2 Crack propagation in Kauswagan soil sample.

MATERIAL AND METHODS
Source of the Expansive Soil Sample
The soil sample used in this study was extracted
from Kauswagan, Lanao del Norte, Philippines. The
sample was extracted at a depth of at least 2 meters
ensuring that no organic matter will be included. The
area was used for a proposed landfill site. Whiledealing with moist samples, color streaks and some ofthe soil were left when forming bigger clumps of soil
as seen in Figure 1. These traits were attributed to clay
minerals in soils [9]. On the other hand, the soil
exhibited cracks and loss in total volume when ovendried for 24 hours at 105 degrees Celsius as can be in
Figure 2.

Fig. 3 Recycled powdered gypsum used in the study.
Source of Rice Husk Ash

Source of Gypsum

The rice husk ash used in this study was collectedfrom a biomass powerplant in Muntinlupa City,
Metro Manila. Huge hauls of rice husk from theCentral Luzon provinces were collected and brought
to the said powerplant to incinerate. The resulting
product of the said incineration process was the ricehusk ash. Figure 4 shows the rice husk ash used in thisstudy.

The gypsum used in this study was sourced froma hardware store, selling it as plaster. This form of
gypsum was grounded to powder and heated in
production plants at a temperature depending on theirapplication. Typically, gypsum wastes are collectedfrom plasterboard excess and rejects during
production. These wastes are then recycled byrecycling companies and repurposing for plaster andtile grout used. Figure 3 shows the gypsum used.
70

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 3 Summary of Subbase Requirements
Subbase Course
Laboratory Test
Requirement
Grain Size Distribution Percent passing no. 200
sieve shall not begreater than two-thirdsof the percent passing
no. 40 sieve
Atterberg Limits
Percent passing no. 40
sieve shall have PI <12 and LL < 35
Fig. 4 Rice husk ash sample used in the study.

CBR Test

EXPERIMENTAL PROGRAMTEST RESULTS

Considering the objectives of the study, theessential parameters to be determined are the soaked
CBR index and coefficient of permeability of theuntreated and treated soil specimens. But before
conducting the main experimental program,
preliminary experiments per ASTM Standards must
be done. These experiments are summarized in Table
1.

Grain Size Analysis
The soil in this study was tested for its grain size
distribution and the average percent passing sieve no.
200 is 59.14% [5]. Based on the results of tests done,
all mix proportions passed the grading requirement
for subgrade materials. The particles of treated soil
samples have a range of 1.28% to 5.54% passing no.
200 sieve. Moreover, upon calculating the two-thirdsof percent passing no. 40 sieve and comparing it to
the percent passing of no. 200 sieve, all treatedsamples passed the grading requirements of subbase
layer. Figure 5 shows the grain size distribution curveof 15% gypsum + 10% RHA + soil mix proportion.

Table 1 Preliminary Experiments
Laboratory Test
Grain Size Analysis
Moisture-Density RelationshipMaximum Index Density
Minimum Index Density

Soaked CBR values
not less than 30%

ASTM Standard
ASTM D422
ASTM D698
ASTM D4253
ASTM D4254

Using the results of these tests, the soaked CBRindex and coefficient of permeability of all the soil
specimens were analyzed and compared. The
parameters obtained in these tests were also used todetermine if the soil passed the requirements
mandated by the Department of Public Works andHighways (DPWH). Table 2 and Table 3 summarize
the requirements for the subgrade and subbase layer.

Percent Passing, %

100
80
60
40
20
0
10.000

Table 2 Summary of Subgrade Requirements
Subgrade course
Laboratory Test
Requirement
Grain Size Analysis
All particles shall pass
75 mm square
openings and not morethan 15% passing no.
200 sieve
Atterberg Limits
Plasticity Index of not
more than 6. Liquid
Limit of not more than
30.

1.000
0.100
0.010
Grain Size, mm (log scale)Fig. 5 Grain Size Distribution Curve of Kauswagan
Soil + 15% Gypsum + 10% RHA.
Moisture-Density Relationship
Table 4 summarizes the results of Proctor
compaction tests conducted for each mix proportion.
Based on the results, it is noticeable that there is anincrease in the maximum dry density (MDD) and adecrease in optimum moisture content (OMC) as
gypsum is added to the soil sample. The same trend is71

GEOMATE – Kyoto, Japan, 3-5 November 2021

true as a constant value of 10% RHA is introduced in
the specimen. Typically, the compaction curves ofclays are bell-shaped [10]. This can be observed in thecompaction curve of plain soil and soil with 15%
gypsum specimens. On the other hand, the addition of
RHA in the soil specimens induced the shifting ofcompaction curves to the left. This kind ofcompaction curve is common to silt with sand [11].
The change in the trend of the compaction curves
proves the effectivity of the admixtures used. Better
visualization of the comparison of curves is shown in
Figure 6.

all trials for each mix proportion were adopted. Using
these values, the masses of each mix proportion to be
used in the permeability test were determined. Table
5 summarizes the resulting values for emax and emin.
Table 5 Maximum and Minimum index densities ofeach Soil Mix Proportion

Table 4 Average Optimum Moisture Content andMaximum Dry Density of each Soil Mix Proportion
Material

OMC(%)
42.71937.37434.435MDD(kN/m3)11.77212.681
13.173

10% Gypsum + 10% RHA +80% Soil

32.19013.247

15% Gypsum + 10% RHA +75% Soil

31.496

13.695

Kauswagan Soil
15% Gypsum + 85% Soil
5% Gypsum + 10% RHA +85% Soil

Material
Kauswagan Soil

emax
2.554

emin
1.616

15% Gypsum + 85% Soil
5% Gypsum + 10% RHA +85% Soil

2.529
2.515

1.727
1.736

10% Gypsum + 10% RHA +80% Soil

2.484

1.754

15% Gypsum + 10% RHA +75% Soil

2.476

1.692

California Bearing Ratio Test
In assessing the effectiveness of the additives usedin this study to stabilize expansive soils, one
important factor to consider is its effect on thestrength of the soil. In this study, the bearing strengthsof the untreated and treated soil specimens were
determined using the soaked CBR test per ASTM
D1883 which is the standard test method for
identifying the CBR value of soils. The soil
specimens were soaked for 96 hours after compacting
them in the CBR mold. Table 6 summarizes theaverage soaked CBR values of the soil specimens at
2.54 mm and 5.08 mm penetrations.
Table 6 Average Soaked CBR Values (%) of eachspecimen at 2.54 mm and 5.08 mm penetration
Material

Fig. 6 Comparison of moisture-density curves of
untreated and treated soil sample.
Maximum and Minimum Index Density Test
The maximum and minimum index density tests
are used to determine the state of compactness of thesoil specimen. From the calculated maximum andminimum index densities, the relative density of thespecimen is determined. In this study, a constantvalue of 90% relative density was used. This is the
maximum relative density that can be achieved bymanual tamping [11]. The resulting maximum value
for emax and the resulting minimum value for emin in

Kauswagan Soil

CBR @2.54 mm
(%)
1.46

CBR @
5.08 mm(%)
1.76

15% Gypsum + 85% Soil

2.74

2.97

5% Gypsum + 10% RHA+ 85% Soil

6.88

7.32

10% Gypsum + 10%
RHA + 80% Soil

10.72

11.13

15% Gypsum + 10%
RHA + 75% Soil

20.43

21.11

Based on the results, the recorded soaked CBRvalue for pure Kauswagan soil is 1.76%. This value isconsidered extremely low for roadway construction
but is completely normal for soils classified as MH
72

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 7 Average Coefficient of Permeability of eachsoil specimen(heavy silt). Soaked CBR values ranging from 1.35%
to 2.20% were recorded for soils classified under MH(heavy silt) [12]. While the addition of 15% gypsum
in the soil specimen yields a 69% increase in thesoaked CBR value of the soil, yet it is still
considerably low to pass the roadway construction
requirements. On the other hand, the introduction ofa constant amount of 10% RHA in the soil specimen
dramatically increases the strength of the soil. At 15%
Gypsum + 10% RHA sample, which yields the peakaverage soaked CBR value of 21.11%, a 600%
increase in the soaked CBR value compared to its
gypsum-only soil mixture counterpart, was recorded.
The bearing strength improvement of 15%
Gypsum+10% RHA soil specimens can be used for
subgrade fill if it will also allow good drainage forwater.

Material
Kauswagan Soil

Permeability, cm/s
𝟑. 𝟗𝟓𝟖 × 𝟏𝟎−𝟒

15% Gypsum + 85% Soil
5% Gypsum + 10% RHA+ 85% Soil

𝟏. 𝟓𝟗𝟗 × 𝟏𝟎−𝟑
𝟖. 𝟗𝟒𝟑 × 𝟏𝟎−𝟒

10% Gypsum + 10% RHA+ 80% Soil

𝟐. 𝟑𝟎𝟖 × 𝟏𝟎−𝟑

15% Gypsum + 10% RHA+ 75% Soil

𝟐. 𝟗𝟔𝟎 × 𝟏𝟎−𝟑

Based on the results, Kauswagan soil recorded acoefficient of permeability value of 3.958 ×
10−4 cm/s which is classified as poor in the table ofdrainage characteristics of soils [13]. The addition of
15% Gypsum in the soil yields a significant increasein the k value and improved the drainage capacity ofsoil into good. Further increase in the permeability
was recorded for gypsum +10% RHA soil specimens.
As the amount of gypsum increases, the coefficient of
permeability also increases. A peak value of 3.114 ×
10−3 cm/s and an average value of 2.960 × 10−3 wasattained by 15% Gypsum+10% RHA soil specimens
which are considered as good drainage capacity. Thisis 647.85% greater than the pure soil specimen and85.11% greater than the gypsum-only soil specimen
counterpart.

Fig. 7 Correlation between the gypsum content andsoaked CBR index at 5.08 mm penetration for both
Gypsum only and Gypsum+10% RHA samples.
In calculating for the CBR index of Gypsum+10%
RHA soil specimens at 5.08 mm penetration, Eq. 1
can be used:2
CBR = 1501 𝑋𝑔𝑦𝑝𝑠𝑢𝑚− 116.45𝑋𝑔𝑦𝑝𝑠𝑢𝑚 + 7.76 (1)Where:CBR – soaked CBR index at 5.08 mm penetration
Xgypsum – the amount of gypsum in decimal form

Falling Head Permeability Test

Fig. 8 Correlation between the gypsum content andCoefficient of Permeability for both Gypsum only
and Gypsum + 10% RHA samples.

The coefficient of permeability, k, of the treatedand untreated soil specimens were determined using
a rigid wall permeameter with an internal diameter of
6.305 cm and a specimen height of 14.6 cm. The
condition of the permeability tests was falling head at
a constant minimum hydraulic gradient of 1.5.
Moreover, a constant relative density of 90% wasachieved in all test specimens as it was the highest
value attainable by manual thumping [11]. The
coefficient of permeability, k, was calculated using
Darcy’s Law. Table 7 shows the summary of thecoefficient of permeability of the untreated andtreated soil specimens.

In calculating the coefficient of permeability ofGypsum + 10% RHA soil specimens, Eq. 2 can beused:
k = 20.555𝑋𝑔𝑦𝑝𝑠𝑢𝑚(2)
Where:
k – coefficient of permeability of Gypsum + 10%
RHA soil specimens
Xgypsum – the amount of gypsum in percentage (%)

73

GEOMATE – Kyoto, Japan, 3-5 November 2021

CONCLUSIONS

the procurement of materials.

Based on this study, the following conclusions canbe drawn:
Physical properties of the soil were described.
Color streaks of the soil were left when molded into
bigger clumps. When dried, visible cracks and loss involume in the soil were also observed. Based on the
Unified Soil Classification System (USCS),
Kauswagan soil is classified as MH (high plasticitysilt). These characteristics proved that the soil is
expansive.
For the bearing capacity, pure soil has a very low
soaked CBR value of 1.76%. The addition of 15%
gypsum in the soil resulted in a 69% increase in thesoaked CBR index but still considerably low to pass
the roadway construction requirements. On the other
hand, the introduction of 10% RHA in the mix
proportion dramatically increased the bearing
capacity of Kauswagan soil. An increasing soaked
CBR index trend was observed as the number ofgypsum increases in the mix proportion. A peakaverage value of 21.11% was recorded for 15%
Gypsum+10% RHA soil specimens. Therefore, theoptimum mix proportion in improving the strength
property of Kauswagan soil is 15% Gypsum + 10%
RHA mix.
Kauswagan soil was also tested for its coefficientof permeability, and it showed that it has a poor
drainage capacity. The addition of 15% gypsum
reduces the number of voids present in the soil but
slightly increases the permeability of the soil. This
unusual effect on the soil remains consistent as 10%
RHA was added to the soil. As the amount of gypsum
increases in Gypsum+10% RHA soil specimens, thevoid ratio and coefficient of permeability of the soil
increases. A peak value of 3.114 × 10−3 cm/s and an
average value of 2.960 × 10−3 was attained by 15%
Gypsum+10% RHA soil specimens which areconsidered as good drainage capacity. Therefore, theoptimum mix proportion in improving thepermeability characteristics of Kauswagan soil is
15% Gypsum + 10% RHA. The addition of RHA in
the mix proportion, which is bigger than the soil,
reduced the cohesion among soil particles which
creates micropores in the soil specimen
Considering the improvement of the strength andpermeability characteristics of Kauswagan soil due tothe combination of gypsum and RHA, the soil isrecommended for subgrade use.

REFERENCES[1] Dang, L., Hasan, H., Fatahi, B., Jones, R., &Khabbaz, H., Enhancing the Engineering
Properties of Expansive Soil Using Bagasse Ashand Hydrated Lime. International Journal of
GEOMATE, Vol. 11, Issue 25, 2016, pp. 24472454.
[2] Shi, B., Zheng, C., & Wu, J., Research Progress
on Expansive Soil Cracks under Changing
Environment. The Scientific World Journal, 2014.
[3] Ahmed, A., Ugai, K., & Kamei, T., Investigationof recycled gypsum in conjunction with waste
plastic trays for ground improvement.
Construction and Building Materials, Volume 25,
pp. 208-217.
[4] Ahmed, A., Ugai, K., & Kamei, T., Laboratory
and Field Evaluations of Recycled Gypsum as a
Stabilizer Agent in Embankment Construction.
Soils and Foundations, Volume 51, Issue 6, 2011,
pp. 975-990.
[5] Tan, J. F., & Adajar, M. A. Q., Recycled Gypsum
and Rice Husk Ash as Additives in the
Stabilization of Expansive Soil. International
Journal of GEOMATE, Volume 18, Issue 70,
2020, pp. 197-202.
[6] Behak, L., Soil Stabilization with Rice Husk Ash.
Rice Technology and Production, 2017, pp. 31-45.
[7] Yadav, A. K., Gaurav, K., Kishor, R., & Suman,
S. K., Stabilization of alluvial soil for subgradeusing Rice Husk Ash, Sugarcane Bagasse Ash,
and Cow Dung Ash for Rural Roads. International
Journal of Pavement Research and Technology,
Volume 10, 2017, pp. 254-261.
[8] Claisse, P. A., & Ganjian, E., Gypsum: Prospects
for Recycling. Proceedings of the ICE –Construction Materials, Volume 159, Issue 1,
2006, pp. 3-4.
[9] Finch, H. J. S., Samuel, A. M., & Lane, G. P. F.,
Lockhart & Wiseman’s Crop Husbandry
Including Grassland, 9th ed., Woodhead
Publishing, 2014.
[10] Budhu, M., Soil Mechanics and Foundations, 3rded., John Wiley & Sons, Inc., 2010.
[11] Dungca, J. R., & Jao, J. L., Strength andpermeability characteristics of road base materials
blended with fly ash and bottom ash. International
Journal of GEOMATE, Volume 12, Issue 31,
2017, pp. 9-15.
[12] Nagaraj, H. B., & Suresh, M, Influence of clay
mineralogy on the relationship of CBR of finegrained soils with their index and engineering
properties. Transportation Geotechnics, Volume
15, 2018, pp. 29-38[13] Liu, C., & Evett, J. B., Soil Properties: Testing,
Measurement, and Evaluation, 6th ed., Pearson
Education, Inc., 2009.

ACKNOWLEDGMENTS
The authors would like to express their deepest
gratitude to DOST-ERDTR for providing thenecessary resources to complete this research, ABC
Infrageoservices, and to Engr. Jonathan Tiongson for74

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

OPTIMIZATION OF THE STRENGTH PROPERTIES OF EXPANSIVE
SOIL STABILIZED WITH AGRICULTURAL WASTES
Mary Ann Q. Adajar1 and Kigia R. Valbuena1
1

De La Salle University, Manila, Philippines
ABSTRACT

Expansive soils are problematic soils that exhibit shrink-swell behavior. Previous research has shown that 20%
rice husk ash (RHA) effectively reduces the swelling potential of expansive soils, but it does not increase thestrength of the soil. An improved admixture composed of RHA and a binder is used to treat the expansive soil.
Different types of binders are used; three of which are agricultural wastes namely coconut shell ash (CSA), ricestraw ash (RSA), and sugarcane bagasse ash (SCBA). Ordinary Portland cement (OPC) is used as a comparison
for the other binders. The untreated and treated soil mixtures were evaluated mainly through the unconfinedcompressive strength (UCS) and expansion index (EI). The effectivity of the admixture is evaluated through
ASTM D4609. The treated specimens exhibited an increase in the strength by as much as 745.38 kPa, 859.90 kPa,
799.46 kPa, 2428.31 kPa for the mixtures with CSA, RSA, SCBA, and cement, respectively. A response surface
methodology was performed for the UCS of the soil mixtures with the binder types CSA, RSA, and SCBA. The
binder content and curing period are the numerical factors and the response is the UCS. Contour plots and response
surface plots show that the optimum strength for the mixtures with CSA, RSA, and SCBA is at the highest curing
period (35 days) and lowest binder content (5% content). The binder type that has the highest maximum value forthe predicted response is the RSA, therefore, the optimum mixture is the soil with 5% RSA.
Keywords: Expansive soils, Agricultural wastes, Unconfined compressive strength, Expansion index, Response
surface methodology

INTRODUCTION

materials can replace cement even up to 70% whileproviding environmentally safe, sound, and low-cost
structures [4]. RHA has already been found to be
successful in mitigating the swelling potential of
expansive soils; however, it does not increase thestrength of the soil alone [5]. A binding agent should
be used together with RHA to improve its quality andefficiency as a soil stabilizer.
Agricultural wastes are pozzolanic, making themgood supplementary cementitious materials [6]. In thePhilippines, large quantities of agricultural wastes areabandoned yearly, creating a pollution problem. Ricehusk, sugarcane bagasse, and coconut shells produced
more than three million metric tons, over five million
metric tons, and more than two million metric tons ofwaste biomass from 2010-2011, respectively. Ricestraw produced a staggering amount of over sixteen
million metric tons during the same period [7]. The
environmental burden caused by agricultural wastes
can be reduced by using them as admixtures.
There is great potential for using agricultural
wastes as binders particularly CSA, RSA, and SCBA
due to their pozzolanic characteristic [8]-[10].
Coconut shells subjected to uncontrolled combustion
to produce CSA were found to be a suitable partial
replacement for cement in the production of concrete[8]. RSA satisfies the minimum requirements of a
pozzolana according to the ASTM standards, making
it a good replacement for cement [9]. SCBA has ahigh percentage of silica which allows it to have a

Expansive soils are clay soils that shrink and swell,
exhibiting an extreme change in volume. The
damaging effects of expansive soils are exacerbatedin fluctuating climates such as the Philippines.
Temperature changes trigger the shrink-swellphenomena in the soil that will lead to the failure of
the soil and ultimately the structure above it.
Expansive soils are prevalent in the Philippines dueto the extensive deposition of volcanic ash [1]. The
Philippines is also abundant in fertile soils, among
which include smectite type of clays, which are verymuch prone to shrinking and swelling upon drying
and wetting [2]. Expansive soils can cause extensive
damage to structures if undetected and overlooked
before construction. It is stipulated in the National
Structural Code of the Philippines (NSCP) that where
expansive soils are present under foundations, theyshould be removed or stabilized around and beneaththe structure [3].
There are already numerous existing mechanicalor chemical solutions for expansive soils. However,these are not always practical or economical.
Removal and replacement of expansive soils can also
be done but it is not always preferred due to its highcost. A sustainable and more cost-effective
alternative would be to make use of admixtures
composed of agricultural waste that havecementitious properties. It has been found that waste
75

GEOMATE – Kyoto, Japan, 3-5 November 2021

binding property. In a study, an increase in thestrength of expansive soil was observed when SCBA
and lime were used. Further, the addition of SCBA tolime is more effective than lime alone in decreasing
the swelling potential of the soil [10].
The main objective of this study is to determine
the optimum amount of admixture in treating
expansive soils under dry curing conditions that will
reduce the swelling potential and produce themaximum strength of the soil. The RHA-soil mixture
for controlling soil expansion will be enhanced byadding different proportions of CSA, SCBA, and
RSA to find the optimum mix design. The effectivityof the admixture is evaluated using the Expansion
Index (EI), Atterberg limits, Maximum Dry Density(MDD), Optimum Moisture Content (OMC), andUnconfined Compressive Strength (UCS). The
response surface methodology (RSM) was used to
optimize the response of interest, the UCS, which is
influenced by the type of binder, binder content, andthe curing period.

moisture-density relationship. The effectiveness of
the admixture is evaluated through ASTM D4609.
The indicators for improvement are in Table 3.
Table 2 Laboratory Tests
Experimental Program
Specific Gravity
Grain Size Analysis
Atterberg Limits
Moisture-Density
Relationship
Expansion Index
Unconfined Compressive
Strength

ASTM D698
ASTM D4829
ASTM D2166

Table 3 Criteria to Gauge the Effectiveness of
Admixture (ASTM D4609)
Property
Liquid Limit
Plasticity Index
Maximum Dry
Density
Optimum Moisture
Content
UnconfinedCompressive
Strength

MATERIALS AND METHODS
Material Sourcing
The expansive soil was obtained from Kauswagan,
Lanao del Norte. It was already used in a previous
study that confirmed its shrink-swell tendencies [11].
RHA was obtained from Restored Energy
Development Corporation in Muntinlupa City. CSA
was produced in Morong, Bataan. RSA was sourced
from local rice farmers in Anao, Mexico, Pampanga.
SCBA was bought from a commercial establishment.

Indicator for Improvement
Significant reduction
Significant reduction
Increase by more than 80
kg/m3
Decrease by more than
15%
Increase by 345 kPa or
more

Mixture Preparation
Dry mix proportioning made use of thepreliminary data. The soil mixtures were created byreplacing a percentage of the untreated soil’s volume
with RHA and binder. The RHA content is always
20%; the binder content ranges from 5% to 20% inincrement of 5%. Twenty percent RHA is used in thisstudy as the stabilizer because it is the recommended
value to effectively reduce the swelling potential of
expansive soil [5].
The dry mixture was mixed with water at
optimum moisture content to make the total soil
mixture for the EI test and the UCS test. For the UCS
test, there were five specimens prepared for eachvariation of the soil mixture.
The samples were placed in airtight containers tocure for at least 16 hours. The cured samples werecompacted into molds then extruded. Immediately
after casting, the specimens were sealed with plastic
wrap for at least seven days. The specimens were
unsealed after seven days and subjected to the drying
curing condition. The specimens were cured up to 14,
21, 28 and 35 days from casting.

Experimental Procedure
The index properties of the untreated soil wereobtained for soil classification. The index properties
were evaluated using the NSCP specifications for
expansive soil in Table 1 to determine the potential
expansion of the soil.
Table 1 NSCP Specifications for Expansive Soil [3]Property
Percent Passing No. 200
Sieve (%)
Percent Passing 5 μm (%)
Liquid Limit, LL (%)
Plasticity Index, PI (%)
Expansion Index, EI

Test Standard
ASTM D854
ASTM D422
ASTM D4318

Required Value
>10%
>10%
>50%
>15%
>20

Standard procedures from ASTM were followed
for all of the laboratory tests shown in Table 2. These
tests were performed on both untreated and treatedsamples. The preliminary data includes the specificgravity, grain size analysis, Atterberg limits, and the76

GEOMATE – Kyoto, Japan, 3-5 November 2021

TEST RESULTS

plasticity of the soil [12]. On the other hand, mostmixtures with CSA and SCBA have values forplasticity index higher than the untreated soil. In a
study that used coconut shell powder, the plasticity
index also increased as the additive increased [13]. In
another study that used SCBA, the liquid limit andplasticity index decreased as the additive increased[14]. This difference in the trend observed could be
because the SCBA used in this study was not burnedin a controlled condition. Overall, the RSA andcement improved the soil while the CSA and SCBA
did not.

Soil Classification
The index properties of the soil used in this study
met most of the criteria set by the NSCP for expansive
soils as shown in Table 4. The soil is classified as
expansive because of its expansion index that is thegoverning property to classify expansive soils. Its
expansion index value of 98 indicates that it has highexpansion potential. Based from the Unified Soil
Classification System (USCS), the soil sample iselastic silt (MH).

Table 5 Summary of Results for Atterberg Limits
Table 4 Summary of Soil Classification
Property
Percent
Passing No.
200 Sieve (%)
Percent
Passing 5 μm(%)
Liquid Limit,
LL (%)
Plasticity
Index, PI (%)
Expansion
Index, EI

Required
Value

ResultRemarks

>10%

54.82

Pass

>10%

N/A

Fail

>50%

76.93

Pass

>15%

32.85

Pass

>20

98

Pass

BinderContent
0%5% CSA
10% CSA
15% CSA
20% CSA
5% RSA
10% RSA
15% RSA
20% RSA
5% SCBA
10% SCBA
15% SCBA
20% SCBA
5% OPC
10% OPC
15% OPC
20% OPC

Evaluation of Effectiveness of Admixture
Atterberg Limits
The behavior of the soil under varying moisture
contents can be determined using the Atterberg limits.
According to ASTM D4609, an admixture is effective
when there is a significant reduction in the liquid limit
and the plasticity index. The summary of the test
results for the Atterberg limits is shown in Table 5.
The liquid limit and the plasticity index values ofthe mixtures are generally decreasing. The reductionin the liquid limit and plasticity index is due to thereplacement of clay particles with non-plastic
materials.
The liquid limit dropped by as much as 7.23% and17.77% for the mixtures with RSA and cement,
respectively. Conversely, it could be observed that the
mixtures containing CSA and SCBA have liquid limit
values significantly higher compared to the untreatedsoil. The microstructure of the CSA and SCBA
allowed more water to be absorbed.
The plasticity index was reduced by as much as
11.89% when RSA acted as the binder. Cement
mixtures also show a reduction by as much as 39.73%
in the plasticity index. A study that used cement and
RHA as additives also saw a reduction in the

LiquidLimit,
LL (%)
77
93
83
83
82
75
75
74
72
81
80
79
77
74
72
67
65

Plastic
Limit,PL (%)
44
49
46
47
45
46
45
45
42
45
46
43
47
46
47
43
41

Plasticity
Index,PI (%)
33
44
37
36
37
29
29
30
30
36
34
36
30
27
25
24
24

Moisture-Density Relationship
Most soils show a relationship between theirmoisture content and dry density when placed undera compactive effort. The optimum moisture content(OMC) and maximum dry density (MDD) are
performance indicators of the admixture as per
ASTM D4609. Table 6 shows the summary of theresults obtained through the Standard Proctor Test.
The reduction in the MDD observed for all
mixtures could be attributed to the lower specificgravity of the stabilizer and binder compared to thesoil. The RHA used in this study has a specific gravityof 1.555 [5]. The CSA, RSA, and SCBA have specificgravity values of 1.154, 1.844, and 1.544,
respectively, while the soil has a specific gravity of
2.713.
The OMC of the soil increased by 2.24% to34.23% when incorporated with the stabilizer anddifferent types of a binder. The increase in the OMC
77

GEOMATE – Kyoto, Japan, 3-5 November 2021

is attributed to the porosity of the RHA [12]. The
presence of 20% RHA in all the mixtures greatly
affected the results of the moisture-density
relationship test. Overall, none of the binders showed
an improvement in the compaction characteristics ofthe soil.

content to achieve a strength that may not always be
as high. OPC is not an ideal binder for expansive soils
not only for being uneconomical but also because of
its poor and inconsistent performance when subjectedto the dry curing condition.

Table 6 Summary of Results for Moisture-Density
Relationship

Binder Content
0%5% CSA
10% CSA
15% CSA
20% CSA
5% RSA
10% RSA
15% RSA
20% RSA
5% SCBA
10% SCBA
15% SCBA
20% SCBA
5% OPC
10% OPC
15% OPC
20% OPC

Maximum Dry
Density(kN/m3)
14.03
9.55
10.06
9.61
9.36
11.03
11.20
11.38
11.67
9.53
9.84
9.66
9.50
11.83
12.23
12.76
12.38

Optimum
Moisture
Content (%)
35.73
44.9
46.93
46.53
47.35
40.08
40.23
39.2
38.85
47.96
46.94
48.57
47.76
30.58
36.53
37.31
37.88

Fig. 1

Increase in strength after 35 days

Expansion Index
With the addition of the stabilizer and binder, thepotential expansion of the mixtures became very low.
The EI of the mixtures ranged from 0 to 2 only. This
reaffirms the effectiveness of 20% RHA as astabilizer. It also indicates that the binder had little tono effect on the expansion of the soil, even though themicrostructures of CSA, RSA, and SCBA allow morewater to be absorbed by the mixture.
Optimization of the Strength Using Response
Surface Methodology

Unconfined Compressive Strength
Most of the soil mixtures met the required
provisions set by ASTM D4609 for the indication of
improvement in strength. The treated specimens
exhibited an increase in strength by as much as745.38 kPa (227.82%), 859.90 kPa (262.82%),
799.46 kPa (187.61%), 2428.31 kPa (569.85%) forthe mixtures with CSA, RSA, SCBA, and cement,
respectively. The consistency of the specimenschanged from very stiff to hard under the drying
curing condition and with an increased curing period.
A similar study also found that the specimens under
drying conditions yielded a higher compressive
strength than that of the sealed condition due tocarbonation and suction [15].
The mixtures with cement did not show a trend forthe curing period and binder content because the drycuring condition inhibited the hydration process. The
increase in strength after the 35-day curing period isrepresented in Fig. 1. A high strength could beachieved by the soil mixtures with RSA and SCBA
with the minimum binder content while the soil
mixtures with cement require the maximum binder

The optimum mixture is chosen based on the
response surface method. The response surface
methodology was performed using the Design-Expert
software. The curing period and binder content are thenumerical factors. The minimum value of the curing
period is 14 days while the maximum is 35 days. The
minimum value for the binder content is 5% while themaximum is 20%. Cement is not considered in the
optimization because it only served as a comparison
for the other binders in terms of effectiveness. A
response surface quadratic model was produced foreach binder type. Statistical analysis revealed that themodels are significant and adequate to represent therelationship between the response and the
independent variables. There is only one response
which is the unconfined compressive strength.
Three-dimensional (3D) surface plots areproduced by the models shown in Figs. 2 to 4. The
highest strength is always achieved in the upper left
portion of the surfaces, which means that the
optimum strength is at the highest curing period (35days) and lowest binder content (5% content) for the78

GEOMATE – Kyoto, Japan, 3-5 November 2021

binder types CSA, RSA, and SCBA. The strength of
the CSA, RSA, and SCBA mixtures increases overtime due to the drying curing condition that gives the
specimens a hard consistency. Additionally, thecohesion of the particles is improved over time. The
ashes are non-cohesive and they have a
microstructure full of voids; therefore, the lowest
binder content is desirable.

The equations of the UCS generated from the
ANOVA were used to obtain the predicted maximum
value of the strength for each binder type. The
equations produced by the models are as follows:

UCSCSA = 1432.809 — 49.460(BC)— 18.361(CP) — 0.27(BC)(CP)+ 1.746(BC2 ) + 0.555( CP2 )(1)

UCSRSA = 1582.026 — 16.791(BC)— 39.551(CP) —0.695(BC)(CP)22— 0.242(BC ) + 1.385( CP )(2)

UCSSCBA = 426.154 + 9.277(BC)+ 35.513(CP) — 0.840(BC)(CP)2
2— 0.329(BC )+ 0.075( CP )(3)

Where:UCSCSA = UCS of Mixtures with CSA (kPa)UCSRSA = UCS of Mixtures with RSA (kPa)
UCSSCBA = UCS of Mixtures with SCBA (kPa)BC = Binder Content (%)
CP = Curing Period (days)

Fig. 2 Response surface plot of curing period
and binder content (CSA as binder type)

The UCS can be obtained using the final equationin terms of the actual factors. The equations for thepredicted response in terms of the actual factors wereused to compare the maximum UCS values as shownin Table 7. The values used for the factors are thosethat would produce the maximum, which is 5% for
the binder content, and 35 days for the curing period.
The binder type that has the highest maximum value
for the predicted response is the RSA, therefore, theoptimum mixture is the soil with 5% RSA.
Table 7 Maximum Values for the Predicted UCS for
Each Binder Type
Fig. 3 Response surface plot of curing period
and binder content (RSA as binder type)
Binder Type
CSA
RSA
SCBA

BinderContent(%)
55
5Curing
Period(days)
35
35
35

Predicted
UCS(kPa)
1219.120
1682.124
1651.952

CONCLUSIONSThe soil used in this study was sourced from
Kauswagan, Lanao del Norte. It is identified as an
expansive soil due to its index properties meeting thecriteria set by the NSCP for expansive soils. 20%
RHA was used as the stabilizer for the soil since it has
been proven to effectively reduce the swelling
potential of the soil. CSA, RSA, SCBA, and OPC

Fig. 4 Response surface plot of curing period
and binder content (SCBA as binder type)79

GEOMATE – Kyoto, Japan, 3-5 November 2021

were added in varying percentages from 5% to 20%
in steps of 5% as the binder.
With the addition of the stabilizer and binder, thepotential expansion of the mixtures became very low.
Only the mixtures with RSA and OPC showed
improvement in the consistency limits, and none of
the mixtures showed improvement in the compaction
characteristics. Improvement in strength wasobserved for all binder types. OPC is not an ideal
binder for expansive soils because of its poor
performance in drying conditions.
A response surface methodology was performed
for the UCS of the soil mixtures. The optimum
strength for each binder is at the lowest binder
content, and the strength increases with the curing
period. The optimum mixture is the soil with 5% RSA
because it yielded the highest maximum value for thepredicted UCS.
It is recommended to investigate the long-term
strength of the treated expansive soils and withcuring methods that will simulate the actual condition
on site.

[7] Baconguis, S. R., and Pasagdan, A. R., Waste
biomass statistics in the Philippines. Canopy
International, 2013, pp. 8-9.
[8] Adajar, M., Galupino, J., Frianeza, C., Aguilon, J.,
Sy, J., and Tan, P., Compressive Strength andDurability of Concrete with Coconut Shell Ash as
Cement Replacement. International Journal of
GEOMATE, Vol. 18, Issue 70, 2020, pp. 183-190.
[9] El-Sayed, M. A., and El-Samni, T. M., Physicaland Chemical Properties of Rice Straw Ash and
Its Effect on the Cement Paste Produced fromDifferent Cement Types. Journal of King Saud
University - Engineering Sciences, Vol. 19, Issue
1, 2006, pp. 21-29.
[10] Hasan, H., Dang, L., Khabbaz, H., Fatahi, B., andTerzaghi, S., Remediation of Expansive Soils
Using Agricultural Waste Bagasse Ash. Procedia
Engineering, Vol. 143, 2016, pp. 1368-1375.
[11] Tan, J. F., and Adajar, M. Q., Recycled Gypsum
and Rice Husk Ash as Additives in the
Stabilization of Expansive Soil. International
Journal of GEOMATE, Vol. 18, Issue 70, 2020,
pp. 197-202.
[12] Basha, E. A., Hashim, R., Mahmud, H. B., andMuntohar, A. S., Stabilization of Residual Soilwith Rice Husk Ash and Cement. Construction
and Building Materials, Vol. 19, Issue 6, 2005,
pp. 448-453.
[13] Oluwafemi, I. J., Laseinde, T. O., and
Akinwamide, J., Data Showing the Effects ofGeotechnical Properties of Lateritic Soil Mixedwith Coconut Shell Powder in Ado-Ekiti, South
Western Nigeria. Data in Brief, Vol. 24, 2019.
[14] Barasa, P. K., Too, D., Jonah K., and Mulei, S.,
Stabilization of Expansive Clay Using Lime andSugarcane Bagasse Ash. International Journal of
Science and Research, 2015, pp. 2112-2117.
[15] Ho, L. S., Nakarai, K., Ogawa, Y., Sasaki, T., andMorioka, M., Strength Development of Cementtreated Soils: Effect of Water Content,
Carbonation, and Pozzolanic Reaction under
Drying Curing Condition. Construction and
Building Materials, pp. 703-712.

ACKNOWLEDGMENTS
Thank you to my adviser, Dr. Mary Ann Q. Adajar
for the guidance throughout this research. Thank you
to Engr. Joenel G. Galupino, Engr. Miller DL. Cutora,
and Dr. Erica Elice S. Uy for answering my queries. I
would like to thank DOST-ERDT as well for thefunding.
REFERENCES[1] Morales E. and Morales M. K., Expansive Soils Identification, Detection and Remediation
Strategies,
Retrievedfrom
http://www.pgatech.com.ph, 2007.
[2] Carating, R. B., Galanta, R. G., and Bacatio, C. D.,
The Soils of the Philippines, Springer Science &Business, 2014.
[3] Association of Structural Engineers of thePhilippines (ASEP), National Structural Code of
the Philippines, 2010.
[4] Khan, R., Jabbar, A., Ahmad, I., Khan, W., Khan,A., and Mirza, J., Reduction in Environmental
Problems using Rice-husk Ash in Concrete.
Construction and Building Materials, Vol. 30,
2011, pp. 360-365.
[5] Adajar, M. Q., Aquino, C. P., Dela Cruz II, J. D.,
Martin, C. H., and Urieta, D. G., Investigating theEffectiveness of Rice Husk Ash as Stabilizing
Agent of Expansive Soil. International Journal of
GEOMATE, Vol. 16, Issue 58, 2019, pp. 33-40.
[6] Alex, J., Dhanalakshmi, J., and Ambedkar, B.,
Experimental investigation on rice husk ash as
cement replacement on concrete production.
Construction and Building Materials, Vol. 127,
2016, pp. 353-362.
80

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

DEVELOPMENT OF SITE FACTOR FOR SURFACE SPECTRALACCELERATION CALCULATION AT THE ALLUVIAL SOILS
Windu Partono1, Masyhur Irsyam2, Ramli Nazir3, Frida Kistiani1, Undayani Cita Sari1
1

Engineering Faculty, Diponegoro University, Semarang 50275, Indonesia; 2Faculty of Civil andEnvironmental Engineering, Bandung Institute of Technology, Bandung 40132, Indonesia; 3Centre of
Tropical Geoengineering, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Malaysia
ABSTRACTCalculation of surface spectral acceleration (SMS) is one of the important steps in seismic design. The SMS
value can be obtained from bedrock spectral acceleration and multiplied it with site factor. According to ASCE/SEI
07-16, for short period spectral acceleration (0.2 sec / SS) greater than 1g and long period (1 sec / S1) greater than
0.2g, site specific propagation analysis (SSA) shall be used for site factor development. SSA can be performed
using three different data, bedrock elevation, dynamic soil profile and acceleration time histories. The site factor
can be calculated by comparing the surface to bedrock spectral acceleration obtained from SSA. This paper
describes the development of site factor at the alluvial area (soft soil area) at Semarang city, Indonesia. The site
factor was calculated at 23 boring positions. Microtremor tests were conducted at this area to predict soil dynamic
profile. Five different acceleration time histories having magnitude from 6.5 Mw to 7 Mw and epicenter distanceless than 10 km were collected for SSA. The average FPGA, Fa and Fv values developed at this area were smaller
compared to the site factor calculated using 2019 Indonesian Seismic Code.
Keywords: Acceleration Time Histories, Bedrock, Microtremor, Site Factor, Site Specific Propagation Analysis

INTRODUCTION

Analysis. This paper describes 1D (one dimension)
Site Specific Propagation Analysis (1D-SSA) for
developing site factor at the study area. The analysis
was performed at 23 boring positions at the alluvial
area. Figure 1 shows the study area and the related
soil investigations performed at the study area. The
1D-SSA was performed using bedrock elevation
observed from single and array microtremor at the
study area [4]-[7]. The study was performed at thenorthern part of Semarang, Indonesia. Based on thegeological map information, the majority of soft soil
area is located at the alluvial area at the northern partof the City. Figure 2 shows the geological map of thecity.
According to the previous 2015–2020 research
conducted at the study area the bedrock elevation waspredicted at a minimum of 225 meters below thesurface level. Figure 3 shows the predicted bedrock
elevation contour map developed based on the single
station microtremor investigation. A comparative
investigation was also performed using arraymicrotremor. The investigation was performed at 5
different locations. The purpose of the arraymicrotremor investigation was to verify the bedrock
elevation at the study area. Figure 4 shows two
different results of the bedrock elevation
investigation. As can be seen in these figures the
bedrock elevation was predicted at a minimum 250
meter below the surface level. The shear wave
velocity (Vs) values were used as a parameter for
bedrock elevation measurement. Rock sample having
at least 1500 m/sec was conducted as a parameter

Site factor is one of the important values used fordevelopment surface spectral acceleration. Threedifferent site factor FPGA (Peak Ground Acceleration),
Fa (short period or 0.2 sec) and Fv (1 sec) are required
for seismic design of buildings. According toIndonesian seismic code 2019 [1] these three site
factors can be obtained using Risk-targeted
Maximum Considered Earthquake MCEG, MCER-SS
and MCER-S1, respectively. The problem of Fa andFv calculation was occurred during the developmentof new 2019 Indonesian Seismic Code. ASCE/SEI 716 [2] as one of the references used by Indonesian
Seismic Code introduced a different method fordeveloping Fa value for soft soil (SE) area having
MCER-SS greater than 1 g and for Fv value having
MCER-S1 greater than 0.2 g. Site SpecificPropagation Analysis (SSA) should be conducted fordeveloping Fa and Fv at SE (soft soil) area. Not all
Civil Engineering in Indonesia familiar with this new
method. An alternative approach for developing thesetwo values were applied following the research
conducted by [3].
Two different problems occurred during thedevelopment Site Specific Propagation Analysis atsite class SE area. The first problem related with theinvestigation of bedrock elevation and the soil profile
from bedrock elevation up to the surface. The second
problem related with the acquisition of acceleration
time histories of specific earthquake events data
which can be used for Site Specific Propagation
81

GEOMATE – Kyoto, Japan, 3-5 November 2021

used for bedrock elevation. The predicted soft-rockelevation (Vs ≥750 m/sec) or SB soil class [1], [2] ispredicted in between 100 to 250 m below the surface
level.

Due to the difficulties in development of soil
profile, a 250 meters soil model was conducted for
1D-SSA. The distribution of soil dynamic parameters
was calculated based on the empirical correlation ofVs (shear wave velocity) and NSPT (Standard
Penetration Test) and also between G (Shear
Modulus) and NSPT.

(a)Fig. 1The study area and soil investigations(boring, single microtremor and arraymicrotremor) performed at the study area(b)

Fig. 4 Predicted bedrock depth based on arraymicrotremor investigation at Array C
position
METHODOLOGY

Fig. 2Geological Map

Fig. 3

Predicted bedrock depth

The site factor FPGA, Fa and Fv development at the
study area were conducted following three basic steps,
de-aggregation analysis, response spectral matchingand 1D-SSA. The objective of de-aggregation
analysis is to find the predicted magnitude (M) anddistance (R) of earthquake scenario in terms ofacceleration time histories that can be used for 1DSSA. The second approach related with the matching
analysis of time histories collected from earthquake
event. The spectral acceleration MCEG, MCER-SS andMCER-S1 were conducted as a target spectral
acceleration used for spectral matching analysis.
These three spectral accelerations were calculated
based on Indonesian seismic code 2019. The final
steps of site factor calculation related with thedevelopment of 1D-SSA. The site factor can be
developed by comparing the surface and bedrock
spectral acceleration.
Figure 5 shows an example of de-aggregation
analysis results of three different spectral acceleration(PGA, 0.2 sec and 1 sec) of shallow crustal fault
seismic source model. The de-aggregation analysis
was performed for the whole part of the city.
According to these de-aggregation charts, theearthquake magnitude that can be used for the study
area is in between 6.5 and 7 Mw. The correlation
earthquake epicenter distance developed based on thede-aggregation analysis is in between 0-40 km. The
alluvial area within the city is located at the north sideof the Semarang fault trace [8]. The maximum
distance of the study area against the seismic source82GEOMATE – Kyoto, Japan, 3-5 November 2021

is less than 10 km. According to these seismic
earthquake scenarios, 5 (five) shallow crustal fault
earthquake time histories were collected for site
factor development. All earthquake data and time
histories were collected based on reverse fault seismic
model from Pacific Earthquake Engineering
Research ground motion database. Table 1 shows theinformation of 5 earthquake events used in this study.

and MCER-S1 calculated at boring no 15 position.
These three spectral accelerations were calculated
based on Indonesian Seismic Code 2019. Fig. 7
shows an example of two directions original
acceleration time histories of Chuetsu-Oki
earthquake data conducted at boring no 15. Fig. 8
shows the corresponding matched time histories
developed from matching spectral acceleration
analysis. The original and matched acceleration time
histories developed for all 5 acceleration time
histories were conducted at bedrock elevation.

(b)

(a)

Fig. 6

Matching spectral acceleration for 5 (twodirection) acceleration time histories atboring no 15

Fig. 7

Chuetsu-Oki original time histories

Fig. 7

Chuetsu-Oki matched time histories atboring no 15(c)Fig. 5De-aggregation analysis results of shallow
crustal fault source model for spectral 1 sec(a), spectral 0.2 sec (b) and PGA (c).

Table 1 Earthquake event and related magnitude andepicenter distance used for site factor
calculation
Earthquake Event
Nahanni (Canada, 1985)
Niigata (Japan, 2004)
Chuetsu Oki (Japan, 2007)
Northridge (USA, 2004)
San Simeon (California, 2003)

M
(Mw)
6.76
6.63
6.8
6.69
6.52

R
(km)
4.93
6.27
5.0
5.26
5.07

The final analysis for site factor calculation was
performed to obtain the surface spectral acceleration.
One soil profile model having predicted bedrock
elevation 250 meter was conducted for 1D-SSA. Fourdifferent Vs-NSPT correlation model was elaborated
for developing VS profile of each boring position.
Table 2 shows 4 different Vs-NSPT (shear wave
velocity and N standard penetration) correlation. The“a” and “b” values displayed in this table represents
correlation coefficients as displayed in Eq. 1. The
NSPT (N Standard Penetration Test) are N60 standard
penetration value. Eq. 2 shows the empiricalcorrelation of shear modulus of soil and NSPT. Three
shear modulus and NSPT correlation was applied fordevelopment of shear modulus (G) values. Table 3

The response spectral matching analysis was
conducted to 5 (North-South/270 and East-West/360)
direction ground motion data. Three Risk-targeted
Maximum Considered Earthquake (MCEG, MCERSS and MCER-S1) used as a spectral acceleration
target. Figure 6 shows matching spectral acceleration
result conducted at boring no 15. Three spectral
acceleration 0.3722 g, 0.8394 g and 0.3667 g
displayed in this figure represent MCE G, MCER-SS
83

GEOMATE – Kyoto, Japan, 3-5 November 2021

shows “c” and “d” values used for development of thesoil shear modulus.
The 1D-SSA conducted at the study area was
performed at 23 soil boring positions. The 1D-SSA
was performed to obtain the FPGA, Fa and Fv site
factor. Fig. 8 shows three example soil parameters atboring no 15. Fig 9 shows the related soil profile
developed from bedrock up to surface elevation.
𝑉𝑠 = 𝑎𝑁 𝑏(1)

𝐺 = 𝑐𝑁 𝑑(2)

The MCEG, MCER-SS and MCER-S1 calculated at
23 boring positions shown at Fig. 10. The MCEG and
MCER-S1 calculated at 23 boring positions are almost
equal, and distributed in between 0.30-0.40 g. The
MCER-SS calculated at 23 boring positions are almost
constant and distributed form 0.7 up to 0.89 g. Basedon ASCE/SEI 7-16, for MCER-SS greater than 1 g andthe building located at soft soil area (average N30 lessthan 15), the site factor Fa for spectral acceleration
0.2 sec shall be calculated using site analysis. Eventhough the MCER-SS at 23 boring positions less than
1 g, the comparative study for site factor calculation
was conducted at the study area.

Table 2 Vs and NSPT correlation parameters

Soil Surface

Correlation Model
Ohta & Goto (1978) [9]Imai &Tonouchi (1982) [10]Ohsaki & Iwasaki (1973) [11]Partono et al. (2021) [12]

a
82.1
93.7
78.0
105.98

b
0.35
0.31
0.39
0.2643

Sandy-silt and softclay
VS= 260-400 m/sec, Gmax = 37.3-220 Mpa,
g = 16.7-17.3 kN/m3

±0m
-40 m

Stiff to hard clay
VS= 400-750 m/sec, Gmax = 220 - 1300 Mpa,
g = 17.3-22 kN/m3-100 mTable 3 G and NSPT correlation parameters
Correlation Model
Ohsaki & Iwasaki (1973) [11]Imai &Tonouchi (1982) [10]Seed et al. (1983) [13]

c
11500
14070
6220

Soft rock (site soil SB)
VS= 750 m/sec, Gmax = 1300-3000 Mpa,
g = 22 kN/m3

d
0.8
0.68
1.0

-250 mBedrock
VS= 1500 m/sec, Gmax = 3000 Mpa,
g = 22 kN/m3

Fig. 9(a)Sample soil profile and parameters used for
1D-SSA at boring no 15(b)
Fig. 10 MCEG, MCER-SS and MCER-S1 calculated at
23 borehole position
The predicted bedrock elevation at this boring
position is 250 m. The thickness of each boring
investigation for all 23 boring positions are not equal.
The minimum depth of boring investigation is 30 m.
However, the maximum soil boring investigation is
60 m. Most of the soil boring investigations
performed at the study area were conducted following
the requirement for foundation design. The FPGA, Fa
and Fv site factors were calculated by comparing thesurface to bedrock peak ground acceleration andspectral acceleration at 0.2 sec and 1 sec period.
Figure 11 shows an example of bedrock and surface
spectral acceleration obtained from 1D-SSA at boring(c)Fig. 8

Soil parameters used for 1D-SSA at boring
no 15, soil density (a), shear wave velocity(b) and soil shear modulus (c)84GEOMATE – Kyoto, Japan, 3-5 November 2021

position no 15. Based on this sample 1D-SSA
analysis result, the average FPGA, Fa and Fv site factor
calculated from two directions Chuetsu Oki scenario
earthquake are 1.4646, 1.1592 and 2.8369,
respectively. The analysis was conducted using NS
and EW acceleration time histories of Chuetsu-Oki
earthquake.

factor FPGA and Fa developed using 1D-SSA andIndonesian seismic code 2019 are almost equal.
However, the Fv site factor developed using 1D-SSA
are smaller compared to the same site factor
calculated based on Indonesian seismic code 2019.
Figure 14 shows the differences of three site factors
developed using 1D-SSA and Indonesian seismiccode 2019.

RESULT AND DISCUSSIONSThe site factors developed using 1D-SSA analysis
and conducted at 23 boring positions were evaluatedand compared to the same site factor obtained fromIndonesian seismic code 2019. Fig. 12 shows thethree site factors FPGA, Fa and Fv developed based onIndonesian seismic code 2019. According to thisfigure the site factor developed at the study area arealmost constant. The FPGA site factor are distributed
in between 1.39 and 1.59. The Fa site factor obtained
at 23 boring positions are also constant and the value
are distributed in between 1.17 and 1.38. However,the Fv site factor calculated at 23 soil boring positions
are distributed in between 2.45 and 2.73. The averagevalues of FPGA, Fa and Fv calculated using Indonesian
seismic code 2019 are 1.46, 1.23 and 2.54
respectively.

Fig.12 FPGA, Fa and Fv distribution developed at
23 boring positions calculated using
Indonesian seismic code 2019

Fig.13 FPGA, Fa and Fv distribution developed at
23 boring positions calculated using 1DSSA

Fig.11 Surface and bedrock (SB) spectral
acceleration developed from 1D-SSA atboring position no 15
Fig. 14 FPGA, Fa and Fv distribution developed at
23 boring positions calculated using 1DSSA and Indonesian seismic code 2019

Fig. 13 shows the three site factors distribution
developed using 1D-SSA. All site factors developed
using 1D-SSA are not constant and the values arevaried between 0.6 and 3. The site factor FPGA are
distributed between 0.97 and 2.74. The site factor Fa
are distributed from 0.68 to 1.85. And the Fv site
factor are distributed in between 1.97 and 2.32. Inaverage the FPGA site factor developed using 1D-SSA
is greater than the Fa and Fv site factor. In average theFPGA, Fa and Fv calculated at the study area are 1.62,
1.23 and 1.48, respectively. Table 3 shows averagesite factor calculated using 1D-SSA and 5 different
seismic scenarios.
Compared to the site factor developed using
Indonesian seismic code 2019, in average the site

Table 3 Average FPGA, Fa and Fv site factor
calculated using 1D-SSA and 5 earthquake
scenarios
SA
PGA
Ss
S1

Ch
1.93
1.16
1.55

Ni
1.44
1.01
1.11

Na
1.95
1.75
1.76

No
1.37
1.15
1.40

Sa
1.38
1.09
1.57

Note: Ch: Chuetsu Oki; Ni: Niigata; Na: Nahanni; No: Northridge
and Sa: San Simeon Earthquake. SA: Spectral acceleration. PGA:
Peak Ground Acceleration. Ss and S1: spectral acceleration 0.2 sec
and 1 sec respectively

85

GEOMATE – Kyoto, Japan, 3-5 November 2021

30 m Soil Deposit Model, Jurnal Teknologi, 78:85, pp. 31-38, 2016.
[4] Sengara I. W. and Komerdevi Det, 2020, SiteSpecific Response Analysis (SSRA) and pairs ofground- motions time-history generation of a sitein Jakarta, E3S Web of Conferences 156, 03009(2020), Volume 156, 2020, 4th International
Conference on Earthquake Engineering &Disaster Mitigation (ICEEDM 2019).
[5] Nguyen V. Q., Aaqib M. , Nguyen D. D., Nguyen
V. L. and Park D., A Site-Specific Response
Analysis: A Case Study in Hanoi, Vietnam,
Applied Science, MDPI, Vol. 10 3972, 2020.
[6] Natepra P., Boonpichetvong M., Pannachet T. andChintanapakdee C., Seismic Response of
Deteriorated Residential RC Buildings in The
Northeasthern Region of Thailand, Intenational
Journal of Geomate, Vol. 9, 2020.
[7] Fathani T. F. and Wilopo W., Seismic
Microzonation Studies Considering Local Site
Effects for Yogyakarta City, Indonesia,
International Journal of Geomate, Vol. 12, 2017.
[8] National Center for Earthquake Studies,
Indonesian Seismic Sources and Seismic Hazard
Maps 2017, Centre for Research andDevelopment of Housing and Resettlement,
Ministry of Public Works and Human
Settlements), ISBN 978- 602-5489-01-3, 2017, pp.
1-377.
[9] Ohta, Y. and Goto, N., Empirical Shear Wave
Velocity Equations in Terms of Characteristic
Soil Indexes, Earthquake Engineering andStructural Dynamics, 6, pp. 167-187, 1978.
[10] Imai, T. and Tonouchi, K., Correlation of Nvalue with S-Wave Velocity and Shear Modulus,
Proc. Of second European Symposium on
PenetrationTesting, Amsterdam, The Netherlands,
pp. 67-72, 1982.
[11] Ohsaki Y. and Iwasaki R., On Dynamics Shear
Moduli and Poisson’s Ratio of Soil Deposits, Soiland Foundations, JSSMFE, Vol. 13, N.4, Dec.,
pp.59-73, 1973.
[12] Partono W., Asrurifak M., Tonnizam E. , KistianiF., Sari U.C. and & Putra K. C. A., Site Soil
Classification Interpretation Based on Standard
Penetration Test and Shear Wave Velocity Data,
J. Eng. Tech, Sci., Vol. 53, No. 2, 2021.
[13] Seed H.B., Idriss I.M. and Arango I., Evaluationof Liquefaction Potential Using Fields
Performance Data, Journal Geotechnic Eng. Div.,ASCE, Vol 109 No. 3, March, pp. 458- 482, 1983.

According to these two calculation methods indeveloping site factor FPGA, Fa and Fv, the site factor
developed using 1D-SSA at alluvial or soft soil area
as already suggested by ASCE/SEI 7-16 should betaken into account. Even-though the Ss values
calculated at 23 boring positions at the study area areless than 1 g. The Fv values are smaller compared tothe same site factor calculated using Indonesian
seismic code 2019.
CONCLUSIONS
Site factor calculation at the alluvial area wasconducted at 23 different boring positions. According
to the Indonesian seismic code 2019, the average N30
at these 23 boring positions are less than 15. The
alluvial area is located at soft soil area.
The three site factors FPGA, Fa and Fv calculated
and analyses based on Indonesian seismic code are
not equal compared to the same site factor developed
using 1D-SSA. The FPGA and Fa calculated based onthese two approaches are almost equal. However, theFv site factor calculated using 1D-SSA are smaller
compared to the same value calculated based onIndonesian seismic code 2019.
The site factor Fa and Fv for building upper
structures calculation at the alluvial or soft soil area
shall be evaluated due to the difference results
calculation. Two different approaches shall be
performed to get the real Fa and Fv site factor at
alluvial area.
ACKNOWLEDGMENTS

This study was financially supported by the
Directorate Research and Community Devotion,
Deputy of Research Empowerment andDevelopment, Ministry of Research andTechnology / National Research Council andInnovation, through research grant 2021.
REFERENCES[1] SNI 1726:2019, Seismic Resistance Design
Codes for Building and Other Structures,Indonesian National Standardization Agency,
2019.
[2] ASCE/SEI 7-16, Minimum Design Loads andAssociated Criteria for Buildings and Other
Structures. American Society of Civil Engineers,
2017.
[3] Partono W., Wardani S. P. R., Irsyam M. andMaarif S., Seismic Microzonation of Semarang,
Indonesia Based on Site Response Analysis Using86

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

METROPOLITAN MANILA SEISMIC HAZARD MAP USING
MIDORIKAWA & HORI SITE AMPLIFICATION MODEL
Jonathan R. Dungca1 and Mariamae Francia G. Montejo2
1,2

Civil Engineering Department, Gokongwei College of Engineering, De La Salle University, Philippines

ABSTRACT
Since Metropolitan Manila is known to be susceptible to seismic hazards, it is necessary to consider multiple
factors in assessing this hazard. Aside from ground motion, the properties of soil which lead to the amplificationof seismic waves should be considered. This study aimed to develop a seismic hazard map for Metro Manila
considering the soil properties in the area by using Midorikawa & Hori site amplification model. Probabilistic
seismic hazard assessment was conducted using earthquake data within 250 km of Metropolitan Manila for 10%
and 2% probabilities of exceedance. The site amplification factors were computed using calculated shear wave
velocities from SPT-N values. The calculated ground motions were amplified using the site amplification factors
considering short-period and mid-period amplifications. The constructed maps of amplified peak groundacceleration (PGA) values showed how soil properties affect the ground motion. It was found that for a shortperiod amplification, the average PGAs are 0.538 g, and 0.659 g for 10%, and 2% probabilities of exceedance,
respectively. While for mid-period amplification, the average PGAs are 0.589 g, and 0.830 g for 10%, and 2%
probabilities of exceedance. Using the amplified seismic hazard maps, a better approximation of seismic hazards
can be generated for future use.
Keywords: Site amplification, Shear wave velocity, Peak ground acceleration, Probabilistic seismic hazard
assessment, Metro Manila

INTRODUCTION

however, the model used is only applicable to strains
less than 1% and for periods that are less than or equal
to 0.5 seconds. With this, the study aims to develop
seismic hazard maps considering site amplification of
Metro Manila using a site amplification model [5]that is designed for higher strains and different
periods. The developed seismic hazard maps show
the amplified peak ground accelerations in the study
area for probabilities of exceedance of 10% and 2%.
With this, a reference and a basis are provided todetermine how local site conditions affect groundmotion intensities. Moreover, the maps will be able toprovide a more accurate representation of seismic
hazards for future planning and assessments.

Since the Philippines is situated within the Pacific
Ring of Fire, the country is considered highly
vulnerable to natural hazards such as earthquakes.
Typically the damage caused by earthquakes isdefined by the magnitude of the event and the
distance from the site to the source. However, it isalso necessary to consider the varying geologicalconditions in the area as it also affects the hazards
caused by earthquakes [1]. These varying geologicalconditions include the stability of slopes in the area,
its liquefaction susceptibility, and the possibility ofsite amplification.
Site amplification refers to the amplification of
the earthquake waves as they travel from the source
through the soil due to the local geological conditionsin the site [2]. Only a few research in the country havebeen conducted which consider the effect of site
amplification on earthquake hazards due to a general
shortage of data for soil profiles and shear wave
velocities. Since Metropolitan Manila or Metro
Manila is regarded to be susceptible to earthquake
hazards and site amplification due to its variablegeology [3], it is necessary to include the effects ofthe soil conditions in the evaluation to provide a moreaccurate representation of ground motion intensities.
In a previous study [4], a reference for site
amplification of Metro Manila was developed,

METHODOLOGY
The location of the study covers the area of Metro
Manila, formally known as the National Capital
Region (NCR). The region has a total of sixteen (16)cities and one (1) municipality. In this study, aprobabilistic seismic hazard assessment (PSHA) was
conducted to determine the peak accelerations at
bedrock (PArock) within the study area. The site
amplification factors were then identified using the
average shear wave velocities (VS30) in Metro Manila
and were used to amplify PArock and obtain the peakaccelerations at the ground surface.

87

GEOMATE – Kyoto, Japan, 3-5 November 2021

Probabilistic Seismic Hazard Assessment

exceedance. By combining the seismic hazard curvewith the Poisson model [6], the peak accelerations at
bedrock (PArock) for probabilities of exceedance of
10% and 2% in 50 years were computed.

In conducting PSHA, the equation of theprobability theorem [7] is shown in Eq. (1). The
equation shows the computation of the probability ofexceeding a value of a ground motion parameter,
considering the possible magnitudes or locations ofan earthquake.
𝑁𝑆 𝑁𝑀 𝑁𝑅𝜆𝑦 ∗ = ∑ ∑ ∑𝑖=1 𝑗=1 𝑘=1

Evaluation of Site Amplification
The average shear wave velocities (VS30) and thePArock were used to quantify the site amplification. To
calculate VS30, the SPT-N data or RQD values fromthe borehole data in Metro Manila were gathered andprocessed.
For SPT-N values, the model by [10] is used to
calculate the shear wave velocity (VS ) as shown in
Eq. (2).

𝑣𝑖 𝑃[𝑌 > 𝑦 ∗ |𝑚𝑗 , 𝑟𝑘 ]𝑃[𝑀 = 𝑚𝑗 ](1)
𝑃[𝑅 = 𝑟𝑘 ]where:𝜆𝑦∗ = total mean annual exceedance rate for peakacceleration level 𝑦 ∗ ;𝑁𝑆 = total number of potential earthquakes source 𝑖in the study area;
𝑁𝑀 = total number of magnitude intervals 𝑗
considered;
𝑁𝑅 = total number of distance intervals 𝑘considered;𝑣𝑖 = average rate of magnitude exceedance forsource 𝑖;
𝑃[𝑌 > 𝑦 ∗ |𝑚𝑗 , 𝑟𝑘 ] = probability that a ground motion
parameter 𝑌 will exceed the peak acceleration level𝑦 ∗ at a specific magnitude interval 𝑚𝑗 and a specificdistance interval 𝑟𝑘 ;𝑃[𝑀 = 𝑚𝑗 ] = probability that a magnitude 𝑀 will be
included in the magnitude interval 𝑚𝑗 ;𝑃[𝑅 = 𝑟𝑘 ] = probability that a distance 𝑅 will be
included in the distance interval 𝑟𝑘 .

𝑉𝑆 = 77.13𝑁 0.377(2)where:
𝑁 = raw standard penetration resistance.
For RQD values, interpolation is done using thetable of typical values of VS for a given RQD by [11].
After computing VS for each soil layer, the VS30
for each site is computed. For soil data with depths
greater than 30 meters, the equation by [12] in Eq. (3)was used, otherwise, an extrapolation shown in Eq.
(4) was conducted as proposed by [13].
𝑉𝑆30 =∑𝑁𝑖=1 ℎ𝑖
ℎ𝑖
∑𝑁
𝑖=1 𝑣
𝑖

log 𝑉𝑆30 = 𝑐0 + 𝑐1 log 𝑉𝑆𝑍 + 𝑐2 (log 𝑉𝑆𝑍 )2

In this study, the earthquake sources within 250
kilometers (km) of Metro Manila were considered
and categorized as either linear sources or areal
sources. For linear sources, the probability density
function by [6] is used. For areal sources, themagnitude-fault area relation [7] was used. The area
was further divided into square elements and theprobability distribution was obtained considering theshortest distance from the center of each element tothe site being considered.
The probability distribution for magnitude wascomputed using the probability density function by[6]. In computing the probability distribution of theexceedance of a ground motion parameter, the groundmotion prediction equations (GMPE) by [8] wereused for shallow earthquakes. The GMPE by [9]shown was used for earthquakes generated by
subduction zones.
The value of 𝑃[𝑌 > 𝑦 ∗ |𝑚𝑗 , 𝑟𝑘 ] is computed using
the equation by [6]. The value of 𝐹𝑌 (𝑦 ∗ ) was thencomputed using the standard normal variate. Whenthe values of 𝜆𝑦∗ were computed considering thetarget accelerations from 0.05 g to 1.0 g, the seismic
hazard curve was constructed by plotting theaccelerations against the mean annual rate of

(3)

(4)

where:ℎ𝑖 = thickness of soil layer;
𝑣𝑖 = shear wave velocity at the 𝑖 th layer at the top 30
meters;
𝑉𝑆𝑍 = average shear wave velocity at termination
depth;
𝑐𝑛 = regression coefficients [13].
The shear wave velocity map of Metro Manila wasthen constructed in a mapping software using thecomputed VS30 values. The VS30 and PArock values
were used to compute the site amplification factors
using the model by [5] shown in Eq. (5).
ln 𝐴𝐹 = 𝑎 ln 𝑉𝑆30 + 𝑏(5)where:
𝐴𝐹 = amplification factor;𝑎, 𝑏 = regression coefficients in the function of
𝑃𝐴𝑟𝑜𝑐𝑘 .
The regression coefficients [5] are computed
depending on whether the amplification being
considered is short-period amplification or mid88

GEOMATE – Kyoto, Japan, 3-5 November 2021

period amplification. Short-period amplification (Fa)covers a period of 0.1 to 0.5 seconds while mid-period
amplification (Fv) covers a period of 0.4 to 2.0
seconds.
The amplification factors were used to amplify thepeak acceleration values computed from the GMPEs,
from which new seismic hazard curves wereconstructed. With this, the amplified peak groundacceleration (PGA) values are computed for 10% and
2% probabilities of exceedance.
RESULTS AND DISCUSSION
In this study, earthquake data with a minimum
magnitude of 4.0 and spanning from the years 1907
to 2020 were obtained from the Philippine Institute ofVolcanology and Seismology (PHIVOLCS). The
geotechnical data, on the other hand, were collectedfrom past studies by [4] and [14]-[18].
Seismic Hazard Maps
In developing the seismic hazard maps,
Geographic Information System (GIS) was usedsimilar to [19]. Considering the probabilities of
exceedance (POE) of 10% and 2%, the PArock values
in Metro Manila were computed. It was found that the
average PArock values in Metro Manila vary from as
low as 0.296 g in Navotas and as high as 0.475 g in
Muntinlupa for 10% POE. Considering 2% POE, theaverage PArock values range from 0.437 g to 0.706 g,
which are also in Navotas and Muntinlupa,
respectively. Graphical representations of the actual
values of PArock are shown as maps in Fig. 1 and Fig.
2

Fig. 2

Map of PArock in Metro Manila for 2% POE

As shown in the maps, the Muntinlupa area has
the highest PArock values. The contour then spreads
outwards, decreasing gradually as the distance fromthe center increases. The highest PArock is at the center
due to the presence of the West Valley Fault, which
also shows that this fault has the biggest influence inthe seismic behavior in Metro Manila. The PArock also
increases as the probability of exceedance decreases,
since large-scale earthquakes are less common thansmaller earthquakes.
Shear Wave Velocity Map
To create the shear wave velocity map, a total of1412 borehole data were collected, 1372 of which arewithin Metro Manila and 40 are gathered from nearby
provinces to complete the interpolation. Relating thenumber of data used and the area of the study area, adensity of 2 borehole data for every square meter wasused. Using the different geotechnical data gathered
within Metro Manila, the VS30 in the study area werecomputed and mapped. From the computation, it was
found that the average VS30 values in Metro Manila
range from 206.48 m/s in Manila up to 554.08 m/s inQuezon City. These values describe the stiffness ofthe soil in each location, which is also attributed tothe types of soil present in the area, and theircorresponding SPT-N or RQD values. Figure 3 shows
the VS30 map of Metro Manila.

Fig. 1

Map of PArock in Metro Manila for 10% POE
89

GEOMATE – Kyoto, Japan, 3-5 November 2021

and Fv for increasing VS30 values, assuming that
PArock is constant.
Table 1 Resulting
amplification
increasing VS30 values
VS30 (m/s)
150
250
350
450
550

Fig. 3

Fa
2.737
2.135
1.813
1.605
1.456

factors

for

Fv
3.405
2.399
1.905
1.603
1.397

Based on the data in Table 1, the values of Fa andFv decrease as VS30 increases. This shows that softer
soils cause stronger amplification of ground motion
compared to stiff soils. It can also be observed thatconsidering the amplification factors at the highest
and lowest VS30, the value of Fa is 1.88 times smaller,
while the value of Fv is 2.43 times smaller. This showsthat VS30 dependency is stronger for mid-period
amplification since the rate of change of its value isgreater.
On the other hand, Table 2 shows the values of Fa
and Fv for increasing PArock values, assuming that
VS30 is constant.

Map of VS30 in Metro Manila

Table 2 Resulting
amplification
increasing PArock values

As seen in Fig. 3, the west and east sides of Metro
Manila are almost similar with low VS30 values, while
the center has high VS30 values. The low values on thewestern side, comprised of Navotas, Malabon, Manila,
and Pasay, can be attributed to the area being near
Manila Bay. The west is also comprised of quarterly
alluvial soil where SPT-N values are very low at thesurface and are average at deeper layers [14].
The areas in the eastern part, comprised of
Marikina, Pasig, Pateros, and Taguig. have slightly
higher VS30 values when compared to the west since
the areas are far from Manila Bay. However, the soil
composition in these areas is similar to the west, withalmost the same SPT-N patterns.
The remaining cities aligned at the center, which
are Caloocan, Valenzuela, Quezon City, San Juan,
Mandaluyong, Makati, Muntinlupa, Las Piñas, andParañaque have the highest VS30 values. This is due tothe tuff and rock formations that are common in thecentral part of the region [14]. With this, the SPT-N
values are generally higher, and refusal can beachieved even in shallow depths.

PArock (g)
0.25
0.35
0.45
0.55
0.65

Fa
1.813
1.627
1.445
1.269
1.104

factors

for

Fv
1.905
1.873
1.845
1.819
1.797

From the data in Table 2, Fa and Fv also decrease
as PArock increases. This is due to the nonlinearity ofsoil behavior [20]. When the ground accelerations are
high, the soil response is governed by its highdamping ratio, which results in low amplification in
the soils. This also shows that amplification is greaterfor earthquakes with low magnitudes at farther
distances. Also, similar to Table 1, the data in Table
2 show that the mid-period amplification is generally
greater than the short-period amplification.
Comparing the amplification factors at the highest
and lowest PArock values, it can be seen that Fa became
1.65 times smaller and Fv became 1.06 times smaller.
This shows that short-period amplification is more
dependent on the change in the PArock values. After
incorporating the amplification factors into the PSHA,
the amplified PGAs are computed and mapped.

Amplified Seismic Hazard Maps
Using the PArock values and the VS30 values, thesite amplification factors Fa and Fv were computed.
To see the relationship between PArock, VS30, and theamplification factors Fa and Fv, theoretical values forthe first two parameters are used to compute theamplification factors. Table 1 shows the values of Fa
90

GEOMATE – Kyoto, Japan, 3-5 November 2021

the shape of the contour remains the same. The maps
in Fig. 6 and Fig. 7 show that the west and east of theregion have the highest PGAs. The main difference
between the short-period and mid-period
amplification is that, for the latter, the areas with thehighest PGAs for 10% POE are still the same areas
with the highest PGAs for 2% POE. This shows that
mid-period amplification is more dependent on VS30
which is constant regardless of the POE considered.

Fig. 4

Fig. 5Map of amplified PGAs in Metro Manila(10% POE, short-period amplification)

Fig. 6

Map of amplified PGAs in Metro Manila(10% POE, mid-period amplification)

Fig. 7

Map of amplified PGAs in Metro Manila(10% POE, mid-period amplification)

Map of amplified PGAs in Metro Manila(2% POE, short-period amplification)

Figure 4 and Fig. 5 show the maps of amplified
PGAs considering short-period amplification. It canbe seen that he contour in Fig. 4 is more random, withthe highest accelerations at the west and east. The
areas with the highest PGAs are also the areas withthe lowest VS30, which shows that areas with softer
soils experience greater amplification.
In Fig. 5, it is observed that the contour of the map
is more similar to Fig. 2, where the accelerations arehigher at the center, and gradually decrease as they
move away towards the sides. This shows how shortperiod amplification is more influenced by PArock.
Figure 6 and Fig. 7 show the maps of amplified
PGAs considering mid-period amplification. Here,

Based on the results, the average amplified PGAs
in the region are 0.538 g and 0.589 g for short-period
andmid-period
amplification,
respectively,
considering 10% POE. For 2% POE, the amplified
91

GEOMATE – Kyoto, Japan, 3-5 November 2021

PGAs for short-period and mid-period amplification
are found to be 0.659 g and 0.830 g, respectively.

Relationships for Subduction Zone Earthquakes,
Seismological Research Letters, 1997, pp. 58-73.
[10] Marto, A., Soon, T. and Kasim, F., A Correlationof Shear Wave Velocity and Standard
Penetration Resistance, Electronic Journal of
Geotechnical Engineering, Vol. 18, 2013, pp.
463-471.
[11] Hunt,R.,
GeotechnicalEngineering
Investigation Manual, Mc Graw Hill Book Co.,
1984.
[12] Ahmad, I., Hashash, Y., Khan, A. and Waseem,M., Site Amplification Factor at Mardan, Journalof Himalayan Earth Sciences, Vol. 44, Issue 2,
2011, pp. 61-70.
[13] Boore, D., Thompson, E. and Cadet, H.,
Regional Correlations of VS30 and Velocities
Averaged Over Depths Less Than and Greater
Than 30 m, Bulletin of the Seismological Societyof America, Vol. 101, Issue 6, 2011, pp. 30463059.
[14] Dungca, J., Concepcion, I., Limyuen, M., See, T.
and Vicencio, M., Soil Bearing Capacity
Reference for Metro Manila, Philippines,
International Journal of GEOMATE, Vol. 12,
Issue 32, 2017, pp. 5-11.
[15] Dungca, J. and Chua, R., Development of a
Probabilistic Liquefaction Potential Map for
Metro Manila, International Journal of
GEOMATE, Vol. 10, Issue 2, 2016, pp. 18041809.
[16] Dungca, J., Pua, R., Raynold, N., Sangalang, A.
and Tan, A, Mat Foundation Design Reference
for Metro Manila Reference, International
Journal of GEOMATE, Vol. 15, Issue 47, 2018,
pp. 42-47.
[17] Dungca, J., A Reference for the Allowable Soil
Bearing Capacities in Quezon City, Philippines,
International Journal of GEOMATE, Vol. 19,
Issue 71, 2020, pp. 42-47.
[18] Dungca, J., Gozum, N., Telan, J., Torres, V. andUytengsu, T., Deep Foundation Reference forMetro Manila, Philippines, International Journalof GEOMATE, Vol. 14, Issue 45, 2018, pp. 1621.
[19] Galupino, J., Garciano, L., Paringit, M. andDungca, J., Location Based Prioritization of
Surigao Municipalities Using Probabilistic
Seismic Hazard Analysis (PSHA) and
Geographic
Information
System(GIS),
Conference proceedings, in HNICEM 2019 – 9thInternational Conference on Humanoid,
Nanotechnology, Information Technology,
Communication and Control, Environment andManagement, 2017.
[20] Kumar, A., Harinarayan, N. and Baro, O., HighAmplification Factor for Low Amplitude Ground
Motion: Assessment for Delhi, Disaster
Advances, Vol. 8, Issue 12, 2015, pp. 1-11.

CONCLUSION
Due to the susceptibility of Metro Manila toseismic hazards, it is necessary to consider the local
soil conditions which intensify the damages fromthese hazards. It was seen that short-period
amplification has a stronger dependency on PArock,while mid-period amplification is more dependent on
VS30. Considering short-period amplification, Metro
Manila has an average PGA of 0.538 g and 0.659 g
for 10% and 2% POE, respectively. Meanwhile, for
mid-period amplification, the region has an average
PGA of 0.589 g and 0.830 g for 10% and 2% POE.
REFERENCES[1] Monahan, P., Levson, V., McQuarrie, E., Bean,
S., Henderson, P., and Sy, A., Relative
Earthquake Hazard Map of Greater Victoria,
Showing Areas Susceptible to Amplification of
Ground Motion, Liquefaction, and Earthquakeinduced Slope Instability, Geoscience Map, 2000,
Ministry of Energy and Mines, British Columbia.
[2] Navidi, S., Development of a Site Amplification
Model for Use in Ground Motion Prediction
Equations, The University of Texas at Austin,
2012.
[3] Sali, A., Seismic Hazard Assessment of Metro
Manila (A Foundation Engineering Analysis),
Geological Society of the Philippines, 2004.
[4] Dungca, J. and Macaraeg, C., Development of aReference for Seismic Amplification: The Caseof Metro Manila, Conference proceedings, in 19th
IABSE Congress Stockholm, 2016, pp. 10151022.
[5] Midorikawa, S. and Hori, A., Nonlinear Site
Amplification Model Derived From Strong
Motion Records Including Records of the 2011
Tohoku, Japan Earthquake, Conference
proceedings, in 16th European Conference on
Earthquake Engineering, 2018.
[6] Kramer, S., Chapter 4, Geotechnical Earthquake
Engineering, Prentice Hall, 1996, pp. 117-138.
[7] Singh, S., Bazan, E. and Esteva, L., Expected
Earthquake Magnitude from a Fault, Bulletin of
the Seismological Society of America, Vol. 70,
1980, pp. 903-914.
[8] Joyner, W. and Boore, D., Peak Horizontal
Acceleration and Velocity from Strong Motion
Records Including Records from the 1979
Imperial Valley, California Earthquake, Bulletinof the Seismological Society of America, Vol. 71,
Issue 6, 1981, pp. 2011-2038.
[9] Youngs, R., Chiou, S., Silva, W. and Humphrey,J., Strong Ground Motion Attenuation92

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

DEVELOPMENT OF SOIL TYPE REFERENCE OF METROPOLITAN
MANILA USING GENETIC ALGORITHM
Jonathan R. Dungca1 and Izzhar Christian P. Sumagaysay2
1

Faculty, De La Salle University, Philippines; 2 Graduate Student, De La Salle University, Philippines

ABSTRACT
A soil type reference of Metropolitan Manila was developed using an optimization algorithm called Genetic
Algorithm (GA). In this study, a program was created to perform a Genetic Algorithm on each meter soil layer
ranging from 40 meters below mean sea level up to 100 meters above mean sea level for specified points with 2km
grid intervals. The program utilized the Unified Soil Classification System (USCS) and a borehole database of thestudy area and surrounding provinces to predict the soil types. Two variables were chosen to assess the fitness ofthe prediction: the likeness of the soil type with surrounding boreholes, and the distance of the boreholes from a
specified grid point. A total of 216 points were used in the study each containing 40 to 130 soil layers. The results
were then compiled and plotted to create a total of 38 soil profiles of the study area for the longitudinal andtransverse directions. The profiles followed the expected soil types based on the geologic zones of the study area:
sands and clays for the coastal lowlands, rocks and sands for the central plateau, and clays and silts for Marikina
Valley. Validation of the profiles suggests the Genetic Algorithm as a suitable tool for the development of soil
type reference of the study area.
Keywords: Genetic Algorithm, USCS, Soil Types, Metropolitan Manila
type reference to map the locations and boundaries ofthe different soil layers present in the study area.
Mapping of geotechnical parameters is notuncommon as there were several studies alreadymaking use of Graphic Information System (GIS) to
create maps and projections of the different soil
characteristics [3]-[13]. However, current GIS
software has no functions fit for mapping the profiles
needed for this study, as such, the soil type reference
was patterned to the soil profiles presented by
Galupino and Dungca [11] in their study on the
Quezon City soil profile referenceTo create the soil profile reference of Metro
Manila, an algorithm was used to predict the existingsoil type in specified locations. The use of machinelearning and other algorithms is becoming common
in the field of Geotechnical Engineering [15]-[17].
One of the fast and easy algorithms is the Genetic
Algorithm (GA). GA follows the idea of survival of
the fittest wherein fitter individuals have a higher
chance to survive and pass on their genes to the next
generation. Several studies [15], [18] have alreadyapplied GA to geotechnical engineering and theresults are very promising. The application of GA,
however, is unique for each problem. With this, afitness function was created for this specificapplication of GA to the prediction of soil types.
This study aims to create a soil type reference forMetropolitan Manila that will aid engineers,
policymakers, and other stakeholders in knowing the
expected soil type in their respective project sites.

INTRODUCTION
Metropolitan Manila, or more locally known as
Metro Manila, is the capital region of the Philippines.
It has about 13% of the total population of the country
and it is where the majority of government offices
along with important business and commercial
infrastructures are located. It has a land area of 619.54
square kilometers bordered by the Manila Bay at thewest and the Laguna de Bay at the east.
Due to its unique location and the shifts in tectonic
plates in its geologic past, a fault system was formed
leading to the frequent earth movements up until thepresent [1], [2] As such, there is a need for proper
planning and design for all infrastructure.
Furthermore, policymakers and disaster response
teams need to know these hazards to create a robust
disaster reduction and management plan. Currently,
there were a lot of studies [3]-[10] that tried toevaluate the geotechnical properties of the study area.
However, even being a major component of thesestudies, there was no available soil type reference forMetropolitan Manila except for the city of Quezon[11] The discussion on the effect of soil type on theirrespective studies was only discussed concerning thedifferent geologic zones of the study area. Usually,
researchers would divide Metro Manila into several
zones with the Central Plateau, Marikina Valley, andthe Coastal lowlands as the most prominent zones [5].
The problem with this, however, is that a detailed soil
profile is absent along with the thicknesses of eachsoil layer. Thus, this study focuses on creating a soil
93

GEOMATE – Kyoto, Japan, 3-5 November 2021

The soil types were grouped into classes based on
the Unified Soil Classification System. The classes
were adjusted to cover the different soil type
reporting given by the borehole logs. A total of 24 soil
types were used in this study, including the 15
original soil types from USCS, unspecified or general
Gravel, Sand, Clay, and Silt, and 5 classifications forRock. The summary of the soil types with their short
description was shown in Table 1.

METHODOLOGY
Borehole logs located in Metropolitan Manila andother surrounding provinces are collected. Erroneous
borehole logs were removed and a representative
borehole was determined for sites with multiple
borehole logs. A grid system with 2km intervals was
also placed in the study area. the grid intersections
will be the location where the Genetic Algorithm will
be deployed. The grid was labeled alphabeticallyfrom west to east and numerically from north tosouth. The borehole locations and the grid systemwere presented in Fig. 1.

Table 1. Summary of soil types.
General Soil Type SpecificDescription
Soil Type Origin Soil TypeUSCS
GW Well-graded gravel
Poorly-graded
USCS
GP
gravelG
USCS
GC
Clayey gravel
GravelUSCS
GM Silty gravel
Added
G
Unspecified gravel
USCS
SW Well-graded sand
USCS
SP
Poorly-graded sand
S
USCS
SC
Clayey sand
Sand
USCS
SM Silty sand
Added
S
Unspecified sand
USCS
CH
High-plasticity clay
C
USCS
CL
Low-plasticity clay
Clay
Added
C
Unspecified clay
USCS
MH High-plasticity silt
M
USCS
ML Low-plasticity silt
Silt
Added
M
Unspecified silt
Boulders and largeAdded
GR
sized gravel
Added
SR
Sandstone
R
Added
CR
MudstoneRock
Added
MR Siltstone
Tuff and other
AddedR
unspecified rock
PT
USCS
PT
Peat
Peat
High-plasticity
USCS
OH
O
organic fines
Organic
Low-plasticity
Fines
USCS
OL
organic fines

Fig. 1. Borehole locations and grid system ofMetropolitan Manila.
The elevations of the borehole logs and the gridintersections were plotted to create a 3D Elevation
map shown in Fig. 2. This was crucial as eachborehole was adjusted to its correct meter elevations
with the mean sea level as the reference elevation.

The process of a Genetic Algorithm (GA) starts
with the creation of a random initial population
followed by the fitness evaluation then the crossoverand mutation operations to create the next generation
population. The new generation replaces the initial
population and the process is repeated until a given
set of generations or if the convergence criteria are
attained [17]. The method is shown in Fig. 3.

Fig. 2. 3D elevation Map of Metropolitan Manila.
94

GEOMATE – Kyoto, Japan, 3-5 November 2021

Population Generation(Randomized)

The genetic algorithm was deployed in each gridintersection point for each meter soil layer. The
extents of the elevations were set at 40 meters below
sea level up to 100 meters above sea level. The
program skips those grid intersections without
surrounding boreholes under a 2.5km radius. Once all
elevation points were completed for all gridintersections, the results were compiled and soil
profiles were plotted in CAD.

Highest Fitness
Individual is Chosen
Yes

Fitness Evaluation

Population reached theConvergence Criteria?
No

Generation of New
Population

Operators (Cross-overand Mutation)

RESULTS
Metropolitan Manila is composed of different
geologic zones due to its locations. The study area canbe divided into 4 main areas: Central Plateau
composed of mainly Guadalupe Tuff, Marikina
Valley composed of delta deposits from the Marikina
River, the coastal lowlands composed of delta
deposits from Pasig River, and the Coastal
swamp/wetlands on the north-west [5], [7]. It issurrounded by Manila Bay on the west, and Lagunade Bay on the east. It is bordered by the province of
Bulacan in the north, Rizal to the north-east, and theprovinces of Cavite and Laguna in the south.
For this specific study, the highest groundelevation from the borehole logs inside the study area
was determined to be 88 meters above sea level,
however, there were borehole logs in the neighboring
province of Rizal that exceeded 100 meters above sea
level. These excess soil layers were disregarded asthey were beyond the set extents of the study area.
For the plot of the soil profiles in CAD, the soil
layers were grouped and color-coded based on theirgeneral soil types following Table 1. Gravels wereshaded with dark green, sands were shaded in light
blue, clays with orange, silts with light green, rocks
with brown, and organic fines as pink. There was no
predicted soil type for peat. The generalization of thesoil types was done for the simplicity of the plotting
as well as to easily observe the trend and movementof the soil types throughout the study area.

Genetic Algorithm Loop
Fig. 3. Genetic Algorithm Flowchart
The GA used in the creation of the program was
deployed in the grid intersection. GA was used as its
process is a fast and easy way for the program toconverge into a prediction of the soil type. The
program scores the prediction based on their accuracy
to surrounding borehole logs. The maximum distanceof the referenced borehole logs was set at 2.5km fromthe respective grid intersections. Furthermore, theinfluence of the borehole logs in the scoring of thepredictions was adjusted in relation to their distanceto a specific grid point. Usually, Euclidean distance isused in these applications [11], however, due to thesize of the study area, the great-circle distance wasused.
For GA to function, it required a set of variables
to optimize, its mutation rates and the fitness function
to quantify the accuracy of the prediction. The createdprogram used two variables: the soil type and the
maximum distance of boreholes to be considered.
The range of this distance was set from 500 meters to2500 meters from the grid intersection points. Thiswas implemented to lessen the irrelevant data points
for the comparison of the prediction. The soil typevariable was set as categorical data following thespecific soil type presented in Table 1. The mutation
rates for these variables are set to be 0.10 and 0.30 for
the soil type and the maximum distanced considered.
Lastly, the fitness function incorporated the weights
used in k-Nearest Neighbor by Galupino and Dungca[11], and a ‘likeness’ function. The fitness formula
was shown in Eq. (1).
𝐹 = 𝐿𝑖𝑗 𝑊𝑖𝑗 =∑𝐿𝑖𝑗 𝑒 −𝐷𝑖𝑗∑𝑒 −𝐷𝑖𝑗

Longitudinal Sections
Due to the Guadalupe Tuff formation, different
rock classes were observed to be prevalent in the
study area with the only exception for this was on thewestern and eastern sides near the bodies of water.
Samples of the soil profile in the longitudinal
direction were shown in Fig. 4 and Fig. 5.
Longitudinal grid H, shown in Fig. 4, passed
through the entire length of the central plateau area
along with the cities of Quezon, Mandaluyong, San
Juan, Makati, Taguig, and some parts of Muntinlupa.
It was also the longest grid present in the area. In thearea of Quezon City, It was observed to be composedof different rock types, which was expected based on
past studies [5], [11]. The topsoil, however, did not(1)

Where: 𝐿𝑖𝑗 is the ‘likeness’ function of the specificsoil layer of grid intersection point 𝑖 to the 𝑗𝑡ℎborehole under the training data set. 𝐿𝑖𝑗 has a value of
1 if the specific soil layer has the same soil type as the
𝑗𝑡ℎ borehole, otherwise, the value is 0. Meanwhile,
𝑊𝑖𝑗 is the distance weights introduced by Galupino
and Dungca [11] and 𝐷𝑖𝑗 is the great-circle distance
between grid intersection point 𝑖 to the 𝑗𝑡ℎ borehole95

GEOMATE – Kyoto, Japan, 3-5 November 2021

conform with the predictions of Galupino and Dungca[11] as a different technique was used in their study.

As mentioned before, the three areas of coastal
lowlands, the central plateau, and the Marikina Valley
were visible in this soil profile. The differences in thesoil composition of these three main areas wereobserved in Fig. 6. The western coast has clay androck layers overlain by sands and silts, meanwhile,the eastern coast has silt and sand layers overlain byclays. Gravels were also present in the lower layers of
Grid G5. The soil types in the western coast were
expected from the Pasig River Delta deposits draining
to Manila Bay. Additionally, tide changes and ocean
movement in Manila bay may have contributed to thesoil types present on the western coast. As for the soil
types in the Marikina Valley, it was as expected from
the Marikina River Delta deposits. Surrounding areasof the Marikina Valley were also composed of thecentral plateau and the Antipolo Plateau of theprovince of Rizal. These surrounding areas werepreviously identified to be mostly composed of Tuff
formations [11]Fig. 4. The soil profile of longitudinal grid H.
Looking at a grid nearer the western coast,
longitudinal grid E, presented in Fig. 5, showed aprevalent sand top layer, especially in the central areain grid E10 to E15. These grid points were located in
the cities of Manila and Pasay where the mouth of the
Pasig River was located. Furthermore, some parts ofthese locations were also reclamation areas. Traveling
further south, revealed a transition from clay to a
mixture of rocks and silts as the soil type underneaththe sand topsoil.

Fig. 6. The soil profile of transverse grid 11.
Genetic Algorithm Analysis and Validation
A total of 551 borehole logs were used in the
program to predict the soil profile. 56 borehole logs
were put aside for the validation of the results of the
GA program. The results of the profile werecompared to this validation subset. Since there were
varying depths of boreholes, it was better to compare
the accuracy of prediction on each meter depth. Fromthese 56 borehole logs, a total of 1113 soil layers wereobtained. These soil layers were compared to theclosest grid intersection point in relation to theirrespective elevations. The number of correct soil
layers was tallied and grouped per city. Overall, theaccuracy of the prediction is 52.83% and 74.75% for
the specific and general soil types respectively.
The higher accuracy of the general soil types was
expected as there were multiple soil types under asingle general soil type. Confusion matrices, shownin Fig. 7 and Fig. 8, were created to visualize these
inaccuracies. The number of predicted layers undereach actual layers were given in these matrices. The
distribution was then shaded to highlight theconcentration of the prediction.

Fig. 5. The soil profile of longitudinal grid E.
The different parts of the study area followed the
expected soil types based on the geologic zones.
Other parts of the central plateau not shown by Fig. 4
and Fig. 5 were mainly composed of rock types witha 5-meter topsoil depth composed of mixtures of sand,clays, and silts. Furthermore, the Marikina Valley
showed prevalent clay and silt layers. The topsoil forthis area was a mix of clay, silt, and sands.
Transverse Sections
The transverse soil profiles gave the perspectiveof the different geologic zones of the study area. The
central plateau was easily seen together with thecoastal lowlands on the west and the Marikina Valley
in the east. Shown in Fig. 6 was Grid 11.
96

GEOMATE – Kyoto, Japan, 3-5 November 2021

CONCLUSION
This study was able to create a soil type referenceof Metropolitan Manila using a Genetic Algorithm
and a compilation of borehole logs collected from thestudy area and surrounding provinces. This soil
profile hopes to serve as a reference for engineers,
policymakers, and other stakeholders.
A program was created to perform a Genetic
Algorithm to predict the soil type. The soil type wasclassified using a revised Unified Soil Classification
System (USCS). This program was deployed at
specific points with 2km grid intervals from 40
meters below mean sea level up to 100 meters above
mean sea level. The genetic algorithm was programed
to predict the soil type based on the soil types ofsurrounding boreholes and the distance of theseboreholes to the grid intersection points. A fitness
function was presented which incorporates theweights used on k-Nearest Neighbor by Galupino andDungca [11] and a ‘likeness’ function. The results
were then compiled and the soil profiles were plottedin CAD.
The soil profiles followed the expected soil types
based on the geologic zones of the study area. Rocks
were prevalent in the Central Plateau, Clays overlain
by sands and silts were observed in the western
coastal lowlands, and silts overlain by clays were
found in the Marikina Valley. Validation of theresults was done by comparing a subset of theborehole database to the closest grid intersection
point of the soil profiles. It was determined that theFig. 7. Confusion matrix for the general soil types.
In Fig. 7, the confusion matrix for the general soil
types was shown. The main diagonal had a higher
value than other parts and was reflective of the overall
74.75% accuracy of the prediction. Only organic fines
had a lower value for the main diagonal. Most of theorganic fines were predicted as clays or sands. Thisprediction error can be attributed to their location asorganic fines were commonly found in the western
coastal lowlands surrounded by sands and clays.
Looking at Fig. 8, this confusion matrix shows thedetailed distribution of the prediction for the specificsoil types. The low accuracy of the prediction wasobserved in this matrix as there were prediction
deviation from the main diagonal. Some of thesewrong predictions, however, were not as severe suchas high-plasticity clays being predicted as lowplasticity clays and vice versa. As such, it is stillnecessary for engineers to perform additional tests todetermine the different geotechnical parameters toproperly design a safe and economical foundation.

Fig. 8. Confusion matrix for the specific soil types
97

GEOMATE – Kyoto, Japan, 3-5 November 2021

predictions were 74.75% and 52.83% accurate forgeneral and specific soil types. Confusion matrices
were also created to show the distribution of theprediction across the soil types. This accuracy was
satisfactory considering the size of the Metropolitan
Manila and the distribution of borehole logs
throughout the study area

GEOMATE, Vol. 10, Issue 2, 2016, pp. 18041809[9] Uy, E. S., Dadios, E., P., and Dungca, J. R.,
Preliminary Assessment of Liquefiable Area in
Ermita, Manila Using Genetic Algorithm,
Conference Proceedings, in IEEE 8th International
Conference
Humanoid,
Nanotechnology,
Information Technology Communication andControl,
Environment
andManagement(HNICEM), 2015[10] Velasco, J. N., Tirao, J. M., Enoria, A. S., San
Juan, S. Q., and Ubaldo, H. F., Liquefaction
Potential Mapping of the City of Valenzuela,
Philippines, Conference Proceedings, in IEEE
11th International Conference Humanoid,
Nanotechnology,
Information
Technology
Communication and Control, Environment andManagement (HNICEM)[11] Galupino, J. G., and Dungca, J. R., Quezon City
Soil Profile Reference, International Journal of
GEOMATE, Vol. 16, Issue 58, 2019, pp. 48-54[12] Khatri, S., and Suman, S. Mapping of Soil
Geotechnical Properties using GIS, Geotechnicaland Geo-Environmental Engineering (ICGGE2019, Prayagraj, India, 2019[13] Galupino, J., Garciano, L., Paringit, M., andDungca, J., Location Based Prioritization of
Surigao Municipalities Using Probabilistic
Seismic Hazard Analysis (PSHA) and Geographic
Information
System(GIS),
Conference
Proceedings, in 9th International Conference
Humanoid,
Nanotechnology,
Information
Technology Communication and Control,
Environment and Management (HNICEM), 2019[14] Elevado, K. J. T., Galupino, J. G., and Gallardo,
R. S., Artificial Neural Network (ANN)
Modelling of Concrete Mixed with Waster
Ceramic Tiles and Fly Ash, International Journalof GEOMATE, Vol. 15, Issue 51, 2018, pp. 154159[15] Alamanis, N., Uncertainties and Optimization in
Geotechnical Engineering, American Scientific
Research Journal for Engineering, Technology
and Sciences (ASRJETS), Vol. 38, Issue 1, pp.
92-111[16] Suguna, N., and Thanushkodi, K., An Improved
k-Nearest Neighbor Classification Using Genetic
Algorithm, IJCSI International Journal ofComputer Science Issues, Vol. 7, Issue 4, 2010,
pp. 18-21[17] de Santos, C., Backanalysis Methodology basedon Multiple Optimization Techniques for
Geotechnical Problems, Universitat Politencia deCatalunya – BarcelonaTECH, 2015[18] Fasna, M., Fathima Zuhara, T., andKrishnankutty, S. V., Prediction of Optimum
Mosture Content using Genetic Algorithm, Asian
Journal of Engineering and Technology, Vol. 5,
Issue 3, 2017, pp. 71-76

ACKNOWLEDGEMENTS
The researchers would like to thank theEngineering Research and Development (ERDT) of
the Department of Science and Technology (DOST)of the Philippines for funding this study, Dr. Erica
Elice Uy for introducing Genetic Algorithm, andJoaquin Ramos and other previous researchers under
the Department of Civil Engineering of DLSU forsharing their borehole logs for the study area This
would have not been completed without their support.
REFERENCES[1] Kurita, K., Kinugasa, Y., Deguchi, T., Rimando,
R.E. and Watanabe, M., Monitoring Aseismic
Surface Creep along the Western Valley(Marikina) Fault, 11th International Workshop on
Seismic Microzoning and Risk Reduction,
Granada, Spain, 2016[2] Ramos, E., Shifts in Tectonic Stresses around
Metro Manila, 2019 Annual Geological
Conference of the Geological Society of thePhilippines, doi:10.13140/RG.2.2.10532.91528[3] Dungca, J. R., A Reference for the Allowable Soil
Bearing Capacities in Quezon City, Philippines,
International Journal of GEOMATE, Vol. 19,
Issue 17, 2020, pp. 42-47[4] Dungca, J. R., and Macaraeg, C. M., Developmentof a Reference for Seismic Amplification: The
Case of Metro Manila, 19th Congress ofInternational Association for Bridge andStructural Engineering, Stockholm, Sweden, 2016
[5] Dungca, J. R., Concepcion, I., Limuren, M., See,T., and Vicencio, M., Soil Bearing Capacity
Reference for Metro Manila, Philippines,
International Journal of GEOMATE, Vol. 12,
Issue 32, 2017, pp. 5-11
[6] Dungca, J. R., Gozum, N. L. C., Telan, J. A. S.,
Torres, V. C. F., and Uytengsu, T. S. Y., Deep
Foundation Reference for Metro Manila,
Philippines, International Journal of GEOMATE,
Vol. 14, Issue 45, 2018, pp. 16-21[7] Dungca, J. R., Pua, R. Y., Que, N. Q., Sangalang,
A. K. M., and Tan, A. N., Mat Foundation Design
Reference for Metro Manila, International Journalof GEOMATE, Vol. 15, Issue 47, 2018, pp. 42-47[8] Dungca, J. R., and Chua, R. A. D., Developmentof a Probabilistic Liquefaction Potential Map for
Metro Manila, International Journal of98

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

PREVENTION OF SLOPE FAILURE USING NATIVEMICROORGANISMS
Ryosuke Kawano, Md. Azizul Moqsud
Faculty of Engineering, Yamaguchi University, Ube city, Japan
ABSTRACTIn this study, the solidification of soil by using native microorganisms living in Japan has been carried out. The
slope soil failure is one of the main geo-disasters in Japan and the traditional methods to improve the slope soil
are not environmental friendly. Microbially induced calcium precipitate (MICP) is becoming popular for the biogeotechnical engineering purposes in the world. In this study , the native microorganisms were used to bio-stabilize
the slope soil. Laboratory experiments were conducted in the constant room temperature to solidify the slope soil
by using the native bacteria for 21 days. Direct shear tests were conducted for the measurement of the strength. A
model slope was also prepared and solidified with the native bacteria. The generated calcium carbonate wasmeasured. It was observed that the strength has increased significantly after 21days of bio-stabilization by using
the native bacteria. The calcium carbonate amount was found maximum in the bottom part of the model slope
which indicated the bio-stabilization was more effective at the bottom than the upper part of the slope.

Keywords: Microbial induced calcium precipitation, Slope soil, Model slope, Native bacteria

INTRODUCTION

The calcium carbonate method is a method ofsolidifying soil by binding soil particles to each otherby precipitating calcium carbonate from carbon
dioxide produced by the metabolism of
microorganisms and calcium existing in the soil.

In Japan, where the ratio of topography in themountains and hills of the country occupies about
70%, and the area above 500 m above sea leveloccupies a quarter of the total, a magnitude 6
earthquake occurred in 20% of the world [1]. In
addition, 120 million people live in Japan, which has
an area of 380,000 km2, and many people live near
the mountains, and the climate is temperate and rainy,
so it is susceptible to erosion. There are geo-disasters
such as slope failure and landslides occurred eachyear. With these background, it can be said that it isnecessary to improve the technology to prevent thecollapse of mountain slopes.
Currently, many of them use concrete as ameasure to prevent the collapse of mountain slopes.
This is very effective, but not very suitable given its
appearance and impact on the ecosystem. However,slope stabilization by using chemical cement is notenvironment friendly. MICP is a promisingtechnology in where natural bacteria can be used tosolidify the soil by producing the calcium carbonate
and binds the soil particle and hence increase thegeotechnical properties of the soil [2,3,4]. The MICP
method by using foreign bacteria is not good for biosafety and many times they do not active in different
weather condition. In recent years, it has been
attracting attention that ground improvement
technology by microbial metabolism may have less
impact on the environment. In recent years, researchon the calcium carbonate method has been
progressing as the technology.

・ Urea hydrolysis
𝑪𝑶(𝑵𝑯𝟐 )𝟐 + 𝟑𝑯𝟐 𝑶 → 𝟐𝑵𝑯𝟒 + + 𝟐𝑶𝑯− + 𝑪𝑶𝟐(1)
・ Calcium precipitation reaction
𝑪𝑶𝟐 + 𝑯𝟐 𝑶 → 𝑯𝑪𝑶𝟑 − + 𝑯+
(2)
𝑯𝑪𝑶𝟑 − + 𝑪𝒂𝟐+ + 𝑶𝑯− → 𝑪𝒂𝑶𝑯𝟑 + 𝑯𝟐 𝑶
(3)
So the main objective of this research is to use thenative bacteria to solidify the slope soil in the
Chugoku region of Japan by conducting the direct
shear test and model slope MICP method.

MATERIALS AND METHODS
Culture Solution
The culture solution was prepared using purechemicals, which are generally used in standard
laboratory experimentation. The compositions od theculture solutions are clearly described and presented
in Table 1. The amounts were expressed for 1 liter ofdistilled water. All the chemicals were added to the
distilled water and shook adequately to mix. The
prepared mix was filtered before applying it to the soil.
Cementation Solutions
The cementation solution was prepared by using
99

GEOMATE – Kyoto, Japan, 3-5 November 2021

pure chemicals. The detail of the composition of
cementation solutions is summarized in Table 2. The
composition is shown for 1 liter of distilled water.
The chemicals were mixed with the distilled waterand filtered.

Direct shear tests were conducted as it is a simple
method for measuring the strength according to
ASTM D 3080. In general, the test is performed bydeforming a specimen at a controlled strain rate on ornear a single shear plane determined by theconfiguration of the apparatus. Three or more
samples were tested, each under different normal
stresses, to determine the effects upon shear
resistance and displacement and strength properties.
In this research, the condition of shear displacementof 7 mm, speed of 0.2 mm/min, and the three normalloads ware 50,100 and 150 kN, respectively. The loss
on ignition test was conducted to check the generated
calcium carbonate amount.

Table 1 Composition of the culture solution

Subdtance
Amonium sulfate
Yeast Extract
Tris-buffer
Distilled water

Amount (g)
10
20
15.7
1L

Scanning electron Microscope Images

Table 2 Composition of cementation solution

Substance
Calcium chloride
Urea
Nutrient broth
Distilled water
Sodium Acetate

Scanning electron microscope images werecompleted with a Hitati S-4100 field emission SEM
at an acceleration voltage of 2 kV and magnificationof 100x. Before imaging, cemented samples wereoven-dried for at least 1-day. Soil samples werecollected from the bio-cemented and un-cemented
soil samples.
The above formula expresses the process of carbon
dioxide emission by hydrolysis of urea and themechanism of calcium carbonate precipitation byreacting with calcium ions in the soil. As a measuringmethod, after the solidification of the soil iscompleted, the calcium carbonate content is
examined by an ignition loss test.
The amount to calcium carbonate was measured byusing the ignition loss test. The SEM was also done
to see the change of the calcium carbonate between
the soil particles.

Amount (g)
55.5
30
3
1L
7

The soil used in this study is the slope soil on thepremises of the Faculty of Engineering, Yamaguchi
University, Ube city, Yamaguchi prefecture, Japan
which is located in the Chugoku region and tocompare the natural slope soil commercial available
Mikawa silica sand was also used. The
microorganism used was Psychrobacillus, which iseasily found in the slope soils in Japan [5]. Many
researchers of MICP used Pasteurii, however, thereason why this study uses a microorganism called
Psychorobacillus is that Pasteutii does not inhabit inJapan. Biohazard and other problems will come up if
it will be used in the field. Therefore, as an alternative,
native microbes will be used.

Amount of calcium ions during the experiment
By measuring the calcium ion concentration
during the experiment, it is possible to roughly grasp
the degree of microbial propagation and the degree ofcalcium carbonate precipitation reaction during theexperiment period.
The experiment was conducted for 21 days, and theamount of calcium ions at that time was measured
using LAQUAtwin.

Direct Shear Test

Ignition weight loss test
The ignition loss test is a test to determine thecontent of organic matter contained in soil, and is atest used to obtain the physical and mechanicalproperties of soil.

Fig. 1 Direct shear tester

100GEOMATE – Kyoto, Japan, 3-5 November 2021

Microbial culture conditions
Add 5.0 g of soil containing Psychrobacillus, amicroorganism used in this study, and 45 mL of
distilled water into a beaker to prepare a 10-folddiluted sample solution. Collect 5 mL from the
prepared 10-fold diluted sample solution and mix itwith 45 mL of distilled water in a beaker to prepare a
100-fold diluted sample solution. At the same time,
an agar medium is prepared in a petri dish. The
material of the agar medium is the same material as
the culture solution and 10 g of agar, and the material
is autoclaved to prepare. Apply a 100-fold diluted
sample solution to a petri dish containing agar
medium thinly on the surface with a large stick, andpropagate in an incubator for 2 days at 30 ° C.

Upper

36.5°

Middle

10cm

Lower

13.5cmFig. 2 Schematic diagram of the model slope
From the results, it can be seen that the
microorganisms were actively working in the lower
layer because the content in the lower layer was
higher than the content of calcium carbonate in theupper layer. In this experiment, the nutrient solution
and the like easily permeated, and it is considered thatthe microorganisms themselves were gathered in thelower layer because of the large amount, or that thelarge amount of the microorganisms flowed out
together with the liquid.
In addition, in the calcium carbonate precipitation
method, the content is generally about 3 to 5%.
Therefore, the content of decomposed granite soil in
Ube was almost as expected, but the calcium
carbonate content of Mikawa silica sand was
extremely low. Regarding this, Ube's decomposed
granite soil originally used natural soil, so
microorganisms could easily propagate in the soil, but
Mikawa silica sand was bought soil and has a finerparticle size than Ube's decomposed granite soil.
Since there was no variation and it was constant, thepropagation of microorganisms did not reach thedetails. Therefore, it is considered that even if anutrient solution was added and a precipitation
reaction was expected, it did not work and such a
result was obtained.

Preparation conditions for specimens
Divide into 5 layers in a 60 mm x 20 mm mold,and compact each layer 100 times. The culture
solution is permeated with 8 ml only on the first day,and the nutrient solution is permeated with 8 ml oncea day at a predetermined time. This is done for 21days, and after 21 days, the sample is frozen and thespecimen is taken out. Photo 1 shows a specimen
made of decomposed granite soil in Ube on the left
and a specimen made of silica sand in Mikawa on theright.
The other type is made in a specimen with a widthof 13.5 cm, a height of 10 cm, and a depth of 10 cm.
The culture solution is permeated with 45 ml only on
the first day, and the nutrient solution is permeatedwith 45 ml once a day.

RESULTS AND DISCUSSION
Picture 1 Specimens of decomposed granite soil
(left) and silica sand Mikawa (right)

Direct Shear Test
A constant pressure direct shear test was
performed at restraint pressures of 50, 100, and 150
kN / m².

Biocementation of model slope
Measured separately in the upper layer, middle
layer, and lower layer.

101

GEOMATE – Kyoto, Japan, 3-5 November 2021

250

Shear stress τ (kN/m²)

60

Shear stress τ (kN/m²)

50

40

30

20

10

Biocementation after 21days
No biocementation

0

150

100

50

Biocementation after 21days
No biocementation

0

Vertical displacement (mm)0(c) 150 kN/m³ normal stress
0.000

Vertical displacement (mm)200

-0.020-0.040-0.060-0.080

-0.05
-0.1

-0.15
-0.2
-0.25
-0.3
-0.35
-0.4
-0.45

-0.5
0

-0.1001

2

3

4

Shear displacement (mm)-0.120-0.140This figure compares before and after the-0.160
0

1

2

3

4

Shear displacement (mm)Fig. 3 Variation of vertical displacement withshear displacement(a) 50 kN/m² normal stresscalcium carbonate experiment on a specimen of
decomposed granite soil in Ube. As can be seen fromthe figure, the shear stress of the sample in which theculture solution and the nutrient solution were
impregnated with microorganisms for 21 days waslarger than that of the sample before the experiment.
In other words, it was found that soil solidification by
microorganisms is possible. In particular, the shear
stress of the specimen with a confining pressure of
150 kN / m² increased significantly.

90

70
60
50

250

40

After Treatment
30
20

Shear stres τ(kN/m²)

Shear stress τ (kN/m²)

80

Biocementation after 21days
No biocementation

10

0

Vertical displacement (mm)

0
-0.1
-0.2

Before Treatment

200

150

100

50

-0.3

0
0

-0.4

50

100

150

200

250

Normal stress σ(kN/m²)

-0.5

-0.6
0

1

2

3

4

Fig. 4 Relationship of shear stress and the normal
stressFrom Fig. 4, the adhesive strength of the specimen on
the first day was 7.5 kN / m² and the effective stress
φd was 30.4 °, and the adhesive strength of thespecimen soaked with nutrient solution for 21 days
was 22.5 kN / m² and the effective stress was 36.6 °.
became. Both the adhesive strength and the effective
stress are increased in the specimens infiltrated with
the nutrient solution. From this, it is considered thatthe experiment in this study can raise the soil strength.

Shear displacement (mm)(b) 100 kN/m² normal stress102

GEOMATE – Kyoto, Japan, 3-5 November 2021

may not have penetrated into the details of the soil, or
it may have been difficult to penetrate. It is also
possible that, although unlikely, the soil in the upper
layer had solidified, and the air did not reach the soil,
hindering the reaction of microorganisms.
From these, reduce the amount of nutrient solution
and culture solution from 45 ml, change the size ofthe specimen because the scale of the specimen was
small and it was difficult to perform an experiment tomeasure the strength of the specimen, and once
propagate the microorganisms in Mikawa silica sand
Improvements such as starting the experiment afterthat are necessary.

Ignition weight loss test
The precipitation rate of calcium carbonate after
21 days was 4.97% for decomposed granite soil and
2.72% for Mikawa silica sand. In the Ube
decomposed granite specimen, the precipitation rateof calcium carbonate is slightly higher. Even if it is
not saturated, it exceeds the precipitation rate, so it isconsidered important to allow microorganisms topropagate on the first day in solidifying the soil. Since
it was confirmed that the Mikawa silica sand was
precipitated at a low rate of 2.72%, it was found thatcalcium carbonate was precipitated even in soil witha substantially constant particle size.

CONCLUSIONS

Calcium ion amount for 21 days

Since it was confirmed that the calcium carbonate
content increased due to the change in culture
conditions, the strength of the solidified soil can be
expected to improve.
Calcium carbonate precipitation rate is lower thanthat of the cylindrical model because thesolidification cannot be maintained in the upper layer
mainly in the inclined model.
As the number of curing days increases, theprecipitation rate of calcium carbonate also increases.
The action of microorganisms could be confirmed
from the calcium ion concentration for 21 days when
the culture conditions were changed.
It was found that the shear stress of the cylindricaltest piece increased due to changes in culture
conditions.
As the number of curing days increases, theprecipitation rate of calcium carbonate also increases.

Fig. 5 Variation of calcium ion with time
The maximum content during this period was
660ppm for decomposed granite soil in Ube on the
5th day and 170ppm for Mikawa silica sand on the
5th day. Other papers also measured the highest
content on 5-7 days, which was in line withexpectations. However, at the scale of the specimen
in this study, the maximum content should have beenmeasured around 1000ppm. It is considered that themain reason for this is that the specimen could not be
kept saturated because it was a specimen with a slope.
In addition, the specimens using Mikawa silica
sand are far below the expected values. This isbecause the particle size of Mikawa silica sand isalmost constant, so it is thought that the propagationof microorganisms in the soil did not go well.
Regarding the fact that the graph has a convex
shape with the apex on the 5th day, themicroorganisms reached the specimen on the 5th day
in the scale of the cylindrical specimen in this study.
As a result, it is considered that the reaction of the
microorganisms appeared remarkably when the
nutrient solution was infiltrated. In addition, thereaction gradually decreased in the latter half of theexperimental period. This is related to the fact that it
took longer for the nutrient solution to penetrate in thelatter half. In other words, the precipitation reaction
was active in the upper part of the specimen in thefirst half of the experiment, so the nutrient solution

ACKNOWLEDGMENTS
The authors acknowledged the financial help of theGeo-science Center Foundation for conducting the
research.
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GEOMATE – Kyoto, Japan, 3-5 November 2021

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[8]. Dhami, N.K., Reddy, M.S., Mukherjee, A.
Significant indicators for biomineralization insand of varying grain sizes. Constr. Build. Mater.
104, 2016, pp. 198-207[9]. Stocks-fisher, S., Galinat, J.K., Bang, S.S.
Microbiobial precipitation of CaCO3. Soil
Biology and Biochemistry Vol. 31(11),199, pp.
1563-1571[10]. Bao, R., Li, J., Chen, L.. Effect of microbial
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10-18.

104

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

DESIGNING A TRAPEZOIDAL MODULAR BLOCK WALL WITHNONLINEAR OPTIMIZATION
Vuttichai Chatpattananan1, Somchai Prayongphan2, Thanadol Kongsomboon3, andVatanavongs Ratanavaraha4
1,3School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Thailand;
Department of Civil Engineering, Kasetsart University, Kamphaeng Saen, Nakhon Pathom, Thailand;
4
Suranaree University of Technology, Nakhon Ratchasima, Thailand

2

ABSTRACTA rectangular wall is better in terms of stability and ease of calculation than a trapezoidal wall. However, a
trapezoidal wall is sometimes inevitable such as a retaining wall construction near rockface. FHWA provides
simplified rules to design a trapezoidal wall. However, FHWA does not give an example to follow, and the rules
need trial and error to implement. BS8006 gives an exact dimension of the block heights, but designing still needs
to adjust the block widths. A 16-meters high modular block wall project near rock face in Thailand as an example
to illustrate a calculation detail in external stability checking follow FHWA simplified rules and BS8006. The
illustrations are trapezoidal walls with two zones, three zones, and four zones. Nonlinear optimization models arealso used to minimize the wall base length to facilitate the construction instead of jacking the near rock face tobuild a rectangular wall. Optimization models also help to relax FHWA simplified rules and BS8006 guidelines.
Using an optimization model can decrease the base length from 0.7H to 0.6H for a rectangular wall or even 0.5H
for a rectangular wall with competent foundation soil. Optimization models can also achieve a base length down
to 0.48H with a decrease in the cross-sectional area down to 0.92 for a three zones trapezoidal wall. A simple threezones wall with exact dimensions is also proposed in the competent foundation soil conditions.
Keywords: Nonlinear optimization, MSE wall, Trapezoidal wall, Uneven reinforcement lengths

INTRODUCTION

preliminary minimum reinforcement length (L) of
0.7H. FHWA also recommends L/H to be not greaterthan 1.1. Shored MSE wall [3] suggests that thereinforcement length can be as narrow as 0.3L/H.
Design for external stability for a rectangular
MSE wall is simply to specify the reinforcement
lengths to handle the sliding, overturning, and bearing
capacity. The design for internal stability is slightly
different for the SPCP wall and MBW wall. SPCP
wall, which is an inextensible wall, adjusts thenumber of metal strips, spacings, and lengths to resist
the rupture and pullout mainly from lateral earth with
its trapezoidal (or bilinear) maximum tension forceline. MBW wall, which is an extensible wall with lessrigid reinforcements than SPCP wall, adjusts thegeogrid tensile strengths with its triangular (or linear)
maximum tension force line to resist the pullout.
Global stability design is implemented by using either
the limit equilibrium method (LEM) or finite element
method (FEM).

A mechanically stabilized earth wall or MSE wall
is a retaining wall that allows more displacement thana rigid reinforced concrete wall. There are two MSE
wall types commonly used, which are segmental
precast concrete panel (SPCP) and modular blockwall (MBW). SPCP uses precast concrete panel as its
facing unit and metal strips as its reinforcements,
while MBW uses modular block as its facing unit andgeogrid as its reinforcements. Reinforcements help to
add tension because the tensile strength of soils is
negligible.
Designing a Rectangular MSE Wall
An MSE wall has to resist the failure modes inexternal stability, internal stability, and global
stability. External stability requires this rigid body
MSE wall to pass the safety factor in sliding,overturning and bearing capacity. Internal stability
prevents the failure inside the MSE wall by setting
safety factors in rupture and pullout. Global stability
considers all other failures that may occur anywhere,
especially outside the MSE wall.
Designing MSE deals with specifying thereinforcement length, reinforcement length spacing
reinforced soil strength property. For a rectangular
MSE wall, FHWA [1] and BS8006 [2] give a

Designing an Uneven Length Reinforcement MSE
Wall
Uneven length reinforcement MSE wall is needed
due to the limited right of way base, near rock facethat is difficult to widen the base, and shored MSE
wall is also not an optional. FHWA gives simplified
rules used in external stability checking for uneven
105GEOMATE – Kyoto, Japan, 3-5 November 2021

length reinforcement MSE walls as follow: 1) thewall is represented by a rectangular block (Lo, H)
having the same total height and the same crosssectional area as the trapezoidal section for externalstability calculations, as shown in Fig. 1 (left). 2)
minimum base length (L3) is 0.4H, with the differencein length in each zone being less than 0.15H. 3)
Internal stability is designed using the same methods
as the rectangular MSE wall. 4) Global stability
analysis is also needed in the design. 5) Trapezoidal
wall is considered given a rock base or competent
foundation soil.
BS8006 gives more specific trapezoidal wall
dimensions. Fig. 1 (right) shows the three zones wall
where Z1 = 0.5H, Z2 = 0.75H, Z3 = H, L1 > 0.4H,  L<
0.15H. Dm is wall embedment length. For the twozones wall, BS8006 recommends minimum
reinforcement lengths at the top at 0.7H and at thebottom at 0.4H or 3 meters. BS8006 also recommends
0.55 < L/H < 0.75 and 0.125 < Sv /H < 0.222 where
Sv is reinforcement vertical spacing.

H

L2
L3

FHWA

From these studies, there is no example showing
a design of uneven reinforcement lengths MSE wall.
There exist guidelines such as BS8006 and thesimplified rules provided by FHWA to design thisunconventional trapezoidal wall geometry. Hence,
this study aims to provide an example in designing
this uneven reinforcement lengths MSE wall.
A 16-meter high retaining wall project in Thailand
is chosen to illustrate the design. Four different
geometries of MSE walls are proposed. The first wall
is a rectangular MBW wall. The second wall is a 2block trapezoidal MBW wall. The third wall is a 3block trapezoidal MBW wall. The fourth wall is a 4block trapezoidal MBW wall.
Nonlinear optimization using GAMS (General
Algebraic Modeling Software) program with NLP
solver [9] is also used to dimension these MBW walls.
The optimization models mainly follow the
simplified rules from FHWA and BS8006. The
nonlinear optimization models consider both external
and internal stability checking. Internal stability canbe adjusted by varying the geogrid reinforcement
lengths and spacings for each block. Global stability
is omitted due to space limitations.
This study helps illustrate the design of an unevenreinforcement length MBW wall. The optimization
models proposed also give an approach indimensioning the 2-block, 3-block, and 4-block
trapezoidal MBW walls. A simple 3-block wall withexact dimensions is also proposed. Optimality andconvergence problems for nonlinear optimization are
also addressed.

Z2
Z3

L2
L3

L0

Fig. 1H

Aim, Limitations, and Significance of the Study

Z1

L1

L1

meters high and 4 meters base width.

L L

Dm
BS8006

Dimensioning uneven reinforcement lengths
wall guided by FHWA [1] and BS0006 [2]Nonlinear Optimization Techniques in Designing
Uneven Length Reinforcement MSE Wall

WALL GEOMETRY AND PARAMETERS
Wall Geometry

Optimization techniques have been applied
popularly in civil engineering for both practitioners
and researchers. The techniques are well suited tohandle problems on hand that demand solutions underlimitations. Uneven MSE wall design, which is aretaining wall design, is also an optimization problem
that seeks a safe and the most economical solution
under its narrowest base limitation.
Numerous optimization techniques have been
applied to retaining wall designs, such as Dungca [4],which applies linear optimization of soil mixes indesigning vertical cut-off walls. Optimization is also
applied in some other areas, such as Bala [5], in mix
design optimization of asphalt mixtures.
Narrow retaining walls near rock faces are
addressed in some studies. Chen [6] pays attention to
its translational mode. Leshchinsky [7] developed adesign chart for narrow SPCP walls. Dai [8]illustrated a project of uneven reinforcement lengthsof trapezoidal walls, which is a shored MSE wall 8

Geometry for nonlinear optimization models isshown in Fig. 2. Those models are the rectangular (1block) wall, the trapezoidal walls with two zones (2block), three zones (3-block), and four zones (4block). Those walls are MBW walls, not the SPCP
wall, because its resistant zone is triangular (linear)according to FHWA.
45+/2Fig. 2

Wall geometry for the three walls

Parameters
106GEOMATE – Kyoto, Japan, 3-5 November 2021

M is the overturning moment in kN-m/m.
MR is the resisting moment kN-m/m
MRBP is the resisting moment in the bearing pressurein kN-m/m.
𝐿′ is the effective bearing width in m.
V is the maximum bearing pressure in kN/m2.
utl is the ultimate bearing capacity of the foundationsoil in kN/m2.

Materials
The reinforced soil density, r, is 18 kN/m3
The reinforced soil friction angle, r, is 32 degrees
The back soil density, b, is 18 kN/m3
The back soil friction angle, b, is 30 degrees
The foundation soil density, f, is 18 kN/m3
The foundation soil friction angle, f, is 35 degrees
The soil cohesion, c, is 0 kPa.

The design calculation details from FHWA [1] areas follows.

Bearing capacity parameters
The bearing capacity parameters are defined by thefollowing equations from Meyerhof [10].
For the foundation soil, Nqf = 33.3, Ncf = 46.1, Nf =37.2. For the reinforced soil, Nqr = 23.2, Ncr = 31.7,
Nr = 22.

Factor of safety - sliding
𝐹𝑆𝑆 =

𝑃𝑅

𝐹𝑆𝑂 =

𝑀
𝑀𝑅𝑂

RECTANGULAR MBW WALL=

⁄2𝛾𝑏 𝐻 2 𝐾𝑎𝑏 +𝑞𝐻𝐾𝑎𝑏𝑉(𝐿⁄2)(1)(2)

𝐹(𝐻⁄3)+𝐹𝑞 (𝐻⁄2)𝐿

𝑀𝑅𝐵𝑃 −𝑀

2

𝑉+𝑉𝑞

𝐹𝑆𝐵 =

The geometry of the rectangular MBW wall or 1block wall, as well as its force diagram, is shown in
Fig. 3.

𝐿

𝐻

𝐻

𝐿

𝛾𝑏 𝐻𝐿(2)−𝐹( 3 )−𝐹𝑞 ( 2 )2𝛾𝑏 𝐻𝐿+𝑞𝐿

= −

(3)

𝜎𝑢𝑙𝑡
𝜎𝑉

=

0.5𝐿′𝛾𝑓 𝑁𝛾𝑓(𝑉+𝑉𝑞 )/𝐿′

0.5(𝐿−2𝑒)𝛾𝑓 𝑁𝛾𝑓= (𝛾𝑏 𝐻𝐿+𝑞𝐿)/(𝐿−2𝑒)(4)

Under FHWA [1] design criteria for externalstability checking, L > 0.7H, FSS > 1.5, FSO > 2.0, e< L/6 and FSB > 2.5. FHWA also recommends global
stability factor of safety > 1.3.

q

1-block Wall Optimization Models
F
The optimization model for 10 meters high 1block wall is done by relaxing the design criteria of L
> 0.7H and tries to minimize L. Note that theconstraint L > 0.3H is added to guarantee theoptimality.

b, bHFq

V

f , fR

𝛾𝑏 𝐻𝐿𝑡𝑎𝑛∅𝑓=1

Factor of safety – bearing capacity

1-Block Wall Geometry

r , r

𝐹+𝐹𝑞

Eccentricity (m)
𝑒= −

Vq

𝑉𝑡𝑎𝑛∅𝑓

Factor of safety - overturning

Other parameters
The wall height, H, is 16 meters.
The surcharge, q, is 10 kN/m2.
The retained soil backslope, , is 0 degrees.
The active earth pressure coefficient is from Rankine
equation [10]. Kab for back soil is 0.31, and Kar for
reinforced soil is 0.33.

q

=

𝑃𝐷

e
1-block L/6 optimization model (1-block L/6)
L
Minimize
Subject toFig. 3 1-block rectangular wall force diagram
FHWA Design Criteria
Additional notations
V is the vertical load in kN/m.
Vq is the vertical surcharge load in kN/m.
F is the horizontal load in kN/m.
Fq is the horizontal surcharge load in kN/m.
PR is the resisting force in kN/m.
PD the driving force in kN/m.

L
FSS in (1) > 1.5
FSO in (2) > 2.0
e in (3) < L/6
FSB in (4) > 2.5
L > 0.3H(5)

FHWA also recommends maximum allowableeccentricity for competent foundation soil at L/4.
Another 1-block optimization model for competent
foundation soil is developed by replacing e in (3) <107

GEOMATE – Kyoto, Japan, 3-5 November 2021

L/6 in (5) with e in (3) < L/4.
Using an optimization model can decrease thebase length from 0.7H to 0.6H for a rectangular wallor even 0.5H for a rectangular wall with competent
foundation soil.

outside the global stability with 1.3 factor of safety;2) Ensure that the reinforcement lengths are beyond
the MBW internal stability resistant zone at𝑡𝑎 𝑛(45 + ∅2 ⁄2) ; 3) Adjust to the actual
construction restriction such as the slope needed to
avoid jacking the rock surface at the back of the wall;4) Balance the moment equilibrium at point O’ in Fig.
4 that geogrid hold enough strength to hold the rightwing of the top block to not fall due to the insufficientbearing capacity of the foundation soil. The authors
use reason 4) in setting the rear slope angle or the top
block height by taking the moment equilibrium atpoint O’ in Fig. 4, stated as follows:

2- BLOCK TRAPAZOIDAL MBW WALL
2-Block Wall Geometry
The geometry of the 2-block trapezoidal MBW
wall or 2-block wall and its force diagram is shown in
Fig. 4.
FHWA Simplified Rules Optimization Models(FHWA Model)q

𝐿𝐵 𝐻𝐵 + 𝐿 𝑇 𝐻𝑇 = 𝐿𝑜 𝐻

x
HT

HB

LT/2
VT
e O’
T

RT
R

FT

Fq
F



e

LB
Lo
LT

Fig. 4 2-block trapezoidal wall force diagram

Different zone length(7)

Top block height

Another FHWA simplified rule is the minimum
base width, or base reinforcement length (LB) is 0.4H,
stated as:

1
𝐻𝑇𝐻𝑇
𝐾𝑎𝑟 𝛾𝑟 𝐻𝑇 2 ( ) + 𝐾𝑎𝑟 𝑞𝐻𝑇
≥
2
3
2
𝐿 −𝐿
𝐿 −𝐿
𝛾𝑟 𝐻𝑇 (𝐿 𝑇 − 𝐿𝐵 ) 𝑇 𝐵 + q(𝐿 𝑇 − 𝐿𝐵 ) 𝑇 𝐵
22(9)

Minimum base length
𝐿𝐵 ≥ 0.4𝐻

V

H

(6)

where the minimum Lo is 0.7H.
This simplified rule is simply to guarantee that thetrapezoidal wall gains a little less sliding factor ofsafety in (11) than the original rectangular wall. The
decrease in the trapezoidal factor of safety is due tothe extra length (LT – Lo) of the surcharge load (q).
FHWA simplified rule also limits the different
zone lengths to be less than 0.15H. This rule can bestated as:

𝐿 𝑇 − 𝐿𝐵 ≤ 0.15𝐻

q

Vq

Simplified rules from FHWA are applied todesign this uneven reinforcement length wall. The
equal sectional area can be stated as:
Equal areas

In (9), the moment from geogrid strength derived
from the lateral earth pressure and the surcharge(MORW) must be greater than the moment from theright-wing weight of the top block (MRRW). To keep
good stability, the overturning factor of safety of thisright-wing block (FSORW) should be close to 1.
Internal stability is also in consideration. FHWA
requires the reinforcement lengths at all layers in theresistant zone to be at least 1 meter, stated as follows.

(8)

Accompanying with FHWA simplified rules in(6), (7), and (8) to specify the base layer height (HB)
and the top layer height (HT) in Fig. 4, one more
relationship between the top layer height (HT) and the
top layer width (LT - LB) is needed. This relationshipcan be set through the top rear slope angle (T) in Fig.
4. BS8006 in Fig. 4 for 3-block wall also implies thatthe upper block height should be greater than thelower block height and the different zone lengths as
Z1 = 0.5H, Z2 = 0.75H, Z3 = H, L1 > 0.4H,  L< 0.15H.
The rear slope is from 1.67V1H to 3.33V1H.
In the authors' opinion, the rear slope is set for thefollowing reasons; 1) Facilitate the slip plane to be

Internal stability
𝐿𝐵 ≥ 𝐻𝐵 tan(45 + ∅𝑟 ⁄2) + 1(10)

An optimization model for a 2-block wall
108

GEOMATE – Kyoto, Japan, 3-5 November 2021

following FHWA simplified rules for externalstability to minimize the base width (LB) is set as
follows:
2-block FHWA optimization model (2-block FHWA)
Minimize
Subject toeccentricity) and internal stability in (10) are checkedat both the top block and the whole 2-block wall. The
2-block proposed model is similar to the 2-block
FHWA in (11) and BS8006 model except that theequal areas constraint in (6) and minimum top
reinforcement length at 0.7H are relaxed. Wallstability is guaranteed through external and internalstability of the top block and the whole 2-blocks.

Base length (LB)
FSS in (1) of top block > 1.5
FSO in (2) of top block > 2.0
eT in (3) of top block < LT/6
FSB in () top block > 2.5
FSS in (1) of 2-block > 1.5
FSO in (2) of 2-block > 2.0
e in (3) of 2-block < 𝑥̅ /3
FSB in (4) 2-block > 2.5
Equal areas in (6)Different zone lengths in (7)Minimum base in (8), LB > 0.4H
Top block height in (9)Internal stability in (10)LT > 0.7H
LT - LB > H/20
(11)

Comparison of 2-Block Wall Models
The optimized result for the three optimized
models for 2-blocks walls is shown in Table 1. The
different zone lengths (7) and top block height (9) arebinding constraints for all three models. The
additional binding constraint for the FHWA model in(11) is the equal areas (6). The additional constraint
for the BS model is the top reinforcement length. The
additional constraint for the Proposed model is theeccentricity.
Table 1 2-block wall output

In (11), 2-block means the whole block, including
the top block and the bottom block. 𝑥̅ in (11) shownin Fig. 4 is the center of mass for the whole block.
Meyerhof's effective bearing length for the 2-blockwall is replaced 𝐿′ = 𝐿 − 2𝑒 with 𝐿′ = 𝑥̅ − 2𝑒. For
top-block wall, 𝐿′ 𝑇 = 𝐿 𝑇 − 2𝑒𝑇 .
The minimum different zone length LT - LB > H/20
and the minimum bottom block height HB > 0.15H
are added to set lower bounds for this nonlinear
optimization model to avoid the optimality andconvergence problems. H/20 in the minimum
different zone constraint is set following FHWA in
designing superimposed MSE walls that if thedifference is offset for the upper wall and the lower
wall is less than H/20, the two-tier wall can be treatedas one wall. The bottom block height can be adjusted
lower or higher according to the rock face at the backof the wall.

Description

FHWA

BS

Proposed

LB /H

0.64

0.55

0.52

LT /H

0.79

0.7*

0.67

(LT – LB )/H

0.15*

0.15*

0.15*

HT/H

0.42*

0.42*

0.42*

FSORW HT

1.0*

1.0*

1.0*

2-block FSS

2.75

2.41

2.30

2-block FSO

4.04

3.11

2.84

2-block e (m)

1.33

1.52

1.59*

2-block al. e (m)

1.88

1.65

1.59

2-block FSB

5.68

3.79

3.21

179.2*

156.9

150.0

1.0*

0.88

0.84

2

Area (m )2Area/(0.7H )Note: 2-block means the whole block. Top means the top block. al.e means allowable eccentricity. * is the binding constraint.

BS8006 Optimization Model (BS Model)
BS8006 recommends the top block reinforcement
length to be at least 0.7H, the bottom reinforcement
to be at least 0.4H, and the different zone lengths tobe less than 0.15H. Equal areas constraint is not
imposed as FHWA. The 2-block BS optimization
model is similar to the 2-block FHWA model in (11)except that the equal areas constraint in (6) is relaxed.

The top block external stability is sliding,overturning, and bearing capacity are not the criticalconstraints in the design. This can be seen from its
factors of safety and eccentricity. Practically, top
block stability can be ignored in the design.
3-BLOCK AND 4 BLOCK TRAPEZOIDALMBW WALLS

Relaxing Minimum Top Length Model (Proposed
Model)The 3-block and 4-block MBW wall use the same
design parameters as the 2-block MBW wall. Its force
diagram is similar to Fig. 4 of the 2-block wall, exceptthat there is one middle block between the top and thebase block for the 3-block model, and there are two
middle blocks for the 4-block model. Table 2

In decreasing the base length, the minimum top
length at 0.7H is relaxed. Omitting LT > 0.7H still
guarantees the wall stability since both externalstability (sliding, overturning, bearing, and109

GEOMATE – Kyoto, Japan, 3-5 November 2021

compares only the proposed model for rectangular 1block, trapezoidal 2-block, 3-block, and 4-block
optimized models.
From Table 2, 4-block performs better than 3block, 2-block, and 1-block models in base length(LB) with the trade-off in increasing top length (LT).
In terms of the area, 1-block and 2-block are better
than 3-block and 4-block because they have fewer
constraints to achieve. For trapezoidal 2-block, 3block, and 4-block, foundation soil bearing capacity(FSB) is a binding constraint. The follows therecommendation by FHWA that trapezoidal wallshould be considered under competent foundationsoil conditions.

three zones wall seems to be the best option in termsof the minimum base length and optimality difficulty.
The three zones wall proposed by this study can
reduce the base length ratio (LB/H) to 0.48 and reducethe cross-sectional area down to 0.92. from the areaof 1.0 of rectangular walls with the reinforcement
ratio of 0.7L/H. A simple three zones wall without
optimization following BS8006 guideline with
0.5125H base length is also recommended.
REFERENCES[1] FHWA-NHI-00-043, Mechanically stabilized
earth walls and reinforced soil slopes design andconstruction guidelines, U.S. Department ofTransportation–
FederalHighway
Administration, 2001.
[2] BS8006,
Codeofpracticeforstrengthened/reinforced soils and other fills, The
British Standards Institution, 2010.
[3] FHWA-CFL/TD-06-001, Shored Mechanically
Stabilized Earth (SMSE) Wall Systems Design
Guidelines, U.S. Department of Transportation–Federal Highway Administration, 2006.
[4] Dungca J., Galupino J., Sy C., Chiu S. F., Linear
Optimization of Soil Mixes in the Design ofVertical Cut-off Walls. International Journal of
GEOMATE, Vol.14, Issue 44, 2018, pp.159-165.
[5] Bala N., Napiah M. and Kamaruddin I.,
Application of Response Surface Methodology
for Mix Design Optimization of Nanocomposite
Modified Asphalt Mixtures, International
Journal of GEOMATE, Nov., Vol.13, Issue 39,
2017, pp.237-244.
[6] Chen F., Yang J. and Lin, Y., Active Earth
Pressure of Narrow Granular Backfill against
Rigid Retaining Wall Near Rock Face underTranslation Mode, International Journal of
Geomechanics, Vol. 19, Issue 12. 2019,[7] Leshchinsky D., Hu, Y. and Han, J., Limited
Reinforced Space in Segmental Retaining Walls,
Geotextiles and Geomembranes, Vol. 22, No. 6,
2004, pp. 543-553.
[8] Dai C., and Livingston J., MSE Wall with
Uneven Reinforcement Lengths (Trapezoidal
Walls) – Design Development and Challenges,
Proceedings of China-Europe Conference on
Geotechnical Engineering pp 1668-1671.
[9] Brooke A., Kendrick D., Meeraus A., Raman R.,
GAMS: A User’s Guide, GAMS Development
Corporation, 1992.
[10] Bowles J. E., Foundation Analysis and Design,
McGraw-Hill, 2001.

Table 2 Comparison of the Proposed model output
Description

1

2

3

4

LB /H

0.6

0.52

0.48

0.48

LT /H

0.6

0.67

0.78

0.87

FSS

2.34

2.30

2.66

2.79

FSO

2.90

2.84

3.82

4.26

e (m)

1.59*

1.59*

1.36

1.28

al e (m)

1.59

1.59

1.84

1.96

4.76

2.5*

2.5*

2.5*

152.6

150.0

165.5

181.9

0.85

0.84

0.92

1.02

FSB
2

Area (m )2Area/(0.7H )Note: 1 is the rectangular wall in (11). 2 is the 2-block wall. 3 is a
3-block wall. 4 is the 4-block wall. * is the binding constraint

The authors suggest using three zones block is thebest option since the 4-block needs to use a subjective
upper bound of the second top height to avoid
infeasibility. A simple rule recommended by theauthors is to combine both the FHWA simplified
rules in equal areas and BS8006 guidelines resulted
in a simple three zones block with Z1 = 0.5H, Z2 =0.75H, Z3 = H,  L= 0.15H, L1 = 0.5125H. The
dimension of this three zones wall is simple to use
with sufficient stability under competent foundationsoil conditions.
CONCLUSIONS
Optimization techniques can help design MSE
walls with economic and safe solutions. The
reinforcement length can be decreased from 0.7H to
0.6H for or even 0.5H for a rectangular wall withcompetent foundation soil. For a trapezoidal wall, the11011th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

ESTIMATION OF SUBSURFACE STRUCTURE OF LANDSLIDE AREA
BASED ON MICROTREMOR OBSERVATION IN THE HOJOJIMA,NAWASHIRO AND AMEDAKI AREA, TOTTORI, JAPANTatsuya Noguchi1, Isamu Nishimura1 and Takao Kagawa1
1

Faculty of Engineering, Tottori University, Japan
ABSTRACT

Investigating the ground structure of a landslide area is important in considering the hazards of slope disasters
in that area. In this study, microtremor exploration was carried out in three areas (Hojojiima, Nawashiro andAmadaki) in Tottori prefecture that were judged to be landslide topography, and the ground structure was estimated.
The target areas are based on the landslide topography distribution map published by the National ResearchInstitute for Earth Science and Disaster Prevention. As a result, it was found that the characteristics of tremors in
the target area differ between landslide moving mass, main scarp, and original ground areas. In addition, thevelocity structure and thickness distribution of the landslide sedimentary layer in each area could be estimated. In
the Hojojima area, where the building was damaged by the 2016 Central Tottori Earthquake, it was found thatlandslide moving mass was deposited on the alluvium. In the Nawashiro area, landslides occurred in three areas,
and it was found that the geology and sedimentary layer thickness differed in each area. In the Amedaki area, it
was found that the soft sedimentary layer of the moving mass was thin and widely distributed. In the future, it will
be necessary to evaluate landslide hazards based on this ground structure information.
Keywords: Landslide area, Microtremor, Subsurface structure, Tottori Prefecture

INTRODUCTION

ground motions triggered widespread landslides in
the area. Therefore, it is important to investigate theground structure in landslide areas in order toconsider slope hazards in those areas.
In this study, microtremor surveys wereconducted in three areas (Hojojima, Nawashiro andAmedaki area) in Tottori Prefecture, Japan (Fig.1)
that were determined to be landslide terrain based on
the landslide topography distribution map publishedIn recent years, earthquakes have caused largescale landslide damage. Strong ground motions
caused widespread landslides in the 2009 West
Sumatra Earthquake [1] and the 2018 Hokkaido
Eastern Iburi Earthquake [2]. Volcanic ash andweathered rock strata exist in the area, and landslides
may have occurred repeatedly in the past. The strong

Fig. 1 Location of observation area (red frame)111GEOMATE – Kyoto, Japan, 3-5 November 2021

observations were conducted to estimate the S-waves
velocity structure. A three-component accelerationtype seismometer (JU410) was used for theobservations. JU410 is a device that can record
microtremors with a period of 0.05 - 10 s.
A total of 80 sites three-component single-site
observations were conducted at Hojojima (35 sites),
Nawashiro (28 sites), Amedaki (17 sites), at intervalsof 100 m to 200 m. The sampling frequency for themeasurement was 200 Hz, and the observation timewas 10 min for each site.
Array observations were carried out at 4 sites: 2
sites at Hojojima (HSAR01, HSAR02), Nawashiro(NSAR01), and Amedaki (AMAR01). In Hojojima,
the ground structure is assumed to be different
depending on the topography, and array observation
points were set up at two locations with different
topography. In the array observation, one point wasplaced at the center of the circle and three points wereplaced at equal intervals on the circumference of thecircle, and the radius of the circle (array radius)
ranged from 1 to 20 meters. Each device wassynchronized using a GPS clock. Since the power ofthe microtremors was expected to be small, three tofour people jumped at the same time at about 5 mfrom the outer seismometer to give an artificial
seismic source and observed the waveform. The
sampling frequency for the measurement was 200 Hz,
and the observation time was 10 - 15 min for eacharray.
ANALYSIS
For the analysis of the three-component singlesite observation data, at least 10 sections of 20.48 s
periods without artificial noise were selected from thearray observation records. The spectra were smoothedusing a log-window [6] with a coefficient of 20. The
averaged Fourier spectra of the selected data for eachcomponent was used to compose the horizontal
components and the horizontal-to-vertical spectral
ratio (H/V spectrum) was calculated. The
predominant periods were read from the H/V spectra
obtained at each site. The H/V spectra obtained fromthe seismometer at the center of the array observation
are shown in Fig. 2 (NS, North-South; EW, EastWest; Comp, composition, where the red dashed lines
are the theoretical values of the Rayleigh waves using
the subsurface structural model). The distribution of
H/V spectral predominant periods for each region
area shown in Fig. 3.
For the array observation data, Using the open
analyzing tool [7], the phase velocity dispersion
curves were estimated based on the CCA method [8].
The conditions for the analysis are as follows: At least
five sections were selected through an automatic
extraction using the RMS value of the microtremor
record of each seismograph with a segment length ofFig. 2 H/V spectra of microtremors
by the National Research Institute for Earth Scienceand Disaster Prevention [3] [4]. In the Hojojima area,
where the building was damaged by the 2016 Central
Tottori Earthquake [5]. The microtremor data wereanalyzed to understand the ground motion
characteristics in the target area. In addition, the layer
thickness distribution of the landslide moving mass
was determined by estimating the ground structure.
OBSERVATION
For microtremor observations, single-site
observations of three-components were conducted todetermine the dominant period and amplification
characteristics of the subsurface structure, and array112

GEOMATE – Kyoto, Japan, 3-5 November 2021(a) Hojoshima area(b) Nawashiro area

Fig. 4 Dispersion curves of phase velocity
10.24 s. The power spectrum of these sections was
smoothed using a Parzen window with a bandwidthof 0.3 Hz and then averaged. Using the power
spectrum of each seismograph, the phase velocity
dispersion curve was obtained based on the CCA
method [8]. The dispersion curves of the phasevelocity for each site are shown in Fig. 4 (solid lines
are curves obtained for each radius. Red dashed lines
are the theoretical values of the Rayleigh waves using
the subsurface structural model). We determined thesubsurface structures to satisfy the H/V spectra andthe dispersion curves of the phase velocity by using
the fundamental mode of Rayleigh waves within thefrequency range of 2 - 40 Hz. The subsurface(c) Amadaki area
Fig. 3 Distribution of predominant period
113

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1 Subsurface structural models
HSAR03

HSAR01Thickness
(m)
1.5
6
100
200
∞

ρ
(g/cm3)
1.7
1.9
2.0
2.1
2.2

Vp
(m/s)
1420
1500
1950
2300
2600

Vs
(m/s)
120
200
600
900
1200

Thickness
(m)
3
4
13
15
35
150
∞

Vs
(m/s)
200
350
600
900
1200

Thickness
(m)
5
8
100
250
∞

NSAR01Thickness
(m)
6
15
100
250
∞

ρ
3

(g/cm )
1.7
1.8
1.9
2.1
2.2

Vp
(m/s)
1460
1560
1970
2300
2620

ρ

Vp

(m/s)
(g/cm3)
1.6
1450
1.7
1480
1.8
1620
1.9
1730
2.0
1840
2.1
2170
2.2
2280
AMAR01ρ
3(g/cm )
1.6
1.7
1.9
2.1
2.2

Vp
(m/s)
1460
1570
1970
2300
2620

Vs
(m/s)
145
170
300
400
500
800
900

Vs
(m/s)
130
250
600
900
1200

Table 2 Average S-wave velocity

HSAR01
HSAR02
NSAR01
AMAR01

S-wave velocity (m/s)
184
315
307
204

structures were determined using forward
calculations based on geological situations from
borehole data [9]. The parameters of the subsurface
structure models are the number of layers, density, Pwave velocity: Vp, S-wave velocity: Vs, and the layer
thickness. Densities were set referring previous
research in Japan [10], and P-wave velocities were set
from S-wave velocities [11]. The parameters of theobtained subsurface structure model are shown in
Table 1.
The predominant period of the H/V spectrum isconsidered to correspond to the layer thickness of thesedimentary layer of the landslide moving mass.
Therefore, the average S-wave velocity of thesedimentary layer of the landslide moving mass wascalculated from the obtained subsurface structure
model. The average S-wave velocity was calculated
by a weighted average of the layer thickness. The
thickness of the layer was estimated from the average
S-wave velocity and the predominant period based on
the 1/4 wavelength rule. The average S-wave velocity
used to calculate the layer thickness in each region isshown in Table 2. For the calculation of Hojojima, theaverage S-wave velocity of either HSAR01 or
HSAR02 is used because the ground structure isdifferent depending on the topography. Which Swave velocity is used is determined by location andtopography. Fig. 5 shows which model was used forFig. 5 Location of average S-wave velocity of
HSAR01 or HSAR02 at each site in Hojojima
each station. The distribution of thickness of landslide
moving mass for each region area shown in Fig. 6.
RESULTS
Distribution of predominant periods
The predominant period distribution in the
Hojojima area (Fig. 3(a)) is about 0.1 s with a shortperiod for scarp (purple line) and mountainous areas,
and about 0.2 - 0.5 s with a long period for landslide
moving mass (yellow-green line, cream-colored
areas) and flat areas. The dominant period
distribution in the Nawashiro area (Fig. 3(b)), whichis topographically divided into three blocks (A, B,
and C), shows a random distribution of short and long
period points in the range of 0.1 - 0.3 s. The
distribution trend is independent of the areas of scarp
and landslide moving mass. In the Amedaki area (Fig.
3(c)), the predominant period is about 0.1 - 0.3 s forthe landslide moving mass. In the area of the scarp,
114

GEOMATE – Kyoto, Japan, 3-5 November 2021

predominant period was not found from H/V spectra.
The geology of each area is as follows: Hojojima isdominated by andesite, Nawashiro is dominated byandesite with localized mudstone, and Amedaki isdominated by andesite and tuff conglomerate.
Subsurface structure
From the ground model in Table 1, the S-wave
velocity structure is as follows. In Hojojima(HSAR01, HSAR02), the S-wave velocity of thesedimentary layer of the landslide moving mass isquite low (120m/s - 200m/s) and the layer thickness
is about 7m. It is considered that the S-wave velocityof the rock mass distributed around HSAR02 is
gradually changing due to weathering and othereffects. In Nawashiro (NASR01), the S-wave velocityof the sedimentary layer of the landslide moving mass
is high (200m/s - 350m/s) and the layer thickness is
21m, which is thicker than the other two regions.
NASR01 model shows the subsurface structure ofblock B, and it is considered that other blocks are
similar. In Amedaki (AMAR01), the S-wave velocityof the sedimentary layer of the landslide moving mass
is low (130m/s - 250m/s) and the layer thickness is 13
m. The area is an extensive landslide terrain, and theobservation point is almost in the middle of themoving object.
The layer thickness distribution is as follows. In
Hojojima (Fig. 6(a)), the sedimentary layers are
thickened from the southwestern scarp to thenorthwestern landslide moving mass, with amaximum thickness of about 30 m. In Nawashiro (Fig.
6(b)), the sedimentary layer tends to be thicker in thecenter of the moving mass at lower elevations andthinner closer to the scarp, and the thickness of thelayer exceeds 20 m at some points. In Amedaki (Fig.
6(c)), the sedimentary layer tends to be thicker fromthe west to the east, with a maximum thickness ofabout 15 m.

(a) Hojojima area(b) Nawashiro area

CONCLUSIONS
Based on the landslide topography distribution
map, microtremor surveys were conducted in threeareas in Tottori Prefecture that were determined to belandslide terrain. As a result, the distributions of
microtremor period, the subsurface structure model,
and the layer thickness distributions of thesedimentary layer of the landslide moving mass wereobtained for each region. Microtremor observation isan excellent method for investigating the subsurface
geological structures, especially for estimating thepredominant period and S-wave velocity structure of
the subsurface. These results provide essential
information for the evaluation of earthquake groundmotions in landslide areas.

(c) Amadaki area
Fig. 6 Distribution of thickness of sediment layer
115

GEOMATE – Kyoto, Japan, 3-5 November 2021

ACKNOWLEDGMENTS

Space, Vol69, DOI10.1186/s40623-017-0689-0,
2017.
[6] Konno K. and Ohmachi T., A smoothing function
sutable for estimation of amplification factor of
the surface ground from microtremor and its
application, Journal of JSCE, No.524/1-33, 1995,
pp.247-259 (in Japanese with English abstract).
[7] Cho I., Tada T. and Shinozaki Y., Possibility of
microtremor array opened up by general theory:
Release of analysis tool BIDO, SeismologicalSociety of Japan Fall Meeting in 2009, 2009 (inJapanese).
[8] Cho I., Tada T. and Shinozaki Y., Centerless
circular array method: Inferring phase velocities
of Rayleigh waves in broad wavelength ranges
using microtremor records, Journal ofGeophysical Research, 2006, Vo.111, B09315.
[9] National Research Institute for Earth Science andDisaster
Resilience,
Geo
Station(https://www.geo-stn.bosai.go.jp), [Viewed onJune 14, 2021][10] Noguchi T., Nishikawa H., Yoshida S. andKagawa T., Estimation of subsurface structure
and characteristics of ground vibration based on
microtremor and seismic observation in thecentral Tottori Prefecture, Japan, Journal ofJapan Association for Earthquake Engineering,Vol. 19, Issue 6, 2019, pp. 258-271. (in Japanesewith English abstract)[11] Kitsunezaki C., Goto N., Kobayashi Y., Ikawa,T., Horike M., Saito T., Kurota T., Yamane K.,
Okuzumi K., Estimation of P- and S- wave
velocities in deep soil deposits for evaluating
ground vibrations in earthquake, Natural disasterscience, 1990, 9-3, 1-17. (in Japanese withEnglish abstract)

Part of this research is based on the results of thegraduation thesis by Mr. Atsushi Shimada, who
belonged to the Faculty of Engineering, Tottori
University. This work was supported by JSPS Grantin-Aid for Scientific Research JP20K05028(Principal Investigator: Tatsuya Noguchi).
REFERENCES[1] Faris F. and Fawu W., Investigation of theinitiation mechanism of an earthquake- induced
landslide during rainfall: a case study of theTandikat landslide, West Sumatra, Indonesia,
Geoenvironmental
Disasters
2014
1:4.,
doi:10.1186/s40677-014-0004-3, 2014.
[2] Osanai. N, Yamada T., Hayashi S., Kastura S.,
Furuichi T., Yanai S., Murakami Y., Miyazaki T.,
Tanioka Y., Takiguchi S., Miyazaki M.,
Characteristics of landslides caused by the 2018
Hokkaido Eastern Iburi Earthquake, Landslides,
DOI:10.1007/s10346-019-01206-7, 2019.
[3] National Research Institute for Earth Science andDisaster Prevention, Landslide Maps, Series 21“The Miyazu and Tottori Region”, Explanationsof Landslide Distribution Maps, Vol.260, 2005
(in Japanese).
[4] National Research Institute for Earth Science andDisaster Prevention, Landslide Maps, Series 25“The Matsue and Takahashi Region”,
Explanations of Landslide Distribution Maps,
Vol.278, 2005 (in Japanese).
[5] Kagawa,T.,
Noguchi,T.,
Yoshida,S.
and
Yamamoto, S., Effect of the surface geology onstrong ground motionsdue to the 2016 Central
Tottori Earthquake, Japan, Earth Planets and116

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

SUBSURFACE STRUCTURES BASED ON MICROTREMOROBSERVATIONS IN LANDSLIDE AREA OF TANDIKAT,WEST SUMATRA, INDONESIA
Isamu Nishimura1, Tatsuya Noguchi1, Yusuke Ono1 and Masanori Kohno1
1Graduate school of Engineering, Tottori University, Japan
ABSTRACTThe 2009 West Sumatra Earthquake (M7.5) caused landslides in Padang Pariaman, West Sumatra, Indonesia,
and severe damage was occurred in Tandikat. Previous studies of the damage reported that the sliding surfaces are
pumice layers with about 2 m thickness on hard clay layers. In this study, we carried out microtremor observations
at the landslide areas to estimate ground motion characteristics from H/V spectral ratios and subsurface structuresfrom phase velocity dispersion curves of array measurements.
The predominant periods of H/Vs are distributed in the range of 0.1-0.4 seconds. The H/Vs in the damaged
area have sharp unimodal peaks and the peak values tend to be smaller in area without landslides. They mightreflect the difference of the velocity structures between the areas with and without landslides through the 2009
earthquake. Harder subsurface structures are suggested in the areas without landslides. From the subsurface
structure models derived from array observations, however soft surface layers with S-wave velocity 200 m/s orless are estimated over the whole areas even in the areas without landslides. The layers are considered to be pastlandslide deposits including pumice layers. We are afraid future coseismic landslides due to the deposit even in
the areas without landslides under this event. We hope the results contribute to landslide disaster prevention in thearea.
Keywords: Microtremor observation, Subsurface structure, Landslide, The 2009 West Sumatra Earthquake

INTRODUCTIONOBSERVATIONS

At local time 5:16 p.m. on September 30, 2009, amagnitude 7.5 earthquake occurred off the coast of
Padang in western Sumatra, Indonesia, and caused
strong motions (Fig.1) and extensive damage mainly
in Padang and Pariaman in western Sumatra [1,2].
Figure 2 shows the distribution of landslides causedby the earthquake. In Tandikat, Padang Pariaman,
where the damage was particularly severe. Many flatroofed houses and schools collapsed due to theearthquake induced landslides, and many people were
killed [3]. According to Wang et al. [3], the slip
surfaces are composed of about 2 m of pumice layers
deposited on top of solid clay layers.
As shown above, coseismic landslides havecaused large-scale damage in recent years. It is
important to clarify the ground motion characteristics
of landslide areas because landslides often occur
repeatedly in the particular areas due to the pastlandslide deposits. Microtremor surveys have been
widely used to evaluate the ground conditions inlandslide areas [e.g.,4]. This is due to the fact that the
sediments above the slip surfaces are very soft and thevelocity contrast against basement is distinct. In thisstudy, we carried out microtremor observations at
Tandikat where earthquake induced landslide
occurred, to estimate landslide deposits.

Single-point observations of microtremors wereconducted at 11 sites in Tandikat mainly in the areaof observed landslides. JU410, three-component
accelerometer with a 24-bit recorder, was used fordata acquisition. We used the following specifications,amplification factor of 100, sampling frequency of
200 Hz, and recording time between 10-15 minutes.
Observation at TDK08, TDK10 and TDK11 werecarried out at the same time.
As shown in Fig. 3, we made array observations
at two sites where landslides were observed(TDKAR1 and TDKAR3) and another site at higher
elevations (TDKAR2). The measurements wereconducted using four JU410s synchronized by GPS
clocks and four geo-phones (UD component only,
natural frequency 4.5 Hz) and a data logger HKS9700(resolution 27 bit). One seismometer was placed at
the center of a circle, and the other three were set
around the circumference to form an equilateraltriangle. The specifications were 200-Hz sampling
frequency, radius of the array 1 to 10 m, and recording
duration around 15 minutes. The single-point
observation records at TDK08, TDK10 and TDK11
were to form an irregular triangular array withsynchronized by GPS to guarantee simultaneous
observations. The results of this analysis are used in
TDKAR3 case.

117

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 1 Estimated seismic intensity distribution
for the 2009 West Sumatra Earthquake [2]Fig. 2 Landslide distribution prepared by
the Geological Survey of Padang, Indonesia[3]

ANALYSIS

subsurface structures [e.g.,9]. Therefore, subsurface
structure models were estimated by trial and error sothat these phase velocity dispersion curves and themicrotremor H/Vs obtained at the center of the arraymatched the theoretical phase velocity dispersion
curves and the theoretical H/Vs based on the Rayleighwave fundamental mode, respectively. S-wave
velocities of the first layers were estimated from theminimum and constant phase velocity in higher
frequencies, and the base layer was fixed for
Vs=700m/s. As shown in Fig. 4, number of layers
were determined with reference of previous studies[3]. Then, modeling was performed by modifying the
thicknesses of layers. P-wave velocities were derived
from S-wave velocities [10]. The resulting subsurface
structure model is presented in Table 1. Figure 5
shows the phase velocity dispersion curves of thefundamental mode of Rayleigh waves overlaid on
those derived from array observations. Figure 6
shows observed and theoretically calculated
microtremor H/V spectrum at the center point of arrayobservation sites.

Fourier spectra were calculated from single-point
observation records of 20.48 sec length and weresmoothed using a log window with a coefficient of 20[5]. Average spectra were evaluated from at least 10
stable sections. Horizontal to vertical (H/V) spectral
ratios were calculated from the Fourier spectra, andthe predominant periods were visually estimated.
Many H/Vs were identified as unimodal with a single
distinct peak. The predominant period distribution in
Tandikat is shown in Fig. 3.
An analysis of the array observations was
conducted with the following procedure: the phasevelocity dispersion curves of the array observation
records were estimated based on the SPAC method[6] or the CCA method [7]. Using the open analysis
tool [8], at least ten sections were selected byautomatic extraction using the root mean squarevalues of microtremor recordings with 10.24-s length
segments. Only for the irregular triangle array of
TDKAR3, 20.48-s length segments were used. Then,the power spectra of those sections were smoothedwith a 0.3-Hz bandwidth Parzen window, and theaverage was estimated. Finally, the phase velocity
dispersion curves were determined for an array witha radius. And those obtained at different radius were
integrated at each observation site, with respect totheir continuity.
Assuming that microtremors are Rayleigh waves,their propagation characteristics allow us to estimate

DISCUSSIONS
Predominant Periods of The Horizontal to
Vertical Spectral Ratios of Microtremors
Figure 3 shows that the predominant periods areabout 0.4-s at TDK01, TDK05, and TDK07, those are118GEOMATE – Kyoto, Japan, 3-5 November 2021

Landslide Area

Fig.3 H/V spectral ratios and predominant period distribution
far from the landslide areas. And the periods areshorter at the other stations. Around TDK08 to 11
sites, the dominant period changes even in small
areas, which may reflect the quick change of layer
thickness deposited by the landslide. According to
Fikri and Wang [1], the landslides occurred along
several valleys eastward from TDKAR2 in Fig. 3.
And the areas around TDK02, TDK03 and TDK06
are considered to be the top of landslide mass. In
addition, the sedimentary conditions are expected tobe very complicated because of confluence of several
valleys. TDK01 is located on a flatland far from thecollapse sites, and is assumed to be long-period dueto the thick layers on the river terraces. Focusing on
the spectral shape of microtremor H/Vs, we observed
many single-peak H/Vs in the range of 0.1-0.4
seconds. The peak ratios of TDK05 and TDK07 aresmaller than those of other stations. This suggests thatthe velocity contrast against the base layers is smaller
than that at the area observed landslides.

Fig.4 Interpretation of landslide sequences fromgeological surveys [3]119GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1 Subsurface structures based onarray observations
Site
TDKAR1TDKAR2TDKAR3Thickness density
Vp(m/s) Vs(m/s)(m)(t/m³)
5

1.7

1430

200

∞

2.1

2070

700

6

1.6

1400

100

10

1.8

1620

300

∞

2.1

2070

700

3

1.5

1350

50

∞

2.1

2070

700

Fig. 5 Phase velocity dispersion curves

Fig.6 H/V spectral ratios at the center of the array observation sites
Estimation of Subsurface Structures

the thick intermediate layer with S-wave velocity of
300 m/s. Therefore, the intermediate layers is
considered to be sediments that did not collapsed dueto the landslide in this area, and there is concern about
the next coseismic landslides in the future.

Table 1 shows that TDKAR1 and TDKAR3 at themountainsides have several meters of low-velocity
layers with S-wave velocities of 100 m/s or less on
the surface, and their S-wave velocities are different
even at very nearby stations. In TDKAR2, in addition
to this low-velocity layers, an intermediate layer with
S-wave velocity of 300 m/s was identified with 10 mthickness. Previous studies reported that the sliding
surfaces are pumice layers with about 2 m thickness
on hard clay layers containing gravel [3]. Soft surface
layers with S-wave velocities of 200 m/s or less are
estimated over the whole areas including the areas
even without landslides from the subsurface structure
models in this study. The layers are considered to bepast landslide deposits including pumice layers.
TDK05 and TDK07 (TDKAR2) have longer
predominant periods than the other sites, because of

CONCLUSIONS
This
research
investigatedmicrotremor
observations in the areas of landslide damaged due tothe 2009 West Sumatra Earthquake in Tandikat. The
relationship between subsurface structures andearthquake induced landslide damages wereconsidered. Consequently, the following findings arenoted.
The predominant periods of microtremor H/Vs
are distributed in the range of 0.1-0.4 seconds, and thepeak ratios of H/Vs tended to be small in the area
120

GEOMATE – Kyoto, Japan, 3-5 November 2021

without landslide damage. They might reflect thedifference in the velocity structures between thelandslide deposits with sediments such as clay andpumice layers and bedrock layers. The subsurface
structures without landslides are considered to beharder.
Surface layers with S-wave velocity 200 m/s orless were estimated overall the area derived fromarray observations. An intermediate layer with an Swave velocity of 300 m/s was estimated at the sitewhere no landslide occurred this time. We are afraid
future coseismic landslides due to the deposit. We
would like to mention the need for continuous
monitoring of the deposits.

No.4, July, pp.215-220, 2011. (in Japanese withEnglish abstract)[4] Takahashi K., Tsuji T., Ikeda T., Nimiya H.,
Nagata Y. and Suemoto Y.: Undergroundstructures associated with horizontal sliding at
Uchinomaki hot springs, Kyushu, Japan, during
the 2016 Kumamoto earthquake, Earth, Planets
and Space volume 71, Article number: 87, 2019[5] Konno K. and Ohmachi T.: A smoothing
function suitable for estimation of amplification
factor of the surface ground from microtremor
and its application, Jornal of the Japan Society of
Civil Engineers, No525/I-33, 1995, pp247-259.
(in Japanese with English abstract).
[6] Aki K.: Space and time spectra of stationary
stochastic waves, with special reference tomicrotremors, Bull.Earthq.Res.Inst., 35, pp415456, 1957.
[7] Cho I., Tada T. and Shinozaki Y.: Centerless
circular array method: Inferring phase velocities
of Rayleigh waves in broad wavelength ranges
using microtremor records, J. Geophys. Res.,
111, B09315, 2006.
[8] Cho I., Tada T. and Shinozaki Y.: Possibility of
microtremor array opened up by general theory:
Release of analysis tool BIDO, SeismologicalSociety of Japan Fall Meeting in 2009. (inJapanese)[9] Ohmachi T., Konno K., Endou T. and Takumi
Toshinawa T.: Refinement and application of an
estimation procedure for site natural periods
using microtremor, Jornal of the Japan Society of
Civil Engineers, No489, 1994, pp251-260. (inJapanese with English abstract).
[10] Kitsunezaki C., Goto N., Kobayashi Y., Ikawa,T. Horike M., Saito T., Kurota T., Yamane K.
and Okuzumi K.: Estimation of P- and S- wave
velocities in deep soil deposits for evaluating
ground vibrations in earthquake, Natural disasterscience, 9-3, 1-17, 1990. (in Japanese withEnglish abstract)

ACKNOWLEDGMENTS
We would like to express our gratitude to allindividuals for their cooperation in the preparation ofthis paper. We appreciate to whom supported ourinvestigations. This research was supported by JSPS
Grants-in-Aid for Scientific Research (Grant-in-Aid
for Scientific Research (B), PI: Yusuke Ono, ProjectNo.: 18H01523).
REFERENCES[1] Fikri F. and Wang F.: Investigation of theinitiation mechanism of an earthquake- induced
landslide during rainfall: a case study of theTandikat landslide, West Sumatra, Indonesia,
Geoenvironmental
Disasters
2014
1:4.,
doi:10.1186/s40677-014-0004-3, 2014.
[2] USGS：M 7.6 - southern Sumatra, Indonesia,<https://earthquake.usgs.gov/earthquakes/event
page/usp000h237 /impact>[Viewed on 1 June
2021]
[3] Wang F., Muhammad W. A. N., Zhang F. andTakeuchi A.: Tandikek and Malalak rapid andlong runout landslides triggered by West
Sumatra earthquake 2009 (M7. 6) in Indonesia,
Journal of the Japan Landslide Society Vol.48,

121

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

INVESTIGATION OF TUNNEL RESPONSE DUE TO THE EFFECT OF
ADJACENT LOADED PILE ROW BY 3D SIMULATION ANALYSIS
Puckjira srikaewsai1, Narunat Heama1, Prateep Lueprasert1, and Suchatvee Suwansawat1
Department of Civil Engineering, School of Engineering, King Mongkut’s Institute of Technology
Ladkrabang, Bangkok, Thailand

1

ABSTRACT
Construction of tunnels for transportation, wastewater, or electricity, etc., in the major cities hasflourishing. In the meantime, other infrastructures such as elevated trains, flyovers, are continuallyconstructed for urbanization. Under a soft ground condition, they are supported by the pile foundations
required pile rows which are possibly located along the existing tunnel. A common challenge encountered
the loaded pile row of the new adjacent structures is inevitably induces soil stress changes and deformationof existing tunnels. Thus, an assessment of the impact of piles under loading on the integri ty of the existingtunnels is essential. The three-dimensional finite element analyses (3D FEA) with two pile conditions werecarried out. The tunnel responses in terms of the transversal deformation and additional forces are presented.
The maximum response of those were shown at the monitoring section (center of pile row section),
Meanwhile, the torsion deformations are maximum as the 10 m and 27 m away from monitoring section. For
both cases of short and long pile, in transverse section, the existing tunnel show obvious asymmetricalcharacteristic, and the maximum values of additional forces were at θ of approximately 105o − 120o and 45o .
In longitudinal direction, the tunnel gradually rotates in range of 0 - 27 m. In addition, the pile length may
not affect on tunnel responses in longitudinal direction.
Keywords: Transversal deformation, Torsional deformation, Tunnel response, 3D finite element analysis

INTRODUCTION

section and the torsion behavior are determined [6],[7].
During the past few decades, many investigationsof the influence of loaded pile on adjacent or
underlying existing tunnels have been concerned. The
parameters of adjacent loaded pile (i.e., pile
diameter, pile length, pile spacing, and number ofpile) were considered to only evaluate the tunnel
transversal deformation and structural forces intunnel lining in soft soil profile. The analysis results
were used to suggest the mechanisms behind tunnelsoil-pile interaction and safe clearance between thetunnel and pile or pile row [8]-[10]. However, thetunnel responses due to adjacent loaded pile were
presented in only one transverse section atmonitoring section or center of the model. Theirresults at different transverse section and torsional
deformation have been neglected.
Specifically, this research focuses on the effectof adjacent loaded pile row on existing tunnel. The
simulations were carried out by 3D finite element(FE) models. There were two simulation models: 3D
FE full models with short pile row and long pile row.
For the pile row, there were simulated by embedded
pile. The existing tunnels responses (e.g., the lining
deformation and additional structural forces) arepresented and discussed. Typically, the torsion
behavior of the existing tunnel after pile row underloading is highlighted.

Construction of tunnels for transportation,
wastewater, electricity, or communication cables,
etc., is flourishing around the major cities in theworld and has become focus of urban infrastructuredevelopment. In the meantime, other infrastructures
such as elevated trains, flyovers, crossing bridges,
are continually constructed for urbanization. Undera soft ground condition, they are supported by thepile foundations. A common challenge encounteredis that the loaded piles of the new adjacent structures
are inevitably close to the existing tunnels.
Typically, the displacement of soil generated by pilesettlement (under loading) induces additional
deformation and change in structural forces toadjacent existing tunnel. Such changes may lead tothe extremely negative effects on the integrity andserviceability of the tunnels. Thus, an assessment oftunnel response due to the impact of piles underloading is crucial.
The numerical analysis has become anincreasingly popular and powerful analytical tool formodeling. A three-dimensional finite element
analysis (3D-FEA) is often used to study in thetunneling works [1]-[5]. In analyses of existingtunnel response due to a new construction (i.e., deep
excavation, tunneling), the internal forces,
deformation monitored at different transverse

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GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1 Parameters for soil layer of 3D and 2D simulation models.
Layer
γsoil
c' (kPa)∅' (o)
Ψ ' (o )refEoed (MPa)1MadeGround
18
1
25
0
45.6

E50 (MPa)
Erefur (MPa)
vur
m
Knc
0
Rf
Gmax (MPa)γ0.7
Analysis type

45.6
136.8
0.2
1
0.58
0.9
DrainedSoil type(kN/m3)

ref

2

16.5
5
27
0
5

3
Medium
Clay
17.5
15
27
0
20

4
1st Stiff
Clay
19.5
25
28
0
60

5
1st Clayey
Sand
19
0
33
5
80

6
2nd Stiff
Clay
20
30
28
0
60

7
Hard
Clay
20
40
28
0
60

8
2nd Clayey
Sand
20
0
36
5
80

5
15
0.2
1
0.6
0.9
15
0.08
Undrained

20
100
0.2
1
0.6
0.9
45
0.11
Undrained

60
180
0.2
1
0.5
0.9
80
0.12
Undrained

80
240
0.2
0.5
0.55
0.9
200
0.014
Undrained

60
180
0.2
1
0.5
0.9
80
0.12
Undrained

60
180
0.2
1
0.5
0.9
130
0.15
Undrained

80
240
0.2
0.5
0.5
0.9
240
0.02
Undrained

Soft Clay

Table 2 Material properties of the 3D and 2D FE
simulation models.

Tunnel lining
Loaded pile
EPB shield
Grouting layer

Young’smodulus (E,kN/m2)

Poisson’s
ratio (v)Unit weight(γ, kN/m3)

31 x 106
31 x 106
210 x 1061 x 106

0.20
0.20
0.28
0.30

24
24
78
21

The soil profile at Sukhumvit station, one of the
MRTA Blue Line Project’s stations (section CS-8C of
the project), was chosen to simulate and analyze in the
study. Figure 2 depicts the geological conditions, which
include (1) Made Ground or fill material at 0–2.5 m, (2)Soft Clays at 2.5–12 m, (3) Medium Clays at 12–14 m,(4) 1st Stiff Clays at 14–20 m, (5) A thin seam of 1st
Clayey Sand is found at 20-21.5 m, (6) 2nd Stiff Clays at
21.5-26.5 m, (7) Hard Clays at 26.5-54 m, and (8) 2nd
Clayey Sand at 54–80 m. The groundwater condition is
piezometric drawdown due to the over pumping of theground water in the past as shown in Fig. 2.

FINITE ELEMENT SIMULATIONThe continuous tunnel lining and embedded pile row
were modeled by Plaxis 3D software version 2018. In
engineering practice, the tunnel is constructed bysegmental lining. However, in case of the tunnel where
the location is shallow, the modeling of continuous
lining is sufficient for consideration of the structuralforces in tunnel lining [11]. The pile row was modeledby embedded pile row which can reduce the complexityof a high number of pile models [12]. The wished-inplace bored piles are modeled on one side of tunnel. The
total displacement was reset to zero, and thus only thetunnel deformation attributable to adjacent loaded pilerow was investigated. The working load is 40% of theultimate pile capacity determined from α-method [13].
The pile row consisted of 13 piles to eliminate the edge
effect [14].

Fig. 1

Geometric parameters in the modelingFig. 2

Soil profile and pore water pressure

Characteristics of Model
In Fig. 1, the lining thickness, T, of 0.3 m, outer
diameter, Dtunnel, of 6.3 m, and the center of the tunnel
below the ground surface, Ztunnel, of 20.5 m are fixed
throughout this study. The diameter of bore pile, Dpile, of
1.0 m, and clearance, C, of 0.5 m are also fixed. The pile
spacing, S, of 3 m, positions of pile tips, Zpile, are at the
17.35 m and 57.0 m below the ground surface,
representing the short pile and long pile conditions fordetermining the existing tunnel response, respectively.
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GEOMATE – Kyoto, Japan, 3-5 November 2021

unit weight of embedded pile ( γembedded ) was
subtracted by unit weight of soil (γsoil ) [12].
Model Validation
Fig. 3 Loaded single pile simulation

The static pile load test of a 23-storey residential
complex at or near the CS-8C section (It is locatedapproximately 500 m away) was used to validates
against the computed single-pile head settlementsfrom FE model.
Figure 3 shows comparing between the static pileload test and simulated pile head settlements, given
the pile diameter of 1 m and pile length of 57.0 m.
The results were generally agreeable. This implies
that the properties of soil and pile are reasonable tosubsequently evaluate the effect on existing tunnel
due to adjacent loaded pile row for this study. Notethat the simulation of 17.35 m pile length (short pile)
was not compared due to unavailability of field static
pile load test data of the short pile.
3D Simulation Model

Fig. 4

Boundary and FE meshFig. 5

Torsion behavior of tunnel due to adjacentloaded pile row (pile length of 17.35 m)

From a previous study, the sufficient boundary of
3D numerical model with TBM tunneling suggested
by Mroueh and Shahrour [18], was the advancementof 4.0Dtunnel behind and ahead of the face of tunnel
excavation and lateral distance from the edge of thetunnel of 3.5Dtunnel. Thus, the lateral distance from atunnel edge of 4.25Dtunnel, and the advancement of
5.5Dtunnel behind and ahead of the monitoring sectionin this study as shown in Fig. 4, are enough to fullymodel the 3D tunneling problem.
On the lateral boundaries of the 3D model, thelateral displacements in the x- and y-directions were
fixed, while vertical displacement in the z-direction
was permitted. The horizontal and verticaldisplacements were both fixed at the bottom
boundary. For a good level of accuracy result, finer
meshes are generated in zone around tunnel and pile.
The total number of elements was approximately
260,000, and the total number of nodes wasapproximately 320,000. The loaded pile row was
located on one side of the existing tunnel.

Model Properties
In the 3D simulation models, the hardening soilmodel with small-strain stiffness (HSS) was adopted tomodel all layers of soils except the Made Ground Layer
which is modeled by Hardening Soil model (HSM).
Table 1 presents the parameters of soil profile of thecapital Bangkok. The properties of the Bangkok
subsoil were adopted from previous study [15], [16].
The tunnel lining, EPB shield, grouting layer and boredpile were modeled as a linear elastic material. The
material parameters listed in Table 2 were used in the
study. The interface friction value (Rinter) between the
structural elements (EPB, tunnel lining, or bored piles),and the surrounding soil was chosen to be 0.9 assuggested in previous research [9], [17].
The soil layer and grouting layer were discretized
into the volume elements or the 10-node tetrahedral
elements. The 6-node triangular plate element wasused to model the tunnel lining and EPB shield. The
embedded pile consists of beam elements which canbe 3-node line elements. For the embedded pile, theinput parameters consist of diameter (D), the unit
weight (γ) and young’s modulus (E). In the model, the

SIMULATION RESULTS AND DISCUSSION
Transversal Deformation
In this study, the tunnel deformations in transverse
direction were investigated along the longitudinal
direction of the existing tunnel as shown in Fig. 5.
This figure presents the tunnel deformation. intransverse direction with 12 m of horizontal distance
between cross-sections (section I to section IX). The
reference of tunnel lining is represented by bold line,
the tunnel deformation along longitudinal direction
with a magnification ratio of 1:1000. Note that the
deformation after tunneling is excluded.
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GEOMATE – Kyoto, Japan, 3-5 November 2021

(a) Pile length of 17.35 m (short pile)(b) Pile length of 57.0 m (long pile)Fig. 6 Simulated tunnel deformation as a result of adjacent loaded pile row
It can be seen that, for both short and long pile
conditions, the lining at the section III to section VII
distorts into a kidney or ellipse shape inclined to theside closer to the pile. The tunnel deformation slightly
changes at the sections I, II, VIII and IX as shown in
Fig. 6, it is attributed to the different degrees ofadjacent loaded pile row effect. With the distanceconsidered from the monitoring section (section V),
the transversal deformations of the tunnel lining
decrease with increasing distance.
Considering the pile conditions, the effect of long
pile row is larger than short pile row. This is similar
to the previous study [9]. However, it is worthy noted
that, with the dual influences of adjacent loaded pilerow, the tunnel deformation slightly changes at thesection II and VIII in both short and long pile cases.
The pile length may not affect on transversal
deformation in longitudinal direction.

slightly increases as the distance away frommonitoring section in range of 0 – 12 m. Then, the 𝜔
gradually decrease in range of 12 – 24 m. The tunnel
gradually turned from anticlockwise rotation toclockwise rotation with increase of the distance awayfrom 24 m. The maximum and minimum of 𝜔 locatedabout 10 m and 27 m away from the monitoring
section, respectively.
In long pile case, the tunnel rotates clockwise
along the longitudinal direction. The 𝜔 increase andgradually decrease in range of 0 - 14 m and 14 – 28
m. Then 𝜔 increases with increasing distance. The
minimum of 𝜔 located about 27 m. In this research,
the rotation of the existing tunnels caused by the
nearby loaded pile in ranges from 0.07×10−4 to 0.02×10−4 and -0.01×10−4 to -0.07×10−4 for short pileand long pile cases, respectively. These results revealthat the maximum or minimum of 𝜔 are not locatedat the monitoring section.

Torsion Behavior
Figure 7 shows the distribution of rotationindexed by ω of the existing tunnel along thelongitudinal direction due to nearby loaded pile row
in both short and long pile condition. In order to
identify the rotation behavior of the tunnel, aqualitative evaluation method is proposed as follows
[6]:
u −u
 = tan  = x 2 x1
(1)
Dtunnelwhere, 𝜔 is rotation index. α is the rotation angle ofthe tunnel. ux1 and ux2 are the horizontal displacementof the crown and invert of the tunnel, respectively(see Fig. 1). Dtunnel is the diameter of the tunnel.
The 𝜔 is assumed to be positive when tunnelrotates anticlockwise and negative when it rotatesclockwise. In short pile condition, the tunnel rotates
anticlockwise at the monitoring section. The 𝜔

Fig. 7125

Rotation of existing
longitudinal direction

tunnelalong

GEOMATE – Kyoto, Japan, 3-5 November 2021

(a) bending moment: pile length of 57 m(b) axial force: pile length of 57 m
Fig. 8 additional lining forces in circumferential direction
figure that the  M decreases when distanceconsidered from the monitoring section (section V)
increases. In the sections I, II, VIII and IX, the  Mslightly changes or remains almost constant.
Figure 8(b) illustrate the N in circumferential
direction, given pile length of 57 m. The N only
increases with maximum value at θ of approximately
105o for both short and long piles. For comparison inboth short and long pile conditions, N are similarto  M . Their results closely resembled the tunnel
deformation in longitudinal direction. This supportsthat the effect on existing tunnel in longitudinal
direction may not depend on pile length.

In some tunnel works, structural forces in tunnel
lining are the main concern, and torsional deformation
may be neglected [19]. However, the 𝜔 due toadjacent loaded pile row may also experience large
torsional deformation, which may lead to track
distortion and even pose a risk to safe operation oftrain. Therefore, the effect of adjacent loaded pilerow on existing tunnel, not only be careful to thestructural forces in tunnel lining but also the torsional
deformation of tunnel should be concern.
Additional Forces in Tunnel Lining
Figure 8 shows the additional bending moment(  M ) and axial force ( N ) in circumferential
direction due to adjacent loaded pile row, given pilelength of 57 m. The tunnel crown is an origin (θ = 0o ).
In the figure, the  M and N were obtained at
different cross-sections (section I to section IX) as thesame the section assigned to monitoring the tunnel
deformation. The calculation of the additional
bending moment and axial force are shown in Eq. (2)and (3). When M1, N2 are structural forces in tunnel
lining before pile loading and M2, N2 are structuralforces after pile loading. In this research, a positive
value of ∆M and N represents an increasing value,
whereas a negative value represents a decreasing
value.

M = M 2 − M1
N = N2 − N1

CONCLUSIONSThis study investigated the effect on existingtunnel due to adjacent loaded pile row in terms oftunnel deformation, torsional deformation, andadditional lining force (bending moment and axial
force). The simulations were carried out by Plaxis 3D
software, with 13 piles within the row. The simulation
results were discussed. The pile diameter was 1 m,
and the pile lengths were 17.35 m (short pile) and 57.0
m (long pile), respectively. The pile spacing (S) andedge-to-edge clearance (C) were fixed at 3 m and 0.5m, respectively. The conclusions are as follows:
1. The adjacent loaded pile row leads to tunnel
responses (deformation and variation of internal
forces) in transverse direction accordingly. The
tunnel deformation and additional forces(bending moment and axial force) become
significant at the center of pile row. Meanwhile,the torsion deformations are maximum as the 10(  1.5Dtunnel) m and 27 m (  4.5Dtunnel) awayfrom center of pile row.
2. The unrecoverable torsional deformation of thetunnel was induced by loaded pile row, which isdistinct from the perpendicular case. For both
short and long pile conditions, the tunnel
gradually rotates in range of 0 - 27 m. (  0Dtunnel- 4.5Dtunnel)

(2)
(3)

The  M increases in the range of θ = 285o to 0o
and 70o to 140o and decrease in the range of θ = 0o to
70o and 140o to 285o for both short and long piles. The
maximum positive and negative  M were at θ ofapproximately 105o and 45o for the short pile andapproximately 120o and 45o for the long pile as shownin Figs. 8(a). By comparison, the  M obtained fromshort pile case were similar tendencies to long pilecase, with smaller magnitude. It is seen from the126

GEOMATE – Kyoto, Japan, 3-5 November 2021

due to adjacent loaded pile by 3D finite element
analysis, International Journal of GEOMATE, Vol.
12, Issue 31, 2017, pp.63-70.
[9] Lueprasert P., Jongpradist P., Jongpradist P., andSuwansawat S., Numerical investigation of tunnel
deformation due to adjacent loaded pile and pile-soiltunnel interaction, Tunnelling and UndergroundSpace Technology, 70, 2017, pp.166-181.
[10] Nematollahi M. and Dias D., Three-dimensional
numerical simulation of pile-twin tunnels
interaction – Case of the Shiraz subway line,
Tunnelling and Underground Space Technology,
86, 2019, pp.75-88.
[11] Michael K., Dimitris L., Ioannis V., and Petros, F.,
Development of a 3D finite element model forshield EPB tunnelling, Tunnelling and UndergroundSpace Technology, 65, 2017, pp.22-34.
[12] Tschuchnigg F. and Schweiger H. F. (2015), The
embedded pile concept - Verification of an efficient
tool for modelling complex deep foundations,
Computers and Geotechnics, 63, 2015, pp.244–254.
[13] Skempton A. W., Cast In-Situ Bored Piles in
London Clay, Géotechnique, 9, 1959, pp.153–173.
[14] Heama N., Jongpradist P., Lueprasert P., andSuwansawat S., Investigation on pile-soil-tunnel
interaction due to adjacent loaded pile row by 3D
FEM,
The International
ConferenceonEngineering,
AppliedSciences andTechnology (ICEAST), 192, 2018, pp.792-795.
[15] Likitlersuang S., Teachavorasinskun S., Surarak C.,
Oh E., and Balasubramaniam A., Small strain
stiffness and stiffness degradation curve of Bangkok
Clays, Soils and Foundations, 53, 2013, pp.498–
509.
[16] Rukdeechuai T., Jongpradist P., Wonglert A., andKaewsri T., Influence of Soil Models on Numerical
Simulation of Geotechnical works in Bangkok
subsoil, Journal of Research and Development, 20,
2009.
[17] Mathew G. V. and Lehane B. M., Numerical backanalyses of greenfield settlement during tunnel
boring, Canadian Geotechnical Journal, 50, 2013,
pp.145–152.
[18] Mroueh H. and Shahrour I., Three-dimensional
finite element analysis of the interaction between
tunneling and pile foundations, International
Journal for
Numerical
and Analytical
Methods in Geomechanic, 2002.
[19] Chen H. H., Li J. P., and Li L., Performance of azoned excavation by bottom-up technique inShanghai soft soils, Journal of Geotechnical andGeoenvironmental Engineering, Vol. 144, Issue)11(, 2018.

3. The tunnel deformation and additional forces intunnel lining in transverse sections slightly
changes or remains almost constant as the
distance away from 36 m (  6Dtunnel) in bothcases of short and long pile.
This study, the pile diameter (Dpile) and pile spacing (S)were fixed and tunnel lining was simulated based oncontinuous lining. The future work considering theeffects of joints and varying Dpile and S should be done
to extent the scope of study.
ACKNOWLEDGMENTS
The authors wish to express their thankfulness toFaculty of Engineering, King Mongkut’s Institute ofTechnology Ladkrabang and TRF Basic Research forthe financial support through Grant KREF016317 andContract PHD60I0032.
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induced ground settlement using pressure relaxation
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Chaiyasarn K., Sharma M., Ali L., Khan W., andPoovarodom N., Crack detection in historicalstructures based on convolutional neural network,
International Journal of GEOMATE, Vol. 15, Issue
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Lueprasert P., Jonpradist P., and Suwansawat S.,
Tunneling simulation in soft ground using shellelements and grouting layer, International Journal of
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Zheng G., Pan J., Li Y., Cheng X., Tan F., Du Y., and
Li, X. (2020), Deformation and Protection of
Existing Tunnels at an Oblique Intersection Angle toan
Excavation”,International
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Lin X. T., Chen R. P., Wu H. N., and Cheng H. Z.,
Deformation behaviors of existing tunnels causedby shield tunneling undercrossing with obliqueangle, Tunnelling Underground Space Technol. 89,
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Zheng G., Pan J., Li Y., Cheng X., and Li X.,
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127

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

VERTICAL LOADING TESTS ON LOCAL SCOURED SPREAD
FOUNDATION ON ALUMINUM RODS MODEL GROUND
Yuna Sasaki1 and Hidetoshi Nishioka2
1, 2

Faculty of Science and Engineering, Chuo University, Japan
ABSTRACTIn recent years, the damage to bridge foundations caused by heavy rainfall and scouring has increased. In somecases, the entire bridge is carried away, whereas, in others, damage mainly occurs in the foundation, such as
settlement and inclination. In the latter cases, it is expected that the damaged bridge is shortly reused after minor
repairs are performed only on the superstructure while retaining the residual displacement of the foundation.
However, it is difficult to predict whether a foundation with residual displacement possesses the bearing capacity
needed to resume emergency use of the damaged bridge. This study focuses on the residual bearing capacity of ashallow foundation after scouring. We performed vertical loading tests on scoured shallow foundations on analuminum rod ground model to clarify the mechanism. The scouring of the ground was simulated by manuallypulling out the aluminum rods. The residual displacement induced by the dead load was measured, and the loading
was continued until the residual bearing capacity became clear. We systematically evaluated the effects of scouring
volume and shape on the behavior of the foundation. It was found that the residual displacement increased withscouring, but the residual bearing capacity did not necessarily decrease. These findings suggest the possibility of
expanding the conditions for the early resumption of damaged foundations and will be helpful for future emergency
disaster operations of damaged river bridges.
Keywords: Bearing Capacity, Scour, Ground Spring Constant, Residual Performance, Image Analysis

INTRODUCTION

settlements and inclinations. For early resume
operations with the reuse of damaged piers, these
resistances must be evaluated. However, mostprevious studies on bridge scouring have focused onscouring, and a few studies have focused on the
bearing capacity of scoured foundations. Therefore, it
is unclear whether residual resistance with residual
displacement retains the bearing capacity required forreuse.
In this study, we performed vertical loading tests
on a scoured foundation on an aluminum rod groundmodel to reveal the mechanism of the residual bearing
capacity of a scoured foundation. The target structure
was limited to a river bridge with a shallow
foundation, and the target conditions were limited to
local scours. By varying the local scour depth, we
considered the expected condition in which theresidual bearing capacity required for resuming
operations will exist. We also performed an image

Background and objective of study
Scouring is caused by the erosive action offlowing water that removes the soil around hydraulicstructures, such as bridge piers. This phenomenon
often occurs with heavy rainfall and typhoons. The
number of scouring disasters has been increasing inrecent years owing to global warming. Half of thebridge scour cases are classified as medium-scale
scours, which mainly damage foundations, such as
settlements and inclinations without significant
damage, for example, the outflow of girders [1].
Figure 1 shows an example of the medium-scale
damage, the Hino Bridge across the Tama River,whose foundation has settled, owing to scours
produced by the typhoon in Eastern Japan in October
2019. The road was closed because of settlement, andthe transportation network was interrupted. The
damaged pier was not reused because its long-term
safety could not be guaranteed. Instead, the damaged
pier and two girders were removed, and a new girder
double the span length was fabricated and erected. It
took seven months for the bridge to be reopened.
In this case, the bridge is partially rebuilt.
However, it is unrealistic to apply the rebuilding
operation to all damaged bridges in medium-scale
scouring cases. In some cases, the bridge is expected
to be used after immediate repair operations, evenwhen its foundation is damaged, such as throughFig. 1
128

Example of medium-scale damage

GEOMATE – Kyoto, Japan, 3-5 November 2021

analysis to clarify the mechanism.
The findings of this study suggest the possibilityof expanding the conditions for the early resumptionof bridges with damaged foundations and is valuable
for future emergency disaster operations of damaged
river bridges. Finally, these findings will improve theresilience of transportation networks.
Performance of residual bearing capacity ofscoured foundation
This study focused on the residual bearing
capacity of scoured foundations based on thefollowing concept (Fig. 2) [2] [3].
The load–settlement curve starting from theorigin represents the performance of the bearing
capacity before the disaster. After a scoured disaster,
the performance changes, and the curves can have awide variety of shapes. We believe that theperformance of the residual bearing capacity can be
determined by identifying the shape of the curve.
After the disaster, we can easily determine theresidual settlement and dead load as a single point(Fig. 2). However, it is difficult to predict thefollowing load–settlement curve from this single
point; thus, it is almost impossible to determine
whether the bridge pier can be repaired.
If the damaged bridge is reused, emergency
restoration, such as jacking up and additional repair
concrete, will be performed. Therefore, safety
confirmation loading tests, such as static loading tests
and slow train passing tests, were conducted in thisstudy. In addition, the settlement was measured toobtain information as a second point in the load–
settlement curve.
Through this process, it is possible to limit thecandidates for the load–settlement curve of thedamaged foundation. The performance of the residual
bearing capacity after a disaster can then be evaluated.

Fig. 2Concept of change in performance ofbearing capacity by scouring disasterFig. 3Outline of experimental devices

Fig. 4

Aluminum rod ground model

VERTICAL LOADING TESTS ON
ALUMINUM RODS
Experimental devices
The experimental devices used for the tests areshown in Figs. 3 and 4. Vertical loading tests were
performed on the aluminum rod ground model
produced by mixing two types of aluminum rods withdiameters of 1.6 and 3.0 mm and layered with 60%
and 40% weight content, respectively. The groundmodel was generated in an experimental container
with a width of 600 mm and a depth of 220 mm.
Compaction was performed for each spreading
depth of 50 mm using a hand vibrator machine. The
weight per unit volume  and angle of repose r of theground model were measured using a measuring box(width = 250 mm; depth = 50 mm). Here, the angle
between the measuring box tilt and the aluminum rod

collapse (Fig. 5) is defined as the angle of repose r.
After five tests, average values of  = 21.3 kN/m3 andr = 29° were obtained.
A rigid plate with a thickness of 10 mm, width of
120 mm, and length of 50 mm was used as the shallow
foundation model. A linear guide was installed on the
top surface of the foundation to enable the foundation
to rotate and slide horizontally. It was loaded
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GEOMATE – Kyoto, Japan, 3-5 November 2021

80

settlement

70

10

load

50

8

40

6

30

4

20

settlement (mm)1260load (N)

14

2

10
0

0
0

200

400

600

800

1000

1200

1400

1600

time (second)load

Fig. 5

Measurement of angle of repose

Fig. 6

Connection structure between load jackand foundation model

vertically using a wedge at the point of the verticaldisplacement-controlled jack (Fig. 6).
The load cell was set between the wedge and thevertical jack to measure the vertical load. In thisexperiment, the actual load resistance was the totalload value output by the data logger and the selfweight of the foundation (3.9 N) because thefoundation was not connected to the vertical jack. The
displacement of the jack was measured using acontact-type displacement transducer. The angle ofinclination of the foundation was obtained bymeasuring the displacement of both sides of thefoundation using laser displacement transducers.

settlement

Fig. 7

Time-history curves of load anddisplacement of model before scouring

Fig. 8Modeling of Local scour by removing
aluminum rods

Fig. 9Test cases of local scour area

Loading procedure
After the aluminum rod model ground was
produced, the foundation was placed on the ground.
At this time, the values of the load and settlement
were zero. First, a 22.5 N load was applied to simulatea dead load. This condition represents the state of thefoundation before the scouring damage occurs. Thisvalue (22.5 N) was obtained by dividing the ultimate
bearing capacity obtained in the previous experiment
without scouring by a safety factor of 3.
During the tests, loading and unloading wererepeatedly applied several times until thedisplacement of the jack exceeded 10% of thefoundation width. The loading speed in eachsettlement phase was controlled to 0.25 mm/min in 0–
3 mm, 0.5 mm/min in 3–6 mm, and 1 mm/min in morethan 6 mm. Figure 7 shows the variations in thesettlement and load with time for vertical loading

Fig. 10 Modeling of loading position after
emergency restoration
tests performed for the model before scouring.
Modeling of local scour
Aluminum rods in the local scour area were
removed to simulate a situation in which local
scouring occurred just below the foundation on the130GEOMATE – Kyoto, Japan, 3-5 November 2021

upstream side (Fig. 8). The shape of the local area wasan isosceles triangle with an angle of repose (29°) andthe depth of the local scour. The loading tests were
performed for four cases by varying the depth of thelocal scour, d = 11, 17, 22, and 33 mm (Fig. 9).
While the displacement of the vertical jack wasmaintained, aluminum rods were removed to simulate
local scouring. During the removal of the aluminumrods, most of the reaction force was lost because theposition of the vertical jack was fixed. Subsequently,
the displacement of the vertical jack increased again,
and the load–settlement curve after the disaster wasplotted.
In practice, the dead load is eccentric because thefoundation is inclined after a local scour appears.
However, this eccentricity was not modeled in ourstudy because of the following reasons. In emergency
restorations, the girders are generally jacked up andmoved horizontally to return to their original position
and then fixed by adding repair concrete (Fig. 10). In
other words, the loading position of the live load
before and after the disaster remained unchanged.

Fig. 11 Load–settlement curves

EXPERIMENTAL RESULTS
Load–settlement curves
The load–settlement curves obtained from thetests are shown in Fig. 11. For d = 33 mm, the curve
could not be plotted because the foundation could notcarry its self-weight and moved by rotating from its
initial position during the aluminum rod removal. In
the other three cases, each curve showed a slight
gradient during the initial loading phase. However,from the middle phase of loading, the curve warped
up, indicating a high load value. This increase
occurred when all foundation base widths reached theground after the foundation was inclined along theslope owing to local scouring.

Fig. 12 Changes in residual settlement andinclination with depth of local scour

Residual settlement and residual inclination
In Fig. 12, the horizontal axis indicates the local
scour depth, and the vertical axis represents thesettlement and inclination when the load reaches thedead load after local scouring. These settlements andinclinations correspond to the residual settlement andinclination that occur in actual situations after afoundation is damaged by disasters because thesettlement and inclination increase with local
scouring under dead loads. The graphs indicated thatthe residual settlement and inclination increased withthe local scour depth.

Fig. 13 Changes in inclination of foundation withsettlement
loaded with 22.5 N after removing the aluminum rods.
These graphs indicate the residual settlement causedby the dead load immediately after the scouring
disaster. In each scouring case, the inclination of thefoundation under the dead load decreased gradually,
and it decreased by at least 1° after the test when thesettlement reached 12 mm. It was assumed in thisstudy that repair would be conducted to return theloading position (which moved slightly owing toscouring) to the initial position (Fig. 10). The
inclination of the foundation returned to the stabledirection, suggesting that there is minimal concern

Inclination of foundation
Figure 13 shows the changes in the inclination of
the foundation with an increasing settlement. The
plots (●) in the graph represent the state when131

GEOMATE – Kyoto, Japan, 3-5 November 2021

about exceeding the limit state of inclination when thefoundation is reused with appropriate repair (Fig. 13).

In summary, in cases where the foundation does
not collapse under the dead load, it is likely for its
ultimate bearing capacity to not decrease even if the
it is damaged by local scouring. Therefore, thefoundation of the bridge might be safe, with there also
being the possibility of its reuse. In addition, thedecrease in the ground spring constant at reloading is
expected to remain within the acceptable range after
resumption. However, the sum of the residual
deformation and increment at initial loading must be
less than the value of the maximum limit that therestoration work can accommodate.

Ultimate bearing capacity and ground spring
constant
In Figure 14, the horizontal axis indicates the
local scour depth, and the left vertical axis shows theultimate bearing capacity. In this study, the ultimate
bearing capacity was defined as the maximum valueof the load before the settlement reached 12 mm,
which was 10% of the foundation width. The ultimate
bearing capacity maintained a steady value (Fig. 14),which was almost the same as that for normal groundloading.
The right vertical axis in Figure 14 represents theground spring constant k, defined as the slope of theload–settlement curve from the dead load (22.5 N) to
1.2 × the dead load (27.5 N). This load increment was
simulated as a live load. We calculated two types ofground spring constants (Fig. 11): the ground spring
constants at the initial loading immediately after local
scouring and during reloading. On the one hand, theground spring constant at the initial loading can beused to calculate the increase in settlement during
restoration work, such as the jack-up, adding repair
concrete, and static loading tests. On the other hand,
the ground spring constant at reloading can be used toevaluate the behavior under live load after the
resumption of service.
Both ground spring constants tended to decrease
with local scour depth (Fig. 14). Although the groundspring constant at the initial loading decreased
significantly, the incremental settlement at therestoration work was smaller than the overall residual
settlement owing to the dead load only (Fig. 11). This
increment can be handled easily through restoration
work. Moreover, the ground spring constant at
reloading had a higher value than the ground spring
constant at the initial loading in all cases. The effectof the local scour depth on the ground spring constantat reloading was less significant than that at initialloading.

IMAGE ANALYSIS
We performed image analyses to calculate thedisplacement and visualize the slip lines on the
ground for all cases. In the image analysis, tracking
points were set on the image for the tests on thealuminum rod ground model. The displacements ateach point were calculated by changing the points. In
addition, slip lines were observed to chase thetrajectories of each point.
Figures 15 and 16 show the image analysis results,
and we compared the changes in the slip lines under
the dead load and 10% of the foundation width (12mm). In all cases under the dead load, tiny slip lines
were observed. In contrast, at 12 mm settlement, more
extensive slip lines appeared than those under thedead load. For d = 17 and 22 mm, significant slip lines
were observed at the unscoured side (right side) of thefoundations, as shown in Fig. 16. On the scoured side,a slip surface was observed, although the movement
was slight. This surface indicated that the groundresistance at the scoured area at the bottom of thefoundation recovered. It was also confirmed from theimage analyses that the bearing capacity increased
during the loading tests, even when the foundation
scoured.
CONCLUSIONSIn this study, we performed vertical loading tests
on a scoured spread foundation on aluminum rods andconducted image analysis to clarify the mechanism of
the bearing capacity of the locally scoured foundation.
The results suggest that it is possible to resume theservice of bridges because the ultimate bearing
capacity will remain, even if the foundation isdamaged if the residual settlement and inclination arerecoverable through simple emergency restoration,and if the reduction in the ground spring constant islimited. Additionally, the inclination of thefoundation will be gentle, and the settlement will notincrease when the repair, such as fixing the loading
position to the original location before resuming, isperformed.
In future research, we will perform tests by

Fig. 14 Changes in ultimate bearing capacity andground spring constant with local scour
depth
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GEOMATE – Kyoto, Japan, 3-5 November 2021

a) Case of normal grounda) Case of normal groundb) Case of d = 11 mmb) Case of d = 11 mmc) Case of d = 17 mmc) Case of d = 17 mmd) Case of d = 22 mmd) Case of d = 22 mm
Fig. 15 Grounddisplacement
settlement

at

Fig. 16 Ground displacement from residual
settlement to 12 mm

residualvarying the diameters of aluminum rods and the size
distributions of the aluminum rods. Moreover, we
will analyze the bearing capacity mechanism in detailthrough image analysis.

[2] H. Nishioka, T. Sanagawa, and Y. Otake,
Reliability Assessment for Residual Bearing
Capacity of a Railway Bridge Foundation after
Scouring, In Proceedings of 7th Asian-PacificSymposium on Structural Reliability and Its
Applications (APSSRA2020), October 4-7, 2020,
Tokyo, Japan, pp.467–472[3] H. Nishioka, M. Shinoda, Y. Sumi, and H.
Yamate, The Vertical Loading Test to the Bridge
Pier of Spread Foundation which was Damaged
by Scouring Settlement, In Proceedings of Japan
National
Conferenceon
GeotechnicalEngineering 2013, July 23-25, 2013, Toyama,
Japan, pp.203-204 (in Japanese)[4] M. Amada, On Trial of Bearing Capacity
Apparatus in Geotechnical Engineering –Experiments by Using Ground Material of
Aluminum Rods, Journal of Japanese Society forEngineering Education, March, 1992, Vol. 40,
Issue 2 (in Japanese)

ACKNOWLEDGMENTS
This work was supported by the JSPS KAKENHI
Grant-in-Aid for Scientific Research (C) (Grant
Number JP20K04687).
We
would
like
to
thank
Editage(www.editage.com) for English language editing.
REFERENCES[1] H. Nishioka, Evaluation Method of Residual
Bearing Capacity of Bridge Pier Foundation
Damaged by Scouring, Monthly Report in
Railway Technical Research Institute, 2018 (inJapanese)https://bunken.rtri.or.jp/doc/fileDown.jsp?Rairac
ID=0040002746
13311th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

THREE-DIMENSIONAL ANALYSIS OF SOIL-PILE-STRUCTURE
INTERACTION PROBLEM ON HIGH RISE BUILDING ON
IMPROVED SOFT SOIL
Thanapon Tipsunavee1, Goran Arangjelovski2
1,2

King Mongkut’s University of Technology Thonburi, Bangkok, Thailand
ABSTRACTThis study presents the numerical simulation of flexible wall barrel soil-pile-structure interaction analysis. SoilStructure Interaction (SSI) is usually assumed beneficial during an earthquake. However, SSI can also increase
permanent deformation and decrease the stiffness of the pile foundation system which affects seismic responseand displacement in the overall structure, especially high-rise buildings. Non-linear 3D FEM models with agap/slap mechanism have been developed in ABAQUS to simulate the effect of soil-pile-structure interaction(SPSI). The objectives of this research are to study the effect of SPSI on soil response, kinematic properties of thesuperstructure, and the modeling of SPSI problem with the superstructure, and to increase horizontal stiffness offoundation system by using the cement-mixing method. The analyses in this study include superstructure analysis
with fix-based, SPSI and SPSI with cement-mixing. The constitutive models in this study are Mohr-Coulomb forsoil material and Linear-elastic for the upper structure. The calibration of the flexible wall barrel modelingtechnique was successfully calibrated with a shaking table reference case with some variation of the results. It isobserved that SPSI provides flexibility and damping to the superstructure. The gap/slap mechanism is clearlyobserved on the contact surfaces between soil and superstructure at a shallow level. Cement-mixing soil
improvement increases horizontal stiffness resulting in a stiffer response of superstructure and reduction of pilestress during earthquakes. The influence of SPSI on superstructure and comparisons between simulation cases arethen discussed. However, this study does not consider consolidation and the contraction properties have been
simplified.
Keywords: 3D FEM/ ABAQUS/ Seismic/ Soil-pile-structure interaction
INTRODUCTION

influenced from far distance earthquakes. Since SSI
has high influence on the upper structure subjected toseismic load, post-seismic records in the past such as
1985 Mexico City, 1994 Northridge, and 1995 Kobe
earthquakes provided adequate reasons to prove that
the SSI effects should be investigated [4], [5].
Cement mixing is one of the cost-effective soil
improvement technique that can improve the strengthand stiffness of the weak soil area [6]. The improved
area of the surrounding soil can potentially reduce thenegative effect of SSI by increasing the horizontal
stiffness. Moreover, additional numerical analyses tovalidate this approach are needed.
In the past, most of the numerical research about
SSI was simplified to be linear elastic in order toreduce the complication of the problem. As computer
hardware improved a lot over the past few years,
FEM software also improved and allow computers toanalyze the non-linear problem with great efficiency.
Even though some of the previous research
implements a non-linear soil model with non-linear
Winkler spring models to the SSI problem, the effectof SSI still not clearly understand. Winkler springmodel is the spring model based on the Winkler
spring theory which uses beam elements to model thepile and uses the spring element to model the soil
along the embedded pile surface. This model willconsider only the load and displacement of the soil

Nowadays, a number of high-rise buildings in
Thailand are increasing dramatically, especially in
Bangkok, the capital of Thailand. Most of the design
methods in Thailand are based on the assumption thatthe foundations of the building are assumed to be
rigid or fixed. This kind of simplification can reducethe complication of the problem and also reduce thetime required to analyze the problem, thus thissimplification can help engineers to work easier andalso save the cost for designing the building. However,this simplification is excluding soil-structureinteraction and the flexibility of soil and foundation
will be ignored.
Many design codes suggest neglecting the soilstructure interaction. Neglecting SSI is usuallyconsidered beneficial because the soil-structureinteraction can increase the damping of the structural
system. In addition, a lot of previous research foundthat SSI is reasonably neglected for low-rise
structures and structures supported with very stiff soil.
However, SSI will have a significant influence on a
structure that is supported with soft soil, especially forhigh-rise buildings. [1]-[3].
Even though the Bangkok area is located in a low
seismic hazard zone, most of the soil profile at ashallow depth are soft soils which are potentiality
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GEOMATE – Kyoto, Japan, 3-5 November 2021

and springs are independent of each other which
exclude shear coupling between the springs.
In this study, a powerful engineering numericalsimulation program based on the finite element
method, ABAQUS, is adopted to simulate the 3D SSI
behavior of pile foundations subjected to seismic loadwith the continuum SSI model. This continuum
model will include normal pressure, shear drag, andgap/slap mechanism between soil and pile. The
response of the structure is then observed.

Where μ is known as the coefficient of friction.
The coefficient of friction, μ, is equal to tan (δ) whereδ is the interface frictional angle of the soils [8]. Theinterface frictional angle (δ) was estimated using theequation as shown in Eq. (2) [9].
𝛿𝛿 = 𝑡𝑡𝑡𝑡𝑡𝑡−1[ 𝑠𝑠𝑠𝑠𝑠𝑠 𝜙𝜙 ′ 𝑐𝑐𝑐𝑐𝑐𝑐 𝜙𝜙 /(1 + 𝑠𝑠𝑠𝑠𝑠𝑠2 𝜙𝜙)](2)

Simulation of Shaking Table

APPROACH
FOR
SOIL-STRUCTURE
INTERACTION IN NUMERAICAL ANALYSISTo demonstrate the soil-structure interaction
behavior such as gap/slap mechanism, the numericalanalysis approaches are calibrated by simulating thephysical model shaking table test. A flexible wallcontainer on a shaking table is proven to be accurate
and provides the best response spectra whencompared to other types of shaking table containers.
Thus, a single pile model with a flexible wallcontainer shaking table test was adopted as referencecase [10]. The single pile model with superstructureis the best model to observe soil-pile-structureinteraction behavior. The single pile model with headmasses of 25, 100, and 160 lbs. The piles were
subjected to Loma Prieta earthquake motion with amaximum horizontal acceleration of 0.16g. The
numerical model assembly of shaking table are shownin Fig.1.

Soil-Structure Interaction Interface Properties
According to previous research, most suitableinterface properties for SSI is combination of normal
behavior (Pressure-Over closure) and Tangential
behavior [7]. In the past, most of SSI model was basedon Winkler spring theory, but recently many
researchers developed SSI model to compatible with
continuum model such as gap element. The behavior
of SSI was modeled as surface contact behavior
between subsoil and pile. The master surface andslave surface are assigned to pile and soil respectively.
Normal Behavior (Pressure-Over Closure)
Normal behavior is defined as Pressure-Over
closure that is transmitted between soil and pile whileboth surfaces are in contact with each other. The mostappropriate type of normal behavior is “hard contact”.
Hard contact means that there is a zero-penetration
condition between each surface. In addition, if
surfaces are in contact, any contact pressure can betransmitted between them. The surfaces separate if
the contact pressure reduces to zero. Separated
surfaces come into contact when the clearance
between them reduces to zero. in other words, thisbehavior will allow gap/slap mechanism to occur.
Tangential Behavior
The most suitable tangential behavior for SSI ispenalty friction. Unlike the spring model, this type offriction can provide drag force to the system. This
friction model base on Coulomb friction. As the basic
theory of the Coulomb friction model, two surfacesthat contacting to each other can carry shear dragstress up to a certain point across their interface
before they start sliding relative to one another. The
Coulomb friction model defines this critical shear
stress, τ, at which sliding of the surfaces starts as afraction of the contact pressure, p.

𝜏𝜏𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = 𝜇𝜇𝜇𝜇

Fig.1 Numerical model assembly of shaking table.
The boundary conditions in the numericalmodel are the same as a flexible wall barrel container.
At the bottom of each confining ring and rubber
membrane, the displacements are restrained in thevertical direction and will allow to move freely in thehorizontal direction. The combination of confining
ring and rubber membrane confined the soils and help

(1)

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GEOMATE – Kyoto, Japan, 3-5 November 2021

the soil maintain its geometry. The soil model isdisplacement restrained with roller support in thevertical direction at the bottom of the model.
The result from the simulation shows a goodcorrelation with the observed from the experiment asshown in Fig.2. Most of the soil-structure interaction
effect can be observed such as Gap/slap mechanismand shows the influence of the kinematic forces fromthe soil to the pile, and the influence from the inertial
forces from the super-structure.

ground level. The superstructure composes ofcolumns, beams, slabs, elevator shafts, and footings.
The columns have two different sizes of a crosssection which are 0.4x0.8 m. and 0.4x0.15 m. The
slab thickness is 0.25m. The beam cross-section
including the slab thickness is 0.5x0.25m. Figure 4
shows the foundation system of the superstructure
consists of several types of pile and piled-raft footings,
with a pile length of 40m., and a pile cap thickness of
1.7m.

Fig.4 Foundation system layout of the superstructure.
Fig.2 Acceleration time history at the pile head with160 lbs. mass.
Figure 3 shows gap/slap mechanism which causes
free vibration and reduction of pile stiffness. Even
though some results have a small deviation, it can be
confirmed that physical shaking table test withflexible wall barrel container can be successfully
simulated by using 3-D FEM program, ABAQUS.

The soil layer boundaries in this study are 235m
deep and 100m in diameter. The superstructure has 18
stories with a combined height of 55.5 m. The
building has a width of 18.8 m. and a length of 40.4m.
as shown in Fig. 5. The boundary conditions in thispart are similar to the previous one. The soil
parameters in this study are adopted from previous
researches [11], [12]. The material properties and soil
parameters are shown in Table 1.
Superstructure

Membrane

Confining ring(a)(b)
Fig.3 Gap deformation (a) during earthquake motion(b) permanent deformation.
ANALYSISOF
INTERACTION

235 m.

Soil

SOIL-PILE-STRUCUTRE

The modeling technique used the contact
properties from the calibration model to simulate theeffect of SSI on the Pile-foundation system andsuperstructure. This model includes a pile foundation
system and an 18-floor building with natural periodsof 1.79 and 1.44s in x and y direction, respectively.
The superstructure in this study is simplified from anexisting 18-floor building, for the purpose to achieve
symmetry in both axes. The earthquake motion isapplied to both directions of the building. Floor tofloor height in of the superstructure was 3 m., with
1.5 m. clearance between the first floor and theFig.5 Assembly of SPSI model with high-risebuilding.
Figure 6 shows three cases that consider in thisstudy which are (1) structure only; (2) structure withsoil layers; and (3) structure with cement-mixing
improved soil.
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GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1 Material properties and soil parameter (Likitlersuangetal et al., 2013; Arai and Yamazaki, 2002)[11], [12]γSuFriction
Eu, E’
Depth
Rayleigh
Material
Model
v
(m)
damping(kN/m3)(kPa)
Angle(ϕ)
(mPa)

Soil
Soft Clay1
Soft Clay2
Medium Clay
Stiff Clay1
Sand
Stiff Clay2
Hard Clay1
Hard Clay2
CementmixingConcrete
Confining Ring
Membrane

0-7.5
7.5-12
12-14
14-20
20-21.5
21.5-26
26-60
60-235

MCM
MCM
MCM
MCM
MCM
MCM
MCM
MCM

16.5
16.5
17.5
19.5
19
20
20
20

20
39
55
80
120
240
400

27
-

10
20.5
27.5
40
53
72
240
1350

0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5

5%
5%
5%
5%
5%
5%
5%
5%

-

MCM

16

300

-

221.9

0.5

5%

LEM
Rigid
LEM

23
0
0

-

35000
Vary*

0.2
-

5%
-

-

* Elastic modulus of the Membrane is depends on the soil layer** MCM = Mohr-Coulomb Model** LEM = Linear Elastic Model
0.6

Accleration (g)The third case attempts to increase the horizontal
stiffness of the foundation system of thesuperstructure by using a soil cement mixing method[12]. The interaction was applied to all contact
surfaces including the contact surface between
cement mixing soil and existing soil.

0.3
0.0
-0.3
-0.6

0

5

10

15

Time (s)

20

25

30

Fig.7 Acceleration time history of Loma Prieta
Earthquake, Gilroy Array, 1989.
RESULTS

Case 1-aCase 2-a

The acceleration time history and spectral
acceleration of both cases are shown in Fig. 8 and Fig.
9. In the case2-a, superstructure vibrates in modes 1
through 4. Due to SPSI, the frequency of vibration forcase 2 was lower than the case 1. In case 2, where
SPSI is included in the analysis, the vibration
frequency of the superstructure was shifted from
0.57Hz to 0.5Hz for mode 1 and 0.69Hz to 0.53Hz formode 2. Figure 9 shows a comparison of acceleration
time history between case 1 and case 2. The soilstructure-interaction provides additional damping andflexibility to the superstructure, thus the amplitude ofacceleration was lower and the period increased [1].
The SSI also reduced the relative displacement of thesuperstructure. Gap/slap mechanism can be observedin Fig.10. Once the gap is forming the unconfinedarea of the piles and footings increases, which leads
to an increase in the natural period of thesuperstructure.
The acceleration time history and spectral
acceleration for cases 2 and 3 are shown in Fig. 11
and Fig. 12. The vibration characteristic between case
2 and case 3 is almost similar, however, ground
improvement by using cement-mixing can increase
the stiffness of the weak soil layer which resulting ina stiffer respond from the super structure.

Case 3-aFig.6 Assembled 3-D model and mesh of analysis
case 1-3.
The model was subjected to the Loma Prieta
earthquake as shown in Fig.7. The earthquake time
history is applied at bottom of the model in Case 2
and Case 3 while the acceleration time history
obtained from the free-field analysis is applied to thebottom boundary of Case 1. The earthquake wave waspropagating from the bottom to the soil surface. As
the wave travels through different soil layers, thewave was modified by transfer functions as shown in
free-field motion analysis. Thus, the wave
propagation in this model was more realistic than themodel that applied the earthquake motion at the sideboundary of the model. After the model was subjectedto earthquake time history, permanent gap formations
are clearly observed on most of the footing and piles.

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GEOMATE – Kyoto, Japan, 3-5 November 2021

8

mixing soil improvement, the efficiency of cementmixing also depends on the non-improved soil. The
elastic modulus and cohesion between improved areaand non-improved soil are significantly different, thus
the gap can be observed between the contract surfaces
as shown in Fig. 15.

Fixed Base
SSI

6

Acceleration (g)

4
2
0
-2
-4

4

Without Cement Mixing
With Cement Mixing

-6
0

5

10

15

20

25

2

30

Acceleration (g)-8Time (s)Fig.8 Comparison of acceleration time history
between case 1-a(Fixed Base) and case 2a(SSI) at the top of the superstructure.

0-2

Amplitude

0.6-4

Fixed Base
SSI

0.5
0.4

0.1
0.0
3

4

5

6

Frequency (Hz)

7

8

9

10

15

20

25

30

0.4

Amplitude2

10

Fig.11 Comparison of acceleration time history
between case 2-a and case 3-a at the top of thesuperstructure.

0.2

1

5

Time (s)

0.3

0

0

Fig.9 Comparison of Spectral acceleration between
case 1-a(Fixed Base) and case 2-a(SSI) at the
top of the superstructure.

Without Cement Mixing
With Cement Mixing

0.3
0.2
0.1
0.0
0

1

2

3

4

5

Frequency

6

7

8

9

10

Fig.12 Comparison of Spectral acceleration between
case 2-a and case 3-a at the top of thesuperstructure.
60

Axial Stress (MPa)

40
20
0
-20

Without Cement Mixing
With Cement Mixing-40

Fig.10 Permanent gap around footings in case 2-a.

-60

0

5

10

15

20

25

30

Time (s)Since there is a very high number of piles in thisstudy, some of the piles were selected from each typeof footings to show the change of vertical stresses
during the earthquake. The vibration of thesuperstructure generates a significant amount ofbending moment on the piles. The stresses in eachpile are shown in Fig.13 and Fig.14. The cementmixing soil improvement increases the horizontal
stiffness of the soil which resulted in better axial
stress distribution among the piles. When the gap isformed, the unconfined area increasing and theinfluence from the moment from the superstructure to
the pile also increasing. As the cement-mixing
increases the strength and stiffness of the surroundingsoil, the gap deformation is reduced. Although mostof the stress in the piles decreased due to the cement-

Fig.13 Comparison of the axial stresses between case2-a and case 3-a for the pile in the middle ofpile raft.

Axial Stress (MPa)

20

0

-20

Without Cement Mixing
With Cement Mixing

-40
0

5

10

15

20

25

30

Time (s)Fig.14 Comparison of the axial stresses between case2-a and case 3-afor the pile of 2-pile footing.
138

GEOMATE – Kyoto, Japan, 3-5 November 2021

Journal of Earthquake Engineering, Vol. 4 No.3
2000.
[4] Mizuno, H., Iiba, M. and Hirade, T., Pile damage
during the 1995 Hyogoken-Nanbu earthquake inJapan, 11th World Conf. on Earthquake
Engineering, Association for Earthquake
Engineering (IAEE), Tokyo, No. 977, 1996.
[5] Phanikanth, V., Choudhury, D. and Reddy, G.,
Behavior of Single Pile in Liquefied Deposits
during Earthquakes, International Journal of
Geomechanics, 2013, pp. 454–462.
[6] Rollins, K. and Brown, D., “Design Guidelines for
Increasing the Lateral Resistance of HighwayBridge Pile Foundations by Improving Weak
Soils,” National Cooperative Highway Research
Program Report, Washington, D.C., 2011, pp. 697[7] Lv, Y., Liu, H., Charles, W.W. Ng, Ding X. andGunawan, A., Three-dimensional numericalanalysis of the stress transfer mechanism of XCC
piled raft foundation, Computers and Geotechnics,
Vol. 55, 2014, pp. 365-377.
[8] Lee, C. J., Bolton, M. D. and Al-Tabbaa, A.,
Numerical modelling of group effects on the
distribution of dragloads in pile foundations,Geotechnique, Vol. 52, No. 5, 2002, pp. 325-335.
[9] Randolph, M.F. and Wroth, C.P., Application of
the failure state in undrained simple shear to theshaft capacity of driven piles, Geotechnique, Vol.
31, 1980, pp. 143–57.
[10] Meymand, P.J., Shaking Table Scale Model
Tests of Nonlinear Soil-Pile-SuperstructureInteraction In Soft Clay, Doctor of Philosophy
Dissertation, University of California, Berkeley,
1998.
[11] Likitlersuang, S., Surarak, C., Wanatowski, D.,
Oh, E. and Balasubramaniam, A., Finite element
analysis of a deep excavation: A case study fromthe Bangkok MRT, Soils and Foundations, Vol.
53, No.5, 2013, pp. 756-773.
[12] Arai, H. and Yamazaki, F., Exploration of SWave Velocity Structure Using Microtremor
Arrays in the Greater Bangkok,Thailand, EDM
Technical Report No.15, Earthquake Disaster
Mitigation Research Center (EDM), 2002.
[13] Muraleetharan, K.K., Ravichandran, N. and
Norton, S.L., Fully Ccoupled Dynamic SoilStructure Interaction Analysis of Pile Foundationin Weak Soil, 4th International Conference on
Earthquake Geotechnical Engineering, No. 1177,
2007.

Fig.15 Permanent gap between cement-mixing andenvironmental soil in case 3-a.
CONCLUSION
Soil-pile-structure interaction behavior can be
successfully simulated by using 3-D finite element
software “ABAQUS”. The gap/slap mechanism wasclearly observed. Soil-pile-structure interaction
provides damping and flexibility to the superstructure.
The natural period of the superstructure was increased.
The acceleration and displacement of thesuperstructure were significantly decreased. Cementmixing soil improvement increases the horizontal
stiffness of the foundation system which results inbetter stress distribution among the piles and the gap
position was changed from the contract surfaces
between soil and piles to contact surfaces between
non-improved soil and cement-mixing soil.
ACKNOWLEDGMENT
The authors would like to express their gratitude
for financial support from King Mongkut’s
University of Technology Thonburi (KMUTT)
through The Petchra Pra Jom Klao PhD scholarship
under contract Grant No. 2/2559.
REFERENCES[1] Guin, J. and Banerjee, P. K., Coupled soil-pilestructure interaction analysis under seismic
excitation, Journal of Structural Engineering, Vol.
124, No. 4, 1998, pp. 434-444.
[2] Han Y., Seismic response of tall building
consideringsoil-pile-structureinteraction,”
Earthquake Engineering and Engineering
Vibration, Vol. 1, 2002, pp. 57–64.
[3] Mylonakis, G. and Gazetas, G., Seismic soil
structure interaction: Beneficial or Detrimental,

139

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

RELATIONSHIP BETWEEN LIQUEFACTION STRENGTH OF SAND
WITH FINE FRACTION AND VARIOUS VOID RATIOS
Kentaro Ishii 1, Wen LIU1, and Shoji KAMAO2
1

Graduate Student, Department of Civil Engineering, Nihon University, JAPAN
Associate Professor, Department of Civil Engineering, Nihon University, JAPAN

2

ABSTRACT
Liquefaction damage has been reported in the ground containing fine fractions. It is necessary to investigate theeffect of soil containing fine particles on liquefaction strength. This study aims to clarify the relationship between
the liquefaction strength and the skeleton void ratio, representing the skeletal structure of the soil and the fine
fraction void ratio. For the utilized samples, Toyoura sand was used as the coarse fraction, Kaolin clay worked as
the fine fraction, and mixed soil in which the mixing ratio was altered in the range of 0 to 40% was applied.
Besides, considering the difference in the fine fraction properties, the experimenter made use of Fujimori clay,
Kasaoka clay, and the three combined soils. The specimens were prepared using the dry tamping method andrepeatedly subjected to cyclic undrained triaxial tests. As a result, when the skeleton void ratio does not exceed
the maximum void ratio of sand, it is located on the same line as the liquefaction strength regardless of the typesof fine fractions. We can declare that it is a unique relationship. When the skeleton void ratio exceeds the maximum
void ratio of sand, there is an excellent correlation between the fine fraction void ratio and the liquefaction strength.
However, it is represented by two curves of Kaolin mixed soil and other mixed soil due to the distinction in themagnitude of the plasticity index, and it can be remarked that the fine fraction void ratio is not distinctly related to
the liquefaction strength.
Key words: liquefaction strength, fines content, undrained cyclic test, sand clay
fraction is dominant has not been clarified. So far, theauthors have repeatedly conducted the undrained
cyclic triaxial tests using a mixed sample in which thevoid ratio e is constant and the fine fraction content ischanged in the range of 0 to 40% [6] [7]. In the region
with a skeleton void ratio es that does not exceed the
maximum void ratio of sand only (es<emax), theliquefaction strength R20 is categorically related to the
Skeleton void ratio es, notwithstanding the type offine fraction. With a skeleton void ratio es above themaximum void ratio for sand only (es>emax), theliquefaction strength R20 has a good correlation withthe "Fine fraction void eff" in the region where the fine
fraction is predominant. An intermediate region exists
between these two regions. The R20 is approximately
equal to the R20 when the skeleton void ratio es is total
up to the maximum void ratio of sand only.
The purpose of this study is to clarify the effect offine fraction on liquefaction characteristics byperforming undrained cyclic triaxial tests on
specimens with the same method from small to large
fine fraction. In this study, the skeleton void ratio es [6][7] [8] [9]is defined as the void ratio of all the fine grainsin the sand as pore space. The fine fraction void ratio
eff [6] [7] [8] [9] is the void ratio that ignores the volumeof the coarse grains and focuses only on the fine
fraction. It is an indicator of the degree of fine fraction
blockage.

INTRODUCTION
The main grounds where seismic liquefaction
events occur are generally recognized as loosely
deposited, saturated sand grounds. However,liquefaction of fine fraction has been confirmed by
the 1995 Hanshin Earthquake and the 2011 Great EastJapan
Earthquake[1]，whichcausedsevereliquefaction of reclaimed land in the Tokyo Bay areafrom Shinkiba to Urayasu. And the researches havebeen carried out to clarify the liquefaction mechanismof fine fraction and its effects on liquefaction strength.
As a result of these studies, it was found that (1) The
liquefaction strength decreases with the increase offine fraction content, and there is a fine fraction
content with the lowest liquefaction strength [2] [3]. (2)There is a good correlation between the skeleton void
ratio and the liquefaction strength, where a part of thefine grain content is considered as a void [4] [5].
However, there is no unified view on the effect of fine
fraction of liquefaction strength, due to different
methods of density control and specimen preparation
among researchers. In particular, few studies havebeen conducted on samples that contain a large
amount of fine fraction up to the region where fine
fraction are dominant. And the effect of fine fraction
on liquefaction strength in the region where fine140

GEOMATE – Kyoto, Japan, 3-5 November 2021

EXPERIMENT OVERVIEW

20%. Each mixed soil is called K5, F3, or KS5,
according to the acronym of the fine fraction' nameand the mixing ratio. This study mixing ratio is the

In this study, undrained cyclic triaxial tests werecarried out to determine the liquefaction strength ofeach sample. An overview of the experiment is given
below

Table 3SampleTest conditions for cyclic triaxial tests

Density
SampleFor the base material sample used in this study,
Toyoura sand was used as the coarse fraction, andKaolin clay, Fujinomori clay, and Kasaoka clay wereused as the fine fraction. The sample used was
Toyoura sand whose grain size was adjusted to 75 μm
to 2 mm as the coarse fraction content, and kaolin clay
as the low plastic fine fraction content, and the mixing
ratio. The author used kaolin-mixed clay in which
was changed by 5% in the range of 0% to 40%. In
addition, Fujinomori clay and Kasaoka clay wereused to examine the effect of different properties offine fraction. The author used Fujinomori clay whose
grain size was adjusted to 75 µm or less. The fine
fraction content of the Fujinomori mixed soil is 3%,
5%, 10%, 15%, 20%, and 25%. The fine fraction
content of the Kasaoka mixed soil is 5%, 10%, andTable 1

ToyouraK5

K10Main soil property values of Toyoura sandSoil particle
densityCorse fraction

3

Toyoura sandTable 2MeanFine fraction
diametercontent

Loose (40)

0.828

0.5

0.12，0.13，0.15

Medium (60)

0.757

0.5

0.16，0.17，0.18

Dense (80)

0.687

0.5

0.21，0.22，0.24

Loose

0.828

1

0.09，0.10，0.14

Medium0.757

10.12，0.14，0.16

Dense

0.687

1

0.15，0.17，0.18

Loose

0.828

5

0.08，0.10，0.12

Medium0.757

50.08，0.10，0.12

Dense

0.687

5

0.10，0.11，0.12

Loose

0.828

12

0.08，0.10，0.12

Medium0.757

120.08，0.10

24

0.07，0.08，0.09

K25

Medium0.757

240.08，0.09

K30

Medium0.757

240.10，0.12，0.15

Loose

0.828

24

0.08，.0.09

Medium0.757

240.09，0.11，0.13

Loose

0.828

24

0.09，0.13

Medium0.757

240.11，0.13，0.15

F3

Medium0.757

0.5

0.2，0.17，0.125

F5Medium0.757

10.16，0.14，0.12

F10Medium0.757

50.12，0.11

F15

Medium0.757

150.11，0.09

F20

Medium0.757

180.105，0.115

F25

Medium0.757

240.11，0.12

KS5Medium0.757

10.17，0.13

KS10

Medium0.757

50.15，0.12，0.10

KS20Medium0.757

150.11，0.10

2.64

0.161

0

0

FC (%)

R(=σ d /2σ c ')0.757

CC (%)IPt (hour)

MediumFC (%)

ρs (g/cm3)

e0

K15

Main soil property values of fine-grained
soil

Fine fraction（ ）indicates D r (%)

K20

D50

Plastisity Fine fraction
index
content

Cyclic stressratio

Clay fraction
contentρs (g/cm )Soil particle
density

Initial Consolidation
void ratio
time

K35

Clay fraction
content
CC (%)

Kaolin clay

2.714

13.7

100

64

Fujinomori clay

2.535

20.4

92

37

Kasaoka clay

2.710

30.8

99.8

46

K40

*

The Confining pressure of Consolidation is 100 kPa

Fig. 1 grain size accumulation curves the sample
141

GEOMATE – Kyoto, Japan, 3-5 November 2021*

The load repeats with a sinusoidal wave of 0.1 Hz frequency at
the specified cyclic stress ratio

between is called "Medium". When only Toyoura
sand was used, in order to take into account the effectof density change, the relative densities were adjusted
in three ways: Dr=40% (Loose), Dr=60% (Medium)and Dr=80% (Dense). When the same test was
conducted using mixed soil, the density wascontrolled so that the initial pore ratio e0 was e0=0.828(Loose), e0=0.757 (Medium), and e0=0.684 (Dense),
respectively, which was the same value as the initial
pore ratio e0 when the density was adjusted using only
Toyoura standard sand. Therefore, the density control
method in this study can be considered as a constantvoid ratio.

percentage of the dry mass of the mixed fine-grained
sample to the total dry mass. Each mixed soil was
prepared by mixing each sample in an air-dried state
until it became homogeneous in a container. main
physical properties and particle size distributions ofeach sample are shown in Table 1 and Table2, Fig.
1.
Experimental Method
The specimen is a cylindrical sample of 5 cm indiameter and 10 cm in height. The specimens weredivided into ten equal parts and then divided into ten
layers, each layer of which was solidified with arammer. After the specimens were prepared at apredetermined density, the specimens were firstallowed to stand on their own at a constraint pressureof 20 kPa, and was saturated by aerating carbon
dioxide, passing sufficient degassed water, and thensaturated by applying 200 kPa of back pressure,which is about three times the volume of thespecimens. Subsequently, consolidation was
performed with effective constraint pressure σc'=100kPa. The consolidation time was set as shown in
Table 3 for each sample, taking into account that theconsolidation could be completed sufficiently. Afterthe consolidation was completed, the B-value wasmeasured to confirm the saturation, and the backpressure was further applied to make the B-value
more than 0.95. After confirming that the B-value
was more than 0.95, the material was repeatedly
loaded with a sinusoidal wave of 0.1 Hz frequency at
the prescribed cyclic stress ratio R(=σd/2σc'). The test
conditions for each sample are shown in Table 3.

Liquefaction Strength Curve
Figure. 2(a)~(d) shows the liquefaction test resultsfor each sample. It shows the relationship between thecyclic stress ratio R and the number of cycles N
required to reach both amplitude axial strains DA=5%.
Fig. 2(a) shows the liquefaction strength curve when
the undrined cyclic triaxial test was repeatedly
performed using Toyoura standard sand. As thedensity of Toyoura sand increases, the cyclic stressratio at a certain number of loading cycles increases,
the curve moves upward, and the resistance toliquefaction increases. In Dense, the liquefaction
intensity curve, commonly seen in dense sand, tends
to rise. Fig. 2(b) shows the cyclic strength curves forK5 to K15 with varying densities; as for K5, the curve
moves upward with increasing density, as in the caseof Toyoura sand, but it becomes weaker as the content
of K10, K15 and Kaolin clay increases. For the samedensities, the cyclic strength curve is located at thebottom of the curve as the fines content increases. Fig.
2(c) shows the cyclic strength curves for the cyclic
triaxial tests using from K20 to K40. It was found thatthe curve was located upward for samples with higherfines content, and the resistance to liquefaction was
higher. Fig. 2 (d) shows the cyclic strength curves forthe cyclic triaxial tests using from F3 to F25 and KS5
to KS20. In Fujinomori mixed soil, the liquefaction
strength curve moves to the bottom as the fine
fraction content increases, and the liquefaction
strength gradually increases beyond FC = 15%,
increasing the resistance to liquefaction. In addition,
the Kasaoka mixed soil shows the same tendency as
the Fujinomori mixed soil.

Density Management Method
In the field of liquefaction, the relative density Dr
is often used as a parameter expressing the density of
specimens, and even in study using samples mixed
with fine fraction, the relative density controls thedensity, and the same relative density is used. There
are many examples of comparing densities with eachother. For example, the reason why the relative
density is often used is that in the case of sand, therelationship between the relative density and theliquefaction strength is proportional. but the"minimum/maximum density test of sand (JIS)" fordetermining the relative density the range of soil towhich A 1224) is applied is said to be sand in which
95% or more remains in a 75 μm sieve, and whenusing a sample containing a large amount of fine
fraction as used in this study, the relative density It isdifficult to control the density using, and it isconsidered inappropriate.
Therefore, in this study, the most densely packed sandis called "Dense", the most loosely packed sand iscalled "Loose", and the sand with a density in

Relationship Between Fine Fraction Content And
Liquefaction Strength
In this study, the cyclic shear stress R was used as"liquefaction strength R20" to indicate the resistanceto liquefaction when both amplitudes of strain
DA=5% were reached at 20 times of cyclic loading(shown as dashed line in Fig.2). The relationshipbetween the liquefaction strength, R20, and the Fine
fraction content FC, is shown in Fig.3. From the142GEOMATE – Kyoto, Japan, 3-5 November 2021

figure, the R20 of kaolin mixed soil Medium

Fig. 2 Cyclic strength for each sample
decreased with the increase of Fine fraction content,
showed the lowest R20 at FC=20%, and thenincreased. In the range of FC=0~15%, the R20 ofKaolin mixed soil increases with the increase ofdensity of specimen, but the effect of density of
specimen on liquefaction strength gradually
decreases with the increase of fine grain content. at
FC=15%, the strength of Loose and Medium arealmost the same. The R20 of Fujinomori mixed soil
Medium decreased with the increase of Fine fraction
content. The lowest R20 was observed at FC=15% andthen increased. The R20 of Kasaoka mixed soil
Medium decreased with the increase of FC. After
FC=10%, under the same density of specimen,
Fujinomori mixed soil and Kasaoka mixed soil show
higher liquefaction strength than Kaolin mixed soil.

Fig. 3 Effect of fine fraction content on liquefaction
strength R20
Relationship Between Various Void Ratios And
Fine Fraction Content
When investigating the strength of a soil with a grain
size intermediate between sand and clay, such as the
sample used in this study, it is very important to pay
attention to the skeletal structure of the soil.
Therefore, in this study, the effect of fine fraction onliquefaction properties was considered using "Skeleton void ratio es" and "Fine fraction void ratio
eff ". The skeleton void ratio es is the void ratio when
all fine fractions are considered as pores while theskeleton void ratio es is the void ratio. The fine
fraction void ratio eff is a void ratio that ignores thevolume of coarse-grains and focuses only on the fine
fraction content, which is an indicator of how well thefine fraction content is plugged. The void ratio can becalculated by the following equations (1) and (2).
Equations (1) and (2) use the symbols shown in Fig.
4.
Skeleton void ratio 𝒆𝒔 =143

𝑽𝒗 +𝑽𝒔(𝒔𝒊𝒍𝒕) +𝑽𝒔(𝒄𝒍𝒂𝒚)
𝑽𝒔(𝒔𝒂𝒏𝒅)…(1)

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fine fraction void ratio 𝒆𝒇𝒇 =Fig. 4

𝑽𝒗
𝑽𝒔(𝒄𝒍𝒂𝒚) +𝑽𝒔(𝒔𝒊𝒍𝒕)…(2)

Fig. 6 Relationship between fine fraction content
and fine fraction void ratio eff

Schematic of mixed samples

Relationship Between Various Void Ratios And
Liquefaction Strength

Fig. 5 and Fig. 6 show the changes in the skeleton
void ratio es and the fine fraction void ratio eff due tothe increase in the fine fraction content in the mediumof each mixed soil. The various void ratios in the
figure are the values at the time of preparing thespecimen. From Fig. 5, the value of the skeleton void
ratio does not change regardless of the type of fine
fraction, and the skeleton void ratio increases as thefine fraction content increases. The skeleton void
ratio es exceeds the maximum void ratio emax of sandonly (broken line in Fig. 5) near FC = 10%. Therefore,
before FC = 10%, a skeleton of sand particles isformed and fine fraction are formed. After FC = 10%,
it is considered that the fine fractions enter between
the sand particles to form a skeleton, or the sandparticles are floating in the fine fraction. Fig. 6 shows
the relationship between the fine fraction void ratio eff
and the fine fraction content FC. As FC increases, thefine fraction void ratio eff decreases.

The relationship between the Skeleton void ratio es
and liquefaction strength R20 is shown in Fig. 7. When
the Skeleton void ratio es does not exceed the
maximum void ratio of Toyoura sand only e max(dashed line in the figure), the Skeleton void ratio e s
and liquefaction strength are on the same curve
regardless of the type of fine fraction or the density ofthe specimen. This indicates that the skeletal structureof the sample is formed by sand and fine fraction does
not affect the liquefaction strength, and R20 isdetermined by the Skeleton void ratio es, within therange of the maximum void ratio of sand only.
Furthermore, the Skeleton void ratio es gradually
increases after emax. The liquefaction strength starts toincrease after reaching the lowest point, suggestingthat the skeletal structure of the soil is similar to thatthe fine fraction does not fit into the spaces between
the sand grains, and the sand grains float in the fine
fraction.
Therefore, in such a state, the liquefaction strength
R20 is presumed to be influenced by the state of fine
fraction and is considered to be closely related to theFine fraction void ratio eff. The relationship between
the Fine fraction void ratio eff and the liquefaction
strength R20 is shown in Fig. 8. Focusing on the
kaolin mixed soil in Fig. 8, R20 is located on almost
the same line regardless of the fine fraction content
and density, and R20 increases sharply from around
FC = 30%. From this, it was found that there is a goodcorrelation between the liquefaction strength R20 and
the Fine fraction void ratio eff in the region where thefine particles of FC = 30 to 40% are predominant. The
same change tendency is shown in the Fujinomori
mixed soil and the Kasaoka mixed soil. However, due
to the difference in the magnitude of the plasticity
index Ip, the results were represented by two curves,
kaolin mixed soil (Ip ＜ 15) and other mixed soil (Ip＞ 20).

Fig. 5 Relationship between fine fraction content
and skeleton void ratio es

144

GEOMATE – Kyoto, Japan, 3-5 November 2021

void ratio for sand only (es>emax), The liquefaction s
trength and the Fine fraction void ratio eff are not uni
quely related, and the liquefaction strength tends to change depending on the difference in the plasticity index of fine fraction.
REFERENCES[1] Kazama M., Kagatani T., Yanagisawa E.,
Peculiarities of liquefaction resistance ofmasado, Journal of JSCE No.645/Ⅲ-50 pp153160, 2000. (In Japanese only)[2] Kuwano J., Iimura H., Nakazawa H., Sugihara
K., Liquefaction strength of sand containing
kaolin clay ， 50th JSCE Annual meeting,pp.506-507, 1995. (In Japanese only)[3] Yazima J., Numata Y., Nkane A., A Study onliquefaction strength evaluation methods andliquefaction properties of fine containing sand,
Journal of JSCE No.624/Ⅲ-47, pp.113-122,
1999.6 (In Japanese only）[4] Ishikawa S., Masayuki H., Ikuko S., Rolando O.,
Nakata Y., Norimasa Y., Cyclic shear properties
of non-plastic low plastic fine fraction mixed
soil, 42nd JGS Annual meeting, pp.158-159,
2007. (In Japanese only)[5] Nabeshima Y., Nagasaw T., Tamotsu M.,
Evaluation of liquefaction strength of sands
containing fines based on clay fraction, 38th
JGS Annual meeting, pp.239-240, 2003. (InJapanese only)[6] Liu W., Kamao S., Yokoyama K., Effect of fines
content on liquefaction characteristics, 10th Int.
Conf. on Geotechnique, Construction Materials
and Environment, Melbourne, Australia, 11-13
November 2020
[7] Liu W., Ishii K., Kamao S., Effect of fines
content
on liquefaction characteristics,
International Journal of GEOMATE, June.,
2021, Vol.20, Issue 82, pp.40-45[8] Sato M., Oda M., Kazama H., Ozeki K.,
Fundamental study on the effect of fines onliquefaction strength of reclaimed ground,
Journal of JSCE No.561/Ⅲ-38, pp.271-282,
1997. (In Japanese only）[9] Thevanayagam S., Shenthan T., Mohan S.,
Liang J., Undrained fragility of clean sands, silty
sands, and sandy silts, Journal of geotechnicaland geoenvironmental engineering 128 (10),pp.849-859, 2002.

Fig. 7 Relationship between Skeleton void ratio es
and Liquefaction Strength R20

Fig. 8 Relationship between Fine fraction void ratio
eff and liquefaction strength R20
CONCLUSION
The authors investigated an evaluation method
using various void ratios as indicators for theliquefaction strength of sand and sandy soil throughexperiments. As a result, the following findings wereobtained.
(1) In the region with a skeletal pore ratio es that does not exceed the maximum pore ratio of sand only (es<emax), the fine fraction makes no difference to the li
quefaction strength. And the liquefaction strength R2
0 is categorically related to the "Skeleton void ratio es", notwithstanding the type of fine fraction.
(2) With a skeleton void ratio es above the maximum
void ratio for sand only (es>emax), the liquefaction st
rength R20 has a good correlation with the "Fine fract
ion void eff" in the region where the fine fraction is p
redominant.
(3) With a skeleton void ratio es above the maximum
145

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

MONITORING AND PREDICTION OF SETTLEMENT USING ASOKAMETHOD AND HYPERBOLA METHOD WITH PREFABRICATEDVERTICAL DRAIN AS GROUND IMPROVEMENT METHOD
Muhammad Haziq Taqiuddin bin Rezuwan1, Saiful Azhar bin Ahmad Tajudin2
1

Faculty of Civil Engineering and Built Environment, University Tun Hussein Onn, Malaysia,
ABSTRACTSurcharge preloading together with prefabricated vertical drains (PVDs) have been widely used to improve
properties of thick clay deposits to accelerate consolidation settlement. To assess the performance of soil
improvement works, the average degree of consolidation needs to be estimated. Based on the formula, Hyperbola
method and Asoka method is proposed to predict the ultimate settlement and calculate the coefficient ofconsolidation using field settlement monitoring data. This research compares the effectiveness and accuracy ofHyperbola method against Asoka method compared to predicted design settlement through the analysis of thedegree and time of consolidation. Comparisons between the Hyperbola method and the Asoka method indicatethat Hyperbola method give a less than 1.0% higher ultimate settlement than the Asoka method if initial data on
60% consolidation were not recorded. The results show a close relationship between the stability of predicted
results by curve fitting methods when monitoring work done between days 380 till days 540.
Keywords: Settlement, Asoka, Hyperbola,
INTRODUCTION
SITE DESCRIPTION AND MEASUREMENT
For clay deposit improvement, preloading method
is generally used by using surcharge fill andPrefabricated Vertical Drains (PVDs) [1]. As analternative method to improve soft soil condition [2],PVDs are made to counter structural failure due tosettlement by increasing the soil shear strength,
decreases compressibility of soil and impede on
reduction of substantial consolidation [3], [4]. Degreeof soil improvement is evaluated by estimating the
average Degree of Consolidation. Degree ofconsolidation is current settlement percentage toultimate primary consolidation settlement [5]. There
are several empirical and predictive models of curve
fitting systems, such as the Hyperbola method and the
Asoka method [6], [7].

This research is focus on evaluate, predict anddetermine the settlement using Asoka method andHyperbola method. The study is Located at Kampung
Jawa, Parit Jalil, Batu Pahat, Johor. Figure 1 shows
the location of the field test at Kampung Jawa, Parit
Jalil, Batu Pahat, Johor

However, the potential and effectiveness for bothmethods are still in study to justify its accuracy.
Especially on cases using prefabricated vertical drain
for marine clay [5], [8]. Several justifications statedthat Hyperbola method are more accurate than Asoka
method yet over predicted. On the other hand, Asoka
method are much more accurate, but it predicted thevalue slight smaller than predicted settlement [9]–[11]. Thus, from this observation which method are
more accurate when predict settlement and the result
are close by to actual designed settlement. However,in this research initial data on 60% consolidation were
not recorded on settlement gauge and settlement
marker. Furthermore, settlement marker was installed
after surcharge rest period. This condition will bring
controversy on accuracy for both method andallowable adjustment during data analysis.

Fig. 1

146

Site location and detail at Kampung Jawa

GEOMATE – Kyoto, Japan, 3-5 November 2021

𝑆𝑡𝑗 = 𝛽0 + 𝛽1 𝑆𝑡𝑗−1(1)

Since β0 and β1 indicate undetermined constants
and independent, the time series data will be plotted
via same straight line. When Stj equals Stj-1, final
settlement S∞ can be calculated by formula:
𝑆∞ = 𝛽0 / (1 − 𝛽1 )(2)Fig. 2 Cross-section of ground treatment with PVD
and surcharge
Generally, this site consists of 5 settlement gauge
and 11 settlement markers install in the site as shownin figure 1 and figure 2. Three boreholes were drilled
to provide information as reference for settlement
analysis. The structures of the soil for the site wereconsistent with clayey type of soil. Surcharge andPrefabricated vertical drain (PVD) were applied
through the whole site. The detail for both ground
improvement method was summarized as below:
Fig. 3Graphical plot of Asoka method [9]Fig 4

Asoka method for settlement prediction [12]Table 1: Surcharged and Prefabricated Vertical Drain(PVD) summary
No.
1
2
3
4
5
6
7
8

Description
PVD Spacing
PVD Length
Clay thickness
Surcharge
HeightEmbankment
HeightTotal height
Predicted
settlement
Degreeconsolidation

BH1
1.1 m
21 m
21 m
1.3 m

BH2
1.1 m
21m
21 m
1.3 m

BH3
1.1 m
21 m
18 m
1.3 m

2.5 m

2.5 m

2.5 m

3.8 m
1.034
m
90 %

3.8 m
1.101
m
90 %

3.8 m
0.993
m
90 %

GRAPHICAL METHOD IN PREDICTIINGGROUND SETTLEMENT

The accuracy of Asoka method depends on timeinterval. The longer the time, the higher the accuracy.

In this research, two field tests are conducted tomonitor the settlement. Hence, other relevant theories
were used to justify and predict the ultimate
settlement.

HYPERBOLA METHOD
It is assumed that the settlement rate of grounddecreases with time in hyperbola method [6], [14],[15], the settlement calculation formula of hyperbola
method [14], [11] is as follows:

ASOKA METHOD
Asoka method [12] is based on the one-way
consolidation equation of Terzaghi [6], its linear
fitting equations (Fig. 3 & Fig. 4) as follows [13]:𝑆𝑡 = 𝑆0 +

147

𝑡− 𝑡0
𝛼+ 𝛽(𝑡− 𝑡0 )(3)

GEOMATE – Kyoto, Japan, 3-5 November 2021

obtain were analyze and summarized in table 2 andtable 3.

In the formula, t0 and S0 respectively indicate theobservation time and the settlement of the starting
point in the fitting calculation; t and St respectively
indicate any time point of fitting curve and thecorresponding settlement; α and β respectively
indicate the intercept and slope of the line. When theparameter t tends to be infinite, final settlement S∞
could be obtain by using [16]:𝑆∞ = 𝑆0+

1

ASOKA METHOD
The results show that predicted settlement for
settlement gauge and settlement marker weredifferent by using Asoka and Hyperbola method. As
a result of different initial data record for settlement
marker and settlement gauge, the predicted data using
both method for both instruments were also
contribute to different result.

(4)𝛽After a period of loading, residue settlement ΔS
can be calculated by formula:∆𝑆 = 𝑆∞ − 𝑆𝑡

Settlement marker were installed and monitored
after embankment are almost filled while settlementgauge was installed and monitored during initial
ground level. From this case, the monitoring record
affect the prediction of the ultimate settlement using
Asoka and Hyperbola method as shown in table 2 andtable 3.

(5)

The S60 and S90 slope can be determine by:
𝑆60 = 𝑆𝑖∝60
∝𝑖and 𝑆60 = 𝑆𝑖

∝60
∝𝑖

(6)

Table 2: Ultimate Settlement for Settlement Marker
using both method

Where ultimate primary settlement can beobtained by following ratio:

Predicted
𝛿𝑓 =

∝𝑖
𝑆𝑖

=

𝛿60
0.6

=

𝛿60
0.9

(7)
Soil
Instrument

DesignSettlement
Sult (mm)

SG 01
SG 02
SG 03
SG 04
SG 05

1034

1100
993

Method
Asoka
Sult
(mm)

Hyperbola

Sult (mm)1100

2380.41225

2297.2

1060

1943.81050

1997.61386

2632.2

Table 3: Ultimate Settlement for Settlement Gaugeusing both method
Fig.4
[9]

Hyperbola method for settlement prediction
Soil
Instrument

In reality, due to the fact that settlement
assessment at the beginning of consolidation is often
not taken, it is more efficient to use (𝑡 − 𝑡𝑖 )/(𝑠 −𝑠𝑖 ) versus (𝑡 − 𝑡𝑖 ) , where Si are settlement after
time as shown in Fig. 4. This method of plotting is
also useful as it allows the results to be reinitialized
whenever appropriate [17].

Predicted
DesignSettlement

MethodAsoka

Hyperbola

Sult
(mm)

Sult (mm)

SM 01

650

1152

SM 02

650

856

410

707

Sult (mm)

SM 03

1034

PREDICTION OF ULTIMATE SETTLEMENT

SM 04

690

1064

The calculation results of ultimate settlement ofsettlement gauge and settlement marker based on two
methods studied in this research. Monitoring work
were done between days 380 till days 540. The data

SM 05

620

798

643

1120

520

-

SM 06
SM 07
148

1100

GEOMATE – Kyoto, Japan, 3-5 November 2021

SM 08

595

963

SM 09

560

1124

700

1458

600

1296

SM 10
SM 11

993

Based on Asoka plot for settlement gauge (Fig 5,
Fig 6, Fig 7, Fig 8 and Fig 9) and settlement marker(Fig 10, Fig 11, Fig 12, Fig 13, Fig 14, Fig 15, Fig 16,
Fig 17, Figure 18, Figure 19, Figure 20) shows that
settlement are linearly increase proportional tosettlement [18].
According to Asoka method [12], the predicted
ultimate settlement were obtain from previous
observation time series of settlement data. from Eq.
1, a line of best fit was constructed, and the predicted
ultimate settlement determined from the interceptionof 𝜷𝟏 with the 45o line.

Fig. 6

Asoka plot for Settlement Gauge, SG 02

Fig. 7

Asoka plot for Settlement Gauge, SG 03

Fig. 8

Asoka plot for Settlement Gauge, SG 04

However, Table 2 and Table 3 shows that thepredicted ultimate settlement obtained fromsettlement gauge were more identical than settlement
marker. Since time interval of settlement monitoringfor settlement gauge were much longer thansettlement marker, the accuracy to attain predicted
ultimate settlement were much higher considering
self-reliant period of 𝜷𝟏 and 𝜷𝟎 .
The time interval of monitoring for settlementgauge were begin after 386 days while settlement
marker was on initial stage of 14 days. Therefore, thepredicted ultimate settlement of settlement gauge wasin average of 1000mm to 1300mm while settlement
marker was in between 600mm to 650mm.

Fig. 5

Asoka plot for Settlement Gauge, SG 01

149

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 9

Asoka plot for Settlement Gauge, SG 05

Fig. 12 Asoka plot for Settlement Marker, SM 03

Fig. 10 Asoka plot for Settlement Marker, SM 01
Fig. 13 Asoka plot for Settlement Marker, SM 04

Fig. 11 Asoka plot for Settlement Marker, SM 02
Fig. 14 Asoka plot for Settlement Marker, SM 05

150

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 15 Asoka plot for Settlement Marker, SM 06

Fig. 18 Asoka plot for Settlement Marker, SM 09

Fig. 16 Asoka plot for Settlement Marker, SM 07
Fig. 19 Asoka plot for Settlement Marker, SM 10

Fig. 17 Asoka plot for Settlement Marker, SM 08
Fig. 20 Asoka plot for Settlement Marker, SM 11

151

GEOMATE – Kyoto, Japan, 3-5 November 2021

HYPERBOLA METHOD

expected because during filling surcharge work on 42
days. The settlement marker was missing and unable
to relocate and retrieved. So, a new settlement marker
07 were establish and off set 1 meter from original
location. Therefore, the settlement reading afterward
were unable to comply with first reading fromoriginal location. Hence, effect the best fit line of
hyperbola curve even though the value of thesettlement is modified.

According to Hyperbola method [14] thepredicted ultimate settlement were obtain based onfield data which in this case, the collected data during
monitoring work. From Eq. (4), it is more reasonable
to use to obtain 𝑺𝒊 because it allows to re-compute theresult on any needed occasion and if another load is
added. Especially on consolidation with nonhomogeneous and non-linear properties.
Based on the hyperbolic plot for settlement
marker (Fig 26, Fig 25, Fig 26, Fig 27, Fig 28, Fig 29,
Fig 30, Fig 31, Figure 32, Figure 33, Figure 34), it isshows that the data follows a hyperbola pattern andpossible to predict the ultimate settlement, ultimate
settlement at 60% and 90% stages [15]. However, due
to the monitoring record did not consist of initial levelof the field reading, the hyperbolic plot misses theearly stage of information to construct a best fit curve.
Especially for settlement gauge (Fig 21, Fig 22, Fig
23, Fig 24, Fig 25).

Fig. 21 Hyperbola plot for Settlement Gauge, SG 01

Therefore, settlement gauge hyperbolic line did
not intersect with 𝒕𝟔𝟎 and 𝒕𝟗𝟎 line while settlement
marker only intercept on 𝒕𝟗𝟎 line during initial plot
both data. Although the analysis may be not accurate,
hyperbola method [14] were still possible although
the pattern were nonlinear and initial stage arenonhomogeneous.
Considering 90% of max settlement must be
achieved to obtain the ultimate settlement [6], [17],by increase the maximum value of time oversettlement, 𝒕𝒔 the 𝒕𝟔𝟎 and 𝒕𝟗𝟎 value that
corresponding to 𝒕𝟔𝟎/𝒔 and 𝒕𝟔𝟎/𝒔 from settlementgauge hyperbola graph were obtain althoughmaximum value of 𝒕𝒔 must be based on current
maximum value of 𝒕𝒔 . From this modification, theresult as shown on settlement gauge hyperbola plot
(Fig 21, Fig 22, Fig 23, Fig 24, Fig 25) clearly
indicate that hyperbola method was not bounded tolimited settlement.

Fig. 22 Hyperbola plot for Settlement Gauge, SG 02

Therefore, the justification influences therespective settlement gauge data as well as values ofintersection of 𝑺𝟔𝟎 and 𝑺𝟗𝟎 when intersect with
Hyperbola curve. Hence, to attain the value of 𝒕/𝜹𝟔𝟎
and 𝒕/𝜹𝟗𝟎 that correspond to the intersection point of
𝑺𝟔𝟎 and 𝑺𝟗𝟎 and the hyperbolic curve. Next, the valueof 𝜹𝟔𝟎 and 𝜹𝟗𝟎 are calculated which give thedetermination of ultimate settlement, ultimate
settlement at 60% and 90% stage. However, due tothe maximum value of time over settlement, 𝒕𝒔
increase, the predicted ultimate settlement will also
increase.

Fig. 23 Hyperbola plot for Settlement Gauge, SG 03

Moreover, settlement marker 07 were unable toplot hyperbolic graph. This case was able to be152GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 24 Hyperbola plot for Settlement Gauge, SG 04

Fig. 28 Hyperbola plot for Settlement Marker, SM
03

Fig. 25 Hyperbola plot for Settlement Gauge, SG 05
Fig. 29 Hyperbola plot for Settlement Marker, SM
04

Fig. 26 Hyperbola plot for Settlement Marker, SM
01
Fig. 30 Hyperbola plot for Settlement Marker, SM
05

Fig. 27 Hyperbola plot for Settlement Marker, SM
02
153

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 34 Hyperbola plot for Settlement Marker, SM
09

Fig. 31 Hyperbola plot for Settlement Marker, SM
06
Fig. 35 Hyperbola plot for Settlement Marker, SM
10

Fig. 32 Hyperbola plot for Settlement Marker, SM
07
Fig. 36 Hyperbola plot for Settlement Marker, SM
11
CONCLUSION
Based on the settlement record, it can beconcluded that both methods require settlement data
beyond 60% consolidation stage to provide accurate
predictions. However, if the initial settlement datafrom settlement marker and settlement gauge were
unrecorded, the prediction using Asoka method were90% more accurate than Hyperbola method.
Fig. 33 Hyperbola plot for Settlement Marker, SM
08

Asoka method graph consist of current settlement
versus previous settlement plot and simpler. It had
more advantage and accurate when predicting shortterm settlement. This is because, it relies on previous
monitoring data. However, if the time interval islonger, the prediction will more accurate and identicalwith actual design settlement. Although the dataobtain were only a few months it manages to predict
the settlement, but it will underestimate the prediction
as the data time interval were shorter.
Hyperbola method had disadvantage on thiscondition. Due to hyperbola method relies on
construction settlement data that need more detail and154

GEOMATE – Kyoto, Japan, 3-5 November 2021

Kouretzis, and J. Huang, “Outcomes of the
Newcastle symposium for the prediction ofembankment behaviour on soft soil,” Comput.
Geotech., vol. 93, pp. 9–41, 2018.
[9] H. Il Park, K. S. Kim, and H. Y. Kim, “Field
performance of a genetic algorithm in thesettlement prediction of a thick soft clay depositin the southern part of the Korean peninsula,”Eng. Geol., vol. 196, pp. 150–157, 2015.
[10]M. Zhang, J. J. Li, and C. H. K. Qin, “Analysis
and discussion on post-construction settlement ofsoft soil foundation treatment test section of
highway,” IOP Conf. Ser. Earth Environ. Sci.,
vol. 81, no. 1, pp. 0–7, 2017.
[11]W. Zhu, J. Yan, and G. Yu, “Vacuum preloading
method for land reclamation using hydraulic filled
slurry from the sea: A case study in coastal
China,” Ocean Eng., vol. 152, no. February, pp.
286–299, 2018.
[12]A. Asaoka, “NII-Electronic Library Service,”
Soils Found., vol. 18, no. 4, 1978.
[13]S.-H. C. Siew-Ann Tan, “Comparison of theHyperbolic and Asoka Observational Method of
Monitoring Consolidation with Vertical Drains,”
Chem. Pharm. Bull., vol. 17, no. 11, pp. 1460–
1462, 1994.
[14]T. Tan, A. Member, T. Inoue, and S. Lee,
“Hyperbolic Method for Consolidation Analysis,”
Geotech Eng., vol. 117, no. 11, pp. 1723–1737,
1992.
[15]T. Shin, Yeon-Sil Mori, Takeshi Okita,
Masatsugu Gemma, Tsuyoshi Kai, Chieko
Mikami, “Validation of Hyperbolic Method for
Settlement in Clays with Vertical Drains,” Soils
Found., vol. 35, no. 1, pp. 101–113, 1995.
[16]A. J. Puppala, P. Ruttanaporamakul, and S. S. C.
Congress, “Design and construction oflightweight EPS geofoam embedded geomaterial
embankment system for control of settlements,”
Geotext. Geomembranes, vol. 47, no. 3, pp. 295–
305, 2019.
[17]M. A. Al-Shamrani, “Applying the hyperbolic
method and Cα/Cc concept for settlement
prediction of complex organic-rich soil
formations,” Eng. Geol., vol. 77, no. 1–2, pp. 17–
34, 2005.
[18]R. Nazir, H. Moayedi, P. Subramaniam, and S.
Ghareh, “Ground improvement using SPVD and
RPE,” Arab. J. Geosci., vol. 10, no. 23, 2017.

numbers of data. This is because the plot consists oftime over settlement versus time. The longer theperiod the tendency to predict of the settlement willmore accurate although it was slightly tedious andcomplex. From this observation, if hyperbola methoddata are not satisfying it tend to overestimate theultimate settlement.
ACKNOWLEDGEMENT
Special thanks to Hidayat, Ain, Ayme, Ashraf,
Iqbal, Faid and Faiq for their help during soil
sampling and monitroring work. Hence, special
appreciation and recognition to those who contributedirectly and indirectly in this finding.
REFERENCES[1] M. W. Bo, A. Arulrajah, S. Horpibulsuk, A.
Chinkulkijniwat, and M. Leong, “Laboratory
measurements of factors affecting discharge
capacity of prefabricated vertical drain materials,”
Soils Found., vol. 56, no. 1, pp. 129–137, 2016.
[2]J. D. Hussin, “Methods of soft ground
improvement,” Found. Eng. Handb., pp. 529–
565, 2006.
[3] A. Suhendra, R. P. Putra, and F. Absari,“Effectiveness study of prefabricated verticaldrain using vacuum preloading and surcharge
preloading,” Earth Environ. Sci., 2018.
[4] L. G. Lam, D. T. Bergado, and T. Hino, “PVD
improvement of soft Bangkok clay with andwithout vacuum preloading using analytical andnumerical analyses,” Geotext. Geomembranes,
vol. 43, no. 6, pp. 547–557, 2015.
[5] W. Guo, J. Chu, and W. Nie, “An observational
method for consolidation analysis of the PVDimproved subsoil,” Geotext. Geomembranes, vol.
46, no. 5, pp. 625–633, 2018.
[6] B. Ahcène and B. Mohamed, “Comparative studyof four consolidation settlement estimation
methods of a railway,” vol. 06002, pp. 1–15,
2017.
[7] S. G. Chung, H. J. Kweon, and W. Y. Jang,“Observational method for field performance ofprefabricatedverticaldrains,”Geotext.
Geomembranes, vol. 42, no. 4, pp. 405–416,
2014.
[8] R. B. Kelly, S. W. Sloan, J. A. Pineda, G.

155

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

AN EXPERIMENTAL STUDY ON TEMPERATURE EFFECTS ON
ELASTIC AND CREEP DEFORMATION BEHAVIOURS OF A SAND
Yodphao Punya-in1, and Warat Kongkitkul2
1,2

Department of Civil Engineering, Faculty of Engineering,
King Mongkut’s University of Technology Thonburi, Thailand
ABSTRACT
An automated triaxial loading system that can accurately control both the loading and temperature histories
was developed. A series of unconventional triaxial compression tests with an air-dried sand sample were performed.
Various loading histories consisting of: i) continuous monotonic loading (ML); ii) sustained (creep) loading (SL);and iii) cyclic loading (CL) with small amplitude were employed in this study. Shearing the sand sample withthese different loading histories was performed under different but constant controlled temperatures surrounding
the sample to study the temperature effects on the elastic and creep deformation behaviours of the sample. The
followings are found from the experimental results. The peak and residual shear strengths tend to decrease with anincrease temperature. The equivalent elastic Young’s modulus increases with increasing stress level in a hypoelasticity manner, but at the same stress level, it decreases significantly with increasing temperature. Creep strain
is obvious and it increases with the increasing stress level and temperature.
Keywords: Triaxial test, Shear strength, Elastic, Creep, Temperature, Sand

INTRODUCTION

geomaterials on various factors have been
investigated by many researches. For example,
Kohata [18] performed a series of triaxial
compression tests on Hostun sand sample employing
a comprehensive loading history (e.g., cyclic loading
during otherwise monotonic loading) and showed thatthe elastic deformation characteristics of granularmaterials depend on the stress state. Hitcher [19]investigated the elastic properties of various soil types(e.g., sand, gravel, clays) by subjecting them to verysmall strains in triaxial compression and extension.
Elasticity was found to be nonlinear because theelastic modulus depends on the mean effective stressin a nonlinear manner. Although there are manystudies on the dependency of elasticity on the stressstate in the literature, little has been attempted toinvestigate the effect of temperature on the elasticity
of granular materials, especially for sands.
On the other hand, it is known that geomaterials
also exhibit creep deformation upon sustained
loading. Creep deformation depends on soil type,stress level, and so on. Tatsuoka [20] explained thatcreep is a deformational response due to the material
viscous properties, and can be explained by a nonlinear three-component model, by which creep
depends on the initial strain rate, viscosity type, ratesensitivity, stress level, and decay characteristic. The
study by Lu [21] showed that the change oftemperature can affect to the creep deformation ofsaturated marine deposit. Understanding creep
deformation is an important key for predicting thelong-term
deformationoffoundationsofmegastructures, whereas the immediate settlement
can be predicted by the elastic properties.

In a number of geological and geotechnicalengineering projects such as nuclear waste
repositories [1], high voltage electric cables [2],energy piles [3] - [5], thermally-active embankments[6], thermally-active retaining walls [7], geothermal
tunnel linings [8], highway pavements [9],geosynthetic reinforced soil (GRS) structure [10], thesoils are subjected to non-isothermal conditions [11].
That is, the ambient temperature surrounding themchange cyclically or arbitrarily. Therefore, for such a
circumstance, it is necessary to understand the stressstrain behaviours, which are affected by the change inthe temperature. In the literature, the thermal effects
on yield stress of clays have been widely investigatedthroughout temperature-controlled conditions such as
triaxial compression test, one dimensional
compression test and isotropic compression test [12]- [15].
The deformation characteristics of granularmaterial (i.e., gravel and sand) at a small-strain levelare important for many geotechnical engineering
problems. For instance, they are necessary for a
precise prediction of the settlement of the foundation
supporting a megastructure or the ground movement
caused by a deep excavation. In such cases, strains
mobilised in the ground by working loads are usuallysmaller than about 0.1%, or at the largest, about 0.5%[16], [17]. At such a small-strain level, the soil
behaviours are significantly elastic, and therefore theelastic deformation properties, i.e., the elastic
stiffness, are essential design parameters.
The dependency of elastic properties of156

GEOMATE – Kyoto, Japan, 3-5 November 2021

Nevertheless, to the best of the authors’ knowledge,
little has been known on how the temperature alters
the creep deformation of geomaterials, especiallywith the dried sand.
Among the limitedly available studies on thetemperature effects on sands, most of them focused
only on the shear strength behaviours. For instance,
Liu [22] performed hollow cylinder tests on Fujian
standard quartz sand sample. The sample was heatedunder a drained condition, and then isotropicallyconsolidated, and finally sheared under an undrained
condition. Their test results showed that the shearinduced pore pressure of the heated quartz sandgreatly increased, and thus weakening the shear
strength. Shoa-Heng He [23] performed another test
on a calcareous sand so as to compare with Fujian
standard quartz sand. The sample was heated under adrained condition, subjected to K0 consolidation, andthen sheared under a drained condition. Their results
showed that with increasing the temperature, thestrength and dilatancy of the quartz sand are
improved, but the opposite was found with thecalcareous sand. This is because an increase in thetemperature aggravated the particle breakage for thecalcareous sand during shearing, while for the quartz
sand the discharge of pore water and thedensification.
In view of the above, this paper aims to study
temperature effects on elasticity and stress - strain
deformation behaviours of a sand by performing aseries of special triaxial compression tests. An airdried sample was used so that the complexity ofsetting up the triaxial loading system with atemperature control unit was minimized. This was
also to avoid pore pressure induced by the heating,consolidation, and shearing stages. The sample once
erected was subjected to heat until reaching the target
temperature, after which held constant thorough thetest. During the above-mentioned three stages, thesample was allowed to drain by connecting thespecimen’s inside to the atmosphere. The stress-strain
deformation behaviours were studied by subjecting
the sample to various loading patterns (i.e.,
monotonic loading, sustain loading, cyclic loading)under different constant controlled-temperatures.

controller unit and a circulating pump. A temperaturetransducer (k-type thermocouple) was installed inside
the triaxial cell and connected to a computer forrecording. Complicated shear loading histories (e.g.,
small-strain cyclic loading, creep) can be successfully
performed by the precise gear loading system which
can response sharply to control the loading directions
and the speed [24]. After finishing the specimen
preparation, the triaxial cell was then sealed andconnected to the temperature control unit. Next, thecell water temperature was elevated to the target
value by circulating the water throughout a heater
located outside. The heater is automaticallycontrolled by the temperature controlling unit. After
achieving the target value, the temperature wasmaintained constant with an accuracy of ± 0.5 ◦C as
shown in Fig. 2.

The developed system in this study

Fig. 1

Temperature-controlled
triaxial
apparatus newly developed

test

70
65

o

Temperature, C

60
55
50
45
40
35
30
25
20

TEST APPARATUS AND MATERIAL

0

30

60

90

120

150

Time, t (min)Test apparatus

Fig. 2A new temperature-controlled triaxial test
apparatus shown in Fig. 1 has been developed in thepresent study. It consists of: i) a cylindrical cell ofwhich the cell water pressure is automaticallycontrolled by an electro-pneumatic (E/P) transducer
and the cell water temperature by temperature control
unit; and ii) an automatic axial loading system with a
precise gear. The newly developed temperature
control unit consists of a heater, a temperature

Typical time histories of the measured
temperature of the cell water

Test material
A river bed sand in Thailand was used as the test
material in the present study. It was treated by sieving
and only the portion that passed through the sieve no.
40 but retained on the sieve no. 100 was used. Then,it was cleaned by flushing with tap water to remove

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GEOMATE – Kyoto, Japan, 3-5 November 2021

any remaining fines attracted on the particle’s surface,
and then oven-dried. This sand is sub-angular in thegrain shape. It has specific gravity (Gs) of 2.640, an
effective particle size (D50 )of 0.285 mm, and acoefficient of uniformity (Cu) of 1.879 .The
maximum and minimum void ratios are 1.035 and0.665, respectively.

Test program
In this study, a comprehensive series of shear
loading histories were employed to investigate theelastic and creep deformation behaviours of the test
sand. The details are listed in Table 1 and shown in
Fig. 4, which can be described as follows.
Loading type a)-ML: Continuous monotonic
loading (ML) is performed at a constant strain rate of
0.03 %/min.
Loading type b)-SL-CL: ML with the strain rateof 0.03%/min is performed until reaching the target
stress ratio (R), after which the sustained loading (SL)is hold for three hours for allowing the test specimen
to exhibit creep deformation. At the end of SL, thespecimen is considered behaves significantly only
elastic behaviour, by which cyclic loading with adouble deviator stress amplitude (q) of 15 kPa (orequal to R of 0.5) is applied for 10 cycles forevaluating the quasi-elastic Young’s modulus (Eeq).
After finishing CL, ML is restarted to the next target
R value. For this loading type, the target R values areequal to 2.0, 3.5, and 5.0.
Loading type c)-SL-CL: This loading type is thesame as the loading type b), except the values oftarget R are equal to 2.75, 4.25, and 5.75.
For each loading type mentioned above, the cellwater temperature surrounding the test specimen iscontrolled constant at either 30 oC, 45 oC, or 60 oC
throughout the shearing process. This range oftemperature is consistent with the values used in thestudies with sands by Hong Liu [22] and Shao Henghe [23]. In addition, there was also limitation of thetriaxial loading system used in the present study inthat: i) there was no system to cool down thetemperature below the room temperature; and ii) the
maximum temperature was limited at 60 oC foravoiding any damage to the measuring devices (e.g.,pressure transducer).

TEST METHOD
Specimen preparation
A split mould with the inner diameter of 70 mm
and height of 150 mm was used to prepare the test
sample. After adhering the membrane with the mouldand placed on the pedestal, the sand was filled in byusing the air pluviation technique [25] using a
multiple-sieving apparatus so as to achieve highuniformity. The relative density in a range of 88 - 95
% was achieved by this method. Table 1 lists thevalues of initial void ratio and relative density for all
the test specimens. After sealing the membrane withthe top cap, a partial vacuuming with 20 kPa wasapplied to specimen so as to be able to remove themould. The specimen was then waited for reaching anequilibrium stage for 30 min (Fig. 3a)). After sealing
the triaxial chamber, the partial vacuuming was
replaced by the cell water pressure by gradually
increasing the cell water pressure and reducing thepartial vacuuming at the same time until the cell waterpressure reached 20 kPa. After that, the specimen was
further isotropically consolidated until the confiningpressure reached 30 kPa (Fig. 3b)). By maintaining
the cell pressure constant at 30 kPa, the cell water wasthen circulated to the heater of the temperature
control unit until reaching the target temperature.
Another waiting period of 90 min was employed forallowing the specimen to achieve an equilibrium
stage, and them the shearing process commences
while holding the cell water temperature constant.

Table 1 Triaxial compression test program withdifferent shearing histories and different
temperatures

70 mm.
150 mm.

Test
No.
1
2
3
4
5
6
7
8
9

Thermocouplea)Fig. 3b)Preparation of the sand sample for triaxial
compression test upon: a) applying a partial
vacuuming of 20 kPa; and b) having been
isotropically consolidated by the cell waterpressure of 30 kPa.

e0
(-)
0.686
0.695
0.707
0.703
0.717
0.714
0.689
0.683
0.691

Dr
(%)
94.39
92.02
88.52
89.73
85.87
86.72
93.41
95.10
93.05

Loading
Type
a)-ML
a)-ML
a)-ML
b)-SL-CLb)-SL-CLb)-SL-CLc)-SL-CLc)-SL-CLc)-SL-CL

T
( C)
30
45
60
30
45
60
30
45
60
o

TEST RESULTS AND DISCUSSION
158

TargetR
2, 3.5,
5
2.75,
4.25,
5.75

GEOMATE – Kyoto, Japan, 3-5 November 2021

Effect of temperature on shear strength

obvious, it does not degrade the shear strength,
unlike the temperature.

Fig. 5 shows the relationships between the stressratio (R) and axial strain (εa) obtained with loading
type a)-ML for different temperatures. With
increasing temperature surrounding the test
specimen, the R - εa relationships observably degrade.
The shear strength of the test sand can be expressedin terms of internal friction angles at the peak state,
ϕpeak, from R at the peak state, and at the residual state,ϕres, from R at the residual state defined at the εa equal
to 12%. Due to the sand used in the experiment wasvery clean without any fines, the cohesion was
assumed null. Thus the internal friction angle can be
determined from the test employing only a single
value of confining pressure as shown in Eq. (1). The
values of the R and  at the peak and residual states
are listed in Table 2. It can be seen that both ϕpeak andϕres tend to decrease with increasing temperature. In
addition, it may be seen from Fig. 5 that not only theshear strength but also the stiffness of the R - εarelationship decreases with an increase in thetemperature. This issue is discussed in detail later inthis paper.

R −1 
 = sin −1  R +1 

3.

For the same temperature, creep deformation
increases with increasing R value. That is, SL
performed at the stress state nearer to the failure
results in more creep deformation. Most
interestingly, for the same R value, creep
deformation increases with an increase in thetemperature.
8

Loading Typea) - ML

Stress Ratio, R

7b) - SL-CL

(1)

ϕpeak
48.73
48.18
47.81

Rres
5.752
5.067
4.465

c) - SL-CL

Creep (SL)for 3 hrs

6
5
4
3
2

Cyclic loading (CL)q =15 kPa R=0.5

10Time, t (s) (not to scale)Fig. 4

ϕres
44.73
42.09
39.35

Illustration of time histories of stress ratio(R) for loading types a), b) and c) according
to the test program.

9
Drained CD-TC test
8 Sample: Dry Sand, 3 = 30 kPa
Loading Type: ML

7

Stress ratio, R

Rpeak
7.052
6.849
6.720

The R-εa relationships immediately after therestart of ML at the end of CL show a very highstiffness, which is very close to the elastic
stiffness.

9

Table 2 Strengths of test KMUTT sand obtained
from ML with different constanttemperatures
T (oC)
30
45
60

2.

Stress-strain relationships during cyclic loading
Fig. 6a) and 6b) show the relationship between R
and εa for different constant temperatures withloading type b)-SL-CL and c)-SL-CL, respectively.
In these tests, CLs were performed by unloadingreloading for 10 cycles with a small stress-amplitude(∆q) of 15 kPa at different specified R values, applied
after SL has been completed, during otherwise ML ata constant strain rate. The trends of R - εa behaviours
from loading types b) - SL-CL and c) - SL-CL can be
described as follows.

o6

T = 30 Co

T = 45 C

5o

T = 60 C

4
3

TEST

2
1
0

Fig. 5e0

Dr0(%)#1, 0.686, 94.387#2, 0.695, 92.016#3, 0.707, 88.519
2

4

6

8

10

Axial strain a ()1214R-εa relationships for different constanttemperatures with loading type a)-ML.

Elastic stiffness
1.

The R-εa relationships for different temperatures
tend to rejoin to the respective ones that could beobtained by continuous ML tests without
intermission of SL and CL. This importantly
implied that the peak shear strength ismaintained, while is not degraded with SL and
CL applied at the pre-peak loading history. Thus,
it can be postulated that although creep strain is

Determination of elastic stiffness
Fig. 7 shows an enlarged portion of Fig. 6a)around the R value equal to 5.0. In this figure, the Rvalue presented on the vertical axis was converted tothe major principal stress (1). This figure shows
typical stress-strain characteristics during SL andthen CL as well as upon the restart of ML at the end
159GEOMATE – Kyoto, Japan, 3-5 November 2021

temperatures as shown in Fig. 9. Here, σ0 is thereference stress equal to 100 kPa. It can be seen that
Eeq is not constant but increases with an increase of
σ1/σ0 ratio. The dependency of elastic modulus withthe stress level can be expressed with hypo-elasticity
expressed by Eq. (2). With respect to the hypo-elastic
properties, the trend that the elastic stiffness ofgeomaterial increases with an increase in the major
principal stress for which the elastic modulus isdefined has been observed in triaxial tests of many
types of unbound geomaterial [26] - [28]. The lines
were best-fitted to the test data points, and the valuesof E0 and m for respective temperatures are shown in
Fig. 9a). With different temperatures, the Eeq - σ1/σ0
relationships exhibit the same trend of behaviours.
For the same value of the σ1/σ0 ratio, it is clearly
noticed that the Eeq decreases with increasing
temperature.

of CL of the present study. It can be clearly observedthat the behaviour during small unload-reload cycles
is highly linear-elastic, as noted by the facts that: i)the stress-strain loops generated during the respective
unload-reload cycles are very small, exhibiting
negligible energy dissipation; and ii) the residualstrain developed by the respective unload-reload
cycles was very small.
Fig. 8a) - 8c) show the last five unloading σ1 - εabranches by CLs at R of 5.0 (Fig. 7) for different
temperatures of 30oC, 45oC and 60oC, respectively. It
can be seen from these figures that the unloading
branches exhibit a highly linear-elastic behaviour for
the whole stress amplitude (15 kPa). Thus, it isrelevant to evaluate the equivalent elastic Young’s
modulus (Eeq) from a linear relation fitted to therespective portions of the unloading σ1 - εa branches
presented in Fig. 8. The value of Eeq is fairly constantduring the last five unload-reload cycles for therespective CL stages. This fact indicates that the
deformation during these unloading σ1 - εa branches
is essential elastic.

 1 
 0 o

T = 30 C

Loading Type: b) - SL - CLo

T = 60 Co

T = 45 C
5
4
3
TEST
2

e0

Dr0(%)#4, 0.703, 89.725#5, 0.717, 85.873#6, 0.714, 86.723

1
0.00

1.00

2.00

3.00

4.00
170

Axial strain a ()a)Major principal stress, 1 (kPa)Stress ratio, R

6

8
Drained CD-TC test
7 Sample: Dry Sand, 3 = 30 kPa
Loading Type: c) - SL - CL

Stress ratio, R

160

oT = 30 Co6

T = 60 Co

T = 45 C

4o

T = 45 Co

T = 60 C

140

3
TEST
2

e0

Dr0(%)

1300.70#7, 0.689, 93.411#8, 0.683, 95.104#9, 0.691, 93.050

1
0.00

Fig. 6oT = 30 C

150

5

b)

(2)

Considering at the m value, it could be seen thatthe m values obtained from the tests with different
temperatures are quite similar. This implies that thecharacteristics of increasing Eeq with σ1 for different
temperatures are very similar. For this reason,
averaging the m values was attempted and theaveraged m value (mavg) of 0.624 was obtained. Then,
regression analysis was re-performed for different
temperatures using Eq. (2) but with the fixed value ofm = mavg, as shown in Fig. 9b). The coefficient ofdetermination (R2-value) value for each respective
temperature is also shown in Fig. 9b).

8
Drained CD-TC test
7 Sample: Dry Sand, 3 = 30 kPam

Eeq =E0 

1.00

2.00

3.00

0.80

0.90

1.00

1.10

1.20

Axial strain a ()4.00

Axial strain a ()Fig. 7R - εa relationships for different constanttemperatures with: a) loading type b) - SLCL; and b) loading type c) - SL-CL

Enlarged portion of Fig. 6a) showing σ1 - εarelationships at R = 5.0.

Creep deformation
Fig. 10 shows the time histories of creep strain
during 3-hr SL at different stress levels for thetemperature equal to 30 °C. The trend of increasing
creep axial strain with increasing stress ratio is
obviously noticed.

Determination of elastic stiffness
The averaged Eeq and σ1/σ0 relationships were thenplotted in the full-log scale for respective
160

GEOMATE – Kyoto, Japan, 3-5 November 2021

Drained CD-TC test
Sample: Dry Sand, 3 = 30 kPa
TEST No. #4

6
7
8
9
10

3463 0.974
3542 0.979
3428 0.975
3443 0.974
3392 0.975
Eeq,avg = 3453 kPa

140
135
130
0.904

0.906

0.908

Major principal stress, 1 (kPa)

0.910

0.912

0.914

0.916

0.918

Axial strain, a (%)
160
155

Drained CD-TC test
Sample: Dry Sand, 3 = 30 kPa
TEST No. #5

Eeqm

T30, 0.624,
T45, 0.636,
T60, 0.613,

2E0(MPa)R

2.731,
2.342,
1.953,

0.99480.96840.9692

Eeq = E0 ()m
10

1
0.5

1.0

1.5

2.0

Vertical stress ratio, 
100
Eeq

mavg

T30, 0.624,
T45, 0.624,
T60, 0.624,

2E0(MPa)R

2.731,
2.344,
1.951,

0.99350.96100.9619Eeq = E0 ()m
10

1
0.5

1.0

1.5

2.0

Vertical stress ratio, 

b)

150

Fig. 9145

Loops

Eeq(kPa)

2

R

6
7
8
9
10

2986 0.979
2936 0.983
2824 0.976
3066 0.976
2960 0.989
Eeq,avg = 2955 kPa

140
135
130
0.942

0.944

0.946

0.948

0.950

0.952

0.954

0.956

Axial strain, a (%)b)
Major principal stress, 1 (kPa)

Equivalent elastic modulus, Eeq (MPa)

Loops Eeq(kPa) R2

145

100

a)

150

a)

c)

Equivalent elastic modulus, Eeq (MPa)

160
155

Determination of equivalent elastic Young’s
modulus (Eeq) from unloading branches ofsmall-amplitude unload-reload cycles at R =5 presented in Fig. 7 for temperatures equal
to: a) 30 °C; b) 45 °C; and c) 60 °C.

Fig. 8

Creep axial strain, a,cr (%)

Major principal stress, 1 (kPa)

Fig. 12 compares the creep strains at the end of 3hr SL for different R values at which the SL was
performed and different temperatures. The trend ofincreasing of creep axial strain with an increase in thetemperature as well as with an increase in the stresslevel can be clearly seen. However, it can be seen thatthe stress level has more influence than temperatureson creep strains.
The end of strain during sustained loading for aperiod of 3 hours at different stress ratio levels froma)-SL-CL and b)-SL-CL tests with respective
temperature. The trend of increasing of creep axial
strain with an increase in temperature can be seen.
However, it clearly seen that the stress ratio level has
a larger influence than the temperature on the creep
strain development.

160
155

Drained CD-TC test
Sample: Dry Sand, 3 = 30 kPa
TEST No. #6

150
145

Loops

2

R

6
7
8
9
10

2532 0.929
2630 0.655
2825 0.985
2566 0.969
2552 0.979
Eeq,avg = 2621 kPa

140
135
130
1.070

Eeq(kPa)

1.072

1.074

1.076

1.078

1.080

1.082

Dependency of Eeq on the stress level andtemperature with respective trend lines fitted
by using: a) respective m values; and b)
common averaged m value.

0.25
Creep axial strainoT = 30 C

R = 5.75

0.20

0.15

R=5

0.10
R = 4.25
R = 3.5

0.05

R = 2.75
R=2

0.00
0

2000

4000

6000

8000

10000

12000

Elapsed time during creep, tcr (sec)Fig. 10 Time histories of creep strain by 3-hr SL at
different stress levels with temperature equal
to 30.

1.084

Axial strain, a (%)

161

GEOMATE – Kyoto, Japan, 3-5 November 2021

temperature histories in an automated manner
was successfully developed.

Creep axial strain, a,cr (%)

0.05
Creep axial strain at R = 2o

T = 60 C

0.04

0.03

With increasing stress level, the Eeq value
significantly increases, exhibiting a hypo-elastic
behaviour. On the other hand, by comparing at
the same stress level, the Eeq value noticeably
decreases with increasing temperature.

4.

Creep strain increases with increasing the stresslevel and the temperature. The effect of stresslevel on the creep strain development seems to be
more dominant than that of temperature.

0.02
oT = 30 C

0.01

0.00
2000

4000

6000

8000

10000

12000

Elapsed time during creep, tcr (sec)
0.45

Creep axial strain, a,cr (%)

3.

o

0

Creep axial strain at R = 5.75
0.40o

T = 60 C

0.35
0.30

o

T = 45 C

0.25

ACKNOWLEDGMENTS

0.20

o

T = 30 C
0.15

The authors would like to express their gratitude
for financial support from King Mongkut’s
University of Technology Thonburi (KMUTT)
through The Petchra Pra Jom Klao PhD scholarship
under contract Grant No. 3/2559.

0.10
0.05
0.00
0

2000

4000

6000

8000

10000

12000

Elapsed time during creep, tcr (sec)b)

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Fig. 11 Comparison of time histories of creep axial
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performed at R equal to: a) 2.0; and b) 5.75.

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Creep axial strain, a,cr (%)

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T = 45 Ca)

End of Creep axial straino0.35

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CONCLUSIONS
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2.

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163

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

ANALYSIS OF SOIL-PIPE INTERACTION SUBJECTED TO NORMAL
AND REVERSE FAULTING USING THE DISCRETE ELEMENTMETHOD
Yusuke Ono1
Faculty of Engineering, Tottori University, Japan

1

ABSTRACT
Although buried pipelines are essential components of lifelines, they have been repeatedly damaged byearthquakes. Fault displacement is one of the major factors that cause damage to buried pipelines. In this study,
the deformation and cross-sectional forces of buried conduits subjected to ground displacement caused by reverseor normal faults were calculated using the distinct element method, which can appropriately model the nonlinear
behavior of the ground. By comparing the results obtained from the individual element method analysis with theresults of finite element analysis, which is widely used in practice, the characteristics of deformation, bending
moment, and axial force in buried pipelines subjected to these fault displacements are summarized. In the case ofa buried pipe subjected to a reverse fault, the coupled effect of the pipe deformation and the axial force should beappropriately considered, and in the case of a buried pipe subjected to a normal fault, it is crucial to evaluate theground reaction force to prevent the pipe from uplifting.
Keywords: Buried pipeline, Soil-pipe interaction, Fault movement, Discrete element method, Finite element
methodINTRODUCTION

displacement by solving the elastic equation of thebeam with the range of yielding of the ground as theunknown in a model in which the buried pipe isrepresented as an elastic beam and the ground aroundthe pipe as elasto-plastic springs. Talebi and Kiyono[10] developed a new governing equation including
exact nonlinear axial and transverse soil-pileinteraction terms for a strike-slip fault crossing. At
present, finite element analysis, in which the pipe ismodeled as a beam element and the ground as anonlinear spring, is commonly used in design practice
to evaluate the cross-sectional force generated inburied conduits subjected to fault displacement. The
restoring force characteristics of the nonlinear springsin the ground can be set based on the guidelines
released by American Lifelines Alliance (ALA) [11].
Yoshizaki et al. [12] conducted large-scale
experiments to analyze in detail the effects ofpermanent ground displacement (PGD) on buried
steel conduits. Ha et al. [13]–[15] and Abdoun et al.
[16] conducted centrifuge experiments on a reduced
model to investigate the relationship between the
cross-sectional forces generated in a buried pipe
subjected to fault displacement and the characteristicsof the pipe and the surrounding soil in detail. In thesestudies, the effectiveness of the finite element model
analysis was also verified by comparing theexperimental results.
Numerical studies have also been carried out toinvestigate the effects of experimental limitations andmany loading conditions. Most of them are based on
finite element analysis. Finite element analysis was
also used in the experimental studies described above[12]-[16], and the consistency between experiment

Fault displacement is one of the most common
causes of damage to buried pipelines during
earthquakes. For example, in the 1971 San Fernandez
earthquake, more than 1,400 buried water, sewage,
and gas pipelines were damaged due to the fault
displacement near the ground surface [1]. In the 1999
Chi-Chi earthquake in Taiwan, it was confirmed
some steel pipes subjected to fault displacement were
deformed in a z-shape [2]. In the 2014 Kamishiro
Fault Earthquake in Nagano Prefecture, a sewer pipethat intersected a reverse fault was damaged by avertical displacement of about 80 cm and a horizontal
displacement of about 40 cm. In the 2016 Kumamoto
earthquake, a ductile iron pipe for water supply
crossing the fault in Mashiki Town was bent
significantly and pulled out at the joint [4].
The design of buried pipelines against fault
displacement has been developed in the past.
Newmark and Hall [5] developed a method to
calculate the pipe elongation by considering the nonuniform distribution of frictional forces between thepipe and the surrounding ground under fault
displacement. Kennedy et al. [6] extended the methodof Newmark and Hall [5] to calculate the bending
moment of the pipe. Wang and Yeh [7] proposed amethod to model the ground as an elastic spring andthe pipe as a beam. Takada et al. [8] developed a finite
element analysis method that combines shell andbeam elements to accurately determine the strains in
the pipe at fault crossings. Suzuki [9] developed amethod for calculating the deformation and crosssectional force of a buried pipe subjected to fault
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GEOMATE – Kyoto, Japan, 3-5 November 2021

and analysis has been confirmed. Rahman andTaniyama [17] proposed a hybrid model that treats
the ground as a discretized element method and thepipe as a finite element method using beam elements
and investigated the characteristics of the interaction
between the pipe and the ground near the faultintersection. They discussed the effects ofdisplacements due to reverse and lateral faults and thebending stiffness of the pipe. Their analysis was
performed on a limited number of models, and it isnecessary to perform discretized element analysis on
many models to clarify the characteristics of theinteraction between the pipe and the ground acting on
the pipe subjected to fault displacement.
In this study, the deformation and cross-sectional
force characteristics of a buried pipeline are analyzed
by using the three-dimensional discrete element
method. The analyzed models are different fromthose of Rahman and Taniyama [17]. The fault
displacement analyzed is pure vertical displacement.
A high-density polyethylene (HDPE) pipe is
examined. The burial depths of the pipes analyzed are
2.4, 4.8, and 7.2 times the outer pipe diameter.

contact. In this study, the ground is also modeled as aset of spheres.
The contact between spheres is determined by the
relationship between their radii and the distance to thecenter of gravity; when the radii of the two spheres
are 𝑟𝑟1 and 𝑟𝑟2 , respectively, and the distance between
the centers of gravity is 𝑟𝑟12 , the two spheres are incontact if they satisfy the relationship as follows:(1)

𝑚𝑚𝑥𝑥𝚤𝚤̈ = 𝐹𝐹𝑖𝑖

(2)

𝐼𝐼𝑖𝑖 ω̈ 𝚤𝚤 = 𝑀𝑀𝑖𝑖

(3)𝑠𝑠
= 𝜇𝜇𝐹𝐹 𝑛𝑛
𝐹𝐹𝑚𝑚𝑚𝑚𝑚𝑚(4)

When contact occurs, virtual springs and dashpots are
generated in the tangential and normal directions of
the contact point, respectively, and the contact force
acting on the sphere is calculated. The equation of
motion of the sphere is given by the following
equation,where 𝐹𝐹𝒊𝒊 is the i-directional component of the sum of
all contact forces acting on the sphere; 𝑚𝑚 is the massof the spere; 𝑥𝑥𝚤𝚤̈ is acceleration. In the discrete element
method, not only the translational motion of theobject but also the rotational motion is considered.
The equation of motion for rotation is given by,(a) Vertical dislocation

where i is the index representing the central axis ofrotation, 𝐼𝐼𝑖𝑖 is the rotational inertia, ω̈ 𝚤𝚤 is the rotational
acceleration, and 𝑀𝑀𝑖𝑖 is the moment.
𝑠𝑠
is determined as
The maximum shear force 𝐹𝐹𝑚𝑚𝑚𝑚𝑚𝑚
follows:(b) Reverse fault

where 𝐹𝐹 𝑛𝑛 is the normal contact force, and μ is thefrictional coefficient between the spheres.
The computer code used in this study is the opensource software Yade 2020.01a [19]. Sphericalelements are used for the ground, while a chained
cylinder element is used to represent the buried pipe.
The chained cylinder model deforms according to theelastic beam theory. However, the chained cylindermodel [20], [21] implemented in the Yade does notsupport a hollow cross-section such as a pipe.
Therefore, we needed to change the original sourcecode slightly.

(c) Normal fault
Fig. 1

𝑟𝑟1 + 𝑟𝑟2 ≤ 𝑟𝑟12

Types of fault movements considered in thepresent study.

METHODThe Discrete Element Method

Analytical Model

The Discrete Element Method (DEM) [18] iswidely used in geotechnical engineering as anumerical tool that can directly deal with the granular
properties of the ground. The deformation of theground is represented by a result of the motion ofmany rigid bodies. In general, spheres are used as arigid body because of their simplicity in detecting

The ground model of 14 m length, 1 m width, and
1 m height are placed in a rigid box, as shown in Fig.2.
The box is divided into two parts, one of them is fixed,
and the other is movable. For normal or reverse fault
analysis, the fault plane was tilted 45 degrees to the
fixed side. The movement of the fault is simulated bymoving the movable side of the box at a constant165

GEOMATE – Kyoto, Japan, 3-5 November 2021

speed of 0.5 m/s. The maximum fault movement δ is0.30 m. The box is modeled using the facet elements
provided by Yade. In the actual analytical mode, a flat
plate is placed at the bottom of the box to prevent thespherical elements from spilling through the gap
created by the movement of the fault.

elements used in the analysis are all equal. When
analyzing the ground using the discrete element
method, it is desirable to keep the size of the sphericalelements as small as possible. However, if the size ofthe spherical elements is too small, the number ofrequired elements becomes too large, and the analysis
cannot be performed realistically. In this study,
therefore, the diameter of the spherical element was
set to 0.04 m, as a guideline that one analysis case canbe completed in a few hours. As a result, the numberof spherical elements used was 319,726.
An HDPE pipe with an outer diameter of 0.125 m
is analyzed. The parameters of the pipe are shown in
Table 2. Although HDPE is a strain-rate-dependent
material, it is treated as an elastic material. The
variation of the cross-sectional shape of the pipe is notconsidered. The modeled pipe is buried horizontallyin the center of the width according to the specified
depth. The ends of the pipe are rigidly fixed to thewalls of the rigid box.
The frictional coefficient 𝜇𝜇 between the surface ofthe pipe and the spherical elements is determined by
Eq.(5) using the internal friction angle 𝜑𝜑 between thespherical elements,(a) Vertical dislocation type(b) Normal and reverse fault typeFig. 2Dimension of the analytical model.

Table 1 Parameters for the spherical elements
Parameter
Radius (m)Density (kg/m3)
Elastic modulus (N/m2)
Poisson’s ratio
Internal friction angle

𝜇𝜇 = tan 𝑓𝑓𝑓𝑓

Value
0.040
2.4×103
3.0×1070.15
35.0°

where 𝑓𝑓 is the coating coefficient of the pipe, andwhich is set to 0.6 according to the ALA guideline[11].
Analytical Cases
The analysis was conducted for nine cases shownin Table 3. The three types of faults are verticallongitudinal dislocation, 45-degree reverse fault, and45-degree normal fault as shown in Fig. 1. The pipe
burial depths (distance from the ground surface to thecentral axis of the pipe) are 0.30 m, 0.60 m, and 0.90m.

Table 2 Parameters for the pipe modelParameters
Diameter (m)Thickness (m)Elastic modulus (N/m2)
Poisson’s ratio

Value
0.125
0.0114
1.0×109
0.46

Simulation Procedure

Table 3 Summary of the analytical cases
Case
111
112
113
121
122
123
131
132
133

Fault type
Vertical dislocation
Reverse fault
Normal fault
Vertical dislocation
Reverse fault
Normal fault
Vertical dislocation
Reverse fault
Normal fault(5)First, spherical elements of a specified size wererandomly generated in the prepared box, depositedunder gravity, and stabilized until the motion of thespherical elements was sufficiently settled. The
number of spherical elements to be generated was
adjusted by trial and error so that the height of theground after stabilization was approximately 1.0 m.
At this point, the friction between the elements was
set to zero so that the spherical elements would be as
dense as possible. Next, the spherical elements were
removed from the buried position of the pipe, and thepipe was placed in the vacant space. After confirmingthat the motion of the spherical element was
sufficiently settled under the action of gravity, thefault displacement was applied. Figure 3 shows the

Buried depth (m)
0.30
0.30
0.30
0.60
0.60
0.60
0.90
0.90
0.90

The buried depth is defined by the depth from the ground surface
to the centerline of the pipe.

Table 1 summarizes the parameters of thespherical elements. The sizes of the spherical
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GEOMATE – Kyoto, Japan, 3-5 November 2021

ground model just before the fault displacement wasapplied. The spherical elements are colored in a grid
pattern so that the deformation of the ground can beobserved. The porosity of the ground at this time was
0.490.

Fig. 3ground could not follow the movement of the fault
sufficiently and the pore of the ground became large.
Near the right boundary, the ground could not keep
up with the movement of the boundary, and a gap was
created between the wall and the ground.

Side view of the ground model before
application of the fault movement.

(a) Vertical dislocation(a) Vertical dislocation(b) Reverse fault(b) Reverse fault(c) Normal fault
Fig. 4

Deformation of the ground models after
application of the fault movement

RESULTS AND DISCUSSION
Deformation of the Ground Model
Figure 4 shows a side view of the ground model
after a fault displacement of 0.30 m was applied.
Depending on the burial depth of the pipe, there aresome differences in the ground surface deformation.
However, the pipe with a burial depth of 0.30 m isshown here as an example. In the model with vertical
dislocation, a gentle slope was formed just above thefault due to the flow of the spherical elements. In themodel with reverse faulting, the ground wassignificantly compressed above the fault plane. The
ground on the fixed side was hardly deformed, while
the ground on the moving side was deformed over awide area. In particular, the ground near the surface
was deformed up to the rightmost boundary. In thecase of the normal fault, the subsidence of the groundsurface was smaller than that of the vertical directionof the bottom of the moving side. This is because the(c) Normal fault
Fig. 5

Deformation of the pipe along the axial
direction.

Deformation of the Pipe
The deformation of the buried pipe when thedisplacement of each fault movement pattern reaches
0.30 m is shown in Fig. 5. The pipe shows
characteristic deformation according to the fault
pattern.
In the case of vertical dislocations, the pipe
deformed slightly upward from just above the fault tothe moving side. The deformation of the pipe on the167

GEOMATE – Kyoto, Japan, 3-5 November 2021

fixed side occurs only near the fault. The vertical
upward displacement of the pipe is more significant
when the depth of burial is shallower. This is because
the downward force acting on the pipe is smallerwhen the depth of burial is shallower. In the case ofreverse faulting, the pipe bends upward significantly
on the moving side near the fault.
In the case of reverse faulting, the pipe bends
upward significantly on the moving side of theground near the fault. In this case, the pipe iscompressed by the fault movement, so the upward
deformation of the pipe is larger. In this case, theupward deformation of the pipe is larger because thepipe is compressed by the fault movement. The
downward restraining force acting on the pipe issmaller when the burial depth is shallower, so the pipe
is bent larger when the burial depth is shallower. In
the case with the shallowest burial depth, the pipe
deforms even near the boundary of the moving box(left side of the figure), indicating that the boundary
affects the analysis results.
In the model with normal faults given, the pipe
deforms gently, unlike the models with the other two
fault types. In the case with the shallowest burial
depth, the deformation of the pipe continues to the left
boundary, unlike the cases with different burial
depths.

to the fault on the moving side as the burial depth
becomes deeper.
In the case of normal faulting, the bending
moment generated in the pipe is smaller than the
results of the case with vertical dislocation or reverse
faulting. The bending moment is negative for the
fixed side and positive for the moving side, which is
opposite to the other two cases. The tendency of themagnitude of the bending moment to increase withthe depth of burial is the same as in the other cases.

(a) Vertical dislocation

Bending Moment of the Pipe
The bending moment generated in the pipe isshown in Figure 6. In the case where the vertical
discrepancy is given, the bending moment on the
fixed side is positive and that on the moved side isnegative. The bending moment of the pipe is almost
zero at the intersection with the fault. The bending
moment of the pipe is larger when the depth of burial
is deeper. The point where the bending moment is thelargest on the fixed side is farther from the fault
location as the burial depth becomes shallower. On
the other hand, the position of the maximum bending
moment on the moving side is almost the same
regardless of the burial depth. The maximum value ofthe bending moment is larger on the moving side thanon the fixed side.
The shape of the distribution of the bending
moment of the pipe in the results of the reverse
faulting is similar to that of the results of the vertical
dislocation. The position where the pipe intersects
the fault and the position where the bending moment
becomes zero are almost the same. The bending
moment of the pipe is positive on the fixed side andnegative on the moving side. The maximum
magnitude of the bending moment is larger when thepipe is buried deeper in both the fixed side and themoving side. The position where the maximum
bending moment appears is constant regardless of theburial depth on the fixed side, while it becomes closer(b) Reverse fault(c) Normal fault
Fig. 6

Bending moment of the pipe at the different
buried depths.

Axial Force of the Pipe
Figure 7 shows the axial force of a pipe. The axial
force is positive in tension and negative incompression. In the model with vertical dislocation
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GEOMATE – Kyoto, Japan, 3-5 November 2021

and the model with normal fault, tensile force isacting on the pipe. On the other hand, in the modelwith reverse faults, compressive forces are acting on
the pipe.

force decreases with the depth of burial. The axial
force at the right boundary is larger than that at theleft boundary, which may be due to dynamic effects.
In the reverse fault model, the compressive force
on the pipe at the intersection with the fault is thelargest at the deepest case132. In case 132, where thedepth of burial is the deepest, the compressive force
on the pipe is the largest at the intersection with thefault, and the graph flattens near the maximum
compressive force, indicating that the ground reaction
force has reached its maximum. On the other hand, incase112, where the soil cover is the shallowest, thecompressive force increases monotonically from theleft boundary to the right boundary, showing a
different trend from the case with the deepest burial
depth. In the intermediate case122, the compressive
force is maximum near the fault and decreases toward
the boundary. The rate of change of the compressive
force is different between the fixed and moving sides.
In the normal fault model, the tensile force ismaximum at the boundary on the moving side anddecreases toward the boundary on the fixed side. The
rate of change of tensile force is different on bothsides of the intersection of the pipe and the fault, andthe rate of change of tensile force on the moving side
is lower than that on the fixed side.

(a) Vertical dislocation

Comparison to the Finite Element Analysis
The deformations, bending moments, and axial
forces of the pipes obtained by the discrete element
method were compared with the results obtained byfinite element analysis. The finite element analysis
software used was OpenSeesPy [22] that enables tocontrol OpenSees [23] from Python script.

(b) Reverse fault

Finite element model
The pipe is modeled as an elastic beam element
and the ground as soil springs. The ground springs areapplied in the axial and the transverse directions. The
transverse soil springs generate reaction forces
against the upward or downward movement of thepipe. Both ground springs are modeled as elasticperfectly-plastic models following the ALA
guidelines [11]. The maximum force 𝐹𝐹max and yielddisplacement δ𝑦𝑦 of each soil spring are determined asfollows, respectively, according to the ALA
guidelines [11].
The maximum force per unit length 𝐹𝐹max and theyield displacement δy (m) on the axial direction are
given by,(c) Normal fault
Fig. 7

Axial force of the pipe at the different buried
depths.

In the model with vertical dislocation, the tensile
force acting on the pipe is large near the fault anddecreases as it approaches the lateral boundary. The
axial force is almost constant near the fault, indicatingthat the reaction force acting from the ground hasreached its maximum value. Near the fault, the axial
force is larger as the depth of the pipe increases. From
the difference in the slope of the axial force graph, it
can be seen that the reaction force acting from theground is larger as the pipe burial depth becomesdeeper. Near the right and left boundaries, the axial

𝐹𝐹max = πDHγ�δy = 0.003

1+𝐾𝐾0
2

tan(fϕ)(6)(7)

where 𝐷𝐷 is the outer diameter of the pipe, 𝐻𝐻 is thedepth of the centerline of the pipe from the ground169

GEOMATE – Kyoto, Japan, 3-5 November 2021

surface, γ� is the unit weight of the ground, 𝐾𝐾0 is thecoefficient of pressure at rest, ϕ is the internal
friction angle of the ground, and 𝑓𝑓 is the factor of pipe
coating that is set to be 0.6 in the study.
For the vertical uplift soil spring, the maximum
force per unit length and the yield displacement δy isgiven by,𝐹𝐹max = �

ϕ𝐻𝐻

44𝐷𝐷

in the finite element method than in the discreteelement method. For the peak bending moment
caused by the normal fault, the finite element method
results are higher than the individual element method
results, contrary to the case of the reverse fault.

(8)� γ�HD(9)𝛿𝛿𝑦𝑦 = 0.01𝐻𝐻

The maximum force per unit length 𝐹𝐹max and theyielding displacement δy for the vertical bearing soil
spring are,ϕ𝐹𝐹max = 𝑒𝑒 (π𝑡𝑡𝑡𝑡𝑡𝑡ϕ) 𝑡𝑡𝑡𝑡𝑡𝑡2 �45 + � + 𝑒𝑒 (0.18ϕ−2.5) γ2

δy = 0.1𝐷𝐷

𝐷𝐷22(a) Vertical dislocation

(10)
(11)

This finite element analysis model does notconsider the coupling of bending moment and axial
force in the pipe. In addition, the coupling between
axial (horizontal) and transverse (vertical) groundsprings is not considered.
Results
Figure 8 shows the deformation of the pipe foreach fault type. For the vertical dislocation model, theresults from the discrete element method and thefinite element method are in general agreement.
However, in the discrete element analysis, the pipe isslightly raised at the fault intersection. The results ofthe discrete element method and the finite element
method for the pipe deformation in the model withreverse faulting are very different. The upward
convex deformation of the pipe at the faultintersection seen in the results of the distinct element
method is not seen in the results of the finite element
method. This is because the finite element method
does not take into account the effect of the axial force
on the bending of the pipe. In the normal fault results,
the deformations of the pipes on the moving side aredifferent from each other. The finite element method
results show that the pipe is pushed down to a deeper
position than the discrete element method results.
This means that the force exerted on the pipe by theground above the moving pipe is excessive in thefinite element method.
Figure 9 compares the distribution of bending
moments of the pipes. The results of the discreteelement method and the finite element method arevery similar for the bending moment of the vertical
dislocation model. For the reverse fault model, thepeak values of the bending moments are different for
both the fixed and moving sides. In particular, thepeak bending moment on the fixed side is much lower(b) Reverse fault(c) Normal fault
Fig. 8Comparison of the pipe deformation to thatfrom the FE analysis.

Figure 10 shows the axial force generated in thepipe. In this figure, the axial force obtained from theresults of the discrete element method varies along
the pipe, while the value obtained from the finite
element analysis is constant. This is due to the factthat the axial slip between the pipe and the groundoccurs at a relative displacement as small as 3 mm in
the ground spring used in the finite element analysis,
and the effect of the force acting perpendicular to the170GEOMATE – Kyoto, Japan, 3-5 November 2021

axis of the pipe on the maximum friction force is nottaken into account.

evaluate the ground reaction force acting in the
direction perpendicular to the pipe axis that prevents
the pipe from uplifting.

(a) Vertical dislocation(a) Vertical dislocation(b) Reverse fault(b) Reverse fault(c) Normal fault
Fig. 9(c) Normal fault

Comparison of bending moment of the pipe
to that from the FE analysis.

Fig. 10 Comparison of axial force of the pipe to thatfrom the FE analysis.

CONCLUSIONS

It is well known that the results of grounddeformation analysis by the discrete element method
depend on the size of the elements used. It isnecessary to carefully confirm that the findings
obtained in this study do not depend greatly on thesize of the spherical elements. In addition, individual
element analysis requires a large number of elements
to be used, so the computation time is large. In
addition, it is not easy to set the ground conditions
such as porosity and unit volume weight as expected.
Therefore, there are still many problems to be solved
before the individual element method can be used as

In this study, the deformation, bending moment,
and axial force generated in a buried pipe subjected tovertical dislocation, reverse fault, and normal fault,
respectively, were calculated using the threedimensional discrete element method. For
comparison, a finite element analysis was also
conducted. The main findings are as follows: for theburied pipe subjected to reverse faulting, the couplingof axial force and bending deformation should beconsidered appropriately; in the case of a buried pipe
subjected to a normal fault, it is necessary to properly
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GEOMATE – Kyoto, Japan, 3-5 November 2021

a major tool in practical design. In the future, we will
try to incorporate the knowledge obtained in thisstudy into the method of setting ground springs usedin finite element analysis with high computational
efficiency.

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[16] Abdoun, T.H., et al., Factors influencing the
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[17] Rahman M.A., and Taniyama H., Analysis of aburied pipeline subjected to fault displacement:
A DEM and FEM study, Soil Dynamics andEarthquake Engineering, Vol.71, 2015, pp.49-62.
[18] Cundall P.A., and Strack O.D.L., A discrete
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[19] Kozicki J., and Donze F.V., YADE-OPEN
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THE APPLICATION OF STRUCTURE FROM MOTION METHODFOR ROCKFALL ANALYSIS, A CASE STUDY ON ENDE-MAUMERE
ROAD KM 18+600, EAST NUSA TENGGARA
Cahya Ahmad Gumilar1 and Muhammad Rizaldi Nuraulia2
1,2

Geotechnical, Tunnel and Structure Division, Directorate General of Highways, Ministry of Public Worksand Housing Republic of Indonesia
ABSTRACTRockfall often happens on Ende-Maumere Km 18+600 road, Ende Regency, East Nusa Tenggara because thatlocation has an intense geological structure condition, such as fault, joint, and plane discontinuity. That condition
affected the rock slope is vulnerable to rockfall occurrences. Rockfall identification and analysis such as rock mass
rating (RMR) and slope mass rating (SMR) to identify rock slope characteristics and kinematic analysis todetermine the type of rockfall failure must be conducted properly. Structure from Motion (SfM) is an image-based
method to identify the characteristics of discontinuity plane from the photogrammetric using UAV/Drone togenerates high-resolution 3D images rock surfaces and outcrops with a lower cost compared to Terrestrial Laser
Scanning (TLS) and can map the location that can’t be done by geological compass. The measurement using ageological compass and SfM on the toe of slope compared, and the results show no significant differences of
discontinuity with differences of only ± 10°. The photogrammetric images show RMR and SMR values between
50° to 70°. The average strike/dip of discontinuity using the SfM method at the toe of the slope is N 190° E/55°.
This data is then used as input parameters for kinematic analysis, resulting in the type of rockfall failure wedge
and toppling.
Keywords: structure from motion, rock mass rating, slope mass rating, kinematic analysis

INTRODUCTION
The development of the structure from motion(SfM) method has acquired high-resolution
topographic data cheaper and more manageable. SfM
allows acquisitions using ordinary digital camerasthat can be done alone, thereby significantly reducing
the number of experts required to collecttopographical models using this method (Micheletti
et al., 2015).
In this study, the SfM method was used to createa three-dimensional model of a slope segment on theEnde-Maumere road Km 18+600, Ende Sub-District,Ende Regency, East Nusa Tenggara.

Fig. 1GEOLOGICAL CONDITIONS

Geological Map of the Ende (Suwarna et al.,
1989)

ROCK MASS RATING (RMR)

Regional stratigraphy was taken based on the
Geological Map of the Ende Quadrangle, East Nusa
Tenggara (Fig. 1) (Suwarna et al., 1989).
Old Volcanic Results (QTv): consists of lava,
breccia, agglomerates with local inserts of tuff andtuff of lapilli, a thin layer of silt and coral limestone.
Lava is composed of andesitic pyroxene, locallyshowing a columnar and sheeting joint structure.
Andesitic and basalt fragments and agglomerates,
loose material. In some places, this unit exhibits acolumnar structure. This unit was deposited in ashallow water environment (Suwarna et al., 1989).

RMR was established by Bieniawski (1973-1989)
to evaluate the quality of rock masses forunderground projects. The RMR system consists ofﬁve basic parameters that represent different
conditions of the rock and its discontinuities. These
parameters are (1) UCS of intact rock, (2) RQD, (3)spacing between discontinuities, (4) condition of
discontinuities, and (5) groundwater. This RMR
system is known as “the basic RMR” and gives avalue ranging from 0 to 100 (Bieniawski, 1989).
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Table 1 Classification parameter and their ratings
Parameter

Range of values> 10

4 – 10

2–4

1–2

For the low range,
uniaxial compression
test is preferred> 250

100 – 250

50 – 100

25 – 50

5 – 2515
90 – 10020>2m

12
75 – 9017
0.6 – 2 m

7
50 – 75
13
20 – 60 cm

4
25 – 50
8
6 – 20 cm

2

Rating

20

10

8

4

Condition of discontinuities(see Table 2)

Very roughsurfaces
Not continuous
No separation
Unweatheredwall rock

Slightly roughsurfaces
Separation < 1
mmHighlyweatheredwalls

Slickensided
surfaces or
Gouge < 5 mm
thick orSeparation 1 –5 mm(Continuous)

30

20

10

0

None

< 10

10 – 25

25 – 125> 125

5

RatingInflow per 10 m
tunnel length(L/min)Groundwater
Ratio of joint waterpressure to major
principal stress
General condition
Rating15
Slightlyroughsurfaces
Separation <1 mm
Slightly
weathered
walls
25

0

< 0.1

0.1 – 0.2

0.2 – 0.5

> 0.5Completely dry
15

Damp
10

Wet
7

Dripping
4

Flowing0

1

2
3

Point-load strength
index (MPa)Strength of
intact rockmaterial

Uniaxial
compressive
strength (MPa)
RatingDrill core RQD (%)
RatingSpacing of discontinuities

1–5

<1

1
< 25
3
< 6 cm0

5Soft gouge > 5 mm
thick orSeparation > 5 mm
(Continuous)

Note: After Bienawski, 1989

Table 2 Guidelines for classification of discontinuity conditions
Parameter
Discontinuity length(persistence/continuity)
Separation (aperture)
Roughness

<1m
6
None
6
Very rough

1–3m
4< 0.1 mm
5
Rough

6

5

Ratings
3 – 10 m
2
0.1 – 1 mm
4Slightly rough

10 – 20 m
1
1 – 5 mm1
Smooth> 20 m0> 5 mm0
Slickensided

3

1

0

Hard filling
None

Infilling (gouge)< 5 mmSoft filling> 5 mm< 5 mm> 5 mm

6

4

2

2

0

Unweathered

Slightly weathered

Moderately weathered

Highly weathered

Decomposed

6

5

3

1

0

Weathering
Note: After Bienawski, 1989

Table 3 Rock mass classes from total ratings
Rating100 – 81
80 – 61
60 – 41
40 – 21
< 20

Class
I
II
III
IV
V

SLOPE MASS RATING (SMR)

Description
Very good rock
Good rock
Fair rock
Poor rock
Very poor rock

SMR is a classical lump-rating classiﬁcation
system for rock slopes developed by Romana (1985).
The SMR system is derived initially from the RMR
system, where adjustment parameters representing
the discontinuity orientations concerning the slope
attitude are added to the basic RMR and the effect of
the excavation method. SMR score is obtained by

Note: After Bienawski, 1989

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Table 6 SMR classes

subtracting a factor from RMR depending on the
joint-slope relationship, and adding an element
depending on the excavation method, as expressed in
the following equation.
𝑆𝑀𝑅 = 𝑅𝑀𝑅 + (𝐹1 × 𝐹2 × 𝐹3 ) + 𝐹4

Class
I
II
III
IV
V

(1)

Where 𝐹1 is an adjustment factor, which depends
on the parallelism between the joint strike (𝛼𝑗 ) (or theplunge direction of the intersection line of two planes(𝛼𝑖 ) and the slope face strike (𝛼𝑠 ). It ranges from 1,
when the joint and the slope face strikes are nearparallel, to 0.15, when the angle between strikes is
30° (see Table 4).
𝐹2 refers to joint dip angle ( 𝛽𝑗 ) in the planar
failure or the plunge of the line of intersection of twoplanes (𝛽𝑖 ) in the wedge-type failure mode. It varies
from 1 for joints dipping more than 45° to 0.15 forjoints dipping less than 20° (see Table 4).
𝐹3 reﬂects the effect of the angle between theslope face dip (𝛽𝑠 ) and the joint dip (𝛽𝑗 ) or the plungeof the intersection line of two planes (𝛽𝑖 ). It ranges
from 0 (very favourable) when 𝛽𝑗 − 𝛽𝑠 or 𝛽𝑖 − 𝛽𝑠 ismore than 10°, to – 60 (very unfavourable) when 𝛽𝑗 −𝛽𝑠 or 𝛽𝑖 − 𝛽𝑠 is less than – 10°.
𝐹4 is an adjustment factor that depends on the
excavation method. The values are selected
empirically as shown in Table 5.

Description
Very good
Good
Fair
Bad
Very badStability
Completely stable
StablePartially stable
UnstableCompletely unstable

Note: After Romana, 1985

RESEARCH METHODS
Field Geology Method
Morphology description of the outcrop and thetype of rock is carried out, and then the scanline
method is measured by the stretching of themeasuring tape along a length of 50 meters where thestrike dip value is calculated from each discontinuity
that is passed by the tape measure (Fig. 2).
Descriptions of rock mass rating (RMR) and slope
mass rating (SMR) are also carried out by observing
outcrops in the field.
Structure From Motion Method
First, the outcrop photo captured was done using
a DJI Mavic 2 Pro drone with a 20 megapixel
Hasselblad camera with global coordinates. Image
captured was done using DroneDeploy software tocreate flight routes and outcrop areas to be captured.
(Fig. 3). The resulting 253 photos were processedusing Agisoft Photoscan Professional software.

Table 5 Adjustment factor 𝐹4 for excavation method
Method of excavation
Natural slope
Pre-splitting
Smooth blasting
Normal blasting or mechanicalexcavation
Deficient blastingSMR
81 – 100
61 – 80
41 – 60
21 – 40
0 – 20𝑭𝟒 value
15
10
8
0
-8

Note: After Romana, 1985

Planar-type and toppling-type failure modes are
considered in the SMR system. Anbalagan et al.
(1992) added a wedge failure to Romana’sclassiﬁcation. Wedge-type and planar-type failuremodes will be addressed separately, where theinclination and the direction of the line of intersectionof two planes are considered for wedge failure
analysis. This modiﬁed SMR (Anbalagan et al., 1992)
is used in this paper, and three types of structurally
controlled failures, i.e., planar, wedge, and toppling,
are used. These are identiﬁed in Table 4 as ‘P’ forplanar, ‘W’ for wedge and ‘T’ for toppling. Different
classes of SMR scores that describe the slope and its
stability condition are listed in Table 6.

Fig. 2

Field observations using tape and compass

Then, the global coordinates are converted to
UTM 48S projection coordinates. After that, thephotos were aligned to produce a sparse point cloud
which has 120,857 points. Then the sparse point
cloud is then processed into a dense point cloud with
16,223,542 points (Fig. 4).

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Table 4 Adjustment ratings for 𝐹1 , 𝐹2 , and 𝐹3
Case of slope failureP|𝜶𝒋 − 𝜶𝒔 |T
|𝜶𝒋 − 𝜶𝒔 − 𝟏𝟖𝟎°||𝜶𝒊 − 𝜶𝒔 |
W
P/T/W
𝑭𝟏
P
|𝜷𝒋 |
|𝜷𝒊 |
W
P/W
𝑭𝟐
T
𝑭𝟐
P
𝜷𝒊 − 𝜷𝒔W
𝜷𝒋 − 𝜷𝒔T
𝜷𝒋 + 𝜷𝒔
P/T/W
𝑭𝟑

Very favourable

Favourable

Fair

Unfavourable

Very unfavourable> 30°

30° – 20°20° – 10°10° – 5°

< 5°

0.15

0.4

0.7

0.85

1

< 20°

20° – 30°

30° – 35°

35° – 45°

> 45°

0.15
1

0.4
1

0.7
1

0.85
1

1
1

> 10°

10° – 0°0°0° to –10°

< –10°< 110°
0110° – 120°
-6

> 120°
-25

–

–

-50

-60

Note: After Romana, 1985 modified by Anbalagan et al., 1992

KINEMATIC ANALYSIS
Field Geology Method

Fig. 3

The results of the strike and dip calculations
carried out in the field as shown in Table 7 wereanalyzed using the Rocscience Dips software. The
wedge failure as shown in Figure 5 occurs at 1084
points of joint intersection with a significance of
13.57 % of the total points of joint intersection. The
pink area on the stereonet indicates the critical area
for wedge failure, while the yellow zone indicates thesemi-critical area for wedge failure.

Flight routes to capture the photos

Then, the dense point cloud is processed into a 3Dmodel through a meshing process. Furthermore, the3D model is exported in ASPRS LAS (.las) format for
further analysis using CloudCompare software.

Table 7 The difference between the calculation of
the field model and the SfM model
Field Model SfM Model
StrikeDip
Strike Dip
235
47
245
39
34
42
29
49
205
60
210
64
30
37
36
41
217
64
224
66
215
67
209
60
210
55
205
53
270
40
263
34
340
33
330
27
200
54
196
45
205
38
215
46
217
48
212
41
195
50
200
57
197
55
203
63
190
62
197
64

Dense point cloudFig. 4

SfM method data processing using Agisoft
Photoscan Professional

Strike and dip calculations can be performed
using the Compass plugins found in the
CloudCompare software.

Difference
Strike Dip
+10
-8
-5
+7
-5
+4
+6
+4
+7
+2
-6
-7
-5
-2
-7
-6
-10
-6
-4
-9
-10
+8
-5
-7
+5
+7
+6
+8
+7
+2

Note: This only shows 15 data examples; 127 data of the fieldmodel and 250 data of the SfM model.

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GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 5

Wedge failure configuration on field modelFig. 7

Wedge failure configuration on SfM modelFig. 8Toppling failure configuration on SfM
model

The direct toppling failure as shown in Figure 6
occurs at 800 points of joint intersection with asignificance of 10.01 % of the total points of joint
intersection. The pink area on the stereonet indicatesthe critical area for direct toppling failure, while theyellow zone indicates the semi-critical area for
oblique toppling failure.

DISCUSSION
Strike Dip Measurements
Fig. 6

Toppling failure configuration on fieldmodel

In this study, the strike dip measurement was
carried out by direct measurement in the field andusing the SfM method (Table 7). The difference in thecalculation with a range of values of ± 10°. Thisdifference is due to some discontinuity planes
minimal surface area and the lack of firm boundaries
on the discontinuity planes.
Therefore, data acquisition using the SfM method,which is only based on the front view of the outcrop,
also causes the resulting depth maps to be lessaccurate due to the shadow effect indirectly capturedby the camera.

Structure From Motion Method
The results of the strike and dip calculations
carried out in the field as shown in Table 7 wereanalyzed using the Rocscience Dips software. The
wedge failure as shown in Figure 7 occurs at 4614
points of joint intersection with a significance of
14.83 % of the total points of joint intersection. The
pink area on the stereonet indicates the critical area
for wedge failure, while the yellow zone indicates thesemi-critical area for wedge failure.
The direct toppling failure shown in Figure 8
occurs at 2797 points of joint intersection with asignificance of 8.99% of the total points of joint
intersection. The pink area on the stereonet indicatesthe critical area for direct toppling failure, while theyellow zone indicates the semi-critical area for
oblique toppling failure.

RMR and SMR Calculation
In this study, there were two calculations of RMRand SMR, which were carried out through fieldobservations and using the SfM method. The
comparison on RMR and SMR calculations shown on

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Table 8 Comparison of the calculation RMR values of the field model with the SfM model
Model

Field
Model

SfM
Model

Strength of intact
rock material
UCS
Rating100-250MPa

97 MPa

12

7

RQD
%

75 – 90

73.57

Rating17

13

Spacing of
discontinuity
Spacing Rating

Condition of
discontinuity
Description
Rating6 – 20cm

8Discontinuity
length 3-10
m, separation
1-5 mm, no
filling,slightly
weathered8

Discontinuity
length 3-10
m, separation
1-5 mm, no
filling,slightly
weathered

6 – 20cm

Groundwater
Condition

Rating

19

Dry

15

71

19

Dry

15

61

Note: Based on Bieniawski, 1989

Table 9 Comparison of the calculation SMR values of the field model with the SfM model
Model

Field
Model

SfM
Model

RMR
values

71

61

𝑭𝟏

1.00

1.00

Slope angle recommendation and class
𝑭𝟐𝑭𝟑𝑭𝟒
SMR Class

0.15

0.15

-60

-60

0

0

62°

52°

Slope support
recommendationIIb

Toe ditch or
fence. Nets.
Spot orsystematic
bolting

IIIa

Toe ditch and/or
nets
Spot orsystematic
bolting
Spot shotcrete

Note: Based on Romana, 1985 modified by Anbalagan et al., 1992

Table 8 and Table 9 are more conservative using the
SfM method. This happens because the SfM methods
calculations generated more data, 250 data in the SfM
method, and 127 data in field observations, shown in
Table 7 notes.

The possibilities for wedge failure are more extensive
than toppling failure because the joint measurement
results in a more intersected joint, which means thewedge failure are more influential in this type of
failure on the slope.
Both models produce the same three types offailure, wedge failure, direct toppling failure, andoblique toppling failure. However, there are
significant differences in the kind of failure in eachmodel. This happens because the discontinuity dataobtained through the SfM method is more numerous
than through field observations.
In addition, with the help of the SfM method,
engineering geologists can observe outcrops in moredetail and more freely in a 3D model.

Kinematic Analysis
In this study, two kinematic analysis calculations
were carried out through field observations and using
the SfM method. Comparison of the kinematics
analysis results through field observations and using
the SfM method shows the calculation results are
more conservative, with 14.83 % chances for wedge
failure in the SfM method and 13.57 % in the fieldmodel. In toppling failure, with 8.99 % chances in the
SfM method and 10.01 % in the field observations.
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RMR
valuesGEOMATE – Kyoto, Japan, 3-5 November 2021

CONCLUSIONSACKNOWLEDGMENTS

Based on the results of data analysis anddiscussion that has been described previously, thefollowing conclusions are obtained:
1. The SfM method approach is suitable for smallscale outcrops such as outcrops in the study area
because of its flexibility in delineating thediscontinuity area.
2. The research area has two sets of joint set withan average orientation N 203°E / 70° in set 1 and
N 22°E / 42° in set 2. The measurement using ageological compass and SfM on the toe of slope
compared, and the results show no significant
differences of discontinuity with differences of
only ± 10°.
3. There are differences in the calculation resultsof the RMR value using the field model, which
has 71 values, and the SfM model has 61 values.
Meanwhile, the difference in the calculation of
the SMR values using the field model has 62°
SMR values and the SfM model with 52° SMR
values.
4. The field model is classified in IIb classes withrecommendations for supporting using toe ditchor fence, nets, spot, or systematic bolting, while
the SfM model is classified in IIIa classes withrecommendations for supporting using toe ditch
and/or nets, spot or systematic bolting, spot
shotcrete.
5. Slope failure in the study area has three types offailure that may occur, wedge failure, direct
toppling failure, and oblique toppling failure.
There is a significant result from the field
method and the SfM method, where the SfM
method is more conservative because the chanceof failure is bigger with 14.83 % compared to
13.57 % in the field model.

Thank you to the Head of the Geotechnical,
Tunnel and Structures Division, Directorate Generalof Highways, Ministry of Public Works and Housing
Republic of Indonesia for granting permission andproviding funds for this research to complete thispaper.
REFERENCES[1] Anbalagan R., Sharma S., Raghuvanshi T. K.
Rock mass stability evaluation using modified
SMR approach. In: proceeding of the 6th National
Symposium on Rock Mechanics; 1992. p. 258-68.
[2] Bieniawski Z. T. Classification rock masses forengineering. John Wiley & Sons; 1989.
[3] Hafiz A., Setianto A. Aplikasi Metode Structurefrom Motion dalam Penentuan Kedudukan
Bidang Gelincir di Desa Ngoro-Oro, Kecamatan
Patuk, Kabupaten Gunungkidul, Daerah Istimewa
Yogyakarta. Prosiding Seminar Nasional
Kebumian ke-12 Universitas Gadjah Mada; 2019.
p. 1362-1375.
[4] Micheletti N., Chandler J., and Lane S. Struturefrom Motion (SfM) photogrammetry. British
Society for Geomorphology; 2015.
[5] Romana M. New adjustment ratings forapplication of Bieniawski classification to slopes.
In: Proceedings of international symposium on
the rock of rock mechanics. Zacatecas: ISRM;
1985. p. 49-53.
[6] Suwarna N. Geological Map of the Ende
Quadrangle, East Nusa Tenggara. Pusat Penelitiandan Pengembangan Geologi; 1989.

179

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

EFFECT OF COCONUT SHELL ASH ON
PORE WATER PRESSURE CHANGES IN SAND
Gabriel Jose G. Lim1, Mary Ann Q. Adajar2, and Erica Elice S. Uy2
1

Graduate Student, De La Salle University, Philippines; 2Faculty, De La Salle University, Philippines

ABSTRACTPrevious studies showed that addition of coconut shell ash (CSA) to soil improved the California Bearing Ratio,
Maximum Dry Density, and Unconfined Compressive Strength. But the effect of CSA in deceasing soil
susceptibility to static liquefaction has yet to be investigated. Static liquefaction is a major concern because it isdefined when soil loses its strength and behaves like a fluid. This causes settlements, damages to buildings, andendangers lives. The loss in soil strength is attributed to the increase in pore water pressure. Pore water pressurebuildup is more likely to happen in loose and saturated sands. This study hypothesizes that the particle size andthe chemical property of CSA can reduce the liquefaction susceptibility of sands by decreasing pore water pressurebuildup when the sand is subjected to vertical loads. For the experiment, the consolidated undrained (CU) triaxial
test was conducted. The Cam-Clay model was used to analyze soil behavior between the control samples and thesamples mixed with CSA. Results show that administering 5 % CSA expanded the soil’s yield surface, improving
soil’s ability to respond elastically to deformations. Five percent CSA decreased pore water pressure buildup in
the samples subjected to 50 kPa and 100 kPa consolidating pressures by 6.53 % and 5.55 %, respectively. However,for the sample subjected to 25 kPa consolidating pressure, 5 % CSA caused an adverse effect by increasing porewater pressure buildup by 10.73 %. The low consolidating pressure negates the effect of CSA and there should bea sufficiently high consolidating pressure to decrease pore water pressure buildup.

Keywords: Coconut Shell Ash, Critical State, Cam-Clay Model, Pore Water Pressure Buildup

INTRODUCTION

electron microscope, the mean particle size of CSA
was 34.3 microns. With its small particle size, CSA
can occupy soil voids and reduce the amount of water
held by the soil, which can decrease pore waterpressure buildup [35]. The effect of CSA as astabilizer for base course of pavements has been
investigated. Dosages of 5 %, 10 %, and 15 % CSA
were tested, and it was found that 5 % CSA produced
the best results [36]. The Philippine Institute ofVolcanology and Seismology (PHILVOCS) declared
the areas of Malabon, Manila, Pasay, Marikina,
Pateros, Pasig and the coastal area of Muntinlupa tohave very high liquefaction susceptibility. Heavyloads from buildings in these areas can trigger porewater pressure buildup [37]. It is the objective of thisstudy to measure and compare the generation of thepore water pressure, under consolidated undrained(CU) triaxial test, among the sand samples at 0 % and
5 % CSA mixtures, at a target relative density. The
objective also includes determining shear strengthparameters and validating stress-strain responses ofmodified soil through obtaining overconsolidation
ratio.

Soil liquefaction is a phenomenon where thestrength of soil is decreased by loads. Soils are madeup of an assemblage of individual soil particles. The
weight of these particles produces contact forcesamong the particles. Contact forces are responsible
for holding the particles together and give the soil its
strength. Liquefaction occurs in saturated soils, which
are soils with voids filled with water. During loading,soils breaks down into a denser configuration. The
water in the voids is compressed, and water pressure
is generated. Water pressure decreases the contact
forces among the soil particles. In severe cases, highwater pressures cause the particles to completely lose
contact with each other. This causes soil to behave
like a liquid [1]-[4]. The current trend in geotechnicalengineering is to improve soil using waste materials
instead of conventional processes and additives, like
fly ash and cement [5]-[16]. Using locally available
waste materials will help the environment and reduce
construction costs [17]-[28]. Previous experiments
concluded that the introduction of additives like slag,
Bassanite, fibers, grout and denitrifying bacteria
decreased pore water pressure generation in soils.
However, past studies have yet to investigate theeffect of coconut shell ash (CSA) in reducing porewater pressure generation [29]-[34]. With a scanning

CAM-CLAY MODEL
The Cam-Clay model is a useful tool for
geotechnical engineers because it incorporates
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GEOMATE – Kyoto, Japan, 3-5 November 2021

volume changes in modeling soil behavior. The CamClay Model states that all soils will fail in a uniquefailure surface defined by deviatoric stress (q),effective mean stress (p’), and void ratio (e). It is used
when sufficient soil tests cannot be conducted orwhen there is a need to estimate soil responses tochanges in loading during and after construction. An
example is when a client makes a last-minute change
by adding another story to a structure but does notwant to finance any further soil testing. The CamClay model is used to determine whether the soil can
support an additional story [38]. It is known that thebuildup of pore water pressure accelerates after soil
yields. With void ratio considered, the Cam-Claymodel defines the range of stresses that would result
to soil yielding. With this, engineers can determine
when to slow down the rate of construction or how tomanage heavy machinery on the construction site, toavoid failure caused by excess pore water pressure
generation [39]. There are other models thatincorporate critical state concepts, like the Modified
Cam-Clay model and the Nor-Sand model. The CamClay model was used because it is simple, consistent,
and arguably still the most influential soil model
proposed [40]. Many sophisticated critical statemodels have been proposed but these models still
adopt the original Cam-Clay model as their backbone[41].
In this study, the original Cam-Clay model wasused for undrained analysis, proving its suitability forthis study. The elements of the Cam-Clay model
include the yield surface, critical state line in the q-p’
space, critical state line in the e-ln p’ space, andnormal consolidation line. These are defined by Eq. 1,
Eq. 2, Eq. 3, and Eq. 4, respectively. The Cam-Claymodel also consists of the total stress path and theeffective stress path. The total stress path and theeffective stress path are plotted using the data fromthe consolidated undrained triaxial test.
𝒒𝑴𝒑′+ 𝐥𝐧 (

𝒑′
𝒑𝒐 ′

)=𝟎

Consolidated Undrained Triaxial Test
The consolidated undrained triaxial test was
performed based on ASTM D4767 – 02. The sample
preparation method used was the air pluviation
method. Two percentages of CSA were adopted inthis study. Zero percent and five percent CSA weremixed with loose Ottawa sand. For the saturation
phase, the cell pressure is kept at a pressure differenceof 5 kPa from the back pressure to prevent sample
swelling. A fully saturation condition is attained
when Skempton’s B-value is greater than 0.95, this isdefined by Eq. 5.
𝑩=(2)

𝒆 = 𝜞 + 𝝀 𝒍𝒏 𝒑′(3)> 𝟎. 𝟗𝟓(5)

For the consolidation phase, the consolidatingpressures were set at 25 kPa, 50 kPa, and 100 kPa. In
this study, the samples are allotted thirty minutes toconsolidate. For the shearing phase, the rate ofloading for an undrained condition is usually 1 %axial strain, but this did not cause any significant
change in pore water pressure. The axial strain was
slowly increased to 2 % and 5 %, before settling for a
10 % axial strain. Ten percent axial strain caused
enough pore water pressure generation required forthe purpose of this study. The strain was applied until
the stroke limit was reached.
RESULTS AND DISCUSSION
Index Properties
Summarized in Table 1 are the index properties of
CSA. The specific gravity of CSA falls within therange of values found from previous studies [42-43].
Table 1. Index Properties
Index Property
Specific Gravity (Gs)
Maximum Void Ratio (emax)
Minimum Void Ratio (emin)(1)

𝒒 = 𝑴𝒑′

∆𝒖
∆𝝈𝟑

Value
1.525
1.778
0.804

Strain Curves
𝒆 = 𝑵𝒑 + 𝝀 𝒍𝒏 𝒑′(4)Larger consolidating pressures allow sand to takemore stress at the same strain. The samples subjectedto the 100kPa consolidating pressure are denser thanthe other samples subjected to 25 kPa and 50 kPa
consolidating pressures. A peak is observed in thesamples subjected to 100k Pa consolidating pressure
because denser soils tend to dilate and exhibit peaks.
Critical state was taken at 10 % axial strain. The stressstrain curves for the samples are shown in Fig. 1 andFig. 2.

EXPERIMENTAL PROGRAM
Index Tests
The index properties of the CSA were obtained inaccordance to ASTM standards. The following are
the tests performed: Specific Gravity Test (ASTM
D854), Maximum Void Ratio Test (ASTM D4253),
and Minimum Void Ratio Test (ASTM D4254).

181

GEOMATE – Kyoto, Japan, 3-5 November 2021

significant increase in friction angle in the effective
stress condition.
Table 2. Friction Angles
CSA
0%
5%

ϕ
31.7595°
38.1076°ϕ'38.2902°38.2073°

Fig. 1 Stress Strain Curve for 0% CSA Group

Fig. 3 Pore Water Pressure-Strain Curve at 25kPa
Consolidating Pressure
Fig. 2 Stress Strain Curve for 5% CSA Group
Pore Water Pressure Buildup
The pore water pressure-strain curves of thesamples are shown in Fig. 3 to 5. It is observed thatpore water pressure fluctuates much more in samples
that were subjected to the 25kPa consolidatingpressure. Samples that were subjected to the 100kPa
consolidating pressure exhibited less fluctuations andmuch flatter curves. Larger consolidating pressuresdecreased pore water pressure changes. Observing
Figure 3, the graph of 0% CSA resembled a flat line
once 5 % axial strain was reached, while the graph of
5 % CSA exhibited relatively more fluctuations.
While both samples were subjected to 50kPa
consolidating pressure, the sample mixed with 5 %CSA exhibited a flat stress-strain curve. Five percent
CSA was shown to decrease fluctuations, thus
resulting to more stable pore water pressure-strain
curve behavior. The same is observed from the graphs
in Figure 4 and Figure 5.

Fig. 4 Pore Water Pressure-Strain Curve at 50kPa
Consolidating Pressure

Shear Strength Parameters
The Mohr Coulomb failure envelope for both total
and effective stress conditions are shown in Fig. 6 andFig. 7. The best fit tangent line is drawn to obtain thefriction angles. The effective friction angle is used tofind the slope of the critical state line in the Cam-Claymodel. Tabulated in Table 2 are the friction angles inthe total stress and effective stress condition. The five(5) percent CSA caused the friction angle in the total
stress condition to increase from 31.7595° to38.1076°. The five (5) percent CSA did not cause

Fig. 5 Pore Water Pressure-Strain Curve at 100kPa
Consolidating Pressure
Cam-Clay Model
The Cam-Clay model in the q-p’ space for the
sample at 5 % CSA and subjected to 25 kPa
consolidating pressure is presented in Fig. 8. The
182

GEOMATE – Kyoto, Japan, 3-5 November 2021

stress paths lie to the right of the critical state line.
The same is also observed in all the other samples.
This indicates that all samples are normallyconsolidated and lightly overconsolidated soils. Thus,
the samples will compress and undergo strain
hardening. For the sample at 5% CSA and subjected
to 25 kPa consolidating pressure, the equations for theyield surface and critical state line in the q-p’ space
are defined by Eq. 6 and Eq. 7, respectively.
𝒒𝟏.𝟓𝟓𝟖𝒑′+ 𝐥𝐧 (

𝒑′𝟏𝟐𝟐.𝟔𝟏𝟗

𝒒 = 𝟏. 𝟓𝟓𝟖𝒑′)=𝟎or at denser soil states. The Cam-Clay model in the qp’-e space for the sample at 5 % CSA and subjected
to 25 kPa consolidating pressure is presented in Fig.
9. For the same sample, the critical state line in the eln p’ space and normal consolidation line are definedby Eq. 8 and Eq. 9, respectively.
𝒆 = 𝟎. 𝟒𝟒𝟏 + 𝟎. 𝟎𝟏𝟓𝟔 𝒍𝒏 𝒑′(8)

𝒆 = 𝟎. 𝟒𝟓𝟔 + 𝟎. 𝟎𝟏𝟓𝟔 𝒍𝒏 𝒑′

(9)

(6)
(7)

Fig. 9 3D Cam-Clay Model at 5% CSA and 25kPa
Consolidating Pressure

Fig. 6 Mohr’s Circle and Failure Envelope at 0% CSA

The friction angle for dense sands range between
36° and 41°. The samples in the study have a relative
density of 70 %, indicating dense sand. The friction
angles obtained fall within the range for dense sands,
verifying that the samples are in the dense state [44].
Summarized in Table 4 are the pore water pressurebuildup at failure for the samples. Five percent CSA
decreased pore water pressure buildup by 6.53 % and5.55 % for the samples subjected to consolidatingpressures of 50 kPa and 100 kPa, respectively. The
decrease in pore water pressure buildup is attributed
to the small particle size of CSA, which occupy thesoil voids and reduces the amount of water thatoccupy the soil voids. However, an adverse effect wasobserved in the sample subjected to 25 kPa
consolidating pressure when mixed with 5 % CSA.
The sample exhibited a 10.73 % increase in porewater pressure buildup. The low consolidatingpressure negated the effect of CSA. Sufficientconsolidating pressure is required for 5 % CSA to
decrease pore water pressure buildup. The effect of
CSA on the yield surface is analyzed by comparing
the yield surfaces at 0 % CSA and at 5 % CSA. Plastic
deformations are unrecoverable and occur whenstress states lie beyond the yield surface. Within theyield surface, soils exhibit elastic responses todeformations. An expanded yield surface indicatesthat soil is more able to respond elastically to heavier
loads. In other words, soil deformations becomemuch more recoverable. It was observed that for 5 %CSA mixture with 25 kPa and 100 kPa consolidationpressure their yield surface expanded. However, for
50 kPa consolidation pressure 5 % CSA did not

Fig. 7 Mohr’s Circle and Failure Envelope at 5% CSA

Fig. 8 Mohr’s Circle and Failure Envelope at 5% CSA
and 25kPa Consolidating Pressure
The Cam-Clay model in the q-p’-e space is a 3Dmodel which shows the boundary surface. All
combinations of deviatoric stress, effective meanstress, and void ratio within the boundary surface will
not result in soil failure and are considered safe. The
boundary surface shows that the yield surface
decreases as the void ratio increases. Soil becomes
more susceptible to failure at larger void ratios, or atlooser soil states. In other words, the soil’s ability to
exhibit elastic responses improves at lower void ratios,183

GEOMATE – Kyoto, Japan, 3-5 November 2021

experience expansion. It was observed uponinspection of the sample after the experiment most of
the CSA settled at the bottom. This was due to thelack of contact of CSA to the soil sample.

Table 3. Cam-Clay Model Parameters and Void
Ratio After Consolidation Phase
Sample
0%CSA
25kPa
0%CSA
50kPa
0%CSA
100kPa
5% CSA
25kPa
5% CSA
50kPa
5%CSA
100kPaFig. 10 Yield Surfaces at 25kPa Consolidating
Pressure

M

Γ

ec

λ

1.562

0.463

0.429

0.0108

1.562

0.416

0.365

0.0144

1.562

0.436

0.415

0.0052

1.558

0.441

0.390

0.0156

1.558

0.477

0.388

0.0256

1.558

0.472

0.402

0.0181

Table 4. Pore Water Pressure Buildup at Failure
Consolidation
Pressure[kPa]
25
50
100

Fig. 11 Yield Surfaces at 50kPa Consolidating
Pressure

From the Cam-Clay model graphs, Eq. 10 isformulated to calculate the pore water pressurechange at failure (Δuf) [38]. The critical stateparameters and the void ratio after consolidation
phase (ec) of the samples are summarized in Table 3.
𝑴

𝜞−𝒆𝒄

𝟑

𝝀

])

(10)

𝒑𝒐 ′
𝝈𝟑

%Decrease-10.73
6.53
5.55

Table 5. Overconsolidation Ratios

With Eq. 1, the preconsolidation stress (po’) isestimated from the deviatoric stress and effective
mean stress at critical state. It is then used to computefor the overconsolidation ratio (Ro) with Eq. 11. The
overconsolidation ratio is used to validate if thesamples are normally consolidated and lightly
overconsolidated soil.
𝑹𝒐 =Δu5%CSA
[kPa]
57.46072.442
107.429

Summarized in Table 5 are the preconsolidationstresses, the current consolidating pressures ( σ3 ), andthe overconsolidation ratios. Ro values less than orequal to 2 define normally consolidated and lightly
overconsolidated soil. From the Ro values obtained,
all samples fall into the classification of normallyconsolidated and lightly overconsolidated soil.
Normally consolidated soil is expected to have astress-strain behavior that does not exhibit a linear
elastic state. For this type of soil, strain hardening ismore pronounced and it behaves as an elastoplastic
material. For a lightly overconsolidated soil is
expected to exhibit a linear elastic state then followed
by strain hardening. Also for this type of soil, it
behaves as an elastoplastic material.
To aconsiderable extent, the stress-strain curves of thesamples, shown in Fig. 1 and Fig. 2, resemble theexpected behavior of both a normally consolidated
and lightly soil, respectively.

Fig. 12 Yield Surfaces at 100kPa Consolidating
Pressure∆𝒖𝒇 = 𝝈𝟑 + ( − 𝟏)(𝐞𝐱𝐩[

Δu0%CSA
[kPa]
51.89077.506113.746

Sample0%CSA 25kPa
0%CSA 50kPa
0%CSA 100kPa
5% CSA 25kPa
5% CSA 50kPa
5%CSA 100kPa

(11)
184

po’ [kPa]67.008126.627123.086122.619107.662235.364

σ3 [kPa]
63.00094.100138.100
69.80088.000130.500

Ro
1.1
1.3
1.0
1.8
1.2
1.8

GEOMATE – Kyoto, Japan, 3-5 November 2021

CONCLUSION[4] Ann Adajar, M., Galupino, J., Frianeza, C., Faye
Aguilon, J., Sy, J. B., & Tan, P. A. (2020).
Compressive strength and durability of concretewith coconut shell ash as cement replacement.
International Journal of GEOMATE, 17, 183190.
[5] Dungca, J. R., Galupino, J. G., Alday, J. C.,
Barretto, M. A. F., Bauzon, M. K. G., &Tolentino, A. N. (2018). Hydraulic conductivity
characteristics of road base materials blended
with fly ash and bottom ash. International JournalofGEOMATE,
14(44),
121-127.
doi:10.21660/2018.44.7145[6] Elevado, K. J. T., Galupino, J. G., & Gallardo, R.
S. (2019). Compressive strength optimization of
concrete mixed with waste ceramics and fly ash.
International Journal of GEOMATE, 16(53),135-140. doi:10.21660/2019.53.14268[7] Galupino, J., Adajar, M. A., Uy, E. E., Koa, N.
C., Lao, A. L., Lao, R. N., & Tan, J. C. M. (2020).
Performance of concrete mixed with fly ash andplastic when exposed to fire. International
Journal of GEOMATE, 19(74), 44-51.
doi:10.21660/2020.74.9198[8] Galupino, J. G., & Dungca, J. R. (2015).
Permeability characteristics of soil-fly ash mix.
ARPN Journal of Engineering and Applied
Sciences, 10(15), 6440-6447.
[9] Adajar, M. A. Q., Aquino, C. J. P., dela Cruz, J.
D., Martin, C. P. H., & Urieta, D. K. G. (2019).
Investigating the effectiveness of rice husk ash as
stabilizing agent of expansive soil. International
Journal of GEOMATE, 16(58), 33-40.
doi:10.21660/2019.58.8123[10] Dungca, J. R., & Jao, J. A. L. (2017). Strengthand permeability characteristics of road basematerials blended with fly ash and bottom ash.
International Journal of GEOMATE, 12(31), 915. doi:10.21660/2017.31.6508[11] Dungca, J. R., Lao, W. D. T., Lim, M., Lu, W. D.,& Redelicia, J. C. P. (2019). Radial flow
permeameter: A proposed apparatus to measure
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S. (2018). Artificial neural network (ANN)modelling of concrete mixed with waste ceramictiles and fly ash. International Journal of
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15(51),
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doi:10.21660/2018.51.58567[14] Dungca, J. R., & Galupino, J. G. (2017).

No liquefaction occurred in all samples because
the pore water pressure buildup for all samples wereinsufficient to induce liquefaction. The five (5)percent CSA resulted to decreased fluctuations in thepore water pressure-strain graphs. A reduction inpore water pressure generation was also observed
from the graphs. Five percent CSA effectively
expanded the yield surface, which makes soil moreable to respond elastically to heavier loads andrecover from deformations. Five percent CSA
reduced the pore water pressure buildup in thesamples subjected to 50kPa and 100kPa consolidatingpressure by 6.53% and 5.55%, respectively.
Therefore, five (5) percent CSA is effective indecreasing pore water pressure generation in sands at
70% relative density. To a considerable extent, theexpected stress-strain behavior of soil is in agreementwith the actual soil behavior of the samples. The
Cam-Clay model validated the samples to benormally consolidated and lightly overconsolidated
soil.
ACKNOWLEDGMENTS
I would like to express my gratitude to my thesis
adviser, Dr. Mary Ann Q. Adajar, and to Dr. Erica
Elice S. Uy, for giving me the opportunity to conduct
this research study. Their expertise proved vital for
conducting this study. Without their continuous
guidance, this paper would not have been possible. I
express my appreciation to my parents for theirconstant support and encouragement throughout theentire process of this paper. Their confidence in me
have proven to be an important factor for thecompletion of this research. I would also like toexpress my gratitude to the Engineering Research andDevelopment for Technology and the Civil
Engineering Department of De La Salle University.
And of course, to God, who has equipped me withperseverance and discipline.
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11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

EXPERIMENTAL STUDY ON THE EFFECT OF ANISOTROPICPRESSURIZING AND UNPRESSURIZING THE TBM-SEGMENTS
Haruka Mashiko1, Naotaka Kikkawa2, Nobutaka Hiraoka2 and Kazuya Itoh3
1, 3

Architecture and Civil Engineering, Tokyo City University, Japan; 2National Institute of Occupational
Safety and Health, Japan
ABSTRACTIn recent years, there has been an increasing demand for the TBM method, which is considered to be aneffective method for tunnel construction in urban areas in Japan. Although morphologies and construction
methods have been established for various geological and construction conditions, it is difficult to cover all safety
criteria in their design, and unexpected external forces during construction may cause the segments to collapse,
resulting in accidents. The purpose of this study is to understand the behavior of segments under various loading
conditions and to indicate the safety criteria. In this paper, a small segment model was fabricated, and each segment
was subjected to anisotropic pressurizing and un-pressurizing from the circular direction, and the segment behavior
was evaluated using the failure criteria of bending moment and normal force. The differences between thesegments with and without reinforcement and joints were also investigated. The results showed that underanisotropic pressure, the bending moment and normal force exceeded the failure criteria, part of the concrete in
the segment was damaged, and the bending moment increased not only in the segment under anisotropic pressure
but also in the adjacent segments (Ring). Furthermore, based on the value of the maximum bending moment, thebending moments were dispersed and reduced when it was assumed that there was a joint. Therefore, it can beseen that in situations where the pressure is unbalanced due to external forces, a stronger joint between thesegments will result in a safer design. 245words (150~250)
Keywords: Shield segment, Bending moment, Normal force, Failure envelope

INTRODUCTION

structure deformation. The segment behavior undervarious compressive loads from the circular direction
was investigated using the failure criteria of bending
moment (M) and axial force (N) [3] to investigate thecollapse mechanism. In addition, four different
models were made and compared to confirm thechange in segment behavior due to the tightness of thejoint and the effect of reinforcement. The durabilityof the segment was also verified.

The TBM method involves excavating a tunnel
while avoiding the collapse of the surrounding groundand constructing a segment ring of A-, B-, and Ksegments in the tunnel. The mechanical behavior ofthese segments depends on the cross-sectional shapeof the tunnel and the tunnel construction method, in
addition to ground conditions such as isotropic earthpressure from the ground, water pressure, and groundreaction forces. During segment assembly,
anisotropic pressure may be generated due tocompetition between the tunnel boring machine andthe segment during construction.
It has been found that the compressive action of
the tunnel boring machine concentrates cracks andfailures in critical and adjacent segments, andunderstanding the behavior of segments due to theattitude of the tunnel boring machine is one of theimportant factors to be studied (Mo et al. 2008)[1].
In order to avoid the collapse of segmental rings,
maximum safety should be considered at the design
stage of the tunnel, and it is important to identify the
structure regarding the composition of the joints, theinteraction between the ground and the structure, andthe load redistribution capability of the lining with theadjacent rings (Molins et al. 2011)[2].
In this study, a small segment model was used toreproduce the segment behavior caused by the tunnel

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OUTLINE OF EXPERIMENTFor the model tests in this study, a miniaturephysical segment model was produced using a PVC
mold with an outer diameter of 150 mm and an innerdiameter of 137.5 mm as shown in Fig.1.
Preparation Procedure for Miniature PhysicalModel of Segments
The dimensions of the models are shown in Table
1. The dimension of segment models were 6.25 mm
in thickness and 37mm in width. The segments
consisted of six segments: K-, A1-, A2-, A3-, B1- andB2-segments. After coating the mold-releasing agent
to the surface of the mold, the mass ratio of mortar
188

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig.2 Segments

Fig.1 Mold for making a physical model ofsegment/ring
Table 1 Dimensions of the miniature segments/ring

Each item

Segment

Outer diameter (mm)
Inner diameter (mm)Thickness (mm)Width (mm)Number of segments
comprising the ring

150
137.5
6.25
37.5
6

Ring
(Integrated
segment)
150
137.5
6.25
37.5
-

Fig.3 Ring (Integrated segment)(Toyoura sand : high early-strength Portland cement :water = 2.0 : 1.0 : 0.65) was poured. “Reinforced”
segment model with reinforcing steel inserted asreinforcement and an "Unreinforced" segment model
without reinforcing steel inserted in the segment were
produced, respectively. Furthermore, the segments
shown in Fig. 2 and the segmental ring (integrated
segments) shown in Fig.3 were prepared. The ring isdesigned to simulate the condition where thesegments are connected to each other by joints.
Four types of segments and rings were produced: "(1)Segments without reinforcement," "(2) Ring without
reinforcement," "(3) Segments with reinforcement,""(4) Ring with reinforcement." After the model was
removed from the mold, it was cured in water, and thetotal number of curing days was 28 days from casting.

segments by the water pressure. By changing the airpressure in the tank in stages, the device canpressurize and un-pressurize the segments and ring. It
is assumed here that the pressure of the air supplied
to the pressure-resistant tank is not equal to thepressure acting on the segments. The value of thepressure acting on the segments was obtained by
dividing the load value of the small load cell installedby the cross-sectional area of the loading jig. For bothpressurizing and un-pressurizing, the pressure can beapplied from 0 kN/m2 to a maximum of about 320
kN/m2 (0.32 N/mm2).

Procedure of Experimental TestFirst, the procedure of isotropic pressurizing andun-pressurizing is shown below.
(1) Pressurizing up to 320 kN/m2 in the entire area.
(2) Un-pressurizing up to 0 kN/m2.
The schematic diagram of the isotropicpressurizing and un-pressurizing is shown in Fig.5.
Next, the procedure of the anisotropicpressurizing and un-pressurizing is described. The
following is the procedure of the anisotropicpressurizing and un-pressurizing to the K-segment.
(1) The pressure is applied isotropically up to 160
kN/m2. (2) Pressurizing up to 320 kN/m2 with thehydraulic cylinders of No. 2 to No. 11 fixed and of
No. 1 and No. 12 unfixed. This simulates that the tailof TBM pressurizes the K-segment or the position ofExperimental caseIn this experiment, we used a pressurizing/unpressurizing system to apply load from the circular
direction as shown in Fig. 4. A total of 12 hydrauliccylinders were installed to position the segment
models, and by simultaneously or individuallyextending and retracting them, loads can be applied
from any direction, reproducing the condition of
unbalanced pressure on the segments.
Pressurizing and un-pressurizing device
Water is connected to a pressure-resistant tank,and high-pressure air is fed into the tank using acompressor to reproduce the load acting on the189GEOMATE – Kyoto, Japan, 3-5 November 2021

J12

J12

J1

J11

J11

J2

J10
B2 K

J9

A1
A2

J1

J10

J2
K

B1
B2

B2
B1

B1

J3

J9

J3

A3
A1

A3
A1
A3
A2

J4

J8
J7

J4

J8

J5

J7

J5
J6

J6

Fig.4 Sketch of pressurizing/un-pressurizing
deviceFig.5 Sketch of Isotropic pressure
J12
J11

Table 2 Segment position of applied anisotropicpressure and the jack to be controlled at that time

J1

J2

J10
K

B1
B2

Position ofpressurizing/unpressurizing
Isotropic
K
B2
A1
A2

B2
B1

UnfixedhydrauliccylinderNone
J12, J1
J10, J11
J8, J9J6, J7

J9

J3

A3
A1
A1
A3
A2
J8

J4

J5

J7
J6

Fig.6 Sketch of anisotropic pressure
plate, the grease was pasted and the teflon sheets were
installed.

K-segment of the ring. This stress state becomes anisotropic in the segments or ring. (3) From the stateof (2), remove the pressure to 0 kN/m2. That simulates
the state of the segments or ring at the opposite
directions of (2). (4) Pressurizing up to 160 kN/m2
once again and then the segments or ring becomes at
the isotropic stress condition again. (5) Releasing to
fix the hydraulic cylinders of No. 2 to No. 11, andremove the pressure to 0 kN/m2 isotropically.
As an example, here is a schematic diagram of thetiming of loading and unloading pressure on the K
segment is shown in Fig.6.
Other anisotropic pressures were also applied to
the B2, A1, and A2 segments locations. The
anisotropic pressures for B2, A1, and A2 wereapplied in the same procedure as described above, andthe unfixed jacks are shown in Table 2. Therefore, atotal of 5 cases of pressurizing and un-pressurizing
tests were carried out for each of them.

RESULTS AND DISCUSSIONS REGARDING
EXPERIMENTAL
TEST
BY
FAILUREENVELOPE
Failure Envelope of Bending moment andNormal force
The M-N failure envelope is shown in Fig.7. The
red dotted line is the failure envelope of concrete andthe light blue solid line is the failure envelope ofreinforced concrete. First, uniaxial compression tests
were conducted on cylindrical specimens made withthe same mixing ratio as the segments to calculate thecompressive and tensile strengths. The compressive
and tensile forces were calculated by multiplying bothstrengths by the cross-sectional area of the segments.
The failure points of compressive and tensile forceswere at the intersection of the horizontal axis and thedotted line in Fig.7.
Next, the failure point of pure bending wereobtained from the average values of the single
bending tests conducted on A1, A2 and A3-segments.
It corresponds to the intersection of the dotted line in
Fig.7 and the vertical axis. The failure point balanced
between compression and tension was calculatedusing Equations (1) and (2) from Reference [3].

Preparation for the experimental test
A small load cell and a plate bent to the same
shape as the periphery of the segments (long side: 400
mm, short side: 300 mm) were installed between thehydraulic cylinder and the segments to reduce theintensive load and to monitor the loading. In order toreduce the friction between the segments and the190

GEOMATE – Kyoto, Japan, 3-5 November 2021

𝝈′𝒄 =
𝝈𝒕 =

𝑵′
𝑨
𝑵′
𝑨

+
−

𝑴
𝒁
𝑴
𝒁

𝟏

𝒆

𝑨
𝟏

𝒁
𝒆

𝑨

𝒁

= ( + ) 𝑵′

(1)

= ( − ) 𝑵′

(2)

yield strength of reinforcement (= 345 N/mm2), 𝑓𝑦 :tensile yield strength of reinforcement (= 345
N/mm2), b: section width (= 37.5 mm), 𝑎𝑏 : equivalentstress block length (= 2.28 mm), 𝑑1 : distance from topedge distance from top edge to tension bar (=4.25mm), 𝑑2 : distance from top edge to compressionbar (= 2.00 mm), 𝐴𝑠1 : cross-sectional area of tension
bar (= 2.61 mm2), 𝐴𝑠2 : cross-sectional area ofcompression bar (= 2.61 mm2), 𝑦𝑡 : length from centerof gravity to edge (= 3.125 mm), 𝑓′𝑐𝑑 : design valueof material strength (here 𝑓𝑐′ ), 𝛽1 : ratio of average
stress to maximum stress (k3fc’) in concrete (here set
to 0.8), 𝜀′𝑐𝑢 : ultimate compressive strain of concrete(= 0.0035), 𝐸𝑠 : elastic modulus of reinforcement (=200,000 N/mm2).

Here, 𝜎𝑐′ : compressive strength of concrete (=46.2N/mm2), 𝜎𝑡 : tensile strength of concrete (=4.62N/mm2), N': normal compressive force (N), M:
bending moment (N∙mm), A: cross-sectional area (=234mm2), Z: cross-section coefficient (= 244mm2), e:
eccentricity.
Next, the failure envelope of reinforced concrete
is developed. The compressive and tensile strengthsof the reinforced concrete are obtained by adding thecompressive and tensile strengths of thereinforcement to those of the concrete. The failure
point balanced between compression and tension was
calculated using equations (3), (4) and (5) from thereference [3].
𝒂𝑴𝒖𝒃 = 𝒌𝟑 𝒇′𝒄𝒅 𝒃𝒂𝒃 (𝒅𝟏 − 𝒃) + 𝑨𝒔𝟐 𝒇′𝒚 (𝒅𝟏 − 𝒅𝟐 ) − 𝑵′𝒖𝒃 𝒚𝒕
𝟐

𝑵′𝒖𝒃 = －𝒌𝟑 𝒇′𝒄𝒅 𝒃𝒂𝒃 －𝑨𝒔𝟐 𝒇′𝒚 ＋𝑨𝒔𝟏 𝒇𝒚𝒂𝒃 =𝜷𝟏 𝜺′𝒄𝒖
𝜺′𝒄𝒖 +𝒇𝒚 ⁄𝑬𝒔𝒅𝟏

Results of Pressurizing/Un-pressurizing
Experimental Test

(3)
(4)

The results of the segmented type experimentconducted in the previous experiment [4] are used tocompare with the results of the ring type experimentin this study. The results of anisotropic pressurizing
and un-pressurizing at K-position for "(1) Segments
without reinforcement" and "(2) Ring without
reinforcement" are shown in Fig.8 and 9. "(3)Segments with reinforcement" and "(4) Ring withreinforcement" are shown in Fig.10 and 11,
respectively. The results of anisotropic pressurizing
and un-pressurizing at the A1 position for (3) and (4)are shown in Fig.12 and 13, respectively. The tension
side is positive and the compression side is negativevalue in the x-axis (the normal force, N).
At first, it was confirmed that the M-N curves ofeach segment returned to near the origin after theisotropic load un-pressurizing. The same trend was(5)Here, 𝑀𝑢𝑏 : ultimate bending moment balanced′
between compression and tension (N∙mm), 𝑁𝑢𝑏:ultimate normal force balanced between compression
and tension (N), 𝑘3 : ratio of maximum stress inconcrete to compressive strength of standard
specimen 𝑓𝑐′ (here set to 1.0), 𝑓𝑐′ : compressive
strength of concrete (= 46.2 N/mm2), 𝑓𝑦′ : compressive

Table 4 Simplified table of parameter values for
failure envelope of reinforced concreteFig. 7

Notation
′
𝒇𝒄
′
𝒇𝒚
𝒇𝒚
b
𝒂𝒃
𝒅𝟏
𝒅𝟐
𝑨𝒔𝟏
𝑨𝒔𝟐
𝒚𝒕
𝜷𝟏
′
𝜺𝒄𝒖
𝑬𝒔

Failure envelope (red-dotted line: failureenvelope of concrete, blue-solid line: failureenvelope of reinforced concrete)

Table 3 Simplified table of parameter values
for failure envelope of concrete
Notation
′
𝝈𝒄
𝝈𝒕
A
Z

Value
46.2
4.62
234
244

Unit
N/mm²
N/mm²
mm²
mm³

191

Value
46.2
345
345
37.5
2.28
4.25
2.00
2.61
2.61
3.125
0.8
0.0035200,000

Unit
N/mm²
N/mm²
N/mm²
mm
mm
mm
mm
mm²
mm²
mm
−
−
N/mm²

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig.8 Bending moment and normal force behavior(anisotropic pressure to K-segment at the “(1)Segments without reinforcement”)

Fig.9 Bending moment and normal force behavior(anisotropic pressure to the position of K-segment in the“(2) Ring without reinforcement”)

Fig.10 Bending moment and normal force behavior(anisotropic pressure to K-segment at the “(3)Segments with reinforcement”)

Fig.11 Bending moment and normal force behavior(anisotropic pressure to the position of K-segment in the“(4) Ring with reinforcement”)

Fig.12 Bending moment and normal force behavior(anisotropic pressure to A1-segment at the “(3)Segments with reinforcement”)

Fig.13 Bending moment and normal force behavior(anisotropic pressure to the position of A1-segment in the“(4) Ring with reinforcement”)

observed for all segments (1) to (4), indicating
elastic deformation. It was found that in the case ofanisotropic load pressurizing and un-pressurizing at
position K for the "(1) Segments without
reinforcement," the B1 segment significantly
exceeded the failure envelope after unloading,indicating that the concrete may have been damaged
or destroyed. On the other hand, in the "(2) Ring
without reinforcement," each segment was within the
failure envelope. This indicates that the impact on the"(1) Segments without reinforcement" is larger thanthe "(2) Ring without reinforcement." It was also
found that the behavior of the B1 and B2-segments
tended to be larger in both (1) and (2) for the
anisotropic un-pressurizing to the K position.
Next, for the steel-reinforced segments, the B1segment exceeded the failure envelope in theanisotropic pressurizing at the K position for "(3)Segments with reinforcement." In addition, the B1segment exceeded the failure envelope in (4),although no damage could be confirmed visually. It
was found that the behavior of the B1 and B2192

GEOMATE – Kyoto, Japan, 3-5 November 2021

segments tended to be larger even when the steel bars
were included. The A1-segment exceeded the failureenvelope only in (3), although the maximum bending
moment of the A1 and A2-segments increased in both
(3) and (4). The same pattern was observed in theother cases of anisotropic pressurizing and unpressurizing on B2, A1 and A2-segments. This
indicates that the applying of anisotropic pressure not
only increases the bending moment of the segment
where the anisotropic pressure is applied, but also
increases the bending moment of the adjacentsegments. It can also be seen that the M-N behaviorof the ring type is smaller than that of the unreinforced
and reinforced types when compared with thesegments and rings in Figures 8 and 9 and 10 and 11.
Results of maximum bending moment
The measured values of maximum bending
moment and normal force for unreinforced andreinforced segments and rings are shown below.
Table 5 shows the values of maximum bending
moment and normal force for (1) and (2), and Table 6
shows the values of maximum bending moment andnormal force for (3) and (4). Comparing theunreinforced (1), (2) and reinforced (3), (4) ofsegment and ring, the values of maximum bending
moment for both are larger for the segment type,suggesting that the force is more concentrated, while
the force is relatively distributed for the ring type.
Comparing (1) to (4), the values of maximum bending
moment are smaller in the order of "(1) Segment
without reinforcement", "(3) Segment withreinforcement", "(2) Ring without reinforcement",and "(4) Ring with reinforcement".

Table 5 Maximum bending moment
Position ofpressurizin
g/unpressurizing
Isotropic
K
B2(1) Segments without
reinforcement
BendingNorma
momentl force
-2426
14950
−

-1136
10504
−

A1

−

A2

−

(2) Ring without
reinforcement
Bending
Normalmoment
force
-1228
2201
-1923

-835
134
-806

−

1715

-544

−

-1584

-292

Table 6 Maximum bending moment value
Position ofpressurizin
g/unpressurizing(3) Segments withreinforcement(4) Ring withreinforcement

Bending
momentNormal force

Bending
momentNormalforce

Isotropic
K
B2

1075
2950
2750

-892
158
-20

1381
2367
1287

-434
357
113

A1

3769

-249

1596

-205

A2

2061

-718

-1524

-232

I would also like to thank Mr. Furuhata Takuma
for his careful guidance on the experimental
approaches.
REFERENCES[1] Mo, H.H., Chen, J.S., “Study on inner force anddislocation of segments caused by shield machine
attitude”, Science Direct, Tunnelling andUnderground Space Technology 23, 2008
pp.281–291[2] Molins, C., Arnau, O., “Experimental andanalytical study of the structural response of
segmental tunnel linings based on an in situ
loading test. Part 1: Test configuration andexecution”, Tunnelling and Underground SpaceTechnology 26, 2011, pp.764-777[3] Yoshikawa, H., Analysis and Design ofReinforced Concrete, Tokyo, 1995, pp.97-122. (inJapanese)[4] Furuhata, T, “M-N behavior of a shield segment
model”, The 47th Kanto Branch is one of the
Branches of JSCE, Ⅲ-42, 2020. (in Japanese)[5] Liao, L., de la Fuente, A., Cavalaro, S.H.P.,
Aguado, A., Carbonari, G., “Experimental andanalytical study of concrete blocks subjected toconcentrated loads with an application to TBMconstructedtunnels”,
TunnellingandUnderground Space Technology 49, 2015, pp295-306

CONCLUSIONSIn this study, the behavior of each segment/ringmodel was confirmed and evaluated from thebehavior of the bending moment and normal force of
each. This experiment showed that the concrete may
be damaged by exceeding the failure envelope whena segment is an-isotropically pressurized/unpressurized, and that the bending moment of not only
the segment at the position of the anisotropic pressure
but also the adjacent segments increases. It wassuggested that the values of maximum bending
moment measured under anisotropic pressure/unpressure were smaller for reinforced concrete than for
unreinforced concrete, and for ring(integrated
segment) than for segmented ones.
In other words, the physical joints between
segments are very effective.
ACKNOWLEDGMENTS

193

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

MONITORING OF ROCKFALL NET FENCE TECHNOLOGY BYAERIAL PHOTOGRAPHY TECHNOLOGY IN ENDE, NUSATENGGARA TIMUR, INDONESIA
Ihwan Fauzi1, Cahya Ahmad Gumilar2 and Muhammad Rizaldi Nuraulia3
1,2,3

Office of Geotechnical Tunnels and Structures, Directorate of Road and Bridge Engineering
Development, Directorate General of Highways, Ministry of Public Work and Housing, Indonesia
ABSTRACT
Different types of technological solutions to protect road against rockfall has developed. It is needed to prevent
transportation from material detaching from rock walls. Barrier technology is important for controlling,intercepting or deviating the material during rock fall movement. Ende is one of district which has many rock wallas geological structure that baypassed by national road. To reduce the impact of rock falls on the road it need tobuild a rockfall net fence. This study describes the characteristics of the rocks in Ende using aerial photographyusing drones, hammer test sampling on random rackwall. Aerial photography used a data cloud that can produce
a 3D model and shows the rack wall image for rock classification. The geometry of the rock wall can be measured
from the results of aerial shooting data processing. Furthermore, the performance of the net fence that has been
installed can be assessed on its condition quickly and safely. So that the existing rockfall net fence andtransportation safety can be maintained.
Keywords: Rockfall, Aerial Photogrphic, geological, Net Fence

INTRODUCTION

human activities, e.g. inappropriate land use ordeforestation. Human activities, such as road cuts,
can also trigger landslides. However, the mostcommon triggering factor for landslides is heavy
rainfall.
The
geologicalandtopographic
characteristics of Kabupaten Ende are in general very
favorable for landslides to occur, it could be seen on
Fig 1. The most severe types of landslides in
Kabupaten Ende are debris flows. Relatively recent
events occurred in November 1988 and April 2003,
when extremely heavy rainfall caused manylandslides, debris flows and floods in Ende District.
Especially debris flows endanger many villages in theregion. In addition to the immediate danger for people
and buildings, the impacts of these disasters are also
indirect through disrupted roads, telecommunicationlines, water supplies, to name a few. Among the areas
hit most severely in the recent past were theKecamatans Ende, Detusoko, Ndona Timur,
Nangapada, Maurole, Wolowaru and South Ende [4].
No exception in a number of areas in East Nusa
Tenggara (NTT) experiencing landslides due to rain.
In Ende Regency, Flores, a landslide hit a road section,
precisely at KM 18 heading for Ende-Maumere. As aresult of this landslide, 9 districts on the mainland of
Flores were isolated.

Geographic position potentially effected
Indonesia as a country with many natural hazards.
Rockfall are one of the natural disasters that often
occur in hilly areas. As a result of this natural disaster,of course, has an impact on human activities. An
extreme hazard in transportation such as roads andrailways in steeply mountainous terrain should be
affected by rockfall [1]-[2]. Rockfall is a potentiallyhazardous mountain areas affect to transportation
infrastructure [3]. Rockfall that hit the road will affectthe safety of road users. In addition to causing
casualties, rockfall that cover the road will result in
the paralysis of cities and regencies connected by theroad.
Landslides are among the most frequent natural
disasters occurring in Indonesia. They occur regularly
throughout the entire Kabupaten Ende. The term
landslides comprises a lot of different types of mass
movements, including rock falls, debris flows,
slumps,andmanyothers.
Geological,
geomorphological, geographical, and land use
characteristics define their predisposition. They are
triggered either by heavy rainfall or earthquake
related shaking. Landslides are natural geologicalprocesses but their predisposition can be increased by194

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 1 Study area on Ende Landslide susceptibility map [4]Research on rockfall related topics is an importanttask and advances are clearly visible. In addition,
structural countermeasures also based on uncertainty
models are also of practical interests [5]. Rockfall canbe described as the quick bouncing, rolling andsliding movement of one (or several) boulders downa slope which can reach significant kinetic energy as
it (they) travels, speed values range from a few metres
per second to up to 25-30 m/s [6]. The rockfall
handling technology that has been implemented inEnde, precisely at KM 18, is the rockfall net fence
made of metallic meshes. The design of rockfall net
fences had a high tensile wire and this is a newtechnology been constructed in Indonesia.
Monitoring rockfall hazardous is needed to assist in
the management and maintenance of rockfall
protection devices during their lifetime. Ensuring risk
mitigation would be fulfilled by identifying loss inefficiency and monitoring slope of rock wall [7]. In
this paper the results of a site survey on 60 meters
length rockfall net fence at KM 18+000 EndeDatusuko, national road. The use of Unmanned Aerial
Vehiucle (UAV) technology in monitoring work
provides convenience and safety at work at rock slope
locations. This study we use UAV to obtain
information on the geometry of rock slopes at thelocation of the application of rockfall net fence
technology. Local geometry is main factor of
installation rockfall protection net fences problems[8].
Aerial photography using UAV data producesorthomosaic map and Digital Elevation Model(DEM). Through image processing orthomosaic
image, we can classify the distribution of rocks
located on the rock slopes above the rockfall net fence.
Classification using unsupervised and supervised

methods on Geographic Information System (GIS)
software.
MATERIAL AND METHOD
Rockfall Net Fence
Rockfall net fence systems are used to mitigate
rockfall and generally consist of flexible nets or
panels that are connected to a post system with energy
absorbing braking elements. Rockfall fence systems
are currently designed and given an energy ratingbased on full-scale field testing to determine theenergy capacity or energy reduction of a single
rockfall event with variable considerations forserviceability after specific impacts [9].
A number of variable factors influence the rockfall process. The factors that influence the occurrence
and behavior of individual falling rocks can bedivided into internal parameters and external
influences. Internal parameters means the source rockmass and slope intrinsically. External influences areconditions sometimes changing very rapidly, andchange the force acting on the rock source so that ittriggers the rockfall [10]. Internal parameters: rockmass properties (rock type, discontinuities, strength,
block size/shape), slope morphology (height, angle,
profile shape), slope materials, vegetation,
groundwater. External influences: weathering,erosion, climate (rain, snow, freeze/thaw), seismic
activity, human activity (excavation, blasting, watertable changes, surface water control, deforestation).
Together these factors Fig. 2 influence the frequencyof rockfall and the path, speed, mode of travel (egbouncing, rolling, etc) and travel distance of rocks aswell as their resistance to breaking apart during travel.

195

GEOMATE – Kyoto, Japan, 3-5 November 2021

it is important to understand and document thesefactors as they play a role in the selection and designof rockfall mitigation measures.

Fig. 2Ende-Datusuko national road. Rock slope with aheight of 100 meters with a slope angle above 65
degrees. A rockfall barrier with a net fence is installed
at a height of 8 meters above the road. The
distribution of rock types on rock slope bodies is very
diverse. The possibility of rock falling from rockfall
sources can be mapped using aerial photography
showed Fig.4. To ensure work safety at rock slope
locations, the use of UAVs to document the rockfall
net fence during monitoring can be seen in Fig. 5
oblique photos can be taken using a UAV, so that theslope conditions of the rockfall net fence can be seen.

Factor that influences the rockfall [10]Technological solutions have been developed toprovide the need for protection against rockfall. Oneof the devices technology to intercept and stop blocks
detaching from rock walls most frequently used to is
net fence Rockfall net fence is able to both prevent
blocks from and to control, intercept or deviate theblocks during movement. [6]. Rockfall Fences Fig.3
referred to as flexible rockfall barriers, are designed
to stop individual rockfall events, rockfall fences
typically consist of panels (interception structure),
infrastructure (support structure), friction brakes(energy-absorbing devices or connections), andfoundations. These systems are commonly tested andcertified by various European testing agencies and are
rated between 100 and 8,000 kilojoules (kJ) for asingle rockfall event [9].

Fig. 3Fig. 4

Aerial photography of Rockfall Fence
KM18 Ende-Datusuko, Flores Indonesia

Fig. 5

Monitoring by UAV of Rockfall Fence
KM18 Ende-Datusuko, Flores Indonesia

The rockfall net fence installed on KM18 EndeDetusuko national road can withstand rockfall energyof 3000 kJ. It consists of several types depending on
the amount of energy or the weight of the rock to beretained. GBE Rockfall Protection Barrier from 100
kJ to 3000 kJ showed on Fig. 6.

Rockfall Net Fence KM18 Ende-Datusuko,
Flores Indonesia

Figure 3 shows rockfall fences applied at KM18

196

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 6

GBE Rockfall Protection Barrier (100 kJ to 3000 kJ) [11]Fig. 7

RXE Rockfall Protection Barrier (500 kJ to 8000 kJ) [11]RXE Rockfall Protection Barrier dari 500 kJ sampai
dengan 8000 kJ showed Fig. 7

and width of cracks at regular time intervals could
occur the crack. It could reduce potential hazard [13].
On photogrammetry using UAV taken a block ofimages by flying in straight lines on flight plan.
Overlap at least 60% of each another to be able to get
orthofoto [12]. On this monitoring we use 2 types ofdrone, DJI Phantom 4 Pro and DJIMavic 2 Pro.

Unmanned Aerial vehicle (UAV)An unmanned aerial vehicle (UAV) is a type of
aircraft that is remote controlled by pilot on the
ground, the most common types used in small-format
aerial photogrammetry; Quadcopters and Wing
planes [12]. Various fields to facilitate work currently
apply to this technology. Monitoring activity used
UAV for site survey, inspection, thermography,
infrared, photogrammetry, transport applications,marketing activities [13] and monitoring of natural
hazards in mapping and monitoring slope failures[14]. UAV is able to give monitoring system to detect
the warning effectively. Loose and potentially
dangerous on rock slope could be checked by visualinspection using UAV. The deterioration slope
caused by weathering, erosion, cutting and blastingdamage could be identified. Recording the number(a)Fig. 8(b)(a) DJI Phantom 4 Pro; (b) DJI Mavic 2 Pro[12]

The DJI Phantom 4 is an onboard cameras
quadcopter drone, could be shown on Fig. 8 (a) An
uprated camera is equipped with a 1-inch 20megapixel sensor capable of shooting 4K/60fps video
and Burst Mode stills at 14 fps. The adoption of197

GEOMATE – Kyoto, Japan, 3-5 November 2021

titanium alloy and magnesium alloy construction
increases the rigidity of the airframe and reduces
weight, making the Phantom 4 Pro similar in weightto the Phantom 4. The Flight Autonomy system adds
dual rear vision sensors and infrared sensing systems
for a total of 5-direction of obstacle sensing and 4direction of obstacle avoidance [15].
The DJITM MAVICTM 2 Pro features
omnidirectional Vision Systems and Infrared Sensing
Systems*, and a fully stabilized 3-axis gimbal with a1" CMOS sensor camera (jointly developed by DJI
and Hasselblad) that shoots 4K video and 20megapixel photos shown at Fig. 8 (b) DJI signaturetechnologies such as Obstacle Sensing and Intelligent
Flight Modes like HyperLapse, ActiveTrackTM 2.0,
QuickShot, Panorama, and Advanced Pilot
Assistance Systems, help you capture complex shots
effortlessly. The Mavic 2 Pro boasts a maximum
flight speed of 44.7 mph (72 kph) and a maximum
flight time** of 31 minutes [16].

throughout the whole image. Accuracy assessment of
the ML classification is determined by means of theconfusion matrix (sometimes called error matrix),
which compares, on a class-by-class basis, therelationship between reference data (ground truth)
and the corresponding results of a classification [20].
This approach works by computing weighted
distances and/or likelihood D relate to an unknown
vector X belong to known classes. However, Mc isregarding Bayesian equation [19]:𝐷 = ln(𝑎𝑐) − [0.5 ln([𝐶𝑜𝑣 𝑐])] − [0.5(𝑋 −𝑀𝑐)𝑇(𝐶𝑜𝑣 𝑐 − 1)(𝑋 − 𝑀𝑐](1)
Where, C is a particular class, ac refers to thepercent probability for any candidate pixel of class C.
when Covc refers to pixels covariance matrix in classc. However, ǀCovcǀ is a Covc determinant, (Covc-1)
inverse of Covc, the T refers to transposition function[19].

Rock Size Classification for RGB Images

RESULT AND DISCUSION

The unsupervised and supervised algorithms used
to identify rock size from RGB images are taken from
UAV. The visual camera assigns to each pixel a valueof the primary colors Red, Green and Blue [17]. This
trichromy is additive: this means that secondary
colors are generated by the summing of three values
corresponding to each pixel. The main elements
noticeable from an RGB image of rock by size.

UAV images data are processed by Dronedeploy,and visualized by GIS Software. The result are
Orthomosaic Map, Digital Terrain Model, Cross
section and Classification by Maximum Likelihood
supervised.

(a)

(b)

Fig. 10 Orthomosaic Map

Fig. 9Grain size [18]A maximum-likelihood (ML) algorithm is one of thewide classifiers has been used for supervised image
classification in different remote sensing application[19]. ML classification [20] is used this present,
regions of interest (ROIs) associated with the training
pixels for 4 classes of rock wall were determinedbased on difference rock sizes Fig. 10.
Training areas were established by choosing one
or more polygons for each rock size class. In order toselect a good training area for a class, the important
properties taken into consideration are its uniformity
and how well they represent the same class

Fig. 11 Rock size classification map
Orthomosaic map used to check may fall rockheld by rockfall net fences on Fig. 10 (a) is the result
198

GEOMATE – Kyoto, Japan, 3-5 November 2021

of mapping using the DJI Phantom 4 Pro, whileFig.10 (b) is surveyed by aerial photos using the DJI
Mavic 2 Pro at different times. Small size rockfall net
fences can be seen, rocks with smaller sizes fall under
the net. Rock size classification is obtained from thesupervised maximum likelihood process in GIS
software Fig. 11.

[7]

F. Rinaudo, F. Chiabrando, A. Lingua, and aSpanò, Politecnico di Torino Porto Institutional
Repository, ISPRS - Int. Arch. Photogramm.
Remote Sens. Spat. Inf. Sci., vol. XXXIX n. B,
2012, pp. 583–588.
[8] A. Luciani, M. Barbero, D. Martinelli, and D.
Peila, Maintenance and Risk Management ofRockfall Protection Net Fences ThroughNumerical Study of Deteriorations, Nat.
Hazards Earth Syst. Sci. Discuss., vol. 0, no.
March, 2016, pp. 1–12.
[9] B. Arndt, B. Arpin, J. D. Higgins, and P.
Thompson, Guidelines for Certification andManagement of Flexible Rockfall Protection
Systems, Guidel. Certif. Manag. Flex. Rockfall
Prot. Syst., 2016, no. December 2015.
[10] M. Easton, ROCKFALL Design Considerations
for Passive Protection Structures, 2004, pp. 1–
71.
[11] A. H. Putra, Metode Pelaksanaan Pekerjaan
Rockfall Protection Barrier, 2021, Jakarta.
[12] S. Syring and J. Nylund, Introducing UAS andPhotogrammetry for Surveying and Surveilling
New Project Sites, 2018.
[13] N. Ngadiman, I. A. Badrulhissham, M.
Mohamad, N. Azhari, M. Kaamin, and N. B.
Hamid, Monitoring Slope Condition Using
UAV Technology, Civ. Eng. Archit., vol. 7, no.
6, 2019, pp. 1–6.
[14] S. K. Nagendran, M. A. M. Ismail, and W. Y.
Tung, Integration of UAV Photogrammetry andKinematic Analysis for Rock Slope Stability
Assessment, Bull. Geol. Soc. Malaysia, vol.
2019, no. 67, 2019, pp. 117–123.
[15] Dji, Phantom 4 Pro/Pro+Series, vol. 3304, no.
January. 2012.
[16] Mavic Air User Manual 2021.05. 2021.
[17] P. Cinat, S. F. Di Gennaro, A. Berton, and A.
Matese,Comparisonof
Unsupervised
Algorithms for Vineyard Canopy Segmentationfrom UAV Multispectral Images, Remote Sens.,
vol. 11, no. 9, 2019, pp. 1–24.
[18] Earth Sience Australia, Grain Size, [Online].
Available:
http://earthsci.org/mineral/rockmin/identificati
on/identification.html. [Accessed: 15-Jun2021].
[19] Z. Abbas and H. S. Jaber, Accuracy Assessmentof Supervised Classification Methods for
Extraction Land Use Maps Using Remote
Sensing and GIS Techniques, IOP Conf. Ser.
Mater. Sci. Eng., vol. 745, 2020, no. 1.
[20] A. Ahmad and S. Quegan, Comparative
Analysis of Supervised and Unsupervised
Classification on Multispectral data, Appl.
Math. Sci., vol. 7, no. 73–76, 2013, pp. 3681–
3694.

CONCLUSIONSThe use of UAV technology in monitoring rockslopes with a rockfall net fence guarantees worksafety. Rock classification using the maximum
likelihood supervised with images from the UAV
provides an overview of the size distribution of rocks
on the rock slopes so that it can be estimated what size
they fall. Furthermore, by taking into account the rockmass, the rockfall net fence's ability to withstandfalling rocks can be more measured.
ACKNOWLEDGMENTS
The authors would like to express their sincere
appreciation to Mr. Fahmi Aldiamar, Head of Officeof Geotechnical Tunnels and Structures, Directorateof Road and Bridge Engineering Development,
Directorate General of Highways, Ministry of PublicWork and Housing, Indonesia.
REFERENCES
[1]

[2]

[3]

[4]

[5]

[6]

P. T. Van and K. Maegawa, Experiments And
Numerical Modeling Of A Rockfall Protective
Wire Rope Fence, Int. J. GEOMATE, vol. 2, no.
2, 2012, pp. 219–226.
G. Lollino et al., Engineering Geology For
Society and Territory – Volume 2: Landslide
Processes, Eng. Geol. Soc. Territ. - Vol. 2
Landslide Process., no. January, 2015, pp. 1–
2177.
M. Grošić, Ž. Arbanas, and D. Udovič,
Designing and Constructing Rockfall Barriers Experiences in Republic of Croatia, Rock Eng.
Difficult Gr. Cond. - Soft Rocks Karst - Proc.
Reg. Symp. Int. Soc. Rock Mech. EUROCK
2009, no. May 2014, 2010, pp. 703–708.
M. Resources, B. Geologi, and G. Agency,
Geological Hazard and Risk Assessment
Kabupaten Ende , Nusa Tenggara Timur, 2010,
no. 57.
A. Volkwein et al., Rockfall Characterisationand Structural Protection - A Review, Nat.
Hazards Earth Syst. Sci., vol. 11, no. 9, 2011,
pp. 2617–2651.
D. Peila and C. Ronco, Technical Note: Designof Rockfall Net Fences and The New ETAG 027
European Guideline, Nat. Hazards Earth Syst.
Sci., vol. 9, no. 4, 2009, pp. 1291–1298.

199

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

PERMEABILITY OF THE DIKE 1’S MATERAILS OF KAENGKRACHAN DAM, THAILAND
Uba Sirikaew1, Witthawin Hopeancharoen2 and Uma Seeboonruang3
1,2,3

School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Thailand
ABSTRACT

Dike 1 of Kaeng Krachan Dam located in Phetchaburi province of Thailand constructed in 1966 with thereservoir capacity of 710 million m3. This large-scale project provides more than 55 years of irrigation purposes.
Risk evaluation of dam is needed to be carried out. Calibration of engineering properties of the Dike 1 is conductedbecause there is no database of those properties. The existing Dike 1 cross section is a soil model, used for
calculated. Piezometric level and flow rate obtained from the dam instruments was calibrated with the hydraulichead and the flowrate determined by SEEP/W model. The permeability coefficient of the Dike 1 materials can beanalyzed by calibration technique. The data of the dam instruments is a helpful information and computer programis friendly use. The coefficient of permeability of soil of the Dike 1 of Kaeng Krachan Dam is determined andapplied for risk analysis.
Keywords: Permeability, Soil Model, Calibration and Dam Instruments

INTRODUCTION

zone comprised of greywacke, claystone, mudstoneand shale. Third layer is natural ground, lean clay(CL) showed impervious one like the core zone.
Fourth, quarry-run-rock (QR) mainly consisted ofcoarse aggregate and rock fragment made fromgreywacke or high strength rock mass. Aggregate’s
permeability showed 1×10-3 to 1×10-1 m/s. Fifth,
Random zone (R) comprised of silty gravel (GM) or
clayey sand (SC) showed permeability as 1×10-8 to
1×10-6 m/s. Sixth, the chimney drain zone (SP)presents permeability as 1×10-7 to 1×10-5 m/s.
Seventh, selected random zone (SR) shows very highvalue of permeability as 1×10-3 to 1×10-1 m/s. Eighth,transition zone shows silty gravel (GM) with 1×10-7
to 1×10-5 m/s.

N

Dike 1 of Kaeng Krachan, located in Phetchaburiprovince of the southern of Thailand, with 33 mheight and 305 m length with capacity of 704,000 m3
of earth fills. Dike 1 was constructed in between 1966
and 1968. It is fully operated more than 55 years
which under the dam safety program of Royal
Irrigation Department of Thailand (RID). Since thelack of permeability data of the construction materials
and there is no budget for soil testing, therefore, theobjective of the study is to determine the permeabilityof soil by calibrating the calculated and measured
data using mathematical model. The measured data
was monitored by the piezometers, installed along thealignment of the Station +130 km and seepage flowat the down-stream, as shown in Fig. 1. The study
scoped with the trial calculated value by assuming therepresentative one for calculation. The monitored
data obtained by piezometer and seepage flow meter
was collected at a certain period at the water level of+96.000 m (msl). 2D SEEP/W with the mesh size of
0.5 to 3.0 m was applied to determine the seepage.

50
0

100 m

DESCRIPTION AND INSTRUMENT
The soil model of Dike 1 consisted of clay core,selected outer core zone, chimney and blanket drain
as shown in Fig.2. Eight zones of the earth
embankment can be summarized in Table 1,
separated by permeability coefficient. First, the corezone (C), soil group of core zone has permeability inthe range of 1×10-9 to 1×10-7 m/s which value closed
to the impervious state. Second, the foundation rockFig.1 Dike 1’s site plan and dam instruments
Vibrating wire piezometers P1, P2, and P3 areinstalled at the core zone, off set from the center lineof Dam 4.00 m at the elevation of 59.940, 70.000 and
85.000 m (msl), respectively. Piezometer P4 and P8200

GEOMATE – Kyoto, Japan, 3-5 November 2021

are installed and monitored the foundation off set
from the center line of dam at 25.00 and 50.00 m atelevation of 60.079 and 75.050 m (msl). Piezometer
P5, P6 and P7 are installed at the random zone off set
from the center line 25.00, 50.00 and 50.00 meters, atthe elevation of 85.079, 58.000 and 75.000 m (msl),
respectively as showed in Table 2.

offset 250 m and 200 m from the center line of Dike
1 to downstream (Fig. 1). Subsurface water level was
depicted and used for determine hydraulic gradientsat the Dike 1’s toe. Local RID office provided
maintenance program of dam instrument and dam
safety inspection. Dike 1’s instrument for seepage
analysis, purpose and maintenance are tabulated inTable 3.
Monitored and recorded data were obtained by
Argus software, checked its reliability. It supposedthat change in water pressure of the vibrating wire
piezometers, flow seepage meter and observation
well depend on changing of the reservoir water level.
However, there is an error due to sensor mounting, abroken electric wire, and sensor’s calibration.

Fig.2 Cross section of Sta.1

Table 3 Dike 1’s instrument for seepage analysis

Table 1 Zoned materials of Dike 1

Instrument
Piezometer
Seepageflow meter
Observation
Well

Reference [1]USCS k, m/sec
CL 10-9 - 10-7
CL
10-9 - 10-7
GW
10-3 -10-1
SC
10-8 -10-6
SM
10-7 -10-5
GW
10-3 -10-1
GM
10-7 -10-5

Dike 1’s
Zone
Soil/Rock type
1. CoreCL, GC
2. Foundation claystone
3. Ground
CL, SC
4. Quarry rock Aggregate
5. Random
GM, SC
6. Filter/drain
SP, SM
7. Selected rock Fragment
8. Transition
GM, SM

Zone

P1, 59.940
P2, 70.000
P3, 85.000
P4, 60.079
P5, 85.079
P6, 58.000
P7, 75.000
P8, 75.059

core
core
core
foundation
randomfoundation
randomrandomPurposePressure head
Flow rate
Flow rate
Hydraulics
gradient

SITE CHARACTERIZATION
Existing data obtained from the drawing of Kaeng
Krachan Dam was carried out by RID andEngineering Consultants Inc. Denver Colorado
U.S.A., defined zone of core and filter as impervious
and sand and gravel, no permeabilities’ testing resultsof construction materials and rock foundation [3].
Soil boring logs of the adjacent areas show clayey(SC), clayey and silty sand (SC and SM) lean clay(CL) in majority. Dense to very dense coarse grainsand with fines, SC and SC-SM, contain with fines in
the range of 13 % to 44 % with the median value of
33 % of fines contents. Fines show liquid limit andplastic index in the range value of 15.90-21.45% and3.15-8.34 %. About three meters thick of groundpresented the natural moisture content, dry unit
weight, compressive strength from pocket
penetrometer and N-value in the range of 5.9512.86%, 1.96-2.08 t/m3, 4.5 ksc and 35-95 blows/ft.,
respectively. At the depth greater than 3 meters, very
dense to dense silty gravel (GM) with the non-plastics
fines has been found in thin layer. It shows finecontents, natural moisture content and dry unit weightof 13.72-17.30%,6.91-10.47% and 1.95-1.96 t/m3,
respectively.
Sandy Clay (CL) consists of about 70 % fines with
30% sand with low plasticity. Two meters of stiff clay
layer presents moisture content, dry unit weight andN value of 21.34%, 1.34 t/m3 and 16 blows/fts.
Medium dense to dense poorly graded sand (SP) was
found below 3 meters of ground. Percentage of fines,
natural moisture content, dry unit weight and N-value

Table 2 Detail of Piezometer
RepresentativeType
Vibrating Wire
Sensor andV notch weir
Bore hole withsensorOff-set- Elevation
dam, m m, msl
4/DS+59.9404/DS+70.000
4/DS+85.000
25/DS+60.07925/DS+85.07925/DS+58.000
50/DS+75.000
50/DS+75.059

Note: from Argus Web Monitoring System

Seepage flow meter (SF2), V-Notch weir was
installed on ground at Sta.0+120 of Dike 1 which
located 150 m downstream (DS) from the center lineof dam. The seepage flow can be monitored by sensor
and measured by manual. There is no significant errorof flowrate counter measured from sensor and direct
measuring. Dike 1’s seepage flow rate increased withreservoir water level, which similar to the seepage
condition of Khassa Chai Dam, Iraq [2].
There are two observation wells, OW5 and OW6
which located at Sta.0+150 and Sta.0+250 which201

GEOMATE – Kyoto, Japan, 3-5 November 2021

shows in the range of 3.06 - 4.77%, 7.72-8.66%, 1.732.02 t/m3 and 28-93 blows/ft, respectively [4].
It remarks that soil groups in adjacent area is thesame as shown in drawing of Kaeng Krachan Dam
and as soil group classified based on ASTM D 2488.
The open-end tests perform at the representative
random zone showed permeability of 7.5× 10-7 -5.3 ×
10-8 m/s of sandy clay (CL). Field test and visualmanual procedure for soil classification can be
operated only on shallow surfaces. Core material andrandom zone can be classified as lean clay, sandy clay(CL) and clayey sand (SC) and sandy clay (CL).
Spillway located at the right abutment of Dike 1
shows good rock mass of mudstone, shale and finegrained graywacke which conform with work of
Department of Mineral Resources [5]. Dike 1’s andgrounds’ materials has been tabulated in Table 4.

good rock foundation as water tightness and highquality and quantity of earthen construction materials
for Dike 1.

Table 4 Materials of Dike 1∆h = Ha − Hb

LocationCore (rightabutment)
Random zone
(D/S)

Rock mass at
spillway
Adjacent
D/S

Spection
methodVisualmanualVisualmanual &open-end
test
Rock mass
ratingSEEPAGE ANALYSIS IN EARTH DAM
Bernoulli’s equation and Darcy’s law are applied
for understanding water flow through porous media.
Bernoulli’s equation can be explained as inp𝒗𝟐

H = z + 𝜸 + 𝟐𝒈

(1)𝒘

Where, 𝑯 is the total head, z is the elevation head,
P is pressure head, v is velocity head and 𝒈 is
gravitational acceleration.
For steady-state flow between two points (a-b),
Eq. (1) expressed the head loss equation as in(2)

Where, ∆h is head loss, or head change between
point a to point b, Ha is the total head at point a, Hb
is the total head at point b.
Combination of Eq. (2) and Darcy’s law can beused to define hydraulic conductivity of soil as inClassified
Lean Clay (CL), hardclay with sand.
Clayey Sand (SC)&
Sandy clay, k=7.5×
10-7 - 5.3 ×10-8 m/sec

mudstone, shale
graywacke, good && tight joints
VisualSandy Clay& Leanmanual and soil symbols; CL, SC
review
SC-SM, GM, SM, SP

𝑸 = 𝑲𝒊𝑨𝜸hydraulic conductivity, 𝑲 = Ki 𝒘

(3)
(4)

∆h
hydraulic gradient, i=
∆L

(5)



Where, Q is the flow rate ( volume over time) , K
is the hydraulic conductivity, Ki is the intrinsic
permeability, w is the unit weight of water,  is waterviscosity, i is the hydraulic gradient, Δh is the headloss, ΔL is the horizontal distance between two points,
A is the cross-sectional area [6].
There are many computed tools used for study
dam behavior which calculated results agree withmeasured data [7], [8] and [9]. This uses 2D SEEP/W
to analyze seepage through Dike 1 of Kaeng KrachanDam. In practice, hydraulic conductivity of soil is an
important factor for seepage analysis besides
consideration of the boundary conditions. Finite
element program, 2D SEEP/W program provides 3
parts, data input, solving and result output. Input partcomposed of defying the dam model, boundary
condition and mesh. It depends on user’s requirement
to select function of transient flow or steady state,saturated or unsaturated condition, for instance.
Solving part is an analysis of total head, hydraulicgradient and flow rate which depend on the methodof selection. An output results generate flow net of theearth dam mode, total head of water and flowrate of
the model section.
The calculated seepage flow is calibrated withflow rate measured by SF2 which V notch weirpresented the calibration equation as in

Based on geological map showed rock unit ofmudstone, shale and graywacke, colluvium andalluvium at Dike 1 and adjacent area. Rock unit has
been found at the elevation of +80 - +250 m (msl).
Generally, the drawing stated that rock quarry had
been performed by blasting the mountain located in
the reservoir area. Aggregate and block of graywacke,
mudstone and shale were mainly used in number 4
zone of quarry run rock, number 7 zone of selectedmaterial and riprap. Residual soils altered from thoseparent rocks mostly consisted of lean clay, clayey
sand and lateritic soil, predominantly. It is about 2-3
meters thick and impermeable soil. Colluvium soil
mostly consisted of rock fragments, gravel, sand, silt
and clay which scattered along the slope and hilly
area which covered at the elevation of +60-+80 m(msl). It is medium to very dense silty-clayey sand.
Alluvium mainly consisted of silty sand (SM) andpoorly graded sand (SP). It is loose to medium dense
sand layer which scattered along the canal and river.
This flood plain shows the elevation +45 – +60 m(msl). Site characterization meets requirements of202

GEOMATE – Kyoto, Japan, 3-5 November 2021

𝑸 = 𝑪𝒘H5/2(6)

Irrigation Department which based on USBR manual
has been conducted at the shallow subsurface groundof the Dike 1 [10]. According to no permission on
drilling for investigation purpose, five locations are
randomly chosen, 3 from Dike 1 and 2 from the two
abutments.

Where, Q is flow rate (liter/min), Cw is dischargecoefficient of weir (82,800), H is height in milli meterof water measured from V notch weir’s sharp edge.
STUDY METHOD(1) Start GeoStudio
2D Seep/W

Study methods purposed for achieving theobjectives which tabulated in Table 5. It consisted ofreviewing the existing data of Dike 1, seepage
analysis by 2D SEEP/W, verification of materials’
permeability in Dike 1 and report writing. The
existing data was reviewed to understand engineering
properties of construction materials of Dike 1 and site
and to classify the drawing and dam instrument.
Seepage analysis gives the result of hydraulics
conductivity of Dike 1’s soil model. Field
permeability test has been carried out to verify thecalibrated hydraulic conductivity which obtained
from SEEP/W. Finally, this study is reported andestablished to the local organization who response fordam safety program. It confirmed the Dike 1’s
behavior and instruments which work properly andneeded to be regularly inspected.

(2) Define soil model &boundary condition(3) Key in material
property & Trial K Valueby
Estimation(4) Run
2D SEEP/W(5) Compare with
instrument data

Table 5 Study method and result
Study Method
Reviewing data
Seepage analysis
Verification ofcalibrated k value by
field permeability test
Report writing

Resultsite characterization andreliability of data
Calibrated k value ofDike 1’s materials
Permeability of soil
(random sampling)No(6) If total head andflow rate frommodel close to the
instrument data

Yes(7) End of seepage
analysis

Dike 1’s safety plan

Seepage analysis diagram of Fig. 3 briefly
presents the detailed calibration of hydraulicconductivity of Dike 1’s materials. Step (1) applying
SEEP/W of GeoStudio program limits for the twodimensions. Step (2) finite element model was
generated and defined mesh size and boundary
condition. Key in material property and trial k value
in the soil model as given step (3). Step (4) The
program of 2D SEEP/W had been executed. Step (5)shows comparison of computed data and the value
from instrument data as earlier mentioned in Table 3.
Trial and error process, step (6), has been considered
if there is the different value in those compared data.
End of seepage analysis is completely done when thecomputed data is closed to the measured data. This
study limits with the cross section Sta 0+130 which
the piezometer had been installed. The computed
flow rate of Dike 1 is calibrated with the seepage flow
meter (SF2).
The standard field permeability test of RoyalFig.3 Seepage analysis diagram
Duration of field test is rainy season, July 2019
which made high moisture content of the shallow
ground. testes section was developed by using 3
inched diameters of auger, drilled into the groundwith the depth of 1.5 meter. Shallow bore hole wasinstalled with the 3 inches diameter of PVC pipe with
about 2.5 meters length. The length of PVC pipe is 1
meter above the surface. Clean the hole bottom andfill with clean water, left soil fully saturated and plug
the PVC case about 24 hours. Period of test is about
20 minutes per set, test 3 sets with the constant headand measure the flow rate. Leakage of water during
testing needed to be avoided. Average permeability ofsoil can be calculated as in

𝑘=
203

𝑄
𝑐𝐻𝑟

(7)

GEOMATE – Kyoto, Japan, 3-5 November 2021

Where, k is average permeability in cm/sec, Q isflow rate (cm3/s), c is dimensionless parameters as afunction of test length and radius of hole (use 5.5 forabout 3 inches diameters), H is total head (section of
constant head in centimeter), r is radius of the PVC
pipe in centimeter.

measurement was compared in Table 7.
Table 6 Total head of piezometers at certain time
Piezometer,
EL, location
P1, 59.940, c
P2, 70.000, c
P3, 85.000, c
P4, 60.079, f
P5, 85.079, r
P6, 58.000, f
P7, 75.000, r
P8, 75.059, r
RWL
EL m msl

RESULT AND DISISCUSSION
The existing data of the Dike 1 provided someengineering properties of soils which applied as thepermeability functions of soil models (Table1 andFig.2). Those permeability values were input to soilmodels for analysis by SEEP/W and adopted by trialand-error method. Thus, the completion of seepage
analysis means the soil model presented thecalculated flow rate and total head in the same rangeof the measured data obtained from seepage flow
meter and from piezometers. This study used data ofyear 2007, and check the reliability of measurement.
The eight piezometers were compared with thewater reservoir and with the elevation of theirinstallation for checking the function as shown in
Table 6. It remarked that there is no water pressure
develop at random zone of the downstream, because
the total head of P5, P7 and P8 are lower than thoseof installation elevation. It implied that designed
chimney and blanket drains allowed all seepage flows
through these path. On the other hand, these
piezometers were installed in shallow depth which
could not detect the water pressure at the downstreamof Dike 1. In case these piezometers work well, it is
the good sign for safety of the dam.
The five of piezometers installed in core zone andfoundation show different value of water headcompare with the elevation of reservoir water level.
These piezometers work functionally. It noted that
local officer read and recorded the data of piezometer,
manual process, in 2009 the dam instruments had
been gradually changed to the automation system. As
water pressure plays role for earth dam, observation
wells, OW5 and OW6 was used for calculation of the
filed average hydraulics gradient of Dike 1. At
downstream of Dike 1, calculated hydraulics
gradients show about 0.12 to 0.14, which was smaller
value than the critical hydraulic gradient about 7 to 8
times. Groundwater condition in OW5 and OW6 isclear. This evidence implies there was no erodible soil.
Seepage flow meter is properly work which give therelationship between flow rate and reservoir waterlevel as inTotal head, m msl
19/9/2007 5/12/2007 3/7/2009
79.10780.39576.81083.03282.29179.22188.24787.73885.71870.72670.88567.16484.40284.691
84.88669.32769.64665.55773.75173.63972.91274.59474.84174.06394.99096.14091.030RWL = Reservoir Water Level, c = core, f = foundation,r = random

Table 7 Calibration of flow rate
Flow rate
SEEP/W, m3/day
6.323(2.93×10-7 m3/s/m)

Seepage Flow Range
meter, m3/day m3/day
6.740
5.7-7.0

After calibration of flowrate of the model which
computed by SEEP/W. The calculated total head,from Seep analysis was correlated with measured
total head of P1 as shown in Fig.4, for instance.
Calculated and Measured of P1 at Dike 1

Measured (m msl)

81.00
80.00

R² = 0.92

79.00
78.00
77.00
76.00
73.00 74.00 75.00 76.00 77.00 78.00
Calculated (m msl)

Fig.4 Calibration matching graph of P1 at Dike 1
The calibration graphs of P1, P 4 and P6 show
linear correlation coefficient as 0.92, 0.82 and 0.60
respectively. Linear correlation coefficient of thecalibration graphs of P2 and P3 are lower than 0.3. It
probably due to the sensitivity of sensor andimpervious core. Calibrations of total head data from
SEEP/W and from piezometers are tabulated in Table
8. The requirements stated that the calculation value
obtained by SEEP/W should be in the range value ofthe measured data. This soil model is accepted andestablished the permeability of soil and k-function,
saturated water content, residual water and anisotropy
ratio as shown in Table 9.

Q = 1.1088RWL − 92.98(8)
Where, Q is flow rate in m3/day, RWL is reservoir
water level in m. The coefficient of correlation (R2) is0.989.
Measured seepage flow rate was used as
calibrated value with those computed from SEEP/W.
The flow rate of Dike 1 from SEEP/W and from204

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 8 Total head from piezometers and SEEP/W
Piezometer,
Total
location
Piezometer
P1, Core
80.395
P2, Core
82.291
P3, Core
87.738P4, Foundation
70.885P5, Random
no water
P6, Foundation
69.646
P7, Random
no water
P8, Random
no wateras conceptual design and technical specification.
SEEP/W was applied with clear understanding on
hydraulics function of materials and seepage flow
through dam. The result of the calculated models has
plotted with implicated dam’s instrument data to findout how similar it was, like calibration technique.
This case shows the application of dam instruments
and seepage analysis by SEEP/W, specified by thepermeability values from the references and verified
randomly by field tests.

Head, m Range in
SEEP/W meter
79.273(79-81)81.151(80-83)88.039(87-88)69.447(69-71)84.60468.964(87-88)68.33773.859-ACKNOWLEDGMENTS
We would like to express our sincere gratitude to
the Dam Safety Branch of Royal Irrigation
Department for all supports.

Based on the computed results, installation ofpiezometers at the upstream of Dike 1 wasrecommended to the owner for calibration and fordam safety program. Calculated value of core zone’s
permeability shows 1×10-9 m/s which was verified byfield permeability test, gravity method which showed
the value as 0 to 7.9×10-9 m/s. Visual manual
procedure defines those soil samples as Lean Clay(CL). Random zone, downstream of Dike 1 shows the
filed permeability value 2.27×10-8 to 1.3×10-7 m/sfrom three locations. It is sandy clay, mainly.

REFERENCES

Table 9 Calculated permeability by SEEP/W
Saturated Residual
k,
Water
WC
m/sec
Core
0.50
0.40
1.0×10-9
Foundation
0.45
0.40
5.0×10-8
Natural Ground 0.40
0.20
1.0×10-8
Quarry Rock
0.30
0.01
5.0×10-3
Random0.40
0.05
1.0×10-7
Chimney
0.35
0.01
1.0×10-6
Selected
0.30
0.05
5.0×10-2
Transition
0.35
0.10
1.0×10-6
Grouting
0.60
0.10
7.0×10-10
Zone

ky/kx
0.20
0.25
0.40
0.50
0.33
0.60
0.60
0.50
0.10

CONCLUSIONSThe study used SEEP/W software to generate
seepage model of Dike 1 of Kang Krachan dam byassumed condition of foundation rock ishomogeneous in 2-dimension with steady state ofnormal reservoir level. The trial-and-error method
was adopted to calibrate the calculated total head andseepage flowrate vs the monitored by piezometersand seepage flow meter. Calculated permeability
values of Dike 1 core and random materials presented
the values in the reasonable range performed bygravity method of field permeability tests. In practice,there are limitation factors on homogeneous materials
in the same zone and construction technology, forexample, water spray, thick of compacted layer andcompaction technique. Thus, there were various
engineering soil properties in the same zone of dam.
However, quality control of construction is processed
205[1] Burt G. Look, Handbook of Geotechnical
Investigation and Design Tables, Taylor &Francis, London, UK, 2007, pp. 93-96.
[2] Nassrin Jassim Hussien Al-Mansori , Thair
Jabbar Mizhir Al-Fatlawi, Nariman Yahya
Othman and Laith Shaker Ashoor Al-Zubaidi,
Numerical Analysis of Seepage in Earth-Fill
Dams, Civil Engineering Journal, Vol.6, No.7,
2020, pp. 336-1348.
[3] Royal Irrigation Department, Technical Recordof ECI’s reports, RID. Library, 1966.
[4] Department of Public Works and Town&
Country Planning Petchaburi Province’s soil
report, 2007, 6 p.
[5] Department of Mineral Resource, TechnicalSeminar; Biostratigraphy and Karst Morphology
of Satun Aspiring Geopark, 2017, 21 p.
[6] Matthew R. Broaddus, Performing in a SteadyState Seepage Analysis Using SEEP/W: a Primer
for Engineering Students, Electronic Thesis,
University of Louisville, 2015, 64 p.
[7] Al-Janabi, A. M. S., Ghazali, A. H., Ghazaw, Y.
M., Afan, H. A., Al-Ansari, N., & Yaseen, Z. M.
Experimental and numerical analysis for earthfill dam seepage. In Sustainability (Switzerland)Vol. 12, 2020, pp. 1–14.
[8] Kamal Ganjalipour and Seyed Mahmod Fatemi
Aghda, Calibration of seepage analysis sections
based on observation well monitoring data: acase study on Darian dam abutments Kermanshah province, Iran, Modeling Earth Systems andEnvironment, 2019, 5: pp. 159–173.
[9] M. Javanmard, F. Amiri, S. M. Safavi,
Instrumentation Readings versus Numerical
Analysis of Taham Dam, International Journal ofEngineering (IJE), IJE Transactions A: Basics
Vol. 32, No. 1, 2019, pp. 28-35.
[10] Royal Irrigation Department, Work Manual of
Soil Engineering Exploration, 2017, pp. 16-17.

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

PERFORMANCE OF REINFORCED BASE COURSES OF FLEXIBLEPAVEMENTS OVERLYING SOFT SUBGRADES: INSIGHTS FROMLARGE-SCALE MODEL EXPERIMENTS
Ramu Baadiga1, Umashankar Balunaini2, and Sireesh Saride3
1

Doctoral Student, Indian Institute of Technology Hyderabad, India; 2,3 Faculty, Indian Institute ofTechnology Hyderabad, India

ABSTRACTIn this study, large-scale model pavement experiments (LSMPEs) were performed to assess the performanceof geosynthetic-reinforced bases of flexible pavements overlying soft subgrades (with California bearing ratio
equal to 1% and 5%). Two configurations of reinforcement were considered – (1) base layers reinforced with
geocell (GC) alone, and (2) base layers reinforced with geocell and geogrid (GG) combinations (GC+GG). Model
pavements were constructed inside the test chamber measuring 1.5m (length) × 1.5m (width) × 1.0m (height).
Wheel loads on the pavements were simulated by applying repetitive load equivalent to the tire contact pressureof 550 kPa through an actuator on a rigid circular plate (diameter equal to 300 mm). The reinforcement
combination with GC+GG was found to perform better than GC alone due to additional support offered by geogrid
when used as a base or basal reinforcement underneath the geocell mattress. The performance was evaluated interms of cumulative permanent deformations and Traffic Benefit Ratio (TBR) over 100,000 repetitive load cycles.
TBRs of GC+GG combination showed values greater than 50 for test conditions considered. Furthermore, test
results showed that the GC+GG combination reduced permanent deformation by up to 78%, while GC alone
reduced it by about 52% compared to the unreinforced section. TBRs of geocell alone and geocell with basal
geogrid combinations indicated extended service life of the pavement.
Keywords: Soft subgrade, Geocell, Geogrid, Permanent deformations, Traffic benefit ratio
overcome such problems generally, a geogrid
reinforcement layer underlying a geocell mattress isplaced to arrest downward deflections and provide
extra support [8, 12, 13].
The geogrid basal reinforcement is used toincrease the pavement's service life, decreasing basecourse thickness or arresting longitudinal cracks inthe pavements [14]. The published studies suggest
significant improvement in load-bearing capacity due
to additional support with basal geogrid placed
underneath geocell [8, 12]. The overall performanceof the basal reinforcement accounts due to the tensile
strength mobilization and membrane support [8, 13].
Previous researchers indicated the necessity ofadditional support with basal geogrid underneath thegeocell mattress to increase the overall performanceof the pavement [12, 13]. On the contrary, its benefit
was reported to be marginal with an increase in theheight of the geocell and was found to give superior
performance when the limited size of geocell andappropriate geogrid is used [15]. The increase inheight was reported to show buckling of geocell walls
leading to a decrease in the load-bearing capacity [15].
The studies mentioned herewith have focusedsolely on evaluating the bearing capacity ofreinforced foundations or pavements. However,studies have not focused on quantifying the overall

INTRODUCTION
Pavements overlying soft subgrades cause
excessive permanent deformation under repetitive
loads. The subgrades associated with wetlands andheavy rainfall regions are a practical concern forimprovement. Available conventional methods suchas cement or lime stabilization or fiber reinforcement
used to treat soft subgrades yield a good platform forthe construction of pavement layers, but consume
enormous time and money. Alternatively,
geosynthetics are highly preferred due to their costeffectiveness and ease of construction. Geosynthetic
reinforcement materials, viz., geogrid, and geocell
offer various benefits in flexible pavements such aslateral restrainment, load-carrying capacity, andmembrane support [1-4]. Similarly, a threedimensional honeycomb structure called geocell
offers overall confinement and higher load carrying
capacity [1, 5-7]. However, their benefit in terms of
reduced deformations and improved pavement
performance over soft subgrades is observed whenreinforcement is placed near the loading region [8-11].
In most cases, when soft subgrades are encountered,single reinforcement such as geogrid or geocell
placement in the base layers may not be adequate tocontrol the pavement deformations. Hence, to206

GEOMATE – Kyoto, Japan, 3-5 November 2021

pavement performance of geocell and geogrid basal
reinforcement, such as improved service life,
especially when soft subgrades are encountered. The
improved service life of the flexible pavement isgenerally quantified in terms of Traffic Benefit Ratio(TBR) [1]. TBR is defined as the ratio of number ofload cycles sustained by base reinforced pavement at
the defined permanent deformation to the number ofload cycles sustained by the unreinforced pavement
section at the same defined permanent deformation
with the same material constitutions and thicknesses.
Permanent deformation is also called cumulative
permanent deformation or rutting. Thus, in this study,
the influence of bases reinforced with geocell alone
and combination of geocell and geogrid is evaluatedin terms of cumulative permanent deformation and
TBR of reinforced pavements overlying soft torelatively fair subgrades (with California bearing
ratio (CBR) equal to 1% and 5%). The overallpurpose of this study is to quantify the enhanced
pavement performance in terms of increased service
life by the inclusion of geosynthetics when soft
subgrades with a CBR ≤ 5% are encountered.

High-density polyethylene (HDPE) geocell (GC)of height equal to 100 mm with weld spacing of 356
mm and a seam peel strength of 1420 N was used.
Geogrid (GG), made of polypropylene with peaktensile strength of 30 kN/m in biaxial directions andaperture size equal to 40 mm × 40 mm, was used asbasal reinforcement placed right below the geocell
mattress. The geogrid had a stiffness of 503 kN/m ×
509 kN/m at 2% tensile strain in the biaxial
directions. Figures 1 (a) and (b) show the geocell andgeogrid used in the study.

MATERIALS AND METHODOLOGY
Subgrade soil, granular base and subbase, andasphalt layer
In this testing program, crushed aggregate, dense
bituminous macadam, and clayey sand were used toprepare granular layers, asphalt layer, and subgrade,
respectively. Similarly, geocell and geogrid made upof high-density polyethylene and polypropylene are
selected for base reinforcement.
Locally available clayey sand was used as the
subgrade material. The Standard Proctor test results
showed a maximum dry unit weight of 19 kN/m3 andan optimum water content of 14.5%. Soft andrelatively fair subgrade conditions (CBR=1% and5%) were simulated in the test chamber by varying
water content and the compacted unit weight. Basedon extensive calibration studies, targeted CBR =1%
and 5% were achieved for the chosen subgrade when
prepared at compacted dry unit weights and molding
water contents of 16.55 kN/m3 and 18.5%, and 17.40
kN/m3 and 17.2%, respectively. The base and subbase
layers were prepared following the gradation of wetmix macadam with crushed stone aggregates, meetingthe standard specifications mentioned in theAmerican Society for Testing and Materials [16] andMinistry of Road Transportation and Highways [17],Government of India. Aggregates used for base andsubbase layers had a maximum dry unit weight of
22.7 kN/m3 and optimum water content of 5.5%. Theasphalt material used in the study as a binder course
had a compacted unit weight of 23.4 kN/m3.

Fig.1 Photo snaps of geocell and geogrid used in the
study (a) geocell and (b) basal geogrid used
underneath the geocell mattress
EXPERIMENTAL PROGRAM
Pavement layers were prepared using a vibratory
plate compactor inside the test chamber measuring
1.5 m × 1.5 m × 1.0 m [3]. The total thickness of
granular layers (base plus subbase) and subgradewere maintained as 600 mm and 400 mm,
respectively, overlying a soft subgrade of CBR=1%.
In the case of pavements underlying relatively fair

Geocell and geogrid
207

GEOMATE – Kyoto, Japan, 3-5 November 2021

subgrade with CBR=5%, a total thickness of granularand subgrade layer was selected as 440 mm and 500
mm, respectively. These thicknesses were finalizedbased on Indian Road Congress guidelines for designof flexible pavements [18]. Base layer thickness of
225 mm was maintained commonly for all the
pavement sections. Over the prepared granular layer,
a 50 mm thick asphalt layer was placed. Pavement
layers were prepared to achieve 98% of their targeted
densities. The expanded geocell mattress size equal to1.45 m × 1.45 m in length and width was placed at
0.15D from the top of the base layer, where D is thediameter of the circular plate (i.e., 300 mm). In thecase of basal reinforcement application, geogrid ofsize equal to 1.45 m × 1.45 m in length and width wasplaced right below the geocell mattress. Prepared
testbed with complete instrumentation is shown in
Fig. 2. In total, six large-scale model pavements wereconducted. Two sections of unreinforced, two test
sections with geocell alone, and two test sections with
geocell and geogrid as a basal reinforcement were
tested. The prepared testbed was ensured for properconsistency in unit weight and water content for all
the test cases; the maximum deviation in themeasured properties were within 3%. As shown in
Fig. 2, repetitive load of haversine load pattern with 5
Hz frequency was simulated and applied over the
prepared bed with a typical tire contact pressure of
550 kPa using 100 kN actuator on 300 mm rigid
circular plate placed on the center of the testbed [2,3]. Figure 3 presents the typical load pattern appliedon the prepared testbed. The permanent deformation
data was measured using an inline displacementtransducer inbuilt with the actuator system (refer Fig.
2).

with actuator mounting

Fig. 3 Repetitive load pattern applied on the testbed
RESULTS AND DISCUSSION
Influence of single geocell and combination of
geocell and basal geogrid on permanent
deformations
Figures 4(a) and (b) present the permanent
deformation vs. load cycles for geocell alone andgeocell plus geogrid reinforced pavement bases
overlying soft (CBR=1%) and relatively fair subgrade(CBR=5%), respectively. In all test cases, repetitive
loads were applied up to 100,000 load cycles (N) due
to test constraints related to the enormous amount oftime it takes to apply repetitive loads. Figure 4(a)shows that the unreinforced section exhibits morethan 20 mm of permanent deformation; thus
exceeding the failure criterion in rutting. The
relatively high permanent deformation of theunreinforced pavement section might have occurred
to large vertical and lateral deformations of granularmaterial overlying soft subgrade conditions under
repetitive loads. During initial repetitive load cycles,
reinforcement mobilization is low; however, as theload cycles increase further, deformations weredrastically reduced for GC alone and GC+GG, asshown in Fig. 4(a). The superior benefit is witnessed
for GC+GG combination. For example, at 100,000
load cycles, unreinforced section, GC, and GC + GG
showed deformations of 24 mm, 11 mm, and 5 mm,
respectively. Compared to an unreinforced section, areduction in measured deformations were found to beabout 54% and 73% corresponding to GC and
GC+GG at the end of load cycles, respectively. In thecase of pavement section reinforced with geocell
alone, reduced deformation might have resulted due
to overall confinement. In the case of GC+GG
reinforcement combination, it is evident that due tothe extra structural support offered throughmembrane and interlock action of basal geogrid,
further reduction in permanent deformation wasFig. 2 Test setup showing prepared testbed system
208

GEOMATE – Kyoto, Japan, 3-5 November 2021

observed. Beyond the 30,000 load cycles, a nearconstant permanent deformation was seen with the
GC+GG reinforcement combination. Therefore,
geocell with basal geogrid combination might be a
viable solution to control deformation over very soft
subgrades.
On the other hand, permanent deformations
observed for pavements overlying relatively fair
subgrade condition is presented in Fig. 4(b). In
pavements underlying fair subgrade condition(CBR=5%), the resulting deformations were lower
than those for a soft subgrade (CBR=1%) for a given
applied load cycle. Between GC and GC+GG, thereinforcement benefit was found to be only marginal.
Nevertheless, GC+GG reinforcement combination
performed better showing lower deformations than
the geocell alone. Almost consistent deformations
were observed beyond 30,000 load cycles for geocell
alone and a combination of geocell and basal geogrid.
Based on the permanent deformations presented inFig. 4(b), it can be inferred that the reductions werein the order of 51% and 66% for GC and GC+GG,
respectively, over 100,000 load cycles. In the case ofpavements encountered with relatively fair subgradeconditions, geocell alone might be used to reinforce
the base layers as no significant improvement in
reducing the deformations were observed for
GC+GG.

Fig. 4 Permanent deformation versus repetitive load
cycles of unreinforced, GC reinforced base layer and
GC+GG reinforced base layer for (a) pavement
overlying CBR = 1%, and (b) pavement overlying
CBR = 5%
Traffic benefit ratios (TBR) of reinforced baselayersTBR is also called traffic improvement life, whichindicates the extended life of the pavement. Figures
5(a) and (b) show the TBRs of reinforced pavements
overlying soft and relatively fair subgrade conditions.
In this study, about 11 mm maximum permanent
deformations were observed for reinforced sections.
Hence, the TBRs were calculated within specified
permanent deformation; however, to witness TBRs at
higher deformation, it might require higher repetitive
load cycles than those applied in the study. From Figs.
5(a) and (b), it can be noticed that pavements
overlying soft and relatively fair subgrade conditions,
GC reinforced base sections resulted in TBR as highas 12 and 8.3, respectively. Likewise, TBRs of bases
reinforced with GC+GG combination was found to beas high as 150 and 38 over soft and relatively fair
subgrade condition, respectively (refer to Fig. 5(b)).
Initially, TBRs increase, and then with an increase infurther permanent deformations, TBRs attenuates for
GC reinforced section alone. However, this trend is
expected as TBR is calculated as a ratio of reinforced
cycles to unreinforced cycles at the same defined
permanent deformation. In the case of GC+GG
reinforced section, resulting load-carrying cycles
were more, thereby leading to higher TBR than the
GC alone. Higher TBRs were observed in the case ofpavement overlying soft subgrades, and lower values
were observed for fair subgrades. In the available
literature, TBRs ranged as high as 670 forgeosynthetic reinforced pavement bases [1].
However, TBRs recommended for design at failure
deformations were reported between 1 to 4 [1].

209GEOMATE – Kyoto, Japan, 3-5 November 2021

The flexible pavement bases overlying soft
(CBR=1%) to relatively fair subgrade (CBR=5%)
conditions were reinforced with geocell alone (GC)and a combination of geocell plus geogrid (GC+GG)
to witness the overall pavement performance. The
repetitive load tests were conducted to evaluate theload-response of geosynthetic reinforced flexible
pavements. The permanent deformation behavior
versus the applied repetitive load cycles were
evaluated for geocell alone, and geocell plus geogrid
combination placed in the base layer of the flexible
pavement overlying soft and relatively fair subgrades.
Further, the extended life of the pavement due toreinforcement is quantified in terms of traffic benefit
ratio (TBR) by considering permanent deformation
observed over 100,000 load cycles. The main
conclusions obtained from the present study are
discussed below.

Fig. 5 TBR versus permanent deformations for (a)geocell alone for pavement overlying subgrades with
CBR=1% and 5%, (b) combination of geocell plus
basal geogrid for pavement overlying subgrades with
CBR = 1% and 5%

Recommendations
The combination of geocell and geogrid placed
below the geocell was found to be a viable solution
over soft subgrades. The reinforcement benefit can beobserved majorly in two ways. Firstly, the reduced
cost due to a decrease in thickness of a granular layer,and secondly, increased service life and reduced
maintenance cost if the same thickness of thepavement is maintained. On the contrary, theunreinforced pavement section frequently requires
rehabilitation and asphalt overlays leading to hugecost incurrences; this activity might be minimal in thecase of reinforced pavements. Over relatively fair
subgrade conditions, geocell alone might help in
improving pavement performance.
It can be noted that in the present study, reported
TBR values ranged as high as 150 corresponding to
low permanent deformation values (i.e., below 10mm). However, this value may decrease further athigher permanent deformations (rutting) or atdesignated rutting failure criterion. For example, at astandard rutting criterion of 20 mm [18] of thepavement overlying subgrade with CBR=1%, thecomputed load cycles of unreinforced pavement andextrapolated load cycles at the same rut depth for GC[refer to Fig. 4(a)] alone resulted in about 76,593 and1,014,862 cycles, respectively.
Thus,
the
corresponding TBR is 13.25. Hence, it isrecommended to adopt the TBR values pertaining to
the rutting failure criterion. The design-related
aspects that involve reducing base course andimproving the pavement's service life can be found in
Berg et al. [1] and Holtz et al. [19] studies.









The reduction in pavement deformation due togeocell (GC) alone and geocell plus basal
geogrid (GC+GG) ranged as high as 54% and73%, respectively, over soft subgrades(CBR=1%). Likewise, the corresponding
reductions were 51% and 66% for pavements
over relatively fair subgrades (CBR=5%).
Geogrid placed right underneath the geocell was
found to arrest lateral movement of granularmaterial effectively leading to higher reductions
in deformations. In other words, the additional
basal reinforcement right underneath the geocell
mattress leads to sustain higher load cycles thanthe pavement base system supported with geocell
alone for the same rut deformation.
GC+GG combination was more effective thanGC alone for pavements overlying soft subgrades(CBR=1%).
However,nosignificant
improvement of GC+GG compared to GC alone
was observed for pavements overlying fair
subgrades (CBR=5%).
Higher TBRs were witnessed for a GC+GG due
to extra support offered by geogrid underneaththe geocell.
Traffic Benefit Ratio (TBR) of GC alone ranged
as high as 12 and 8.3, respectively, for pavements
overlying soft and relatively fair subgrades.
TBRs of geogrid placed underneath a geocell
mattress (GC+GG) showed as high as 150 and 38
for pavement overlying soft and relatively fair
subgrades, respectively. However, it may be
noted that the reported TBR values correspond to
relatively low permanent deformation values(i.e., within 12 mm).

ACKNOWLEDGEMENTCONCLUSIONS
Authors would like to thank the National Highway
Authority of India for the research grant under grant
number: NHAI/TIC/R&D/108/2016.

In this study, essential insights obtained from largescale model pavement experiments were discussed.
210

GEOMATE – Kyoto, Japan, 3-5 November 2021

[10] Sireesh S., Mole P. A. F., Madhav M. R., andKumar R. V., Non-linear Response of
Geocell Reinforced Dense Granular Layerover Weak Soil Under Circular Loading. Int.
J. Geotech. Eng., Vol.10, No.1, 2016, pp.23 –30.
https://doi.org/10.1179/1939787915Y.00000
00014[11] Thakur J. K., Han J. and Parsons R. L.,
Factors Influencing Deformations ofGeocell-Reinforced
Recycled
Asphalt
Pavement Bases Under Cyclic Loading. J.
Mater. Civ. Eng., Vol.29, No.3, 2017, pp.1–
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https://doi.org/10.1061/(ASCE)MT.19435533.0001760[12] Saride S., Sitharam T.G., and Puppala A.J.,
The Function of Basal Geogrids in
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https://doi.org/10.1061/41165(397)475[13] Hegde A., and Sitharam T. G., 3-Dimensional
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DOI:10.1080/17486020600900596[16] ASTM .2020. Standard Classification for
Graded Aggregate Material for Bases or
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[17] MORTH. Specifications for Road and BridgeWorks, (5th Revision), Ministry of Road
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S.W., Geosynthetic Reinforcement of theAggregate Base/subbase Courses ofPavement Structures. Geosynthetic Materials
Association White Paper-II, 2000, Roseville,
Minnesota, USA.
[2] Baadiga R., Saride S., Balunaini U. andMadhira R. M., Influence of Tensile Strengthof Geogrid and Subgrade Modulus on Layer
Coefficients of Granular Bases. Transport.
Geotech., Vol.29, 2021, pp.100557,
https://doi.org/10.1016/j.trgeo.2021.100557[3] Baadiga R., Balunaini U., Saride S. andMadhira R. M., Influence of Geogrid
Properties on Rutting and Stress Distribution
in Reinforced Flexible Pavements under
Repetitive Wheel Loading, J. Mater. Civ.
Eng., 2021, 10.1061/(ASCE)MT.19435533.0003972[4] Saride S. and Baadiga R., New Layer
CoefficientsforGeogrid-reinforced
Pavement bases. Indian Geotech. J., Vol.51,
No.1, 2021, pp.182 – 196. DOI:
10.1007/s40098-020-00484-6[5] Pokharel S.K., Han J., Manandhar C., Yang
X., Leshchinsky D., Halahmi I. and Parsons
R., Accelerated Pavement Testing of
Geocell-Reinforced Unpaved Roads overWeak Subgrade. Transp. Res. Rec., J. Transp.
Res. Board., Vol.2204, No.1, 2011, pp.67–
75. https://doi.org/10.3141/2204-09[6] Han J. and Thakur J. K., Sustainable
Roadway Construction using Recycled
Aggregates with Geosynthetics. Sustainable.
Cities Soc., 14, 2014, pp.342–350.
https://doi.org/10.1016/j.scs.2013.11.011[7] Hegde A., Geocell Reinforced Foundation
Beds-Past Findings, Present Trends andFuture Prospects: A State-of-the-Art Review.
Constr. Build. Mater., Vol.154, 2017,
pp.658–674.
https://doi.org/10.1016/j.conbuildmat.2017.0
7.230[8] Dash S. K., Sireesh S. and Sitharam T. G.,
Model Studies on Circular Footing Supported
on Geocell Reinforced Sand underlain bySoft Clay. Geotext. and Geomembr., Vol. 21,
No.3,
2003,
pp.197–219.
https://doi.org/10.1016/S02661144(03)00017-7[9] Sitharam T. G., Sireesh S. and Dash S. K.,
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703. https://doi.org/10.1139/t04-117

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11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

EFFECT OF BENDING RIGIDITY OF PRECAST L-TYPE RETAINING
WALL ON LATERAL PRESSURE CAUSED BY REPEATED WHEELLOADING
Yukimine Otomo1, Osamu Maruyama2
1

Seikitokyu Kogyo Co., Ltd., Japan; 2Department of Urban and Civil Engineering, Tokyo City University, Japan
ABSTRACTThe lateral earth pressure acting on the precast L-type retaining wall installed in the embankment section of
the road is thought to be the result of the interaction between the retaining wall and the backfill soil. However, indealing with earth-retaining concrete structures, few reports have clarified the mechanism of deterioration due torepeated loads of passing vehicles, and no diagnostic method for damage has been established. In this report, we
focused on the bending rigidity of the wall and measured, by changing the bending rigidity of the wall, the lateralpressures that act when the test wheel is repeatedly loaded on the backfill soil. In the experiment, two types ofwalls were used; one was a wall of a box made of light gauge steel with its bending rigidity being three times thatof the precast L-type retaining walls and the other a pseudo L-type retaining wall having almost the same bending
rigidity. As a result, it was found that the magnitude of the lateral pressures depends on the bending rigidity of the
precast L-type retaining wall.
Keywords: Precast L-type retaining wall, Repetitive loading, Lateral pressures, Bending rigidity
the stem is in the range from 851kN-m2 to 8002kNm2. The relationship between the stem height andbending rigidity is shown in Fig. 1, and Eq. (1) can beobtained by approximating with a cubic equation.

INTRODUCTION
Precast L-type retaining walls installed in theembankment section of the road are repeatedly
exposed to various lateral pressures from passing
vehicles through the backfill soil. These lateralpressures occur because of the interaction between
the retaining wall and the backfill soil, and arethought to be affected by the bending rigidity of thestem [1]. However, few reports have clarified themechanism of deterioration caused by repeated loads
of passing vehicles, and no diagnostic method fordamage has been established. In this paper, thebending rigidity of the wall was changed, and thelateral pressures and top displacements were
measured when the backfill material was repeatedly
loaded with test wheels. For the specimens, one wasa 20cm high, 50cm wide wall made of light gauge
steel with its bending rigidity being three times thatof the precast L-type retaining wall. Another was a
20cm high, 15cm wide pseudo L-type retaining wallwith its bending rigidity of 1.25 times the L-type’s.

y = 771.36x3 + 272.46x2 + 51.031x – 1.8939(1)

Bending Rigidity

Where, x is the height of the stem and y is thebending rigidity. From the approximate formula, thebending rigidity was estimated to be 25.4kN-m2 when
the wall height was 0.2m. The moment of inertia of
area for the precast L-type retaining wall was also
calculated with the extension of 15cm, which is thesame as the wall width of the pseudo L-type retaining
wall. The bending rigidity was obtained by taking into
account the value of 25.4 abovementioned and thedifferent widths between the two retaining walls as
follows; 25.4 × 15 / 50 = 7.6kN-m2.

BENDING RIGIDITY OF PRECAST L-TYPE
RETAINING WALLS
It is stated that in case the stem height of the
precast L-type retaining wall is in the range of 0.91m
to 2.06m, its thickness corresponds to 9cm to 19cm
[2]. The design standard strength of the stem is 30N /mm2 and the modulus of elasticity is 28000N/mm2.
And when the moment of inertia of area is calculatedwith the extension of 50cm, which is the same as thewall width of light gauge steel, the bending rigidity ofPrecast L-type retaining wall standard(extend 50cm)
Approximate curve of stem height and bending rigidity(kN-m2) y = 771.36x3 + 272.46x2 + 51.031x - 1.8939
10,000
8,000
6,000
4,000
2,000
25.4 00 0.2 0.5
1
1.5
2
2.5
Stem Hight (m)Fig. 1 Relationship between stem height and bending
rigidity
METHOD OF REPETITIVE LOADING
EXPERIMENT USING WALL SURFACE OF

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GEOMATE – Kyoto, Japan, 3-5 November 2021

LIGHT GAUGE STEEL

Loading Method and Loading Weight

Bending Rigidity of Wall of Light Gauge SteelThe box was fixed so that the minimum horizontal
distance from the pressure gauges to the center of thetest wheel was to be 4cm (Fig. 2). For the repetitive
loading test, a water-immersed wheel tracking
machine was used. Its test wheel was of a diameter of
20cm and a width of 5cm. The moving distance of thewheel was set to be 35cm so that the test wheel could
pass in front of, and cover, all the pressure gauges
arranged in the lateral direction. The loads on the test
wheel were 1472N, 1275N, 1079N, and 883N. The
test wheel was manually operated and repeatedly
moved in parallel with the wall.

Specimen is a box mostly made of light gauge
steel. It is 50cm (25+25) wide, 25cm (11+14) longand 20cm deep (Fig. 2). The sidewalls as well as the
50cm wide wall and the bottom plate were fixed with
M10 bolts. The bending rigidity of the wall, EI is
obtained to be 76.0kN-m2 by assuming the modulus
of elasticity of the two light gauge steels, which form
the wall, being 2.0×107N/cm2, and calculating the
moment of inertia of area around the weak axis as
I=2×19=38cm4. Therefore, the ratio of bending
rigidity of the wall to that of the precast L-type
retaining wall turns out to be 3.0 times (76/25.4=3.0).
Method of Measurement of Lateral Pressures and
Top Displacement
Nine pressure gauges, with the diameter being
7.6mm, thickness 2mm and capacity 3MPA, were
pasted on a thin metal plate. Then, a washer wasplaced around each of the pressure gauges to fix themon the metal plate. It was placed horizontally or
vertically in the center of the box for the pressure
measurement (Fig. 2). In this process, the pressuregauges and wires were covered with a 5mm thick NR
sponge rubber. For the correlation of pressure withand without sponge rubber, a linear equation was
obtained using a compression testing apparatus as isshown as Eq. (2), by which the measured pressure x(kN/m2) was converted to the pressure y (kN/m2)
without sponge rubber. The measurement of lateralpressures and a top displacement started from thetime of backfilling and continued until the specified
number of loading was completed.
y = 1.042x + 34.3(b) B-B cross section(2)

Backfill Material(a) A-A cross section
Fig. 2 Box with light gauge steel
Percentage Passing

Part of gradation-controlled crushed stone, which
passed through a 5.6mm sieve, was used as backfill
soil. The maximum dry density was 2.153g/cm3, theoptimum moisture content was 7.9%, and the soil
particle density was 2.685g/cm3. The backfill
material was gravelly soil as seen from the grain size
distribution (Fig. 3), and the angle of shear resistancewas 35°. Generally, the optimum moisture content
that maximizes the dry density is different from themoisture content that maximizes the strength.
It is said that the maximum strength is on the dryside of the optimum moisture content [3], leading to
the selection of the moisture content of 6%. The
backfill material was compacted with a tamper so thatthe compacted thickness of one layer was to be 5cm
or less. The wet density after tamper compaction was
18.7kN/m3 and the degree of compaction was 83%.

(%)

100
80
60
40
20
0
0.001

0.01

0.1
1
Grain Size (mm)

10

100

Fig. 3 Grain Distribution Curve
LATERAL PRESSURE ACTING ON WALL
SURFACE OF LIGHT GAUGE STEEL ANDDISPLACEMENT OF TOP
213

GEOMATE – Kyoto, Japan, 3-5 November 2021

Lateral Pressures in Vertical Direction

Lateral Pressures in Horizontal Direction and
Displacement of Top

Lateral Pressure (kN/m2)

The transition of the lateral pressures is shown in
Fig. 4 as related to the time during which the wheelload of 1472N was manually and repeatedly loaded,
800 times in total. They were measured by thepressure gauges placed at specified depths. The
figures with colored lines on the graph represent thedepth at which the gauges were placed. It took about
34 minutes before the measurement for backfillingand compacting the backfill material. The combinedlateral pressure due to the wheel load and thecompaction using the tamper is the largest at a depthof 30mm, and the lateral pressures tend to decrease as
the depth increases from 30mm. The peak of residual
stress was 32 loadings at depths of 30 to 45mm (point
A in Fig. 4) and 4 loadings at depths of 60 to 120mm.
It has been reported by Spangler and Mickle [4]that in case a wheel loading is statically acted on the
backfill soil of the retaining wall, the lateral pressures
acting on the stem after removing the loading is often
larger than the lateral pressures before loading. In therepetitive loading test, it was confirmed as well at all
the pressure gauges installed at each depth that theresidual pressures increased and the values gradually
decreased after reaching the maximum. The larger the
initial residual pressures value, the more remarkable
the decrease in the residual pressures. In addition, thedifference between the maximum value of lateralpressures and the residual pressures (hereinafterreferred to as the amplitude of pressures) tends togradually decrease with each loading.
Granular soil becomes a particle structure
corresponding to the applied load after repeatedloading. It is stated that as the stress in the soil
increases, plastic deformation occurs due to thechange of the internal structure [5]. The experimental
results showed that plastic deformation occurredfrom the start of repeated loading. In addition, theincrease of residual pressures came to end when theplastic deformation converged. It is presumed that themagnitude of the amplitude of pressures isdetermined by the amount of plastic deformation.
Both pressures after the convergence of plastic
deformation seem to naturally decrease even if
repetitive loading is applied.

200
180
160
140
120
100
80
60
40
20

180mm
60mm
A

150mm
45mm

120mm
30mm

95mm
15mm

Lateral Pressure (kN/m2)

The transition of the lateral pressures and top
displacement are shown in Fig. 5 and Fig. 6,
respectively, as related to the time during which thewheel load of 1275N was repeatedly loaded, 120
times in total. The measurements were made at ninepressure gauges placed at a depth of 4cm. The legend
above the graph in Fig. 5 shows the distance from thepressure gauge at the end. In the measurement, it took
10.4 minutes for backfilling and compacting thematerial. The numbers in the Fig. 5 represent the
number of loadings. The lateral pressures at the
beginning of repeated loading were solely the
amplitude of pressures. The ratio of residual pressures
gradually increased with repeated loadings. The
residual pressures and the amplitude of pressures
hardly increased at and after 37 loadings andgradually decreased after 69 loadings.
In addition, the displacement of top end increased
by 0.45mm from 5.11mm to 5.56mm during theloading. It was confirmed that the increase rate ofdisplacement at the top also changed with that of theresidual pressures (Fig. 6).使用するデータ
165mm
45mm
240
210
180
150
120
90
60
30

135mm
30mm

105mm
80mm
60mm
15mm
0mm
100 12050 70
20 30 40 60 80 90 110

1010.010.410.811.211.612.012.412.813.213.614.014.414.815.215.616.016.416.817.217.6

10

11

12

13
14
15
Elapsed Time (min)1617Fig. 5 Lateral pressures in horizontal direction (wheel
載荷開始時変位5.1mmから載荷69回5.55mmまで0.45mm増加
load 1275N,
depth 4cm, 120loadings)
No. 329 繰り返し載荷と天端変位の関係(1275N、距離4cm、深さ4cm，120回載荷)
Displacement (mm)Displacement of Top

75mm

6.0
5.5

5.0
4.5

10.010.410.811.211.612.012.412.813.213.614.014.414.815.215.616.016.416.817.217.6

10

11

12

13

14

15

16

17

Elapsed Time (min)Fig. 6 Displacement of top (wheel load 1275N, depth
4cm, 120 loadings)
METHOD OF REPETITIVE LOADING
EXPERIMENT USING PSEUDO L-TYPE
RETAINING WALL

105 115

Bending Rigidity of Pseudo L-type Retaining Wall

Fig. 4 Lateral pressures in vertical direction (wheelload 1472N, 800 loadings)

Eight pseudo L-type retaining walls were madefor the experiment from precast inlets of thefollowing properties; internal dimensions were 30cm

35

45

55
65 75 85
Elapsed Time (min)

95

25
27
30
32
34
36
39
41
43
45
48
50
52
54
57
59
61
63
66
68
70
72
75
77
79
82
84
86
88
91
93
95
97
100
102
104
106
109
111
113
115

25

214

GEOMATE – Kyoto, Japan, 3-5 November 2021

× 30cm, 3cm thick and 15cm deep. The design
standard strength was 30N/mm2 and the maximum
aggregate size was 20mm. The stem of the processed
wall was 20cm high and the heel was 16cm wide. The
pseudo-L-type retaining walls contained one normal
iron wire with a diameter of 2.6mm.
The similarity between the pseudo L-type and real
precast L-type retaining walls is confirmed as follows.
The modulus of elasticity of the pseudo L-type
retaining walls is 28000N/mm2, and the bending
rigidity of the stem and heel is 9.5kN-m2. Therefore,
the bending rigidity of the pseudo L-type retaining
walls were considered to be 1.25 (9.5 / 7.6) times thatof the real products.

moment was acting, was estimated to be 1875N using
the bending moment that acted when the loadings
1472N to 883N were repeatedly loaded on the wall
surface of the light gauge steel. And then, a set of
proportional loads were selected and applied. Theirratios against 1875N were set to be in the range of
110.6% to 90.2% (2073N, 2019N, 1967N, 1911N,
1855N, 1809N, 1757N, 1692N).

Method of Measurement of Lateral Pressures and
Top Displacement
The pseudo L-type retaining wall was placed in
the box of light gauge steel, and square steel pipes
were arranged linearly on both sides of the pseudo Ltype retaining wall. Fig. 7 shows the layout and crosssection of the pseudo L-type retaining wall. A metal
plate with nine pressure gauges pasted on it was
attached vertically on the stem, and the front surfacesof the pressure gauges and wiring were protected with
5mm thick NR sponge rubber. Measurements oflateral pressures and top displacement at two points
continued from the backfilling to breakage of thepseudo L-type retaining wall. The pressures
measured in repetitive loading experiment wereconverted to those pressures without sponge rubber
using the Eq. (2).

Fig. 7 Layout and cross section of the pseudo L-type
retaining wall
RESULTS OF REPETITIVE LOADING
EXPERIMENTON
PSEUDO
L-TYPERETAINING WALLS
Shape of Damaged StemIn the repetitive loading experiments, cracks
occurred at the end of the haunch only when thewheel load was 1692N. Cracks occurred at the haunchin the case of the remaining seven stems like when thewheel load was 2019N (Photo-1).

Backfill Material
The backfill material used was the same as one
that was used for the wall of light gauge steel. The
thickness after compaction by tamper and the watercontent were the same, too.
Loading Method and Loading Weight
A pseudo L-type retaining wall was placed so thatthe minimum horizontal distance from the pressuregauges to the center of the test wheel was to be 4cm(Fig. 7). The wheel tracking tester was used for therepetitive loading test. The test wheel was a solid tirewith its diameter of 20cm and width of 5cm. Inchanging and adjusting the load, steel plate of 18.1 to
95.6N per one piece was piled up above the test
wheel. The moving distance was set to be 23cm sothat the extension of the pseudo L-type retaining wall
could be passed. The running speed was eighttimes/minute.
The bending moment at which the stem of thepseudo L-type retaining wall statically failed wasestimated to be 135.4Nm from the static fracture test.
The repetitive load, under which this bending

Wheel load of 1692N
Wheel load of 2019N
Photo 1 Damaged shape after repetitive loading
Relationship between Top Displacement of Stemand Number of Loadings
When the wheel load of 1692N was repeatedly
loaded, the wall failed at 69 loadings. The top
displacement of the stem at that time is shown in
Fig.8. The legend shows displacement No.1 anddisplacement No.2 representing the values measured
by transducers installed at the top of the stem. It took
27.6 minutes for backfilling and compaction until thestart of repetitive loading. The top displacement that215

GEOMATE – Kyoto, Japan, 3-5 November 2021

that the plastic deformation continued again due tothe displacement of the stem after 8 loading, and theresidual pressures and the amplitude of the pressurescontinued to increase.
圧力1692Nの時の時間と天端変位の関係
Displacement(mm)was 0mm before backfilling indicated 0.4mm on the
average when loading was started.
When repetitive loading began, the top
displacement gradually increased until the elapsed
time reached 30 minutes, after which it increased to
1.6mm when the stem failed.
Relationship between Residual Pressures at Each
Depth of Stem and Number of Loadings
The fluctuation of the lateral pressures by time ateach depth of the stem when the wheel load 1692N
was repeatedly loaded is shown in Fig. 9. The figure
above the graph shows the depth from the groundsurface, and the figures in the graph for the depth of
45mm indicate the number of loadings. The residualpressures at depths deeper than 60mm reached the
maximum value after 8 loadings or so and thengradually decreased. The residual pressure at a depthof 45mm reached its maximum value at 11 loadings,
and remained almost constant until it failed at 69
loadings. The residual pressures at depths shallower
than 30mm reached the maximum value at 58
loadings, and then gradually decreased.

Displacement No.1

Displacement No.2

3
2.5
2
1.5
1
0.5
0
27

30

33
36
Elapsed Time(min)39

Pressure(kN/m2)

In case the heel plate is fixed, the displacement of
the stem increases as it approaches the top. And thedisplacement of the stem promotes the plastic
deformation of the backfill material. What can beseen from Fig. 8 and Fig. 9 are as follows. At a depthof 60mm or more from the ground surface, the plastic
deformation converged after 8 loadings, so it isconsidered that the residual pressures and theamplitude of the pressures naturally decreased evenafter repetitive loadings. At a depth of 45mm, it isestimated that the plastic deformation continued
again due to the displacement of the stem after 11
loadings, and only the amplitude of pressuresincreased. At a depth of 30mm or less, it is presumed

Pressure(kN/m2)

Increase of Lateral Pressure due to Difference ofDisplacements of Stem

Pressure(kN/m2)

Similarly, looking at the fluctuation in theamplitude of pressures in Fig. 9, the amplitude ofpressures at depths of 60mm or more reached the
maximum at two loadings. After that, it gradually
decreased with each additional loading. The
amplitude of pressure at a depth of 45mm reached its
first peak at two loadings and then decreased. Afterreaching the minimum value at 30 loadings, it
gradually increased until it failed. The amplitude ofpressures at depths less than 30mm decreased after
reaching the first peak at one loading. After reaching
the minimum value at 11 loadings, it reached the
maximum value at 66 loadings.

330
280
230
180
130
80
30

230
180
130
80
30

Pressure(kN/m2)

Relationship between Amplitude of Pressures at
Each Depth of Stem and Number of Loadings.

330
280
230
180
130
80
30

230
180
130
80
30

Pressure(kN/m2)

Fig. 8 Top displacement of the stem (1692N, 69
loadings)

180

15mm

27

30

33
36
Elapsed Time(min)

39

30mm

27

30

33
36
Elapsed Time(min)

39

45mm

10
27

30

20

30

40

50

33
36
Elapsed Time(min)

60

69

39

60mm

27

30
155mm

33
36
Elapsed Time(min)
135mm

115mm

39
95mm

75mm

130
80

30
27

30

33
36
Elapsed Time(min)39

Fig. 9 Lateral pressures at each depth (1692N, 69
loadings)
Variations of Lateral Pressures Due to Differencein Bending Rigidity of Wall
Comparison was made between the wall surfaceof light gauge steel and the pseudo L-type retaining
216

GEOMATE – Kyoto, Japan, 3-5 November 2021

wall, which revealed that the residual pressures andthe amplitude of pressures increased at the depths
45mm or less after 30 loadings in the case of the latter.
The difference was considered to be caused by thedisplacements of the stem. The reason for this isbased on a laboratory experiment report that says thatwhen the stem repeatedly undergoes minutedisplacement due to outside force, such as seasonalvariation, its deformation tends to progress toward theactive direction and the backside earth pressures
gradually increases [6].
The bending rigidity of the real precast L-type andpseudo L-type retaining walls is almost similar.
Therefore, it is considered that the stem is subject tohigher residual pressures and amplitude of pressures
acting on it and they increase more at points closer tothe top affected by its displacement.

The magnitude of residual pressures and theamplitude of pressures was determined by that of therepetitive loading and the depth from the groundsurface. In the case of the wall of the light gauge steel
with high bending rigidity, the residual pressures andthe amplitude of the pressures had peaks and thendecreased even if the loading continued.
The smaller the repetitive wheel load on thepseudo L-type retaining wall was, the greater thenumber of loadings was required for failure. It failed
even under the repetitive load of 1875N that was
supposed to cause failure.
The lateral pressures acting on the stem depended
on the bending rigidity of the stem. If the bending
rigidity of the stem was low, the lateral pressurescaused by the displacement of the stem were added to
the lateral pressures due to the repetitive loading. In
the case of the pseudo L-type retaining walls with low
bending rigidity, plastic deformation of the backfill
material increased the displacement of the stems,
which in turn promoted plastic deformation of thebackfill material. Both of them accelerated the failureof the stems by inducing the increase of residualpressures and amplitude of pressures. In this case,they became higher as they approached closer to theground because of the effect of the displacement of
top of the stem.

Relationship between Weight of Repetitive Loads
and Number of Loadings Leading to Failure

Repetitive Load (N)Shown in Fig. 10 is the relationship between therepeated loads and the number of loadings to thefailure in the repetitive loading experiment of thepseudo L-type retaining walls. The load is estimatedto be 1875N that makes the stem to fail after 30 to 40
loadings. With some exceptions, the smaller therepetitive load, the greater the number of loadings
required before failure.
By looking at the general tendency shown in Fig.
10, it would be possible to presume that the plastic
deformation of the backfill material becomes main
factor for the failure of the pseudo L-type retaining
walls with repetitive loads larger than 1875N. In
addition, it is considered that the pressures caused by
the displacement of the stem is gradually added to thepressures due to the repetitive loading on the stem
starting from 8 loadings or so. Therefore, it isconsidered that the smaller the repetitive loads are,the greater the ratio of lateral pressures due to thedisplacement of the stem gradually increases andfewer the number of repetitive loadings for failure
becomes. 繰返し荷重と破壊回数の関係
Measured value
2,200
2,100
2,000
1,900
1,800
1,700
1,600

REFERENCES[1] British Standards Institution, British Standard,
Eurocode 7: Geotechnical design, Part 1:
General rules, British Standards Institution, 2004,
pp. 102-103.
[2] Shikoku Technical Office, Shikoku Regional
Development Bureau, Ministry of Land,
Infrastructure, Transport and Tourism, Design
and Construction Manual for Precast L-Type
Retaining Walls (Draft), Revised version, 2001,
pp. 31-37. (Japanese).
[3] Japanese Geotechnical Society, GeotechnicalEngineering - Practice Series No.30, Soil
Compaction, Maruzen Publishing, 2012, pp. 7-17.
(Japanese).
[4] Spangler, M. G., and Mickle, J. L., Lateral
Pressures on Retaining Walls Due to Backfill
Surface Loads, Highway Research Board
Bulletin, 141, 1956, pp. 1-15.
[5] Hidetoshi Ochiai, Effect of Initial Density on
Compressibility of Granular Soils, Journal of theJapanese Society of Soil Mechanics andFoundation Engineering, Vol. 15, No.2, 1975, pp.
89-95. (Japanese).
[6] Takashi Sumiyoshi, Behavior of U-type Retaining WallApproximate curve

1,875Ｎy = 2073.1e-0.003x
1

10
Number of Fracture (times)

100

Fig. 10 Relationship between weight of repetitive
loads and number of loadings leading to failure (stemheight 20cm)

Subjected to Cyclic Displacement, Annual Report I. C.
E. of Tokyo Metropolitan Government, 2005, pp. 6978. (Japanese).

CONCLUSIONS

217

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

INVESTIGATION AND STABILITY ANALYSIS OF SLOPE FAILURE
AT EXTREMELY SHALLOW LAYER AFTER SNOW MELT IN
SNOWY COLD REGIONS
Naoki Miura1, Dai Nakamura2, Takayuki Kawaguchi3 and Shunzo Kawajiri4
1

Graduate School of Engineering, Kitami Institute of Technology, Japan
2,3,4
Faculty of Engineering, Kitami Institute of Technology, Japan
ABSTRACTIn snowy cold regions such as Hokkaido, slopes can collapse owing to the frost heave phenomenon, freezethaw actions in winter, or snowmelt in spring. Kitami City in Hokkaido recently suffered from such slope failures
when the extremely shallow layer eroded and slid after a snow melt in early spring of 2020. The depth of this slope
failure was about 0.20 to 0.25 m, which was very shallow compared with general surface failures. In this research,
we conducted a site investigation of the road slope, and collected undisturbed soil samples from a non-collapsed
area near the slope failure to conduct a constant pressure direct shear test. For the direct shear tests, specimens
with natural water content at the time of sampling and forcibly saturated specimens were used. The results of theslope stability analysis with the strength parameters obtained from the direct shear test showed that the slope failure
was caused by snowmelt penetration, mainly due to a decrease in cohesion.
Keywords: Snowy cold region, Slope failure, Direct shear test, Slope stability analysis

INTRODUCTION
In snowy cold regions such as Hokkaido in Japan,
slopes can collapse owing to the frost heave
phenomenon, freeze-thaw actions in winter, or
snowmelt in spring. Many incidents of slope failure
along roadways of Hokkaido were reported (e.g.
Subramanian et al. [1]). Such a slope failure also
occurred in early spring of 2020 on a road slope in
Kitami City, Hokkaido. The depth of this slope failure
was about 0.20 to 0.25 m, which was very shallow
compared with general surface failures. In thisresearch, we conducted a site investigation of the roadslope, and collected undisturbed soil samples from a
non-collapsed area near the slope failure to conduct a
constant pressure direct shear test. In addition, slope
stability analysis of the infinite length assumptions
without seepage was conducted using the strengthparameters obtained from the direct shear tests, andthe factors that led to the failure were discussed.

Fig. 1

Overall appearance of the road slope
where the failure occurred

Meteorological conditions before and after the day
when the slope failure was observed

Fig. 2

Daily mean temperature, snow depth, anddaily precipitation around April 3, 2021

The slope failure was observed on April 3, 2020.
Figure 1 shows the overall appearance of the roadslope where the failure occurred. Since the failuresurface is a little dry, it is assumed that the slope
failure occurred a little earlier than this date.
Figure 2 shows the daily mean temperature, snow

depth, and daily precipitation around April 3, 2020,
when the slope failure was observed. The
meteorological data were observed at the AMeDAS
Kitami station [2]. From this figure, it can be seen thatthe snow depth has been decreasing rapidly since
mid-March, when the daily mean temperature became

SITE INVESTIGATION OF THE ROADSLOPE WHERE SURFACE FAILURE
OCCURRED218

GEOMATE – Kyoto, Japan, 3-5 November 2021

Slope Surface

0.1 m
0.2 m
0.3 m
0.4 m
0.5 m

Fig. 3Fig. 4

Vertical cross section of a non-collapsed
area near the slope failure

Fig. 5Grain size accumulation curve

Cross-sectional view of the slope failure
obtained by surveying

positive. On the other hand, daily precipitation before
the date of confirmed slope failure is low. Based on
the above meteorological data, the trigger for thisslope failure is thought to be the infiltration ofsnowmelt.
Overview of the investigated road slopes
The site investigation, including collection ofundisturbed soil samples, was conducted from May
15 to June 5, 2020. The road slope is a cut slope facing
south. The height is about 4.4 m, the angle is 47 to
49 °, and the upper part of the slope is flat and used
as cultivated land. The surface of the slope wascovered with vegetation work to prevent erosion, anda metal net was laid on top of it.
Figure 3 shows a cross-sectional view of the slope
failure obtained by surveying. As shown in the figure,the depth of the failure surface (vertical depth) was
about 0.20 to 0.25 m. This figure also shows thelocations where the soil samples were collected.

0.0

Depth (m)

0.1
0.2
0.3
0.4

Soil test results of collected soil samples

0.5
0Figure 4 shows a vertical cross section of a noncollapsed area near the slope failure (2.7 m below theslope top). From site observations, it was confirmedthat the soil layer from the slope surface to a depth of
0.5 m was slightly weathered sandy soil. The soil
samples were collected from this section at intervalsof 0.1 m in the vertical direction, and the grain size
distribution, soil particle density, and water content
were measured. All of the respective soil tests wereconducted in accordance with the Japanese
Geotechnical Society Standards [3-5].
Figure 5 shows the grain size accumulation curveof soil samples collected from the non-collapsed area.

10

20 2.4

Fine fraction content
Fc (%)

Fig. 62.6

2.8 20

Soil particle densityρs (%)

40

60

Water content
w (%)

Fine fraction content Fc, soil particle
density s and water content w at eachdepth

In this study, the grain size distribution wasdetermined by sieve analysis for grain sizes larger
than 0.075 mm and by the laser diffraction method forsmaller grain sizes.
Figure 6 shows the fine fraction content Fc, soil
particle density s and water content w at each depth.
219

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1 Experimental conditions for the four cases conducted in this study

Collection depth (m)Moisture condition
Normal stresses (kN/m2)

Case 1
0.2
Unsaturated
10, 30, 50

Case 20.2
Saturated
5, 10, 30, 50

Case 3
0.4
Unsaturated
15, 30, 50

Case 40.4
Saturated
15, 30, 50

Normal stress s

The soil samples from 0.1 to 0.4 m depth contained asmall amount of fine-grained material, while soil
samples from 0.4 to 0.5 m depth contained no finegrained material and a small amount of coarsegrained material.

Pressure plate

Upper shear box

Specimen

20 mm

Shear Plane

Collection of undisturbed soil samples

Spacer
The undisturbed soil samples for the constantpressure direct sheer test were collected from the noncollapsed area. An acrylic cylinder with an innerdiameter of 60 mm attached to a sharp-edged shoe
was used. Soil samples were collected undisturbedly
by pressing this cylinder slowly and perpendicularly
against the slope surface.

Lower shear box

Displacement meter

Flooding box

WeightEnlargement

Fixed plate

Horizontal load cell

CONSTANT PRESSURE DIRECT SHEAR
TEST

Loading direction of shear force

Rail

Flooding box
Linear Guide

Specimens

Fig. 7In this study, the constant pressure direct shear
tests were conducted to obtain the strength parameters(cohesion c, internal friction angle ).
Table 1 summarizes the experimental conditions
for the four cases conducted in this study. Two typesof soil samples were used in the experiments: one wascollected from a depth of 0.2 m near the failuresurface, and the other was collected from a depth of
0.4 m, deeper than the failure surface. And also, twotypes of soil moisture conditions were used in theexperiments: one was a natural water content state(unsaturated state), and other was a forcibly saturated
state.
The specimens were prepared by the following
procedure. First, the bottom surface of theundisturbed soil sample in the acrylic cylinder was
cut and smoothed. Then, the sample was extruded
from the acrylic cylinder using a piston andtransferred directly into a shear box with the same
inner diameter and a depth of 20 mm. Finally, the top
surface of the specimen in the shear box was cut andsmoothed. The size of the specimen was 60 mm indiameter and 20 mm in height, which is the Japanese
Geotechnical Society Standards [6].
Some of the specimens were forcibly saturatedusing the following procedure. First, the specimen
was placed in an acrylic cylinder, sandwiched
between porous plates at the top and bottom, andsubmerged in a container filled with distilled water.
Then, the container was placed in a separate pressure-

Vertical load cell

Direct-drive motor

Schematic diagram of the constantpressure direct shear test apparatus

resistant container and degassed with a vacuum pump.
This degassing process took about 96 hours. Finally,
the specimen was placed in distilled water for morethan a week to increase the saturation.
Test apparatus and test method
Figure 7 shows the schematic diagram of theconstant pressure direct shear test apparatus.
In this study, the change in normal stress on theshear plane due to frictional forces on the innersurface of the shear box was monitored by a verticalload cell. During the direct sheer test, the constantpressure was controlled by increasing or decreasing
the weight to reflect the change in normal stress.
Table 1 also summarizes the vertical stresses
established in the direct shear test. Normal stresses
were applied to the specimens from the consolidation
process until the end of the direct shear test. Amongthe specimens collected near the failure surface, thesaturated specimens were tested with a small normal
stress of 5 kN/m2 because the change in normal stressdue to an increasing friction force was small (Case 2).
On the other hand, the minimum normal stress for the
specimens collected deeper than the failure surface
was 15 kN/m2 due to the large change in normal stressdue to the increase in frictional force (Case 3&4).
Shearing of the specimens was performed at a220

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 2 All direct shear test results conducted in this study
Case 1
Normal stressσ (kN/m²)
Water content
w (%)
Dry densityd (g/cm³)
Wet densityt (g/cm³)
Void ratioeDegree ofsaturation
Sr (%)
Maximumshear stressmax (kN/m²)

Fig. 8

Case 2

Case 3

Case 4

10

30

50

5

10

30

50

15

30

50

15

30

50

30.1

31.6

32.3

64.4

49.4

51.3

44.3

39.0

38.1

36.0

55.9

49.4

50.1

1.04

1.03

1.00

1.09

1.18

1.12

1.20

1.07

1.12

1.05

1.08

1.11

1.08

1.35

1.35

1.33

1.78

1.76

1.70

1.73

1.49

1.60

1.42

1.68

1.66

1.62

1.45

1.48

1.55

1.35

1.17

1.27

1.15

1.39

1.22

1.46

1.38

1.31

1.37

52.8

54.5

53.3 100.0 100.0 100.0 99.1

71.8

80.2

63.5 100.0 96.9

93.8

18.3

42.0

66.9

31.5

39.0

54.0

42.7

4.4

9.3

26.1

42.4

14.4

29.2

Comparison of normal stress s, vertical displacement H and shear stress  with shear displacement
 for each specimen under constant pressure direct shear test

constant speed by a direct-drive motor installedoutside the flooding box. The volume change during
the test was measured by a displacement meterattached to the vertical loading axis. The top andbottom shear box spacing was 0.2 mm, the shear
displacement rate was 0.02 mm/min, and the
maximum horizontal displacement was 7 mm. And
the shearing position was set at 10 mm above thebottom of the specimen.
The shearing process was conducted after aconsolidation process of about 24 hours. For thesaturated specimens, the flooding box was filled with

distilled water before consolidation.
Results and discussion of Constant Pressure
Direct Shear Test
Table 2 summarizes the all test results conductedin this study.
Figure 8 shows the comparison of normal stress s,vertical displacement H and shear stress  withshear displacement  for each specimen underconstant pressure direct shear test. It is confirmed thatthe normal stress s during the direct shear test can be
221GEOMATE – Kyoto, Japan, 3-5 November 2021

2

Maximum shear stress τ max（kN/m ）70

Table 3 Input conditions for the stability analysis(a) Collection depth: 0.2 m

60Case 1
Unsaturated
c=5.9450

ϕ=50.4740
30

Internal friction angle (°)

Case 2
Saturated
c=0.49ϕ=40.1820
100
0Cohesionc (kN/m²)

10

20

30

40

Unit volume weightin a wet condition
t (kN/m3)

50

2

Normal stress σ （kN/m ）Assumedslip surface angle
 (°)

2

Maximum shear stress τ max（kN/m ）70(b) Collection depth: 0.4 m
60Case 3
Unsaturated
c=20.92

50

30

Case 4
Saturated
c=3.27ϕ=38.71

20
10
0
0

10

20

30

40

All cases

48

SLOPE STABILITY ANALYSIS OF THE
INFINITE LENGTH ASSUMPTIONSWITHOUT SEEPAGE

502Normal stress σ （kN/m ）Fig. 9

5.94
0.49
20.92
3.27
50.47
40.18
32.90
38.71
13.17
16.91
14.72
16.23

collected from near the failure surface, both cohesionc and the internal friction angle  decrease withincreasing saturation (Fig. 9 (a)). On the other hand,for specimens collected from a depth deeper than the
failure surface, the internal friction angle  increasedslightly with saturation, but cohesion c decreased
significantly (Fig. 9 (b)).

ϕ=32.90

40

Case 1Case 2
Case 3
Case 4
Case 1Case 2
Case 3
Case 4
Case 1Case 2
Case 3
Case 4In order to investigate the effect of changes instrength parameters due to snowmelt infiltration on
the stability of the slope, stability analysis of theinfinite length assumptions without seepage shown in
Eq. (1) was conducted using strength parameters
obtained from the direct shear tests.

Relationship between the maximum shear
stress max and the normal stress s

controlled to be almost constant in all tests.
The comparison of the shear stresses  in theunsaturated and saturated states of the specimens
collected from near the failure surface (Fig. 8 (a), (b))
shows that the maximum shear stress max decreases
as the specimens become saturated. This is because
the suction decreases and disappears as the specimen
becomes saturated. Even for specimens collectedfrom a depth deeper than the failure surface (Fig. 8(c), (d)), the maximum shear stress max decreases as
the specimen saturates.
Comparing the shear stress  in the saturation
state of the specimens collected from near the failuresurface and those collected from a depth deeper than
the failure surface (Fig. 8 (b), (d)), it can be clearlyseen that the maximum shear stress max of thespecimens collected from a depth deeper than the
failure surface is higher.
Figure 9 shows the relationship between the
maximum shear stress max and the normal stress s atthat time for each test shown in Fig. 8. The figures
also show cohesion c and the internal friction angle
 obtained from each specimen. For the specimens

Fs =

c
tan 
+
・z
・sin  cos tan t(1)where Fs is the factor of safety, c is cohesion,  is theinternal friction angle, z is the assumed slip surface
depth, and t is the unit volume weight in a wet
condition,  is the assumed slip surface angle. The
infinite length assumptions are widely used to predict
the slope failure probability (e.g. Haefeli. [7],Skempton and DeLory. [8]).
Table 3 shows the input conditions for thestability analysis. In this analysis, the average angleof this slope 48 ° was used as the assumed slip surface
angle .
Figure 10 shows the results of the slope stability
analysis of the infinite length assumptions without
seepage. It can be seen that the factor of safety Fs in
the saturated state is clearly lower than that in the222

GEOMATE – Kyoto, Japan, 3-5 November 2021

(Fig. 10 (b)). This reason could be that the soil deeper
than 0.4 m was not subjected to freezing and thawing
history during the winter of 2019. Considering thefact that the slope failure occurred during thesnowmelt season, the freezing and thawing history is
considered to have had a significant influence. In the
future, we would like to clarify the effects of frost
heaving and other phenomena.
CONCLUSIONSIn this research, we conducted a site investigationof the road slope, and collected undisturbed soil
samples from a non-collapsed area near the slope
failure to conduct a constant pressure direct shear test.
For the direct shear tests, specimens with natural
water content at the time of sampling and forcibly
saturated specimens were used. The results of theslope stability analysis with the strength parameters
obtained from the direct shear test showed that theslope failure was caused by snowmelt penetration,
mainly due to a decrease in cohesion.
We will continue to investigate slope failuresand conduct soil tests to study the mechanism of slope
failures at the extreme surface layer that occur insnowy cold regions.
REFERENCES[1] Subramanian S. S., Ishikawa T. and Tokoro T.
Stability assessment approach for soil slopes inseasonal cold regions, Engineering Geology, Vol.
221, 2017, pp.154-169.
[2] Japan Meteorological Agency,
http://www.data.jma.go.jp/obd/stats/etrn/index.p
hp[3] Japanese Industrial Standards, JIS 1204:2020,
Test method for particle size distribution of soils,
2020.
[4] Japanese Industrial Standards, JIS 1202:2020,
Test method for density of soil particles, 2020.
[5] Japanese Industrial Standards, JIS 1203:2020,
Test method for water content of soils, 2020.
[6] Japanese Geotechnical Society, JGS 0561-2020,
Method for consolidated constant pressure directbox shear test on Soils, 2020.
[7] Haefeli R. The stability of slopes acted upon by
parallel seepage, in Proc. 2nd Int. Conf. on Soil
Mechanics and Foundation Engineering, Vol. 1,
1948, pp.57–62.
[8] Skempton A.W. and DeLory F.A. Stability ofnatural slopes in London clay. in Proc. 4th Int.
Conf. on Soil Mechanics and Foundation
Engineering, Vol. 2, 1957, pp. 378–381.

Fig. 10 Results slope stability analysis of theinfinite length assumptions without
seepageunsaturated state for both specimens collected fromthe failure surface and specimens collected from adepth deeper than the failure surface. In particular, forthe specimens collected from the failure surface in thesaturated state, the factor of safety Fs is below 1 when
the depth was deeper than 0.25 m (Fig. 10 (a)). Thissuggests that the surface layer of the slope wasextremely unstable.
It can be understood that the decrease in cohesionc contributes significantly to the decrease in the factorof safety Fs due to the increase in saturation. These
results suggest that the main cause of slope failure inthis study is the decrease in cohesion c caused by the
infiltration of snowmelt.
On the other hand, the factor of safety at a depthof 0.4 m for specimens collected from the failuresurface in the saturated state is always greater than 1223

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

ANALYTIC CONSIDERATIONS ON TWO-DIMENSIONALMODELING OF PARTIAL FLOATING SHEET PILE METHOD
Nanase OGAWA1, Kakuta FUJIWARA2 and Kentaro NAKAI3
1

GIKEN Ltd., Japan; 2Department of Civil Engineering, Tokai University, Japan
3
Civil and Environmental Engineering, Nagoya University, Japan
ABSTRACTThe partial floating sheet pile method is a steel sheet pile method combined of the end bearing sheet pile (longsheet pile) and the floating sheet pile (short sheet pile), which was developed as a countermeasure against land
subsidence when constructing embankments on soft ground. The authors conducted a three-dimensional (3D)
analysis to evaluate the usefulness of the PFS method during liquefaction and confirmed that it has the effect of
decreasing the ground settlement. It has shown the behaviour of the ground movement beneath the floating sheet
pile was also visualized in the 3D results. 3D analysis, its use is increasing with the spread of the software, but still
more limited than 2D analysis, and it is not easy to carry out analysis. Therefore, 3D analysis and 2D analysis
using the same program were performed, and the deformation state of the sheet pile wall was compared. As aresult, it was confirmed that the average behavior of the 3D behavior can be grasped by averaging the rigidity ofthe steel sheet pile over the cross section in 2D analysis.
Keywords: Numerical analysis, Retaining wall, Partial floating sheet-pile method, Liquefaction

INTRODUCTION

subsidence due to the earthquake when reinforcementby the PFS method compared to the noncountermeasure embankment has been reduced [2].
Nakai et al. (2021) reported the PFS method using 2D
numerical analysis assuming the actual ground, andreported the characteristics of ground deformationand the usefulness of a liquefaction countermeasuremethod by the PFS method [3, 4]. In Fujiwara (2021)[5], 3D analysis was carried out using the structural
cross section constructed by the PFS method, and the
behavior of the wall during an earthquake and the
behavior of the ground under the floating sheet pile
were confirmed.

The Partial Floating Sheet Pile Method
The steel sheet pile method has been developed asa construction method for temporary structures suchas earth retaining walls and temporary coffering walls.
But, in recent years, construction methods that areused for both temporary construction and permanent
construction have been developed, and their use as
permanent construction is increasing. One of them isthe Partial Floating Sheet Pile Method (PFS meth-od).
The PFS method is also a stress shut off method,
is a steel sheet pile method developed for the purposeof suppressing deformation of the surrounding
ground that occurs when constructing anembankment structure on soft ground. Figure 1 showsa construction image of the PFS method. The
structure is a combination of a long sheet pile
“conventional sheet pile” that is embedded to the
support layer and a short sheet pile “floating sheet pile”
that is embedded only halfway through the soft
ground. It’s economical because the number ofconventional sheet piles to be embedded up to the
support layer is reduced, and the workability isimproved that the interlocking length is also decreaseby the floating sheet piles are increased.
Technical note for the PFS construction method
was published by the PFS method association in 2005
[1], thereafter the number of construction cases isincreasing mainly in the Kyushu region. Kasama et al.
(2020) has reported that the subsidence of riverembankments damaged by the 2016 Kumamoto
earthquake, if an embankment does not liquefy, theFig. 1Model of PFS method.

In this paper, we report the result of examining to
reproduce the result of 3D analysis by 2D analysis.
Chapter 2 explains the conditions of the analysis
model. Chapter 3 shows the results of comparing the224

GEOMATE – Kyoto, Japan, 3-5 November 2021

results of 2D and 3D analysis, and Chapter 4 shows
the summary of this paper.

NUMERICAL ANALIYSIS MODEL
The program used for the analysis is LIQCA2D17[1, 6, 7]. Liqca is a numerical code developed basedon the effective stress analysis method, and it ispossible to determine the process of liquefaction anddeformation of the ground due to an earthquake, as
well as the dissolution process of excess pore waterpressure after vibration. The program is provided in
2D and 3D, and this paper uses the 2D code.
Numerical model for the PFS method

Fig. 2Concept of cross section area of a sheet pile.

Fig. 3

Concept of cross section area of the PFS
Method.

The PFS method has a shape that combines aconventional steel sheet pile and a floating sheet pile.
When creating an analytical model, the 3D model can
reproduce the steel sheet pile as expected, but the 2Dmodel cannot directly set the change in the length of
the sheet pile in the depth direction. In this paper, thePFS method was reproduced by the following method.
Firstly, as a precondition, in order to compare theresults of modeling the 3D analysis results under thesame conditions, the steel sheet pile model also useda plane strain element and an elastic model. As sameas the 3D analysis, the steel sheet pile was made into
one steel sheet pile with two elements theembankment side and the free ground side as shownin Figure 2. The values averaged from Eq. (1) was
adopted so that the physical property values of thesteel sheet pile would be equivalent in the model crosssection.
Esheetpile Isheetpile＝Emodel Imodel(1)

Steel sheet piles exist continuously in the depth
direction up to the bottom of the floating sheet piles
and the conventional sheet piles. The physicalparameters for these sections were determined to beequivalent to the rigidity of the steel sheet pile in across section with a width of 0.2 m and a depth of 1
m. In the cross section from the bottom of the floating
sheet pile to the conventional sheet pile, as shown in
Figure 3, one element has been a steel sheet pile andthe others have composed of the ground. In this paper,
the idea of averaging in consideration of the depth
direction (y-axis) has used, and the rigidity is reduced
as the distance between the columns becomes wider.
The elastic modulus (Es) of the steel material was
200G Pa. The Imodel can be obtained from the equation(BH3/12) in the case of the rectangular cross-sectionalarea, the Emodel in the model was calculated from theequation 1.
A list of analysis conditions is shown in Table 1.

Table 1 Numerical cases.

Note: Distance between a long sheet pile and the next long sheet
pile.

225

GEOMATE – Kyoto, Japan, 3-5 November 2021

The length of the short sheet piles of the PFS method
is set to 4 m, and which the number of short sheet
piles placed between the long sheet piles are changed

in 4 cases. In addition, with the conventional wall andwithout countermeasures are calculated 6 cases intotal.

Table 2 Numerical parameters.

Liquefaction layer, Dr = 40%

Non Liquefaction layer, Dr =90%

Cyclic elastic -plastic model
Initial void ratioe0
Unit weight of soil
γt
kN/m3
3
Satturated unit weight of soil
γsat
kN/m
Elements depthmCompression index
λSwelling index
κ
Normalized shear moduls
G0/σ'm0
Effective stress under OCR*
Coefficient of earth pressure atrest under ODR*
Permeability cofficient/ Unit
weight of water
Gravity

0.833
19.2
20.2
1.0
0.018
0.006
873
1.0

m/s/kN/m3gm/s

Effective stress
σ'v
Coefficient of earth pressure at
K0
rest
Phase transformaion stress
M*m
ratio
*
Failure stress ratio
Mf*
Harding parameter
B0

γsat
ν
σ'v

0.0 Permeability cofficient/ Unit

3

kN/m

weight of water
1.0
Bulk modulus of water
Elements depth
0.9090

B*1
Cf

Bulk modulus of water

Kf

Parameter of anisotropy
Tentative overconsolidation
ratio
Dailatancy parameter
Dailatancy parameter
Reference strain parameter,
Plastic
Reference strain parameter,
Elastic
Yield criterion

Cd

2000

OCR*

1.0

30
0
kN/m2

2.00E+06

P*

γ r
γE*r

a
b
c
φ
a
b
e0

33665
0.5
0.00
42
0.30
2.30
0.6830

ks

kN/m

2

2.00.E+04

Normal stiffness modulus

kn

kN/m2

2.00.E+08

Effective stress
Coefficient of earth pressure at
rest
Cohesion
Friction angle
Tension strength
Elements depth

σ'v

kN/m

2

K0

0
0

kN/m2
deg.
kN/m2
m

deg.

k/γw m/sec/kN/m3 6.73.E-07
Kf

kN/m2
m

2.00E+06
100.27-1.0.E+08
1

226

0
1.0
0
9.81

γt

3

kN/m

34.8

γsat

kN/m3

34.8

2

2.00E+06
1
5.34.E+07
2.75.E+07

e0
Kf

0.1
kN/m
m

λ
μ

Lamé constantsλμcase 1
5.3.E+07 2.8.E+07
case 25.3.E+07 2.8.E+07
case 3
1.3.E+07 6.9.E+06
case 44.4.E+06 2.2.E+06
case 5
1.5.E+06 7.6.E+05
case 64.4.E+05 2.3.E+05※To change for the each cases.

Joint element
Shear stiffness modulus

kN/m2

Elastic model
2
Effective stressσ'v
kN/m
Coefficient of earth pressure at
K0
rest
Permeability cofficient/ Unitk/γw m/sec/kN/m3
weight of water
2
Gravitygm/s

Unit weight of soil
2.5
1.5 Satturated unit weight of soil
Initial void ratio
0.002
Bulk modulus of water
0.01 Elements depth
Lamé constants
Von Mises Lamé constants

*

D0
n

20
0.33
50

kN/m

Sheet pile1.1220
1800

18

3

0.5

9.81 Initial void ratio

2

kN/m3

K0

2.17E-04 R-O parameter

Harding parameter
Harding parametercφ-R-O model
γtSatturated unit weight of soil
Paisson's ratio
Effective stress
Coefficient of earth pressure at
rest
Parameter of shear elastic
modulusCohesion
1.0 Friction angle

kN/m3

k/γw

Unit weight of soil

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 4

Analysis cross section.

Soil conditions

drainage boundary. A joint element is provided
between the sheet pile element and the groundelement in order to consider peeling and slippage.

The model ground consists of two layers, aliquefied layer with an upper layer of 8 m and a nonliquefied layer with a lower layer of 4 m as shown in
Figure 4. The liquefied layer is used the repetitive
elastoplastic model with a relative density of 40% of
Toyoura sand, and the non-liquefied layer is used an
RO model with a relative density of 90% of Toyoura
sand. The physical parameters are shown in Table 2.
These values were determined with reference to theresults of the non-drainage triaxial compression test.

Input motion
As shown in Figure 5, the wave with a maximum
acceleration of 9 m/sec2 at a frequency of 3 Hz isapplied for 10 seconds. In the model ground, such alarge load is applied in order to surely liquefy theground and confirm the behavior of the steel sheet
pile when an excessive force is applied.
For the dynamic analysis, the Newmark method isused, the coefficient β (0.3025), γ (0.6), the Reyleighdamping is initial stiffness dependence, and thecoefficient is 0.003. The incremental time interval
during analysis is 0.001 seconds.
In order to observe the extinction process of theexcess pore water pressure generated by the vibration,
the acceleration is maintained at 0 for 90 seconds after
the vibration is performed for 10 seconds.

Load conditions
In the half-section model, the assumed
embankment load is applied 3 m on the upper side, 13
m on the lower side, and 5 m in height, and the body
volume weight is 1.8 kN / m3. In the analytical model,a more simplified and evenly averaged load (q) is
applied as the nodal load. The model is a half-section,a load of 1/2q is applied to the central node.
Boundary conditions
The analysis cross section is shown in Figure 4. In
the model, the origin (0,0) on the XY plane is set as
the center point of the lower end of the half-section
model. The nodes (0, Y) are the central surface of theembankment, and are used as the hinge fulcrum inconsideration of line symmetry. The nodes (X, 0) are
the lower end of the model and are the fixed point,
and the nodes (40, Y) are the hinge fulcrum. These
three sides are non-drainage boundaries. The watertable is at the same depth as the ground surface, andthe ground surface is a drainage boundary.
The boundary surface between the steel sheet pileelement and the ground element is also a non-Fig. 5227

Input motion.

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 6

Horizontal displacement in 2D.

x-axis in 2D, 3D [m]2.0

Fig. 7

Case 3_3D
Case 3_2D

1.5

Case 4_3D
Case 4_2D

Comparison of 2D and 3D PFS results.

Case 5_3D
Case 5_2D

Case 6_3D
Case 6_2D

1.0
0.5

0.0
-15

Fig. 8-10-50y-axis in 3D [m]

5

10

15

Comparison of displacement of steel sheet pile on x-y plan at depth of 4m.

ANALYSIS RESULTS

to 6 with broken lines and the 3D results with rigid
line. It is confirmed that the deformations of Case 0
and Case 1 showed the same tendency, and PFS
analysis is performed. The 2D results tended toapproach the 3D results as the spacing between thelong sheet piles increased. In this method, thedeformation deeper than the short sheet pile in Case 6
was the same, but the other cases showed a tendency
that the amount of deformation in 2D analysis was
small overall.
One of the problems in 2D analysis is that it is notpossible to express the slip-through of the groundunder the short sheet pile. Figure 8 shows the resultof this 2D analysis is superimposed on the
deformation diagram [3] of the sheet pile at thebottom of the short sheet piles. At the boundary
between the sheet pile and the ground, it is found thatthe values were equivalent to the average enemy
behavior of one unit. Figure 9 shows the relationshipbetween the deformation by 2D analysis on the

Horizontal displacements
The horizontal displacement of the sheet pileelement in each case is shown in Figure 6. In Case 0,
where no countermeasure is taken, the maximum
displacement occurs near a depth of 2 m. Case1 has
consisted only a long sheet pile and has the smallest
horizontal displacement. In Case 2 with only a shortsheet pile, the displacement of the ground element
deeper than 4 m without the sheet pile element is thesame, but at shallower then 4m with the sheet pileelement, the sheet pile shows rigid deformation, andthe maximum horizontal displacement occurs on the
ground surface. Cases 3 to 6 are PFS cases as a
combination of long and as the distance between thelong sheet piles increases, the deformation of Case 2
approaches.
Figure 7 shows the 2D analysis results of Cases 3
228

GEOMATE – Kyoto, Japan, 3-5 November 2021

vertical axis and the average of the deformation by
3D analysis on the horizontal axis, at a depth of 4m.
From the 2D analysis, the result with high correlationof the amount of deformation at the lower end of theshort sheet pile was shown.

expansion of applicability and assessment of seismic
performance of PFS method. Authors express our
deep appreciation for their advices and discussion.

REFERENCES[1] PFS method association, Technical note for the
PFS method, 2005, in Japanese.
[2] Kasama K., Yamamoto S., Ohno M., Mori H.,
Tsukamoto S. and Tanaka J., Seismic damage
analysis on the river levees reinforced with steel
sheet pile by the 2016 Kumamoto earthquake,Japanese Geotechnical Journal, Vol.15, No.2,
2020, pp395-404.

Fig. 9[3] Nakai, K., Noda, T., Taenaka, S., Ishihara, Y.
and Ogawa, N., Seismic assessment of steel sheet
pile reinforcement effect on river embankment
constructed on a soft clay ground, Proceedings ofthe First International Conference on Press-in
Engineering, 2018, pp.221-226.

Horizontal deformation distribution at thebottom of floating sheet pile.

[4] Nakai, K., Fujiwara, K. and Ogawa, N., Seismic
performance evaluation of PFS method by soilwater coupled finite deformation analysis,
Proceedings of 11th International Conference on
Geotechnique, Construction Materials andEnvironment, GEOMATE 2021.

CONCLUTIONS
Aiming at simplification of the design study of the
PFS method, which is a wall body that combines along sheet pile and a short sheet pile, we examined
the reproduction by 2D analysis.
It was confirmed that the rigidity of the elementsfrom the depth deeper than the short sheet pile to thelower end of the long sheet pile can be expressed by
the method of averaging the rigidity of the sheet pileby the area to grasp the average behavior of the 3D
behavior.
In this paper, the influence of the length of theshort sheet pile is not considered. We would like tocontinue to study the effects of the ratio of long sheet
piles to short sheet piles, consider the efficient
application of the PFS method, and contribute to its
widespread use.

[5] Fujiwara K., Ogawa N. and Nakai K., 3-D
Numerical Analysis of Partial Floating Sheetpile Method as Countermeasure for liquefaction.
Journal of JSCE, Vol. 9, 2021, pp.138-147.
[6] 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.
[7] Oka F., Yashima A., Tauchi Y. and Yamashita
A., A cyclic elasto-plastic constitutive model forsand considering aplastic-strain dependence ofthe shear modulus, Geotechnique, Vol.49, 1999,
pp.661-680.

ACKNOWLEDGMENTS
This research was supported by the IPA- TC3(Chair: Prof. Jun Otani); Technical committee on229

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

USAGE LIMIT OF LUNAR SOIL SIMULANT
Keito Endo1, Ryoichi Fukagawa2 and Taizo Kobayashi2
1

Graduate School of Science and Engineering, Ritsumeikan University, Japan;
2
College of Science and Engineering, Ritsumeikan University, Japan
ABSTRACTIn recent years, lunar exploration has been given higher priority than other space exploration programs. To set up
a base on the Moon, various countries have focused on grasping information on the geotechnical characteristics oflunar soil. The Moon surface is covered by lunar soil; it is called the “Lunar Regolith.” The Lunar Regolith was
brought back from the Moon in the Apollo program. However, soil samples are scarce and thus very precious;
therefore, some researchers have developed several types of Lunar Regolith Simulants (RS). We used one of thesesimulants, FJS-1 [1], in our study, and we found that RS used often in experiments have different physicalproperties than those of the new RS. In this study, we clarified the following issues. The relationship between RS
usage history and self-standing height was investigated by repeatedly compacting unused RS and measuring thechange in self-standing height of specimens. In addition, we performed grain-sized analysis and shapemeasurements of RS to investigate the cause of the decrease in self-standing height. We estimated the apparent
cohesion of RS particles using Rankine’s earth pressure theory from the test results and proposed a usage limit for
RS based on this value.
Keywords: Lunar Regolith Simulant, Apparent cohesion, Screw auger, Excavation, Self-standing height
INTRODUCTIONLUNAR REGOLITH AND ITS SIMULANTIn recent years, the scope of human activity has
expanded beyond Earth to include space. Amongthem, human interest in the Moon is high, and thepossibility of building a lunar base for further
research is increasing. As a result, it is becoming
increasingly important to understand the mechanicalproperties of lunar surfaces. In the FukagawaKobayashi laboratory, we developed a method toestimate the soil strength parameters from the
excavation response of a screw auger. In this study,
we used a lunar RS. We found that the usage history
of RS had a significant effect on the reproducibilityof the experiment. Figure. 1 shows the borehole
formation after screw auger excavation. In the case of
the old RS, the holes completely collapsed, but in thecase of the new RS, the holes are still clearly visible.
We attribute this difference to the scattering of fineparticles and the change in particle shape during theprocess of model ground preparation. In this study,
we examine the effect of RS usage history on theproperties of RS and propose a usage limit for RS.

The lunar surface is covered with a deposit offine, angular particles called the Lunar Regolith. The
Lunar Regolith is formed by the continuous impact oflarge and small meteoroids and exposure of cosmic
rays from the sun and stars [2]. Figure. 2 shows amicroscopic photograph of the Lunar Regolith [3].
The Lunar Regolith is loosely deposited on thesurface of the Moon, but the relative density rises
sharply at a depth of approximately 5 cm. The relative
density exceeds 90% at about 30 cm and remains thesame at greater depths [4]. Since the Lunar Regolithbrought back from the Moon by the Apollo programis rare and valuable, several kinds of RS, a simulated
Lunar Regolith, have been developed. In our previous
research, we also used RS (FJS-1[1]). Table 1 shows
the chemical composition of the Lunar Regolith andits simulant [5], [6]. FJS-1 uses lava basalt as its mainmaterial, mixed with pulverized olivine and othermaterials to match its chemical composition andphysical properties such as particle size and density.

Fig. 1 Borehole formation after screw auger
excavation for model RS grounds

Fig. 2 Microscopic photograph of Lunar Regolith [3]230GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1 Chemical composition of Lunar
Regolith and its simulant[5], [6]Chemicalcomposition
SiO2 (%)
TiO2 (%)
Al2O3 (%)
Cr2O3 (%)
Fe2O3 (%)
FeO (%)MnO (%)
MgO (%)CaO (%)Na2O (%)
K2O (%)
P2O3 (%)
Total (%)

Apollo16
sample
45
0.54
27.3
0.33
5.1
0.30
5.7
15.7
0.46
0.17
0.11
99.9

Apollo15
sample
46.8
1.4
14.6
0.36
14.3
0.19
13.0
10.3
0.33
0.19
0.19
99.4

FJS-1
46.8
1.6
17.8
0
3.8
8.3
0.3
3.9
11.2
3.7
1.0
0
98.4

Fig. 4 Results of grain-size analysis for old and new RS
Excavation test
The model ground of the RS created in a circularsoil bin was excavated using a screw auger. The
excavation test was carried out in a speciallycontrolled manner because we wanted to estimate thestrength parameters of soil for every 25 mm depth. A
cycle of excavation consists of 25 mm rotary
excavation and 5 second rotation without excavation,
which is the process of no-axial excavation androtation. This cycle was repeated for the depth
direction. Details of the test can be referred to withinreference [7].
Figure. 1 shows the drilled hole after the
excavation test. The other excavation conditions are
as follows: Digging speed v=4 mm/s, rotational speed
=0.5 (r/s). The model ground was prepared with90% relative density to reproduce the lunar ground.
The test results are shown in Fig. 5. From the test
results, the excavation resistance of the new RS was
higher than that of the old RS. This is thought to be
due to the apparent cohesion. Apparent cohesion was
also considered to have a strong effect on the shapeof the borehole.

COMPARISON OF THE PROPERTIES OF
OLD AND NEW RS
In this section, we compare the properties of theold and new RS, as shown in Figure. 1. Although theold RS is a used RS, we can only express it
qualitatively because we cannot quantitatively
express its usage history. The old RS was repeatedlyused for at least one year. To compare the old and newRS, we conducted grain-size analyses and excavation
tests.
Grain-size analysis
For the grain-size analysis, we used a Shimadzu
SALD-2300 laser diffraction particle size analyzer, as
shown in Figure. 3. This analyzer is effective fortesting rare and valuable materials such as RS
because it can perform grain-sized analysis with asmall amount of soil sample. The results of the grainsize analysis are presented in Fig. 4. The old RS lostthe fine grain content contained in the new RS. This
result indicates that fine particles were scatteredgradually during the repeated compaction process.

Fig. 3 Laser diffraction particle size analyzer
SALD-2300

Fig. 5 Excavation resistance duringcontrolled excavation for old and new RS
231

GEOMATE – Kyoto, Japan, 3-5 November 2021

EXPERIMENTS TO INVESTIGATE THE
EFFECT OF USAGE HISTORY ON
BEHAVIORS OF RS
Self-standing test
Based on the results of the excavation tests, wehypothesized that the apparent cohesion of the RS
would provide an indication of the usage history of
the RS. In this section, we present a specimen of RS
and compare its self-standing height with the physicalproperties of RS. Grain-size and particle shapeanalyses were used to evaluate the physical properties.
For the above purposes, we conducted a series of selfstanding tests using the two-split mold shown in
Figure. 6. Matsushima and Ishikawa (2014) originallyproposed the idea of self-standing height [8]. They
made the self-standing condition of the specimen bypushing the sample out from the sampling tube
upward. They investigated the relationship between
the void ratio and self-standing height for new FJS-1
specimens. We believe the method to use the split
mold is much better than the method of pushing thesample out from the sampling tube, because theformer can avoid the influence of friction between the
inner side of the sampling tube and the soil sample.
The mold in Figure 6 is 10 cm high and 5 cm indiameter, and we compact RS in this mold. An
overview of the test method is shown in Fig. 7. If thespecimen does not collapse at all, it is assumed to be
self-standing. If the specimen collapses even slightly,
it is assumed to have collapsed. The initial height ofthe specimen corresponded to the compaction height.
If the specimen did not collapse after the mold wasremoved, the self-standing height was the same as theinitial height of the specimen. Fig. 8(a) shows anexample of a self-standing case, and Fig. 8(b) shows
an example of a collapsing case.
A series of tests were conducted in the following
manner. First, we compact the material in the mold ata height of 10 cm using a new RS. The relative density
was found to be 90%. After that, we slowly removed
the split mold to determine whether the specimen
could stand on its own. The specimen was repeatedly
made until it did not stand on its own at a compaction
height of 10 cm. When it does not stand on its own,
the compaction height is decreased by 1 cm, and theprocess is repeated until the compaction height is 1
cm. The relative density was set at 90% to simulate
the lunar ground and to accelerate the degradation
rate of the RS. A small amount of soil sample wastaken at each self-standing height for grain-size
analysis and particle shape measurement.

Fig. 6 Split mold for estimating self-standing heightof RS specimens

Fig. 7 Procedures of tests for definition of self-standingheight(a) Self-standing case(b) Collapsing case111
Fig. 8 Definition of self-standing and collapsing
conditions
Grain-size analysis
The results of the grain-size analysis are shownin Figure. 9. Some results on the self-standing height
have been omitted from this figure for clarity. In
addition, Fig. 10 shows the amount (%) of particles
less than 10 μm that pass through. As the selfstanding height decreased, the fine grain content in
the RS decreased.
232

GEOMATE – Kyoto, Japan, 3-5 November 2021

approximately 100 samples gives a steady estimation
result, so that the number of samples in this analysis
is selected as 150 at each height. Fig. 13 shows therelationship between the mean and standard deviationof the coefficient from unevenness and each selfstanding height. Both data fall within the small range,
and there is almost no relationship with the selfstanding height. This means that the coefficient of FU
does not change significantly with repeatedcompaction.

Fig. 9 Results of grain-size analysis

Fig. 11 Enlarged photo of RS

Fig.10 Relationship between Particles passing below
10m and self-standing height
Particle shape measurement
Coefficient of form unevenness
To investigate the change in particle shape due tocompaction, we measured the coefficient of form
unevenness [9] and maximum length at each selfstanding height. Magnified photographs were taken
and measured using ImageJ software [10]. The
enlarged photograph used in the analysis is shown in
Figure. 11. Equation (1) shows the definition for thecoefficient of form unevenness (FU) [8].
4 A
(1)FU = 2l
FU is calculated from area A and perimeter l ; usingFU, the particle shape can be expressed as 0–1. The
closer FU is to 1, the more the particle is a perfectcircle, and the more uneven it is, the smaller the value.
The smaller the particle, the less useful the value is;thus, FU was obtained by omitting particles with anarea of less than 20 μm2. The relationship between thenumber of samples and the average value of thecoefficient of FU is shown in Fig. 12. It can be seenfrom this figure that the analysis based on more thanFig. 12 Effect of sample numbers on coefficient ofform unevenness

Fig. 13 Average and standard deviation of thecoefficient form unevenness233

GEOMATE – Kyoto, Japan, 3-5 November 2021

Maximum lengthof times specimen-making increased, the selfstanding height decreased almost linearly. This may
be due to the poor interlocking of particles due to thescattering of fine particles caused by the compactionof RS, as shown in Figure 10.

Next, the maximum length was considered. The
maximum length is the value of the longest side
length of the rectangle circumscribed to each particle(see Figure. 14). The relationship between maximum
length and self-standing height is shown in Fig. 15.
This graph shows that the maximum length tends todecrease as the self-standing height decreases, except
for the 10 cm self-standing height. This means that it
is possible that repeated compaction results in the
failure of soil particles.

Fig. 14 Definition of maximum length ofparticle

Fig. 16 Relationship between number of times for
specimen-making and self-standing heightDISCUSSION
Causes of degradation of RS
As shown in Figures 1 and 5, the change in themechanical properties of RS due to usage history has
a significant effect on the reproducibility of theexperiments. Let us consider the elements that cause
the degradation of RS in this section.
As shown in Fig. 10, the fine grain content of RS
decreased as the specimen was repeatedly formed bycompaction. Dry and fine particles easily flew away
during this compaction process. In addition, Figs 13
and 15 show that the coefficient of FU does notchange significantly, and the particle size decreases
with the progress of the degradation of RS.
Considering these results, the degradation of RS
is mainly caused by the decrease in the fine grain
content. The degradation of RS results in a decrease
in self-standing height. The self-standing height isclosely related to the apparent cohesion, “c.”Therefore, let us consider the relationship between
self-standing height and apparent cohesion of RS.

Fig. 15 Relationship between maximum length andself-standing height
RELATIONSHIP BETWEEN USAGEHISTORY OF RS AND SELF-STANDING
HEIGHT
From the above test results, we can recognize therelationship between the self-standing height and thephysical properties of RS. However, the influence ofusage history on the self-standing height of RS has
not yet been clarified. In this section, a series of tests
using the two-split mold were performed again to
investigate the relationship between the self-standingheight and usage history. The number of times the
specimen was made was used as an indicator of theusage history, and the relationship between the selfstanding height and the number of times for making
the specimen is shown in Figure. 16. As the number

Estimation of the apparent cohesion of RS
The apparent cohesion of the RS can be estimatedusing Rankine's earth pressure theory. According tothis theory, soil materials having “c” have selfstanding height zc expressed by Eq (2).
(2)

234

GEOMATE – Kyoto, Japan, 3-5 November 2021

where c is the apparent cohesion, t is the unit weightof the soil sample, and  is the internal friction angle.
The apparent cohesion, “c,” can be obtained bysubstituting t = 1.99 (gf/cm³),  = 31.7 (deg) [11],and each self-standing height value z c into Eq (2).
The estimation results are presented in Figure. 17.

(2) To obtain quantitative data on use history, selfstanding tests were conducted. As a result of thegrain size analysis and particle shapemeasurement for each self-standing height, it
was found that as the compaction was performed
many times, the fine particles scattered and theparticles became smaller while maintaining acertain shape, and the self-standing height(apparent cohesion) of RS decreased because of
the poor interlocking of particles.
(3) To investigate the relationship between the selfsupporting height and the number of times the
specimen was made of, self-standing tests wereconducted again. Consequently, a linear
relationship was found between the two.
(4) From the results of the self-standing tests, wederived the apparent cohesion of the RS. The ｓ
ｙapparent cohesion of RS was compared withthat of the Lunar Regolith, assuming a lunar
surface, and we confirmed that RS can be used
as a sample up to a self-standing height of 12 cm.
REFERENCES

Fig. 17 Relationship between apparent cohesion andself-standing height[1] Kanamori, G., Udagawa, S., Yoshida, T.,
Matsumoto, S., and Takagi, K.: Properties ofLunar Soil Simulant Manufactured in Japan, Proc.
of 6th Int. Conf. on Eng. Construction andOperations in Space, ASCE, pp. 462-468, 1998.
[2] Heiken. G. H., Vaniman. G. H. and French. B. M.:
Lunar Sourcebook, A User's Guide to the Moon,
New York, Cambridge University Press, p. 285,
1991.
[3] Heiken, G. H., et al.: op. cit., p. 297, 1991.
[4] Heiken, G. H., et al.: op. cit., p. 499, 1991.
[5] Heiken, G. H., et al.: op. cit., p. 349, 1991.
[6] Kanamori, G. and Shinoda, Y.: Basic Research on
the Production of Construction Materials from
Lunar Resources, Proc. 34th ISAS Space Energy
Symposium, 6th March, 2015.
[7] Kurihara, K. and Fukagawa, R.: A Study on
Estimation of Mechanical Properties of Soil
Using
Construction
Information
duringExcavation by Screw Auger, p. 90, 2018.
[8] Matsushima, T. and Ishikawa, T.: Particle
Grading Effect on Mechanical Properties of Lunar
Soil Simulant FJS1, Proc. Earth & Space 2014,
ASCE, pp. 60-68, 2014.
[9] Yoshimura, Y. and Ogawa, S.: A Simple
Quantification Method of Grain Shape ofGranular Materials Such as Sand, JSCE , pp. 95103, 1993.
[10] Rasband, W.S., ImageJ, U. S. National
Institutes of Health, Bethesda, Maryland, USA,
http://rsb.info.nih.gov/ij/, 1997-2012.
[11] Kurihara, K. and Fukagawa, R.: op. cit., p.88,
2018.
[12] Heiken, G. H., et al.: op. cit., p. 506, 1991.

Proposal of the usage limit of RS
We compare the apparent cohesion of the Lunar
Regolith brought back by past Apollo missions tosuggest the usage limit of RS. The cohesion of the
Lunar Regolith is expected to be 0.1-1 (kPa) [12].
Using this intermediate value as a reference, theapparent cohesion that RS should have is 0.55 (kPa).
From Figure. 17, the corresponding self-standingheight against c=0.55 (kPa) is approximately 3 cm;
however, we must consider the difference in gravity
between the Earth and the Moon. If the gravity is onesixth, zc on the Moon surface becomes 6 times 2 cm,
which is 12 cm. The number of times the specimen
was made corresponding to the value of 12 cm wasestimated by extrapolation to 10-12 cm, as shown in
Fig. 16. Although we must consider the difference inthe compaction process in self-standing tests and inother tests using model ground, we think that a selfstanding height of 12 cm is an effective sign for theusage limit of RS.
CONCLUSIONSIn this study, we examined the effect of RS use
history on the properties of RS and proposed a usage
limit for RS. The main conclusions are as follows:(1) First, we compared the grain-size analysis andexcavation resistance of the RS with varying use
histories. The results showed that the RS used
had fewer fine grains and less excavation
resistance than the new RS.
235

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

MONITORING DEFORMATION WITH WIRELESS SENSOR
NETWORK AND EVALUATION OF MECHANICAL STABILITYBY FDM SIMULATION FOR SLOPE
Satoshi Sugimoto1 and Yoichi Ishizuka1
1

Faculty of Engineering, Nagasaki University, Japan
ABSTRACTIn recent years, torrential rains have occurred frequently due to abnormal weather and the rainy season, andmany slope failures have occurred accordingly. When it rains, the infiltration of rainwater and the inflow ofgroundwater from the upstream may reduce the shear resistance in the slope and the infiltration water pressure
may lead to slope failure. In addition, the change in groundwater is large on the natural slopes, and the amount ofgroundwater inflow from the upstream increases, which may lead to slope failure. The purpose of this study is toreproduce and stabilize the groundwater level rise on the target slope by numerical analysis, and to consider theeffect of the hydraulic conductivity on the groundwater level rise and the safety factor. And we proposed themethod of observing the penetration of groundwater due to rainfall and the change of the groundwater depth using
a wireless sensor network for sedimentation grounds Through the observation, we clarified the progress of theslope deformation and correlations between the rainfall, fluctuation of groundwater depth and the slope
deformation.
Keywords: Wireless sensor network, Monitoring, Slope, FDM

INTRODUCTION

previous studies [1]-[3] on an artificially reclaimed
slope in Sasebo City, Nagasaki Prefecture, Japan. The
purpose of this study is to quantitatively clarify the
progression of slope deformation through continuous
data collection and analysis and to clarify thecorrelation between rainfall, changes in groundwater
depth, and the amount of ground deformation.

In recent years, due to the tight financial cost of
local administration in Japan, there has been a needfor unmanned management of local social
infrastructure facilities. On the other hand, even withthe recent development of infrastructure, many slope
failures and landslide accidents caused byearthquakes and heavy rains have occurred in various
regions. As a countermeasure against such slope
disasters, it is thought to be effective to constantly
monitor the signs of the disaster before it occurs in thepredicted disaster area, and to implement
countermeasures such as prioritizing high-risk areas
for construction. However, most of the monitoring
systems in use have been wired informationcollection systems, which are difficult to install in
undeveloped areas such as slopes. In addition, highprecision and expensive sensors are often used formeasurement, which limits the number ofmeasurement points due to the cost of the system andthe difficulty of installation. In addition, the difficultyof securing a power source to operate the system fora long period has been a major obstacle to monitoringin undeveloped areas such as slopes. As a result, thelimitation of the amount of acquired measurement
data and the localization of measurement locations
have become problems for the data that are effective
in determining the priority of proactive measures as
described earlier.
In this study, slope deformation and changes ingroundwater depth due to rainfall were observed
using a wireless sensor network developed in

MEASUREMENT BY REMOTE
MONITORING SYSTEM
Outline of Target Slope
The target slope shown in Fig. 1 is a former stableindustrial waste disposal site located in Sasebo City,and consists of sediment-dominated industrial waste
layers, talus deposit layers, and bedrock, in orderfrom near the ground surface. Heavy rains in 2013
caused deformations from the middle to the lower
part of the slope. In addition, a large-scale crack
Upper area

Middle area

Lower
area

Fig. 1 Geological cross-section chart.
236

Soil cover
Waste layer
Talus deposits
Bedrock

GEOMATE – Kyoto, Japan, 3-5 November 2021

occurred near the middle section. As a result, theslope has been gently graded by cutting back the soil
and intercepted by covering the surface layer with 0.5
m of soil. The current slope profile has a maximum
slope gradient of about 35 degrees and a height ofabout 40m.

2021, the result of k-12 shown in Fig. 2, which has anespecially large amount of deformation, was taken
and analyzed during the period. Figure 3 shows the
cumulative angle. Figure 4 shows the cumulative
angle for each month. In the case of k-12, a large
amount of deformation was observed in 2018 and2020, and the amount of deformation increased over
the years, indicating that the deformation in the
direction perpendicular to the slope was large. The
cumulative amount of deformation is represented by
two-dimensional vectors for k-1 to k-13, and thevisualization of the slope movement is shown in Fig.
5. From this figure, the relative size of theOverview of The Monitoring System

Cumulative angle (deg)Figure 2 shows the various monitoring
instruments and their installation locations. The
structure of this monitoring system is as follows [4],[5]. (1) observation data is acquired by using
observation devices such as a water level meter, rain
gauges and tiltmeters, (2) data is collected by routers
and a coordinator in the field through a wirelesssensor network (hereinafter referred to as WSN), and(3) data is transmitted to the server through a mobile
communication network. The monitoring can be
carried out remotely, which enables to ensure thesafety of the target slope and to analyze the current
status in real-time by analyzing the sensing data. The
water level meter used is measuring at a depth ofabout 10 meters from the ground surface. The water
depth can be measured at a minimum depth of 0.01m
and a maximum depth of about 20m. In addition,
tiltmeters for detecting slope deformation are
installed at 0.5 m below the ground surface at 13
locations (k-1 to k-13) on the target slope. The
tiltmeter has a measurement range of -30 degree to+30 degree with a resolution of 0.02 degree.

※2018/9/27より計測開始

Fig. 3 Change in cumulative angle over time.
X direction
y軸(度)(deg)direction (deg)Y x軸(度)-2.5

Jan. 20182018年10月

-0.5
0.5
1.5

Monitoring of Slope Surface Deformation by
WSN

k-12

-1.5

Mar. 20202020年3月

Apr. 20202020年4月

May 2020
2020年5月

2.5
1.5

From the data of June 13, 2017, to January 31,

0.5

-0.5

Fig. 4 Trajectory of cumulative angle by month.

Tiltmeter
X axis (+)
Y axis (+)
Coordinator
Porewaterpressure gauge

50m

Fig. 5 Deformation vector estimated frommeasurement data.

Fig. 2 Layout of installed instruments.

237

-1.5

GEOMATE – Kyoto, Japan, 3-5 November 2021

Variation of Groundwater Depth with Rainfall

地表面からの地下水位(m)・地下水位 ―降雨量
Groundwater depth
Hourly rainfall

2020/6/19~2020/8/31
June 2020 ~ Aug. 2020

1

50

2
40

3

4

30

5

20

6
10

7
8
2020/6/8

0

2020/6/28

2020/7/18

2020/8/7

2020/8/27

Fig. 6 Relationship between groundwater depth andrainfall.
water level (m)Rise of ground
上昇水位(m)

3

2017

2018

2019

2020

2.5

2
1.5

1
0.5

0
0

50

100

150

総降雨量(mm)Total
rainfall (mm)

200

250

Fig. 7 Relationship between rainfall and water level
rise.
150

・2018 ・2019 ・2020

120

Cr

90
60
30
0
4

4.5

5

5.5

6

6.5

降雨前の地表面からの地下水位(m)Ground water depth (m)Fig. 8 Relationship between groundwater depth
before rainfall and rainfall coefficient.
1.2
tilt angle (deg)Cumulative
変状量（度）The relationship between rainfall andgroundwater depth was analyzed using the data from2017 to 2020 for a water level meter installed at thelower part of the slope shown in Fig. 2. Figure 6
shows the relationship between rainfall andgroundwater depth in 2020. Each color band in thisfigure means the period during which the increase anddecrease of the groundwater depth due to one rainfall
was confirmed. Figure 7 shows the relationshipbetween rainfall and groundwater depth, and also
shows the period from 2017 to 2020. The
groundwater depth did not decrease every year, but it
can be regarded as an overall decreasing trend during
the four years from 2017 to 2020. This may indicatethat the permeability of the layer where groundwaterflows is increasing. It is also possible that some of therainfall does not infiltrate into the ground but flows
out to the surface. The relationship between therainfall coefficient Cr [6] and the groundwater depth
from the ground surface before rainfall is shown in
Fig. 8. Cr is a parameter defined by Sugiyama et al.
This is the percentage of the value obtained by
dividing the amount of rainfall in a certain period by
the amount of change in the groundwater depth.
Figure 8 shows the results of the analysis of therelationship between Cr and the groundwater depth
before rainfall. The rainfall coefficient Cr is the ratioof the amount of rainfall to the increase in the
groundwater depth after the rainfall. In thedescending curve, the higher the groundwater depth
from the ground surface before rainfall, the smaller
the rise in groundwater depth. In the rising curve, therise in groundwater depth was small despite a large
amount of rainfall. This may be since rainfall
occurred before the water depth dropped to a steadystate in response to the preceding rainfall, which may
have affected the change in the rising water depth. It
is expected that the correlation between rainfall andgroundwater depth changes will be clarified byaccumulating more data.

60

1時間降雨量(mm/h)
rainfall (mm/h)
Hourly0Ground water depth (m)deformation can be confirmed. It is considered thatthe downward deformation of the slope is linked tothe mid-slope deformation. On the other hand, it isalso possible that the lower part of the slope appears
to be stationary due to the parallel movement of theslope, while the lower part is moving. A large amountof deformation in k-11 and k-12 can be explained by
the fact that they are located near the slope where thefailure is actually in progress. From these results, it isconsidered that the local deformation trend of theslope can be captured in a plane.

1

X axis2018 x軸
axis
2019yY軸

Y axis2018 y軸X axis2020 x軸

X axis2019 x軸
Y axis2020 y軸

0.8
0.6
0.4
0.2
0
6500

6700

6900

7100

7300

7500

Cumulative 水位上昇累積度数(m*h)
frequency of water level rise (m*h)

Fig. 9 Relationship between cumulative degree ofwater level rise and slope angle.
238

GEOMATE – Kyoto, Japan, 3-5 November 2021

Relationship between Groundwater Depth
Change and Slope Deformation

focusing on the conditions of slope deformation,
rainfall and groundwater depth. Slope stability
analyses based on the shear strength reduction
method [7] were conducted under different conditions
for the constituent geological features of the slope.
The simulation flow can be divided into four majorparts: (1) creation of the slope model and setting ofphysical properties, (2) reproduction of the steadystate of groundwater flow, (3) reproduction of rainfall
and groundwater flow, and (4) determination of slope
failure by gravity analysis.

The relationship between the groundwater depth
and the amount of slope deformation is shown in Fig.
9 which shows the cumulative frequency of waterlevel rise and the inclination angles of x and y axes
from June to August in the above period. The
cumulative frequency of water level rise (m*h) is thefrequency of water level rise by accumulating the
groundwater depth from the basement rock for acertain period. The results show that the x-axis
deformation was large in 2018 in the direction
perpendicular to the slope, while the y-axis
deformation was large in 2020. This indicates thetendency that the direction of easy movement differsfrom year to year. In addition, it was confirmed thatthe amount of deformation was excessive even if thevalue of the cumulative frequency of water level rise
was relatively small.

Rain intensity

EVALUATION OF SLOPE STABILITY BY
NUMERICAL SIMULATION
The finite difference method and the finite
element method are typical numerical analysis
methods that consider the target ground as acontinuum. The Finite Difference Method (FDM) is
more advantageous than the Finite Element Methodin performing stress-deformation calculations at eachtime increment based on explicit formulations
because the constitutive laws are easier to formulate[7]-[9]. In this study, we used FLAC [10], a largedeformation finite-difference analysis code based on
the explicit method developed by P.A. Cundall et al.
FLAC has the capability of performing seepage
analysis for porous media and can be used
independently of stress-deformation analysis. FLAC
can also perform coupled stress-osmosis flow
analysis. A two-dimensional slope model wasdeveloped to perform elasto-plastic FDM analysis

Location of groundwater level monitoring
Ground water inflow

Soil cover
Waste layer
BedrockEmbankment
Talus deposits 1
Talus deposits 2
Talus deposits 3

DrainageinterfaceFig. 10 Cross-section of the analysis model.

Fig. 11 Observation data of rainfall and groundwater
depth.

Table 1 Physical properties.

Dry density

Shearresistanceangle

Cohiesionr d (kg/m3)

f (deg)

2

2300
1700
1700
1700
1400
1400
2300

35
35
35
37
15
-

Layer type

Poisson's
ratio

Deformation
modulusCoefficient of permeability
Case1

Bedrock
Talus deposits 1
Talus deposits 2
Talus deposits 3
Waste layer
Soil cover
Embankmentc (kN/m )n (-)

E(MN/m )

0
0
0
8
10
-

0.15
0.3
0.3
0.3
0.3
0.3
0.15

1000
300
300
300
200
200
300

239

2

Case2

Case3

k (m/s)5.0×10-7-5
5.0×10
5.0×10-5-5
5.0×10-5
3.0×10
5.0×10-7-5
3.0×10

5.0×10-7-5
5.0×10
2.5×10-2-2
2.5×10-5
3.0×10
5.0×10-7-5
3.0×10

5.0×10-7-5
5.0×10
2.5×10-2-2
1.0×10-5
3.0×10
5.0×10-7-5
3.0×10GEOMATE – Kyoto, Japan, 3-5 November 2021

Outline of The Slope Model

Reproduction of Steady Groundwater Flow and
RainfallTo reproduce the slope of interest, a numericalmodel with the same dimensions of 140 m in depth
and 61.7 m in height (Fig. 10) was created. The
physical properties used in the analysis were set basedon previous studies [11], [12], as shown in Table 1.
Figure 11 shows the groundwater depth and rainfall
observed in the field shown in the previous section. It
can be seen that the groundwater depth on the target
slope rises by about 1 to 3 meters during a few tens of
millimeters of rainfall down the slope. In addition, thegroundwater depth fluctuates immediately after
rainfall, indicating a sensitive response. To reproduce
this situation, the following three analyses wereconducted: Case 1: analysis of input conditions basedon past geotechnical investigations, Case 2: analysis
assuming that groundwater flowed more easily in thecliff cone sediment layer around the base surface, andCase 3: analysis assuming that permeability at the endof the slope became worse in addition to Case 2. The
effect of the permeability of the cliff cone sedimentlayer on the groundwater level rise and the safety
factor was examined.

Groundwater flow was applied from the high sideof the slope to simulate a steady groundwater surface.
Then, rainfall intensity of 15 mm/h and total rainfallof 150 mm were applied from the slope surface toreproduce the slope with rainfall. The groundwater
inflow from the high side of the slope was also
increased with rainfall. Figure 12 shows therelationship between rainfall and groundwater depth.
Comparison with Fig. 11 shows that the amount ofgroundwater level rise and the groundwater level rises
immediately after rainfall was reproduced in cases 2
and 3.
Evaluation of Slope Stability by Gravity Analysis
In this study, the overall safety factor was
calculated using the shear strength reduction method.
The effect of the increase in groundwater inflow fromthe high side of the slope on the safety factor was also
confirmed. Figure 13 shows that a slight decrease in
the hydraulic conductivity at the end of the slope
significantly reduces the amount of groundwater
inflow required for the safety factor to fall below 1.0,
which increases the risk of slope failure.
CONCLUSIONSIn this study, the ground deformation behaviorof the slope was analyzed by monitoring the slope
using WSN. In addition, it was clarified that thedirection of slope deformation is not necessarily
uniform even if the water depth changes to the same
extent from year to year.
Numerical simulations based on the monitoringdata reproduced the rapid rise of groundwater after
rainfall due to the formation of localized highpermeable zones in the layers forming the
groundwater table. The results indicate that the
groundwater depth is likely to rise and the safety
factor is likely to decrease under 1.0, increasing therisk of collapse when the downstream permeability
becomes poor in the future.

Fig. 12 Relationship between rainfall duration andgroundwater depth.

ACKNOWLEDGMENTS
The authors gratefully appreciate a lot of supportand understanding for this research by Ryota ETO
and Naoki YAMADA in their graduation theses. And
this research was supported by a research grant fromthe Japan Construction Information Center
Foundation (JACIC).
Fig. 13 Relationship between groundwater inflow
and slope safety factor.
240

GEOMATE – Kyoto, Japan, 3-5 November 2021

REFERENCES

District, Japan - An analysis of water level
fluctuations (part 1)-, Geological Survey
Monthly Report, Vol.32, No.3, 1981, pp.133-150.
(in Japanese)[7] Ishida J., Jiang Y., Omine K., Sugimoto S.,
Higashi Y., Ogata Y., Numerical study on slope
stability in consideration of the influence of
weathering by two-phase flow analysis,
Proceeding of EUROCK 2015 – ISRM European
Regional Symposium – the 64th Geomechanics
Colloquy, 2015, pp.997-1002.
[8] Ishida J., Sugimoto S., Jiang Y., Seepage Flow
Behavior in Slope Model Test and Verificationof Reproducibility by Numerical Simulation,
Proceedings of the 8th Asian Joint Symposium
on Geotechnical and Geoenvironmental
Engineering, 2016, pp.75-80.
[9] Sugimoto S., Jiang Y., Omine K., Ishida J.,
Higashi Y., Numerical studies on slope stability
in torrential rainfall by using two-phase flow
analysis, Proceeding of The 15th Asian Regional
Conference on Soil Mechanics and GeotechnicalEngineering, 2015, JPN-105(6 pages).
[10] Itasca Consulting Group, Inc., FLAC, Fast
Lagrangian Analysis of Continua, Theory andBackground, 2000.
[11] Chang H., Sugimoto S., Jiang Y., Omine K.,
Study on stability evaluation of slope groundwith rainfall and groundwater flow, 2019,
pp.335-336. (in Japanese)[12] Nakamura M., Sugimoto S., Jiang Y., Omine K.,
Study on evaluation of mechanical stability ofslopes with rainfall and groundwater flow,
Western Branch of Japan Society of Civil
Engineers 2020 Annual Meeting, 2020, pp.415416. (in Japanese)[1] Nishikawa Y., Sasamura T., Ishizuka Y.,
Sugimoto S., Iwasaki S., Wang H., Fujishima T.,
Fujimoto T., Yamashita K., Suzuki T., Kurihara
K., Design of Stable Wireless Sensor Network
for Slope Monitoring, 2018 IEEE TOPICALCONFERENCE ON WIRELESS SENSORSAND SENSOR NETWORKS, 2018, pp.8-11.
[2] Nishikawa Y., Sasamura T., Ishizuka Y.,
Sugimoto S., Fujishima T., Fujimoto T., Iwasaki
S., Kurihara K., Yamashita K., Problems withindependent power supplies in wireless sensor
networks, IEICE technical report, Vol. 116, No.
429, EE2016-53, 2017, pp. 27-32. (in Japanese)[3] Sugimoto S., Sasamura T., Ishida J., Ishizuka Y.,
Fujishima T., Fujimoto T., Iwasaki S., YamashitaK., Kurihara K., Basic research on thedevelopment and application of slope groundmeasurement systems using wireless sensor
networks, Japan Society of Civil Engineers 2016
Annual Meeting, 2016, pp.27-28. (in Japanese)[4] Ishida C., Sugimoto S., Jiang Y., Omine K.,
Ishizuka Y., Takaesu R., Iwasaki S., Basic
research on the development of multi-point
observation method for slope, Western Branch ofJapan Society of Civil Engineers 2019 Annual
Meeting, 2019, pp.347-348. (in Japanese)[5] Nakamura T., Sugimoto S., Jiang Y., Omine K.,
Ishizuka Y., Takaesu R., Iwasaki S.,
Development of monitoring system for multipoint observation of slope, Western Branch ofJapan Society of Civil Engineers 2020 Annual
Meeting, 2020, pp349-350. (in Japanese)[6] Sugiyama Y., Tsukuda E., Katoh K., Ikeda K.,
Analyses of groundwater data from observation
wells for earthquake prediction in the Tokai241

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

UNDERSTANDING THE EFFECT OF DEGREE OF SATURATION ON
COMPRESSIVE STRENGTH BEHAVIORS FOR RECONSTITUTEDCLAYEY SOIL OF DHAKA
Sultan Al Shafian1, Sk. Abu Talha2, Musaddik Hossain3 and Hossain Md. Shahin4
1

MSc Student, Islamic University of Technology, Bangladesh; 2Phd Student, University of Cincinnati, USA;
Faculty, Islamic University of Technology, Bangladesh; 3,4
ABSTRACT

Using clayey soil to fill typical earthwork is a common practice in Bangladesh. Disturbed clayey soils at site
are usually subjected under precompression load to achieve desired level of compaction for stabilization. Degreeof saturation (DOS) affects the compressive behavior of clayey soil to a great extent. This study aims to investigate
the effect of degree of saturation on unconfined compressive strength (UCS) of reconstituted clayey soil to imitate
disturbed soil behavior. To this end, disturbed clay samples were collected from three different sites in Dhaka city.
The collected soil samples had a liquid limit (LL) range of 28-41 and a plastic limit (PL) range of 18-25. The soil
samples were reconstituted using slurry consolidation technique simulating a common practice carried out in
Bangladesh to apply precompression on the subgrade layer of a pavement. USC tests were conducted on thereconstituted clay sample with varying degree of saturation. The considered range for degree of saturation in thisstudy was 60-100%. From the test results, it was observed that the UCS increased with the decrease of degree ofsaturation. The relationship between the unconfined compressive strength and degree of saturation within thespecified range was in agreement with the non-linear equation 𝑦 = 𝑎𝑒 −𝑏𝑥 . The stress-strain relationship of theclay specimen also clearly indicated that the post-peak strain softening and ductility are largely dependent on the
DOS of the specimens.
Keywords: Degree of saturation, Atterberg limit, Unconfined compressive strength, Clayey Soil

INTRODUCTION

strengths and pore water pressure of natural andremolded clay through tri-axial test[4]. W Norhaliza
et al in his study on shear strength of remolded
samples using three different molding methodsproctor compaction method, hand operated method
and miniature mold method concluded that all threemethods conform to a desired shear strength though
due to easiness and comfort hand operated method ismore suitable[5]. Jianjun Ma et al. proposed an
equation based on numerical models andexperimental results to describe the intrinsic
compressibility of reconstituted clay with different
initial water contents[6]. Hong, Z. S et al [7], Liu, M.
D [8], Sridharan, A et al [9] have notable works
focusing the compressibility of reconstituted clay . To
this end most of the studies mainly focused on themethods of reconstitution and compressibility ofreconstituted clay but the unconfined compressive
strength is a basic soil mechanical aspect to gain afirst-hand information about the soil quality. This
research aims to find a substantial relationshipbetween compressive strength and degree ofsaturation of reconstituted clay. Besides, the behaviorof stress-strain curve with respect to DOS will also be
explored.

Earthwork filling for civil engineering
constructions (e.g foundation,embankment etc.) is themost essential task for any project.[1].In
Bangladesh,specially in Dhaka ,mostly soils arecohesive (clayey soils)[2] and so for subgrade filling
activities this soil has been used widely in various
projects.To attain proper bearing capacity atsite,necessary mechanically compaction is done ismeasured to ensure the quality at site.The
compressive behaviour is affected by degree ofsaturation to a great extent.As this filling works aredone usually disturbed soil and later compacted using
mechanical forces,there is a scope to study thecompressive behaviour of this kinds of soil.To
understand and analyze the behaviour of the disturebd
soils,reconsituted clay is an excellent option.
“A reconstituted clay is one that has beenthoroughly mixed at between 1 and 1.5 times theliquid limit and preferably consolidated onedimensionally”:Burland[3]. Reconstituted clay ismost commonly used processed clay that has beenused for engineering infrastructures for decades dueto the unavailability of undisturbed clay. So far, many
researchers have conducted significant experiments
to investigate the engineering properties ofreconstituted clay. Graham et al. conducted an
extensive study to compare among various soil
properties like Critical state line, compressibilities,

COLLECTION OF SOIL SPECIMENSFor this research, Dhaka soil has been selected for
preparing reconstituted clay. The soil samples were242

GEOMATE – Kyoto, Japan, 3-5 November 2021

collected from three different location from a depthof 1.3-2.5m. The description of soil specimens
collected for this study is summarized in Table 1. As
presented in Table 1, the soil samples had specificgravity of around 2.6. The liquid limit (LL) and theplastic limit of the collected specimens were within
the range of 30-41 and 19-24, respectively.
Table 1
LL
PL

LocationSp

Hatirpool(A)Ajimpur(B)

2.6
2.59

41
35

Polashi(C)

2.61

30

PI

USCS

24
23

17
12

CL
CL

19

11

CL

to drain out the pore water to facilitate theconsolidation.

Note: Sp=Specific Gravity; LL=Liquid Limit, PL=Plastic Limit;PI=Plasticity Index

RESEARCH METHODOLOGY
The research follows three steps: a) reconstitutethe soil slurry by slurry-consolidation technique, b)
preparation of soil specimen for UCS testing, and c)
UCS testing.
The collected clay samples were first air dried andcrushed into powdered form in the laboratory fortesting. Laboratory tests were carried out to determine
the physical properties such as liquid limit, plastic
limit, plasticity index, and specific gravity. Later, all
the samples were then reconstituted using Ko
consolidation technique. Fig 1 shows the schematic
setup diagram for the soil reconstitution process.

Fig. 1 a) Ko-Consolidation of soil b) reconstituted soil
specimen c) soil sample kept insidedesiccator, and d) Failure plane after testing.

Reconstitution of Soil

Each mold has perforated base plate below and topof the mold to bolster two-way drainage. The soil
slurry was confined by Geo-textiles to prevent theparticle migration. Lubrication was used on the innerwall to prevent any type of friction between soil andthe wall of the mold. A schematic diagram showing
various parts Ko Consolidation of have been shown in
Figure 2.

Soil samples were reconstituted using the slurryconsolidation technique suggested by Sheeran andKrizek (1971). The reconstitution of the soil includes
two major steps: preparation of slurry and Ko
consolidation. The following subsections have a brief
descriptions of these steps.
Preparation of Slurry
The soil was first air dried and crushed into
powdered form. Then the soil was sieved through #40
sieve in order to remove any big chunk rocks or
impurities which may affect the consolidation. The
samples were then mixed with a water content equal
to 1.15 times of liquid limits which was found as
sufficient to yield uniform and homogeneous slurry.
Ko Consolidation
The soil slurry of each soil sample was thenpoured into three specific cylindrical molds one
having diameter of 20cm and the others 26cm. Eachmold containing the soil slurry has a drainage system

Fig. 2

243

Schematic diagram showing the set up for
slurry- consolidation technique for sample
preparation.

GEOMATE – Kyoto, Japan, 3-5 November 2021

and hence degree of saturation was calculated.
To determine the unconfined compressive
strength, unconfined compression test was performedin accordance to ASTM D2166 (2000). A typicalstress strain curve obtained from UCS test is shownin Fig 4.

100000

0

50

8
100

7

6
150

UCS (Kpa)

Dial guage reading (1 unit=.01mm)

1Time in min (log scale)
10
100
1000
10000

5

4
3

Fig. 3 A typical load-deformation curve

2

The prepared soil slurry was then poured inside
the molds. Initially, the slurry was allowed toconsolidate by the self-weight and the weight of theporous metal discs for about 48 hours. Then load was
gradually applied with an increment of 50kpa. Each
increment was held into position until the primaryconsolidation was fully completed. The time forprimary consolidation was obtained from thedeformation vs. time curve (Figure 3) for eachincremental load. This process was repeated untilpressure reached about 400kpa. It took almost 90 days
for the soil samples to accomplish the primaryconsolidation. The time between each increment wasdelayed intentionally to prevent the instability of thesoil structure.

1

0
0

5

10

15

Axial strain (%)

Fig. 4. A Typical stress-strain curve obtained in UCS
test.
RESULTS AND ANALAYSIS
Influence of degree of saturation on UCS
Figure 5 shows the results of unconfinedcompressive tests under various degree of saturationsfor Sample A, sample B and sample C. It is apparent
that the unconfined compressive strength increases
with the reduction of degree of saturation for each of
the sample. It is interesting to note that, for all threesamples, initially the rate of increase of UCS are
small. However, a drastic increase in UCS is observed
once the degree of saturation drops below 90%.
Therefore, care must be taken while measuring the
UCS for samples having degree of saturation less than
90% since a slight decrease in degree of saturation
may alter the UCS significantly. This phenomenon ismore pronounced for sample B and C, where theplasticity indices were comparatively lower.
As mentioned earlier, in all three cases it wasobserved that initially the rate of increase is steady but
when the degree of saturation is below 90% the UCS
increases drastically. Based on this observation,
relationships were developed between UCS anddegree of saturation. The relationships for all thesamples are summarized in Table 2.
As shown in Table 2, the relationship foundbetween degree of saturation and UCS is exponential
and can be expressed in the following form.

Preparation of soil specimen for UCS
Once the primary consolidation was completed,
the reconstituted soil sample was extracted from themold and kept inside a desiccator for 24 hours. The
soil sample was then cut and trimmed into smaller
cylindrical samples for UCS test.
Determining DOS and UCS
The reconstituted soil specimen, after extractingfrom the mold, was kept initially in desiccator for 24hours. Then the sample was divided into several
smaller samples to facilitate the study. All thesamples had height to diameter ratio greater than 2(ASTM D2166-00). The sample was initially 100%
saturated and degree of saturation decreased as thetime went by and to foster the rate of decrease the lidof the desiccator was left open intentionally. For the
first few samples unconfined compression test was
conducted at 6 hours interval and later 24 hours.
Every time after the unconfined compression test soil
samples was oven dried to find the moisture content

244

GEOMATE – Kyoto, Japan, 3-5 November 2021

60

Sample A

60

Sample B

100% saturated
92% saturated
77% saturated

50

40

97% saturated
85% saturated
67.5% saturated

40

UCS (Kpa)

Compressive Strength (kpa)

Sample A
Sample C

20

30
20
10

0
60

70

80

90

100

110

0
0

Degree of Saturation (%)

Fig. 5 Variation of UCS with respect to degree ofsaturation
𝑈𝐶𝑆 = 𝑎𝑒−𝑏∗𝐷𝑂𝑆

(1)

B
C

17
12
11

Table 2
Relationship between
UCS and DOS
𝑼𝑪𝑺= 𝟒𝟑𝟕𝟓 𝒆𝟎.𝟎𝟔𝟎∗𝑫𝑶𝑺
𝑼𝑪𝑺= 𝟐𝟏𝟖𝟖 𝒆𝟎.𝟎𝟔𝟐∗𝑫𝑶𝑺
𝑼𝑪𝑺= 𝟓𝟔𝟎𝟕 𝒆𝟎.𝟎𝟕𝟔∗𝑫𝑶𝑺

15

96% saturated
83% saturated
62% saturated

UCS (Kpa)

40

30

20

10

0
0

Coefficient ofregression (𝑹𝟐 )0.95

5

Axial stain

10

15

Sample C

60

100% saturated
90% saturated
75.5% saturated

50

97.5% saturated
82% saturated
63% saturated

0.98
40

0.97

UCS (Kpa)

A

PI

10

100% saturated
90% saturated
74% saturated
70% saturated

50

ID

Axial stain

Sample B

60

Where ‘a’ and ‘b’ are the coefficient of the
exponential equation. ‘a’ represents the shifting of thegraph with respect to UCS axis and ‘b’ represents therate of change of UCS with respect to DOS. It isinteresting to note that the value of ‘b’ increases withthe decrease of plasticity index. Lower plasticity
index represents higher percentage of silt in clay.
Therefore, the rate of increase in strength in clayeysoil due variation of DOS may depend on thepercentage of silt in the soil. From Table 2, it is also
observed that the coefficient of regression varies
between 0.95 to 0.98, indicating a very strong
relationship between UCS and DOS.

5

Shear stress-strain behavior

30

20

The stress-strain curves for all the specimens areshown in Fig 6. Each curve in the figure represents
the stress vs strain curve for a particular DOS. As
shown in Fig 6, the soil sample became brittle withthe decrease in DOS for all three samples. In otherwords, the strain required to mobilize the peak shear
stress dicreased with the decrease of DOS. In addtion,
post-peak strain softening behavior was also observed
as the DOS decreased. As indicated by Fig 6, the post

10

0
0

5

10

15

Axial stain

Fig. 6 Load vs strain curve for all DOS(Sample
A,B,C)
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GEOMATE – Kyoto, Japan, 3-5 November 2021

peak behavior was observed once the DOS of thespecimens were below 90%. However, it was more
promiment when the DOS was below 80%. It is also
worth mentioning that the distinct failure planes wereobserved of the clay samples when the DOS was
below 80%, while the failure criteria observed forSpecimens with higher DOS was bulging. In general,
the results indicate that when the DOS of clay sample
is below 90 %, it may start loosing its ductility andbelow 80% it may become brittle.

MSc thesis, Bangladesh University ofEngineeringandTechnology(Buet).
http://lib.buet.ac.bd:8080/xmlui/handle/1234567
89/1264[3] Burland, John. (1990). Thirtieth Rankine
Lecture: On the compressibility and shear
strength of natural clays. Geotechnique. 40. 329378. 10.1680/geot.1990.40.3.329. Graham,James & Li, E.. (1985). Comparison of Natural
and Remolded Plastic Clay. Journal of
GeotechnicalEngineering.
111.
10.1061/(ASCE)0733-9410(1985)111:7(865).
[4] Graham, James & Li, E.. (1985). Comparison of
Natural and Remolded Plastic Clay. Journal of
GeotechnicalEngineering.
111.
10.1061/(ASCE)0733-9410(1985)111:7(865).
[5] Norhaliza, W., Ismail, B., Azhar, A. T. S., &Nurul, N. J. (2016). Shear Strength ofRemoulding Clay Samples Using Different
Methods of Moulding. IOP Conference Series:
Materials Science and Engineering, 136(1).
https://doi.org/10.1088/1757899X/136/1/012018[6] Ma, J., Qian, M., Yu, C., & Yu, X. (2018).
Compressibility Evaluation of Reconstituted
Clays with Various Initial Water Contents.
Journal of Performance of Constructed Facilities,
32(5),04018077.https://doi.org/10.1061/(asce)cf.1943-5509.0001221[7] Hong, Z. S., Bian, X., Cui, Y. J., Gao, Y. F., &Zeng, L. L. (2013). Effect of initial water content
on undrained shear behaviour of reconstituted
clays.
Geotechnique,63(6),
441–450.
https://doi.org/10.1680/geot.11.P.114[8] Liu, M. D., Zhuang, Z., & Horpibulsuk, S. (2013).
Estimation of the compression behaviour ofreconstituted clays. Engineering Geology, 167,
84–94.
https://doi.org/10.1016/j.enggeo.2013.10.015[9] Sridharan, A., & Nagaraj, H. B. (2000).
Compressibility behaviour of remoulded, finegrained soils and correlation with index
properties.

CONCLUSIONSThis study presented the results from theunconfined compressive strength test conducted on
three different clayey soil samples with different DOS.
The results of the study clearly indicated that the UCSof clay is significantly dependent on DOS and their
non-linear relationship can be described withexponential equation. It was observed that the rate ofincrease in UCS is dependent on the PI of the clay
samples. Sample with the lowest PI showed thehighest rate of increase in UCS. Also, for all the soil
samples, below 90% DOS, a steady increase in UCS
was observed, whereas the rate of increase in UCS
with the decrease of DOS was drastic for DOS below
90%. From the stress-strain curve, it was obvious thatthe post-peak strain softening of clayey soil is largely
dependent on the DOS. The samples started showing
post-peak strain softening once the DOS was below
90%. This behavior was more pronounced when DOS
was lower than 80%. It was also obvious that below
80% DOS the clay soil specimens lost its ductility andbehaved like a brittle material.
REFERENCES[1] Mamat, R. C., Kasa, A., & Razali, S. F. M.
(2019). A review of road embankment stability
on soft ground: Problems and future perspective.
IIUM Engineering Journal, 20(2), 32–56.
https://doi.org/10.31436/iiumej.v20i2.996[2] Abul Bashar, M. (2000). Geotechnical
Characterization Of Dhaka Metropolitan Area.

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11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

SAFETY FACTORS VALUE OF ROCK MASS SLOPE AFTERDIFFERENT APPROACH OF ROCK FAILURE CRITERIA BY
LIMIT EQUILIBRIUM METHOD
Arief Rachmansyah1, Eko A Suryo1, Hasyim Alhadar2, Retno P Rini2
1

Geotechnical Laboratory, Department of Civil Engineering, Brawijaya University, Indonesia
Master’s Program of Civil Engineering, Faculty of Engineering, Brawijaya University, Indonesia

2

ABSTRACT
Weathering zone, geological structures and discontinuities in the rock are weak areas and as
groundwater infiltration pathways. The existence of these phenomena will reduce the stability of rock massand the main implication is to increase the slope failure. The purpose of this study was to determine the typeof rock failure, rock mass classification, and the stability of the rock slopes. The study was conducted on
Miocene age of basaltic andesite lava and high discontinuity. After the kinematic analysis of rock failure thisrocks failure can be categorized as planar rockslide. The value of Rock Mass Rating of andesitic rock is 53
and classified as medium rock mass. These characteristics show qualitative stability of rock slope. Slopestability analysis for andesite rock based on the generalized criteria Hoek - Brown failure was obtained a safety
factor value of 1.55 with a slope angle of 60, however analysis based on Mohr - Coulomb criteria obtained asafety factor value of 1.59 with a slope angle of 70.
Keywords: slope stability, kinematics analysis, rock mass rating, rock failure criteria, safety factor.

INTRODUCTION
Road of Ponorogo to Pacitan on East Java
Province Indonesia is known as a dangerous
route, because the roads have been developed on
the edge of a cliff. Rock and soil slides along
the route occur often, especially during the rainyseason. This paper will discuss the types ofrockslides, and the application of rock massclassification system for different numericalanalysis of the slope stability.
Base on Physiographic of East Java Islandthat proposed by Van Bemmelen (1949)
Ponorogo – Pacitan Road located in theSouthern Mountains Zone. In Miocene this zone
is the east - west magmatic arch as a product of
subduction of Indo-Australian to Eurasian Plates[1]. During the Late Miocene to Pliocene, itturned into a shallow sea that produced
carbonate rocks. The tectonic setting causes thiszone to be dominated by volcanic facies
sediment, lava intrusive rocks, and dense
geological structures. The plate collision fromthe south produces shear faults trending
Southwest – Northeast, and Northeast Southeast. Folded structures are also found inseveral places with a West – East axis. These
conditions were also followed by mineralizationof epithermal gold and porphyry copper inseveral places. [2].
Slope failure of rock and soil are
generally influenced by conditions ofinconsistency between the existence of theformation process in nature and the bondconditions between rocks that are identified as
unconformity. The effect of non-conformity canaffect the value of shear strength and thestrength of the slope forming material [3]. Rockcollapse usually starts and go along
discontinuities of rock mass, such as joints,
fractures, bedding planes, faults, and other typesof cracks in the rock. According to, geologicalstructures and discontinuities in rocks are weak
areas and groundwater infiltration pathways [4].
The existence of geological structures anddiscontinuities will reduce the level of rockshear strength and the main implication is toincrease the chance of landslides.
Type of Rock Failure
To determine the potential failure type ina rock slope, it is necessary to map theorientation of the discontinuity before and after
the rock slope is exposed [5]. In general, theintegration of rock discontinuity orientations

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GEOMATE – Kyoto, Japan, 3-5 November 2021

will form the main types of slides in rocks,
namely:• Plane failure, if the sliding plane isconsidered flat. The sliding area can be in
the form of fractures, faults or rock layers.
• Wedge failure is happened when two or
more weak planes intersect in such a way
as to form a wedge against the slope. This
wedge failure can be divided into 2 types oflandslides, namely: single sliding anddouble sliding. For single landslides, theslide occurs in one plane, while the form ofmultiple landslides occurs at theintersection of the two planes.
• Toppling failure is generally occur on steep
slopes and on massive rock masses where
the weak areas are column shaped.
Toppling failure occur because the weak
areas on the slope are in the opposite
direction to the slope direction. As a resultof the opposite tilt direction, the material
cannot support its own mass, resulting in
toppling failure.
• Circular failure is the most common mass
movement in nature, especially on soil anddebris material., and high weathered rockmass so they almost resemble soil. As thename implies, the landslide field is arc
shaped. In hard rocks, circular failure canbe happened on the rock with has
weathered and with very tight spacing
fracture or discontinuous.
To determine the type of rockslide, it isanalysis by drawing the surface plane of theslope and the discontinuity that acts as the causeof the failure on stereographic diagram.
Currently, there are many software that can
describe the position of the two planes on astereographic.

Rabewicz, Muller and Pacher (1964); Patching
and Coates (1968); Deere et al (1967),
Wickham, et al (1972); Bieniewski (1973),
Barton et al (1974), Hoek (1994), andPalmstorm (1995). Among the many
classifications that have received much attention
related to the numerical analysis of rock massslope stability are Rock Mass Rating (RMR)
proposed by Bieniewski, and GeologicalStrength Index (GSI) proposed by Hoek.
Rock Mass Rating
Rock Mass Rating (RMR) is used todetermine the stability of rock mass empiricallyby providing an assessment of rock mass withweight and parameters based on geologicalconditions [8]. The RMR value is obtained bymatching the results of field observations of rockmass characteristics on the table. The following
five parameters are used to classify rock masses
using the RMR system [9].
a. Uniaxial compressive strength (UCS)The strength of intact rock in RMR isexpressed by Uniaxial Compressive Strength(UCS). UCS is the strength of the intact rockobtained from the uniaxial compressive strength
test results in the laboratory.
b. Rock Quality Designation (RQD)
The presence of discontinuity in the rockmass often adversely affects its mechanicalproperties so that the quantitative magnitude ofthe discontinuity plane needs to be known.
Parameters that can indicate rock quality before
excavation are Rock Quality Designation(RQD) that is obtained from core of exploration
drilling.
To quantify the core of the box, the RQD
must be calculated. RQD is calculated based on
the percentage of core drill bits obtained with aminimum length of 10 cm and the number ofcore cuttings is usually measured at a 2 m longcore. an example of RQD calculation of drill
core. Cuts due to drilling handling must be
ignored from the calculation and the core a softand not good drill weight = 0 and the calculation
is as follows.

Rock Mass Classification System
Rock Mass Classification is the process todetermine rock masses into groups or classes indefined relationships and assigning a uniquedescription (or number) to it based on similar
properties / characteristics, so that the behaviorof rock masses can be predicted [6]. Rockmasses are called in situ rock material separated
by rock discontinuities, mostly by joints,
bedding planes, intrusions and faults, and othergeology process.
Many experts propose rock massclassifications [7], including: Terzaghi (1946);

RQD =Σ Length of core pieces ≥10 cm
Total length of the core (cm)× 100%(1)

If core drills are not available, the RQD
can be calculated indirectly by measuring the248

GEOMATE – Kyoto, Japan, 3-5 November 2021

orientation and distance between discontinuities
in the rock outcrop. It is proposed an equation todetermine the RQD from the line stretch data as
follows [6].
RQD = 100 𝑒 −0.1𝜆 (0.1𝜆 + 1)

underground excavation in hard rock. These
criteria start from the properties of the intact
rock and then introduce factors to reduce theseproperties based on discontinuity characteristics
in a rock mass [12].

(2)Where, λ is the ratio of the number of
discontinuities per meter.
c. Discontinuity Distance
Discontinuity distance is determined fromthe average distance between rock fractures
along the scanline measurement span.

a. Hoek - Brown Generalized Failure Criteria
These criteria are derived from the resultsof research into intact rock collapse by Hoek andstudies of rock mass behavior model by Brown.
There are 2 rock collapse criteria based on Hoek
& Brown's proposal. This criterion became
known as the Generalized Hoek-Brown criterionwith the equation [13]:𝜎1′ = 𝜎3′ + 𝜎𝑐𝑖 (𝑀𝑏d. Discontinuity Field Conditions
Discontinuity conditions are determinedfrom the description of each discontinuity plane,in the form of weathering level, discontinuity
plane surface roughness, continuity of thefracture plane, opening width, and discontinuity
field fill material.

𝜎3′
𝜎𝑐𝑖

𝑎

+ 𝑠)

(4)

Where σ1' and σ3' are the maximum andminimum effective stresses at failure, σci is thecompressive strength (UCS) of the intact rock.
Meanwhile, Mb is the reduction factor of therock type constant mi, and s and a are the rockmass constants obtained by the following
equation.

e. Groundwater Conditions
The presence of this water will reduce theshear strength between the two discontinuity
surfaces. The weight of groundwater parameters
can be determined in several ways, one of which
is direct observation in the field by determining
the general condition of groundwater.

Mb = mi exp (𝑠 = exp (
1

1

2

6

𝐺𝑆𝐼−100
24−14𝐷

𝐺𝑆𝐼−100
9−3𝐷
𝐺𝑆𝐼

)

(5)

)

(6)
20

𝑎 = + (𝑒 − 15 − 𝑒 − 3 )Geologiy Strength Index
In 1995 Hoek et al included the conceptof the Geological Strength Index (GSI) which
provides an estimate of the reduction in rockmass strength due to differences in geologicalconditions. GSI was introduced and developed
as tool for classification inability of Rock Mass
Rating system to determine the rock quality with
bad quality [10].
The GSI value is a rock strength index
classification obtained in two ways, the first is
by field observing the rock mass characteristics
with the help of the GSI value estimation chart.
The second is by reducing the RMRclassification results through the following
equation [11].
GSI = RMR - 5(7)

The values of Mb, s, and a are used indetermining the strength of rock mass, both for
Hoek - Brown or Mohr-Coulomb criteria [14].
b. Mohr-Coulomb Criteria
Most geotechnical software still applicate
the Mohr-Coulomb collapse criteria, so it isnecessary to determine the inner shear angle andthe cohesion value for each rock mass. The
adjustment process requires a balance between
the upper and lower areas on the Mohr-Coulomb
plot, resulting in an equation to obtain the valueof the inner shear angle and cohesion [15]:𝑐′ =𝜎𝑐𝑖 [(1+2𝑎)𝑠+(1−𝑎)𝑚𝑏 𝜎′3𝑛 ](𝑠+𝑚𝑏 𝜎′3𝑛 )𝑎−1
(1+𝑎)(2+𝑎)√1+(6𝑎𝑚𝑏 (𝑠+𝑚𝑏 𝜎′3𝑛 )𝑎−1 )/((1+𝑎)(2+𝑎))(3)𝜙′ = sin−1 [6𝑎𝑚𝑏 (𝑠+𝑚𝑏 𝜎′3𝑛)𝑎−12(1+𝑎)(2+𝑎)+6𝑎𝑚𝑏 (𝑠+𝑚𝑏 𝜎′3𝑛 )𝑎−1

Rock Failure Criteria
Hoek and Brown introduced their failure
criteria in an attempt to provide input data forthe analyzes necessary for the design of
249

]

(8)

(9)

GEOMATE – Kyoto, Japan, 3-5 November 2021

Determination of the value of σ3max on
different slopes and tunnels, for slopes the valueof σ3max is obtained based on the formula [15]:𝜎′3𝑚𝑎𝑥𝜎′𝑐𝑚= 0.72 (𝜎′𝑐𝑚 = 𝜎𝑐𝑖𝜎′3𝑛 =

𝜎′𝑐𝑚 −0.91
𝛾𝐻)(𝑚𝑏+4𝑠−𝑎(𝑚𝑏−8𝑠))(•

RESULTS AND DISCUSSION
Geology
Based on the geological map sheet
Pacitan scale 1:100000 the study location iscomposed of basaltic andesite lava which is partof the Watupatok Formation. This Miocene
formation is composed of andesitic lava,
volcanic breccia, and volcanic sandstone [16].
Basaltic andesite lava has a sheathing joint
structure that has undergone a low to moderate
weathering process. The soil formed is reddish
brown and slightly plastic (Figure 2).

(10)
𝑎−1
𝑚𝑏
+𝑠)
4

2(1+𝑎)(2+𝑎)𝜎′3𝑚𝑎𝑥𝜎𝑐𝑖

Numerical modelling of rock slope stability
by using limit equilibrium method with thehelp of software Rockplane.

(11)

(12)

In the calculation of rock slope stability,
this failure criterion is only suitable for circular
failure types caused by very tight joints, but also
can be used for planar type.
STUDY LOCATION AND METHOD
Location
The research location was conducted on
Ponorogo - Pacitan road KM 232.5,
administratively on Tegalombo District, Pacitan
Region (Figure 1).

Figure 2: Slope failure on basaltic andesite rockwith sheeting joint
Type of Slides
To identify the types of rockslides thefield observation of rock mass and measurementof the orientation of joints was carried out. To
determine the type of rockslide was carried out
by kinematic analysis method using Dips
software. The data obtained was a strike dip ofslope of N154E / 66. Orientation of somestrike and dip of joints is N138E/48.
It can be concluded that the type ofrockslide that occurred was planar, because
there is only 1 joint set, and the strike of planes
is almost parallel to the slope. The difference
between two planes is less than 20. The resultsof stereographic analysis can be seen in Figure
3.

KM 232

Figure 1 : Study Location
MethodThe research steps include:• Secondary data collection from difference
institution• Field survey which includes fieldobservations and measurement of theorientation of geologic structure and otherdiscontinuity.
• Laboratory tests for physical properties ofrocks, and uniaxial compressive strength.
• Kinematic or stereographic analysis todetermine the types of rockslides.
• Rock mass classification analysis using theRock Mass Rating (RMR) and Geology
Strength Index (GSI).

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GEOMATE – Kyoto, Japan, 3-5 November 2021

discontinuities is 20 cm. It is known that theweight of the distance between discontinuities
on the slope is 8.
d. Discontinuity Field Conditions
Based on field observations, the conditionof the discontinuity plane is rather rough withthe separation of discontinuities less than 1 mm
and the rock conditions are very weathered.
Based on RMR Table it is known that the weightof the discontinuity conditions on the slope is 13.
e. Groundwater Conditions
Groundwater parameters are directly
observed in the field by observing the generalcondition that the slopes have found conditions
on the slopes, namely humid. Based on the fieldobservation there is springs water, and the rockmass is moist. The weighting of groundwaterconditions is 10.

Figure 3. The results of the joint projection
stereographic analysis using the Dips software.
Rock Mass Rating
Rock mass classification of slope isanalyzed after Rock Mass Rating developed by
Bieniawski (1989) using five parameters.

f. RMR total weight classification
Each result of the weighting of the fiveparameters that has been obtained is then added
up to get the total RMR weight value that will be
used to classify the rock mass class.

a. Intact rock strength
The intact rock strength parameters werecarried out using laboratory testing, namely the
Uniaxial Compressive Strength (UCS) test. In
this case the rock type is andesite, the samples
taken were tested by UCS, so the rockcompressive strength for andesite rock types
was 6.5 Mpa. So that the strength of the intact
rock is obtained at 2.

RMR value = 2 + 20 + 8 + 13 + 10 = 53
Based on the results of the total RMR
weighting, then the rocks on the slope of KM
232.5 are included in the classification of class
III rock masses (medium rocks). It can beconcluded that the rock slopes belonging to theclass III, and are susceptible to weathering, sothey are classified as prone to landslides.

b. RQDThe calculation of RQD is carried out by
direct observation in the field by looking at themuscular structure on the slope. Observations
were made on a slope span of 4 meters, in whichobservations obtained a total number offractures along the length of 20 fractures, thenthe ratio of the average number of fractures (λ)
can be calculated.

4.4. Slope Stability
Slope stability analysis is calculated bydifferent rock failure criteria, namely Hoek Brown's general criteria and Mohr - Coulomb
criteria.

20

a. Hoek & Brown Failure
The general failure criteria of Hoek &Brown include several parameters, namely rockcompressive strength (σci) from thecompressive strength test results, the GSI value
obtained from the RMR reduction (Bieniawski,1989), rock mass constant (mi) based on rock
type, and the value of the disturbance factor (D)based on stress relaxation.
The GSI value can be determined fromthe reduction in the weighting result of the RMRclassification according to equation (3).

𝜆 = 4 = 5 joint / meter
Then the RQD can be calculated by using
equation (2) of 90.87%. After this calculation
weighting value of RQD is 20.
c. Distance Between Discontinuities
Determination of the distance between
discontinuities was carried out using thescanline method, namely measuring theorientation of the rock mass in the field. Basedon the measurement data of rock massorientation, the average distance between
251

GEOMATE – Kyoto, Japan, 3-5 November 2021

Previously, the RMR was obtained at 53, thenthe GSI value is 48.
As for several other parameters, namely
Mb, s, and a which are calculated based on thevalue of mi, GSI and the disturbance factor (D)using the formulas (5), (6), and (7). Then thevalue of Mb = 0.609, s = 0.0002, and a = 0.507.
The data is inputted into the Rockplane
software, and then slope modeling performed
with this software. The results of slope stability
analysis with this software are shown in Figure
4.

standard, namely SF = 1.55 for the slope angleof 60 and SF = 1.79 for the slope angle of 55.
Table 1. The safety factor value base on

variations of the slope angle.
Slope ()
80
75
70
65
60
55

Safety factor
1.21
1.26
1.33
1.42
1.55
1.79

Information
Not safe
Not safe
Not safe
Not safe
Safe
Safe

b. Mohr-Coulomb Criteria
The Mohr-Coulomb criterion parameters
used in the slope stability analysis are cohesion(c ') and inner shear angle (') determined by theequations of Hoek - Brown. The cohesion value
is calculated using equation (8) of 0.098 Mpa
and the inner shear angle is calculated using
equation (9) of 32.25.
The value of cohesion (c') and the inner angleof shear (') is then inputted into the Rockplane
software for slope stability analysis. The resultsof the slope stability analysis are shown in
Figure 5.

Figure 4. Analysis model of slope stability after
Hoek – Brown criteria for slope angle of 80o .
To make more stable slope it is necessary
to reduce the slope angle. The slope angle wassimulated until the safety factor more than 1.5,
with the specified variation of 75, 70, 65, 60,and 55. The safety factor of rock mass slope
after simulation of the slope angle are shown in
Table 1.
Based on these results, if the slope angle
is reduced or smaller than the actual slope angle,
the slope will approach a stable condition. At the
specified slope angle variation, the slope
becomes stable at an angle of 60 and 55 withthe value of the safety factor that meets the

Figure 5. Analysis model of slope stability after
Mohr - Coulomb criteria for slope angle of 80o.
After the results analysis, the safety factorof slope is 1.33, it can be said that the slope isunstable. This is because the slope SF value is252

GEOMATE – Kyoto, Japan, 3-5 November 2021

smaller than the standard or does not meet the
specified standards, namely SF ≥ 1.5.
To make the stable slope it is necessary tochange the slope angle. Changes in the slope
angle of the slope are made with several
variations in the angle until the slope becomesstable, with the specified slope angle variationof 75, 70, 65, 60, and 55. The results of theanalysis of the safety factor of slope stability
based on variations in the slope of the slope areshown in Table 2.
Based on these results, if the slope angle
is reduced than the actual slope angle, the slope
will approach a stable condition. At the specified
slope angle variations, the slope becomes stable
at an angle of 70 and 65 with the value of thesafety factor that meets the standard, namely SF= 1.59 for the slope angle of 65 and SF = 1.83
for slope angle of 65.

the slope is said to be stable at a slope of 70
with the SF value = 1.59. With reference to therock mass classification, the result of the safety
factor is taken based on the general criteria for
Hoek & Brown collapse with a slope angle of60.
CONCLUSIONS AND SUGGESTIONS
Conclusion
1. After stereographic kinematic analysis rock
failure on study location can be classified
as planar rockslides.
2. The weight value of Rock Mass Rating(RMR) of slope on site is 53 and can beclassified as a medium rock class. The GSI
value is 48.
3. Based on safety factor analysis with Hoek Brown's general failure criteria rock mass
will be stable, if SF is 1.55 and the slope
angle is 60. Meanwhile, based on the
Mohr-Coulomb criteria the stability of rockmass can be reached if the SF value is 1.59
and the slope angle is 70.

Table 2. The safety factor value base on
variation of slope angle.
Slope ()
80
75
70
65
60
55

Safety factor
1.33
1.44
1.59
1.83
2.26
3.30

Information
Not safe
Not safe
Safe
Safe
Safe
Safe

Suggestion
1. It isneeded to more detailed
geotechnical survey of the rock mass to
obtain probability of hazard and its
overcoming.

From the two methods used in analyzing
the safety factor of slope stability, namely the
Hoek & Brown collapse general criteria and theMohr-Coulomb criterion, different safety factor
values were obtained. The value of the safety
factor of the two methods is shown in Table 3.

2. Periodic monitoring of slopes movement isnecessary to detect other symptom of
instability, so preventive efforts can bemade immediately.
REFERENCES[1] Purwanto, H.S. 1997. Analisis Dan Genesa
Pembenrukan Srruktur Geologi Pada
Batuan Berumur Oligosen - Miosen Di
DaerahPacitanDan
Sekitarnya,
Kabupaten Pacitan, Jawa Timur. Thesis
Magister. ITB.
[2] Sudrajat, A. and Jong-IK, 1993. Report on
The Join Mineral Exploration In The
Pacitan Ponorogo Area, East Jawa, theRepublic of Indonesia, IndonesianGeological Survey. Unpublish.
[3] Priest, S. D. 1993. The collection andanalysis of discontinuity orientation data
for engineering design, with examples.
In Rock
Testing
and
Site
Characterization (pp.167-192), Pergamon.

Table 3. The value of the safety factor is basedon the general criteria for the failure of Hoek &Brown and the value of the safety factor is basedon the Mohr-Coulomb criteria.
Slope ()
80
75
70
65
60
55

SF
Hoek - Brown's
general criteria
1.21
1.26
1.33
1.42
1.55
1.79

SF
Mohr-Coulomb
criteria
1.33
1.44
1.59
1.83
2.26
3.30

Based on the table above, for the generalcriteria for Hoek - Brown failure the slope is said
to be stable on a slope of 60 with the SF value= 1.55, while for the Mohr - Coulomb criteria
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GEOMATE – Kyoto, Japan, 3-5 November 2021

[4] Priest, S. D. 2012. Discontinuity analysis[12] Hoek, E. and Brown, E.T. 1980. Empiricalfor rock engineering. Springer Science &Business Media.
[5] Hoek, E., & Bray, J. D. 1981. Rock slopeengineering. 3rd. London: Institute of
Mining and Metallurgy CRC Press.
[6] Bieniawski, Z. T., 1976. Rock MassClassification in Rock Engineering.
Proceeding Symposium of Exploration forRock Engineering, 1, 97-106. Cape Town:
Balkema.
[7] Palmstrom, A., 2000. Rock MassClassification Systems. Proceedings Geo
Eng. 2000.
[8] Romana, M. 1985. New adjustment ratingsforapplicationofBieniawski
classifications to slopes. Proceeding ofRock Mechanics. Zacatecas, Mexico, pp
49-53.
[9] Bieniawski, Z.T. 1989. Engineering rockmass classifications. New York: Wiley.
[10] Marinos, P., Hoek, E. & Benissi, M. 1998.
Applicability of the Geological Strength
Index (GSI) classification for very weak
and sheared rock masses. The case of theAthens Schist Formation. Bulletin ofEngineering
GeologyandtheEnvironment, Vol 57 (2), pp 151-160.
[11] Hoek, E., Carter, T. G., & Diederichs, M.
S., 2013. Quantification of the geologicalstrength index chart. In 47th US rock

Strength Criterion for Rock Masses.,
Journal Geotech. Engineering Div., ASCE
106 (GT9), pp 1013-1035.
[13] Hoek, E. and Brown, E. T., 1988. The
Hoek-Brown failure criterion - a 1988
update. In Rock Engineering for
Underground Excavations, Proceeding
15th
CanadianRock
Mechanics
Symposium, (ed. J.C. Curran), 31-38.
Toronto: Dept. Civil
Engineering,
University of Toronto.
[14] Hoek, E., Carranza-Torres C T., Corkum,
B. Hoek-Brown Failure Criterion, 2002
edition. In: Proceedings of the 5th North
American Rock Mechanics Symposium.,
Toronto, Canada, 2002: 1: pp 267–73.
[15] Cheng, Y. M., & Lau, C. K. 2014. Slopestability analysis and stabilization: newmethods and insight. CRC Press.
[16] Samodra,H.;
Gafoer,S.;
dan
Tjokrosapoetro, S. 1992. Peta geologi
lembar Pacitan. Jawa. Sekala I : 100.000.
Puslitbang Geologi. Bandung.
[17] Sophian, R. I., & Muslim, D. (2018).
Pengaruh Geological Strength Index (GSI)
Terhadap Nilai Faktor Keamanan Melalui
Simulasi Kestabilan Lereng Tambang,
Kecamatan Batu Kajang, Kabupaten Paser,
Kalimantan
Timur,
Geoscience
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AmericanRockMechanics
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254

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

IMPACT OF UNDERGROUND DISPLACEMENT BEHAVIOR ON
SLOPE FAILURE INITIATION DUE TO RAINFALL BASED ON
EXPERIMENTAL STUDY
Xin Qin1, Masamitsu Fujimoto2 and Yoshifumi Masamitsu3
1

Dept. of Civil Engineering, Ritsumeikan University; 2,3 Dept. of Civil Engineering, Ritsumeikan University
ABSTRACT

Rapid and intermittent soil movement due to precipitation events can occur within a matter of seconds. Even asingle disaster of this type can cause heavy economic losses and casualties. However, studies of sliding initiation
time are scarce, which are most relevant to the early warning of collapse. In this study, we conducted physicalmodel experiments to discuss the deformation behavior due to displacement and slope collapse. This study is
attempted to clarify the relationship between the two phenomena and the mechanism of the initial collapse, toimprove the possibility of accurate prediction before the occurrence of the collapse. Three kinds of model slopesof a same shape with different densities were made under condition of the presence or absence of the weatheredsoil bedrock zone (low-permeability rock). We place a total of 12 accelerometer on two longitudinal line (left andright) with three points. The accelerometers were embedded at depths of 2 cm and 10 cm under the soil surface, torecord the deformation of the model slope during rainfall. The accelerometer can detect significant displacementchanges for low-density soil before the initial collapse. After the rainfall, the displacement was observed
continuously. These results indicate that the possibility of predicting the collapse phenomenon can be improvedby discussing underground displacement.
Keywords: Slope callops, Deformation behavior, Rainfall, Laboratory experiment
INTRODUCTION

widely put into practical application in Japan [4].
Also, in Japan, disasters waring will be issued whenheavy rain increases risk of sediment-relate disasters,
the criteria of issue of the waring and advisory arebased on rainfall intensity and soil water index.
Although multiple measurement techniques andtheories are constantly being refined, collapsing is
still having a complex physical mechanical process.
Excluding the intensity of rainfall, disaster warnings
for slope failures are mostly established by measuring
the changes in moisture or groundwater level in thesoil and the displacement of the topsoil [5]. Slopes innatural environments have complex soil formations,and the distribution of groundwater is also influenced
by these formations, showing great spatial variability.
[6] These extreme criteria on current early warning
systems are highly empirical. A better understandingof the processes during the collapse event can provide
a correct assessment for treatment method and reducethe damage caused.
The objective of this study was to determine
whether measured changes in slope acceleration are
adequate for predicting collapse events. We
performed laboratory experiments using a modelslope of varying density, and placed accelerometers
within the slope to record triaxial acceleration
changes at multiple points. Then, we determined theeffects of slope deformation on the early stages of

Due to global climate change, the frequency andintensity of precipitation has recently increased inJapan, leading to severe sediment disasters caused byslope collapse and debris flow. More than 1,000
sediment disasters occur in Japan every year, with afatality rate of 62% among those in the affected areas[1]. Sediment disasters are difficult to predict
accurately and usually occur within a short period oftime; they carry a risk of significant loss of life andproperty due to suboptimal evacuation times.
Therefore, it is important to obtain accurate
information about the vulnerability of different
regions to sediment disasters, particularly in Japan,
which has fragile geological conditions.
Slope collapse is influenced by various
environmental factors, which in turn affect physicalslope properties such as shear force and pore waterpressure [2]. When the shear strength or water content
of unsaturated soil changes slowly, it is possible toprevent the resulting progressive slope movement;
however, rapid and intermittent soil movement canoccur within seconds, without warning. Measuring
the deformation behavior of slopes is considered to be
one of the effective methods to assess the degree ofslope stability [3]. Measurement of developing
displacement to predict slope collapse has also been
255

GEOMATE – Kyoto, Japan, 3-5 November 2021

slope collapse.

bedrock zone. For each experiment, we added soil
onto the model vertically, in 10-cm layers, andcompacted the soil evenly to ensure that the model
would maintain uniform density. Six observation
point were installed axis metrically at the right andleft sides of the model slope, at distances of 25, 42,
and 80 cm from its base (Fig 1). The accelerometers
were embedded at depths of 2 cm (group A) and 10
cm (group B) under the soil surface, to record thedeformation of the model slope during rainfall. We
performed simulations for Cases 1–3, varying thedensity of the surface layer as 1.2, 1.4, and 1.6 g/cm³,
respectively. The density of the internal bedrock zone
remained constant at 1.8 g/cm³ in all experiments. A
camera was placed at the front of the slope to record
ground surface changes and evaluate the phase of thecollapse during artificial rainfall.

METHODSIn this study, we used a slope model to simulate
natural slope collapse due to rainfall (Fig. 1). The
experimental setup consisted of an artificial rainfall
device, a model slope, and sensors set at various
points within the slope. Artificial rain was released
from directly above the model slope at an intensity of
50 mm/h; the raindrops resembled fog particles. The
model slope was composed of weathered granite,which is widely distributed on the mountains of Japan.
The model slope was composed of dry soil, with awater content of only 5%. The total height of themodel slope, including a 10-cm drainage layer, was
60 cm. The model slope had a width and length of 60
and 100 cm, respectively. The artificial rainfall device
was set directly above the model slope, and theraindrops were in the form of mist with an intensityof 50m/h.
To reproduce the multi-layered structure of slopesin the natural environment, the model slope wascomposed of two layers, a surface layer, and anunderlying permeable layer, which simulated the soil
Surface layer
GroupB
GroupA

0

25

44

10cm
2cm

80

RESULTS AND DISCUSSION
Each laboratory experiment produced 12 set ofsoil deformation behavior observations for various
locations and soil depths. Triaxial acceleration wasmeasured at the onset of artificial rainfall, andthroughout the experiment at a frequency of 24 times
per second. Each measurement included bothdynamic and static acceleration changes, where
dynamic acceleration is caused by displacement andstatic acceleration by gravitational acceleration. Thus,
if there was no change in acceleration between two
measurements, the acceleration value of the axis did
not return to zero; instead, the acceleration wasstabilized due to projected intrinsic gravitational
acceleration. Such acceleration changes caused byinternal displacement produced a gradient thatchanged over time. The results for all experiments areshown in Fig.2-4.

(cm)
60
50
40
30
20
10
0
100 (cm)

Slope failure and deformation behavior
In Case 1, surface cracks were first observed 72
min after the start of rainfall. A long crack was
observed at the top edge of the model slope nearobservation point 1. The crack developed gradually,
and the surface soil slid downward from the crack.
Accelerometers B3 and B6, at the front of the slope,
showed significant deformation starting at 3 min after
the onset of rainfall, and all accelerometers showed
deformation within 15 min after the onset of rainfall.
All shallow accelerometers (group A) showed clear
changes in acceleration along the X-axis, withgradients approaching 90° at A1, A2, A3, A5, and A6.
The deformation gradually stabilized as rainfall
continued; a few minutes before the first crack
formed, the X-axes of A1, A5, A6, B2, and B3; theY-axes of A1 and B1, the X-axes and Y-axes of A6
and B6 underwent large acceleration changes.
In Case 2, the first crack was observed between
observation points 5 and 6 at 75 min after the onset of

Observation point

Fig. 1 Schematic of the model slope used in thisstudy. Numbers indicate the positions ofaccelerometers placed axis metrically within themodel slope, at distances of 25, 42, and 80 cm fromthe base, and depths of 2 cm (group A) and 10 cm(group B).
256

GEOMATE – Kyoto, Japan, 3-5 November 2021

rainfall. A lateral crack developed gradually and cut
off the entire sloping surface. The lower soil layer slidoff, followed by the individual upper soil layers,
eventually leading to collapse of the entire slope.
Almost simultaneously with the rainfall, B4 began torecord a sustained deformation behavior. Within 30
min after the onset of deformation, all accelerometers
recorded deformation.
Among all cases, Case 3 showed the shortest timeto initial cracking. A small part of the soil layer
cracked near observation point 3, at the foot of theslope, followed by gushing of groundwater from thesaturated soil. The cracks expanded upward from theinitial crack, gradually leading to collapse of theentire slope. The crack and slope collapse in Case 3

occurred rapidly and abruptly. On the other hand, inCase 3, all accelerometers were constant at theirinitial values until the initial crack, except Y-axis of
A1 and X-axis of A2 recorded subtle changes in theminutes before the initial cracking occurred. In a
relatively loosely constructed soil layer such as inCase 1 (ρ=1.2g/cm³) and Case2 (ρ=1.4g/cm³), thedeformation behavior can be observed continuously.
On the contrary, in a soil layer with a hard structure
as Case 3(ρ=1.6g/cm³), the deformation behavior
then becomes insignificant.
Comparison with deformation behavior in threecases
To summarize the experimental results for all

Grounp A

Grounp B

Acceleration (cm/s²)

0.15
0.10 A1
0.05
0
-0.05
-0.10
0.15 A2
0.10
0.05
0
-0.05
-0.10
0.15 A3
0.10
0.05
0
-0.05
-0.10
0

30

60

90

Fig. 260

90

Fig. 3

z
B4

A5

B2

B5

A6

B3

B6

0

30

60

90

0

30

60

90

0

x

y

A4

B1

A5

B2

B5

A6

B3

B6

0

30

60

90

0

30

60

90

0

B4

Time (min)Results of the internal deformation behavior of Case 2257

initial crack

30

Time (min)Results of the internal deformation behavior of Case 1
Grounp B

Acceleration (cm/s²)

30

y

B1

Grounp A

0.15 A1
0.10
0.05
0
-0.05
-0.10
0.15 A2
0.10
0.05
0
-0.05
-0.10
0.15 A3
0.10
0.05
0
-0.05
-0.10
0

x

A4

z

60

90

initial crack

30

60

90

GEOMATE – Kyoto, Japan, 3-5 November 2021

Grounp A

Grounp B

A4

Acceleration (cm/s²)

0.15
A1
0.10
0.05
0
-0.05
-0.10
0.15 A2
0.10
0.05
0
-0.05
-0.10
0.15 A3
0.10
0.05
0
-0.05
-0.10 0

30

60

90

z

B5

A6

B3

B6

0

30

60

90

0

Time (min)

30

60

90

initial crack

B4

B2

0

30

60

90

Results of the internal deformation behavior of Case 3

three cases, the rate of change along the Z-axes was
smaller than those along the X- and Y-axes. For
example, in Case 1, Z-axis values at A3 and B1 were
nearly constant with respect to the X- and Y-axes,
whereas in Case 2, this contrast was more pronounced
at observation points A1, A2, A4, A5, B2, B3, and B4.
Thus, the deformation movements of the soil layer atthese observation points resembled rotational rather
than lateral movement.
For accelerometers installed at the same depth, weobtained similar trends but with different magnitudes
at certain observation points. For example, in Case 1,
the similar trends of variations were obtained for theX- and Y-axes at A1 and A2, the X-axes at A3 andA6 , etc. In Case 2, the similar trends of variations
were obtained for the X-axes at A2 and A5 and Xaxes at A3 and A6. Meanwhile, similar trend ofvariations was obtained at different depths for
Y-axisno-motion

initial crack
remotion

45

75

accelerometers at the same observation point. For
example, in Case 1, the similar trend of variations was
obtained for the Y-axes at A1 and B1, the X-axes at
A4 and B4, etc. In Case 2, similar trends of variations
were obtained for the X- and Y-axes at A1 and B1,
A5 and B5 , etc. The deformation behavior showed it
has a propagation range, the accelerometers whichcloser to the deformation point can recorded withlarger variation. Therefore, for deformation behavior,
it is not comprehensive to assess the stability of thewhole slope by the measurements at a local point. In
addition, for example, the deformation moves of A4
and B4, A2 and B2, located at different depths in thesame location in Case 2, are more pronounced at
depth than at shallow depths. These results show themeasurement of deformation movements not only on
the surface but also deep in the slope allows a better
assessment of the stability of the slope. Also, theX-axismotion0.15
Acceleration (g/cm³)

yA5

Fig. 4

X-axismotionx

B1

Y-axisno-motion

moving stage change
remotion

0.10
0.05
0

-0.05
-0.10

0

15

30

60

90

115

Time (min)

Fig.5

0

15

30

45

Time (min)Examples of classification in the deformation stage.

258

60

75

GEOMATE – Kyoto, Japan, 3-5 November 2021

measurement of multiple locations and multiple
depths provides a better understanding of thechanging conditions of the slope.

still be changing. When the value obtained in at least
one axis does not stabilize and is still changing, thepoint where the accelerator is located is considered to
be in motion.
As show in Fig.5, for example, in Case2-A1, themotion process of X axis can be divided into threestage
process:
motion–stationary(no-motion)–remotion. The Y-axis, on the other hand, only goes
through two phases: motion and stationery. The
motion patterns of Case2-A1 can be classified as in
the right side of Fig.5: The initial movement started 7
minutes after the rainfall and lasted until 59 min. 59
min to 71 min were stationary(no-motion) and after
71 min were observed to be in re-motion.
As mentioned above, the deformation behavior ismore pronounced in soils possessing relatively loose
structures, so we classify the results of thedeformation behavior obtained in Case 1 and Case 2
in stages, that have a relatively low-density

Moving stage classification of deformation behavior
When comparing the deformation behavior ofeach accelerator in the same case, it can be found thatthe deformation behavior can be divided into several
different stages. We calculated the slope change ofthe measured values in the graphs of the experimental
results of the deformation behavior, using the curve
fitted to the average per unit time to assess whether
the soil was moving or stationary at any given point
in time.
The variation in three directions obtained by eachaccelerometer is not all the time in same state of
motion. While the value in one direction is in aconstant state, the value in the other direction could

Moving stage

B1

A1

B2

A2

B3

A3

B4

A4

B5

A5

B5

A6

motionno-motion

remotion

60

75

MotionNo-motion

Remotion
0

15

30

Fig.6

Moving stage

45
Time (min)Classification of the deformation stage of Case1

B1

A1

B2

A2

B3

A3

B4

A4

B5

A5

B6

A6

motionno-motion

remotion

45

60

75

MotionNo-motion

Remotion
01530Time (min)Fig.7

Classification of the deformation stage of Case1

259

GEOMATE – Kyoto, Japan, 3-5 November 2021

CONCLUSION

soil.( Fig.6 ~ 7)
In Case 1, as rainfall proceeded, the recorded
acceleration changes gradually stabilized, with lesspronounced changes than those recorded during theprevious phase. Except for B4 and B5, which stopped
recording deformation behavior after stabilization at
36 and 37 min after the rainfall, respectively, allaccelerators went through three-stage process:motion–stationary(no-motion). During the period of
45-65 minutes after the rainfall, almost all
accelerators were in a stable state. On the other hand,and before this time, the deformation behavior of B1
located deep at the top of the slope and B3 at the footof the slope were once again active. Near observation
point 1, where accelerator B1 is located, we observed
the occurrence of initial cracks.
In Case 2, all accelerometers observed
deformation behavior 28 minutes after the onset of
rainfall, and at A1, B1, A2, B2, A3, B3, A4, A5, B5,
and A6 underwent a motion–stationary(no-motion)–remotion change as described above as the rainfall
progressed. In, A5, B5, A6 and B6, at least one axis
was observed to be continuously deforming until the
initial crack occurred. Especially around the initialcollapse, at the locations of observation points 5 and6, the deformation behavior is continuously occurring.
It can be assumed that local changes will affectthe overall force balance, and small-scale changes
will cause a larger chain reaction. Therefore, local
deformation appears to be an important trigger ofoverall slope collapse. It is also indicated that there isa higher probability of observing initial cracks in thevicinity of places where deformation is continuously
occurring or where continuous deformation isobserved again after settling down for a while.
Together, these results indicate that internal
deformation movement reflected wide variation in thestrength of the soil layers. In low-density soils
structure, internal deformation behavior occurred
after rainfall and before collapse; this was acontinuous, readily observable process. Internal
deformation behavior that spreads within a small
distance. Deformation closer to the observation point
had a greater impact and was more likely to alter thebalance of forces within the soil layer at that point.
Therefore, local deformation movement is an
important factor that can trigger overall slope collapse.
In low-density soils structure, deformation movement
prior to initial cracking occurred in three stages.
These stages can be used to estimate the potential
collapse location for an unstable slope after the onsetof rainfall.

In this study, we examined the characteristics ofdeformation behavior within a slope model during
artificial rainfall. Our findings suggest that, in a lowdensity soil structure, deformation movement within
the slope can serve as a criterion for predicting thelocation of initial slope collapse. These deformations
are continuous, pronounced, and can be divided into
several stages. The measurement of the deformation
behavior inside the slope can be one of the initial
criteria to determine the location of the initial
cracking occurrence. The deformation has a certain
propagation in distance, so the local deformation
behavior can be considered as an important factor to
trigger the overall collapse. In some instances, thedeformation behavior at depth are more significant
than those at the surface, and the measurement of
deformation behavior at different depths can provide
a better understanding of the stability of the slope.
The surface area of the experimental model usedin this study was not large and accommodated alimited number of sensors. The detection of
deformation movement at greater depths, and over alarger spatial scale, would be useful for designing
more effective evacuation warning procedures tomitigate sediment damage from disasters.
REFERENCES[1] Japan Ministry of Land and Transport
Infrastructure, “Number of sediment-related
disasters in 2018”, 2018.
[2] Muntohar Setyo Agus, “Rainfall infiltration:
infinite slope model for landslides triggering by
rainstorm”, Nat Hazards, vol.54, 2010, pp.967984.
[3] Sasahara K. and Sakai N, “Shear Deformation
Development and the Increase of Pore Pressure
due to Rainfall Infiltration in Sandy Model
Slope under Different Inclination”, Int. J. of
GEOMAT, vol. 1, Oct. 2011, pp.64-70.
[4] Noki Sakai, “Advanced Trends in Slope
Measurement”, National research institute for
Earth science and disaster Prevention, 2016.
[5] Kazuhiko Nagata, “The Importance of Data
Quality Control in Disaster Prevention and
Mitigation”, Meteorological Agency Japan,
2010.
[6] Fujimoto, Masamitsu; Kosaki, Kenichiro; Baba,
Nokia, “Flow characteristics of basement rockgroundwater using tracers in weathered granite
mountains.”, The Japanese Forest Society, 2013260

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

THREE-DIMENSIONAL ANALYSIS OF GEOGRID REINFORCEDFLEXIBLE PAVEMENT USING FINITE DIFFERENCE PROGRAM
FLAC3D
Rojimol J1, Umashankar B2
1

Department of Civil Engineering, IIT Hyderabad, India, 2 Department of Civil Engineering, IIT
Hyderabad, India
ABSTRACT

India has the second-largest road network in the world after the United States. But most of the roads are lessdurable resulting in a large number of potholes, cracks, and ruts. In recent years, geosynthetics have created an
impact on enhancing the life of the pavement. Numerous studies have been done on the application ofgeosynthetics on the flexible pavement to reduce rutting behavior and fatigue cracks. Laboratory and field studies
have limitations such as cost and time constraints. These limitations can be overcome by making use of numericalmodels. This research focuses on the numerical analysis of biaxial geogrids in flexible pavement. Proper
knowledge of geogrid-pavement layer interaction is essential to obtain an understanding of strains, stresses, anddeflections in pavement layers. In this study, a three-dimensional model of the geogrid reinforced unpaved road isdeveloped using the finite difference program - FLAC3D. Appropriate constitutive models are selected for the
pavement layers. Geogrid is modeled using a linear elastic ‘geogrid’ element in FLAC 3D. Pavement layer
thicknesses in the analysis are taken as per the codal provisions of IRC: 37-2012. The study shows that geogrid ismore efficient for settlement ratio (settlement under load with respect to width of loading) higher than 2% for weak
subgrade. The optimal location of geogrid is proposed at one-third base thickness from the top of the surface ofthe pavement. This study is useful for construction sites where competent aggregates are not available and subgrade
CBR is less than 5%.
Keywords Geogrids, optimal location, static loading, FLAC3D

wheel
load.
Consequently,
time-consuming
laboratory and field tests can be avoided. There areseveral numerical methods used by researchers suchas Multilayer elastic theory, Finite difference
methods (FDM), Finite element methods (FEM),
Boundary element methods (BEM), etc., to estimate
stress, strain and surface deformation in pavement.
Duncan [2] did pioneering work on the analysis of
unreinforced flexible pavement using the FEM
technique. He validated the model with elastic
solutions and highlighted that this method is also
capable of simulating the actual behavior of thepavement with inherent nonlinear material properties.
Researchers have used FEM softwares, such as
ANSYS, ABAQUS, ADINA, PLAXIS, etc., fornumerical modeling of pavement. Wathugala [14]modeled reinforced pavement using finite element
software ABAQUS by placing geogrid at the basesubgrade interface and found significant reduction inrut depth. Ling [7] used PLAXIS2D to perform theanalysis of reinforced pavement subjected to static,
cyclic, and dynamic loading. Leng [6] used ABAQUS
to conduct analysis of geogrid reinforced granularbases overlying soft subgrade. Saad [16] had used
ADINATM for three-dimensional finite element
analysis of geosynthetic reinforced flexible pavement
subjected to dynamic loads. It was recommended thatgeogrid should be kept at the lower one-third of the

INTRODUCTION
Geosynthetics have been successfully applied invarious civil engineering problems, especially toenhance the long-term performance of pavement.
Construction sites where competent aggregates are
not readily available, geosynthetic reinforcement
technique can be a viable option. Commonly usedgeosynthetics in pavement applications include
geotextiles, geogrids, and geocells. Researchers haveexamined the improvement in structural performanceof pavement using these products via large-scalelaboratory tests, full-scale field tests, and numericalmodeling. Perkins [11] highlighted that geogrid whenplaced in base layer improved the performance ofpavements. It was found that stabilization of
pavements using geosynthetic reinforcement has led
to a reduction in granular base layer thickness ([4],[6], [9], [11]), rutting and fatigue strains ([3], [14]),and reflective cracking ([1]), thereby increasing thepavement service life.
Laboratory and full-scale field tests are often
preferred but are subjected to cost and time
constraints. With the development of advanced
computational facilities, numerical modeling ofpavement can aid in calculating strains, stresses, anddeflections occurring in pavement layers and properly
study the geogrid-pavement layer interaction under261

GEOMATE – Kyoto, Japan, 3-5 November 2021

base layer thickness irrespective of subgrade strength.
Ahirwar [13] conducted analysis of reinforced
pavement using PLAXIS2D and Mohr-Coulomb
model was used for various pavement layers.
This study considers finite difference method forthe analysis of unpaved road subjected to wheelloading. Finite difference method is one among thenumerical methods used in the analysis of flexible
pavement. Fast Lagrangian Analysis of Continua(FLAC3D), being an explicit FDM program, solves
all equation of motion and has a variety of nonlinear
constitutive models suitable for modeling nonlinear
stress-strain characteristics of pavement materials.
Goud [10] conducted numerical analysis ofreinforced unpaved road using two-dimensional finite
difference program FLAC2D. Three dimensional
analysis provides a better understanding of behaviorof pavement under wheels of different configuration[16]. However, the present study focuses on theanalysis of unreinforced and reinforced unpaved
roads under stationary wheel loading. In this study,
explicit finite difference program FLAC3D is used todevelop a three-dimensional comprehensive model ofgeogrid reinforced unpaved road. This study uses a
simple constitutive material model- Mohr-Coulomb
model -for modeling the nonlinearity of base andsubgrade. The developed numerical model issubjected to static loading. The study also focusses onsuggesting the optimal position of geogrid based on
the load improvement factor and the effect of axial
stiffness on efficiency of geogrid reinforcement. This
study can be further used for analysis under various
wheel configurations and moving wheel loads andconsidering more advanced material models fordifferent pavement layers.

given in Table 2. Geogrid-soil interface properties arechosen based on Hedge [17] and Itasca [18].
Table 1. Properties of base course and subgrade
Granularbase
MohrCoulomb

Property
Material model
Mass density, d,(kg/m3)
Elastic modulus, E,
MPa
Poisson’s ratio, 
Cohesion, c, kPa
Friction angle, ,degrees
Dilation angle, ,degrees

Subgrade
MohrCoulomb

2100

1800

50

10

0.35
35

0.42
20

40

4.9

10

0

Table 2. Properties of various reinforcements

Properties
Material model
Elastic modulus,
E, MPaPoisson’s ratio, Thickness, t, mm
Coupling springcohesion,
cs_coh, kPa
Coupling spring
Friction angle,
cs_fri, deg.

MATERIAL PROPERTIES
A typical unpaved road section consists of
granular base course layer and subgrade layer. One of
the challenges in constructing unpaved roads is theavailability of competent aggregate for granular bases.
In this study, typical marginal aggregate with elastic
modulus of 50 MPa is taken. Thickness of aggregate
layer is chosen as 250 mm, as recommended byIndian Road Congress [5]. According to [7], [11],geosynthetics have shown predominant effect inimproving weak subgrade. Therefore, relatively soft
subgrade with elastic modulus of 10 MPa withthickness 750mm is considered. The material
properties of pavement layers and geogrid are chosenfrom a similar study [6]. Table 1 gives the propertiesof the pavement layers. Axial stiffness of geogrid is amajor factor in finding the efficiency of geogrid
application in pavement [13], [10]. Properties ofvarious geogrid reinforcements used in the model are

Reinforcement
1Linearelastic
1e8

Reinforcement2Linearelastic
4e8

Reinforcement
3
Linearelastic
8e8

0.35
3

0.35
3

0.35
3

30

30

30

25

25

25

NUMERICAL MODELINGFLAC3D is capable of modeling complex behavior ofcontinuum models including non-linear material
behavior, large displacements and strains.
Considering the symmetry, one-fourth section of
unpaved road is modelled. Boundary conditions arechosen so that fixed support is provided at the bottom
and roller support at left and right boundaries. Lateralboundaries are kept at 0.75 m from center and bottom
boundary is kept at 1.0m vertically down as in [6]. Fig.
1 shows the numerical model developed in FLAC3D.

262

GEOMATE – Kyoto, Japan, 3-5 November 2021

FIG.1 Numerical model in FlAC3D

FIG. 3. Vertical displacement contours of reinforced
unpaved section

Granular base course and subgrade are modelledusing the Mohr-Coulomb plasticity model. Modelingof biaxial geogrid is done using linear elastic ‘geogrid’element available in FLAC3D. Uniform loading of 550
kPa is applied. Large-strain mode is activated in orderto account of the significant plastic deformations by
unpaved road [12]. The convergence of the model in
FLAC3D is ensured by finer discretization of mesh andsatisfying the criterion of reaching maximum
unbalanced force ratio of 1e-6 for the equilibrium. In
the analysis of reinforced pavement models, a refinedmesh size around the interface equal to 0.8 times themesh size of surrounding region is used in order tostudy the effect of geogrid. Vertical displacement
contours for unreinforced and reinforced models areshown in Fig. 2 and 3. Unreinforced and reinforced
pavement model developed is validated withavailable study in literature [6] by comparing the
maximum surface displacements as given in Fig. 4.

Leng [6] has placed geogrid at the interface ofthe base and subgrade. Whereas this study focusses
on placing the geogrid at various locations in baselayer in order to study the optimal location at which
maximum efficiency of geogrid is observed. This
study is useful for pavements with thicker granularbases overlying soft subgrades [8]. In addition,
reinforcement stiffness is varied to find the range ofstiffness for which settlement reduces.

FIG. 4. Comparison of maximum surface
displacement from this study with Leng [6].
RESULTS AND DISCUSSION
Analysis shows that geogrid reinforced unpaved
section have lesser deformation compared to
unreinforced unpaved sections for given base layer
thickness under same loading. Fig. 5 shows thecontour of vertical displacement of unreinforced andreinforced pavement section under 700 kPa. It isfound that presence of geogrid has reduced maximum
surface deformations by 12%.

FIG. 2. Vertical displacement contours of
unreinforced unpaved section

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GEOMATE – Kyoto, Japan, 3-5 November 2021

defined as the ratio of the bearing pressure underfooting of reinforced section to unreinforced section

under for the same settlement.
FIG. 6. Load-settlement ratio curve
unreinforced and reinforced unpaved roadfor

FIG. 5. Comparison of vertical displacements at 700
kPa for a) unreinforced and b) reinforced pavement
model
Fig. 6 shows the bearing pressure vs. settlement
ratio at various locations of geogrid in base layer.
Inclusion of geogrid was found to reduce the surface
deformations. It was observed that as load increases,
the effect of geogrid was more significant. In addition,
geogrid was more efficient at higher settlement ratio(greater than 2%). Geogrid was placed at threelocations, viz., upper one-third base, mid depth base,and at the interface of base and subgrade. This wasshown to improve the load-settlement behavior. The
optimal location of geogrid in base layer is very
important to assess the maximum benefit imparted bygeogrid. In this study, placing the geogrid at upper
one-third resulted in least deformations (Fig. 6).
Axial stiffness of reinforcement was varied bykeeping geogrid at proposed location. As the axial
stiffness of reinforcement increases, the load taken by
the pavement for a given-settlement ratio was foundto increase. Various geogrid stiffness were used forthe study, viz., 300, 1200, 2400 kN/m. Fig. 7 shows
load vs settlement ratio for varied geogrid stiffness.
Stiffness above 1200 kN/m showed significant effecton load-settlement curve. Load improvement factor isFIG. 7. Load-settlement ratio curve with various
geogrid axial stiffness
Table 3 shows the load improvement factor forvarious reinforcement axial stiffness. Load
improvement factor is more significant for settlement
ratio higher than 2% as reported in [12]. For axial
stiffness equal to 300 kN/m, the load improvement
factor ranges from 1.03-1.06%. Goud [10] observed aload improvement factor of 1.06 for settlement ratio
equal to 8% for the case of reinforcement with axial
stiffness of 292 kN/m, which matches with present
study. Whereas at higher stiffnesses of 1200 and 2400
kN/m, the load improvement factor varies from 1.051.19% and 1.11-1.25%, respectively. For unpaved
roads, Goud [10] has reported load improvement
factors ranging from 1.08 to1.28 for settlement ratiosof 4-16% corresponding to geogrid axial stiffness
ranging from 292-29,200 kN/m. Thus, a stiffness of1200 kN/m give better performance and hence
suitable for pavement with marginal aggregates.
264

GEOMATE – Kyoto, Japan, 3-5 November 2021

of design method, J. Geotech. Eng., Vol. 130, No. 8,
2004, pp. 775-786.
[4] Haas R., Walls J., and Carroll R.G., Geogrid
reinforcement of granular bases in flexible pavements,
Transportation Research Record. 1188, No.1, 1988,
pp. 19-27.
[5] IRC: 37-2012. Guidelines for the design offlexible pavements, Indian Roads Congress, New
Delhi. 2012.
[6] Leng J., & Gabr M., Numerical analysis ofstress-deformation response in reinforced unpaved
road sections, Geosynthetics International, Vol. 12,
No. 2, 2005, pp. 111-119.
[7] Ling H., and Liu H., Finite element studies ofasphalt concrete pavement reinforced with geogrid. J.
Eng. Mech., ASCE, Vol. 129, No. 7, 2003, pp. 801–
811.
[8] Mousavi S.H., Gabr M.A. and Borden R.H.
Optimum location of the geogrid reinforcement in
unpaved road, Can. Geotech. J., Vol. 54, No. 7, 2017,
pp.1047-1054[9] Abu-Farsakh M.Y., Akond I., and Chen, Q.,
Evaluation of Performance of GeosyntheticReinforced Unpaved Roads using Plate Load Tests,
93rd TRB Annual Meet, 2014.
[10] Goud G.N., Mouli S. and Umashankar B.,
Numerical modelling of geosynthetic-reinforced
unpaved roads using FLAC 2D, Proceedings of the
11th International Conference on Geosynthetics,
South Korea, 2018.
[11] Perkins S.W., and Edens M.Q., Finite
element and distress models for geosyntheticreinforced pavements, Int. J. Pavement Eng., Vol. 3,
No. 4, 2002, pp. 239-250.
[12] Benmebarek S., Remadna M.S., andBelounar L., Numerical modeling of reinforced
unpaved roads by geogrid, Turkish Online Journal of
Science and Technology, Vol. 3, No. 2, 2013, pp.
109-115.
[13] Ahirwar S.K., and Mandal J.N., Finite
Element Analysis of Flexible Pavement with
Geogrids,
Transportation
Geotechnics
andGeoecology, Procedia Eng., 189, 2017, pp. 17-19.
[14] Wathugala G.W., Huang B., and Pal S.,
Numerical simulation of geogrid reinforced flexible
pavements, Transportation Research Record 1534,
Washington, D.C., 1996, pp. 58–65.
[15] Zornberg J.G., Advances in the use ofgeosynthetics in pavement design, Geosynthetics
India, 2011, pp. 3-21.
[16] Saad B., Mitri H., and Poorooshasb H.,
Three-dimensional dynamic analysis of flexible

Table 3. Load improvement factor for various
reinforcement stiffness
S
l.
N
o
1
2
3
4

Load improvement factor
Settle
ment
ratio
(%)
4
6
8
10

Reinforce
ment(J=300
kN/m)
1.03
1.05
1.06
1.06

Reinforce
ment(J=1200
kN/m)
1.05
1.12
1.15
1.19

Reinforce
ment(J=2400
kN/m)
1.10
1.15
1.19
1.25

CONCLUSIONS
Three-dimensional analysis of unreinforced andreinforced unpaved model was conducted using
FLAC3D. The reinforced unpaved section showed
12% lesser surface deformations in comparison withunreinforced section corresponding to a settlement
ratio equal to 5%. The optimal location of geogrid in
granular base layer is proposed as one-third depth
from the surface. Another characteristic governing
the reinforcement efficiency is choosing theappropriate axial stiffness of geogrid. As per thepresent study, reinforcement stiffness higher than
1200 kN/m was found to be more efficient than lower
stiffness values. Judiciously choosing the optimal
location and geogrid axial stiffness can save hugeamount of construction cost and time. This study is
relevant at places where competent aggregatematerial is not readily available and for soil subgradeof low CBR ranging between 2 and 5%. Also, theproposed research can be used for advanced material
models and study the behavior under multiple wheelloads.
REFERENCES[1] Khodaii A., Fallah S., and Nejad F.M., Effects
of geosynthetics on reduction of reflection cracking
in asphalt overlays, Geotext. Geomembr., Vol. 2,
No.1, 2009, pp. 1-8.
[2] Duncan J.M., Monismith C.L., and Wilson
E.L., Finite element analyses of pavements. Highway
Research Record 228, Transportation Research Board,
No.38, 1968, pp. 18–33.
[3] Giroud J.P., and Han J., Design method for
geogrid-reinforced unpaved Roads. 1: Development

265

GEOMATE – Kyoto, Japan, 3-5 November 2021

conventional pavement foundation, J. Transp. Eng.,
Vol. 131, No. 6, 2005, pp. 460-469.
[17] Hegde A., and Sitharam T.G., 2015a. 3Dimensional numerical modelling of geocell
reinforced sand beds. Geotext. Geomembr., Vol. 43,
No. 2, pp. 171–181.

[18] Itasca Consulting Group, I., FLAC3D-Fast
Lagrangian Analysis of Continua in Three
Dimensions, Version 6.0 User’s Guide. Minneapolis,USA. 2019266

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

NUMERICAL STUDY ON THE AXIAL AND RADIAL SWELLINGBEHAVIORS OF UNSATURATED COMPACTED BENTONITE
Shinya Tachibana1, Daisuke Hayashi2, Tomoko Ishii2, Tomohide Takeyama3 and Atsushi Iizuka1
1

Research Center for Urban Safety and Security, Kobe University, Japan
Radioactive Waste Management Funding and Research Center, Japan
3
Graduate School of Engineering, Kobe University, Japan

2

ABSTRACTIn this study, the swelling behavior with swelling limit of unsaturated bentonite specimen according to thedirection of swelling, i.e. limited axial and radial swelling, is analytically investigated through unsaturated
soil/water coupled finite element analyses. The results of the analysis, which includes changes in the distributionof dry density in the specimen formed in the direction of swelling and changes in swelling pressures generated not
only in the same direction as the swelling direction that starts to generate after the gap is filled but also in the
direction perpendicular to the swelling direction, are presented. The analysis results show that the distribution ofdry density and swelling pressure in the specimen differs depending on the direction of swelling, even if thebentonite specimen reaches a saturated and equilibrium state after gap filling and has same average dry densityand dimension. In the case of radial swelling, it is shown analytically that each of the three principal stresses forms
a non-uniform distribution in the radial direction of the specimen.
Keywords: Bentonite; Swelling; Finite element Analysis; Elasto-plastic constitutive law

INTRODUCTION

formation of a non-uniform dry density distribution
within the bentonite. After the gap is closed, theexcess pore water pressure (or suction) gradient in the
bentonite dissipates and the dry density distribution
becomes more uniform, but some inhomogeneity
seems to remain even after the bentonite is saturated
and the mechanical equilibrium is reached. The
presence of gaps has been found to affect, at the sametime, the mechanism of swelling pressure generation.
In this study, finite element simulations ofproblems related to saturation-induced swelling and
gap filling of cylindrical unsaturated compacted
bentonite specimens were carried out to investigate
analytically the differences in the development of
non-uniform distribution of dry density and theswelling pressure in each direction depending on theswelling direction of the specimen due to the positionof the gap. Two types of boundary conditions weredealt with, i.e. both axial swelling with an upper gap
and radial swelling with an outer side gap. In order tofocus on the effect of swelling direction, the initial
and geometrical conditions were set so that the initial
volume and initial dry density of the bentonite
specimens, as well as the specimen volume andaverage dry density after swelling, are common.

Compacted bentonite is expected to be used as anengineered barrier or backfill in radioactive waste
repositories and is part of a multi barrier system thatslows the migration of radionuclides from the waste
body. When blocks of compacted bentonite are
installed as a buffer material and stacked around thewaste body, gaps are created between the blocks or
between the blocks and the surrounding host rock or
waste body. Although such gaps, referred to as"technological voids", can become water channels
allowing groundwater flow if they remain intact, oneof the reasons to utilize bentonite as a buffer material
is that it has a sealing function to close the gaps owing
to its saturation-induced swelling with groundwater
infiltration.
There has been considerable interest inquantitatively understanding the state changes ofbentonite originating from the presence of gaps, andseveral experimental efforts have been reported [1-7].
These studies have focused on the changes in the dry
density distribution within the bentonite, theassociated permeability of the bentonite and theswelling pressure acting on the surfaces in contact
with the bentonite, during the process of filling thegaps and afterwards until the bentonite reaches
equilibrium. The results of these experimental studies
have shown that the average dry density of bentonite
decreases during the swelling process to close thegaps. It has also been measured that this reduction
generally occurs first in the vicinity of the gaps, i.e.
close to the seepage surface, resulting in the

ANALYSIS METHOD OF UNSATURATED
SOIL/WATER COUPLING PROBLEM
The finite element code DACSAR-I [8] was used
to simulate the limited axial and radial swelling of
unsaturated compacted bentonite specimens. The
code is based on the theoretical framework of
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GEOMATE – Kyoto, Japan, 3-5 November 2021

In Eq. (2), pc and ps were formulated in a form
similar to the Cam-clay model to describe the plastic
volume change along the normal consolidation line
(NCL) and plastic rebound line (PRL) shown in
Figure 1, respectively, as follows:

unsaturated soil mechanics [9], capable of dealing
with the coupling problem in which the soil deforms
under the condition where its pore is partiallysaturated with water. In a general unsaturated
coupling problem, the fields of pore-water and poreair pressures are approximately obtained in addition
to the displacement field by simultaneously solving
the spatially discretized equations for the weak formof the equilibrium equation and the continuity
equations for pore water and pore air. This study,
however, assumes the fully drained condition for pore
air where the pore-air pressure is always equal to theatmospheric pressure everywhere in the specimen,
allowing us to disregard Darcy’s law and thepermeability coefficient, which govern the pore air
flow, in carrying out the analysis. The following
sections give an overview of the material models
employed in the analysis.

 1 + e 0 p p = p 0 exp  v  ;  = c,s  −
where  is a compression index denoting the slopeof both NCL and PRL,  is a swelling index
 anddenoting the slope of ‘elastic’ swelling line, pc0
ps0 are initial values of pc and ps , respectively. ec0
and es0 are the reference void ratios corresponding to on the NCL and ps0
 on the PRL, respectively.
pc0

 c and s in Eq. (2) are parameters that control
the expansion/shrink of the yield surface due to thecontribution of effective degree of saturation, and areformulated by the following equations, respectively:

Elasto-plastic constitutive model for unsaturated
bentonite
The elasto-plastic constitutive model for saturated
bentonite proposed by Tachibana et al. [10] wasextended to describe the mechanical behavior of theunsaturated state and used in the analysis. The yieldsurface of the model which has an elliptical shape inthe effective stress space, analogous to the modified
Cam-clay model [11], can be formulated asf ( p, q, pc , ps ) =c = exp (  ( Se ) − 1) ln

material constant which determines the position ofelastic swelling line as shown Fig. 1. It is noted thatthe elastic swelling line is defined as a straight line
connecting the points of pc on NCL and ps on PRL
in e − ln p space at any time. Therefore, the swelling
index  is naturally modelled as a function of thedegree of saturation and is required to satisfy thefollowing relation, in the combination with Eqs. (4)and (5), as)σ  = σ net + sSe 1 and s ( = σ  − p1) are the effective
stress tensor and its deviatoric part, respectively,
where σ net is a net stress tensor (equivalent to thetotal stress σ in this study), s is a matric suction( = pa − pw ; pa : pore air pressure and pw : pore

 =  ( Se ) =   ( Se )water pressure), and S e is an effective degree ofsaturation and Sr0 : its residual value at the state
where s →  ).
Adopting the idea of the model proposed by Ohno
et al. [12] that the size of the yield surface depends on
both plastic volumetric strain  vp and effective degree

condition  = 1 at Se = 1 . In addition,  is required
to monotonously increase according to the decreaseof S e since the model assumes the expansion of yieldsurface due to desaturation. Thus, the function 
was formulated as a candidate satisfying the abovementioned requirement in the formof saturation S e , the following hardening law wasformulated for the evolution of pc and ps :

)

( )

(6)

where the function  determines the slope of elasticswelling line depending on the effective degree ofsaturation. In order for  to reduce  at a fully
saturated state, the function  has to satisfy thesaturation ( = ( Sr − Sr0 ) (1 − Sr0 ) ; S r : degree of((5)

ratio of pc to ps , i.e pc ps = (1 +  )  .  is also a(1)stress ( = trσ / 3 ), q is a stress deviator ( = 3 2 s ).

p = p  vp , Se = p  vp   ( Se ) ;  = c,s(4)

where  is a material constant which determines thewhere M% is a material parameter determining theellipticity of yield surface, p  is an effective mean(

1+  
; (c  1)
 +   s = exp (  ( Se ) − 1) ln; ( 0  s  1)
+  2q+ ( p − pc )( p − ps ) = 0
M% 2

(3)

(

(2)

)

 =  ( Se ) =  1 − Sel + 1

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GEOMATE – Kyoto, Japan, 3-5 November 2021

where  and l are input parameters; the former
determines the maximum magnification ratio of yieldsurface at a driest state ( Se = 0 ), while the latter

Permeability model for pore water flow
The flow of pore water is governed by the Darcy’s
law. In this study, we simply considered only thedependence of the void ratio and adopted thefollowing permeability model proposed by
Börgesson et al. [13].

controls the degree of influence of S e on the swelling
index  .
Elastic region for Se (< 1.0)eElastic region for Se = 1.0ps0k = k0 ( e e0 ) k

ps0

pc0



es0



where k 0 is the hydraulic conductivity correspondingpc0

to the reference void ratio e0 . k is a material
parameter that controls the dependence of the void
ratio on the hydraulic conductivity.



ec0
1−





es



ec

Soil-water characteristic curve modelpsFig. 1

ps

pc

pc

Among
variousmodelsofsoil-water
characteristic curve (SWCC) which describe therelationship between the soil matric suction and thedegree of saturation, the logistic curve model
proposed by Sugii and Uno [14] was used. The curveis characterized by the following equation.

ln pEvolution of elastic region with change insaturation.

(

Sr = Sr0 + (1 + Sr0 ) 1 + s B exp A

The following nonlinear elastic constitutive
relationship, consistent with the elastic swelling linewith the dependence of effective degree of saturation
described above, was used.
σ& = D e : ε&e − KSe S&e 12 
De =  K − G  1  1 + 2GI
3 (1 +  ) p1 
KSe = −p ln Se( +  ) pc

3 (1 − 2  )1 + ec0p and G =K
2 (1 +  )


)

(14)

where A and B are material parameters that
determine a shape of SWCC. Since the present study
basically deals with a monotonically increasing
response of degree of saturation, the hysteresis ofsoil-moisture characteristics was not considered.

(8)
(9)

NUMERICAL SIMULATION OF LIMITEDAXIAL AND RADIAL SWELLING(10)

The simulation was carried out by setting ideal
and well-defined boundary conditions in which water
infiltration and deformation occur in either axial orlateral direction of a cylindrical unsaturated
compacted bentonite specimen. The mixture of Nabentonite (Kunigel V1) and silica sand, in a
proportion of 70/30 in dry mass and with a specificgravity of 2.736, was assumed to be the material to beanalyzed.

in which the elastic bulk modulus and the shear
modulus are respectively given by

K=

(13)

(11), (12)where   is Poisson’s ratio.
In the elastic state, where the current stress islocated inside the yield surface, Eq. (8) is used as theconstitutive relation. On the other hand, in the elastoplastic state, the elasto-plastic constitutive relation
derived by imposing the consistency condition on theyield surface and applying the associated flow rule isused. The derivation of the constitutive relation isomitted for the sake of space, but it can be derived
through the similar procedure to in the generalframework of elasto-plasticity theory.

Analysis condition and input parameters
Fig. 2 show the geometric and boundary
conditions for axial and radial swelling. In axial
swelling, the diameter remains constant and theheight changes, while in radial swelling, the heightremains constant and the diameter changes. In bothcases, the initial dry density of the bentonite specimen
was set to 2.0 Mg/m3, and the initial dimensions were
set so that the swollen bentonite specimen was 25 mm
high, 40 mm in diameter, and had the average dry

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GEOMATE – Kyoto, Japan, 3-5 November 2021

density of 1.6 Mg/m3 after filling the gap. The friction
with the inner wall of the container was notconsidered in the simulation. The permeable surface
was set to be only on the top of the specimen for axial
swelling and only on the side of the specimen forradial swelling. By setting the pore water pressure to
zero on the permeable boundary, a hydraulic gradientis created and water infiltration is promoted as longas there is suction (negative pore water pressure) in
the specimen. The state changes at the numberedlocations in order of proximity from the permeableboundary will be of interest in the next section.
Permeable boundaryGap:
G = 5 mm

CL
R6R5R4R3R2R1

Table 1 Material parameter input in the simulation

A1
A2 Height:
A3 H = 25 mm
A4
A5
A6

Initial height:
H0 = 20 mm

Fig. 2

specimen swells earlier until the gap is closed,
resulting in non- uniformity of the void ratio. After
closing the gap, the volume of the specimen remains
constant, but the distribution is formed in such a waythat the volume change between denser and looser
regions is always balanced with respect to the average
void ratio. In other words, the closer region to thepermeable boundary is re-consolidated, while theregion farther away continues to swell, resulting in
the elimination of the non-uniformity. However, evenafter the entire specimen is saturated and reaches anequilibrium state, the non-uniform distribution of thevoid ratio remains without being completely
eliminated.

Diameter: D = 40 mm

Gap:
G = 2.11 mm Initial diameter: D0 = 35.78 mm(a) Limited axial swelling(b) Limited radial swelling

Notation and symbol
Critical state parameter, M
Compression index, Swelling index at Se = 1 , Poisson’s ratio,  
Reference void ratio on NCL, ec0Pre-consolidation pressure, pc0
Plastic rebound parameter, Sizing parameter, Positioning parameter, 
Se-Dependency adjustment, lReference void ratio for k , e0Hydraulic conductivity at e0 , k 0
e-dependency adjustment, kResidual of S r , Sr0
SWCC parameter, A
SWCC parameter, B

Geometry and boundary conditions.

Table 1 summarizes the values of the material
parameters used in the analysis. Among theparameters, those related to the elasto-plastic
constitutive model were determined from the resultsof consolidation tests, undrained shear tests, andswelling pressure tests on the silica sand mixed
bentonite to be analyzed. The parameters related tohydraulic conductivity and SWCC were also
determined from the results of hydraulic conductivity
and water retention tests on the materials,
respectively.
The initial condition of the bentonite specimens issummarized in Table 2. The specimen was initially
uniform and its stress state was assumed to be
isotropic. The initial value of suction was determinedbased on SWCC, corresponding to the initial value ofdegree of saturation. Since a stress-free initial stressstate was assumed ( p = p net = 0 ), the initial effective
mean stress was calculated from the product ofsuction and effective degree of saturation from Eq. (1).

Value
0.63
0.14
0.028
0.40
0.350
6.795 MPa
0.45
6.37
0.65
2.23
0.368
1.18e-13 m/s
2.15
0.10
-3.00
4.85

Note: M% in Eq. (1) can be obtained by M% = (1 + 2 ) M . For A ,the value corresponding to the suction unit MPa is shown.

Table 2 Initial condition of bentonite specimen
Notation and symbol
Dry density, d,iVoid ratio, ei
Water content, wi
Degree of saturation, S r,iMatric suction, si
Mean total stress, pi
Effective mean stress, pi

Numerical results
Fig. 3 shows the evolutions of the specimen heightin axial swelling and the specimen radius in radialswelling. For reference, the results of the calculation
without the swelling limit are shown in dashed lines.
Comparing the time required for the bentonite
specimen to close the gap, it can be seen that the radialswelling is shorter. Nevertheless, different results
would be obtained if, for example, the aspect ratio of
the specimen is changed.
Fig. 4 shows the isochrones of void ratio
represented along the initial coordinate for both cases.
The closer region to the permeable boundary of theValue
2.0 Mg/m3
0.368
10.0 %
74.4 %
1.54 MPa
0 MPa1.10 MPa

The swelling pressure generation in axial swellingis shown in Fig. 5. Due to the equilibrium of the axial
forces in the specimen, the axial stress is always thesame at all axial positions (black line). It only begins
to increase when contact pressure is applied to the top
surface of the specimen after the gap has been closed.

270

Height of specimen (mm)2.0(a) Axial swelling28

1.9
Gap closure

27

1.8

26

1.7

25

1.6

24

1.5

23

1.4

22

1.3

21
20

1.2
0

10

20

40

Elapsed time (day)25
24

Radius of specimen (mm)

30

1.1

2.1
2.0

(b) Radial swelling23

1.9
Gap closure

22

1.8

21

1.7

20

1.6

19

1.5

18

1.4

17

1.3

16
15

1.2
0

10

20

30

40

1.1

stresses at equilibrium are the same at a point slightly
inside the top surface of the specimen, and the stressstate reaches isotropic.
2.0

Axial and radial swelling pressuresa, r (MPa)

2.1

29

3
)
Average dry density of specimen (Mg/m30

3)
Average dry density of specimen (Mg/mGEOMATE – Kyoto, Japan, 3-5 November 2021

Void ratio e

0.9
0.8
Average after a gap closure: 0.71 (Dry density: 1.60 Mg/m3)

0.7
0.6
0.5
0.4
0.3
2

4

6

8

10

12

14

16

18

20

16

18

20

Initial z-coordinate (mm)1.3
1.2(b) Radial swelling

1.1

Initial distribution: t = 0 (day)Distribution at the moment of gap closure: t = 4.05 (day)Final distribution: t = 40 (day)Void ratio e

1.0
0.9
0.8

Average after a gap closure: 0.71 (Dry density: 1.60 Mg/m3)

0.7
0.6
0.5
0.4
0.3
0

2

4

6

8

10

12

14

Initial r-coordinate (mm)Fig. 4

0.0

a
10

20

30

40

Generation of swelling pressure along theaxial direction in axial swellingFig. 6 shows the generation of swelling pressure
along the radial direction in the radial swelling. In thiscase, even before the gap is closed, there is not only atime delay but also a difference in the magnitude ofeach stress component generated depending on theradial position. In this case too, the axial andcircumferential stresses orthogonal to the swelling
direction show peaks before the gap is closed, but thepeak values become smaller as the position is further
away from the permeable boundary. The only
position where the radial stress is zero is R1, which is
closest to the permeable boundary, and closer to thecentral axis, the greater the negative radial stress.
After closing the gap, the axial and radial stresses
converge to larger values the closer the position is tothe central axis. On the other hand, the residual valueof the circumferential stress is still the largest at theposition close to the central axis (R6), but it is thesmallest at the position slightly inside the side of thespecimen (R2). In the equilibrium state, the stressstate is  r   a =  θ at position R1 and  a   r =  θat position R6, which means the maximum andintermediate principal stresses seem to differ
according to the radial position.

Initial distribution: t = 0 (day)Distribution at the moment of gap closure: t = 19.36 (day)Final distribution: t = 40 (day)0

Gap closure

0.5

Fig. 5(a) Axial swelling

1.0

A5 A6

Elapsed time (day)

1.3

1.1

A4

1.0

0

Evolutions of (a) the height in axial swellingand (b) the radius in radial swelling.

1.2

A3

-0.5

Elapsed time (day)Fig. 3

A1 A2

1.5

Isochrones of void ratio in (a) axial swellingand (b) radial swelling.

CONCLUSIONSThis study investigates analytically thedifferences in the development of non-uniform
distribution of dry density and the swelling pressurein each direction depending on the swelling directionof the specimen due to the position of the gap, i.e.
limited axial and radial swelling. For both axial andradial swelling, the analysis results show that the nonuniform distribution of dry density (or void ratio) in
the specimen formed during the gap filling process
becomes more uniform during the process ofequilibrium after gap closure, but does not reach aperfect uniform distribution in both cases. The
mechanism of generation of the swelling pressureorthogonal to the swelling direction differed between
axial and radial swelling already in the swelling

The radial stress begins to generate immediately after
the water infiltration, starting from the position where
the saturation increases more quickly, i.e. closer to thepermeable boundary, and rises to about 1.3 MPa
before the closure of the gap, after which it turns todecrease. There is only a time lag in the axial response
until the gap is closed. On the other hand, after closing
the gap, it is found that the radial stress at the position
where the swelling preceded the closing increases due
to reconsolidation, while the radial stress at theposition where the swelling occurs later decreases.
The radial stress at equilibrium, as well as the void
ratio, varies along the axial direction of the specimen,
and the closer to the permeable boundary, the smaller
the residual value. The values of axial and radial
271

GEOMATE – Kyoto, Japan, 3-5 November 2021

process before the gap was closed. This seems to berelated to the fact that in axial swelling theaxisymmetric stress condition is maintained, i.e. thetwo principal stresses other than the axial stress are of
the same value, whereas in radial swelling the threeprincipal stresses are different from each other. The
swelling pressure at the equilibrium state after the gap
is closed is also non-uniform, and the radial swellingshows not only non-uniform distribution of axialpressure in the radial direction, but also non-uniform
distribution of radial and circumferential pressure. As
the degree of non-uniformity may vary depending on
the initial and boundary conditions of the bentonite
specimen, more comparisons and investigations arefurther needed.

Axial swelling pressure a (MPa)2.0of the 17th Int. Conf. on Soil Mechanics andGeotechnical Engineering, 2009, pp. 2495-2498.
[2] Gens A., Valleján B., Sánchez M., Imbert C.,
Villar M. V., Van Geet M., Hydromechanicalbehavior of a heterogeneous compacted soil:
experimental observations and modeling,
Géotechnique, Vol. 61 (5), 2011, pp. 367-386.
[3] Dueck A., Goudarzi R., Börgesson L., Buffer
homoginization, status report 3. Technical Report
TR-14-25. Swedish Nuclear Fuel and Waste
Management Co., 2016.
[4] Wang Q., Tang A. M., Cui Y., Delage P.,
Barnichon J, Ye W., The effects of technologicalvoids on the hydro-mechanical behavior ofcompacted bentonite-sand mixture, Soils andFoundations, Vol. 53, Issue 2, 2013, pp. 232-245.
[5] Bian X., Cui Y. J., Li X. Z., Voids effect on theswelling behaviour of compacted bentonite,
Géotechnique, Vol. 69, (7), 2019, pp. 593-605.
[6] Jia L. Y., Chen Y. G. Ye W. M., Cui Y. J., Effects
of a simulated gap on anisotropic swellingpressure of compacted GMZ bentonite,
Engineering Geology, Vol. 248, 2019, pp. 155163.
[7] Watanabe Y., Yokoyama S., Self-sealing
behavior of compacted bentonite-sand mixturescontaining technological voids, Geomechanics forEnergy and the Environment, Vol. 25, 2021,
100213.
[8] Takeyama T., Tachibana S., Furukawa A., A
finite element method to describe the cyclic
behavior of saturated soil, Int. J. of Mater. Scienceand Engineering, Vol. 2, Issue. 1, 2015, pp. 20-25.
[9] Iizuka A., Tachibana S., Takeyama T., Sugiyama Y.,
Nomura S., Ohta H., Extension of unsaturated soil
mechanics and its applications, Geotech. Research,
Vol. 6, Issue. GR3, 2019, pp. 156-176.
[10] Tachibana S., Ito S., Iizuka A., Constitutive modelwith a concept of plastic rebound for expansive soil,
Soils and Foundations, Vol. 60, 2020, pp. 179-197.
[11] Roscoe K. H., Burland J. H., On the generalized
stress-strain behavior of ‘wet’ clay, In: Engineering
Plasticity, Cambridge Univ. Press, 1968, pp. 535609.
[12] Ohno S., Kawai K., Tachibana S., Elasto-plastic
constitutive model for unsaturated soil applied
effective degree of saturation as a parameter
expressing stiffness, J. JSCE, Vol. 63, Issue. 4,
2007, pp. 1132-1141. (in Japanese)[13] Börgesson L., Johannesson L. E., Sanden T.,
Hernelind J., Modeling of the physical behavior ofwater saturated clay barriers. Laboratory tests,
material models and finite element applications,Technical Report TR-95-20. Swedish Nuclear Fueland Waste Management Co., 1995.
[14] Sugii Y., Uno T, Modeling of hydraulic properties
for unsaturated soils, Proc. Symp. Permeability of
Unsaturated Ground, 1996, pp. 179-184. (inJapanese)

Gap closure
R1
R2

1.5

R3

R5

R4

R6

1.0

0.5

0.0

-0.5

Radial swelling pressure r (MPa)

2.0

0

10

20

30

40

20

30

40

20

30

40

1.5
Gap closure

1.0

R5
R1

0.5

R6

R2

0.0
-0.5
R3

R4

Circumferential swelling pressure  (MPa)

-1.0
2.0

0

10
R1

1.5

Gap closure
R2
R3

1.0

R5

R4

R6

0.5
0.0
-0.5
-1.0
0

10

Elapsed time (day)Fig. 6

Generation of swelling pressure along theradial direction in radial swelling

ACKNOWLEDGMENTS
This study was conducted in “The project forvalidating near-field system assessment methodology
in geological disposal system (FY2020)”
commissioned by Agency for Natural Resources andEnergy in Ministry of Economy, Trade and Industryof Japan.
REFERENCES[1] Komine H., Self-sealing capacity of somebentonite buffers in artificial seawater, In: Procs.

272

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

INFLUENCE OF RICE HUSK ASH WITH MINIMAL DOSAGE OFCEMENT ON SHEAR STRENGTH AND MICROSTRUCTURE OF SOIL
Najmun Nahar1, 2*, Sayful Kabir Khan1, 3, Alex Otieno Owino1, Zakaria Hossain1 and Noma Tamaki4
1

Graduate School of Bioresources, Mie University, Japan
Life and Earth Science, Jagannath University, Bangladesh
3
Directorate of Food, Dhaka, Bangladesh
4
Make Integrated Technology Company, Osaka, Japan

2

ABSTRACT
Based on recent research, rice husk ash (RHA) is used as a secondary cementitious material for soil stabilization.
Nominal dosage cement also can improve the engineering properties of soil. Usage of only RHA is unable toenhance the expected strength properties of soil. The present study attempted to investigate the effects of RHA
with the nominal dosage of cement on shear strength and microstructure development of soil. In total, 10
combinations of soil-RHA-cement mixtures comprising of A-2-4 type soil with 5%, 10%, 15% of RHA, and 0.2%,
0.4%, 0.6% of cement were investigated. The Standard Proctor Test, Consolidated Drained Triaxial Test, andScanning Electron Microscopy (SEM) were performed for study purposes. All specimens were moisture-cured for
1, 7, and 90 days. The test results revealed no significant shear strength of the admixtures was identified at
specimens age one day and seven days due to slow chemical reaction. The highest strength was observed for thesoil with 5% RHA and 0.6% cement at 90 days. The SEM test results also exhibited the best microstructuraldevelopment by the chemical reaction among RHA, cement, and water after 90 days of curing for the same mix
type, recommended as the optimum amount for practical purposes.
Keywords: Rice husk ash, shear strength; Calcium silicate hydrate; Scanning electron microscopy

INTRODUCTION

percentage of soil weight to improve soil properties[6,7, 9-14]. Hossain and Sakai [14] added less than
1% cement with clayey soil to improve soil strength,
but that study used cement only, and no RHA wasreported. Several studies were performed on the RHA
and cement stabilized soil, but very little or no work
was conducted on the RHA effects with minimal
cement dosage. Technical and analytical study of soil,
RHA with little cement, provides excellent benefits
for the building the eco-friendly structure, e.g., roads,
dams, paddy field ridges, and other agricultural
construction. Therefore, the present study presents
the effectiveness of RHA with the nominal dosage of
cement, considering the curing periods on the shear
strength and microstructure of the soil. All specimens
were assessed by triaxial test and Scanning Electron
Microscopy (SEM) test with the curing periods of 1,
7, and 90 days of moist curing. Results on the stressstrain relationship, shear strength, failure pattern, andmicrostructure development of soil mix with 5%,10%, 15% of RHA, and 0.2%, 0.4%, 0.6% cement aredescribed in the study.

Rice husk ash (RHA) is an agricultural waste
abundantly obtainable in many rice-producing
countries globally [1]. It creates environmental
pollution due to its being very lightweight after
dumping in open places, roadsides, riversides andmakes public health problems due to local air
pollution [2]. RHA has the highest amount ofamorphous silica (80%-95%) among all agro-wastes[3]. It can be substituted cement and used as asecondary cementitious material in ground
improvement to improve soil strength properties andreduce project cost, waste disposal, andenvironmental contamination [4]. Using soil with
RHA on its own does not give sufficient strength andstability to the soil as having a low pozzolanic
substance [5]. Primarily, cement or lime additives areused with RHA for soil stabilization [6]. Cement is
widely used with many soil types, but lime isappropriate for clay soil [7]. After mixing the RHA,
cement, and optimum water with soil, calcium
hydroxide is produced in the first step of the chemical
reaction. Calcium Silicate Hydrate (CSH) andCalcium Aluminate Hydrates (CAH) gels are formedin the second phase of the reaction, which createsbonding within soil particles and gives the soil
strength [8].
Many researchers have used RHA 5-15% andmore with (2-10% cement) or more cement

MATERIALS AND METHODS
Materials
Soil, as-obtained RHA, and ordinary Portland
cement were used in this study. The soil sample wascollected from the Handa Area of Tsu City in Mie
273

GEOMATE – Kyoto, Japan, 3-5 November 2021

Prefecture in Japan. The significant properties of soil
are shown in Table 1. The particle size distribution
curve of soil (Fig. 1) showed that the soil was wellgraded.

The ordinary Portland cement (OPC) wascollected from the market in Japan. The specificgravity of OPC is 3.15 g/cm3. Usually, the OPC is
composed of 62.7% lime, 21.7% silica, 5% alumina,
2.9% magnesia, etc. [16].

Table 1: Major properties of soil
Preparation of the Specimens with Mixed Design
Parameters
Specific gravity
MDD
OMC
Sand (75 μm - 2 mm)
Silt (5 - 75 μm)
Clay < 5 μm
Liquid Limit, LL
Plastic Limit, PL
Plasticity Index, PI
Textural
USCS
AASHTO classification

Values2.70 g/cm3
1.696 g/cm3
17.50%
87.9%
8.9%
3.2%
37.5%
29.7%
7.8%
Silty sand
SM
A-2-4

Soil particle size

As a part of soil sample preparation, the soil wasdried at 25°C temperature in a room and sieved toeliminate the undesirable leaves and roots from thesoil. In this investigation, 10 soil-RHA-cement
combinations were made from the mixing proportionsof soil with 0%, 5%, 10%, 15% RHA, and 0%, 0.2%,
0.4%, and 0.6% cement. The necessary amount ofsoil, RHA, and cement were gently mixed manuallyfor 10 to 15 minutes in a large tray. About 2% water
was reduced from OMC for ensuring better cement
hydration and added the water content in the soilRHA-cement mixtures. All triaxial and SEM test
specimens were moist cured and reserved in a
desiccator for 1, 7, and 90 days.

RHA particle size

Percent finer (%)

100

Experimental Method

80

Triaxial test method60

The triaxial compression test is one of the mostconsistent techniques for determining soil shear
strength [17]. The tests were performed following thestandard of the Japanese Geotechnical Society (JGS
0520-0524) [18]. The triaxial test specimens of thesoil-RHA-cement
admixturesaremanuallycompacted in a mold, with a height and diameter of
12.5 cm and 5.0 cm, respectively, using a handrammer with a 4.9 cm diameter, a mass of 1.0 kg, anda falling height of 30 cm. Three layers of thespecimens were compressed, with each layer
receiving 20 blows. The consolidated drained tests
were demonstrated under three confining pressures(50 kPa, 100 kPa, 150 kPa). Both graphical andmathematical techniques were used to compute thecohesion (c) and angle of internal friction (φ) of
triaxial test data. As a graphical technique, the MohrCoulomb failure criterion was used, in which
specimens fail due to a critical combination of normal
stress (𝜎𝑓 ) and shear stress (𝜏𝑓 ). The failure envelope
line's equation is as follows:𝝉𝒇 = 𝒄 + 𝝈𝒇 𝒕𝒂𝒏 𝝋
(𝟏)

40
20
0
0.001

0.01

0.1

1

10

Particle diameter (mm)Fig. 1 Particle size distribution curve of soil and RHA
The readymade RHA was collected from Make
Integrated Technology (MIT) company of Osaka Cityin Japan. The significant properties of RHA are
presented in Table 2. The particle size distribution
curve of RHA is shown in Fig. 1. It is noticed thatabout 86.97% of RHA particles passed through a 0.25
mm sieve, indicating an enormous number of fineparticles in the RHA sample.
Table 2 Major properties of RHA [15]Parameters
Specific gravity
Burning temperature
Burning time
Average particle size
Silica (SiO2)
Alumina (Al2O3)
Lime (CaO)
Magnesia (MgO)
Carbon dioxide (CO2)

Values2.12 g/cm3
650°C-700°C
27 hours
0.001-0.3 mm
91.10%
0.03%
0.57%
0.16%
4.35%

where, 𝝉𝒇 is the shear stress, and 𝝈𝒇 is the normal
stress on the failure plane. The cohesion (c) and angle
of internal friction (φ) were calculated using thefollowing equation:
𝝈𝒂 = 𝝈𝒓 𝒕𝒂𝒏𝟐 (𝟒𝟓 + 𝝋/𝟐) + 𝟐𝒄 𝒕𝒂𝒏 (𝟒𝟓+ 𝝋/𝟐) (𝟐)274

GEOMATE – Kyoto, Japan, 3-5 November 2021

where, 𝝈𝒂 is the major, and 𝝈𝒓 the minor effective
principal stress.
Scanning Electron Microscopy (SEM) Test
Methodachieved peak value on 2-3% axial strain except forthe control specimen (Fig. 2). The axial strain
decreased after adding RHA and cement content
compared to the control specimen (Table 3).
The ultimate principal stress difference was
reduced with the increment of cement content for thesame amount of RHA. All soil-RHA-cement
specimens achieved the highest principal stressdifference (σa-σr) under the confining pressure of 150
kPa. However, the maximum principal stressdifference also diminished for all soil-RHA-cement
combination types except the S+5R+0.6C specimen
with the increase of the curing days. Perhaps below
0.6% cement and more than 5% RHA was unable toimprove the shear strength of the soil. The surplus
RHA with a minimal dosage of cement in the soil
prohibited the cement hydration due to the water
absorption by the microporous RHA with the time.

The SEM test specimens of soil-RHA-cement mix
types were collected from compacted triaxial test
specimens. All SEM images were captured at ×500
magnification and a scale of 60 µm to understand themicrostructure development and changes of theparticles in the specimens.
RESULTS AND DISCUSSIONS
Triaxial Test Results
Effects of RHA and cement with curing periods onthe differential stress and axial strain curve of soil

σa-σr (kPa)

The relationship between principal stressdifference (σa-σr) and axial strain (ℇa) curves ofcontrol specimen, soil with 5%, 10%, 15 RHA with
0.2, 0.4, 0.6% cement mix type are shown in Table 3.
These graphs also focused on the relationshipbetween axial strain (ℇa) and principal stressdifference (σa-σr) of the combination types with 1, 7,
and 90 days curing period. It was observed that the
behavior of soil-RHA-cement specimens was
significantly affected by the addition of RHA andcement percentage with the age of the specimens. The
relationship between axial strain (ℇa) and principal
stress difference (σa-σr) of all specimens showed thatpeak strength and brittleness behavior changed due toseparate or combined effects of RHA and cement
percentages. All the soil-RHA-cement specimens

700
600
500
400
300
200
100
0

50kPa
100kPa150kPa
0

2

4

6

8

10 12 14 16

εa (%)
Fig. 2 The principal stress difference (σa-σr) andaxial strain (ℇa) curves of control specimen

Table 2 The relationship between axial strain (ℇa) and principal stress difference (σa-σr) of various soil-RHAcement combination types at 1, 7, and 90 days curing.

700
600
500
400
300
200
100
0

50kPa
100kPa150kPa0

2

4

6

8 10 12 14 16

700
600
500
400
300
200
100
0

50kPa
100kPa150kPa0

εa (%)

90 days2

46σa-σr (kPa)

7 daysσa-σr (kPa)

σa-σr (kPa)

1 day

700
600
500
400
300
200
100
0

50kPa
100kPa150kPa0

8 10 12 14 16
εa (%)

2

4

6

8 10 12 14 16
εa (%)

02

4

6 8 10 12 14 16
εa (%)

700
600
500
400
300
200
100
0

50kPa
100kPa150kPa0

2

4

6 8 10 12 14 16
εa (%)

275

σa-σr (kPa)

50kPa
100kPa150kPaσa-σr (kPa)

σa-σr (kPa)

Soil + 5% RHA + 0.2% cement (S+5R+0.2C)
700
600
500
400
300
200
100
0

700
600
500
400
300
200
100
0

50kPa
100kPa150kPa0

2

4

6

8 10 12 14 16
εa (%)

GEOMATE – Kyoto, Japan, 3-5 November 2021

Soil + 5% RHA + 0.4% cement (S+5R+0.4C)
Table 2 Continued

50kPa
100kPa150kPa

700
600
500
400
300
200
100
0

0 2 4 6 8 10 12 14 16
εa (%)

90 days

50kPa
100kPa150kPa0

2

46

σa-σr (kPa)

7 daysσa-σr (kPa)

σa-σr (kPa)

1 day
700
600
500
400
300
200
100
0

700
600
500
400
300
200
100
0

50kPa
100kPa150kPa0

8 10 12 14 16
εa (%)

2

4

6

8 10 12 14 16
εa (%)

700
600
500
400
300
200
100
0

50kPa
100kPa150kPa0 2 4 6 8 10 12 14 16
εa (%)

0 2

4 6

σa-σr (kPa)

50kPa
100kPa150kPaσa-σr (kPa)

σa-σr (kPa)

Soil + 5% RHA + 0.6% cement (S+5R+0.6C)
700
600
500
400
300
200
100
0

700
600
500
400
300
200
100
0

8 10 12 14 16
εa (%)

50kPa
100kPa150kPa0

2

4

6

8 10 12 14 16
εa (%)

700
600
500
400
300
200
100
0

50kPa
100kPa150kPaσa-σr (kPa)

50kPa
100kPa150kPaσa-σr (kPa)

σa-σr (kPa)

Soil + 10% RHA + 0.2% cement (S+10R+0.2C)
700
600
500
400
300
200
100
0

700
600
500
400
300
200
100
0
0 2

0 2 4 6 8 10 12 14 16
εa (%)

0 2 4 6 8 10 12 14 16
εa (%)

50kPa
100kPa150kPa4 6

8 10 12 14 16
εa (%)

0

2

4 6

700
600
500
400
300
200
100
0

50kPa
100kPa150kPaσa-σr (kPa)

50kPa
100kPa150kPaσa-σr (kPa)

σa-σr (kPa)

Soil + 10% RHA + 0.4% cement (S+10R+0.4C)
700
600
500
400
300
200
100
0

700
600
500
400
300
200
100
0

0 2 4 6 8 10 12 14 16
εa (%)

8 10 12 14 16
εa (%)

50kPa
100kPa150kPa0

2

4

6

8 10 12 14 16
εa (%)

700
600
500
400
300
200
100
0

50kPa
100kPa150kPa0

0 2 4 6 8 10 12 14 16
εa (%)

2

4

6

σa-σr (kPa)

50kPa
100kPaσa-σr (kPa)

σa-σr (kPa)

Soil + 10% RHA + 0.6% cement (S+10R+0.6C)
700
600
500
400
300
200
100
0

700
600
500
400
300
200
100
0

8 10 12 14 16
εa (%)

50kPa
100kPa0

2

4

6

8 10 12 14 16
εa (%)

02

4 6

8 10 12 14 16
εa (%)

700
600
500
400
300
200
100
0

50kPa
100kPa150kPa0 2 4 6 8 10 12 14 16
εa (%)

276

σa-σr (kPa)

50kPa
100kPa150kPaσa-σr (kPa)

σa-σr (kPa)

Soil + 15% RHA + 0.2% cement (S+15R+0.2C)
700
600
500
400
300
200
100
0

700
600
500
400
300
200
100
0

50kPa
100kPa150kPa0

2

4

6

8 10 12 14 16
εa (%)

GEOMATE – Kyoto, Japan, 3-5 November 2021

Soil +1 5% RHA + 0.4% cement (S+15R+0.4C)

Table 2 continued

7 days024

6

8 10 12 14 16
εa (%)

90 days

700
600
500
400
300
200
100
0

50kPa
100kPa
0

2

4

6

700
600
500
400
300
200
100
0

σa-σr (kPa)

50kPa
100kPaσa-σr (kPa)

σa-σr (kPa)

1 day
700
600
500
400
300
200
100
0

50kPa
100kPa8 10 12 14 16
εa (%)

0

2

4

6

8 10 12 14 16
εa (%)

Soil + 15% RHA + 0.6% cement (S+15R+0.6C)
Effects of RHA and cement with the curing periods
on cohesion and angle of internal friction of soil

Hydrate (CSH) gel by the chemical reactions among
SiO2 and CaO in the soil through hydration process,which developed the microstructure of soil.

c (kPa)Fig. 3 shows the cohesion of soil-RHA-cement
mix types considering the curing periods (1, 7, 90days). The cohesion of untreated soil was 76.0 kPa. It
was noticed that there were significant effects ofcuring time on the cohesion of the soil-RHA-cement
combination types. An improvement of cohesion wasobserved for all soil-RHA-cement combination types
at 90 days. The mechanical interlocking among thesoil, 5-10% RHA, and cement particles in the soil
increased the cohesion value of the admixtures overtime. However, 15% RHA content reduced thecohesion value compared to specimens consisting of
5-10% RHA due to less mechanical linkage of theparticles by increasing the inter-particle distancesamong soil, RHA and cement particles.
Fig. 4 represents the angle of internal friction of
all soil-RHA-cement combination types. The angle ofinternal friction of the control specimen was 32°, andit diminished with the increment of the curing days ofthe specimens. Perhaps the fine particles of RHA andcement particles decreased the angle of internal
friction due to the anti-synergetic action between theangle of internal friction and cohesion over time
[17,19].

160
140
120
100
80
60
40
20
0

1 day

7 days90 daysMix types

φ (°)

Fig. 3 Cohesion of soil-RHA-cement combinations
40
35
30
25
20
15
10
5
0

1 day

7 days90 days

Effects of RHA and cement with the curing periods
on the microstructure of soil
Mix types

The SEM images showed that soil microstructure
was changed with the increment of RHA and cement
content over time for all soil-RHA-cement mix types.
The SEM images of the control specimen are shownin Fig. 5. It was observed from the figure that the air
voids of the soil particles are reduced after
compaction. Compared to the control specimen,
significant microstructural changes and development
were observed in the S+5R+0.6C combination 90-day(Fig. 6) curing. The mechanical joining of soil, RHA,
and cement particles in that mixture changed themicrostructure of soil. Moreover, 5% RHA and 0.6%
cement content with water produced Calcium Silicate

Fig. 4 Angle of internal friction of soil-RHA-cement
combinations

277

GEOMATE – Kyoto, Japan, 3-5 November 2021

rice husk ash to explore an economical way to recycle
and reuse agricultural waste. Reviews inEnvironmental Science and Bio/Technology, Vol. 15,
Issue 1, 2016, pp. 47-65.
[3] Ahmad, J., Rosli, M. I. F., & Rahman, A. S. A., The
Effect of Rice Husk Ash (RHA) Mixtures on
Geotechnical Properties of Soil. In Regional
Conference on Science, Technology and Social
Sciences (RCSTSS 2016) (pp. 345-352). Springer,
Singapore.
[4] Xu, W., Lo, T. Y., and Memon, S. A., Microstructure
and reactivity of rice husk ash. Construction and
Building Materials, Vol. 29, 2012, pp. 541-547.
[5]

Choobbasti, A. J., Samakoosh, M. A., and Kutanaei, S. S.,
Mechanical properties soil stabilized with nano calcium
carbonate and reinforced with carpet waste fibers.
Construction and Building Materials, Vol. 211, 2019, pp.
1094-1104.
[6] Basha, E. A., Hashim, R., Mahmud, H. B., and Muntohar, A.
S. Stabilization of residual soil with rice husk ash and cement.
Construction and Building Materials, Vol. 19, Issue 6, 2005,
pp. 448-453.
[7] Hossain, A. S. M. M., Cement and cement-rice husk ash
stabilization of selected local alluvial soils, 1986, A Masters
dissertation in BUET, Dhaka, Bangladesh.
[8] Singh, B., Rice husk ash. In Waste and Supplementary
Cementitious Materials in Concrete, 2018, pp. 417-460,
Woodhead Publishing.
[9] Rahman, M. A., Effects of cement-rice husk ash mixtures on
geotechnical properties of lateritic soils. Soils andFoundations, Vol. 27, Issue 2, 1987, pp. 61-65.
[10] Ali, F. H., Adnan, A., and Choy, C. K., Geotechnicalproperties of a chemically stabilized soil from Malaysia withrice husk ash as an additive. Geotechnical & GeologicalEngineering, Vol. 10, Issue 2, 1992, pp. 117-134.
[11] Alhassan, M., and Mustapha, A. M., Effect of rice husk ash
on cement stabilized laterite. Leonardo Electronic Journal ofPractices and Technologies, Vol. 11, 2007, pp. 47-58.
[12] Ahmad, J., Rosli, M. I. F., and Rahman, A. S. A., The Effectof Rice Husk Ash (RHA) Mixtures on Geotechnical
Properties of Soil. In Regional Conference on Science,
Technology and Social Sciences, 2016, pp. 345-352.
[13] Nguyen, D. T., and Nu, N. T., Effect of different types of ricehusk ash on some geotechnical properties of cement-admixed
soil. The Iraqi Geological Journal, 2020, pp. 1-12.
[14] Hossain, M. Z., and Sakai, T., The effectiveness of nominal
dosage of ordinary cement on strength and permeability ofclayey soil, Journal of the Japanese Society of Soil Physics,
Vol. 110, 2008, pp. 25-35.
[15] MIT, What is plant-based (rice husk) silica? 2018, (inJapanese).
Availableat:https://www.mitcorp.biz/business/biosilica/.
[16] Lee, S. H., Hong, K. N., Park, J. K., & Ko, J. (2014).
Influence of aggregate coated with modified sulfur on theproperties of cement concrete. Materials, 7(6), 4739–4754.
[17] Rachmawati, S. H., Hossain, Z., and Shiau, J., Shear strengthof soil by using clam shell waste as recycle aggregate. Journalof Agricultural Engineering, Vol. 51, Issue 3, 2020, pp. 155160.
[18] JGS 0520-0524, Triaxial compression test of soil, Guidance
and Basic - Soil Test (in Japanese), The Japanese
Geotechnical Society, 2010, pp. 135-150.
[19] Hossain, M. Z., Narioka, H., and Sakai, T., Effect of Ordinary
Portland-Cement on Properties of Clayey Soil in Mie
Prefecture. Journal of the Japanese Society of Soil Physics,
Vol. 130, 2006, pp. 31-38.

Fig. 5 SEM image of control specimen

Fig. 6 SEM image of S+5R+0.6C at 90-day curing
CONCLUSIONSThe study investigated the effects of RHA witha nominal dosage of cement with the different curingdays at A-2-4 type soil. The study can be concluded• An improvement of shear strength was observed
for the S+5R+0.6C combination at 90 days ofcuring.
• Cohesion values of all soil-RHA-cement
combinations increased at 7 and 90 days.
• The angle of internal friction values of all
combination types was decreased sharply also for
7 and 90 days.
• The SEM image on the S+5R+0.6C combination
showed the best microstructural development for
90 days.
• S+5R+0.6C combination can be used for some
applications of ground improvement considering
the curing period.
REFERENCES[1] Pode, R., Potential applications of rice husk ash wastefrom rice husk biomass power plant, Renewable andSustainable Energy Reviews, Vol. 53, 2016, pp. 14681485.
[2] Kumar, A., Sengupta, B., Dasgupta, D., Mandal, T.,
and Datta, S., Recovery of value added products from278

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

EFFECTS OF BASALT FIBER ON STRENGTH AND PERMEABILITY
OF RHA TREATED EXPANSIVE SOILSAlex Otieno Owino1, Najmun Nahar1, 2, Saiful Kabir Khan1, 3, Zakaria Hossain1 and Noma Tamaki4
1

2

Graduate School of Bioresources, Mie University, Japan
Life and Earth sciences, Jagannath University, Bangladesh
3
Directorate of Food, Dhaka, Bangladesh
4
Make Integrated Technology Company, Osaka, Japan
ABSTRACTThe application of stabilized soil is in demand in construction works due to the improved geotechnicalproperties. This research focused on improving the compressive capabilities, and the permeability characteristics
of RHA treated clayey soils using basalt fiber. Important variables included basalt fiber length (3mm, 6mm, and12mm), RHA percentages (5% to 15%), and cement percentage (3%) in the stabilized soil matrix. The samples
were prepared at optimum water content, OMC obtained from standard proctor compaction tests, and thensubjected to unconfined compression strength (UCS) test, constant head permeability test, and scanning electron
microscope (SEM) test. It was observed that the unconfined compression strength of the RHA treated clay
increased with an increase in the length of the basalt fibers and significantly increased when the samples wereallowed to wet cure for 28 days. Similarly, adding fibers into the soil improved the permeability coefficient,improving water flow in the soil. Scanning electron microscope (SEM) test showed the existence of a porous
morphology that increased permeability. Furthermore, through SEM, the randomly oriented basalt fibers' meshingportrayed the reinforcing phenomenon related to improved compressive strength.
Keywords: Soil Stabilization, RHA, Basalt Fiber, Unconfined Compressive Strength, Permeability coefficient
INTRODUCTION

Pozzolanic stabilization technique

Ground improvement is a fundamental step ingeotechnical engineering to improve the strength ofnaturally occurring soils. The application of such
improved soils is in the rise in areas like the subgrade
layers [1] and fill material [2] and stability for shallow
foundations [3] [4]. Major parameters such ascompressibility behavior, deformation coefficients,
failure planes, permeability, and the soil
microstructure interaction must be examined tosupport engineering structures. Several methods havebeen proposed to produce high-strength soils forengineering construction purposes within the last
decade. Soil improvement can be throughmodification of the existing properties or bystabilization [5]. Stabilization methods includedensification techniques using cement-rich mixtures,pozzolans to stabilize the soil chemically, andreinforcement techniques using different types offibers. In most practices worldwide, the uses ofdensification practices by cement have been
considered, but this has led to a tremendous increasein construction costs and environmental impacts
associated with the manufacture of cement [6]. Thisencounter has led to the sustainable use of industrial
wastes like RHA and fibers as potential replacementsor reduction aggregates to conventional cement
densification techniques, as described in the next
section.

One of the predominant pozzolanic materials inengineering use today is rice husk ash (RHA). Ricehusk is a by-product of the rice grain production
process. Upon the controlled burning of rice husks,
we obtain RHA, which is extensively used to improvesoils' geotechnical properties due to its abundance
and is superiorly inexpensive compared to otherconventional stabilizing agents such as cement andlime [7]. Pozzolans are those materials that are rich insiliceous and aluminous compounds and which inthemselves have very minimal cementitious
properties until chemically reacted with calcium
hydroxide from materials such as cement [8] [9].
Using the rice husk ask in soil stabilization is anadvantageous method in terms of improved soil
strength, durability, environmental impacts related tothe uncontrolled disposal of agricultural waste, and areduction in carbon dioxide emissions fromuncontrolled burning in fields [6].
Rahman [11], in his research on the effects of ricehusk ash alone on the UCS of stabilized soils, showedthat with an increase of RHA to 20% increased the
UCS of lateritic soils, after which they started todecrease. Noor [12] also examined the rice husk ashcement ratios and how they positively influenced the
proposed mix ratios, unconfined compressive
strength, and durability. Muntohar [13] results
showed that mixing rice husk ash with 6% lime
significantly reduced swelling and increased279

GEOMATE – Kyoto, Japan, 3-5 November 2021

durability on such improved soils. The literature also
showed that rice husk ash had an optimum percentage
above which the UCS of improved soils decreased.
Alhassan [14] showed that adding rice husk ash
percentages between 0 to 4% to clay soils and lime
specimens significantly increased the UCS value at
specified lime contents. On the other hand, from thesame research, increasing rice husk ash percentages
from 6 to 8% decreased the UCS value considering
the curing period in both cases.
Soil stabilization by rice husk ash involves apozzolanic reaction between the Ca(OH)2 rich cement
or lime and the SiO2 rich rice husk ash in an alkaline
environment leading to the formation of calcium
silicate hydrate (CSH) gel [15]. James [16]investigated the setting process for a lime-excess anda lime-deficient mixture, and the product of thereaction showed the CSH gel by a combination ofthermal analysis, XRD, and electron microscopy. In anutshell, the formation of CSH gel accounted for thestrength of lime-rice husk ash and cement, as shownin Eq. (1).
Ca (OH)2 + SiO2 = CaO0.8-1.5SiO2(H2O)1.0-2.5+ CaO1.5-2.0SiO2.2(H2O)(1)

influence of discrete fiber reinforcement on theuniaxial compression response found out that sandy
clay reinforced with polypropylene fibers and cement
increased the stiffness, deformation coefficient, andcompression strength of the mixtures for every
cement content. Similar results were also determinedby Maher [22] and Consoli [23]
Scope of the Study
This present study focused on stabilization by
pozzolanic reactivity of rice husk ash andreinforcement method by randomly distributed basalt
fiber in the proposed soil matrix. The inclusion ofbasalt fibers improved the strength of the soil RHA
mixtures since, at low cement values, the compressive
strength of RHA treated soils was reduced due to thelimited catalytic Ca(OH)2 elements for the pozzolanic
reaction. Basalt fibers (lengths 3mm, 6mm, and12mm), cement (3% of the dry weight of soil), andrice husk ash (5%, 10%, and 15% of the dry weightof soil) were added to clayey soils and mixed by handto a homogenous matrix for the three sets of RHA
percentages. Samples were then subjected to an
unconfined compressive strength test (UCS),
permeability test and microstructure examination byscanning electron microscope (SEM) to obtain thegeotechnical properties for ground improvement,
considering curing periods of 1 day, 7days and 28
days. The results presented in this study show asignificant increase in the compressive strength andimproved permeability in the new composite soil
matrix.

Fiber stabilization techniques
Fibers inclusion in a soil matrix has proved to add
more intensification in the compressive abilities ofsoils due to the reinforcing technique between the soil
particles. Several pieces of research have been
investigated considering different fibers and theirapplicability in enhancing soil strength from theliterature. Kumar [17] investigated the UCS gain on
the soil by mixing polyester fibers and soft clay andobserved that the compaction degree affected thepresence of fiber reinforcement, the unconfinedcompressive strength of clay increased with theaddition of fibers, and it further increased when fibers
were mixed in the clay-sand mixture.
Hossain [18] also examined the structural
improvement on cement-based matrices using varied
carbon fiber lengths, improving durability andcompressive strength. With the diversity in the fiber
available for soil stabilization, polypropylene andrecycled carpet have also been used before. Where
bender element tests on 126 cylindrical samples of
cement-treated clay with various cement and fiber
contents were analyzed to distinguish therelationships between fiber and cement content andthe small-strain mechanical properties [19]. Park [20]further showed that compacting polyvinyl alcohol(PVA) fiber in different layers inside a cylindricalriver sand specimen improved the compressive
strength as the number of fiber layers increased. Withfibers evenly distributed throughout the five layers, areinforced specimen was twice as strong as a nonfiber-reinforced specimen. A more comparable study
to this research was done by Cristelo [21], where the

MATERIAL AND METHODS
Material properties
The materials used in this research included soil,
rice husk ash (RHA), cement, and basalt fiber (BF).
Figure 1 is a pictorial representation of theengineering materials. A detailed explanation of theproperties of the materials is as discussed below.

Soil

RHA

CementBF 3mm

BF 6mmBF 12mmFig. 1 Experiment materials
Soil
280

GEOMATE – Kyoto, Japan, 3-5 November 2021

Burning time, hour
Silica (SiO2), %Carbon dioxide (CO2) %Potassium Oxide (K2O), %Calcium Oxide (CaO), %Iron Oxide (Fe2O3), %Alumina (Al2O3), %Others, %Basalt Fiber
Fiber diameter, (μm)
Properties
Density, (g/cm3)
Tensile strength, (MPa)Elastic modulus, (GPa)Fiber Lengths, (mm)Fracture elongation rate, (%)

The soil used in this research was collected from
Handa Area, Mie Prefecture, Japan. The soil was airdried for 3weeks then sieved through the 2mm sieve,
after which sieve analysis and hydrometer analysis
were conducted on a 500 grams sample to classify thesoil. According to the American Association of State
Highway and Transportation Officials (AASHTO),
the soil was classified as A-7-5 clayey soils with acomposition of 6.2% coarse sand, 52.6% medium
sand, 39.2% clay. More detailed properties are shownin Table 1.
Rice husk ash (RHA) properties
Rice husk ash (RHA) was obtained from Make
Integrated Technology Co., Ltd, Osaka, Japan. The
RHA was controlled-burned ash at 650-800 °C withhigh purity and high silica content of 91.10%. In thisresearch, unground ash was used, keeping it in its
natural particle size distribution of 0.07 to 0.3mm.
The most dominant particles were between 0.07 and
0.106mm. Table 1 shows the detailed physical andchemical properties of the rice husk ash.
The particle distribution curves from sieve
analysis and hydrometer analysis for soil and sieve
analysis only for RHA are shown in Fig. 2.

27
91.10
4.35
2.40
0.57
0.05
0.03
1.50
6-30
2.63-2.8
4100-4840
93.1-110
3, 6 & 12
3.1

100% Finner

80

60

A-7-5 (Clayey Soil)
Rice Husk Ash (RHA)

40

20

0
0.001

Basalt Fibre (BF) Properties

0.01

0.1

1

Particle Size, D (mm)Basalt fiber is made from basalt rock. The basalt rockis washed and melted, then extruded through small
nozzles to produce the continuous filaments calledbasalt fibers [25]. Its application in engineering
construction reinforcement works is derived from thehigh tensile strength of between 4100～4840 MPa,high elastic modulus ranging from 93.1 ～ 110GPa[26], durability, alkali resistance, and thermal
stability compared to other fibers [27]. Table 1
illustrates the fundamental properties related to thisstudy.Table 3 Basalt fiber (BF) properties

Properties
Soil
Properties

RHA
Properties

Parameters
Specific Gravity, (g/cm3)
Dry Density, (g/cm3)
Optimum water content, %Sand (75 μm - 2 mm), %Silt (5 - 75 μm), %Clay < 5 μm, %Liquid limit, LL, %Plastic limit, PL, %Plasticity Index, PI, %AASHTO classification
Average Particle Size, mm
Loss of Ignition, %Specific Gravity, g/cm3
Burning Temperature, °C

Fig. 2 Particle size distribution for Soil and RHA
Ordinary Portland Cement, OPC
OPC cement was chosen for this research due toits availability in the market.
Testing methods
Standard proctor compaction test
A standard proctor test was done following the JIS
A 1210:2010 standards to evaluate the samples' dryunit weight (MDD) and the optimum water content(OMC). The soil samples (herein, sample refers to a
mixture of soil, RHA, cement, and basalt fiber invaried ratios) were compacted in three layers inside
the compaction mold measuring 10cm diameter and12.68 cm height, using a 2.5kg rammer at a fallingheight of 30cm. A record of 25 blows was registeredfor each layer. A representative sample was taken
and tested for moisture content, and the processrepeated for all water increments.
The codes for the samples under study are
explained in table 2:

Values
2.751
1.646
24.00
6.20
55.56
39.22
58.20
31.05
27.15
A-7-5
0.001-0.3
4-6
2.12
650-700

Table 4 Mix ratios and codes.
Mix CodeS281

Mix description
Soil (Control specimen)

GEOMATE – Kyoto, Japan, 3-5 November 2021

S:5R
S:10R
S:15R
S:5R:3C:1BF3
S:10R:3C:1BF3
S:15R:3C:1BF3
S:5R:3C:1BF6
S:10R:3C:1BF6
S:15R:3C:1BF6
S:5R:3C:1BF12
S:10R:3C:1BF12
S:15R:3C:1BF12

experimental procedures followed Japan standards,
JIS A 1218.

Soil+5%RHA
Soil+10%RHA
Soil+15%RHA
Soil+5%RHA+3%cement+1%
basalt fiber 3mm
Soil+10%RHA+3%cement+1%basalt fiber 3mm
Soil+15%RHA+3%cement+1%basalt fiber 3mm
Soil+5%RHA+3%cement+1%
basalt fiber 6mm
Soil+10%RHA+3%cement+1%basalt fiber 6mm
Soil+15%RHA+3%cement+1%basalt fiber 6mm
Soil+5%RHA+3%cement+1%
basalt fiber 12mm
Soil+10%RHA+3%cement+1%basalt fiber 12mm
Soil+15%RHA+3%cement+1%basalt fiber 12mm

Scanning electron microscope
Microscopic examination was necessary tounderstand the microstructure interaction of the
proposed composite ratios. A scanning electron
microscope was used for all the samples, considering
the curing periods and the percentages of the rice husk
ash in the proposed mix ratios. A sample withdimensions 3mm by 3mm by 0.5mm was collectedfrom the center of the cylindrical samples used in the
UCS test. Soil-RHA mix ratios were examined at amagnification of x500, while the soil-RHA-C-BF mix
ratios were taken at a magnification of x150 toprovide a wider area for spotting the basalt fibers
random distribution in the soil composite matrix.
RESULTS AND DISCUSSION

Unconfined Compression Strength (UCS) Test
Compaction test
UCS test was done on a cylindrical sample withdimensions 5cm diameter and 12.5 cm height toexamine the compressibility behavior of the mix
ratios above. The samples were prepared using acylindrical mold and a rammer weighing 1kg at afalling height of 30cm. All samples were prepared at
the optimum moisture content obtained fromcompaction tests and wet cured for 1day, 7days, and28days at a constant temperature of 25 degrees
Celsius before testing. The data sets obtained fromeach sample were then analyzed for the stress-strain
curve plots used to evaluate the compressive stress(qu) and axial strain (Ԑ) relationship and thedeformation coefficient E50. The testing procedures
were following Japan standards, JIS A 1216:2010.

Compaction test refers to soil densification byremoving air and rearranging the soil particles
through mechanical energy. The degree ofcompaction is measured in terms of the maximum dry
density, γdmax, and the optimum water content, OMC[28]. Figure 3 shows a plot of the compaction curves,emphasizing the maximum values for the optimum
water content, OMC, and the dry unit weight γdmax for
all the sample mix ratios.

Dry unit weight of compaction, γd (g/cm3)

1.9

Permeability test
In this study, a constant permeability test was
carried out to examine the coefficient of permeability,k, for all the sample mix ratios considering curing
periods 1day, 7days, and 28days. Each sample wascompacted in three layers in the compaction mold(10.4 cm diameter and 6.3cm length) at 20 blows per
layer to maintain constant compaction energy acrossall the samples in this study. The samples were wet
cured while maintaining the water content at OMC,
which allowed for enough hydration and pozzolanic
activity in the samples. The constant water head wasmaintained at 45m, simulated by the air pressure of
0.45 MPa in the water chamber on top of thespecimen. Water flowed through the sample until it
was saturated before taking the first reading. Four
readings were taken by measuring the volume of
percolated water and the corresponding time taken forflow. Afterward, the average k of each flow was used
to evaluate the final permeability coefficient (k). The

1.8

Sr = 100

1.7

-

1.6

-

S
S:5R
S:10R
S:15R

1.5
1.4
1.3

S:5R:3C:1BF
S:10R:3C:1BF
S:15R:3C:1BF

1.2
1.1
1.0
10

15

20

25

30

35

40

45

50

Optimum Moisture content OMC, w (%)

Figure 3 Compaction curves
The optimum water content increased with anincreased percentage of rice husk ash. Similarly, uponadding cement into the mix ratios, there was a further
increase in OMC.
On the contrary, the dry unit weight decreased
with the addition of rice husk ash and cement. The
increase in optimum water content was due to thehigh-water affinity by the increasing percentage ofrice husk ash and the hydration effect with cement
mixtures [29]. Furthermore, the decrease in dry
282

GEOMATE – Kyoto, Japan, 3-5 November 2021

density was attributed to flocculation andcementitious compounds in the new composite soil
mixtures.

500

Compressive Stress σ (kN/m2)

Unconfined Compression Test, UCS
This section shows the relationship between
compressive stress, qu and axial strain, ɛ for 5%, 10%,and 15% RHA, and different basalt fiber lengths. In
figure 4, the maximum compressive stresses, qu wereachieved with 5% RHA mix ratios at low axial
strains, ɛ with mix ratio S:5R:3C:1BF12 giving thehighest compressive stress than the control sample,
soil only, S. In Figures 5 and 6, adding RHA to 10%
and 15% respectively, reduced the compressive
stresses with a slight increase in the axial strain. This
increase in axial strain was due to the rearrangementof soil particles and the excess fine RHA particles
filling the voids. Nevertheless, S:10R:3C:1BF12 and
S:15R:3C:1BF12
samples produced significant
stress-bearing characteristics compared to the soil
only S sample.

Compressive Stress σ (kN/m2)

200

350
300
250
200
150
100

0
0

2

4

6

8

10

Vertical Strain Ԑ (%)

Fig. 6 Stress-Strain relationships for 15% RHA
For more reliability of the data sets obtained,curing period was considered for 1 day, 7days, and 28
days as shown in figure 7. From the graph, a slight
improvement in qu was observed for all the samples.
For example, sample S:5R:3C:1BF12 had a qu of 292
kN/m2 after one day, and the value increased to 318
kN/m2 at 7 days of curing. After 28 days of curing,
there was a significant increase in the UCS value for
S:5R:3C:1BF3, S:5R:3C:1BF6 and S:5R:3C:1BF12
with values 438 kN/m2, 453 kN/m2 and 463 kN/m2
respectively. The soil matrix also gained strengthwith an increase in the length of basalt fibers which
enhanced the bonding of soil particles from the meshgenerated by the fibers.

S-Control (1D)
S:5R (1D)
S:5R:3C:1 BF3 (1D)
S:5R:3C:1 BF6 (1D)
S:5R:3C:1 BF12 (1D)
S:5R (7D)
S:5R:3C:1 BF3 (7D)
S:5R:3C:1 BF6 (7D)
S:5R:3C:1 BF12 (7D)
S:5R (28D)
S:5R:3C:1 BF3 (28D)
S:5R:3C:1 BF6 (28D)
S:5R:3C:1 BF12 (28D)

300

400

50

500

400

S-Control (1D)
S:15R (1D)
S:15R:3C:1 BF3 (1D)
S:15R:3C:1BF6 (1D)
S:15R:3C:1 BF12 (1D)
S:15R (7D)
S:15R:3C:1 BF3 (7D)
S:15R:3C:1BF6 (7D)
S:15R:3C:1 BF12 (7D)
S:15R (28D)
S:15R:3C:1 BF3 (28D)
S:15R:3C:1BF6 (28D)
S:15R:3C:1 BF12 (28D)

450

500

100

450

S
-

0
0

2

4

6

8

qu (kN/m2)

Vertical Strain Ԑ (%)

Fig. 4 Stress-Strain relationships for 5% RHA
500
S-Control (1D)
S:10R (1D)
S:10R:3C:1 BF3 (1D)
S:10R:3C:1BF6 (1D)
S:10R:3C:1 BF12 (1D)
S:10R (7D)
S:10R:3C:1 BF3 (7D)
S:10R:3C:1BF6 (7D)
S:10R:3C:1 BF12 (7D)
S:10R (28D)
S:10R:3C:1 BF3 (28D)
S:10R:3C:1BF6 (28D)
S:10R:3C:1 BF12 (28D)Compressive Stress σ (kN/m2)

450
400
350
300
250
200

S:5R
S:5R:3C:1BF3
S:5R:3C:1BF6
S:5R:3C:1BF12

400

10

350
-

S:10R
S:10R:3C:1BF3
S:10R:3C:1BF6
S:10R:3C:1BF12

300
250
-

S:15R
S:15R:3C:1BF3
S:15R:3C:1BF6
S:15R:3C:1BF12

200
150
100
1D

7D

28D

Curing Period, (Days)

Fig. 7 qu versus curing period

150

The high-strength samples in terms of basalt fiber
length were those incorporated with 12mm longfibers. For example, for 5%, 10% and 15% RHA
samples the highest qu’s were obtained from
S:5R:3C:1BF12 (463 kN/m2), S:10R:3C:1BF12 (362kN/m2), S:15R:3C:1BF12 (297 kN/m2), respectively.
The qu increased due to fiber reinforcements andchemical reaction in the soil matrix, leading to silica
gel formation [17] that aided in developing thecementitious properties compared to the control
specimen S, which had compressive stress of 147
kN/m2.

100
50
0
0

2

4

6

8

10

Vertical Strain Ԑ (%)

Fig. 5 Stress-Strain relationships for 10% RHA

283

7.09

8

7.09

From this study, 5% RHA produced the mostapplicable compressive strengths for engineering
construction works. Figure 10 summarises the failure
planes for the 5% RHA samples; S, S:5R,
S:5R:3C:1BF3, S:5R:3C:1BF6, and S:5R:3C:1BF12. The first three samples experienced a simple shear
failure upon maximum compression load, while thehigher strength mix ratio S:5R:3C:1BF12 showed atension failure as shown.

7.09

GEOMATE – Kyoto, Japan, 3-5 November 2021

7
S

6

S:10R

2

3.18
2.38
2.36
2.01

3

S:10R:3C:1BF3

2.80
2.37
2.01

4

3.59

3.59

-

2.82
2.39
2.02

Ԑ (%)

5

1D

7D

28D

S:10R:3C:1BF6
S:10R:3C:1BF12

1
0
--

--

Curing Period (Days)

d

e

7.09

c

7.09

8

b

a

7.09

Fig. 8 Strain values for 10%RHA mixtures atdifferent curing periods

7
S

6

-

5

-

S:15R

2.42
2.75
2.42
3.13

3.59

1D

7D

28D

S:15R:3C:1BF6
S:15R:3C:1BF12

2
1
0
--

Fig. 9 Strain values for 15%RHA mixtures atdifferent curing periods
30S-

E50 (MN/m2)

25

S:5R
S:5R:3C:1BF3
S:5R:3C:1BF6
S:5R:3C:1BF12

20
-

S:10R
S:10R:3C:1BF3
S:10R:3C:1BF6
S:10R:3C:1BF12

15
-

10

S:15R
S:15R:3C:1BF3
S:15R:3C:1BF6
S:15R:3C:1BF12

7
S

6

S:5R

1D

-

2.36
2.37
1.97
2.75

Ԑ (%)

S:5R:3C:1BF3

2.76
2.35
1.99
2.75

2

0

-

2.80
2.38
2.00
3.19

3

5

-

5
4

--

Curing Period (Days)

7.09

7.09

8

7.09

It was observed that cracks occurred in the verticalplane for sample a at 7% strain, and this strain value
reduced considerably to less than 3% for samples b,c, and d after adding basalt fibers. This reduction in
axial strain is shown in Figures 8, 9, and 10 for 5%,
10%, and 15% RHA, respectively. Amongst thebasalt fiber samples, 12mm long fibers had a 1%
increase in axial strain compared to BF 3mm and BF
6mm. This increase was due to the slight extra
deformations needed before the strengthening
benefits were realized [30]. The axial strains reduced
slightly after 28 days of curing, indicating shear
strength development in the soil samples by adding
fibers and cementation from the pozzolanic reaction.

-

2.80
2.75
2.82

3

S:15R:3C:1BF3

3.96

4

2.82
2.78
2.82

Ԑ (%)

Fig 10 Failure patterns (a) S, (b) S:5R, (c)S:5R:3C:1BF3, (d) S:5R:3C:1BF6, (e)S:5R:3C:1BF12

S:5R:3C:1BF6
-

28D

--

--

Fig. 10 Deformation coefficient E50 of different
samples considering curing periods.

S:5R:3C:1BF12Lastly, deformation coefficients, E50 were also
examined for all the samples considering curing
period, basalt fiber length, and rice husk ash
percentages, as shown in Fig. 10. Adding rice husk
ash, cement, and basalt fiber had a tremendous
influence on E50. A significant increase was observed
after 28 days of curing with 5% mix ratios giving thecedbhighest deformation coefficients. For mix ratios, 5R,

0
7D

28D

Curing Period (Days)

1

1D

7D

--

Curing Period (Days)

Fig. 7 Strain values for 5%RHA mixtures atdifferent curing periods284GEOMATE – Kyoto, Japan, 3-5 November 2021

S:5R:3C:1BF3, S:5R:3C:1BF6, S:5R:3C:1BF12 thedeformation coefficients were 16 MN/m2, 20 MN/m2,
26 MN/m2, and 29 MN/m2, respectively, which weremuch higher compared to control specimen S(3MN/m2). S:5R:3C:1BF12 gave the highest value
due to the intense interconnectivity of the longer
fibers compared to the 3mm and 6mm.

verified the significant increase in permeability
coefficient, k
200μm

Permeability test
Fiber mesh joints
Permeability test examines the water flow rate
through a soil medium and is expressed as thecoefficient of permeability, k. This water flow rate
has a significant impact on soils' properties when it
comes to drainage of subgrade material. In this study,
the soil had a very low permeability of 7.1x10 -6,
which was not considered suitable for subgrade orshallow foundation use. Adding basalt fibers into thesoil increased the permeability coefficient, k to 10 -5
for all 5% to 10%RHA samples and 10-4 for 15%RHA
samples, as shown in figure 12. From 1 day to 7 daysof curing, k increased due to the rapid flocculation ofparticles in the sample [31]. It was also evident that k
declined at 28 days of curing due to the cementation
effect that reduced the number of voids in thesamples.

Randomly oriented basalt fibers
Fig. 12 Randomly oriented basalt fiber mesh in the
improved soil matrix (Magnification x150)
60μm

Coefficient of Permeability, k (cm/s)

2x10-4
S
2x10

-

-4

S:5R:3C:1BF3
S:5R:3C:1BF6
S:5R:3C:1BF12

1x10-4-5x10-5-

Porous morphology

S:10R:3C:1BF3
S:10R:3C:1BF6
S:10R:3C:1BF12

Fig. 14 Soil matrix showing a porous morphology

S:15R:3C:1BF3
S:15R:3C:1BF6
S:15R:3C:1BF12

0
1D

7D

28D

CONCLUSIONS
Unconfined compressive strength of compressible
clay having 5% rice husk ash increased with theaddition of basalt fibers from BF3mm to BF6mm to
BF12mm. A similar increase in UCS value wasevident for 10% and 15% rice husk ash but with lower
values than the superior 5% rice husk mixtures.
Clay soil only had a, qu of 146.79 (kN/m2) and a
Deformation coefficient E50 of 3.34 (MN/m2), but
upon improvement, the higher strength mixture,
S:5R:3C:1BF12, showed a significant increase inboth qu and E50 with values of 463 (kN/m2) and29(MN/m2) respectively, after 28 days of curing at 25
֯C. The axial strain within the soil sample duringloading was reduced significantly by the addition ofbasalt fibers. This reduction demonstrated the tensile
ability of the fibers to hold the sample particles
together with minimal deformation.
This mix ratio, S:5R:3C:1BF12, SEM image
confirmed the interconnected microstructure,enhanced by the basalt fiber (BF) mesh between the--Curing period (Days)

Fig. 11 Coefficient of Permeability, k of different
mix ratios considering curing periods.
Scanning electron microscope
Figure 12 shows the meshing of basalt fibers at
low resolutions of x150 and a scale of 200μm. Meshjoints in the S:R:C:BF matrix increased thecompressive stresses in the sample due to the hightensile strength of the basalt fibers. In addition, fiber
meshing also considerably reduced the axial strain
within the sample, as explained earlier. The mixed
chemical reaction between soil, rice husk ash, andcement and the reinforcing behavior of basalt fibers
led to the production of a composite material
inhibiting a porous morphology, as shown in Fig. 13
at a magnification of x500 and a scale of 60μm. Thisporous morphology and the fiber reinforcing points
285

GEOMATE – Kyoto, Japan, 3-5 November 2021

soil particles and the cementitious soil mass, hence
improved compressibility. The pozzolanic reactionthat produced the cementitious matrix can beillustrated in equation 2 below, with 2Ca2 (SiO3nH2O)
representing the CSH Gel.

[10] Ali, F. H., Adnan, A. and Choy, C. K.,
Geotechnical Properties of a chemicallystabilized soil from Malaysia with rice husk ash
as an additive. Geotechnical & GeologicalEngineering 10, no. 2, 1992. Pp. 117-134.
[11] Rahman, MD. A., The potentials of somestabilizers for the use of lateritic soil inconstruction. Building and Environment 21, no.
1, 1986, pp. 57-61.
[12] Noor, M. J. M. M., Aziz, A. A., and Suhadi, R.
U. R., Effects of cement-rice husk ash mixtureson compaction, strength, and durability ofMelaka Series lateritic soil. The Professional
Journal of the Institution of Surveyors Malaysia
28, no. 3, 1993, pp. 61-67.
[13] Muntohar, A. S., Uses of RHA to enhanced limestabilized clay soil. In International Conferenceof Geotechnical Engineering, University of
Sharjah, United Arab Emirate, 2004, pp. 356357. 2004.
[14] Alhassan, M., Permeability of lateritic soil
treated with lime and rice husk ash. Assumption
University Journal of Thailand 12, no. 2, 2008,
pp. 115-120.
[15] Rogers, C. D., and Glendinning S., Lime
requirement for stabilization. Transportation
Research Record 1721, no. 1, 2000, pp. 9-18.
[16] James, J., and Rao M. S., Reaction product oflime and silica from rice husk ash. Cement andConcrete Research 16, no. 1,1986, pp. 67-73.
[17] Kumar, A., Walia B. S., and Mohan, J.,
Compressive strength of fiber-reinforced highly
compressible clay. Construction and building
materials 20, no. 10, 2006, pp. 1063-1068.
[18] Hossain, M. Z., and Awal, A. A., Flexural
response of hybrid carbon fiber thin cement
composites. Construction and Building Materials
25, no. 2, 2011, pp. 670-677.
[19] Fatahi, B., Fatahi B., Le T. M., and Khabbaz H.,
Small-strain properties of soft clay treated withfiber and cement. Geosynthetics International 20,
no. 4, 2013, pp. 286-300[20] Park, S., Effect of fiber reinforcement anddistribution on unconfined compressive strengthof fiber-reinforced cemented sand. Geotextiles
and Geomembranes 27, no. 2, 2009, pp. 162-166.
[21] Cristelo, N., Cunha, V.M., Dias, M., Gomes,
A.T., Miranda, T. and Araújo, N., Influence of
discrete fiber reinforcement on the uniaxial
compression response and seismic wave velocity
of a cement-stabilized sandy-clay. Geotextiles
and Geomembranes 43, no. 1, 2015, pp. 1-13.
[22] Maher, M.H., and Gray D. H., Static response ofsands reinforced with randomly distributed
fibers. Journal of geotechnical engineering 116,
no. 11, 1990, pp. 1661-1677[23] Consoli, N.C., Prietto P. D., and Ulbrich L. A.,
Influence of fiber and cement addition on behaviorof sandy soil. Journal of geotechnical and(Si2O5, Al2O2) + 3Ca (OH)2 + nH2O = Ca2(Al2SiO7,
nH2O) + 2Ca2 (SiO3nH2O)(2)
Nevertheless, the inclusions of randomly oriented
Basalt fiber into the soil, combined with the rapidly
hardening CSH Gel, improved the geotechnicalengineering behavior of the new material and can beproposed for use as a subgrade material forpavements and as fill material in shallow foundations.
ACKNOWLEDGMENTS
Sincere thanks to MEXT Scholarship and Mieuniversity for according the chance to pursue thisresearch journey.
REFERENCES[1] Crockford, W.W., Grogan, W.P. and Chill, D.S.,.
Strength and life of stabilized pavement layers
containing fibrillated polypropylene,1993, (No.
1418).
[2] Festugato, L., Fourie, A. and Consoli, N.C.,
Cyclic shear response of fibre-reinforced
cemented paste backfill. Géotechnique Letters,
3(1), 2013, pp.5-12.
[3] Ibraim, E., Diambra, A., Wood, D.M. andRussell, A.R., Static liquefaction of fibre
reinforced sand under monotonic loading.
Geotextiles and Geomembranes, 28(4), 2010,
pp.374-385.
[4] Mitchell, J.K., Soil improvement-state of the art
report. In Proc., 11th Int. Conf. on SMFE Vol. 4,
1981, pp. 509-565.
[5] Fattah, M.Y., Rahil, F.H. and Al-Soudany, K.Y.,
Improvement of clayey soil characteristics using
rice husk ash. Journal of Civil Engineering and
Urbanism, 3(1), 2013, pp.12-18.
[6] Alhassan, M., Potentials of rice husk ash for soil
stabilization. Assumption university journal oftechnology, 11(4), 2008, pp.246-250.
[7] Aprianti, E., Shafigh, P., Bahri, S. and Farahani,
J.N., 2015. Supplementary cementitious
materials origin from agricultural wastes–A
review. Construction and Building Materials, 74,
pp.176-187.
[8] Malhotra V. M., and Mehta, P. K., Pozzolanicand cementitious materials. CRC Press, 2004.
[9] Basha, E. A., Hashim, R., Mahmud H. B., andMuntohar A. S., Stabilization of residual soil
with rice husk ash and cement. Construction andbuilding materials 19, no. 6, 2005, pp. 448-453.

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geoenvironmental engineering 124, no. 12, 1998,
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[24] Tavenas, F., Leblond, P., Jean, P. and Leroueil, S.,
1983. The permeability of natural soft clays. Part I:
Methods of laboratory measurement. Canadian
Geotechnical Journal, 20(4), pp.629-644.
[25] Lopresto, V., Leone, C. and De Iorio I., Mechanicalcharacterization of basalt fiber reinforced plastic.
Composites Part B: Engineering 42, no. 4, 2011, pp.
717-723.
[26] Berozashvili, M., Continuous reinforcing fibers arebeing offered for construction, civil engineering,and other composites applications. Adv Mater Com
News, Compos Worldwide 6, 2001, pp. 5-6.
[27] Sim, J. and Park, C., Characteristics of basalt fiber
as a strengthening material for concrete structures.
Composites Part B: Engineering 36, no. 6-7, 2005,
pp. 504-512.

[28] Robert, W.D., Soil testing manual: procedures,classification data, and sampling practices. Press of
Ohio, OH, USA, 2008.
[29] Anupam, A.K., Kumar, P. and Ransinchung, G.D.,
Permeability study on fly ash and rice husk ash
admixes with subgrade soil for pavement
construction. In Proceedings of International
Conference on Advances in Architecture and Civil
Engineering (AARCV 2012),Vol. 21, 2012 p. 489.
[30] Lawton, E.C., Khire, M.V. and Fox, N.S.,
Reinforcement of soils by multioriented
geosynthetic inclusions. Journal of GeotechnicalEngineering, 119(2), 1993, pp.257-275.
[31] Hossain, M.Z. and Sakai, T.,. The effectiveness of
nominal dosage of ordinary cement on strength andpermeability of clayey soil. Journal of the JapaneseSociety of Soil Physics, 110, 2008, pp.25-35.

287

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

SEISMIC PERFORMANCE EVALUATION OF PFS METHOD BY
SOIL-WATER COUPLED FINITE DEFORMATION ANALYSIS
Kentaro Nakai1, Kakuta Fujiwara2 and Nanase Ogawa3
1

Faculty of Engineering, Nagoya University, Japan
Faculty of Engineering, Tokai University, Japan3
Giken Ltd., Japan

2

ABSTRACTThe PFS method (Partially Floating Sheet pile method) has been frequently used as a countermeasure to reduce
settlement during embankment construction due to the edge cutting effect. However, the aseismic effect has not
yet been fully discussed. Therefore, to expand the applicability of the PFS method, seismic response analyses forriver embankments on sand - clay alternating ground was conducted. As a result, it was shown that the PFS method
can significantly reduce the earthquake damage compared to the floating sheet pile method, although it is not as
good as the end-bearing sheet pile method. However, when the embedment depth of the floating sheet pile of the
PFS method was short, the aseismic effect was reduced; that is not only the amount of settlement of theembankment increase but also the amount of lateral displacement of the soft clayey layer increases.
Keywords: PFS method, seismic response analysis, lateral displacement

INTRODUCTION

aseismic effect of the PFS method was conducted, as
well as the effect of settlement and lateraldisplacement at the time of static loading due toembankment construction. The analysis code
employed in this study was the soil-water coupledfinite deformation analysis GEOASIA [1], which
incorporates an elasto-plastic constitutive model,
namely SYS Cam-clay model [2], that allows adescription of the behavior of soils ranging from sandthrough intermediate soils to clay within the same
theoretical framework. This analysis code can beapplied to both ground deformation and failure andcan be used to analyze co-seismic behavior over atime range of a few seconds to a few minutes andpost-seismic behavior, in which case the time range isfrom a few years to a few centuries.

Steel sheet piles have been used for temporary
construction to suppress lateral deformation of theground and ensure stability. However, at present, it isalso used as a permanent structure not only to prevent
slip failure but also to reinforce embankments andsuppress lateral flow and settlement in the soft groundunder embankment construction. As for thesettlement countermeasure method using steel sheet
piles, the end bearing sheet pile method has been used
so far, in which all the piles are embedded into the
supporting layer. However, if the soft ground is thick,the pile length becomes very long and causes costproblem. In addition, the floating sheet pile method,which does not allow the piles to penetrate to the
supporting layer, is economically effective and can be
expected to have a certain effect on settlement
countermeasures, but it also has the disadvantage ofsettlement of the pile itself. Based on these problems,
the PFS method (Partially Floating Sheet pilemethod) shown in Fig. 1 has been developed.
The PFS method has many achievements in riverembankments on the soft ground of the Kumamoto
Plain. In the 2016 Kumamoto earthquake, there is areport that the PFS method, which was implemented
as a countermeasure against settlement and lateraldisplacement, also suppressed the deformation during
the earthquake. However, the aseismic effect has not
yet been fully discussed for the PFS method.
Therefore, to expand the applicability of the PFS
method, seismic response analyses of riverembankment on sand - clay alternating ground was
conducted.
In this paper, a numerical investigation on theFig. 1 Schematic figure of PFS method

288

GEOMATE – Kyoto, Japan, 3-5 November 2021

ANALYSIS CONDITION
Figure 2 shows the finite element mesh used inthis analysis. Target ground was soft sand-clay layer
ground near the estuary of the Kikuchi river inKumamoto plain. The ground was assumed to be
initially horizontally layered, with an alternate layerof sand and clay. The hydraulic boundaries for the
subsurface were designated as drained boundaries,
and the bottom end and the two lateral faces were
designated to be under undrained conditions. Periodic
boundaries were realized by tying two nodes with thesame height between both lateral ends.

Fig. 3 Undrained shear behavior of clay

Fig. 2 Finite element mesh used in the analysis
Table 1 shows the elasto-plastic parameters(material constants and initial conditions) of the soils
used in this analysis. These parameters were alldetermined based on the results of laboratory testing
using undisturbed samples obtained from in-situ
boring surveys. The elasto-plastic parameters were
determined by replicating the results of thesemechanical behaviors using the SYS Cam-Clay
Model. Figures 3 and 4 show some examples of the
reproduction results. The parameters were identified
by reproducing the shear behavior to the compression
behavior with a single set of material constants. For
detail, please see reference [3].

Fig. 4 One-dimensional compression behavior of clay
Table 2 shows the elastic parameters of sheet piles
used in this analysis. The two-dimensional modelingof the PFS method was considered as follows [4, 5].
Although the sheet pile shows a complicated shape, it
is considered as a mixture of the sheet pile and thesurrounding ground and modeled as a one-phaseelastic body so that the rigidity and weight per unit
depth are equal. In addition, in the case of the PFS
method under plane strain conditions, sheet piles andground exist discretely in the same plane. Therefore,
we considered the space between the end-bearing
sheet piles as one unit and assumed average therigidity within the unit. It was modeled as a twophase elastic body and the hydraulic conductivity is
assumed to equal to the ground.

Table 1 Elasto-plastic parameters of soils
Emb

Sand

Clay

Bedrock

Critical state index M

1.60

1.60

1.65

1.80

NCL intercept N

1.70

2.28

1.90

2.00

Elasto-plastic parameters~

Compression index 
Swelling index ~

0.05

0.165

0.243

0.05

0.005

0.025

0.020

0.005

Poisson’s ratio 

0.30

0.20

0.40

0.10

Degradation index of structure a

1.00

1.00

0.60

0.01

Ratio of − D v

cs

1.00

1.00

0.07

1.00

Degradation index of OC m

5.00

3.00

10.0

20.0

Rotational hardening index br

0.00

0.00

0.00

0.00

Limit of rotational hardening m b

0.00

0.00

0.0

0.00

Evolution parameterspto D s p

Initial conditions

Table 2 Elastic parameters for steel sheet pile

Specific volume v

-

-

-

-

Stress ratio  0

0.545

0.545

0.545

0.545

ActualSheet pile

Sheet pile

Degree of structure 1 / R * 0

1.0

1.05

20.0

1.00

sheet pile

1-phase mat.

2-phase mat.

Degree of overconsolidation 1 / R0

200.0

30.0

1.80

500.0

Density  (g/cm3)

7.80

1.80

1.80

Degree of anisotropy  0

0.00

0.00

0.00

0.00

Poisson’s ratio 

0.3

0.3

0.3

Soil particle density  s (g/cm3)

2.65

2.65

2.65

2.65

200

43.15

7.2

Mass permeability index k (cm/s)

1.0 × 10-5

1.0 × 10-4

1.0× 10-6

1.0 × 10-7

Young’s modulus E (GPa)
Mass permeability index k (cm/s)--

1.0× 10-6

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GEOMATE – Kyoto, Japan, 3-5 November 2021

assumed. Moreover, two types of PFS methods withdifferent floating sheet pile lengths were assumed.

4

Fig. 6 Settlement distribution in the depth direction

2

Fourier amplitude (m/s *s)2

Acceleration (m/s )Numerical analyses were carried out in thefollowing steps.
1) A steel sheet pile was modeled by replacing thesoil with a linear elastic material.
2) Embankments 3.4 m in height with slope
gradients of 2:1 were constructed on the initial
ground by adding finite element meshes.
3) The seismic waveform was input in the horizontal
direction equally on all nodes of the bottom faceof the ground. The seismic motion was theassumed Nankai trough earthquake prepared by
the Cabinet Office. As you can see from Fig. 5, in
addition to large acceleration, seismic motion
contains many long-period components and has along duration.
4) After the earthquake, consolidation has proceeded
until the deformation converged.

2
0
-2
-4
0

50

100
Time (sec)

150

100
Period (sec)

101

10

5

0 -1
10

Fig. 5 Input seismic motion (expected Nankai troughearthquake by Cabinet Office)
ANALYSES CASES
Figure 6 shows the distribution of settlement in
the depth direction during the embankment
construction. The ground is the case of nocountermeasures, and the measurement point of thesettlement was at the embankment toe. According to
the PFS method manual [6], the embedment depth of
the floating sheet pile in the PFS method is calculated
as the neutral point of the settlement distribution map.
Therefore, the embedment depth of the floating sheet
pile was determined as 20m in this research.
Figure 7 shows five analysis cases. The left sideof the figure (assuming the outer land side of theembankment) is assumed to have end-bearing sheet
piles in all cases. In addition to the (a) no
countermeasure ground, (b) the end bearing sheet pilemethod, and (c) the floating sheet pile method wasFig. 7 Analysis cases (different countermeasures)

LATERAL DISPLACEMENT SUPPRESSION
EFFECT
DURINGEMBANKMENTCONSTRUCTION
Figures 8 and 9 show the settlement at theembankment crown and lateral displacement at theembankment toe caused by the embankment
construction.

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GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 8 Settlement at the embankment crownFig. 10 Settlement during and after the earthquake atthe embankment crownFig. 9 Lateral displacement distributions to depth
direction at the embankment toe

Fig. 11 Lateral displacement distributions to depth
direction at the embankment toe 300 seconds after theoccurrence of an earthquake

Compared to no countermeasures for both thesettlement and the horizontal displacement, theamount of deformation can be suppressed in the orderof the floating sheet pile method, the PFS method, andthe end-bearing sheet pile method. This tendency isalso consistent with the reference [7]. There is no
significant difference in deformation depending on
the type of countermeasure, but this is thought to be
due to the large sand layer thickness of 10 m. This isconsidered to be due to the dispersing effect of theembankment load by the thick sand layer.

Unlike the static behavior during embankmentloading, the dynamic deformation during anearthquake
differs
dependingonthecountermeasures. In the case of no countermeasures,the embankment will sink 90 cm and cause a lateraldisplacement of about 60 cm near the ground surface.
However, in the case of the end-bearing sheet pilemethod, the amount of settlement will be reduced toabout 2/3. Moreover, there is no lateral displacement
at the embankment toe; it is pulled by theembankment settlement and causes a slight negative
displacement. The floating sheet pile method and the
PFS method are located between no countermeasuresand end bearing sheet pile method. However, thefloating sheet pile method has a larger amount of
deformation and is close to no countermeasures. As
can be seen from Fig. 12 below, in the case of thefloating sheet pile method, the shear strain isconcentrated at the tip of the sheet pile, and it can beseen that the clay layer near the tip was disturbed. The
PFS method has a smaller amount of deformation
than the floating sheet pile method and is close to an
end-bearing sheet pile method. In particular, theeffect of suppressing lateral displacement near the

ASEISMIC EFFECT OF THE VARIOUS STEELSHEET PILE METHODS
Figure 10 shows the settlement at theembankment crown during and after the earthquake.
The initial settlement at time t = 0 is the displacement
during the embankment construction shown in Fig. 8.
Figure 11 shows lateral displacement distributions to
depth direction at the embankment toe 300 seconds
after the occurrence of an earthquake.
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GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 12 Shear strain distributions 300 seconds after the occurrence of an earthquake
ground surface is great. It can be seen that when thedepth of the floating sheet pile is shallow, lateraldisplacement (slip-through that occurs between theend bearing sheet piles) occurs in the deep part of theground. However, when the embedment depth of thefloating pile is 20 m, the amount of slip-through canbe strongly suppressed.
Next, the influence area of seismic deformation
was examined. Figure 12 shows shear strain
distributions 300 seconds after the occurrence of anearthquake. In the case of no countermeasures or thefloating sheet pile method, it can be seen that theshear strain spreads in the embankment land region
on the right side of the figure. In the case of the PFS
method, although it is not as good as the end-bearing
sheet pile method, the shear strain growth to theembankment land was suppressed, and the deeper thedepth of the floating sheet pile, the greater the effect.

Figure 13 shows the maximun bending moment
acting on the sheet pile during embankment
construction and earthquake. Comparing the bending
moments acting on the sheet pile during embankment
construction and during earthquakes, it can be seenthat the bending moments during the earthquake are
overwhelmingly larger than that of embankment
construction. Since this research targeted a huge
earthquake called the Nankai Trough earthquake, itmeans that there is a risk that a large moment will act
during the earthquake and might exceed the yield
and/or permissible values.
From the above discussions, the embankment
deformation caused by the earthquake decreased in
the order of no countermeasure >> floating sheet pilemethod> PFS method > end bearing sheet pilemethod, and the aseismic effect of the PFS method
could be confirmed. In addition, the PFS method is
considered to be more effective in suppressing lateral
flow, especially near the ground surface, than the
floating sheet pile method. In the case of the floating
sheet pile method, the sheet pile itself flows together
with the ground because the ground at the tip of the
sheet pile was disturbed.

CONCLUSIONSIn this paper, a numerical investigation on theaseismic effect of the PFS method was conductedusing soil-water coupled finite deformation analysis.
Although the PFS sheet pile has a complicated threedimensional structure, a simple two-dimensional
plane strain model is derived by giving the average
physical properties (equivalent rigidity andequivalent weight) of the steel sheet pile and theFig. 13 Cross-section performance check of the sheet
pile in case of PFS method with its floating pile 20m

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GEOMATE – Kyoto, Japan, 3-5 November 2021

ground per unit depth. The following conclusions
were obtained.
1) Embankment damage (settlement of theembankment crown and horizontal displacementof the embankment toe) caused by the earthquake
decreased in the order of no countermeasure >>floating sheet pile method> PFS method > end
bearing sheet pile method, and the aseismic effectof the PFS method was confirmed.
2) PFS method was considered to be more effective
in suppressing lateral flow, especially near the
ground surface, than the floating sheet pilemethod. In the case of the floating sheet pilemethod, the sheet pile itself flows together withthe ground because the ground at the tip of the
sheet pile was disturbed.
Since this study was conducted under thecondition that the sand layer thickness was relatively
large. Therefore, it is necessary to investigate when
the sand layer thickness was thinner, and when thesoil layer conditions were different. In addition, since
the seismic motion was very large as the Nankai
trough earthquake, we should further work withdifferent input seismic motion characteristics. Basedon these additional studies, we would like to evaluate
the aseismic performance of the PFS method andpromote the expansion of the application of the PFS
method.

REFERENCES[1] Noda, T., Asaoka, A., and Nakano, M., Soilwater coupled finite deformation analysis basedon a rate-type equation of motion incorporating
the SYS Cam-clay model, Soils and Foundations,Vol. 48, No. 6, 2008, pp. 771–790.
[2] Asaoka, A., Noda, T., Yamada, E., Kaneda, K.,
and Nakano, M., An elasto-plastic description of
two distinct volume change mechanisms of soils,
Soils and Foundations, Vol. 42, No. 5, 2002, pp.
47–57.
[3] Nakai, K., Noda, T., Taenaka, S., Ishihara, Y.
and Ogawa, N., Seismic assessment of steel sheet
pile reinforcement effect on river embankment
constructed on a soft clay ground, Proceedings ofthe First International Conference on Press-in
Engineering, 2018, pp.221-226.
[4] Fujiwara, K., Ogawa, N. and Nakai, K., 3-D
numerical analysis for partial floating sheet-pilemethod under liquefaction, Journal of JSCE, Vol.
9, No.1, 2021, pp.138-147.
[5] Ogawa, N., Fujiwara, K. and Nakai, K., Analytic
considerations on two-dimensional modeling ofpartial floating sheet pile method, Proceedings of
11th International Conference on Geotechnique,
Construction Materials and Environment,
GEOMATE 2021.
[6] PFS method association, Technical note for the
PFS method, 2005. in Japanese.
[7] Kasama, K., Fujiyama, H. and Otani, J., 3D fem
analysis of partial floating steel sheet piling
method on two-layered ground, Proceedings of
2nd International Conference on Press-in
Engineering (ICPE) 2021.

ACKNOWLEDGMENTS
This research was supported by the International
Press-in Association TC3 (Chair: Prof. Jun Otani).
The authors express deep appreciation for thecooperation and useful discussion.

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Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

STRENGTH EVALUATION OF CEMENT-TREATEDSAND-EXPANDED POLYSTYRENE BEAD LIGHTWEIGHT FILL
Khola Iqbal1, Hammad Siddique2, Waleed Iqbal2, Taro Uchimura1 and Zain Maqsood3
1

Department of Civil and Environmental Engineering, Saitama University, Japan; 2 Department of Civil
Engineering, Mirpur University of Science and Technology, Mirpur, Pakistan; 3 School of Civil andEnvironmental Engineering, National University of Sciences and Technology, Islamabad, Pakistan
ABSTRACTEPS (expanded polystyrene) is a light weight geo-synthetic material which can be utilized for various
geotechnical applications including the desired compressive strength of soil, construction on the soft soils,
reduction of the soil settlement, construction cost reduction, enhancement of slope stability and reduction in thelateral earth pressure. This study investigates the variation of physical and mechanical properties of the lightweightbulk filling material prepared with sandy soil mixed with different contents of Ordinary Portland Cement (OPC)and expanded polystyrene (EPS) beads. The influence of EPS beads and cement content on density and strengthof this lightweight bulk filling material was examined by a series of laboratory tests including Modified Proctor
Tests and Unconfined Compressive Strength (UCS) tests on cylindrical specimens. The test results showed thatthe inclusion of EPS beads in the mixture significantly reduces its density i.e. addition of 0.25% EPS beads reduces
the density of the mixture by 10%. Further, the compressive strength of the mixture decreases with the increase in
the EPS beads, however, the desired strength can be achieved by adding an optimum amount of cement content.
Keywords: Cement; Expanded polystyrene beads; Embankments; sand; Lightweight fill material.
INTRODUCTION

comparing the costs of this with that of generalembankments. It was concluded that geo-foam
proved to be a safe and economical method of dealing
with the embankments on soft soil.
Expanded Polystyrene (EPS) is a lightweightmaterial that has been used in recent years as a geofoam to improve the properties of soft soils forconstructing embankments. It has been noted to
improve the bearing capacity of the stabilized soil,
decrease permeability and lower the unit weight ofthe stabilized soil thus producing a lightweightcomposite material with better engineering properties[9]. Reference to EPS Industry Alliance (2012), EPS
is approximately 1% of the soil weight, and is lessthan 10% the weight of other lightweight fillalternatives. This makes EPS embankments lightercompared with compacted soil embankments, andaccordingly reduces the dead loads imposed onunderlying soil. This in turn also allows to constructcost effectively and timely, and simplicity of
construction as EPS blocks are easy to handle without
the need for special equipment, and can be easily cut
and shaped on site. Owing to the availability of EPS
geo-foam in various densities, it can be selected by
the designer depending on specific geotechnicalapplication. As it retains its physical properties undervarious conditions, its durability and service span arecomparable with various other construction materials[10].
However, the use of EPS geofoam is limited
sometimes by its inability to fit irregular spaces. In
such cases EPS- soil composite is an alternative. This

Excessive soil Settlement, bearing capacity
failures, slope instability are some major challenges
that are faced during the construction of
infrastructure particularly during the construction ofembankments on soft soils. Several ground
improvement techniques are in practice in order tomitigate these problems including the haulage ofgood quality soil from considerable distances which
greatly affect the cost of the projects. However,researchers nowadays are trying to develop lightweight fill materials that would reduce the weight ofthe embankments to ensure that soft soils are notoverloaded. This serves as major solution for
mitigation of settlement problems in poor capacity
soils.
From shredded tyres to geo-foam and air-foams
various kinds of lightweight materials have beendeveloped over the passage of time and have beenused in my projects [1-6]. Engstrom and Lamb [7]came up with the idea of using the shredded tyres asa lightweight fill material in road construction tominimize and reduce the environmental issues
because of waste tyres, this method was first adopted
in Minnesota during 1985. They concluded that thiskind of fill material not only proves to be beneficial
for the environment but is also useful in construction.
A research report was submitted by Stark et al. (2004)[8] to the Transportation Research Board highlighting
the use of geo-foam (e.g. expanded polystyrene (EPS)
blocks) as fill material in highway embankments and294

GEOMATE – Kyoto, Japan, 3-5 November 2021

research was therefore conducted to investigate how
the different properties of the sandy soil are affectedby varying the contents of EPS beads and cement inregard to its suitability for a foundation material.

EPS beads used in this study are presented in table 1.
Based on the desired mixing ratio, the materials were
weighed by electronic scale with an accuracy of 0.01
g. The mixing procedure used is presented as follows:
Dry sand and Portland cement were mixed first.
Then water was added into the mixture and stirred
properly; EPS beads were lastly added into the
mixture and stirred to ensure the uniformity of the
mixture. EPS beads had to be added after water wasadded in the soil and cement mixture for even
distribution of water in the mixture since the EPS
beads cover the mixture completely owing to theirgreater volume and adhesive nature. The wholemixing process took about 30 minutes.

EXPERIMENTAL PROGRAM
Material Properties and Mixing Properties
The light weight material used in this research wasmade of sand, Portland cement, expanded polystyrene(EPS) beads, and water. The sand used was procured
from Mangla, located in the State of Azad Jammu andKashmir, Pakistan. Fig. 1 presents the physicalproperties and the particle-size distribution curve of
this sand.

Table 1 Mix proportions used in this study
EPS Beads (%)

Cement (%)

0, 0.25, 0.5, 1

5, 10, 20

Modified Proctor Tests
To obtain the optimum water content and the
maximum dry density of the lightweight fill, modified
Proctor tests [11] were undertaken. Besides
determining OMC and MDD of each mix proportion
for the sample preparation of unconfinedcompression tests, the effects of varying contents of
EPS beads and cement on the soil composite werealso examined.

Fig. 1 Particle size distribution of sand used in the
study

Unconfined Compression Strength Tests

EPS beads are white, rounded particles withvarying diameters depending on the manufacturing
procedure. The EPS beads used in this study, shownin Fig. 2, were 2-5 mm in diameter having specificgravity of 0.044.

The unconfined compressive strength (UCS) tests[12] were conducted on cylindrical specimens with adiameter of 50 mm and height of 100 mm at the OMC
and MDD condition determined earlier. The
unconfined compression test device used is automaticand computer-controlled; the deformation rate was
0.5 mm/min.
Specimen Preparation
The moulds for the specimens were preparedmanually by cutting the PVC pipes into the desired
dimensions as shown in Fig. 3. The specimens werecompacted with a metallic tamper in 5 layers. Eachlayer was given 25 blows. After preparation of
specimen, it was wrapped properly, making it airtight.
After the curing period, the specimens were removed
from the moulds and tested.

Fig. 2 EPS Beads used in study
Cement used was Ordinary Portland Cement tobind the cohesionless sand and EPS beads such thatthey would be easier to compact as well as stable andsufficiently strong to carry the required load after anappropriate curing time. Water was added forcarrying out the hydration reaction and facilitate themixing process.
The proportions of each material were taken by
percentage weight. Varying contents of cement and295

GEOMATE – Kyoto, Japan, 3-5 November 2021

(a)

(b)
Fig.4 Effect of EPS Beads content on compaction
parameters at cement content of (a) 10% (b) 20%
Effect of Cement Content(b)Fig. 3 (a) Specimen during compaction (b) Prepared
Specimen

To investigate the effect of the cement content on
the optimum water content and the maximum dry
density of the lightweight fill, soil composites wereprepared at cement contents of 5, 10 and 20% while
keeping the EPS content constant and tested using theModified Proctor test. The test results showed that theincrease in cement content increases the maximum
dry density while having no significant effect on the
OMC of the composite as depicted in Fig. 5 which isin contradiction with the results of Brooks et al.
(2010) which showed that the MDD and the OMC ofthe sample could be decreased and increased,
respectively, when the amount of pozzolanic material
is increased. However, bearing in mind that thecompaction behavior of the mixture depends on
different factors including the material type andcomposition.

RESULTS AND DISCUSSION
Modified Proctor Tests
Effect of EPS Beads Content
The effect of the varying content of EPS beads on
OMC and MDD was investigated by conducting
modified proctor tests. Four soil composites wereprepared for this purpose with EPS beads content of
0%, 0.25%, 0.5% and 1% by weight of the composite.
The compaction behavior of the mixture in Fig. 4,
indicates that increase in the EPS beads has no
significant impact on the OMC, while it remarkably
reduces the MDD of the samples. This is because
firstly, the density of EPS is much less than the othermaterials and secondly, EPS beads are damping someof the compaction energy. The reduction is attributed
to the EPS particles, of lower density, occupying thespace that would have been occupied by the sandparticles. Owing to the low volume mass ratio of EPS,
the stabilised sample re-duces in the MDD (Illuri
2007)

Fig. 5 Effect of cement content on compaction
parameters at EPS Beads content of 10%
Unconfined Compression Strength Tests
According to the test results, increase in EPS
beads content decreases the peak compressive stress,qu, of all samples Fig. 6. This can be due to the higher

(a)
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GEOMATE – Kyoto, Japan, 3-5 November 2021

compressibility of EPS beads compared to otherconstituents of the mixture. Moreover, EPS beads
hinder the bond between the constituents due to its
inability to absorb water.

Offshore and Polar Engineering Conf., ISOPE,
CA, 670–674.
[2] Tsuchida, T., and Kang, M. S. (2002). “Use oflightweight treated soil method in seaport andairport construction projects.” Proc., Nakase
Memorial Symp., Soft Ground Engineering in
Coastal Areas, Balkema, London, 353–365.
[3] Gan, C. H., and Tan, S. M. (2003). “Someconstruction experience on soft soil using
lightweight materials.” Proc., 2nd Int. Conf. on
Advance in Soft Soil Engineering andTechnology, Universiti Putra Malaysia Press,
Serdang Selangor Darul Ehsan, Malaysia, 1–8.
[4] Tsuchida, T., and Egashira, K. (2004). “Thelightweight treated soil
method: New
geomaterials for soft ground engineering incoastal areas.” A. A. Balkema, London.
[5] Wang, F., and Miao, L. C. (2009). “A proposed
lightweight fill for embankments using cementtreated Yangtze River sand and expanded
polystyrene (EPS) beads.” Bull. Eng. Geol.
Environ., 68(4), 517–524.
[6] Miao, L. C., Wang, F., Lv, W. H., and Li, J. (2010).
“Study on cement-treated Yangtze hydraulic sandmixed with expanded polystyrenes (EPS) beads as
backfill material in highway embankments.” Proc.,
GeoShanghai Int. Conf., ASCE, Shanghai, China,
372–378.
[7] Engstrom GM, Lamb R (1994) Using shredded
waste tires as a lightweight fill material for roadsubgrades. Report MN/RD-94/10 to Minnesota
Department of Transportation[8] Stark, T. D., Horvath, J. S., and Leshchinsky, D.
(2004). “Geo-foam application in the design andconstructionof highway embankments.” TRB
Project No. 24-14, TRB, Washington, DC.
[9] Stark, T. Bartlett, S. &Arellano, D. 2012.
Expandedpolystyrene(EPS)
geofoamapplications & technical data.EPS Industry
Alliance.
[10] AbdelSalam, S.S., Azzam, S.A.: Reduction oflateral pressures on retaining walls using geofoam
inclusion. ICE Geosynth. Int. 23(6), 395–407(2016)[11] ASTM (2012), Standard Test Methods forLaboratory Compaction Characteristics of Soil
Using Modified Effort (56,000 ft-lbf/ft3(2,700
kN-m/m3)),
ASTM
D1557-12e1,
WestConshohocken, Pennsylvania, U.S.A.
[12] ASTM (2016), Standard Test Method for
Unconfined Compressive Strength of Cohesive
Soil,ASTM
D2166/D2166M-16,
West
Conshohocken, Pennsylvania, U.S.A.

Fig. 6 Effect of EPS Beads on Compressive Strengthof the composite having 10% cement content
CONCLUSIONSThis study investigated the suitability of using alightweight fill material composed of sand, EPS
beads and cement for construction of embankments
over soft soils. Mechanical properties of thecomposite were evaluated with laboratory tests
including the modified proctor tests and unconfinedcompression strength (UCS) tests. The main findingsof the study are as follows:• Increasing the amount of cement increased the
MDD with no remarkable effect on OMC of the lightweight fill
• During compaction tests, bleeding was observedwith the increase in the amount of moisture content.
•An increase in the EPS beads content resulted in
decline of compressive strength of the light weight fillwhich, however, can be compensated by an increasein cement content in optimum quantity.
ACKNOWLEDGMENTS
The authors wish to acknowledge Muhammad
Kamran Attique and Munib Arif for their assistancein experimental work and interpretation of results.
REFERENCES[1] Oh,W. S., Lee, J. K., Kwon, Y. C., and Lee, B. J.
(2002). “Bearing capacity of light weight soil
using recycled Styrofoam beads.” Proc., 12th Int.

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11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

ADHESIVE FRACTURE MODES BETWEEN STEEL ANCHOR AND
INJECTABLE MATERIAL AND CONCRETE IN CONSIDERATION OF
WEDGE-SHAPED EFFECT
Tanaka Hidenori1
National Institute of Technology (KOSEN), Gunma College

1

ABSTRACT
We have used some deformed steel anchors to install fans and steel columns on the concrete structures.
There was significant problem that we couldn’t precisely define the fracture modes between steel anchor andinjectable material and concrete in structures such as interfacial, shear and bearing of steel anchor’s node. They
depended on the size of specimen and the treatment of surface with brush in injectable hole. In this research, thepushing out test method was adopted by universal testing machine. Changing the experimental factors such as
wedge angle, surface treatment and specimen’s diameter. As the result, there were effective wedge angles toimprove adhesive resistance more than twice as much as that of normal case. And we could show the evolutionof cracking from the steel anchor’s node and along the interfacial surface between the straight-line of steel
anchor and the concrete considering axisymmetric problem in two dimensions.
Keywords: Adhesive resistance, Wedge effect, Inorganic material, Fracture mode
INTRODUCTION

injectable hole and specimen’s diameter.
As the results, the most effective angle was
between 3 to 5 degrees. And the maximum effect ascompared with that of normal cylindrical case became
more than doubled. We found three mixed modes thatwere bond-splitting and shear in injectable material
and interfacial between injectable material andconcrete regardless of thin thickness.

We have used organic material like epoxy andurethane resin to glue steel or plastic plates toconcrete structures. Especially, epoxy resin is usefulfor cracking repair or injectable material. but, thesematerials have some flaws for example, deterioration
by UV light, nonconformity under water construction
and non-filling in case of the upward injection in hole.
Recently, the inorganic material has made up forthe defects and has been used for injectable material
to connect to a steel anchor and concrete structures incase of the thin thickness.
Adhesive resistance force has been easily definedby interfacial area of two materials or hypothesis ofshear or diagonal fracture. However, these patterswere within narrow limits. we have to consider otherfracture modes like bond-splitting and mixed shear
and bond-splitting. On top of that, some studies haveconsidered the structures which was more than 400(mm) thicknesses and never the ratio of span andshape of injectable hole [1], [2].
In this study, we conducted some experiments toclear the adhesive force between deformed steel
anchor and injectable material, or injectable materialand concrete block considering fracture modes bypushing out shearing test.
We already have recognized the effect of wedgeshaped injectable form to improve adhesive force inprevious research [3]. Here, we showed theexperimental results by the wedge - shaped injectableform compared with those of the normal cylindricalcases, Changing wedge angles, surface treatment of

EXPERIMENT
Experimental method
The pulling out test method was generally
conducted to pull the top of anchor. In this case, itneeds special steel jigs and tools [1], [2], [4]. And thismethod can’t check the influence of structural size
(thickness, support length), bending or shear
deformation. For the above reasons, we adopted thepushing out test method. And then, we controlled
constantly the displacement speed of specimen by
universal testing machine at 2.0 (mm/min).
In this research, we used the cylindrical concrete
specimens (diameter(D) = 150 and 300,500(mm)).
the compressive strength is 40 (N/mm2) of concreteand 50 (N/mm2) of injectable material. The injectable
length that was 70 (mm) that was equal to thespecimen thickness and the wedge- angles(α) werechanged from 3 to 5 and 7 (degrees). And then, theinjectable topped diameter was equal to 48(mm). The
steel anchor protrusion length from top or bottom
surface of specimen was 20 (mm) respectively. Oneof the specimens and the concept of pushing out test
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GEOMATE – Kyoto, Japan, 3-5 November 2021

method were shown in Fig. 1.

We set three experimental factors. The wedge-angle
and the treatment by brush of injectable hole were
expected to improve interfacial resistance between
injectable material and concrete.
Next, the specimen’s diameter has the influence on
bending or shear fracture mode of specimen. Because
the length from support point to loading point was
important factor to evaluate shear capacity ofconcrete structure [5].
Table 3 Experimental factors
Factor
Case1
Case2
Case3

wedge-angle (0,3,5,7 (degrees))with or without treatment of side surface ininjectable holespecimen's diameter (150,300,500 (mm))

RESULTS OF EXPERIMENT
Fig.1 Typical of specimen and loading method

Effect of wedge-angle

Material properties

We showed the maximum loads for all cases
without treatment of side surface in hole in Fig.2.

We used almost same compressive strength
concrete and injectable material. Because we would
like to evaluate the effect of wedge-angle andtreatment on side surface in injectable hole. The mix
proportion of concrete and material properties werelisted in Table 1 and Table 2.
Here, we set the air 7 (%) in concrete in order tokeep the workability in fresh state and the durability
against freezing and thawing in northern area of
Gunma prefecture. And we set same value of
mechanical properties both concrete and injectablematerial (refer to NUMERICAL ANALYSIS).
Table 1 Mix proportion of concrete
Fig.2 Relationship between max. load and angle
Gmax
(mm)

Slump
(cm)

W/C
(％)

Air
(％)

s/a
(％)

20

10

47

7

39

In the case of a diameter of 150 (mm), The
maximum loads were proportion to wedge-angle.
And there were the most effective angles for adhesive
resistance between 5 and 7 degrees. And next, thefracture mode was radial tension due to circulartensile stress (refer to Fig.3). The bond-splitting
fracture along deformed bar and the shear fracture in
injectable material and the interfacial fracture were
found for all cases from cut planes of specimen indetail (refer to Fig.4,5,6). We presumed that thebond-splitting fracture was occurred at first. And then,the shear and the interfacial fracture was occurred insequence. Finally, they led to radial fracture mode.
However, the interfacial fracture between injectablematerial and concrete was only occurred in 0 degrees(refer to Fig.7). We could determine the wedge-effectto build up the adhesive resistance without treatment.

Quantity of material per unit of concrete volume (kg/m3)

W

C

S

G

Sp

169

357

673

1183

2.86

Table 2 Mechanical properties

Young Modulus(kN/mm2)

Poisson's
ratio

25

0.2

Experimental factors
The experimental factors were shown in Table3.
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GEOMATE – Kyoto, Japan, 3-5 November 2021

3 degrees was the maximum load among others. As
in the case of 150(mm), We presumed that the bondsplitting fracture was occurred at first. And then, theshear and the interfacial fracture was occurred insequence (refer to Fig.4 and Fig.8).

Fig.3 Bottom of specimen (5 degrees)Fig.8 Cut plane of 3 degrees
The cases of 5 and 7 degrees were almost same
maximum load. It was considered that the radial
tension fracture due to bending deformation was
occurred before the shear and interfacial fracture.
Therefor, the fracture mode would be shifted bywedge effect. We assumed that the wedge angle had
little effect on the maximum load.
The bottom surface was shown in Fig.9.

Shear crack

Fig.4 Cut plane of 3 degrees

Interfacial crack

Fig.5 Cut plane of 5 degrees

Fig.9 Bottom surface of 5 degrees
In the case of a diameter 500 (mm), there were notany effects on the maximum load due to wedge angle.

Bond-splitting crack

Fig.6 Cut plane of 7 degrees

Effect of treatment by brush in hole
There was the relation between maximum load
and wedge angle varying with or without treatment
by brush in hole in Fig.10and Fig.11.
In the case of a diameter 300 (mm), the cases of 0
degrees and 3 degrees had significant effects on the
maximum loads and fracture modes. At 0 degrees,
the maximum load with treatment was 1.8 times
bigger than that of without treatment. And the fracturemode without treatment was interfacial between
injectable material and concrete. The other hand, withtreatment was radial tension due to circular tensilestress. At 3 degrees, the maximum load without

Interfacial crack

Fig.7 Cut plane of 0 degrees
In the case of a diameter of 300 (mm), The case of301

GEOMATE – Kyoto, Japan, 3-5 November 2021

treatment was 1.3 times bigger than that of withtreatment due to wedge angle effect. And the fracturemode with treatment was radial tension due tocircular tensile stress. Finally, at 5 and 7 degrees,
there were little effect on the maximum load due towedge angle. These cases were radial tension due tocircular tensile stress. And, there were little effect on
the maximum load due to wedge angle with treatment.
In the case of a diameter 500 (mm), regardless ofwhether there was a treatment or not, the maximum
loads were almost same value due to wedge angle.
The fracture modes were bending due to circulartensile stress (refer to Fig.12).

Effect of diameter
We introduced the important parameter that socalled the ratio of shear span to classify the fracturemode of the reinforced concrete structure [6]. In thissection, we defined the parameter r/t. Here, r was aradius of specimen (75,150,250 (mm)) and t was a
thickness (70(mm)).
The relation between the maximum load and ratioof shear span without the treatment in hole in Fig. 13.

Fig.13 Max. load and ratio of shear span No.1
Fig.10 Max. load and wedge angle (D=300 mm)
The wedge angle had effect on the maximum loadfrom the case of ration of shear span 1.07 to 2.14. It
showed the remarkable value at 1.07. The maximum
load was almost same at 0 degrees because ofinterfacial fracture mode. All cases at 3.57 werebending fracture mode because of geometric shape.
The same graph with treatment was shown in
Fig.14. We excluded the case of a diameter 150 (mm)because it has not been conducted yet.
There was little or no difference between any wedge
angles. We recognized that the treatment by brush inhole was important factor to have an influence on thefracture mode of adhesive deformation.
Fig.11 Max. load and wedge angle (D=500mm)

Bending crack
Fig.12 Bottom of specimen (without treatment)

Fig.14 Max. load and ratio of shear span No.2
302

GEOMATE – Kyoto, Japan, 3-5 November 2021

Where the following notations were used.
𝝈𝒆𝒒 ; Effective Drucher-Prager’s equivalent stress
𝝈𝒀 ; The yield stress, 𝛂; Material parameter
𝑰𝟏 ; The first invariant of stress , 𝑱𝟐 ; The second
invariant of stress deviator

NUMERICAL ANALYSIS
Analysis caseIn this section, we showed the numerical F.E
analysis factoring in an axisymmetric problem in two
dimensions by damage mechanics [7].
We selected the case of a diameter 150 (mm) and awedge angle 5 degrees whether the bond-splitting,shear and interfacial fracture modes could be
evaluated or not. The half size model with meshdivision was shown in Fig.15.

𝒀 𝑺𝟐
𝒅𝒑
𝑺𝟏
𝝈𝟐𝒆𝒒
𝒀=
𝟐𝑬(𝟏 − 𝑫)𝟐𝒅𝑫 =𝒅𝝈 = (𝟏 − 𝑫𝒅𝝈 − 𝝈𝒅𝑫 = 𝑫𝒆𝒑𝒅 𝒅𝜺
𝑫𝒆𝒑𝒅 = (𝟏 − 𝑫)𝑪 − (𝟏 − 𝑫)𝑪

𝝈

𝝏𝑭𝑫
𝝏𝒀

𝑯

𝝏𝑭𝒑
𝝏𝝈

+

𝝏𝑭𝒑 𝑻
𝑪
𝝏𝝈
𝝏𝑭𝒑 𝑻 𝝏𝑭𝒑 𝝈𝒆𝒒 𝝏𝑭𝑫𝑪𝝏𝝈 𝟏 𝑫 𝝏𝒀𝝏𝝈(3)

𝑫𝒆𝒑𝒅 meant elasto-plastic and damageable
stiffness tensor, 𝑪 is general elastic tensor.
The detail parameters were referred to thereference [3].
Results of analysis
The distribution of equivalent stresses was shownin Fig.16. The analysis was carried out to control thedisplacement at the top of deformed anchor.

Fig.15 Model for analysis (5 degrees)
Constitutive equation
It was important to evaluate the equivalent stressand strain of material in numerical analysis. The
constitutive equation was defined as followed [3].
𝑭 = 𝑭𝒑 (𝝈, 𝜸, 𝑫) + 𝑭𝑫 (𝒀, 𝒑, 𝑫)=𝝈𝒆𝒒 − 𝛄 − 𝝈𝒀 + (𝑺𝑺𝟏
𝟐 𝟏)(𝟏 𝑫)𝒀 𝑺𝟐 𝟏𝑺𝟏(𝟏)Where 𝑭𝒑 is the potential for growth of plasticstrains, which is a function of the effective stress 𝝈,
the plastic hardening parameter 𝛄(= 𝐊𝒑𝒏 ) and thescalar damage variable 𝑫.𝑭𝑫 is the potential for theevolution of damage, which is a function of strainenergy release rate 𝒀, material parameter 𝑺𝟏 , 𝑺𝟐
and the equivalent plastic strain 𝒑 and scalar damage
variable𝑫. In the formulation of the constitutive
equation and the yield function were assumed asfollows.
𝒇 = 𝝈𝒆𝒒 − 𝜸 − 𝝈𝒀 = 𝟎
𝝈𝒆𝒒 = 𝝈𝒆𝒒 /(𝟏 − 𝑫)𝟏

𝝈𝒆𝒒 = 𝜶𝑰𝟏 + (𝑱𝟐 ) 𝟐(2)303

GEOMATE – Kyoto, Japan, 3-5 November 2021

(4) The wedge and treatment effects were little inthe case of where the ratio of shear span was bigger
than 3.57 due to bending fracture.
(5) We could define that the bond splitting crack
was generated from the tip of deformed bar’s node
to injectable material as the results of experiments
and numerical analyses.
(6) In case of normal case without treatment, thetreatment was useful for adhesive resistance.
(7) We could not trace the shear crack in injectablematerial and the interfacial between interfacial
material and concrete in analysis.
ACKNOWLEDGMENTS
Author appreciates Sumitomo Osaka Cement
Co.,L.t.d. for providing the injectable inorganic
material so called “Cemeforce anchor”. And helping
from Mr.Kurita, Mr.Yamanouchi, Mr.Kawahara andMr.Nakamura who were mainly in charge of theexperiments.
REFERENCES[1] Andoh, S. Nakano,K. Tanuma,T.Yuki,K..
Relationship between crack width of concrete anretention ratio of ultimate load for cast-in-placeanchor and inorganic post-installed anchor.
Architecture of Technology and Design, 59(25),2019, pp.51-54.
[2] Andoh, S. Nakano,K. Waranabe, K.,Influence odenvironmental and construction condition on theadhesive strength of post-install rapid hardening
cement bonded anchor, Proceedings of the Japan
Concete Institute,Vol.35,No.2,2013,pp.535-540.
[3] Hidenori Tanaka, ADHESION MECHANISM
BETWEEN STEEL ANCHOR, INJECTABLE
INORGANIC MATERIAL AND CONCRETE.
Advanced in Construction Materials Proceedingsof the ConMat’20, 2020, pp.372-382.
[4] Ministry of Land, Infrastructure, Transport andTourism, Construction guideline of post-installanchor and continuous fiber reinforcement.2006.
[5] Japan Society of Civil Engineers, STANDARD
SPECIFICATIONS FOR CONCRETESTRUCTURES-2012, Design, pp.180-187.
[6] Kobayashi Kazuo, Miyagawa Toyoaki et al,Concrete structural study, Morikita Publishing2017,pp.80-90.
[7] EA de Souza Neto, D Peric, DRJ Owen,
COMPUTATIONAL METHODS FOR
PLASTICITY, Morikita Publishing, 2012.

Fig.16 Distribution of equivalent stress (N/mm2)
Firstly, the cohesive fracture was occurred at the
top of interfacial area along the deformed bar. And
next, the evolution of cracking from the deformed
bar’s node and along the interfacial surface between
the straight-line of it in sequence. Finally, the bondsplitting fracture was found along the deformed bar.
In this analysis, we could not evaluate the shear
fracture in injectable material and the interfacial
fracture between injectable material and concrete.
CONCLUSIONSThe knowledge found in this research were as
below.
(1) 5 degrees for the case of 150 (mm) or 3 degrees
for 300 (mm) was the most effective wedge angle toenhance the adhesive force without treatment.
(2) At the case of 0 degrees, there were little effecton the maximum load for all specimens.
(3) The maximum loads for 5 or 7 degrees in a 300(mm) were almost same with or without treatment.

304

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

MODELING OF THE FLEXURAL BEHAVIOR OF CORRODED RC
BEAMS STRENGTHENED BY CFRP SHEET
Hoai Anh Tran1,2, Ngoc Tan Nguyen1, Trung Kien Nguyen1, Hoang Giang Nguyen1
1Faculty of Building and Industrial Construction, National University of Civil Engineering, Vietnam
2
The South Agency, Ministry of Construction, Vietnam
ABSTRACTIn the present study, a series of numerical modeling is performed in order to investigate the structural behavior
of corroded reinforced concrete beams externally strengthened to tension area with CFRP (Carbon Fiber
Reinforced Polymer) with the aid of the finite element method adopted. A three-dimensional model employing thenonlinear characteristics of materials (e.g. elasto-plastic material model of steel reinforcement and damaged
concrete model due to corrosion, bond stress-slip behavior between steel and concrete in good and deteriorated
conditions) is created and validated. The numerical results obtained from the finite element analysis (FEA) arecompared with the experimental results for six RC beams with different degrees of corrosion. The accuracy of thefinite element models is based on comparison with the experimental curves of load-deflection. Results obtained
from FEA showed a good correlation between the predicted residual flexural capacity and experimental results.
The parametric study is then conducted to examine the capability of the FE model on assessing the structural
behavior of the FRP-strengthened corroded RC beams in various strengthening conditions.
Keywords: Corroded beams, Steel corrosion, CFRP sheet, Strengthening, Modeling

INTRODUCTION

fabrics, are significant in compressive, shear andflexural performances have been obtained. The
overall performance of the RC beam under service
load is slightly improved and the load-carrying
capacity is remarkably increased when strengthening
FRP sheets are used [5]–[8].
Furthermore, the finite element method (FEM) is
extremely effective to solve the engineering problems
especially structures having complex boundary
conditions. It has been used widely for the structural
analysis of reinforced concrete structures including
FRP strengthened RC structures. Many previous
numerical research works had demonstrated good
agreement with experimental results [9]-[11].
However, from the literature, less attention has
been paid to study the structural performance ofcorroded RC beams strengthened by FRP composites
employing FEM. In this paper, the numerical results
obtained from the FEA are compared with theexperimental results for two non-corroded RC beams,
two corroded RC beams and two corroded CFRP
strengthened RC beams. The failure modes of thestrengthened corroded beams are governed mainly by
the bond between the CFRP sheet and concrete cover
because it is the means for the transfer of stresses todevelop composite action. The numerical analysis isthen broadened, a parametric study is conductedconsidering the number of CFRP layers and thelength of the CFRP sheets. Finally, the FEA
confirmed the suitability of the model to predict themechanical behavior of corroded RC members
strengthened in flexure with the CFRP material
considering many variables.

Steel corrosion is one of the main causes ofdeterioration in reinforced concrete (RC) structures.
Many research works suggest that high durability
requirements are not always achieved in practice due
to corrosion of reinforcement [1]. The principal
effects of corrosion on structural elements are loss ofreinforcement cross-section and altering its
mechanical properties, owing to the volumetric
expansion compared to the base reinforcing bars, thegeneration of internal pressure induces splitting
stresses. This stress-induced can lead to cracking andthe reduction of compressive strength of the concrete
cover when it reaches the tensile capacity of theconcrete. As the splitting cracks develop, theconfinement action of the concrete is rapidly
decreased thereby reducing the bond strength at theinterface between steel and concrete [2]–[4].
Therefore, deteriorated structures should be repaired
to ensure proper functioning, to extend their service
life, and to accommodate changes in increased loads.
As such, one alternative way to rehabilitate thedeteriorated structures is to utilize fiber-reinforced
polymer (FRP) composite materials, which are
currently viewed by structural engineers as new andhighly promising materials in the industry [1].
Advantages of FRP materials include a high strengthto-weight which is more applicable in confined space,excellent corrosion resistance, and high tensile
strength. Regarding the application of FRP
composites on corroded structures, several
applications involve externally bonded composite
305

GEOMATE – Kyoto, Japan, 3-5 November 2021

NUMERICAL MODELING

Concrete, steel plates and CFRP sheet

A three-dimensional nonlinear model isdeveloped using DIANA FEA software. In analyses,
the smeared crack concept was used with a rotatingcrack model based on total strain. The constitutive
models of materials with element types applied ingeometric modeling are used to develop the FE modelwith the validation from an experimental study withthree sets of tested RC beams [11].

Three-dimensional CHX60 element, a twentynode isoparametric solid brick element, is employed
for the concrete, CFRP laminates, support, andloading steel plates as illustrated in the DIANA FEA
library [14]. Steel plates are added at support andloading locations in the finite element models (as in
the actual beams) in order to avoid stressconcentration problems.

Finite Element Model (FEM)

Steel reinforcements

In this study, the mesh size of concrete elements
was investigated. Furthermore, the average mesh size
corresponding to three times the diameter ofmaximum aggregates has been recommended [12].
The minimum mesh size is usually deemed the
maximum size of coarse aggregates due to theheterogeneity of the concrete material. The threedimensional (3D) analysis can be calculated withaccuracy employing an acceptably large mesh size
[13].
Hence, the load-deflection curves responded fromFE analysis of non-corroded beams with different
mesh sizes ranging from 20 to 100 mm were
performed and illustrated in Fig. 1. Based on thecomputational time and accuracy of the obtained
results, the mesh size of 50×50×50 mm has been
selected for numerical modeling as in Fig. 2.

In the model of non-corroded RC beam,
embedded bar elements are assigned for thelongitudinal reinforcement and stirrups. They wereembedded in the continuum element and their strainsare calculated based on the displacement field of thesurrounding continuum element.
When the failure mode is not governed by thebond between steel and concrete, the behavior ofstructural tested beams can be predicted using theperfect bond assumption. In case, the steel-concretebond controlled the failure mechanism and structural
performance of corroded RC beam as demonstrated
in many previous experimental studies, the steel bars
are modeled as bond-slip reinforcements. A line-solidinterface element is employed to simulate theinfluence of bond stress-slip behavior.
Concrete-CFRP interface

50

A tie constraint is used to model the interaction
between the different materials. The assumption that
all materials had a perfect bond to one another is
made.

Load (kN)

40
30
EXP
FEM 20mm
FEM 50mm
FEM 100mm

20
10Materials ModelingConcrete00Fig. 110

20
30
Deflection (mm)In the adopted rotating crack model, the concrete
behavior in tension is described by the stress-strain
curve of JSCE tension softening defined by the tensile
strength and mode-I tensile fracture energy. The
parabolic stress-strain curve in DIANA software isused for compression considering the reduction
model due to lateral cracking and stress confinement
model [14]. The post-peak response of this curve
employing fracture energy and crack bandwidth asrecommended [15]. The damaged concrete model due
to corrosion was proposed by a reducing stress-strain
relationship as suggested in [16].

40

Convergence study

CFRP sheet

Steel reinforcements and steel plates
Fig. 2Mesh discretization of the beam, CFRP sheet
and the load, support conditions

The FEM employed linear elastic isotropic
material to model steel plates at support and loading
positions. An elasto-plastic material with linear strain
306

GEOMATE – Kyoto, Japan, 3-5 November 2021

hardening was assigned to reinforcements. The
tangent modulus in the plastic stage is set equal to
one-hundredth of the modulus of elasticity. The
appearance of pitting corrosion of reinforcements
across their lengths has been shown to affect thestrength and ductility of corroded reinforcement inprevious studies [17], [18]. Since it is difficult toincorporate the actual variability of steel corrosion
into a numerical model, an alternative method is tomodel the corroded steel bar over a length employing
average cross-section loss.

they proposed the deteriorated bond stress-slip curveby reducing maximum bond strength. Moreover, theassumption is made for the unchanged behavior along
the deterioration region as illustrated in Fig. 3.

CFRP sheet
Although the CFRP sheet is an orthotropic
material that has higher modulus and strength inwoven directions than through the thickness
directions), it could be modeled as an isotropic
material within the plane. This is because the loadingon the CFRP sheet is spreading within the sheet plane.

Fig. 3

Bond stress-slip behavior of CEB-FIP 1990
[19] and bond strength reduction model by
Tørlen and Horrigmoe [20]Damaged steel-concrete interface

NUMERICAL VALIDATIONThe bond between reinforcements and concrete
covering plays a significant role in the mechanicalresponse of the structures attacked by corrosion [2][4]. Moreover, pullout failure often seen in the
structure where steel reinforcements embedded inwell-confined concrete. Therefore, the bond stressslip curve characterized pullout failure mode asshown in Fig. 3 from CEB-FIP Model Code 1990 [19]is adopted to model the bond between steel andconcrete.

Details of Beam Specimenss =  max ( )s1

for 0  s  s1(1)

 =  max

for s1  s  s2

(2)

for s2  s  s3

(3)

for s3  s(4)

 = ( max −  f )(
 = fs − s2)s3 − s2

The first set comprised the two non-corroded RC
beams named S1-NC and S2-NC that wereconsidered as control beams. The second set has twocorroded RC beams named S3-C, S4-C with acorrosion degree of approximately 10%. Lastly, thethird set also has two corroded RC beams named S5CFRP, S6-CFRP subjected to a 10% degree ofcorrosion, the corroded beams are then sanded andspread with epoxy resin on the surface for coveringcorrosion cracks before strengthening. All tested
beams, which are 2200 mm long are tested in a fourpoint-bending test as illustrated in Fig. 4. Six beams
had the overall depth, the width of 200 mm, 150 mm,
respectively. Each beam was reinforced in tension
and compression with two ϕ12 mm longitudinal steel
bars, whilst using ϕ6 mm stirrups with a spacing of
150 mm. The thickness of a single CFRP layer
provided by the manufacturer was 0.111 mm, while
the sheet width and length are 150 mm and 2000 mm,
respectively [11].

The bond stresses between the reinforcing bar andconcrete can be calculated as a function of the relative
displacement (denoted s) as from Eq. (1) to Eq. (4).
Where: τmax is the maximum bond strength calculated
as  max = 1.25 f c ; τf is the residual bond strength f = 0.4 max ; fc is the characteristic value of theuniaxial compressive strength of concrete measured
on cylindrical specimens. The parameters used inthese equations are defined in CEB-FIP Model Code1990 [19]. In the numerical study of Tørlen andHorrigmoe [20], in order to characterize thedeterioration of bonds in corroded reinforcement,

Fig. 4Four-point bending test on a typical beamspecimen

The compression test of cubic specimens of
150mm obtained the average 28-day concrete307

GEOMATE – Kyoto, Japan, 3-5 November 2021

compressive strength of 40.9 MPa with a coefficientof variation of 1.6% from the average value. The
tensile strength and elastic modulus of the CFRP
sheet were 3400 MPa and 245000 MPa, respectively.
Lastly, the elastic modulus of ϕ12 mm longitudinal
steel bars is equal to 200 GPA, while the yield andultimate tensile strengths are 374.3 and 543.2 MPa,
respectively.

in Fig. 5. The experimental (EXP) and FEA loading
capacity are given in Table 1. As it can be seen, thestiffness of non-corroded specimens was close up toa load level of 28 kN both in experimental andnumerical analysis. Similar nonlinear behavior was
extended up to the maximum loading capacity of 38.5
kN of beam S2-NC and 35.0 kN beam S1-NC in theexperiment compared with the value of 35.1 kN in
finite element simulation. It shows the good
agreement between the two results for the loaddeflection curves as shown in Fig. 5(a).

FEM Prediction

50
Table 1 Experimental, FE-predicted maximum load

Load (kN)40
Beamnotation30
EXP S1-NC
EXP S2-NC
FEM S-NC

20
10

S1-NC
S2-NC
S3-C
S4-C
S5-CFRPS6-CFRP

0

0

10

20
30
Deflection (mm)
(a)

40

50

Load (kN)40
30

EXP S3-C

10

EXP S4-C
FEM S-C

0
0

10

20
30
40
Deflection (mm)(b)

Deviation
(%)
0.3
8.8
10.8
9.4
3.7
5.1

The experimental curves of load – deflection of
corroded beams S3-C and S4-C are approximately
linear up to crack formation at the corresponding load
25kN. Subsequently, the curves turned nonlinear as aresult of stiffness deterioration due to corrosion
cracking. The deterioration continued with increased
loading until failure occurred with the maximum
loading capacity of 32.1 kN on average. Fig. 5(b)clearly demonstrates that there is a very good match
between the experimental and FEA load-deflection
behavior from initial loading up to beam failure.

50

20

Maximum load (kN)
EXP
FEA
(Test)
35.0
35.1
38.5
35.1
32.4
28.9
31.9
28.9
59.9
62.1
59.1
62.1

50

70

60
Load (kN)

50
(a)

40
30

EXP S5-CFRP

20

EXP S6-CFRP

10

FEM S-CFRP

00

Fig. 5

10

20
30
40
Deflection (mm)
(c)

50

(b)
Fig. 6

EXP and FEA load-deflection curves

The experimental and numerical variations ofmaximum loading capacity with mid-span deflection
under loading points for the tested beams are depicted

Comparison of typical cracking patterns on
S5-CFRP beam in (a) experiment and (b)FEA

Lastly, as the CFRP sheet bonded to the corroded
beams C5-CFRP and C6-CFRP, higher yield loads
and maximum loads were achieved. For example, the308

GEOMATE – Kyoto, Japan, 3-5 November 2021

maximum load for beam specimen S5-CFRP was
59.9 kN which approximately doubled the yield loadof corroded beams S3-C and S4-C (32.4 kN and 31.9kN). A similar trend was observed for the maximum
capacity of 62.1 kN in the finite element simulation.
It is reported in the experiment that the beam suffersfrom a bonding failure mechanism in the shear span.
The crack due to cover separation between concrete
and CFRP sheet propagates upwards until meeting thecorrosion crack. It continues to travel up to theloading point forming the inclined crack as shown in
Fig. 6. On the other hand, the failure mode obtained
from FE analyses is a flexural failure with thecrushing of concrete in the compression zone. These
differences in failure mode were due to the smeared
crack model of concrete used in FEM could not
perfectly reflect the influence of localized cracking on
laminates. Therefore, it is needed to extend thesimulation by using a prescribed discrete crack modeland bond-slip model between materials to achieve
accuracy in simulating the debonding failure
mechanism of strengthened CFRP beams.

considerable reduction of ductility in beams
strengthened with more layers of CFRP sheets. Thisfinding shares the same agreement with the researchworks on the multiple layers of FRP of RC beams
conducted by previous experimental works [21].
Effects of CFRP sheets length
For the strengthened beams with different CFRP
sheet lengths ranging from 1200 mm to 2000 mm, thedevelopment of the load-carrying before cracking isalmost the same. In the case of a single layer, thelength of the CFRP sheet has a slight effect on thechange of load-carrying capacity as illustrated in Fig.
8. By reducing from 2000 mm to 1600 mm length of
the strengthening sheet, the load-carrying capacity
difference reported is around 3.24 kN. There is a more
remarkable loss from the 1200 mm length CFRP
sheet (800 mm shorter than the original) which isapproximately 12.2 kN. As the length of the CFRP
sheet decreases, the ductility of the beams decreases
as well. This is because the cracking pattern is more
evenly distributed over the area where RC beam isbonded with the CFRP sheet. Therefore, the overallsum of all crack widths is reduced and the ductility
will be improved.

FEM Parameter Analysis
In this section, the parametric study on the varying
CFRP layers and the CFRP sheet length will be
conducted to assess their influence on structural
behavior. The FEM of specimen C5-CFRP and C6CFRP are used with the assumption that the samematerial, geometry described in the experimental
section.

80

Load (kN)60

Effects of CFRP layers

402000 mm1600 mm
1200 mm20100
0Load (kN)

80

0

60

40
20
0

0

Fig. 7

Fig. 8One layer
Two layers
Three Layers
Five layers

10

20
30
40
Deflection (mm)

50

20
40
Deflection (mm)60

Effect of CFRP lengths on the structural
behavior of strengthened corroded beams

CONCLUSIONSThe paper contributes to a more understanding of
the flexural behavior of RC beams with reinforcement
corrosion strengthened with externally bonded CFRP
sheets.
A FE model for RC corroded beams flexuralstrengthened with CFRP sheets was successfully
validated using published experiments to predict theload-carrying capacity of tested beams with relatively
small deviation.
A parametric study is done to examine thecapability of the applied FE simulation for many
variables. It indicated that the predicted flexuralstrength enhancement was remarkably influenced by
the increase of a limited number of CFRP layers while

60

Effet of the CFRP layers on the structural
behavior of strengthened corroded beams

Fig. 7 indicates that the stiffness, yield load, theload-carrying capacity of the beam are enhanced with
the increase of layers. The load-carrying capacity
when using two, three, and five layers improved by
13.1%, 25.8%, and 44.8% in comparison with a single
layer. The post-peak response in the load-deflection
curve is becoming steeper as the number of FRP
layers increased. That behavior indicates the309

GEOMATE – Kyoto, Japan, 3-5 November 2021

it was smaller affected by the varying CFRP sheet
lengths. The strengthened beams behave more ductilewhen externally bonded with more than one CFRP
sheet and the ductility also increases along with the
reduction of sheet length.

Composites for Construction, Vol. 11, Issue 6,
2007, pp.640–649.
[10] Kishi N., Zhang G. and Mikami H., Numericalcracking and debonding analysis of RC beams
reinforced with FRP sheet, Journal of
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experimental study on the flexural strengtheningof corroded RC beams using externally CFRP
sheet, Journal of Science and Technology in
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pp.1–16 (in Vietnamese).
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fracture of concrete, Materials and Structures,Vol. 16, 1983, pp.155–177.
[13] Maekawa K., Pimanmas A. and Okamura H.,
Nonlinear mechanics of reinforced concrete,
Spon Press, London, 2003.
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[15] Nakamura H. and Higai T., Compressive fractureenergy and fracture zone length of concrete, In:
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structures under seismic loads, Shing P. andTanabe T-A., Ed.., American Society of Civil
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[16] Coronelli D. and Gambarova P., Structuralassessment of corroded reinforced concretebeams: modeling guidelines, Journal of
Structural Engineering, Vol. 130, Issue 8, 2004,
pp.1214–1224.
[17] Stewart M.G., Mechanical behaviour of pitting
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pp.19–30.
[18] Chen E., Berrocal C.G., Fernandez I., Löfgren I.
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[19] Fib. CEB-FIP model code 1990. London:
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[20] Tørlen A. and Horrigmoe G., Modelling of bond
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[21] Bsisu, K., Sargand, S., and Ball, R., The effect ofwidth, multiple layers and strength of FRP sheets
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2015, pp.33-42.

ACKNOWLEDGMENTS
The authors would like to thank Toray
International, Inc., Japan for providing the materialsof this research.
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[5] Abuodeh O.R., Abdalla J.A. and Hawileh, R.A.,
Prediction of shear strength and behavior of RC
beams strengthened with externally bonded FRP
sheets using machine learning techniques,Composite Structures, Vol. 234, 2020, 111698.
[6] Chen W., Pham T.M., Sichembe H., Chen L. and
Hao H., Experimental study of flexural
behaviour of RC beams strengthened bylongitudinal and U-shaped basalt FRP sheet,
Composites Part B: Engineering, Vol. 134, 2018,
pp.114–126.
[7] Dong J., Wang Q., and Guan Z., Structural
behaviour of RC beams with external flexuraland flexural–shear strengthening by FRP sheets,
Composites Part B: Engineering, Vol. 44, Issue
1, 2013, pp.604–612.
[8] McSwiggan C. and Fam A., Bio-based resins for
flexural strengthening of reinforced concretebeams with FRP sheets, Construction and
Building Materials, Vol. 131, 2017, pp.618–629.
[9] Godat A., Neale K.W. and Labossière P.,
Numerical modeling of FRP shear-strengthened
reinforced concrete beams, Journal of310

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

SUCCESSIVE OVER RELAXATION METHOD FOR SOLVING HEATEQUATION OF THERMAL CONDUCTIVITY OF CONCRETE
Dalal Adnan Maturi 1
1

Departement of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia
ABSTRACTAny building regardless of its size always begins with development. The goal is to design not only the outward
appearance of the future structure, but also the calculation of the main geometric letters. After all, the main task of
construction is to build sturdy buildings that can help maintain a comfortable environment, without additionalcosts. Therefore, when choosing the raw materials used in the construction of buildings and structures, the thermal
conductivity of the materials used in construction is taken into account, including all kinds of concrete. Concrete
is a mixture of raw materials made up of sand, aggregate (or the tooth, which means breaking stones) and cement,with the addition of water to it. In this paper we will use the method of Successive Over Relaxation (SOR), tosolve the two-dimensional heat equation with the thermal conductivity of concrete for the stable and unstable state.
When comparing the results of the explicit and implicit methods, the explicit method converges faster and withless iterations. The explanation for this is that the implicit has more than one unknown, making it more difficult tosolve, even though it is unconditionally stable at higher timer steps. Of the three iterative solvers (Jacobi, Gauss
Seidel, and SOR), the SOR approach has the fastest convergence. Therefore, the use of iterative methods andnumerical simulations allow us to choose suitable building materials depending on the thermal conductivity of thechosen material and this conductivity depends on the porous structure, density and humidity.
Keywords: Method of Successive Over Relaxation, Heat Equation, Thermal Conductivity of Concrete, Matlab.

INTRODUCTION
1. Flexibility of use: Soft concrete is poured into
molds to obtain different forms of concrete
depending on the shape of the mold used.
2. Economical maintenance work: Metal
structures need periodic painting works, and theconnections of stone structures are usually affected by
frost, while basic reinforced concrete structures do
not need maintenance work.
3. Fire resistance: Concrete is a material withpoor thermal conductivity, in addition to that it hasless thermal expansion than other building materials
such as clay and building stone. The low thermal
expansion factor reduces the appearance of cracks
when the concrete temperature rises, and the poor
thermal conductivity prevents the heat of the fire fromreaching the interior of the concrete block to reach thereinforcement steel. Experiments have shown that the
exposure of the outer surface in a block of concrete toa high temperature in one hour does not lead to a
noticeable rise in the surface layer temperature at a
depth of about 2.5 cm in the mentioned surface, while
the temperature rise is almost negligible in the layers
at a depth of 7.5 cm. cm. Finally, the convergence ofthe thermal expansion factors of concrete and steel
reinforcement allows reinvestment of the structure of
reinforced concrete after surface repairs in cases oflight or non-long-term fire, while the metal structure,for example, and in the same conditions can become
uninvestable.
4. Resistance to emergency loads: Reinforced

One of the most important methods for solving
large linear system equations is successive overrelaxation (SOR). Fluid dynamics, mathematicalprogramming, linear elasticity, and machine learning
are just a few of the areas where it can be used. The
study of steady heat conduction, turbulent flows,boundary layer flows, or chemically reactive flows
are examples of SOR applications in Dynamics. As aresult, both scholars and corporate policymakers
should use the SOR method.
Time is always valuable in the real world, and noone wants to waste it; while solving systems ofequations, it is sometimes preferable to have a nearapproximation of the solution rather than the precise
solution for this reason, among others. The sequential
over-relaxation approach (SOR) can help in thissituation. Gaussian elimination, the industry standard
for discovering accurate techniques, requires roughly
n×n operations to solve the system, which becomestime demanding as n grows larger. While SOR justprovides an approximation, it does so significantly
faster than Gaussian elimination. David Young and H.
Frankel created SOR in 1950 for use on digitalcomputers.
FEATURES OF CONCRETE AND
REINFORCED CONCRETE
These features can be summed up as follows:
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SEE - Kyoto, Japan, Nov.18-20, 2020

concrete is less affected by the change in theapplication of live loads on it than the rest of theconstruction materials, due to its high self-weight.
That is, the least resistant parts undergo sufficient
deformations to transfer forces to the more resistantparts.
5. Possibility of obtaining prefabricated parts:
The precast concrete parts can be implemented in thefactory, and then they are assembled at the work site.

(𝑘)

𝑥𝑖

=

(𝑘)

(𝑘−1)

𝑥𝑖

(𝑘−1)

− ∑𝑛𝑗=𝑖+1 𝑎𝑖𝑗 𝑥𝑗

= 𝑥𝑖

+𝑤{

1

𝑎𝑖,𝑖

(𝑘)

[𝑏𝑖 − ∑𝑖−1
𝑗=1 𝑎𝑖𝑗 𝑥𝑗∑𝑛𝑗=𝑖+1 𝑎𝑖𝑗 𝑥𝑗(𝑘−1) ] − 𝑥𝑖(𝑘−1) }The Gauss-Seidel iteration model was altered tocreate it.
1. Given 𝐴𝑥 = 𝑏, where 𝐴 and 𝑏 are known and𝑥, 𝑥0 , is a first estimate.
2. 𝐿∗ 𝑥𝑘+1 = 𝑏 − 𝑈𝑥𝑘 Where 𝐿∗ denotes thematrix 𝐴 's lower triangular components, 𝑈 denotes
the matrix 𝐴′ 𝑠 upper triangular components, and 𝑏
denotes our 𝑏 𝑥𝑘+1 is the next iteration of 𝑥, and 𝑥𝑘+1
is the 𝑥𝑘+1 𝑘𝑡ℎ approximation of 𝑥. Hadjidimos [1]presented the accelerated Overrelaxation method as a
two-parameter modification of the sequential
Overrelaxation (SOR) approach for numericalsolution. This is how the SOR technique works.
1. Given 𝐴𝑥 = 𝑏 where A and b are known, x
unknown, and an initial guess for 𝑥, 𝑥0 .
2. Let 𝐴 = 𝐷 + 𝐿 + 𝑈 where 𝐷 is the main
diagonal of 𝐴, 𝐿 the lower triangle components of 𝐴
and 𝑈 the upper triangle components of 𝐴.
3.
𝑤
𝑥𝑘+1 = (1 − 𝑤)𝑥𝑖 + (𝑏𝑖 − ∑𝑗<𝑖 𝑎𝑖,𝑗 𝑥𝑗(𝑘+1) −

]

−

(5)

The term in the bracket
1

{

𝑎𝑖,𝑖

(𝑘)

[𝑏𝑖 − ∑𝑖−1
𝑗=1 𝑎𝑖𝑗 𝑥𝑗(𝑘−1)− ∑𝑛𝑗=𝑖+1 𝑎𝑖𝑗 𝑥𝑗
(𝑘−1)

𝑥𝑖

}

]−
(6)

is just difference between the variables of theprevious and present iterations for the Gauss-Seidel
method
{

1

𝑎𝑖,𝑖

(𝑘)

[𝑏𝑖 − ∑𝑖−1
𝑗=1 𝑎𝑖𝑗 𝑥𝑗

(𝑘−1)

𝑥𝑖

(𝑘)

} = [𝑥𝑖(𝑘−1)− ∑𝑛𝑗=𝑖+1 𝑎𝑖𝑗 𝑥𝑗

(𝑘−1)

− 𝑥𝑖

]−

]𝐺𝑎𝑢𝑠𝑠−𝑆𝑒𝑖𝑑𝑒𝑙(7)

APPLICATIONS OF SUCCESSIVE OVERRELAXATION (SOR)
The staedy state equation for 2D heat conduction is
by:

𝑎𝑖,𝑖

𝛼(

∑𝑗>𝑖 𝑎𝑖𝑗 𝑥𝑗𝑘 )(1)
Where 𝑥𝑘 is the kth approximation of 𝑥, 𝑥𝑘+1 isthe next iteration of 𝑥, 𝑎𝑖,𝑗 is the corresponding
element of matrix A, b is our vector, and is our
relaxation factor, and 𝑎𝑖,𝑗 is the corresponding
element of matrix 𝐴.

𝜕2 𝑇
𝜕𝒙𝟐

+

𝜕2 𝑇
𝜕𝒚𝟐

)+𝑓 = 0(8)

𝜶 is thermal diffusivity, T is temperature, and f is thesource term in the preceding equation. Because thesource term is omitted for simplicity, the final
equation is:

When we get to the following part, we'll discuss
more about choosing an acceptable relaxation factor,
but for now, remember that if 𝑤 = 1, we get theGauss-Siedel method. Because the value at a given
iteration is made up of a mixture of the old value andthe freshly calculated value, the convergence isimproved.
𝑥𝑖𝑛𝑒𝑤 = 𝑤𝑥𝑖𝑛𝑒𝑤 + (1 − 𝑤)𝑥𝑖𝑜𝑙𝑑
(2)

𝜶(

𝝏𝟐 𝑻
𝝏𝒙𝟐

+

𝝏𝟐 𝑻
𝝏𝒚𝟐

)=𝟎

(9)

Finite difference equation is:
𝑻𝒊+𝟏,𝒋 −𝟐𝑻𝒊,𝒋 +𝑻𝒊−𝟏,𝒋(∆𝒙)𝟐

𝑻𝒊,𝒋+𝟏 −𝟐𝑻𝒊,𝒋+𝑻𝒊,𝒋−𝟏

+

(∆𝒚)𝟐

=𝟎

(10)

The Unsteady state equation for 2D heat conduction
is by:The SOR method is similar to the Gauss-Seidel
method, with the exception that it uses a scaling factor
to reduce approximation error. Take a look at theequations below.
𝑖 = 1,2, ⋯ , 𝑛(𝑘)[𝑏𝑖 − ∑𝑖−1
𝑗=1 𝑎𝑖𝑗 𝑥𝑗(4)It's worth noting that when calculating 𝑥𝑖 , the
Variables with a lower index than 𝑖are iterated (k)times. while variables with indices greater than𝑖 remain unchanged the previous iteration (𝑘 −1). The SOR equation is as follows: is offered as amethodSUCCESSIVE OVER-RELAXATION (SOR)∑𝑛𝑗=1 𝑎𝑖𝑗 𝑥𝑗 = 𝑏𝑖 ,

1
𝑎𝑖,𝑖

𝝏𝑻
𝝏𝒕

(3)

= 𝜶(

𝝏𝟐 𝑻
𝝏𝒙𝟐

+

𝝏𝟐 𝑻
𝝏𝒚𝟐

)+𝒇

(11)

Again, f is ignored, thus the equation is:

The values at the k iteration of the Gauss-Seidel
method are provided by

𝝏𝑻
𝝏𝒕

312

= 𝜶(

𝝏𝟐 𝑻
𝝏𝒙𝟐

+

𝝏𝟐 𝑻
𝝏𝒚𝟐

)

(12)

SEE - Kyoto, Japan, Nov.18-20, 2020

Finite difference equation is:𝒏𝑻𝒏+𝟏
𝒊,𝒋 −𝑻𝒊,𝒋∆𝒕=

𝑻𝒊+𝟏,𝒋 −𝟐𝑻𝒊,𝒋 +𝑻𝒊−𝟏,𝒋
(∆𝒙)𝟐

+

𝑻𝒊,𝒋+𝟏 −𝟐𝑻𝒊,𝒋 +𝑻𝒊,𝒋−𝟏
(∆𝒚)𝟐

(13)

Let 𝒌𝟏 = 𝜶∆𝒕⁄(∆𝒙)𝟐 and 𝒌𝟐 = 𝜶∆𝒕⁄(∆𝒚)𝟐
The number of grid points taken in both the X andY directions along the square plate domain is 10 in
the program. The time step size utilized in the explicit
technique is 1e-2 and 1e-3 to see how the time step
influences the solutions. To check the stability of the
Implicit technique at higher time steps, 1e-2 isutilized as the time step size, and iterative solvers are
utilized to converge the solutions so that the error falls
within the stated tolerance requirements. Omega(scaling factor) is represented as an array of numbersin the Implicit SOR algorithm (0.8 to 1.2).

Fig. 1

Fig. 2

Fig. 3This is the Jacobi method at ∆𝑡 = 0.010

Fig. 4This is the Gauss-Seidel method at ∆𝑡 =0.010

Fig. 5This is the Successive Over Relaxation(SOR) method at ∆𝑡 = 0.010, Ω = 0.8

This is the Explicit method at ∆𝑡 = 0.0010 .

This is the Explicit method at ∆𝑡 = 0.0100

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SEE - Kyoto, Japan, Nov.18-20, 2020

Fig. 6This is the Successive Over Relaxation(SOR) method at ∆𝑡 = 0.010, Ω = 0.9

Fig. 9This is the Successive Over Relaxation(SOR) method at ∆𝑡 = 0.010, Ω = 1.2

Fig. 7This is the Successive Over Relaxation(SOR) method at ∆𝑡 = 0.010, Ω = 1.0

1. Steady-state conduction is a type of conductionin which the temperature differential that drives the
conduction is consistent across time. In a transientstate condition, on the other hand, the temperature at
the boundary fluctuate over time and finally settles
into a steady state if the temperature isn't modified
any further.
2. The solution is considered to be converged if
the error term obtained is less than the tolerance
value. We find an error term below the tolerance
value(1e-4) for each case in the aforementioned issue,
which is solved in steady and unsteady-state
conditions, indicating that the solution is converged.
3.The answer is achieved utilizing three
approaches in the implicit approach for steady-state
and transient-state analysis, namely Jacobi(J), GaussSeidel (GS), and successive over-relaxation(SOR).
SOR, the third of these three approaches, converges
to the solution with less iterations than the other two
approaches, assuming that the value of omega is
acceptable. When compared to the other two
methods, the Jacobi approach converges to thesolution with the most iterations in both
circumstances.
4. We may conclude that the sequential overrelaxation approach is the most effective approachsince it converges the solution in fewer iterations andhence takes less time to solve than the Jacobi andGauss-Seidel methods. This is mostly determined by
the value of omega; if an adequate value is chosen,
the solution will converge quickly; otherwise, it willtake longer.

Fig. 8This is the Successive Over Relaxation(SOR) method at ∆𝑡 = 0.010, Ω = 1.1

CONCLUSIONS
We have emphasized the relevance of using the
SOR interactive solve method for expediting thesolution of real-world problems in this study. Itshould come as no surprise that the SOR method isused to solve matrix systems 𝑨𝒙 = 𝒃.

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SEE - Kyoto, Japan, Nov.18-20, 2020

More research will be required to develop a SOR
algorithm that produces better, more accurate
outcomes.
However, the SOR technique excels in terms ofspeed and ability to solve any n x n system without
the requirement for the coefficient matrix A to havean inverse or to store the matrix fully, saving both
space and time.

Second Kind Using Maple, Journal of Advances
In Mathematics, Vol9, No.1, pp.18681875,2014.
[10] Dalal A. Maturi., Application of Adomian
Decomposition Method for Solving of Fredholm
Integral Equation of the Second Kind, European
Journal of Science and Engineering, Vol9, No.2,
pp.1-9,2014.

ACKNOWLEDGMENTS[11] H.H. Sorkun., S. Yalcinbas., Approximate
solutions of linear Volterra integral equation
systems with variable coefficients, Applied
Mathematical Modeling, 34(2010) 3451-3464.

This paper was supported by the Deanship ofScientific Research (DSR), King Abdulaziz
University, Jeddah.

[12] Dalal A. Maturi., Honaida M. Malaikah.,Numerical Solution of System of Three
Nonlinear Volterra Integral Equation Using
Implicit Trapezoidal, Journal of Mathematics
Research; Vol. 10, No. 1; February 2018, ISSN
1916-9795doi:10.5539/jmr.v10n1p44URL:
https://doi.org/10.5539/jmr.v10n1p44.

REFERENCES[1] Mayooran T., and Light E., Applying the
Successive Over-relaxation Method to a RealWorld Problems. Am. J. Appl. Math. Stat. 4,
2016 pp.113–117.
[2] Hadjidimos A., “Successive Overrelaxation(SOR) and related methods,” Journal ofComputational and Applied Mathematics, vol.
123, no. 1, pp. 177-199, 2000.
[3] Moitsheki R. J., and Harley C., Transient heattransfer in longitudinal fins of various profiles
withtemperature-dependent
thermalconductivity and heat transfer coefficient;
Pramana Journal of Physics 77,2011, pp.519-532.
[4] A.M. Wazwaz., The variational iteration method;a reliable tool for solving linear and nonlinear
wave equations, Comput. Math. Appl., 54 (2007)
926–932.
[5] A.M. Wazwaz., A First Course in Integral
Equations, World Scientific, Singapore, (1997).
[6] A.M. Wazwaz., Suheil A. Khuri, The variational
iteration method for solving the Volterra integrodifferential forms of the lane-Emden and theEmden-Fowler propblems with initial andboundary value conditions, DE GRUYTEROpen Eng. (2015) ;5:31-41.
[7] D.A. Maturi., Numerical Solution of System of
Two Nonlinear Volterra Integral Equations,International Journal of Computers &Technology, Vol12, No.10, pp.3967-3975,2014.

[13] Dalal A. Maturi., The Modified Decomposition
Method for Solving Volterra Integral Equation
Of The Second Kind Using Maple, International
Journal of GEOMATE, Oct., 2019 Vol.17, Issue
62, pp. 23-28.
[14] Dalal A. Maturi., Amal I. Aljedani., Eman S.
Alaidarous., Finite Difference Method For
Solving Heat Conduction Equation Of The
Granite, International Journal of GEOMATE,
Sept., 2019 Vol.17, Issue 61, pp. 135 -140.
ISSN: 2186-2982 (P), 2186-2990 (O), Japan.
[15] Dalal A. Maturi., Finite Difference Method For
Solving Heat Conduction Equation Of The
Brick, International Journal of GEOMATE,
April, 2020 Vol.18, Issue 68, pp. 114-119.
[16] Dalal A. Maturi., Eman Ahmed M. Simbawa.,
The Modified Decomposition Method For
Solving Volterra Fredholm Integro-Differential
Equation Using Maple, International Journal of
GEOMATE, March 2020 Vol.18, Issue 67, pp.
84-89.

[8] D.A. Maturi., A.Z. Bajamal., B.M. AlGethami.,
Numerical Solution of Volterra Integral Equationof Second Kind Using Implicit Trapezoidal,
Journal of Advances In Mathematics, Vol8,
No.2, pp.1540-1553,2014.
[9] Dalal A. Maturi., Adomian Decomposition
Method of Fredholm Integral Equation of the315

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

RESPONSE SURFACE MODELLING OF PERFORMANCE OF
CONCRETE WITH BAUXITE LATERITE SOIL AND FLY ASH
Joenel Galupino1, Mary Ann Adajar1, John Jacob Abejuela2,
Ernst Kenneth Alcantara2, and Philippe Daniel Ebreo2
1

Faculty, De La Salle University, Manila, Philippines
Undergraduate student, De La Salle University, Manila, Philippines

2

ABSTRACTThe study investigated the use of waste materials such as fly ash and bauxite laterite soil in concrete in order toaddress the wastes' disposal issues and lower the cost of concrete products. Bauxite laterite soil is a byproduct ofaluminum mining, whereas fly ash is a byproduct of coal power plants. To make bauxite laterite concrete, fly-ash
was utilized as a partial substitute for cement in a 20% proportion and bauxite laterite soil was used as a fineaggregate substitute in 10%, 20%, 30%, and 40% quantities. We evaluated and compared the mechanical propertiesof bauxite laterite concrete to those of standard concrete. The test findings indicate that a 10% replacement ofbauxite laterite soil is the optimal percentage substitution. The mixture attained 58 percent increase of thecompressive strength compared to ordinary concrete after 28 days of curing. Additionally, bauxite laterite concrete
displayed high early compressive strength and a unit weight reduction of approximately 6% to 8%. However, as
bauxite laterite soil was increasingly substituted, the workability of the concrete mix decreased. A Response
Surface Model was developed to forecast the compressive strength of concrete based on several parameters. It may
be concluded that bauxite laterite soil and fly ash are both viable alternatives to fine aggregates and cement.
Keywords: Bauxite laterite soil, Fly-ash, Compressive strength, Response surface modelling

INTRODUCTION

Both of the aforementioned materials were usedas alternatives for components used in concrete
manufacturing in the study. Due of the intrinsic
pozzolanic and cementitious qualities of fly ash, it
was used. The researchers' goal is to find a new
purpose for things that are currently deemed garbage.
Recycling is carried out in order to give an alternate
source of raw materials, most notably cement and fineaggregates. While the relatively low replacement %may look inconsequential, when applied to large
projects, significant savings might be achieved. The
amount of waste that will be disposed of may reduce
disposal issues. Additionally, it may eventually resultin a reduction of pollutants produced by the mining
and power generation industries.
One way of giving a relationship with theproperties of these wastes and its subsequent results
is to use modelling [28]-[32]. Response surface
modelling will forecast or predict the causal
relationship of using fly ash and bauxite laterite waste
in this study, specifically its compressive strength andthe days of curing. Thus, in this study, it is aimed tocreate a response surface model that will forecast thecompressive strength of concrete given thepercentage of fly ash and bauxite laterite used and its
days of curing. Validation is important in a study[33]-[37], an equation for the compressive strength of
concrete can be determined and used for estimating
the concrete strength at a given percent substitution
and days of curing.

Meyer (2009) [1] estimates that the world annual
production of concrete is 10 billion tons. Concrete's
huge demand necessitates vast volumes of raw
resources and energy.
Fortunately,
numerous
researchersareinvestigating the use of other materials [2]-[21] andmethods [23]-[27] to improve construction materials.
Consider the possibility of cement substitution.
Cement is generally composed of limestone, clay, andshale. Silica, alumina, and lime are used as raw
materials. They are calcined to make clinker incement kilns. After that, the clinker is pulverized toan extremely fine powder. Cementitious or
pozzolanic materials can be used in place of cement.
Fly ash is an example of this; it is abundant in thePhilippines. Fine aggregates are another componentof concrete that is utilized in considerable quantities.
Fine aggregates are frequently obtained throughriver dredging and, in some situations, through thecrushing of rocks. Substitute materials withcomparable particle sizes and surface roughness canbe used in place of fine aggregates. Due to its
resemblance to sand, bauxite laterite soil is one of thematerials that could be used.
Extraction of bauxite necessitates the clearance ofenormous areas of land in order to reach the mineralbeneath the ground. Surrounding ground surfaces arecontaminated with mineral waste and are rendereduseless following bauxite mining.
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GEOMATE – Kyoto, Japan, 3-5 November 2021

bauxite laterite soil is red in color and has a particle
size distribution that is predominantly fine.
The soil underwent Atterberg Limit Test, Specific
Gravity Test and Particle Size Distribution Curves inaccordance with the ASTM Standards.

METHODOLOGYDesign of Experiment
The experimental research approach establishedthe causes and effects of partially substituting fly ash
for cement and fine aggregates for bauxite laterite soilin the production of concrete on its mechanicalproperties. The experimental design considers adesign mix containing a constant 20% cement
replacement for fly ash and varied amounts of fineaggregate in place of bauxite laterite soil.
The compressive strength is the dependent
variable in this study. The amount of bauxite laterite
soil used as a partial fine aggregate substitution andthe curing days affect all strength parameters. The
study's control specimen is with the mix of Portland
cement type 1b and fly ash class F (20 percent) to be
used in place of cement.
The percentages of bauxite laterite soil substituted
were 10%, 20%, 30%, and 40%, represented as LS0,
LS10, LS20, LS30, and LS40. Laterite Soil (LS) andthe number following it denotes the percentage
replacement of fine particles.
Compressive strength of 25 MPa was used to
calculate the design mix, shown in Table 1. A
constant water cement ratio of 0.57 with a slump of
25mm-50mm was considered. Due to the specificgravity of fly ash class F being identical to that of
cement, a partial substitution of cement was made interms of weight. Due to the varying specific gravity
of bauxite laterite soil and river sand, the fineaggregate replacement was done by volume at 10%,
20%, 30%, and 40%. Compressive strength
specimens were made in cylindrical molds with adiameter and height of 150mm by 300mm,
respectively.

Concrete Works and Test
Concrete was cast and cured in accordance withASTM C192 standards. The concrete mixes wereconducted to the slump test, the concrete mix was
carefully put to the pan. The slump test enables us toevaluate and quantify the workability of fresh
concrete, allowing researchers to assess if theconcrete mix has an adequate amount of water. The
slump height was determined by measuring from the
top of the mold to the top of the concrete.
The Compressive Strength of Concrete Test was
performed on the concrete sample (based on ASTM
C39). The test was designed to measure the sample's
resistance to compressive forces up to the point ofcollapse. The concrete cylinder's two bases werecovered and vertically positioned inside the Universal
Testing Machine. The specimen was subsequently
subjected to an axial compressive load till failure.
Table 2 Number of Specimen
Mix
Control
LS0
LS10
LS20
LS30
LS40

LS0
0.205
0.092
0.023
0.000
0.358
0.303

Days of Curing
14
21
5
5
5
5
5
5
5
5
5
5
5
5

28
5
5
5
5
5
5

Response Surface Modelling

Table 1 Design Mix
Mix (m3)
Water
CementFly AshLS
Gravel
Sand

7
5
5
5
5
5
5

LS10
0.205
0.092
0.023
0.030
0.358
0.270

LS20
0.205
0.092
0.023
0.061
0.358
0.240

LS30
0.205
0.092
0.023
0.091
0.358
0.210

Lastly, a response surface model was used tocreate a forecasting model to determine which
variables have an impact on the compressive strengthof the concrete mixed with Laterite Soil. An equalityline was also used to validate the predicted versus theactual data.

LS40
0.205
0.092
0.023
0.122
0.358
0.180

RESULTS AND DISCUSSIONS
Index Tests

Index Tests

To determine the particle size distribution of theoven-dried specimen, sieve analysis was used. The
bulk of particles, 96.85 percent, that passed throughthe #4 sieve and were retained on the #200 sieve wereclassed as sand and were used as a substitute for fineaggregate. Fines were identified as a very minor
percentage of the 0.97 percent that passed through the

The bauxite laterite soil was mined at Samar's
aluminum mines. The mining locations are currently
undergoing soil exploration, which has identified
various layers of laterite soil.
The soil used in the study was extracted two
meters below the earth to guarantee that it was free ofcontaminants. Visual examination revealed that the317

GEOMATE – Kyoto, Japan, 3-5 November 2021

#200 sieve.
The soil is well-graded, as indicated by auniformity coefficient, Cu, of 9.09. Additionally, thecoefficient of curvature, Cc, value of 0.9, which is
close to one, indicates that the delivered soil is wellgraded sand. Due to the fact that our soil is wellgraded, our sample contains particles of varied sizes.
Which is great for substituting for fine aggregatessince it aids in the interlocking of particles, which isnecessary for concrete strength.
Bauxite laterite soil has a specific gravity of 2.05,
which is lower than the specific gravity of fineparticles. The liquid limit is determined to be 50.61
percent water, whereas the plastic limit is determined
to be 35.92 percent water. The plasticity index for thissoil sample is 14.69 percent, indicating that it is of
medium plasticity. The soil's flexibility can be linked
to the presence of clay particles in the sample.

in clay minerals, to retain more water than theordinary sand used as fine aggregates in concrete. It
was discovered during the mixing process that water
is progressively absorbed by the bauxite laterite soil,
drying portions of the concrete mix.
The control specimen failed in failure categories I
and II, while the concrete specimen failed violently
due to concrete's brittleness. The failure modes
observed for LS0 and bauxite laterite concrete are
Type II and IV.
The average compressive strength of concrete
specimens was determined at various curing times.
The control specimen has an average strength of
24.24 MPa, which meets the structural concrete
minimum standards. The increased use of fineparticles weakens the concrete specimen. LS0 has the
maximum compressive strength when compared tobauxite laterite concrete, which has a compressive
strength of 16.52 MPa. LS0 had a compressive
strength of just 68% of that of the control specimen.
The compressive strength of the LS10 specimen was
marginally lower at 13.42 MPa. While thecompressive strengths of LS20, LS30, and LS40 are
decreasing at 12.65 MPa, 9.08 MPa, and 7.70 MPa,
respectively. Additionally, it is worth noting that theearly strength of LS10 and LS20 is greater than thatof LS0, which increased strength through subsequentcuring periods.

Concrete Tests
Compressive, tensile, and flexural tests were
performed on concrete samples to investigate theeffect of bauxite laterite soil as a partial substitution
for fine aggregates and fly ash as a 20% substitute forcement. Compressive strength development wasdetermined on concrete sample after 7, 14, 21, and 28
days of curing.
The workability of bauxite laterite concretedecreases in direct proportion to the percentage
substitution, as measured by the slump value. The
concrete mix LS0 has the maximum slump due to theaddition of fly ash as a plasticizer. On LS40, thelowest slump value was obtained. Due to thedecreased workability of the concrete sample, shownin Figure 1, the researchers mixed fresh concrete
manually. During the mixing process, it became
apparent that the workability decreased due to thecohesiveness of the fresh concrete.

Figure 2 Compressive Strength vs. Curing Days
The control and LS0 specimens gained increasing
amounts of strength over time, particularly during thelater curing periods. As a result, both control and LS0
specimens demonstrated improved long-term
strength. LS10 and LS20 build strength rapidly,
attaining 69.37 percent and 65.77 percent strength
after seven days, respectively. Although bauxite
laterite concrete demonstrates a linear strength rise,
this contributes to the material's poor 28-daycompressive strength. On bauxite laterite concrete
specimens, it is noted that the strength develops
slowly.

Figure 1 Workability of the Soil Mix
Clay is a form of soil that has a greater capacity forwater retention than sandy soils [38]; this attribute ofclay relates to the ability of laterite soil, which is rich
318

GEOMATE – Kyoto, Japan, 3-5 November 2021

Long-term strength can be linked to the hydration
process, in which water causes concrete to harden byforming cementitious components. The chemical
reaction between cement components and waterproduces hydration products, which are found in
concrete [39]. Laterite soils are predicted to include
clay, which has a better water holding capacity thansandy soils. Due to the low workability of laterite
concrete, it is prone to include a large number of voids.
While the curing process may have aided in thehydration of the specimen, due to water's
incompressibility, it is unable to support loads, which
may have resulted in the bauxite laterite concrete's
decreased compressive strength.
The researchers noted that several concrete
examples had uneven top and bottom surfaces. It wasdetermined to be caused by air voids, settling, andmoisture loss in the concrete. When the specimen wasplaced on the Universal Testing Machine, thespecimen's uneven surface was seen. The initial focusof the applied force was on the partial cross-sectional
region it impacted. Without having to optimize thestrength, the top piece eventually failed. Side
fractures occurred on either the top or bottom faces ofthese specimens.

Figure 4 Equality Line for Validation
CONCLUSIONSThe parameters of the bauxite laterite soil, sand,gravel, fly ash, and cement were determined prior to
the production of bauxite laterite concrete. After
analyzing the index properties of bauxite laterite soil,
it was determined that its sand-sized particles made ita good substitute for fine aggregate. The bauxite
laterite soil is found to have uniform particle size
distribution with a specific gravity of 2.05.
Increased partial substitution of bauxite laterite
soil resulted in a decrease in the concrete's strength
and workability. Thus, LS10 is the preferred %substitution specimen due to its superior combinationof compressive, split-tensile, and flexural strength
when compared to the other percentage substitution
specimens. After seven days of curing, it regained
69.37 percent of its compressive strength. At 13.42
MPa, the compressive strength of LS10 was 57.80
percent that of the control sample. Due to the lower
specific gravity of bauxite laterite soil compared toriver sand, the unit weight of bauxite laterite concrete
was up to 8% lighter than traditional concrete.
The study was able to create a Response Surface
Model for the compressive strength of concrete for
estimating the concrete strength at a given percent
substitution of the Laterite Soil and days of curing.
The Response Surface Model has an R2 value of0.7542 which shows an agreement between the modeland the observed parameters.

Response Surface Modelling and Validation
A Response Surface Model for the compressive
strength of concrete was used for estimating theconcrete strength at a given percent substitution of theLaterite Soil and days of curing.
A Response surface model graph was used toillustrate the strength development curve for eachpercent substitution, shown on Figure 3.

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32111th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

NUMERICAL SIMULATION OF PERVIOUS CONCRETE PILE IN
LOOSE AND SILTY SAND AFTER TREATING WITH MICROBIALLY
INDUCED CALCITE PRECIPITATION
Hayder A. Hasan1, Sabah H. Lafta2, Muhannd W. Majeed3, Hadi Khabbaz4 and Mehdi Aghayarzadeh5
1,2,3Faculty of Engineering, Mustansiriyah University, Iraq; 4,5 School of Civil and Environmental
Engineering, University of Technology Sydney, Australia
ABSTRACT

It is essential to provide a stable foundation system for construction projects to reduce the geotechnical risk offailure due to static or dynamic loads. Pile foundations are recommended to increase bearing capacity and decrease
the dynamic oscillations of soils. Recently, soil stabilisation using microbially induced calcite precipitation(MICP) was widely used to increase shear strength parameters and reduce the hydraulic conductivity of sand. In
this study, the technique of using MICP was reviewed based on previous studies and analysed using Plaxis 3D toevaluate the enhancement of a single pervious concrete pile under static, free vibration and earthquake stages ofloose and silty sand. In the static stage, under the applying load to reach prescribed displacement 76 mm, the resultsof loose sand demonstrate that the static load capacity was increased from 470 kN of untreated loose sand to 582,
598 and 612 kN after treating by MICP along the shaft and tip of a concrete pile with 0.5,0.75 and 1 m, respectively.
In earthquake stage, the result of treated loose sand such as vertical and lateral displacement were insignificant
compared with untreated loose sand. The Plaxis 3D models were clarified the benefit of using MICP with pilefoundation model.
Keywords: Soil improvement, Pervious concrete pile under static and dynamic load, Numerical analysis

INTRODUCTION

to the urease enzyme activity of bacteria in existenceof urea and calcium chloride, the calcite (calcium
carbonate) is formed after a series of chemical
reactions. Umar [5] clarified that the principle of
MICP method is as follows: the reaction of hydrolysis
of urea , CO(NH2 )2 , with water leading to theformation of ammonia (NH3) and carbon dioxide(CO2 ).

Post-grouting methods have been used to improve
the load-carrying capacity of deep foundations byincreasing shaft and/or tip resistances of the pile.
Post-grouting was widely utilized to improve the tip
resistance of drilled pile. However, the grouting
process around the pile shaft is complex and difficult
quality control [1], [2]. According to Suleiman [3], apervious concrete pile is used as an injection point toimprove the soil-pile interaction and capacity load.
Lin [1], [2] has been used permeable pile (pervious
concrete pile) to simplify the grouting procedure of
MICP along the shaft and tip of foundation systems
without applying pressure.
The results of [1] research demonstrated that the
improving of pile capacity under axial compression
was up to 2.5 times higher the amount of pile without
bio-grouting. Moreover, a CaCO3-cemented zone
around the pile was extended from the effective pile
diameter (76 mm) to approximately 165 mm. The pilelength was increased roughly by 25 mm. Soil clumps
were noted surrounding the pile tip, which pointed out
the breaking and failure of the cemented soil aroundthe pile tip. It could be concluded that the failure wasoccurring near the pile tip of the cemented soil.
Microbially induced calcite precipitation (MICP)
is a biological process that uses to improve granularsoils due to increasing shear strength parameters.
MICP has emerged as a green, low cost, andsustainable technique for soil improvement [4]. Due

𝐶𝑂(𝑁𝐻2 )2 + 𝐻2 𝑂 ⟶ 2𝑁𝐻3 + 𝐶𝑂2(1)

The ammonium (NH4+ ) and hydroxyl ions (OH − )induced by diversion of ammonia (NH3) in thepresence of water. Then, the carbon dioxide(CO2 ) which generated by urease activity or
respiration, decomposes rapidly to produce
bicarbonate (HCO3− ) and hydrogen ion (H + ) afterreacting with water [6].
2𝑁𝐻3 + 2𝐻2 𝑂 ⟶ NH4+ + 2OH −𝐶𝑂2 + 2𝐻2 𝑂 ⟶ 𝐻𝐶𝑂3− + 𝐻 +

(2)
(3)

The bicarbonate (HCO3− ) combined with hydrogenand hydroxyl ions to create carbonate ions (CO2−
3 ),which then combines with calcium ions Ca2+ (fromdissolved calcium chloride) to precipitate calcium
carbonate (CaCO3 ).
𝐻𝐶𝑂3− + 𝐻 + + 2OH − ⟷ 𝐶𝑂32− + 2𝐻2 𝑂
𝐶𝑎2+ + 𝐶𝑂32− ⟶ 𝐶𝑎𝐶𝑂3
322

(4)
(5)

GEOMATE – Kyoto, Japan, 3-5 November 2021

Calcium ions quickly combine with urea hydrolysis
and water to produce calcium carbonate andammonium.
𝐶𝑂(𝑁𝐻2 )2 + 2𝐻2 𝑂
𝐶𝑎𝐶𝑂3

+

𝐶𝑎2+ ⟶ NH4+ +The comprehensive experimental tests werecarried out by [10]-[12] to investigate the influence of
MICP technique on poorly graded sand treated withdifferent percentage of silt. Zamani [10] studied the
behaviour of monotonic and cyclic undrained direct
shear tests of untreated and bio-cemented. Also, thechange in permeability was examined on Nevada
sand classifying as uniformly graded and Nevada
sand containing 15% silt classifying as silty sand. A
result of calcite precipitation due to MICP, thenormalized shear strength increased from 0.15 to 0.38
for sand and from 0.23 to 0.38 for silty sand.
Moreover, the reduction in permeability was reflected
directly on excess pore water pressure. For example,
the excess pore water pressure reduced about 75%
when the permeability reduced from 5.04×10 -5 m/s to
4.47×10-6 m/s of Sand. Also, the excess pore waterpressure decreased 35% with reduction inpermeability from 2.56×10-5 m/s to 2.45×10-6 m/s ofsilty sand.
The Seep/W and Sigma/W programs used toevaluate the changes in injection rate and theinfluence radius of MICP process progress [12]. The
results of numerical modelling presented that the rates
of injection could be increased of Nevada sand.
However, it remains unchanged of Nevada sand with
15% silt content. In addition, the higher levels of porewater pressure were generated due to the presence offines (silt) during the injection process, which
requires higher strength enhancement to prevent thedevelopment of excessive plastic strains. The key
parameter to determine the radius of treatment is toimprove the shear strength and stiffness relative to thequantity of the hydraulic conductivity level and itschange rate.
The purpose of this study is to focus on using
Plaxis 3D program to assess the effect of MICP
treatment on the ultimate loading capacity and thedisplacement of constant load under free vibrationand earthquake. The research offers a promising wayof using MICP as an eco-friendly stabiliser fortreating a pervious concrete pile that is particularly
embedded on loose sand.

(6)

MICP processes are affected by many factors
including bacterial species and strains, bacterial
solution concentration, temperature, pH, cementation
solution composition and concentration, grouting
technology, and soil properties [7]. Bacillus andSporosacina bacteria are commonly used in MICPtreated geotechnical technology due to having an
excellent ability to create calcium carbonate. The best
conditions for microbial to improve the geotechnicalproperties are temperature around 20 and 40 °C, pH
ranges from 7 and 9.5 and the adequate grain size ofsoil particles ranging from 10 to 1000 μm [7].
Soon [8] used microorganism technique toimprove shear strength and reduce hydraulicconductivity of two different soils under 85%, 90%
and 95% of maximum densities. The first sample
classified as Sandy Silt with high plasticity (MS),while the second sample classified as Well Graded
Sand (WS). The research explored the bio-clogging
and bio-cementation development depended on
bacteria, cementation reagents (e.g., urea and calcium
chloride) and soil densities. The best results ofundrained shear strength parameter (𝑐𝑢 ) andhydraulic conductivity (𝑘) were under 95% densities
of Sandy Silt specimen. The 𝑐𝑢 improved from 26.2
kPa (untreated soil) to 69.2 kPa (treated soil with
MICP). The 𝑘 decreased from 1.0×10-7 m/s ofuntreated soil to 2.6×10-8 m/s of treated soil. Thechanging in parameters of 𝑐𝑢 and 𝑘 were inflicted bycreating the calcite (1.711%) after treatment system.
In sandy soil, the effective internal friction angle (∅′ )was 50° with treated soil while the ∅′ was 48.8° withuntreated soil under 95% of maximum index density.
In addition, hydraulic conductivity (𝑘) decreased
from 4.4×10-4 m/s of untreated soil to 6.7×10-5 m/s oftreated soil. The reason for this change is that thecalcite content is 2.935% because of MICP treated.
MICP technique suffers from some drawbacks
when using with fine grains because of bacteria
incapable of growth in very low permeability tooxygen and moving freely in tiny pore space [9].
Behzadipour [9] addressed the impacts of plastic andnon-plastic fines on the bio-mediated soil
improvement of shear strength parameters of sandy
soils. The results presented that the presence of fineparticles can decrease the efficiency of MICP
treatment. The effect of created calcite on shear
strength reduces as the fine content increases. The
MICP efficiency to treat sandy soils was reduced
more than with adding plastic fine compared with
non-plastic fines.

CONSTRUCTIONPARAMETERSFINITE ELEMENT MODEL SETUPAND

The dimensions of the model have been set with40 m in X direction, 5 m in Y direction and 40 m in Z
direction to minimize the boundary condition effectof dynamic steps that is assumed to have an effective
action through the X direction.
The representative properties and model of soil
and pile were constructed based on previous studies
and recommendations. Tables 1 and 2 illustrate theproperties of soils using in this study. The
geotechnical properties of untreated and treated soils
were gotten from [12]. The pervious concrete pile wasmodelled depending on reference [1] after scaling the323

GEOMATE – Kyoto, Japan, 3-5 November 2021

dimensions to 10 times. For example, the diameter ofpile is 0.76 m while the length of pile is 10.7 m
including an embedded length 9.16 m. The treatedsoil was assumed extending to 0.5, 0.75 and 1 m along
the shaft and tip of a concrete pile.

at X component with fixing Y and Z components. The
data of earthquake, which is available in Plaxis, wasused for the dynamic excitation under accelerationand drift correction options, as shown in Fig. 2.
The element distribution in Mesh mode was set tomedium because of the calculation under dynamic
taking more than one day to complete. The processof calculation includes the initial conditions phase,simulation of concrete pile with untreated and treatedsoil, prescribed displacement load under 76 mm, freevibration analysis and earthquake analysis. In
prescribed displacement load and earthquake phases,
the reset displacement was selected to zero.

Table 1 Soils properties*
Identification
Model
Drainage type
Unsaturated
Unit Weight, 𝛾(kN/m3)
Saturated Unit
Weight, 𝛾(kN/m3)
Void ratio, eYoung’smodulus (kPa)
Poisson ratio, 𝜈Friction Angle,
𝜙°Permeability(m/day)Sand

TreatedSand

Treated
Silty
sand

Silty
sand

Mohr-Coulomb
Drained
17

17

17

17

19.25

20

19.25

20

0.709

0.647

0.709

0.650

5400

16200

8400

12900

0.27

0.29

0.27

0.29

32.5

36

32

34.5

4.52

2.29

0.386

0.212

*The data were taken or derived from [12].

Table 2 The properties of concrete pile
Concrete pile
Linear elastic
DrainedFig. 1 The geometry of treated soil with 1 m aroundthe shaft and tip of a concrete pile as half
symmetric using Plaxis 3D.

22
220.149•
20 x106
0.1
1.04x106•3
Dynamic multiplier(acceleration)

Identification
Model
Drainage type
Unsaturated Unit Weight, 𝛾(kN/m3)
Saturated Unit Weight, 𝛾 (kN/m3)
Void ratio, eYoung’s modulus (kPa)
Poisson ratio, 𝜈Permeability (m/day)• The data were extended taken from [1].

To calculate the ultimate static load capacity toreach 76 mm prescribed displacement (10% of pile
diameter) and dynamic states of untreated and treatedsoil, the design steps have been followed. The soil
layer (sand or silty sand) was extended until -40 m.
The untreated and treated soils were modelled as
Mohr-Coulomb model with drain condition. The
phreatic level assigns at the ground surface. The pile
was modelled as half symmetric to reduce thecalculation time. The interface elements were used toincorporate with interface strength reduction factor(Rinter) 0.8 to decrease the skin resistance of theinterface that allows sliding between the pile and soil
nodes. The geometry of treated soil with 1 m aroundthe shaft and tip of a concrete pile is demonstrated inFig. 1.
The earthquake was assumed as 0.5 m uniform
prescribed displacement across the bottom boundary

2
1
-1
-2
-3
0

4

8
12
16
Dynamic time (secend)

20

24Fig. 2 Acceleration-time used in study (Plaxis
program).
The free vibration or earthquake phases wasstarted after completing prescribed displacement
load. The free vibration analysed as dynamic option
with 5 sec. time interval under boundary conditionsin X directions and Z min as viscous while in Y
directions and Z max as none. In earthquake phase,the dynamic option with 20 sec. time interval was
selected. The boundaries were set to Free-field in the324

GEOMATE – Kyoto, Japan, 3-5 November 2021

X direction, to None in Y and Z max, and toCompliant base in Z min.

sand exhibits a higher vertical load compared withuntreated loose sand. It could be attributed to thehigher contacts between silty sand particles. As
shown in Fig. 5, the enhancement of load capacity isaround 313 kN when the soil is treated with 0.5m
along the shaft and tip of a concrete pile. However,the load capacity is insignificantly increased with
0.75 and 1 m treated.

RESULTS AND DISCUSSION
Figure 3 illustrates the increase of half pile
displacement due to the increasing load of untreated
and treated loose sand with MICP. In untreated state,
the load versus displacement curve is gradually
decreased until the load is approximately reached 135
kN with a vertical displacement around 17 mm. The
curve is then reduced sharply until reachingdisplacement 76 mm with a load 235 kN of half pile.
The maximum load capacity of a complete pile is 470
kN. As you can see from the treated soil curves with
0.5, 0.75 and 1 m around the shaft and tip of aconcrete pile, the gradient of the load-displacement
graph is smaller than untreated soil. In addition, thecurves of treated soil are reduced continuously up to
approximately the load and displacement reaching
160 kN 13 mm, respectively. The curves of half pile
are then dramatically decreased until reaching 76 mm
displacement when load capacity of treated soil with
0.5, 0.75 and 1 m are 291, 299 and 306 kN,
respectively. The enhancement ratio of treated soil
with 0.5, 0.75 and 1 m is increased to 1.24, 1.27 and
1.3, respectively. In addition, the load capacity
development is slightly increased after treatment with
0.5 m.

(a)(b)

Vertical load (kN)
0

100

200

300

400

Vertical displacement (mm)

0

-20

-40

Fig. 4 Vertical displacements uz to reach -0.076 m: (a)of untreated loose sand, (b) of treated loose
sand with 1 m (scaled up 20 times).

-60

0

100

Vertical load (kN)
200
300

400

0

-80

Treated with 0.5 m
Treated with 1 m

Vertical displacement (mm)Untreated
Treated with 0.75 m

Fig. 3 Load – displacement relationship of untreated
and treated loose sand.
The vertical displacement as shading of untreated
and treated with 1 m loose sand is presented in Fig. 4.
The displacement on the pile tip is more influencecompared with the pile shaft. From anotherperspective, the intensity of displacement of treatedloose sand around the pile shaft is higher thanuntreated sand. It could be because of more load(stress) requiring to attain 76 mm displacement.
Figure 5 presents the effect of MICP on thevertical pile load in silty sand. The maximum verticalload of untreated silty sand is 277 kN to extend 76
mm of displacement. As expected, the untreated silty

-20

-40

-60

-80
Untreated
Treated with 0.75 m

Treated with 0.5 m
Treated with 1 m

Fig. 5 Load – displacement relationship of untreated
and treated silty sand.

325

GEOMATE – Kyoto, Japan, 3-5 November 2021

Figure 6 shows the horizontal displacement (ux) offree vibration versus time on the top of the pile centre(0, 0, 1.54). The greatest ux of untreated sand is 7.798
x 10-1 mm at 0.955 sec. while the highest ux of treatedsand with 1 m along the shaft and tip of a concrete
pile is 6.869 x 10-1 at 1.405 sec.

of untreated sand while uz is reduced to 40 mm at 1.3
sec. of treated sand. It could be happened because of
the same reason that mentioned at free vibration step.
40
30
ux (mm)1.0

Untreated
Treated

0.8

20
10

ux (mm)0.6
0Untreated
Treated

0.4
-10
0.2

0

5
10
Dynamic time (secend)

15

20

0.0

Fig. 8 Horizontal displacement (ux) versus dynamic
time on the top of the pile centre (0, 0, 1.54).

-0.2
0

1

2

3

4

5

Dynamic time (secend)

Dynamic time (second)Fig. 6 Horizontal displacement (ux) of free vibration
versus dynamic time.

0

The vertical displacement (uz) – dynamic time
relationship under free vibration is illustrated in Fig.
7. The free vibration step calculated after reaching 76mm prescribed displacement due to vertical load. The
uz of untreated sand is decreased to 117 mm at 0.73
sec while uz of treated loose sand is reduced to 120mm at 0.73 sec. It might be because the pile undertreated state was carried more load compared with theuntreated state when obtaining 76 mm verticaldisplacement.

uz (mm)

-10

1

2

3

4

10

15

20

Untreated
Treated

-20
-30
-40
-50

Fig. 9 Vertical displacement (uz) – dynamic time
relationship under earthquake.

Dynamic time (second)
0

5

0

5

-70

uz (mm)-80

CONCLUSIONS

Untreated
TreatedThis paper reports the outcomes of finite element
modelling and analysis of a single pervious concrete
pile in loose and silty sand before and after treatedwith MICP. The pile was subjected to free vibrationand earthquake after evaluating the maximum verticalload. The following conclusions could be drawn as aresult of this study.

-90
-100
-110
-120

Fig. 7 Vertical displacement (uz) – dynamic time
relationship under free vibration.

The results reveal that using MICP has asignificant effect on the pile bearing capacity of loose
sand compared with silty sand. For example, theultimate load capacity of the complete pile to displace
76 mm was increased from 470 kN to 582,598 and612 kN when the loose sand treated with 0.5, 0.75 and1 m, respectively. On the other hand, the maximum
load was improved from 554 kN of untreated silty
sand to 620 kN of treated silty sand with 0.5 m aroundthe shaft and tip of the pile. Moreover, theenhancement of load capacity was insignificant
increased after 0.5 m treated. The horizontal (ux) andvertical (uz) displacement were marginally changed

Figure 8 illustrates the horizontal movement (u x)with time on the top of the pile centre (0, 0, 1.54). It
observes that the peak value of ux of untreated andtreated sand is an approximately close pattern.
The vertical displacement (uz) versus time ofuntreated and treated loose sand under seismic
condition is shown in Fig. 9. The displacement of thepile under earthquake condition determined after
resetting the displacement to zero in Plaxis program.
It appears that uz is decreased to 35 mm after 1.3 sec.
326

GEOMATE – Kyoto, Japan, 3-5 November 2021

under free vibration and earthquake conditions of
untreated and treated loose sand.

[10] Zamani, A. and Montoya, B., Shearing andHydraulic Behavior of MICP Treated Silty Sand.
Geotechnical Frontiers GSP 281. ASCE, 2017,
pp.290-299.
[11] Zamani, A. and Montoya, B., Undrained
Monotonic Shear Response of MICP-TreatedSilty Sands. Journal of Geotechnical andGeoenvironmental Engineering, Vol. 144, Issue 6,
2018, p.04018029.
[12] Zamani, A., Montoya, B. and Gabr, M.,
Investigating Challenges of in Situ Delivery ofMicrobial-Induced
Calcium
Carbonate
Precipitation (MICP) In Fine-Grain Sands andSilty Sand. Canadian Geotechnical Journal, Vol.
56, Issue 12, 2019, pp.1889-1900.
[13] Meena, N. and Nimbalkar, S., Effect of Water
Drawdown and Dynamic Loads on Piled Raft:
Two-Dimensional Finite Element Approach.
Infrastructures, Vol. 4, Issue 4, 2019, p.75.

ACKNOWLEDGMENTS
The authors would thank Mustansiriyah
University (www. uomustansiriyah.edu.iq) BaghdadIraq for its support in the present work.
REFERENCES[1] Lin, H., Suleiman, M., Jabbour, H., Brown, D. andKavazanjian, E., Enhancing the Axial
Compression Response of Pervious Concrete
Ground Improvement Piles Using Biogrouting.
Journal of Geotechnical and Geoenvironmental
Engineering, Vol. 142, Issue 10, 2016, p.
04016045.
[2] Lin, H., Suleiman, M., Jabbour, H. and Brown, D.,
Bio-grouting to Enhance Axial Pull-out Responseof Pervious Concrete Ground Improvement Piles.
Canadian Geotechnical Journal, Vol. 55, Issue 1,
2018, pp. 119-130.
[3] Suleiman, M., Ni, L. and Raich, A., Developmentof Pervious Concrete Pile Ground-Improvement
Alternative and Behavior under Vertical
Loading. Journalof
GeotechnicalandGeoenvironmental Engineering, Vol. 141, Issue
10, 2015, p.07015025.
[4] Mujah, D., Shahin, M. and Cheng, L., State of the
Art Review of Biocementation by Microbially
Induced Calcite Precipitation (MICP) for Soil
Stabilization. Geomicrobiology Journal, Vol. 34,
Issue 6, 2016. pp.524-537.
[5] Umar, M., Kassim, K. and Ping Chiet, K.,
Biological Process of Soil Improvement in Civil
Engineering: A Review. Journal of RockMechanics and Geotechnical Engineering, Vol. 8,
Issue 5, 2016, pp.767-774.
[6] Pakbaz, M., Behzadipour, H. and Ghezelbash, G.,
Evaluation of Shear Strength Parameters of Sandy
Soils
upon
Microbial
Treatment.
Geomicrobiology Journal, Vol. 35, Issue 8, 2018,
pp.721-726.
[7] Tang, C., Yin, L., Jiang, N., Zhu, C., Zeng, H., Li,
H. and Shi, B., Factors Affecting the Performanceof Microbial-Induced Carbonate Precipitation(MICP) Treated Soil: A Review. Environmental
Earth Sciences, Vol. 79, Issue 5, 2020, p. 94.
[8] Soon, N., Lee, L., Khun, T. and Ling, H., Factors
Affecting Improvement in Engineering Properties
of Residual Soil through Microbial-Induced
Calcite Precipitation. Journal of Geotechnical andGeoenvironmental Engineering, Vol. 140, Issue 5,2014, p.04014006.
[9] Behzadipour, H., Pakbaz, M. and Ghezelbash, G.
Effects of biocementation on strength parametersof silty and clayey sands. Bioinspired,
Biomimetic and Nanobiomaterials, 2019, pp.1-9.
327

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

AN EXPERIMENTAL STUDY ON THE CREEP OF CONCRETE MADE
WITH RECYCLED COARSE AGGREAGES: CASE STUDY IN HANOI
Thanh Quang Nguyen1,2, Hoang Giang Nguyen1,*, Ngoc Tan Nguyen1, Viet Cuong Tran1, Ha Tan Nghiem1
1Faculty of Building and Industrial Construction, Hanoi University of Civil Engineering, Vietnam;
2
Vietnam Paper Corporation, Hanoi, Vietnam
ABSTRACTThe properties of recycled coarse aggregates (RCA) from construction and demolition waste (CDW) depend
highly on its source. Normally, the quality of RCA is lower than that of the natural aggregate, as it has lower
particle density, lower resistance to fragmentation, and higher water absorption. The quality and proportion usedof RCA can compromise the performance of recycled concrete. The deformation of recycled concrete is higher
and more varied in comparison with ordinary concrete. This paper aims to determine creep and shrinkage evolution
in time of recycled concretes that were made with RCA from CDW collected in an old-age reinforced concretebuilding in Hanoi, Vietnam. The substitution percentages of coarse natural aggregate by RCA were 0%, 50%, and100%. The other compositions were remained constant in the mixture for all cases. The obtained results during
180 days of the measurements show that the creep coefficient of RC is about 1.32 - 1.45 times higher than that ofNA, depending on the ratio of using RCA to replace NCA.
Keywords: Construction and demolition waste, Recycled coarse aggregate, Creep, Compressive strength,
Modulus of elasticity.

INTRODUCTION

aggregate (RCA) which is often used with different
rates of 30%, 50%, 70% and up to 100% [5]. The
compressive strength of recycled concrete (RC) at 28
days of age is reduced by less than 35% whencompared with natural aggregate concrete (NC).
However, the strain of recycled concrete is usuallyhigher and more variable than that of natural
aggregate concrete. Similarly, the studies of Silva etal. (2014) [6, 7] also showed that the mechanicalstrength of RC depends on the quality and the content
of the recycled aggregates used. Hansen (1986) [8]showed that the creep of concrete produced RCA isgreater than concrete produced from NCA from 30%
to 60%. This statement is also consistent with theresults obtained from other studies that have been
carried out [9-12]. These results are not surprising as
recycled aggregate concrete has up to 50% more
grout volume. The creep of concrete is directly
proportional to the mortar content in the concrete[13]. However, Gomez-Soberon (2002) [14] indicatesthat with the test load corresponding to 40% of thecompressive strength of concrete, the creep strain of
RC is larger than NC from 35% to 51%, since therecycled aggregate particles contain old mortar, it isconsidered as a factor affecting the difference in creep
between RC and NC. In which, the elastic modulus
and characteristics of the old mortar are determined
to be the decisive criteria for this difference. In fact,creep leads to stress relaxation of elastic strain andsubsequent reduction of tensile stress in the case oflimited shrinkage.
The properties and composition of RC affect the
short-term and long-term mechanical behavior of
concrete. In particular, the dry creep of RC is

Demolition of old constructions is carried out
during renovation and/or implementation of new
construction projects and is especially common inlarge cities where urbanization is high. Every year,
850 million tons of construction and demolition(CDW) are generated in Europe, accounting for about
31% of the total waste generated. In the United Statesalone, the waste generated from the demolition ofbuildings is about 123 million tons per year [1].
Typically, CDW is transported to landfills, causing
significant environmental impacts and health hazards.
Furthermore, land shortages and increasing landfill
costs are exacerbating environmental problems.
Sustainable development is currently an important
issue worldwide and now becomes a guiding
principle in the construction sector [2]. The recycling
and reuse of CDW is an inevitable trend and a feasible
and potential technical solution to use waste as arenewable resource, thereby minimizing pressure on
mining natural resources. Currently, the recycling of
CDW contributes to the development of theconstruction engineering industry and plays animportant role in regenerating the environment, as a
new way to protect natural resources and reduce theamount of material discharged into landfills [3, 4].
In the world, many studies have been carried out
to determine the mechanical properties of concrete
made with recycled aggregates from CDW. Research
by Tam et al. (2015) synthesized research results
obtained from more than 26 studies that have been
performed previously, it is showed that natural coarse
aggregate (NCA) can be replaced by recycled coarse
328

GEOMATE – Kyoto, Japan, 3-5 November 2021

significantly affected when the rate of replacing NCA
with RCA is greater than 30%. The research results
obtained on concrete samples of B20 and B35
compressive strength using 30% and 60% RCA
indicate that the creep of RC increases compared toNC from 1.6 to 2.4 times. Meanwhile, the shrinkageof the RC is equal to or less than that of NC [15].
Therefore, it can be concluded that, when increasing
the rate of replacement of recycled aggregate, thecreep of concrete increases and increases more for dry
creep when compared to basic creep.

having the size ranging from 10 to 20 mm.
CDW is collected at a demolition site in Hanoi,
Vietnam, then transported to the crusher system
located in Dong Anh district (Fig. 1). The crushingline system allows obtaining recycled aggregate witha particle diameter of 0 - 20 mm, of which therecycled fine aggregate has a diameter of 0 - 5 mm,
and the RCA has a diameter of 5 - 20 mm as shownin Fig. 2. In this study, only RCA particles with thediameter ranging from 5 – 20 mm were used.

CDW RECYCLING AND MANAGEMENT IN
VIETNAMIn Vietnam, specific objectives on the
management, collection, and recycling of CDW havebeen specified in the National Strategy [16]. Until
now, there is only standard TCVN 11969:2018 [17]on RCA for concrete. However, the incompletenessof the system of technical standards related torecycled materials, as well as the habit of using
natural materials leads to the application of recycled
aggregates is very limited. In recent times, several
studies on the current status of management andcomposition of CDW have been carried out [18–21].
Meanwhile, research studies on recycled materials
and structures using recycled materials have not beeninterested. A number of studies have been carried out
showing that local recycled aggregates derived fromthe demolition of old structures are a factor affecting
the quality of concrete and the development ofcompressive strength, elastic modulus properties [22].
Therefore, an experimental study to investigate
different content of RCA is essential.
In this study, block concrete waste was collectedfrom an old building demolished in Hanoi, Vietnam.
It is crushed at the factory to obtain RCA with aparticle diameter of 10 - 20 mm. RCA is used toreplace NCA in the aggregate composition, withratios of 0%, 50%, and 100% respectively. In thelaboratory, an experimental program was carried out
to determine the compressive strength, modulus ofelasticity, and the long-term compressive behavior of
concrete. The obtained results allow determining theinfluence of the ratio of using RCA to replace NCAon the development of compressive strength, elastic
modulus, and creep strain of RC with time.

The physical properties of RCA material such as
water absorption, volumetric mass, specific gravity
and dry density are done on RCA as per Vietnamesestandards. The test results are shown in Table 1.

EXPERIMENTAL PROGRAM

Table 1. Physical properties of RCA

Materials

Water
absorption
(%)
3.53

Fig. 1. Crushing CDW materials at site

Fig. 2. RCA collected from crushing machine

The materials used are Portland cement PC40
with the technical properties to ensure therequirements for properties and quality according to
the standard TCVN 2682:2009 [23], river sand of size
passing through 5 mm with the technical parametersof meet the standard TCVN 7570:2006 [24], NCA

Volumetricmass(g/cm3)
2.43

Specific
gravity(g/cm3)
2.62

Dry
density(g/cm3)
2.38

Concrete mix
In the laboratory, an experimental program was

329

GEOMATE – Kyoto, Japan, 3-5 November 2021

carried out on three concrete mixtures that werenamed CP0 (control concrete), CP50, and CP100(recycled concretes) corresponding to the RCA
substitution percentage of 0%, 50%, and 100% for
NCA replacement, respectively. Control concrete
CP0 is designed to achieve compressive strength class
C30/37 at 28-day. The compositions of each concretestudied are presented in Table 2 consisting ofPortland cement (C), river sand (S), natural coarse
aggregates (NCA), recycled coarse aggregates (RCA),
water (W), and Superplasticizer Silka viscocrete
30M-2000 (A).

control concrete CP0 has stabilized, not significantly
increased, reaching the compressive strength R90 of
48.2 MPa. Meanwhile, two RC such as CP50 and
CP100 have the compressive strength R28 values of
40.2 and 43.3 MPa, respectively, which decrease by
9.5 - 16.0% when compared to that of control
concrete. Similar to the NA, the compressive strengthof RC is stable and does not increase significantly
after 28 days of age.
Modulus of elasticity
The test to determine the modulus of elasticity
was performed on a 50-tonne hydraulic compressor
combined with an electronic load-cell and a Datalogger TDS530, allowing continuous and automatic
data recording during the experiment. The test pieceis loaded to a compressive force equal to 30% of thespecimen failure force. Elastic modulus tests werecarried out according to the guidelines of RILEM
CPC8 [25]. Each specimen is compressed for fivecycles of loading and unloading at a constant speed.
The modulus of elasticity at time t (day), denoted Et(GPa), is determined by Eq. (2), with σt being thestress in the concrete,  being is the axial
compressive strain.

Table 2. Mix proportions (unit: kg/m3)
Mix
CP0
CP50
CP100

W
190
190
190

C
490
490
490

S
700
700
700

NCA
1050
525
0

RCA
0
481
943

A
4.9
4.9
4.9

RESULTSTo determine the mechanical properties ofconcrete specimens with and without recycled coarse
aggregates, cylinders of 150 mm diameter and 300
mm length were cast, cured and tested at 3, 7, 28, 60,
90 and 120 days after casting.

Et =Compressive strength
The compressive strength of concrete cylindrical
specimens was determined by conducting
compression test. The maximum load was measured,and the compressive strength (fc in MPa) was
calculated by formula (1), with P (kN) being the
maximum load, F (mm2) being cross-sectional area.

(2)

45

Modulus of elasticity GPa)

P
fc =
F

t
t

(1)

CP0

CP50

CP100

14

28

40

35
30
25
20
15
10
5

Compressive strength (MPa)

60

CP0

CP50

0

CP100

3

50

7

60

90

Age (day)40

Fig. 4. Elastic modulus of concretes

30

The results in Fig. 4 show that, for each concrete
used, the modulus of elasticity increases steadily withtime. At 28 days, the elastic modulus of control
concrete CP0 reached an average value E28 of 39.0
GPa. Meanwhile, the two recycled concretes CP50
and CP100 achieved the average E28 values of 34.0
and 29.5 GPa, respectively, which decrease by 12.8 24.2% compared to that of control concrete. After 28
days of age, the elastic modulus of all concretes tends
to stabilize, increases by 4 - 8% at 90 days of age
compared with the E28 value.

20

10
0

3

7

14
28
Age (day)60

90

Fig. 3. Compressive strength of concretes with time
The results of the compression test are shown in
Fig. 3 for three concretes CP0, CP50, and CP100.
From 28 days of age, the compressive strength of330

GEOMATE – Kyoto, Japan, 3-5 November 2021

Creep of ordinary and recycled concretes

results show that the temperature varies from 18.7 to
27.6oC with an average temperature of 24.8oC.
Meanwhile, the relative humidity is more variable,
ranging from 42 to 81%, with an average value of
57.1%.
The dry creep deformation evolution of concretes
used under compression has been calculated andshown in Fig. 7. For each concrete mixture, themeasurements were carried out on a set of threecylinders with a diameter of 150 mm and a height of
300 mm, consisting of two loaded specimens and one
unloaded specimen.

All test samples were stored and tested underidentical
indoorenvironmental
conditions(temperature, humidity) of the laboratory. The load
acting on the creep test specimens is taken as 30% of
the compressive strength of each concrete at 28 daysof age shown in Fig. 5.

Fig. 7. Creep deformation of concretes used underlong-term compression

Fig. 5. Set-up of concrete creep measurements

The experimental results show that the creep
strain of RC is smaller than NC. Even the creep strainof recycled concrete CP50 is only about 48 - 60% of
the creep value of control concrete CP0. These results
do not totally reflect the traditional argument when
RC normally has a smaller elastic modulus than NC,
so there will be a larger strain. The result can be
explained by the presence of recycled coarse
aggregates contain old mortar, which is considered asa factor affecting the difference in creep between RC
and NC [14]. This result conforms to theinstantaneous elastic modulus values shown in Table
4.
Table 4. Instantaneous elastic modulus

Fig. 6. Record of creep strain data in time
The creep rate is derived from Eq. (3) as follows: = (1/ E ) + F ( K ) loge ( t + 1)

Mix

Compressive
stressσc (MPa)

Instant strain
ε (x10-6)

CP0
CP50
CP100

14,4
12,1
13,0

596
285
404

(3)

Where:
ε is the total load-induced strain per megapascal;
E is the instantaneous elastic modulus, in MPa;
F(K) is the creep rate;
T is the time after loading, in day.

Instant
elastic
modulus Ei
(GPa)
24,1
42,3
32,2

In this case, recycled concrete CP50 has thelargest instantaneous elastic modulus, so it has thesmallest compressive strain. In contrast, control
concrete CP0 has the smallest instantaneous elastic
modulus, so it has the largest compressive strain.

During the experiment, the temperature andhumidity were regularly checked and recorded. The

331

GEOMATE – Kyoto, Japan, 3-5 November 2021

of elasticity of 80-90% of the value of the NC.
Meanwhile, for recycled concrete CP100, themodulus of elasticity is reduced to less than 80%.
The study determines creep deformation of
concrete when subjected to compressive stress equal
to 30% of the compressive strength over time. The
obtained results show that the creep coefficient of RC
is about 1.32 - 1.45 times higher than that of NA,
depending on the ratio of using RCA to replace NCA.
The present results were obtained from a limited
number of RCA concrete specimens. Thus, extending
the experimental works in a vast program is necessary
to comprehensively confirm the potential of concrete
made with local CDW in Vietnam.

Characteristic creep and coefficient of creep
The characteristic creep and creep coefficient of
concretes used have also been calculated and shownin Figs. 8 and 9. The results show that the creep
coefficient of control concrete is usually smaller thanthose of recycled concretes. The creep coefficient of
CP0 is determined as 1.91. The experimental values
are completely consistent with the values specified inthe Vietnamese design standard TCVN 5574:2018[26]. Meanwhile, creep coefficients of recycledconcretes CP50 and CP100 at 90 days of age were
determined to be 2.77 and 2.52, respectively.

ACKNOWLEDGMENTS
This research was supported by the project of theJapan Science and Technology Agency (JST) - Japan
International Cooperation Agency (JICA) on Scienceand Technology Research Partnership for Sustainable
Development (SATREPS) (No. JPMJSA1701).
REFERENCES[1] Malešev M., Radonjanin V., Marinković S.,
Recycled concrete as aggregate for structuralconcreteproduction,
Sustainability
2,
2010,1204–1225.
[2] Limbachiya M.C., Koulouris A., Roberts J.J.,
Fried A.N., Performance of recycled aggregate
concrete, in Proc. of RILEM International
Symposium on Environment Conscious
Materials and Systems for Sustainable
Development, 2004, Koriyama, Japan, 127–136.
[3] Blengini G.A., Garbarino E., Resources andwaste management in Turin (Italy): the role ofrecycled aggregates in the sustainable supply
mix, J. Cleaner Prod., 18, 2010, 1021–1030.
[4] Rodrigues F., Carvalho M., Evangelista L.B.,
Physical-chemical
andmineralogicalcharacterization of fine aggregates fromconstruction and demolition waste recycling
plants, J. Cleaner Prod., 52, 2013, 438–445.
[5] Tam V. W. Y., Kotrayothar D., Xiao J., Longterm deformation behaviour of recycled
aggregate concrete, Const. Build. Mat., 100,
2015, 262-272.
[6] Silva R.V., de Brito J., Ravindra Dhir, R.K., The
influence of the use of recycled aggregates on thecompressive strength of concrete: a review, Euro.
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[7] Silva R.V., de Brito J., Dhir R.K., Properties andcomposition of recycled aggregates fromconstruction and demolition waste suitable forconcrete production, Const. Build. Mat., 65,2014, 201-217.

Fig. 8. Characteristic creep of concretes under longterm compression

Fig. 9. Creep coefficients of concretes
CONCLUSIONS
Using RCA with good input quality, the strength
development of RC is similar to that of NA. By thetime, the compressive strength of RC reaches 80% to100% of that of the NC. Moreover, the effect of RCA
on compressive strength is less than that of elastic
modulus.
The elastic modulus of RC decreases significantly,
almost linearly with increasing RCA content in theconcrete mix. Recycled concrete CP50 has a modulus

332

GEOMATE – Kyoto, Japan, 3-5 November 2021

[18] Hoang N.H., Ishigaki T., Kubota R., Tong T.K.,
Nguyen T.T., Nguyen H.G., Yamada M.,
Kawamoto K., Waste generation, composition,and handling in building-related construction anddemolition in Hanoi, Vietnam, Waste
Management, 117, 2020, 32-41.
[19] Nghiem H.T., Phan Q.M., Kawamoto K., Ngo
K.T., Nguyen H.G., Nguyen T.D., Isobe Y.,
Kawasaki M., An investigation of the generationand management of construction and demolition
waste in Vietnam, Detritus, 12, 2020, 135-139.
[20] Nguyen V.T., Tong T.K., Dang T.T.H., Tran
T.V.N., Nguyen H.G., Nguyen T.D., Isobe Y.,
Ishigaki T., Kawamoto K., Current status of
construction and demolition waste managementin vietnam: challenges and opportunities,
International Journal of GEOMATE, 15 (52),2018, 23–29.
[21] Ngo K.T., Tran H.S., Le V.P., Nguyen X.H.,
Nguyen T.K., Vu V.H., Tran V.C., Study on
current situation of construction and demolition
waste management in Vietnam, Journal of
Science and Technology in Civil Engineering
12(7): 107-116. (in Vietnamese).
[22] Tong T.K., Study on the use of constructionwaste in concrete manufacturing. PhD thesis,
National University of Civil Engineering, 2017,
Vietnam. (in Vietnamese).
[23] Vietnamese standard TCVN 2682: 2009,
Portland cements - Specifications. (inVietnamese).
[24] Vietnamese standard TCVN 7570: 2006,
Aggregates for concrete and mortar Specifications. (in Vietnamese).
[25] RILEM CPC8, Modulus of elasticity of concretein compression, RILEM Recommendations forthe testing and use of constructions materials,
1994, 25-27.
[26] Vietnamese standard TCVN 5574: 2018, Designof concrete and reinforced concrete structures.
(in Vietnamese).

[8] Hansen T.C., Recycled aggregates and recycled
aggregate concrete second state-of-the-art reportdevelopments 1945–1985, Materials andStructures, 19, 1986, 201–246.
[9] Gómez-Soberón J.M., Relationship between gas
adsorption and the shrinkage and creep ofrecycled aggregate concrete, Cement, Concreteand Aggregates, 25, 2003, 42-48.
[10] Domingo-Cabo A., Lázaro C., López-Gayarre F.,
Serrano-López M.A., Serna P., Castaño-Tabares
J.O., Creep and shrinkage of recycled aggregate
concrete, Const. Build. Mat., 23 (7), 2009, 25452553.
[11] Fathifazl G., Ghani Razaqpur A., Burkan Isgor
O., Abbas A., Fournier B., Foo S., Creep anddrying shrinkage characteristics of concrete
produced with coarse recycled concrete
aggregate, Cement and Concrete Composite, 33(10), 2011, 1026-1037.
[12] Marinkovic S., Radonjanin V., Life cycle
assessment of concrete with recycled aggregate,
In: Handbook of recycled concrete anddemolition waste, Ed. Pacheco-Torgal F. et al.,Woodhead Publishing, 2013.
[13] Lamond J.F., Campbell R.L. (Sr.), Campbell
T.R., Cazares J.A., Giraldi A., Halczak W., Hale
H.C. (Jr.), Jenkins N.J.T., Miller R., Seabrook
P.T. (2001). Removal and Reuse of Hardened
Concrete. ACI Committee 555.
[14] Gomez-Soberon J.M.V., Creep of concrete with
substitution of normal aggregate by recycledconcrete aggregate, ACI Material Journal, 209,
2002, 461-474.
[15] Waste and Resources Action Programme(WRAP). An investigation into the effects ofrecycled aggregates on the engineering
properties of concrete, 104, 2007.
[16] DecisionNo.
491/QĐ-TTg,
Approving
adjustments to national strategy for generalmanagement of solid waste to 2025 with vision
towards 2050, The prime minister of Vietnam,
2018. (in Vietnamese)[17] Vietnamese standard TCVN 11969: 2018,
Recycled coarse aggregate for concrete. (inVietnamese)333

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

INFLUENCE OF EACH MIXTURE PARAMETER ON
FRESH PROPERTIES AND COMPRESSIVE STRENGTHOF GEOPOLYMER MORTAR
Takumi Osedo1, Yoichi Mimura2, Itaru Horiguchi3
1

Advanced Course, Project Design Engineering, National Institute of Technology, Kure College, Japan;
Department of Civil Environmental Engineering, National Institute of Technology, Kure College, Japan;

2, 3ABSTRACT
Huge amount of Portland cement is manufactured as a binder of concrete. CO2 emitted by manufacturing
process of cement causes global warming, development of construction material with low CO2 emissions isimportant issues for infrastructure engineers. Geopolymer (GP) is a relatively new generation construction material
developed without using cement, and is hardened by the condensation polymerization of active filler based on
industrial by-products and alkali solution instead of hydration of cement. Process of mixture design method of GP
has not been established due to various mixture parameters, and its fresh properties are high viscosity and low
workability. In this study, the effect of each mixture parameter on fresh properties and compressive strength of GP
mortar was investigated. In this research, water glass and caustic soda solution were used as alkali solution, fly
ash and blast furnace slag were used as active fillers, and high-performance AE water reducing agent and air
entraining aid for fly ash were used as admixtures. The main mixture parameters of this study are alkali/water ratio(A/W), silicon/alkali ratio (Si/A), water/binder ratio (W/B), blast furnace slag substitution rate (BFS substitutionrate) and admixture addition rate. Result of the mortar flow test indicates that the fluidity was increasing withhigher W/B, and viscosity decrease with lower A/W and Si/A. However, GP mortar in this study was more viscousthan cement mortar based on cement concrete with 8cm of slump. GP mortar had higher compressive strength bylowering W/B or increasing BFS substitution rate.
Keywords: Geopolymer, Fresh properties, Viscosity, Fluidity

GP has various issues to be solved for wide usage
as a construction material. The establishment of
mixture design method is one of them [2]. This isbecause the effects of materials used in GP and each
mixture parameter in the mixture design have notbeen clarified on fresh properties and mechanicalproperties of GP. In addition, viscosity of GP ishigher than general concrete and its workability is
inferior, so improvement of workability contributes
more utilization of GP.
In this study, the effects of water/binder ratio(W/B) and BFS substitution rate and addition of
admixture on fresh properties (fluidity and viscosity)and compressive strength of GP mortar were
investigated after preliminary test on the effect of unit
content of water glass for GP mortar.

INTRODUCTION
Global warming and reducing greenhouse gas
emissions are worldwide issues. CO2 is major
greenhouse gas, and all industries should take action
in reducing CO2 emissions. Huge amount of Portland
cement is manufactured as a binder of concrete. CO2
emitted by the chemical decomposition of limestone
and the consumption of energy in manufacturing
process of cement. Geopolymer (GP), which contains
no cement has been developed as an alternative
construction material to cement concrete [1].
GP is a general term for a condensation polymer
formed by reaction of alkali solution and active filler.
By mixing active filler with alkali solution, metal ionsof aluminum (Al) and silicon (Si) are eluted from
active filler by alkali component. When the eluted
metal ions come into contact with water glass in alkali
solution, the metal ions cross-link the silicate ions(monomers) present in water glass to form a polymer.
In this process, active filler particles are solidified
with an inorganic adhesive, and sponge-likemicropores characteristic of gels are generated.
Generally, sodium silicate solution (Na2SiO3, waterglass, WG) or sodium hydroxide (NaOH, causticsoda) is used as the alkali solution, and fly ash (FA)or blast furnace slag (BFS) is used as the active filler.

MATERIALS AND METHOD OF TEST
Materials
Active fillers used in this study were FA (JIS IItype), which is easy to obtain and widely distributed,
and BFS (specific surface area: 4000 cm2/g) in order
to increase strength. Both materials are commerciallyavailable in Japan. Water glass and caustic soda
solution (CS) were used as alkali solutions. For the334

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1 Mixture parameters and flow of GP mortar.

water glass, JIS No.1, which is generally used, wasused in these tests. The caustic soda solution used inthis study was prepared by adding caustic soda flakes
to tap water. If the molarity of the caustic soda is low,large amount of caustic soda solution is required for
GP and the water content of the alkali solutions canbe exceed the unit content of water which is major
mixture parameter. In the present study, the molarityof caustic soda solution was set to 15 mol/L. The
water content of caustic soda solution and water glass
may not reach unit content of water as a mixture
parameter and the water content of GP was controlled
by the additional water. Fine aggregate (S) was
crushed sand with a density of 2.62 g/cm3 and afineness modulus of 2.71. A high-performance AE
water-reducing agent (Ad1), which has main
component of polycarboxylate-based dispersant, wasused as admixture, and is generally adopted for
cement concrete. Unburned carbon contained in FA
prevents performance of Ad1, so an air entraining
agent for FA concrete (Ad2) was also used for
restraining the influence of the unburned carbon.

A/W
(mole
ratio)
0.15
0.1

No.
M1
M2

Si/A
(mole
ratio)
0.8
0.4

flow (mm)015bounce bounces
168
173
170
213

Notes: W=240kg/m3, W/B*=0.44, BFS sub rate=10%* B=FA+BFS

Table 2 Mix proportion of GP mortar.
No.

FA
476
476

M1
M2

BFS
69
69

kg/m3
WG CS
258
57
86
90

water
101
149

S
1244
1328

300

flow (mm)250

Method of Flow Test

A/W＝0.15, Si/A＝0.8
A/W＝0.1, Si/A＝0.4

200
150
100

A mortar mixer was used for mixing, and mixing
volume for one batch was 1 L. First, FA, BFS, and S
were mixed for 30 seconds, then an alkaline solutionof water glass, caustic soda solution, and water wasadded, and the mixture was mixed for 1 minute. After
mixing, the mortar flow test was immediately
conducted.
The flow cone was placed on the center of theflow table, and 1/2 of the volume of the flow cone wasfilled with GP mortar. Then GP mortal wascompacted 15 times with a tamping rod. This
procedure was repeated for the second lift. After theflow cone was completely filled with mortar, top
surface was leveled with cone top opening. After that,the flow cone was lifted vertically, and diameters ofmortar were measured in 5 mm increments at the most
spread and at right angle to it. The average of themeasurement is the mortal flow in 0 bounce. Then,the flow table was bounced 15 times with one bounce
per second, and diameters of mortal were measured
again. The mortal flow in 15 bounce was evaluatedfrom the average of diameters after bounces.

50
0Fig. 1

cement

M1

M2

Flow of GP mortal and cement mortal.

a unit content of water, and the mass of H2O in thealkaline solution. W/B is the mass ratio of the unit
water content to binder, the binder in the present
study is an active filler included FA and BFS. BFS
substitution rate (BFS sub rate) means a volume ratioof FA that is replaced by BFS.
This paper investigates the effect of W/B andadmixture on fresh properties and strength of GP
mortal. First, control mix proportion of GP mortal
was determined by the mortal flow test. Table 1
shows mixture parameters of GP mortal, and Table 2
shows mix proportion. A/W of M1 in this test was set
to 0.15, because high A/W could cause fast setting
and low workability of GP mortal due to its
condensation property in high alkali region. Freshproperties of GP mortal with wide range of Si/A from
0.3 to 1.0 have reported by the technical committee
report in JCI [2]. In this test, Si/A of M1 was set to
0.8. Increasing the unit water content induces to theimprovement of fluidity of GP. However, water in GP
has little contribution to the reaction of an alkali
solution and an active filler, so increase in unit watercontent could cause the decrease in strength of GP.
Therefore, the unit water content was set to 240 kg/m3.
W/B in this test was 0.44, referring to the previous

DETERMINATION OF A/W AND Si/A
MixtureMixture design of GP requires various mixture
parameters, such as alkali/water ratio (A/W),
silicon/alkali ratio (Si/A), unit water content (W),water/binder ratio (W/B) and BFS substitution rate.
A/W (Na/H2O) is a mole ratio of sodium to water inan alkaline solution. Si/A (Si/Na) represents a moleratio of silicon to sodium in an alkaline solution. W is335

GEOMATE – Kyoto, Japan, 3-5 November 2021

report [2]. BFS substitution rate was set to 10%,which is generally used in GP mortars.
GP mortal of M1 had high viscosity, this might bedue to the viscosity of WG. A/W and Si/A of M2 were
set to have lower unit content of WG than M1, eachvalue was 0.1 and 0.4, respectively. The others of
mixture parameters were the same as in M1: W=240
kg/m3, W/B=0.44 and BFS substitution ratio =10%.

Table 3 Mixture proportion and adhesion amount of
GP mortar.

No.
M1
M2
M3
M4
M5
M6
M7
M8
M9
M10
M11
M12
M13
M14
M15
M16
M17
M18
M19
M20

Result and Discussion
Result obtained from the mortar flow test is shownin Fig. 1. The mortar flow in 0 bounce and 15 bounces
is demonstrated in as dotted and solid lines,
respectively. For comparison with the GP mortar, themortar flow test was also conducted with the cement
mortar with 55% of water cement ratio. The concretewith this cement mortar has the targeted slump of 8cm. While the 0 bounce flow of the cement mortar
was 115 mm, the 0 bounce flow of M1 was 168 mm
and the mortar of M1 had higher fluidity than the
cement mortar. The 15 bounces flow of the cement
mortar increased by 83 mm from the 0 bounce flow.
Increase in mortar flow of M1 due to bounce of flowtable was 5 mm, which was only 6% of cement mortar,and high viscosity of GP mortar of M1 could be
visually observed. These results indicate little
increase in mortar flow of M1 due to flow tablebounce was caused by high viscosity in spite of highfluidity.
The 0 bounce flow of M2 with a smaller unit
content of WG was 170 mm, which was almost equal
to that of M1, and GP mortar of M2 had equivalent
fluidity to M1. In addition, the 15 bounces flow
increased from 173 mm of M1to 213 mm of M2 andthe improvement of viscosity was confirmed byvisual observation. Such results on mortar flow andvisual observation indicate that reduction of WG by
decreasing A/W and Si/A contributes to low viscosityof GP mortar. However, the flow increase of M2 due
to flow table bounce was lower than that of thecement mortar, so GP mortar of M2 requires the
improvement of viscosity.
In this study, the effect of W/B, BFS substitution
rate and admixture on the flow and the compressive
strength of GP mortar was investigated based on M2,
which had lower viscosity than that of M1.

W/B
(mass
ratio)

BFS
sub
rate
(vol%)

0.33
10

0.35

20
30
10

0.37

20
30
10

0.44

20
30

Ad1
(%)

Ad2
(%)

adhesionamount
(g)

0
3
0
3
0
3
0
3
0
3
0
3
0
3
0
3
0
3
0
3

0
5
0
5
0
5
0
5
0
5
0
5
0
5
0
5
0
5
0
5

0.8
0.5
0.7
0.7
14.7
0.8
1.1
0.6
1.6
13.1
115.0
1.3
9.0
2.0
5.5
6.8
19.0
3.0
64.7
2.7

Nates: A/W＝0.1, Si/A＝0.4, W＝240kg/m3

W/B was set to 0.33, 0.35, 0.37 and 0.44. In this study,
A/W, which is related to the molarity of alkali
solution, was set low, so GP with low molarity alkali
solution could have lower strength than general
concrete according to Uehara et al [3]. In order toincrease strength of GP, the BFS substitution rate wasincreased from 10% to 20 or 30%. The rate of
admixture was 3% or 5% by mass of active filler.
These rates are the maximum of the manufacturer's
standard usage. The mix proportion of M15
corresponds to that of preliminary flow test.
Method of Tests
In this study, mortar flow test with the sameprocedure of the preliminary experiment describedabove and compressive strength test were conducted.
The flow cone, which inside is coated with fluorine
resin, was used for mortar flow test in order to prevent
excessive adhesion to flow cone inside due to highviscosity of GP mortar. In addition, the amount of GP
mortar adhered to flow cone without inside coating
was measured in order to investigate the viscosity of
GP mortar. The adhesion amount of GP on flow cone
was evaluated by the increase in its weight in mortar
flow test.
In this study, three cylindrical specimens with 50
mm diameter and 100 mm height were used for each
mixture in compressive strength test. GP mortar was

TEST ON W/B AND BFS SUBSTITUTION
RATE AND ADMIXTURE
MixtureMixture proportions of GP mortar and amount of
GP mortar adhered to flow cone in this study are
shown in Table 3. The amount of GP mortar adhered
to flow cone will be described in detail below. A/W,
Si/A and W were determined as 0.1, 0.4 and 240
kg/m3 based on preliminary flow test, respectively.
336

GEOMATE – Kyoto, Japan, 3-5 November 2021

0.33

0.37

without admixture

0.44
(20)

(10)

300
flow (mm)

0.35

with
(10)

(30)

compressive strength( )in BFS sub rate
(20)

(30)

50

250

40

200

30

150

20

100

Fig. 2

M20

M14

M8

M18

M12

M6

M16

M10

M4

M2

M19

M13

M7

M17

M11

M5

M15

0

M9

0

M3

10
M1

50

compressive strength (N/mm2)

W/B

Effect of W/B on mortar flow and compressive strength.

filled by two layers. Each layer was compacted fivetimes by using a tamping rod with 9 mm of diameter,
referring to JIS A 1132 on method of making concrete
specimen. The compaction holes were closed by
jigging. The top surface of specimen was leveled andsealed with plastic wrap to prevent from water
evaporation. The specimen was cured in an electric
furnace at 60 °C for 24 hours [4]. After that, curing
temperature in an electric furnace was sustained at
20°C. The curing temperature was automaticallycontrolled using a thermostat.
Compressive strength test in this study was
conducted at the age of 2 days in accordance with JIS
A 1149. Loading rate was set to 0.2 N/mm2 persecond. The compressive strength of the GP mortar
was the average of results obtained from three
specimens

0.8g

0.7g

(a) M1 (W/B=0.33)(b) M3 (W/B=0.35)

1.6g

5.5g

(c) M9 (W/B=0.37)(d) M15 (W/B=0.44)

Result and Discussion

Fig.3

Influence of fluorination on flow cone

restrain GP mortar adhesion to flow cone and toevaluate fresh property of GP mortar with highviscosity.

The adhesion amount, which is evaluated by themortar flow test using the flow cone without coating,is shown in Table 3. Most of GP mortar in this study
had less adhesion amount obtained from flow test byusing coated flow cone than that without coating. The
GP mortar of M19 had the most difference due tocoating, the adhesion amount with and without
coating was 36.5 g and 64.7 g, respectively. This
result indicates that the coating with fluorine resin is
effective to restrain GP mortar adhesion to flow cone.
The effect of fluorine resin coating on mortar flow
was also investigated by mortar flow test using the GP
of M15. Although mortar flow at 0 bounce obtained
from flow cone without coating was 170 mm, themortar flow using coated flow cone was 160 mm. The
coating inside with fluorine resin reduced the 0bounce mortar flow by 10 mm. Decreasing GP mortar
adhered to the flow cone due to coating was alsovisually observed. These results indicate that coatingwith a fluorine resin on flow cone is effective to

GP mortar just after lifting the flow cone.

Influence of W/B
The effect of W/B on mortar flow and compressive
strength of GP mortar in this study is shown in Fig. 2.
Mortar flow shown in Fig. 2 are results of mortar flow
test using the fluorine-coated flow cone. As shown in
Fig.2, GP mortar with higher W/B had more mortar
flow at both bounces of 0 and 15 times except forM17. Figure. 3 shows GP mortar of M1, M3, M9, andM15 just after lifting flow cone. The adhesion amountof GP mortar on flow cone is also demonstrated in Fig.
3. As shown in Fig. 3, the GP mortar with higher W/B
spread more just after lifting flow cone. This may be
due to the decrease in the rate of powder (= FA +BFS) in GP mortar as W/B increases, and increase in
W/B could cause higher fluidity. Increasing W/B by
0.02 from 0.33 to 0.37 was obviously more effective
to fluidity of GP mortar than BFS substitution rate337

GEOMATE – Kyoto, Japan, 3-5 November 2021

and admixture, which will be described below.
Although GP mortar with higher W/B had higher
mortar flow, higher W/B caused the increase in theadhesion amount of GP mortar such as M1-M3-M9 at
BFS substitution rate of 10% without admixture
shown in Fig. 3, M7-M13-M19 at the BFS
substitution rate of 30% without admixture, M6M12-M18 at BFS substitution rate of 20% with
admixture and M8-M14-M20 at BFS substitution rateof 30% with admixture.
Compressive strength was obviously dependent toW/B shown in Fig. 2 and increase in W/B caused
decrease in compressive strength. This may be due todecrease in active filler, which is required for
hardening of GP, to alkaline solution.

amount at W/B=0.35 (M3-M5-M7) and W/B=0.37(M9-M11-M13), and the GP of W/B=0.44 (M15M17-M19) had the most adhesion amount at BFS
substitution rate of 30%. The GP mortar with
admixture and 10% of BFS substitution rate had themost adhesion amount W/B=0.37 and 0.44. The
adhesion amount was independent to BFS
substitution rate at the GP mortar with W/B=0.35.
These results indicate an appropriate BFS substitution
rate for the adhesion amount differs in accordancewith mixture parameters such as W/B and admixture.
Compressive strength of GP mortar obviously
increased as BFS substitution rate increased. The
compressive strength of the GP mortar with BFS
substitution rate of 20% was from 46% to 164%
greater than that of 10%, although the GP mortar with
BFS substitution rate of 30% had 8-28% higher
compressive strength than that with BFS substitution
rate of 20%. In addition, compressive strength of the
GP mortar with admixture had more strength
development due to increase in BFS substitution rate,compared with the GP mortar without admixture.

Influence of BFS substitution rate
The effect of BFS substitution rate on mortar flow
and compressive strength of GP mortar is shown in
Fig. 4 except for M1 and M2. The GP mortar without
admixture had equivalent mortar flow, though BFS
substitution rate was different. Before mortar flow
test, it was expected that mortar flow would decrease
as BFS substitution rate increased because BFS has amore non-uniform particle shape than FA sphericalparticles. However, mortar flow was similar,
indicating that BFS substitution rate could cause little
effect on mortar flow. Unburned carbon contained in
FA absorbs admixture such as high-performance AE
water-reducing agent and prevent performance ofadmixture. In GP mortar with the admixture, it was
expected in advance that reduction in unit content of
FA by substitution with BFS could lead to an increasein mortar flow. Contrary to expectations, the GP
mortar with higher BFS substitution rate had lower
mortar flow.
The effect of BFS substitution rate on theadhesion amount varied to W/B and admixture. The
examples of results are shown below. In the GP
without admixture, BFS substitution rate of 20% was
the least effective to the reduction in the adhesion{0.35}

300
flow (mm)

10

20

30

{0.37}The effect of admixture on mortar flow and thecompressive strength is shown in Fig. 5. In someresults of mortar flow test, mortar flow was increased
by admixture. In particular, admixture was more
effective to improvement of fluidity in the GP with
BFS substitution rate of 10%. However, themaximum increase due to flow cone bounces was
53mm, and less than the result of 83 mm obtainedwith cement mortar shown in Fig. 1. GP in the present
study had higher viscosity than cement mortar forconcrete with the slump of 8cm.
The effect of admixture on the adhesion amount isshown in Fig. 6. The results in which the difference
due to the admixture was less than 3g are not includedin Fig. 6. Although the GP with W/B=0.37 and BFS
substitution ratio of 20% had the adhesion amount of
115.0g, the adhesion amount was decreased to 1.3g

without admixture with{ }in W/B

{0.35}{0.37}{0.44}

compressive strength{0.44}

50

250

40

200

30

150

20

100

Fig. 4

Effect of BFS substitution rate on flow and compressive strength.
338

M20

M18

M16

M14

M12

M10

M8

M6

M4

M19

M17

M15

M13

M11

M9

0

M7

0

M5

10
M3

50

compressive strength (N/mm2)

BFS substitution rate

Influence of admixture

GEOMATE – Kyoto, Japan, 3-5 November 2021

300

{0.33}(10){ }in W/B，( )in BFS sub ratewith admixture{0.35}
(20)

(10)

(30)

{0.37}
(20)

(10)

(30)

compressive strength{0.44}
(20)

(10)

(30)

flow (mm)

250

50
40

200

30

150
20

100

Fig. 5

M20

M19

M18

M17

M16

M15

M14

M13

Effect of admixture on flow and compressive strength.
on the effect of unit amount of water glass on GP
mortar. Based on test results in this study, it can beconcluded as follows:( ) in BFS sub rate120

adhesion amount (g)

M12

M11

M10

M9

M8

M7

M6

M5

M4

0

M3

0

M2

10

M1

50

compressive strength (N/mm2)

without admixture

(20)

100
80

W/B
0.35
0.37
0.44

(30)

60
40

20

(1) The GP mortar with higher W/B has moremortar flow. Some results indicate that fluidityof GP mortar can be increased by using highperformance AE water reducing agent forcement concrete.
(2) Although viscosity of GP mortar can be reduced
by decreasing A/W and Si/A, viscosity of GP
mortar in this study was higher than that of
cement mortar.
(3) Compressive strength of GP mortar can beincreased by lowering W/B or increasing BFS
substitution rate. Some results indicate thatcompressive strength of GP mortar increased byusing a high-performance AE water reducing
agent.

(20)
(30)

(10)

0
without admixture
Fig. 6

with admixture

Effect of admixture and adhesion amount.

by adding admixture. The GP with W/B=0.44 and
BFS substitution ratio of 30% had also similar result.
These GP (M11-M12 and M19-M20) had increase inmortar flow due to the bounce of flow table was
improved by admixture. These results indicate that
adding admixture to GP is effective restraining
viscosity of GP mortar.
A high-performance AE water-reducing agent
caused the increase in compressive strength in someGP as shown in Fig. 5. Although admixture was used
for improvement of fluidity and viscosity in thepresent study, its effect was less than that of W/B and
BFS substitution ratio.

REFERENCES[1] Zhang Z., Provis J. L., Reid A. and Wang H.,
Geoplymer foam concrete: An emerging
materialforsustainable
construction,
Construction and Building Materials, Vol.56,
2014, pp.113-127[2] JCI-TC155A, Technical Committee report on
Application of Geopolymer Technology toConstruction Field, Japan Concrete Institute,2017 (in Japanese)[3] Uehara M., Minami K., Hirata H. and Yamasaki
A., Effect of mix proportions and production
methods on hardened geopolymer, Proceedingsof the Japan Concrete Institute, Vol.37, No.1,
2015, pp.1987-1992 (in Japanese)[4] Zhang P., Zheng P., Wang K. and Zhang J., A
review on properties of fresh and hardened
geopolymer mortar, Composites Part B, Vol.152,
2018, pp.79-95

CONCLUSIONS
GP, which has been developed as an alternative
construction material with low CO2 emissions, hasvarious issues such as establishment of mixture
design method. In this study, the effects ofwater/binder ratio (W/B) and BFS substitution rate
and admixture on fresh and strength properties of GP
mortar were investigated by the mortar flow test andthe compressive strength test, after preliminary test339

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

TENSILE CREEP BEHAVIOR CONSIDERING STIFFNESSDEVELOPMENT OF HIGH STRENGTH CONCRETE AT EARLY AGE
Takuya Hara1, Yoichi Mimura2 and Itaru Horiguchi3
1

Advanced Course, Project Design Engineering, National Institute of Technology, Kure College, Japan;
Department of Civil Environmental Engineering, National Institute of Technology, Kure College, Japan;

2,3

ABSTRACTHigh strength concrete has more unit cement content and higher temperature rise than ordinary concrete,
therefore thermal crack is caused by the volume change by hydration of cement. The thermal crack affects longterm durability of concrete structures, so thermal stress is predicted by numerical analysis such as FEM. Numericalanalysis (FEM) requires various thermal and mechanical properties of concrete. Accurate prediction of thermal
stress including the relaxation due to the tensile creep at early age contributes effective control of thermal
stress/crack. However, little researchers focus on the tensile creep behavior considering development of concrete
strength and stiffness at early age. In this study, tensile creep tests by using high strength concrete with water
cement ratio of 45% were carried out with 2 sets of loading stress. The tensile creep tests in the present study
started loading at the age of 3 days and the loading was sustained for 14 days. The loading stresses were 30% or
40% of the splitting tensile strength at loading start. The test results of high strength concrete loaded 30% ofstrength indicate that each specific creep was in the range of ±25% of the average and the averaged specific creep
was equivalent to that of concrete with ordinary strength (water cement ratio of 55%). On the other hands, most
specific creeps with stress/strength ratio of 40% was equivalent to or greater than that of 30%, although someresults with stress/strength ratio of 40%were smaller than that of 30%.
Keywords: High strength concrete, Thermal crack, Tensile creep, Water-cement ratio, Stress-strength ratio

Basic creep of mature concrete based on
experiments can be evaluate assuming a constantelastic strain during creep test with a constant load.
However, the elastic strain in creep test at early age
decreases due to stiffness development, so creep
behavior evaluated by a constant elastic strain isdiffer to actual one. Little researchers have beenreported on tensile creep behavior at early age
considering developments of some mechanicalproperties such as concrete stiffness. In particular,
there is even less research on tensile creep of highstrength concrete with the low water cement ratio(W/C), although the low W/C increase the potential
for cracking due to higher temperature rise at earlyage.
In this study, the tensile creep experiments at early
age were carried out on high strength concrete with
W/C = 45%, which has higher unit cement content
and higher temperature rise than ordinary concretewith general strength. In the tensile creep test of thepresent study, the both ends of the dog-bone shaped
specimen were connected to the loading apparatus
with a pin in order to restrain of bending stress as
possible. The tensile creep behaviors of concrete withdifferent strength at early age are evaluated based on
the experimental data obtained from the present study
and the previous studies on the ordinary concrete with
W/C = 55%. The tensile creep behaviors were
investigated with the creep tests with different
loading stress of 30% and 40% of the splitting tensile

INTRODUCTION
High strength concrete has been widely used forconstruction of structures with long span and large
cross-section. Volume change due to hydration heatcauses initial cracks in high strength mass concrete,
which has more unit cement content. Such thermal
crack occurs in a few weeks after casting and induces
to degrade long term durability, so preventing thecracks or controlling the crack width is important
issues for concrete engineers.
The finite element method (FEM) is generally
conducted to analyze thermal stress in order toprevent thermal cracking due to temperature changes
and temperature distribution. Accurate estimation of
the thermal stresses contributes to appropriate
selection of materials, mixture proportions andconstruction methods and to ensure the long term
durability of concrete structures. Accurate FEM
requires various input data which include mechanicalproperties such as strength, Young's modulus andcreep. Effect of these properties development at earlyage should be considered for thermal stress analysis.
This study focuses on the tensile creep behavior at
early age in order to accurately predict tensile stress.
The prediction of tensile stress without the relaxation
due to the tensile creep might be overestimated thanactual thermal stress, so the tensile creep behavior at
early age is an essential factor to evaluate the stressrelaxation in thermal stress prediction.
340

GEOMATE – Kyoto, Japan, 3-5 November 2021

strength at loading start.

290
120

70

15

75

Materials and Mixture Proportions

70

Table 1 shows the materials used for concrete andmix proportion in this study. The mix IDs of N and H
represent the ordinary concrete mix with W/C =55%
and the high strength concrete mix with W/C = 45%,
respectively as shown in table 1. The targeted slump
and targeted air content of high strength concrete is8±2.5 cm, 4.5±1.5%, and they are the same level asthat of ordinary concrete. An AE water reducing
agent, which has the principal components of lignin
sulfonate and oxycarboxylate was added at 1% of thecement mass for normal concrete and at 2% of thecement mass for high-strength concrete in order toensure the targeted slump and air content.

unit：mm100

15

MATERIALS AND TEST PROGRAM

Strain gauge (50mm long)

Fig. 1

Dog-bone shaped specimen

Steel arm

Load

Water Unloaded
specimen
Loaded specimen

Specimen
Fig. 2The dog-bone shaped specimen and loading
apparatus used in the tensile creep test of this study
are shown in Fig. 1. A jig is attached to the enlarged
section of the dog-bone shaped specimen andconnected to the loading apparatus. Both ends areconnected with pins to minimize the bending stressdue to eccentricity of the load. A small mold strain
gauge having a temperature measurement function
and a length of 50 mm was embedded in the center ofthe cross section of the dog-bone shaped specimen. In
order to evaluate developments of the strength and thestiffness during the loading period of tensile creep
tests, splitting tensile strength tests (conform to Japan
Industrial Standards A 1113) and compression tests
were conducted using a cylindrical specimen with
100 mm diameter and 200 mm height. For the strain
measurement in compression tests, a strain gauge
with length of 60 mm was attached at the center ofboth side faces of each specimen.
The temperature at mixing in this study was theoutside air temperature. After casting, the specimens
were carried into a curing room to be cured at a room

Loading apparatus

temperature of 20 ± 1 degree Celsius for 24 hours
before demolding. After that, the specimens were
cured underwater in tank (16 ± 1 degree Celsius)
installed in the curing room until loading.
Method of Tensile Creep Test
Figure 2 shows the loading apparatus used in thetensile creep test in this study. Tensile creep was
loaded underwater, as shown in Fig .2. The loading
apparatus was installed in the water tank in the curing
room with the temperature remained at 20 ± 1 degree
Celsius and the temperature in the water tankremained at 16 ± 1 degree Celsius. In this test, loading
was carried out in water to eliminate the effect ofdrying shrinkage. Therefore, tensile creep in thisstudy is targeted for basic creep without moisturetransfer.
In this study, tensile creep experiments wereconducted using two loading apparatus. Two dog-

Table 1 Materials and mix proportion

Property

Materials

Mix ID
Water cement ratio
Water (W)
Cement (C)Fine aggregate (S)Coarse aggregate (G1)
Coarse aggregate (G2)
Admixture (Ad)

Tap Water
Ordinal Portland Cement
Crushed rock sand
Crushed rock (20-15 mm)
Crushed rock (15-5 mm)
AE water reducing agent

Proportion
Ordinary
High strength
concreteconcrete
N
H
55%
45%
165 kg/m3
165 kg/m3
3
300 kg/m
367 kg/m3
3
844 kg/m
812 kg/m3
499 kg/m3
486 kg/m3
3
499 kg/m
486 kg/m3
33.00 kg/m
7.34 kg/m3

341

Density

1.00 g/cm3
3.16 g/cm3
2.62 g/cm3
2.67 g/cm3
2.67 g/cm3
1.07 g/cm3

GEOMATE – Kyoto, Japan, 3-5 November 2021

y = 12714x - 3280.7(1)

y = 17962x - 4357.0(2)

Where x is tensile stress loading to dog-bone shaped
specimen (N/mm2), y is weight of mass (g)．
Mimura [1] have reported that the compressive
Young's modulus calculated using the stress up to thetensile strength and the corresponding strain was
equivalent to the tensile Young's modulus, even in the
stress-strain relationship obtained by compression
experiments. In this study, the compression test was

Table 2 Measured fresh properties

Slump(8±2.5 cm)
Air content(4.5±1.5%)

Compressive strength (MPa)

70

H30

H40-a

H40-b

5.5 cm

5.5 cm

6.0 cm

3.5%

3.6%

3.3%

W/C=55%60

W/C=45%

50
40

30
20
10

0
0

5

10
age (days)

15 17

splitting tensile strength (MPa)Fig. 3 Compressive strength during creep test

5

W/C=55%

W/C=45%510age (days)

4
3
2
1
0
0

15 17

Fig. 4 Splitting tensile strength during creep test

50
Young’s modulus (GPa)bone shaped specimens were prepared for eachloading apparatus. Loading specimen was subjected
to constant and continuous loading, while theunloading specimen was installed in the sameenvironment as the loaded specimen.
In this study, the tensile loading was started at theage of 3 days, because thermal stress changes fromcompression to tension around these ages. The
loading period of 14 days was determined from theoccurrence of the initial cracks in approximately two
weeks after casting. According to Davis-Granville’s
principle, creep strain is roughly proportional toloading stress in the range of one-third of concrete
strength. Therefore, the loading stress in this study
was set to 30% (H30) and 40% (H40) of the splitting
tensile strength at the loading-start age (3days).
Tensile creep experiments were conducted once for
H30 and twice for H40 (H40-a, H40-b). Table 2
shows the measured fresh properties of the tested
concrete. As shown in Table 2, the tested concrete had
equivalent fresh properties in the range of the targetedslump and air content. The strain and temperature of
the loaded and unloaded specimens wereautomatically measured at one hour interval by a datalogger during the tensile creep test, and the watertemperature in each water tank was also measured aswell as the strain and the temperature.
In this study, the stiffness of the concrete develops
during the loading period of the tensile creep
experiment due to active hydration at early age.
Therefore, the elastic strain decreases with thedevelopment of stiffness though the load is sustained
at a constant. The creep strain behavior at early age
cannot be accurately evaluated unless suchdecreasing elastic strain is appropriately obtained. Inorder to measure the elastic strain during the tensile
creep experiment, the strain was measured bytemporary unloading every 24 hours.
In this study, tensile creep experiments wereconducted in water, so the loading stress could not bemeasured by a load cell. The weight mass wascalculated from the following equations related theweight mass placed on the steel arm tip and the tensilestress to the dog-bone shaped specimen, as shown inEquation (1) and Equation (2). Each equation is foreach loading apparatus.

W/C=55%

W/C=45%

40
30

20
10

0
0

5

10
age (days)

15 17

Fig. 5 Young’s modulus during creep test
conducted by the same method in the previous study[1] in order to obtain the compressive strength and342

GEOMATE – Kyoto, Japan, 3-5 November 2021

Young’s modulus during the tensile creep test. The
stress and the strain were measured ten times at thesame stress interval until the loading stress reaches
the splitting tensile strength at the tested age. The
loading was temporally removed just after themeasurement of the stress and the strain and theresidual strains was also measured in order toconsider the effect of inelastic strain in the stiffness
evaluation. The Young's modulus was obtained bylinear regression of the relationship between strain
and stress obtained from the compression test. The
splitting tensile strength test and the compression test
were conducted at the age of 3, 5, 7, 10, 14, and 17
days. The compressive strength, splitting tensile
strength and Young's modulus obtained from thesetests are shown in Fig. 3, Fig. 4 and Fig. 5,
respectively. In these figures, the results in theprevious study [2] on the ordinary concrete with
W/C=55% are also shown. As shown in Fig. 3, thecompressive strength of the concrete with W/C=45%
was greater than that of the ordinary concrete of
W/C=55%, and the concrete had a high compressive
strength more than 50 MPa at the age of 10 days. The
splitting tensile strength was also greater than that ofordinary concrete, and had less difference between
both the concretes than the compressive strength. The
Young's modulus of the high strength concrete wasless than 5% different from that of the ordinary
concrete at the age of 5 days, and the difference
gradually decreased. The Young’s moduli of the highstrength concrete and the ordinary concrete were
almost equal at the age of 17 days. Table 3 shows the
splitting tensile strength and loading stress at the ageof 3 days when the tensile creep test was started. The

loading tensile stresses in this study were set to 30%
or 40% of the splitting tensile strength at age of 3 days,
and were sustained for 14 days. As shown in Fig. 4,
the splitting tensile strength develops during thetensile creep test in the present study, so the stressstrength ratio gradually decreases though the loading
stress is a constant.
RESULTS AND DISCUSSION
Tensile Creep Experiment results
The measured strains obtained from the tensile
creep tests are shown in Fig. 6. The loaded strain andthe unloaded strain shown in Fig. 6 are measured
from the loaded specimen and the unloaded specimen,
respectively. In H40-a, the measured strain from 12days were obviously inappropriate due to the failureof the data logger, so such inappropriate strains in
H40-a were removed for evaluation of tensile creep
behavior. The measurement values of H40-b werealso temporarily unavailable due to out of recording
paper on the 3 days after the start of loading.
The loaded strain gradually increased with thesustained loading, and the unloaded strain also
increased as shown in Fig. 6. The tensile creep test
was conducted underwater, so the strain increase in
the unloaded specimen might be caused by the
expansion strain due to water absorption.
The elastic strains during the tensile creep test are
shown in Fig. 7. The elastic strain in the present study
was evaluated as the average of the difference
between the strain at temporary unloading and theloaded strains before and after the unloading. The
data between the loaded strain and unloaded strain are
the strains at temporary unloading every 24 hours. As
shown in Fig. 7, the elastic strains behaviors in H30
were almost equivalent, and the elastic strains of H40
had greater difference than that of H30. In all
specimen, the decrease was significant up to 2 days
after the start of loading, and the average decrease inelastic strain during the tensile creep test was 23% for
H30, 27% for H40-a and 23% for H40-b. The

Table 3 Loading stress in tensile creep experiment
Mix
ID
H30
H40-a
H40-b

Age
(days)
3

140

Loading
stress (MPa)
0.770
1.176
1.081

〇:H40-a1△:H40-a2〇:H30-1△:H30-2

120

Strain(×10-6)

Splitting tensile
strength (MPa)
2.57
2.94
2.70

100

〇:H40-b1△:H40-b2

Unloaded strain

80

Loaded strain

60
40
20
0

0

7
time (days)

14

0

7
time (days)

Fig. 6 Behaviors of loaded strain and unloaded strain.
343

14

0

7
time (days)

14

GEOMATE – Kyoto, Japan, 3-5 November 2021

60〇:H30-1△:H30-2

40
30

20
10
0

0

H30

specific creep (×10-6/MPa)strain (×10-6)

50

100
90
80
70
60
50
40
30
20
10
0

7
time (days)(a) H30

14〇:H30-1△:H30-2□:H30 average

0

7time (days)

14

Fig. 8 Specific creep behavior of H30.
60

strain (×10-6)〇:H40-b1△:H40-b2

100
90
80
70
60
50
40
30
20
10
0

40
30
20

10
0

0

Fig. 7

7time (days)(b) H40

14

Elastic strain behavior during tensile creep
test.

H40

specific creep (×10-6/MPa)〇:H40-a1△:H40-a250

H40-b1

H40-b2

H40-a2

H40-a10

H30 average

7time (days)

14

Fig. 9 Specific creep behavior of H40.
at 4 days was 51% of that at the end of loading, while
the specific creep of H30-2 tended to converge. The
averaged specific creep of H30-1 and H30-2 isdemonstrated in Fig. 8, and each specific creep wasin the range of ±25% of the average.
The specific creep behavior of H40 is shown in
Fig. 9. The averaged specific creep of H30 is also
shown in Fig. 9. The specific creep behavior of H40a2 was similar to that of H30. The specific creep ofH40-b1 was almost equivalent to that of H30 at theend of loading, though the behavior of H40-b1
differed to the result of H30 from loading to around
10 days. The specific creep of H40-a1 was obviously
smaller than that of H30 through the loading period.
The result of H40-b2 was obviously greater than thatof the others and the specific creep at the end ofloading was around 1.7 times greater than H30. The
value of H40-a1 was obviously small, and the valueof H40-b2 was large from the beginning of loading,and the specific creep at the end of loading was about
1.7 times larger than H30. Such specific creep of H40
had various behaviors. This may be due to over the
linear range between loading stress and creep strain.
Loading stress more than around 1/3 of concrete
strength induces microcrack development, so
nonlinear relationship between loading and creep

decrease in elastic strain was approximately
independent to the stress/strength ratio.
In this study, the measurement interval of theelastic strain was 24-hours, while the strains were
measured every hour. The elastic strains every hour
were required for hourly creep evaluation, and were
estimated from the partially linear regression of themeasured elastic strain.
Specific Creep Comparison
In this study, the creep strain was evaluated bysubtracting the unloaded strain and the regressed
elastic strain shown in Fig. 7 from the loaded strain.
The specific creep of H30 obtained in this study isshown in Fig. 8. The specific creep is the creep strain
per unit stress, which is calculated by dividing thecreep strain by the loading stress. Specific creep isgenerally used for comparison of creep behaviors
obtained from the experiments with different loading
stresses, and is also required for the thermal stressestimation considering relaxation effect because
thermal stress has time dependent. The specific creep
behaviors of H30 shown in Fig. 8 were similar fromloading to around 4 days. The specific creep of H301 had also increased since then and the specific creep
344

GEOMATE – Kyoto, Japan, 3-5 November 2021

100
90
80
70
60
50
40
30
20
10
0

□:H30 average
△:N30

0

7
time (days)□:H40□:H30 average
〇:N40
△:N40

specific creep (×10-6/MPa)

specific creep (×10-6/MPa)

100
90
80
70
60
50
40
30
20
10
0

14

Fig. 10 Specific creep behavior of N30 and H30.

0

7time (days)

14

Fig. 11 Specific creep behavior of N40 and H40.

strain is caused by microcrack development. The
microcrack development due to the loading stresswith 40% of the splitting tensile strength could cause
the different creep behavior, because the microcrackdevelopment is sensitive to a bit difference in the
microstructure in concrete.
Figure. 10 shows the averaged specific creep
behavior of H30 and N30 reported in the previous
study [2]. Although the high strength concrete had thedifferent strength development from the ordinary
concrete as shown in Fig. 3 and Fig. 4, the averaged
specific creep behavior of the high strength concretewith W/C=45% was almost equal to that of theordinary concrete with W/C=55%. This result
indicates that the specific creep behavior, which is
caused by the loading stress with the stress/strength
ratio of less than 30%, is independent to 10%
difference of W/C and the specific creep of concretewith W/C from 45% to 55% can be represented by thesame equation.
Figure. 11 shows the specific creep of N40 and
H40 which were obtained from the tensile creep testwith the stress/strength ratio of 40%. As mentioned
above, the specific creep of H40 varied from
specimens. As shown in Fig. 11, the specific creep
behavior of each specimen with the ordinary concretewith W/C=55% also had significant difference. The
averaged specific creep of H30 is shown in Fig. 11.
Some results of H40 and N40 were smaller than H30,
and most results of H40 and N40 were equivalent toor greater than that of H30. In particular, the specificcreep of one of N40 [2] was more than twice of H30.
These results indicate that the loading stress morethan 1/3 of the concrete strength causes the nonlinear
relationship between loading stress and creep strain
due to microcrack development. Such microcrackdevelopment is so sensitive to microstructure ofconcrete in spite of concrete strength that the specificcreep obtained from creep test can include large
errors. Therefore, accurate evaluation of tensile creep
behavior requires more experimental results.

CONCLUSIONSThis paper presents an investigation of the tensile
creep behavior of high strength concrete with aconsideration of the stiffness development at early
age of concrete. The tensile creep tests using dogbone shaped specimens were conducted to obtaintensile creep. Based on the test results in this study, it
can be concluded as follows:(1) Tensile creep which is caused by the loading
stress with stress/strength ratio of 30% is similarin the range of W/C from 45% to 55%, although
the strength development characteristics at early
age are different, and the difference in watercement ratio has little effect.
(2) Although the specific creep with a stress-strength
ratio of 40% could be equal to or higher than thatof 30%, the specific creep of high strength
concrete loaded with a stress-strength ratio of
40% differed greatly among specimens. Such
result was also obtained from ordinary concretewith W/C=55%. The difference among
specimens in the tensile creep test loaded with astress/strength ratio of 40% could be caused by
microcrack development which is sensitive to a
bit difference in microstructure in concrete.
REFERENCES[1] Mimura Y., Yoshitake I. and Horituchi I.,
Experimental study on Young's modulus for crack
analysis at early age, Proceedings of the Japan
Concrete Institute, Vol.36, No.1, 2014, pp.400405 (in Japanese)[2] Mimura Y., Vimonsatit V., Horituchi I. andYoshitake I., Tensile mechanical properties of fly
ash concrete at early age for thermal stressanalysis, Journal of Infrastructure Preservation
and Resilience, Springer, Vol.1, No.14, 2020.11.

345

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

EFFECTS OF POLYETHYLENE TEREPHTHALATE (PET) PLASTICS
ON THE MECHANICAL PROPERTIES OF FLY ASH CONCRETE
Mary Ann Adajar1 and Irene Olivia Ubay-Anongphouth1
1

Civil Engineering, De La Salle University, Philippines
ABSTRACT

Concrete is a favored construction material for its high compressive strength and low maintenance cost. With
the global interest on sustainable construction materials, continuous research is being done on alternative concretematerials. Research is geared towards the improvement of concrete properties as well as to contribute to waste
utilization. A study was conducted by incorporating polyethylene terephthalate plastics (PET) into fly ash concrete
to investigate the effects on compressive and flexural strengths. As the addition of fly ash as partial replacementof concrete is known to improve concrete strength, the addition of PET as partial substitute for fine aggregates
could be advantageous. A constant of 30% fly ash was used in the concrete mix proportions for test specimens,
while the amount of PET introduced varied from 5% to 15%. Specimens were prepared and tested in accordancewith ASTM standards. It was observed that increasing the amount of PET in fly ash concrete mix also increased
workability and decreased the unit weight of concrete. Compressive strength test results show that 5% to 10% PET
plastic substitution increased the compressive strength of fly ash concrete. Test specimens containing 5% PET led
to the increase of the flexural strength of fly ash concrete and further increase of PET content indicated a decreasein flexural strength. The results of this study show that the inclusion of PET in fly ash concrete could lead toincreased workability, decrease in unit weight, and improved compressive and flexural strength without the use of
admixtures.
Keywords: Concrete, Fly ash, Polyethylene terephthalate, Compressive strength, Flexural strength

INTRODUCTION

Polyethylene terephthalate (PET) plastic is one of
the most heavily produced waste materials. However,
only a small percentage of these waste are recycledleaving the rest dumped in landfills, oceans, and theenvironment. Studies by Nikbin [7] and Jo et al. [8]indicated that PET plastics could be used as partial
substitute for fine aggregates in concrete, which
resulted in lightweight concrete due to the reductionin unit weight. Borg [9] stated that PET plastics could
cause insignificant changes in the strength of concrete
if used in small percentages. Borg [9] suggested thatpartial substitution of PET plastics up to 8% without
contributing significant detrimental effects on thecompressive strength of concrete.
A study on the combined effects of polyethylene
terephthalate plastics (PET) and Class F fly ash (FA)as partial substitutes for fine aggregates and cement,
respectively, on concrete strength was conducted. As
initially stated, these waste materials continuously
increase over time in landfills and bodies of water.
With this study, not only will it provide additional
information for the field of civil engineering, but also
contribute to the reduction and utilization of the wastematerials.

Concrete is one of the most common building
material used worldwide. It is a favored construction
material owing to its high compressive strength andlow maintenance cost. Numerous research on
alternative substitutable materials for cement as well
as for both fine and coarse aggregates is continuously
conducted in order to further improve concrete
properties and contribute to waste utilization with theuse of alternative concrete materials. Fly ash [1]- [3],palm oil fuel ash [4], and coconut shell ash [5] aresome examples of viable substitute for cement.
Recycled solid wates like wastes from aggregate
quarries has been found as a possible replacement for
conventional fine aggregates [6].
It was anticipated that there would be new coal
power plants that will be constructed in thePhilippines over the next five years to account for theelectrical consumption needs of the country. The
large demand for power would then increase theamount of fly ash produced annually. The abundanceof fly ash motivated several researchers to investigate
its effects on concrete properties. Studies haveaffirmed that the addition of fly ash can be beneficial
to the mechanical properties of concrete. The
incorporation of until 40% fly ash, as partial
substitute to cement, can increase the compressive
and flexural strength of concrete as well as improve
its stress-strain behavior [1], [2].

EXPERIMENTAL PROGRAM
To evaluate the combined effects of thesubstitution of PET plastics and Class F fly ash on its
strength properties of concrete, alternative concrete346

GEOMATE – Kyoto, Japan, 3-5 November 2021

mix proportions were generated in accordance with
ACI 211.1 [10]. The amount of fly ash used as apartial substitute to cement was constant at 30 percent.
The amount of PET plastics used to substitute fineaggregates varied from zero to 15 percent. The mix
design used for test specimens are shown in Table 1.

slump of concrete due to its glassy and smoothtexture. This caused less intergranular friction
between the fresh concrete therefore causing themeasured slump to increase.
Table 2 Workability and unit weight of concrete
samples

Table 1 Mix design used for tested samples
Component

0F0P

30F0P

30F5P

30F10P

30F15P

Water, kg
Cement, kg
Sand, kg
Gravel, kg
Fly ash, kg
PET, kg

184.0
384.9
722.7
930.0
0
0

184.0
269.5
672.8
930.0
82.5
0

184.0
269.5
639.1
930.0
82.5
19.3

184.0
269.5
605.5
930.0
82.5
38.7

184.0
269.5
571.8
930.0
82.5
58.0

Concrete
Mix
0F-0P
30F-0P
30F-5P
30F-10P
30F-15P

Slump,
mm
81.25
82.50
90.25
95.25
103.50

Unit Weight,
kN/m3
22.38
22.14
22.01
21.85
21.67

Another notable observation was that the unit
weight was affected by the incorporation of PET
plastics in the fly ash concrete mix. As shown in
Table 2, the unit weight decreased insignificantly
when fly ash was incorporated into the mix. However,a sudden change in unit weight was noted when PET
plastics were introduced. The largest decrease in unit
weight was 5.14% with 15% PET plastic substitution.
Results indicated that the unit weights were less than
the typical normal concrete unit weight of 22 to 24
kN/m3 but were still greater than 18 kN/m3, which is
the typical value for lightweight concrete. The
decrease in unit weight was due to PET plastics being
lighter than sand. The specific gravity of PET plastics
was determined to be 1.33 which was almost half ofthat of conventional sand used in concrete.

Note: F = Fly ash, P = PET

The compressive strength of concrete wasinvestigated using cylindrical specimens of 150 mm
in diameter and 300 mm in height and subjected tocompression tests with a loading rate of 0.34 MPa/s
in accordance with ASTM C39 [11]. The 7th, 14th,
21st, and 28th day compressive strengths were
documented to compare the strength development
between the different mix designs and to determine
the effect of fly ash and PET plastics on thecompressive strength of concrete. Beam specimens
with dimensions of 150 mm x 250 mm x 500 mm
were tested under the third-point loading condition
following the procedure in ASTM C78 [11] todetermine flexural strength. Strain gauges wereattached unto beam specimens to observe the stressstrain behavior of the samples. The flexural strengthof the samples was determined at the 28th day ofcuring to represent the theoretical maximum flexuralstrength of concrete. A total of 100 cylindrical
specimens and 15 beam specimens were investigated.
Scanning Electron Microscopy (SEM) test wasused to visually observe the chemical bonding of thematerials used in the concrete mix. Samples for the
aforementioned test were randomly taken from thepieces used from the strength tests performed.

Compressive Strength
Table 3 presents the 28th day compressive
strength of the different mix designs tested. The
control samples (0F-0P) surpassed the target strengthof 28 MPa. As exhibited in Table 3, the addition of
fly ash reduced the compressive strength of concrete
while the incorporation of PET plastics increased thecompressive strength of concrete.
Table 3 Average 28th day compressive strength oftested samples

EXPERIMENTAL RESULTS
Concrete Mix
0F-0P
30F-0P
30F-5P
30F-10P
30F-15P

Physical Properties
The physical properties observed in the study
were the workability and unit weight of fly ash
concrete as these properties have been noted to beaffected by the incorporation of PET plastics. The
results are summarized in Table 2.
The incorporation of fly ash slightly affectedworkability as seen in Table 2. Meanwhile, theaddition of PET plastics could significantly increase
the slump of concrete. PET plastics affected theCompressive Strength, MPa
29.12
24.13
26.05
26.73
24.73

Fly ash concrete samples (30F-0P) manifested adecrease of 17.14% in strength with respect to thecontrol samples. This was contradictory to previous
studies stating that the incorporation of fly ash would
347

GEOMATE – Kyoto, Japan, 3-5 November 2021

increase the strength of concrete [7]. The reduction ofstrength could be attributed to the cement used in the
study having substances such as fly ash alreadyincorporated, which caused the fly ash compositionof the cement to have gone over the optimum of 30%.
Thus, also reducing the strength of concrete. PET
plastics were observed to increase the strength of
concrete with respect to the samples with 0% PET
plastics and 30% fly ash.
Fly ash concrete samples with 5% PET plastics
manifested an increase of 7.96% and samples with
10% PET plastics displayed an increase of 10.77%,
both with respect to the 0% PET plastics and 30% fly
ash samples. Meanwhile, samples with 15% PET
plastics exhibited a decrease of compressive strength.
The decrease in compressive strength at 15% denotesthat an increase in PET plastics would not contributeto a direct increase in strength. Rather, the trend
suggests that PET plastics would be beneficial forcompressive strength of concrete only to a certain
amount. With the trend manifesting in the study, theoptimum PET percentage suggested to be between
5% and 15% PET plastic partial substitution for sand.
Regression analysis on the compressive strength
results returned a polynomial function with an R2 of
0.976 as shown in Eq. 1,
y = – 0.0392x2 + 0.6376x + 24.058

Compressive Strength, fc' (MPa)

40

30

0F-0P

30F-0P

30F-5P

30F-10P

30F-15P

20

10

0
0

10

20

30

Sample Age (Days)

Fig. 1

Compressive strength development.

Flexural Strength
Table 4 shows the flexural strength of the samples
tested in the study. As observed, the incorporation of
fly ash reduced the strength of concrete likely becauseof the presence of fly ash in the cement powder
causing a reduction in strength. On the other hand, theincorporation of PET plastics caused the concrete
samples to have increased flexural strength. This
result agrees with the observations made by Borg [9]in wherein PET plastics were observed to sustain
larger peak loads than control samples.

(1)

where: y is the compressive strength in MPa and x isthe amount of PET plastics in percent. From Eq. 1,
the amount of PET plastics that would generate
optimum compressive strength of concrete with 30%
fly ash was determined to be approximately 8.13%.
Pearson correlation between theoretical andexperimental compressive strength values per
substitution of PET plastics up to 15% generated alinear relationship with an R2 of 0.853 and R of 0.923.
This indicated that the generated Eq. 1 was adequate
to be used as a tool to estimate expected compressive
strength up to 15% PET plastic substitution in
concrete with 30% fly ash.
Figure 1 shows the strength development curve ofthe tested samples, which illustrated that PET plastics
and fly ash do not significantly affect strength
development as they simply manifest the same
strength development trend as traditional concrete.
The effect of PET plastics and fly ash on thecompressive strength of concrete was seen to besignificant through Analysis of Variance (ANOVA).
The analysis was performed with an alpha (α) valueof 0.05. The F value of 9.04954 was greater than thecritical F value of 2.8661. This indicated that the twomaterials (PET and FA) significantly affect thecompressive strength of concrete. A P value (0.0002)
less than the alpha value further strengthened theclaim that the addition of PET plastics and Class F fly
ash has significant effect on the compressive strengthof concrete compared to the typical concrete mix.

Table 4 Average 28th-day flexural strength of tested
samples
Concrete Mix
0F-0P
30F-0P
30F-5P
30F-10P
30F-15P

Flexural Strength, MPa
2.40
2.08
2.98
2.52
2.47

A trend similar to the results obtained from thecompressive strength test was observed in which theflexural strength of the concrete beams increased fora partial substitution of 5% PET plastics. However, adrop was observed in flexural strength when partial
substitution of PET reached 10%. Regression
analysis on the determined flexural strength was also
conducted. The analysis returned a third-degree
polynomial equation with an R2 of 0.887 as shown in
Eq. 2,
y =0.0024x3 – 0.0626x2 + 0.4342x + 2.0777(2)

where: y is the flexural strength in MPa and x is theamount of PET plastics in percent. From Eq. 2, it wasdetermined that 4.78% PET plastics would produce
348

GEOMATE – Kyoto, Japan, 3-5 November 2021

optimum flexural strength in concrete with 30% flyash. Pearson correlation analysis between thetheoretical and experimental flexural strength ofsamples with PET plastics until 15% gave a linear
relationship with an R2 of 0.891 and R of 0.944.
Similar to compressive strength results, the Pearson
correlation analysis has shown that Eq. 2 wasacceptable as a tool to estimate expected flexuralstrength of concrete with 30 fly ash for PET plastic
substitution of up to 15%.
Figure 2 illustrates the stress-strain graphs of thetested samples in the study. The incorporation of fly
ash was observed to improve concrete stiffness, as the
sample was able to sustain greater loads with lessstrain [12]. This was further supported by themodulus of elasticity values shown in Table 5 assamples with 0% PET and 30% fly ash having thegreatest stiffness at approximately 30 GPa, as
opposed to the control specimens with approximately
24 GPa. The result was in agreement with the resultof the study of Uysal and Akyuncu [12] whichclaimed that fly ash may be used to improve theflexural performance of concrete due to its ability toincrease the modulus of elasticity of concrete. In
addition, fly ash concrete samples with PET plastics
behaved like conventional concrete. However, theaddition of PET plastics in fly ash concrete resulted
to decreased stiffness. This can be attributed to the
reduced frictional resistance between particles, as
PET plastics are smooth and glassy in texture.

the observed less desirable stress-strain behavior in
the beams with PET plastics, in comparison to thesamples with 0% PET and 30% fly ash. However, thedetrimental effects were observed to be counteracted
by the addition of fly ash. This compensation allowed
samples with PET plastics to simply manifest similar
deflection characteristics as the control samples. This
indicated that the combination of PET plastics and fly
ash can improve the flexural strength and without
negatively affecting the flexural behavior of concrete.
Similar to the effect of PET plastics on thecompressive strength of fly ash concrete, the effect on
flexural strength of concrete was also proven to besignificant using ANOVA. The F value of 19.908 wasseen to be much greater than the critical F value,
3.478, which verifies that the two materials did
significantly affect the flexural strength of concrete.
A P value less (9.407x10-5) than the alpha value of
0.05 further strengthens the claim that the addition of
PET plastics and Class F fly ash has an effect on the
flexural strength of concrete compared to typicalconcrete.
Concrete Microstructure
The micro-fabric structure of the concrete samples
was observed with the use of the Scanning Electron
Microscope (SEM). The microstructure of theconcrete in the SEM photographs were merelycomposed of sharp, angular, and flakey surfaces withsmooth or rough textures. Smooth textured surfaces
were identified as PET plastics, while grainy surfaces
were recognized as concrete [13].
Figures 3, 4, and 5 shows the microscopic view of
fly ash concrete samples with 0%, 5%, and 15% PET
plastics, respectively. Figure 3 shows thehoneycomb-like pattern of plain concrete as well as
the spherical particles, which indicate the presence of
fly ash in the mix. The SEM image shown in Fig. 4
exhibits that the incorporation of PET plastics
reduced the intergranular voids of concrete incomparison to fly ash concrete (Fig. 3). The reduction
in intergranular voids manifests better bonding whichthen results into better strength. However, when the
PET plastic substitution reached 15% (Fig. 5), theintergranular voids began to increase in size
indicating weak bonding. Figure 5 also shows thatsome of the parts of the PET plastic are no longer
bonded to fly ash concrete. This weaker bonding
could be the reason for of the reduction ofcompressive and flexural strength in fly ash concretewith 10 to 15 percent PET plastics as was mentioned
in the previous sections. It is possible that the increasein PET plastics (within the range of 10 to 15 percent)resulted in the reduction of calcium silicate hydrate(C-S-H) gel in the surrounding PET plastics within
the concrete mix, thus leading to weaker bonding andreduced compressive and flexural strength.

Flexural Strength (MPa)

2.5

2
1.5

0F-0P
30F-0P
30F-5P
30F-10P
30F-15P

1
0.5
0
0

50

100

150

Strain,ε (µε)
Fig. 2Stress-strain curve of tested samples.

Table 5 Modulus of Elasticity of tested samples
Concrete Mix
0F-0P
30F-0P
30F-5P
30F-10P
30F-15P

Modulus of Elasticity, GPa
23.70
29.63
23.70
28.15
27.65

Results may show that PET plastics could be
detrimental to the flexural behavior of concrete from349

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 3

SEM image of 30F-0P test sample.

Fig. 4

SEM image of 30F-5P test sample.

Fig. 5SEM image of 30F-15P test sample.

incorporation of PET plastics into fly ash concretealso resulted to a 3.14% decrease in unit weight.
Thus, PET plastics and fly ash produced concretewith higher workability with reduced unit weight.
Considering that the substitution of fly ash isconstant within the other samples, it was observedthat 5 and 10 percent of PET plastic substitution
resulted to an increase in compressive strength of 8%
and 11% from the control samples. Thus 5-10% of
substitution of PET plastic promotes beneficial
increase to the compressive strength of concrete. The
flexural strength of concrete was observed to increase
with a 5% PET plastic substitution as compared to a
0% PET plastic substitution. It was evaluated tomanifest a 43% increase in flexural strength. Thus,
addition of PET plastic will result to a noticeable
increase to the flexural strength of the concrete.
Generated equations for predicting thecompressive strength and the flexural strength of
concrete with varying PET plastic percentage up to
15% was also verified to be valid and adequate withthe use of external data obtained specifically forverification. Results indicate that a suggested amountof approximately 8.13% and 4.78% PET plastic
substitution for fine aggregates in concrete with 30%
fly ash would produce optimum compressive andflexural concrete strength, respectively.
The incorporation of both recyclable materials
was observed to have no effect to the stress-strain
behavior of concrete as well as the strength
development of the concrete samples. With this,concrete with PET plastics and fly ash can be treatedsimilarly to conventional concrete when used on site
in terms of strength development and in observing
deformation behavior due to applied loads. Therefore,
PET plastics and fly ash may be used as materials toincrease both the compressive strength and flexuralstrength of concrete without affecting its stress-strain
behavior.
PET plastics is a viable substitute for fineaggregates up to 10% substitution with 30% fly ash
as cement replacement in concrete mix withoutcompromising the strength and quality of concrete
products. However, it is recommended for further
study the long term behavior of hardened concrete
especially when it is expose to elevated temperature.
It is also recommended to investigate the compressive
and flexural strengths of concrete with fly ash atlower percentage and PET plastics higher than 10%.
ACKNOWLEDGMENTS

CONCLUSIONSThe experimentation of this research paper was
made possible by the participation of De La Salle
University – Civil Engineering students, Rafael
Crisostomo, Steven Cruz, Cris Fernandez, andBenedict Imperial as part of their undergraduate
thesis under the supervision of the authors.

The utilization of fly ash as partial cement
substitute and PET plastics as partial fine aggregate
substitute yielded noticeable effects on theperformance of concrete and the following
conclusions were made:An evident increase in workability up to 27% was
observed as PET plastic percentage increased. The
350

GEOMATE – Kyoto, Japan, 3-5 November 2021

REFERENCES

Waste in Construction Industry, Int. Journal of
GEOMATE, Vol. 12, Issue 31, March 2017, pp.
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[7] Nikbin, I. M., Feasibility study of waste Poly
Ethylene Terephthalate (PET) particles as
aggregate replacement for acid erosion ofsustainable structural normal and lightweight
concrete. Journal of Cleaner Production, Vol.
126, 2016, pp. 108 - 117.
[8] Jo, B., Park, S., and Park, J., Mechanicalproperties of polymer concrete made withrecycled PET and recycled concrete aggregates.
Construction Building Materials, Vol. 22, 2008,
pp. 2281-2291.
[9] Borg, R., Early age performance and mechanicalcharacteristics of recycled PET fibre reinforced
concrete. Construction of Building Materials,
Vol. 108, 2016, pp. 29 - 47.
[10] American Concrete Institute, ACI manual of
concrete practice. Farmington Hills, MI:
American Concrete Institute, 2013.
[11] American Society for Testing and Materials
Annual, Annual Book of ASTM Standards.
Philadelphia, PA, USA, 2010.
[12] Uysal, M., and Akyuncu, V., Durability
performance of concrete incorporating class F
and class C fly ashes. Construction and Building
Materials, Vol. 34, 2012, pp. 170-178.
[13] Martin, L.H., Winnefeld, F., Tschopp, E., Müller,
C. J., and Lothenbach, B., Influence of fly ash on
the hydration of calcium sulfoaluminate cement.
Cement and Concrete Research, Vol. 95, 2017.

[1] Chandru, C., and A, D. C., Experimental Studyon Effect of Addition of Flyash on the Properties
of Plastic Fibers Reinforced Concrete. IJERTInternational Journal of Engineering Researchand, Vol. 4, Issue 5, 2015.
[2] Thomas, M., Optimizing fly ash content forsustainability, durability, and constructability.
Coventry University and The University of
Wisconsin Milwaukee Center for By-Products
Utilization, Second International Conference on
Sustainable
Construction Materials andTechnologies, 2010, pp 1-11.
[3] Arifi E, Zacoeb A, and Shigeishi M, “Effect of
fly ash on the strength of concrete made fromrecycled aggregate by pulsed power”, Int. J. of
GEOMATE, Vol. 7, No. 1 (Sl. No. 13), Japan,
Sept. 2014, pp. 1009-1016.
[4] Awal A.S.M., Ibrahim M.H.W., Ali A.Z.M.A.,
and Hossain, M.Z., Mechanical Properties andThermal Properties of Two-Stage ConcreteContaining Palm Oil Fuel Ash, Int. Journal of
GEOMATE, Vol. 12, Issue 32, 2017, pp. 166175.
[5] Adajar, M., Galupino, J., Frianeza, C., Aguilon,
J., Sy, J., and Tan, P., Compressive Strength andDurability of Concrete with Coconut Shell Ash
as Cement Replacement, Int. Journal of
GEOMATE, Vol. 18, Issue 70, 2020, pp. 183 190.
[6] Adajar, M., De Guzman, E., Ho, R., Palma Jr., C.,
and Sindico, D., Utilization of Aggregate Quarry351

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

GRINDING PROCESS INFLUENCES TO THE PHYSICAL AND
MORPHOLOGICAL CHARACTERISTICS OF ULTRAFINE TREATEDRICE HUSK ASH (UFTRHA) AS REACTIVE MINERAL ADDITIVECONSTRUCTION MATERIAL
Siti Asmahani Saad1, Aida Nabila Jamaluddin1, Siti Aliyyah Masjuki1, Salmia Beddu3 and Nasir Shafq2
1

Faculty of Engineering, International Islamic University Malaysia, Malaysia; 2Faculty of Engineering,
University of Technology PETRONAS, Malaysia; 3College of Engineering, Universiti Tenaga Nasional,
Malaysia
ABSTRACT
It is known that rice husk ash (RHA) contains high amount of silica that is beneficial in concrete strength
development. Despite of numerous studies on conventional RHA were done since 1970’s, usage of conventional
RHA is still unfavourable in current concrete industry. This paper aims to extend the investigation of producing
highly reactive additive material in ultrafine size, with particle diameter of less than 5μm through mechanicalactivation using planetary ball mill. In light of the mechanical activation process, most of studies were done to test
materials such as metakaolin and fly ash. However, study on the mechanical activation effect via planetary ballmill to conventional RHA or treated RHA are limited. In this research, raw rice husk (RH) was treated using heated
low concentration of acid, i.e. 0.01 - 0.1M hydrochloric acid (HCl). Then, the treated RH was combusted at 600 800oC. The treated RHA was ground using planetary ball mill for 15 - 60 minutes. In terms of material average
particle size, it was also observed that, burning temperature of 600 oC, treated at four hours, ground at 300rpm for
15 minutes had finest size of ultrafine treated RHA (UFTRHA) with value of 4.012µm. Largest specific surface
area value was obtained at 222.9125 m2/g with the similar treatment condition. In terms of the UFTRHA
morphology, particle agglomeration was observed for samples with grinding duration more than 15 minutes.
Therefore, grinding parameters involved in the process are proven to affect the physical and morphologicalattributes of UFTRHA produced.
Keywords: Ultrafine Treated Rice Husk Ash (UFTRHA), Mechanical Activation Process, Morphology of UFTRHA,
Particle Size Distribution, Specific Surface.
INTRODUCTION

production promotes environmental and economic
relief. It has greater reaction with Ca(OH)2 in
pozzolanic reaction, contributing towards increment
in compressive strength value and durability of highperformance concrete (HPC) and ultra-highperformance concrete (UHPC) instead of fume andfly ash [2].
This paper is focused on the mechanical activation
process to produce ultrafine size (<5μm) of RHA inorder to achieve the maximum reaction of RHA as
additive material in concrete production.

According to International Energy Agency(2015), steel and cement industry are responsible for
8% and 15% of total global energy consumption andtotal global anthropogenic carbon dioxide expose in2012. As a consequence, the emissions hike fromthese sectors occurred rapidly between the 20 yearsof duration [1].
In this regard, most of researchers are currently
working on the utilization of agricultural wastes as
pozzolana to replace cement in concrete production.
Pozzolana has cementitious property act as a binderand able to react with calcium hydroxide (Ca(OH)2)
formed from the hydration process of cement.
In light of incorporating rice husk ash (RHA) as
an additive material into concrete production isregarded as one of them due to high silica content.
Production of RHA is done via burning process of therice husk from agricultural waste.
The mineral additive materials which possess
pozzolanic behavior are favorable to be added into
cement mixture due to its properties that promotes theconcrete strength development. The usage of RHA in
concrete is favorable in terms of material cost
reduction. Thus, incorporation of RHA in concrete

EXPERIMENTAL METHOD
In this study, the raw rice husk (RH) is acquired
from a single local rice milling plant (BERNAS) in
Sungai Ranggam, Perak. Then, the raw RH undergo
several processes to produce ultrafine size of treatedrice husk ash (UFTRHA) which are thermochemical
pretreatment process, incineration, and mechanicalactivation process.
However, this study emphasizes the effect ofusing a planetary ball mill in the mechanicalactivation process as an important process to produce
high quality and performance of UFTRHA.

352

GEOMATE – Kyoto, Japan, 3-5 November 2021

Thermochemical Pretreatment Processactivated by high energy planetary ball mill for 15
minutes at 300 rpm and ball-to-powder ratio (BPR) of
15:1 successively.

The thermochemical process includes treatmentusing diluted hydrochloric acid (HCl) and heating
element at 0.01M, 0.05M and 0.1M. The soaking
temperature are varied from 30 – 80oC for 1 – 5 hours.
There is a significant difference between metallic
impurities removal activities using HCl and distilledwater (0M, denoted by DW) treatment was observed
e.g. an increment of 400% potassium concentration
removal using 0.1M HCl than DW treatment at 80oC
for 4 hours. This observation indicates that treatment
process using acid is superior compared to the DW
treatment process.

RESULT AND DISCUSSION
Mechanical Activation Process Influence to the
Physical Characteristics of UFTRHA
The laser particle size analysis was employed inorder to find the effect of grinding on the particle size
distribution (PSD) of both NTRHA and UFTRHAFor PSD analysis, the most important informationis D50 as this value give an indication of average
particle size of the tested material in powder form. At
Table 1, the observed trend of average particle size
pattern for UFTRHA-600℃ at milling speed of
300rpm was contradicting to the other two speeds.

Incineration process
For the incineration process, the raw RH treated at
80oC using 0.1M HCl was selected for incineration
process. There are three different combustion
temperatures used which are 600oC, 700oC and 800oC
from 1 up to 12 hours by using the laboratory furnace.
Variation of burning period was introduced toobserve the impact of prolonged combustion to SiO 2
content in both of the samples i.e., UFTRHA and nontreated rice husk ash (NTRHA).
From this process, it was evident that the sodium
dioxide (SiO2) content for UFTRHA were consistentthan that of the NTRHA despite of extended burning
period up to 12 hours. Overall, the UFTRHA
produced at 600oC for 1 hour burning period hadyielded a maximum content of SiO2 which is about
98.6%. Meanwhile, only the NTRHA sample
prepared at 600oC had only 70.1% of SiO2 content at
the same burning duration period.
This is again due to significant drop in thepotassium oxide (K2O) content for UFTRHA is
anticipated that make SiO2 much less susceptible toearly crystallization during strong and prolonged
combustions.

Table 1 PSD details of optimum UFTRHA-600℃
ground with BPR value of 15:1 at various speed andduration.
Grinding
speed
(rpm)
100

200

300

Grinding
duration
(Min)
15
30
45
60
15
30
45
60
15
30
45
60

Particle size (µm)
D10
D50
D90
3.251
1.444
1.514
1.483
1.603
1.285
1.250
1.323
1.325
0.598
1.393
1.295
1.460

15.973
5.383
5.300
5.289
5.044
5.071
5.074
5.044
5.027
4.012
4.380
4.473
4.676

53.97318.46915.95917.57414.07420.79121.97517.881
16.61815.73514.14415.25414.768Mechanical Activation Process
However, the UFTRHA-600℃ at 300rpm forabout 15 minutes has the lowest D50 value. The highspeed of the grinding process has a greater velocity ofthe ball movements in the milling bowl. Therefore,shearing impact between grinding media (balls) andfeed stock (UFTRHA) occurs vigorously in thesystem hence the smaller particle size obtained withinshort period of time. This is an evident that long
milling duration at high speed lead towards particle
agglomeration through excessive grinding process[3].
Compared to UFTRHA-600oC powder sample,
NTRHA-600oC ground at 0rpm to 300rpm possesseda relatively larger D50 values at grinding duration of
15 minutes compared to UFTRHA-600oC samples asshown in Table 2. The specific PSD of UFTRHA able
to be achieved by either minimizing the grinding

Mechanical grinding is known as one of feasible
method to be used in order to reduce average particle
size and eventually increase the specific surface area
(SSA) of a particle in powder form. Particle size
reduction and large SSA value of UFTRHA is vitalfor excellent pozzolanic reaction and hence greater
performance of the material as superior additive
material in concrete application.
This mechanical activation mechanism isattainable using planetary ball mill. In this research,
the UFTRHA-600℃ was ground at different grindingtime i.e, 15, 30, 45 and 60 minutes using grinding
speeds of 100, 200 and 300 rpm in order to identify
the effect of the physical and morphological attributesof UFTRHA produced.
Overall, the UFTRHA was then mechanically353

GEOMATE – Kyoto, Japan, 3-5 November 2021

speed and escalate the grinding duration or vice versa
as reported elsewhere [4].
In addition, the UFTRHA-600℃ grain yielded
was having smaller length than that of the NTRHA600℃ ash. The metallic impurities presence on thesurface of the NTRHA that caused difficulties ofremaining carbon content in the NTRHA to be
removed out during combustion process, hence define
the colour difference. The trapped carbon content
easily dissolved and remained in such ternary of Si–
Na–O as well as Si–K–O oxides that melted on thesurface of SiO2 during the burning procedure [5].

This phenomenon was due to higher unburnt
carbon content in NTRHA that increased rigidity to
the NTRHA structure, making it a harder material tobe milled. However, based on BJH evaluation on porecharacteristics in Table 4, all UFTRHA samples
classified as mesoporous structured material since theaverage pore diameter recorded were less than 9 nm.
Mesoporous is defined as porous materials withpore size between 2 to 50nm. Usually, distribution ofpore size for the mesoporous structured materials had
a narrow range values i.e. between 2 to 22 nm withthe average pore diameter value from 2 to 9 nm [6],[7].

Table 2 PSD details of optimum NTRHA-600℃
ground with BPR value of 15:1 at various speed andduration.
Grinding
speed
(rpm)
100
200
300

Table 4 Pore features of NTRHA and UFTRHA
prepared with various combustion temperature.

Particle size (μm)
D10

D50

D90

3.213
1.115
1.442
1.339

28.002
7.807
5.951
5.158

194.8226.17
24.47118.704

Samples

NTRHA600
NTRHA700
NTRHA800
UFTRHA600
UFTRHA700
UFTRHA800

Effect of Mechanical Activation Process on
Specific Surface Area (SSA) of UFTRHA andNTRHA
The specific surface area (SSA) and porecharacteristics of the porous structure of NTRHA andUFTRHA were assessed using the Brunauer Emmet
Teller (BET) and Barret Joyner Halenda (BJH)
techniques. The NTRHA and UFTRHA preparedwith various combustion temperature at optimum
grinding parameters (grinding at 300rpm speed with
BPR 15:1 for 15 minutes).
From Table 3, it was evident that UFTRHA-600
possessed remarkably highest SSA value (222.9125
m2/g) among all the samples due to the smallest
particle size of this sample (D50 = 4.012µm). There isa huge difference of SSA values were recorded forboth NTRHA and UFTRHA.

NTRHA-600
NTRHA-700
NTRHA-800
UFTRHA-600
UFTRHA-700
UFTRHA-800

3.91100.020130.540334.26700.145612.5530

25.5360

0.107522.8062167.6350

0.24235.5593151.2060

0.23306.2720154.5550

0.23516.2203Overall, the mesoporous UFTRHA-600 with an
optimum grinding pramaters was identified to havethe highest SSA, pore surface area, pore volume andlowest average pore diameter values.
Effect of Mechanical Activation Process on
Morphology of UFTRHA and NTRHA

Table 3 Specific Surface Area (BET-SSA) of
NTRHA and UFTRHA prepared with various
combustion temperature.
Samples

Pore features (BJH)
Adsorption
Adsorption Adsorption
cumulative
cumulative cumulative
average
pore
pore
pore
surfacevolumediameter
2
3
area (m /g)(cm /g)(nm)

Based on the field emission scanning electron
microscopy (FESEM) image of NTRHA ash as
illustrated in Fig. 1, it had a corrugated, rough surface,rigid structure with cone-shaped protuberances. The
outer surface of the RHA is dense and look like netstrip with arranged conical protrusion. Moreover,high content of unburnt carbon contributed towards
rigidity of the NTRHA ash as well.
On the other hand, unground UFTRHA FESEM
image in Fig. 2 gave an indication of soft structure ofSpecific surface area
(BET-SSA) (m2/g)
4.829140.3132
28.1305
222.9125205.1350
219.5759
354

GEOMATE – Kyoto, Japan, 3-5 November 2021

the obtained ash. It was evident that the structure wasalready broke into small pieces even though nogrinding aid had been introduced to the ash powder.
The pore was already exposed, filled by agglomerated
finely particles of UFTRHA even with no mechanicalgrinding imposed to the powder sample. This is dueto the acid treatment process that successfully breaks
the rigid structure of lignicellulosic material [8].

shape and the material arrangement form isundescribed [9].

Figure 3
FESEM images UFTRHA-600 groundfor 15 minutes, with grinding speed of 300 rpm andBPR value of 15:1

Figure 1
NTRHA-600 FESEM image captured
100X magnification

Figure 4
FESEM images of UFTRHA-600
ground for 30 minutes, with grinding speed of 300
rpm and BPR value of 15:1

Figure 2
UFTRHA-600 FESEM image captured
100X magnification
In order to quantitatively analyze the effect ofgrinding duration to the morphology of UFTRHA,FESEM technique was used at high magnification of
1000X. The morphologies of the UFTRHA particles
with respect to grinding duration variation are shownin the Fig. 3 to 5.
From the captured FESEM images, it can be seenthat the powders have agglomerated particles with anirregular shape. Agglomeration of particles occurred
due to excessive grinding aid. Therefore, extended
duration of grinding process i.e. more than 15 minutes
should be avoided.
Based on the explanation elaborated by Tanaka etal. (1999), amorphous is the state such a condition‘chaos just start to settle’. The word is derived fromthe Greek language which means ‘without a definite
shape’. According to this description it can be said
that the amorphous material does not possess a fixed

Figure 5
FESEM images of UFTRHA-600
ground for 45 minutes, with grinding speed of 300
rpm and BPR value of 15:1

CONCLUSIONSIn a nutshell, the mechanical activation process isproven to be an effective process to produce a highly
reactive of UFTRHA. Based on the analysis
conducted, the mechanical activation process reduce355

GEOMATE – Kyoto, Japan, 3-5 November 2021

cement paste,” Constr. Build. Mater., vol. 89,
pp.
90–101,2015,
doi:
10.1016/j.conbuildmat.2015.04.030.
[4]
H. K. Venkatanarayanan and P. R. Rangaraju,“Material Characterization Studies on Lowand High-Carbon Rice Husk Ash and Their
Performance in Portland Cement Mixtures,”Adv. Civ. Eng. Mater., vol. 2, no. 1, p.
20120056,
2013,
doi:
10.1520/acem20120056.
[5]
J. Umeda and K. Kondoh, “High-purificationof amorphous silica originated from ricehusks by combination of polysaccharidehydrolysis and metallic impurities removal,”Ind. Crops Prod., vol. 32, no. 3, pp. 539–544,
2010, doi: 10.1016/j.indcrop.2010.07.002.
[6]
Z. A. Alothman, “A review: Fundamental
aspects of silicate mesoporous materials,”
Materials (Basel)., vol. 5, no. 12, pp. 2874–
2902, 2012, doi: 10.3390/ma5122874.
[7]
W. Wang, J. C. Martin, X. Fan, A. Han, Z.
Luo, and L. Sun, “Silica nanoparticles andframeworks from rice husk biomass,” ACS
Appl. Mater. Interfaces, vol. 4, no. 2, pp.
977–981, 2012, doi: 10.1021/am201619u.
[8]
H. V. Lee, S. B. A. Hamid, and S. K. Zain,“Conversion of lignocellulosic biomass tonanocellulose: Structure and chemical
process,” Sci. World J., vol. 2014, 2014, doi:
10.1155/2014/631013.
[9]
K. Tanaka, E. Maruyama, T. Shimada andH. Okamoto, Amorphous Silicon. Wiley,1999.

the ultrafine sized particle (average particle size<5µm) with high BET-SSA value was obtained
through grinding speed of 300 rpm, Ball-to-powder(BPR) ratio of 15:1 at only 15 minutes grinding
duration. Extended grinding period caused particle
agglomeration and hence larger particle size andlower BET-SSA value yielded.

ACKNOWLEDGMENTS
The author would like to express highest gratitude
to the Ministry of Higher Education Malaysia(MoHE) under Fundamental Research Grant Scheme,
project ID FRGS19-171-0780.

REFERENCES
[1]

[2]

[3]

B. J. Van Ruijven, D. P. Van Vuuren, W.
Boskaljon, M. L. Neelis, D. Saygin, and M.
K.
Patel,“Long-term
model-based
projections of energy use and CO2 emissions
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11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

REVIEW ON CURRENT SITUATION OF GENERATION AND MANAGEMENT OF
COAL ASH IN VIETNAM
Tran Viet Cuong1, Ken Kawamoto2, Nguyen Hoang Giang3
1 Department of Civil Engineering, National University of Civil Engineering, Hanoi, Vietnam.
2 Saitama University, Japan.

ABSTRACTDue to rapid industrialization and economic growth, the generation of coal ash from coal-fired thermal power
plants is increasing in Vietnam. At present, the coal ash waste is mostly dumped at landfills and stored at stockyards, however, the capacity of landfilling and stock sites can be estimated to be insufficient in near future. In
Vietnam, there are two main residential coal-burning technologies used in thermal power plants as pulverized
combustion and circulating fluidizing bed (CFB) and 26 coal-fired power plants were generated 12.2 million tonsof coal ash waste as of 2017. The generation amount can be expected to increase to up to 30 million tons according
to the master plan for electricity development: 52 coal-fired power plants will be in operation by 2030. Therefore,
the coal ash will be increased from 109 million tons in 2020 to 248 million tons in 2025 and 422 million tons in
2030. The coal ash impact on the environment problems while only 31% (~ 5 million tons) of the total is currently
treated and recycled. Based on the survey results on legal framework and regulations, present treatment
technologies, and management practice of coal ash, the major difficulties were summarized: i) there is no technicalstandards for handling, reuse and recycle of coal ash, ii) the thermal plants do not possess sufficient storage
capacity of coal ash (especially, for small scale- and old plants that faces the risk of closure, iii) lack ofcollaborative system and framework among enterprises, government (central and local), and research institutes(including universities) to initiate actions for promoting an effective reuse and recycling of coal ash in Vietnam.
Keywords: Coal ash, coal-fired power plant, reuse and recycling, Vietnam.

INTRODUCTION

soil improvement. In Vietnam, at present, the most ofcoal-fired power plants even have measures to reuse
and treatment coal ash yet. Only a little quantity ofcoal ash meets the input standards for use as cement,
concrete and unburn construction materials whilemost of the rest is discharged directly to the dumping
site. At the same time, the dust is arising duringstorage and transportation from coal-fired power
plants to the dumping site by truck [3]. The trucks
have not cover full truck crates and factories still notcompleted the transportation system, storage areas forash have not met the requirements of environmental
protection, polluting the environment around theplant [4]. Besides, many enterprises and localities
need to use large amounts of coal ash to makeconstruction materials and levelling but there are nostandards and technical regulations that cannot be
implemented [5].

In 1993, less than half of Vietnamese households
had use to electricity. By the end of 2015, more than
98% have been use it [1]. With the rapid growth of
economic is parallel to the development of electricity
has provided immense benefits to the economy andsociety. Besides these benefits came with effectenvironmental in recent year. According to therevised Seventh Power Development Plan of Vietnam,
in 2017, 26 commercially operated thermal power
plants generated 12.2 million tons of coal ash and is
expected to increase to 30 million tons with 52 coalfired power plants coming into operation by 2030 andthe scale of coal in the structure of power resources
for electricity production will gradually increase from
49.3% in 2020 to 56.4% in 2030. Most of this waste
is disposed into the environment without any
treatment and dumped in landfill sites even theircapacity is overload [2].
The developed countries in the world always
encourage the use of coal ash from coal-fired power
plants if they have the standards used in construction
materials. For instance, according to JCOAL, inJapan, the annual generation of coal ash is 12.35
million tons and effective use and recycling areachieving around 98% including 64 % of coal ash isused in cement and concrete field, and 14% of coal
ash is used in the field of civil engineering including

COAL ASH FROM COAL-FIRED POWER
PLANTS IN VIETNAM
Electricity from coal-fired power plants
accounts for nearly 53.2% in total electricity
production, consumed about 129 million tons ofcoal in 2030 [6]. Figure 1 show the map of coalfired power plants in Viet Nam. Most focus along
the beach and the economic centre in Vietnam.

357

GEOMATE – Kyoto, Japan, 3-5 November 2021

Coal combustion technology
Currently, coal-fired thermal power plants in
Vietnam use one of two types of burningtechnologies: pulverized combustion (PC) andcirculating fluidizing bed (CFB).
PC technology
The coal is finely ground on crushing machineand dried to a particle size of less than 0.09mm
then move to the storage tanks. The temperaturein the furnace is about 1400 oC- 1600oC for the
purpose of heating to create the pressure feed thepower electricity. Burning coal leaves coal ash,part of the molten superheat turns into slag fallingto the bottom of the furnace, cooled by water,most of the fine coal ash follows the flue wind to
filter electric dust to retain, clean gas discharged
into the environment [8].
CFB technology
This technology is used when the coal is low
quality and high in sulphur. Coal and limestone
are process on hammer crushing machine to aparticle size is less than 10mm, stored in tanks has
feeding and dosing at the bottom. Coal andlimestone are fed into the circulating fluidized
bed furnace to generate heat and feed the water
into high-pressure steam to create electric. Fine
ash into electrostatic dust filter to collect fly ash,clean gas discharged into the environment
through the chimney. Large particles of coal ash
and limestone, whether or not reactive will fall tothe bottom of the kiln to form bottom ash. For
plants using CFB technology, gypsum always
mix with fly ash. Using CFB technology makes it
difficult to separate gypsum from fly ash, itdifficult to use as raw materials for construction
materials production [8].

Fig. 1. Map on coal-fired power plants [7]The process of burning coal to operate thermal
power plants generates combustion productcalled coal ash. Coal ash consists of bottom andfly ashes. Bottom ash is also called slag, is coarse,
large particles obtained at the bottom of thefurnace; Fly ash is fine fly ash particles collectedin dust filter. Normally, the amount of fly ash
accounts for about 80 - 90%, while the slag
accounts for only about 10-20%.
As of 2016, the total electricity capacity is 13,110
MW, the total amount of coal ash generated is about
15,784,357 tons [8]. The ratio of fly ash and bottom
ash is shown in Fig. 2.

Properties of coal ash at thermal power plants
in Vietnam
Properties of coal ash depend on the burningtechnology. Table 1 show the components of coal
ash in some thermal power plants in Vietnam [8].
As for the coal ash of PC technology, coal ash
composition of coal-fired power plants is similar;
the slag obtained at the bottom of kiln has a lossof ignition less than 8%, it is match with thestandard of TCVN 6882:2016 [9] for mineral
additives for cement. As for the coal ash of CFB
technology, coal ash from Cam Pha and Son
Dong can be classified as Class F according to
ASTM C618-19 [10]. Coal ash with high loss on
ignition (LOI), CaO, CaSO 4 content is the cause

Fig. 2. Rate of generation of coal ash in 2016 [8]358

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1. The components of coal ash in some thermal power plants.
No

Thermal power plans

1
2
3
4
5
6
7
8
9
10

Uong Bi 1, Uong bi 2
Pha Lai 1, Pha Lai 2
Quang Ninh 1, Quang Ninh 2
Hai Phong 1, Hai Phong2
Ninh Binh
Duyen Hai 1
Nghi Sơn 1
Vung Ang 1
Vinh Tan 2
FMS Dong Nai

1
2
3
4
5
6
7

Na Duong
Cao Ngan
Cam PhaSon Dong
Mao KheMong Duong 1
An Khanh

Capacity
SiO2 + Al2O3(MW)+ Fe2O3 (%)
PC Technology
735
85
4x110, 2x300
64.5-71.3
4x300
83-85.2
4x300
83-87.1
400
70
2x622
84
2x300
76.5
2x600
2x622
3x150
88.6
CFB Technology
2x55
58.7
2x57.5
45.61
2x340
78.0
2x110
85.6
2x220
84.3
2x540
72
2x58
83

of difficulty for their use in cement production as
well as construction materials.

SO3

CaO

LOI (%)

0.58
0.12-0.06
0.29
0.32
0.12-0.06
0.32<0.008<0.008<0.0080.63<0.0080.636-8
22.6-25
6-8
10-14
22
12
14-17
<6
12
<6

21.7
13.5
5.5
1.3
2.0
7.42
1.96

4-8
24-27
8-12
10-14
6-8
11.0
6.6

0.68
10.6
10.5
2.4
1.1
0.7
1.87
0.7

tons per year. However, after 7 years of operation,
the dumping ground has become nearly occupied
for other purposes; Ha Long Bay are sufferingfrom the 7 million tons of coal ash produced fromthe nearby coal-fired power plants, as shown in
Figure 3. [12].

Current situation of coal-fired some thermal
power plants
As the development of economic in recent
years, the share of coal in the structure of power
resources for electricity production will gradually
increase from 49.3% in 2020 to 56.4% in 2030,
according to the revised Seventh Power
Development Plan of Vietnam [11]. It makes alarge of coal ash exist at the dump site.
Table 2 show the total amount of coal ash exist
if not reuse in the future.
Table 2: Total amount of coal ash exist if not
reuse [8, 11].

No

Year

Total Coal Ash(Tons/year)1
2016
15,784,357
2
2018
20,612,500
3 2020*25,441,770
4 2025*29,371,100
5 2030*38,314,500*
Estimated values.

Total amount of
Coal Ash exist
if not reuse(Tons)
22,705,558
61,515,750
109,980,500
248,978,800
422,663,000

Fig. 3. A dumping site besides Ha Long Bay [12]Mong Duong 1 thermal power plants operate
at the end of 2015, is facing possible closure innear future due to overload of the coal ash
problems. The capacity of plant is 1,080MW
consumes about 3 million tons of coal per year
and releases 1 million tons of coal ash. At thepresent, only 40% of coal ash could sale forcement manufacturing and concrete mixing,
while fly ash meet difficulty to find buyer as wellas treatment [13]. The dumping site of the plant

In Quang Ninh, there are 7 coal-fired power
plants with total capacity of 4,150MW electricity(16% of the total electricity country). There are 2
more plants will operate in 2020 even through the
dumping ground for coal ash is currently
overloading. For instance, there are 2 plants in
Cam Pha with total capacity of 600MW andspend 34 ha to dumping coal ash with 1 million
359

GEOMATE – Kyoto, Japan, 3-5 November 2021

has a total volume of 2.25 million m 3 but it has
already spent 1.8 million m 3.
EFFECT
OF
COAL
ASH
ENVIRONMENTAL
POLLUTIONPOTENTIAL RISK TO HUMAN LIFE

causing air pollution that significantly impact
local people [1].

ONAND

The amount of coal ash remained currently is
big problems such as: spending large dumping
space, overloading the current dumping spaceleading to the closure of the plants, threatening toenvironment and people.
Monitoring by Ministry of Natural Resourceand Environment (MONRE) [14] shows that at
100% of coal mining and processing facilities, theconcentration of dust in the air is above theVietnamese standard from 30 to 300 times [15].
Electricity generation from coal is responsible for
producing airborne pollutants throughout its life
cycle from coal mining, processing, andtransportation to coal burning and coal ash
handling (see Figure 4). Study in Mao Khe coal
power plant shows concentrations of heavy
metals and toxic in Gao Stream exceed
Vietnamese standards. More than 75% of peopleinterviewed around the Duyen Hai Coal PowerCentre declared that they had to change their daily
activities due to low air quality condition such as
closing windows and sweeping the house more
often.

Fig. 4. Coal ash transportation [3]The Vietnamese standards on Ambient Air
Quality (QCVN05:2013/BTNMT) [16] set thenational standards for PM10 concentration at 50
μg/m3 (measured as an annual mean) and 150
μg/m3 (measured as a daily concentration).
Standard for PM2.5 is 25 μg/m 3 and 50 μg/m3,
respectively. The concentration of PM2.5 ishighest in the region where coal thermal power
plants are concentrated [17].
CURRENT SITUATION AND SOLUTION
OF RECYCLE AND MANAGEMENT OF
COAL ASH
Current situation of recycle of coal ash in theworld.
In the world, the total amount is estimated in2001 to about 480 million tonnes. In Europe(EU25) about 95 million tonnes of coal ash were
produced in 2003. In Europe, around 53% use inconstruction and underground mining, 36% areused for the restoration of open cast mines,
quarries and pits. Only 12% of the total CCPs
were temporarily stockpiled (8%) or had to bedisposed of in disposal grounds (4%) [18]. In
Japan, In 2016, thermal coal ash output of thecountry was about 12.35 million tons and its
useful amount was 12.35 million tons, equivalent
to 99.3%. Coal ash have been used in construction
as raw material for cement, road pavement
materials, ground-leveling materials or applied in
the field of agriculture, forestry and fishery [19].

Fig. 4. Impact of coal to air quality from its whole
life cycle [1]Coal ash is affecting air quality by its
transportation and storage. An example of poor
coal ash management severely impact air quality
is from Vinh Tan Coal Power Plant in Binh Thuan
Province. Vinh Tan produces 4,000 tons of coalash per day. Coal ash is transported to the dumpsite by trucks and piled up without any coverage(Figure 5). Dust is carried by strong winds

Current situation of recycle of coal ash in
Vietnam.
Decision No. 1696/QD-TTg dated September
23, 2014 of the Prime Minister on a number of360

GEOMATE – Kyoto, Japan, 3-5 November 2021

solutions to treat and use coal ash from thermal
power plants as raw materials for the productionof building materials [20]. However, according toactual survey data and reporting units, the total
amount of coal ash consumed is not large, only
about 25% - 30% (around 5 million tonnes) of thetotal amount discharged annually. There arefactories that have sold most of the coal ash
discharged, while there are factories that have todischarge all to the site.
Vietnam government launched a program touse coal combustion residuals (CCR), a waste
product from thermal power plants to makebuilding materials. The program demanded that atleast 25% of CCR must be recycled by 2015.
However, only 18% of CCR was recycled at theend of 2015 according to the survey of the
Building Materials Institute [21]. However, someof plants are doing well in recycling coal ash suchas Cao Ngan plant. Each year, Cao Ngan coalfired power plant discharges about 200,000
tons/year. Until the end of 2015, the Cao Ngan
factory delivered nearly 32,000 tons of fly ash forthe testing of cement production at the Quan
Trieu Cement Plant. The average monthly
amount was around 6500 to 7000 tons [2]. And,
they can save about 2 ha (20,000 m 2) dumping
area each year.
However, most of the coal-fired power plants
are currently cannot find a solution for solving thecoal ash problems [21]. In addition, some of newtechnologies such as the integrated gasification
combined cycle (IGCC) and pressurized fluidized
bed combustion (PFBC) will allow increases of
50% in efficiency rates in the future [22]. Ultraclean coal (UCC) technology with newprocessing solutions will reduce fly ash to below
0.25%, and sulfur to a very low level [23]production of building materials and inconstruction works must be aimed at protecting
the environment, and reducing both the use ofdump areas and mineral resources for building
materials, to ensure sustainable development.
Specifically, to make mineral additives for
cement production about 14 million tons;
replacing a part of clay to produce about 8 million
tons of cement clinker; replacing a part of clay to
produce baked clay bricks of about 7 million tons;
as a mineral additive for the production of
concrete and unburnt bricks, about 2 million tons;
building levelling materials for works,
reconstructing mines and making roads about 25
million tons.
Besides that, decision No. 452/QD-TTg
encourages and funds research and developmenton using of coal ash, boiler slag, and gypsum forproduction of building materials. Basically,
improve the treatment technologies and bringthese technologies into operation ahead of June
2019. By 2020, ensure that an area of each
dumping ground will not exceed the amountwastes released within two years of mean
production. This regulation shall be specificallyapplied such as: (1) active plants from which
proposals for treatment and use of ash, slag andgypsum produced have been submitted before
December 31, 2018; (2) projects that are underconstruction, require modified proposals andsubmission of the accepted proposals before
officially being put into operation; (3) projects
that are at the stage of pre-investment
arrangement,
proposalformulation
andsubmission of the request for approval of the
proposal and the investment project. State-funded
investment and construction projects must prefer
to use ash, slag and gypsum or building materials
made from ash, slag, FGD and PG gypsum
provided these materials or products have thesame economic and technical efficiency as otherones.
To promote the processing and consumptionof ash, slag, and plaster, on December 28, 2018,
the Ministry of Construction issued Document
No. 3314/BXD-VLXD [25] on guiding the use ofash and slag as raw materials in construction andin construction works. Then Vietnamese’s
government promulgate TCVN 12249:2018:
Coal ash of thermal power plant using as backfill
material - General requirements [26].
Most recently, On 22 February 2021, theMinistry of Industry and Trade of Vietnam(MOIT) released the draft proposal for thenational power development plan for the periodof 2021-2030, with a vision to 2045 (“Draft
PDP8”) for public comments. This proposal
focuses on some structural changes in the powerindustry especially coal power industry. In which,Acts, regulation, strategies and official
documents related to Coal ash in Vietnam
In recent year, Vietnamese government
provide many laws and decisions to reused andrecycled coal ash use in construction that shownin Table 3. The first law was published in 1993 on
environmental management and the newest in
2018 has directives on the reuse of coal ash inconstruction.
Most prominent, decision No. 452/QD-TTgof the Prime Minister [24], dated 12 th April 2017,
approved a project which accelerates thetreatment and reuse of ash slag and gypsum frompower plants and fertilizer chemical factories as
raw materials for construction. The goal of theVietnamese government is to recycle at least 60%
of ash released from power plants for building
materials by 2020. This decision also states thatthe recycling of coal ash as a material for the361

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 3. Acts, regulations, decisions related to treatment and recycled coal ash
YearType ofdocument

Name of document

Remarks

Law on Environmental
Protection

Environmental regulation shall consist in thereview of natural and human elements andfactors of the environment, observation andrecording of their alterations, as well as thesupervising of the causes of these alterations.

1993

Law

2005

Law

2007

Decree2009

QCVN

QCVN 07: 2009/BTNMTNational Technical Regulation on Hazardous
Waste Thresholds

2009

VietnameseStandard

Fly ash - Methods ofchemical analysis

Prescribes chemical analysis methods for fly ash

2014

Decision

Decision No.1696/QD-TTg

2014

VietnameseStandard

TCVN 10302:2014.
Activity admixture - Fly
ash for concrete, mortar
and cement

2017

Decision

Decision No.452/QD-TTg

2018

VietnameseStandard

2018

Decision

Law on Protection of theenvironment
No.59/2007/ND-CP.
Decree on solid waste
management

TCVN 12249:2018. Coalash of thermal power plantusing as backfill material
Decision No.3986/QĐBNN-XD

from 2020 to 2030, there will be no additional
development of new coal-fired thermal power.
The power capacity structure will be reducing
coal-fired thermal power from 34% in 2020 to
27% in 2030 and the remaining 17-18% in 2045.
The ratio of coal-fired thermal power will
gradually be decreased from 40% in 2030 toabout 30% in 2045 [27].

Update from Law 1993
Initial guideline for managing solid waste

Encouragement of recycling coal ash as building
material (guideline, incentive, international
cooperation)
To applies for mineral admixtures of fly ash(hereinafter referred to as fly ash) that have been
through dry or wet sorting treatment technology
for concrete, mortar and cement.
Encouragement of recycling coal ash as building
material (guideline, incentive, international
cooperation)
This standard applies to coal ash from coal-fired
thermal power and is used for leveling forconstruction and technical infrastructure.
Instructions for the use of fly ash in irrigation
concreteconstruction materials to replace raw materials is
one of the active solutions, bringing highefficiency to solve this issue.
Worldwide, renewable energy technologies
such as solar and wind power are mature,commercial and expanding much more rapidly
than fossil fuel generation technologies. Not yet
in Vietnam. Energy efficiency and renewableenergy sources should be given more attention, in
terms of technology development and economic
incentives.
- The government should step up its interministerial efforts to organize the socioeconomic
readiness conditions for a large-scale expansionof renewable energy.
- The development of renewable energy andhigher efficient use of energy should be paid more
attention by both direct and indirect subsidies andsupports.
Basically, these mechanisms are necessary inthe short run, especially when the fullenvironmental costs of coal are not yet fullyinternalized.

CONCLUSIONSThe Vietnam electricity has grown rapidly,reflecting the country’s economic development. It
is supposed to increase two to three times over the
next two decades. The electricity peak requires
growing an average of 12% annually. Currently,
Vietnam’s power industry is to be more heavily
weighted toward coal-fired thermal plants as
indigenous hydro resources become more limited.
But the coal-fired power generation isaccompanied by environmental protection
challenges that are primarily caused by emissions
and ash. Government needs to strongly promotes
and solutions as well as supporting policies forrecycling businesses of coal ash. At present, theuse of coal ash from coal-fired power plants as

ACKNOWLEDGMENTS
362

GEOMATE – Kyoto, Japan, 3-5 November 2021

This research was supported by JST–JICA
Science and Technology Research Partnership for
Sustainable Development Program (SATREPS)
project (No. JPMJSA1701).

Available at https://www.vir.com.vn/16-billionthermal-power-plant-faces-possible-closureover-ash-problems-51889.html[14] MONRE. (2014) National Environmental Report2013 - Air Quality.
[15] Green ID. (2015) Survey on some coal fired
power plant in Vietnam.
[16] QCVN05:2013/BTNMT (2013). National
Technical Regulation on Ambient Air
Quality.
[17] Koplitz, Shannon. (2015). “Burden of Disease
from Rising Coal Emission in Asia.” presented
at the International Symposium on Climatechange and Coal, Tokyo, Japan, May 29.
Availableat:
http://acmg.seas.harvard.edu/presentations/201
5/koplitz_japan_symposium_20150529.pdf.
[18] Report EN-10. Residues from combustion of coalfor energy production[19] The 2nd Japan – Viet Nam Coal Energy Working
Group Conference, held in Hanoi in two days of
8 – 9 November 2018, by Viet Nam Electricity(EVN) and Japan Coal Energy Center (JCOAL).
https://en.evn.com.vn/d6/news/Japan-efficientlyreuses-over-99-thermal-coal-ash-6-121186.aspx[20] Decision No.1696/QD/TTg (2014). Taking
measures to treat ash, slag and gypsum fromthermal power, chemical or fertilizer plants forthe production of building materials.
[21] Hong, H. T. V and Ji, W. A. (2018). Overview ofcoal-fired power plant ash situation and cement
industry in Vietnam. Journal of Energy
Engineering, Vol. 27, No. 3, pp.57~62.
[22] Global CCS Institute. (2017) Clean Coal
Technologies,
CarbonCaptureandSequestration.
Available
online:http://www.world-nuclear.org/informationlibrary/energy-and-theenvironment/clean-coaltechnologies.aspx.
[23] Rubiera, F., Arenillas, A., Arias, B., Pis, J.J.,
Suárez-Ruiz, I., Steel, K.M., Patrick, J.W.
(2003). Combustion behaviour of ultra clean
coal obtained by chemical demineralization.
Fuel 2003, 82, 2145–2151.
[24]
Decision
No.452/QD-TTg.
(2017)
Approving the proposal to boost treatment
and use of ash, slag and gypsum discharged
from thermal power plants, chemical andfertilizer plants for production of building
materials and for use in construction
projects.
[25] No. 3314/BXD-VLXD. (2018). Guidelines on
the use of ash and slag as raw materials inconstruction and in construction works.
[26] TCVN 12249:2018. (2018). Coal ash ofthermal power plant using as backfill
material - General requirements.

REFERENCES[1] Minh, H. D., An, H. T., And Hoang, A. T. N.
(2016) Synthesis Report on Socioenvironmental Impacts of Coal and Coalfired Power Plants in Vietnam.
[2] Thriveni, T., Nguyen. T. M. N., Lai, Q. T., Trinh,
H. S., Ho, H. H., Dang, T. N. T., Nguyen, T. T.
T., Duong, T. T. T., Doan, T. N. H., Tran, T. V.,
Ramakrishna, C., and Ji, W. A. (2018)
Technological Solutions for Recycling Ash Slagfrom the Cao Ngan Coal Power Plant in
Vietnam.
[3] Le Truong (2015). “Live Together with Coal AshandDust.”
Nld.com.vn.
April
6.
http://nld.com.vn/news20150406215144374.htm[4] Hoang Linh (2016). “Vinh Tan 2 Coal Power
Plant Cause Pollution in Binh Thuan Province.”
Laodong.com.vn. January 26.
http://laodong.com.vn/chung-toi-len-tieng/nhietdienvinh-tan-2-treu-nguoi-tinh-binh-thuan511896.bld.
[5] Management solutions, using ash, coal slag in ourcountry's thermal power system (2017), availableat:
http://hoivlxdvn.org.vn/news/giai-phapquan-ly-sudung-tro-xi-than-trong-he-thongnha-may-nhiet-dien-nuoc-ta-40902.html[6] Decision No. 428/QĐ-TTg (2016). The approvalof revisions to the national power development
plan from 2011 to 2020 with visions extended to2030.
[7] Report on Graphic by Greening the Grid (2018).
WRI Global Power Plant Database.
[8] Le Van Quang (2019). Report on analysis
technology. Trends of application of coal ash
in the production of building materials.
[9] TCVN 6882:2001. Mineral additive forcement.
[10] ASTM C618-19. Standard Specification for
Coal Fly Ash and Raw or Calcined Natural
Pozzolan for Use in Concrete.
[11] Jongyeol, L., Taeyoon, K., Mina, S., Hong, H. T.
Vu., Kyung, N. S., and Ji, W. A. (2020) An
Integrative
ApproachtoInternational
Technology Transfer for Recycling Vietnam Coal
Ash with Consideration of the Technological,
Legal, and Network Perspectives.
[12] Ash & slag from thermal power plants threatens
Ha Long Bay (2017), available at:
http://english.vietnamnet.vn/fms/environment/18
7712/ash---slag-from-thermal-power-plantsthreatensha-long-bay.html.
[13] Trang, V. (2017) $1.6-billion thermal power
plant faces possible closure over ash problems.
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[27] Draft PDP8. “Key highlights of new draft ofnational
power
developmentplan”.
https://www.globalcompliancenews.com/202
1/03/13/vietnam-key-highlights-of-newdraft-of-national-power-development-plandraft-pdp8-04032021-2/[28] Law on Environmental Protection (1993,2005).
[29] Decree No.59/2007/ND-CP (2007). Decree on
solid waste management.

[30] TCVN 8262:2009. Fly ash - Methods ofchemical analysis
Prescribes
chemicalanalysis methods for fly ash.
[31] TCVN 10302:2014. Activity admixture - Fly ash
for concrete, mortar and cement.
[32] Decisions No.3986/QĐ-BNN-XD (2018).
Instructions for the use of fly ash in irrigation
concrete.

364

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

POROSITY AND PERMEABILITY OF PERVIOUS CONCRETE USINGCONSTRUCTION AND DEMOLITION WASTE
Kim Tuan NGO1, Quang Minh PHAN1, Hoang Giang NGUYEN1, Tien Dung NGUYEN1
Ken KAWAMOTO1,2
1
National University of Civil Engineering, Vietnam;
2
Graduate School of Science and Engineering, Saitama University, Japan
ABSTRACTThe use of recycled aggregates (RA) from construction and demolition wastes to produce non-structuralconcrete is considered as a sustainable solution with dual environmental benefits. This paper aims to expand thepotential use of RA to manufacture pervious concrete for permeable pavement systems. The ratio of concrete
aggregates and clay brick aggregates, design porosity, and the mix design are chosen as the mix variables andevaluated their contributions in pervious concrete block. Porosity characteristics, hydraulic conductivity andmechanical properties of the pervious concrete were investigated in this study. The relationship between total andeffective porosities and water permeability is also performed. Experimental results show that the mix using blended
aggregate has lower mechanical properties than that containing concrete aggregate, while the clay brick aggregate
leads to the lowest of compressive and flexural strength. However, the composition of the aggregate mixture has
a negligible influence on the effective porosity and permeability of pervious concrete. The effect of the clay brickcontent on total porosity is significant. Increasing clay brick content in blended aggregate increases the total
porosity of the pervious concrete and increases the water permeability coefficient. A relationship between
permeability and effective pore characteristics over the total porosity and mechanical strength of pervious concrete
was drawn.
Keywords: Pervious concrete, recycled aggregate, porosity, water permeability.
INTRODUCTION

connected pore structure inside [6]. Further, pervious
concrete is an environmentally friendly material that
improves skid resistance, sound absorption, andreduces the ‘‘heat island effect” [7].
The experimental investigation shows that thehydraulic conductivity of any porous material isinherently dependent upon the pore features such as
pore size, specific surface area of pores, porosity andthe tortuous flow path [8]. However, theinterconnected pore system (the main factor
determining the drainage capacity) is most affectedby the type of aggregate, not the size of the aggregate[9]. Pervious concrete using recycled aggregates withmany angles often has porosity larger than the design
porosity and higher than when using normal
aggregates (from 2-3%) [10]. The pore system isenlarged and the water permeability of porous
concrete using recycled aggregate is increased due tothe thinner cement paste covering the aggregate
particles. This phenomenon is due to the higher
specific surface area of recycled aggregate thannatural aggregate [11][12]. Another study showedthat recycled concrete did not affect to porosity but
reduced density and increased water permeability[13]. The water permeability of pervious concrete
achieved range from 0.1 to 3.3 cm/s, it is perfectly
suitable for making drainage water purposes [10].
The author [14] also obtained permeability
coefficients up to 55 and 80 mm/s when using
recycled aggregates to make concrete with large
porosity. However, the compressive strength is only
3.5 MPa. In some cases, although the porosity is

Population growth, infrastructure, and housing
construction activities generate large amounts ofconstruction and demolition waste (CDW), and as aresult, dire demand for recycling construction waste.
Construction waste created during all phases of abuilding's life cycle: construction, renovation anddemolition [1]. In many countries, the proportion ofrecycled aggregate used is very small, while the land
for landfill becomes scarce and the world demand for
aggregate reaches a massive 40 billion tons per year.
Therefore, more solutions to this problem are needed.
[2]. Japan and other developed countries are leading
the way in recycling CDW, achieving 98% recycling,
and the recycled CDW is used in many applications
thanks to high taxes on natural aggregate anddevelopment techniques [3].
Recycled aggregate is commonly used as bulk
backfill, in sub-base, base or surface material in road
construction, sidewalk and hydraulically boundmaterials and in the manufacture of new concrete
structure, fresh concrete [2][4]. Due to the weakmechanical characteristics and the high variability inquality, the use of recycled aggregates should be
carefully considered in terms of applicability inproportion to the mechanical strength achieved.
Pervious concrete is a potential application ofrecycled materials. It has a porosity and median pore
diameter in the range of 0.15 – 0.3 and 2–4 mm,
respectively [5]. Pervious concrete displays better
water permeability characteristics due to its
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GEOMATE – Kyoto, Japan, 3-5 November 2021

equivalent to using natural aggregates, the drainage
capacity is still increased [12]. Remarkable results
when using 50% fine glass waste (2.36 – 5 mm) and
50% coarse aggregate from concrete, pervious
concrete can be obtained that can meet both strengthand drainage according to Japanese standards JIS A
5371 [15] for pedestrian road [16].
The available research results also have different
views and results on the influence of recycled
aggregates on the porosity and drainage
characteristics. Further, the study of the influence ofclay brick content in the recycled aggregate mix on
the porosity and permeability properties as well as thestrength of permeability concrete has not been
mentioned by many studies. In this paper, therelationship between porosity, permeability, andstrength of pervious concrete with different
proportions of aggregate mixture of clay brickrecycled concrete will be clarified.

workability [17]. The coarse aggregate size No. 8(ASTM C33/C33M) was used in this study. The
water-cementitious material ratio (w/cm) is an
important consideration for obtaining desired
strength and void structure in pervious concrete.
Table 1 Material properties of recycled aggregate
from concrete and clay brick demolition waste

MATERIALS AND METHODS
Materials
There are three main materials in the fabricationof pervious concrete: cement, coarse aggregates, andwater. Ordinary Portland cement with a specificgravity and specific surface area of 3.1 g/cm3 and3300 cm2/g, respectively, was used for thisinvestigation. The 28-day compressive strength ofcement is 47 MPa. Two types of single-size coarse
aggregates with 5–10 mm diameter were used in thisstudy. The first was a recycled aggregate fromcrushed structural concrete (RC), and the last was a
recycled aggregate from crushed broken clay brick(RB), two types obtained from a local construction
site. No treatment was done on the RA to minimize
the production cost of pervious concrete. Figure 1
shows the coarse aggregates used in this study andtheir properties are detailed in Table 1. The dryrodded density of RC and RB were lower than that ofNA (about 1550 kg/m3) while the water absorptions
were higher as expected.

Properties

Recycled
concrete(RC)

Recycled
brick (RB)Particle size (mm)Specific gravity(g/cm3)
Bulk density(kg/m3)24h water
absorption (%)
Los Angeles
abrasion (%)

5.0 – 10.0
2.60

5.0 – 10.0
2.55

1620

1280

5.5

14.6

38

46

Experience has shown that w/cm in the range of 0.26
to 0.45 will provide the best aggregate coating andpaste stability. The w/cm was 0.33 in all mixture. To
ensure that water will percolate through pervious
concrete, the void content, both in the design of the
mixture and measured as the percent air by ASTM
C138/C138M (the gravimetric method) should be
15% or greater. The design porosity was in the rangeof 15%, 20% and 25% in this research. Base on void
content and lightly compacted was used, thepercentage of paste content was determined. Figure 2
shows the mix proportions of pervious concrete.
Testing methods
The compressive and flexural strengths ofpervious concrete were determined at the age of 28days. The porosity and water permeability coefficientof pervious concrete were tested using rectangularspecimens of 210 x 100 x 60 mm, the reported values
were the average of three specimens.
The pervious concrete porosity was calculated bytaking the difference in weight of oven dry andsaturated, submerged under water [18].
The total porosity, ɸT, can be determined by two

Fig. 1 Coarse recycled aggregate

methods. Assuming no isolated pores in the sample,
the total porosity (%) was calculated using Eq.(1):T =(1 −(a) RC aggregate(b) RB aggregate

d) *100 (%)
Gs *  w(1)

If according to a water displacement method [18][19], the T can be calculated using Eq.(2):Mix proportion
The process of design mixture proportions for
pervious concrete according to the ACI 522R - 10. In
general, the overarching philosophy of mixture
proportioning for pervious concrete is to achievebalance between voids, strength, paste content, andM d − M sub) *100 (%)(2)
VT *  w
The effective porosity eff represents the openT =(1 −366

GEOMATE – Kyoto, Japan, 3-5 November 2021

(connected) pores of the sample [20] [21]; and can be
determined by using Eq.(3):M suf − M sub eff =(1 −M sub is the weight of specimen under water (g),M suf is saturated surface dried weight of the(3)

specimen (g), M d is weight of dry sample;  d is dry

If it is assumed that the tested sample has a bimodal
pore system consisting of inter-aggregate (outeraggregate) and intra-aggregate pores [22] andabsorbed water in the water adsorption test fills fully
in intra-aggregate pores of aggregates and binders,the porosity of intra-aggregate pore 
can bedensity (g/cm3), and VT volume of specimen (cm3),

VT *  w) * 100 (%)

G s is the specific gravity, and  w is the density ofwater
Hydraulic conductivity is calculated by the following
equation, and et-up as shown in Fig. 2 (JIS A 53712016 of Japanese standard) [15].

int racalculated using measured water absorption (%):

int ra =(M suf − M d
VT *  w) *100 (%)

K=

(4)

t
Q
1


(mm/s)
h A  30 100(5)where K is hydraulic conductivity (mm/s); t is the
thickness of sample (mm), Q is the quantity of waterwhere eff is the effective void (%), int ra is the(mm3),  h is water head difference (mm),inter-aggregate porosity, T is the total porosity;

A is

2

area of sample (mm )Table 2 Mix proportions of pervious concrete
Aggregate type

100%RC

100% RB

50%RC + 50%RB

Designporosity(%)
15 (RC15)
20 (RC20)
25 (RC25)
15 (RB15)
20 (RB20)
25 (RB25)
15 (M15)20 (M20)25 (M25)

Recycled
concreteaggregate(RC) kg/m3
1519
1519
1519
0
0
0
754
754
754

Recycled brickaggregate(RB) kg/m3

Cement (C)kg/m3

Water (W)
kg/m3

0
0
0
1121
1121
1121
565
565
565

414
337
270
414
337
268
414
337
268

138
112
89
137
112
88
137
111
88

Fig. 2 Test set-up of water permeability [15]RESULTS AND DISCUSSIONS
Density, compressive and flexural strength

to the high void of pervious concrete. The smallest
density of pervious concrete sample was 1394 kg/m3
which using 100% clay brick aggregate (RB) and
25% design porosity was 1394 kg/m3. The highest
density of 1957 kg/m3 was determined in the sample
using 100% concrete aggregate (RC) and 15% design
porosity. As the design porosity increases, the densityof pervious concrete decreases.

The results of density, compressive strength, andflexural strength tests of pervious concrete are shownin Figure 3. The density of pervious concrete depends
on type aggregate and is directly related to the void
content of a given mixture, were between 1394 and1957 kg/m3, which were lower than that of theconventional concrete (about 2400 - 2500 kg/m3) due
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GEOMATE – Kyoto, Japan, 3-5 November 2021

For a given porosity, the compressive strength ofpervious concrete using clay brick aggregate (RB)were lower than that of concrete aggregate (RC).
These results are also consistent with previous results[8][10]. The compressive strength of pervious
concrete using RC and RB varies from 7.5 – 15.5
MPa and 3.5 – 7 MPa, respectively, depending on thedesign porosity. For example, at 20% designed
porosity, the compressive strength of concrete using
100% concrete aggregate and 100% clay brickaggregate were 11.3 MPa and 5.2 MPa, respectively.
20.0
y = 0.0204x - 25.064
R² = 0.9689

Strength (MPa)16.0

Flexural strength
Compressive strength

12.0
8.0
y = 0.0036x - 4.0488
R² = 0.9533

4.0
0.0
1300

1500

1700

1900

2100

Density (kg/m3)
Fig. 3 Relationship between compressive and flexuralstrength and density.
The same trend was found at the other designed
porosity. The highest compressive strength of 15.5
MPa was obtained with 15% designed porosity andusing 100% RC, while the concrete sample used
100% RB and 25% designed porosity shows thelowest strength with 3.5 MPa. This result confirmsthat designed porosity is the most important factor
affect to the compressive strength of pervious
concrete [23] [24].

Void content

20

Strength (MPa)1612Flexural strength
11.3
7.5

8
4

The method for determining the porosity ofpervious concrete in this research involves avolumetric procedure in which the mass of water
filling a sealed pervious concrete sample is converted
to an equivalent volume of pores. As shown by the
results in Fig. 5, 6, 7, three different porosities values
were present: total porosity, effective porosity, andporosity of intra-aggregate pores. The relationshipbetween designed porosity and measured porosity ofall specimens is shown in Fig. 5. Thereby, it can beconcluded that the total porosity and the effective
porosity increase with the increase of designed
porosity, while the intra-aggregate pores seem to beunaffected. Unlike smooth round aggregate, pervious
concrete using RA often obtains a porosity value
greater than the design porosity due to the shape andangles of aggregate [10]. The results show thatmeasured effective porosity is higher than designed
porosity (15%, 20% and 25%). In the case of using

Compressive strength

15.5

8

7
5.7

3.1

2.4 2.0

4.5

1.5 1.4 1

7.3
5.5
2.1 1.65

The influence of design porosity on the compressive
strength of concrete using brick aggregate is lower
than when using concrete aggregate. Specifically, thedesign porosity increased from 15 to 25%, thecompressive strength of concrete using RB decreased
from 7 to 4.5 MPa respectively, equivalent to 36%.
While that of pervious concrete using RC decreased
from 15.5 to 7.5 MPa, equivalent to 52%. Thisphenomenon can be explained that, due to increasing
porosity, the strength of concrete depending more on
the aggregate strength and adhesion strength between
aggregate and cement paste. With low strengthcharacteristics of RB, almost failures undergo occur
due to aggregate breakage. Meanwhile, the strengthof pervious concrete using RC is larger than that using
RB, and it relies on the adhesion strength of thecement paste and recycled aggregate, which is
influenced by the design porosity and attached mortar
in RC. The compressive strength of pervious concrete
using 50%RB and 50%RC is higher than that ofpervious concrete using 100%RB and lower than thecase of using 100%RC. The strength of pervious
concrete is directly related to the void content and theweakness of the attached mortar in recycled aggregate.
Therefore, it leads to a reduction of strength of thepervious concrete. The relationship between density,
compressive strength and flexural strength of all
specimens is shown in Fig. 3. An increase in density
enhanced the strength development of all mixes.
When comparing the effect of aggregate type anddesign porosity on the compressive strengths, flexuralstrength within each mix design were quite similar.
As shown in Fig. 4, it is obvious that with the increasein the porosity, the flexural strength of the pervious
concrete decreases. For example, the flexuralstrengths of RC15 and RB15 were 3.1 MPa and 1.5
MPa, while those of RC25 and RB25 were 2.0 and
1.0 MPa, respectively.

1.3

0

Fig. 4 Compressive and flexural strength of pervious
concrete with difference designed porosity

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GEOMATE – Kyoto, Japan, 3-5 November 2021

brick aggregate RB and mixing aggregate M, the total
porosity is significantly higher than the effective
porosity and designed porosity. Meanwhile, whenusing RC aggregates, the total porosity is slightly
higher than the design porosity (from 1 to 1.2%).
After 24 h of water immersion, the voids in theaggregate have been filled with water due to the water
absorption properties of the RC aggregate.
50

Porosity (%)

40

equations. The total porosity determining by Eq. (1)larger than that of Eq. (2). For example, for a given
design porosity of 20%, the total porosity of thepervious concrete samples using concrete aggregate,
clay brick aggregate and blended aggregate
corresponds to Eq. (1) were 24%, 34%, 27.5%,
respectively, while these figures when applying Eq.
(2) were 22.6%, 32.1%, 26.9%, respectively. The
difference in the two calculation formulas is because
Eq. (2) only calculates the total porosity when thosevoids are filled with water. In fact, after 24 hours of
immersing the sample in water, there are still someisolated pores or small capillary tubes along with the
air pressure in the pores preventing the water fillingcompletely. Even in the case of soaking longer than
24 hours. With the increase in the porosity, thestrength of pervious concrete is reduced.
Figure 8 shows the correlation between
compressive strength, porosity and water
permeability of pervious concrete. It displays thetendency of compressive strength to decrease whenthe void ratio increase.

Total porosity
Effective porosity
Intra-Aggregate porosity

30
20
10
0

Water permeability coefficient
The pervious concrete mixtures that were
expected to have highest permeability rates were
those constructed from single-sized aggregates. The
permeability of the pervious concrete mixtures isshown in Table 3 and illustrated in Figs. 7 and 8. Aswith the effective porosity, the trends were opposite
to those of the concrete strength.

Fig. 5 Total porosity, effective porosity and intraaggregate porosity with difference designed porosity
45.00

Porosity (%)

35.00

Total porosity (Eq. 2)
Total porosity (Eq. 1)

10.0

Water permeability (mm/s)

40.00

30.00
25.00
20.00
15.00
10.00

8.0
6.0

y = 4.5014ln(x) - 6.4982
R² = 0.6813

4.0
2.0
0.0

Fig. 6 Total porosity calculated by two different
equations

10

15

20

25

30

Effective porosity (%)

The voids have been filled with water after 24h
immersion in water contributing the total porosity.
However, intra-aggregate porosity does notcontribute to the water permeability properties.
Effective porosity excludes intra-aggregate pore andclosed voids. The RB25 mix had the highest total
porosity of 39%, while the smallest total porosity was
16% for RC15. Samples using blended aggregates(50%RC-50%RB) have a total porosity of 22, 26.9
and 31.7% corresponding to 15%, 20% and 25%
designed porosity.

Fig. 7 Relationship between water permeability andeffective porosity
The results confirm that the water permeability ofpervious concrete rises with an increase in the void
ratio. On the other hand, with the increase of total
porosity and effective porosity, the compressive,
flexural strength decreases. RC15 mixture (using
concrete aggregate and 15% designed porosity) has
significantly lower hydraulic conductivity than RC25
(25% designed porosity) with 3.5 mm/s and 6.6 mm/s,
respectively.

From the experimental test results, Fig. 6 shows
the total porosity calculated by two different
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GEOMATE – Kyoto, Japan, 3-5 November 2021

The high values of water permeability coefficientsof 5.7– 8.0 mm/s were achieved with pervious
concrete using RB aggregate. The water permeabilityof RC aggregate was lower than that of RB aggregatewith 3.5 – 6.6 mm/s. In general, using RB aggregate
for pervious concrete can increase the drainage
capacity, although the porosity is equivalent to theother aggregate as RC aggregate or mix aggregate.
This point was confirmed by Gaedicke [12].
10y = 3.0945e0.0287x
R² = 0.7684

15

8
6

10
4
5

2

y = 35.446e-0.057x
R² = 0.8931

0

0
12

17

22

27

32

37

Water permeability (mm/s)

Compressive strength MPa)20

the permeability of pervious concrete using therecycled aggregate from crushed concrete waste (RC)and crushed clay bricks waste (RB). It is clear fromthe test results that the porous of pervious concretewith brick aggregate shows some different
phenomena from that of the recycled concrete
aggregate and conventional aggregate.
- The density of pervious concrete mixtureschanges from 1394 kg/m3 to 1957 kg/m3 as total
porosity decreases from 37.8% to 17% when clay
brick content in the blended aggregate range from
100% to 0%. Density decreases with an increase of
total porosity and decreasing strength of pervious
concrete. However, the obtained compressive
strengths of 3.5 –15.5 MPa were within the typicalstrength distribution reported [13] [8]. Linearity canbe used to describe the relationship among density,
compressive strength and flexural strength, thecorrelation coefficient reaches 0.97 and 0.95,
respectively.
- Porosity is the most important factor
determining
pervious
concrete
properties(permeability, compressive strength, flexuralstrength). Higher porosity improves permeability but
decreases the mechanical properties of the samples.
The total porosity not only influences in thepermeability of the mix, but also illustrating thecharacteristics of coarse aggregate. The clay brickaggregate and the attached mortar in recycled
aggregate contributes significantly to total porosity
thank to an intra-aggregate porosity.
- The water permeability, on the other hand, notbe affected by intra-aggregate porosity. Effective
porosity and shortest paths (connected pores) are a
crucial factor to permeability. The use of recycled
aggregate had slight effect on permeability depending
on compacting method and aggregate angular. In thisstudy, water permeability coefficients of 5.7– 8.0mm/s and 3.5 – 6.6 mm/s were achieved withpervious concrete using RB and RC aggregate,
respectively.

42

Total porosity (%)
Compressive strength

Water permeability

Fig. 8 Relationship among water permeability andcompressive strength and total porosity
The explanation for such consequences can begiven by the fact that the angular-sized aggregates
possess larger shortest paths which have resulted in ahigher water permeability coefficient. The level ofporosity and permeability at all designed porosity
illustrate a slightly decrease with a decrease in thepercentage of clay brick content. Thank to lightcompaction, there is no tendency of brick aggregate
was crushed into finer particles, which fills theavailable void spaces as well as provides better
packing of aggregates [8]. For example, for a givedesigned porosity, water permeability of pervious
concrete is 7.5, 7.3 and 6.0 mm/s corresponding to100%, 50% and 0% clay brick were used.
From the experimental test results, it is understood
the relationships among porosity, strength, andpermeability for pervious concrete. It is used toestimate the void content needed for mixtures tosatisfy the specification requirement for permeability
and strength of concrete. By determining the void
ratio, it could be possible to obtain proper
permeability and compressive strength.

ACKNOWLEDGEMENT
This research was supported by JST-JICA
Science and Technology Research Partnership for
Sustainable Development (Satreps) project (No.
JPMJSA1701) and National University of Civil
Engineering (NUCE).
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Based on the experimental investigation on thepervious recycled aggregate concrete, the effects ofcoarse aggregate types and designed porosity on
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drawn:-This study deals with the analysis of porosity and370

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371

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

ADSORPTION PROPERTIES OF MATERIALS ADSORBING
SELECTIVELY ARSENIC FROM AQUEOUS SOLUTIONSCONTAINING MULTIPLE CHEMICAL SPECIES
Ayane Yanaka1, Yuri Takeshita2, Keiichiro Shibata3, Yoshihiro Suenaga4 and Hidenori Yoshida4
1

Graduate School of Engineering, Kagawa University, Japan; 2 T-NET JAPAN CO., Ltd.; 3 Kajima Corporation,
Japan; 4 Faculty of Engineering and Design, Kagawa University, Japan

ABSTRACT
Arsenic contamination has been confirmed in groundwater around the world. In particular, health hazard byarsenic occurs frequently in developing countries such as Bangladesh. In these countries, it is urgent to remove
arsenic from water sources such as groundwater from the viewpoint of the security of safe and sufficient drinking
water because the population growth is predicted in the future. Especially, inexpensive and simple technology forremoving arsenic is required at arsenic-contaminated sites. As a simple method for removing arsenic, theadsorption method is mentioned. However, the adsorption method has two problems. One is that the cost required
for the adsorption method is higher than that for other methods, and the other is that it is difficult to adsorb only
arsenic selectively when arsenic is mixed with other chemical species. Thus, in this study, the inexpensive
adsorbent (improving hydroxyapatite derived from fishbone [Patent No. 6351008 given by Japanese Patent Office],
which has a low material cost) for the purpose of removing arsenic is developed, and the effect of other chemical
species on the adsorption performance for the arsenic of the adsorbent is examined. In the case of no chemical
species mixing (arsenic only), the case of mixing one or more kinds of chemical species, the adsorption test by theshaking test is carried out, and the adsorption performance is evaluated by the adsorption isotherm. From the test
results, it is proven that arsenic is selectively adsorbed, even if the chemical species are mixed.
Keywords: Arsenic, Selectively adsorption performance, Chemical species, Adsorbent

INTRODUCTION

contamination is serious, the population growth ispredicted in the future, so it is essential to secure safe
and sufficient drinking water. Therefore, it is urgent
to remove arsenic from water sources such as
groundwater.
There are mainly two methods in the current
arsenic removal technologies, the aggregation
method (coprecipitation method) and the adsorption
method. According to Tonokai [5], thecoprecipitation method has been used since the 1970s
and is the arsenic removal technology generally
adopted, since some flocculants used in the
coprecipitation method are inexpensive and easily
available. However, as shown in [6], a large amountof waste such as excess sludge is generated because alarge amount of flocculant is required to reduce thearsenic concentration to the standard value dependingon the flocculant used. That is why the disposal of thegenerated waste is costly. In addition, thecoprecipitation method has many problems asfollows: it is necessary to secure a large installation
area in order to construct treatment facilities and it
takes a lot of effort to manage them due to thecomplexity of the processing equipment, and so on.
On the other hand, the adsorption method is a very
convenient one compared with other methods forremoving arsenic and the large-scale treatment

In recent years, arsenic contamination in
groundwater has been frequently confirmed all over
the world. For example, according to the research
survey by Ohno et al. [1], it has been found that thearsenic concentration in all wells of the 10 sites in thecentral area of Nawabganji City in Bangladesh
exceeded 0.01 mg/L which is the WHO guideline
value (the guideline value) for drinking water quality.
In addition, Bretzler et al. [2] reported that 14.6 % ofdrinking water wells in rural areas of Burkina Faso,
West Africa, have arsenic concentrations exceeding
the guideline value of 0.01 mg/L. According to thisfact, about 560,000 people, one-fifth of 2.8 million
people who live in areas with a high risk of arsenic
contamination in the rural areas, may be exposed toarsenic-contaminated groundwater. Similarly, arsenic
concentrations exceeding the guideline value havebeen detected in arsenic-contaminated groundwaterof other developing countries in Asia and Africa.
Additionally, in these countries, there are many
patients with chronic arsenic poisoning such as
keratosis and skin cancer due to continued intake ofarsenic-contaminated groundwater as drinking water
as described in [3], [4]. Furthermore, in thesecountries where the health hazard caused by arsenic
372

GEOMATE – Kyoto, Japan, 3-5 November 2021

facilities are not required. However, depending on theadsorbent used, pretreatment such as pH adjustment
may be required according to the past study [7].
Additionally, in the adsorption method, themanufacturing cost of the adsorbent is expensive, andthe adsorption performance of the adsorbent isstrongly affected by the substance (chemical species)
which coexists with arsenic in the solution as anadsorption object. It is desirable to adopt a simple
method for removing arsenic in addition to lowmaterial costs in developing countries in Asia andAfrica where arsenic contamination is serious. Whenthe adsorption method is selected with an emphasis
on simplicity, the following three are required for theadsorbent. The first is that the adsorbent is cheap, thesecond is that the pretreatment is unnecessary in theadsorption treatment, and the third is that it has theadsorption performance which is not affected by otherchemical species which coexists with arsenic.
According to [1], [2], [8], [9], it is clarified that
sodium (Na), magnesium (Mg), potassium (K),calcium (Ca), manganese (Mn), iron (Fe), bicarbonate
ion (HCO3), sulfate ion (SO4) and nitrate ion (NO3)
are contained in a relatively high concentration as
chemical species coexisting with arsenic in the
groundwater of developing countries in Asia andAfrica where arsenic contamination has been
confirmed.
Therefore, in this study, an inexpensive adsorbent
for the removal of arsenic is developed by improving
low-cost fishbone hydroxyapatite [Patent No.
6351008 given by Japanese Patent Office]. At thesame time, the effect of other chemical species (Na,Mg, K, Ca, Mn, Fe, HCO3, SO4, and NO3) coexistingwith arsenic on the adsorption performance forarsenic is investigated.

simulated solution is poured into a centrifuge tube towhich 20 mg of FFP is added. These are thespecimens in the test. Then, these specimens areshaken for 24 hours at 200 rpm at a temperature of
20 °C. After that, the solutions are centrifuged at 2000
rpm for 3 minutes, and the residual concentration ofarsenic in the solutions is analyzed by an ICP-AES.
Here, Table 1 shows the chemical species detectedin the arsenic-contaminated areas of developing
countries as mentioned above in Asia and Africa andtheir concentrations. Each concentration shown in
Table 1 is the average value of the concentration ineach chemical species listed in [1], [2], [8], [9]. In thisstudy, 9 types of chemical species listed in Table 1
and their corresponding concentrations are adopted as
test conditions. Regarding 9 types of chemical species,
chloride-based solutions (sodium chloride solution,
magnesium chloride solution, potassium chloride
solution, calcium chloride solution, manganesechloride solution and iron chloride solution) are used
for cation (Na, Mg, K, Ca, Mn, Fe), and sodium-based
solutions (sodium bicarbonate solution, sodium
sulfate solution and sodium nitrate solution) are used
for anion (HCO3, SO4, NO3). In addition, in the testwith mixed multiple chemical species, a typical three
types of water quality that have been confirmed inarsenic-contaminated areas of the developing
countries in Asia and Africa are assumed. Table 2
Table 1 The concentration of each chemical
species
Chemical species

Concentration (mg/L)

Na

491.7

Mg

30.9

ADSORPTION TEST (SHAKING TEST)K

5.5

The selective adsorption properties of thedeveloped arsenic adsorbent (Functional Fishbone
Powder, FFP) for arsenic in the solution containing
not only arsenic but also other chemical species areverified. Therefore, the adsorption test by the shaking
test is carried out in the case of no chemical species
mixing (arsenic (As) only), the case of mixing one
kind of chemical species, and the case of mixing
multiple kinds of chemical species assuming thewater quality in the actual arsenic contamination site.
After that, the adsorption performance of the FFP for
arsenic is evaluated based on the adsorption isotherm.
In the test, an arsenic standard solution (As2O3・
NaCl (0.05%) ・HCl acidic solution) is used. First,
the arsenic standard solution is prepared to seveninitial concentrations (3 ppm, 4 ppm, 5 ppm, 6 ppm,
7 ppm, 30 ppm, 60 ppm), and the prepared solutions
are mixed with a chemical species (one type or
multiple types) set to a predetermined concentration.
This prepares simulated solutions. Next, 10 mL of the

Ca

56.1

Mn

241.7×10-3

Fe

1.6

HCO3

267.7

SO4

1371.3

NO3

19.3

Table 2 The combination of chemical species

373

Sample1

Sample2

Sample3

Mn, Fe, HCO3

Na, Mg, K, Ca,
HCO3, SO4,
NO3

Na, Mg, K, Ca,
Mn, Fe,HCO3, SO4,
NO3

GEOMATE – Kyoto, Japan, 3-5 November 2021

shows the combinations of chemical species in eachwater quality.
In the study, the adsorbed amount per unit mass ofthe FFP is employed as the evaluation of the test
results. The adsorbed amount per unit mass of the FFP
is defined by the following equation (1).
𝑾𝒊 (𝒎𝒈⁄𝒈) =𝑽(𝑪𝒐 − 𝑪𝒊 )𝑴𝒊axis are in three patterns. One is that the adsorbed
amount increases logarithmically as the equilibrium
concentration increases, i.e., an upward convex curve,
another is that the adsorbed amount increases
exponentially as the equilibrium concentration
increases, i.e., a downward convex curve, and theother is that a linear relationship exists between theequilibrium concentration and the adsorbed amount,i.e., a straight line which tends to rise to the right. The
upward convex curve indicates that an attractive force
acting between the FFP surface and As promotes
adsorption of As by FFP, while the downward convex
curve indicates that the attractive force which acts
between the FFP surface and As is weak. Additionally,
when a linear relationship is established between theequilibrium concentration and the adsorbed amount,it means the adsorption of As by FFP from a very
dilute solution, which indicates the competitive
adsorption of the solvent and As on the FFP surface.
On the other hand, the Langmuir model expresses
whether the adsorption mode is monolayer adsorption
or not. The applicability of the Langmuir model
depends on whether there is a linear relationshipbetween the equilibrium concentration of As and thevalue of equilibrium concentration divided by theadsorbed amount of As.
Fig. 1 to Fig. 4 indicate that the adsorption
isotherm shows an upward convex curve in mixing
any one kind of chemical species, although theadsorbed amount is inferior to the case withoutchemical species. From this, it can be said that an
attractive force acting between the FFP surface andAs promotes adsorption of As by FFP. In addition, thecoefficient of determination R2 listed in Table 3 showshigh values of 0.9 or more in each case. From theseresults, it is considered that the adsorption mode of
FFP for As is monolayer adsorption and that As ischemically adsorbed on the FFP surface (such as
chemical bonding), even when no chemical species(As only) or one kind of chemical species is mixed.

(𝟏)

where, “V” is the amount of the solution, “Co” is theinitial concentration of the solution, “Ci” is theresidual concentration (equilibrium concentration)
after the tests and “Mi” is the mass of the FFP. In orderto ensure reproducibility, the mean value of three tests
is adopted as a test result. In addition, the test results
are applied to the Langmuir model so as to clarify theadsorption mechanism of the adsorbent for arsenic.
The Langmuir model is typical adsorption
equilibrium equations in liquid-phase adsorption. The
Langmuir model is defined by the following equation
(2).
𝑪𝒊
𝟏
𝟏
=
+
𝑪
𝑾𝒊 𝒂𝑾𝒔 𝑾𝒔 𝒊(𝟐)where, “a” is the adsorption equilibrium constant and“Ws” is the amount of saturation adsorption.
ADSORPTION TEST RESULTS
In the case of mixing one chemical species, theadsorption isotherms plotted the test results obtained
from the shaking tests are shown in Fig. 1 (cation, Na
and K), Fig. 2 (cation, Mg and Ca), Fig. 3 (cation, Fe
and Mn) and Fig. 4 (anion), respectively. In addition,
Table 3 shows the coefficient of determination
obtained by applying the results of shaking tests with
one chemical species to the Langmuir model. The
horizontal and vertical axes in Fig. 1 to Fig. 4 are theequilibrium concentration of As, and the equilibrium
concentration divided by the adsorbed amount (C/W),
respectively. The adsorption isotherm, as described in[10], indicates the relationship between the soluteconcentration in equilibrium under certain
temperature conditions and the adsorbed amount atthat time. The adsorption isotherm shows various
types of curves depending on the combination of theadsorbent (refers to FFP in this study) and theadsorbate (a substance to be adsorbed, refers to As in
this study). Its shape shows how As is adsorbed to
FFP, i.e., the physical or chemical interaction between
FFP and As. The adsorption isotherms obtained byplotting the residual concentration (equilibrium
concentration) of the solute analyzed after theadsorption test on the horizontal axis and theadsorbed amount per unit mass of FFP on the vertical

Adsorbed amount (mg/g)

14
12
10
8
6
4

Non-mixed (As only)
Na
K

2
0
0

20

40

60

Equilibrium concentration (mg/L)
Fig. 1 Adsorption isotherm (Mixing one chemical
species (Na, K))
374

14

14

12

12

Adsorbed amount (mg/g)

Adsorbed amount (mg/g)

GEOMATE – Kyoto, Japan, 3-5 November 2021

10
8
6
4
Non-mixed (As only)
2

Ca
0

20

6
4

Non-mixed (As only)
Sample1
Sample2
Sample3

0
40

0

60

20

40

60

Equilibrium concentration (mg/L)
Fig. 5 Adsorption isotherm (Mixing multiple
chemical species)

Equilibrium concentration (mg/L)
Fig. 2 Adsorption isotherm (Mixing one chemical
species (Mg, Ca))

Table 3 Coefficient of determination obtained byappling test results to the Langmuir model (Mixing
one chemical species)

1412Adsorbed amount (mg/g)

8

2

Mg

0

10

Chemical species

R2

Non-mixed (As only)

0.9977

Na

0.9699Mg

0.9164

K

0.9176

Ca

0.953460

Mn

0.9019Fig. 3 Adsorption isotherm (Mixing one chemical
species (Mn, Fe))Fe

0.9892

HCO3

0.9641SO4

0.9064

NO3

0.919310
8

6
4

Non-mixed (As only)

2

Mn
Fe

0
0

20

40

Equilibrium concentration (mg/L)

Adsorbed amount (mg/g)

14

12
10

Table 4 Coefficient of determination obtained byappling test results to the Langmuir model (Mixing
multiple chemical species)8
64

Non-mixed (As only)
HCO3
SO4
NO3

2
0
0

20

40

Chemical species

R2

Non-mixed (As only)

0.9977

Sample10.9341

Sample20.9154

Sample30.913560

Equilibrium concentration (mg/L)
Fig. 4 Adsorption isotherm (Mixing one chemical
species (Anion))
375

GEOMATE – Kyoto, Japan, 3-5 November 2021

On the other hand, it can be seen that the coefficientof determination R2 in the case of mixing K, Mg, Mn,
SO4 or NO3 respectively is lower than that in the caseof no chemical species mixing (As only) (< 0.95).
These results suggest that the chemical species of Mg,
K, Mn, SO4 and NO3 may affect the adsorption
performance of FFP for As. Further examinations areneeded to gain clearer insights into the effect of thesechemical species on the adsorption performance of
FFP for As.
In the case of mixing multiple chemical species,
the adsorption isotherms plotted the test results
obtained from the shaking tests are shown in Fig. 5,
and the coefficient of determination obtained byapplying the test results to the Langmuir model isshown in Table 4. The horizontal and vertical axes ofFig. 5 are the same as those of Fig. 1 to Fig. 4. Similar
to Fig. 1 to Fig. 4, Fig. 5 indicates that the adsorption
isotherm shows an upward convex curve in any
combination patterns of chemical species, although
the adsorbed amount is inferior to the case withoutchemical species. In addition, the coefficients ofdetermination R2 listed in Table 4 as well as thoselisted in Table 3 show high values of 0.9 or more ineach case. However, it is found that the coefficient ofdetermination R2 in the case of mixing multiple
chemical species is lower when more than one
chemical species is mixed in any combination patternof chemical species than when no chemical species ismixed (As only). Since the coefficient ofdetermination R2 decreases as the number of chemical
species increases, the adsorption performance of FFP
for As may be affected by the number of chemical
species.

even without the addition of acid or alkali.
Therefore, it can be an adsorbent which utilizablesafely. In addition, it is an environmentallyfriendly adsorbent because FFP is developed
using fishbone, which is food waste.
 From the value of the coefficient of determination
R2 obtained by applying the results of shaking
tests to the Langmuir model, it is considered thatthe chemical species of K, Mg, Mn, SO4 and NO3
may affect the adsorption performance of FFP for
arsenic. Further examinations are needed to gain
clearer insights into the effect of these chemical
species on the adsorption performance of FFP for
arsenic.
From these results, in order to clarify the effect of
the chemical species of K, Mg, Mn, SO4 and NO3 on
the adsorption performance of FFP for arsenic, it isnecessary to analyze the changes in the concentrationof K, Mg, Mn, SO4 or NO3 respectively when thearsenic adsorption test is conducted in the case of
mixing each chemical species and verify how muchchemical species are adsorbed on FFP. Additionally,
it is needed to carry out an adsorption test in which
the concentration of mixed chemical species (K, Mg,
Mn, SO4 and NO3) is changed.
ACKNOWLEDGMENTS
This work has been supported by JSPS
KAKENHI Grant Number JP20K04684. Also, incarrying out this study, Nihon Kogyo Co., Ltd.
provided FbP. We express our gratitude here.
REFERENCES

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and Magara Y., Relation between arsenic andother metals and ions in groundwater in
Bangladesh,
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Engineering
Research, Vol. 41, 2004, pp. 591-600.
[2] Bretzler A., Lalanne F., Nikiema J., Podgorski J.,
Pfenninger N., Berg M. and Schirmer M.,
Groundwater arsenic contamination in Burkina
Faso, West Africa: Predicting and verifying
regions at risk, Science of the Total Environment,
Vol. 584-585, 2017, pp. 958-970.
[3] Ando M., Arsenic contamination of ground waterin India and Bangladesh and its health effect,
Journal of the National Institute of Public Hearth,Vol. 49, No. 3, 2000, pp. 266-274.
[4] Mazumder. D. N., Ghosh A., Majumgar K. K.,
Ghosh N., Saha C. and Mazumder N. R., Arsenic
Contamination of Grand Water and its HealthImpact on Population of Distinct of Nadia, West
Bengal, India, Indian Journal of Community
Medicine, Vol. 35, Issue 2, 2010, pp. 331-338.
[5] Tonokai K., Method of purification of arsenic
contaminated groundwater, Global Environment,

In this study, the selective adsorption property of
FFP for arsenic was verified in the solution
containing not only arsenic but also other chemical
species (Na, Mg, K, Ca, Mn, Fe, HCO3, SO4 and NO3).
Concretely, the adsorption test by the shaking test was
carried out in the case in which arsenic alone was
mixed, the case in which one kind of chemical species
was mixed except for arsenic, and the case in which
multiple kinds of chemical species were mixed on theassumption of the water quality in an actual arsenic
contamination field. The adsorption performance of
FFP for arsenic was evaluated based on theadsorption isotherms obtained from the test results.
The results obtained are as follows.
 In the case of mixing one or more kinds ofchemical species, the adsorption isotherm showed
an upward convex curve in mixing either kind ofchemical species, though the adsorbed amount ofarsenic was a little inferior to the case in which
only arsenic was mixed.
 FFP has an adsorption performance for arsenic
376

GEOMATE – Kyoto, Japan, 3-5 November 2021

Vol. 22, No. 1, 2017, pp. 91-98.
[6] Uyama H., Shan J. and Yano T., Development ofSecure and Convenient Remediation Technology
for Arsenic-contaminated Water, Manufacturing& Technology, Vol. 60, No. 3, 2008, pp. 81-84.
[7] Yanagida T., Jiang Y. and Nakamura M., Arsenic
Adsorption Properties of New Iron Hydroxide,
Journal of Japan Society on Water Environment,
Vol. 36, No. 5, 2013, pp. 149-155.
[8] Berg M., Stengel C., Trang K. T. P., Hung V. P.,
Sampson L. M., Leng M., Samreth S. andFredricks D., Magnitude of arsenic pollution in
the Mekong and Red River Deltas – Cambodia
and Vietnam, Science of the Total Environment,
Vol. 372, Issue 2-3, 2007, pp. 413-425.
[9] Malana A. M. and Khosa A. M., Groundwaterpollution with special focus on arsenic, Dera
Ghazi Khan-Pakistan, Journal of Saudi Chemical
Society, Vol. 15, Issue 1, 2011, pp. 39-47.
[10] Abe I., Chemistry of Adsorption, Oreo Science,Vol. 2, No. 5, 2002, pp. 275-281.

377

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

EFFECT OF SCREW ON THE AXIAL PERFORMANCE OF
COLD-FORMED STEEL
Desy Setyowulan1, Eva Arifi2, Devi Nuralinah3, and Edwardo Pradana A.N.4
1,2,3,4Engineering, Universitas Brawijaya, Indonesia
ABSTRACT

Along with the development of the construction world in Indonesia, the use of cold-formed steel (CFS) as aconstruction material is also increasing. The connection is an important element that must be considered in thistype of material. Structural elements that are joined experience stress concentration around the hole resulting in achange of the behavior of structure that becomes more complex. To predict the failure mode of the connection
section, this study conducted an experimental test to study the capacity of cold-formed steel against tensile loads.
The test specimens used in this study is canal type which is usually used as material construction, with threedifferent configuration of screw positions. Effect of screw will be compared with original model without screw.
Main failure modes for all types of screw connection are categorized by tilting and pull-out failure. Increasing
space between screw will also increase the maximum load by 6.67% and 10.81% for connection with three andfour screws, respectively. In addition, the specimens with one and four screw connection have an average extension
which was 300% larger than that of without screw.
Keywords: Axial Performance, Cold Formed Steel, Connection, Screw, Failure model

INTRODUCTION

screw is a common type of connections that is used
due to the thinness of CFS sections. In addition, areview on CFS frame connection was also studied
previously [6]. Based on the review assessment, theresults highlighted that all types of connections
except adhesive connections have shown the proper
behavior that can trigger the change of any designcodes.
The failure modes of connections were
investigated by researchers. The results have shownthat screwed CFS connections can be subjected tosimilar fracture and different moment–rotation
capacities under loading [7]. An experimental
investigation and FEA of CFS bolted connections
with single and multiple bolts under shear loadingwere investigated [8]. The comparison results showthat current AISI (2012) design provisions for the
CFS provides the safe bearing resistances for thebolted connections with washers on oversized holes
in single bolted, double bolted and quadruple bolted
connections.
This study conducted an experimental test to
study the axial performance of cold-formed steel
connections with different screw configurations,
including the capacity and failure modes of eachmodel test. The results of this study will be used as
the basis for analyzing whether the formulations inIndonesian Standard of 7971: 2013 are in accordancewith the real conditions of using cold-formed steel
profiles in the market.

Nowadays, steel has become one of the mostwidely used construction materials around the world.
The use of steel material will be effective in reducing
the consumption of cement as the main binder ofconcrete. It means that the air pollution due to CO2emissions will be reduced to realize the concept ofgreen building and sustainable construction inIndonesia. Structural engineering applications ofcold-formed steel is increasing steadily. Manyadvantages can be achieved by using this material inbuilding construction, such as easily assembled into awide range of structural and architectural forms,
easily assembled and modified on site, Efficient use
of material leads to competitive construction and tosave in material costs, etc. However, the used of thinwalled sections and cold-forming manufacturing
effects can results in special design problems, such as
buckling strength (including local, global,
distortional, shear), torsional rigidity, web crippling,
ductility and plastic design, connections, fire
resistance, and corrosion [1].
Many researches have been undertaken related tothe behavior of cold-rolled steel in order to improveits design problem, such as numerical andexperimental study related to the axial capacity,
flexural capacity, and connection [2, 3, 4].
Connections are the critical components in coldformed steel structures. Review on cold-formed steel
connections is investigated [5]. The performance ofscrew connections, storage crack connections,welded connections, and bolted connection werediscussed. From this review, it was concluded that378

GEOMATE – Melbourne, Australia, 11-13 November 2020

METHODSRESULTS AND DISCUSSIONS

Experimental model of CFS connection withscrew configurations are shown in Fig. 1. In thisanalysis, we used three different screw configurations.
The block shear behaviors of double-shear screw
connections fabricated with CFS were planned toinvestigated.
The common dimension for specimens is in thefollowing; screw diameter d was 4 mm, thickness foreach specimen of 0.6 mm, and space between eachscrew was determined by 4d and 5d. Eachconfiguration has three specimens. The total lengthfor specimen without connection as shown in Figure1(a) was 200 mm, and for other specimens were 250
mm, as shown in Figure 1(b, c, d). The detaildimension for specimen without connection were 50
mm, 12.7 mm, 12.5 mm, 200 mm, 57 mm, 60 mm,
and 20 mm for G, W, R, L, A, B, C, respectively. As
shown in the configuration and assembly of test
specimen of Figure 1, the specimen consist of two
coupling CFS plate in the connection with length isadjusted to the space used between the plate.
The tensile strength test procedure wasdetermined based on Indonesian National Standard(SNI 7971:2013), as shown in Fig. 2. The externaltensile load was produced by Universal Testing
Machine, which increased gradually until failure ofeach specimen. It indicated that the specimens cannot
withstand the external load.

Tensile Testing Stages
The tensile testing stage for CFS with a screw
connection is shown in Fig. 2. In this figure, thetensile testing stage can be seen clearly. These stages
include the initial condition, balanced level before
loading, tilting failure, and pull-out failure. When twomaterials of the same thickness are screwed together,or when the thicker material is against the screw head,tilting failure occurs [9]. Hancook [10] illustrates thisshortcoming in Fig. 3.

(a)

(b)

(c)

(a)

(d)

(e)

(b)
Fig. 2. Tensile testing stage for CFS using screw
connections, including (a) initial condition, (b)balanced level, (c) and (d) tilting failure, (e) pull out
failureSource: Author’s documentation, 2021

(c)

(d)
Fig. 1. Configuration and assembly of test
specimen; (a) without connection, (b) 1 screw, (c) 3screws, and (d) 4 screws.

Figure 3. Tilting failure of screwed connections
[10]

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GEOMATE – Melbourne, Australia, 11-13 November 2020

Failure Modes

Previous study has shown that the end distance of
3d is enough to avoid the tear out failure for the
stainless-steel bolted connections [11]. In this study,
we used the space of 4d and 5d in accordance with
SNI 7971:2013 with the minimum spacing should be≥ 3d.
The main failure modes for all types of screw
connection are categorized by tilting and pull-out
failure. For the specimen without connection, fracture
occur. This type of failure produces stretching of thehole on one side of the screw, while the plate isbunched together on the other side of screw. Becauseof the commonly thickness of CFS, Rogers andHancook [12] demonstrated that using washers underthe bolt head and nut can considerably boost aconnection's resilience to bearing failure.

Effect of edge distance and screw spacing to thefailure modes of CFS connection were investigated,
as shown in Fig. 4.

(a) Without connection

Maximum Load(b) One screw (4d)Different variation for screw configuration
number gives an impact to the maximum axial load,
as shown in Table 1. From these results, it could be
depicted that the average maximum load for tensile
test without connections are similar with connection
in one screw, shown as 6000 N. Increasing space
between screw will also increase the maximum loadby 6.67% and 10.81% for connection with three andfour screws, respectively.

(c) One screw (5d)Table 1. Maximum load(d) Three screws (4d)(e) Three screws (5d)(f) Four screws (4d)(g) Four screws (5d)Fig. 4. Failure modes for tensile specimens
without connection
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GEOMATE – Melbourne, Australia, 11-13 November 2020

Load-extension Curve

CONCLUSIONS

Table 2 depicted the extension for each specimen.
According to this data, connection with one screw and
4 screws have a similar result of 21 mm. Furthermore,minimum result occurs for specimen control without
connection of 5 mm. The specimens with one andfour screw connection have an average extension
which was 300% larger than that of without screw.
This capacity was limited by the shear capacity of thescrew.
A load-extension curve for one screw connection
is illustrated in Figure 5. The larger space (5d) showsa less ductile failure compared the smaller space (4d).

This study used an experimental test to determine
the capacity of cold-formed steel against tensile loads
in order to anticipate the failure mechanism of theconnection section. The test specimens in this study
were canal type, which is commonly utilized as amaterial construction, and had three distinct screw
configurations. The screw's effect will be comparedto the original model without the screw.
The following conclusions can be drawn from thisresearch:
1. Tilting and pull-out failure are the most common
failure modes for all types of screw connections.
2. For connections with three and four screws,
increasing the spacing between screws increases
the maximum load by 6.67% and 10.81%,
respectively.
3. The specimens with one and four screws have an
average extension that is 300 percent greater thanthe ones without screws.

Table 2. Maximum extension for specimens

ACKNOWLEDGMENTS
The authors greatly indebt to “BPPM FTUB”(Research and Community Service Agency, Facultyof Engineering, Universitas Brawijaya) for providing
financial support through this research. Special
thanks to the Civil Engineering Department,
Universitas Brawijaya, for supporting thisopportunity.

CITATION AND REFERENCE LIST
[1]. Dubina, D., Ungureanu, V., Landolfo, R.
(2012). Design of Cold-formed Steel
Structures, Eurocode 3: Design of Steel
Structures, Part 1-3: Design of Cold-formed
Steel Structures. Wiley-Blackwell, Ernst &Shon (A Wiley Company), Portugal.
[2]. Pandey, M., Young, B. (2020). Structural
performance of cold-formed high strength
steel tubular X-Joints under brace axial
compression. Engineering Structures, Vol.
201.
[3]. Nandhakumar, C.S., Ramesh, R., Sreevidya,
V. (2020). Investigation of cold formed steel
beam subjected to torsion. Materials Today:
Proceedings, Vol. 21, pp. 425-429.
[4]. Ye, J., Hajirasouliha, I., Becque, J. (2018).
Experimental investigation of local-flexuralinteractive buckling of cold-formed steel
channel columns. Thin-Walled Structures,Vol. 125, pp. 245-258.
[5]. Lee, Y.H., Tan, C.S., Mohammad, S., Tahir,
M.Md., Shek, P.N. (2014). Review on Cold-

Figure 5. Load-extension curve381

GEOMATE – Melbourne, Australia, 11-13 November 2020

Formed Steel Connections. Hindawi, Article
ID 951216, pp. 1-11.
[6]. Komara, I., Wahyuni, E., Suprobo, P.
(2017). A Study on Cold-Formed SteelFrame Connection: A Review. The Journal
for Technology and Science, Vol. 28, Issue
3, pp. 83-89.
[7]. Maali, M., Sagiroglu, M., Solak, M.S.
(2015). Experimental Behavior of Screwed
Beam-to-Column Connections in ColdFormed Steel Frames. Arabian Journal of
Geosciences, Vol. 11: 205.
[8]. Konkong, N. (2017). An Investigation of the
Ultimate Strength of Cold-Formed Steel
BoltedConnections.
Engineering,Technology & Applied Science Research,
Vol. 7, No. 4, pp. 1826-1832.
[9]. Zeynalian, M., Shelley, A., Ronagh, H.R.
(2016). An experimental study into thecapacity of cold-formed steel truss
connections. Journal of Constructional Steel
Reearch, Vol. 127, pp. 176-186.
[10]. Hancock, G.J. (1998). Design of Coldformed Steel Structures: To Australian/New
Zealand Standard As/NZS 4600: 1996.
Australian Institute of Steel Construction.
[11]. Salih, E.L., Gardner, L., Nethercot, D.A.
(2011). Bearing failure in stainless steelbolted connections. Engineering Structures,Vol. 33, Issue 2, pp. 549-562.
[12]. Rogers, C.A., Hancook, G.J. (1999).
Screwed connection tests of thin G550 and
G300 sheet steel. J. Struct. Eng., 125.

382

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

PERFORMANCE OF INTERLOCKING BRICK WALLSAGAINST OUT-OF-PLANE EXCITATION
Aiko Furukawa1, Johanes Jefry Prasetyo2 and Junji Kiyono1
1

Department of Urban Management, Kyoto University, Japan; 2Pacific Consultants CO., LTD., Japan
ABSTRACT

Masonry structure is one of the most commonly used building types in developing countries due to its
inexpensiveness and material availability. While masonry structure is intrinsically strong in resisting gravitational
force, it is relatively weak in resisting lateral forces such as an earthquake. Therefore, reinforcement for masonry
structures in earthquake-prone areas is necessary. This paper introduced an interlocking brick as one of thepromising reinforcement methods for masonry structure as it does not require extra material and easy to implement.
The performance of a lego-shaped interlocking brick in resisting dynamic lateral forces was investigated through2-dimensional dynamic finite element analysis. The created lego-shaped interlocking brick wall was analyzed
under the out-of-plane dynamic loading. Its performance was compared with the performance of the rectangularbrick wall. The analysis results showed that the lego-shaped interlocking brick wall had better performance inresisting out-of-plane loading than the rectangular brick wall since the blocks interlock each other.
Keywords: Interlocking brick, Rectangular brick, Out-of-plane excitation, Dynamic finite element analysis
INTRODUCTION

In this study, a reinforcing method that does not
require reinforcing materials is considered formasonry structures. The method uses interlocking
bricks instead of regular rectangular bricks.
Interlocking bricks are bricks that utilize aninterlocking system to reinforce against loading.
When a joint failure occurs in a rectangular brick wall,the external force is resisted by only friction force(Fig.1(a)). In comparison, when a joint failure occurs
in an interlocking brick wall, the external force isresisted by both friction force and the interlocking
mechanism (Fig. 1(b)). By utilizing this concept, theinterlocking brick is assumed to be able to reinforce
masonry structures. Additional benefits of thismethod are that it does not require additional
reinforcement material and easy to implement.
Researches on the performance of interlocking
bricks against earthquake have been conducted [8-11].
Research [11] showed that interlocking bricks could
reinforce masonry walls against the in-plane static
lateral load. However, since earthquakes are dynamic
loads, which also act in the out-of-plane direction,
research on interlocking bricks' performance against
dynamic lateral load in the out-of-plane direction isnecessary. However, such researches have not been

Masonry structures are one of the most commonlyused building types in developing countries due totheir inexpensiveness and accessibility as
construction material. While masonry structure is
intrinsically strong under gravitational load, it is weakagainst lateral forces such as an earthquake.
Historically, the collapses of structures due toearthquakes account for almost 75% of theearthquake fatalities [1], and the collapse of masonry
structures accounts for a large number of fatalities
among them [2]. Masonry structures are stillcommonly used in earthquake-prone areas indeveloping countries because they are inexpensive
and easy to construct. To reduce causalities due to thecollapse of the masonry structures in developing
countries, reinforcing the masonry structure isdeemed necessary.
The usage of steel reinforcing bars for masonry
structures is one of the most commonly usedreinforcement methods in developed countries. Itsapplication, however, is not always easy indeveloping countries due to the lack of resources andknowledge [3]. Consequently, reinforcement
methods for masonry structure which are costefficient and easy to implement are much preferable.
In the past several years, many kinds of researchon the reinforcement of masonry structures have been
conducted. Among them, reinforcement methods byutilizing vertical steel ties [4], fiber-reinforced plastic[5], tires [6] (as a tensioned material), and PP bands[7] are some of the examples which are easy to
implement, and utilizing readily available materials,
but require additional reinforcing material.

(a)Rectangular brick (b)Interlocking brickFig.1 Difference in the resistance mechanism against
lateral load between rectangular and interlocking
brick walls
383

GEOMATE – Kyoto, Japan, 3-5 November 2021

30

100

100

conducted yet. With this background, this paper aims
to investigate the performance of interlocking bricks
in resisting out-of-plane lateral excitation through the
dynamic finite element analysis.

30
45 35 45

125

Rectangular brickLego-shaped brick(a)Out-of-plane wall model

100

100
250

ANALYSIS CONDITIONS

30

Analysis models

30

In this study, two-dimensional dynamic analysis
of masonry walls was performed using the generalpurpose finite element analysis software, MSC Marc
[12].
There are four analysis models, two one-wallmodels, and two two-wall models. Two one-wallmodels and two two-wall models are divided into a
rectangular brick type and an interlocking wall type.
Dimensions of bricks for each model are shown in
Fig. 2. Rectangular bricks and lego-shaped bricks are
considered. In past research [8-11], I-shaped bricks
and wave-shaped bricks were considered, which havethe interlocking effect only in one direction. Since
earthquake acts both in the in-plane and out-of-plane
directions, the lego-shaped brick was consideredsince they have the interlocking effect in both
directions.
Two-dimensional analysis models are shown in
Fig.3. Analysis models in Fig. 3 are examples of theinterlocking brick walls. Analysis models for therectangular brick wall were also created similarly.
Fig. 3(a) is a thin wall model with only one brickin the horizontal direction, and Fig. 3(b) is a two-wallmodel with thicker walls. Assuming that the roof is
supported more than two walls in actual buildings,
Fig.3(b) is considered.
The lego-shaped bricks are only used for the wallpart. The protrusions of bricks on top of the wall were
neglected, and the top surface was smoothed. In thesame way, holes for engaging with protrusions at thebottom of the wall are neglected.
Actual buildings have three-dimensional shapes
and show three-dimensional behavior. However, thisstudy used two-dimensional analysis to investigate
the out-of-plane vibration since the wall is weakagainst the out-of-plane direction.
All nodes at the bottom are fixed in bothhorizontal and vertical directions. The horizontal
inertia force was applied to all free nodes when
inputting horizontal acceleration.
Fig. 4 indicates the example of how therectangular and interlocking bricks are divided intoelements. Plane strain 4-noded elements were utilized.
The size of each element for the rectangular brick,roof, and steel elements is 5 cm  5 cm. The legoshaped brick was also divided into elements of asimilar size.
Material properties are shown in Table 1(a). Thebase and roof are made of steel so that theirdeformation can be neglected.

35 55 70 55 35

Rectangular brickLego-shaped brick(b) Out-of-plane two-wall modelFig.2 Assumed bricks (unit: mm)

0.3m

Displacement
output point
Brick
Brick

0.125
m

2.5
m

Base

Roof

0.3
m

0.25m

(a)Out-of-plane wall model
Displacement output point

3.3 m

0.3
m
Roof
0.5m

Left Wall

2.5 m

Right Wall

Base
0.3
m
1.1cm

Base
1.1 cm(b) Out-of-plane two-wall model

Fig.3 Wall models made of lego-shaped bricks(a)Rectangular brick(b)Lego-shaped brickFig.4 Division of brick into elements384

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1 Analysis Parameters(a)Material properties
Value
Brick Base & Roof
3
-6
-6
Density (kg/mm )1.9  10 8.05  10
2Young Modulus (N/mm ) 19,000
200,000
Poisson’s Ratio
0.216
0.2

20gal and 120gal were input to the one-wall models
and two-wall models in the out-of-plane direction.
Three frequencies, 0.5Hz, 1.0Hz, and 2.0Hz, wereused to investigate the performances under various
frequencies.
The single-step Houbolt time integration method
was used (1 = 1.5 and  = −0.5).

(b)Mortar properties
Value
Mortar2
0.3
Tensile strength (N/mm )2

Shear strength (N/mm )Friction coefficient

RESULTS OF NUMERICAL ANALYSIS
Overview

0.3
0.463

The analysis results of out-of-plane lateral
excitation are discussed. The performance of thelego-shaped brick wall and the rectangular brick wall
are compared concerning the displacement history
and deformation process.
One-wall models
In one-wall models, sinusoidal accelerations of
20gal with the frequency of 0.5Hz, 1.0Hz, and 2.0Hz
were input in the horizontal direction of Fig. 3(a).
The horizontal displacement history for all
frequency cases is shown in Fig. 5. The walls arevulnerable, and both models collapsed by thesinusoidal acceleration of even 20gal irrespective of
the frequency.
Since all models collapsed and showed similar
behavior, the deformation process of the 1.0Hz caseis shown in Fig. 6 as an example. The walls got
fractured at the middle height and broken into twoparts. The color contour of Fig. 6 indicates thehorizontal displacement. The red color is the
maximum displacement, and the blue color is theminimum displacement. The right side is the positive
direction of the displacement. The top of the modelhas the largest displacement. After the walls arebroken into two pieces, the middle part showed theminimum displacement since it moved to the left.
Since there is only one brick in the out-of-plane
direction, the interlocking effect is small and could
not resist the out-of-plane excitation.

Fig.5 Horizontal displacement history of one-wallmodels(a)Rectangular brick(b)Lego-shaped brickFig.6 Deformation process of one-wall models (1Hz)
Modeling of failure
It is considered that bricks are connected withmortar, and failure occurs at the mortar. Therefore, it
was set that all bricks do not shear the nodes withother bricks. The bricks are glued with the adjacent
bricks at the interface. The bricks move together withthe adjacent bricks unless the glue does not get broken.
The mortar failure is modeled by the failure of theglue between elements. When the normal or shear
stress at the interface between elements exceeds thetensile or shear strength, the glue is broken. After the
failure, only compression and friction force acts
between the elements. Mortar properties are shown in
Table 1(b).

Two-wall models
Dominant frequency
The one-wall model was too thin and got easily
fractured even with a slight acceleration of 20gal. It
was found the interlocking system does not work insuch cases where the wall itself is vulnerable.
Next, two-wall models are analyzed. Their wall
width is 50 cm, which is four times thicker than thatof the one-wall models. Furthermore, the roof is
supported by two walls, which means more stability.
It is considered that the two-wall models are more
realistic since the roof weight is usually supported bymore than two walls in the actual buildings.

Input accelerations
Sinusoidal accelerations with the amplitude of385

GEOMATE – Kyoto, Japan, 3-5 November 2021

Therefore, the input acceleration cases weredivided into three categories, the case lower than thedominant frequency (0.5Hz), the case around thedominant frequency (1.0Hz), and the cases higher
than the dominant frequency (2.0Hz).
When the sinusoidal acceleration of 20gal is input,
both models experienced no failure. When no failure
occurs, no difference is observed between therectangular and lego-shaped brick models since there
is no need for the interlocking system to work.
Therefore, the lateral load was increased up to 120gal,
where a noticeable performance difference between
the two models was observed.

Fig.7 Horizontal displacement history of two-wallmodels (0.5Hz)

Result for 0.5 Hz
The horizontal displacement history for 0.5 Hz isshown in Fig. 7. The lego-shaped brick wall showeda slightly larger deformation than the rectangularbrick wall, but its value is small.
The deformation process and displacement
contour for 0.5 Hz are shown in Fig. 8(a)(b). Bothmodels showed similar behavior until 2.5 sec, but thedifference occurred at 3.5 sec. The enlarged figures at
3.5 sec are shown in Fig.8(c)(d).
Non-smoothness of displacement contour
indicates the occurrence of failure. As shown in Fig.
8(c), the rectangular brick wall experienced failurearound the top of both walls. In contrast, the legoshaped brick wall shows a smooth displacement
contour (Fig.8(d)), and no failure was observed. It
was found that the interlocking system could avoid
failure around the top of the walls.

(a)Rectangular (0.5, 1.5, 2.5, and 3.5 sec, from the left)(b)Lego-shaped (0.5, 1.5, 2.5, and 3.5 sec, from the left)(c)Failure observed at the (d)No failure observed to
top of both walls of the the lego-shaped brick wall
rectangular brick wall (3.5 (3.5 sec)
sec)
Fig. 8 Deformation process and horizontal
displacement contour of two-wall models for 0.5Hz(red: maximum, blue: minimum)

Result for 1.0 Hz
The horizontal displacement history for 1.0 Hz isshown in Fig. 9. Compared to the result for 0.5 Hz,
both models showed more significant horizontal
displacement. The horizontal displacement of therectangular brick wall model was gradually shifted to
the negative direction, which means the roof moved
to the left.
The deformation process and displacement
contour for 1.0 Hz are shown in Fig. 10(a)(b). Fig.
10(a) shows that the sliding movement of the roof
occurred in the rectangular brick wall since thehorizontal cracks occurred on the upper walls. On theother hand, no sliding displacement occurred in thelego-shaped brick wall owing to the interlocking
system (Fig. 10(b)).
The enlarged figures at 2.2 sec are shown in
Fig.9(c)(d). As shown in Fig. 9(c), the rectangularbrick wall experienced horizontal cracks at the top ofboth walls and diagonal cracks at the bottom of bothwalls. In contrast, the lego-shaped brick wall
experienced no diagonal cracks at the bottom of thewalls. Mortar failure occurred at the top of both walls,
but no sliding between the walls and the roof occurred
due to the interlocking system.

Fig.9 Horizontal displacement history of two-wallmodels for 1.0Hz
The dominant frequencies of the two-wall models
were obtained from their free vibration responses.
The models were excited by inputting an impulsive
acceleration, where the acceleration amplitude for the
first 0.01 sec is 1gal and that for the left 4.99 sec is 0
gal. Then the displacement histories at the top centerof the roof were Fourier transformed, and the
predominant frequencies were read. The dominantfrequencies of both two-wall models were about 1.25
Hz.
386

GEOMATE – Kyoto, Japan, 3-5 November 2021(a)Rectangular (0.4, 1.0, 1.6, and 2.2 sec, from the left)(b)Lego-shaped (0.4, 1.0, 1.6, and 2.2 sec, from the left)
Fig. 13 Comparison of the maximum displacement
for two-wall models
Result for 2.0 Hz
The horizontal displacement history for 2.0 Hz isshown in Fig. 11. They showed a similar tendency tothe case of 1.0 Hz even though the horizontal
displacement of 2.0 Hz is small. The horizontal
displacement of 2.0 Hz is smaller since 2.0 Hz isfarther from the dominant frequency of 1.25 Hz than
1.0 Hz.
The deformation process and displacement
contour for 2.0 Hz are shown in Fig. 12(a)(b). Fig.
12(a) shows that the sliding movement of the roof
occurred in the rectangular brick wall since thehorizontal cracks occurred on the upper walls. On theother hand, no sliding displacement occurred in thelego-shaped brick wall, and horizontal displacement
could be kept small due to the interlocking system(Fig. 12(b)).
The enlarged figures at 1.2 sec are shown in
Fig.12(c)(d). In the rectangular brick wall model, theupper part of both walls got fractured. In the legoshaped brick wall model, no failure occurred, and themodel deformed as a united body.

(c)Failure observed at the (d)No failure observed to
top of both walls of the the lego-shaped brick wall
rectangular brick wall (2.2 (2.2 sec)
sec)
Fig. 10 Deformation process and horizontal
displacement contour of two-wall models for 1.0 Hz(red: maximum, blue: minimum)

Fig.11 Horizontal displacement history of two-wallmodels for 2.0Hz

Summary
The interlocking system did not work for thin
one-wall models, while it worked for thick two-wallmodels.
The comparison of the maximum displacement
for two-wall models is shown in Fig. 13. In all
frequencies, the rectangular brick wall model showedlarger displacement than the lego-shaped brick wallmodel.
The displacement suppression effect of theinterlocking system was the largest at 1.0 Hz whenboth models had the largest displacement due toresonance, and the displacement of the lego-shaped
brick wall was about 1/3 of that of the rectangularbrick wall.
In the case of 2.0 Hz, the large displacement
response and failure occurred to the rectangular brickwall. In contrast, no failure occurred, anddisplacement was kept small in the lego-shaped brickwall since the interlocking system worked.
In the case of 0.5 Hz, the displacement response
was small and slight failure occurred to the(a)Rectangular (0.3, 0.6, 0.9, and 1.2 sec, from the left)(b)Lego-shaped (0.3, 0.6, 0.9, and 1.2 sec, from the left)(c)Failure observed at the (d)No failure observed to
top of both walls of the the lego-shaped brick wall
rectangular brick wall (1.2 (1.2 sec)
sec)
Fig. 12 Deformation process and horizontal
displacement contour of two-wall models for 2 Hz
(red: maximum, blue: minimum)
387

GEOMATE – Kyoto, Japan, 3-5 November 2021

rectangular brick wall. Since there was not muchroom for the interlocking system to work, thedisplacement suppression effect of the lego-shaped
brick wall was small. No damage occurred in thelego-shaped brick wall, and the interlocking effectwas observed in damage prevention.
It could be concluded that the lego-shaped brickwall shows better performance in resting the lateraldynamic load even in the out-of-plane direction when
the wall is not too thin.

Humanitarian
Affairs):www.unocha.org.
[Accessed April 16, 2021].
[2] Coburn, A., and Spence, R., Earthquake
protection 2nd Edition, Chichester, John Wileyand Sons, 2002.
[3] McWilliams, H., and Griffin, C.T, A criticalassessment of concrete and masonry structures forreconstruction after seismic events in developing
countries, Structures and Architecture: Concepts,
applications, and challenges, Proceedings of theSecond International Conference on Structures
and Architecture, CRC Press, 2013, pp. 857-864.
[4] Darbhanzi, A., Marefat, S., and Khanmohammadi,
M., Investigation of in-plane seismic retrofit of
unreinforced masonry walls by means of verticalsteel ties, Construction and Building Materials,
Vol. 52, 2014, pp. 122-129.
[5] Marcari, G., Manfredi, G., Prota, A., and Pecce,
M., In-plane shear performance of masonry panels
strengthened with FRP, Composites: Part B, Vol.
38, 2007, pp. 887-901.
[6] Turer, A., and Golalm, M., Scrap tire as low-cost
post-tensioning
materialformasonry
strengthening, Materials and Structures, Vol. 41,
2011, pp. 1345-1361.
[7] Sathiparan, N., Sakurai, K., and Meguro, K.,
Experimental study of PP-band retrofitted
masonry structure made of shapeless stone.
SEISAN-KENKYU, Vol. 61, Issue 6, 2009, pp.
1051-1054.
[8] Sanada, Y., Nakamura, Y., Yamauchi, N., andAkano, Y., Seismic performance of masonrywalls using interlocking units, Proceedings of theFirst European conference on earthquake
engineering and seismology, Paper No. 508, 2006.
[9] Furukawa, A., Kimura, S., and Kiyono, J., Studyon failure mechanism and strength increment
effect of masonry walls made of interlocking
bricks, Journal of Japan Society of Civil
Engineers, Vol. 74, No. 4, 2018, pp. I_699-I_711(in Japanese).
[10] Furukawa, A., Masuda, K., Daru, G.T.S., andKiyono, J., Shape effect of interlocking blocks onforce-displacement relationship and failure
behavior of masonry wall, Journal of StructuralEngineering, Vol. 64A, 2018, pp. 241-249 (inJapanese).
[11] Furukawa, A., Prasetyo, J.J., Kiyono, J., Failure
process and load-displacement relationship ofrectangular block and interlocking block walls
during in-plane lateral loading, Journal of Japan
Society for Natural Disaster Science, Vol. 38,
2019, pp.25-41.
[12] MSC
SoftwareCorporation:Marc,
www.mscsoftware. com/product/marc. [Accessed
April 16, 2021.]

CONCLUSION
In this study, the performance of the interlocking
brick walls under lateral dynamic load in the out-ofplane direction was investigated using the 2dimensional finite element analysis. The lego-shaped
brick was chosen, and its performance was comparedto the rectangular brick. Two models were compared.
The first is a one-wall model of a single thin wall
supporting a roof, and the second is the two-wallmodel of two thicker walls supporting the roof.
Sinusoidal acceleration was input to the models withthree frequencies of 0.5 Hz, 1.0 Hz, and 2.0 Hz.
In the one-wall models, both rectangular andinterlocking brick wall models quickly got fractured
and collapsed irrespective of the input frequencies. It
was found that the interlocking system does not work
for a thin wall subjected to the out-of-plane loading
since the wall itself was vulnerable.
In the case of thick two-wall models, theinterlocking system worked. The rectangular brickwall got failure, but the failure was avoided, anddisplacement response was kept small in theinterlocking brick wall. When the displacement
response is small, the effect of the interlocking system
is small. As the displacement response larger, theinterlocking system worked, and the displacement
suppression effect was noticeable. The interlocking
system caused the brick wall to deform together as aunit, eliminating the unwanted sliding movement due
to failure.
It was confirmed that the lego-shaped brick walls
showed a better performance in resisting the out-ofplane dynamic load due to the interlocking system if
the wall is thick enough to exert the interlocking
effect.
ACKNOWLEDGMENT
This work was supported by JST aXis Grant
Number JPMJAS2020, Japan.
REFERENCES[1] OCHA(OfficefortheCoordination

of

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11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

INFLUENCE OF FREEZING AND THAWING CONDITIONS ATINITIAL AGE ON THE STRENGTH DEVELOPMENT OF MORTARSPeter Kaba1, *Shushi Sato2, Masahiro Yamamoto3 and Keikichi Takahashi41

United Graduate School of Agricultural Sciences, Ehime University, Japan; 1,2Faculty of Agriculture, Kochi
University, Kochi, Japan; 3ASTON Company Limited, Japan; 4KAIHATSU Concrete Company Limited,Japan.

ABSTRACTAgricultural irrigation canals are widespread across Japan and extensively used in productive farming. Thereis an urgent need to extend the service life of these facilities to reduce their maintenance cost. Therefore, thepurpose of this study is to experimentally explore the effect of low-temperature history on the long-term strengthof polymer cement mortar, especially during the early stage of the mortar's life, thus, 3 days of age. In this study,
two types of polymer cement mortar were used: powdered ordinary polymer cement mortar and powdered fasthardening polymer cement mortar, thus 100P and 100PS respectively. The experimental results indicate that thecompressive strength analysis of the polymer cement mortar is lower than the standard cured, whenever the initial
curing process was subjected to rapid temperature changes (-10ºC to 5ºC) every 3 hours; subsequently, its purposeof extending the service life of agricultural canals cannot be fulfilled. We again noted that the compressive strengthof the specimens subjected to low temperature during the initial curing period does not show positive strength on
the 91st day, and depending on the timing of the repair work, the waterway may lack sufficient compressive
strength. Finally, under a constant low-temperature environment where freezing and thawing do not occur (-10ºC), the fast-setting polymer cement mortar shows sufficient strength up to 91 days. The results from this work
will provide a reference to experimentally clarify the effect of low-temperature history on the long-term
compressive strength of typical polymer cement mortar.
Keywords: Compressive strength, Polymer cement mortar, concrete, Freez-Thaw

water tightness, and weak chemical resistance,
although it also has some advantages, such as easy
manufacturing and application in construction andhigh compressive strength [3].
To overcome the disadvantages of ordinary
cement concrete and mortar with Portland cement,
researchers have attempted over the last 60 years todevelop a new type of concrete and mortar using
polymer that demonstrates excellent performance [3,4]. Today, various polymers are being tested to
improve cement’s properties as a binder; this newconcept concrete or mortar has better physicalproperties and durability than ordinary cement
concrete and is termed polymer-modified
concrete/mortar or polymer cement concrete(PCC)/mortar (PCM).
Agricultural water utilization facilities, thus river
canals in Japan were mainly constructed between theend of World War II and the period of rapid economic
growth, and nearly 80 years have already passed.
There is an urgent need to extend the service life ofthese facilities and to reduce their cost.
Agricultural open waterways are scatteredthroughout Japan[5]. Whenever there is adeterioration in the agriculture canal even if the

1. INTRODUCTION
The first canals were built between the Middle
Ages and the Renaissance to harness the driving forceof water. Their development during the 18th and 19th
centuries aimed to address the growing need for waterin Mediterranean farming. Canals have aided thedevelopment of irrigated agriculture since the late
1800s, allowing for increased production, increased
crop surfaces, and the introduction of new crops. [1].
Agricultural water use, as the largest form ofwater use, accounts for 70% of the total water
consumption in the world and is a vital part of thesocial water cycle, and even in regions with relatively
abundant water resources, the agricultural water
demand cannot be met completely [2]. Intense water
resource exploitation and utilization have resulted insevere changes in natural hydrological cycle
processes in many agricultural watersheds.
As a result, irrigation canal scheduling is criticalfor achieving high crop yields and improved water
use efficiency while lowering environmental impact.
Concrete made of ordinary Portland cement has
disadvantages such as slow hardening, large
shrinkage, weak tensile and flexural strengths, lack of389

GEOMATE – Kyoto, Japan, 3-5 November 2021

deterioration is of the same kind, it may be difficult
to repair them using the same repair material due tovarious conditions such as environmental conditions,usage conditions, and location within the waterway.
In addition, agricultural irrigation canals are often
conducted during the off-season, that is winter, andthe possibility of construction failure caused by low
temperatures is considered to be high. Damage
caused by frost expansion is a primary concern when
designing concrete structures in cold weather regions.
Ordinary portland cement concrete has a range ofbenefits, including low costs, high compressive
strengths, and flexibility in fabrication and casting,but its durability concerns are gaining more attention
these days, as the majority of concrete structures areexperiencing performance degradation. Concrete
performance is affected by a variety of factors,
including material composition, curing schemes andtemperatures, hazard species, harsh conditions, andexternal loads [6].
Therefore, in this study, we focused on the repairof cracks in the concrete canal walls of agricultural
canals as one of the aging problems of agricultural
water utilization facilities.
Therefore, the purpose of this study was toexperimentally elucidate the effect of lowtemperature history on the long-term strength ofpolymer cement mortar, especially during the earlystage of the mortar's life up to 3 days of age. The
purpose of this study was to experimentally clarify
the effect of low-temperature history on the long-term
compressive strength of polymer cement mortar.

plamold cases. The water-cement ratio was the value
recommended by the manufacturer, with a pH valueof 7. A total of 90 mortal samples were prepared,
which was subsequently subdivided, thus based on
the properties of cement mortar used. All mortar
specimens were prepared by hand-kneading using astirrer.
The curing conditions of mortar specimens were
all standard water curing after 3 days of material age
except for intentional temperature change. An
incubator capable of providing arbitrary lowtemperature history was used to provide lowtemperature levels up to 3 days of material age. Since
the volume of the mortar was observed to expand withtime, the top of the filled plamold case was sealedwith plastic immediately after casting and the top load
was applied to prevent any change from thepredetermined volume.
In this experiment, the temperature history of thespecimens during curing was set at 20°C (hereinafterreferred to as control), which was recommended by
the manufacturer, as the standard condition, and allother mortar specimens were subjected to lowtemperature history immediately after curing. The
minimum temperature was set at -10ºC and the
maximum was set at +5ºC. The reason why theminimum value of the low temperature was set to 10°C is that it is the lowest temperature in cold
regions in Japan, and the maximum value of thetemperature history was set to +5°C based on theassumed daily difference.

2. MATERIALS AND METHODS
Materials
In this study, two types of polymer cement mortar
were used: powdered ordinary polymer cement
mortar and powdered fast-hardening polymer cement
mortar. The former will be referred to as 100P and thelatter as 100PS. An electronic balance scale was used
for measuring the cement powder. A CF 1033 tablevibrator was used to level and release existing air inthe mortar. Makita UT1305 cement mixer machine
was used to mix the mortar in a bucket to control air
bubbles that are entrained in the process of mixing. A
Plamold plastic mold with a diameter of 50 mm and aheight of 100 mm was adopted for casting. A concrete
compression testing machine was used to measure thecompressive strength employed in the fracture test at
the final material age.
Methods
Firstly, grease was applied inside the plastic moldfor easy removal of the made mortar. A 4kg of eachpolymer cement mortar, thus 100PS and 100P wasmeasured using the electronic balance into theFig.1 workers repairing a deteriorated river canal in
Kochi.

390

GEOMATE – Kyoto, Japan, 3-5 November 2021

In general, freezing and thawing effects on
hardened concrete cause volume expansion andcontraction, which are the failure mechanisms infreezing damage. However, the effect of freezing andthawing immediately after casting on the long-term
strength of cement hydrates has not yet been clarified.
In this experiment, the temperature was repeatedly
changed between -10°C and +5°C for 12 hours, 6hours, and 3 hours with the aid of an incubator. The
age of the specimens was set at 7, 28, and 91 days
(365 days), and three specimens were prepared foreach temperature history and age. The test results
were basically the average of the three specimens.
Table 1 below shows the temperature differences,
duration, and the number of cycles for a cement
mortar in an incubator.

kN) at 7 days of age, and only about half of thecompressive strength of the control specimen (20.0kN) as shown in Fig 3(A). On the other hand, thedifference between 100PS and the control specimen
was minimal, and the strength of 100PS was about 3.7
times higher than that of 100P under the same
condition as shown in Fig 3(B).

Table 1.
Temperature(ºC)
Standard 20ºC

Duration

Cycle

Fig. 2 A well repaired river canal repair in Kochi-10ºC

3 daysKeep in an incubatorfor 3 days at acontrolledtemperature of -10ºC-10 ºC +5 ºC

12 hours, 3
days-10 ºC +5 ºC

6 hours, 3
days+5ºC for 12 hours
and
-10ºC
for
12hours (a cycle aday)+5ºC for 6 hours and-10ºC for 6 hours (4cycles a day)-10 ºC +5 ºC

3 hours, 3
days

(A)
Compressive Strength(MPa)+5ºC for 3 hours and-10ºC for 3 hours (9cycles a day)Furthermore, as shown in Fig 3(A) and Fig 3(B),not much difference ware recorded for 100P and
100PS polymer cement mortars compared to thecontrol specimen at 28 days of mortar age. This may
be due to the constant temperature adopted during theinitial curing process for 3 days of the mortar life.
At 91 days of age, the average compressive
strength of both types of specimens for Fig 3(D) was
22.9 MPa, which was the least recorded among thespecimens at the same mortar age. Both types of
specimens that underwent freeze-thaw at 12 and 6hour pitches showed an increase in strength with time,
but the average compressive strength was lower inboth conditions.

3. RESULTS AND DISCUSSIONFirstly, comparing the normal strength
development characteristics of 100P and 100PS, it
can be read that the compressive strength increases
with time for both. However, the strength
development characteristics were different, with thecompressive strength of 100PS being greater at 7 daysof age, but showing little change at 91 days of age.
In order to investigate the effects of freezing andthawing (freeze-thaw) cycles the specimens were
subjected to a low-temperature history of -10°C for 3
days during initial curing as shown in. The
compressive strength of 100P was very small (10.0

20℃ standard condition

50
40
30
20
10
0
0

10 20 30 40 50 60 70 80 90 100

Days
100p

100ps

Fig. 3(A). 20 ºC standard condition in water
For the specimens subjected to freeze-thaw at a 3hour pitch, both specimens showed relatively highcompressive strength up to the 7th and 28th day, but
391

GEOMATE – Kyoto, Japan, 3-5 November 2021

the compressive strength on the 91st day was lower
than the compressive strength on the 28th day as
shown in Fig 3(E).

-10℃ 3day(s)

(B)
50
40
30
20
10
0
0

10 20 30 40 50 60 70 80 90 100

Days
100p

100ps

Fig. 3(B). Compressive strength of -10ºC for 3 days

-10℃〜5℃ 12h 3day(s)Compressive Strength(MPa)

(C)

60
40
20
0
0

10 20 30 40 50 60 70 80 90 100

Days
100p

100ps

40
30

Fig. 3(E) Compressive strength of -10 ºC~ 5 ºC for 3
hours
We also confirmed that the freeze-thaw effectduring the initial age of the specimens had a negative
effect on the compressive strength, which was due tothe increase in the volume of water in the specimens
caused by freezing and the destruction of thehardened parts by fine cracks due to the hydration
reaction. This suggests that the compressive strengthof the specimens was not sufficient due to the increasein the volume of water in the specimens caused byfreezing and the destruction of the hardened parts byfine cracks due to the hydration reaction.

20
10
0
0

10 20 30 40 50 60 70 80 90 100

Days
100p

100ps

Fig. 3(C) Compressive strength of -10ºC~5 ºC for 3
hours

(D)
Compressive Strength(MPa)-10℃〜5℃ 3h 3day(s)(E)
Compressive strength(MPa)Compressive Strength(MPa)of 100PS even at the age of 7 days. One possible
reason for this is that the manufacturer recommended
a water-mortar ratio (water/polymer cement mortar,
weight %) in the range of 15.6~17.2% for 100P and
16~18% for 100PS.
In this study, the amount of water used for both isunified and set at 17%, which falls within thecommon range, and may have caused the difference
between the strength expression in this experiment.

-10℃〜5℃ 6h 3day(s)

30
20
10
0
0

10 20 30 40 50 60 70 80 90 100

Days
100p

100ps

Fig. 3(D) Compressive strength of -10ºC~5 ºC for 3hours
It was confirmed that 100PS, which is a fastcuring type, has a larger initial strength developmentrate. According to the manufacturer's measurement,
the compressive strength of 100P was higher than that

Fig. 4. Map of Japan showing freezing damage risk.
Source:[7]392GEOMATE – Kyoto, Japan, 3-5 November 2021

2 Wang, W., Hu, P., Wang, J., Zhao, J., Liu, H.,
and Yang, Z.: ‘Scenario analysis for thesustainable development of agricultural water in
the Wuyuer River basin based on the WEP modelwith a reservoir and diversion engineering
module’, Science of The Total Environment, 2021,
758, pp. 143668
3 Jo, Y.-K.: ‘Adhesion in tension of polymer
cement mortar by curing conditions using polymer
dispersions as cement modifier’, Construction and
Building Materials, 2020, 242, pp. 118134
4 Huseien, G.F., Mirza, J., Ismail, M., Ghoshal,S., Hussein, A.A.J.R., and Reviews, S.E.:‘Geopolymer mortars as sustainable repair
material: A comprehensive review’, 2017, 80, pp.
54-74
5 Sakura, K.J.I., Society, and Glick, L.T.t.T.F.:‘The relationship between urban structure andwaterways in Edo, old Tokyo’, 2015, pp. 924-934
6 Peng, Y., Zhao, G., Qi, Y., and Zeng, Q.: ‘Insitu assessment of the water-penetration
resistance of polymer modified cement mortars
by μ-XCT, SEM and EDS’, Cement and Concrete
Composites, 2020, 114, pp. 103821
7 Takeshi Narita, S.-i.K.a.H.M.: ‘Frost Damage
Risk Map Based on Local Climate Condition andSite Investigation’, Concrete Research andTechnology, 2008, Vol. 19, (1), pp. 29-38

The decrease in compressive strength at 91 day(s)was caused by freeze-thaw action more times than thespecimens with 12-hour and 6-hour pitches,indicating that freeze-thaw action after casting has anegative effect on long-term strength development,even for the mortars subjected to this study.
4. CONCLUSION
In this study, we focused on polymer cement
mortar, one of the inorganic repair materials, in thecold climate of Japan, where the temperaturesometimes drops below 0°C. The following three
things were obtained by confirming the compressive
strength of polymer cement mortar subjected tovarious low-temperature histories.
First, if the initial curing process is subjected to
rapid temperature changes every three hours, thecompressive strength tends to drop, and the purposeof extending the service life of agricultural canals
cannot be fulfilled.
Secondly, the compressive strength of thespecimens subjected to temperature changes during
the initial curing period does not develop easily on the
91st day, and depending on the timing of the repair
work, the waterway may be used without sufficientcompressive strength.
Finally, under the low-temperature environment
where freezing and thawing do not occur, the fastsetting polymer cement mortar shows sufficientstrength up to the 91st day.
REFERENCES
1 Aspe, C., and Jacqué, M.: ‘Agricultural
Irrigation Canals in Southern France and New
Urban Territorial Uses’, Agriculture andAgricultural Science Procedia, 2015, 4, pp. 29-39

393

Environment394

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

TEMPORAL CHANGES IN THE INDOOR RADON CONCENTRATION
AS AN EARTHQUAKE PRECURSOR
Jae Wook Kim1, Han Young Joo1, Young Seo Kim1, So Yun Jeong1 and Joo Hyun Moon1
1

Department of Energy Engineering, Dankook University, Gyeongju, Republic of Korea
ABSTRACTThis paper reviews the changes in indoor radon concentrations, measured in a university building in Gyeongju,
Rep. of Korea, to find the relationship, if exists, between the indoor radon concentrations and the seismic activities
in the area neighboring Gyeongju. The measurement period was from June 1, 2017 to May 31, 2019. During thisperiod, numerous seismic activities occurred in the southeastern region of the Korean peninsula. Considering amagnitude and distances from our measurement place to epicenters, 11 earthquakes for analysis were chosen.
Among this, nine earthquakes were found to have radon anomalies before their occurrences. Three earthquakes
with magnitude greater than 4.0 were scrutinized for the correlation between radon anomalies. Effects of theenvironmental variables such as relative humidity, barometric pressure, rainfall and inlet temperature were also
examined. Similar to those found in our previous study, spike-like patterns were also found in the indoor radon
concentration distributions before the earthquakes.
Keywords: Indoor radon concentration, Gyeongju, Seismic activities, Pohang, Radon anomaly

INTRODUCTION

earthquake, gradual decrease before the earthquake,and sudden drop on the day of the earthquake if theinterval between successive earthquakes wasmoderately longer, for example, 3 days [7]. In case of
the Pohang earthquake, on the other hand, we
observed no noticeable patterns between earthquake
and indoor radon concentration due to the long
distance between the epicenter and the measurement
spot [8].
This study is an enlarged version of our previous
studies. Our previous studies investigated therelationship between radon concentration andearthquake during specific period which main shock
and its aftershocks occurred. However, this study
investigated the changes in the indoor radon
concentrations before and after earthquakes for two
years from June 1, 2017 to May 31, 2019, to see if asimilar pattern of indoor radon concentration isrecurred prior to the earthquake, as in our previous
study. We also scrutinized three earthquakes withmagnitude 4 or greater and radon anomalies prior toeach one.

Radon is an inert radioactive gas generated by aseries of the decay of uranium, and released to the airfrom the soil, groundwater, and cracks in buildings[1]. Due to cracks in the crust prior to seismic activity,radon gas emits highly than usual [2]. Using thisphenomenon, lots of research have studied on radon
concentrations as an earthquake precursor. Yasuoka
et al. observed that, in the earthquake in Kobe, Japan,
a rise in the atmospheric radon concentration wasevident prior to the earthquake [3]. Sunarno et al.
found that radon concentration increased 3 days
before earthquake in Situbondo, Indonesia [4].
In general, radon concentrations in theatmosphere are affected by meteorological variables.
Planinic et al. presented radon concentrations overfour years along with the barometric pressure,rainfall, and air temperature, and investigatedwhether the weather variables affected changes in theenvironmental radiation [5]. Virk et al. measured therelease of radon gas in the wake of an earthquake in
the Himalayas’ main boundary fault and explainedthat the soil, air temperature, rainfall, pressure,humidity, and wind speed were related to radon gas
abnormalities [6].
Those studies focused on the outdoor radon
concentrations in the air, soil or groundwater.
However, unlike these previous studies, our previous
studies investigated the relationship between theindoor radon concentration and earthquake occurredin Gyeongju and Pohang, Republic of Korea. In caseof Gyeongju earthquake, we observed similar spikelike patterns between them: a sudden increase in thepeak indoor radon concentration 1–4 days before an

MATERIALS AND METHODS
Measurement Device and Procedure
As shown in figure 1, we used a RAD7 detector tomeasure the indoor radon concentration continuously.
The RAD7 can measure radon concentration in therange 0.1–20,000 pCi/L, with approximately 5%
uncertainty [9]. The protocol of the U.S.
Environmental Protection Agency was applied tomeasure indoor radon concentration [10]. The
measurement place was the first floor of the395

GEOMATE – Kyoto, Japan, 3-5 November 2021

M = 2. 4 log 10 D − 0 . 43

(1)

DMAX = 10 (M +0. 43 ) /2. 4(2)Engineering Hall at Dongguk University-Gyeongju,
Rep. of Korea. Because the RAD7 was equipped next
to the central entrance of the Hall, where hundreds ofpeople come in and out the building a day, we
assumed that there was no influence on ventilation.
Measurements were made every 30 minutes, and themeasurement data were collected and analyzed using
the computer program for RAD7.

Where
M = magnitude of earthquake (Richter scale)
Dmax = maximum measurable distance from theepicenter (km)Fig. 1 Structure of a RAD7 detector.

Dmax in equation (2) means the maximum distancefrom epicenter, which radon anomaly could bemeasured. It means that, if the M-magnitude quakes
occurred and the measurement place is located within
Dmax, radon anomalies could be observed.
For analysis, we employed the value of ‘seasonal
average radon concentration + 2 × standard deviation(σ)’ as a criterion to assume radon anomaly. That is,
if radon concentration exceeded this value, we could
consider it as effects of earthquakes notenvironmental impacts [12].

Data Analysis Method

RESULTS AND DISCUSSIONS

Gyeongju is located 360 km southeast of Seoul in
Korean Peninsula, and Pohang is located 20 km northof Gyeongju. After a 5.8-magnitude quake (Gyeongju,
September 12, 2016) and a 5.4-magnitude quake(Pohang, November 15, 2017) occurred, theiraftershocks have still occurred in Gyeongju andPohang. In order to identify the relationship indoor
radon concentrations and the seismic activities, weinvestigated daily radon concentrations with seasonal
average radon concentrations for two years from June
1, 2017 to May 31, 2019.
Earthquakes for analysis were screened byapplying the following formula proposed by
Hauksson and Goddard [11]:Reviewing theearthquakes

Measurement

Data

Table 1 shows the information of the earthquakes
selected by using eq. (2). If the distance were equal toor less than Dmax, that is, if the distance difference
were 0 or higher, the radon anomalies could be foundat the measurement place as a precursor of theearthquake. Magnitude of those earthquakes ranged
from 2.5 to 5.4. There were 14 earthquakes from June
1, 2017 to May 31, 2019. In case of two or more
earthquakes occurred on the same day, however, we
regarded that only the largest magnitude earthquake
occurred on that day. Finally, the 11 earthquakes wereanalyzed to identify the relationship between
earthquake and radon concentrations.

Table 1 Information of the selected earthquakes using eq. (2)NumberDate

Magnitude

Distanceb
[km]

Dmax
[km]

1
2

17.06.11
17.10.16
17.11.15
17.11.15
17.11.15
17.11.15
17.11.16
17.11.19
17.11.20
17.12.25
18.02.11
18.04.09
19.01.10
19.02.10

2.5
2.5
5.4
3.6
3.5
4.3
3.6
3.5
3.6
3.5
4.6
2.4
2.5
4.1

13.5
11.8
29.0
28.0
26.9
30.1
30.1
30.1
32.2
29.0
25.9
14.3
8.1
71.5

16.6
16.6
268.6
47.8
43.4
93.5
47.8
43.4
47.8
43.4
124.7
15.1
16.6
77.2

3a
4
5
6
7
8
9
10
11

and

396

Distance
Differencec
[km]
3.2
4.8
239.6
19.8
16.5
63.4
17.7
13.3
15.6
14.4
98.8
0.8
8.5
5.7

GEOMATE – Kyoto, Japan, 3-5 November 2021

a

Regards that 5.4 magnitude earthquake occurred only on that day.
Distance means the straight distance between the measurement point and the epicentercDistance difference = Dmax – Distanceb

Because we are not able to show the measurements
all together in one figure, we presented themeasurements in the two figures: Fig. 2 and 3. In
figure 2, we observed ten radon anomalies that reachedor exceeded ‘seasonal average + 2σ’ and nine
earthquakes. In figure 3, we found thirteen radon
anomalies and two earthquakes. We observed 23 radon
anomalies marked in small orange circle for our
measurement period in total, and the details of theanomalies were summarized in Table 2. In Table 2,
difference means how much the radon concentration
exceed the seasonal average + 2σ. Next, we checkedthat if any anomalies occurred before seismic activities,
and found that radon anomalies occurred before thenine among the eleven earthquakes as shown in Table
3. The earthquakes on November 19 and 20, 2017
were assumed to have the same radon anomaly
because they happened over two consecutive days. It
was interesting that no radon anomaly which exceed
the value of ‘seasonal average radon concentration +2σ’ was observed though the 5.4-magnitude
earthquake occurred on November 15, 2017 and the
distance between the epicenter and the measurement
place was relatively short. We think that this was
because radon released already due to a 2.5-magnitude
quake on October 16, 2017, a month before the 5.4magnitude quake.

Fig. 2 Daily radon concentrations and earthquakes from June 1, 2017 to May 31, 2018.

Fig. 3 Daily radon concentrations and earthquakes from June 1, 2018 to May 31, 2019.
Table 2 Radon anomalies which exceed or reached seasonal average radon concentrations + 2σ
397

GEOMATE – Kyoto, Japan, 3-5 November 2021

Radon anomaly
concentration[Bq/m3]869.7
768.7
1129.2982.471027.71022.01428.811445.9926.2
909.2
1057.31221.11106.81219.71214.21151.91276.21310.21321.61406.61146.31084.51084.4Date
17.06.19
17.07.13
17.09.24
17.10.26
17.11.17
17.11.30
18.01.14
18.02.27
18.03.03
18.03.10
18.07.27
18.08.15
18.11.04
18.11.21
18.11.24
18.11.25
18.12.02
19.02.05
19.02.06
19.02.28
19.03.08
19.03.09
19.03.29
aDifferencea[Bq/m3]

Seasonal average + 2𝛔
[Bq/m3]

130.2
29.2
139.8
-7.0
38.2
32.6
-4.7
12.4
55.6
38.5
73.6
237.4
57.2
170.0
164.6
102.3
-9.0
24.9
36.3
121.3
85.5
23.7
23.6

739.4

989.5

1433.5870.7
983.7

1049.61285.31060.8Difference = Radon anomaly – (seasonal average + 2𝛔)

Table 3 Earthquakes that are accompanied by radon anomaliesa

NumberEarthquake
occurrence dateMagnitude

1
2
3
4
5
6
7
8

17.10.16
17.11.15
17.11.19
17.11.20
17.12.25
18.02.11
18.04.09
19.01.10

2.5
5.4
3.5
3.6
3.5
4.6
2.4
2.5

9

19.02.10

4.1

17.09.24
17.10.26

Radon anomaly
concentration[Bq/m3]1129.2982.4717.11.17

1027.717.11.30
18.01.14
18.03.10
18.12.02
19.02.05
19.02.06

1022.01428.81909.2
1276.21310.21321.6

Radon anomaly
occurrence date

Time intervala
[Day]
22
20
2
3
25
28
30
39
5
4

Time interval between occurrence date of earthquake and that of radon anomaly

Scrutiny ofConcentration

ChangeinIndoorRadon

at inlet of the RAD7, rainfall and relative humidity(RH) inside the building. We checked if the fluctuationin the indoor radon concentration could have mainly
For three of the nine earthquakes with magnitude
resulted from variation of any environmental variables
greater than 4.0, we scrutinized the indoor radon
except seismic activity. Though the rainfall could
concentrations over 39 days including the day of thepotentially influence the indoor radon concentration,
earthquake from 34 days before the earthquake to 4
we measured the radon concentration inside thedays after it. Table 4 shows the indoor radonbuilding using absorbents to avoid the effects ofconcentrations and environmental variables including
moisture and the rainfall had no significant impact.
relative humidity (RH), barometric pressure, rainfall
As shown in figure 4, there were limited changes
and inlet temperature. Analysis periods were Octoberin the environmental variables measurements over the
12–November 19, 2017, January 8–February 15, 2018,
same period, from October 12 to November 19, 2017.
January 7–February 14, 2019, respectively.
The indoor radon concentrations for October 12–23,
Figures 4, 5, 6 show the daily indoor radon
2017 changed with small deviation, but it fluctuatedconcentration and measurements of the environment
after October 24 with a spike-like pattern that was alsovariables such as barometric pressure, air temperature
identified in our previous study [7].
Table 4 Indoor radon concentrations and environmental variables over 39 days for earthquakes of magnitude ≥4.0
398

GEOMATE – Kyoto, Japan, 3-5 November 2021

Earthquake
occurrence
date
17.11.15
18.02.11
19.02.10

Analysis
period17.10.1217.11.19
18.01.0818.02.15
18.01.0718.01.14

Average radon
concentration[Bq/m3]

Maximum/minimum
Daily radon
concentration[Bq/m3]Air inlet
temperature[℃]

Relative
humidity[%]

Rainfall[mm]

631.7 ± 191.0

1027.7 / 327.5

21.3 / 11.4

4.0 / 2.0

24.0 /
0.0

903.1 ± 249.0

1428.8 / 440.5

12.8 / 7.5

2.6 / 1.0

9.5 / 0.0

823.1 ± 198.6

1321.6 / 389.6

14.8 / 10.6

3.9 / 1.4

6.5 / 0.0

Barometricpressure[mmHg]
1023.2 /1006.41026.9 /1005.21026.9 /1010.2the indoor radon concentration reached 1428.8 Bq/m3,
just 4.7 Bq/m3 lower than ‘seasonal average + 2σ’.
After that day, the indoor radon concentrations had
kept low with a little deviation.
RH and barometric pressure were almost constantfor this period. For the four days of this period, it
rained, but it had no influence on the radon
concentrations and radon fluctuation. In contrast, theinlet temperature seemed to synchronize with the dailyradon concentration. However, that could not accountfor the peak and high radon concentrations during
January 14-22, 2018.

Fig. 4 Daily indoor radon concentration over 39 days
October 12-November 19, 2017.
The temperatures at the inlet of the RAD7
gradually decreased overall throughout the same
period since the measurement time was during achange of season from autumn to winter. In addition,radon concentrations were measured inside thebuilding and the range of temperature fluctuations
were limited. The temperature change trend did notsynchronize with the indoor radon concentration
change.
The RH was only 4%, even on the day when therainfall was highest at 24 mm for the measurement
period, which indicated that the rainfall did not impact
the indoor radon concentration.
The barometric pressure was also relatively
constant. Even though was observed an abnormal
pattern in the indoor radon concentration patterns
starting on October 24, it had changed little. Therefore,
the abnormal pattern of the indoor radon concentration
could not have resulted from a change in thebarometric pressure.

Fig. 6 Daily indoor radon concentration over 39 days
January 7-February 14, 2019.
As shown in figure 6, three spike-like patterns wereobserved in this period, for January 7–February 14,
2019. On January 19, that was 21 days before theearthquake, the daily radon concentration remarkably
increased and reached near the ‘seasonal averageradon concentration + 2σ’. Next, On February 3, theindoor radon concentration rose sharply to 1264.9
Bq/m3 and it decreased to 762.2 Bq/m3 on February 4.
The next day, it increased again to 1310.2 Bq/m3 andthen gradually decreased to 564.6 Bq/m3 on February
9, which was one day before the earthquake.
The temperature change trend did not synchronize
with that of the indoor radon concentration change
overall. The barometric pressure and RH changes were
somewhat irrelevant to the indoor radon concentration.
The only notable difference between figs. 4, 5 and
6 was the occurrence time of the radon anomalies
before earthquake. The daily indoor radon
concentrations over 39 days for three earthquakes
were compared in fig. 7. For the earthquake on
November 15, 2017, the spike-like patterns were

Fig. 5 Daily indoor radon concentration over 39 days
January 8-February 15, 2018.
As shown in figure 5, on January 14 2018, peak of399

GEOMATE – Kyoto, Japan, 3-5 November 2021

observed on October 26, 2017, 20 days before theearthquake. The peak radon concentration wasmeasured on January 14, 2018, 28 days before theearthquake on February 11, 2018. Finally, there were
the four peaks measured before the earthquake on
February 10, 2019. The first occurred 22 days before
the earthquake, and others occurred seven, five, fourdays before the earthquake. For comparison, ourprevious study found that radon anomalies occurred amonth before the 5.8 magnitude earthquake on
September 12, 2016, in Gyeongju, and a few days
before its aftershocks [7].

ACKNOWLEDGMENTS
This work is an enlarged version of our previous
study [7], [8].
This work was supported by the National Research
Foundation of Korea (NRF) grant funded by the
Korean government (Ministry of Science and ICT).
(No. 2020M2D2A2062436).
REFERENCES[1] Timothy M.K., Geological Hazards: A Sourcebook,
Greenwood Press, 2003.
[2] Sac M. M., Harmansh C., Camgoz B., and Sozbilir H.,
Radon Monitoring as the Earthquake precursor in fault
line in western turkey. Ekoloji. Vol. 20, No. 79, 2011,
pp.93–98.
[3] Yasuoka Y., Igarashi G., and Ishikawa T., Evidence ofprecursor phenomena in the Kobe earthquake obtained
from atmospheric radon concentration. Applied
Geochemistry, Vol. 21, No.6, 2016, pp.1064–1072.
[4] Sunarno, Firdaus H. L., Luckyarno Y. F., Waruwu M.
M., and Wijaya R., Detection system for deterministic
earthquake prediction based on radon concentration
changes in Indonesia. Journal of Engineering scienceand technology, Vol. 15, No. 3, 2020, pp.1787-1798.
[5] Planinić J., Radolić V., and Vuković B., Radon as anearthquake precursor. Nuclear Instruments andMethods in Physics Research, Vol. 530, No.3, 2004,
pp.568–574.
[6] Virk H. S., and Singh B., Radon anomalies in soil-gas
and groundwater as earthquake precursor phenomena,
Tectonophysics, Vol. 227, No. (1-4), 1993, pp.215–224.
[7] Kim J. W., Joo H. Y., Kim R., and Moon J. H.,
Investigation of the relationship between earthquakes
and indoor radon concentrations at a building in
Gyeongju, Korea. Nuclear Engineering and Technology,
Vol. 50, No. 3, 2018, pp.512–518.
[8] Joo H. Y., Kim, J. W., and Moon J. H., Investigation ofchanges in indoor radon concentrations before and after
seismic activities in Gyeongju and Pohang, Korea.
International Journal of GEOMATE, Vol. 16, No. 56,
2019, pp.98–103.
[9] Durridge Company Inc., RAD7, Electronic Radon
Detector User Manual, 2009.
[10] U.S Environmental Protection Agency (U.S.EPA),
Indoor Radon and Radon Decay Product Measurement
Device Protocols, Office of Air and Radiation, U.S EPA
Publication, 402-R-92-004. 1992.
[11] Hauksson E., and Goddard J. G., Radon precursor
studies in Iceland. Journal of Geophysical Research, Vol.
86, No. B8, 1981, pp.7037–7054.
[12] Gregorič A., Zmazek B., Deroski S., Torkar D., andVaupotič J., Radon as an Earthquake Precursor –Methods for Detecting Anomalies, InTech, Rijeka,
Croatia, 2012.

Fig. 7 Comparison of the daily indoor radon
concentration over 39 days for the earthquakes ofmagnitude ≥4.0.
CONCLUSIONSThis study scrutinized the changes in the indoor
radon concentrations that were continuously measured
in a university building in Gyeongju, Rep. of Korea to
see whether a similar pattern recurred in the indoor
radon concentration prior to the earthquake or not,which was found in our previous study [7]. We
analyzed the 11 earthquakes, and identified that, innine cases of them, they have radon anomalies before
they occurred. For three of the nine earthquakes withmagnitude greater than 4.0, we checked whatenvironmental variables mainly caused the fluctuationin the indoor radon concentration. From this, we could
not find any environmental variable that seemed tohave large influence on the indoor radon concentration.
As in our previous study [7], we also identified similar
spike patterns prior to earthquakes.
The recurring identifications imply that the indoor
radon concentrations inside a building dozens ofkilometers from the epicenter might have changed due
to seismic activities prior to the earthquake, which
could be a precursor and provide indications of an
upcoming earthquake. We hope that our study could
help identify a clearer relationship between them.

400

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

PARAMETRIC MODEL OF LINE TRANSECT SAMPLING FOR
GROUPED DATA SET
Noryanti Muhammad1,2 and Gamil.A.A.Saeed 1
1

Center for Mathematical Sciences, College of Computing and Applied Sciences, Universiti Malaysia
Pahang, Lebuhraya Tun Razak, 26300, Gambang Kuantan Pahang, Malaysia;
2
Centre of Excellence for Data Science & Artificial Intelligence, Universiti Malaysia Pahang,26300 Gambang,
Malaysia.

ABSTRACTThe density (𝐷𝐷) of certain object of the population is important for many areas such as biologists, ecologists
and others. The line transect distance technique is used frequently by biometricians and researchers for estimating
the population density of the wild population. In this research, we have restricted attention to considering aparametric model for the line transect sampling for grouping data set. The maximum likelihood estimation method
is used to estimate the density function 𝑓𝑓(0) and the interested object. The parametric model has been applied to
Laake Stakes real data as the grouped data set. Then, the performance of the proposed estimator is investigated.
The result shows that the proposed estimator performance is quite well, but further research should be considered.
Keywords: Grouped data, Line transect sampling, Parametric model.
Then, the density function f ( y ) is defined on [ 0, w )INTRODUCTION

given by Burnham and Anderson [5] asThere are several methods to estimate thepopulation abundance. One of the methods used is theline transect sampling. This technique is an ideal
technique to estimate the wild population density due
to its advantages. In the line transect samplingtechnique, the density D of the interested objects on
certain area relies upon on the measured of the
distances y1 ,..., yn between the detected objects andthe line of transect, L . The main assumptions for linetransect distance sampling are the detectabilty of theobjects on the line transect should have a perfectprobability and the observed objects should be
detected at their original place. The distance sampling(DS) provides an unbiased estimator for population
abundance if these two assumptions are valid as
mentioned in Buckland et al. [3]. This lead to definethat the concept of detectability function plays themain part of the line transect sampling which can be
represented asf ( y) =g ( y)w∫ g (u)du(2)0

By assuming that all objects present on the line
and have perfect probability, Burnham and Anderson[5] proved that the density D of the observed objects
in surveyed area is related to the probability density
function (pdf) f ( y ) which determined at y = 0 as

D=

E ( n) f ( y = 0 )2L(3)where E ( n ) is the expected value of observed
objects. The density D is estimated by fˆ (0) due tothe density D depends to f (0) . According to
Burnham and Anderson [6] and Buckland et al. [2],[4], the density D is estimated byg ( y ) = P ( observing object / its perpendicular distances y ) .     

(1)The detectability function g ( y ) is assume to be a

n ˆ
Dˆ =
f (0)
2L

decreasing function on [ 0, ∞ ) . The probability ofdetection should close to one as the distance from theline increases from zero, which means that thedetection function satisfied the shape criterion( g (0) = 1) . Let y1 ,..., yn denoted to the grouped(4)

Several parametric models have been proposed toestimate f (0) , for example in Gates et al. [7],Hemingway [8], Ababneh and Eidous [1] and Saeed
et al. [10].

observed perpendicular distances which selected
randomly and independently from the line transect.
401

GEOMATE – Kyoto, Japan, 3-5 November 2021

The Model

For such a scheme it is natural, at least in large
samples, to use the unbiased estimator of Pi , therelative frequency is given as

Let y1 ,..., yn random variables and identicallyand independent distributed (iid) of size n observed
perpendicular distances which have probability
density function (pdf) f ( y ) and detection functionmPˆi = i .
ng ( y ) . Saeed et al. [10] introduced a detection
function,(g ( y; σ 2 ) =2 − e− y2 2σ 2)e− y 2 2σ 2; y ≥ 0, σ 2 > 0.

For grouped line transect data using maximum
likelihood estimator (MLE), Burnham and Anderson[6] stated that if f ( y ) is follows any pdf(s) for theungrouped perpendicular distances data, an estimatorof f ( y = 0) can validly be based on the model usinggrouped data.
Let the perpendicular distances be grouped into kintervals by cut points, a0 < a1 < ... < ak ; the i thintervals is ( ai −1 , ai ) with a0 = 0 and ak = w , w may
be chosen to be larger than the largest perpendiculardistance. Each detection object will fall into one of
the k mutually exclusive intervals, then theprobabilities satisfy the condition(5)

and the corresponding pdf is given by Saeed et al.
[10] asf ( y; σ 2 )=(2 − e( 2 2 − 1) π σ
22− y 2 2σ 2)e

− y 2 2σ 2; y ≥ 0, σ 2 > 0

(7)

(6)

The model in Eq. (6) has been studied by Noryanti etal. [9] to fit the ungrouped line transect data.

k∑ P = 1.

MATERIAL AND METHODSi =1

Parametric Model and Grouped Data
Grouped data is distances of detected objects
which has been organised in group. Distances usuallyare grouped which been binned with intervals definedby cut points. Rounding errors in measurements oftencause the data to be grouped to some degree, but it
must be analysed either it had been recorded
accurately or grouped further, in attempt to reduce theeffects of rounding on bias. Distances are often
assigned to be predetermined distance intervals,
therefore must be analysed using an appropriate
method developed for frequency grouped data.
Burnham et al. [6] gave two reasons for considering
to analysis of the grouped data. First, line transect
data are sometimes recorded in a grouped. Second,
the grouping is sometimes necessary to achieverobustness due to problems during data recording or
inaccurate field measurements.
Assume that the perpendicular distances weretaken in the field only by intervals, or for somereasons, the perpendicular distances must be analysed
as grouped data. Consider n perpendicular distances
are grouped into intervals (or bins) corresponding towhether it independently falls into one of the intervals.
The number of perpendicular distance counted
in
k
n
=
m
i
,
i
=
1,...,
kdistance interval i is miand∑1 iwhere n is the total sample size. Assume thati =eachdistance independently falls into one of the intervals
with probability Pi ; i = 1,..., n , then the distribution of
mi is multinomial with probabilities Pi ; i = 1,..., k .

(8)iConsider the perpendicular distances as a grouped
data, then, the multinomial likelihood function given
by Buckland et al. [3] as

P ( m1 , m2 ,..., mk ) =kn!
Pi mi ,∏
m1 !m2 !...mk ! i =1

(9)where mi is the number of objects counted in thekdistance interval i, i = 1,..., k , n = ∑ mi , and
i =1

=
Pi

ai

y ) dy F ( a ) − F ( a ) ,∫ f (=
i

i −1

(10)

ai −1

=
a0 0,=
a1 ,..., ak w are the cut points between
wherebins and F ( y ) is the cumulative distribution (CDF)of the pdf f ( y ) . In this case, the proposed modelf ( y ) is fitted to the grouped data by linking themodel in Eq. (6), Pi has been expressed as function ofσ2.
The Maximum Likelihood Estimator
For a given data set, the log likelihood function is
given by Buckland et al. [3] as402

GEOMATE – Kyoto, Japan, 3-5 November 2021


 k
n!
ln L (σ 2 ) ln = + ∑ mi ln Pi ,
 m1 !m2 !...mk !  i =1

The perpendicular distances for the 68 stakes
found are reproduced in Table 1.

(11)

Laake Stakes Data. Note: The
Table 1
data shown are perpendicular distances in meters
and appear on page 62 of Burnham et al. [6].

In this case, each cell probability Pi has been
expressed as

Pi
=

ai

) dy F ( a ) − F ( a ) ,∫ f ( y; σ =
2

i

i −1

(12)

ai −1

where F ( y ) is the CDF of the corresponding density

function f ( y; σ 2 ) which is given in Equation (6).

The MLE of the σ is the solution of the following
equation,2d ln L (σ )
=
dσ 2
2

k

mi

Pi

.
0.
∑=
P dσ
2

i =1

(13)

i

No

yi

yi

No

1

2.02

2

0.45

18

1.61

19

31.31

3

10.40

20

6.50

No

yi

No

yi

35

3.79

52

8.49

36

15.24

53

6.08

37

3.47

54

0.40

4

3.61

21

8.27

38

3.05

55

9.33

5

0.92

22

4.85

39

7.93

56

0.53

6

1.00

23

1.47

40

18.15

57

1.23

7

3.40

24

18.60

41

10.05

58

1.67

8

2.90

25

0.41

42

4.41

59

4.53

9

8.16

26

0.40

43

1.27

60

3.12

10

6.47

27

0.20

44

13.72

61

3.05

11

5.66

28

11.59

45

6.25

62

6.60

12

2.95

29

3.17

46

3.59

63

4.40

(11) with respect to σ 2 and solve the Eq. (13). Hence,
the MLE estimator of fˆ (0) and the density of the

13

3.96

30

7.10

47

9.04

64

4.97

14

0.08

31

10.71

48

7.68

65

3.17

15

11.82

32

3.86

49

4.89

66

7.67

interested object D based on the parametric model in
Saeed et al. [10] are given as
2fˆ ( 0 ) =,(14)
2 2 − 1 π σˆ 2

16

14.23

33

6.05

50

9.10

67

18.16

17

2.44

34

6.42

51

3.25

68

4.08

The MLE of the σ 2 is obtained numerically inorder to estimate the fˆ (0) by differentiate the Eq.

(

)

and
n ˆ
Dˆ =
f (0).
2L

RESULTS AND DISCUSSION
The stakes data set has been grouped to find thevalue of the proposed estimator fˆ (0) as in Eq. (13).
The Stakes data pooled into six intervals, eachinterval with group width is equals to 3.195. The
corresponding frequency of intervals is demonstrated
in Table 2. Based on the table, the frequency
histogram of the perpendicular distances which weregrouped into six intervals is shown in Fig.1.

(15)

Data Description
The Laake stakes data set which is given in
Burnham and Anderson [6] is reanalyzed in this study
by applying the parametric model in Saeed et al. [10].
The data were collected by using line transect
technique to estimate the density of the stakes. These
data are the perpendicular distances (in meters) ofdetected stakes from the line transect. In thisexperiment, the investigator was placed 150 stakes at
random in an area of L = 1000 meters long. Out of
150 stakes, 68 stakes were detected. The true pdf
f ( y ) for the perpendicular distances is unknown,

Table 2 The interval boundaries of six
intervals for perpendicular distances of Stakedata and the corresponding number ofobservations.
Interval
Number1
2
3
4
5
6

but the true density ( D ) of stakes is known and equals
to 0.00375stakes / m 2 , which gives the true value off ( 0 ) = 0.110294 . The value of f ( 0 ) can beobtained by using the following equation
n
D=
f (0)
2L

403

Boundaries
of intervals
0.000-3.195
3.195-6.295
6.295-9.395
9.395-12.495
12.495-15.595
15.595-31.31

Number ofobservations
23
19
14
5
3
4

GEOMATE – Kyoto, Japan, 3-5 November 2021

parametric model to fit the line transect by only
considering a grouped data set. Currently, the otherestimation methods and different interval areconsidered.
ACKNOWLEDGMENTS
We gratefully thanks to Ministry of Higher
Education of Malaysia (FRGS Grant RDU 1901190)(FRGS/1/2019/STG06/UMP/02/11) and Universiti
Malaysia Pahang for financial support under Doctoral
Research Scheme (DRS). We also thanks Ministry of
Yemen Higher Education for sponsor this researchstudy.
REFERENCES

Fig. 1 The frequency histogram of Stakes
data which grouped into six equallyintervals.
The MLEs of

D and[1] Ababneh, F. and Eidous, O. M. 2012 A weighted
exponential detection function model for linetransect data. Journal of Modern Applied
Statistical Methods. Vol. 11, No. 1, 144-151.
[2] Buckland, S. T., Anderson, D. R., Burnham,
K. P. and Laake, J. L. 1993 Distance sampling
Chapman and Hall, London.
[3] Buckland, S. T., Anderson, D. R., Burnham, K.
P., Laake, J. L. Borchers, D.L., and Thomas, L.
2001. Introduction to distance sampling Oxford
university Press, Oxford.
[4] Buckland, S. T., Rexstad, E. A., Marques, T. A.
and Oedekoven, C. S. 2015 Distance sampling:methods and applications Springer.
[5] Burnham, K. and Anderson, D. (1976).
Mathematical models for nonparametric
inferencesfrom line transect data. Biometrics,
pages 325–336.
[6] Burnham, K. P., Anderson, D. R., and Laake, J.
L. (1980). Estimation of density from linetransect sampling of biological populations.
Wildlife monographs, (72):3–202.
[7] Gates, C. E., Marshall, W. H., and Olson, D. P.
(1968). Line transect method of estimating
grouse population densities. Biometrics, pages
135–145.
[8] Hemingway, P. (1971). Field trials of the linetransect method of sampling large populations ofherbivores. The scientific management of animaland plant communities for conservation, pages
405–411.
[9] Noryanti Muhammad, Gamil.A.A. Saeed and
Wan Nur Syahidah binti Wan Yusoff
2018 ASM Sci. J. Special Issue 2018(1).
[10] Saeed, G. A. A., Muhammad, N., Liang, C. Z.,
Yusoff, W. N. S. W. and Salleh, M. Z. 2017 J.
Phys.: Conf.Series 890 IOP Publishing (p. 0121

f ( 0 ) , and thecorresponding D̂ and fˆ (0) respectively, based on the
proposed model in Saeed et al. [10], using Eq. (14)and (15) are computed depending on σˆ 2 . Table 3
shows the values of the estimators σˆ 2 , fˆ (0) and D̂ .
Based on the table, the values of the proposed
estimators fˆ (0) and D̂ quite close to the true
parameters. However, after remove the cut point of
wk = 31.31 , the values of the proposed estimatorsfˆ (0) and D̂ are improve to 0.097654 and 0.00327
stakes/hectare, respectively.
Table 3The MLE of σˆ 2 , fˆ ( 0 ) and D̂ forgrouped data.
Number ofobserved distances
Actual68
Proposed
68
67

σˆ 2

fˆ ( 0 )D̂na
44.965939.93710.110294
0.092031
0.097654

0.00375
0.003130.00327

CONCLUSIONSAs conclusion, the concept and the essential roleof distance sampling (DS) is important in theestimation of the population densities has beenpresented. Based on the results, the proposed
estimators perform better in case of n = 67 compared
to n = 68 , this due to outlier which has been
removed. Overall, the values of proposed estimatorsfˆ (0) and D̂ are quite close to the true values off ( 0 ) and D for both sample size. However, in thispaper, the work has been restricted to use the404

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

SEDIMENT ACCUMULATION ASSESSMENT DURING WET AND
DRY SEASONS IN FISH CAGE CULTURE AREA: A CASE STUDY OF
THE RANGSIT CANAL 13 IN THAILAND’S PATHUM THANIPROVINCE
Boontarika Thongdonphum1, Kittima Vanichkul1, Sangob Srimueang1 and Adun Bunchaleamchai2
1

Faculty of Agricultural Technology, Rajamangala University of Technology Thanyaburi, THAILAND
2
Faculty of Medical Technology, Rangsit University
ABSTRACTThis research investigates the sedimentation rate for assessment of sediment accumulation in fish cage culturearea, Rangsit canal 13 in Thailand’s central province of Pathum Thani. Water and sediment qualities wereconducted during September 2017 (rainy season) and March 2018 (dry season). The temperatures, dissolved
oxygen, and pH ranged between 30.5-31.5 OC, 5.54-7.78 mg L-1, and 7.7-8.1, respectively. The results revealed
that the dissolved oxygen varied from season to season (p0.05). The results indicated that the water qualities
varied in response to the seasonal variability and alarmingly higher than Thailand’s minimum threshold foraquaculture. In term of sediment qualities, variations in water content, total organic matter, and sedimentation rate
ranged between 37.4-62.4%, 67.6-97.6 mg g-dry weight-1, and 438.8-3,768.8 g m-2day-1, respectively. The results
revealed that water content and total organic matter had not different significance (p>0.05) while sedimentation
rate varied significantly (p0.05) with seasons. Such levels of total organic matter and sedimentation rate werehighly in the area, especially in the rainy season. The finding also showed that mass transfer and water inflow in
the area, especially in the rainy season, influenced on the sediment output in the water system, indicating theadequate of sediment input-output balance. The water mass transfer will require consideration for the assessmentof sediment accumulation and water resource management in the study area.
Keywords: Sedimentation rate, Accumulation, Fish cage culture

INTRODUCTION

as the continuous accumulation of sulfides, which are
toxic to benthic organisms [8]. As a result, one of themost important factors affecting the quality of fisheryproduction is the problem of sediment quality in theculture area.
Fish cage culture's impact on sediment quality. It's
necessary to monitoring and assess the sedimentquality at a level that does not have an impact onwater resources. Thus, this current research aims tocomparatively investigate the effects of seasonalvariability (the rainy and dry seasons), the sedimentquality and sedimentation rate in the fish cage area.
The findings are expected to avoid any negative
impact on the ecosystems of water resources, which
will be used for aquaculture and the production offishery products for farmers in the future.

The south Rangsit canal is located in Thailand’s
central province of Pathum Thani, about 50 km northof the capital Bangkok ]1[ . The canal is on the eastof the Chao Phraya River and approximately 54 km
in length [2]. Land utilization along the canal iscategorized into four main categories: urban areas,
agriculture, aquaculture, and industry [3]. The
Rangsit canal 13 is one of the main canal’s branch. In
fact, water resources are used in more than 1,000
cages for fish farming, such as raising tilapia, catfish,
and other freshwater fish throughout the year [4].
Aquaculture using the natural systems saves thefinancial cost on water preparation and improvement[5]. By the way, in the event of a flood, this could
have an impact on production risks. The sediment in
the water, as well as pollution from human activities,causes cloudiness. and fish disease epidemics [6]. The
canal's deteriorating biochemical composition,
including excessive nutrient loadings, is a result of
increased area utilization [7].
The process of sedimentation from the surface
water to the bottom. It can reflect the area's activity
and physical condition, as well as the effects ofprecipitation on the sediment's chemical quality. The
occurrence of anaerobic conditions in the soil, as well

MATERIALS AND METHODS
Sampling sites
In this research, eight localities with along theRangsit canal 13 were selected as the samplingstations (Fig.1) and were clustered in a ‘fish cagearea’ (Stn. 1, 4, 7, and 8), and ‘without fish cage’ (Stn.
2, 3, 5, and 6) in sampling site. The water andsediment sampling were conducted in two separate
405

GEOMATE – Kyoto, Japan, 3-5 November 2021

time periods to take account of the effects of seasonalvariability: September 2017 (rainy season) and March2018 (dry season). Under the influence of monsoon
winds, Thailand has three seasons: rainy season(May-September), winter season (October-February)and dry season (March-April) [9]. In addition, theRangsit canal 13 is under the jurisdiction of the North
Rangsit Irrigation Office, which is responsible formanagement and maintenance of the canal water for
local demand [10]. Here, water management
depending on the situation and agricultural activities.
During the dry season, for example, the water
discharge level was 64.8x106 m3/month under normal
conditions. When wastewater is present, the water
discharge rate can be increased to 181.4x106
m3/month.

freeze-dried technique, respectively.
The sedimentation rates were determined using
sedimentation traps, including two attached cylinders(30 cm height and 10 cm diameter) and made up of
two cylinders with an open top and a closed bottom.
Each cylinder was placed at the bottom and was taken
through after 24 hours. The particulate matter
generated during the investigation was measured insamples collected in sedimentation chambers
installed below all the net cages. Sedimentation trapsamples were collected and placed in polyethylenebags that kept frozen at -20 oC for no longer than twomonths before being defrosted for analysis. The
sedimentation rates were perform using thegravimetric method, consists of measuring the weightloss of components. The sedimentation rate was given
in g m-2 day-1. The water content was determinedusing an oven-drying method at 110 oC for 48 hours.
After heating the dried sediment at 660 oC for 3 hours,
the organic matter content was measured by weight
difference.
Data analysis
Physicochemical properties of the water andsediment samples associated with both sampling
periods September 2017 (rainy season) and March2018 (dry season) were determined using descriptive
statistics and presented as means ± standard
deviations (SD). T-test was used to verify statisticaldifferences between the two study periods, withp0.05.
RESULTS
Table
1
shows
the
physicochemical
characteristics of the water samples for both study
periods (rainy and dry seasons). Results revealed thatwater temperature and pH varied minimally fromseason to season (p>0.05). Sampling locations (st.112) and seasonal variability had no significant impact
on pH levels which remained relatively constantthroughout (Fig. 2c).
Table 1 Physicochemical properties of water samples(mean±SD)

Fig. 1 Sampling stations (UTM; 47P) from thesatellite image of the Rangsit canal 13 with the eightsampling stations (St.1-8)
Sample collection and analysis
The temperatures, dissolved oxygen (DO), andpH of the water samples were measured with a multiparameter probe (YSI-6600 Sonde instrument) at the
sampling sites (st.1-8). In the nutrient analysis,
surface water samples (30 cm deep) were collected
and pre-filtered using GF/F (Whatman) and retained
at 4 oC for further analysis. The ammonium (NH4+),
nitrite and nitrate (NO2-+NO3-) and orthophosphate(PO43-) concentrations were determined using a
SKALAR segmented flow analyzer, given thecorresponding detection limits of 0.70-57.14; 0.7014.28 and 0.03-3.87 µmol L-1. Samples to determine
the levels of chlorophyll a and total suspended solids(TSS) were collected and placed in polyethylene bags
that were kept away from direct light and heat. The
samples were filtered through pre-weighed GF/F.
Chlorophyll a and TSS measurements were analyzed
according to a spectrophotometric method and a

Parameters
Temp (oC)DO (mg L-1)
pH
NH4+ (mol L-1)
NO2-+NO3(mol L-1)
PO43- (mol L-1)
Chlorophyll a (g L-1)
TSS (mg L-1)

Study period
SeptemberMatch 20182017 (rainy)(dry)
30.8±0.2a
30.9±0.3a
4.9±0.4a
5.9±0.8b
7.9±0.1a
7.8±0.1a
7.6±4.3a
4.0±1.7b5.6±1.5a

6.5±0.6a

0.7±0.3a
9.2±1.9a
59.0±15.7a

1.0±0.1b
20.2±5.7b
19.8±6.0a

Note: The values in the same row with different superscript letters
are significantly different (p0.05).

406

GEOMATE – Kyoto, Japan, 3-5 November 2021

The DO varied from 4.5 to 5.6 and 5.1-7.8 mg L-1
in rainy and dry season, respectively, and showed in
response to seasonal variability (p0.05) atalarmingly higher than Thailand’s minimum
threshold of 4 mg L-1. Average levels of NH4+ varied
significantly (p0.05) with the season. Maximum
concentrations of NH4+ found in rainy season.
Observations also revealed that NO2- +NO3- and PO43were high during the dry season (p0.05).

which may have been a result of this sampling point
being situated just near an urbanization. In addition,
the average levels of chlorophyll a showed higher
than 10 g L-1 in dry season. Such levels reflected
signs of plankton blooming and the watercolor was
visibly changed.
High concentrations of TSS were found during therainy season and varied from 39.6 to 91.5 mg L-1.
Higher TSS concentrations in the st.3 area reflected
greater anthropogenic particulate loads from canaldredging. Sediment overloading as a result of landand stream erosion has serious consequences for theenvironment.

(a)

(a)

(b)

(b)
Fig. 2

Table 2 shows the physical characteristics of thesediment samples for both study periods (rainy anddry season). Water content could be defined texture
and structure of sediment, such as soil, rock, clay. WC
varied from 37.4 to 59.8% and 41.7 to 62.4% in rainy
and dry season, respectively (p>0.05). High levels
were apparent at Stn. 8 in both seasons.
TOM shows high levels, varied from 70.2 to 97.6
and 67.6 to 97.0 mg g-dry weight-1 in rainy and dry
season, respectively. Sedimentation rate varied from
1,031.3 to 3,768.8 and 438.8 to 1,699.4 g m-2 day-1 inrainy and dry season, respectively.

(c)Fig. 2Distribution of Chlorophyll a (g L-1) andTSS (mg L-1) in the Rangsit canal 13
surveyed during September 2017 and March
2018

Distribution of (a) water temperature (oC),(b) dissolved oxygen (mg L-1) and (c) pH in
the Rangsit canal 13 surveyed during
September 2017 and March 2018

Levels of chlorophyll a varies from 7.8 to 13.6 g
L-1 and 8.4 to 25.4 g L-1 in rainy and dry season,
respectively. Chlorophyll a varied significantly(p0.05) by season. High levels were apparent at St.
2 (>10 (g L-1) in rainy season, where somewhat high,407

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 2 Physical properties of sediment samples(mean±SD)

Parameters
WC (%)TOM (mg g-1 dryweight)
Sedimentation rate

(g m-2 day-1)

blooming and the watercolor was visibly changed.
Higher TSS concentrations could be reflected greater
anthropogenic particulate loads in those areas [13]and freshwater inflow and precipitation-induced
drainage influenced the TSS level. Fish cage culture,along with other activities such as agriculture, pondaquaculture, livestock, paddy fields, and household
sewage discharge, all contributed to changes in thecanal's water quality [18]. Excessive release of thisnutrient into the aquatic environment can cause
eutrophication and a variety of conflicts related to theuse of affected ecosystems [17].
Solid waste, also known as particulate organic
matter, is frequently composed of feces or uneaten
food. Organic wastes are present in the recirculation
system in three forms: settled solids, whichaccumulate on the tank's bottom; suspended solids,
which float in the water column and will not settle outof water; and fine and dissolved solids, which float in
the water column and can cause gill irritation andhealth damage to fish [19]. The potential for
sedimentation rate may be greatly enhanced by thedischarge of large pulses of water. According to [10],During the dry season, the water discharge level canbe increased to control wastewater and support
agricultural areas. The water's homogeneity could bea result of the canal's shallowness of 2.5-4.0 meters
and the water flow that flushed solid waste
downstream via the sluice gate [18]. The accumulated
water in reservoirs disrupts the river's hydromorphodynamic continuity, accumulating not only
water but also sediment transported by the river [20].
Fish farming contributes the organic matter to theenvironment. The finding indicated the high level of
total organic matter in the area. The tilapia cultivationin net cages increased the sedimentation rates ofnutrients and particulate matter [17]Study period
September 2017
Match 2018
(rainy)(dry)
46.6±9.4a
48.8±8.3a
86.9±10.1a

84.0±9.3a2,719.6±1,068.1a1,133.6±469.5b

Note: The values in the same row with different superscript letters
are significantly different (p0.05).

Fig. 2Distribution of Sedimentation rate in theRangsit canal 13 surveyed during September
2017 and March 2018DISCUSSIONS
Research showed that the temperature of thechannel water varied minimally for seasons, while thepH was consistent throughout the entire period. The
findings revealed the alarmingly higher thanThailand’s minimum threshold of 4 mg L-1, rendering
it unfit for the aquatic animals [11]. In water quality
assessment, NH4+ is an important indicator of thewater quality [11]. The NH4+ levels in water bodies
should be below 1 mg L-1 (or 71.4 mol L-1) [12], and
the PO43- levels below 1 mol L-1 to avert
eutrophication [13]. The PO43- levels in the Rangsit
canal 13 were in excess of the limit during dry season.
In addition, land use could contribute to nitrogenenrichment in the river [14], while phosphorus
emission from wastewater is prevalent in highly
populated areas [15]. Nutrients, particularly nitrogenand phosphorus, are directly related to theeutrophication of waters [16]. The wastes from fish
farming can cause problems due to the highsedimentation load of total dissolved solids andnutrients such as nitrogen and phosphorus [17]. Whenthe flow is less than 40x106 m3 month-1, the water
quality is poor [18].
Chlorophyll a showed higher than 10 g L-1 in dry
season. Such levels reflected signs of plankton

CONCLUSIONS
Effects of seasonal variability (rainy and dry
seasons) and sediment accumulation patterns on thefish farming, the Rangsit canal 13 in Thailand were
investigated. The DO showed in response to seasonalvariability at alarmingly higher than Thailand’s
minimum threshold of 4 mg L-1. Average levels of
NH4+ varied significantly (p0.05) with the season
and lower than NH4+ limitation in water bodies. The
PO43- levels were in excess of the limit during dry
season. The levels of total organic matter andsedimentation rate were highly in the area.
Sedimentation rates was high during the rainy season.
The sediment accumulation was found to be seasondependent. The results revealed that the amount of408

GEOMATE – Kyoto, Japan, 3-5 November 2021

water that flows through the system during the rainyseason is higher than the dry season. This increases
the likelihood of water bodies sediment erosion. The
water release level of the area is controlled comparedto the dry season.

Department, Thailand, assessed :https://www.tmd.go.th/info/info.php?FileID=53(April 15, 2021)[10] North Rangsit Irrigation Office, Water
Resources Management. accessed :http://ridceo.rid.go.th/pathum/rangsitn/about/abo
uthead.html, (April 5, 2019)[11] Pollution Control Department, Water Quality
Standard, assessed :http://www.pcd.go.th/info_serv/reg_std_water06.
html (March 20, 2021)[12] PHILMINAQ, Water Quality Criteria andStandards for Freshwater and Marine
Aquaculture, Mitigating Impact from
Aquaculture in Philippines, assessed:
https://www.researchgate.net/file.PostFileLoader.html?id=571b93653d7f4b012861d0a1&assetKe
y=AS%3A354056255098880%40146142499773
9. (Mar 2, 2016)[13] Thongdonphum B., Meksumpun S. and
Meksumpun C., Nutrient Loads and their Impacts
on Chlorophyll a in the Mae Klong River andEstuarine Ecosystem: An Approach for Nutrient
Criteria Development. Water Science andTechnology, Vol. 64, Issue 1, 2011, pp. 178-188.
[14] Li R., Liu S., Zhang G., Ren J. and Zhang J.,
Biogeochemistry of Nutrients in an Estuary
Affected by Human Activities: the WanquanRiver Estuary, eastern Hainan Island, China,
Continental Shelf Research, Vol. 57, 18-31.
[15] Mockler E.M., Deakin J., Archbold M., Gill L.,
Daly D. and Bruen M., Sources of Nitrogen andPhosphorus Emission to Irish Rivers and Coastal
Waters: Estimates from a Nutrient Load
Apportionment Framework, Science of theEnvironment, Vol. 601-602, 2017, pp. 326-229.
[16] Júlio César da Silva Cachoa, Rodrigo Sávio
Teixeira de Mourab, Gustavo Gonzaga HenrySilva., Influence of Nile tilapia (Oreochromis
niloticus) Fish Farming in Net Cages on the
Nutrient and Particulate Matter Sedimentation
Rates in Umari Reservoir, Brazilian semi-arid,
Aquaculture Reports, Vol 17, 2020, pp. 1-7.
[17] Montanhini R.N. and Ostrensky A., Nutrient

ACKNOWLEDGMENTS
The authors would like to extend their deep
gratitude to the Research Institute and Faculty ofAgricultural Technology, Rajamangala University ofTechnology Thanyaburi (RMUTT) for financial
support.
REFERENCES[1] Pathum Thani Office Center, Development Planof Pathum Thani Province, assessed :http://www2.pathumthani.go.th/551017.pdf,(April 3, 2019)[2] RIO 11, Data Project, South Rangsit Irrigation
Project, Regional Irrigation Office 11, Royal
Irrigation Department , Ministry of Agriculture
and Cooperatives, assessed :http://ridceo.rid.go.th/pathum/rangsits/Data.html,(Aug 22, 2012)[3] Thongkumpow J. and Sumpanpanit P., Water
Use Patterns of the Irrigation Area in Klong
Rang Sit Tai, Environmental research institute,
Chulalongkorn university intellectual repository,
1997.
[4] Pathum Thani Provincial Fisheries office.
(2016). Number of Farmers. Department of
Fisheries. Assessed :http://www.fisheries.go.th/fpopathumtani/t052.html, (April 28, 2016).
[5] Kasamesiri, P. and Thaimuengphol, V., Trophic
status of Nile Tilapia Cage Culture Areas in theCentral Part of Chi River. Burapha Science
Journal, Vol 20, Issue 2, 2015, pp. 48-55.
[6] Lebel, P., Leudpasuk, S., Lebel, L. and Chaibu, P.,
Fish Cage Culture in Upper Part of Ping River.
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Issue 2, 2007, pp. 160-170.
[7] Thongdonphum B., Meksumpun S., Meksumpun
C., Sawasdee B. and Kasemsiri P., The Predictive
Model for Biochemical Component of
Phytoplankton in the River and Estuary System of
the Mae Klong River, Thailand, International
Journal of Environmental and Rural Development,
Vol. 4-1, 2013, pp. 13-18.
[8] Faculty of Fisheries, Final report on Bang Pakong
Electricity Generating Authority of Thailand on
the Impact of Drainage from the Cooling Systemof the Bang Pakong Power Plant on Water Quality,
Sediment, and Biological Resources in the Bang
Pakong River, Kasetsart University, Bangkok,
Thailand, 2003.
[9] TMD, Climate of Thailand, Thai Meteorological

Load Estimation in the Waste of Nile Tilapia Oreochromis
niloticus (L.) Reared in Cages in Tropical Climate
Conditions, Aquaculture Research, Vol 46, 2015,
pp.1309-1322.

[18] Ingthamjitr S., Paankhao N., Lueangtongkham
W. and Oopariktatipong K., The Impact of Fish Cage Cultureon Water Quality of Taasarn-Bangpla Canal, Nakhon Pathom
Province, Thailand, Journal of Fisheries and Environment,
Vol 41 (1), 2017, pp. 37-44.
[19] Cao L., Wang W., Yang Y., Yang C., Yuan Z.,
Xiong S. and Diana J., Environmental Impact of Aquaculture
and Countermeasures to Aquaculture Pollution in China,
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pp. 452-462.
[20] Reisenbüchler M., Bui M.D., Skublics D. andRutschmann P., Sediment Management at Runof-River Reservoirs Using Numerical Modelling,
Water, Vol 12, pp. 3-17.
409

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

WATER POLLUTION OF THE JYUSHI-GAWA RIVER IN
YOKKAICHI-CITY
Yukimasa Takemoto1, Masaaki Takahashi1, Maki Ooyagi1, Shigeaki Inagaki2, Atsushi Suzuki3
1

Faculty of Environmental and Information Sciences, Yokkaichi University, Japan; 2Tomida-Chiku
Machizukuri Kyougikai, Jyushi-Gawa to Kankyou wo Mamorukai, Japan; 3Mie Prefecture Environmental
Conservation Agency, Japan
ABSTRACTThe Jyushi-Gawa river is a very short river, which flows down through the northern part of Yokkaichi-City
to Ise-Bay. Recently, around the catchment area, many factories and stores have been constructed, and waterpollution is concern. In order to confirm the influences of these factories and stores, investigations on thewater quality of the river were carried out for 10 years. In the middle of the river, inflow of the wastewater isobvious, and many pollutants (Nutrients; COD, NO3-N, PO4-P) were found.
Key words; Jyushi-Gawa, domestic water, water pollution, Yokkaichi

been built on the upstream side. In this area, apublic sewage system is not provided, andfactories are treating their wastewater using own
water treatment facility. The reservoir (space
8336m2, volume: 20325 m3) was constructed in2009 between St5 and St4, and rain water isstored there during heavy rain fall, but its effecton improving water quality is not clear.
At the midstream of the river, supermarkets,
restaurants and many houses were constructed,
and the sewage system is not provided, so eachstore is treating the domestic wastewater using theprivate water treatment equipment. However,some residents in this area that do not have theprivate sewage tank, discharge wastewater
directly into the river. Around the area (between
St3 to St4) a sewage system is currently underconstruction.
At the downstream of the river, a sewage
system has already been provided for many years,
and also, the drains of the streets are notconnected to the river for the above reasons.

INTRODUCTION
The Jyushi-Gawa river is a small river(length about 4 km) which flows down throughthe northern part of Yokkaichi city. Many cherrytrees are planted on the bank, and the river attracts
many citizens. The catchment area of the river islocated near the Higashi-Meihan Highway
Interchange. Around the upstream and midstreamof the river, waste disposal sites, factories andstores are constructed. From those developments,
water pollution of the river is considered serious[1] [2]. In order to confirm the level of thepollution, investigations were carried out for 10
years [3].
FEATURE OF THE CATCHMENT AREA
OF JYUSHI–GAWA RIVER
Almost all of the catchment area is spreadaround the upstream (around St5, in Fig.1) andmidstream (St3 to St4 in Fig.1) of the river. On
the contrary, the downstream (St1 to St3 in Fig 1)
has very small catchment area because of the river
structure (raised bed). In order to prevent floods,bank and river bed are covered with concrete. The
flow rate of the river (based on the data of thisresearch show in Fig.2) apparently increases at
St3 mainly in autumn and winter. The increase inflow rate is due to the introduction of the AsakeYousui irrigation.
The upstream side of the river is locatedaround the hillside (about 40m height), and iscovered with fields or forests. Near here the
Oyachi-Heizu waste disposal site was constructed
about 40 years ago, but to now closed. For over
10 years, many factories and warehouses have

METHODS
Investigation Period and Sites
The investigations were carried out from Jul.
2011 to Nov. 2020 twice a year. For 2 years (2015,2016) it was 4 times. The investigations werecarried out on the 5 sites (St1 to St5) shown in
Fig.1. The features of the every site are describedas follows.
St 1: Houei-Bashi bridge, near of the mouth of theriver. The current of the river is very slow, anddepth of the river is influenced by the tidal change.
St 2: Near Yokkaichi-High School. Cherry trees
410

GEOMATE – Kyoto, Japan, 3-5 November 2021

are planted around the bank. St 3: HigashikawaraBashi bridge (Ikaruga-chou district). St 4:
Upstream of the Itsuki-Bashi bridge. The width of
the river is wide, and usually the depth of theriver is very shallow. St 5: Upstream of the
Hokusei-Bypath road, and the width of the river isvery narrow.
.
Table 1

Parameters and the Analytical Methods
The outline of the survey items, method andinvestigation period is shown in Table 1.

Parameters and Analytical Method

Item
Analytical method
pH
Glass-electrodeDO
Winkler method
EC
Platinum electrode
COD
Acidic potassium per manganese method
NO3-N
UV light absorption method
NH4 ionIndo Phenol method
PO4-P
Molybdenium Ammonite method
Cl ionSilver nitrate titration
SS
Glass filter method
Flow rate
Current meter
Flow rate was calculated by current velocity, depth and width.

Investigation period (year)
2011 to 20202011 to 20202011 to 20202011 to 20202016 to 20202016 to 20202011 to 20202014 to 20202014 to 20162011 to 2020

Remarks

Except for St1

NagoyaJyushi –Gawa River

Kintetsu Tomida Station

N

Yokkaichi -City

Fig. 1

Map around the investigation site

(Yahoo Map)

411

GEOMATE – Kyoto, Japan, 3-5 November 2021

Flow rate (m3/s)
0.12

0.1

0.08

0.06

0.04

Fig.2 The flow rate of the river
0.02

0

St2

St3

St4

St5

2011.7.4
2011.10.11
2012.4.17
2012.10.30
2013.6.14
2013.11.15
2014.6.10
2014.11.4
2015.5.29
2015.8.27
2015.11.19
2016.2.18
2016.5.31
2016.8.19
2016.11.18
2017.2.16
2017.5.17
2017.8.23
2017.11.7
2018.5.22
2018.11.27
2019.6.4
2019.11.14
2020.6.25

Fig. 2 Flow rate of the river

downstream. This is considered to be theinfluence of the wastewater (Fig.5).
PO4-P: At St4 a high value was found, and it
decreases toward the downstream (Fig.6). The
reason is considered to be the influence of thewastewater.
Flow rate: At St5, the flow rate was very low,
and it has increased at St3 due to the introductionof the Asake-Yousui irrigation.
Cl ion: Some times, high values are found at
St1, and this may be due to the influence of thesea same as EC.
SS: At St4 and St5, a relatively high value
was found, which shows the influence of thewater discharged from the factories.
Relation between flow rate and COD: In order
to confirm the effect of the introducing waterfrom Asake-Yousui irrigation, the relation
between the COD value and the flow rate at St3
was investigated. The result shows (Fig.7) that
COD tends to decrease sharply with the higher
flow rate.
The outstanding pollution caused by nearby
the Ooyachi –Heizu waste disposal sites was notfound.

RESULTS AND DISCUSSION
Temperature: The water temperature became
higher at St4, and the inflow of relatively hot
wastewater and the riverbed structure areconsidered to be the cause. The temperature getslower as it goes downstream.
pH: At St5, the pH value is about 8, and tends
to increase from St4 to St2, because of theinfluence of the aquatic plants of the river [4].
EC: EC tend to be lower from St5 toward
downstream. This is considered to be due to thatthe factories discharge salty wastewater, and EC
decreases with dilution. At St1 sometimes highEC value was found, which is thought to be theinfluences of the sea.
DO: DO tend to become higher toward thedownstream especially at St2, which is thought tobe due to the influence of the aquatic plants.
However, it became low at St1 which isconsidered to be the influence of the sea (Fig.3).
COD: COD became high at St4, which isconsidered due to the inflow of the wastewateraround St4, and it gets lower toward downstream
by dilution (Fig.4).
NO3-N: At St4, relatively high values were
found, and it gradually decreased toward the412

GEOMATE – Kyoto, Japan, 3-5 November 2021

DO (mg/L)
25
20
15
10
5
0
St1

Fig.3

St2

St3

St4

St5

2011.7.4

2011.10.11

2012.4.17

2012.10.30

2013.6.14

2013.11.15

2014.6.10

2014.11.4

2015.5.29

2015.8.27

2015.11.19

2016.2.18

2016.5.31

2016.8.19

2016.11.18

2017.2.16

2017.5.17

2017.8.23

2017.11.7

2018.5.22

2018.11.27

2019.6.4

2019.11.14

2020.6.25

2020.11.27

DO of each site

COD (mg/L)
14

2011.7.4

2011.10.11

2012.4.17

2012.10.30

12

2013.6.14

2013.11.15

10

2014.6.10

2014.11.4

2015.5.29

2015.8.27

82015.11.19

2016.2.18

6

2016.5.31

2016.8.19

2016.11.18

2017.2.16

4

2017.5.17

2017.8.23

2

2017.11.7

2018.5.22

2018.11.27

2019.6.4

2019.11.14

2020.6.25

0
St1

St2

St3

St4

St5

2020.11.27

Fig.4 COD of each site

NO3-N (mg/L)
6

2016.5.31
2016.8.19

5

2016.11.18

4

2017.2.16

32017.5.17
2018.5.22

2

2018.11.27

1

2019.6.4
2019.11.14

0

St1

St2

St3

St4

NO3-N
Fig. 5 NO3-N of each site

413

St5

2020.6.25
2020.11.27

GEOMATE – Kyoto, Japan, 3-5 November 2021

PO4-P (mg/L)
2011.7.4
2012.4.17
2013.6.14
2014.6.10
2015.5.29
2015.11.19
2016.5.31
2016.11.18
2017.5.17
2017.11.7
2018.11.27
2019.11.14
2020.11.27

1
0.8
0.6
0.4
0.2
0

St1

St2

St3

St4

St5

2011.10.11
2012.10.30
2013.11.15
2014.11.4
2015.8.27
2016.2.18
2016.8.19
2017.2.16
2017.8.23
2018.5.22
2019.6.4
2020.6.25

Fig. 6 PO4-P of each site

Flow rate (m3/s)
0.12
0.1
0.08
0.06
0.04
0.02
0
0

2

4

6

8

10

12

COD (mg/L)
Fig. 7 Relation between flow rate and COD (St3)Some pollution was found at the Jyushi-Gawariver, however, the pollution is less serious than inother rivers in Yokkaichi city [5][6], and water
quality is improving due to the construction of the
sewage system.

CONCLUSIONSIn order to determine the environmental
influence caused by the development around the
Jyushi-gawa river, ten years of the investigations
were carried out. On the upstream side,
significant pollution was not found. Typicalpollutants ((Nutrients; COD, NO3-N, PO4-P) were
found in the middle of the river, which werecaused by the waste water of the surrounding area.
The pollution gradually decreases as it flows
down downstream. The introduction of theirrigation water is considered particularly
effective in improving the water quality. At St1,
which situates near the mouth of the river, theinfluence of sea water was observed. Outstanding
pollution caused by the Ooyachi-Heizu waste
disposal sites was not found.

ACKNOWLEGEMENT
The authors wish to express their appreciation
to Dr. Eric Bray (Professor of Yokkaichi
University) for his advice on making this article.
REFERENCE[1] Masaaki Takahashi, Kayoko Awaya, Mikihiro
ioka, Yukimasa Takemoto: Water and soil
pollution near Ohyachi-heizu Waste Disposal
Site, The Journal of Yokkaichi University, Vol.
11, No. 2, 2008, pp.27-31414

GEOMATE – Kyoto, Japan, 3-5 November 2021

[2] M. Takahashi, Y. Tkemoto, K. Awaya, M.
Ioka and P. Guo: Water pollution around the
Ohyachi-heizu District of Yokkaichi City, The
Journal of Yokkaichi University, Vol. 15, No.
2,2012, pp.83-89[3] Yukimasa Takemoto, Masaaki Takahashi,
Maki Ooyagi, Shigeaki Inagaki, Reigirou
Hashimoto, Kazuo Hirata and Tadamichi
Hattori, Water pollution of the Jyushi-Gawariver, The Journal of Yokkaichi University, Vol.
30, No. 2, 2018, pp.153-174[4]Dissolved Oxygen (DO) Kankyou Chousa
ButyouOkano
Takeshi,https://www.dir.co.jp/research/report/esg/keyw
ord/20130918_007694.html, Accessed Apr.
2021
[5] Sachie Takeuchi, Masaaki Takahashi,
Yukimasa Takemoto, Maki Oyagi, Satoshi

Chiba, Yuzou Morikawa, Jia Zhou, Kunihide
Miyaoka, Kaoru Ito, Takanori Terazawa:
WATER POLLUTION of KAMATANIRIVER BASIN in YOKKAICHI, SeventhInternational Conference on Geotechnique,
Construction Materials and Environment, Mie,
Japan, Nov 2017, pp. 21-24[6] Yukimasa Takemoto, Masaaki Takahashi,
Maki Ooyagi, Seiji Iwasaki, Masashi
Sakakibara, Jirou Ito, Naoki Shimomura,
Takanori Terazawa and Tadashi Kobayashi:
Water pollution of the Kaizo-Gawa river, 9thInternational Conference on Geotechnique,
Construction Materials and Environment,
Tokyo, Japan, November, 2019, pp20-22415

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

EXPERIMENTAL STUDY ON GROUND STRENGTHENING AND
ELECTRIC POWER GENERATION WITH USING RECYCLED
ASPHALT PAVEMENT MATERIAL
Shoji Yokohama1
1Faculty of Engineering, Hokkaido University, Japan
ABSTRACT

In order to secure a safe living environment against frequent natural disasters, mechanical strengthening the
ground and stable supply of electric power are required. Reusing resources is essential for developing a sustainablesocial living system. In this study, a total system that realizes ground strengthening and electric power generationby using recycled asphalt pavement material is proposed. In the proposed total system, newly made T-shaped unit
is the core tool for both of improving the bearing capacity and generating the electric power. T-shaped unit isinserted into the ground and the ground surface is covered by recycled asphalt pavement material (RAP). Bearing
capacity of the ground is improved and deformation of the ground is restrained by T-shaped unit and RAP laid on
the on the surface of ground. On the other hands, Peltier elements are built into the T-shaped unit. The electric
power can be generated by Thermoelectric effect by converting the temperature difference between the groundsurface and underground. In this study, the bearing capacity of the strengthened ground and power generation
behavior are investigated by the laboratory tests and field monitoring, respectively. It is shown that the bearing
capacity of the ground is improved by the RAP laying and inserting the T-shaped unit by the results of laboratory
tests. The electric power generation can be recognized from the result of field monitoring. Based on theseexperimental results, the feasibility of a total system for ground strengthening, power generation and constructionwaste material reusing will be discussed.
Keywords: Recycled asphalt pavement material, Ground protection, Power generation, Electromagnetic force
INTRODUCTION
In recent years, damages caused by natural
disasters have become enormous and prolonged due
to both the occurrence ground collapse and stopping
the supply of energy required for daily life by damage
to the various infrastructure. In order to deal withsuch a situation, it is beneficial for engineering thatsignificance to suggest and to research for practicaluse a method that achieves both ground protection
and stable energy supply. In addition, it is important
to make effective use of construction materials in thefield of civil engineering in order to build asustainable social system.
In this study, a concept to realize groundprotection and power generation at the same time
while reusing the recycled asphalt pavement material
is suggested and its feasibility is tried to discuss
through the experimental studies. Regarding groundprotection, a method to improve the bearing capacityof the ground by inserting a reinforcing tool into theground and generating a magnetic force around thereinforcing tool is proposed in present study. The
bearing capacity of the ground inserted thereinforcing tool were examined by the laboratory tests.
In this study, a power generation unit built inthermoelectric elements was also prepared. The
effectiveness of a power generation method of the
prepared unit utilizing the ground temperature wasFig.1 Protecting ground surface by RAP
investigated by field monitoring. Based on the resultsof the laboratory tests and the field monitoring, thefeasibility of a system that realizes both groundprotection and power generation by utilizing recycledasphalt pavement materials and ground surface
temperature are discussed.
APPLICATION OF RECYCLED ASPHALTPAVEMENT MATERIAL FOR GROUNDSURFACE PROTECTION
Before starting this study, investigating theconstruction case where the recycled asphalt416

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig.3 Concept of ground protection and power
generation by using RAP
Fig.2 Soil hardness at the protected ground surface
by RAPand protect the ground. RAP has several mechanicaladvantages [1]. The advantages are as follows;
1. The cyclic strength of sandy soil mixed with
RAP is higher than that of the plain sandy soil.
2. The bonding occurs between soil particles when
the RAP specimen is compressed with a low
confining pressure at the triaxial compression
test.
RAP is adopted as the testing material in thisstudy because these advantages are effective inrealizing the suggested system [1], [2]. In this study,
the tool used for the proposed system that realizes
ground reinforcement and power generation is called
PG unit. The PG unit has a shape in which plate andpile are connected. It is thought that groundreinforcement is realized by inserting the PG unit into
the ground and exerting friction between the PG unit
and the ground. On the other hand, the PG unit, which
is a built-in Peltier element [3] attempting to generate
the electric power using the temperature difference
between ground surface and underground. RAP
covering the ground surface is exposed to sunlightirradiation and plays a role in increasing thetemperature difference on surface of the PG unit.

pavement material (hereinafter referred to as RAP) isused as a ground surface protection material havebeen performed. Fig.1 shows the situation of theprotection of the ground surface, including the slope,using recycled asphalt pavement material, which was
carried out in Uryu-cho, Hokkaido, JAPAN. The
figure was taken in September 2020. The whole ofground surface was covered by RAP material. It isseen the ground surface was kept on stable from thisfigure. This ground protection work was carried out
in June 2009 to prevent mud situation on the groundsurface when the water content is high and to
facilitate passage of vehicle for construction work.
The compaction control at the RAP layer was notplanned.
Fig.2 shows the value of soil hardness on thesurface of slope (see the area of dotted line in Fig.1)
covered with RAP. The soil hardness was measured
by using a soil hardness tester. When the soil hardness
tester was sticked into the ground surface, the cone
was moving into the tester and the cone penetration
length could be read by the scale. The high value ofthe cone penetration length could be read if theground surface was hard. From this figure, it was
found that the soil hardness value in each spring
season was slightly lower than the value in otherseasons, but the soil hardness value was around 30
mm from 2016 to 2020. This result suggests that thelong-term protection of ground surface is possible byutilizing recycled asphalt pavement material.

MECHANICAL PROPERTIESPROTECTED GROUND

OF

THE

The ground behavior protected by the PG unit andrecycled asphalt waste material (RAP) was examinedby a laboratory test. Fig. 4 illustrates the test
apparatus. The test apparatus mainly consists of anacrylic cylindrical cell with an inner diameter of 200
mm, a loading plate, and a loading frame. Toyoura
sand (T sand) was used as the testing material, and aground with a height of about 200 mm was preparedby the multiple sieves pluviating method (MSP
method) [4]. A loading plate with a diameter of 54
mm was placed in the center of the ground surface,and a vertical force was loaded on it. The verticalloading was carried out by increasing the verticalforce by about 23 N in 10 minutes. The vertical force
and the displacement of load plate were measured
with a load cell and dial gauge connected to the test

CONCEPT OF SYSTEM FOR GROUNDPROTECTION, POWER GENERATION AND
REUSING RECYCLED ASPHALT PAVEMENT
MATERIAL
Fig.3 illustrates the concept of total system forground protection - power generation - constructionwaste material reusing. In the proposed system, theground surface is covered with RAP, and T-shaped
unit (PG unit) is inserted into the ground to reinforce
417

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig.6 Grain size distributions of RAP and T sandFig.4 Test apparatus for bearing capacity of theprotected groundFig.7 Appearance of RAP and T sandTable 1 The index of physical properties of samples

the PG unit was supplied from a power source inwhich six nickel-metal hydride batteries (6500 mAh,
1.2 V) were connected in series. The purpose of
supplying the electric current to the PG unit is togenerate a magnetic force from the PG unit and attract
the iron sand in T sand to the PG unit to improve thefriction with the ground. Fig.6 and Fig.7 show theparticle size distribution and appearance of thesamples (RAP and T sand) used in the study. Fromthese figures, it can be seen that T sand is fine sandwith a uniform particle size, while RAP has a blacksurface and a uniformity coefficient of 10 or more.
The RAP was a sample collected from a plant in
Sapporo City in August 2014, and the maximum
particle size was adjusted to 9.5 mm for this study.
The index of physical properties of each sample areshown in Table 1. It has been confirmed in advancethat the iron sand content of T sand used in this study
is about 0.43 % of the total mass of T sand.
Fig. 8 shows the relationship between the loading
stress sv and the normalized settlement of the loadingplate DH/H0 in PG unit, PG unit-EM, and T sand cases.
Where, DH and H0 are the settlement of loading plate

Fig.5 Test conditions in the laboratory test
equipment.
Fig. 5 shows the test conditions adopted in the
study. In this study, the T-shaped reinforcing tool
(called the PG unit) made of iron was inserted into theground. Enameled wire with length of 30 m anddiameter of 1.6 mm was wound in a coil around thelower pile of the PG unit. At PG unit case, the PG unit
was inserted into the testing ground. At PG unit-EM
case, the electric current was passed through theenameled wire of the inserted PG unit. The conditions
that the ground above the PG unit was replaced withrecycled asphalt pavement material (RAP) arereferred to RAP-PG unit case and RAP-PG unit-EMcase. The ground behavior was observed without any
strengthening at T sand case. The electric current to418

GEOMATE – Kyoto, Japan, 3-5 November 2021

The behavior of the sand ground protected by
RAP layer is examined. Fig. 9 shows the relationshipbetween the loading stress sv and the normalized
settlement DH/H0 obtained in the RAP-PG unit and
RAP-PG unit-EM cases. In both cases, the relative
density of the T sand ground Dr is about 85 %. For
comparison, the results of PG unit and PG unit–EM
case (see Fig. 8) are also shown in Fig.9. From thefigure, it is found that in the RAP-PG unit and RAPPG unit-EM cases, the increase in loading stress at thestage where the normalized settlement DH/H0 is small
is slower than in the test cases without the RAP layer(PG unit and PG unit–EM case), and when DH/H0
becomes about 1.1% or more, a higher loading stressvalue is exhibited compared to PG unit and PG unit–
EM cases. In the results of RAP-PG unit and RAPPG unit-EM, it could not be possible to measure thepeak value of the loading stress clearly due to themeasurement capacity of the load cell mounted on thetest apparatus, but a tendency for increasing thebearing capacity of the ground by using RAP layer is
described.

Fig.8 Bearing capacity for T sand ground protectedby PG unit

POWER GENERATION BY USING RAP AND
GROUND TEMPERATURE
Fig.10 shows the configuration of the power
generation equipment (referred to as the PG unit for
power generation) used in the field monitoring of
power generation behavior in this study. The PG unit
for power generation is mainly composed of an upper
plate made by sandwiching a Peltier element between
aluminum plates (thickness 0.3 mm, width 300 mm,
length 200 mm) and a group of members for coolingthe back surface of the upper plate. Inside the upper
plate of the PG unit for power generation, 30 Peltierelements are connected in series and arranged side byside. The Peltier element is a square with a side of 40
mm and a thickness of 4 mm, its maximum heatabsorption is 51 W, and the maximum voltage thatcan be input to the Peltier element from the outside is
15.4 V. Thermometers were attached to the front andback surfaces of the upper plate of the PG unit to get
the temperature data.
On the surface of the upper plate of the PG unit
for power generation, a heat collecting box made of aheat insulating material with a thickness of 20 mm
and an aluminum plate was attached, and recycledasphalt pavement material (RAP) was spread inside
the heat collecting box to raise the surface
temperature. A cooling agent (a commerciallyavailable product containing a highly waterabsorbent resin, etc.) was attached to the back surfaceof the upper plate, and two brass fixing bases wereattached to cool the PG unit for power generation.
When setting the PG unit outdoors, two stainless steel
tumblers filled with tap water were prepared andFig.9 Bearing capacity for T sand ground protected
By PG unit and RAP
and the initial ground thickness, respectively. The
relative density Dr of the ground is around 90 %. It
can be found that the maximum load stress in the PG
unit and PG unit–EM cases is higher than that in the
T sand case, and the maximum load stress in the PG
unit–EM is highest of the all. From the result, it canbe inferred that in addition to the effect of inserting
the PG unit, the magnetic force generated around the
PG unit exerted an effect of contributing to theimprovement of the bearing capacity in the sandy
ground.
Next, the residual state after the maximum valueof the loading stress appears is being focused. It isseen that the loading stress in the residual state in the
PG unit-EM reaches about twice the value in the T
sand and PG unit cases. This fact indicates that themagnetic force generated around the PG unit affected
the stress distribution in the ground due to the effectof attracting iron sand in the ground and the value ofthe loading stress in the residual state became higher
than in the case without magnetic force. From theseresults, it was clarified that the friction between thesand ground and the PG unit was increased by theinsertion of the PG unit and the magnetic force
generated around the PG unit, and the bearing
capacity was improved.
419

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig.10 Configuration of the PG unit for power generationaFig.11 Situation of field monitoringat [Oy] and [Sh]b

Fig.12 Voltage and temperature difference of PG unit, (a) : [Oy](Open circuit), (b) : [Sh](Closed ciicuit)inside the heat collection box.
Fig.12 (a) and (b) show the measured voltage andtemperature difference of PG unit DT at [Oy] and [Sh].
The temperature difference DT is defined as thedifference between the temperature on front surface
and that on back surface of the upper plate of the PG
unit. From Fig.12 (a), it was seen that the output
voltage was corresponding to the fluctuation of thetemperature difference DT, and the voltage sometimes
could reach 5000 mV. Such voltage behavior
indicates that the step-up DC-DC converter can be
run by the output electric power from the PG unit
when the temperature difference can be secured.
From Fig.12 (b), it was found that the temperature
difference DT reached a maximum of 13 oC, and thevoltage at the resistor of 330 W connected to thecircuit reached 400 mV. Although the voltage output
behavior depends on the season and time, according
to the suggested method in this study, it is possible tosecure the temperature difference of about 10 oC for
the PG unit, and the electric power can be generated
by running the step-up DC-DC converter.

buried in the ground so that the protrusions of the
fixed base attached to the PG unit could be inserted.
In this study, two types of electric circuits wereprepared. In each circuit, a 330 W resistor and a stepup DC-DC converter (maximum output 5V) wereconnected to the wiring from the Peltier’s element.
One of them is to connect a voltmeter with the circuitfrom the step-up DC-DC converter as an open type(referred to open circuit) in order to confirm thatwhether the step-up DC-DC converter operates. On
the other hand, a 330 W resistor and a voltmeter wereconnected to the output circuit from the step-up DCDC converter, and the power generation behavior wasinvestigated (referred to as a closed circuit).
Field monitoring were carried out at two locationsof Oyachi (denoted as [Oy]) and Shinoro (denoted as[Sh]) in Sapporo city, JAPAN. In [Oy], PG units with
open circuit was used. In [Sh], the PG unit with closed
circuit was used. Fig. 11 shows the set-up situation of
the PG unit in [Oy] and [Sh]. A tumbler filled withtap water is buried near the ground surface, the PG
unit is put on it. The recycled asphalt pavement
material is spread with a thickness of about 50 mm
420

GEOMATE – Kyoto, Japan, 3-5 November 2021

ab

Fig.13 Relationship between voltage and temperature difference, (a) : [Oy](Open circuit), (b) : [Sh](Closed
circuit)
The relationships between the output voltage andthe temperature difference DT are shown in Fig.13 (a)and (b). According to the results of [Oy] (see Fig.13(a)), when the temperature difference DT was 3 °C or
higher, the step-up DC-DC converter was activatedand the output voltage of about 5 V was observed. On
the other hand, there were cases that the output
voltage was 400 to 500 mV even when thetemperature difference DT was 5 °C or higher in Fig.
13 (b). From these results, it is recognized that theelectric power required for the running of the step-up
DC-DC converter can be produced when thetemperature difference DT could be larger than acertain value. But it is also noted that the other factor
from the temperature difference DT should beconsidered for the stable running of the step-up DCDC converter.

surface of the power generation unit was covered
with recycled asphalt pavement material. It wasalso clarified that it is necessary for achieving
stable power generation to take keeping thetemperature difference between the front andback surfaces of the power generation unit larger.
From the above results, if a reinforcing tool that is
equipped with a thermoelectric element and will not
be damaged by acting some stresses can be
manufactured, it may be possible to embody thesystem for ground protection and power generation
by utilizing the recycled asphalt pavement material.
ACKNOWLEDGMENTS
Author received generous support of the field
monitoring for the ground protection and the electric
power generation from Mr. Juichi Hatakeyama in
Uryu-cho, Hokkaido, Japan. This study was
supported by JSPS KAKENHI Grant Number
JP18K04341.

CONCLUSIONSIn this study, the concept for the implementationof ground reinforcement and power generation byusing recycled asphalt pavement material was
suggested. In order to confirm embodying the concept,
the laboratory tests on ground reinforcement and field
monitoring on power generation were carried out.
The main results obtained from the study are as
follows;
1. It was shown that when recycled asphalt
pavement material is laid on the ground surface
and a reinforcing tool is inserted into the ground,the bearing capacity of the ground can be
improved. It was also shown that the bearing
capacity can be further improved by generating amagnetic force around the reinforcing tool
inserted in the ground.
2. The output of electric power could be seen byusing the power generation unit with built-in thethermoelectric devices. The power generation
unit was put on near the ground surface and its

REFERENCES[1] Yokohama S. and Sato A., Cyclic mechanicalproperties of sandy soil by mixing recycledasphalt pavement material, International Journalof GEOMATE, Vol.16, Issue 58, 2019, pp.41-47.
[2] Soleimanbeigi, A. Tuncer B. Edil, and Craig H. B.,
Creep response of recycled asphalt shingles,
Canadian Geotechnical Journal, Vol.51, 2014,
pp.103-114.
[3] Ahiska R. , Mamur H., A review :Thermoelectric
generators in renewable energy, International
journal of Renewable Energy Research, Vol.4,
No.1, 2014, pp.128-136.
[4] Miura S. and Toki S., A sample preparation
method and its effect on static and cyclic
deformation-strength properties of sand, Soils andFoundations, Vol. 22, No.1, 1982, pp.61-77.

421

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

STUDY ON CHANGES OF PUBLIC TRANSPORT USE ANDCONSCIOUSNESS DURING THE COVID-19 INFECTION PERIOD
Ryotaro Kinoshita1, Tetsuo Morita1, Tomohiro Miyazaki 1 and Shinya Tsukada2
1

Maebashi Institute of Technology, Japan; 2Maebashi City Municipality, Japan
ABSTRACT

People who stay indoors are increasing with the COVID-19 and the spread of computers. We analyzed
changes in the public transport and consciousness and behavior during the COVID-19 infection period, using
the results of surveys in May and October in 2020, and January 2021, for perceptions on the risk of coronavirus,
attitude towards public transportations and activities. First, we analyzed the chronological relationship between
the subjective risk of infection when using public transportation and the announcement of the state of
emergency by the Japanese government. The state of emergency announcement reduced trips by people andraised the awareness of infection risk. It was also found out that people tend to overestimate the risk whenusing public transportation. Next, Factor analysis showed the potential factors behind the results of respondents’attitudes toward lifestyle and the government measures. Furthermore, changes in scores of extracted factors
were analyzed according to gender, place of residence, and time series.
Keywords: COVID-19, transportation choices, living consciousness, factor analysis, quantification method Ⅱ

INTRODUCTION

Shimada and Daito showed that among elderly or
handicapped people who need assistance whengoing out, those who frequently go out and men are
more likely to use public transportation, and womenand the older generation were less likely tounderstand the use of public transportation [4].
Yabusaki, Morishige and Morimoto analyzed
changes in bicycle usage trends from the 1960s to
the 2000s regarding the shifts of transportation
choices. They showed that the number ofmunicipalities which have a high proportion of useof bicycles as the major means of transportation,
decreased in suburban areas and increased in urban
areas from the 1980s to the 2000s. They also
showed that the number of municipalities where
citizens use bicycles frequently for trips over long
distances increased from the 1990s to the 2000s [5].
Imai, Okushima and Kondo analyzed from anenvironmental point of view and showed that selfsacrifice intention to prevent global warming was afactor related to the shifts of commuting means [6].
Regarding the relationship between COVID-19
pandemic and frequency of going out, Hiroi showedthat despite the state of emergency in April 2020
had the effect of reducing the trips for commuting
purposes, it made little change in trips for meals andsightseeing purposes. Hiroi also clarified that thehigher the frequency of going out of the person, thehigher the face-to-face rate [7]. In addition, Li, Ko,
Chang, Yen and Chen showed that there was apositive correlation between risk perception forcoronavirus infection and the degree ofcountermeasure implementation [8]. In addition,
Moriwaki, Hiyoshi and Murota analyzed an opinion
survey conducted in November 2020 and concluded

During the state of emergency due to the spreadof the new coronavirus infection, the Japanese
government called for people to avoid three Cs(Crowded, Close contact, Confined and enclosed
space), and recommended online homecoming andworking from home. As a result, people have thetendency to aware of infection risk when going one
place to another. This led to the decrease of public
transportation users, and the utilization rate of
public transportation services such as railway, bus,
ship, aviation is much lower than usual [1].
The Purpose of the Study
The purpose of this study is to clarify shifts inpublic transportation use and living consciousness
during the period of the COVID-19 pandemic
concerning the relationship between personal
attributes, place of residence, consciousness.
RESEARCH SIGNIFICANCE
Kawakami, Sugita, Morio and Morita studied
traffic behavior by cohort analysis and concluded
that the share of automobile use by women was on
the rise and that the baby boomer generation formen and the bubble generation for women peaked
and the net generation intensity began to decrease[2]. Ishii, Mori, and Aono analyzed characteristicsof the traffic behavior and concluded that thefrequency of going out and the number of trips
differ depending on occupations, annual income,and access to traffic services [3]. Yanagihara,422

GEOMATE – Kyoto, Japan, 3-5 November 2021

References for transportation choices andlifestyle consciousness before the pandemic
the 2015 National Survey of Urban Traffic
Characteristics,
the 2015 NHK National Living Time Survey

The number of cases

10000
8000
6000
4000
2000
0

References for transportation choices andlifestyle consciousness in May, 2020
the 1st Behavior / Awareness Survey on
Coronavirus (by the Committee ofInfrastructure Planning and Management)

Fig. 2 The number of COVID-19 cases in Japan

References for transportation choices andlifestyle consciousness in October, 2020the 2nd Behavior / Awareness Survey on
Coronavirus

relationship between transportation choices andlifestyle consciousness during the pandemic as of
May 2020 and October 2020, based on the results of
the 1st, 2nd and 3rd Behavior / Awareness Survey
on Coronavirus (by the Committee of Infrastructure
Planning and Management) [10]. (Fig. 1)

References for transportation choices andlifestyle consciousness in January, 2021
the 3rd Behavior / Awareness Survey on
Coronavirus

CHANGES OF THE COVID-19 CASES AND
MEASURES BY THE GOVERNMENT

Comparison for transportation choices andlifestyle awareness based between 3 surveys

The number of COVID-19 cases
The number of COVID-19 cases in Japan haschanged as shown in Fig. 2.
The first wave (March to May last year), thesecond wave (July to September last year), the third
wave (December to February last year), the fourthwave (March to May this year) The time wasdivided by comparing the increase and decrease in
the number of infected people to a wave. We also
use the terms of the 1st to 4th waves in this research.

Fig. 1 Research proceduresthat decrease in life satisfaction was seen in many
respondents, and the cause was decrease in thefrequency of going out [9].
It is clear that the means of transportation andfrequency of trips change depending on individual
attributes (gender, car ownership, family type), thepurpose of travel, and health consciousness, buthygiene have not been considered as a change factor.
There are mainly two characteristics of thisresearch: The first is to consider unprecedented
infectious disease as the factor of shifts of modal
choices and frequency of trips. The second is social
significance since this research dealt with shifts intransportation and now is an important turning pointin changing attitudes towards transportations in the
future.

The state of emergency by the government
The Japanese government has announced a stateof emergency in line with the increase in the numberof infected people. (See Table 1) The state of
emergency is an emergency measure based on the
Act on Special Measures against Novel Influenza,etc. The first announcement was in April, 2020, andit covered seven prefectures where cases wererapidly spreading. Along with this, the government
designated four prefectures as requiring special
caution due to their large number of infections. As
the number of the cases have increased nationwide,
the government increased the coverage ofprefectures, and the government eventually applied
the state of emergency nationwide. In contrast, the
2nd state of emergency was aimed at particularprefectures. Note that there was no state of
emergency during the second wave.
In this research, prefectures are classified at thetime of the announcement of the state of emergency,
and the prefectures corresponding to (1), (2), and (3)in Table 1 are designated as Area1, Area2, andArea3, respectively. (See Fig. 3)

RESEARCH METHODSIn this study, The COVID-19 infection period isdefined as the term starting from January 16, 2020
to the last day when the infection case was notconfirmed for two consecutive weeks.
We studied the relationship between
transportation choices and lifestyle consciousness
before the infection period, based on the results ofthe 2015 National Survey of Urban Traffic
Characteristics (by Ministry of Land, Infrastructure,
Transport, and Tourism) and the 2015 NHK
National Living Time Survey (NHK, Japan
Broadcasting Corporation). Then, we analyzed the423

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1 Government measures and state of
emergency
May-20State of emergency(Announcement /lifting date)Announcement (1)(April 11, 2020)
Prefectures
requiring special
caution
Additional
announcement(April 16)
lifting(May 14)lifting(May 21)
lifting(May 25)
Announcement(January 7, 2021)
Additional
announcement(January 13)
Lifting(February 2)
Additional Lifting(February 26)
Additional Lifting(March 8)

Applicable prefectures

Oct-20Saitama, Chiba, Tokyo,
Kanagawa, Osaka,
Hyogo, Fukuoka (Area 1)
the prefectures in (1),
Hokkaido, Ibaraki,
Ishikawa, Gifu, Aichi,
Kyoto (Area 2)(Area 1), (Area 2),
The rest of the Prefectures(Area 3)39 prefectures other than
Hokkaido, Saitama,
Chiba, Tokyo, Kanagawa,
Osaka, Kyoto, and Hyogo

Jan-210%Completely 0%
Approx. 10％
Approx. 30％
Approx. 50％
Approx. 70％
Approx. 90％
Completely 100%

50%

100%

Approx. 0％
Approx. 20％
Approx. 40％
Approx. 60％
Approx. 80％
Approx. 100％

Fig. 4 Temporal difference in subjective infection
risk during travel

Osaka / Kyoto / Hyogo

TEMPORALDIFFERENCESIN
SUBJECTIVE INFECTION RISK OF USING
PUBLIC TRANSPORTATION

Saitama, Chiba, Tokyo,
Kanagawa, Hokkaido
Saitama, Chiba, Tokyo,
Kanagawa
Tochigi, Gifu, Aichi,
Kyoto, Osaka, Hyogo,
Fukuoka

In 3 surveys, there were questions to fill in theperception of the probability of infection while
taking bus, train, or taxi. The response results areshown in Fig. 4. In the 2nd survey in October 2020,
the rate of respondents who estimated that the riskof infection was lower increased compared to the
1st survey in May. Then the rate of respondents who
estimated infection risk higher increased again in
the 3rd survey in January 2021. This shows that thestate of emergency announcement not only reduces
trips by people but also has a psychological effectof raising the awareness of infection risk. However,
the actual value was 1% [10], which wassignificantly different from the subjective risk ofinfection among the people.

Tochigi
Gifu / Aichi / Osaka /Hyogo / Fukuoka
Saitama, Chiba, Tokyo,
Kanagawa

Area 1
Area 2ANALYSIS OF CONFORMITY AND
POLICY SUPPORTArea 3Extraction of Factors
We conducted a factor analysis on the questions
and answers that were asked to evaluate going out
for private purpose and the announcement of thestate of emergency. At this time, it was judged thatthe number of factors was 2 based on the screen plot
and the materials of the Committee of Infrastructure
Planning and Management [10]. The factor
estimation method was the maximum likelihood
method. The rotation method was oblique rotation(Promax rotation), and the analysis targets were 10
variables common to the 1st to 3rd times. The
results are shown in Table 2.

Tokyo

Osaka
Fig.3 The classification of prefectures

424

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 2 The result of factor extraction
variables
Do you think private
meetings are bad?
Do you think it is bad when
you see people who go out
for private purposes?
Do you think going out for
private purposes is bad?
Do you think it is bad when
you see people who meet for
private purposes?
Do you think going out on
business is bad?
Should shops that do notcomply with self-restraint
requests be blamed?
Should people who infectedcoronavirus be blamed?
Do you support guidelinesthat contact with people
should be reduced by 80%?
Do you support the extensionof the state of emergency by
the end of May nationwide?
Do you support
announcement of the state of
emergency in Tokyo, Osaka,and Fukuoka?
Characteristic value
Contribution ratio
Cumulative Contribution
Name of factor

1st survey
May-20

2nd survey
Oct-20

3rd survey
Jan-21

0.871

0.025

0.941

0.162

0.948

-0.007

0.842

0.028

0.886

0.173

0.788

0.079

0.842

0.058

0.927

0.167

0.948

-0.003

0.835

0.025

0.880

0.155

0.781

0.080

0.517

-0.144

0.767

0.019

0.675

-0.135

0.415

0.140

0.469

0.328

0.490

0.179

0.391

-0.073

0.642

0.088

0.505

-0.081

0.064

0.886

0.189

0.673

0.007

0.714

-0.041

0.811

0.138

0.943

-0.0470.947-0.077

0.730

0.105

0.911

0.016

0.877

3.96
39.6%
39.6%
Cnfr.

1.56
15.6%
55.2%
Pol. Sup.

4.67
46.7%
46.7%
Cnfr.

2.11
21.2%
67.8%
Pol. Sup.

4.39
43.9%
43.9%
Cnfr.

1.86
18.6%
62.5%
Pol. Sup.

Note: Abbreviations: Cnfr. = Conformity, Pol. Sup. = Policy Support

The Results of Factor Analysis

the sense of conformity.

There were no variables that linearly combine.
Factor 1 included items such as "Do you thinkprivate meetings are bad?" "Do you think it is badwhen you see people who go out for private
purposes?" "Should shops that do not comply with
self-restraint requests be blamed?", thus we named
the factor "Conformity". Factor 2 was named"Policy Support" because it contained variables
related to "guidelines that contact with people
should be reduced" and "the state of the emergency".
Since the cumulative contribution rate after factor
extraction also exceeded 50%, we judged theapproximate accuracy was maintained.
As shown in Table 2, the contribution rate ofpressure to private meetings and going out was highin May 2020. In contrast, in October 2020 and in
January 2021, the contribution rate of fear to private
meetings and going out was high.
Therefore, it can be seen that the proportion of
consciousness for private behavior was raised under

The Relationship between the Extracted
Factors and Gender and Place of Residence
The relationship between the extracted factors
and individual attributes was calculated by theaverage score for each attribute, and the time-serieschanges were analyzed. At this time, it was pooled
once and factor analysis was performed. The higher
the factor score, the stronger the conformity or the
more supportive the policy.
Also, Fig.4 shows that women have higher
conformity than men and were more likely tosupport policy. In chronological order, it was foundthat October 2020, when no state of emergency wasannounced, had the weakest conformity for both
men and women among the three points.
According to Fig. 5, it was Area 2 residents who
had strong conformity in their place of residence
and they were likely to support the policy. The
prefectures in Area 2 include many prefectures with425

GEOMATE – Kyoto, Japan, 3-5 November 2021

0.2

0.1

Support for Policies

0.3

May - Women

Jan - Women

Oct - Women

Conformity

0
-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3
0.4
May -Men

0.5

0.6

-0.1

Jan - Men

-0.2
Oct - Men

-0.3

Note: May=May 2020, Oct=October 2020, Jan=January 2021.

Fig. 4 Extracted factors and Gender
0.3

0.1

Support for Policies

0.2

May - Area2

May - Area 3
Jan -Area 2
May - Area1
Conformity

0
-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

Oct - Area2
-0.1

-0.2
Oct - Area1

Jan - Area1

Jan - Area 3

Oct - Area 3-0.3

Note: May=May 2020, Oct=October 2020, Jan=January 2021.
Prefectures in each Area are shown in Fig.3.

Fig. 5 Extracted factors and Place of residencea smaller population, and the state of emergency
was applied later than prefectures in Area 1. Due tothe rapid spread of infection, measures similar to
those in Area 1 were taken, and as a result,awareness for others and policies became stronger.

As for chronological changes, as with gender,
respondents had the strongest sympathetic pressure
and supports the policy in May. It can be judged thatthe declaration of the state of emergency raised theconformity.
426

GEOMATE – Kyoto, Japan, 3-5 November 2021

Transport, The Public Transport Management
Report, 2021 [in Japanese][2] Kawakami S., Sugita H., Morio J., Morita T.,
Analysis on Changes in Travel behavior
characteristics Focused on Life Stage, Period
and Generation -Based on the Five Time Data ofTokyo Metropolitan Area PT Survey-, Journalof Traffic Engineering, Vol.55, No.2, pp.174181, 2020.
[3] Ishii R., Mori Y., Aono S., Study on Traffic
Behavior
Characteristics
Focusing
on
Transportation Service and Personal andHousehold Attributes Using Web Questionnaire
Survey in Tokyo Metropolitan Area, Journal of
Traffic Engineering, Vol.5 ， No.2, 2019, pp.
B_49-B_58.
[4] Yanagihara T., Shimada M., Daito T.,
Consideration About the Special Quality
between the Going out Frequency and Travel
Behavior of the Elderly or People with
Restricted Mobility, Journal of Japan Society of
Civil Engineering D3, Vol.3, No.5, 2017, I_761I_769.
[5] Yabusaki R., Morishige Y., Morimoto A.,
Analysis of long-term Changes in the Bicycle
Use by Use of the PT Survey Data, Journal of
Traffic Engineering, Vol.6, No.2, 2020, pp.
A_97-A_104.
[6] Imai Y., Okushima M., Kondo A., Structural
Analysis of Environmental Consciousness withSocial Interaction Related to Modal Shift ofCommuting Trip, Vol.68, No.5, 2012, I_607I_614.
[7] U H., A Study on the Effect of Japanese-style
Lockdown on Self-restraint Request for
COVID-19, Journal of the City Planning
Institute of Japan, Vol.55, No.3, 2020, pp.902909.
[8] Dian-Jeng Li, Nai-Ying Ko, Yu-Pimg Chang,
Cheng-Fang Yen, Yi-Lung Chen, Mediating
Effects of Risk Perception on Association
between Social Support and Coping with
COVID-19: An Online Survey, International
Journal of Environmental Research and Public
Health, Vol.18, No.4, 2021
[9] Moriwaki Y.,Hiyoshi N., Murota M., A study
on changes in going out behavior, outdoor
space usage and life satisfaction with COVID19, Reports of the City Planning Institute ofJapan, No.19, pp.435-438.
[10] Japan Society of Civil Engineering, theCommittee of Infrastructure Planning andManagement, the Behavior / Awareness
Survey on Coronavirus [in Japanese][11] Hirayama N., Factors that influence on theinfection
prevention
measuresduringpandemic period, Journal of Society for
Environmental Economics and Policy Studies,
Vol.14, No.1, 2021, pp_43-pp_46 [in Japanese]

CONCLUSION
We analyzed the means of transportation andchanges in consciousness as of May 2020, Octoberof the same year, and January 2021, according to theopen data of the Infrastructure Planning ResearchCommittee.
As a result, the following 2 points were found.
First, the subjective risk of infection when using
public transportation was the highest in May andJanuary when the state of emergency wasannounced, and it was thought that the risk ofinfection tends to be overestimated due to theincrease in the number of infected people and theannouncement of the state of emergency.
Second, the potential factors of sympatheticpressure and policy support were extracted, andscores of each factor change according to gender,
place of residence and time series. Moreover, andthe sympathetic pressure that affects subjective risk
perception was the lowest in October among thethree points in time.
Furthermore, it was found that the state of
emergency influences subjective risk perception
and changes in the frequency of going out. We will
look into changes in frequency of activities such ascommuting, shopping, or entertainment, before
self-restraint, in May and October 2020, and in
January 2021.
We showed the attributes and ideas that lead toestimate the higher infection risk when using public
transportation, the characteristics of people who arelikely to switch to automobiles, and who continue
to use public transportation in the future. As a result,when a company plans an event, it is possible toeffectively narrow down the target customer. For
example, automobile companies can narrow down
their new target customers to women, young people,
and the elderly are more likely to switch their meansof transportation to automobiles. On the other hand,operators for public transportations would be able
to plan for trains or buses departing from and arrivein populated areas (Area 1, in this study) targetingmen and people who have a higher tendency of nondaily activities. Thus, this study can be used as atool in the urban economy.
Although the number of trips has decreased ingeneral, it remained to be clarified in terms of theratio of transportation modes in this study. Thus, wewould like to work on changes in transportation use
in each Area. It will also be necessary to analyze
how subjective risk perception changes dependingon the number of the announcement of the state of
emergency.
REFERENCES[1] The Research Institute for Local Public427

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

THE ISO DESIGN GUIDE FOR GEOSYNTHETIC BARRIERS AND
THE USE FOR SAFE AND ECONOMIC BARRIER SOLUTIONS
Kent von Maubeuge1 and Jake Stoyle2
1

NAUE GmbH & Co. KG, Germany; 2NAUE Geosynthetics Ltd., UK
ABSTRACTOver the past 40 years, the advantages in utilising geosynthetic barriers versus traditional barrier materials havebeen well documented, e.g., greater project economy, extended service lives, enhanced environmental protection,and greater site safety. Achievements such as conserving water resources and enabling beneficial site reuse haveeven given geosynthetic engineering a level of importance. As such, the use of geosynthetic barriers has
increasingly been required. However, there are regions and applications in which the use of these barrier
technologies should be more widely adopted. This paper highlights the principles and practices of design using
geosynthetic barriers and takes into account a number of different parameters. An ISO design guide aims to assist
the process by identifying the various characteristics of barrier types and comparing them with the requirementsof a variety of different applications. It also offers design advice to professionals involved in the design of civilengineering and construction solutions using geosynthetic materials. Overall, the intent is to encourage appropriate
selection of materials and design methods to suit particular applications, rather than to redesign projects to suit
predetermined materials. This paper will describe both the process and outcomes of a new ISO design guide forbarrier systems, some of the challenges and make recommendations how it can be used to improve regulations.
Further, an example of successful use of barrier systems for encapsulation of debris from Fukushima with aninnovative barrier system is described.
Keywords: Geosynthetic clay liner, Barrier, Multicomponent, Guideline, Liner

INTRODUCTION

The use of geosynthetic barriers continues to growinternationally, but more regulatory support is needed
and also a better understanding of the limitations
when designing with these types of geosynthetic
barriers as a wrong design can cause a risk to thedesign without the geosynthetic being the pulling
trigger.

Geosynthetic barriers are an established productgroup in the geo-environmental industry. They
include factory-made polymeric geomembranes (e.g.,
HDPE), bituminous barriers (bitumen attached togeotextile), and geosynthetic clay liners (withclay/bentonite core). These geosynthetic materials are
accepted as barrier solutions for landfill caps and baseliners, under roadways and railways, and with various
containment structures such as dams, canals, ponds,
rivers, and lakes. They are also used forwaterproofing of buildings and similar structures.
Advantages of geosynthetic barrier systems vs.
traditional designs include:
•
•
•
•
•
•
•
•
•

BRIEF HISTORY OF GEOSYNTHETICBARRIER SYSTEMS
Geomembranes (ASTM D4439 [1] definition: anessentially impermeable geosynthetic composed of
one or more synthetic sheets) can be either smooth or
textured on the surface, are essentially impermeable
and are used as fluid barriers. Textured surfaces
provide an enhancement of frictional characteristics,
which allows designs on steeper slopes or whereshear stress occurs. Geomembrane liner materials
belong to the group of geosynthetic polymericbarriers (GBR-P) and the terminology of these typesof products are defined by ISO 10318 [2] as follows:
Factory-assembled structure of geosynthetic
materials in the form of a sheet in which the barrier
function is fulfilled by polymers other than bitumen.
Geosynthetic clay liners (GCL) (according to
ASTM 4439: a manufactured hydraulic barrier
consisting of clay bonded to a layer or layers ofgeosynthetic materials) also known as geosynthetic
clay barrier (GBR-C) according to ISO 10318 [2] are:More economical to produce, transport, andinstall (quicker and simpler)
Enable predictability designs
Reduced excavation required (e.g., less fillrequired, less land disturbed)
Clear, established quality controls fromproduction through installation
More homogeneous than soil and aggregatesLess environmentally sensitive and lower
environmental impact
Improved performance and durability
Better sustainability
Better resilience
428

GEOMATE – Kyoto, Japan, 3-5 November 2021

Factory-assembled structure of geosynthetic
materials in the form of a sheet in which the barrier
function is fulfilled by clay.
Most recently, multicomponent GCLs (Fig. 1) areintroduced to the market and enable to challenge
particular site conditions (prevent desiccation,
perform against critical chemical fluids, reduce root
penetration, under extreme hydraulic gradients, etc.)
where the use of GCLs has previously been limited.
The following definition proposals are currently
being used in ASTM D35 standards: multicomponent
GCL, a GCL with an attached film, coating, or
membrane decreasing the hydraulic conductivity orprotecting the clay core or both.

benefits and it is these benefits that are attracting
more clients and construction professionals to theiruse. Geosynthetic barriers can often reduce theamount of excavation and fill material. They also
provide a number of design benefits both technicaland aesthetic. These benefits can have an effect on thecost of a project, and many solutions using
geosynthetic barriers as opposed to more traditional
methods have resulted in reduced costs. In addition tocost, sustainability of a construction solution is also
very important. Hopefully with government targets
and legislation, designers, owners, operators andmanufacturers are being driven towards reducing
their carbon footprint and carbon emissions (Fig. 2).
And besides the design benefits provided bygeosynthetic barriers, they have also been shown to
reduce the carbon footprint of a number of
construction projects [3]. Geosynthetic barriers arewidely used in all types of applications where liquid
or fluid tightness is required to prevent any kind of
inflow or outflow. In the following the most known
applications are described.

BARRIER APPLICATIONSWith the increasing number of geosynthetic
barriers and the increasing potential of barrier
applications, geosynthetic barrier systems are
becoming a very important part of the constructionindustry. Geosynthetic barriers provide a number ofFig. 1

Multicomponent geosynthetic clay liner – attaching an extruded polymer coating to the GCL

Fig. 2 Comparison of CO2 emissions of a GCL (left) and a compacted clay liner (right) in a 36,000 m² large
landfill cap [3]429

GEOMATE – Kyoto, Japan, 3-5 November 2021

Landfill Barrier Systems

laws apply, country by country; but the mining
industry is unique in that it increasingly adopts
geosynthetic barriers primarily for economic
advantages.

Many modern landfills constructed over the past
decades are performing very well and this is due tothe fact that the content of landfills is consideredhazardous for the environment and that landfills are
typically located in cities or at least nearby to them,so that the effect on the environment is noticeably inan early stage.
Further regulations have been developed in thelast decades, ensuring proper design and proper
requirements to ensure that the geosynthetic barriersystem in the base or as a cap will last far beyond theestimated design life of the landfill structure. There
are several factors which contribute to the protectionof the environment, but one key factor is the use of apolymeric geomembrane and/or a geosynthetic clay
liner (GCL).

Environmental Protection in Infrastructure
Applications
Groundwater protection is generally required
where a road enters a groundwater sensitive area, toavoid damage from winter maintenance with de-icing
salt, everyday pollution arising from motor vehicles,
and to protect the area from accidents with thepossible release of polluting substances [4]. This
German Guideline of the RiStWag [5] was one of theearlier guidelines on this topic (first publication 1982).
The guideline describes, among other things,
geomembranes and GCLs for environmental
protection (Fig. 3).

Surface Impoundments
Encapsulation of Contaminated Soils
Numerous national regulatory bodies have passed
wide-reaching clean water legislation. Many of theseregulations require the use of geomembrane liner
systems in treatment lagoons at publicly operated
wastewater treatment plants. These are also being
used in potable water reservoirs (e.g., liners andfloating covers). Here, these materials and systems
have helped conserve water annually by minimisingwater seepage.
Also, storm water retention and detention
management increasingly require smart lining
solutions and include geomembranes, geosynthetic
clay liners (GCLs) and multicomponent lining
systems.
Geosynthetic products can also be used forpractical or decorative pond liners at golf courses,
amusement parks, and resorts, as well as inagriculture and aquaculture to create healthier, more
efficient, and cost-effective systems.

Road noise and view-blocking barriers alongroads and railway lines can be built with lightly
contaminated soils or recycling material [6]. This
may include slag, ash, contaminated soils fromremediated sites, and residue from construction waste
recycling or industrial processing. These wastes mustmeet certain environmental-chemical requirements
and must be provided with a surface sealing cap(Fig. 4) to prevent water penetration and any
contaminant transport. Suitable sealing materials forthese purposes include GCLs and geomembranes.
Water Conveyance in Canals
Government agencies such as the United States
Bureau of Reclamation (BuRec) indicate that seepage
from unlined irrigation canals and waterways may besubstantial and costly; and that geosynthetic barriers
offer economically flexible and highly effective
performance enhancement for canals. They are
effective alternatives to concrete, asphalt orcompacted clay soils. Stark and Hynes [7]summarised
numerous
geosynthetic
barrier
installations in canal systems, including single
geomembranes (various polymers), exposed andburied installations, and composite systems, such as
geomembrane with geotextile protection or concrete
cover.
In Germany, all important technical information
on waterway lining systems has been collated in the
new guideline, “Recommendations for the use oflining systems on beds and banks of waterways.” The
guideline, taking into account local boundary
conditions, provides liner system selection
information to be used by agencies. The focus isprimarily on the underwater installation of GCL.

Mining Applications
The daily mining rates, scale of single-site
operations, and costs associated with mining increase every year. Advances in extraction
technologies have greatly increased recovery rates
from ore bodies. Geomembranes and GCLs under thelarge leach pads prevent the loss of valuable metalladen chemical solutions while protecting soils andgroundwater.
Geosynthetic barriers are also used to recapture
and recycle harmful chemicals on site and insecondary containment applications.
Geosynthetics can aid in channelling surface
water run-off and in preventing rainwater intrusion
into heap leach pads, thus minimising solution
dilution. In general, few regulations govern mining
usage of geosynthetic barriers. Basic environmental
430

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 3Groundwater protection for a road dam construction [according to 5]

Fig. 4 Typical cross section of geosynthetic barrier system for the encapsulation of contaminated soils in road
constructions
Dams – Levees – Dykes
Geosynthetic barriers provide easy-to-implement
solutions for dams and flood defences, with project
records extending back 40 years or more. Whenwaters rise in a levee system, the integrity of the levee
itself may be at risk. If water permeates the earthen
structure, it weakens the dam stability andprecipitates failure. Geosynthetic barriers resolve
threats like this, often in ways that can be adapted
easily to the local conditions. For flood protection anddam designs (Fig. 5), geosynthetic barriers(geomembranes and GCLs) provide long-term
sealing performance, prevent permeation through theearthen construction and may reduce the dam or levee
stability.

Fig. 5 Geosynthetic clay liner as hydraulic barrier
in a water storage dam

431

GEOMATE – Kyoto, Japan, 3-5 November 2021•

Coal Ash Storage
In recent years, there have been several tragic
failures of coal ash storage facilities worldwide andinmany
documented
cases
groundwatercontamination at or nearby coal ash storage facilities.
This contributed to the US EPA‘s issuance of new
regulations for the storage of coal ash which require
the use of geosynthetic materials. The US EPA
regulations for coal ash storage propose the mostefficient and effective barrier system as a composite
liner system using a primary geomembrane (GM)liner, in combination with a compacted clay liner(CCL), approx. 500mm thick, or a needle-punchedgeosynthetic clay liner (GCL).

••

THE ISO DESIGN GUIDEThis new design guide ISO/DTR 18228-9 [8] isintended to offer advice to designers as to what toconsider when using geosynthetics in a particular
civil engineering or construction design [9]. As suchit needs to cover a range of applications, material
types, climatic and geological issues. Further, it
covers installation and site preparation / completion
recommendations. The emphasis is on choosing themost appropriate type(s) of material(s) for theapplication rather than changing the design to suit aparticular material.
A possible whole process (Fig. 6) is organised intoa basic flow chart to guide designers through theprocess of choices which need to be considered when
designing with a geosynthetics barrier and thepossible actions are described below.

•

•

•

One topic which the standard does not address iscosts, not because these are not important but they canvary enormously according to the availability of typesof materials, transport distances and costs, installation
expertise etc. Good quality design needs to consider
the cost effectiveness of any solution but must firstqualify and meet all technical and service
expectations of the stakeholders in the end use.
Additionally, as not mentioned yet, a good quality
control and assurance plan from the manufacturing to
the installation is also required. One set of good guidespecifications and quality control on the
manufacturing side is documented in the GSI
specifications and they have seen worldwide
implementation and use. The test methods include
ASTM and ISO standards and have harmonised
ASTM and ISO tables. The specifications are underFig. 6 Organised basic flow chart for designers
when considering a geosynthetic barrier••

Investigation of site-specific parameters: This isa very important part, as all site-specificconditions need to be evaluated and they haveinfluence on the performance of the barriersystem. It includes soil types on site, inclinations,weather conditions, vegetation, burrowing
animals, etc.
Installation considerations: Geosynthetic barriersystems are likely to be less sensitive to
installation than traditional barrier systems likecompacted clay liners, but they are still
considered as relatively thin elements. Therefore,
the installation process as well as the surrounding
soil types need to be considered as well.
Selection of barrier system: Balancing the
combination of often conflicting performance
criteria and different GBR materials to the
proposed installation is always a complex matter.
This inevitably comes down to professional
judgement which geosynthetic barrier system
suits best for the selected application.
Define long-term and durability values: For a
few applications there are well federal regulated
geosynthetic barrier requirements. This processensures national consistency and minimum
standards while providing flexibility to states in
implementing rules. It should be noted that thebaseline of known requirements is pretty strict inmaterial properties (e.g., GRI-GCL-3 [10] andGM-13[11]) and design selections are also
sometimes available (e.g., GRI-GCL5 [12]).
Select possible additional safety factors: Areassuch as subgrade preparation, slope stability,
climatic conditions, protection and hydraulicuplift, installation parameters and types of CQA
are also considered as to what effect these may
have on the design as well as what additional
factors of safety they may offer.
Specify entire barrier system: Obviously, a well
prepared and written complete specification will
avoid issues and questions on site.

Selection of application: The first part of theprocess asks to select the application. With that itis necessary to check local regulations andrequirements.
Evaluation of design life: During this evaluation
process, it is necessary to find out what the life
time expectations should be, knowing that noconstruction method lasts forever. Realistically,
they can be 5 (e.g., temporary solutions), 25 (e.g.,secondary containment), 50 (e.g., containment
non-landfills) or 100 years (e.g., landfills).
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GEOMATE – Kyoto, Japan, 3-5 November 2021

constant review and are updated frequently(http://geosynthetic-institute.org/specs.htm).

approach of the ISO design guide is a good start what
the expectations are.
Designers, authorities or other involved parties
should recognize that geosynthetics cannot be
specified purely on properties. It is important to see
the big picture and consider all facts of the project,such as the German RiStWag guideline indicates inFig. 7.

GEOSYNTHETICS NEED TO BE INCLUDEDIN REGULATIONSThere is every reason to believe that geosynthetics
will continue to be adopted into regulations aroundthe world. No other field of engineered materials hasdeveloped as rapidly or gained such wide-spread
acceptance as geosynthetics. This has much to dowith the innovation and quality control measures inmanufacturing and care of handling in the field. It
also has much to do with geosynthetics being used in
two primary situations: to perform better and/or more
economically than traditional geotechnical designs.
With a large record of data in support of cost andperformance measures, and with secondary benefits
such as decreased project carbon footprints withgeosynthetics, the field’s growth is assured.
It is absolutely necessary that regulations include
application accepted geosynthetics in theirregulations to allow cost-effective and highperforming solutions as they are already state-of-theart and state-of-practice.

CASE STUDY: FUKUSHIMAWASTE STORAGE

NUCLEARIn March 2011, the Fukushima Nuclear Power
Plant was severely damaged by an earthquake andtsunami. The subsequent radioactive leakage at thesite led to the event being classified as a Level 7 on
the International Nuclear Event Scale (INES). To date,the only other disaster to equal this radioactive threat
to a population was the 1986 Chernobyl disaster. The
epicentre of the earthquake was 72 km from the areain the Pacific Ocean. At 9.0 – 9.1, it ranks as the mostpowerful earthquake ever recorded in Japan. The
tsunami waves produced by the quake were up to 40
m high.
The waves that reached the Fukushima Nuclear
Power Plant were roughly 14 m high, which allowed
them to overtop the seawall around the plant. Fourreactors were flooded and the emergency generators
failed, shutting off the coolant circulation pumps.
With the cores not being cooled, the plant
experienced nuclear meltdowns and hydrogen
explosions in three of the four reactors that had
suffered flooding. Radiation was released over aseries of days. More than 154,000 people were
evacuated from the area.

ISO DESIGN GUIDE AS THE BASIS FORREGULATIONS
In many parts of the world, landfills are very well
regulated by the federal government agencies throughthe process known as rulemaking. Such processes
should also be introduced in other application
regulations to ensure national consistency andminimum standards while providing flexibility tostates in implementing rules. In general, following the

Fig. 7 Additional aspects for barrier systems requested by the German RiStWag guideline for groundwater
protection in infrastructure projects [5]433GEOMATE – Kyoto, Japan, 3-5 November 2021

In the years since the disaster, engineers haveworked to design temporary and final disposal sites
for the contaminated soils and waste from Fukushima.
Geosynthetic containment has been a key part of theInterim Storage Facility (ISF) plan to enable waste to
be stored without negatively impacting the soils
beneath the ISF itself.
The primary ISF, which is in operation, will
ultimately house 14.1 million m³ of contaminatedmaterial until a final disposal site is finalised. The
contaminated material arrives at the initial soil
separation facility in flexible bulk bags. A series of
conveyor belts feeds the bag through a bag opener,
sieving machine, soil improvement, removal of bag
waste, a secondary sieving machine, removal ofplant-based material, and finally the separated soil
piles that are ready for interim storage.
In the soil storage area, the separated soil isdeposited from trucks onto covered conveyor belts
that deposit the waste in the lined disposal cell. A
leachate collection system controls seepage andtransports any leachate to a special treatment facility.
Due to the importance of the barrier function, the ISF
disposal cell is lined with a geosynthetic clay liner(GCL). Approximately 20,000 m² of amulticomponent GCL material was supplied in
March 2018 (Fig. 8).

Fig. 8This particular GCL is one of the newer types
within this barrier class. It is a multicomponent GCL
that utilises an extruded, polyethylene coating on one
side to enhance the material’s performance against
risks of desiccation, cation exchange, gas emission,and bentonite erosion. The special coating ensuresthat the GCL’s ability to perform its impermeablebarrier function is optimised. For a highly sensitive
waste disposal site requiring long-term containment,
it is a strong design strategy. The ISF is expected toreceive all planned soils and waste by the end of 2021.
The ISF project is not the first use of geosynthetic
clay liners in the management of contaminated soils
from the disaster. In 2013, one of the early projects
involved the installation of a GCL cap overcontaminated soils. That project used a needlepunched GCL that has additional bentonite powder inthe entire cover nonwoven to create self-sealing
overlaps for a quicker, even more secure overlap
sealing.
The use of GCL for lining the base and slopes of
the contaminated soil storage facility and for capping
contaminated soils to prevent the migration ofpolluted material emphasises how much trust
engineers and environmental regulators have in thesebarrier systems.

Installation of the PE extrusion-coated multicomponent geosynthetic clay liner

434

GEOMATE – Kyoto, Japan, 3-5 November 2021

CONCLUSION

C 2K10; 3rd International Symposium Geosynthetic Clay Liners, Germany, 2010[4] von Maubeuge, K. P., and Post, A., Regulated
groundwater protection in roads withgeosynthetic barriers, Proceedings of the 17th
African Regional Conference on Soil
Mechanics and Geotechnical Engineering. 7, 8
& 9 October 2019 – Cape Town
[5] RiStWag FGSV Nr. 514, Richtlinien fürbautechnische Maßnahmen an Straßen in
Wasserschutzgebieten (translated: Guidelines
for Construction Projects in Waterways ofProtected Areas), Forschungsgesellschaft für
das Straßen- und Verkehrswesen, Germany,
2016[6] MTSE FGSV-Nr. 559, Merkblatt überBauweisenfürtechnische
Sicherungsmaßnahmen beim Einsatz von Böden und
Baustoffen mit umweltrelevanten Inhaltsstoffen im Erdbau (Recommendation on
construction methods for technical safetymeasures when using soils and building
materials with environmentally relevant
substances in earthworks). Forschungsgesellschaft für das Straßen- und Verkehrswesen, Germany, 2017[7] Stark, T.D. and Hynes, J.M., Geomembranes for
Canal Lining. Proceedings of Geosynthetics
2009,
Industrial
Fabrics
Association
International, Roseville, Minn., USA[8] ISO/DTR 18228-9, Design using geosynthetics— Part 9: Barriers, 2021
[9] von Maubeuge, K. P., Geosynthetic barriers in
regulations and recommendations in line with
the ISO design guide? Innovative Infrastructure
Solutions, Springer Nature Switzerland AG
2018[10] GRI-GCL3, Standard Specification for Test
Methods, Required Properties, and Testing
Frequencies of Geosynthetic Clay Liners(GCLs), Geosynthetic Institute, Folsom, PA,
USA, 2016[11] GRI-GM13, Test Methods, Test Properties andTesting Frequency for High Density
Polyethylene (HDPE) Smooth and Textured
Geomembranes, Geosynthetic Institute, Folsom,
Pennsylvania, USA, 2021
[12] GRI-GCL5, Test Methods, Required Properties,
and Testing Frequencies of Geosynthetic ClayLiners (GCLs). Geosynthetic Institute, Folsom,
Pennsylvania, USA, 2013

In many parts of the world governmental agencies
have mandated the use of geosynthetics in many
applications. The strongest sector seems to belandfills.
It is essential that their use is made mandatory in
regulations in order to have efficient solutions withlarge potential on cost savings, safety of the designed
structures and a minimum of environmental pollution
including large reduction of the use of natural
resources.
Most regulations describe material properties indetail or refer to existing specifications. However,some regulations show a shortage on other relevant
design parameters, such as design issues, external
effects, durability issues, installation considerations
and/or quality control/assurance. The current versionof the ISO design guide (approved and ready for
publication) ISO/DTR 18228-9 “Design using
geosynthetics — Part 9: Barriers” is an international
standard containing recommendations and guidance
for the design of geosynthetic barriers in geotechnicalapplications. It provides design guidance over various
applications, design lives, material types, parametersand site-specific conditions. Obviously professional
judgement is still needed in all designs and this guide
will not substitute that as the document is intended
only to assist in the process by identifying parameterswhich are relevant.
Good quality design needs to consider the costeffectiveness of any solution but must first qualify
and meet all technical and service expectations of the
stakeholders in the end use. All engineers must havea current working knowledge of the sort of costs
incurred by their designs but the view was taken thatto try and incorporate such parameters into thestandards would be virtually impossible and almost
certainly inaccurate.
REFERENCES[1] ASTM D4439. Standard Terminology for
Geosynthetics, ASTM International, WestConshohocken, PA, United States, 2020
[2] ISO 10318, Geosynthetics – Part 1: Terms anddefinitions, 2015[3] Egloffstein, T. A., Heerten, G., von Maubeuge,
K. P., Comparative life cycle assessment forclay geosynthetic barriers versus clay liners andother sealing systems used in river dykes, canals,
storm water retention ponds and landfills. GBR-

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11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

STUDY ON THE RELATIONSHIP BETWEEN THE CHANGE OF
RIVER MOUTH SANDBAR AND RIVER DISCHARGE AND OCEANWAVE AT THE OMONO RIVER
Junya Taniguchi1, Kazuya Watanabe1 and Noritoshi Saito1
1,2,3

Graduate School of Engineering Science, Akita University, Japan
ABSTRACTThe sandbar exists at the river mouth of the gentle flow river. The river mouth sandbar topography was affectedby combined forces, such as river discharge, sea waves, and wave direction, etc. River mouth sandbar interrupts
salt and wave intrusion and affects the navigation of cruisers and fishing vessels. Field investigation was carriedout for change of a river mouth sandbar in Omono River which locate on Akita Prefecture in Japan. The
topographic surveys using UAV were conducted. Then, analysis of the sandbar change process was conducted on
how sandbar area and river mouth width were calculated from reconstructed topography by the SfM technique.
Moreover, an investigation of a relationship between sandbar area and river mouth width and river discharge,
cross-shore, and longshore direction waves were carried out. As a result, the following results were confirmed
from this study. (1) Right side sandbar has the sand spit which faced the south or left side sandbar develop verymuch in winter. (2) Sandbar area and river mouth width have a positive interrelation between river discharge andcross-shore waves. (3) Sandbar area has a strong negative interrelation between river mouth width.
Keywords: Omono River, Sandbar area, River mouth width, River discharge, Ocean wave

INTRODUCTION

after a flood was monitored using UAV and was
carried out experiences [7]. However, it is necessary
for understanding the change of sandbar in detail toincrease the frequency of the field observation andstudy quantitatively relationship between sandbar andexternal force which river discharge and ocean wave.
In this study, first, sandbar was reconstructedconducting UAV survey and using SfM (Structurefrom Motion) technique. Second, change of rivermouth sandbar topography was observed and sandbar
area and river mouth width were calculated. Finally,
it was studied about the relationship between sandbar
which change of sandbar shape, sandbar area, andriver mouth width and external force which riverdischarge and ocean wave.

Sandbar repeatedly develops or reduces at rivermouth of gentle flow river [1]. Developing sandbar
prevents ocean waves and salinity from intruding andaffects the navigation of cruisers and fishing vessels.
On the other hand, these kinds of reasons, thebehavior of river mouth topographic change was
required to understand for river management. Thereis a seasonal characteristic at the river on the Sea ofJapan coast. Sandbar was grown up in winter becauseriver discharge was decreased, and ocean wave wasdeveloped by the monsoon from Northwest. Sandbar
was decreased in spring, summer, and Autumn
because river discharge was increased by snowmelt
or heavy rain [2].
In previous studies, sandbar area, recovery of
river mouth sandbar on Nanakita River wereresearched to take aerial photographs and analyticalsolution of one-line model [3]. Variation of sandbar
on Yura River was studied to observe shape andsandbar area and conducted flume experiment andnumerical simulation [4]. Reformation of sandbar at
Tenjin River after a typhoon event was studied byfield investigation on sandbar topography using UAV(Unmanned Aerial Vehicle) and RTK-GNSS [5].
Sandbar was grown up from both sides at the rivermouth on the Omono River. So, few studies werecarried out about the river mouth sandbar on the
Omono River. Dynamics of sand movement between
river mouth terrace and river mouth was studied using
aerial photographs, sandbar shape, and submarine
topography map [6]. Recovery process of sandbar

STUDY AREA
Omono River is located in Akita prefecture in
Japan. Figure 1 shows the location of Omono Riverand investigated points of data. The size of thecatchment area is 4710 m2 and the channel extension
is 133 km. The channel bed profile in the downstream
area is 1/4000~1/5000, so Omono River is the gentle
flow river. Estimated high water discharge on theriver mouth is 9300 m3/s. This river flows towards the
Sea of Japan and there is long sandy coast on bothsides of the river mouth.
River discharge, significant wave height, andwave direction were observed by Ministry of Land,
Infrastructure, Transport and Tourism. River
discharge was observed for every 1 hour at
Tsubakigawa station which was located at the upper
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GEOMATE – Kyoto, Japan, 3-5 November 2021

13.1 km, significant wave height H1/3 and wave
direction were observed for every 20 minutes at Akita
Port. Anyway, the wave of 1-188 degrees from thenorth clockwise toward the sea from land because
wave which toward the land from sea was considered[8]. Cross-shore wave H1/32cos and longshore wave
H1/32sin were calculated because the square of wave
height is proportional to wave energy. Figure 2 showsdefinition of cross-shore wave and longshore wave.

dcu

N

400km

STUDY METHODFirst, UAV surveying that aerial photographs ofsandbar were taken to overlap 80% horizontal andvertical from ground to 150 m height were carried out[9].
Second, sandbar topography was reconstructedusing SfM technique from taken aerial photographs.
Figure 3 shows the reconstructed sandbar. Anyway,
sandbar area A, sandbar area at the right side AR,
sandbar area at the left side AL, and river mouth width
B were calculated from the reconstructed sandbar.
Also, shoreline was not modified considering sea
level because tidal range is small on seashore on the
Sea of Japan [10].
Finally, to examine the influence that sandbar area
and river mouth width were affected by riverdischarge, cross-shore wave, and longshore wave, acomparison was carried out between sandbar data andexternal force.

N

Fig. 1 Location of Omono River and investigatedplaces of data.

RIVER DISCHARGE, CROSS-SHORE WAVE,
AND LONGSHORE WAVE

N

Figure 4 shows river discharge, longshore, andcross-shore wave 2017-2020 year. River discharge
was used data for every 1 hour. Cross-shore wave andlongshore wave were used data averaged for 24 hours.
Regarding longshore waves, the positive number
shows wave direction from south to north, negative
number shows wave direction from north to south.
River discharge decreased 100-500 m3/s in winterevery year. After that, river discharge was increased
700-1500 m3/s by snowmelt in spring and greatly
increased by heavy rain in summer and autumn.
Cross-shore waves were excelled in comparison
with longshore waves. Cross-shore waves tended todecrease from spring to summer. It was increased
from autumn to winter and grew up most strongly in
winter. Longshore waves tended to become strongest
in winter and go ahead to north from south mainly.
to
CHANGE OF SANDBAR

Fig. 2 Definition of longshore wave and crossshore wave.

Fig. 3 River mouth sandbar.
curved to southwest in winter. River discharge wasdecreased most in winter and cross-shore wave wasincreased most from autumn to winter as indicated in
Figure 4. Therefore, it was considered that sand spit
toward south was formed because cross-shore wave

Figure 5 shows change of shape of sandbar from2017 to 2020. In winter, sand spit was formed toward
south from the right side sandbar and river toward
south from right side sandbar and river channel was437

GEOMATE – Kyoto, Japan, 3-5 November 2021

andwinterDirection of shore

North

South
2017

2019

2018

year

2020

Fig. 4 River discharge, cross-shore wave, and longshore wave.
area, river mouth width, sandbar area at the right side,and sandbar area at the left side 2017-2020 year.
Sandbar area was increased from autumn to winterand arrived at the maximum, approximately 80000 m 2,
in winter. It was begun to decrease from spring, thenarrived at the minimum in summer or autumn.
Therefore, it was considered that sandbar area wasdecreased because sand was drained from sandbar byriver flow when river discharge was increased causedby snowmelt in spring or heavy rain in summer andautumn. Moreover, it was decreased because sandwas carried by cross-shore waves from offshore anddeposited on sandbar. It was found out that sandbar
area change is similar to topographic change. Thus, it
was proved that topographic change of sandbar wasable to evaluate quantitatively to monitor using UAV
and SfM technique.
River mouth width was begun to increase fromspring, then reached the maximum in summer orautumn. It was decreased from autumn to winter andreached the maximum, approximately 50 meters in
winter. These results show that sand was drained fromsandbar caused by river flow in spring, summer, andautumn. Furthermore, river mouth width was got
narrow because sand was carried from offshore anddeposited on sandbar. According to this result, rivermouth sandbar on Omono River was found to havethe characteristic like other rivers which flow to the
Sea of Japan [11], [12].
Sandbar area on the right side was similar to
sandbar area as illustrated in Figure 6 that these wereincreased from autumn to winter and reachedand river flow was balanced and sand transported
from offshore was deposited.
River discharge was increased caused bysnowmelt and cross-shore wave was decreased.
Hence, sand spit was reduced or disappeared, andriver mouth width was extended in spring.
River mouth width was extended more becauseriver discharge was sometimes increased greatly by
heavy rain in summer and autumn. In particular, rivermouth width was extended greatly on July 29, 2017,
after river discharge was increased July 24, 2017, andon May 21, 2018, after river discharge was increased2904 m³/s on May 19, 2018.
Furthermore, it was found out that developed
sandbar from right side or left side toward offshore
was bent to north on September 4, 2017, May 21,
2018, June 1, 2018, and November 19, 2020, as
illustrated in Figure 5 and longshore wave go-ahead
to north. These show that sandbar was affectedslightly by longshore waves toward north.
Shoreline was repeated advancing andretrograding both sides. To focus on winter, it was
found out that shoreline was very different every year.
Thus, it was not able to find relationship between
shoreline and season.
CHANGE OF SANDBAR AREA AND RIVER
MOUTH WIDTH
Sandbar Area and River Mouth Width
Figure 6 shows change of river mouth sandbar
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GEOMATE – Kyoto, Japan, 3-5 November 2021

winterFig. 4 Change of sandbar area and river mouth width from 2017 to 2020.

2017

2018

year

2019

2020

Fig. 6 Change of sandbar area, river mouth width, sandbar area at the right side, and sandbar area at the left side.
maximum in winter. After that, it was decreased fromspring and reached a minimum in summer or autumn.
The factor which sandbar area on the right side
became greatly big in winter was that sand spit wasformed on the right side. On the other hand, sandbar
area on the left side was not similar to sandbar area

and was not able to confirm seasonal characteristics.
Moreover, the right side sandbar is important for the
management of the river mouth sandbar because theright side sandbar was changed more intensely thanthe left side.

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GEOMATE – Kyoto, Japan, 3-5 November 2021

Recovery of Sandbar
Recovery of sandbar was examined by studying
change of sandbar after the flood. It was defined as
the flood when river discharge was exceeded 1500
m³/s. Figure 7 shows change of sandbar area after theflood. Anyway, when the flood has happened was set
with 0 days and sandbar area in the observation day
immediately after the flood was set of 0 m³/s.
Explanatory notes in Figure 7 show the maximum
river discharge in the flood and the day when theflood has happened. Sandbar area was not suddenly
increased just after the flood regardless of season andwas increased gently until 60 days later. By thechange after August 25, 2017, and October 13, 2019,
sandbar area was suddenly grown up after about 90
days.

Fig. 7 Change of sandbar area after the flood.

RELATIONSHIP BETWEEN VARIATION OFSANDBAR AREA AND RIVER MOUTH
WIDTH WITH WAVE

Equation (2) is river mouth width per day.
𝒅𝑩 𝑩𝒚 − 𝑩𝒙
=
𝒅𝒕
𝒚−𝒙

Variation of sandbar area per day from x to y wasdefined as dA/dt and river mouth width per day fromx to y was defined as dB/dt. Equation (1) is variationof sandbar area per day.
𝒅𝑨 𝑨𝒚 − 𝑨𝒙
=
𝒅𝒕
𝒚−𝒙

(2)

Mean of cross-shore wave from x to y that
H1/32cos Ave and mean of longshore wave from x to ythat H1/32sin Ave were calculated. Furthermore,
relationship between variation of sandbar area andriver mouth width with wave was studied. Figure 8
shows relationship between variation of sandbar area

(1)

spring, summer andautumnspring, summer andautumnFig. 8 Relationship variation of sandbar area with mean of cross-shore wave and longshore wave.

spring, summer andautumnFig. 9 Relationship variation of river mouth width with mean of cross-shore wave and longshore wave.
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GEOMATE – Kyoto, Japan, 3-5 November 2021

with cross-shore wave and longshore wave. Figure 9shows relationship between variation of river mouth
width with cross-shore wave and longshore wave.
Anyway, data within six days were removed that thedays of the interval on an observation day because it
was considered the sudden change of area and rivermouth width.
Variation of sandbar area per day was -500-+400
m², and variation of river mouth width was -2-+2 m.
On the other hand, it was not able to confirm
relationship between variation of sandbar area andriver mouth width with cross-shore wave andlongshore wave.
Being plus in winter and minus in spring andsummer, sandbar area was confirmed that wasincreased in winter and decreased in spring andsummer.
Variation of river mouth width was positive inspring, summer, and winter. However, it was negative
regardless of season. According to these results, rivermouth width was extended by strong river flow inspring, summer, and autumn, just after that, sand was
deposited on river mouth sandbar.

Changes at the Mogami River, Proceeding of
Coastal Engineering, JSCE, Vol. 37, 1990, pp.
374-378. (in Japanese)[3] Hoang C. V., Tanaka H. and Mitobe Y.,
Morphological Evolution of Beach Adjacent to
the River Mouth After the 2011 Tsunami,
Proceeding of the 36th IAHR World Congress,
2015, pp. 4379-4385.
[4] Miwa H., Kanda K., Ochi T. and Kawaguchi H.,
Dynamic state of river-mouth bar in the Yuragawa
River and its control under flood flow conditions,
Proceedings of the 13rd International Symposium
on River Sedimentation, 2016, pp. 710-718.
[5] Katayama T., Kuroiwa M., Kajikawa Y. andKurasige
H.,
Field
InvestigationforMorphodynamic of River Mouth Bar Using UAV
and RTK-GNSS, Proceedings of the 10th
International Conference on Asian and Pacific
Coasts, Vol. 74, No. 2, 2018, pp. 793-798.
[6] Uda T. and Matsuda H., Relation between theFormation and Disappearance of a River Mouth
Bar and a River Mouth terrace in the Omono
River, Proceedings of Coastal Engineering, JSCE,
Vol. 42, 1995, pp. 566-570. (in Japanese)[7] Watanabe K., Dinh V. D., Noguchi K. and Tanaka
H., Study on Recovery Process of Sediment
Deposit and Mechanism in the Formation of River
Mouth Terrace, Proceedings of Coastal
Engineering, JSCE, Vol. 74, issue 2, 2018, pp.
787-792. (in Japanese)[8] Matsutomi H. and Fujita M., Verification of
Correspondence of Long-term Trend of Shoreline
Position and Wind Wave Height Changes on the
Akita Southern Coast, Journal of Japan Society of
Civil Engineer, Ser. B2 (Coastal Engineering),
Vol. 70, No. 2, 2014, pp. 731-735. (in Japanese)[9] Taniguchi J., Watanabe K. and Saito N., Study on
the Behavior of Sandbar in a River Channel at theBabamegawa River, International Journal of
GEOMATE, Vol. 20, issue 78, 2021, pp.115-120.
[10] Matsutomi H. and Kondo Y., Kimura T. andTanaka H. Long- and Short- term Trends ofShoreline Position Change near the New Mouthof the Omono, Proceedings of Coastal
Engineering, JSCE, Vol. 55, 2008, pp. 636-640(in Japanese)[11] Maekawa K. and Kon A., On Change in the
Topography at the Mouth of the Aka River,
Bulletin of the Yamagata University. Agriculturescience, Vol. 11, No. 4, 1993, pp. 891-898.(inJapanese)[12] Ochi T., Kanda H., Miwa H., and Nakamura F.,
Research on the Dynamic State of River-Mouth
Bar under Flood Conditions in the Yuragawa
River, E-proceedings of the 36th IAHR World
Congress, 2015, pp. 2428-2439.

CONCLUSIONS
It would be concluded as follows.
The cycle in Omono River was confirmed thatsandbar was eroded by strong river flow causedby snowmelt in spring and heavy rain in summer
and autumn. After that, sand was carried from
offshore and deposited on sandbar by crossshore waves in autumn and winter like otherrivers on the Sea of Japan.
2) Sandbar area was grown up mostly in winter anddecreased most in summer or autumn. However,
river mouth width was got narrow in winter andextended most in summer or autumn.
3) Sandbar area was changed -500-+400 m2 perday. River mouth width was changed -2-+2 m
per day.
1)

ACKNOWLEDGMENTS
The author would like to thank Ministry of Land,
Infrastructure, Transport and Tourism for theircontributions to this study.
REFERENCES[1] Shimatani H., Sasamoto M., Kasai S., Ohba K.,
Fuse Y. and Sakai S., River Mouth Fluctuation of
Yoneshiro River, Proceeding of Coastal
Engineering, JSCE, Vol. 44, 1997, pp. 571-575.
(in Japanese)[2] Nishikawa K., Sasaki M., Maekawa K., Uda T.
and Kunimatsu H., River Mouth Topography441

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

PREDICTION ACCURACY OF RETRAINED SELF-ORGANIZINGMAP BASED SURROGATE MODEL FOR A REAL COASTALAQUIFER
Dushyantha Wagale1, Bithin Datta2
1,2

College of Science & Engineering, James Cook University, Australia
ABSTRACT

Salinity intrusion is an issue in coastal aquifers around the world. It can be controlled through appropriate
management strategies, and optimal sustainable pumping strategy for groundwater use is always preferred due
to high demand and scarcity of freshwater sources in coastal areas. Search based simulation optimization
algorithms used in formulation and development of such prescriptive management strategies require surrogate
models in place of computationally expensive numerical flow and transport simulation models. This ensurescomputational feasibility. This study evaluates the prediction accuracy of Retrained Self-Organizing Map(SOM) based surrogate model. The surrogate model is developed for a real life coastal aquifer – Rita Island in
North Queensland, Australia. It is a deltaic island in Burdekin River with an approximate area of 60 square
kilometers. Sugarcane farming has been carried out in the area since early nineteenth century and utilizing
groundwater pumping for irrigation. Monitoring well readings have indicated presence of saline waters incertain parts of the aquifer. The developed hydraulic simulation model for the aquifer was calibrated andvalidated and found to be in good agreement with filed data. It was used to generate training and testing data
for Retrained SOMs. Several SOMs were created during the training process under different training
parameters to understand the effect of these parameters on prediction accuracy of the saltwater intrusion
process. Analysis of results indicate that prediction errors are in the order of 10 -6.
Keywords: Coastal aquifers, Self-Organizing Maps, Surrogate models, saltwater intrusion, Burdekin River

1. INTRODUCTION

the extreme computation burden inherited byhydraulic simulation models. When surrogate
models are employed in place of hydraulic models,the efficiency of the optimization process increases
significantly [2] [3].
The use of replacement models significantly
reduce the calculation burden during the
optimization process and, if not, the optimization
process is uneconomical in terms of calculation
time to converge into an optimal solution [4]. One
such potential replacement model is Retrained Selforganizing Maps (RSOM). Once properly trained
and tested, SOM can be used to predict unknown
variables[5] [6]. In this study a calibrated andvalidated real aquifer model has been built for a real
coastal aquifer – Rita Island, a deltaic island in
Burdekin River delta in North Queensland,
Australia. The data generated from this model wasused to train and test the prediction accuracy of
RSOM based surrogate model.

The management of coastal aquifers have been
given substantial attention in recent years due to theincreased environmental consciousness and theireconomic importance. The coastal aquifers are vitalfor economic activities all over the world as they
provide water for human needs and 50% of theworld’s population do live within 60 km narrow
strip of land from sea shore[1]. Costal aquifers aredifferent from other aquifers as they have hydraulicconnection with the sea. In costal aquifers thelighter fresh water is flowing towards the sea
through upper layers; while dense saline water
encroaches towards the land in lower layers of theaquifer. The density difference of the saline waterand the freshwater governs the configuration of theaquifer. This configuration requires groundwatermodelers to model the aquifer by means of density
dependent flow and transport models to achieve
accurate predictions. Though, in return these
density dependent flow and transport simulation
models are computationally expensive especiallywhen used in optimization algorithms utilizing
search methods using linked simulation
optimization. Use of surrogate models areunavoidable in formulation of prescriptive
management strategies for coastal aquifers due to

2. RETRAINED SELF-ORGANISING MAPS(RSOM) AS A SURROGATE MODEL
SOMs are neural networks with the ability ofspatial adaptation [7]. The training process of SOM
is a recursive non-parametric iteration process.
SOM is a low order representation of higher order442

GEOMATE – Kyoto, Japan, 3-5 November 2021

dimensional data enabling better visualization,clustering and analysis [8][9][10][11]. Exploitationof SOM’s prediction capabilities are not common,but in past it has been used successfully forprediction of unknown variables.
A Retrained Self-Organizing Map is obtained
through repeating the training process over and overagain but with different training parameters toensure that the topology of the training data is welltransferred to the SOM.
The use of RSOM as a surrogate model isfeasible once properly trained and tested. To train a
RSOM, a properly selected data set has to be usedin order to obtain reliable and robust RSOM basedsurrogate model. Training data set must be beyond
the upper and lower bounds of the interested
constraint variables to ensure there is no effect due
to the bounds under which RSOM has been trainedon the predication capability. The mathematical
approach for the use of trained RSOM as areplacement models for a general case for an aquifer
management period with pumping patterns with nelements for a and resulting concentration values atn monitoring locations is discussed below.
Let the vectorial representation of neurons in a
SOM, which has m number of vector points thatcarry pumping at different wells and resulting
concentration at monitoring locations as their vector
elements be denoted as:

Where;
𝑆th̅̅̅
𝑖𝑥𝑝𝑛 = n pumping element in 𝑾𝒊𝐼th𝑥𝑝𝑛 = n element of the input pumping
realization
Once the BMU corresponding to the pumping
elements on the SOM is known, then the remaining
elements of the BMU vector-concentration values
are directly read from the SOM.
To investigate the predication capability of
RSOM as a replacement model instead of anumerical flow and transport simulation model, acoastal aquifer model was developed to generate
training and testing data for the evaluation.
3. RITA ISLAND AQUIFER MODEL
To train and test the RSOMs two separate data
sets were generated; training and testing data set.
These data sets were generated by running ahydraulic simulation model for a real aquifer, theRita Island. It is a portion of larger lower Burdekin
alluvium, in North Queensland Australia [12].
Rita Island is a deltaic island with an area of
6000 ha (60 km2). Rita Island carries clear
hydraulic and transport boundaries. The eastern
hydraulic boundary of the aquifer is the Coral Sea.
The southern boundary is Burdekin River and thenorthern boundary is Anabranch. The disjunction of
the Anabranch and the Burdekin River lies
approximately 10 km inland to the Coral Sea. In the
Burdekin area, sugar cane farming has been
continuing since early 19th century and groundwater has been used to irrigate the sugarcane fields
since the commencement of farming[13]. The
deltaic island has encountered salinity issues and atpresent some of the monitoring wells indicate
higher level of salinity in ground water with readingover 1200 mg/L. Figure 1 shows a 3-dimensional
image of Rita Island and its surroundings.

̅̅̅𝟏 , 𝑾̅̅̅𝟐 , 𝑾̅̅̅𝟑 ,…, 𝑾̅̅̅𝒊 , … , 𝑾
̅̅̅𝒎 , and a representative𝑾neuron in this SOM can be expressed as:̅𝑾̅̅𝒊 = ( 𝒊𝒙𝒑𝟏 , … , 𝒊𝒙𝒑𝒏, , 𝒊𝒙𝒄𝟏 , … , 𝒊𝒙𝒄𝒏 )(1)

Where,
̅𝑾
̅̅𝒊 = ith vector in SOMth̅̅̅
𝒊𝒙𝒑𝒏 = n element of the pumping pattern in 𝑾𝒊th̅̅̅
𝒊𝒙𝒄𝒏 = n element of concentration in the 𝑾𝒊
Let the input pumping realization for which thecorresponding concentration values are required tobe;𝑰

𝑿𝒑 = ( 𝑰𝒙𝒑𝟏 , 𝑰𝒙𝒑𝟐 , … , 𝑰𝒙𝒑𝒏, )N(2)

The 𝑰𝑿𝒑 pumping realization is checkedagainst all the SOM point vectors, only taking into
consideration vector elements of pumping, to findthe best matching unit (BMU) following theargument in Eq. (3).
𝑁
2
𝑚𝑖𝑛 √
arg
‖ ∑( 𝑆𝑖𝑥𝑝𝑛 − 𝐼𝑥𝑝𝑛 ) ‖𝑖

Fig.1 Rita island 3D representation of terrain andwater bodies around it

(3)

𝑛=1

The area receives annual average rainfall of
993mm and is located within the dry tropics region

for i = 1,2,3,…,m443

GEOMATE – Kyoto, Japan, 3-5 November 2021

of Northern Queensland. By analyzing boreholedata [14], it was concluded that the average
thickness of the aquifer varies between 60-80m. It
was also concluded that it is reasonable to represent
the aquifer by two material layers, the upper layer
similar to sandy loam and the lower layer similar togravelly sand. Aquifer material was assigned to behomogeneous, although the hydraulic conductivity
had been taken to be transversely anisotropic.
Horizontal components of hydraulic conductivity(Kx and Ky values) were taken to be the same whilethe vertical component of hydraulic conductivity(Kz) were selected to be less than that of horizontal
values. To simulate the hydraulics and contaminanttransport, a three-dimensional density dependent
flow and transport simulation model; FEMWATER[15] was used. A finite element model was
generated to represent the aquifer, with sufficientnumber of elements with appropriate size, shapeand orientation in such a way that results would be
reliable.
The upper layer of the aquifer was sub divided
in to two layers of elements while the underlying
layer was subdivided into three layers of elements.
Along the aquifer boundaries the vertices were
distributed in such a way that the element size isbeing maintained close to but less than 500 m.
Around the wells the element were refined to havesize of 350m or 200m depending on the location of
the well. The finite element model had 3525 gridelements with 2250 nodes. Figure 2 illustrates thefinite element mesh used in the model.

is the eastern boundary of the aquifer. It was
assigned to be a specific head boundary at mean sea
level, the transport boundary condition, along it was
set to be at a saline concentration of 35,000 mg/L.
The Northern boundary, Anabranch and thesouthern boundary, the Burdekin River, weremodelled as varying specific head boundary. Headat the disjunction of the Anabranch from the
Burdekin River was assigned to be at 3m based on
the topography of the area. The transport boundary
conditions for the freshwater bodies were defined
based on conductivity readings of the BurdekinRiver. After a conversion of conductivity to salinity[16] the fresh water was adopted to be at a salinity
concentration of 160 mg/L. The salinity value wasincrementally increased for the freshwater bodies,from 160 mg/L to 35,000 mg/L downstream to thesand barriers which are constructed across the
Burdekin River and Anabranch. This was toaccommodate influence of tidal effects. The vertical
recharge was modelled as an influx (infiltration)over a polygon. Aquifer recharge was assumed to
be 20% of the rainfall and daily recharge averaged
over 3 month periods were used in the model
calculations.
4. MODEL CALIBRATION
The model was calibrated using available geohydrological, meteorological and crop data. The
pumping from the aquifer was assigned to theaquifer as a negative flux as there are over a hundred
wells within the aquifer. The pumping demand was
calculated based on the soil water balance. Daily
water balance was calculated using the daily
evapotranspiration and rainfall combined with crop
coefficient, root zone depth and soil type. It was
assumed that the irrigation is carried out each time
the readily available water reaches 25% of the total
available water. The pumping was taken to be anegative flux. The resulting value after combining
the positive recharge flux and negative pumping
flux was applied to the model. When selecting thehydraulic conductivity values for testing the trial
range was kept within prescribed values for
Burdekin Delta, 25-100 m/d [1]. For the calibration
run the initial conditions were generated following
a two-step process. The first step was to generate thepre irrigation conditions. The pre irrigation
condition represents the steady state conditions of
the aquifer prior to the commencement of irrigation.
The total simulation period adopted for the first stepwas 120 years. Generated results, head andconcentration values were used as the initialcondition for step two. The second step was togenerate the resulting head and concentrations
under pumping which was carried out for 120 years.
The rainfall influx was combined with the pumping
requirement and was applied as a resultant flux overFig. 2 material layers and finite element mesh usedin the model
The model’s boundary conditions were
determined based on the physical characteristics ofthe aquifer and hydrological features around it.
Logically, the upper bound of the aquifer waschosen to be the terrain. Terrain elevation data was
extracted from google maps. The lower bound of
the aquifer material was derived from the bedrock
elevation derived from borehole data. The Coral Sea
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GEOMATE – Kyoto, Japan, 3-5 November 2021

the aquifer. Resulting water levels were checkedagainst the field observations for early 2012. Rita
Island has network of monitoring locations with acontinuous record of well head and conductivity
readings over the past several decades. Figure. 3
shows the monitoring location with monitoring wellnumbers. Field observed values and adopted values
in the model were in good agreement. It wasobserved that the max deviation is 2.6m over anapproximately 75m ground water column except for
one outlier.

heads is acceptable.

Fig.4 Calibration results for monitoring locations
groundwater heads
Figure.5 provides comparison of calibration run
results with the field observed concentration values.
Monitoring location MW3 has been given forcompletion of data but in the analysis it wasremoved. According to the analysis the maximum
deviation is 252 and it is 37%. The second largest
deviation is 184 which is 18% deviation from theobserved values. The maximum percentage error isobserved at MW9 where the observed values are
small – 95 mg/L. The average percentage error is21%. This is accepted to be good as the match
between the hydraulic heads are good.

Fig.3 Monitoring locations in Rita Island
The filed observed concentrations in early 2012were interpolated to the finite element mesh andwas used as the initial conditions. Analysis ofinterpolated values showed, that the monitoring
locations identified as MW3 had high observed
concentrations compared to the distributed value.
Considering the location of the M3 (Fig. (3)), it
was concluded that observed salinity concentrations
at this location are not resulted by salinity intrusion.
It is also noted that the monitoring locations to theeast of MW3 have less concentration readings.
Hence, it was concluded that this anomaly is due toconnate water resides deep in the fissures as thelocation of the monitoring locations and the salinity
concentrations does not go hand in hand.
In Fig. 4 calibration run results are comparedwith the observed head at the end of the 3 year
period from 2012 January to 2015 January. It isnoted that field observed and model run results forconcentration at M3 are not in good agreement. But
the difference of head values at this location is only
0.466 m and considered to be acceptable calibration
results. The maximum difference between
calibration run results and filed observations forhydraulic head is 2.63 m. This difference is over a
ground water column of 76.586 m at monitoring
location M8. The average error is 1.515 m, which asa percentage is 2.0%. The maximum error is 3.4%.
Hence, it is concluded that the calibration run for the

Fig. 5 Calibration results for monitoring locations –saline concentrations
5. MODEL VALIDATION
Model validation was carried out in the samemanner that the calibration was done, except the
adopted time period was 2013 to 2016. The
validation run results are given in Fig. 6 and Fig. 7.
In Fig. 6 it is clear that the match between the fieldobserved head and the validation run results have agood match with very little variation. The maximum
deviation is 2.66m and the average deviation ofhead is 0.874m; as a percentage error it is 1.2%.

445

GEOMATE – Kyoto, Japan, 3-5 November 2021

6. DATA GENERATION FOR TRAINING
AND TESTING RETRAINED SOM BASE
SURROGATE MODEL
The farming area within Rita Island was divided
in to approximately fourteen equal areas, with awell assigned for each area. These fourteen wells
were assigned random pumping in between 2500 to
15000 m3/day. Five monitoring wells were chosen
within the aquifer along the eastern boundary of thecultivated area. The adopted monitoring wellconfiguration allows for establishment of the
maximum allowable pumping, and if the salinity
front moves towards the arable area, monitoring
wells will pick this change and remedial measures
can be implemented. Hence, these locations are best
to assign constraint values for a management model.
During salinity intrusion, the saline waterfront
which migrate from coastline to inland will bepicked up by these monitoring wells prior to thepumping wells being affected by saline water, andany adverse impacts on aquifer can be avoided. Fig.
8 provides the pumping wells (Well 1 – 14) fieldalong with the monitoring wells (M1 – M5).

Fig. 6 Validation results heads
Figure 7 shows the validation results forconcentration values. When comparing theconcentration values simulated and observed at theend of validation period, it is noted that the average
difference is 107 mg/L, and as a percentage it is23%. The comparison is accepted as four of themonitoring locations have differences less than orequal to 40 mg/L, while another two are under 185
mg/L and the remaining two under 245 mg/L. The
validation results are concluded to be very good
given that the match of heads are very good and theconcentration values are acceptable.

Fig.8. Pumping wells (W1 to W14) and monitoring
well (M1 to M5) for the regional aquifer model

Fig.7 Validation results concentrations
The Table 1 provides the adopted parametervalues in the model. Some of them are adopted frompast literature.

An individual management scenario was taken
to be spanning over a three-year period. A pumping
season was assumed to be three months, resulting in
12 pumping seasons over the management period.
For 1 management scenario, a random pumping
pattern with 168 pumping values was required as
there were 14 pumping wells. To train the SOM,
some 2000 of such patterns were used. Pumping
patterns were generated by sampling from Haltonsequence[17] by using MATLAB. Halton sampling
can generate unbiased samples [18].
Thesepumping values are fed to the FEMWATER aquifer
model and resulting concentration values are read.
Similarly, a separate test data set, with 250 pumping
patterns, was generated using the same process.

Table 1 Parameters used in the model
Value
Parameter
Hydraulic conductivity (m/day)
Kxx
Kyy
Kzz
Lateral dispersivity (m)Longitudinal dispersivity (m)Molecular diffusivity (m2/s)
Bulk density of the soil (kg/m3)
Effective porosity (%)
Density reference ratio

Upper
layer

Lower
layer

35
35
3
0.5
2.5
10-9
1600
41

55
55
4
0.5
2.5
10-9
1600
41
7.14 x 10-7446

GEOMATE – Kyoto, Japan, 3-5 November 2021

7. GENERATION OF RETRAINED SELF
ORGANIZING MAPS BASED SURROGATE
MODELS

respective training steps.
Table 3 Number of iterations and Alpha values
Number of
iterations

Training Step

Six RSOMs were created to evaluate theprediction capability. Created maps were ofdifferent size. Table 2 provides basic information on
generated maps.
Table 2 Retained SOMs generated
Map

Number of vector points in the SOM

25 by 25625

50 by 502,500

75 by 75

5,625

100 by 100

10,000150 by 150

22,500200 by 200

40,000Alpha

Training Step 1

10,0000.5

Training Step 2

10,0000.5

Training Step 3

10,0000.5

Training Step 4

100,000

0.05

Training Step 5

1,000,000

0.05

Training Step 6

1,000,000

0.05

Training Step 7

1,000,000

0.05

The generated SOMs were used in prediction ofconcentration values at monitoring locations forpumping realizations in test data by using equation
4. These predictions were used to calculate theprediction error by using Eq. (4).
𝑁

1
| 𝑆𝑥𝑐𝑛 − 𝑇𝑥𝑐𝑛 |𝑅𝐸 = ∑ ()
𝑆
𝑁
𝑥𝑐𝑛

While training the maps some of the training
parameters were kept constant within the similar
training step (for e.g. training step 1) for all themaps. But these parameters were changed from one
training step to the other, for example the number of
iterations and Alpha – the learning rate parameter.
Table 3 provides the number of iterations and Alpha
adopted in training. Table 4 provides the radius ofneighborhood adopted for different maps in

(4)

𝑛=1

Where:𝑅𝐸 = relative error𝑺

𝒙𝒄𝒏 = predicted concentration by RSOM
𝒙𝒄𝒏 = simulated concentration results in test data

𝑻

Table 4. Radius of neighborhood adopted in the training of different SOMs
Map

Training
Step 1

Training
Step 2Training
Step 3Training
Step 4Training
Step 5Training
Step 6

Training
Step 725 by 25

25

12

6

4

3

2

2

50 by 50

50

25

12

6

4

3

2

75 by 75

75

40

20

10

5

3

2

100 by 100

100

50

25

12

6

3

2

150 by 150

150

75

35

17

8

4

2

200 by 200

200

100

50

25

12

6

2

this figure it is clear that the errors are large formaps 25 by 25, 50 by 50 and 75 by 75 regardless of
how much they were trained. On the other hand formaps 100 by 100, 150 by 150 and 200 by 200 theprediction accuracy increases with retraining. Thisis significant after training step 4.

8. RESULTS
It is noted that 25 by 25 map carries 625 vector
points, which is less than that of the number oftraining data. On the other hand the largest map 200
by 200 had 40,000 vector points in it.
Figure 9 shows variation of relative error forprediction against the map size and training step. In

447

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 9 Variation of relative prediction error with map and training step
It is also noted that for the 100 by 100 RSOM’serrors are better than that of 75 by 75 RSOM though
not as good as 150 by 150 after training step 4. But
the difference between the errors in 150 by 150 and
200 by 200 RSOMs are more or less similar to eachother at corresponding training steps. The 200 by
200 RSOM at the end of training step 7 has anaverage error of 1.2x10-6, while for the same
training step the 150 by 150 map carries an average
error of 1.6x10-6.

improvement in prediction can be attributed to thenumber of iterations. In the figure it is also notedthat at the seventh step there is only three outliers
and all of them are in the rage of 10-5.
9. CONCLUSION
A finite element based density dependent
coupled flow and transport simulation model was
implemented for a real aquifer – Rita Island in
Burdekin River delta in North Queensland
Australia. This model was calibrated and validated
against hydraulic head and salinity levels at ninemonitoring wells within the island. Validated model
was used to generate training and testing data to
generate retrained self-organizing map basedsurrogate models of different sizes to understand theeffect of map sizes, and extent of training on
prediction accuracy. It was concluded that RSOMs
with vector points 10 times the data set can retain
the topology of the training data set for the aquifer
model. It is also concluded that prediction accuracy
increases with the number of iterations in the
training process.

Fig. 10 Box plot for distribution of prediction error
for 200 by 200 map for each training step

10. ACKNOWLEDGMENTS
Figure 10 shows the box plot of prediction error
distribution for 200 by 200 RSOM. By analyzing
Fig. 10 it can be concluded that there is an
improvement in the error after training step 4. In
Table 3 it is clear that training step 5 and above had
1,000,000 iterations compared to training step 4
which had only 100,000 iterations. This

Authors would like to thank the Department of
Natural Resources and Mines, Ayr office staff and
Burdekin Water Board staff for their support whilecollecting data for the aquifer model.

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GEOMATE – Kyoto, Japan, 3-5 November 2021

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DOI:
10.1145/355588.365104.
[18] Roy, D.K., and B. Datta, Modelling andmanagement of saltwater intrusion in acoastal aquifer system: A regional-scale
study. Groundwater for sustainable
development, 2020. 11: p. 100479.

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11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

EFFECT OF PACLOBUTRAZOL CONCENTRATIONS OF FOLIARAPPLICATION ON FLOWERING OF ‘MAHACHANOK’ MANGO
Yaowarat Wongsrisakulkaew1, Unaroj Boonprakob1 and Lop Phavaphutanon1
1

Faculty of Agriculture at Kampang Saen, Kasetsart University, Thailand
ABSTRACT

Paclobutrazol (PBZ) is a growth regulator, has been widely marketed throughout to stimulate mango flowering.
In the recent years, many reports showed that application of PBZ by soil drenching can cause permanent stuntingof flushes, vegetative and root growth was reduced. ‘Mahachanok’ mango trees were used in this study. The
experimental design was CRD (completely randomized design) with 10 treatments and 4 replications. Mango trees
received two and three PBZ foliar applications, applied at 15 days after shoot pruning with 0, 1,000 2,000, 3,000
and 4,000 mg/L. and repeated at 30 and 45 days with PBZ 2,000 mg/L. The results showed that PBZ could
stimulate earlier flowering and higher percentage of flowering shoots than the control trees. Significantly
differences were obtained on the time to flowering, percentage of flowering shoots and panicle length. Phyto toxic
symptoms of shoots and panicles were observed on the PBZ treatments at 4,000 mg/L.
Keywords: Paclobutrazol, Foliar application, ‘Mahachanok’ mango

INTRODUCTION

growth in mango [9]. Studies have shown that PBZ isneeded to be applied annually to increase mango fruit
yields [10]. In the recent years, many reports showedthat application of PBZ by soil drenching produced
uneven distribution of the chemical throughout theplants as noticed by the uneven size of the panicles in
the lower versus the upper part of tree. Overdose may
cause undesirable effects such as stunting of flushes,
panicle malformation, vegetative and root growth ofmango was reduced [11]. In areas where PBZ isapplied regularly, there may be risk of environmental
contamination due to its residues persisting in soil fora very long time [12].
Then, the aim of this work was to determine theeffects of PBZ on flowering of Mahachanok mango.
Foliar application was used in this study to insure
uniform flowering and reduce the detrimental side
effects.

Mango is a commercially important tropical fruit
crop in Thailand. The cultivation of mango trees ishighly popular among growers in all regions of thecountry, since there are lots of mango varieties being
cultivated for different consuming purposes
depending on variety some of them are grown for ripe
fruit production and others are used for pickled fruits
and edible fresh fruits like apple fruits, its annual
production of fruits plays an important role in theThai economy. The well known commercial cultivar
is Mahachanok.
Mango flowering is a physiological process that
onset the fruit production [1] and it is the first of
several events that set the stage for mango production
each year. It also influences the quality and quantityof fruits [2]. Mango trees flower in response to theage of the last vegetative flush in the tropicalconditions. Synchronization of the vegetative of tree
canopies is a necessary first step in the flowering
management program. Synchronization growth isbest accomplished by tip pruning all of the stems on
the tree [3]. The next step is to decide whether to use
Paclobutrazol (PBZ) or not. PBZ has been used withconsiderable success to induce flowering in several
fruit crops such as apple [4], pummelo[5], apricot [6]and mango [7]. PBZ inhibits gibberellin biosynthesis
reduces vegetative growth and induces water-stress
tolerance as well as increases total non-structuralcarbohydrates (TNC) [8]. PBZ has been widelymarketed throughout the tropics to stimulate mango
flowering. Soil application of PBZ has been found to
be more responsive in regard to suppressing thevegetative growth and enhancing the reproductive

MATERIALS AND METHODS
Experimental details
This experiment was carried out at Tropical Fruit
Research and Development Center Kasetsart
University, Kamphaeng Saen Campus Nakhon
Pathom Province, Thailand. Mango trees (cv.
Mahachanok), at the age of 6 year-old, were selected.
Tip pruning has been done to induce new flush fromall the stems on the tree (Fig.1). Foliar application at
15 days after pruning with PBZ at 0, 1,000, 2,000,
3,000 and 4,000 mg/L and it was repeated at 30 days
after pruning with 0 and 2,000 mg/L. and repeatedagain at 45 days after pruning with 0 and 2,000 mg/L.

450

GEOMATE – Kyoto, Japan, 3-5 November 2021

30 days with 2,000 mg/L and repeated again at 45days with 2,000 mg/L of PBZ; T9) spray with 3,000
mg/L of PBZ at 15 days and repeated at 30 days with2,000 mg/L and repeated again at 45 days with 2,000
mg/L of PBZ; and T10) spray with 4,000 mg/L of
PBZ at 15 days and repeated at 30 days with 2,000
mg/L and repeated again at 45 days with 2,000 mg/L
of PBZ. Ten shoots per tree are randomly tagged
around the canopy of each tree and monitored forflower in determination after foliar application of
PBZ. The parameters measured were percentage offlowering shoots, time taken from shoots pruning toflower emergence, panicle length (panicle length wasmeasured at 50% bloom) and phenological changesof shoots. The data were subjected to analysis ofvariance and the means were separated by Duncan’s
new multiple range test (DMRT) at P < 0.05.

15 DAP(a)

RESULTSThe results showed that the use of PBZ could
stimulate higher percentage of flowering shoots thanthe control trees. Trees receiving 1,000 mg/L at 15
days and repeated at 30 days with 2,000 mg/L of PBZ
had the highest percentage of flowering shoots of
72.5%, whereas those control trees showed the lowest
flowering percentage of 0% (Table 1, Fig.2).

30 DAP

Flowering shoots(%)(b)

45 DAP
(c)

80
70
60
50
40
30
20
10
0

72.5a
55b

32.5de45bc

37.5cd

40bc

25e

12.5f

7.5fg

0g
T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

Treatment

Fig.1 New flush at 15 days (a), 30 days (b) and 45days (c) after pruning in Mahachanok mango.
Experimental design and data analysis

Fig.2 Percentage of flowering shoots of Mahachanokmango.

Completely randomized design (CRD) was usedin this study. There were four replications with ten
treatments: T1) control; T2) spray with 2,000 mg/L of
PBZ at 30 days; T3) spray with 1,000 mg/L of PBZ
at 15 days and repeated at 30 days with 2,000 mg/L
of PBZ; T4) spray with 2,000 mg/L of PBZ at 15 days
and repeated at 30 days with 2,000 mg/L of PBZ; T5)
spray with 3,000 mg/L of PBZ at 15 days andrepeated at 30 days with 2,000 mg/L of PBZ; T6)
spray with 4,000 mg/L of PBZ at 15 days andrepeated at 30 days with 2,000 mg/L of PBZ; T7)
spray with 1,000 mg/L of PBZ at 15 days andrepeated at 30 days with 2,000 mg/L and repeatedagain at 45 days with 2,000 mg/L of PBZ; T8) spraywith 2,000 mg/L of PBZ at 15 days and repeated at

PBZ could stimulate earlier flowering. Timetaken from application to flower mergence was highly
significant among the PBZ treated trees. Trees
receiving 1,000 mg/L at 15 days and repeated at 30
days with 2,000 mg/L of PBZ had earliest flowering
at 106.5 days from application (Fig.3 and Table 1).
There was a significant difference on the lengthof panicle. Trees treated with PBZ at the rate of 2,000
mg/L and above had a significant shorter panicle
compared with the control. However, the higher
concentration of PBZ gave shorter panicle. Panicles
from the trees receiving with 2,000 mg/L of PBZ at
15 days and repeated at 30 days with 2,000 mg/L andrepeated again at 45 days with 2,000 mg/L of PBZ
had the shortest panicle of 15.5 cm (Table 1, Fig.4).
451

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1 Effect of PBZ on percentage of flowering shoots, time to flowering and length of panicle of Mahachanokmango after foliar application of PBZ at different rate.
Treatment

Flowering shoots(%)

Time to flowering(days)

Length of panicle
(cm)

T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
F-testcv.(%)

0.00 g12.50 f72.50 a32.50 de37.50 cd
55.00 b25.00 e45.00 bc
40.00 cd
7.50 fg*
22.28

172.75 a111.25 c
106.50 d
107.25 d107.75 d107.50 d
107.25 d107.75 d107.50 d
162.25 b*
1.26

42.13 a33.00 c37.75 b28.63 d28.50 d16.28 f24.65 e15.50 f26.65 de25.50 e*4.81

Time to flowering(days)

Means followed by the same letter at the same column were not significantly different at the 5% level by DMRT.
* = significantly different at P < 0.05

180
160
140
120
100
80
60
40
20
0

In this study, the phyto toxic symptoms ofshoots and panicles were observed on the PBZ
treatments at 2,000 mg/L. The trees receiving the
PBZ at 2,000 mg/L were stunting of flushes andpanicle malformation (Fig.5, Fig.6).

162.3a111.3b 106.5c 107.3c 107.8c 107.5c 107.3c 107.8c 107.5c

DISCUSSION
0d
T1

T2

T3

T4

T5

T6

Treatment

T7

T8

T9

Application of PBZ affected vegetative growth
and reproductive growth of Mahachanok mango. The
vegetative growth of mango trees was observed 2 wks
after treated with PBZ. Shoot number was notdifferent but shoot length showed significant
differences among treatments. In the present study,
PBZ application was effective in reducing shootlength since the PBZ inhibits GA biosynthesis byblocking the step in the oxidation of ent-kaurene to
ent-kaurenoic acid [13] resulting in unelongatedshoots, even though cell division still occurs.
Triazoles compounds are anti-giberellic could alter
the levels of gibberellins in shoot buds. Paclobutrazol
application increased the number of flowering shoots
due to lower vegetative growth and higher reserves inthe tree. Similar results were also obtained by Silva etal., [14] in mango cv. Kent. Alphonso is a variety
often enters in to alternate bearing habit it can be
possible with break that alternate bearing with growth
retardants. The triazole products help to shift thevegetative phase into flowering in ‘Tommy Atkins’ acultivar difficult to change the phase [3]. Similar
results have been reported in apple [4]. In addition,
PBZ could also induce flowering in mango trees
because it reduced vegetative growth. The

T10

Length of panicle (cm)Fig.3 The time to flowering of Mahachanok mango.
45
40
35
30
25
20
15
10
5
0

42.13a37.75b33c28.63d 28.5d

16.28f

T1

T2

T3

T4

T5

26.65de 25.5e

24.65de

T6

15.5f

T7

T8

T9

T10

Treatment

Fig.4 The length of panicle in Mahachanok mango.

The trees receiving the PBZ application showed
its effect by decreasing shoot and panicle length
significantly (Table 1).
452

GEOMATE – Kyoto, Japan, 3-5 November 2021

involvement of the GA in mango was also
demonstrated by Yamashita et al.[14] who reported
that GA acts as a flowering inhibitor. The use of PBZ
could stimulate higher percentage of flowering shoots
than the control trees. This result was supported by
Yeshitela et al. [8] who found that PBZ produced thehighest number of flowers per panicle in mango. The
trees receiving the PBZ at 2,000 mg/L by foliar
application were stunting of flush and panicle
malformation.

stimulate earlier flowering and higher percentage offlowering shoots than the control trees. Significantly
differences were obtained on the time to flowering,
percentage of flowering shoots and panicle length.
ACKNOWLEDGMENTS
This research was supported by a grant from theprogram Strategic Scholarships for Frontier Research
Network form the Higher Education Commission,
Thailand.

CONCLUSIONSThe study of using paclobuctrazol applying as
foliar spray to mango trees cv. Mahachanok could

(a)

(b)

(c)

(d)

Fig. 5 The control tree (a) and phyto toxic symptoms of shoots (b)- (d) in Mahachanok mango.

(a)

(b)

453

(c)

GEOMATE – Kyoto, Japan, 3-5 November 2021

(d)

(e)

(f)

Fig. 6 The control tree (a) and phyto toxic symptoms of panicles (b)- (f) in Mahachanok mango.
REFERENCES[8] Yeshitela, T., P. J. Robbertse and P.J.C. Stassen.
Paclobutrazol suppressed vegetative growth andimproved yield as well as fruit quality of ‘TommyAtkins’ mango (Mangifera indica) in Ethiopia.
NZ J. Crop Hort. Sci. Vol. 32, 2004, pp. 281–293.
[9] Singh, D. K. and Ram, S. Level of paclobutrazol
residues in shoot and fruit of mango Indian J.
Plant Physiol. Vol.5, 2000, pp. 186-188.
[10] Yadava, R.B.R., Singh, V.K. Long term effectsof paclobutrazol (PP 333) on yield and qualityof Dashehari mango (Mangifera indica L.). Ind.
J. Plant Physiol.Vol.3, No.2, 1998, pp.166–167.
[11] Singh, Z. Effect of (2RS, 3RS) paclobutrazol ontree vigour, flowering, fruit set and yield inmango. Acta Hort. Vol. 525, 2000, pp. 459–462.
[12] Debi Sharma and Mahesh D. Awasthi. Uptake ofsoil applied paclobutrazol in mango (Mangifera
indica L.) and its persistence in fruit and soil.
Chemosophere. Vol. 60, 2005, pp. 164-169.
[13] Sterrett, J.P. Paclobutrazol: A promising growth
inhibitor for injection into woody plants. J.
Amer. Soc. Hort. Sci. Vol.110, No.1, 1985, pp.
4–8.
[14] Silva, G. J. N., E. M. Souza, J. D. Rodrigues, E.
O. Ono and Mouco, M. A. C., Uniconazole on
mango floral induction cultivar ‘Kent’ atsubmediosaofrancisco region, Brazil. Acta Hort.,Vol. 884, 2010, pp. 677- 682.
[15] Yamashita, K., K. Kitazono and S. Iwasaki.
Flower bud differentiation of Satsuma Mandarin
as promoted by soil-drenching treatment with
IAA, BA or paclobutrazol solution. J. Japan.
Soc. Hort. Sci. Vol.66, No.1, 1997, pp. 67–76.

[1] Ramírez, F. and Davenport, T.L., Mango(Mangifera indica L.) flowering physiology,
Scientia Hort. Vol. 126, Issue 2, 2010, pp. 65-72.
[2] Tiwari, D.K., Patel, V.B. and Pandey, K., Floral
induction in mango: physiological, biochemical
and molecular basis, Int. J. Chem. Studies. Vol. 6,
Issue 1, 2018, pp. 252-259.
[3] Davenport, T. L., Reproductive physiology ofmango, Brazz. J. Plant Physiol. Vol.19, 2007, pp.
363-376.
[4] Zhu, L.-H., A. Peppel, X.-Y. Li and M. Welander.
Changes of leaf water potential and endogenous
cytokinins in young apple trees treated with orwithout paclobutrazol under drought conditions.
Scientia Hort. Vol.99, 2004, pp. 133–141.
[5] Phadung, P., K. Krisanapook and Phavaphutanon,
L. Paclobutrazol, Water Stress and Nitrogen
Induced Flowering in ‘Khao Nam Phueng’
Pummelo. Kasetsart J. (Nat. Sci.) Vol.45, No.2,
2011, pp. 189 – 200.
[6] Arzani, K. and H.R. Roosta. Effects of
paclobutrazol on vegetative and reproductive
growth and leaf mineral content of mature apricot(Prunus armeniaca L.) trees. J. Agric. Sci.
Technol. Vol.6, 2004, pp. 43–55.
[7] Wongsrisakulkaew
Y.,
B.
Boonprakob,
R.Sethpakdee, and N. Juntawong., Effect of
paclobutrazole concentrations and time of foliar
application on flowering of ‘Namdokmai-Sitong’mango. International Journal of GEOMATE. Vol.
12 Issue 30, 2017, pp. 23–27.

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11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

THE RELATIONSHIP BETWEEN AQUATIC ORGANISMS AND
ENVIRONMENTAL PARAMETERS IN URBAN RIVER WITH
IMPROVEMENT
Michiko Masuda1, Koichi Takada2 and Fumitake Nishimura3
1,2

Faculty of Engineering, Nagoya Institute of Technology, Japan; 3 Faculty of Engineering, Kyoto
University, Japan
ABSTRACT

Many habitat models about organisms have been constructed by many researchers. Most of the models areconstructed using only a year data. We collected over 5 years data about organisms and environmental factors in
the Yamazaki River, an urban river in Nagoya city at the center of Japan. Using the data, we constructed a generallinear model at each year. It was revealed that the river improvement at every year affected the distribution of thealien species, especially the vegetation cover changing. The model of shrimps with low ability of moving was very
predictable of the distribution. But in the model of the species with high ability of moving, it occurred the goodmodel was made by chance. It was revealed that the distribution of the river species is always changing therefore
so the good model will be made by chance. In order to predict the distribution of the species, we must investigate
for some years.
Keywords: General linear model, Alien species, Native Species, Year fluctuation

INTRODUCTION

Fishes and other species with low survival rate atjuvenile are always fluctuate the population, because
the increasing rate (r-strategy). On the other hand, thespecies with high survival rate at juvenile is always
stationary (K-strategy). But there are few reports
about the year fluctuation about the number ofjuvenile fishes. Is the fluctuation of the fish
population among years caused by environmental
changing or r-strategy of fecundity? Then, wecollected the environmental factors and the number offishes for 4 years. Finally, we realized the ideal
environmental condition for native species, and makethe guideline in order to maintenance the rivers.
First, we made some habitat prediction modelusing General Linear Models (GLM) to further define
the environment of each native and alien fish species
habitat. Second, for past 5 years river maintenancechanged the environmental factors, then we analyze
between the influence of the environmental changing
and the fish population changing with each GLM
among years.

In Japan, at the maintenance of the river the target
species has been set up from the viewpoint ofenvironment by local public entity [1]. The Ministryof Land, Infrastructure, Transport suggested that theriver management should be done not only for thesafety of people life but also the natural environment.
The Yamazaki River is an urban river in Nagoya
city, in the center of Japan, with many fishes,
Plecoglossus altivelis (native species), Oryzias
latipes (endangered species). But some alien species
live in the river, for example Zacco platypus, Lepomis
macrochirus and Cyprinus carpio [2]. And the target
species of the river is Rhinogobius spp. But few
numbers of the species were observed, nevertheless
Nagoya city managed the river maintenance with theenvironmental factors, such as the water flow rate, thedepth of the river and the materials of the riverbottoms which were controlled for the species.
Perhaps other environmental factors are needed for
Rhinogobius spp.
From the factor of the living species, themaintenance of the river system should be done inorder to conserve the native species. What wereenvironmental factors needed for the native species?
The maintenance of the Yamazaki River, i.e.
excavation of sandbanks, the riverbank reinforcementand deforestation, has been done for past 5 years.
Continuous maintenance affects seriously damages tohabitat of living things in the river.
Generally, the number of the species is always
changing among years, especially migratory fish
species [3] because of r-strategy of the fishes [4].

MATERIALS AND METHODS
Study site
The place of the investigation was the Yamazaki
River, located in east side of Nagoya City at center ofJapan (Fig. 1). The river is 13.6km, the size of acatchment area is 26.6km2. At the mouth of the river,
the sea water goes up to 5km from the mouth of theriver. This river level and width are always controlled
by the gate of pond at the source of the river.
From the source of the river to the mouth of the455

GEOMATE – Kyoto, Japan, 3-5 November 2021

river, 9 stations were set up for the investigation. Eachstation has 100m length with 5m x 5m quadrat. St.1
is narrow river, so the 20 quadrats were set up. The
width from St.2 to St.8 was almost 10m, so the 40
quadrats were set up at each station. The width of theSt. 9 was 15m, 60 quadrats were set up. We
investigate at a total of 360 quadrats along the river.

area by vegetation or concreate block or somethings
were measured by tape measure. The vegetation
cover degree was 4 levels. 0 degree is no vegetation,
1 degree is 0-20%, 2 degree is 20-40% and 3 degree
is over 40%. Materials of the river bottom wereobserved by eyes, C: concrete, M: main gravel, S: sub
gravel, T: transit gravel, D: mud. Materials of river
bed was observed by eyes, c: concrete, r: rock, s: sand,n: no river bed. The dissolved oxygen was measured
by a portable meter (Hanna Instruments Japan Co.
HI914N). Loss on ignition was measured by sampling
river bottom materials. After sampling the materials
was into an incubator at 120oC for 2days in order toloss the water. After pretreatment, the weight wasmeasured and they were burned in a muffle furnace(800oC 1day) to measure loss on ignition.

Study site

Statistical Analysis

Fig. 1

We performed analysis using GLM (using quickR: correspondence analysis program) to elucidate thehabitation density of each fish species and its
relationship to environmental factors in each year.
The environmental parameters used in this study
assumes eight kinds of measurement data. The
population of each species at each quadrat was usedas the distribution research data.
The environmental factors should be standardized
in order to compare with each factor, standardization
is used for easily comparing among different average,
distribution and units. The multicollinearity is
confirmed by the value of variance inflation factors(VIF>10: there is the multicollinearity). We confirm
the possibility of VIF.
The response is the population density of eachspecies in the study. Thus, we supposed that theprobability distribution of the response obeyed
negative binomial distribution. Then the link equationis the logarithm equation. To explain the distributionof density, our model assumes the main effect term.
The environmental factors are two types, one is alinear numeric factor and the other is factor type. We
must exchange the factor type to numeric factor. The
number of numeric factor is N, then we prepare N-1
dummy variable.

The map of study site, and quadrat at St.2.

Population Research
In order to know the population of each species in
the Yamazaki River, we conducted roughinvestigation with rectangle fish capture net (40cm x25cm) with 5mm mesh. At each quadrat one personchased from the edge of quadrat and other person
received fishes. The investigation was done 6 times(from June to November) at each year (2013, 20172020). We collected all the fishes and benthos in thewater tank after catch, we distinguished species andcounted.
Environmental Factor Measurement

𝟎
𝟏
𝟏
𝟐
𝟑
= 𝟎
⋮
𝟎
𝐍−𝟏
( 𝐍 ) [𝟎We measured each environmental factor at eachquadrat once a year in Fall in 2013, from 2017 to 2020.
Eight environmental parameters, the velocity of aflowing fluid (v), the depth of the river (D), the closed
area rate over the water surface (N), vegetation coverdegree (P), materials of river bottom (RB), materialsof riverbed (RS), dissolved oxygen (DO) and loss on
ignition (HL) were measured in each quadrat. The
velocity of a flowing fluid was measured by thecurrent meter (Kenek Co. VE10). The depth of theriver was measured using pole for leveling (Myzox
Co. N-150) at three points at center line at quadrat.
The length and the width of the cover at the closed

𝟎
𝟎
𝟏
⋮
𝟎
𝟎

𝟎
𝟎 ⋯
𝟎
⋱
𝟎
⋯
𝟎

𝟎
𝟎
𝟎
𝟏
𝟎

⋮

𝟎
𝟎
𝟎

(1)

𝟎
𝟏]

The left side is the level of factors, and the right sideis the dummy variable. Interaction terms in the case,
we performed centration for 2 variables beforehand
and incorporated it as an explanatory variable. In
addition on the main effect variable without dummy
variable. As a standard to evaluate the good model,
we used Akaike’s information criterion (AIC).
Evaluation of Biodiversity
456

GEOMATE – Kyoto, Japan, 3-5 November 2021

To evaluate the biodiversity, the species diversity
index is mainly used. The Simpson index (2) reflects
not only the number of species, the amounts ofspecies. We used the Simpson index to evaluate thebiodiversity in the Yamazaki River.

2013

𝑺

𝑺𝒊𝒏𝒅𝒆𝒙 = 𝟏 − ∑ 𝒑𝟐𝒊

(2)

𝒊=𝟏

𝒏𝒊
𝑵

(3)
Velocity of river flow (cm/sec)𝒑𝒊 =S: the number of species, species i priority degree ispi, pi is brought to equation (3). N is the number of
total organisms, ni is the number of species i.
RESULTS
Field census
The population size of each species varied among
5 years. The result of each year is compiled in Table
1. In 2017, 2018, 2020, over a half of the populationis alien species. The habitat of the Yamazaki River is
occupied by alien species. The number of Cyprinus
carpio was stable for years. There was over twice
fluctuation at the number of each species, especiallyin Gambusia affinis where eight times difference wasobserved.

2020

Table 1 The number of each genus from 2013 to 2020
Genus
Cyprinus

2013
270

2017
401

2018
256

2019
230

2020
241

Gambusia

4194

32598

8873

6841

6469

Palaemon

2028

8400

4129

4422

3049

Zacco

8598

1296

1466

2295

2751

Fig. 2The distribution of velocity of river flow ateach Station in 2013 and 2020

32

54

218

422

164

331

313

1585

185

99

46

112

93

130

106

No. of total

15897

46717

17004

14703

13100

Total No.
of alien
Total No.
of native
Total No.
of species

11301

10468

7722

7755

6214

4596

36249

9282

7151

6886

22

21

25

26

25

There were some differences among years in each
St. For example, Fig. 2 and 3 showed the distributionof each station in 2013 and 2020. The velocity of St.
1-3 was faster than the other stations, and the velocity
at St. 9 was the slow stream. Except St. 4 there is noclear difference between the years. The maintenanceof the river changed the velocity at St. 4. The upper
river flow was faster than that of the end of the river.

Velocity of river flow (cm/sec)

St. 4
Pseudorasbora
Gymnogobi
usOryzias

2013

2017

2018

2019

2020

Fig. 3 The distribution of the velocity of river flow at
St. 4 from 2013 to 2020.

457

溶 存酸素 DO
流速V
水深D
遮蔽率N
植生度数P
2020 年
河 床 材 料 RB
表- 5 コイ生息モデル
河 岸 材 料 RS
強 熱減量 HL
溶 存酸素 DO

9.65
17.13
19.50
0.22
0.15
M-T
R
1.05
9.40

HL
1.25
9.40 10.20 10.20 10.20 10.05 8.70 Autumn
8.20
9.50 *****
DO
12.50 7.68
3.87
6.90
8.82
7.03
9.70
3.95 9.65
2018
Summer***2020 29.00 20.50
V
17.00 41.00 83.50 45.33 45.45
27.8317.13
GEOMATE
Kyoto,0.05
Japan,
2021
0.10
0.17 –0.03
0.063-5 November
0.09 Autumn
0.14
0.14
*
***
D
19.5
1.18
0.68
0.40
0.53
0.35
0.33
1.40
0.13
N
0.22
2019 Summer
***
S
S-T
T
S-T
T-S
T-S
S
M-D-S
P
0.15
** ***CR
C
C
C-S
C-S Autumn
S
S-C-R
1.07
1.08
1.13
1.14
1.09
1.13 RB
1.13
1.16 M-T
2020 Summer
***
9.15
9.10
9.15
8.80
8.80
9.20 RS
8.80
9.00
R

0.99

0.77

0.74

0.73

0.71

9.40

10.20

10.20

10.20

10.05

12.50***

7.68

3.87

6.90

8.82

17.00

41.00

85.3

45.33

45.45

0.10**

0.17

0.03

0.05

0.06

1.18**

0.68

0.40

0.53

0.35

S

***S-T***

T

S-T

T-S

C

C-S

1.14

1.09

8.80

8.80

C
R bottomC up
higher carnivore.
The
providesthe
Autumn*** model*****
HL web of the
1.05
1.07 network.
1.08
1.13
of the foodecological

Appendix showed the summary of the parameterdata from 2013 to 2020.

***: P <0.001, **: P <0.01, *: P <0.05, if the coefficient is minor,
DO
9.40
9.15
9.10
9.15
the star 3is red.
Table
The habitation model of G. affinis of each

The Habitation Modelparameter
カダヤシ
From the data of field census, the population sizeof 6 species was sufficient to create new models, twoalien species, Cyprinus carpio and Gambusia affinis,
four native species, Palaemon sp., Zacco platypus,
Pseudorasbora
parva
and
Gymnogobius
petschiliensis.
The habitation model of C. carpio was shown in
Table 2. Only parameters that have the significant
difference were shown in Table 2. And RSC, RSR
and RSS mean the dummy variable from equation (1).
There were few differences among 5 years. The
species chooses deep and likes rock, concrete andsand river bed materials area, and don’t chooses faststream in both all seasons and all years. C. carpio
likes the habitat where the slow stream and deep area,
therefore we can control the population of the species.

ParameterYear

Season

V

D

**

**

Autumn***2017 Summer***

Autumn**2018 Summer
Autumn**N RBS RBT RSR RSS RSN P*** ****

Autumn *** ********* *********** ***

*

***

***

2018 Summer ***Autumn *2019 Summer *Autumn *

*** *
**

***
***

2020 Summer
Autumn

***
**

***

*** **

**
***

*
***

***: P <0.001, **: P <0.01, *: P <0.05, if the coefficient is minor,
the star is red.

Table 4 The habitation model of Palaemon sp. ofeach parameter

RSC RSR RSS HL
*

**

*

***
***

***
***

**

***

**

2020 Summer***

Autumn

***

***
**

DO

*** ***

***

***
*

2019 Summer
AutumnN

D***

AutumnParameter2013 SummerV

2017 Summer ***Table 2 The habitation model of C. carpio of eachparameterYear

Season2013 Summer *** ***

***

***

***: P <0.001, **: P <0.01, *: P <0.05, if the coefficient is minor,
the star is red.

The second habitation model of Gambusia affinis
was shown in Table 3. The prediction of the species
was not stable, because of the fluctuation of thesignificant value among years. From 2013 to 2017 thespecies liked shallow with no stream. After 2018
there was no significant preference observed. In 2019and 2020 new parameter, dissolved oxygen wasmeasured. The preference of the high dissolved
oxygen area was observed. And the mass of the
population in 2017 was very high, therefore the modelof the 2017 reflects the high number populationpreference. In autumn the species likes the surface ofthe river or shallow area, in summer the species likes
no stream area.
The third habitation model of Palaemon sp. wasshown in Table 4. The preference of the species
depends on the vegetation cover. The Palaemon sp. isvery important to the food web because of the feed of

The fourth habitation model of Zacco platypus
was shown in Table 5. The population of species was
not stable among years. In 2017 at autumn there wasno significant data about the population. The
significance of the parameter was not stable, it wasalways changing. And the preference of the species
was not stable. We cannot predict the habitat of thespecies beside of the categorized seasonality
parameter.
The fifth habitation model of Pseudorasbora
parva was shown in Table 6. P. parva generally lived
in pond for the agricultural water supply. The
preference of the species would be estimated low
velocity area. Sometimes the preference of the low

458

***

GEOMATE – Kyoto, Japan, 3-5 November 2021

velocity was observed at summer in 2017 and 2018,
but in the other season that was not observed.

site.
Table 7 The habitation model of Gymnogobius
petschiliensis of each parameter

Table 5 The habitation model of Zacco platypus ofeach parameter

1.0
Table 6 The habitation model of Pseudorasbora
parva of each parameter

a)
a

0.8

0.6

Simpson index

0.4

0.2

0
1.0

b)

0.8

0.6
The last habitation model of Gymnogovius
petschiliensis was shown in Table 7. This species isalways lived in the mouth of the river with vegetation
cover and low loss of ignition. Then they like low
velocity area. There was no significant parameter
observed through 5 years. The habitation model is notsuitable for the species.

0.4

0.2

0

Biodiversity of the Yamazaki River

St.1 St.2 St.3 St.4 St.5 St.6 St.7 St.8 St.9

Fig. 4 The distribution of Simpson index in eachstation from 2013 to 2020, a) summer b) autumn

The biodiversity was shown in Fig. 4. The
biodiversity was changing seasonally at each study
459

GEOMATE – Kyoto, Japan, 3-5 November 2021

DISCUSSION

improvement and other species decrease accordingly[12]. As a result of the field census and GLM model,
the results were different from the character of thespecies, which prefers a gentle flow velocity. In
addition, the difference in selection characteristics
from year to year becomes clear, and although amodel that fits well in every year could be created, it
was not possible to create a consistent habitat modelthat differs from year to year.
P. parva lives a wide range of freshwater areas
such as lakes, downstream areas of rivers, irrigation
canals, reservoirs, and paddy fields, and lives ingroups in shallow bottoms with abundant aquaticplants and gentle flow [13]. Then the species was
expected that the model would be negative at the flow
velocity, and positive at mud river bed and vegetation.
But only 2 years model has followed the fact. Due toits alert nature, it may have been captured in adifferent location than its habitat.
There was wide variation of habitation models of
G. petschiliensis among years. The number of thespecies in the Yamazaki River is declining, especiallyin early summer. In early summer the juvenile fishes
swim down to the mouth of the river, therefore thepopulation of the species is expected to increase.
Maybe the reproduction of the species does not
succeed in the Yamazaki River.
Using GLM has the advantage that importantenvironmental factors for the target species are likely
to be highlighted. Therefore, as shown in the modelfor each species, we were able to clarify theenvironmental materials that the Yamazaki Riverlacks. However, it was found that the correlation of
the models varies from year to year, and although it isa simple method, it does not lead to an absoluteevaluation.

Some Habitation Model of river biology by GLM
were existed ever [5]-[8]. Every model was able to bepredictive for the habitat of each species. But all
studies were conducted using only a year data. There
was no study about GLM model with long-term data.
Then we made habitation models each year by GLM
and compared each model among years at same
species. The result of that, the prediction of thenumber of species with low moving ability could be
predicted by GLM models through years. For the
species with high ability of migration, there werecases where it was possible to create a valid modelthat matched the original ecology by focusing only on
one year. However, multi-year comparisons of suchspecies distribution have revealed that GLM models
can sometimes produce seemingly appropriate
models. From the fact, it can be said that whencreating a habitat model using GLM, it is necessary
to avoid erroneous model creation by comparing overmulti years.
About C. carpio habitation models, there was few
differences about the tendency of habitation model
among years. Especially St.4 provide good habitat forthe species. C. carpio has been selected as one of the
100 worst invasive alien species in the world andcauses problems such as competition with native fish[9]. The habitation model of the species is appropriate
because the tendency of the habitat did not change
among years. It is possible to reduce the population
by creating an environment in which the species isdifficult to stay.
The habitation model of G. affinis was not stablethrough 5 years. This alien species are sometimes
found in shallow area with no flow and sometimes ingentle streams. The habitat of the species was notclear. The species affect the population of Oryzias
laties(endangered
species),
therefore
conservationists want to clean up the species. But the
habitation was not clear by GLM models, we must
look for other environmental factors affecting thehabitat of the species.
According to the habitation model of Palaemon sp.
it was shown that the riverbank material, river bed
material and vegetation degree had a great influence.
The population of Palaemon sp. has a life cycle withlow moving ability. The presence of plants thatthrives in water is considered important for Palaemonsp. [10]. Palaemon sp. is important species that feeds
large organisms in the ecosystem [4], the presence ofthe species is desirable. By improving the habitat of
the species, a network of river ecosystem can beconstructed.
As Z. platypus grows, it expands its habitat fromshallow areas near the shore to open areas with noobstruction [11]. It is said that an environment where
the flow is slow is suitable for habitation and thesecases where this species increases after river

CONCLUSIONS
The following conclusions are drawn from thestudy.
1) The GLM model is effective to more fully
understand the habitation area of river species
easily.
2) However, it is necessary to take the data someyears because there is a year variation in the
habitat situation.
3) From the predictive model with consistent data
among year, we can create the good habitat for
native species.
4) Using the GLM, it is possible to predict theincrease or decrease of the population from theriver improvement for some years.
ACKNOWLEDGMENTS
We send thanks to Prof. T. Kitano who advised
the analysis of statistical method. We also thank to thecolleague at our laboratory and the group of ‘the460

GEOMATE – Kyoto, Japan, 3-5 November 2021

Clean Up Yamazaki River’, they helped the field
census.

[7] Sugiyama Y., Nakamura M., Senda S, andMasudaM.,
Environmental
parameters
controlling the habitat of the brackish water clam
Corbicula japonica identified by predictivemodelling. International Journal of GEOMATE,
Vol. 17, Issue 59, 2019, pp.68-73.
[8] Masuda M., Morioka T., and Nishimura F., The
environmental parameters controlling the habitatof invasive alien species, Spartina alterniflora
identified by predictive modeling. International
Journal of GEOMATE, Vol. 20, Issue 79, 2021,
pp. 62-67.
[9] IUCN, 100 of the world’s worst invasive alien
species, ISSG office, 2000.
[10] Kawaguchi Y. Saiki M., Mizuno T., and Kayaba
Y., Evaluate the function of waterfront vegetation
for aquatic organisms in experiment river.
Conference of Japan Society of Civil Engineering,Vol. 59, 2004, pp. 543-544.
[11] Takamura K., Utilization of habitat by young
pale chub, Zacco platypus (Temminck et
Schlegel). Japanese Journal of Ecology, Vol. 29,
Issue 3, 1979, pp. 295-306.
[12] Shimatani Y. Oguri S., and Kayaba Y., Impact of
stream modification on habitat component andfish in Tagawa River. Vol. 38, 1994, pp 337-344.
[13] Jackson M.C., Britton J.R., Variation in therophicoverlap of invasive Pseudorasbora parva
and sympatric cyprinid fishes. Ecology ofFreshwater Fish, Vol. 22, Issue 4, 2013, pp. 654657.

REFERENCES[1] The Ministry of Land, Infrastructure, Transport,
the report ‘Development to the making of multinature river’. 2006.
_
https://www.mlit.go.jp/kisha/kisha06/05/050530/
00.pdf[2] Nagoya City, The living species andenvironmental factors in River in Nagoya city,
2016,_
https://www.city.nagoya.jp/ryokuseidoboku/cmsfiles/contents/0000080/80159/sinaikasenn(1).pdf[3] Shimada K., Goto K., Yamamoto K. and Wada Y.,
Forecasting ascending population sizes ofamphidromous juvenile ayu Plecoglossus altivelis
in the Nagara River. Nippon Suisan Gakkaishi,
Vol. 73, Issue 4, 2006, pp. 665-672.
[4] Begon M., and Townsend C.R., Ecology: fromindividuals to ecosystems, Wiley, 2021.
[5] Inui R., Hitotsumatsu A., Akamatsu Y., and KonoT., Development of model prediction for fishbiomass in Saba River, Japan. Journal of Japan
Society of Civil Engineers ser. BI Hydraulicengineering, Vol. 72, Issue 4, 2016, pp. 997-1002.
[6] Hirayama T., Endo T., Kanou K., Usui S., andKaneko S., Invasion of channel catfish into small
rivers connected to Lake Kitaura, Ibaraki
Prefecture, Japan. Nippon Suisan Gakkaishi, Vol.
84, Issue 1, 2018, pp. 136-138.

461

GEOMATE – Kyoto, Japan, 3-5 November 2021

Appendix Environmental parameter of each station from 2013 to 2020, the number if the mean of each station462

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

EVALUATION OF THE VISIBILITY OF STAIRCASES IN UMEDA
UNDERGROUND STREET BY USING DEPTHMAP
Yuanhang SONG1, Akira TAKAHASHI2, Kensuke YASUFUKU2 and Hirokazu ABE2
1

Global Architecture, Osaka University, Japan; 2 Cybermedia Center, Osaka University, Japan
ABSTRACT

When an earthquake occurs, the tsunami caused by the earthquake can threaten people's lives. Especially for
low-lying terrain and densely populated areas, it is necessary to evacuate people to a safe place as soon as possible.
The predicted water level of the Osaka Umeda Underground Street during the tsunami flood will reach 0.5-1.0
meters. And people inside the underground street need to reach the ground through stairs or evacuate from theexits connected to the surrounding buildings. There are a large number of stairs distributed inside the Umeda
Underground Street. This study uses spatial syntax theory and depthmapX software to quantitatively analyze andorganize the visibility of the staircases, so as to evaluate whether the stairs at different locations are easy to bediscovered by the crowd during evacuation. And then lay the foundation for further evacuation research. In thispaper, the following conclusions can be drawn: The visibility of staircases under different radius 。restrictions isdifferent. On the whole, the stairs near the integration center and the square nodes have better visibility, and thosestairs which require multiple turns and have deeper visual depth are more difficult to find and use.
Keywords: Tsunami, Staircases, Visibility, Integration

INTRODUCTION

seen that in the Umeda area, the depth of water causedby the tsunami was about 0.5-1.0 meters when theearthquake occurred (Fig. 1). And the tsunami refugebuilding directly connected to the underground street
is only one, the Osaka Fukoku Life Hall (No. 8). [1]Therefore, the most important thing during adisaster is to let people in the underground street findthe nearest exit to the ground or the surrounding
building. This leads to the analysis and evaluation of
the staircases in the underground street.
Accessibility theory

Fig. 1

Accessibility refers to how easy it is to reach alocation in space. In 1980, William. H. Whyte
described the relationship between the accessibility ofurban space and the communication activities that
occur in urban space in his book "The Social Life of
Small Urban Spaces". Among them, accessibility islisted as the most important feature of a successful
space. [2]Firstly, accessibility has a visual meaning. At thelevel of visibility, only the "visible" space has thepossibility of "reachable". Secondly, accessibility has
spatial significance. Accessibility reflects the ease ofcommunication between spaces and expresses thelogical relationship between spaces. [3] In addition,accessibility is also affected by people's psychology,
and the psychological guidance and hints to people insome spaces will also affect accessibility.

Flooding in the Umeda area of Osaka at thetime of the tsunami

When a huge earthquake occurs, theaccompanying tsunamis often threaten the lives ofpeople in the city. When a disaster occurs, people
located in an underground shopping street need to use
the shortest route to reach a safe place which they canevacuate as quickly as possible. According to theflood hazards issued by the Osaka City Hall, it can be

RESEARCH BACKGROUND PURPOSE AND
SIGNIFICANCE
463

GEOMATE – Kyoto, Japan, 3-5 November 2021

find the dangerous parts, thereby obtaining
evacuation inspiration.
The significance of this research is to evaluate the
distribution of the staircases by summarizing andanalyzing the overall passable roads inside theUnderground Street. To predict the stairs that areprone to be crowded in situations of disasters, andformulate targeted evacuation plans and response
measures when necessary. It also lays the foundation
for further research on disaster simulation.

Research background
As Japan is one of the countries with frequent
earthquakes, disasters such as tsunamis caused byearthquakes have always been important issues forresearchers. How to minimize the loss of people's
lives and property caused by natural disasters is a taskthat researchers should continue to study and think
about.
The 2011 earthquake of the Pacific coast ofTōhoku occurred on March 11, 2011, the magnitudeof the earthquake reached 9.0 and was the fifth largest
earthquake in history. The huge tsunami triggered by
the earthquake caused devastating damage to thecoastal areas of northeastern Japan and also caused
heavy casualties. Now in 2021, 10 years have passed
since this huge earthquake. While commemorating
and mourning the victims of the earthquake, it’s alsomeaningful for us to take this as a topic to study theevacuation plan in the case of an earthquake or atsunami, and to deal with similar problems in the
future.
When a tsunami occurs, low-lying and densely
populated areas are more susceptible to disasters andface greater evacuation pressure. Especially as thedensity of cities is increasing and the threedimensional transportation system is greatly
developed today, the commercial street around theunderground station is the area where the evacuation
problem should be addressed. The Umeda area inOsaka is one of such typical areas.
And in recent years, the Space Syntax theory
proposed by Bill Hillier of the University of London
has entered the vision of more people. As a new
method of describing and analyzing space, Space
Syntax believes that "space itself is not important, butmore important is the relationship between spaces." [4]This theory, which studying space logic withmathematical methods, is gradually becoming widelyused in the study of road systems in cities andneighborhoods.

RESEARCH OBJECTS AND METHODS
Research object and scope
The research area is located in the Umeda
Underground Street, Kita Osaka, around JR Osaka
Station and Hankyu Umeda Station. It mainly
analyzes the visual accessibility of the undergroundpassable parts except for stations and shops (Fig. 2).
And it also analyzes the staircases and exits that canreach the ground or surrounding buildings.

Fig. 2The scope of the Umeda Underground Streetof the research

In the Fig.2, The purple part is the scope of
research in this paper. In this research, I believe thatwithin these parts of the underground street, the sightof people can travel through the space without
obstruction theoretically. Therefore, the parts with alarge height difference, which need to be traversed
through multiple stairs, and the shop area with a clear
sense of spatial separation are not considered as
research objects.
To study the visibility of stairs and exits in theentire underground street, and to compare andevaluate them, firstly mark all the stairs and exit
positions inside the underground street to facilitate
subsequent discussion. [5] (Fig. 3)

The purpose and significance of the research
The Umeda area is in danger of being damaged byfloods due to its low altitude in the case of tsunamis.
In addition, the Umeda Underground Street isconnected to the subway system and there are also
many shops located in the Umeda Underground Street.
It can be imagined that there’s always a large numberof people here. This research aims to evaluate andsimulate the location of stairs and exits in theunderground streets by analyzing the visual
integration of the Umeda underground area, and then464

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 3The locations of all the stairs and exits

Space Syntax Theory

As for the definition of the depth of sight, we canset it on a straight street. If point b can be directly seenfrom point a, then from a to b is the distance of one
depth of sight. However, when the street turns, pointe cannot be seen directly from point c. If you want toreach point e from c, you must first go to point d. In
this case, the distance between c and e is two sight
depths. (Fig. 4)

The space syntax theory was first proposed in the
1970s by Bill Hillier of UCL, England. Now a
relatively complete and mature theoretical system andmethodology, as well as technologies and software
for spatial analysis, have been formed. Based on theanalysis of spatial accessibility at the theoretical level,
it is also further researching various indicators ofspatial accessibility from a quantitative perspective.
In this article, depthmapX software is used toquantitatively analyze the research area from the
aspect of visual reachability. Visual accessibility is
the attribute of whether things can be seen in sight. It
can be studied from the perspective of spatialconnectivity and isovist area.
The analysis of the visibility in the space is to
examine the distribution of the depth of the sight in aspace.

Fig. 4

Definition of the depth of sight

Besides, we can also set the maximum distancethat the sight can reach. For example, if the farthest
visible distance in space is 150 meters, and the
distance from a to b is already 200 meters, then a to b
465

GEOMATE – Kyoto, Japan, 3-5 November 2021𝑛 −𝑛

is a closed interval of [n1, n2]. Take 2 1 as the width,
10
divide this value segment into ten equal parts, andassign red to the highest value segment, blue to thelowest value segment, and the middle-value segmentfrom top to bottom from warm to cold. It can be seenthat the closer the position to the center of the wide
space, the higher the connectivity value. These
positions are usually similar to nodes and small
squares in the space.
Based on the numerical feature distribution ofconnectivity, a spatial isovist feature map can begenerated. The isovist map means that looking out
from an element a, the value of the area that can beseen is fed back to a. At this time, if point b and point
a are in different positions, but their value is in thesame value range, then a and b will show the samecolor. (Fig. 6)

is already 2 sight depths distance.
Convex Map model
The convex map model is to understand thearchitectural space as a system composed of convex
spaces. Convex space means that within a space, any
two points can see each other. In the research andcalculation of space syntax, we transform the spacethat is not convex space into a system composed ofseveral convex spaces. The convex space involved in
the calculation is the "element". In the following
research, an infinitely subdivided square grid will be
used to replace the original spatial system. [6]
RESULTSThe convex space analysis method is used to study
the interior of the Umeda Underground Street,
starting from the analysis of the connectivity, andfurther analyzing the isovist area. Then use thedepthmapX software to compare the visual
integration under different radius, and find out
whether each staircase can be seen by the crowdunder different visual ranges.
Result of connectivity and isovist area
Use an infinitely subdivided convex space grid toreplace the spatial plan. This is the first step "set grid"process. In this study, the grid scale is set to 1000.
And then set the farthest range of sight on this basis,
I set 10 meters here. After the calculation is
performed, the connectivity of the space will beobtained. (Fig. 5)

Fig. 6

Isovist area map

Because the Isovist area map calculates the visiblearea, the Umeda Underground Street we studied isgreatly affected by the spatial shape and road width,
and the results displayed by the Isovist area andconnectivity are quite different. For squares and roads
in a wider area, the visible range of sight in the isovist
map tends to be wider.
Result of Visual Integration

Fig. 5Using the depthmapX software to analyze andcalculate the spatial visibility relationship. In thisstudy, the numerical distribution of the spatial sightintegration degree is mainly calculated. Visual
integration, that is, "accessibility of sight". It
describes how many turns people need to make one
point to another. And in space designing and planning,the "shortest distance" is always one of the mostmeaningful contents to think about. That is why I
choose the Visual Integration [HH] value to get theresult.
Visual Integration [HH] means the global visual
depth after removing all the influencing factors inConnectivity in the area

The connectivity generated based on the gridmeans that from a certain element in the space, a total
value of several other elements can be seen is addedup, and then fed back to that element. After eachelement has a value, these values are regarded as asequence of numbers, and the interval of the sequence466

GEOMATE – Kyoto, Japan, 3-5 November 2021

topology. The higher the value, it means that thiselement only needs fewer turns to see other elementsin the entire system. The lower the value, it indicatesthat starting from this element, if you want to see
other elements, you need more turns. Simply say, thehigher the value of Visual Integration [HH], the easier
it is to attract people's attention, and the easier it is forpeople to use that place.
When analyzing, the first step is to set the radiusof the analysis, that is, how many sight depths ouranalysis is limited to. For different space systems, theappropriate range is also different. Here I choose R3,
R5, R7, R9, R11 (corresponding to the range of 30
meters, 50 meters, 70 meters, 90 meters, and 110
meters), these situations for analyzing. (Fig. 7-11)
It can be seen that under the conditions ofdifferent radius restrictions, the results obtained are
not the same. From R3 to R11, with the gradualexpansion of the radius, some nodes with better viewsin the space gradually become obvious, and thepossibility of each staircase being discovered bypeople also changes.

Fig. 9Visual Integration [HH] R7

Fig. 10 Visual Integration [HH] R9Fig. 7Visual Integration [HH] R3

Fig. 11 Visual Integration [HH] R11

Fig. 8Then count the visual integration value of all thestairs and exits. And draw charts and statistics on the
results obtained. (Fig. 12-15)
On the whole, the visual integration value of thestaircases decreases from R3 to R11, which also
shows that the larger the range, the lower the

Visual Integration [HH] R5467

GEOMATE – Kyoto, Japan, 3-5 November 2021

probability that the stairs will be discovered by people.
As a result, it is necessary to ensure that within acertain range, there are enough stairs to meet theevacuation needs.

VISUAL INTEGRATION-A
R5

R7

R9

R11

6-1
6-2
6-10
6-13
A-3
6-20
A-1
6-19
6-25.1
A-11
6-25.2
6-30
6-34
6-31
6-36
6-40
6-48
6-46
6-47
6-57
6-71
6-77.1
6-77.2
C-57
5-2
C-1
C-61
C-83
C-49
C-69
4-8
C-80
C-93
C-84
C-7
C-92
C-72
C-60.2C-60.1

VISUAL INTEGRATION [HH]

R3
8
7
6
5
4
3
2
1
0

STAIR NUMBER (1-39)

Fig. 12 Comparison of Visual Integration-Part A

R5

R7

R9

R11

8
7
6
5
4
3
2
1
0
17
5-10
11-4/5
C-30
16
D-1.1
15
D-1.2
D-13
14
5-22
D-14
D-21
D-23
D-24
D-27
D-41
D-39
D-31
5-30
D-55
D-51
D-57
11-21
D-33
D-49
11-23
D-58
D-56
D-52
D-44
D-50
D-40
5-38
E-2
5-42
8-5
10-7
7-30
18

VISUAL INTEGRATION [HH]

VISUAL INTEGRATION-B
R3

STAIR NUMBER (40-79)

Fig. 13 Comparison of Visual Integration-Part B
VISUAL INTEGRATION-C
R5

R7

R9

R11

E-8
E-15
10-9
13
5
E-17
6
E-14
4
F-14
8-17
E-25
19
E-20
E-33
E-35
E-26
21
20
E-18
E-32
E-34
11-41
12
2
22
3
10-21
E-22
H-1
11-44
F-34
11-43
5-64
1
H-2
3-31
5-66
H-8
5-68

VISUAL INTEGRATION [HH]

R3
8
7
6
5
4
3
2
1
0

STAIR NUMBER (80-119)

Fig. 14 Comparison of Visual Integration-Part C

468

GEOMATE – Kyoto, Japan, 3-5 November 2021

VISUAL INTEGRATION-D
R5

R7

R9

R11

8
7
6
5
4
3
2
1
0
F-40
G-1
H-43
H-16
H-49/51
H-60
H-58
H-41
H-40
8
H-81
7.1
H-28
H-32
H-52
7.2
H-30
24
23
H-76
7-58
H-80
H-82
9
J-3
J-2/4
11
7-66
7-65
10
7-72
7-79
M-9
M-5
M-1
M-13
M-2
M-6
M-10
M-14

VISUAL INTEGRATION [HH]

R3

STAIR NUMBER (120-159)

Fig. 15 Comparison of Visual Integration-Part D
From the overall trend, with the expansion of thesight radius, the trend of the integration value
distribution of each staircase is gradually regular,
however, under the radius of R3 (small range), thevalue of the integration degree of the staircases
fluctuates greatly.

roads with more turns from the main roads, such as
stairs No. 4-8, C-61, 10, 5-66, 7-79, H-76, C-57, D41 and C-49. (Fig. 16) This suggests that people are
more prone to choose staircases that are easier toreach from the main road, for the reason that thesestairs have better visibility.

DISCUSSION
In addition, from the obtained data results, somedistinctive staircase positions can also be found andshow that it is necessary to pay attention to them.

Fig. 17 Distribution of stairs with good visibility
Next, there are some stairs with higher integration
values under different radius ranges, such as M-9, M10, M-13, 6-47, H-2, 6-77.2, C-92, M-14, 6-30, A-3(Fig. 17). By analyzing the map, the locations of thesestairs are very close to the small centers of integration,such as the intersections or the vicinity of small
squares. But correspondingly, it’s important to notethat in the process of evacuation, it may be easier tocause congestion at the locations of these stairs.

Fig. 16 Distribution of stairs with poor visibility
Firstly, there are some stairs, even though they arewithin the radius of 30 meters, their visibility is still
very low. It means that these stairs are still not easy
to find in a small area. Inspecting its specificdistribution location, it can be found that most ofthese stairs with poor visibility are located in branch

CONCLUSION

469

GEOMATE – Kyoto, Japan, 3-5 November 2021

This study analyzed the visibility of the staircases
inside the Umeda Underground Street and got thefollowing conclusions:
1. From the perspective of connectivity, theconnectivity between open road intersections andsquare nodes is higher than that of narrow andterminal roads. Correspondingly, from the isovist
area map, the value of connectivity plus the area value
within the visual range of the location also shows thatthe area of sight on squares and wide roads is larger
than that of narrow places, that is to say, narrow roads
are not only more likely to be crowded, but also moredifficult to find safe exits within sight.
2. From the perspective of visual integration,
under different limits of the radius, the visual
integration center, and the visual integration value atthe location of each staircase are different, but theoverall integration center distribution trend is similar.
With the enlargement of the sight radius, the center ofintegration becomes more obvious and concentrated.
3. Through the analysis of the specific values ofthe staircase locations, it is possible to find and locate
some stairs that are difficult to find, and some stairs
that are easier to be found - the stairs that are close to
the square and nodes and can be reached without
many turns are easier to use; and the stairs with manyturns and far away from the main roads have low
visibility in people's vision.
In general, most of the stairs’ visibility can be seen
to be relatively average, and that guides me to do thesimulation of the crowd to find more patterns in
specific evacuation situations in future research.

[4] B. Hillier, Space is the machine: fabricconstruction theory, January, 2008.
[5] Osaka City Osaka Station Area UndergroundSpace Inundation Countermeasure Plan Ver.1(Material-Second Half), 2017.11.4.
[6] Space Syntax: a very short introduction, July,
2013. pp.9–42.
[7] Rina Uogashi, Aimi Itokawa, Yuichi Yamaguchi,
Analysis of evacuation behavior in Umeda
underground shopping center, Osaka.
[8] Junpei Ueno, Tatsuya Kishimoto, An analysis ofpedestrian movement in multilevel complex by
Space Syntax theory – In the case of Shibuya
station, Journal of the City Planning Institute ofJapan, No.43-3, October, 2008, pp.49–54.
[9] Fumiko ITO, Yoshifumi FUJIKI, Analysis of theRelation between Complexity and Impression ofStreet Network, Journal of the City Planning
Institute of Japan, Vol.48 No.3, October, 2013,
pp.327–332.
[10] Eiji Nakamura, Koike Norimitsu, Prediction ofresidents and proposals for evacuation guidancefrom evacuation simulations based on visitor
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Park as an example-, JSCE Proceedings F6(Safety Issues), Vol.74 No.2, 2018, pp.93–100.
[11] Toshiyuki KANEDA, Akira OTA, Junya
KOBAYSHI ， A STUDY ON FACTORANALYSES OF PEDESTRIANS’ SPATIAL
DISTRIBUTION IN KANAYAMA DISTRICT,NAGOYA – By applying Agent Analysis inSpace Syntax studies, J. Archit. Plann, AIJ.
Vol.85 No.767, Jan., 2020, pp.121–129.
[12] Kei YASUDA, Ken MIURA, PLANNING OF
SHARED SPACE IN SENIOR CITIZENS’LIVING FACILITY USING VISIBILITYANALYSIS – Case study on elderly housing withsupportive services, J. Archit. Plann, AIJ. Vol.86
No.781, Mar., 2021, pp.727–737.
[13] Hu Bin, Ni Zhenjie, Lyu Yuan, Optimization
Design for Transfer Space Orientation in Subway
Station Based on Depthmap, Metropolitan RailTransit, Vol.29 No.1, Feb., 2016, pp.30–34.
[14] MA Chenxiao, SUN Yi, PENG Fangle, Space
Syntax Based Space Analysis of Wujiaochang
Underground Pedestrian System, Shanghai,
MODERN TUNNELLING TECHNOLOGY (S2),
Vol.55 (S2), Nov., 2018, pp.1255–1262.
[15] Rongrong Yuan, Michael J. Ostwald, Spatiovisual experience of movement through the
Yuyuan Garden: A computational analysis basedon isovists and visibility graphs, Frontiers ofArchitectural Research (2018) 7, pp.497–509.
[16] Hou Xiana, Zheng Lipeng, Simulation ofPedestrian Flow in Traditional Commercial
Streets Based on Space Syntax, Procedia
Engineering 205 (2017), pp.1344–1349.

ACKNOWLEDGMENTS
In writing this paper, I have benefited from thepresence of my teachers and my friends. They
generously helped me collect the materials I needed
and made many invaluable suggestions. I hereby
extend my thanks to them for their kind help, without
which the paper would not have been what it is.
Particularly, I am deeply indebted to my Professor,
Hirokazu ABE, who guided me throughout mywriting of this thesis. And teachers Akira
TAKAHASHI and Kensuke YASUFUKU, also gave
me a lot of help.
REFERENCES[1] Osaka City, "To protect lives from tsunami andflood damage" Flood hazard map, 2021.4.19.
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Urban Spaces. Washington D.C.: Conservation
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470

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

THE OCCURRENCE OF MICROPLASTICS ON THE START-UP
PROCESS OF AN ANOXIC BIOFILM BATCH REACTOR
Ansiha Nur1, Mhd Fauzi2, Prayatni Soewondo3, Ahmad Soleh Setiyawan3, Katharina Oginawati3
Environmental Engineering Doctoral Program of Institut Teknologi Bandung, Indonesia; 2 Environmental
Engineering Master Program of Institut Teknologi Bandung, Indonesia; 3 Environmental Engineering of Institut
Teknologi Bandung, Indonesia
1

ABSTRACT
Activated sludge from several sources is often used to initiate bacterial growth on the start-up of the biologicaltreatment system and it contains microplastic impurities. Currently, there is no information regarding the presenceof microplastics in a biological reactor during the start-up process. The aim of this study was to investigate theoccurrence of microplastics during the start-up process in an anoxic fixed-biofilm batch reactor. Three units of 42
L laboratory-scale reactor fed with synthetic biodegradable substrates was inoculated with activated sludge fromfecal sludge of residential areas. Each unit filled with PET media with three different specific surface ara (SSA).
The results showed that the stable bioﬁlm formation rates were associated with removal of COD after 75 days.
The source of microplastics in the reactors during the start-up process comes from fecal sludge (x̅ ± SD, n = 3)
7.666,67 ± 513.16 MP/kg (w/w). Approximately 7.96 – 9.24% of microplastics were adsorbed on the biofilm. No
PET as secondary microplastic was found during 75 days of the start-up process. Two types of microplastics foundin the reactor were fibers and fragments with the amount of 80.87 ± 44.8% and 19.13 ± 10,1%, respectively. The
types of polymers detected by the ATR-FTIR spectroscopy were PES, PE, PET and PS. SSA affects the adhesionof microplastics on PET media. In a larger SSA, more microplastics are adsorbed on the biofilm.
Keywords: Anaerobic, Fecal sludge, Fixed-biofilm batch reactor, Microplastics, Start-up process

INTRODUCTION

g of stool in median concentration. The abundance of
microplastics in human feces found 1 particle/g to 36
particles/g (size 20 – 800 µm) in Beijing, China [9].
Microplastics, a type of an emerging
environmental pollutant, are small particles less than
5 mm in size [10]. Based on the origin, themicroplastics classified as primary and secondary.
Primary microplastics were originally manufactured
in very small sizes, such as microbeads (facial scrub
cleansers), industrial ‘scrubbers’ used to blast cleansurfaces aand plastic powders used in molding [11].
Secondary microplastics are the result of
decomposed, degraded, and fragmented of larger
plastics into sizes smaller than 5 mm due toenvironmental
mechanical,
photo-oxidative,
physicochemical, and biotic factors [12].
To date, there have been limited studies on theoccurrence of microplastics during the start-up of
fixed attached growth systems. The purpose of thisstudy was to identify the occurance of microplastics
in the fixed-bed reactor during the start-up process asa result of the use of fecal sludge as inoculum. This
research uses PET plastic bottles as support material.
This is based on its advantages such as lightweight,
inertness, large surface area, does not rot, and can
attach bacteria [13] – [15]. Despite its advantages, thepotential solubility of PET as a microplastic during
the start-up process was investigated in this study.
PET scraps have the potential as microplastics [16] inthe reactor.

In recent years, the biological wastewater
treatment process is still the most widely used method
to remove organic pollutants and nutrients because ofhigh treatment efficiency and cost-effectiveness [1]–[2]. The use of attached growth systems, such as
fixed-bed biofilm reactors (FBBRs) or moving bed
biofilm reactors (MBBRs), has a promisingalternative to remove organic and nutrient fromwastewater, such as domestic wastewater [3].
Seeding is the initial biofilm adhesion to
supporting media in attached growth systems during
the start-up of process. The start-up process is definedas the biofilm development rate period to reach astable value, it can vary 1 – 6 weeks [4]–[5]. Fecalsludge from residential is potential used for seedsludge because of a high VSS value. Behind theadvantages, the occurance of microplastic on start up
process in a reactor are still unknown. Most studies
are still focused on the bacteria accumulation,
biomass adhesion, biofilm growth to support media inorder to reducing the start-up duration within reactor.
Currently, publications on microplastics raised
concerns in the food chain as potential intake byhumans and harm health [6] – [7]. Estimation of
microplastics consumption within food and beverage
range 39.000 to 52.000 particles annually by humanin America [6]. Schwabl [8] have been investigatedmicroplastics in human stool was 20 particles per 10471

GEOMATE – Kyoto, Japan, 3-5 November 2021

MATERIAL AND METHODS
Reactor and biofilm media
This study was carried out in a lab-scale fixed-bed
circulating batch reactor (CBR), made fromtransparent acrylic material with a working volume of
42 L (Fig. 1). The CBR reactor was equipped with arecirculation pump to make the substrate andactivated sludge mixed well, in order to make thebiomass attached to the PET surface during seeding
process. The experiment used three units of CBR
under anoxic condition. Each reactor was filled withPET modules with three different specific surface
area (SSA); Anx-1 (SSA-1; 555.78 m2/m3), Anx-2(SSA-2; 444.53 m2/m3), and Anx-3 (SSA-3; 374.68
m2/m3) as shown in Fig. 2.
To obtain anoxic conditions in the CBR, the DOlevel in the wastewater was artificially removed byinjecting N2 gas into the reactor for 15-20 minutes[17]. The N2 gas injection pressure was set at ± 4.5
lbf/in2. Measured DO less than 1 mg O2/L to 0.2 mg
O2/L. During the experiment period, the operating
temperature kept in a constant temperature room.
After the reactor was run, to determine the presenceof microplastics in the reactor, the microplastics massbalance was calculated in the initial liquid (day 0) andat the end of seeding (day 75). The initial liquidsample (0 day) was taken at the beginning of theseeding process when the activated sludge andwastewater were completely mixed after the pump
was turned on. At the end of seeding (75 day), liquidand biofilm samples were taken.

a = resirculation pump, b = feeding valve, c = siphon
tube, d = aerator, e = sampling point, f = discharge
valveFig. 1 Anoxic Circulating Batch Reactor

(a)

(b)

(c)

Fig. 2 Different SSA on CBR. a) Anx-1, b) Anx-2,c) Anx-3
by checking the pH periodically and operating thereactor at room temperature, 25 – 27 0C. DO
conditions under anoxic conditions were maintained
not to exceed 2 mg/L

Synthetic wastewater
Synthetic wastewater consisted of glucose as asource of carbon, NH4Cl as a source of nitrogen, andKH2PO4 as a source of phosphate, and tap water of
the
Water
Quality
Research
Laboratory,
Environmental Engineering Institut Teknologi
Bandung (ITB) as mixing water was used. Before use,the characteristics of tap water are checked fororganic and nutrient content. The result showed thattap water was free from organic and nutrient content
and its characteristics are relatively constant.
Microplastic content is also checked.

Identification and quantification of microplastics
There is no standard method for the quantificationand identification of microplastics in a sample.
However,varioussampleprocessingforidentification and quantification methods for
microplastics were employed. There are 3 types ofsamples processing for microplastics; artificial
wastewater, sludge, and biofilm. To remove naturalorganic from samples, 30% H2O2 solution was added
to the samples, digest using a magnetic stirrer underheat 600C until H2O2 fully evaporated [20]. Artificial
wastewater filtered using a glass microfiber filter
Whatman GF/C and dried in an oven heat 60 0C for
30 minutes [21]. Different processing from artificial
wastewater, before sludge and biofilm samples digest
with 30% H2O2, the samples were dried in an ovenheat 60 0C over 24 h [22]. When it dried, mixed with
distilled water. The next step was using density
separation by using ZnCl2 1,5 mg/cm3 [23] for 24

Seeding
Activated sludge in this study taken from a septictank of one household, Bandung City. The C:N:P
substrate ratio used for optimum bacterial growth
conditions, 100:5:1 for anoxic [18]. To monitor
bacterial growth, VSS and COD were measured
routinely according SMEWW (Standard Methods for
Examination of Water and Wastewater) 2540 E and5220 I. During the seeding process, the optimum pH
for bacterial growth was maintained at 6.5 – 7.5 [19]472GEOMATE – Kyoto, Japan, 3-5 November 2021

hours. The supernatant of both, was filtered undervacuum onto a glass microfiber filter paper(Whatman GF/C).
Filter papers were observed by visual observation
using an Olympus CX-21 light binocular microscopewith 100x magnification coupled with Optilab
Viewer 3.0 software. Observations on the surface ofthe filter paper in a zigzag manner [24]. The
measurement of particle by using Image Raster 3.0
software. To ensure that the particle was microplastic,
then the chemical structure was identified using the
Attenuated Total Reflection-Fourier Transform
Infrared (ATR-FTIR) at the Environmental
Engineering Instrument Laboratory, ITB. ATR-FTIR(Bruker Alpha II Platinum ATR).

a)

1001-5000 µm
7%501-1000 µm
7%20-100 µm
42%301-500 µm
16%101-300µm
28%b)
fragment
19%

RESULTS AND DISCUSSION
Activated sludge as a source of microplastics in thereactor
Although fecal sludge was a source of bacterial
seeds needed during seeding, microplastics derived
from activated sludge are the largest contributor to thepresence of microplastics in the seeding process in an
anoxic batch reactor. The average of microplastics inthe activated sludge (x̅ ± SD, n = 3) was 7,666.67 ±513.16 MP/kg (w/w). No microplastics were found in
artificial wastewater.
Figure 3 shows the distribution of size, shape,
polymer, and color of microplastics in the activatedsludge used. The dominant of microplastic
characteristics were microplastics with less than 300µm in size, in the form of fibers (80.87 ± 44.8 %),transparent (55%) in color, and polyester (PES)
polymer (39%). PES types are mostly found in thisactivated sludge, then polyethylene terephthalate(PET) and polystyrene (PS) types. This was because
its density was greater than water, so a lot of it settled
in the sludge. Density of PS 1.04 – 1.1 g/cm3,
Polyethylene (PE) 0.92 – 0.97 g/cm3, PET 1.37 – 1.45
g/cm3, PES 1.24 – 2.3 g/cm3 and water 1.0 g/cm3.
Feces is a waste product of the human digestive.
One of the potential routes of microplastics into thehuman body is through the ingestion process,consumption of food and beverages [6], [7], [25]. The
food chain is strong evidence of the transfer ofmicroplastics to humans [26]. The presence of
microplastics found in human feces in Beijing 1 – 36
particles/gr (20 – 800 µm), PET (83.3%), polystyrene(PS) (50%), polyethyelene (50%) [27], human fecesin Europe and Asia, an average of 20 particles/10 g(50 – 500 µm) [8]. Schwabl [8] found sizes 50 – 500µm and polypropylene (PP) (62.8%), PET (17%)
polymers were the most commonly found. Its similar
size with microplastics obtained in this study, less

fiber
81%

c)
Brown
8%

Green
2%

Purple4%

Others3%
Transparent
55%

Red
8%

Blue
20%

Others
14%

d)

PES
39%
PS
19%

PET
24%

PE
4%

Fig. 3 Distribution of microplastic characteristics inactivated sludge. a) size, b) shape, c) color, d)
polymer473

GEOMATE – Kyoto, Japan, 3-5 November 2021

than < 500 µm. Also similar size of the human stool
found by Schwabl [8] and Zhang [27]. Comparing
with the previous study, the differences in polymers
that were found in this study, as an effect of thedifference in food and beverage consumption in acountry.
Several studies on investigating the potential
sources of microplastics from various types of foodand beverage have been reported. Microplastics were
found in foods and beverages commonly consumedby humans such as sea salt, lake salt [28], seafood[29], fish [27] [30], vegetables and fruit [31]-[32],piped water [33], [27], drinking water [34], mineralwater from various countries, such as Indonesia,
Thailand, China, India, Mexico, Brazil, USA,
Thailand, Lebanon [35]. Microplastics found in PET
bottled water was < 10 m [36]-[38].

In Fig. 4 can be seen that there were no
microplastics added at the end of the start-up process.
It means that there is no addition of secondary
microplastic from PET media in a short period (75days. Figure 4 and Table 1 show SSA affects theadhesion of microplastics to PET media. In larger
SSA, more microplastics are adsorbed on the biofilm.
At the beginning of seeding, all microplastics are
suspended and completely mixed in the artificial
wastewater. At the end of seeding, 7.96 – 9.24% of
microplastics were adsorbed on the biofilm growing
on PET.
Identification of PET polymer
Even though from the mass balance known that
there was no addition of microplastics during theseeding process, identification of PET from bottles
using ATR-FTIR was still carried out by matching itwith the standard spectrum of PET from plastic
bottles. For this purpose, a standard spectrum of PET
bottles was made, then stored in a spectrum library,
making it easier to identify PET polymers fromsamples of microplastic particles that were read by
ATR-FTIR. The sample spectra were matched withthe spectrum library, as well as the information thatappeared on the monitor screen showing themolecular formula and images of the PET functionalgroups. The PET functional groups that appear on themonitor screen are adapted to the PET functionalgroups described by Crawford and Quinn, 2017 andAndardy, 2017.

Mass balance microplastics in the reactor
The presence of PET as secondary microplastic aslong as the start-up period was detected from the massbalance of the presence of microplastics in the reactor(Table 1).
Tabel 1 Mass balance of micoplastics in the reactor(MP/L)
ReactorTime

LiquidBiofilm

Anx-1

Day 0

1190

0

Day 75

1080

110

Day 0

1304

0

Day 75

1200

104

Day 0

1130

0

Day 75

1040

90

Anx-2
Anx-3

Microplastics (MP/L)

1400

1200
1000
800
600
400
200
0
Day 0 Day 75 Day 0 Day 75 Day 0 Day 75
Anx-1

Liquid

Anx-2

Anx-3

Biofilm

Fig.4 The occurence of microplastics during seeding
processFig. 5 Types of microplastic sample polymer foundby the ATR-FTIR

474

GEOMATE – Kyoto, Japan, 3-5 November 2021

Figure 5 shows the types of microplastic polymers
found in ATR-FTIR. The types of microplastic
polymers identified by ATR-FTIR in the seeding
reactor samples are PE, polystyrene (PS), polyester(PES), and PET. In addition, other types of polymers
such as polypropylene (PP), polyamide (PA) werealso found in small amounts. The red spectrum graphshows the sample spectrum and the blue one shows
the standard spectrum in the library. Figure 6 shows
the polymer distribution in the anoxic reactor during
the seeding process in the liquid phase and biofilm.
Differences in density, shape, and size of eachparticle in the sludge affect their presence in thereactor during the seeding process.
PET polymer was found 17.38 ± 5.72% in theanoxic reactor, less abundant than PES and PE
polymers. The difference in particle density affects its
distribution in the liquid and biofilm. PE polymer
found in biofilms more dominant than others, because
its density is smaller than water, so it's easieradsorbed on biofilms.

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Fig. 6 Size, shape and color of microplastics in thereactor; a) red fiber, b) blue fiber, c) transparent fiber,d) red fragment and blue.

CONCLUSIONSThe source of microplastic in the anoxic reactor
for 75 days of start-up process came from fecal sludge
taken from housing with a concentration of 7,666.67± 513.16 MP/kg (w/w). At the end of seeding, around7.96-9.24% of microplastics were adsorbed on thebiofilm. The most identified types of microplastics by
the ATR-FTR were PES and PET polymers. Based on
the mass balance calculation, there was no
microplastics addition at the end of seeding, meaningthat no PET was dissolved as secondary microplastics
during the seeding process.
ACKNOWLEDGMENTS
This work was supported by Riset Unggulan ITB,
2021.
475

GEOMATE – Kyoto, Japan, 3-5 November 2021

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476

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

CHANGES IN RIVER ENVIRONMENT AT THE DOWNSTREAM
AREA DUE TO MAINTENANCE FLOW DISCHARGE FROM THE
HIRAIDE DAM LOCATED ON KATASHINA RIVER
Naoki Miyazato1, Yuko Kakegawa2, Keiji Nakajima2, Hiroyuki Aizawa3, Yuya Saito4,Yuishi Yagi5,
Akihiro Horio1, Toru Aoi1 and Yoshikatsu Yamamoto1
1

National Institute of Technology (KOSEN), Gunma College, Japan
2
Organization-Kawagera-no-kai, Japan3
2-7-3, Bunkyo-cho, Maebashi, Gunma, Japan
4
Yaritanago restoration project team, Japan
5
Branch office of JASHA, Japan
ABSTRACTIn 2011, a new hydroelectric power station was built by Gunma Prefecture to improve the river environment.
The water of river maintenance flow is used by the new hydroelectric power station.
In this study, water quality, diatoms, aquatic insects, and freshwater fish in the low flow section in 2010 andthe maintenance flow discharge section at Katashina River in 2015 were examined. The results are summarised,First, Nitrogen concentrations of Katashina River water were as high as 0.15mg-N/l at all sampling sites. Second,
The amount of Melosira varians in the maintenance flow discharge section was decreased comparing that in the
low flow discharge section. Third, the most amount of freshwater fish was Tribolodon hankonesis at Katashina
river. These results indicated that Katashina River environments were improved slightly.
Keywords: decreased flow rate section, water quality, diatoms, aquatic insect, freshwater fish

INTRODUCTION

environment.

Until 2010 in winter seasons, the Katashiina River,a tributary of Tone River (Gunma Prefecture, Japan)
has suffered a decreased flow rate section of 2.7 km
due to flow diversion to the hydroelectric power
station of Hiraide Dam, resulting in deterioration of
the river environment as a wildlife habitat. In 2011,a new hydroelectric power station (Shin-Tonami
electric power station) was built by Gunma Prefecture
to improve the river environment. The water of river
maintenance flow is used by the new hydroelectric
power station [1]-[3].
Conversely, diatoms are widely used for the
biological assessment of rivers, along with aquatic
insects and fish [4]-[7]. Diatoms are used in a variety
of ways to show the impacts of environmental
disturbance. Effects of dams on diatom communities
along the Katsuura River were investigated tounderstandtherelationshipbetweentheenvironmental factors and the characteristics ofdiatom communities [8].
In this study, water quality, diatoms, aquatic
insects, and freshwater fish in the low flow section
and the maintenance flow section in Katashina River
were examined. The purpose is to investigate theaquatic organisms and water quality at the Katashina
River for five years, after the construction of the
Shintonami Power Station and the start ofmaintenance flow discharge, and to grasp andevaluate the improvement status of the river

MATERIAL AND METHOD
Katashina River and sampling sections
Figure 1 shows that Katashina River is located in
the northern part of Gunma Prefecture. Figure 2
shows that five survey areas were selected on the
Katashina River. The river water was sampled fromthe Katashina River occasionally. The water surface
width and water depth in the figure are the conditions
when the Gunma Prefectural Enterprise Bureau
conducted the test release [1]. However, in this survey,

Figure 1 Katashina River was located innorthern of Gunma prefecture, Japan

477

GEOMATE – Kyoto, Japan, 3-5 November 2021

species and family.
Fish survey
Fishes were collected at each survey site No.1-2
and 4-5 using a scoop net with a diameter of about 30
cm. In addition, electric shockers, cast nets, andfishing were also used. The number of fish obtained
was counted for each species, the total length wasmeasured, and then the fishes were released on the
spot.

Figure 2 Five sampling sites, number 1 to 5
on Katashina River

Water quality survey
The water temperature (°C) and electricalconductivity (EC: mS/m) at survey points No. 1 to 2
and 4 to 5 were measured. 1 liter of river water wascollected. In our laboratory, pH was measured using
pH meter. Ammonia nitrogen (NH4-N), nitrite
nitrogen (NO2-N), oxidized nitrogen (NOx-N),
phosphate phosphorus (PO4-P) were analyzed using
an autoanalyzer (BLtec, Quattro-II).

Figure 3 survey site No.3
it was predicted that there would be danger incollecting samples at survey site No. 3 (Figure 3) due
to changes in riverbank conditions (effects of gravel,
weeds, etc.), so we abandoned the survey.

RESULTS AND DISCUSSION
Biological survey
Biological survey
Diatom survey
Diatom survey results
The diatom survey was conducted on June 21,
2015, October 25, November 15, and December 6,
2015. The diatoms observed in this survey are 30
species in a total of 15 families.
Table 1 shows the observed diatom species.
At all survey sites, the main species that appeared
were Achnanthes minutissima, Achnanthes japonica,
Encyonema minutum, and Navicala cryptotenella.
Achnanthes
minutissima
belongingtoeurysaprobic taxa and Achnanthes japonica
belonging to saproxenous taxa showed at survey sites.
The number of confirmed saprophilous taxa,
eurysaprobic taxa, and saproxenous taxa at eachsurvey site are shown in figure 4. At each survey site,
it was confirmed that there were a large amount ofeurysaprobic taxa. Furthermore, the abundance ratiosof the confirmed diatom species were determined for
aprophilous taxa, eurysaprobic taxa, and saproxenous
taxa. Figure 5 shows the abundance ratio compared
Furthermore, the abundance ratios of the confirmed
diatom species were determined for aprophilous taxa,
eurysaprobic taxa, and saproxenous taxa.

Analyze the diatom community attached to thegravel at survey sites No. 1 and 2 and 4 to 5 to clarify
the type and composition of diatoms at the survey
points. The diatom survey will also be conductedwhen a fish survey was conducted. In this survey,
diatoms adhering to the gravel will be collected at thesite and stored in 70% ethanol. After that, we will takeit back to our laboratory and investigate thecomposition of diatoms. After treating the sample
with concentrated sulfuric acid, seal it with proof flux
to prepare a permanent preparation. The samples
were examined under an optical microscope (using anoil-immersion lens) and made a 2000x photograph.
The diatom list was created by counting each sample.
Habitat survey of aquatic insects
This survey will be conducted at survey sites
No.1-2 and 4-5. At each survey site, aquatic insects
will be collected at 4 locations (4 times) at 1 site using
a 25 cm x 25 cm square frame and nylon net. The 0.25
m2 quantitative survey will be conducted. The
collected aquatic insects are taken out from the nylon
net using tweezers. They are soaked in 70% ethanoland stored in a 25 ml glass bottle. The wet weight wasmeasured to take out aquatic insects soaked in ethanol,
wipe the surface using Kimwipe, and measure by478

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1 Diatom in survey site No. 1 to 5
month/day/year
6/21/15
species / site No.
No.1 No.2 No.4 No.5
convergens
48
60
48
28
minutissima
38
154
94
68
Achnanthes
japomica52
112 12048
exigua4
3
16
clevei6
20
minutum
33
184
63
32
Encyonema
lange-bertalotii
21
96
42
24
capitellata
128
28
60
Fragilaria
capucina
29
56
39
56
dissipata
10
148
60
60
Nitzschia
inconspicua
16
6
12
foutica
428
15
16
inaequalis
420
Synedraangusta2
ulna
3
3
Asterionella
formosa
58
60
48
33
cryptotenella
22
116
66
24
Novicalaangusta43cyclotellastelligera
38
39Diploneisboldtiana
12
93
Cocconcis
placentula
12
15
47
kuetzingii
8
6
Diatomamesodon1
4
mexicana
126
Cymbellaturgidula var.nipponia
8
Melosira
varians3
12
6
angustatum
14Gomphonema
parvulm17
clevei11
Rhoicosphenia
abbreviata
20
21
6

Saprophilous taxa

Eurysaprobic taxa

10/15/15
No.1 No.2 No.5
108
33
108
243
99
246
144
96
18
18
3
81
201 135 225
48
84
168
111
9
8
15
3
0
69
12
3
45
0
12
3
12
33
12

11/15/15
No.1 No.2 No.4 No.5
12
5
63
21
159
66
120 222108 144 162
24
0

3
252

3
240
36
51
6
6
12

108
9
24
6
21

18

0
72
33
39
3
3

33
18
3
32
20

3
3
0
0

12

12

36

4

Saproxenous taxa

the number of diatom

1200
1000
800

600
400
200

0
No.2

No.4

No.5

site No.

Figure 4 the comparison of results that speciesof Saproxenous taxa (blue), Eurysaprobic taxa
(green), Saprophilous taxa (red) are shown at
all survey areas in 2010 and 2015
2010

9
57
39

24
48
24
6
18
96
12
24

75
30
3

15
18
6

12
99
3
9

96
6
9

108
36
18
24
18
42

180
30
24
15
42
24

87
39
12
6
6
18

78
39
15
9
24
12

6
3
6

18

87

57

3

45
6

6

12

69

24

27

9

48

12

6

6

6

3

132
18
18
6
24
24

132
18
36
18
6
30

57
24

36

31

12

Saprophilous taxa
Saprophilous taxa
Eurysaprobic taxa
Saprophilous taxa
Eurysaprobic taxa
Saprophilous taxa
Eurysaprobic taxa

18
Saprophilous taxa
Saprophilous taxa
Saprophilous taxa
Eurysaprobic taxa
Eurysaprobic taxa
Eurysaprobic taxa
Saprophilous taxa
Saprophilous taxa

number of eurysaprobic taxa, which accounted forabout 75% before the start of maintenance flow in
2010, has decreased. In addition, saproxenous taxa
(this species that can be confirmed mainly in the
upstream area) increased, accounting for about 30%.
It was considered that this result was largely due tothe decrease in Melosira varians belonging toeurysaprobic taxa, and the increase in Achnanthes
japonica belonging to saproxenous taxa. It wassuggested that the maintenance flow discharge may
have improved the water quality of the Katashina
River.

1400

No.1

150
84
24

12/6/15
taxa
No.1 No.2 No.4 No.524
6
36
21 Saprophilous taxa
174 11484
59 Eurysaprobic taxa
216
84
300
76 Saproxenous taxa
Eurysaprobic taxa
Saprophilous taxa
75
126
96
54 Saproxenous taxa
30
48
30
41 Saproxenous taxa
27
24
12 Saprophilous taxa
Saprophilous taxa
91242
15 Saprophilous taxa
30
72
81
138 Eurysaprobic taxa
18
Eurysaprobic taxa
15
24
6
6
Saprophilous taxa

aquatic insects survey
Comparison with the results of the preliminary survey(2010/12/02) conducted before the start ofmaintenance flow

2015

Saproxenous

The results of this survey are shown in figure 4.
Compared with the results of the 2010 survey before
the start of maintenance flow, it can be confirmed thatthe number of species and individuals increased insite No. 1 and No. 2. However, the number of species
and individuals decreased in site No. 4 and No. 5.
Aquatic insects were probably washed away in No. 4that occurred in 2015.
This result suggests that it was not possible to andconfirm the increase of aquatic insects due to themaintenance flow discharge. The increase in aquatic
insects was confirmed in survey site No. 1 where thetaxa
Eurysaprobic
taxa
Saprophilous
taxa

Figure 5 the percentage of species that are
Saproxenous taxa (blue), Eurysaprobic taxa
(green), Saprophilous taxa (red) are shown at all
survey areas.
Figure 4 shows the abundance ratio comparedwith the results of the 2010 survey and the 2015
survey. In the 2015 survey, it was confirmed that the479

GEOMATE – Kyoto, Japan, 3-5 November 2021

the number of species and individuals

species

Table 1 Comparison appling Bilogical Monitoring
Working Party for japanese version

individuals

350

class / family
Dipteromimidae
Ameletidae
Isonychiidae​Ephemeroptera
Heptageniidae
BaetidaeEphemerellidae
Ephemeridae
Nemouridae​Plecoptera
Perlodidae
Perlidae
Neuroptera
Corydalidae
Stenopsychidae
Hydropsychidae
Trichoptera
Rhyacophilidae
Leptoceridae
Tipulidae
Simuliidae
Diptera
Chironomidae
Ceratopogonidai
Opisthopora
Oligochaeta
Hirudinea
family number
TS
ASPT

300
250
200
150
100

50
0
2010 2015 2010 2015 2010 2015 2010 2015
No.1

No.2

No.4

No.5

survey site number and year

Figure 6 the number of species and individuals at
survey sites in Katashina riverNo. 5 due to the large impact of Typhoon No. 18. The
discharge was released from the dam due to the
influence of the typhoon. The survey site No. 2 where
the flow was slow and the dam was not easily affected
due to the typhoon. It was suggested that the number
and population of aquatic insects increased due to themaintenance flow discharge.
Comparison due to the Japanese version averagescore method (number of departments) MWP(Bilogical Monitoring Working Party) for japanese
ver.

score
10
8
8
9
6
8
8
6
9
9
9
9
7
9
8
8
7
6
7
4
24
198
25
7.92

MWP(Bilogical Monitoring Working Party) for japanesever.
TS:Total Score
ASPT:Average Score Per Taxon)

Table 2 Comparison EPT value

In the Japanese version of the average score
method, the scores given to the appearance
departments are added to calculate the total score, andthe average score per taxon (ASPT) is divided by the
number of appearance departments to evaluate.
ASPT indicates that the upstream environment isin good condition at a score 10-8 and that thedownstream river environment is polluted below
score 4.
The collected aquatic insects were scored by theJapanese average scoring method and compared. The
scoring value is based on [11]. The results are shownin Table 1. The average score (ASPT) was 6.55 in the2010 survey. However, the score in the 2015 survey
is 7.03. The result suggests that the river environmentis improving in the upstream area.

class, MM/dd/year

12/2/2010

12/6/2015

Ephemeroptera​Plecoptera
Trichoptera
TPT

9
0
13
22

17
5
11
33

*the number of species

and Caddisflies are benthic organisms that live inclean waters, and these species are abundant in cleanwaters.
The results are shown in Table 2, the EPT value
was 22 in the 2010 survey, but it was 33 in the 2015
survey. The EPT value indicates that the aquatic
insects are diversified in Katashina rever environment.
Fish survey in Katashina riverComparison due to EPT value (species number)

Outline of each survey site for fish

Table 2 shows the results of comparison by EPT value(species number)[10]-[11]. The total number of
species belonging to the order Ephemeroptera, theorder Plecoptera, and the order Caddisfly Trichoptera
is used as the EPT value. Ephemeroptera, Stoneflies,

Two Lethenteron spp. and eleven Tribolodon
hakonensis were confirmed at 3-time surveys in site
No.1. The survey points are generally low in gravel
and the bedrock is exposed, and there are few suitable
places for fish. The two species were obtained from a480

GEOMATE – Kyoto, Japan, 3-5 November 2021

small amount of gravel in the slow stream andaccumulated fallen leaves.
Rhinogobius sp. was confirmed at 3-time surveys
in site No.2. A large pool was developed, and theplace where it could be collected by the net was onlya small part near the shore.
Two Lethenteron spp., five Opsariichthys
platypus, five Phoxinus lagowskii steindachneri, 39
Tribolodon hakonensis, a peace of Gnathopogonelongatus
elongatus,
two
Misgurnus
anguillicaudatus, six Cobitis sp. BIWAE type C, 2
two Rhinogobius sp., and Gymnogobius urotaenia
were confirmed at site No.4. The center of the stream
at site No.4 is a state in which about 50 cm of boulders
is piled
A total of 6 Lethenteron spp., one Phoxinus
lagowskii steindachneri, two Tribolodon hakonensis,
six Cobitis sp. BIWAE type C, two Rhinogobius sp.
OR, and four Gymnogobius sp. 1 were confirmed atthree time surveys in site No.5.

Table 3 Water quality at surve site in Katashina river
y/m/d

2015.6.21

2015.10.25

2015.11.15

2015.12.7

sait No.

pH

EC

NH4-N

NO2-N

NOx-N

Inorg-N

PO4-P

No.1

9.86

9

0.017

0.010

0.870

0.887

0.042

No.2

8.75

5

0.017

0.010

1.127

1.144

0.045

No.4

8.30

9

-

-

-

-

-

No.5

7.31

6

0.017

0.011

1.305

1.322

0.032

No.1

8.28

14

0.072

0.016

0.944

1.016

0.022

No.2

8.12

12

0.299

0.011

1.007

1.306

0.066

No.4

-

-

-

-

-

-

-

No.5

7.19

21

0.037

0.015

1.401

1.438

0.061

No.1

8.76

11

0.000

0.016

0.979

0.979

0.227

No.2

8.54

11

0.027

0.016

0.869

0.896

0.036

No.4

8.11

10

0.017

0.015

0.950

0.967

0.028

No.5

8.06

10

0.017

0.015

1.272

1.289

0.025

No.1

8.27

11

0.016

0.005

1.01

1.026

0.016

No.2

8.48

10

0.033

0.005

0.97

1.003

0.000

No.4

8.59

10

0.017

0.004

1.07

1.087

0.083

No.5

8.43

11

0.020

0.004

1.41

1.430

0.000

Inorg-N concentration (mg/l)

2015.6.21
2015.11.15

Fish survey consideration
The scientific names of fish based on Sekine, K.,
et. al. [12] and Hosoya, K., and Uchiyama, R., [13]were applied. Ten species belonging to four familiesof fish and jawless fish were confirmed on surveys(11 times in 2015) at four survey sites. Lethenteronspp., Phoxinus lagowskii steindachneri, Tribolodon
hakonensis, Cobitis sp. BIWAE type C, Rhinogobius
sp. OR are major fish at rivers in Gunma Prefecture.
They were confirmed at a few sites at this time.
These fish inhabited widely in this area andvarious sizes (young fish and adult fish) fish wereconfirmed. These results suggest that they were also
breeding. In addition, it seems that breeding for
Opsariichthys
platypusandMisgurnus
anguillicaudatus is possible to consider the rivermorphology.
On the other hand, Gnathopogon elongatuselongatus,
Gymnogobius
urotaenia
andGymnogobius sp.1 are originally fish that live on the
sandy mud bottoms of small and medium-sized rivers,
agricultural waterways, and ponds. This result
suggests that outflow from the Hiraide-dam lake
carried these species.
In this survey, ten species were confirmed, but thisnumber of each individual was not large. It is inferredthat these results are related the situations that few
slow-flowing parts of the gravel bottom are favorable
for fish habitat.

2015.10.25
2015.12.7

8
7
6
5
4
3
2
1
0
No.1

No.2
No.3
No.4
survy site number

No.5

Figure 7 Inorganic nitrogen concentration
at survey site 1 ~ 5 in Katashina river on 2015

River- water quality survey
Table 4 shows the results of water quality analysis
at each survey point in this survey.
In addition, figure 7 shows the concentration of
inorganic nitrogen at each survey site.
However, at survey points No. 4 on June 21 andOctober 25, water sampling was difficult due to thecondition of the river, and the analysis was notpossible. The results showed that the nitrogenconcentration was high at all the survey points wherewater could be sampled. The inorganic nitrogenconcentration was about 1.0 mg/L at all survey sites.
In the northern part of Gunma prefecture, it has beenreported that one of the causes is that nitrogen oxides
contained in the exhaust gas emitted from cars
running in the metropolitan area are carried by thewind and are poured down by rainfall [9]. That effectis also suggested in the Katashina River basin. In
addition, the value of inorganic nitrogen

481

GEOMATE – Kyoto, Japan, 3-5 November 2021

Inorg-N(mg/l)

6

2010/12/20
2013/12/24

2011/12/7
2015.12.7

2012/12/7

5

[2]

4
3
2
1

[3]

0
No.1

No.2

No.3

No.4

No.5

section no.

Figure 8 Inorganic nitrogen concentrationat survey site
1 ~ 5 in Katashina river on 2010 ~ 2013[1]-[3], 2015

[4]

concentration of 1.0 mg / l or more is higher than TN=0.15 mg/L, which is the standard foreutrophication.
According to the reports of Katashina river water
quality written by Gunma prefecture Oze highschool[10], the water quality of the Katashina River
from Ozenuma is not artificially affected, and thenitrogen concentration is low. The results previously
reported (Figure 8,[1]-[3]) and these results suggestthat there is almost no effect of the inflow of domestic
wastewater in the survey area of Katashina River.

[5]

[6]

[7]

CONCLUSIONS[8]

Five years have passed since the completion of the
Shintonami Power Plant, and the start of maintenanceflow discharge in 2015. These results in the survey
suggest that the environmental condition of theKatashina River with a sufficient amount of water isenriched mainly due to the ecosystem of diatoms andaquatic insects in the winter season. Although wehave not conducted a thorough survey, the Katashina
River has few slow-flowing areas with gravel bottoms,
and the results of this survey suggest that the amountof water has not improved the habitat for fish.
The river water quality data suggest that the riverenvironmental condition is favorable. It is necessary
to investigate the habitat for fish due to long-term
continuous surveys in the future.

Aoi, T., and Yamamoto, Y., Research of riverenvironments comparing the low flow section and themaintenance flow section in Katashina Rive,
Proceeding of 5nd IWA-ASPIRE Conference andExhibition in Daejeon, 2013, oral-11C2-4 in USB
Miyazato, M., Ajisawa, Y., Kakegawa, Y., Nakajima,K., Yagi, Noguchi, K., Aoi, T., and Yamamoto, Y.,
Effects of maintenance flow water for riverenvironmental at Katasina river, the 48th annual
conference of japan society on water environment in2014, p.4, in Japanese
Miyazato, M., Ajisawa, Y., Y., Kakegawa, Y.,
Nakajima, K., Yagi, Y., Aoi, T., and Yamamoto, Y.,
The effect of maintenance flow for water quality andaquatic organisms in Katashina river, the 49th annual
conference of japan society on water environment in2015, p.4, in JapaneseAmisah, S., Cowx, I,G., Response of the fish
population of the River Don in South Yorkshire towater
qualityandhabitatimprovements,
Environmental Pollution Vol.108, No.2, 2000 ,pp.191-199
Delgado, C.,
Pardo, I.,
Garcia, L., Diatom
communities as indicators of ecological status inMediterranean temporary streams (Balearic Islands,
Spain), Ecological Indicators, Vol.15, Issue 1, 2012,
pp.131–139
Growns, I., Is genus or species identification of
periphytic diatom required to determine the impacts ofriver regulation?, Journal of Applied Phycology,
Vol.11(3), 1999, pp.273-283
Matsuno, T., Ohkubo, M., Toriiminami, Y., Tsushima,M., Sakaguchi, S., Minami, T., Maoka, T.,
Carotenoids in food chain between freshwater fish andaquatic insects, Vol.124, Issue 3, 1999, pp.341-345.
Shiratori, M., Kozuki, Y., Kurata, K., Haseda, M.,
Kofuji, M., Murakami, H., Characteristics andcolonising processes of periphytic diatom
communities downstream of dam sites along the
Katsuuta River, Tokushima Prefecture, Environmental
Engineering Research, Vol. 40, 2003, pp.117-126. inJapanese.

[9] Aoi, T. Nitrogen pollution in the upper basin ofTone river without artificial sources, Global
Environmental Research, Vol. 15(2), 2010, pp.145152, in Japanese[10] Ministry of Land, Infrastructure, Transport andTourism,https://www.mlit.go.jp/common/000046619.pdf
June.11. 2021, in Japanese[11] Nozaki, T., Biological Assessment Based on
Macroinvertebrate Communities : Average
Score System for Japanese Rivers, Journal ofJapan Society on Water Environment 35(4),2012, pp.118-121, in Japanese[12] Sekine, K., Gunma-ken no doubutsushi, Gunmahigh school education and research association,
1985, in Japanese[13] Hosoya, K., Uchiyama R., Nihon no Tansuigyo,
2019, Yama-kei Publishers co.,Ltd. in Japanese[14] http://www.oze-ic.gsn.ed.jp/data/H18katashinagawa.pdf， June.10. 2021, in Japanese

ACKNOWLEDGMENTS
The authors wish to express their thanks tograduate students at National institute technology(KOSEN), Gunma college, and Gunma prefecture for
their technical assistance.
REFERENCES[1] Miyazato, M., Kakegawa, Y., Nakajima, K., Yagi, Y.,482

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

DEVELOPMENT OF ANFIS MODEL FOR AIR QUALITYFORECASTING IN DHAKA CITY
A. A. Abeer, J. Farzana and M. T. Rahman
Civil Engineering Department, Military Institute of Science & Technology, Bangladesh
ABSTRACTThis study aims to forecast the daily concentrations of PM 10 and PM2.5 in the atmosphere of Dhaka city using
the Adaptive Neuro-Fuzzy Inference System (ANFIS). In Dhaka, particulate matter (PM) is the most harmful air
pollutant to public health. So, to forecast the concentration of PM in the atmosphere is important for thedevelopment of cost-effective control strategies to alert and protect the population. In this study, the ANFIS model
predictor considers five meteorological factors (pressure, temperature, relative humidity, wind speed, and rainfall)
and the previous day's pollutant concentration in different combinations as the inputs to predict the same day and1-day advance air pollution concentration. In order to reduce the computational cost and time, the collinearity tests
and forward selection (FS) technique are applied to remove the dispensable input variables and select the different
combinations of input variables, respectively. Root mean square error (RMSE), Coefficient of determination (R2),
Normalized Mean Square Error (NMSE), Index of Agreement (IOA), and Fractional Bias (FB) are applied for the
performance testing of the model. Since ANFIS is a combination of neural and fuzzy logic is capable ofrepresenting knowledge acquired from human experts and which further improves the prediction. From this study,
considering the better values of the indices the PM2.5-PDPM2.5-T & PM10-PDPM10-P model performed wellfor prediction.
Keywords: ANFIS Model, Forward search, Collinearity, Computational cost, Air pollutants

INTRODUCTION(1997) [3].In the ANFIS model, crisp input series areconverted to fuzzy inputs by developing amembership function for each input series [4]. The
membership function can be any shape but it depends
on the data set. Many researchers used the FS method
to develop proper prediction models [5]. The ANFIS
model with a reduced input combination lowers
computational cost and predicts with less errors. It
seems that timely forecasting of air quality lets
authorities alarm the residents and helps them manage
its detrimental effects. Thus, the present study aims todevelop an ANFIS model for forecasting of daily air
quality of Dhaka city.

Air, the most essential aspect of the living world
gradually losing its quality due to anthropogenic
practices all over the world. In Bangladesh, the air
pollution situation is getting aggravated with the rapid
growth in urbanization and industrialization. Dhakathe center of all administrative, economic and culturalactivities of Bangladesh was the second most air
polluted city in 2020 as the majority of processing
plants in two industrial towns on the periphery do notfollow emission requirements. The problem isexacerbated further by transport mega-projects suchas a metro rail system and an elevated expressway,which generate massive quantities of pollution,
leading to pollution from old cars, private building
sites, and open garbage burning. Particulate matterwith an aerodynamic diameter of less than 10 μm candeeply penetrate the human lung and cause diseases
such as bronchitis, impaired lung function, andcardiopulmonary problems [1], [2]. Hence it isnecessary to create air monitoring networks anddevelop air quality management strategies (which
include measurements, forecasting, regulations, etc.).
A neural network modeling scheme provides anefficient computational tool for mapping input-output
or cause-effect relationships and establishes
intelligent what-if scenarios based on robust learning
mechanisms. One such example is the Adaptive
Neuro-Fuzzy Inference System (ANFIS), which has
a hybrid algorithm and was proposed by Jang et al.

OBJECTIVESThe study aims to find out the best combination of
weather parameters to develop an ANFIS model, toforecast air quality based on the best combination andto evaluate developed hybrid models with all input
variables to show that FS techniques reduce not only
the output error but also computational complexity
and cost due to less number of inputs.
METHODOLOGY
Input & Output Parameter Selection
In the present study, five meteorologicalparameters (temperature, pressure, wind speed,
relative humidity, and rainfall) along with the483

GEOMATE – Kyoto, Japan, 3-5 November 2021

previous day's pollutant concentrationconsidered as input variables.

wereusing a generalized bell-shaped membership function.
Data Normalization

Multi-collinearity test
To increase the accuracy of the results anddecrease the range of data the normalization method
is utilized. This normalization process brings thevalues of variables in between 1 and -1. The
normalization of data was done using the following
formula:X = [(2((A − Amin )/(Amax − Amin))) − 1] (1)Where, X=Normalized Value & A=Observed value.

A multi-collinearity test was conducted toeliminate the redundant input variables [6].
Collinearity means that two or more of the
independent/ explanatory variables in a regression
have a linear relationship. The close relationship ofthe variables makes the isolation difficult. A multicollinearity diagnosis was conducted throughregression analysis using SPSS software version 25.
Multi-collinearity can be diagnosed based on two
factors. These are variance inflation factors (VIF) andtolerance. If the VIF values of the independent
variables are more than 10 and tolerance is less than0.1, then we can say that the model is suffering frommulti-collinearity [7].

Model Training & Testing
The data sets are divided into two parts (training
data set and testing data set) after normalization.
Training data contain 70% of the data and the testingdata set contains 30% of the data. Both the training
data sets & testing data sets are loaded in the ANFIS
toolbox of MATLAB software version 8.3. After dataloading, membership number and type for each input
and output variables were assigned to generate the
FIS system. After that, the models are trained for each
input combinations as described above and theperformance of the models are tested using the testingdata sets.

Forward search method
As the number of variables is high, the number of
combinations will require high computation cost.
Step-by-step algorithms such as forward search (FS)can be extremely beneficial in terms of loweringcomputing costs. FS method has been successfully
used by many researchers in order to build robust
prediction models. A linear regression model is thebasis of this approach. Ordering of the explanatory
variables according to their correlation with thedependent variable (from the most to the least
correlated variable). Then, the explanatory variable,
which is best correlated with the dependent variable,
is selected as the first input. All remained variables
are then added one by one as the second input
according to their correlation with the output and thevariable which most significantly increases thecorrelation coefficient (R2) is selected as the second
input [8].

Performance Test of Models
The deviation of the predicted data (FIS output)
from the observed value is determined for the
performance evaluation of models. Five statisticalindices- Fractional Bias (FB), Root Mean Squared
Error (RMSE), Coefficient of determination (R2),
Normalized Mean Square Error (NMSE), and Index
of Agreement (IOA) were used for performance
evaluation of the model.
Best Model Selection
Based on the statistical performance tests, the bestmodels were chosen having the highest index ofagreement and good performance test output.

Assigning Membership Function Type & Number
Expressing linguistic values in the form of fuzzy
sets is the first thing to do for generating a FIS system.
The membership functions can be constructed fromseveral basic functions such as piecewise linear
functions, the sigmoid curve, the Gaussian
distribution function, quadratic and cubic polynomial
curves. The range of input and output variables canbe divided into several linguistic values. In this study,
the function computes fuzzy membership values

RESULTS
Description of Analysis & Data Source
The method explained for the development of the
ANFIS model in methodology is applied for air
quality forecasting in Dhaka city, Bangladesh. The
daily ambient air pollutant concentrations PM10 (24484

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1 Descriptive Statistics of Air Pollutant conc.
(PM2.5)PM2.5 in µg /m3
2016
Maximum
Minimum
MeanStandard Deviation
2017
Maximum
Minimum
MeanStandard Deviation
2018
Maximum
Minimum
MeanStandard Deviation
2019
Maximum
Minimum
MeanStandard Deviation

PM10 concentration-24hr avg. (µg/m3)

Days

Fig. 1

PM10 in µg /m3
2014
Maximum
Minimum
MeanStandard Deviation
2015
Maximum
Minimum
MeanStandard Deviation
2016
Maximum
Minimum
MeanStandard Deviation
2017
Maximum
Minimum
MeanStandard Deviation

2014
2015
2016
2017

Daily variation of PM10 concentration

450
400
350
300
250
200
150
100
50
0

1
21
41
61
81
101
121
141
161
181
201
221
241
261
281
301
321
341
361

PM2.5 conc. in µg/m^3

PM2.5 concentration-24hr avg. (µg/m3)

2016
2018
2019
2017
Daily variation of PM2.5 concentration
Days

Fig. 2289.417
12.46
85.27162.768289.4178.2
80.10761.7058285.7
10.7
87.47566.016381.25485.75265.157

Table 2 Descriptive Statistics of Air Pollutant conc.
(PM10)

700
600
500
400
300
200
100
0

1
24
47
70
93
116
139
162
185
208
231
254
277
300
323
346

PM10 Conc. in µg/m^3

hrs average) and PM2.5 (24-hrs average) are used forthe study. Air pollutant data have been collected fromthe Department of Environment (DoE) and the US
Embassy, Bangladesh. The daily mean values of
meteorological data (temperature, pressure, relative
humidity, wind speed & rainfall) are obtained from
Bangladesh Meteorological Department (BMD). The
annual average of PM10 concentration in 2014, 2015,2016 & 2017 are respectively 160.03 µg/m3, 149.88µg/m3, 142.37 µg/m3 and 144.95 µg/m3 .This
indicates that the annual average concentration ofPM10 has been decreased from 2014 to 2016 andslightly increased in 2017. The annual average of
PM2.5 concentration in 2016, 2017, 2018 & 2019 arerespectively 85.27 µg/m3, 80.11 µg/m3, 87.48 µg/m3
and 85.75 µg/m3. This indicates that the annual
average concentration of PM2.5 has been decreased
from 2016 to 2017 and increased during 2018.
Figures 1 and 2 represent the daily variation of PM10
and PM2.5 respectively.

480.31319.963160.033
110.328420.603
7.176
149.881104.070599
15.056152.367
114.983
538.501
14.065144.946105.847

The multi-collinearity was diagnosed using thetwo factors (VIF and tolerance level). The results ofthe multi-collinearity test are represented in table 3
indicating there is no collinearity problem in thesevariables.
Table 3 Collinearity Statistics

Descriptive statistics of PM2.5, PM10 are shown in
Tables 1 and 2 respectively.

485

GEOMATE – Kyoto, Japan, 3-5 November 2021

Air
PollutantParameters

Tolerance

VIF

PM2.5

Rainfall (R)Temperature(T)
Relative
Humidity(H)
Pressure (P)Wind
Speed (WS)Rainfall (R)Temperature(T)
Relative
Humidity(H)
Pressure (P)Wind
Speed (WS)

0.84
0.38

1.18
2.61

0.65

1.53

0.29
0.96

3.38
1.04

0.82
0.35

1.21
2.83

0.62

1.61

0.26
0.93

3.72
1.07

Table 5 Input Parameter Subset for Different Models

PM10

PM10

PM2.5

Model 1Model 2Model 3Model 4Model 5Model 6
Model 1Model 2Model 3Model 4Model 5Model 6

PM10-PDPM10,P,T,H,R,WS
PM10-PDPM10,P,T,H,R
PM10-PDPM10,P,T,H
PM10-PDPM10,P,T
PM10-PDPM10,P
PM10-PDPM10
PM2.5-PDPM2.5,T,P,H,R,WS
PM2.5-PDPM2.5,T,P,H,R
PM2.5-PDPM2.5,T,P,H
PM2.5-PDPM2.5,T,P
PM2.5-PDPM2.5,TPM2.5-PDPM2.5

Correlation analyses were done for thedetermination of correlation coefficients between air
pollution concentration and each input variable
including the previous day's pollution concentration.
The result of correlation analysis is represented inTable 4.
Table 4 Correlations among
Meteorological Parameters

PollutantandAirpollutants
PM2.5

RainfallTemperature

Relative
Humidity-0.257-0.743-0.518PM10-0.295-0.677-0.499Air
pollutants
PM2.5

PressureWind Speed

PDPM

0.734

-0.191

0.88

PM10

0.729

-0.2490.863

Fig. 3

Input parameter subsets for different models areshown in Table 5. The ANFIS model results indifferent input combinations are shown in Table 6.
The scatter plots between observed and predictedvalues of PM10 & PM2.5 conc. are indicated in Figs. 3
and 4 respectively.
486

Scattered Plots for Correlation (R) for sameday forecasting of PM10

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 4

Scattered Plots for Correlation (R) for same-day forecasting of PM2.5

Table 6 ANFIS model results in different input combinations
It is seen that Model 01 with input variables
Same Day Forecasting PM10
INDICES

Model-06(PM10PDPM10 )Model-05(PM10PDPM10,P)
0.15850.79480.0972-0.02200.9862Model-04(PM10PDPM10,P,
T)
0.16950.77
0.1111-0.02140.9844Model-03(PM10PDPM10,P,
T,H)
0.237
0.604
0.2221-0.04240.9700Model-02(PM10PDPM10,P,T,H,R)
0.24210.63610.2311-0.04170.9693Model-01(PM10PDPM10,P,T,H,R,
WS)
0.50320.34901.0433-0.08540.8831

RMSE
R2
NMSE
FB
IOA

0.15900.79280.0970-0.01430.9860

INDICES

Model-06(PM2.5PDPM2.5 )Model-05(PM2.5PDPM2.5,P)

Model-04(PM2.5PDPM2.5,P,T)

Model-03(PM2.5PDPM2.5,P,
T,H)

Model-02(PM2.5PDPM2.5,P,T,H,
R)

Model-01(PM2.5PDPM2.5,P,T,H,R,WS)

0.14710.80280.06620.0266
0.98920.1536
0.78810.0711
0.04100.98830.18870.69600.11010.01550.98280.18250.71730.10420.00440.98390.4133
0.34650.52160.02790.9274

One Day Advance Forecasting PM2.5
RMSE
0.17590.1698R2
0.71130.7305NMSE
0.09420.0868FB-0.0087
0.0027
IOA
0.98470.98570.17500.71650.0929-0.0051
0.98510.20250.64310.1241-0.00230.98050.19410.66070.1139-0.00100.98190.28400.46640.2518-0.0336
0.9636Same Day Forecasting PM2.5
RMSE
0.1484R2
0.7973NMSE
0.0691FB
0.0012
IOA
0.9891487

GEOMATE – Kyoto, Japan, 3-5 November 2021

PDPM10, P, T, R, and WS for forecasting same day
PM10 has RMSE of 0.5032 which is not close to 0, R2
was only 34.90% which is not that satisfactory and
IOA is 0.8831 which is not that much close to 1. On
the other hand, it is observed Model 05 with input
variables PDPM10, P for forecasting same day PM10
had RMSE of 0.1585 which is much closer to zero,
R2 is 79.48% which indicated quite excellent result
and IOA is 0.9862 which is much closer to 1.For same
day forecasting PM2.5 it is observed that Model 05
with input variables PDPM2.5, T for forecasting same
day PM2.5 had RMSE of 0.1471 which is much closer
to zero, R2 is 80.28% which indicates quite excellent
result and IOA is 0.9892 which is much closer to 1.
Again for 1-day advance forecasting for PM2.5 Model
5 performs better. All other models’ values were in
between those two models. For all models, NMSE &FB were calculated to check the models’ conditionand values came out quite satisfactory.

to existing models based on time series like ANN.
This technique can analyse and interpret any kind of
information. It seems that timely forecasting of air
quality lets authorities alarm the residents and helps
them manage its detrimental effects.The ANFIS
model predictor considers the value of meteorologicalvariables like pressure, temperature, relative
humidity, wind speed, rainfall and previous day's
pollutant concentration in different combinations as
the inputs for the prediction of the same day air
quality concentration. Collinearity tests and FS
techniques reduced input combination that lowers
computational complexity and predicts with lesserrors.

DISCUSSION

REFERENCES

The input combination used in MODEL 5-PM10& MODEL 5-PM2.5 can be selected for optimal
forecasting. It is observed that with the data ofprevious day’s PM2.5/PM10 concentration, current
PM2.5/PM10 concentration and with only one
environmental parameter among five different
parameters (Temperature for PM2.5 & Air pressure forPM10), a better forecasting can be done. This process
saves computational time. By plotting of real PM(PM2.5/PM10) concentration data and predicted PM(PM2.5/PM10) concentration for best model, a similar
trend is seen. In case of PM10, it was notable that onlywith input parameter previous day PM10 and pressure
value prediction of PM10 came out well whereas incase of PM2.5, input parameter- previous day PM2.5
and temperature value prediction of PM2.5 showed
better output. In both cases previous day’s particulatematters value were needed but because of differencein particulate matter size second input might bevarying. FS method successfully reduced thecomputational complexity and output error. There
are a few limitations of this forecasting model. For
example, weather parameters are considered here
only but PM2.5/PM10 concentration can fluctuatebecause of urbanization (construction work),
industrialization (brick kilns) & traffic emission etc.

[1] Dockery D.W., Pope C.A., 3rd Acute respiratory
effects of particulate air pollution. Annual
Review of Public Health 1994, Vol.15, pp.107132.
[2] Xing Y. F., Xu, Y. H., Shi M. H.,& Lian Y.
X.,The impact of PM2.5 on the humanrespiratory system. Journal of thoracic disease,
Vol.8 (1), 2016, E69-E74.
[3] Jang J.S.R., Sun C.T., Mizutani E., Neuro-fuzzy
and soft computing: a computational, approachto learning and machine Intelligence, IEEET
Automat Control, Vol.42, No.10, 1997.
[4] Tektas M.,Weather Forecasting Using ANFIS and
ARIMA- A Case Study for Istanbul,
Environmental Research, Engineering andManagement, Vol. 1 (51), pp. 5–10, 2010.
[5] Cerioli A., Farcomeni A., Riani M., Strong
consistency and robustness of the Forward Search
estimator of multivariate location and scatter.
Journal of Multivariate Analysis, Vol.126, 2014.
[6] Chen G. J., A simple way to deal with multicollinearity, Journal of Applied Statistics, Vol.39,
Issue.9, 2012, pp.1893-1909.
[7] Hair, J., Black, W. C., Babin, B. J., & Anderson,
R. E. (2010). Multivariate data analysis (7th ed.).
Upper saddle River, New Jersey: Pearson
Education International.
[8] Prasad K., Gorai A.K., Goyal P., Development of
ANFIS models for air quality forecasting and
input
optimization
for
reducing
thecomputational cost and time, Atmospheric
Environment, Vol. 128,2016,pp. 246-262.

ACKNOWLEDGEMENTS
Authors are thankful to US Embassy, Dhaka,Bangladesh for making air pollution data available on
the website for public use.

CONCLUSION
Neuro-fuzzy logic is a probabilistic approach used
for forecasting purposes providing a good alternative488

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

THE RECYCLING WASTE FROM SUGAR INDUSTRY: THE
OPTIMALIZATION OF CO2 ADSORPTION CAPACITY ON
SUGARCANE BAGASSE-BASED BIOCHAR AND ZEOLITE
Ambar Pertiwiningrum1, Margaretha Arnita Wuri2, Widyawati Luhur Pambudi3, Andang Widi Harto4, Alva
Edy Tontowi5 and Rachmad Nur Besari6
1,3,6

Faculty of Animal Science, Universitas Gadjah Mada, Indonesia; 2Center for Energy Studies, Universitas
Gadjah Mada, Indonesia; 4,5Faculty of Engineering, Universitas Gadjah Mada, Indonesia

ABSTRACTThe recycling of biomass waste for CO2 adsorption in biogas purification is still rare. This study wasinvestigated the CO2 adsorption capacity by sugarcane bagasse-based biochar and natural zeolite with a volume
ratio of 50%:50%. The CO2 adsorption test carried out using biogas purificator in the difference adsorption time
(10, 15, 20 and 25 minutes). The biogas was purified by adsorbing CO 2 in the mixture gases. Adsorption CO2 onsugarcane bagasse-based biochar and zeolite have been investigated at a room temperature and gas pressure
ranging from 6-8 bar. The CO2 adsorption capacity was further reduced by increasing adsorption time. The
combination of sugarcane bagasse-based biochar and zeolite exhibited a high CO 2 removal capacity of 78.49% at
10 minutes of adsorption time. The CO2 adsorption capacity was saturated by the increase in time adsorption. The
lowest CO2 adsorption capacity was performed at 25 minutes adsorption time with CO2 removal capacity of
12.34%.
Keywords: Adsorption, Biochar, Carbon dioxide, Sugarcane bagasse, Zeolite

INTRODUCTION

known as bagasse [5]. Nelson et al. [6] reported thatbagasse yield in every 1000 tons sugarcane is 27%.
Munagala and Yogendra [7] also reported that one tonof sugarcane yields 0.3 tons of bagasse. The bagasse
is composed of 40-50% of cellulose, 30-35%
hemicellulose, 20-30% lignin and small percentage
other material [8]. Utilization of bagasse usually for
producing energy, paper, and cattle feed [5].
Lignocellulosic feedstock in bagasse also can be used
for energy [7] or the other application like adsorbent.
Valorization of bagasse as adsorbent can be used inbiogas purification by carbonization process. This
conversion leads to implement circular bioeconomy
concept for sustainable management in sugary
industries. Conversion of waste from sugary
industries is revealed reduced carbon dioxide
emissions [9].
Wojciech [10] explained that adsorption is one of
methods in biogas purification the efforts in. By
adsorption biogas produces high methane andcalorific value. Himjyoti and Charu [11] explainedthat natural zeolite and clay are nature-basedmaterials that can be used as CO2 absorbing
adsorbents. In addition, activated carbon, biochar,and flying ash from biomass waste have been widely
developed as alternative adsorbent.
Bagasse can be used as precursor of biogas
adsorbent because of its abundant commodity in theenvironment so that it can reduce production. The
converting bagasse to biochar aims to increase theadsorption ability [12]. Rizkyarief [13] stated that theBased on Indonesia Energy Outlook [1],Indonesia has biomass energy potential of 32,654
MW that comes from animal, edible and non-edible
plants. These resources can be converted to biogas
energy for supplying national energy. Biogas
becomes an alternative energy that substitute to LPG(liquid petroleum gas). In Indonesia, LPG demand is
expected increase from 7.2 million tons in 2017 to
17.4 million tons in 2050 [2], whereas currently LPG
production only 2 million tons. The increasing importof LPG is feared to burden Indonesia’s current tradebalance so that to meet the demand, it needs otheralternative energies. Therefore, an alternative energy
like biogas for cooking in households needs to be
encouraged.
One of the large potentials of biomass inIndonesia is waste from sugarcane industries.
Sugarcane industries are one of acro-based industries
that produce huge quantity of waste during sugar
production. Until 2016, Indonesia has 63 sugar mills
owned by 18 companies with a total running capacityof 245,900 tons per cane per day [3]. In 2019, a total
running capacity in sugarcane industries in Indonesia
of 278,411 tons per cane per day [4]. It means wastefrom sugary industries will increase with the increasein production capacity of sugar in Indonesia.
First step sugar production from sugarcane is juice
extraction or cane crushing using crushing rollers.
After extraction of the juice, the fibrous material is489

GEOMATE – Kyoto, Japan, 3-5 November 2021

advantage of biochar is its good capacity inadsorption, biochar has pore structure and chemicalfunctional groups on the surface. Therefore, biochar
can absorb carbon levels well.
Many factors effect on biogas purification
performance. Difference in the physical properties ofadsorbent generate differences in the adsorption
performance [14]. The carbon dioxide adsorption
undergoes a physical and chemical adsorption at thesurface of adsorbent. The important factors
influencing on carbon dioxide adsorption are thebasicity and pore size of adsorbent [15]. Besideinternal factors, external factors like temperature, airpressure and contact time also influence on carbon
dioxide adsorption. According to A, Boonpoke et al.
[10], for the temperature range of 30-150oC, carbon
dioxide adsorption capacity on activated carbon
decreased as adsorption temperature and air pressure
increased. The same results also reported by Masoudand Fatemeh [16]. Masoud and Fatemeh and Diogo etal. [17] are reported that the optimum pressure forcarbon dioxide adsorption at range of 5-7 bar. For
contact time, Anne et al. [18] observed that the kinetic
carbon dioxide adsorption process occurred very
quickly in the first ten minutes and slowed down afterten minutes and flattened at 60 minutes. It means thatthe equilibrium has been reached at 60 minutes
contact time. The same phenomenon also reported by
Lalhmingsanga and Pinakeswar [19] and Xiaoyun etal. [20].
In this study, we investigated the influence ofadsorption contact time on biochar-based sugarcane
bagasse. The investigation conducted for finding the
optimal contact time in biogas purification.

Table 1 The composition of adsorbents in every
column (%volume)
Items

ZZ
ZB1
ZB2
ZB3
ZB4

Biochar-based
sugarcanebagasse
(%)
0
25
50
75
100

Zeolite

(%)
100
75
50
25
0

The results of the best composition adsorbent thatseen from the highest decrease in carbon dioxide isused for contact time variation treatment. The
variation of contact time was carried out for 10, 15,
20 and 25 minutes.
RESULTS AND DISCUSSION
Carbon Dioxide Adsorption in The Difference
Composition of Adsorbent
The level of carbon dioxide as impurity gas inbiogas is an important indicator to determine thequality of the biogas. The lower level of carbon
dioxide shows that a better quality of biogas
produced. In this study, the results of carbon dioxide
decreasing in the difference compositions (Table 2)
will be used as a reference in the difference contact
time experiment.
Table 2. The decrease carbon dioxide in the
difference composition of adsorbents
Items
BeforeAfter
Theadsorption adsorption reduction
(ppm)
(ppm)
of CO2METHODOLOGY
Biochar-based Sugarcane Bagasse
Sugarcane bagasse that collected from thesurrounding environment was still in a wet condition
so it needs to be dried under sunlight for several days.
Then sugarcane bagasse pyrolyzed at a temperatureof 255°C for 3 hours.

(%)

ZZ
ZB1
ZB2
ZB3
ZB4

Biogas Purification – Carbon Dioxide Adsorption

214,700
261,360
178,720
221,020
180,830

229,000
213,540
38,445
203,720
212,020

-6.00
17.33
77.66
7.33
-17.00Note: - means an increase in carbon dioxide composition.

Carbon dioxide adsorption was conducted at roomThetemperature and pressure range of 5-7 bar.
Adsorption column type that used in this study ispacked second-level bed column with a column
length of 20 cm and a diameter of 3 cm. Every column
filled adsorbent with the formulation in Table 1. The
column directly connected with biodigester andbiogas holder. Biogas mixture will be passed throughthe column for carbon dioxide adsorption process by
air compressor.

490

The results that presented in Table 2 indicate thatthe highest decrease in carbon dioxide level was
performed by the adsorbent composition of 50%
sugarcane bagasse-based biochar and 50% of naturalzeolite (ZB2) with the decrease level of 77,66% andthe lowest decrease was in the treatment performedby 100% sugarcane bagasse-based biochar (ZB4)
with the decrease level of -17,00%. A balanced
combination of zeolite and biochar will maximize thecarbon dioxide adsorption because they have

GEOMATE – Kyoto, Japan, 3-5 November 2021

different properties in its absorption but complement
each other. Abdul and Masrukhil [21] state thatzeolite is hydrophilic and polar which is able to bindoxygen while biochar is hydrophobic and nonpolar
which is able to bind carbon, so that the combinationof the two absorbents will maximize absorption. A.
Alonso-Vicaro [22] stated that natural zeolites andbiomass-based absorbents need to be further activatedin order to have wider pores so that they can moremaximize in carbon dioxide adsorption.

adsorption, the pores were filled with more carbon
dioxide molecules so that the adsorption capacity
decreased. This condition can even reach thesaturation point [15-16]. Investigation to find thesaturation point in adsorption time need to beinspected at the next research.
CONCLUSION
The results of this study, on optimizing carbon
dioxide adsorption with sugarcane bagasse-based
biochar and zeolite, concluded that the highest
decrease in CO2 occurred in the use of 50% sugarcane
bagasse-based biochar and 50% zeolite (ZB2) with adecrease carbon dioxide level of 77,66% and wasused as an indicator of time variation treatment. This
study also revealed that contact time of adsorption
effected on the decrease carbon dioxide level. The
longer adsorption time resulted more saturated pointin carbon dioxide adsorption. The use of 50%
sugarcane bagasse-based biochar and 50% zeolite(ZB2) in carbon dioxide adsorption in biogas
purification has saturation point at 25 minutes ofadsorption time.
So, in general, this study states that differences in
the composition of the adsorbent and the contact time
affect the carbon dioxide adsorption capacity. The
longer in the adsorption contact time, the lower the
carbon dioxide adsorption capacity.

Carbon Dioxide Adsorption in The Difference
Contact Time
Many reports showed that the difference ofadsorption contact time influenced on adsorption
capacity. In this study, the influence of contact timeon biochar-based
sugarcanebagassewasinvestigated. The aim of this study is finding the
optimal condition for biogas purification using
biochar-based sugarcane bagasse. In this study, thehighest carbon dioxide adsorption capacity in theprevious study was used for next experiment, thedifference of contact time. Based on the results on
Table 2 showed that the using of combination of 50%
volume of zeolite and 50% of biochar (ZB2)
performed the best carbon dioxide adsorption
capacity of 77.66%. The combination of 50% volume
of zeolite and 50% of biochar (ZB2) used for next
experiment in the variation of contact time of 10, 15,
20 and 25 minutes.
After the experiment, gas samples before and after
adsorption were analyzed by gas chromatography.
The results of gas chromatography analysis were
showed in Table 3.
Table 3 The decrease carbon dioxide
difference adsorption contact timeTime(minutes)
10
15
20
25

Beforeadsorption(ppm)
178,715.98
219,024.58
289,014.54
287,056.84

Afteradsorption(ppm)
38445.64
113,079.69
191,709.38
251,638.30

ACKNOWLEDGMENTS
We gratefully acknowledge the support from
BRIN - Ristek Grant through the Research
Directorate of Universitas Gadjah Mada with acontract number of 2134/UN1/DITLIT/DITLIT/PT/2021. We also thank the Center of AgroTechnology Innovation for contribution in thisresearch and college students of Faculty of Animal
Science Universitas Gadjah Mada for helping incollecting the data.

in theThe
decreasein CO2
78.46
48.28
33.13
11.19

REFERENCES[1] Secretariat General National Energy Council,
Ministry of Energy and Mineral Resources, 2019,
pp.1-75.
[2] Center of Assessment of Process Industry andEnergy, Agency for the Assessment andApplication of Technology, 2019, pp.1-90.
[3] Aris T. and Triantarti., An Overview of Sugar
Sector in Indonesia. Sugar Technology, Vol.18,
2016, pp.636-641.
[4] Garret M. and Sugiarti M., Indonesia Sugar
Annual Report 2019, USDA Foreign Agricultural
Service, 2019, pp.1-10.
[5] Amit B., Kavindra K.K. and Narasinha S.,

Results in Table 3 presents that the variation
contact time effected on the decrease in carbon
dioxide. The highest carbon dioxide adsorption
performed by combination of 50% volume of zeolite
and 50% of biochar (ZB2) with adsorption contact
time for 10 minutes. It can be concluded that thelonger adsorption time, the lower level of carbon
dioxide decreasing. It occurred due to the maximal
adsorption capacity in first ten minutes. The contact
time more than 10 minutes, carbon dioxide adsorption
capacity decreased. With the increasing time of491

GEOMATE – Kyoto, Japan, 3-5 November 2021

Multidisciplinary Approaches to Handling
Wastes in Sugar Industries. Water Air Soil
Pollution, Vol.227, Issue 11, 2016, pp.1-30.
[6] Nelson L.N., Franklun J.A., Franklin A. and Frank
P.A., International Thomson Publishing Company,
1998, pp.1-748.
[7] Munagala M. and Yogendra S., Sustainable
Valorization of Sugar Industry Waste: Status,
Opprtunities, and Challenges. Bioresource
Technology, Vol.303, 2020, pp.1-10.
[8] Harish M.N., Nagendra M.S. and Amaresh P.,
Utilization of Sugar Industry Wates in Agriculture.
Agriculture and Food e-Newsletter, Vol.2, Issue 1,
2020, pp.14-23.
[9] Fateme M., Anne R., Mohammad A.A., Majid A.
and Hossein V., Life Cycle Assessment (LCA) of
The Energetic Use of Bagasse in Irianian Sugar
Industry. Renewable Energy, Vol.145, 2020,
pp.1870-1882.
[10] Wojciech M.B., A Review of Potential
Innovations for Production, Conditioning andUtilization of Biogas with Multiple-Criteria
Assessment. Renewable and Sustainable Energy
Reviews, Vol.54, Issue 2, 2016, pp.1148-1171.
[11] Himjyoti D. and Charu L.M., Traditional
Parboiled Rice-Based Products Revisited: Current
Status and Future Research Challenges. Rice
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[12] Ade A., The Used of Sugarcane Bagasse
Charcoal as An Adsorbent of Metal Ions Cd, Cr,
Cu and Pb in Wastewater. Syarif HidayatullahState Islamic University Jakarta, 2010.
[13] Rizkyarief E., The Use of Carbon Active
Bagasse for Reducing Concentration of Metal Pb
In Aqueos Solution, University of Semarang,
2017.
[14] A. Boonpoke, S. Chiarakorn, N. Laosiripojana, S.
Towprayoon and A. Chidthaisong, Synthesis of
Activated Carbon and MCM-41 from Bagasse
and Rice Husk and Their Carbon Dioxide
Adsorption Capacity. Journal of Sustainable
Energy and Environment, Vol.2, 2011, pp.77-81.

[15] Danielle B., Mourad K., Patrick N., Murielle M.,
and Robert H., Advances in Principal Factors
Influencing Carbon Dioxide Adsorption on
Zeolites. Science Technology Advanced
Materials, Vol.9, 2008, pp.1-7.
[16] Masoud M. and Fatemeh G., Gas Adsorption of
CO2/CH4 System using Zeolite 5A. Microporousand Mesoporous Materials, Vol.200, 2014, pp.110.
[17] Diogo P.B., Ronan S.O., Rodrigo S.V., Celio
L.C.Jr., and Diana C.S.A., Adsorption of CO2 on
Nitrogen-enriched Activated Carbon and Zeolite
13X. Adsorption, Vol.17, 2011, pp.235-246.
[18] Anne E.C., Bin G. and Ming Z., Carbon Dioxide
Capture using Biochar Produced from Sugarcane
Bagasse and Hickory Wood. Chemical
Engineering Journal, Vol.249, 2014, pp.174-179.
[19] Lalhmingsanga H. and Pinakeswar M., Carbon
Dioxide Adsorption on Zeolites and ActivatedCarbon by Pressure Swing Adsorption in a Fixed
Bed. International Journal Energy Environment
Engineering, Vol.5, 2014, pp.349-356.
[20] Xiaoyun X., Yue K., Ling Z. and Xinde C.,
Chemical Transformation of CO2 during Its
Capture by Waste Biomass Derived Biochars.
Environmental Pollution, Vol.213, 2016, pp.533540.
[21] Abdul M.R. and Masrukhi M., Optimalization ofMethane Biogas from Cow Manure by Using
Difference Absorbents. Journal of AgriculturalEngineering, Vol.10, Issue 2, 2017, pp.8-17.
[22] A. Alonso-Vicar, Jose R.O., S.G. Gil-Rio, O.
Gomez-Jimenez-Aberasturi, C.A. Ramirez-Lopez,
J. Torrecilla-Soria and A. Dominguez,
Purification and Upgrading of Biogas by Pressure
Swing Adsorption on Synthetic and Natural
Zeolites. Microporous and Mesoporous Materials,
Vol. 134, 2010, pp.100-107.

492

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

REPRODUCTIVE ECOLOGY OF THE ENDANGERED PYRUS
CALLERYANA DECNE IN URBAN PARKS
Naoya Motoori1and Michiko Masuda2
1,2

Faculty of Technology, Nagoya Institute of Technology, Japan
ABSTRACT

Recently urban development has invaded conservation-important biotic communities, including theendangered Pyrus calleryana Decne, which exists in urban parks, in the Center of Japan. The condition of urban
parks is not suitable for the species so the number of the species is decreasing in most habitat. In order to conserve
the species, the reproductive elements, the number of flowers, fruits and seeds, and survival rate of the seeds were
measured in some parks for 10 years. Then the reproduction is not successful in the half of the parks. The numberof the flower and fruit were fluctuating among years. And the half of the seeds was not useful for the survival rate.
The result of that, the seedlings of the species were not produced. For the self-incompatibility of the species preventthe production of seeds. The absence of the pollinators was expected because of the agriculture chemical is usedin urban parks for the people.
Keywords: Pyrus calleryana Decne, Number of flowers, Number of fruits, Year fluctuation

INTRODUCTION

dangerous as IA, but is at risk of extinction in the wildin the near future is extremely high [4]. In addition,
this species, along with Vaccinium sieboldii Miq, isthe most critical of the plants that characterize Aichi
Prefecture, and requires systematic protection
measures [6].
The Pyrus calleryana Decne population, which islarge enough for breeding, is distributed in Inuyama,
Owariasahi, Kuwana, and Komaki. Pyrus calleryana
Decne has different ecological characteristics andflower scents depending on the local population.
Therefore, it is highly possible that they areecologically differentiated. Understanding the uniqueecological characteristics is important in considering
genetic diversity.Therefore, the purpose of this study
is to create a foundation for conservation activities
suitable for the habitat by conducting a counting
survey of the number of flowers and fruiting in the
habitat of Callery pear in Komaki City and clarifying
the characteristics of the habitat.

In recent years, attention has been focused on theimportance of biodiversity. In October 2010, the 5th
Conference of the Parties to the Cartagena Protocol
on Biosafety of the Convention on Biological
Diversity (COP-MOP5) and the 10th Conference of
the Parties to the Convention on Biological Diversity(COP10) were held in Nagoya City, Aichi Prefecture.
At COP10, the new global goal "Aichi Targets(Strategic Plan 2011-2020)" by 2020 was formulated,
and the "Nagoya Protocol", which is an internationalframework for ABS (acquisition of genetic resources
and profit sharing) adoption was raised．
In the Tokai region, including Aichi prefecture,
many spring swamps are distributed and endemic
flora exists. This endemic flora is called the "Tokai
Hill Element" [1]. It has been reported that the
number of existing Tokai hill elements is small due tothe development of hills [5]. Due to the recognitionof the importance of the environment in which theabove flora grows, various conservation groups are
currently giving conservative consideration. On theother hand, these floras are often designated asprotected areas because of their rarity. For this reason,
it is difficult to investigate, there is a lack of
ecological knowledge, and even if transplanted, it
often dies.
Pyrus calleryana Decne, which was the subject of
this study, is a type of Tokai hill element that is
distributed only in Aichi, Gifu, and Mie prefectures.It
is designated as Critically Endangered IA (CR) in
Aichi Prefecture and Endangered IB (EN) in Mie
Prefecture.Critically Endangered IA (CR) is atextremely high risk of extinction in the wild in thevery near future, Endangered IB (EN) is not as

MATERIALS AND METHODS
Study Plant
The subject of this study is Callery pear, which
belongs to the subfamily Rosaceae and subfamily
Pear (Fig. 1). It is closely related to Pyrus pynifolia
Decne. It is a deciduous small tree that branches welland has a height of 8-10 m. The bark is gray-purpleblack with vertical cracks. Leaves alternate on long
branches or mostly bundled on short branches, broadovate, oval or oval oblong, sharp-pointed at the tip,
usually round at the base, 4-9 cm long, 3-6 cm wide,
has fine blunt serrations on the edges, and initially has
493

GEOMATE – Kyoto, Japan, 3-5 November 2021

white vellus hair, but later becomes hairless. The
flowering season is from March to April, the flowers
are white and about 2.5 cm in diameter, with 5 petals,
2-3 styles, and 6 ovules.

strain is attached to the pistil and pollinated, but thereaction is such that no fruit is produced. Finally,pollen of the same strain is attached to the pistil andpollinated to produce fruits, but the seeds do not
function normally. It is known that seeding is difficultin populations that have lost genetic diversity.
It is known that Pyrus calleryana Decne has
different ecological characteristics depending on the
population. Therefore, it is highly possible that they
are ecologically quite differentiated. As for thepolyneta, the population of Nagoya City has thelargest number of honeybees, and the population of
Komaki City has the largest number of honey fly.
About the same number of honeybees and honey fly
were found in the population of Kuwana City. The
presence of pollinators is important because of thenature of self-incompatibility. It is thought that therange of gene flow by pollen is 5 km in the case ofWestern honeybee and about 100 m in the case ofhoney fly. Seed dispersal was expected to be carriedout by birds, but it was pointed out that it was noteaten frequently. For this reason, it is thought thatmany of them are transported by free fall or water
flow. In recent years, spraying by raccoon dogs has
been confirmed, and it is known that a large amountof seedlings are germinated from raccoon dog
droppings (Fig. 3). The seed dispersal range isconsidered to be about 5 km from the raccoon dog's
range of action. It is thought to be about 10 km whensprayed by bulbuls and starlings.

Fig. 1 Photo of Pyrus calleryana Decne
The fruit is almost spherical, about 1 cm indiameter, yellowish brown with many small round
lenticels, and is a thorny tree, so it may be cut down
as an obstacle [6] (Fig. 2).

Fig. 2 From left: Pyrus calleryana Decne,
intermediate species, callery pear fruit
Pyrus calleryana Decne has the property of selfincompatibility. Self-incompatibility is a general term
for several types of genetic properties that prevent
self-pollination in order to maintain genetic diversity.
Self-incompatibility can be divided into three levels.
First, even if pollen of the same strain is attached tothe pistil, the pollen tube does not be extend andpollination does not occur. Next, pollen of the same

Fig. 3 Seedlings germinating from raccoon dog
droppings
Predation by bulbuls and starlings is relatively
common in Moriyama and Seto, and the spraying
style varies from region to region.
Study Site494

GEOMATE – Kyoto, Japan, 3-5 November 2021

be maintained in relation to humans.
The survey site is Komaki City, which is locatedin the northwestern part of Aichi Prefecture (Fig. 4).

MethodFlowering number, Fruiting number survey
The survey period is 2013-2020. The number offlowers was measured for flowering individuals fromMarch to May, which is the flowering time of Pyrus
calleryana Decne. In addition, the number of fruit set
was measured for all fruiting individuals from
October to November, which is the fruiting time of
Pyrus calleryana Decne.
The measurement method is rough because thenumber of measurements is enormous. Count thereference 100 sets and count how many of them there
are. Count with 2-3 people, and let the average value
be the number of flowers and the number of fruits.

Fig. 4 Komaki City
The survey site in Komaki City is the habitat of
Pyrus calleryana Decne, a large grass on the east
coast of the Dairakami pond (Fig. 5).The Daira pond
is one of the largest agricultural ponds in the city. The
pond on the north side is called the Dairakami pond,
the south side is called the Shimodaira pond, andthere are paddy fields in between. In the hills of Dairapond, which is dotted with agricultural land,
residential land, and thickets, a factory complex has
been constructed on the east side since 2010. In
addition, there is Toukadai New Town on the westside, and development projects have been carried out
from 1972 to 1999.

RESULT
Flowering Number, Fruiting Number Survey
Table 1 shows the number of flowers per individualin 2013-2020, and Table 2 shows the number of fruit
set per individual in 2013-2020.
Table 1 Flowers number for each individual in 20132020

Fig. 5 Position of the individual in the Dairakami
pond, which is the survey site of Komaki City.
The habitat of Pyrus calleryana Decne is often
located at the edge of the pond, suggesting that it may

Table 2 Fruit number for each individual in 20132020
495

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 3 Fruiting rate for each individual in 20132020

In 2013, the number of flowers per individual was
0-4850, and the number of fruits was 0-3100. In 2014,
the number of flowers per individual was 0-60000,
and the number of fruits was 0-40000. In 2015, thenumber of flowers per individual was 0-60000 andthe number of fruits was 0-7000. In 2016, the numberof flowers per individual was 0-31000 and thenumber of fruits was 0-2000. In 2017, the number offlowers per individual was 0-16000 and the numberof fruits was 0-15000. In 2018, the number of flowers
per individual was 0-60000, and the number of fruits
was 0-3000. In 2019, the number of flowers per
individual was 10-395400, and the number of fruits
was 0-14000. In 2020, the number of flowers per
individual was 0-66370, and the number of fruits was
0-25000. The number of individuals that did not
produce fruit despite flowering was 3 in 2013, 1 in2014, 1 in 2015, 2 in 2016, 0 in 2017, 9 individuals in
2018, 1 individual in 2019, 2 individuals in 2020. The
value obtained by dividing the number of fruits by the
number of flowers was used as the fruiting rate, andthe ratio of fruits produced among the flowers thatbloomed was shown. Table 3 shows the fruiting rates
for each individual in 2013-2020. The number ofindividuals with a fruiting rate exceeding 50% is 2 in2013, 12 in 2014, 1 in 2015, 0 in 2016, 20 in 2017, 0
in
2018,
4
in
2019,
3
in
2020.

Table 4 shows the number of flowers, the numberof fruits, and the rate of fruits in the entire habitat foreach year. The number of flowers, the number offruits, and the rate of fruits in the entire habitat varyfrom year to year. Of the eight years, the highest
number of flowers was reached in 2019, and thehighest number of fruits was reached in 2014. In 2013,both the number of flowers and the number of fruits
were the lowest. Regarding the fruiting rate, themaximum value was 66.9% (2017) and the minimum
value was 2.2% (2018).
Table 4 Number of flowers, fruits and fruiting rateof the entire habitat in 2013-2020

Table 5 shows the comparison results of thenumber of fruits set by year. It was found that there
was a significant difference (Steel-Dwass. Test P<0.05) in the number of fruits in 2013 compared with2017, 2019 and 2020. It was found that there was no
significant difference (Steel-Dwass. Test N.S.) in thenumber of fruits in 2013 compared with 2014, 2015,2016 and 2018.It was found that there was asignificant difference (Steel-Dwass. Test P <0.05) inthe number of fruits in 2014 compared with 2016 and2018.It was found that there was no significant
difference (Steel-Dwass. Test N.S.) in the number of496

GEOMATE – Kyoto, Japan, 3-5 November 2021

fruits in 2014 compared with 2015, 2017, 2019 and2020.It was found that there was a significant
difference (Steel-Dwass. Test P <0.05) in the numberof fruits in 2015 compared with 2017, 2019 and2020.It was found that there was no significant
difference (Steel-Dwass. Test N.S.) in the number offruits in 2015 compared with 2016 and 2018.It was
found that there was a significant difference (SteelDwass. Test P <0.05) in the number of fruits in 2016
compared with 2017, 2019 and 2020.It was found that
there was no significant difference (Steel-Dwass. Test
N.S.) in the number of fruits in 2016 compared with2018.It was found that there was a significant
difference (Steel-Dwass. Test P <0.05) in the numberof fruits in 2017 compared with 2018.It was foundthat there was no significant difference (Steel-Dwass.
Test N.S.) in the number of fruits in 2017 comparedwith 2019 and 2020.It was found that there was asignificant difference (Steel-Dwass. Test P <0.05) inthe number of fruits in 2018 compared with 2019 and2020.It was found that there was no significant
difference (Steel-Dwass. Test N.S.) in the number offruits in 2018 compared with 2020.

Therefore, there is a possibility of underestimation in
the counting result of the number of flowers. In the
future, in order to correctly compare the counting
results of the number of flowers in the continuous
survey, it is necessary to establish a unified survey
method. From the results of this study and the feelingsof the investigators involved in the survey, it isconsidered that the degree of flowering in the entire
habitat varies from year to year. In order to recognize
the significant difference from year to year, wethought that it would be useful to establish a unified
survey method and to grasp the cycle of the year when
the number of flowers is rich and the year when thenumber of flowers is poor, which will help to lay thefoundation for future conservation activities.
It can be judged that the number of fruit set varies
from year to year because a significant difference isoften observed in the yearly comparison results. It issaid that all species of plants have biennial bearing,and biennial bearing is the property of repeating
abundant and poor harvests of fruits. No such reporthas been made on Pyrus calleryana Decne, and it has
been considered a so-called "yearless species".
However, this study found 8150-259291 annual
variations in the number of fruits, revealing that Pyrus
calleryana Decne has years that produce significantly
more fruit. In general, if the biennial bearing is strong,a good harvest and a poor harvest are repeated every
other year, but the cycle of the year of birth isunknown in the 8-year survey, and it is consideredthat continuous survey is necessary in the future.
The average annual fruiting rate was 2.2-66.9%,
and in 2017, more than half of the habitat populations
showed a fruiting rate of 50% or more. Therefore,
compared to the average fruiting rate of 0.0-21.8% in
the habitats of Inuyama City, Owariasahi City, andKuwana City in 2020, the habitat of Komaki City has
a large number of fruiting relative to the number offlowers. [11]. Summarize the current status and future
prospects of each habitat learned through thisresearch. Individuals in the habitat of Komaki City
have a large number of flowers and fruit set, showing
a high fruit set rate. As the investigation progressed,
the seedlings and the growth of young trees wereconfirmed, so it is considered that the environment issuitable for the growth of Pyrus calleryana Decne.
Conservation groups, local governments, andneighboring companies are gradually working toprotect Pyrus calleryana Decne's habitat. However, it
is unclear whether there are any scientifically-based
conservation activities suitable for Pyrus calleryana
Decne. It is considered that conservation activities
based on the survey are necessary. As a future policy,

Table 5 List of p-values (Steel-Dwass. Test P<0.05) by comparing the number of fruits in 20132020

CONSIDERATION
The purpose of this study is to clarify thecharacteristics of the habitat of Pyrus calleryana
Decne in Komaki City and to lay the foundation forconservation activities suitable for the habitat.
Regarding the number of flowering, there is aconcern that the results of the number of flowering ineach year cannot be compared correctly because thesurvey method is not unified between the years.
However, the method of conducting the survey is alsoslightly different. In 2017, 2019, and 2020, we
conducted a flowering number survey and a flowering
phenology survey in parallel, so we were able tocount evenly during the flowering period. On theother hand, in 2013, 2014, 2015, 2016 and 2018, theflowering phenology survey was not conducted, so
the flowering number survey was conducted only ona few days of the flowering period. Since the time offull bloom differs depending on the individual, not allindividuals can be counted at the time of full bloom.

497

GEOMATE – Kyoto, Japan, 3-5 November 2021

we would like to continue the survey so far, consider
new survey items, and build a foundation forconservation activities suitable for each habitat.

[6] Aichi Prefectural Environmental Research Center,Endangered wildlife in Aichi Prefecture, Plant
Edition, Red Data Book Aichi 2009, 2009, pp. 94.
[7] Aichi prefecture, 2011 Environmental, White
Paper, 2011, pp. 1-2.
[8] Mikio Watanabe, evening edition on March 12,
Chunichi Shimbun, 2013.
[9] Niki
Tanaka,Interrelationshipbetweenconservation activities and endangered plants,
Master’s thesis, Nagoya Institute of Technology,
2018.
[10] Wataru Ueda, Ecological characteristics ofendangered callery pear in Owariasahi, Komaki,
and Inuyama, Graduation thesis, Nagoya
Institute of Technology, 2019.
[11] Naoya Motoori, Comparative ecology in all
Callery pear distribution areas, Graduation thesis,
Nagoya Institute of Technology, 2020.

REFERENCES[1] Kunihiko Ueda, Phytogeography of Tokai hillelements I. Plant Taxonomy, Geography Vol. 40,
1989, pp. 190-202.
[2] Kunihiko Ueda, Magnolia stellata, Planter
Volume 7, 1990, pp. 77-81.
[3] Kunihiko Ueda, Natural History of Plants,
Hokkaido
University
Book
Publishing
Association, 1994, pp. 3-8.
[4] Richard B, Primack and Hiromi Kobori,Recommendation of conservation biology,
Bunichi Comprehensive publishing, 2008.
[5] Shozo Hiroki, Satoyama Ecology, Nagoya
University Press, 2002, pp. 42-96.

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11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

CONSIDERATION ON THE DYNAMICS OF PARK USERS UNDER
COVID-19 CRISIS; TAKING SHIKISHIMA PARK IN MAEBASHI CITY
AS AN EXAMPLE
Shinya Tsukada1, Xingyu Tao2, Tetsuo Morita3and Toshikazu Nishio4
1

Maebashi City Office, Japan; 2,3Maebashi Institute of Technology, Japan;
4
Takasaki Technical High School, Japan
ABSTRACTThis study considers the dynamics of park users under COVID-19 crisis taking Shikishima Park in Maebashi
City, Gunma Prefecture as an example. In this study, we analyzed the traffic volume of users measured by installinga camera in the parking lot of Shikishima Park. An analysis of user behavior during each infection period showedthat the number of pedestrians increased in the second wave period despite the spread of infection. The mask
wearing rate of park users has been stable at 60% to 80%, and it is considered that the habit of wearing masks has
become more common. In addition, despite the spread of infection, the number of pedestrians increased during theconsecutive holidays in May and the summer vacation. As mentioned above, the dynamics of park users under
COVID-19 crisis were obtained. We believe that the research results will be useful as an archive of the actual stateof park use in under COVID-19 and for the park management plan of the "With COVID-19 pandemic era".
Keywords: COVID-19, urban park, number of pedestrians, constant observation survey

INTRODUCTION

Under such circumstances, it is meaningful toknow how the use of parks has changed inconsidering the future of parks. To date, muchresearch has been done on COVID-19 crisis and parkuse. Takeyama et al. analyzed the actual number ofpark users during the period of infectious disease
control implemented by Hyogo Prefecture and otherperiods using big data collected from the location
information of smartphones [4]. Takeuchi et al.
analyzed the actual usage of large-scale parks inTokyo by conducting hearing surveys and grasping
the number of park users using line census data [5].
Hashimura et al. clarified the relationship between
preschoolers and their mothers' use of parks andchild-rearing stress under COVID-19 crisis from aquestionnaire survey in Tokyo [6]. Osamura et al.
conducted a questionnaire survey of university
students in Tokyo to understand the changes in sports
implementation and usage space during the COVID19 expansion period [7].

The new coronavirus (COVID-19), which wasreported in Wuhan City, Hubei Province, China in
December 2019, has spread all over the world and has
not subsided even now. In Japan, the infection has
spread rapidly since the first infection was reported in
January 2020. In April 2020, a state of emergency
was issued due to the spread of COVID-19, and it was
requested to refrain from going out unnecessarily. It
is presumed that the spread of this infectious disease
has significantly changed people's lifestyles andbehavior patterns. Therefore, it is possible that theusage pattern of the park, which is a familiar green
open space, will change.
RESEARCH SIGNIFICANCE
As the infection spread, the national and local
governments took measures against infectious
diseases through press conferences and websites.
Hiroi showed that despite the state of emergency inApril 2020 had the effect of reducing the trips forcommuting purposes, it made little change in trips formeals and sightseeing purposes [1]. In addition, Li,
Ko, Chang, Yen and Chen showed that there was apositive correlation between risk perception forcoronavirus infection and the degree ofcountermeasure implementation [2]. In addition,
Moriwaki, Hiyoshi and Murota analyzed an opinion
survey conducted in November 2020 and concluded
that decrease in life satisfaction was seen in many
respondents, and the cause was decrease in thefrequency of going out [3].

Fig. 1

499

Location ofPrefecture

Maebashi

City,

Gunma

GEOMATE – Kyoto, Japan, 3-5 November 2021

Soccer field
Survey area
Athletics stadiumTennis court
Auxiliary athletics stadium

Cafe
Swimming pool
Bus rotary
Baseball ground

500m

0

Fig. 2

Facility overview of Shikishima Park

Fig. 4

Parking entrance

Table 1 Survey outline

Range of Shooting
Measurement line

CameraMeasurement line

Parking lot
Cafe

25m

Fig .3

Picture of Survey location (Shooting range
from the camera, see Fig. 3)

Survey point

Investigation
periodMarch 30, 2020～
September 27, 2020

SurveymethodCapture changes in traffic volume
during COVID-19 period in still
images taken with a camera

Totalization(i)Hourly pedestrians, bicycles, cars(ii)Mask wearing status of pedestrians
and cyclists

It is known that the citizens of Maebashi City havea great relationship between water and greenery [8].
In addition, Maebashi citizens are interested in park
management [9]. There are 425 city parks in
Maebashi City, Gunma Prefecture, with a total areaof 395.78 ha, which is 11.8 m2 in terms of park area
per capita. There are two large-scale parks in
Maebashi City, Maebashi Park and Shikishima Park.
Open garden activities were taking place around
Shikishima Park [10].
In this study, we focused on Shikishima Park,
which was the most well-known in the citizen
questionnaire. Shikishima Park is an athletic park
(area 36.6ha) that serves as the backbone park of thecity. The facility scale and facility contents are thesame as those of the Komazawa Olympic Park (area41.1ha, athletic park) in Tokyo, which was the subjectof previous research [4].
Shikishima Park is a city park with a long history
that was established in 1922 by the Maebashi City
Council as a park around the river site called
Koidegawara. It is an athletic park located about 3km
northwest of the center of Maebashi City andbordering the Tone River on the west side. Full-scaleathletic facilities such as a baseball field, including a
400m track and field stadium, are installed. Gunma
Prefecture manages the park facilities (Fig. 2).

All of the above studies were conducted on the useof parks in large cities such as Tokyo and Hyogo
prefectures. But there are not many studies conducted
on local cities such as Gunma prefecture. Therefore,
the originality of this study is to capture the traffic
dynamics of COVID-19 by grasping the traffic
volume of park users through constant observation
surveys in Maebashi City, Gunma Prefecture. The
location of Maebashi City, Gunma Prefecture is asshown on the previous page (Fig. 1).
Here, we will grasp the changes in pedestrian
traffic volume in city parks during the COVID-19
expansion period in local cities. The purpose of thisstudy was to obtain basic materials useful forarchiving the actual state of park use under COVID19 crisis and for the park management plan of the"With Corona era".
RESEACH METHODS
Maebashi City is the prefectural capital of Gunma
Prefecture. Maebashi has a population of 334,204 anda total of 152,466 households (as of 2021.6.30). The
population ratio of Maebashi City by industry is 1.2%
for agriculture and forestry, 21.9% for industry, and76.9% for service.
500

GEOMATE – Kyoto, Japan, 3-5 November 2021

The survey was conducted on a record of crosssectional traffic volume of park users by installing acamera in the parking lot of Shikishima Park (Fig. 3).
Differences could be seen depending on the surface
on which the target pedestrians, motorcycles, buses,
and automobiles pass. Therefore, we measured twocross-sections, one(roads) for pedestrians, bicycles
and buses and the other (parking lot entrances andexits) for automobiles and motorcycles (Fig. 4).
By installing an automatic measurement camera,
we photographed pedestrians, bicycles, buses, cars,and motorcycles entering the shooting area. It wasverified whether it was possible to grasp the crosssectional traffic volume in the captured still image. It
was also found that it is possible to grasp the wearing
situation of the mask in the still image.
We also tried to determine the age of the users andwhether or not they were wearing masks by checking
the captured still images. As a result, it was
impossible to determine age. However, it wasdifficult to determine the age of an individual by
wearing a mask.
Table 1 shows the outline of the survey. We
thought that the use of the park under COVID-19
crisis was influenced by three factors: the infection
situation, government measures, and the press.
In this study, the traffic volume was defined as thenumber of pedestrians and the number of park facility
users. In addition to the traffic volume of pedestrians
and bicycles, the wearing status of masks was alsotabulated. In advancing the research, the analysis
period was set as follows.
In Japan, the "First Wave" of COVID-19 is frommid-March to mid-May 2020 (Red line in the fig.). In
Japan, the "Second Wave" of COVID-19 is from lateJune to September 2020 (Yellow line in the fig.).
In this study, the analysis period was set fromMarch 30 to September 30, 2020, including the firstand second waves (Other than Blue line in the fig.).

Nunmder of pedestrian person/Day

400

5/11～

4/6～

300

200

3/30～
100

7/13～
6/29～

0

0

500

1000

1500

PCR test positive Number（Japan） Person/Week

Fig. 5

Relationship between the number of PCRpositive people and the number ofpedestrians

100%

%

5/11～

Mask wearing rate

80%

7/13～
4/6～

60%

40%

6/29～

20%

3/30～

0%

0

500

1000

1500

PCR test positive Number（Japan） person/day

Fig. 6

Nunmber of park users Person/Day

DYNAMICS OF PARK USERS UNDER
COVID-19 CRISISThe Relationship between The Number of PCRPositive People
Fig. 5 show the relationship between the number of
PCR-positive people (Japan) on the X-axis and thenumber of pedestrians in the park observed by thecamera on the Y-axis. From the figure, therelationship between the number of PCR-positive
people and the number of pedestrians showed that the
number of pedestrians increased from July to Augustwhen the second wave of infection was spreading.
Fig. 6 show the relationship between the number of
PCR-positive people (Japan) on the X-axis and themask wearing rate of pedestrians on the Y-axis.

Relationship between the number of PCRpositive people and the wearing rate of
masks

3000

7/27～

2000

7/13～

3/30～
1000

6/29～

0

0

500

5/11～

1000

1500

4/6～

PCR test positive Number（Japan） person/day

Fig. 7

501

Relationship between the number of PCRpositive people and the number of park users

0.30

COVID-19
measures200

Case/Day

Number ofpedestrians

300

0.20

0.10
100

0.00

Relationship between COVID-19 measuresand the number of park users in Gunma
Prefecture
400

4.00

200

3.00

News Coverage

2.00

100

1.00

0

0.00

Press Case/Day

Number of pedestrians
300

Fig. 9number of park facility users increased from July toAugust. From the above, it is considered that thereason why the number of park users from July toAugust increased despite the spread of infection in thesecond wave is that many people used the park during
the summer vacation. Regarding the pedestrian mask
wearing rate, the pedestrian mask wearing rateincreased from March to early April of the first wave.
However, with the cancellation of the state of
emergency on May 13, it was found that the rate ofpedestrians wearing masks decreased. In addition, therate of pedestrians wearing masks remained at about
the same level after the end of June, the period of
spread of the second wave of infection.
The Relationship between The Number of parkusers

3/30-4/5
4/13-4/19
4/27-5/10
5/18-5/24
6/1-6/7
6/15-6/21
6/29-7/5
7/13-7/19
7/27-8/9
8/17-8/23
8/31-9/6
9/14-9/20

Number of pedestrian

Person/Day

Fig. 80Counter plan400

3/30-4/5
4/13-4/19
4/27-5/10
5/18-5/24
6/1-6/7
6/15-6/21
6/29-7/5
7/13-7/19
7/27-8/9
8/17-8/23
8/31-9/6
9/14-9/20

Number of pedestriansPerson/Day

GEOMATE – Kyoto, Japan, 3-5 November 2021

Relationship between News Coverage andthe number of park users in Gunma
Prefecture

From the figure, regarding the relationshipbetween the number of PCR-positive people (Japan)
and the mask wearing rate of pedestrians, the mask
wearing rate of pedestrians increased from March toearly April in the first wave period. After that, it
declined after the cancellation of the state of
emergency on May 13. In addition, it was flat during
the second wave period of infection spread.
Fig. 7 show the relationship between the number of
PCR-positive people (Japan) on the X-axis and thenumber of park facility users provided by the
manager of Shikishima Park on the Y-axis. From thefigure, regarding the relationship between the numberof PCR test positives and the number of park users,
while the infection spread in the second wave, theFig. 8 show the relationship between the number ofmeasures taken by Gunma Prefecture and the numberof pedestrians [11]. The number of COVID-19 crisis
countermeasures issued by Gunma Prefecture was
grasped by three types: "Declaration," "Request," and"Policy." Regarding the measures taken by Gunma
Prefecture, we searched the "Gunma Prefecture
Homepage" on the Internet and extracted it from the"Summary of Coronavirus Infectious Diseases" page.
In addition, the measures against COVID-19 crisis
taken by Maebashi City were not included in thenumber of extractions because they were included in
the measures taken by Gunma Prefecture. Amongthem, there are "state of emergency", "request for
self-restraint", and "policy to prevent the spread of
COVID-19", and each of the extracted items is
counted as one case. It was found that measures weretaken by Gunma Prefecture during each of the firstand second waves of infection. However, during theGolden Week holidays in May, the number ofpedestrians around the park was increasing.
Fig. 9 show the relationship between the number ofpedestrians and the number of news articles on
COVID-19 by the Jomo Shimbun, a local newspaper
in Gunma Prefecture. To extract news articles, I
searched the local newspaper "Jomo Shimbun" inGunma prefecture on the Internet, opened thehomepage, and entered "Gunma Corona" in the
search word [12]. The number of reports on COVID19 in the Jomo Shimbun during the second wave
period was decreasing. On the other hand, the numberof pedestrians increased during this period.
CONCLUSIONSThis study uses an observation camera toinvestigate the actual traffic conditions of park users,
taking Shikishima Park in Maebashi City as an
example. The results of the study are as follows.

502

GEOMATE – Kyoto, Japan, 3-5 November 2021

(1) We analyzed the walking behavior of city parks
during COVID-19 infection period. The number ofusers of Komazawa Olympic Park, which is similarin scale and facility, decreased significantly in May
and continued to decrease after June (previous study[2]).However, in the second wave of the infection,
the number of pedestrians increased despite thespread of the infection.
(2) It is considered that the reason why the number ofpedestrians and users of the park increased during
the Golden Week holidays in May and from July toAugust was that many people used the park on longholidays such as summer vacation. In addition, as
for the pedestrian mask wearing rate, it was foundthat the rate decreased with the cancellation of thestate of emergency on May 13.
Behind the results, there are many "public
transportation such as trains" as the means of accessto parks in Tokyo. It was thought that the access tothe park in Maebashi City was "private car", and thatthe self-control over the use of the park was looser in
Maebashi City than in Tokyo (previous study [7]).
In the use of parks in After COVID-19, the time
will come when we will consider equalizing the useof parks. In that case, it is effective for us to think
about the park usage plan while grasping the actual
conditions of the park access means and the numberof park usage. However, after the second wave, thepedestrian mask wearing rate was stable at 60% to
80%.From this, it is considered that pedestrians havebecome accustomed to wearing masks after thesecond wave. Furthermore, in order to understand theimpact of information management on park users, weanalyzed the announcement of measures against
COVID-19 infectious diseases in Gunma Prefecture
and the coverage of the Jomo News paper. As a resultof the analysis, the measures taken by Gunma
Prefecture and the coverage of COVID-19-related
news by the Jomo Shimbun increased during each of
the first and second waves. However, despite themeasures taken in Gunma Prefecture and newspaper
reports, the number of pedestrians around the park
tended to increase during the Golden Week holidays
in May. In the future, it will be necessary to carry out
a more detailed analysis and understand the usage
status of the park in relation to other urban facilities.

Effects of Risk Perception on Association
between Social Support and Coping with
COVID-19: An Online Survey, International
Journal of Environmental Research and Public
Health, Vol.18, No.4, 2021
[3] Moriwaki Y.,Hiyoshi N., Murota M., A study onchanges in going out behavior, outdoor space
usage and life satisfaction with COVID-19,
Reports of the City Planning Institute of Japan,
No.19, pp.435-438.
[4] Takeyama H., Wakai S., Yamamoto S. ，
Yakushiji T. and Nakase I., A Study on the Useof Urban Parks Using Location-Based Big Data
under the Coronavirus Crisis, Japanese InstituteLandscape Architecture, Vol. 84, No. 5, 2021, pp.
475-478.
[5] Takeuchi T. and Kuma A., Analysis of Changes
in the Number of Park Users and the Facility
Usage Restrictions in Tokyo Metropolitan Parks
under the COVID-19 Pandemic, JapaneseInstitute Landscape Architecture, Vol. 84, No. 5,
2021, pp. 479-484.
[6] Hahimura C., Amemia M., Hata N. and
SHIMADA T., The Relationship between
Changes in Park Use by Mothers with PreschoolChildren and Parenting Stress of those Mothers
under the COVID-19 Pandemic, JapaneseInstitute Landscape Architecture, Vol. 84, No. 5,
2021, pp. 485-490.
[7] Osamura Y., Fukuoka T., Study of Changes in
University Students’ Exercise Routine andLocations pre and during COVID-19 Outbreak,Japanese Institute Landscape Architecture, Vol.
84, No. 5, 2021, pp. 491-494.
[8] Tetsuo M., Shinya T., Yoshihito K., Hiroshi S.,
Tsuyoshi B., Shinya T., Naoki M., A Study on
Evaluation of Quality of Life in Consideration of
Water/Green Environment, Int. Journal of
GEOMATEVol.2(2), 2011, pp.241-246[9] Shinya T., Tetsuo M., A Study of Public Opinion
on Green Spaces using data from Free-Text
Descriptive Responses- A Case Study ofRegional City in Japan, Int. Journal of
GEOMATE Vol.14(46), 2018, pp.21-25[10] Shinya T., Kazuya T., Tetsuo M., Residents’Evaluation and Continuity in The case of Shikisima
Open Garden in Maebashi City, Int. Journal of
GEOMATE Vol.18(68),2019, pp.108–113

REFERENCES[11] https://www.pref.gunma.jp/[12] https://www.jomo-news.co.jp/[1] U H., A Study on the Effect of Japanese-style
Lockdown on Self-restraint Request for COVID19, Journal of the City Planning Institute of Japan,
Vol.55, No.3, 2020, pp.902-909.
[2] Dian-Jeng Li, Nai-Ying Ko, Yu-Pimg Chang,
Cheng-Fang Yen, Yi-Lung Chen, Mediating[13] Shinya T., May K., Tetsuo M. and Akira Y., A study
influence by Quality of life captured from Travelvalued of Shikishima park, Japanese InstituteLandscape Architecture, Vol. 81, No. 5, 2018, pp. 495500.

503

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

CONVERSION OF BLAST FURNACE SLAG INTO HYDROGARNETFOR HUMIC ACID REMOVAL
Takaaki Wajima1
1

Graduate School of Engineering, Chiba University, Japan
ABSTRACTThe discharge of humic acid in aqueous environments is a key health and esthetic issue. In this study, blast furnace(BF) slag, a byproduct of iron- and steel-making plants, is converted into a hydrogarnet product under ahydrothermal reaction for humic acid removal. The BF slag is pulverized and passed through sieves of four
different sizes, i.e., 100, 100–300, 300–500, and 500–1000 μm. Subsequently, these BF slags are added to NaOH,
KOH, and LiOH solutions with 1–4 M NaOH solution/BF slag (= 20, 10, and 4 mL/g) and heated at 80, 120, and
160 °C for 24 h, with or without Al(OH)3 addition. In the absence of Al(OH)3, portlandite is synthesized in alkali
solutions, and the highest crystallinity of portlandite in the product is obtained in 3 M NaOH at 160 °C. With theaddition of Al(OH)3, katoite, which is a hydrogarnet, as well as tobermorite and hydroxysodalite are synthesizedfrom BF slag. As the NaOH solution/BF slag ratio increases, the peaks of hydroxysodalite and tobermorite increase,
whereas those of katoite decrease. As the BF slag size decreases, the peaks of katoite increase because of theincrease in Ca supply from the BF slag. The product, including katoite, can remove humic acid.
Keywords: Blast furnace slag, Hydrogarnet, Humic acid removal, Alkali hydrothermal synthesis

INTRODUCTION

interactions between HA and the material surface is
key to improving the adsorption properties of HA
removal systems. To manipulate various adsorption
sites for HA at the material surface, the hydroxyl
groups must have a diverse range of binding energies.
It is expected that these groups can be easily tunable
as sites for reaction with HA. The hydrogarnet series
Ca3Al2(SiO4)3-x(OH)4x (0 < x < 3) is a solid solution
comprising grossular (x = 0) and katoite (x = 3). The
increased substitution of (SiO4)4- in this structurecauses a decrease in the lattice constants, resulting indisorder in the cation-centered polyhedral [8], and ahydrogarnet with a number of hydroxyl groups on thesurface being able to adsorb HA from aqueous
solutions [9].
Hydrogarnets are known as the primary hydration
product in solidified cement pastes [10-12]; they canbe synthesized by the hydrothermal treatment of
byproducts such as coal ash or molten slag [13, 14].
Blast furnace (BF) slag is the dominant byproduct ofiron- and steel-making processes, and more than 23
million tons of BF slag is generated annually in Japan[15]. Although BF slag is widely used for cement
production, road construction, and as a concrete
aggregate, the capacity for the use of recycled BF slagin these fields has become saturated, and thedevelopment of value-added products from such
waste slag has become an important issue forsustainability. Therefore, various recycling processes
have been investigated recently [16, 17].
In this study, hydrogarnets were synthesized from
BF slag using hydrothermal treatment to remove HAfrom aqueous solutions. Hydrogarnets weresynthesized from BF slag using an alkali

Humic acid (HA) is a ubiquitous natural organic
polyelectrolyte formed by the breakdown of animaland vegetable matter in aqueous environments. The
presence of significant amounts of HA in water isindicated by a yellowish to brown color, and thechlorine used during water purification reacts with
HA to form carcinogenic materials [1]. Therefore, thedischarge of HA in water is a major environmental
and health concern.
Several methods, including membrane separationand coagulation techniques, have been used toremove HA from effluents [2, 3]. Furthermore, HA
can be removed from aqueous solutions via
adsorption using micropores and functionalized
substrate filters, which serve as adsorption sites. In
activated carbon, the pores can be obstructed by theadsorption of HA [4]; therefore, carbon filters withmesopores were prepared to overcome this problem[5]. Meanwhile, kaolinite exhibits higher adsorptionfor HA than montmorillinite, although their surfacefunctional groups (Al–OH and Si–OH) are similar [6].
The adsorption of HA on Mg/Al-layered doublehydroxides occurs via a ligand exchange reaction
with surface groups [7]. Therefore, it is believed thatthe binding energies of the surface functional groups
significantly affect HA adsorption properties.
HA contains various organic functional groups
such as hydroxyl, carboxylic, and phenolic groups,
resulting in reaction sites with different potential
energies. It is difficult to achieve a complete removalof HA from aqueous environments because of its
complex structure. Increasing the number of504

GEOMATE – Kyoto, Japan, 3-5 November 2021

hydrothermal reaction, and the ability of the productto remove HA from aqueous solution was analyzed
for its possible application in wastewater treatment.
EXPERIMENTAL

Synthesis

SampleThe raw slag was first ground in a mill and sieved into
four slags of different sizes: S-1, 100–250 μm; S-2,
250–500 μm;, S-3, 500–1000 μm; and S-4, 1–2 mm.
The slag (0.5, 1, and 2.5 g) with or without Al(OH)3(Wako) was added to 10 mL of 1–4 M NaOH, KOH,
and LiOH solutions in 50 mL pressure vessels, andheated at 80 °C, 120 °C, and 160 °C for 24 h in anelectric oven. After heating, the solid was filtered,washed with distilled water, and dried in an oven at
60 °C overnight to obtain the product. The productphases and morphologies were analyzed using XRD.
The change in the amount of mineralogical phases in
the product was represented as a relative crystallinity(RC) by calculation using the intensity of the major
XRD peaks in the product at specified diffraction
faces, i.e., portlandite (0 0 1), as follows:

BF slag can be classified into air- and watercooled slags, based on the cooling method. The slags
contain crystalline and amorphous phases,
respectively. To ensure that the phases in the slag
were converted into functional phases by alkali fusion,
the BF slag used in this study (received a steelproducing plant in Japan) was water cooled. The BF
slag was gently ground using a mill and sieved to lessthan 2 mm. The chemical and mineralogicalcompositions of the raw slag were obtained via X-ray
fluorescence spectrometry (Shimadzu, XRF-1700)
and X-ray diffraction (XRD) (Rigaku, RINT-2500),
respectively, as shown in Table 1 and Fig.1. The raw
slag was composed primarily of CaO (42.9%), SiO2
(34.5%), and Al2O3 (13.7%) in the amorphous phaseand minor crystalline phase, and gehlenite(Ca2Al2SiO7). Other oxides, such as MgO, SO3,
Fe2O3, TiO2, K2O, and MnO, were present in lower
amounts.

RC =(Intensity of major peaks in the product)  100 (1)(Intensity of major peaks in the standard)Commercial Ca(OH)2 (Wako) were used as thestandard.

Table 1 Chemical composition of BF slag.
Adsorption
CaO
SiO2
Al2O3
MgO
SO3
Fe2O3

Content (wt.%)
42.9
34.5
13.7
6.1
1.1
0.6

5000

The HA adsorption capacity of the product wasevaluated using 0.25 g/L of product. The HA solution
was prepared using a method first reported by
Moriguchi et al. [18]. Briefly, HA was dissolved in adiluted NaOH solution, and the solution was filteredusing a membrane filter with 0.45 μm pores.
Subsequently, the solution was added to a diluted HClsolution for precipitation and centrifuged to obtain
HA. After the dissolution of the resultant HA in
diluted NaOH, diluted HCl was added to shift the pHof the HA solution to 7 to simulate real natural water.
The product was soaked in HA solution at 0–30 mg/L
for 24 h, and the supernatant obtained by centrifuging
the slurry solution was analyzed via UV/Vis
spectroscopy by monitoring the change in absorbanceat 400 nm. Blank samples without a product were
measured for each adsorption test as a reference. The
adsorption amount of HA (qe) was calculated using
the following equation:▲: Gehlenite [Ca2Al2SiO7]

Intensity [cps]

4000
3000
▲

2000
1000
0

Fig. 1

0

10

20
30
40
2θ[CuKα (degree)]

qe =

50

(C 0 − C )  Vw(2)

where C0 (mg/L) and C (mg/L) are the initial andmeasured concentrations, respectively; V (L) is the

XRD patterns of BF slag.
505

GEOMATE – Kyoto, Japan, 3-5 November 2021

volume of the solution; w (g) is the weight of theadded sample.

160 °C for 24 h. The XRD patterns of the productfrom the slag with various particle sizes are shown in
Fig. 5. As the particle size, tobermorite appeared in
the product synthesized from the slag with particles
measuring less than 500 μm, and katoite, one of thehydrogarnets, appeared in the product from the slagwith particles measuring less than 100 μm.
These results indicate that hydrogarnets can besynthesized from slag with particles measuring lessthan 100 μm in 3 M NaOH solution at 160 °C for 24
h. It is noteworthy that the insufficient Al content in
the solution rendered the synthesis of hydrogarnets
difficult.

RESULTS AND DISCUSSION
Hydrogarnet Synthesis
The effects of the alkali species, concentration,and temperature on the product were investigated. It
is noteworthy that 2.5 g of raw slag (< 2 mm) was
treated with 10 mL of 1–4 M NaOH, KOH, and LiOH
solutions at 80 °C, 120 °C, and 160 °C for 24 h. All
obtained products contained portlandite (Ca(OH)2)
without hydrogarnets. The typical XRD patterns of
the product are shown in Fig. 2.

(a)
160

10000

Intensity [cps]8000

Temperature [oC]●: Portlandite [Ca(OH)2]

●

6000

(b)
120

4000
●
2000

80

●

1

0

0

10

20

30

40

50

4

2
3
KOH concentration [M]42
3
LiOH concentration [M]4(b)2θ[CuKα (degree)]160

XRD patterns of product from BF slag in 3
M NaOH solution at 160 °C.

oTemperature [ C]

Fig. 2

2
3
NaOH concentration [M]Figure 3 shows the RC of the portlandite phase of
the product. The abscissa is the concentration of thesolution, and the ordinate is the heating temperature.
The white zone indicates the high crystallinity of
portlandite in the product, and the black zone
indicates no portlandite phase in the product. The
crystallinity of portlandite in the product using NaOH
solution was higher than that using KOH and LiOH
solutions. As the NaOH solution and heating
temperature increased (from bottom left to top right),
the crystallinity of portlandite increased, and the
product with the highest crystallinity of portlandite
was synthesized in 3 M NaOH solution at 160 °C for
24 h.
The effect of the solid/solution ratio on the
product was determined. It is noteworthy that raw
slag (0.5, 1, and 2.5 g) was treated with 3 M NaOH
solution (10 mL) at 160 °C for 24 h. The XRD
patterns of the product when the solution/slag ratios
were 20, 10, and 5 mL/g are shown in Fig. 4. As thesolution/slag ratio increased, tobermorite appeared in
the product with portlandite, but hydrogarnet peaks
were not observed.
The effect of the particle size on the product wasinvestigated. The slag (2.5 g) with various particle
sizes was treated with 3 M NaOH solution (10 mL) at

(c)
120

80
1

(c)

o

Temperature [ C]

160

120

80
1

Fig. 3506

Crystallinity of portlandite synthesized from
BF slag in (a) NaOH, (b) KOH, and (c)LiOH solution at various temperatures.

GEOMATE – Kyoto, Japan, 3-5 November 2021

For hydrogarnet synthesis, 2.5 g of slag (< 2 mm)
and 1 g of Al(OH)3 were added to a 1–3 M NaOH
solution (10 mL) at 160 °C for 24 h. The XRD
patterns of the product synthesized from the slag with
Al(OH)3 added are shown in Fig. 6. Portlandite wassynthesized in the absence of Al(OH)3. With theaddition of Al(OH)3, katoite can be synthesized in the
product. The peaks of katoite and bohemite wereconfirmed in the product synthesized in 1 M NaOH
solution, and as the NaOH concentration increased,
the peak intensity of bohemite decreased. The productcontaining katoite and hydroxysodalite can besynthesized from the slag in 3 M NaOH solution.

▼: Tobermorite 11Å [Ca5(Si6O18H2)・4H2O]●●: Portlandite [Ca(OH) ]20 mL/g

2

Intensity [a. u.]

●
▼

●
▼▼ ▼

▼
●

10 mL/g

●
▼ ▼

●▲ ●
▲

▼▲

▼

▲: Tobermorite 9Å [Ca5(Si6O18H2)]

▲

▲

●

1M NaOH■: Katoite[Ca Al SiO (OH) ]32
48●: Bohemite [AlOOH]

4 mL/g

●

●

■
●

■
■

0

Fig. 4▲

Intenisty [a. u.]

■
●▲ ●
▲

▲

●
■

●

●

10
20
30
40
2θ[CuKα (degree)]50

XRD patterns of product from BF slag in 3
M NaOH solution at 160 °C whensolution/slag ratios were 20, 10, and 5 mL/
g.

■■

■

■

2M NaOH◆: Hydroxysodalite [Na 4Al3Si3O12OH]■▼: Dolomite [(Mg,Ca)(CO ) ]
3 2

■

■
◆
●

◆

▼
■

■

■

◆■■

■

◆

3M NaOH■■< 100 μm●: Portlandite [Ca(OH) 2]●◆▲: Tobermorite 9Å [Ca5(Si6O18H2)]■: Katoite [Ca3Al2(SiO4)(OH)8]▲

Intensity [a. u.]100-300 μm

▲

■▲ ■
●

■▲ ■

●
■▲ ■●
▲ ■■ ▲ ■
■

0

■

●

●

●

●

●

●

500-1000 μm●

0Fig. 51020
30
40
2θ[CuKα (degree)]

◆

■

■
■

■

20
30
40
2θ[CuKα, degree]■

50

XRD patterns of products synthesized from
BF slag in 1–3 M NaOH solution at 160 °C
for 24 h with Al(OH)3 added.

The effect of the solution/slag ratio on the productsynthesized with Al(OH)3 added was investigated.
The slag (5 g) and Al(OH)3 (2 g) were mixed, and the
mixture (0.6, 1.2, and 3.5 g) was treated with 3 M
NaOH solution at 160 °C for 24 h. The XRD patternsof the product synthesized from the slag Al(OH)3
added at various solution/slag ratios are shown in Fig.
7. All products contained katoite, and as thesolution/slag ratio increased, the peaks of katoite
decreased, whereas those of hydroxysodalite andtobermorite increased. It was assumed that thesynthesis of hydrogarnets at 4 mL/g would yield
better results than those at 10 and 20 mL/g.

●

300-500 μm

▲

■

■●

Fig. 6■●▲ ■●▲ ■■ ▲ ■
■

10

■

●

50

XRD patterns of product from BF slag withvarious particle size in 3 M NaOH solution
at 160 °C.
507

GEOMATE – Kyoto, Japan, 3-5 November 2021

20 mL/g▲: Tobermorite 9Å[Ca5Si6O16(OH)2]■: Katoite [Ca3Al2SiO4(OH)8]■: Katoite [Ca3Al2SiO4(OH)8]◆: Hydroxysodalite [Na 4Al3Si3O12OH]■< 100 um◆: Hydroxysodalite [Na 4Al3Si3O12OH]
◆

■

◆

■

■

■
▲
◆

■
▲

■ ▲▲
◆

■

◆

■■

■◆

▲

■◆

◆

■
▲

■
◆

Intensity [a. u.]

Intensity [a. u.]

■
▲

■

■

■

■■

▲ ▲ ■◆

4 mL/g

▲

◆ ■

■

◆ ■

■
■■
■◆

■

■◆

100-300 um

10 mL/g

◆

■

■◆

◆

■

◆

■
■■ ■ ◆ ■300-500 um

■
■ ■

■◆

■

■ ■

■

■

■

500-1000 um
◆

■

■

■
◆

■

◆ ■

■■

■◆

■◆
■

■

0

Fig. 7

10
20
30
40
50
2θ[CuKα (degree)]XRD patterns of product from BF slag with
Al(OH)3 in 3 M NaOH solution at 160 °C for
solution/slag ratios of 20, 10, and 4 mL/ g.

0

Fig. 81020
30
40
2θ[CuKα (degree)]50

XRD patterns of product from mixture of BF
slag of various particle sizes and Al(OH)3 in
3 M NaOH solution at 160 °C.

30
HA adsorption (mg/g)

The effect of particle size on the productsynthesized with Al(OH)3 added was investigated.
The slag (2.5 g) and Al(OH)3 (1 g) were mixed, andthe mixture was treated with 3 M NaOH solution at
160 °C for 24 h. The XRD patterns of the productsynthesized from the slag of various particle sizes
with Al(OH)3 added are shown in Fig. 8. All products
contained katoite, and as the particle size decreased,
the peaks of katoite and hydroxysodalite increased. It
was assumed that the synthesis of hydrogarnets fromslag with smaller particles would yield better results,
due to the higher supply of Ca to mineral phases fromsmaller particles, as shown in Fig. 5.

20

10

0

0

10

20

30

Equilibrium concentration (mg/L)

Fig. 9These results suggest that the product, includinghydrogarnets, can be synthesized from slag withparticles measuring less than 100 m by adding
Al(OH)3 in 3 M NaOH solution at 160 °C for 24 h.

Isotherm of HA adsorption for product.

The linear forms of the Langmuir and Freundlichmodels are expressed as follows:
Ce
C
1
(3)
=
+ e
qe qmax  K L qmax

HA Removal

1ln( qe ) = ln( K F ) + ln(C e ) ,n

The HA adsorption of the product is shown in Fig.
9. The product was synthesized from 2.5 g of the slag(< 100 μm) with Al(OH)3 addition (1 g) in 3 M NaOH
solution (10 mL) at 160 °C for 24 h. As the HA
concentration increased, HA adsorption increased
gradually.
The HA adsorption of the product was determinedbased on isotherm models. Several isotherm models
can be used to describe the equilibrium sorption
distribution, in which two models are used to fit theexperimental data, i.e., Langmuir and Freundlichmodels.

(4)

where qe is the HA adsorption amount at equilibrium(mg/g); qmax (mg/g) and KL (L/mg) are Langmuir
constants related to the maximum adsorption capacity
corresponding to the complete coverage of available
adsorption sites and a measure of adsorption energy(equilibrium adsorption constant), respectively. KF
and n are Freundlich constants.
The results of the isotherm analysis are presented
in Table 2. Our experimental results indicate acorrelation regression coefficient (R2), which is the508

GEOMATE – Kyoto, Japan, 3-5 November 2021

measure of goodness-of-fit, of 0.975 and 0.978 whenthe Langmuir and Freundlich models were used,
respectively. The Freundlich model indicated a better
fit than the Langmuir model, as the former had ahigher R2 than the latter.

Adsorption of Humic Acid, Langmuir, Vol. 33,
2017, pp. 6769-6777.
[6] Feng X., Simpson A. J. and Simpson M. J.,
Chemical and Mineralogical Controls on Humic
Acid Sorption to Clay Mineral Surfaces, Org.
Geochem., Vol. 36, 2005, pp. 1553-1566.
[7] Vreysen S. and Maes A., Adsorption Mechanismof Humic and Fulvic Acid Onto Mg/Al Layered
Double Hydroxides, Appl. Clay Sci., Vol. 38,
2008, pp. 237-249.
[8] Mercury J. M. R., Turrillas X., Aza A. H. andPena P., Calcium Aluminates Hydration in
Presence of Amorphous SiO2 at Temperatures
Below 90 °C, J. Solid State Chem., Vol. 179,
2006, pp. 2988-2997.
[9] Maeda H., Kurosaki Y., Nakayama M., Ishida E.
H. and Kasuga T., Adsorption Behavior of
Hydrogarnet for Humic Acid, R. Soc. Open Sci.,
Vol. 5, 2018, 172023.
[10] Kobayashi S. and Shoji T., Infrared Analysis of
the Grossular-Hydrogrossular Series, Mineral. J.,
Vol. 11, 1982, pp. 331-343.
[11] Passaglia E. and Rinaldi R., Katoite, A New
Member of the Ca3Al2(SiO4)3-Ca3Al2(OH)12
Series and a New Nomenclature for theHydrogrossular Group of Minerals, Bull.
Mineral., Vol. 107, 1984, pp. 605-618.
[12] Sacerdoti M. and Passaglia E., The Crystal
Structure of Katoite and Implications Within the
Hydrogrossular Group of Minerals, Bull.
Mineral., Vol. 108, 1985, pp. 1-8.
[13] Fujita S., Suzuki K. and Shibasaki Y., The Mild
Hydrothermal Synthesis of Hydrogrossular from
Coal Ash, J. Mater. Cycles Waste Manage., Vol.
4, 2002, pp. 41-45.
[14] Fujita S., Suzuki K., Shibasaki Y. and Mori T.,
Synthesis Hydrogarnet from Molten Slag and its
Hydrogen Chloride Fixation Performance at
High-temperature, J. Mater. Cycles Waste
Manage., Vol. 4, 2002, pp. 70-76.
[15] Nippon Slag Association (NSA), The Annual
Report of Statics of Iron and Steel Slag 2018,
http://www.slg.jp/statistics/report.html[16] Wajima T. and Munakata K., Synthesis of 11A
Tobermorite from Blast Furnace Slag Using
EDTA, J. Ion Exch., Vol. 21, 2010, pp. 171-174.
[17] Wajima T., Oya K., Shibayama A., Sugawara K.
and Munakta K., Synthesis of Hydrocalumitelike Adsorbent from Blast Furnace Slag Using
Alkali Fusion, ISIJ Int., Vol. 51, 2011, pp. 11791184.
[18] Moriguchi T., Yano K., Tahara M. and YaguchiK., Metal-modified Silica Adsorbents for
Removal of Humic Substances in Water, J.
Colloid Interface Sci., Vol. 283, 2005, pp. 300310.

Table 2 Parameters and correlation regression forLangmuir and Freundlich models
Langmuirqmax
KLR2
192.3 0.006 0.975

Freundlich
n
KF
R2
1.09
1.50 0.978

CONCLUSIONS
BF slag was converted into a product that
included hydrogarnets to remove HA from theaqueous solution. The results are summarized asfollows:‧BF slag with smaller particles can be converted into
a product including hydrogarnets via hydrothermal
treatment with 3 M NaOH solution at 160 °C for 24 hwith Al(OH)3 addition, when the solution/slag ratio
was 4 mL/g.
‧The product can remove HA from an aqueous
solution.
‧The HA adsorption of the product was fitted to the
Freundlich isotherm model.
This study demonstrated that a novel hydrogarnetcontaining adsorbent capable of removing HA from
aqueous solutions can be synthesized from BF slag
through an alkali reaction with Al(OH)3 addition.
These results suggested that new recycling
technology for steel making and new adsorbent for
HA removal in water treatment.
REFERENCES[1] Tang W. Z. and Tassos S., Oxidation Kinetics andMechanisms of Trihalomethanes by Fenton’s
reagent, Water Res., Vol. 31, 1997, pp.1117-1125.
[2] Jucker C. and Clark M. M., Adsorption of Aquatic
Humic Substances on Hydrophobic Ultrafiltration
Membranes, J. Membr. Sci., Vol. 97, 1994, pp.
37-52.
[3] Wei J., Gao B., Yue Q., Wang Y., Li W. and Zhu
X., Comparison of Coagulation Behavior andFloc Structure Characteristic of Different
Polyferric-Cationic Polymer Dual Coagulants in
Humic Acid Solution, Water Res., Vol. 43, 2009,
pp. 724-732.
[4] Chen J. P. and Wu S., Simultaneous Adsorptionof Copper Ions and Humic Acid Onto an
Activated Carbon, J. Colloid Interface Sci., Vol.
280, 2004, pp. 334-342.
[5] Libbrecht W., Verberckmores A., Thybaut J. W.,
Voort P. V. D. and Clercq J. D., Tunable Large
Pore Mesoporous Carbons for the Enhanced
509

GEOMATE – Kyoto, Japan, 3-5 November 2021

[19]

510

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

INTEGRATED APPROACH BASED ON FLUID PHENOMENAFOR BENTONITE SUBJECTED TO THERMAL ACTION
Tomoyoshi Nishimura1
1

Department of Civil Engineering, Ashikaga University, Japan
ABSTRACT

Thermal characterization of geotechnical materials is essential for many applications to mechanical prediction
such as deformation, seepage and strength capacity, which is possible to reflect to safety design associateenvironmental structure consist of soils. Interprets to thermal characterization have been supplied and computed
to ecosystem service recently. Focusing on the computation of thermal properties, analyzing the effect, which isimportant for solution of environmental geotechnical problems. This study conducted out some experimental tests
using developed apparatus to compacted unsaturated sodium bentonite, and it is most important to represent
thermal characteristic for material related to geotechnical environmental practice. Thermal characteristic
parameters consist of strength, occurrence of fluid pressure, hydro-conductivity such as air/liquid flow. Sodium
bentonite has been widely used for thermal-hydro-mechanical-chemical properties to barrier system in radioactive
disposal waste facility, which is constructed at quite deep underground. Some experimental works have contributed
to interpret of thermal properties such as decreasing strength and developing of hydro conductivity (i.e. coefficientof permeability). The strength properties have indicated only as thermal proletaries. Producing fluid pressure in
void structure such as macro-micro aggregate is required for establishing mathematical models in order to predict
safe design to barrier system. This study verifies increment of fluid pressure due to heating under isotropic
conductivity for temperature range is from 20 C to 80 C. Increment of fluid pressure induced that absorbing
performance offer reduction, as well as decreasing of strength resistance.
Keywords: Bentonite, Heating, Volume change, Pore pressure, Unconfined compressive strength, Relative
humidity

INTRODUCTION
Kunigel V1 was used in this testing program
which was sodium bentonite, and is product by
Kunimine Co. Ltd. Mechanical properties for Kunigel
V1 was measured as one of representative materials
consist of artificial barrier system in previous many
works. Having high content of montmorillonite on
chemical properties, a bentonite had absolutely finedcontent larger than 95 %. measured density of soil
particles was 2.733 Mg/m3.
Moreover, SiO2 occupied 62 % as chemicalcomponents. For mixture, silica sand, named Iitoyo
No.4, was used as compositing testing specimen,
which had highly unique grain size distribution
obtained from grain size analysis test. Maximum size
was evaluated as 2.0 mm. The mixture ratio of sandand bentonite was 7:3 in dry weight.
Subsequently the bentonite-sand was adjusted toa required water content of 17.0 %, specimens wascompacted statically in the stiffness steel mold. Sizesof specimens were a diameter of 3.8 cm and a heightof 7.6 cm, respectively with creating a ratio of 2.0 todiameter against height. Initial specimens had a dry
density of 1.600 Mg/m3, void ratio of 0.710 anddegree of saturation of 65.61 % as physical variables.

Thermal characterization of a bentonite such as
geotechnical materials is essential necessary toevaluate maintaining of safety for high level radio
active waste disposal facility constructed at further
deeply underground [1]. This study conducted out
some experimental tests, in which were callibration
test in order to define relationship between
temperature and relative humidity associate tomodified vapor pressure technique, decision of porepressure at undrained condition, investigation ofincreasing for pore pressure through absorbing water
process, deformations properties induced by heating
and observations of reduction to shear resistance.
TESTING PROCEDURE
This testing programs composite that thermalhydration-mechanical properties for unsaturatedsaturated bentonite is mainly investigated in various
measurement using some test apparatus. All of
specimens are Na type bentonite (Sodium bentonite)mixture with sand. Soil materials, and modified
apparatus demonstrated, and testing process isverified as following below sections.
Soil materials used in this study510

GEOMATE – Kyoto, Japan, 3-5 November 2021

Measurement
humidityoftemperatureandrelative●Potassium Sulfate 〇Potassium Nitrate▲Ammonium Dihydrogenphosphate△Sodium Chloride■Potassium Chloride□Magnesium Nitrate ◆Magnesium Chloride◇Lithium Chloride
Relative humidity is measaured using samll tem & RH
smal logger (Producted by KN-Lab.com

Relative humidity, ％To measure temperature and relative humidity
surrounding bentonite is necessary to consider theeffort to mechanical properties for deformation whileheating process. A small temperature & humidity
logger (Product by KN-Labs Co. Ltd), in which had adiameter of 17.0 mm, thickness of 6.0 mm and aweight of 3.3 g. Instructions are exhibited asfollowings; temperature range is from minus twenty
C to positive eighty C and relative humidity ispermitted from zero % to 95 % depending on
environmental temperature. Each physical quantity isenough accuracy that 0.1 C in temperature and 0.5 %in relative humidity. Various chemical substances arelocated with a small temperature & humidity logger
into steel chamber, which have a subjection to create
specialized relative humidity, and relative humidity
against chemical substance is indicated into testing
code: JGS 0151-2009 corresponding to vapor
pressure technique for Test method for water
retentivity of soils. Subsequently, the logger wasplace into specimen, and it was composed two
separated specimens. Decision of relative humidity
for void structure revealed with processing increment
temperature.

100
75
50
25
0
0

20

40

60

80

100

Temperature, degree Celsius

Fig. 1

Calibrationofrelativecorresponding to temperature.

humidity

all mechanical tests associated to heating efforts havefocused on resistance reduction and translation ofstress-strain behavior which many cracks in bentonite
caused by heating at least in common. Thermal efforts
not only caused producing cracks, but also significant
deformations for bentonite is observed that is
obviously influence to changing of shear resistance or
stability. This testing program prepared to conductedout applying heating and controlling relative
humidity that unsaturated bentonite placed into a steel
mold with difference chemical substance above
mentioned in order to control relative humidity basedon vapor pressure technique as one of representative
suction controlling methods.

Measurement of pore pressure under isotropic
heating conductivity
Radioactive waste in packing induced significant
high heating due to chemical reaction, which is madein high carbon steel, has quite strong stiffness. It is
predicted that some efforts applied to bentonite withinbarrier system. This study conducted out that porepressure in further small void structure are measured
using developed pore pressure sensor (PHL-A3MPA-A, Product by KYOWA Co. Ltd). Instruction
for the sensor provided that capacity is 3.0 MPa,
permitted temperature range is from minus 40 C to150 degrees Celsius and accuracy is less than 0.3 %RO. Tow sensors were prepared and installed into
upper and lower portion on steel mold. A steel moldwith the developed pressure sensors locate inthermostat oven, and atmospheric temperature
controlled from twenty degrees Celsius to eighty
degrees Celsius with step by step. Requiretemperatures are 20, 40, 60 and 80 C, and thedifference is 20 C.

TEST RESULTS
Test results provide calibration of relationshipbetween temperature and relative humidity using atemperature & humidity small logger up to 80 C.
Producing increment of relative humidity in specimen
with temperature observed, and pore pressure in
specimen increased due to isotropic conductivity
heating. Deformations for unsaturated bentonite
induced by both heating and relative humidity
alternation obviously verified, and unconfinedcompressive strength properties described according
to heating efforts.

Measurement of deformation due to heating andchanging of relative humidity

Calibration for relative humidity variation withincrement temperature using temperature &humidity small logger

It is often found out that interpretations of shear
strength for unsaturated compacted bentonite havebeen moderated on previous experimental works
corresponding to difference heating methodology as
thermal properties in couple phenomena. Almost of

A steel mold was prepared that some chemical
substances were put in order to investigate relative
humidity changing. The mold was placed in
511

GEOMATE – Kyoto, Japan, 3-5 November 2021

Relative humidity in specimen, ％100

75〇Heating from 20 degrees
Celsius▲Cooling from 80 degrees
Celsius50
Kunigel V1 mixture sandMizture ratio 7:3
Dry density 1.600Mg/m3
Water content：17.0％
Diameter 38mm
Height76mm

25

Heating triaxial apparatus
Isotorpic conductivity
Undrained-unexhast condition
samll tem & RH smal logger (Producted by
KN-Lab.com

Photo. 1 Measurement of relative humidity in
specimen using a small tem & RH logger.

0
0

20

40

60

80

100

Temperature, degress Celsius in specimen

Fig. 2

achievement to maximum temperature of eighty
degrees Celsius. While cooling process as opposite
process, relative humidity was measured, and it had
determined that decreasing for relative humidity wasconfirmed such as opposite behavior comparison toheating process. A few differences are identified
though heating process and cooling process, andexisting a reversibility for relative humidity
associated to change the temperature is applicable on
experimental measurement. Finally, when back totwenty C, relative humidity of 75.0 % was determined,
and it was coincident to initial relative humidity
before heating.

Changing of relative humidity in unsaturated
bentonite with temperature increment anddecrement.

thermostat oven, in which permitted a range fromtwenty degrees Celsius to eighty degrees Celsius.
Eights difference chemical substances were used toverify relative humidity corresponding to changing oftemperature that obtained result was described asshown in Fig. 1. Determined relative humidity has arange from 95 % to 9.0 %. All of chemical substances
have common tendency which is reduction withincrement of temperature regardless of relative
humidity at twenty degrees Celsius. Much reductionof relative humidity was indicated in such tendency
when Magnesium Nitrate was placed in the mold,which showed 54.7 % - 40.2 % reduction in relative
humidity. Calibrated results are considerable useful topredict or evaluate relative humidity on heatingprocess.

Process of pore pressure due to isotropic
conductivity heating for unsaturated bentonite
It is generally that investigation andinterpretations for swelling pressure properties of
unsaturated bentonite have been studied on
experimental works, establishing mathematicalmodels and suggesting further parameters to
prediction with safety aspects. Thus, swelling
properties is possible to applicable to prediction
correct of safety for artificial barrier system as one ofthermal-hydration-mechanical-chemical
coupling
constitutive behavior. Other hands, investigation ofevaluation the pore pressure in bentonite due to highheating is necessary to achieve completely
understanding scientific phenomena to bentonite
associated to various influence factors. This testing
programs included that directly measurement was
adopted using a developed steel mold, in which had
two pressure sensors was install on the lateral surface.
The developed steel mold was placed into athermostat oven as shown in Photo. 2, which had anoven space of 154 liters (i.e. a wide of 600 mm, a
length of 600 mm and a height of 430 mm), and atemperature controlling range 60 C to 260 C inpossible. A distance is 19.0 mm from each top andbottom of the mold extremity that equipped pressure
sensor was correct. The mold had supplied isotropic
conductivity heating in the oven. An inspection hall
was produced carefully, and two electrical codes
having coating connected to pressure sensor, watersupply nylon tube having heat resistance are though

Changing of relative humidity in unsaturated
bentonite
increment
anddecrementintemperature
Beyond establishing calibration relative humidity
above mentioned, measuring relative humidity in a
specimen simultaneously that illustration was shownin Photo. 1, and consist of two mass and small logger,
and they was covered by membrane with a thicknessof 2.5 mm. Subsequently, the specimens were put ona pedestal carefully, and membrane covered them.
Heating controlling apparatus applied heating to
specimens at isotropic thermal conductivity up to
eighty C. To measure relative humidity accordingwith increasing the temperature in specimen issignificant procedure, and it promoted to convey
physical factor corresponding to thermal-hydration
phenomena for bentonite. Initially, relative humidity
measured a 75 % in rough, and subsequently, slightincrement of temperature had produced insignificant
increasing of relative humidity till 54.5 C as shown in
Fig. 2. Relative humidity of 88.4 % was measured at
61 C beyond 54.5 C that it was distinct increasing. It
found out that small growing was remained during
512

GEOMATE – Kyoto, Japan, 3-5 November 2021

Absorbed water, cc

10
7.5
5
〇 20 degrees Celsius□ 40 degrees Celsius△ 60 degrres Celsius

2.5
0Photo. 2 Installed pore pressure sensors at steel mold,thermostat
ovenandburetteformeasurement of absorbed water.

0

Fig. 5

10

15

Deference of absorbed water behavior withvarious temperature.

200
40〇 Distilled water in mold
□ Atmosphere in moldPore pressure at low portion,kPa

Pressure in steal mold, kPa

5
Elapsed days

80 degrees Celsius

100

0
0

Fig. 35

10
Elapsed days

15

20

Occurrence of pressure in mold at 80 C.

Fig. 6500
Pore pressure in saturation,kPa〇 Distilled water□ Salt water: Sodium concentration 3.5 ％80 degrees Celsius
Saturated specimen
Kunigel V1 mixture sandMixture ratio 7:3
Dry density 1.600Mg/m3
Water content：17.0％
Diameter 38mm
Height76mm

400
300
200

0

Fig. 410

20
Elapsed days

Occurrence of pressure
bentonite at 80 C.

30

40

for

saturated

Pressure senso at
upper portion
measured that porewater pressure
remained zero kPa

20
10

0
0

5
Elapsed days

10

15

Changing of pore pressure at 20 degrees
Celsius.

prepared in the mold, in which absorbed water was
variance in fact. One is distilled water and other one
was salt water with 3.5 % of salinity concentration, in
which is similar to that of nature sea water. It wascausing that eighty degrees Celsius produced excess
pore pressure to two saturated specimens as shown in
Fig. 4. Further increments of pore water pressure wasprovided to both containing and no containing insalinity components as comprehension. Maintained
pore pressure of 36 kPa was determined at end of test
for two saturated bentonites, and it was similar. twosaturated bentonites, and it was similar.

100

0

20 degrees Celsius

30

Producing pore pressure with accompanying ofadsorbed water

the surface of wall in thermostat oven.
Firstly, two cases were prepared that distilledwater was full up into mold, and other one was empty(i.e. atmospheric only) in order to measure eachproducing pressure to two difference fluids.
Beginning of heating had practice from steady twenty
degrees Celsius that it clearly found out increment atonce as shown in Fig. 3, and it was easy to consider
pressure phenomena for obviously growing thepressure. Comparison two pressure behavior withelapsed day that distilled water was rapidly growing,
and had a remaining large pressure than that of
atmospheric (i.e. data sets distribution is indicated toatmospheric data sets.). The difference pressure
maintained 26 kPa at least while a testing. Then,decision pore pressure in saturated bentonite wasadopted that two differences saturated bentonite were

It is very interesting to see pore pressure withabsorbing of water as swelling properties underisotropic conductivity heating. Heating was applied tounsaturated bentonite in steel mold as shown in Fig.
2, and water supply was adopted from bottom of
specimen through nylon tube with temperature
resistance. The tube connected to burette installed a
difference voltage sensor, in which placed outside ofthermostat oven. Magnitude of absorbed water into
specimen was automatically measured, and twopressure sensors as explained above measured porepressure accuracy to each temperature (i.e. 20, 40 and
60 degrees Celsius). Well, adsorbed water changing
was indicated under difference temperatures withswelling as shown in Fig. 5. Large absorbing that513

GEOMATE – Kyoto, Japan, 3-5 November 2021

15

40Volume strain, ％Pore pressure, kPa

40 degrees Celsius

30〇Pore pressure at upper
portion□ Pore pressure at lower
portion20

Increment in pore pressure wasprovided due to heating from 20
degrees Celsius to 40 degrees
Celsius

10

12.5
10
7.5
5
2.5
0

0
0

Fig. 7

5
Elapsed days

10

0

15

Changing of pore pressure at 40 degrees
Celsius.

Fig. 95
10
Axial strain, %15

Relationship between axial strain andvolume strain.
15

60 degrees Celsius

30

Volume strain, ％Pore pressure, kPa

40〇Pore pressure at upper
portion□ Pore pressure at lower
portion20

Increment in pore pressure wasprovided due to heating from40 degrees Celsius to 60
degrees Celsius

1000Fig. 8

Shrinkage

5
Elapsed days

10

10
Shrinkage

5

0

15

0

5
10
Strain in radius, ％15

Changing of pore pressure at 60 degrees
Celsius.

Fig. 10 Relationship between strain in radius andvolume strain.

decline was rapidly at twenty degrees Celsius, andsubsequently increments were smoothly whencommencement at 40 degrees Celsius. Similarity,
specimen with 60 degrees Celsius described smoothincrement, and tow absorbed phenomena was closely.
Simultaneously, pore pressures at two measurement
points were measured that pore pressures indicatedvariation with elapsed day at lower portion of
specimen as shown in Fig. 6. Neglection that no
measurement was at upper potion at twenty degrees
Celsius. Also, pore pressures were observed at tow
portions (i.e. upper potion and lower potion) as shownin Fig. 7. Pore pressures grew rapidly at beginning ofheating approaching to 40 degrees Celsius. Porepressures approached to 11.0 kPa as maximum
pressure, and it was steady phenomena during fivedays. Moreover, temperature increased to 600
degrees Celsius as shown in Fig. 8, and pore pressure
increased to 16 kPa, and pore pressure at lower
portion was larger comparison to pressure upper
portion. It was common tendency with limitation in
20, 40 and 60 degrees Celsius.

pre-consolidation pressure) and modulus rigidly (i.e.
stiffness). It is however few investigations that
deformation for unsaturated bentonite due to thermal
effort verified on experimental works and solve using
prediction mathematical models [2]. It is lack tosuggest some parameters associated to volume
shrinkage tendency and volume shrinkage index. This
study realized through volume change test forunsaturated bentonite, which both heating andrelative humidity changing applied to all specimens
in a steel mold place in a thermostat oven. Threedifference chemical substances were put into the steel
mold, that were Magnesium Chloride, Sodium
Chloride and Ammonium Dihydrogen phosphate.
Each relative humidity was corresponding to 55.0 %,75.0 % and 93.0 % at twenty degrees Celsius. As
above mention (Fig. 1), each relative humidity varies
depending on temperature. After equilibrium eachtemperature and relative humidity, a diameter andheight were collected, and calculated strain to radius
way and axial way. In addition to compose each strain
to estimate total volume shrinkage strain. To better
find some tendency regard to producing deformation
for unsaturated bentonite is so interesting and isfurther effectively to understanding bentonite
properties deeply. Volume shrinkage strains
corresponding to each axial strain and strain in radius
way are shown in Figs. 9 and 10. Strain in radius
exhibit to be close to volume shrinkage strain
compare to axial strain, and it was 3 times to strain inradius way. It has been moderated the producing

Volume shrinkage for unsaturated bentonite
induced by heating and changing of relative
humidity
General speakers have often demonstrated that
unmatured benitoite subjected to heating issignificant reduction in shear resistance, changing
stress-strain properties, decreasing yield stress (i.e.
514

GEOMATE – Kyoto, Japan, 3-5 November 2021

Discussion for shear resistance subjected toheating and changing of relative humidityAsmechanicalcomponent,
unconfinedcompression test conducted out for unsaturated
bentonite subjected to heating and developed relative
humidity, and all specimens had further cracks asshown in Photo. 3. All specimens were applied
required temperatures using a heating triaxial
compression triaxial apparatus, while temperatures
were 20, 40, 60 and 80 degrees Celsius. Reductionsof unconfined compressive strength was provided
through four difference temperatures as one tendency
as shown in Fig. 12. Volume shrinkage and muchcracks are significant factor to decrease the resistanceof unsaturated bentonites.

Photo. 3 Observed cracks on surface for unsaturated
bentonite subjected to heating.

Volume strain, %18
Magnesium
Chloride

Ammonium
Dihydrogenphosphate

12

SodiumChloride6

CONCLUSIONS

Shrinkage

0
0

20

40

60

80

This study conducted out some experimental
tests that the subjections were to realize thermal
properties for bentonite, and some developed tests
revealed important characters including heating
efforts. The obtained resukts are summaried asfollowing: Growing of pore pressure was induced byheating for saturated bentonite, that is confirmed toboth distilled water and salinity water. Pore pressureaccomplishing with absorbed water through sweing
process obvioulsy increased according to incremantof temperature. Further volume shrinkage wasderived due to heating and changing of relative
humidity, that relationship between volume shrinkage
strain and strain in radius way verified as one uniquetendency. Fiinally, the unsaturated benotite subjected
to heating effort and chnaging relative humisity
exhibited reduction to unconfined cmompressive
strength.

100

Relative humidity, %Fig. 11 Increment of shrinkage with decreasing ofrelative humidity.

Unconfined compressive
strength, kPa

8000●〇Kunigel V1 mixture sandMizture ratio 7:3
Dry density 1.600Mg/m3
Water content：17.0％
Daiameter 38mm Height76mm

6000▲ Kunigel V1 mixture sandMizture ratio 7:3
Dry density 1.72Mg/m3
Water content：17.0％
Daiameter 38mm Height76mm●Magnesium Chloride▲Ammonium Dihydrogenphosphate〇Sodium Chloride

4000

2000

0
0

20
40
60
Temperature, degree Celsius

Fig. 12 Reduction of unconfinedstrength due to heating.

80

100

compressive

ACKNOWLEDGMENTS
The author is grateful to Dr. Shuichi
YAMAMOTO (Obayashi Corporation, Japan) for
supporting to establish pore pressure measurement
system using developed pressure sensor. Also, thisstudy has been supported by a Grant-in-Aid for
Scientific Research (No. 21K04259) from JSPS.

cracks for unsaturated bentonite subjected to heating
or increment of temperature, and significant cracks
are confirmed in this study with similar to previous
investigation as shown in Photo 3. Many cracks arewidely sprayed on the surface of specimen, and they
are either continuous distribution or discontinuous
distribution. Volume shrinkage strain is discussed
like a thus cracks distribution. Relationship between
relative humidity and volume shrinkage strain wasverified with three groups used in Magnesium
Chloride, Sodium Chloride and Ammonium
Dihydrogen phosphate as shown in Fig. 11. It isseemed to be reduction in volume shrinkage strain
with increasing of relative humidity that has widely
scatter, and it is difficult to establish applicable trend.

REFERENCES[1] Bucher, F. and Müller-Vonmoos, M., Bentonite asa containment barrier for the disposal of highly
radioactive wastes, Appl. Clay Sci. 4, No. 2, 157–
177, 1989.
[2] Lloret, A. and Villar, M., Advances on theknowledge of the thermo-hydro-mechanicalbehaviour of heavily compacted ‘FEBEX’bentonite. Phys. Chem. Earth, Parts A/B/C 32, No.
8, 701–715, 2007.

515

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

STUDY OF SOIL ERODIBILITY UNDER THE POTATO FARMINGSYSTEM IN THE UPSTREAM LEMBANG WATERSHED
Aprisal1, Bambang Istijono 2, Irwan Darpis1, Mimin Harianti1, and Teguh Huria Aditia3.
1

Soil science of Agriculture Faculty Andalas University, Padang Indonesia
Hydrology laboratory technique Faculty of Andalas University Padang Indonesia
3
Postgradute of Soil Science of Agriculture Faculty Andalas University Padang, Indonesia
2

ABSTRACT
Farmers cultivate potatoes intensively in the upper Lembang watershed, i.e. cultivate the land every planting
season. The soil management system does not apply soil conservation methods, so it is suspected that there has
been a decline in soil fertility. The purpose of this study was to examine the soil properties of potato farming inseveral land forms. The method used is the land survey method. Soil sampling was done by purposive random
sampling and descriptive. Soil samples taken were disturbed soil samples for analysis of soil physical properties(texture, bulk density, total pore space, permeability) while direct measurements in the field were effective soil
depth and infiltration rate. Meanwhile, undisturbed soil samples were used to analyze the chemical properties ofC-organic soil. Soil analysis was carried out in the laboratory of the Department of Soil, Faculty of Agriculture,
Andalas University. To determine the soil characteristics of the landform is to analyze the soil parameters using
Minitab 17 software. The results showed that the value of soil erodibility in the production center area is strongly
influenced by c-organic variables, dust, bulk density, total pore space, fine sand fraction, fraction clay and soil
permeability. The value of soil erodibility includes low to moderate criteria. It was concluded that the soil condition
was still quite good.
Keywords. Potato farming, soil erodibility, soil degradation, conservation
INTRODUCTION

gentler slope will cause the erodibility of the soil tobe higher. This will causethe amount of fine soil
particles carried by surface runoff [3].Therefore, soil
erodibility is the sensitivity of the soil to erosion, thehigher the erodibility value of a soil, the easier the soil
is to erode. Soil erodibility is influenced by soil
texture, soil structure, organic matter, andpermeability [4]. Soil erodibility factor shows theresistance of soil particles to peeling and transport ofsoil particles by the kinetic energy of rainwater [5].
Furthermore, it was found that the average soil loss
was negatively correlated with clay content but
positively correlated with very fine sand and silt plus
very fine sand content [6].
Soil physical andchemical properties are often influenced by organic
matter content, by building aggregations, soil organic
matter affects soil erodibility.
This study aims to study the dominant factors that
affect the value of soil erodibility in potato production
centers in the upstream sub-watershed of Lembang.

Intensive land use in agricultural land, causeschanges in soil properties, such as soil resistance tothe destructive power of rain kinetic energy or also
known as rain erosivity energy. Soil resistance to thedestructive power of raindrop energy greatly
influences soil erosion or erosion of the soil surface,causing the soil surface layer, especially the tillage
layer to become thinner. This is because erosion
carries parts of the soil from one place to another,
especially on lower slopes. According to the soil loss
equation, the rate of soil erosion can be more
influenced by rainfall characteristics, land slope andslope length, land cover, and management than soil
properties. However, some soils erode more easily
than others despite the similarity of the factors of thesoils. This difference, influenced by the properties ofthe soil itself, is called soil erodibility [1]. Soilerodibility is strongly influenced by soil properties
such as: soil texture (very fine sand, silt, and clay),soil structure, organic matter content, andpermeability [2].
The shape of the land cultivated by farmers now
is wavy, so this causes a variety of soil properties. The
cause is the effect of erosion on land with sloping
slopes and accumulation of erosion material on lower
or flatter slopes transported by surface runoff. The
soil is easily eroded by surface runoff due to its
height. Varied landforms and intensive use will cause
soil erodibility values to also vary. Land that has a

MATERIALS AND METHODS
Research sites
This research was conducted in a potato
production center located in the upstream of the
Lembang sub-watershed. More precisely in thevillages of Kampung Batu Dalam and Batu Bajanjang
with a geographical position of 100o42'0” E 100o44'0”E and 1o0'0”S-1o0'2”S (Figure 1), Solok
district, West Sumatra, Indonesia.
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GEOMATE – Kyoto, Japan, 3-5 November 2021

Figure 2. Map the research location.

Inseptisol (Table 1). The study was conducted on V
groups of farmers based on the physiography of theland, the groups are; groups I, II, III, IV and V. GroupI is flat, II is slightly sloping, III is sloping, IV is flat,
V is sloping. Therefore, it is the center of potato
production. Soil samples taken are undisturbed soil
samples with a ring sample for analysis of soil
physical properties. For analysis of soil chemical
properties, disturbed soil samples were taken using a
Belgian soil drill.

Research methods
Soil sampling.
This research was conducted by means of a fieldsurvey in the potato production center area in theUpper Lembang sub-watershed. Samples were taken

Table 1. Groups, soil types and sampling points
Location
I
II
III
IV
V

Type ofsoil
Andisol
Andisol
Inceptisol
Inceptisol
Inceptisol

Soilsampling
32422plan type
Potatoes
Potatoes
Potatoes
Potatoes
Potatoes

Soil analysis in the laboratory.
Soil samples from the field were analyzed in thesoil laboratory, for analysis of soil physical properties
for variables; texture and permeability, bulk density,
total pore space. The variable soil chemical properties
analyzed were organic matter.

Figure 2. Photos of potato fields in the research
location.
based on five groups of land units (Table 1). Based on
the soil map, the potato production center area isdominated by two soil orders, namely Andisol and
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GEOMATE – Kyoto, Japan, 3-5 November 2021

Soil Variable Calculation
Soil erodibility.
Soil variable data from the results of laboratory
analysis is used to calculate the value of soil
erodibility using the soil erodibility formula [1]. Soil
Erodibility (K) The calculation, soil erodibility is
obtained from the results of field observations andlaboratory analysis. The soil properties tested wereorganic matter content, texture, structure, soil
permeability, which were then calculated using theequation 1:

RESULTS AND DISCUSSION
Soil properties
From the soil analysis in the laboratory, soil
variables were obtained to calculate the soil
erodibility value (Table 2). The results of the analysisof the physical and chemical properties of the soil at
the research site (Table 2) indicate that the soil in thisarea is dominated by soil with a dusty loam texture
and crumb soil structure, so that potatoes are very
suitable to grow in this area. The soil is quite crumbly
and plants can grow and develop well. In this soil
condition, potato roots and tubers can develop
optimally, so that potato yields are optimal. Soils thathave loose properties are usually easily dispersed by
the kinetic energy of rainfall.

description: M = value of (% dust + % very fine sand)(100 - % clay)a = organic matter (% C organic x 1.724)
b = soil structure valuec = value of permeability

Soil Erodibility
Based on the variable data of soil properties(Table 3), the soil erodibility value was calculatedusing the formula. From the calculation results of thesoil erodibility value, the soil erodibility value isobtained as shown in Table 4. This soil erodibility
value is compared with the criterion value (Table 2).
It turns out that the value of soil erodibility in thispotato production center area is dominated by very
low and low criteria. This means that the soil in the
potato production center area has a fairly good ability
to withstand the threat of soil erosion. This is due tothe high influence of organic matter on the soil so thatit can become a cemented agent for soil fractions.
Further research [6]. in Iran also stated that organic
matter by coating soil particles and creating a waterrepellent layer that prevents soil release and keeps
soil particles flocculated, thereby reducing erodibility
and soil erosion. In addition, the factor seen from therelationship between organic matter and erodibility
has a negative correlation, meaning that increasingorganic matter will reduce the value of soil
erodibility..

Table 2. Criteria for soil erodibility value
No
1
2
3
4
5
6

Class
Very low
Low
Currently
A bit highTallVery highCriteria
0.00 – 0.10
0.11 – 0.20
0.21 – 0.32
0.33 – 0.43
0.44 – 0.55
0.56 – 0.64

The erodibility value obtained from thecalculation of the soil properties in each land unit iscompared with the criteria for the erodibility value ofthe soil (Table 2).
Principle component analysis
Analysis of the main components of the soil fromthe erodibility characteristics of the soil. Principal
Component Analysis (PCA), is used to: determine
what factors most influence the value of soil
erodibility. The researcher also uses this method todetermine the main factors that affect the value by
using PC with an eigenvalue of more than 1 as theindependent variable. Therefore, this value is highly
correlated with the independent value in PC, [7].
Researchers used Minitab 17 to analyze data
variables.

Further research [8] showed that soil organic
matter and clay content were the main factors
affecting soil anti-erodibility in the Loess Plateau andthe percentage of stable aggregate to water was thebest indicator.

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GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 3. Soil properties from the location of the potato production center area in the upstream
sub-watershed of Lembang.
Location of
The
C-org
Sand
Dust
Clay
Bv
Permeability Total Poresampling
%
%
%
%
g/Cm3
(cm/hour)
%
1
2
3
4
5
6
7
8
9
10
11
12
13

6.94
7.08
4.45
6.99
8.31
9.26
7.56
9.9
10.18
9.4
11.59
11.74
8.62

6.1
12.87
20.25
4.29
5
5.78
5.68
5.68
7.67
13.79
6.62
5.26
6.75

68.86
70.1
57.87
62.94
62.48
57.83
61.52
51.51
66,48
67.44
51.13
44.73
47.38

24.84
16.7
18.26
26.1
22.23
33.25
23.9
23.9
8.02
15,17
24.21
42.07
17.92

0.53
0.54
0.7
0.66
0.65
0.85
0.46
0.65
0.78
0.56
0.97
0.92
0.91

24.79
2.86
7.31
7.54
26.18
1.95
0.35
12.51
4.44
21.85
2.14
10.96
21

80
76.02
73.58
75.09
75.47
67.92
82.64
75.47
70.57
78.87
63.4
65.28
65.66

Table 4. Soil properties and soil erodibility values in the potato production center area in the upstream
Lembang sub-watershed
Locationsampling
1
2
3
4
5
6
7
8
9
10
11
12
13

fine sand
%
6.1
12.87
20.25
4.29
5
5.78
5.68
5.68
7.67
13.79
6.62
5.26
6.75

Dust
%
68.86
70.1
57.87
62.94
62.48
57.83
61.52
51.51
66,48
67.44
51.13
44.73
47.38

Clay
%
24.84
16.7
18.26
26.1
22.23
33.25
23.9
23.9
8.02
15,17
24.21
42.07
17.92

a

b

c

M

K

6.94
7.08
4.45
6.99
8.31
9.26
7.56
9.9
10.18
9.4
11.59
11.74
8.62

3
2
2
2
3
2
2
3
2
3
2
3
3

1
3
2
2
1
4
5
1
3
1
3
1
1

5634.06911.46385.54968.35247.94246.05113.94352.26820.36890.74376.92895.94443.00.15 r
0.13 r
0.19 r
0.12 r
0.12 r
0.09 SR
0.15 r
0.07 SR
0.05 SR
0.09 SR
0.01 SR
0.02 SR
0.11 rDescription: K = erodibility, M = value of (% dust + % very fine sand) (100 - % clay) a = organic matter (% C organic x 1.724)
b = soil structure value c = permeability value, SR = very low, r = low

Table 5. The eigenvalues from the analysis of soil variables with PCA
Eigenvalue 4,7864 1,2779 1,1122 0,9514 0,4367 0,3426 0,0749 0,0109 0,0070
Proportion 0,532 0,142 0,124 0,106 0,049 0,038 0,008 0,001 0,001
Cumulative 0,532 0,674 0,797 0,903 0,952 0,990 0,998 0,999 1,000

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GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 6. The loading matrix value from analysis of soil biophysical properties
Variable
Location
K
C-org
Sand
Dust
Clay
Bv
Permebility
Total Pore

PC1
-0,3750,401-0,3950,213
0,366-0,209-0,402
0,066
0,392

PC2
0,098
0,057
-0,1740,670-0,064-0,4250,280-0,376-0,323PC3-0,3560,265-0,301-0,034-0,2640,634
0,107-0,461-0,123

PC4
0,086
0,317
-0,2730,205-0,384
0,122
0,185
0,748
-0,140Principle Component Analysis (PCA)

PC5
0,512
0,040
0,099
0,326
-0,4070,194-0,416-0,1810,459

PC6-0,267-0,351
0,308
0,594
0,311
0,461
-0,0320,216-0,024

PC7
0,605
0,286
-0,167-0,029
0,618
0,323
0,163
-0,037-0,098PC8-0,038
0,503
0,501
0,004
-0,007-0,029-0,462-0,011-0,530PC90,123-0,454-0,515-0,047-0,0010,048-0,5480,045-0,454variable of soil properties, namely c-organic, anddust. The second factor (PC2) is sand, and clay. While
the third factor (PC3) is the rate of permeability. His
previous research [9]. This means that these threecomponents are representative for all variations that
affect the value of soil erodibility in the potato
production center area. The first factor (PC1) is avariable of soil properties, namely c-organic, anddust. The second factor (PC2) is sand, and clay. While
the third factor (PC3) is the rate of permeability. His
previous research [9]. stated otherwise, sand content
has a negative relationship with erodibility andsediment load. Because the mass of the sand particles
is greater, runoff cannot transport them. On the other
hand, the presence of sand particles in the soil causes
an increase in macro pores, water infiltration, andconsequently a decrease in susceptibility to erosion.

Eigenvalues
The determining factor based on the eigenvalue
greater than 1 is retained, but if it is less than 1 thenthe factor is excluded from the model. An eigen value
shows the contribution of the factor to the variance of
all original variables. Only factors with variancegreater than 1 are included in the model. Factors witha variance of less than 1 are not good because theoriginal variables have been standardized, whichmeans the average is 0 and the variance is 1 (Table 5).
Main component
To determine which soil properties are more
dominant, the soil erodibility factor is determinedbased on the results of the principle component
analysis (PCA) and can be seen in table 6. From the
PCA analysis, there are three main components that
determine the diversity of soil erodibility, namely
PC1, PC2 and PC3. . Table 6 shows that there arethree factors (PC) having eigenvalues greater than 1,
namely PC1, PC2 and PC3. Of the three factors, it isable to explain the variation of the total data that more
influences the value of soil erodibility in the potato
production center area in the upstream Lembang subwatershed. From the extract of many data variables,
PC1 with an eigen value of 4.78 gives the proportionof influence of 53.2 percent. Then PC2 with an eigen
value of 1.27 gives a proportion of 14.2 percent, andPC3 with an eigen value of 1, 11 contributed a
proportion of 12.4 percent. These three components
cumulatively contribute to explaining the diversity ofthe total data by 79.7 percent. This means that thesethree components are representative for all variations
that affect the value of soil erodibility in the potato
production center area. The first factor (PC1) is avariable of soil properties, namely c-organic, anddust. The second factor (PC2) is sand, and clay. While
the third factor (PC3) is the rate of permeability. His
previous research [9]. This means that these threecomponents are representative for all variations that
affect the value of soil erodibility in the potato
production center area. The first factor (PC1) is a

CONCLUSION
Based on the results of the study, several conclusions
can be drawn;
1. That the resistance of the soil to the erosion of rainerosion is strongly influenced by several variables ofsoil properties, namely c-organic, fine sand, dust, clay
and soil permeability.
2. The value of soil erodibility in the corn production
center area is still in very low to low criteria. Thismeans that the soil in this area is not easily damagedby the erosive energy of rain.
ACKNOWLEDGMENTS
The researcher wishes to express his profoundgratitude to the rector of Andalas University for the
support and providing all the facilities needed to carryout this study. This research was funded by Andalas
University under Research Grant No SP DIPA
023.017.2.677513/2021, 23 Novemver 2020
REFERENCES[1]. Wischmeier, W., Johnson, C., Cross, B. A landof the nomograph erodibility for farmland and520

GEOMATE – Kyoto, Japan, 3-5 November 2021

construction sites. Journal of Soil and Water
Conservation Vol.26,No.3,1971, pp.189-193[2]. Bhandaria, KB, Charles P. Westa, Veronica
Acosta-Martinezb, Jon Cottonb, and Amanda
Cano. 2018. Soil health indicators as affected bydiverse forage species and mixtures in semi-arid
pastures. Applied Soil Ecology 132 (2018) 179–
186[3].. Aprisal, Istijono. Taifur, WD, Indradwipa. 2020
Study effect of land management ofhorticultural on soil erodibility at the upstreamof sumani watershed. International Journal of
GEOMATE, March, 2020, Vol.18, Issue 67, pp.
175-181 ISSN: 2186-2982.
[4]. Arsyad, S. 2000. Soil and water conservation.
IPB Press. Bogor.
[5]. Asdak, Chay. Hydrology and river basin
management. Yogyakarta: Gadjah Mada
University Press.2004.pp1-61[6]. Mazllom, U., Emami, H., Hossain, G., Haghnia.
2016. Prediction the soil erodibility andsediments load using soil attributes. Eurasian J
Soil Sci 2016, 5 (3) 201 - 208[7]. Santoso, S. 2002 "SPSS Multivariate Statistics",
Jakarta: PT. Elex Media Kompitundo[9]. Wang, YM, Guo, PC, Gao, WS, 1994. A study
on soil anti-erodibility in Loess Plateau. Journalof Soil Erosion and Water Conservation 8(4):11-16 [in Chinese].
[10]. Santos, FL, Reis, JL, Martins, OC, Castanheira,
NL, Serralheiro, RP, 2003 Comparative
assessment of infiltration, runoff and erosion ofsprinkler irrigated soils. Biosystem Engineering
86(3): 355-364

521

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

A STUDY ON APPLICATION OF DEEP LEARNING TO WATERENVIRONMENT PLANNING
Shengping Zhang1 and Jie Qi2
1

Professor, Meijo University, Japan; 2Professor, Utsunomiya University, Japan
ABSTRACTThe water environment of the most important watersheds has generally not been improved upon in aconsiderable manner in the last two decades although the Japanese government has made significant management
and improvement efforts, such as increasing sewerage system coverage rates nationwide and installing advanced
wastewater treatment systems. The marginal effects of these direct efforts have been diminishing. This study seeks
to discover the most effective water environment improvement measures in a wider range other than those direct
measures. An artificial intelligence (AI) model has been constructed with Deep Learning technology by applying
the watershed information from the 104 watersheds as the teacher data to train the AI model. The well-trained AI
model is used to identify the effectiveness of all the direct and indirect water-environment-related factors, ranging
from geological/geographical factors, hydrological/hydraulic factors to socio-economic factors. This study
concludes by pointing out that Deep Learning is able to reveal and simulate the complicated relationships between
river management goals and diverse water environment factors and it is also able to make the sensitivity analysis
and the selecting of effective water environment improvement measures much more convenient and reliable. The
aim of this study is to establish a more reliable river environment planning and management methodology.
Keywords: Water Environment Evaluation, Artificial Intelligence (AI), Deep Learning, Sensitivity Analysis

INTRODUCTION

rivers.
The survey has shown that the requirements of theenvironmental quality standards for human healthhave been achieved in almost all monitoring sites forthe last thirty years, and the achievement rate was
99.1% in 2018 [2]. As for the environmental quality
standards for conservation of the living environment,
their achievement rate of BOD had improved
significantly in the first three decades after the survey
started, and compliance rate reached 90% in 2004 forthe first time since the survey started [3].
However, the compliance rate of theenvironmental quality standards for conservation of
the living environment was still 94.1% in 2018 [2],and the water environment of the most importantwatersheds, almost all of which are designated as the
direct-administrative-control areas of Class-A rivers,
generally has not been improved in a considerable
manner in the last two decades, although the Japanese
government has made significant management aswell as improvement efforts, such as increasing
sewerage system coverage rates nationwide,
installing advanced wastewater treatment systems. It
is believed that the marginal effects of these direct
efforts have been diminishing [4]. This study seeks todiscover the most effective water environment
improvement measures in a wider range outside ofthese direct measures. An artificial intelligence (AI)model has been constructed with Deep Learningtechnology by applying the watershed informationfrom the 104 Class-A watersheds as the teacher datato train the AI model.

Water environment evaluation and planning havebeen mainly depended on 1) mathematic models thatsimulate all the physical, chemical and biochemical
processes leading to water environment changes overtime and space and 2) expertise of planners on a
specific river [1]. Mathematic models connect all
direct water environment factors to management
goals, and the final evaluation and decision-making
are usually based on the expertise of planners. In thisplanning process, most of the indirect environment
factors have not been taken into consideration in areasonable and proper manner, and expertise tends to
put much more weights on characters of a specificriver and ignore the common characters of all theother rivers in the same region. How to evaluate andapply the experiences and lessons all the rivers withsome common characters can provide has beenremained to be answered. This study approaches thisproblem by applying the water environment big data
to environment planning.
In Japan, a water quality survey of public water
areas focusing on items that are used inenvironmental quality standards (EQSs) for theprevention of water pollution has been performed
since 1971 under the provisions of the “Water
Pollution Control Law” (enacted in December 1970).
This survey has been conducted by local governments(including prefectures and designated cities) under
the Water Pollution Control Law and also, in the caseof direct-administrative-control areas of Class-A
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GEOMATE – Kyoto, Japan, 3-5 November 2021

O : output from the output layer unit.
Z : input to the output layer unit.
w𝑗 : connection weight between the hidden
layer unit 𝑗 and the output layer unit.
Θ : threshold value of the hidden layer unit 𝑗

ARTIFICIAL INTELLIGENCE MODEL
An artificial intelligence model, specifically aneural network model has been adopted to compose awater environment evaluation method for evaluationor prediction problems due to the suitability of neuralnetwork models [5], [6].

For the unit output function 𝑓(∙) , some
expressions have been proposed. The following
Sigmoid function has been applied frequently.
However, it is not necessarily the best one in terms oflearning efficiency. A testing process for different
output functions is strongly suggested. In this studied
this Sigmoid function has been finally adopted after
careful tests.

Structure of A Neural Network [6]A neural network is a network system constructed
artificially by idealizing the neurons (nerve cells), andconsists of a number of nodes and lines that are called
units and connections (or links) respectively. Basedon the differences in network structures, neuralnetworks generally are classified into two types:
layered networks and interconnected networks. It has
been shown that a layered network is suitable for
evaluation/prediction problems due to its abilities inlearning (self-organization) and parallel processing of
information.
A typical layered neural network, which has alayer of input units at the top, a layer of output units
at the bottom, and a number of hidden layers between
the input layer and the output layer. Connections exist
only between the units in the adjacent layers, andconnections within a layer or from higher to lower
layers are forbidden.

𝑓(𝑥) =

1
1 + 𝑒 −𝑥(5)

Theoretically, the neural network model
expressed by Eqs. (1) through (5) is able toapproximate any non-linear relationship between
inputs and outputs with any degree of accuracy byusing enough hidden layer units and setting
connection weights and thresholds to be appropriate
through proper learning processes [6]. The potentialof this model has been verified with similar problem
to this study [4].
Learning Process of Neural Network Model

Modelling A Neural Network
For a neural network model, the process of setting
the connection weights unit thresholds is called
learning. The term learning here means the selforganization process through which the neuralnetwork model automatically adjusts all theparameters (i.e. all the connections and thresholds) tothe appropriate values, when a series of samples of
input-output data (called teacher data or teacher
signals) are shown to the model. If we consider the
information processing in a neural network model asa transformation of input data to output data, thenmodel learning can be considered to be a process
through which the neural network model gradually
becomes capable of imitating the transforming
patterns represented by the teacher data.
A lot of learning algorithms have been proposed,
and among them the Error Back Propagation
Algorithm is the most widely used and mostsuccessful algorithm. The following is the summaryof the Error Back Propagation Algorithm [7].
Suppose T sets of teacher data are given.

For the sake of simplicity, consider a neuralnetwork consisting of three layers.
Let the unit numbers of the input layer, hidden
layer and output layer be N, M, and 1, respectively.
When an input {𝐼𝑖 , 𝑖 = 1,2, ⋯ , 𝑁 } is given to theunits of the input layer, the inputs and outputs of thehidden layer units as well as the output layer units are
represented as follows.
𝑌𝑗 = 𝑓(𝑋𝑗 ), 𝑗 = 1,2, ⋯ , 𝑀(1)𝑁

𝑋𝑗 = ∑ 𝑤𝑖𝑗 𝐼𝑖 + 𝜃𝑖 , 𝑗 = 1,2, ⋯ , 𝑀

(2)

𝑖=1

𝑂 = 𝑓(𝑍)(3)𝑀

𝑍 = ∑ 𝑤𝑗 𝑌𝑗 + 𝜃

(4)

𝑗=1

Where 𝑌𝑗 : output from the unit 𝑗 of the hidden layer.
𝑋𝑗 : input the unit 𝑗 of the hidden layer.
𝑓(∙) : unit output function.
w𝑖𝑗 : connection weight between the input
layer unit 𝑖 and hidden layer unit 𝑗.
𝜃𝑗 : threshold value of the hidden layer unit 𝑗

(𝑡)

(𝑡)

(𝑡)

{𝐼1 , 𝐼2 , ⋯ , 𝐼𝑁 , 𝑂 (𝑡) ; 𝑡 = 1,2, ⋯ , 𝑇}

(6)

Notice that the teacher data consists of two parts: the(𝑡) (𝑡)(𝑡)input part {𝐼1 , 𝐼2 , ⋯ , 𝐼𝑁 ; 𝑡 = 1, 2, ⋯ , 𝑇} andthe output part{𝑂 (𝑡) ; 𝑡 = 1,2, ⋯ , 𝑇} .
Now consider an initial value523

GEOMATE – Kyoto, Japan, 3-5 November 2021

learning procedures are defined as follows.
[𝑘][𝑘] [𝑘]𝑤𝑖𝑗 , 𝑤𝑗 , 𝜃𝑗 , 𝜃 [𝑘]

, 𝑘=0

(7)

𝛿 [𝑘](𝑡) = (𝑂(𝑡) − 𝑈 [𝑘](𝑡) ) ∙ 𝑂(𝑡) ∙ (1 − 𝑂(𝑡) )for each of the connection weights and thresholdvalues, respectively. Notice that the superscript [k]indicates the number of learning iterations and [k=0]means the initial values for all the parameters directly
preceding the start of the learning process. Then the
outputs corresponding to the inputs of the teacher data(𝑡) (𝑡)(𝑡){𝐼1 , 𝐼2 , ⋯ , 𝐼𝑁 ; 𝑡 = 1, 2, ⋯ , 𝑇} can be obtained
from Eq. (1) ~ Eq. (5). Let these outputs be{𝑈 [𝑘](𝑡) ; 𝑡 = 1, 2, ⋯ , 𝑇 𝑎𝑛𝑑 𝑘 = 0} .
Clearly,{𝑈 [𝑘](𝑡) ; 𝑡 = 1, 2, ⋯ , 𝑇 𝑎𝑛𝑑 𝑘 = 0} are different
from the outputs of the teacher data {𝑂(𝑡) ; 𝑡 =1,2, ⋯ , 𝑇}, and an error function can be defined withthe two different kinds of outputs as follows.

[𝑘](𝑡)𝛾𝑗𝑅

2= ∑(𝑂(𝑡) − 𝑈 [𝑘](𝑡) ) , 𝑘 = 0

(8)

𝑡=1

In order to apply the neural network model
proposed above to a water environment evaluation
problem, the model has to be trained appropriately
through a deep learning process by using waterenvironment-related data.
In this study, the data obtained from the water
quality survey conducted for the 109 Class-A riversof Japan are used for the deep learning process [8].
After a careful data verification process, only 104
rivers out of 109 are chosen to be included in theteacher data set for deep learning because there arequite a few of data missing for the other 5 rivers. For
each river the data includes 58 water environment
items as shown in Table 1. The data records used inthis study are from 1998 to 2018 with a duration of 21
years long.
The 58 environment items are divided into twoparts to form a teacher data set, evaluation goal
variables and explanation variables. The evaluation
goal variables include the five environment items thatare used to define The Water Environment Standards
for Rivers as shown in Table 2 [9], which are pH,
BOD, SS, DO and Total coliform. All theenvironment items are used to explain how theachievement of water environment standards are
impacted.
The achievement of water environment standards
has been scored with an effort to make the final scoresfor different standard classes that are corresponding
to different water use purposes a uniform distribution,
which is expected to maximize the score distance
between different standard classes. The results areshown in Table 3.

The Error Back Propagation Algorithm makes useof the connection weights and threshold values thatminimize the above error function 𝑅 [𝑘] . Usually anon-linear programming method is required to solve
the optimization problem along with an iteration
process in order to obtain the optimal (but possibly
suboptimal) connection weights and threshold values.
The final iteration procedures derived from a nonlinear programming method known as the Method of
Gradient Descent are as follows.
𝑇
[𝑘]

= 𝑤𝑗

[𝑘](𝑡)− 𝜂 ⋅ ∑(𝛿 [𝑘](𝑡) ⋅ 𝑌𝑗

(9)

)

𝑡=1
𝑇

𝜃 [𝑘+1] = 𝜃 [𝑘] − 𝜂 ⋅ ∑ 𝛿 [𝑘](𝑡)

(10)

𝑡=1
𝑇
[𝑘+1]

𝑤𝑖𝑗

[𝑘]

[𝑘+1]

= 𝑤𝑖𝑗 − 𝜂 ⋅ ∑(𝛿 [𝑘](𝑡) ⋅ 𝑤𝑗[𝑘](𝑡)⋅ 𝛾𝑗(𝑡)⋅ 𝐼𝑖 )

(11)

𝑡=1
𝑇
[𝑘+1]

𝜃𝑗

[𝑘]

= 𝜃𝑗

(14)

Teacher Data[𝑘] [𝑘]𝑤𝑗 , 𝜃𝑗 𝑎𝑛𝑑 𝜃 [𝑘] are given.

[𝑘+1]

)

TRAINING OF NEURAL NETWORK MODEL

Obviously, 𝑅 [𝑘] is a function of connection
weights and threshold values because {𝑈 [𝑘](𝑡) ; 𝑡 =[𝑘]1, 2, ⋯ , 𝑇 𝑎𝑛𝑑 𝑘 = 0} are calculated after all 𝑤𝑖𝑗 ,𝑤𝑗[𝑘](𝑡)∙ (1 − 𝑌𝑗

In order to avoid the overfitting (or over-learning)problem, a criterion is usually required to make a
judgement when the iterative learning process shouldbe terminated. In this study the learning process will
be stopped when the Mean Relative Error (MRE) of
the outputs is less than a specified relative error
expectation for prediction/evaluation results, which is
a common treatment for a learning process of teacher
data with random errors (i.e. white noise). Needlessto say, this error expectation should be set according
to the required accuracy of the problem which is
being dealt with. In this study we have set the error
expectation to 2%, which is considered an accuracy
that is good enough for the expected result.

𝑇[𝑘][𝑘](𝑡)

= 𝑌𝑗

(13)

[𝑘+1]

− 𝜂 ∙ ∑(𝛿 [𝑘](𝑡) ⋅ 𝑤𝑗[𝑘](𝑡)

⋅ 𝛾𝑗

)

(12)

𝑡=1

where the superscript [k] indicates the number oflearning iterations as mentioned earlier, and η is asmall positive number that indicates the step size of
the Method of Gradient Descent for optimization
iteration process, and we have set η = 0.25 in thisstudy. The other variables which occurred in the final

524

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1 Water environment items included in the teacher data for deep leaning
Category
Water Environment Item
Number of Items
Time of Sampling

River/Flow Conditions

Watershed Conditions

Water Quality Indexes For The
Living Environment

Water Quality Indexes About
Human Health

Water Quality Index For Inflow Of
Domestic Wastewater;

OthersYear/Month/Day/HourPlace of Sampling
Weather
Water Level
Quantity of Flow
Total Water Depth
Water Depth of Sampling
Temperature
Water Temperature
Visual Appearance:
1) Water Color
2) Flow Strength3) Turbidity (Muddiness)
4) Floating Waste/Garbage
Water Smell
Vertical Visibility
Horizontal Visibility
Time of Low Tide of Sampling Day
Time of High Tide of Sampling Day
Length of Main Stream
Catchment Area
Catchment Population
Number of Tributaries
Annual Average Stream Flow
Number Of Dams
Number Of Hydraulic Power Plants
pH
BOD
COD
SS
DO
Saturation Degree of DO
Total Coliform
The Amount of N-Hexane Extract(Oil)Total Nitrogen
Total Phosphorus
Cadmium
Cyanogen
Lead
Hexavalent Chromium
Arsenic
Total Mercury
Alkyl Mercury
PCB
Dichloromethane
Ammonium Nitrogen
Chromaticity
Turbidity
Evaporation Residues
Total Hardness
Potassium Permanganate Consumption
SodiumIron
Manganese
Aluminum
Residual Chlorine

4

17

7

10

9

1

10

Total:

Table 2 Water environment standards for rivers [9]

525

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GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 3 Scoring of achievement degree ofthe environment quality standards
Water quality class ranked withthe environmental quality
Score
standards for conservation of theliving environment
AA Class
0.9
A Class with indexes
0.8
ranked in AA
A Class
0.7
B Class with indexes
0.6
ranked in A or higher
B Class
0.5
C Class
0.4
D Class
0.3
E Class
0.2
Below E Class
0.1

regarding each variable as follows.
𝑆𝑖 =𝜕𝑂(𝑋1 ,𝑋2 ,⋯,𝑋𝑁 )𝜕𝑋𝑖

|𝑎𝑡 𝑎 𝑔𝑖𝑣𝑒𝑛 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 𝑣𝑎𝑙𝑢𝑒 𝑠𝑒𝑡(15)

where 𝑆𝑖 is the sensitivity coefficient of variable 𝑋𝑖 at
a given variable value set.
For all the variables, the sensitivity coefficients
have been evaluated for the average variable value ofthe last data year 2018. This sensitivity analyses haveidentified 7 variables that have the most significant
and meaningful impacts on the achievement degree ofwater environment standards as shown in Table 4. As
for all the other variables, the sensitivities were notgreat enough to treat them as variables with aconsiderable impact in terms of average variablevalues.
Needless to say, the sensitivity is defined as thedifferentiation of the evaluation function with respect
to each factors at a given time(the final year of thedata records in this study), and just means how
sensitive the evaluation function is to the change ineach factor. It is reasonable to view the sensitivity asa relative index to compare different factors, but it is
not designed to make a sense in terms of absolute
impacts of each factor on the evaluation function.

Training of Neural Network Model
The neural network model has been trained (putunder a learning process) with the collected teacher
data explained above. The training process is basedon the learning procedures as described prior, but it isstill a process of trial and error because there are still
many details that remain undecided, such as a suitablestep size of optimization, a suitable output function,an efficient order to present the teacher data to theneural network model, and a proper initial network
size (layers and units in each layers). The learning
process was stopped after the trained neural networkmodel is able to reproduce the entire teacher data withan acceptable error, which was set in this study to be
below 2% in mean relative error.

Discussions
The environment items with the greatest impacts
on the achievement degree of water environment
standards can be classified into three categories:
natural factors, human factors and mixed factors.
Natural factors include month of sampling andannual average stream flow. The fact that the monthof sampling has great impacts means that water
environment has a clear tendency of seasonal change.
This is because of the subtropical climate pattern inJapan with a clear rainy season and a typhoon season.
The seasonal rainfall change is considered the maincause for the seasonal change tendency in river water

WATER ENVIRONMENT EVALUATION
Sensibility Analyses of Variables
The well-trained neural network model has beenused to carry out a sensibility analysis for all thevariables to identify how much each variable impacts
on the achievement of water environment standards.
The sensibility coefficient of a variable is defined as
the partial derivative of the achievement score

Table 4 The environment items with the greatest impacts on achievement of water environment standards
Environment Item
Sensitivity
Descriptions
It means that water environment has a clear
Month of Sampling
0.16
tendency of seasonal change.
It is reasonable and expected that water quantity
Annual Average Stream Flow
0.09
has a tremendous impact on water quality.
This just reconfirmed that human activity is one
Catchment Population
0.07
of the main factors that are able to make a great
difference on water environments.
Visual turbidity usually gets remarkably worse
Visual Appearance: Turbidity
right after storms that cause landslides or debris
0.04(Muddiness)flow in mountain areas, soil erosion in farmland
and sewage overflow in urban areas.
Total nitrogen in water environment is mainly
Total Nitrogen
0.03
contributed by sewage inflow and agriculture
drainages.
Dichloromethane is almost entirely from
Dichloromethane
0.02
industrial wastewater.
Power plants can change river flow, andNumber Of Hydraulic Power Plants
0.01
consequently water environments
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GEOMATE – Kyoto, Japan, 3-5 November 2021

intelligence model with a deep learning technology
can treat both numerically continuous variables suchas annual average stream flow and categoricalvariables such as visual turbidity with the discretevalues yes/no without substantial effort orhinderances. This implies a significant potential
application of the artificial intelligence model to thewater environment management area.
With this well-trained neural network model, thenext step is to identify the most influential
environment factors for each river, and find the mosteffective combination of water environment
improvement measures.
The biggest difference between a traditional water
environment model and an artificial intelligencemodel is the difference of the knowledge base used inenvironment evaluations. Traditional models aremainly based on the knowledge and experiences (orexpertise) with regard to a specific target river.
Artificial intelligence models, however, are using thefull knowledge and experiences that are hidden in theenvironment survey data of all rivers with similar
characters. More analyses are required to show whatkind of differences this character of artificial
intelligence model can make in water environment
evaluation and management in the future. These
results combined together in their entirety will
demonstrate that artificial intelligence is an effective
and efficient tool for water environment evaluation.

environment. This is consistent with the fact that theannual average stream flow is ranked as the second
most important factor to water environment. This isalso a reasonable result expected from the common
sense that water quantity has a tremendous impact onwater quality.
Human factors include catchment population,
total nitrogen, dichloromethane and number ofhydraulic power plants. Catchment population
implies that human activity is one of the main factorsthat can make a significant difference on water
environments. Total nitrogen in water environment ismainly contributed by sewage inflow and agriculture
drainages (overuse of fertilizers), and Dichloromethane is almost entirely from industrial wastewater.
Power plants are the most controversial factor. The
conflicts between power generation and conservationof river environment have been problems in mostbasins and a priority problem of river flow fordifferent purposes is remained to be resolved.
The only mixed factor is turbidity or muddiness interms of visual appearance. Visual turbidity usuallygets remarkably worse right after a heavy storm thatcauses landslides or debris flow in mountain areas,soil erosion in farmland and sewage overflow inurban areas. Both catchment natural condition andfarming or urban human activities are contributing
indirectly to river flow turbidity during rainy time.

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Urban Water and Green Space, Kyoto University
Press, 2010.
[2] Ministry of The Environment of Japan, Resultsof the FY 2018 Water Quality Survey of Public
Water Areas, 2019.
[3] Ministry of The Environment of Japan, Resultsof the FY 2004 Water Quality Survey of Public
Water Areas, 2005.
[4] Zhang S. P. and Kido Y., A Study on theEnvironment Evaluation method for Aise Riverand the Effectiveness of River Environment
Improvement Measures, Urban Science Studies,No. 21, 2016, pp. 45-56.
[5] IPA, WHITE PAPER Artificial Intelligence
2019, Kadokawa publisher, 2018.
[6] Asou H., The Information Processing by Neural
Network Models, Sangyo Publisher, 1988.
[7] Rumelhart D. E., Hinton G. E., and Williams R.
J., Learning Representations by Backpropagating Errors, Nature, Vol. 323, No. 9,
1986, pp. 533-536.

CONCLUSIONSWith the purpose of developing a bettermethodology for water environment evaluation andmanagement of rivers, a neural network model has
been proposed for water environment evaluation inthis study.
The neural network model was trained through a
deep learning process with the water environment
data of 104 Class-A rivers that are under the direct
administrative control for 58 environment items from1998 to 2018.
The well-trained neural network model wasapplied to a sensitivity analysis of environment items.
The sensitivity analysis results have identified 7
variables that have the most significant andmeaningful impacts on the achievement degree ofwater environment standards, of which there arenatural factors (season and annual average streamflow), human factors (catchment population, total
nitrogen, dichloromethane and number of hydraulicpower plants) and a mixture of both (Visual
turbidity). These results are reasonable and areconsistent with the expectation derived from common
knowledge.
It is more meaningful and important that the above
analysis results have shown that an artificial[8] Ministry of Land, Infrastructure, Transport andTourism of Japan, Water Information System,
http://www1.river.go.jp/ (an Online Open Source
Database).
[9] Ministry of Environment of Japan, Environmental
White Paper, 2020.

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Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

ESTIMATION OF USEFUL SPECIES OF BENTHIC ANIMALS AND
WATER PLANTS FOR AN INDEX OF AL AND CR CONTAMINATIONFOR RIVER IN THE KINOKAWA RIVER CATCHMENT
Takuma Kubohara1 and Hiroyuki Ii2
1

Kinokawa City Office, Japan; 2Faculty of Systems Engineering, Wakayama University, Japan
ABSTRACT

Useful species for an index of Al and Cr contamination for river were estimated in the Kinokawa River
catchment because there are few studies on whether benthic animals and water plants are useful for an index of Al
and Cr contamination for river. Al concentrations of crane fly larva and bryophyte at the closed Cu mine were high(260 to 5,260 and 640 to 20,000 mg/kg-dry for crane fly larva and bryophyte). Cr concentrations of dragonfly larva
and bryophyte at the serpentinite were high (3 to 30 and 3 to 91 mg/kg-dry for dragonfly larva and bryophyte).
Crane fly larva for Al (260 to 5,260 mg/kg-dry) and dragonfly larva for Cr (0.6 to 30 mg/kg-dry) in benthic animals
and bryophyte for Al and Cr (220 to 20,000 and 2 to 91 mg/kg-dry for Al and Cr) in water plants had high and awide range of metal concentrations. Therefore, it was thought that crane fly larva for Al, dragonfly larva for Cr
and bryophyte for Al and Cr were useful species for an index of metal contamination. Moreover, bryophyte had
the highest and a wide range of Al and Cr concentrations among benthic animals and water plants. Therefore,
bryophyte was useful species for an index of Al and Cr contamination among benthic animals and water plants.
Keywords: Al, Cr, Bryophyte, Crane fly larva, Dragonfly larva

INTRODUCTIONSTUDY AREA

To investigate the influence of heavy metals foraquatic life in river, metal concentration of river waterand river sediment are measured. Generally, metal
concentration of river water is always very low.
Moreover, it is changeable depending on the changeof flow rate. And then, heavy metal concentration ofriver sediment does not represent the heavy metal
mass transferring in river (soluble only) because
heavy metals of river sediment are soluble andinsoluble. Therefore, the metal concentrations of riverwater and river sediment were not always useful for
an index of the influence of heavy metals for aquatic
life in river. On the other hand, it is reported that themetal concentration of benthic animal and water plantis useful for investigating the influence of heavy
metals for aquatic life in river [1]-[6]. However, there
are few studies on whether benthic animals and water
plants are useful for an index of Al and Cr
contamination for river. Therefore, Al and Cr
concentration of benthic animals and water plants
were measured in the Kinokawa River catchment andthen useful species for an index of Al and Cr
contamination for river were estimated. Usefulspecies as an index of Al and Cr contamination needs
a high concentration for Al and Cr in a contaminatedarea and a low concentration for them in a noncontaminated area. Moreover, it needs a high and awide range of their concentration and also common.

Figures 1 and 2 show study area. The KinokawaRiver is located in the center of Kinki district and isclassified into A river based on the Ministry of Land,
Infrastructure, Transport and Tourism of Japan. The
length and total area of the Kinokawa River are 136
km and 1,750 km2 [7]. It flows into the Kii Channel
through the Kii plain from South Nara. The Izumi
Group composed of sedimentary rocks, sandstone,
mudstone and conglomerate is distributed in thenorthwest part of the catchment. The Sanbagawa Belt
composed of metamorphic rocks, serpentinite andcrystalline schist is distributed in the southwest partstudy area

Fig.1 Location of study area.
catchment.
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GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig.2 Kinokawa River catchment.
and the northeast part of the catchment. The
Hidakagawa Group composed of sedimentary rocks,
sandstone and shale is distributed from the southernpart to the northeast part of the catchment. The
Chichibu Belt composed of sedimentary rocks,
sandstone, mudstone, limestone and chert is
distributed in the eastern part of the catchment [8].
The Ryoke Complex composed of plutonic rocks,
granite and gneiss is distributed in the northeast partof the catchment. In the Kinokawa River catchment,
there are the closed Cu mines and serpentinite. The
closed Cu mine produced a lot of Cu and Fe sulfide
ore and the waste water was low pH and highconcentration of sulfate with metal. The chemistry ofserpentinite is quite different from the other rocks andin particular Mg, Ni, Cr concentrations of serpentinite
are high.

Japanese knotweed was not sampled in the normalarea.
In the laboratory, the sampled benthic animals andwater plants were rinsed with ultrapure water at first.
Secondly, they were desiccated by dryer. Afterdrying, they were dissolved with concentrated nitricacid and it was filtered with the membrane filter with
0.45 micrometer of pore size. After filtering, Al andCr concentrations of benthic animals and water plants
were measured by ICP-OES (AMETEK, Inc.,
SPECTRO ARCOS) in the laboratory of Wakayama
University. The actual detection limit of ICP-OES is
0.01ppm for Al and Cr. Water plants excluding
bryophyte were divided into leaf, upper part stem,
middle part stem, lower part stem and root and eachpart was separately analyzed.
RESULTS AND DISCUSSIONSTUDY METHODAl Concentration of Benthic Animals and Water
PlantsBenthic animals and water plants in the river bed
were sampled in the Kinokawa River catchment.
Sampling points were shown in fig.2. The number ofsampling points was 73 points. Investigation period is
July 2013 to April 2016. The kinds of sampled
benthic animals were Japanese freshwater crab,
caddice-worm, dobsonfly larva, dragonfly larva,
stonefly larva, crane fly larva, mayfly larva andcorixidae. The kinds of sampled water plants werereed, fern, bryophyte, Japanese knotweed and coix.
Sampling points were classified into three groups, theserpentinite area, the Cu mine area and the normal
area. The serpentinte area is a sampling point aroundthe serpentinite. The Cu mine area is a sampling pointaround the Cu mine. The normal area is a sampling
point excluding those areas. Crane fly larva and reed
were not sampled in the serpentinite area. Moreover,Figures 3 and 4 show Al concentrations of benthic
animals and water plants. Al concentrations ofdragonfly larva and crane fly larva in the Cu minearea were 151 to 3,100 and 260 to 5,260 mg/kg-dry,
respectively. Al concentrations of dragonfly larva andcrane fly larva in the other areas were 88 to 1,100 and
530 to 3,100 mg/kg-dry, respectively. Al
concentrations of reed root, leaf and root of fern,
bryophyte, upper and middle part stem and root ofJapanese knotweed and coix root in the Cu mine area
were 610 to 1,600, 260 to 3,200, 640 to 20,000, 270
to 3,000 and 380 to 500 mg/kg-dry, respectively. Al
concentrations of reed root, leaf and root of fern,
bryophyte, upper and middle part stem and root ofJapanese knotweed and coix root in the other areas
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GEOMATE – Kyoto, Japan, 3-5 November 2021

10000010000010000
Al(mg/kg-dry)

1000

corixidae

100

root

bryophyteroot

stem(lower part)stem(lower part)reedstem(middle part)

leaf

root

stem(lower part)stem(middle part)10

Fig.3 Al concentrations of benthic animals.
River catchment.
were 440 to 590, 180 to 1,600, 220 to 13,000, 190 to
1,300 and 340 mg/kg-dry, respectively. Therefore,
their Al concentrations in the Cu mine area werehigher than those in the other areas. Aluminum iswidely distributed as feldspar and clay minerals inrocks in the crust, where it is the third most abundantelement following oxygen and silicon. The solubilityof aluminum silicate and aluminum hydroxide inrocks is large in low or high pH condition. The closed
Cu mine produce low pH waste water. Chemical
equations of aluminum silicate and aluminumhydroxide with water in low pH condition are shown
as below [9].

fern

100000Al(mg/kg-dry)

10000

serpentinite area
Cu mine area
normal area

1000

100

Japanese knotweed(2)stem(middle part)stem(upper part)root

stem(lower part)stem(middle part)

leaf

10

𝐴𝑙2 𝑆𝑖2 𝑂5 (𝑂𝐻)4 + 6𝐻 + = 2𝐴𝑙 3+ + 2𝐻4 𝑆𝑖𝑂4 + 𝐻2 𝑂
(1)
𝐴𝑙(𝑂𝐻)3 = 𝐴𝑙 3+ + 3𝑂𝐻 −1000

stem(upper part)

mayfly larva

crane fly larva

stonefly larva

dragonfly larva

dobsonfly larva

caddice-worm

10Japanese
freshwater crab

100serpentinite area
Cu mine area
normal area

stem(upper part)Al(mg/kg-dry)

10000

serpentinite area
Cu mine area
normal areacoix

Fig.4 Al concentrations of water plants.
animals River catchment.
140 to 410, 200 to 710 and 260 to 350 mg/kg-dry,
respectively. Al concentrations of Japanese
freshwater crab, caddice-worm, dobsonfly larva,
stonefly larva, mayfly larva, reed stem, middle partstem of fern and upper part stem of coix in the otherareas were 43 to 1,400, 142 to 2,000, 121 to 2,200, 64
to 1,100, 320 to 1,300, 200 to 900, 260 to 1,100 and
380 to 870 mg/kg-dry, respectively. Therefore, their
Al concentrations in the Cu mine area were not higher
than those in the other areas.
Al concentrations of Japanese freshwater crab,
caddice-worm, dobsonfly larva, dragonfly larva,
stonefly larva, crane fly larva, mayfly larva andcorixidae were 43 to 1,300, 142 to 2,000, 121 to
2,200, 88 to 3,100, 64 to 1,100, 260 to 5,260, 320 to1,300 and 280 to 1,400 mg/kg-dry, respectively.
Therefore, crane fly larva had the highest and a wide
range of Al concentration among benthic animals. Al
concentrations of reed, fern, bryophyte, Japaneseknotweed and coix were 140 to 1,600, 180 to 3,200,
220 to 20,000, 190 to 3,000 and 260 to 870 mg/kg-

Therefore, it was thought that high Al
concentrations of dragonfly larva, crane fly larva,
reed root, leaf and root of fern, bryophyte, upper andmiddle part stem and root of Japanese knotweed andcoix root in the Cu mine area were caused by waste
water from the closed Cu mines.
On the other hand, Al concentrations of corixidae,
lower part stem of fern, lower part stem of Japaneseknotweed and middle and lower part stem of coix in
the Cu mine area were 1,300, 450 to 690, 200 to 260
and 420 to 710 mg/kg-dry, respectively. Al
concentrations of corixidae, lower part stem of fern,
lower part stem of Japanese knotweed and middle andlower part stem of coix in the other areas were 280 to
1,400, 250 to 610, 250 and 470 to 720 mg/kg-dry,
respectively. Therefore, their Al concentrations in the
Cu mine area were almost the same as those in theother areas. Al concentrations of Japanese freshwater
crab, caddice-worm, dobsonfly larva, stonefly larva,
mayfly larva, reed stem, middle part stem of fern andupper part stem of coix in the Cu mine area were 48
to 210, 560 to 1,500, 1,300, 237 to 720, 720 to 760,
530

GEOMATE – Kyoto, Japan, 3-5 November 2021

dry, respectively. Therefore, bryophyte had thehighest and a wide range of Al concentration amongwater plants.
From the above results, crane fly larva had thehighest and a wide range of Al concentration among
benthic animals. Bryophyte had the highest and awide range of Al concentration among water plants.
Moreover, Al concentrations of crane fly larva andbryophyte were high in the Cu mine area and were
low in the other areas. Therefore, crane fly larva andbryophyte were useful species for an index of Al
contamination.

100

Cr(mg/kg-dry)

serpentinite area
Cu mine area
normal area
10

1

Cr(mg/kg-dry)bryophytestem(lower part)

root

root

stem(lower part)stem(upper part)root

stem(lower part)stem(middle part)stem(upper part)leaf

stem(middle part)coix

Fig.6 Cr concentrations of water plants.
animals River catchment.
freshwater crab, dobsonfly larva, stonefly larva,
corixidae, fern, Japanese knotweed stem and coix inthe serpentinite area were 0.2 to 8, 1 to 7, 0.7 to 4, 4,
2 to 10, 2 and 2 mg/kg-dry, respectively. Cr
concentrations of Japanese freshwater crab,
dobsonfly larva, stonefly larva, corixidae, fern,
Japanese knotweed stem and coix in the other areas
were 0.2 to 5, 2 to 7, 0.6 to 5, 0.8 to 5, 1 to 10, 2 and
2 to 3 mg/kg-dry, respectively. Therefore, their Cr
concentrations in the serpentinite area were almost
the same as those in the other areas.
Cr concentrations of Japanese freshwater crab,
caddice-worm, dobsonfly larva, dragonfly larva,
stonefly larva, mayfly larva and corixidae were 0.2 to8, 0.3 to 19, 1 to 7, 0.6 to 30, 0.6 to 5, 1 to 20 and 0.8
to 5 mg/kg-dry, respectively. Therefore, dragonfly
larva had the highest and a wide range of Cr
concentration among benthic animals. Cr
concentrations of fern, bryophyte, Japanese knotweed

corixidae

mayfly larva

stonefly larva

dragonfly larva

dobsonfly larva

1Japanese knotweed

serpentinite area
Cu mine area
normal area

caddice-worm

10

0.1

1

Japanese
freshwater crab

leaf

serpentinite area
Cu mine area
normal area

10

crane fly larva

Cr(mg/kg-dry)

fern

100

100

0.1

root

reed

stem(middle part)

Figures 5 and 6 show Cr concentrations of benthic
animals and water plants. Solid mark indicates Cr
concentration of benthic animal and water plant under
the detection limit, 0.01 ppm of concentration forsample solution.
Cr concentrations of caddice-worm, dragonfly
larva and mayfly larva in the serpentinite area were 9
to 19, 3 to 30 and 20 mg/kg-dry, respectively. Cr
concentrations of caddice-worm, dragonfly larva andmayfly larva in the other area were 0.3 to 10, 0.6 to 9
and 1 to 5 mg/kg-dry, respectively. Cr concentrationsof bryophyte and Japanese knotweed root in theserpentinite area were 3 to 91 and 27 to 60 mg/kg-dry,
respectively. Cr concentrations of bryophyte andJapanese knotweed root in the other area were 2 to 53
and 26 mg/kg-dry, respectively. It is known that theserpentinite contains a large amount of Cr. It isreported that Cr concentrations of the serpentinite
were 400 to 31,300 ppm [10]-[11]. Therefore, it wasthought that high Cr concentrations of caddice-worm,
dragonfly larva, mayfly larva, bryophyte andJapanese knotweed root in the serpentinite area werecaused by the serpentinite.
On the other hand, Cr concentrations of Japanesestem(lower part)

Cr Concentration of Benthic Animals and Water
Plantsstem(middle part)stem(upper part)

0.1

Fig.5 Cr concentrations of benthic animals.
River catchment.
531

GEOMATE – Kyoto, Japan, 3-5 November 2021

and coix were 1 to 10, 2 to 91, 2 to 60 and 2 to 3
mg/kg-dry, respectively. Therefore, bryophyte had
the highest and a wide range of Cr concentration
among water plants.
From the above results, dragonfly larva had thehighest and a wide range of Cr concentration among
benthic animals. Bryophyte had the highest and awide range of Cr concentration among water plants.
Moreover, Cr concentrations of dragonfly larva andbryophyte were high in the serpentinite area and were
low in the other areas. Therefore, dragonfly larva andbryophyte were useful species for an index of Cr
contamination.

other areas. Cr concentrations of dragonfly larva andbryophyte in the serpentinite area were higher thanthose in the other areas. Moreover, crane fly larva for
Al and dragonfly larva for Cr had the highest and awide range concentration among benthic animals.
Bryophyte had the highest and a wide range of Al andCr concentrations among water plants. Therefore,
crane fly larva for Al and dragonfly larva for Cr were
useful species for an index of metal contamination
among benthic animals. Bryophyte was useful species
for an index of Al and Cr contamination among water
plants.
Bryophyte had the highest and a wide range of Al
and Cr concentrations among benthic animals andwater plants. Therefore, bryophyte was useful species
for an index of Al and Cr contamination among
benthic animals and water plants.

Most Useful Species for Al and Cr contaminationof RiverFrom figs.3 and 4, Al concentrations of benthic
animals, bryophyte and other water plants were 43 to
5,260, 220 to 20,000 and 140 to 3,200 mg/kg-dry,
respectively. From figs.5 and 6, Cr concentrations ofbenthic animals, bryophyte and other water plants
were 0.2 to 30, 2 to 91 and 1 to 60 mg/kg-dry,
respectively. Therefore, bryophyte had the highest
and a wide range of Al and Cr concentrations among
benthic animals and water plants. Moreover, Al
concentrations of bryophyte in the Cu mine area werehigher than those in the other areas. And then, Cr
concentrations of bryophyte in the serpentinite area
were higher than those in the other areas. It is known
that bryophyte absorbs heavy metals from the wholesurface of the body [12]. Therefore, it is thought thatbryophyte is easy to absorb heavy metals comparedwith benthic animals and other water plants.
Moreover, it is thought that bryophyte is little metal
consumption because the growth rate of bryophyte is
generally slow [13]. From the above reasons, it wasthought that bryophyte had the highest and a wide
range of Al and Cr concentrations among benthic
animals and water plants. Therefore, bryophyte was
useful species for an index of Al and Cr
contamination among benthic animals and water
plants.

REFERENCES[1] Hatakeyama S., Satake K., and Fukushima S.,
Flora and Fauna in Heavy Metal Polluted Rivers
Ⅰ
DensityofEpeoruslatifolium(Ephemeroptera)
and
Heavy
Metal
Concentrations of Baetis spp. (Ephemeroptera)
Relating to Cd, Cu and Zn Concentrations,
Research Report from the National Institute forEnvironmental Studies, No.99, 1986, pp.15-33.
[2] Ii H., and Nishida A., Effectiveness of Using
River Insect Larvae as an Index of Cu, Zn and As
Contaminations in Rivers, Japan, International
Journal of Geomate, Vol.12, No.33, 2017,
pp.153-159.
[3] Kubohara T., and Ii H., Cu Co and Ni
Contamination Index for River Using River
Insects and River Plants, International Journal of
GEOMATE, Vol.11, Issue 26, 2016, pp.26512658.
[4] Kubohara T., and Ii H., Evaluation of Metal
Contamination for River using Bryophyte in the
Kinokawa River Catchment, International
Journal of GEOMATE, Vol.12, Issue 37, 2017,
pp.135-142.
[5] Kubohara T., and Ii H., Zn and Fe Contamination
Index for River using River Insects and Water
Plants in The Kinokawa River Catchment,International Journal of GEOMATE, Vol.19,
Issue 75, 2020, pp.76-83.
[6] Satake K., Elemental Composition of Water andAquatic Bryophytes Collected from the Central
Part of Kyushu (Mt. Kuju, Mt. Aso and the Cityof Kumamoto), Proceedings of the BryologicalSociety of Japan, Vol.3, No.9, 1983, pp.137-140.
[7] Ministry of Land, Infrastructure, Transport andTourism, https://www.mlit.go.jp/river/toukei_cho

CONCLUSIONSAl and Cr concentration of benthic animals andwater plants in the Kinokawa River catchment were
investigated in order to estimate useful species for an
index of Al and Cr contamination for river. Usefulspecies as an index of Al and Cr contamination needs
a high concentration for Al and Cr in a contaminatedarea and a low concentration for them in a noncontaminated area. Moreover, it needs a high and awide range of their concentration and also common.
Al concentrations of crane fly larva and bryophytein the Cu mine area were higher than those in the532

GEOMATE – Kyoto, Japan, 3-5 November 2021

usa/kasen/jiten/nihon_kawa/0604_kinokawa/060
4_kinokawa_00.html[8] Wakayama City Children’s Science Museum,
Wakayama no Ishi, 2002, pp.7-13.
[9] Ii H., Hirata T., Matsuo H., Tase N. andNishikawa M., pH and Chemistry of Nitrogen,
Phosphate, Sulfar and Aluminum in Surface
Water near Tea Plantation, Doboku Gakkai
Ronbunshu, No.594, 1998, pp.58-63.
[10] Mizuno N., Studies on Chemical Characteristics
of Serpentine Soils and Mineral Deficiencies andToxicities of Crops, Report of Hokkaido
Prefectural Agricultural Experiment Stations,No.29, 1979, 87p.

[11] Bamba T., Genetic Study on the Chromite
Deposits of Japan. Based on Ultra-Basic Rocksand Chromite Deposits of Hokkaido, Chugoku
and Shikoku Districts, Reports, Geological
Survey of Japan, No.200, 1963, 68p.
[12] Yoshio H., Okayama University of Science,
http://had0.big.ous.ac.jp/thema/moss/moss.htm[13] Graduate School of Agricultural and Life
Sciences,
The
UniversityofTokyo,
https://www.a.u-tokyo.ac.jp/topics/2018/2018
0327-3.html

533

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

FLOATING AUTOMATED FACTORY SYSTEM FOR EFFECTIVEUTILIZATION OF SEABED RESOURCES AND REDUCTION OF CO2
EMISSIONS
Shinji Sato1 and Ryo Deyama2
1

Department of Oceanic Architecture and Engineering, College of Science and Technology, Nihon
University, Japan; 2NIHON SEKKEI, INC., Japan
ABSTRACTThe economy of Japan is growing to become the third-largest in the world by 2020 in terms of GDP. Japan is
one of the world’s largest consumers of electrical energy. However, Japan’s self-sufficiency rate of energy
resources, excluding nuclear power, is only 9.6%; thus, they currently rely on importation for more than 80% of
their energy demands.
On the other hand, under the seabed of Japan’s exclusive economic zone, there are methane hydrates, nearly
100 times larger than Japan’s annual consumption of natural gas. Methane is a clean energy source with CO2emissions 30% lower than that of oil and about 45% lower than that of coal.
In the current development of submarine resources, the deeper the water depth, the higher the cost for extracting
resources. To minimize cost, from the extraction to the processing of submarine resources, it is most efficient to
set up a base directly above the sea area where the resources are located, and power can be generated after the
extraction and conversion to energy. In the future, it would be possible to reduce the overall cost, from productprocessing to finished product manufacturing, by siting various factories around the base of the resources as
floating structures.
Here, we propose a floating structure that can consolidate the movement of people, products, and logistics fromthe seabed to the sea and its surroundings for the effective use of energy resources and reduction of CO2 emissions.
Keywords: Energy resources, Methane Hydrates,Marine Space, Vertical production city, Floating structure
INTRODUCTION

Plan in Japan” in July 2001. This development plan
was a long-term plan that covered the period from2001 to 2018. In March 2013, the Agency for Natural
Resources and Energy conducted a gas production
experiment with methane hydrates and recorded thefirst success in the world. With this experiment, thetransition to commercial production of methane isanticipated in the 2020s [2].
In this paper, we propose an island city of afloating automated factory system using methane
hydrates as the main energy source. It is anticipatedto be the new domestic energy resource.

Following the period of high economic growth in
the 1970s, Japan has grown into a wealthy country.
With this social development, power consumption
has grown dramatically, and today, Japan is the third
highest consumer of energy in the world. However,
the energy resources self-sufficiency rate of Japan is
below 9.6%, and the country relies on importation for
more than 80% of its energy resources.
With the Fukushima Daiichi Power Plant accident
during the Great East Japan earthquake, Japan faces aturning point in energy policies. In the future, there
would be a need for less dependence on imported
resources and the securing of safe and clean domesticenergy sources. Thus, currently, seabed resources
around Japan have attracted attention.
Methane hydrates, the most representative seabedresources, are natural gas resources called “fire ice.”
Compared to conventional fossil fuels, CO2 emissions
during methane combustion is about half. Thus, it isa potential clean energy source. About 6 trillion cubic
meters of methane reserve in the waters of the Nankai
Trough were also reported [1].
Based on this survey, to commercialize methane
hydrates as a new domestic energy resource, theMinistry of Economy, Trade, and Industry
established the “Methane Hydrates Development

PLAN BACKGROUND
Energy In Japan
While Japan is a major energy consumer, its selfsufficiency rate of energy resources, excluding
nuclear power, is only 9.6%; thus, the country
depends on importation for the majority of its energy
needs (Fig. 1, 2).
From the energy resource explorations to energy
production in Japan, consider oil as an example; Oil
accounts for half of Japan’s energy needs.
The process is as follows: exploration overseas →primary refining process → loading on ships at a port
→ transport by sea → unloading at a port → final
534

GEOMATE – Kyoto, Japan, 3-5 November 2021

refining process at a petrochemical complex →production at each factory → cities and consumers.
Shipping oil from the Middle East, where 90% of
oil supply comes from, involves risks as ships must
pass through the Persian Gulf, which has unstable
political situations, and the narrow Strait of Hormuz
and Strait of Malacca. In addition, since the ships
travel a long distance of 12,000 km, the cost oflogistics is quite high. It takes about 45 days to makea round trip, during which a large amount ofgreenhouse gases are emitted (Fig. 3).

power. Natural energy has many challenges, such as
varying output by climate conditions, the low energy
density per area, and high cost. In Japan, the solutions
to these challenges would take much time; thus, thehope is currently put on the seabed resources buriedin the oceans around Japan.
Methane Hydrates As An Energy Resource

In Japan, which depends on importation for mostof its energy resources, such as oil and coal, it has
been a challenge to find new energy resources thatcan replace the limited resources. Following the
Fukushima Daiichi nuclear power plant accident, theworld demands a stop to the dependence on nuclear
power, and there is increasing anticipation forrenewable natural energy, such as solar and wind

Methane hydrates have recently gained attention
as next-generation energy resource. It has beenreported that there is a rich reserve of methane
hydrates in the oceans surrounding Japan. Thispotential is suggested by what is known as bottom
simulating reflector (BSR). As the deep seafloor has
low temperature and under high pressure, methane
hydrates are stable. Since the lower limit of the stable
range for methane hydrates can be estimated using
BSR, the location of the reserves can be exploredonce the existence of methane hydrates is confirmed.
Marine production tests confirmed BSR along theshoreline of Shikoku to Kyushu, south of Hokkaido,

Fig. 1

Power consumption rate by country [3].

Fig. 2

Fig. 3

Process of oil resource mining to production for Japan

Outlook Of Domestic Energy Production

535

Self-sufficiency rate of energy resources bycountry [2].

GEOMATE – Kyoto, Japan, 3-5 November 2021

anticipated [7].
Use Of Marine SpaceJapan has the 6th widest exclusive economic zone
on the world, which is 10 times the land area, it isfilled with potential for marine development. Thus, tocreate space on water, a very large floating structure,mega-float, is being developed. The TechnologicalResearch Association of Mega-Float, established in
1995, advanced technological development, andduring the Great East Japan earthquake, a mega-float
was used as a floating disaster prevention base for
Muroran. Thus, mega-floats are used for various
facilities [8].
Preparation Against Disasters

Fig. 4

Japan is one of the most earthquake-affected
countries in the world, and has experienced many
major earthquakes, such as the Great Hanshin–Awaji
and Great East Japan earthquake. Earthquakes thatare predicted to have more than 60% probability ofoccurrence in next 30 years include Tokai/Tonankai
earthquake, the earthquake directly below Tokyo, theTokachi earthquake, and the Miyagi Offshore
earthquake. Specifically, the Nankai Troughearthquake is estimated to be M8 or larger, andvarious measures are being taken to prepare for it.
During the Great East Japan earthquake, tsunamis
stopped the operation of several power plants along
the shoreline, which led to a large-scale power
shortage. With such experiences, a base with disasterprevention function on the sea, which is less likely tosuffer disaster damage, would be vital in recovery.

BSR distribution in oceans around Japan [4]and offshore of Boso and Noto Peninsulas. The
estimated amount of methane gas contained in themethane hydrates found at the seafloor of “the eastern
Nankai Trough,” located offshore of Aichi and Mie
Prefectures, is equivalent to 10 years’ worth of natural
gas imported. If other locations are added, theresource is anticipated to exceed 100 years (Fig. 4).
Though Japan relies on imports for the majority ofnatural gas, the successful commercialization of
methane hydrates makes it possible for Japan to
become the next-generation energy resource
powerhouse [5], [6].

FLOATING PRODUCTION FACILITY
Characteristics And Plans For A Floating
Structure
Here, we propose a floating marine production
facility that is mainly supplied with methane hydrates,
which is anticipated to be widely used as a domesticenergy resource in the future. The plan will fully
utilize the properties of a floating structure [9].
1. A floating structure does not require land and canbe placed in an area with the likely presence ofrich marine resources in order to obtain a stablesupply.
2. Methane and hydrogen gases require completely
sealed space. Thus, gas tanks can utilize theinternal space of a mega-float.
3. A floating structure is easy to relocate, move, andprocess; thus, it can accommodate the expansionof a project.
4. Expansion through floating structures, such as
mooring of production facilities, factories, andhotels, is easy.

Uses Of Methane Hydrates
Methane gas obtained from methane hydrates is
the main component of liquid natural gas currently
imported to Japan. It emits less sulfur oxide, nitrogenoxide, and CO2 compared to oil and coal, and it ispredicted to be an environmentally friendly resource.
Methane gas can be used as an energy source invarious applications, such as city gas, fuel for power
generation, natural gas vehicles, and fuel cells.
The steam-reforming method, in which methane
reacts with water, is widely used in the industry. Thismethod allows easy manufacturing of hydrogen as a
new energy for the 21st century, and its diverse use is536

GEOMATE – Kyoto, Japan, 3-5 November 2021

Plan With Power Generation Function

2.

By combining a floating structure and power
generation function in a plan, the following
advantages are anticipated (Fig. 5).
1. Independent production and operation on theocean become possible, allowing for long-term
activities.

3.
4.
5.

Majorities of the automotive industry located on
the shoreline anticipate exports. Cooperating andlinking with a floating structure, it would bepossible to share power and produce on the ocean.
It is possible to manufacture hydrogen throughthe methane-steam-reforming method.
Combining with lithium in the seawater, lithiumion batteries can be manufactured, providing a
power storage function.
A floating structure is not vulnerable toearthquakes. Therefore, during a disaster, it canplay a role of the recovery base by providing
power to the affected area, and so on.

BASIC PLAN
Site Selection
Based on the abovementioned plans and policies,
the following site selection criteria are proposed: thesea is in a calm condition; the area is rich withresource reserve to provide a stable supply.
Planned Marine Area
With these selection criteria, the seabed of“eastern Nankai Trough,” located offshore of Aichi
and Mie Prefectures (Fig. 6), was selected. This area

Fig. 5Functional configuration diagram of afloating production facility

Fig. 6

Plan site: Eastern Nankai Trough at 30 km offshore [4], [5].
537GEOMATE – Kyoto, Japan, 3-5 November 2021

is not affected by the Kuroshio Current throughout
the year, and it is located 30 km from Atsumi
Peninsula where the Chūkyō Industrial Area issituated.
Produced methane gas is used to generate power
within the facility, converted and processed as
secondary energy media, such as lithium-ion batteries
and hydrogen, and shipped to surrounding floating
factories or the Chūkyō Industrial Area.

work temporarily, leisure building for workers on
break, and sport building dedicated to indoor sports(Fig. 7).

Selection Of A Floating Structure- FPSO
A floating production, storage, and offloading
system (FPSO) is a facility where oil and gas areproduced on the sea and stored in a tank located in thefacility. Then, they are loaded in transport tankers. It
is the most popular type of production facility (60%or more of floating marine oil/gas production
facilities). Today, there are about 160 FPSO bases inoperation around the world. These facilities are built
inside a mega-float.

Fig. 7

Drilling For Methane Hydrates

Overall facility configuration

CONCLUSIONS

We employ the CO2-in-Water Emulsion Injection
method, which is also used to extract natural gas
resources. The CO2-in-Water Emulsion Injection
method involves drilling to a methane-hydrate layer
at around 1000 m below the seafloor, which breaks
down methane hydrates into methane gas and water,which the gas is collected. It can provide a stablesupply under various conditions [10].

Reviewing Renewable Resources
Energy resources in Japan are extremely limited;
thus, much of the energy resources used are imported.
The dependence on overseas is, directly andindirectly, attributed to the increased cost of
production and other costs. In a country like Japan,
which is highly dependent on imports, this is a severe
issue that demands urgent solutions.
Thus, we propose a system that verticallyintegrates the process of mining the seafloor energy
resource in the nearby ocean, followed by purificationand distribution as energy instead of long andhorizontal movement of energy resource.

CONSTRUCTION PLAN
Introduction Function/Scale Calculation
Following the plan background and basic policies,
we perform the introduction function and scalecalculation. As the major function of the proposed
facility, four functions, including residence/activity
department,
power
generation
department,
production department, and storage department, areintroduced.

Energy-Producing Island City
The highest advantage of the proposed system isthe extremely low production cost and absence of risk
usually incurred when moving resources. Since amega-float is a floating structure, its position can bechanged according to the mining conditions of theseabed resources. Furthermore, it can be combinedwith a manufacturing factory, power storage, hotel,
residential building for workers, and so on, as
modules above or near the floating structure. Such asystem with limited movements of energy resources,
objects, and people can be called a vertical production
city.
The advantages of this city include extremely low
cost for production and processing, low CO2 emission,and less time required for administrative proceduresto perform environmental assessments of the plantsite. Furthermore, by preparing various facilities as
modules, the facilities can be extended or reduced,

Facility Plan That Makes Life Comfortable On
The Sea
Infrastructure maintenance is performed for along-term life on the sea. Based on the above
introduction and scale calculation, we proposed to
install five facilities with the residences at the core
where 270 staff resided.
The plan has five facilities with residences at thecore. It includes food production building, in which
exhaust heat and CO2 from the power generation
facilities are effectively utilized for cultivation in theplant factory. It also includes accommodationbuilding, where researchers and workers can stay and538

GEOMATE – Kyoto, Japan, 3-5 November 2021

depending on the function.
The disadvantage of floating structures is that they
are costly because they are not currently massproduced. However, since it is not premised on
moving, it does not require equipment such as
steering, engine, and fuel like ships, so it is expectedthat the cost will decrease if it is mass-produced.

[2] Agency for Natural Resources and Energy,
Energy in Japan 2018, https://www.enecho.meti.
go.jp/about/pamphlet/energy2018/html/001/#section1.
[3] The Federation of Electric Power Companies ofJapan, https://www.fepc.or.jp/enterprise/jigyou/
shuyoukoku/index.html.
[4] MH21-S R&D consortium, The distribution mapof BSRs related to methane hydrates, offshore
Japan, 2009, https://www.mh21japan.gr.jp/pdf/
BSR_2009.pdf.
[5] Fujii T., Suzuki K., Tamaki M., Komatsu Y., andTakayama T., Reservoir properties andheterogeneity of turbidite sediment revealed fromthe exploration of methane hydrate in the eastern
Nankai Trough, Japan. Journal of the Japanese
Association for Petroleum Technology, Vol. 8,
Issue 1, 2016, pp.84-95.
[6] Tanaka S., Brief mention of the first phase ofJapan’s Methane Hydrate R&D Program. Journalof the Japanese Association for Petroleum
Technology, Vol. 74, Issue 4, 2009, pp.265-269.
[7] Hirata K., Katagiri K., Maeda T., Shinoki T., andFunaki J., Hydrogen Production by EthanolSteam Reforming Using Cu/ZnO/Al2O3 Catalyst.
Transactions of the Japan Society of MechanicalEngineers. B, Vol. 79, Issue 808, 2013, pp.25872591.
[8] Sato C., Principal Characteristic of Mega-Float
and It’s Structure Planning Flow. Journal of theJapan Welding Society, Vol. 69, Issue 4, 2000,
pp.304-308.
[9] Sato C., and Kawamura A., A Study of Ultra-large
Floating Marine Structure and the Result (Part 3):
Floating Structure Design Techniques, A Study ofDurable Technologies: Chapter 4 A Study of
Floating Structure Design Techniques. Techno
Marine, Vol. 841, 1999, pp.512-529.
[10] Tanaka Y., Sasaki T., Miyake S., Masuda Y.,
Naganawa S., and Nagano J., Experimental Studyon CO2-in-Water Emulsion Injection Method forEnhanced Methane Hydrate Recovery, CSMH-5
2013 (5th Comprehensive Symposium on
Methane Hydrate). Methane Hydrate ResearchCenter, AIST, 2013, pp.1-4.

Island City Prepared For A DisasterIn the proposed facility, located 20–30 km
offshore of Japan, the impact of earthquakes andtsunami would be extremely small; thus, theadvantages of the facility during disasters, such as
earthquakes and tsunamis are significant. In otherwords, the module design of such a floating structure
can be called a novel 21st-century disasterpreventative city.
Notes:[Note 1] The survey conducted by the Ministry of
Economy, Trade, and Industry was implemented in2006. The resource reserve in the oceans surrounding
Japan was evaluated.
[Note 2] The methane-hydrate development planin Japan: 1) clarification of the theoretical reserve andcharacteristics of methane hydrates in the oceanssurrounding Japan; 2) estimate of potential methanegas reserve in the areas with theoretical methanehydrate reserves; 3), selection of the methane-hydrate
field from the candidate reserves and examination of
its economic potential; 4) marine production
experiment in the selected methane-hydrate field; 5)
updates to technologies for commercial production;6) establishment of a system that considers
environmental protection.
REFERENCES[1] Agency for Natural Resources and Energy,
Natural Resources and Fuel Department,
Promotion of Resource Development in Oceans
Around Japan, 2017, https://www.meti.go.jp/
shingikai/enecho/shigen_nenryo/pdf/021_04_00.
pdf, May 2017, pp.13-19.

539

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

THE EFFECT OF POROUS CONCRETE HANDLING ON EROSION
REDUCTION IN SLOPED SOIL AND SANDY CLAY CONDITIONS
Bimo Brata Aditya1), Putranto, Dinar DA2, Anis Saggaff2), Hanafiah2)1Doctor degree program, Faculty of Engineering, Sriwijaya University; 2 Department of Civil Engineeringand Planning, Faculty of Engineering, Sriwijaya University, Jl. Raya Inderalaya Km. 32, Inderalaya District,
South Sumatra, Indonesia

ABSTRACT
Erosion is the process of eroding the surface of the topsoil caused by the intensity of the rainfall that occurs
and causing splashes due to the working kinetic energy (J/m2), coupled with runoff due to the slope factor, whichis influenced by soil type and land cover. The characteristics of soil type and topography cause damage to the
topsoil and push the sedimentation volume to increase from time to time. Therefore Road safety infrastructurebuildings, such as cliff protection on a slope, irrigation building walls, and so on, require good security to avoid
collapse or loss of slope stability. Reducing the occurrence of kinetic energy in the topsoil surface layer andreducing erosion can be done by making a surface layer with a porous concrete pavement structure. However, first,
it necessary to analyze the amount of kinetic energy acting on the soil surface. The positive effects of porous
concrete layers on slope stability are discussed using: i) geo-mechanical effects, namely soil reinforcement withshaft concrete; ii) soil hydrological effects, namely the suction regime of the soil, which is influenced by theabsorption of water flowing through the concrete shaft. A one-dimensional vertical groundwater dynamics modelis assumed to simulate the soil suction regime, representing soil covered with shaft concrete on slopes with various
gradients. It was found that in various soil types and different slopes, in the case of sandy loam soils, geomechanical effects tend to be more relevant than soil hydrological effects during the rainy season.
Keywords: porous concrete, kinetic energy, geo-mechanical, rain intensity, slope

INTRODUCTION

Therefore, it is necessary to engineer a layering of thebasic soil structure with special treatment, tominimize the stripping of the soil layer due to erosion
caused by water flow
The topographical modelling approach for erosionand deposition is illustrated using the upstream
Lematang Sub-watershed, South Sumatra Province,
Indonesia. Geographically, the research area islocated at 2o 45 '- 4o 20' South Latitude and 103o 05 '104o 20' East Longitude. The upper Lematangwatershed consists of steep undulating hills, withslope angles of between 30% - 50%. To meet theirrigation water needs for the Lematang Irrigation
Area in center Dempo District Pagar Alam City,
South Sumatra Province, the Lematang Weir has been
built, which is located in the hilly area of the upstream
Lematang sub-watershed. Meanwhile, the hillsides
that tilt towards the river flow in some places form
steep slopes (the angle is almost 50%), such as thepedestal hill to the left of the weir, while the rightpedestal hill is formed gentler slopes [5].
The most important thing in looking at the studyof weir construction in the hill area is the need tocalibrate the characteristics of the river in thewatershed (DAS) of the study area. This is very
important considering that the shape and magnitude

In the development of human civilization, thedecline in the socio-economic conditions of society is
primarily due to the unwise exploitation ofenvironmental resources [1].
In the development of the industry to date, humanactivity can be said to be an essential agent inecological change. According to a 2005 study by
Wilkinson, human activity is the most critical agentof geomorphic change, compared to natural processes
that gradually work on the land surface.
Furthermore, Price, Ford, Cooper, and Neal [2]stated that the annual change in sediment caused byhuman activities exceeds that carried by river flows.
In the treatment process carried out by humans inmining activities, many topographic trimmings witha reasonably extreme slope have caused many
topographic changes, resulting in an erosion process
in the area [3]. Likewise, in infrastructuredevelopment such as roads, dams, and so on [4],much topography is trimmed to increase erosion in
the area, which will increase the incidence of
sedimentation. On this basis, it is essential to reducethe increase in ecological damage in infrastructuredevelopment such as roads, built areas, and dams.
540

GEOMATE – Kyoto, Japan, 3-5 November 2021of the flood hydrograph are highly dependent on thecharacteristics of the watershed. The watershedfeatures become the basis for reducing the scour rate
at the bottom of the channel [6].
In a study conducted by Sulaeman et al [4], thenumerical analysis uses 3D Delft to predict patterns,
flow velocity, and morphological changes upstream
and downstream of the weir. The results showed atrend of change at the bottom of the river, namely
degradation of the outer bend channel upstream of theweir and a decrease in the left side of the riverbed,
while sedimentation occurred on the rightdownstream of the riverbed the weir.

assessment of watershed stability in terms of themutual interaction between erosion and deposition iscarried out by comparing the contribution of sedimentfrom the Upper Lematang watershed.
RESULTS
Figure 1 presents the distribution of erosion anddeposition in the Upper Lematang sub-watershed for
four different combinations of factors using the
USPED decline and deposition model [7]. Initially,
the analysis was carried out only paying attention to
topographical factors, then combined with soil
erosion factors and land cover factors. They wereanalyzed simultaneously. Thus, there are three factors
used in conducting analysis using the erosion
mechanism of the runoff model for comparison
purposes. Overall (see Table 1), the classification of
the magnitude of the erosion distribution is minus (0-100) tons/ha/year, spreading over an area ofapproximately 1,331.35 Km2 in a room with a land
slope of around 17o – 34o [8].

METHOD
The approach used is first to compare the
distribution of sediment transport values, erosion, andsoil deposition with different parameters: a) only
paying attention to topographic factors, b) topography
and soil erosion, and c) topography, water seepage,and layering experiments. Second, a generalFig. 1 The upper Lematang sub-watershed and the location of the Lematang Dam
Meanwhile, the amount of deposition is between+(0-100) tons/Ha/Year spreads over an area ofapproximately 1,278.41 Km2, with a land slope ofaround 0o-17o. Meanwhile, sheet erosion of more than
500 tons/ha/year occurs on an area of approximately
71.26 Km2. This magnitude of the erosion anddeposition processes with high intensity is relatively
low. The distribution also changes with the additionof topographic factors into the model. With a
topographic slope of more than 34o, the amount oferosion is more than 500 tons/ha/year, occurring on
an area of 33.54 Km2. If the vegetation factor isremoved, the erosion rate will increase significantly
up to 0.5% [9].

River Channel Change Analysis
Based on speed distribution
Changes in the river bed upstream anddownstream of the Lematang weir can occur due tothe high flow velocity during drainage, of 2.3 m/s at
a 5-year return period discharge downstream of theweir with an average river bed material around thedownstream of the weir of 2 mm [5]. Based on the
Hjulstorm graph, namely the relationship between the
diameter of the material grains and the flow velocity(Figure 2), the tendency for scour to occur results ina decrease in the riverbed (blue line).

541

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1 Erosion classification and deposition oferosion rates in the upper Lematang sub-watershed
Classification(Ton/Ha/Year)< - (2500)-(2500-1000)-(1000 – 500)-(500 – 250)-(250 –100)(100 - 0)(0 – 100)(100-250)(250-500)
> 500
Total

Area (Ha)273.75
1,576.31
3,354.00
7,125.50
15,693.13
133,134.75
127,840.88
13,411.19
5,599.94
4,364.25

Area
(Km2)
2.74
15.76
33.54
71.26
156.93
1,331.35
1,278.41
134.1156.00
43.64
2,123.74

the downstream channel of the weir, has a shear force
value of 43 N/m2 with a material diameter of 2 mm
D50 (red line). This explains that with a 5-year return
period, discharge of 26.3 m3/s can erode the riverbed
downstream of the weir.

%
0.09
0.50
1.07
2.28
5.02
42.62
40.93
4.29
1.79
1.40
100.00With the shape of the river that bends sharply
upstream and downstream of the weir, it is possible
for scouring to occur at the foot of the outer riverbank. When depicted on the Hjulstorm graph, with a
5-year return period discharge and a flow velocity of
0.9 m/s, the grain size of the D50 material is 0.8 mm.
At the upstream of the weir (red line), with a speed of
1.4m/s, at the downstream bend with a material
diameter of D50 (2 mm), it is predicted that riverbed
scour (green line) will occur (Figure 2).

Fig. 3 Graph of critical shear stress relationship withriverbed grain diameter (2mm)
Similarly, upstream of the dam, the water flowfrom the left upstream hitting the cliff to the right
upstream has a shear force value of 15 N/m2 with amaterial diameter of D50 is 0.8 mm shown in the blueline. This explains that the 5-year return discharge of26, 3 m3/s can erode the riverbed upstream of theweir. The sheer force of the flow increases with theincrease in release at the return period, and it will also
increase the potential for greater scouring of theriverbed [5]Discussion
Sediment transport rate and spatial distribution oferosion and sediment affected by topographicalfunctions
In reducing the sediment transport capacity for the
upstream Lematang sub-watershed, terrain geometry
plays the most critical role. In addition, there is a fixedpattern underlying the sediment transport capacity,which is controlled by topographical conditions.
When modified by vegetation cover conditions andsoil type distribution, topographical geometricalproperties (slope) determine the spatial distribution of
sediment transport capacity in the sub-watershed[10]. Sheet flow is usually characteristic of areas withgood vegetation cover. However, it can occur in very
compacted soils, where soil detachment and theformation of natural flow can be prevented bycompaction. The increasing contribution to the slopearea, combined with high local slope values, isindicated by high levels of sediment transport. Areas
with high sediment transport rates associated withconcave slope profiles and valleys will accelerate
convergent sediment transport rates. Comparing
sediment transport rates between sheet erosion and

Fig. 2 Graph of the relationship between flow
velocity and the grain diameter of the river bed
material (0.8 mm)
Based on the flow shear stress
One of the parameters to predict scouring is theflow shear stress parameter (τo). By comparing theflow shear stress (τo), that occurs and the criticalgrain shear stress (τcr), the prediction of scouring ordeposition will occur. From the results of plotting theflow shear stress on the graph of the relationshipbetween the diameter of the sediment material and theshear stress in Figure 3, it is found that the shear force
that occurs at the five-year return discharge, in theposition after leaving the stilling pond, to the right of542

GEOMATE – Kyoto, Japan, 3-5 November 2021

natural erosion shows that the actual flow is turbulent.
As a result, it can carry sediment further in the flow
and be more concentrated along the valley and in the
concave part of the hillside than if the flow wasdispersed by vegetation in sheet flow.
Sediment transport rate divergence (qs) can be
identified in areas where the sediment transport rateincreases in the direction of flow (leading to erosion),decreases (leads to deposition), or remains constant(no corrosion or deposit). It is essential to emphasize
the difference between the quantities calculated using
the Erosivity Index (E) and Transport Capacity (T)equations, i.e., sediment transport rates and erosionand deposition rates: which can detect areas of highmass-carrying capacity. While the second allows thedetection of erosion and deposition patterns
determined by the distribution of incoming sedimentsupply to local transport capacity [11].
The resulting erosion/deposition map (based on
topography only) shows that the estimated high-risk
erosion areas are located at the top of the hillside, inbasins and valley centers with concentrated flows.
The depositional area usually occurs in the lower partof the hillside and the concave portion of the valley.
This situation is consistent with previous results
showing that the highest erosion rates correlate withdivergent elements and deposition with convergent
avalanche elements [12]. Maximum soil loss occurs
on slopes with a slope degree of more than 34o, andtotal soil erosion occurs on both sides of the traversed
pitch.

covered 9.92% of the study area (Table 1). Regionswith maximum erosion risk are concentrated along
steep slopes (42.62%), which also have large soil
erosion values, while deposition occurs in areas withlower pitches and occurs along valleys [8].

Fig. 4 Erosion and Sedimentation as a function of
topography and soil erodibility
Reducing the effect of water seepage, which can
reduce the amount of erosion
Experiment using a pond in a different case to test
water seepage in a watercourse [14][15]. Six pondswith a capacity of 100 m3 observed the rate ofseepage during the rainy season and after the rainyseason. During the rainy season, surface water is kept
at the pond's bottom, limiting the vertical flow ofwater and sometimes damaging seepage. One of theponds was coated with a low-density polyethylene
film to see the effect of the absence of seepage. The
result provides the lowest level of seepage comparedto pools lined with broken brick material or pools
lined with a mixture of soil and cement.
Meanwhile, Wang et al conducted a study on
seepage loss in channels with various coating
materials [16]. The seepage loss analyzed by the
Inflow-Outflow method varies from 0.225 to 0.315
ft3/ft2/hour. At the same time, the process ofobserving the water level in the pond obtained results
rangingfrom
0.1954ft3/ft2/hour
to0.2584ft3/ft2/hour. The rate of constant seepage loss
over 200 ft in the earth channel was found to varyfrom 12 to 18 percent of the inflow to the outlet, i.e.,
0.23 to 0.41 ft3/ft2/hr [17].
By conducting a semi-field trial using thefollowing composition, [18] : (1) Cement-gravelsand-gravel concrete (0.58 : 0.15 :5:10); (2) Cement– fly ash-sand-gravel concrete (0.8: 0.2: 5: 10 ); (3) A
layer of sand – asphalt – cement on the soil base (0.85:
0.1: 0.05); (4) Sand-bitumen – cement layer on the

Sediment transport rate and spatial distribution oferosion and sediment as a function of topography
and soil erosion
Overall, by including the K-factor in the analysis,
the spatial pattern of sediment transport shows theinfluence of areas with high erosion. However,
because the distribution of soil types is highly
correlated with topography, the design is strongly
dominated by topography. Thus sediment flows will
have lower values over a more prominent location
across the landscape than they would have very highvalues concentrated in high-sloping sunken areas[13].
The inclusion of soil erosion patterns also
modified the spatial distribution of erosion anddeposition. This will increase the area of the area thatis at greater risk of decline. Although the percentage
of sites with erosion/deposition values is less than orequal to the case where the topography is the only
triggering factor (42.62%). It should be noted the
distribution of erosion and deposition values for both.
Erosion and deposition cases were analyzed by taking
into account the soil erosion factor, and the erosion
percentage ranged from 5.02% to 4.29%. At the sametime, the topographic factor has a much moresignificant influence on erosion (40.93 to 42.62) %.
The corresponding erosion range from (-0 to 100)543

GEOMATE – Kyoto, Japan, 3-5 November 2021

cement mortar base (0.85: 075: 0.075 ) and (5)without treatment. The results obtained are that the
maximum seepage occurs in the uncoated channel,
which is 45 m3 per 1000 m2. While the minimum
seepage in treatment No. 2 is 2.76 m3 per 1000 m2.
On this basis, to reduce water seepage due to theflow process in areas that significantly influenceslopes and high erosion potential due to cliff erosion,
it is necessary to design using coatings. It is
recommended that infrastructure development, which
can lose water or reduce surface erosion in general,
should be equipped with a layer in all future projects.
Zhang et al [19] experimented on seepage loss
through experiments with field conditions coatedwith different coating materials. The seepage ofvarious coating specifications varies from almost nil
to 0.3 m3/Mm2 in a double layer tile layer with a layerof 1:3 cement sand plaster on the slope side and single
layer bricks on the base 6.8 cm thick asphalt concreteon the side—each layer. The seepage loss through the
precast beams of single-layer cement mortar 1:3
bricks ranged from 0.04 to 0.16 m3/Mm2.
Preliminary laboratory tests have been carried out
on a layer of asphalt concrete consisting of pea jelly,
quarry dust, fine aggregate, rock dust (20: 40: 25: 10),and 60/70 asphalt 9 percent by weight and cement
concrete 1:4:8 with 13 percent bentonite.
Furthermore, it concluded that the asphalt concrete
layer could function as a suitable coating material.
Experimentally Mclaughlin et al [20] proved thatemulsified asphalt mixed with soil had shownpromise as a coating material, reducing the level of
erosiveness in a topsoil layer.
Worstell [21] conducted tests on various coating
materials to study the seepage loss and found therange of seepage loss through different coating
materials as below : (1) Concrete (0.009-0.29)
m/day); (2) Compacted soil (0.003 to 0.29 m/day); (3)Asphalt cement (0.003 to 0.92 m/day; (4) Soil cement(100 : 5 ) with (0.009 to 0.06 m/day; (5) Chemical
Sealant (0.1-2.53 m/day); (6) Sediment Seal (0.12 to0.40 m/day); (7) Unlined (0.003 to 5.37 m/day).
The Maharashtra Engineering Research InstituteNasik [22] develop an inexpensive channel coating
material in mortar-stabilized soil tile and fly ash
concrete. It has been observed that flash has uniform
properties except for its fineness, although the coalused has very variable properties.
It was observed that cement could be replaced by
fly ash up to 10 percent for 1:3 and 1:5 mortars, andsand could be replaced by fly ash up to 20 percent for
1:3 and 1:5 mixtures. The study was made based on
the strength criteria, and it was found that if 90 days
is a strength criterion, then 20 percent of cement will
be replaced for significant works such as dams, and if
28 days is a strength criterion, 15 percent of glue canbe replaced with fly ash. Fly ash has been used instable soils as support for tiles. This technique has

been adopted in the Jaikwadi and Girna Command
Areas.
CONCLUSIONS
1. Comparison of USLE, LS and topographic factors
and the erosion/deposition index shows that awater-based destructive power approach is more
appropriate for erosion modelling on a landscapescale. Especially when the location of both regions
is at risk of erosion and with potential deposition,
it is very likely to occur
2. With this analysis has improved the approachbased on water damaging power units byincreasing DEM calculations using smaller pixel
sizes and by perfecting the flow direction using
vector-grid algorithm
3. Retaining walls to hold the soil on the cliff fromscouring due to flow patterns and flow velocity as
well as natural collapse caused by water scouring
and slope slope
4. It
isrecommended
that
infrastructuredevelopment, which can lose water or reducesurface erosion in general, should be equippedwith a layer in all future projects.
ACKNOWLEDGMENTS
Thank you to Sriwijaya University, through theInstitute for Research and Community Service,through funding for innovation research. Which isintended to increase the role of Sriwijaya University
in supporting climate change monitoring in the Musi
River Basin. And Increasing Independent Learning of
the Independent Campus..
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[2] N. Haregeweyn et al., “Comprehensive
assessment of soil erosion risk for better land use
planning in river basins: Case study of the Upper
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[3] A. L. Yuono and D. D. P. Sarino, Sarino,“Analisis Spasial Kondisi Lingkungan dan
Hidrologi Sub DAS Komering Hulu Kaitannya
dengan Penurunan Ketersediaan Air,” no.
September, pp. 978–979, 2019.
[4] F. Sulaeman, A., Harianto., Kaharudin, H. K.,
Pramana, “Kajian morfologi sungai akibat
perletakan bendung Lematang,” in Seminar
Nasional Teknik Sumber Daya Air, 2016, pp. 1–
8.
[5] F. Pramana, A. Saggaff, and F. Hadinata, “AN
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vol. 9, no. 03, 2020.
[6] N. Mulatu, Atshushi, “Exploring land use_land
cover changes, drivers and their implications incontrasting agro-ecological environments ofEthiopia _ Enhanced Reader.pdf.” pp. 1–15,
2019.
[7] a Pistocchi, G. Cassani, and O. Zani, “Use of the
USPED model for mapping soil erosion andmanaging best land conservation practices,”
Proc. First Bienn. Meet. Int. Environ. Model.
Softw. Soc. Integr. Assess. Decis. Support, vol. 3,
pp. 163–168, 2002.
[8] D. D. A. Putranto, Sarino, and A. L. Yuono,“Spatial distribution level of land erosion
disposition based on the analysis of slope on
Central Lematang sub basin,” in AIP Conference
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[9] Sarino, A. L. Yuono, and D. A. Putranto Dinar,“Spatial pattern of sediment transport for analysis
of precipitation direction and magnitude in theupper Lematang river sub-basin,” IOP Conf. Ser.
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[10] P. R. I. Putranto Dinar DA, Titis Pratami,“ASSESSMENTOFSPATIAL
DISTRIBUTION OF LAND BASED ON
ANALYSIS OF SLOPE AND WATERCONSERVATIONPROGRAMS,”
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agriculture (ICOSA), 2017, pp. 1–9.
[11] X. C. J. Zhang and Z. L. Wang, “Interrill soil
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[12] V. Belton and T. Stewart, Multiple Criteria
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[14] B. Tian, T., Balzer, D., Wang, L., Torizin, J.,
Wan, L., Li, X., . . . Tong, “N Landslide hazard
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[20] McLaughlin N.B.; Dyck F.B.; Sommerfeldt, “Anasphalt incorporator and packer for lining
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Resources, 2012, pp. 246–283.

545

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

PROPOSAL OF ISOLATION MEASURES FOR MOUNTAINVILLAGES BASED ON ANALYSIS OF DISASTER PREVENTIONDRILLS
Zhijun Tang１,2, Takeyasu Suzuki1
１

2

The University of Yamanashi，Japan
Chuo Kaihatsu Corporation，Japan
ABSTRACTAs measures against the isolation of mountain villages, it is the mainstream to enable two-way communication
between residents and the local government by installing satellite mobile phones and answer back function ofdisaster prevention radio, that is, to secure information transmission means. However, due to sediment-related
disasters, isolation may occur even within the village, the heavy snowfall or sediment may prevent the installationof satellite mobile phones and disaster prevention radios. Therefore, we propose to arrange a wirelesscommunication network and an information terminal that has a WiFi function and is easy to operate and to proceedwith the construction of a wireless communication network (public assistance) along with the safety confirmation(mutual assistance) by the residents of the village, and confirmed by a practical disaster prevention drill.
Keywords: Isolation measures，Mutual assistance，Public assistance，Safety confirmation ，Mountain village

INTRODUCTION

less susceptible to line congestion and power outages.
However, satellite mobile phone has some demerits,
firstly, it must point their antennas in the direction of
the artificial satellite to capture the radio signal.
Secondly, it cannot be used in a room without awindow unless taken outdoors, and it is impossible to
make a call on a cloudy day. finally, since satellite
mobile phones are generally installed in public halls,
it is necessary to go there to use them.
Therefore, we setup local wireless network andinformation system, examined the effects ofdifferences in information transmission means suchas mobile phones, landlines, satellite mobile phones,
and disaster prevention administrative radios on
disaster response such as safety confirmation andrescue requests in disaster prevention drills.

Many types of natural disasters occur in Japan suchas tsunamis, earthquakes, typhoons, and landslides.
More than 17,000 villages may be stranded due tothese natural disasters in national land, for example,
in Yamanashi Prefecture, 493 villages may beisolated due to road blockages caused byearthquakes[1].
Disaster Countermeasures for being stranded are
not only hard measures but also soft measures likeearly warning evacuation. When a sudden disasteroccurs, it should be aiming to evacuate to a safe
community center. Living in an unfamiliar shelter can
be stressful, especially for the elderly. It is better If
can evacuate to the village public hall, because it caneasily get the supplies from the home nearby, and if
the home is not damaged, it is possible to live at home.
In addition, the life of the village public hall is not sostressful because it is a neighbor who is acquainted
with each other. If someone is injured, or suddenly ill,
it needs to be taken to a hospital. if there is a shortageof supplies such as food and medicine, support must
be requested.
Local governments are promoting the introductionof satellite mobile phones to villages that may beisolated to secure communication means in the eventof a disaster. Since satellite mobile phones
communicate via satellites, unlike fixed-line phones
and mobile phones, they have the advantage of being

SURVEY SITE
The survey sites are located at Hachinoshiri district,Ichikawa Misato Town, Yamanashi Prefecture,
shown in Fig.1. It is a mountain village and alandslide area, if a large earthquake occurs, the roadmay be blocked by the landslide and the village may
become isolated. The evacuation center is in the
gymnasium of Elementary School and Junior HighSchool in the Kurosawa area at the foot of themountain.
In January 2014, a practical disaster prevention
drill in these sites was conducted on the condition that546

GEOMATE – Kyoto, Japan, 3-5 November 2021

the district was isolated and mobile phones could not
be used due to line congestion. "Safety confirmation
using ICT, rescue request" was executed in "Safety
confirmation system derived by residents
themselves". Risk Communication using the CAUSE
Model has been conducted in disaster isolation
measures, to make the residents get the consensus ofevacuating from the village when a disaster occurs.

Along with improving self-help and mutual
assistance, building disaster countermeasures incollaboration with residents and the government willlead to the improvement of regional disasterprevention capabilities. "Workshop on home
evacuation as an isolation measure" was held as an
initiative to carry out Risk Communication using the
CAUSE Model.
The CAUSE model is a method proposed byProfessor Rowan of George Mason University in the
United States for Risk Communication education forregional crisis managers. CAUSE is an acronym for
Confidence, Awareness, Understanding, Satisfaction
with proposed Solutions, and Enactment arranged in
the order of C, A, U, S, E[2].
Targeting the Hachinoshiri area of the mountainvillage, the local community and the government
collaborated to form a consensus on evacuation athome[3]. First, we built a relationship of trust with
university teachers, students, municipal disasterprevention officers, and residents (Confidence).
Next, make people aware that they have the option of

Fig.1 The survey site in Yamanashi Prefecture

Issues of self-help and mutual help. ⇒Work on your own
Examination and construction of safety confirmation system

RISK COMMUNICATION
Risk Communication, in general, means that
accurate information on the risks surrounding society
is shared among governments, experts, companies,and citizens to communicate with each other.
Risk Communication is required when regarding
problems that involve certain risks such as fostering
public understanding of disasters and environmental
problems and still require sharing of awarenessamong related parties, and when it is necessary torecognize safety measures and share cooperative
relationships.
Risk Communication in disaster prevention
shares information on disaster risk among
stakeholders such as the government and citizens, andmutually understands the information provided byresidents to the government and the information andservice contents provided by the government to theresidents. It is a process of working together topromote and mitigate risks as much as possible. In
this process, the parties concerned do not hide theinformation, share all the good and bad information,and discuss and solve the bad things.
By communicating with each other and building acooperative system through Risk Communicationeven before a risk arises, it is possible to mitigate therisk when it occurs.

Public assistance issues ⇒ Have them understand the proposal
Proposal of information transmission means

1.Confirmation of safety with a smartphone
2.Confirmation of safety by videophone
3.Inter-building communication antenna (left) and outdoor WiFi routerFig.2 safety confirms by communication means
evacuating at home in the event of a disaster(Awareness). Finally, make people understand theadvantages and disadvantages of staying in a village
in the event of an earthquake disaster (Understanding).
If the problem is solved, stay in the village
depending on the degree of damage (Solution). The
residents discussed by themselves and took out thesolutions also conducted.
WIRELESS NETWORKS AND INFORMATION SYSTEMS
Considering the characteristics of mountainous
areas, it is considered that a wireless network thatbuilds a wireless connection by radio waves insteadof cables in the event of a disaster is applicable. It is547

GEOMATE – Kyoto, Japan, 3-5 November 2021

2.4GHz wireless network

Fig.3 Wireless network built in the villages
expected that fixed-line phones will gradually be
replaced by mobile phones (smartphones), andwireless communication will be carried out by anyone
anywhere.
It is indispensable to build a wireless network as acountermeasure against the isolation of mountainvillages instead of satellite mobile phones. But thewireless network has not been constructed in the
Bessho area and the Okimura area, because of many
radio obstacles in this mountainous area.
Professor Suzuki proposed a method of designinga wireless network in advance on a desk that reflects
topographical conditions to build a wireless network
as a countermeasure against the isolation of mountainvillages[4]. Group the mountain villages, obtain andprepare the necessary data in advance for the wirelessnetwork of the mountain villages where it is difficult
to conduct on-site reconnaissance and radio wave
surveys, and then limit the number of relay stations to
two between base stations and use GIS. So, we
proposed an algorithm for designing a wirelessnetwork using it, shown in Fig.3.
Then, the wireless network design method wasapplied to 29 isolated villages in Ichikawamisato
Town. As a result, we were able to construct awireless network that connects nine villages by
installing three relay stations and a wireless networkthat connects six villages by installing one relay
station. In particular, regarding the wireless networkof the group including the Hachijiri area, the wirelessnetwork could be constructed in Okimura and Bessho,
which could not be constructed even if thetelecommunications carrier examined it locally, andthe user could be proved.

PHONES IN THE VILLAGE
Before the actual disaster prevention drill, we visited
all households one by one in the Hachinoshiri district,in order to understand the means of informationtransmission for each household. Hachinoshiri
district consists of Miyazawa, Nakamura, Bessho /Okimura, and the number of households in each
district is 10, 15, and 12 households.
On November 17, a survey on information andcommunication methods for residents conducted
home visits to 34 households in the Hachinoshiri area,
only 3 households could not visit at home(Table 1).
Regarding the results of the survey, among all
Hachinoshiri households, 7 households have 15
Table 1 Households in the Hachinoshiri area
Total
VisitedAbsent37
34
3
Table 2 Ordinary mobile phone in 23 households
au
softbank
Total
docomo38 units
23 units
13 units
2 units
Table 3 Smart mobile phone in 7 households
au
softbank
Total
docomo16 units
4 units
10 units
2 units
Table 4 About the fixed-line phone
All fixed-line phone
Use of mobile mail function
Don't use email at all
Terminals that can receive
Earthquake Early Warning Area Mail

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31
10
10
21

GEOMATE – Kyoto, Japan, 3-5 November 2021

smartphones, and 23 households have 38 ordinary
mobile phones. Regarding the contracted mobile
phone companies, DoCoMo has 27 cases, au has 20
cases, and SoftBank has 2 cases. For model types, see
DoCoMo's MicroSD, f-10a, f88ies, f-08c. There wereau MicroSD, Koos, sony Xperia, sol21, etc, shown in
Table 2 and Table 3.
We checked from the homepages of the threecarriers and found that only 21 units could receive theEarthquake Early Warning Area Mail. It was also
confirmed that 31 households have 31 fixed-line
telephones as another means of communication. It isshown in Table 4.
Before the actual disaster prevention drill, theresidents of Hachinoshiri used four smartphone
phones and five videophones borrowed from
Yamanashi University to register their safety andreport the damage situation and found out theschedule.

Fig.4 Disaster prevention drill in Bessho

RESULTS OF DISASTER PREVENTION DRILLSOn January 11, 2014, a practical disaster prevention
drill started at 9 o'clock in the Hachinoshiri district ofIchikawamisato Town, Yamanashi Prefecture, shownin Fig.4 and Fig.5. The occurrence of an earthquake
and the issuance of evacuation advisories werecommunicated by the voice from the disasterprevention radio. At the same time, emergency
earthquake messages were transmitted by mail andSNS.
Since we registered the information on theestablishment of the disaster countermeasures
headquarters and the issuance of evacuation
advisories, it could be started to confirm the safety ofresidents. The safety of the residents was confirmedby the system decided by each village and reported to
the government office by IP phone, SNS, videophone,and satellite mobile phone.
After the training starts, the residents implemented
the safety confirmation system for each group andpromptly confirmed the safety. Some residents in thegroup have a videophone or smartphone, and when
they confirm their safety, they register the safety
information on the Ichikawamisato SNS and informthe town of the safety information. In addition, theinjured residents and the residents whose houses havecollapsed and whose safety is unknown are found,and a rescue request is made to the town.
The precondition is that the mobile phone callfunction cannot be used due to line congestion or thedestruction of the base station due to an earthquake.

Fig.5 Disaster prevention drill in Nakamura
In addition, a simulated disaster was generated in the
district according to the scenario.
Signs, traffic cones, and people closed the road toblock the passage of residents. It was assumed that all
the roads leading to the outside of the area were cut
off and the Hachinoshiri area was isolated. A practicaldisaster prevention drill using ICT (Information andCommunication Technology) was conducted for the
residents of Hachinoshiri. Planned for the first timeso that information can be exchanged smoothly in theevent of a disaster. Residents used smartphone
phones and other devices to register their safety andreport on the damage situation using the information
sharing system being developed by the University of
Yamanashi.
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GEOMATE – Kyoto, Japan, 3-5 November 2021

It was confirmed that the safety confirmation basedon the safety confirmation system was carried out(Enactment) in each group from the behavior
observation and questionnaire survey of each
inhabitant. It was confirmed that 95% of the residents
carried out the safety confirmation.
(1) All the residents were not aware of the voice
speakers of the disaster prevention radio, emergency
bulletin mail, IP phones (videophones, smartphones),and SNS, and the certainty was not satisfied. By
adding a call (safety confirmation), it was found that
all the residents in the Yanojiri area were able to beaware of the evacuation information.
(2) As a result of investigating all mobile phone
types owned by residents in the Hachinoshiri area andsending an Earthquake Early Warning Area Mail, inareas where the radio waves of mobile carriers are
relatively weak, it is also within the area. However, itturned out that the Earthquake Early Warning Area
Mail could not be received.
(3) When evaluated in the entire Yanojiri area, inthe case of calls using IP phones and response actions
using SNS, safety confirmation in the village,
information transmission to the town hall,
information sharing between the town hall and thefire department, safety information on SNS, It was
confirmed that the input of damage information was
effectively achieved.
EVALUATION
MEANS

OF

INFORMATION

in the Hachinoshiri district practical disasterprevention drill. The time required for response
actions using satellite mobile phones and disasterprevention administrative radio, and the time required
for rescue requests from the government office to thefire department is set as table 5 and Fig.6.
Table 5 The time required in different methods
Methods
Calls using ICTEnter safety information anddamage information into SNS
Calls using mobile phones
Calls using landlines
Calls using satellite mobile
phones and disaster prevention
administrative radio
Based on SNS information, thegovernment office requests thefire department to rescue theresidents
Based on the report by
telephone, the government
office requests the fire
department to rescue theresidents

minutesTime
3 Minutes
1 Minute
3 Minutes
3 Minutes
5 Minutes

1 Minute

5 Minutes

The time takes for the government office to request
support from the fire department after the disaster

150
100

TRANSMISSION

500From the questionnaires conducted in the Yanojiri
district practical disaster prevention drills, thesemeans can be used for disaster response such as safety
confirmation/rescue requests. The evaluation ofcommunication means as a disaster isolation measure
in mountainous villages is described as the following
indexes.
(1) "Time" from the disaster to the time when thegovernment office contacts the fire department
The total time taken for the response action in theprocess of the government office contacting the fire
department from the disaster for each condition of thecommunication means is defined as the "time" fromthe disaster until the government office contacts thefire department.
The time required for response actions using ICT
and mobile phones was set based on the results of the
training survey. The time required for a walk visit wascalculated using the time required for walking (80m /min). Landline phone that could not be investigatedIP phones IP phones IP phones Mobile
-some
-all
,SNS-all phones
residents residents residents

Mobile Satellite
phones mobile
phones,andlandlines radioFig.6 Time of different communication means(2) "Number of activities" from the disaster to thetime when the government office contacts the fire
department
The latest route from the disaster to the officecontacting the fire department is extracted, and it isdefined as the "number of activities" from the disasterto the office contacting the fire department. The typesof activities are shown below.
1.Gather at the meeting place, confirm the safety
2.Visit on foot, confirm the safety
3.Use communication equipment to confirm safety
4.Enter safety information by SNS
5.Resident’s report safety to the government office
6.Rescue from the fire department
550

GEOMATE – Kyoto, Japan, 3-5 November 2021

phones and response actions using SNS, isolatedhouseholds could not be confirmed the safety.
In "Calls using mobile phones and landlines," allresidents in the Yanojiri area were able to confirm
their safety. Therefore, if the usability of software
such as IP phones and SNS is improved, the safety
confirmation of all residents will be confirmed. It isthought that the "time" from the disaster to thegovernment office requesting support from the fire
department can be shortened and the "number ofactivities" can be reduced.
From the above, a system in which residents andgovernments collaborate in a WiFi environment built
on a local area network using wireless is effective as
a communication means for disaster isolation
measures in mountain villages.

Number of activities
20
15
10
5
0
IP phones IP phones IP phones Mobile
-some
-all
,SNS-all phones
residents residents residents

Mobile Satellite
phones mobileandphones,landlines radio

Fig.7 Number of activities(3) The "stage" from the disaster to the time when
the government office contacts the fire department
The contents of the response actions at each stage,
which are color-coded, are organized, and theevaluation index is whether the government office issurely performing the rescue request to the fire
department, shown in Fig.8.

SUMMARY
A practical disaster prevention drill in the
Hachinoshiri area was conducted. As a measureagainst the isolation of mountain villages, it wasclarified that it is important to establish a safety
confirmation system and secure a means ofcommunication between the government office andthe village by wireless communication.
To reduce the damage to people caused by disasters,
different communication means are necessary, andlocal area network using wireless is effective as acountermeasure for the isolation of mountain villages
on disaster response such as safety confirmation andrescue requests.
REFERENCES[1] “Cabinet Office of Japan.Disaster Managementin Japan. Available online:,” 2015,
http://www.bousai.go.jp/1info/pdf/saigaipamphle
t_je.pdf.
[2] K. E. Rowan, “Why Rules for Risk
Communication Are Not Enough: A ProblemSolving Approach to Risk Communication,” Risk
Anal., vol. 14, no. 3, pp. 365–374, 1994, doi:
https://doi.org/10.1111/j.15396924.1994.tb00253.x.
[3]T. Suzuki, T. Watanabe, and S. Okuyama,“Facilitating community risk communication for
wide-area evacuation during large-scale floods,”
Int. J. Environ. Res. Public Health, vol. 16, no. 14,
2019, doi: 10.3390/ijerph16142466.
[4] T. Suzuki, “Influence of Communication Tools
on Transmission Characteristics based on the
Evacuation Information Transmission Test,” JSTAGE, no. 13, pp. 48–56, 2015, doi:
https://doi.org/10.24709/jasdis.13.0_48.

Fig.8 The flowchart of stage
Using the three evaluation indexes of "time","number of activities", and "stage" from the time ofthe disaster until the government office contacts thefire department, the impact of the differences in thefive information transmission methods on disaster
response was confirmed.
When evaluated in Miyazawa, Nakamura, Bessho/ Okimura, and Yanojiri district, in the case of callingusing IP phones and responding actions using SNS,safety confirmation in the village, informationtransmission to the town hall, It was found that thethree stages of information sharing between thegovernment office and the fire department were
integrated into one stage of inputting safety
information and damage information into SNS.
Under the preconditions of residents who do nothave phones. Therefore, in the case of calls using IP
551

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

SPATIAL STATISTICAL ANALYSIS OF THE POROUS MEDIUM
EXTRACTED FROM VIRTUAL PACKED GRAINS WITH RANDOMSIZESYu Song1, Junichiro Takeuchi1, Yuto Takeuchi1 and Masayuki Fujihara1
1

Graduate School of Agriculture, Kyoto University, Japan
ABSTRACT

Hydraulic properties such as water retention properties and permeability, which are macro characteristics ofporous media, are determined in a bottom-up manner by pore properties including pore shape, pore-size
distribution, and pore connectivity. Hydraulic properties could be reproduced from local physical phenomena
such as capillarity in the pores using a pore-network model. Spatial distribution of pores could affect thesehydraulic properties. In this research, global and local Moran’s indexes, which are spatial statistics, are used toanalyze the spatial distribution of pores in porous media packed with both single-size and various-size grains.
The obtained results of global Moran’s indexes show that pore distribution has positive autocorrelation in all the
media we packed, and those of local Moran’s indexes show that larger pores tend to be formed around larger
pores, and that those form large-pore clusters. These results imply that hydraulic properties of porous media
could be affected by the spatial distribution of pores.
Keywords: Water retention property, Pore-network, Spatial statistics, Global and local Moran’s indexes
INTRODUCTION
It is well known that soil water relations are an
important aspect of the soil environment of the plant.
The hydraulic properties of soils such as water
retention properties and permeability, which aremacro characteristics of porous media, play animportant central role in determining the movement
and storage of water in soil and thus have a large
influence on crop growth.
Soil water retention and transport properties arealtered in response to changes in pore properties
including pore shape, pore-size distribution, and poreconnectivity [1]. Pore-network models could be
utilized to reproduce the hydraulic properties fromlocal physical phenomena such as capillarity in the
pores [2,3]. It was presented that the water retention
property of the original pore-network model extracted
from a porous medium model is different from that ofpore-network models generated randomly based on
the same pore-size distribution, which suggests thatpores have a certain spatial structure in the porous
medium [4].
In this research, we used the spatial statistics called
global and local Moran’s indexes [5] to analyze thespatial pore-size distribution of porous media packed
with both single-size and various-size grains, to findthe global spatial autocorrelation in the pore
distribution.

pattern. Moran’s I statistics are one of the statisticaland computational methods for examining spatial
autocorrelation on a network, that is, the correlation
between attribute values of the same kind at different
locations on a network or on different subnetworks(including line segments and links) forming the
network [6]. Moran’s I statistics include two types:
global Moran’s I statistic and local Moran’s I
statistic. The former shows the average level ofspatial autocorrelation over the whole network; thelatter examines the correlation between the attributevalue of a specific network and those of its
surrounding network points.
Global Spatial Autocorrelation
The global Moran’s I statistic on a network,
network global Moran’s I statistic, is used for global
spatial autocorrelation, which is defined as:nn w  x  x  x  x I  i 1 j 1

ij

i

j

m2 S0

,

(1)

where n is the number of the data, xi is the attributevalue that denotes the value taken on by the variable
x of interest at location i and x denotes the mean ofthese values, which is defined as follows:

SPATIAL AUTOCORRELATIONx

Spatial autocorrelation can be defined in thephenomenon that occurs when the spatial distributionof the variable of interest exhibits a systematic

1 n
 xi ,n i 1m2 and S0 are defined as:
552

(2)

GEOMATE – Kyoto, Japan, 3-5 November 2021

m2 

2

1 n
 xi  x ,n i 1



n



(3)

n

S0   wij .

(4)

i 1 j 1

In the present study, xi is the diameter of the pore
body (PB) whose index is i , and n is the number of
all PBs in the interest pore-network.
Spatial autocorrelation statistics deal with thecorrelation between nearby attribute values. wij
denotes the elements of the spatial weights matrix
W corresponding to the location pair (i, j ) and canformalize the nearness mathematically. In thisresearch, wij  1 if network points are adjacent;otherwise, wij  0 .
When Iis positive, it suggests a positive
autocorrelation; when I is negative, it suggests a
negative autocorrelation. Statistical test is based on
Z I values, computed by subtracting E[ I ] from I
and dividing it by the standard deviation of I , which
is given by:I  E I 

ZI 

Var ( I )

 N  0,1 .

(5)This equation can express both the deviation fromthe average of the point’s own value and that of thesurrounding points’. That means, I L is positive whenboth of them are greater than or less than the average,
and I L is negative if either one is greater or smaller
than the average.
In terms of the network global Moran’s I statistic,
this equation is written as:nI   I iL / S 0 .

This equation shows that the global Moran’s I
statistic is proportional to the average of network
local Moran’s I statistics over all network points.
The same as those of the global Moran’s I
statistic, the expected value and variance of I L under
the null hypothesis of no global spatial
autocorrelation, which can be utilized for identifying
statistically significant points, are derived as:
ZIL 
i

(6)

2
n 2 S1  nS 2  3S02
Var  I    E  I  ,22
 n  1 S0(7)

I iL  E  I iL 
Var  I iL 

 N  0,1 ,E  I iL   
Var  I iL  

wi*
,
n 1

2
1 n n
1 n
2
w

w
,
S



 ij ji  wi*  w*i  ,2
2 i 1 j 1
2 i 1

n

n

j 1

j 1

wi*   wij , w*i   w ji .

n 1

m2

 x  x .
j 1

ij

j

2wi ( kh )  2b2  n 

 n  1n  2  E I  ,
2

(14)

n

wi (2)   wij 2 , wi ( kh ) 

(8)

(9)

The local Moran’s I statistic can examine thecorrelation between the attribute value of a specificnetwork point and those of its surrounding network
points, which is defined as follows: xi  x  n w

andLocal Spatial Autocorrelation

I iL 

(13)

wi (2)  n  b2 

j 1

S1 

(12)

where

The expected value and variance of I under thenull hypothesis of the normal variate spatial
randomness are derived as:

1
E I   
,
n 1

(11)

i 1

(10)

b2 

1 n n
 wik wih ,2 k 1 h 1

m4
1 n
4
m

,
 xi  x  .

42m2
n i 1(15)(16)

Moran Scatter Plot
Spatial distribution of the attribute values can bevisualized through classification by the scatter plot
with the original attribute values on the horizontal
axis and the surrounding values on the vertical axis
[7].
In the Moran scatter plot, the attribute values are
divided into four quadrants, each of which represents
a different kind of spatial association: Spatial
categories in the upper right (HH, high-high) andlower left (LL, low-low) quadrants, and spatial
categories in the lower right (HL, high-low) andupper left (LH, low-high) quadrants.

553

GEOMATE – Kyoto, Japan, 3-5 November 2021

NUMERICAL EXPERIMENTS
Pore-network Models
Pore-network models extracted from porous
media are utilized to conduct the numericalexperiments in this research. The virtual porous
media are prepared by using the discrete element
method, which are composed of various kinds ofspherical grains packed randomly in the cubic
containers. The method used for pore network
extraction is known as the watershed segmentation
algorithm, which has been popular these days due tothe effectiveness in working with porous media
images of any porosity and its easier implementation[8]. In this research, we put SNOW algorithm [9], an
efficient algorithm for extracting networks using only
standard
image
analysistechniques,
into
implementation. Details on network extraction using
this method can be found in the reference [9].
A pore-network consists of pore bodies (PBs) andpore throats (PTs). PBs are relatively large, however,PTs that connect two PBs are relatively small. To
make networks sufficiently large that exceed therepresentative element volume (REV), we tried
packing spherical grains from 1000 to 7000 and
extracted the pore networks under the conditions of
the voxels from 2003 to 7003. By observing
histograms of the frequency distributions of poresizes and coordinate number, the number of PTs thata single PB connects, we find that when the numberof grains is larger than 2000 and the voxels exceed
5003, packed porous media and the extracted
networks are sufficiently large and resolved fornumerical experiments.
In this research we packed porous media withfour grain size distributions (A-D) and three different
samples were made in each grain size distribution:
Sample A1-3 are packed with single-size sphericalgrains; the others are packed with uniformly
distributed spherical grains. The numbers of grains all
exceed 3000, and we choose voxels of 6003 to get
more delicate models. The sample holders (mm) ofthem are A-44.13, B-32.63, C-45.13 and D-48.13.
Table 1 shows the properties of packed porous
media and the extracted pore networks. Figure 1
shows the virtual porous media and pore-networks of
A1 and D1. The former is packed with single-size
spherical grains; the latter is packed with uniformly
distributed spherical grains. To eliminate theinfluence of boundary PBs and PTs, we make thefrequency distributions of pore-sizes and coordinate
number of the internal pore-network models D1-3,
which are shown in Fig. 2, and it was confirmed thatsimilar pore-size and coordinate number distributions
were obtained from different grain-packing in the
cubic container. Moreover, the following numericalexperiments are all applied on the internal porenetwork models.
Table 1 Properties of porous media and porenetworks
N
A
M
E
A1
A2
A3
B1
B2
B3
C1
C2
C3
D1
D2
D3

3615
3603
3619

No.
of
PB-internal/
PB-all9720/11441
9693/11450
9753/11551

No.
of
PT-internal/
PT-all34255/35976
33893/35650
34256/36054

3728

9765/11530

33956/35721

3725

9749/11484

34111/35864

3714

9663/11422

33727/35486

3557
3525
3549
3342
3301
3311

9280/11054
9204/10907
9336/11000
7949/9417
7787/9304
7857/9505

32103/33727
31592/33293
32009/33823
27468/28652
26782/28299
27092/29024

Size ofgrains(mm)

No.
ofgrains3
3
3
1.772.57
1.772.57
1.772.57
2-4
2-4
2-4
1-5
1-5
1-5

Results
The global Moran’s I of internal pore-networkmodels are shown in Table 2 as well as the Z  scoreand P  value of them. When the results of I
distribute around 0, it indicates the normal variate
spatial randomness and shows that pore-size
distributions are random ones and that those has no
global spatial autocorrelation.
The global Moran’s I of A1-3, B1-3, C1-3, andD1-3 shows strong spatial autocorrelations, and thenull hypothesis that there is no spatial autocorrelation
was rejected at a significant level of 1%, whichindicates that there are same clusters of larger and/or
smaller PBs in the porous media.
Regarding the local Moran’s I , the numbers ofsignificant PBs at the 5% statistically significant levelin each category are shown in Table 2. Figure 3
denotes the Moran scatter plots of A1, B1, C1 and D1;
Figure 4 shows the spatial distributions of categories
for both all PBs and significant PBs of A1 and D1.
As we can see in Fig. 3, the numbers of statisticallysignificant PBs in HH region are obviously higher
than LH, LL and HL, and the numbers of significant
PBs in LL and HL regions are extremely small,which indicates that large PBs tend to distributearound large PBs.

554

GEOMATE – Kyoto, Japan, 3-5 November 2021

(a) Packed grains of A1(b) Pore-network of A1(c) Packed grains of D1(d) Pore-network of D1

Fig. 1 Virtual porous media and pore-networks (A1, D1)(a) Coordinate number (-)(b) Pore body radius (0.1mm)(c) Pore throat length (0.1mm)(d) Pore throat radius (0.1mm)

Fig. 2 Frequency of coordinate number and pore-sizes (D1-3, Internal)

555

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 2 Global Moran’s I and number of significant/all PBs-Internal in each category
Pore-networks
A1
A2
A3
B1
B2
B3
C1
C2
C3
D1
D2
D3

I
0.473
0.488
0.482
0.498
0.489
0.495
0.474
0.474
0.506
0.483
0.494
0.478

Z  score
29.77
30.57
30.30
31.57
30.89
31.18
29.32
29.22
31.45
27.69
28.09
27.31

P  value
Neary 0
Neary 0
Neary 0
Neary 0
Neary 0
Neary 0
Neary 0
Neary 0
Neary 0
Neary 0
Neary 0
Neary 0HH
996/2677
920/2700
942/2807
1058/2706
1030/2744
1022/2769
999/2642
1055/2640
967/2551
877/2351
808/2277
844/2301

LH
104/2379
91/2346
89/2321
107/2311
105/2250
91/2250
88/2159
110/2067
81/2031
119/1976
111/1799
110/1830(a) A1(b) B1(c) C1(d) D1
Fig. 3 Moran scatter plot

556

LL
11/3563
2/35664/3501
12/3624
7/363231/3601
12/3411
7/3482
6/3696
10/2832
10/2975
16/2940

HL
1/1101
2/1081
0/1124
6/1124
2/1123
2/1043
1/1068
3/1015
1/1058
1/790
2/736
0/786

GEOMATE – Kyoto, Japan, 3-5 November 2021

(a) All PBs - A1(b) Significant PBs - A1(c) All PBs - D1(d) Significant PBs - D1
Fig. 4 Spatial distributions of categories for PB-Internal
CONCLUSIONSIn this research, global and local Moran’s indexes,
which are spatial statistics, are used to analyze thespatial distribution of porous media packed with both
single-size and various-size grains. The obtained
results of the spatial statistics show that there is aspatial autocorrelation in the pore distribution, both in
the porous media packed with single-size sphericalgrains and uniformly distributed spherical grains. In
addition, the numbers of significant PBs at the 5%
statistically significant level that distributed in HH
category are obviously higher than LH, LL and HL
regions, which indicates that large PBs tend to
distribute around large PBs. All of these imply thathydraulic properties of porous media could be
affected by the spatial structure of pores.
REFERENCES[1] Horton, R., M. D. Ankeny, and R. R. Allmaras,
Effects of compaction on soil hydraulic properties,
Developments in Agricultural Engineering, Vol.
11, 1994, pp.141-165.
[2] Takeuchi, J., Sumii, W., and Fujihara, M.,
Modeling of fluid intrusion into porous media
with mixed wettabilities using pore-network,

International Journal of GEOMATE, Vol. 10,
2016, pp.1971-1977.
[3] Takeuchi, J., Sumii, W., Tsuji, H., and Fujihara,M., Estimation of permeability of porous media
with mixed wettabilities using pore-networkmodel, International Journal of GEOMATE, Vol.
11, 2016, pp.2241-2247.
[4] Takeuchi J. and Fujihara M., Spatial Statistical
Analysis of Pores in Single-size Grains Packing,Proceedings of JRCSA 2017 Annual Congress,
2017, pp.18-21 (in Japanese).
[5] Moran, P. A., Notes on continuous stochastic
phenomena, Biometrika, Vol. 37(1/2), 1950,
pp.17-23.
[6] Okabe, A., and Sugihara, K., Network spatial
autocorrelation, Spatial analysis along networks:
statistical and computational methods. John Wileyand Sons, 2012, pp.137-151.
[7] Pisati, M., sg162: Tools for spatial data analysis,
Stata Technical Bulletin, Vol. 60, 2001, pp.21-37.
[8] Rabbani, A., Jamshidi, S., and Salehi, S., An
automated simple algorithm for realistic pore
network extraction from micro-tomography
images. Journal of Petroleum Science andEngineering, Vol. 123, 2014, pp.164-171.
[9] Gostick, J. T., Versatile and efficient pore
network extraction method using marker-based
watershed segmentation, Physical Review E, Vol.
96(2), 2017, p.02330.

557

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

GROUNDWATER FLUORIDE HAZARD ZONATION IN THE
VOLCANOGENIC POLLUTED AREA, EAST JAVA, INDONESIA
Dhandhun Wacano1,2, Lutfia Isna Ardhayanti2, Suphia Rahmawati2, Ihsan Diyahulhaq Fadhlurahman2,
Mawarti Sekar Ratri Nastiti2, Auniatul Aulia2, Minoru Yoneda1
1

Department of Environmental Engineering, Graduate School of Engineering, Kyoto University, Japan;
2
Department of Environmental Engineering, Universitas Islam Indonesia, Yogyakarta, Indonesia
ABSTRACT

Volcanic activity can release numerous hazardous contaminants, including toxic elements such as fluoride.
Despite the relevance of fluorine to human nutrition, the ingestion of excessive doses can have adverse healtheffects such as fluorosis disease. Asembagus, Banyuputih and Jangkar (ABJ) subdistrict in East Java Indonesia
have been polluted by fluoride, where the Asembagus subdistrict is known as a fluorosis endemic area in Indonesia.
The high groundwater fluoride concentrations in this area were reported from effluent of hyper-acid water lake of
Kawah Ijen volcano. In order to prevent and mitigate severe chronic effects of prolonged exposure of fluoride in
ABJ subdistrict, the zonation of polluted area was developed. Spatial multi criteria evaluation (SMCE) approachand geospatial analysis technique was applied to visualize fluoride hazard zone. Moreover, spatial hazard modelwith 5x5 m2 grid resolution showed that 56 % area in very low zone, 12% area in low zone, 21 % area in high zone,
and 11 % area in very high zone respectively. Cross operation technique also revealed that 9 villages were in thehigh hazard zone with potential for dental fluorosis and 5 villages were in the very high hazard zone with potential
for skeletal fluorosis chronic effect. Based on the condition of drinking water source, development of clean water
networks and groundwater quality improvement in accordance with standards of drinking water is needed.
Furthermore, risk communication about fluoride exposure hazard should be addressed to reduce and mitigate
fluoride pollution risk in Asembagus, Banyuputih, and Jangkar subdistrict.
Keywords: Asembagus, Groundwater fluoride pollution, Risk mitigation, Kawah Ijen volcano

1. INTRODUCTION

this highly acid and fluoride-polluted water for theirrigation process has been changed the agriculture
soil [4] and groundwater quality [1]-[2].
Fluoride is an essential micronutrient need by thehuman body, especially for teeth and bones. A certain
amount of fluoride is found in drinking water within
the permissible limit of concentration between 0.5 to
1.5 mg L-1 as prescribed by the World HealthOrganization (WHO) [5]. Even so, fluoride
contamination in water becomes a significant
problem globally. Some article states that there will
be a negative impact on the human body when
exposed with higher fluoride intake. Most of them
reported dental and skeletal fluorosis as the impact of
the higher fluoride intake [6]-[7].
Even though fluoride health is still well controlled
in some countries, many dental fluorosis cases havebeen reported [6]. Zuo [8] reportedly that
accumulation of fluoride in the environment could
affect health risk for plants, animals, and humans. In
the study area, the total daily F intake exceeds thepossible limit that can be causal factor for dental andskeletal fluorosis [2]. Furthermore, the groundwater
is used for the drinking water source by the local
residents. This practice has caused many healthproblems especially dental and skeletal fluorosis tothe local residents.

Kawah Ijen volcano in East Java, Indonesia, is thelargest hyper-acid volcanic crater lake in the world.
The effluent of hyper-acid water from crater lake
through Banyupahit-Banyuputih river channel isreported as the source of natural pollution in thenorthern part of Kawah Ijen volcano [1]. The polluted
areas are Asembagus, Banyuputih, and Jangkar (ABJ)
subdistrict. This acid water is entering polluted areas
not only from the watershed system, but also via
irrigation channel that loading toxic element
including fluoride.
In the Banyupahit-Banyuputih river, the fluoride
concentration is in the range between 5.5 and 14.2 mg
L-1 and near this area, the concentration of fluoride ingroundwater reached 4.2 mg L-1 [2]. The high fluoride
concentration in the water of the study area isoriginated from Kawah Ijen crater lake which the pHof the water is lower than 0.3 and the concentration offluoride is up to 1500 mg L-1.
The water from Banyuputih river is also used for
irrigating agriculture field in Asembagus and Jangkar
area. The irrigation practice is carried out during thedry season. Approximately 2800 kg of fluoride isdischarged per day into the soil in this area during theirrigation process [3]. The long-term application of558

GEOMATE – Kyoto, Japan, 3-5 November 2021

Due to the potential health risk that caused by thehigh concentration of fluoride in groundwater, thisstudy presents hazard map from groundwater fluoride
in ABJ subdistrict. Spatially, hazard quotient analysis
in Asembagus was calculated by [2] and showedbased on the point of well samples and village
community. However, this study used different
approach and technique to complement the previous
study. The spatial pattern of fluoride concentration is
explained using drainage system approach, while
spatial analysis with multi criteria parameter has beenused in order to develop fluoride hazard index.

season of 1999 and 2000. During dry season 2019, 17
water wells were sampled in this study to compareand validate the previous samples. It is clear that thetemporal difference between the two types ofgroundwater samples is about 20 years. The 2019
samples were taken mostly within Banyuputih
watershed as the main sources of high fluoride
concentration in the river water from Kawah Ijen
crater lake. The consideration of the sample location
is based on the information that acid river is morepotential as the pathway of fluoride pollution in the
study area.

2. MATERIALS AND METHODS

2.2. Samples Analysis

2.1. Study Area and Samples Location

Fluoride samples concentration from [2] wasmeasured with a Dionex DX120 ion chromatographcombine with Pulse Electrochemical Detector. In
order to analysis 17 samples of 2019, SPADNSSpectrophtometric Method was applied.
The
principle of this method based on the process ofreaction between fluoride and zirconium. Fluoride
will react with Zr-SPADNS and resulting coloured
complex then measured in a spectrophotometer at 570
nm.

The study area is located in the Situbondo
Regency, in the northern part of Kawah Ijen volcano,
East Java province, Indonesia Fig.1. This area
consists of three subdistricts namely Asembagus,
Banyuputih and Jangkar which will be abbreviated as
ABJ subdistrict for convenience. Morphologically,
the ABJ subdistrict is coastal plain with the volcanic
soil as dominant surface material. Five watershedsystems are bordered in the study area i.e.,
Sokmaelang watershed, Jangkar Watershed,
Banyuputih watershed, Leket watershed andKlompret watershed with drainage networks are
categorized as acid river of Banyuputih, acidirrigation channel mostly in Asembagus subdistrict,neutral irrigation channel and neutral river stream.

Fig. 12.3. Spatial Analysis
Geographic information system (GIS) was used todevelop fluoride hazard index. ArcGIS 10.8 and
ILWIS 3.3 Academic software packages werecombined in order to analyze both of type vector andraster data set. Vector based analysis was applied
mostly for index parameter preparation steps. A
georeferenced corner was created to determine therelation between row and column numbers in a rastermap and XY-coordinates, along with the 5x5 m2 grid
resolution are generated.
A total of 49 groundwater fluoride samples from[2] was interpolated using inverse distance weighted(IDW) interpolation to show the general spatial
pattern of fluoride concentration distribution in the
study area. This interpolation technique was chosen
due to their easy and merely show the factual data
pattern. This result of interpolation then classified
with chronic health effect concentration following[9]-[10].
Drainage networks were analyzed using distancecalculation technique. This process is crucial toidentify the correlation of groundwater fluoride
concentration with drainage networks both acid andneutral channel.
Spatial multi-criteria evaluation (SMCE) is atechnique for supporting stakeholders to makedecision. The complexity of parameters in thedecision process is simplified by the systematichierarchy. Parameter maps as input for SMCE weregrouped, standardized, and weighted in a criteria tree
Fig 2.

Map of study area and sample location.

Groundwater fluoride samples are divided into
two categories based on the data sources. A total of
49 groundwater samples is selected from [2] in dry
559

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 2surface lithological condition. This characteristic ofmaterial drives the acid water more easily to infiltrate
into groundwater. The other explanation is period
time of exposure between acid river and irrigation
river. The acid river has flowed for more than ahundred years [3], while the acid irrigation is shorter
period of time. This clear indication that fluoride
exposure will intensively occurring with duration
time of exposure from the acid river.

The criteria tree of fluoride hazard index

5

Fluoride concentration (mg L-1)

3. RESULTS AND DISCUSSION
3.1. Groundwater Fluoride Pattern
Cross correlation between groundwater fluoride
concentration and irrigation distance shows anegative correlation Fig. 3. The correlation isrelatively weak that indicated not clear influence ofacid irrigation to fluoride concentration in
groundwater. Moreover, many samples withconcentration of 0.1 mg L-1 are present at almost
every distance from closest to farthest. This data
condition explained unclear correlation that acidirrigation is the main source of groundwater fluoride
concentration in study area.

4
3
2
1
0
0

5000
Distance from acid river (m)Fluoride concentration (mg L-1)

5

Fig. 4

4

2

1

0
2000

4000

6000

Distance from acid irrigation (m)Fig. 3Graphic plot F concentration versus distanceof acid river

The groundwater fluoride pattern also can be
explained by the drainage length (Dl) and drainage
density (Dd) characteristic. Drainage length
calculation in the Table 1 shows comparison between
acid drainage length and neutral drainage length
within every watershed system. The result of
calculation shows Jangkar watershed has the longest
acid drainage length, followed by Banyuputih andSokmaelang watershed. The longest acid drainage
length will affect the range of fluoride dispersion in
the soil surface and into groundwater. Klompret and
Leket watershed will not be polluted by fluoride since
the acid drainage does not flow in these twowatersheds, showed in zero value of drainage length.

3

0

10000

Graphic plot F concentration versus distanceof acid irrigation

Table 1 Drainage length in watershed system
Different from the previous cross correlation in
Fig 3, the graphic at Fig. 4 shows relatively a strong
negative correlation. Despite the low groundwater
fluoride with concentration of 0.1 mg L-1 is alsopresent in every distance, strong negative correlation
indicates that acid river has a strong influence tocontribute fluoride concentration in the groundwater.
This condition may be affected by the basecharacteristic of acid channel. Base of acid irrigation
channel usually constructed by cement concrete thatwill protect the acid water from infiltration process.
The acid river base is naturally from soil or

Watershed
Banyuputih
Jangkar
Klompret
Leket
Sokmaelang

560

Acid length
(km)
14.18
55.34
0
0
12.16

Neutral length
(km)
9.91
3.53
10.06
42.58
116.23

GEOMATE – Kyoto, Japan, 3-5 November 2021

Drainage density calculation in the Table 2 shows
comparison between acid drainage density andneutral drainage density within every watershedsystem. The result of calculation shows Jangkar
watershed has the highest acid drainage density,
followed Banyuputih and Sokmaelang watershed.
The highest drainage density will affect theconcentration of fluoride dispersion from the soil
surface into the groundwater. Otherwise, the highest
neutral drainage density will contribute to the dilution
process of fluoride becoming lower its concentration
before reach groundwater aquifer system.

The groundwater fluoride concentration in the
study area varied from 0.1 to 4.2 mg L-1 with a mean
concentration of 1.9 mg L-1 from [2] data, andbetween 1.4 - 3.3 mg L-1 with a mean concentrationof 2.4 mg L-1 from survey data in 2019. Based on thechronic health effect concentration of groundwater
fluoride, the study area samples were classified into
four pollution zones Fig. 5e: low category (0-0.5 mg
L-1), medium category (0.5-1.5 mg L-1), high category(1.5-4.0 mg L-1), and very high category (4.0-6.0 mg
L-1). Analysis of samples revealed that 26 %concentration in the range of dental caries suffer,
12 % in the recommended health range, 56 % in the
fluorosis-mottled teeth potential health chronic effect,
and 6 % in dental fluorosis and skeletal fluorosis
potential chronic health effect.
Fluoride concentration increases with the mean ofincreasing is 0.7 mg L-1 and cover about 47 % ofsamples Fig. 6. In the other hand, decreasing offluoride concentration cover 53 % of total samples
with the average of decreasing concentration is 0.6
mg L-1. The condition of change concentrations ingroundwater indicates that after 20 years interval time,
there is a fluctuation of fluoride concentration in the
groundwater.

Table 2 Drainage density (Dd) in watershed system
Watershed
Acid-Dd
Neutral-Dd
Banyuputih
0.000650.00046
Jangkar0.001420.00009
Klompret
00.00094
Leket
00.00261
Sokmaelang
0.000190.00183
3.2. Parameter and Sub-parameter map
Parameter and sub-parameter maps are used togenerate fluoride hazard index. In this study, fiveparameter maps were prepared Fig 5. Buffer map is aparameter that give information about classified
distance from the drainage both acid and neutral, withthe 6 class sub-parameter Fig. 5a-d. These buffer map
including acid river buffer, acid irrigation buffer,
neutral river buffer and neutral irrigation buffer.

Fluoride concentration (mg L-1)

5
4
3

2
1

F-IDW
F-Survey00 1 2 3 4 5 6 7 8 9 101112131415161718
Number of sample
Fig. 6Graphic plot of F-IDW and F-Survey

3.3. Fluoride Hazard Index and Zonation

Fig. 5

Fluoride hazard index result shows that the range
index is from 0.0 to 1.0 Fig. 7a. Small value index
indicates the low hazard potential and the highest
value is in the high hazard potential. The hazard
zonation was classified into four pollution zones: very
low category (<0.51), low category (0.51-0.67), highcategory (0.67-0.84), and very high category (>0.84).
The hazard map was crossed with the settlements area
to analyze the number of villages in ABJ subdistrict
that potentially polluted by groundwater fluoride.
Village number 1-10 is in Asembagus subdistrict,Parameter map (a) acid river buffer, (b) acidirrigation buffer, (c) neutral river buffer, (d)neutral irrigation buffer, (e) fluoride chronic
concentration
561

GEOMATE – Kyoto, Japan, 3-5 November 2021

number 11-13 is in Banyuputih subdistrict and village
number 14-20 is in Jangkar subdistrict Fig 7b.

The percentage of fluoride concentrations indrinking water and its chronic health effects
supported dental caries and dental fluorosis-mottled
teeth as the result of fluoride groundwater polluted in
ABJ Subdistrict Table 3. Very high hazard category
is Sumberejo village followed by Bantal village andBanyuputih village. All of village in Table 3 isconfirmed previous information that a total 14 village
receive polluted water from Banyuputih river,whereas 3 villages are not including in hazard map
resulted in this study.
Unfortunately, the problem of confidence among
the local people to consult their health condition in
Health Clinic lead the lack of fluorosis data in ABJ
Subdistrict. This situation makes the validation ofmodel becoming more difficult. The condition
become severe when the municipality in 1990 failed
to solve this drinking water problem in ABJ
subdistrict [11]. Most of the inhabitants Fig. 8
preferred to drink water from their own well because
taste feeling and economically reasons.

Fig. 7

Number of household

4000

Well

Water company

3000

Map of (a) fluoride hazard index and (b)fluoride hazard category zonation

2000

3.4. Fluoride Hazard and Risk Mitigation

1000

Cross correlation analysis revealed that 26 %concentration is potentially make dental caries suffer
which is not correlated with fluoride concentration in
drinking water [2] but strongly related to dental
fluorosis. This is proven with 56 % of samples is in
the fluorosis-mottled teeth potential health chronic
effect, while about only 12 % in the recommended
health range. The skeletal fluorosis also potentiallyoccurs in ABJ subdistrict due to 6 % samples is inskeletal fluorosis potential chronic health effect.

0
1 2 3 4 5 6 7 8 9 10 11 12 13

Village number
Fig. 8

Daily water source of household (1-10 in2018 and 11-13 in 2017)

Dry season also enhances the risk of fluoride
pollution in water [12]. Age also becomes the roleplay in the fluoride-health problem as children have ahigher risk of the fluoride-health problem than adults.
For children, the high amount of fluoride in the bodywill impact neurodevelopment than for an adult, thehigher amount of fluoride will affect the dental andskeletal problem [13].

Table 3 Hazard category area in the villages
VillageHigh area
Very high
(km2)
(km2)
Bantal507.08Perante38.88
Awar – Awar
191.33
41.43
Kedunglo
0.35
Trigonco
260.30
Wringinanom
885.70Asembagus
212.58
Gudang356.45
Sumberejo
311.85936.78
Banyuputih
624.98222.25
Palangan
82.13-

4. CONCLUSIONS
Groundwater fluoride hazard zonation in the
study area was classified into four categories namely
very low (<0.51), low (0.51-0.67), high category(0.67-0.84), and very high category (>0.84). Spatialmodel showed that 56 % of study area in very low
category, 12% area in low category, 21 % area in highcategory, and 11 % area in very high category.
Furthermore, spatial analysis also revealed that 9
villages were in the high category with potential for
dental fluorosis and 5 villages were in the very high562

GEOMATE – Kyoto, Japan, 3-5 November 2021

category with potential for skeletal fluorosis chronic
effect. Based on the condition of drinking water
source, development of clean water networks andgroundwater quality improvement in accordance withstandards of drinking water is needed. Moreover, risk
communication about fluoride exposure hazard
should be addressed to reduce and mitigate fluoride
pollution risk in Asembagus, Banyuputih, andJangkar subdistrict.

The case of Asembagus, Java, Indonesia. Journalof Geochemical Exploration, Vol. 96, 2008,
pp.53 –68.
[5] WHO, Guidelines for drinking-water quality:
Fourth edition incorporating the first addendum.
Geneva, 2017, pp.370-373.
[6] Ali, S., Thakur, S.K., Sarkar, A., and Shekhar, S.,
Worldwide Contamination of Water by Fluoride.
Environmental Chemistry Letters, Vol. 14, 2016,
pp.291-315.
[7] Dasaiah S., Kurakalva R.M., and Pindi P.K.,
Data on fluoride concentration profile ingroundwater of rural habitats in Mahabubnagar
district, Telangana, India. Data in Brief, Vol. 32,
2020, pp.1-6.
[8] Zuo H., Chen L., Kong M., Qiu L., Lu P., Wu P.,
Yang Y., and Chen K., Toxic Effects of Fluorideon Organisms. Life Sciences, Vol. 198, 2018,
pp.18-24.
[9] Dissanayake, C.B., The fluoride problem in the
ground water of Sri Lanka: environmental
management and health. International Journal ofEnvironmental Studies Vol. 38, Issue 2-3, 1991,
pp.137–156.
[10] Edmunds W.M., Smedley P.L. Fluoride in
Natural Waters. in Essentials of Medical
Geology, 2013, pp 311-336.
[11] Budiparmana E.S., Hapsoro A., Irmawati E.S.,
and Kuntari S., Dental Fluorosis and Caries
Prevalence in the Fluorosis Endemic Area of
Asembagus, Indonesia. International Journal of
Paediatric Dentistry, Vol. 12, 2002, pp.415-422.
[12] Zhang Q., Xu P., Qian H., and Yang F.,
Hydrogeochemistry and Fluoride Contaminationin Jiaokou Irrigation Subdistrict, Central China:
Assessment based on multivariate statistical
approach and human health risk. Science of the
Total Environment, Vol. 741, 2020, pp.1-15[13] Liu J., Peng Y., Li C., Gao Z., and Chen S., A
Characterization of Groundwater Fluoride,
Influencing Factors and Risk to Human Health in
the Southwest Plain of Shandong Province,
North China. Ecotoxicology and Environmental
Safety, Vol. 207, 2021, pp.1-10.

ACKNOWLEDGMENTS
Authors would like to thank the Department ofEnvironmental Engineering Kyoto University, andDepartmentofEnvironmental
Engineering
Universitas Islam Indonesia for supporting thisresearch. The author would also like to thank students
from the Kawah Ijen volcano group for their support
during the survey campaign in 2019.
REFERENCES[1] Löhr, A.J., Bogaard, T.A., Heikens, A., Hendriks,
M.R., Sumarti, S., Van Bergen, M.J., Van Gestel,
C.A.M., Van Straalen, N.M., Vroon, P.Z.,
Widianarko, B., Natural pollution caused by theextremely acidic crater Lake Kawah Ijen, East
Java, Indonesia. Environmental Science and
Pollution Research, Vol. 12, Issue 2, 2005,
pp.89–95.
[2] Heikens, A., Sumarti, S., van Bergen, M.,
Widianarko, B., Fokkert, L., van Leeuwen, K.,
Seinen, W., The impact of the hyperacid Ijen
Crater Lake: risks of excess fluoride to humanhealth. Science of the Total Environment, Vol.
346, Issue 1–3, 2005, pp.56–69.
[3] Delmelle, P., Bernard, A., Downstream
composition changes of acidic volcanic waters
discharged into the Banyupahit stream, Ijen
caldera, Indonesia. Journal of Volcanology andGeothermal Research, Vol. 97, Issue 1 –4, 2000,
pp.55–75.
[4] van Rotterdam-Los, A.M.D., Heikens, A.,
Vriend, S.P., van Bergen, M.J., van Gaans,
P.F.M., The effect of naturally acidified
irrigation water on agricultural volcanic soils.

563

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

TRANSITION TOWARDS SUSTAINABLE ENERGY IN THAILAND:
DRIVERS, BARRIERS AND CHALLENGES OF SOLAR ENERGYINITIATIVE AT KRABI PROVINCE
Chariya Senpong1 and Dawan Wiwattanadate2
1

Environment, Development and Sustainability, Interdisciplinary Program Graduate School,
Chulalongkorn University, Bangkok, Thailand; Faculty of Engineering
2
Chulalongkorn University, Bangkok, Thailand
ABSTRACT

Solar energy investment initiative is a leading pathway of renewable energy transition towards sustainableenergy in Thailand. This study conducted a survey for potential barriers and drivers for solar energy initiatives
at Krabi province. The study found that the potential of solar rooftops in Krabi province is more than a thousand
MW and the highest portion compared with others like biomass, biogas and waste to energy. However, Krabi
has land limitations for solar farm development, except shifting of most lands of rubber and palm oil tree
plantation. While a solar rooftop potential of a thousand MW was observed, which is the highest portion ofrenewable energy potential in the province. To prioritise solar rooftop investment is experienced on somesuccess projects and drives the relevant barriers to reach the target, including unlocking the national solar
energy policy, the price of solar energy purchasing agreement and motivative measures for investment, suchas financial start-up for solar energy’s loan for solar rooftop prosumers and the new framework of its contributeto the cooperative’s members to enhance the solar rooftop on their residential. However, supporting policy forsolar energy from the government is still necessary. The following key factors to challenge solar energy
initiatives at Krabi province needed to be considered: political, legal, economic transition and disruptive
phenomenon.
Keywords: Solar energy, Energy transition, Krabi province, Renewable energy, Success and barrier

INTRODUCTION[2]. The largest increase in renewable energy
installation mostly solar PV in many countries
adopted policies encouraging the measurement offeed-in-tariff for the long term power producer
agreement (PPAs), the net metering for the solar
rooftop, public utility policy and the intensive
regulatory for decentralized system pathway.
Corporates and prosumers are driving theenergy storage technologies. Renewable energy
plays an important role protecting the environment
and mitigating climate change [3] including
reducing air pollutants in the energy sector [4]. That
implement innovation and investments support thepillars of sustainable development goals (economic,
social and environmental development [5]including the impact of job creation. To achieve thegoals, the systematic policy development plan andfinancial support planning are the initiative andessential program for driving the country's
ambitious scenario [6] and the dynamics of social
behaviors, the role of social innovations,community involvement and social acceptance [7].

The renewable energy transition has also
occurred in Krabi province as one of the top tourism
destinations and earns high income from those
relevant businesses. The average of electricity
consumption has been continuously increasing
annually following the data of tourism seasonal
peak. Green tourism is the priority to reach out thequality of natural conservation serving the tourism
industry and people’s life. How to win green
tourism and energy is challenging the province's
planning and implementation for long term
development. This article will illustrate the study ofKrabi energy transition which is a part of Thailand’s
renewable energy initiatives.
Drivers, barriers as well as drivers to achieve
Krabi’s sustainable energy will be described in thispaper. The renewable energy transition trend has
grown rapidly in the power sector in recent years inlocal communities, urban, peri-urban and rural
areas. The total capacity of more than 200 gigawatts
globally power sector was 26.4 percent of
renewable share of total final energy consumption[1] and the expectation for solar PV growth is toreach approximately 107 gigawatts in 2020 evenwith the uncertain energy policy in ASEAN country

RESEARCH METHODOLOGY
The research aims to study the electricity
demand and supply of Krabi province and its

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GEOMATE – Kyoto, Japan, 3-5 November 2021

potential for electricity generation from renewable
resources, drivers, and barriers for the renewableenergy transition towards Krabi’s sustainableenergy related to national and provincial levels.
Drivers as well as barriers and challenges taking
part in renewable energy transition were
investigated in this study by using the qualitative
method. Potential as well as the existing solar
energy investment was collected from thesecondary data from the energy agencies including
the provincial and national level and seeking therelevant studies. Qualitative method applied thecase study theory [8] as the single case focusing to
explain the phenomenon of solar energy transitionand point to the significance of time and past events
since the beginning of solar energy was emerging.
Barriers and drivers were collected by in-depth
interviews as well as onsite-observation. The
researcher used the explanatory method for thethoughts, views and perceptions of interviewees of
Krabi solar energy transition, applied thedescriptive approach from the open-ended
questions, grouping the interview’s wording,analysis to the typical information by considering ofeach word and specific relational or what it is called
and diversity of relationship and convert the thefrequency wording to the success factors, barriersand challenges and describe how people participate
in energy transition.

2013, was high at the beginning of solar energy
development at 8 baht (+-$0.26) per unit for VSPP
and SPP for the long term support of 7 years andextended to 10 years. In 2010, the adder deduction
policy was forced down to 6.50 baht (+-$0.21) per
unit by NEPC and transmitted to FiT policy. Adder
intensive policy was distributed with a huge budget
as the beginning to encourage the new internationaland national solar energy investors and at theturning point, when the solar energy’s market wascompetitive reflecting the dropped solar energy cost.
The later adopted policy in 2013 was feed-in
tariff (FiT) for solar PV rooftop and theeffectiveness of this program depended on the rateof energy price, design and implementation details[13]. In 2014, the business model of solar power
was growing capacity mainly from utility-scaleinstallation of the solar farms investment [14]distributed location in the central and northeast of acountry with a high concentration factor. Price of its
purchase was 5.66 baht (+-$0.18) per unit within the
25 years contract period for governmental agencies
and agricultural cooperatives [15]. Moreover, thespecified price for the household sector was 6.96
baht (+-$0.22) per unit, 6.55 baht (+-$0.21) forsmall businesses and 6.16 baht (+-$0.20) for
medium and large business scale.
The country’s pilot phase of solar rooftop
applied during 2019 officially opened the quota for
100 MW household sector [16] and the additional
100 MW for the business and industrial sector with
the FiT long term contract. A pilot project of solar
rooftop had mainly created the household target toself-energy
generating
supporting
theirconsumption and sold the left to the grid system, thetotal installation capacity not exceeding 10
kilowatts per each and totally less than 100 MW
across the country and reaching a 10 years
purchasing agreement of net billing measurement[17]. In 2019, Solar for All projects [18] wereannounced by the Energy Regulatory Commissionof Thailand, ERC opening the quota of solar rooftop
for the household sector for the 10 years powerpurchasing contract under the net billingmeasurement, separately the quota 30 and 70 MW
for Metropolitan Electricity Authority (MEA) andProvincial Electricity Authority (PEA) respectively.
In late 2019, Thailand Solar Fund [19] had
solidarity with the 15 wider networks including
environmental, consumer and green developmentgroups raising crowdfunding across the country tosupport the solar rooftop installation as the initiative
solar hospital and technician schools projects.
Although a country could deliver the generated
solar energy on grid the remaining barriers [20] of
solar power purchasing agreement (PPA) was
uncleared status of a third-party developer could
sell electricity to the consumer as a scale up
business model, the sizing of solar rooftop system

RESULT AND DISCUSSION
Thailand’s Renewable Energy Transition
The first solar energy investment in Thailand
was installed in 1987 by the governmental policy
support Later, an adder scheme was applied toencourage renewable energy investment during
2007-2013, since then a feed-in-tariff scheme has
been applied instead.The earlier phase of thedevelopment adder program applied in 2007 to
benefit the start-up renewable energy investor in acountry and the systematic model would attract thesolar farm streamline application generating
electricity to the grid. This initiative program wasdrafted by the Ministry of Energy (MoE) andproposed to the National Energy Policy
Commission (NEPC) [9].
The attractive adder rate raised the capacity inthe pipeline and targeted a country to further
establish the formulation and regulation ofrenewable energy. The National Energy Policy
Commission (NEPC) continually promoted solar
energy purchasing policy in 2007 with a total targetof 500 MW according to the Alternative Energy
Development Plan (AEDP) and planned for the next
10 years, to reach the total target 3,000 MW underthe Alternative Energy Development Plan (AEDP)[10, 11, and 12]. The price of energy purchasing in
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GEOMATE – Kyoto, Japan, 3-5 November 2021

was limited and not allowing the excessing energy
production flow to the grid, the potential policy ofnet metering regulatory has not been yet
implemented even the customer’s bill saving areloading benefits [21], the financial scheme andbusiness model had less support to motivate solar
rooftop market and prosumers and the mindset ofsolar e-waste and requires the plan to offset
environmental burden [22] even solar energy
created the less impact compared to fossil fuel based
electricity life cycle assessment [23].

solar street lights have been installed by Krabi
Provincial Administrative since 2019. Total Krabi's
renewable energy is COD at 59.06 MW, 44.4 MWof which are biomass and biogas from rubber andpalm oil industry, and the rest (nearly 15 MW.) aresolar energy [26].
According to the data from Provincial
Electricity Authority of Thailand (PEA) in June
2020, the highest potential of renewable energy inthe province was biomass and biogas as the
majority areas of palms and rubber trees. Since
2014, the initiative of solar rooftop on the grid wasfrom the household sector by Wanna Chaideker’s
home with 0.0068 MW in Krabi province andrapidly growing in 2018 with the capacity of solar
farms for agricultural cooperation, total installation
almost 15 MW. Furthermore, local governmental
administration and private sectors continually
generated electricity from solar energy including
schools, marine tourism port, governmentalbuilding, and supermarket in fig1. And the latest
solar energy development was under the hundred
street lights projects of Krabi Provincial
Administrative authority. Even though the potentialof solar energy was high, especially in rooftop
investment, its purchasing policy had not motivatedthe electrical consumers enough to shift supply. The
capacity of accessing technology transfer affected
their mindset to invest in solar rooftop forhouseholds and business. During the beginning ofsolar investment, communities were concerned
about solar cell reflection as vision and healthy
impact and the proof of the potential of solar
production at day time.
National solar energy policy was the obstacle ofits development at the provincial level as well,especially the price of selling back to gridtransmission much lower than buying. Saving
money from solar energy installation was thought
first for the business sector and led to duplicate
investment for general prosumers across a province.
Transition pattern of solar energy was initiated by
the private sector who had capacity for financial
support and imported its technology. Tourism
sector and local governmental agencies were thefront line of the renewable energy transition that
followed the green concept of green tourism andgreen energy to promote and conserve gross income
growth.

Krabi’s Renewable Energy Transition
The research found that Krabi’s renewableenergy transition had been implemented since 2007
by investing in biomass energy, later growing thepotential of biogas from the left of biomass fuelsource and the latest was solar energy. The target
was to fulfill the energy consumption of Krabi
province and was expected to be increased
continually every year. Considering the last ten
years in 2010, the peak load reached 111 MW
annually and gradually growing approximately 5
to10 MW in each year. In 2019, the peak load
significantly increased to 159 MW as the historical
record [24]. During the recovery of the covid-19
epidemic for some period the consumption inprovince became nearly up to the usual rate of
consumption and the main factor was related to thetourism season of the province as the priority energy
consumption and income growing.
The potential of Krabi’s solar rooftop wasstudied in 2018 [25] to install on the rooftop of theresidential, hotel and business building prioritise
reducing electrical bill and transition loss for thelong distance and to benefit the ownership of thedecentralisation system. The study calculated thecapacity of Krabi’s rooftop for the household sector,
business SMEs and large building sectors with a
total capacity of 1,125 MW. The 91,362 households
in the province were calculated with the roof area ataverage 20 sq.m. and the result with the housing
potential was 634 MW and the small, medium andspecialised businesses were for the average 50 kW
per unit with the potential total of 468 MW The
large building sector averaged 1,000 kWh per placeand 23 MW potential. However, as of 2020 the
installed grid connected solar rooftop reported by
PEA was found to be totally 2.23 MW. In addition,

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GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig.1 Solar Energy Development of Krabi Province, 2020
In 2018, solar farms for agricultural cooperation
have been prioritized for joint venture investment
between the Ministry of Energy, Ministry ofAgriculture and Cooperative and solar energy
business sectors. This phenomenon concretelychanged solar energy investment in Krabi provinceand people’s mindset when benefits sharing was thepriorities proposing. The selected projects gained
land use for solar farm installation, the income ofelectricity purchasing was agreement between theinvestors and each cooperatives, and the right to sellelectricity and the fines if the investors break thecontract was under the cooperative authorizedunder the 25 years feed-in-tariff long term
agreement. Two main regulatory enforcement tomanage the solar farm projects were the cooperative
management fund and cooperative welfare fund.
And the additional qualification for solar farmcooperatives was under the conditions of ERC
regulation. In table1, the capacity of solar energy
production has been quite stable and slightly
decreasing during the heavy raining season in MaySeptember as usual.

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GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1 Krabi’s VSPP Solar Farm in 2018-2020, Provincial Electricity Authority of Thailand, 2020

Name
Mar Solar Company(Hua Plunang Rubber Tree Fund Cooperative)
Smart Solar Power Company(Lam Thap Agriculture Cooperative)
Thai Green Company(Ban Dinna Rubber Tree Fund Cooperative)Name
Mar Solar Company(Hua Plunang Rubber Tree Fund Cooperative)
Smart Solar Power Company(Lam Thap Agriculture Cooperative)
Thai Green Company(Ban Dinna Rubber Tree Fund Cooperative)Name
Mar Solar Company(Hua Plunang Rubber Tree Fund Cooperative)
Smart Solar Power Company(Lam Thap Agriculture Cooperative)
Thai Green Company(Ban Dinna Rubber Tree Fund Cooperative)

Year
2018
2019
2020
2018
2019
2020
2018
2019
2020

Year
2018
2019
2020
2018
2019
2020
2018
2019
2020
Year
2018
2019
2020
2018
2019
2020
2018
2019
2020

Key Drivers and BarriersJan
877,680928,980
653,985
787,815
636,840
741,840

May
781,860
790,740
631,485
627,615
622,800
583,140Sep
694,200
567,630
602,100-

COD (kWh/Month)FebMar944,580
986,280
866,040 1,040,460
661,185732,060
739,800
874,215
678,360
764,220
667,680790,020
COD (kWh/Month)JunJul
710,820751,020643,200
700,620
600,885648,720557,910
589,905551,280
565,620492,720535,440
COD (kWh/Month)Oct
Nov683,520
697,080
541,665545,940
572,940
591,540-

Apr
840,240
844,440
703,665
661,725
690,840
620,100

Aug660,600
729,780
592,740
633,330
555,060
552,900
Dec
95,760807,300
93,105653,040
134,520664,500-

collaborating with the Krabi vision 2020 [27,28]
they were working together with different
stakeholders in a province such as local
governmental agencies, tourism associations,fishery groups, agricultural networks and othercommunities. Driving the master plan of theprovince was conducted for years with the eightaccreditation of provincial committees in the
different responsibilities and roles. Head of theprovince was the leader of the plan and was
responsible for the direction of the long term vision.
The first draft of the plan was signed by more than
200 organizations and networks under the
Declaration of Green Tourism and completed thefull vision of Krabi 2020 to lead the economy,
environment and the quality of life. From the green
tourism plan to Krabi vision 2020 plan, Krabi had
also studied the feasibility of renewable energy of

The qualitative research studied the key
stakeholders of solar energy transition of Krabi
province including political, environmental,
economic, technological and social aspects. The
researcher had an in-depth interview with thestakeholders of each aspect and sought the relevant
solar energy data, and momentum event analysis
covering the connection of key solutions, barriersand challenges in fig 2.
Key drivers
The present study found that energy policy andstrengthening of the private sector in the province
were significantly driving renewable energy
investment even though it took more than 10 years
of investment and movement across the province. In
2014, Krabi was the first province of a country
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GEOMATE – Kyoto, Japan, 3-5 November 2021

the province and during the opposition of the newcoal power plant project since 2012.
Aside from the driving of provincial policy
planning and people's movement to derail the newplant, the market trend was a disruptive
phenomenon that pushed the challenge to a
province to make a decision of energy demand andsupply shifting. Linked to technological factors,
when solar energy had been installed across acountry the trend of citizens in the province wasinterested so the first local solar company wasestablished in 2015. The potential of solar energy
proved energy security and the return of investment
shortly at the present within 5-6 years so thehousehold sector, especially tourism business andsupermarkets were escalated to save their electricity
consumption and the cost of electricity bills. And
the governmental agencies projects such asbuilding, schools, tourism pier and street lighting
transferred their budget to invest more solar energy.
The company also created job opportunities foryoung graduates from technician schools andpeople from the local communities by training andknowledge sharing. Political aspect was theimportant part of the solar energy transition in Krabi
province and lifting the potential of its installation
to approximately 15 MW. in the present.
The collaboration between the Ministry ofEnergy and Ministry of Agriculture andCooperatives initiated the solar energy project that
benefited the grid supply. When the national energy
policy unlocked the capacity of solar energy
production, the flow of the implementation plan in
the province continually drove to achieve the planand naturally supported the vision of local
governmental agencies and boosting the private
sector and investors deciding to agree on the plan.
And in the depth of power analysis, the strong
vision and relationship of the leaders, key
influencers and private investors in the province
motivated the followers to be on a track of thepolicy implementations. The legal factors had also
been released to respond to the energy policy
movement, the power purchasing agreement
addressed the price per unit of solar energy, theamount of quota in each timeline and feed-in-tariffmeasurement. In the case of solar energy forcooperatives, the qualification of them was essential
to be considered joining a project across thecountry, especially the transparency management
which identified the grade of each cooperatives byinternal checks with the Ministry of Agriculture andCooperatives agencies.
Economic factors impacted as a majority subject
could build the income and domestic growth in theprovince. Creating direct and indirect jobs fromsolar energy investment such as construction, solar
cells installation, technician and engineering,gardening, cleaning and security guards. Benefit

sharing encouraged the members of cooperatives tomake a decision to accept the solar energy project.
Those income would be distributed to thecooperative's funding for hundreds members’
welfare in each, including the land rental of the
project regulated to rent from the members of its
cooperatives in the long term period following thesolar project contract. The fast track of solar energy
investment required the financial support that the
party of contract had capacity to deal with.
Social aspect was much mentioned in theprocess of energy transition and the way to movethe vision of the province to be real practice on the
ground. Public participation was the vital process of
solar energy investment especially solar farmprojects of cooperatives. The process had not justonly to know its details but also the members of thecooperative had a right to make their own decision
to approve the project. The ownership of the solar
farm joint investment would change people's
mindset and be aware of their concerns of its
impacts during the process of public hearing held bygovernmental
agencies,
companies
andcooperatives in the community. And thetransparency process of bidding that was
implemented after the public hearing was importantto them because of the benefits and income for the
25 years energy purchasing agreement.
The last one was the environmental aspect, themindset and acceptance of renewable energy were
avoiding the opposition to new projects. Solarenergy had less impact and green energy, bothreasons had been integrated to the vision of theprovince that prioritizes on green tourism. At thestage of project consideration, the environmental
agencies in the province were required to approve
the solar farm project covering the impact
assessment, city planning and the risk management
such as landslide, water management and land use
authority.
Key barriers
Krabi province had a capacity to increase more
solar energy supply to the grid according to therelevant studies as mentioned above. The quota
system was one of the national solar energy policies
that blocked the investment potential of bothcooperative institutions and the household sector.
This policy significantly limited the potential of thecooperative's solar energy projects and solar rooftop
because the opening round of promotion was fixedby the number of solar projects and installation’s
capacity. The limited supply affected the selection
process of solar energy for the cooperative group as
well, that means the qualified would be eliminatedby the randomness process.
A solar farm project to support the cooperative
investment required the review of land use
permission policy because there was one project of569

GEOMATE – Kyoto, Japan, 3-5 November 2021

solar energy farm that has been sued to theadministrative court. The potential of a solar farm,about 5 MW has not been supplied to the national
grid even though the project has been completely
installed on the land of cooperative settlement. The
loss of energy supply to the grid system and benefits
to a company and cooperative was an unexpected
policy that significantly impacted the investment.
This problem was a conflict between the agenciesof the Ministry of Energy and Ministry ofAgriculture and Cooperatives. And the unlocked
cooperative enforcement was able to grow thepotential of solar energy investment for example,
the cooperative, as the owner of the project had
authority to borrow the money from the financial
institution to invest in their own project.
Financial sector has opened the limited
opportunities for the customers such as thehousehold sector and cooperative institutions toapply for any financial support implementing solar
energy projects. There are some banks exploring thenew policy of solar energy loan and mostly service
for the private sector such as property owners andtourism associations. Lacking financial flowing, thesolar energy investment in both solar rooftop andsolar farm has not been growing fast. Finally, thelimited internal experts forced the province to seek

and import the solar energy professional from theoutside and took time to share the relevant
knowledge and the effective investment. The
growth of prosumer and the potential of solar
investment still require fast development andrespond to the consumer demands.
Key challenges
Krabi is a tourism province of Thailand having
continuously invested in electricity generated fromrenewable sources since 2007. Key drivers for the
renewable investment are "electricity demand
increasing" to fulfill the tourism sector and theprovince's Krabi Goes Green vision. While facing
various barriers such as the extension of solar farmprojects is the challenge and will have a potential
effect on the capacity of biomass and biogas energy
production because of the collapse of land use
management, the requirement of internal grid
system extension and management require support
for energy security supply and demand sides, theneed of smart grid and decentralization
implementation plan to invest more budget tofeeding the renewable energy production andconsumption.

Fig. 2 Drivers, barriers and challenges of Krabi’s solar energy transition, 2021
the highest potential and some drivers like
supporting the household’s potential and thepathway of the cooperative's members that urges
them to install solar cells reducing the cost of

CONCLUSION
The study found that solar rooftop tends to be
570

GEOMATE – Kyoto, Japan, 3-5 November 2021

electricity bill and agricultural production cost,wider prosumers will lead the technology
knowledge exchanging in the southern and also
across the country as the golden opportunity,
regulate the intensive cost of solar energy
purchasing will be the one factor to fill the gap ofrenewable energy investment and motivate theinternal economy from selling the electricity energy
across the province especially in the area ofAndaman tourism provinces to value the green
tourism and energy, support the decentralizationand energy storage will be a priority for the futuresolar energy transition both on land and the island,
must be well considered.

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sustainable energy transitions in Thailand andthe Philippines, Energy Research and Social
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ACKNOWLEDGMENTS
This study is partly funded by the Doctorate
Scholarship Program “The 100th Anniversary
Chulalongkorn University Fund for Doctoral
Scholarship”, in the Environment, Development
and Sustainability international program, Graduate
School of Chulalongkorn University.
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572

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

CLIMATE VARIABILITY’S IMPACT ON RAINFED-SUGARCANE
PRODUCTION IN THAILAND’S NORTHEAST
Piyapong Wongkhunkaew1, Supasit Konyai 2 and Vichai Sriboonlue 3

1

Graduate Student of Agricultural Engineering, Faculty of Engineering, Khonkaen University, Thailand;
Associate Professor of Agricultural Engineering, Faculty of Engineering, Khonkaen University, Thailand;
3
Associate Professor of Agricultural Engineering, Faculty of Engineering, Khonkaen University, Thailand.

2

ABSTRACT
Sugarcane is one of Thailand's most valuable cash crops. However, besides being used for sugar production,
sugarcane can also be used to manufacture biofuel, resulting in increase in demand for sugarcane. Although
Northeast’s Thailand has the most sugarcane production and cultivation areas, but it receives less yield than otherparts. The climate fluctuation has both direct and indirect effects on sugarcane yields. It aimed to investigate theeffects of climate variability on sugarcane yields and productivity in northeastern Thailand. Actual
evapotranspiration is mechanism that occurs during plant transpiration and soil surface water evaporation, its
causes plants to absorb nutrients and water from the soil, which results in plant growth. The study relied on ENSO
index data, meteorological data, and sugarcane cultivation data from 2008 to 2017.
The results showed that ONI and SEaI's return period were 3.3 and 4-10 years, respectively and ENSO events
happen 3-4 months before SEaI events, according to spectrum analysis. The fluctuation in SEaI will occur more
rapidly than the ENSO. For comparison, SEaI 1, 3, 6, and 12 months were implemented. The best time scale of
SEaIs with yields is 6 and 12 month time scale. The highest yield was in correlation with SEaI at 6 months at 0.78
on Nakhonphanom station, with an increase in yield with SEaI for every station, but the production and harvested
area were inconsistent with SEaI. It has been shown that yield depends on amount of soil moisture and the rainfall.
However, sugarcane production and the harvested area also depend on other factors, etc. political, prices, farmmanagement.
Keywords: Climate variability, Standardized actual Evapotranspiration Index, Sugarcane production and yield.

INTRODUCTION

appropriate cane production. Because it receives a lotof rain during the wet season, Southeast Asian
countries have one of the best climates for sugarcane
cultivation. During the months of May to October, thenortheast of Thailand receives a lot of rain from twosources: the southwest monsoon from the Indian
Ocean and tropical cyclones from the Pacific Ocean.
The upper part of the Northeast is unique in that the
Phetchabun, Dong Phaya Yen, and Sankambeng
mountain ranges have an orographic effect that
postpones the flow of the Southwest monsoon into theregion. Before reaching the northwestern region,
tropical cyclones from the Pacific must pass throughVietnam, Laos, Cambodia, and the eastern part of theNortheast. When the southwest monsoon is weak andthere are no tropical cyclones, the region typicallyreceives less rainfall than the rest of Thailand. During
a year with a strong summer monsoon and a large
number of tropical cyclones, however, the area can be
flooded. El Nino Southern Oscillation has indicatedthat these dry and wet years are the result of
atmospheric and Pacific Ocean interaction (ENSO).
These lowland rainfed cane fields get their water fromrainfall and runoff, and they lose it toThailand is the world's fifth largest producer ofsugarcane and the world's second largest exporter ofsugar, dropping only Brazil. Sugarcane is one ofThailand's most important economic crops. There are
57 sugar mills in Thailand, except the southern
region. Other parts of sugarcane can be used for otherpurposes, such as biomass from bagasse and ethanolfrom cane leaves, in addition to producing granulatedsugar. As a result, the demand for sugar and canegrows year after year. This demonstrates the sugar
cane and sugar industry's continued growth. For along time, climate variability has had a direct andindirect impact on sugarcane and sugar production.
For example, severe drought events in 1997/1998 and2015/2016 resulted in a significant decrease insugarcane production, which was caused by climate
variability. The relationship between climate
variations, cane production, and sugar production in
Thailand should be studied. Understanding trends anddirections, including the effects of climate variability
on cane and sugar production, and using thisinformation to develop policy planning strategies for573

GEOMATE – Kyoto, Japan, 3-5 November 2021

evapotranspiration, which is a combination of
evaporation and crop transpiration. Actual
evapotranspiration,or
standardized
actualevapotranspiration index, is a moisture availability
indicator for sugarcane crops (SEaI). The SEaI index
outperforms other indices in detecting both wet anddry soil extremes. Our aims and objectives are tocompare and correlate the cane production and yieldsof the eight upper provinces in Thailand's northeast
with their SPEIs and then with ENSO..

Hydro-meteorological, ENSO index, andsugarcane cultivation data are the three types of datathat will be used. The Thai Meteorological
Department provided hydro-meteorological datafrom 2008 to 2017. (TMD). Daily maximum
temperature, daily minimum temperature, daily dew
point temperature, daily solar radiation, daily
maximum relative humidity, daily minimum relative
humidity, daily wind speed, and daily precipitation
are all needed for SETaI evaluation. The ETa must be
calculated using the Morton CRAE formula (Eq. (6)),
which requires a variety of meteorological data.

STUDY AREA AND DATA COLLECTION
Our study area is in Thailand's upper northeast,
between 15 31' and 18 21' north latitude and 100 83'
and 103 66' west latitude. The eight provinces
covered are NongKhai, Nongbualamphu, Khonkaen,
Udonthani,
Sakonnakhon,
NakhonPhanom,
Mukdaharn, and Loei. The Northeast region is a highlevel plain and one of the large sub-basins of the
Khorat plateau on the Mekong River's right bank. The
Phu Phan mountain range, which runs northwestsoutheast, separates the northeastern region into twobasins. The Sakonnakhon Basin, a large high-level
plain in the west and Khorat Basin, which is smallerand slopes toward east, is another. Interestingly, ourresearch area covers both basins: Khonkaen is in the
Khorat Basin, while Udonthani, NongKhai, Sakon
Nakhon, Nakhon Phanom, Mukdaharn, and Loei arein the Sakol Nakhon Basin. Nongbualamphu,
however, covers both basins.

METHODOLOGY

Fig. 1 Topography area of the eight-province study
region in Thailand.

Calculation Procedure of Standardized Actual
Evapotranspiration Index

We reasoned that actual evapotranspiration should
be proportional to the amount of soil water available
for crop growth. Crop water supply determines thetiming and quantity of evapotranspiration. Drought
was exacerbated by excessive evapotranspiration.
Crop production and yield are affected by the drought.
The Standardized actual Evapotranspiration Index(SEaI), which is a function of actual
evapotranspiration calculated using the Morton
CRAE method, is calculated using ETa. We also
believe SEaI is a new drought index that is fully
dependent on the variability of ETa estimatescalculated using the complementary relationship(CR) method.

The SEaI is a classification system for climate
variables based on hydrometeorological data. It wasdeveloped by replacing rainfall with actual574

GEOMATE – Kyoto, Japan, 3-5 November 2021

evapotranspiration in the same case that thestandardized precipitation index (SPI) was developed[1], [2]. Monthly actual evapotranspiration is used as
the input data for the SEaI. The Morton CRAE
method is used to calculate the ETa, which is difficult
due to the involvement of numerous parameters suchas daily temperature, daily dew point temperature,daily solar radiation, daily maxi-min relative
humidity, daily wind speed, and daily precipitation[3]. The probability distribution function is calculated
from the long-term data by fitting a function to it,which is then transformed to a normal distribution
with a mean of zero and standard deviation of one
using equal probability, resulting in SETaI values thatare truly in standard deviations.

is a constant (W m-2 mbar-1) and 𝜉 is a dimensionlessstability factor estimated from (Eq. 4):

Morton CRAE (Complementary Relationship
Areal Evapotranspiration)

The conventional definition of potential
evapotranspiration is the same as the wetenvironment areal evapotranspiration. To account foratmospheric advection, a term (b1) was added to thePriestley–Taylor equation to estimate wetenvironment areal evapotranspiration from Eq. 5:ξ=Estimating wet-environment areal
evapotranspiration (ETwet)

ETwet=

Estimating potential evapotranspiration (ETp)
The potential evapotranspiration by Morton
CRAE’s model for a catchment or a large vegetatedsurface is to solve the energy-balance and the vapor
transfer equations respectively for potential
evapotranspiration and the equilibrium temperature(Eq. 1 and 2):
1

ETp=

1

λ

λ

{Rn -[γpfv +4εs σ(Te +273)3 ](Te -Ta )}(1){fv (v*e -v*D )}

(2)

𝑝

0.5 𝑓

𝑝

𝜉

𝑧

1
λ

{b1 +b2Rneγpe(1+∆ )}(5)

Where ETwet is the wet-environment areal
evapotranspiration (mm.day-1), Rne is the net
radiation (Wm-2) for the soil/plant surface at theequilibrium temperature Te (◦C), γ is thepsychrometric constant (mbar ◦C-1), p is atmosphericpressure (mbar), ∆e is slope of the saturation vapour
pressure curve (mbar ◦C-1) at Te, b1 and b2 are theempirical coefficients (Wm-2) for Morton’s
procedure.
Estimating actual areal evapotranspiration (ETa)

Where ETp is potential evapotranspiration (mm.
day-1), λ is the latent heat of vaporization (Wdaykg-1),
Rn is net radiation for soil/plant surfaces at air
temperature (Wm-2), 𝛾 is the psychrometric constant(mbar°C-1), 𝑝 is the atmospheric pressure (mbar), fv isthe vapour transfer coefficient (Wm-2mbar-1) and is afunction of atmospheric stability, εs is the surface
emissivity, σ is the Stefan–Boltzmann constant (Wm2 -4
K ), Te and Ta are the equilibrium temperature (◦C)
and air temperature (◦C) respectively, v*e is saturation
vapour pressure (mbar) at Te and, v*D is the saturation
vapour pressure (mbar) at dew point temperature.
fv is given by (Eq. 3):
𝑓𝑣 = ( 𝑠)noting that ξ ≥1 (4)where v*D is the saturation vapour pressure (mbar)
at dew point temperature, v*a is the saturation vapour
pressure (mbar) at air temperature, ∆ is the slope ofthe saturation vapour pressure curve (mbar°C-1) at air
temperature and, 𝑏0 = 1.0 for the CRAE model

The CRAE model consists of three components:
potential evapotranspiration, wet environment areal
evapotranspiration and actual areal
evapotranspiration [4],[5].

ETp=

1
v*
Rn Δ
0.28(1+ D
)+
p 0.5
v*a
[γp( ps ) b0 fz(v*a -v*D )]The actual areal evapotranspiration (ETa) (mmday-1), one uses the results from the complementary
relationship method as follows Eq. 6 and Eq. 7 [6]:ETa = 2ETwet – ETp
ETa=

1
λ

[2 {b1 +b2

(6)

Rne

γp
e

} - {fv (v*e -v*D )}](1+ ∆ )

(7)

Where ETwet is the wet-environment areal
evapotranspiration (mm.day-1) and ETp is potential
evapotranspiration (mm.day-1)
Accurate temperature and relative humidity
estimates from a representative land-based location
are required to calculate the Morton CRAE model,
which suggested a 5-day limit as the minimum time
step for analysis.

(3)

Where 𝑝𝑠 is the sea-level atmospheric pressure(mbar), 𝑝 is at-site atmospheric pressure (mbar), 𝑓𝑍

575

GEOMATE – Kyoto, Japan, 3-5 November 2021

Correlation Coefficient

numerous frequencies. The technique of separating
the spectrum in the frequency domain in order todetermine the time series' dominant frequency is
known as spectral analysis.
To see the dominating spectrums, spectral
analyses were performed on the time series of ONI
and SEaIs, as shown in Fig. 3. The dominating return
periods of ONI and SEaIs of the eight provinces areprovided in Table 1 and the maximum peak curves
are shown as wavelengths (Fig. 3 a)). Table 1 showsthat the ONI recurrence time should be around 3.3
years, which is similar to the SEaIs for Nongkhai,
Sakon Nakhon, and Loei, which are 4 - 5 years, andUdonthani, NakhonPhanom, KhonKaen, Mukdaharn,and NongBuaLamPhu, which are 9-10 years.

The statistical metrics were used to quantify andcompare the performance of the SEaIs with that ofother drought indices. The Pearson correlation
coefficient, which was calculated as Eq. 8, was used
to test the correlation between the drought indices
SEaIs, SPIs, and SPEIs in different months.
Px,y =

Cov(X,Y)√Var(X)√Var(Y)(8)

where X, Y represent two variables forcomparison; Cov represents the covariance of X andY; and Var represents the variance of X or Y.
RESULT AND DISCUSSIONS

Table 1 The spectral analysis results of agricultural
drought indices.

The data from Nongkhai are the only ones wehave, although the results from the other eightprovinces are remarkably comparable. The ONI
fluctuates slowly over time, but the ETa fluctuates
rapidly. In Fig 2, the EL Nino condition is represented
by a positive ONI (dry condition), whereas the La
Nina condition is represented by a negative ONI (wetcondition) (La Nina years in 2010-2011 and El Nino
year in 2015-2016). The wet soil is represented by apositive ETa, whereas the dry soil is represented by anegative ETa. When the ETa values from 2015-2016
were compared to the El Nino event, they were foundto be inconsistent. This suggests that when ONI isstrong, the value of ETa decreases, meaning that if
ETA falls, drought or low soil moisture conditions
will occur. On the other hand, if there is a lot of rain
or a lot of soil moisture, the ETa value will be hightoo though. As a result, ETA values could be used to
predict soil moisture content based on meteorologicalconditions [7], [8].

ENSO
Index

SEaI

Return Period

HighestSpectrum

Frequency

ONI

0.392

NK
L

Mont
h

Year

0.025

40

3.3

0.198

0.019

52.6

4.4

0.163

0.016

62.5

5.2

UD

0.256

0.009

111.1

9.3

SK

0.163

0.017

58.8

4.9

NP

0.170

0.008

120.5

10.0

KK

0.204

0.008

120.5

10.0

MD

0.180

0.008

126.6

10.5

NB

0.237

0.008

120.5

10.0

In Table2 and Fig 3 b), we found that thestrongest correlations occur in the h-negative region,
indicating that a higher average value of ONI is likely
to lead to a lower average value of “SEaI” in
Nongkhai, Loei, Sakolnakhon, Nakhonphanom, andMukdahan about 4 months earlier. And,approximately 4 months later, a lower average of ONI
is related to a likely higher-average SPEI value. The
higher average value of ONI in Udonthani, Khonkaen,
and Nongbualamphu is likely to lead to a lower
average value of “SEaI” about 3 months earlier.
A distinguishing feature of important SEaIs is thetime scale format. Pearson's correlation coefficient
was used to investigate the relationship between
sugarcane production, harvested area, and yield in
Thailand and SEaIs. Nongkhai, Sakol Nakhon,
Udonthani, Nakhonphanom Mukdahan, and Loei are
all in the Sakol Nakhon Basin, but Khonkaen is in the
Khorat Basin, and Nongbualamphu is in both basins[9].

Fig 2. Time series of climate variability 3 condition
and actual evapotranspiration value in Nongkhai.
Because the ENSO index and the drought index
are both time series with sinusoidal forms, both
require time-series analysis in both the time andfrequency domains to completely appreciate them.
Every time series is the result of the addition of576

GEOMATE – Kyoto, Japan, 3-5 November 2021

The optimal time scale for sugarcane production
and yield for the same period (t) was a 12-month timescale, with the best yield correlations of 0.28, 0.56,
and 0.34 for Nong Khai, Loei, and Udonthani,
respectively, and the best sugarcane production
correlations of -0.28, -0.27, and -0.28 for Nong Khai,
Loei, and Udonthani, respectively. At a 6-month timescale, the best correlation for yields was 0.68, 0.78,
and 0.28 for Sakol Nakhon, Nakhonphanom, andMukdahan, respectively, and production was 0.59,
0.41, and 0.33. At the 12-month time scale, the best
correlations for Khonkaen's production and yield are-0.29 and 0.20, respectively.Only the cane yield of
Nongbualamphu corresponds with SEaI at 0.39 of a
12-month time scale, but not the production.

Table 2 The cross-correlation results of ENSO

and SEaI in 1-month time scale in UpperNortheast Thailand during 2008-2017.
Highest Cross
Correlation
-0.36
-0.43
-0.38
-0.29
-0.40
-0.20
-0.33
-0.34

NK
L
UD
SK
NP
KK
MD
NB

Lag (months)
-4
-4
-3
-4
-4
-3
-4
-3

5. CONCLUSIONSThe sugarcane is the most important economic
crops in Thailand. The sugarcane productions andyields from 8 provincial regions of upper-northeast,
Thailand. Correlation between SEaI and ONI using
Pearson's correlation coefficient. It was found that thetwo variables had opposite relationships. It shows thatwhen ONI is positive (El Nino), SEaI will havenegative value, on the contrary, if La Nina (heavyrain) occurs, SEaI value will be positive (high soil
moisture. When using spectrum analysis to find thecorrelation, ONI's return period was 3.3 years and
SEaI's return period was 4-10 years. The best timescale of SPEIs and found that the yields wereagreeable with SEaI-12 nicely especially in NK, L,
UD, KK and NB. SK, NP and MD were agreeablewith 6-month time scale. However, production andharvested area value did not follow all time. The
findings will help the stakeholders of sugar
production to understand, plan, and manage
sugarcane farming accordingly to obtain better yields.
Fig. 3. The a) spectrums of SEaI in 1-month timescale and b) cross correlation between ONI and SPEI
1-month time scale on Nongkhai from 2008-2017
period.

ACKNOWLEDGMENTS
The authors would like to thank the Graduate
School and Applied Engineering for Important Crops
of North East Research Group, Khon Kaen University
for providing facilities, the Water Engineering Group,
Faculty of Engineering, Khon Kaen University forfunding and data supporting.

Table 3 The Pearson's correlation coefficients of
SPEIs with rice production and yield at several timescales.
Time scale

Production
(ton)

Yield
(ton/ha)

NK

L

UD

SK

NP

KK

MD

NB

1

-0.27

-0.17

0.05

0.21

0.37

-0.10

-0.26

0.00

3

-0.11

0.00

-0.16

0.26

0.36

-0.23

0.05

0.00

6

-0.04

-0.03

0.25

0.59

0.41

-0.10

0.33

-0.10

12

-0.28

-0.27

-0.28

0.46

0.18

-0.29

0.01

0.07

1

0.16

0.39

0.01

0.60

0.71

-0.09

0.02

0.36

3

-0.01

0.46

0.10

0.60

0.71

-0.20

0.02

0.39

6

0.28

0.51

0.23

0.68

0.78

-0.08

0.28

0.34

12

0.28

0.56

0.34

0.44

0.67

0.20

0.16

0.39

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11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

IMPLEMENTATION OF GREEN CAMPUS AT
UNIVERSITIES IN INDONESIA
Any Eliza1, Dinda Fali Rifan2, Lindrianasari3, Nur Wahyu Ningsih4
1,2,4

Faculty of Economic and Islamic Business, Islamic State University of Raden Intan, Lampung, Indonesia
3
Faculty of Economics and Business, University of Lampung, Indonesia
ABSTRACTThis study aims to describe the implementation of green campus for universities in Indonesia. Using the
ranking of the Most Sustainable University category at the national level UI GreenMetric World University
Rankings, three universities were selected as samples. This type of research is field research and uses theresults of interviews and documentation from sustainability reports and university website. The
implementation of Green Campus is measured based on 4 instruments, namely Environmental Awareness,
Environmental Involvement, Environmental Reporting, and Environmental Auditing. The findings of thisstudy indicate that three universities have met the stages of environmental awareness, environmental
involvement, and environmental reporting, but have not met the stages of environmental auditing. The
university's participation in UI Green can be an indication that the university has awareness in the university
and its surroundings about the importance of sustainability issues. University X has the highest percentage ofachieving 4 green campus implementation instruments. The theory of legitimacy can explain that anorganization must be able to adapt to the value system that has been implemented by society and theenvironment because the more forms of responsibility it takes to its environment, the better the image andreputation of the organization will be.
Keywords: green campus, universities in Indonesia, environmental management accounting
INTRODUCTION
The
Sustainable
Development
Goals(TPB/SDGs) prioritize the implementation
principle with inclusiveness, which involves bothgovernment and non-government elements such as
business actors, philanthropists, community
organizations, as well as academics anduniversities so that more people are involved.
Universities play a very important role inachieving TPB/SDGs and must be able to becomea center of excellence in the field of science inaccordance with their core competencies, prioritize
SDGs in the education/teaching process, become apartner of the central and regional governments
and other stakeholders in monitor and evaluate theimplementation of the SDGs, as well as provide
recommendations. The university is strategically
positioned to make agreements on key areas for
actual action, in line with the “triple bottom line”
or “3 Es” concept of equality, economy,
environment, green buildings and education forsustainable development.
In 2010, the University of Indonesia (UI)
initiated a World University Ranking whichbecame known as the “UI GreenMetric World
University Rankings” to determine the university's
sustainable efforts. The UI GreenMetric world
university ranking was created as a tool foruniversities to address sustainability issues facing
the world today. Universities can work together toreduce the impact of environmental problems andUI GreenMetric is a non-profit organization so alluniversities can participate free of charge. In
general, UI GreenMetric uses the concept of asustainable environment which has 3 components,
namely environmental, economic and social.
Environmental aspects include the use of natural
resources, environmental management andpollution control where the economic aspects
include profit and efficiency. While the social
aspects include education, community and social
involvement. These three aspects are used as UIGreenMetric criteria. Currently the UIN
GreenMetric network consists of 719 universities
from Asia, Europe, Africa, Australia, and Oceania
as well as 1,997,294 teaching staff, 16,413,522
students with US$ 7,529,29,073 research funds forenvironment and sustainability [1].

Fig. 1 UI GreenMetric Criteria
The university's participation in UIGM canhelp raise awareness in the university and its
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GEOMATE – Kyoto, Japan, 3-5 November 2021

surroundings
about
the
importanceofsustainability issues. UI GreenMetric uses theimportant role of higher education institutions toraise awareness by assessing and comparing theefforts made by the world of education towards
sustainable development, sustainable research,greening campuses and their social impact.
Disclosure of the role of universities inrealizing a sustainable campus can be seen throughsustainability reporting and information presented
on the university website. Research results invarious countries show that some universities havenot focused on environmental reporting withalmost no economic and social disclosures [2] [3][4] [5] [6]. UI GreenMetric encourages
Universities to create and publish Sustainability
Reporting which is reflected in questionnairequestions, which can be an evaluation for theuniversity, Make it easier for other universities to
get information and to adapt existing policies at
the university, Make it easier for UI GreenMetric
Assessors to clarify answers and evidence
provided in the questionnaire. Universities that are
members of the UIGM ranking Publish
Sustainability Reporting through the disclosure ofindicators in UI GreenMetric: Arrangement andInfrastructure, Energy and Climate Change, Waste,
Water, Transportation, Education and Research.
However, until now in Indonesia, there is no
regulation that requires universities to disclosesocial information related to the activities carriedout, especially the responsibility of universities tothe environmental impact of the activities carriedout, while the benefits for the ethics of institutionalsustainability are enormous. Disclosure of the roleof universities in realizing a sustainable campus isvoluntary and has been carried out by universities
that are members of the UI GreenMetric World
University Rankings. Based on this, the research
objective is to analyze the implementation ofGreen Campus at universities in Indonesia.

The term green campus can be defined as an
ethical quality policy in the activities of theuniversity community (students, lecturers,
administrative
staff),
through
responsible
management of the educational, cognitive,
workforce, and environmental impacts of theuniversity, in participatory dialogue with thecommunity to promote human development.
sustainability in four steps: (1) commitment, (2)self-diagnosis,(3)compliance,and (4)accountability [8]. Green campus can beconsidered as a university philosophy as an ethicalapproach to developing and engaging with local
and global communities in order to sustain social,ecological,
environmental,
technical,
andeconomic development. Green campus can be
implemented well if the campus can implement
environmental management accounting correctly.
Environmental management accounting is limited
in use and fragmented in organizations at thereactive and preventive stages except for using
environmental management accounting for costsavings and efficiency improvements. However,the findings suggest that and when organizations
progress into higher levels of cleaner production
strategy development, there is a relatively highlevel of use of environmental management
accounting in terms of integrative tools, and forcontrol and stewardship purposes [9].
The practice of social responsibility ofuniversities or green campuses has also attracted
the attention of the Asian region. The Associationof Southeast Asian Nations Network (ASEAN)
proposes a conceptual framework for AUN
University Social Responsibility and Sustainability
with the following main components: (1) teaching
and learning, research and academic services, (2)university governance and administration, (3)community involvement, and (4) campus life topromote green campus practices and addresssocial, economic, and environmental challenges
faced in the ASEAN Community.
Research of Chen et.al. [10], found that most of
the actual practice and strategic plans were focusedon achieving academic service. Thus, further
investigation of green campus components,
practices, and how exactly to create such a green
campus impact should be examined in the future.
The process of implementing environmental
management accounting at the University of
Bologna has been able to implement its mainconcepts, namely academic autonomy andfreedom, as a new form of social responsibility.
This accountability to the whole of society
involves personal improvement in the interest ofsociety and for its main concerns: climate change,
global injustice, environmental protection andrecycling [11]. Lindrianasari et.al [12] found thatthe theory of regulation (particularly for public

LITERATURE REVIEW
Legitimacy and stakeholder theory state thatorganizations must be able to adapt to the value
system that has been applied by society and theenvironment. Among other things, the company's
business is realized through social disclosure. Thisis carried out with the aim that the activities andactivities of the organization are legitimized in theeyes of the community. The more forms of
responsibility an organization undertakes to its
environment, the better the image and reputation of
the organization will be. Ahadis et.al. [7] argued
that mining regulations issued by the government
improve community welfare and confirm
regulatory theory, especially the theory of public
interest.

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GEOMATE – Kyoto, Japan, 3-5 November 2021

interest theory) can clearly explain the reasons
why the four variables research have increased
after the environmental regulations issued. Theirresearch found similar results in all companies
listed on stock exchanges in three developing
countries, namely Indonesia, Malaysia andThailand, related to new policies on social andenvironmental responsibility.
An organization can be categorized as taking
part in protecting the environment if it has
Environmental
Awareness,
Environmental
Involvement, Environmental Reporting, andEnvironmental Auditing. Therefore, it can beconcluded that at least four factors are needed interms of organizational responsibility for theenvironment, namely environmental awareness,environmental
involvement,
environmental
reports, and environmental audits [13] [14] . Evanaet.al [15] said that public acceptance of mining
companies is influenced by several factors,
including the cost of living, quality of theenvironment, community involvement, improved
infrastructure, the impact of other industries, andpublic welfare.

UIGM ranking publish Sustainability Reporting
through the disclosure of categories andassessment indicators for the UI GreenMetric
World University Rankings: Arrangement andInfrastructure, Energy and Climate Change, Waste,
Water, Transportation, Education and Research.
The categories and weights of points in the UIGreenMetric World University Rankings are as
follows

RESEARCH METHODSThis research is a field research. The
population in this study are universities inIndonesia which are included in the top 50
greenest campuses in Indonesia according to the
UI Green Metric in 2019. The universities sampled
in this study are the University of Indonesia,
Raden Intan State Islamic University, Lampung,Muhammadiyah University of Yogyakarta, andState University Yogyakarta. The considerations
are representatives of higher education institutions
that are included in the category of the greenest
campus in Indonesia according to the 2019 UIGreen Metric version which are willing to be used
as research samples.
Data collection techniques used interviews anddocumentation of leaders, lecturers, education staff,
and students from universities that were sampled.
The measurement of data is based on the
respondent's tendency towards 4 indicators of thestages of higher education in implementing Green
Accounting, namely Environmental Awareness,
Environmental
Involvement,
Environmental
Reporting, and Environmental Auditing. After thedata collected, then they are processed
systematically according to the problem
formulation as well as analyzed in a qualitativedescriptive manner.

Based on information of the table above,
category of Energy and Climate Change (EC) has
the highest score in the UI GreenMetric World
University Rankings assessment, followed by thecategory of Waste (WS), Transportation (TR),
Education (ED), Setting and Infrastructure (SI),and Water (WR). ). UI GreenMetric World
University Rankings is managed by a team under
the Rector of Indonesia University. The teammemberscomefromvarious academic
backgrounds
andexperiences,suchas
Environmental
Sciences,
Engineering,Architecture and Urban Design, Computer
Engineering, Dentistry, Public Health, Statistics,
Chemistry, Physics, Linguistics and Cultural
Studies.
Based on the 2019 UI GreenMetric Guidelines,
the Setting and Infrastructure (SI) category
provides an overview of campus tendencies
towards a green environment and aims to trigger
participating universities to provide more green
open spaces for reforestation and protecting theenvironment as well as sustainable energy. The
Energy and Climate Change (EC) category
provides an overview of the use of energy-efficientequipment,
implementationofsmart
building/automation building/intelligent building,policies on the use of renewable energy, total useof electrical energy, energy conservation
programs, elements of green building, adaptation
to climate change and mitigation programs,
policies for reducing greenhouse gas emissions
and carbon footprints. The Waste (WS) category is
related to waste treatment and recycling activities

Table 1 Categories and Their Weighting Used in
UI GreenMetric World University Rankings
No
Categories
Percentage of Total
Points (%)
1
Setting and
15
Infrastructure (SI)2
Energy and Climate
21
Change (EC)3
Waste (WS)18
4
Water (WR105Transportation (TR)18
6
Education (ED)
18
TOTAL
100%
Source: UI GreenMetric Guidelines, 2019DISCUSSION
The university's participation in the UIGreenMetric World University Rankings can be anindication of the university to realize a Green
Campus. Universities that are members of the581

GEOMATE – Kyoto, Japan, 3-5 November 2021

in creating a sustainable environment. The Water(WR) category is related to water conservation
programs, water recycling programs, the use ofwater-saving equipment, and the use of treatedwater. The Transportation (TR) category is related
to the transportation system that plays an importantrole in carbon emissions and pollution levels on
campus. The Education (ED) category relates tothe availability of sustainability-related courses
offered on campus, research funding forsustainability
research,indexedscientificpublications related to environment andsustainability publishedannually,
campusevents/activities (e.g. conferences, workshops,
awareness raising , training, etc.) related toenvironmental and sustainability issues, student
organizations at both the faculty and university
level related to environmental sustainability,
university sustainability websites, and theavailability of university sustainability reporting.
Several stages that can be carried out are throughthe four stages, namely Environmental Awareness,
Environmental
Involvement,
Environmental
Reporting, and Environmental Auditing. The
stages of the Environmental Awareness Indicators
are as follows

2.
3.
4.
5.
6.

Build infrastructure and facilities that refer togreen building standards;
Build facilities by prioritizing reforestation;
There is a recycling program in the treatmentof waste, water, plastic, glass;
Provide zero emission transportation;
Organize community service activities andenvironmental-based research designs;

Efforts to build environmental awareness arecarried out through posters, campaign activities
and appeals. To foster a sense of environmental
concern, the campus has socialized the call for nosmoking, maintaining cleanliness, an appeal tosave light and air conditioning when not in use, acarbon reduction campaign, a plastic waste
reduction campaign and other non-binding appeals
throughout the campus area. However, University
Z has not yet had a written rule issued by theuniversity leadership which includes obligations
and sanctions related to violations of these appeals.
In general, based on the presentation of theresults of interviews, study documentation on the
Sustainability Report and information available on
the special green campus website, as well as theawards that have been achieved in recent years
including the acquisition of the UI Green Metric
World University Ranking, it can be stated that theleadership of University Z has high commitment tobuild environmental awareness and become asustainable campus. At university X according to
the facts on the ground with the provencommitment of the university to uphold good
governance of the university and the new jargon at
the university "Greening Your Campus, Greening
Your Life". This jargon is in line with theuniversity's vision as stated in the statute, namely
the realization of the university as an internationalreference in the development of integrativemultidisciplinary Islamic science with anenvironmental perspective in 2035. In addition, thecampus also held a "Car Free Day". In order toimprove the management of green campuses andaccelerate the realization of the Vision, theuniversity gives awards to study programs andfaculties related to environmental management.
This award is known as the Environmental
Management Awards (EMA). At university Y, theuniversity leadership has a high commitment tomaking the university an environmentally friendly
campus. This is evidenced by a university issuing a
Rector's Regulation on green campuses. The stagesof the Environmental Involvement Indicators are
as follows

Table 2 Stages of Environmental AwarenessIndicators in Planning for Green Campus
Implementation in Indonesia
Indicators
X Y
Z
Environmental Awareness
There are regulations regarding v
v
x
the environment
Philosophy
ofuniversity v
vv
leadership
The support of Human Resources vvv(HR) and Economic Resources(SDE)
Support for research master plan vvv
and community service
The Environmental Awareness stage is to build
awareness and commitment to the importance ofpreserving the environment. The University
leadership strongly supports the existence of theuniversity as an environmentally friendly campus.
University leaders generally have a philosophy
about the environment. Based on the results of aninterview with one of the managers of theuniversity's green campus Z, the leadership's
commitment to building environmental awarenessis manifested in the form of:
1. Place the UI green metric ranking as one ofthe performance indicators of university
leaders and use the UI GreenMetric World
University Rankings form as a green campus
document;

Table 3 Stages of Environmental Involvement
Indicators in Planning for Green Campus
Implementation in Indonesia
Indicators
X Y
Z
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GEOMATE – Kyoto, Japan, 3-5 November 2021

Work
governanceregardingtheimplementation of green campus refers to theDecree (SK) of the appropriate bureau officials aswell as the Decree (SK) and duties for ad hocbodies or special committees. At university Y theteam is still ad hoc and the team coordinates
directly with the Vice Chancellor for Division 2
regarding funding and infrastructure development.
The campus provides a special budget or
funding for the green campus program as stated in
the University RKT. The university's budget for
infrastructure, facilities, personnel costs, including
Research Funds Dedicated to Sustainability
Research is disclosed in the university's
Sustainability Reporting and is one of the fillingsin the questionnaire in the UI Green Metric World
University Rankings.
Since 2011, Z University has special programsin the context of conserving natural resources andthe environment, such as setting most of its open
space area with trees growing, constructing more
smart buildings, paperless behavior and efficientenergy appliances (Air conditioning with the VRVtechnology, LED lighting in most buildings),
recycling program in the management of waste,water, plastic, glass, Providing zero emission
transportation.
Regarding education and curriculum design,
the University through several study programs has
opened several courses related to the environment.
The trend of data on environmental involvementshows that the indicators of environmental
involvement are in the high category because
many components or environmental conditions at
University Z are already good. In 2019, University
X formed a Sustainable and Environmentally
Insight Campus Development Team (TPKBBL)
through the Chancellor's Decree. The Stages ofEnvironmental Reporting Indicators are as follows

Environmental Involvement
The existence of environmental v
xx
units in universities
Availability
of
funds
for v
v
v
environmental protection
Availability of programs or vvv
procedures
that
reduceconsumption of natural resources
Availability of environmental vv
v
safety studies if the entity uses
machines in its operational
activities
Environmental involvement can be seen
through the existence of a special team that isformed directly under the supervision of theappointed university leadership and evaluates theimplementation and measures the effectiveness ofgreen campus implementation. University Z does
not yet have a special division/work unit that is
specifically responsible for handling green campus
implementation, but it is attached to related
bureaus/units, such as:

The General Bureau regulates water, green
space cleanliness, bicycles, electricity, andothers; Bureau of Information Systems makes
programs/applications for paperless office ;Special activities regarding green campuses
are carried out by ad hoc bodies orcommittees, for example carbon reduction
campaigns,
plastic
waste
reduction
campaigns, etc;Infrastructure development refers to green
building standards designed by a special
team;

Activities related to green campus rankings
are carried out by the Strategic Data andInformation Center, the Planning andDevelopment Agency under the coordinationof the Vice Chancellor for Governance,
Cooperation and International Affairs.;

Routine activities related to environmental
regulation are adjusted to the appropriateorganizational structure and governance;Temporary activities are also adjusted to theappropriate organizational structure andgovernance.

Table 4 Stages of Environmental Reporting
Indicators in Planning for Green Campus
Implementation in Indonesia
Indicators
X Y
Z
Environmental Reporting
Environmental
performance v
xv
reportPerformance reports are reported v
xv
continuously
Performance report publishedv
xv
The environmental performance v
xv
report is attached with publication
Environmental reporting is required for thedecision-making process as well as for the purposeof disclosing material and relevant information
about the university. Activities in order to support
the implementation of the green campus arecontained in the Sustanability Report which

Fig. 2 The Implementation of Solid Waste
Management in Y University
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GEOMATE – Kyoto, Japan, 3-5 November 2021

generally contains the achievements/indicators of
the UI Green Metric World University Ranking.
The Sustainability Report is published by
Universities X and Z every year on the relevant
green campus web. Documentation is carried out
on videos stored on YouTube, social media, andthe university's green campus web. The Stages ofEnvironmental Auditing Indicators are as follows:
Table 5 Stages of Environmental
Indicators in Planning for Green
Implementation in Indonesia
Indicators
X
Environmental Auditing
There is an audit report regarding x
the implementation of a green
campus at the university
There is an internal supervisory x
unit (SPI) that audits theimplementation of the green
campus at the university

Auditing
Campus
Y

Z

x

x

x

x

Fig. 3 graph of the percentage of achievements inthe stages of implementing green campus inIndonesia
The university's participation in UI Green canbe an indication that the university has awarenessin the university and its surroundings about theimportance of sustainability issues. University X
has the highest percentage of achieving 4 green
campus implementation instruments. Based on thegraph above, at the environmental awareness stage,
University X and Y have a higher percentage than
University Z. At the environmental involvement
stage the highest percentage value is owned by
University X. At the environmental reporting
stage, University Y has not yet done At theenvironmental awareness stage, environmental
reporting, but universities X and Z have done
environmental reporting. Meanwhile, no university
has implemented environmental auditing yet.

The Environmental Auditing stage is carriedout to ensure whether the performance ofenvironmental conservation programs run by
universities has been carried out effectively andefficiently is the existence of environmental
performance audits. This is measured based on
answers to the availability of environmental
performance audit reports or university green
campus implementations. Until now, the Internal
Audit Unit or the University's Internal Supervisory
Unit does not yet have a special program related toauditing programs, funds, and environmental
performance carried out by the University. In otherwords, there has never been an audit of theperformance of the green campus implementation,
both from internal and external parties.
Based on 4 stages, universities in theirplanning to implement Green Campus have met
the stages of environmental awareness,environmental involvement, and environmental
reporting. All universities have not met theenvironmental auditing stage. The following is agraph of the percentage of achievements in thestages of implementing green campus inIndonesia:

CONCLUSION
The implementation of Green Campus ismeasured based on 4 instruments, namely
Environmental
Awareness,
Environmental
Involvement, Environmental Reporting, andEnvironmental Auditing. The findings of this study
indicate that three universities have met the stagesof environmental awareness, environmental
involvement, and environmental reporting, buthave not met the stages of environmental auditing.
The university's participation in UI Green can be
an indication that the university has awareness inthe university and its surroundings about theimportance of sustainability issues. University X
has the highest percentage in achieving 4 green
campus implementation instruments. The theory oflegitimacy can explain that an organization must
be able to adapt to the value system that has been
implemented by society and the environment
because the more forms of responsibility it takes to
its environment, the better the image andreputation of the organization will be.
Through the implementation of a green
campus, universities must pay attention toeconomic, social, and environmental aspects so
that
their
existenceandenvironmental
sustainability will be maintained. Legitimacy
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GEOMATE – Kyoto, Japan, 3-5 November 2021

theory can explain that organizations must be able
to adapt to the value system that has been appliedby society and the environment because the more
forms of responsibility that are carried out on theirenvironment, the better the image and reputation of
the organization will be.

[7]

[8]

ACKNOWLEDGMENTS
This research can be done well due to the funds
provided by the government of the Republic ofIndonesia through a research grant scheme at theIslamic State University of Raden Intan, Lampung.
We would like to thank for the financial support ofthis research. In addition, we would also like tothank our colleagues at Universities in Indonesia toaccess the data. Participant feedback andsuggestions in The 11th International Conference
on Geotechnique, Construction Materials andEnvironment (GEOMATE), Kyoto, Japan, Nov. 35, 2021.

[9]

[10]

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GEOMATE, vol. 19, no. 72, 2020, doi:
10.21660/2020.72.9148.

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

METALAXYL RESISTANCE, PATHOGENICITY, AND CROSSINFECTIVITY OF PHYTOPHTHORA PALMIVORA FROM DURIAN
AND PARA-RUBBER DISEASES IN SOUTHERN THAILAND
Pornprapa Kongtragoul1 and Thammanoon Phelawan1
1

Management Technology for Plant Production, Department of Agricultural Technology,
King Mongkut’s Institute of Technology Ladkrabang, Prince of Chumphon, THAILAND

ABSTRACTDurian and para-rubber are the economic crops of Southern Thailand. The serious pathogen of both is
Phytophthora palmivora. Metalaxyl has been commonly used to control this pathogen. The objectives of this study
were to detect the metalaxyl resistance of P. palmivora, their pathogenicity test and cross-infectivity test. Ten
isolates of parental isolate [PI] were successfully isolated from para rubber cv. RRIM600 and durian cv. Monthongby tissue transplanting technique. The metalaxyl-resistant assay was conducted on V8 agar amended withmetalaxyl by poisoned food method. The results showed that 8 isolates of PI were highly resistant to metalaxyl[MetHR], which they were able to grow at > 500 µg/ml, and 2 isolates of PI were sensitive to metalaxyl [MetS],
which they were able to grow at ≤10 µg/ml. Moreover, the hyphal tips isolate [HI] derived from PI isolates 6month preservation showed the results indicated that all Met HR isolates of PI were still MetHR phenotype. All HI
isolates were studied for pathogenicity and cross-infectivity by the detached leaves technique. The results showedthat the lesion diameters of all isolates from durian disease were significantly wider than all isolates from pararubber disease after inoculation for 4 days. All isolates from durian disease were pathogenic to un-wounded hostleaves, but all isolates from the para-rubber disease were not pathogenic to un-wounded host leaves. The isolatesof DF03-MetHR and DS07-MetS showed the highest percent infection after inoculation for 4 days at 16.66% and13.88%, respectively. However, all isolates were pathogenic to wounded cross-host leaves, and the PRS01-MetHR
isolate showed the widest lesion diameter after inoculation for 4 days at 23.16+3.36 mm. On the other hand, allisolates were not pathogenic to un-wounded cross-host leaves.
Keywords: Fungicide resistance, Phenylamides, Plant disease, Durian, Para rubber

INTRODUCTION

polymerase I of nucleic acids metabolism thatoomycetes have developed resistance to this group ofcompounds. Fungicide resistance action committee(FRAC) reported that metalaxyl is a high-risk
resistance in various Oomycetes but mechanism
unknown [3], [4]. However, using fungicides may be
the acquired appearance of resistant strains in the
durian and para-rubber orchard. It may be one of thesignificant limiting factors for poor disease control
because of pathogen resistance development. This
resistance is transferrable to descendants. Moreover,the side effects of fungicides cause risks to humans,
animals, and the environment. Resistance had
occurred in some fungi because metalaxyl was used
intensively before the dangers of resistance were
understood. Therefore, knowledge of fungicide
resistance, pathogenicity, and cross-infectivity isimportant for effective pathogen control. The
resistance of Phytophthora spp. to metalaxyl has beenreported in many regions of the world [5]-[11].
However, limited information is available on thesensitivity of P. palmivora to metalaxyl in Thailand.
The objectives of this research were to [i]determine the occurrence of metalaxyl-resistant P.
palmivora vegetative reproduction causing durian

Durian and para-rubber are the importanteconomic crops of Thailand. They have the mostplantations in Southern Thailand, and Chumphon
province is ranked among both production areas [1].
However, the most serious pathogen of both crop
production is the genus Phytophthora. It causes fruit,
stem, and root rot diseases in durian; black strip andleaf fall in para-rubber [2]. They are the key limiting
factors of durian and para-rubber production.
Fungicides are still important for plant production.
Some fungicides were used to reduce the
Phytophthora diseases because almost farmers
believed that fungicides could successfully control
the plant pathogen better than other methods. They
applied the systemic fungicides such as metalaxyl forcontrolling this pathogen. It has been widely used formany years ago in durian and para-rubber orchard.
Therefore, its long term may induce mutant isolates
in the field.
Metalaxyl is the most active of the phenylamides
fungicides, and it has curative as well as protective
activity, and rapid uptake and translocation to new
growth. The mode of action is so specific at RNA
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and para-rubber diseases and [ii] their pathogenicityof P. palmivora on the host plant and [ii] crossinfectivity of P. palmivora on durian and para-rubber.

parental isolate [PI] was cut from the margin after
isolated from infected tissue. The hyphal tips isolate[HI] derived from PI isolates were kept on V8 agar
for 6 months for metalaxyl-resistance assay. The
fungicides applied for P. palmivora control in durian
and para-rubber diseases were metalaxyl for 40g/ 20
l water [25% w/v]. Resistant reaction to metalaxyl[25%WP w/v] of PI and HI isolates was determinedthrough in vitro comparison of each isolate's mycelial
growth on the final concentrations of metalaxyl at 0.1,
1, 10, 100, 500 [field-use recommended rate], and
1,000 a.i. µg/ml amended V8 agar and un-amended
V8 agar with the growth of the same isolate on
medium. Metalaxyl was added to V8 agar after
autoclaving, and the plates were incubated at RT. The
mycelial plugs of 5 mm diameter were cut from themargin of a 5 day old culture and placed with themycelium in contact with the test medium [5replicates for each isolate at each concentration].

MATERIALS AND METHODS
Isolation and identification pathogen causingpara-rubber and durian diseases
The disease samples of durian cv. Monthong andpara-rubber cv. RRIM600 that showed infection
naturally were collected from orchards in Chumphon
provinces, Thailand. PAR[PH]-V8 selective medium
was made for purification purposes by adding
antibiotics and fungicides and cooled clarified V8
juice medium followed Jeffers [12].
The diseased plant tissue was cut into pieces andsurface sterilized by soaking in 10% Clorox®, rinsed
in sterile distilled water, and blotted dry on sterile
paper. The dried tissues were placed on PAR[PH]-V8
selective medium and incubated at room temperature[RT] of approximately 28-30oC. The parental isolates[PI] were cut from the hyphal tip of a 4-days-old
culture after isolated from single discrete lesions andtransferred onto clarified V8 juice agar for 6 months.
Three isolates [PRS01, PRS02, and PRS03] wereisolated from black stripe symptoms on the naturally
infected para-rubber stem [Figure 1a]. Five [DS02,
DS03, DS06, DS07, and DS10] and two [DF01,
DF03] isolates were isolated from stem and fruit rot
symptoms on naturally infected durian, respectively[Figure 1b, 1c].

[a]

[b]

The response of resistance to metalaxyl wascategorized which modified from Kongtragoul et al.
[13] as follows:• sensitive to metalaxyl [MetS]: the culture was able
to grow at ≤10 µg/ml• resistant to metalaxyl [MetR]: the culture was able
to grow at 10 < EC50 ≤ 100 µg/ml• highly resistant to metalaxyl [MetHR]: the culture
was able to grow at > 500 µg/ml
Pathogenicity and Cross-infectivity in vivo test
Pathogenicity test on a hostAll HI isolates of P. palmivora were pathogenic
on host leaves. The healthy leaves were detachedwithout applying the fungicide orchard in KMITL
Chumphon campus, Thailand. The healthy leaves
were washed thoroughly under running tap water
before being surface sterilized with 10% Clorox® andblotted slightly on paper towels to remove excess
moisture. The base of each detached leaf was coverwith a piece of moist cotton to reduce leaf desiccation.
Wound and un-wound inoculation were performed
for pathogenicity tests on the leaves of the ‘Monthong’durian and ‘RRIM600’ para-rubber. Then they wereplaced in a plastic box. A circular inoculation site,with a 5 mm diameter, was marked on the leaf surface.
The Mycelium disc of each isolate was cut at the
peripheral colony and transferred to the wounded andun-wounded area. Moistened paper towels wereplaced in the plastic box containing the inoculated
leaves and incubated at RT. The lesion diameters were
measured after four days of incubation on wounded
inoculation. For un-wounded inoculation wereobserved the number of the lesion and calculate thepercentage of infection.

[c]Fig. 1 Symptoms of black stripe disease [a] in pararubber, stem rot disease [b], and fruit rot disease [c]in durian.

Metalaxyl-resistance assay
The resistance to metalaxyl was studied for all
isolates of P. palmivora by phenotypic assay. The
resistibility of each isolate to metalaxyl was observed
from the growth of the colony. For each isolate, the587

GEOMATE – Kyoto, Japan, 3-5 November 2021

Cross-infectivity

metalaxyl resistance has been almost found on P.
infestans in Northern Thailand [14] and othercountries such as; China [5, 6], Mexico [10], USA[15], Uganda [7], Morocco [8], Cameroon [9], Russia[16], Estonia [17], and Poland [18]. This result is oneof the reasons for increasing difficulties in disease
control. Furthermore, the information on fungicide
resistance of P. palmivora must be study in themolecular level. It may help in understanding in thefungicidal mechanism.

The determination of the cross-infection of P.
palmivora isolates from durian and para-rubber wasused to inoculate each of the above host combinations.
The isolates from durian diseases were inoculated bymycelium disc on wounded and non-wounded pararubber leaves, and the isolates from para-rubber
diseases were inoculated by mycelium disc on
wounded and non-wounded durian leaves.
RESULTS AND DISCUSSION

Table 1 Metalaxyl-resistant assays of Phytophthorapalmivora, causing durian and para-rubber diseases
on V8 agar amended with metalaxyl.

Isolation and identification pathogen causingpara-rubber and durian diseases
Ten isolates were isolated from para-rubber anddurian diseases. The colony of all isolates was white[Figure 2a]. Mycelia were branched, non-septate, andhyaline [Figure 2b]. They formed sporangium withpapilla by growing on clarified V8 agar for 7 days
[Figure 2c]. These morphological characteristics
were identified as P. palmivora [12]. This situation issimilar to that in other durian and para-rubber
producing countries such as Malaysia, Vietnam, andIndonesia, etc., where the fungus, P. palmivora, hasbeen reported to cause both crops [2].

Isolate
code

DF01

0.1
1
10 100 500* 1,000
100 100 97.78 58.45 38.66 17.34
100 100 95.55 62.22 41.48 20.74
100 100 96.76 66.67 36.78 16.77
100 100 98.14 78.14 40.74 23.70
100 100 98.54 66.76 34.16 16.86
100 100 95.5 70.37 44.81 22.77
100 100 100 71.48 33.33 17.70
100 100 100 77.77 36.38 25.00
100 100 100 61.38 29.16 28.33
100 100 100 77.77 40.00 22.66
100 100 100 57.77 26.94 13.05
100 100 100 71.33 34.4 22.66
98.34 10.00 7.7
000
98.44 14.40 000097.77 13.55 7.7
000
98.00 12.44 0000100 100 100 68.16 33.88 16.38

MetHR
MetHR
MetHR
MetHR
MetHR
MetHR
MetHR
MetHR
MetHR
MetHR
MetHR
MetHR
MetS
MetS
MetS
MetS
MetHR

DF03

HI
PI

100
100

MetHR
MetHR

PRS02
PRS03
DS02
DS03

DS07
DS10

[b]

[c]

Resistantlevels

PI
HI
PI
HI
PI
HI
PI
HI
PI
HI
PI
HI
PI
HI
PI
HI
PI

PRS01

DS06

[a]

Percentage growth
Metalaxyl concentrations [mg/l]

100 97.50 76.39 44.81 27.40
100 100 73.3 34.44 23.61

HI 100 100 94.81 70.84 40.37 22.96 MetHR
Field-use recommended rate, parental isolates [PI], hyphal tips
isolate [HI]Fig. 2 Morphological characterization of
Phytophthora palmivora; colony color on V8 agar[a], hypha [b], and sporangium [c].

*Pathogenicity and Cross-infectivity in vivo test

Metalaxyl-resistance assay

The pathogenicity test and cross-infection
confirmed that the vegetative reproduction of P.
palmivora after kept for 6 months was pathogenic toboth the durian and para-rubber on wounded leave.
However, all isolates from durian disease were
pathogenic to un-wounded host leaves, but all isolates
from the para-rubber disease were not pathogenic to
un-wounded host leaves. The lesion diameters of all
isolates from durian disease were significantly wider
than all isolates from para-rubber disease after
inoculation for 4 days. DS02-MetHR, DF03-MetHR,
and DS07-MetS showed the widest lesion diameter
after inoculation for 4 days at 18.61+0.88, 18.52+1.24,
and 17.94+2.38, respectively. The isolates of DF03MetHR showed the highest of percent infection after
inoculation for 4 days at 16.66%.
Moreover, all isolates were pathogenic to

The results showed that three isolates wereclassified as the MetHR phenotype, of which 3 [PRS02,
PRS01, PRS03] and 5 [DS02, DS03, DS06, DF01,
DF03] were obtained from para-rubber and durian,
respectively. All MetHR isolates were obtained fromtreated fungicide orchards. All PI isolates, 2 isolates
from durian [DS07, DS10], were classified as the
MetS phenotype, of which both isolates wereobtained from untreated fungicide orchards. After
kept PI isolates for 6 months on V8 agar, themycelium of all isolates was repeated the resistibility
to the metalaxyl test. The result shows that all PI
MetHR isolates could still grow on metalaxyl at thefield-use recommended concentration. All MetHR of
PI were classified as the MetHR of HI after kept
mycelium for 6 months [Table 1]. In other reports,
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GEOMATE – Kyoto, Japan, 3-5 November 2021

wounded cross-host leaves, and the PRS01-MetHR
isolate showed the widest lesion diameter after
inoculation for 4 days at 23.16+3.36 mm. On theother hand, all isolates were not pathogenic to unwounded cross-host leaves [Table 2, 3]. This implies
that the wound is a significant factor in pathogenic tohost and cross-host of P. palmivora. Moreover, themetalaxyl response group is not significant toinfection.

CONCLUSIONS

Table 2 Lesion diameters of host leave after
inoculation with Phytophthora palmivora on
wounded leaves for 5 days and percent of infection onhost leaves after inoculation on un-wounded leaves
for 4 days.

ACKNOWLEDGMENTS

Isolate code

REFERENCES

This study demonstrated that P. palmivora
vegetative reproduction is highly resistant tometalaxyl. All tested isolates from durian were
pathogenic to host leaves and not pathogenic to unwounded para-rubber leaves. We conclude that theapplication of metalaxyl fungicide should be
carefully planned for durian and para-rubber disease
management.

We express my sincere thanks to King Mongkut's
Institute of Technology Ladkrabang, Prince of
Chumphon Campus, Thailand for support.

Lesion diameter on host Percent of[mm + standard
infection1/
deviation]
on ‘RRIM 600’ para-rubber leaves
PRS01-MetHR
7.87+0.15d
0.00b
PRS02-MetHR
7.90+0.37d
PRS03-MetHR
11.81+2.59c
on ‘Monthong’ durian leaves
DS02-MetHR
17.94+2.38aDS03-MetHR
17.23+0.85ab
DS06-MetHR
16.75+1.01ab
DS07-MetS
18.52+1.24aDS10-MetS
17.52+0.43ab
DF01-MetHR
15.27+1.28b
DF03-MetHR
18.61+0.88a[1] Office of Agricultural Economics Departmentof Agriculture Thailand, Quantity and value ofimports of agricultural hazardous substances,
2018,(Online)Available
http://oldweb.oae.go.th/economicdata/pesticides.html[2] Drenth, A., and Guest D. I., Diversity andManagement of Phytophthora in Southeast Asia,
Canberra Australia Australian Centre forInternational Agricultural Research, 2004, pp.1238.
[3] Fungicide Resistance Action Committee, List of
Resistant Pathogenic Organisms Resistant toDisease Control Agents, 2018, (Online)
Available
http://www.frac.info/docs/defaultsource/publications/list-of-resistant-plantpathogens/list-of-resistant-plant-pathogenicorganisms_may-2018.pdf[4] Hollomon D. W., Fungicide Resistance: Facing
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51, Issue 4, 2015, pp. 170-176.
[5] Wang W., Liu X., Han T., Li K., Qu Y., and GaoZ., Differential Potential of Phytophthora
capsici Resistance Mechanisms to the Fungicide
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Zhang Z., and Wang Y., Distribution,
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[7] Mukalazi J., Adipala E., Sengooba T., Hakiza J.
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[8] Hammi A., Msatef Y., Bennani, A., Ismaili A.
E. L., and Serrhini M. N., Mating Type

0.00b
0.00b
8.33ab
5.55ab
9.72ab
11.10ab
9.72ab
4.16b
16.66a1/

Average of four replications [18 sub-replications]. The meanfollowed by the same letter is not signification different by leastsignificant difference [LSD].

Table 3 Lesion diameters and cross-host leave after
inoculation with Phytophthora palmivora on
wounded leaves for 4 days.
Isolate CodeLesion diameter on host Percent of[mm + standard
infection
deviation]1/
on wounded ‘Monthong’ durian leaves
PRS01-MetHR
16.66+2.14a
0
PRS02-MetHR
5.84+0.95e
0PRS03-MetHR
14.29+1.70abc
0
on wounded ‘RRIM 600’ para-rubber leaves
DS02-MetHR
12.35+2.99bcd
0DS03-MetHR
10.18+1.29d
0DS06-MetHR
11.47+1.40cd
0DS07-MetS
13.37+1.33abcd
0DS10-MetS
12.91+1.46bcd
0DF01-MetHR
15.22+1.54ab
0DF03-MetHR
10.93+1.22d
0
1/

Average of four replications [18 sub-replications]. The meanfollowed by the same letter is not signification different by leastsignificant difference [LSD].

589

GEOMATE – Kyoto, Japan, 3-5 November 2021

Metalaxyl Resistance and Aggressiveness of
Phytophthora infestans (Mont.) de Bary in
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Isolates Obtained from Garden Huckleberry,
Potato and Tomato in Cameroon, Crop
protection, Vol. 24, Issue 5, 2005, pp. 449-456.
[10] Grünwald N. J., Sturbaum A. K. and Montes G.
R., Selection for Fungicide Resistance within a
Growing Season in Field Populations of
Phytophthora infestans at the Center of Origin.
Phytopathology, Vol. 96, Issue 12, 2006, pp.
1397–1403.
[11] Earnshaw D. M., and Shattock R. C., Sensitivityof Progeny of Phytophthora infestans to
Fungicides, Asian Journal of Agricultural
Sciences, Vol. 4, Issue 3, 2012, pp. 213-224.
[12] Jeffer S. N. Identifying Species of
Phytophthora, Clemson University (Online)
2006,
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https://fhm.fs.fed.us/sp/sod/misc/culturing_spe
cies_phytophthora.pdf[13] Kongtragoul P., Nalumpang S., Miyamoto Y.,
Izumi Y., and Akimitsu K., Mutation at Codon
198 of TUB2 Gene for Carbendazim Resistancein Colletotrichum gloeosporioides Causing
Mango Anthracnose in Thailand, Journal Plant
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377–383.

[14] Chiampiriyakul P., Sopee J., and Mekmok T.,
Evaluation of Metalaxyl Sensitivity Among
Phytophthora infestans by Poisoned Food
Technique Using Corn Agar, Proceeding of 49th
Kasetsart University Annual Conference Plants
1-4 February 2011, pp. 480-487.
[15] Parra G., and Ristaino J. B. Resistance toMefenoxam and Metalaxyl Among Field
Isolates of Phytophthora capsici Causing
Phytophthora Blight of Bell Pepper, Plant
Disease, Vol. 85, Issue 10, 2001, pp. 1069-1075.
[16] Elansky S., Apryshko V. P., Milyutina D. I., andKozlovsky B. E., Resistance of Russian Strainsof Phytophthora infestans to Fungicides
Metalaxyl and Dimethomorph, Moscow
University Biological Science Bullatin, Vol. 62,
Issue 1, 2007, pp. 11-14.
[17] Runno‐Paurson E., Fry, W. E., Remmel, T.
Mänd, M. and Myers K. L., Phenotypic andGenotypic Characterisation of Estonian Isolatesof Phytophthora infestans in 2004–2007,
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2010, pp. 375–384.
[18] Sobkowiak S., Śliwka J., Chmielarz M., Lebecka
R., and Zimnoch-Guzowska E., Resistance toMetalaxyl of Phytophthora infestans Isolates
Occurring in Poland in 2006-2010, Plant
Breeding and Acclimatization Institute –National Research Institute Research Divisionat Młochów Młochów Poland, Phytopathologia,
Vol. 61, pp. 29-35.

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11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

3D NUMERICAL ANALYSIS OF TSUNAMI–INDUCED FLOW
AND TOPOGRAPHY CHANGE IN AN ABRUPT EXPANSION AREA
WITH DIFFERENT SHORELINE CONDITIONS
Yuki Kajikawa1 and Masamitsu Kuroiwa2
1,2Faculty of Engineering, Tottori University, Japan
ABSTRACTThe purpose of this study is to clarify the applicability of a three-dimensional numerical model to predicting
flow and topography changes induced by tsunamis in an abrupt expansion area with different shoreline conditionsof overflow and reflection. In the numerical model, the tsunami flow was calculated using the three-dimensional
Reynolds-averaged Navier–Stokes equations with the shear-stress transport k–ω turbulence model using theCartesian coordinate system. The topography change was estimated by the sediment continuity equationconsidering the nonequilibrium bed-load and suspended-load transport. Moreover, the fractional area–volume
obstacle representation method was introduced into the governing equations. To investigate the applicability ofthe model to the phenomena, it was applied to laboratory experiments conducted by Arimitsu and Kawasaki (2017).
By comparing the calculated results and the experimental data, it was clarified that it is critical to set the appropriate
side wall roughness to accurately reproduce the tsunami flow in the abrupt expansion area using three-dimensional
numerical models. Moreover, the model developed in this study was found to reproduce the flow and topographychanges in an abrupt expansion area with different shoreline conditions.
Keywords: Tsunami, Abrupt expansion area, Shoreline condition, 3D numerical analysis, Topography change
various turbulence models have been proposed.
Additionally, in turbulence models in which themodel constants are determined almost uniquely,
such as the k-ε model, it is preferable to maintain theconstants so that the phenomenon can be reproduced.
Therefore, it is necessary to aim at reproducing thephenomenon by setting other conditions. However,the conditions to be set have not been clarified, andthe applicability of 3D models to this phenomenon isstill unclear.
Accordingly, the purpose of this study is to clarify
the applicability of a 3D RANS numerical model tothe prediction of flow and topography change induced
by a tsunami in an abrupt expansion area withdifferent shoreline conditions of overflow andreflection. Specifically, the authors first developed a
3D RANS numerical model and conducted thetsunami flow analyses with the side wall roughnesschanged for the experiments conducted by Arimitsu
and Kawasaki [4]. Then, after determining the sidewall roughness, analyses for the movable bed
experiments were performed.

INTRODUCTION
From the perspective of disaster prevention, it isextremely important to predict flows and topographychange induced by a tsunami in an inland sea leadingfrom a narrow pass to an abrupt expansion area, suchas a harbor. Therefore, numerous studies on theprediction of the phenomenon have been conducted
experimentally [1], [3], [4] and numerically [1]–[3],[5]. Arimitsu et al. [3] experimentally clarified thatthe circulation flow formed in the abrupt expansion
area will be the same scale regardless of the depth of
the area behind the expansion area. Furthermore, theyalso clarified, using the shallow water equations
based on the zero-equation turbulence model, thatsetting the eddy viscosity coefficient is very
important in accurately reproducing the flow andtopography change in the abrupt expansion area.
Incidentally, inside the circulation flow formed in
the abrupt expansion area, the flow near the bed is
known to be more inclined to the vortex center thannear the water surface due to the development of the
Ekman layer. This flow near the bed transports sandto the vortex center and prompts sedimentaccumulation. Therefore, a three-dimensional (3D)
numerical model is essential to predicting the flow
with vertical distribution and topography change
induced by the circulation flow accurately. However,in the 3D numerical model based on the Reynoldsaveraged Navier–Stokes (RNAS) equations, aturbulence model that can appropriately predict thephenomenon has not yet been elucidated, although

NUMERICAL MODEL
Governing Equations for Tsunami flow
In the tsunami flow model, 3D RANS equations
using the Cartesian coordinate system was adopted.
Furthermore, the fractional area–volume obstacle
representation (FAVOR) method [5], [6], which cansmoothly impose boundary conditions at complex
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GEOMATE – Kyoto, Japan, 3-5 November 2021

uBjuBj); qBe is the equilibrium bed-load transport rate;ζ is the spatial lag distance; ζe and uBei are the ζ anduBi for equilibrium condition, respectively; m* = μk s g
cos θb/(s+1+CM); μk is the dynamic friction coefficientof sediment; s is the specific gravity of sediment inwater (= σ/ρ – 1, σ is the density of sediment); θb is
the maximum bed angle; CM is the coefficient ofadded mass (= 0.5); hB = qBuB; γi = qBi/qB; γei = qBei/qBe;
qB* = qB/(sgd3)0.5; qBe* = qBe/(sgd3)0.5; and d is thesediment diameter.
The equilibrium bed-load transport rate, qBe, in Eq.
(3) was calculated using the following formula
proposed by Ashida and Michiue [9]:boundaries, was introduced into the governing
equations. The momentum and continuity equations
based on 3D RANS equations are as follows:ui 1  A j u j ui 
1 P    g 3i 
t V  x j  xi ui u j  
1    A j     t  V x j  x j xi  
 A( j )u j   0
x j

(1)

(2)

where t is time; i = 1, 2, 3; j = 1, 2, 3; (x1, x2, x3) = (x,y, z) in the Cartesian coordinates (x, y, and z denotethe horizontal, cross, and vertical coordinates,
respectively); ui is the velocity in the xi direction [(u1,u2, u3) = (u, v, w)]; V is the fractional volume rate
occupied by the fluid in an calculation grid; A(j) is thefractional area rate occupied by the fluid on the gridboundary surface in the xj direction [(A(1), A(2), A(3))= (Ax, Ay, Az)]; g is the gravitational acceleration; δ is
the Kronecker’s delta; ρ is the fluid density; P = p +2/3k; p is the pressure; ν is the kinematic viscosity of
fluid; k is the turbulent kinetic energy; and νt is theeddy viscosity coefficient.
In this model, the shear-stress transport (SST) k–ω turbulence model [7] was adopted for theevaluation of the eddy viscosity coefficient νt toaccurately reproduce the circulation flow in theabrupt expansion area.

 K  K c c  17 3 2 1  c c  1   
sgd



qBe

3

where τ* and τ*c are the non-dimensional and critical
bed tractive forces [10], respectively; and Kc is thecorrection factor attributed to the influence of bed
inclination on sediment motion [11].
Moreover, the suspended-load transport rate perunit area qsu was defined using the following formula
proposed by Itakura and Kishi [12]:    gd
q su  K    w f 

u






qBi 1  L j uBj qBi  t S  x j




e





 m hB   ei   i 

uB sgd  3.3(qB )1 3 , uBe sgd  3.3(qBe )1 3
23
hB d  0.3(qB ) d  600(qBe ) 2 3 ,  d  600 max(qBe , qB )2 3
 e



B





1







2



 2  1
B
2
exp  d





(6)

(7)

where wf is the settling velocity of the sediment given
by Rubey’s formula [13]; α' = B*/τ* – 2; α* = 0.14; K= 0.008; and B* = 0.143.
The advection and diffusion processes of thesuspended load were calculated using the following
suspended-sediment transport equation, into which
the FAVOR method was introduced:

In the topography change model, both the bedload and suspended-load transport were considered.
The bed-load transport rate was estimated from thefollowing nonequilibrium bed-load transport model
proposed by Uchida and Fukuoka [8]:qB u Bi1
exp    d
 



Governing Equations for Topography ChangeqBe u Bei(5)

c 1  A j u j c A3 w f c  1  
c 
 


 A j  Dt
 (8)t V  x j
x3  V x j x j 

(3)

where c is the concentration of the suspended load; Dt
is the eddy diffusion coefficient (= νt/σt); and σt is theturbulent Schmidt number (= 0.8).
The topography change was calculated using thefollowing continuity equation of sediment transport
into which the FAVOR method was introduced:(4)

where qBi is the bed-load transport rate in the xi
direction (qB2 = qBiqBi); S is the fractional area rate
occupied by the movable bed on a two-dimensional
horizontal (2DH) calculation grid; L(j) is the fractionalline rate occupied by the movable bed on the 2DH
grid boundary in the xj direction; uBj is the sedimentparticle velocity of bed load in the xj direction (uB2 = L j  qBj

zb
1

 S  qsu  wf cb    0
t S 1     x j(9)where zB is the bed elevation; λ is the porosity of the
bed material; and cb is the near-bed concentration of592

GEOMATE – Kyoto, Japan, 3-5 November 2021

Wave
Generator

S1

S3

S2

Shoreline Boundary
S4
Block
Movable Bed

1/50

0.7 m

MODEL APPLICATION AND DISCUSSION
Outline of Targeted Experiments
0.6 m

1/10
14.25 m

0.3 m

y

3m 3m

7.5 m

Block

0.75 m0.86 mx

P1

P0

Numerical analyses were performed for thelaboratory experiments with an abrupt expansion
area; the different shoreline conditions from theexperiments conducted by Arimitsu and Kawasaki [4]were used. The experimental setup is shown in Fig. 1.
Uniform sand with grain diameter d = 0.064 mm wasused in the movable bed area. The boundary
conditions of the shoreline is illustrated in Fig. 2. The
experiments were performed under two types ofboundary conditions: overflow and reflection. The
generated wave profile measured at S2 (indicated inFig. 1) is shown in Fig. 3. The time on the horizontal
axis in this figure was set to 0 s when the water level
reached its maximum at S2. This wave profile wasgiven at the offshore boundary in the calculations.
As the calculation conditions, the horizontal grid
sizes (Δx and Δy) and the vertical grid size (Δz) were
set to 0.02 m and 0.005 m, respectively. The
calculation time step Δt was set to 0.0025 s, andManning’s roughness coefficient at bed nb was fixed
at 0.010 s/m1/3.

6m

0.75 m

P4

P7 0.07 m

P3

P6

P2

P5

0.5 m

2.5 m

Fig. 17.5 m

0.75 m0.75 m

P9
0.07 m1m

P8

3m

Experimental setup (Top: Elevated view;
Bottom: Top view of movable bed area).
Overflow(Slope)Reflection(Vertical Wall)

0.1 m (Initial Water Depth)

0.1 m (Initial Water Depth)

0.1 m (Sand)

0.1 m (Sand)
0.6 m

Boundary conditions of shoreline (Left:
Overflow; Right: Reflection).

Fig. 2

Water level (m)

0.06
0.05
0.04
0.03
0.02
0.01
0
-0.01
-0.02

Discussion on Reproducibility of Tsunami Flow

-15

Fig. 3

-10

-5

0

5

10

Time (s)

15

20

25

First, the numerical analyses with different sidewall roughness n is performed for the experiment of
overflow condition with fixed bed. Fig. 4 and Fig. 5
show the comparison of measured and calculated
water levels and flow velocities at each station
indicated in Fig. 1, respectively. Here, the measured
and calculated velocities are given for 0.04 m heightfrom the bed. Regarding the water level, thecalculated results generally replicates the measured
ones regardless of the side wall roughness. However,in terms of velocities at P5 and P8, the calculated
results with the small side wall roughness n = 0.014
are considerably different from the measured data. In
contrast, the results with the large side wall roughnessn = 0.019 are in good agreement with the measured
results. In other words, it is very important to set
appropriate side wall roughness to accurately
reproduce the tsunami flow in the abrupt expansion
area.
Next, the analysis with the fixed side wallroughness n = 0.019 was conducted for theexperiment under reflection condition. Fig. 6 and Fig.
7 show the comparisons of measured and calculated
water levels and flow velocities at each station
indicated in Fig. 1, respectively. The calculated
results for both the water levels and flow velocities
are in good agreement with the measured results.
Thus, the developed numerical model was found to
sufficiently reproduce the tsunami flow in the abrupt
expansion area by setting the side wall roughness
appropriately, even if the conditions of the shoreline

30

Wave profile at the offshore boundary.

the suspended load.
Numerical Method
A collocated grid system was adopted for thecomputational grids of the tsunami flow. The fifthorder weighted essentially non-oscillatory (WENO)
scheme [14] was applied to discretize the advection
terms of the momentum equation in Eq. (1), and the
hybrid scheme [15] was applied to discretize the SST
k–ω equations and the suspended-sediment transport
equation in Eq. (8). The velocities at the gridboundaries and the pressure in the grid center were
calculated using the highly simplified mark-and-cell
method on the collocated grid system (C-HSMAC)[16].
As boundary conditions, frictional resistance wasconsidered according to the logarithmic law at thevertical wall and bed boundaries. The turbulent
quantities were given by the wall functions at the bed
boundary and by the slip condition at the vertical wall.
Moreover, the time variation of the wave profile
measured in the experiment was provided at theoffshore boundary.
593

GEOMATE – Kyoto, Japan, 3-5 November 2021

Measured P0Cal. P0 (n=0.014)
Cal. P0 (n=0.019)

Measured P2
Cal. P2 (n=0.014)
Cal. P2 (n=0.019)

0.05
0.00

-0.10
5

15

20

25

Time (s)

30

35

40

45

0.05
0.00

0

50

Measured P8
Calculated P8 (n=0.014)
Calculated P8 (n=0.019)

5

0.10

Water level (m)Water level (m)10Measured P5
Calculated P5 (n=0.014)
Calculated P5 (n=0.019)

0.10
0.05
0.00

10

15

20

25

Time (s)

30

35

40

45

Measured P5

Measured P8

Calculated P5 (n=0.019)

Calculated P8 (n=0.019)

50

0.05
0.00

-0.05

-0.05
-0.10

-0.10
0

5

10

15

20

25

Time (s)

30

35

40

45

0

50

Measured P1
Calculated P1 (n=0.014)
Calculated P1 (n=0.019)

1.5

Measured P2
Calculated P2 (n=0.014)
Calculated P2 (n=0.019)

5Velocity (m/s)

0.5
0.0

-0.5

15

20

25

Time (s)Measured P1
Calculated P1 (n=0.019)

1.5

1.0

10

30

35

40

45

50

Comparison of measured and calculated
water levels at each station under reflection
condition (Top: P0, P1, P2; Bottom: P5, P8).

Fig. 6Comparison of measured and calculated
water levels at each station under overflow
condition (Top: P0, P1, P2; Bottom: P5, P8).

Fig. 4Velocity (m/s)

Measured P2
Cal. P2 (n=0.019)

-0.10

0

Measured P2
Calculated P2 (n=0.019)

1.0
0.5
0.0

-0.5

-1.0

-1.0

-1.5

-1.5
0

5

10

15

20

25

Time (s)Measured P5
Calculated P5 (n=0.014)
Calculated P5 (n=0.019)

1.5

30

35

40

45

50

0

Measured P8
Calculated P8 (n=0.014)
Calculated P8 (n=0.019)

5

1.5

1.0

Velocity (m/s)

Velocity (m/s)

Measured P1
Cal. P1 (n=0.019)

-0.05

-0.05

0.5
0.0

10

15

20

25

Time (s)

30

35

40

45

Measured P5

Measured P8

Calculated P5 (n=0.019)

Calculated P8 (n=0.019)

50

1.0
0.5
0.0

-0.5

-0.5

-1.0

-1.0

-1.5

-1.5
0

Fig. 5

Measured P0Cal. P0 (n=0.019)

0.10

Water level (m)Water level (m)0.10Measured P1
Cal. P1 (n=0.014)
Cal. P1 (n=0.019)

5

10

15

20

25

Time (s)

30

35

40

45

0

50

Comparison of measured and calculated
velocities at each station under overflow
condition (Top: P1, P2; Bottom: P5, P8).

Fig. 7

5

10

15

20

25

Time (s)

30

35

40

45

50

Comparison of measured and calculated
velocities at each station under reflection
condition (Top: P1, P2; Bottom: P5, P8).

circulation flow is formed at the back of the abrupt
expansion area, and the eddy viscosity coefficient νt
indicates a large value—800 times greater than the
kinematic viscosity ν along the side wall from thenarrow pass. Conversely, in the case of n = 0.019, theposition of the circulation flow is almost at the centerof the abrupt expansion area, and the situation is ingood agreement with that observed in the experiment[4]. Thus, it was clarified that the reproducibility of
the circulation flow in the abrupt expansion area
greatly differs depending on the influence of the sideboundary change.
The horizontal flow velocity vectors at a height of
0.04 m from the bed at time t = 15 s is illustrated inFig. 8. The eddy viscosity coefficient contours arealso shown. Here, the eddy viscosity coefficient νt isdimensionless for the kinematic viscosity ν = 1.01 ×
10–6 m2/s. By comparing the results of n = 0.014 andn = 0.019 under the same shoreline condition of
overflow, it was found that the position of thecirculation flow center was quite different. In the caseof the small side wall roughness n = 0.014, the594

GEOMATE – Kyoto, Japan, 3-5 November 2021

νt/ν :
0

0
1

3

y (m)

200

300

400

500

600

700

800

900

4

5

x (m)

Δzb (cm) :6

Overflow(n = 0.019)

3

0
4

5

x (m)

4

5

x (m)

6

Fig. 9100 cm/s
Reflection(n = 0.019)

0Fig. 83-1

1

100 cm/s

-0.5

0

0.5

1

5

x (m) 6

y (m)

y (m)

Overflow(n = 0.014)

0
1

100

100 cm/s

y (m)

1

1

2

3

4

Comparison of measured and calculated bed
variation contour under overflow condition(Top: Measured; Bottom: Calculated).

6

Calculated flow velocity vectors with eddy
viscosity coefficient contours (Top: n =0.014 with overflow; Middle: n = 0.019 withoverflow; Bottom: n = 0.019 with reflection).

Δzb (cm) :
-1

1

-0.5

0

0.5

1

5

x (m) 6

Fig. 10 Comparison of measured and calculated bed
variation contour under reflection condition(Top: Measured; Bottom: Calculated).

Discussion on Reproducibility of Topography
ChangeCONCLUSIONSy (m)wall roughness. Moreover, comparing the results forthe overflow and the reflection conditions under thesame roughness n = 0.019, the position of thecirculation flow under the reflection condition isfound to be closer to the block than that under theoverflow condition. These situations are also in good
agreement with those observed in the experiments,
thereby confirming the validity of the model.

0

1

2

3

4

In this study, the authors investigated theapplicability of a 3D RANS numerical model for theprediction of flow and topography changes inducedby tsunamis in an abrupt expansion area withdifferent shoreline conditions of overflow andreflection. In the developed model, the SST k–ω
turbulence model was adopted to accurately evaluate
the circulation flow in the abrupt expansion area, andthe nonequilibrium bed-load and suspended-loadtransport were considered in the topography change
model. The model was applied to the laboratory
experiments conducted by Arimitsu and Kawasaki [4].
Consequently, the importance of setting theappropriate side wall roughness to accurately
reproduce the tsunami flow in the abrupt expansion
area using 3D RANS models was clarified. Moreover,the developed numerical model was found toreproduce the tsunami flow and topography changes
in an abrupt expansion area with different shoreline
conditions. However, in the calculated results,
erosion was noted on the opposite side of the narrow
pass, which was not observed in the experiments. In

Topography change analyses under the movable
bed condition were performed with the side wallroughness fixed at n = 0.019. Fig. 9 and Fig. 10 show
the comparison of measured and calculated bed
variation contours under the overflow and reflection
conditions, respectively. In the experiments, salienterosion was observed in the narrow pass where a highflow velocity occurs under both shoreline conditions.
Additionally, although severe sedimentation anderosion occurred on the shoreline side from the blockin the case of overflow condition, they occurred on
the offshore side from the block for the reflection
condition. The calculated results are found toreproduce these phenomena well. However, in thecalculations, erosion not observed in the experiments
occurred near x = 4 m on the opposite side of thenarrow pass. The cause of this erosion has not yet
been clarified. Therefore, we intend to investigate thecause of this erosion in the future by introducing a
new estimation method of the bed shear stress oranother sediment transport formula.

595

GEOMATE – Kyoto, Japan, 3-5 November 2021

turbulence model, Turbulence, Heat and Mass
Transfer, Vol. 4, 2003, pp. 625-632.
[8] Uchida, T. and Fukuoka, S., Numericalcalculation for bed variation in compoundmeandering channel using depth integrated
model without assumption of shallow water flow,
Advances in Water Resources, Vol. 72, 2014, pp.
45-56.
[9] Ashida, K. and Michiue, M., Study on Hydraulicresistance and bed-load transport rate in alluvial
stream, Proceedings of the Japan Society of Civil
Engineers, Vol. 206, 1972, pp. 59-69. (inJapanese).
[10] Iwagaki, Y., Fundamental study on criticaltractive force (I), Hydrodynamical study on
critical tractive force, Trans. Japan Soc., Civil
Engineer, Vol. 41, 1956, pp. 1-17.
[11] Takebayashi, H. and Okabe, T., Geometriccharacteristics of braided channel on nonuniform sediment bed, The Int. Cong.
INTERPRAVENT, 2002. in the Pacific Rim,
Congress Publication, 1, 2002, pp. 79-89.
[12] Itakura, T. and Kishi, T., Open channel flow withsuspended sediments, J. of Hydraulic Division,
ASCE, Vol. 106, No. 8, HY8, 1980, pp. 13251343.
[13] Rubey, W. W., Settling velocity of gravel, sand,and silt particles, American Journal of Science,Vol. s5-25, No. 148, 1933, pp. 325-338.
[14] Shu, C.-W., High-order finite difference andfinite volume WENO schemes and discontinuous
Galerkin Methods for CFD, International Journalof Computational Fluid Dynamics, Vol. 17, No.
2, 2003, pp. 107-118.
[15] Patankar, S. V., Chapter 5, Numerical heattransfer and flow, McGraw-Hill, New York,1980.
[16] Ushijima, S. and Nezu, I., Computational method
for free-surface flows on collocated grid withmoving curvilinear coordinates, J. of Japan
Society of Civil Eng., Vol. 698, No. II-58, 2002,
pp. 11-19. (in Japanese with English abstract).

our next work, we intend to clarify the cause of thiserosion by modifying the model.
ACKNOWLEDGMENT
The authors appreciate the valuable commentsfrom the reviewers. This work was supported by JSPS
KAKENHI Grant Number JP21K04274.
REFERENCES[1] Yoshii, T., Ikeno, M., Matsuyama, M. and Fujii,N., Pick-up rate of suspended sand due totsunami, Coastal Eng. Proc., Vol. 1, No. 32, 2010,https://doi.org/10.9753/icce.v32.sediment.12[2] Kihara, N., Fujii, N. and Matsuyama, M., Threedimensional sediment transport processes ontsunami-induced topography changes in a harbor,
Earth, Planets and Space, Vol. 64, No. 10, 2012,
pp. 787-797.
[3] Arimitsu, T., Kawasaki, K. and Takahashi, T.,
Numerical simulation of bottom topographychange due to tsunami in coastal sea area withabrupt expansion region, Journal of Japan
Society of Civil Engineers, Series B2 (Coastal
Engineering), Vol. 72, No. 2, 2016, pp. I_607I_612. (in Japanese with English abstract).
[4] Arimitsu, T. and Kawasaki, K., Tsunamiinduced topography change in coastal sea areawith narrow pass and influence of shoreline
condition, Ann., Journal of Japan Society of Civil
Engineers, Series B3 (Ocean Engineering), Vol.
33, No. 2, 2017, pp. I_666-I_671. (in Japanesewith English abstract).
[5] Kajikawa, Y. and Kuroiwa, M., Numericalsimulation of 3D flow and topography change inharbor caused by tsunami, Proc. 10th int. conf.
on Asian and Pacific Coasts, 2019, pp. 183-190.
[6] Hirt, C. W. and Sicilian, J. M., A Porosity
technique for the definition obstacle inrectangular cell meshes, Proc. 4th int. conf. on
Numerical Ship Hydrodynamics, 1985, pp. 1-19.
[7] Menter, F. R., Kuntz, M. and Langtry, R., Ten
years of industrial experience with the SST

596

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

EVALUATION OF UNDERGROUND SPACE FOR TSUNAMIEVACUATION SAFETY BY AGENT-BASED SIMULATION
Akira Takahashi 1, Kensuke Yasufuku 1 and Hirokazu Abe 1
1

Cybermedia Center, Osaka University, Japan
ABSTRACTThis study quantitatively evaluates the safety of an evacuation guidance plan created for a large undergroundfacility in preparation for the flooding that would result from earthquake-related tsunamis and examines theeffects of evacuation route obstacles via simulations. The main findings are as follows:
1. In some scenarios, the occurrence of evacuation route obstructions will cause delays in evacuation
completion times due to evacuee congestion on stairways.
2. By comparing the results of each scenario with the evacuation safety plan prepared by the facility
management company, the impacts on safety could be evaluated quantitatively.
3. When considering evacuation route obstacles, evacuation efficiency impacts differed depending on which
stairways and/or corridors became unavailable.
4. The scenarios in this study showed that evacuation time impacts were more severe in cases involving
blocked corridors than in cases involving blocked stairways.
Keywords: Evacuation simulation, Large-scale underground mall, Flooding, Evacuation guidance plan,
TsunamiINTRODUCTION

With the above points in mind, it is crucially
important to prepare alternative evacuation routes in
anticipation of cases where the optimal evacuationroutes are no longer available during a disaster.
However, evacuation routes are often determined
empirically, and various conditions impose
limitations on the ability of facility managers toevaluate their evacuation safety plans. To assist such
efforts, multi-agent simulation systems, which are
increasingly being used to evaluate the evacuation
safety of buildings, can quickly be adapted to predict
the evacuation properties of underground facilities
under various conditions [2].
For example, using a multi-agent crowd
simulator, Yamada et al. (2013) reported on humanbehaviors during the evacuation of an undergroundfacility directly connected to an anonymous terminal
station after an earthquake disaster [3]. However,that study did not consider inflows from connectedunderground malls that may host numerous
commercial facilities. Separately, Kiyono et al.
(2001) and Nakamura et al. (2018) conducted
detailed traffic surveys of actual underground malls
and used the collected data to conduct evacuation
simulations [4],[5]. However, these studies did notconsider flood damages that would result fromtsunamis or torrential rains.
In other studies, Takizawa (2015) and Goda(2014) estimated the evacuation capacity ofconnected buildings in their target areas and thenumber of pedestrians during rush hour periods for alarge-scale underground shopping mall. They thenused that information in basic simulations of vertical

In Japan, many large cities have constructed
extensive underground facilities in areas below sea
level, and such spaces now cover widespread areas.
These developments have resulted in a need forsocial and economic risk management plans toprevent or minimize human casualties and damage
to infrastructure in underground facilities such as
subways in the event of large-scale flooding caused
by torrential rains and earthquake-related tsunamis.
For example, in the Great East Japan Earthquakethat occurred on March 11, 2011, a massive tsunamithat exceeded predictions caused extensive flooding
damage over a widespread area, thereby forcing afundamental review of previous disaster prevention
measures [1].
The resulting Act on Development of Areas
Resilient to Tsunami Disasters 2011 requires the
managers of underground facilities such as basement
shopping malls, etc., in areas designated as Tsunami
Disaster Prevention Zones to prepare and publish
evacuation safety plans detailing measures that will
ensure smooth and rapid evacuations from theirfacilities in the event of a tsunami. However, whenattempting to verify whether all affected persons canevacuate safely in the case of a major earthquakerelated tsunami, it is necessary to consider various
route obstacles that may result when the disaster firststrikes. For example, evacuation routes may beblocked by falling shelves or damage to glass
windows, walls, or ceiling materials, which would
affect evacuation safety and efficiency.
597

GEOMATE – Kyoto, Japan, 3-5 November 2021

evacuations to the connected buildings underassumed flooding conditions. Their results showed
severe evacuee congestion on certain staircases,
which delayed evacuation completion times, and thatthe number of evacuees would exceed the capacities
of some connected buildings [6],[7].
Yamamoto (2019) proposed a mathematicalprogramming problem to minimize the evacuation
completion time of an underground shopping mall inOsaka City, along with a workflow to convert
general geometric spatial data into graphical data.
Ultimately, the accuracy of the proposed method
was confirmed by multi-agent simulations [8]. In
another study, in order to verify the evacuation
safety of buildings in the event of a major
earthquake, Yasufuku (2017) proposed anevacuation analysis method that uses a multi-agent
system to consider multiple path obstructions causedby damage to non-structural materials [9].
However, the abovementioned studies did notevaluate evacuation cases using surface-level exits
and specific evacuation safety plans prepared byfacility management companies.
Accordingly, this study quantitatively evaluates
the safety of the evacuation guidance plan of a large
underground facility prepared by the facility
management company in anticipation of the flooding
hazards that can result from earthquake-related
tsunamis and examines the effects of evacuationroute obstacles via simulations.

Fig. 1 Floorplan of underground mall
Evacuation Safety Plan
The evacuation safety plan prepared andpublished by the facility management company has
been revised several times to improve evacuation
safety and efficiency [11]. According to the newest
version, the evacuation locations in the event of atsunami disaster include the surface area to the eastof the underground shopping mall that is outside theexpected flooding zone and the second or higher
floors of mall-connected buildings. The primaryroutes to these evacuation sites are, in principle, via
the nearest evacuation stairways.
However, the Osaka City Underground Flooding
Prevention Council has estimated that the maximum
number of people that could be evacuated from thetarget underground mall and surrounding stations tothe second and higher floors of connecting buildings
at 18:00 on weekdays at approximately 1,700 people,
which would make it difficult to evacuate all theaffected visitors and commuters to connecting
buildings [12].
As for the guidance method, although the facility
evacuation safety plan states that when the plan is ineffect, evacuees are to be led to the nearest safe
stairways and persons outside are to be prevented
from entering the underground mall, the plan does
not provide a detailed description of the evacuation
guidance method. However, according to a facility
management company official interviewed inconjunction with this study, even though the

RESEARCH OBJECT
Underground mallOur study object is an underground mall (Whity
Umeda) located near JR Osaka Station in Osaka City,
Japan, which is part of an even larger undergroundcomplex called the Osaka Umeda Underground Mall.
The subject underground mall is connected to themain stations of two Osaka Metro Co., Ltd. subway
lines and serves approximately 400,000 daily users,
which is the highest number of all the undergroundmalls and underpasses in Osaka City. However, inthe event of the long-predicted Nankai TroughEarthquake, the area around this underground mall is
expected to be inundated by floodwater up to twometers deep [10].
Figure 1 shows the floor plan and outline of thesubject facility, which has a 31,336 m2 total floorarea, two basement levels, 25 stairways to thesurface level, 27 building connections, and 182
stores and restaurants. The nine areas in Fig. 1 show
how the floor area was divided by the facility
management company for the purpose of estimating
the number of people in the underground mall.

598

GEOMATE – Kyoto, Japan, 3-5 November 2021

stairways (EGSs) in the timeline was limited to six(Table 1).
Once an evacuation has been announced,
evacuees are first expected to gather and wait at thefour evacuation squares (ESs), established by thefacility management company, shown by the red
dotted circles in Fig. 1. Then, after facility
management personnel have confirmed the safety ofthe evacuation stairways and the surface level exits,
they will be directed to evacuate to the EGS
sequentially while following the specific evacuation
instructions provided.

evacuation safety plan does not contain a specificevacuation guidance method, it is expected that theevacuation will proceed in accordance with the
Evacuation Timeline that was prepared in the eventof a major earthquake.
In that timeline, evacuation to connecting
buildings is not anticipated because the estimatednumber of people that can be accommodated inthose buildings is likely to be too small in relation to
the number of people in the mall requiring
evacuation. Therefore, in principle, the tsunami
evacuation will proceed based on horizontal
movements that lead people to the east side of theunderground mall, which is outside the anticipatedflooding area.
Figure 2 shows the depth of anticipated floodingin the vicinity of JR Osaka Station, including theunderground mall, in the event of a major Nankai
Trough earthquake. The target line for horizontal
evacuation is the area more than 300 m east of ShinMido-Suji street, which is indicated by the black
dotted line in the figure. Here, it should be noted thatphysically challenged and injured persons who havedifficulty walking will be evacuated to connectedbuildings, but this matter is outside the scope of thispaper.
One of the purposes of the timeline underconsideration here is to simplify evacuation
guidance. This is because such guidance is primarily
expected to be provided by personnel who normally
perform daily security and facility management
duties, and the number of such persons is limited.
This, in turn, imposes a limit on their ability tocheck the safety of the evacuation stairways,
including those to surface level, in a limited amountof time. After considering stairway widths, access tothe horizontal evacuation target line, theunderground mall structure, and available humanresources, the number of evacuation guidance

Table 1 Width of each EGS
EGS1 EGS2 EGS3 EGS4 EGS5 EGS6
Width(mm)

3000

1800

5400

5400

1850

3585

Evacuation Timeline

Shin-Midōsuji

Figure 3 shows the specific steps in the
Evacuation Timeline. After a disaster occurs,
evacuees will first act to protect themselves (Act 01).
Next, they are expected to move to the nearest ES
while following instructions provided by store staff
members and public address system announcements
given by underground mall facility managers (Act02). Simultaneously, facility managers will confirm
the safety of the EGSs and divide the areas withropes to prevent the evacuation flow from crossing
into Area 9 (Fig. 4). This is to ensure smoothevacuation guidance in Area 9, where the largest
concentration of evacuees is expected.
Next, the managers place staff members at each
ES to handle crowd control. Because it will takemanagement staffers approximately eight minutes toconfirm the safety of each EGS and its surface area
exit after the disaster starts, the evacuation to surface
level will begin when this process is complete. The
evacuees will then be guided from each ES to thesurface level exits via the EGSs.
More specifically, evacuees in ES1 and ES2 will
use EGS1 and EGS2, respectively (Act 05), whileevacuees in ES3 and ES4 will use EGS3 and EGS4
(Act 06). Evacuees in ES3 on the south side, which
will be divided by ropes, will use EGS5 and EGS6
(Act 07) (Fig. 4). Evacuee inflows are expected fromthe West Plaza and Higashi Umeda station (HUstation), which are connected to Area 9. West Plaza
inflowing evacuees (Act 03) will be directed tofollow the path of Act 06 evacuees, and evacuees
from HU station (Acts 04, 08, and 09) will follow
the path of Act 07 evacuees.
After evacuees arrive on the surface (Act 10),they will be instructed to horizontally evacuate(walk) until they pass the target line into areas where
flooding is not expected (Act 11). In this study, theevacuation simulation concludes with Act 10. Since

Fig. 2 Map of assumption of flooding [12]Note: This figure was added and modified by the author from thefigure in Ref. 12.

599

GEOMATE – Kyoto, Japan, 3-5 November 2021

the expected arrival time of a tsunami in the JR
Osaka Station area is estimated to be 150 minutes
after the Nankai Trough Earthquake occurrence, thetarget time for Act 11 completion is 140 minutes.
The distance from the furthest EGS to the target
line for horizontal evacuation is about 500 meters,
which is the distance an average person cannormally be expected to walk in about 10 minutes.
However, considering the enhanced need for safety
and the potential for confusion on the surface due tothe earthquake, we assume that the travel time will
be about 20 minutes. Therefore, the target time tocomplete the Act 10 evacuation is 120 minutes after
the earthquake occurrence.

SIMULATION SOFTWARE
Pathfinder simulation software (Thunderhead
Engineering Consultants, Inc., Manhattan, KS) is
used in this study [13], [14]. Pathfinder is an agentbased simulation software that can create model
structures with two-dimensional navigational
geometry. This navigational geometry is based on
simulating the flow of motions in a room and issuitable for reproducing complex shapes.
In the simulation, navigation geometries aregrouped by rooms, and adjacent rooms areconnected by doors and stairways. To assign
individual behaviors to each occupant, attributes
called profiles are used. When setting up an
individual profile, various characteristics such as
body size, movement speed, and collision behavior
can be defined. Behavioral patterns can also be used
to define various instructions to the occupants. Thus,
occupants can be classified by profiles and behavior
patterns.
Simulator algorithm
When an occupant in the simulation moves to a
specific destination, the process involves three steps:
path planning, path generation, and path following.
Path planning is the process of determining thepath to a destination. Each occupant has cost
information for local and global targets and the
expected waiting times. Since the shortest route is
not necessarily the fastest, cost calculations are
made for each occupant’s route to their destination.
In other words, by calculating the route to the room
exit and the route to the destination after exiting theroom separately, the optimum route can be selectedbased on cost. In addition, the door queue cost isalso calculated, after which a path to that door thatthe occupant can follow is generated.
Once a local target is selected by path planning,a path to the target is needed. To produce it, thePathfinder application uses the A* search algorithm
and a triangulated navigation mesh [15]. The
resulting path is represented by a series of points on
the triangular edges of the mesh. In order for theoccupants to move smoothly along the resulting path,they proceed to points on the mesh called waypoints(Fig. 5) [16].
Path following is conducted using a combinationof inverse steering [17] and collision handlingalgorithms. These algorithms enable occupants todeviate from the path in order to avoid obstacles
while still heading in the correct direction toward
their goal [14]. The inverse steering method
evaluates several discrete directions of movement
based on the cost factors between the old and newpaths.
Potential new paths are calculated by various
cost factors, including velocity, acceleration, andFig. 3 Evacuation timeline flowchart

Fig. 4 Division of Area 9 (ES3)600

GEOMATE – Kyoto, Japan, 3-5 November 2021

waiting time until the path with the lowest cost
factor is identified and set as the alternative path.
While moving, each occupant tries to maintain apredetermined behavior pattern with regard to
distance from walls and other occupants, walking
speed, obstacle avoidance, and turning.

The first is the inflow from HU station, which is
expected to come in two waves. The primary wave
(beginning 20 minutes after the earthquake) is
assumed to be people temporarily stopped in thestation concourse and platforms, and the second
wave (beginning 40 minutes after the earthquake)
will consist of passengers evacuating from trains that
stopped between stations after the earthquake.
The second inflow, which will be people fromthe West Plaza connected to Area 9, is calculated
based on the number of people staying in the plaza
and the number of people coming from the stations.
This inflow is assumed to consist of about 3,898
people in five minutes. West Plaza is connected tomany of Osaka City’s major stations, including
Metro and private railway terminal stations, as wellas large department stores, making it a majorcrossway of the extended Osaka underground mallarea.

Fig. 5 An occupant’s path and a waypoint on themesh
In this experiment, referring to the evacuation
safety verification method supported by the Ministryof Land, Infrastructure, Transport and Tourism(MLIT), the maximum walking speed was set to 1.0
m/s for level surfaces, and 0.45 m/s was set the
maximum speed for traveling up stairways [18]. For
the other required path selection parameters, theinitial Pathfinder software values were used. For
more details, please refer to the Pathfinder Technical
Reference Manual [14].

Fig. 6 Numbers of mall visitors by time

Simulation data

Table 2 Number of people in simulation(Weekday/18:00)

The underground mall data modeled in our
simulation was created from the overall floor plan of
the facility, the floor plans of each store, andstairway data provided by the facility managementcompany.
The input data was the number of people in thefacility, which was calculated from a pedestriantraffic volume survey conducted by the facility
management company. That pedestrian-traffic
volume survey was conducted on two weekdays andtwo holidays in 2020, and the average survey valueof those days was selected as the traffic volume. The
number of visitors and commuters was calculated by
dividing the underground mall into nine areas basedon the points surveyed in the pedestrian-traffic
volume survey, as shown in Fig. 1.
The input data for the number of evacuees weretaken at 18:00 on weekdays when the number ofpeople in the underground mall is normally thehighest, and the initial positions of each evacuee
were randomly assigned, as shown in Fig. 6. Table 2
shows the input values for each area and the inflow
numbers of evacuees used in the simulation. The
primary evacuee inflows are expected to come fromtwo locations.

Area 1

158

Area 2

73

Area 3

1,089

Number Area 4

1,149

of People Area 5

1,122

in Areas Area 6577

Area 7

517

Area 8764

Area 9

6,823
total

12,272
114

Number ofon the platform

People inflow

1 st inflow(after 20 min)647

from HU Station

2 nd inflow(after 40 min)

1,296

total

2,057

Number of People inflow
from West Plaza (in 5 minutes)

3,898

Evacuation route obstructions
Figure 3 shows Scenario 0, which is the baselineof the Evacuation Timeline. The following two
601

GEOMATE – Kyoto, Japan, 3-5 November 2021

conditions are considered in each of the five
scenarios shown below: situations in which the
EGSs cannot be used due to a blocking obstacle suchas a collapsed ceiling, and situations in which thecorridors that serve as evacuation routes cannot be
used because they are partially blocked. Scenarios 1
through 5 are set as shown below:

efficiency is seen in S3, and the slope in Fig. 7
becomes shallower shortly after the start ofevacuation.
In Figure 8, it can be seen that in S3, evacuee
congestion occurs in the vicinity of EGS5 and EGS6.
Hence, in order to more efficiently distribute theevacuees, it might be advisable to direct a portion of
the EGS5 and EGS6 evacuees to EGS1 and EGS2.
In the case of S4, where EGS5 was not available, it
can be seen that the Fig. 7 slope was almost
unaffected. In contrast, in the case of S5, where
EGS6 was not available, the Fig. 7 slope rapidly
became shallow around 2000 seconds after thedisaster onset.
From the number of evacuees processed via
EGS5 and EGS6, it can be seen that EGS6, which is3585 mm wide, was able to provide a sufficientthroughflow. In contrast, EGS5, which is thenarrowest of the evacuation stairways (1850 mm),was considered to have insufficient flow capacity forS4 and S5, as shown in Fig. 8. Therefore, if EGS6
becomes unavailable, a flexible response such as
redirecting evacuees to EGS3 and EGS4 will be
required.
Comparing S2 (where the corridor is partially
blocked) and S3 (where the corridor is completely

 Scenario 0 (S0): Evacuation Timeline baseline(no incident) Scenario 1 (S1): EGS4 blocked by an obstacle
 Scenario 2 (S2): Route in Area 8 partially
blocked by an obstacle
 Scenario 3 (S3): Route in Area 8 blocked by an
obstacle
 Scenario 4 (S4): EGS5 blocked by an obstacle
 Scenario 5 (S5): EGS6 blocked by an obstacle
The Area 8 corridor width is about 5000 mm,
and in Scenario 2, it is assumed that a 2500  3500
mm area is no longer passable (Fig. 1).
RESULTS AND DISCUSSION
Table 3 shows the evacuation completion time,
average evacuation time, and average congestion
time for each scenario. The evacuation completiontime indicates the time when the last evacuee exitedthe facility. The average evacuation time indicatesthe average time it took for each evacuee to exit thefacility, while the average congestion time indicatesthe average of the total time during which thewalking speed of each evacuee was less than 0.25
m/s.
Table 3 shows that, in completion timecomparisons, S0 and S4 were almost the same; S1and S2 increased 7% and 10% over S0, respectively;
while S3 and S4 increased significantly to 62% and
55% over S0, respectively. However, for all
scenarios, the evacuations were completed within
the target evacuation time of 120 minutes after theearthquake occurrence.
Both the average evacuation time and theaverage congestion time generally showed the same
trends as the evacuation completion time, but for S5,
the ratio of increase in the evacuation andcongestion completion time averages were smaller
than the increase ratio in the evacuation completion
time. When compared to S0, S3 showed an increaseof more than three times in the average congestion
time.
Figures 7 and 8 show the total number of
evacuees by time and EGSs for each scenario,
respectively. The slope in the figures indicates theevacuation efficiency, with a steeper indicating amore efficient evacuation. These figures indicatethat some evacuation routes suffered more
unavailability impacts than others. For example,
compared with S0, the largest impact on evacuation

Table 3 Results of the evacuation simulation eachscenario
Completedtime forevacuation
(sec)

/S0
(%)

Scenario 0 (S0) 2,875

Averagetime forevacuation
(sec)

(%)

1,248

(sec)

/S0
(%)

436

108

1,606

129

Scenario 2 (S2) 3,254

113

1,597

128

766

176

Scenario 3 (S3) 4,666

162

2,029

163

1,343

308

Scenario 4 (S4) 2,875

100

1,356

109

536

123

Scenario 5 (S5) 4,475

156

1,590

127

767

176

Number of evacuees in the underground mall

Scenario 1 (S1) 3,092

18000

761

175

Scenario 0

16000

Scenario 1

14000

Scenario 2

12000

Scenario 3

10000

Scenario 4

8000

Scenario 5

6000
4000
2000
0

Time in seconds

Fig. 7 Number of evacuees by time
602

/S0

Averagetime forcongestion

EGS1
10000

EGS2

8000

EGS3
EGS4

6000

EGS5

4000

EGS6

2000
0

12000
10000

EGS2

8000

EGS3

8000
6000
4000

EGS4

6000

EGS5
EGS6

4000
2000
0

12000

0
Time in seconds

EG
S1
EG
S2
EG
S3
EG
S4

10000
8000
6000
4000
2000
0
Time in seconds

Scenario 4 (S4)
EGS
1
EGS
2
EGS
3
EGS
4

10000

2000

Scenario 2 (S2)Time in seconds

Cumulative Number of Evacuees

Cumulative Number of Evacuees

10000

Scenario 3 (S3)
EGS
1
EGS
2
EGS
3
EGS
4

12000

EGS1

Time in seconds

12000

Scenario 1 (S1)

Cumulative Number of Evacuees

Scenario 0 (S0)

8000
6000
4000
2000
0

12000

Cumulative Number of Evacuees

12000

Cumulative Number of Evacuees

Cumulative Number of Evacuees

GEOMATE – Kyoto, Japan, 3-5 November 2021

10000
8000
6000
4000

Scenario 5 (S5)
EGS
1
EGS
2
EGS
3
EGS
4

2000
0

Time in seconds

Time in seconds

Fig. 8 Number of evacuees by stairways in each scenario
4.

closed), it can be seen that the S3 evacuation
completion time was 1.4 times longer. In thesimulation, the evacuation efficiency rates of EGS3
and EGS4 were strongly affected by the flow rate
congestion that resulted from a bottleneck at the endof the corridor connecting Area 9 to Area 8.
Therefore, when only half the corridor width isavailable, the evacuation efficiency of EGS3 and
EGS4 is approximately 50%. In S2, the final
evacuees were in the direction of EGS3 and EGS4,
so if the passageway flow rate of Area 8 is reduced
due to a larger blockage area, the impact is expected
to be more severe.

When considering evacuation route obstacles,
we found that the impact on the evacuation guidanceplan differed significantly depending on which
stairways and corridors became unavailable.
Therefore, when route obstacles appear, it isimperative to consider redirecting evacuees awayfrom congested stairways and towards otherstairways in order to improve evacuation safety.
However, in this experiment, the most severe
impact was seen in the scenario in which thecorridor was blocked, thus indicating that thepossibility of blocked corridors requires more
attention, primarily because the number of stairways
to which evacuees can be redirected will be limited.

CONCLUSIONSThis study was conducted to quantitatively
evaluate the safety of evacuation plans for theinundation hazards in large underground spaces thatmight result from earthquake-related tsunamis and to
examine the effects of evacuation route obstacles.
The main findings are as follows:
1.

2.

3.

The scenarios in this study showed that impacts
on evacuation time were more severe in cases
involving closed corridors than in cases ofclosed stairways.

ACKNOWLEDGMENTS
This work was supported by the Japan Scienceand Technology Agency (JST) Future Society
Creation Project JPMJMI20D1 and Osaka Chikagai
Co., Ltd., to whom we would like to express our
sincere gratitude.

In some scenarios, evacuation routeobstructions caused delays in evacuation
completion times due to stairway congestion inareas where evacuees were most concentrated.
By comparing the results with the evacuation
safety plan prepared by the facility
management company and the results ofsimulation each scenario, we could evaluate
safety impacts quantitatively.
Whenconsidering
evacuation
routeobstructions, the impact on evacuation
efficiency differed depending on the stairways
and corridors that became unavailable.

REFERENCES[1] Cabinet Office, Government of Japan: Disaster
Management in Japan, DIRECTOR GENERALFOR DISASTER MANAGEMENT CABINET
OFFICE, GOVERNMENT OF JAPAN, 3. 2015
http://www.bousai.go.jp/1info/pdf/saigaipamphl
et_je.pdf (accessed 23. 3. 2021 )603

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[2] Urban Transport Facilities Division of City
Bureau of Ministry of Land, Infrastructure,
Transport, and Tourism: Guideline for Safety
Evacuation Measure for Underground Malls,
2020. 3 (in Japanese)[3] Yamada, T., Ohmori, T., Hiroi, U., Fukui, K.:
Study of Evacuation from the UndergroundSpace around a Large-Scale Station Using the
Crowd Simulation, Proceedings of the
Symposium on Underground Space, Vol. 18, pp.
137-144, 2013. 1 (in Japanese)[4] Kiyono, J., Toki, K., Inukai, N., Takeuchi, T.:
Evaluation of Safety of Evacuation Behavior
from an Underground Space during an
Earthquake, Proceedings of JSCE 689, pp. 31-43,
2001. 10 (in Japanese)[5] Nakamura, E., Koike, N.: Crowd Congestion
Estimation and Evacuation Planning Based on
Real Number of Underground Mall Visitors –Case Study of Central Park in Nagoya, Journal ofJapan Society of Civil Engineers, Ser. F6 (Safety
Problem), Vol. 74, No. 2, pp. 93-100, 2018 (inJapanese)[6] Takizawa, A., Takagi, N., Taniguchi, Y.: A
Multi-Agent Based Simulation of Evacuationfrom the Umeda Underground Malls toConnected Buildings at the Time of Flooding,Summaries of Technical Papers of Annual
Meeting, Architectural Institute of Japan, E-1,
pp. 749-750, 2015. 7 (in Japanese)[7] Goda, S., Taniguchi, Y., Yoshinaka, S.,
Takizawa, A.: Study on Tsunami Evacuation
Plan at the Underground Mall in front of Osaka
Station, Summaries of Technical Papers ofAnnual Meeting, Architectural Institute of Japan,
B-2, pp. 147-148, 2014. 7 (in Japanese)[8] Yamamoto, R., Takizawa, A. Partitioning
Vertical Evacuation Areas in Umeda
Underground Mall to Minimize the Evacuation
Completion Time. Rev Socionetwork Strat 13,
209–225(2019).
https://doi.org/10.1007/s12626-019-00037-1[9] Yasufuku K and Nagano Y, Evacuation
Analysis of Several Impassable Routes byUsing Multi-agent System, Summaries ofTechnical Papers of Annual Meeting,Architectural Institute of Japan, pp. 387-388,
2017. 7 (in Japanese)[10] Osaka City Underground Flooding Prevention
Council: Osaka City Underground Flooding
Prevention Guidelines, 2018. 3 (in Japanese)[11] Osaka Chikagai Co.,Ltd.: Umeda UndergroundSpace Evacuation and Flooding Prevention Plan,
2006.12 (2017.7 revision) (in Japanese)[12] Osaka City Underground Flooding Prevention
Council: Flooding Prevention Plan for
Underground Spaces around Osaka Station Ver.
1, 2016. 3 (in Japanese)[13] Thunderhead Engineering Consultants Inc.:
Pathfinder
User
Manualhttps://support.thunderheadeng.com/docs/pathfi
nder/2020-3/user-manual/ (accessed 2021. 6. 3)[14] Thunderhead Engineering Consultants Inc.:
Pathfinder Technical Reference Manualhttps://support.thunderheadeng.com/docs/pathfi
nder/2020-5/technical-reference-manual/(accessed 2021. 6. 3)[15] Hart P, Nilsson N, Raphael B: A formal basis
for the heuristic determination of minimum cost
paths. IEEE Trans Syst Sci Cybern 4:100–107,
1968 https ://doi.org/10.1109/TSSC.1968.30013
6[16] Geraint Johnson: Smoothing a Navigation MeshPath, AI Game Programming Wisdom 3, ed. S.
Rabin. pp. 129-139, 2006.
[17] Heni Ben Amor, Jan Murray, and Oliver Obst:
Fast, Neat, and Under Control: Arbitrating
Between Steering Behaviors, AI Game
Programming Wisdom 3, ed. S. Rabin. pp. 221232, 2006[18] Ministry of Construction: Ministry ofConstruction Notification, No. 1441, 1-4, 2000.
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31
(in
Japanese)604

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

ON GEOGRAPHIC INFORMATION MAP BY THE SPATIALATTRACTIVENESS
Ami Hasegawa1, Kazunari Tanaka1
1

Osaka Institute of Technology, 5-16-1, Omiya, Asahi-ku, Osaka, Osaka, Japan
ABSTRACT

Currently, various districts have been redeveloped in Japanese cities to make the city comfortable. However,this change sometimes reduce the spaces and playgrounds that children can freely use. Creating a city that is easierand more enjoyable for children to live in is an issue for future urban development. In order to address this, it isnecessary to understand the image of the city that children will find attractive. The purpose of this study is toclarify the elements and shapes of the cityscape that influence the image of the city by comparing the spatial
cognition between children and adults in the town where they live. As for the research method, we conducted apaired comparison method using photographs of roads that actually exist and a cognitive map survey of elementary
school students on spatial cognition of the streets that they use on a daily basis, and analyzed the results. In thepaired comparison method, it found that the path preferred by children and the path preferred by adults tended tobe different. In the children's cognitive maps, the places where direction changes, such as curves and intersections,were drawn disproportionately larger than they actually are. And the lively spaces where shops are grouped close
together, such as shopping streets, are drawn proportionately wider and shorter than actual street width and length.
Keywords: Cognitive Map, Color, Image, Street

INTRODUCTION

roads, extract the components of each photograph,and compare them with the results of thepsychological experiment. We will then find if they
like them or not. The second is to conduct a cognitive
map survey using the basic drawing method, toexamine the distortion between the elements ofextraction and actual elements, with the difference
between the drawn cognitive space and the actual
space. We will find what kind of place they usually
see and what elements you can easily remember.
The Developmental Stage according to the studyof J. Piaget is 12 years and over. In the experiment inthis study, 18 years old and over are the subjects asadults. The children, 10 to 12 years old are targeted inconsideration of the specific investigation stage andformal operation period. Regarding the image of thecity, we conducted research based on Chapter 2
Cognitive Space and Research Targets from the studyof urban distortion in children's spatial cognition
conducted by Sakai et al [1].
Research on children's cognitive space has been
conducted for a variety of purposes and methods[2][3][4][5]. In this study, these results are also
referred to.

In modern times, various areas and districts inJapan have been redeveloped in cities and suburbs tomake our life comfortable. However, the alteration
sometimes reduce the spaces and playgrounds thatchildren can freely use. We think that children cangain a lot from interacting with nature, people, andvarious physical spaces and things in their childhood.
By thinking and playing around for themselves, thechildren make new discoveries and grow. Changes inthe environment centered on public spaces have astrong impact on the physical and mental health ofchildren. In order to solve such problems in the future,
we should investigate the differences in objects andplaces that adults and children find attractive, andpropose urban space designs that both adults andchildren find attractive.
PURPOSE AND METHOD
Creating a city that is easier for both children andadults to live in and enjoy is an issue for future urban
development. In order to create them, we need tounderstand the image of a city that children will findattractive.
The purpose of this study is to clarify the elements
and shapes of the cityscape that influence the imageof the city by comparing the spatial cognition
between children and adults in the town where theylive.
The research method will be divided into two forchildren and adults according to their purpose. One isto conduct a route selection experiment via the paired
comparison method using photographs of actual

ROUTE SELECTION EXPERIMENT
Experiment Contents
A paired comparison method was performed withthe cooperation of 150 adults aged 18 and over and184 children in grades 5 and 6 from two elementary
schools, using 13 photographs of actual roads. From605

GEOMATE – Kyoto, Japan, 3-5 November 2021

the 13 photographs, the ratio of the area occupied byspatial components such as plants, buildings, androads in the photographs was calculated, andexperiments were conducted by comparing the resultsof the paired comparison method with adults andchildren. Furthermore, based on the results of thecomparison, the photographs were visually analyzed.

2).

Experimental result

Visual analysis of photographs

Comparison of selectivity and each component

Next, from the results of visual analysis of thephotos, we observed that what the top three rated
photos had in common for adults was that greeneryand plants were manicured and maintained (Fig. 3).
On the other hand, the bottom three had unmaintained
greenery. In other words, the streets were notorganized and there was a lack of cleanliness (Fig. 4).

Also, regarding the comparison of the spatial
components between adults children, overall the adultgraph shows a cohesiveness in each item, while thechild graph does not. Regarding the proportion ofbuildings, there was no tendency for either adults norchildren.

Fig.1 and Fig.2 shows the area ratio occupied byeach spatial component in the photographs from the
13 photographs, and compares them between adultand child together with the results of the paired
comparison method.

Fig. 1
adultsFig. 3

The top three photos for adults

Fig. 4The bottom three photos for adults

Results of paired comparison method of

The top three for children were ones where the sky
is visible and unobstructed (Fig. 5). The bottom threewere where the sky was obstructed or not visible, andnot open space (Fig. 6).

Fig. 2 Results of paired comparison method ofchildren
For adults, the top three rated photos comprised ofabout 40% plants, and the 4th to 10th ranks are 15% or
less. Regarding roads, the top half from 1st to 7thcomprised of 30-40%. Regarding the ratio of plants toroad, the top three are close to 100% (Fig. 1). Next,
for children, the bottom three had less than 30%
plants. The top six photos had 30 to 40% roads. In
addition, although it is not shown in the graph, whenpaying attention to the actual road width, it was foundthat the bottom three were around 2 meters wide (Fig.

Fig. 5606

The top three photos for children

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 6The bottom three photos for children

Consideration
From the results, we conclude that children are
attracted to roads with good visibility, and adults are
attracted to roads that are manicured. In addition,
adults had a tendency in the upper ranks, but children
tended to have a tendency in the lower ranks, so I
think that the selection criteria for choosing a pathmay differ between adults and children.

Fig. 8

Cognitive map of elementary school B

Experimental result
Analysis of elements

COGNITIVE MAP SURVEYFirst, we compared the number of elements on the
cognitive map with the average number per person.
Fig. 9 shows the number and ratio of the averageelements of the characters and pictures drawn on the
cognitive map. From the results, the number of
elements that had a combination of pictures andletters was 12.67 per adult, 6.43 for children at School
A, and 8.63 for children at school B, 1.5 to 2 times
more for adults. Next, when comparing the number of
picture elements, the results were 4.11 per adult, 1.88
at school A, and 3.11 at school B. Comparing thenumber of character elements, the results were 8.56
per adult, 4.55 at school A, and 5.53 at school. (Fig.
9)

Experiment Contents
In the cognitive map survey, we conducted it withchildren aged 10-12 going to school A or school B
and adults aged 20 and over who attended a university.
Children drew a map of the road they take to school
and its surroundings on an A2 size blank piece of
paper and 12 colored pencils. (Fig. 7, 8) Urban
elements were extracted and analyzed for each zoneof the elementary school. For the Adults, we asked
them to draw a map of the university surroundings on
a A2 size blank piece of paper and 12 colored pencils.
Urban elements were extracted and analyzed. For theelements, what was drawn on the cognitive map was
extracted by dividing it into letters and pictures.

Fig. 9 Percentages and averages of elements on the
cognitive map
Fig. 7

Both adults and children had more letter drawn
elements than pictures, as about 30% of the elements
were pictures. Fig.10, Fig.11, and Fig.12 show the
proportions of all the elements drawn on the cognitive
map by item and by adult and elementary school.

Cognitive map of elementary school A

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GEOMATE – Kyoto, Japan, 3-5 November 2021

For adults, it can be seen that the ratio of shops
and restaurants is large when excluding the university
because they drew the area surrounding the university.
For children, if we exclude the elementary school andhome because they drew the road to the school, theratio of parks and apartments is large in school A
located in the suburbs, while many traffic lights, roadmarkings, and roads were drawn at school B locatedin the city.
Focusing on the results of adults, it was found thatmost of the shops and restaurants in particular weredrawn in letters. On the other hand, in the painting, it
was found that many highways, banks, and stairs on
the banks were drawn. Focusing on the results ofchildren, most of the parks, which had a highproportion in elementary school A, were drawn inletters. The proportion of parks in the text elements
and the proportion of parks in the picture elements
were almost the same. All apartments, which also hada high proportion, were drawn in letters. It was foundthat most of the traffic lights and road markings,
which were common in elementary school B, weredrawn with pictures, and the roads were drawn with
about half pictures and half letters.
In addition, it was found that plants were 100%
drawn with pictures, and most of the shops andrestaurants were drawn with letters. From the results,
what elementary school A and B have in common isthat about half of the pictures and letters are written
for their homes, and that the elementary school has
both pictures and letters. It turns out that the numberof elementary schools drawn as letters is nearly
double that of elementary schools drawn as pictures.
In addition, while many children drew only the sitewhen drawing an elementary school, most adults
drew each school building on the site when drawing
a university. While no traffic lights, pedestriancrossings, traffic signs, etc., were drawn on the adultcognitive maps, most children drew traffic lights,
pedestrian crossings, traffic signs, etc., on their
cognitive map. In the elements drawn in letters forboth children and adults, there were many places
where the usage was clear, such as restaurants, shops,
and schools.
On the other hand, many of the elements depictedin the drawings had a characteristic shape. For
example, for adults, these were large structures suchas highways and banks, and for children, they weretraffic lights and roads with "stop" signs, pedestriancrossings and other symbols and patterns, plants andfences. Comparing the places that block the view suchas buildings and plants with the places that can open
such as parks and vacant lots, for adults about 70% of
the drawings were things that block the view, andonly about 15% were the open spaces. On the other
hand, it was found that about 50% of the children’s
drawings in both elementary schools were objects that
obstruct the view, and 25% were open spaces.

Fig. 10 Percentage of elements on the adultcognitive map

Fig. 11 Percentage of elements on the school A
cognitive map

Fig. 12 Percentage of elements on the school B
cognitive map

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GEOMATE – Kyoto, Japan, 3-5 November 2021

Consideration

as road markings that adults do not pay attention to,and things that are displayed with patterns such as
signboards. We think that this is because attention ispaid to the shape close to the size and athletic abilityof one's body.
As a result of the above, it goes without sayingthat buildings with various functions and uses in town
development have a great influence on the image ofthe city. In addition to that, it can be seen that theheight and display are important, for example, onfamiliar roads where children walk. In the future, it isnecessary to look at the composition andclassification of such elements.

The differences in the number of elements drawn
between adults and children is thought to be due tothe difference in the number of places they normallyuse. While the top elements drawn on the adultcognitive maps were shops and restaurants, the top
elements drawn on the child cognitive maps were notshops and restaurants. We think it may be thedifference in frequency and interests. Also, whilemost children draw traffic lights and road markings,
adults almost did not draw these at all, so it is possiblethat children are more interested in objects relative totheir body size than adults. For both adults andchildren, there are many places where the usage of theelements drawn as letters is clear, and many of theelements drawn as pictures are large objects, shapes,
patterns, etc. I think that large structures arememorized in form, and children may memorize
things that exist in illustrations and shapes.

REFERENCES[1] T. Sakai, On the Relationship Between the Spatial
Elements and Perceptual Space of Children, 6thInt. Conf. on GEOMATE, 2016.
[2] H. Tsukaguti and K. Matsuda, Pedestrian routeselection behavior analysis, Japan Society of Civil
Engineers Papers, Vol. 2002, Issue 709, 2002, pp.
117-126.
[3] J. Zhang and H. Imada, A Study on City
Landscape Based on Free Description Written
about "My Favorite Scenery" by Children, Social
information science: journal of the Faculty ofSocial Information Science, Hiroshima Bunka
Gakuen University 22, 45-52, 2017[4] E. Kumazawa and T. Awahara, A Study on'Children's play space' through the Analysis ofHand Drawn Maps V: On effects by Geographical
Conditions2, Urban Plannning (2013), 247-248(7124), Architectural Institute of Japan, 2013
[5] K. Umekawa, H.Yokoyama and Y. Miyagishi, A
study on the playgrounds for the children and thespace cognition in the city, Conf. on Architectural
Institute of Japan Kinki (49), 365-368 (7020),
Architectural Institute of Japan, 2009

CONCLUSION
From the results of the paired comparison method,
there was an overall tendency in the results fromadults, but it was difficult to see the tendency in thechildren's results. Although the route is selected, it isconsidered that the child selects the route by focusing
on the part that is not a component of the road.
In the cognitive map survey, many restaurants
were drawn by adults, and for children, many places
to play with friends, such as parks and friends' houses,
were drawn. Regarding the cognition of the street
space, we think that the difference in the place thatthey usually use with their friends is reflected in the
difference between adults and children. Also, while
the adults drew large structures, the children' did not
draw many large structures, and even if they weredrawn, the structures were not drawn in great detail.
This is probably attributed to varying line-of-sightfrom height differences between adults and children.
With large structures being harder to see, children are
attracted by things that are drawn on the ground, such609

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

ON EVALUATION OF ENVIRONMENTAL SOUND BY DEGREE OF
HUMAN CONCENTRATION
Haru Kanda1, Kazunari Tanaka2
1

Architecture, Civil Engineering and Urban Design, Graduate School of Engineering, Osaka Institute ofTechnology, 5-16-1, Omiya, Asahi-ku, Osaka, Osaka, Japan
2
Osaka Institute of Technology, 5-16-1, Omiya, Asahi-ku, Osaka, Osaka, Japan
ABSTRACT

Currently, COVID-19 is changing the learning environment of students and the working environment of office
workers. They spend more time at home. In this study, we focused on the environmental sound, clarifying thedifference in the environment of residential areas, which is different from educational districts and office areas,from the viewpoint of concentration. We applied two methods of psychological experiment and physiologicalexperiment. In the psychological experiment, five grades were used to evaluate how concentrated the collectedenvironmental sounds were. As for the physiological experiment, an electroencephalogram measuring device(EMOTIV EPOC X) was used to detect concetration. Testers were requested to answer 12 simple arithmetic
operations by mental arithmetic, while they were under the specific sound stimuli for each question. As a result,the difference in concentration due to the difference in sound was extracted. The data obtained in the physiologicalexperiment and the psychological experiment were compared. Of the four, the one that hindered concentration
most was the human voice. We also found that the sound of turning paper and the chirping of clocks and birds do
not interfere with concentration. There was some differences between the psychological and physiologicalexperiments. Both experiments detected human voice as an adverse effect on concentration.
Keywords: Sound, pollution, brain waves, concentration
INTRODUCTION

sound environment. When evaluating "comfort" withregards to, working environment, we focused onwhether or not the concentration was improved.

There are many studies from the viewpoint ofsoundscape, but there are very few studies on the
comfort of space (especially focusing on
concentration) from the type of sound. Therefore, thecomfort of the space will be examined based on thetype of environmental sound.

PURPOSE AND METHOD
PurposeThe purpose of this study is to clarify the noisethat affects our concentration in various environments.
In addition, it is to find indicators to get the level ofthe noise. Ultimately, we aim to improve the healthy
environment of the city and create a good urban space.

BackgroundToday, there are seven typical pollutions in Japan.
Out of the complaints received for pollution, noise
pollution, which is one of these, is the highest
received. [1] The definition of noise is ambiguous,and noise is often referred to as unwanted soundrather than volume. In other words, noise exists in ourdaily lives and is always present as environmental
sounds surrounds us.
The world has been disrupted by the influence of
COVID-19, and changes in the environment are
occurring for many Japanese people. This means thatwork and study have switched to online and more
people are working and studying in unusualenvironments. In other words, the sound environmentchanges as the environment changes, and many
people are not accustomed to working in a different
space than usual. We investigated what kinds ofsounds have adverse effects and what kinds of sounds
have positive effects in order to build a comfortable

MethodAs a research method, after conducting apreliminary survey, we conducted research from two
points, psychological and physical aspects.
Preliminary Survey Method
Before conducting the psychological experiment, aquestionnaire was conducted for undergraduate andgraduate students as a preliminary survey. [3] In thequestionnaire, we asked two questions, "What is thesound you hear when you are in the house, and at
school?" We collected as much as possible from theexperiments under same situation.
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GEOMATE – Kyoto, Japan, 3-5 November 2021

Psychological Experiment Method

However, I couldn't find a specific sound source suchas the sound of “a mechanical pencil” falling in mysearch, so I recorded those myself. [6] As for the typesof environmental sounds, the same 12 types ofenvironmental sounds used in the psychologicalexperiment are used. First, each sound is played at thesame time, and only one sound is set louder. Using
PowerPoint, ask them to perform a simple fourarithmetic operation problem that appears on thedisplay during the time when the sound is being heard.
The time required for each question was set to 10
seconds, and the response time was set to 2 seconds.
A total of 157 seconds of brain waves were measured,
includingtheresting
time.
Since
the
electroencephalograph picks up a weak electric
current and affects the waveform, we did not havesubjects use their hands to fill in the answer, and tried
to keep body movement to a minimum. In addition,
when determining concentration, it is generally
suggested that when low β waves and α waves appear,
they are concentrated, so brain waves in O1 and O2,
where α waves and β groups are likely to appear, aremeasured. Then, Fourier analysis was performed
using EMOTIV PRO, and the maximum values ofbrain waves for each problem were compared.

The
psychologicalexperiment
presents
environmental sounds and asks the subject to evaluate
whether or not the environmental sounds can beconcentrated in five stages. At this time, the subject is
not actually asked to listen to the sound [4], but rather
asked if he-/-she would likely be able to concentrate.
The environmental sounds evaluated by the subjects
were classified by the author and used in thephysiological experiment. The environmental sounds
were divided into a total of seven sounds: naturalsounds, sounds emitted from humans, human
movement sounds, animal sounds, mechanicalsounds, external sounds, and other sounds. Since
there are a considerable number of classified
environmental sounds and it is difficult for the
subjects to evaluate all of these sounds, representative
sounds were selected in these seven types and used in
psychological experiments. In psychologicalexperiments, these environmental sounds areevaluated on a five-point scale as to whether or notthey are likely to be prevalent at university or home.
Physiological Experiment Method
In this chapter, unlike the psychological aspect
performed in Psychological Experiment, the subject
is actually given a sound stimulus, and theconcentration is verified from their physicalreaction.[5]In the physiological experiment, the concentration
was evaluated using an electroencephalograph. In theevaluation, the intensity of the frequency band suchas α wave and β wave is observed from theelectroencephalogram spectrum measured by theelectroencephalograph, and the degree ofconcentration is analyzed from the activity level. [2]The one used for this experiment was EMOTIVEPOCX, and the analysis software was EMOTIV PRO.
In FIG. 1, the upper half of the waveform is a Fouriertransformation of the measured brain wave, and thelower half shows the band power for each frequency
band from the waveform.

RISULTS AND DISCUSSION
Results and Considerations of Preliminary Survey
The respondents were 21 males (55.3%) and 17
females (44.7%), for a total of 38 respondents. As aresult, there were 106 types of environmental sounds
in the house and 70 types of environmental sounds inthe university.
13.7%

9.4%
10.9%

14.8%

29.7%

6.3%

15.2%

Sound of natureSound when a person is moving

Mechanical soundSounds made by people

Animal sounds

Outside soundUnclassifiable

Fig. 2 Percentage ofenvironmental sounds
Fig. 1 Measured Brain Wave and Fourier Transform
The sound source used was a sound stimulus
created from free materials existing on the Internet.
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each

group

of

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GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1 Value of Environmental Sounds in House

3.4% 5.1%
16.0%
0.6%

30.3%

Environmental soundThunderRain
CoughHuman voice
The sound of turningpaper
The sound of writing
letters with a mechanicalpencilClock
Air conditioner
Car running noise
The sound of amechanical pencil falling
Bird chirping
Crow barks

24.0%

21.1%
Sound of natureSound when a person is moving

Mechanical soundSounds made by people

Animal sounds

Outside soundUnclassifiable

Fig. 3 Percentage of each group of university
environmental sounds
The ratios of the seven sounds-classifications areshown in Fig. 2 and 3. As a result, in the houses,
26.1% of the total were sounds emitted from people
and the sound of human movement, and 54.3% at theuniversity, and the result was that the university
recognized more environmental sounds for people.
Another major difference is the external sound. The
outside sounds at the university were 16.0%, while
the outside sounds at home were 29.7%, which was
about twice the difference. From this result, it was
found that the house is more conscious of outside
sounds.
It is thought that the way of thinking about space isdifferent between the university and the house. At
university, the range of personal space is narrow, and
it is easy to focus on the surrounding sounds.
Therefore, it is easy to hear sounds that are relatively
close to oneself, such as sounds emitted from humans.
On the other hand, at home, because the range of
personal space is bigger, the sound inside the personal
space is not a concern, and the outside sound is easierto hear. In other words, it is easy to hear externalsounds that are considered to be outside the personal
space.
Since it was found that the recognizable ranges aredifferent, the next step is to investigate the effects ofauditory image on concentration.

Table 2 Value ofUniversity

2.82
3.79
2.92
2.89

standard
deviation
1.27
1.28
1.09
1.02

3.89

1.17

3.79

1.15

3.61
3.66
3.24

1.14
0.98
1.29

3.16

1.09

3.74
3.32

1.23
1.28

average

Environmental

Environmental soundThunderRain
CoughHuman voice
The sound of turningpaper
The sound of writing
letters with a mechanicalpencilClock
Air conditioner
Car running noise
The sound of amechanical pencil falling
Bird chirping
Crow barks

Sounds

in

2.65
3.76
2.54
2.19

standard
deviation
1.21
1.01
1.14
1.15

3.68

1.16

3.76

1.14

3.97
3.41
2.89

1.07
1.04
1.02

3.11

1.33

3.84
3.14

1.01
1.23

average

From this data, a t-test was performed by one-sided
test. In the t-test, when the P value was less than 0.05,
it was considered statistically significant. From Table
3, it was found that the environmental sound of lessthan 0.05 corresponds to the human voice.

Results and Considerations of Psychological
SurveyTables 1 and 2 below show the average value andstandard deviation of the five-grade evaluation foreach environmental sound.

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Table 3 The P Value in the T-test
Environmental soundP valueThunder

0.2821

Rain

0.4516

Cough

0.0733

Human voice
The sound of turningpaper
The sound of writing
letters with amechanical pencil
Clock0.0034Air conditioner

0.1431

Car running noise
The sound of amechanical pencil
falling
Bird chirping
Crow barks

0.1028From the above results, it can be seen that there
may be differences in these three types of sounds
imaged from words depending on the subject.
The group with a high evaluation of the threesounds regards these sounds as continuous sounds. It
is considered a sound that can be heard continuously
at a volume that does not bother you. On the contrary,
the subjects had negative images of unexpectedsounds such as sounds that cannot be continuously
heard, that is, sudden coughing and fallingmechanical pencils. We can imagine the sudden loudcall of a crow and the voice of a person as a suddensound. From this, it is possible that these subjects
perceive the sounds of birds chirping and paperturning expressed in letters as exceptions.
③ is considered to have the same tendency.
Another way to look at it may be whether the subject
can concentrate or not, depending on whether thesound of rain at the university is perceived as if itwere from inside the house or as a loud sound heard
in the classroom.

0.20970.45130.07780.43030.35050.2687

Results and Considerations of Physiological
SurveyFrom the results of the questionnaire, there aresounds in which the groups were divided into twohigh and low evaluation. They are the "bird chirping"
and the "paper-turning sound" heard from house, andthe "rain sound" heard at university. Hereafter, the"bird chirping" is referred to ①, the "paper-turningsound" in the house is-_ ②, and the "rain sound" atuniversity is-_③. The correlations between theevaluations of the three sounds and the other sounds
are as follows. The subjects who gave high marks in① selected sounds best able to concentrate most on
rain (80.0%), the sound of turning paper (60.0%) andthe sound of writing with a mechanical pencil(60.0%). On the other hand, - the subjects who did notevaluate well in ① evaluated that they could notconcentrate with cough (73.0%), mechanical pencil
falling sound (54.7%), and crow calls (63.7%).
Subjects who gave high marks in ② tend to givehigh marks to the sound of writing letters with amechanical pencil (81.2%), rain (62.5%), chirping ofbirds (56.3%), clocks (50.1%). Conversely, subjects
who did not evaluate ② well were coughing (60.1%),
human speaking (50.2%), writing with a mechanicalpencil (50.2%), air conditioner (50.2%), bird chirping(50.2%) and the crow calling (50.2%). There were noclear tendencies.
Subjects who highly evaluated ③ tended to highly
evaluate the clock (63.6%), the chirping of birds(63.6%) and the sound of writing letters with amechanical pencil (54.5%). - Subjects who rated ③
low, rated the cracking of lightning (53.3%), airconditioners (53.3%), and crow calls (53.3%).

According to the results of the α waves in Fig. 4, it
was observed that the sounds of the mechanical pencil
falling and the car running detected a large amount of
the α wave. In addition, it was confirmed that all thesubjects had a decrease in alpha waves when thehuman voice became louder. Further, FIG. 5 shows
the result of the Low β wave. There was a reaction to
three points: the sound of turning paper, the sound of
a car running, and the chirping of birds. Similarly, it
can be seen that the value of human speech is low.
25

20

15

10

5

0
Crow
barks

Pencilfalling

Writing
letters
with apencilRain

Air
Turning
Cough conditio
paper
ner

Clock

Car
Hum anBirdrunning
Thunder
voice chirpi ng
noi seSubject A

5.39

12.54

6.88

8.70

10.00

5.44

7.14

8.68

17.80

6.14

7.42

9.66

Subject B

3.94

76.68

2.99

3.65

2.63

2.41

2.34

7.74

4.39

1.99

5.12

2.00

Subject C

4.09

2.14

2.15

1.43

1.32

1.80

1.52

1.58

1.96

1.30

1.33

1.56

Subject A

Subject B

Subject C

Fig. 4 Comparison of the highest value of alpha wave

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GEOMATE – Kyoto, Japan, 3-5 November 2021

noise of the person is louder than the noise of the car,and when it approaches yellow, the noise of the car is
louder than the noise of the person. Judging from theresults of the experiment, it may be more difficult forpeople living in areas close to blue to concentrate on
their work. In addition, I thought that a better soundenvironment could be constructed by conducting suchan analysis and conducting city planning.

25

20

15

10

5

0
Crow
barks

Pencilfalling

Writing
letters
with apencilRain

Air
Turning
Cough conditio
paper
ner

Clock

Car
Hum an Bird Thunderunning
voice chirpi ngr
noi seSubject A

5.74

7.29

4.14

4.65

5.43

3.17

11.71

5.23

7.14

4.20

11.58

3.93

Subject B

1.58

140.57

2.19

1.12

1.65

1.43

1.00

7.98

3.03

0.86

4.51

1.25

Subject C

1.15

1.13

0.68

1.11

1.63

1.30

1.83

0.89

1.31

0.99

0.72

0.80

Subject A

Subject B

Subject C

Fig. 5 Comparison of the highest values of Low β
waves
When a lot of α waves and Low β waves appear, it
can be said that they are in a concentrated state, so thesound of driving a car, the sound of a mechanicalpencil falling, the sound of turning paper, the chirpingof birds, clocks- etc, have some commonality for the
subjects. It can be said that it is in a state of being seen
and concentrated. However, in general, the runningnoise of a car is often regarded as noise pollution, so
there are still some doubts about this result. The
running sound of the car used this time was the soundof only one car running without making such a loudnoise, so this may have been a factor. In addition,
there was an opinion from the subject that the soundof the car running and the sound of waves were very
similar after the experiment. Natural sounds often
have 1 / f fluctuations, which are sound fluctuations
whose spectral density is inversely proportional to thefrequency f, and it is said that wave sounds also
include these 1 / f fluctuations. Although it waslimited to the sound source used this time, the author
thinks that the running sound of the car contained 1 /f fluctuation and acted on the brain waves. According
to Samaki et al, it has been clarified that 1 / f
fluctuation reduces stress with ultrasonic waves.[7] Inother words, it is suggested that 1 / f fluctuation
affects the body.
Based on this result, we interpolated theautomobile noise (dB) in the city with QGIS using thedata of the Ministry of the Environment, andexamined where the voice of a person would have an
adverse effect. [8] According to the Ministry of theEnvironment, it is said that a person's voice is about
50 to 61 dB when engaged in a normal conversation,
but it is said to be about 88 to 99 dB when theconversation becomes loud due to excitement. In
figure 5, the noise data of the car is interpolated by
the reverse distance load method and contour lines are
drawn. The area is where the densely populated urban
areas of Moriguchi City, Kadoma City, andNeyagawa City in Osaka Prefecture are concentrated.
In this figure, the bluer areas represent,- the quieter
car noise, and the closer it is yellow, the louder thenoise. In other words, when the blue color is dark, theFig. 6 Adverse Effect Zone by Human Voice forConcentration
CONCLUSION
The research results and future developments are
summarized below.
Summary
In this study, in order to design a comfortablesound space, we examined comfort from two
perspectives: the sound formed by the auditory image
and the actual sound stimulus. As a result, it was
found that the voice of a person has an adverse effectwhen viewed from the viewpoint of concentration. In
addition, although the results were not sufficiently
significant, the sound of turning paper, clocks, andchirping of birds tended to have a positive effect on
concentration.
Future Tasks
We determined a car affected concentration in a
psychological experiment but not in a physiologicalexperiment, while rain and the sound of writing witha mechanical pencil, affected concentration in a
psychological experiment but not in a physiologicalexperiment. I would like to work hard to find out thecause of the discrepancy when comparing the runningnoise of a machine. Although it is the running noiseof a car that is generally considered to be one of thecauses of noise pollution, it was suggested that it
improves concentration when measuring brain waves.
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GEOMATE – Kyoto, Japan, 3-5 November 2021

However, since the running sound of the car used thistime was the running sound of only one car, we would
like to consider how it changes with the sound of
multiple automobiles running at the same time. In
addition, it is necessary to consider a method that canconsider 1 / f fluctuation.

[4] Tahara K., Tsuneo H., Kobayashi Y., Katada A.,
Tyoukakushougaisya ni okeru Kankyouon no
Tyoukakuimeiji ni kansuru Kenkyuu, Audiology
Japan, vol.59, 2016, pp.198-206[5] Yamazaki K., Hotta K., Saitou M., Ogawa M.,
Keiryuu no Oto ni fukumareru Tyouonpa ga
Ningen no Seiri ni ataeru Eikyou ni tuit,
Acoustical Society of Japan, Vol64, No.9, 2008,
pp.545-550[6] Shimai S., Tanaka M., Kankyouon no KaiFukaihyouka to Onatsu no Kankei, AcousticalSociety of Japan, Vol49, No.4, 1993, pp.243-252[7] Mori Y., Samaki Y., Tada H., Watanabe S.,
Shirahama N., Nakaya N., Tomita M., Effects oflistening to Ultrasonic Sounds with 1/f
Fluctuation for the Autonomic Nervous Activity,
Biomedical Fuzzy Systems Association, Vol.19,
No.1, 2017, pp.67-72[8] Constant monitoring results of automobile noise,
Ministry of the Environment

REFERENCES[1] About noise regulation law enforcement situation
investigation in 2018, Ministry of Environment[2] Hirai F., Yoshida K., Miyaji I., Comparison
analysis of the thought and the memory at thelearning
time
by
the
simpleelectroencephalograph, Multimedia, Distributed,
Cooperative, and Mobile Symposium, vol.2013,
no.2, pp.1441-1446[3] Hashimoto Y., Shindo M., Tanaka Y.,
Considerations of a Cognition Test ofEnvironmental Sounds, Shitugosyo Kenkyuu,
Vol.9, No.4, 1989.12, pp.227-236

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11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

ON CHANGES IN THE PSYCHOLOGICAL “AFFORDANCE” IN
URBAN SPACE DUE TO THE INFLUENCE OF COVID-19
Kazuki Miyake1, Kazuknari Tanaka2
1

Architecture, Civil Engineering and Urban Design, Graduate School of Engineering, Osaka Institute ofTechnology, 5-16-1, Omiya, Asahi-ku, Osaka, Osaka, Japan
2
Osaka Institute of Technology, 5-16-1, Omiya, Asahi-ku, Osaka, Osaka, Japan
ABSTRACT"Affordance" is a coined word, defined by J. Gibson and is the nature of forms and spaces. This meanscharacteristics of forms and spaces that people get information intuitively, understand how to use it and act by justlooking at and touching the shape and material without thinking.
The chair "affords" to sit and the pencil "affords" to write. Similarly, people act by getting information fromtheir environment, such as where they can sit comfortably in a university lecture room or ride comfortably on a
train. From the influence of COVID-19, telecommuting, online lectures, and delivery services became widespread.
In public spaces, it is common for people to wear masks. In addition, it is recommended to avoid closed
spaces, crowded places, close-contact settings etc. In urban spaces, there are physical restrictions
on people’s behavior, such as restrictions on admission to a store, bench usage restrictions, and requirements fordisinfection and temperature measurement prior to admission. It is thought that people's psychological behavior
has changed and is restricted unknowingly. In this study, we compared the behaviors of people before and after
the spread of COVID-19 and investigated various "affordance" changes. The purpose is to find new actions andmovements of people to design public spaces. From the behavioral surveys, we found the behavior change whenpeople encountered a person wearing a mask. Moreover, the difference of personal space was measured. There
was a clear difference between before and after the spread of COVID-19.
Keywords: Mask, COVID-19, Affordance, Personal space

INTRODUCTION

restricted unknowingly. In addition, it has becomecommon for people to wear masks. So, I think thesignificance of masks has changed since the COVID19 outbreak.

Background“Affordance" is a coined word, defined by J.
Gibson and is the nature of forms and spaces.
This means characteristics of forms and spaces thatpeople get information intuitively, understand how to
use it and act by just looking at and touching the shapeand material without thinking. The chair "affords"
to sit and the pencil "affords" to write. Similarly,
people act by getting information from theirenvironment, such as where they can sit comfortably
in a university lecture room or where they can ride
comfortably on a train. Signs that indicate allperceptible signs that convey appropriate behavior to
the subject are called “signifiers”
From the influence of COVID-19, telecommuting,online lectures, and delivery services became
widespread. In public spaces, it is common for people
to wear masks. In addition, it is recommended to
avoid closed spaces, crowded places, close-contact
settings etc. In urban spaces, there are physicalrestrictions on people’s behavior, such as restrictions
on admission to a store, bench usage restrictions,and requirements for disinfection and temperature
measurement prior to admission. It is thought thatpeople's psychological behavior has changed and is

Purpose and Method
In this study, we compared the behaviors ofpeople before and after the spread of COVID-19 andinvestigatedvarious "affordance" changes. The
purpose is to find new actions and movements ofpeople to design public spaces. In this study, theeffect that masks have on the human action isinvestigated from the action investigation(Experiment 1 and 2). Next, the difference before andafter COVID-19 generation of the personal space isclarified by the investigation of the action space(Experiment 3).
EXPERIMENT
Experiment１
In this study, the classification of space by
Edward T. Hall is defined as being composed of four
elements: proximity distance, personal distance,social distance, and public distance. The personal
space is defined as the individual distance based on
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the past studies and references shown in the figure
below [1].

Fig. 3Fig. 1

Past research

The difference between two questionnaires was
judged by t test. The results showed that there was adifference between the two, and that there werechanges in people's behavior choices, such as
standing or sitting, depending on whether they had
masks or not.
Next, the following three questionnaires werecarried out in order to examine the action selection of
the seat position by the existence of a mask in close
distance.

In order to investigate changes in people's
behavior with and without masks at proximity
distances (0 ~ 45 cm), we focused on the seats of a
train where the distance between each seat is 43 cm[2].

Fig. 2

Questionnaire (1)Train type

We look at seat selections on trains and use theresults to explore how masks can change people's
behavior at close range. Questionnaires with andwithout masks were administered to 56 male andfemale university students, and questionnaires
without masks were administered to 48 male andfemale university students. The conditions forselecting an object were defined as follows. ① We
selected a photograph similar to the seat style of theTanimachi subway line, which is familiar to studentsof Osaka Institute of Technology. In order toinvestigate the distance, bench-style seats wereselected instead of facing seats. ③ As for the survey,
it's conditional on being able to stand, sit, or both,rather than sit on crowded trains. ④ Option 7 means
to stand. ⑤ Concerned about any age and gender bias,
they used black-lacquered people. Due to concerns
over the spread of COVID-19, we decided not to
conduct an on-site survey, but instead to use photos
obtained online. First, we investigated whether there
is a difference in the behavior of sitting and standing
depending on the presence or absence of a mask.

Fig. 4

Questionnaire (2)The following table shows the distances of people
wearing and not wearing a mask (The choice of donot sit is as infinity distance.)
Table 1 Experimental results
Experimentdistance from someone wearing amaskdistance from someone without a
mask
617

Distance
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From the results, it can be understood that riders
want to keep a distance from people who don't wear
masks. Therefore, it was proven that the existence ofthe mask affected the action selection in near distance.

other, and the distance which the participant become
worried when the counterpart walked from left side,
left oblique front, front, right oblique front, right side
toward the participant was measured.

Experiment 2
In order to investigate the behavior change of
people with and without masks within the range
corresponding to the individual distance (45 ~ 120cm), we focused on the waiting position on the station
platform. The difference in the distance between thewaiting positions was examined according to thepresence or absence of a nearby person with a mask,
and was expressed numerically. A photograph wastaken from the second floor of the station, and the
distance was calculated by measuring the mark of theleg which shows the position of the platform. JR
Osaka station was selected as an object place withmany users.

Fig. 5

Fig. 6

Experimental method

Four experiments were conducted with different
subjects: when the approaching person wore a mask
(3 males and 3 females) and when the approaching
person did not wear a mask (3 males and 3 females).
Although the experiment was scheduled to be carriedout on the same station platform as the first andsecond investigations, it was carried out in the Osaka
Institute of Technology considering spacerequirements for the experiment and risk of COVID19 infection. The experimental results are shown inthe diagram below.

Home standby position

Based on the survey results, the dimensions on thephotographs were measured from the actual
dimensions (20.5 cm) of the foot marks indicating thepositions where the platforms were lined up, and thescales were calculated to calculate the averages. The
position of the person standing behind the person
wearing a mask was 65.7 cm, and the position of theperson standing behind them wearing a mask was
62.7 cm. In other words, there was no change inbehavior selection with masks at individual distances.
However, it was considered that they did notrecognize the presence of a mask as information
unrelated to themselves, because they did not knowwhether the person in front of them was wearing themask, and because many people were focused on theirmobile phones in the station.

Approaching person is wearing a mask.

Experiment 3
Approaching person isn`t wearing a mask
Therefore, we conducted a survey to determine
the distance which you are not concerned when you
are facing a person (when you can see a person's face),that is, within a personal space (individual distance).
In addition to the changes in mask-induced behavior
within individual distances, also revealed changes inpeople's personal space after the COVID-19 outbreak.
In the experiment, 6 male university students and 6
female university students participated by facing eachFig. 7

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Table 2 Validation results
Direction
Left
Left front
Front
Right frontright

Mask(cm)
92.33
111.67
146
116
94.67

3

CONCLISION

From the results of the above study, it is within
individual distance that the behavior changes
according to the information of the mask, and themask indirectly affects the behavior of the person
who is not wearing the mask. I think it is because of
the change of feeling that wearing a mask has become
normal due to the occurrence of COVID-19. In
addition, it turns out that the rise of COVID-19 andthe spread of social distance's terminology havechanged personal space from individual distance tosocial distance. In the future, the team will look into
the possibility of new designs based on this, whilelooking to identify other Afford Factors after the
COVID-19 pandemic.

No mask(cm)
98.67
131
140
119.33
97.33

From the above results, it became clear that there
was no change in the actions depending regarding
mask wearing in the individual distance. According
to past research, the average personal space before theprevalence of COVID-19 was about 60 cm, andaccording to the reference book, the personal space
has a radius of about 70 cm from the center of thesubject, and the personal space is defined as
individual distance. As can be seen from this table,
the personal space calculated in this study is 140 cm
or more in the forward direction, which corresponds
to the social distance (120 ~ 360 cm). For this reason,
the definition of personal space for people after the
COVID-19 epidemic corresponds to the social
distance before the COVID-19 epidemic. The reason
for this is thought to be that the word social distance,
which is recommended around the world, has become
widely known and people have become more
physically distant.

REFERENCES[1] Murata S., Morihara K., and Koda T., Analyses ofEffect of Personality on Personal Space in RealWorld and Metaverse. The Japanese journal of
Psychology, Vol. 52(2), 1981, pp. 124-127.
[2] Konishi H., Nozawa K., Study on Seating
Behavior in the Train; the Influence of Prediction
about Crowd on Seating Behavior. The annual
bulletin of Musashino University Institute of
Human sciences, Vol. 7, 2018, pp. 37-39.

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11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

DATA MODELING WITH TRANSFORMATION IN LAPLACEAUTOREGRESSIVE MODEL USING REVERSIBLE JUMP MCMC
Suparman1, A.M. Diponegoro2
1

Department of Mathematics Education, Universitas Ahmad Dahlan, Indonesia;
2
Department of Psychology, Universitas Ahmad Dahlan, Indonesia
ABSTRACT

Autoregressive (AR) is a model that is often used to model various types of data. For confidential data, data
confusion is very important to be protected so that the data is not known by other unauthorized parties. This
study aims to find data modeling with transformations in the AR model. In this AR model, noise is assumed tohave a Laplace distribution. AR model parameters include AR model order, AR model coefficient, and noise
variance. AR model parameter estimation is carried out in a Bayesian framework using the Markov Chain Monte
Carlo (MCMC) reversible jump algorithm. This study shows that the posterior distribution of AR model
parameters has a complicated equation so that the Bayes estimator cannot be determined analytically. Bayes
estimators for AR model parameters are calculated using the MCMC reversible jump algorithm. This algorithm
produces an AR model that satisfies the stationary conditions. The results of this research can be applied tomodeling and forecasting confidential data in various sectors. The novelty of the research results is the use oftransformations in the AR model which has Laplace noise. This research can be further extended to the moving
average autoregressive model (ARMA).
Keywords: Autoregressive, Bayesian, Confidential data, Laplace noise, Reversible jump MCMCINTRODUCTION

modeled data when the research results are
published. This article focuses on AR models with
Laplacian noise. This Laplacian noise was chosenbecause the characteristics of the AR model with
Laplacian noise have not been studied much. This
article has several objectives, namely: (a) finding atransformation for the AR model with Laplaciannoise, (b) finding an order estimation procedure forthe transformed AR model, (c) finding the procedure
for estimating the coefficients of the transformed AR
model, and (d) finding the procedure for estimating
the noise variance of the transformed AR model.

Autoregressive (AR) is one of the stochastic
mathematical models. The definition andcharacteristics of the AR model can be found in [1].
AR models have been widely applied in various
fields, including time series and signal processing.
The AR model contains a noise which is assumed tohave a distribution. Generally, the AR model isassumed to have a normally distributed noise, forexample [2]. In order for the AR model to be appliedin a wider field, several authors have developed AR
models in which noise is assumed to be nonnormally distributed, for example AR models with
Pareto noise [3], AR models with exponential noise[4]-[5] and AR models with Laplace noise [6].
However, for confidential data, the AR model needs
to be transformed. So that the confidentiality of thedata is protected when the research results are
published.
Variable transformation is a method to change arandom variable into another random variable. Thistransformation is often used in parametric data
analysis when parametric assumptions are not met,for example [7]. In this article, variabletransformation is proposed to transform the AR
model. If the AR model is transformed to another
AR model, then the AR model noise is automaticallytransformed into another noise. The transformed
noise will have a different distribution.
In this article, variable transformation is used in
the AR model to protect the confidentiality of the

LITERATUR STUDY
Laplace Distribution
The random variable z is said to have a Laplace
distribution with parameters 𝜇 and 𝛽 , written as𝑧~𝐿𝑎𝑝𝑙𝑎𝑐𝑒(𝜇, 𝛽) , if and only if the probability
density function of 𝑧 is𝒇(𝒛|𝝁, 𝜷) =

𝟏
𝟐𝜷

𝒆𝒙𝒑 −

|𝒛−𝝁|
𝜷

.

The mean and variance of the random variable 𝒛 are
𝝁 and 𝟐𝜷𝟐 , respectively. If 𝒛~𝑳𝒂𝒑𝒍𝒂𝒄𝒆(𝝁, 𝜷) thenthe random variable 𝒆 = 𝒂𝒛 + 𝒃~𝑳𝒂𝒑𝒍𝒂𝒄𝒆(𝒂𝝁 +𝒃, 𝒂𝜷).
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The posterior distribution of parameter 𝜽 is

Let 𝑥1 , … , 𝑥𝑛 be a data set where 𝑛 is thenumber of observations. The data is said to have anautoregressive model of order 𝑝, written as 𝐴𝑅(𝑝),if this data satisfies the following stochastic equation[1]:

𝑳(𝒚𝟏 , ⋯ , 𝒚𝒏 |𝜽)𝝅(𝜽)𝝅(𝜽|𝒚𝟏 , ⋯ , 𝒚𝒏 ) = ∑𝑳(𝒚𝟏 , ⋯ , 𝒚𝒏 |𝜽)𝝅(𝜽)if the parameter 𝜽 is discrete.

𝒑𝐱 𝐭 = − ∑𝒊=𝟏 𝝓𝒊 𝒙𝒕−𝒊 + 𝐳𝐭

Reversible Jump MCMC

where 𝒑 is the order of the autoregressive model and
𝝓𝟏 , 𝝓𝟐 , ⋯ , 𝝓𝒑 is the coefficient of theautoregressive model. The random variable
𝒛 𝟏 , … , 𝒛𝒏
isnoise.
In
thisarticle,𝒛𝒕 ~𝑳𝒂𝒑𝒍𝒂𝒄𝒆(𝟎, 𝜷).

Suppose 𝐸 is the set of states and 𝜋 is theprobability on 𝐸 . The reversible jump MCMC
algorithm [9] produces a Markov chain on 𝐸 which
has a stationary probability close to the probability
𝜋 . Let 𝑞 be the auxiliary kernel on 𝐸 . From thestarting point 𝑤 ∈ 𝐸, take a new point 𝑤 ∗ according
to 𝑞(𝑤, 𝑤 ∗ ) . Next, the point 𝑤 ∗ is accepted withprobability

Variable Transformation
Variable transformation is a method to change arandom variable into another random variable. Let 𝑥
be a continuous random variable and 𝑓(𝑥) be aprobability density function of the random variable
𝑥 . If 𝑦 = ℎ(𝑥) is an increasing or decreasing
function, then the random variable 𝑦 = ℎ(𝑥) has aprobability density function [8]:𝒈(𝒚) = 𝒇[𝒉−𝟏 (𝒚)] |𝝆(𝒘, 𝒘∗ ) = 𝒎𝒊𝒏 {𝟏,𝝅(𝒘)𝒒(𝒘,𝒘∗ )}.

In the reversible jump MCMC algorithm, Markov
chains can move between spaces of different
dimensions.

𝒅𝒉−𝟏 (𝒚)𝒅𝒚𝝅(𝒘∗ )𝒒(𝒘∗ ,𝒘)

METHODOLOGY|.

This article uses time series data that can bemodeled as an AR model with Laplace noise. The
following are the steps of data modeling. Let 𝑥𝑡(𝑡 = 1, 2, ⋯ , 𝑛) De time series data. The data istransformed by using 𝑦𝑡 = 𝑎𝑥𝑡 , The time series data
𝑥𝑡 is modelled as an AR model with Laplace noise.
The likelihood function of times series data isdetermined. The prior distribution of the parameters
is selected. The posterior distribution of theparameters is determined. Bayes estimators forparameters are calculated using the Reversible Jump
MCMC algorithm. The data modeling procedure isgiven in Figure 1.

Bayesian ApproachLet 𝜃 be a parameter. In the Bayesian approach,the parameter is considered as a random variable
that has a prior distribution. If 𝑦1 , ⋯ , 𝑦𝑛 is n
random samples and 𝐿(𝑦1 , ⋯ , 𝑦𝑛 |𝜃) is a probability
function of random samples 𝑦1 , ⋯ , 𝑦𝑛 . Suppose that𝜋(𝜃) is the prior distribution of parameter 𝜃, thenthe posterior distribution of parameter 𝜃 is𝑳(𝒚𝟏 , ⋯ , 𝒚𝒏 |𝜽)𝝅(𝜽)∫−∞ 𝑳(𝒚𝟏 , ⋯ , 𝒚𝒏 |𝜽)𝝅(𝜽)𝒅𝜽𝝅(𝜽|𝒚𝟏 , ⋯ , 𝒚𝒏 ) = ∞if the parameter 𝜽 is continuous.

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Fig. 1 Flow chart𝑎𝑥. This new data is then analyzed and published.
Thus, the confidentiality of the original data is
protected. The statistical model for the original data
can be obtained by inverse transformation on thestatistical model for the new data. For example,
Figure 2 plots the original data and Figure 3 plots
the new data using the equation 𝑦 = 5𝑥.

RESULTS AND DISCUSSION
Likelihood Function
Let 𝑥 = (𝑥1 , … , 𝑥𝑛 ) be the original data. The
original data is modelled by AR model with Laplacenoise. To secure the original data, the original data istransformed into new data using the equation 𝑦 =Fig. 2 Original data

Fig. 3 New data
Let 𝒚 = (𝒚𝟏 , … , 𝒚𝒏 ) be the new time series that is
the result of the transformation. The probability
function of 𝒚 is
𝟏 𝒏−𝒑𝒇(𝒚|𝒑, 𝝓(𝒑) , 𝜷) = (
)
𝒆𝒙𝒑
𝟐𝐚𝛃
𝒏
𝒑
𝟏
−
∑
|∑ 𝝓𝒊 𝒚𝒕−𝒊𝒂𝜷 𝒕=𝒑+𝟏
𝒊=𝟏

In this article, the noise 𝒛𝒕 ~𝑳𝒂𝒑𝒍𝒂𝒄𝒆(𝟎, 𝜷) for 𝒕 =𝟏, … , 𝒏. Therefore, the probability function of 𝒙𝒕 canbe written as𝒑𝒇(𝒙𝒕 |𝜷) =𝟏|∑ 𝝓𝒊 𝒙𝒕−𝒊 + 𝒙𝒕 |𝒆𝒙𝒑 − 𝒊=𝟏𝟐𝛃𝜷+ 𝒚𝒕 |

Time series 𝒙𝒕 is transformed to time series 𝒚𝒕 =𝒂𝒙𝒕 . Therefore, the probability function of 𝒚𝒕 is𝒇(𝒚𝒕 |𝜷) =

𝟏
𝟐𝐚𝛃

𝒑

𝒆𝒙𝒑 −

where 𝝓(𝒑) = (𝝓𝟏 , … , 𝝓𝒑 ).
AR model with order 𝒑 that satisfies the stationary
condition is very useful. However, the stationarity|∑𝒊=𝟏 𝝓𝒊 𝒚𝒕−𝒊 +𝒚𝒕 |
𝐚𝜷

.

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GEOMATE – Kyoto, Japan, 3-5 November 2021

(𝜆, 𝛽, 𝑣) Ir given the value (𝑝, 𝑟 (𝑝) ) . Second,
simulation of conditional distribution of (𝑝, 𝑟 (𝑝) ) if
given the value (𝜆, 𝛽, 𝑣) . Since the conditional
distribution of (𝜆, 𝛽, 𝑣) if given the value (𝑝, 𝑟 (𝑝) )can easily recognized, then the simulation of theconditional distribution of (𝜆, 𝛽, 𝑣) if given thevalue (𝑝, 𝑟 (𝑝) ) can be done as follows:

condition is difficult to determine when the order 𝒑is large. In order to solve this problem,
reparameterization of the AR model is required. Let
𝒓𝟏 , 𝒓𝟐 , ⋯ , 𝒓𝒑 be partial autocorrelation functions
corresponding to AR model with order 𝒑. Next, let 𝑭
be a transformation from (𝛟𝟏 , … , 𝛟𝐩 ) ∈ 𝑺𝒑 to(𝒓𝟏 , 𝒓𝟐 , ⋯ , 𝒓𝒑 ) ∈ (−𝟏, 𝟏)𝒑 where 𝑺𝒑 is thestationarity region of the AR model [10]. In the newparameter, probability function for the time series 𝒚becomes•𝜷 ∼ 𝑰𝑮 (𝒏 − 𝒑 + 𝟐, 𝒗 +𝟏

𝟏 𝒏−𝒑𝒇(𝒚|𝒑, 𝒓(𝒑) , 𝜷) = (
)
𝒆𝒙𝒑
𝟐𝐚𝛃
𝒏
𝒑
𝟏
−
∑
|∑ 𝑭−𝟏 (𝒓𝒊 )𝒚𝒕−𝒊
𝒂𝜷 𝒕=𝒑+𝟏
𝒊=𝟏••

where 𝑭−𝟏 si the inverse transformation of 𝑭.

Prior Distribution
The binomial distribution with parameters 𝑝𝑚𝑎𝑥
dan 𝜆 is chosen as the prior distribution of the orderof the AR model. While the uniform distribution on
the interval (−𝟏, 𝟏)𝒑 is chosen as the priordistribution of 𝒓(𝒑) . And, the distribution 𝑰𝑮(𝟐, 𝝂) is
chosen as the prior distribution of 𝜷. In the priordistribution, there are parameters 𝜆 and 𝑣 . The
uniform distribution on the interval (0,1) is chosen
as the prior distribution of 𝜆 . And, the Jeffreys
distribution is chosen as the prior distribution of 𝜈.
Finally, the joint prior distribution of all parameters
can be written as

1 𝜈2

2

𝛽3

𝑒𝑥𝑝 −

𝑣1
𝛽𝜈

.

Posterior Distribution
Using Bayes’ Theorem, the posterior
distribution of (𝑝, 𝑟 (𝑝) , 𝜆, 𝛽, 𝑣) can be expressed as1𝜋(𝑝, 𝑟 (𝑝) , 𝜆, 𝛽, 𝑣|𝑥) = ( )

𝑛−𝑝

1

𝑛−𝑝+3

( )

𝑒𝑥𝑝 −

2𝑎
β
∑𝑛𝑡=𝑝+1|∑𝑝𝑖=1 𝐹 −1 (𝑟𝑖 )𝑦𝑡−𝑖 + 𝑦𝑡 | 𝐶𝑝𝑝𝑚𝑎𝑥 𝜆𝑝 (1 −
𝑎𝛽
𝑣
𝑝
−𝑝 1
1

𝜆) 𝑚𝑎𝑥2𝑝

𝝀 ∼ 𝑩(𝒑 + 𝟏, 𝒑𝒎𝒂𝒙 − 𝒑 + 𝟏), and𝟏𝝊 ∼ 𝑮(𝟐, ).
𝜷The results of the study were in accordance withwhat was expected. Data modeling using AR modeltransformation so that the confidentiality of theoriginal data is protected when the research results
are published. The data used in the publication ofresearch results is new data which is transformation
data. The model for the original data is obtained byapplying the inverse transformation.
If the results of this study are compared with theresults of research in [2] then this study has
similarities and differences. The similarity between
the results of this study and the results of research in[2] is that both use the same prior distribution. But
the difference between the results of this study andthe results of research in Suparman is that this study
uses Laplace noise while research in [2] uses
Gaussian noise. If the results of the research in thisarticle are compared with the results of research in[6] then this study also has similarities anddifferences. The equation is that they both use thesame prior and noise distribution. However, thedifference is that the results of research in [6] are a
special form in this research when thetransformation is identity transformation. In thisstudy, the transformation used is the transformation𝒚 = 𝒂𝒙. In order to increase the confidentiality ofthe original data, this study was developed using amore complicated transformation.
Research results can be applied in a wider field
because research results can not only be used infields where data is not confidential but research
results can be applied to fields where data is
confidential. The results of this study are𝜋(𝑝, 𝑟 (𝑝) , 𝜆, 𝛽, 𝑣)𝑝∑𝒏𝒕=𝒑+𝟏|∑𝒑𝒊=𝟏 𝑭−𝟏 (𝒓𝒊 )𝒚𝒕−𝒊 + 𝒚𝒕 |),However, the conditional distribution of (𝒑, 𝒓(𝒑) ) if
given the value (𝝀, 𝜷, 𝒗) has a complex form, thenthe simulation of the conditional distribution of(𝒑, 𝒓(𝒑) ) if given the value (𝝀, 𝜷, 𝒗) is simulatedwith the reversible jump MCMC algorithm. For thispurpose, the reversible jump MCMC algorithm uses
3 types of transformations, namely: changes to thecoefficient of the AR model, birth of order of the AR
model, death of order of the AR model [6].

+ 𝒚𝒕 |= 𝐶𝑝 𝑚𝑎𝑥 𝜆𝑝 (1 − 𝜆)𝑝𝑚𝑎𝑥−𝑝 𝑝𝒂𝜈 𝑒𝑥𝑝 − .
𝛽Reversible Jump MCMC Algorithm
The posterior distribution is simulated in 2stages. First, simulate the conditional distribution of623

GEOMATE – Kyoto, Japan, 3-5 November 2021

recommended for modeling time series data that isconfidential. So that when the research results are
published, the confidentiality of the original data is
protected.

the research can be carried out. Furthermore, theauthors also thank the reviewers of the 11th
International
Conferenceon
Geotechnique,
Construction Materials and Environment in Kyoto,
Japan, for their comments and suggestions toimprove the quality of this article.

Model Validation

REFERENCES

The model generated by the reversible jump
MCMC algorithm is validated through a simulation
study. First, the values of the model parameters are
set. Then, simulated data is created based on thevalue of this model parameter. Furthermore, thereversible jump MCMC algorithm is implemented
on the simulated data to estimate the value of themodel parameters. Finally, the estimators of themodel parameters are compared with the predefinedvalues of the model parameters. The model is said tobe correct if the estimator of the model parameters is
close to the value of the model parameters that havebeen set previously.

[1]

[2]

[3]

CONCLUSIONSThis article examines the AR model estimation
procedure with Laplace noise to ensure theconfidentiality of the data so that it is not accessedby other parties. This article uses the transform
method to hide the original data. This article focuseson the transformation of the linear through the centerof the coordinates. This article has not investigated
the use of non-linear transformations, so this topic isinteresting to be studied further. This article
provides benefits on data security. This article has
two advantages. The first advantage is the use oftransformations to ensure data confidentiality. The
second advantage is that the order of the AR modelis treated as a parameter and the order of the modelis estimated using data so that the AR model used ismore accurate than if the order is determinedsubjectively. Research results can be applied to awider field because research results can not only be
used to model non-confidential data, but research
can also be applied to model confidential data. For
data that is confidential, it is recommended to use
data modeling that protects data confidentiality.

[4]

[5]

[6]

[7]

[8]

[9]

ACKNOWLEDGMENTS
The authors would like to thank the Directorateof Research and Community Service (DRPM),
Ministry of Education, Culture, Research andTechnology (Indonesia) for the basic research
scheme research grant that has been provided so that

[10]

624

G. E. P. Box, G. M. Jenkins, G. C. Reinsel,
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Forecasting and Control. John Wiley &Sons, 2016.
Suparman and M. S. Rusiman, “Hierarchical
Bayesian
Estimationfor
Stationary
Autoregressive Models Using Reversible
Jump MCMC Algorithm,” Int. J. Eng.
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www.sciencepubco.com/index.php/IJET.
J. Jureckova, H. L. Koul, and J. Picek,“Testing the tail index in autoregressivemodels,” Ann Inst Stat Math, vol. 61, pp.
579–598, 2009.
L. Larbi and H. Fellag, “Robust Bayesian
Analysis of an Autoregressive Model with
Exponential Innovations,” Afr. Stat., vol. 11,
no. 1, pp. 955–964, 2016.
Suparman, “A new estimation procedureusing a reversible jump MCMC algorithm
for ar models of exponential white noise,”Int. J. GEOMATE, vol. 15, no. 49, pp. 85–
91, 2018, doi: 10.21660/2018.49.3622.
Suparman and A. M. Diponegoro,“Mathematical
Model
with
Laplace
Autoregressive Process for Heart Rate
Signals,” 2020.
D. K. Lee, “Data transformation: A focus on
the interpretation,” Korean J. Anesthesiol.,vol. 73, no. 6, pp. 503–508, 2020, doi:
10.4097/kja.20137.
D. D. Wackerly, I. Mendenhall, and R. L.
Scheaffer, Mathematical Statistics with
Applications. Thomson Higher Education,
2008.
P. J. Green, “Reversible jump Markov chain
Monte Carlo computation and Bayesianmodel determination,” Biometrika, vol. 82,
no. 4, pp. 711–743, 1995.
O. Barndorff-Nielsen and G. Schou, “On the
Parametrization of Autoregressive models
by partial autoccorelation,” J. Multivar.
Anal., vol. 3, pp. 408–419, 1973.

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

A COMPARATIVE STUDY ON THE CHANGE OF TOWNSCAPE
IN HISTORICAL BLOCKS BETWEEN XI’AN AND KYOTO
Kun YUAN1, Hirokazu ABE2, Kensuke YASUFUKU2,
Akira TAKAHASHI2
1Dept. of Global Architecture, Osaka University, Japan; 2 Cybermedia Center, Osaka University, Japan

ABSTRACTThis paper uses space syntax analysis to analyze similarities and differences in the urban spatial structurechanges of two of the world’s historical and cultural cities during the process of modernization. Axial line mapsof the main urban areas of Xi'an, China, and Kyoto, Japan, are compared from the start of their respective
periods of modernization to the present day. Changes in the cities’ topological centers are analyzed, and theglobal and local relationships in their main urban areas are explored. The primary outcomes of the study are as
follows:
1. With the expansion of the urban area, the topological centers of the two main urban areas also changed.
From 1902 to 2020, the topological centers of Kyoto expanded outward simultaneously in both the horizontal
and vertical directions. Xi'an also expanded, from one topological center centered on the bell tower in 1949 to
two topological centers in 2019. In both cities, the global integration value (hereafter, Int.V global) grew(especially in Kyoto) during the period of modernization and long-distance accessibility increased, indicatingthat the two cities were heading toward motorization.
2. The average Int.V global change rates in the local historical blocks of the old Xi'an city show differencesin the growth rates of local change and the global growth rate of urban space, which indicate the local
development feature of block evolution is evident. The Int.V global change rates in the three wards of Kyoto are
not significantly different from the global change rate, indicating a consistent change in global integration for
Kyoto and a greater consistency of global street construction.
3. The correlation of global integration and local integration, as represented by intelligibility (R2), increased
in both Xi’an and Kyoto, indicating that the solidarity of the respective regions increased during themodernization process. This is especially true for Kyoto.
Keywords: Capital Cities, Morphological Change, Space Syntax, Xi’an, Kyoto
INTRODUCTION

dramatically to the East, South and West from theold city, growing from 13.2 to 724 square kilometers;
during this period, the population has also increased,from 2.27 to 10.2 million [1]. With the rapid
expansion and development of the city area, theform and structure of the main urban area within thecity wall has changed significantly, and a large
number of historical alleys have been rebuilt whileothers have disappeared in the historic blocks of themain city.
Japan began its modernization construction in
the Meiji period (1868-1912), during which time the
new Japanese government began to restore Kyoto[3]. A number of urban planning measures werecarried out, widening streets and creating a ring road，
all of which had the effect of accelerating the city’smodernization. In the Showa period (1926-1989),
the city merged with surrounding towns, rapidly andsignificantly expanding its urban area from 31 to
210 square kilometers. The population increased
from 0.28 million in the Meiji period to 1.41 million[4], [5]. The city’s urban development is still mainly
characterized by grid planning, and the main urban

Xi'an, China, and Kyoto, Japan, are ancient
cultural centers whose main urban areas havedeveloped around a chessboard network of roads. In
terms of their morphology, the two have similarities,
but they also have had their own distinct
development features over the course of theirmodernization.
The Ming City District is the core area of Xi'an,
which was built on the basis of the imperial city of
the Sui and Tang Dynasties during the Hongwu
Period of the Ming Dynasty, and which has retained
many of its original areas [1]. The Ming City
District is the center of Xi'an’s urban humansettlements, especially in its southwest area’s
remaining historic and cultural block of Beiyuan
Gate and Sanxue Street, which inherited the best
historical characteristics of the region and streets.
With the founding of the People's Republic of China(1949), China began its modernization construction.
[2] Since that time, Xi'an has experienced four
master plans, and its urban areas have expanded

625

GEOMATE – Kyoto, Japan, 3-5 November 2021

area is dominated by Kamigyo Ward, Nakagyo
Ward and Shimogyo Ward. As in Xi’an, themodernization construction in Kyoto has greatlychanged the urban area and street morphology of thecity through urban planning.
Previous studies of the urban morphology of
the two cities include a study by Onishi [2], who
conducted a detailed investigation of the changes inthe townscape of Xi’an’s historic center andcompared them to developments in Kyoto. Kang [6]studied the spatial morphology characteristics ofXi’an’s Historic District using traditional spatial
morphology analysis, mostly in terms of spatiallayout, texture, street scale and plane form.
Compared to investigative analyses that focus on
traditional morphological characteristics, more
recent spatial morphology analysis is mainly carriedout using quantitative methods and is mainly
focused on space syntax with a GIS data platform.
In an empirical and quantitative study of large-scaleurban streets and roads in three separate periods,
Huang [7] investigated the complex evolution of themorphological characteristics of the typical urban
Hui culture from the morphological middle, street
structures and network systems perspective, andexamined the shift in the topological relationshipbetween historic block space and urban whole space;however, there are few comparative studies between
similar blocks.
Space syntax is important for extracting the
topological structures of a city, particularly whencomparing two or more cities. Kigawa [3] conducted
an analysis of Kyoto’s spatial configurations
throughout the ages and its urban planning projectbased on space syntax, revealing that streets had astrong impact on Kyoto’s transformation from its
early modern to modern form. Kigawa [8] also used
space syntax to analyze the process by which thecastle cities had been reformed. In studying theprocess of urban modernization in several of thecastle cities of Japan, he found distinct differencesamong the cities. In China, a comparative study ofurban space based on space syntax was conductedby Wang et al. [9], who selected cities with wide
differences in road networks and land configurations.
Analyzing the axial line, the study exploreddifferences and commonalities. Such studies help us
to understand core area aggregation and urban
morphology
evolution
inertia.
However,comparative studies involving cities in two different
countries that have a similar space structure initially
but which later experience different development
patterns are rare.
This paper compares the urban spatial structurechanges in Xi'an and Kyoto in their respective
modern city construction. In the process, we not
only make a horizontal comparison of similar cities
in different development stages, but also provide a
vertical analysis of the relationship between theirglobal and local aspects. Using a comprehensive
quantitative analysis of the current urban
development
situationanditsdistinctive
characteristics, we compare axis maps of the main
urban areas of Xi'an and Kyoto in different years,
and analyze the changes in their topological centers
as well as the global and local relationship in theirmain urban areas.

Fig. 1 Map of Xi’an (2019)

1902

Fig. 2 Map of Kyoto (2020)
METHODSpace syntax is a set of theories and analytical
tools introduced in “The Social Logic of Space” by
Hillier and Hanson in 1984 [10]. Topologicalanalysis focuses on the connection between spaces.
We examined the street network of thehistorical capital districts in two steps:
First, we use an axial analysis to investigate thestreet network, then compare the axis maps of Xi'an
and Kyoto in different years, tracing the changes intheir topological centers. (The axial map isconstructed by taking an accurate map and drawing
a set of intersecting lines through all the spaces of
the urban grid so that the grid is covered and allrings of circulation are completed.)
Because the urban morphologies of the two626GEOMATE – Kyoto, Japan, 3-5 November 2021

cities have undergone dramatic changes in modern
times, we chose the beginning period of themodernization construction of the two cities as thecontrastive object. The starting point examined inthis study for Xi’an is the founding of the new China
in 1949; the starting point for Kyoto is the year of
the earliest measured map (1902) in the Meiji periodof the new Japanese government.
The study area for Xi’an focuses on the main
urban areas that served as the Ming castle city (13.2
km2). The main study area for Kyoto (21.2 km2)
consists of three districts (Kamigyo Ward, Nakagyo
Ward and Shimogyo Ward). With respect to Xi’an,the key objects used in the study include four mapsof narrow streets in the old city 1949 road network,
together with a remote sensing image of Xi'an in
2019. In the case of Kyoto, the key objects are afield survey map from 1902 and a cadastral map
from 2020.
In order to analyze the configuration layout ofeach city, we translated the actual spatial structure
into an axial map (Fig. 3), which is the least set of
longest lines drawn tangent to vertices that can see
each other. For our topological analysis, the axial
map was projected onto a justified graph in which
the axial lines are represented as nodes, and theintersecting lines are the connections between thenodes [10].
Fig. 3 shows a justified graph that has been
converted from an axial map. A justified graph is
one in which a node is drawn at the base, and all the
points of various depths are drawn above it.
Specifically, all the points at depth 1 from thestarting point are aligned horizontally immediately
above it, all the points at depth 2 from that point are
drawn above those at depth 1, and so on, until all
levels of depth from the initial point are accountedfor. The topological total depth and mean depth canthen be determined from the justified graph.
A depth value represents the convenience ofmoving from one space to another. The topologicaldistance between two adjacent nodes is one step.
The shortest topological distance between any two
nodes—thatis,thenumberofspatialtransformations—is expressed as the depth value
between the two nodes. The larger the value, thedeeper the element is hidden in the network of thestudy area.
Topological total depth (TD) is the cumulative
total of the fewest topological depth paths between
all pairs of nodes. MD (mean depth) is the averageof the minimum number of steps from one node to
all the other nodes in the system, which eliminates
the effect of numbers.
Next, integration is introduced as an importantresult of the axis analysis. Its value is derived fromthe value of the topological depth, as in Fig. 4. By
analyzing the integration value, it is possible todetermine the degree of a street and how

topologically shallow or deep the entire system isfrom a point on the street.
An integration value essentially describes the"accessibility" of an element in the study area
network. The higher the depth value, the lower is theFig. 3

Axial analysis [11]Fig. 4 Flow Chart from Depth to Integration [10]n: total number of vertices
degree of integration, that is, the worse is theconnectivity, and vice versa. More specifically, axial
local integration is defined as the integration valuesof the axial lines at radius 3 (root plus two
topological steps from the root) which can be used torepresent a localized picture of integration. Axial
global integration is defined as the integration valuesof axial lines at an infinite radius which can be used
to represent the integration pattern at the largest
scale.
Finally, the integration core can be used torepresent the change and transfer of the topologicalcenter, consisting of the most integrating 10% of thelines [10,12].
In the second step, the results of the topologicalanalysis conducted in the previous step are used toanalyze the global and local relations of therespective main urban areas.
Axial intelligibility indexes the degree to which
the number of immediate connections that a line has
is a reliable guide to the importance of that line in
the system as a whole. (Specifically, it is thecorrelation between local and global elements). Thus,
intelligibility is introduced as a way to describe the627

GEOMATE – Kyoto, Japan, 3-5 November 2021

correlation between local integration and global
integration and as the means to measure whether the
local space and the global space environment are
related and unified. This relationship indicates
whether an area is intelligible in its global context[13, 14].

 I  I I  I  I  I   I  I 

which are lower than the 1.91 global average.
However, the percentage change for the Beiyuan
Gate block (18%) is greater than the global average(12%), while the percentage change for the Sanxue
Street Block (10%) is slightly lower. The indicationis that the historical blocks in the main city area
experienced a high degree of local change withurban global space development. The local
development in the evolution of the historic blocks
is clear.

2Intelligibility:

R2 

3

3

3

3
2

n

n

2

n

(1)

n

The value of intelligibility (R2) is calculated by
Equation 1, where In is the global integration of any
axis in space, and I3 is the three-step integration
value of any axis in space. I n and I 3 are the meanvalues of global integration and three-step
integration, respectively.
For Eq. (1), if the intelligibility of the local
space is high, the indication is that the centrality of
local space can be integrated into the global spatial
structure. The natural movement of local space can
not only bring movement economy; it can also
produce a greater multiplier effect in the process offunctional interaction with the movement economy
of global space. On the other hand, if theintelligibility of local space is low, the indication isthat the local centrality of the regional urban spacecannot be integrated with the global space and isthus relatively isolated [12,15].

1949

RESULTS AND DISCUSSION

Changes in the main urban areas of Xi'an andKyoto
1) Main urban area of Xi’an
The two axial maps of Xi’an (1949 and 2019)shown in Fig. 5, allow us to examine the change inthe urban topological center. With the developmentof lattice sub-trunk road planning, many narrow
alleys were expanded and new roads throughout the
castle district were constructed. The global
integration value (Int.V global) of Lianhu Road, East
5th Road and West 5th Road to the north of thecentral axis, Jiefang Road and Heping Road to theeast of the Central Axis is high. This suggests thatthe topological center of the street network has
extended to the north side roads of the main roadcentered on the bell tower.
The remaining historical and cultural blocks of
Beiyuan Gate and Sanxue Street in the western andsouthern areas of Ming city, with low Int.V global
values, were far from the topological center of themain cross road centered on the bell tower and thenortheastern part of the castle city in 1949. The
average global integration values of the two
historical blocks are 1.56 and 1.58, respectively,
which is lower than the average global value of 1.71
in 1949. By 2019, the average global integration
value had increased from 1.71 to 1.91, over whichtime the values for the two historical blocks
increased to 1.88 and 1.74, respectively, both of

2019

Fig. 5 Axial Maps with Global int.V for Xi’an in1949 and 20192) Main urban area of Kyoto
Fig. 6 shows the 1902 and 2020 axial maps
with Int.V global for Kyoto. The streets with highintegration values based on the results of the axial
analysis are more darkly shaded.
In 1902, the highest integration values are for
Karasuma Street, Horikawa Street and San-Jyo
Street, which constitute the urban topological center,as seen in the two maps. The highest vertical axial
global Int.V extended outward and westward to
Nishiohji street in 2020. The highest horizontal axial
global Int.V expanded from San-Jyo Street to shiJyo Street. High connectivity is also seen for Go-Jyo
Street and Shichi-Jyo Street. The topological center
extended outward in both the horizontal and verticaldirections simultaneously. The average global
integration values of the three Kyoto wards
increased from 1.38, 1.62 and 1.54, respectively, in
1902 to 2.18, 2.46 and 2.37 in 2020, corresponding
to respective change rates of 55.7%, 51.8% and53.9%. The fact that the change rates are not628

GEOMATE – Kyoto, Japan, 3-5 November 2021

Scatter diagram analysis

substantially different from the global change rate
(53.6%) indicates a strong consistency in global
street construction.

Scatter diagrams for Xi’an showing Int.V
global on the horizontal axis and Int.V local on
the vertical axis are provided in Fig. 7. It isapparent that the distribution shapes of the scatter
points in 1949 and 2019 show little similarity. In
1949, Int.V global was relatively low, and thescatter points are concentrated in the lower left
portion of the diagram. In 2019, the concentrationof the scatter points has shifted toward the middle,
and the average Int.V global has increased,
indicating that the Int.V global of Xi'an’s axial
line became stronger in the process of the city’smodern urban spatial development.
Fig. 8 gives the Int.V (global and local)
scatter diagrams for Kyoto. As shown, the centerof the dense area of the scatter points moves
rightward over the period of 1902 to 2020. The
average value of Int.V global increased markedly
as the global integration value of most of thescatter points was greater than 1.0. Such results
indicate that Int.V global grew substantiallystronger and that long-distance accessibility
increased during the modernization period.

1902

2020

Fig. 6

Global integration values for Kyoto in1902 and 2020

As can be seen here, the topological centers ofboth cities experienced an outward expansion. In themain urban area of Kyoto, the consistency of street
construction is stronger and the change in global
accessibility is more obvious. In Xi’an, the globalchange in the main city is not nearly as clear. While
Xi’an’s main urban area has been strongly affectedby the urban expansion outside of the castle city, theresidential areas inside the city wall also haveobvious characteristics of local development. The
external driving force of urban global spacedevelopment and the internal traction of block
evolution appear to have interacted with each otherto jointly promote the deepening development of thestructure of the historic blocks.

Fig. 7 Int.V (global and local) scatter diagramsfor Xi’an in 1949 and 2019
Table 1 gives key parameters of the scatterplots in Fig. 7 and Fig. 8. Of particular note is theslope of the regression line fitted to the Xi’an (Fig. 7)
scatter points, which decreases from 1.905 in 1949
to 1.725 in 2019. The slope of the regression line forthe Kyoto scatter diagrams (Fig. 8) also decreases,

629

GEOMATE – Kyoto, Japan, 3-5 November 2021

from 1.868 in 1902 to 1.454 in 2020. The fact thatthe slopes decrease means that the scatter points
move rightward (i.e., Int.V global becomes stronger).
In general, urban networks with a higher Int.V
global are better suited for long-range accessibility,
since Int.V global is calculated with up to all
topological depths in Depthmap, and is closely
related to motor vehicle traffic. In contrast, urban
networks with a higher Int.V local are better suited
for short-range accessibility related to pedestrian
traffic. From the basic of these, it can be inferredthat the modernization of the two cities was heading
toward motorization.

local centrality of the regional urban space cannot be
integrated with the global space and is thus
relatively isolated [15]. The value of intelligibility(R2) increases in both Xi’an and Kyoto, meaningthat the solidarity of the respective regions has
increased during the modernization process.
CONCLUSIONSThis paper sought to uncover similarities anddifferences in the evolution of the historical cities of
Xi’an and Kyoto by using space syntax. Throughaxis analysis of the two main urban areas in different
years, the topological centers and the global andlocal relations of the main urban areas werecompared. The analysis produced three important
findings:
1. With the expansion of the urban area, thetopological centers changed correspondingly. The
topological center in the main urban area of Kyoto
clearly expanded outward from 1902 to 2020 inboth the horizontal and vertical directions
simultaneously. In Xi'an, the area expanded from
one topological center centered on the bell tower in1949 to two topological centers in 2019. Int.V global
increased markedly for the two cities (especially for
Kyoto), and long-distance accessibility increased
during the period of modernization. The implicationis that the modernization of the two cities washeading toward motorization.
2. The average Int.V global change rates of the local
historical blocks in the old Xi'an city show adifference in the local change and the global growthof its urban space, and make evident the local
development feature of block evolution. On theother hand, the Int.V global change rates for thethree wards in Kyoto are similar to the global change
rate, indicating a consistent change in global
integration and a strong consistency of global street
construction.
3. The global and local integration correlation of thetwo major urban areas is reinforced by thecharacteristic curve and index analysis, with the two
exhibiting strong similarity. The intelligibility (R2)value increases in both Xi’an and Kyoto, meaningthat the solidarity of the respective regions has
increased during the modernization process. In thisregard, Kyoto is clearly stronger than Xi'an.

Fig. 8 Int.V (global and local) scatter diagramsfor Kyoto in 1902 and 2020
Table1 Parameter comparison of Fig. 7 and Fig. 8

The intelligibility (R2) value for Xi’an
increased from 0.8229 (1949) to 0.8509 (2019),
while the value for Kyoto increased from 0.8054(1902) to 0.9319 (2020). Hillier suggests that
intelligibility is used to describe the correlation
between local integration and global integration andmeasures whether the local space and the global
space environment are related and unified. This
relationship presents if an area is intelligible in its
global context [13].
Hillier also suggests that if the intelligibility
value of local space is low, the implication is that the

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11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

MID-INFRARED SPECTRA OF CHLORIDE IONIMMOBILIZED ON CEMENT
Yu Hashimoto1, Shinichiro Okazaki2, Kenji Wada3, Ichiro Ishimaru4 and Koji Kanasaki5
1Graduate School of Engineering, Kagawa University, Japan;
Faculty of Engineering and Design, 3Medicine, Kagawa University, Japan;
5
Nisshin Kikai Co.,Ltd., Japan

2,4

ABSTRACTA strategy based on infrared spectroscopy is prospected to be used as a novel nondestructive inspection method
to assess the risk of chloride attack on reinforced concrete (RC) structures. The characteristics of the infrared
spectra of bound chloride ions in cement in the mid-infrared region were investigated in this study to examine theconcentration of chloride ions on the surface of the concrete using the mid-infrared spectroscopic camera. The
reflection FTIR technique was used to measure the infrared spectra of cement paste samples with varying chloride
ion concentrations. Furthermore, multivariate statistical methods were applied to the obtained mid-infrared spectra
to critically examine the wavelength bands of the peaks with characteristic values reflecting differences in chloride
ion concentrations. As a result, if we can develop an instrument that can measure around 7300 nm, the wavelengththat reflects the difference in chloride ion concentration may be around 7300 nm.
Keywords: Mid-infrared spectroscopy, infrared spectrum, Bound chloride ions in cement

INTRODUCTION

been developed [4–7] and tested, but it has been notedthat sunlight may interfere with outdoor
measurements [8].
The authors have elemental technology related toa lightweight, compact, and vibration-resistant
spectrometer (Fig. 1, hereinafter referred to as the"compact device") [9] that uses imaging 2-D Fourier
spectroscopy in the mid-infrared region, which is lessaffected by low solar radiation. The purpose of thisstudy is to investigate in detail the wavelengths thatare correlated with the concentration of chloride ion
immobilized on cement in the mid-infrared region,
assuming that this device will be used, and to obtain
information that will help with future measurements
using this device.

In recent years, various fields have made efforts
to realize a sustainable society as outlined in the
Sutainable Development Goals. For example, in civilengineering, infrastructures are being shifted frombreakdown maintenance to preventive maintenancemanagement to improve sustainability. In addition,
various nondestructive testing methods have beendeveloped in recent years and are actually inoperation in the field.
Reinforced concrete is frequently used for theconstruction of infrastructure. When the sea breeze
carries in chloride, it enters the concrete throughcracks. When this chloride reaches the steel bars,
corrosion begins, and the structure's life is cut short.
To assess the degree of chloride deterioration ofreinforced concrete, the chloride ion concentration at
the location of the reinforcing bar must be measured,and a formula has been proposed to estimate theinternal chloride ion concentration by measuring theconcentration on the concrete surface [1][2].
With the establishment of the formula for
calculating the internal chloride ion concentration, it
became possible to predict the concentration at the
bar's location by measuring the surface chloride ion
concentration. Thus, focusing on measuring theconcentration on the concrete surface, a method for
estimating the concentration using near-infrared
spectroscopy was proposed [3]. As a result of thisstudy, it was found that there is a peak in the nearinfrared spectrum around 2,266 nm that correlateswith chloride ion concentration [3]. Based on thesefindings, various near-infrared spectrometers have

Device Body

Lense

Connection Part to PC

Fig.1
Spectrometerusing
imaging
twodimensional Fourier spectroscopy (Compact Device)
INFRARED SPECTROSCOPY
Infrared spectroscopy is a spectroscopic method
based on absorption, diffuse reflection, and emissionof light in the infrared wavelength range. When amaterial is irradiated with infrared light, themolecules that make up the material absorb the lightenergy, causing a state transition in molecular motion.
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GEOMATE – Kyoto, Japan, 3-5 November 2021

The energy of the infrared light transmitted orreflected from the material is smaller than that of theirradiated infrared light used for the state transition of
the molecular motion. This energy is calledabsorbance, and it is excellent for identifying
chemical substances because each molecular
structure exhibits a unique waveform. The advantagesof this measurement method include the fact that it isnondestructive and non-contact and that nopretreatment is required because no chemicals areneeded. In addition, the measurement time is short,which makes it easy even when the number of test
pieces is large.

time of measurement often cause the entire absorption
spectrum to rise and fall. This is called baseline shift,and it makes it challenging to detect apparent
differences. Therefore, Standard Normal Variate(SNV) is often used [10]. Let y (v, p) be the datasetof acquired spectral y(v). If the spectral variable in thep-th spectral is v, the mean value of the spectral is𝑦̅(p), and the standard deviation is σ(p), then SNV isexpressed as

EXPERIMENTAL METHOD

Since this process unifies the baseline of all spectral
data and makes it easier to detect apparent differences,the processed data were used in the analysis.

𝒚𝑺𝑵𝑽 (𝒗, 𝒑) =Overview of Device and Measurement Methods
In this experiment, the powdered samples were
measured using Fourier Transform Infrared
Spectrometer (hereafter, this is called "FTIR"). In thisstudy, the diffuse reflection method was used for themeasurement. To examine the results in more detail,the number of integrations was 64 times. The FTIR4200 type A spectrometer manufactured by JASCO
Co. was used for the measurement.

̅(𝒑)𝒚(𝒗,𝒑)−𝒚𝝈(𝒑)(1)Analysis Methods
Multivariate analysis is used to detect apparent
differences, particularly, principal component
regression and partial least squares regression
analyses are often used in infrared spectroscopy [10].
In this study, these two types of analysis methods
were used. The analysis was performed in the rangeof 5000-17000 nm, including the Compact Device's
wavelength range. The Unscrambler X from Camo
Analytics Co. was used for the analysis.

Samples
Ordinary Portland cement was used to prepare thesamples, and the weight ratio of water to cement(W/C) was 50%. Two samples were prepared, one for
FTIR and the other for later measurement by theCompact Device. The samples were placed in asample form to a height of 5cm, cured for 24 hours,
and then de-molded and sealed for 21 days. After
curing for 24 hours, the sample was unmolded andsealed and cured for 21 days. The reason for using a
5cm sample was to facilitate measurement with theCompact Device. After the curing was completed, thesamples were ground into powder using a metal
mortar for FTIR analysis Although it has been
confirmed that the mid-infrared region is affected bymoisture, we did not completely dry the powder
because it might change the physical properties.

Principal component regression analysis (PCR)
In PCR, the amount of chloride is calculated fromspectral data X using the coefficient of principal
component T, which is estimated to maximize thevariance of X by an information compression method
known as principal component analysis and bycreating a regression equation using least-squares
regression. The following equation expresses themodel equation for principal component analysis withthe weight P assigned to each wavelength:
𝑋 = 𝑇𝑃𝑡(2)

The superscript t represents the transpose matrix.
For least-squares regression, the target variable isonly the amount of chloride, so it is a simple
regression. If the amount of chloride is y, theregression coefficient is q, the initial value is f; leastsquares regression is expressed by the following
equation, which is calculated to estimate the amountof chloride.

ANALYSIS
Spectral Processing
To improve the analytical accuracy of
instrumental analysis, it is necessary to remove
disturbances other than the actual information that
inevitably enters the analysis as much as possible. In
infrared spectroscopy, environmental factors such as
fluctuations in the light source and humidity at the𝑦 = 𝑇𝑞 + 𝑓
633

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GEOMATE – Kyoto, Japan, 3-5 November 2021

Partial least squares regression analysis (PLSR)ANALYSIS RESULTSIn PLSR, the principal component T is calculated
to optimize the covariance between the spectral data
X and the amount of chloride y. Thus, the model
equation is E for the residual of X, q for thecoefficient of the principal component, and f for the
residual of y.

PLSR and PCR generated the regression
equations, and the mean squared errors of thepredictions of chloride ion concentrations werecompared. The PLSR results showed a smaller meansquare error than the PCR results, and the distinctive
wavelengths were identified from the PLSR results.
First, comparing the predicted values based on the
regression equation and the initial chloride ion
concentration shows that the prediction is highly
accurate (Fig.4). In addition, the factor contribution
rate of this regression equation exceeds 70% at thethird principal component (Fig.5).

𝑋 = 𝑇𝑃𝑡 + 𝐸
𝑦 = 𝑇𝑞 + 𝑓

(4)
(5)

The principal component T is calculated from Eq.
(4) and Eq. (5). The chloride content is estimatedfrom Eq. (5)MEASUREMENT RESULTS
In the FTIR measurement results, each spectrum
is shifted up and down, making it challenging todetect characteristic differences (Fig.2). However, byusing SNV, the baseline is aligned, making it easierto see the characteristic differences in the spectra(Fig.3).

Fig.4
Comparison of standard and predictedchloride ion concentrations

Fig.2

Original spectral data acquired by FTIR

Fig.5
Cumulative
principal components

contribution

ratio

of

From Fig.3, it can be said that there is acharacteristic difference in the wavelengths withlarge absolute values of loading among the first to
third principal components. The wavelengths withparticularly large absolute values are 7300, 8800,
9200, 10700, 15000, and 16300 nm (Fig.6, 7, and 8).
Fig.3 Spectral data acquired by FTIR after
SNV processing.

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GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig.6

Fig.8

Loading of the first principal component

Loading of the third principal component

DISCUSSION OF ANALYSIS RESULTS

Fig.7
Loadingcomponent

Fig.9

of

the

secondBy comparing the spectra of the analysis results
and the spectra after SNV processing, we will
examine in detail where the wavelengths that reflect
the differences in chloride ion concentrations arelocated. First, from the magnified images of the
wavelengths with the largest absolute values, theabsorbance of only the area around 7300 nm isarranged in order of concentration (Fig.9).
Next, the absorbance at 7339 nm, where theabsolute value of loading was huge among the 7300
nm region, is negatively proportional to the chloride
ion concentration (Fig.10).This suggests that 7339
nm is a characteristic wavelength that reflects thedifference in chloride ion concentration.

principal

Absorbance near the wavelength where the absolute value of loading was large
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GEOMATE – Kyoto, Japan, 3-5 November 2021

absorbance was not ordered by chloride ion
concentration. (Fig.13); therefore, we could not detect
a clear difference in absorbance.

Fig.10Relationship between absorbance andchloride ion concentration at 7339nm
MEASUREMENTDEVICE

BY

THE

COMPACT
Fig.12 Spectral data acquired by the Compact Device
after SNV processing

Experimental Method
A light source, a sample mounting apparatus,
and the Compact Device were used for themeasurements. The light source was a halogen lamp
manufactured by HawkEye Technologies Co. The
positional relationship is shown in Fig.11.

Fig.13 Spectral data around 7300 nm after SNV
processing acquired by the Compact Device.
CONCLUSION

Fig.11
A scene of measurement using theCompact Device

To develop and propose a chloride ion
concentration detection system using mid-infrared
spectroscopy as a new method for investigating
chloride induced damage to RC structures, this study
reports on the infrared spectra of cement paste withsequentially changing chloride concentrations using
mid-infrared spectroscopy.
As a result of FTIR measurement andmultivariate analysis, it was confirmed that the
wavelength reflecting the difference of chloride ion
concentration existed around 7300nm. At 7339 nm,
the correlation between chloride ion concentration
and absorbance was strong. However, as a result of
measurement using the Compact Device, it was
impossible to measure around 7300 nm accurately,
and the difference in chloride ion concentration could
not be determined. In the future, it will be necessary
to develop a compact device that can accurately
measure around 7300 nm.

The first step in the measurement procedure is toplace an unwanted sample on the sample mounting
apparatus and check and adjust whether sufficientluminance can be measured with the Compact Device.
Once adequate illumination is obtained, the sample
and gold plate are replaced to obtain a reference. Then,the measurement is carried out while replacing eachsample. When setting up the samples, themeasurement was carried out carefully so that thesurface to be measured would not shift back and forth.
Measurement Results
The measurement range of the Compact Device
is 8000-14000 nm, and the area around 7300 nm isbarely measurable. The wavelength 8000-14000 nm
showed a waveform similar to that of FTIR (Fig.12),
but the area around 7300 nm was quite noisy and the636

GEOMATE – Kyoto, Japan, 3-5 November 2021

REFERENCES

Water and Chlorine Content in Cement Paste
using Near-infrared Spectroscopy, Construction
and Building Materials, 196(30), 2019, pp.95-104[7] K. Wada, S. Okazaki, and I. Ishimaru,
Visualization of Chemical Composition and
Chloride Ion Distribution by Novel NIR Imaging
Spectroscopic System, Journal of the Imaging
Society of Japan, 58(6), 2019, pp.664-650[8] Y. Sakakihara, Y. Kabeyama, K. Hamada, S.
Okazaki, K. Kanasaki, K Wada, I. Ishimaru and
T. Saito, Development of Two-dimensional
Imaging System of Surface Chloride Ion ofConcrete Structure, Journal of Japan Society of
Civil Engineers, Ser. B3 (Ocean Engineering),
75(2), 2019, pp. I_157-I_162[9] N. Yamamoto, T. Saito, S. Ogawa, and I. Ishimaru,
Middle Infrared (wavelength range: 8 𝜇m-14 𝜇m)
2-dimensional Spectroscopy (total weight with
Electrical Controller: 1.7 kg, total cost: less than
10,000 USD) so-called hyperspectral camera forunmanned air vehicles like drones, Proc. SPIE
9840, Algorithms and Technologies forMultispectral, Hyperspectral, and Ultraspectral
Imagery XXII, 2016[10] K. Miyamoto, Near-Infrared Spectroscopy III.
Quantitative
Analysisby
Near-Infrared
Spectroscopy, Journal of the spectroscopicalresearch of Japan, 53(3), pp. 192-203[1] T. Saeki, T. Shima, and S. Nagataki, Prediction of
Chloride Penetration into Concrete by Equivalent
Diffusion Coefficient, Proceedings of the Japan
Concrete Institute, 20(2), 1998 pp. 859-864,[2] G.K. Glass, and N.R. Buenfeld, The influence ofchloride binding on the chloride-induced
corrosion risk in reinforced concrete, Corrosion
Science 42, 2000, pp. 329-344[3] Y. Ishikawa, H. Kanada, Y. Kato, and T. Uomoto,Development of quantitative analysis of chloride
content in concrete using near-infrared method,
Monthly journal of the Institute of Industrial
Science, University of Tokyo, 58(3), 2006, pp.
281-284[4] Y. Nakajima, Y. Shirane, S. Suenaga, I. Hanya,
and R. Sakimura, A Development on the
Inspection System for Chloride Ion Content on theConcrete Surface Using Near-Infrared Spectrum
and Its Application to the Maintenance of the RC
Structures, Report of technical research institute,
Maeda Corporation, 57, 2016, pp.06[5] K. Toda, Y. Nakamura, and T. Kurata, System for
Diagnosing
Concrete
Deterioration
withSpectroscopic Analysis, IHI Engineering Review,45(1) 2012, pp.31-36[6] A. Watanabe, H. Furukawa, S Miyamoto, and H.
Minagawa, Nondestructive Chemical Analysis of637

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

TILLER AND RHIZOME GROWTH ON EXCAVATED SLOPESIN TWO Carex SPECIES DERIVED FROMCOLONIES ON LANDSLIDES
Teruo Arase1, Tetsuo Okano1, Tetsuoh Shirota1, Masaaki Furuno2 and Taizo Uchida3
1

Faculty of Agriculture, Shinshu University, Japan; 2Graduate School of Engineering, Kyusyu Sangyo
University, Japan; 3Faculty of Architecture and Civil Engineering, Kyusyu Sangyo University, Japan
ABSTRACTTo assist with the revegetation of excavated slopes, experimental revegetation sites were established using
two terrestrial sedges, Carex oxyandra and C. satsumensis, that typically colonize landslides in the Kiso
Mountains, Japan. The experimental design comprised a randomized block design with a two-way layout; 2
species × 2 soil types (black-colored soil and weathered granite soil) × 2 replicates. The plot size was set to 1.5 ×
0.9 m. Rootstocks of the sedges were collected and 15 rootstocks per plot (1 to 3 tillers per rootstock) weretransplanted in May 2019. The number of tillers per ramet was measured at one-month intervals. Rhizome
growth was measured by sampling two ramets from each replicate in December 2020. Based on the increase oftillers, both species were capable of growth on both soil types; C. oxyandra showed superior growth on theblack-colored soil, while C. satsumensis grew better on the weathered granite soil, and both species showed poor
growth in the plot under shaded conditions. Rhizome growth was influenced significantly by species, soil type
and their interaction; the total rhizome length per ramet in C. oxyandra and C. satsumensis was 15.9 to 28.6 cm
and 182.1 to 746.2 cm, respectively. The former species produced short rhizomes and dense tussocks fromconcentrated tillers, while the latter produced numerous extremely long rhizomes and sporadic foliage fromhorizontally scattered tillers. Since the characteristics of growth differed between the two species, using an
optimum combination of both species is considered suitable for early revegetation of excavated slopes.
Keywords: Carex oxyandra, Carex satsumensis, Tiller, Rhizome, Excavated slope

also urgently required in order to protect andstabilize susceptible areas., Revegetation projects
focusing on stabilizing these areas in Japan haveformerly employed exotic pasture grasses, but these
species are regarded as problematic at present
because their rapid and uniform growth may bedifficult to limit once established. [1]. Two
approaches can be adopted when using native plant
species for revegetation: developing technologicalimprovements to promote rapid vegetational
succession, and searching for native species withexcellent growth characteristics. In the present study,
we employed the latter approach and focused onterrestrial sedges native to Japan.
Sedges, herbaceous species in the genus Carex(Cyperaceae), are perennials with rhizomes andtussock foliage. Sedges are very familiar to Japanesepeople; the foliage of some Carex species has beenused as a traditional fiber to make straw lampshadehats, raincoats, and sacred festoons in some areas,
and variegated-leaf varieties are planted as
ornamental garden plants [4]. Of the approximately
2,000 Carex species that have been described to date,approximately 250 species are found in Japan where
they inhabit environments such as riparian areas,
wetlands, grasslands, forests, alpine areas andcoastal areas [5]. While the growth characteristics ofsedges are relatively similar to those of grasses

INTRODUCTION
Wild plant species that are indigenous to specificareas are attracting attention for revegetation
projects given the important roles they play in theconservation of biodiversity and landscape ecology[1]. While some wild plants, such as Japanese
pampas grass (Miscanthus sinensis) and Japanese
mugwort (Artemisia princeps), have already beenused for revegetation projects [1], using wild plant
species is often complicated by the lack of basic
information on their ecology, germination andgrowth, and reproduction. In addition, compared tocultivated pasture grasses, wild plants often show
considerably less uniformity in their germinationand growth patterns as they need to survive innatural areas affected by occasional disturbances. It
is therefore necessary to collate information on how
these wild plant species can be applied torevegetation projects.
The increased frequency of severe typhoons andheavy rainfall events in recent years has meant thatlandslide disasters are also increasing across Japan[2]; for example, a record of 3,459 landslidedisasters occurred in the country in 2018 [3]. Whilesaving human life and providing aid to people
adversely affected by these events is a universal
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GEOMATE – Kyoto, Japan, 3-5 November 2021

(Poaceae), most revegetation projects using sedges
have been limited to riparian areas and wetlands [1,
6–7].
Studies on Carex species have generally focusedon aspects such as taxonomy and phytogeographicaldistribution [8–9], seed dispersal [10], growth
responses to water [11–12], and vegetational
succession [13]. Within the context of practicalapplications, some aquatic and hydrarch species
have been studied for use in riparian areas andwetlands [1, 6–7], as mentioned above; however, a
recent study examined the application of sedges towater purification [5]. Most of the studies that havebeen conducted on terrestrial sedge species in Japan
have focused on their undesirable characteristics,
such as the way in which they behave like weeds
and cause nutritional deterioration of pasture [14],and how they can suppress the regeneration of tree
seedlings in natural forests [15–16].
However, some terrestrial sedge species havebeen reported to be pioneer plants that colonize
landslides in riparian areas [9] and bare ground inabandoned mine quarries [13]. The species thatcolonize disturbed areas as pioneers could
potentially play an important role in maintaining
plant community structure and in protecting the soil,
at least in the early stages of vegetational succession.
Given the broad range of habitats colonized byCarex species, it is considered that there must be
some species that are well suited to revegetating
disturbed terrestrial areas (i.e., areas in addition toriparian areas or wetlands), such as landslides or
excavated slopes.
In this study, we established experimental
revegetation plots using two terrestrial Carex species
on excavated slopes. The species were selectedbased on the results of a previous study on Carex
flora found in the subalpine and alpine zones of theKiso Mountains, Central Japan [9]. By monitoring
the increase in tillers and elongation of rhizomes
after planting, the growth characteristics of these
species were analyzed and their potential for
application to revegetation projects is suggested.

Fig. 1

Carex oxyandra in natural habitat at anelevation of 1,430 m in the Kiso Mountains
in May 2019.

Fig. 2Carex satsumensis in natural habitat at anelevation of 1,230 m in the Kiso Mountains
in May 2019.

METHODS
Carex Species Used in the Present Study
The flora and habitat of Carex species werepreviously investigated in the subalpine and alpine
zones (at elevations of approximately 1,230 to 2,650m) of the Kiso Mountains in central Japan [9]. A
total of 14 Carex species were observed, and ofthese, two species (C. oxyandra and C. satsumensis)
were evaluated as being potentially suitable forrevegetation applications as they typically colonizedareas comprising accumulated detritus or gravelassociated with landslides.
Carex oxyandra (Fig. 1) had a broad vertical
distribution at elevations ranging from 1,300 to
2,650 m (from low-mountain to the alpine zone) in
the Kiso Mountains [9]. In addition, this species has
been reported to have colonized the bare ground of
an abandoned mine quarry [13]. This species has
also been reported to have suppressed the growth oftree seedlings [15-16].
Compared to C. oxyandra, C. satsumensis (Fig.
2) had a narrower vertical distribution and wasfound at elevations of 1,200 to 1,400 m [9]. Little
information is available for this sedge, except that it
emerged as a pioneer species in a clearing
experiment conducted in an alpine grassland [17].

RESEARCH SIGNIFICANCE
The significance of the present study is that it
focuses on two native terrestrial Carex species andassesses whether they can be applied to practicalrevegetation experiments on excavated slopes in two
different soil types. Specifically, the increase inaboveground tillers was measured at monthly
intervals over an 18-month period after planting, andthe development of underground rhizomes and buds
was monitored. The obtained data and findings are
considered to be useful for promoting the use ofnative terrestrial Carex species to revegetation
projects.

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GEOMATE – Kyoto, Japan, 3-5 November 2021

interaction) was estimated by analyses of variance(F-test). Significant differences among the averages
were then estimated using Tukey’s HSD test.

Establishment of Experimental Sites
Experimental revegetation sites were establishedin two areas with different soil types: black-coloredsoil (Andosol; derived from volcanic ash) on theCampus Research Farm, and weathered granite soilin the Terasawayama Research Forest, both of which
belong to the Faculty of Agriculture at Shinshu
University. In each area, excavated slopes covered
with almost no vegetation were selected as
experimental sites.
The experimental design comprised arandomized block design with a two-way layout; 2
species × 2 soil types (black-colored soil andweathered granite soil) × 2 replicates. The plot size
was set to 1.5 × 0.9 m, where 1.5 m was set along
the slope direction. Table 1 shows the environmental
conditions at the experimental sites.
Rootstocks of the sedges were collected fromtheir natural habitats in the Kiso Mountains and 15
rootstocks per plot (1 to 3 tillers per rootstock) weretransplanted at the end of May 2019. The experiment
was started on June 1, 2019.
Table 1 Environmental
conditions
experimental plots

of

Soil

Incline(°)

Plots

Elevation
Slope(m)direction
Black-colored soil
(Research Farm on university campus)
Upper slope
760
NE
Lower slope
759
NE
Weathered granite soil
(Research forest in Terasawayama)Open slope
1,059
SW
Shaded slope
1,061
SW

RESULTS AND DISCUSSION
Aboveground Growth
Each of the two species survived in both soil
types during the experimental period. Colonizationby each species was not observed in 2019 (i.e., 6months after planting). Except for the plots on theshaded slope with weathered granite soil, the initialrootstocks became rather indistinguishable fromeach other due to an increase in foliage in 2020.

the

25
16

36
37

Plant height and number of tillers per rootstock
were measured at one-month intervals from June2019 to December 2020; no sampling was
performed over the winter season as the sites werecovered by snow.
To measure underground growth, two ramets persite (since the rootstocks developed rhizomes andnumerous tillers after planting, we use the word‘ramet’ hereafter to refer to the rootstock) werecollected at the site with the black-colored soil
(upper slope) and at the site with the weathered
granite soil (open slope) in December 2020 i.e., 18
months after planting. The rhizomes were thenuntangled and straightened so that the condition of
branching and the length of every rhizome section(from one bud to the next) could be recorded. The
locations of apical and lateral buds on the rhizome
were also recorded.
For the statistical analysis, the significance ofeach factor’s effect (species, soil types, and theirFig. 3Growth of tillers per ramet after
planting. Vertical bars indicate the leastsignificant difference among averages(Tukey’s HSD, p<0.05).

Fig. 3 shows the increase of tillers per ramet ineach of the two species. Based on tiller development,
C. oxyandra showed superior growth in the blackcolored soil, while C. satsumensis showed superior
growth in the weathered granite soil; in December
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GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 4

Examples of ramet growth of Carex oxyandra (left) and C. satsumensis (right) at 18 months after
planting. The upper row shows photos (black rulers are 10-cm long), and the lower row shows thecorresponding depictions of rhizome sections and buds.

2020, the average number of tillers per ramet of C.
oxyandra was 18.4 to 39.9 in the black-colored soil
and 1.5 to 13.3 in the weathered granite soil,
whereas that of C. satsumensis was 10.4 to 12.7 in
the black-colored soil and 1.0 to 21.0 in theweathered granite soil. Each species showed poor
growth in the plot on the shaded slope in theweathered granite soil.
In examining the aboveground growth of tillers,
it was found that each species was able to grow forat least 18 months in both of the soil types. However,regarding long-term survival, the poor growthobserved in both species in the plots under shaded
conditions is considered to be problematic because,
as vegetational succession advances, tall herbaceous
plants and arboreal species will shade out smaller
plants in the lower layer. Since the habitat of the twoterrestrial Carex species used in the present study
comprised open landslides, shady conditions might
restrict their germination and growth as Carex
albata, a weedy sedge in pasture whose seeds needs
light (i.e. gap formation) for breaking the dormancy[14]. Consequently, when considering vegetational
succession at disturbed sites, shade-resistant Carex
species will be required for revegetation projects.

Rhizome GrowthFig. 4 shows representative examples of ramet
growth of C. oxyandra and C. satsumensis at 18
months after planting. Although elongation of therhizomes did not always proceed linearly, therhizomes depicted in the figure were extended
linearly to facilitate interpretation of the differencesin rhizome length.
In C. oxyandra, rhizome sections were densely
arranged with numerous buds and tillers, whereas inC. satsumensis, rhizome sections were more loosely
spread out with small clumps of buds and tillers
scattered throughout (Fig. 4).
Table 2 shows the average measurements
obtained for underground growth at 18 months after
planting. The reason why the number of buds seems
larger than the average number of tillers (Fig. 3) isbecause the former contains both developed tillers
and young buds. Carex oxyandra showed similar
underground growth patterns in both black-colored
and weathered granite soils (44.0–44.5 buds and
17.7–20.8 cm-length rhizome, respectively).
Conversely, C. satsumensis showed a markedly
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GEOMATE – Kyoto, Japan, 3-5 November 2021

superior underground growth pattern in theweathered granite soil (20.8 buds and 763.9 cmlength rhizome) than in the black-colored soil (19.5
buds and 191.1 cm-length rhizome); rhizome lengthand the number of buds were approximately fourtimes larger in the weathered granite soil than in theblack-colored soil. However, even in the inferior
growth, the length of the rhizomes in C. satsumensis
was several tens-of-times greater than the plantheight, which is comparable to Japanese lawn grass(Zoysia japonica) and alang-alang (Imperata
cylindrica) [18], both of which can be widely
observed in Japan in frequent-cut grassland such as aridge between rice fields.

A logarithmic regression curve was obtained foreach soil type, in which the number of internodes
increased as the length of rhizome section increased.

Table 2 Number of buds and total rhizome length
per ramet of the two Carex species at 18
months after planting
Item

Species

Soil
Blackcolored soil

Number of buds
C. oxyandra
44.5 abC. satsumensis
19.5 b
Total rhizome length (cm)C. oxyandra
17.7 cC. satsumensis
191.1 bWeatheredgranite soil
44.0 ab72.5 a20.8 c763.9 aAverage values with different letters were significantly different(Tukey’s HSD, p<0.05).

Fig. 5 shows a histogram of rhizome section
length in ramets of each species. In C. oxyandra, thedata have a lognormal distribution with a peak at 100.2
to 10-0.4 cm (i.e., 0.40 to 0.63 cm) in both theblack-colored soil and the weathered granite soil.
However, in C. satsumensis, the data have a bimodal distribution, with a small peak at 10-1 to 10-0.5
cm (i.e., 0.10 to 0.31 cm) and a large peak at 101 to
101.5 cm (i.e., 10.0 to 31.6 cm) in both soil types.
Maximum rhizome length was 55.0 cm in theweathered granite plot. These results imply that C.
satsumensis develops two types of rhizomes, a small
number of very short rhizomes and a large numberof long rhizomes. This differentiation of rhizome
sections is considered to correspond to thedifferentiation of functions in long shoots (the spacecapture strategy) and short shoots (the light-capture
strategy) in terms of the general shoot development
patterns of trees [19].
Concerning the marked variation observed in theexceedingly long rhizome sections of C. satsumensis,
we attempted to clarify the cause of elongation.
Twelve long rhizome sections were sampled and thenumber of internodes (accumulation of internodes
comprises a rhizome section) were counted in bothsoil types. Fig. 6 shows the relationship between thelength of rhizome section and number of internodes.
642

Fig. 5

Histogram of rhizome-section length ina ramet at 18 months after planting.

Fig. 6

Relationship between the length of
rhizome section and the number ofinternodes in Carex satsumensis

GEOMATE – Kyoto, Japan, 3-5 November 2021

developed in C. oxyandra, while long widely spreadrhizome sections with small scattered clumps ofbuds developed in C. satsumensis. Rhizome growthin C. satsumensis changed dramatically according tosoil type.
Thus, the characteristics of tiller and rhizome
growth in the two species were markedly different;
C. oxyandra benefits from steadily producing dense
rhizome tillers around the mother rootstock, whereas
C. satsumensis benefits from rapidly spreading its
rhizomes far from the mother rootstock. Since thesecharacteristics are considered to complement eachother, using an optimum combination of both
species is considered to be well suited to the earlystages of revegetating excavated slopes.

An analysis of covariance detected a significant
difference between the y-axis intercepts (p<0.01, Ftest),with the intercept for the black-colored soil
being larger than that obtained for the weathered
granite soil (-26.7 and -32.6, respectively). The
coefficient of log x was similar between the tworegression curves and the difference was not
significant. Thus, the relationship shown in Fig. 6
implies that the relationship between an increase in
the number of internodes and rhizome elongation isalmost the same in both soil types, but a larger
number of internodes is likely to be produced (i.e.,each internode becomes slightly smaller in theblack-colored soil compared to the weathered
granite soil.
These findings clarified the differences of growthproperties between the two species; C. oxyandra
benefits from steadily producing dense rhizome
tillers around the mother rootstock, and underground
growth showed no noticeable differences between
the two soil types. Conversely, C. satsumensis
benefits from rapidly spreading its rhizomes far fromthe mother rootstock, and the underground growthchanged dramatically in the two soil type. Thisdifference observed in the growth of C. satsumensis
in different soils suggests that soil type plays animportant role in deciding whether to use thisspecies for revegetation applications. Since thepersistence (i.e., life span) of Carex communities
has been reported to differ among species [20], it ispossible that differences in underground growth may
be responsible. Consequently, soil type should be
carefully considered when using C. satsumensis forrevegetation purposes.

ACKNOWLEDGMENTS
This study was funded by a Grant-in-Aid for
Scientific Research from the Ministry of Education,
Culture, Sports, Science and Technology, Japan(Grant No. 18K05723, fiscal year 2018 to 2020 toT.A.).
REFERENCES[1] Kobayashi, T. and Kuramoto, W. eds.,
Handbook of Revegetation for Biodiversity
Conservation. Chijin Shokan Co., Ltd., 2006,
323 pp. (in Japanese)[2] Japan Meteorological Agency, A Search for
Past Meteorological data Home Page (http:data/jma.go.jp/obd/stats/etrm/index.php).
Referred in 25th May, 2021.
[3] Ministry of Land, Infrastructure, Transport andTourism, Public Information Home Page
(http:milt.go.jp/report/press/sabo02_hh_000091.
html). Updated in 25th March, 2021.
[4] Saito, S., The World of Sedges –The sedges
Indigenous to Fukushima Prefecture. Rekishi
Shunju Publishing Co., Ltd., 2001, 162 pp. (inJapanese)[5] Kitamura, S., Murata, G. and Koyama, T.,
Colored Illustrations of Herbaceous Plants ofJapan III. Hoikusha Publishing Co., Ltd., 1964,
pp. 256-303. (in Japanese)[6] Tsuji, M., Yamada, K. and Hiratsuka, A., Water
Purification Characteristics of Native Plant
Species whose Height does not Obstruct the
View in Planted Canal –A Case Study of Carex
dispalata, Carex thunbergii and Phalarisarundinacea. Transactions of the JapaneseSociety of Irrigation, Drainage and Reclamation
Engineering, No. 243 (Vol. 74, No. 3), 2006, pp.
33-40.
[7] Kurita, K. and Aso, M., Studies on the
Revegetation Method of Snow-Patch Vegetation
at Heavy Snow Montane Region. Journal of theJapanese Society of Revegetation Technology,

CONCLUSION
To assess whether the native terrestrial sedges
Carex oxyandra and C. satsumensis, both of whichhave been found to colonize landslides in the Kiso
Mountains of central Japan, could potentially be
used to revegetate disturbed areas, this study
established revegetation sites on excavated slopes in
May 2019. The experimental design employed atwo-way layout; 2 species × 2 soil types (blackcolored soil (Andosol) and weathered granite soil) ×
2 replicates. The number of tillers per ramet wasmeasured at one-month intervals and rhizome
growth was measured in December 2020. The
results were as follows:
1. As to the aboveground growth and increase intillers, each species was capable of growth on bothsoil types; C. oxyandra showed superior growth on
the black-colored soil, while C. satsumensis grew
better on weathered granite soil. However, eachspecies showed poor growth in the plot under shaded
conditions.
2. As to the underground growth of rhizomes andbuds, dense rhizome sections and numerous buds
643

GEOMATE – Kyoto, Japan, 3-5 November 2021

Colonization and Morphological Changes of a
Sedge Restricting Regeneration after Wind
Damage in a Natural Forest. International
Journal of GEOMATE, Vol. 12, Issue 31, 2017,
pp. 100-104.
[16] Arase, T., Okano, T. and Shirota, T., Methodsof Suppressing Colonizing Sedge to Help toEstablish Tree Seedlings in a Natural Forest.
International Journal of GEOMATE, Vol. 12,
Issue 32, 2017, pp. 19－24.
[17] Chou, C. and Lee, Y. (1991) Allelopathic
Dominance of Miscanthus transmorrisonensis in
an Alpine Grassland Community in Taiwan.
Journal of Chemical Ecology, Vol. 17, No. 11,
1991, pp. 2267-2281.
[18] Numata, M. and Yoshizawa, N. eds., Weed
Flora of Japan Illustrated by Colour. Zenkoku
Noson Kyouiku Kyokai Publishing, 1978, 414 pp.
(in Japanese with English legend)[19] Shirota, T. and Okano, T., Comparison of ShootDesign among Four ERICACEAE Understory
Species in Pinus densifolia Forest. Bulletin
Shinshu University Alpine Field Center, No. 8,
2010, pp. 1-8. (in Japanese with English
summary)[20] Arase, T. and Uchida, T., Growth andPersistency of Five Terrestrial Sedge Species
Established on a Cutting Slope. Journal of theJapanese Society of Revegetation Technology,
Vol. 35, No. 1, 2009, pp.119-122 (in Japanese)Vol. 20, No. 4, 1995, pp. 223-233. (in Japanesewith English summary)[8] Hoshino,
T.,
KaryomorphologicalandCytogenetical Studies on Aneuploidy in Carex.
Journal of Science of the Hiroshima University.
Series B. Div. 2, Botany, Vol. 17, 1980, pp. 155238.
[9] Arase, T. and Uchida, T., Vertical Distributionof Carex Species (Cyperaceae) in the Riparian
Zone in Nishikoma Research Forest at Shinshu
University. Bulletin Shinshu University Alpine
Field Center, No. 17, 2019, pp. 37-43. (inJapanese with English summary)[10] Tanaka, T., Yamawo, A., and Yano, O., Seed
Dispersal by Ants in Carex oxyandra var.
oxyandra (Cyperaceae) from Japan. The Journalof Japanese Botany, Vol. 90, No. 2, 2015, pp.
129-133.
[11] Molina, A., Acedo, C. and Llamas, F., The
Relationship between Water Availability andAnatomical Characters in Carex hirta. Aquatic
Botany, Vol. 85, No. 3, 2006, pp. 257-262.
[12] Bubier, J. L., The Relationship of Vegetation to
Methane Emission and Hydrochemical Gradientsin Northern Peatlands. Journal of Ecology, Vol.
83, 1995, 403-420.
[13] Tsujimura, A., The Ecology of Carex oxyandra
II. The Behavior of Seedling and Tillers.
Ecological Research, Vol. 2, 1987, pp. 279-288.
[14] Watanabe, N., Nishiwaki, A. and Sugawara, K.,
Effects of Bare Ground Formed in Pasture on
Dormancy Breaking of Carex albata Boott Seeds.
Grassland Science, Vol. 45, No. 3, 1999, 233237.
[15] Arase, T., Okano, T. and Shirota, T.,644

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

NUMERICAL STUDY OF PARTICLE FLOW(PM10) IN LOW-VOLUMEIMPACT SEPARATOR: PARTICLE’S PATH AND COLLECTIONPERFORMANCE
Pannita Phirommark1, Watcharapong Chookaew1, Chakrit Suvanjumrat1, Sakchai Uapipatanakul2,
Machimontorn Promtong1
1

Department of Mechanical Engineering, Faculty of Engineering, Mahidol University,
Salaya, Nakorn Pathom, 73170, Thailand
2
Kinetics Corporation Ltd. 388 Ratchadapisek Rd.32 Chandrakasem,
Chatuchak Bangkok 10900, Thailand

ABSTRACTDue to a health issue, an inspection of soot particle size quantity in ambient air is essential. This research
focuses on studying the small particles (PM 1, PM10, and PM100) flow behavior while is separated by a lowvolume impact separator. A computational Fluid Dynamics (CFD) method was introduced to analyze the flow
complex of particle flow. In the simulation, the actual operating flow rates and considered particle sizes wereadopted as the initial conditions and material properties. The flow pattern was numerically observed and evaluatedthe collection performance. Moreover, the particle’s paths of different sizes were compared and discussed. The
simulation results showed that the air velocity influenced the particle traces and their distributions in the separator
PM10 head, especially the smaller sizes (PM1 and PM10). The residence time and the number of escaped particles
were used to evaluate the performance. Within 5 seconds, the predicted collection performance of 1 µm,10 µm,
and 100 µm were 44.2%, 37.6%, and 0%, respectively. In future work, the validation study will be performed, andthe effect of the internal structure that could affect the separator performance will be further investigated. In
addition, the aggregations of the particles due to the flow vorticities that could cause the dispersions will beobserved as well.
Keywords: Soot Particle Flow, Computational Fluid Dynamics (CFD), Discrete Phase Modelling (DPM),
Performance evaluation, Separation Technique

INTRODUCTION

isolate PM2.5 by establishing a novel static chambersystem. The polydisperse aerosol was involved toevaluate the performance of separators tested thecritical parameters of the system. It also compared theseparation efficiency curves of three cyclone
separators (VSCC-A, SCC-A, and SCC112). The
results showed that the performance of the VSCC-A
was optimal (with a slightly sharper cutoff curve).
Tongling Xia and Chun Chen [10] studied theevolution of incense particles on nanofiber filter
media. The results show that the PM2.5 removal
efficiency of nanofiber filter media decreased with anincrease in the incense particle loading mass. The
liquid aerosols were found to interact with thenanofiber network and enlarged the pore sizes.
When the loading mass was sufficiently large,
the PM2.5 removal efficiency tended to be a constant.
Zhanpeng Sun [3] studied a static cyclone classifier
and observed the flow characteristics. It was foundthat the primary flow could characterize by an upper
vortex and a lower reverse vortex. The primary andsecondary air occupied a separate area. Regardless of
the inlet air velocity, the upward vortex represented ahigh flushing effect, reducing the retention of fineparticles caused by higher-size particles. Prashant

Due to an impact on public health, monitoring
many harmful particulates (PM10, PM2.5, PM1)
suspended in the air has become a critical task.
Practically, sizing and recognizing the ingredients ofthese particles are also another necessary task because
it may give information regarding the sources, i.e.,burning of crops, pollutions due to construction, andautomotive. In certain circumstances, like an indoor
environment, the particles could become a virus
carrier (COVID-19); thus, spreading infectedparticles could lead to a viral infection which could
directly affect the health of the living [1, 2]. Particle
collectors/separators have been invented to collectharmful particles and report real-time information [3,4]. However, to accurately classify and effectively
collect the particle size, the proposed particle sizers
may require investigating a collection performance[5-9].
Recently, many researchers have invented andimplemented the developed techniques (i.e., cyclonicand impact methods) into the particle separators toclassify the ultrafine particle sizes [6, 8]. For
example, Peng [8] studied the cyclone's efficiency to645

GEOMATE – Kyoto, Japan, 3-5 November 2021

Patel [11] investigated a PM2.5 high-volume
impactor (HVI) with a new inlet design. Also, theoptimized D50 cutoff size of 2.52 μm wasinvestigated experimentally under the ambientconditions. The performance of the developed PM2.5
HVI sampler was investigated under various massloading conditions. As a result, the developed PM2.5
HVI sampler gave a comparable performancecompared with commercial PM2.5 high- and lowvolume samplers.
Instead of experimental testing, a Computation
Fluid Dynamics (CFD) method is a promising tool forinvestigating complex flow and use for redesigning
the thermal system [12-16], and particle flow
circulations[17-23]. Kaltenbach and Laurien [18]studied the diffusion of radioactive particles inside
the reactor building. As mentioned, the cycle during
a catastrophic accident was simulated using a CFD
technique. Different droplets and particle size groups
were introduced in this three-dimensional modeling.
Ahmed [24] studied the Venturi scrubber, anessential element of the Filtered and Closed
Ventilation System (FCVS), which removes aerosols
from polluted air. In the simulation, ANSYS CFX
was used to investigate the removal efficiency of
Venturi scrubbers operating in self-priming mode.
Titanium oxide (TiO2) particles with a size of 1
micron were used as the size of powder particles. The
removal efficiency was evaluated under the inlet air
velocities of 1-3 m/s. It was found that higher inlet air
velocity, removal give higher efficiency.
Peng [25]proposed a hydraulic separator to
remove pollutant particles. Ansys FLUENT program
was used to simulate the hydrodynamic separator
under complex initial conditions. As a result, theoptimal angle between the overflow tube and the inlet
tube in removing the polluting particles was found.
Fang [26] simulated the stone powder separator (SPS)
using Ansys FLUENT software. The Discrete Phase
Model (DPM) was introduced to simulate thecrusher's airflow distribution and particle trajectory.
The structure of the stone separation device and thesuitable volume were optimized. Also, the simulation
results were compared with experimental data.
This research will introduce the Discrete Phase
Model (DPM) to observe the flows inside thecommercial impact separator. The observation will
particularly investigate how different particle sizes
distributed while passing the classifying chamber. In
addition, the performance of the collector inclassifying the particle smaller than 10 microns(PM10) will also be discussed. In general, theobjective of this research is to utilize theComputational Fluid Dynamics (CFD) technique;i) to investigate the effect of internal
configurations and the flow characteristics in acommercial impact particle separator,ii) to capture the particle (PM1, PM10, andPM100) paths under actual operation conditions,iii) to study the effect of particle sizes, internal
configuration and address the critical factors related
to particle collection performance.
Apart from the flow variables and the particle's
paths, the residence time and the number of particles
escaped will additionally compare to evaluate thecollection performance of the impact separator.
Hopefully, a more understanding regarding thecurrent design and the possible solutions to enhance
the current performance could be found andsuggested.
FLUID AND PARTICLE FLOW EQUATIONSContinuity Equation
The continuity equation reflects the fact thatmass is conserved (as shown in Eq. (1)). The equation
is developed by adding up the rate at which mass isflowing in and out of a control volume, and setting
the net in-flow equal to the rate of change of mass
within it. Since the mass velocity is continuous, thispartial differential equation is usually called as thecontinuity equation. Sometimes the first term can beomitted when the fluid flow is in the steady state.
𝝏𝝏𝝏𝝏
𝝏𝝏𝝏𝝏

+ 𝝆𝝆

� 𝒊𝒊 )
𝝏𝝏(𝒖𝒖𝝏𝝏𝒙𝒙𝒊𝒊= 𝟎𝟎(1)

Momentum Equation
The momentum transfers within a control
volume is conserved, hence the momentum equation
can describe as shown in Eq. 2.
� 𝒊𝒊
� 𝒊𝒊
�
𝝏𝝏𝒖𝒖
𝝏𝝏𝒖𝒖
𝝏𝝏𝒑𝒑
� 𝒋𝒋
+ 𝝆𝝆𝒖𝒖=−𝝏𝝏𝝏𝝏𝝏𝝏𝒙𝒙𝒋𝒋𝝏𝝏𝒙𝒙𝒊𝒊
� 𝒋𝒋
� 𝒊𝒊 𝝏𝝏𝒖𝒖𝝏𝝏𝝏𝝏𝒖𝒖′ 𝒖𝒖′ �������
+�𝝁𝝁 �+
� − 𝝆𝝆𝒖𝒖
𝒊𝒊 𝒋𝒋𝝏𝝏𝒙𝒙𝒋𝒋𝝏𝝏𝒙𝒙𝒊𝒊𝝏𝝏𝒙𝒙𝒋𝒋

(2)

Where
� 𝒋𝒋
𝝏𝝏𝒖𝒖
� 𝒊𝒊
𝟐𝟐
′ 𝒖𝒖′ = 𝝁𝝁 �𝝏𝝏𝒖𝒖
������
𝝉𝝉𝒊𝒊𝒊𝒊 = −𝝆𝝆𝒖𝒖+� − 𝜹𝜹𝒊𝒊𝒊𝒊 𝝆𝝆𝝆𝝆𝒊𝒊 𝒋𝒋
𝒕𝒕
𝝏𝝏𝒙𝒙𝒋𝒋𝝏𝝏𝒙𝒙𝒊𝒊𝟑𝟑(3)

� is theIn Eqs. (1) - (2), 𝝆𝝆 is a fluid density and 𝒑𝒑
���𝒋𝒋 are the average velocity
𝒖𝒖𝒊𝒊 𝒖𝒖
system pressure. Here ���,components and ������
𝒖𝒖′𝒊𝒊 𝒖𝒖′𝒋𝒋 is the velocity fluctuation. The
𝒙𝒙𝒊𝒊 , 𝒙𝒙𝒋𝒋 terms are the coordinate axis. The 𝒈𝒈𝒋𝒋 and 𝝁𝝁
terms represent the gravitational acceleration vector
and kinetic viscosity, respectively. Equation (3)′ 𝒖𝒖′ )
������
shows the Reynolds-stress term (−𝝆𝝆𝒖𝒖𝒊𝒊 𝒋𝒋
Moreover, Boussinesq approximation isincreased in term which calculated the difference
property of fluid. And this equation can be resolved
by turbulent models. The term of turbulence model isused RNG 𝒌𝒌 − 𝜺𝜺 (Re-Normalization Group kepsilon) to calculate the air flow by value show in Eq.
646

GEOMATE – Kyoto, Japan, 3-5 November 2021

(5) and Eq. (6). and then bring to calculate theviscosity due to turbulence model in Equation (4).

𝒌𝒌 Equation:𝝏𝝏𝝏𝝏𝒙𝒙𝒊𝒊𝝁𝝁𝒕𝒕 = 𝝆𝝆𝑪𝑪𝝁𝝁(𝝆𝝆𝒌𝒌𝒖𝒖𝒊𝒊 ) +

𝝏𝝏

𝝏𝝏𝝏𝝏

𝝏𝝏

(𝝆𝝆𝒌𝒌) =

(9)

𝒌𝒌𝟐𝟐

(4)

𝜺𝜺

𝝏𝝏𝒙𝒙𝒋𝒋Re is the relative Reynolds number is given by��𝝁𝝁 +

𝜺𝜺 Equation:

𝝁𝝁𝒕𝒕

�

𝝏𝝏𝝏𝝏

𝝈𝝈𝒌𝒌 𝝏𝝏𝒙𝒙𝒋𝒋𝝆𝝆𝝆𝝆

� + 𝑷𝑷𝒌𝒌 −

𝝏𝝏
𝝏𝝏
𝝏𝝏
𝝁𝝁𝒕𝒕 𝝏𝝏𝝏𝝏
(𝝆𝝆𝜺𝜺𝜺𝜺𝒊𝒊 ) + (𝝆𝝆𝝆𝝆) =��𝝁𝝁 + ��
𝝈𝝈𝜺𝜺 𝝏𝝏𝒙𝒙𝒋𝒋
𝝏𝝏𝒙𝒙𝒊𝒊𝝏𝝏𝝏𝝏𝝏𝝏𝒙𝒙𝒋𝒋𝜺𝜺Where+𝑪𝑪𝟏𝟏𝟏𝟏 𝑷𝑷𝒌𝒌 − 𝑪𝑪∗𝟐𝟐𝟐𝟐 𝝆𝝆And𝑪𝑪∗𝟐𝟐ε = 𝑪𝑪𝟐𝟐ε +𝒌𝒌

η = 𝑺𝑺𝑺𝑺/ε

𝜺𝜺𝟐𝟐
𝒌𝒌

(10)

(5)
Where 𝒅𝒅𝒑𝒑 is the particles diameter.
Turbulence model

Turbulence model is a model that helps tocalculate continuity equations and the Reynoldsaveraged Navier-Stokes equation (RANS) in
turbulent flow. It is used to determine Reynolds
stress. A good turbulence model must be able toaccurately calculate various flow behaviors. The most(6)𝑪𝑪µ η𝟑𝟑 (𝟏𝟏 − η/η𝟎𝟎 )𝟏𝟏 + βη𝟑𝟑

The model constants are

popular turbulence model in turbulence simulation is
the Launder and Spalding model called the Standardk −ε model. [27].
In this research, the Standard k - ε turbulencemodel was chosen because it was a turbulence model
whose results were close to the reality by the Standard
k - ε turbulence model equation as in Equation 11.

𝑺𝑺 = (𝟐𝟐𝑺𝑺𝒊𝒊𝒊𝒊 𝑺𝑺𝒊𝒊𝒊𝒊 )𝟏𝟏/𝟐𝟐

𝑪𝑪𝝁𝝁 = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎, 𝑪𝑪𝟏𝟏𝟏𝟏 = 𝟏𝟏. 𝟒𝟒𝟒𝟒, 𝑪𝑪𝟐𝟐𝟐𝟐 = 𝟏𝟏. 𝟔𝟔𝟔𝟔, η𝟎𝟎 =𝟒𝟒. 𝟑𝟑𝟑𝟑,
𝝈𝝈𝒌𝒌 = 𝟎𝟎. 𝟕𝟕𝟕𝟕𝟕𝟕𝟕𝟕, 𝝈𝝈𝜺𝜺 = 𝟎𝟎. 𝟕𝟕𝟕𝟕𝟕𝟕𝟕𝟕, β = 𝟎𝟎. 𝟎𝟎𝟎𝟎𝟎𝟎𝟎𝟎(11)

The last term in equation (6), 𝝆𝝆𝝆𝝆 represents thedestruction rate, and 𝑷𝑷 are the shear
production buoyancy production term, which are
given by:
𝑷𝑷 = 𝝁𝝁𝒕𝒕

𝝏𝝏𝒖𝒖𝒊𝒊𝝏𝝏𝒙𝒙𝒋𝒋𝝏𝝏𝒖𝒖𝒊𝒊�𝝏𝝏𝒙𝒙𝒋𝒋+𝝏𝝏𝒖𝒖𝒊𝒊
𝝏𝝏𝒙𝒙𝒊𝒊Flow equation of particles phase�

where k is the kinetic energy of turbulence, ε isthe rate of reduction in kinetic energy of turbulence,
𝑮𝑮𝒌𝒌 is the production term of turbulence kinetic
energy.

(7)(12)

The particle motion equation is obtained by the
integration of the equilibrium force acting on theparticle. which is in the Lagrangian frame (Fluent 6.2
User's guide. 2005). On the left with the terms of theforce acting on the particle can be written as

RESEARCH METHODOLOGY
The research methodology is illustrated in Fig. 1.
Our main task is to perform the simulation of theparticle flow in the particle separator. Nevertheless,before using the CFD method to simulate the particle
flow, the steady-state flow's initial stage is firstrequired (running the case without the particles).
Afterward, the considered size of the particles was
adopted into the simulation, and the flow's paths aremonitored. The results, including the velocity pattern
the motions of each particle size, the residence time,
are mainly discussed regarding the separation
performance. By adopting different operating
conditions, hopefully, the ideas for further
development and design could be found.

(8)
Where
𝒖𝒖 is the fluid phase velocity
𝒖𝒖𝒑𝒑 is the particle velocity
μ is the molecular viscosity of the fluidρ is the fluid density𝛒𝛒𝒑𝒑 is the density of the particles
𝑭𝑭𝒙𝒙 is an additional acceleration
𝑭𝑭𝑫𝑫 is the drag force per unit particles mass

647

GEOMATE – Kyoto, Japan, 3-5 November 2021

Reviewing the parameters
affecting the particle
separator performance

Running the simulation
using the actual
suggested flow rate

Collecting the operating
condition of TE-Wilbur 2.5
separator and creating the
3D modelAdopting the consideredparticle sizes (PM1,
PM10, PM100) into thesimulation

(a)

(b)

Performing the particle
flow under various
conditions and collecting
the interested results.

(c)Results
converged

Not reach the convergence criteria
Cyclone inlet
jet

Evaluating the collection
performance and suggesting for
future work

Spiralflow

Fig. 1 The flow chart representing the researchmethodology

Outlet
tube

Grit
Pot
Sampleexit

Details of the particle separator
Fig. 2 PM10 and PM2.5 particle size sorting head (a)with internal structure details (b) at the PM10 size
sorting point and PM2.5 size sorting point (c).

There are two available functions of the TEWilbur 2.5 particle separator. The first is to collect
PM10 dust (sizes from 10 microns down) and works
based on the impact technique (Figure 2).
Specifically, the size is equal to or smaller thanPM10; when they hit the solid plate, it will bounce
down to the screening tube and flow-through anotherfunction (PM2.5 dust screening unit). For the second
mode, the separation of dust particles that are equal
or smaller than PM2.5 size separated using thecyclonical object (based on the rotation principle).
The cyclonic separator will send the particle sizes
smaller than PM2.5 into the sampling room and larger
sizes to the other collector.
Meshing
The 3D separation head after the meshing process
is shown in Fig. 2. It should be noted that the mesh
independence study was conducted, and the velocity
at the center of the chamber was compared between
the various number of the mesh considered. As aresult (Fig. 3), the velocity was insignificantly
changed when using the mesh element at about
150,000 [23]. Therefore, this number of mesh sizes
was adopted for further numerical investigation.
Furthermore, 16.67 L/min (0.0675 m/s) of thevolumetric flow rate was adopted for the velocity
inlet and this number is suggested by the
manufacturer for the real operation.

Fig. 3 The 3D separation head (PM10) after themeshing process

648

GEOMATE – Kyoto, Japan, 3-5 November 2021

a)b)Fig. 4 Total mesh used for the separator model (themesh independence study).

RESULTS AND DISCUSSIONSIn this section, the simulation results of theparticle separator will be presented and discussed.
Firstly, the velocity profiles in the separator system
will be given. The section part discusses the flow
paths of different particle sizes (PM1, PM10, andPM100). Last, the particle's escaped number will be
discussed to evaluate the collection performance. It
should be noted that only the PM10 separator system
was investigated and evaluated the impact technique,especially in terms of sizing performance.

c)Fig. 5 Velocity streamlines a) having normal
velocity b) having normal velocity c) reduced to ahaft-velocity
Fig. 6 compares the flow pattern in theseparation chamber when there is only a haft of thevelocity inlet. As shown, when the airflow in thechamber impacts the bottom wall and flows to thewall causing the circulations inside the collecting
room. Because of having a small chamber, currently,
it is hard to recognize a difference in the flow
circulation in the separation room.

The Velocity Pattern
Usually, the local flow velocity could lead theparticle’s motions in the separator system, and hence
observing the flow pattern in the primary chamber isnecessary. The velocity streamlines of the PM10 inthe separator system are shown in Fig.5. It is clearlyseen that the vortexes occurred at the top of the
primary chamber (see in Fig. 5a and Fig. 5b). Because
Inside the system, the lower velocity appeared near
the chamber surface (Fig. 5b), and when having highinlet velocity, the velocity difference could lead to apresence of the circulations [28].
It should be noted that without the suitably
designed cone inside the separator head, the air
circulations could not happen [29]. The maximum
velocity found at the connecting tube that links to theseparation chamber from the figures. The maximum
velocity was about 4.30 m/s.
Fig. 5c presented the flow velocity when the inlet
velocity was halved. Averagely, the velocity values in
the primary chamber were reduced almost a haft.
When there is a smaller gap between the inlet velocity
and the near-wall velocity, the smaller vortexes were
found near the top of the chamber. Moreover, themaximum velocity in the connecting tube wasdecreased as well.

a)b)Fig. 6 Velocity streamlines in the separationchamber a) having normal velocity and b) reduced toa haft-velocity
The Particles’s PathIn this section, the particle’s paths of different
sizes are presented (under the same operating
condition). Fig.7a show the particle’s path of 1 µm
and 10 µm, and Fig. 7b show the particle’s path of 1
µm and 100 µm. For both cases, the particles were649

GEOMATE – Kyoto, Japan, 3-5 November 2021

tracked from the inlet and the final time captured was
at 5s.
It is clearly seen that when the particles were
injected, the large dust particles (10 µm and 100 µm)
were fallen to the middle of the primary chamber due
to a dominant inlet velocity. On the other hand, theparticle particles at 1 µm micron kept swirling at the
top for longer than 10 µm and 100 µm near the top
region of the chamber. This could be a result of being
push away from the heavy-turbulent regions.

a)b)Fig. 8 The particle’s path of 1 µm and 100 µm
when the collecting tube has a) an original heightand b) reduced to the half-height.

a)

The Collection performanceb)
In Fig. 9-11, the Particle Residence Times (PRT)of each particle size (1 μm, 10 μm, and 100 μm) arepresented. It should be noted that the ending time forthis tracking was 5s; hence it is possible to have aslight change in terms of the number of escaped andstayed particles in the separator.
Within the 5 seconds, the large particles 100 μm
were unable to be separated from the separationchamber. This is also relevant to the design objectivethat this system should allow only the particle sizes
equal and/or smaller than 10 µm flow through. This
could be because the flow kept pushing the particle toflow near the floor of the separation chamber.

Fig. 7 The particle’s path of a) 1 µm and 10 µm
and b) 1 µm and 100 µm
Fig.8a and Fig. 8b present the particle’s path dustof 1 µm and 100 µm. It should be noted that the caseof fig. 8b, the pipe length remains only a half of theoriginal height. According to the residence time(shown on both figures), it can confirm that thesmaller particle takes a longer time (kept swirling) toleave the separation chamber in the case of having thehaft-height. This could result from the height of theunsuitable collecting area, tube to collect the smaller
particle. In the case of 100 µm, although the particle
could enter the separation chamber, however, within
45s, the particle is still in the separation unit, notescaped out [30].
More details regarding the time required to enter
the separation chamber and the time required toescape from the separator are given in Table 1.
Interestingly, it shows that the escaped time required
for the smaller particle for the case of having originaltube length was smaller than the case of the haftheight. Nevertheless, at this point, it requires moreevidence to conclude the influence of the collecting
tube height.
Table 1 Comparisons for particle separation 1 µm and100 µmTime to enter
EscapedCase
separation
Particles
time
study
chamber
1 µm
19.2s
25.1s
Normalheight100 µm
4.9s
1
µm
39.2s
45.4s
Half
height100 µm2.9s-

Fig. 9 Particle Residence Time (PRT) of particles
size 1 µm

650

GEOMATE – Kyoto, Japan, 3-5 November 2021

particle-sized smaller than 10 microns (PM 10) canbe separated by the collector, not for the 100 microns(PM100). Also, by introducing the computational
Fluid Dynamics (CFD) technique, the separation
performance and the particle's path could be
investigated. Overall, the conclusions can be drawn
as follows:i) The secondary flow (vortexes) occurred near
the top of the primary chamber. The flow circulations
could result from high inlet velocity and the internal
geometry of the impact system. The average velocity
inside the primary chamber was less than 0.5 m/s;
however, the velocity inside the connecting tube
before entering the impacting room was as high as
4.37 m/s.
ii) By tracking the particle paths of PM1, PM10,
and PM100, it was found that smaller particles (PM 1and PM10) kept swirling inside the primary chamber
longer than higher particles (PM100) before entry theseparation chamber.
iii) When reducing the length of the separatingtube (remaining only a haft) in the separationchamber, it was found that the residence time of small
particles was longer than having the original length.
This may be because of the velocity profiles(circulation) in the separation chamber.
iv) At the end of the collection time (5 seconds),
the collection performance of the impact stem for 1
micron, 10 microns, and 100 microns particles were
44.2%, 37.6%, and 0%, respectively.
In future work, the experimental study will be
conducted, and the validation study will be
performed. Further investigations would be theoptimization of the operating condition and internal
structure of the impact and cyclonical systems of theparticle separator.

Fig. 10 Particle Residence Time (PRT) of particles
size 10 µm

Fig. 11 Particle Residence Time (PRT) of particles
size 100 µm
Table 2 The calculated collection performance when
the tracking time ended at 5 seconds.

ACKNOWLEDGMENTS
Size
(µm)

No. of (#)
the particles
injected#
Stayed#EscapedCalculated
collection
performance

1
10
100

708
706
706

395
440
706

313
266
0

44.2%
37.6%
0%

Many thanks to the Mechanical Engineering
Department, Faculty of Engineering, Mahidol
University, for allowing us to use the laboratory.
Thanks to CADFEM SEA Pte. Ltd. - A certified Elite
Channel Partner to ANSYS - for providing support
and access to the CFD program named ANSYS
Fluent 2021 R1.
Also, many thanks to Kinetics Corporation Ltd.
and National Research Council of Thailand (NRCT)
for financial support.

As displayed in Table 2, some of 1 µm and 10µm were separated and left the chamber for about
44% and 37%, respectively. More details regarding
the number of particles leaving and staying in theseparation chamber can be seen from the Table. 2. Itshould be bear in mind that the escaped number of thesmall particles (1 μm and 10 μm) number could
increase following the times.

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653

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

IMPACT OF THE GREAT EAST JAPAN EARTHQUAKE AND
NUCLEAR ACCIDENT ON LAND PRICE FLUCTUATIONSTakato Azegami1, Akira Takahashi2, Kensuke Yasufuku2, Noriko Otsuka3, Tomoko Miyagawa4and Hirokazu Abe2
1

Takenaka Corporation, Japan; 2Osaka University, Japan; 3ILS Research Institute for Regional and Urban
Development, Germany, 4Wakayama University, Japan
ABSTRACT

Using a hedonic regression approach, this research aims to identify what factors affected land price fluctuations
in Fukushima Prefecture following the Great East Japan Earthquake and the Fukushima Daiichi Nuclear Power
Station accident in 2011. In this paper, taking a long-term perspective, the analysis focuses on Iwaki City in
Fukushima, where the real estate market behaved unexpectedly following the two catastrophic events, and land
price fluctuation rate were rising abnormally. Results based on analysis of data for eight-year indicate thefollowing: The impact of radiation air dose rate and distance from the nuclear power plant on land price fluctuation
rate changed significantly from the fourth year after the disaster. Being located in a reconstruction base area had anegative impact on land price fluctuation rate, while access to sewers and city gas had a positive impact. It appears
that the stigma associated with the accident had a little disappeared after approximately four years.
Keywords: Land price, Hedonic Approach, Great East Japan Earthquake, Nuclear Accident
INTRODUCTION

prices at all points in the commercial area continued
to decrease. However, two years after the earthquake,the official land prices in Iwaki City showed anabnormal rate of increase, not only for Fukushima
Prefecture but for all of Japan. This had a substantial
impact on the housing supply available to victims.
The radioactive contamination and the subsequentevacuation orders resulting from the nuclear accident
were unique events in the world. Accordingly,
attempting to understand the large fluctuations in land
prices that occurred in the disaster area is a significant
part of understanding the massive effects of thisunprecedented catastrophe.
The purpose of this paper is to identify thosefactors that influenced post-disaster land pricefluctuations in Iwaki City, where land prices roseabnormally following the Great East Japan
Earthquake and the Fukushima Daiichi Nuclear
Power Station accident.
Previous research related to earthquake disasters
and land price fluctuations include a study by Nomura
et al. [1], which analyzed the impact of the Great
Hanshin Earthquake in 1995 on land prices using ahedonic approach. It was found that in 1996, the year
immediately following the earthquake, the decreasein land prices was directly proportional to the degreeof damage to buildings (i.e., the greater the damage tobuildings, the lower the land price). However, in 1997,
land prices stopped falling in areas where buildings
had been completely destroyed or burned to theground, while in areas with high half-destruction rates,
land prices continued to fall.

Ten years have passed since the Great East Japan
Earthquake. At this point, many of the countermeasures of the reconstruction/creation period havebeen completed. The number of evacuees has fallen
to approximately one-tenth of the number
immediately after the earthquake, and thereconstruction of housing in the damaged area has
been progressing at a fairly rapid pace. The supply of
new or re-constructed buildings has reached 18,000
private units and 30,000 units of public housing.
However, in Fukushima Prefecture, it has been not
only the damage caused by the earthquake andtsunami, but also the radioactive contamination
resulting from the Fukushima Daiichi Nuclear Power
Plant accident that has had long-term effects on thephysical, psychological and social well-being of thevictims. This was a completely different case frompast earthquakes in Japan, and unprecedented
measures have been required. Given the severity of
the radioactive contamination, it was impossible torespond immediately to the problems and anxieties of
the residents, and many victims were forced toevacuate the area, seeking a new living base either
within or outside the prefecture. Reaction to theradioactive contamination also radically affected
Fukushima Prefecture's land prices.
In 2012, immediately after the earthquake, land
prices in all residential and commercial areas in
Fukushima Prefecture fell significantly. In 2013, land654

GEOMATE – Kyoto, Japan, 3-5 November 2021

Regarding the relationship between land pricefluctuations after the earthquake and real estate sales
in Fukushima Prefecture, Kawamura [2] pointed outthat the spatial movement and settlement of many
victims was a significant factor. Saito [3] analyzed theimpact of accepting evacuees from the nuclear
accident on city planning in Iwaki City, focusing onchanges in population and land use He found thathouses were scattered on a small scale and built inplaces that were not intended to be residential areas,
pointing to the need to develop a new living
environment after such a disaster.
These studies focused on factors such as thenumber of real estate transactions in the disaster area,
the rate of increase in land prices, and changes in landuse. There are few studies discussing the relationshipbetween land prices and the stigma of radioactive
contamination caused by a nuclear accident. Hirota[4] considered the outlook for the real estate market
in the year of the Great East Japan Earthquake,highlighting concerns about the decline in real estatevalue and the associated stigma with soil pollution
from diffused radioactivity. As an overseas reference
example, he also introduced a research report byGambling and Downing et al. [5] describing theimpact on land prices of the Three Mile Island
accident in the United States.

represented by seven regions of Fukushima
Prefecture. The land price used here is the average ofthe official land price and benchmark land price. (The
official land price is the land price announced on
January 1st every year based on an appraisal by two
or more real estate appraisers for each plot set by the
Ministry of Land, Infrastructure, Transport andTourism. The benchmark land price is the land pricedetermined by the prefecture on July 1st every year
based on an appraisal by one or more real estateappraisers per plot.) Land price fluctuations rate
which are compared to the previous year were
negative until 2011 and then turned positive in all
regions except Minami-aizu in 2013 after theearthquake.
However, the movements show different patterns
in each region. The fluctuation in land prices in
Minami-soma and Iwaki changed significantly after
the earthquake, and the fluctuation in Iwaki roseabnormally from 2013 to 2016 compared to otherregions. Possible reasons for this are the acceptanceof approximately 24,000 evacuees in the Futaba
District and the transfer of workers who worked at thenuclear power plant while accommodated in Iwaki
city
duringthecountermeasureprocess.
(approximately 10% of the population of Iwaki City).
However, this is not sufficient to explain the complex
relationship between the earthquake and nuclear plant
disaster and land price fluctuations.

METHOD
As noted, the purpose of this paper is to identify
how the Great East Japan Earthquake and the
Fukushima Daiichi Nuclear Power Station accident
affected the real estate market in the region. Focusing
on fluctuations in land prices in Iwaki City, the study
quantitatively analyzes the effects of various social
factors caused by the earthquake from a long-term
perspective using a hedonic regression approach. We
first investigate changes in land price fluctuation rateand changes in the air dose (radiation dose of thesurrounding environment emitted from radioactive
materials) throughout Fukushima Prefecture
following the disaster, and describe the position of
Iwaki City within the prefecture. Next, we create aland price volatility function using multivariate
analysis based on the official land prices in Iwaki City.
We then establish how various factors associated withthe earthquake affected land price fluctuation rate in
the disaster area.

Fig. 1

Fluctuation of land prices in seven areas of
Fukushima Prefecture.

On the other hand, unlike other areas, Minamisoma, where many victims were forced to evacuate
due to the nuclear accident, experienced rapidchanges in land price fluctuations. There were no
major changes in land price fluctuations in Aizuwakamatsu, Minami-aizu, and Shirakawa.

POST-EARTHQUAKE LAND PRICES AND
AIR DOSES IN FUKUSHIMA

Air dose rate after the earthquake

Land prices after the earthquake

Figure 2 shows the changes in the air dose rate from2011 to 2018 in the seven representative cities of theregions of Fukushima Prefecture. We used the air
dose rates (μSv/h) for the seven representative cities
that were listed on the website of the Fukushima

Figure 1 shows the fluctuation of land prices inseven cities and towns (Fukushima City, Koriyama
City, Shirakawa City, Aizu-wakamatsu City,
Minami-aizu Town, Minami-soma City, Iwaki City),
655

GEOMATE – Kyoto, Japan, 3-5 November 2021

price fluctuations in 2011, indicating that higher air
dose rates are associated with lower land pricefluctuations. However, in 2012, the correlation
coefficient became quite small, after which it becameslightly positive.

Reconstruction Station. (Measured location is thejoint-government building in each city, theprefectural north health and welfare office building inFukushima city) On this site, the air dose ratemeasurement results in Fukushima Prefecture arerecorded by measuring the air dose every hour in eacharea. Here, the average of the measurement results at
00:00 on the first day of each month of the year istaken as the air dose rate for the year.

Table 1 Correlation coefficients measuring thecorrelation of land price fluctuations and air
dose rates from 2011 - 2018
year
Correlation
coefficientFig. 2

2011

2012

2013

2014

2015

2016

2017

-0.66

0.04

0.34

0.11

0.38

0.20

0.80

Changes in air dose rate in seven areas of
Fukushima Prefecture.

Since the radioactive materials diffused by the
Fukushima Daiichi Nuclear Power Plant accident
flowed towards the west by the wind, the values inFukushima City and Koriyama City, which arelocated west of the nuclear power plant, are quite high,at more than 1.0 μSv/h. Especially in 2011,
Shirakawa City, Minami-soma City, and Iwaki City
had air dose rates exceeding 0.23 μSv/h. The Ministryof the Environment has determined that therequirement for an area to be included in thedecontamination implementation plan is an area withan air dose rate of 0.23 μSv/h or more. This value iscalculated by converting the additional exposure doseof 1 mSv/y into the radiation dose per hour and adding
the natural radiation dose.
Since 2013, the air dose rate in all seven regions
has decreased due to decontamination and attenuation,and in 2017, the air dose rate at the monitoring postsin the seven areas was below 0.23 μSv/h.
Table 1 shows the correlation coefficients
indicating the degree of correlation between land
price fluctuations and air dose rates in the sevenregions of Fukushima Prefecture from 2011 to 2017.
Figure 3 shows a scatter plot of the air dose rates andland price fluctuations in the seven representative
cities in FY2011.
Fukushima City and Koriyama City, which had
high air dose rates in 2011, saw large declines in land
price fluctuations. On the other hand, the decline issmall in Shirakawa City and Minami-aizu Town,
where the air dose rate is low. Table 1 also shows anegative correlation between air dose rate and land

Fig. 3

Scatter plot of air dose rates and land pricefluctuations in the 7 cities of Fukushima in
2011

In 2017, a strong positive correlation was found,resulting in higher land price fluctuation rate in areas
with higher air doses. Immediately after theearthquake, it is probable that the anxiety caused by
the lack of information on radioactive contaminationand the damage it could inflict on the human body
drastically reduced land price fluctuation rate.
However, by 2013 (approximately), when
decontamination
work
andnational
measures/reconstruction plans were underway, the air
dose rate had decreased, at least to some extent, andthe land market appeared not to react significantly tothe air dose rate. It can be said that the land priceincreased regardless of the air dose rate due to the
population concentration in Iwaki City, Fukushima
City, and Koriyama City, which were major
destinations for residents who had received
evacuation orders.
LAND PRICE FLUCTUATIONS AFTER THE
EARTHQUAKE IN IWAKI CITY
It is believed that the rise in land prices in Iwaki
City after the earthquake is mainly due to the656

GEOMATE – Kyoto, Japan, 3-5 November 2021

Joban district, Uchigo district, Yotsukura district,
Tono district, Ogawa district, Yoshima district,
Sanwa district, Taito district, Kawamae district,
Kunohama/Oku district (a total of 13 items, all ofwhich are converted to dummy variables)4) variables directly related to nuclear power plant;
Air dose, distance to the nuclear power plant,
tsunami inundation area, and reconstruction base area
(a total of 4 items)
In total of 55 explanatory variables have been set.

acceptance of evacuees and the presence of workers
at the nuclear plant. We analyzed the situation using
the hedonic approach in order to quantitatively
determine what factors actually affected the land
price fluctuations in Iwaki City.
Analytical method (hedonic approach)The hedonic method is a regression techniqueused to estimate the prices of qualities or models thatare not available on the market in particular periods,
but whose prices in those periods are needed in order
to be able to construct price relatives. [6]. Because thehedonic approach is based on the capitalization
hypothesis, In this paper, we consider that land prices
are determined by a variety of factors, including thesurrounding environment, and express them asvectors for characteristics that contribute to land price[7]. Multiple regression analysis is used to implement
this approach. In this study EXCEL multivariate
analysis Ver.6.0, Esumi Co., Ltd. was used.

Examination of each explanatory variable andmulticollinearity
Among the explanatory variables above, there
may be multicollinearity that could adversely affectthe estimation accuracy of the model. To ensure thatthe hedonic function produces a highly explanatorymodel, the presence or absence of multicollinearity isestablished using a correlation coefficient table. If thevalue of VIF (Variance Inflation Factor) calculated
after the multivariate analysis is greater than 10, thenthe presence or absence of the effect of that variable
is re-examined.
Table 2 shows the final set of explanatory variables
after excluding factors which were causing themulticollinearity. With respect to the quantitative
variables shown in the table, since 12 stations besides
Iwaki Station were used well, we chose to omit
“distance to Iwaki station” (DS) as an explanatory
variable.
Regarding air dose (AD), since the values at eachsurvey site were not measured, we applied the air
dose values at the monitoring post closest to the site.
The monitoring post uses the Fukushima PrefectureRadioactivity Measurement Map operated by
Fukushima Prefecture. When the closest monitoring
post changes, it is changed every year. (For themeasured values of air dose rate ( ㏜ /h) at themonitoring post, the Fukushima Prefecture website isreferred.
Among the qualitative variables shown in thetable, the front road width (Road-4,6,8) at the surveysite includes widths of 0 to 4 m, 4 to 6 m, and 6 to
8 m. Dummy variables are set for each range.
As for site shape (Square, Rectangle, Trapezoid),building structure (ST-W, LS, RC, S, Other) and landuse (Residence, Industrial, Store, Bank, Office, Farm,
Workshop), the VIF value at the time of the analysis
greatly exceeded 10. Moreover, when these variables
were excluded, the coefficient of determination andthe p-value value were significant. Consequently,
these factors were excluded from the set of

Setting surveyed sites
The surveyed sites in this paper are the standard
land of the official land price set by the Ministry ofLand, Infrastructure, Transport and Tourism (103
sites) and the standard land of benchmark land priceset by the local government (77 sites), for a total of
180 sites in Iwaki City (2018).
Objective variables and explanatory variables
The objective variable in the hedonic approach
used in this paper is the land price fluctuation (rate ofland price in a given year compared to the previousyear) at each surveyed site in the analysis. The
explanatory variables are the various environmental
factors that potentially affected the real estate market
during the period from the earthquake to thereconstruction of the study site in the following
criteria.
1) building and infrastructure related variables;
Land area, floor area ratio, front road condition,
lifeline (city-gas or sewer), distance from the nearest
station, distance to Iwaki station, along national orprefectural road, site shape, building structure,building usage status (a total of 24 items)2) built environment related variables;
Surroundings and surveyed site surroundings (a totalof 24 items)3) different districts;
Taira district, Onahama district, Nakoso district,
657

GEOMATE – Kyoto, Japan, 3-5 November 2021

residential area), ASA (site is in a farm village area),
CA (site is in a commercial area), Dev-M (site is in a
medium-sized development area), Dev-L (site is in alarge-scale development site), and Adjust (a land
readjustment is involved). However, since there wereno survey sites corresponding to Dev-M and Dev-L,
these two variables were excluded from the final setof explanatory variables.
The inundation area dummy (FD) indicates that thesurvey site is within the tsunami inundation areaaccording to data on the 2011 Great East Japan
Earthquake released by the Geospatial Information
Authority of Japan. The reconstruction base dummy(RD) refers to being located in the Onahama
hinterland tsunami reconstruction base development
project in the reconstruction plan of Iwaki City.
Table 3 shows the correlation coefficients among
the quantitative explanatory variables. For the
explanatory variables, there were no significantly
high correlation coefficients (r > = 0.8).

explanatory variables.
The list of explanatory variables related tosurrounding buildings initially included ARResidence (houses around the survey site), AR-Farm(farmhouses around the survey site), AR-Industrial(factories around the survey site), AR-Public (public
facilities around the survey site), AR-Vacant (vacant
lot around the survey site), and AR-Store (when there
is a store around the survey site). However, since
there were no survey sites corresponding to ARPublic, AR-Public was excluded from the final set of
explanatory variables.
Table 2 Explanatory variables
Abbreviatio

MeasureDescription

final decision Reason

Objective variable PLP

Land price

Yen/m²

Official land prices for 2010-2018○

ACLand aream²

Area of one survey site○

FAR

Floor-area ratio％/100

Quantitative variablesnStation
DS
AD

DSNPP

Qualitative variables

Explanatory variable

Units

Distance to the nearest
stationDistance to "Iwaki
Station"Air dose rate
Distance to the nuclear
power plant

m
㎞
μsv/h

ｍ

Ratio of maximum allowable floorarea to the area of the survey site
Distance from the survey site to thenearest station
Distance to "Iwaki Station"
Annual average value of themonitoring post closest to survey site
Distance to Fukushima Daiichi
Nuclear Power Station

○
○
×

○

m

Front road width is 0-4m

Road-6

Front road widthｍ

Front road width is 4-6m○

Road-8Front road widthｍ

Front road width is 6-8m, Yes○

Along national or※１○

Front road width

Table 3 Correlation coefficients for explanatory
variables○

Along national or prefectural roads○

GOS

Lifeline dummy

Supply by city gas or sewer○

SquareSite shape

The site shape is square×※２

prefectural roads

Rectangle

Site shape

The site shape is rectangular×※２

Trapezoid

Site shape

The site shape is trapezoidal

×

※２

ST-W

Architectural structure

The building structure is wooden

×

ST-LS

Architectural structure

The building structure is light steel

×

※２

ST-RC

Architectural structure

The building structure is RC

×

※２

ST-S

Architectural structure

The building structure is steel

×※２

ST-OtherArchitectural structure

Other building structures

×

※２

※２

Residence

Land use

Land use status is residential

×※２

Industrial

Land use

Land use status is factory

×

※２

Store

Land use

Land use status is store

×

※２

Bank

Land use

Land use status is bank×

OfficeLand use

Land use status is office

×

※２

Farm

Land use

Land use situation is rice field×※２

WorkshopLand use

Land use status is the workplace

×※２

AR-Resid

Surrounding buildings

Dwelling in the vicinity○

AR-Farm

Surrounding buildings

Farmers in the vicinity○

AR-InduSurrounding buildings

Factories in the vicinity○

AR-Public

Surrounding buildings

Public facilities in the vicinity

×

AR-VacantSurrounding buildings

vacant land in vicinity○

AR-Store

Surrounding buildings

Stores in the vicinity○

DLA

Surrounding area

located in area of house for
installment sale

Surrounding area

located in Urbanized area○

RA

Surrounding area

located in Residential area○

ASA

Surrounding area

located in Farm village area○

CA

Surrounding area

located in Commercial area

○

Dev-M

Surrounding area

Dev-L

Surrounding area

located in Medium-scale
development site
located in Large-scale development
site

※２

※3

○

BUA

×

※3

×

※3

AdjustSurrounding area

located in Land readjustment○

DV-TAI

District dummy

Taira district dummy○○

DV-ONA

District dummy

Onahama district dummy

DV-NAK

District dummy

Nakoso district dummy○

DV-ZYO

District dummy

Joban district dummy○

DV-NAI

District dummy

Uchigo district dummy○

DV-YOK

District dummy

Yotsukura district dummy○District dummy

Tono district dummy○

DV-OGA

District dummy

Ogawa district dummy○

DV-YOS

District dummy

Yoshima district dummy○

DV-SAN

District dummy

Sanwa district dummy○

DV-TAJ

District dummy

Tabito district dummy○

DV-KAW

District dummy

Kawamae district dummy

DV-KUN

District dummy

Hisanohama / ohisa district dummy○

FD

Flooded area dummy

Tsunami inundation area○

※４

Reconstruction base○※５

Reconstruction base
dummy

There was no corresponding area
The damage caused by the tsunami caused by the earthquake was great in Iwaki City.

※５

Samples belonging to the area around Onahama

2012 PLPAC
FAR
Station AD
DSNPP
PLP
1.00
AC
-0.02
1.00
FAR
-0.17
0.00
1.00
Station0.11
0.00 -0.32
1.00
AD
0.18 -0.01 -0.22
0.01
1.00
DSNPP -0.14
0.07
0.01
0.08 -0.63
1.00

2016 PLPAC
FAR
Station AD
DSNPP
PLP
1.00
AC
-0.11
1.00
FAR
-0.27
0.00
1.00
Station -0.07
0.00 -0.32
1.00
AD-0.10 -0.03
0.08 -0.05
1.00
DSNPP -0.06
0.07
0.01
0.08 -0.42
1.00

2013 PLPAC
FAR
Station AD
DSNPP
PLP
1.00
AC
-0.10
1.00
FAR
-0.27
0.00
1.00
Station -0.05
0.00 -0.32
1.00
AD
0.11 -0.04 -0.01
0.05
1.00
DSNPP -0.14
0.07
0.01
0.08 -0.39
1.00

2017 PLPAC
FAR
Station AD
DSNPP
PLP
1.00
AC
-0.11
1.00
FAR
-0.24
0.00
1.00
Station -0.07
0.00 -0.32
1.00
AD-0.07 -0.02
0.09 -0.10
1.00
DSNPP -0.12
0.07
0.01
0.08 -0.48
1.00

2014 PLPAC
FAR
Station AD
DSNPP
PLP
1.00
AC
-0.10
1.00
FAR
-0.23
0.00
1.00
Station -0.10
0.00 -0.32
1.00
AD
0.09 -0.04
0.06 -0.07
1.00
DSNPP -0.04
0.07
0.01
0.08 -0.41
1.00

2018 PLPAC
FAR
Station AD
DSNPP
PLP
1.00
AC
-0.07
1.00
FAR
-0.06
0.00
1.00
Station -0.08
0.00 -0.32
1.00
AD-0.09 -0.03
0.08 -0.07
1.00
DSNPP -0.02
0.07
0.01
0.08 -0.47
1.00

Table 4 shows the coefficients of determination,
adjusted coefficients of determination, and sample
sizes from 2011 to 2018. For 2011 and 2012, thecoefficients of determination (R² values) were 0.33
and 0.22, respectively, with adjusted R-squared
values (Adjusted R²) of 0.06 and -0.07, indicating apoor goodness-of-fit for the model.

○

The VIF value was high, and the coefficient of determination did not change in the analysis excluding this variable.

※４2015 PLPAC
FAR
Station AD
DSNPP
PLP
1.00
AC
-0.11
1.00
FAR
-0.29
0.00
1.00
Station -0.09
0.00 -0.32
1.00
AD-0.05 -0.03
0.05 -0.04
1.00
DSNPP
0.01
0.07
0.01
0.08 -0.48
1.00

RESULTS AND DISCUSSIONIn Iwaki City, the nearest stations on the JR Joban Line and interchanges on the motorway are mainly used.

※３

2011 PLPAC
FAR
Station AD
DSNPP
PLP
1.00
AC
0.04
1.00
FAR
0.00
0.00
1.00
Station -0.17
0.00 -0.32
1.00
AD
0.06 -0.05 -0.18
0.03
1.00
DSNPP -0.29
0.07
0.01
0.08 -0.53
1.00

Coefficient of determination and model validity

DV-TON

RD

※２

ment

Road-4N-P-Road※１

Variable name

Table 4 Coefficients of determination (2011-2018)

Similarly, the initial list of explanatory variables
related to the surrounding area included LA (surveysite is located in an area of houses for installment sale),
BUA (site is in an urbanized area), RA (site is in a

2011

2012

2013

2014

2015

2016

2017

2018

0.334

0.225

0.562

0.598

0.713

0.726

0.658

0.525

Adjusted R²

0.062-0.063

0.403

0.468

0.630

0.647

0.567

0.402

Number of samples

125

134

136

148

161

163

171

176

Coefficient
determination (R²)

658

GEOMATE – Kyoto, Japan, 3-5 November 2021

1%-5% level from 2013 to 2017, thus suggesting thatthe floor area ratio actually had a negative impact onland price fluctuations. This would seem to run
counter to what would normally be expected. Since,
in general, a larger floor area ratio means that a larger
floor area can be constructed, the floor area ratio is
typically a variable that contributes positively to a risein land price fluctuation rate; however, here it appears
to work in the opposite way. This result is thought tobe due to the high demand for low- and middle-risehousing in Iwaki City during this time.
Distance from the nearest station (Station) was
shown to have a significant negative effect (at the
10% significance level) in 2015, 2017, and 2018. This
result indicates that the shorter the distance from thenearest station, the greater the land price fluctuationis.
Air dose (AD) was not significant until 2014.
However, it had a significant negative effect (at the
1%-10% level) between 2015 and 2018, suggestingthat this environmental factor negatively impacted
land price fluctuation rate. Moreover, since thestandardized partial regression coefficient for air doseis rather large, it can be inferred that its influence onland price fluctuation rate was substantial. The likely
reason for this is that the excessive demand for
housing had begun to settle down, and air dose
considerations came to constitute a large portion of
the judgment factors affecting the residents’ choice ofa place to live.
A significant positive effect (at the 5% level) was
found for distance from the Fukushima Daiichi
Nuclear Power Station in 2013; however, asignificant negative effect (at the 10% level) was
found for this same variable in 2017 and 2018.
In 2013, the larger the distance from the
Fukushima Daiichi Nuclear Power Station, the higher
was the land price fluctuation. In contrast, in 2017 and2018, the larger the distance to the Fukushima Daiichi
Nuclear Power Station, the lower the land pricefluctuation was. Immediately following theearthquake, evacuees were concerned about theunknown dangers of the nuclear power plant andgenerally looked for a place to live that was far fromthe Fukushima facility. However, several years after
the earthquake, many became less anxious or lessconcerned about the dangers. It appears that thestigma associated with the accident had a little
disappeared after approximately four years.
There is great variation among the district
dummies (DV-TAI, ONA, NAK, ZYO, NAI, YOK,
TON, OGA, YOS, SAN, TAJ, KAW, KU). In 2015
and 2016, a significant positive effect (at the 5%-10%level) was found in the Taira and Onahama areas.

This would appear to be largely due to the rapid
decrease in land price in Iwaki City immediately after
the earthquake. The various environmental factorsthat determine normal land price fluctuation rate may
not have responded clearly in the turmoil of the landmarket during this period. On the other hand, from2013, land price fluctuation rate show an upward
trend, and the coefficients of determination for themodel are all above 0.5. The adjusted coefficients ofdetermination are also substantially higher. Thus, themodel’s explanatory power, as reflected in its better
goodness-of-fit, appears to be much stronger.
Consequently, it is considered an effective means for
estimating land price fluctuations after 2013.
Multiple regression analysis
Table 5 shows the standardized partial regression
coefficients and significance levels from 2011 to
2018. No significant results were produced for theland area (AC) variable during the estimation period,
suggesting that the size of the land area did not affectthe fluctuation in the land price during thereconstruction period.
Table 5 Standardized partial regression coefficients(2011-2018)
Explanatory variablevariables

Qualitative variables

Quantitative

logprice

2011

2012

2013

2014

2015

2016

2017

2018

AC

0.123

-0.032-0.031-0.060-0.053-0.052-0.046-0.030FAR-0.020-0.070-0.320-0.237-0.235-0.201-0.220-0.020Station

0.033

0.164

0.015

-0.022-0.140-0.067-0.134-0.1580.720-0.148-0.0420.024-0.163-0.203-0.191-0.156

DSNPP

0.159

0.177

0.778

0.287

-0.151-0.203-0.695-0.557

DV-TAIAD-0.342

0.211

0.525

0.300

0.414

0.232

-0.036-0.276

DV-ONA-0.155-0.027

0.050

0.165

0.452

0.349

0.323

0.083

DV-NAK-0.252-0.047-0.558-0.183

0.159

0.044

0.180

-0.065

DV-ZYO-0.160

0.040

0.026

-0.0540.035-0.002-0.042-0.216

DV-NAI-0.310

0.176

0.123

-0.0230.036-0.077-0.130-0.228

DV-YOK-0.571

0.036

0.082

-0.019-0.0030.131-0.140-0.364

DV-TON-0.3630.022-0.210-0.201-0.107-0.155-0.135-0.139

DV-OGA-0.024

0.133

0.147

0.046

-0.001-0.064-0.188-0.200

DV-YOS-0.208

0.085

0.250

0.090

0.092

0.041

-0.084-0.221

DV-SAN-0.451

0.037

0.009

0.037

0.067

0.007

-0.038-0.019

DV-TAJ-0.4570.066-0.204-0.171-0.019-0.085-0.053-0.115

DV-KAW-0.128

0.101

0.078

-0.096-0.184-0.186-0.289-0.307

DV-KUN-0.595

0.264

0.185

0.008

-0.085-0.136-0.262-0.306FD-0.127

0.033-0.095-0.085

0.063

0.062

0.113

0.161

RD

0.124

-0.171-0.206-0.093-0.175-0.163-0.114-0.089Road-4-0.009-0.222

0.001

0.021

0.209

0.341

0.300

0.195

Road-6-0.258-0.423

0.333

0.403

0.597

0.863

0.800

Road-8-0.287-0.192

0.416

0.381

0.340

0.653

0.663

0.488

GOS

0.043

0.095

0.334

0.310

0.226

0.281

0.176

0.161

N-P-Road

0.088-0.050-0.124-0.174-0.114-0.080-0.106-0.027

AR-Resid

0.192

0.035

0.014

0.072

0.014

0.040

0.058

-0.016

AR-Farm

0.117-0.043-0.034-0.138-0.112-0.148-0.274-0.2410.599

AR-Indu-0.021

0.103

0.078

0.041

0.043

0.046

0.065

0.133

AR-Vacant-0.057-0.210-0.159-0.110-0.009-0.065-0.100-0.056

AR-Store-0.116-0.174-0.0370.018-0.043

0.018

0.042

0.123

DLA

0.016

-0.073-0.058-0.083-0.003

0.038

0.036

0.142

BUA

-0.163-0.0680.023-0.001

0.071

0.027

0.133

0.172

RA

-0.213

0.184

0.242

0.315

0.295

0.256

0.331

0.483

ASA

0.046

0.135

-0.007-0.032-0.121-0.063-0.027-0.055

CA

0.036

0.179

0.073

0.044

0.062

-0.0440.010-0.067

Adjust-0.027-0.093

0.004

0.046

0.050

0.057

0.064

-0.005

10％level

5％level

1％level

10％level

5％level

1％levelLand price volatility
riseLand price volatility
declineCoef.

On the other hand, the effect of the floor area ratio(FAR) was found to be negative and significant at the659

GEOMATE – Kyoto, Japan, 3-5 November 2021

These areas are among the most reconstructed areas
in Iwaki City. This was also the time when many
disaster-related public housing and group relocation
promotion projects were completed.
A significant negative effect (at the 1%-5% level)
can be seen in 2011 for areas in the northern part of
Iwaki City, such as the Yotsukura, Hisanohama, andKawamae districts; however, the coefficient ofdetermination is quite small from 2011 to 2012,
making it difficult to evaluate based on the model.
In 2017 and 2018, a significant negative effect (atthe 1%-10% level) was found in most areas, with the
exception of the Onahama area. As the reconstruction
progressed and the demand for housing slowed down,
it seems that the land market fell to the level that wasseen in the local cities before the earthquake.
For the flooded area dummy (FD), no significant
results were produced until 2016; thereafter, asignificant positive effect (at the 5%-10% level) was
found in 2017 and 2018. The positive impact of thetsunami inundation variable since 2017 may be due tothe progress of the tsunami countermeasures, such asseawalls.
The Reconstruction Base Area dummy (RD) was
negative significant effect (at the 5% level) in 2013,2015 and 2016. Despite the business vision, land
price fluctuation rate fell near the Onahama Porttsunami reconstruction center development project.
The influence of front road width (Road-4,6,8)
was found to be positive and significant (at the 1%5% level) in 2015, 2016 and 2017. A sufficiently wide
front road is advantageous in terms of access toparking lots and day lighting conditions and thus
seems to be preferred as a condition for choosing a
residence.
For the lifeline dummies (GOS), a positive
significant effect (at the 1% level) was found from2013 to 2018. The construction of sewers and city gaslines is a very important factor in the rapid
reconstruction of living space after a disaster, and has
a great positive impact on land price fluctuations.
Among the surrounding buildings variables (ARResidence, Industrial, Store, Bank, Office, Farm,
Workshop), the effect of AR-Farm was negative andsignificant (at the 1%-5% level) between 2016 and2018, and the effect of the AR-Vacant was negativeand significant (at the 10% level) in 2013. Thissuggests that land price fluctuation rate will decrease
if there are farms or vacant lots around the survey area.
Finally, Finally, regarding the area around thesurveyed site (DLA, BUA, RA, ASA, CA, Dev-L,
Dev-M, Adjustment).
DLA (house for installment sale area) had asignificant positive effect (at the 5% level) in 2018.

BUA (urbanized area) had a significant positive effect(at the 5%-10% level) in 2016/2017, and RA(residential area) had a significant positive effect (atthe 1%-5% level) from 2014 through 2018. After thedisaster, it seems that Iwaki City experienced anincrease in demand by evacuees seeking a place ofresidence.
CONCLUSIONS
Focusing on Iwaki City, where the real estatemarket behaved unexpectedly after the Great East
Japan Earthquake, and land price fluctuation rate
were rising abnormally, this paper used a hedonic
regression approach to identify those factors thatsignificantly affected area land price fluctuations. The
results are as follows:1.

From 2013 to 2016, when an abnormal rise inland price fluctuation rates was observed, theexplanatory variables having a significant
positive effect were the Taira and Onahama area
dummies, Front road width, Lifeline, andSurrounding area.

2.

Regarding distance from the Fukushima Daiichi
Nuclear Power Station, it was found that in 2013,
the larger the distance, the higher the land pricefluctuation rate was. In contrast, in 2017 and2018, the larger the distance, the lower the land
price fluctuation rate was.

3.

With regard to reconstruction bases (RD), land
price fluctuation rates decreased in all such areas.
As for the impact of lifelines (GOS), a significant
positive effect was found between 2013 and 2018.

4.

Air dose (AD) was not significant until 2014.
However, it had a significant negative effect (atthe 1%-10% level) between 2015 and 2018,suggesting that this environmental factor
negatively impacted land price fluctuation rate.

The effect of the air dose and distance from thenuclear power plant variables changed significantly
after the 4th year since the earthquake. On the other
hand, regardless of the time of year, a negative impact
on land price fluctuation rate was shown for thereconstruction base variable, while a positive impact
was shown for lifelines. It suggests that the stigma forland that existed after the accident was a little
resolved in roughly four years.
We have issues to be addressed in the future. Since
the coefficient of determination (R²) in 2011 and 2012
was rather small, the model for Iwaki City was ill
fitting for 2011 and 2012. We intend to improve theexplanatory power of the hedonic model by adding
variables that reflect the regional characteristics of the660

GEOMATE – Kyoto, Japan, 3-5 November 2021

survey area, the rate of collapse of houses, and the rateof population decline.

[3] Mitsuhiro Saito, An Approach to Influence on
City Planning by Accepting Evacuees due to
Nuclear Accident, 2018 Vol.53 Issue 3, pp.919926[4] Yuji Hirota, Nuclear Power Plant Accident Due tothe Great East Japan Earthquake and Appraisal ofReal Estate, The Appraisal Journal Summer 2011Vol.53, No.3 pp25-40、2011.07[5] Gamble, H.B., and R.H. Downing, "Effects of
Nuclear Power Plants on Residential Property
Values," Journal of Regional Science, 1982, 457478.
[6] OECD Glossary of Statistical Terms,
Organization for Economic Co-operation andDevelopment,
https://stats.oecd.org/glossary/detail.asp?ID=1225, (10th june2021)[7] Koji Shimada, Tomohiko Yoshida, Urban andEnvironmental Policy Assessment for Kyoto,
Osaka and Kobe City by The Hedonic Price
Method, Proceedings of Annual Meeting ofEnvironmental Systems Research, Vol.40
PP.373-378, 2012

ACKNOWLEDGMENTS
We are grateful for a Grant-in-Aid for Scientific
Research (18K18602) for supporting this research.
REFERENCES[1] Koji NOMURA, Miho OHARA and Kimiro
MEGURO, Analysis of Change of Land Price
After an Urban Earthquake -Case Study in
Affected Area Due to The 1995 Kobe
Earthquake-, Journal of Japan Society of Civil
Engineers A1、Vol.65, No.1, p. 655-660, 2009[2] Shinya KAWAMURA, Relationship between
Real Estate Transactions by Fukushima Refugees
and Land Price Changes after The Great EastJapan Earthquake, Proceedings of the General
Meeting of the Association of Japanese
Geographers, 2013a(0), 100141, Session ID : 316 ,
2013

661

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

NUMERICAL INVESTIGATION INTO MOLTEN METAL FLOW ANDITS SOLIDIFICATION UNDER GRAVITY SAND MOULDING IN
PLUMBING COMPONENTS: POURING TEMPERATURE AND
RUNNER STRUCTURE
Sirawit Namchanthra1, Watcharapong Chookaew1, Chakrit Suvanjumrat1,
Wichuphan Wijitdamkerng, and Machimontorn Promtong1
Department of Mechanical Engineering, Faculty of Engineering,
Mahidol University, Salaya, Nakorn Pathom, Thailand

ABSTRACT
Metal casting using the solidification of molten metal flows involves complex heat transference during
production. This can cause defects on the workpiece, affecting the efficiency of production. This research will use
the Computational Fluid Dynamics (CFD) technique to investigate the filling, solidification, and cooling stages ofthe production of a component for plumbing. The CFD program FLOW 3D CAST v5.03 tracked the leading edgeof the molten metal liquid (based on the Volume of Fluid (VOF) method). A two-equation k–ɛ turbulence wasadopted to provide a Reynold term, and it should be noted that the geometry of the molten liquid and internal
chamber are the same as in the actual production line. The simulation shows how different pouring temperatures
effect the runner, and that the pouring temperature influenced the behavior of the molten metal flow, resulting in
defects on the workpiece. Moreover, the runner and its number affected the liquid metal flow and affected thedefect's location during the solidification period. Future work will verify the results between the simulation andexperiments, and further investigate possible defect locations on the surface of the workpiece, which could be a
critical criterion in further discussions.
Keywords: Gravity sand casting, Solidification, Computational Fluid Dynamics (CFD)INTRODUCTION

internal cavities affect the mechanical properties,
resulting in low-quality casting that is unfit for
purpose. For decades, research has investigated theinfluence of pouring temperature, filling rate, andpressure transfer (during the solidification period),
cooling rates, mould coating material, and air
entrapment behavior [1, 4-9] on the quality. Someworks focused on minimizing residual stress and thehealing of possible bifilms during solidification [10,11]. Moreover, the magnetic fields added to thesolidification process to eliminate shrinkage defects[12]. Because of the parameter optimization (i.e.,pouring temperature and pouring time), amathematical model based on regression analysis was
proposed recently [13].
The Computational Fluid Dynamics (CFD)
technique is widely used in the casting process
design, especially for parametric optimization [1422]. Basically, this promising tool can help redesign
the mould layout and eliminate defects such as
shrinkage, porosity, and the discovery of hot spot
regions [23], significantly, the liquid phasetransformation to the solid phase, which could also
capture using the CFD technique. This metallurgicalprocess could point out critical criteria of defectoccurrence [20]. Some researchers have observed theeffects of the feeder and discussed concerns regarding
the filling characteristics [23]. Moreover, the heat

In the manufacturing of plumbing components,
gravity sand casting is a commonly used production
process. The casting process starts with the
preparation of a sand mould, into which molten metal
is poured [33]. The metal solidifies during the coolingprocesses, after which the mould is removed, and theworkpiece quality checked for shape and size. If there
are defects, the component cannot proceed to thefinishing section, and will be recycled to save thematerials cost. However, the energy and labor costs
are already spent and cannot be retrieved, so thisprocess can be more efficient. Many techniques havebeen proposed to enhance the economic efficiency ofmolten metal casting, such as the production ofseveral castings in a single mould and theintroduction of lost foam technology. [1]. This canincrease the efficiency of material use and saveproduction costs, but it requires a deep understandingof mould layout design, material pouring techniques,and physical molten metal heat transfer process.
Defects in castings may arise from poor designof the mould, an unsuitably sized runner, or
carelessness during the pouring process [2, 3]. Fig. 1
shows plumbing components produced by casting as
defective workpieces (Fig. 1c shows the defects(porosity) within the workpiece). The external and662

GEOMATE – Kyoto, Japan, 3-5 November 2021

transfer and the interface between solids and liquids
were identified related to various casting speeds [15].
In addition, apart from the fluctuated surface ofmolten metal, the temperature distribution during thefilling and solidification was monitored [18]. The
validation study, and especially the casting process,became mandatory tasks so the evaluation was
accurate [2, 6, 24-30].

To perform the casting simulation, FLOW 3D
CAST will be used as it is a program that calculates
based on the Fractional Area/Volume Obstacle
Representation (FAVOR) method, which is based on
the finite difference for the generated mesh. This
technique may improve the accuracy of the numericalsolution near the mould walls when compared to thestandard finite difference. For an incompressible,
viscous fluid study, FAVOR equations areformulated using the flowing equations:𝛁 ∙ (𝑨𝒖) = 𝟎
𝝏𝒖
𝝏𝒕

(1)

𝟏

𝟏

𝟏

+ 𝑽 (𝑨𝒖 ∙ 𝛁)𝒖 = − 𝝆 𝛁𝒑 + 𝝆𝑽 (𝛁𝑨) ∙ (𝝁𝛁)𝒖 + 𝒈

(b)

(2)
𝝏𝑯
𝝏𝒕

(a)

𝟏

𝟏

+ 𝑽 (𝑨𝒖 ∙ 𝛁)𝑯 = 𝝆𝑽 (𝛁𝑨) ∙ (𝒌𝛁𝑻)
(3)

(c)

where

Fig. 1 Plumbing Joints Produced in the Plumbing
Manufacturing Process (Thailand); a) An Example of a
Defect, b) A Defect Causing Porosity occurrence , andc) detail view of porosity occurrence.

𝑨𝒖 = (𝑨𝒙 𝒖𝒙 , 𝑨𝒚 𝒖𝒚 , 𝑨𝒛 𝒖𝒛 )(𝛁𝑨) = (This research uses the Volume of Fluid (VOF)
method to capture the molten metal flows in thegravity sand mould. It will observe the effects ofdifferent pouring temperatures and different internal
structures on the traveling distance and filling time.
In addition, the curve of average temperature during
the solidification and cooling stages will be
discussed. The objective of this work is to use theComputational Fluid Dynamics (CFD) technique:i) to investigate the molten metal flows
during the filling stage, as well as the influence of
pouring temperature,ii) to capture the casting process, especiallythe solidification, when operating under different
pouring temperatures,iii) to analyze the effects of the size andnumber of runners and discuss possible solutions to
reduce the cavity defects.
It should be noted that a CFD program named
FLOW CAST v5.03 was used to capture the filling,solidification, and cooling sequences. Therefore, thetime for each stage will be included in additional
discussions under the considered controlling
conditions, from which insights may be observed.
This will lead to suggestions for future
improvements, especially reducing the cavity defects,
reduction of production time, and increase in quality.

𝝏
𝝏
𝝏
𝑨𝒙 ,
𝑨𝒚 , 𝑨𝒛 )𝝏𝒙𝝏𝒚𝝏𝒛

𝑯 = ∫ 𝑪(𝑻)𝒅𝑻 + 𝑳(𝟏 − 𝒇𝒔 )(4)

Where Ai is the open area fraction associated with theflow in the ith direction. V, ρ, and p are the open
volume fraction, density, pressure. Where ui isvelocity component and μ is the fluid viscosity
coefficient. g gravity, H fluid enthalpy, T fluid
temperature, fs solid fraction, L latent heat. C and k
are the fluid-specific heat and thermal conductivity
coefficient, respectively.
With regard to the temperature focus, the energy
equation and form are as follows:
𝝏𝑻𝒎
𝝏𝒕

𝟏

= 𝝆𝑪 𝑽 (𝛁𝑨𝒄 ) ∙ (𝒌𝒎 𝛁𝑻𝒎 )
𝒎 𝒄

(5)

where m indicates the parameter related to the mould
and c indicates quantities that are complements of thevolume and area fractions. At the metal/mouldinterface, the heat flux, q, is calculated by
𝒒 = 𝒉(𝑻 − 𝑻𝒎 )where h is the heat transfer coefficient.

Volume of Fluid (VOF) Method
FLUID FLOW EQUATIONS AND VOLUME
TRACKING METHOD
Fluid Flow Equations
663

(6)

GEOMATE – Kyoto, Japan, 3-5 November 2021

In the mould filling process, the boundaries
between the molten casting metal and the surrounding
air can be tracked using the tracking free surface
technique. In terms of numerical development, themost popular method to describe the free surfaces anddeformations are the Volume-of-Fluid (VOF)
method. The equation for the VOF method isrepresented as the F function, which is shown as
follows:

in Fig. 3, and the thermo-physical properties of thecast iron and sand mould are shown in Table 1. The
surface roughness of the mould was 25 μm, and thecontact angle was 180◦. The volume flow rate at thesprue was 0.0016 m3/s, which was calculated using
the total weight of the molten metal and the filling
time.

𝝏𝑭

Mesh Independence Study𝝏𝒕𝟏+ 𝑽 𝛁 ∙ (𝑨𝒖𝑭) = 𝟎

RESULTS AND DISCUSSIONS(7)

To minimize the calculation time, a mesh
independence study was performed. In this study, theaverage temperature at the completion of the fillingstage was derived from a comparison of different
meshes. Fig. 3 shows how the multi-blocks areapplied for the mesh of the plumbing component. The
mesh was the smallest available size in the plumbing
block (denoted by the blue box).

The boundary conditions on the free surface
have no normal and tangential stresses so the time
step must assess the accuracy and stability. As in Eq.
8, the accuracy of the results is constant when the time
step is less than the minimum value of the calculated
ratio. The time increment for stability is necessarily
governed by two restrictions, firstly the fluid material
cannot move through more than one cell in the onetime step because the different equations assume
fluxes between adjacent cells only. Therefore, thetime increment must satisfy the inequality below.
∆𝒙

∆𝒚

𝒊,𝒋

𝒊,𝒋

𝒋
∆𝒕 < 𝒎𝒊𝒏 {|𝒖 𝒊| , |𝒗 |}

Sprue

(8)

where the mesh increments, ∆𝒙 and ∆𝒙, u and v,
velocity component, and the time increment, ∆𝒕. The
minimum is calculated with respect to every cell inthe mesh. Typically, ∆t is equal to one-fourth to onethird of the minimum cell transit time. Secondly,
where there is a non-zero value for kinematic
viscosity, momentum must not be diffused by morethan approximately one cell in one-time step. A linear
stability analysis shows that this limitation by
𝟏 ∆𝒙𝒊 𝟐 ∆𝒚𝒋 𝟐

𝒗∆𝒕 < 𝟐 ∆𝒙 𝟐+∆𝒚 𝟐𝒊

RunnerFig. 2 A 3D Model of the Plumbing Component
where the Red Area is the Cast Iron Input Boundary,and the Pink Area is the Environment Boundary.

(9)𝒋Four different meshes (3,600,000, 1,500,000,
250,000 and 20,000 elements) were tested and theiraverage molten cast iron temperature compared(shown in Table 2).

with ∆t satisfying the two inequalities above.
The last parameter ensures numerical stability is α,
the upstream differencing parameter. The properchoice for α is𝐮𝐢,𝐣 ∆𝐭𝐯𝐢,𝐣 ∆𝐭𝐢𝐢

𝟏 ≥ 𝛂 > 𝐦𝐚𝐱 {| ∆𝐱 | , | ∆𝐲 |}(10)Further information regarding the VOF method
can be found in research from Hirt [31] andAniszewski [32].
DESCRIPTION OF THE NUMERICAL STUDY
The plumbing component is used to simulate thestudy and it was created in the stereolithography(STL) format and imported into the FLOW 3D CAST
software. The model’s multi-block meshes are shown

Fig. 3 The Multi-Block Meshes of the Plumbing
Component (the Mould Mesh is Not Displayed).

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GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1 Thermo-Physical Properties of the Casting & Mould used in the simulations
Material
Casting

Property
Thermal Conductivity of theLiquidThermal Conductivity of theSolidSpecific Heat of the Liquid
Specific Heat of the Solid
Surface Tension Coefficient of
the Liquid
Kinematic Viscosity

Symbol
kl

Value
39.59

Unit
W/(mK)

ks

34.39

W/(mK)

Cl
Cs
σ

897
770
1.871

J/(kg K)
J/(kg K)kg/s2

μ0.004516*
0.0045*0.00448539*
Density of the Liquid
ρl6856
Density of the Solidρs
7100
Latent Heat
L
216
Liquidus TemperatureTl
1230.5
Solidus TemperatureTs
1146.5
Sand Mould
Thermal Conductivity
km
0.61
Volumetric Specific Heat
ρC
1700
* Note: Three viscosities are for Molten Cast Iron at 1329, 1429 and 1529 deg-C Respectively.
The pouring temperature was 1429 deg-C(which is relevant to the real operation), and after theliquid was poured, the average temperature was
reduced to a certain value due to the heat transfer tothe mould. As shown in Table 1, the averagetemperature created by the three different meshes aredifferent, with the maximum approximately 0.5%.
Therefore, the smallest mesh size (250,000) was
introduced for further study as the plumbing partcomponent is small so the average temperature will
not have much variance.

250,000
1,500,000
3,600,000

Averagetemperature on
finish time (deg-C)
1401.491395.221394.70

kg/m3
kg/m3
kJ/m3
deg-C
deg-C
W/(mK)
kJ/(kg K)

liquid gets into the plumbing region. From Fig. 4b and4c show the melt entering to the mould cavity andthen completed filling the cavity about 9s. Afterward,
the liquid keeps running back to runner andcompletely fills the mould and ending at 10.861s (see(Fig. 4d). It should be noted that in this simulation thefill fraction criteria for stopping the calculation was
set at 0.999. At the finishing time of filling process,the lowest temperature in the mould was lower thanthe pouring temperature about 35 deg-C.
Fig. 5. shows the solidification stage of theplumbing part, after the filling period. This process isvery important because if having the entrapped air inthe mould it could lead to the defect occurrence. From
the figure, the melt solidification is started frombottom part, to the runner and the pouring area. As
shown in Fig. 5a., reaching of 0.6 solid fraction, it
takes about 324s. The solidification reaches 0.9 of thesolid fractions when the time is 529s, at this time, justonly the basin region is not completely solidified (seeFig. 5b). In Fig. 5c, it shows the time consumed andthe solidified volume fraction. According to thegraph, the solidified process slowly increases from 0
to 0.05 the beginning (0-100s) and rapidly increased
from 0.05 to 0.9 (100s-500s). After that, there has aslightly increase until get 0.999 of the solid fractions(~840s or ~ 14 mins).

Table 2 The Percentage Difference of the Average
Temperature at the End of the Filling Stage ofDifferent Mesh Sizes
Mesh

m2/s

Error (%)

0.447
0.037
-

Simulation of Plumbing Part
The filling of the molten liquid in the mould at
different time are shown in Fig. 4. The cast iron melt
at 1429 deg-C enters the mould at the poring cup andkeep filling up the plumbing part. Fig. 4a show thefilling at 1s, at this time only a small volume of the665

GEOMATE – Kyoto, Japan, 3-5 November 2021

(a)

(b)

(c)

(d)

Fig. 4 The Filling for the Plumbing Component at Different Times: (a) 1sec, (b) 5sec, (c) 9sec, and (d)10.861sec.

(a)

(b)

(c)

Fig. 5 The Solidification of the Plumbing Component at (a) 0.6 and (b) 0.9 Solid Fraction. The Graph Shows
the Solidified Volume Fraction Through the Solidification Process

666

Average metal temperature (deg=C)

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 5 illustrates the average temperature andtime consumed for the filling, solidification, andcooling stages. According to simulation results, thefilling required 11 seconds, and the averagetemperature was reduced from 1429 °C to beapproximately 1395 °C. During the solidification
stage, 14 minutes (840 secs) were required. The
solidification process was halted when the wholedomain had 0.999 of the solid fractions, when the
average temperature was found at 962 °C. The
cooling stage was longer than the other stages at morethan five and a half hours before it reached ambient
temperature.

1600
1400
1200

Filling
Solidification
Coolling

1000
800
600
400
200
0
0

5000

10000

15000

20000

25000

Time (s)Fig. 6 Average Temperature Occurrence During theFilling, Solidification, & Cooling Stages(pouring 1429 deg-C).
(a)

(b)

(c)

(d)

Fig. 7 Contour of Distance Traveled by the Molten Metal on 9s at (a) 1329 deg-C, (b) 1529 deg-C of the pouring
temperature, (c) an up-close view of 1329 deg-C, and (d) an up-close view of 1529 deg-C

The Influence of the Pouring Temperature

Fig. 8 Comparison of the Solidified Volume Fractionof Each Pouring Temperature Through the
Solidification Process

Due to a requirement to study the influence of the
pouring temperature, thus, in this simulation two
different cases having the temperature at 1329 deg-C
and 1529 deg-C were investigated. Fig.7 shows the
distance traveled by the fluid at the pouring time at 9 s.
From the figures, it can be noticed the position ofmelted cast iron at 1529 deg-C flows faster than 1329
deg-C as confirmed by Fig. 7c and 7d. This could be
because the higher pouring temperature has lower
viscosity, hence the melt can flow faster.
In this study, the solidification of melt cast iron on
1329 deg-C and 1529 deg-C were also compared anddiscussed about the time consuming.

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GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 3 The Solidification of the Plumbing Component at 0.6 and 0.9 Solid Fraction at 1329 deg-C
and 1529 deg-C.
Solid
Fraction

Molten Cast Iron Input Temperature (deg-C)
1329

1529

0.6

0.9

was found at the lowest point of the plumbing
component, at the runner, and at the last area tosolidify, as shown in Table 3. The solidification graph(Fig. 8) shows that the solidification of the twodifferent pouring temperatures were similar. at the
beginning of the period, but the higher temperaturecase had slower growth compared to the case of
having lower pouring temperature. This can affect thequality of the final workpieces, but further study isrequired to confirm this prediction.

Table 3 shows the results at 0.6 and 0.9 of thesolid fractions for the different pouring temperatures.
The time consumed for each case to reach 0.6 of thesolid fractions are approximately 200 seconds and450 seconds respectively. For the case of reaching 0.9
of the solid fractions, the time required for thedifferent pouring temperatures is approximately 376
seconds and 687 seconds at 1329 deg-C respectively.
In general, the first solidified volume
(a)

(b)

(c)

Fig. 9 Defect net volume on 1329 deg-C of the case (a) 3 branches, (b) 6 branches and (c) 15-degree inclined
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GEOMATE – Kyoto, Japan, 3-5 November 2021

Influence of the Runner Structure

Moreover, authors would like to thank Chaipong
Engineering Co., Ltd. for allowing to use thefacilities.
Thanks to Robert McEvoy, from the Faculty of
ICT at Mahidol University for editing andproofreading the manuscript.

The cavity found in the plumbing component
was removed. Other possible defects occurred, andmodifications to the runner were attempted. Threedifferent angles were investigated, and the pouring
temperature of the molten cast iron was 1329 deg-C.
More details of the modified runners can be seen in
Fig.9.
After the solidification was completed, the defectnet volume (the volume of void) in the mould and its
position were identifiable. It was found the defectlocation was the same in all cases, and the defect net
volume was also similar, at approximately 1.0x10-4(see Fig. 9). Therefore, the new modifications to therunner did not reduce the cavity defects in theplumbing component, possibly because of having no
the riser. Further investigation regarding the runnermodifications study will be conducted in future work.

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and S.V. Smetanyuk, Influence of Temperaturesof Melt Overheating and Pouring on the Qualityof Aluminum Alloy Lost Foam Castings. Russian
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CONCLUSIONS AND RECOMMENDATIONSThe simulation of casting molten iron in theplumbing component was successfully performed
using the Computational Fluid Dynamics (CFD)
technique. The parameters affecting the quality ofcasting related to the cavity defect were investigated.
Overall, the conclusions are:i) At the temperature of 1429 deg-C, the filling
time required is 11 seconds, the solidification stage
required 14 minutes, and the cooling time was aroundfive and half hours.
ii) At different pouring temperatures (1329 and
1529 deg-C), the distance was travelled faster bymolten cast iron with a higher temperature as it wasless viscous. When comparing the solidification
behavior, the time required to become solid for thecase having a higher pouring temperature (1529 degC) was required nearly double the lower temperature.
iii) Applying three different types of runner has
no effect in terms of reducing the number of defects
at the present work.
In future work, the experiment of pouring
temperature will be conducted, an experimental study
regarding the production of plumbing components
will be compared with simulation results. The
parameters affecting the reasons behind the cavity
defect will be discussed and a lower cost of
production sought.
ACKNOWLEDGMENTS
This research was supported by the MechanicalEngineering Department, Faculty of Engineering,
Mahidol University.
Thanks to CADFEM SEA Pte. Ltd. - A Certified
Elite Channel Partner to ANSYS - for providing
support and access to the CFD program named
ANSYS Fluent 2021 R1.
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GEOMATE – Kyoto, Japan, 3-5 November 2021

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11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

MOTORCYCLE ACCIDENT MODEL BASED ON MOTORCYCLISTCHARACTERISTICS FOR DESIGN THE DEDICATED LANE
Sobri Abusini1 and Lasmini Ambarwati2
1

Faculty of Natural Science, Brawijaya University, Malang, East Java; 2Faculty of Engineering, Brawijaya
University, Malang, East Java, Indonesia

ABSTRACT
A motorcycle is the most popular transport mode and requires a relatively inexpensive cost for travelingwithin the city. The growth of the transport mode is quite high and 87% of the total vehicles overburden a largepart of the highway network in Malang District, as the study case area. As a consequence, more than 70% of
accident victims are motorcyclists. An accident prediction model based on motorcyclist characteristics isrequired in the effort to minimize the occurrence of accidents. The model is expected to predict the causalities ofmotorcyclists and to design dedicated lane requirements. This research aims to identify motorcyclist
characteristics, to develop an accident model, and to plan the best solution in reducing motorcycle accidents.
Motorcyclist characteristics were identified from questionnaires. Most respondents (±70%) had collided withother motorcycles or other vehicles with socio-economic background, i.e., male, only having motorcycle as atransport mode, traveling with a travel time of average a half-hour and once in a day, rarely carrying heavy
luggage, as well often passing another vehicle from the right side. The results are found out that the motorcycle
accident probability is influenced by several characteristics, i.e. age, amount of luggage, and motorcyclist
knowledge. A dedicated lane for motorcycles should be prepared with a clearance distance with other vehicles ofmore than 2.18 m. The government should facilitate the dedicated lane standard taking into account clearance
distance for safe travel.

Keywords: Accident model, Accident victims, Dedicated lane, Motorcyclist characteristics, Motorcycle
interactions.
data, several black spots are Geneng Kepanjen,
Sudirman/ Mojosari Kepanjen, Hayam Wuruk
Gondanglegi, Raya Bululawang, Sultan Agung
INTRODUCTION
Kepanjen, Brigjen Abdul Manan Pujon, Mojorejo,
Panglima Sudirman Batu, Raya Areng-areng andTraffic volume in urban areas increases every
Patimura Batu Streets as the research locations.
year. This can be seen from increasing the density of
The increase in the number of vehicles in Malangroad traffic. The increase in traffic density has anDistrict is also followed by rising in the number ofimpact on raising the traffic accidents on theaccidents. There was the largest increase in the
highway.
number of accidents that occurred in 2016 with an
In Indonesia, the population of motorized
increasing 232 incidents from 542 to 774. Based on
vehicles in 2018 was around 141,428.052 units,
the number of victims, the number of deaths and81.58% of which were motorcycles. The risk factor
minor injuries also experienced a significant
with the involvement of motorcycles in traffic
increase from 2015 to 2016 The number of victims
accidents is increasing due to minimum motorcycle
who died was 219 persons and 1,031 persons
infrastructure and an increase in the number ofsuffered minor injuries.
motorcycles. In 2018, there were 196,457 accidents
A motorcycle is a two-wheeled motorized
or 73.49% of traffic accidents involving motorcyclist
vehicle that is designed in such a way for the easiest
as a victim.
and cheapest transport mode for mobility among
According to [1], the number of vehicles
other motorized vehicles [2]. Motorcycles with slim
increases every year, from 2012 the number of
two-wheeled vehicles are considered to have highvehicles was only 196,000, while the number offlexibility for the rider. On the other hand,vehicles had reached 779,000 in 2016 or increased
motorcycles have a greater risk of an accident
almost 4 times in less than 5 years. Of thesecompared to four-wheeled vehicles because
vehicles, the number of motorcycles dominates
motorcycles are more prone to slipping or falling.
approximately 680,000 units or 87% of the total
Motorcycle riders are relatively in danger of being
vehicles in Malang District. Based on 2016-2019
victims of traffic accidents [3]-[4].
67111th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

Motorcyclist characteristics have greatly an
impact on traffic accidents such as the age group of
26-59 years being the highest victim in traffic
accidents [5]. A driving offense committed by
motorcyclists is one factor in accidents. . Higher age
groups have better levels of discipline [6]. The levelof education and knowledge also affects thediscipline of motorcyclists when driving. Driving
knowledge are about good driving methods such as
knowledgeofhuman-vehicle-environment
interaction, developing driving skills, and positively
influencing driver behavior [7]. Driving experience
also influences driver behavior. An experience indriving directs the extent to which the driver's ability
to control his vehicle both under normal conditions
and in sudden conditions that require a quick
response [6]. Furthermore, This study underlies
research on motorcyclists' behavior and theirinteractions with other vehicles.
A motorcycle accident for highland areas isinfluenced by geometry and traffic flow, therefore,
the efforts to reduce the number of accidents should
be considered with improving these factors [8].
Motorcyclists tend to ride motorcycles at higher
speeds on roads with dedicated motorcycle lanes
than on roads without this lane [9]. Setting andformulation of motorcycle accident models are
required to predict accidents and to reduce the riskof accidents that occur [10]. In India, motorcycle
accidents are increasing every day. Therefore, it isneeded to identify factors causing the accident
through further research analysis on the preventionof motorcycle accidents [11].
There are not many studies that understand the
reduction in motorcycle accidents from the
motorcyclists' behavior on roads with therequirement of dedicated motorcycle lanes. To
achieve this goal, it is necessary to research
motorcyclist characteristics and behavior as the basis
for determining motorcycle lanes. Understanding
motorcyclists' characteristics and behavior aim to
reduce the number of accidents and to improve thesafety of motorcyclists.
Attention to assess the factors related to
motorcyclist characteristics and behavior, to develop
motorcycle accidents, to design dedicatedmotorcycle lanes that aim to reduce motorcycle
accidents as a victim. This research aims to identify
motorcyclist characteristics, to develop an accident
model, and to plan the best solution in reducing
motorcycle accidents.
The outline of the paper is: research framework
is elaborated in ‘‘Methods” section consisting of
background information on the existing condition of
the motorcycle accident, motorcyclist characteristics
and behavior, the road accident on the certain roads
as the research locations, design of dedicated lane
for motorcycle. The ‘‘Results” section explains theanalysis of motorcyclist characteristics and behavior

as a victim, and setting up motorcycle accident
models.
Section‘‘Conclusions
andrecommendations” presents conclusions andrecommendations for further research.

METHODS
The research area is in Malang District where thecity surrounding Malang Municipality in East Java
Province, Indonesia. The locations of the survey are
adjusted to the location of the motorcycle accident
as “Black Spot”. The locations were chosen due tothe high motorcycle accident rate. Traffic accident
data was supported by the Malang Police
Department in 2016 – 2019.
Collecting Data
The research survey was conducted by
distributing questionnaires to respondents randomly
on ten roads which are “Black Spots". The samples
have amounted to 150 respondents who had
experienced in traffic accidents or as a victim. The
contents of the questionnaire include generalcharacteristics of respondents (social-economic
background), travel characteristics, and riding
behavior.
Data Analysisa.

Descriptive analysis is an analysis that describes
the situation or event that is only limited todescribing the facts.
b. An accident model by applying the logistic
regression method was developed to determine
the accident probability involving motorcycle
riders through the motorcyclist characteristics,
travel, and behavior characteristics ofmotorcyclists.
The logit model is based on the formation of the
cumulative logistic probability function as follows:
Pi = F (βo + β1X1i) =𝟏𝟏+ 𝒆−𝒛=𝟏
𝟏+ 𝒆−(𝜷𝒐+𝜷𝟏𝑿𝟏𝒊)(1)

𝟏P(MCA)= 𝟏+ 𝒆−(𝜷𝒐+𝜷𝟏𝑿𝟏+𝜷𝟎+𝜷𝟐𝑿𝟐+ 𝜷𝟎+ 𝜷𝟑𝑿𝟑+⋯.) (2)which :P(MCA) = Probability of motorcycle accidente = natural number (2.71828)
X = explanatory variable
As seen in Fig.1, steps for developing model
aims to identify a relationship between the number672

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

of a motorcycle accident (response variable) andmotorcyclist characteristic also behavior variables(explanatory variables). This study will determine amodel between a response variable and explanatory
variables on areas surrounding ten road sections in
the city of Malang identified as black spots (the highnumber of motorcycle accidents). Those explanatory
variables can forecast the number of motorcycle
accidents for the future years. The analysis is doneby using the number of motorcycle accidents (MCA)
as the dependent variables or response variables.

•
•
•
•
•
•

Primary data:
Road geometriccondition
Traffic flow
Socio Economic
characteristics
Motorcyclist behavior
Travel characteristics
Accident characteristics

statistical value of -2log-likelihood andNegelkerke R Square.
f. Formulating the model.
After the F test and t-test, the model isformulated from all explanatory variables and a
response variable (dependent variable) as the
proposed model in Eq. (2).
Factors that cause accidents should be predicted

RESULTS AND DISCUSSIONS
Motorcyclist Characteristics
Travel characteristics

Secondary data:
Road network map
Motorcycle
Accident data

•
•

and

Behavior,

Motorcyclist characteristics are important factors
in planning and design for motorcycle infrastructuresuch as exclusive motorcycle lanes to minimize thenumber of motorcycle accidents. Motorcycle
characteristics are defined as explanatory variables(X1-X71) which experience in the accident withother vehicles involving motorcyclists as the victim(Y), as explained in Table 1.
The characteristics of motorcyclists in the
Malang District are as follows:a. Most of the respondents who ride a motorcycle
are aged 15-25 years (58%), and most of them
are male. Most of the respondents are graduatedfrom senior high school (55%), as private
employees (40%).
b. Travel Characteristics. In general, motorcyclists
travel for working with frequency in every day
needed average 27 minutes for traveling, so it isrequired good pavement condition for highway
network.
c. Accident Characteristics. Fifty-six percent ofrespondents ever had one accident with a light
injury which occurs at noon and evening. A
motorcyclist has a crash with the other riders or
due to their own mistake.
d. Behavior
characteristics
beforeriding.
Motorcyclist always checks breaking devices,fuel, and electrical devices before riding, while
they rarely check motorcycle wheel andrearview mirror.
e. Behavior
characteristic
duringriding.
Motorcyclist rarely uses a helmet, trouser, andjacket. They sometimes ride together, giving
signs during overtaking and rarely using a
dedicated lane for motorcycles.
f. Behavior
characteristic
duringriding.
Motorcyclist rarely uses a helmet, trouser, andjacket. They sometimes ride together, giving
signs during overtaking and using a dedicatedlane for motorcycles.

Black spot

Accident model based on motorcyclist
characteristics and behavior

Safe DistanceDesign Dedicated lane for motorcycle

Fig. 1 The research framework
The statistical tests are explained as follow:
Correlation Test, the correlation coefficients are
more than 0.05 then H0 is rejected, H1 isaccepted or vice versa, which is H0:
Simultaneous independent variables have noeffect on the probability of an accident, and H1:
The independent variable simultaneously affects
the probability of an accident
b. Model Significance Test, a significance test of
the model is defined from the results ofsimultaneous testing on the effect of the
explanatory variables.
c. Regression Model Feasibility Test, asignificance value is more than 0.05 then H0 is
accepted, H1 is rejected or vice versa. H0: Themodel used is feasible, H1: The model used is
not feasible
d. Chi-Square Test (X2), the chi-square countshould more than chi-square table, then these
attributes are related to the probability of an
accident or vice versa.
e. Coefficient of Determination Test. The basis for
decision-making for this test is based on thea.

673

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

g.

Safety knowledge. Most of the motorcyclist
does not understand about the function of thededicated lane, even though they will use thededicated lane when the government facilitatesthese lanes with two lanes, designing withtraffic marking, and average 2.18 meter width.
The minimum and average safe distances for a
motorcycle with the other vehicle are 0.75 m
and 2.18 m respectively. Motorcyclists hope thegovernment gives attention to safety
infrastructures and policies for motorcycles.
This distance is in line with the previous
research revealed that the motorcycle
interactions with the other motorcycle and car
requiring critical pore space 3.12 m and 3.21 m
respectively [10].

Cost for injury treatment

Development of Accident Model
Table 1 Significance test for explanatory variables
Explanatory variables
Notation Significance
Sex
IncomeSocio-economic background
X1
X2

0.973
0.169

Age

X3

0.004

Education

X4

0.559

Job
The age which is the firsttime driving

X5

0.122

X6

0.332

Motorcycle(MC) ownership

X7

0.167

The number of MC
Type of MC

X8
X9

0.301
0.255

Family member using MC

X10

0.845

Transport cost a month

X11

0.595

Travel characteristics
X12
Frequency in a week
X13
Activity time

0.624

X14

0.761

X15
Travel time
X16
Travel propose
X17
Accompanied person
X18
Type of road
Pavement condition of the
X19
road
X20
Other vehicle ownership
Other vehicles used except
X21
Motorcycle (MC)Accident experience
X22
Frequency of accident

0.171

X23

0.999

Travel distanceType of injury

0.863

0.268
0.106
0.497
0.808
0.735
0.163
0.999

0.999

X25
Motorcycle repairing
The time when the accident
X26
occurred
X27
Accident time
X28
Accident cause
X29
Other vehicle involved
Motorcyclist Knowledge
Knowledge about Motorcycle lane
X30
X31
Requirement of motorcycle lane
Willingness to use motorcycle
X32(MC) lane
Design of MC lane
X33
The number of MC lane
X34
Safe distance with other vehicles
X35
when moving
Using motorcycle even though
X36
having other vehicles
Checking Motorcycle equipment
X37
Checking rearview mirror
X38
Bell
X39
Major lamp
X40
Breaking lamp
X41
Sign lamp
X42
Speedometer
X43
Exhausted equipment
X44
Wheel
X45
Reflected light
X46
Motorcycle license
Behavior before driving
Checking the motorcycle
X47
coil and spark plug wires
X48
Checking brake condition

0.999

X49
Checking wheel condition
checking the condition of
X50
motorcycle chains and gears
X51
Checking battery
X52
Checking oil
X53
Checking rearview mirror
Behavior during driving
X54
Using helmet
X55
Using gloves
X56
Using shoes
X57
Using trousers
Wearing jacket
X58
X59
Ride with another person
Riding motorcycle in a
X60
group
X61
Joking when driving

0.653

X62

0.550

Breaching traffic light
Bringing heavy or big
luggage
674

X24

X63

0.999
0.999
0.999
0.997
0.255
0.000
0.857
0.000
0.000
0.002
0.359
0.999
0.61
1.000
1.000
0.999
0.61
0.999
0.792
1.000
0.453
0.905
0.83

0.856
0.472
0.508
0.577
0.956
0.464
0.351
0.876
0.951
0.118
0.111
0.104
0.000

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

Overtaking from the left side

X64

0.941

Signing when overtaking

X65

Traveling when raining

X66

0.726
0.708

of MC lane), X34(The number of MC lane), andX35 (Safe distance with other vehicles whenmoving). When X31, X33, X34, and X35 areanalyzed together in the model, there is one variable
X33 that must be deleted due to no relation to themodel. Then the regression function is developed bythree variables (X31, X34, and X35). The regressionfunction is
YMK = 3.063+4.829X31 – 3.28 X34(1) -3.924 X34(2) –1.037 X35, then the probability of accident due tovariables of the requirement of motorcycle lane
(X31), X33 (Design of MC lane), and X35 (Safedistance with other vehicles when moving) is

0.645
X67
Riding on motorcycle lane
Motorcyclist perception to the government policy
related to motorcycle safety program
The government already consider
X68
0.267
safety program
0.433
X69
Considering other drivers
Felling ignored by the other
0.524
X70
vehicle driver
Requirement for a special safety
0.134
X71
program for motorcycle
Ever crashing with other vehicles
Y
1.000(experience in an accident)

Pi

=

F(βo

+

𝟏

β1X1i)=

𝟏
𝟏+ 𝒆−𝒀 𝑺𝑬

F(βo+β1X1i)

1=

1+ 𝑒 −𝑌 𝑀𝐾

1=1+𝑒 −(𝟑.𝟎𝟔𝟑+𝟒.𝟖𝟐𝟗𝑿𝟑𝟏 – 𝟑.𝟐𝟖 𝑿𝟑𝟒(𝟏) −𝟑.𝟗𝟐𝟒 𝑿𝟑𝟒(𝟐) – 𝟏.𝟎𝟑𝟕 𝑿𝟑𝟓)

From behavior during driving (BD), theprobability of motorcycle accidents is influenced by
X63 (Bringing heavy or big luggage). Then, theregression function is as follows
YBD = -2.944+3.977X63, then the probability ofaccident is

From Table 1, it can be concluded that thevariables X3 (age) from socio-economic backgroundis proven to be related to the experience in theaccident (dependent variable). The explanatory
variables from travel characteristics (X12-X21) areomitted from the model because they had a p-valueof significance greater than 0.05. And all
explanatory variables from accident experience(X22-X29) are omitted from the model. From
motorcyclist knowledge, the variables X30(knowledge
about
motorcyclelane),
X32(Willingness to use MC lane), and X36 (Using a
motorcycle even though having other vehicles) areomitted from the model. On the other hand, X31(Requirement of motorcycle lane), X33 (Design ofMC lane), X34(The number of MC lane), and X35(Safe distance with other vehicles when moving) areverified on influencing to the dependent variable.
In a similar analysis, the explanatory variablesfrom motorcycle equipment (X37-X46) are left fromthe model and also all variables from behavior
before driving (X47-X53). Most explanatory
variables from behavior during driving (X54-X67)
are omitted from the model. X63 (Bringing heavy or
big luggage) can be proven to influence the model.
All explanatory variables from motorcyclist
perception to the government policy related to themotorcycle safety program (X68-X71) are ignoredfrom the model.
The model development is set from socioeconomic characteristic (SE) is influenced by age(X3) with a positive coefficient which means thatvariable X3 increases the probability of an accident.
YSE = -2.576+0.093 X3, then the probability ofaccident due to age of motorcyclist is
Pi

=

Pi

=

F(βo

+

1
1+𝑒 −(−𝟐.𝟗𝟒𝟒+𝟑.𝟗𝟕𝟕𝑿𝟔𝟑,)

β1X1i)=
1

1
1+ 𝑒 −𝑌 𝐵𝐷== 1+𝑒 𝟐.𝟗𝟒𝟒−𝟑.𝟗𝟕𝟕𝑿𝟔𝟑,

The combination of all significant variables is set
by five explanatory variables that are X3 (age), X31(the requirement of motorcycle lane), X34(Thenumber of MC lane), X35 (Safe distance with othervehicles when moving), and X63 (Bringing heavy or
big luggage). The regression function is defined asfollows:
YComb. = 0.662+0.008 X3 + 4.247X31 – 2.633 X34(1) 3.489 X34(2) – 1.072 X35 +2.471X63, then theprobability of accident due to age of motorcyclist is1
Pi = F(βo + β1X1i) =
=
−𝒀
1

1+ 𝑒

𝒄𝒐𝒎𝒃.

1+𝑒 −(𝟎.𝟔𝟔𝟐+𝟎.𝟎𝟎𝟖 𝑿𝟑 + 𝟒.𝟐𝟒𝟕𝑿𝟑𝟏 – 𝟐.𝟔𝟑𝟑 𝑿𝟑𝟒(𝟏) −𝟑.𝟒𝟖𝟗 𝑿𝟑𝟒(𝟐) – 𝟏.𝟎𝟕𝟐 𝑿𝟑𝟓 +𝟐.𝟒𝟕𝟏𝑿𝟔𝟑)

The probability of a motorcycle rider obtain an
accident P(i) is influenced by X3 (age), X31 (therequirement of motorcycle lane), X34(The numberof MC lane), X35 (Safe distance with other vehicles
when moving), and X63 (Bringing heavy or big
luggage). From the model, the odds ratio for age is e0.008= 0.99, the probability of a driver having an
accident is 0.99 times with adding the age ofmotorcyclist. Odd ratio of the requirement ofmotorcycle lane, e-4.247 = 0.014, the probability offacilitating motorcycle lane in an accident is 0.014
times, meaning that possibility of a motorcycle
accident can increase with a small value of 0.014 byproviding motorcycle lane. On the other hand, Odd
ratio of the one motorcycle of e-(-2.633) = 13.9, so theprobability of the driver having an accident can be
reduced by providing one motorcycle lane.
Establishing two motorcycle lanes can reduce the=𝟏+𝒆−(𝟎−𝟐.𝟓𝟕𝟔+𝟎.𝟎𝟗𝟑 𝑿𝟑)From motorcyclist knowledge (MK), theprobability of motorcycle accident is influenced by
X31 (Requirement of motorcycle lane), X33 (Design675

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

probability of an accident by 32.75 times. While,
Odd ratio of the safe distance with other vehicles ofe-(-1.072) = 2.92, so the probability of motorcycle
accident is approximately 2.92 times with increasingclearance distance between the motorcycle and theother vehicle. The odds ratio of the carrier/loader(X63) is e2.471 = 0.085, meaning that the driver who
carries a large number of goods can increase theprobability of an accident only 0,085 times.
In brief, the number of motorcycle lanes has asignificant effect on reducing motorcycle accidents,
and a wide distance between the motorcycle and theother vehicles also will decrease motorcycle
crashing with the other vehicle.

University. We also want to thank Riski Nur
Istiqomah Dinnullah, the doctoral student in the
Department of Mathematics, Brawijaya University
for assisting to collect and processing data.
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(2015). Peran dan Pola Penggunaan Sepeda
Motor Pada Masyarakat Berpendapatan Rendah
di Kawasan Perkotaan Yogyakarta. Jurnal
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Risiko Kecelakaan Lalu Lintas Berdasar
Pengetahuan, Penggunaan Jalur, Dan Kecepatan
Berkendara. Jurnal Berkala Epidemiologi. 4(2):275–287.
[4] Susanto, B., Malkhamah, S., & Suparma, L.B.
(2019). Risiko Kecelakaan Sepeda Motor Pada
SimpangPrioritas.
JurnalTransportasi.
19(3):161-170.
[5] Ibrahim, M.M., Adi, M. S., & Suhartono.
(2018).
Gambaran
Distribusi
Kejadian
Kecelakaan Lalu Lintas Pada Pengendara
Sepeda Motor. Jurnal Ilmiah Permas: Jurnal
Ilmiah STIKES Kendal. 8(2): 82 - 91.
[6] Lady, L., Rizqandini, L.A. & Trenggonowati,
D.L. (2020). Efek Usia, Pengalaman
Berkendara, Dan Tingkat Kecelakaan Terhadap
Driver Behavior Pengendara Sepeda Motor.
Jurnal Teknologi. 12(1): 57-64.
[7] Imanulloh, R.F., & Prihutomo, N.B. (2019).
Kajian Karakteristik Pengendara Sepeda Motor
Di Kota Depok. Seminar Nasional Teknik Sipil
Politeknik Negeri Jakarta. 1(1): 380-386.
[8] Abusini, S., & Ambarwati, L.. (2017).
Motorcycle Accident Model on the Road
Section of Highlands Region by Using
Generalized Linear Model. International Journalof Civil Engineering and Technology (IJCIET).
8(10):1249-1258.
[9] Ambarwati, L., Pel, A.J., Verhaeghe, R., & vanArem, B., (2014a). Empirical analysis ofheterogeneous traffic flow and calibration of
porous flow model. Transp.Res. Part C: Emerg.
Technol. 48, 418–436.
[10] Kiran, B. N., Swamy, N. K., & Sashidhar, C.
(2017). Prediction of Road Accident Modellingfor Indian National Highways. International
Journal of Civil Engineering and Technology(IJCIET). 8(1): 789-802.
[11] Singh, J., et al. 2017. Analytical Hierarchy
Process for Road Accident of Motorcycle inIndia: A Case Study. International Journal of
Mechanical Engineering and Technology(IJMET), Volume 8, Issue 7, July 2017, pp.
1348–1356.

CONCLUSIONS
The majority of motorcycle riders in Malang
District have experienced an accident while riding a
motorcycle by 56%. Based on socio-economic
characteristics, the drivers are male (70%), aged 1525 years (58%), high school education background(55%), and private employees (40%). Based on thetravel characteristics, the most of the motorcycle
riders intend to working place (78%) with anaverage travel time of approximately 27 minutes.
Based on behavioral characteristics, it was
found that drivers always check breaking devices,fuel, and electrical devices before riding, while theyrarely check motorcycle wheels. For behavior
characteristics during riding, motorcyclist rarely
uses a helmet, trouser, and jacket. They sometimes
ride together, giving signs during overtaking andrarely using dedicated lanes for motorcycles.
The probability of accident model involving
motorcycle riders is influenced by X3 (age), X31(the requirement of motorcycle lane), X34(Thenumber of MC lane), X35 (Safe distance with othervehicles when moving), and X63 (Bringing heavy or
big luggage). The accident model involving
motorcycle riders has the greatest opportunity is old
riders, bringing luggage, no motorcycle lane, thenarrow distance between a motorcycle or the othervehicle. The requirement of a dedicated motorcycle
lane is more than 2.18 meters based on motorcyclist
behavior.
This study recommends that safety campaign
programs to improve the safety of motorcycle
drivers in the city of Malang should be undertaken
based on motorcyclist characteristics and behavior.

ACKNOWLEDGMENTS
This research has supported a grant from theFaculty of Natural Science, Brawijaya University,
Malang, Indonesia. We would like to express deep
thanks to the Faculty of Natural Science, Brawijaya676

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Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

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11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

STUDY OF DISTANCE AND NUMBER OF REBARS ON VELOCITY
MEASUREMENT USING NON-DESTRUCTIVE TEST
Christin Remayanti Nainggolan1, Indradi Wijatmiko2, Hendro Suseno3, Ananda Insan Firdausy4
1,2,3,4

Department of Civil Engineering, Brawijaya University, Indonesia
ABSTRACTThe quality of constructions can be evaluated by using non-destructive test (NDT) method, one of them is anultrasonic pulse velocity (UPV) test. Velocity is one of the parameters that can indicate density of material thatwill affect the strength of material. The difference in density of concrete and rebars could affect the velocity
measurement of reinforced concrete. It is common knowledge as there are factors that affect the velocity
measurement and some of them are the position of transducers and the number of rebars. An experiment was
conducted to get better understanding on the velocity measurement of reinforced concrete determined by positionof the transducers, number of rebars and distance between rebars and transducers using UPV test. The specimens
were reinforced concrete beam with different number of rebars, however had the same tensile strength anddiameter. The number of rebars used for specimens are 4; 8 and 12 rebars with varied position of transducers. It
can be concluded that the pulse velocity of reinforced concrete when measured with perpendicular position lower
than the parallel position and also the average transmission time in the parallel position was 52% faster thanperpendicular. On the case of rebar parallel to pulse path, the velocity of reinforced concrete decreased as the
distance between rebar and transducers increased, however the number of rebars did not affect the velocity. On
the other hand, in case of rebar perpendicular to pulse path, the number of rebars resulted insignificant effect tothe velocity measurement (less than 1%).
Keywords: Velocity, Rebar, Ultrasonic pulse velocity, Non-destructive test

INTRODUCTION

structure especially reinforced concrete structureusing UPV because even though this technique iseasy to conduct however it is affected by various
factors. These factors can be divided into two
categories: (1) factors from concrete properties suchas type and amount of aggregate, cement type, watercement ratio, admixture and age of concrete, curingcondition [7] [8]; and (2) other factors such astransducers contact, size of the specimen andpresence of steel reinforcement. From several factorsthat have already been mentioned, this study
discusses further specifically to the presence of steel
reinforcement.
According to some research, elasticity propertiesand density of rebar are higher than concrete,
therefore the pulse velocity in rebar is 1.4 to 1.7 times
than the concrete [9]. The pulse velocity of steel
medium is generally up to 5.90 km/s, however thevalue decreases as well as the diameter [10]. The
pulse velocity of rebar at reinforced concrete is lower
than the rebar in the air because it is affected by thevelocity of concrete and the bond condition between
rebar and concrete.
Several research on UPV measurement have been
carried out on reinforced concrete and the presence ofrebar commonly affected the pulse velocity ofreinforced concrete. The effect can be an increase or
decrease [11] in the measurement of pulse velocity
where this value is also influenced by the condition of
the concrete [12], the quality of the concrete and rebar

Every structure must be in good condition andmeet the safety standard therefore it can perform agood service ability. To evaluate the condition ofexisting structure then it is needed reliable inspection
techniques. Recently, non-destructive test (NDT)
methods is an assessment technique that getting
popular among engineers and often used to evaluatebuildings. NDT methods represent approach of
quality and condition of materials in the structure,
further more they are used to diagnose and predict thestructure condition. In addition, NDT methods have amajor advantage in their capability to evaluate an
existing building [1] and become promisingtechniques for in-situ examination [2]. Even now,these methods are still being developed to meet the
expectation of the user.
Ultrasonic Pulse Velocity (UPV) is one of NDT
methods that frequently used to evaluate the qualityof materials especially concrete. Many researchers
propose the relationship between UPV measurement
and compressive strength to estimate the strength of
concrete [3]-[6]. Therefore, it is important to know
the validity of the UPV results by studying andevaluating many kinds of UPV measurements.
The basic idea of UPV methods is using pulse
wave propagation which have velocity that depend onelastic properties and density of the materials.
Therefore, it is a challenge to evaluate an existing677

GEOMATE – Kyoto, Japan, 3-5 November 2021

[13]. In addition, the position between the rebar andthe transducers will also affect the pulse velocity
value and the transmission time because at a certain
position, the first pulse wave received by thereceiving transducers is partly pass through theconcrete and partly pass through the rebar.
There are many things that affect themeasurement of the pulse velocity of reinforced
concrete therefore it still needs to be studied further.
This present experiment focus on the effect of rebar
to the pulse velocity and the transmission time of thepulse wave of reinforced concrete based on proximityof the measurements to the rebars, the number ofrebars and the position of the transducers (parallel andperpendicular to rebars).

𝑘 = 1−

𝑎 1 𝑉𝑠 − 𝑉𝑐
> √𝐿 2 𝑉𝑠 + 𝑉𝑐

(6)

The effect of rebar is considered to exist when 𝑎⁄𝐿 <0.5 in high strength concrete or 𝑎⁄𝐿 < 0.25 in low
strength concrete.

UPV measurement on reinforced concrete should
not at the point where the rebar is located to preventthe pulse wave path pass through the rebar. However,if this is unavoidable, then it is needed correction due
to the influence of rebar. BS 1881-203:1986 [14]provides a formula of correction factor for the pulse
velocity of reinforced concrete where theapproximate pulse velocity of concrete will be
reduced due to the influence of rebar.
(1)

Where 𝑉𝑐 is the pulse velocity in concrete (km/s) , 𝑘
is the correction factor and 𝑉𝑚 is the measured
apparent of pulse velocity (km/s)
The correction factor will be affected by the
distance between the transducers and the nearest rebar,
the diameter of rebar and the pulse velocity
surrounding concrete. When the pulse path is parallelto the rebar then the correction factor can be obtainedby the following formula:𝑎𝑘 = 𝛾 + 2 ( ) √1 − 𝛾 2𝐿(5)Where 𝐿𝑠 is the length of the bar (mm).
When the position on rebar is parallel to thetransducers then the velocity ratio can be obtained
according to Figure 1, while Figure 2 can be used forthe case when rebar perpendicular to the transducers.
The influence of rebar on reinforced concrete isconsidered non-existent if:

STUDY LITERATURE

𝑉𝑐 = 𝑘𝑉𝑚

𝐿𝑠(1 − 𝛾)𝐿

Fig. 1 Relationship between velocity ratio and bar
diameter for bar parallel to pulse path [10,14](2)

in which
𝛾=

𝑉𝑐
𝑉𝑠

(3)

Where 𝛾 is velocity ratio, 𝑉𝑠 is pulse velocity in rebar(km/s), 𝑎 is distance from the surface of the rebar to
the nearest point of both transducers, 𝐿 is the direct
path length between transducers (mm).
If the value of 𝑎 is greater than two times the
distance from the end of the concrete to the end of therebar (𝑏) then Eq. 2 can apply, otherwise
𝑘 = 𝛾 + 2(

√𝑎2 + 𝑏 2 − 𝛾𝑏)𝐿

(4)Fig. 2 Relationship between velocity ratio and bar
diameter for bar perpendicular to pulse path [10,14]

If the rebar is in line with the transducers (𝑎 = 0)
then
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GEOMATE – Kyoto, Japan, 3-5 November 2021

3 (c)-(d))✓ BC4, 12 deformed bar - Ø 22 mm as the rebar (Fig.
3 (e)-(f))
Figure 3 (a)-(f) show the arrangement of rebars :Fig. 3 (a), Fig. 3 (c) and Fig. (e) are the long sectionof the specimens and Fig. 3 (b), Fig. 3 (d) and Fig. (f)are the cross section of the specimens.
Before the UPV test was carried out, the first thing
to do was perform a bar scanner on the specimens tofound out the exact location of rebars. Afterward,
determined the location of the transducers placement.
The UPV were tested with 2 transducers
positions: (1) parallel to the rebar with a variety
distance (𝒂); (2) perpendicular to the rebar.

EXPERIMENTAL METHODUPV test was conducted to the reinforced
concrete and unreinforced concrete with dimensionsof specimens as follows: length, 60 cm; width, 30 cm,
and height, 40 cm. The rebars were deformed bar of
the same diameter (22 mm) and plain bar (diameter 8mm) as the stirrup. The rebars had the same tensile
strength and the concrete had the same mixture.
The specimens differ from each other depend on
the number of the bars and the arrangement of thetransducers position as follows:✓ BC1, unreinforced concrete beam (Fig. 3 (g)-(h))✓ BC2, 4 deformed bar - Ø 22 mm as the rebar (Fig.
3 (a)-(b))✓ BC3, 8 deformed bar - Ø 22 mm as the rebar (Fig.
transducers

Fig. 3 (a)-(f) Reinforced concrete; (g)-(h) unreinforced concrete

RESULTS AND DISCUSSION

velocity of concrete were inserted at Fig.1 to estimate
the velocity ratio and afterward, used to calculate thepulse velocity of rebar. Table 1 shows that rebar givehigher effect to the pulse velocity of reinforced
concrete when the transducers is in the center of rebar(𝑎 = 0) than the other value of 𝑎. At that point (𝑎 = 0),
the pulse wave fully pass through the rebar as shownin Fig.4.

The pulse velocity of reinforced concrete ( 𝑉 )when measured with position of the rebar is parallelto the transducers can be seen in Table 1 – 3.
Moreover, it can also be seen the results of thecalculation of Eq. 2 – 4 for the value of correction
factor. The diameter of the rebar and the pulse
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GEOMATE – Kyoto, Japan, 3-5 November 2021

When it is compared to the pulse velocity of
unreinforced concrete, which is 4.31 km/s, there wasan increase by 13% due to the presence of rebar.
Moreover, in this point, the pulse velocity of rebar
was 5 km/s which is smaller than the pulse velocityof rebar in the air because of the influence of the bond
between rebar and concrete that can not be define. At
the measurement point where 𝑎 = 12 mm, there was adecrease in pulse velocity of reinforced concrete by
9.8% compared to when 𝑎 = 0 and when compared tothe pulse velocity of unreinforced concrete, there wasan increase by 2%. At this point, the pulse wave only
pass through the concrete, however the influence ofrebar was still extant and made the pulse velocity ofreinforced concrete increased although not significant.

70 mm. As for the specimen BC4, the result can beseen at Table 3. In specimen BC4, the value of
distance was 20 mm and 40 mm. This value wassmaller than the specimen BC3 because of the limited
space between rebar on BC4. Same as the specimen
BC3, the pulse velocity of reinforced concreteslightly decreased as the distance 𝑎 increased.
Table 3 Measured condition: parallel to transducers
on specimen BC4
𝑎
(mm)
20
40

transducers

Table 1 Measurement condition: rebar parallel totransducers on specimen BC2

12
32
52
72
92

4.41
4.41
4.36
4.31
4.31

𝑉𝑠
(km/s)
5

𝑎⁄
𝐿

0.84

𝑉𝑐
(km/s)
4.1

0.85
0.89
0.94
0.96
1

3.73
3.9
4.1
4.13
4.29

4.54
4.75
4.99
5.03
5.23

0.03
0.08
0.13
0.18
0.23

𝑘

0

Table 2 Measurement condition: rebar parallel totransducers on specimen BC3
𝑎
(mm)
50
70

𝑉
(km/s)
4.31
4.26

𝑘
0.93
0.95

𝑉𝑐
(km/s)
4.03
4.07

𝑉𝑠
(km/s)
4.90
4.95

𝑎⁄
𝐿
0.13
0.18

Table 2 gives information about the results ofspecimen BC3 when measured with condition ofrebar parallel to transducers. In this specimen, thepulse velocity of reinforced concrete measured at 𝑎 =50 mm was nearly the same when it measured at 𝑎 =pulse velocity (km/s)

Fig. 4 Cross section of reinforcement concrete withrebar parallel to transducers

𝑉(km/s)
4.89

𝑘
0.87
0.91

𝑉𝑐
(km/s)
4
4.08

𝑎⁄
𝐿

𝑉𝑠
(km/s)
4.81
4.9

0.05
0.10

Figure 5 illustrates the relationship between pulse
velocity (reinforced concrete and concrete) and ratioof distance and path length. When the distance
between the transducers and rebar increased, it
followed by the decreased in the pulse velocity ofreinforced concrete (𝑽). This happened because theposition of the rebar was far from the transducers thatcaused the pulse wave only pass through concretematerial. On the other hand, the farther the distance
between rebar and transducers, the higher the pulse
velocity of concrete (𝑽𝒄 ), although the value was still
smaller than the pulse velocity of reinforced concrete.
At certain distance, the presence of rebar on thereinforced concrete was still considered give effect.
Based on the regulation BS 1881-203:1986 (Eq. 6),
there is a condition on the distance of rebar that canaffect the pulse velocity of reinforced concrete. In thisstudy, the length of the specimen had a constant value
which was 40 cm, while the distance from the rebar
to the transducers had a various value. From Eq. 6 was
obtained the requirement value of 𝒂⁄𝑳 = 0.16,
therefore the UPV measurements at the value of 𝒂 =70 mm, 72 mm and 92 mm were considered notaffected by the presence of rebar.

rebar

𝑎
(mm)
0

𝑉
(km/s)
4.6
4.45

a/L = 0.16

5
4.9
4.8
4.7
4.6
4.5
4.4
4.3
4.2
4.1
4
3.9
3.8
3.7

V

0

0.05

Vc

0.1

a/L

0.15

0.2

Fig.5 Relationship of pulse velocity and 𝑎⁄𝐿 for test
condition : rebar parallel to transducers
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GEOMATE – Kyoto, Japan, 3-5 November 2021

1.05

Table 4 shows that the effect of rebar on the UPV
measurements in perpendicular position is not assignificant as in parallel position because the pulse
wave does not completely pass through the rebar as
shown in Fig. 7.
The pulse velocity of unreinforced concrete (4.32km/s) was not much different from the pulse velocityof reinforced concrete BC2, BC3 and BC4 withdecreased of 2%, 1.6% and 2% respectively.
Although the pulse velocity of reinforced concrete did
not differ between specimens, however the additionof more rebars gave reduction effect to the correction
factor by 8,2% - 8,6%. Increased the number of rebar
caused increased to the length of bars and reduced thecorrection factor, consequently higher effect of rebar
to the UPV measurement. In this measurement
position, the magnitude of rebar influence was still
uncertain because there was another factor that affectthe reinforced concrete which was the bond between
rebar and concrete.

1

k

0.95
0.9
0.85
0.8
0

0.1

0.2

0.3

a/L
Fig. 6 Relationship of correction factor 𝑘 and 𝑎⁄𝐿for test condition : rebar parallel to transducers
From Fig. 6 it can be seen that as the ratio of
distance and path length increases, the value ofcorrection factor increase gradually. This could
signify that the effect of rebar to the UPV
measurements became smaller. Effect of correction
factor can be seen in Fig. 5 where the pulse velocity
of unreinforced concrete increases as the length 𝑎increases and the values is nearly the same with thepulse velocity of reinforced concrete at position 𝑎 =92 mm which the value of 𝑘 = 1.
The measurement results with the position of therebar perpendicular to the transducers can be seen in
Table 4. The value of correction factor was obtainedby used Eq. 5 and to obtained the value of velocity
ratio, interpolation is carried out on the graph in Fig.
2.

Table 5 Results of transmission time (T0)

Table 4 Measured condition: rebar perpendicular totransducers
Specimen
BC2
BC3
BC4

𝐿𝑠
(mm)
44
88
132

𝑉
(km/s)
4.23
4.27
4.23

𝑘
0.93
0.85
0.78

𝑉𝑐
(km/s)
3.92
3.64
3.30

BC1
BC2
BC3
BC4

𝑳
(mm)
600
600
600
600

T0
(µs)
140
142.2
144.1
145.1

Measurement
position
Rebar
perpendicularwithtransducers

BC1
a = 0 mm
a = 12 mm
a = 32 mm
a = 52 mm
a = 72 mm
a = 92 mm

400
400
400
400
400
400
400

93.7
86.8
92.4
97.5
96
94.8
96.3

Rebar
paralleltotransducers

Variation

𝑉𝑠(km/s)
4.41
4.06
3.89

The presence of rebar in reinforced concrete also
influenced the value of transmission time. Table 5shows the transmission time when the first pulse
wave detected (T0) between 2 points (transmittertransducer and receiver transducer) with different
length depends on the measurement position. The
smaller the value of transmission time, the faster thepulse is received by the receiver and signified that thedensity of material was high. The transmission time
when measured with the transducers position
perpendicular to the axis of rebar was higher thanwhen measured with the transducers position parallelto the axis of rebar. This indicates that the timerequired to receive pulse wave in perpendicular
position was longer than in parallel position. This
caused by the difference in path length 𝐿 as well as
the homogeneity and density of the material where
the pulse wave pass through when the transducers
was in parallel position and perpendicular position.
The transmission time when measured on parallel

tranducers
rebar

Fig.7 Cross section of reinforcement concrete withrebar perpendicular with transducers
681

GEOMATE – Kyoto, Japan, 3-5 November 2021

position and the rebar was in line with the transducers( 𝑎 = 0) was the smallest then the other distances
because in this position, the pulse wave pass throughthe rebar. Rebar density is higher than concrete
therefore the time needed to detect pulse wave isfaster than concrete. At the distance 𝑎 = 12 mm, thetransmission time increased because the pulse wave
path only on the concrete. At several distance, thetransmission time fluctuated due to the level ofhomogeneity of the concrete material. While on thetransducers position perpendicular to the rebar, theaddition of more rebars caused the transmission timeincreased slightly.

Experience Achieved with Case Studies, MATEC
Web of Conferences 149, 01015, 2018, pp.1-8.
[3] Kannan A., Relationship Between Ultrasonic
Pulse Velocity and Compressive Strength of Self
Compacting Concrete Incorporate Risk Husk Ash
and Metakaolin, International Journal ofEngineering and Applied Sciences, Volume 2,
Issue 5, 2015, pp.66-71.
[4] Mahure N.V., Vijh G. K., Sharma P., Sivakumar
N., Ratnam M., Correlation Between Pulse
Velocity and Compressive Strength of Concrete,
International Journal of Earth Sciences andEngineering, Volume 4, No. 06 SPL, 2011,
pp.871-874.
[5] Hedjazi S., Castillo D., Relationship Among
Compressive Strength and UPV of Concrete
Reinforced With Different Types of Fibers,
Heliyon 6, 2020.
[6] Aribawa, B. B., Wijatmiko I., Simatupang R. M.,
Effect of Concrete Quality on Concrete Strength
Using Non-Destructive Test and Destructive Test,
Rekayasa Sipil, Volume 13, No. 3, 2019, pp.184192. (in Indonesian)[7] Lorenzi A., Tisbierek F. T., Filho L. C. P. S.,
Ultrasonic Pulse Velocity Analysis in Concrete
Specimens, Conferencia Panamericana de END
Buenos Aires, 2007.
[8] Godinho J.P., De Souza Junior T. F., Medeiros M.
H. F., Silva M. S. S., Factor Influencing
Ultrasonic Pulse Velocity in Concrete,
IBRACON Structures and Materials Journal,
Vol.13, Number 2, 2020, pp. 222-247.
[9] Malhotra V.M., Carino N.J., Handbook on
Nondestructive Testing of Concrete, 2nd ed., CRC
Press LLC, 2004.
[10] Bungey J. H., Millard, S. G., Testing on Concretein Structures, 3rd ed., Blackie Academic &professional An Imprint of Chapman & Hall,1996.
[11] Lencis U., Udris A., Korjakins A., Decrease of
The Ultrasonic Pulse Velocity in Concrete Caused
by Reinforcement, Journal of Materials Scienceand Engineering, A 1, 2011, pp.1016-1028.
[12] Kencanawati, N. N., Akmaluddin, Anshari B.,
Paedullah A. G., Shigeishi M., The Study of
Ultrasonic Pulse Velocity on Plain and
Reinforced Damaged Concrete, MATEC Web ofConference 195, 2018.
[13] Fodil N., Chemrouk M., Ammar A., The
Influence of Steel Reinforcement on Ultrasonic
Pulse Velocity Measurements in Concrete ofDifferent Strength Ranges, IOP Conf. Series :Materials Science and Engineering 603, 2019.
[14] British Standard, BS 1881-203-1986, Testing
Concrete Part 203 : Recommendations for
Measurement of Velocity of Ultrasonic Pulses inConcrete, pp.7-10.

CONCLUSIONS
From the experiment, several conclusions wereobtained:
1. When UPV measured with condition rebar
parallel to the transducers:a) the farther the transducers from the rebar (𝒂),the pulse velocity of reinforced concrete will
decreased and the other hand the value of thecorrection factor ( 𝒌 ) will increased andfollowed by the pulse velocity of concrete.
b) with the dimension of specimen, therequirement of 𝒂⁄𝑳 was 0.16, therefore theeffect of rebar was considered not existed in
the specimen with 𝒂 = 70 mm, 72 mm and 92mm.
2. When UPV measured with position of therebar perpendicular to the transducers, it was
known that the addition of more rebars gave aslightly increased (less than 1%) to the pulse
velocity of reinforced concrete.
3. The pulse velocity of reinforced concrete whenmeasured with perpendicular position lower thanthe parallel position due to the difference in path
length (𝑳) and the level of homogeneity in thepulse path.
4. The position of the transducers and the number ofrebars also affect the transmission time (T0)
where the average transmission time in theparallel position was 52% faster thanperpendicular due to the difference in the level ofhomogeneity and density of the material in thepulse wave path.
REFERENCES[1] Verma S.K., bhadauria S.S, Akhtar S., Review of
Nondestructive Testing Methods for Condition
Monitoring of Concrete Structures. Journal ofConstruction Engineering, Volume 2013, Article
ID 834572, 2013, pp.1-11.
[2] Tavukçuoğlu A., Non-Destructive Testing for
Building Diagnostics and Monitoring :682

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

NON-DESTRUCTIVE EVALUATION OF VARIOUS FLOOR COATING
USING ULTRASONIC PULSE VELOCITY AND ULTRASONIC PULSE
ECHO: A COMPARATIVE STUDY
Indradi Wijatmiko1, Christin Remayanti Nainggolan2, Sugeng Prayitno Budio3, Ming Narto Wijaya4 andIndra Waluyohadi5
1,2,3,4,5

Department of Civil Engineering, Faculty of Engineering, Universitas Brawijaya, Indonesia
ABSTRACTThe present study was conducted to evaluate the effect of floor coating on the assessment of non-destructive
test (NDT). Floor coating has been known as simple and low-cost method in modifying the surface’s membranedistillation (MD) to improve durability or strength, hence the coating material affect on the transmitted NDTenergy has not been clearly studied. It is necessary to investigate the effect of coating material on NDT assessment
to get better understanding on the quality of inspected structures. In this study, ultrasonic pulse velocity (UPV)and ultrasonic pulse echo (UPE) techniques were performed to evaluate the degree of disruption of coating materialon the NDT measurement. Various floor coating was used, i.e., water shield coating, waterproof coating, cement
resin coating, and floor hardener coating to investigate the differences on the waveform length, amplitude, andvelocity. The comparison of waveform between with and without epoxy floor coating showed 1-2% differencesof signal amplitude and wave velocity, in respect to the material coating and surface smoothness. Further, thewaveform of coated ones indicates longer wavelength in term of peak and period.
Keywords: Non-destructive test, Floor coating, UPE, UPV

INTRODUCTION

wave of NDT. In order to get better understanding on
the effect of various floor epoxy coating. This study
to investigate the effect of epoxy floor coating to thereading of ultrasonic pulse velocities and echoes on
the non-destructive measurement.

The vastly growth of construction industry
demanded effective and efficient method through allconstruction sites. The use of epoxy on floor has been
a common action to provide a high-performance,smooth, and durable surface that can last many years
and withstand heavy loads. One of the advantages of
epoxy floor is the resistance ability to stains and water,
therefore some application tended to use it as water
shield and waterproof type, meanwhile some others
are used it as floor hardener due to its durability. The
thickness and uniformity of material coating tend toaffect the quality of the structure.
Non-destructive test (NDT) has been widely usedon the existing structure to monitor and assess the
structure quality[1]. Moreover, there are many NDT
method which depend on the material and structural
condition, such as visual testing, eddy current testing,infra-red thermographic testing, acoustic emission
testing and ultrasonic testing. Among of thesetechniques, ultrasonic testing is considered one of themost capable NDT methods used for testing of solid
component, such as beam, column, and plate.
Many research reviews on the non-destructive test
have been published to achieve higher accuracy on
the prediction of structural quality[2],[3]. Thus, thestudy on the epoxy coated floor holds a few reviews
article. Though the epoxy materials may create an
addition layer above the structural surface, which
could give significant impedance or amplify the pulse

EXPERIMENTAL SETUP
Samples
Concrete blocks of 0.5m x 0.2m x 0.2m andcylindrical samples of diameter 0.15m x 0.3m height
were casted with various coating materials as seen in
Table 1.
Table 1 Sample attributes.
Sample Sample
Code
Shape
N.C.
Block
N.C.R. Block

Coating

None
Cement resin(thick 5mm)
H.N.C. Block
None
H.C.E1 Block
Water shield
H.C.E2 Block
Waterproof
H.C.E3 Block Floor Hardener
S.N.C. Cylinder
None

Hole

Unit

No
No

3
3

Yes
Yes
Yes
Yes
No

3
3
3
3
8

As seen in Fig.1 several concrete blocks were
casted with hole to make clearer on distinguishing the683

GEOMATE – Kyoto, Japan, 3-5 November 2021

NDT reading differences between with coating andwithout ones.

transducers was used to measure the wave acoustic
and velocity parameter, which in this case is thelongitudinal wave. The compressional wave in which
the particle motion is parallel to the wave propagation
direction was measured by using direct method asseen in Fig. 2.
The measured ultrasonic velocity (V) is relatedwith the Young’s modulus (E), Poisson’s ration (σ)
and density of sample (ρ), as described on Eq. (1).

Fig. 1. Schematic view of concrete block with hole
Coating Material𝑬(𝟏 − 𝝈)𝑽=√
𝟐𝝆(𝟏 + 𝝈)(𝟏 − 𝟐𝝈)Concrete blocks of 0.5m x 0.2m x 0.2m andcylindrical samples of diameter 0.15m x 0.3m height
were made with various coating materials toinvestigate the effect of epoxy coating to the readingof non-destructive test result. The various types of
epoxy coating were used to get clearer understanding,i.e., water shield, water floor, and floor hardener type.
As additional comparison, cement raisin coated was
also introduced on this study.

(1)

Ultrasonic Pulse Echo (UPE) Testing
The generated propagation of the ultrasound pulse
waves is being bounced back by the target into thereception to be detected. Thus, the bounce back pulse
provides parameter of signal processing andrepetition frequency. Echoes are generated when theultrasound wave intercept inhomogeneities in a
medium which reflect part of the incident energy backto the receiver, as seen in Fig. 3.

NON-DESTRUCTIVE TECHNIQUE
Ultrasonic Pulse Velocity (UPV) Testing
The propagation of sound waves requires somephysical elastic medium, such as gas, liquid or solid.
The propagation of ultrasonic sound waves creates
stress on applied surface, which cause the internal
particle to restore the system to its original
equilibrium by vibrating back and forth [4][5]. The
vibration of particle triggers the surrounding particles
to vibrate in relation to the sound wave propagation
directions continuously until the mechanic energy
transmission dissipate.
Ultrasonic transmission
method used to detect defects in the concrete bymeasuring the wave acoustic parameter (amplitudeand frequency) and the wave velocity parameter [6].

Transmitter

Receiver

SampleAngular soundpath
Fig. 3. The angular sound path of UPE
When an ultrasonic echo travel into a boundary
between two media, a part of energy is beingreflected, while the rest is being transmitted. The
percentage of transmission and reflection energy isrelated with acoustic impedance of material, as
follow-on Eq. (2).
𝒁 = 𝝆. 𝒄(2)With z is the acoustic impedance (N s/m3), ρ is thedensity of the material (kg/m3) and c is the velocity ofsound in the material (m/s).
The greater the impedance difference, the greater
the percentage of energy that will be reflected at theinterface or boundary between one medium andanother. Thus, the reflection coefficient can becalculated as Eq. (3).

Fig. 2. The non-destructive test direct method.

𝒁𝟐 − 𝒁𝟏 𝟐
𝑹=()
𝒁𝟐 + 𝒁𝟏

In this study, the Pundit PL-200 sonicator fromthe Swiss company Proceq SA with 54 kHz P-wave

684

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GEOMATE – Kyoto, Japan, 3-5 November 2021

Where Z1 and Z2 are the acoustic impedances of
the two materials at the interface.
RESULT AND DISCUSSION

The results shown in Table 2 and Fig. 5 indicatesgood agreement between hammer test results andcompressive test results on measuring thecompressive strength of samples.

Compressive Strength

Ultrasonic Pulse Velocity

Table 2 show the result of compressive strengthand the indicated strength from the concrete blocks
by using hammer test. The compressive strength
obtained from the compressive machine (Fig. 4) of
cylindrical samples from each concrete mixture.

Figure 6 shows the measurement position oftransducer in the sample block.

Hole

Fig. 4. Compressive test on cylindrical samples.
Table 2. Result of Compressive Strength
Fig. 6.
SampleCode
S.N.C.
N.C.R.
H.C.E1.
H.C.E2
H.C.E3Compressive Strength (MPa)
MachineHammer Test
3143
31.11
32.08
31.42
32.16
32.09
32.48
32.64
32.81
32.82
32.97
33.16
34.02
33.92
33.78
34.50

Measurement position of transducer.

Direct method of UPV were used to measure thewave velocity on 10 different locations as seen in
Fig.6. The result of wave velocities was compared
between coated surfaces and without coated ones as
illustrated on Fig. 7 and Fig. 8. The comparison in
Fig.7 shows that the wave velocity of non-coatedsurface tended to achieve higher velocity compared towith cement resin coated. The decreasing of wave
velocity may occur due to air existence between
cement resin and concrete block.

Hammer Test (MPa)

35.00
34.50
34.00

y = 1.2874x - 9.4122
R² = 0.9261

33.50
33.00
32.50
32.00
31.50
31.00
31.00

32.00

33.00

34.00

35.00

Compressive Machine (MPa)Fig. 5.

Correlation value of hammer test andcompressive test.

Fig. 7. Wave velocities of normal concrete andnormal concrete with cement resin coated.
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GEOMATE – Kyoto, Japan, 3-5 November 2021

Meanwhile in term of epoxy coated materials, theused of epoxy as water shield, waterproof, and floor
hardener purposes did not create significant effects on
the wave velocity. However, among all epoxy coated,water shield epoxy type produces higher reduction ofwave velocity value with approximately 3.4%.

However, the introduction of epoxy coated on theconcrete did not give significant different on the
waveform amplitude as seen in Fig. 11 and Fig. 12,
which are the water shield and waterproof coatedrespectively.

Fig. 11. Waveform comparison between normal
concrete with hole and concrete water shield coated.
The wave pattern of both with and without coatedin Fig. 11 and Fig. 12 did not significantly change,
thus the wavelength tended to be slightly longer incase of coated ones.

Fig. 8. Wave velocities of normal concrete with holeand various epoxy coated concrete with hole.
Figure 9 shows the measurement of initial wave
on pulse waveform in the reading of non-destructive
test UPV.

Fig. 12. Waveform comparison between normal
concrete with hole and concrete waterproof coated.
The epoxy floor hardener coated have a tendency
to significantly increase the wavelength, although theinitial wave time trave was similar with the concrete
without coated as seen in Fig. 13. Further, the epoxy
coated gave slight reduce on the wave amplitude as
the wave traveling longer.

Fig. 9. Measured initial wave on the waveform.
As seen in Fig. 10, the comparison of waveform
between normal concrete and concrete with cement
resin coated shows that the amplitude of wave wasgreatly reduced due to the existence of cement resin.
However, there were no significant different on theinitial wave travel time.

Fig. 13. Waveform comparison between normal
concrete with hole and concrete floor hardenercoated.
The measurement of maximum amplitude on
pulse waveform has been set on the first wave
maximum point, in which the placement point of nondestructive test UPV reading can be seen in Fig. 14.

Fig. 10. Waveform comparison between normal
concrete and concrete coated with cement resin.

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GEOMATE – Kyoto, Japan, 3-5 November 2021

ones, a layer was observed on the depth of 0.0-0.02m
which were separated by the thin air between cement
resin and the concrete block. Thus, the thin air bounce
most of the echo wave, which resulted on the lesssignal strength through the sample block and cause
the contrast signal from the inhomogeneities
materials at the bottom side became dimmer.

Fig. 14. Measured maximum amplitude on the
waveform.
The comparison of maximum amplitude value
and time can be seen in Table 3a and Table 3b.
Table 3a. Measured time of maximum amplitude

Comparison

T max (µs)
N.C.

N.C.R.

Diff.

N.C. vs N.C.R.

168.1

168.4

0%

Comparison

H.C.

H.C.E

Diff.

H.C. vs H.C.E1

203.3

208

2%

H.C. vs H.C.E2

215.9

205.9

-5%

H.C. vs H.C.E3

125.9

359.6

65%

Fig. 15. UPE result on the normal concrete sample.

A layer thin air
separated cement
resin with concrete
sample block, cause
echo wave bounced.

Table 3b. Measured maximum amplitude
Comparison

Fig. 16. UPE result on the normal concrete sample
with cement resin.

A max (%)
N.C.

N.C.R.

Diff.

N.C. vs N.C.R.

86.1

38.8

-122%

Comparison

H.C.

H.C.E

Diff.

H.C. vs H.C.E1

79.2

80.3

1%

H.C. vs H.C.E2

100.1

98

-2%

H.C. vs H.C.E3

98.9

75.3

-31%

Fig. 17. UPE result on the normal concrete sample
with hole.

Ultrasonic Pulse Echo
The detection of backscattered pulse-echo was
performed to acquire the echo waveforms whichshows the spatial and contrast of each measurement.
As described, the measurements were conductedhorizontally perpendicular to the hole on each sample
block. B-Scan method was used with the increment of
0.01m for 35 points. All signal gains and voltage oneach measurement were set at the same value to getbetter understanding on the difference between
sample comparison.
Figure 15 and 16 shows the comparison result
between normal concrete and the ones with thecement resin coated. In Fig. 15, the thickness ofsample block can be indicated by the yellow color
between the red ones at the depth of 0.20m. Though
the red color on the depth of 0.18-0.20m indicated thematerial were not homogeneities due to rough surface
finishing. Meanwhile on the cement resin coatedHigher contrast color
indicated the hole(air) position on thecenter of sample.

Fig. 18. UPE result on the concrete sample withhole, coated with water shield epoxy.
Both Fig. 17, Fig. 18 and Fig. 19 shows thatcoated epoxy materials (water shield type andwaterproof type) increased the signal strength of echo
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GEOMATE – Kyoto, Japan, 3-5 November 2021

and generating higher contras color on the each
inhomogeneities materials, especially the hole (air)on the center of the concrete sample. It was due to thematerial of epoxy reduce the impedance of echo
signal and smoother the measurement surface thatcaused solid contact between transducer and surface.

epoxy with the increase signal around 15-21%.
Table 4a. The comparison wave amplitude
Comparison

Higher contrast color
indicated the hole(air) position on thecenter of sample.

Amplitude (%)
N.C.

N.C.R.

Diff.

N.C. vs N.C.R.

45

42.2

-7%

Comparison

H.C.

H.C.E

Diff.

H.C. vs H.C.E1

68.5

86.2

21%

H.C. vs H.C.E2

60.6

71.2

15%

H.C. vs H.C.E3

73.2

75

2%

CONCLUSIONS
Fig. 19. UPE result on the concrete sample withhole, coated with waterproof epoxy.

The used of cement resin as coated materials tended
to reduce the wave velocity and reducing wave
amplitude approximately 7% due to the existence of
air as seen in the UPE result. Further, the cement resin
coating tended to significantly decrease the maximum
value of amplitude.
In the other hand, the used of epoxy coating material
such as, water shield, waterproof, and floor hardenertype did not create significant effect on the wave
velocity (max +3.4%) and wave form pattern.
However, the introduction of epoxy coating produce
higher contrast on the UPE readings (+15-21%).

Similar result can be found in the case of floor
hardener epoxy coating, as seen in Fig. 21. However,
the amplified signal was not as robust as water shieldepoxy coated nor waterproof epoxy coated.

Slightly higher
contrast color to
identify theinhomogeneities
materials.

REFERENCES[1] Verma S.K., bhadauria S.S, Akhtar S., Review of
Nondestructive Testing Methods for Condition
Monitoring of Concrete Structures. Journal ofConstruction Engineering, Volume 2013, Article
ID 834572, 2013, pp.1-11.
[2] Bungey J. H., Millard, S. G., Testing on Concretein Structures, 3rd ed., Blackie Academic &professional An Imprint of Chapman &Hall,1996.
[3] Aribawa, B. B., Wijatmiko I., Simatupang R. M.,
Effect of Concrete Quality on Concrete Strength
Using Non-Destructive Test and Destructive
Test, Rekayasa Sipil, Volume 13, No. 3, 2019,
pp.184-192. (in Indonesian)[4] Tavukçuoğlu A., Non-Destructive Testing for
Building Diagnostics and Monitoring :Experience Achieved with Case Studies,
MATEC Web of Conference 149, 01015, 2018,
pp.1-8.
[5] Kencanawati, N. N., Akmaluddin, Anshari B.,
Paedullah A. G., Shigeishi M., The Study of
Ultrasonic Pulse Velocity on Plain and
Reinforced Damaged Concrete, MATEC Web ofConference 195, 2018.
[6] Malhotra V.M., Carino N.J., Handbook on
Nondestructive Testing of Concrete, 2nd ed.,CRC Press LLC, 2004.

Fig. 19. UPE result on the concrete sample withhole, coated with floor hardener epoxy.
The comparison of initial echo wave time travel
can be seen in Table 4a. In which it showedinsignificant difference between with and withoutcoated samples. The cement resin materials tended toreduce the echo wave velocity, in the other hand theeffect on epoxy coated tended to slightly increase it.
Table 4a. The comparison of initial wave time travel
Comparison

T (µs)N.C.

N.C.R.

Diff.

N.C. vs N.C.R.

230.5

225

-2.44%

Comparison

H.C.

H.C.E

Diff.

H.C. vs H.C.E1

302.7

308.7

1.94%

H.C. vs H.C.E2

313

318.3

1.67%

H.C. vs H.C.E3

307.5

311.5

1.28%

As seen in Table 4b, the significant effect can befound on the water shield and waterproof type of688

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

ANALYTICAL OF MULTI CRITERIA APPROACH FOR
IDENTIFYING THE WEIGHT AND FACTOR OF RURAL ROAD
MAINTENANCE PRIORITIZATION.
Pawarotorn Chaipetch1, Chisanu Amprayn2 Paijit Pawan3 and Vatanavongs Ratanavaraha4
1, 2, 3

School of Engineering, Sripatum University, Thailand; 4Suranaree University of Technology, Thailand
ABSTRACTThe Department of Rural Roads' (DRR) annual budget prioritization for road maintenance is now based on acomparison of the benefits of reduced road user costs (Vehicle Operation Cost, Value of Time, and Accidental
Cost) and maintenance costs, which is mainly based on two factors: traffic volume and international roughness
index (IRI). As a result, the budget allocation for these factors may be inappropriate for a low-traffic road. The
main objective of this paper is to modify DRR's road maintenance budget prioritization, which is related to the
DRR's strategic driving plan. The factors in the study are divided into three groups in order to investigate andevaluate factors which have impact on road maintenance in rural road: economic, engineering, and social. The
weight of each factor and the priority value of each strategic driving plan were found out by using the Analytic
Hierarchy Process (AHP). The priority values of DRR’s strategic driving plan (from highest to lowest) are
strategic for logistic and integrated transport systems, strategic for reducing traffic congestion in urban areas,
strategic for developing transportation to strengthen competitiveness and rural economic development, andstrategic for tourism respectively. The economic factor is the most important factor that every strategy considers
because it reflects the value of an investment. It involves evaluating all of the costs of maintenance and thebenefits of reduced vehicle operating costs, travel time savings, and accident reduction to determine theirmonetary value. The Engineer calculated priority value for IRI, V/C ratio, traffic volume, volume of heavy truck,
lifeline network and connectivity to other road networks, the result of engineering factor showed thatconnectivity to other road networks is an important factor in every strategy because it provides network
connectivity and ensures traffic continuity. transporting goods and services and making public services andattractive places more accessible. While IRI is the only factor that is prioritized in the tourism strategy. Because
the tourist road network must be maintained to ensure that it remains in good condition, is comfortable, and iseasily accessible to tourist attractions and strategic of logistic and integrated transportation system is the only
one that prioritize the volume of heavy truck as the most important factor. In terms of social factors, the numberof people, tourist attractions, business centers, government offices, hospitals, and industrial areas were allcounted along both sides of the road for 2 kilometers, indicated that all strategies prioritize consistent andappropriate road usage behavior, population density, tourist attractions, and business hub is a factor that every
strategy prioritizes. As a result, priority factors should be investigated so that road hierarchy is consistent andappropriate budget allocation.
Keywords: Prioritization, AHP, IRI, Low Volume RoadINTRODUCTION

truck, rainfall and topographical gradient [3,4]. DRR
allocated funding for road network maintenance inaccordance with road strategies and the nation's
development policy, including strategic for logistic
and integrated transport systems, tourism, reducing
traffic congestion in urban areas, and developing
transportation to strengthen competitiveness andrural economic development.
The annual budget for road maintenance isprimarily based mostly on engineering and asset
management criteria to guide investment and work
to reduce life cycle agency and road user costs suchas travel time, operating costs, and safety [5].
Benefit-cost analysis is used to assess the impact ofvarious funding levels on the economic cost to users
and agencies [6], as well as analyzing treatment
alternatives from a financial perspective. Database

The World Bank considers roads to be important
assets for reducing poverty levels and considers
them to be keys to raising living standards. [1].
Better roads improve social outcomes by loweringtransportation costs and fostering economic linkages
that improve agricultural and industrial production
while also facilitating access to public service
facilities.
The Department of rural roads (DRR) in
Thailand is responsible for road maintenance 48,974
kilometers, which includes 2,563 kilometers ofconcrete, 45,868 kilometers of asphalt, and 542
kilometers of gravel [2]. The deterioration of
pavement is in exponential function and is affectedby pavement age, traffic volume, volume of heavy
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GEOMATE – Kyoto, Japan, 3-5 November 2021

of existing road assets, use patterns, asset
deterioration rate, project cost, user costs ofdeteriorated condition, and investment triggers or
minimally tolerated conditions are all included inthese models minimally tolerable conditions [7]. As
a result, the allocated budget may be insufficient or
inappropriate for maintenance needs in each area,
especially the low volume road.
The most widely known method for determining
the economic value of road maintenance investments
is cost-benefit analysis (CBA), which involves
evaluating all of the costs and benefits of projects todetermine their monetary worth. The most importantmonetary benefits are reduced vehicle operating
costs, travel time savings, and accident reduction.

Fig. 1Chanon and Wisanu (2009) studied analyticalframeworkofroad
serviceability
level
corresponding to road strategies found that theeconomic analysis approach is suitable for road that
accommodate high traffic volume. According to thelife cycle cost analysis, an investment with an
Annual Average Daily Traffic (AADT) greater thanor equal to 3,500 vehicles per day and an
International Roughness Index (IRI) greater than orequal to 3.0 meters per kilometer will be effective[8]. When this criterion is compared to current
AADT data from DRR, it was found that over 84
percent of roads have AADT less than 3500 vehicles
per day (Fig.1).

Cumulative Traffic Volume of Department of Rural Road
(Department of Rural Road, 2020).

Because of its sole focus on economic efficiency,
this method's effectiveness as a decision-making toolis limited. The effects on the environment andcommunity, as well as the transportation factors thatdrive non-travel productivity, are not quantified.
Because the project list included many rural roadsections with lower traffic volumes, this approach
was not chosen. The benefits of low-volume roads
may be insufficient to justify the investments. There
are various other socioeconomic benefits associatedwith the development of rural road. These areadvantages related to increased economic activity interms of connectivity, accessibility, regional growth,and a key component of rural development, such asaccess, which is important both for goods movement
and labor markets, and is a contributing factor tohigher income. Finally, the budget allocation should
be based on the Strategic Driving Plan of DRR. As aresult, priority factors should be investigated andevaluated so that road hierarchy is consistent andappropriate, considering economic, engineering, andsocial factors.

The objective of the study was to use ananalytical hierarchy process (AHP) to analyze thepriority factors based on the Department of Rural
Road's Strategic Driving Plan in Thailand, and toprioritize the main and secondary factors byconsidering economic, engineering, and social
factors in order to classify the rural road network
and apply it to appropriate budget allocation.
AN ANALYTICAL HIERARCHY PROCESSThe analytic hierarchy process (AHP), also
known as the analytical hierarchy process is a
structured method for organizing and analyzing
complex decisions that is based on math andpsychology. It was created in the 1970s by Thomas
L. Saaty [9] and it has been extensively studied andrefined since then. It is a precise method ofquantifying the weights of decision criteria. Pairwise comparisons are used to estimate the relative
magnitudes of factors based on individual experts'
experiences.
Usingaspecially
designedquestionnaire, each respondent compares the relative
importance of each pair of items. AHP is suitable tocomplex decisions involving the comparison of

OBJECTIVE

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GEOMATE – Kyoto, Japan, 3-5 November 2021

difficult-to-quantify decision elements. It is based on
the assumption that when challenged with a complex
decision.

This study examines the factor that should beconsidered when prioritizing road maintenance, and
it is divided into three groups based on the findingsof a literature review. [10,11]

cost ratio, the engineering factor included IRI, V/Cratio, traffic volume, volume of heavy truck, lifeline
network and connectivity to other road networks.
Finally, social factor is the number of people, tourist
attractions, business centers, government offices,
hospitals, and industrial areas were all counted along
both sides of the road for 2 kilometers. The
population was purposive in authority of budget
planning and prioritization on road maintenance ordecision – making authority on planning policy,
which is 20 respondents.

Economic factor

Analysis of Priority Strategic Factor

It is an evaluating all of the costs and benefits ofprojects to determine their monetary value. The mostimportant monetary benefits are reduced vehicle
operating costs, travel time savings, and accident
reduction compared with maintenance andrehabilitation cost. As a result, the benefit-cost ratio
must be greater than 1.0.

According to an analysis of priority strategic
factors as a component of road maintenance budget
allocation, the highest priority value with a priority
value of 27 is strategic for logistic and integrated
transport systems and strategic for reducing traffic
congestion in urban areas. Second, with a priority
value of 24, strategic for developing transportation
to strengthen competitiveness and rural economic
development, and finally, with a priority value of 22,
strategic for tourism.

A LITERATURE REVIEW ON FACTOR TO
PRIORITIZATION ROAD MAINTENANCE

Engineering Factor
It's a direct factor in the design, management,
maintenance, and rehabilitation of a road, and it
includes physical, functional, distress, condition,connectivity, and accident data. As an example,• International Roughness Index (IRI).
• Traffic Volume and Volume of Heavy Truck.
• Volume Capacity Ratio (V/C Ratio)• Lifeline Road.
• Connectivity to road network.
• Accident Data.

Analysis of Priority main and secondary Factor
An analysis of main and secondary factor is a
result of priority factor of economic, engineeringand social factor, including a secondary priority
factor of engineering and social perspective.
Strategic for logistic and integrated transportsystems. (LIS)

Social Factor
The highest priority factor is the economic factor,
which has a priority value of 46, followed by theengineering factor, which has a priority value of 29.
In descending order, the secondary factors areconnectivity to the road network, heavy truck
volume, accident data, traffic volume, and V/C ratio.
Industrial area, population density, and tourist spots
are the priority secondary factors in descendingorder, with a social factor having the lowest priority
value of 25.

It’s a direct factor that related to social,environment, land use, community area andattractive point of Interest. As an example,• Population density.
• Tourist spots.
• Government office, School, Collegesand Hospital.
• Business Center, Market.
• Industrial area, Warehouse
ANALYSIS

Strategic for reducing traffic congestion in urban
areas. (TCA)

This research is a study on the strategic, mainand secondary factor analysis based on Departmentof Rural Road's Strategic Driving Plan in Thailand.
The methodology of research was analytic hierarchy
process which compared each pair of factors inmatrix table. An analysis calculated the priority
value for the economic factor in term of a benefit

The highest priority factor is the economic factor,
which has a priority value of 40, followed by social
factor, which has a priority value of 32. In
descending order, the secondary factors are
population density, business center, industrial area
and tourist spots.

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GEOMATE – Kyoto, Japan, 3-5 November 2021

An engineering factor is a lowest priority valueof 28 with a priority secondary factor are indescending order is accident data, V/C ratio,connectivity to the road network and traffic volume.
Strategic for tourism (TSM).

Strategic for developing transportation to strengthen
competitiveness and rural economic development(CED).
Finally, economic is a highest priority factor
same as others strategic with a value of 45, followedby the social factor, which has a priority value of 35.
In descending order, business center, tourist spots,
population density and industrial area.
Accident data, connectivity to the road network,
V/C ratio, traffic volume and lifeline road are thepriority secondary factors in descending order, witha social factor having the lowest priority value of 20.

Economic factor is a highest priority value of 44
and 33 for social factor with a priority secondary
factor are in descending order is tourist spots,business center, hospital and population density.
Accident data, connectivity to the road network,
V/C ratio, traffic volume and IRI are the priority
secondary factors in descending order, with anengineering factor is lowest priority value of 23.

Table 1 Summary of the analysis of Priority Factor.
LIS
TCA
Factor/Strategic
Priority Value Priority Value
27
27
Economic
46
40
Engineering
29
28
IRI
V/C Ratio
17
24
Traffic Volume
19
21
Volume of Heavy Truck
21
Lifeline
Connectivity to the road network
23
24
Accident data
20
31Social25
32
Population density
19
29
Tourist spots.
18
20
Business Center
23
26
Government office.
Hospital
Industrial Area
40
25
DISCUSSION AND CONCLUSION

TSM
Priority Value
22
43
23
13
21
16
25
25
33
17
40
23
20

CED
Priority Value
24
45
20
17
15
14
27
27
35
25
26
27
22

with the development of rural road. These areadvantages related to increased economic activity interms of connectivity, accessibility, regional growth,and a key component of rural development, such asaccess, which is important both for goods movement
and labor markets, and is a contributing factor tohigher income. The budget allocation should bebased on the Strategic Driving Plan of DRR. As aresult, priority factors should be investigated andevaluated so that road hierarchy is consistent andappropriate, considering economic, engineering, andsocial factors.
This research is a study on the strategic, mainand secondary factor analysis based on DRR's
Strategic Driving Plan. The methodology of research
was analytic hierarchy process which compared eachpair of factors in matrix table. According to an
analysis of priority strategic factors, the highest
priority value is strategic for logistic and integrated
transport systems (LIS) and strategic for reducing
traffic congestion in urban areas (TCA). Second,

The Thai government has allocated funding forroad network maintenance in accordance with roadstrategies and the nation's development policy,
including strategic for logistic and integrated
transport systems, tourism, reducing traffic
congestion in urban areas, and developing
transportation to strengthen competitiveness andrural economic development. The DRR annual
budget prioritization for road maintenance is now
based on a comparison of the benefits of reduced
road user costs (Vehicle Operation Cost, Value ofTime, and Accidental Cost) and maintenance costs,
which is mainly based on two factors: traffic volume
and international roughness index (IRI). As a result,the budget allocation for these factors may beinappropriate for a low-traffic road.
The benefits of low-volume roads may beinsufficient to justify the investments. There arevarious other socioeconomic benefits associated692

GEOMATE – Kyoto, Japan, 3-5 November 2021

strategic for developing transportation to strengthen
competitiveness and rural economic development(CED) and finally, strategic for tourism (TSM).
The economic factor is the most important factor
that every strategy considers because it reflects thevalue of an investment. In terms of social factors,
TCA, CED and TSM are secondary considerations
because it's a factor that reflect the land use, social
characteristics, and usage behavior. However, LIS is
the only one with secondary engineering factor
significance.
The engineering factors found that connectivity
to other road networks is an important factor in
every strategy because it provides network
connectivity and ensures traffic continuity.
transporting goods and services and making public
services and attractive places more accessible.
Accident data is the primary secondary factor that
almost every strategy recognizes. The V/C Ratio is asecondary priority factor because it is related to thequality of traffic flow and congestion, particularly inurban areas.
Finally, secondary social factors indicated thatall strategies prioritize consistent and appropriate
road usage behavior, for example, LIS prioritizes
industrial areas. Population density is considered
important for TCA. The number of tourist spots wasemphasized in TSM. The importance of businesscenters is strategically valued for CED. Furthermore,
it was found that the secondary factor in population
density, tourist spots, and business center is a factor
that every strategy prioritizes, but the order of eachstrategy differs.
Following a detail analysis of main andsecondary factors in each strategy, LIS is the only
one that prioritize the volume of heavy truck as themost important component. Because roads of thatstrategic support the transportation of goods andservices, as well as connectivity to other networks.
Because routes enhance the growth of transportationof services and goods while also connecting to othernetworks. As a result, to develop spatial logistics
management, network creation, and the connectivityof raw materials source, production base, and market
to improve and increase logistics and supply chain
management.
Furthermore, to decrease transportation costs andimprove the efficiency of transportation services, aswell as to integrate the logistic network, such as
roads, rails, waterways, and air, and to strengthen
the private sector's competitiveness. Furthermore, abusiness center and industrial area are also priority
factors in the social secondary factor. Finally,
according to the third Thailand logistic development
plan (2017-2022) of the office of national economic
and social development council, road asset
management is a priority factor in the strategic oflogistic and integrated transportation system to be
more efficient.

Moreover, TSM as an important service sector,
develop a transportation facilitation toward thetourist attraction and create value-added products
and travel service and is the only one that prioritizes
IRI as a maintenance management factor. Because
the tourist road network must be maintained toensure that it remains in good condition, is
comfortable, and is conveniently accessible to tourist
attractions. Furthermore, the tourism strategy's
network has significant high traffic volume
exclusively during only the festival season or during
the vacation. Hospital is a secondary priority value
for social factors in tourism strategy. There is apossibility and risk of an accident during thevacation season due to the high volume of traffic andincreasing travel speed. Assemble the engineeringfactor, which prioritizes the accident data, reflects
the importance of being consistent and heading inthe same direction that can respond to sustainable
and environmentally friendly tourism.
Private car ownership and road usage are
increasing faster in urban areas and large cities thanin rural areas. This implies significant traffic
congestion in many large cities, as well as air
pollution and car emissions. As result, thepopulation density, business center and industrial are
the most significant social factor in TCA. As aresult, by integrating the v/c ratio, accident data andconnectivity to the road network with traffic
management and maintenance management inmetropolitan areas may be more effective. These
characteristics can help to reduce traffic congestion
and making travel more convenient and safer.
According to the priority analysis of CED, theroads network in this strategy is a low-volume road,but it has a variety of other socioeconomic benefits
associated with its development. These are benefits
related to increased economic activity in terms ofconnectivity, accessibility, regional growth, and a
key component of rural development, such as public
service access, which is important for both goods
movement and labor markets, and is a contributing
factor to higher income.
Finally, the priority value of each factor that was
analyzed by AHP was used to calculate the score
and order of each road by considering economic,
engineering, and social factors in order to classify
the rural road network and apply it to appropriate
budget allocation. Details of priority analysis ofsocial and engineering factor shown in fig. 2 and fig.
3
ACKNOWLEDGMENTS
I would like to thank Bureau of Road
Maintenance, Department of Rural Road and Centerof Excellence in Infrastructure Management,
Chulalongkorn University for their valuable data,expert opinion and constructive suggestions.
693

GEOMATE – Kyoto, Japan, 3-5 November 2021

Fig. 2

Fig. 3

Priority analysis of Engineering factors

Priority analysis of social factors

REFERENCES

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[7] Margiotta, R., The Highway Economic
Requirements System (HERS): How HERSWorks, 2016, Cambridge Systematics. Presented
to Committee for a Study of the Future Interstate
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[8] Amornchaisakda, C., and Subsompon, W.,
Analytical Framework of Road Serviceability
Level Corresponding to Road Strategies, 2009,
Proceeding of the 14th National Civil
Engineering Convention, Engineering Institute ofThailand, pp. 635-642.
[9] Satty, L. T., Fundamental of Decision Making
and Priority Theory with The Analytic Hierarchy
Process, 2014, RWS Publication, USA.
[10] Moya, M., Gallego, E., Garcia, A. I., and Ayuga,
F., A Proposed Methodology for theManagement of Low-Volume Roads in Spain,
2011, The Baltic Journal of Road and Bridge
Engineering, Vol. 6, No. 3, pp. 153-162.
[11] Menendex, J. R., Budget Allocation Planningfor Small Road Systems in Peru, 2015,
Proceedings of the 25th World Road Congress Seoul 2015: Roads and Mobility - Creating New
Value from Transport.

[1] Gannon C. A., and Zhi Liu, Poverty andTransport, TWU discussion papers, TWU-30, 1997,

World Bank, Washington D.C.
[2] Department of Rural Road, Annual Report of
Bureau of Road Maintenance, 2020, Thailand.
[3] Subsompon, W., et. al., Road Detrioration
Model based on International Roughness Index(IRI), 2000, Proceeding of the 6th National Civil
Engineering Convention, Engineering Institute ofThailand.
[4] Subsompon, W., and Chaipetch, P., Work
Estimation Model for Pavement Routine
Maintenance, 2010, Research and Development
Journal, Vol. 21, No.4.
[5] National Academics of Science. Engineering,and Medicine, Investment Prioritization Methods
for Low-Volume Roads, 2018, The National
Academies Press, Washington D.C.
[6] Duncan, C. and Schroeckenthaler, K., NCHRP
Synthesis 510: Resource Allocation Available
Funding to Programs of Work, 2015,
Transportation Research Board of the National694

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

TRANSPORT MODE CHOICE SPLIT FOR COMMUTER LINESIN PANDEMIC COVID-19 SITUATION
Lasmini Ambarwati1 and Achmad Wicaksono2
1,2

Faculty of Engineering, Brawijaya University, Malang, East Java, Indonesia

ABSTRACTThe Covid-19 pandemic is a global disease at the end of 2019 caused by the SARS-Cov-2 virus. WHO issuedthat people should maintain a social distance, use masks and wash their hands to minimize the disease spread. To
minimize the widespread of Covid-19, the Indonesian government executes a national lockdown program.
Movement and capacity restrictions on public transport are implemented. Commuter lines that accommodate
passengers in Jabodetabek (Jakarta, Bogor, Depok, Tangerang, Bekasi) are also limited to passenger capacity of
up to 50%. As a result, there is a change in passenger behavior and transport mode of commuter line passengers
to other vehicles. The study aims to determine passenger characteristics and passenger behavior in choosing
transport mode as well to predict the split of transport mode from commuter lines to motorcycles in Bekasi City
as a case study area. Data is collected by Google form that distributes to passengers of three Bekasi routes due tomany passengers on the commuter lines. There is a change in passenger behavior in maintaining hygiene andhealth protocols when traveling on a commuter line in a Covid-19 pandemic. Changes in transportation modes
occur from commuter rail to motorcycles based on travel time and cost. Humanitarian factors are also factors inchoosing transport mode, i.e., social distance and health protocol. Government policies related to public
transportation operational should be taken based on the factors.
Keywords: Capacity restrictions, Commuter passengers, Covid-19 pandemic, Humanitarian factors, Transportmode choice.

INTRODUCTION

waiting times, and decreased accessibility, resulting
in changes in the behavior of commuter line users.
Several variables that will affect the behavior ofCommuter Line (KRL) users in traveling, such as
time, routes, fares, comfort, and security, are thedeterminant factors in choosing public transportation[1]. To improve the Jabodetabek Commuter Line,the manager develops and implements high-priority
technical requirements as a quality improvement
strategy. This strategy is expected to improve theservice quality of commuter lines as well as toincrease the level of customer [2].
In Lagos, Nigeria, the manager of public
transport increase tickets by 53.5%. The government
should provide financial support to the public
transport operator to prevent the increasing ticket
which is burdensome the passengers [3]. During
pandemic covid-19, there was a decline in commuterline travel by 58% for the Greater Tokyo Region [4].
During the Covid-19 pandemic, many cities
worldwide have decreased air pollution and cleaner
air due to lockdowns, restrictions on activities
outside the home, and other physical distancing
requirements. Its impact has also occurred on thetransport sector on a rail, disrupting the regularrhythm of the European rail supply industry [5].
Das [6] informed that the total revenue for IndianRailways, down 41% compared to the same time lastyear due to the Covid-19 pandemic. There is a

Commuter line, KRL (‘Kereta Rel Listrik”)
Jabodetabek (Jakarta, Bogor, Depok, Tangerang,
Bekasi) has important impact on reducing traffic
congestion in Jakarta and its surrounding areas. The
commuter line is interested in the local community
due to the consideration of travel time, punctualityof departure, mass accommodation, and affordable
fares. On the other hand, the commuter line is still
overcapacity in peak hours (06.00-08.00.a.m and16.00-18.00.p.m). This situation has an impact on
uncomfortable for the passengers, which is jostlingwithout safe distance in the train carriages. Thiscondition is contrary to the pandemic situation,
which requires a social distance of 1.5-2 meters
width.
Lockdown is undertaken in those areas(Jabodetabek), which decreases the number ofcommuter passengers. There is a split mode choicefrom commuter line to motorcycles due to the
lockdown. During the lockdown, limitation ofpassenger capacity until 50% is done to reduce
massive density in the train carriages as well as
arranging at the station area during train departures,
limitation of passenger flow at the main entrance ofthe station, and the implementation of healthprotocols. The station arrangement has led todecreased movement/mobility, long queues, long695

GEOMATE – Kyoto, Japan, 3-5 November 2021

change of behavior by increasing the use anddependence on cars for people's movement behavior.
In the USA, a survey of the panel revealed thatof transit riders, 75% reported taking transit lesssince the pandemic, due to a combination of being
affected by transit service changes, concerns about
infection risk on transit, and trip reductions due toshelter-in-place rules. The study results havesignificant implications for understanding how
welfare has been affected for lower-income riders
during the pandemic and insight into the recovery of
U.S. public transit systems [7].
COVID-19 pandemic in Bangladesh causes
significant variations in transport mode preferences
but minor variations in the frequency of trips. Most
people continue to use the bus for health costs due tothe lack of cheaper transport modes. The
government should ensure proper hygiene practices
on public transport and non-motorized paratransit
vehicles.
Information
andCommunication
Technology (ICT), pedestrian and bicycle facilities
also need to be improved [8].
Increasing the use of cars concerning with thehigher risk of infection during heavy use of public
transport occurred by doubled use of parking lots forschool trips from February to March 2020 in theTokyo District. More people may switch to usingcars during the longer pandemic [9]. The strategy
related to sustainable transportation development,
improvement of P.T. systems reduces emissions
rates of each emission parameter by 66% (H.C.),
71% (C.O.), and 82% (NOx) [10]. So theimprovement of the P.T. system with considering
healthy and humanitarian factors should be
implemented during the pandemic.
Attention to assess the factors on transport mode
choice and the change in the transport behavior ofcommuter passengers during the pandemic will bedone in this study. The objectives of the study are todevelop a mode choice model during normal,
lockdown, and post-lockdown or new normal
periods. The specific objectives are to identify
passengers' characteristics and changes of commuterline user behavior for Bekasi routes during the
pandemic.

or new normal conditions, are informed from theresult of this study.

METHODS

A Stated Preference (S.P.) technique is madeusing a numerical scale and specific information
adjusted to the choice of transportation mode, i.e.,
between commuter line (KRL) and motorcycle
according to some attributes influencing the choiceof transport mode during the pandemic. Differencesin total travel time (X1) and cost (X2) between
commuter lines and motorcycles are factors assessed
according to passengers' preferences.
Total travel time for a motorcycle includes
preparation time, the delay/waiting time in traffic
flow, and trip time. The total travel time forCommuters Line consist of reparation time, waiting

Primary data:••

Secondary data:
Commuter line
passenger during the
pandemic

Socio-economic and travel
characteristics
Mode transport choice

Descriptive
analysisi ofusers of KRL

Analysis of Binary
Logic Model

Transportmode split
Emission

Public Transport Improvement,
During the pandemic

Fig. 1 The research framework
The first research steps are conducted bysurveying primary and secondary data to obtaincommuter line user characteristics and information
about implementing social restrictions and healthprotocols on public transportation operations during
the pandemic, as shown in Fig.1. A survey method is
conducted by distributing a questionnaire or a webbased questionnaire survey. Data were obtained
from 390 respondents (130 persons for each route)as a representative of the total passengers ofCommuter Line from Bekasi City (298,395
passengers/day), West Java Province, Indonesia.
Questionnaires are distributed to users of CommuterLine with Bekasi-Jakarta, Bekasi-Depok, andBekasi-Bogor routes with a high number ofpassengers from Bekasi City. The questionnaire
consists of the respondent's background about thesocial-economic background, trip characteristics,
behavioral characteristics related to social
restrictions and health protocols on public
transportation operations during the pandemic, andfactors influencing transport mode choice.
The Choice of Transport Mode Model

Research Framework
The impact of the Covid-19 pandemic period on
the implementation of the social restriction programand health protocols on public transportation
operations, especially the Commuter Line (KRL), isinvestigated. The characteristics and behavior ofusers of the Commuter Line from Bekasi City under
different conditions, such as conditions before thepandemic or normal, lockdown, and post-lockdown
696

GEOMATE – Kyoto, Japan, 3-5 November 2021

time, queuing time to check health protocols on
public transportation operations, and traveling. Total
travel costs include parking and fuel cost for a
motorcycle, whereas KRL cost comprises ticket
price, parking cost, or feeder transport cost fromhome to station and post-trip (from station todestination place).
Before analyzing the transport mode choice
data, firstly, correlation coefficient test (R) and t-test
were carried out. After testing the hypothesis bycalculating the coefficient correlation (R) and t-test,
the model of transport mode choice is carried out by
applying the binomial logit model to get therespective probabilities of each transport mode. The
choice probability of each mode is obtained by using
the equation:
U1 – U2 = b0 + bn (𝛥X)
(1)
P1 =
P1

eU 1
e + eU 2

new normal period. It means that checking healthy
protocol needs 5-10 minutes for each passenger.
Transportation costs are 250,000-500.000 IDR
during three conditions. However, there is anincrease in travel costs below 250,000 IDR during
the lockdown and new normal, due to the social
restrictions causing some activities to be limited,such as office activities done from home, and so on
as well as lead to lower transportation costs.
Uncertain frequency is dominated by CommuterLine passengers, approximately 61.28%, 72.31%,
and 70.26% during before pandemic, lockdown, andnew normal conditions. Some activities are allowed
to be done during the new normal, increasing thefrequency 1-2 times a week.
The reasons for respondents choosing to use theCommuter Line are cost and travel time for all
conditions. In addition, respondents also paid
attention to safety and health factors, humanitarianfactors, especially during the lockdown, and newnormal trips.
Feeder transport mode mostly used by CommuterLine passengers is online motorcycle mode, which is62.31% before pandemic decreasing 51.79% during
the lockdown and new normal trips. Furthermore,
the use of motorcycles increases during the
lockdown and new normal trips related to safety andhealth reasons and flexible transport mode. Reasonsfor cost and travel time also dominate respondents inchoosing feeder transport mode.

U1= 1- P2(2)(3)

which isU1 – U2 = Utility function of transport modeb0
= constantbn= coefficient𝛥X= difference of each attributeP1= the probability of Commuter Line choiceP2= the probability of motorcycle mode

RESULTS AND DISCUSSIONS
Motorcyclist Characteristics,
Travel Characteristics

Behavior

Table 1 Trip characteristic
passengers

and

Trip
characteristic

Passengers' characteristics can be explained as
informed in Table 1. Respondents are male (> 53%
for each route) in 17-25 years (52.8%) and 26-35
years (42.05%), meaning that passengers can pay theticket and as private employees and university
students. They have background education
graduating high school and bachelor's degrees. They
have an income of 4.5-7.5 million IDR and spendtransport costs in a month ± 250.00-500.00 IDR (4447%) and 500.00- 1 million IDR (30-34%).
Passengers of Commuter Line mostly areclarified working at the private company (35.13%)
and university students (38.97%) who need amorning and evening peak hour trip. This situation
should be arranged by managing less waiting timeand checking healthy protocol in peak hours to
reduce queuing at Commuter Line stations.
Table 1 informs that the travel time required by
Commuter line passengers is more than 60 minutes
for one trip, i.e., 64.62% for travel time before thepandemic, 69.23% during the pandemic, and 71.79%

NormalLock down
New normala.

NormalLock down
New normal

NormalLock down
New normal

697

Commuterline

Travel time (Minutes)

< 30
5.9 %
4.9 %
4.1 %
<250,000

NormalLock down
New normal

of

30-60
>60
29.5%
64.6%
25.9%
69.2%
24.1%
71.8%
b. Travel cost (IDR)
250,00500,000

500,0001 million>1
million16.41%
45.64%
34.36%
3.59%
21.28%
47.44%
30.00%
1.28%
21.03%
44.10%
32.31%
2.56%c.
Frequency to use Commuter Line
Every
1-2
3 times
>3
Uncer
day
times
times
tain
time
5.64% 5.62%
5.38%
22.1% 61.3%
3.08% 7.6%
4.36%
12.6% 72.3%
3.33% 9.2%
1.54%
12.3% 71.5%
d. Reason to choose commuter Line
comfort
Safe andTravelcost
Fixhealthtimeschedule
64.36% 81%
14.36% 37.95%
5.9%
62.56% 81%
16.41% 36.67%
8.97%
62.05% 80%
17.95% 37.44%
9.49%
e.
Feeder transport mode
Motorcycle
Other
car
bike
Onlinemodes
vehicleGEOMATE – Kyoto, Japan, 3-5 November 2021

NormalLock down
New normal

NormalLock down
New normal

NormalLock down
New normal

NormalLock down
New normal

NormalLock down
New normal

NormalLock down
New normal

3.33% 27.95%
0.26% 62.31% 6.2%
7.18% 34.87%
0.77% 51.79% 5.38%
7.18% 34.36%
0.77% 51.79% 5.90%
f.
Reason to use feeder transport mode
comfort
Fix
Trave
cost
Safe
schedule
l timeandhealth
74.6% 77.4% 16.67% 22.82% 9.49%
74.4% 77.4% 16.41% 16.41% 13.59%
74.6% 75.4% 18.97% 22.56% 14.87%
g. Impact of KRL operational
Less
Inflexi
Queuing
HealthyMorecomfortble
atprotocolTrave& safetytime
stationprobleml time74.6% 64.36% 55.1% 63.85% 14.62%
69.5% 26.92% 51.3% 84.1%
14.62%
69.7% 24.62% 51,5% 84.1%
14.87%
h. Passenger preference to healthy protocol
Agree
Not agree
97.18%
2.82%
96.67%
3.33%
95.38%
4.62%
i.
Realizing healthy protocol by using
Stand
Keep the No
Washin Bringard
distancetalk in g hand
hand
mask
KRL
sanitizer
92.6% 51.03%
85.1% 70.77% 51.03%
93.1% 49.74%
85.4% 72.05% 52.05%
93.9% 54.62%
86.9% 69.23% 46.15%
j.
Transport mode choice due to implementing
the healthy protocol
Using KRL
Split toSplit to othermotorcycle
publictransport
74.62%
18.46%
6.92%
75.90%
16.15%
7.95%
64.62%
26.15%
9.23%

Based on Table 1, the pandemic's impact on theoperation of the Commuter Line is the queuing at thestation, increasing from 63.85% to 84.10%.
Increasing queues at the stations are due torestrictions of Commuter Line operations such astransport capacity causing longer waiting time inchecking healthy protocol.
Respondents' preferences for implementingsocial restrictions and health protocols in CommuterLine operations are agreed, 97.18% and 96.67 %during the lockdown and new normal. Healthprotocols are done by passengers using a standard
mask, no talk inside commuter line, washing hands,
and keeping a safe distance from other passengers.
Most of the respondents' behavior due toapplying health protocols in Commuter Line
operation is to continue using Commuter Line.
Meanwhile, there is a significant increase of some
respondents using or split to a motorcycle.

and new normal because passengers consider
humanitarian factors in realizing healthy protocol
using commuter Line.
To develop the mode choice model, theresearcher uses total travel time (X1) and cost (X2) tobe assessed according to passengers' preferences byapplying a binomial logic model.
Correlation analysis was done, and the results are
informed that R values are more than 98% of travel
time and cost attributes for three routes. Withsignificance value (α), 0.05, the t-test values are
more than the t-test standard, 1.9787 for travel timeand cost variables for all routes. The utility model
will determine the probability of each transportmode chosen between the Commuter Line andmotorcycle.
A. The Change of Travel time variable (  X1)
a. Bekasi-Jakarta route:
The utility function can be determined based on
statistical analysis in Table 2.
Table 2. Significance values of each attribute  X1

The probability of commuter line choice is set as
follows:
PKRL

e 0.503 - 0.059 X1=
1+ e 0.503 - 0.059 X1(4)1

Probability

0.8
0.6

0.4
0.2
0

-30

-20

-10

0

10

20

30

∆X1 (Difference of travel time in minutes)
Probability of Commuter Lines

Probability of Motorcycle usage

Fig. 2 Transport mode choice model based on travel
time variable for Bekasi-Jakarta routeFig. 2 explains the travel time change between
Commuter Line (KRL) and motorcycle (MC).
Passenger tends to choose KRL within the differenceof travel time less than 8.525 minutes. It means that
the Commuter Line system is expected to havehigher speed and less waiting time than amotorcycle. This result is in line with the fact that
consumers prefer to choose flexible transportation
modes with minimum travel time.

The change of Transport Mode
Based on descriptive analysis of passenger
characteristics in trip characteristics, cost and travel
time are expected as major factors in transport mode
choice. Other factors are comfort, and safe andhealthy significantly increase during the lockdown
698

GEOMATE – Kyoto, Japan, 3-5 November 2021

Bekasi-Depok route:
PKRL=

e 0.471 - 0.001 X2
1+ e 0.471 - 0.001 X2

e 0.845 - 0.056 X 1=
1+ e 0.845 - 0.056 X1 (5)

-20

-10

0.8
0.6
0.4
0.2
0
-3000

-2000

0

Probability of Commuter Lines

10

20

1000

2000

3000

Probablity of motorcycle

Fig. 4 Transport mode choice model based on costvariable for Bekasi-Jakarta route

Probability of Motorcycle

Fig. 4 informs the probability of choosing KRL
and motorcycles based on travel cost variables.
Passengers will choose KRL with differences oftravel cost minimal 471 IDR. It means thatgovernment should be subsidized Commuter Line up
to passenger ability to pay the ticket.
b.

Bekasi-Depok route:
PKRL=

Bekasi-Bogor route:

e 0.848 - 0.058 X 1
1+ e 0.848 - 0.058 X1 (6)e 0.785- 0.001 X2
1+ e 0.785- 0.001 X2

Equation (6) explains the change of travel time
between Commuter Line (KRL) and motorcycle(MC). Passengers tend to choose KRL within the
difference of travel time approximately 0-14.62
minutes. It shows that respondents use KRL within a
travel time of fewer than 15.09 minutes.

-3000

(8)

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

Probability=

0

Probability of Commuter Lines30

Fig. 3 Transport mode choice model based on travel
time variable for Bekasi-Depok routeFig. 3 explains that a balance situation is
obtained between Commuter Line and motorcycle
probability with a ratio of 50% for each transportmode if the difference in travel time (X1) is 15.09
minutes. Similar analysis with other routes, thedifference of travel time between Commuter Line
(KRL) and motorcycle (MC) less than 15.09
minutes, passengers prefer to choose KRL.

PKRL

-1000

∆X2 (Difference of travel cost in IDR)

∆X1 (Difference of travel time in minutes)

c.

(7)

1

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

Probability
-30

PKRL

Probability

b.

-2000

-1000

0

1000

2000

3000

∆X2 (Difference of travel cost in IDR)

B. The Change of Travel Cost variable (  X2)

Probability of Commuter lines

Probability of Motorcyclea.

Fig. 5 Transport mode choice model based on travel
cost variable for Bekasi-Depok route

Table 3. Significance values of each attribute  X2

Fig. 5 shows that a balance situation is obtained
between Commuter Line and motorcycle probability
with a ratio of 50% for each transport mode if thedifference in travel cost (X2) is 785 IDR.

Bekasi-Jakarta route:
Travel cost attribute affects a utility function formode choice, which can be determined based on
statistical analysis in Table 3.

c.

Bekasi-Bogor route:
PKRL=

e 0.798- 0.001 X2
1+ e 0.798- 0.001 X2 (9)Equation (9) explains that passengers tend tochoose Commuter Line within the difference oftravel cost approximately minimal 798 IDR. It

The probability of commuter line choice is set as
follows:
699

GEOMATE – Kyoto, Japan, 3-5 November 2021

means that respondents will use Commuter Line to
make a trip activity with a cost difference of lessthan 798 IDR.

REFERENCES[1] Pratandari, S. 2019. Analysis of factors
influencing people's behavior in choosing public
transport Commuter Line Indonesia (Bachelor's
thesis, FISIP UIN Jakarta).
[2] Dianawati, F., Hanif, H. and Maiciptaani, L.
2019. December. Strategy of service quality
improvement for commuter line Jabodetabek
train using integration methods of SERVQUAL
and Kano Model into the house of quality. In
AIP Conference Proceedings (Vol. 2194, No. 1,
p. 020021). AIP Publishing LLC.
[3] Mogaji, E. 2020. Impact of Covid-19 ontransportation in Lagos, Nigeria, Transportation
Research Interdisciplinary Perspectives 6(2020) 100154.
[4] Parady, G., Taniguchi, A., Takami, K. 2020. A
Travel behavior changes during the Covid-19
pandemic in Japan: Analyzing the effects of risk
perception and social influence on going-out
self-restriction,
Transportation
Research
Interdisciplinary Perspectives 7 (2020) 100181.
[5] Citroën,
P.
26
March
2020.
https://www.globalrailwayreview.com/article/98
741/covid19-european-rail-supply-industry/[Assessed on October 27, 2020].
[6] Das, M., New Delhi | Updated on April 22.
2020 Published on April 22, 2020.
https://www.thehindubusinessline.com/economy/logistics/covid-19-impact-36-drop-inrailways-cargo-in-earlyapril/article31407380.ece#[Assessedon
October 27, 2020] .
[7] Parker, M. E., Li, M., Bouzaghrane, M. A.,
Obeid, H., Hayes, D., Frick, K. T., & Chatman,
D. G. (2021). Public transit use in the UnitedStates in the era of COVID-19: Transit riders'travel behavior in the COVID-19 impact andrecovery period. Transport Policy.
[8] Anwari, N., Ahmed, M. T., Islam, M. R.,
Hadiuzzaman, M., & Amin, S. (2021).
Exploring the travel behavior changes caused by
the COVID-19 crisis: A case study for adeveloping country. Transportation ResearchInterdisciplinary Perspectives, 9, 100334.

CONCLUSIONS
The majority of Commuter Line passengers are
male (> 53% for each route), aged 17-25 years(52.8%) and 26-35 years (42.05%) high school andbachelor degree for education background highschool and bachelor degree. They are explained
working at the private company and university
students who need a trip in the morning and evening
peak hour.
Travel time is more than 60 minutes for one trip
for three conditions. It means that checking healthy
protocol needs 5-10 minutes for each passenger. The
reasons for respondents choosing to use theCommuter Line are cost and travel time for all
conditions. In addition, respondents also paid
attention to safety and health factors, especiallyduring the lockdown and new normal trips. Here, theuse of motorcycles increases during the lockdown
and new normal trips related to safety and healthreasons and as a flexible transport mode. The impactof the pandemic on the operation of the CommuterLine is the increase in queuing at the station.
Increasing queues at the stations are due torestrictions of Commuter Line operations such astransport capacity causing longer waiting time inchecking healthy protocol. Furthermore, thegovernment should ensure proper hygiene practices
for public transport operators.
By considering the difference of travel time andcost between Commuter Line and motorcycle,
passenger tends to choose Commuter line within the
difference of travel time approximately 8.525, 15.09
and 14.62 minutes, as well as travel cost minimal
471, 785 and 798 IDR for Bekasi-Jakarta, BekasiDepok, and Bekasi-Bogor routes respectively.
The probability choice model between theCommuter Line and other motorized vehicles can be
set as a reference to operate the Commuter Lineduring the pandemic. The study results can berecommended for further studies in predicting thepotency of passengers demand with considering
humanitarian factors influencing the use ofCommuter Line during the pandemic.

[9] Shafi, R., Delbosc, A., Rose, G., 2020. Travel
attitudes and mode use among Asianinternational students at an Australian
university. Transportation Research Part D, 80,
102259.
[10] Ambarwati, L. and Indriastuti, A.K. 2019.
Improvement of Public Transport to Minimize
Air Pollution in Urban Sprawl. Geomate
International Journal, 17(59), pp.43-50.

ACKNOWLEDGMENTS
Thanks to Rizky Pratama, the bachelor student in
the Department of Civil Engineering, Brawijaya
University, for collecting and process data.

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11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

PREDICTION OF THE COMPRESSIVE STRENGTH OF FOAM
CONCRETE USING THE ARTIFICIAL NEURAL NETWORK
Husnah1, Rahmat Tisnawan2, Harnedi Maizir3 and Reni Suryanita4*
Fakultas Teknik, Universitas Abdurrab, Pekanbaru, Indonesia1,2
Fakultas Teknik, Sekolah Tinggi Teknologi Pekanbaru, Pekanbaru, Indonesia 3
Fakultas Teknik, Universitas Riau, Pekanbaru, Indonesia4*

ABSTRACT
Foam concrete experiments require a lot of time and money. Therefore, a brand new modeling system isneeded. A system which is not depending on the experiments but can predict the strength of foam concrete
accurately. In this study, an Artificial Neural Network (ANN) was used as a solution to predict the compressive
strength of foam concrete. The ANN method uses the feed-forward backpropagation architecture and theLevenberg-Marquardt training algorithm it consists of three layers, namely the input layer, hidden layer, andoutput layer. The input layer consists of cement, sand, water, foam, slum flow, and density, while the output
layer consists of the compressive strength of foam concrete. The number of data used in this study was 90 data.
The results indicated that the Artificial Neural Network had 6 input layer neurons, 13 hidden layer neurons, and1 output layer neurons and they had an accuracy rate of 98.7%. It can be concluded that the Artificial Neural
Network method can be used to predict the strength of foam concrete with an accuracy level close to 100
percent.
Key Words: Foam Concrete, Backpropagation, Artificial Neural Network, Compresive Strength, Accuracy Level

INTRODUCTION(ANN) is able to classify complex input-output
relationships in foam concrete mixtures accurately.
The use of ANN has been researched extensively inrecent years to predict lightweight concrete.
However, there are not many prediction foam
concrete. For instance, the use of ANN onlightweight bricks[4], high-performance foam
concrete [5], and porous concrete [6] and theresearch on multivariate adaptive regression splines
optimized by water on foamed cellular lightweight
concrete (FCLC)[7], the SVM method onlightweight foam concrete (LFC)[8]–[10] and theuse of genetic programming in foam concrete[11].
This study predicts the compressive strength offoam concrete with varied constituent materials
consisting of mortar and foam which has a complex
impact on foam concrete properties using ANN.
This study used ANN as a method of predicting thecomposition of foam concrete constituent materials
fulfilling the requirements of a density value and it issmaller than the specific gravity of water and thecompressive strength of foam concrete that can be
determined before being applied. Although manystudies have been devoted to predicting foam
concrete, we need to pay more attention to thecompressive strength of foam concrete, not to that ofthe density of foam concrete. The foam concrete has
its main advantage, the lightweight. Thus the foam
concrete will float if it is immersed in water.
This paper reports the results obtained frompredicting the compressive strength of foam

Foam concrete is a type of lightweight concrete
widely used in building construction. The use of
foam concrete is apparently the latest innovation inbuilding construction material since foam concrete islighter and more environmentally friendly, startingfrom themanufacturing
process
to
its
implementation. Foam concrete, one type oflightweight concrete, consists of Portland cement or
mortar which has a hollow structure formed from air
bubbles and has a specific gravity of (400 – 1,600)
kg/m2 [1]. Foam concrete is used as a temperatureand sound insulation material and is easy to produce.
The use of foam concrete in civil building
construction should be carefully and effectively
planned, as foam concrete must be able to fulfill therequired technical specifications [2], therefore theright mixture composition is essentially needed to be
applied to civil building construction. The
composition of the foam concrete material mix will
greatly affect its weight and strength, thus it isnecessary to test the properties of the material andproduce test samples in the laboratory [3]. The foam
concrete sample is tested using a free compressive
strength test. This test requires a large number of test
samples, this is not efficient and it takes a long time,
also this experimental test is not able to describe therelationship between the components of the foam
concrete mixture and the compressive strength
produced. Therefore, Artificial Neural Network
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GEOMATE – Kyoto, Japan, 3-5 November 2021

concrete using the ANN method which fulfills therequirements for foam concrete density thus it canbe applied in fields that require light construction.
The benefit of this study is that it can predict thecompressive strength of lightweight foam concrete
without conducting experimental research that
requires a lot of costs and a long time.
LITERATURE REVIEW
Previous research that has been done to predict
foam concrete is in research [7] on foamed cellular
lightweight concrete, by developing and evaluating
the Multivariate Adaptive Regression Splines
optimized using Water Cycle Algorithm (MARSWCA) model. which this study has a weakness inmodeling optimization so that it takes an artificial
intelligence-based method with an algorithm withhigh-speed convergence. In addition, there is also alightweight Foamed Concrete (LFC) study with a
Support Vector Machine (SVM)[8] which adopts aradial basis function characterized by a minimum
mean squared error relative to other functions andtraditional regression. This study reports having aminimum standard deviation so that the prediction
results get high precision values at all points in thedata set. The research proposed by previous
researchers such as [5], [8], [9], [12], [13],[14] all
predicts the compressive strength of foam concrete
to study quickly and more accurately in improving
the performance of methods that predict the strengthof the foam concrete. Compressive strength of foam
concrete without considering the effect of density on
foam concrete where the predicted foam concrete
not only has the targeted compressive strength offoam concrete but also lightweight foam concrete.
Therefore, this study offers to explore the use of
ANN with the backpropagation (BP) algorithm. The
neurons have activation according to formulas (1)and (2).
𝑛𝑒𝑡𝑘 = ∑ 𝑊𝑘𝑗 𝑂𝑗(1)

𝑌𝑘 = 𝑓(𝑛𝑒𝑡𝑘 )

(2)

Input
Layer

HiddenLayer

OutputLayer

Fig. 1 Artificial neural network architecture with
3 layers, namely 7 inputs, 8 hidden
nodes, 1 output
The author also refers to research [6] that uses anartificial neural network model with the Levenberg
Marquardt (LM) algorithm that will be used in thisstudy where the LM algorithm can capture complex
interactions between input/output variables in asystem without prior knowledge of the nature of theinteraction. and without having to explicitly assume
a model. The research describes the existing
research data, data selection, network model training
process, and validation. The results showed that thecompressive strength of porous concrete can bepredicted more accurately, easily, and quickly fromthe density of porous concrete, the ratio of sand andcement, and the distribution of sand particle size.
According to [8],[15] the neural network method has
good performance in overcoming the problem ofnonlinear data, but the neural network has
limitations in dealing with high noise data. So tosolve this problem, a bagging method is needed toreduce data noise in the neural network method. In
the question of estimating the compressive strengthof the concrete lifted, this study combines thebagging and neural network methods to estimate thecompressive strength of foam concrete.

netk is the activation of k neurons, j sets ofneurons in the previous layer. Wkj of the connection
between neuron k and neuron j is the output of
neuron j, and yk is the output that can be calculated
in both sigmoid and logistic transfer functions. The
ANN structure can be seen in Figure 1 Artificial
neural network architecture with 3 layers, namely 6
inputs, 13 hidden nodes, 1 output

METHODS
Experimental data were obtained fromexperimental research results from Abdurrab
University in collaboration with Riau University.
The data used in this study were 90 samples of foam
concrete with variations of cement, sand, and foam
which were used to test the predictive model of the
ANN method. The back-propagation algorithm. This
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GEOMATE – Kyoto, Japan, 3-5 November 2021

The ANN used is feed-forward with a total of 13
hidden layer nodes, the activation function from the
input layer to the hidden layer is tansig and theLevenberg -Marquardt ANN algorithm.
d. Determination of training parameters
e. Training process
f. Display final weightg. Saving ANN, the Best network will be used

research uses input data, namely cement, sand,water, slum flow, foam, and density. While thetarget is the free compressive strength of foam
concrete. This input and target data are for training
with ANN. Training with ANN is carried out using
the MATLAB R2021a software with the following
stages:a. Initialize input and target
b. Normalization of input and target datac. ANN initialization
4. RESULTS AND DISCUSSION

experimental
ANN

2

qu (MPa)

1.5
1
0.5

0
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89
Number of Specimen
Fig. 2 ANN calculation result with experimental data

(a)

(b)

(c)
Fig. 3 Training, validation, test and all regresion
plot in matlab to predict the compressive strengthof foam concrete(d)Figure2. shows the prediction results withexperimental data using ANN with 13 hidden
nodes, it can be seen that the prediction results ofthe compressive strength of foam concrete using
703

GEOMATE – Kyoto, Japan, 3-5 November 2021

primary data as input as many as 90 samples did
not deviate too far from the results of the tests
carried out. This shows that the developed ANN
can predict the effect of the composition of theconstituent elements on the compressive strength offoam concrete with an acceptable level of accuracy.
Figure 3 shows that for training and validation
with ANN using 13 hidden nodes, the R-value(coefficient of determination) is 0.96775 and
0.98747, respectively. This R-value shows therelationship between the output and the target in the
training and validation process. An R-value that ismore than 0.97, means that the amount of training
data that has been recognized is large. So it showsthat the output data generated in the training andvalidation is close to the target data entered in theANN. While the blue and green fit lines which areclose to the dotted line indicate that the Y value(output) is also close to the T value (target).

Mean Squared error (mse)Best validation performance is 0.0044992 at epoch 16

study resulted in a small mse value which is shownin Figure 4, and the amount of training data thatwas successfully recognized was large in Figure 3.
CONCLUSIONS
Based on the research and observations that havebeen carried out on the ANN system for predicting
the compressive strength of concrete using feedforward backpropagation architecture and theLevenberg-Marquardt training algorithm, the best is
obtained at hidden node 13, with a validation MSE
of 4.4992×10-7, the authors can conclude that by
The ANN method can be used to predict thecompressive strength of foam concrete based on the
input amount of the foam concrete-forming mixture.
So that costs in planning the formation of foam
concrete can be minimized because they no longer
depend on concrete experts who offer services athigh prices. The ANN in this study showed that theaccuracy of the 90 training data tested reached98.7%, indicating that the amount of training datathat was successfully recognized was large.
However, further research is needed to optimize theprediction results of lightweight foam concrete andthe required compressive strength of foam concrete.

Fig. 4 Error value: Best Validation Performancevs Mean Square Error (MSE)
Figure 4 shows that the results of training anddata testing using ANN have produced the besterror value (Mean Squared Error, MSE) of
4.4992×10-7. ANN will be of good value if the
MSE value is close to 1. From the training using
input data from primary data by varying the hidden
node ANN, the results of the mean square error oftraining and validation are obtained. as shown infigure 4. It can be seen from the training results thatthe number of hidden nodes 13 obtained thesmallest validation mean square error, then hidden
node 13 had the best training MSE value. Thus
ANN can be trained to recognize the compressive
strength of foam concrete with this architecture.
The architecture of this network is 6 neurons in the
input layer, 13 neurons in the hidden layer, 1
neuron in the output layer. Therefore, the network
used in this study is a network with 13 hidden
nodes. The ANN architecture used can be seen in
Figure 1.
The results of this ANN research are strongly
influenced by the input data and the target that
becomes the learner in ANN training takes a longtime to get the best training results. Forecasting
research conducted [7] the most recently conducted
there are limitations in terms of the combination ofmaterials and components of the mixture. One ofthe drawbacks of the MARS-WCA model is that its
implementation is time-consuming due to theparameter setting of modeling optimization. This

ACKNOWLEDGMENTS
Gratitude to the Ministry of education and culture
research and technology of Indonesia Grant forHigher Education Cooperation Year 2021–2022,
and Civil Engineering Study Program at Abdurrab
University.
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[2]
[3]

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T. Nguyen, A. Kashani, T. Ngo, and S.
Bordas, “Deep neural network with high‐order neuron for the prediction of foamed
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Le, and B. T. Pham, “Investigation andoptimization of the C-ANN structure inpredicting the compressive strength of
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64, 2018.

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

STUDY ON EVALUATION OF HUMAN DAMAGE FROM TSUNAMICONSIDERED CONGESTION OF EVACUEE
Ken-ichi Fujita1 and Harumi Yashiro2
Faculty of Engineering, Nagasaki Institute of Applied Science, Japan
School of Systems Engineering, National Defense Academy of Japan, Japan1

2

ABSTRACTIn evacuation behavior from tsunami, street-blockage caused by building collapse due to earthquake increase
the number of human damage because of decrease of walking speed by congestion of evacuee. When area-wide
mesh as macro evaluation is used to evaluate the human damage, the congestion of evacuee has to be appropriately
considered. In this study, the stochastically evaluation method of human damage in evacuation from tsunami usingarea-wide mesh proposed by the authors is extended to the method which can be considered both the congestionof evacuee and relation between the street-blockage and building damage. The congestion is assumed to beoccurred from the street-blockage caused by building collapse due to earthquake. The congestion degree is definedby number of people per square meter. The decrease of walking speed in evacuation is expressed by the congestion
degree. Moreover, using the proposed method, the variations of the number of the human damage in evacuatingfrom tsunami by difference of the evacuation awareness and the congestion by the street-blockage caused bybuilding collapse are discussed.
Keywords: Tsunami Evacuation, Human damage probability, Congestion, Street-blockage, Area-wide mesh
INTRODUCTION

tsunami inundation speed have been considered in thestudies. The street-blockage has been represented bydecrementofsidewalkwidth.
However,consideration of decrease of walking speed bycongestion of evacuee is left for a future work.
This study presents a stochastically evaluation
method on the human damage in evacuation fromtsunami taking account of the congestion of evacuee.
The congestion is assumed to be occurred from thestreet-blockage caused by building collapse due toearthquake. The congestion degree is defined bynumber of people per square meter. The decrease ofwalking speed in evacuation is expressed by thecongestion degree. The area-wide mesh is used tovisually represent the distribution of the humandamage in evacuation from tsunami. Moreover, thevariations of the number of the human damage inevacuating from tsunami by difference of theevacuation awareness and the congestion due to thestreet-blockage are discussed using the proposed
method. The originality of this study is in the point ofevaluating of the human damage in evacuating fromtsunami using stochastically considering variances
walking speed and tsunami run-up speed.

The damage estimation for large-scale natural
disaster caused by earthquake or tsunami which may
occur in the future has been carried out in Japan. To
reduce the damage caused by the natural disaster,
disaster prevention and evacuation plans have beenplanned by using the estimation results in manymunicipalities. The evaluation of the human damage
in the damage estimation is one of an important item.
The human damage for tsunami has been usually
evaluated for evacuation behavior from tsunami.
However, in many studies and damage estimations on
the human damage, the effects of building damage,liquefaction of the ground and street-blockage caused
by earthquake have not been considered. The
evaluation result of the human damage considering
the effect of earthquake damage is considered to bedifferent because walking speed of evacuee isdecreased due to obstacles such as rubbles andcollapsed objects on evacuation route caused byearthquake.
As the one of the most impact on evacuation fromtsunami, street-blockage caused by building collapse
is considered. The evaluation of the human damage
considering the impact on the street-blockage toevacuation from tsunami in the previous studies canbe divided into the following two methods. One is thenumerical method such as the multi-agent model, andthe other is the method using the area-wide mesh. The
authors have proposed a stochastically evaluation
method of the human damage using the area-wide
mesh [1], [2]. Variances of walking speed and

OUTLINE OF EVALUATION OF HUMANDAMAGEThe area-wide mesh is used to set up evaluation
conditions and to show evaluation results. Mesh size
is 250m x 250m. Population, number of buildings,
road network, and geographical conditions are
introduced of GIS (Geographic Information System)
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GEOMATE – Kyoto, Japan, 3-5 November 2021

data. For seismic intensity and tsunami inundation
depth, the average value in mesh area is adopted.
Evacuation distance from evacuation mesh toevacuation facility including the difference ofelevation between mesh is assumed to be 1.5 times oflength of plane distance between the centers of mesh.
The human damage probability is evaluated byusing relation between the evacuation time and thetsunami arriving time after earthquake occurrence.
The human damage ratio is evaluated by using thetsunami fragility [3].
The street-blockage is occurred by the building
collapse due to earthquake. The blockage ratio isevaluated by using the fragility of buildings forearthquake [4].
The walking speed in evacuation assumed to be
decreased by the congestion of evacuee by the streetblockage. The congestion is defined by the number ofpeople per unit area [5].
From the above, the evaluation flow on the humandamage in evacuation from tsunami can be shown in
Fig.1.
The following assumptions are used in theevaluation.
1) The human damage is counted when person iscaught up with tsunami.
2) The evacuation method is only walk. The walking
is only permitted on sidewalk.
3) The evacuation direction is not toward to coast.
4) The evacuation facility is designated for each
evacuation mesh. The damage of evacuation

facilities by earthquake is not occurred.
5) Random variables with respect to speed and time
are assumed to follow normal distribution.
6) Mean value of walking speed is only decreased by
the effect of the congestion. Standard deviation of
the walking speed is assumed to be not variable forthe congestion.
EVALUATION METHOD OF HUMANDAMAGE
Human Damage Probability
The human damage in evacuation from tsunami isevaluated for each evacuation mesh. The humandamage is analyzed by using the difference between
total time in evacuation behavior after the earthquake
occurrence FE(t) and the time from tsunami
generating to arrival at evacuation facility FT(t). FE(t)and FT(t) are assumed to be follow normal
distribution N(µE, σE) and N(µT, σT), respectively. Itshould be noted that µE and σE are the mean value andstandard deviation of TE and µE and σE are the meanvalue and standard deviation of TT.
The mean values of TE and TT are expressed by thefollowing equations;

Population,
elevation

TsunamihazardSeismic
hazardTsunamiinundationarea

Damage ratio ofbuildings
Street-blockage ratio

Area-wide mesh

Effective area ofsidewalk

Evacuation
conditions

Decrement ratio of
walking speed

Evacuationtime

Tsunami arriving
time

Damageprobability

No Damage
Survive(1)µT = t P + t R(2)

where, tS is the start time of evacuation after
earthquake, tW is the mean value of random variablesof the walking time TW obtained by the walking speed
and the distance to evacuation facility, tP is thetsunami propagation time to coast, and tR is the meanvalue of random variables of the tsunami run-up time
TR obtained by the run-up speed and the distance toevacuation facility.
The mean values and the standard deviations of tW
and tR in this study have to be evaluated as the inverse
function because the walking speed in evacuation VWE
and the tsunami ran-up speed VT defined as normal
distributions are used. The mean value µ1/X and thestandard deviation σ1/X of an inverse function of apositive normal variable X with mean value µX andstandard deviation σX are given by the following
equations [6];Scenario
earthquakeGISµ E = t S + tW

Tsunami fragility forhuman damage




2

 σ X   µ X
 µ X  

µ1 / X = 1 + (3)DamageInjury(survive)

σ1/ X =

No survive

σX
µX 2

  σ X  2  
1 −   µ X  

(4)

The mean value and standard deviation of TW and

Fig.1 Evaluation flow of human damage
707

GEOMATE – Kyoto, Japan, 3-5 November 2021

TR can be evaluated by Eq. (3) and Eq. (4),respectively.
The human damage probability PD in evacuation
can be provided by the following equation.

µM PD = 1 − Φ

σM 

μE

where, Φ is the standard normal distribution function
with N(0, 1), µM and σM are given by the following
equations;

σ M = σ +σ

1

(7)

0.6
0.4
0.2

Decrease Ratio of Walking Speed by Congestion

0

Decrease of walking speed is assumed to beoccurred by congestion of evacuee by street-blockage.
The decrease of walking speed is evaluated for each
evacuation mesh. The street-blockage is occurred bybuilding collapse. Fragility curves of buildings forseismic motion shown in Fig.3 [7] are used to theevaluation of the street-blockage. In Fig.3, C.D. iscompletely destroyed, C.P.D. is completely andpartially destroyed. The fragility is evaluated for eachstructural type, construction year, and destruction
type.
The street-blockage ratio is evaluated for eachmesh using destruction ratio of buildings obtained by
the fragility curve. Evaluation equation for the streetblockage ratio Rd is given as follows [5];(WS < 3m)
(3m ≤ WS < 5.5m)(5.5m ≤ WS < 13m)

D pr
2

4

4.5

5
5.5
6
6.5
Instrumental seismic intensity

7(a) Wooden structure
1‐1960 (C.D.)
1961-1980 (C.D.)
1981‐ (C.D.)‐1960 (C.P.D.)
1961-1980 (C.P.D.)
1981‐ (C.P.D.)

Destruction ratio

0.8
0.6
0.4
0.2
0

4

4.5

5
5.5
6
6.5
Instrumental seismic intensity

7(b) Non wooden structure(8)

Fig.3 Fragility curve for buildings
following procedure.
1) Evaluating the sidewalk area in usual condition Asw
by subtracting the roadway area Ar and the roadshoulder area Ars from the total road area in mesh.
2) Evaluating the sidewalk width in usual condition
Wsw by dividing the side walk area Asw by the road
length Lr in mesh.
3) Evaluating the sidewalk width by the streetblockage Wswb by the difference between Wsw andWsb.
4) Evaluating the sidewalk area considered the streetblockage Aswb by multiplying Wswb and evacuationroute length in mesh Lr.
5) Evaluating the congestion degree Rc by dividing
population in mesh Np by Aswb.

where, WS is the road width and Ddr is the destruction
ratio given by the following equation [5];Ddr = Dcr +‐1960 (C.D.)
1961-1980 (C.D.)
1981‐ (C.D.)‐1960 (C.P.D.)
1961-1980 (C.P.D.)
1981‐ (C.P.D.)

0.8

The survival probability can be obtained by 1-PD.
The survival probability curve and the probability
density function of fE(t) and fT(t) of probability
functions FE(t) and FT(t) are shown in Fig.2.

 1.28Ddr

Rd = 0.604 Ddr
0.194 Ddr


μM/σM

Fig.2 Survival probability curve

(6)
2
E

fE(t)：Probability density
function ofevacuation time.
fT(t)：Probability density
function oftsunami arriving time.

μT

0Destruction ratio

2
T

fT(t)

fE(t)

(5)

µ M = µT − µ E

1-PD

1.0

(9)

where, Dcr and Dpr is the completely and the partiallydestruction of building, respectively, which can beevaluated by using the fragility. The width of streetblockage Wsb can be obtained by multiplying Ddr andWs.
The decrease ratio Rwd of the walking speed by thecongestion is evaluated for each mesh by the708

GEOMATE – Kyoto, Japan, 3-5 November 2021

Decrease ratio Rdw

1.2

Inundation
depth (m)Rdw = 1 (0 ≤ Rc < 1.5)
110
Rdw = − Rc +(1.5 ≤ Rc < 6)
5
13
1
Rdw =
(6.0 ≤ Rc )
5 Rc

1

0.8
0.6
0.4
0.2
0

0

1
2
3
4
5
6
7
Congestion degree Rc (Person/m2)

Fig.7 Tsunami inundation depth

8

condition, hazard map information of tsunami andseismic intensity, and statistical data of the target area
are used to the mesh.
Tsunami inundation depth and seismic intensity
obtained by damage estimation result for a Yaeyama
islands southern offshore earthquake are used [7].
The moment magnitude of the earthquake Mw isestimated to be 9.0.
Tsunami inundation depth is shown in Fig.7. In
addition, the maximum tsunami height is 22m and thearriving time of the first wave of the tsunami is 16
minutes [7]. The mean value of tsunami run-up speedvT is evaluated by using the following equation [8];Fig.4 Decrease ratio of walking speed6) Obtain the decrease ratio of the walking speed Rwd
by using Rc.
7) Evaluating the mean value of walking speed in
evacuation vWE by multiplying the mean value ofwalking speed in usual condition vW and Rwd.
The relation between decrease ratio of thewalking speed Rwd and the congestion degree Rc is
given by Fig.4 [5].
EVALUATION OF HUMAN DAMAGE(10)vT = 1.1 gh

Evaluation Conditions

where, g is the gravitational acceleration and h is theinundation depth. The standard deviation σT of the
run-up speed is assumed vT/10 in this study.
Seismic intensity of the area by the earthquake is
6- which indicates in the range of greater or equal 5.5
and less than 6.0 of measured seismic intensity
defined by Japan Meteorological Agency. The
destruction ratio of building can be obtained by usingfragility curve shown in Fig.8. The destruction islarge in west and part of east. The ratios are 0.1 to
0.15 for the mesh with large number of buildings and0.05 to 0.1 for the mesh with small number of

The human damage in evacuation from tsunami
using the proposed method is discussed by differenceof the evacuation awareness and the effect of thecongestion by the street-blockage.
The area-wide mesh of a target area is shown in
Fig.5. The mesh size is 250m x 250m. The population
in mesh and the evacuation facilities are also shownin the figure. The population is 26,250 persons. The
population is large in the west and part of the east.
Building distribution is shown in Fig.6. The numberof buildings is 8,088, wooden buildings is 813 andnon-wooden is 7,275. In addition, geographicalSeismic
damageratio ofbuilding(-)

Number ofpeoplePopulation: 26,250: Evacuation facilityN

Fig.8 Destruction ratio of building
Fig.5 Area-wide mesh and population distribution:Evacuation direction

Number of building: 8,088 Number ofbuilding

Fig.6 Building distribution

Fig.9 Evacuation route709

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1 Population ratio for evacuation awareness

Table 3 Evaluation results of human damage

Evacuation behavior
EvacuationNo
awareness Immediately After work Urgency
evacuation
level(High)
(Low)
(Low)
(Low)
High
80%
10%
5%
5%
Normal
50%
25%
15%
10%
Low
15%
35%
30%
20%

Evacuation
awareness
level
High
Normal
Low

After
work

5 min.

15 min.

Without congestion

Congestion

7,130
13,501
20,941

9,918
15,245
21,465

Human damage: 7,130 (person) Humandamage(person)Table 2 start time of evacuation
Immediately

Damaged population (persons)

Urgencytsunami arrivingtime(a) High evacuation awarenessbuilding. The number of completely destroyedbuilding is 404 and that of partially one is 750.
Evacuation routs are shown in Fig.9. The redarrow in the figure is the evacuation direction.
The evacuation behavior is divided into four type:evacuation immediately, evacuation after work,urgency evacuation and no evacuation. The
evacuation awareness is categorized into three types;
high, normal and low awareness. Population for theseawareness is grouped by combining the four types ofthe evacuation behavior [4]. The population ratio forevacuation awareness is shown in Table 1. The start
time of evacuation for several conditions is shown in
Table 2.
The mean value vW and the standard deviation σW
of walking speeds are used statistical results for
middle aged in the previous study [9]. vW is 1.34m/s
and σW is 0.167m/s.

Human damage: 13,501(person) Humandamage(person)(b) Normal evacuation awareness
Human damage: 20,941(person) Humandamage(person)Evaluation Results(c) Low evacuation awarenessEvaluation Results without Congestion

Fig.10 Humancongestion

The human damage under the condition neither
the congestion nor the street-blockage are discussed.
Evaluation results of the human damage
distribution for different evacuation awareness areshown in Fig.10 and the number of the humandamage is shown in Table 3. Number of the humandamage becomes large with decreasing evacuation
awareness. The number of the human damage
increases in some meshes for high level of evacuation
awareness. The number of the damage increases in
the west and part of the east for low level of theawareness.

damagedistribution

without

The congestion degree for each evacuation meshby the street-blockage caused by the building collapse
is shown in Fig.11. The congestion tends to increasein the evacuation mesh with the large number of
population and passing the route in the high damageof building. The walking speed in evacuation
decreases for the applicable mesh.
Evaluation results of the human damage
distribution for different evacuation awareness areshown in Fig.12 and the number of the humandamage is also shown in Table 3. The human damage
considering the congestion is larger than that of theresult without the congestion in spite of difference ofthe evacuation awareness. The human damage for thehigh awareness is 1.39 times larger than that of theresult without the congestion. For the normalawareness and the low awareness are 1.13 times and

Evaluation Results with Congestion
Evaluation results of the human damage under
the condition with the congestion by the streetblockage are discussed.
710

GEOMATE – Kyoto, Japan, 3-5 November 2021

Human damage: 9,918(person) Humandamage(person)Congestion
degree ofpeople(person/m2)

Fig.11 Congestion degree by street-blockage(a) High evacuation awareness1.03times larger than those of the results without thecongestion. The human damage considering thecongestion in the evacuation increases regardless thedifference in the evacuation awareness.

Human damage: 15,245(person) Humandamage(person)

CONCLUSIONS
A stochastically evaluation method of the humandamage in the evacuation from tsunami considering
the congestion due to the street-blockage by thebuilding collapse was presented. The number of thehuman damage by difference of the evacuation
awareness and the congestion are discussed.
The following conclusions can be drawn.
1) The human damage considering the congestion
increases than that of the result of without thecongestion regardless the difference of theevacuation awareness.
2) The impact of the congestion is especially large.
Even if the evacuation awareness is high, thehuman damage increase due to a decrease in thewalking speed.
3) To consider the congestion due to the streetblockage caused by building collapse is importantin the evaluation of the human damage inevacuation from tsunami.
The future work of this study is that the validityof the proposed method is verified using comparison
with the result of damage estimation using area-wide
mesh published by the local government in Japan.

(b) Normal evacuation awareness
Human damage: 21,465(person) Humandamage(person)(c) Low evacuation awarenessFig.12 Human damage distribution with congestion
Engineers, ser. B, Vol.65，No.4，2009, pp.320331. (in Japanese)[4] Cabinet office of Japan, Damage Estimation of
Nankai Trough Earthquake (1st. Report), 2012.
(in Japanese)[5] Cabinet office of Japan, Simulation Results on
Return Home Action, 2007. (in Japanese)[6] Nakamura T., On the Moment of Positively
Truncated Normal Distribution, J. of Japan
Statist. Soc., Vol.10, No.2, 1980, pp.139-134. (inJapanese)[7] Okinawa Pref., website, Report of Survey on
Estimation of Earthquake damage in Okinawa
Prefecture in 2013, https://www.pref.okinawa.jp/site/chijiko/bosai/h25jishinhigaisoutei.html(Reference: 2021.5.13)[8] Shuto N, Imamura F., Koshimura S., Satake K.,
Matsutomi H., eds., Encyclopedia of Tsunami,
Asakura Publishing Co., Ltd., 2007. (inJapanese)[9] Matsumoto N., Kiyota S., Ito M., Relationshipbetween the Characteristics of Streetscape andWalking Speed, Journal of Architecture andPlanning, Vol.74, No.640, 2009, pp.1371-1377.
(in Japanese)

REFERENCES[1] Fujita K., and Yashiro H, A Study on Human
Damage in Evacuation from Tsunami
Considering Street-Blockades Caused by
Destroy of Buildings, International Journal of
GEOMATE, Vol. 19, Issue 72, 2020, pp.1-7.
[2] Fujita K., and Yashiro H, Human Damage
Estimation in Evacuation from Tsunami Caused
by a Common Scenario Trench-Type Earthquake,
Proc. of the 30th European Safety and Reliability
Conf. and the 15th Probabilistic Safety
Assessment and Management Conf., 2020,
pp.508-516.
[3] Koshimura S., Namegawa Y., Yanagisawa H.,
Fragility Functions for Tsunami Damage
Estimation, Journal of Japan Society of Civil

711

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

EXPERIMENTAL STUDIES ON THE TAYLOR-COUETTE FLOW OFSHEAR-THINNING FLUIDS
Yuki TANO, Tatsuya KAWAGUCHI and Takushi SAITO
1Faculty of Engineering, Tokyo Tech, JAPANABSTRACTTwo concentric cylinders which rotating inner cylinder make the circumferential flow with Taylor vortex under
the certain Reynolds number. It is called Taylor-Couette flow. It has been focused on the extensive study for
Newtonian fluids but does not for non-Newtonian fluids since flow transition mechanism is remained and its use
in various industrial applications. Many kinds of apparatus using Taylor-Couette flow of redesign and scale up are
still under investigation. The objective of the present study is firstly to specify the transition area of the Taylorvortex flow and Wavy vortex flow with the non-Newtonian working fluid. In the experiment, two type of shearthinning fluids that have different structural viscosity index. Particle Image Velocimetry (PIV) was employed as
the flow visualization technique for a wide range of Reynolds number. Water solution of guar gum (n=0.7) resulted
the six-cell stable mode in the range of Taylor-vortex flow and wavy-vortex flow. The other working fluid with
Xanthan gum (n=0.52) resulted the vertical oval vortex enlarged to axial direction with the same Reynolds number
range. Both results were significantly different from the Taylor-Couette flow with Newtonian fluid. As aconclusion, we confirm that the shear-thinning effects of working fluid affects the vertical oval vortex drafting toaxial direction.
Keywords: Non-Newtonian fluids, Taylor vortex Flow, Wavy vortex Flow, Shear-thinning Fluids, Structuralviscosity index
INTRODUCTION

experimental data on shear-thinning fluids.
From that background, the purpose of thisresearch is to figure out flow transition
mechanism for non-Newtonian fluids. The

Two concentric cylinders in which one orboth are rotating make the circumferential
flow with Taylor vortex under the certain
Reynolds number. It is called Taylor Couette
flow. Taylor Couette flow has been applied tovarious industries including food, chemical,
medical fabrication industries and has
improved many industrial applications,
including filtration[1], protein shearing[2],blood
detoxification[3],liquid-liquid
extraction[4],bioreactors[5],emulsionpolymerization[6],crystallization[7],cultivation of animal cells[5], andphotocatalytic reactions[8]. Many industrial
applications of Taylor-Couette flow use nonNewtonian fluids. Ashrafi[9] pointed out thatin terms of study of complex fluids, TaylorCouette flow of non-Newtonian fluids plays
an important role of rheology in flow
instabilities. As mentioned above, there aremany case studies on complex fluids and manykinds of apparatus using Taylor-Couette flowof redesign and scale up are still underinvestigation. Many Taylor-Couette flowbased applications employ shear-thinning
fluids and some researchers[9]-[12] havepointed out that there are insufficient

theoretical equation for determining the transition
Reynolds number of Taylor Couette flow in
Newtonian fluids has been established, but it has
not yet been established for non-Newtonian fluids.
Therefore, if it becomes possible to identify thetransition region and determine the transition
Reynolds number theoretically, it will be useful forthe design of devices in various industrial fields.
For that reason, the objective of the present study
is to specify the transition area of the Taylor-vortex
flow and Wavy vortex flow with the shear-thinning
fluids using experimental methods.
Taylor-Couette flow has many flow regimes
which are controlled by the Reynolds number and has
surprisingly complex dynamics. Considering themost common condition, the inner cylinder is rotating
and the outer cylinder is fixed. Reynolds number isdefined below.
𝑅𝑒 =𝜌𝑑𝑟𝑖 Ω
𝜇𝑒𝑓𝑓(1)Where 𝜌 is the fluid density, 𝜇𝑒𝑓𝑓 is the effective
viscosity, Ω is the rotational speed of the innercylinder, 𝑑 is the gap between the cylinders, 𝑟𝑖 is theinner cylinder radius. One of differences between
Newtonian and shear thinning flow is a712

GEOMATE – Kyoto, Japan, 3-5 November 2021

circumferential velocity gradient. The shear stressacting on the fluid is given by the following equation.
𝑑𝑢 𝑛𝜏 = 𝜇∗ ( )𝑑𝑦(0 < 𝑛 < 1)

and is caused by the shear thinning property of thefluid. Cagney and Balabani[17] examined four fluidswith different structural viscosity index(n=1, 0.68,
0.62 and 0.52). Three of them are shear thinning andviscoelastic. They confirmed that shear thinninglowered the critical Reynolds numbers for TVF and
WVF compared with Newtonian case and calculated
the amplitudes of inflow(moving toward the innercylinder) and outflow(moving toward the outercylinder)using
particle
image
velocity
measurement(PIV) for TVF and WVF. They reported
results that the magnitude of vorticity and the strengthof the outflow are both reduced in shear thinning
fluids to relative to the Newtonian case. In the WVF
for Newtonian fluids, the magnitude of inflow arelower than those at the outflow, but the magnitude of
inflow and outflow both tend to be significantly larger
for shear thinning fluids. They also observed slow
drifts in the axial positions of vortices and spatial
variations in the wavy instability which are notobserved in Newtonian fluids.
The most parts of dynamics for shear thinning fluidsof Taylor Couette flow are still unexplained andneeded more experimental data. The previous
research[17] got the critical Reynolds number of TVF
and WVF for shear thinning fluids. However,
elucidation of the mechanism remains an issue forflow mode transition. This paper describes theidentification of transition regimes using PIV forshear thinning fluids and have consideration for them.

(2)

where 𝜏 is shear stress, 𝜇 ∗ is reference viscosity, 𝑢 isvelocity, 𝑦 is coordinates perpendicular to the flow, 𝑛
is structural viscosity index. From the above equation,
the velocity gradient boundary layer increases as 𝑛decreases, that is, high shear thinning.
Andereck and Swinney[13] observed the flowtransitions between states were determined by 𝑅𝑖 and𝑅𝑜 (inner- and outer-cylinder Reynolds number). Fig
1[13] shows regimes observed in flow between
independently rotating concentric cylinders. In thisresearch, outer cylinder is fixed, flow regimes are
Circular-Couette
Flow(CCF),
Taylor-Vortex
Flow(TVF), Wavy-Vortex-Flow(WVF), ModulatedWavy-Vortex-Flow(MWF) and Turbulent Taylor
Vortex Flow. CCF is laminar and axisymmetric at the
low Reynolds number. As the Reynolds number isincreased beyond a critical value 𝑅𝑒𝑐1 , the flow
becomes unstable and CCF trans to TVF. TVF isdominated by a series of steady toroidal vortices. A
second critical Reynolds number 𝑅𝑒𝑐2 exists and aseries of toroidal vortices becomes unsteady andpulsates at a single frequency. This state is WVF.
Further increase in Reynolds number is associatedwith more transitions in which include MWF pulsatesat two frequencies, ultimately leading to turbulence.

EXPERIMENTAL DETAILSThe appearance of the experimental device isshown in Fig.2. Cylindrical coordinates, 𝑟, 𝜃, 𝑧

Fig.1 Flow regimes of two concentric rotating
cylinder [13]Study of Taylor-Couette flow was mainly initiatedby Taylor[14] for Newtonian fluids, and has been thefocus of considerable attention until now. Fromnumerical work of Lockett, Richardson, andWorraker[15], Khali, Nebbali, and Bouhadef[16] andAlibenyahia et al[11], Ashrafi[9] predicted that shear
thinning characteristic of working fluid lowered thecritical Reynolds number for TVF compared with
Newtonian fluids using a deterministic approach toanalyze the nonlinear flow stability and reported
results clearly show the dynamics that is completely
different in Taylor-Couette flow of Newtonian fluids

Fig.2 Schematic diagram of experimental equipment
represents the radial, the circumferential, and axial
coordinates respectively. The material of the cavity isan acrylic and a water jacket is designed around theouter cylinder. Table 1 shows the dimensions of theexperimental equipment. The inner cylinder isconnected to the motor driver via a belt. The stepping
motor is good for Taylor Couette flow device. In thispaper, the velocity distribution was measured by PIV.
713

GEOMATE – Kyoto, Japan, 3-5 November 2021

𝑘(𝑛, 𝜂) = 77.05𝑛0.32 𝜂 2 − 88.73𝑛0.31 𝜂 + 26.85𝑛0.21 (4)A schematic diagram of the PIV experiment is shownin Fig.3. The laser sheet was inserted into the verticalcross section. The tracer particles are DNTEC
DYNAMIC S-HGS, Sliver Coated Hollow Glass
Spheres. The particle size is 10 μm and the particle
density is 0.68 g/cm3 . The shooting frame rate is
500 fps and the resolution is 1280 x 768 pixels. Afterconfirming that the flow was steady, 2177 frames
were taken.
Laser sheet

The effective viscosity is shown using Carreau modelby the following equation.
𝑛−1𝜇(𝛾̇ ) = 𝜇∞ + (𝜇0 − 𝜇∞ )(1 + (𝜆𝑐 𝛾̇ 2 ) 2

where 𝜇0 is zero viscosity, 𝜇∞ is infinity viscosity 𝜆𝑐is relaxation time. From the above results, theidentification of the transition region is evaluatedusing the Reynolds number in Eq (1).

CameraRHELOGICAL PROPERTIESTo see how shear thinning influence flow
field, two fluids are made with different structuralviscosity index which are guar gum 0.1%wt(n=0.7)
and xanthan gum 0.05%wt(n=0.52). The two fluids
were prepared by mixing them at 700 rpm for 30
minutes using a stirrer with blades and then storingthem in the refrigerator for one day. The rheologicalproperties of both fluids were measured with arheometer. The measurement results are shown in
Fig.3 and Fig.4, and the important parameters in thispaper are shown in Table 2. The shear rate band in
this research is 10~150.

Water jacket
Fig.3 A schematic diagram of the PIV experiment
Table 1 Device dimensions
Parts
Radius of inner cylinder 𝒓𝒊 [mm]Radius of outer cylinder 𝒓𝒐 [mm]Height 𝒉 [mm]Gap 𝒅 [mm]Radius ratio 𝜼 = 𝒓𝒊 /𝒓𝒐aspect ratio 𝚪 = 𝒉/𝒅

(5)

Unit
64
80
96
16
0.8
6

METHOD OF DETERMINING EFECTIVE
VISCOSITY
The model of shear-thinning fluid is shown
by Eq(2). For Newtonian fluids, the velocity
distribution and the viscosity are constant because
𝑛 = 1 . However, for shear thinning fluids, thevelocity distribution is exponential and the viscosity
depends on the shear rate. Determining effective
viscosity is required to get effective Reynolds number
because the viscosity varies depending on thepositions. Masuda, Horie, Hubacz, Ohta and Ohmura[18] suggested method of determining effective shear
rate in order to determinate effective viscosity. The
effective shear rate is given the following equation.
𝛾̇𝑒𝑓𝑓 = 𝑘ΩFig.4 Viscosity v.s. shear rate.

(3)

where 𝑘 is proportional coefficient. The proportional
constant is determined by numerical work comparedwith experimental data. 𝑘 is shown by following
equation and is determined by 𝑛 and 𝜂, but if 𝜂 ≥ 0.7
it can be effective because wider gap has larger
viscosity ratio between inner and outer walls thannarrow gap.

Fig.5 𝛿° = tan−1 𝐺‫״‬/𝐺‫ ׳‬v.s. the Reynolds number(𝐺‫ ״‬is elastic modulus, 𝐺‫ ׳‬is storage modulus)714

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 2 Fluids property used in the experiment.
Fluids
GUAR0.1%wt
XTG0.05%wt

n
0.7
0.52

𝛍𝟎 [𝐦𝐏𝐚 ∙ 𝐬]
51
820

upper and lower roll pairs become vertically enlarged
depending on the Reynolds number and the roll pairof the central cells look crushed at the WVF. The
relationship between the axial vortex length of the roll
pair compared with Newtonian case and the Reynolds
number is shown in Fig.7. The vertical axis is the ratioof axial lengths based on the case of Newtonian fluid.
The first and second vortex center positions from the
top are 𝒁𝟏 and 𝒁𝟐 , they offset upper depending on the
Reynolds number at the WVF. The relationshipbetween 𝒁𝟏 and 𝒁𝟐 and the Reynolds number isshown in Fig.8.

𝛌𝐜 [s]
0.55
0.76

RESULT OF FLOW VISUALIZATION
Each working fluid was visualized up to
Reynolds numbers 650(n=0.52) and 850(n=0.7) using
a visualization tracer. Fig.6 shows the flow
visualization results. The Taylor vortices on the upperand lower end faces are slightly different in size dueto the influence of the Ekman boundary layer. For
Newtonian fluids, a series of toroidal vortices are six
cells mode and the each cell size is almost same areobserved at the TVF and WVF in the previous
research[19]. If the cylinders are infinitely long, aseries of vortex trains infinitely are expected for
Newtonian fluids and a series of vortex trains which
size are irregular infinitely are expected for non
Newtonian fluids. For guar gum 0.1%wt fluid(n=0.7),
the flow is beginning with a simple circumferential
Couette flow and a series of steady toroidal vortices
are six cells mode and the each cell size is almost
same are observed at the Re=~90. At the Re=90~850,
the flow becomes WVF and the each cell sizefluctuates are observed. For xanthan gum 0.05%wt
fluid(n=0.52), the flow is beginning with a simple
circumferential Couette flow and a series of steadytoroidal vortices are four cells mode and the each cellsize is almost same are observed at the Re=0~65. At
the Re=65~650, the flow becomes WVF and a seriesof toroidal vortices are significantly different with
Newtonian case are observed. The

Fig.7 The ratio of axial lengths based on the case of
Newtonian fluid vs Reynolds numbers

Fig.8 𝒁𝟏 and 𝒁𝟐 vs the Reynolds numbers
DETERMING THE CRITICAL REYNOLSDSNUMBER

Fig.6 Flow visualization at the Re=600(above n=0.7,
below n=0.52)

Velocity distributions were obtained
from the images acquired by PIV in order todeterminate the critical Reynolds numbers for TVF
715

GEOMATE – Kyoto, Japan, 3-5 November 2021

and WVF. Here 𝑅𝑒𝑐1 , 𝑅𝑒𝑐2 are the critical Reynolds
numbers for TVF and WVF respectively. 𝑅𝑒𝑐1 isdetermined from the velocity distribution whensteady toroidal vortices are observed. The velocity
distributions for both fluids are shown in Fig.9. 𝑅𝑒𝑐2is determined from the spectrum analysis result byacquiring the velocity time series data of eachwindow from the velocity distribution and spatiallyaveraging it. WVF has the peak frequency and the
Taylor vortex pulsates at the peak frequency. The
relationship between the peak frequency and the
Reynolds number is shown in Fig.10. From the above
process, each critical Reynolds number is shown intable 3 compared to the Newtonian case[19].

Fig.9

Table 3 Rec1 , Rec2 for both fluids
n
1
0.7
0.52

Rec1
95
92.6
65

Rec2
1330
608.7
93.1

f(WVF) [Hz]
~1
3.67
0.68

SPECIFAYING THE TRANSITION AREA
For shear thinning effects of working
fluids, significantly different velocity fields areobserved, unlike the case of the Newtonian fluids.
The shape of the Taylor vortices are not the same in
the axial and radial directions. Those are a tendency
not seen in the case of Newtonian fluids. For n=0.52,
four cell mode and the size of upper and lower cells
are larger than central of the cells are observed. Fig.7
show that as the Reynolds number increases, the sizeof the vortex increases in the axial direction, and thecore of the vortex is offset in the axial direction at the
WVF. Table 3 show that the critical Reynolds
numbers for shear thinning fluids and they are lower
than the critical Reynolds number for the Newtoniancase. Cagney and Balabani[17] got the critical
Reynolds number for TVF and WVF for shear
thinning fluids experimentally. A large difference
was observed between the critical Reynolds numberof Cagney and Balabani[17] and the critical Reynolds
number of this experiment. The possible reason istheir definition of the Reynolds number is different.
They prepared four kinds of fluids which one of themis the Newtonian fluid and three of them havedifferent viscosity index. They confirm that shearthinning makes the critical Reynolds number lower
than the Newtonian case, but there are no large
differences. In this experiment, the critical Reynolds
numbers for TVF are no large difference. However,shear-thinning make the critical Reynolds number for
WVF significantly lower than the Newtonian case.
Shear-thinning makes significantly increases theviscosity difference between the vicinity of the wall
surface and the distance, because the velocity
gradient in the circumferential direction is
exponential as shown by Eq (2). The Taylor vortices
become easy to pulsate when wall binding force isreduced by the larger viscosity difference.

Velocity distributions

CONCLUSIONS
We performed experiments examining
Taylor Couette flow of two shear-thinning fluids, inwhich the Reynolds number was increased, such that
the CCF, TVF and WVF were observed. The flow
dynamics were examined using PIV to measure thevelocity field in the vertical cross section.
The time-averaged velocity field indicated that shear
thinning makes the shape of Taylor vortex verticallyFig.10 Peak frequency vs the Reynolds number
716

GEOMATE – Kyoto, Japan, 3-5 November 2021

Reactor for Water Purication,” Chem. Eng. Sci.,
59, 5249–5259 (2004)

long and the position of the vortex core is verticallyoffset as the Reynolds number increase at the WVF.
The critical Reynolds numbers for transition to TVF
and WVF both showed that shear-thinning makes
them lower than the case of the Newtonian fluids,
especially the critical Reynolds number for transition
to WVF is significantly low depending on theviscosity index. As a hypothesis, he Taylor vortices
become easy to pulsate when wall binding force isreduced because shear-thinning makes the larger
viscosity difference.
We analysis the flow field of vertical cross section
for two shear-thinning fluids experimentally, but
analysis of the circumferential velocity field isremained yet.

[9]N. Ashrafi, “Stability analysis of shear-thinningflow between rotating cylinders,” Appl. Math.
Modell. 35, 4407–4423 (2011).
[10]O. Coronado-Matutti, P. R. Souza Mendes, and
M. S. Carvalho, “Instability of inelastic shearthinning liquids in a Couette flow between
concentric cylinders,” J.Fluids Eng.126,385–390(2004).
[11]B. Alibenyahia, C. Lemaitre, C. Nouar, and N.
Ait-Messaoudene, “Revisiting the stability ofcircular Couette flow of shear-thinning fluids,” J.
Non-NewtonianFluidMech.183-184,37–51(2012).

ACKNOWLEAGES
Sansho Co., Ltd. who provided the medicine is
gratefully acknowledged.

[12]M. P. Escudier, I. W. Gouldson, and D. M. Jones,“Taylor vortices in Newtonian and shear-thinningliquids,” Proc. R. Soc. London, Ser. A 449, 155–
175 (1995).

REFERENCES[1]U.B.Holeschovsky and C.L.Cooney, “Quantitativedescription of ultrafiltration in a rotating filtration
device,” AIChE J. 37, 1219–1226 (1991).

[13]C. D A V I D ANDERECK, AND HARRY L.
SWINNEY “Flow regimes in a circular Couettesystem with independently rotating cylinders” J .
Fluid Mech (1986), vol. 164, p p . 15S183[2]E. K. Hill, B. Krebs, D. G. Goodall, G. J. Howlett,and D. E. Dunstan,“Shear flow induces amyloid
fibril formation,” Biomacromolecules 7, 10–13(2006)[14]Taylor.G.I 1923 “Stability of a viscous liquidcontained between two rotating cylinders.” Phil.
Trans. R. Soc. Lond. A 223, 289[3] G. A. Ameer, E. A. Grovender, B. Obradovic, C.
L. Cooney, and R. Langer, “RTD analysis of anovel Taylor-Couette flow device for blood
detoxification,” AIChE J.45, 633–638 (1999).

[15]T. J. Lockett, S. M. Richardson, and W. J.
Worraker, “The stability of inelastic nonNewtonian fluids in Couette flow between
concentric cylinders: A finite-element study,” J.
Non-Newtonian Fluid Mech. 43, 165–177 (1992).

[4] S. Vedantam and J. B. Joshi, “Annular centrifugal
contactors—A review,” Chem. Eng. Res. Des. 84,
522–542 (2006).

[16]S. Khali, R. Nebbali, and K. Bouhadef,“Numerical investigation of non-Newtonian
fluids flows between two rotating cylinders using
lattice-Boltzmann method,” Int. Scholarly Sci.
Res. Innovation 7, 1999–2005(2013).

[5] B. Haut, H. Ben Amor, L. Coulon, A. Jacquet, and
A. Halloin, “Hydrodynamics and mass transfer ina Couette-Taylor bioreactor for the culture ofanimal cells,”Chem.Eng.Sci. 58, 777–784 (2003).

[17]N. Cagney and S. Balabani “Taylor-Couette flowof shear-thinning fluids” Cite as: Phys. Fluids 31,
053102 (2019)[6]Kataoka, K., N. Ohmura, M. Kouzu, Y. Simamura
and M. Okubo; “ Emulsion Polymerization of
Styrene in a Continuous Taylor Vortex Flow
Reactor,” Chem. Eng. Sci., 50, 1409–1416 (1995)[18]Hayato Masuda, Takafumi Horie, Robert Hubacz,
Mitsuhiro Ohta and Naoto Ohmura “Prediction of
onset of Taylor-Couette instability for shearthinning fluids” Rheol Acta (2017)[7]Jung, T., W. S. Kim and C. K. Choi; “Eect ofMonovalent Salts on Morphology of Calcium
Carbonate Crystallized in Couette–Taylor
Reactor,”Cryst.Res.Technol., 40, 586–592 (2005)[19]Yuki Noguchi, Takushi Saito and Tatsuya
Kawaguchi “Flow Structure Analysis of TaylorCouette Flow in Low Aspect Ratio Cell
Department of Mechanical Engineering, Tokyo
Institute of Technology”[8]Dutta, P. K. and A. K. Ray; “Experimental
Investigation of Taylor Vortex Photocatalytic717

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

NUMERICAL BEACH EVOLUTION MODEL CONSIDERING
SEDIMENT TRANSPORT IN SWASH ZONE
Shuhei Miki1, Masamitsu Kuroiwa2 and Kajikawa Yuki3
1,2,3

Department of Management of Social Systems and Civil Engineering, Tottori University, Tottori, Japan
ABSTRACTThe objective of this study was to develop a three-dimensional morphodynamic model that considers thesediment transport rate in the swash zone and shoreline change. First, the setting of the run-up height andequilibrium beach slope parameters in the swash zone of the sediment transport model were investigated. Next,
the beach profile changes computed using the proposed sediment transport model in the swash zone werecompared with previous experimental results obtained using a large wave flume, showing good agreement inpatterns of beach profile erosion and sedimentation. Finally, the applicability of the proposed model to theanalysis of the morphodynamics around submerged breakwaters was investigated at a field site, and the results
showed good agreement with the observed erosion and sedimentation patterns.
Keywords: sediment transport, numerical model, topographic change, swash zone

INTRODUCTION

investigated. First, to improve the accuracy of thesediment transport model in the swash zone, theappropriate expressions for the equilibrium slope
and run-up height parameters were investigated asthey exert a critical influence on sediment transport
rates. Next, the sediment transport model was
evaluated using previously obtained experimental
results for various erosion and sedimentation types
with a constant slope topography. Finally, theproposed model was applied to the simulations of afield site, and the computed results were comparedwith the observed shoreline change.

In Japan, many coastal structures such asdetached breakwaters, groins, and submerged
breakwaters have been built to prevent coastal
erosion. However, the erosion of sandy beaches canstill occur as a result of the complicated waves andnearshore currents generated around such coastal
structures. In addition, beach erosion is also causedby the severe waves that accompany typhoons. The
retreat of a shoreline can therefore occur as the waterlevel rises and the wave characteristics change overtime. To implement appropriate beach protection, it
is necessary to improve the accuracy of numericalbeach evolution models by calculating the complex
waves, currents, and sediment transport rates aroundcoastal structures and near the shoreline. Though theshoreline change model is effective for long-term
prediction over a wide area, the three-dimensional
morphodynamic model is more effective forpredicting short-term topographic changes caused by
sediment transport due to waves and currents aroundstructures. Many laboratory experiments [1]-[3] andnumerical analyses [4], [5] have been performed to
study sediment transport in the swash zone. In
particular, detailed numerical analyses have been
performed using the discrete element method and theparticle tracking method [6]. However, in practice, it
is still difficult to apply these methods to local
analyses of beach evolution.
The objective of this study was therefore todevelop a three-dimensional morphodynamic modelthat considers the sediment transport rate in theswash zone as well as the shoreline change. In thisstudy, the beach evolution model with a sedimenttransport model in the swash zone, developed by
Miki [7], was modified and its applicability was

NUMERICAL MODEL
Outline of Numerical Model
The numerical beach evolution model developedby Miki [7] was modified in this study. As shown in
Fig. 1, this model consists of four modules: the wave
module, nearshore current module, sedimenttransport module, and topographic change module.
The wave module is based on the wave action
balance equation with energy dissipation for thewave breaking and wave diffraction terms [8]. The
nearshore current module is based on a hybrid of the
Q-3D and 2DH models, proposed by Kuroiwa [9].
Initial bathymetry
UpdatedSediment transport module
Wave module
Beach evolution module

Nearshore current module
Feedback
Final bathymetry

Fig. 1 Outline of numerical modeling modules
718

GEOMATE – Kyoto, Japan, 3-5 November 2021

Sediment Transport Model in Swash Zone

using the equation provided by Larson [11], KR is
the scaling coefficient, uˆ R is the amplitude of the
wave orbital velocities at the shoreline, T is the wave
period, and θw is the wave angle relative to the shore
normal.
The values of tanβ and tanβeq are determined by

The sediment transport model in the swash zone
was based on the model developed by Leont'yev[10] to reproduce the formation of beach scarps andwas improved considering the shoreline change. The
model for sediment transport distribution in the
cross-shore direction is shown in Fig. 2.
The swash zone was defined as the region from xs
to xr, where xs is the point where the water depth is
the same as the offshore wave height and xr is the
maximum run-up point. xc is the shoreline point. The
total sediment transport rates in the cross-shore andalongshore directions (qx and qy, respectively) canthen be represented by
qxc , q yc
qx , q y = qxc + qxr , q yc + q yrqxr , q yrx  xsxs  x  xc
xc  x  xr

  x − x 1s qˆ xr   xc − xs 
qxr , q yr = 2qˆ  xr − x  yr  x − x 
c 
  r

K R uˆR3 tan βeqqˆ yr

0.1K R uˆ R2

(1)

(2)

α1

R
Ho

xc  x  xr

Wf T

(6)

Hs

1.47

tan β

0.79

H o / Lo(7)
where Ho is the equivalent deepwater wave height,
and Lo is the equivalent deepwater wave length.
The values of KR and uˆ R are determined by(3)

R tan θw
T tan β(4)Shoreline
Cross-shore direction

KR

0.5 10 7

uˆ R

σH c
T sinh kh

T gH sd(8)(9)

where d is the median sediment diameter, σ isrelative angular frequency, k is wave number, h iswater depth and Hc is the wave height at xc.
VERIFICATION OF PARAMETERS

where tanβ is the beach slope, tanβeq is theequilibrium slope, R is the run-up height determined

The equilibrium slope and run-up height are very
important parameters for estimating the sedimenttransport rates in the swash zone in Eqs. (3) and (4)and for predicting beach profile changes. We
accordingly investigated the influence of wave andbottom material characteristics on the estimation of
the equilibrium slope in Eq. (6) and the run-up
height in Eq. (7).

R
x

qxc

tan βeq(5)xs  x  xc

tan β

BottomHs R
xr xs

where Wf is the settling velocity, Hs is the wave
height at xs, and α1 is a constant coefficient
determined according to the type of beach evolution
as per Horikawa and Sunamura [12]. In this study,
1 was set to 0.06.
The value of R is determined bywhere qxc and qyc represent the sediment transport
rates from the offshore boundary to the shoreline,
presented by Kuroiwa [9]; qxr and qyr are thesediment transport distributions in the swash zone; γ1and γ2 are coefficients that determine the sedimenttransport rate distribution in the swash zone, andwere set to 0.5 and 1.0, respectively; and. qˆxr andqˆ yr are respectively determined by

qˆ xr

tan β

Swash zone

qxc + qxr

Equilibrium Slope

qxr
xs

xc

xr

x

The response of the equilibrium slope calculatedusing the proposed model was investigated using
various wave and sand particle size conditions. Thus,

Fig. 2 Sediment transport model in swash zone719

GEOMATE – Kyoto, Japan, 3-5 November 2021

the incident significant wave height Hs, wave period
T, and sand grain size d were set to 0.5–2.0 m, 5.0–
9.0 s, and 0.1–0.4 mm, respectively.
Figure 3 shows the relationship between theequilibrium slope and wave steepness Hs/Lo, in
which it can be seen that the equilibrium beach slope
increased with the applied sand grain size. Figure 4
shows the relationship between equilibrium slope
and sand grain size. The coefficient of determination
R2 obtained from the relationship in Fig. 4 was
0.7006, which is similar to that obtained by Noshi[13], who also indicated that sand grain size plays animportant role in determining the equilibrium slope.

Eq. (7) for different beach slopes was overestimatedwhen the beach slope was large and underestimatedwhen the beach slope was small. In order to modify
the relationship accordingly, the use of a newcorrection coefficient α2 was proposed as follows:

Run-up Height

Table 1 Calculation conditions

R
Ho

1.47 α2

0.79

(10)

H o / Lo

Where correction coefficient α2 is

To investigate the accuracy of the run-up heightcalculation, the results computed using Eq. (7) werecompared with the experimental results of Gono [14],Sawaragi [15], and Mizuguchi [16], defined as Case
1, Case 2, and Case 3, respectively, as shown in
Table 1.
Figure 5 shows a comparison of the calculated
and experimental results for run-up height R. In thisfigure, it can be observed that the R calculated using

Case 1Case 2
Case 3Wave
heightHs (cm)2.30–5.10
2.55–6.81
24.2–77.1

Wave
periodT (s)0.80–1.25
0.70–1.50
4.40–10.9

Beach
slope
1/20–1/5
1/40–1/5
1/60

0.300
case1(Gono et al.,1996)
case2(Sawaragi et al.,1976)
case3(Mizuguchi et al.,1986)

computed results R(m)0.1tanβeq

tan β

0.01

0.200

0.100

d=0.1mmd=0.2mmd=0.3mm

0.000
0.000

d=0.4mm
0.01

0.200

0.300

0.1

Hs/Lo

Fig. 5 Comparison between calculated results andexperimental results

Fig. 3 Relationship between equilibrium slope andwave steepness Hs/Lo

0.300
case1(Gono et al.,1996)
case2(Sawaragi et al.,1976)
case3(Mizuguchi et al.,1986)

computed results R(m)

0.05

0.04

tanβeq0.100

experimental results R(m)

0.001
0.001

0.03

0.02

0.200

0.100

0.01

0.000
0.000

R² = 0.7006
0
0.0

0.1

0.2

0.3

0.4

0.100

0.200

0.300

experimental results R(m)

0.5

d(mm)

Fig. 4 Relationship between equilibrium slope andsand grain size

Fig. 6 Comparison between calculated results withcorrected coefficients and experimental results
720

GEOMATE – Kyoto, Japan, 3-5 November 2021

1 3.0
1 5.0

α2

tan β 0.02
0.02 tan β

tan β
tan β

Table 2 Calculation conditions in experiment of
Maruyama [17]

1/ 50
1/ 50

Figure 6 shows comparison between thecalculated R obtained using Eq. (10) and theexperimental results. Clearly, the use of thecorrection coefficient α2 resulted in calculated runup heights that agreed well with the experimental
results.
COMPARISONWITHRESULTS
IN
THE
DIRECTION

Case 4
Case 5

Wave
period
T (s)
3.1
3.1

sand grainsize
d (mm)
0.27
0.27

Beach
slope
1/20
3/100

result nearer the shoreline exhibited a large
discrepancy with the experimental results. However,the computed sand bar height and position
qualitatively agreed with the experimental results.
After 15 h, Fig. 8 shows that the computed results
agreed well with the experimental results; thepresented model was able to reproduce the erosion
exhibited by the experimental result in the swash
zone.
Figures 9 and 10 show comparisons of the beachprofile after 30 h for Case 4 and Case 5, respectively.
In Case 4, the computed result in the swash zone
was in good agreement with the experimental result.
In Case 5, although the computed magnitudes of
erosion and sedimentation were smaller than thoseof the experimental result, the numerical modelshows qualitative agreement with the experimental
results. Thus, the numerical model for the beachprofile changes effectively agreed with theexperimental results regardless of the deposition
condition.

EXPERIMENTAL
CROSS-SHORE

To investigate the applicability of the proposed
beach evolution model with sediment transport in
the swash zone, the computed results were comparedwith the results of wave flume tests evaluating beachprofile changes under regular waves, conducted at
the Central Research Institute of Electric Power
Industry [17]. The length, width, and height of the
wave flume were 205, 3.4, and 6.0 m, respectively.
The experimental conditions for the sedimentationand erosion types are defined in Table 2 based on
Maruyama [17].
Figures 7 and 8 show the computed results after
7 h and 15 h, respectively, in Case 4. Figure 7
indicates that the experimental results did not show
erosion in the swash zone, and that the computed

-1
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
4

h(m)

h(m)

-1
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
4

Wave
heightHs (m)
1.62
1.61

Exp.
t=0h
t=7h

40

50

60

70
80
90
100
shoreward distance(m)

110

120

130

Exp.
t=0
t=30h
40

Fig. 7 Comparison of computed results withexperimental results by Maruyama [17] in Case
4 (after 7 h)

50

60

70
80
90
100 110
shoreward distance(m)

120

130

Fig. 9 Comparison of computed results withexperimental results by Maruyama [17] in Case 4(after 30 h)
-1

-1
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
4

-0.5

h(m)

h(m)

0

1

Exp.
t=0
t=15h

40

50

60

70
80
90
100
seaward distance(m)

110

120

0.5
Exp.
t=0
t=30h

1.5
2
40

130

50

60

70
80
90
100 110
shoreward distance(m)

120

130

Fig. 10 Comparison of computed results withexperimental results by Maruyama [17] in Case
5 (after 30 h)Fig. 8 Comparison of computed results withexperimental results by Maruyama [17] in Case
4 (after 15 h)
721

GEOMATE – Kyoto, Japan, 3-5 November 2021

APPLICATION TO FIELD SITE

by these results.

The applicability of the numerical model underfield conditions was investigated using fieldobservations collected in the Tomimasu area of
Kaike Beach in Tottori Prefecture, Japan from
August 2017 to November 2017.
The wave conditions and time series of theobserved waves are presented in Table 3 and Fig. 11.
The wave conditions were based on the wave datameasured offshore of the Tomimasu area; wave
heights of less than 0.5 m were neglected. Until the
final bathymetry was obtained by the computations,the bathymetry was updated every 1 days, and thecomputations were performed 45 times. The mediansand particle diameter d was 0.2 mm. Figures 12 and
13
show
the
observed
andcomputed
erosion/deposition plots, respectively. Figure 14
shows a comparison between the computed andobserved topographies in the cross-shore direction at
500 m in the alongshore direction.
The computed erosion/deposition patterns can beseen in Fig. 13 to be very similar to the observed
results in Fig. 12, indicating that the computed
topographical changes on the west side and behindthe submerged breakwaters are reasonable. Thus, it
was found that the computed result near the
shoreline was in good agreement with the observed
result in the swash zone. However, Fig. 14 showsthat the erosion behind the sand bar wasoverestimated in the computed results. Still, in theswash zone, the computed result was in good
agreement with the observed results. The
applicability of the model was therefore confirmed

CONCLUSIONSIn this study, we first reexamined in detail the
run-up height and equilibrium slope parameters of
the sediment transport model for erosion andsedimentation in the swash zone. Then, theperformance of the revised numerical beachevolution model including the sediment transportmodel was investigated on a constant slope
topography. Finally, the proposed model wasapplied to a field site and the results were comparedwith field observations. The final results of thisstudy were as follows:(1) It was confirmed that the equilibrium slope
parameter obtained from Eq. (6) yields
reasonable results. The results calculated using
the modified run-up height model were in good
agreement
withprevious
conventional
experimental results.
4 m 3.2 m 2.4 m 1.6 m 0.8 m 0 m -0.8 m-1.6 m-2.4 m-3.2 m -4 m
800m
600m

9
8
7
6
5
4

9

400m
12

123

12

3

6

37

5
4

2
134

4

200m

2
1

8

9

4 5

3

0m
200m 400m 600m 800m 1000m 1200m 1400m 1600m 1800m 2000m

Fig. 12 Observed erosion/deposition plot
4 m 3.2 m 2.4 m 1.6 m 0.8 m 0 m -0.8 m-1.6 m-2.4 m-3.2 m -4 m

Table 3 Wave conditions

Wave 1
Wave 2
Wave 3
Wave 4

600m

9
8
76
5

9

8

400m
3
124

200m

12
3

64 3
57
12

12

2

23
1

4

Wave
direction
θ( )
-10.0
-10.0
-10.0
-10.0

8

Wave
period
T (s)
6.325
6.407
8.309
10.025

5

Wave
height
H (m)
0.781
1.181
2.014
3.089

800m

0m
200m 400m 600m 800m 1000m 1200m 1400m 1600m 1800m 2000m

Fig. 13 Computed erosion/deposition plot

3.5

-4

3

initial topographyobserved result
computed result

-2

2

h(m)

H(m)

2.5

1.5

0

1

2

0.5

4

0

1

5

9

13

17

21

25

29

33

37

41

45

6

250

steps

Fig. 11 Time series of waves

722

200

150
100
seaward distance(m)500Fig. 14 Comparison between calculated andobserved result in the cross-shore direction

GEOMATE – Kyoto, Japan, 3-5 November 2021

(2) The results of a comparison between theexperimental and computed topography change
in the cross-shore direction indicated that the
proposed sediment transport model was able toaccurately compute the beach profile patterns oferosion and sedimentation.
(3) A comparison between the computed results andfield observations of erosion/deposition in the
Tomimasu area of Kaike Beach indicated thatthe result obtained using the proposed model
agreed reasonably well with the observed data.

T., Phase Averaging Wave Prediction Modelwith Breaking and Diffraction Effects in Wavecurrent Coexisting Field, Annual Journal of
Coastal Engineering, JSCE, Vol.51, No.1, 2004,
pp.6-10.
[9] Kuroiwa M., Matsubara Y., Kuchiishi T., andKato K., Prediction System of 3D Beach
Evolution with 2DH and Q3D Hydrodynamic
Modes, Proceedings of 16th International
Offshore and Polar Engineering Conference,
2006, pp.751-757.
[10] Leont'yev I.O., Modeling the Morphological
Response in Coastal Zone for Different
Temporal Scales, Advances in Coastal
Modeling edited by V.C. Lakhan, 2003, pp.
299-335.
[11] Larson M. and Kraus N.C., SBEACH:
Numerical Model for Simulating Storm-induced
Beach Change, Report 1, Empirical foundationand model development, Technical Report
CERC-89-9, US Army Engineer Waterways
Experiment Station, CERC, Vicksburg, MS,
1989.
[12] Horikawa K., Sunamura T. and Kondo K.,
Experimental Study for 2DH Beach Changes
with Waves, Annual Journal of Coastal
Engineering, JSCE, Vol.21, 1976, pp. 193-199.
(in Japanese)[13] Noshi Y., Kobayashi A., Kumada T., Uda T.
and Serizawa M., Calculation Method of the
Local Cross-shore Slope Based on the Sediment
Grain Size Composition, Annual Journal of
Coastal Engineering, JSCE, Vol.51, 2004, pp.
406-410. (in Japanese)[14] Gono K., Hattori S. and Sugimoto N.,
Experimental Study for Motion Mechanism of
the Runup Height of Swash Waves, Annual
Journal of Coastal Engineering, JSCE, Vol.43,
1996, pp. 141-145. (in Japanese)[15] Sawaragi T., Iwata K. and Morino A.,
Characteristics for the Runup Height of Swash
Waves on a Mild Slope, Annual Journal of
Coastal Engineering, JSCE, Vol.23, 1976, pp.
164-169. (in Japanese)[16] Mizuguchi Y., Karibe Y. and Hotta S., Runup
Height of Swash Waves in Local Coast, Annual
Journal of Coastal Engineering, JSCE, Vol.30,
1983, pp. 109-113. (in Japanese)[17] Maruyama K. and Shimizu T., Verification of
Improved Model for Prediction of On-Offshore
Beach Change, CRIEPI Research Report,
Report Number U86014, 1986.

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[2] Alsina J.M., Caceres I., Brocchini M. andBaldock T.E., An Experimental Study on
Sediment Transport and Bed Evolution Under
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Conditions, Coastal Engineering, Vol.68, 2012,
pp. 31-43.
[3] Murakami S., Mizuguchi Y., Kaneko Y.,
Koyanagi Y., Nakazawa Y. and Nomoto H.,
Laboratory Study on Equilibrium Slope andSedimenr Transport in Swash Zone by
Nonbreaking Waves, Annual Journal of Coastal
Engineering, JSCE, Vol.71, No2, 2015, pp. 535540.
[4] Kuchiishi T., Kuroiwa M., Kato K., Matsubara
Y. and Noda H., Prediction of 3D Beach
Evolution with Shoreline Change Around
Coastal Structures Using Q-3D Nearshore
Current Model, Annual Journal of Coastal
Engineering, JSCE, Vol.20, 2004, pp. 503-508.
[5] Bakhtyar R., Ghaheri A., Yeganeh-Bakhtiary A.
and Barry D.A., Process-based Model for
Nearshore Hydrodynamics, Sediment Transportand Morphological Evolution in the Surf andSwash Zones, Applied Ocean Research, Vol.31,
No.1, 2009, pp. 44-56.
[6] Harada E. and Goto H., Numerical Simulation
for Vertical Sorting in Sheetflow by ParticleLaden Turbulence Flow Model, Annual Journalof Coastal Engineering, JSCE, Vol.65, No.1,
2009, pp. 516-520.
[7] Miki S., Kuroiwa M. and Kajikawa Y., Threedimensional Morphological Model with
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pp. 499-504.
[8] Mase H., Yuhi M., Amamori H. and Takayama

723

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

ANTIBACTERIAL ACTIVITY OF HELIOTROPIUM INDICUM CRUDEEXTRACTS AGAINST STAPHYLOCOCCUS AUREUS FROM
CLINICAL AND SUBCLINICAL MASTITIS IN DAIRY COW
Duangkamol Taemchuay1, Nareerat Phangsub2, Kanokpon Bunya-atichart2 and Wimonmat Boonmee3
1

Department of Animal Production Technology and Fishery, School of Agricultural Technology,
King Mongkut’s Institute of Technology Ladkrabang, Bangkok, 10520, Thailand
2
Department of Agricultural Technology, King Mongkut’s Institute of Technology Ladkrabang,
Prince of Chumphon Campus, Chumphon, 86160, Thailand
3
Department of Biology, School of Science, King Mongkut’s Institute of Technology Ladkrabang,
Bangkok, 10520, Thailand
ABSTRACTBovine mastitis is an economic disease. The disease is affected to milk productivity and milk quality.
Staphylococcus aureus is a contagious pathogen in clinical mastitis and subclinical mastitis in dairy cow. The
conventional antibiotic treatment is intramammary infusion into infected quarters of the udder. The treatment has
been caused of antibiotic residue in marketed milk. Therefore, medicinal plants are interested in a new alternative
treatment from natural resources. Heliotropium indicum is the weed that has a wide range of medical properties.
This study aimed to evaluate antibacterial activity of H. indicum crude extracts against S. aureus. The methanol
extracts were prepared from leaf and whole plant of H. indicum. Crude extracts were tested for antibacterial activity
using by disc diffusion and the modified resazurin methods. The results of disc diffusion method showed theinhibition zones of 11.58 mm for the methanol extract of leaf part and the inhibition zones of 7.33 mm for thewhole plant extracted with methanol and ethyl acetate. The MIC and MBC values of the methanol extract fromleaf part were 8 and 64 mg/ml, respectively. Conclusion, the leaf part of H. indicum with methanol extracts could
impeded S. aureus growth from clinical mastitis and subclinical mastitis in dairy cows.
Keywords: Antibacterial activity, Heliotropium indicum, Staphylococcus aureus, Mastitis, Dairy cow

INTRODUCTION

Heliotropium indicum L. is an interesting plant to be
used against S. aureus in mastitis. It is in the
Boraginaceae family, locally also known as Yahnguang-chang in Thai [9]. H. indicum is a small
annual with a height of 15–50 cm. The leaves arealways opposite. The hairy layers cover throughtout
the stem and root. The flowers are calyx green withflowering around the whole year [10]. This medicinal
plant contains many phytochemical constituents suchas alkaloids, flavonoids, tannins, phenols, saponins,
glycosides and volatile oil. The main substances in
the alkaloid group are heliotrine and indicine N-oxide,that they have been isolated from the aerial parts of H.
indicum [11, 12]. The pharmacological substanceeffect depends on the extraction solvent; water,
chloroform, ethanol, methanol, ethyl acetate andpetroleum ether [13]. Boye et al. (2012) found that H.
indicum was extracted by ethanol and aqueous had apotential application for analgesic treatment [14]. In
addition, the methanol extract of H. indicum showedanti-inflammatory activity against acute paw edema
and sub-acute inflammation [15], whereas the ethanol
extract of whole plant showed the anti-microbialactivity against S. aureus [16]. In Thailand, the dried
and powdered plant is used for this disease treatment[17].
This research investigated appropriate solvents

Bovine mastitis is a disease that is primary
importance in dairy cows, causing damage to dairy
farmers. When the milk secretory cells are damaged
resulting in decreasing milk yield and also having thecost of medicine, treatment and culling of cattle [1].
This disease is an inflammation of the mammary
glands with the cause of physical trauma andpathogenic infections [2]. Clinical mastitis shows thatthe dairy cows have a fever, pain, anorexia, udderswollen, redness, and being able to find abnormal
milk whereas subclinical mastitis shows nosymptoms in udder of cow and visible unchanged inmilk [3]. Staphylococcus aureus is often the mostcommon infection of cow udders. The main reservoirof S. aureus is not only the infected quarter, but also
be able to transmiss from cow to cow during milking[4]. This pathogen recognized as a causative agent ofsubclinical mastitis, leading to an increase a numberof somatic cell count in milk [5], which cause clinicalmastitis [6]. Treatment of S. aureus infection is quitedifficult due to it is often resistant to antibiotics
resulting in treatment failure [7, 8].
Therefore, traditional medicine with herbs has
been the most sensible and interest source ofalternative therapeutic in the bovine mastitis.
724

GEOMATE – Kyoto, Japan, 3-5 November 2021

and parts of plant for extraction, include antibacterial
activity of H. indicum against S. aureus.

After that, the discs were dried at room temperature
overnight.
Preparation of bacteria, the selected colonies into 5 ml of Mueller-Hinton broth (MHB). Then thebroth were incubated at 37°C for 3-5 h until 0.5
McFarland standard turbidity using the densitometer.
The suspensions of 150 l were added to 5 ml of
Mueller-Hinton agar (MHA). The final inoculum
concentration was approximately 106 CFU/ml. The
discs were transferred on MHA and incubated at 37°C
18-24 hr. Negative controls used pure DMSO, 95%
ethanol, methanol, and ethyl acetate as while thepositive control used cephalotin (30 g, Oxoid) andamoxicillin-clavulanic acid (30 g, Oxoid). The
diameter of the inhibition zone were recorded and themeans were calculated. The experiments were donein 6 replicates.

MATERIALS AND METHODS
Preparation of crude extracts
H. indicum sample was obtained from Chumphon
Province in the single batch collection. The wholeplants of H. indicum (4 kg) were cleaned with waterand the leaf part (5.3 kg) got from H. indicum (11 kg).
Then, they were dried at 50°C in the hot air oven for
24 hr. and finely grounded into powder by the cutting
mill machine. After that, the powder of whole plant(615.20 g) and leaf part (612.20 g) were individual
extracted with 95% ethanol, methanol, ethyl acetate(1:10) and then macerated at room temperature for 7
days. The whole plant and leaf part extracts derived
from ethanol, methanol and ethyl acetate refered to
EW, EL, MW, ML, EaW and EaL, respectively. The
powder-solvent mixturess were hand-squeezed andfiltered by using a filter paper (Whatman No.1),
respectively. To obtain the crude extracts, all solvent
was removed using a rotary evaporator. The crude
extracts were kept at -20°C until use.

Determination of the MIC by the modified resazurin
assay
The sterile 96 well plates (Greiner bio-one,
Germany) modified resazurin assay was performed
for MIC determination. Fifty l of MHB was added
into all wells. Fifty l of the initial concentration;
1,024 mg/ml, of crude extracts prepared were added
into the first well. After that, two-fold serial dilutions
were done into nine consecutive wells. Fifty l of the
0.5 McFarland cultures were added into all wells. The
plates were incubated at 37°C for 18-24 hr. Afterincubation, growth of the bacteria was evaluated byadding 50 l of resazurin solution; 5 mg of one
resazurin tablet in sterile distilled water (50 ml). The
plates were further incubation for one hour. The color
change from purple to pink indicated that (was thenassessed). The MIC values; the bacteria growth
inhibit, the lowest concentration at which color
change. DMSO, 95% ethanol, methanol and ethyl
acetate were used as the negative controls.

Isolation of S. aureus from bovine mastitis
One ml of milk sample was added into 10 ml of
Tryptic Soy Broth (TSB) with 1% pyruvate and 10%
sodium chloride. The culture was incubated at 37°C
for 24-48 hr. After that, 2-3 loops of the culture werestreaked onto Baird-Parker agar (BPA) and incubatedat 37°C for 24-48 hr. The circular, smooth, convex,
and gray to jet-black and surrounded by opaque zone
indicated the colonies of S. aureus.
Identification of S. aureus
The coagulase positive test was used for S. aureus
confirmation. The colonies of S. aureus weretransferred into 2 ml of Brain Heart Infusion Broth(BHI). The culture were incubated at 37°C for 18-24
hr. The coagulase test was carried out by 20 ml ofsolution from BHI into 200 ml of coagulase plasma
solution. After that, it was incubated at 37°C for 24
hr. S. aureus was also approved by Mannitol Salt
Agar (MSA).

Determination of the MBC by the Mannitol Salt Agar
The MBC; the lowest concentration that kill thebacteria. The method was determined by one loop
touching from each well of MIC plate and streaking
on MSA plate. The plates were then incubated at
37°C for 18-24 hr. The unchanged media color isdefined as the MBC. S. aureus growth will turn media
yellow.

Antibacterial activity by disc diffusion test

Statistical analysis

Preparation of the crude extract by diluted in thesolvent (dimethyl sulfoside; DMSO for ethanol
extracts) to concentrations of 1,000, 800, 400, 200,
100 and 50 mg/ml. Thus, the concentration of eachdisc contained 20, 16, 8, 4, 2 and 1 mg of crude extract,
respectively. Twenty l of each crude extract was
dropped on sterile 6 mm-diameter filter paper disc.

The experimental designed with factorial incompletely randomize design (CRD) consists of two
factors. These factors of the crude extract were aconcentration in 6 levels and The powder-solvent
mixturess in 6 kinds.
The statistical analysis was compared theinhibition zone was performed using ANOVA with
725

GEOMATE – Kyoto, Japan, 3-5 November 2021

Duncan’s multiple comparison test. Inhibition zones
were expressed as mean ± SD.

measured from the inhibition zone diameter as shownin significantly at p- value ≤ 0.01 (Table 2).

RESULTS

Table 2 Antibacterial activity of the crude extracts
from H. indicum against S. aureus and S. aureus
ATCC 25923

The different solvents; ethanol, methanol andethyl acetate were used for extraction of H. indicum.
The yields (%) were calculated based on the initial
weight, 200 g of the powder of leaf and whole plant.
The results showed the weight of the ethanol extractsof leaf part and whole plant were 12.43 and 13.89 g,
the methanol extracts of leaf part and whole plant
were 17.99 and 13.15 g, and the ethyl acetate extractsof leaf part and whole plant were 9.89 and 7.82 g (datanot shown). The yields of each crude extracts of H.
indicum from the powder of leaf part and whole plantwith ethanol, methanol and ethyl acetate were 6.22,
6.95, 9.00, 6.58, 4.95 and 3.91, respectively (Table 1).
The selected solvent and plant part used to obtain the
crude extracts, the highest yield of the extract derived
from the methanol extract solvent with the leaf part.

Concentrationsof crude
extract(mg/disc)

mixtures

Inhibition zone (mm)S. aureus
S. aureus
ATCC
25923
6.00±0.00g
6.00±0.00j
6.00±0.00g
6.00±0.00jg
6.00±0.00
6.00±0.00jg
6.00±0.00
6.00±0.00jg
6.00±0.00
6.00±0.00jg
6.00±0.00
6.00±0.00jg
6.00±0.00
6.00±0.00jg
6.00±0.00
6.00±0.00jg
6.00±0.00
6.00±0.00j
6.00±0.00g
6.00±0.00j
6.00±0.00g
6.00±0.00jg
6.00±0.00
6.00±0.00jg
6.00±0.00
6.00±0.00jd7.50±0.45
6.00±0.00jg
6.00±0.00
6.00±0.00jg
6.00±0.00
6.00±0.00jg
6.00±0.00
6.00±0.00jg
6.00±0.00
6.00±0.00jg
6.00±0.00
12.92±1.02b
8.17±0.26c
14.92±0.49a
7.17±0.26ef
10.25±0.61eg
6.00±0.00
8.25±0.42gg
6.00±0.00
10.83±0.75dg
6.00±0.00
9.00±0.63ff
7.00±0.00
12.50±1.00bb
10.00±0.63
12.50±0.55bde
7.33±0.26
11.83±1.13c
f7.00±0.00
7.17±0.41i
7.08±0.20ef
8.08±1.02gh
7.00±0.00f
8.83±0.52f
de
7.33±0.26
10.17±0.41e
a11.58±0.49
11.42±0.49cc
8.25±0.42
13.08±0.74b
ef
7.08±0.20
7.58±0.92hi
de7.33±0.26
7.00±0.00i
de
7.33±0.26
7.83±0.52gh
**
**

EL
ML
1
EaL
EW
MW
EaW
EL
ML
2
EaL
EW
MW
EaW
EL
ML
4
EaL
EW
MW
EaW
EL
ML
8
EaL
EW
MW
EaW
EL
ML
16
EaL
EW
MW
EaW
EL
ML
EaL
20
EW
MW
EaW
Concentration of
crude extracts
Powder-solvent****
mixtures
Concentration x****
Powder-solvent
mixtures
In column, means followed by a different letter are
significantly different by DMRT 0.05** are significantly difference at probability ≤ 0.01

Table 1 Yields of crude extracts from H. indicum
Crude extracts
EL
ML
EaL
EW
MW
EaW

Powder-solvent

Extract yields (%)
6.22
9.00
4.95
6.95
6.58
3.91

The solvent of our experiment selected has apotential to extract active substance which have allthese medicinal properties; analgesic activity, antiinflammatory activity and anti-microbial activity [13,14, 15 and 16].
The yield of ethanol extraction of whole plant of
H. indicum showed the similar result [16], which our
result was obtained 6.95 % and the previous reports
was 7.20%.
Solvent Extraction is the separation of substancesby the solubility between the solvents and the active
substances in herbs. This is based on the principle ofthe polarity dissolving of both the solvents and thesubstances. Importantly, the active substances can be
dissolved in any solvent only if the polarity of theactive substances and the solvents are similar [18].
Due to the ethanol and methanol belong to thegroup of moderately polar solvents, therefore theygave higher percentage of yield than ethyl acetate,
which is in the group of non-polar solvents. As theresult, ethyl acetate extraction yield obtained from theleaf and the whole plant gave a lower percentage ofyield.
The crude extracts showed antibacterial effectproperty against S. aureus depending on theconcentration and the powder-solvent mixer which is726

GEOMATE – Kyoto, Japan, 3-5 November 2021

The inhibition zone diameter of crude extracts
gave the better result at the higher concentrations thanthat of the lower concentrations. The methanol extractof leaf had the highest antibacterial activity, which
was at the concentration 20 mg/disc and 11.58 mm ofinhibition zone diameter observed. Whereas, at 2
mg/disc of other extracts had the lowest antibacterial
activity which was 6.00 mm of inhibition zone
diameter.
Additionally, the antibacterial activity of thecrude extracts at concentration 16, 8, 4 mg/disc had
inhibition zone of the leaf extracted with methanol
10.00, 8.17 and 7.50 mm, respectively. As the result
indicates that the lower concentration of crude extract
the less effective antibacterial activity, and vice versa.
The antibacterial activity at concentration 16, 20
mg/disc had no significant different as the numbers
show in the table 2. As the results of the experiment,
it can be seen that the methanol extracts of the leafpart had better results than the whole plant of H.
indicum.
By contrast, the leaf and the whole plant extracts
obtained from ethanol and ethyl acetate showed thesimilar trend of inhibition zone diameter which thefigure were range between 7.00-7.33 mm except theethyl acetate extract of leaf.
The similar trend result on the antibacterial
activity effect against S. aureus and S. aureus ATCC
25923 were observed in aspects of concentration andthe powder-solvent mixtures (Table 2). These results
emphasize the fact that the leaf extract with methanol
gave the highest antibacterial activity.
The MIC values of the extracts were determinedby the modified resazurin microtiterplate. The DMSO
95% ethanol, methanol and ethyl acetate used ascontrols showed no inhibitory effect. The MIC values
were range 8-256 mg/ml for S. aureus and 32-512
mg/ml for S. aureus ATCC 25923. The results of MIC
values show in Table 3.

Table 4 The MBC values of H. indicum crude extracts
against S. aureus and S. aureus ATCC 25923
Crude
extracts
EL
ML
EaL
EW
MW
EaW
CONCLUSIONSThe crude extracts of H. indicum impeded thegrowth of S. aureus isolated from bovine mastitis.
The antibacterial activity of crude extracts from H.
indicum was observed in vitro. The most extensive
inhibition of crude extract against S. aureus derived
from the methanol extraction of leaf. These results
will bring about our further study on in vivo
application, and also being able to advance the teatdipping product from H. indicum extracts.
ACKNOWLEDGMENTS
We gratefully thank you to the staff of the
Department of Veterinary Public health andDiagnostic Service and The Animal Disease
Diagnostic Unit, Faculty of Veterinary Medicine,
Kasetsart University, Kamphaeng Saen Campus forproviding the facilities and all instruments. Finally, I
would like to thank King Mongkut’s Institute ofTechnology Ladkrabang for the grant support in thisresearch.
REFERENCES[1] Gomes F. and Henriques M., Control of bovine
mastitis: old and recent therapeutic approaches,
Curr. Microbiol, 72, 2016, pp. 377- 82.
[2] Wei N.C. and Sung G.H., Bovine mastitis: risk
factors, therapeutic strategies, and alternative
treatments. A review, Asian-Australas J Anim Sci,
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[3] Khan M. and Khan A., Basic facts of mastitis indairy animals: a review, Pak Vet J, 26, 2006, pp.
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Bovine Mastitis in Eight Countries: Genotypes,
Detection of Genes Encoding Different Toxins
and Other Virulence Genes, Toxins, 10, 2018, pp.
247.

Table 3 The MIC values of H. indicum crude extracts
against S. aureus and S. aureus ATCC 25923
Crude
extracts
EL
ML
EaL
EW
MW
EaW

MBC value (mg/ml)
S. aureus
S. aureus
ATCC 25923
512
256
64
64
256
128
512
512
512
512
1,024
1,024

MIC value (mg/ml)
S. aureus
S. aureus
ATCC 25923
64
64
8
32
128
64
128
256
128
512
256
256

The MBC values of the extracts were determinedon S.aureus and S. aureus ATCC 25923. The MBC
values of both bacteria were identical which range
between 64-1,024 mg/ml. The result of MBC values
shows in Table 4.

727

GEOMATE – Kyoto, Japan, 3-5 November 2021

[5] Abebe R., Hatiya H., Abera M., Megersa B., andAsmare K., Bovine mastitis: prevalence, risk
factors and isolation of Staphylococcus aureus indairy herds at Hawassa milk shed, South Ethiopia,
BMC Vet Res, 12, 2016, pp. 270.
[6] Vasudevan P., Nair M.K.M., Annamalai T., andVenkitanarayanan K.S., Phenotypic and genotypiccharacterization of bovine mastitis isolates of
Staphylococcus aureus for biofilm formation, Vet
Microbiol, 92, 2003, pp. 179-185.
[7] Rainard P., Foucras G., and Fitzgerald J.R.,
Knowledge gaps and research priorities in
Staphylococcus aureus mastitis control, TransboundEmerg Dis, 2018, 65 (Suppl 1), pp. 149-165.
[8] Kappeli N., Morach M., Corti S., Eicher C.,
Stephan R., and Johler S., Staphylococcus aureus
related to bovine mastitis in Switzerland: Clonal
diversity, virulence gene profiles, and antimicrobial
resistance of isolates collected throughout 2017,
J. Dairy Sci., 2019, Vol. 102 No. 4, pp. 3274–3281.
[9] Chandan S., Milon M., Bilkis K., Monir Md.,
Hossain Md., Solayman H., Antoni S.,
Muhammad T.I., Miquel M., Manoj K., Javad
S.R., Harrasi A.A., and Rawahi A.A.,
Heliotropium indicum L.: From Farm to a Sourceof Bioactive Compounds with Therapeutic Activity,
Evidence-Based Complementary and Alternative
Medicine, 2021, Article ID 9965481, 21 pages.
[10] Osungunna M.O. and Adedeji K.A., Phytochemical
and antimicrobial screening of methanol extractof Heliotropium indicum leaf, Journal of
Microbiology and Antimicrobials, 2016, Vol. 3,
No. 8, pp. 213–216.
[11] Nawaz A.H., Hossain M., Karim M., Khan M.,
Jahan R., and Rahmatullah M., An ethnobotanicalsurvey of Rajshahi district in Rajshahi division,
Bangladesh, American-Eurasian Journal of Sustainable
Agriculture, 2009, Vol. 3, No. 2, pp. 143–150.
[12] Kugelman M., Liu W.C., Axelrod M., McBride
T.J., and Rao K.V., Indicine-N-oxide: the antitumor
principle of Heliotropium indicum, Lloydia,
2015, Vol. 39, No. 2-3, pp. 125–128.
[13] Roy A., Pharmacological activities of Indian
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[14] Boye A., Koffuor G.A., Amoateng P., Ameyaw
E.O., and Abaitey A.K., Analgesic activity andsafety assessment of Heliotropium indicum Linn.
(Boraginaceae) in rodents, International Journalof Pharmacology, 2012, Vol. 8, No. 2, pp. 91–100.
[15] Shalini S., Kaza R., and Shaik F., Study on theanti-inflammatory activity of Heliotropium
indicum, Journal of Innovative Trends inPharmaceutical Sciences, 2010, Vol. 1, No. 1, pp. 43.
[16] Premnath D. and Gomez P., Antifungal and antibacterial activities of chemical constituents from
Heliotropium indicum Linn. Plant, Drug Invention
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M.F., A comprehensive review of ethnopharmacologicallyimportant medicinal plant species from Mauritius,South African, Journal of Botany, 2019, Vol. 122,
pp. 189–213.
[18] Pandey A. and Tripathi S., Concept ofstandardization, extraction and pre phytochemical
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No. 5, pp. 115-119.

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11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

ANALYSIS OF LOCAL RESIDENTS' MOVEMENTS FOR DAILY LIFE
IN URBAN AREAS IN JAPAN
Hibiki MOTOI1, Yukihiro YAMASHITA2, Daisuke KOBAYASHI 3 and Yukikazu YAMAGUCHI4
1

Graduate Course in Architecture, Civil Engineering and Urban Design, Osaka Institute of Technology,
Japan; 2Himeji City, Japan; 3Osaka City, Japan;4Faculty of Engineering, Osaka Institute of Technology,
Japan
ABSTRACTIn recent years, as Japan's urban population and the birthrate have declined as the population has aged, thenumber of vacant houses in urban centers has increased. This increase in house vacancy is an urban problem thatis difficult to solve using conventional town and transportation planning methods. In particular, with regard to
local transportation, the number of elderly people who have difficulty traveling is increasing as public
transportation services are being cut due to profitability issues. The purpose of this study was to clarify the issues
affecting local transportation in daily life. The analysis considered local residents' evaluations of public
transportation and an assessment of local residents' movements in Moriguchi City, Osaka Prefecture. The resultsof the analysis showed that local residents' satisfaction with transportation services was correlated with the distanceto the nearest train or metro station, but not to the nearest bus stop. Also, the transportation network may need tobe redeveloped in the future because of differences in daily activities, even within Moriguchi City.
Keywords: Local residents, Daily life, Local transportation, Public transportation, Transportation network
INTRODUCTION

transportation networks.

In recent years, as the Japanese population has
declined and aged, vacant houses and other underusedor unused land have randomly appeared inside cities,
causing economic decline and public safety problems[1]-[2]. On the other hand, even in areas where thepopulation is concentrated, it is becoming difficult tomaintain public transportation, as buses are being
discontinued due to a decrease in the number of users.
In addition, as the number of elderly drivers increases,
traffic accidents have also increased, and the numberof elderly people surrendering their driver’s licenses
is increasing, despite public transportation services
often being inadequate. As a result, there are concerns
about an increase in the number of people with low
mobility. Furthermore, the spread of the coronavirus
is expected to bring about major changes in thetransportation environment in daily life, includingchanges in work styles and lifestyles. To date, local
governments have subsidized the operation of local
community buses, and demand-based transportation
services [3]. It is necessary to develop a transportation
system that is tailored to the specific requirements oflocal residents in order to ensure that they canmaintain their daily activities.
The purpose of this study was to clarify the
problems facing local transportation by analyzing
local residents' evaluations of the current public
transportation system and their travel history in
Moriguchi City, Osaka Prefecture, to provide basic
information for improving the existing transportation
system, and to propose improvements to the existing

METHODOLOGY
Target Area of Case Study
The study area is Moriguchi City, Osaka
Prefecture (See Fig. 1). The city, which has apopulation of approximately 140,000 people and anarea of 12.71 km2, has been experiencing a decline inboth population and economic activity since 1975.
The city is served by trains, subways, and monorails.
It takes about 15 minutes to travel from Moriguchi
City to the center of Osaka City and about 35 minutes
to Osaka Airport. In addition, private buses and local
community buses run at a frequency of about one bus
every hour [4]-[5].
Questionnaire
Movements

Surveyon

Local

Residents’

Table 1 shows an overview of a questionnaire
survey on local residents’ movements. The questions
assessed whether respondents had a driver’s license,
private car, evaluation of their movements in the city,and their movement history. Based on data publishedby Moriguchi City, the city was divided into eastern,
central, and southern regions [4]-[5]. The evaluation
results for daily movement and movement history
were analyzed.
RESULTS & DISCUSSION

729

GEOMATE – Kyoto, Japan, 3-5 November 2021

Table 1 Overview of a questionnaire survey on of
local residents’ movements

PurposeTo u n d e r s t a n d t h e e v a l u a t i o n o flocal residents' movements and thehistory of movements in order toexamine the means oftransportation for the future aging
population

Study
Area

M o r i g u c h i C i t y, O s a k a P r e f e c t u r e

PeriodMethodTa r g e t
PeopleCollection
Results

Neyagawa City
Subway Tanimachi Line

Eastern Area

Central Area
Moriguchi City

Keihan Line

September 26, 2020 - October 19,
2020
By mail
1 , 0 0 0 r e s i d e n t s , a g e d 1 6 o r o l d e r,
randomly selected from Basic
resident register network system inMoriguchi City
1000 distributed, 244 responses.
24.4% valid response rate

Osaka City

Kadoma City

Southern Area

Fig. 1 The study area.
0%
GenderResults of analysis of local residents' movements

Age

Evaluation of local residents’ movements in the city

20%

Male 39%

Private car

60%

80%

100%

Female 59%

30-69 years old Over 70 years old
38%
55%

Driver's license

As can be seen in Fig. 1, the questionnaire survey
was conducted in Sep and Oct 2020. 1,000
questionnaires were distributed to local residents ofMoriguchi City in Osaka, Japan, and 244 responses
were received. Figure 2 shows the personal attributesof respondents to the survey. A total of 59% of the
respondents were “female,” and 55% of the
respondents were aged “60 or older”. The percentageof respondents who “possessed a driving license” was
55%, and the percentage of those who owned “privatecar” was 44%. A total of 39% of the respondents rated
their movements in the city as “inconvenient” or“slightly inconvenient”. As shown in Fig. 3, thenumber of respondents who rated transportation in thecity as being “inconvenient” was 29% for “shopping”,
19% for “going to the hospital”, 18% for “gettingfrom their home to the nearest station”, and 16% for“entertainment/learning”.

40%

Have 55%
Have 44%

Don't have 40%
Don't have 51%

Movements within
25% 14%
20% 12% 24%
Moriguchi City
Convenient
Inconvenience
Neither
LittleLittleconvenient inconvenient
Unknown answer(n=244)

Fig. 2 The personal attributes of respondents.
0% 5% 10%15%20%25%30%35%
Shopping

Attitudes of private car owners toward surrendering
driver’s license
Figure 4 shows the results of the questions onalternative means of transportation for private carowners after they surrendered their license. Amongthe private car owners surveyed, 53% intended toreturn their license, of which 32% planned to do so“by the time they were 75 years old” and 64% “by thetime they were 80 years old”. After returning theirlicense, their primary mode of transportation was tobe “public transportation” for 37% of the respondents,
followed by a “cycling” for 19%, and “shuttle bus orlifts by family members” for 14%.

29%

Hospital

19%

Nearest station from home

18%Entertainment and lessons

16%

Job-related

5%

Lifts by family members

4%

Nearest station from officeor school3%

Commuting to work andschool (Privatetransportation)
Commuting to work andschool (Public
transportation)3%
1%(n=232)

Fig. 3 The number of respondents who rated
transportation in the city as being “inconvenient” /multiple answers.
730

GEOMATE – Kyoto, Japan, 3-5 November 2021

(n=107)
Over 85
0%0% 16%

16% 32%

Lifts byfamilymembers(n=57)

Analysis results of local residents' movements

Walking(n=147)
Multipleanswers
Unknown answer

Purpose of travel in three areas of Moriguchi City
0% 20% 40% 60% 80% 100%

Fig. 4 alternative means of transportation for private
car owners after they surrendered their license.

8% 11% 13%
Public transportation 18% 24%
15% 10%(n=97)
12% 8%
12%8%
Taxi
16% 24%20%(n=25)
9%
9%
12%
12%
Lifts by family members
30%
21%
7%
(n=43)
9%
15% 11%
9%Bicycle17%26%
11%(n=53)
10%
15% 8%Walking 15%
15%
23%
15%
(n=40)
Low cost
Fast

Figure 5 shows the reasons why private carowners selected these alternative means oftransportation after surrendering their license. Mostof the respondents emphasized “short travel time” and“catching transportation immediately in front ofhome”. Those who selected public transportation
placed more importance on “short travel time”, while
those who selected “taxi”, “lifts by family members”,“cycling”, and “walking” placed more importance on“being able to travel immediately from in front of thehome”.

Seamless
No strangers
Familiar

From the front of the house
HealthyFig. 5 Reasons why private car owners selected these

Evaluation of travel within the city by distance to thenearest station

alternative means of transportation after surrendering
their license. (Multiple answers)

Figure 6 shows the ratings obtained for the“convenience” or “inconvenience” of travel in thecity as a function of distance from home to the nearest
train station. The number of people who rated the
distance from their home to the nearest station as“inconvenient” exceeded the number of people who
rated it as “convenient” when the distance was
between 600 m and 800 m.
Figure 7 shows a map of the “inconvenient” or“convenient” responses to travel by area. In the
Eastern Area, most respondents rated travel within
the city as being “inconvenient”. This was because
the average distance to the nearest station in the
Eastern Area was more than 1000 m. Further, the only
options available to residents in the Eastern Area are
the subway located near the border with the Central
Area, or the train station in Kadoma City, which lies
outside Moriguchi City.
In the Eastern Area, only few stations exist, and(Number of people)
25

Convenient

20

Inconvenient

15
10
5

0~199m0(n=161)

2000m~

80 to 84
Cycling

1800~1999m

Taxi

1600~1799m

Publictransportation

0%

1400~1599m

0%0%1200~1399m

70 to 74
Means oftransportation

No 53%
75 to 790%

all station are located near the boundary with theCentral Area. Consequently, those residents who live
near the stations tend to rate travel within the city as“convenient”, while many other people rate it as“inconvenient”.
In the Central Area, more respondents rated it as
being “convenient” than “inconvenient”. This may besince there are many subway stations in the area, andthe distance between the residential area and the train
station is shorter than it is in other areas.

100%

1000~1199m

0%

80%

800~999m

Yes 53%65 to 69

Age ofsurrendering
driver's license60%

600~799m

Surrendering
driver's license

40%

400~599m

20%200~399m

0%

Fig. 6 The ratings obtained for the convenience orinconvenience of travel in the city as a function of
distance from home to the nearest train station.

731

GEOMATE – Kyoto, Japan, 3-5 November 2021

Eastern Area

As shown in Fig. 11, "cycling" and "walking"account for about 60% to 70% of the means oftransportation in all areas on weekdays and holidays.
In addition, for both weekdays and holidays, there
were more trips by “private car” to the Eastern andCentral Areas than to the Southern Areas. The
combined usage rate of “train” and “subway” wasabout 10% on weekdays and about 5% on weekends.

Central Area

Southern Area0%

Nearest station f rom home
(Convenient)
Nearest station f rom home
(Inconvenient)
Walking d istance (From thetrain stat ion)

LegendWeekdays
Eastern Area

Fig. 7 Map of the inconvenient or convenient
responses to travel by area.
Figure 8 shows the purpose of travel for trips inMoriguchi City only. And map of the purpose of
travel for trips on weekdays and holidays are shown
separately on the map in Fig. 9 and Fig. 10,
respectively.
As shown in Fig. 8, “shopping” was the mostcommon reason for travel on weekdays in all areas,
followed by “commuting to work/school” and “goingto hospital”. On weekends and holidays, “commuting
to work/school” and “going to hospital” decrease, and“shopping” accounts for half of all trips. “shopping”
was the most common activity in the Eastern Area,
where there is a large commercial district, and in theSouthern Area, where there are numerous department
stores. “hospital visits” was the most common
activity in the Central Area, where there are several
regional hospitals.
Figures 9 and 10 show the following trends intravel to particular areas. On weekdays, most of theresidents in Moriguchi City went to the Central Area
for “hospital visits”, accounting for 44% of the total.
Of these, more than 60% traveled to Matsushita
Memorial Hospital. On weekdays, most “commuters”
in respondents in Moriguchi City travel to theSouthern Area around Moriguchi Station, accounting
for 35% of all commuters in respondents. On
weekends and holidays, most of the respondents went
“shopping” at Dainichi Aeon Mall in the Eastern Area
and Keihan Department Store in the Southern Area.

20%

40%

60%

80%

Work or
Shopping Hospital
School20%

Central Area

25%

Southern Area

25%

Weekends
Eastern Area 9%
Central Area 7%
Southern Area 13%

47%
23%

100%

Other

10% 22% (n=49)
28%

25%

(n=40)

34%

20%

20% (n=44)

57%

7%

26%

43%

7%
56%

43%

(n=54)(n=14)

6% 25% (n=32)

Fig. 8 Purpose of travels in Moriguchi City only.
Eastern Area

Central Area
Matsushita
Memorial
Hospital
Dainichi Aeon Mall

Moriguchi
City
StationKeihan Department

Southern Area

Commuting
Shopping
Hospital vis its
SubwayTanimachi L ine
Keihan L ine
Other Subways
Supermarket
HospitalFig. 9 Map of the purpose of travels on weekdays.

Eastern Area
Central Area
Matsushita
Memorial
Hospital
Dainichi Aeon Mall

Moriguchi
City
StationRepresentative means of transportation in Moriguchi
City

Keihan Department

Figure 11 shows the representative means oftransportation in Moriguchi City by arrival point, andFig. 12 and Fig. 13 shows that for travel by “train”,“subway” and “private car” for weekdays andholidays.

Southern Area

Commuting
Shopping
Hospital vis its
SubwayTanimachi L ine
Keihan L ine
Other Subways
Supermarket
HospitalFig. 10 Map of the purpose of travels on weekends.

732

GEOMATE – Kyoto, Japan, 3-5 November 2021

0%

20%

40%

60%

80%

100%

were more likely to visit Dainichi Aeon Mall in the
Eastern Area and Keihan Department Store in theSouthern Area in respondents.
On weekdays, respondents that used the “train”
tended to visit the areas between the Eastern andSouthern Areas of the city, while respondents thatused “private car” tended to move along the mainstreet. 50 percent of the respondents that used the“train and subway” in the Eastern Area traveled fromareas outside a 500 m radius of the train and subway
stations, and about 60% of the respondents that used“private car” also traveled from areas outside a 500 m
radius of the train and subway stations. Based on thisresult and the fact that the majority of the respondents
will surrender their driver's license in the future, as
shown in Fig. 4, it is necessary to secure a new meansof transportation for zone (1) in the Eastern Area,
because it will be difficult for these residents to movearound in the future.

Bus
Taxi Bicycles
Train
LegendPrivate car Walking
Subway Suttle bus
Weekdays
Eastern Area 8% 17%
39%
26% (n=76)
Central Area

5% 18%Southern Area 7% 7% 11%

44%

26%

(n=61)

45%

23%(n=56)

27%(n=86)

25%(n=28)

Weekends
Eastern Area

3% 24%

Central Area 4%Southern Area

44%

36%
12%

32%
40%

36%

(n=42)

Fig. 11 The representative means of transportation in
Moriguchi City by arrival point.
(1)

Matsushita
Memorial
Hospital

Transportation to the nearest station to get to Osaka
City
Dainichi Aeon Mall

We then examined “traveling from Moriguchi
City to Osaka City”, which is the most popular
destination for commuters “going to work or school”
on weekdays. Most travel on holidays was for“entertainment” or “sightseeing”. Figure 14 shows
the results for “travel to Osaka City” on weekdays andweekends and the means of transportation used.
Figure 15 shows the mode of travel used to get to thenearest station in order to travel to Osaka City on
weekdays and weekends combined, and thepercentage of elderly people in 2035 by area.
Figure 14 shows that the most used mode oftransport in all areas are “the train and subway”. In
the Eastern Area, the percentage of respondents who
used “the subway” to travel to Osaka City was thehighest, at 52%. On the other hand, in the Southern
Area, which is relatively close to Osaka City, more
people use “cycling” than any other area, at 26%.

Train
SubwayPriva te car
Subway Tan imachi Line
Keihan L ine

Keihan Department

Other Subways
Supermarket
Hospital
Walking d istance(From the train s tat ion)Fig. 12 Map of travels by train/subway and private
car in Moriguchi City on weekdays.
(1)

Matsushita
Memorial
Hospital

Dainichi Aeon Mall
Train
SubwayPriva te car
Subway Tan imachi Line
Keihan L ine

Keihan Department

Other Subways
Supermarket

0%

Hospital
Walking d istance(From the train s tat ion)

20% 40% 60% 80% 100%Train
Bus Private car Walking
LegendTaxi
SubwayBicycleFig. 13 Map of travels by train/subway and private
car in Moriguchi city on weekends.
Figures 12 and 13 show that on weekdays, travelby “private car” were more likely to arrive at
Matsushita Memorial Hospital in the Central Area
and Dainichi Aeon Mall in the Eastern Area in
respondents. On weekends, travel by “private car”

Eastern Area

21%

52%

7%14% 7%(n=29)

Central Area

29%

33%

21% 8%8%(n=24)

Southern Area

42%

13%10% 26% 10% (n=31)

Fig. 14 The means of transportation used for“travel to Osaka City” on weekdays and weekends
combined.

733

GEOMATE – Kyoto, Japan, 3-5 November 2021

(2)

-

(3)

0% to 10%
10% to 20%
20% to 30%
30% to 40%
40% to 50%
50% or more
Cycling
Train
SubwayWalking d istance(From the train s tat ion)Fig. 15 The means of transportation used for “travel
to Osaka City” on weekdays and weekends
combined, and the percentage of elderly people in2035 by area.

-

Figure 15 shows that in the Eastern Area, mostpeople used Dainichi Station on the Osaka Metro; in
the Central Area, most people used stations on the
Osaka Metro; and in the Southern Area, most people
used stations on the Keihan Line.
For the Eastern Area in particular, accessibility toboth stations will need to be improved in the future
because the future percentage of elderly people in theentire Eastern Area is estimated to be 42% and 52%
of subway and rail users, respectively [6]. These
people travel from areas outside a radius of 500 m
from Osaka Metro Dainichi Station and Keihan Line
Owada Station (See Fig. 15 (2)). On the other hand,in the Southern Area, the percentage of people
traveling to the nearest station from areas outside a
500 m radius of the train or subway station is 29%,
which is less than that in other areas. However, since
the future aging rate for the entire Southern Area isestimated to be 40%, it is possible that those
respondents who currently travel to Osaka City by
cycling from areas outside a 500 m radius of a train
or subway station will have difficulty in doing so due
to aging or other reasons (See Fig. 15 (3)). Therefore,
it is considered that accessibility to train and subway
stations will need to be improved in the Southern
Area in the future.

In the future, we would like to accumulate data on
actual travel conditions and analyze changes in thenumber of trips and the of local residents’ movements
in order to consider the most sustainable means oftransportation for Moriguchi City. In doing so, we
hope to ensure that the transportation needs of thetransportation-weak are met in an aging society.
REFERENCES[1] Ministry of Land, Infrastructure, Transport andTourism, Current situation and problems ofvacant houses, 2014.
http://www.hrr.mlit.go.jp/kensei/machi/akiya/kai
sai03/02_honsho.pdf[2] Ministry of Land, Infrastructure, Transport andTourism, Promotion of measures against urban
spongification, 2019.
https://www.mlit.go.jp/common/001290033.pdf[3] Ministry of Land, Infrastructure, Transport andTourism,A
Guideto
Demand-BasedTransportation, 2013.
https://wwwtb.mlit.go.jp/hokushin/content/0001
04104.pdf[4] Moriguchi City,2018.
http://www.city.moriguchi.osaka.jp/ikkrwebBro
wse/material/files/group/35/rittitekiseikakeikaku
honpen.pdf[5] Moriguchi City,2014.
http://www.city.moriguchi.osaka.jp/ikkrwebBro
wse/material/files/group/13/kyotenshisetu_kihon
keikaku.pdf[6] Ministry of Land, Infrastructure, Transport andTourism, Future Population and Household
Projection Tool V2, 2015.
https://www.geospatial.jp/ckan/dataset/cohortv2

CONCLUSION
In this study, we were able to clarify the following
by analyzing the current movements of local residents
in Moriguchi city.
-

who rated it as “convenient” when the distancewas between 600 m and 800 m.
On weekdays, “ shopping” was the mostcommon reason for travel in all areas, followedby “commuting to work/school” and “going tohospital”. On weekends and holidays,“commuting to work/school” and “going tohospital” decrease, and “shopping” accounts forhalf of all trips. On weekdays, most of theresidents in Moriguchi City went to the Central
Area for “hospital visits”, accounting for 44% of
the total. Of these, more than 60% traveled to
Matsushita Memorial Hospital.
In the Eastern and Southern Areas, accessibility
to stations and subways will need to be improvedin the future, as the aging of the population andother reasons may make it difficult to get around.

The number of people who rated the distancefrom their home to the nearest station as“inconvenient” exceeded the number of people734

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

THE DEDICATED LANE FOR MOTORCYCLE BASED ON
MOTORCYCLIST CHARACTERISTICS AND SPACE DEMAND
Sobri Abusini1 and Lasmini Ambarwati2
1

2

Faculty of Natural Science, Brawijaya University, Malang, East Java; Faculty of Engineering, Brawijaya
University, Malang, East Java, Indonesia

ABSTRACT
A motorcyclist is the largest victim in the accident (more than 70%). The number of motorcycle increase by
3.6% every year, on the other hand, the safety of motorcyclist is still neglected. The effort to minimize
motorcycle accidents should be realized by designing the demand for a dedicated lane. This study aims todevelop the accident probability for the motorcyclist, to design the dedicated lanes based on the scape need, andto evaluate the existing dedicated lanes on several roads. Motorcyclist characteristics were identified fromquestionnaires. Evaluation of the existing dedicated lane based on the space requirements and highway
performance. The results are found out that the probability of motorcycle accident is influenced by several
characteristics, i.e. socio-economic characteristics, travel characteristics, and behavior characteristics. The
dedicated lane requirement for motorcycle should be determined based on the innocuous movement of a
motorcycle on the highway network. The existing dedicated lanes are not properly facilitated and do not pay
attention to the safety and continuity of motorcycle movement. Based on the results, the government should
facilitate the dedicated lane standard by considering the main factor, safety for the motorcyclists.

Keywords: Accident probability, Accident victims, Dedicated lane, Motorcycle accident, Motorcyclist
characteristics.

that are not designed to protect the rider(unprotected riders), are not equipped with covers toprotect the driver, so motorcyclists must equipthemselves with safety equipment such as helmets,
weather-resistant jackets, and strong shoes. The
impact of a motorcycle accident will result in higher
losses compared to a car accident.
The phenomenon of the high number ofaccidents involving motorcyclists has occurred inmost big cities in Indonesia, one of which is Malang
District. The number of traffic accidents every year
has increased. The high number of motorcycles
causes a high number of accidents involving
motorcyclists as victims. According to [4], thenumber of vehicles in Malang District increases
every year, from 196,000 units up to 779,000 units,
almost 4 times increase in less than 5 years (20122016). The number of motorcycles dominates
approximately 87% of the total vehicles in Malang
District. The increase in the number of vehicles in
Malang District is also followed by an increase in
the number of accidents. The largest increase in thenumber of accidents occurred in 2016. There was anincrease in motorcycle accidents from 542 to 774 in
2016. Based on the number of victims, the numberof deaths and minor injuries experienced asignificant increase from 2015 to 2016. The death

INTRODUCTION
A motorcycle has several advantages and canmove easily in traffic jams. This transport mode has
efficiency in movement and low operational cost [1].
According to [2], several factors cause accidents, i.e.
human, traffic, vehicle, highway, and environment.
The human factor is the main factor influencing
accidents approximately more than 95% because ofthis factor [3].
Economic development influences the growth of
the number of motor vehicles by approximately 3%
per year, particularly the growth of motorcycles by
5%. The number of accidents increases by 7% due toan increase in the number of motorcycles every year
[4].
Motorcycle riders have a higher risk of accidents
than other motorized vehicle users. First, from thestability of its movement, the motorcycle is only
supported by two wheels and the balance of its
movement depends on the ability of the driver tocontrol the motorcycle. If the driver is not careful
and has good skills, it is very easy to slip or crash.
The second is the ability of the motorcycle to moveat high speed which will result in serious injury in an
accident or collision. Third, motorcycles are vehicles
735

GEOMATE – Kyoto, Japan, 3-5 November 2021

victims and minor injuries were 219 people and1,031 people respectively.
According to [2], Article 108 explains that slowmoving vehicles should use the left lane to ensure
the safety and smoothness of traffic on the road.
Motorcycle lanes are provided for the safe andsmooth movement of motorcycles, improving roadperformance and reducing the rate and number ofmotorcycle accidents as victims [5]. A dedicatedmotorcycle lane is something that should befacilitated for each road section and the approachintersection, which separates motorcycles lanes fromcar lanes. With the existence of a dedicatedmotorcycle lane, it is expected that disturbancesfrom motorcycles to cars can be reduced, thenpassenger cars and motorcycles can reach optimal
speeds and for the safety of each type of vehicle [6].
The realization of dedicated motorcycle lanes inthe simulation model at the intersection has an
impact on reducing the average delay at thesignalized intersections. The special stopping spacefor motorcycles in the simulation model can reduce
the average delay experienced by cars. In addition, a
dedicated stopping space for motorcycles is effective
in reducing the average delay experienced by
motorcycles [7].
Road sections with dedicated motorcycles lane
influence the motorcyclist behavior in increasing
speed. Here, space and minimum pore size are set in
the road section to minimize the number ofmotorcycle accidents [6].
Generally, accidents occur based on several
factors as follows: road user factors, traffic factors,
vehicle factors, road factors, and environmental
factors [8]. There are various relationships between
the causes of road accidents. These factors maketransportation safety as a focus of attention.
According to [9], the behavior of male motorcyclists
was recorded at 80.6% compared to other categories
and needs to be considered as the cause of traffic
accidents with the highest percentage compared toother identified causes.
Inadequate research concerns on behavior andcharacteristics of motorcyclists causing the accident
by facilitating the dedicated lane based on the needsof motorcyclists for safe driving. To achieve thisgoal, it is necessary to investigate the need for a
dedicated lane for motorcyclists based on theircharacteristics and behavior, as well as roadperformance. Understanding the need for dedicatedlanes based on the characteristics and behavior of
motorcyclists aims to reduce the number ofaccidents and to increase the safety of motorcyclists.
This study aims to identify the need for a
dedicated lane by considering the safe distance of
motorcycle movement based on the characteristics
and behavior of motorcyclists.
The remaining paper is arranged as follows. The
characteristics and behavior of motorcyclists andspace requirements for designing dedicated lanes aredescribed in Section 2. Methods, clarifying the
behavior and characteristics of motorcycle andevaluating road performance in the presence ofdedicated lane is described in the following sections.
The Results and Discussions section explains how to
describe the design of dedicated lanes related tohighway performance. This section also evaluates
the road performance with or without dedicatedlanes. Finally, the study concludes the information
and recommends the basis for the dedicated lane
design by considering motorcyclist safety.
METHODS
Malang District, a city around Malang
Municipality in East Java Province, Indonesia is theresearch area. The survey locations are adjusted to
the location of the motorcycle accident as “BlackSpot”. The locations were chosen because of thehigh rate of motorcycle accidents. Traffic accident
data was supported by the Malang Police
Department for 2016 – 2019 periods. The blackspots are Geneng Kepanjen, Sudirman/ Mojosari
Kepanjen, Hayam Wuruk Gondanglegi, Raya
Bululawang, Sultan Agung Kepanjen, Brigjen Abdul
Manan Pujon, Mojorejo, Panglima Sudirman Batu,
Raya Areng-areng and Patimura Batu Streets as theresearch locations.
Collecting Data
The research survey was conducted by
distributing questionnaires to respondents at randomon ten roads which are “Black Spots". The samples
are 150 respondents who have experienced a traffic
accident or as a victim and investigated roadgeometry, traffic volume, and speed. The contents of
the questionnaire include general characteristics of
respondents (social-economic background), travel
characteristics, and driving behavior.
Data Analysis
a.

b.
c.

736

Descriptive analysis is an analysis that describes
the condition of the road section as a black spot
and the characteristics and behavior of
motorcyclists.
Analysis of the determination/potential of
motorcycle lanes, design of motorcycle lane.
Analysis of the need for motorcycle riding
space with a pre-design of motorcycle lane
including a safe space for driving whentraveling alone, overtaking, and side by sidewith other motorcycles. Space requirement canbe determined from the physical characteristics
of motorcycles as well as the characteristics andbehavior of motorcyclists.

GEOMATE – Kyoto, Japan, 3-5 November 2021d.

Analysis of motor vehicle speed (heavy andlight vehicles, motorcycles) on the geometry of
the existing road and road condition with thedesign of a dedicated motorcycle lane.
According to [10], road performance withservice level (LOS) C is used as the basis fordetermining motorcycle lanes.








Primary data:
Road geometriccondition
Traffic flow & speed
Road geometry
Socio Economic
characteristics
Motorcyclist behavior
Accident characteristics

drivers. The safety campaign programs to improve
the safety of motorcycle drivers in Malang District
should be carried out based on the predeterminedsafe distance.
RESULTS AND DISCUSSIONS
Characteristics and Behavior,characteristics of Motorcyclist

andTravelPlanning and designing motorcycle infrastructuresuch as exclusive motorcycle lanes to minimize thenumber of
motorcycle
accidents requires
information on the characteristics and behavior of
motorcyclists. Their characteristics include age, sex,
education, income, career, number of motorcycles,
and ownership.
The characteristics of motorcyclists in Malang
District are as follows:a. Most of the respondents are aged 15-25 years(58%), and most of them are male. They
graduated from senior high school (55%), as
private employees (40%).
b. Accident Characteristics. Fifty-six percent ofrespondents have experienced at least one
accident with a minor injury that occurred
during the day and the evening. Motorcyclists
get into accidents with other riders or throughtheir fault.
c. Behavior
characteristics
beforeriding.
Motorcyclist always checks breaking tools, fuel,and electrical tools before riding, while theyrarely check motorcycle wheels and rear-view
mirrors.
d. Behavioral characteristics while driving.
Motorcyclists rarely use a helmet, trouser, andjacket. They sometimes ride together, giving
signs when overtaking and using dedicatedlanes for motorcycles.
e. Safety knowledge. Most motorcyclists do notunderstand the function of dedicated lanes, eventhough they will use dedicated lanes when thegovernment facilitates these lanes with twolanes, designed with traffic markings, and an
average width of 2.18 meters for the safety of
motorcyclists. The minimum and average safe
distances for motorcycles with other vehicles
are 0.75 m and 2.18 m respectively. They hopethat the government will pay attention to
infrastructures and policies for motorcycles'
safety. This distance is in line with the previous
research which revealed that the interaction of
motorcycles with other motorcycles and carsrequires a critical pore space of 3.12 m and 3.21
m respectively [6].

Secondary data:
Road network map
Motorcycle
Accident data

Black spots

Safe distance

Dedicated lane at the existing condition and scenario

Design Dedicated lane for motorcycle

Fig. 1 The research framework
As shown in Fig.1, the design of dedicated lanes
with the existing road conditions and scenarios
based on the road performance on the road section as
the black spot is the main analysis of this research.
This study will examine the road performances
without and with dedicated lane design. Analysis ofroad performance in scenario conditions was carriedout by taking into account the existing conditions ofroad performance and land use at the research
location as well as analysis of motorcycle spacerequirements and road performance before and after
the scenario.
To design the dedicated motorcycle lane, it isnecessary to analyze the safe distance for motorcycle
movement. This study investigates the area aroundten road sections in Malang District which are
identified as black spots (high rates of motorcycle
accidents). The survey was conducted to collect dataon road geometric conditions, traffic flow, and speed,road geometry, socio-economic characteristics,
motorcyclist behavior, and accident characteristics.
The safe distance is based on the characteristics andbehavior of motorcyclists. The design of thededicated lane will be promoted by implementing a
campaign to improve the safety of motorcycle

Motorcyclists’ knowledge of safety is taken into
737

GEOMATE – Kyoto, Japan, 3-5 November 2021

consideration when designing the dedicated lane,from their knowledge, the development of an
accident model is defined where the probability of
motorcycle accident is influenced by the variables
X31 (motorcycle lane requirements), X34 (Numberof MC lane), and X35 (Safe distance with othervehicles when moving). Then the regressionfunction is developed by three variables (X31, X34,
and X35). The regression function is
YMK = 3.063+4.829X31 – 3.28 X34(1) -3.924 X34(2) –1.037 X35, therefore the probability of accident due
to the variables need for motorcycle lane (X31), X34(Number of MC lane), and X35 (Safe distance withother vehicles when moving) is

Pi

=

F(βo

+

β1X1i)==––The regression function is fit because the t-test
rejects the null hypothesis with a 95% confidence
level. Table 1 presents the results of statisticalanalysis of motorcycle knowledge about safety that
influences the accident. The R-squared for thesevariables shows a reasonable calibration fit. It is
expected that the estimation model can beconsidered to improve motorcycle facilitation suchas the existence of dedicated lanes in the highway
network.

Table 1 Explanatory Variables in the model
B
Step 1aX31(1)

S.E.

4.829

Wald

1.001

X34

df

Sig.

Exp(B)

23.283

1

.000

8.127

2

.017

125.074

X34(1)-3.280

1.404

5.461

1

.019

X34(2)-3.924

1.384

8.039

1

.005

.020

X35

-1.037

.429

5.840

1

.016

.355

Constant

3.063

1.590

3.712

1

.054

21.383.038

Table 2 Road performance before and after the design of dedicated motorcycle lane
Volume capacity
ratio

traffic volume (vehicle/hr)road section
motorcyclecartruck & bus

speed(km/hr)lane
roadshoulderwidthcapacity
width (m)without with MC
(m)
(pcu)
MC lanelane

Improvement of road performance & MC safety
MC lane widh (m)modefication of lane
width (m)scenario 1 scenario 2

Hayam Wuruk
Gondanglegi

Raya Bululawang
Sultan Agung
Kepanjen
Panglimasudirman
Kepanjen

1,744

240

60

41.97

6

2.7

2,517

0.47

0.12

2.75

4.18

3,548

840

210

42.34

6.5

1.25

2,517

1.14

0.43

2.75

4.18

2,498

496

124

41.22

5

3

2,517

0.75

0.34

2.75

4.18

2,418

804

201

40.27

5

2

2,517

0.90

0.55

2.75

4.18

Raya Genengan

2,176

724

181

36.24

5

1.5

2,517

0.81

0.49

2.75

4.18

Brigjen Abdul
Manan Pujon

1,349

391

98

48.68

6

1.75

1,887

0.63

0.27

2.75

4.18

Raya Mojorejo
Batu

1,681

576

144

29.51

6

1.5

2,489

0.64

0.40

2.75

4.18

Panglima
Sudirman batu

Pattimura
Batu
Raya Arengareng (Raya
Dadaprejo/
Soekarno)

6.0

need to widen lane
scenario 1

scenario 2

unnecessary
widen laneneed 1.5 m
widen lane
each wayneed 1.25 m need 2.7 m
widen lane
widen lane
each way
6.0 each way
need 1.2m
unnecessary widen lane
5.0 widen lane
each wayneed 0.75m need 2.2m
widen lane
widen lane
5.0 eachneed way
1.25m eachneed way
2.7m
5.0 widen laneneed 0.5m
widen lane
5.0 each way
need 0.75m
widen lane
5.0 each way

widen laneneed 2m
widen lane
each wayneed 2.2m
widen lane
each wayneed 2.7m
widen lane
each wayneed 3.3m
widen lane
each wayneed 2.7m
widen lane
each way

601

206

51

50.32

9

0.5

2,766

0.21

0.09

2.75

4.18

1,217

417

104

30.64

6

1

2,436

0.47

0.22

2.75

4.18

need 1.25m
widen lane
7.0 each way
need 1.75m
widen lane
6.0 each way

4.18

need 0.25m
widen lane
7.0 each way

1,134

389

97

47.22

10

1

2,708

738

0.40

0.15

2.75

GEOMATE – Kyoto, Japan, 3-5 November 2021

dedicated motorcycle lanes related to motorcycle
safety. It is expected that transportation safety andmotorcyclist awareness will be achieved through a
motorcyclist safety action program. Therefore, thegovernment should establish the dedicatedmotorcycle lane for motorcycle safety and minimize
motorcyclists as victims of accidents.

Table 2 describes traffic volume for each type of
vehicle, speed, lane width, shoulder width, roadcapacity, road performance before and after thedesign of dedicated motorcycle lane, dedicatedmotorcycle width, and the modification of lane
width due to the presence of motorcycle lane.
Several road performances in the existing
condition have a low level of service, i.e. Raya
Bululawang
Kepanjen,
Panglima
Sudirman
Kepanjen, and Raya Genengan Pakisaji Streets(VCR>0.8). This is due to the high volume of
motorcycles. To improve traffic performance in theexisting conditions, it is necessary to improve roadperformance analysis by making several scenarios.
In this study, two scenarios were created bydesigning a dedicated lane with a minimum distance(Scenario 1) and an average safe distance (Scenario
2) for motorcycles with the other vehicles. Scenario
1 is determined by planning the dedicated lane of
2.75 m (0.75m (minimum safe distance) + 2x 0.75m(motorcycle width) + 2 x 0,25m (distance with thebarriers/ obstructions)). Scenario 2 is decided byplanning the dedicated lane of 4.18 m (2.18m(average safe distance) + 2x 0.75m (motorcycle
width) + 2 x 0,25m (distance with the barriers/
obstructions)). With the realization of the dedicatedlane, all roads performances are at a good level as
indicated by VCR <0.6 for Scenario 1 and Scenario
2.
Several roads, i.e. Hayam Wuruk Gondanglegi
and Sultan Agung Kepanjen, do not require roadwidening with a dedicated lane width of 2.75 m. For
the convenience of movement and safety of
motorcycles, it is necessary to pay attention to
Scenario 2 especially roads with a high volume of
motorcycles. In both scenarios, there is a change inroad geometry conditions.
The result of this study also supports previous
research on the effort to minimize motorcycle
accidents by reducing vehicle volume and speed, as
well as improvement of road geometry, i.e. lane
width, shoulder width, number of lanes, number ofaccess, and walkway width [11]. The results of theprevious study recommend an increase of the lane
width by facilitating the dedicated lane toaccommodate a high volume of motorcycles,
furthermore, the movement of the motorcycle will
be safe and comfortable.
The dedicated motorcycle lanes to reduce thenumber of accidents involving motorcycles is theresult of research based on an analysis of
motorcycle’s safe distance when moving orinteracting with other vehicles. The behavior andawareness of motorcyclists and other vehicles
regarding the use of dedicated motorcycle lanes isthe basis for the development and arrangement of

CONCLUSIONSIn Malang District, 56% of motorcyclists haveexperienced an accident while riding a motorcycle.
Based on socio-economic characteristics, drivers are
male (70%), aged 15-25 years (58%), high school
education background (55%), and private employees(40%). Behavioral characteristics of motorcyclists
were found that drivers always check breaking tools,
fuel, and electrical equipment before driving, while
motorcycle wheels are rarely checked. For behavior
characteristics during driving, motorcyclists rarely
use a helmet, trouser, and jacket. They sometimes
ride together, giving signs during overtaking andrarely using dedicated lanes for motorcycles.
The study suggests that the minimum andaverage safe distances for a motorcycle with theother vehicles are 0.75 m and 2.18 m respectively.
They hope the government to pay attention to safety
infrastructures and policies for motorcycles morethan 2.18 meters based on the behavior of
motorcyclists.
Two scenarios are created by designing thededicated lane with a minimum distance (Scenario
1) and an average safe distance (Scenario 2) for a
motorcycle with other vehicles. Scenario 1 is set at2.75 m and Scenario 2 is determined at 4.18 m. With
the realization of the dedicated lane, all roadperformances are at a good level as indicated by
VCR <0.6 for Scenario 1 and Scenario 2.
Scenario 2 especially roads with the high volume
of motorcycles should be considered for theconvenience of movement and safety of motorcycles.
In both scenarios, there is a change in the conditionof the road geometry. Therefore, the government
should establish a dedicated motorcycle lane formotorcycle safety and minimize motorcyclists asvictims of accidents.

ACKNOWLEDGMENTS
A grant from the Faculty of Natural Science,
Brawijaya University, Malang, Indonesia has
supported this research. We would like to express
deep thanks to the Faculty of Natural Science,
Brawijaya University.
We also want to thank Riski Nur Istiqomah
Dinnullah, the doctoral student in the Department of739

GEOMATE – Kyoto, Japan, 3-5 November 2021

Mathematics, Brawijaya University for assisting tocollect and processing data.

[7] Zukhruf, F., Frazila, R. B., Wiowo, S. S.,(2010)
EvaluationofSignalized
Intersection
Performance with Signalized Intersection
Management Scenarios Using Micro Simulation
Model, Simposium XIII FSTPT, Universitas
Katolik Soegijapranata Semarang, 8-9 Oktober
2010.
[8] Enggarsasi, U. & Sa’diyah, N.K. (2017). Studyof the Factors that Cause Traffic Accidents as
an Effort to Improve Traffic Accident
Prevention.. Perspektif. 22(3): 238-247 (inIndonesian).
[9] Idris, A., Hamid, H., & Hua, L. T. (2019).
Factors contributing to motorcycle accidents in
Malaysia. In IOP Conference Series: Earth andEnvironmental Science (Vol. 357, No. 1, p.
012039). IOP Publishing.
[10] IHCM (Indonesian Highway Capacity Manual).
Department of General Work, Directorate
General of Highway, 1997.
[11] Abusini, S., & Ambarwati, L. (2020).
Motorcycle accident model for highlands
regions by applying Generaliazed Linear Model(GLM). Lowland Technology International, 20(3,Dec),331-340.

REFERENCES[1] Permanawati, Sulistio, H., T. and A.
Wicaksono. (2010). Motorcycle Accidents on
the Highway Network, Malang: Brawijaya
University, Bachelor thesis, unpublished (inIndonesian).
[2] Anonymous, Traffic Law no. 22/2009, (2009)
Law of the Republic of Indonesia number 22 of2009
concerning
Road
Traffic
andTransportation and its implementing regulations(in Indonesian).
[3] Warpani, Suwardjoko P. (2002). Traffic
Management and Road Transport: ITB (inIndonesian)[4] Statistic Bureau of Malang City, the Figures ofMalang City in 2018 (in Indonesian).
[5] Idris, M., (2010). The Criteria for Motorcycle
Lane for Secondary Arterial Road (inIndonesian), Technical Report. Puslitbang Jalan
dan Jembatan, Bandung.
[6] Ambarwati, L., Pel, A.J., Verhaeghe, R., & vanArem, B., (2014a). Empirical analysis ofheterogeneous traffic flow and calibration of
porous flow model. Transp.Res. Part C: Emerg.
Technol. 48, 418–436.

740

11th Int. Conf. on Geotechnique, Construction Materials & Environment,
Kyoto, Japan, 3-5 November 2021, ISBN: 978-4-909106063 C3051

FLOOD DAMAGE ASSESSMENT: A REVIEW OF MULTIVARIATEFLOOD DAMAGE MODELS
Sumiliana Sulong and Noor Suraya Romali
Faculty of Civil Engineering Technology, Universiti Malaysia Pahang, Gambang, Kuantan, Pahang
ABSTRACT
Nowadays, more emphasis is given to flood risk management for dealing with flood disaster. To assess floodrisk, damage from flood is crucial component to be considered. Flood damage function is a commonly accepted
approach for the estimation of flood damages worldwide, where it combines the element of hazard, vulnerability
and exposure. However, this method usually considers only the flood depth and the type of buildings at risk. The
effect of other flooding conditions (impact parameters) and the resistance parameters to the degree of flood
damages are normally neglected. In fact, flood risk assessment should cover all damage dimensions in order toobtain an extensive description of flood damages. Multivariate damage modelling can be applied to examine therelationship between flood damages and other flood influencing factors. This paper presents a review of methods
applied to generate multivariate flood damage models, which includes the Multiple Linear Regression (MLR),
Bayesian Network (BN), Artificial Neural Network (ANN) and the Random Forest (RF) analysis. Moreover, themultivariate models are also found capable in generating synthetic data to counter the problems of flood damage
data scarcity experienced by the developing countries. Identifying damage influencing factors especially resistanceparameters is important as a comprehensive flood damage model based on the local conditions and property
characteristics of study area can assist in the future damage assessment works, as well as offering decision-makers
with an indispensable tool for managing strategies related to flood risk.
Keywords: Flood risk, Flood damage assessment, Flood damage influencing factors, Multivariate analysis

INTRODUCTION

to all varieties of damages produced by floods and iscategorised into two groups: tangible and intangible
damages [4]–[8]. Tangible damages may bequantified in monetary terms, while intangible
damages, are often measured using non-monetary
metrics such as the ecosystem, the environment,
health, and psychological impacts. Furthermore, eachgroup of damage is classified further as either directorindirectdamages[9].
Penning-Rowsell and Chatterton [10] in the
United Kingdom and Smith [11] from the Universityof New South Wales, Australia, were among thepioneer researchers on flood damage assessment.
Although flood vulnerability assessment studies havebegun over 40 years ago, it is still new in developing
countries. Many works are to be done, which relate toseveral issues, such as limited knowledge on thefactors influencing vulnerabilities, damage data
scarcity, and uncertainties in flood damage analysis[4], [12]. Past studies on flood damage assessment
typically deliberated on the relationship between
flood damage and hydrological impacts parameters,such as flood depth, which has been regularly chosen
as the most appropriate flood damage variable [13].
The variation of flood damages to properties could
not be explained based on flood depth alone [8]. The
outcomes were uncertain and did not significantly
affect the total damage cost predictions. Therefore,
proper estimation of flood damage is challenging andrequires careful consideration of some effects such as

Floods are the most prevalent natural catastrophes
that may endanger lives, destroy property, disrupt
social and economic activities, and the environment
and ecology in both rural and urban regions. Global
climate change and rapid urbanization are explicitly
identified as major catastrophe hazard drivers in the
Sendai Framework 2015 - 2030 [1]. Althoughboundless focus is positioned in executing structural
flood mitigation approaches, non-structural measures
including flood risk assessment still need to beextensively managed, since comprehension of thedamage evaluation remains inadequate [2]. Flood risk
assessment is concerned with the combination of thehazard and its vulnerability. Estimation of flood
hazard combining two essential flood characteristics,
namely flood extent and flood depth whilevulnerability is a notion that includes exposure andrefers to the impacts of floods. The primary tools invisualizing flood risk assessment are the flood hazard
map, flood damage assessment, and flood
vulnerability map [2] – [4].
Flood vulnerability assessments has become
increasingly relevant all around the world.
Meanwhile, flood damage assessment is one of themain tools of flood vulnerability assessment. Its
framework serves as a decision support tool for
decision-makers who must prioritise and execute bestpractices in flood risk reduction. Flood damage refers
741

GEOMATE – Kyoto, Japan, 3-5 November 2021

the effects on socio-economic conditions and theresistance parameters to the degree of flood damage,especially for loss estimation in monetary terms.
Many studies have shown that the influence ofresistanceparameters(such
asbuilding
characteristics and unitary prices) should be given
more attention as it affects the accuracy of flood
damage and makes the outcomes adaptable for otherareas [12]. Apart from that, with appropriate
modifications, flood damage assessment can also bemade on other assets and properties such as building
contents in residential, commercial and industrial
buildings [14]. Therefore, the study on therelationship between socio-economic conditions andproperty characteristics that may affect the extent offlood damage should prioritised. This paper intends
to deliver a concise review of the multivariate flood
damage models that may aid in establishing aframework for assessing the flood damages byconsidering the effects of damage factors frommultiple dimensions. To this end, a review of flood
damage assessment framework is provided to
improve the understanding of flood damage
estimation.

commonly involves the estimation of the damages toeach property (or unit) using a questionnaire survey
on the affected populations and properties to assess
the sustained damage or average values [16]. The
losses for each unit (property) are then summed up to
estimate the total event loss. The other method is themodelling approach method which may be divided
into two main techniques: an interactive computer
package model based on a web application or a standalone version; and statistical analysis techniques[17]–[19]. Some examples of well-known
computerized model are Integrated Software Package
for Flood Damage Analysis (FDA package) from
United States, Rapid Appraisal Method (RAM) and
ANUFLOOD from Australia and INSYDE models in
Italy [14]. The statistical analysis techniques include
a damage function model that utilizing therelationship between hazard parameters (hydrologicalcharacteristics), and resistance parameters such as
property characteristics and socio-economics
situation to the expected damages [12], [14].
Recently, several researchers have recently
created multivariate flood damage models utilizing
statistical approaches, such as the Multiple Linear
Regression, the Bayesian Network, the Artificial
Neural Network (ANN), and the Random Forest
analysis [15], [20], [23]–[27]. Multivariate flood
damage models have become increasingly common
in data-scarce areas [19]. In addition to modelling
applications, relevant new data points may be
produced to counter the issues of data scarcity issues.
As a result, together with subsequent works
addressing the synthetic approach, model
development is less reliant on accessible datasets.
Therefore, creating a site-specific model that reflects
the flood and socioeconomic circumstances in thedata scarcity region is appropriate.

FLOOD DAMAGE ASSESSMENT
Throughout recent decades, risk-based methods
have been progressively acknowledged and utilized
in managing flood risk. Flood damage assessments
are important component of flood risk assessment,
and the outcomes offer decision-makers withessential tools for preparing more effective
approaches of reducing risk [15]. Consequently, thecurrent understanding of flood impacts and theiroutcomes can be improved towards developing amore reliable damage estimation, and efficiently
reduce flood risk. The effectiveness of flood damage
assessment depends on its framework, which
encompasses hazard, vulnerability, and exposure.
The concept has been adapted by numerous studies inboth developed and developing countries and has
progressively obtained global reception as thestandard approach for assessing urban flood damages
in urban areas. The key differences among theexisting methodological frameworks for damage
assessment are linked to the context of flood damage
assessment (purpose, required accuracy andreliability data and resource availability, time
consumption, spatial scale approach (macro-, meso-,
and micro-scale), exposure and element at risk
analysis, damage influencing parameters, anddamage categories) [9].
Two main ways of assessing the flood damage are
the unit loss and the modelling approach [16]. The
unit loss approach refers to the direct computation ofloss to individual properties, which is then combined
to create a total loss number for the event underconsideration. For losses resultant of flooding, thisFLOOD DAMAGE MULTIVARIATE MODEL
Flood Damage Influencing Variables
Torgersen [26] asserted the necessity to identify
patterns and interactions among multi-parametersthat impact various damage categories for the purposeof developing reliable flood damage models that
represent the relationship between flood damage andresistance parameters such as socioeconomic andproperty characteristics. The influencing factors offlood damage may be divided into two categories:
impact (hazard) parameters; and resistance(vulnerability) parameters. Impact parameters that
influence flood damage include water depth, thevelocity of water flow, the duration of flood, thecontamination in flood water, debris and thefrequency of flooding. Meanwhile, the resistanceparameters include property type, property quality,
building material, property value, precautionary
measures and socio-economic circumstance (e.g.,742

GEOMATE – Kyoto, Japan, 3-5 November 2021

income, educational background and ownershipstatus).
Nevertheless, the relationship between flood
damage and resistance parameters such as socioeconomic and property characteristics has been
insufficiently emphasized [18]. Several studies
discovered that the disparity in flood damage toproperties cannot be solely explicated by the depth offlood water [18]. The flood damage assessment does
not depict the distribution of losses throughout aflooded area and does not reflect the total impact of
the flooding since the various resistance parameters
disregarded. As a result, other additional damage
influencing parameters must be included in theconstruction of reliable flood damage modelling[28]. The multivariate flood damage model has
demonstrated an improved forecast accuracy,
allowing for validity with any flood occurrence anddiverse locations in flood damage assessments [28].

(RF) have been used in establishing the multivariate
flood damage assessment model for the purpose ofidentifying the importance flood damage influencing
parameters and generating synthetic data for usefulpredictions of the flood damage estimation. The
description of the techniques is further elaborated in
the following sections.
Multiple Linear Regression
Multiple Linear Regression has been commonly
employed to aid in flood damage assessment. Win Zin[15] established a flood loss estimation model of
direct damages to residential buildings in Bago,
Myanmar, using the multivariable linear regression todetermine the most influencing factors to the flood
damage. This model incorporates more than one
factor of hazard and resistance variables that affectdamage, such as the depth of floodwater, the durationof flooding, building material, age of the building,building condition, number of stories and flood level.
The multiple linear regression models the linear
relationship between the independent variable(hazard and resistance variables) and the dependent
variable (flood damage). The coefficient ofdetermination (R2) value and p-value measures thesignificant outcome, and the output was displayed as
an equation. On the other hand, Velasco [4] andWagenaar [23] also developed a flood loss estimation
model using the linear-regression-based approachcompared to the other regression approaches using
supervised learning techniques to identify theapproach that produces the best result.

Multivariate Flood Damage Models
Recently, flood damage models have been
established with multivariate of impact parameters(e.g., water depth, flow velocity, flood duration, andcontamination) and resistance parameters (e.g.,building material, type of building, socio-economic
status, flood warning, and preparedness) [15], [20],[23]–[27]. Table 1 summarized the techniques usedby the researchers in multi variate models. Statisticalanalysis techniques such as Multiple Linear
Regression (MLR), Bayesian Network (BN),Artificial Neural Network (ANN) and Random Forest

Table 1 Multivariate flood damage models techniques and variables.
Authors[4]

Multivariate flood damage
models techniques

Flood damage variables
Impact (hazard) variables
Resistance variables
Flood depth
Building structural, building
fabric, building inventory andclean-up cost

[15]

Multiple Linear Regression,
Random Forest and Artificial
Neural Network
Multiple Linear Regression[18]

Random Forest

Flood depth and duration offlooding
Flood depth

[23]

[28]

Multiple Linear Regression,
Bayesian Network and Random
ForestRandom Forest

Flood depth, the velocity offlood water and duration offlooding
Flood depth[33]

Artificial Neural Network

The extent of flood, durationof flooding and flow velocity[36]

Random Forest

Flood depth, duration offlooding and contamination

743

Building characteristics
Building structural, building
fabric and building inventory
Building characteristics

Building structural, building
fabric and building inventory
Building material, building
quality, building age, building
type, location, and value ofthe affected buildings
Size of company, emergency
preparedness, building
structural and equipment

GEOMATE – Kyoto, Japan, 3-5 November 2021

Bayesian Network (BN)generate synthetic data in order to estimate thepotential economic costs of flood events to residentialbuildings in Italy. In the study, apart from taking theflood depth into consideration, the contribution ofmultivariables is considered, such as the detailed
hazard data (e.g., the extent of flood, its duration andits flow velocity) and comprehensive vulnerability
data including building characteristics (e.g., building
material, building quality, building age, building type,location, and value of the affected buildings). All ofthese multivariables are significantly correlated todamage factor.
ANN models are excellent for a probabilistic
analysis, i.e., a procedure in which the variation in
input variables may be explicitly represented in amodel and may include the quantitative analysis ofenormous quantities of data. Other benefit is ANN
can extrapolate configuration since the output rangeof the model is not restricted to the range of
normalized values. The input data is distributed into
three unique sets at random, namely training,validation, and test. The training data set is used tocalibrate the ANN model, signifying that the weight
connections among neurons are changed in relation to
the data set. The validation set is applied to end thetraining process in in the prevention of overtrainingor overfitting of ANN model; thus, the test set is not
supplied to the model during training since it is only
utilised to check the effectiveness of a trained ANN
when stipulated by new data.

Wagenaar [23] adapted the Bayesian Network(BN) technique to model flood damage and generate
high-quality synthetic data as a substitute to the datadriven structure. The BN technique is a probabilistic
graphical model that represents a set of randomvariables and provisional dependencies in a Directed
Acyclic Graph (DAG) structure [35]. Every variablein the network may be perceived as a preceding
probability distribution, and dependencies between
variables are represented with edges representing
joint probability distributions. The edges in the BN
are focused on the influence of one variable that flows
to the other. From this network, extrapolation can be
performed to apply knowledge of one variable forpredicting other variables. It demonstrates theversatility and value of combining multi-parameters
to predict system behaviour for multiple data output.
The ability of the BN technique to assimilate data
allows them to integrate several multi-parameterssimulations from developed models for a combined
assessment of many scenarios and characteristics.
Wagenaar [23] performed the multi-variable flood
damage model by considering the hazard variables(e.g., the depth of flood water, the duration offlooding and the velocity of flood water) andresistance variables (e.g., building characteristics).
One of the most significant advantages of employing
BN is due to its ability to analyse correlations
between variables using data contained in conditional
probability tables. Because conditional probability
tables are designed with the connected relationships
between variables, the BN may also be used to answer
queries regarding sector behaviour [31]. By means,
BN may quantify the distribution obtained inresistance parameters and assess the flood damage
with the induced hazard characteristics. However, thedisadvantage of the BN technique is its poor
performance in cases with a limited data set and poor
data quality [23].

Random Forest (RF)Random Forest (RF) analysis based on regression
approach is increasingly popular in modelling theflood damage assessment. Regression analysis ismainly used in understanding the relationshipbetween dependent variables (hazard) andindependent variables (vulnerability). Wagenaar [23]and Velasco [4] assessed flood damage for a variety
of damage factor variables using the Random Forest(RF) regression technique. The regression tree-basedmodel generates data for each tree using abootstrapping resampling method [35]. Branches are
created in each tree by separating the data set using
binary recursive partitioning, such as partitioning data
based on whether the average wind speed is greaterthan a specified value. The RF analysis method does
not use all explanatory factors for each tree but rather
pursues the optimal splits among a fixed set of
explanatory variables being assessed. The number ofsampled features is the square root of the total numberof features in the data sets. The best splits areregression trees that separate the training data until
the results do not vary as much. The mean value ofthe predictions from each regression tree is the
anticipated value for the entire RF model. Thus, themore decision tree a random forest model includes,
the more robust and accurate its result becomes.

Artificial Neural Network (ANN)
Rumelhart [34] pioneered the ANN approach,which was inspired by the way the human brain
analyses information [35]. ANNs discover theconnections between variables in a data collection.
The model constructed using ANNs is composed ofnumerous (hidden) layers of neuron-like processing
units coupled in a network-like model. Each neuron
is linked to every other neuron in the layer
immediately before and following it. Coefficients areapplied to measure every value passing through theneuron. The coefficients of the neurons are computedby an optimization approach that reduces error on the
training data set.
Amadio [33] applied the Artificial Neural
Network (ANN) to evaluate flood damage and744

GEOMATE – Kyoto, Japan, 3-5 November 2021

Nafari [18], Malgwi [32], Wagenaar [35], Siege[36] and adapted the RF analysis to develop a flood
damage function model for the classification andregression of continuous dependent variables. All ofthese researchers relate the damage to many otherinfluencing factors including hazard and resistanceparameters. Nafari [18] derived the flood damage
model by considering the relationship between multiparameters, consisting of impact variables (e.g.,
flood depth, water contamination and flow velocity)and resistance variables which include emergency
precaution, preparedness, building characteristic(e.g., building material, building quality and buildingsize) and socio-economic situations (e.g., ownershipstatus and household income) to flood damage. The
results from the RF simulations showed that factors
other than flood depth such as flow velocity, watercontamination, precaution measure, size of building,the value of the affected building, and building
quality also significantly influence flood damage.
Apart from modelling application, this regression
technique can also generate appropriate new data
points to alleviate data scarcity problems [36].
Malgwi [28] reported that the RF model which use
data in a site-specific region shows the same highaccuracy and consistency result when tested onanother region.

ACKNOWLEDGEMENTS
The authors would like to thank Ministry of
Education of Malaysia (MOE) for providing financial
support under fundamental research grant No.
FRGS/1/2019/TK01/UMP/02/2(University
reference RDU1901155) and Universiti Malaysia
Pahang for technical support and encouragement.
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Nowadays, flood vulnerability assessment has
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scarcity in performing damage assessment works.

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