GEOMATE 2022 BANGKOK, THAILAND 22-24 NOVEMBER, 2022
GEOTECHNIQUE, CONSTRUCTION MATERIALS AND ENVIRONMENTiPROCEEDINGS OF THE TWELVETH INTERNATIONAL CONFERENCE – GEOMATE 2022
GEOTECHNIQUE, CONSTRUCTION MATERIALS AND ENVIRONMENT, BANGKOK,THAILAND 22-24 NOVEMBER, 2022

Geotechnique, Construction
Materials and Environment
Edited byProf. Zakaria Hossain
Department of Environmental Science and Technology
Graduate School of Bioresources
Mie University, Japan

Prof. Suksun Horpibulsuk
School of Civil Engineering,
Faculty of Science and Engineering
Suranaree University of Technology, Thailand

THE GEOMATE INTERNATIONAL SOCIETYii

Copyright @ 2022 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
system, or transmitted in any form or by any means without prior permission in writing from the publisher.
Disclaimer: The editors and the publisher have tried their best effort to ensure the integrity and the quality ofthis publication and information herein. However, they give no warranty of any kind, expressed or impliedwith regard to the material contained in this book, and will not be liable in any event for the consequences of
its use.
Published by:
The GEOMATE International Society
Tsu city, Mie, Japan
E-mail: society@geomate.org
http://www.geomate.org/
ISBN Number: 978-4-909106087 C3051

iii

Twelveth International Conference on Geotechnique, Construction Materials and Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

Table of Contents

ID

Prefacexii

Organization

xiii

Keynote Papers

1

IMPROVED MECHANISTIC PERFORMANCE OF CEMENT- NATURAL RUBBER LATEX STABILIZED SOILS AT

k1

VARIOUS TEMPERATURES

2

Suksun Horpibulsuk
SOIL BIO-ENGINEERING: A SUSTAINABLE GROUND IMPROVEMENT TECHNIQUE FOR STABILIZATION OF

k2

MARGINAL SOILS

3

Bashir Ahmed Mir

Technical Papers
ID

13

Geotechnique14

g12105FINITE ELEMENT ANALYSIS OF BEAM RESTING ON FOOTING
Radhi Alzubaidi, Husain M Husain and Samir Shukur

15

g12106ANALYSIS AND DESIGN ON VARYING THE OFFSET DISTANCES IN SUPERIMPOSEDMECHANICALLY STABILIZED EARTH WALLS
Vuttichai Chatpattananan and Vatanavongs Ratanavaraha

23g12114

NUMERICAL ANALYSIS FOR ESTIMATING PULL-OUT RESISTANCE OF FLIP-TYPE GROUNDANCHORS IN SAND GROUNDS
Shota Yoshida, Xi Xiong and Tatsunori Matsumoto

29g12118FIBER-BASED MEASUREMENT OF THE ACTIVATION OF GEOSYNTHETICS IN SOIL
Jan Derksen, Jeanette Ortega, Kira Heins, Thomas Gries and Raul Fuentes

35

g12121SATELLITE-BASED GROUND DEFORMATION MONITORING IN NAGA CITY, CEBU ISLAND IN THE
PHILIPPINES
Ryan A. Ramirez, Rajiv Eldon E. Abdullah and Christabel Jane P. Rubio

41

g12124FINITE ELEMENT PARAMETRIC STUDY OF CONTIGUOUS PILE WALL REINFORCEMENT IN A
DEEP EXCAVATION
Fachrizal Naufal Indrawan, Fikri Faris and Akhmad Aminullah

47

g12126NUMERICAL ANALYSIS OF INTERSECTING URBAN METRO TUNNELS IN ROCK MASS
Isan Dey and Ramanathan Ayothiraman55

iv

g12130SLOPE STABILITY DURING EXTREME DAILY RAINFALL: A COMPARATIVE STUDY USINGSTATIONARY AND NON-STATIONARY GENERALIZED EXTREME VALUE MODEL
Thapthai Chaithong59

g12133

ESTIMATION OF THE ALLOWABLE BEARING CAPACITY OF SOIL IN SOME MUNICIPALITIES OF
THE PROVINCE OF PAMPANGA USING NEURAL NETWORKS
Carmela Marie A. Lingad and Erica Elice S. Uy

65

g12138DYNAMIC CENTRIFUGE MODEL TESTS FOR SOIL-BENTONITE VERTICAL CUTOFF WALLS WITH
EARTHQUAKE-PROOF PERFOMANCE
Koji Watanabe, Daisuke Ueno and Masaaki Hasegawa

72

g12140

REPRODUCTION OF DYNAMIC BEHAVIOUR AND CLASSIFICATION OF FAILURE PATTERNS OFSTONEWALLS BY DISTINCT ELEMENT METHOD SIMULATION
Satoshi Sugimoto, Kosuke Yamaguchi and Maho Yamaguchi

78

g12141MPS-CAE SIMULATION FOR JET-GROUTING TECHNIQUE IN GROUND IMPROVEMENTS
Sudip Shakya and Shinya Inazumi

84g12142PROSPECTS AND MERITS OF USING CALCIUM PHOSPHATE COMPOUNDS FOR SOIL
IMPROVEMENT: A REVIEW
Maksym Avramenko, Kazunori Nakashima and Satoru Kawasaki

90

g12145ESTIMATION OF SUBSURFACE STRUCTURE OF LANDSLIDE AREA BASED ON MICROTREMOR
AND SEISMIC OBSERVATION IN THE SAJI AREA, TOTTORI, JAPANTatsuya Noguchi, Isamu Nishimura and Takao Kagawa

96

g12148EXPERIMENTAL STUDY ON STATE BOUNDARY SURFACE OF COMPACTED SILTY SOIL
Tufail Ahmad, Riko Kato and Jiro Kuwano102

g12150PERFORMANCE AND BEHAVIOR OF DIAPHRAGM WALL OF UNDERGROUND AUTOMATIC CARPARK PROJECT IN BANGKOK SUBSOIL
Jirat Teparaksa

108

g12151

NATURALLY DERIVED CEMENTS FOR CONSTRUCTION AND BUILDING MATERIALS LEARNEDFROM THE WISDOM OF ANCESTORS: A LITERATURE REVIEWSu Zhan, Kazunori Nakashima and Satoru Kawasaki

114

g12154

EFFECTIVENESS OF SAND COMPACTION PILE AND PREFABRICATED VERTICAL DRAINS IN
IMPROVING SOFT SOIL FOR PAVEMENT SUBGRADE
Tanim Shahriar and Shah Md Muniruzzaman

119g12158XRD PATTERNS OF TURBIDITY IN SEEPAGE WATER THROUGH TWO SOIL LAYERS
Shota Takano, Motoyuki Suzuki, Taichi Ishimaru and Asahi Komori

128

g12159ESTIMATION OF SUBSURFACE STRUCTURES AND GROUND MOTION CHARACTERISTICS OF
ARTIFICIALLY IMPROVED LAND FROM MICROTREMOR OBSERVATIONS – CASE STUDY IN
TOTTORI CITY, JAPANIsamu Nishimura, Tatsuya Noguchi and Takao Kagawa

134

g12162EVALUATION OF VERTICAL BEARING CHARACTERISTICS FOR SOIL-CEMENT COMPOSITE PILES
Shohei Koga, Koji Watanabe and Tadahisa Yamamoto

140

g12169REVIEW ON CONCEPTS, RESEARCH, AND CHALLENGES FOR THE ADOPTION OF PERMEABLEPAVEMENT SYSTEM IN VIETNAM
Van Nam Pham, Hoang Giang Nguyen, Tien Dung Nguyen and Ken Kawamoto146

g12171

INFLUENCE OF FOOTING SIZE AND RELATIVE DENSITY IN ULTIMATE BEARING CAPACITY
FORMULA OF STRIP FOOTING ON SANDY SOILS
Tahir Iqbal, Satoru Ohtsuka, Koichi Isobe, Yutaka Fukumoto and Kazuhiro Kaneda

152

g12177SUSTAINABLE SOLUTION TO EXPANSIVE SUBGRADE AND EXISTING PAVEMENT USINGGEOCONFINEMENT SYSTEM
Lokanantham Logitharan and Umar Ali

158v

g12184

EXPERIMENTAL STUDY ON DATA TRANSMISSION USED FOR CONSTRUCTION QUALITY
CONFIRMATION OF EMBEDDED PILE CONSTRUCTION METHOD
Yasuhide Mochida and Tatsuki Maniwa

164g12201NUMERICAL SIMULATION FOR SEDIMENTATION OF SAND PARTICLES IN EXCAVATIONSTABILIZERS
Hiroya Asano, Koki Nakao, Tomotaka Morishita, Toshihiko Miura, Yasuharu Wachi, Kazuhiro
Watanabe and Shinya Inazumi

170

g12205

STRENGTH PROPERTES OF BENTONITE SUBJECTED TO CYCLIC LOADING STRESS
Tomoyoshi Nishimura, Seiichi Narushima, Yasunori ARAI and Yuki Sakoda

176

g12208PROPERTIES AND PERFORMANCES OF SOIL CEMENT MODIFIED WITH CONCENTRATED
PARA-RUBBERSupathinee Kowsura, Susit Chaiprakaikeow, Apiniti Jotisankasa, Suphawut Malaikrisanachalee,
Supakij Nontananandh, Korakod Nusit, Auckpath Sawangsuriya and Shinya Inazumi

182

g12209NUMERICAL STUDY OF A HYBRID COUNTERMEASURE FOR RIVER EMBANKMENT IN ACTUAL
FIELD CASE
Kakuta Fujiwara and Enayat Mallyar

189

g12210A YIELD FUNCTION AND UNDRAINED BEHAVIOR OF K0 CONSOLIDATED CLAYS
Yoshihito Imai, Salisa Chaiyaput, Koichi Iinuma, Masaru Akaishi and Motohiro Sugiyama

195

g12211FAILURE INITIATION AND MODES OF HOEK-BROWN ROCK MASSES SURROUNDING
UNDERGROUND STORAGE WITH HIGH INTERNAL PRESSURE
Apiwish Thongraksa and Pornkasem Jongpradist

202

g12212EVALATION OF DISPLACEMENT AT GROUND SURFACE DURING GROUNDWATER RECOVERY
Sutasinee Intui, Jittiphan Jindawutthiphan, Apinya Rungrueang, Choknimit Leelananthawong,Thanapatsa Srisarn Trpkovski and Kearkkeart Apischotecawankit and Shinya Inazumi

208

g12215

A BASIC STUDY ON ESTIMATING PARTICLE-SIZE DISTRIBUTION BY AUGER EXCAVATION SOUNDUSING MACHINE LEARNING
Keito Endo, Taizo Kobayashi and Ryoichi Fukagawa

214

g12217

MODEL TESTS ON CONCRETE BLOCKS SINKING INTO SEABED GROUND WITH UPWARDSEEPAGE FLOW GENERATED
Tatsuya Matsuda, Kinya Miura, Naoto Naito and Yuki Ando220

g12218MODEL TESTS ON SAND BOILING AROUND SHEET PILE REGARDING PROPERIES OF GROUND
MATERIAL
Tatsuya MATSUDA, Naoto NAITO and Kinya MIURA

226

g12219DISCRETE ELEMENT MODELING OF SLOPE FLOW BEHAVIORS OF DRY GRANULAR MATERIALSWITH DIFFERENT COLLAPSE CONDITIONSNaoto Naito, Tatsuya Matsuda, Kinya Miura, Yasuhiro Yamada and Takumu Omura

232

g12220

MODEL ROCK-SLOPE FAILURE TESTS ON FINAL RUNOUT DISTANCE OF DRY GRANULARAVALANCHE WITH SECONDARY SLOPE FAILURE
Naoto Naito, Tatsuya Matsuda, Kinya Miura, Takumu Omura and Arif Daniel Bin Azmi

238

g12224

INFLUENCE OF TEMPERATURE ON ELASTIC STIFFNESS AND TIME-DEPENDENT DEFORMATIONBEHAVIOURS OF HOSTUN SAND IN TRIAXIAL COMPRESSION TEST
Kosit Jariyatatsakorn and Warat Kongkitkul

244

g12236NUMERICAL ANALYSIS FOR VERTICAL RESPONSE OF SHALLOW SUCTION PILE
Donghyun Lee, Jaewoo Jung, Yoowon Lee, Jaehun Ahn and Jongwon Jung254

g12237

MODELLING LIGHTWEIGHT DEFLECTOMETER BASED ON ANALYTICAL AND NUMERICAL
MODELS
Tram Huyen Nguyen, Yunje Lee, Hoa Phuong Thi Nguyen, Sangwook Kang and Jaehun Ahn

257

g12252FEASIBILITY STUDY ON SOIL CLASSIFICATION FROM SOIL IMAGES USING DEEP LEARNING
Tomoki Abe and Taizo Kobayashi

261

vi

g12260

A COMPARISON BETWEEN PAVEMENT RESPONSES FROM THE FALLING WEIGHT
DEFLECTOMETER AND THOSE FROM TRUCK LOADING BASED ON THE LAYERED ELASTICANALYSIS
Thinn Thinn and Auckpath Sawangsuriya

265

g12261PERFORMANCE OF BEARING LAYER CONSTRUCTED USING LIGHTLY CEMENTED CLAY
Juan Wei Koh, Soon Hoe Chew, Yeow Chong Tan, Cheng Soon Teo, Shanyin Kee and Danette
S.E. Tan270

g12264

CORROSION OF UNDERGROUND STEEL BETWEEN SOIL AND CLAY AND ITS PREVENTION
Keiyu Kawaai and Takahiro Nishida276

g12269PILE INSTALLATION EFFECTS ON THE STRESS AND DEFORMATION STATE OF SURROUNDING
SOIL: REVIEWWorku Firomsa Kabeta

280

g12303

A CORRELATION BETWEEN ONE-DIMENSIONAL CONSOLIDATION COEFFICIENTS WITH BASALTFIBER LENGTH AND RHA-CEMENT CONTENT IN FIBER-REINFORCED STABILIZED EXPANSIVE
SOILSAlex Otieno Owino and Zakaria Hossain

288

Construction Materials

302

ID
g12112

PARAMETRIC MODELING OF RECYCLED BRICK AGGREGATE CONCRETE (RBAC) USINGARTIFICIAL NEURAL NETWORK (ANN) AND MULTIPLE LINEAR REGRESSION (MLR)
Albert Jr Griño

303

g12146

RAW MATERIAL OPTIMIZATION WITH NEURAL NETWORK METHOD IN CONCRETE
PRODUCTION ON PRECAST INDUSTRY
Ranti Hidayawanti and Yusuf Latief309

g12156

EFFECT OF ADMIXED POLYPROPYLENE FIBERS ON CONCRETE PROPERTIES AND SHEAR
STRENGTH OF REINFORCED CONCRETE BEAMS
Osama Daoud and Reham Ibrahim Ahmed Ibrahim

316

g12157

ADDITION OF SEWAGE SLUDGE ASH TO CONCRETE AS SUBSTITUTE FOR PORTLAND CEMENT
Jean-Frank Wagner322

g12161STUDY OF THE HEAT EQUATION AND THE EFFECT OF TEMPERATURE INSIDE AN ELECTRIC
CABLE CONSISTING OF ALUMINUM AND COPPER METALS
Dalal Adnan Maturi

326

g12214ANALYSIS OF RESOURCE REQUIREMENTS IN A CONSTRUCTION SAFETY PROGRAM TO
ESTIMATE COST OF ARCHITECTURAL WORK IN FLATS BUILDING
Rossy Armyn Machfudiyanto, Yusuf Latief, Ratih Fitriani and Amira Syifa

331

g12221

EXPERIMENTAL STUDY ON SURFACE PROTECTION OF RESTORATIVE BRICK FOR CULTURALPROPERTIES IN AYUTTHAYA
Yuko Ishida, Masaru Koemoto, Mika Yamada and Hajime Ito338

g12223

SELECTION OF RAILWAY BALLAST BASED ON CEMENTING POTENTIAL: A CASE STUDY IN
THAILAND
Phitsanu Pholkainuwatra, Sitthiphat Eua-apiwatch and Siam Yimsiri

344

g12244

RECOVERY AND VALORIZATION OF DEMOLITION AND CONSTRUCTION WASTES SPREAD OVER
ALL THE CITY OF CASABLANCA (MOROCCO)
Mourad MORSLI, Mohamed TAHIRI, Youssef HALIMI and Mehdi MAANANE

349

g12250

THE EFFECT OF LDPE PLASTIC WASTE ON THE COMPRESSIVE STRENGTH OF PAVING BLOCKS
Fauzan, Rani Fahmi Zakaria, Dyan Adhitya Nugraha M and Zev Al Jauhari355

g12251FRAGILITY CURVE DEVELOPMENT OF SCHOOL BUILDING IN PADANG CITY WITH ANDWITHOUT RETROFITTING DUE TO EARTHQUAKE AND TSUNAMI LOADS
Fauzan, Ruddy Kurniawan, Nandaria Syahdiza and Zev Al Jauhari

361

vii

g12254

REMEDIATION OF AL-QATIF EXPANSIVE CLAY USING CEMENT KILN DUST, INITIAL ASSESSMENT
Sultan Abdulaziz Almuaythir and Mohamed Farid Abbas

367

g12266

EFFECT OF MACRO-SYNTHETIC POLYPROPYLENE FIBER ON DRYING SHRINKAGE OF
SELF-CONSOLIDATING CONCRETE
Aris Aryanto and Andy Muliohardjo

372

g12267ACTIVATION ENERGIES OF CHLORIDE INDUCED CORROSION OF STEEL IN CONCRETE
Takahiro Nishida, Keiyu Kawaai and Nobuaki Otsuki

378

g12275APPLICATION OF DIGITAL IMAGE CORRELATION METHOD IN RC AND FRC BEAMS UNDER
BENDING TEST
Messa Revolis, Aris Aryanto, Yuki Oribe and Hibino Yo384

g12276

A STUDY ON METHODS OF SELF-HEALING ASPHALT PAVEMENT
Enohmbey Chubisong Moses and Yoshitaka Kajita

390g12302PRODUCING VALUABLE ‘SAND-LIKE’ MATERIAL FROM EXCAVATED MARINE CLAY USING
MID-SIZED ROTARY KILN
Sathya Subramnian, Juan Wei Koh and Soon Hoe Chew, Y. C. Tan, C. S. Teo, M. Y. C. Koh, T. H. H.
Cheung, H.B. G. Foo

396

ID

Environment

401

g12104MATERIAL SOURCE OF MUARAJAMBI TEMPLES, INDONESIA
Sondang Martini Siregar, Edy Sutriyono, Ari Siswanto and Agus Aris Munandar402

g12109DESALINATION BEHAVIORS FROM SEAWATER USING NATURAL ZEOLITE AND CALCINED CA-FE
LAYERED DOUBLE HYDROXIDE FOR CULTIVATION
Takaaki Wajima and Fumika Sekihata409

g12116

MEASUREMENT OF CRACK DISPLACEMENT USING DIGITAL PHOTOGRAMMETRY
Afia S. Boney, Satoshi Nishiyama and Teruyuki Kikuchi

415

g12120STABILITY OF THE MUNICIPAL SOLID WASTE LANDFILL ON SLOPING LAND IN BATU CITY, EAST
JAVA, INDONESIA
A Rachmansyah, A P Putra, A Darmawan and Harimurti

420

g12123

FACTORS INFLUEANCING ANAEROBIC DIGESTION PROCESSES OF MARKET WASTE AND COW
DUNG AS RAW MATERIAL FOR RENEWABLE ENERGYEllina Sitepu Pandebesie, Susi Agustina Wilujeng and Trifena Karunia Marbun

426

g12131

TiO2 PHOTOCATALYST SUPPORTED ON FLY ASH PARTICLES USED IN WASTEWATER TREATMENT
LOADED WITH FOOD DYES
M. Visa and I. Visa432

g12137A NOVEL ROUGH FUZZY BASED DELPHI METHOD FOR HIGHWAY PROJECTS RISK ANALYSIS: THE
SOE ASSIGNMENT SCHEME CASE STUDY
Gilang Ardi Pratama, Yusuf Latief and Lukas Beladi Sihombing

438

g12139RESULTS OF RECOVERY PROJECT ON WETLANDS WITH LOST BIODIVERSITY
Michiko Masuda, Koichi Nagarekawa and Fumitake Nishimura

444

g12143

PREPARATION OF GEOPOLYMER CEMENT FROM LUNAR ROCK SAND USING ALKALI FUSION,AND ITS EVALUATION OF RADIATION SHIELDING ABILITY
Osamu Toda and Takaaki Wajima

450

g12152PROPOSAL OF A FLOATING OFFSHORE BASE FOR DISASTER PREVENTION AND MULTIPURPOSE
USE
Shinji Sato and Kai Nagatomi

454

viii

g12153

STUDY ON THE ALLEY IN THE CITY BASED ON PEDESTRIAN’S IMAGE
Shonosuke Kajita and Kazunari Tanaka

460

g12155A STUDY ON THE RELATIONSHIP BETWEEN VISUAL STIMULI AND THERMAL SENSATION IN A
TROPICAL REGION – TARGETING SHORT TERM RESIDENTS –Kenta Fukagawa, Yoshihito Kurazumi, Ariya Aruninta and Yoshiaki Yamato464

g12165

THE RECYCLING BIOCHAR BASED-MUSHROOM GROWING MEDIA FOR SOIL ENRICHMENT IN
CORN CULTIVATION
Ambar Pertiwiningrum, Margaretha Arnita Wuri, Alva Edy Tontowi and Andang Widi Harto

470

g12167g12168STRATEGIES FOR INCREASING ACCESS TO WATER AND SANITATION IN A WATER-SENSITIVE
AREA
Shella Zahrawani, Ahmad Soleh Setiyawan, Prasanti Widyasih Sarli, Prayatni Soewondo andDion Awfa
DETERMINATION FACTORS FOR SELLING LOCATIONS, GENDER, AND MIGRATION STATUS OF
STREET VENDORS IN EAST JAKARTA IN ACHIEVING ECONOMIC RESILIENCE DURING THE
COVID-19 PANDEMIC
Beti Nurbaiti, Chotib, Kemas Ridwan K, Mia Siscawati and Elisabeth Ratu Rante Allo

474

480

g12172

ON MOVEMENT OF PEDESTRIANS IN THE STATION SQUAREHaru Kanda and Kazunari Tanaka

485

g12173

CEMENT SOLIDIFICATION CHARACTERISTICS OF COAL CINDERS-MIXED SOIL
Shoji Kamao, Kentaro Ishii, Satoshi Shigemura and Yuta Murakami

489

g12175

ROAD TRAFFIC EVALUATION FORCUSHING ON VELOCITY AND FORM
Shion Muramoto and Kazunari Tanaka

495

g12176METHOD FOR EVALUATING URBAN COMFORT SPACES FOCUSING ON ENVIRONMENTAL
SOUND USING EEG
Shotaro Otsuji and Kazunari Tanaka

499

g12180APPLICATION OF THREE-DIMENSIONAL POINT CLOUDS FOR RIVER MANAGEMENT USINGDRONE SURVEYING
Nanoka Akiyama and Satoshi Nishiyama503

g12185ASSESSMENT OF DOMESTIC WASTEWATER MANAGEMENT PROGRAMS IN RIVERBANKSETTLEMENTS
Moch Zaelani Pebriansyah, Ahmad Soleh Setiyawan, Dyah Wulandari Putri and Ken Aryu
Ruska Yuniar509

g12186

THE IMPACT OF COVID 19 ON CHANGE OF MONTHLY INCOME IN INDONESIA
Aditya Maulana Mugiraharjo and Chotib

515

g12193

MULTIMODAL GOODS TRANSPORTATION POLICY MODEL: TRANSPORTATION POLICYENHANCEMENT IN NORTH COAST LINE OF JAVA
Zony Yulfadli, Achmad Wicaksono, Ludfi Djakfar, Muhammad Zainul Arifin and Moch. Abdillah
Nafis

518

g12194

SMART, INTEGRATED SUSTAINABLE AND ENVIRONMENT FRIENDLY TRANSPORTATIONINFRASTRUCTURE CONNECTIVITY TO THE CAPITAL CITY OF NUSANTARA
Achmad Wicaksono, Rosa Agustaniah and Ludfi Djakfar

524

g12196EVALUATING IMPACTS OF OVER-DIMENSION AND OVERLOADING TRUCKS (CASE STUDY IN
ARTERIAL ROADS)
Achmad Wicaksono and Meriana Wahyu Nugroho534

g12197APPLICATION OF THE IDEAL FLOW NETWORK (IFN) METHOD TO EVALUATE THE LEVEL OFSERVICE ARTERIAL ROADS
Susilowati, Achmad Wicaksono, Ludfi Djakfar and Solimun

540

g12203

THE EFFECT OF CHANGES IN LAND USE ON THE PREDICTION OF CRITICAL LAND DISTRIBUTION
IN THE RAWAS WATERSHED (SOUTH SUMATRA PROVINCE, INDONESIA)
Zainuddin Muchtar, Dinar Dwi Anugerah Putranto, Febrian Hadinata, Lawin Bastian and Julian
fikri

546

ix

g12216

COPPER RECOVERY FROM WASTE WIRE HARNESS USING POTASSIUM HIDROXIDEKoto Kagawa and Takaaki Wajima

555

g12222

INFLUENCE OF COMPOSITION ANALYSIS ON UNIT WEIGHT OF SYNTHETIC MUNICIPAL SOLID
WASTE
Vidit Singh and Prof.Taro Uchimura

559

g12238SPATIAL STATISTICS AND PERCOLATION PROBABILITY OF PORE-NETWORK IN POROUS MEDIA
WITH AGGREGATE STRUCTURE
Junichiro Takeuchi, Yu Song, Yuto Takeuchi and Masayuki Fujihara

566

g12239NONLINEAR TIME SERIES ANALYSIS OF IRREGULAR OSCILLATION INDUCED BY SALINEINTRUSION IN GROUNDWATER WITH LAB-SCALE EXPERIMENT
Theara Seng, Junichiro Takeuchi and Masayuki Fujihara572

g12242

MODELLING THE EFFECT OF CLIMATE CHANGES ON COASTAL AQUIFERS IN OMAN
Javed Akhtar, Ahmad Sana, Syed Mohammed Tauseef and Shakila Javed

578

g12243SIMULATION OF SMOKE DISPERSION AND TEMPERATURE DISTRIBUTION ON KEBON MELATISUB-DISTRICT FIRE USING COMPUTATIONAL FLUID DYNAMICS
Deffi Ayu Puspito Sari, Agnes Setioningrum and Dani Harmanto

584

g12277

GEOMETRIC SHAPE FOR IRRIGATION SEDIMENT TRAPS VORTEX DESILTING BASIN
Muhammad Isnaeni, Muhammad Syahril Badri Kusuma, Joko Nugroho, Mohammad Farid and
Muhammad Cahyono

596

g12279PROFILING EXHALED VOLATILE ORGANIC COMPOUNDS FROM SEMERU ERUPTION REFUGEESBY USING E-NOSE
Arinto Yudi Ponco Wardoyo, Eko Teguh Purwito Adi, Hari Arief Dharmawan, Susanthy
Djajalaksana, Arif Budianto, Ngakan Putu Putra, Aditya Sri Listyoko, Fitri Indah Sari and Raden
Dicky

605

g12280EFFECT OF DIATOMACEOUS EARTH ON DESICCATION CRACKING OF EXPANSIVE SOILS
Alemshet B. Tadesse, Y. Fukubayshi, A. Koyama and D. Suetsugu

613

g12284URBAN HEAT HAZARD MODEL BASED ON LOCAL CLIMATE ZONE
Adi Wibowo, Nadira Retno Abisha, Eko Kusratmoko and Ratna Saraswati

621

g12286URBAN HEAT SIGNATURE AS MONITORING OF ENVIRONMENTAL HEALTH
Adi Wibowo, Iqbal Putut Ash Sidiq, Mariney Binti Mohd Yusoff and Tengku Adeline Adura Binti
Tengku Hamzah

627

g12288STUDY ON THE TEMPERATURE MITIGATION EFFECT BY A CULTURAL HERITAGE IN JAPAN-TARGETING A SHRINE LOCATED NEAR THE CENTER OF FUKUOKA CITY
Kenta Fukagawa, Yoshihito Kurazumi, Ariya Aruninta and Yoshiaki Yamato633

g12297

SOME QUESTIONS ABOUT GEORGIA'S LANDSCAPES DYNAMICS (ON THE EXAMPLE OF
SAMTSKHE-JAVAKHETI)
Maia Tskhavardze, Dali Nikolaishvili, Lia Matchavariani, Lamzira Lagidze and Vazha Trapaidze

639

g12293

STRENGTH PARAMETERS AND THE RATE PROCESS THEORY APPLIED TO COMPACTED FADAMA
SOILS
Ola, Samuel Akinlabi, Fadugba, Olaolu George and Nnochiri, Emeka Segun

645

g12300AN EMPIRICAL STUDY OF FLEXURAL STRENGTH OF BEAMS MADE OF RECYCLED AGGREGATE
CONCRETE FROM CONSTRUCTION AND DEMOLITION WASTE IN HANOI, VIETNAM
Ha Tan NGHIEM, Tran Viet CUONG, Nguyen Ngoc TAN, Phan Quang MINH, Nguyen Tien
DUNG, Ken KAWAMOTO and Nguyen Hoang GIANG

652

g12310LEGALIZATION OF THE USE OF MEDICAL MARIJUANA AS A TREATMENT IN THE EFFORT OF
RENEWING THE NATIONAL HEALTH LAW IN INDONESIA.
Siska Elvandari, Yandriza, A. Irzal Rias, Tenofrimer and Ansiha Nur

658

g12225FLASH CALCINATED SEDIMENT USED IN THE CEM III CEMENT PRODUCTION AND THE
POTENTIAL PRODUCTION OF HYDRAULIC BINDER FOR THE ROAD CONSTRUCTION – PART I:
CHARACTERIZATION OF CEM III CEMENTS
Mahfoud BENZERZOUR, Duc Chinh CHU, Joelle KLEIB, Mouhamadou AMAR, Nor-Edine ABRIAK

664

x

and Jaouad NADAH

g12311

g12312

g12313

MICROSCOPIC INVESTIGATION ON ATMOSPHERIC PARTICLES IN CHELYABINSK, SOUTH URALREGION, RUSSIA
Olga V. Rakova, Tatyana G. Krupnova, Kirill A. Bondarenko, Svetlana V. Gavrilkina and Valerii N.
Udachin
CAN TREES HELP REDUCE LEAD IN URBAN AIR? А CASE STUDY OF GREENING IN A RUSSIAN
INDUSTRIAL CITY
Tatyana G. Krupnova, Olga V. Rakova, Susanna V. Berentseva, Svetlana V. Gavrilkina and Valerii
N. Udachin
FUNDAMENTAL PROPERTIES OF SOIL AND RICE HUSK ASH COMPOSITE FOR GROUNDIMPROVEMENT – A REVIEW
Abd Elmageed Atef, Alex Otieno Owino, Md. Yachin Islam, Md. Mahabub Alan, Md. Soybur
Rahman and Zakaria Hossian

xi

671

677

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Twelveth International Conference on Geotechnique, Construction Materials and Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

PrefaceOn behalf of the GEOMATE 2022 Organizing Committee, we would like to welcome you in attending the
Twelveth International Conference on Geotechnique, Construction Materials and Environment held at
Swissôtel Bangkok Rachada, Thailand in conjunction with School of Civil Engineering, Suranaree
University of Technology, 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 2022
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

Even with the COVID-19, this year we have received many submissions from different countries. The
participated countries are Bangladesh, Georgia, Germany, India, Indonesia, Japan, Morocco, Oman,
Philippines, Romania, Saudi Arabia, Singapore, South Korea, Sudan, Thailand and United Arab Amirates.
The technical papers were selected from the vast number of contributions submitted after a review of theabstracts. The final papers in the proceedings have been peer reviewed rigorously and revised as necessary by
the authors. It relies on the solid cooperation of numerous people to organize a conference of this size. Hence,
we appreciate everyone who supports as well as participate in this joint conference.
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 2022. 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)

Prof. Suksun Horpibulsuk,
School of Civil Engineering,
Suranaree University of Technology, Thailand,
Chairman (Program)xii

Twelveth International Conference on Geotechnique, Construction Materials and Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 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 (General)
Prof. Suksun Horpibulsuk, Suranaree University of Technology, Thailand (Program)Conference Organizing Committee:
Prof. Dr. Zakaria Hossain, Mie University, Japan (General)
Prof. Suksun Horpibulsuk, Suranaree University of Technology, Thailand (Program)
Prof. Dr. M Ibn Ibrahimy, Prof., Int. Islamic Univ. (Co-Chair)Prof. Dr. Toshinori Sakai, Mie University, Japan (Co-Chair)
E/Prof. Dr. Takamitsu Kajisa, Mie University, Japan (Co-Chair)
Dr. Vivi Anggraini, Lecturer, Monash Uni Malaysia (Co-Chair)Dr. Masaaki Kondo, Mie University, Japan (Co-Chair)
National & International Advisory Committee:
Dr. Fumio Tatsuoka, E/Prof., Tokyo University of Science, Japan
Dr. Junichiro Takeuchi, Prof., Kyoto University, Japan
Dr. Kingshuk Roy, Prof., Nihon University, Japan
Dr. Sai Vanapalli, Prof., University of Ottawa, Canada
Dr. Musharraf Zaman, Prof. Univ. of Oklahama, USA
Dr. Rafiqul Tarefder, Prof. University of New Mexico, USA
Dr. M. Bouassida, Prof., National Sch. of Engg. of Tunis
Dr. L.R. Austriaco, E/Prof., Angles Univ. Found., Philippines
Dr. M. Ibn Ibrahimy, Prof., Int. Islamic Univ., Malaysia
Dr. Mohammad Shariful Islam, Prof., BUET, Bangladesh.
Dr. Bujang B.K. Huat, Prof., Univ. Putra Malaysia
Dr. Nemy Banthia, Prof., UBC, Canada
Dr. Ian Jefferson, Prof., Univ. of Birmingham, UK
Dr. John Bolander, Prof., Univ. of California, USA
Dr. Shamsul Chowdhury, Prof., Roosevelt Univ., USA
Dr. Isabel Pinto, Prof., University of Coimbra, Portugal
Dr. Mark Jaksa, E/Prof., University of Adelaide, Australia Dr. Jim Shiau, A/Prof., USQ, Australia
Dr. Hj. Ramli Bin Hj. Nazir, A/Prof., UTM, Malaysia
Dr. H.M. Shahin, Prof., Islamic University of Technology, Bangladesh
Dr. Md. Ariful Islam, A/Prof. Ohio State University, USA
Dr. Md. Nurul Amin, Prof. Dhaka University, Bangladesh
Dr. Chan Chee-Ming, Prof. Universiti Tun Hussein Onn Malaysia
Dr. Ahmed H. A. Dabwan, A/Prof. TATI Univ. College, Malaysia

xiiiInternational Technical Program Committee:
Prof. Adolf Heinrich Horn, Geological Institute - Federa University of Minas Gerais, Brazil
Prof. Bang-Fuh Chen, National Sun Yat-sen University, Taiwan
Prof. Bindeshwar Singh, Kamla Nehru Institute of Technology, India
Prof. Catherine Mulligan, Concordia Institute of Water, Energy and Sustainable Systems, Canada
Prof. Chi-Min Liu Chienkuo Technology University, Taiwan
Prof. Daffalla Rabih, Kenana Sugar Company, Sudan
Prof. Essaid Bilal, Ecole Nationale Superieure Des Mines De Saint Etienne, France
Prof. Hakan Caliskan, Usak University, Faculty of Engineering, Turkey
Prof. Ibrahim Maiyza, National Institute of Oceanography & Fisheries, Egypt
Prof. Loc Nguyen, Sunflower Soft Company, Vietnam
Prof. Marilia Hagen, Indiana University, United States
Prof. Md Najib bin Ibrahim, Universiti Teknologi MARA, Malaysia
Prof. Md. Abdul Baset Mia, BSMR Agri. Univ., Bangladesh
Prof. Mihaela Popescu, University of Craiova, Romania
Prof. Mohamed Abdou, Faculty of Education Department of Mathematics, Egypt
Prof. Mohamed Tahiri, Présidnce de l'Université Hassan II de Casablanca, MoroccoProf. Nazar Oukaili, University of Baghdad, Iraq
Prof. Radim Cajka, Technical University Ostrava, Faculty of Civil Engineering, Czech RepublicProf. Rajaraman Jambunathan, AMET University, India
Prof. Saad Farhan Ibrahim Alabdullah, University of Almustansiriyah, Iraq
Prof. Salem Alsanusi, Benghazi, Libya
Prof. Sudhir Kumar Das, Retired Senior Project Manager of Indian Railways, India
Prof. Zachary Senwo, Alabama A&M University, United States
Prof. Imed Jabri, University of Tunis, Tunisia
A/Prof. Bindeshwar Singh Kamla Nehru Institute of Technology, India
A/Prof. Hasi Rani Barai, Yeungnam University, South Korea
A/Prof. Jamaluddin Mahmud, Universiti Teknologi MARA, Malaysia
A/Prof. Mohamed Ramadan, University of Hail, Saudi Arabia
A/Prof. Najam Hasan, Dhofar University, Oman
A/Prof. Nosina Krishna Chaitanya, Jawaharlal Nehru Technological University, India
A/Prof. Nurbek Saparkhojayev, Almaty Management University, Kazakhstan
A/Prof. Pandian Vasant, Universiti Teknologi Petronas, Malaysia
A/Prof. Teodor Lucian Grigorie, University of Craiova, Romania
A/Prof. Zawawi Daud, Universiti Tun Hussein Onn Malaysia
A/Prof. Abdull Halim Abdul, OIl and Gas department, Malaysia
A/Prof. Baoping Cai, China University of Petroleum, China
A/Prof. Dariusz Jakóbczak, Koszalin University of Technology, Poland
A/Prof. Edgar Allan Mendoza, University of the Philippines
A/Prof. Lakhveer Singh, Universiti Malaysia Pahang (UMP) Malaysia, Malaysia
A/Prof. Lidia Sas Paszt, Research Institute of Pomology, Poland
A/Prof. Mahmood Barbooti, University of Yechnology, Iraq
A/Prof. Majid Mirzaei, Universiti Tunku Abdul Rahman, Malaysia
A/Prof. Najeh Lakhoua, University of Carthage, Tunisia
A/Prof. Ryan Joseph Calinao, Lyceum of the Philippines University-Laguna
A/Prof. Sarawut Thepanondh, Mahidol University, Thailand
A/Prof. Yasir Al Hussein, Jerash University, Faculty of Engineering, Jordan
A/Prof. Grigorie Teodor Lucian, University of Craiova, Romania
A/Prof. Hêriş Golpîra, Islamic Azad University, Sanandaj, Iran
A/Prof. Muhammad Aslam, King Abdulaziz University, Saudi Arabia
A/Prof. Tomasz Plech, Medical University of Lublin, Poland
A/Prof. Fellah Mamoun, Abbes laghrour University, Algeria
A/Prof. R. S. Ajin, GeoVin Solutions Pvt. Ltd., India
A/Prof. Roman Szewczyk, Industrial Research Institute for Automation and Measurements, Poland
Dr. Abolghasem Akbari, University Malaysia Pahang, Malaysia
Dr. Ahmad Safuan A Rashid, Universiti Teknologi Malaysia, Malaysia
Dr. Akinola Johnson Olarewaju, Federal Polytechnic Ilaro, Ogun State, Nigeria

xivDr. Alexandre Costa, Federal University of the valleys of Jequitinhonha and Mucuri, Brazil
Dr. Angelo Gallone, Scotland’s Rural College (SRUC), United Kingdom
Dr. Azizul Azhar Ramli, Universiti Tun Hussein Onn Malaysia
Dr. Bashir Dar, University of Kashmir Delina Baramulla J&K India, India
Dr. Bassam Abdellatif, National Authority for Remote Sensing and Space Sciences, Egypt
Dr. Binh Phu Nguyen, National University of Singapore, Singapore
Dr. Cazacu Gabriela, S.C. Geotech Dobrogea, Romania
Dr. Chengen Yang, Intel Corporation, United States
Dr. Dayang Norulfairuz Abang Zaidel, Universiti Teknologi Malaysia
Dr. Evgeni Starikov, KIT, Karlsruhe, Germany; Chalmers, Gothenburg Sweden, Germany
Dr. Fatma Khanchel, University of Tunis El Manar, Tunisia
Dr. Hamidreza Khataee, Griffith University, Australia
Dr. Hêriş Golpîra, Islamic Azad University, IranDr. Iskhaq Iskandar, Dept. Physics, University of Sriwijaya, Indonesia
Dr. Jingwei Zhao, University of Wollongong, Australia
Dr. Jitendra Agrawal, Rajiv Gandhi Proudyogiki Vishwavidyalaya, India
Dr. Liza Patacsil, Malayan Colleges Laguna, Philippines
Dr. Mohamed Amine, Ferrag Guelma University, Algeria
Dr. Mohd Afendi Rojan, Universiti Malaysia Perlis, Malaysia
Dr. Mohd Altaf, University of Kashmir Delina Baramulla J&K India, India
Dr. Mohd Hairy Ibrahim, Sultan Idris Education University, Malaysia
Dr. Mostafa Khater, Egypt - El Sharqia - Zagazig, Egypt Dr. Najam Hasan, Dhofar University, Oman
Dr. Namir Alkawaaz, University of Almustansiriyah, Iraq
Dr. Nashrul Fazli Mohd Nasir, Universiti Malaysia Perlis, Malaysia
Dr. NaufaL Mansor Kampus Uniciti Alam, Universiti Malaysia Perlis (UniMAP), Malaysia
Dr. Obed Majeed Ali, Northern Technical University, Iraq
Dr. Piyapong Janmaimool, King Mongkhut' University of Technology, Thailand
Dr. Po-Sheng Chiu, National Cheng Kung University, Taiwan
Dr. Prabu Mohandas, Adhiyamaan College of Engineering, India
Dr. Raman Kumar, D A V Institute of Engineering and Technology, India
Dr. Riccardo Colella, University of Salento, Italy
Dr. Rolando Javellonar, Romblon State University, Philippines
Dr. Shikha Agrawal, Rajeev Gandhi Technical University, India
Dr. Stefania Tomasiello CORISA, University of Salerno, Italy
Dr. Sumiyyah Sabar, Universiti Sains Malaysia, Malaysia
Dr. Suphaphat Kwonpongsagoon, Mahidol University, Thailand
Dr. Wei Hong Tan, Universiti Malaysia Perlis, Malaysia
Dr. Yoshiro Fujii, Shin Kobe Dental Clinic, Japan
Dr. Yuk Feng Huang, Universiti Tunku Abdul Rahman (UTAR), Malaysia
Dr. Zongyan Zhou, Monsh University, Australia
Dr. Purnanand Savoikar, Goa Engineering College, India
Dr. Ahmed Toaha Mobashsher, University of Queensland, Australia
Dr. Chupong Pakpum, Maejo University
Dr. Emanuele Quaranta, Politecnico di Torino, Italy
Dr. Jiangling Yin, Apple Inc., Cupertino, CA, United States
Dr. Khor Shing Fhan, Universiti Malaysia Perlis, Malaysia
Dr. Mario Chauca, Ricardo Palma University, Peru
Dr. Santosh Gaikwad, Model College, Ghansawangi, India
Dr. Tse Guan Tan, Universiti Malaysia Kelantan
Dr. Vikas Panthi, National Institute of Technology, India
Dr. Watoo Phrompittayarat, Naresuan University, Thailand
Dr. Hamidreza Namazi, Nanyang Technological University, Singapore
Dr. Parichat Phumkhachorn, Ubon Ratchathani University, Thailand
Dr. Subhasis Roy, University of Calcutta, India

xv

Conference 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. Suksun Horpibulsuk
Engr. Alex Otieno Owino
Engr. Md. Aminul Islamxvi

Keynote Papers

112th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

IMPROVED MECHANISTIC PERFORMANCE OF CEMENTNATURAL RUBBER LATEX STABILIZED SOILS AT VARIOUS
TEMPERATURES
Suksun Horpibulsuk, PhD
Suranaree University of Technology
Nakhon Ratchasima, Thailand
ABSTRACT
Thailand is an agricultural country, which is ranked the top in the world for the production and export of natural
rubber. This study investigated the influence of natural rubber latex (NRL) replacement on the mechanistic
performance improvement of cement stabilized soil. The mechanical strengths were investigated via unconfinedcompressive strength (UCS), indirect tensile strength (ITS), flexural strength (FS), and indirect tensile fatigue tests.
The UCS, ITS, FS and indirect tensile fatigue life (ITFL) values were found to increase when increasing the NRL
replacement ratio, up to the highest values at the optimum NRL replacement ratio. The mechanistic performance
improvement was examined through the scanning electron microscopy (SEM) and energy dispersive X-ray
spectroscopy (EDS) analyses. NRL replacement was found to significantly enhance the mechanistic performanceof cement-NRL stabilized soil. Even though the NRL films within the soil-cement matrix improved the cohesionof the soil matrix, it was found to retard cementation bonding. As such, the excessive NRL replacement not only
reduced the compactability but also retarded the cement hydration; hence the strength reduction.
The increased temperature at the early state of curing affects the fatigue properties of pavement base materials
especially at high cement contents. The cement-NRL stabilized soil was found to have superior ITS, ITFL andindirect tensile resilient modulus (IT Mr) values, as compared to cement stabilized soil for the same cement content,
whereas having the same rate of reduction in ITS due to the raised temperature. For the NRL replacement ratio on
the dry side of optimum, the cement-NRL stabilized soil had lower rate of IT Mr reduction than the cement
stabilized soil though they had the same ITS due to the higher toughness. Therefore, the rate of ITFL reduction of
cement-NRL stabilized soil was lower than that of the cement stabilized soil. It was proven in this research that
the NRL replacement could reduce the thickness (superior IT Mr) of cement-NRL stabilized base course for agiven traffic volume and service life, and therefore the construction (material and operation) cost by 17.26%
benchmarked to cement stabilized base course. The outcome of this research will promote the usage of NRL as asustainable alternative to imported synthetic latexes for improving the mechanistic performance of cement
stabilized soil for pavement bases.

2

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

SOIL BIO-ENGINEERING: A SUSTAINABLE GROUNDIMPROVEMENT TECHNIQUE FOR STABILIZATION OF MARGINAL
SOILS
B. A. Mir, Ph. D., MASCE (USA), MISSMGE (UK), MIGS (I), C. Engg. (I)Professor, Deptt. of Civil Engineering, National Institute of Technology Srinagar-190 006, Kashmir, J&K (India)
E-mail: p7mir@nitsri.net, bashiriisc@yahoo.com, ORCID: 0000-0002-9360-9223
ABSTRACT
Soil Bio-engineering is a novel technique for enhancement of stability of steep banks/slopes by means ofvegetative cover and reinforced structures. This technique enables the researchers and designers to work together
for sustainable management of ecosystem for the overall benefit of human beings and to safeguard the naturalenvironment. Many researchers have developed sustainable soil additives/stabilizers for ground improvement toreplace conventional tradional and non-tradional stabilizers, which are ecologically adverse and cause heavy
carbon foot print. Among these soil additives, soil bio-cementation by means of carbonate precipitation has beenfound most suitable and eco-friendly ground improvement technique for enhancement of geotechnical propertiesof marginal soils. Soil improvement using bio-engineering techniques, which include vegetative covers,
microbial induced carbonate precipitation (MICP) and enzyme induced carbonate precipitation (EICP) are ecofriendly and sustainable in nature. In this paper, an innovative approach of MICP has been adopted to improvethe unconfined compressive strength and CBR of dredged soils. Dredged soils are dredged from water bodies
and cause tremendous environmental problems if not utilized scientifically. Therefore, to avoid these problemsby dredged soils, two bacteria’s, Bacillus subtilis (B.S) and Bacillus megaterium (B.M), were used undercontrolled conditions at an optical density of 2.0 by bio-augmentation approach for improving the properties ofthese soils. Based on test results, it has been seen that the dredged soils can be stabilized by MICP technique.
Thus, using soil bio-engineering technique reduces the cost of stabilization of marginal soils and also improves
their engineering properties for safe construction of Engineering Structures.

Keywords: Marginal soils, Soil bio-engineering; Vegetative cover; MICP; Environment; Remediation
INTRODUCTION

activities in Kashmir [1]. However, this River is also
the main cause of large and small-scale flooding in
the region and one of the most disastrous floods
occurred in September 2014. In view of this, various
flood control measures have been recommended andone among them is flood spill channel which carryexcess water from Jhelum River during flooding.
But unfortunately, the present capacity of thechannel has decreased due to the encroachments andsediment deposition in the river and flood spillchannel over the years. Therefore, the scheme forshoreline dredging of flood spill channel was carriedfortheeco-regeneration
andsustainable
development of the flood spill channel. However,the dredging of the flood spill channel generated thedredged soil in large quantity posing serious disposal
and environmental problems.
The scarcity of stable soil deposits and inherent
properties of dredged soils has put pressure on
geotechnical engineers for characterization andstabilization of this soil as a resource for various
beneficial applications for the eco-friendly andsustainable development of various infrastructures.
Many researchers have reported that the dredged soil
is no longer being regarded as a "spoil" or "waste"
but as a resource [2]-[5]. Its mineralogy andGeotechnical properties qualify it for use in the

Soil is universally available as a construction
material or as a foundation medium for building ofvarious infrastructures safely. However, there is
scarcity of good construction sites as well as
construction materials due to huge population
increase and rapid urbanization around the world.
Therefore, there is lot of pressure on GeotechnicalEngineers to characterize marginal soils forconstruction of various infrastructures. On the other
hand, due to rapid industrialization, lot of solid
waste materials are produced every day, which need
to be disposed-off in a scientific way to avoid any
environmental or health problems. Thus, there is adire need of identification of the sources and typesof solid waste materials produced for the sustainable
management and eco-friendly development. There
are various types of solid wastes generated such as
residential, commercial, institutional, construction
and demolition, municipal services, treatment plantsites, industrial, agricultural and dredging of water
bodies such lakes and rivers. Among various such
solid wastes, this study deals with the solid wastematerial generated by dredging the flood spillchannel of Jhelum River, Srinagar. Jhelum River isessentially the backbone of all its economic
3

GEOMATE – Bangkok, Thailand, 22-24 November 2022

manufacture of high value, beneficial use products.
However,insuchasituation,
modification/stabilization of these soils throughground strengthening is usually beneficial for ecofriendly and sustainable development [6]. Also,
since few decades before, the marginal soils havebeen improved extensively for the construction ofvarious infrastructural developments all over the
globe due to scarcity of stable soil deposits [7]-[10].
Soft clay deposits with high water content exhibit
high compressibility characteristics and low bearing
capacity. Due to huge population and urbanization,
there is increasing demand for more and more
infrastructural developments for which very limited
stable construction sites are available. Thus, there isdire need for improvement of soft soil deposits bymeans of various ground improvement techniques.
Although, large number of conventional ground
improvement techniques such as Mechanical Ground
Improvement Methods, Ground Treatment Methods
using Traditional/Non-Traditional admixtures andGround Reinforcement Methods have beendeveloped to improve poor ground conditions.
However, conventional ground improvement
methods are ecologically adverse and cause heavy
carbon emissions [11]-[14]. Therefore, groundtreatment using Soil Bio-Engineering Methods are
increasingly being used for soil stabilization and are
replacing the conventional methods. Soil bioengineering is a well-recognized component of
ecological engineering, defined as “the design ofsustainable systems, consistent with ecologicalprinciples, which integrate human society with its
natural environment for the benefit of both”. Soilbioengineering technique can also be used asreinforced structures in the construction projects toprovide soil reinforcement such as living retaining
walls and live reinforced earth walls. The concept ofusing Bio-Chemical or Biological process in soil
improvement, which is also known as bio-mediatedsoil improvement method has proven as an ecofriendly and a sustainable ground improvement
technique for stabilization of marginal soils [15][18]. The processes of Soil bioengineering being
investigated range from use of living plant materials
to stabilize complex slopes [16], [19] to microbiallyinduced calcite precipitation (MICP) for solidifying
clays and sands to bio-film slimes generated toreduce hydraulic conductivity [20]-[22]. Soil
microorganisms are broadly defined as a group of
microscopic life forms that include bacteria, archaea,
viruses, and eukaryotes like fungi.
Many researchers [23]-[24] have reported thatthe microbially induced calcite precipitation (MICP)
is an eco-friendly and sustainable soil stabilization
technique, which is being explored by the
researchers all-over the globe. Also, Mitchell andSantamarina [25] have reported that the biochemical methods of soil stabilization is a novel andemerging research area in geotechnical engineering
for the sustainable development of various
infrastructures. Among various bio-chemical
methods, MICP technique can alter/enhance thephysical and mechanical properties of the marginal
soils, which can support the superstructures safely[26]. Reichle [27] reported that the microbes
constitute about 70% to 85% of the living
component in the soil matrix/systems, which can be
successfully utilized as a sustainable means of soil
stabilization. However, researchers need tounderstand about the metabolic rate for proper
forecasting and understanding of mechanism andworking principle of how the microorganisms
behave under different conditions [28]. The various
Bio-Engineering Techniques for treatment ofmarginal soils are shown in Fig. 1.

Fig. 1. Bio-Engineering Techniques for treatment of marginal soils
4GEOMATE – Bangkok, Thailand, 22-24 November 2022

This CO32− combines with calcium ions (Ca2+) fromthe Cementing solution molarity (CSM) to form
calcite (CaCO3) which induces strength to the soils
by cementing the particles together (Eqn.1):

In the present study, two different bacteria,
Bacillus subtilis (B.S.) and Bacillus megaterium (B.
M.) at an optical density (OD) of 2.0 were chosen
for stabilization of dredged soil. The soil specimens
for UCS and CBR tests were prepared at 0.85 γd(max)and optimum water content (OMC) to ensure thatsufficient pore space is available for the bacteria andcementing solution to percolate into the soil matrix.
The UCS samples were treated in both fabricatedbrass moulds and full contact flexible moulds(FCFM) made of non-woven geo-textiles undercontrolled conditions and tested as per standard
codal procedures. The increase in UCS for 0.5
cementing solution molarity from 197 to 544kPa and
197 to 637 kPa for B.S. and B.M. bacteria treatment,
in fabricated brass moulds and 197-603kPa and 197
to 674kPa for B.S. and B.M. bacteria in FCFMs wasappreciable. The CBR values increased from 1.3%
to 8.4% for B.S. and 9.1% for B.M. bacteria
treatment in soaked conditions respectively. The
experimental results were also supported byscanning electron microscopy (SEM) and Fouriertransform infrared spectroscopy.

CO (NH 2 )2 + 2 H 2O →2 NH 4+ + CO32 −CaCl 2 → Ca 2 + + 2Cl −Ca

2++ CO32 −(1)→ CaCO3  ( precipitated )MATERIALS AND METHODS OF SAMPLEPREPARATION AND TESTING
Soil Used in this Study
Disturbed and undisturbed soil samples werecollected from three distinct sites distanced 300m
apart on the flood spill channel of Jhelum River(Fig. 3). The study area also falls near aninternationally recognized wetland, Hokersar in theoutskirts of Srinagar city, India. The process of soil
sample collection is shown in Fig. 4. The
undisturbed soil samples were carefully packed andtransported to Geotechnical soil testing laboratory sothat the in situ state of soil samples is not altered.

MECHANISM OF BIO‑CEMENTATIONThe Mechanism and working principle of Bacteria
for Bio‑cemented weak soils have been reported by
Wani and Mir [29]. Bio-cementation is a naturalprocess that occurs over millions of years andutilizes microbes, nutrients and natural procedures toefficiently develop the engineering properties ofsoils. Mechanism of bio-cementation starts withmixing of a urease producing bacteria to the ovendried soil and then treatment with external
cementing solution (CS). Through hydrolysis, urea
(CO(NH2)2) is hydrolyzed by the enzyme into
ammonium (NH4+) and carbonate ions (CO32−). The
deposition of CaCO3 crystals within the soil grains
by MICP is shown in Fig. 2.

Fig. 3. Sample collection site from flood channel

Fig. 2. Illustration of MICP technique mediated byurease and carbonic anhydrase

Fig. 4. Process of dredging,
undisturbed, disturbed soil samples

5collectionofGEOMATE – Bangkok, Thailand, 22-24 November 2022

The disturbed soil samples were air-dried andpulverized in the laboratory for conducting various
basic tests such as soil grading, compaction, shear
strength and CBR tests. All the soil samples were
tested as per standard procedures [30]-[33]. The
particle size distribution (PSD) curves of three sites
are shown in Fig. 5 and the physical properties are
given in Table 1 respectively.

Thus,
based
on
basic
geotechnicalcharacterization of the soil samples, the weakest
sample (S-2) out of the three chosen sample wasselected for stabilization.

Bacteria Used in this StudyIn this study, two urease producing bacteria
namely Bacillus Subtilis (B.S) and Bacillus
Megaterium (B.M) Bacillus along with a cementing
solution molarity (CSM) were used for improving
the unconfined compressive strength (UCS) and
CBR of dredged soils.

Bacteria culture
The bacteria culture process has already been
discussed in detail by Wani et al. [34]. In this study,
two bacteria`s Bacillus Subtilis (BS) and Bacillus
Megaterium (BM) were cultivated in the laboratory
as per standard procedure provided by the supplier.
The stock of bacteria was revived on LB-agar Plate
at 37°C overnight. Accordingly, single bacteriacolonies were patched on LB agar plate and grownin a 37°C incubator. The bacteria cells were theninoculated in a 5 ml LB broth and grown with180
rpm at 37°C overnight. Similarly, the sub-culturing
process was carried out by adding 2 ml of thesaturated culture in a 200 ml of LB broth and grownwith 180 rpm at 37°C overnight. The bacteria cells
were checked for optical density (O.D.) of 2.0(equivalent to 2.108 cells/ml) at 600nm till thedesired O.D was attained. The bacteria colony
formation units were projected by the equation given
by previous researchers [35]:Fig. 5. PSD curves for different site samples
Table 1 Properties of soil samples used in the study
Properties

Site 1Site 2 Site 3

In situ water content (%)
In situ dry unit weight(kN/m3)Specific gravity (G)Percentage finer than 75 µm
Clay (%)
Silt (%)
Sand (%)
Liquid limit (%)
Plastic limit (%)
Plasticity index (%)
P.I, A-line
P.I, U-line
Clay mineral
Classification
Maximum dry unit weight,
MDU (kN/m3)

31.2

31.7

27.9

14.3

13.6

15.6

Optimum moisture content,
OMC (%)
In situ UCS, qu (kPa)
UCS at OMC, qu (kPa)
CBR, Un-Soaked (%)
CBR, Soaked (%)

2.66
99
7.5
91.5
01
36.2
25.1
11.1
11.8
25.4
MI

2.61 2.65
95
92
02
04
93
88
05
08
33.9 29.7
24.6 20.1
9.3
9.6
10.1 7.1
23.3 19.5
Kaolinite
ML
ML

16.2

15.7

16.5

21.4

16.3

15.7

25.7
39.2
6.3
3.1

15.5
33.3
5.1
1.3

15.9
33.8
6.9
3.7

(

C = 8.59  107  D1.3627)(2)

Where: D = reading at OD600 (the optical densityof a sample measured at a wavelength of 600 nm,
and C = concentration (cells per ml).
Cementing solution molarity (CSM)
In this study, the urea, CaCl2·2H2O (hydrated
calcium chloride) and distilled water were used for
preparation of cementing solution molarity. A CSM
of 0.5 for UCS tests was prepared by mixing 30 g ofurea and 73.5 g of calcium chloride in a litre of
distilled water to make a litre of CSM solution.
Similarly, a CSM of 1.0 for CBR tests was preparedby mixing 60 g of urea and 147 g of calcium
chloride to make a litre of CSM solution. The
aforesaid selected CSM values were chosen basedon extensive literature, which revealed that there issignificant enhancement in soil properties at lower
values of CSMs. However, many researchers in thepast have used different concentrations of media forin-depth studies involving microbes [36]-[37].

The test results show that the soil is poorly gradedsilty soil with low to medium compressibility.
Hence, the soil is not suitable in its in-situ state as aconstruction or foundation material.

6

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fabrication of MICP setup

Specimen preparation

In this study, a customized setup was required
for microbial induced carbonate precipitation(MICP). Accordingly, the MICP setup was
fabricated in the Institute workshop, which consistedof unconfined compression test moulds, wooden
stand, over head supply tank and control knobs. The
customized setup is shown in Fig. 6a. The UCS
moulds were fabricated of brass material to avoid
rusting during continuous treating of soil samples
up to 48 hours. The moulds had screw arrangement
at the top and bottom to facilitate compaction atdesired density. Provision for accommodating
porous stones was provided inside the mould and thefinal extruded sample was of standard size, 76mm
height and 38mm diameter. Also, full contact
flexible mould (FCFM) (Fig. 6b) was made fromnon-woven geo-textiles for treatment of soil
samples.

The remolded soil specimens were prepared at
0.85 γd(max) and corresponding water content for
conducting UCS and CBR tests for stabilized soil.
The dry unit weight (γd(max) ) was chosen dry of
optimum to make sure that sufficient pore void
space is made available for the bacteria and thecementing solution (CS) to percolate into the soil
specimen. Also, the purpose of using reduced
density was to ensure that there is homogeneity inthe distribution of calcite in the soil specimen. Two
different treatment procedures were adopted for
preparation of UCS samples [32]. In the first method,
the soil specimens were treated from bottom to top in
fabricated brass mould for 48 hours and then tested as perstandard procedure [38]. In the second method, the soil
specimens were treated in full contact flexible mould(FCFM) made from non-woven geo-textiles. Both thetreatment methods are shown in Fig. 6a&b
respectively. Likewise, CBR tests on soil specimens
were conducted for both unsoaked and soaked
conditions. For unsoaked CBR tests, 48 hours
treatment was given to soil specimens by keeping
the soil moulds in CS buckets. These soil specimens
were tested after 24h rest period. Similarly, soakedsoil samples moulds were kept in CS buckets for
96h and the tested as per standard procedure [33].
RESULTS AND DISCUSSIONS
Effect of Biological Processes on the UCS Valuesof Treated Soil
To study the effect of microbial induced carbonate
precipitation on the UCS values of treated soil
samples for site-2, the unconfined compression tests
were carried out on soils samples for different curing
periods. The soil specimens were compacted at
0.85dmax and corresponding water contents on thedry side of optimum and treated with “BS & BM”
bacteria and Cementing solution (CS) in fabricatedbrass and full contact flexible moulds. The stressstrain curves for “BS & BM” treated soils for both
treatment methods are shown in Figs. 7 (a-b) & 8 (ab) respectively. The test results showed that the UCS
values increased from 197 to 544kPa for B.S.
treatment at 2.0 OD and 0.5 CSM for 3 days ofcuring, whereas an increase from 197 to 637kPa wasobserved for B.M. treatment for the same testing
condition in brass moulds. Similarly, the UCS values
increased from 197 to 603kPa for B.S treatment
with 2.0 OD and 0.5 CSM for 7days of curing,whereas an increase from 197 to 674kPa wasobserved for B.M. treatment for the same testing
condition in full contact flexible moulds (FCFM).
This overall increase in UCS is attributed due to theformation of calcite which bonds the soil particles

Fig. 6. MICP setup for bacterial treatment of soil
samples7

GEOMATE – Bangkok, Thailand, 22-24 November 2022

together. From Fig. 7(a-b), it can be seen that“UCS” decreased after 3 days of curing period,
which can be attributed due to the access salt
accumulation in brass fabricated moulds andinactive microbes in the soil matrix. Lee et al.
[2013] has reported that access salt accumulation isunfavorable to bacteria growth and as such leads touneven calcite precipitation. Therefore, in thisstudy, to over-come this short coming, the soil
samples were treated in FCFM moulds by means offull immersion methodology. This can also be
observed from Fig. 8(a-b) that “UCS” values
increased after 3 days of curing period, which canbe attributed due to the fact that a uniform
distribution of precipitates in FCFM moulds. It may
be noted here that 2 days of treatment was given inbrass moulds whereas continuous treatment wasprovided for FCFMs. This treatment procedure waschosen for the expulsion of bacteria from themoulds over large span of time. The test results
indicate that the treated soil samples failed at lower
strain compared to untreated soil, which can be
attributed due to stiffer behavior of treated soil
specimens. Wani and Mir [17] have also reported
similar behavior or bio-cemented soils. The
variation of “UCS” and modulus of elasticity withincreased curing period is given in Table 2. From
Table 2, it is observed that with increase in curing
period, the behavior of untreated soil from ductile tobrittle behavior for treated soil. The change in soil
behavior can also be observed from Figs. 7(a-b) and8(a-b), which is clearly seen that the failure strain ofstabilized soil specimens is decreasing with increasein unconfined compression strength and the curing
period for both treatment methods.

Fig. 7. Stress versus strain plots (Brass mould) for: a
B.S-treated soil; b B.M-treated soil

Fig. 8. Stress versus strain plots (FCFM) for: a B.Streated soil; b B.M-treated soil

8

2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0

534
544
541
560
585
603
620
637
632
645
659
674

3.429
5.571
6.143
6.429
7.000
7.428
8.000
8.571
9.142
9.285
9.500
13.333Treatment
Approach

0
3
7
0
3
7
0
3
7
0
3
7

Bacteria used

Modulus ofelasticity
(MPa)

0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5

UCS (kPa)

CSM OD

Curing days

Table 2 Variation of “UCS” & modulus of elasticityof stabilized soils with different curing days

BS

In UCS
moulds

BS

In
FCFM

BM

In UCS
moulds

BM

In
FCFM

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Effect of Biological Processes
Microstructure of Treated Soil

on

the

Effect of Biological Processes on the CBR Valuesof Treated Soil in both Unsoaked and Soaked
Conditions

SEM tests were conducted on untreated and post-teststabilized soil samples at a magnification ranging
from 2 to 3k. SEM images for untreated sample (Fig.
9a) showed less interaction between soil particles
and changes in soil microstructure are
predominantly seen in treated samples (Fig. 9b-d).
The flocculated structure and reduced pore size
along with formation of
new cementitious
materials after treatment are also seen in bio-treateds o i l samples (Fig. 9b-d).Similar test results havealso been reported by various researchers for the
bio-cemented soils [40]-[41].

Based on previous research finding, a predetermined
CSM of 1.0 for treatment of soil samples for CBR[42]. This was done in agreement with the fact that
CSM is directly proportional to the amount ofprecipitate [43]. The load-settlement curves for
CBR tests for site-2 under different treatment
conditions are shown in Fig. 10. From Fig. 10, it isseen that the CBR value increased from 1.3% to8.4% for BS treated soil and 1.3% to 9.1% for BM
treated soil in soaked conditions. Likewise, CBR
value increased from 5.1% to 13.1% for BS treatedsoil and 14.1% for BM treated soil in unsoaked
conditions respectively.

Fig. 10. CBR curves for untreated and treated soil
samples (site-2)
The increase in CBR may be attributed due to thereaction of bacteria with the cementation reagent toproduce calcite, which binds the soil together andfill the void spaces in the granular soil to increase its
strength [44]-[45]. Dejong et al. [15] attributed
increase in CBR to be due to higher relative density
and increased number of contacts per particle within
the dense sand, which contributed to the higher
CBR value. The higher precipitation of calcite at 1
CSM must be responsible for enhanced CBR values
as the precipitate fills the pores and increases
cohesion amongst the grains. The effect of biocementation on CBR can also be correlated to thebearing capacity of the same site location as has
been studied by the authors wherein the bearing
capacity increased up to 2 times in the treated state[17]. The effects on CBR are comparatively low inthe unsoaked state when compared to otherstabilization techniques like cement, lime or use ofgeo-synthetics, but still in accordance with IndianRoads Congress for rural roads [46]-[47]. Thisdecrease in CBR can be attributed due increase of

Fig. 9. SEM image for: a untreated sample, b–dbacteria- treated soil samples (Site-2)
9GEOMATE – Bangkok, Thailand, 22-24 November 2022

moisture content in the soil specimen, which is
given 24h of rest at room temperature (20–25°C)
after treatment. The post-test variation of moisture
content in the soil specimen with different curing
periods for both treatment methods is given in Table
3. From Table 3, it can be observed that the
moisture content of “UCS” soil specimen decreases
marginally with increasing curing period. However,
the CBR soil specimens have higher water holding
capacity due to their large area compared to “UCS”
specimens. Thus, the CBR soil specimen exhibit
low CBR values, which are permissible as perstandard Codes, and proves beneficial for the field
applications.

3. At 1.0 CSM and 2.0 OD bacterial concentrations,
CBR values increased from 5.1% to 13.1% for
B.S. treated soil and 14.1% for B.M. treated soil
in unsoaked conditions respectively. The increasein CBR values may be attributed mainly becauseof the precipitate filling the void spaces andforming a thin crust of calcite over the sample.
4. As the OD, CSM and curing time is increased,
the amount of precipitate in the form of calcite
increases proportionally. The values for B.M.
were considerably more in comparison with B.S.
5. The SEM analysis showed reduced pore size andagglomeration of particles in the stabilized soils,
which supports the test results.
The above experimental procedures havescale effects, and as such, in situ response might
be different as the samples used in the laboratories
were small and homogeneous. The improvementin the properties of soil largely depends on calcite
precipitation which is in turn dependent on manyother factors like pattern of precipitation, pH,
development of flow paths, temperature, mixing ofbacteria with the soil, etc. Taking all the above
factors into consideration, the optimization of
calcite distribution is not possible by means ofmodel testing in the laboratory. Therefore, thepresent results can only be taken to be qualitativein nature.

0.5
0.5
0.5
1.0
0.5
0.5
0.5
0.5
0.5
0.5
1.0

UCS
UCS
UCS
UCS
UCS
UCS
CBR
UCS
UCS
UCS
UCS
UCS
UCS
CBR

15.0
14.5
14.0
14.5
14.5
14.0
28.0
14.5
14.5
14.0
14.5
14.5
01.0
27.0

Treatment
Approach

0
3
7
0
3
7
0
3
7
0
3
7
-

Bacteria used

Moisture content
after
testing (%)

0.5
0.5
0.5

Testing

CSM

Curing days

Table 3 Post-test variation of moisture content withdifferent curing periods test methods

In UCS
mouldsACKNOWLEDGMENTSBSIn FCFMIn FCFMThe investigation reported in this paper formed partof the research carried out in the GeotechnicalEngineering Laboratory-I and II of National Instituteof Technology, Srinagar, India. The Author would
like to thank Institute Administration and Deptt. of
Civil Engineering for providing the necessary
facilities for carrying out this work and SKUAST-K,
Shalimar for proper guidance on bacterial culture
preparations and for providing laboratory facilities.

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CONCLUSIONS
Based on the test results and discussions, thefollowing conclusions were drawn:
1. The UCS values increased from 197 to 544kPa
and 197 to 637kPa for B.S. and B.M. treatedsamples with 0.5 CSM for 3 day curing period
by Bio-cementation technique for soil samples
treated in fabricated brass moulds.
2. The UCS values increased from 197 to 603kPa
and 197 to 674kPa for B.S. and B.M. treatedsamples with 0.5 CSM for 7 days curing period
by Bio-cementation for soil samples treated in
FCFMs.
10

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12

Technical Papers

13Geotechnique1412th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

FINITE ELEMENT ANALYSIS OF BEAM RESTING ON FOOTING
1

1

Radhi Alzubaidi, 2Husain M Husain and 3Samir Shukur

Professor of Civil engineering, college of Engineering, University of Sharjah, Sharjah, UAE.
2
Formally professor of civil engineering, University of Baghdad, Baghdad, Iraq.
3
MSc student, College of Engineering, University of Kufa, Iraq.

ABSTRACT
Unreinforced concrete members used in construction projects lead to low costs and easy construction, a nonlinear
three –dimension finite element technique utilized in present research to explore the load deflection of unreinforced
concrete beam rest on strip footings by using the computer program(ANSYS 5.4). , the criteria used by solving thenon-linear equation by conducting incremental and iterative method running load procedure. The developed Newton–Raphson method been conducted for non-linear solution algorithm and load or displacement that used as aconvergence criterion. A vast Investigation been conducted to explore the effects on such parameters such as depth,width and length of foundation. The results of the experimental and theoretical load – deflection curves showed agreat agreement with those analyzed by four applications using the finite element technique. The present finite element
and available experiential results showed a good agreement. the percentage of differences not exceeding 6% in theultimate load prediction and 15.6% in deflection in case of using interface element and critical state model.
Keyword: Strip footing, Finite element, Deflection, Depth, Length
including diaphragm wall, diagonal braces and boredpiles. They concluded that the types of elements
implemented to model the structures distinctively
affect the analysis of finite element.
[4]) presented a finite element modeling of beam
resting on a two-parameter layered soil, in their model
analysis, they consider the strain energy solutions, theshear strain of the beam element and soil foundation.
They concluded that the shear deformations showedcrucial influence on the beam, on the structure and on
the interface behaviors. [5] studied the developmentof a methodology for the identification of optimal
design parameters for a system of beams resting on astone
column–improved soft soil. They conducted a finite
difference–based simulation model and an
evolutionary multiobjective optimization model. In
their study tried to minimize the settlement at thecenter of the beam and the maximum shear force. They
concluded that the evaluation of the system showedthat stiffness of the stone columns or modular ratio andflexural rigidity of the beam are the most importantparameters for optimal design. [6] presented adeveloped method for nonlinear analysis of EulerBernoulli beams resting on heterogeneous
multilayered soil.

INTRODUCTION
Design of different type of foundations has led to theconcept of transmitting the load to soil throughdevelopment a special member system. The
foundations types are varied according to different
requirements. Concrete or steel members like beams orplates rest on layered soils have attracted the attentionof both structural and geotechnical engineers andmerges the effect on both superstructure andsubstructure element.[1] presented his theory of themodulus of subgrade reaction, the basic assumption ofthis model is the relation between the applied surfacepressure and the vertical surface deflection at every
point. [2] studied interaction analysis for the design ofopen plane frame resting on soil, the soil has beenmodeled as 4-node isoparametric element, a planestrain approach is adopted for representing the soil. A
comparison is made between the more realistic halfspace continuum and the plane-strain approach to
examine the approximation involved in the letter typeof representation of the soil. They concluded sagging
moments in superstructure beams as obtained using
plane-strain model were always greater than thoseobtained from the elastic half. [3] investigated into
numerical analysis of most deep excavation and statedthat most designers careless about structuralmodelling. They modify a 3D finite element method tomodel in their solutions . The case study was a building
project located in Bangkok .The structure models used
15

GEOMATE – Bangkok, Thailand, 22-24 November 2022

assumptions concerning the characteristics of soil –structure interface :1- The interface is perfectly rough ,with no possibility
for slip between the soil and the structure .
2- The interface is perfectly smooth ,with nopossibility for shear stress that would retard relative
movement between the soil and the structure ([12].
Many types of finite elements have been developed foe
representing the soil- structures interface in finite
element analysis in more realistic fashion. [13] In theirstudy to analyze the seismic of soil- structureinteraction in rigid rock ,applying the finite element
method ,there are many methods modified to input
earthquake on the lateral boundary of finite element
model. They concluded that the formulations and
ABAQUS implementations of these boundaries wereused . The accuracy properties of these boundaries arethen compared by numerical examples including thefree-field and SSI problems. The comparison studies
indicate that the free and VS boundaries failed toreproduce the free-field and SSI responses when arelatively small size SSI model is employed. [14]studied the developments in the particle finite element
method (PFEM) that used for analysis of many
complex coupled problems in mechanics involving
fluid–soil–structure interaction (FSSI). They showedsome examples of application of the particle finite
element method to solve fluid–soil–structureinteraction problems like, the motion of rocks by water
streams, the erosion of a river bed near to a bridgefoundation, the stability of breakwaters andconstructions sea waves and the study of landslides.
[15] in their study to review the non-linear analysis ofsoil structure interaction problem. They discussed thecoupled finite element modeling of soil structureinteraction (SSI) system with soil non-linearity andinterface element modeling. In their reviewing study
stated that the focusing is on advantages anddisadvantages of the methods discussed according totheir applicability, accuracy and caliber to idealize thesuperstructure and soil.

They obtained the governing differential equations forbeam and soils displacements using the virtual work,
the equations been solved using one-dimensional
finite-element and finite-difference methods, theanalysis showed that beam responses with accuracy
comparable with those obtained from equivalent twodimensional finite-element analysis are obtained
within seconds. [7] studied a boundary element
method to analyze the elastic foundation finite beams
on 2D plane-strain and 3D multilayered isotropic soils.
They tried to explore the solution of multilayeredelastic soils that obtained to be a kernel function of
BEM analysis. They found that with the displacement
and stress condition of coordination between beam andsoil, the solution is acquired for beams resting on
multilayered
FOUDATION ANALYSIS METHOD[8] established a computer program capable ofanalyzing combined footing and irregular with
openings or notches and non –prismatic sections andresting on layered medium ,by using the finite element
method . In his study used “the thin plate bending
theory”. [9]) analyzed the combined footings and matsfoundations utilized the finite grid method andprepared nomograms which can aid the designer in
rapid prediction of displacements of displacements andbending moments. The results of the study comparedwith those obtained by finite element and Hetebyi
solution. He also studied the properties of each soil andfoundation ,variation of thickness and configuration offoundation, effect of adding stone columns andprobable existence of soil voids. [10] proposed a nonlocal viscoelastic foundation model to analyze thedynamics of beams with different boundary conditions
using the finite element method. They conclude thatthe case study, prdicted that the finite element
technique is efficient for the dynamic analysis ofbeams with non-local viscoelastic foundations.
[11] investigate into behavior of shallow foundation
resting on multi layered and homogeneous soil underdynamic loading. They used 2-dimensional finite
element software Cyclic TP to modeling the soil
foundation system. In their conclusion ,they compared
the results to understand the effect of layered soil on
dynamic load response of soil foundation system.

MODELLING OF MATERIAL
The most powerful representation that incorporates
for concrete and soil behavior is the elastoplastic
model. The model exhibits nonlinearities, failure, andresidual strain upon loading to initial stress condition.
Several location of yield points are introduced due tovarious material types , however yield is the starting
point of plastic behavior and the end points of elastic
behavior for the elastic- plastic material . the criteria
for deciding which combination of stress will cause
yielding and this called yield criterion. In present study
the concrete is modeled by Willam and Warnk yield

INTERFACE MODEL
The use of finite element for analysis of soil
structure interaction problems has been limited tosome extent, however due to difficulties in represent
the interface between soil and structure ,most analysis
been performed using one of two limiting

16

GEOMATE – Bangkok, Thailand, 22-24 November 2022

surface, while the soil is modeled by Druker- Prager
yield surface.

µ= coefficient of friction
fn= normal force

MODELING OF CONCRETE MATERIAL

fn= Kn (un,j – un,i + d )There are many constitutive models which have beendeveloped to predict the response of plain andreinforced concrete under various stress states. The
main constitutive models are the elasticity basedmodels and plasticity based models. The elasticity –based models describe the concrete as a linear elastic
or non-linear elastic material. The elasticity models
have been used to study the non-linear response ofplain and reinforced concrete beams, panels ,and shells
in which the main nonlinearity is introduced by thecracking and widening of cracks in the concrete[16] .
Elastic model for concrete in compression can besignificantly improved by assuming a nonlinear elastic
stress-strain relationship. [17] proposed a nonlinear
elastic –strain relationships for concrete materials, they
can generally classify into

Kn = normal stiffness
un,j=displacement of node j in normal direction
un,i= displacement of node i in normal direction
d= distance between nodes.
Also
fs = Ks (us,j – us,i –uo )fs= sliding force
Ks=sticking stiffness
us,j= displacement of node j in sliding directionus,i= displacement of node i in sliding direction

1- The total stress-strain formation in the formof secant stiffness relation
2- The incremental stress-strain formulation in
the form of tangential stiffness formulation.

uo= distance that nodes i and j have slide with respect
to each other
Writing:

Finite element representation of interface{F}= [K]{∆}(1)

The present study describes a technique for solving
two and three dimensional interface problems. The
elements (contact 52) shown in Fig. 1 are adopted inpresent researchfn

The elements (contact 52) shown in Fig. 1 are adopted
in present research.

fsy
fsz

Y
Z

fn
{F}=

X
X
Z

(2)

j

i

fsy
fsz

Y

Fig. 1 CONTAC52 3-D point –to- point(ANSYS Manual 1997)

Where :
fn = normal force

The interface element includes normal and sliding
forces [18] and [19]. This element has one of two
condition if the elastic COULOMB friction is used :fs = stick force (in y and z direction)

1- Stuck element (no sliding)µ|fn |˃|fs |Where:
17

GEOMATE – Bangkok, Thailand, 22-24 November 2022

[20]in the present study used a computer program
ANSYS 5.4 to analyze plain concrete beams rested on
soil. The program has the capacity to solving linear andnonlinear problems including the effect of cracking,crushing, yielding of reinforcement (if existing) ,creep,bond slip, temperature change ,with 165 different
elements conducted in the program.

ui
vi
wi
{∆} =

uj

Numerical applications(3)
Numerical actual cases are conducted to compare theresults obtained by the present method of finite
elements with those obtained from experimental or
analytical solutions.
A number of numerical examples have been also
analyzed by the computer program ANSYS 5.4. The
examples also used to check the validity of the material
models used and the applicability and capability of theanalysis method adopted in present research where
different types of elements are employed. The
theoretical study is approximate in nature due todifferent factors mainly:
1- Approximation in the material modeling of concreteand soil.
2- Approximation inherent in discretization in finite
element technique.
3- Approximation in the integrations used in the
numerical analysis.
4- Approximation introduced due to the type ofprocedure used in solving the nonlinear system of
equations.
The main results obtained by the solution introduced
through present study are the load –deflection
response, crack propagation and stress distribution.

vj
wj

kn

0

0
0

-kn

0

0

ks 0

0

- ks

0

0

ks

0

0

-ks

0 0

kn

0

0

[K]= - kn

0

0

- ks

0

0

ks

0

0

0

-ks

0

0

ks

(4)

1. Sliding elementIfµ|fn |=|fs |Then sliding occurs in both directions, the stiffness
matrix (in element coordinate) is:

BEAM REST ON STRIP FOOTING

[K]=

kn 0

0

-kn

0

0

0

0

0

0

0

0

0

0

0

0

0

0

-kn

0

0

kn

0

0

0

0

0

0

0

0

0

0

0

0

0

0

A long strip footing with uniformly distributed loadof 450 MPa lies on a surface of homogeneous isotropic
linear elastic half space was analyzed numerically by[21]. Table 1 , showed the details of the problem.
According to the symmetry, only one- half of the caseconsidered. A mesh of 1338 elements was used tomodel the concrete , soil and interface . The details ofgeometry and the finite element mesh are shown in Fig.
1 and Fig. 2 respectively. Fig. 3 shows the load –deflection results obtained by the present study andcompared with results predicted by[21]) using thefinite element and infinite elements for the soil, theresults showed a good agreement. The cracks patterns
at the bottom surface of the beam at initial cracking
and at failure are shown in Fig.4 . It can be noted thatfirst cracking appeared at a load 300 KPa , the initial
cracks appeared at the bottom region of the beam. As
the load increased, the value of stress in tension zone

(5)

ANSYS Computer Program

18

GEOMATE – Bangkok, Thailand, 22-24 November 2022

increased and cracks extended towards the top andsides. distribution stress.
Parametric Study
There are some parameters that affect the behaviorof the plain concrete strip foundation on soil under
static load been studied. These parameters include theeffect of beam’s dimension like length, width anddepth.
Effect of Beam Depth
The effect of depth of beam on deflection been
investigated by [20], where 6 values of depth been usedwhile the length and width are kept constant, equal to
6 m and 0.5 m respectively. Fig. 5 showed that there
is a distinctive decrease in deflection when increasing
the depth of the beam, under the same load. This result
seemed to be reasonable for increasing the flexural
rigidity for strip footing
Fig.1 Geometrical details of strip footing

Effect of Beam Width
The investigation of beam width on deflection
carried out using 8 values of beam width while
keeping the length and depth constant. Fig. 6 showedthat there a considerable increase in deflection of thebeam with increasing the width, this may attribute to
the increase of the concrete mass.
Effect of Beam Length
The effect of length of the beam studied byconsidering 7 values of length while keeping the widthand depth

Table. 1 Material properties of strip footing
Concrete

Soil

Interface

Young's modulus
Compressive
strength
Tensile strength
Poisson' ratio
Young's modulus
Poisson' ratio
CohesionAngle of internal
friction
Factor friction

Ec(/mm2)23000
fc (N/mm2)25ft (N/mm2)2.5
vc0.2
Eso (N/mm2)12
Vso0.3
C (N/mm2)038

Fig. 2 Finite element mesh of strip footing

0.3

constant, the results as shown by Fig. 7 that the
deflection decreased as the length increased, this also
due to increase of the mass of the concrete.
19

Load (kPa)

500
450
400
350
300
250
200
150
100
50
0

Maximum deflection (m)GEOMATE – Bangkok, Thailand, 22-24 November 2022

PresentStudy(finiteelement)

190
170
150
130
110

Lengh =6 m
Width=0.5 m

90
70

50
0

10 Defelection
2030(mm) 40

0.3

50

0.5
0.7
Thickness of beam (mm)Fig.3 Load -deflection curve of strip footing
Fig.5 Effect of thickness on load-deflection curve for

Maximum deflection (mm)strip footing

Fig.4 Crack patterns of strip footing

150
100
50

Thickness=0.6 m
Length=4 m0
0.3 0.4 0.5 0.6 0.7 0.8 0.9
Width of beam(m)1

Fig.6 Effect of width on load- deflection curve forstrip footing

20

GEOMATE – Bangkok, Thailand, 22-24 November 2022

REFERENCES[1] Winkler (1967), E. “Die Lehre Von Der
Elastizitatune Festigkeit “ Dominicus, Prague,
1976.
[2] Rao P, Rambabu, K.V, and Allam, M.M. (1995).
'Representation of Soil Support in Analysis ofOpen Plan Frames'. Computers and Structures,
56(6), .917-925[3]Likitlersuang, S, Chheng, and Keawsawasvong, S.
(2019). 'Structural Modelling in Finite Element of
Deep Excavation' , Journal of GeoEngineering,14(3), 121-128.
[4] Boudaa ,S, Khalfallh, S, and Bilotta, E. (2019)'Static interaction analysis between beam andlayered soil using a two-parameter elastic
foundation'. International Journal of Advanced
Structural Engineering.,11, 21–30.
[5] Deb ,K. and Dhar, A. (2013). ' Parameter
estimation for a system of beam resting on stone
column-reinforced soft soil'. International Journalof Geomechanics ,13(3).
[6] Haldar, S and Basu , D.(2016). 'Analysis of Beams
on Heterogeneousand Nonlinear Soil ' .
International Journal of Geomechanics. 16(4).
[7]AI,Z , Li, Z, and Cheng ,Y(2014) “ BEM analysis
of elastic foundation beams on multilayered
isotropic soils “ Soils and Foundations,54(4):667674.
[8] Munther, J.(1971). 'Mats and combined Footings
Analysis by Finite Element Method'. ACI
Journal,68(12), 945-949.
[9]Noori, F.(1990). 'Effect of Foundation Soil
Rigidity on Concrete Pressure Distribution
Beneath Rafts Foundations', MSc Thesis,University of Technology ,Bagdad , Iraq.
[10] Friswell , M, Adhikari , S and Lei,Y. (2007) .
'Vibration analysis of beams with non-local
foundations using the finite element method'.
International journal for numerical methods inengineering, 71, 1365-1386[11]Verma,A and Mohanty ,S. (2018). 'Finite element
analysis of foundation on layered andhomogeneous soil deposit under dynamic loading'.
Indian geotechnical conference, Bengaluru, India[12]Clough ,G and Duncan ,J. (1971) . ' Finite Element
Analysis of Retaining Wall Behavior' , Journal of
Soil Mechanics and Foundation Division,ASCE,97(SM 12)[13]Li,Y, Zhao, M , Xu, C, Du,X, and Li,Z .(2018) .
'Earthquake input for finite element analysis ofsoil- structure interaction on rigid bedrock’.
Tunneling and Underground Space Technology,,79, 250-262[14] Oñate, E, Celigueta, M, Idelsohn,S, Salazar,F, andSuárez,B.(2011). 'Possibilities of the particle

Maximum deflection in (mm)

200

150

100

Thickness=0.8 m
Width=0.4 m

50
4

6
8
Length of beam in (m)10

Fig.7 Effect of length on load- deflection curve

CONCLUSIONS
A numerical method of analysis for strip beamfoundation rest on soil under static load beenpresented. The analysis implemented 8-nodebrick has been used to model the concrete
foundation. The soil been modelled by 8-nodebrick element, while the interaction between thesoil and the concrete foundation is simulated bycontact interface element. Based on the present
finite element developed program ,which been
conducted through the present work , thefollowing conclusions can be drawn as follows:
1-The ability of using unreinforced concreteeams in the foundation with a good performanceeven after the occurrence some cracks.
2-The results obtained from the present finite
element method showed that the computationalmodel adopted in present research is suitable forpredictionofload-deflection
behaviorof
unreinforced concrete foundation under staticload. The comparison between the numericaland the available experimental results showed agood agreement.
3- Increasing the depth of the strip footing leads
to decreasing the deflection under the same
static load. This seems reasonable due toincrease in the flexural rigidity.
4-The depth of the strip footing when increased,theinflection
willexperienced
distinctive
increase due to increase in concrete mass.
5- When increasing the length of the strip
footing ,showed increase in the deflection of thebeam due to decrease in stiffness of the strip
footing beam.
21

GEOMATE – Bangkok, Thailand, 22-24 November 2022

finite element method for fluid–soil–structureinteraction
problems'.
Computational
Mechanics,48[15] Dhadse, G, Ramtekkar, G and Bhatt, G. (2021).
'Finite Element Modeling of Soil StructureInteraction System with Interface: A Review'.
Archives of Computational Methods inEngineering , 28, 3415–3432.
[16]Chen, W., and Saleeb, A . (1982) . Constitutive
Equations for Engineering Material, John Willey
and Sons, New York.
[17]Yasuhiro ,C and Chen ,W. (1987). ' Hypoelastic –Perfectly Plastic Model for Concrete Materia'l .
Journal of Engineering Mechanics, ASCE,
113(12),840- 1858.

[18]Mazurkiewics M, and Ostachowicz, W. (1983).
'Theory at Failure Element Method for Elastic
Contract Problems of Solid Bodies'.Computers andStructures,17(1),51-59.
[19]ANSYS Manual (1997) ,Version 5.4 , Swanson
Analysis System .Inc., Houston , Pennsylvania ,[20]Shukur, S. (2002). 'Analysis of Unreinforced
Concrete Beams on Soil By Finite Elements'.
MSc thesis, University of Kufa, Iraq.
[21] Sitharm ,T and Kamar, I.(1998). 'Non-Linear
Analysis of Geomechanical Problems Using
Coupled Finite and Finite Element'. Geotechnicaland Geological Engineering,16,129-149

22

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

ANALYSIS AND DESIGN ON VARYING THE OFFSET DISTANCES IN
SUPERIMPOSED MECHANICALLY STABILIZED EARTH WALLS
Vuttichai Chatpattananan1 and Vatanavongs Ratanavaraha2,
1

King Mongkut's Institute of Technology Ladkrabang, Thailand;
2
Suranaree University of Technology, Thailand
ABSTRACTSuperimposed MSE walls or multi-tier MSE walls are frequently used due to their eye-pleasing, economical,
and safety requirements compared with single tall MSE walls. Designing an MSE wall requires checking the failuremodes in its external stability, internal stability, and global stability. Calculating the external stability and global
stability for superimposed walls is well explained. Internal stability, however, is more complicated in calculating
the additional vertical stress from the upper wall as an equivalent surcharge on the lower wall with its magnitude
determined by the offset distance. By varying offset distances, the additional vertical stress can be categorized into
three cases. Case 1 is the maximum additional vertical from the upper wall load when the offset distance is lessthan HLtan(45-r/2). The additional vertical stress is zero in case 3 where the offset distance is greater than
HLtan(90-r). In case 2, the additional vertical stress is a hyperbola function that can be calculated easily using the
proposed equation i = [(HL-z1)/ (z2-z1)](HL+q) in Eq. (7) with geometric and algebraic explanations are usedthrough a numerical example in designing a superimposed wall.
Keywords: Additional vertical stress, Internal Stability, Mechanically stabilized earth walls, Multi-tier walls,
Superimposed walls.

INTRODUCTION

designs.

Mechanically stabilized earth walls or MSE walls
are retaining walls consisting of facing units,
reinforcements using metal strips or geogrids, andreinforced backfill soil. MSE walls can be single
walls or complex geometric walls like superimposed
walls. Superimposed walls or multi-tier walls are
frequently used due to their eye-pleasing,
economical, and safety requirements compared withsingle tall MSE walls. Designing an MSE wall
requires checking the failure modes in its externalstability, internal stability, and global stability.
FHWA [1] and AASHTO [2] provide details
guidelines and clear calculation examples fordesigning single walls. However, [1] provides only
two pages as shown in Fig. in designingsuperimposed MSW walls.
FHWA/TX-2 [3] suggest design methods for
multi-tiered MSE retaining walls by treating theupper-tier wall as an equivalent surcharge on thelower-tier wall with its magnitude determined by theoffset distance. Different offset distances determine
different equivalent surcharges. Figure 1 shows that
there are three cases in which calculating theadditional vertical stress depends on the offset
distance.
FHWA/TX-1 [4] provides calculation examples
but with some parts of the internal stability checking
which is rather complicated and may lead to mistaken
designs that are evidenced in some superimposed walldamages due to misunderstanding concepts or

Fig. 1

Design rules for superimposed walls [1]An example of this complicated design concept isthat FHWA [1] mentions that a footing located23

GEOMATE – Bangkok, Thailand, 22-24 November 2022

𝜎𝑖 is the additional stress on the ith layer ofreinforcement caused by the upper wall.
𝜎𝑓𝑖 is the additional stress at the wall face on the ith
layer of reinforcement caused by the upper wall.

outside case 1 is not a surcharge on the lower wall in
Fig. 1 while an upper wall in case 2, that is outsidecase 1, is a surcharge distributed to the lower wall.
This reflects the main question in designing a
superimposed wall is how much the superimposed
load or the additional vertical stress in Fig. 1 is atdifferent offset distances.
This study provides concepts and proposed aneasier formula than FHWA/TX [4] for calculating theadditional stress imposed on the lower wall. A
numerical example is used for calculation details forthe internal stability checking for 2 tiers ofsuperimposed walls at different offsets. Externalstability and global stability are omitted due to space
limitations.

Equation (1) is from a similar triangle in Eq. (6).
This similar triangle is also shown later in Fig 6.
(𝛾𝐻𝑈 +𝑞)−𝜎𝑓(𝑧2 −𝑑𝑖 )𝑡𝑎𝑛(45−∅𝑟/2)=

𝜎𝑖 −𝜎𝑓𝐿𝑎(6)

By substituting Eq. (2), Eq. (3), Eq. (4) in Eq. (5),or Eq. (6), 𝜎𝑖 shown in Eq. (7) remains constant for
all reinforcement layers and does not depend on thelayer depths, 𝑑𝑖 .
(𝐻 −𝑧 )𝜎𝑖 = (𝑧 𝐿−𝑧 1) (𝛾𝐻𝐿 + 𝑞 )2

DESIGN CONCEPT(7)

1

Case 3) If 𝐷 ≥ 𝐻𝐿 𝑡𝑎𝑛(90 − ∅𝑟 ) , 𝜎𝑖 = 0 . Thiscase is the same as the external stability case 3.

Let 𝐷 be the offset distance shown in Fig. 2. 𝐻𝑈
or 𝐻1 Fig. 2 is the upper wall height. 𝐻𝐿 or 𝐻2 Fig. 2
is the lower wall height. ∅𝑟 is the reinforced soilinternal friction angle of both the upper wall and thelower wall. 𝛾 is the reinforced soil density of both theupper wall and the lower wall. 𝑞 is the surcharge on
the upper wall.

Combining external stability and internal stability
When considering both external stability andinternal stability, there are four cases to calculate
additional stress ( 𝜎𝑖 ) as follows: case 1) 𝐷 ≤(𝐻𝑈 + 𝐻𝐿 )/20 , case 2) (𝐻𝑈 + 𝐻𝐿 )/20 ≤ 𝐷 ≤𝐻𝐿 𝑡𝑎𝑛(45 − ∅𝑟 ) , case 3) ) 𝐻𝐿 𝑡𝑎𝑛(45 − ∅𝑟 /2) ≤𝐷 ≤ 𝐻𝐿 𝑡𝑎𝑛(90 − ∅𝑟 ) and case 4) 𝐷 ≥ 𝐻𝐿 𝑡𝑎𝑛(90 −∅𝑟 ).
Let 𝐷1 = (𝐻𝑈 + 𝐻𝐿 )/20, 𝐷2 = 𝐻𝐿 𝑡𝑎𝑛(45 − ∅𝑟 /2) , 𝐷3 = 𝐻𝐿 𝑡𝑎𝑛(90 − ∅𝑟 ).The offset distances D1,
D2, D3 are shown in Fig. 3. In case 1, D is less than𝐷1 , both the upper wall and lower wall will be treatedas one wall. In case 4, D is greater than D3, both theupper wall and lower wall will be treated separately
as two walls. In case 2, D is between D1 and D2, theadditional stress is simply 𝜎𝑖 = 𝛾𝐻𝑈 . In case 3, D isbetween D1 and D2, the additional stress 𝜎𝑖 =(𝐻𝐿−𝑧1 )(𝛾𝐻𝐿 + 𝑞 ) as mentioned in Eq. (7).
(𝑧 −𝑧 )

External stability
For external stability checking, there are 3 cases
as follows:
Case 1) If 𝐷 ≤ (𝐻𝑈 + 𝐻𝐿 )/20, the wall should bedesigned as a single wall with a height 𝐻 = 𝐻𝑈 + 𝐻𝐿 .
Case 2) If (𝐻𝑈 + 𝐻𝐿 )/20 ≤ 𝐷 ≤ 𝐻𝐿 𝑡𝑎𝑛(90 −∅𝑟 ), the upper wall in a surcharge acting upon thelower wall.
Case 3) If 𝐷 ≥ 𝐻𝐿 𝑡𝑎𝑛(90 − ∅𝑟 ) , there is nosuperimposed load from the upper wall to the lower
wall.
Internal stability
For internal stability checking, there are 3 cases asfollows:
Case 1) 𝐷 ≤ 𝐻𝐿 𝑡𝑎𝑛(45 − ∅𝑟 ) , 𝜎𝑖 = 𝛾𝐻𝑈 . Thiscase includes the external stability case 1 and somepart of the external stability case 2 where (𝐻𝑈 + 𝐻𝐿 )/20 ≤ 𝐷 ≤ 𝐻𝐿 𝑡𝑎𝑛(45 − ∅𝑟 )

2

1

D3
D2
D1

Case 2) 𝐻𝐿 𝑡𝑎𝑛(45 − ∅𝑟 /2) ≤ 𝐷 ≤ 𝐻𝐿 𝑡𝑎𝑛(90 −∅𝑟 ),(𝛾𝐻𝑈 +𝑞)−𝜎𝑓

𝜎𝑖 = (𝑧 −𝑑 )𝑡𝑎𝑛(45−∅ /2) 𝐿𝑎 + 𝜎𝑓(1)

𝜎𝑓 = 𝑧 −𝑧 (𝛾𝐻𝑈 + 𝑞 )

(2)

2
𝑖
𝑑𝑖 −𝑧1
2

1

𝑟

90-r
45-r/2

𝑧1 = 𝐷𝑡𝑎𝑛∅𝑟(3)𝑧2 = 𝐷𝑡𝑎𝑛(45 + ∅𝑟 /2)(4)
𝐿𝑎𝑖 = (𝐻𝐿 − 𝑑𝑖 )𝑡𝑎𝑛(45 − ∅𝑟 /2)(5)
𝐿𝑎𝑖 is the active zone length of the ith layer ofreinforcement.
𝑑𝑖 is the depth of the ith layer of reinforcement..

Fig. 2

Offset distances showing D1, D2, D3

A NUMERICAL EXAMPLE
The soil parameters, surcharge, and wall geometry
for this illustrated example are shown in Table 1. The
24

GEOMATE – Bangkok, Thailand, 22-24 November 2022

total height, 𝐻 = 𝐻𝑈 + 𝐻𝐿 = 3 + 4 = 7 meters. For thereinforcement length [1] with 𝐻𝑈 = 3 meters, theupper wall reinforcement length is 𝐿𝑈 = 0.7𝐻𝑈 =0.7x3 = 2.1 meters. The lower wall reinforcement
length is 𝐿𝐿 = 0.7(𝐻𝑈 + 𝐻𝐿 ) = 0.7H = 0.7x7 = 4.9
meters. The minimum lower wall reinforcement [1]𝐿𝐿 = 0.6(𝐻𝑈 + 𝐻𝐿 ) = 4.2 meters is not enough that islater shown in Table 5 at layer 8 representing the top
reinforcement of the lower wall that LT = 4.89 meterswhich is less than 4.2 meters. With 𝐷 = 3 meters.
𝑧1 = 𝐷𝑡𝑎𝑛∅𝑟 = 3tan(28) = 1.60 meters from (3).
𝑧2 = 𝐷𝑡𝑎𝑛(45 + ∅𝑟 /2) = 3tan(45+28/2) = 4.99
meters. The dimension of this 2-tier superimposed
walls is shown in Fig. 3.
Table 1 Soil parameters,
geometry.
Item
Soil cohesion
Soil internal friction
Soil density
Surcharge
Upper wall height
Lower wall height
Offset distancesurcharge,

Symbol
c

r

q
HU
HL
D

and

Value
0
28
18
10
3
4
3

The reinforcement layers are shown in Fig. 4. The
reinforcement spacing is 0.5 meters where the top
layers at both the upper wall (layer 14) and the lower
wall (layer 8) are started at 0.25 meters below theirsurfaces. The reinforcement layer depths are also
shown as 𝑑𝑖 in Table 4. The active zone length foreach reinforcement layer is also shown in Table 4
using Eq. (5).
The reinforcement depths for 14 layers in bothwalls are shown in Fig. 4 denoted as d1 to d14 where
the lower wall has layer 1 to layer 8 and the upper
wall has layer 9 to layer 14. 𝑧1 from Eq. (3) and 𝑧2
from Eq. (4) are also included.
Layer 14Layer 13d14
Layer 12 d13
Layer11Layer 10d10Layer 9

wall

Unit
kN/m2
degree
kN/m3
kN/m2
meter
meter
meter

Layer 8Layer 7Layer 6Layer5Layer 4Layer 3Layer2
Layer 1z1

z2

Note: All the retained soil, reinforced backfill soil, and thefoundation soil have the same properties

d9

d8
d7

d2
d1

Fig. 4q

Reinforcement layers for internal stability

Calculation of the additional stresses (𝝈𝒊 )For the superimposed lower wall, allreinforcement layers 1 to 8 fall in to the internalstability case 2 because 𝐷2 (= 2.4) ≤ 𝐷 (= 3.0) ≤𝐷3 (= 7.52). The additional stresses can be calculatedusing either Eq. (7) or Eq. (1) in any layer. All gives
the same result as shown as follows. For example,(𝐻 −𝑧 )
Using Eq. (7), 𝜎𝑖 = 𝐿 1 (𝛾𝐻𝑈 + 𝑞 )HU
45 + r/2 r
H

z1
HL
z2

(4−1.60)

LL

Using Eq. (1) for layer 6 as shown in Fig. 6.
𝑑 −𝑧
From Eq. (2), 𝜎𝑓 = 𝑧 𝑖 −𝑧1 (𝛾𝐻𝑈 + 𝑞 )LU
Fig. 3(𝑧2 −𝑧1 )= (4.99−1.60) (18𝑥3 + 10) = 45.30 kN/m2.

Dimension of the superimposed wall
exampleD
LL - D

1.25−1.60

2

1= 4.99−1.60 (18𝑥4 + 10) = - 6.50 kN/m2.
From Eq. (5), 𝐿𝑎 = (𝐻𝐿 − 𝑑𝑖 )𝑡𝑎𝑛(45 − ∅𝑟 /2)= (4 − 1.25)𝑡𝑎𝑛(45 − 28/2) = 1.65 m.

Compared with Fig. 2, 𝐷1 = (𝐻𝑈 + 𝐻𝐿 )/20 =(3+4)/20 = 0.35 meters. 𝐷2 = 𝐻𝐿 𝑡𝑎𝑛(45 − ∅𝑟 /2) =4tan(45-28/2) = 2.4 meters. 𝐷3 = 𝐻𝐿 𝑡𝑎𝑛(90 − ∅𝑟 ) =4tan(90-28) = 7.52 meters.

(𝛾𝐻𝑈 +𝑞)−𝜎𝑓

From Eq. (1), 𝜎𝑖 = (𝑧 −𝑑 )𝑡𝑎𝑛(45−∅ /2) 𝐿𝑎 + 𝜎𝑓2𝑖𝑟

(18𝑥4+10)−(−6.50)= (4.99−1.25)𝑡𝑎𝑛(45−28/2) 1.65 + (−6.50)= 45.30 kN/m2.
Using Eq. (1) for layer 3 as shown in Fig. 6.
From Eq. (2), 𝜎𝑓 = 21.75 kN/m2 at di = 2.75 m.
From Eq. (5), 𝐿𝑎 = 0.75 m.

ADDITIONAL STRESSES
Reinforcement layers
25

GEOMATE – Bangkok, Thailand, 22-24 November 2022

For layer 3, 𝑦1 = (𝛾𝐻𝑈 + 𝑞 ) − 𝜎𝑓3 , 𝑦2 = 𝜎𝑖 − 𝜎𝑓3 ,and 𝑥1 = (𝑧2 − 𝑑3 )𝑡𝑎𝑛(45 − ∅𝑟 /2), 𝑥2 = 𝐿𝑎4
For layer 6, 𝑦3 = (𝛾𝐻𝑈 + 𝑞 ) − 𝜎𝑓6 , 𝑦4 = 𝜎𝑖 − 𝜎𝑓6 ,and 𝑥3 = (𝑧2 − 𝑑6 )𝑡𝑎𝑛(45 − ∅𝑟 /2), 𝑥4 = 𝐿𝑎6

From Eq. (1), 𝜎𝑖 = 45.30 kN/m2.
As shown in Table 4 and in Fig. 5, the additional
stress at the wall face, 𝜎𝑓 , is negative if thereinforcement depth, di, is less than z1. 𝜎𝑓 , is positive
if di, is greater than z1.

Figure 7 shows that this similar triangle is also
(𝐻 −𝑧 )
held for Eq. (7) where 𝜎𝑖 = 𝐿 1 (𝛾𝐻𝐿 + 𝑞 ) .
(𝑧2 −𝑧1 )Additionally, similar triangle in Eq. (2) where 𝜎𝑓 =𝑑𝑖 −𝑧1(𝛾𝐻𝑈 + 𝑞 ) holds true with Eq. (7) where 𝜎𝑖 =𝑧2 −𝑧1(𝐻𝐿−𝑧1 )

f8
f7
f6
f5
f4
f3

f2
f1

HU

i

i

HU
HU
HU

i

HU
HU

i

i
i

(𝛾𝐻𝐿 + 𝑞 ) in which di in Eq. (2) is HL in Eq.

(7).
Layer 8Layer 7Layer 6Layer 5Layer 4Layer 3Layer 2
Layer 1

HU

i
i

(𝑧2 −𝑧1 )

 HU

HU

HU+q

45-r/2 z1

i
r

HL

HL-z1
45 + r/2
Fig. 5z2

z2-z1
45-r/2

Offset distances showing D1, D2, D3
Fig. 7

Similar triangle in Eq. (7)Additional stresses outside the internal stability
case 2

x3
x4

x1
x2

Eq. (1) or Eq. (7) to calculate the internal stability
are valid only 𝐷2 ≤ 𝐷 ≤ 𝐷3 . Suppose that the offset
distance, D, is 2 meters, this superimposed wall falls
in the internal stability case 1 where 𝐷 ≤ 𝐷2 at 2.4
meters. Thus, 𝜎𝑖 = 𝛾𝐻𝑈 + 𝑞 = 18x3 +10 = 64 kN/m2.
However, if the additional stress, 𝜎𝑖 , is wrongly
calculated using Eq. (1) or Eq. (7), then(𝐻 −𝑧 )
Using Eq. (7), 𝜎𝑖 = (𝑧 𝐿−𝑧 1) (𝛾𝐻𝑈 + 𝑞 )

y3 y4
d6

z1
d3

y1 y2
45 + r/2

2

1

From (3), 𝑧1 = 𝐷𝑡𝑎𝑛∅𝑟 = 2tan(28) =1.06
From (4), 𝑧2 = 𝐷𝑡𝑎𝑛(45 + ∅𝑟 /2)=2tan(45+28/2) = 3.33
(4−1.06)(18𝑥3 + 10)
From (7), 𝜎𝑖 =z2(3.33−1.06)

Fig. 6= 82.97 kN/m2.
A layer can be picked to calculate 𝜎𝑖 . For example,
arbitrarily pick layer 6, thenUsing Eq. (1) for layer 6
𝑑 −𝑧
From Eq. (2), 𝜎𝑓 = 𝑖 1 (𝛾𝐻𝑈 + 𝑞 )Similar triangle in Eq. (6)Similar triangle of the additional stresses
Figure 6 shows that the additional stresses, 𝜎𝑖 , forall the layers in the superimposed lower wall gives thesame value because they all possess the same similar
triangle properties. In this example, 𝜎𝑖 for layer 1 to 8
using Eq. (1) is 45.30 kN/m2. Fig. 5 also shows that
𝜎𝑖 for layer 1 to 8 have the same magnitude.
Figure 6 also shows the similar triangle using Eq.
(𝛾𝐻𝑈 +𝑞)−𝜎𝑓(6) where (𝑧 −𝑑 )𝑡𝑎𝑛(45−∅ /2) =2𝑖𝑟

𝜎𝑖 −𝜎𝑓𝐿𝑎

1.25−1.06

𝑧2 −𝑧1= 3.33−1.60 (18𝑥4 + 10) = 5.27 kN/m2.
(𝛾𝐻𝑈 +𝑞)−𝜎𝑓

From Eq. (1), 𝜎𝑖 = (𝑧 −𝑑 )𝑡𝑎𝑛(45−∅ /2) 𝐿𝑎 + 𝜎𝑓2𝑖𝑟

(18𝑥4+10)−(5.27)= (3.33−1.25)𝑡𝑎𝑛(45−28/2) 1.65 + 5.27
= 82.97 kN/m2.
This value at 82.97 N/m2 is incorrect since the.

26

GEOMATE – Bangkok, Thailand, 22-24 November 2022

similar triangle does not hold. In fact, Eq. (1) or Eq.
(7) are starting to be valid when D is D2 which is 2.4.
Suppose that D is 8 meters which is in the internalstability case 3 where 𝐷 ≥ 𝐷3 at 7.52 meters. Using
Eq. (7) or Eq (1), 𝜎𝑖 = -1.79 kN/m2 where the correct
value is 𝜎𝑖 = 0.
In conclusion, for internal stability case 1 where
𝐷 ≤ 𝐷2 = 𝐻𝐿 𝑡𝑎𝑛(45 − ∅𝑟 /2) , 𝜎𝑖 = 𝛾𝐻𝑈 + 𝑞 . Forinternal stability case 3 where 𝐷 ≥ 𝐷3 =𝐻𝐿 𝑡𝑎𝑛(90 − ∅𝑟 ), 𝜎𝑖 = 0

vertical stresses from the upper wall, then the
additional stress ratio is defined as 𝜎𝑖 /𝜎𝑣𝑈 . Also, letdefine the offset distance ratio as 𝐷/𝐻𝐿 . Varying
both the offset distances (D) and the internal friction
angles () is shown in Fig. 8 where  is increased by
2 degrees starting from 24 degrees to 34 degrees.
Figure 9 is further clarified in Table 2 and Table
3. Table 2 shows the offset distance ratios of D2/H
and D3/H at different . At  = 28 degrees, forexample, the offset ratio of D2/HL at 0.60 means that
if the offset distance (D) is less than 0.6 of lower wallheight, the lower wall will bear the full verticalsuperimposed load from the upper wall (𝜎𝑣𝑈 ). Also,
D3/HL at 1.88 for  = 28 degrees means that D must
be more than 1.88 times the lower wall height to getaway from the upper wall load.
Table 3 implies that at  = 28 degrees, the lower
wall with a typical reinforcement length from 0.7L/H
to 1.1L/H [1] is not enough to bear the upper wallload. 0.7L/H lower wall still needs to bear the
additional stress at 0.79 times the upper wall load.

Additional stresses inside the internal stability
case 2
The additional stress (𝜎𝑖 ) by varying the values ofthe offset distance (D) to be inside the internalstability case 2 or 𝐷2 ≤ 𝐷 ≤ 𝐷3 is shown in Fig. 8.
Substitute Eq. (3) and Eq. (4) into Eq. (7) leads to(𝐻𝐿−𝐷𝑡𝑎𝑛∅𝑟)(𝛾𝐻𝑈 + 𝑞 )𝜎𝑖 = (𝐷𝑡𝑎𝑛(45+∅(8)⁄2)−𝐷𝑡𝑎𝑛∅ )𝑟𝑟

in which 𝜎𝑖 is a function of  and D where 𝐷2 ≤ 𝐷 ≤
𝐷3 , 𝜎𝑖 is a hyperbola function.

i (kN/m2)  = 28

Table 2 Offset distance ratios of D2/H and D3/H atdifferent .

70

60



50

D2/HL
D3/HL

40
30

24
0.65
2.25

28
0.60
1.88

32
0.55
1.60

36
0.51
1.38

Table 3 Additional stresses ratio (i/vH) at different
D/H and different .

20
10
0

Fig. 8



D (m)

0

1

2

3

4

5

6

7

8

0.7D/H
D/H
1.1L/H

Additional stresses by varying D at  = 28

32
0.68
0.32
0.24

36
0.57
0.22
0.15

The result from the internal stability calculation isshown in Table 4 and Table 5, in which

1.0
0.8

𝜎𝑣𝑖 = 𝛾𝑑𝑖 + 𝜎𝑖𝜎ℎ𝑖 = 𝑘𝑎 𝜎𝑣𝑖
𝑘𝑎 = 𝑡𝑎𝑛2 (45 − ∅𝑟 /2)
𝑇𝑚𝑎𝑥−𝑖 = 𝜎ℎ𝑖 𝑉𝑖
𝐿𝑒𝑖 = 1.5𝑇𝑚𝑎𝑥−𝑖 ⁄𝐶𝑡𝑎𝑛(∅𝑟 )𝛾𝑑𝑖 𝑅𝑐 𝛼
𝐿 𝑇𝑖 = 𝐿𝑎𝑖 + 𝐿𝑒𝑖

0.6

 = 24

 = 36

0.2
0.0
0.0

28
0.79
0.41
0.33

INTERNAL STABILITY

i/vU

0.4

24
0.90
0.51
0.42

0.5

1.0

1.5

2.0

(8)
(9)
(10)
(11)
(12)
(13)

where
𝜎𝑣𝑖 is the vertical stress at the ith layer ofreinforcement.
𝜎ℎ𝑖 is the horizontal stress at the ith layer ofreinforcement.
𝑘𝑎 is the active earth pressure coefficient.
𝑇𝑚𝑎𝑥−𝑖 is the maximum tension at the ith layer of

D/HL

Fig. 9 Additional stresses ratio (i/vH) by varying
offset distance ratio (D/HL) and  increment by 2°
From Eq. (8), suppose 𝜎𝑣𝑈 = 𝛾𝐻𝑈 + 𝑞 is the27

GEOMATE – Bangkok, Thailand, 22-24 November 2022

reinforcement.
𝑉𝑖 is the tributary area at the ith layer of reinforcement.
𝐿𝑒𝑖 is the embedment length in the resisting zone at
the ith layer of reinforcement.
C is 2 for geogrid.
𝑅𝐶 is coverage ratio = 1.
 is scaling ration = 1.
𝐿 𝑇𝑖 is the total length at the ith layer of reinforcement.

reinforcement layers can be determined using Eq.
(11) and the reinforcement length can be determinedusing Eq. (13).
The external stability needs to check the sliding,overturning, and bearing capacity. FHWA/TX-1 [4]suggests that only the part of the upper walls directly
above the lower wall is treated as a surcharge. Global
stability in the limit equilibrium method or finite
element method is also omitted because of the space
limitation.

Table 4 Internal stability showing additional
stresses, vertical stresses, and horizontal
stresses.

CONCLUSIONS
Layer
8
7
6
5
4
3
2
1
Emb

di
(m)
0.25
0.75
1.25
1.75
2.25
2.75
3.25
3.75
4.0

Lai
(m)
2.25
1.95
1.65
1.35
1.05
0.75
0.45
0.15

fi

vi

hi

(kN/m2)-25.34-15.92
-6.50
2.91
12.34
21.75
31.17
40.59

(kN/m2)
49.80
58.80
67.80
76.80
85.80
84.80
103.80112.80(kN/m2)
17.98
21.23
24.48
27.73
30.98
34.23
37.47
40.72

The key point for internal stability checking indesigning superimposed MSE retaining walls is totreat the upper wall as an equivalent surcharge on thelower wall with its magnitude determined by theoffset distance. By varying offset distances, theequivalent surcharge magnitude or the additionalvertical stress can be categorized into three cases. The
additional vertical stress is maximum in case 1 whichis the full upper wall load where the offset distance isless than HLtan(45-r/2). The additional verticalstress is zero in case 3 where the offset distance isgreater than HLtan(90-r). In case 2, the additionalvertical stress is a hyperbola function that can becalculated easily using the proposed equation i =[(HL-z1)/ (z2-z1)](HL+q) in Eq. (7) with the geometricand algebraic explanations.

Note: di is the reinforcement layer depth. Lai is the active zone
length. i is the additional stress at the wall face. fi is the additional
stress on the reinforcement layer. Emb is the embedment depth.

Table 5 Internal stability showing maximum
reinforcement tensions and reinforcement
lengths.
Layer
8
7
6
5
4
3
2
1
Emb

di
(m)
0.25
0.75
1.25
1.75
2.25
2.75
3.25
3.75
4.0

Vi
(m)
0.375
0.5
0.5
0.5
0.5
0.5
0.5
0.5

Tmax-i(kN/m)
6.74
10.61
12.24
13.86
15.49
17.11
18.74
20.36

Lei
(m)
2.64
1.39
0.96
0.78
0.67
0.61
0.56
0.53

REFERENCES[1] FHWA-NHI-00-043, Mechanically Stabilized
Earth Walls and Reinforced Soil Slopes Design
and Construction Guidelines, 2001, U.S.
Department of Transportation - Federal Highway
Administration.
[2] AASHTO, Standard specifications for highway
bridges, 1998, American Association of State
Highway and Transportation Officials.
[3] FHWA/TX-05/0-4485-2, Design Guidelines forMulti-Tiered MSE Walls, 2004, Texas
Department of Transportation and the Federal
Highway Administration.
[4] FHWA/TX-05/0-4485-1, An Examination of
Design Procedures for Single- and Multi-Tier
Mechanically Stabilized Earth Walls, 2004,
Texas Department of Transportation and theFederal Highway Administration.

LTi
(m)
4.89
3.34
2.61
2.13
1.73
1.36
1.02
0.68

Note: di is the reinforcement layer depth. Vi is the reinforcement
tributary area active zone length of the reinforcement layer. Tmax-i isthe maximum tension of the reinforcement layer. Lai is the active
zone length. LTi is the minimum reinforcement length.

The calculation detail can be followed as in [1]that is not shown here. The tensile of the28

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

NUMERICAL ANALYSIS FOR ESTIMATING PULL-OUT
RESISTANCE OF FLIP-TYPE GROUND ANCHORS IN SANDGROUNDS
Shota Yoshida1, Xi Xiong2 and Tatsunori Matsumoto3
1

Daisho Co., Ltd., Japan; 2 Kanazawa University, Japan; 3 Professor Emeritus, Kanazawa University, Japan
ABSTRACT

Flip-type ground anchors (hereinafter referred to as “flip anchors”) are a kind of ground anchors, which do not
require grouting to obtain the pull-out resistance. The flip anchors are directly driven into the ground from theground surface with their anchor heads closed. When pull-out forces act on the anchors, their anchor heads rotate
and open in the ground. Unlike ordinary pre-embedded plate anchors, there are few studies on flip anchors. Thus,
to investigate the pull-out behavior of flip anchors, and to estimate their pull-out resistance in sand grounds,
numerical analyses using the finite element method (FEM) were conducted. The Mohr-Coulomb model was
employed to model the soil. The numerical analyses were conducted to simulate full-scale field pull-out
experiments. In the field experiments, 5 sizes of actual flip anchors embedded or driven in the sand ground were
pulled out vertically. Pull-out force and vertical displacement were measured while pulling the anchors. In the
numerical analyses, not only the pull-out resistance but also the displacement of the ground due to the pull-out of
the anchor was investigated. By comparing the experimental results under various conditions with the calculated
results of the FEM analyses, a method of estimating the pull-out resistance of flip anchors by FEM was proposed.
Keywords: Ground anchor, Flip anchor, Numerical analysis, Sand ground
INTRODUCTION

open in the ground when tensile force acts. For thisreason, percussion anchors (Figs. 2 & 3) are called“flip anchors” in this study.

Anchors have been widely used for supporting
structures on the ground mainly called earth anchors
or ground anchors. The types of anchors can bebroadly divided into those that are used with grouting
or those without grouting. Among the anchors
installed without grouting, there are mainly 2 types ofanchors. One is pre-embedded in the ground and theother is driven or penetrated into the ground.
Figure 1 is examples of pre-embedded plateanchors. The plate anchors are expected to have pullout resistance due to earth pressure acting on theanchor plates. The plate anchors can be used for awide variety of purposes, such as embankment
reinforcement, retaining wall reinforcement, tower
support, and so on.

Fig. 2 Examples of flip anchors [2].

Fig. 3 Installation procedures of a flip anchor [2].
Although the flip anchors have convenient
workability, estimation of the pull-out resistanceaccording to ground conditions is more difficultcompared to general pre-embedded plate anchors, due
to their particular resistance mechanism. A number ofprevious research on empirical estimation of pull-out
resistance of plate anchors were conducted so far,such as [3]-[5]; however, there are few studies on flip
anchors [6].
Pull-out resistance of plate anchors has been
estimated based on the ground failure models [7]-[9].
As shown in Fig. 4, the ground failure models need toFig. 1 Examples of pre-embedded plate anchors [1].
Among anchors that are driven or penetrated into
the ground, there are commonly 2 types of anchors,which are often called “percussion anchors” and“helical anchors”. The pull-out resistance of
percussion anchors is obtained by earth pressures
acting on the anchor plates like the plate anchors, but
it differs from the plate anchor in that it rotates to29

GEOMATE – Bangkok, Thailand, 22-24 November 2022

vibration tamper to have a relative density Dr ofaround 80% and a dry density d of around 1.745 t/m3.
As shown in Figs. 5 & 6, five sizes of actual flip
anchors made of ductile iron were used in theexperiments. The length of the anchor L, the breadthof the anchor B, and the projected area of the anchor
A are different for each anchor. Each anchor wasburied in the ground during ground preparation atdesignated embedment depths H under Opened or
Closed head conditions. In driven installation
condition, the anchors were driven into the groundfrom the ground surface. After that, each anchor wasvertically pulled out with a hydraulic jack, and pullout force F and pull-out displacement w were
measured.

be divided into “Shallow anchor” model or “Deepanchor” model according to the embedment ratio H/L(H = embedment depth of anchor head, L = length ofanchor plate). It is because the slip lines extend fromedges of an anchor plate do not reach the groundsurface when the anchor is installed deeper than thecritical embedment ratio (H/L)cr. If H/L is smaller
than the (H/L)cr, the failure pattern appears as a
“Shallow anchor” model, and if H/L is equal or
greater than (H/L)cr, the pattern appears as a “Deepanchor” model. In practice, (H/L)cr can be set inrelation to the friction angle of soil ' (Table 1).

B

L
Fig. 4 Ground failure models above a plate anchor.
Table 1 (H/L)cr depending on ' [10]'(H/L)cr

20°
2.5

25°
3

30°
4

35°
5

40°
7

Fig. 5 Actual flip anchors used in experiments.
45°
9

48°
11

Merifield [11] estimates anchor break-out factorN for horizontal square and circular anchors byrigorous numerical analysis for various friction
angles of soil '. The effectiveness of numericalanalysis to estimate pull-out resistance of plateanchors has been verified.
In this study, pull-out resistance of flip anchors insandy ground is calculated by numerical analysis
using the finite element method (FEM). And thecalculated values are compared with measured valuesof field experiments. According to the calculated
results, ground failure modes are also investigated.

Fig. 6 Three installation conditions of flip anchors.
Experimental Results
Maximum pull-out resistance Fmax of flip anchors
increased as the projected area of anchors A increased.
Fmax of Driven anchors were equivalent to Fmax ofClosed anchors. Fmax of Driven anchors, which iscorresponding to the practical installation conditionof flip anchors, reached about 80% of Fmax ofhorizontally embedded plate anchors (Opened
anchors). From the results of field experiments, w
required for the flip anchors to open sufficiently in thedense sand could be the same amount as plate length
L or about 1.5 times L.

FIELD PULL-OUT EXPERIMENTS OF FLIPANCHORS EMBEDDED OR DRIVEN IN A
SAND GROUND
Experimental Conditions
A full-scale model sand ground was prepared in atest pit of 4.0 m in length, 4.0 m in width, and 2.5 m
in height in a field. The peak internal friction angle 'of the sand, which was measured by direct shear tests,
was 42 degrees. The ground was compacted using a
30

GEOMATE – Bangkok, Thailand, 22-24 November 2022

CALCULATION OF FMAX OF FLIP ANCHORSBY NUMERICAL ANALYSIS (FEM)

Table 2 Dimensions of approximated rectangularshapes of flip anchors for the calculation

Modeling of the Ground and Plate Anchors

Flip anchor
H50
H110
HG100
HG180
HG320

The above field pull-out experiments were
simulated by the finite element method (FEM) to
examine the effectiveness of FEM for estimating pullout resistance of flip anchors. A FEM analysis
software Plaxis 3D was adopted.
As shown in Fig. 7, the ground with a length of
4.0 m, a width of 2.0 m, and a depth H of more than
H/L = 20 was modelled for each anchor used in thefield experiments. For the calculation, because theflip anchor has a unique shape, the shape wasapproximated to a rectangle having the same A byadjusting the breadth of anchor B as Bc (Table 2 & Fig.
8). For simplicity, the model was split in half, where
the anchor plate was halved (Bc/2). As thedisplacement boundary conditions, the horizontal
displacement of the side surface and the verticaldisplacement of the bottom surface were fixed. MohrCoulomb model was applied to the soil constitutive
law.
Table 3 shows the parameters of the ground, basedon the ground conditions of the field experiments.
Residual internal friction angle 'r was adopted in the
FEM analysis. Modulus of elasticity E' wasempirically estimated by E' = 2700 N (kPa) based on
measured SPT N-values of the ground. Table 4 showsparameters of an anchor plate based on the actual flip
anchors used in the experiments (Fig. 5). Interfaces
were set between the bottom of the anchor plate andthe sand for tension cut-off.
m
2.0

H

B (m)
0.050
0.110
0.100
0.180
0.320

A (m2)
0.007
0.013
0.030
0.049
0.103

Bc

L
actual shape offlip anchor

B

Fig. 8 An outline of an approximated rectangularshape of a flip anchor for the calculation.
Table 3 Parameters of the sand ground for FEM
analysis
Item
Unit weight of the unsaturated soil, unsat(kN/m3)
Initial void ratio, einit
Modulus of elasticity, E' (kPa)
Poisson’s ratio, 
Cohesion, c (kPa)
Residual int. friction angle, 'r (deg)Dilatancy angle, (deg)The earth pressure coefficient at rest, K0

H/L=1

anchorplate*Bc (m)
0.043
0.079
0.088
0.143
0.235
rectangularanchor model

4.0
m

sand
ground

L (m)
0.160
0.160
0.340
0.340
0.440

H/L=3
H/L=5
H/L=7
H/L=9
H / L = 11
H / L = 13
H / L = 15

Value
17.10
0.540
8400
0.30
0.0
35
0.0
0.43

Table 4 Parameters of the anchor plates for FEM
analysis
Item
Unit weight,  (kN/m3)
Thickness, d (mm)Young’s modulus, E (GPa)Poisson’s ratio, 

H / L = 20

Fig. 7 Modelling of the ground and plate anchors for
FEM analysis.

Value
71.54
50.0
176.0
0.27

Calculation of Fmax of Flip Anchors by FEM
Pull-out force F vs. pull-out displacement w

The following procedure were applied to simulate
the pull-out experiment.
Step 1: K0 self-weight analysis on horizontal ground
was performed with K0 = 1 – sin'.
Step 2: An anchor plate was set.
Step 3: Upward vertical displacement that is equal to
L was applied to the anchor plates.

Figures 9-13 show the relationships of F vs. w foreach H/L. F becomes larger with an increasing H/L.
The tendency of the relationship changes withincreasing H/L. When H/L ≤ 5, Fmax was clearly
appeared; whereas when H/L > 5, Fmax cannot be
defined because F kept increasing with increasing w.
31

GEOMATE – Bangkok, Thailand, 22-24 November 2022

The tendency of the relationship of F vs. w seems
to shift from general shear failure to local shear
failure with increasing H/L. And (H/L)cr is regarded
as a turning point of the tendency. Thus for Deepanchors [(H/L)cr ≤ H/L], maximum allowable w isneed to be defined to determine Fmax. Some references[12] regard w where the gradient (Fw) becomesto the minimum as the maximum w at Fmax. For flip
anchors in this study, the amount of maximum
allowable w for Deep anchor is defined as L/2.
Figure 14 shows Fmax calculated by FEM vs.
measured Fmax. The calculated and measured values
are in good agreement. Fmax of each anchor in deepanchor condition was estimated from F at w = L/2.
Fmax becomes larger with increasing A or H/L. The
increasing tendency of Fmax changes from an
exponential increase tendency to a linear increasing
tendency at (H/L)cr as the conversion point.

Pull-out force, F (kN)

300

o

60
40

L = 0.160 (m)2
A = 0.007 (m )

0

0

c=0
o
=0
E ' = 8400 kPa

40
20
0

0

Pull-out force, F (kN)

Pull-out force, F (kN)o

40 80 120 160 200 240 280 320 360
Pull-out displacement, w (mm)HG180 anchor
250  ' = 35o

L = 0.340 (m)2
A = 0.049 (m )Cal. by FEM
H / L = 20
H / L = 15
H / L = 13
H / L = 11
H/L= 9
H/L= 7
H/L= 5
H/L= 3
H/L= 1c=0o
=0E ' = 8400 kPa

200
150
100
50
0

40 80 120 160 200 240 280 320 360
Pull-out displacement, w (mm)

600

80

60  ' = 350Fig. 12 F vs. w by FEM simulations for HG180
anchor.

Fig. 9 F vs. w by FEM simulations for H50 anchor.
L = 0.160 (m)2
A = 0.013 (m )50

020 40 60 80 100 120 140 160 180
Pull-out displacement, w (mm)H110 anchor

Cal. by FEM
H / L = 20
H / L = 15
H / L = 13
H / L = 11
H/L= 9
H/L= 7
H/L= 5
H/L= 3
H/L= 1

100

300

Cal. by FEM
H / L = 20
H / L = 15
H / L = 13
H / L = 11
H/L= 9
H/L= 7
H/L= 5
H/L= 3
H/L= 1

20

150

L = 0.340 (m)2
A = 0.030 (m )Fig. 11 F vs. w by FEM simulations for HG100
anchor.

Pull-out force, F (kN)

Pull-out force, F (kN)

H50 anchoro ' = 35c=0o
=0E ' = 8400 kPa

200

0

80
 ' = 35c=0o
=0E ' = 8400 kPa

HG100 anchor

250

Cal. by FEM
H / L = 20
H / L = 15
H / L = 13
H / L = 11
H/L= 9
H/L= 7
H/L= 5
H/L= 3
H/L= 1o ' = 35c=0o
=0E ' = 8400 kPa

400
300

L = 0.440 (m)2
A = 0.103 (m )Cal. by FEM
H / L = 20
H / L = 15
H / L = 13
H / L = 11
H/L= 9
H/L= 7
H/L= 5
H/L= 3
H/L= 1200100
020 40 60 80 100 120 140 160 180
Pull-out displacement, w (mm)

HG320 anchor

500

0

80
160 240 320 400
Pull-out displacement, w (mm)480

Fig. 13 F vs. w by FEM simulations for HG320
anchor.

Fig. 10 F vs. w by FEM simulations for H110 anchor.

Max. pull-out force, Fmax (kN)

Displacement of the GroundFigures 15 and 16 show the results of FEM
analysis on displacements of the ground when the
HG180 anchor was pulled until the w at Fmax. The
anchor displacements affected the ground surface
when H/L = 1; however when H/L = 5 or more, theanchor displacements did not affect the groundsurface. From these results, H/L = 5 can be regardedas (H/L)cr. This result certainly agrees with theempirical values in Table 1.

500
400
300

o

 ' = 35c=0o
=0E' = 8400 kPa

200
100
0
0

Cal. by FEM
H50
H110
HG100
HG180
HG320
Measured(Opened)
H50
H110
HG100
HG180
HG320

2 4 6 8 10 12 14 16 18 20 22
Embedment ratio, H / L

Fig. 14 Measured Fmax vs. calculated Fmax (by FEM).
32

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Pull-out force, F (kN)300

HG180 anchor

250

L = 0.340 (m)2
A = 0.049 (m )o
 ' = 35c=0o
=0E ' = 8400 kPa

200
150

o

half-opened,  = 45
full-opened,  = 0

100
50
0

0

o

Cal. by FEM
H / L = 20
H / L = 15
H / L = 13
H / L = 11
H/L= 9
H/L= 7
H/L= 5
H/L= 3
H/L= 140 80 120 160 200 240 280 320 360
Pull-out displacement, w (mm)Max. pull-out force, Fmax (kN)

Fig. 17 F vs. w by FEM simulations for full openedor half-opened HG180 anchor.
Fig. 15 Displacements of the ground at w = 20 mm
calculated by FEM (HG180, H/L = 1).

500

HG180 anchor

400 ' = 35c=0o
=0E ' = 8400 kPa

300

o



200
100
0
0

(a) H/L = 5oMeasured (Opened,  = 0 )o
FEM  = 0o
FEM  = 45

2 4 6 8 10 12 14 16 18 20 22
Embedment ratio, H/L

Fig. 18 Measured (full-opened) vs. calculated (full &half-opened) Fmax by FEM.
Displacement of the Ground(b) H/L = 7
Fig. 16 Displacements of the ground at w = 170 mm
calculated by FEM (HG180).
(a) H/L = 3

Calculation of Fmax of Half-Opened Flip Anchors
by FEM
Pull-out force F vs. pull-out displacement w
Figure 17 shows the comparison of F vs. w ofOpened (horizontal, = 0°) and half-opened (=45°) anchors pulled vertically. The inclined anchor
imitates the anchor in the middle of opening or theanchor half opened. As shown in Fig. 18, if the anchor
half opened, F tended to be smaller than F of thehorizontal (completely opened) anchor. Fmax of thehalf-opened anchor was about 2/3 of Fmax of theanchor completely opened.

(b) H/L = 5
Fig. 19 Displacements of the ground calculated by
FEM (at w = 170 mm, HG180).
33

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 19 shows the results of FEM analysis on
displacements of the ground when pulling the halfopened anchor vertically. Although the basic
tendency is not so different from that of the fully
opened anchor, the displacement area seems to be
distorted and slightly smaller. That might be thereason for the smaller Fmax. Thus, in practice, it isparticularly important to ensure that the anchor can be
fully opened to be perpendicular to the pull-out
direction. However, even if the anchor plate is half
opened, it could reach 70% of Fmax of the fully opened
anchor.

analysis certainly changes from “Shallow anchor”
to “Deep anchor” at (H/L)cr = 5, which was incommon with the value assumed empirically in[10].
4) In FEM analysis, F at w = L/2 is defined as Fmax inthe deep anchor condition.
5) When designing H of flip anchors, the amount ofw equivalent to L or 1.5 times L that is required
for flip anchors to open sufficiently, need to be
added to designed H for the installation depth.

What required to calculate Fmax of a flip anchor in
the same way as a plate anchor by FEM.

[1] Niroumand H., Kassim K. A. and Nazir R.,
Anchor plates in two-layered cohesion less soils,
American Journal of Applied Sciences, Vol. 7,
No. 10, 2010, pp. 1396-1399.
[2] Anchoring Rope and Rigging Pty. Ltd.,
https://www.arandr.com.au, 2022.
[3] Baker W.H. and Kondner R. L., Pullout load
capacity of a circular earth anchor buried in sand,Highway Research Record, Vol. 108, 1966, pp.
1-10.
[4] Das B.M. and Seeley G. R., Breakout resistanceof shallow horizontal anchors, Journal of the
Geotechnical Engineering Division, Vol. 101,
No. 9, 1975, pp. 999-1003.
[5] Dickin E.A., Uplift behavior of horizontal anchor
plates in sand, Journal of GeotechnicalEngineering, Vol. 114, No. 11, 1988, pp. 13001317.
[6] Niroumand H. and Kassim K.A., Pullout
capacity of irregular shape anchor in sand,Measurement, Vol. 46, No. 10, 2013, pp. 38763882.
[7] Balla A., The resistance to breaking-out of
mushroom foundations for pylons, Proceedingsof the 5th international conference on Soil
Mechanics and Foundation Engineering, Vol. 1,
1961, pp. 569-576.
[8] Majer J., Zur berechnung von zugfundamenten,
Osterreichister, Bauzeitschift, Vol. 10, No. 5,
1955, pp. 85-90.
[9] Mors H., Das Verhalten von Mastgruendungenbei Zugbeanspruchung, Bautechnik, Vol. 39, No.
10, 1959, pp. 367-378.
[10] Meyerhof G. G. and Adams J. I., The ultimate
uplift capacity of foundations, Canadian
Geotechnical Journal, Vol. 5, No. 4, 1968, pp.
225-244.
[11] Merifield R.S., Lyamin A.V. and Sloan S.W.,
Three-dimensional lower-bound solutions for thestability of plate anchors in sand, Géotechnique,Vol. 56, No. 2, 2006, pp. 123-132.
[12] Das B.M. and Puri V. K., Load displacement
relationship for horizontal rectangular anchors insand, Proc. 4th Int. Conf. Civil Struct. Eng.
Computing, London, Civil-Comp Press, Vol. 2,
1989, pp. 207–212.

REFERENCES

The above results agreed well with measured Fmaxof flip anchors; however these simulations do not take
into consideration the unique mechanism for flip
anchors that rotate and open in the ground after being
driven in closed state. The field pull-out experiments
have shown that flip anchors can attain about 80% ormore of Fmax of horizontal plate anchors having thesame A. Thus, if the amount of w required for flip
anchors to open sufficiently is taken into
considerations as Fig. 20, Fmax of flip anchors can be
estimated by FEM.
Compared to pre-embedded plate anchors at sameH, flip anchors should be installed (Driven) deeper byat least L. If the anchor plate is not fully opened, Fmax
will be about 70% as shown in Fig. 18. It is necessary
to estimate Fmax with such possibility in mind.
H

L

plate anchor

tailLflip anchor

Fig. 20 Difference in H between plate anchor and flip
anchor in calculation
CONCLUSIONSIn this study, the pull-out resistance of flip
anchors in sandy ground was calculated by FEM. The
ground failure modes were observed and thecalculated values were compared with measured
values of field experiments. Main conclusions in thepaper are as follows:
1) Fmax of flip anchors can be reasonably estimated byFEM.
2) By FEM, the behavior of the ground affected by
the pull-out of flip anchors, and relationship F vs.
w can also be obtained.
3) The ground failure patterns calculated by FEM
34

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

FIBER-BASED MEASUREMENT OF THE ACTIVATION OFGEOSYNTHETICS IN SOIL
Jan Derksen1, Jeanette Ortega2, Kira Heins2, Thomas Gries2 and Raul Fuentes1
1

Institute of Geomechanics and Underground Technology, RWTH Aachen University, Germany;
2
Institut für Textiltechnik, RWTH Aachen University, Germany
ABSTRACTThe integration of monitoring systems in critical infrastructure is becoming increasingly important forassessing the safety and reliability of these structures. In geomechanics, a large number of these infrastructures
such as dams, dikes, embankments or bridge abutments contain geosynthetics as reinforcements. This paper
presents a novel approach to capture geogrid activation in terms tensile strains by using a fiber-based electricalsensor system. The electrical properties of fiber materials as well as their ability to be integrated into or on a textile
justify a high potential for this fiber-based measurement concept. First, the metrological response of the fiberbased electrical sensor materials to tensile strain is determined and transferred into calibration curves. The
measurement system was applied in geogrid tensile tests in air. Moreover, geogrid pullout tests are performed withdifferent confining stresses to verify the applicability of the fiber-based electrical sensors to be used on
geosynthetic-reinforced earthworks. The test results with redundant measurements of Linear Variable Differential
Transformers (LVDTs) prove that the novel measurement system is generally applicable for measuring
geosynthetic strains, but has to be improved in terms of reliability for monitoring purposes.
Keywords: Geogrid, Sensor Fibers, Tensile Test, Pullout Test

INTRODUCTIONRWTH Aachen University in Aachen, Germany. In
order to avoid measurement disturbance throughmoisture in the soil or environment, bicomponent
thermoplastic filaments are spun, with a conductive
core and insulating sheath. The melt spinningparameters and the materials used are described in [5].
The characteristic properties of the employedfilaments are shown in Table 1 as well as a crosssectional microscopy image in Fig. 1.

Geosynthetic-reinforced soil (GRS) is an
important component of infrastructure as it is widelyused for retaining structures for slopes, cuttings,
dams, walls and abutments, to name but a few
applications [1]–[3]. Typically, geogrids are used asgeosynthetic reinforcements to enhance the overall
stability and to improve the settlement performanceof the structures by interaction mechanisms describedin [4]. Once the geogrids are embedded in the soil, the
GRS is a black box for the designer, as there is usuallyno information available about the mobilization of thereinforcement in the structure. However, theintegration of monitoring systems in criticalinfrastructure is becoming increasingly important forassessing the safety and reliability of these structures.
In this context, this paper presents a novel fiberbased electrical sensor material to measuregeosynthetic tensile strains in GRS structures. The
new measuring technique was tested in tensile tests in
air and in pullout tests in soil with the fiber attachedto the geosynthetic.

Table 1 Properties of the sensor filament
Property
Fineness
DiameterLinear resistivity
Tensile strengthMax. Elongation
Elastic limit

Units
dtex
µm
MΩ/m
cN/tex%%Value
1280 (± 83)
408.82 (± 6.55)
3.9 (± 0.5)
5.37 (± 0.34)
697.89 (± 44.11)
1.805 (± 0.043)
Insulating

Conductive

MATERIALS AND METHODS
Materials
Fibers200 µmThe sensor fibers employed in this work aredeveloped at the Institut für Textiltechnik of theFig. 1 Cross-sectional microscopy image of thesensor filament.
35

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Geogridstrain rate of the later measurements. The
experimental set up for the calibration curve
generation can be seen in Fig. 2.

In this study, a biaxial geogrid made frompolyester (PET) was used. The general specifications
are summarized in Table 2 for machine direction(MD) and cross-machine direction (CMD).
Table 2 Characteristic properties of the geogrid
Property
Polymer
Aperture size, dA
Mass per unit area
Ultimate tensile strength,
Tult in MD/CMD
Strain at ultimate tensile
strength, ε in MD/CMD
Tensile stiffness, J0–1%

Units
mm
g/cm³

Value
PET
25×25
160

kN/m

≥ 20/20%≤ 10/10kN/m

357

Fig. 2

Experimental setup for the calibration

Soil

Application of fiber on geogrid

A dry sand was used as fill material. According to
the Unified Soil Classification System (USCS) [6],the soil is classified as a poorly graded sand (SP). The
characteristic properties of the sand are given in
Table 3 for a relative density of Dr = 89%, which wasused in the subsequent pullout tests.

The application of the fiber-based sensor
technology on the geotextile is an essential issue for
ensuring the functionality of the system. The
application must ensure the transmission of the forces
and strains to the sensor fiber without influencing theproperties of the sensor fiber at the connection points.
In addition, the application method must be able towithstand the stresses that occur during handling,
installation and operation. Regardless of theapplication method, the sensor fiber must be appliedin a stretched state, so that stretching of the substrate
results in immediate stretching of the sensor fiber.
Bonding by means of individual clamps or textile
bonding methods such as sewing or embroidery are
ruled out because of the effort involved and the lackof reliability of the bond. In addition, these methods
involve an increased risk of premature damage to thesensor fiber due to mechanical clamping loads. In
comparison, the utilized adhesive bond is
uncomplicated to apply and avoids any additional
mechanical stress on the sensor fiber.

Table 3 Characteristic properties of the soil
Property
Grain density, ρsMean particle size, d50
Void ratio, emin/emax
Friction angle, ϕ’c’=0
Stiffness, Eoed

Units
g/cm³
mm
°
MN/m²

Value
2.65
0.5
0.482/0.842
43.9
52.9

Testing methods
Calibration curves
In order to understand the sensor response of thefilament, calibration curves are produced, which arethen used for the data interpretation of the trials in soil.
The filament is first contacted, in order to receive theelectrical signal from the core of the filament. To dothis silver paint was applied to the end of the filament.
This increases the accessible conductive area. Thissilver-tipped filament is then connected to a standard
copper wire with a mechanical crimp.
To record the change in resistance during
straining, a digital multimeter is used (Keithley 2100
Series: 6,5 Digit USB Multimeter, Tektronix, Inc.,
Oregon, USA). The strain is induced using a tensile
tester, zwickiLine Z2.5, ZwickRoell GmbH & Co.
KG, Ulm, Germany. The strain rate for the calibration
curves was held at 1 mm/min, in order to match theTensile tests in air
In order to investigate possible changes in thesensor response due to the application process, thefilaments applied the geogrid are also tested in air
before performing pullout tests in soil. For these tests
the tensile tester ZMART.PRO, ZwickRoell GmbH& Co. KG, Ulm, Germany is employed. The
maximum tensile force of this device is 20 kN, which
is needed to apply strain to the geogrid. The strain rateis held at 1 mm/min and the clamping length is
250 mm. The test setup for the tensile test in air of thesensor filament applied to the geogrid can be seen in
Fig. 3.

36

GEOMATE – Bangkok, Thailand, 22-24 November 2022stress was applied with an actuator loading the top
plate. Then, the geogrid specimen was pulled out of
the confined soil at the right end by a clamp moving
displacement controlled with 1 mm/min. The test
series comprises three tests with varying normal
stresses of σn = 7.5, 15 and 30 kN/m². At each test, thepullout load and displacement of the clamp wererecorded. Additionally, the fiber-based measurement
system was tested in the measurement section (A – B),
shown in Fig. 5. At the neighboring longitudinal
member conventional steel wires connected to
LVDTs outside the box were used as a redundant
control measurement.

Sensorfilament

50
mm

250
mm

Geogrid
Contact

10cm

steel wires

Fig. 3 Test setup for tensile test of applied filamenton the geogrid in air

contact A

control
A

(a)

B

fiber

contact B

Pullout tests in soil

steel wire

Pullout tests are typically used to quantify theinterfacial efficiency of geosynthetic reinforcementswith the fill. Its wide application is due to the ease ofexperimental realization and the standardized
framework [7]–[8]. The experimental setup of thepullout tests of this study is shown in Fig. 4.

fiber
(b)

B

Fig. 5

Detail of measurement section

Load cell
30 cm
21 cm

RESULTS AND DISCUSSION(a)43.7 cmσnClampucl; Fcl

20 cm

(b)

First, calibration curves of the fiber material aredeveloped that relate the electric resistance to thetensile strain. Second, the performance of the sensor
fiber is evaluated in geogrid tensile tests in air. Third,
the novel measuring system is tested for geogrids insoil subjected to pullout.

Pullout
direction

Calibration curves
75 cm
Load plate

Clamp

In Fig. 6, the electrical response of the filamentalone is shown for 7 repetitions.

LVDT

2

LVDT

Actuator(normal stress)

ΔR/R [-](c)

1,5

Actuator(pullout load)

10,5Fig. 4 Experimental setup for pullout tests: (a) top
view, (b) side view, (c) final setup

0

In the tests, geogrids with dimensions of
750 × 210 mm in length and width were used. The
mid-section of the geogrid was embedded in the testbox filled with soil and the rear ends were led out of
the box. On top of the specimen, a constant normal

0

Fig. 637

5Strain [%]10

Initial calibration of the filaments alone

GEOMATE – Bangkok, Thailand, 22-24 November 2022

It is clear that the filaments have a slight increase
until 5%, then a rapid increase after this point. The
filaments are relativity homogenous at low strains (0–5%), but deviate after this point. The focused regionof 0–5% can be seen in Fig. 7. The sensor response
can be considered linear at the lower strains andhomogeneous. This is only partially relevant for theapplication at hand, since strains up to 10% can beexperienced by the geogrid as stated in Tab. 2.

3,0
to 10% pretensioned

ΔR/R [-]

2,5

y = 0,2493x - 0,0099
R² = 0,3943

2,0
1,5
1,0
0,5
0,0

(a)

0

1

2
3
Strain [%]

4

5

4

5

3,0

y = 0,0091x + 3E-05
R² = 0,9562

0,06
0,04

0,02
0
0

Fig. 71

2
3
Strain [%]

4

1,5

0,0
0

(a) 10%
to 10% pretensioned

15

ΔR/R [-]

10
5
0

0

5
Strain [%]10(b) 1 MΩ

3,0

3,0

2,5

2,5

2,0

2,0

1,5

1,5

1,0

1,0

0,5

0,5

0,0
5
Strain [%](c) Filament
0,08
0,06
0,04
0,02

0,0
0

8

2
3
Strain [%]It is clear to see that the sensor response isstronger for the 10% pretensioned (larger slope), butthat the response is more homogenous for the 1 MΩ
pretensioned samples (larger R²). When comparing
the response of the single filament with the applied
filament, it can be seen that the response is much
lower for the single filament, but very homogenous.
These differences are compared in Fig. 10.

20

0,00
0

5
Strain [%]05Strain [%]to 1 MΩ pretensioned

4

Fig. 10 Comparison of the homogeneity andsensitivity, (a) prestrained to 10%, (b) prestrained to
1 MΩ, (c) not prestrained single filament

2

Performance evaluation in soil

6

ΔR/R [-]

1

Fig. 9 Sensor response for applied filaments only
0–5%, (a) to 10%, (b) to 1 MΩ

The sensor response is then repeated for thefilaments which are applied to the geogrid toinvestigate possible changes due to application. In
order to ensure reproducible results, the filaments areapplied in a pretensioned state. This is evaluated for apretension to reach a specific strain (10%) andspecific resistance (1 MΩ). The resulting graphs ofthe electrical response of the filaments in both states
can be seen in Fig. 8 and 9 (zoomed in to 0–5%).

0(b)y = 0,1221x - 0,0237
R² = 0,5791

1,0

(b)

Performance evaluation in air

ΔR/R [-]

2,0

0,5

5

Sensor response 0–5%

(a)

to 1 MΩ pretensioned

2,5

ΔR/R [-]

ΔR/R [-]

0,08

0

5
Strain [%]Fig. 11 shows the tensile force-displacement
curves of the clamp, pulling out the geosynthetic fromthe soil for the three confining stresses. Each test was
repeated (Test-1 and -2) and the good agreement of
the response curves confirmed the tests. Geogrid
pullout occurred for normal stresses up to 15 kN/m².

10

Fig. 8 Sensor response for applied filaments, (a) to10%, (b) to 1 MΩ
38

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Contrary, rupture at the clamp was observed at
30 kN/m², because the pullout resistance exceeded
the ultimate tensile strength of the geogrid. The
rupture is evident from the rapid decrease of tensile
load at a displacement of about ucl = 17 mm.
Test-120

σn = 30 kN/m²

Fcl (kN/m)

16

Once the geogrid was pulled out of the soil, thegeogrid strain remained relatively constant, eventhough a slight decrease was found at 7.5 kN/m² andan increase was detected for 15 kN/m². Overall, thegeosynthetic strains were found between 2–7%,
where no clear tendency of strain magnitude wasfound for different confining stresses.
As can be seen from Fig. 13, the fiber
measurement technique resulted in geosynthetic
strains that were reasonably consistent (a, c) with thecontrol measurement, or significantly deviate fromthe strains determined with LVDTs (b). For a normal
stress of 7.5 kN/m², both measurements represent
similar tensile strains up to clamp displacement of
15 mm. Then, the fiber recorded a rapid increase intensile strain, which was not detected by the LVDTs.
The reason can be explained with a local stretching of
the fiber, which will be discussed in detail in thechapter “limitations”. For a normal stress of 30 kN/m²,
the fiber measurement represented the tensile strainof the LVDT measurement accurately during themobilization of the pullout resistance (up to
ucl = 15 mm). From there, a constant tensile strain
was measured, which was, about 100% higher thanrecorded by the LVDTs. For a normal stress of
15 kN/m², the fiber measurement did not correspondto the geosynthetic strain at any clamp displacements.

Test-2σn = 15 kN/m²

12
8
σn =
7.5 kN/m²

4
0
0

5

10 15 20 25 30 35
ucl (mm)Fig. 11 Results pullout tests
The interface coefficients were calculated fornormal stresses up to 15 kN/m² according to the total
area method, which assumes an average shear stressalong the full anchorage length on the top and bottomof the geogrid. This assumption applies for thepresent tests since pullout failure of the entire geogrid
was observed. In Fig. 12, the internal shear strengthof the soil and the maximum mobilized shear stresses
at the geosynthetic-soil interface from pullout tests
are plotted. By relating the interfacial shear stress tothe shear strength of the soil, the interface efficiency
λ was determined to 0.95 and 0.91 for normal stressesof 7.5 and 15 kN/m², proving a high interaction
efficiency of the geosynthetic with the soil.

ε (%)

fiber
8
6
4
2
0

σn = 7.5 kN/m²

0

5

10

(a)

Internal shear
strength of soil

20

ε (%)

τ (kN/m²)

30

sand vs. geogrid(rupture at clamp)
sand vs. geogrid (pullout)

10

5

10

15 20 25σn (kN/m²)

30

35

Fig. 12 Interface efficiency from pullout tests

8
6
4
2
0

(c)

25

30

35

5

10

15 20
ucl (mm)

25

30

35

25

30

35

σn = 30 kN/m²

0

The fiber-based measurement of the geogrid
strains was tested in the measurement section asshown in Fig. 5. The results of the measured geogrid
strains are summarized in Fig. 13 for the three
confining stresses, where the dotted lines represent
the control measurement with wires connected to
LVDTs and the and continuous lines represent thefiber measurement. For the control measurementswith LVDT, the measured geosynthetic strains
increases as the pullout resistance was mobilized.

15 20
ucl (mm)

σn = 15 kN/m²

0

ε (%)

0

8
6
4
2
0

(b)

0

LVDT

5

10

15 20
ucl (mm)Fig. 13 Results pullout tests for normal stress of (a)7.5 kN/m², (b) 15 kN/m², (c) 30 kN/m²
In conclusion, the pullout tests revealed that thefiber measurement technique is generally applicablefor measuring geosynthetic strains (Fig. 13 a, c).
39

GEOMATE – Bangkok, Thailand, 22-24 November 2022

However, it should be emphasized that at the fiberused in this study, was not reliable enough torepresent the tensile strain in all tests and significant
deviations were detected during one of the three tests.

response is not only more accurate but more sensitive.
Furthermore, the material used can be modified, as alower resistivity can actually contribute to a higher
sensitivity of the sensor. For this, the particle
concentration can be lowered in order to find the
maximum sensitivity of the sensor filament.
Additional work must also be carried out in thesensor calibration. Possible other environmental
changes, such as pressure, moisture or temperature
can alter the electrical properties and must becompensated. The sensors should be tested in cyclic
stress as well as relaxation, in order to understand thelong-term changes of the electrical resistance when
the filament is pretensioned during application.

LIMITATIONS
The test results of the previous section haveclearly revealed that current development stage of thefiber measurement technique is not reliable enough to
be used in geotechnical structures as monitoring
system. The limitations of the system can besummarized as follows:• Inhomogeneous cross section of fiber resulting indeviating electrical resistance due to not
optimized spinning process• Difficult contacting of the filament to cable andgeogrid due to small dimensions• Sensitivity of the filament to mechanical strains
outside of the testing area (Fig. 14)• Filament relaxation from application to testing• Sensitivity of the filament to mechanical impactof the surrounding soil.

Undrawn filament

The authors would like to thank Huesker
Synthetic GmbH for providing geogrids for this study.
REFERENCES[1] Tatsuoka, F., Tateyama, M., Koseki, J. andYonezawa, T. 2014. Geosynthetic-Reinforced
Soil Structures for Railways in Japan.
Transportation Infrastructure Geotechnology, 1,
3–53.
[2] Allen, T. M., Bathurst, R. J. and Berg, R. R. 2002.
Global Level of Safety and Performance ofGeosynthetic Walls: An Historical Perspective.
Geosynthetics International, 9, 395–450.
[3] Skinner, G. D. and Rowe R. K. 2005a. Design andbehaviour of a geosynthetic reinforced retaining
wall and bridge abutment on a yielding foundation.
Geotextiles and Geomembranes, 23, 234–260.
[4] Derksen, J., Ziegler, M. and Fuentes, R. 2021.
Geogrid-soil interaction: A new conceptual modeland testing apparatus. Geotextiles andGeomembranes, 49, 1393–1406.
[5] Ortega, J., & Gries, T. 2022. Investigation of
production influences on the properties ofresistive based filament sensors. Fiber Society
Spring Conference, 30 May – 1 June, 2022,
Leuven, Belgium.
[6] ASTM D 2487. Standard Practice forClassification of Soils for Engineering Purposes(Unified Soil Classification System), American
Society for Testing and Materials, WestConshohocken, USA.
[7] ASTM D6706 (2013): Standard Test Method for
Measuring Geosynthetic Pullout Resistance inSoil. ASTM Standards, West Conshohocken,
USA.
[8] DIN
60009(2017):
Geosynthetics
Determination and testing of the interaction
coefficient to the soil using pullout test, Beuth,
Berlin, Germany

5 mmNon-sensing area
Necking

ACKNOWLEDGMENTS

Sensing area

Drawn filament

Necking from drawing

200 μmFig. 14 Deformation of the filament
CONCLUSIONS & OUTLOOKThe results of this work are promising in showingthat the measurement of strains in geostructures isprincipally possible when employing filament-based
sensors. However, the need for process optimization
has been demonstrated. In the next steps, theprocessing parameters of the spinning trials for thefilament production will be further investigated andimproved. Afterwards, it is expected that the sensor

40

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

SATELLITE-BASED GROUND DEFORMATION MONITORING IN
NAGA CITY, CEBU ISLAND IN THE PHILIPPINES
Ryan A. Ramirez1,2, Rajiv Eldon E. Abdullah1 and Christabel Jane P. Rubio 1,2
1Faculty of Engineering, University of Santo Tomas, Manila 1015, Philippines; 2National Research Councilof the Philippines, Taguig 1631, Philippines
ABSTRACT

Human activities are increasingly altering subsurface conditions leading to unwanted ground deformations.
Excessive ground deformations endanger buildings, linear systems, underground facilities, and ultimately,
people’s safety. Thus, mapping and monitoring ground deformation are critical in managing disaster risk andmitigating socio-economic damage from possible geohazards. Accessible and archived satellite data and thecontinual development of advanced remote sensing technologies can now provide valuable information about theearth’s surface for various applications. This study applied the descending Sentinel-1 Persistent Scatterer
Interferometric Synthetic Aperture Radar (PSInSAR) technique for long-term remote ground deformation
monitoring over the coastal city of Naga in Cebu Island in the Philippines. The Sentinel-1 PSInSAR techniqueprovided relatively dense ground monitoring points, specifically over built-up areas. The maximum groundsubsidence along the radar line-of-sight (LOS) estimated throughout the monitoring period from October 2014 toSeptember 2018 exceeds –50 mm, whereas the LOS mean displacement velocity exceeds –15 mm/yr. The twoparameter hyperbolic model well fitted the PSInSAR results to describe the nonlinear behavior of grounddeformation. Ground deformation over the coastal city continues to evolve, and stabilization is yet to occur. This
study suggests that the Sentinel-1 PSInSAR technique can provide technical support in mapping and monitoring
ground deformation over vast areas with less human labor and efficient cost.
Keywords: Sentinel-1 PSInSAR, Ground deformation monitoring, Hyperbolic model, Coastal city, Cebu Island

INTRODUCTION

since 2014, which the scientific community is
enjoying for various earth and geoinformation
applications. Specifically, the Persistent Scatterer
Interferometric SAR (PSInSAR) has beensuccessfully applied in various geohazard
applications [4]. Theoretical and technical reviews ofdifferent PSInSAR algorithms are available in theliterature [5]–[6]. PSInSAR provided the opportunity
to detect, map, and monitor ground deformations
along the coasts caused or enhanced by natural andanthropic activities [7]–[12]. In low-lying coastal
urban areas in Metro Manila, Philippines, grounddeformation from 1993-to-2010 has been
investigated using differential InSAR (DInSAR) [13].
Nevertheless, limited studies are available in theliterature exploiting the S1-PSInSAR technique inother coastal cities in the Philippines experiencing
short- to long-term nonlinear ground deformations
reflecting the complex interaction among different
natural and anthropic processes.
This study applied the PSInSAR technique forlong-term remote ground deformation monitoringover the coastal city of Naga in Cebu Island in thePhilippines using descending S1 SAR dataset
acquired between October 2014 and September 2018.
The cumulative line-of-sight (LOS) displacement
acquired from the S1-PSInSAR analysis was modeledusing a two-parameter hyperbolic model to describe
the nonlinear ground deformation process over a

Reclamation projects along coastal cities forindustrial, residential, and commercial use haveallowed land expansions. Human activities aiding
continual urban sprawl over reclaimed lands are
increasingly altering subsurface conditions resulting
in unwanted ground deformations. Excessive grounddeformations endanger structures, linear systems,
underground facilities, and ultimately, people’ssafety. Thus, ground deformation mapping and longterm monitoring over coastal cities are critical toprevent disaster risk and mitigate socio-economic
damage from such geohazards [1].
Ground-based geodetic methods such as precise
leveling and terrestrial measurements such as Global
Positioning System (GPS) are standard monitoring
methods providing accurate and reliable grounddeformation [2]–[3]. However, these methods are
inefficient to deploy over vast areas and long term
because of the workforce needed and the high costassociated. Meanwhile, accessible and archived
satellite Synthetic Aperture Radar (SAR) data and thecontinual development of advanced remote sensing
techniques have emerged as viable solutions forgeohazard mitigation. The launch of the Sentinel-1(S1) mission under the Copernicus Program of theEuropean Space Agency (ESA) and European
Commission (EC) has provided accessible SAR data
41

GEOMATE – Bangkok, Thailand, 22-24 November 2022

multipurpose complex zone built over reclaimed land
in Naga city.

tube. The image acquired on July 31, 2017, was set as
the master image to mitigate the decorrelation effect.

STUDY AREA AND SAR DATASET
Study Area
Cebu Island’s geological structure comprises a
Jurassic-Cretaceous igneous and metamorphic
basement complex and Cenozoic sedimentary andvolcanic rock units [14]–[15]. The island is classified
as a tropical rainforest [16]. The Philippine
Atmospheric, Geophysical and Astronomical
Services Administration (PAGASA 2022) reported amean annual rainfall of 1,565 mm for Cebu Island.
Naga city is located on the southeastern coast of Cebu
Island, characterized by karst terrain. Ridges andvalleys converged to a coastal plain in thesoutheastern part of the city. Land use ispredominantly cultivated lands with quarry zones.
Small villages are located inland, whereas built-up
areas are concentrated mainly along the coastal plain.
The nearest potentially active faults are the South andCentral Cebu faults; the latter passes through Nagacity. Between October 2014 and September 2018, thePhilippine Institute of Volcanology and Seismology(PHIVOLCS 2018) recorded one earthquake ofmagnitude 5.2, 7 earthquakes of magnitude between
4 and 5, 45 earthquakes of magnitude between 3 and4, and 173 earthquakes of magnitude between 2 and3 that shook Cebu Island. There were also 14
earthquakes with a magnitude below 2.

Fig. 1 S1 SAR dataset. The vertical and horizontal
axes represent the perpendicular and temporalbaselines of interferometric pairs.
METHODS
Advanced Time Series InSAR Analysis
The S1 SAR images were processed following a
two-staged processing workflow, namely (1) multiple
differential interferogram formation and (2) timeseries InSAR analysis. In Stage 1, the SeNtinel
Application Platform (SNAP), freely distributed by
ESA, was utilized, whereas, in Stage 2, the opensource software package of Stanford Method for
Persistent Scatterers (StaMPS) was used [18].

SAR Dataset
Stage 1: SNAP pre-processing
The SAR dataset exploited in this study was
acquired by the S1 mission operated jointly by ESA
and EC under the Copernicus Program. The S1
mission consists of two right-side looking satellites:
S1-A, which was launched in 2014, and S1-B, which
was launched in 2016. S1 sensors operate at awavelength λ equal to 5.6 cm (C-band). The twin
constellation has a near-polar sun-synchronous orbit
with a 6–12 days repeat cycle over the exact location.
S1 SAR products acquired in the interferometric wideswath mode have a ground range coverage ofapproximately 250 km, thanks to the Terrain
Observation by Progressive Scans (TOPS) mode [17].
The geometric resolution in the range and azimuth
directions is 5 m × 20 m, respectively.
This study collected 139 S1 single look complex
SAR images from October 21, 2014, to September 18,
2018. The images were acquired by the S1descending satellite (track 61), whose flight is from
north to south direction. The minimum and maximum
time gap between each successive image are 6 and 48
days, respectively, as shown in Fig. 1. Allinterferometric pairs have a perpendicular baseline ofless than 150 m, thanks to the improved S1’s orbital

First, a master image from the S1 SAR dataset isselected under good weather conditions. To this aim,the InSAR Stack Overview tool in SNAP was used.
Then, the open-source snap2stamps package tool
automated the pre-processing steps [19]. The area ofinterest (AOI) was selected during the image splitting
and orbit file correction to reduce the computational
effort. Subsequently, the interferogram formation
followed immediately after the coregistration of slave
images to the geometry of the master image using
back-geocoding with Enhanced Spectral Diversity(ESD). Afterward, the Shuttle Radar Topography
Mission digital elevation model (SRTM DEM) 1
arcsec data in the SNAP library allowed topographic
phase removal. Then, debursting of the coregisteredimages and differential interferograms followed to
produce seamless image products. These products arethen exported in Stage 2.
Stage 2: StaMPS processing
After exporting the SNAP pre-processed image
products into the format supported for StaMPS
42

GEOMATE – Bangkok, Thailand, 22-24 November 2022

processing in Matlab, an amplitude dispersion index(ADI) of 0.42 was used to select and load initial PS
candidates (PSCs). Then, the phase noise value foreach PSC in every differential interferogram wasestimated iteratively. Based on the estimated phasenoise characteristics, final PSs were selected, andthose PSs deemed noisy were weeded afterward. The
final PSs in the wrapped phase were then corrected
for DEM error. The corrected phase information isthen unwrapped using the open-source software,
Statistical-Cost, Network-Flow Algorithm for Phase
Unwrapping (SNAPHU) [20]. Then, the spatiallycorrelated look angle (SCLA) error is estimated andremoved from the unwrapped data. Finally, phaseerrors attributed to atmospheric perturbations are
mitigated using spatiotemporal filters.
The immediate results after Stage 2, such as the
LOS mean displacement velocity map and timeseries, are relative measures concerning a selectedreference area. For this purpose, a preliminary S1PSInSAR analysis with an ADI of 0.60 was
performed, introducing more PSCs corrupted withnoise. Then, the AOI was inspected for a stablereference area by evaluating the LOS mean
displacement velocity and time series of PSs groups
within a moving window with a 20-m radius. The
mean velocity within the selected reference area must
be between ±1 mm/yr with a nearly horizontal
displacement time series.

derived using regression analysis. The root-meansquared error (RMSE) and coefficient ofdetermination (R2) were optimized to constraint thetwo constants.
RESULTS AND DISCUSSION
Spatial Distribution of Measurement Points
After the S1-PSInSAR processing, 5,767 quality
PSs were recognized over the AOI. Figure 2 shows
the LOS mean displacement velocity map. PSs wereplotted relative to the selected reference stable area
(i.e., negligible ground deformation). Warm colors
represent a movement away from the radar (i.e.,
subsidence), and cold colors indicate a movement
towards the radar (i.e., uplift). Most PSs weredetected over built-up areas, characterized by houses,
buildings, and roads. On the one hand, there is thecomplete absence of PSs over vegetated areas andwater bodies, which are normally subjected to rapidchanges between two S1 SAR image acquisitions.
These rapid surface changes lead to temporaldecorrelation.

Hyperbolic Modeling
Due to the success of applying and validating
advanced time series InSAR using various archived
SAR datasets, modeling the long-term temporal
evolution of ground deformation has been possible
with several mathematical functions [21]–[22]. The
fitted model describing the settlement curve obtained
from advanced time series InSAR is typically thehyperbolic function. Advanced time series InSAR
results from existing literature utilized distributed
scatterers (DSs), whose phase behavior are different
from those of PSs. Generally, DS-based time series
InSAR techniques are preferred for non-urban areas
with considerable vegetation cover or when few SAR
images are available. On the one hand, the PSInSAR
technique is more suitable for ground deformation
monitoring in urban areas. Therefore, in this study, a
two-parameter hyperbolic model was fitted to our S1PSInSAR results to describe the nonlinear
deformation phenomenon over Naga city, specificallyon the reclaimed land where a multipurpose complex
zone is built. The displacement d at a specific time t
described by the hyperbolic model is expressed in Eq.
(1), as shown below [23].

d = t /( + t )Fig. 2 LOS mean displacement velocity map over thecoastal area in Naga city, Cebu Island, Philippines.

(1)
Subsiding areas were detected and mapped during
the analysis period over the reclaimed land, thewhere α and β are the PS point-specific constants
43

GEOMATE – Bangkok, Thailand, 22-24 November 2022

relatively high R2 (>0.90).

eastside of the coastline in Fig. 2, where amultipurpose complex zone is built. However,groundworks on the east side of the reclaimed land
during the monitoring period resulted in a lack of PSs.
The maximum subsidence rate exceeded –15 mm/yr.
The inland, located west side of the coastline, wasgenerally stable, with most of the PSs having a LOS
mean displacement velocity between ±5 mm/yr. The
distribution of PSs concerning the ground movement
type is shown in Fig. 3.

Fig. 3 Distribution of LOS mean displacement
velocity for PSs over the AOI. The inset shows thenumber of PSs with subsidence rate ≤–15 mm/yr.
Temporal Evolution of Ground Deformation
Four representative PSs or measurement points
were selected within the subsiding zone near thecoastline over the AOI. These four points are labeled
as PSs D1–D4 in Fig. 2. PSs D1, D2, D3, and D4 arelocated over the sports arena, public market,
industrial flour milling company, and national road.
The subsidence rate of these points exceeds –10
mm/yr. The cumulative LOS displacement time seriesof the PSs are plotted in Figs. 4a–d. Accumulated
subsidence of the PSs varies depending on theirlocation over the AOI. At the end of the monitoring
period in September 2018, the subsidence values of
PSs D1, D2, D3, and D4 were –86.807 mm, –70.193
mm, –53.146 mm, and –45.074 mm, respectively.
The maximum subsidence was recorded for PS D1,
located over the reclaimed land. On the one hand, PSs
recognized over the inland (i.e., PS D4) record lower
subsidence values than PS D1.
The hyperbolic curve fitted to the S1-PSInSAR
displacement time series of PSs D1–D4 is also plottedin Figs. 4a–d. Table 1 summarizes the optimized
hyperbolic model constants. The constant α is highly
dependent on the spatial location of the PSs than thatof the constant β. The modeled cumulative LOS
displacements are in excellent agreement with the S1PSInSAR results with low RMSE (<4 mm) and

Fig. 4 Cumulative LOS displacement time series of
PSs D1–D4, including the fitted hyperbolic curve.
Table 1 Optimized hyperbolic model constants
PS
No.
D1
D2
D3
D4

Model constantsαβ–9.367 –0.0049–6.634 –0.0088–13.965 –0.0087–27.809 –0.0045

RMSE

R2

2.691
2.847
3.222
2.747

0.99
0.98
0.95
0.94

The modeled displacement time series of the four
PSs implies that subsidence may have occurred
before the start of the satellite-based monitoring
period. However, the hyperbolic function might nothave captured the commencement stage of thesubsidence phenomenon [23]. Nevertheless, themodeled results suggest that subsidence slowly
decelerates faster over time for PSs D2 and D3. For
PSs D1 and D4, the modeled results suggest thatstabilization is yet to occur over the coastal area.
Using the Google Earth Street View function, a44

GEOMATE – Bangkok, Thailand, 22-24 November 2022

walkthrough over the AOI showed signs of active
subsidence, such as hairline cracks on buildings, roadpavements, and masonry fences.

validate our observations, is indispensable.
Nevertheless, the S1-PSInSAR results reported
here recommend that continuous ground deformation
monitoring is needed until the ground has beenstabilized completely.

Potential Contributory Factors of Subsidence

CONCLUSIONS

The modeled cumulative LOS displacement timeseries of PSs D1–D4 was plotted with the monthly
average precipitation from October 2014 toSeptember 2018 in Fig. 5 to verify the cause of
nonlinear subsidence over the coastal area. The
weather monitoring station of PAGASA in Cebu
Island is located at Mactan Station (10°19'18.15"N,
123°58'33.7"E). The collected precipitation datashows good relation with the modeled displacement
time series.

This study demonstrated the S1-PSInSAR
technique for long-term ground deformation
monitoring over the coastal city of Naga in Cebu
Island in the Philippines. The satellite-based SAR
data and advanced remote sensing technique detected,
mapped, and monitored the nonlinear grounddeformation in the multipurpose complex zone built
over reclaimed land. The two-parameter hyperbolicmodel was fitted to the S1-PSInSAR displacement
time series to describe the nonlinear grounddeformation behavior. Overall, the modeled results
show excellent agreement with the S1-PSInSAR
results, suggesting maximum ground subsidence over
–50 mm and subsidence rate over –15 mm/yr. Grounddeformation over the reclaimed land continues to
evolve related to natural and anthropogenic
processes, and stabilization is yet to occur. This study
suggests that the S1-PSInSAR technique can provide
technical support in mapping and monitoring grounddeformation over vast areas under different
meteorological conditions with less human labor andefficient cost.
ACKNOWLEDGMENTS
The authors would like to thank the National
Research Council of the Philippines (NRCP) for thefinancial assistance provided through the Support to
Research Dissemination in Local and International
Platforms (RDLIP) component of the Basic ResearchInformation Translation for Empowerment in the
Regions (BRITER) Program.

Fig. 5 Relation of modeled cumulative LOS
displacement of PSs D1–D4 with average monthly
precipitation from October 2014 to September 2018.
Most of the highest monthly average
precipitations (>200 mm) were recorded between
January 2017 and July 2018. In the case of PSs D2
and D3, subsidence may have decelerated after
precipitation has recharged the groundwater [24]–[25]. In the case of PSs D1 and D4, subsidence may
be related to precipitation and other contributory
natural factors, including the geology of Naga city,
the thickness of compressible soil layers along thecoastal area, and the swarm of seismic activities
during the monitoring period. Anthropogenic
processes or human interventions such as excessive
groundwater exploitation, continual urbanization,industrialization, and quarrying may have also
contributed to the different ground deformations over
the AOI. However, detailed analyses were notpossible due to the lack of available ancillary datacollected during the study period. Further
investigation, including in-situ measurements to

REFERENCES[1] Galloway D.L., and Burbey T.J., Review:
Regional Land Subsidence Accompanying
Groundwater Extraction. Hydrogeology Journal,
Vol. 19, 2011, pp.1459–1486.
[2] Abidin H.Z., Andreas H., Djaja R., Darmawan D.,
and Gamal M., Land Subsidence Characteristics
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46

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

FINITE ELEMENT PARAMETRIC STUDY OF CONTIGUOUS PILE
WALL REINFORCEMENT IN A DEEP EXCAVATION
Fachrizal Naufal Indrawan1, Fikri Faris2 and Akhmad Aminullah3
1

Department of Civil and Environmental Engineering, Universitas Gadjah Mada, Indonesia
ABSTRACT

Deep excavation for underground structures has become a common practice in the world. This study discusses
the parametric study of contiguous pile wall (CPW) reinforcement in a deep excavation in dry medium dense sandsoils. The parametric study focuses on studying the effect of excavation dimension ratio, pile diameter (d), andcap beam dimensions on lateral displacement and bending moment behaviour of CPW reinforcement. The researchmodel is set as an example case of a square excavation in dry medium dense sand with a depth of excavation (he)7.50 m, and the pile length is 18.50 m, with 11.00 m penetration depth (D). 3-D and 2-D finite elements are used
to support this research. Rocscience RS3 was used for 3-D analysis and RS2 for 2-D analysis. The result showedthat 3-D analysis is effective in a square excavation with a small dimension ratio (B/L ≤ 2), while 2-D analysis is
more suitable for a square excavation with a large dimension ratio (B/L ≥ 3). Increasing the pile diameter reduces
the lateral displacement of the pile, although it increases the bending moment. The cap beam on the pile head
reduced the lateral displacement on the pile by 45% and the bending moment by 54% compared to the pile without
the cap beam. Increasing the cap beam dimensions also reduced the lateral displacement of the pile but increased
the bending moment, although not significantly.

Keywords: Parametric study, Contiguous pile wall, Deep excavation, Finite element

INTRODUCTION

excavation dimension ratio and cap beam on lateraldisplacement and bending moment of the CPW
reinforcement were less studied.
This study focused on studying the lateraldisplacement and bending moment behavior of CPW
reinforcement based on variations in several
parameters such as excavation dimension ratio, pile
diameter, and cap beam dimensions in 3-D and 2-D
models using Rocscience RS3 and RS2. In 3-D
analysis, this study assumed the pile and the cap beammodel as a beam element to approach the actual
condition of the reinforcement configuration in thefield. The 2-D analysis was used to verify variationsin the ratio of excavation dimensions in the 3-D
analysis.

The main problem in deep excavation work andsub-structure of high-rise buildings in dense urban
areas is the narrow space and adjacent buildings. If
forced, it will result in excessive ground movement,
deformation, subsidence, and even damage toexisting structures around the excavation site [1]. Analternative solution to the problem of deep excavationand sub-structure of high-rise buildings in narrow
land and adjacent buildings is contiguous pile walls(CPW) reinforcement. CPW is an expensive
reinforcement, but it can save space and time,
especially in dense urban areas.
The study of deep excavation with CPW
reinforcement is a popular topic, especially in thegeotechnical field. Several researchers used twodimensional (2-D) and three-dimensional (3-D) finite
elements (FE) to analyze the complexity of deep
excavation problems. The 2-D analysis has provenmore economical and relevant in continuous
modeling (plane strain), fast modeling, andcalculation. 2-D FE also does not require a high-specs
computer as required in 3-D. A 3-D analysis is themost realistic analysis and approaches the actual
conditions in the field compared to the 2-D analysis[2]. Many studies have explored the behavior of soil
and reinforcement in deep excavations using FE,
including [2-17], advantages of 3-D analysis over 2D [18-23], and soil-structure interaction behavior:[24-26]. In these previous studies, the effect of

NUMERICALSIMULATIONPARAMETERS USEDAND

Deep excavation design requires carefulconsideration of the strength and stability of various
structural elements at all stages during theconstruction process [25]. Excavated wall
deformation is crucial in selecting a stabilization
system [9]. In this study, deep excavation modelingwith CPW reinforcement in homogenous dry medium
dense sand was carried out to represent theconstruction sequences, the excavation andreinforcement behavior, soil-structure interaction,and conditions in the field. The Mohr-Coulomb (MC)47

GEOMATE – Bangkok, Thailand, 22-24 November 2022

constitutive model and the reinforcement parameters
used were determined based on the existing literature.
Figure 1 illustrates the model of a deep excavationcase in sandy soil with a square geometry used in thisstudy. It shows a model with an excavation depth (he)of 7.50 m, and the pile length is 18.50 m, with 11.00
m pile penetration depth (D). A surcharge load of
10.00 kN/m2 with a width of 10.00 m was applied toeach side of the excavation. The equation for
determining the pile distance (s) is:𝒔 = 𝒅 + 𝟎. 𝟏𝟎

physical and mechanical properties of medium-dense
sand are shown in Table 1.
Table 1 Physical and mechanical properties of soilParameter

Value
3

Unsaturated unit weight (kN/m )3

Saturated unit weight (kN/m )2(1)

Since the excavation did not consider the wall
impermeability, the pile distance (𝑠) was determinedby Eq. (1). Hence, the optimal pile configuration canbe obtained while still being easy and realistic toimplement in the field. The excavation dimensions
will be varied on the primary side (L) while thecomplementary side (B) is fixed. Variations were also
made on the diameter of the pile ( 𝑑 ) and thedimensions of the cap beam. Rocscience RS3 andRS2 simulated all execution sequences, such as
reinforcement installation, excavation, and surcharge
loading in 3-D and 2-D models.

20.00

Young’s modulus (kN/m )25 x 103

Poisson’s ratio

0.30
2

where: 𝒔 = Pile distance (m), 𝒅 = Pile diameter (m).

17.00

Cohesion (kN/m )1.00

Friction angle (degree)

35.00

Dilatancy angle (degree)

5.00

Material behaviour

drained

The excavation support system in this study
consists of a contiguous pile wall and a cap beam at
the head of the pile, which were modeled in 3-D and
2-D conditions. Reinforced concrete piles used indeep excavation analysis with 3-D and 2-D FE would
apply four types of diameters ( 𝒅 ) variations,
including d-1 = 0.60 m, d-2 = 0.80 m, d-3 = 1.00 m,
and d-4 = 1.20 m. The distance between the piles (𝒔)is determined by Eq. (1).
The pile length was 18.50 m with 11.00 pilepenetration depth (D), and the excavation height (he)was 7.50 m. Piles in the 3-D analysis were modelled
as circular vertical beam elements with linear elastic
behaviour.
In the 2-D analysis (plane strain), theconfiguration of the CPW reinforcement must be
equivalent due to the limitations of the 2-D model.
Equivalence was carried out by modeling the pilerows as a continuous structure (liner/plate). It was
assumed that the moment of inertia of the pile rows in
the 3-D model would equal the moment of inertia ofcontinuous structure (liner/plate) in the 2-D model.
Cap beam (CB) modeling on the pile head in the
3-D analysis was assumed to be a horizontal beam
element. Cap beam was only used for 3-D analysis
because, in the 2-D plane strain model, thereinforcement is supposed to be a continuous
structure (liner/plate). The dimensions of the cap
beam are adjusted to the diameter of the pile (𝒅). Fig.
2 shows the typical cap beam dimensioning as
suggested by [28]. Several variations of thedimensions are used including: CB1: 0.825 x 0.90 m,
CB2: 1.025 x 1.10 m, CB3: 1.225 x 1.30 m, and CB4:
1.425 x 1.50 m.

Fig. 1 Excavation layout modelling with FE

Soil Constitutive Model and Material Properties
Medium dense sand soil analysis was carried out
under drained conditions using effective stress. MohrCoulomb (MC) constitutive models in RS3 and RS2
were applied for sand idealization. A linear-elastic
perfectly plastic Mohr-Coulomb (MC) idealization
for sandy soil was adopted from several previous
studies such as those [2, 6, 11, 26]. Some propertiesof medium dense sand, such as unit weight, Poisson's
ratio, friction angle, and Young's modulus, werechosen as suggested by [27]. The determination of thecohesion and the dilation angle of sandy soil using[11]. The analyses used a low cohesion value (𝒄 =𝟏. 𝟎𝟎 𝐤𝐍⁄𝐦𝟐 ) for sandy soil to avoid numericalinstability and errors during calculations. The

48

GEOMATE – Bangkok, Thailand, 22-24 November 2022

and medium-quality mesh for the boundaries area
where the stress is low. The 3-D excavation model isshown in Fig. 3.

Fig. 2 Cap Beam (CB) typical design (Wells, 2009)

Fig. 3 Finite element model in 3-D

At the surface level, a surcharge load (q) of 10.00
kN/m2 with 10.00 m width was applied to eachexcavation side. The application of surcharge load tothe model is shown in Fig. 1. Surcharge loads
represent external loads such as heavy equipment,
excavated soil, and other loads (SNI 8460:2017).

The boundary conditions for the 2-D model wereset at 105.00 m x 37.50 m, with a surcharge load
applied to each side of the excavation. The 6-noded
triangular grade was used as a meshing configurationin the 2-D.

PARAMETRIC STUDY VARIABLES
The parametric study focused on studying theeffect of excavation dimension ratio (B/L), pile
diameter (𝑑 ), and cap beam dimensions on lateraldisplacement (Utot) and bending moment (Mx)behavior of CPW reinforcement. The variation
excavation dimension of 3-D models are model A (15x 15 m2), model B (15 x 30 m2), model C (15 x 45m2),
model D (15 x 60 m2), while model E is twodimensional. Each model was run by variation of pile
diameter which are d-1 (0.60 m), d-2 (0.80 m), d-3(1.00 m) and d-4 (1.20 m). Cap beam effect analysis
was only carried out on the pile with d1 = 0.60 m andexcavation (B): 15.00 x 30.00 m2.

Fig. 4 Finite element model in 3-D
RESULTS AND DISCUSSION
The analyses results in Fig. 5 consistently showthat with the more significant excavation dimensions
ratio (B/L), the lateral displacement (Utot) occurs in all
variations of pile diameters (d-1, d-2, d-3, and d-4) also
increases. The 3-D analysis has proven to be more
effective for a square excavation with small
dimension ratios (B/L ≤ 1:2), such as excavation in
conditions (A) and (B). The 2-D analysis is moresuitable for a square excavation with large dimension
ratios (B/L ≥ 1:3), such as excavation in conditions(C) and (D).
Lateral displacement (Utot) value in 3-D analysis
for excavation (C), on pile d-1= 0.60 m was 0.0743 m,
d-2= 0.80 m was 0.0550 m, d-3= 1.00 m was 0.0455
m, and for d-4= 1.20 m was 0.0396 m. For excavation(D), lateral displacement (Utot) on pile d-1= 0.60 m
was 0.0783 m, d-2= 0.80 m was 0.0583 m, d-3= 1.00
m was 0.0489 m, and for d-4= 1.20 m was 0.0436 m.

NUMERICAL SIMULATION
Numerical simulations were conducted using 3-D
and 2-D models, as discussed before. The first step
was applying the surcharge load at the ground levelon each excavation side, and then the second step was
installing the pile and cap beam. Step-by-step
excavation was carried out after the installation of thereinforcement. The result of lateral displacement andbending moment used in the output graph is the
maximum value for the pile located at the middle ofthe excavation in each analysis (critical pile).
The boundary conditions in the 3-D model varied
depending on the dimensions of the excavation to beanalyzed. The meshing configuration used was a 10noded tetrahedron graded with high-quality mesharound the excavation area where the stress is high49

GEOMATE – Bangkok, Thailand, 22-24 November 2022

square excavation with large dimension ratios (B/L ≥1:3), such as excavations (C) and (D), is close to theresult of lateral displacement (Utot) of excavation (E)or 2-D (plane strain).

In the 2-D analysis, the lateral displacement (Utot) that
occurs on pile d-1= 0.60 m was 0.0789 m, d-2= 0.80
m was 0.0597 m, d-3= 1.00 m was 0.0518 m, and for
d-4= 1.20 m was 0.0479 m. This shows that the valueof lateral displacement (Utot) in 3-D analysis for a
Lateral Displacement (m)
0.03
0.06

0.09

0

0

0

2

2

4

4

6

6

8

8

Z (m)

Z (m)

0

10
Excavation (A): 15 x 15 m

12

Excavation (A): 15 x 15 m

12

Excavation (B): 15 x 30 m

14

Excavation (C): 15 x 45 m
Excavation (D): 15 x 60 m

16

Excavation (E): 15 m 2-D

18

Excavation (D): 15 x 60 m

18

Excavation (E): 15 m 2-D(b) d-2= 0.80 m

0

0.09

0

0

2

2

4

4

6

6

8

8

Z (m)

Z (m)

Lateral Displacement (m)
0.03
0.06

10
12

Excavation (A): 15 x 15 m

Lateral Displacement (m)
0.03
0.06

0.09

10
12

Excavation (B): 15 x 30 m

14

Excavation (C): 15 x 45 m

16(a) d-1= 0.60 m

0

0.09

10

Excavation (B): 15 x 30 m

14

Lateral Displacement (m)
0.03
0.06

Excavation 15 x 15 m
Excavation 15 x 30 m

14

Excavation (C): 15 x 45 m

Excavation 15 x 45 m

16

Excavation (D): 15 x 60 m

16

18

Excavation (E): 15 m 2-D18(c) d-3= 1.00 m

Excavation 15 x 60 m
Excavation 15 m 2-D(d) d-4= 1.20 m

Fig. 5 Lateral displacement of CPW reinforcement in medium dense sand (He=7.50 m, q=10.00 kN/m2).
The cap beam at the pile head significantly affects
the lateral displacement (Utot) and bending moment(Mx) that occurs in a pile. It shows that the value oflateral displacement (Utot) for piles without cap beam
(NO-CB) yielded lateral displacement (Utot) of 0.0698
m and bending moment (Mx) of -181.26 kN.m. In
contrast, the cap beam dimension (CB1) reduced thelateral displacement (Utot) significantly by 45% to0.0378 m. The bending moment (Mx) was decreasedby 54% to 83.240 kN.m. Figure 6 shows that
increasing the cap beam dimensions also reduced thelateral displacement (Utot). However, it showed anincreased bending moment (Mx), although it is not
significant.
These results show the effectiveness of cap beam
as CWP reinforcement and stiffener. Enlarging
inertial stiffness of the cap beam ability in therefore
can potentially replace the use of invasive ground50

GEOMATE – Bangkok, Thailand, 22-24 November 2022

anchor or a space consuming battered pile. However,the use of large cap beam should still consider the
specific environmental and architectural constraints.

The use of a cap beam on the pile head alsochanges the displacement pattern that occurs in the
CPW reinforcement.
Fig. . shows the displacement pattern of CPW
reinforcement without a cap beam, the piles tend towork individually while the use of a cap beam withthe minimum dimensions (CB1) in

Fig. . shows a more concentrated displacement
pattern on the primary side (L) of the excavation
which indicates the reinforcement works in monolith.

Bending Moment (kN.m)

Lateral Displacement (m)0.01875 0.0375 0.05625 0.075-240 -180 -120
0

2

2

4

4

6

6

8

8

Z (m)

Z (m)

0

0

10
12

18

60

12
NO-CB
14

CB1
CB2

16

0

10

NO-CB
14

-60

CB1
CB2

16

CB3
CB4

18

(a) Lateral Displacement (Utot)

CB3
CB4

(b) Bending Moment (Mx)

51

120

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 6 Lateral displacement and bending moment of CPW reinforcement in medium dense sand (Excavation (B):
15.00 x 30.00 m, d-1= 0.60 m, He=7.50 m, and q=10.00 kN/m2).

beam. Increasing the dimensions of the cap beam also
reduces the lateral displacement (Utot) that occurs, but
it shows an increase in the bending moment (Mx),although it is not significant.
Based on the results of deep excavation parametricstudies with several parameters such as excavation
dimension ratio (B/L), pile diameter ( 𝑑 ), and cap
beam dimensions, it shows FE 3-D and 2-D have theirrespective advantages in the analysis. Therefore, it isnecessary to be careful in considering the method ofdeep excavation analysis with 3-D or 2-D FE toachieve an efficient design.

CONCLUSION
In excavations with a square geometry in medium
dense sands, the ratio of excavation dimensions (B/L)
in the 3-D analysis proved to be very influential forthe lateral displacement (Utot). 3-D FE has proven to
be very effective in square excavations withdimension ratios (B/L ≤ 2), such as excavations (A)and (B). 2-D FE (plane strain) is more suitable for use
in the case of excavations with a dimension ratio (B/L≥ 3), such as excavations (C) dan (D).
Using a cap beam on the pile head in 3-D analysis
significantly reduced lateral displacement (Utot) andbending moment (Mx) of the pile. Based on the 3-D
analysis, a cap beam is proven to reduce lateraldisplacement (Utot) in a pile by 45% and bending
moment (Mx) by 54% compared to without a cap

Fig. 7 CPW reinforcement lateral displacement pattern: (a) without a cap beam; (b) with cap beam CB1
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1–13.
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Interaction. Arabian Journal of Geosciences,13(12), 1–13.
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The authors are very grateful for the facilities andsupport from the Department of Civil andEnvironmental Engineering, Universitas GadjahMada, Yogyakarta, which has supported this research.
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http://www.ijser.org[13] Schwamb, T., & Soga, K. (2015). Numerical
Modelling of a Deep Circular Excavation at
Abbey Mills in London. Geotechnique, 65(7),604–619. https://doi.org/10.1680/geot.14.P.251[14] Khoiri, M., & Ou, C. (2013). Evaluation ofDeformation Parameter for Deep Excavation in
Sand through Case Histories. Computers andGeotechnics, 47, 57–67.
[15] Ramadan, E. H., Ramadan, M., Khashila, M. M.,& Kenawi, M. A. (2013). Analysis of Piles
Supporting Excavation Adjacent to Existing
Buildings. 18th International Conference on Soil
Mechanics and Geotechnical Engineering:
Challenges and Innovations in Geotechnics,
ICSMGE 2013, 4, 2835–2838.
[16] Finno, R. J., Blackburn, J. T., & Roboski, J. F.
(2007).
Three-Dimensional
Effectsfor
Supported Excavations in Clay. Journal of
GeotechnicalandGeoenvironmental
Engineering, 133(1), 30–36.
[17] Lin, D. G., & Woo, S. M. (2007). Three
dimensional Analyses of Deep Excavation in
Taipei 101 Construction Project. Journal of
GeoEngineering,2(1),29–42.
https://doi.org/10.6310/jog.2007.2(1).4[18] Chheng, C., & Likitlersuang, S. (2018).
Underground Excavation Behaviour in Bangkok
using Three-Dimensional Finite Element
Method. Computers and Geotechnics, 95(July
2017),
68–81.
https://doi.org/10.1016/j.compgeo.2017.09.016[19] Lee, F. H., Hong, S. H., Gu, Q., & Zhao, P.
(2011). Application of Large Three-Dimensional
Finite Element Analyses to Practical Problems.
12th International Conference on Computer
Methods and Advances in Geomechanics 2011,11(6),
529–539.
https://doi.org/10.1061/(asce)gm.19435622.0000049[20] Iwata, N., Nakai, T., Zhang, F., Inoue, T., &Takei, H. (2007). Influences of 3D effects, Wall
Deflection Process and Wall Deflection Mode in
Retaining Wall Problems. Soils and Foundations,47(4),
685–699.
https://doi.org/10.3208/sandf.47.685[21] Zdravkovic, L., Potts, D. M., & St. John, H. D.
(2005). Modelling of a 3D Excavation in Finite
Element Analysis. Geotechnique, 55(7), 497–
513. https://doi.org/10.1680/geot.2005.55.7.497[22] Hong, S. H., Lee, F. H., & Yong, K. Y. (2003).
Three-dimensional pile-soil interaction insoldier-piled excavations. Computers andGeotechnics,
30(1),
81–107.
https://doi.org/10.1016/S0266-352X(02)000289[23] Yu Ou, C., Dar Chang, C., & Tzong-Shiann, W.
(1996). Three-Dimensional Finite Element
Analysis of Deep Excavations. Journal of
Geotechnical Engineering, 122(May), 337–345.
https://doi.org/https://doi.org/10.1061/(ASCE)0733-9410(1996)122:5(337)53

GEOMATE – Bangkok, Thailand, 22-24 November 2022

[24] Dong, Y. P., Burd, H. J., & Houlsby, G. T. (2018).
Finite Element Parametric Study of The
Performance of a Deep Excavation. Soils andFoundations, 58(3), 729–743.
[25] Dong, Y. P., Burd, H. J., & Houlsby, G. T. (2016).
Finite-element analysis of a deep excavation casehistory.
Géotechnique,66(1),
1–15.
https://doi.org/10.1680/geot.14.p.234[26] Hsiung, B. C. B. (2009). A Case Study on the
Behaviour of a Deep Excavation in Sand.
Computers and Geotechnics, 36(4), 665–675.

[27] Look, B. G. (2014). Handbook of Geotechnical
Investigation and Design Tables (2nd ed.). CRC
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Institution of Structural Engineers, 87(23), 23–26.

54

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

NUMERICAL ANALYSIS OF INTERSECTING URBAN METROTUNNELS IN ROCK MASS
Isan Dey1, Ramanathan Ayoythiraman2
1

M.Tech. Student, Department of Civil Engineering, IIT Delhi, India;
2
Professor, Department of Civil Engineering, IIT Delhi, India
ABSTRACT

Underground excavation for urban tunnels is one of the most important areas of study as it poses a greatchallenge with respect to variation in geology, surface conditions and alignment of the tunnels. Due to spiderweb
network of metros undergoing in almost every major city across the world, it is seen that tunnels of different metro
lines could intersect at different heights and also at different angles. The urban tunnels are generally twin tunnels
having 6 m diameter each, with different pillar widths. 3D FEM models are prepared for shallow depth tunnel at
10 m, 15 m, 20 m and 25 m i.e., incremental depths of every 5m for the shallow tunnel while deeper tunnel depth
at 40 m with angles of intersection at 30°, 60° and 90° to understand the effect of settlements on the surface, anddeformations of the shallow tunnel with respect to progression of deeper tunnel. It is found that the construction
sequence, separation distance with respect to depth between tunnels and angle of intersection have profound effects
on the existing tunnel response.
Keywords: Metro Tunnel, Rock, Intersecting Tunnels, Numerical Analysis.
INTRODUCTION
The necessity for tunnels and the benefits they bringcan be visualized in the modern-day very vividly.
Tunnels improve connections and shorten lifelines.
The utilization of underground space for continuous
traffic, storage, power and water treatment plants,
civil defense, and other activities is often a must give
limited space, safe operation, environmental
protection, and energy-saving. Of course, theconstruction of tunnels is risky and expensive andrequires a high level of technical skills.
For urban metro tunnels it is seen that theshallower tunnel is generally constructed earlier due
to the availability of space, but the deeper tunnel is
excavated at a later stage to cater to the proposed
requirement at a later stage. As a result of this, it isexpected that there would be change in the stresses,
deformations and settlements of the shallow tunnel aswell on the surface due to the effect of theconstruction of the deeper tunnel. Henceforth, itshows that the intersection of the tunnels at various
depths is the major area of concern in the present
scenario of a spiderweb network of tunnels in mostareas worldwide. As a result of this, it is very
important to study the effect of tunneling due tovarying depths, and angles of intersection between
the tunnels. Though there are studies reported oninteraction of twin tunnels and tunnel-pipeline
interaction [1 – 8], studies on intersecting tunnel are
limited.
In the present study, the effect of angle ofintersection and depth of the tunnels are investigatedthrough three-dimensional analysis. The shallower
tunnel shall be excavated for different tunnel depths
starting from 10 m and incremented by every 5 m

until a depth of 25 m while the deeper tunnel shall be
at a depth of 40 m. The settlement and deformation
results are required to be obtained from twin
intersecting tunnels for different angles ofintersection of 30°, 60° and 90°.
GEOTECHNICAL DATA
Lithological data pertaining to one of metros in India
are considered. Top layers are mainly comprised of
backfilled soils followed by Silty/ Sandy clay forabout 4 to 5 m. Below this, highly to moderately
weathered rock formations are observed. Major rocktypes occurring in the area are fine-grained, greenish
basalt to black colored associated with acidic andbasic tuffs, volcanic breccia.
The rock is identified as highly weathered
Volcanic Breccia to slightly weathered/ Fresh
Volcanic Breccia with core recovery varying from 13
to 100 % and Rock Quality Designation (RQD) is
varying from 13 to 100 %. Bulk rock density isvarying from 22 to 26 kN/m3. The design parametersof the soil adopted in the analysis are shown in
Table 1.
Table 1: Soil shear strength parameters
Thickness
layer
0-2m

Density(kN/m3)19c(kN/m2)
10

f
(°)
28

E
(MPa)
14

The design parameters of the different grades ofBreccia rocks as per the weathering grades whichhave been obtained from Rock lab software [9] andsummarized in Table 2.
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 2: Rock shear strength parameters
Grade

Densityc(kN/m3) (kN/m2)
IV(2-6m)
22
26
III (6-56m)
22 108
II (56-100m) 24 262

f
(°)
25
51
59

E
(MPa)
111
912
3310

MODELLING OF TUNNELS
The tunnels having 6 m diameter have been excavatedin FEM analyses followed by installation of linerswhich have a thickness of 400 mm and a modulus ofelasticity of 31622 MPa. The surcharge loads havebeen applied on the surface with an average load of
10 kPa per floor. The in-situ stress condition has been
considered as unity for the analyses in FEM.
The construction methodology of the analyses has
been considered such that the tunnel on left is
excavated followed by application of liner.
Correspondingly the same approach is used for theright-hand side tunnel. Along the length of tunnel,
excavation length of 10 m is adopted withcorresponding application of liner.
The typical geometry of the problem analyzed forintersecting twin tunnels is shown in Fig. 1 fordifferent angles of intersection of 30°, 60° and 90°.

(c)Fig. 1. The typical layout of the problem analyzed
for different intersecting tunnels at (a) 30°, (b) 60°,
and (c) 90°.
RESULTS AND DISCUSSION
Surface Settlement
The surface settlement results have been derived fromthe FEM models and the same have been plotted inFig. 3. In Fig. 2, the maximum surface settlement isshown in Y-axis and Ez/D is plotted in X-axis where
Ez is the vertical clear distance between the tunnels at
the intersecting location and D is the diameter oftunnel for different angles of intersection. It isinferred from the figure that the s settlement
decreases as the depth increases, i.e., as the clear
distance between the tunnel increases. The percentage
increase in settlement due to intersecting tunnels is
calculated and is shown in Fig. 3. From Fig. 4, it isseen that the maximum settlement increases byalmost 85 to 250% with the excavation of deeper
tunnel. Such a trend of settlement increase is reported
for existing pipeline and tunnel interaction in [1] and
[5].

(a)

Fig. 3. Variation of maximum surface settlementwith normalized clear distance between theintersecting tunnels.

(b)

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

It can also be deduced from Fig. 5 that the
maximum settlement decreases with the overall
increase in the angle of intersection with acorresponding increase in the influence area. Also, for
90° intersecting tunnel, the influence area is more
gradual than the 60° intersecting tunnel. But thesettlement increases after a certain point because of
the settlement occurring due to excavation of the
deeper tunnel.
Deformation of existing tunnel
The existing shallow tunnel would undergo
deformation due to the construction of deeper new
tunnel. The deformation of crown of shallow tunnel
with the normalized clear distance is shown in Fig. 6.
From Fig. 7, it is noticed that the maximum tunnelcrown deformation increases by almost 10-80 % withthe excavation of the deeper tunnel.

Fig. 4. Variation of increase in settlement (%) withnormalized clear distance between the intersecting
tunnels.
The variation of maximum surface settlementwith angle of intersection for different depths oftunnel is shown in Fig. 4. From Fig. 5, it is observedthat as the angle of intersection increases the
maximum settlement decreases, and the average
decrease of settlement is around 10 to 15% withincrease of the angle of intersection. The trend of
decrease is similar for tunnels located at all depths
analyzed.

Fig. 7. Variation of shallow tunnel deformation withnormalized clear distance between the intersecting
tunnels.
The variation of deformation at crown of shallow
existing tunnel with angle of intersection is shown in
Fig. 7. It is clearly evident from Fig. that the angle
of intersection has considerable effect on the
deformation of shallow existing tunnel which could
not have been captured in two-dimensional finite
element analysis. It is also interpreted from the figurethat there is about 15% to 20% reduction in tunnel
deformation occurs with an increase of angle ofintersection.

Fig. 5. Variation of maximum surface settlementwith angle of intersection.
The surface settlement troughs obtained from theanalyses are plotted in Fig. 5. It is evident from
Fig. 6 that with an increase of the degree ofintersection, the influence area of the settlement
trough also increases for different angle ofintersections (30°, 60° and 90°).

Fig. 7. Variation of deformation of tunnel crown with
angle of intersection.

Fig. 6. Settlement troughs obtained for the different
cases.
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

SUMMARY AND CONCLUSIONS

Excavation that Obliquely Crosses Existing
Pipelines, Arabian Journal of Geosciences,(IF1.827), Pub Date: 2022-01-22.
[2] Xiaolu Gan, Jianlin Yu, Xiaonan Gong, Nianwu
Liu, Dongzhu Zheng, Behaviors of existing shieldtunnels due to tunnelling underneath considering
asymmetric ground settlements, UndergroundSpace
Technology,
https://doi.org/10.1016/j.undsp.2021.12.011.
[3] Rui Zhanga, Yao Xiaob, Minghua Zhaob, Heng
Zhaob, Stability of dual circular tunnels in a rockmass subjected to surcharge loading, Computersand geotechnics, 108 (2019) 257–268.
[4] Luo-bin Lin, Yan-ping Lu, Fu-quan Chen , Dayong Li , Analytic study of stress anddisplacement for shallow twin tunnels subjected
to surcharge loads, Applied Mathematical
Modelling, 93 (2021) 485–508.
[5] Xinrong Liu, Linfeng Wang, Xiaohan Zhou,
Jiming Wang, Zuliang Zhong, Peng Liu, Fei
Xiong, Chunmei He, E-M calculation model andits application of calculating deformation in a
new tunnel orthogonally undercrossing an
existing tunnel, Tunnelling and UndergroundSpace Technology, 123 (2022) 104418.
[6] Zhiyong Liu, Jianfeng Xue, Jianzhong Ye, Jiangu
Qian, A simplified two-stage method to estimate
the settlement and bending moment of upper
tunnelconsideringtheinteractionofundercrossing twin tunnels, Transportation
Geotechnics 29 (2021) 100558.
[7] Mingfeng Lei, Jiyao Liu, Yuexiang, Shallow
Tunnel Excavated in Soft Clay with High
Plasticity, Hindawi Advances in Civil
Engineering, Volume 2019, Article ID 7483628,
14 pages.
[8] Lianyang Zhang, Engineering Properties ofRocks, Butterworth-Heinemann (2016).
[9] Hoek-Brown and Generalized Hoek-Brown
Material Models, https://static.rocscience.cloud.

The effect of construction of new and deeper tunnel
intersecting the shallow existing tunnel isinvestigated using three-dimensional finite element
analysis. The angle of intersection and depth ofshallow and deeper tunnels have been varied in theanalysis. Based on the results of numerical analysis
the following conclusions are drawn:
1. The construction of deeper tunnel is foundto have considerable deformation ofexisting shallow tunnel as well as surface
settlement.
2. The angle of intersection of two tunnels andthe clear distance between the two tunnels
are having pronounced effect on both
surface settlement as well as existing tunnel
deformation.
3. Three-dimensional analysis could capture
the effect of angle of intersection
efficiently. The settlement trough at surface
and the zone of influence/point of inflectionof trough are significantly affected by theangle of intersection of tunnels of different
lines.
4. It showed that the maximum surface
settlement was encountered for 30° while
the minimum settlement was encounteredfor 90°.
The study considered limited geotechnical data andfurther detailed parametric study is required for
different rock mass conditions so that generalized
guidelines can be arrived at for quantifying the effectof intersecting tunnels.
REFERENCES[1] Sheng‑Wu Zhao, Xiao‑Li Li, Xin Li1, Li‑Guang
Chen, Tong Li1, Suo‑Hui Li1, Yu Bail, Analysis
of Pipeline Deformation Caused by Shield Tunnel

58

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

SLOPE STABILITY DURING EXTREME DAILY RAINFALL: A
COMPARATIVE STUDY USING STATIONARY AND NONSTATIONARY GENERALIZED EXTREME VALUE MODEL
Thapthai Chaithong11Faculty of Social Sciences, Kasetsart University, Thailand
ABSTRACT

Extreme rainfall data are valuable for assessing an area’s susceptibility to landslides. Previous research
generated an Intensity–Duration–Frequency (IDF) curve of extreme rainfall by using a generalized extreme value(GEV) distribution. The stationary generalized extreme value model is the most common method for generating
an extreme rainfall dataset; however, extreme rainfall may also occur under non-stationary conditions, requiring
the trend to be analyzed from past to present. Therefore, the non-stationary generalized extreme value model may
be an alternative method for analyzing extreme rainfall data. This study compares the stability of residual soil
slopes under extreme daily rainfall using stationary and non-stationary generalized extreme values. The study
analyzes the daily rainfall at the Chiang Mai meteorological station from 1952 to 2021. The three return periods
explored were 5-, 100-, and 1000-year. The residual soil slope is analyzed using the analytical framework for thestability of infinite slopes under steady unsaturated seepage conditions. The results show that an increasing annual
maximum of daily rainfall has been found in Chiang Mai. The study finds that the safety factor differs for slopes
under stationary and non-stationary conditions based on the amount of rainfall. The safety factor is lower for the
residual soil slope under non-stationary conditions than in stationary conditions. Moreover, the safety factor gap
between stationary and non-stationary conditions increases over time.
Keywords: Slope stability, Extreme rainfall, Generalized extreme value, Landslide

INTRODUCTION

conditions.

Extreme rainfall is a significant triggering factor
in natural and man-made slope failure [1].
Historically, disastrous landslides have occurred inmany parts of Thailand as a result of heavy monsoon
rains. Nowadays, Thailand has experienced anincrement of heavy or extreme rainfall events due toclimate change and climate variability. IPCC 2021
reported an increase in heavy rainfall in the SouthEast Asia region [2]. This may increase theoccurrence of landslides in Thailand.
Considering extreme value theory, a generalized
extreme value (GEV) distribution is widely used to fit
the distribution of extreme rainfall. The GEV is basedon the block maxima method of extreme value theory.
The block maxima method considers and extracts the
maximum value from periods of equal length. The
assumption of GEV can be divided into two forms ofanalysis: stationary and non-stationary conditions.
For a non-stationary assumption, the parameters of
the GEV distribution function are time-dependent incontrast to the parameters for a stationary assumption,
which are time-independent. [3]–[5]Given this context, this study aims to compare thestability of residual soil slopes under both thestationary and non-stationary assumptions of GEV
distribution. The method of slope stability analysis isbased on the analytical framework for the stability of
infinite slopes under vertical steady seepage

METHOD AND DATA
There are three main steps in this study. First, thehistorical rainfall data at Chiang Mai meteorologicalstation from 1952 to 2021 are analyzed using
stationary and non-stationary GEV distributions.
Second, the pore water pressure is generated based on
the vertical steady seepage conditions. Finally, thestability of the residual soil slope is analyzed using an
infinite slope stability model.
Extreme Daily Rainfall Analysis
In the first step of the analysis, the study used a
generalized extreme value (GEV) distribution. The
historical daily rainfall data were measured from1952 to 2021 at Chiang Mai meteorological station.
The annual daily maximum rainfall data wereextracted and fitted using a generalized extreme value(GEV) distribution. The GEV distribution consists ofthree types: (1) Gumbel distribution (  = 0), Fréchet
distribution (  < 0) and the Weibull distribution (  >0). The GEV distribution function is given by [5]−1  x −     
F ( x ) = exp − 1 +       
 

59

(1)

GEOMATE – Bangkok, Thailand, 22-24 November 2022

 s = − ( ua − uw )where  ,  , and  are the location, scale, andshape parameters,
Used to estimate the Intensity–Duration–
Frequency (IDF) curve of extreme daily rainfall, thequantiles of the GEV distribution are given by 
 1 
xT =  − 1 − − log 1 −    
 T 

−





s =

(2)

Fs =

(3)

Vertical Steady Seepage Analysisctan    s tan  +−( tan  + cot  ) (9) sat H sin  cos tan  sat HThe study area located in Chiang Mai province inthe northern region of Thailand. The daily rainfall
data from 1952 to 2021 were measured by the Thai
Meteorological Department at Chiang Mai
meteorological station. The selected return period forboth stationary and non-stationary models in the
study is 5-, 100-, and 1000-year. Moreover, toestimate the GEV distribution parameters, themaximum likelihood estimation (MLE) method isused to fit the data. Table 1 shows the soil parametersin this study.

(4)

where u w is pore water pressure, k is the unsaturated
hydraulic conductivity dependent on matric suction,
q is ground surface flux,  w is the unit weight of water.
To describe the unsaturated hydraulic conductivity, it
can be expressed as [7]Table 1 Summary of soil properties(5)
Parameterkswhere ua-uw is the matric suction, ks is the saturated
hydraulic conductivity,  is inverse of the air entry
pressure.
With the combined equation (4), equation (5), andthe boundary condition of zero suction at the watertable and a steady-state flow rate, a one-dimensional
steady-state matric suction profile can be expressedas [7]ln (1 + q / k s ) e − w z − q / k s 

(8)

Data

The pore water pressure profile under the steadystate condition can be written as [6]( n −1) / n

where Fs is the factor of safety, c  is the effective
cohesion,   is the effective friction angle,  sat is thesaturated unit weight of soil and  is the slope angle.

where  0 ,  t are parameters to be estimate using asimple linear regression.

−1nBased on the extended Mohr-Coulomb failure
criterion for an unsaturated soil slope [6], the safety
factor for infinite slopes under steady vertical seepage,for both saturated and unsaturated conditions, is givenbylocation (  ) varies linearly over the time.

ua − u w =)− q / ks 

Infinite Slope Stability Modeland shape ( ) parameters are remained constant. The− ( ua − uw )−  w z

where  s is the suction stress.

investigate the non-stationary model, the scale ( )k = ks e(1 + − ln (1 + q / k ) e; ua − u w  0

and shape ( ) parameters are constant. In order toduw=  w ( q / k ) − 1
dy



(7)

ln (1 + q / ks ) e − w z − q / ks swhere T is return level.
For stationary model, the location (  ) , scale ( ) , ( t ) = 0 + 1t1; ua − uw  0

w
n
c


 sat


(6)

Value
0.0231
0.2
9.81
5
10.29
40.13
18.53
30

Unit
mm/sec
kPa-1
kN/m3
kPa
DegreekN/m3
DegreeRESULTS AND DISCUSSION
Trend of Extreme Daily Rainfall

Considering the suction stress, the profile ofsuction stress under the surface flux conditions can beshown as [8]:Figure 1 shows a plot of the annual maximum
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

daily rainfall: the trend is to increase slightly overtime. The average annual maximum daily rainfall
from 1952 to 2021 is approximately 80.97 (± 26.59)mm.

In addition, to estimate the location parameter for
non-stationary GEV distribution using the simple
linear regression, the equation of location parametercan be written:

 ( t ) = 75.90 + 0.143t(10)where t is (tyear – t0 +1); t0 is 1952
For the scale and shape parameters, the stationary andnon-stationary have the same values.
The IDF plots of annual maximum daily rainfall
in both stationary and non-stationary GEV
distribution conditions are shown in Fig 3 and Fig 4.

Fig. 1

Trend of annual maximum daily rainfall
from 1952 to 2021 at Chiang Mai
meteorological station.

To analyze the changing annual maximum daily
rainfall, the study separated the rainfall data into two
periods: 1952 to 1986 and 1987 to 2021. According
to the analysis, the curves of the probability density
function of rainfall data shifted as shown in Fig 2.

Fig. 2

Fig. 3

IDF curve for annual maximum daily
rainfall in stationary condition.

Fig. 4

IDF curve for annual maximum daily
rainfall in non-stationary condition.

Probability density function and histogram
for two periods of analysis.

To estimate the parameters of GEV distribution
using a maximum likelihood estimation method, theparameters for the stationary model are shown in
Table 2.

Consider to Fig 3 and Fig 4, the graphs present thedifference in the amount of rainfall in the same return
period, in which the amount of rain in the nonstationary condition is higher than in the stationary
condition. It also shows that the amount of rain or IDF
precipitation under non-stationary conditions varies
over time.

Table 2 Parameters for stationary GEV distribution
Parameter
Location
Scale
Shape

Value
68.85
20.90
0.00

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Pore Water Pressure Profiles under Vertical
Seepage Condition

saturated state because the amount of rain in the IDF
curve increases over time, as shown in Fig. 4. In
contrast, the pore water pressure profile of thestationary condition is constant because the amountof rain in the IDF curve is fixed over time, as shownin Fig.3.

The pore water pressure profile for stationary andnon-stationary conditions can be predicted using
Equation 6. The pore water pressure profile is shownfor 1960 in Fig. 5, for 1990 in Fig. 6, and 2020 in Fig.
7.

Fig. 7
Fig. 5

Pore water pressure profile for 1960.

Fig. 6

Pore water pressure profile for 1960.

Pore water pressure profile for 2020.

Fig. 8 presents a comparison of the pore waterpressure profile for a 5-year return period. Fig. 9
presents a comparison of the pore water pressure
profile for a 100-year return period. Fig. 10 presents
a comparison of the pore water pressure profile for a
1000-year return period.

According to the difference in rainfall between
stationary and non-stationary conditions, the porewater pressure profile of non-stationary conditions isless negative than the stationary condition. In addition,
the pore water pressure profile for non-stationary
varies over time at the same return period. The porewater pressure of non-stationary is a shift into theFig. 8 Comparison of pore water pressure profile
for return period 5-year.
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

stationary condition. In contrast, the safety factor
decreases in the non-stationary condition when theamount of rain increases. When comparing the safety
factor in both stationary and non-stationary
conditions of GEV distribution, the results show thatthe safety factor of non-stationary conditions is lower
than in stationary conditions. Moreover, the safety
factor gap between both stationary and non-stationary
conditions of GEV distribution is increasing overtime. Fig.11 presents the plots of the safety factor inthis study.

Fig. 9 Comparison of pore water pressure profile
for return period 100-year.

Fig. 11 Comparison of factor of safety.
CONCLUSIONS
The amount of rainfall is a key controller of thestability of the soil slope. Comparing the stationary
and non-stationary conditions of GEV distribution,
this study found that the selection of historical data to
generate IDF curves is the essential step for slope
stability modelling because, if the modeller selects
the low rainfall period to generate IDF curves, theresults of the slope stability are high, and thesusceptibility of slope failure is low. It may lead toneglecting the prevention of soil slope failure. In
addition, changes in extreme rainfall are important
issues of imperative concern for landslide or slope
failure risk management.
ACKNOWLEDGMENTS
Fig. 10 Comparison of pore water pressure profile
for return period 1000-year.

The authors gratefully acknowledge the rainfall
data provided by the Thai Meteorological Department(TMD).

Slope Stability Analysis

REFERENCES

Slope stability analysis in the study is based on an
infinite slope stability model: the depth of failure
plane is shallow, and the failure plane is parallel tothe surface. According to the slope stability analysis,
the results show that the safety factor of residual soil
slopes decreases when the return period increases.
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in extreme daily rainfall distribution on thestability of residual soil slopes. Big Earth Data,
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Contribution of working group I to the Sixth
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[4] Xavier A.C.F, Rudke A.P, Fujita T, Blain G.C,
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Martins L.D, Souza R.A.F, Freitas E.D, andMartins J.A., Stationary and non-stationary
detection of extreme precipitation events andtrends of average precipitation from 1980 to2010 in the Paraná River basin, Brazil.
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pp.1–16. DOI: 10.1002/joc.6265[5] Wi S, Valdes J.B, Steinschneider S, and Kim

T.W., Non-stationary frequency analysis ofextreme precipitation in South Korea using
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doi:10.1029/2008WR006976

6412th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

ESTIMATION OF THE ALLOWABLE BEARING CAPACITY OF SOIL
IN SOME MUNICIPALITIES OF THE PROVINCE OF PAMPANGAUSING ARTIFICIAL NEURAL NETWORK
Carmela Marie A. Lingad1, Erica Elice S. Uy2
1

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

Urbanization is evident in some municipalities of Pampanga specifically in San Fernando and Santo Tomas.
Those municipalities being developed, requires an information of the load-bearing capacity of soil. Predicting thesoil bearing capacity provides an estimation of how much loads can the soil carry. The bearing capacity wascalculated using the local shear failure equation of the Terzaghi’s bearing capacity formula. Also, the bearing
capacity was predicted using Artificial Neural Network for those locations which has available data with the N 60
value, friction angle, unit weight, and footing width as dependent parameters. The results show a coefficient ofcorrelation of approximately equal to 1 and a mean squared error of at least 0 for a hidden layer of 10 which provesthat ANN is an efficient way in predicting the bearing capacity. Based on the sensitivity analysis, it was found outthat the unit weight is the most significant parameter affecting the value of the bearing capacity. A relationshipbetween the N60 value, soil classification, and the bearing capacity was observed. It was concluded that the N 60
value and soil classification are the two determining factors on how the value of the bearing capacity will be,
because it affects the consistency of the soil and most of the parameters are dependent on those two variables. The
value of the bearing capacity ranges from a minimum of 20 kPa to a maximum of 630 kPa for a specific area.
Keywords: Allowable Bearing Capacity, Artificial Neural Networks, Geographic Information System, SPT-NINTRODUCTION

value in the means of the mean square error and thecoefficient of correlation.
This paper used artificial neural network tosimulate an estimation of the allowable bearing
capacity of soil in the municipality of San Fernando
and Santo Tomas, Pampanga.

Urbanization starts to arise in the Philippines.
Also, some of the provinces being near to Metro
Manila start to get urbanized and be developed. Oneof the provinces that are starting to get urbanized isthe province of Pampanga.
According to Das and Sobhan [6], bearing
capacity are usually analyzed when it comes todesigning foundations. Bearing capacity is the loadcarrying capacity with respect to the shear failure ofsoil of shallow foundations. A wrong estimation of
the bearing capacity often leads to a wrong design ofthe foundation.
Chao, Zhang, Zhu, and Hu [2] stated that artificial
Neural Network (ANN) is the duplication of thephysiological structure and mechanics of humanbrain. They stated that ANN is mostly used incomputing some geotechnical engineering problems.
It was also said that using ANN for solving isbeneficial because of the following factors: a) it has
high speed when it comes to calculation; b) it has
strong ability when it comes to fault-tolerance; and,c) it has dexterous capability when it comes withdealing with convoluted solving rules [2].
The determination of the allowable bearing
capacity allows the easier design of the proper
dimension of a footing that can carry such loadings.
In addition, the use of artificial neural network for theestimation of the bearing capacity provides a moreaccurate value because it also provides a validation

METHODOLOGY
For the study, 52 borehole data was used toestimate the bearing capacity. The borehole data was
obtained from the municipalities of San Fernando andSanto Tomas, Pampanga. Most areas in the said
location are sand. 52 borehole data was obtained
because of the availability of the borehole data. The
borehole data was plotted to provide an estimation of
the bearing capacity of the area within 1 km radius.
The standard penetration test – N (SPT – N) value wasobtained in the borehole data provided by the local
government units and some private testing companies.
The SPT–N value was corrected using Eq. 1 which
was provided by Das and Sobhan [6].
𝑵𝟔𝟎 =𝑵𝜼𝑯 𝜼𝑩 𝜼𝑺 𝜼𝑹
𝟔𝟎(1)

Input Parameters/Independent Parameters
The input parameters for the calculation of the
bearing capacity are the following: SPT-N values,
friction angle, unit weight of soil, and footing width.
65

GEOMATE – Bangkok, Thailand, 22-24 November 2022

The friction angle and unit weight of soil was
calculated according to the corresponding corrected
SPT-N value. The value of the friction angle wasinterpolated using Table 1 which was proposed by
Kulwahy and Mayne [10]. The value of the footing
width was considered as 1 m for all embedment
depths considering the limitations of the Terzaghi’s
bearing capacity equation [6].

where B is the width or diameter of the base of thefooting, L is the length of the footing, q is the verticaleffective stress from the natural ground level to thebottom of the footing, 𝛾𝐵 is the effective verticalstress from the bottom of the footing to the ‘B’projection, Nc, Nq, and 𝑁𝛾 are the Terzaghi’s bearing
capacity coefficient, and c is the cohesion in whichaccording to Kulhawy and Mayne [10] can be1
expressed as c = [0.06(N)(Pa )] , where N is the2
SPT-N value, and Pa is the atmospheric pressure.
The allowable bearing capacity was computed
using a factor of safety of 3.

Table 1. SPT-N value vs. Friction angle relationshipfrom Kulhawy and Mayne [10]SPT – N value
0–4
4 – 10
10 – 30
30 – 50> 50

Friction angle< 28
28 – 30
30 – 36
36 – 41> 41

Artificial Neural Network
Artificial Neural Network was used to predict theallowable bearing capacity with the independent
parameters (SPT-N value, friction angle, unit weight,
footing width) as input values and the dependent
parameter (allowable bearing capacity computed
using Terzaghi’s bearing capacity equation) as output
value. For the simulation of training of artificial
neural network, the dataset was trained using a
different number of hidden layers such as 5, 10, 15,
and 20. The value of the coefficient of correlation andthe mean square error was obtained to validate theefficiency of ANN in predicting the dependent
parameter.
A sensitivity analysis was done to determine
which independent parameter is highly significant
and greatly affects the accuracy of the dependent
parameter. According to Mrzyglod, Hawryluk, Janik,
and Olejarczyk-Wozenska [13] to know thesignificance of one parameter to the desired output, asensitivity analysis should be done. Sensitivity
analysis can be computed by obtaining the error of themeasured and predicted outputs. The equation of theerror is presented in Eq. 4 and can be denoted as,

A study done by Puri, Prasad, and Jain [15]provided a prediction of the geotechnical parameters
using machine learning, one of which is the densityof soil using SPT-N value. The following equations(Eq’s 2-a to 2-e) were used for the calculation of thedensity of soil which is then multiplied to theacceleration due to gravity to obtain the unit weightof the soil which will be used for Eq. 3.
a) For Coarse Grained soil,a.1) Dry Density with 1 ≤ N ≤ 50,
𝝆𝒅 = 𝟎. 𝟎𝟎𝟔𝟖(𝑵) + 𝟏. 𝟓𝟓𝟓𝟒

(2-a)a.2) Bulk Density with 1 ≤ N ≤ 39,
𝝆𝒃 = 𝟎. 𝟎𝟎𝟗𝟔(𝑵) + 𝟏. 𝟓𝟎𝟎𝟏

(2-b)a.3) Bulk Density with 40 ≤ N ≤ 50,
𝝆𝒃 = 𝟎. 𝟎𝟏𝟒𝟏(𝑵) + 𝟏. 𝟑𝟕𝟐𝟔

𝐸𝑟𝑟𝑜𝑟 =

(2-c)

1
𝑛

2
∑𝑛𝑖=1(𝑦𝑖 − 𝑦̂)𝑖𝐸𝑟𝑟𝑜𝑟𝑖(4)b) For Fine Grained soil,𝑊=b.1) Dry Density with 1 ≤ N ≤ 50,

where n is the number of output values, yi is themeasured/computed values, 𝑦̂𝑙 is the predicted values
from ANN. Eq. 5 represent the value of W. W is theratio (sensitivity of the parameter), Errori is thecomputed error without the specific parameter, andError is the total error including all parameters. If thevalue of W is less than or equal to 1, hence, theparameter is insignificant; in other words, it does notaffect the value of the output.

𝝆𝒅 = 𝟎. 𝟎𝟏𝟏𝟒(𝑵) + 𝟏. 𝟐𝟒𝟖𝟖

(2-d)b.2) Bulk Density with 1 ≤ N ≤ 50,
𝝆𝒃 = 𝟎. 𝟎𝟎𝟖𝟎(𝑵) + 𝟏. 𝟕𝟐𝟎𝟐

(2-e)

Output Parameter/Dependent Parameter
The ultimate bearing capacity was calculated using
the Terzaghi’s bearing capacity equation (Equation 3)
considering local shear failure [6].
𝑞𝑢(𝐺𝑆) = 1.3𝑐𝑁𝑐 + 𝑞𝑁𝑞 + 0.4𝛾𝐵𝑁𝛾

𝐸𝑟𝑟𝑜𝑟

(5)

Artificial Neural Network Validation
According to Rezaei, Nazir, and Momeni [18] tovalidate the predicted values for ANN, the coefficientof correlation and the mean square error should be(3)66

GEOMATE – Bangkok, Thailand, 22-24 November 2022

obtained. Both the coefficient of correlation and meansquare error are displayed upon the ANN simulation.
An article written by Elcicek, Akdogan, and Karagoz[8] provided an equation for the mean square error
and coefficient of correlation. It can be computed as,
1

𝑀𝑆𝐸 =

𝑁

∑𝑁
𝑖=1(𝑥𝑜𝑏 − 𝑥𝑝𝑟 )2

Eq. 3 and the allowable bearing capacity using afactor of safety of 3 [7]. All the values of theparameters required for the ultimate bearing capacity
equation was computed first such as the cohesion,unit weight, q, and Nc, Nq, Nλ.
Table 4. Sample Calculation of the Bearing Capacity
in a school in Bulaon, San Fernando City

(6)
2

𝑁
2
𝑅2 = [∑𝑁𝑖=1(𝑥𝑜𝑏 − 𝑥̅𝑜𝑏 ) − ∑𝑖=1(𝑥𝑜𝑏 − 𝑥̅𝑝𝑟 ) ] ×
2 −1
[∑𝑁
(7)
𝑖=1(𝑥𝑜𝑏 − 𝑥̅𝑜𝑏 ) ]where MSE is the mean square error, R2 is thecoefficient of correlation, N is the total number ofdata, xob are the actual computed values, xpr are thepredicted values using ANN, 𝑥̅𝑜𝑏 is the mean of theactual computed values, and 𝑥̅𝑝𝑟 is the mean of thepredicted values using ANN.
RESULTS AND DISCUSSION

Table 2. Sample borehole analysis of a bridge inbarangay Bulaon, San Fernando City

1st

2nd

SPT
N-value

N60

Soil
Classification

0

1

22

12.375

SM

1

2

22

12.375

SM

2

3

15

8.4375

SM

3

4

21

11.8125

SM

4

5

21

11.8125

SM

5

6

18

10.125

SM

N60

Ø'
(°)

δ
(kN/m3)

B
width

1st

2nd

0

1

12.375

21.61

16.08

1

1

2

12.375

21.61

16.08

1

2

3

8.4375

20.65

15.82

1

3

4

11.8125

21.47

16.05

1

4

5

11.8125

21.47

16.05

1

5

6

10.125

21.08

15.93

1

Soil
Class

Ultimate
Capacity
(kPa)

Allow
Capacity
(kPa)

0

1

12.38

SM

171.04

51.65

1

2

12.38

SM

312.30

93.38

2

3

8.44

SM

406.89119.63

3

4

11.81

SM

585.04173.67

4

5

11.81

SM

724.28214.73

5

6

10.13

SM

ANN Validation
Since different values of hidden layers were used,the values of the coefficient of correlation wereobtained for all hidden layers (HL) (5, 10, 15, 20) in
both locations.
The lowest value of the mean square is found when
there are 10 hidden layers. Though all the other 3
returns a value of mean square error of approximately
0, yet the lowest value of MSE is found at 10 hidden
layers. Table 5 represents the value of the MSE and R
of all the hidden layers with their corresponding
embedment depths.
Figure 1 shows the regression fit graph of the
training, validation, and testing of the data in both
locations with their corresponding coefficient ofcorrelation with a hidden layer of 10. As it can be seenin Figure 1, the regression fit line and data lies on thefitting line which explains the accuracy of the output
values predicted using ANN with 10 hidden layers.
The regression fit line also provided the values of thecoefficient of correlation which shows that it isapproximately equal to 1.
There are 52 available borehole data. The dataset
was divided into a 70% – 15% – 15% distribution for
the training, validation, and testing, respectively;
hence, 36 data was used for the training, 8 data forvalidation and testing.

Table 3. Input parameters for the calculation of the
bearing capacity
Depth

N60

To validate the results of the computed allowablebearing capacity, the computed values werecompared to the allowable foundation pressure
provided in the National Structural Code of thePhilippines 2015 [14]. Observing all the computed
allowable bearing capacity for different embedment
depths, it satisfies all the limitations within the code.
Hence, the values computed are acceptable.

From the borehole data, refer to Table 2 for thedata in one of the borehole logs obtained from abridge in Bulaon, San Fernando City. Table 3
represents the values of the input parameters.

Depth

Depth
(m)
1st 2nd

Bearing Capacity Calculation
From the obtained data, refer to Table 3 for the
calculated ultimate bearing capacity equation using
67

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 5. Summary of validation results in ANN with
10 hidden layers
Df
1
2
3
4
5
Df

HL = 5
MSE
R
0.067
1
0.001 0.997
0.009 0.994
0.0018 0.995
0.011 0.956

HL = 15
MSE
R
0.069
0.98
0.002 0.995
0.009 0.954
0.023 0.994
0.003 0.985

HL = 20
MSE
R
0.051
1
0.013 0.991
0.004 0.996
0.0021 0.943
0.003 0.977

Table 6. Summary of the dependent parameters'
weights for both locations
Df
(m)
1
2
3
4
5

ALLOWABLE BEARING CAPACITY (KPA)

1
2
3
4
5

HL = 10MSE
R
010.0003 0.986
0.002 0.991
0.0017 0.98
0.003 0.995

the weight, second is the SPT-N value, and the third
is the friction angle. For the footing width, twoembedment depth returns a value of weight of lessthan 1, but the rest provides a value of more than 1.
In conclusion, it can then be said that the footing
width is still a significant parameter. In contradiction,
it can also be concluded that it is the least significant
because it has the lowest value of the weight.

SPT – Nvalue
25.46421.452
4.863
29.04361.387Weights
Friction
Unit
angle
weight2.057
748.9841.306
224.0524.236
209.623
18.131
382.678
9.709
158.060

1000
900
800
700
600

500
400
300
200
100
0
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52
NUMBER OF BOREHOLE DATA
Computed

Figure 1. Regression fit curve for the training,validation, and testing of both municipalities for Df= 1 m with 10 hidden layers

Footing
Width
0.929
0.794
2.307
9.210
7.500

Predicted

Figure 2. Comparison of results of the computed andpredicted allowable bearing capacity at Df = 1 m

Figure 2 shows the comparison of the computed
values of the allowable bearing capacity using the
Terzaghi’s bearing capacity equation and theestimated allowable bearing capacity using Artificial
Neural Network. It shows how close the values arefrom each other. The figure also proves that theestimated values using ANN are close enough to thecomputed values/

Allowable Bearing Capacity Map
The allowable bearing capacity distribution map
for the municipalities of San Fernando and Santo
Tomas are shown in Figure 3a to Figure 3e. The
values of the bearing capacity were mapped using
Geographic Information System (GIS). The values of
the allowable bearing capacity are within a range ofminimum of 20 kPa to a maximum of at least 635 kPa.

Sensitivity Analysis

Relationship between the allowable bearing
capacity, N60 value, and the soil classification

A summary of the weights for both locations isprovided in Table 6. The sensitivity analysis provided
an idea of whose the most significant parameter
amongst the independent parameters such as the SPTN value, friction angle, unit weight, and the footing
width. As it can be seen, the unit weight is the mostsignificant parameter as it returns the highest value ofRelating the two parameters and the allowablebearing capacity, it can be observed that both N60
value and soil classification are directly related to thevalue of the allowable bearing capacity. It is notable
68

GEOMATE – Bangkok, Thailand, 22-24 November 2022

that the N60 value and soil classification affects theconsistency of the soil. It can be observed that even if
the soil is classified as coarse-grained, if the N60 value
is greater than 11, the soil is already considered as
dense; hence, it can have a greater value of the
bearing capacity. In contradiction, if the soil isconsidered as fine-grained, yet the N60 value is within
0 – 4, the soil has a very soft to soft consistency.
Therefore, even if the soil is cohesive, it does notpossess a higher value of bearing capacity. Hence, it
can be concluded that both the N60 value and soil
classification should be considered as the greatest
contributing factor in the value of the allowablebearing capacity.

(d)

(e)
(a)

Figure 3. Allowable Bearing Capacity Map at (a) Df= 1m; (b) Df = 2m; (c) Df = 3m; (d) Df = 4m; and,(e) Df = 5m

Figure 3. Side-by-side allowable bearing capacity
map and N60 value map for Df = 1m(b)

Figure 4. Side-by-side allowable bearing capacity
map and soil classification map for Df = 1m(c)
69GEOMATE – Bangkok, Thailand, 22-24 November 2022

CONCLUSIONSInternational Conference on Civil and HydraulicEngineering.
[3] Chen, W. F. & McCarron, W. (1991). Bearing
Capacity of Shallow Foundations.
[4] Das, B. M. (2009). Shallow Foundations –Bearing Capacity and Settlement. CRC Press,
Taylor & Francis Group, USA. 2nd Ed.
[5] Das, B. M., & Larbi-Cherif, S. (1983). Soils andFoundations: Bearing Capacity of Two CloselySpaced Shallow Foundations on Sand. JapaneseSociety of Soil Mechanics and Foundation
Engineering. 23(1).
[6] Das, B. M., & Sobhan, K. (2014). Principles ofGeotechnical Engineering. Cengage Learning,
Stamford, USA. 8th Ed.
[7] Dungca, J. (2019). A Reference for the Allowable
Soil Bearing Capacities in Quezon City,
Philippines. International Journal of GEOMATE.
19 (71), 42 – 47.
[8] Elcicek, H., Akdogan, E. & Karagoz, S. (2014).
The Use of Artificial Neural Network for
Prediction of Dissolution Kinetics: Research
Article. The Scientific World Journal.
[9] Fatehnia, M. & Amirinia, G. (2018). A Review of
Genetic Programming and Artificial Neural
Network Applications in Pile Foundations.
International Journal of Geo-Engineering.
[10] Kulhawy, F. H. & Mayne, P. W. (1990). Manualon Estimating Soil Properties for Foundation
Design. Research Project. 4 – 13.
[11] Moayedi, H., Kalantar, B., Dounis, A., Bui, D.
T. & Foong, L. K. (2019). Development of Two
Novel Hybrid Prediction Models Estimating
Ultimate Bearing Capacity of the Shallow
Circular Footing. Applied Sciences.
[12] More, J. J. (2006). The Levenberg-Marquardt
Algorithm: Implementation and Theory. U.S.
EnergyResearchandDevelopment
Administration. 105 – 106.
[13] Mrzyglod, B., Hawryluk, M., Janik, M. &Olejarczyk-Wozenska, I. (2020). Sensitivity
Analysis of the Artificial Neural Networks in a
System for Durability Prediction of Forging
Tools to Forgings Made of C45 Steel. The
International
JournalofAdvanced
Manufacturing Technology. 109, 1385 – 1395.
[14] National Structural Code of the Philippines(NSCP) 2015. Association of StructuralEngineers of the Philippines, Inc. Volume 1, 7 th
Edition, 1st Printing.
[15] Puri, N., Prasad, H. D. & Jain, A. (2017).
Prediction of Geotechnical Parameters Using
Machine Learning Techniques. 6th International
Conference on Smart Computing andCommunications. 509 – 517.
[16] Quoc, V. N. (2008). Numerical Modeling of the
Undrained Vertical Bearing Capacity of
Shallow Foundations. A Thesis. University ofSouthern Queensland.

Urbanization starts to arise in most of theprovinces in the country and one of which is theprovince of Pampanga. Pampanga is one of theprovinces that is just a few hours away from Manila;
hence, developments are evident there. Some of thehighly urbanized municipalities in the province ofPampanga is San Fernando (the province’s capital)
and Santo Tomas just below San Fernando. An
estimation of the allowable bearing capacity wasprovided for both locations using artificial neuralnetwork.
The SPT-N value and soil classification was
obtained from the borehole data and those data wereused to obtain the bearing capacity using the
Terzaghi’s bearing capacity equation. The values of
the allowable bearing capacity are within a range of aminimum of 20 kPa to a maximum of at least 635 kPa.
It is notable as well that as the value of theembedment depth of footing increases, the values ofthe bearing capacity increase, respectively.
Upon obtaining the actual values of the bearing
capacity, the values were trained using the artificial
neural network where the N60 value, friction angle,
unit weight, and footing width are the input values,
and the bearing capacity is the output value. 10
hidden layers was used because it yields to a more
acceptable value of the allowable bearing capacity.
The results were validated by obtaining the meansquare error and coefficient of correlation. It isobserved that all the values of the coefficient ofcorrelation for all embedment depths yields to a valueof approximately equal to 1, which proves theefficiency of the artificial neural network inpredicting the values of the bearing capacity. After
training the data using ANN, the independent
parameters have undergone a sensitivity analysis todetermine its significance in the prediction of the
bearing capacity. The sensitivity analysis has
provided an observation that the most significant
parameter is the unit weight, followed by the N60
value, and lastly is the friction angle.
ACKNOWLEDGMENTS
The author would like to thank the Department of
Science and Technology-Engineering Research andDevelopment for Technology (DOST-ERDT) for
funding the research.
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[2] Chao, Z., Ma. G., Zhang, Ye., Zhu, Y. & Hu, H.
(2018). The Application of Artificial Neural
Network in Geotechnical Engineering. 201870

GEOMATE – Bangkok, Thailand, 22-24 November 2022

[17] Ramesh, S., Suman, R. V., Yashwanth, M. &Prasad, S. K. (2019). Evaluation of Bearing
Capacity of Ground in Transition Zone.
Proceedings of the Indian GeotechnicalConference 2019. IGC – 2019, 1 (451).
[18] Rezaei, H., Nazir, Rm. & Momeni, E. (2016).
Bearing Capacity of Thin-Walled Shallow
Foundations: An Experimental and Artificial
Intelligence-Based study. Journal of Zhejiang
University-SCIENCE A (Applied Physics &Engineering).

[19] Sharma, S. (2017). Epoch vs. Batch Size vs.
Iterations. Retrieved September 23, 2017, fromhttps://towardsdatascience.com/epoch-vsiterations-vs-batch-size-4dfb9c7ce9c9.
[20] Skempton, A. W. (1986). Standard penetration
test procedures and the effects in sands ofoverburden pressure, relative density, particle
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ageingandoverconsolidation.
Geotechnique, 36(3), 425-447.

71

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

DYNAMIC CENTRIFUGE MODEL TESTS FOR SOIL-BENTONITE
VERTICAL CUTOFF WALLS WITH EARTHQUAKE-PROOF
PERFOMANCE
Koji Watanabe1, Daisuke Ueno2 and Masaaki Hasegawa2
1

Department of Civil Engineering, Aichi Institute of Technology, Japan; 2Seikotone Co. Ltd., Japan
ABSTRACT

Vertical cutoff walls using low permeable materials are often constructed at contaminated sites to contain thecontaminants and to prevent their migration in the aquifer. The purpose of this study is to develop the soilbentonite vertical cutoff walls with earthquake-proof performance. The soil-bentonite vertical cutoff wall which
is developed in this study is a rather flexible material compared with other typical barrier materials such as soilcement mixing material. In this study, dynamic centrifuge model tests were performed to confirm theearthquake-proof performance and the impermeable performance of cutoff wall. Four kinds of step-wise shaking
using sine wave were conducted for the soil-bentonite vertical cutoff wall and the soil-cement mixing verticalcutoff wall. The following findings were obtained from this study.
1) It is concluded that the soil-bentonite vertical cutoff wall which was developed in this study has the highearthquake-proof performance because the deformation of soil-bentonite vertical cutoff wall follows the model
ground deformation during the shaking.
2) According to the observation after the dynamic centrifuge model tests, the failure of soil-bentonite cutoff
wall does not occur although the collapse of soil-cement mixing vertical cutoff wall due to the shaking is
confirmed. Therefore, this fact implies that the soil-bentonite vertical cutoff wall has the high earthquake-proof
performance.
Keywords: Soil-bentonite Cutoff Wall, Dynamic Centrifuge Model Test, Earthquake-proof Performance

INTRODUCTION
Industrial waste

Vertical cutoff walls using low permeable
materials are often constructed at contaminated sites
to contain the contaminants and to prevent theirmigration in the aquifer. It is also needed to theleakage control of river embankment and regulating
pond. Figure 1 summarizes the concept ofapplicability on vertical cutoff walls. The usual
countermeasure for the migration and the leakage
control is to construct the soil-cement cutoff wall.
However, it is pointed out that the soil-cement cutoff
wall is damaged when the earthquake happens.
Moreover, the barrier performance of cutoff walldecreases as the crack of cutoff wall occurs.
The purpose of this study is to develop the soilbentonite vertical cutoff walls with earthquake-proof
performance. The soil-bentonite vertical cutoff wall
which is developed in this study is a rather flexible
material compared with other typical barriermaterials such as soil-cement mixing material. There
are some previous researches to examine theperformance of soil-bentonite cutoff walls [1]–[3].
These researches are subjected to construct thevertical cutoff walls by mixing powdered bentonite
and in-situ soil. The results of previous researches
are reported as follows;
1. It is possible to construct the low permeable

Cutoff wall

▽G.W.L
Permeable soil
layer

Impermeable soil
layer(a) Containment of waste soil

Cutoff wall

▽G.W.L
Permeable soil
layer
Impermeable soil
layer(b) Leakage control of regulating pond
Fig. 1 Concept of vertical cutoff walls.
cutoff walls as bentonite and in-situ soil are mixed.
2. The damage of cutoff walls does not occur when
the large deformation happens in the ground.
3. Corrosion and degradation does not occur because
it is stable that the major component of bentonite isinorganic substance such as montmorillonite
4. Sludge amount due to the construction of cutoff
walls decreases as bentonite and in-situ soil are
mixed in the construction process.

72

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Watanabe et al. [4] developed the soil-bentonite
vertical cutoff walls with earthquake-proof
performance. The following findings obtained fromliterature [4];1) The hydraulic conductivity showed the highvalue and decreased with the confining pressure
from the triaxial hydraulic conductivity tests.
2) According to the in-situ full scale test, it isconfirmed from the results of CPT that the soilbentonite vertical cutoff wall can be constructed theconstant quality along to the depth direction byusing TRD (Trench cutting Re-mixing Deep wall)
method.
3) It is examined that the soil-bentonite verticalcutoff wall has the high hydraulic conductivity fromhydraulic conductivity test for the core sampling
specimen of in-situ full scale test.
In this study, the dynamic centrifuge model tests
were performed to confirm the earthquake-proof
performance and the impermeable performance of
cutoff wall. Four kinds of step-wise shaking using
sine wave were conducted for the soil-bentonite
vertical cutoff wall and the soil-cement mixing
vertical cutoff wall.
CONSTRUCTION METHODBENTONITE CUTOFF WALLSOFSpecialty bentonite slurry
Verticalcutoff wall

Specialty
bentoniteslurryFig. 2 Concept of vertical cutoff walls.

Fig. 3 Construction machinery for TRD method.

SOIL-

Rotation arm

The vertical cutoff wall with earthquake-proof
performance is constructed by TRD (Trench cutting
Re-mixing Deep wall) method as shown in Fig. 2.
Figure 3 also indicates the construction machinery
which is applied the construction of vertical cutoff
wall. The construction process is described asfollows;
1. The agitated auger which is chainsaw type
shape connects the base machinery and this auger
inserts into in-situ ground.
2. The specialty bentonite slurry injects into thein-situ ground and the chainsaw type auger agitates
in-situ ground as the chainsaw type auger moves tohorizontal direction.
It may be suggested that the homogeneous cutoff
wall is constructed by using TRD method.

Static platform

Shaking table platform

Fig. 4 Centrifuge apparatus.
Table 1 Specifications of centrifuge apparatus.
Outer diameter of arm
Height of arm
Radius of platform
Maximum payload
Payload weight
Platform space
Test model height
100

Centrifuge Apparatus

80
Grain passing (%)

DYNAMIC CENTORIFUGE MODEL TESTSThe beam type centrifuge apparatus was used tothe model test as described in this paper. This beam
type centrifuge has a pair of parallel arms that holdplatforms on which the model container and aweight for counterbalance are mounted as shown in
Fig. 4. Radius of rotation is 7.01m, which is the
distance from the rotating shaft to the platform base.
The surface of swinging platform is always normal
to the resultant acceleration of the centrifuge
acceleration, nG, and earth’s gravity. The shaking

16.67m
3.00m
7.01m
700g-t7t
2.2m*2.2m
2.5m

60
40
20
0
0.001

0.01

0.1
1
Grain size (mm)10Fig. 5 Grain size distribution of silica sand No. 7.
73

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 2 Physical properties of silica sand No. 7.

Table 3 Compounding condition.

Soil particle density r s (g/cm3 )Mean diameter D 50 (mm)2.654

Maximum void ratio e max

1.193

Case 1Minimum void ratio e min

0.698

Case 2

Soil
type

0.185

Kaolin
clay
(g)

Sand
(g)

Sandy
soil
Composite
soil

-

1713
1486.6

165.2

Water
(g)

CaIonbentonite exchange(g)material (g)

480.4

122.6

4.4

541.6

122.6

4.4

Shaking direction
2000

Case 4Soil-cement cutoff wall

Case 1
Soil-bentonite cutoff wall
(Sandy soil)

Case 3
Soil-cement cutoff wall

688

Case 2
Soil-bentonite cutoff wall
(Composite soil)：Laser displacement transducer：Accelerometer

Shaking direction：Pore-water transducer

PW13

AH10

PW14

PW11

AH11

PW15

PW12

AH12

PW16

80

AH09

PW10

L-5

PW05 AH05 Dr=40％

100 80

PW09

40

80
129

AH03PW03

L-4

100 80

AH02PW02

260

80
100 80389

650

AH01PW01

AH04PW04
AH26

L-3

L-2

L-1

PW06 AH06
PW07 AH07
PW08 AH08

Impermeable layer

Fig. 6 Schematic view of centrifuge shaking table test.
Model soil-bentonite
vertical cutoff wall

table platform as shown in Fig. 4 was used indynamic centrifuge model tests. The specificationsof beam type centrifuge which was used in this study
are summarized in Table 1.

Model soil-cement
vertical cutoff wall

Material Used in Centrifuge Model Tests

Impermeable layer(Acrylic plate)Figure 5 indicates the grain distribution curve of
Silica sand No. 7, and the physical properties of thatare also shown in Table 2. The model ground isprepared by air-pluviation method using Silica sandNo. 7.
The compounding conditions of model soilbentonite cutoff wall are proposed in Table 3. The
constitutional materials are sand (Silica sand No. 5),
Kaolin clay (only Case 2), Ca-bentonite, water andion-exchange material. The making process ofmodel soil-bentonite cutoff wall is described as←Embedded portion
40mm (1m at 25G)

Fig. 7 Detail of embedded portion for model
vertical cutoff wall
follows;
1. The predetermined sand and water are mixed
homogeneously by the rotary-type mixer. (i.e. model
ground material) Here, the predetermined Kaolin
clay is added in the Case 2.
2. The predetermined Ca-bentonite and water are74

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Horizontal displacement (mm)
20
40
60
80

0

100

-100

-80

Horizontal displacment (mm)
-60
-40
-20
0

-1

-1

-2

-2

-3

-3

-4

20

-4

-5

-5
Micro shaking
L1 shaking
L2 shaking
Maximum shaking

Micro shaking
L1 shaking
L2 shaking
Maximum shaking

-6
-7
-8

-6
-7
-8

(a) Sine wave (2.4Hz).
-200(a) Sine wave (2.4Hz).

Horizontal displacement (mm)
20
40
60

80

-80

0

-60

Horizontal displacement (mm)
-40
-20
0
0

-1

-1

-2

-2

-3

-3
Depth (m)Depth (m)00Depth (m)Depth (m)

-20

-4

-4

-5
-6
-7

20

-5
Micro shaking
L1 shaking
L2 shaking
Maximum shaking

Micro shaking
L1 shaking
L2 shaking
Maximum shaking

-8

-6
-7
-8

(a) Sine wave (4.8Hz).
Fig. 8 Depth distribution of horizontal
displacement for Case 1.

(a) Sine wave (4.8Hz).
Fig. 9 Depth distribution of horizontal
displacement for Case 2.

mixed homogeneously. (i.e. injection liquid)
3. The predetermined ion-exchange material andwater are mixed homogeneously. (i.e. injection
activating agent)
4. The injection liquid and injection activating
agent are added to the model ground material.
5. The model soil-bentonite cutoff wall is madeby injecting the model ground.

acrylic plate. The detail of embedded portion for themodel vertical cutoff wall is shown in Fig. 7. For thevertical cutoff wall, the sandy soil-bentonite cutoff
walls was simulated using compounding conditionof Case 1 as described in Table 3, whereas
composite soil-bentonite cutoff wall was simulatedusing compounding condition of Case 2 as describedin Table 3. The model soil–cement mixing walls(Cases 3 and 4) are made by Silica sand No. 5 andcement. The target strength of soil-cement mixing
walls are unconfined strength qu=500kN/m2. It is
confirmed that the strength of soil-cement is almost
qu=500kN/m2 from the unconfined compression test.
The dimension of model cutoff wall are width 20mm,
depth 340mm and length 300mm. As the model
scale ratio was 1:25, a centrifuge acceleration of
25G was used in the shaking table tests. The model
ground is saturated after preparing the dry sandground by using the metolose water which has 25

Experimental Method
Figure 6 shows the schematic view of centrifuge
shaking table test. A rigid soil container was used in
the shaking table test, and the model ground wascreated using air-pluviation method such that therelative density of the Dr was 40%. Silica sand No. 7(Specific gravity of soil, Gs=2.645) was used in
preparing the model ground. The bottom layer was
the impermeable layer which was made by the75

GEOMATE – Bangkok, Thailand, 22-24 November 2022

50

0

60

0

0

-1

-1

-2

-2

-3

-3
Depth (m)Depth (m)0Horizontal displacement (mm)
10
20
30
40

-4

Micro shaking
L1 shaking
L2 shaking
Maximum shaking-6-7

Micro shaking
L1 shaking
L2 shaking-7

Maximum shaking-8(a) Sine wave (2.4Hz).

(a) Sine wave (2.4Hz).
0

10

Horizontal displacement (mm)
20
30
40

0

50

0

0

-1

-1

-2

-2

-3

-3
Depth (m)Depth (m)

50

-4

-6

-8

-4

10

Horizontal displacement (mm)
20
30
40

50

-4
-5

-5

-7

Horizontal displacement (mm)
20
30
40

-5

-5

-6

10

-6

Micro shaking
L1 shaking
L2 shaking
Maximum shaking-7

Micro shaking
L1 shaking
L2 shaking
Maximum shaking-8-8(a) Sine wave (4.8Hz).
Fig. 11 Depth distribution of horizontal
displacement for Case 4.

(a) Sine wave (4.8Hz).
Fig. 10 Depth distribution of horizontal
displacement for Case 3.
times larger viscosity than water. All the tests wereconducted to the saturated sand conditions under
25G centrifuge acceleration field.
The frequency and amplitude of acceleration
were varied using sine wave as input to generate
stepwise shaking. The frequencies of sine wave usedin this experiment are 60Hz and 120Hz to examine
the dynamic behavior due to the difference of
frequency. Thus, the prototype frequencies of sine
wave are 2.4Hz and 4.8Hz respectively. The
parameters measured in the experiment wereacceleration at various depths of the model ground,displacement of the model cutoff wall surface and at
ground level, and the pore water pressure at various
depth of model ground. Moreover, the image
analysis such as PIV (Particle Image Velocimetry) toobtain the deformation of model cutoff wall andmodel ground was carried out after the experimentby tracking the model groundmovement

Fig. 12 Soil-bentonite cutoff wall after shaking.
sequentially. It is possible to examine the failuremode of model cutoff wall and model ground from76

GEOMATE – Bangkok, Thailand, 22-24 November 2022

the results of image analysis.
RESULTS OF CENTRIFUGE MODEL TESTS
The results of centrifuge model tests present the
prototype scale values in this chapter. The depth
distributions of horizontal displacement are shownin Fig. 8~Fig. 11. Here, the input acceleration levels
are described as follows;
Micro shaking: Lower than 100gal
L1 shaking: 200~300gal
L2 shaking: 600~700gal
Maximum shaking: Higher than 1000gal
It is important to estimate the horizontal
displacement of cutoff wall to obtain the earthquakeproof performance. Therefore, the horizontal
displacement of cutoff wall is calculated by PIV
analysis during the shaking. It is confirmed that thehorizontal displacement of soil-bentonite cutoff wallas shown in Figs. 8 and 9 indicates the large value atthe shallow depth of model ground. This fact implies
that the horizontal displacement of model cutoff wall
dominates at the shallow depth of model groundbecause the large horizontal force is acted to themodel cutoff wall at the shallow depth of model
ground. The deformation behavior of model soilbentonite cutoff wall as described above shows thesame trend regardless of the input wave frequency.
On the other hand, the horizontal displacement ofsoil-cement cutoff wall as shown in Fig. 10 and Fig.
11 presents that the difference of horizontal
displacement at the shallow depth of model groundindicates the small value. It is said that the failuresuch as the cracks of cutoff wall occurs in the small
shaking level. Thus, the soil-cement cutoff wallcannot follow the ground deformation during theshaking. The deformation behavior indicates thesame trend regardless of the input wave frequency.
Figure 12 and 13 presents the model cutoff wallafter the shaking. The soil-bentonite cutoff wall asshown in Fig. 12 does not occur the failure such as
the cracks of model cutoff wall. It is concluded thatthe soil-bentonite cutoff wall can follow the grounddeformation in the large earthquake. Moreover, it issaid that the impermeable performance can be kept
after the large earthquake because the large failure is
not examined. Figure 13 indicates the soil-cement
cutoff wall after the shaking tests. Here, the red lines
in Fig. 13 mean the dominant cracks. Form Figure
13, the many cracks of model cutoff wall areobserved. This fact implies that the soil-cement
cutoff wall cannot follow the ground deformation.

Fig. 13 Soil-cement cutoff wall after shaking.
proof
performanceandtheimpermeable
performance of cutoff wall. Four kinds of step-wiseshaking using sine wave were conducted for the soilbentonite vertical cutoff wall and the soil-cement
mixing vertical cutoff wall. The following findings
were obtained from this study.
1) It is concluded that the soil-bentonite verticalcutoff wall which was developed in this study has
the high earthquake-proof performance because thedeformation of soil-bentonite vertical cutoff wall
follows the model ground deformation during theshaking.
2) According to the observation after the
dynamic centrifuge model tests, the failure of soilbentonite cutoff wall does not occur although thecollapse of soil-cement mixing vertical cutoff wall
due to the shaking is confirmed. Therefore, this fact
implies that the soil-bentonite vertical cutoff wall
has the high earthquake-proof performance.
REFERENCES[1] Evans J. C., Shacklford C. D., Yeo S. S. andHenning J., Membrane Behavior of Soilbentonite Slurry-trench Cutoff Walls. Soils andFoundations, Vol. 17, No. 4, 2008, pp. 316-322.
[2] Yeo S.S., Shacklford C. D. and Evans J. C.,
Hydraulic Conductivity and Compressibility of
Soil-bentonite Back-fill Amended with ActivatedCarbon,Journalof
GeotechnicalandGeoenvironmental Engineering, ASCE, Vol. 135,
No. 5, 2009, pp. 664-672.
[3] Takai A., Inui T., Katsumi T., Kamon M. andAraki S., Factors Affected The Hydraulic Barrier
Performance of Soil-bentonite Mixture Cut-off
Wall, Journal of Japan Society of Civil Engineers,
Ser. C (Geosphere Engineering), Vol. 68, No. 1,
2012, pp. 1-14.
[4] Koji W., Shinya M., Daisuke U and Masaaki H.,
Study on Soil-bentonite Vertical Cutoff Walls
withEarthquake-proof
Performance,
Proceedings of International Conference of
GEOMATE, 2020, pp. 241-246.

CONCLUSIONSIn this study, the dynamic centrifuge model
tests were performed to confirm the earthquake-77

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

REPRODUCTION OF DYNAMIC BEHAVIOUR AND
CLASSIFICATION OF FAILURE PATTERNS OF STONEWALLS BYDISTINCT ELEMENT METHOD SIMULATION
Satoshi Sugimoto1, Kosuke Yamaguchi1 and Maho Yamaguchi2
1

Graduate School of Engineering, Nagasaki University, Japan; 2 Nagasaki City Office, Japan
ABSTRACT

While large earthquakes are predicted to occur in the near future, there is no established method for evaluating
the stability of castle stonewalls from an engineering perspective. Therefore, there is an urgent need to elucidate
the mechanism of dynamic stability of castle stonewalls. In this study, we measured the angle of repose, clarified
the relationship between the measurement results and input physical properties, and reproduced the collapse
behavior of stone base types and half-stone base types to investigate the factors affecting the stability of stonewalls.
As a result, the deformation and collapse patterns of stonewalls were classified according to the shear resistanceangle of the block surface and the width of the cobblestone layer.
Keywords: Stonewall, DEM analysis, Dynamic behavior, Displacement

1.

INTRODUCTION

but also as deformable bodies, and nonlinear models
such as elasticity and elasto-plasticity can beconsidered for the deformable bodies. Failure modeand crack propagation simulations can also be
performed.
In a past report [6], we reproduced the collapse
behavior of a stone base type wall under various
conditions. However, the validity of the input
physical property values has not been clarified. In thisstudy, the angle of repose was measured and therelationship between the measurement results and the
input physical property values was clarified. In
addition to the stone base type, the collapse behaviorof the half stone base type was reproduced, and thefactors affecting the stability of the stonewall were
examined.

The 2016 Kumamoto earthquake caused enormous
damage to ground structures such as buildings andstonewalls at Kumamoto Castle. Besides, manystonewalls in Kumamoto Castle collapsed andoverturned, and tensile cracks occurred in the back of
the embankment. The 2016 Kumamoto earthquake ischaracterized by the fact that Kumamoto Castle wasdamaged by a foreshock with a seismic intensity of
5+ and the main shock of 6+. Although the stonewallsof Kumamoto Castle escaped deformation during theforeshock, many cases were found to have been
deformed or collapsed due to the mainshock.
However, despite the fact that large earthquakes are
predicted to occur in the future, a method forevaluating the stability of castle stonewalls as civilengineering structures has not yet been established.
Therefore, there is an urgent need to elucidate themechanism of dynamic stability of castle stonewalls.
Studies have been conducted to follow the dynamic
behavior of stonewalls using various numericalanalysis methods. Hashimoto et al. [1] conducted a
dynamic analysis using the finite element method(FEM) and focused on the response acceleration andresponse velocity obtained from FEM at the stonewall
location to clarify the relationship with damage.
However, since FEM treats a stonewall as acontinuum, it is difficult to represent local collapse
behavior. Therefore, in this study, the distinct element
method (DEM: analysis code UDEC) was used [2],which can analyze local deformation in discontinuous
structures. The distinct element method usuallymodels the analysis target as a set of rigid elements
and traces the movement of individual blocks down
to their very shape [3] [4] [5]. The UDEC used in thisanalysis can treat elements not only as rigid bodies

2.

OVERVIEW OF ANALYSIS MODEL

2.1 Selection of The Stonewall Model
According to past research [7] [8] [9], thestonewalls of Kumamoto Castle can be roughly
divided into three types according to their internal
composition. The stonewall model handled in thisstudy was divided into three types based on theliterature: the "stone base type" consisting of only
stonewall blocks and cobblestones, the "half stonebase type" consisting of stonewall blocks,
cobblestones, and the embankment, and the "stoneless base type" consisting of only stonewall blocks
and the embankment. In this study, we focused on thestone base type and the half stone base type that havecobblestone. Fig.1 shows the internal composition,
shape characteristics, and constituent materials.

78

GEOMATE – Bangkok, Thailand, 22-24 November 2022

2.2 Conditions of The Analysis Model

10.272m
12.05

Figure 2 shows the model outline and Table 1
shows the model conditions. Table 2 shows theseismic waveforms used in the analysis. In this study,
the distinct element method (analysis code: UDEC),
which is an analytical method for solving the motionof a discontinuity model, was used to model theindividual movements of the stonewall blocks andcobblestones. Based on past studies, for the stonebase type, comparisons are made by the magnitude of
the shear resistance angle of the block surface and by
the height of the stonewall. For the half stone basetype, only the slope angle of the stonewall waschanged to 60° and 80° without changing theconditions of the physical properties of each block,and the comparison was made. Next, to make acomparison by the width of the cobblestone zone, we
compare the width of the cobblestone zone in thedepth direction of 3m and 7m without changing thephysical property values and stonewall slope angle ofeach block. Considering that the physical propertiesof the stonewall block and cobblestone surfaces
contribute significantly to the generation of earthpressure, the shear resistance angle values of thestonewall block and cobblestone surfaces were set tothe cases shown in Table 3.

Characteristic

Fig. 1

Stonewall
block,
Cobblestone
Height：low
Slope：steep

3.

Characteristics of each stonewall type

Table 1 Physical properties of each block

3000

Table 2 Seismic waveforms used in the analysis
Excitation
Waveform Period Amplitude
timesine wave

10Hz

1.0m/s：cobblestone(elastic element)：quiet boundary：fixed block：base(fixed element)：sandy soil(elasto-plastic element)：between stonewall block andembankment between
cobblestone and embankment

Cross-section of the analysis model

Stonewall block (°)

10

20

30

40

Cobblestone (°)

10

20

30

40

RESULTS OF
ANALYSIS

DYNAMICBEHAVIOR

The angle of repose was measured on thesimulation to estimate the shear resistance angle as a
block aggregate. According to the GeotechnicalEngineering Dictionary, the angle of repose is theangle of the soil slope when uncohesive soil such as
sand or gravel is dropped from a certain height and isstable without collapsing. Theoretically, the angle ofrepose should coincide with the shear resistance angle(internal friction angle) of loosely packed sand or
gravel under low pressure. The shear resistanceangles of the block surface were set to 10°, 20°, 30°,40°, and 50° when the parameter "round" was set to
0.05, 0.10, and 0.15 on the UDEC to represent theroundness of the block, angle of repose were
measured, respectively. The analytical model was
made to drop a disturbed specimen from a height of
15 meters by applying gravitational acceleration.
Figure 3 shows the results of the comparison of angleof repose and angle of shear resistance. From theseresults, it was found that the angle of shear resistances on the block surface and the angle of repose  are
generally related to s ±5=.Based on these results,
the shear resistance angle of cobblestones behind thestonewall can be set as ≒m.

Stonewall block,Cobblestone,
Embankment
Height：highSlope：gentle

Shear rigidity(N/mm)：stonewall block(elasto-plastic element）

3.1 Measurement of Angle of Repose

Cobblestone

3000

Dashpot

Table 3 Conditions for shear resistance angle ofsurface

Embankment

Vertical rigidity(N/mm)

Free field

Fig. 2Sine wave

Sine wave
30.00m：between stonewall
block and cobblestone

State at thetime ofcollapse

Materials

14.472m

Dashpot

Half stone base type

Stonewall block

Sine wave

Sine wave

80°
Free field

Cobblestone

60°

30.00mStyle
Stonewall block

Free field

Stone base type80°
Free field

Type name12.00m

12.05

2.0 sec

79

GEOMATE – Bangkok, Thailand, 22-24 November 2022

angle of repose(°)

stonewall. When the height of the stonewall is 12m,
the overburden pressure is relatively large due to thehigh height of the stonewall. Therefore, the horizontal
force acting due to the inertia force of earthquake
motion is larger than the soil overburden pressure,and the slope of the slip surface is considered to be
steeper. In the case of the 6 m high stonewall, thelower height of the stonewall causes the cobblestones
to collapse in the form of horizontal lateral slip,
resulting in a gentle slope of the slip surface.

round：0.10

60
50

44.9

36.76

40
30

23.32

35.4

26.4

20
10
0

0

10

20

30

40

50

60

angle of shear resistance(°)

Fig. 3Comparison results of angle of repose andangle of shear resistance

3.2 Creation of Analysis Model
Figure 4 shows how the models were created forthe stone base type and half stone base type with astonewall slope of 70°. Figure 3 shows the model
after cutting the cobblestones. In the stone base type,
two blocks were made inside the stonewall and one
block was made at the top of the stonewall, and theblocks were cut into 40 cm squares. Blocks weremade separately because it is more difficult to create
gaps when blocks are dropped separately than when asingle block is made and dropped. To approximate theactual construction procedure, cobblestones weremade at a certain height, and then free-fallen to fill
the inside of the stonewall. In a past study, free-fallalone caused gaps in the corners of the stonewall.
Based on this reflection, this time gravity was applied
to the left, right, and vertically downward while thestonewall blocks and base were fixed. In the halfstone base type, a large block was created at the topof the stonewall and cut into 40 cm squares. After that,
only the left portion of the cobblestone block wasremoved and allowed to fall freely in order to allow it
to fall smoothly. Then, with the stonewall blocks andthe base fixed, gravity was applied to the left and rightand vertically downward. As a result of this procedure,it can be confirmed that the cobblestones are
sufficiently packed, as shown in the Fig. 4.

Fig. 4

3.3 Analysis Results and Consideration of Stone
Bases Type30°

Schematic diagram of the model
construction of stone base type and halfstone base type
Height:12m
Maximum
displacement:5.348m

Height:6m
Maximum
displacement:2.192m

Maximum
displacement:4.948m

Maximum
displacement:1.908m

Maximum
displacement:4.547m

Maximum
displacement:0.2262m

10°3.3.1 Stonewall height
Based on past studies, analytical models weredeveloped to compare different heights of stonewalls,
with heights of 12 m and 6 m, a stonewall slope angleof 70°, and surface shear resistance angles of 10°, 30°,
and 50°. The deformation diagrams are shown in
Fig.5. Even under the same conditions, there weredifferences in the collapse behavior as the height ofthe stonewall was lowered. The inclination of the slip
surface differs depending on the height of the50°

Fig. 580

Displacement vectors for 12m and 6mheight of stonewall

GEOMATE – Bangkok, Thailand, 22-24 November 2022

did not collapse. However, the vector diagram showsthat force was applied to the entire stonewall block,causing impregnation in the horizontal direction.
Collapse was observed when the width of thecobblestone was 7 m. This may be due to the fact thatthe wider the cobblestone is, the greater the force thatpushes out the stonewall block as a whole, causing it
to collapse.

3.3.2 Surface shear resistance angles
In order to compare the shear resistance angle ofthe stonewall surface, an analytical model was
created for a height of 12 m, a stonewall slope angleof 70°, and shear resistance angles of 10°, 30°,
and 50° on the surface of the stonewall. Figure 6
shows the analytical results. For the 12m height,
collapse was observed for all shear resistance angles
from 10° to 50°. As the angle of shear resistanceincreased, the maximum dis-placement decreased,
indicating that the collapse was less likely to occur.
φs=10°
Maximum
displacement:
5.348mφs=30°
Maximum
displacement:
4.948m・3.0sec・Maximum displacement:
1.500m・3.0sec・Maximum displacement:
7.039mWidth of cobblestone
zone:3m

Width of cobblestone
zone:7m

φs=50°
Maximum
displacement:
4.547mFig. 8Fig. 6 Displacement vector for stonewall height of
12m and stonewall slope angle of 70°

Comparison of different width ofcobblestone zone

3.4.3 Surface shear resistance angles
3.4 Analysis Results and Consideration of Half
Stone Bases Type

Figure 9 shows the deformation of a stonewall witha slope of 80°, cobblestone zone 3m width, and surface shear resistance angles of 10°, 20°, 30°, and 40°.
When the surface shear resistance angle was 10°, theentire stonewall was deformed as if it was pushed out.
When the surface shear resistance angle was 20°, thestone did not collapse, but the upper part of the stone
was deformed as if pushed out. When the surface
shear resistance angle was 30° and 40°, there wasalmost no deformation of the entire stonewall, and thestonewall was stable. Figure 10 shows thedeformation of the stonewall with a slope of 80°,
cobblestone zone 7m width, and surface shear
resistance angles of 10° and 40°. The results showthat both cases collapsed, but the higher the value ofthe shear resistance angle, the less likely it was tocollapse. The difference in deformation is also
apparent. The smaller the value of shear resistanceangle on the surface, the more easily the cobblestones
slide against each other and collapse occurs. The
larger the value of the shear resistance angle, thegreater the static frictional force of the stonewall
blocks than the magnitude of the cobblestones' sliding
down, resulting in a difference in collapse. The angleof repose changes with the value of the shear
resistance angle on the block surface, and the range inwhich the cobblestones can stand on their own may
also change. Figure 11 compares the horizontal displacement of the top of the stonewall under different
conditions. The horizontal axis represents elapsed
time, and the vertical axis represents horizontal displacement. The input seismic wave is indicated by adotted line. The graph shows that when the shear
resistance angles of the block surfaces are 10° and 20°,
the displacement is greatly affected by the seismic

3.4.1 Stonewall slope angle
Figure 7 shows the deformation when the shear
resistance angle between the stonewall face and thecobblestone face is 20° for two cases of stonewall
slope angle of 60° and 80°. Based on past studies andactual damage, the larger the stonewall slope angle,
the more likely it was to collapse. However, in thepresent results, no collapse was observed in all cases
where the shear resistance angle of the block surface
was between 10° and 40°, indicating that the block
was in a stable condition.
・3.0sec・Maximum displacement:
0.487m・3.0sec・Maximum displacement:
0.248mSlope angle:60°

Slope angle:80°

Fig. 7 Comparison of deformation caused bydifferent stonewall slope angle
3.4.2 Width of cobblestone zone
Figure 8 shows a deformation diagram of astonewall with a slope angle of 80° and cobblestone
zone with width of 3 m and 7 m. The shear resistanceangles of the stonewall and cobblestone surfaces areboth 20°. From figure 8, the 3m-width cobblestone81GEOMATE – Bangkok, Thailand, 22-24 November 2022

waves as time passes. However, when the shear
resistance angles of the block surfaces are 30° and 40°,
little change is observed.
φs=10°

φs=20°

φs=30°

60° and 80°. Even with the same values of stonewall
slope angle and surface shear resistance angle,
collapse was observed when the slope angle of theembankment was 60° and no collapse was observed
when the slope angle of the embankment was 80°.
This may be due to the fact that the overburdenpressure of the upper cobblestones is greater than thestatic friction force between the stonewall blocks.

φs=40°

2.0secFig. 9・3.0sec・Maximum displacement:
9.378m・3.0sec・Maximum displacement:
4.522mEmbankment slope
angle:60°

Fig. 12

Shear resistance angles:10°

Fig. 102.0secComparison of different surface shear
resistance angles no.1

Comparison of different embankment
slope angle

3.4.5 Comparison of all analysis results for the halfstone base typeShear resistance angles:40°

Comparison of different surface shear
resistance angles no.2

Figure 13 shows the analysis results of the halfstone base type. In particular, the deformed
stonewalls that did not collapse but were deformed
are the types that are often found in actual damaged
stonewalls. Therefore, it is necessary to elucidate theconditions for the occurrence of this type of
deformation behavior through this analysis in the
future.
4.

CONCLUSION

In this study, we focused on a half stone base typestonewall, modeled its collapse, and comparedvarious conditions. Although the slope of thestonewall was considered to be a factor in the collapse
in past studies, no collapse was observed even if theslope was steep, depending on the width of thecobblestones. It was found that the wider the
cobblestones zone, the more likely it was to collapse.
The same model also showed a change in the collapse
rate by changing the magnitude of the shear resistanceFig. 11 Horizontal displacement of stonewall stone
tops under various conditions
3.4.4 Embankment slope angle
Figure 12 shows deformation diagrams for astonewall slope angle of 80°, a surface shear
resistance angle of 30°, and embankment slopes of
φs=10°

Embankment slope
angle:80

φs=20°

Width of cobblestone zone

2m

3m

7m

Fig. 13 Comparison of all analysis results for the half stone base type82

φs=30°

φs=40°

GEOMATE – Bangkok, Thailand, 22-24 November 2022

angle. These results indicate that the dynamic
behavior of the half stone base type and stone basetype stone structures is characterized by theirmechanical stability based on the relationshipbetween the slope of the stonewall blocks, the widerof the cobblestone zone, and the roughness of theblock surface.

Engineering Society Proceedings C (Geomatics),
Vol. 64, No. 4, 2013, pp.444-456. (in Japanese)[4] Kasa H., Nishida K., Nishigata T., Morimoto H.,
Awatani Y., Yamamoto H.: A study on stability
analysis of castle masonry walls by using thedistinct element method, Civil History Research
Papers, Vol.27, 2008, pp.45-51. (in Japanese)[5] Nishimura T., Noma Y., Kasa H., Yamamoto H.,
Nishigata T.: Examination of deformation
behavior of shaking table test by stability
analysis of castle wall using distinct element
method, Proceedings of 66th JSCE Conference(CD-ROM), 2011, pp.325-326. (in Japanese)[6] Sugimoto S., Yamaguchi M., Yamanaka M.:
Surface shape measurement and evaluation of
mechanical stability by DEM simulation fordamaged traditional stonewalls, International
Journal of GEOMATE, Vol.20, Issue 80, 2021,
pp.121-127.
[7] Sugimoto S., Yamanaka M.: Reports of damaged
earth structures in Kumamoto Castle,
Proceedings of 54th Natural disaster and science
symposium, 2017, pp.45-51. (in Japanese)[8] Katsuda Y., Jiang Y., Omine K., Sugimoto S.:
Research of damaged stone structures inKumamoto Castle by the 2016 Kumamoto
Earthquake, Proceedings of 72th JSCE
Conference (CD-ROM), 2017, pp.479-480. (inJapanese)[9] Katsuda Y., Sugimoto S., Yamanaka M.: Reports
of a survey on damaged stonewalls in Kumamoto
Castle by the 2016 Kumamoto Earthquake,
Proceedings of the Japan National Conference on
Geotechnical Engineering, 2018. (in Japanese)

5. ACKNOWLEDGMENTS
The authors gratefully appreciate a lot of supportand understanding for this investigation from all
officers of Kumamoto Castle Survey and ResearchCenter after shortly after the 2016 Kumamoto
earthquake. And our gratitude extends to all
cooperators of this study with several information.
6. REFERENCES[1] Hashimoto T., Isobe Y., Matsuo T., Ishizukuri
K.: 3D geological modeling and 2D seismic
response analysis of Ishigaki in Kumamoto
Castle, Department of Science and Engineering,
National College of Science and Engineering
Abstract No. 14, 2021, pp.113-119. (in Japanese)[2] Fujii I., Yang L., Jiang Y., Li S., Tanahashi T.:
Evaluation of the dynamic behaviors of the
bedrock containing discontinuities underimportant buildings based on numericalsimulations, Proceeding of 2011 JSCE west
conference (CD-ROM), 2011, pp.411-422(inJapanese)[3] Noma Y., Yamamoto H., Nishimura T., Kasa H.,
Nishigata T., Nishida K.: Study on seismic
deformation prediction and dynamic stability
evaluation of castle masonry wall, Civil

83

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

MPS-CAE SIMULATION FOR JET-GROUTING TECHNIQUE IN
GROUND IMPROVEMENTS
Sudip Shakya 1 and Shinya Inazumi 2
1

Graduate School of Engineering and Science, Shibaura Institute of Technology, Japan;
2
College of Engineering, Shibaura Institute of Technology, Japan

ABSTRACTThis study focuses on the recreation of the jet grouting construction simulation by MPS-CAE method. Jet
grouting method is one of the innovative ground improvement techniques in which the ground improvement
procedure is conducted at grout injection. In this study, the simulation ground model is created by computer-aided
engineering (CAE) system and the construction simulation is recreated by the MPS method. Since the actual
construction procedure is surrounded by various uncertainties due to the lack of visuality in development progress;
it is the objective of this study to solve those uncertainties involved in the construction. The ground model was
designed while making comparison to the existing ground condition and development of soil-improvement bodywas visualized. After confirming the successful construction of the soil-improvement body, the construction
simulation is verified by calculating the mud discharge quantity and its density. It is expected that the result
obtained from this simulation can be used as guideline for the future jet grouting construction.
Keywords: Cement slurry, Computer-aided engineering, Jet-grouting technique, Moving particle semi-implicit
INTRODUCTION

the difficulty in proving the validity of design. Only
if the improved body formation phenomenon by highpressure injection could be analytically verified basedon the mechanical theory, it would contribute to the
explanation of the construction mechanism, theoptimization of the construction method, and theimprovement of the visualization technology for the
improved body construction.
This study focuses on the application of acomputer-aided engineering (CAE) system thatsimulates the performance of the middle-pressure jet
grouting method. Specifically, as one of the CAE
systems, the moving particle semi-implicit (MPS)CAE method [6],[7],[8], which can handle groundfailure phenomena and high-velocity fluids, is used.
Employing the MPS-CAE with the “Particleworks”
software [6], an attempt is made to visualize andevaluate the development situation of the columnar
soil-improved body by the middle- pressure jet
grouting method.

Technology for the columnar soil-improved bodyusing high-pressure jets to spray cement slurry was
first developed in 1970 in Japan as a single-pipe typeof high-pressure jet grouting method [1],[2],[3]. Since
then, this technology has been widely used for the
improvement of social infrastructures, such as rivers,
ports, roads, railways, and water and sewer systems[4],[5]. The single-pipe type of high-pressure jet
grouting method [1],[2] is one of the groundimprovement methods for developing a columnar
soil-improved body. In this method, the ground is cut
with cement slurry sprayed at high pressure and, atthe same time, the cut ground (soft soil) is mixed withthe cement slurry. In recent years, by applying thistechnology and using silica sand or mechanicalagitation and mixing in combination with jets thatspray cement slurry, it is possible to develop a higher
quality columnar soil-improved body even withrelatively low-pressure jets of cement slurry. One ofthe problems generally encountered in groundimprovement methods is the sophistication of thedesign and performance evaluation methods of theground-improvement method itself. Since thisimproved body construction mechanism is acomplicated and "invisible" phenomenon as thedevelopment of it is conducted in an in-situ ground, it
has been put into practical use by making
speculations experimentally and empirically forconstructions and finally checking the quality after
construction. Thus, not only the inability ofconfirming the construction status visually but also

CAE WITH MPS METHOD
Computer-aided engineering (CAE) is atechnology that simulates and analyzes prototypes on
a computer created by CAD (computer-aided design)and so on, considering the site conditions [9]. In thefield of geotechnical engineering, CAE can be used tovisualize the inside of the ground and the stress acting
on the inside of the ground, and to understandexperiments that would require huge costs and/or
phenomena that would be difficult to reproduce. In
addition, by performing appropriate post-processing,84

GEOMATE – Bangkok, Thailand, 22-24 November 2022

it is possible to communicate with other people in avisually easy-to-understand manner. In this study, theauthors attempt to apply CAE using 3D-CAD and
MPS, one of particle methods, as an elemental
technology for the visualization of the inside of theground during soil-improvement construction and the
reproduction of a series of constructions. Specifically,
based on the reproduction of the middle-pressure jet
grouting method by MPS-CAE with the“Particleworks” software [6] the construction
specifications can be examined according to theground conditions.
In this study, the authors apply MPS method[6],[7],[8] to analyze the behaviour of fluid particles.
The governing equations for the incompressible flowused in the analysis are the mass conservation law of
Eq. (1) and the Navier’s stroke law of Eq. (2)considering surface tension.
𝑫𝝆
=𝟎
𝑫𝒕

(1)

𝑫𝒖
𝟏
𝟏
= − 𝛁𝑷 + 𝝑𝛁𝟐 𝒖 + 𝒈 + 𝝈𝒌𝜹𝒏
𝑫𝒕
𝝆
𝝆

(2)

whose grid size is the same with the diameter of theparticles is called the criterion of particle number
density (𝒏∗ ).
OUTLINE OF MIDDLE-PRESSURE JETGROUTING METHOD
The middle-pressure jet grouting method, which
is a cement slurry jet grouting method [4],[5] that uses
middle-pressure jets of 20 MPa or less. The first one
is a method using a single-tube type of rod equippedwith a jet nozzle to loosen the ground by a horizontal
water jet during the penetration of the rod. Next, therod with the jet nozzle is pulled up and rotated,
spraying the cement slurry at middle-pressure todevelop a columnar soil-improved body with amaximum diameter of 1.0 m.
Meanwhile the latter one uses a special mixing
blade attached with an escape prevention plate at theend to combine the soil and cement slurry sprayed at
middle pressure mechanically. The middle-pressure
jet grouting method is a ground-improvement methodthat allows the selection of both of the abovementioned construction methods. Up to now, the fulllength confirmation of the columnar soil-improved
body sampled by boring after full-scale experiments
and construction has been performed. Although thequality of the construction has been verified byunconfined compression test and so on, it has yet tobe verified analytically. Therefore, in this study, theauthors apply the MPS-CAE to the middle-pressure
method and verify it analytically.

Where, 𝝆 is the density of the fluid, 𝒖 is thevelocity vector, 𝑷 is the pressure, 𝝑 is the kinematic
viscosity coefficient, 𝒈 is the gravity vector, 𝝈 is thesurface tension coefficient, 𝒌 is the curvature, 𝜹 is thedelta function for the surface tension to act on thesurface, and 𝒏 is the unit vector in the direction
perpendicular to the surface.
In the MPS method, each differential operator(slope, divergence, and Laplacian) of the governing
equation, as shown in Eq. (2), is discretized by aweighting function [6],[7],[8]. The weighting
function depends on the interparticle distance r andthe influence radius ( 𝑹 : 2.1 to 4.1 times theinterparticle distance) in each particle interaction
model.
The weighting function (𝒘) is expressed by Eq.
(3), and it is a function of the distance (𝒓) between
particles and the influence radius (𝑹) of the support
domain.
𝑹− 𝟏 (𝟎 < 𝒓 ≤ 𝑹)
𝒘(𝒓) = 6 𝒓:(𝑹 ≤ 𝒓)𝟎

METHODOLOGY
Ground Modelling and Analysis Condition
Figure 1 shows the actual scale models for the jet
grouting method. They were created as the analyticalmodels by three-dimensional AutoCAD. In the jet
grouting method, the jet material was sprayed in one
horizontal direction from the jet nozzle in acylindrical ground with a diameter of 2 m and a heightof 1.5 m.
Table 1 shows the analysis condition for the jet
grouting construction. The water will be sprayed forfirst 20 s while digging until it will reach the desired
depth. Then, the injection fluid is switch to the cement
grout and the jet grouting string is pulled upward
while rotating. The total time of cement grout
spraying is 58 s and the height retracted upward is 0.5
m only and not up to the surface level.

(3)

The sum of the weight functions in the support
domain is commonly known as the particle number
density (𝒏𝒊 ) and it is defined as Eq. (4).
𝒏𝒊 = ; 𝒘𝒊𝒋
𝒋$𝒊

Material Parameters(4)

Table 2 shows the material parameters of thewater, cement slurry, and ground particles thatcomprised the target materials of the analysis.

Where, 𝒘𝒊𝒋 = 𝒘.𝒓𝒊𝒋 , 𝑹0 is the weight function
between particle (𝒊) and (𝒋). The particle number
density when particles are located on a regular grid85

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Lifting

Height of ground: 1.5 m

reproducible analysis of an unconfined compression
test [10],[11]. Bingham fluid has a property whereby
it begins to flow when the shear stress exceeds theyield stress. Therefore, until the shear stress exceeds
the yield stress value, it is regarded as being in an
immobile state, but if the strain ratio is 0, an analysis
becomes impossible. In this study, as a coping
method, a bi-viscosity model is adopted; it treats thefluid as a highly viscous fluid when it is less than theyield stress value. The constitutive equations when
flowing and when immobile are expressed by Eqs. (5)and (6), respectively:

Ground model

QSJ rod
Rotation

Cement slurry jet

Jet nozzle

Diameter of ground: 2.0 m

Fig. 1 Jet grouting method model

𝑻𝒊𝒋 = −𝑷𝜹𝒊𝒋 + 𝟐 >𝜼𝒑 +Table 1 Analysis condition
Dimension of the ground model
Material of jet

2m diameter 1.5m height
CementWater *1
slurry *2
20 s
58 s

Time of spraying
Length of penetration while
0.5
blanking (m)Length of penetration while
0.5
improving soil (m)Amount of jet (L/min)
80
Pressure of jet (MPa)9.4
Velocity of jet (m/s)
137.5
Velocity of penetration while
3.0
blanking (m/min)
Velocity of penetration while
3.0
improving soil (m/min)
Velocity of lifting while improving
soil (m/min)
Velocity of rotation (rpm)
20 *1*1while penetrating *2 while pulling up

𝑻𝒊𝒋 = −𝑷𝜹𝒊𝒋 + 𝟐 E𝜼𝒑 +

0.5
90
18.0
155.0
*2

20

Water

Density (kg/m3)
w/c
Yield value (Pa)
Plastic viscosity (Pa·s)
Yield parameter (-)
Surface tension (N/m)

1000
0.1
Newtonfluid

Fluid model

CementSlurry
1500
1
10
0.28
0.0001
0.1
Binghamfluid

𝜫 > 𝜫𝒄

(5)

𝝉𝒚
+𝜫𝒄

F 𝜺̇ 𝝂𝒊𝒋𝜫 < 𝜫𝒄(6)𝜫 = 𝟐𝜺̇ 𝒊𝒋 𝜺̇ 𝒊𝒋(7)𝜫𝒄 = (𝟐𝝅𝒄 )𝟐(8)where, 𝝅𝒄 is the flow limit strain ratio.

Table 2 Material parameter
Parameters𝝂𝒑@ 𝜺̇ 𝒊𝒋

where 𝑷 is the pressure, 𝜼𝒑 is the plastic viscosity, 𝝉𝒚𝝂𝒑is the yield value, 𝜺̇ 𝒊𝒋 is the strain ratio when flowing,𝜺̇ 𝝂𝒊𝒋 is the strain ratio when immobile, and 𝜫𝒄 is theyield reference value for the fluid state and theimmobile state. It should be noted that 𝜫𝒄 isexpressed by Eqs. (7) and (8), respectively, using theflow limit strain rate:

0 *2

0.52

𝝉𝒚
√𝜫

Setting Probe Region and Boundary
GroundThe probe boundaries are set at the top of theground model to calculate the quantity of mud
discharge during the simulation at the beginning andat the end from the top view and front view
respectively. The total three different dimension
probe boundary is placed at the top. The cylindrical
disc of 1.8 m, 1.6 m and 1.4 m diameter and height
0.1 m and width 0.1 m each are set at the just above
the top of ground model and the number of cement
particles, water particles and soil particles collected at
the end of the simulation is computed. The 1.8m,
1.6m and 1.4m diameter probe region is indicated byred, green and black color respectively.

1600
60000
17000
0.0001
0.002
Binghamfluid

Water is the Newtonian fluid meanwhile cement
slurry and ground are assumed to be Bingham fluid.
The water particles used the general value of a
Newtonian fluid, and the cement slurry particles usedthe value of a Bingham fluid with the plastic viscosity
being measured by a B-type viscometer.
For cement slurry, other values can be measured
by the standard test. Since the cement slurry is
assumed to be the Bingham Fluid, it should possesscertain value of yield point at which it finally starts toflow from the stationary state. But, since the watercement ratio is taken as 1, the cement slurry will flow
before applying any kind of additional stress.
Meaning the yield point for the sample cement slurry
is very low and equivalent to the zero.
For the ground particles, the material parameters
were set using the value of a Bingham fluid by a

RESULTS AND DISCUSSION
Validation of Analytical Ground Model
Figure 2 shows the result of stress-strain curve
obtained by the simulation of the unconfinedcompression test for the material parameter from
Table 2 and is compared with the two real unconfinedcompression test data. The result shows that the soil
yielded at the compression strength of 61.24 kN/m2
and 12.59 % strain value. The graph indicated by the86GEOMATE – Bangkok, Thailand, 22-24 November 2022

triangular plot point is the result of the unconfinedcompression test for the soil sample from Aomori
prefecture, Tsugaru city. It is extracted from the depthof 7 to 7.85 m below the ground and is silty soil withthe average soil diameter of 0.0036 mm whichyielded at the 64.69 kN/m2 and strain of 4.73 %. The
graph plotted with the square plot point represents theresult obtained for the unconfined compression test
result of soil sample from Mikawa town, Yamagata
prefecture. The core sample was taken from the depthof 4 to 4.7 m below the ground. The soil sample issandy soil with the average soil diameter of 0.034 mm
which yielded at 61.55 kN/m2 and strain of 0.55 %.

It is found that the densities of soil have noinfluence on the ground modelling but the size of soil
considered during simulation does influence. Figure
3 shows the stress-strain relation generated for thetwo different soil size and compared to the model
adopted in this study. For 0.5 mm soil sample, yieldvalue of 61.07 kN/m2 is obtained when input
plasticity viscosity is 5500 Pa·s and it yield at 2.11 %strain value. For 1 mm soil sample, yield value of
61.03 kN/m2 is obtained at 3.8 % strain value when
input plasticity viscosity value is 9000 Pa·s. For 2 mm
soil sample, the ground modelling parameter did notchange much but the time of calculation is increasedby 1.5 times that for 3 mm soil sample. Meanwhilefor 1 mm and 0.5 mm, it was 2 and 42 times more
respectively. Thus, if these models are adopted thesimulation might have been failed due to highcalculation load so despite being comparatively lessaccurate, this study is conducted for 3 mm soil
particles.

70

Compression strength (kN/m2)

60
50
40
30
20

Reproduction of Development Situation of
Columnar Soil-Improved Body

Yamagata Data
Aomori Data

10

Simulated Data

Figure 4 shows the development progress of thecolumnar soil-improved body. Ground particles areshown in transparent yellow color, water particles inlight blue color and slurry particles in red color.
About 2 s after the start of the construction water
starts spraying and continues till it reaches the
maximum depth of 1 m. The time required to reachthis depth is set to be 20 s. Water particles are seen to
be moving to the surface as mud discharge and someparticles are even seen to scatter into the open space.
When the jet grouting string reaches the desired
maximum depth of 1m, then it starts rotating incounter-clockwise direction and the rod starts pulling
upward while spraying cement grout. It can be seenthat the water particles have scoured the groundmodel below the desired depth; although it is not theintention of forming soil-improvement body below
the lowest tip of jet grouting string. The velocity of

0
0

5

10
Strain %15

20

Fig. 2 Simulation result compared to known data
70

Compression strength (kN/m2)

60
50
40
30
20

3mm
1mm

10

0.5mm
0
0

5

10
Strain %15

20

Fig. 3 Simulation result for different soil size

Fig. 4 Reproduction of the development condition of soil-improvement body87

GEOMATE – Bangkok, Thailand, 22-24 November 2022

accurate shaped soil improved body can be formed bymaking amendment in the construction specifications.

1000

Total mud discharge particles

900

1.8m

1.6m

1.4m

800

Computation of the mud discharge quantity

700
600

Figure 5 shows the total mud discharge particles
accumulating pattern at each time frame in different
probe region. It is seen that the total number ofparticles keeping accumulating throughout theprocess and attains maximum value at the end of theprocess. However, it is expected that the largest probe
region would automatically attains maximum numberof the discharged mud particles at every time frame
as the lower probe region already is within theboundary of it. There are cases where each probe
region contains highest number of particles at certain
time frame. The number is highest for the probe
region of 1.8 m diameter at 50 s and 60 s, for the probe
region of 1.6 m diameter at 40 s and 78 s and for theprobe region of 1.4 m diameter at 20 s and 30 s.
The water was injected at 80 L/min for 20 s andcement grout at 90 L/min for 58 s so, total injectedamount is about 0.113 m3. However, from the
maximum number of particles at 78s if converted into
volume results only to 0.000024 m3 and as for thedensity it is equivalent to 1600kg/m3 because 99% of
the ejected mud is composed of ground particles. The
main reason for the low mud discharge volume can be
as follows:
1) The data suggests the number of particles
present at the given instant rather than the total
accumulated quantity.
2) Mud discharge particles are scattering
throughout the whole process instead of
remaining stationary and few found its way outof the boundary of probe region too.
3) Calculation of water particles are either notincluded or being overlapped with the soil
particles.
In future, it is necessary to develop the efficientmethod of counting total mud discharge particles formore relatable comparison.

500
400
300
200
100
0
20

30

40
50
Time frame

60

78

Fig. 5 Total mud discharge particles comparison

pulling the rod while spraying cement grout is muchslower compared to the time during digging. At 78 s,
the rod reaches the desired height of 0.5 m from thelowest point of digging and the construction
procedure is completed. The cement grout is sprayed
for the total height of 0.5 m only but it can be seenthat the cement grout has spread beyond the sprayedarea and is distributed unevenly throughout the target
area and outside. Almost all of the water particles
sprayed during digging at the beginning within the
ground have been removed as mud discharge. The
mud discharge is mostly water particles that had been
replaced by the higher density cement grout particles.
The overall simulation can be said to be
successful as it has been able to recreate the realistic
phenomenon of mud discharge and formation of thesoil improved body. In real case scenario, the statusof the jet grouting construction is determined by the
mud discharge phenomenon and by confirming thediameter of the soil improved body formed at the end.
In this simulation, both phenomena can be confirmed
at each stage without waiting for the completion of
the construction. Mud discharge in real case scenariocontains the soil particles and certain amount of
cement grout carried to the surface by the upstream
water particles; same is confirmed in the simulation
too.
The ideal shape of the soil improved body isgeometrically accurate cylindrical shape but it can beobserved that the shape is cylindrical in nature withdifferent diameter at each level. The target design
shape is shown by the blue rectangular frame in Fig.
4. The maximum diameter is observed at the lowest
drilled level and is measured to be 0.9 m in diameter.
Then it starts decreasing gradually in both upwardand downward direction. The diameter at the target
top surface is measured to be 0.6 m. The total heightof the soil improved body formation is 0.86m but theobjective was to form only of 0.5 m height. The heightof 0.1 m and average diameter of 0.5 m soil
improvement body is formed above the target area
meanwhile the height of 0.26 m and average diameterof 0.5 m soil improved body is formed below thetarget area. It is expected that more geometrically

CONCLUSIONSThis simulation has been successful to recreate therealistic jet grouting construction phenomenon andcan be used as the guideline for future research but
there are still certain limitations that this research
paper failed to address properly. The major
conclusions that can be drawn from this research islisted below.
(1) Ground modelling for the 60 kPa soil issuccessful in this simulation as indicated by thevalidation test. Hence, unconfined compression
test simulation can be used for checking thevalidity of the ground model and also changing
the perspective; the reverse can be used to findthe correct parameters of the ground.
88GEOMATE – Bangkok, Thailand, 22-24 November 2022

Generalized approach for prediction of jet grout
column diameter. Journal of GeotechnicalGeoenvironment Engineering, Vol. 139, Issue 12,
2013.
[4] Harada K., Ohbayashi J., Matsumoto J., Kubo Y., andAkima T., New ground improvement technologies
under restricted conditions in Japan. Japanese
Geotechnical Society Special Publication, Vol. 2,
Issue 32, 2016, pp. 1165-1170.
[5] Pinto A., Tomásio R., and Marques G., Ground
improvement with jet grouting solutions at the new
cruise terminal in Lisbon, Portugal. Procedia
Engineering, Vol. 143, 2016, pp. 1495-1502.
[6] Hattori M., An attempt to apply mathematicalfoundation of Moving Particle Simulation to the MPS
software “Particleworks”. Proc. Symp. Educ. Appl.
Inf. Technol. 2015, 9, 59-62.
[7] Shakibaeini A., and Jin Y. C., MPS mesh-free particle
method for multiphase flows. Computer Methods in
Applied Mechanics and Engineering, Vol. 229-232,
2012, pp. 13-26.
[8] Tanaka M., Cardoso R., and Bahai H., Multiresolution MPS method. Journal of Computational
Physics, Vol. 359, 2018, pp. 106-136.
[9] Inazumi S., Kuwahara S., Jotisankasa A., andChaiprakaikeow S., MPS-CAE simulation on
dynamic interaction between steel casing and existing
pile when pulling out existing piles, International
Journal of GEOMATE: Geotechnique. Construction
Material and Environment, Vol. 18, 2020, pp. 68-73.
[10] Güllü H., A new prediction method to rheologicalbehavior of grout with bottom ash for jet grouting
columns. Soils and Foundation, Vol. 57, Issue 3, 2017,
pp. 384-396.
[11] Güllü H., Cevik A., Al-Ezzi K. M., and Gülsan M. E.,
On the rheology of using geopolymer for grouting: A
comparative study with cement-based grout included
fly ash and cold bonded fly ash. Construction and
Building Material, Vol. 196, 2019, pp. 594-610.

(2) Real case scenario like mud discharge
phenomenon is recreated so the jet grouting
construction simulation is also successful.
(3) The ability of making visual assessment during
and after the construction simulation makes it
easier to make correct speculation about theprobable events and action that might beundergoing or likely to take place. Thus, it will
be helpful in formulating future guidelines andmake correction to achieve the better result.
This simulation is carried out with making
assumption and parameter setting such that thecalculation load is low. This might have been thereason for occurring certain conflict during
simulation such as difference in soil particles andwater particles ejection as mud discharge in visual
data and computational data. This simulation also
tried to calculate the total amount of mud discharge
occurring during the construction. However, it has
failed to compute the accurate result. The immediate
reason for it is the inability to predict the range of theprobe region that need to be set and the unpredictable
scattering trajectory of the ejected mud discharge
particles. The degree of accuracy for the construction
can be calculated by comparing the data fromempirical rules and computational data. Since thissimulation failed to compute the exact volume of mud
discharge, this subject need to be studied further.
REFERRENCES
[1]
[2]

[3]

Modoni G., Croce P., and Mongiovì, L., Theoreticalmodelling of jet grouting. Géotechnique, Vol. 56,
Issue 5, 2006, pp. 335-347.
Nakanishi W., and Nakazawa J., The process ofdevelopment on jet grouting methods and their future.
Soil Mechanics and Foundation Engineering, Vol. 54,
2006, pp. 10-12.
Shen S. L., Wang Z. F., Yang J., and Ho C. E.,89

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

PROSPECTS AND MERITS OF USING CALCIUM PHOSPHATECOMPOUNDS FOR SOIL IMPROVEMENT: A REVIEW
Maksym Avramenko1, Kazunori Nakashima2 and Satoru Kawasaki2
1

Graduate School of Engineering, Hokkaido University, Sapporo, Japan
2

Faculty of Engineering, Hokkaido University, Sapporo, Japan

ABSTRACT
Many new soil reinforcement techniques have recently emerged, the most popular of which are microbialinduced carbonate precipitation (MICP) and enzyme-induced carbonate precipitation (EICP). They areenvironmentally friendly and more sustainable than conventional methods, but during carbonate (e.g., calcite)
precipitation occurs ammonia (NH3) and ammonium (NH4+) emissions into air and groundwater, which arehazardous. There are techniques for removing NH4+ from the soil, but in the case of calcium phosphate compounds(СPCs) emissions of ammonium can be eliminated by over than 90%. Calcium phosphate precipitation occurs
when urea and acid urease or acidic bacteria interact with a source of calcium and phosphorus. Precipitation takes
place in between sand particles enhancing their contact and, therefore, strengthens the soil. Properties rely on typeof CPCs, which is sensitive to the pH of the medium and the concentration of calcium and phosphorus. Given therelatively low strength of CPCs, this review paper aims to look at ways to increase their durability. In addition, it
will also show differences in products when using different methods of obtaining CPCs. Ultimately, prospects forthe use of CPCs for soil reinforcement will be provided.
Keywords: Soil improvement, Calcium Phosphate Compounds (CPCs), Morphology, pH dependency, Ground
improvement.
INTRODUCTION

methods are the oldest, most common techniques andinclude synthetic reinforcement
application.
Displacement and replacement, soil nailing, stage
constructions, stone columns method and preloading,reinforcing bars, fibers, grids, strips are the mostcommonly used techniques [4] - [6].
Because of the rapid growth of Earth's population,
there is a need to find alternative, environmentallyfriendly and harmless methods to strengthen the soil
or improve existing ones. Sustainable engineering
practices rank first worldwide in encouraging andincentivizing use. The construction industry alone
accounts for 23% of the global carbon dioxide (CO2)emissions, with Portland cement production
responsible for 6% of those emissions. This motivatesfor the need to discover new environmentally friendly
methods and these techniques must be implemented
in the field of engineering, including geotechnicalengineering [7].
A relatively new method that is rapidly gaining
popularity is biocementation. Biocementation is ageotechnical process that utilizes microorganisms tobind the soil particles, which significantly increases
the strength of soil [8]. Mainly, this process used in
two major biochemical methods: microbial-induced
carbonate precipitation (MICP) and enzyme-induced
carbonate precipitation (EICP).
MICP is a process where the bacterium produces
a urease enzyme that reacts with urea (CH4N2O),
converting it into ammonium (NH4+) and carbonate

Soil is a porous natural material that needs to becompacted or strengthened before it can be used forengineering purposes. It is a critical, non-renewablematerial for humanity's future existence. Therefore, it
is vital to understand how to use it in environmental
and sustainable conditions [1], [2]. There are manykinds of soils in nature. According to [3], soils forengineering applications can be divided into: Gravels,
Sands, Silts and Clays and Highly Organic Soils. For
each type of soil, there are appropriate techniques tostrengthen it. In general, soil stabilization methods
can be divided into (I) Chemical methods, (II)Biological methods and (III) Mechanical methods.
Chemical stabilization involves adding additives
to the soil to obtain the desired physical andmechanical characteristics. Chemical methods of soil
stabilization consist of on-site work and mixing thesoil with cement, asphalt, silicates, bentonite, ash,polymers, fly ash, silica fume, blast furnace slag,
gypsum, calcium-based chemicals and lime, acids,
salts, petroleum emulsions, resins and lignosulfonates.
Biological methods are a mixture of environmental
practices and engineering techniques and include theuse of bio-organisms and/or their byproducts as astructural component. Mechanical stabilization
involves methods and techniques by which air voids
can be removed from the ground with a nominalchange in water content to strengthen it. Mechanical90

GEOMATE – Bangkok, Thailand, 22-24 November 2022

ions (CO3-2) Eqs. (1) - (2). This process raises the pHof the environment, resulting in higher emissions ofammonia (NH3) into the atmosphere and ammonium
into the water. Calcium ions (Ca+2), in turn, react with
CO3-2 to produce calcium carbonate (CaCO3) at thenucleation sites provided by the bacteria cells Eq. (3).
Precipitation of calcium carbonate can be achieved byurease produced bacteria (MICP) or by using a urease
enzyme directly (EICP). With urease source being thedifferent factor between the two methods, the reaction
pathways are similar [6], [9].
Although MICP and EICP are significantly moreenvironmentally friendly than conventional soilreinforcement methods, they have their limitations.
(I) a byproduct produced during urea hydrolysis
is ammonia (which release to atmosphere) andammonium and hydroxide ions (which pollute groundwater). This occurs according to the following
equations Eqs. (1) - (3):(NH2)2CO + 2 H2O → 2 NH4+ + 2 OH- + CO2 ,(1)

CO2 + 2 OH ↔ CO3 + H2O ,(2)Ca + CO3 → CaCO3↓ .

(3)

-

2+

-2

-2

released into the environment, resulting in pollution[11].

Fig. 1 Dependence of ammonium and ammonia
relative concentration and pH (modified from [15]).
Scientists from all over the world are trying tosolve the problem of NH3 gas and NH4+ produced
during the MICP/EICP process. If their formation isinevitable, it is necessary to control the course of thereactions. Among the effective techniques are rinsing
technique (with NH4+ removals exceeding 97.9%)[16], electro-biocementation [17], the addition ofzeolite [18], absorbance of released NH3 by sulphuricacid [10], [19], precipitation NH4+ as struvite [11],[20].
CALCIUM PHOSPHATES COMPAUNDS(II) relatively fragile bonding of soil particles(III) the use of calcium reagents is less costeffective when compared to convention cement [8],[10].

A potential, alternative method to reduce
emissions of toxic NH3 gas to the environment isbiogrouting. First type of biogrouting occurs due to
CPCs precipitation using phosphate and calcium
stock solutions [21]. Second – due to CPCs
precipitation obtained via reaction between bacteria,
urea and phosphate source [22], [23]. CPCs has anadequate physical strength (unconfined compressive
strength (UCS) 0.1-2.0 MPa) [21], [22], [24] - [31],and are promising geotechnical material.
Calcium phosphates are currently widely used
for dental applications [1], [32], [33], bone tissue
engineering [34], coating of implants, drug delivery
and encapsulation [35], bioceramics [36] - [39] andstrengthen the soil [21], [25] - [31], [40], [41]. The
benefits of using this family of compounds forstrengthening soils are the following:- CPCs are environmentally friendly and nontoxic materials. The basic mineral HA[Ca5(PO4)3OH] with a Ca-to-P molar ratio of 1.55 is
the most documented and investigated calcium
phosphate and is the mineral that makes up bones andteeth [42].
- CPCs become stronger over time through a selfsetting mechanism. Amorphous or gel-like calcium
phosphate turns into a stable form, HA (Fig. 2).
- The solubility of CPCs depends directly on the
pH of the medium, so by controlling the pH solution
you can control the minerals that will form (Table 1,
Fig. 3).
- CPCs are recyclable. CPCs, that remain in thesoil after applying various precipitation techniques

DRAWBACKSOFCONVENTIONALMETHODS OF BIOCEMENTATION
Ammonia (NH3) is a colorless gas with a density
similar to air, 0.73 kg/m³. During MICP/EICP, large
amounts of NH3 and NH4+ are released into
atmosphere and water, respectively. In small
quantities, these substances are not harmful tohumans or animals, but large quantities of NH3 cancause severe illness and even death in animals [11].
This has resulted in the legislation in Japan where themaximum concentration of ammonium in industrial
wastewater <100 g/L [12]. This has led to only ahandful of studies conducted in large-scale
applications [13]. During conventional MICP/EICP,
the pH of the medium increases to 8.8-9.0, resulting
in the precipitation of calcium carbonate [14]. Figure
1 shows that up to pH 7.3, the formed nitrogen is in
its NH4+ form, and after 7.5 it is almost completely
converted into gaseous form, releasing a large amountof gas into the atmosphere, which prevent thesemethods from being applied in the field. During
urease hydrolysis, 1 mol of urea produces 1 mol ofcarbon dioxide (CO2) and 2 mol of ammonia (NH3).
One part of NH3 turns into NH4+ in the solution, thepart goes into gaseous phase and pollutes the air, andthe other part remains in the solution as NH3·H2O.
Therefore, most of the NH3 gas produced during thebiocementation process is uncontrollable and gets91

GEOMATE – Bangkok, Thailand, 22-24 November 2022

can be removed and used as agricultural fertilizer[22], [21].
Along with its advantages, CPCs also have its
disadvantages. They have low strength, and it ischallenging to control the reaction and precipitation.
Compared to UCS soil reinforced with CPC and
MICP/EICP, the strength of the soil is several times
lower (0.1-2.0 MPa and 0.31-14.0 MPa respectfully)[6]. Because of the relatively low precipitation of
CPCs (~28 kg/m3), more calcium-phosphateresources are required to achieve a measurable
strength in soils (~60 kg/m3), resulting in a more
expensive method [22].
Some researchers have used pure chemicals [21],[25] - [30]; and urease hydrolyses [12], [22], [24],[31], [40], [41] to improve soil with CPCs. The
reaction of formation of CPCs during ureasehydrolysis takes place at from weakly acidic toneutral pH, causing formation of almost allammonium in ionic form, rather than in the mostdangerous gaseous form. However, methods should
be used to control and evacuate ammonium ions inthe soil after treatment.

Fig. 2 The dependence of calcium phosphatepresence [21].

Fig. 3 Solubility of each calcium phosphate
compound as a function of pH [21].
Table 1 List of calcium phosphate compounds [29].
Ca/P ratio
0.5
0.5
1.0
1.0
1.33
1.5
1.5
1.2-2.2
1.5-1.67
1.67
2.0

Compound
Monocalcium phosphatemonohydrate
Monocalcium phosphate anhydrate
Dicalcium phosphate dihydrate
Dicalcium phosphate anhydrate
Octacalcium phosphate
α-tricalcium phosphate
β-tricalcium phosphate
Amorphous calcium phosphate
Calcium-deficient hydroxyapatite
Hydroxyapatite
Tetracalcium phosphate

Abbreviation

Formula

MCPM (MCP)

Ca(H2PO4)2·H2O

MCPA (MCP)
DCPD (DCP)
DCPA (DCP)
OCP
α-TCP
β-TCP
ACP
CDHA
HA
TTCP

Ca(H2PO4)2
CaHPO4·2H2O
CaHPO4
Ca8(HPO4)2(PO4)4·5H2O
α-Ca3(PO4)2
β-Ca3(PO4)2
Cax(PO4)y·nH2O
Ca10-x(HPO4)x(PO4)6-x(OH)2-x (0<x<1)
Ca10(PO4)6(OH)2
Ca4(PO4)2O

WAYS TO INCREASE STRENGTH OF CPCS

- the most common method of strengthening
building materials is the use of reinforcement. There
are a wide variety of investigated fibrous materials fibers. Both synthetic, such as glass fiber, plastic fiber,
metal fiber, and natural fiber, such as plant fiber, fiberfrom animal components and mineral fiber, are used[4]. In CPCs, ceramic nanofibers are widely used forreinforcement [44].
- adding some powders, such as tricalcium
phosphate; magnesium phosphate; calcium carbonate
and magnesium carbonate, and scallop shell powders[27], [28], [30].
- in the case of microbial/enzymatic CPC
precipitation, pure chemical reagents can be used to
improve the physical characteristics of thecompounds. The other way to improve soil properties

CPCs are weaker in strength than conventional
methods for soil improving, which in turn limits theiruse for soil enhancement. Solving this problem will
increase the capabilities and variability of CPCs
implementation.
The following methods can be used to increasesoil strength by applying CPCs:- ammonia and ammonium can be turned into
struvite, which not only solves the problem ofcontamination of the environment after the reaction of
CPCs deposition, but also increases the strength of thesoil [11], [43]. Using this practice together with the
CPC solves two challenges at once: low strength ofsoil and ammonium pollution.
92

GEOMATE – Bangkok, Thailand, 22-24 November 2022

is to use bacteria with high urease activity or pureurease with calcium and phosphate chemicals. UCS
of soil can be increased by modifying the way thechemicals are applied to the soil, such as injection
frequency or volume.

environmental engineering. These compounds will
help to solve the issue of toxic emissions in a wide
range of applications. Because of size of enzyme or
chemicals, CPCs can be applied for compact soils
with a small space between particles.
Applying
different fibers at different levels, from micro to nanolevels, can significantly increase soil strength andfacilitate the process of soil injection. By changing theratio of chemicals or using natural sources of calcium
or phosphorus, it is possible to make these methods
more cost effective. Phosphorus is a limited resourcethat decreases every year [46], but using CPCs for soil
strengthening, there is an opportunity to reuse
phosphorus from soil. It is also possible to apply acidbacteria from food and by modifying the urease
activity to improve results. Since these bacteria areharmless to humans, it greatly accelerates the waiting
period before application to the open field.
The application of CPC for soil strengthening is apromising methodology that requires further researchand improvement.

DIFFERENCES IN PRODUCTS WHEN USING
DIFFERENTMETHODOLOGYFOROBTAINING CPCS
The formation of CPCs depends on various
factors, such as the pH of the medium, time, and the
Ca/P ratio in the system.
The reactions leading to the formation of CPCs
are divided into 2 types, the acid-base reaction or thehydrolysis reaction. Only one CaP precursor
compound is involved in the hydrolysis reaction,
which interacts with the liquid phase and transforms
into the hydrate phase. During the acid-base reaction,
the acid base and other bases interact with several CaP
precursor compounds, with the formation of the final
end-product with neutral pH [34], [45].
CPCs can be formed due to acid tolerant ureaseproducing microorganisms by the following DCPA
and HA precipitation reaction Eq. (4) and Eq. (5)respectively [8].

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Ca(H2PO4)2 + CO(NH2)2 + H2O + acid urease →CaHPO4 ↓ + CO2 + (NH4)2HPO4(4)

5Ca(H2PO4)2 + 8CO(NH2)2 + 8H2O + acid urease →Ca5(PO4)3(OH) ↓ + 2NH4HCO3 + 6CO2 +7(NH4)2HPO4(5)

The application of bacteria can control the pH of
the environment, which will control the products thatwill eventually be formed. CPCs can be divided into
two groups: apatitic and brushite CPCs. The final
product, apatitic and brushite, can be predicted
depending on the solubility of CaP precursor and thepH of setting reaction. The final products of apatitic
CPCs are either CDHA or HA. Apatitic CPCs can beformed either by acid-base interactions or byhydrolysis reactions. Brushite CPCs are acidic, andreactions can also occur at pH < 2.5. Thus, this typeof CPC can only be formed due to the reactions of theacid base with DCPD as the final product. Аpatitic
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has a short setting time, so it is more porous and nonstrength. For this reason, most CPCs are apatite CPCs[34], [45].
CONCLUSIONS
The application of CPCs for soil reinforcement isa novel, environmentally safe method in civil and93

GEOMATE – Bangkok, Thailand, 22-24 November 2022

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Phosphorus in Biology and Materials, 2020.
[35] Salama A., Recent progress in preparation
and applications of chitosan/calcium
phosphatecompositematerials,
International
Journalof
Biological
Macromolecules, Vol. 178, 2021, pp. 240252.
[36] Balázsi C., Wéber F., Kövér Z., Horváth E.,
Németh C., Preparation of calcium–
phosphate bioceramics from natural
resources, Journal of the European CeramicSociety, Vol. 27, Issues 2–3, 2007, pp. 16011606.
[37] Pietak A. M., Reid J. W., Stott M. J., Sayer
M., Silicon substitution in the calcium
phosphate bioceramics, Biomaterials, Vol.
28, Issue 28, 2007, pp. 4023-4032.

[38] Xin R., Leng Y., Chen J., Zhang Q., A
comparative study of calcium phosphate
formation on bioceramics in vitro and invivo, Biomaterials, Vol. 26, Issue 33, 2005,
pp. 6477-6486.
[39] LeGeros R. Z., Biodegradation andbioresorption of calcium phosphate
ceramics, Clinical Materials, Vol. 14, Issue
1, 1993, pp. 65-88.
[40] Roeselers G., Van Loosdrecht M. C. M.,
Microbial
Phytase-Induced
Calciumphosphate Precipitation - a Potential Soil
Stabilization Method, Folia microbiologica,Vol. 55, Issue 6, 2010, pp 621-624.
[41] Dilrukshi R. A. N., Watanabe J., KawasakiS., Strengthening of Sand Cemented With
Calcium Phosphate Compounds Using
Plant-Derived Urease, International Journalof GEOMATE, Vol. 11, Issue 25, 2016, pp
2461-2467.
[42] Al-Sanabani J. S., Madfa A. A., Al-Sanabani
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[45] Lodoso-Torrecilla I., van den Beucken J. J.,
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9512th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

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

Faculty of Engineering, Tottori University, Japan
ABSTRACT

It is important to investigate the subsurface structure to consider seismic strong ground motions in landslideareas. In this study, microtremor and seismic observations were conducted in the Saji area, Tottori City, Japan,
based on the landslide topographic distribution map. The seismic ground motion characteristics were determined,
and the ground structure was estimated from the analysis of the observation data. H/V and its predominant period
were obtained from three-component single-point microtremor observations. The predominant period was in therange of 0.1 - 0.4 seconds, indicating that areas with similar periods were concentrated and distributed regardlessof topographic cliffs or moving mass. S-wave velocity structure of the sedimentary layer of the moving mass was
obtained from the array observation of microtremors. The S-wave velocity of the sedimentary layer of the mobile
mass was found to be between 150m/s - 400m/s, with a maximum layer thickness of about 30m. The shape of the
H/V and its predominant period corresponds to the thickness of the sedimentary layer of the moving mass of thelandslide. The H/V of seismic records were obtained from seismic observation, and their characteristics suggested
the influence of the deep structure below the moving mass.
Keywords: Landslide area, Subsurface structure, Microtremor observation, Seismic observation

INTRODUCTION

strong ground motion characteristics and subsurface
structure of the target area were related to theoccurrence of the landslide. In such a landslide area,
it is very important to understand the strong groundmotion characteristics and subsurface structure toconsider the risk of slope disasters caused byearthquakes. In this study, microtremor and seismic
observations were conducted in the Saji area, Tottori

In areas where rocks have become brittle due tovolcanic deposition and weathering, repeatedlandslides may have occurred in the past due toearthquakes and heavy rainfall. The 2009 Sumatra
earthquake caused a large seismic landslide [1], andmicrotremor surveys of the area [2] suggest that theFig. 1 Location of the observation area (upper map) and microtremor observation point (lower map)
96

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 2 Distribution map of predominant period

Fig. 3 H/V of microtremors
City, Tottori Prefecture, Japan, based on the landslide
topographic distribution map [3], [4] to determine
strong ground motion characteristics and estimationof subsurface structure.

single-point observations. SAJAR1 was conducted on
the site of the institution where the seismic stations
are located, as described below. Fig. 1 shows thelocations of the observation points.
A three-component acceleration seismograph,
JU410, was used as the observation equipment. The
array consisted of one at the center of the circle andthree at equal intervals along the circumference of thecircle. The distance between the center point and theseismometers on the circumference ranged from 1 to
15 meters. Because the power of the microtremors
was expected to be low, three to four adults jumped
at a position about 5 m from the outer seismograph toartificially induce vibrations, and their waveforms
were also observed.

OBSERVATION
Microtremor observations
As microtremor observations, three-component
single-point observations were conducted todetermine the predominant period and amplification
characteristics of the subsurface structure, and arrayobservations were conducted to estimate the S-wave
velocity structure. Three-component single-point
observations were made at 95 points on the road at
100 m intervals. Array observations were conducted
at four points (SAJAR 1, SAJAR 2, SAJAR 3, and
SAJAR 4). Array observation points were selectedbased on the assumption that the subsurface structure
differs according to the distribution of the
predominant period based on three-component

Seismic observation
As for seismic observation, a seismograph has
been installed on the foundation in the Saji Astro Parkin Tottori City (same location as SAJAR1) since July
1, 2021 (observation is ongoing). The instruments
used were a three-component integrated velocity
97

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 4 phase velocity dispersion curve

Fig. 5 H/V at the center of the array

seismometer CV-374V, a three-component velocity
seismometer KVS-300, and a data logger HKS-9700.
The specifications of the observations were as
follows: sampling frequency of 100 Hz, time
calibration using a GPS clock, and continuous
observation.

window [5] with a coefficient of 20 was used tosmooth the spectra. The ratio of the spectra of thehorizontal and vertical components (H/V) was
obtained from the obtained Fourier spectra of thethree components. The predominant period was readfrom the H/V obtained at each location. The
distribution of the predominant period is shown in Fig.
2 and some examples of H/V are shown in Fig. 3.
For the simultaneous data of array observations of
microtremors, the phase velocity was estimated basedon the CCA method [6] and SPAC method [7] using
the microtremor analysis package tool BIDO2.0 [8].
More than 10 segments were automatically selectedfrom the RMS values of the observation recordings
with a segment length of 10.24 s. The power spectraof these segments were smoothed by Parzen window

ANALYSIS
Microtremor observation data
For the three-component single-point observation
data, at least 10 stable 20.48 s intervals were selectedvisually, excluding non-stationary portions, and theFourier spectrum of each component was obtained by
FFT, and the average spectrum was obtained. A log
98

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 1 Subsurface structural models (microtremor)
Thickness

(m)
11
25
∞

Thickness

(m)
5
25
∞

Thickness

(m)
5
24
∞

Thickness

(m)
12
25
∞

SAJAR1ρVp
3(m/s)(g/cm )
1.8
1.9
2.2

Vs
(m/s)

1510
1790
2180

200
450
800

SAJAR2
ρVp(g/cm3) (m/s)

Vs
(m/s)

1.8
1.9
2.2

1460
1790
2180

150
450
800

SAJAR3ρVp
3(m/s)(g/cm )

Vs
(m/s)

1.8
1.9
2.2

1460
1620
2070

150
300
700

SAJAR4ρVp(g/cm3) (m/s)

Vs
(m/s)

1.8
1.9
2.2

1400
1790
2180

Fig.6 H/V of the earthquakes

100
450
800

Fig.7 Comparison of H/V observed (black)and theoretical values of inversion (red).
component was selected from the seismic record, anda cosine taper with a half-period time rising by 0.5 s
was applied to the outside of both ends, and then zerodata was added to obtain a time of 20.48 s. From thisdata, the Fourier spectrum was calculated by using
FFT and smoothing these by the Parzen window witha bandwidth of 0.2 Hz, and H/V was obtained. H/V ofthe earthquakes is shown in Fig. 6. From the mean
H/V of all earthquakes, a subsurface structural model
was determined by inverse analysis using the
diffusion wavefield theory [11] and the hybrid
heuristic method [12]. Microtremor arrayobservations were made for the shallow layers
smaller than Vs = 800 m/s, and the initial model forthe deeper layers above Vs = 800 m/s was based on
the previous subsurface structure model [9]. The
inverse analysis was searched for S-wave velocity: Vs,
P-wave velocity: Vp, layer thickness, and damping
constant: h. In the uppermost and lowermost layers,
the initial model values were fixed for modeling. Fig.
7 is the graph of H/V overlaid with observed andtheoretical values from inversion. The obtained
parameters of the subsurface structure model by thisanalysis are shown in Table 2.
The frequency characteristics H/V of theearthquake (Fig.6, Fig7) show peaks around 0.2 s(blue arrows) and 0.8 s (red arrows). The peak around
0.2 s is also seen in the H/V of the microtremor

with a bandwidth of 0.3 Hz, and then averaged. Phase
velocity dispersion curves were obtained using these
spectra. Using the phase velocity dispersion curve
and the H/V of the array center, we estimated thesubsurface structure by forward analysis based on theborehole data. Density was obtained from previous
studies [9], and P-wave velocity was obtained using
the S-wave velocity conversion formula [10]. At eacharray observation point, the phase velocity dispersion
curve in Fig. 4 and the H/V obtained at the center of
the array is shown in Fig. 5. n Fig. 4 and Fig. 5, theobserved values (solid lines) and the theoreticalvalues of Rayleigh wave fundamental modes
obtained from the estimated model (dotted lines) areoverlaid. The colors of the solid lines in Fig. 4
indicate blue: NS/UD, orange: EW/UD, and black:horizontal composite/UD. The solid colors in Fig. 5
show the dispersion curves obtained for each radius.
The obtained parameters of the subsurface structure
model by this analysis are shown in Table 1.
Seismic observation data
Seismic observation data were analyzed for 21
earthquakes of intensity 2 or greater that occurred
within 200 km of the seismic stations between August
11 and December 11, 2021. For data processing,
10.24 seconds of the S-wave portion of each99

GEOMATE – Bangkok, Thailand, 22-24 November 2022(a) Block A(b) Block B and C

Fig.8 Distribution of thickness of sediment layer(SAJAR1 in Fig. 4); the peak around 0.8 s is not seenin the H/V of the microtremor and appears to be
influenced by the deeper ground beneath thesedimentary layer of the moving mass.

Table 2 Subsurface structural models (earthquake)
SAJAR1（Seismic Analysis Model）
ρ
Vp
Vs
h
3
(m)
(m/s)
(g/cm ) (m/s)

Thickness

PREDOMINANT PERIOD AND SUBSURFACESTRUCTURE

11.0
26.8
36.4
60.6
1640.3∞Results from microtremor data analysis
The distribution of the predominant period of
microtremors (Fig. 2) is not so much related to the
topographic areas of sliding cliffs and cliff cone
deposits (moving masses). For each block, Block A
has a short period of less than 0.3 s, Block B has aslightly longer period of 0.2-0.4 s, and Block C has along period of 0.4 s or longer. The distribution of thetime periods in Blocks A and B did not change much,while those in Block C varied greatly.
According to the subsurface structure model inTable 1, the S-wave velocity of the sedimentary layerof the moving mass is small (150m/s~400m/s), andthe layer thickness is about 30m for SAJAR1 and
SAJAR3, and about 4m for SAJAR2. These results
suggest that the predominant period corresponds tothe thickness of the sedimentary layer of the moving
mass with respect to the subsurface structure.
To investigate the areal thickness distribution of
the moving mass, layer thickness: H was determinedbased on the 1/4 wavelength law using thepredominant period of H/V: T and the average Swave velocity: V up to the second layer of thesubsurface structure model at the array points in theblock. The equation is as follows.

𝐻 = 𝑇 ∙ 𝑉 ⁄4

1.70
1.94
1.94
2.02
2.40
2.56

1524
2015
2404
2644
4716
5500

200
655
659
854
2241
3000

0.038
0.090
0.038
0.038
0.030
0.010

SAJ
The average S-waveThickness
velocities are 400 m/s forρ(g/cm³)
Vp(m/s)block A, 370 m/s for block B, and 350
m/s for block(m)
C. Fig. 8 shows the layer thickness distribution. The
11.0
1.70 in these1524
layer thicknesses of the surrounding
cliffsdistribution maps are spatiallycomplemented
by
26.8
1.94
2015
assuming that the thicknesses are 0 m (black dots in
36.4
1.94
2404
the figures).
In block A, the layer isthicker in2.02
the center2644
of
60.6
several small landslide areas, with the maximum
2.40
thickness exceeding 20 m1640.3
in some areas.
In blocks4716
B
and C, the layer thickness is∞thicker in2.56
general, with5500
areas exceeding 50 m at the center of the moving massin block C and the surrounding cliffs. The geology of
the area consists of Neogene sedimentary rocks,
andesitic lava and metamorphic rocks, and thesestrata are thought to exist beneath the layers of cliff
cone deposits.
Results from seismic data analysis
From the estimation of subsurface structure model(Table 2), the correspondence between geology andS-wave velocity is as follows. The first layer is a cliff
cone deposit at 200 m/s, the second to fourth layers
are Neogene sedimentary rocks or andesite lava at Vs

(1)

100

Vs(m/s)
200
655
659
854
2241
3000

GEOMATE – Bangkok, Thailand, 22-24 November 2022

= 650-850 m/s, and the fifth to sixth layers aremetamorphic rocks at Vs = 2200-3000 m/s. In otherareas where moving masses are present, thestratigraphy is assumed to be similar to that at this site.

[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] 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).
[6] 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.
[7] Aki, K., Space and time spectra of stationary
stochastic waves ， with special reference tomicrotremors, Bull. Earthq. Res. Inst., 1957, 35,
pp.415-456.
[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, 2009 (inJapanese).
[9] 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)[10] 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)[11] Kawase, H., Sanchez-Seama, F. J. andMatsushima, S., The optimal use of horizontalto-vertical spectral ratios of earth-quake motions
for plane waves, Bull. Seism. Soc. Am., Vol. 101,
No. 5, pp. 2001-2014, 2011.
[12] Yamanaka, H., Inversion of surface-wave phasevelocity using hybrid heuristic search method,
geophysical Exploration, Butsuri-Tansa, Vol.60,
No.3, pp. 265-275, 2007. (in Japanese withEnglish abstract).

CONCLUSIONSIn this study, microtremor and seismic
observations were conducted in the Saji area, Tottori
City, Japan, based on the landslide topographic
distribution map, to understand strong ground motion
characteristics and estimate of subsurface structure.
The predominant period of H/V distribution was
obtained from single-point microtremor observations,and the S-wave velocity structure of moving masses
was obtained from array microtremor observations.
The layer thickness distribution was found to be
thicker near the center of the moving mass, with amaximum thickness of 50 meters. Furthermore, theH/V of the earthquake was obtained from seismic
observation records, and the estimation of thesubsurface structure of the deep subsurface structure
below the cliff cone sediments was also obtained.
ACKNOWLEDGMENTS
Part of this research is based on the results of thegraduation thesis by Ms. Rino Sasaki, who belonged
to the Faculty of Engineering, Tottori University.
This work was supported by JSPS Grant-in-Aid for
Scientific
Research
JP20K05028(Principal
Investigator: Tatsuya Noguchi). We also used a
seismograph borrowed from the Earthquake ResearchInstitute of the University of Tokyo with the supportof the Ministry of Education, Culture, Sports, Scienceand Technology (MEXT) of Japan, under its
Earthquake and Volcano Hazards Observation andResearch Program (Phase 2). We would like to thankAtsushi Miyamoto, former director of the Saji Astro
Park in Tottori City, and all the staff for providing uswith the installation site and the use of a power supply.
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] Nishimura I., Noguchi T., Ono Y. and Kohno M.:
Subsurface structures based on Microtremor
observations in landslide area of Tandikat, West
Sumatra, Indonesia, International Journal of
GEOMATE, Vol.22, Issue 90, pp.57-62, DOI:
https://doi.org/10.21660/2022.90.gxi282, 2022.

101

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

EXPERIMENTAL STUDY ON STATE BOUNDARY SURFACE OF
COMPACTED SILTY SOIL
Tufail Ahmad1, Riko Kato1 and Jiro Kuwano1
1

Department of Civil & Environmental Engineering, Saitama University, Japan
ABSTRACTTo understand the behavior of unsaturated soil, considerable research has already been done. However, there
is limited research to model the behavior of unsaturated soil in a generalized state boundary surface (SBS). SBS
is the surface outside which a soil stress state can never exist. It is an envelope surface of all the possible soil stresspaths in the p’-q-e space. In order to investigate the SBS of unsaturated soils, a series of triaxial compression tests
were performed in this study under constant water content conditions using a double cell triaxial test apparatus.
The samples were prepared with 80%, 83% and 86% degree of compaction and water contents of 20% (Optimum
water content) and 25%. All the soil specimens were consolidated isotropically under 500kPa confining pressure
before being sheared with constant void ratio under monotonic loading. The results show that constant void ratio
tests depict contour like lines which can help in defining the SBS of unsaturated soil.

Keywords: Unsaturated soil, State Boundary Surface (SBS), Constant water content, Constant void ratio,
Contours

INTRODUCTION

and Hvorslev surfaces using compression andextension triaxial tests on soil samples. The RoscoeHvorslev model, based on elastic-perfectly plastic
behavior, was presented by Houlsby et al. [6]. The
critical state framework for saturated soils is built
around the concepts of yielding and critical state.
Sasitharan et al. [7] found that very loose saturated
sand is loaded slowly under drained conditions cancollapse undrained following a certain stress path. It
was observed that for a particular void ratio andconsolidation stress, the post-peak component of an
undrained stress path specifies the state boundary
above which a stress state cannot exist. Several
researchers have attempted to extend the critical state
concept to unsaturated soils [8]-[13]. The mechanicalbehavior of unsaturated compacted soil has alreadybeen thoroughly researched [8], [14]-[16]. However,
due to the complexity and time-consuming nature of
testing unsaturated soils, there is limited experimental
study to determine the state boundary surface forunsaturated soils.
Estabragh and Javadi [17] performed controlled
suction triaxial tests with constant confining pressure
under drained conditions on samples of unsaturated
compacted silty soil to investigate the existence andfeatures of Roscoe and Hvorslev boundary surfaces
for unsaturated soil. The findings reveal that the
Roscoe and Hvorslev surfaces exist for unsaturated
silty soil as well. With the increase and decrease ofsuction, the Roscoe surface expands and shrinks,
while the Hvorslev surfaces are almost parallel fordifferent suctions.
This study presents experimental data fromtriaxial compression tests performed on samples of

The soil near the surface of the earth ground isusually unsaturated by nature. The processes of
excavation, compaction and soil reshaping used invarious civil engineering structures such as highways,
earth fill dams, embankments and runways result in
unsaturated soils. There are only two phases insaturated soil that are soil and water while forunsaturated soil there are three phases: namely, soil,
air, and water [1].
A novel approach to soil mechanics wasdeveloped in the 1950s and 1960s based on theconcepts of critical state. It establishes a framework
for relating a saturated soil's shear distortion during
yielding to its stress and volume condition [2]-[3]. For
saturated soils, the critical state theory is a threedimensional (3D) approach that is defined by threestate variables: effective mean stress (p’), deviatoricstress (q), and specific volume (v). When the soil issheared, it eventually reaches a critical state condition,
which is located on a unique line in the p’, q and v
space. This surface contains both sets of stress paths.
This is known as the Roscoe surface or state boundary
surface. All drained and un-drained stress paths
appear to lie on a 3D surface for normallyconsolidated soil, bordered at the top by the criticalstate line (CSL) and at the bottom by the normal
consolidation line (NCL). Another state boundary
surface that connects to the Roscoe surface at the CSL
is the Hvorslev surface. The significance of thissurface is that a sample's shear strength is a functionof the specific volume and the mean net stress (p’).
Marto [4] and Moradi [5] demonstrated the Roscoe
102

GEOMATE – Bangkok, Thailand, 22-24 November 2022

unsaturated compacted silty soil under constantvolume and constant water content conditions. The
findings are being utilized to investigate the existence,
shape and the features of the state boundary surfaceof unsaturated soil.

0.98. For specimens prepared with 80% and 83%
degree of compaction, the pressure exerted to the soil
during sample preparation was less than the confiningpressure during the test phase, hence the samples are
known to be normally consolidated. Similarly forsample compacted with 86% degree of compactionand 20% water content, the pressure exerted during
sample preparation is higher than the applied stressduring the test phase, so the sample is deemed to beslightly over-consolidated (OCR ≈ 2). All preparedspecimens were 10 cm in height and 5 cm in diameter.
The features of the specimens used in this study are
shown in Table 2.

EXPERIMENTAL PROGRAMMEThe physical properties of soil, sample
preparation, experimental setup and procedure will be
discussed in this part.
Soil Properties
The commercial term for the soil utilized in thisstudy is DL Clay; it is a fine soil with negligible
plasticity. Freshly and freely deposited DL clay has ayellowish-brown color. According to the Japanese
Geotechnical Society (JGS), it has Medium-Low
compressibility (ML) and is made of 10% clay and90% silt, indicating that the grainsize is larger thanordinary clay. The grain size distribution of this soil
is relatively uniform, with a mean grain size D50 ofabout 0.03 mm [18]. The physical properties of DL
Clay are shown in Table 1.

Table 2 Specimen Properties
PropertiesValue

Dc (%)

80

80

83

83

86

86

d (g/cm3)

1.24

1.24

1.29

1.29

1.33

1.33

w (%)

20

25

20

25

20

25

Sr (%)

46.2

57.8

49.5

62.6

53.6

67.5

e

1.13

1.13

1.05

1.05

0.98

0.98

Compaction
Pressure,(kPa) ≈

200

100

300

210

850

375

InitialSuction,
kPa ≈

20

15

20

15

20

15

Table 1 Physical Properties of DL Clay
Properties

Unit

Value

Density of soil particle, s

g/cm

3

2.635

Maximum dry density, dmax

g/cm3

1.55

Optimum water content%

20Consistency-

Non-Plastic

Sample Preparation

Initial matric suction of soil samples preparedwith 20% and 25% water content and degrees ofcompaction of 80%, 83%, and 86% was around
20kPa and 15kPa, respectively. This is consistent
with the findings of Yang et al. [20], who found thatcompaction energy has a lower impact on soil suction
in soils having a lower clay content. The soil-water
characteristic curve (SWCC) is a representation of therelationship between matric suction and watercontent. For determining SWCC of DL-clay, theinitial suction was measured for several specimens
prepared with variable water content (or initial degreeof saturation) after they were placed on top of asaturated pedestal having a thin membrane filter. The
results are shown in Fig. 1, which show that when thesaturation ratio rises, the suction falls [19].

A static compaction machine with a hydraulicjack was used to prepare homogenous specimens bycompacting the soil in 5 layers in a cylindrical moldhaving diameter of 5cm, each layer being 2 cm thick[19]. The low energy of static compaction can provide
a homogeneous density in the specimen, preventing
the formation of a weaker zone [16]. Water was
mixed with dry DL clay prior to compaction to makespecimens at water contents of 20% and 25% i.e., towet side of optimum water content. Specimens werecompacted to degree of compaction of 80%, 83% and86%. Specimens prepared with an 80% degree ofcompaction represent the loose state of soil with avoid ratio of 1.13, an 83% degree of compaction
represents the medium dense state of soil with a void
ratio of 1.05, and an 86% degree of compaction
represents the dense state of soil with a void ratio of103

GEOMATE – Bangkok, Thailand, 22-24 November 2022

exhaust air valve was also fitted inside the top cap to
reduce the volume of air in the pore air drainage line.
A balance with external load cell is also connected tothe triaxial apparatus to measure the amount of waterdrained or infiltrated into the specimen.
Test Procedure
Before the start of each experiment the bottom
pedestal is fully saturated so that no air bubble
remains inside the porous stone of the pedestal.
During the testing, de-aired water was utilized toreduce inaccuracies caused by air entrapment in thesoil. The water in the external upper and lower tanks
was de-aired for at least 24 hours by applying vacuum
pressure using the suction pump.
After the specimen preparation, the sample waswrapped in a rubber membrane using a membraneholder and placed over the pedestal having a thinmembrane, in order to note the value of initial suction.
The Axis translation technique (ATT) was carried out
after measuring the initial suction and setting allnecessary connections. The main purpose of ATT
was to prevent formation of cavities or voids inside
the pedestal, which would ultimately influence thereading of pore-water pressure. To make pore waterpressure zero, the confining pressure and pore airpressure were both increased simultaneously, leaving
the specimen volume unaltered [22]. During ATT,
volumetric strain should theoretically be zero.
Following ATT, the specimen was isotropicallyconsolidated by applying the required net confiningpressure of 500kPa, to get the sample to the desired
state before start of shearing. To dissipate the excess
pore water pressure, the drain valve was opened
throughout the consolidation. The axial stress was
automatically controlled by the load control system tokeep deviatoric stress (q) equal to zero.

Fig. 1 SWCC for the studied soil [19]Experimental Setup
Figure 2 shows a schematic of the double cell
triaxial apparatus utilized in current research, which
includes a pore water pressure transducer, pore airpressure transducer, cell pressure transducers andloading system for exerting axial load. The device
could measure both pore air and pore water pressuresat the same time. The pore air pressure transducer isinstalled in the top cap and connected to an air
regulator for continuous air supply to the specimen.
A Low-Capacity Differential Pressure Transducer(LCDPT) measured the volume change in the soil
sample as a function of the fluctuation in water levelin the inner cell, while an external Linear Variable
Displacement Transducer (LVDT) measured thevertical deformation of the specimens.

(a)

(b)

ATT

I.S

Fig. 2 Schematic of Double Cell Triaxial

CON SHEAR

Fig. 3 (a) Stress path followed in constant void ratio
test: (b) Test phases involved

For effective control and measurement of porewater and pore air, a thin membrane "Supor 450" anda Polytetrafluoroethylene (PTFE) sheet were utilized.
The thin membrane having a pore size of 0.45 μm,
thickness of 140 μm, and air entry value of 250 kPa
was mounted on the bottom pedestal to allow water
to flow while blocking the air passage [21]. To
oppose the flow of water, the PTFE sheet was pasted
to the bottom of the top cap. A solenoid-controlled

The test phases and stress path followed during
current research is illustrated in Fig. 3. The soil
specimens were sheared with constant void ratio(constant volume) at a strain rate of 0.05 mm/min
under constant water content condition, by keeping
the drainage valve for pore water pressure closed,
while the pore air pressure was constantly drained and104

GEOMATE – Bangkok, Thailand, 22-24 November 2022

controlled. The constant water content test simulates
the state of compacted soil in an embankment or
slope. The sample volume was maintained constantby varying the confining pressure in response to thevolume change (i.e., reading of the LCDPT value) of
the specimen. According to the standards of Japan
Geotechnical Society “JGS 0527-2009”, the shearing
phase was automatically stopped when the axial strainreached 15%.

(a)

EXPERIMENTAL RESULTS
The initial matric suction has a significant impact
on the mechanical behavior of unsaturated soils. It
can be seen from Fig. 4 that specimen prepared with
20% and 25% water contents has different values of
initial matric suction. For samples with lower watercontent, the matric suction is high, and it takes moretime to stabilize the matric suction as compared tosample with high water content.

(b)Fig. 5 Void ratio Vs Mean effective stress for samples
prepared with (a) 20% water content (b) 25%
water content

Fig. 4 Variation in initial matric suction over time.
The sample was sheared at a rate of 0.05% perminute up to an axial strain of 15%. The shearing was
performed under constant water conditions, i.e., waterdrain valve was closed while the air drain valve wasopen. As the samples were to be sheared withconstant void ratio or zero volumetric strain, therefore
the cell pressure was increased or decreased
corresponding to the LCDPT value to keep thesample void ratio constant.
Figure 5 shows the void ratio against meaneffective stress during consolidation and shearing forsamples prepared with 20% and 25% water content.
It can be observed from Fig. 5 that as the degree ofcompaction increases from 80% to 86%, the decreasein void ratio becomes less during the consolidation
for both 20% and 25% water content samples.
Similarly for samples with same degree ofcompaction e.g. (80%, 83% or 86%), the decrease in
void ratio during consolidation is slightly less forsamples with 20% water content as compared to 25%
water content samples. The void ratio is kept constantthroughout the shearing.

(a)

(b)

Fig. 6 Deviatoric stress during shearing for samples
prepared with (a) 20% water content (b) 25%
water content
It can be seen from Fig. 6 that the peak deviatoricstress was slightly more towards the optimum
moisture content than the wet side of optimum
moisture content. This is because of matric suction in
unsaturated soil. When water content in a specimen
increase, the deviatoric stress decrease accordingly.
When there is less water content, water will not be
sufficient to act as a lubricant between soil particles
so they can slide and reorient when stress is increased.
Hence the internal friction between soil particles is105

GEOMATE – Bangkok, Thailand, 22-24 November 2022

increased which increases the deviatoric stress. Also,
the deviatoric stress is more for samples preparedwith high degree of compaction. The effective stressplotted against the axial strain is shown in Fig. 7. The
effective stress value is more for dense samples ascompared to the semi dense and loose samples.

(a)

(a)

(b)

(b)

Fig. 9 Pore air pressure during shearing for samples
prepared with (a) 20% water content (b) 25%
water content
The void ratio (e), net mean effective stress (p’),
and deviator stress (q) is plotted in a 3D space asshown in Fig. 10. All the stress paths lie on the same
failure plane forming the state boundary surface. SBS
separates the possible and impossible stress states. It
can be observed that the constant void ratio test
provides contour like lines which helps in identifying
the shape of state boundary surface. With increase inmatric suction, the SBS extends outwards.

Fig. 7 Mean effective stress during shearing forsamples prepared with (a) 20% water content(b) 25% water content
It can be noted from Fig. 7, there is some unusual
behavior for axial strain range of 0-2%. The effective
stress initially increases slightly followed by a sudden
decrease and then again increases and decreases
continuously until the specimen reaches the criticalstate. This phenomenon may be due to the sudden
loading impact under high confining pressure of 500
kPa during shearing which may cause the soil
particles held together by suction to collapse suddenly
to fill in the gaps and then reorient.
Both pore water and pore air pressure decreased
throughout the shearing phase, as illustrated in Fig. 8
and Fig. 9. However, the decrease in pore waterpressure was more than the pore air pressure. As aresult, the matric suction also increased slightly. The
degree of saturation of samples did not alterthroughout shearing as the water drainage valve wasclosed and the tests were performed with constantvoid ratio.

(a)

(b)

(a)

(b)

Fig. 8 Pore water pressure during shearing forsamples prepared with (a) 20% water content(b) 25% water content

Fig. 10 State Boundary Surface for samples preparedwith (a) 20% water content (b) 25% water content
tested under constant void ratio
106

GEOMATE – Bangkok, Thailand, 22-24 November 2022

CONCLUSIONS

Soil. Geotechnique, Vol. 45, No. 1, 1995, pp. 3553.
[9] Maâtouk, A., Leroueil, S. and La Rochelle, P.,
Yielding and critical state of a collapsible
unsaturated silty soil. Géotechnique, Vol. 45, No.
3, 1995, pp. 465-477.
[10] Loret, B., and Khalili, N., An effective stresselasto-plastic model for unsaturated porous
media. Mech. Mater., Vol. 34, No. 2002, pp. 97116.
[11] Toll, D. G., and B H. Ong, Critical-State
Parameters for an Unsaturated Residual Sandy
Clay. Géotechnique, Vol. 53, No. 1, 2003, pp.
93-103.
[12] Tarantino, A., A possible critical state
framework for unsaturated compacted soils.”
Géotechnique, Vol. 57, No. 4, 2007, pp. 385-389.
[13] Estabragh, A. R., & Javadi, A. A., Critical State
for Over-consolidated Unsaturated Silty Soil.
Canadian Geotechnical Journal, Vol. 45, No. 3,
2008, pp. 408-420.
[14] Cui, Y. J., and Delage, P., Yielding and plastic
behaviour of an unsaturated compacted silt.
Géotechnique, Vol. 46, No. 2, 1996, pp. 291-311.
[15] Jotisankasa A, Coop M, Ridley A, The
mechanical behavior of an unsaturated
compacted silty clay. Géotechnique, Vol. 59, No.
5, 2009, pp. 415-428.
[16] Melinda F, Rahardjo H, Han KK, Leong EC,
Shear strength of compacted soil under
infiltration conditions, Journal of GeotechnicalEng. Div. ASCE, Vol. 130, No. 8, 2004, pp. 807–
817.
[17] Estabragh, A. R., & Javadi, A. A., Roscoe andHvorslev Surfaces for Unsaturated Silty Soil.
International Journal of Geomechanics, Vol. 14,
No. 2, 2014, pp. 230-238.
[18] Chae, Jonggil, Byeongsu Kim, Seong-wan Park,and Shoji Kato., Effect of suction on unconfinedcompressive strength in partly saturated soils.
KSCE Journal of Civil Engineering, Vol. 14, No.
3, 2010, pp. 281-290.
[19] Rasool AM, Kuwano J, Influence of matricsuction on instability of unsaturated silty soil in
unconfined conditions. International Journal of
GEOMATE, Vol. 14, No. 42, 2018, pp. 1–7.
[20] Yang S-R, Lin H-D, Huang W-H, Variation of
initial soil suction with compaction conditions
for clayey soils. Journal of Mechanics, Vol. 28,
No. 3, 2012, pp. 431-437.
[21] Habasimbi P, Nishimura T, Soil water
characteristic curve of an unsaturated soil under
low matric suction ranges and different stressconditions. International Journal of Geosciences,Vol. 10, No. 1, 2019, pp. 39-56.
[22] Hilf JW, “An investigation of pore waterpressure in compacted cohesive soils” U.S.
Department of Interior Bureau Reclamation
Technical Memo. no. 654, 1956.

A series of laboratory element tests were carriedout to investigate the state boundary surface of
unsaturated soil under constant void ratio andconstant water content conditions. It was found thatfor soil samples with higher water content, initialsuction values stabilize in a shorter time interval,
while lower water content soil samples take longer tostabilize. The soil samples prepared at optimum watercontent shows higher shear strength than those
prepared at wet side of optimum water content. Also,
the shear strength of soil increased with increase indegree of compaction. The effective stress increased
and decreased multiple times within axial strain rangeof 0-2%. The matric suction of samples slightly
increased during shearing due to decrease in porewater pressure. The SBS was set up by contour likelines obtained with constant void ratio tests which
separates the possible and impossible stress states.
ACKNOWLEDGEMENTS
The Japanese Ministry of Education, Culture,
Sports, Science and Technology (MEXT) is
gratefully acknowledged for research facilities andfinancial assistance.
REFERENCES[1] Fredlund DG, Rahardjo H, Soil Mechanics for
Unsaturated Soils. New York: John Wiley andSons Inc., Soil Dynamics and Earthquake
Engineering, Vol. 12, No. 7, 1993, pp. 449–450.
[2] Roscoe, K. H., Schofield, A. W., and Worth, C.
P., On the yielding of soils. Géotechnique, Vol. 8,
No. 1, 1958, pp. 22-53.
[3] Schofield, A. W., and Worth, C. P., Critical State
Soil Mechanics. Engineer, Vol. 16, No. 3-4, 1968,
pp. 53-66.
[4] Marto, A., Volumetric compression of a silt underperiodic loading. Ph.D. thesis, Univ. of Bradford,
Bradford, U.K, 1996.
[5] Moradi, G., Symmetric and non-symmetrical
cyclic triaxial loading of silt. Ph.D. thesis, Univ.
of Bradford, Bradford, U.K, 1998.
[6] Houlsby, G. T., Wroth, C. P., and Wood, D. M.,
Predictions of the results of laboratory test on aclay using a critical state model. Proc., Int.
Workshop on Constitutive Behavior of Soil,
Balkema, Rotterdam, Netherlands, 1982, pp. 99–
121.
[7] S. Sasitharan, P.K. Robertson, D.C. Sego, and
N.R. Morgenstern., Collapse behavior of sand.
Canadian Geotechnical Journal. Vol. 30, No. 4,
1993, pp. 569-577.
[8] Wheeler, S. J., and V. Sivakumar, An ElastoPlastic Critical State Framework for Unsaturated

107

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

PERFORMANCE AND BEHAVIOR OF DIAPHRAGM WALL OF
UNDERGROUND AUTOMATIC CAR PARK PROJECT IN BANGKOKSUBSOIL
Jirat Teparaksa1
1Faculty of Engineering, Kasetsart University, Thailand
ABSTRACT

Currently, in Bangkok, the underground automatic car park becomes more popular since it can provide
maximum usage space without ramp. However, the automatic system requires large openings of the basement slab
resulting in possible insufficient axial capacity of the slab which must be rechecked. This paper focuses on one of
the latest underground construction projects in Bangkok city center, True Digital park. There were in total fourbasement stories with 13.20m-maximum depth of excavation including mat foundation. The 800mm-thick
diaphragm wall (D-wall) was proposed to be a soil retaining structure with 3-layers of full temporary bracing.
Finite Element Method (FEM) with Mohr-Coulomb soil model was employed for the D-wall design and horizontal
movement prediction. To date, the construction of basement was completed and the superstructure is being
constructed. The field performance of D-wall construction was evaluated by means of horizontal movement using
installed inclinometers. During construction, every sequence, such as soil excavation and bracing installation was
carefully monitored and strictly controlled to minimize the D-wall movement. The monitoring result shows good
agreement with FEM analysis. This paper describes the project construction and monitoring together with field
performance results.
Keywords: Deep excavation, Field monitoring, Diaphragm wall, Underground automatic car park
INTRODUCTION

floor B2 and B3 as shown in Fig 3. It can be seen that
there are large number of openings closed to the Dwall for the mechanical system and five openings in
the middle for car transferring lifts. This paper
presents soil retaining structural design and its
behavior during construction.

Currently, in Bangkok, as one of the largest
developing cities, there are many undergroundconstruction
projects
includinggovernment
infrastructures and private skyscrapers to optimum
land usage. The most widely designed for private
underground structures is intended to be undergroundcar parking especially an automated car park as itprovides maximum usage space without car ramp.
However, the automatic system requires many large
openings for vehicle elevators resulting in possible
insufficient axial capacity or stiffness of undergroundslab.
High-rise mixed-used building with fourbasement stories of substructure is being constructed.
The basement floors are desired to be an undergroundautomatic carpark as it helps maximizing parking
space eliminating driving path and ramps.
Four basement includes B1 – B4 at elevation EL.10.50 m., -7.65 m., -5.10 m. and -2.00 m. with the
maximum excavation depth of -13.20 m. from groundsurface. Diaphragm wall (D-wall) of 0.80 m. thickwith 21.0 m. length penetrated in stiff clay layer was
employed as a soil retaining structure. During
excavation, the 3 layers of temporary steel bracing
strut was used at EL. -1.50 m., -4.50 m. and -9.00 m.
Typical section of temporary bracing andbasement floor are presented in Fig 1 and Fig 2. In
this project, there are a few openings for both carelevators and mechanical system works in basement

Fig. 1108

Typical section of temporary bracing.

GEOMATE – Bangkok, Thailand, 22-24 November 2022

SOIL CONDITIONS

Fig. 2Six boreholes of 75 m. deep were conducted toinvestigate soil condition in the site. It consisted of a
12 m. thick soft clay layer followed by 3 m. of
medium clay. There was a stiff clay layer up to
approximately EL.-35.00 m. from ground surface. It
should be noted that there was a variation between
EL.-19.00 m. to EL.-23.00 m. where four boreholes
revealed very stiff silty clay while the other twoboreholes showed medium silty sand. Soil profile andsoil properties are presented in Fig 4.
Groundwater condition of Bangkok soft clay is inhydrostatic condition starting from -1.0 m. Deep well
pumping in the past had promoted drawdown of thesoft, medium and the first stiff clay. The piezometriclevel of Bangkok aquifer was reduced and wasconstant at EL.-23.0 m [1]. This is beneficial topracticing geotechnical engineers in terms of higher
effective stress and dry condition during undergroundconstruction. However, as the Thailand’s governmenthas been enforced deep well pumping prohibition law
for about 20 years to solve the problem of land
subsidence, the current piezometric level is increasing
up to -13.0 m. as illustrated in Fig 5 [2].

Typical section of basement.

Fig. 3a B2 floor plan

Fig. 3b B3 floor plan
Fig. 4109

Soil profile and soil properties.

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Young’s modulus(EU) = 500 – 700 Su (Undrained Shear
Strength)• Stiff to very stiff silty clay layer
Eu = 1000 Su• Sand layer
E’ = 2000(N) SPT-N-Value (kN/m2)
The above correlation between Eu-Su, and E’N(value) is based on the back analysis from various
basement excavation project by means of FEM
analysis compared with field measurement proposedby [4].
Surcharge on the diaphragm wall
The ground surface surcharge behind thediaphragm wall during construction was assumed at
10 kN/m2 for 6 m. wide to simulate the machineryload. This ground surface was applied during
excavation, basement casting and completion of thebasement work.
Groundwater table
Fig. 5

The ground water of Bangkok subsoil condition isin the draw downed condition due to deep well
pumping. Recently, as the deep well pumping is notallowed, the recent ground water table has been
increasing to EL.-13.00 m. below ground surface.
This groundwater level was used in the model during
excavation.

Recent Bangkok groundwater conditions
data [2].

Although the sand layer was encountered at EL.19.00 m., the maximum depth of excavation was just
13.20 m. This scenario gives safety factor against
uplift of approximately 1.75, therefore, theexcavation is safe in respect of uplift pressure.

FEM ANALYSIS RESULTSANALYSIS OF DIAPHRAGM WALL
BEHAVIOR BY FINITE ELEMENT METHOD

All the construction sequences starting from thefirst excavation to the final stage after construction
were modelled in the FEM analysis, using MohrCoulomb as a failure criterion. The first sequence wasinitial excavation to install the first strut layer. The
second was after the first strut installation which was
excavation to the second strut layer level. The processcontinued until reaching the final depth as shown interms of deformed mesh in Fig 6. It should be notedthat since there was variation in soil investigation,
two models with stiff clay and medium sand layer
between -19.00 to -23.00 m. were developed.
For the upward construction, it was started bycasting the mat foundation and removal of strut layer
3. The second strut layer was removed in the nextstage after casting basement B2 and B3 floor. Then,
B1 was casted followed by removal of strut layer 1.
The FEM model was completed at the construction
step of that ground water table was at -1.00 m. andconsolidation was taken place for long-term
simulation as presented in Fig. 7.

Design Criteria of Diaphragm Wall
The behavior of diaphragm wall can be predictedby numerical analysis by mean of Finite Element
Method (FEM). The diaphragm wall behavior, resultof FEM, is presented in terms of bending moment andshear force induced in the diaphragm wall as well aslateral displacement. The step of soil excavation,
bracing installation, as well as preloading in the strut
system is simulated in the FEM analysis. The castingof base slab, basement floor and the step of removalof strut system are also combined in the FEM analysis
of diaphragm wall. In this project, the PLAXIS 2D [3]program was used as the FEM program to predict thediaphragm wall behavior.
The Mohr-Coulomb soil modeling was employed.
The Undrained Young’s modulus (Eu) of layer wascorrelated with undrained shear strength (Su) while inthe sand layer, the drained modulus (E’) was
correlated with the Standard penetration SPT N-value.
The correlation of Eu and Su as well as E’ and Nvalue can be conducted as follows.
• For soft to medium clay layer, Undrained110GEOMATE – Bangkok, Thailand, 22-24 November 2022

Soil Face Excavation Face

Soil FaceExc Face

0
-2
-4

at

the

final

-10

depth

-12

1-DB32@0.15

Deformed meshexcavation.

Depth (m)Fig. 6

1-DB25@0.15-8

1-DB32@0.15

-6

-14
-16
-18

Sand
Clay
Reinforcement-20-800 -400

0

400 800 1200 1600 Compressive Strength

Bending Moment Diagram (kN/m) = 280 ksc (cylinder)

Rebar grade = SD40

Fig. 7

Fig. 8

Deformed mesh at the final stage.

Bending moment envelope and D-wall
reinforcement.

Table 1 Trigger level and safety control
Disp.
Safety Criteria
Trigger Level(mm.)
Alarm Level
37.43
Inform designer(70 % of DV)
to review CS
Alert Level
42.78 Inform all parties(80 % of DV)
to review CS
Action Level
48.12 Stop construction(90 % of DV)
and revise the CS
Maximum
53.47
Note: Disp. = Displacement, DV = Design Value,
CS = construction sequence.

All the basement floors were modelled to obtain
the axial forces at every step for the structural
engineer to recheck the reinforcement especially atthe openings closed to the D-wall. The moment andshear envelopes, of both stiff clay and medium sandlayer, were then employed to design the D-wall
reinforcement as illustrated in Fig. 8. In addition, thepresented moment envelope was summarized from all
construction stages including final stage whereconsolidation occurred. It can be seen that thereinforcement covered the predicted maximum
moment in both cases with safety factor of 1.5.
DIAPHRAGM WALL DIAPLACEMENT AND
SAFETY CONTROL
Not only bending moment and shear force thatwere obtained from the FEM analysis but also
horizontal D-wall movement at every construction
step. The maximum horizontal displacement wasused to set the trigger level for safety control during
construction as shown in Table 1. There were four
inclinometers installed at each side as illustrated in
Fig 9. The inclinometers were read after completionof every construction step. The final reading showedhorizontal displacement of 40.64 mm., 37.71 mm.,41.31 mm. and 58.65 mm. for IW1 to IW4
respectively. IW4 started to exceed maximum value
after removal of strut layer 2.

Fig. 9111

Inclinometer location.

GEOMATE – Bangkok, Thailand, 22-24 November 2022

The inclinometer reading of IW4 is shown in Fig
10 together with trigger level and basement levels.
There was large movement at the very initial stages
where the inclinometer reading showed cantilever
behavior. The movement firstly touched the alarmlevel during excavation to the final depth at -13.20 m.
The horizontal movement continued during upward
construction until the completion of basement
construction at 58.65 mm. which was more than thepredicted maximum value.
It can be noticed that after casting the basement
floor B2, B3 and B4, there was movement at thoseelevations which can be implied that the basementslab might be weak. Similar horizontal movement butlarger was also obviously found at Inclinometer IW2
as shown in Fig 11 despite the fact that the overall
movement did still not exceed the alert level. At first,
it was thought that it might be because the basementslabs B2 and B3 at IW2 location are closed to the
opening. However, the movement continued further
at basement B1 even the B1 has no opening.
It was, then, suggested to do visual investigationof all the basement floor but no crack was observed.
Several photograph of basement B2 and B3 areshown in Fig 12 and Fig 13 respectively.

All of the inclinometer readings (IW1 – IW4) arecompared to the FEM prediction as presented in Fig.
13. The movement of IW1, IW2 and IW3 agreed wellwith the FEM analysis with larger movement at topof D-wall observed at IW2 as discussed previously.
The IW4 reading seemed to be different from theothers and FEM prediction. This might be because ofvery large movement at the initial stages due to weak
local strut together with described horizontal
movement during upward construction.
The construction continued to the super-structure
as there was no observed damage to the substructure.
Currently, at the time of this paper, the whole buildingis almost completed.

Fig. 11 Inclinometer reading IW2

Fig. 12 Photo of B3 after removal of bracing layer 2.

Fig. 10 Inclinometer reading IW4112

GEOMATE – Bangkok, Thailand, 22-24 November 2022

CONCLUSIONSThe underground basement construction was
designed to be underground automatic carpark of thehigh-rise mixed-use building. There were many
openings in the sub-structure floors for car elevatorand mechanical systems. The diaphragm wall,
designed using FEM analysis with Mohr-Coulomb
failure criteria, was employed as a soil retaining
structure. All the construction sequences were
simulated in the FEM. The diaphragm wall behavior
and displacement were predicted and compared withthe field inclinometer monitoring data. The FEM
result agreed well with the field performance.
However, the unusual horizontal movements atbasement slab and mat foundation level were detectedduring the upward construction.

Fig. 13 Photo of B2 after removal of bracing layer 2.
0Depth (m)-5

REFERENCES[1] Teparaksa W., Principal and application of
instrumentation for the first MRTA subway
project in Bangkok, 5th International
Symposium on Field Measurement inGeomechanics, Singapore, 1999.
[2] Teparaksa W., and Teparaksa J., Comparison of
Diaphragm Wall Movement Prediction and Field
Performance for Different Construction
Techniques. Underground Space, 2019.
[3] Brinkgreve, R.B.J., PLAXIS 2D Version 8
Manual, A.A. Balkema Publishers, 2002.
[4] Teparaksa W, Displacement analysis ofdiaphragm wall for subway construction, The 7th
National convention in the Civil Engineering
Bangkok,2001

-10

-15

IW1
IW2
IW3
IW4
FEM

-20
0

10

20

30

40

50

60

Horizontal movement (mm)Fig. 14 Comparison of Inclinometer readings andFEM prediction.

113

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

NATURALLY DERIVED CEMENTS FOR CONSTRUCTION AND
BUILDING MATERIALS LEARNED FROM THE WISDOM OFANCESTORS: A LITERATURE REVIEWSu Zhan1, Kazunori Nakashima2 and Satoru Kawasaki 3
1Graduate School of Engineering, Hokkaido University, Sapporo, Japan
2Faculty of Engineering, Hokkaido University, Sapporo, Japan
ABSTRACTFor over a thousand years, many ancient lime mortars have remained durable despite long-term exposure toatmospheric or humid agents. This review paper summarizes technologies of worldwide ancient architectures
which vest remarkable durability to maintain them over thousands of years of constant erosion. It aims toidentify the influence of organic and inorganic additions in altering mortar properties and take the lost andforgotten technologies to the production frontline. The types of additions are usually decided based on the local
environment and use purpose. The ancient Romans built magnificent structures by making hydraulic cement
using volcanic ash. The old Chinese introduced sticky rice and other local materials in addition to improving theproperties of pure lime mortar. A variety of organic and inorganic additions used in traditional lime mortar not
only changes the properties but also improves its durability for centuries. The benefits they bring to lime mortar
may also be useful in Enzyme Induced Carbonate Precipitation (EICP) and Microbially Induced Carbonate
Precipitation (MICP) fields. For instance, sticky rice has been confirmed to play a crucial role in regulating
calcite crystal growth and providing interior hydrophobic conditions which contributed to its reserve, which
may improve the strength and durability of EICP and MICP treated samples as well.
Keywords: naturally derived cement, lime mortar, sticky rice, enzyme induced carbonate precipitation(EICP), microbially induced carbonate precipitation (MICP), ground improvement
INTRODUCTION

was built by Agrippa in about 118–125 AD, has stoodfor nearly two thousand years with only several minor
repairs. Hydraulic mortar used a pozzolanic additive
to replace the pure silica sand in previous lime mortar[3].

Lime has a long history of being used as a building
material. Thousands of years ago, ancient engineers
around the world began to utilize lime to produce
mortar for architecture construction. To satisfy thedemand for different use purposes, the properties oflime mortar were constantly modified by variousorganic or inorganic additions. Some of these give
lime mortar the remarkable strength and durability
that allowed ancient architecture to be well preserved
even over thousands of years of environmental
erosion. This review paper aims to summarize thetypes and influence of various organic and inorganic
additions used in ancient architecture and take thoselost and forgotten technologies to the production
frontline.
DISCOVERED ADDITIONS
Use the Republican period, perhaps in the third
but certainly by the second century B.C., ancient
Romans discovered how to create hydraulic mortar, akind of building material with enormous performance[1], [2], which was more durable than other building
materials they used. The Pantheon in Rome, which

Fig. 1 The Pantheon, Roma. [4].
The history of utilization of lime mortar was mucholder, and evidence of its use for binding stones
seems to have first appeared in Egypt in the third

114

GEOMATE – Bangkok, Thailand, 22-24 November 2022

millennium B.C. [5]. Lime is obtained by thecalcination of limestone at around 1,000℃, and thefinal product calcium oxide, quicklime, is its main
ingredient. Another ingredient of lime mortar is sand[6]. Vitruvius, a Roman architect and engineer
writing around 30 B.C., suggested several different
formulations for lime mortar and specifications ofsands in his book. In addition, he outlined the typesof sands. River sand makes mortar dry more slowly,
which leads to delays in construction, and sea sandcould lead to a salty efflorescence when used instucco. Vitruvius preferred pit sand to river or sea
sand, which, he explains, can crackle when rubbed in
the hand. Later, as conventional mortared rubble
construction became more common in the third
century B.C., builders may simply have found that the
replacement of local volcanic ash for sea sandresulted in a stronger mortar that could set underseawater. When Vitruvius was writing, hydraulicmortar and concrete were still being used sparingly
and tentatively as they were relatively recent
innovations [8]. He described volcanic ash is a kindof powdery sand, which, by its nature, produces
wonderful results. It is found in the neighborhood of
Baiae and in the lands of the municipalities around
Mount Vesuvius. Not only does this material furnish
strength to other buildings when mixed with lime andrubble, but also, when piers are built in the sea, they
set under water, and neither the waves nor the forceof water can dissolve them.
Not long after, Roman engineers quickly realized
the remarkable potential of this new material in viewof its special suitability for the construction of
hydraulic installations, bridge footings, and harbor
structures. Roman engineers could construct harbors
anywhere political, economic, or militaryconsiderations dictated and not simply where
advantageous physical features existed. The resulting
changes in Roman architectural construction havebeen termed a “revolution” [7].
Approximately a century later, the developmentof hydraulic mortar reached its peak [8]. The use of
hydraulic concrete for the construction of harbors andother maritime structures in Italy and in some otherregions of the Mediterranean had virtually becomeroutine. Pliny the Elder specified that pozzolana
should be taken from the hills around Puteoli, and aseries of investigations into the ancient port
confirmed his record. The crushed ceramics represent
an artificial pozzolanic additive that was used tosupplement non-pozzolanic sand, producing a mortar
particularly suitable for waterproofing cisterns.

The remarkable durability of Roman mortar
results from crystalline, calcium-aluminum silicatehydrate (C-A-S-H binder) in the cementing matrix ofthe mortar, which was produced through reaction ofseawater, lime, and volcanic ash [9], [10], [12]. C-AS-H is the poorly crystalline analog of Al tobermorite—a rare, layered, calcium-silicate
hydrate mineral composed of aluminosilicate chainsbounded by an interlayer region and a calcium oxide
sheet [11]. In Portland cement, the inert aggregate
particles tend to lead the expand and growth ofcrack; however, in the Roman concrete, tobermorite(Al-tobermorite) is formed between the aggregate
and the mortar, preventing the cracks from extending
further [10]. Al- tobermorite forms crystals withplaty shapes that interlock to reinforce the cementing
matrix and thus increase the concrete’s resistance tobrittle fracture.
A long time ago, the ancient Chinese began to use
lime and lime-based gelled materials. Archeologicalevidence shows that the application of lime started
long before the prehistoric period. By the mid to lateWest Zhou (1046–771 B.C.) [15], lime materials
were widely used in the reinforcement of pill, ground
improvement, and roofing. After the Qin and Han
dynasties, lime material was more widely adopted.
Pig blood was used on the floor of the Xianyang
Palace site during the Qin Dynasty (221–206 B.C.)[16], mixing with lime and ginger stone into a dark
red, smooth surface with moisture-proof effect.

Fig. 2 The Great Wall, China. [17].
It is worth noting that at least during the Han
Dynasty, inorganic hybrid gelled material commonlyreferred to as “tabia” (composed of lime, loess, andsand) was invented and applied. In the Sixteen States
and the Empire of the Eastern Jin Dynasty (317–420
A.D.) [18], the use of tabia was more widespread, anda tomb constructed by tabia was found in North Yan.
Tongwan city―the capital of the state of Helian—

115

GEOMATE – Bangkok, Thailand, 22-24 November 2022

was also found to be built with tabia. After seeing
Tongwan by himself, Shen Kuo, a scientist of theNorth Song Dynasty, described the city as “hard likestone” in his book, The Dream Rivulet Diary. A
reference in General History of China also said thatthe city was “hard enough to sharpen knife and axe.”
These records show the toughness of tabia [19] – [21].
With the latest analytical technology, researchers[13] found that crystals of aluminium tobermorite
were growing from a silicate mineral called phillipsite,which is frequently found in volcanic rocks. The
reason that tobermorite had grown from the phillipsiteis the reaction of seawater between the concrete,
which increased its degree of alkalinity. Such kindsof crystallisation are highly unusual and indeed are
only found in such places as the volcanic island of
Surtsey [13], which is located in the Vestmannaeyjar
archipelago off the southern coast of Iceland. It isthought that as the tobermorite grows throughout theconcrete, it may provide strength due to its long,plate-like crystals, which allow the concrete to flex—
and not shatter—when stressed.
At least no later than the South-North Dynasty(386–589 A.D.) [22], sticky rice lime mortar was
already a mature technology. The appearance oforganic/inorganic hybrid mortar such as sticky ricelime mortar was great progress. The building work
Tian Gong Kai Wu, written during the Ming Dynasty,
recorded the composition, method of making, andperformance of it in detail: “Sticky rice soup andActinidia chinensis cane juice is added to the mixtureof 1/3 of lime and 2/3 of loess and river sand and wellstirred, and the mortar is produced. The buildings
constructed by it are very strong and permanent, andthis kind of material is named tabia.” This kind of
tabia with added sticky rice is the sticky rice lime
mortar mentioned above. The addition of naturalorganic compounds such as sticky rice soup greatly
improved the performance of building gelled material.
Due to its excellent properties and performance, suchas high adhesive strength, good toughness,waterproofness, and durability [23], [24], traditionalorganic mortar such as sticky lime mortar was valued
by many people after its invention. However, due tolow levels of production, it was not available in large
quantities. Until the Yuan and Ming Dynasties, purelime mortar could be found in brickwork; then, sticky
rice mortar became the most mature and widely usedtechnology in construction [25].
Modern evaluation shows that the bonding
properties of sticky rice mortar were good enough tomatch modern cement. In addition to its high strength,

sticky rice mortar was characterized by amazing
durability [24], and it was used to build importantarchitecture such as city walls, palaces, stone bridges,
dams, and so on. After hundreds or even thousands ofyears, such architecture is still well preserved. Further
research found that sticky rice pulp plays the role ofbiological template, regulating and coordinating thecarbonation of mortar so that it will generate nanosized calcium carbonate crystal with fine structure
and improve its toughness, impermeability, andcompressive strength [25]. The reason why sticky rice
can endure in mortar for a long time in the natural isthe anticorrosion effect of lime. The organic andinorganic compositions wrap and pad each other,similarly to the formation process of products ofbiomineralization, such as bones, teeth, and shells. heincompletely reactive calcium hydroxide, which iswrapped in sticky rice pulp, inhibits the growth ofbacteria and thus protects glutinous rice from decay
for a long time [26] – [28].
In addition to sticky rice soup, blood mortar was
also common in ancient China, and it was mainly
used for building painted floors [29]. According torelevant record, Qin Xianyang Palace is paved withblood mortar. Zhejiang University found that animal
blood plays an important role in the mortar as it adds
air, reduces water, prevents freezing and thawing,
resists cracking, and increases bond strength. The
underlying mechanism is as follows. Firstly, theprotein in the blood is expected to have a foaming
ability; thus, the small bubbles can improve themortar’s workability. Secondly, anions andhydrophilic groups in blood protein can generate
electrostatic repulsion between mortar particles andimprove their dispersion. Thirdly, blood protein isdecomposed in an alkaline environment, connectswith calcium ions in mortar and enhances bondstrength. Fourthly, the amino and carboxyl groups inblood protein provide waterproof ability.
Tung oil mortar is mainly composed of boiledtung oil and lime mortar [30] because it has goodwater resistance, anti-codling, and bond strength.
Thus, in ancient China, it was widely used in water
wells’ hooks, wooden boats’ greasy seams, holefilling, housing grounds, and where buildings had
special requirements in waterproof ability anddurability. The main components of tung oil mortar
are calcium carbonate and calcium carboxylate,which give it better mechanical properties,
waterproof properties, and weathering resistance.
The following admixtures which are listed inTable 1 are used to modify the properties of lime
mortar. The superscript “C” in the table indicates that116

GEOMATE – Bangkok, Thailand, 22-24 November 2022

the type of additive was used in China, and thesuperscript “R” in the table indicates that the type of
additive was used in Roma.

[5] Adam, J-P. (1994). Roman building: Materials
and techniques, Indiana University Press,
Bloomington, Ind.
[6] Vitruvius, M. (1960). The ten books onarchitecture, M. H. Morgan, translator Dover,
New York.
[7] White, K. D. (1984). Greek and Roman
technology, Cornell University Press, Ithaca, NewYork.
[8] Gianfrotta, P. A., 1996, Harbour structures of theAugustan Age in Italy, in A. Raban and K. G.
Holum (eds), Caesarea Maritima:a Restrospective after Two Millennia, 65–76. Leiden.
[9] Jackson, M.D., Vola, G., Všianský, D., et al.
(2012) Cement microstructures and durability inancient Roman seawater concretes. In: Válek, J.,
Groot, C. and Hughes, J. (eds), Historic mortars,
characterisation, assessment and repair, pp. 49–76.
Springer, Berlin.
[10] University of Utah (2017) How seawater
strengthens
ancient
Roman
concrete.
https://unews.utah.edu/roman-concrete/[accessed 11 December 2017].
[11] Jackson, M.D. (2014) Seawater concretes andtheir material characteristics. In: Oleson, J.P. (ed.),Building for eternity: the history and technology
of Roman concrete engineering in the sea, pp.
141–187. Oxbow Books, Oxford.
[12] Dean, A.M. (2017) Rock solid history ofconcrete – how limestone, rocks, and volcanic ash
built
the modern world.
https://www.
concretepavements.org/2017/10/18/rock-solidhistoryof-concrete-how-limestone-rocks-andvolcanic-ash-built-the-modern-world/[accessed
11 December 2017].
[13] Jackson, M.D., Mulcahy, S.R., Chen, H., et al.
(2017) Am. Mineral., 102, 1435–1450.
[14] Witze, A. (2017) Nature News. 3 July.
https://www.nature.com/news/seawater-is-thesecret-tolong-lasting-roman-concrete-1.22231[accessed 11 December 2017].
[15] Yao Y. The History of Xi’an in Han Dynasty(Science and Technology) (in Chinese). Xi’an:
Shanxi People’s Publishing House, 1993.
477―478.
[16] Zuo Q M (Spring and Autumn Period). Zuo
Zhuan (in Chinese). Changsha: Yuelu Press, 2001.
289.
[17] https://www.dreamstime.com/stock-imagesgreat-wall-china-image21049254.
[18] Pu S. North Yan tomb in Yuantaizi village,
Chaoyang country, Liaoning province (inChinese). Cul Rel, 1994, 11: 43―47.
[19] Yao Q Z, Lv Da. On historical evolution of theancient Tongwan City & its architecture (inChinese). J Yan’an Univ (Social Science Edition),
2004, 26 (2): 126―128.
[20] Shen Kuo (Song Dynasty). The Dream Rivulet
Diary (in Chinese). Changchun: Times Wenyi

Table 1 Additions and their purposes used in ancientarchitecture.
The name of additions
Raw Sugar C
Sugar CEgg white C, R
Soybean milk C
Animal Blood C, R
Tong oil C
Straw fiber C, R
Jute fiber C, R
Bean dregs C
Paste(wheat) C
Sand C, R
Ceramic chip R
Brick bat R
Gypsum C, R

The purpose of use
Bonding agent
Prevent shrinkage
Improve strength
Better bonding
Better Viscosity
Waterproof
Better bonding
Better bonding
Better bonding
Better bonding
Improve strength
Improve strength
Improve strength
Work ability

CONCLUSIONSHistorical records have proved the efficiency ofadditives used in lime mortars, especially in terms ofimproving durability for hundreds/thousands of years
with construction quality.
Further research is needed on the performance of
these additives not only for restoring and preserving
existing old buildings but also for their application inmodern and future construction. In recent research,
sticky rice has been proved to play a crucial role inregulating calcite crystal growth and improving its
endurance. The same mechanism may be used in
MICP/EICP technologies and improve the effect oftreated samples as well.
REFERENCES[1] Blackman, D. J. (1982) Ancient harbours in the
Mediterranean, IJNA 11: 79–104, 185–211.
[2] Gazda, E. K. (2001) Cosa’s Contribution to the
Study of Roman Hydraulic Concrete: An
Historiographic Commentary, in N. W. Goldman(ed.), Classical Studies in Honor of Cleo Rickman
Fitch, 145–77. New York.
[3] Delatte, N. (2001). "Lessons from Roman Cement
and Concrete." J. Prof. Issues Eng. Educ. Pract.,
127(3), 109-115.
[4] https://www.dreamstime.com/pantheon-romeancient-pantheon-rome-cloudy-sunrise-italyimage141378165

117

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Press, 2001. 110.
[21] Tang C R. References of Chinese General
History (in Chinese). Beijing: Zhong Hua Press,
1983.
[22] Archeology crew of Henan province. A StickTomb with Colorized Portrait in Deng Country,
Henan Provice (in Chinese). Beijing: Cultural
Relic Press, 1958.
[23] Alexandra Olga Pintea. New types of mortars
obtained by aditiving traditional mortars withnatural polymers to increase physico-mechanicalperformances. Procedia Manufacturing, 32(2019)201–207[24] Wu Q Z. The experience and implication for theflooding control in the ancient towns in China(continued) (in Chinese). City Plan Rev, 2002, 26(5): 76―84[25] Vintilă-Bolchiș, O. The Great Wall of China has
overcome a century due to rice. The Diary.ro.
[Publication Date] 05 06 2010. [Quote: 18 03
2018.]
http://jurnalul.ro/afla-ce-e-nou/marelezid-chinezesc-a-rezistat-sutelor-de-ani-datorita-

orezului-545810.html.
[26] Yang F, Zhang B, Ma Q. Study of sticky rice lime
mortar technology for the restoration of historicalmasonry construction. Acc Chem Res 2010;
43:936–44.
[27] Yuyao Zeng, Zhang Bingjian, Xiaolin Liang.
Study on characteristics and consolidation
mechanism of historical mortar materials. Sci
Conserv Archaeol 2008; 2:1–7 (in Chinese).
[28] Zeng Yuyao, Zhang Bingjian, Liang Xiaolin. A
case study and mechanism investigation of typicalmortars used on ancient architecture in China.
Thermochim Acta 2008; 473:1–6.
[29] Li Hongming. Studies on properties and formulaof traditional lime mortar. Taiwan: National
Cheng Kung University; 2005.
[30] Ma Tao, Chong-bin Bai, Yang Qi. Studies on the
traditional techniques and the conservation
method of polychrome paintings on wooden
structures of Chinese traditional buildings. Wen
Bo 2009; 6:412–2

118

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

EFFECTIVENESS OF SAND COMPACTION PILE AND
PREFABRICATED VERTICAL DRAINS IN IMPROVING SOFT SOIL
FOR PAVEMENT SUBGRADE
Tanim Shahriar1 and Shah Md Muniruzzaman1
1Faculty of Civil Engineering, Military Institute of Science and Technology, Bangladesh
ABSTRACTThe study examines the effectiveness of Sand Compaction Pile (SCP) and Prefabricated Vertical Drains (PVD)
in improving properties of soft soil underlying road pavement. Subgrade improvement techniques are often used
to reinforce the subsoil properties with regards to its bearing capacity, shear strength, settlement etc.
This research scrutinized two interconnecting roads at Jolshiri outskirts of Dhaka city. Physical soil properties
were measured at four different locations, two locations each from both the roads. Average SPT-N values were
found around 3 to 4 before subgrade improvement. Basing on soil properties and SPT-N values, suitabledimensions and parameters of SCP and PVD were designed. SCP was installed in one of the road mainly due toconstrain of time of construction. PVD was conducted at another road as there was flexibly of time. SPT test was
executed again and average SPT-N values were found around 14 to 18 in all four locations. Dynamic Cone
Penetration (DCP) test was performed to check the suitability between SCP and PVD. DCP test results showsthat PVD provides slightly more compacted ground surface than SCP at shallow depth. An economic analysis
was also carried out to check the cost effectiveness. SCP found considerably cheaper when compared to PVD
which needs geotextile and other imported materials.
All soil properties and test results were analyzed to determine the impact of subgrade improvement and compare
the effectiveness. It is found that, SCP and PVD are two technically viable and cost-effective solutions for soils
which are weak in strength and needed treatment.
Keywords: Sand Compaction Pile, Prefabricated Vertical Drain, Dynamic Cone Penetration

1. INTRODUCTION

ground [2].
In road construction on compressible, saturated
soils like loose and soft clay, excessive settlement is
a common problem to deal with. The subgrade
improvement technique using Prefabricated Vertical
Drains (PVD) are one of the most appropriate
methods to overcome this problem. Vertical drainingof fine-grained soils for ground improvement
purposes was first advised and applied in the US in
the late 1920’s [3]. In the early days of the method,
there was some pioneer work done by a Swedish
engineer, Walter Kjellman, who patented a type ofprefabricated cardboard drain and equipment for its
installation. Later on, methods for designing verticaldrains were developed by Barron in 1948. However,the design of vertical drains today is based mainlyon the subsequent
andmore practical,
simplifications and additions to the theory [4].
In Bangladesh SCP and PVD have been used in
few highway projects in subgrade improvement
recently. In this research a venture is taken to lookover the effect of subgrade improvement using SCP
and PVD methods.

The performance of road pavement depends
largely on its subgrade layer that provides support toa whole pavement system. As such, subgrade layer
plays a key role in mitigating the detrimental effects
of geotechnical, environmental and dynamic stresses
generated by the traffic load, and building a stable
subgrade is vital for constructing an effective andsustainable pavement system.
2. BACKGROUND OF THE STUDY
Many subgrade improvement techniques arepracticed around the globe. Out of these subgrade
improvement techniques, some of them are widelypracticed in our country. Among these, SCP is oneof the potential methods for improving groundstability. This method was originally developed inorder to increase the density of loose sandy groundand to increase the uniformity of sandy ground, toimprove its stability or compressibility and toprevent liquefaction failure. It has also been applied
to soft clay ground to assure stability and to reduceground settlement [1]. The principal concept of the
SCP method for application in sandy ground is toincrease the ground density by placing a certain
amount of granular material (usually sand) in the

3. DESCRIPTION OF THE STUDY AREA
Dhaka, one of the fastest growing megacities of
the world experiences huge pressure of mass
119

GEOMATE – Bangkok, Thailand, 22-24 November 2022

population and traffic congestion. Two east boundroads Purbachal Expressway and Madani Avenue
traversed to connect Dhaka city with its Easternregion. Considering inter connectivity, it had been
decided to construct two roads of 3.25 km(Purbachal 300 feet Expressway to Madani Avenue)
and 3.79 km (Madani Avenue extension up to
Shittalakha River) in this area. But both these futureroads situated on the reclaimed area with soft clay
soil underground. These two roads would require
substantial subgrade improvement to withstandfuture designed traffic load. The study area was
located at this reclaimed land of Purbachal
commonly known as Jolshiri Abashon. The area wasbasically a low-lying paddy field which was latersand filled for housing project about 10 years ago.
The surface of the ground seems to be settled andhard enough. But underlying ground were still
comprising very loose, soft clay or sandy layer ofsoil. The main features of this area are two interconnecting under construction roads from Jolshiri to
Shittalakha River and Jolshiri to Purbachal 300 feet
Expressway. Locations of the study area arerepresented in Fig 1.

4. METHODOLOGY
4.1 Standard Penetration Test (SPT)The Standard Penetration Test, commonly
known as SPT, was forwarded to provide subgrade
properties for highway design basis. The test was
brought out in this study area to ascertain the soil
properties of the study area [5]. The results can beused to determine soil properties like specific gravity,
Atterberg limit test, particle size distribution etc.
Typical components of a SPT set up used in thisstudy are shown in the picture of Fig. 2.

Fig. 2 Standard Penetration Test (SPT) setup
4.2 Soil Properties before Ground Improvement
The condition of the soils at the four different
locations are almost similar based on tests. Insideeach of the boreholes at different layers loose silty
sand, lean clay and fat clay type soil was found in all
the four sampling locations.
According to soil properties, it was found thatthe value of specific gravity varied in between 2.65
and 2.75 in all the sampling boreholes. Atterberg
Limit Test assured that, till depth of 3m Silty sandwas identified and the next advancing layers hadlean clay, fat clay at the 4 different locations. The
laboratory test depicted that the liquid limit and theplasticity index of the soils were almost same.

Fig. 1 Locations of study area (marked as dots).
Table 1 SCP and PVD test locations
Location

Chainage (km)Description

1

2+760

2

3+033

300 feet expressway
to Madani Avenue
link road
Same as location 1

3

6+600

4

8+160

Madani Avenue to
Shitalakkhya riverroad
Same as location 3

Table 1 shows the description of four locations of
the study area and the chainages are mentioned inkm.

Fig. 3120

Soil condition at four different locations

GEOMATE – Bangkok, Thailand, 22-24 November 2022

4.3 Sand Compaction Pile (SCP)

ground surface the casing pipe was withdrawn andleft the remaining hole unsupported. The process
was continued until the granular piles wereconstructed up to the ground level [6].

Sand Compaction Pile (SCP) method was used inthis study area to build up loose and soft clay groundby installing sand or a similar material into the soft
ground via a casing pipe manually to assemble
strongly compacted sand piles in the ground.
4.3.1 General specification
Table 2 Design of SCP
Diameter of pile

300 mmLength of pile

8 meter

Spacing
SCP Arrangement

1.4 meter
Triangular Arrangement

A 1500 rpm traditional rig machine and a twoend open casing pipe 8 mm thickness and 300 mm indiameter and 8 m long with a vibro-hammer ofweight 1000 kg. The vibro-hammer was 250 mm indiameter and 3.00 m long. The construction
sequences are described in the following statements.
A two-end open casing pipe, 300 mm in diameterand 8 m long was placed vertically at the designed
point on the natural ground surface for sand pile
construction. The casing Pipe was then inserted
vertically into the ground about 300 mm to 450 mm
depth at its own weight just by applying somepressure manually.
At first a plug is made by the designated sand up
to 750 mm of casing pipe at bottom level. The vibrohammer 250 mm in diameter and 3.0 m long,weighting 1000 kg was placed inside the casing pipe.
The vibro-hammer displaced the soil from beneaththe casing pipe hence the casing pipe was driven byits own weight till reached the designated position(depth) into the ground. Here one casing pipe of 7 mlong was driven inside the ground.
After reaching the designated depth, the sandplug is broken by providing excess energy then thevibro-hammer is withdrawn from the casing pipe.
Casing pipe was then lifted up by about 1m from its
original bottom position. The designated granularmaterials were poured into the hole about 1m layer
thickness measured from the bottom. The poured
granular materials were densified by vibro-hammer
till the required compactness achieved. Casing pipe
was then withdrawn from inside the ground that left
the bottom portion of hole unsupported and the top
portion supported by the casing pipe. It wasobserved that the bottom portion of the hole standing
safely without any lateral support. Then hole waspoured by the selected granular materials in layers
and hence 10~15 drops compacted each layer was
densified by vibro-hammer till the designatedcompactness was reached. After the top of granularpiles were reached about 1.0~1.5 m below theFig. 4 Sand Compaction Pile (SCP) installation
4.4 Prefabricated Vertical Drain (PVD)
Prefabricated Vertical Drains (PVDs) or Wick
Drains are formulated of a plastic core confined by ageotextile for the basis of accelerating consolidationof slow draining soils. They are typically coupledwith surcharging to expedite preconstruction soil
consolidation.
4.4.1 General specification
Table 3 Design of PVD
Average length

15 meter

Average Spacing

1.5 meter

Sand blanket height

0.6 meter (FM >2.5)

Surcharge height

1 meter (FM>1)

Fig. 5 Installation of Prefabricated Vertical Drain
The spacing and pattern of the drains are now
121GEOMATE – Bangkok, Thailand, 22-24 November 2022

fairly well standardized. In most situations,triangular or square grid spacing of 1-4 m is usedwith 1.5-2.5 m being most commonly adopted.
A number of sections was defined and eachsection was indicated by a letter code. For eachsection a predefined installation depth for the drains
was required. This depth was selected according to
the design parameters. A section was set out basedon the information from the detail survey as per site
condition. The four corners of each section weremarked with pegs. The grid, which defined theactual positions of the drains, had a triangular or
square spacing as specified by the design
specification. The individual drain position wasmarked by pulling a nylon string, marked with therequired drain spacing along the alignment of thedrain positions. The anchor plate was used to markthe position of the drain. The drain was placed inposition in the grid, with a tolerance of 150 mm.
The leader was aligned on top the drain location.
The drain was wrapped around the fixture on theanchor plate (dimensions: 140x80x1 mm),dimensions might vary depending on soil conditions
and the folded end of the wrapped drain was pulledback into the mandrel, until the plate rested against
the base of the mandrel. The hydraulic motors of themachine pushed the mandrel to the design depth.
The drains were installed to the depth as defined
above, with a tolerance of 150 mm. The operator
saw on the display of the drain logger, if the mandrel
had been inserted till the required depth. This actual
depth varied due to irregularities in the layer profile.
On reaching the depth of the drains, theoperation was reversed and the mandrel waswithdrawn from the PVD. The anchor plate locked
itself at the driven depth such that the drain was
fixed as the mandrel rises. The drain roll was
mounted on the side of leader allowing the drain to
be fed into the mandrel through a series of rollers,
which prevent damage and minimize friction. Oncethe mandrel cleared the ground surface, the drain
was cut off approximately 250 mm above groundlevel and the drain was installed. The leader wasaligned onto the next drain position and the above
procedure was repeated [7].

appropriate place on the data sheet. The instrument
was held vertical and the weight carefully raised tothe handle. It was carefully ensured that the weight
was touching the handle, but not lifting the
instrument, before it was allowed to drop and thatthe operator let it fall freely and did not lower it withhis hands. It is recommended that a reading should
be taken at increments of penetration of about 10
mm. However, it is usually easier to take a scalereading after a set number of blows.
Therefore, it was necessary to change the numberof blows between readings according to the strengthof the layer being penetrated. For good quality
granular bases readings every 5 or 10 blows arenormally satisfactory but for weaker sub-base layers
and sub-grades readings every 1 or 2 blows may beappropriate. After completing the test, the DCP was
removed by gently tapping the weight upwards
against the handle [8].

Fig. 6 Implementation of Dynamic Cone Penetration(DCP) test
5. RESULTS AND ANALYSIS
5.1 Soil Properties (up to 8 meter depth)
Table 4 Soil properties after SCP installation at
location 1 and location 2

4.5 Dynamic Cone Penetration (DCP)
Dynamic Cone Penetration (DCP) testingis operated to evaluate subgrade’s strength and thethickness and location of subgrade soil layers. In thisstudy, DCP test was also conducted to check thesuitability of SCP and PVD through average CBR
percentage. Figure 5 represents Dynamic Cone
Penetration test in the study area.
After assembly, the zero reading of the apparatus
was recorded. This was done by standing the DCP
on a hard surface, such as concrete, checking that itwas vertical and then entering the zero reading in theProperties

Location 1

Location 2

Specific Gravity

2.56

2.68

Liquid Limit

00

Plastic Limit

NP

NP

Plasticity Index

NP

NP

Table 5
Soil properties after PVD installation at
location 3 and location 4

122

Properties

Location 3

Location 4

Specific Gravity

2.69

2.67

Liquid Limit

00GEOMATE – Bangkok, Thailand, 22-24 November 2022

Plastic Limit

NP

NP

Plasticity Index

NP

NP

7
7.5
8
9
10

5.2 Results of Sand Compaction Pile
Then columns were installed, the area replacement
ratio is defined as,

5
4-

From Table 6, it can be seen that at 3 meter depth
before installation of SCP the SPT-N value was very
low and the value was 4. The soil condition was notsuitable for highway construction and soils were
mostly loose and very soft fat clay. Average SPT-N
value was identified around 4 up to 10 meter depth.
After installation of sand column, subgradeconditions improved and become silty fine sand.
SPT-N values were also increased after execution of
SCP and average SPT-N value was found 18 up to
10 meter depth.

𝑎𝑠 = 𝐶 × (𝑑𝑐 ÷ 𝑠)2
Where, 𝑎𝑠 = area replacement ratio
𝑑𝑐 = diameter of the column𝑠 = Center to center spacing of columns in a squareor equilateral triangular pattern
𝐶 = Constant (0.907 for an equilateral triangularpattern)
Kensetsu Kikai Chosa suggested the average
weighted SPT-N value including the sandcompaction column and the surrounding soil asfollows [9]:Table 7 Comparison between SPT-N values
before and after execution of SCP at location 2

𝑁𝑒𝑞 = 𝑎𝑠 × 𝑁2 + (1 − 𝑎𝑠 ) × 𝑁1

Depth

Where,
𝑁𝑒𝑞 = average weighted (equivalent) SPT N value
𝑁1 = SPT N value in the surrounding soil
𝑁2 = SPT N value in the sand compaction column.
For 1st Location: SPT-N value at 3-meter depth;
SPT-N Value midway between sand compaction
columns, 𝑁1 = 21
SPT-N Value in the center of sand compaction
columns, 𝑁2 = 22
Area Replacement Ratio,
𝑎𝑠 = 𝐶 × (𝑑𝑐 ÷ 𝑠)2= 0.907 (0.3 ÷ 1.4)2 = 0.04165
Now for example at 3 meter depth, the average
weighted SPT-N value including the sandcompaction column and the surrounding soil,
𝑁𝑒𝑞 = 𝑎𝑠 × 𝑁2 + (1 − 𝑎𝑠 ) × 𝑁1
= 0.04165 × 22 + (1 − 0.04165) × 21 = 21

SPT-N
(before
SCP)

1
1.5
2
3
4
4.5
5
6
7
7.5
8
9
10

7
4
2
1
2
1
-

SPT-N
(before
SCP)

1
1.5
2
3
4
4.5
5
6

4
4
3
5

Average
SPT-N(beforeSCP)
4.20 ≈ 4

SPT-N(after
SCP)
18
18
21
19
17
18

Average
SPT-N(beforeSCP)
3.20 ≈ 3

SPT-N(after
SCP)
18
16
17
19
17
19
21
17
7
8

Average
SPT-N(after
SCP)
18

From Table 7, it is observed that at 6 meter depth
before installation of sand compaction pile SPT-N
value was as 1. Average SPT-N value was obtained
3 up to 10 meter depth. After installation of SCP,
Subgrade conditions become Silty Fine Sand. SPT-N
values were also increased after execution of SCP
and average SPT-N value was found around 18 up to
10 meter depth.

Table 6 Comparison between SPT-N values before
and after execution of SCP at location 1
Depth

19
16
8
8

Average
SPT-N(after
SCP)
18.25 ≈
18

5.2.1 Findingsa. After the installation of SCP, the loose tovery loose clay soil becomes Silty Fine Sand.
b. At location 1, SPT-N value was foundaround 4 before the installation of SCP. After the
installation of SCP, the SPT-N value wasinvestigated around 18 up to 10 meter depth.
123GEOMATE – Bangkok, Thailand, 22-24 November 2022

c. At location 2, SPT-N value was foundaround 3 before the installation of SCP. After the
installation of SCP, SPT-N values was found around18 up to 10 meter depth.
d. The SPT-N value after execution of SCP
was found 18 at two different locations which is
sufficient enough for pavement subgrade.
e. SCP can be installed isolate for a smallerarea.
f.
By the installation of SCP, soil becomes
Silty Fine Sand.

locations. Very loose Silty Sand, Yellowish Grayand Lean Clay became mostly Sand after subgrade
improvement at location 3. Average SPT-N value
was found 14 up to 10 meter depth. The
recommended
SPT-N
value
for
highway
construction is around 10. Installation Reduced
Level (RL) was 7.770 meter on June 22, 2020. Plate
Pipe Top RL was found 7.578 meter on Jan 21, 2021.
Soil settlement was found 192 mm (7.56 inch) after
seven months. A graphical representation of
cumulative settlement of soil (in mm) with time after
installation of Prefabricated Vertical Drain (PVD) isshown below in Figure 8.

5.3 Results of Prefabricated Vertical Drain
Table 8Comparison between SPT-N values
before and after execution of PVD at location 3
Depth

SPT-N
(before
SCP)

1
1.5
2
3
4
4.5
5
6
7
7.5
8
9
10

3
4
4
3
3
4
-

Average
SPT-N(beforeSCP)
3.40 ≈ 3

SPT-N(after
SCP)
13
14
18
14
13
12
16
11
12
10

Average
SPT-N(after
SCP)
13.87 ≈
14

Fig. 8 Cumulative Settlement after Installation of
PVD at location 3
Table 9 Comparison between SPT-N values before
and after execution of PVD at location 4

Fig. 7 SPT-N values of before and after execution of
PVD at Location 3

SPT-N value was increased and soil condition
was also improved after installation of PVD in these
124

Depth

SPT-N
(before
SCP)

1
1.5
2
3
4
4.5
5
6
7
7.5
8
9
10

3
4
4
3
3
4
-

Average
SPT-N(beforeSCP)
3.40 ≈ 3

SPT-N(after
SCP)
13
14
18
14
13
12
16
11
12
10

Average
SPT-N(after
SCP)
13.87 ≈
14

GEOMATE – Bangkok, Thailand, 22-24 November 2022

0.3 meter. Average CBR percentage for the full
depth can be taken 24%.

Fig. 9 SPT-N values of before and after execution of
PVD at location 4
At location 4, very loose Silty Sand, Yellowish
Gray and Lean Clay became mostly Silty Sand after
subgrade improvement. Average SPT-N value wasfound also 14. Installation Reduced Level (RL) was
7.759 meter on June 22, 2020. Plate Pipe Top RL
was found 7.595 meter on Jan 21, 2021. Soil
settlement was found 164 mm (6.45 inch) after
seven months.

Fig. 11 Graphical representation of depth (m) vs
CBR (%) at location 1
After installation of SCP at location 2, soil
becomes Silty Sand. Penetration reading of DCP
was taken from 100 mm to 415 mm (total depth 315mm). The 1st layer thickness was 110 mm (4 noblows) and the average CBR percentage was found
7.73. Next layer thickness was 205 mm (17 noblows) and CBR percentage was found 19.11. The
DCP termination depth was taken 0.315 meter.
Average CBR percentage for the full depth can betaken 15%.

Fig. 10
Cumulative settlement after installationof PVD at location 4
5.4 Results of Dynamic Cone Penetration
DCP was done at location 1, 2 and location 3, 4
where SCP and PVD were done before. DCP test
was done to show the comparison between SCP and
PVD, which is suitable for subgrade improvement.
After installation of SCP at location 1, soil
becomes Silty Sand. The penetration reading weretaken at 90 mm to 390 mm (total depth 300 mm) and28 blows were counted. The 1st Layer thickness was
135 mm (8 no blows) and the average CBR
percentage was found 14.94. Next layer thickness
was 165 mm (20 no blows). Here CBR percentage
was found 31.3. DCP termination depth was taken -Fig. 12Graphical representation of depth (m) vs
CBR (%) at location 2
After installation of PVD at location 3, The DCP
penetration reading were taken from 140 mm to 365
mm (total depth 225 mm). The 1st layer thickness
was 95 mm up to 235 mm depth (3 no blows) andthe average CBR percentage was found 6.73. The
2nd layer thickness was 45 mm (4 no blows) and
CBR percentage was found 20.18. Next layer
thickness was 85 mm (13 no blows) and CBRpercentage was found 40.2. The DCP termination
125

GEOMATE – Bangkok, Thailand, 22-24 November 2022

depth was taken as 0.225 meter. Average CBR
percentage for the full depth can be taken 22%.

conducted after ground improvement only tocompare effectiveness of SCP and PVD.
b. From Figs. 4.7 to 4.10, the average CBR(%) was found 19.5% and 21.5% respectively
subgrade improvement done by SCP and PVD
respectively. The average CBR as such was slightly
higher where ground improvement was conducted
through PVD than that of SCP. Therefore, it isunderstood that SCP and PVD provide comparable
CBR at shallow depth.
c. One of the probable causes of higher CBR
due to PVD might be that DCP test was done atshallow depth after the removal of surcharge andthus soil was under compression.
6. ECONOMIC ANALYSIS BETWEEN SCPAND PVD

Fig. 13 Graphical representation of depth (m) vs
CBR (%) at location 3

There are multitude of factors which are required
to be considered for economic analysis between SCP
and PVD such as: diameter of the pile, spacing,length of pile, fineness modulus (FM) are estimatedin subgrade improvement for SCP whereas
Prefabricated Vertical Drain depends upon
composite drain properties, filter fabric properties,
average spacing, length, sand blanket height,
surcharge height etc. This economic analysis isspecific to this area and particular to this design of
SCP and PVD.

After installation of PVD at location 4, the DCP
penetration reading was taken from 125 mm to 420
mm (total depth 295 mm). 26 number of blows werecounted. The layer thickness was 295 mm (26 noblows). Average CBR percentage was 20.87. The
DCP termination depth was taken as 0.295 meter.
The average CBR percentage for the full depth canbe taken 21%.

6.1 Cost for Sand Compaction Pile (SCP)
The following specification has been considered
for SCP at site:
Diameter of pile :300 mmLength (Depth) of pile:
8 meter
Spacing:
1.4 meter
FM of filled sand :2.5
Volume of Sand :0.25 cum/meter
Considering 100 square meter area:
Quantity of SCP in 100 sqm area
= Total No of Borehole x Depth of Pile = 60 x 8= 480 RM
Rate of SCP per RM = 930.49 BDT (11 USD) [10]Total Cost = 480 x 930.49 BDT= 4,46,635.20 BDT (5207.33 USD)

Fig. 14 Graphical representation of depth (m) vs
CBR (%) at location 4

6.2 Cost for Prefabricated Vertical Drain (PVD)
5.4.1 Findings
The following specification has been considered
for SCP at site:
Average Spacing =1.5 meter
Average length =15.0 meter
No of PVD in 100 sqm area =42
Sand Blanket height =0.6 meter (FM >2.5)
Surcharge height = as per filling height say 1 meter(FM >1.0)
Considering 100 square meter area:
Quantity of PVD requires in 100 sqm area

The results of DCP test were used to estimate the
CBR of subgrade soil of both types of sites(improved by SCP and PVD) using the procedures
suggested by ASTM D6951 (2018). The following
observations obtained from DCP test are given
below:
a. The DCP test was performed in shallow
depth (only up to 0.4 meter) to assess the strength ofsubgrade at top surface. The DCP test was126

GEOMATE – Bangkok, Thailand, 22-24 November 2022

= Number of PVD x Length of PVD= 630 RM
Rate of PVD per RM=268.56 BDT (3.18 USD) [10]Sand Blanket required in 100 sqm area = 60 cum
Rate of Sand Blanket per cum = 2830 BDT (33.46
USD) [10]Surcharge required in 100 sqm area = 100 cum
Rate of Surcharge per cum = 420 BDT (4.97 USD)
[10]
Soil Settlement required in 100 sqm area = 100 x0.18 = 18 cum
Rate of Soil Settlement per cum = 181.44 BDT [10]So, Total cost of PVD in 100 sqm area
= (630 x 268.56) + (60 x 2830) + (100 x 420) +(18x181.44) BDT= 6,10,641.92 BDT (7119.49 USD)e. Construction of the sand columns isextremely fast. Several SCP can be constructed very
easily within a day whereas PVD takes around sevenmonths due to soil settlement.
8. CONCLUSION
SCP method is one of the typical ground
improvement methods to densify the ground by
installing compacted sand piles into ground. PVDs
are a type of geo-composites which has got
application in highway construction. It slacks timerequirement and amount of surcharge required in agiven time. It also augments the rate of strength gain
due to consolidation of soft soils when stability is ofmajor concern. In the context of Bangladesh both
SCP and PVD are much efficacious for ameliorating
subgrade improvement techniques. SCP can beconstructed very easily where as PVD takes a longtime due to soil settlement. Both are excellent
techniques in context of Bangladesh.

6.3 Findings from Economic Analysis
Total cost of PVD = 6,10,641.92 BDT (7119 USD)
Total cost of SCP = 4,46,635.20 BDT (5207 USD)
Therefore, difference of cost is (6,10,641 - 4,46,635)= 1,64,006 BDT (1912 USD) per 100 sqm area
The result evidently shows that SCP cost is 1.37
times (37%) higher/costly than PVD cost.

9. REFERENCES[1] Hossain, M. Z., “Effectiveness of Sand
Compaction Piles in Improving Soil Bed Formed ofAlluvial Deposits of Bangladesh”, M. Sc. Engg.
Thesis, Department of Civil Engineering, BUET,
Bangladesh, pp. 1, 2015.
[2] Kitazume, M., “The Sand Compaction Pile
Method”, A.A. Balkema Publishers, London, UK,
2005.
[3] Hansbo, S., “Band Drains”, Ground
Improvement (Second Edition), Blackie Academic
Professional, U.K, pp. 4-6, 1993.
[4] Hansbo, S., “Consolidation of clay by bandshaped prefabricated drains.” Ground Engineering,12(5), pp. 16-25, 1979.
[5] ASTM: D1586-11, “Standard Test Method forStandard Penetration Test (SPT) and Split-Barrel
Sampling of Soils”, American Society for Testingand Materials (ASTM), USA, 2017.
[6] Samanta, M., Sawant, V.A. and Ramasamy, G.
(2010), Ground Improvement using Displacement
Type Sand Piles, Proc. Indian GeotechnicalConference, IGS Mumbai Chapter and IIT Mumbai,
Dec.16-18, 2010, pp. 629-632.
[7] Sakleshpur, V. A., Prezzi, M. and Salgado, R.,“Ground Engineering Using Prefabricated Vertical
Drains: A Review”, Geotechnical Engineering
Journal of the SEAGS & AGSSEA, vol. 49, no. 1,
March 2018.
[8] Sharif, G., Standard Test Procedures, Ministry ofCommunications Roads and Highways Department,
Bangladesh, pp 5.21-5.23, May 2001.
[9] Han, J., “Principles and Practices of Ground
Improvement”, New Jersey: Wiley-Sons, 2015.
[10] “Item of Works and Schedule of Rates”, Dhaka
Zone, LGED, 2019-20.

7. SUITABILITY COMPARISON
Both SCP and PVD are suitable for improving
subgrade strength at soft soil. However, when boththese two methods were applied on ground someadvantages and disadvantages were observed
between these two. According to survey data
analysis and observation, following comparison
between SCP and PVD may be brought out:a. With the installation of SCP at location 1
and 2, subgrade soil condition was improved and
SPT-N values also increased significantly. The SPTN value raised around 18 from 4 at location 1 andraised around 18 from 3 at location 2 up to the depth(8 meter) where sand piles are driven.
b. With the installation of PVD at location 3
and 4, subgrade soil condition was improved and
SPT-N values also increased sharply. The SPT-N
value raised around 14 from 3 at location 3 andraised around 14 from 3 at location 4 up to the depth
examined (8 meter).
c. Average CBR (%) was found around 19.5%
where subgrade improvement has been done through
SCP and 21.5% where subgrade improvement was
conducted through PVD. Average CBR (%) isslightly higher where ground improvement was
conducted through PVD than that of SCP.
d. The primary advantage of SCP is that thesand used is considerably cheaper when compared to
PVD which needs geotextile and other imported
materials. In comparison to Prefabricated Vertical
Drains, SCP is more economic. In this case, PVD
cost is 1.37 times (37%) higher/costly than SCP cost.
127

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

XRD PATTERNS OF TURBIDITY IN SEEPAGE WATER
THROUGH TWO SOIL LAYERS
Shota Takano1, Motoyuki Suzuki1, Taichi Ishimaru1 and Asahi Komori1
1

Graduate School of Sciences and Technology for Innovation, Yamaguchi University, Japan
ABSTRACTTo detect the internal erosion associated with groundwater seepage, it is important to estimate the path of theseepage water flowing under the ground. The authors thought that if the mineral species of the turbidites in theseepage water could be identified by XRD (X-ray diffraction), the flow path of the seepage water could be
estimated. It is expected that the XRD patterns of turbidite in seepage water flowing through multi-layered ground,which is composed of various mineral species, will be complex. However, the characterization of XRD patternsof mineral species in seepage water that has passed through multi-layered ground is not always well understood.
Thus, the authors analyzed the diffraction patterns by XRD using turbidites composed of multiple minerals
discharged by the seepage water passing through two soil layers. In the present study, a model experiment was
conducted in which two layers of soil were placed in a cylindrical container and water flowed from the top. Fourcases of XRD analysis were performed on the turbidites in the drained water. In each case, the type of soil in theupper and lower layers and the density of each layer were changed. The results showed that the diffraction patternof the turbidites changed significantly when the upper and lower soil layers had replaced each other. They also
showed that the diffraction pattern changed with time in each case.
Keywords: Suffusion, Internal erosion, XRD, Seepage

INTRODUCTION
The loss of fine particles due to internal erosion(suffusion) is believed to affect the deformation andstrength properties of the soil [1], [2], [3]. Therefore,
it is important to estimate the path of the seepage flowin order to estimate where suffusion may occur in the
ground.
The authors thought that by analyzing the fine soil
particles contained in seepage water by XRD (X-ray
diffraction) and examining their crystal structure, itwould be possible to determine which point in the
ground these particles are passing and to estimate thepath of the seepage flow. However, when theturbidites contained in the seepage water passing inan actual ground are analyzed by XRD, the XRD
pattern becomes complicated. The reason for thecomplexity of the XRD pattern may be that the
seepage flow in the ground passes through various
soil layers, each time incorporating fine grains ofdifferent mineral species. Figure 1 shows anillustration of the mixing of fine grains.
Although studies have been conducted to trace thesource of the turbidites in seepage water [4], thedischarge behavior of fine grains, when different
types of fine grains are simultaneously discharged byseepage, has not been fully clarified. Therefore, inorder to investigate the temporal variation of thediffraction patterns of turbidites composed ofmultiple fine grains discharged by internal erosion, aconstant water head one-dimensional infiltration
experiment was conducted in this study using two

Fig. 1

Mixing of fine grains

types of soil samples to reproduce two layers of theground, which were vertically drained. The flow rates
and effluent concentration obtained from theexperiment were then measured, and the turbidites inthe effluent were extracted. XRD analysis was thenperformed to analyze the temporal changes in the
crystal structure of the turbidites.
TEST METHOD
Test apparatus
In this study, two types of soil were placed in twolayers (upper and lower) in a cylindrical column
128GEOMATE – Bangkok, Thailand, 22-24 November 2022

Saturated with water

During infiltration experimentFig. 2 Schematic of cylindrical column infiltration apparatus

infiltration vessel made of acrylic to reproduce a twolayer ground, and water flowed through the vessel inthe vertical direction. Figure 2 shows a schematic
diagram of the apparatus.
The inner diameter of the specimen inside thecolumn was 10 cm, and the height of both the upperand lower layers was 10 cm. A porous acrylic plate,with 108 perforations, each with a diameter of 5 mm,
and a replaceable mesh were installed at the bottomof the specimen to adjust the particle size of thedischarged soil particles. In this study, a 425 µmdiameter mesh was used, thereby allowing only finesand to flow out. A funnel-shaped depression in thebottom plate of the specimen facilitated the dischargeof soil particles.

Table 1

Density of soil particles
ρs (g/cm3)
Liquid limit wL (%)
Plastic limit wp (%)
Plasticity index 𝐼p
Table 2

Fig. 3129

KaolinDL clay

2.675

2.741

59.9
35.6
24.3

NP
NP
―

Proportion of host silica sand

Silica sand
No. 1
No. 3
No. 4
No. 5
No. 6
No. 7
Total

Tested material
The soil samples were composed of host silica
sand, which is a mixture of several silica sands ofdifferent grain sizes, and kaolin or DL clay mixed toachieve a fine-grain content of 5 %. Table 1 shows
the physical properties of kaolin and DL clay. Kaolin
and DL clay were used as the fine-grain fraction
because of the difference in grain size and theplasticity of the fine fractions, which would result indifferent discharge behavior during infiltration
experiments. The host silica sand was composed ofsix different grain sizes of silica sand No. 1, No. 3,
No. 4, No. 5, No. 6, and No. 7 in the proportions
shown in Table 2. The soil sample with the host silica
sand and kaolin was named "QK" (quartz sand andkaolin), and the soil sample with the host silica sandand DL clay was named "QDL" (quartz sand and DLclay). Water contents of 11.5 % and 10.6 % wereprepared in the cases of QK and QDL, respectively.

Physical properties of kaolin and DL clay

Grain size (mm)4.8～1.7
3.3～0.8
2.4～0.3
1.7～0.2
0.6～0.07
0.2～―

Percentage finer by weight

Proportion (%)
9
41
16
22
2
10
100

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Definition of skeleton relative density 𝑫𝒓𝒈
The composition of the mixed soil is composed of
the void ratio, fines, and sand, as shown in Figure 4.
In this study, kaolin and DL clay are the fines, and thehost silica sand is the sand. Void ratio 𝑒 is obtainedby the following equation:𝑒=

𝑉𝑣
𝑉𝑠𝑠 + 𝑉𝑠𝑓

(1)

However, based on the idea that the fines are regarded
as part of the leakable component, skeleton void ratio
𝑒𝑔 , devised by Mitchell et al. [5], was adopted in thisstudy. 𝑒𝑔 is obtained by the following equation:𝑒𝑔 =𝑉𝑣 + 𝑉𝑠𝑓
𝑉𝑠𝑠

Fig. 4is then allowed to stand for approximately one day tosaturate the specimens with deaerated water.
Infiltration procedure(2)

In this study, the infiltration procedure of
Ishimaru et al. [6] is employed. Water is injected into
the cylindrical column vessel and a vertical
downward seepage flow is applied to the specimens
while the water level is kept constant by overflow. In
all cases, the height of the water surface in the uppertank of the infiltration device is set to the top of theupper soil layer of the specimen, and the hydraulicgradient is kept constant at 𝑖 = 1. As soon as thewater starts to flow, the bottom plate valve at thebottom of the device is opened, and 100 mL of
seepage water is collected 20 times using a collection
vessel, for a total of 2,000 mL of seepage water. The
time required to collect 100 mL is measured. Afterinfiltration is completed, soil particles are extracted
from the effluent to determine the amount of soil
particles discharged.

The maximum void ratio of the host silica sand isdefined as maximum skeleton void ratio 𝑒𝑔𝑚𝑎𝑥 andthe minimum void ratio is defined as minimum
skeleton void ratio 𝑒𝑔𝑚𝑖𝑛 . The relative density isdefined by 𝑒𝑔𝑚𝑎𝑥 , 𝑒𝑔𝑚𝑖𝑛 and 𝑒𝑔 is defined as skeleton
relative density 𝐷𝑟𝑔 , as in the following equation:
𝐷𝑟𝑔 =𝑒𝑔𝑚𝑎𝑥 − 𝑒𝑔
× 100 [%]
𝑒𝑔𝑚𝑎𝑥 − 𝑒𝑔𝑚𝑖𝑛(3)

In this study, the soil layer was prepared with 𝐷𝑟𝑔 =60 % as the relatively dense condition and 𝐷𝑟𝑔 =30 % as the relatively loose condition.
Saturation process
Once the two soil layers have been placed in thecolumn infiltration vessel, a lid with legs is attachedand the vessel is sealed. The vessel is positioned sothat the upper soil layer side is down, and CO2 gas is
injected upward from the upper soil layer side, as
shown on the left side of Figure 2. The specimen isfilled with CO2 gas for approximately one hour.
Deaerated water is then injected upward through thetubes of the deaerated water tank. The deaerated
water tank is raised up for approximately one hour
until the height of the water surface of the tank is thesame as the height of the bottom plate valve. The tank
Table 3

Schematic of mixed soil

TEST CASE
Table 3 shows the details of the test cases in thisstudy. In total, four cases of infiltration experiments
were conducted, employing two different finegrained and two skeleton relative density conditions.
The thickness of both soil layers in the specimens was
10 cm. The case name means [Upper soil layer type
(QK or QDL) + Lower soil layer type (QK or QDL)
– 𝐷𝑟𝑔 (30 % and 60 %.)].

Specimen preparation conditions
Case nameFines (upper layer)

Fines (lower layer)

𝐷𝑟𝑔 （%)

Case 1UQK+LQDL-30

KaolinDL clay

30

Case 2UQDL+LQK-30

DL clay

Kaolin30

Case 3UQK+LQDL-60

KaolinDL clay60

Case 4UQDL+LQK-60

DL clay

Kaolin60

130GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 4 Examples of seepage water samples (Upper：QDL，Lower：QK，𝐷𝑟𝑔 = 30 %)Container No.

Purified
water

①

⑤

⑩

⑮

⑳

13.20
0.693
7.33

12.35
0.344
3.69

11.98
0.053
0.55

11.45
0.015
0.16

11.53
0.008
0.09

Pictures of seepage water

Time taken to collect (s)Amount of turbidity material (g)Concentration (g/L）

TEST RESULTS
Visual observation of seepage water
Table 4 shows examples of the effluent samples
obtained from Case 2 (upper soil layer: QDL, lower
soil layer: QK, 𝐷𝑟𝑔 = 30 % ). 20 samples of theeffluent are shown with sampling numbers ①, ⑤, ⑩,⑮, and ⑳. Picture of purified water without fineparticles is also given to compare the turbidity of theseepage water. The seepage water of Number ① is soturbid that the other side of the container cannot beseen at all, indicating that it contains a lot of turbid
soil particles. From ⑤ to ⑳, it is found that theamount of turbid matter in the water has decreased,
although the turbidity of the drainage can be seenfrom ⑤ to ⑳.

Fig. 5

Flow rates of turbid water

Fig. 6

Emissions of soil particles

Fig. 7

Accumulated emissions of soil particles

Emissions of soil particles
Figure 5 shows the flow rates of the seepage waterover time. It can be seen that there is a clear differencein the flow rates of the seepage water between thecases with 𝐷𝑟𝑔 = 30 % and 60 %. However, whencomparing the cases with different soil layer
arrangements under the same skeleton relative
density condition, there is no significant difference inthe flow rates. In other words, in the case of twolayered soil, even if the order of the soil layers
through which the water passes is changed, there isno significant difference in the amount of seepage
that is ultimately discharged.
Accumulated emissions of soil particles
Focusing on the discharge rate per container, in allcases, the discharge rates were greater between
container numbers ① to ③, while the discharge rateof the soil particles after that decreased rapidly withthe water flow and almost disappeared after container
number ⑩.
When focusing on the cumulative discharge, it canbe seen that 𝐷𝑟𝑔 = 30 % yields a larger value of
cumulative discharge than 𝐷𝑟𝑔 = 60 % as well as the131GEOMATE – Bangkok, Thailand, 22-24 November 2022

time variation of the flow rate of the seepage water.
However, the effect of the difference in the soil layer
arrangements was also observed, and the discharge
was larger in the percolation case where the finegrains in the upper layer were kaolin and the finegrains in the lower layer were DL clay. The smaller
particle size and higher plasticity index of kaolin may
have allowed it to flow more easily in the pore spaces
than DL clay and to pass through the soil layer below,
even if it was contained in the upper layer. Therefore,
the final discharge was probably larger for the upper
layer than for the DL clay and for the lower layer thanfor the kaolin.
XRD analysis of turbidites
In the XRD analysis, the turbidites in the seepage
water, obtained in the four water flow cases, wereanalyzed for their respective XRD patterns using
XRD to examine the temporal changes in the patterns
in each water flow case. The samples to be analyzed
were container numbers ① to ③ immediately after
the start of the water flow in all cases. The samples ofdischarged sediment after ④ were not included in theanalysis because the amount necessary for XRD
analysis could not be collected. However, sinceapproximately 75-80 % of the total final discharge

volume of turbid material was discharged up to ③, it
can be assumed that most of the dischargeable soil
particles had been discharged by the time of ③.
The turbidites collected from the seepage water byfiltration were dried, then thoroughly ground in an
agate bowl to a powder form for analysis. Under theconditions of the analysis, CuKα X-rays were(a) UQK+LQDL(b) UQDL+LQK
Fig. 9Fig. 8XRD patterns of basic samples of soil

Results of XRD analysis
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

irradiated at diffraction angles from 5° to 75°.

in the lower layer, resulting in a larger change in the
XRD pattern.

Diffraction patterns of basic samples
CONCLUSIONS
Figure 8 shows the diffraction patterns of thekaolin, DL clay, and silica sand powder that may havebeen discharged by the fine-grained runoff. Althoughkaolin and DL clay were used as the fine-grained
components in this study, the maximum particle sizeof the outflowable component was 425 µm. Strictly
speaking, this means that fine silica sand particles in
the host silica sand were also discharged in addition
to the two types of fine-grained components. These
three diffraction patterns were used as reference whenanalyzing the samples obtained from the flowthrough experiment.

In this study, the temporal variation in the XRD
patterns of the turbidites in seepage water, that passed
through a two-layer ground, was investigated. The
main results of the study are summarized as follows:
1.

2.

XRD pattern analysis of two soil layers

3.

Figures 9 (a) and (b) show the results of theUQDL+LQKandUQK+LQDL
analyses,
respectively. The maximum value of the Y-axis scale
is set to 500 in each result so that the maximum
diffraction intensities can be compared, and values
are shown in Figure 9 for the peaks exceeding 500. A
comparison of these figures shows that the XRD
diffraction patterns were clearly different dependingon the arrangement of the soil layers. When
comparing the diffraction intensity of the largest peaknear 26.6 °, the diffraction intensity values werelarger when the lower soil layer was QDL.
Comparing the diffraction patterns of the basic
sample of kaolin and DL clay, the diffraction patternof UQK+LQDL is closer to that of DL clay and thediffraction pattern of UQDL+LQK is closer to that ofkaolin because DL clay has a larger peak near 26.6 °.
In other words, the diffraction pattern of the turbidites
in the seepage water that has passed through the twolayer ground is characterized by the fine grains
contained in the lower layer near the outlet.
In terms of the difference in skeleton relative
density, the XRD patterns for 𝐷𝑟𝑔 = 30 % and𝐷𝑟𝑔 = 60 % were not completely identical; there
were some differences in the diffraction intensity
values. In the case of 𝐷𝑟𝑔 = 30 % , the peaks
appeared and disappeared more frequently. In otherwords, the XRD patterns were more likely to change
from ① to ③ when the relative density was loose. It
is thought that a loose relative density will cause thefine grains in the upper layer to be more easily
discharged and to become mixed with the fine grains

The flow rate of seepage water through twolayers of soil is not affected by the arrangementof the soil layers, but only by the density.
The discharge of soil particles is greater when
the density is loose. In this study, the discharge
was greater when the soil in the lower layer was
QDL.
The XRD patterns of the turbidites varied
greatly depending on the soil layer arrangement,
and it was clear that the two-layer soil was more
likely to produce a special number of fine grainsin the subsoil. In addition, peaks tended todisappear or appear more frequently in the flowthrough case where the relative density of thesoil was smaller.

REFERENCES[1] Sato M., and Kuwano R., Laboratory testing forevaluation of the influence of a small degree of internal
erosion on deformation and stiffness, Soils andFoundations, Vol. 58, Issue 3, 2018, pp. 547-562.
[2] Ke, L., and Takahashi, A., Triaxial erosion test forevaluation of mechanical consequences of internal
erosion, Geotechnical Testing Journal, Vol. 37, Issue 2,
2014, pp. 347-364.
[3] Prasomsri, J., Takahashi, A., The role of fines oninternal instability and its impact on undrained
mechanical response of gap-graded soils, Soils andFoundations, Vol. 60, Issue 6, 2020, pp. 1468-1488.
[4] Murakami T., Suzuki Y., Oishi H., Kenichi I. and Nakao
T., Tracing the source of difficult to settle fine particles
which cause turbidity in the Hitotsuse reservoir, Japan,
Journal of Environmental Management, Vol. 129, 2013,
pp. 37-47.
[5] Mitchell, J.K., Fundamentals of Soil Behaviors, John
Wiley Interscience New York, 1976, pp. 172-189.
[6] Ishimaru T., Suzuki M., and Takano S., Particle size
composition of migrating soil particles and the timechange in water-passing experiments for reproducing
suffusion using turbidity, Journal of GeotechnicalEngineering, Vol. 17, Issue 1, 2022, pp. 47-60 (inJapanese).

13312th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

ESTIMATION OF SUBSURFACE STRUCTURESAND GROUND MOTION CHARACTERISTICS OF ARTIFICIALLY
IMPROVED LAND FROM MICROTREMOR OBSERVATIONS– CASE STUDY IN TOTTORI CITY, JAPANIsamu Nishimura1, Tatsuya Noguchi1 and Takao Kagawa1
1

Graduate school of Engineering, Tottori University, Japan
ABSTRACTIn recent earthquakes, a large number of houses on artificially filled valleys were damaged due to large groundmotions. It is because lower S-wave velocities in the filled valleys than that in earth cut section amplify groundmotions. In this study microtremor observations were carried out at two areas in Tottori City, Japan that aredesignated as large-scale artificially improved lands, in order to understand distribution of ground motion
characteristics. The results show that in both areas, the predominant periods tend to be longer and the peak valuesof H/V spectra are larger in the filled valley sections than in the each cut sections. Furthermore, it was found thatthe peak value of the H/V spectrum of microtremors had a certain correlation with the thicknesses of theembankment regardless of the presence or absence of soft layer before the improvement. Estimated S-wave
velocities based on microtremor array observations and predominant periods from H/V spectra were used toestimate thicknesses of sediments from the quarter-wavelength law. It is found that the estimated layer thicknesses
agree well with the elevation change of topography between present and before the improvements.
Keywords: Microtremor observation, Subsurface structure, Artificially improved land

INTRODUCTION

carried out to build new towns, etc., and there arelarge areas with flat terrain that have been artificiallycreated. Cuts and embankments are intricately
distributed in these areas, and it is assumed that the
ground motion characteristics and potential of
housing damages due to an earthquake differ greatly
from point to point.
The Ministry of Land, Infrastructure, Transport
and Tourism (MLIT) is promoting earthquake
resistance improvement for such large-scale
artificially improved lands by investigating theprediction of fluctuations and preventing landslides
and collapses. As a part of the project, the local
government is preparing cut-and-fill maps in hillside
landform to identify large-scale development sites.
This cut-and-fill map is based on a comparison of theold topographic map and the current topographic map,

Artificially improved lands such as filled valleys
have been constructed on hills and foothills
continuously after 1970’s, the economic grow period
in Japan, and they concern about earthquake damage
in the areas. In the recent series of earthquake
disasters, damage caused by soft embankment groundhas become remarkable, and it has been reported thatthe damage was more severe in the area than that of
the surrounding cut ground in each large earthquake[e.g., 1]. This is because the different characteristicsof the ground in the filled valley and earth cut section,such as the fact that S-wave velocity in valley filledpart is lower than that in cut part, and it tends toamplify ground motions. In the suburbs of cities,
large-scale residential land development has been

Kitazono

Wakabadai

Fig.1 Aerial photographs before and after the development in both areas
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 2 Distribution of cut-and-fill thicknesses based on topographic maps and microtremor array sites(left: Kitazono, right: Wakabadai)
and is published on the websites of local governments
as "Large-scale Embankment Landform Map.” Insome areas, the secondary investigation was
conducted, not only borings but also detailed
investigations such as surface wave surveys, toevaluate stiffness of the embankments.
In this study, microtremor observations werecarried out at two areas - Kitazono and Wakabadai in Tottori City, Japan that are designated as largescale artificially improved lands as shown Figure 1.,
in order to understand ground motion characteristics.

4) Create a rasterized elevation distribution before
the development by interpolation based on
ANUDEM [4].
5) Digital elevation data (DEM) of 5m mesh issued
by Geospatial Information Authority of Japan[5] is used for the current map, and it isinterpolated by the reverse distance load method(IDW) to make a raster data.
6) The difference between the old and current
elevations was calculated spatially using raster
operations, and the value was used as
thicknesses of cut-and-filled.
The distribution of cut-and-fill thicknesses at
Kitazono and Wakabadai are shown in Fig. 2.

ESTIMATING THICKNESSES OF
ARTIFICIALLY IMPROVED LAND BY
USING GISOBSERVATIONS
Figure 1 shows aerial photographs of the site
before and after the development at Kitazono andWakabadai regions. In this study, by taking elevationdifference between the old and the current
topographic maps, cut-and-filled thicknesses were
estimated. For the old topographic maps, a 1/25,000
scale topographic map issued in 1984 [2] was used for
Kitazono and a 1/25,000 scale topographic map
issued in 1975 [3] was used for Wakabadai. ArcGIS
by ESRI was employed to obtain the cut-and-fillthicknesses in each region. The method for estimating
the cutting thicknesses is as follows.
1) Scan and digitize the old topographic maps.
2) Match the digitized data to the current
topographic map using geo-referencing functionof GIS.
3) Obtain elevation values for each contour line on
the old topographic map.

Single-point microtremor observations wereconducted at 53 sites in Kitazono and 94 sites inWakabadai for a total of 147 sites. A JU410 threecomponents accelerometer with a 24-bit recorder wasused for data acquisition. We used the following
specifications: an amplification factor of 100 for 4G
maximum output from the sensor, a sampling
frequency of 200 Hz, and a recording time of 10–15
min. Seismographs were placed at about 50 m
intervals in cut and fill sections based on the cut-andfill thickness distribution.
We also conducted microtremor arrayobservations at 7 sites (KZAR1-7) at Kitazono and 2
sites (WKBAR1-2) at Wakabadai as shown in Fig. 2.
Four JU410s synchronized by GPS clocks were used
to conduct the measurements. One seismometer wasplaced at the center of a circle, and other three were
135

GEOMATE – Bangkok, Thailand, 22-24 November 2022

WKB13
KZ14

WKB69
KZ07

Fig. 3 Predominant period distributions (left: Kitazono, right: Wakabadai)

Fig. 4 H/V spectral ratios

Kitazono

Wakabadai

Fig. 5 relationships between difference in elevation before and after land development and the predominantperiods and peak values
set in an equilateral triangle around the circumference.
Linear arrays with two seismographs wereadditionally conducted at the 4 sites at Kitazono(KZAR3-6) with the equilateral triangular arrays. The
specifications were as follows: an amplification
factor of 100, a 200-Hz sampling frequency, 1–30-m
radius of the array, and a 15-min recording duration.

DISCUSSIONS
Results of microtremor single-point analysis
The analyzing procedure for microtremor singlepoint observation is as follows. Fourier spectra were
calculated from 20.48 s length segments and they
were smoothed using a log window with a coefficientof 20 [6]. Average spectra were evaluated from at
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

least 10 different stable sections. Horizontal tovertical (H/V) spectral ratios were calculated from theaveraged Fourier spectra, and the predominantperiods were visually estimated. The predominantperiod distributions at Kitazono and Wakabadai areshown in Fig. 3.
As results, predominant periods in Kitazono
region were from 0.07 to 0.55 s, while Wakabadai
region were between 0.01 and 0.40 s. Figure 4 shows
typical microtremor H/V spectra of embankment andcut land sites in both regions. The H/V spectra ofembankment sites have clear peak in a range of 0.10.4 seconds. On the other hand, the H/V spectra of the
cut sites have a period of about 0.1 seconds or less,and the peaks are unclear at many points. Particularlyat Wakabadai, the shape of H/V spectra at cut-off
lands are almost flat, and there is a remarkabletendency that no peaks are recognized.
Figure 5 shows the relationship between
difference in elevation before and after landdevelopment and the predominant periods and peakvalues of the microtremor H/V spectra at Kitazono
and Wakabadai observation sites. At Wakabadai
without clear peak, the predominant period is marked
as 0.05 s, and the peak value as 0.8. In addition, thenatural ground sites at Kitazono that did notcorrespond to cut-and-fill operations were excluded.
At Kitazono, there is not a strong correlation between
the predominant periods and the elevation difference
before and after construction, but there is aweatheredbedrock
weatheredigneousrock

remarkable correlation with the peak values. On theother hand, a certain correlation is observed between
the predominant periods and the elevation differenceat Wakabadai. It is considered that Wakabadai has
almost no soft layers near the mountains, and thecorrelation is particularly high with the predominantperiods. On the other hand, Kitazono has weathered
and cultivated lands on natural ground even near themountains.
Estimation of Subsurface Structures
Analysis of the array observations was conductedwith the following procedure: the phase velocity
dispersion curves were estimated from verticalcomponent records based on the SPAC [7] or CCA
methods [8]. Using the published analysis tool [9], atleast ten sections with 10.24 s length were selected byautomatic extraction using the root mean squarevalues of microtremor recordings. Then, the power
spectra of those sections were smoothed by a Parzen
window with 0.3-Hz bandwidth and the average wasestimated. Finally, the phase velocity dispersion
curves were determined, and those obtained at eachradius were integrated at each observation site, withrespect to their continuity.
Assuming that microtremors are mainlycomposed from Rayleigh waves, their propagation
characteristics allow us to estimate subsurface
structures. The subsurface structure models were

weatheredbedrock
weatheredigneousrock

gravelweatheredbedrock

silt
gravelclay
gravelclay

tuff
brecciaFig. 6a S-wave velocity structures based on array observations and borehole data at Kitazono(Red line: cut sites, blue line: fill sites)

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 1 Converted S-wave velosities
Site

Converted Vs(m/s)

KZAR1(cut)267.1

KZAR2(fill)

339.2

KZAR3(cut)

300.0

KZAR4(fill)

200.0

KZAR5(fill)

336.4

KZAR6(cut)

400.0

clay

KZAR7

208.3

bedrock

WKBAR1(cut)534.3

WKBAR2(fill)365.9

embankment

Kitazono

Wakabadai

Fig. 6b Same as Fig. 6a but for Wakabadai

Fig. 7 Layer thicknesses in Kitazono and Wakabadai regions
estimated through trial and error to ensure that thesephase velocity dispersion curves and the microtremor
H/V spectral ratios obtained at the center of the array,to match the theoretical phase velocity dispersion
curves and H/V spectral ratios based on the Rayleighwave fundamental mode, respectively. The total
numbers of sedimentary layers were determined withreference to the borehole data around the observationsites.
The resulting S-wave velocity structure models
and borehole data are presented in Fig. 6a and 6b.
Please note that the borehole data of Kitazono wereacquired before the development. The thickness ofembankment near K2 at Kitazono is considered to be
several meters from Fig. 2. The S-wave velocity ofthe embankment layer was estimated as 200 m/s
based on KZAR5. The S-wave velocity at the surface
layer at KZAR6 was 400 m/s. It confirms that the cut
sites have higher S-wave velocities than the fill sites.
However, KZAR1 and KZAR2 have several metersof layers with S-wave velocities of 150 m/s on thesurface layer regardless whether cut or fill sites, andthey have over 30m depth layers with S-wave
velocities of 400 m/s or less. Therefore, the groundmotion characteristics of this area are affected not
only by the thicknesses of embankment but also by
the weathered layers under the embankment layers.
The thickness of embankment near W1 at
Wakabadai is about 22 m. The S-wave velocity of theembankment layer was estimated to be 250 m/s based
on WKBAR2. At the cut-off site WKBAR1, a layerof Vs=500m/s or more is estimated at a depth of 5mor deeper. It suggests that the site has very hardground.
Estimation of artificially improved land thickness
based on microtremor results
The S-wave velocities and the predominantperiods were used to estimate the layer thicknesses

138

GEOMATE – Bangkok, Thailand, 22-24 November 2022

from the quarter-wavelength law. S-wave velocities
were calculated by weighted average using down to
the top surface of Vs = 500 m/s of the estimatedmodel at Kitazono and the top surface of Vs = 1000m/s at Wakabadai as sediments. Table 1 shows theconverted S-wave velocities at each location used toestimate the layer thicknesses hereafter. The layer
thicknesses at the cut-off sites with no clear peak at
Wakabadai is set to be 5 m. In both regions, dummy
data with a layer thickness of 0 m were added atseveral points in stiff mountainous areas. Afterobtaining the estimated layer thicknesses at each site,
the distribution was rasterized by interpolation basedon the reverse distance load method (IDW).
The distributions of layer thicknesses are shownin Figure 7. It is clear that the points with thicksediment layers in both areas generally correspond to
the embankment areas. The thicknesses of the layers
were confirmed to be about 20 m at the deepest pointat Kitazono and about 30 m at Wakabadai. Thicker
layers are observed in the cut areas at Kitazono. Thisis because the microtremor H/V spectra in the areas
are considered to reflect velocity contrasts of thestructures due to deeper part of the embankment
layers.

ACKNOWLEDGMENTS
Part of this research is based on the results of thegraduation thesis by Ms. Nazuna Nishimura who
belonged to the Faculty of Engineering, Tottori
University. We used seismographs provided by the
Earthquake Research Institute of the University ofTokyo with the support of the "Research Plan for
Earthquake and Volcano Observation to Contribute toDisaster Mitigation (2nd)" by the Ministry of
Education, Culture, Sports, Science and Technologyof Japan.
REFERENCES[1] Mori T. and Kazama T., The damage research offilled-valley residential area in Sendai City Izumi
ward about the 2011 off the Pacific coast of
Tohoku Earthquake, Japanese Geotechnical
Journal, Vol.7, No.1, pp.163-173, 2012. (inJapanese with English abstract)[2] National Institute of Geography, Topographic
map at 1/25,000 scale Tottori Kita, 1984.
[3] National Institute of Geography, Topographic
map at 1/25,000 scale Inabayama, 1975.
[4] Hutchinson, M.F., Optimising the degree of datasmoothing for locally adaptive finite element
bivariate smoothing splines. Australian & New
Zealand Industrial and Applied Mathematics
Journal 42(E): C774-C796, 2000.
[5] Geospatial Information Authority of Japan,
Digital Elevation Model (DEM),
https://www.gsi.go.jp/kiban/index.html (view on2022.6.14)[6] Konno K. and Ohmachi T., A smoothing
function suitable for estimation of amplification
factor of the surface ground from microtremor
and its application, Journal of the Japan Societyof Civil Engineers, No525/I-33, 1995, pp247259. (in Japanese with English abstract).
[7] Aki K., Space and time spectra of stationary
stochastic waves, with special reference tomicrotremors, Bull.Earthq.Res.Inst., 35, pp415456, 1957.
[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, J. Geophys. Res., 111,
B09315, 2006.
[9] 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)

CONCLUSIONSThis study conducted microtremor observationsin the areas - Kitazono and Wakabadai in Tottori City,
Japan - designated as large-scale artificially improved
land. The subsurface structures of the cut-and-fillareas were estimated from the microtremor records,
and the characteristics of ground motion were
examined. Consequently, the following findings arenoted.
The predominant periods of microtremor H/V
spectra on embankment ground shows a highcorrelation with the thicknesses of the embankment
where no soft layer deposited before land
improvement, such as in Wakabadai.
The peak values of the H/V spectra of
microtremors show a certain correlation with the
thicknesses of the embankment regardless of thepresence or absence of soft layers before theimprovement.
The estimated ground structure indicates thatsoft sediments are deposited mainly in the fill area but
not in the cut area. It is considered to be an important
index for predicting the risk of future earthquake
disasters.
Comparison between the estimated surface
thickness distribution and the cut distribution in bothregions showed similar trends. We would like toincrease the number of case studies in other improved
lands.

139

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

EVALUATION OF VERTICAL BEARING CHARACTERISTICS FOR
SOIL-CEMENT COMPOSITE PILES
Shohei Koga1, Koji Watanabe2 and Tadahisa Yamamoto3
1

Obayashi Corporation, Japan; 2 Department of Civil Engineering, Aichi Institute of Technology, Japan
ABSTRACT

There are several pile construction methods with relatively small construction machinery that are suitable forconstruction in narrow urban spaces and areas with low overhead clearance. The Top-drive Boring Hole (TBH)
method, which has a machine height of 4.5m, is often used. The Boring Hole (BH) pile method can handle narrow
spaces and low overhead clearances better than the TBH method. However, because the BH pile method is a direct
circulation method, mud cakes easily form on the hole walls, and slime tends to accumulate at the pile tip. Suchchallenges related to the construction method, bearing capacity and settlement need to be resolved for pile
construction in narrow urban spaces. Pile construction in narrow urban spaces and for existing structures tostrengthen the seismic resistance is subjected to constraints on the construction site and process. Therefore, amethod for soil-cement composite pile construction was developed that uses a compact mechanical agitator for
pile foundations of lightweight structures. In this study, static compression load tests on soil-cement composite
piles that were constructed with the developed ground improvement construction method were carried out toevaluate the vertical bearing characteristics. Furthermore, it was evaluated the performance of vertical bearing
characteristics on the soil-cement composite piles to compare the evaluation method as shown in the design
specifications in Japan.
Keywords: Soil-cement composite pile, Compression load test, Bearing capacity, Evaluation method

INTRODUCTION
There are several pile construction methods withrelatively small construction machinery that aresuitable for construction in narrow urban spaces andareas with low overhead clearance. The Top-drive
Boring Hole (TBH) method, which has a machineheight of 4.5m, is often used. However, there aremany cases where even these construction machines
are interfered with by architectural limitations andexisting structures. In particular, piles near railway
tracks or on rail platforms are currently constructed
after temporary construction is carried out to ensure
construction space. The Boring Hole (BH) pilemethod can handle narrow spaces and low overhead
clearances better than the TBH method. However,
because the BH pile method is a direct circulation
method, mud cakes easily form on the hole walls, andslime tends to accumulate at the pile tip. This lowers
the bearing capacity of the piles, so subsidence ismore likely to occur. Such challenges related to theconstruction method, bearing capacity and settlement
need to be resolved for pile construction in narrowurban spaces.
Pile construction in narrow urban spaces and forexisting structures to strengthen the seismic
resistance is subjected to constraints on theconstruction site and process. Especially, this pile issubjected to the lack of bearing capacity due to theincrease of superstructure weight by strengthening
the superstructure. Furthermore, noise and industrial

Fig. 1 Construction machine of soil-cement
composite pile.
waste need to be considered with regard to theireffects on the surrounding environment. Therefore, amethod for soil-cement composite pile construction
combined with soil cement ground improvement wasdeveloped that uses a compact mechanical agitator for
pile foundations of lightweight structures. The
construction machine is a mechanical agitator with anattached vibrating mechanism and improved drilling
capacity. This developed machine can be used torealize pile construction in narrow spaces whereconstruction is usually difficult. This method ischaracterized by having no need to drill up to a fixed
depth as in the pre boring pile construction method.
Instead, soil is agitated along with the cement milk at
the original position. This greatly reduces the spoil
generated by pile construction. In this method, the140GEOMATE – Bangkok, Thailand, 22-24 November 2022

drilling rod of the developed machine is sent down tothe bearing stratum; after agitating and mixing, thepile is composed of segmental steel pipes that areconnected with bolts on flanges.
In this study, static compression load tests on thesoil-cement composite pile (i.e. H-section steel piles
with soil-cement ground improvement) that wereconstructed with mechanical agitator ground
improvement method were carried out to evaluate thevertical bearing capacity. Furthermore, it wasevaluated the performance of vertical bearing
capacity on the soil-cement composite pile tocompare the evaluation method as shown in thedesign specifications in Japan.

Fig. 2 Pile tip shape of soil-cement composite pile.
H section steel pile
H300×300×10×15
300
150

75

300

75

SUMMARY OF SOIL-CEMENT COMPOSITEPILE CONSTRUCTION

75

75

150
300

Fig. 3 Scale of soil-cement composite pile tip.

In this construction method, strength of the soil
cement is in the ranges of 1000～5000kN/m2. The H
section steel piles were used to be fabricated from JIS
standard steel piles to ensure a low cost and certain
level of quality. The typical construction process withthis method is described as follows;
1) Excavate to the bearing stratum, carry out
agitation and mixing, and lay the soil cement as theground improvement structure. During this time,carry out drilling so that the rod will reach apredetermined depth.
2) Build H section steel pile joints in the ground
improvement structure, which is in loose and poorly
lithified soil.
3) After the pile construction is completed, fix thepile head before the soil-cement hardens.
The above construction can be carried out using
only the developed construction machine shown in
Fig. 1. The planar dimensions of the construction
machine are extremely compact: a 1.55m width and2.32m length. In addition, the construction machineis 3.0m tall, including the leader. Thus, construction
is possible even with low overhead clearances. The
machine has a vibration mechanism to improve theagitation and mixing capability. The results ofconstruction test showed that even strong sandy
stratum with an N value of about 40–50 can beagitated and mixed [1].
In order to increase the end bearing capacity, twoplates were attached to the tip of the H section steel
pile. Fig. 2 and Fig. 3 shows the tip shape of the H
section steel pile. As shown in Fig. 2 and Fig. 3, twoplates were attached to the tip of the H section steel
pile with an inclination of 135deg. from the horizontal
direction.

SPT N-value Type 1, 2 Type 3 Type 4
0 20 40 60

0

Embankment
Loam
Tuffaceous
clay

5

Sand

Depth （GL- m)

Sandy silt
Sand

10

Silty sand
Sand
Silty sandFine sand
Sand

15

Sandy siltSand

H section
pile
Soil-cement：Strain gauge

Medium sand20

：Displacement transducer

Fine sand(m)Fig. 4 Soil profile and test piles.
Table 1. Specifications of test piles.

Type 1
Type 2Type 3
Type 4

Pile diameter

Pile length

Strength of

(mm)

(m)

soile-cement (kN/m )

600
600
800
600

14.0
14.0
14.0
17.0

1000
3000
1000
1000

2

The soil profile and test piles indicates in Fig. 4.
The test ground was composed of loam andtuffaceous clay up to about GL −5.0 m and sandy silt
and medium sand, fine sand below that. Four piles
were constructed for the static load test with the soil
cement diameter, pile length, and soil cement strength
as parameters.
Table 1 presents the specifications of the test piles.
The bearing stratum was adopted to be N=20 and 40

IN-SITU FULL SCALE COMPRESSION LOAD
TEST
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

from the results of ground investigation; the test piles
were embedded in the bearing stratum. The test piles
were pre-drilling soil-cement column diameter of
600mm and 800mm, H section steel shape of H300×300×10×15. The target strength of the predrilled soil cement as the improved ground structure
was 1000kN/m2 with the test pile of Type 1, 3, 4 and3000kN/m2 with the test pile of Type 2. In the corestrength test carried out after the load test, the average
strengths with the all of the test piles were over thetarget strength, respectively.
The arrangement of test piles and reaction piles
presents in Fig. 5. Four test piles and six reaction piles
were constructed. Similar to the test piles, six reaction
piles were also constructed as the H section steel pilewith soil cement by the same construction method as
the test piles. The distance between the test piles andthe reaction piles were set based on the influence
range as described in the Japanese GeotechnicalSociety (“Methods for vertical load test of piles”) [2],the distance are 3D=1800, 2400 (mm). Here, D means
the pile diameter such as soil cement diameter.
The static load test was carried out based on thestandards of the Japanese Geotechnical Society(“Methods for vertical load test of piles”) [2]. A
stepwise multi cycle loading system was employedwith a new load holding period of 30min, hysteretic
load holding duration of 2min, and zero load holding
duration of 15min. The measured parameters were thepile head load, pile head and pile tip displacements,and strain of the H section steel. The pile tip
displacement was measured with the pipe-in pipe
method (i.e. double steel tube method). In order toevaluate the shaft friction of each stratums and theend bearing capacity, the strain were measured at the
tip of pile, the boundary of each soil stratums and topof pile.

3000

Load at pile head (kN)

2500
2000
1500
Type1
Type2
Type3
Type4

1000
500
0
0

5

10

15

20

25

30

Displacement at pile head (mm)Fig. 6 Relationships between load and displacement
at pile head.
-160(a) GL-0.5m～GL-5.5m

Shaft friction (kN/m2)

-140
-120
-100
-80

Type1
Type2
Type3
Type4

-60
-40
-20
0
0

5

10

15

20

25

30

Local pile displacement (mm)(a) Clayey soil: GL-0.5m~GL-5.5m.

SCALE

-250
(b) GL-5.5m～

Shaft friction (kN/m2)

RESULTS OF IN-SITU FULLCOMPRESSION LOAD TEST

Fig. 5 Arrangement of test piles and reaction piles.

Figure 6 shows the relationships between the loadand displacement at the pile head. The maximum
loads were 2500kN for Type 1 and 2700kN for Type2~Type 4. For all of the piles, the curve was steep
from the initial of loading; and all of the piles
possessed a large initial stiffness. The gradient of thecurve then changed with the loading and reached the
maximum load. For the test piles of Type 2~Type 4,
a larger N value for the bearing stratum, a larger pile
diameter and a longer pile length meant a higher
stiffness.
The axial force was calculated by considering

-200
-150
Type1
Type2
Type3
Type4

-100
-50
0
0

5

10

15

20

25

Local pile displacement (mm)(b) Sandy soil: GL-5.5m~.
Fig. 7 Relationships between shaft friction and local
pile displacement.
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

only the H section steel. The strain of the H section
steel obtained from the load test was multiplied with
the Young’s modulus and cross-sectional area of the
H section steel. The relationships between the shaft
friction and local pile displacement which was
obtained by dividing this axial force difference by thecircumferential area are shown in Fig. 7. The
improved diameter of the soil cement (i.e. outer
diameter of soil cement column) was used whencalculating the circumferential area of the pile. Figure7(a) shows that a maximum shaft friction of 50 ~140kN/m2 was reached in clayey soil stratum. In
comparison, the average value for the unconfinedcompressive strength of the corresponding section of
the ground was 57kN/m2. Thus, it can be concluded
that the shaft friction was more than the undrained
shear strength of the ground (=28kN/m2). In the sandy
ground stratum, it is observed that the maximum
shear strength is 100~200kN/m2. Therefore, it is said
that the shaft friction of the corresponding section
shows the large value.
The end bearing capacity was calculated by
dividing the axial force reaching the pile tip by theenvelope area of the H-section steel (BxH) as shownin Fig. 8. The relationships between the end bearing
capacity and displacement at pile tip indicates in Fig.
9. The reference displacement of pile tip is 10% of thediameter of the equivalent circle area which is equal
to the envelope area of H-section steel pile when theend bearing capacity is evaluated. However, themaximum displacement at pile tip does not reach thereference displacement in this load test. Thus, the end
bearing capacity is evaluated at the maximum
displacement at pile tip. The end capacity of
3000~6600kN/m2 is examined in this load test. The
trend indicates that the end bearing capacity is
gradually increasing because the displacement at pile
tip is small and does not reach the reference
displacement to evaluate the end bearing capacity. In
addition, the end bearing capacity of the test pile Type
3 indicates the largest value of four test piles. Thismeans that the large end bearing capacity is mobilized
due to the large diameter of the pile.
EVALUATIONCAPACITYOF

VERTICAL

B

H

Envelope area

Fig. 8 Pile tip area (Envelope area).

End bearing capacity (kN/m2)

-7000
-6000
-5000
-4000
Type1

-3000

Type2

-2000

Type3

-1000

Type4

0
0

5

10

15

20

Displacement at pile tip (mm)Fig. 9 Relationships between end bearing capacityand displacement at pile tip.
applied to the railway structure. The estimation
equations of bearing capacity for the steel pipe soilcement pile and cast in-situ pile are used in this study.
The estimation equations are described as follows;
1) Steel pipe soil-cement pile
Shaft friction for sandy soil:
γ fk = 7 N ≤ 200(kN/m2 )(1)

Shaft friction for clayey soil:
γ fk = 10 N ≤ 200(kN/m2 )(2)

End bearing capacity for sandy soil:
qtk = 150 N ≤ 10000(kN/m2 )(3)2) Cast in-situ pile
Shaft friction for sandy soil:

BEARINGγ fk = 3N ≤ 150(kN/m2 )(4)

Shaft friction for clayey soil:

The end bearing capacity which was obtained
from the developed soil-cement composite pile iscompared to the calculated end bearing capacity as
shown in the design specification of Japan. In thisstudy, the calculated value is estimated by the DesignStandards for Railway Structures and Commentary(Railway Technical Research Institute, 2012) [3]because the developed soil-cement composite pile isγ fk = 6 N ≤ 150(kN/m2 )(5)

End bearing capacity for sandy soil:
qtk = 60 N ≤ 3500(kN/m2 )

143

(6)

GEOMATE – Bangkok, Thailand, 22-24 November 2022

250

12000

200

10000

Steel pipe soil-cement pile150

End bearing capacity (kN/m2)

Shaft friction (kN/m2)

Cast in-situ pile

Type 2100

Type 1
Type 450

Steel pipe soil-cement pile
Cast in-situ pile
Load test results

Type 30Load test results

8000
Type 36000

Type 1
4000

Type 4
Type 2

2000
0

0

5

10
15
20
SPT N value

25

30

0

20

40
60
SPT N value

80

100

Fig. 11 Relationships between end bearing capacityand displacement at pile tip.

(a) Clayey soil.
250
Type 2Type 1200

5000
Type 4

Observed value (kN)

Shaft friction (kN/m2)

6000

150

100

Type 3
Steel pipe soil-cement pile
Cast in-situ pile
Load test results

50

4000
3000
2000
Steel pipe soil-cement pile

1000

0

Cast in-situ pile

0

10

20
30
40
SPT N value

50

60

0
0

(b) Sandy soil.
Fig. 10 Relationships between shaft friction and
SPT N value.

1000 2000 3000 4000 5000 6000
Calculated value (kN)

Fig. 12 Relationships between observed bearing
capacity and calculated bearing capacity.

Where,
N: SPT (Standard Penetration Test) N value
The relationships between shaft friction and SPT
N value for clayey soil and sandy soil indicate in Fig.
10. The estimated values from Eqs. (1), (2), (4) and(5) are also plotted in Fig. 10. According to Fig. 10(a),the shaft friction which is obtained from the load testis larger than the estimated value from the design
specification. It is said that the developed soil-cement
composite pile has the enough shaft friction for theclayey soil. From Fig. 10(b), compared to the shaft
friction from the load test and that from the design
specification, the shaft friction of load test shows thelarge value. Therefore, it is also said that the shaft
friction of the developed soil-cement composite pile
has the enough shaft friction for the sandy soil.
The relationships between end bearing capacity
and SPT N value for sandy soil are plotted in Fig. 11.
The estimated values from Eqs. (3) and (6) are alsoplotted in Fig. 11. It is seen that the end bearing
capacity of Type 1 and Type 3 from the load test islarger than that of the estimated value. However, theend bearing capacity of Type 2 and Type 4 from theload tests is lower than that of estimated value for the
steel pipe soil-cement pile although the end bearing
capacity of load test shows the large value for the castin-situ pile. This means that the end bearing capacity
does not mobilize because the displacement at the pile
tip is too small.
The relationships between observed bearing
capacity and calculated bearing capacity are plottedin Fig. 12. Here, the calculated bearing capacity isevaluated by Eqs. (1)~(6). The trend indicates that thebearing capacity which was obtained from the load
test is larger than the calculated value.
CONCLUSIONSThe static compression load tests on the soilcement composite pile (i.e. H-section steel piles withsoil-cement ground improvement) that wereconstructed with the mechanical agitator ground
improvement method were carried out to evaluate thevertical bearing capacity in this study. Furthermore, it
144

GEOMATE – Bangkok, Thailand, 22-24 November 2022

was evaluated the performance of vertical bearing
capacity on the developed soil-cement composite pile
to compare the evaluation method as shown in thedesign specifications in Japan. The following findings
were obtained from this study.
1) According to the compression load test, boththe shaft friction and the end bearing capacity has theenough performance on the vertical bearing capacity.
2) It is concluded that the shaft friction of the load
test is larger than the estimated shaft friction which is
calculated by the design specification in Japan.
3) Compared to the end bearing capacity which is
obtained from the load test and that of estimated value
from the design specification in Japan, the end
bearing capacity of the load test shows the large value.
However, the partial results is lower than theestimated value. This means that the end bearing
capacity is gradually increasing because thedisplacement at the pile tip is too small.
There are some research issues to address in the
future. More vertical load tests to examine the verticalbearing behavior will be carried out. Moreover, it isneeded to confirm the performance of pile body suchas the bond strength and the effect of reinforcing piletip. We will tackle the research issues described as theabove sentence in the future.
REFERENCES[1] Kitade, K., Watanabe K., Yamamoto T. andKubo T., Verification of The Improvement
Effect of The Ground Improvement Structure
Constructed Using a Low-overhead Clearance
Ground Improvement Method. Proceedings of
the 67th Annual Conference of the Japan Society
for Civil Engineers, 2012, pp.799-800.
[2] Japanese Geotechnical Society, Standards of theJapanese Geotechnical Society (Method for
Vertical Load Test of Piles), Japanese
Geotechnical Society, 2001.
[3] Railway Technical Research Institute., DesignStandard for Railway Structures andCommentary (Foundation Structures), Railway
Technical Research Institute, 2012.

145

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

REVIEW ON CONCEPTS, RESEARCH, AND CHALLENGES FOR THE
ADOPTION OF PERMEABLE PAVEMENT SYSTEM IN VIETNAM
1

Van Nam Pham1, Hoang Giang Nguyen2, Tien Dung Nguyen2, and Ken Kawamoto1,2
Graduate School of Science and Engineering, Saitama University, Japan, 2 Hanoi University of Civil
Engineering, Vietnam
ABSTRACT

To make flood control and to mitigate urban heat islands, numerous studies of permeable pavement systems (PPS)
have been carried out, especially in the fields of road, material, geotechnical, and environmental engineering. In
developed countries, PPS has been mostly applied to roadways with light traffic load, parking lots, and sidewalks.
Additionally, several PPS have been used with heavy loads. Previous studies have also repeated that PPS
contributed to improving stormwater quality and driving safety, and reducing stormwater runoff and volume.
Despite its popularity, the application of PPS is currently not receiving mainstream use throughout much ofdeveloping countries such as Vietnam. This study, therefore, investigated structural design, performance, waterdrainage control, operation and maintenance, case studies, and challenges necessary to examine suitable andapplicable PPS in Vietnam based on the literature survey of previously published research papers and reports, fully
considering technical standards/requirements and case studies in developed countries. This literature survey foundthat some of the most important constraints and corresponding challenges/requirements include site-specific,
technical/engineered, sustainability, economic, environmentally friendly, existing associated facilities, current
policies, and social when applying PPS in Vietnam.
Keywords: Permeable Pavement System (PPS), Flood control, Monitoring, Barriers, Challenges.
publications by US and Japan Road Association,
Google Scholar, and Web of ScienceTM with several
keywords including permeable pavement system(PPS), permeable asphalt pavement (PAP), pervious
concrete pavement (PCP), permeable interlocking
concrete pavement (PICP), and grid pavement (GP)with combination of design, hydrologic, hydraulic,water quality performance, and cost. Based on thisresearch process, the previously published research
papers, reports, technical standards, and case studies
in developed countries were selected for review. The
present review comprises a summary graphicalrepresentation of results, and a table listing barriersand challenges/requirements.

INTRODUCTION
In developing countries, climate change and urban
concretization contribute to the increased risk ofurban flooding [1]. Traditional systems capture thestorm runoff, and subsequently delivered it to nearby
receiving rivers or sewage systems, however, they
have become inefficient and expensive [2]. In that
context, PPS which allows the rainwater to infiltrate
in to the permeable surface and base layers , as wellas the soil subgrade, have been deployed in many
developed countries [3]. Previous studies haveconstantly demonstrated the beneficial performanceof PPS in reducing the surface runoff rate and volume,
improving groundwater quality, and providingsmooth driving conditions [4-5]. Despite its
widespread use in developed countries, it has notreceived adequate attention in developing countries.
Many studies have focused on examining thetechnical design and engineering performances,
however, have not yet shown the barriers andchallenges that need to be overcome for PPS adoption
in developing countries such as Vietnam. This study,
therefore, examined existing research in developed
countries with a particular focus on design concepts,
materials, performances, and economic aspects, andto potentially assess barriers and challenges to PPS
adoption considering various specific factors in
Vietnam.

EXISTING PPS IN DEVELOPED COUNTRIESPermeable pavement system
In addition to the benefits of stormwater
management, PPS also provides pavement forpedestrians and vehicles[5]. PPS are often suitable forlight duty such as parking lots and sidewalks [6-8],
but case studies also have confirmed the applicableuse of PAP and PCP for roadways [9, 10].
Design concepts
The typical structures of PPS were summarized inFig. 1. PPS systems can be designed and constructedto accommodate three drainage conditions including
infiltration, partial infiltration, and no infiltration
pavement [11, 12] and one PPS with a water storage

METHODOLOGYThis review work searched database includes

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

tank [2] as shown in Fig.1. Depending on the site
conditions, and target concepts and setting of PPS
such as stormwater control, pollution control,
groundwater recharge, and UHI mitigation theirdesigns vary greatly. For example, when exfiltration
is not desired or when there is any concern about thepossible migration of pollutants into groundwater (i.e.,
pollution control) the PPS with an impermeable liner

should be constructed (Fig.1c and d).
When targeting the UHI mitigation, PICP and PCP
might be considered due to their lower surface
temperature in the same weather condition instead of
PAP [13-14]. The reservoir thickness is mainly
determined by hydrological design (i.e., rainwater
control). The design process of PPS has been well
documented by several guidelines [2, 5].

Fig. 1 Typical structure of PPS [2, 11]. (a) Full infiltration, (b) Partial infiltration, (c) No infiltration, (d) No
infiltration with a water storage facility.
20 % [9-10] while the removal rate of nutrients (i.e.,
nitrogen species) is somehow less effective (negative
Materials
and positive removal efficiency). Collins et al [6]Generally, materials used for roadbed and surface
examined the transformation and fate of N throughlayers must have appropriate engineering properties
PPS. He concluded that PPS provides suitableand environmental safety [2]. Granular roadbed
conditions for nitrification NH4+ to NO3- based on theapplies to the roadbed structure of roadway, parking
observation that PPS exfiltration had consistently
lot, and sidewalk, while stabilized materials such as
lower TKN and NH4+ concentrations and consistently
asphalt and cement-based can be considered to use forhigher NO3- concentrations than asphalt runoff.
the upper roadbed of the roadway due to their highCollins et al [6] and Yong et al [19] observed that TN
strength [2]. Using recycled materials and industrial
concentrations can be higher in PPS effluent than in
by-products has received much attention recently [15].
asphalt runoff and atmospheric deposition. The pH
Pham et al. [16] used recycled concrete (RC) andfrom exfiltration of PPS has been demonstrated to be
autoclaved aerated concrete (AAC) which is an
basic ranging from 7.7 to 10 [6, 8], while that ofindustrial by-product for roadbed materials. The
rainfall and asphalt runoff tend to be acidic ranging
results showed a significant improvement in waterfrom 6.5 to 7.6 [20-21]. Limited studies examined theretention capacity was found by blending AAC grains
potential for decreasing pollutant removal with age
and the saturated volumetric water contents for 20
after new construction.
and 40% AAC blended RC samples increased
approximately 1.8 to 2.2 times compared to that of
RC 100% while still satisfying the minimum bearing
capacity for road base material.
Performances
Water quality
PPS reduces the total pollutant mass discharged toreceiving systems by diminishing runoff and outflow
volumes and removing pollutants from stormwater[17]. Pollutants of interest include suspended solids,
metals, nutrients, and hydrocarbons [18]. The
pollution removal efficiency of PPS was showed in
Fig.2. In general, PPS contributes to reducing
pollutants concentration, but the removal rate varies
greatly, even with the same type of pavement. Thismight be attributed to not only the pavement structure
itself but also local sources of pollution [18]. It also
can be seen that PPS has been effective to remove
heavy metals such as Pb, Zn, and, and TSS by at least

PAP
PICP
GP
Ref. [33] [10] [6] [17] [34] [9] [8]NH4-N: Ammonia nitrogen, NO3-N: Nitrate as nitrogen, TKN:
Total Kjeldahl nitrogen TPH: Total petroleum hydrocarbons,
TSS: total suspended solids, TN: total nitrogen, and COD:
Chemical oxygen demand.

Fig.2 Pollution removal efficiency of PPS.

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Hydrologic and hydraulic performance
In-situ infiltration
rate (cm/hr)

10000

Climatic conditions (i.e, cumulative rainfall) is
one of the factors that affect hydrologic performance[22]. Figure 3 showed the relationship between runoffand cumulative rainfall at an event. Generally, PPS is
more effective in reducing runoff rate (ranging
between no runoff and 4/3:1 line) than asphalt
conventional pavement (ranging between no runoff
and 4/3:1 and 4:1line). Most technical standards
indicated that PPS is restricted in places with highwater tables or low soil permeability [4-5]. Few
studies, however, have investigated the hydrologic
performance under these conditions.
One of the concerns of PPS is clogging and lossof hydraulic function over time. Fig.4 describes thein-situ infiltration rate as a function of the age ofpavement. The age, in this study, is the year of PPS
since new construction at the time of the in-situ
surface infiltration test. It can be seen that the
infiltration rate decreases rapidly over time although
the minimum permeability requirement is stillsatisfied many years after new construction. However,
without regular maintenance, a complete loss ofhydraulic capacity can occur rapidly. For example,
Boogaard et al. [23] examined the effect of the age of
55 PPS which was installed in the Netherlands andAustralia with the age ranging from 1 to 12 years on
their infiltration function. There is some PPS loss
their hydraulic function only after an age of 1 to 4
years. Measuring in-situ infiltration rate is the only
common method used to access clogging. Therefore,
developing a suitable monitoring system is essential
to access long-term hydraulic performance, but notmuch information in the literature discusses thissystem.

1000

POTENTIAL
VIETNAM

100

120

Runoff (mm)

ADOPTION

OF

PPS

IN

Experience from developed countries shows that
PPS still has limitations such as the applicability of
PPS under unfavorable conditions (i.e., poorly
draining soil and high water table), the potential risksof contaminating groundwater, and loss of long-term
hydraulic function due to clogging. A suitable
monitoring system for clogging assessment has not
yet been established. Besides, the construction of PPS
often requires highly qualified human resources [5].
The cost uncertainty remains a concern. Althoughdesigned to have a lifespan as conventional pavement,
PPS is generally expected to be shorter [24].
All these limitations will become barriers to theadoption of PPS in Vietnam. Even in developed
countries, although widely acknowledged, the above
limitations still exist somewhere. For example, Coteet al [25] assessed the social and economic barriers to
use in residential driveways in Kitchener, Canada,and showed that social perception, price, and

0

80

PICP

Barriers and challenges/requirements for the
adoption of PPS in Vietnam

20
60

PCP

The economic efficiency of PPS remains achallenge. There are limited reports on initialconstruction costs. A report by ASCE (2015) [11]collects the estimated costs for conventional asphalt
pavement and PAP from projects in San Diego
County in 2007. The result showed that the square
food costs of PAP are almost 2 times higher than thatof conventional asphalt pavements, mainly due to thehigher cost of excavation and installation of thesubbase of PPS. The PPS is expected to have a lower
overall price based on the hypothesis of reducing theneed for stormwater infrastructure, and surface water
treatment [4]. However, there are no reports to assess
the efficiency of these costs.

40

40

4
8
12 16 20 24 28
Pavement age at testing (years)

Cost

60

20

1

Fig.4 In-situ infiltration rate as a function of age of
PPS [23, 39].

PPS range

0

10

PAP

Conventional asphalt range

80

Minumum requirement
25cm/hr [5]

0

120

100

100

140

Cumulative rainfall at an event (mm)
PAP
PCP
PICP
GP
Conventional asphalt
Ref.
[35] [36] [37] [17] [38] [34]Fig.3 Runoff as a function of cumulative rainfall at an
event of PPS.

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

technological acceptance are barriers to overcome.
Monrose et al. [22] had an overview of PPS for
applying in Small Island Developing States and also
pointed out limitations of PPS, aggregate choice andavailability, and support from policymakers asimportant parameters when considering PPS adoption.
All the above experiences on the barriers,
requirements/challenges that need to be overcome to
be able to apply PPS in Vietnam have been carefully
reviewed and summarized in Table 1. These barriers
includesite-specific,
technical/engineered,sustainability, economic, environmentally friendly,
existing associated facilities, current policies, andsocial. It is clear that, whether these barriers arenatural factors, such as site-specific (i.e, poorly
draining soil, high water table, and climate) occurring
unpredictably, or other factors that can be controlled
by individuals/communities, the geographic andgeologically, and social factors of Vietnam present
unique parameters for considering the PPS system.

annual rainfall of 1260, 1200, 834, and 538 mm,
respectively [30], while that of Vietnam the lowest of1600 mm in the summer, and the rainy season is up to
2050 mm. To reduce investment costs, using locallyavailable materials is proven to be economically
viable, especially recycled materials, and thesematerials are very abundant and safe in Vietnam [15].
There are concerns about groundwatercontamination and clogging, however many, casestudies still demonstrate perform well for many after
new construction. But, in the long term, it is necessary
to establish a suitable system for assessing clogging.
In Vietnam, the planning of traditional drainage
systems and infrastructure facilities is notsynchronized with the speed of urbanization and does
not take into account the effects of extreme weather
conditions ( i.e, climate change, sea level rise) leads
to many consequences [31], despite the promulgationof current policies on strengthening solutions to deal
with the problems above, taking advantage of natural
conditions [32]. For example, a decision
no.35/1999/QD-TTg dated 1999 on ratifying theorientation for the development of urban drainage in
Vietnam up to the year 2020 considered the urban
flood reduction as a top priority, and it is necessary tohave solutions to take advantage of natural factors
such as soil infiltration. PPS meets this requirement
and can be a premise to adopt PPS in Vietnam. But it
is necessary to make policymakers recognize thisbenefit.

Specific factors to be considered in Vietnam
Vietnam is incorporated by large fertile, low-lying,and densely populated deltas of the Mekong and the
Red Rivers [1]. Vietnam would be classified as aregion physically vulnerable to present day climate
extremes and potentially to changes in the typhoon
regime and sea-level rise [1, 26]. The annual average
rainfall is 2050 mm in the rainy season and the lowest
is 1600 mm in summer [27]. Many areas in the
Mekong delta are under sea level with a meanelevation of only 80 cm above sea level. According to
Asian Development Bank, the population isapproximately 85.8 million in 2009, with an urban
population of 25.4 million (nearly 30% of the total
population). The rate of urbanization has averaged
3.4% per annum since 2000 and is expected to remain
at the same level [28]. Most urban growth is in themetropolises of Hanoi and Ho Chi Minh city [28].
Regarding site-specific barriers, the high watertable is a factor that needs to be fully considered. As
discussed above, some areas of Vietnam are below
sea level, with the average elevation being just 80 cm
above sea level. Moreover, Vietnam is also strongly
influenced by high tides. One of the presented
requirements is the design of the PPS as a detention
system [29]. Although hydrologic has performed wellin developed countries, however, the climate factor isdifferent. Vietnam tends to suffer more annual
rainfall than the reviewed areas. For example, New
Jersey (USA), New Hampshire (USA), Toronto(Canada), and South Australia have an average

CONCLUSIONSPPS are suitable for a wide variety of parking lot,sidewalks, and roadways. They have exhibited
beneficial performance in reducing the surface runoff
rate (i.e., from 25-100% with the cumulative rainfall
less than about 120 mm), improving groundwater
quality (e.g., at least of 20% removal efficiency for
Pb, Zn, Cu, and TSS). Despite its popularity, there arestill concerns regarding environmental, andhydrologic performances in the long-term and underadverse conditions, and cost uncertainty. The socialand policy factors have not been discussed in
guidelines and case studies in developed countries. A
suitable monitoring system for accessing clogging
has not been established. The above concerns arebarriers to PPS adoption in developing countries. To
adopt PPP in Vietnam, all must be carefully
considered in the conditions of Vietnam. This needs
the cooperation of all individuals, scientists, thewhole community, and policymakers.
149

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 1. Barriers and challenges/requirements for the adoption of PPS in Vietnam.
No Item
1 Site specific

2

3
4
5
7
8

9

Barriers
Challenges/requirements
Refs
Poorly draining soil Design a pavement as a detention system[26, 29,and high water table
40]
ClimateEstablish guidance that fully considers the suitable intensity -duration[2]– frequency curve
Aggregate selection Practice sustainable methods to obtain construction aggregates. Local [22]and availability
construction materials are easily available
Technical/
Groundwater
Treated stormwater should subsequently be discharged into a suitable [41]Engineered
contamination
drainage system.
Clogging/
Establish an applicable and suitable monitoring system to assess [42]maintenanceclogging and sign of distress
Ease of construction, Proposed a system is simple to construct and maintain with locally [11]maintenanceavailable human resources and expertise
Sustainable
Durable and longPerform lifespan as traditional pavement and have a capacity to absorb [11]term performancedissolved pollutant in a manner that outlasts the useful life of PPS
EconomicalLess investment
Reduce initial cost by use of available construction materials[22]
Environmentally Use of safe PPS
PPS and its construction materials used do not harm the environment[2]
friendly
AssociatedAsynchronous
Demand should be transferred to the natural renewed solution to cope [31, 43]
facilities
with impervious surface expansion and overload drainage facility
Policy and law Policy on flood
Promote the implementation of current policies on strengthening flood [32]control
prevention and water pollution by taking advantage of available natural
conditions such as the infiltration capacity of soil
SocialAwareness
Policymakers easily recognize PPS practice as being beneficial[25][7] M. Legret and V. Colandini, “Effects of a porous
pavement with reservoir structure on runoff water:
Water quality and fate of heavy metals,” Water Sci.
This research was supported by JST-JICA
Technol., vol. 39, no. 2, pp. 111–117, 1999.
Science and Technology Research Partnership for[8]
T. and R. Conservation, “Performance Evaluation of
Sustainable Development (SATREPS) project (No.
Permeable Pavement and a Bioretention Swale Seneca
JPMJSA1701).
College, King City, Ontario,” 2007.
[9] J. K. Gilbert and J. C. Clausen, “Stormwater runoff
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12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

INFLUENCE OF FOOTING SIZE AND RELATIVE DENSITY IN
ULTIMATE BEARING CAPACITY FORMULA OF STRIP FOOTINGON SANDY SOILS
Tahir Iqbal1, Satoru Ohtsuka2, Koichi Isobe3, Yutaka Fukumoto 4, Kazuhiro Kaneda51

Department of Energy and Environment Engineering, Nagaoka University of Technology, Japan
Department of Civil and Environmental Engineering, Nagaoka University of Technology, Japan
3
Division of Field Engineering for Environment, Hokkaido University, Japan
5
Department of Civil and Environmental Engineering, Chiba Institute of Technology, Japan

2,4

ABSTRACT
For the estimation of the ultimate bearing capacity (UBC) of footings, most contemporary formulas employ a
linear yield function in the shear stress-normal stress space. However, it is well known that the general property inthe failure envelopes of sandy soils manifests the non-linear effect of the stress level on the peak friction angle.
Moreover, the effect of relative density on bearing capacity is often not well considered by the conventional UBC
formulas. The focus of this research study is the assessment of the UBC of surface strip footings ascribed to theeffect of confining stress level and relative density (Dr) on the shear strength of sandy soils. The rigid plastic finite
element method (RPFEM), using the confining stress dependence property of Toyoura sand, is utilized in nonlinear finite element analyses. The ground failure domains in the case of the non-linear shear strength model aregleaned smaller than those in the case of the linear shear strength one. The results of the UBC analyses areascertained to be consistent with those of the centrifuge experiments in the published references. The analysis
results are compared with prevailing guidelines, for instance, the Architectural Institute of Japan (AIJ) and theJapan Road Association (JRA), which are developed for the mean shear strength property of sandy soils. The
precision of the UBC formula is examined by considering the effect of relative density. The applicability of the
UBC formula to effective stress analysis is also discussed, and the modified formula is developed.
Keywords: Ultimate bearing capacity, Strip footing, Size effect with footing width, Relative density, Sandy soils

INTRODUCTION

effect is estimated in terms of footing size only; forthe centric vertical load without surcharge.
Nonetheless, prominent experimental studies, forinstance, Zhu [3], have highlighted the significance ofsoil unit weight () in determining the stress level
effect on UBC in terms of product B. Similarly, thesize effect mechanism has also been attributed to theeffect of relative density in the eminent experimental
studies [4]–[6]. Therefore, this study is focused on theestimation of the size effect of footing on UBC undercentric vertical load in the case of total and effective
stress analysis conditions. RPFEM, using non-linear
shear strength characteristics of Toyoura sand, isutilized in finite element analysis. A modified UBC
formula is put forward, rendering consistency withthe centrifuge experiments in the publishedreferences. Moreover, the efficacy of the proposed
formula is examined through UBC analysis in thecase of various relative densities of Toyoura sand andother granular soils existing in the European region.

Most of the formerly developed well known
bearing capacity formulas, for instance, Terzaghi, do
not appraise the effect of footing size and relative
density on the shear strength property of sandy soils.
On the other hand, numerous experimental studies
have now ratified that the typical yielding
phenomenon of sandy soils is non-linear in shear
stress-normal stress space. The UBC formulas being
commonly used in Japan are those recommended by
AIJ [1] and JRA [2], which consider the same effectof footing size on UBC in the absence of surchargeload. The simplified AIJ bearing capacity formula isindicated in Eq. (1) in the case of surface strip footing
under centric vertical load only.
−1

3
1
q =  B N where  =  B  , Bo = 1 m
2
 Bo 

(1)

Here, N depicts the well known bearing capacity
factor,  denotes the soil unit weight, B and Bo accountfor the footing size and reference value in it,
respectively, and  symbolizes the size effectmodification factor for N.
In both AIJ and JRA formulas, the stress level

CONSTITUTIVE EQUATION FOR RPFEM
The typical non-linear yield function employed
for numerical analysis in this research investigation isindicated in Eq. (2).

152

GEOMATE – Bangkok, Thailand, 22-24 November 2022

f ( ) = aI1 + ( J 2 )n = b(2)Here, a and b express the material shear strengthcharacteristics, i.e., internal friction and cohesion,respectively, while n denotes the non-linearity of theyield function in relation to the first stress invariant,I1, and the second invariant of deviator stress, J2. The
non-linear rigid plastic constitutive equation, as
proposed by Nguyen [7], is used for numericalcomputations.
NON-LINEAR SHEAR STRENGTH OF SANDYSOILSIn this study, Toyoura sand is used to model thenon-linear shear strength property of sandy soils. The
physical characteristics of Toyoura sand are listed inTable 1 after Tatsuoka [8].
Table 1 Physical characteristics of Toyoura sandMinerology

Value

Fig. 1

2.64
0.977
0.605
0.16 mm

Strength test results of air-pluviated Toyoura
sand[9]Dr=88

Dr=47

Dr=74

Dr=30

Dr=65

Dr=20

50

Internal friction angle  [°]Property
Specific gravity ofsolids, Gs
Max. void ratio, emax
Min. void ratio, emin
Mean grain size, d50
Uniformity coefficient,
Cu
Grain shape45

Dr=58

1.46

40

angular to sub-angularquartz (90%), chert
(4%)

35

Figure 1 manifests the influence of confiningpressure on the shear strength of Toyoura sand basedon the experimental study of Tatsuoka [9]. It
demonstrates that the internal friction angle, ,decreases with the increase in confining pressure fora given void ratio, e. In this study, triaxial
compression test results are used to arrange thecorrelation between  and I1 (Fig. 2) with the aim ofestimating the influence of pressure level on . Figure
2 specifies the reduction in internal friction angle with
the increase in the first stress invariant. Moreover, theexperimental data is extrapolated to estimate the yintercept, i.e., internal friction angle, 0, against eachrelative density, Dr, as indicated through dashed lines
in Fig. 2, to normalize the internal friction angle
corresponding to the various stress levels. By using
the least squares method, normalized relationships are
established between /0 and I1 corresponding to eachrelative density as well as the mean trendline (Fig. 3).
Due to the similarity in soil response having different
Dr under various stress levels, it is meaningful toestablish a simplified relation based on the mean
property of Toyoura sand.

30
-3.0

-2.5 -2.0 -1.5 -1.0 -0.5
First stress invariant I1 [MPa]Fig. 2

Relation between  and I1 for Toyoura sand(after Tatsuoka[9])Dr=47%
Dr=30%
Dr=20%
Mean trendline

1.00

0.96

/0

Dr=88
Dr=74%
Dr=65%
Dr=58%

0.0

0.92

0.88

153

0.84
0.0

-2.5

-2.0
-1.5
-1.0
-0.5
First stress invariant I1[MPa]Fig. 3

Normalized relation between /0 and I1

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Based on the relationships plotted in Fig. 3, thenon-linear shear strength parameters are set
corresponding to each relative density and the mean
trendline in Tables 2 and 3, respectively, in the caseof Toyoura sand.

UBC [MPa]20

Table 2 Shear strength parameters for Toyoura sand
Dr
(%)
20
65
88

0
(°)
35.7
41.8
46.8


(kN/m3)
13.6
15.0
15.7

15
10

a

b

n

5

0.217
0.262
0.280

1

0.522
0.526
0.528

0
1

Table 3 Parameters based on the mean property

(°)
30
35
40

a

b

n

0.175
0.216
0.257

1

0.526

RPFEM[DP] - =30°
Terzaghi - =30°
AIJ, JRA - =30°
RPFEM[DP] - =40°
Terzaghi - =40°
AIJ, JRA - =40°

25

5
10
Footing size B [m]2030Comparison of UBC results (=18 kN/m3)

Fig. 4

Bearing Capacity Analysis Using Non-Linear
Shear Strength Property
In this section, UBC analysis is conducted using
the non-linear shear strength parameters set in Table
3. The UBC results through the non-linear (NL)
RPFEM are arranged in Fig. 5 for the given range offooting sizes and shear strength parameters. Figure 5
illustrates that the UBC analysis based on the meanconfining stress property of Toyoura sand wellestimates the size effect. Experimental studies [3]have indicated the stress level effect as a function of
B. Moreover, often practical situations involving
frequent variations in the ground water table comeacross, ultimately affecting the soil strengthcharacteristics. Therefore, it is necessary to broadly
investigate the effect of  on UBC. The typical groundfailure domain in the case of NL analysis is smaller
than that of the DP criterion (Fig. 6) due to the
reduction in  at higher stress levels. Moreover, thefailure mode at =8 kN/m3 is obtained larger than at
=18 kN/m3 in the case of NL shear strength, mainly
because of the difference in the mean effective stressin both cases, in contrast with the DP criterion.

ULTIMATE BEARING CAPACITY ANALYSIS
Drucker-Prager Criteria and Conventional UBC
Estimation
This section analyzes the bearing capacity of strip
footing under plane strain condition by employing the
RPFEM Drucker-Prager (DP) using the shear
strength parameters identical to those gleaned fromconventional triaxial compression tests. The bearing
capacity is estimated for a wide range of footing sizes(1 m to 30 m) and soil shear strength parameters toproperly investigate the correctness of the applied
method. The footing is considered as a perfectly rigid
mass, while the boundary conditions are defined to belarge enough so as not to have any influence ofrigidity on the collapse mechanism. The analysis
results are plotted in comparison with the AIJ, JRA,and one of the conventional UBC formulas, i.e.,
Terzaghi [10], in the case of centric vertical load only
(Fig. 4). As the Drucker-Prager criterion is a
simplified Mohr-Coulomb criterion; thereby, theRPFEM(DP) results are in good agreement with the
Terzaghi [10]; however, there is a marked differencewith those from the AIJ and JRA formulas. Thisdistinction is primarily because both AIJ and JRA
formulas consider the size effect of footing in UBC
estimation in contrast with the Mohr-Coulomb yield
criterion. Numerous experimental studies have also
pointed out the stress level effect on UBC [11]–[14].
However, the correctness of the semi-experimental
AIJ and JRA formulas needs to be well investigatedbased on the shear strength property of real sandy
soils, such as Toyoura sand, thereby endorsing thenecessity of this research study.

9

RPFEM[NL] - =30°
AIJ, JRA - =30°
RPFEM[NL] - =35°
AIJ, JRA - =35°
RPFEM[NL] - =40°
AIJ, JRA - =40°

UBC [MPa]

8
6
4
2
0
1

Fig. 5154

510Footing size B [m]2030Comparison of UBC results (=18 kN/m3)

GEOMATE – Bangkok, Thailand, 22-24 November 2022

CL
B/2

Load

0.75

Color code for "B " in m

0.70

20
10
5
Symbol code:
=8 kN/m3
=12 kN/m3
=15 kN/m3
=18 kN/m3
Mean trendline-RPFEM
AIJ, JRA

0.65
25.0 m2q/BN

Failure domain of
linear shear strength

Failure domain of non-linear
shear strength

0.55
0.50
0.45

Magnitude of strain rate
0

0.40
0.35

50.0 mFig. 60.60

0.5

Ground deformation domains in the case of
=35°, B=10 m and variation in  (kN/m3)



(3)

3.0

3.5

4.0

Plot dataset:
B=1,5,10,20,30 [m]=8,12,15,18[kN/m3]
=30°
=35°
=40°
Mean trendline

0.8

In this study, the size effect modification
coefficient is defined as Eq. (3):

2q
 BN

2.0 2.5
B/pa

1.0

Modification Coefficient for N

 =

1.5

Effect of  on 2q/BN in case of =35°

Fig. 7ULTIMATE BEARING CAPACITY FORMULA
FOR SANDY SOILS

1.0

0.6

0.4

The analysis results are arranged in Fig. 7 in termsof normalization variable B/pa against 2q/BN in thecase of =35° for variation in  . Here, pa symbolizes
the normalization factor i.e., standard atmosphericpressure, pa=101.325 kPa. Figure 7 manifests thedecrease in normalized UBC with the higher stresslevels by increasing  with the mean property of
Toyoura sand, while the AIJ and JRA formulas do notconsider such an effect of . Likewise, previous
studies such as Loukidis [15] have also corroborated
that the bearing capacity factor N decreases with theincrease in . Considering the effect of soil unit
weight and the findings of experimental studies, it ispertinent to figure out the modification coefficient forN. The RPFEM analysis results are plotted in Fig. 8
for an extensive range of shear strength parameters,i.e., , footing sizes, and soil unit weights with themean property of Toyoura sand to perfectly grasp theinfluence of stress level and to propose a relationshipfor the modification coefficient  (Eq. (4)) by setting
the mean trendline using the least squares method.
Although, the range in B/pa encountered in practicelies between 0.1 and 0.6 [15]; however, in this study,
results are investigated for a comparatively wide
range to broadly fathom out the confining stressdependency of shear strength parameters. The
efficacy of the proposed UBC formula (Eq. (5)) is
assessed against RPFEM in Fig. 9 for the wide rangeof B and shear strength parameters. The newly
proposed formula estimates the UBC within 5%.

0.2
0.1

1

2

3

4

B/pa

5 5.5

Relation between  and B/pa for mean
property in case of Toyoura sandFig. 8−1

Proposed UBC formula [MPa]B 3
 = 0.55  pa 
1
q =  BN
2

Symbol code:

B=1m
B=5m
B=10m
B=20m
B=30m

6
4

0    1

(4)
(5)

9 Color code for "" in kN/m3
8 21 18 15 12 8

1:1 line
+5%
-5%

Note:
=30° - Hollow symbols
=35° - Partially filled symbols
=40° - Completely filled symbols

20
0

Fig. 9

155

where

2

4
6
RPFEM UBC [MPa]89Performance of the proposed UBC formula

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Proposed UBC formula [MPa]Effect of Relative Density on UBC
It is pertinent to estimate the UBC for the various
compactness levels of Toyoura sand andcorresponding , i.e.,  changes with Dr to check theaptness of the proposed UBC formula ascribed to thesize effect. The UBC analysis is conducted based on
the non-linear shear strength parameters set in Table
2, and results are arranged in terms of B/pa against
 for various relative densities and the mean
trendline in Fig. 10. Interestingly, the change in with the mean trendline corresponds to the Dr=65%
in the case of Toyoura sand. Moreover, it can benoticed that the size effect becomes less marked witha decrease in the relative density of Toyoura sand andvice versa. This observation is consistent with theexperimental studies [4]–[6]. The proposed formulaestimates the size effect within 20% for the broad
variation in relative density of Toyoura sand (Fig.
11). Moreover, it also estimates the size effect within
10% in the case of other granular soils [5] having
similar mineralogy as that of Toyoura sand (Fig. 12).
1.0

Dr=88
Dr=74%
Dr=65%
Dr=58%

0.9
0.8



0.3
0.2
4

5

Fig. 10 Relationship between  and B/pa in thecase of Toyoura sand+20%
88 74 65 58 47 30 20 1:1 line

12
10

-20%

8

Symbol code:
B=1m
B=2m
B=3m
B=5m
B=10m
B=20m
B=30m

6
4
2
0
0

2

4
6
8
10
RPFEM UBC [MPa]Symbol code:
B=1m
B=2m
B=3m
B=5m
B=10m
B=20m
B=30m

6
4
2
2

4
6
8
10 12
RPFEM UBC [MPa]14 15

400 Color code for "D " in %r

Color code for "Dr" in %Normalized UBC 2q/B

Proposed UBC formula [MPa]14

1:1 line

8

The estimated bearing capacity based on the
RPFEM analysis is compared with the centrifuge
model loading tests on surface strip footings by
Okahara [16] in Fig. 13 for three different relative
densities of 58%, 74%, and 88%. In Fig. 13, RPFEM
analysis results indicate fairly good agreement withthe experimental findings and a similar effect offooting size can be witnessed. Moreover, bearing
capacity results through the proposed UBC formula
are also compared with the centrifuge tests by Zhu [3]in the case of silica sand of Dr=90% and otherconventional UBC formulas (Fig. 14). The unit
weight of the Silica sand used for the centrifuge
experimentation was 15.4 kN/m3 and the confining
stress in the triaxial compression tests ranged from 25
to 2500 kPa. As Toyoura sand is also a kind of Silica
sand; therefore, a good agreement with the proposed
UBC formula is evident from Fig. 14. On the other
hand, formulas by Eurocode and the US Army Corpsof Engineers (USACE) significantly overestimate the
UBC.

Dr=47%
Dr=30%
Dr=20%
Mean trendline

3

-10%

Experimental Validation

0.4

γB/pa

10

Fig. 12 Performance of the proposed UBC formulain the case of various sands

0.5

2

12

0

0.6

1

Toyoura sand, Dr=65%
Eastern Scheldt sand, Dr=55% +10%
Degebo sand, Dr=55%
Darmstadt sand, Dr=85%

0

0.7

0

15
14

12

88

74

58

100

Plots: Centrifuge tests - Okahara [16]Lines: RPFEM[NL]

20
0.1

14

Fig. 11 Performance of the proposed UBC formulafor various relative densities of Toyoura sand

0.2

0.3
γB/pa

0.4 0.5 0.6 0.7

Fig. 13 Comparison of the analysis results with thecentrifuge experiments in the case of Toyoura sand
156

B
toyoura ub
schelt ub
dege ub
darm ub

GEOMATE – Bangkok, Thailand, 22-24 November 2022

15
12

UBC [MPa][3] Zhu B. F., Clark J. I., and Phillips R., Scale Effectof Strip and Circular Footings Resting on Dense
Sand, J. Geotech. Geoenviron. Eng., Vol. 127,
Issue 7, 2001, pp.613-621.
[4] Kimura T., Kusakabe O., andSaitoh K.,
Geotechnical Model Tests of Bearing Capacity
Problems in a Centrifuge, Géotechnique, Vol. 35,
Issue 1, 1985, pp.33-45.
[5] Hettler A., and Gudehus G., Influence of theFoundation Width on the Bearing Capacity
Factor, Soils Found., Vol. 28, Issue 4, 1988,
pp.81-92.
[6] Cerato A. B., and Lutenegger A. J., Scale Effectsof Shallow Foundation Bearing Capacity on
Granular Material, J. Geotech. Geoenviron. Eng.,
Vol. 133, Issue 10, 2007, pp.1192-1202.
[7] Nguyen D. L., Ohtsuka S., Hoshina T., and IsobeK., Discussion on Size Effect of Footing in
Ultimate Bearing Capacity Using Rigid Plastic
Finite Element Method, Soils Found., Vol. 56,
Issue 1, 2016, pp.93-103.
[8] Tatsuoka F., Goto S., and Sakamoto M., Effectsof Some Factors on Strength and Deformation
Characteristics of Sand at Low Pressures, Soils
Found., Vol. 26, Issue 1, 1986a, pp.105-114.
[9] Tatsuoka F., Sakamoto M., Kawamura T., andFukushima S., Strength and Deformation
Characteristics of Sand in Plane Strain
Compression at Extremely Low Pressures, Soils
Found., Vol. 26, Issue 1, 1986b, pp.65-84.
[10] Terzaghi K., Theoretical Soil Mechanics, John
Wiley and Sons Ltd., 1943, pp. 510.
[11] De Beer E. E., Bearing capacity and settlementof shallow foundations on sand, Proc. Symposium
on Bearing Capacity and Settlement ofFoundations Duke Univ., 1965.
[12] Shiraishi S., Variation in Ultimate Bearing
Capacity Factors of Dense Sand Assessed byModel Loading Tests, Soils Found., Vol. 30, Issue
1, 1990, pp.17-26.
[13] Ueno K., Miura K., and Maeda Y., Prediction of
Ultimate Bearing Capacity of Surface Footings
with Regard to Size Effects, Soils Found., Vol. 38,
Issue 3, 1998, pp.165-178.
[14] Kumar J., and Khatri V. N., Effect of Footing
Width on N, Can. Geotech. J., Vol. 45, Issue 12,2008, pp.1673-1684.
[15] Loukidis D., and Salgado R., Effect of Relative
Density and Stress Level on the Bearing Capacityof Footings on Sand, Géotechnique, Vol. 61, Issue
2, 2011, pp.107-119.
[16] Okahara M., Takagi S., Obata H., Mori K., andTatsuta M., Centrifuge Tests on Scale Effect ofBearing Capacity, 42nd Japan Annual Conf. of
Civil Engineers, Vol. 3, 1988, pp. 250-251 (inJapanese).

Eurocode
USACE
AIJ, JRACentrifuge tests - Zhu [3]Proposed UBC formula - Eq.(5)

9
6
3
2
1

2

3
4
Footing size B [m]5Fig. 14 Experimental validation of proposed UBC
formula in the case of =46.7° and =15.4 kN/m3
CONCLUSIONSA UBC formula was examined in detail andmodified based on the mechanical property of
Toyoura sand. The applicability of the analysis
technique for UBC estimations was corroborated
through the centrifuge experiments in the publishedreferences. Based on the numerical survey, the effectof soil unit weight was clarified to be well expressedby normalized variable B/pa regarding size effect;
therefore, the correction factor for N was newly
proposed. Moreover, the applicability of internationalguidelines such as Eurocode and USACE is found tobe limited, ascribed to the size effect of footing on
UBC. The proposed formula estimates the size effecton UBC within 20% in the case of the wide variationin the relative density of Toyoura sand. Moreover, theperformance of the proposed UBC formula was also
assessed by analyzing the bearing capacity of othergranular soils such as Eastern Scheldt sand, Degebo
sand, and Darmstadt sand; and found to estimate thesize effect within 10%.
ACKNOWLEDGEMENTS
This work was supported by JSPS KAKENHI
Grant Number JP18H01533. The authors deeply
acknowledge the financial support of Grant-in-Aid
for Scientific Research (B) in Japan.
REFERENCES[1] AIJ, Recommendations for Design of Building
Foundations, Architectural Institute of Japan,
1988, 2001, pp. 430.
[2] JRA, Road Bridge Specifications (Substructure),
Japan Road Association, 2017, pp. 205-206.

157

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

SUSTAINABLE SOLUTION TO EXPANSIVE SUBGRADE AND
EXISTING PAVEMENT USING GEOCONFINEMENT SYSTEM
Lokanantham Logitharan1 and Umar Ali2
1

Faculty of Health, Engineering and Sciences, University of Southern Queensland, Australia; 2 Departmentof Transport and Main Roads, Australia
ABSTRACTThe Darling Downs region, located over 100kms west of Brisbane, comprises predominately fertile black soil.
Black soil found across the region has characteristics which are extremely problematic to civil engineers and havecaused significant damage to the road infrastructure in the region. The black soil in the region has a high shrink/
swell characteristic which has caused substantial pavement damage through rutting, shoving, wash outs andsettlement issues. Many pavement rehabilitation techniques such as lime stabilisation and foamed bitumen havebeen used to minimise the damage caused by moisture infiltration, however, the issues remain. The Departmentof Transport and Main Roads developed 10 innovative rehabilitation methods as a trial using geocells andgeosynthetics along a 1km section of the main highway in the region. The Gore Highway had been identified as
the ideal location as previous rehabilitation methods achieved limited benefits in reducing pavement failures,
which posed safety concerns.
Keywords: Expansive soil, Geocells, Geocomposite and Geogrid

INTRODUCTIONBACKGROUND

The Darling Downs region consist of roads thatlink the national highways which predominately areused by the tourism and the transport industry fortransporting goods across the nation. The
characteristics of the soils pose a greater threat as theregion is also susceptible to large scale flooding
Most of the area in Toowoomba, soils are formed
from basalt rock and weathered rock. There are two
types of soils that can be found around the region
which are 'red soil and 'black soil'[11]. Most of theroads are built on the black soil, which is sensitive towater. Black soil comprises both alluvial and residualsoils, which formed across the low laying areas
around the region [3]. The black soils around theregion are highly fertile, comprising high waterabsorption, liquid limits, and plasticity. These
characteristics pose significant concerns forengineers as the black soils absorb significant watercontent, resulting in high expansive properties whilst
also shrinking significant when drying out.
This creates serious issues for engineers when
designing and building roads and major infrastructurein the region. The expansive soils around the Darling
Downs have caused significant damage to the road
networks mainly causing large cracking, shoving, andrutting along throughout roads across the region.
Over the years, various trials have been used by
the Department of Transport and Main Roads (TMR)
to counter the properties of the soils. Some of thesetechniques include lime stabilisation and foam
bitumen.

The Department of Transport and Main Roads(TMR) undertook an innovative rehabilitation trial
along the Gore Highway 28A. The trial comprised of
10 various trials over a 1km section of the Gore
highway. Each trial was undertaken over a 100m
section using different innovative techniques, most ofwhich had never been used in Queensland.
The 1km section chosen for the trial posed a safetyconcern for traffic because of the degradation of theexisting pavement. Significant rutting, shoving, washouts and cracking were observed over the selectedarea.
The project site was located 57 km west of
Toowoomba between the suburbs of Pampas to
Brookstead.
The section was previously stabilised using
foamed bitumen and lime stabilisation, however,
because of the significant rainfall and flooding events
the region had experienced, significant pavement
damage had occurred.
Expansive Soil Property
Black soil is a cohesive soil, which is sometimes
referred to as black cotton soil. The typicalcharacteristics of black soil include its colour, which
is typically black or dark grey [12]. The soil is usually
soft to very soft when wet and has high plasticity andliquid limits, which may cause significant swelling as
it has a high-water retention capability. Its ability toretain water results from the high clay content present
within the soil.
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

When drying the soil experiences significant
shrinkage, resulting in cracking as the watermolecules evaporate from the bonding matrix. Blacksoils have low bearing capacity and low shear
strength [7].
Black soils make up about one third of the area ofthe state of Queensland [13]. There are three types of
black soils found in the Darling Downs region, all ofwhich have similar qualities, however, were formed
geologically differently. Black soil from alluvial
origin, was derived from weathering produced fromthe Rolling Downs Group, which was transported
along with sediments from the Tertiary age [3].
Residual black soil was formed directly over theweathered sedimentary rock of the Rolling Downs
group during the Cretaceous age. The third black soil
type found in the region was formed on the Tertiary
basalt deposits [3]. The engineering properties ofthese soils are summarised below.

Geocells and Geosynthetics.
Geosynthetic reinforcement has been used for
subgrade improvement as well base reinforcement forover 40 years around the world [6]. However, inQueensland, geocells had not been used despite thevast areas of weak expansive soils.
Geocells and geosynthetics are used to achieve
five functions which assist with improving the
durability of a road. The five key functions they aredesigned for include providing reinforcement,
stiffness, improved drainage, filtration, andseparation [2].
Because of the above, geocells and geosynthetics
help with mitigating reflective cracking in asphalt,
separate, lateral restraints. Geosynthetics can be usedboth as a subbase treatment and a subgrade treatment.
Some other benefits of using geocells include
limiting the use of lime or foam bitumen and using
recycled material, thus significantly reducing the costof construction and maintenance and as well as
providing an environmentally friendly alternative
solution.
The diagram below shows the benefits of using
geogrids. Load distribution of the axle force and shear
stresses are distributed over a larger area therefore
resulting in reducing the impact on the pavement. The
geosynthetic absorb the forces and remain in tensilestress [2].

Table 1: Engineering properties for black soils [3]Property Alluvial
Residual
Residualon
on Basalt
Cretaceous
Liquid40 – 60
40 -7060 -90Limit
Plastic
25 – 35
25 - 35
40 – 60
Index
Linear
12 -1815 - 20
15 – 25
Shrinkage
USC
CL -CH
CL - CHCH
Geogrids and geosynthetics
Geogrids and geosynthetics have been used acrossthe world for many years with various engineering
applications. TMR were required to find alternative
solutions to the ever-growing problem of pavement
rehabilitation in the region. Various trials andstabilisation techniques, such as lime and foamed
bitumen, were used across the region with various
degrees of success.
TMR began investigating alternative solutions
and began trialing geocells and geosynthetics on asection of the Gore Highway. Using geo-cells on theGore Highway was a first for the state of Queensland,as these had only been trailed in other states inAustralia with different soil conditions.
The trial involved stabilising a 1km length of road
along the Gore Highway 28A, between Pampas andBrookstead approximately 57km westof
Toowoomba. The trial involved using 10 different
geogrids each over a 100m length.
The project, along with the procedures, wasdeveloped between consultation with technicalexperts and TMR.

Figure 1: Stress distribution with and without geogrid in stabilised roads [2].
Research was carried out at the University of
Kansas to investigate the vertical stresses and impactof permanent deformation because of using geocells
in recycled asphalt pavement (RAP) with weak
subgrade material under cyclic plate loading. The
experiment involved using test samples with andwithout geocells in a geotechnical test box applying
cyclic loading replicating a single wheel load force of
40kN. The subgrade material used in the experiment
had a CBR value of 2%, a plastic limit of 22% and aliquid limit of 30%. During the cyclic loading, thesurface deformation and vertical stresses and strains
were monitored, and the results indicated using
geocells improved the life of the pavement by a factor
of up to 19% compared with not using geocells. The
data also indicated using geocells improved theresilient deformation whilst reducing plasticity
properties [5].
Other research using geocells in unpaved roads
with fill material also indicated a reduction in
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

permanent deformation and an increase in stress
distribution angles when using geocells compared [6].
Research into the benefits of using geocells overplanar geosynthetics indicated using geocells wasmore effective in increasing the strength, bearing
capacity, and reducing settlements in various
engineering applications [8].

resistance to pavement damage and resistance topermanent dynamic traffic loads by absorbing anddistributing tensile stress. [4, 10].
In Trial 2, a HaTelit XP geosynthetic was used.
This product is made from polyvinyl alcohol (PVA),
which is known for having good resistance toreflection cracking, high resistance to pavement
damage and traffic loads and can be used on asphalt
surfaces [9].

Trial
The trial involved using different geocells andgeosynthetics in various ways to treat the subgrade
and base along the Gore Highway. The figure below
shows how each trial was undertaken.

Trial 3 and 4.
These trials were focused on treating the baselayers. The trials involved cleaning the existing
surface to remove dust and loose stones, allowing thetack coat to bind better with the surface. Once the tackcoat was placed, the geotextile was laid respectively
over the trial areas.
Geocomposite chip seal grids were used as they
are easy to use, durable, provide high resistance,
through flexibility and rupture resistance which helps
prevent crack propagation [4]. Once the geotextiles
were laid, hot bitumen crack treatment and HSS2 seal
were placed over a 10m width.

Figure 2: Trial techniques used on Gore Highway.
Trial 1 and 2
Trials 1 involved adding the polymeric-PET(HaTelit C) geosynthetic over the full 10m width. The
process involved brooming and cleaning the existing
pavement surface before adding a tack coat to assist
with binding the existing layer with the Hateltic C.
The Hatelitic C polymer was laid using a forklift witha modified frame to lay the product. Once the productwas laid over the subgrade, AC20 was laid over it.
Figure 4: Geotextile on tack coat

Figure 3: Trial 1 using Hatelitic C

Figure 5: HSS2 Seal

HaTelit C is made from reinforced meshcomprising of high-modulus polyester yarn. It is
made by combining ultra-light non-woven fabric withthe polyester yarn. Some advantages of using thisproduct include minimal loss of strength, high160

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Trial 5
The rest of the trials comprised subgrade
treatment trials. Trial 5 involved profiling/ milling out
approximately 400mm of unsuitable material. ClassA MRTS04 material was used to replace soft areas,
allowing for the Hilf test to achieve a minimum of
97% compaction. A basetrac duo geocomposite(Basetrac Duo PET 50 B15) was placed over the layer
to prevent penetration of moisture to the subgrade.
Basetrac Duo was another geocomposite used madefrom polypropylene, polyester, or polyvinyl alcohol.
The manufacturer states the product has a biaxial
tensile strength of up to 75kN/m whilst providing theability to interlock with granular material to prevent
fine soil particles from segregation [4].
The geocells Fortrac 80MDT was placed over the
geocomposite and stretched out and nailed to theedges of the existing pavement surrounding the trial
area. This allowed the geocells to remain in a state of
tension. Fortrac geogrids were also used as they areeasy to use because of the flexibility of the product.
This product also provides high tensile strength madefrom low-creep synthetic materials [4].

Trial 6This trial involved removing 250mm of existingmaterial before the Bastrac Duo PET 50 B15 layer
was placed over 5m X 100m area. Type 4.1 was thenlaid over the geocomposite before the Fortac 80 MDT
was laid. Type 2.1 material was laid and compacted
to 250mm before the primar10mm seal was placed.

Figure 8: Trial 6
Trial 7 and 8
These trials involved milling 150mm of theexisting surface and placing a geosynthetic material(HaTelit XP and C respectfully). A tack coat wasplaced over the geosynthetic before AC20 was placedand compacted.

Figure 6: Placing Geogrids on GeofabricType 4.1 material was used to fill in around thegeocells before being compacted using a smooth
drum, before being sealed off using spray seal
Figure 9: Trial 7 and 8
Trials 9 and 10
These trials involved milling out approximately
60mm of the existing pavement before being primed
and then the geocomposites were placed.
Trail 9 had a polymeric-PET HaTelit C
geosynthetic placed and trial 10 had a polymericPVA HaTelit XP placed. Once these were laid, A/C
20mm was placed and compacted.

Figure 7: Placing material and fill voids

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

To better understand the true impact on theperformance of these products, further analysis isrequired of the trialed section of the Gore Highway.
ACKNOWLEDGMENTS
Authors would like to acknowledge the work done
by Murray Peacock and Department of Transport andstaff involved in developing scope and assisting
during construction. The authors are also grateful to
the Department of Transport and Main Roads forproviding an opportunity to carry out the research andpresent the paper to the international forum.

Figure 3: Trial 9 & 10
LIMITATIONS:
Although research has been undertaken using
geocells and geosynthetics as reinforcements by
subgrades and bases, the research is limited tolaboratory testing trying to simulate real world
scenarios.
Due to time, weather and budget constraints, thetrials were limited to 100m length sections, which
may limit the true reflection of performance of eachgeocell and geosynthetic product. Prior pavement
rehabilitation techniques, such as subgrade treatment(lime and foamed bitumen) may also hinder the true
performance of the products. Likewise, any priorwater infiltration or subgrade damage may not havebeen identified at the time of construction and may
influence the performance of the products.

REFERENCES[1] Patil, D., 2022. Black Cotton Soil | Black Cotton
Soil Properties | Types of Foundation In Black
Cotton Soil | Black Cotton Soil Definition.
[online]Civiconcepts.
Availableat:<https://civiconcepts.com/blog/black-cottonsoil#Black_Cotton_Soil_Properties> [Accessed
11 June 2022].
[2] Geosynthetics Magazine. 2022. PART 2
Functions and applications of geosynthetics inroadways - Geosynthetics Magazine. [online]
Availableat:<https://geosyntheticsmagazine.com/2017/04/01/part-2-functions-and-applications-ofgeosynthetics-in-roadways/> [Accessed 11 June2022].
[3] Vanderstaay, L. and Reeves, I., 2000. Soils ofWestern Queensland. Technical Notes Department of Transport and Main Roads,(WQ32), pp.1-8.
[4] ITM design Werbeagentur, G., 2022. HaTelit.
[online]Huesker.com.au.
Availableat:<https://www.huesker.com.au/geosynthetics/pro
ducts/geogrids/hatelit/> [Accessed 10 June2022].
[5] Thakur, J., Han, J., Pokharel, S. and Parsons, R.,
2012. Performance of geocell-reinforced
recycled asphalt pavement (RAP) bases overweak
subgradeundercyclicplate
loading. Geotextiles and Geomembranes, 35,
pp.14-24.
[6] Thakur, J., Han, J., Pokharel, S. and Parsons, R.,
2012. Performance of geocell-reinforced
recycled asphalt pavement (RAP) bases overweak
subgradeundercyclicplate
loading. Geotextiles and Geomembranes, 35,
pp.14-24.
[7] Toll, D., 2009. Unsaturated soils. London:
Taylor & Francis.
[8] Tafreshi, S. and Dawson, A., 2010. Behaviour offootings on reinforced sand subjected to repeatedloading – Comparing use of 3D and planar

CONCLUSIONSThe trials undertaken along the Gore Highway
provided a much needed alterative to a problem thathas caused a significant impact to the road network in
the Southern Downs region. Finding a long-term
solution to countering the highly expansive soils in aregion suffering both from severe drought andlargescale flooding could have substantial economic
impacts for not only the region but also for the stateof Queensland.
Geocells and geosynthetics provide an alternativeengineering solution to a widespread problem in theSouthern Downs region.
Although limited research is available on theimpacts of these products on black soils, the researchsuggests these products could solve a problem thathas plagued the region.
Some of the expected benefits of using theseproducts include minimizing the ability for moisture
to penetrate through to the subgrade whilst also
improving and increase the strength of the pavement.
Geocells can transfer the shear stresses caused byaxle loading to a larger area of the base and subgrade,
therefore reducing the impact and stress on thoseareas. As a result, reducing the potential for damaging
the structural integrity of the pavement.
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

geotextile. Geotextiles and Geomembranes,
28(5), pp.430-450.
[9] Leite-Gembus, F., Hilpert, D. and Bakrac, M.,
2002.Theory and practical experiences inpavement
rehabilitationusingasphalt
reinforcement grids. Doctoral. Technological
Institute of Aeronautics, São José dos Campos,
Brazil.
[10] Kaya, Z., 2020. Asphalt Reinforcement with
HaTelit and Sami Grid Technology[11] Thompson C.H., and Beckmann G.G., 1959,
Soils and Land use in Toowoomba area, Darling

Downs, Queensland, Soil and Land Use Series
No.28, 1959.
[12] Sivapullaiah,P.V., Sridharan,A., and Bhaskar
Raju,K.V., 2000, Role of amount and type ofclay in the lime stabilisation of soils, proceedingsof the institution of civil engineers, ground
improvements[13] www.tmr.qld.gov.au

163

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

EXPERIMENTAL STUDY ON DATA TRANSMISSION USED FOR
CONSTRUCTION QUALITY CONFIRMATION OF
EMBEDDED PILE CONSTRUCTION METHOD
Yasuhide Mochida1, Tatsuki Maniwa1
1

Ritsumeikan University, Japan 2Graduate School of Science and Engineering, Ritsumeikan University, Japan

ABSTRACT
Maintaining the construction quality of foundation work in construction is an important construction management
act to prevent major defects that occur during building operation such as uneven settlement. In Japan, in 2016, a
defect in the foundation work of an embedded pile in a building caused the reinforced concrete condominium totilt, and it had to be rebuilt. In the embedded pile construction method using preboring, poor quality in construction
caused by insufficient bearing capacity at the tips of piles is a significant problem. To solve this problem, it isnecessary to ensure the quality immediately after construction in the soil, the interior of which is invisible. To doso, a method of measuring the electrical resistivity of the soil cement between the ready-made pile and the holewall in real time and monitoring the construction can be considered. An attempt was made in this study to clarify
the possibility of wireless data transmission in the soil of the measured resistivity value associated with pile
construction. The aim was to help promote quality confirmation immediately after construction in the soil at theactual construction site.
Keywords: Wireless communication, Data transmission, Pile construction method, Construction quality

1.INTRODUCTION
During the construction of a foundation, most of thework in the ground cannot be seen directly, whichmakes it difficult to control the construction quality.
To solve this problem, a method of measuring
management data (electric resistivity) immediately
after construction in the soil in real time andmonitoring the construction would be effective.
However, in current foundation work, the available
communication technology has not been established.
Wired communication is not suitable because of bad
weather, impacts, rotation, and connection work, andan important technical issue to be addressed isunderground wireless communication technology. In
this study, an experiment was conducted on thepossibility of wireless communication under thecondition that the cavity is filled with a medium(water, soil, cement, etc.) in which radio waves areeasily attenuated in the soil.

An attempt was made to clarify the possibility ofusing a coaxial leakage cable (hereinafter referred toas a “coaxial cable”) for wireless communication in
the final wireless communication. In a coaxial cable,
the entire cable becomes an antenna by leaking radio
waves to the outside through a slit made inside. As
shown in Fig. 1, a coaxial cable was placed along theshaft of the earth auger, and a simulated experiment
was attempted assuming wireless transmission withthe measurement electrode attached to the tips of thewings of the auger.

Excavation
auger

Monitoring the construction
Coaxial cable

2. ICT OUTLINE OF THIS RESEARCH

The root
hardeningliquidThe goal of this research is to enable the excavation
with an excavation auger, measure the electricalresistivity while injecting the root hardening liquid
and the fixing liquid around the pile, and monitor theconstruction. To implement partial wireless features
for radio wave transmission in the soil, an experiment
was conducted using water and soil cement as anattenuation medium.

Fig.1

Wirelesstransmission

The measurement
electrode,transmitter, etc.

Detailed drawing of wireless transmission

3. WIRELESS COMMUNICATIONEXPERIMENT USING WATER IN THE SOIL
AS AN ATTENUATION MEDIUM

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

3.1 OUTLINE OF THE EXPERIMENT

and the signal strength level. The radio field intensity
level changed from 9 to 8 at a depth of 340–350 cm,

To investigate the possibility of wirelesscommunication using water in the soil as anattenuation medium, the following three types ofexperiment were conducted to clarify the difference
between frequency in water and radio communication
distance, difference between radio output and radio
communication distance, and possibility of wirelesscommunication using the transmission cable (coaxial
cable).
Experiment 1: Communication limit between
transmitter GTR-920F and GSU-7000 (1 W)
Experiment 2: Radio field strength of transmitter
GSU-7000 output 1, 3, and 5 W
Experiment 3: Transmitter GSU-7000 (1 W) +coaxial cable communication limit
Fig. 2 shows the measurement electrode and theoutline of the experimental device on the transmitting
side, and Fig. 3 shows the outline of the experiment.
Assuming a drilling hole for pile foundation work, adevice was created that incorporates tools such as atransmitter, battery, well pack (electrical resistivity
measuring device), and electrodes. into one set in awaterproof case in a well (Agano City, Niigata
Prefecture, depth of approximately 90 ⅿ, diameter of
200 mm, PVC pipe protection) for observation. An
experiment was conducted by submerging it in thedepth direction and receiving it on the ground tounderstand the wireless communication properties
using water as the attenuation medium. The
equipment used was a 920-MHz band, 20-mW
specified low-power radio station GTR-920F (using
an ancillary antenna), a UHF band (351-MHz band)
digital simple radio station, a data communication
dedicated module (GSU-7000; antenna AZ350S),
which is common; a waterproof case with a diameterof 140 mm and height of 700 mm, a battery (GBL1275; lithium ferrite), a coaxial cable (5D-2V), an
electrical logging device (Well Pack10), and anelectrical resistivity electrode (four-pole method).

Transceiver
2 types(Figure is GSU-7000)
Well
PACK10

12V
Battery

Transceiver
Antenna

Supported by a Cremonarope with a diameter of
9 mm
Fixed with ropes on both the
top and bottom to preventthe top lid from falling off

Fill the gap with cushioning
material such as waste clothor sponge.

The top lid is sealed andfixed with waterproof tapefor inserting equipment.
The electrodes of theelectrical logging are fixed
to the side of the case withvinyl tape so that they are
not tied.

Buoyancy adjustment
with weight
Volume inside the pipe
= approx. 8,600 ㎤Gross equipment weight
Buoyancy adjustment weightFig. 2Outline of the experimental device

and it changed from level 3 to 2 at a depth of 500–510
cm. From the level conversion table (2), the fields
show −88 and −113 dBm. Based on this value, theattenuation constant is 15.6 dB/m. The maximum
underwater communication distance increased seventimes with the change from 920 to 350 MHz.
Fig. 6 shows the relationship between 1, 3, and 5 W
of GSU-7000 and the radio field intensity level inExperiment 2. The depth of change from level 9 tolevel 8 and the depth of level 0 increased to 345 and570 cm at 1 W, 445 and 620 cm at 2 W, and 485 and680 cm at 3 W. The differences were 225, 175, and195 cm, respectively, and the attenuation constants
can be calculated as 15.6, 20.0, and 17.9 dB/m.
Fig. 7 shows a comparison of radio field intensity
levels up to a depth of 20m with and without a coaxial
cable using GSU-7000 (1 W) in Experiment 3. In the

3.2 EXPERIMENTAL RESULTS
Fig. 4 shows the relationship between the depth of
the GTR-920F and radio wave attenuation inExperiment 1. The depth is 210 cm from the water
surface. From 0 to 210 cm, communication is throughthe air, and the total attenuation is 0.43 dBⅿ per 10
cm; after this depth, the communication limit reached(1) at approximately 50 cm in deep water. Whencalculating the propagation loss in water, it can be
estimated that the attenuation coefficient is 54 dB/mfrom the total attenuation of 76 dBm at a depth of 210
cm and 103 dBm at 260 cm. Fig. 5 shows therelationship between the depth of GSU-7000 (1 W)
165

GEOMATE – Bangkok, Thailand, 22-24 November 2022

experiment of (1), it decreased to level 4 at a depth ofapproximately 4 m without the coaxial cable;
however, with the coaxial cable, it became level 9 inthe entire measurement at a depth of 20 m, and almost
no propagation attenuation was observed.

Antenna

PVC pipe φ＝200mm
200mm

Water surface -210cm

3.3 EXPERIMENTAL CONSIDERETION
Waterproof case

3.3.1 RADIO ATTENUATION IN WATER(1) Change
a transmitter

Fig. 8 shows the relationship between the frequencyof electromagnetic waves in water and the attenuation
coefficient (3). In general, when electromagnetic
waves are transmitted in water, absorption attenuationis larger because of the difference in density, and thecommunicabledistanceisshorterwhenelectromagnetic waves are transmitted in water. The
shorter the frequency, the less the susceptibility toattenuation. Furthermore, the propagation loss inwater can be considered as follows.
The electric field E of a plane wave propagating in
the x-direction in a loss medium is
E= E0e-γx(1)where γ＝α＋jβ＝√(jωμ（σ＋jωε）), γ is the
propagation constant, α is the attenuation constant, β
is the phase constant, ω is the plane wave angularfrequency, σ is the medium conductivity, ε is thedielectric constant, and μ is the magnetic permeability.
When the frequency is extremely low, σ >> ωε, andthe displacement current can be ignored, so the
propagation constant can be approximately expressedas γ = √jωμσ, and α = β = √ (ωμσ / 2). Here, theaverage salinity is approximately 76 mg/l, and the
conductivity at 18°C is considered to be about 11.15
mS/m. This conductivity value is treated as the
freshwater conductivity value. The attenuation
constant of fresh water is α = 2.098 × 10-4√f [Np/m]= 1.822 × 10-3√f [dB/m].
The following evaluations can be made as a result:(GTR-920F) α=1.822×10-3×√(920×1000000) ≒ 55
dB/m,
and
(GSU-7000(1W))α=1.822×103×√(351.2×1000000)≒34 dB/m.

(2) Change output
of transmitter

Coaxial cable

PVC pipe φ＝200mm
200mm

Water surface -210cm

Provide clearance
between cable and caseTotal attenuation (-dBm)

Fig. 3Fig. 4

Outline of the experimentThrough the air

Depth (cm)
Relationship between the depth of the GTR920F and radio wave attenuation

Radio field
intensity level3.3.2 FREQUENCY AND COMMUNICATION

Waterproof case

In underwater communication, a short frequency
band leads to a decrease in data transmission speed
andcapacity.
Among
theundergroundcommunication technologies currently in practicaluse, there are communication devices in the range of
10–1000 Hz that use magnetic field lines at extremely
low frequencies. In this experiment, where theobjective is to obtain construction results in real time,
inexpensive and easy-to-use frequency equipment
was used instead of low frequencies.

Depth (cm)Fig. 5166

Relationship between the depth of GSU-7000(1W) and the radio field intensity level

GEOMATE – Bangkok, Thailand, 22-24 November 2022

From the Friis formula, the output of the radio isgreatly related to the transmission distance. In thisexperiment, the difference in transmission power wasinvestigated, and it was confirmed that the larger the
output, the longer the communication distance.
Pr=Gt・Gr・Pt・(λ/4・π・r)2(2)Here, Gt is the transmission gain, Gr is the reception
gain, Pt is the transmission power (W), Pr is thereception power (W), λ is the wavelength (m), and r
is the distance (m).

performance and usage of radios, etc., and thecomposition table of the materials used, respectively.

Radio field
intensity level3.3.3 RADIO OUTPUT AND TRANSMISSIONDISTANCE

Depth (cm)Fig. 6In this experiment, it was assumed that there wassoil cement at the root of the pile tip, and the effect of
the composition and degree of mixing on radio wave
attenuation when sandy soil or cohesive soil is mixed
in a cement slurry of a certain W/C was clarified.
Furthermore, it was clarified whether radio
communication in soil cement is possible at a distanceof 500 mm between the tips of the auger wings andthe core of the auger.

Relationship between 1, 3, and 5 W of GSU7000 and the radio field intensity level

Radio field
intensity level

4. WIRELESS COMMUNICATIONEXPERIMENT USING SOIL CEMENT IN THE
SOIL AS AN ATTENUATION MEDIUM○ With a coaxial cable● Without a coaxial cable
Depth (m)Fig. 7
4.1 OUTLINE OF THE EXPERIMENT

Attenuation coefficient (dB/m)

As shown in Fig. 9, a cubic hole of B × Ð × H = 500
× 500 × 500 mm was excavated on the ground surface,and a cavity with a depth of 500 mm was created byusing a PVC pipe with a diameter of 120 mm facing
the surface. A transmission radio was installed at a
position 250 mm from the surface of the earth.
Assuming the ground of the construction site, twotypes of soil cement, sand and clay, were created.
First, as mentioned above, excavation was done tocreate an underground space. Next, water was poured
into the space to make it a water tank. After draining,
the blended cement slurry was injected. Fifty percent
of cement slurry was replaced with muddy water tomake soil cement. A predetermined amount of silica
sand No. 7 was added to the soil cement (Kasaoka
clay was not added because it was highly viscous, andit became difficult to stir). After 24h, it was confirmedthat the soil cement had hardened. The electricalresistivity was continuously measured in each of theabove-mentioned experimental procedures. In eachexperimental procedure, it was measured step by stepwith a portable resistivity meter in the Well Pack 10.
Two types of muddy water with a specific density of
1.6 (kg/㎤) were used with silica sand No. 7 and two
types of muddy water with a specific density of 1.6(kg/㎤) with Kasaoka clay. The radio field intensity
and resistivity at each procedure and age were
measured. In addition, Table 1 and Table 2 show the

Comparison of radio field intensity levels up
to a depth of 20m with and withouta coaxial cable using GSU-7000 (1W)
visible light
X-ray
SeawaterMicrowave
Ultraviolet rays

Infrared
Pure water
Frequency (Hz)Fig.8

Relationship between the frequency ofelectromagnetic waves in waterand the attenuation coefficient
③
②

④

③
②

Water tank

④

250mm

500mm
PVC pipe①
500mm

Fig. 9167①

PVC pipe*The numbers correspond to the Table1

Plan view and cross-sectional viewof the experimental device

GEOMATE – Bangkok, Thailand, 22-24 November 2022

4.2 EXPERIMENTAL RESULTS

Table 1

Performance and usage of radios

Fig. 10 and Fig. 11 summarize the resistivity andradio wave attenuation derived from the relationshipbetween the resistance value of the Well Pack 10 andthe output of the GTR-24H over time. From thesefigures, the transition of the resistivity measured by
the electrode for measuring resistivity and theportable agrees with the transition of radio wave
attenuation to some extent, and there is a correlation
between the specific resistance and the radio wave
attenuation.

The
numbers
in Fig. 9

Name of radios andmeasurement electrodes①

GTR-920F

4.3 EXPERIMENTAL CONSIDERETION

It accurately measures the resistivity and transmits
data for GTR-24H.

It is a radio device for data transmission.
It measures the radio wave strength between two
GTR-920F and transmits data for GTR-24H.
②

Table 3 summarizes the evaluation of radio fieldstrength for each experimental procedure. Although
there are some areas where communication isunstable, radio communication is possible within thesoil cement at intervals of 500 mm.
In addition, based on the radio wave attenuation
constant of water,α＝√ω・μ・σ/2(3)where ω is the angular frequency of plane wave, μ is
the magnetic permeability, and σ is the conductivityof water. Here, σ was replaced with the conductivityof soil cement to derive the theoretical value of soil
cement in this study. For the damping constant of soil
cement,when transforming Eq. (1), this becomes
β＝√ω・μ・σ/2＝√π・f・μ/ρ
(4)
where F is the plane wave frequency, μ is themagnetic permeability, and ρ is the specific resistanceof soil cement. The damping constant of muddy water
2 in (6) can be evaluated as β1 ＝0.674 ×10-3×√f[Np/m]＝5.85×10-3×√f[dB/m]＝5.85×10-3×√920×10-6[dB/m] ≒178[dB/m]. In addition, thetotal attenuation of muddy water 2 in (6) is 97 dBm −28 dBm = 69 dB, so when the attenuation constant of
the measured value is obtained,
β2＝69 dB/0.5 m＝138dB/m.

③

Electrical resistivity measuring device

GTR-24H

Device to continuously receive and store measured
data in ①②.
Converting radio wave strength to radio wave
attenuation.
④

Well PACK10

It accurately measures the resistivity.
Electrical tester with digital display of theresistance values⑤

Portable resistivity meter
It simply measures the resistivity,
humidity, and pH.

Table 2Composition table of the materials used

Experimental material and Composition
Muddy water 1 [1.6 (kg/㎤)]
Silica sand No. 7 [2.6 (g/㎤)] + water
Muddy water 2 [1.6 (kg/㎤)]
Kasaoka clay [2.5 (g/㎤)] + water
Cement slurry [W/C 60(%)]
Ordinary portland cement [3.15 (g/㎤)]+ water

5. CONCLUSION

Soil cement [Volume ratio 1:1]

In an experiment on the possibility of wirelesscommunication using water as an attenuation medium,the maximum underwater communication distance
was seven times higher at the difference between thefrequencies of 920 and 351 MHz of the equipmentused. As the radio output increased from 1 to 5 W, thecommunicable depth increased from 570 to 680 cm.
It was confirmed that there was no propagation
attenuation up to a depth of 20 m with a coaxial cable.
In an experiment on the possibility of wirelesscommunication using soil cement as an attenuation

Cement slurry + Muddy water 1or 2
medium, radio communication was possible within
an interval of 500 mm, although communication was
unstable in some areas. A coaxial cable was placedalong the shaft of the earth auger, and it was assumed
that wireless transmission would be performed withthe measurement electrode attached to the tips of thewings of the auger. From this, it became clear thattransmission is possible at 500 mm and that wireless168

GEOMATE – Bangkok, Thailand, 22-24 November 2022

(3)

Table 3(5) 1.44(Ω•m)•

Specific resistance(Ωm)

Radio wave attenuation (dBm)

Communication isunstable because ofdrainage.
The blue sheet is torn andcommunication is unstable.

(1) (2)(2)(6)

Relationship between the specific resistanceand the radio wave attenuation(Muddy water 1)(3)*1.87 (Ω•m)
Radio wave attenuation (dBm)

Fig. 10(4) 1.30 (Ω•m)

Communication isunstable because ofdrainage.

Specific resistance(Ωm)•

Stir soil cement
by force.

(1)(4) 1.94 (Ω•m)

Evaluation of radio field strength

Evaluation
Muddy water 1
Muddy water 2(2)
StableRelatively stable
Performance(3)
Limitations ofRelatively stable
RadiosPerformance
Communication is(4)
Limitations ofpossible,Radiosbut unstable
Performance(5)
Limitations ofRadiosPerformance
Performance(6)
Limitations ofLimitations ofRadiosRadios*Numbers correspond to experimental procedures
REFERENCES[1]GTR920F, Materials related to electrolytic
strength and communication distance[2]GSU7000, digital simple radio (351 Hz)
Electrolytic strength test value and communication
status guideline[3]Absorption and attenuation of electromagnetic
waves in water (JAMSTEC 2020 data)[4]G. Komatsu, M. Fujii, et al., Quality Evaluation for
the Foot Protection part of the Bored Precast Pile
using the Electric Resistivity, Summaries ofTechnical Papers of Annual Meeting ofArchitectural Institute of Japan, pp. 621–622,
1998/9[5]Y. Mochida, T. Maniwa, et al., Experimental study
on data transmission for confirming pile
construction quality (wireless communication
properties using water as an attenuation medium),
Summaries of Technical Papers of Annual Meetingof Architectural Institute of Japan, pp. 747–748,
2021/9[6]Procedure manual for radio field strength test of
wireless modem GTR-920F (2019/05/23 Geotech
Service Co., Ltd.)(6)

Fig. 11 Relationship between the specific resistanceand the radio wave attenuation(Muddy water 2)
communication is possible. The radio wave
attenuation was smaller in the experiment than in theevaluation, and it became necessary to predict theattenuation to be slightly larger. In planned future
research, efforts will be made to derive more accurate
theoretical values by investigating such physicalproperties as the magnetic permeability of soil cement
in consideration of the attenuation of free space andclosed space.
This research was carried out as part of the "Study on
quality confirmation method immediately after pile
construction using electrical resistivity”: Grant-inAid for Scientific Research (KAKENHI) (C),20K04782.

169

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

NUMERICAL SIMULATION FOR SEDIMENTATION OF SANDPARTICLES IN EXCAVATION STABILIZERS
Hiroya Asano 1, Koki Nakao 1, Tomotaka Morishita 2, Toshihiko Miura 2,
Yasuharu Wachi 2, Kazuhiro Watanabe 2 and Shinya Inazumi 3
1

Graduate School of Engineering and Science, Shibaura Institute of Technology, Tokyo, Japan;
2
Obayashi Corporation, Tokyo, Japan;
3
College of Engineering, Shibaura Institute of Technology, Japan

ABSTRACTThis study focuses on the behavioral pattern of sand particles in the excavation stabilizers used during
construction of the cast-in-place piles. Sand particles gets mixed as an impurity in the excavation stabilizer,
which will cause a detrimental effect while driving cast-in-place piles. That’s why sand particles should be
removed from the excavation stabilizer. However, it is difficult to confirm the behavior of sand particles in
excavation stabilizer visually. This study attempts to reproduce the sedimentation behavior of sand particles inthe excavation stabilizer by numerical simulation using the CAE coupled with MPS-DEM analysis during theconstruction of cast-in-place concrete pile. The simulation cylindrical column model is created by computeraided engineering (CAE) system and the excavation stabilizer and sand particles are modeled by MPS and DEM,
respectively. After confirming the simulation behavior of sand particles in excavation stabilizer by reproducing
cylindrical column test for sedimentation test and reproduction of CAE coupled with MPS-DEM analysis
simulation, it is expected that the result obtained from this simulation will contribute to more efficient andeconomic construction of cast-in-concrete piles.
Keywords: Excavation stabilizer, Numerical simulation, Sand particle, Sedimentation, Viscosity
INTRODUCTION

However not only it is difficult to confirm theconstruction status visually but also to prove thevalidity of design.
This study focuses on the application of acomputer-aided engineering (CAE) [3-5] system thatsimulates the sedimentation of sand particles in
excavation stabilizers. During construction of castin-place- concrete method, sedimentation behaviorof sand within the excavation stabilizer isstudied/trailed by CAE coupled with DEM-MPS
analysis method, sedimentation behavior vs time isvisualized and evaluated. This study, using
cylindrical column test only for sedimentation test,
reproduction accuracy checking of the simulation.

Most of urban areas in Japan are located on soft
ground, and geo-disasters such as land subsidence
and liquefaction occur frequently. In theconstruction of infrastructures, it is indispensable toapply the foundation structure in the ground. In theconstruction of cast-in-place concrete piles, thestabilizer is filled into the excavated hole which at
the same time will act as medium to prevent thefalling of surrounding sand, and it will be replacedby concrete [1,2]. This study focuses on the
excavation stabilizer used in the cast-in-place
concrete pile method for constructing reinforced
concrete piles at the on-site among the foundation
structures. Figure 1 shows the process of substitution
process of the excavation stabilizer that has been
initially filled into the excavated hole as it will get
mixed with the surrounding sands. Since it isundesirable to have mixing of sand as it will change
the original property of excavation stabilizer. In
order to protect the quality of pile formation by useof this reclaimed excavation stabilizer, it isimportant to control properties like specific gravity,
viscosity, wall building capacity, and sand ratio
contained within it. Since the high concentration ofsand will reduce the quality of concrete, it is desired
to understand the behavioral pattern of sand present
in excavation stabilizer before making substitution.

RecycleSedimentation tank Circulation tankgroundHydraulicpressure
Excavation
stabilizer

Mud membrane

Fig. 1 The good excavation stabilizer substitution
170

GEOMATE – Bangkok, Thailand, 22-24 November 2022

CAE WITH DEM-MPS METHOD COUPLEDANALYSIS (MPS-DEM-CAE)

START

Input calculation conditions

MPS (Moving Particle Simulation) Method

Viscosity setting
based on fluid models

MPS (Moving Particle Simulation) method is
an analytical method dealing with incompressible
flow, which discretizes continuum mechanics using
particles.
In this study, the authors apply MPS method [35] to analyze the behavior of fluid particles. The
governing equations for the incompressible flowused in the analysis are the mass conservation law of
Eq. (1) and the Navier’s stroke law of Eq. (2).
𝑫𝝆

=𝟎

(1)

= − 𝝆 + 𝝂𝜵𝟐 𝒖(⃗ + (𝒈
(⃗

(2)

𝑫𝒕
%%⃗
𝑫𝒖
𝑫𝒕

𝜵𝑷

Explicit∆𝒕((((⃗𝒌 + 𝒈= 𝝂𝜵𝟐 𝒖((⃗Calculate the Poisson equation for pressure∇*𝑃$/0 =Implicit𝜵𝟐 𝑷𝒌-𝟏 =

∆𝒕

=−

𝜵𝑷𝒌%𝟏
𝝆

Calculate the pressure gradient term

Correct the velocity and particle positions

Yes
Proceed time step

No
END

Fig. 2 The calculation algorithm of the MPS
equations of motion. DEM calculates the dynamics
of each individual particles by solving Newton’s
equation of motion. Equations of motion fortranslational and rotational motion of DEM particles
are expressed by Eq. (6) and (7).

(3)

(4)

𝒅
(⃗
𝒎 𝒅𝒕 (𝒗⃗ = 𝑭

(6)

𝒅
((⃗
𝑰 𝒅𝒕 𝝎
(((⃗ = (𝑴(7)(⃗ is the(⃗ is the velocity, 𝑭
Where, 𝒎 is the mass, 𝒗((⃗ is angularcontact force, 𝑰 is the inertia moment, (𝝎((⃗ is the torquevelocity, and (𝑴For a spherical particle, the inertia moment isexpressed by Eq. (8).
𝟐
𝑰 = 𝒎𝒓𝟐(8)

Velocity and position correction by the pressure
gradient is expressed by Eq. (5).
%%%%%%%%%%%⃗%%%%⃗∗
𝒖𝒌%𝟏 *𝒖𝜌 𝑛 ∗ − 𝑛3∆𝑡 * 𝑛3

𝑢$/0 − 𝑢∗∇𝑃 $/0=−∆𝑡𝜌Implicit calculation of pressure is expressed by
Eq. (4).
𝝆 𝒏∗ *𝒏𝟎∆𝒕𝟐 𝒏𝟎𝑢∗ − 𝑢$= 𝜈∇* 𝑢$ + 𝑔⃗∆𝑡

Particle movement

Where, D/Dt expresses a Lagrangian derivation, 𝝆 is(⃗ is the velocity vector, 𝑷 is thedensity of the fluid, 𝒖(⃗pressure, 𝛎 is kinematic viscosity coefficient, and (𝒈is the gravity acceleration.
In the MPS method, the Navier-Stokes
equations are divided into two stages, and all terms
are solved explicitly, except for the pressure term,
which is solved implicitly.
Explicit calculations of terms except thepressure term is expressed by Eq. (3).
%%%%%⃗𝒌%%%%⃗∗ *𝒖𝒖Calculate the gravity and viscosity terms(5)𝟓

Where, n is the particle number density, and n0 is theparticle number density at the initial state, both ofwhich are explained later. Superscript 𝒌 expresses
the time step. Superscript ∗ expresses a physicalquantity at the stage where the explicit calculation
has been completed. Figure 2 shows the calculation
algorithm of the MPS method.

Where, 𝒓 is the radius of the particle. DEM handles
particles as rigid particles that do not deform, but it
allows overlapping of particles to acceleratecalculation.
The contact forces (repulsive force, energy
damping/decay, and slide due to friction) of apowder particle are expressed by a rigid-body springmodel, viscous damper model, and slider model,
respectively, as shown in Fig. 3.
The contact force of normal and tangential aredefined as in Eq. (9) and (10).

DEM (Discrete Element Method) Method
DEM (The Discrete Element Method) is atechnique for analyzing the behavior of powders byfollowing each individual particles through their
171

GEOMATE – Bangkok, Thailand, 22-24 November 2022((((⃗𝒏 − 𝜼𝒏 (𝒗((((⃗((((⃗𝒏2 − ((((⃗
𝑭𝒏 = −𝒌𝒏 𝜹𝒗𝒏3 )(⃗ is the velocity, 𝒓(⃗ is thethe damping coefficient, 𝒗
distance from center of gravity to point of contact,(((⃗ is angular velocity. The upper subscripts nand 𝝎
represents the normal direction component, t is thetangential direction component, and the lower
subscripts i and j represent each particle. The
calculation algorithm for DEM is shown in Fig. 4.

(9)

((((⃗
(((⃗𝒕 − 𝜼𝒕 ;<𝒗((((⃗𝒏2 − 𝒗((((⃗𝒏3 = + (𝒓𝑭𝒕 = −𝒌𝒕 𝜹(((⃗2 × ((((⃗)
𝝎2 +<𝒓(((⃗3 × 𝝎((((⃗=?
3(10)

Where, 𝒌 is the spring constant, (𝜹⃗ is the overlap, 𝜼 is

CAE Coupled with DEM-MPS Method Analysis(MPS-DEM-CAE)
SpringA computer aided engineering (CAE) is analternative technology for large-scale tests,
conducted in a room-situ, using prototypes that havebeen prepared in a study as part of the development
process of “manufacturing”. In other words, CAE isa general term for technology that simulates andanalyzes prototypes on a computer created by acomputer-aided design (CAD) and so on,
considering the site conditions [3-5]. At the sametime, CAE may refer to computer-aided engineering
work or its tools for the prior examination, design,
manufacturing, and products. In the field ofgeotechnical engineering, CAE can be used not only
to visualize the inside of the ground and the stressloading on the inside of the ground, but also toestimate the results of tests that would require hugecosts and/or phenomena that would be difficult toreproduce. In addition, by performing appropriate
post-processing, it is possible to communicate withother people in a visually easy-to-understand manner.
In the MPS-DEM coupled system, the motion ofviscous fluid and powder is expressed by Eq. (12)and (13). Here, for simplification, it is assumed that
there is no external force.

SliderDashpot

SpringDashpot【Normal direction】 【Tangential direction】

Fig. 3 The contact model of DEM(Voigt model)
START

Input calculation conditions

No
Contact determination

Yes

Calculate contact force
𝐹 " = −𝑘 " 𝛿 " − 𝜂 " (𝑣*" − 𝑣+" )𝐹 - = −𝑘 " 𝛿- − 𝜂 "𝑣*" − 𝑣+" + 𝑟* × 𝜔* + 𝑟+ × 𝜔+
𝐹- = 𝜇𝐹"
𝐹-

𝐹-

Calculate external force (gravity etc.)

Correct the velocity and particle positions

%%%%⃗
𝑫𝜺𝒗

𝟎

No
END

%%%%⃗
𝑫𝒗

((((((((((⃗(((((⃗ (((((((⃗
𝒎 𝑫𝒕𝒔 = 𝑭
𝑫𝑬𝑴 + 𝑭𝑫 + 𝑭𝛁𝑷

Fig. 4 The calculation algorithm of the DEM

500$ Evaluation depth%

1200

1100

250

150

(13)

CYLINDRICAL COLUMN TEST FOR
SEDIMENTATION TESTThis study was conducted for checking theaccuracy of simulation. In this study using thecylindrical column shown in Fig. 5, thesedimentation behavior of sand particles in highviscous excavation stabilizer (CASE-1), medium
viscous excavation stabilizer (CASE-2), and water(CASE-3) as shown in Table 1 are measured as sandparticle concentration changes over time at each

750

"mm#

(a) Analytical model$ Analysis%(12)

Where, 𝝆 is fluid density, 𝜺 is volume fraction of the(((⃗𝒍 is fluid velocity, 𝐏 is fluid pressure, 𝛎 is
fluid, 𝒗((((⃗𝒔 is
viscosity of the fluid, 𝒎 is powder mass, 𝒗
powder velocity, ((((((((((⃗
𝑭𝑫𝑬𝑴 is contact force, (((((⃗
𝑭𝑫 is fluid
resistance, and (((((((⃗𝑭𝛁𝑷 is buoyancy.

0

Excavation
stabilizer!
Sand particles𝜺𝝆 𝑫𝒕 𝒍 = −𝛁𝛆𝐏 + 𝛎𝛁𝟐 𝜺𝒗(((⃗𝒍 − 𝒍𝟑 (((((⃗
𝑭𝑫

Yes

Proceed time step(b) Cylindrical column test$ Experiment%

Fig. 5 Analytical model simulating
a cylindrical column
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

As for the rheological models for MPS
excavation stabilizers, the Bingham fluid model, a
non-Newtonian fluid, is defined for the excavation
stabilizers in (CASE-1) and (CASE-2), and a
Newtonian fluid for the water in (CASE-3). All
these physical property parameters refer to and citeprevious tested values. Attention should be paid tothe DEM parameters for sand particles as shown in
Table 2, especially the setting of sand particle
diameter and density. The particle diameter anddensity of the sand used in the cylindrical column
test were 75µm and 2650kg/cm3.
In this study, diameters and densities of sandparticles modeled in the DEM were set while
making consideration of analysis accuracy andcomputational load; so that the particle settling
velocities of typical sand particles and sand particles
modeled in the DEM becomes equal for the Stokes
fluid.

Table 1 MPS parameters for excavation
stabilizers particles
MPS

Parameters

Excavation stabilizer
CASE 1 CASE 2 CASE 3
10Particle size (mm)Density (kg/m3)

1200

1200

1000

Plastic vicosity (Pa･s)

0.306

0.0082-

Yeid value (Pa)

0.042

0.0028Rheology model

Buingham

NewtonTable 2 DEM parameters for sand particles
Parameters

DEM
Soil particles

Particle size (mm)2

Density (kg/m3)

1800

Spring constant

1×108

Damping coefficient

RESULTS AND DISCUSSION

0.7

Result of DEM-MPS Simulation for
Sedimentation of Sand Particles

depth. The viscosity and yield value of eachexcavation stabilizers were measured by a B-type
viscometer.
The procedure of the test is described below.
1) Add 10 g/L of 75 to 150µm sand to the sample(excavation stabilizers or water) andhomogenize it well.
2) Put the sample in the cylindrical column andcollect 1L of sample including sand from thecollection hole of the column at regularintervals.
3) Measure the specific gravity, viscosity, APIwall building capacity, and sand concentrationof the collected sample.

Figure 6 shows a longitudinal section of themodel showing changes of sand particle
sedimentation. In addition, in Fig. 6, only sandparticles are shown after processing the MPS
particles transparently to visually evaluate thesedimentation of sand particles, and the red particles
correspond to sand particles.
Figure 6(a) shows the settling behavior of sandparticles in a highly viscous excavation stabilizer for
90 seconds at 10-second intervals.
However, due to the long time required for the
analysis, it was not possible to show the
sedimentation of sand particles until they converged.
Figure 6(b) shows the settling behavior of sandparticles in a medium viscous excavation stabilizer
for 45 seconds at 5 second intervals. From Fig. 6(b),the settling of sand particles is completely converged
after 45 seconds.
Figure 6(c) shows the settling behavior of sandparticles in water for 6 seconds at intervals of 1
second. Figure 6(c) shows that the settling of sandparticles in water is completely converged after 6
seconds. From these results, it is clear that the
behavior of sand particle sedimentation and thesettling velocity vary depending on the viscosity of
the liquid. Here, focusing on the results of CASE-2
shown in Fig. 6(b), it can be seen that the settling
velocity of sand particles gradually decreased fromthe middle to the lower of the column. This may be
due to the influence of pressure differences between
the upper, middle, and lower parts of the column, as
well as collisions of sand particles or sand particles
with each other and with stable liquid particles. In
addition, the top of the sand particles settles withANALYSIS MODEL AND CONDITIONSIn this study, as shown in Fig. 5, a cylindricalcolumn (height 120 cm, inner diameter 15 cm) isused as the analytical model, where excavation
stabilizer and sand particles up to 110 cm in height
are mixed in the initial state. After modeling the
excavation stabilizer using the MPS method and thesand particles using the DEM method, we will use
MPS-DEM-CAE analysis to qualitatively evaluate
the changes over time in the sedimentation of sandparticles of the stable solution. This study also
focuses on changes over time in the settling behaviorof DEM sand particles in each MPS fluid with threecases of viscosity: high viscosity stable fluid(CASE-1), medium viscosity stable fluid (CASE-2),
and water (CASE-3).
Table 1 shows the MPS parameters for the
excavation stabilizers and water. Table 2 shows theDEM parameters for the sand particle.

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

(a) CASE-1(a) CASE-1(b) CASE-2(b) CASE-2(c) CASE-3
Fig. 6 Change over time of sedimentation of sandparticles in each MPS fluid of different viscosity
Table 3 Stokes' settling velocity of sand particles
calculated from conditions of MPS-DEM-CAE
analysis and column test

CASE
1
2
3

Cylindrical column test
(m/s)
-5

1.45×10
5.42×10-4
5.05×10-3(c) CASE-3
Fig. 7 Sedimentation behavior vs time by
MPS-DEM-CAE analysis and column test

MPS-DEM-CAE(m/s)

Comparison of the cylindrical column test andanalytical results-3

4.27×10
1.59×10-1
1.74×10-0

The Stokes’ low has been commonly used in
the evaluation of sand particle sedimentation in
fluids such as water and stable liquids. The Stokes
low’ low represents the terminal velocity of particles
as they settle in a fluid. The average particle
diameter and density of the sand particles used in thecylinder test were 75 µm and 2650 kg/m3,
respectively, while for the DEM sand particle
parameters used in the CAE coupled with MPSDEM analysis it was 0.002 m and 1800 kg/m3
respectively. The Stokes settling velocity of sandparticles calculated from the cylinder test conditions
and the CAE coupled with MPS-DEM analysis
conditions is shown in Table 3.
The Stokes settling velocity obtained from the
MPS-DEM-CAE analysis is approximately from 295

concave shape for 15 seconds, but after 20 seconds
they settle with convex shape. This is thought to be
due to friction between the wall of the cylindricalcolumn and the sand particles. The convex settlingof sand particles observed after 20 seconds is
considered to be due to an array substitution
between excavation stabilizer particles and sandparticles, in which stable solution particles spreadoutward like ripples from the center at the bottom of
the cylindrical column and move upward along thewall surface while sand particles move downward
along the wall surface.

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

to 345 times larger than the Stokes settling velocity
obtained from the cylindrical column test conditions.
Therefore, in order to treat the Stokes settling
velocity obtained from both as equivalent, theelapsed time in the MPS-DEM-CAE analysis must
be corrected by a factor of 295 to 345.
Figure 7 shows the changes in sand particle
concentration over time in highly viscous excavation
stabilizer, medium viscous excavation stabilizer, andwater at each depth, obtained from the cylindricalcolumn test and MPS-DEM-CAE analysis. The
elapsed time for the MPS-DEM-CAE analysis wascorrected according to the above. Comparison
between test and analysis in Fig. 7, suggests that thesettling time and settling trend of sand particles in
excavation stabilizer may be reproduced (assuming
actual behavior), although there are slight
discrepancies in the settling behavior of sandparticles. The reason for the discrepancies in thesettling behavior of the sand particles between thetwo is that the elapsed time in the MPS-DEM-CAE
analysis was assumed to be the Stokes settling
velocity. Stokes’ settling velocity is the onedimensional settling velocity of a single particle. On
the other hand, the cylinder test and MPS-DEMCAE analysis show a large number of sand particles
of varied sizes mixed together, and phenomenon likecollision, repulsion, and rotation of sand grains
occur during the settling process, that is, the settlingof sand particles is three-dimensional. Therefore, theresults of the cylinder test and the MPS-DEM-CAE
analysis are considered not to be in strict match with
Stokes’ low.

stabilizer by applying Stokes’ low (onedimensional).
(2) By CAE coupled with DEM-MPS analysis,
sedimentation behavior vs time was visualized
and evaluated.
(3) By reproducing cylindrical column test for
sedimentation test, reproduction accuracy of
CAE coupled with MPS-DEM analysis
simulation was confirmed.
(4) By visualizing the behavior of sand particles, it
can be expected to contribute to efficient andeconomical pile driving during construction of
cast-in-concrete piles.
The results of the numerical simulation using
the CAE coupled with MPS-DEM analysis proposed
and implemented in this research will greatly
contribute to the establishment of ICT with higher
precisions and the promotion of DX at construction
sites those will surely be required in the future.
REFERENCES[1] Morishita, T., Miura, T., Wachi, Y. andYoshimoto, K. (2019): Development ofAutomatic
Measuring
Apparatusfor
Stabilization Slurry, Report of the Engineering
Research Laboratory, Obayashi-Gumi, Ltd,
No.83.
[2] Otsuka, Y. and Matsuda, S. (1965): Problems
and Considerations for Excavation Stabilizer
Method, Reports of the Research Institute of
Shimizu Construction Co., Vol.6.
[3] Chang, K.H. (2014): Product Design Modelingusing CAD/CAE: The Computer Aided
Engineering Design Series, Elsevier.
[4] Pan, Z., Wang, X., Teng, R. and Cao, X.
(2016):
Computer-aided
design-whileengineering technology in top-down modelingof mechanical product, Computers in Industry,
Vol.
75,
pp.
151-161,doi:
10.1016/j.compind.2015.05.004.
[5] Inazumi, S., Kuwahara, S., Jotisankasa, A. andChaiprakaikeow, S. (2020b): MPS-CAE
simulation on dynamic interaction between
steel casing and existing 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.

CONCLUSIONSThe sedimentation behavior sand particles in
excavation stabilizer were evaluated using MPSDEM coupled CAE analysis in this study. An
attempt to reproduce a cylindrical column test using
the CAE coupled with MPS-DEM analysis was
made, and qualitatively evaluated the sedimentation
behavior of soil particles in the excavation
stabilizing solution with respect to time.
The results and findings obtained in this study
are shown below.
(1) CAE coupled with MPS-DEM analysis
succeeded in visualizing the sedimentation
behavior of sand particles in excavation

175

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

STRENGTH PROPERTES OF BENTONITE SUBJECTED TO CYCLICLOADING STRESS
Tomoyoshi Nishimura1, Seiichi Narushima2, Yasunori ARAI2 and Yuki Sakoda2
1

Department of Civil Engineering, Ashikaga University, Japan; 2Natural Blanket Institute, Japan
ABSTRACT

Further research works have identified shear characteristic before decade ago, and it is successful to improve
testing in GCL strength testing are suggested. It is provided that an approach for comparing the effectiveness ofgeosynthetic clay liners (GCLs) and compacted clay liners (CCLs) associated with association with geomembranes.
Geomembrane has some activity, which are similar to equivalency demonstrations with barrier system consist of
bentonite, and extremely low hydraulic conductivity and high expansive deformation are delegate components.
This study presented that unsaturated-saturated bentonite indicated stress-strain properties in monotonic loading
and cyclic mobility using cyclic triaxial test for various dry densities. Static triaxial compression test was
conducted to determine strength parameters for establishing safety number. Also, dynamic triaxial test performed
under undrained conditions under various initial stress ratios and loading frequencies. Three different dry densities
are prepared, which are determined using maximum dry density from Proctor compaction method in soil
mechanics theory. Dynamic strength properties have significant effort on depending of dry density.
Keywords: Bentonite, Monotonic loading test, Cyclic loading test, Liquefaction

INTRODUCTION

be sufficiently hydrated. The hydration behavior has
been clearly demonstrated in the laboratory throughthe investigation of advective flow of various
permeants regard to unsaturated clay liner.
Mechanical properties such as strength properties,
compression properties and cyclic loading resistanceare one of some couple phenomena such as hydration,and it is effectively to determine the safety code.
Despite the importance of hydration to the overall
effectiveness of the clay liner system, surprisingly
little research has addressed this aspect of clay line
cyclic loading properties.
Further research works have identified shear
characteristic before decade ago, and it is successful
to improve testing in GCL strength testing aresuggested. It is provided that an approach forcomparing the effectiveness of geosynthetic clay
liners (GCLs) and compacted clay liners (CCLs)
associated with association with geomembranes.
Geomembrane used for waste disposal system hassome activity, which are similar to equivalency
demonstrations with barrier system consist of
bentonite, and extremely low hydraulic conductivity
and high expansive deformation are delegate
components. The contribution of bentonite is toremaining low permeability without advective
leachate flow. Shear strength for a compacted clay
liner including bentonite is of great importance for
landfill stability analysis and safety design on
construction waste disposal in practice.

Various clay liners such as Geosynthetic clay
liners (GCLs) are placed at the bottom of waste
disposal facilities where they hydrate from the subsoil
and eventually from a hydraulic head on
geomembranes (GMs) defects [1][2]. Bentonites
consist of Geosynthetic clay liners (GCLs) whichhave further low permeability and enough expansive
activity. Predicting hydration behavior of bentonite
requires knowledge of hydraulic conductivity,swelling properties and mechanical properties.
Hydraulic conductivity and swelling properties forbentonite have been conducted out in decade ago. It
is verified that dry density and chemical components
such as salinity influenced on couple phenomena.
Then, Seiphoori et al., (2016) [3] investigated thewater retention properties of the Geosynthetic clay
liners (GCLs) subjected to different ranges of verticalstresses along wetting paths (i.e., reduction ofsuction) in laboratory test. To do so, conventional
controlled suction oedometer was used in osmotic
suction control technique. It is successful to compare
results between experimental laboratory test
measurement and modelling simulations in results. A
geosynthetic clay liner (GCL) is a composite
geosynthetic material. It commonly consists of a thinbentonite clay liner (typically of the order of 5-10 mmthick) that is either bound by needle-punchedgeotextiles or bounded to a geomembrane with anadhesive [4].
In order for a clay liner composed by expansive
bentonite to act as an efficient hydraulic barrier, theexpansive bentonite component of the clay liner must

PREVIOUS WORKS ON CLASY LINER
To interpret aspect of strength properties to clay
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

liner (GCL) in a municipal solid waste (MSW)
landfill liner system lead to evaluate safety concept
clay liner system in mechanical properties. Fox and
Stark (2015) [5] appears that some identified factors
such as the slope inclination, slope height, physicalcharacteristic are significant important for remaining
safety situation. Shear strengths of geosynthetic clay
liners (GCLs) and soil liner are necessary for staticand seismic stability analyses in the design of waste
containment facilities and other waste disposal
facilities, which incorporate these materials as
hydraulic barriers. The aspect corresponds toestablish safety design for various disposal systemsuch as the context of landfill bottom liner, coversystems and high-level radioactive waste disposal at
deeply repository barrier system [6][7][8].
Further research works have identified shear
characteristic before decade ago, and it is successful
to improve testing in GCL strength testing aresuggested. The contribution of bentonite is toremaining low permeability without advective
leachate flow. Shear strength or shear resistance for acompacted clay liner including bentonite is of great
importance for landfill stability analysis and safety
design on construction waste disposal. Other hands, it
is few to appear the cyclic behavior of bentonite that
it is possible to refer the like liquefaction behavior
either loose sand or soil having much finecomponents.
Yamamuro and Lade (1997) [9] presented someexperimental results on two different instability soils
with performing both drained and undrained triaxial
compression tests, and all tests were produced undermonotonic loading. It is obviously that complete
static liquefaction phenomena were exhibited for
instability soil. In generally, liquefaction phenomena
have been appeared on cyclic loading triaxial testwith completely full saturation situation that
Yamamuro and Lade (1997) [9] verified staticliquefaction phenomena due to performing
monotonic triaxial compression test associated toexpression effective stress paths. Lade et al. (2009)[10] moderated shear strength and static liquefaction
under undrained conditions for non-plastic fineparticles that some efforts such as minimum andmaximum void ratios, compressibility. Prepared nonplastic soil material had a rage from 0 % to 100 % in
fine components. While liquefaction is determined,
observations
appliedcomparison
betweenliquefaction behavior and stable behavior on void
ratio and fine components scales. Monkul andYamamuro (2011) [11] studied investigates the fines
contents influence on liquefaction potential of asingle base sand mixed with three different essentiallynon-plastic silts through strain-controlled monotonic
undrained triaxial compression test. Test results also
revealed that commonly used comparison bases (i.e.,void ratio, intergranular void ratio, relative density)
are not sufficient for assessing the influence of fines

on liquefaction potential of silty sands. Then, they
represented that current geotechnical engineering
practice mostly considered that the influence of fines
contents on mechanical properties may besignificantly affected by the natural of the fines.
POURPOSE OF THIS STUDYThis study presented that unsaturated-saturated
bentonite indicated monotonic loading properties andcyclic stress-strain behavior such as cyclic mobility
on static-cyclic triaxial test for various dry densities.
Monotonic static triaxial compression test was
conducted to determine strength parameters such as
coefficient of friction, angle of shear resistance andapparent cohesion that it possible to evaluatemechanical properties. The monotonic static triaxial
compression test performed under undrained
conditions in order to simulate effective stress paths
using principal stress and excess pore-water pressure.
It can be seen effective stress paths with/without
instability paths including two effective principal
stress. Also, dynamic triaxial test conducted out under
undrained conditions that various initial stress ratios
and loading frequencies were controlled in order toobserve the influence of assigned cyclic mobility
components on stress-strain behavior. It seemed toindicate slightly liquefaction phenomena according toinitial cyclic stress ratio.
TEST PROCUDER
Soil material
This study used bentonite with maximum grain
size of 4.0 mm that grain size distribution test results
47.6 % of gravel, 52.3 % of sand and clay of 0.1 % in
gradations. D50 is 1.80 mm, D30 is 0.988 mm and D10
is 0.43. Therefore, uniformity coefficient is 0.43 andcoefficient of curvature is 0.919. Compaction
properties determined through in A-c part in Test
method for soil compaction using a rammer (JIS
Code: JIS A 1210:2009) that maximum dry density
was 1.357 Mg/m3 and optimum water content was
27.7 %. Three difference dry densities such as
physical factor was required that were 1.357 Mg/m3,
1.221 Mg/m3 and 1.153 Mg/m3, respectively. Eachdry density corresponds to 100 %, 90 % and 85 % of
maximum dry density. Compaction water content
takes coincident as 27.7 % that is optimum watercontent. As one of physical properties for usedbentonite, previously swelling pressures havedetermined under constant volume, and were 300.2
kPa, 153.6 kPa and 109.6 kPa corresponding to 1.357
Mg/m3, 1.221 Mg/m3 and 1.153 Mg/m3, respectively
for dry density. Then, specimen was prepared in staticcompaction method using stiffness steel mold and ahydraulic jack, which had a height of 76 mm and adiameter of 38 mm, respectively.

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Testing Apparatus

200
Coeffient of friction 0.5607
Angle of internal frictiion (Angle of shear resistance)
14.9 degrees
Apparent cohesion 22.1kPa

180

Principal streee difference kPa

Both unsaturated and unsaturated bentonite withvarious dry densities were tested on the one triaxial
apparatus, while extra components was added to theapparatus for the measurement of cyclic strengthstrain behavior. It is possible to control the vertical
cyclic loading, and had sinusoidal loading withfrequency of 0.5 Hz and 0.05 Hz. Regulator
contribute to remain lateral confining pressure
constant, and had 100 kPa duration cyclic loading
test. It is often that the total mean principal stressconstant test was conducted out, and reported inliquefaction resistance researches. Wang, et al.,(2016) [12] sufficiently explain composing ofmodified apparatus for cyclic loading triaxial test,
which is possible to apply accurately to unsaturatedsaturated specimen phenomena in order to observeliquefaction resistance while the total mean principal
stress constant test. Cyclic loading triaxial test
performed without remaining the total mean principal
stress (p), the influence of changing the total mean
principal stress is not clear to the obtained results
regard to stress-strain behavior duration cyclicloading. The apparatus is possible to measure excess
pore-water pressures by the pressure transducer
connected with triaxial basement, which used forstatic triaxial compression test and cyclic loading test.

160
140
120

100
80
60
Dry density：1.357 Mg/m3
Saturation

40
20
0
0

50

100
150
Effective mean stress kPa

200

Fig. 1 Effective stress paths.
significant factors in cyclic behavior.
STRENGTH PARAMETERS
Within a range of low lateral confining pressures,
undrained triaxial compression test on saturated
bentonite with three difference dry densities
conducted out previously to cyclic loading triaxial
test. These results from a series of saturated
monotonic tests are shown in Fig 1that effective stresspaths defined as effective mean principal stress ofvarying initial lateral confining pressures fall onto a
unique line, which called “instability line” by
Yamamuro et al. (1997) [9]. These lines intersect thestress origin, and meant failure criterion (i.e., failureenvelope). Also, it possible to be characterize threestrength parameters such as coefficient of friction,
shear resistance angle and apparent cohesion.
Yamamuro et al., (1997) [9] demonstrated undrained
soil instability on instability line comparison witheffective stress failure line, and evidenced expansionof instability region according to increment confiningpressure for loose Scaramnto River sand. As shownin Figs 1 to 3, observed saturated bentonite behavior
with various dry densities was seemed to be slightly
instability line. Strength parameters was exhibitedfrom failure criterions as followings; coefficient offriction indicated a range from 0.42 to 0.56, shear
resistance angle had a range from 11.6 degrees to 14.9
degrees, and apparent cohesion result a range from
12.9 kPa to 22.1 kPa. Shear resistance angles werenamely coincident, apparent cohesion dropped with
reduction of dry density.

Testing ProgramThis testing programs consist of main two
triaxial tests that one is static triaxial test, another one
is cyclic loading test. Static triaxial compression test
(i.e. monotonic loading test) performed for saturated
specimens that effective stress paths had
determination under undrained conditions due tovarious lateral confining pressures. Applied lateralconfining pressure had a range from 25 kPa to 100
kPa, and required compression speed correspond tocreate 20 % in axial strain duration 24 hours. Excess
pore water pressures measured and evaluated thechanging effective stresses including the influence of
two lateral confining pressures. Then, effective mean
principal stress was verified to three difference dry
densities for determination of failure criterion, and it
leads to estimate coefficient of friction and strengthparameters such as shear resistance angle andapparent cohesion. While cyclic loading triaxial test
under lateral confining pressure of 100 kPa constant,unsaturated-saturated specimens were used forcomparison of delete suction in specimen. Controlled
initial cyclic stress ration was a range from 0.24 to
1.24, and required external loading frequencies were
0.5 Hz and 0.05 Hz. Observing that deviator stress,
axial strain, excess pore-water pressure wereCYCLIC MOBILITY FOR UNSATURATEDBENTONITE
The cyclic triaxial test apparatus performed wellwhen measuring the axial strain of unsaturated
bentonite having a dry density of 1.153 Mg/m3
corresponding to 85 % of maximum dry density
178

GEOMATE – Bangkok, Thailand, 22-24 November 2022

80

40

20
10

Compression side

0-10Initial cyclic stress ratio: 0.51

-20
-30

Lateral confing pressure：100 kPa

40
20
0

Initial cyclic stress ratio:
0.75（Solid line）

-20
-40

Initial cyclic stress ratio: 1.32（Broken line）-60

Expansion side

Expansion
side

-80

-40
-1

-0.5

0
Axial strain %

0.5

-1

1

Fig. 2 Cyclic mobility at loading frequency of 0.51.

20Compression side

Loading
frequency：0.5 Hz
Frequency formeasurement：
20Hz

0Lateral confingpressure：100kPa

-20
-40
-60

0
Axial strain %Initial cyclic stress ratio：1.09

Initial dry density：
1.221Mg/m3（90% of maximum dry
density ）Saturation

60

Deviator stress kPa

40

-0.5

0.5

1

80

Initial dry density：
1.153 Mg/m3（85 % of mamimum
dry density）
Unsaturation

60

Lateral confining pressure：100kPa

Fig. 4 Reduction amplitude stress (Dry density of
1.153 Mg/m3)

80

Deviator stress kPa

Compression side
Initial dry density：
1.153 Mg/m3（85 % to m,aximum dry
density）
Saturation

60

Deviator stress kPa

Initial dry density：1.153
Mg/m3(85 % of mamimum dry
density）
Unsaturation30

Deviator stress kPa

Loading frequency：
0.5 HzFrequency formeasurement ：20 Hz

40
20
0

Initial cyclic
stress ratio: 0.57
（Solid line）-20

Lateral confiningpressure：100kPa-40
Expansion
side

-60

Expansion side

Compression
side

Initial cyclic stresss ratio1.24（Broken line）

-80

-80
-1

-0.5

0

0.5

1

Axial strain %Fig. 3 Cyclic mobility at loading frequency of 1.09.

-1

-0.5

0
Axial strain %0.5

Fig. 5 Reduction amplitude stress (Dry density of
1.221 Mg/m3)

as described in Figs. 2 and 3. With two externalloading frequencies of 0.51 and 1.09, deviator stress(i.e. amplitude stress) was measured with axial strain
during cyclic loading. It would be appropriate to growaxial strain in compression side, and growing of axial
strain is relatively small value. Then, axial strain
increased over two times due to increase initial cyclic
stress ratio, and it was typical small hysteric loop. It
seemed to be extremely small for plastically
deformations than elastic behavior. The specimen
was not approached to liquefaction failure mode.
Compress stress comparisons to expansion stress, andit surpass during cyclic loading.

density, two figures were prepared, and reported themuch growing of axial strain when dry density was
1.153 Mg/m3. In addition, axial deformation reachedsufficiently to compression side. Therefore, it wasobserved that saturated bentonite with low dry density
described compression deformation due to first cyclicloading.
CYCLIC MOBILITY WITH
LOADINGFREQUENCY OF 0.5 HZ FOR SATURATEDBENTONITE
For saturated bentonite with dry density of 1.357
Mg/m3 cyclic behaviors due to the assigned deviator
stress amplitude were investigated on cyclic stressamplitude, excess pore water pressure against to
cyclic number. Required external loading frequency
was 0.5 Hz. The obtained relationships were shownin Figs 6 to 8. Produced axial deformation notachieved to failure condition due to cyclic loading,because saturated specimen had relatively dense, andcyclic mobility seemed to be not typed flow (i.e. noliquefaction) as shown in Fig. 6. It proved that excess
pore water pressure during cyclic loading was not
much, and measured excess pore water pressure ratio
was less than 0.05 as shown in Fig. 8.

CYCLIC MOBILITY WITH
LOADINGFREQUENCY OF 0.5 Hz FOR UATURATEDBENTONITE
Saturated specimens were prepared that had dry
density of 1.221 Mg/m3 and 1.153 Mg/m3,
respectively. Each dry density corresponds to 90 %and 85 % to maximum dry density. Applied initial
cyclic stress ratio had a range from 0.57 to 1.32 thatexternal loading frequency was common, and it was
0.5 Hz. Figs 4 and 5 show the typical cyclic stressstrain relationships of saturated bentonite,
respectively. For the sake of comparison in dry
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1GEOMATE – Bangkok, Thailand, 22-24 November 2022

200

200

Deviator stress kPa

150
100

50

Initial cyclic stress ratios：0.248,
0.494

Compression side0
Expansion side-50

Lateral confingpressure：100kPa-100
Loading frtequency：0.5 Hz
Frequency for measurement ：
20Hz

-150
-200
-1

-0.5

Initial dry density：
1.357Mg/m3（Maximu dry density）
Saturation

150

Deviator stress kPa

Initial dry density：
1.357 Mg/m3（Maxium dry density）
Saturation

100
50

Lateral confiningpressure ：100kPa

0

Expansion side
Loading
frequency：0.05Hz
Collect frequency：
2 Hz

-50
-100

0

0.5

1

Fig. 6 Reduction amplitude stress (Dry density of
1.357 Mg/m3)

-200
-1

-0.5

1

1.5

Cycle stress ratio

Cyclic stress ratio

0.5

1

2
Loading frequency：0.5 Hz
Frequency for measurement：20Hz

1.5

Compression side
Lateral confing pressure：100 kPa

0-0.5
Expansion side

-1.5

0
Axial strain %Fig. 9 Stress-strain behavior (Dry density of 1.357
Mg/m3)

2-1

Initial cyclic stressratio ：1.09-150

Axial strain %0.5

Compression side

Initial dry density 1.357 Mg/m3（Maximum dry density）
Saturation

-2
0.05

0.5

-0.5

-1.5

Frequency ：0.05Hz
Frequency formeasurement ：2Hz

Initial dry density：
1.357Mg/m3（100 % of maixmum
dry density）
Saturation

-2
0.05

Lateral confiningpressure：100kPa

Expansion side

0.5

50

Fig. 7 Observation of amplitude stress (Dry density of
1.357 Mg/m3)

Compression
side

0

-1

Initial cyclic stressratio: 0.248, 0.494

0.55
Number of cycles

1

Initial cyclic strass ratio: 0.242

550

Numberr of cycles

Fig. 10 Decreasing of amplitude stress
0.05

0.05
0.03

Loading frequency：
0.5 HzFrequency formeasuirement：20 Hz

Excess pore water pressure ratio

Excess pore water pressure ratio

0.07
Latreral confingpressure ：100kPa

0.01
-0.01

Initial cyclic stress ratio : 0.248

-0.03
-0.05
-0.07
0.05

Initial cyclic
stress ratio:
0.494

Initial dry density：1.357Mg/m3（Maximum dry density）
Saturation

0.5

5

Initial cyclic strass ratio 0.242

0.03
0.01
-0.01
-0.03
-0.05
0.05

50

Loading frequency：0.05Hz
Frequency for measurement：2Hz

Initial dry
density：
1.357Mg/m3（ Maxium dry
density）
Saturation

Lateral confining pressure：100 kPa

0.5

5
Number of cycles

Number of cycles

Fig. 11 Measurement of excess pore water pressure.
Fig. 8 Growing of excess pore water pressure (Drydensity of 1.357 Mg/m3)
behavior under undrained condition corresponding tocyclic loading amplitude ratio as shown in Fig. 7.

Excess pore water pressure was namely zero,
when initial cyclic stress ratio was 0.248. Beyond theassigned cyclic stress ratio was 0.484, occurrence ofexcess pore water pressure was obviously andremained same value duration of cyclic loading.
Certainly, excess pore-water pressure

CYCLIC MOBILITY WITH
LOADINGFREQUENCY OF 0.05 Hz FOR UATURATEDBENTONITE

180

50

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Geosynthetic
barriersforenvironmental
protection at landfills, In Geosynthetics, 8thInternational Conference on Geosynthetics, 2006,
pp. 121-152.
[2] Bannour H., Stoltz G., Delage P. and Touze-Foltz
N., Effect of stress on water retention of needle
punched geosynthetic clay liners, Geotextiles andGeomembranes, 42(6), 2014, pp. 629-640.
[3] Seiphoori A., Laloui L., Ferrari A., Hassan M.
and Khushefati W. H., Water retention andswelling behaviour of granular bentonites forapplication in Geosynthetic Clay Liner (GCL)
systems, Soils and Foundations, 56(3), 2016, pp.
449-459.
[4] Beddoe R. A., Take W. A. and Rowe R. K.,
Development of suction measurement techniques
to quantify the water retention behaviour of
GCLs, Geosynthetics International, 17(5), 2010,
pp. 301-312.
[5] Fox P. J. and Stark T. D., State-of-the-art report:
GCL shear strength and its measurement–ten-year
update, Geosynthetics International, 22(1), 2015,
pp. 3-47.
[6] Giroud J. P., Badu-Tweneboah K. and Soderman
K. L., Comparison of leachate flow throughcompacted clay liners in landfill liner systems.
Geosynthetics International, 4(3-4), 1997, pp.
391-431.
[7] Giroud J. P., Rad N. S. and McKelvey J. A.,
Evaluation of the surface area of a GCL hydrated
by leachate migrating through geomembrane
defects. Geosynthetics International, 4(3-4), 1997,
pp. 433-462.
[8] Giroud J. P. and Soderman K. L., Criterion for
acceptable bentonite loss from a GCL
incorporated into a liner system, Geosynthetics
International, 7(4-6), 2000, pp. 529-581.
[9] Yamamuro J. A. and Lade P. V., Staticliquefaction of very loose sands, Canadian
Geotechnical Journal, 34(6), 1997, pp. 905-917.
[10] Lade P. V., Yamamuro J. A. and Liggio Jr, C. D.,
Effects of fines content on void ratio,
compressibility, and static liquefaction of silty
sand, Geomechanics and Engineering, 1(1), 2009,
pp. 1-15.
[11] Monkul M. M. and Yamamuro J. A., Influenceof silt size and content on liquefaction behaviorof sands, Canadian Geotechnical Journal, 48(6),2011, pp. 931-942.
[12] Wang H., Koseki J., Sato T., Chiaro, G. and Tian
J. T., Effect of saturation on liquefaction
resistance of iron ore fines and two sandy soils,
Soils and Foundations, 56(4), 2016, pp. 732-744.

The difference external loading frequency was
attempted to saturated bentonite with dry density of
1.357 Mg/m3 which was 0.05 Hz. Similar factors tofrequency of 0.5 Hz as shown in Figs. 8 to 10 wereobserved, and the obtained results were provide asshown in Figs. 9 to 11. It seemed to be extremely
different cyclic mobility associated to cyclic stress,
and it was worth mentioning that brittle failure at thesmall external loading frequency at initial stress ratioof 1.09. From the cyclic number and stressrelationships, it can be seen that further growing ofstress was observed. Other hands, excess pore waterpressures seemed to be not accumulated. The externalloading frequency compare that the difference
frequency induced further difference cyclic mobility
phenomena for saturated bentonite. Dense saturated
bentonite had the resistance against liquefaction. Notethat excess pore water pressure was certain small, andnot achieve to zero duration cyclic triaxial test. yournative language is not English and if you feel that you
need to go through, please get a native Englishspeaking colleague to proofread your paper.
CONCLUSIONS
This
study
conducted
outmonotoniccyclicmobility triaxiasl test under undarained
condition for bentonite used for geosynthetic clay
liners, which had appeared shear strength parametersand ewxplained interpetation of cyclic strength strain
properties with the influemce of various dry densities.
The obtained results are summary as following.
Determined effective stress paths composite two
total principal stresses and pore-water pressure
descibed typical failure criterion line without
instabiolity point mentioned by Yamamuro et al.
(1997). Estimated angle of internal friction (i.e. shear
resistance angle) was certanly, and the influence ofdry denmsity was not so much. Other hands, apparenycohesion increased according to dense.
For unsaturated bentonite it can be seen thatgrowing of axial strain was silightly in cyclic mobility.
Beyound saturation, increement of initial cyclic stressratio enmphased to produce large deformaions toward
compression side. It was however to measure samll
excess pore-water pressure duration cyclic amplitude
stress, and achieveing of liquefaction can be not seenin this testing coditions.
REFERENCES[1] Kavazanjian Jr. E., Dixon N., Katsumi T.,
Kortegast A., Legg P. and Zanzinger H.,

181

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

PROPERTIES AND PERFORMANCES OF SOIL CEMENT MODIFIEDWITH CONCENTRATED PARA-RUBBERSupathinee Kowsura1, *Susit Chaiprakaikeow1, Apiniti Jotisankasa1, Suphawut Malaikrisanachalee1,
Supakij Nontananandh1, Korakod Nusit 2, Auckpath Sawangsuriya3 and Shinya Inazumi4
1

Department of Civil Engineering, Kasetsart University, Thailand;
Department of Civil Engineering, Naresuan University, Thailand;
3
Bureau of Road Research and Development, Department of Highways, Thailand;
4
Department of Civil Engineering, Shibaura Institute of Technology, Japan
2

ABSTRACTDue to the slump of the price of Para-rubber, the Department of Highways of Thailand has adopted the rubber
to be used in the quality improvement of cement stabilized materials. Hence, this research aims to study thechanges of properties of soil cement modified with concentrated Para-rubber. Lateritic soils were mixed withcement, at the ratio of 1, 3 and 5%, and Para-rubber, at the ratio of 1 and 2% of dry soil weight. The shear wave
velocity (Vs), unconfined compressive strength (UCS), strain at failure, indirect tensile strength (IDT), resilient
modulus (MR), fatigue resistance (FR) and soil permeability (k) were tested after 7 days of curing. The results
indicated that UCS increased with the increasing of cement ratio but tended to decrease with the increasing of
Para-rubber which were consistent with the results of Vs. For the performance tests, IDT and MR tended to decrease
while FR and k tended to increase with the increasing of Para-rubber. It was expected that Para-rubber affected thecompaction quality and, probably, the creation of hydration reaction. Conclusively, the addition of cement
increased the brittleness of the material resulting in higher strength. On the other hand, the addition of Para-rubberincreased the ductility resulting in lower strength but higher deformation and higher fatigue crack resistance.
Keywords: Para-rubber, Cement, Strength, Shear Wave Velocity, Performance

INTRODUCTION(Vs), unconfined compressive strength (UCS), strain
at failure, indirect tensile strength (IDT), resilient
modulus (MR), fatigue resistance (FR) and soil
permeability (k).

The Department of Highways of Thailand (DOH)
has been using cement to improve road structure
quality for a long time. By mixing cement with soil
and water, the compressive strength of soil cement(SC) is increased due to the hydration reaction
resulting in its ability to carry higher load [1]-[4].
However, due to the slump of the price of Pararubber, DOH has been assigned to use Para-rubber intheir missions with the intention to increase theconsumption of Para-rubber in the country. One ofthem is the use of Para-rubber to improve theproperties of SC. Hopefully, adding more amount of
Para-rubber into the mix will help the rubber
plantation farmers to be able to sell their productmore. However, a mixture of natural rubber latex
may affect strength and properties of the materials[5]-[7]. Though, most of the studies used natural latex
that has dry rubber content (DRC) of around 30% by
mass, hence, the use of more Para-rubber ispreferable as it can enhance the consumption rate.
With the purpose of doing that, the study of theeffects of mixing higher amount of Para-rubber into
SC is necessary.
The objective of this study is to study the effectsof Para-rubber on properties and performances of soil
cement modified with concentrated Para-rubber(SCP). The tests comprise of shear wave velocity

BASIC PROPERTIES OF MATERIALSThe soils used in this study were lateritic soils
from 2 sources, Chainat province and Chonburi
province. The soils were tested for their basic
properties according to [8]-[13]. The results of thebasic properties and the soil classifications are shownin Table 1. The grain size distributions are shown in
Fig. 1. The results showed that the lateritic soils fromboth sources met the requirements of [14]-[15].
However, the lateritic soil from Chainat was a moresuitable material for road structure as it has lower
plasticity, lower fine content, higher compacted dry
density, higher California bearing ratio (CBR) value,and lower percent loss by Los Angeles abrasioncompared to the lateritic soil from Chonburi.
The cement used was Portland cement type 1. The
Para-rubber latex used was concentrated latex type
HA (High Ammonia) that contains no less than 60%
of DRC. Nonionic surfactant NP9 (Nonylphenol
Ethoxylate 9 MOLES) was also used to help
preventing the rubber particles from clumping before
mixing with the aggregates.

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 1 Basic properties of lateritic soils
Table 2 Number of samples for FFR and UCS tests
Basic properties
Liquid Limit
Plasticity Index
Maximum Dry
Density (kN/m3)
Optimum Water
Content (%)
Percent Loss (%)
CBR (%)USCS*AASHTO**

Chainat
Non-Plastic
Non-Plastic

Chonburi
26.00
9.51

22.86

20.40

5.30
39.14
46.2
SM
A-1-b

10.00
49.64
14.4
SC
A-2-4

Cement(% byweight)
1
1
1
3
3
3
5
5
5

*Unified Soil Classification System**American Association of State Highway and Transportation

100

Percet Finer (%)

Chainat(Sample)
5
5
5
5
5
5
5
5
5

Chonburi(Sample)
5
5
5
5
5
5
5
5
5

Free-free resonance (FFR) test
Chainat
Chonburi80

FFR test is a low strain, undisturbed, dynamic test
that is simple, reliable and has been widely used todetermine the resonant frequency and shear velocity
of materials [16]-[19]. In this study, the sample wasplaced on the sponge to create a free-boundary
condition. An accelerometer was attachedhorizontally at the side of one end, perpendicularly tothe long side, of the sample. Shear waves weregenerated by vertically tapping the sample at the sideof the other end, perpendicularly to the orientation of
the accelerometer, using a small impact hammer asshown in Fig. 2a. The resonant, maximum peak
amplitude, frequency was recorded, then shear wave
velocity was analyzed using the following equation,

60
40
20
0
100.00Fig. 1

Para-rubber(% byweight)
0
1
2
0
1
2
0
1
2

10.00

1.00
0.10
Diameter (mm)0.01

Grain size distribution curves of lateritic
soils from Chainat and Chonburi

METHODOLOGY

Vs = f  = 2 fL

Shear
Wave
Velocity
andUnconfinedCompressive Strength Determinations

where f is resonant frequency, λ is wavelength and L
is sample length.

Sample preparation for shear wave velocity andunconfined compressive strength determinations

Unconfined compressive strength (UCS) test

(1)

After a completion of FFR test, the same sample
was put into the universal testing machine (UTM) for
UCS test according to [20]. The shearing rate was 2
mm/min. The UCS and strain at failure of the samples
were recorded for further analysis. An example of thetest is illustrated in Fig. 2b.

Samples from both sources were compacted withmodified Proctor test in the mold with an innerdiameter of 101.6 mm and a height of 116.43 mm byconsidering the use of cement content of 1, 3 and 5%
and the use of Para-rubber of 0 (SC), 1 and 2% of dry
soil weight. The surfactant used was 5% of cement
weight. To be noted that the amount of added waterfor compaction was calculated from the optimum
water content subtracted the amount of Para-rubberand surfactant. Five samples per mixture ratio wereprepared to study the effect of cement and Pararubber contents on Vs and UCS as shown in Table 2.
After the compaction, the samples were wrapped in aplastic sheet and cured for 7 days. Then, the samples
were immersed in water for 2 hours before
conducting the free-free resonance (FFR) test andthen UCS test.

Laboratory Performance Tests
Admixture design and sample preparation forlaboratory performance tests
After the UCS-cement content analysis, themixing ratio was determined for laboratory
performance tests, including IDT, MR, FR, k. The
designed cement content, normally, is determined tosatisfy the required UCS, 1724 kPa after 7 days ofcuring time, according to [the DOH standard], of SC.
However, in this study, the amount of cement content
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

was depended on the 125% of normal designed
strength to avoid the potential loss of performance
during the field construction in the future. The
targeted UCS then became 2155 kPa instead. This
criterion was applied for the soils from both sources,
Chainat and Chonburi.
Three samples of SC and 3 samples of SCP from
2 lateritic sources, a total of 12 samples, were used
for each test to study the performance of thesematerials. The soils were compacted and cured for 7
days before the tests. Details of each test are
mentioned in the following topics.

The tested sample was subjected to constantrepetition of force until the specimen underwent
rupture and failure. Coincide to the MR test, thesample was subjected to a Haversine force for 0.1 s
and was allowed to recover for 0.9 s. In this study,
the tested sample was subjected to constant repetitionloads, which equaled to 30-40% and 60-70% of IDT
for soil samples from Chainat and Chonburi,
respectively, until failure. Number of cycles to failure
was recorded and used to compare the FR ofmaterials.
Soil permeability (k) test

Indirect tensile strength (IDT) test
K tests were carried out by constant head tests inaccordance with [24]. The tested soil was compacted,
following the mix design, in the molds with a dimeterof 101.6 mm and a height of 116.43 mm. The amountof water over the measured time was recorded, then
the k of each sample was calculated. Examples of k
tests are shown in Fig. 2d.

IDT tests were performed in accordance with [21].
Samples were compacted, following a modified
compaction, to a diameter of 100 mm and a height of
50 mm. The sample was arranged for testing, asshown in Fig. 2c, and was pressed until failure using
a pressing rate of 50 ± 5 mm/min. The maximum
force at failure (P, in N) was recorded. The IDT (inkPa), then, was calculated using Eq. 2

IDT =2, 000  P T  D(2)

where T is the height of the tested sample (in mm),and D is the diameter of the tested sample (in mm).
Resilient modulus (MR) test
MR tests were conducted in accordance with [22].
The size of the samples and the apparatus were assame as the IDT tests. To measure MR, the sample
was subjected to 5 cycles of constant load repetitions,each cycle was subjected to a Haversine force for 0.1
s and was allowed to recover for 0.9 s before thesecond cycle begins. The standard recommends thatthe optimum loading force in the test should be no
more than 20% of the IDT measured from the same
mixture to ensure that the material still responds
elastically in every cycle of repetitions. The lateralswelling of the sample was also measured withLinear Variable Differential Transformer (LVDT).
After the sample had been subjected to 5 repeatedload cycles, the resilient modulus was calculated
based on the repetitive force and lateral swelling of
the sample according to the equation and procedure
specified in the standard.

Fig. 2 Examples of (a) free-free resonance test (b)unconfined compressive strength test (c) indirect
tensile strength, resilient modulus and fatigue
resistance tests (d) soil permeability test
ANALYSIS AND TEST RESULTS
Effect of Cement and Para-rubber Contents on
Shear Wave Velocity
The effect of cement and Para-rubber contents on
Vs of SC and SCP from Chainat and Chonburi areshown in Fig. 3 and Fig. 4, respectively. It was foundout that Vs increased with the increasing of thecement content which agreed well with [25]-[26].
However, once Para-rubber was added into the
mixture, Vs tended to decrease with an increasing of
Para-rubber content instead. This could be that Pararubber affected the compaction quality and only
partially bound the soil particles, as shown in Fig. 5,
resulting in higher void ratio. The amount of water
replaced by the Para-rubber could also affect theFatigue resistance (FR) test
FR, or also called fatigue life, tests were
performed according to [23]. FR is a value thatindicates the strength of a material against repetitive
loads, typically represents as the number of cycles
that cause the failure of the sample. FR tests also use
the same apparatus and sample size as the IDT tests.
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

cementhydration
reaction,
however,themicrostructure analysis is required for further
investigation.

The effect of cement content and Para-rubber
contents on UCS of SC and SCP from Chainat andChonburi are shown in Fig. 6 and Fig. 7, respectively.
The results presented that at the same amount of Pararubber, the UCS of all materials increases as the
cement content increases, which was consistent with[27]-[30] and the results of FFR tests. The results also
showed that at the same amount of cement content,
the SC and SCP from Chainat had higher UCS thanthe SC and SCP from Chonburi which could be dueto the soil basic properties.
In a similar way to Vs, when considering thesamples that had the same amount of cement content,
it was found out that the addition of Para-rubber into
the mixture tended to lower UCS which agreed wellwith [31]. The loss of UCS could also come from thepoorer compaction quality and, probably, thehydration reaction generation. It is worth noting thatthe amount of Para-rubber had a greater impact onstrength of SCP from Chainat than Chonburi
province. Expecting that the lateritic soil from
Chainat is non-plastic while lateritic soils from
Chonburi has higher plasticity, resulting in a higher
cohesion between soil particles.

Shear Wave Velocity (m/s)

2000

1500

1000

Para-rubber 0% (SC)Para-rubber 1%
Para-rubber 2%

500

0
0

1

2

3

4

5

6

Cement (%)
Fig. 3 Vs of treated soils from Chainat

1500

5000
Para-rubber 0% (SC)Para-rubber 1%
Para-rubber 2%

4000

1000

UCS (kPa)

Shear Wave Velocity (m/s)

2000

Para-rubber 0% (SC)Para-rubber 1%
Para-rubber 2%5002155 kPa (125%),
cement 2.4%

3000
2000

0
0

1

2

3

4

5

6

1000

Cement (%)

1724 kPa (100%),
cement 1.9%

0

Fig. 4 Vs of treated soils from Chonburi

0

1

2
3
4
Cement (%)

5

6

Fig. 6 UCS of treated soils from Chainat
5000
Para-rubber 0% (SC)Para-rubber 1%
Para-rubber 2%

UCS (kPa)

4000
3000
2000
1000
Fig. 5 Bond of Para-rubber of SCP.

2155 kPa (125%),
cement 3.9%

1724 kPa (100%),
cement 3.0%00

Effect of Cement and Para-rubber Contents on
the Unconfined Compressive Strength

1

2
3
4
Cement (%)

Fig. 7 UCS of treated soils from Chonburi
185

5

6

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Effect of Cement and Para-rubber Contents on
Strain at Failure

Strain at Failure (%)

8

The strain at failure also measured from the UCS
tests as shown in Fig. 8 and Fig. 9 for the samples
from Chainat and Chonburi, respectively. The results
showed that the strain decreased with the increasingof cement content suggesting that cement tended toincrease the brittleness of the samples. Considering
at the low amount of cement content, the samples
from Chonburi had slightly higher strain than thesamples from Chainat due to the higher plasticity ofthe soil from Chonburi. However, when considering
at the high amount of cement content, there was notmuch different between 2 material sources as thehydration reaction may govern their behaviors.
Though, the microstructure analysis is preferred.
When Para-rubber was added into the mixture, it
was found that the strain at failure increased, thechange was clearly seen for the addition of 2% of
Para-rubber indicating that Para-rubber tended toincrease the ductility of the samples [32]. This could
be the consequence of the high flexibility of Pararubber. An addition of 1% of Para-rubber, however,provided slightly change on the strain at failure,
expecting that the rubber created interparticle bonds
only at some portions of the samples. The
microstructure analysis is, again, preferred to confirm
the notion.

5

Strain at Failure (%)

2

3
4
Cement (%)

5

6

Ratio

for

Laboratory

The results of UCS of SC were used to determine
the most appropriate admixture ratio for thelaboratory performance tests as shown in Fig. 6 andFig. 7. For the soil from Chainat, the soil required
cement content of only 1.9% of dry soil weight tosatisfy the required UCS, 1724 kPa after 7 days ofcuring time. On the other hand, the soil from
Chonburi required higher cement content, 3.0% of
dry soil weight to satisfy the same required UCS.
Though, due to the reason mentioned prior, the target
designed strength in this study was higher than theminimum requirement, at 2155 kPa. Hence, theoptimum cement content for the soils from Chainat
and Chonburi became 2.4% and 3.9% of dry soil
weight, respectively.
The amount of Para-rubber used for the
performance test was selected at 2% of dry soil
weight for both sources as it tended to increase theductility of the samples and, also, to satisfy theobjective of using high amount of Para-rubber.
Effect of Cement and Para-rubber Contents on
Indirect Tensile Strength

034Cement (%)

1

The
Admixture
Performance Tests

2

2

2

Fig. 9 Strain at failure of treated soils from Chonburi

4

1

4

0

Para-rubber 0% (SC)Para-rubber 1%
Para-rubber 2%

0

6

0

8
6

Para-rubber 0% (SC)Para-rubber 1%
Para-rubber 2%

6

The summary of the performance test results isshown in Table 3, noted that the numbers represent
the mean of 3 samples. The result indicated that themean IDT of SC were generally higher than SCP. The
samples using lateritic soil from Chonburi had higher
IDT than the lateritic soil sample from Chainat.
Expecting that cement content was an importantfactor for the increasing of the IDT of the samples. In
contrast, Para-rubber resulted in a decrease in the IDTof the samples.

Fig. 8 Strain at failure of treated soils from Chainat

Table 3 Summary of the performance test results
Test
186

Chainat

ChonburiGEOMATE – Bangkok, Thailand, 22-24 November 2022

IDT
(kPa)
MR
(MPa)
FR
(No. ofcycle tofailure)k(cm/sec)

soil from Chonburi, it was found out that the k of bothSC and SCP were not much different. It is expectedthat the soil from Chonburi had more fine particles
leading to a less effect on k. Also, as a result, thesamples from Chonburi had lower k than the samples
from Chainat because it had higher fine content andhigher plasticity.

SC

SCP

SC

SCP

424

234

513

428

15,234

10,103

7,435

6,547

64

964

28

458

CONCLUSIONS AND RECOMMENDATIONS

4.3x10-7

3.0x10-6

1.4x10-8

1.3x10-8

The study showed the effect of cement (1, 3 and
5% of dry weight) and Para-rubber (60% DRC at 1
and 2% of dry weight) contents on the properties andperformances of SC and SCP from 2 lateritic soils
that have different basic properties. The results of thestudy showed that the increase of cement content
resulted in higher UCS and Vs. However, the additionof Para-rubber tended to decrease both properties. On
the contrary, the strain at failure increased with theincreasing of Para-rubber. This suggested that the
Para-rubber might affect the compaction quality due
to the partial bonds of Para-rubber and, probably,
affected the creation of hydration reaction due to thesubstitution of water to Para-rubber. Nonetheless,microstructure analysis is recommended for further
study.
The performances of the materials were also
affected by the addition of Para-rubber. The IDT and
MR tended to decrease, while FR and k tended toincrease after adding 2% of Para-rubber into the
mixture. It can be concluded that the use of cement
increased the brittleness of the material, resulting inhigher strength, while the use of Para-rubberincreased the ductility, resulting in lower strength but
could be benefited from higher deformation andhigher fatigue crack resistance.
However, this study did not cover the durability
against wet-dry cycles which is important for the
pavement structure design of tropical countries
Hence, further study on the test is also recommended.

Effect of Cement and Para-rubber Contents on
Resilient Modulus
The test results of MR are also shown in Table 3.
An average MR of SC was generally higher than SCP.
It was also found that MR measured from the samples
from Chonburi was lower than the samples from
Chainat which was in contrast with the results of IDT.
Expecting that the quality of lateritic soils is an
important factor to the increasing of MR ofcompacted samples. Para-rubber, again, resulted in adecrease in MR of the samples.
Effect of Cement and Para-rubber Contents on
Fatigue Resistance
The results, shown in Table 3, indicated that SC
had lower FR than SCP when considering at the sameamount of applied stress ratio. However, it isnoteworthy that the test that uses equivalent appliedstress ratio is the test that many researchers have beenused to compare the performance of studied materials
assuming that those materials have similar response
under the same repetitive load. However, the appliedstress ratio clearly depends on the IDT of eachmaterial. In this study, the applied stress ratio was
40% for the soil from both sources, however, theapplied loads were different, 170 kPa and 94 kPa forthe soil from Chainat and Chonburi, respectively.
This study also only limited to the laboratory
condition as the samples were cured under controlled
conditions, wrapped in room temperature. However,
the materials on the construction sites are normally
exposed so there might be some deterioration due tothe environment, temperature, humidity etc. which
could affect the performance of the materials.

ACKNOWLEDGEMENTS
The authors would like to thank DOH for thefunding support. The kind supports from the staff of
DOH and Kasetsart University during the course ofthis study are also appreciated.
.

7. REFERENCES[1] Terashi M., Theme Lecture: Deep Mixing
Method-Brief State of the Art, In Proceeding of
the 14th International Conference on Soil
Mechanics and Foundation Engineering, Vol. 4,
1997, pp. 2475-2478[2] Horpibulsuk S., Katkan W., Sirilerdwattana W.,
and Rachan R., Strength Development in Cement
Stabilized Low Plasticity and Coarse Grained
Soil: Laboratory and Field Study, Soil andFoundation, Vol. 46, No. 3, 2006, pp. 351-366

Effect of Cement and Para-rubber Contents on
Soil Permeability
The results of k test, presented in Table 3,
indicated that the k of SC prepared from the lateritic
soil from Chainat was lower than SCP, suggestingthat the SCP had higher void ratio than SC. This
could also specify that the Para-rubber affected thequality of compaction or even the generation of
hydration reaction. However, when using the lateritic
187

GEOMATE – Bangkok, Thailand, 22-24 November 2022

[3] Cortez E. R., Moisture effects on the mechanicalbehavior of flexible pavement subgrades, Ph.D.
Dissertation, University of New Hampshire,
Durham, NH, USA, 2007
[4] Ribeiro D., Neri R., and Cardoso, R., Influence ofWater Content in the UCS of Soil-Cement Mixtures
for Different Cement Dosages, Procedia
Engineering, Vol. 143, 2016, pp. 59-66[5] Kantatham K., Horpibulsuk S., Suddeepong A.,
Buritatum A., Hoy M., and Takaikaew T., Effect ofNatural Rubber Latex on the Compressive Strengthand Durability of Cement Stabilized Soil, Suranaree
J. Sci. Technol., 28(3):030054(1-5), 2020
[6] Paotong P., Jaritngam S., and Taneerananon P., Useof Natural Rubber Latex (NRL) in Improving
Properties of Reclaimed Asphalt Pavement (RAP),
Engineering Journal, Volume 24 Issue 2, 2020,
DOI:10.4186/ej.2020.24.2.53[7] Jose A., and Kasthurba A.K., Laterite soil-cement
blocks modified using natural rubber latex:
Assessment of its properties and performance,
Construction and Building Materials, Volume 273,
2021, 121991[8] Department of Highways of Thailand, Standard Test
No. DH-T. 102/ 1972, Method of Test for
Determining the Liquid Limit (LL) of Soils (in Thai)[9] Department of Highways of Thailand, Standard Test
No. DH-T. 103/ 1972, Method of Test for
Determining the Plastic Limit (PL) and Plasticity
Index (PI) of Soils (in Thai)[10] Department of Highways of Thailand, Standard
Test No. DH-T. 205/1974, Method of Test for Wet
Sieve Analysis (in Thai)[11] Department of Highways of Thailand, Standard
Test No. DH-T. 108/ 1974, Method of Test for
Modified Compaction Test (in Thai)[12] Department of Highways of Thailand, Standard
Test No. DH-T. 202/ 1972, Method of Test for
Resistance to Degradation of Coarse Aggregate by
Los Angeles Abrasion Machine (in Thai)[13] Department of Highways of Thailand, Standard
Test No. DH-T. 109/ 1974, Method of Test for
Determining the CBR (in Thai)[14] Department of Highways of Thailand, Standard
No. DH-S. 2 0 4 / 2021, Standard for Soil Cement
Base (in Thai)[15] DepartmentofHighwaysofThailand,Specification for Natural Rubber Modified Soil
Cement Base Course, 2017 (in Thai)[16] Ryden N., and Ekdahl U., Quality control of
cement stabilized soil using non-destructive
seismic tests, Advanced Testing of Fresh
Cementitious Materials, Stuttgart, Germany, 2006[17] Guimond Barrett A., Nauleau E., Le Kouby A.,
Pantet A., Reiffsteck P., and Martineau F., Free freeresonance testing of in situ deep mixed soils,
Geotechnical Testing Journal, vol. 36, no. 2, 2012[18] Verastegui-Flores R.D., Di Emidio G., Bezuijen
A., Vanwelleghem J., and Kersemans M.,
Evaluation of the free-free resonant frequency
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treated soil, Soils and Foundations, 55(5), 2015, pp.
943-950[19] Barus, R.M.N., Jotisankasa A., Chaiprakaikeow S,
and Sawangsuriya A., Laboratory and field evaluationof modulus-suction-moisture relationship for a silty
sand subgrade, Transportation Geotechnics, 19, 2019,
pp. 126–134[20] Department of Highways of Thailand, Standard Test
No. DH-T. 105/1972, Method of Test for Determining
the Unconfined Compressive Strength of Soils (inThai)[21] American Society for Testing and Materials, ASTM
D6931-17, Standard Test Method for Indirect Tensile(IDT) Strength of Asphalt Mixtures[22] American Society for Testing and Materials, ASTM
D7369-11, Standard Test Method for Determining theResilient Modulus of Bituminous Mixtures by Indirect
Tension Test[23] British Standard Institution, Method for determinationof the fatigue characteristics of bituminous mixturesusing indirect tensile fatigue, DD ABF, 1995.
[24] American Society for Testing and Materials, ASTM
D2434, Standard Test Method for Permeability ofGranular Soils (Constant Head)[25] Chaiprakaikeow S., Soponpong C., and Sukolrat J.,
Development of a quality control index of cement
stabilized road structures using shear wave velocity,
In: Proceedings of the 2nd World congress on civil,
structural, and environmental engineering (CSEE’17),
ICGRE 113, April 2 – 4, 2017, Barcelona, Spain[26] Lindh P., and Lemenkova P., Resonant Frequency
Ultrasonic P-Waves for Evaluating Uniaxial
Compressive Strength of the Stabilized Slag–Cement
Sediments. Nordic Concrete Research, 65(2), 2021,
pp. 39-62[27] Kamdee S., Thongmunee S., and Jitsangiam P.,
Effects of Fine Content and Cement Content on
Unconfined Compressive Strength of Lateritic SoilCement, The 25th National Convention on Civil
Engineering, July 15-17, 2020, Chonburi, Thailand (inThai)[28] Saroglou H., Compressive Strength of Soil Improved
with Cement, International Foundation Congress andEquipment Expo, March 15-19, 2009, Orlando,
Florida, United States[29] Horpibulsuk S., Rachan R., Chinkulkijniwat A.,
Raksachon Y., and Suddeepong A., Analysis ofstrength development in cement-stabilized silty clay
from microstructural consideration, Construction and
Building Materials 24, 2010, 2011-2021[30] Jaritngam S., Yandell W.O., and Taneerananon P.,
Development of Strength Model of Lateritic SoilCement, Engineering Journal Vol 17, Issue 1, 2013[31] Pinwiset K., Raksuntorn W., and Witchayangkoon B.,
An Investigation and Test of Natural Rubber Latex
Soil Cement Road, International Transaction Journalof Engineering, Management, & Applied Sciences &Technologies, Vol. 9, No. 2, 2018, pp. 67-74[32] Veena U., and James N., Natural Rubber Latex for
Improving Ductility Characteristics of Soil: A
Preliminary Experimental Investigation, Geotech
Geol Eng, 2022, https://doi.org/10.1007/s10706-02202162-1

188

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

NUMERICAL STUDY OF A HYBRID COUNTERMEASURE FOR
RIVER EMBANKMENT IN ACTUAL FIELD CASE
Kakuta Fujiwara1, Enayat Mallyar1
1

Dept. of Civil Eng., Tokai University, Japan
ABSTRACTIn this study, a hybrid countermeasure for river embankments is proposed consisting of a wall of sheet-piles
installed on one side of the toe of embankment and partial floating sheet-piles (PFS) installed on the other. Previous
studies show that the PFS method is effective against embankment settlement caused by liquefaction of loose sandand consolidation of soft clay. However, such previous studies were carried out on simple model cases, and hybrid
countermeasures on actual embankments have not been thoroughly studied. In this study, the authors have
performed numerical analyses to verify the effectiveness of the hybrid countermeasure applied to actual field cases.
It was concluded that the settlement of embankment was reduced by the hybrid countermeasure in the actual fieldcase. Additionally, authors obtained a result for design that the maximum bending stress among the different lengthof the sheet-piles were almost same after consolidation.
Keywords: Numerical analysis, Liquefaction, Countermeasure, PFS, Three-dimension

INTRODUCTION

numerical analysis to verify the effectiveness of the
PFS method for a river embankment underliquefaction. They concluded that the PFS could
inhibit settlement of the embankment underliquefaction. Furthermore, the relationship between
the parameters of the sheet-pile, such as the distance
between the end-bearing sheet-piles and the length of
the floating sheet-piles, as well as their effectivenessagainst embankment settlement was quantitatively
verified.
However, these analyses were carried out under
several simple assumptions as follows:• Vertical load was applied to the surface groundinstead of modelling of an embankment.
•The ground had only two sand layers.
•A sinusoidal wave with enormous acceleration wasgiven as an input.
•Bending stress of sheet-piles were not discussed formaking a design method
Therefore, in this study, the authors carried out
numerical analyses to investigate the effectiveness ofthe hybrid countermeasure against settlement in an
actual field case. Additionally, the authors discuss thebending stress of the sheet-piles in the PFS for the
purpose of designing this countermeasure.

Loose sand layers along river embankments arecontinuously damaged and settled due to liquefaction
caused by earthquakes. As a conventionalcountermeasure, sheet-piles are installed at both toesof the embankment to potentially prevent damage tothe embankment as well as settlement along theembankment, according to Fujiwara et al [1]. There
are several examples of river embankments in Japanthat demonstrate the effectiveness of sheet-piles
against settlement due to earthquakes (Fig. 1(a)).
However, a layer of soft clay on the ground near
the river embankment settles due to consolidation
caused by the weight of the embankment. Otani [2]studied the partial floating sheet-pile (PFS), which
consists of partially floating sheet-piles and endbearing sheet-piles (Fig. 2), applied to one side of thetoe of the embankment in a residential area (Fig. 1(b)).
Nowadays, a “hybrid countermeasure” (a termcoined by the authors in this paper) is in use (Fig.1(c)), where an embankment having clay and sandlayers has sheet-piles installed in river areas and the
PFS installed in residential areas along both toes ofthe embankment. Yamamoto et al [3] investigatedcertain examples where the damage to a riverembankment, due to consolidation and liquefaction
during long-term including an earth-quake event was
reduced through the use of hybrid countermeasures.
Such hybrid countermeasures could be effective
against soil deformation due to liquefaction andconsolidation. However, the behavior andeffectiveness of these hybrid countermeasures are notfully understood.
As a first step, Fujiwara et al [4] carried out

NUMERICAL CONDITION
The numerical analyses were conducted using
LIQCA3D20. The target embankment is constructedon six horizontal layers of ground shown as Fig.3.
The width and depth of this model was relatively
small for reduction of calculation time. Model size
should be considered in the future. To perform thenumerical analysis, parameters such as dimensions,

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Soils
The soil parameters used in the numerical model
are shown in Table 1. Some pieces of information(soil species, N value, water level) could be gained bya ground survey. Detail information (stress-strain
relationship, liquefaction curve, consolidation test
result, and so on) were not gained. Therefore, theseparameter values were inferred and determined fromthe survey results referring to the manual of LIQCA
by Liquefaction Geo Research Institute [5] and theacademic paper by Fujiwara et al [6].
The embankment and basement (Dg) are modeled
as elastic-perfectly plastic models. The sand layers(As1 and As2) were modeled as elasto-plastic models
created by Oka et al [7], which is often used to modela loose sand layer. This model represents sandbehavior resulting from an increase in excess porewater pressure which causes motion, making the sandsoft, and finally causing liquefaction. An elastoviscoplastic model proposed by Adachi [8], which
can represent an increase in excess pore waterpressure by the load, making the clay soft and causing
consolidation.

(a) Sheet-piles as a liquefaction countermeasure(b) PFS to prevent settlement by consolidation

Countermeasures
The sheet-piles were installed along the toe of theembankment, shown as Fig. 3, in residential area. The
PFS has three floating sheet-piles and one endbearing sheet-pile per one set shown in Fig. 4. The
sheet-piles were modeled by cuboid meshes withlinear elastic material having no yield. The PFS sheetpiles in the residential area were modeled with aflexural rigidity of 8.0×104 kN·m2 (per unit width of1 m) and a thickness of 210 mm, such that the flexural
rigidity was equivalent to that of a IVw steel sheetpile, considering corrosion allowance. The sheetpiles on the river side were modeled with a flexural
rigidity of 4.0×104 kN·m2 (per unit width of 1 m) anda thickness of 300 mm, such that the flexural rigidity
was equivalent to that of a 25H steel sheet-pile,considering corrosion allowance. As the values offlexural rigidity and the section modulus cannot be
the same as the actual values of the sheet-piles, thesection modulus of the cuboid sheet-piles was higher
than the actual value of the sheet-piles. Therefore, thebending stress (discussed herein later) of the cuboid
sheet-piles was lower than the actual value of the
sheet-piles.

(c) Hybrid countermeasure
Fig. 1 Countermeasures for the river embankment

Numerical cases
For Case 1, no sheet-piles were set in the numericalmodel. For Case 2, the target field with the hybrid
countermeasure was modeled, as explained herein
above. As a further study, the length of the endbearing sheet-piles was changed for making a design
method. For Case 3, the length of the end-bearing

Fig. 2 The PFS structuresoil parameters, and countermeasures needed to be
specified. The following sub-sections describe theground condition and these parameters and theircorresponding values.
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Fig. 3 Target embankment
Table 1 Soil parameters
Layer

Embankment
Elastic-

Model

perfectly
plastic

As1
Elastoplastic

Ac1
Elastoviscoplasti
c

As2

Ac2

Elasto-Elasto-plastic

viscoplastic

Ac3

Dg

Elasto-Elastic-

viscoplasti

perfectly

cplastic

Density

r

t/m3

1.8

1.8

1.7

1.9

1.7

1.7

2.1

Young's moduls

E0

kN/m2

14000

9810

5730

30000

7040

7040

1400000.33

Poisson's ratio

n

0.33

0.33

0.33

0.33

0.33

0.33

Cohesion

C

kN/m2

0

0

20

0

50

70

0

Friction anglefdegree

30

30

0

30

0

0

40

Coefficient of permeabilitykm/s

1.0×10-5

1.0×10-8

1.0×10-5

1.0×10-8

1.0×10-8

1.0×10-4

Initial void ratio

e0

0.821

1.25

0.856

1.25

1.25

Compression index

l

0.015

0.341

0.018

0.341

0.341

Swelling index

k

0.002

0.019

0.0055

0.019

0.019

OCR*

1.0

1.0

1.0

1.0

1.0

Initial elastic shear modulus ratio

G0/s'm

1000

75.2

851

75.2

75.2

Stress ratio at critical state

M*m

0.909

1.24

0.909

1.24

1.24

Stress ratio at failure

M*f

1.122

1.24

1.122

1.24

1.24

B*0

7000

100

2200

100

100

B*1

50

40

30

40

40

Cf

2000

10

2000

10

10

Hardening parameter

Viscoplastic parameter

Structural parameters
Scalar hardening parameter

Dilentancy parameter

Reference strain parameter

C1

1/s

1.0×10-5

1.0×10-5

1.0×10-5C2

1/s

3.83×10-6

3.83×10-6

3.83×10-6

m'

24.68

24.68

24.68

b

3.6

3.6

3.6

A*2

5.9

5.9

5.9

1.8

1.8

B*2

1.8

D0

3

5
1.5

n

3

gP*

0.02

0.1

gE*

0.01

0.015

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

sheet-pile was 27 m. For Case 4, the length of the endbearing sheet-pile was 16 m, which was the same asthat of the floating sheet-piles.

elements of the sheet-pile were connected directly tothe soil elements without any joining elements.
Therefore, soil didn’t pass through between the gap
between end-bearing sheet-piles. Accordingly, there
was little difference of settlements among Cases 2, 3
and 4 shown in Fig. 8.

Input motion
The dynamic analyses were carried out from theresults of the initial analyses, using the input motion,
which was observed in the Kumamoto Earthquake in2016 in Japan, as represented by the plot (Fig. 5). The
Japan Meteorological Agency provided the data [9].
This wave had a maximum acceleration of 3.24 m/s2,
and a duration of 43 s. The long-term analyses werecarried out after shaking, using input data of noacceleration up to t = 3.0E+07 s, which isapproximately one year.
Target points
The locations of target points D, P1, P2, and P3(discussed herein later) are selected for discussion
indicated in Fig. 6. These points are located in thecenter of this model. The point D indicates verticaldisplacement located at top of the embankment,
whereas the points P1, P2, and P3 indicate the excess
pore water pressure located at 33m, 25m and 13mfrom bottom of the model, respectively.

Fig. 4 The PFS structure

RESULT AND DISCUSSION
Deformation of groundThe ground deformation with volume strain at t =3.0E+07 s is shown in Fig. 7 (Cases 1 and 2). Timehistories of the settlement at D are shown in Fig. 8.
The horizontal displacements of the soil and sheetpiles at the toe of the embankment along z-direction
at t = 3.0E+07 s in residential area is shown in Fig. 9.
The time histories of the excess pore water pressure
at P1, P2, and P3 are shown in Fig. 10 (Case 1).
For Case 1, the liquefied sand layers laterally
moved outwards, due to the weight of theembankment (Fig. 7(a)). After the shaking, although
the void water drained out from the surface (as seenfrom the data at P1), the excess pore water pressure at
P3 increased in the clay layers (Fig. 10). This isbecause the shear force from the weight of theembankment applied to the clay layers generated thewater pressure at P3. Therefore, the excess pore waterpressure layers remained. The settlement of theembankment was 1.33 m. The settlement would
continue to raise after t = 3.0E+07 s.
For Case 2, the sheet-pies prevented the settlement
up to 1.16 m, this was 13% lesser than that in Case 1
at t = 3.0E+07 s shown in Fig. 8. The volume strain
near the left sheet-pile of the PFS reduced, whichmeans that the friction of the sheet-piles reduced theground settlement (Fig. 7(b)). For Cases 3 and 4, thesettlements were almost same. In this analysis, theFig. 5 Input motion

Fig. 6 Target point
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Excess pore water pressure (kN/m2)

0.05

0.00

(a) Case 1

0.05

200
P1

P2

P3

End of shaking (43 sec.)

150
100
50
0
1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 1.0E+06 1.0E+07
Time (sec.)

Fig. 10 Time histories for excess pore water pressure
at P1, P2 and P3 (Case 1)
36

0.00

30

(b) Case 2

24

z coordinate (m)Fig. 7 The ground deformation with volume strain

0.0
-0.2

18
Case-2

12

Settlement (m)Case-36-0.4

Case-4-0.6

0-100000

Case-1-0.8

Case-2

-1.0

50000

100000Fig. 11 Bending stress for the sheet-pile in depth
direction

Case-4-1.4
1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 1.0E+06 1.0E+07
Time(sec.)

20000
Case-2
Bending stress (kN/m2)

Fig. 8 Time histories for settlement at D
36

30

0

Case-3Case-4-20000-40000-60000-800001.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 1.0E+06 1.0E+07
Time(sec.)24
z coordinate (m)0Bending stress (kN/m2)

Case-3-1.2-50000Fig. 12 Time histories for bending stress at z = 30 m
18

Bending stress of sheet-piles
Fig 11 shows the bending stress of the end-bearing
sheet-pile obtained from Fig. 9 at t = 3.0E+07 s. For
Case 2, that is long end-bearing sheet-piles case, themaximum bending stress was generated in the lower
layer. For Case 4, that is short end-bearing sheet-piles
case, the maximum bending stress was generated inupper layer. For Case 3, the medium result between
Cases 2 and 4 was obtained.
From Fig. 9, a difference in the displacements forCases 2, 3 and 4, could be seen near the top of the
sheet-pile. The top of the short end-bearing sheet-pile
was moved to the right side by the motion of the

12
Case-1Case-26

Case-3Case-4

0
-0.6

-0.4
-0.2
0.0
Horizontal displacement (m)0.2

Fig. 9 Horizontal displacement for the PFS sheet-pilein residential area
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

ground, indicated in Case 1. There were minor
differences of bending stress in the clay layers among
Cases 2, 3 and 4, which was the same as the result ofhorizontal displacements. The clear differences along
the upper side of the sheet-pile were caused by the
deformation of soil, as mentioned earlier in the
explanation of horizontal displacement. For Case 4,
the lower side of the sheet-pile was inclined towards
the left side by the deformation of the clay layers andthe upper side was pushed to the right side, generatinglarge bending stress around z = 30 m. As the longer
the sheet-pile became, the smaller the bending stressbecame at z = 30 m. Above all, Case 4 had the highest
bending stress among Cases 2, 3 and 4.
The time histories of the peak bending stress at z =30 m are shown in Fig. 12. The bending stress forCases 2, 3, and 4 were nearly the same during andafter shaking. As the excess pore water pressure at P2
and bending stress at z = 30 m are in same layer (As2),
these values are strongly correlated, shown in Fig. 10
and Fig. 12. Since all the excess pore water pressure
did not dissipate in this analysis, the bending stresswould continue to change after t = 3.0E+07 s.

Kasama, Shinji Taenaka, Shin Oikawa, Yukihiro
Ishihara) for helpful discussions.
REFERENCES[1] Fujiwara, K., Koseki, J., Otsushi, K. andNakayama, H., Study on reinforcement method oflevees using steel sheet-piles, Foundation and SoftGround Engineering Conference, Thu Dau Mot
University ICTDMU-1, 2013, pp. 281-289.
[2] Otani, J., A new sheet-pile method forcountermeasures against the settlement ofembankment on soft ground (Development of
PFS Method), IPA News Letter, Vol. 2, Issue 3,
2017, pp. 8-10.
[3] Yamamoto, S., Kasama, K., Ohno, M. and Tanabe,Y., Seismic behavior of the river embankment improved with steel sheet piling method,
proceedings of the First International Conference
on Press-in Engineering, Kochi, 2018, pp. 227232.
[4] Fujiwara, K., Ogawa, N. and Nakai, K, 3-D
numerical analysis of partial floating sheet-pilemethod as countermeasure for liquefaction,
Journal of JSCE, Vol. 9, 2021, pp. 138-147.
[5] Oka, F., Yashima, A., Shibata, T., Kato., M. andUzuoka, R., FEM-FDM coupled liquefaction
analysis of a porous soil using an elasto-plastic
model, Applied Scientific Research, Vol. 52,
1994, pp. 209-245.
[6] Fujiwara, K., Ogawa, N. and Nakai, K.,
Quantitative evaluation of PFS (Partial floating
Sheet-pile)methodunderliquefaction.
Geotechnics for Sustainable Infrastructure
Development, 2020, pp. 467-472.
[7] Oka, F., Yashima, A., Tateishi, A., Taguchi, Y.
and Yamashita, A., A cyclic elasto-plastic
constitutive model for sand considering a plasticstrain dependence of the shear modulus,
Geotechnique, 1999, Vol. 49(5).
[8] Adachi, T. and Oka, F., Constitutive equations
for normally consolidated clay based on elasto
viscoplasticity, Journal of Soils and Foundations,Vol. 2, Issue 4, 1982, pp. 57-70.
[9] Japan
Meteorological
Agency(http://www.data.jma.go.jp/svd/eqev/data/)

CONCLUSIONS
A hybrid countermeasure for river embankment
comprising sheet-piles on the river side and the PFS
in the residential area is proposed. Numerical analysis
was carried out using LIQCA3D20 to investigate theperformance of this countermeasure, applied in theactual field, for a period of one year from anearthquake event. The clay layers were modeled byelasto-viscoplastic model. Furthermore, the behaviorof an end-bearing sheet-pile in PFS was investigatedby changing the length of the end-bearing sheet-pile.
The following conclusions were reached:1) During the shaking that accompanied the quake,the sheet-piles prevented the settlement by 13%
compared to no countermeasure case, even in theactual field case.
2) The maximum bending stress was generated in
the sand layers near surface for short end-bearing
sheet-piles case, and that was in the clay layers deep
underground for long end-bearing sheet-piles case.
Above all, short end-bearing sheet-pile had thehighest bending stress in the numerical condition.
3) Soil and sheet-piles are connected directly
without joint elements. Therefore, soil didn’t passthrough between the gap of end-bearing sheet-piles
under floating sheet-piles. Accordingly, there waslittle difference of settlements for countermeasurecases. That should be discussed in a future work.
ACKNOWLEDGMENT
We are grateful to International Press-in
Association TC3 Design WG members (Hidetoshi
Nishioka, Hiroaki Akutagawa, Jun Otani, Kiyonobu
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Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

A YIELD FUNCTION AND UNDRAINED BEHAVIOR OF K0
CONSOLIDATED CLAYS*Yoshihito Imai1, Salisa Chaiyaput2, Koichi Iinuma3, Masaru Akaishi4 and Motohiro Sugiyama4
ONODA CHEMICO Co.,Ltd., Japan, 2Department of Civil Engineering, King Mongkut's Institute ofTechnology Ladkrabang, Thailand, 3OHBA Co.,Ltd., Japan, 4Department of Civil Engineering, Tokai
University, Japan

1

ABSTRACTThe Cam Clay model is one of the typical elastoplastic soil models based on the critical state concept and its
yield function is derived from an assumption regarding the dissipation of energy during the plastic deformation.
The constant line of minimum principal effective stress for normally consolidated clay lies inside the yield surfaceof Cam Clay. An anisotropic critical state model with a yield function is formulated to represent the undrained
path of normally consolidated clays. The parameters required for the model are easy to decide from theconventional test except of one parameter, which makes the soil model practicable. It has been demonstrated thatthe calculated and observed undrained paths of normally consolidated clays are satisfactory for small strain
softening case. In addition, an anisotropic yield function is shown to give a reliable estimation of the elastoplastic
stress boundary surface that can reproduce experimental results.
Keywords: Anisotropic yield function, Earth pressure at rest, Triaxial test, Clay, Cam Clay model
INTRODUCTION

test results it is clarified that the plastic strain actuallyoccurs due to the stress change into the yield surfaceof Cam Clay model.

The greatest pressure under which clays had been
consolidated during its past geological history, is theso-called pre-consolidation pressure and called theconsolidation yield stress in Japan [1]-[2]. The yieldstress in multidimensional consolidation analysis isdefined by the yield function which means the limitof the stress space for the elastic behavior of clays.
The useful mathematical constitutive model for clays
is based on the theories of elasticity and plasticity.
The Cam Clay model developed by Roscoe et al. [3]is one of typical elastoplastic soil models based on thecritical state concept and its yield function is derived
from an assumption regarding the dissipation ofenergy during the plastic deformation. However, forthe validity of its yield function there are not stillenough for the experimental evidence, because a lineof the minimum principal effective stress for K 0
normally consolidated clays lie in the yield surface of
Cam Clay. So long as the stress state stays in the yieldsurface, its behavior must be purely elastic, even if
the applied minimum principal effective stressexceeds the maximum value to which the clay wasever subjected in the past.
The purpose of this study is to present a yieldfunction for K 0 normally consolidated clays, and to
examine experimentally its reliability in the
viewpoint of the relations of the yield stress and theminimum principal effective stress. The validity of
the yield function will be evaluated by the undrained
triaxial test result since the undrained path depends
on the yield function only and independent of theplastic potential. And also from the drained triaxial

YIELD FUNCTION AND UNDRAINED PATH
Yield function
In order to define the yield function of Cam Claymodel, it has been postulated that the external work
acting on a clay element is equal to the dissipationenergy due to the plastic deformation and that the Eq.
(1) is expressed as follow [3].
𝑝 ∗ 𝑑𝜐𝑞 ∗ 𝑑𝜀𝑀 ∗ 𝑝 ∗ 𝑑𝜀1where p is the effective mean stress, q is thedeviator stress, M is the slope of Critical State Line
(CSL), 𝑑𝜐 and 𝑑𝜀 are the increments of the plastic
volumetric and the deviator strain. By assuming that
normality rule exists and substituting Eq. (2) in Eq.
(1) it is possible to drive the yield function F.
Yielding of the clay occurs whenever the stresssatisfies the yield function, i.e., Eq. (3).
𝑑𝑞 𝑑𝜀
∗
𝑑𝑝 𝑑𝜐
𝐹

𝜂

1
𝑀 ∗ log

2
𝑝
𝑝

0

3

where η(=q/p) is the stress ratio and 𝑝 is ahardening parameter and analogous to a preconsolidation pressure.
Dafalias et. al. [4] postulated the following plastic
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CSL

behaves elastically. However, the validity for theseyield functions should be examined by theexperimental evidence.

K0L

Deviator stress q(p0 , q 0)

Undrained path
F=0
Eq.(5)

pc

The volumetric strain increment 𝑑𝜐 of a soil
element is expressed by Eq. (6), as the sum of elastic
and plastic strain components both with superscripts
e and p respectively. Using yield function F andplastic potential Q, the hardening parameter H isgiven by Eq. (7).

Effective meanstress p'

σ3 const.
line

Original
Cam ClayEq.(3)𝑑𝜐𝑑𝜐𝑑𝜐Fig.1 The yield surfaces of normally isotropic andK 0 consolidated clay
𝜕𝐹 𝜕𝑄
𝜕𝜐 𝜕𝑝

H
work dissipation equation to derive the yield function
for normally K 0 consolidated clays.

𝑝
𝐹

d𝜐
𝑞

𝑁 ∗ 𝑑𝜀2 ∗ 𝛽 ∗ 𝑑𝜐 ∗ 𝑑𝜀2∗𝛽∗𝑝∗𝑞 𝛽 ∗𝑝∗𝑞
𝑝∗𝑝
𝑁 ∗ 𝑝

0

1
𝑑𝑝
𝐾
1 𝜕𝐹 𝜕𝑄𝑑𝑞
𝐻 𝜕𝑞 𝜕𝑝6
7

where K is the elastic bulk modulus. It is possible
to calculate the undrained path in the consolidated
undrained triaxial test by making the volumetricstrain increment in Eq. (6) to be equal zero.
With the above condition 𝑑𝜐=0, Eq. (8) and Eq.
(9) yields the p-q relation derived from Eq. (3) and Eq.
(5) respectively. It should be emphasized that theundrained path is not affected by the plastic potential
Q, as 𝜕𝑄/𝜕𝑝 in the volumetric strain is eliminated inthis derivation.

𝑞 ∗ 𝑑𝜀𝑝 ∗ d𝜐

1 𝜕𝐹 𝜕𝑄
𝐻 𝜕𝑝 𝜕𝑝4(5)

where N and 𝛽 are a material constant related tothe shape of yield surface, 𝑝 is the effective meanstress in the particular case of K 0 consolidated clays.
The yield surfaces of normally isotropic and K 0
consolidated clay is shown in Fig. 1 [4]. The yieldsurface of Original Cam Clay by Eq. (3) is wellknown and widely used, and its shape looks like a leaf.
If N=M and 𝛽=𝜂 =0, Eq. (5) corresponds to that ofModified Cam Clay. An anisotropic surface with ablue line is the transformed and rotated ellipse of
Modified Cam Clay. A red line shown in Fig. 1 has
an incline of 3 in 1 in which the minimum principal
effective stress of a normally consolidated clay is held
constant. The red line is located inside of the yieldsurface given by Eq. (3) although Eq. (5) with a blueline close enough to a red line. Even if the stress state
for the over consolidated clay moves to the right sideof the red line, only elastic strains will be predictedby means of the yield function, i.e., Eq. (3). Plasticstrain never occurs even if the minimum principal
effective stress exceeds the past maximum value. As
logical consequence the definition on the yieldfunction is ambiguous.
Although the yield criterion should be noted indetermining the stress level at which plastic
deformation begins, it is not clear whether the shapeof the yield surface depends on the consolidated stressstate. It is difficult to obtain a yield function that canstrictly define the stress space in which the clay

𝜂＝

𝑝
𝑀
∗ ln
𝑝
𝛬

𝑝

𝑝

𝜂
𝜂

2∗𝛽∗𝜂
2∗𝛽∗𝜂

8
𝑁
𝑁

9

where 𝛬 1 𝜅/𝜆, 𝜅 and 𝜆 are the swelling andcompression index, N is stress ratio at the maximum
deviator stress in the undrained triaxial test, β is equal
to the stress ratio 𝜂 (= q0/p0) at K 0 consolidation.
Proposed model expressed by Eq. (9) can explain
the strain softening behavior if N is less than M. The
yield surface shown by the blue line in Fig. 1, isexpressed by using M=1.57 and N=1.35.
An example of the undrained path affected by βvalue with N=M is shown in Fig. 2. Fig. 3 shows theeffect of N value on the undrained path. When 𝜂 <M,
the deviator stress q shows the maximum value, andthen q decreases until 𝜂=M. Reduction of deviationstress, i.e. the peak drop can be calculated.
EXPERIMENTAL PROCEDURE AND CLAYSTESTED
Undisturbed saturated normally consolidated
alluvial clays were taken by the thin wall sampling forthe residential development in the suburbs of Tokyo.
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150

50

Calculated
β
1.2
1
0.844
0

CSL
M=1.57K0 =0.46

100

Deviator stress q (kPa)

Deviator stress q (kPa)100

N=M=1.57
0
0

-50

-100

CSL

150

100

Effectivemean
stress p'
(kPa)

CSL

K0
=0.46
50

M=1.57

0
0

100

-50

σ3 =const.
line
Modified
Cam clay

Effectivemean
stress p'(kPa)

σ3 =const.
line
Modified
Cam clay-100

Fig.2 Effect of β value on undrained paths

Calculated
N β1.57 1.2
1.3 1.2
1.1 1.2
1.57 0

Fig.3 Effect of N value on undrained paths

Table 1 Physical and mechanical properties of clay samples
Site
Kashiwa
Soka
Hitachi

Gs
2.643
2.672
2.657

wL

Ip

(%)

(%)

112
83
121

50
39
69

The physical and mechanical properties of the clay
sample and soil constants used in the calculation are
shown in Table 1.

λ

κ

M

K0

0.200
0.182
0.460

0.030
0.041
0.120

1.48
1.54
1.57

0.40
0.43
0.46

increase of the lateral pressure is investigated by the
undrained path. And the constants M and N wereanddetermined using the maximum stress ratio 𝜂the peak deviator stress 𝑞 , respectively in CU tests.

Consolidated Undrained (CU) Triaxial Test
Consolidated Drained (CD) Triaxial Test

In order to examine the effects of secondary
compression and the stress state in the anisotropic
consolidation on the yield surface, consolidated
undrained triaxial compression and extension tests
are performed by the strain control with the strain rateof 0.1 %/min.
The specimens with a diameter of 5 cm and aheight of 12 cm wrapped with the drain paper are
consolidated under K 0 condition before theundrained shear test and the pore water pressureduring undrained shearing is measured at the bottomof the specimen. The influence of the secondary
consolidation period on the undrained path, that is,
the yield function was investigated as remarkablesecondary compression was observed during K 0
consolidation.
The lateral pressure of some specimens wasincreased after K 0 consolidation, and anisotropically consolidated under constant axial stress for
one day. This stress change is occurred inside of theinitial yield surfaces of Cam Clay. The effect for the

The void ratio change for the over consolidated
clay was measured in order to examine the effects ofdilatancy on the initial yield surface. Overconsolidated clays ought to exhibit the elastic
behavior. No volumetric strain is observed without
the change of the effective mean stress as it is clear
from Eq. (6).
For CD tests, the saturated clay specimens are firstsubjected to K 0 normally consolidated condition.
Each stress increment duration of one day is used forcomplete drainage so that the excess pore waterpressure becomes equal to zero. The conditions forthe axial and radial stress increment with theexperimental results are shown in the latter discussion.
Back pressure is applied of 98.1 kPa in both
triaxial tests during consolidation and shearing.
TEST RESULTS AND DISCUSSIONS
pc in the isotropic consolidation test
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2.4

e0= 2.080

σy=σv= 78.5 kPa

Volumetric strain (%)

2.2
Void ratio e0e0= 2.3602
λ0.53 κ
0.37 0.05
0.36
0.30 0.03

1.8
1.6

101

κ
0.040
0.042
p < 39 kPa

5λ0.38 49 kPa < p0.51 59 kPa < p
10

λ is calculated fromthe range 78.5~157 kPa

1.4

py=50 kPa

102
103
Consolidation pressure σv (kPa)

Original p
Cam clay c =86 kPa
101

102
Effective mean stress p' (kPa)

Fig.4 e-logp curves of one-dimensional
consolidation test with Hitachi clay

Fig.5 e-logp curves of isotropicallyconsolidation test with Hitachi clay

To investigate the relationship between thehardening parameter 𝑝 of the Cam Clay model andthe consolidation yield stress 𝜎 ( 𝑝 ), a onedimensional and isotropic consolidation test was
performed on Hitachi clay. The results are shown in
Figs. 4 and 5. In the one-dimensional consolidation
test, two sets with similar sampling depths, a total offour specimens, were pre-consolidated at an
overburden pressure of 𝜎 =80 kPa. Each one of theclays was then consolidated by incremental loading(white symbols in Fig. 4), and the other clays wereunloaded to 10 kPa and then reloaded by incrementalloading (black symbols in Fig. 4).
In the isotropic consolidation test, initialconsolidation pressures of 10 kPa and 20 kPa wereapplied to two cylindrical specimens, each 5 cm indiameter and 10 cm high, and then 20 kPa loadincrements were applied in stages.
Fig. 4 shows the e-log p relation in onedimensional consolidation test. From the e-log p
curve as shown black symbols obtained by reloading
after the release of consolidation pressure, the void
ratio and compression index decrease but the verticalyield stress hardly change. Therefore, the settlement
prediction by the compression index obtained fromthe laboratory consolidation test may lead to theunderestimation. So, it is difficult to infer the actual
behavior in the field by using the relationshipmeasured from the laboratory sample released theoverburden pressure. The effective mean stress as theconsolidation yield stress obtained from onedimensional consolidation test is equal to 50 kPa
since K 0 =0.46.
According to the results of isotropic consolidation test in Fig. 5 it is possible to confirm the valueof the hardening parameter, 𝑝 because the deviator
stress is kept to zero. The value of 𝑝 as Original Cam
Clay becomes 86 kPa. As shown in Fig. 5, it is clearly
understood that the yield stress is approximately

equal to the effective mean stress obtained from of the
one-dimensional consolidation test and is muchsmaller than the hardening parameter 𝑝 in the case ofOriginal Cam Clay. Therefore, plastic strains are
occurred by the stress change inside of the yieldsurface. This leads to a doubt about the assumption of
the yield function.
Effects of quasi-overconsolidation
Before the undrained compression test, theconsolidation settlement seems to continue almost
infinitely under the sustained load as shown in Fig. 6.
No method has been proposed to determine theappropriate time for the transition to the undrained
shear test, though the undrained strength increases
due to secondary compression.
Figs. 7 and 8 show that the undrained path isaffected by the duration of quasi-over consolidation.
The calculated undrained paths are approachable tothe experimental results according to the value of
constants β in Eq. (9). Therefore, it can be consideredthat the yield surface is also influenced by secondary
compression during K 0 consolidation.
The effective stress path traverses along the path
A⇒B⇒C⇒D as shown in Fig. 9, if the lateral stressduring K 0 consolidation increases with secondary
compression. This stress change is equivalent to akind of quasi-over-consolidation because the verticalstress is kept constant. And after K 0 consolidation
the effective stress is located in the inside of the yieldsurface of Cam Clay.
It is clear that the undrained path is influenced bysuch stress history. It can be considered that there is apossibility of enlargement/deformation of the yieldsurface due to secondary compression.

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1.4

200
Deviator stress q (kPa)

K0 cosolidation

Void ratio e

σv = 98.1→196.2 kPa
End of primary time
60 min1.3

1 day
1 week

1.2
1

10

102

Observed
60 min
1 day
7

103

150

100
100

104

K0 cosol. period before CU test

Time (min)

140

K0L

300
Deviator stress q (kPa)

Deviator stress q (kPa)

130

CSL

60 min7 day

Calculated
by Eq.(5)
β= 1
1.2
1.3

150

200

120

130

σv const.
line
M.C.C
F=0
A
B

100
Calculated
M.C.C

K0 cosol. period before CU test
110

120

Fig.7 Observed undrained paths

Observed

100
100

110

Effective mean stress p' (kPa)Fig.6 Consolidation time curves

200

CSL

0
0

140

Effective mean stress p' (kPa)

C
D

100
200
300
Effective mean stress p' (kPa)

Fig.8 Undrained paths calculated with different
β values

Fig.9 Undrained paths and quasi-overconsolidation

Undrained path in the extension side

Fig. 11 show the yield surface and the change ofthe void ratio caused by the loading and unloadingalong those stress paths (A⇒B(⇒C), B⇒D, B⇒E
and 1⇒2⇒3⇒4). If the stress at a point A in Fig.
11(a) lie on the yield surface and then is loaded
beyond A to B, the clay will yield and cause theelastic and plastic strains corresponding to the normal
consolidation line. The void ratio changes along thepath A⇒B⇒C of Hitachi clay in Fig.11(b) represent
e-log p relation under K 0 compression and swelling.
On the other hand, when a clay sample brought to
B is subjected to unloading paths such as B⇒D and
B⇒E, their paths have to cause purely elastic strain.
The change of the void ratio due to the unloading path
B⇒ E must be zero. As the effective mean stressincrement is equal zero (dp=0) inside of the yieldsurface, the elastic strain increment must be zero.
However, the experimental results of two clays in Fig.
11(b) show the reduction of the void ratio. If these
reductions are coursed by plastic strains, the loading
path B⇒E must lie outside of the initial yield surface.
For the loading path B⇒D, the change of the void

Fig. 10 show the calculated and observed
undrained paths in the triaxial compression andextension tests. The subscripts C and E in the figure
for N and β mean compression and extension,respectively. As seen from these figures, the observed
undrained path lies inside of the yield surface of Cam
clay. The undrained path in the extension side should
be located in a little bit outside of the yield surface as
it can be known from the comparison between Eq. (3)and Eq. (8).
As the undrained path based on Eq. (9) depends
on the constant β described in the figures. The
constant β was varied so that the result of thecalculation was close to the measured one. The
undrained path on the compression side can becalculated close to the observed one though thecalculation is greatly affected by the set value of theconstant β.
Void Ratio Change inside of the Yield Surface

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

200

Observed

Observed

Observed

Calculated
NC =1.5
βC =1.2

Calculated
NC =1
βC =1

200

100

Deviator stress q (kPa)

100

0

CSL
M=1.48NE =M
βE =0.7100

0200
Effective
mean stressp' (kPa)

M.C.C.
Y.S.

Kashiwaclay

CSL
M=1.54NE =M
βE =0.7

CSL
M=1.54100

0200
Effective
mean stressp' (kPa)(a) Kashiwa clay

M.C.C.
Y.S.

CSL

-100

2.6
CSL

Hitachiclay(c) Hitachi clayA

2.4
Void ratio e

K0L

A

C

1
2.2

B

2

D

3
A

2

E
4
B

Kashiwa clay
C
1

D
2

E
3

100
Effective
mean stressp' (kPa)

Hitachi clay

B (p0, q0)

Deviator stress q

50(b) Soka clay
Fig.10 Yield function and undrained paths

F=0
Eq.(5)NE =M
βE =0.7-50

Soka-200 clay

CSL

Eq.(5)

Eq.(5)

-100

-100

-200

50

100

Eq.(5)

Calculated
NC =1.3
βC =1

pc

1.8

4
Effective
mean stress p’

E

D
20

40

100

200

Effective mean stress p' (kPa)(a) stress paths(b) e-logp relation of two samples
Fig.11 Stress paths and e-logp relation under K 0 compression and swelling
ratio is very small. As the loading path B ⇒ D is
located near the constant line of the past minimum
principal effective stress, it can be considered to beequivalent to the yield surface.
The stress path such as 1⇒2⇒3⇒4 of Hitachiclay corresponds to the isotropic compression. The
void ratio decreases rapidly when the mean stressexceeds the past maximum mean stress (𝑝 ). Thismeans that the yield surface may cross the p axis on
the left side of 𝑝 .
These experimental results signify that theassumption for the yield function of Cam Clay is
doubtful and does not define strictly the limit of theelastic behavior. It can be considered that there is alimit in ability of elasto plastic soil models.

CONCLUSIONSThis paper presents a yield function exhibiting
strain softening behavior for K 0 normallyconsolidated clays and evaluates the simulative
capability using the undrained and drained triaxial
test results. Proposed yield function is equivalent tothat of Modified Cam Clay if the constant N=M andthe initial stress ratio 𝜂 (＝𝑞 /𝑝 =0 in the isotropic
consolidated clay. The following conclusion can be
drawn from the comparison of the calculated andobserved triaxial test results.
1) The calculated undrained paths using ananisotropic yield function agree well with observed200GEOMATE – Bangkok, Thailand, 22-24 November 2022

ones of K 0 normally consolidated clays. The strain
softening behavior can be approximated by the
proposed yield function if the degree of the strain
softening is small.
2) Results of the consolidated drained triaxial test
show that the plastic strains are occurred when theapplied minimum principal stress exceeds the past
maximum pressure. It has been shown by theexperimental results that the yield surface for K 0
consolidated clays is located near the constant line of
the minimum principal effective stress.
3) The void ratio and volume of the clay decreased
despite the unloading test into the yield surface of
Cam Clay, which exhibits elastic behavior. This
experimental result can emphasize that the stresschange into the yield surface causes plastic strain in
the clay, indicating that the existence of a yield planes
is questionable.

REFERENCES[1] Tschebotarioff,
G.P.,
Soil
Mechanics,
Foundations and Earth Structures, McGraw Hill
Book Company, Inc., 1951, 102-104.
[2] A. Casagrande, The Determination of the PreConsolidation Load and Its Practical Significance,
Proceedings of the 1st International Conference
on Soil Mechanics, Harvard, Vol. 3, 1936.
[3] Roscoe, K. H. and Burland, J. B.,On the
Generalized Stress-Strain Behavior of 'Wet' Clay,Engineering Plasticity, Cambridge Univ. Press,
1968, pp. 535-609.
[4] Dafalias, F.Y.，Manzari，M.T. and Akaishi，
M.,A Simple Anisotropic Clay Plasticity Model,
Mechanical Research Communication, Vol.29,
2002, pp.241-245.

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12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

FAILURE INITIATION AND MODES OF HOEK-BROWN ROCKMASSES SURROUNDING UNDERGROUND STORAGE WITH HIGH
INTERNAL PRESSURE
Apiwish Thongraksa, Pornkasem Jongpradist *Construction Innovations and Future Infrastructures Research Center, Department of Civil Engineering, Facultyof Engineering, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand *Corresponding author: pornkasem.jon@kmutt.ac.th
ABSTRACTThe critical issue for the design of highly pressurized caverns is the uplift failure. A reasonable evaluationof the failure path including the initial failure location and direction of the crack propagation is needed. A seriesof numerical analyses based on the finite element method was conducted in the framework of the 2D axisymmetricmodel by considering the variety of rock mass properties, cavern depth, and in-situ stress ratio. The wide range ofthe natural rock mass properties (rock strength) and initial stress conditions surrounding the cavern, which include
in-situ stress ratio and depth of tunnel were considered in this investigation. To detect and distinguish the locationof failure initiation and failure modes between tensile and shear failure, the tensile criterion and Hoek-Brow failure
criterion are utilized, respectively. The numerical results indicated that the rock strength has a strong effect on theinitial failure mode while the initial failure location strongly depends on the in-situ stress ratio and rock strength.
The charts of failure mode and initial failure location were created by using the relationship between unconfinedcompressive strength and tensile strength of rock mass and initial stress condition, respectively.
Keywords: Failure mode, Uplift failure pattern, Rock failure, High pressurized cavern.

INTRODUCTION

the analysis.
To gain a better understanding of the failure
behavior of rock and rock mass materials, intact rockis commonly chosen. Many failure criteria have been
proposed to understand the rock behavior. The
popular nonlinear failure model based on the HoekBrown (H-B) criterion [7] was developed. In recent
years, the H-B criterion has been proved to be able tobetter describe the nonlinear rock mass behavior andis widely utilized in engineering practices [8], roadcuts and cliff face stability analysis [9]. However, theH-B failure criterion was developed to capture only
the shear failure mode. Generally, the rock mass canfail both tensile and shear failures [10], which maylead to different uplift failure path evaluations.
The failure behavior of rock mass around apressurized cavern is investigated by considering theinitial failure mode and location. A series ofnumerical analyses are conducted by taking toaccount the variety of rock mass properties, cavern
depth, and in-situ stress ratio. The H-B criterion andtensile criterion are used to determine the rock failure.
A conceptual guideline for predicting the failuremode of rock mass and the possible location of a
failure initiation along the cavern periphery withconsidering the rock strength and initial stress state isestablished in this study.

The large renewable energy storage with lined
rock caverns (LRC) is becoming the popular
alternative solution to provide stable energy sourcesof base load electricity [1]. When the power demand
period is low, the high-pressure nature of air or gas is
absorbed by the driving compressor with excess
electric energy. The high-pressure air or gas is stored
underground. During the peak period, the highpressure air or gas can be released into the generator
to generate electrical energy for supplying theincrease in the power demand. With the high internalpressure storage and relatively small undergroundcavern depth, the stability of rock mass above theunderground cavern can fail caused by the uplift,
which is a critical issue that needs to be considered[2].
In the decade, many researchers have performed
analyses to suggest a reasonable uplift failure pattern[3–6]. According to their study, the initial failure
point along the cavern periphery depends on the insitu stress ratio that can lead to the different uplift
failure patterns [2,4]. However, the linear failure
criterion model, Mohr-Coulomb, is mainly used innumerical analysis due to its simple form. To
investigate the rock behavior with a wide range ofstress, the nonlinear behavior is exhibited. Thus, thelinear Mohr-Coulomb may present unrealistic
behavior, which can affect the factor of safety during
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

FAILURE MECHANISM

different types of rock from many researchers. The
range of the unconfined compressive strength and mvalue of rock exhibited in the range of 37.2 – 264.8
MPa and 3.2 – 54.6. Therefore, in the sensitivity
analyses, the range of unconfined compressive
strength of rock and the m value are consideredbetween 1 to 300 MPa and 5 – 25, respectively.
Moreover, the influencing factor that strongly affects
the initial failure location as discussed in the work of[2], the in-situ stress ratio (k) is taken into account in
the range of 0.3 – 3.0.

The failure mode of rock material, the tension or
shear can be obtained. For tensile failure, when thevalue of tensile stress reaches the critical value of thematerial, the failure occurs. The simple equation canbe written asσ1 -σT =0(1)

When σ1 and σ3 are the major and minor principal
stress, and σT is the tensile strength of the material(tension is positive).

Procedures for Detection of Initial Failure
A series of stress analyses by using the continuum
finite element program ABAQUS (2016) of thecavern under increasing internal pressure is
performed to determine the failure initiation location.
The analysis procedures can be divided into 3 steps
from the initial to operation stages which are thegenerate the in-situ stress state, the silo cavern
excavation, and applied the internal pressure tosimulate the underground cavern storage state. The
level of internal pressure increases until the deviator
stress along the cavern storage achieves the shear
failure criterion, the value of the maximum major
principal stress and the location of maximum σ1 andminimum of σ3HB /σ3 are captured.

For the shear failure criterion, the Hoek and Brown(H-B) model was developed. The general form of HB failure criterion can be mathematically expressed interms of the major and minor principal stress as

σ3HB =σ1 +(-m𝑖 σc σ1 +sσ2c )0.5

(2)

The mi and s are the constant value that depends on
the properties of the rock mass. The unconfinedcompressive strength (UCS) of rock represents by theσc in the equation. For intact rock material, s = 1.0.
The shear failure occurs when the stress state whichis represented by the Mohr circle reaches the shear
failure envelope determined by the H-B model as
displayed in Fig. 1. The minor principal stresscalculated from the H-B model in Eq. 2 (σ3HB ) can be
determined using the stress state and rock strength.
According to Fig.1, the shear failure occurred under
the condition of σ3HB /σ3 equal to 1. The failure ratio
used in this investigation can be expressed as
σ3HB σ1 +(-m𝑖 σc σ1 +σ2c )
σ3

=

σ3

0.5

=1.0

(3)

NUMERICAL ANALYSIS
Fig. 1

Shear failure proposed by Hoek-Brown.

Problem Characteristic
Table 1 Controlled rock properties

In this investigation, the 2D axisymmetricproblem is considered. The analytical model andloading condition are demonstrated in Fig. 2. The silo
cavern has a diameter of 22.2 m. and 33.3 m in high.
The depth of cavern storage is located at a depth in
the range of 60 – 150 m. The bottom boundary wasconstrained in both lateral and vertical directions.
Boundaries on both vertical sides were fixed in only
one direction, which does not allow to move in thelateral direction. The properties of rock mass used in
the numerical model can be divided into 2 groups
including the control properties and varying
properties. The unit weight of rock (), Young’s
modulus (E), and Poisson’s ratio () are fixed asshown in Table 1. Regarding the literature review,[11] collected the properties of rock mass of 28

Quantity

Symbol

Value

Unit

Unit weight
Young modulus
Poisson's ratio

γ
E
ѵ

26
6.66
0.2

kN/m3
GPa
-

The Notion of the Failure Mode Identification
Due to the underground storage with internalpressure inside, the stress state in the rock mass
surrounding the silo chamber gradually changes bothprincipal and deviatoric stresses as the internalpressure increases. The stress along the cavern wall,
radial stress, becomes compression while the stress inthe perpendicular direction, tangential stress,
203

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig.2

the case of cavern depth of 60 m. and k = 1, the data
points which are the relationship between ultimate
compressive strength and tensile strength under shear
and tensile failure occurs simultaneously, are plotted
as shown in Fig. 4. The further investigations wereconducted to get a better understanding of the effectof rock strength by considering rock strength incompressive of 1 to 300 MPa and m-value of 5, 10,
and 25. By collecting all the relationships between theunconfined compressive strength of rock andmaximum major principal stress (indicated the tensile
strength) at the shear and tensile failure occurs
simultaneously in all analysis cases, the boundary line
to distinguish the mode of failure can be obtained asshown in Fig. 5. According to the results, it can beseen that the influence of the rock mass strength has
a strong effect on the initial failure mode. The rockwith the relationship properties between shear andtensile strength locates above the boundary line, thetensile failure is dominated at the initial failure while

The loading and boundary conditions of theanalytical model.

becomes tension as represented as σ3 and σ1 ,respectively. Therefore, the radius of the Mohr circle
increases with the increase of internal pressure. Whenthe Mohr circle reaches the failure envelope surface
which is determined by the H-B model of σ3HB /σ3 =1.0, the shear failure occurred. If the tensile strengthof rock mass is equal to the maximum major principal
stress at σ3HB /σ3 = 1.0, the failure initiation occurs
under shear and tension simultaneously. For rocks
that have greater tensile strength, the shear failuremode can be obtained while the rock with a lower
tensile strength than the value of σ1 at σ3HB /σ3 = 1.0,
the failure initiation caused by tensile mode is
induced.
NUMERICAL RESULTS
Influence of the Rock Properties on Initial Failure
Mode for Underground Cavern Storage
The effect of the rock strength and types of rockmass, which are the ultimate compressive strengthand types of rock, are described in this section. Fig. 3
presents the major principal stress distribution atshear failure occurs under operation state for various
cases of ultimate compressive strength of rock. It canbe noticed that the location of a minimum of σ3HB /σ3
and maximum major principal stress are the same
location for the example case, which is the casehaving the cavern depth of 60 m. and k of 1. With therock having greater ultimate compressive strength,
the required level of pressure inside the cavern
storage (Pcav) is increased. The location of the initial
failure tends to move from cavern crow to cavernsidewall while the location of maximum σ1 andminimum σ3HB /σ3 are still the same locations. If theultimate tensile strength of rock is equal to maximum
σ1, the initial failure is induced under shear and tensile
mode simultaneously. From the simulation results in

Fig. 3204

The location of (a) the minimum value ofσ3HB /σ3 and (b) the maximum σ1 withinternal pressure of 32.6 MPa.

GEOMATE – Bangkok, Thailand, 22-24 November 2022

the strength of rock mass both shear and tensile locate
under the boundary line, the initial failure mode isgoverned by shear. To improve the reliability of theanalysis results, the information on rock mass used in
the physical model test for the underground storage
cavern with high internal pressure was conducted byThongraksa et al.[4] is plotted in the figure byconsidering the scaling factor. In their work, thetriaxial tests were performed to determine thesynthetic rock properties used for the experiment in
the laboratory by reducing the scale by 200 times. By
using the Hoek-Brown failure criterion to determine
the shear failure envelope, the m-value and UCS canbe calculated, which are equal to 17.78 and 43.95 kPa.
In addition, it can be seen that the expected initial
failure mode is a shear failure, which is shown a good
agreement with the previous study. Therefore, thechart of initial failure mode with nonlinear criterion
has been proven to predict the failure of rock mass inunderground cavern storage with high pressure insideproblem.

Fig. 4The Initial Failure Location Assessment ofUnderground High-Pressure Storage
To determine the initial failure location, the depthof the cavern, in-situ stress ratio, and rock strength aretaken into account in the analysis. According to Fig.
4, it can be seen that with the rock strength increase
the required cavern pressure increase. Fig. 6 shows
the initial failure location in the case of cavern depthof 60 m and k of 0.3, 1, and 3, which represent the insitu stress ratio of less and more than 1 and equal to 1
by varying the UCS in the range of 50 to 250 MPa. In
the case of k less than 1, the initial failure occurs at
the cavern roof while for the k larger than 1, the initial
failure is located at the cavern sidewall. In case of k
become unity, the initial failure location can be
captured between cavern roof and sidewall which has
a failure angle in the range of 30 and 40. In addition,
the required pressure level also increases with therock having greater strength. It can be seen that theinitial failure location depends on the rock massstrength. Similar results can be obtained in previous
studies [4,12].

The relationship between shear strength andtensile strength at both shear and tension
occur simultaneously.

Fig. 6

Fig. 5

The relationship between unconfinedcompressive strength (UCS) and failure angle(α) at depth of craven of 60 m.

To get a better understanding of the initial failure
location of the rock, the in-situ stress in a range of 0.3
to 3.0 and the depth of the cavern in the range of 60
to 150 m. are chosen to perform the further
investigation. The analysis data in the initial failure
location are plotted in a radial space concerning therock mass strength along the X-axis and Y-axis fortensile strength and shear strength, respectively,
while the initial failure angle is exhibited on the radial
axis as illustrated in Fig. 7. To eliminate the influenceof cavern depth, both terms shear strength and tensile

The chart for classifying the initial failure
mode.

205

GEOMATE – Bangkok, Thailand, 22-24 November 2022

strength are normalized by the vertical stress. From
the results in Fig. 7, it is seen that the rock strengths
have a strong effect on the initial failure location forcases with k in the range of 0.8 – 1 for the low rockstrength. Moreover, for cases of k less than 0.8, theinitial failure locations are located at the cavern roof
while in cases of k larger than 1, the locations of initial
failure occur at the cavern side wall. With increasing
the rock strength, the location of the initial failure
tends to approach the cavern shoulder which has afailure angle of approximately 40. After all the
influencing factors on the initial failure location areinvestigated, the uncomplicated chart for determining
the initial failure location is proposed as demonstrated
in Fig 8. By knowing the mode of failure from thechart of the mode of failure as presented in Fig 5, theexpected failure location can start with the horizontal
and vertical axis for the tensile failure and shear
failure, respectively. Then, move in thecircumferential direction to the line which represents
the in-situ stress ratio, and determine the initial failure
angle by moving in the radial direction to the value ofthe failure angle.
To validate the proposed chart, the scale-down
model tests for the case of underground storage withinternal pressure were conducted by Thongraksa etal.[4] are chosen. In their work, the initial failure
occurred under shear failure both k of 1 and 3 with adepth of 40 m for full-scale. The initial failure angle
for k of 1 and 3 are 45 and 75, respectively. In Fig.
8, the failure initiation points for both cases are added
to the chart by plotting the relationships between theterm of shear strength and initial failure angle. In thecase of k larger than 1, a good agreement between thephysical model test and the proposed chart can beobtained. The small discrepancy between the failure
angle captured in the experiment and the prediction
chart is exhibited in the case of k equal to 1 because
the failure angle is very sensitive to low rock strengthfor k become unity. It can be concluded that the
proposed chart for determining the failure locationshows reasonable agreement with the experimental.
CONCLUSION
This investigation presents the initial failure
behavior of rock mass surrounding the undergroundcavern storage with high internal pressure inside via
the numerical analysis. The effect of the depth ofcavern, in-situ stress ratio, and rock mass strength(ultimate compressive strength) on the initial failure
and mode of failure by using nonlinear failure
criterion, Hoke-Brown failure are conducted. The
main finding of this investigation can be summarized
as follows;
1. The initial failure mode strongly depends on

Fig. 7

Effect of the rock strength and stress stateon the initial failure location.

Fig. 8Chart for predicting the location of initial
failure, accounting for the rock massstrength, cavern depth, and in-situ stressratio.

rock mass strength which is the relationship between
shear strength and tensile strength. By considering therange of the natural rock properties in terms of rockstrength between shear and tensile strength, theboundary line to separate tensile and shear failuremodes can be obtained. The initial failure mode canbe expected by knowing the rock mass strength.
2. The influencing factors consist of rockstrength, depth of cavern, and initial in-situ stressratio that have influenced the initial failure location.
The depth of the cavern has a slight effect on the206

GEOMATE – Bangkok, Thailand, 22-24 November 2022

initial failure point. In low rock strength, the in-situ
stress ratio has a strong effect on the initial failure
location. For high rock strength, the initial failure
location seems to locate in the zone between the
cavern roof and sidewall.

Initiation and modes of failure. Tunn. Undergr.
Sp. Technol., Vol. 115, 2021, p. 104058.
[5] Tunsakul J., Jongpradist P., Kim HM.,
Nanakorn P., Evaluation of rock fracture
patterns based on the element-free Galerkinmethod for stability assessment of a highlypressurized gas storage cavern, Acta. Geotech.,Vol. 13, 2018, pp. 817–832.
[6] Wang H-T., Liu P., Li S-C., Li X-J., Zhang X.,
Limit analysis of uplift failure mechanisms for ahigh-pressure gas storage tunnel in layered
Hoek-Brown rock masses, Eng. Fail. Anal., Vol.
138, 2022, p. 106274.
[7] Hoek E., Brown E., Underground excavations inrock The Institution of Mining and Metallurgy,
1980.
[8] Zhao J., Applicability of Mohr-Coulomb andHoek-Brown strength criteria to the dynamic
strength of brittle rock, Int J. Rock Mech. Min.
Sci., 2000;37:1115–21.
[9] Lindsay P., Campbell RN., Fergusson DA.,
Gillard GR., Moore TA., Slope stability
probability classification, Waikato Coal
Measures, New Zealand. Int. J. Coal. Geol., Vol.
45, 2001, pp. 127–45.
[10] Paterson MS., Wong T., Experimental rockdeformation — the brittle field, Springer, 2005.
[11] He M., Zhang Z., Zheng J., Chen F., Li N., A
New Perspective on the Constant mi of the
Hoek–Brown Failure Criterion and a New
Model for Determining the Residual Strength ofRock, Rock Mech. Rock Eng., Vol. 53, 2020,
pp. 3953–3967.
[12] Perazzelli P., Anagnostou G., Upper bound limit
analysis of uplift failure in pressurized sealed
rock tunnels, Int J Numer Anal. Methods
Geomech., Vol. 42, 2018, pp. 719–735.

ACKNOWLEDGMENT
This research work was supported by King
Mongkut’s University of Technology Thonburi(KMUTT) under The Petchra Pra Jom Klao
scholarship contract No. 29/2558 and the financial
support provided by the National Research Council ofThailand (NRCT) through grant No. NRCT5RSA63006 and Thailand Science Research andInnovation (TSRI) under Fundamental Fund 2023
(Project: Advanced Construction Towards Thailand
4.0).
REFERENCES[1] Salgi G., Lund H., System behaviour ofcompressed-air energy-storage in Denmark witha high penetration of renewable energy sources,
Appl. Energy, Vol. 85, No. 4, 2008, pp. 182–
189.
[2] Tunsakul J., Jongpradist P., Kongkitkul W.,
Wonglert A., Youwai S., Investigation of failure
behavior of continuous rock mass around cavern
under high internal pressure, Tunn. Undergr. Sp.
Technol., Vol. 34, 2013, pp. 110–123.
[3] Kim HM., Park D., Ryu DW., Song WK.,
Parametric sensitivity analysis of ground uplift
above pressurized underground rock caverns,
Eng. Geol., Vol. 135-136, 2012, pp. 60–5.
[4] Thongraksa A., Punya-in Y., Jongpradist P, Kim
H-M, Jamsawang P. Failure behaviors of rockmasses around highly pressurized cavern:

207

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

EVALATION OF DISPLACEMENT AT GROUND SURFACE DURINGGROUNDWATER RECOVERY
Sutasinee Intui1, Jittiphan Jindawutthiphan2, Apinya Rungrueang2, Choknimit Leelananthawong2,
Thanapatsa Srisarn Trpkovski2 and Kearkkeart Apischotecawankit 2 and Shinya Inazumi3
1

Graduate School of Engineering and Science, Shibaura Institute of Technology, Japan;
2
Faculty of Engineering, Bangkok Thonburi University, Thailand;
3
College of Engineering, Shibaura Institute of Technology, Japan

ABSTRACT
Chao Phraya River basin is located in the central area of Thailand which has a lot of land subsidence issues
due to groundwater pumping. Following the recording data of Department of Groundwater Resources (DGR), thegroundwater level change due to water pumping from 1960 until the present time. In 1997, DGR has issued law
regulating the use of groundwater effect to the characteristic of groundwater level changing. Therefore, thecharacteristic of groundwater level changing was separated into two periods. First period, high groundwater
pumping ratio led to rapidly decrease of groundwater level at about 27 meters from ground surface. After DGR
issued the law, pumping ratio decrease and groundwater level was increase or recovery close to the ground surface.
The groundwater level recovery had an effect to both settlement and rebound of ground surface. This study focuseson the evaluation and prediction of soil displacement during groundwater level recovery in the future based on
Terzaghi’s theory. The research area considers only zone D (10 to 14 meters soft clay thickness) in Bangkok clay
area. The soil profile and the soil boring log were gathered from Department of Public Works and Town & Country
Planning. This study research the displacement behavior of clay layer during groundwater level recovery. All data
will be used to make a 3D soil profile and calculate to predict surface displacement in the future of soil layer. The
results found that the settlement has rebound to ground surface every bored hole during the groundwater recovery.
Keywords: Soil displacement, Bangkok clay, Groundwater recovery, Terzaghi’s theory

INTRODUCTION

water level. The groundwater is rapidly decreaseduring groundwater pumping. The land subsidence iscontinuously increase during that period. Thailand
also occurs land subsidence due to groundwater
pumping same as Jakarta. [4] showed the evaluationof soil surface displacement after groundwater
pumping was controlled. After 1997 to present, bothresearches were predicted that the soil surface
displacement at ground surface, has rebound by using
ABAQUS software. The recovery ratio is
approximately 0.26 to 0.32 centimeters per year [5]studied the soil surface behavior during groundwater
recovery. In period of 1997 to 2037, the soil surface
has rebound around 35 percentage of maximum
displacement.
This research tries to evaluate the soil surface
displacement during groundwater recovery or the
behavior after 1997 in Thailand. The Bangkok clay
have wide area along Chao Phraya River basin,
central area of Thailand. Following the previous
study, summarized the soft soil area follow by the soft
clay thickness in each zone. So, this study chooses
only zone D of soft soil thickness to consider the soil
displacement in Soft clay and Stiff clay layer base on
Terzaghi’s theory of consolidation in both saturated
soil and unsaturated soil.

Before 1997, the groundwater level decrease
continuously due to groundwater pumping in center
part of Thailand. The high demand of groundwater
pumping has affected to land subsidence especially inthe soft soil area or center part of Thailand. After
Department of groundwater resource issued the law
to control the amount of groundwater pumping forindustry, the groundwater level tends to increased or
recovered to nearly ground surface. This is theinteresting point to evaluate the soil surface
displacement refer to the instrument data of someareas still settlement down and some areas occur lift
up of soil surface or called rebound in this research.
The soil displacement during groundwater levelrecovery has affect to the soil surface displacement interm of civil engineering structure issues such as roadcrack, building damage especially the old building
which design at lowest the groundwater level.
The land subsidence is a global problem included
Thailand. Many researchers try to analyze andexplain the soil surface displacement behavior in soft
soil area. Land subsidence occurs around the world insoft soil area. The reason depends on the usage of thatarea such as groundwater pumping, [2] and [3].
Jakarta city in Indonesia located on the lowest sea
208

GEOMATE – Bangkok, Thailand, 22-24 November 2022

BANGKOK SOFT SOIL PROFILE

basin have groundwater level increasing ratio about
0.57 m/yr. to 2.74 m/yr.

Bangkok soft soil area located along Chao Phraya
River basin covering many provinces, Bangkok andurban area. In the past, this area has been the sea. The
soil was deposited to be clay layer alternate with sandlayer alternately. The subsoil layer consists of very
soft clay to soft clay, very stiff to stiff clay, first sand,hard clay, second sand respectively. Department of
Groundwater Resource (DRG), Department of Public
Works and Town & Country Planning and many
researchers have studied the characteristics of subsoil
layer in Chao Phraya River basin area found that thesubsoil layer have variance. However, overall soil
layer still be the clay layer alternates with sand layer.

METHODOLOGYThis study focuses to collect the data of the soil
boring log from Department of Public Works andTown & Country Planning to evaluate thedisplacement from each subsoil layer. The subsoil
layer characteristic was compared and verified by thegroundwater level data from Department of
Groundwater Resource. All data was drawn andpresented subsoil layer in 3D layout. The soil
parameters from 3D layout were used to calculate thesoil surface displacement during groundwater levelchange.
Subsoil profile of zone D

GROUNDWATER LEVEL SITUATION
Characteristics of groundwater level situation in
Bangkok area depends on the behavior ofgroundwater pumping. After 1997, groundwater levelin Bangkok area tend to recover to ground surface
after groundwater pumping was controlled.
Following the Fig.1, showed the behavior ofgroundwater level and land subsidence during
groundwater consumption changing [6]. The graphrepresented groundwater level increase from 1997
and tend to reach ground surface in the future. In
contrast, Land subsidence still occurs and graduallystable after 2008.

-20
-30
-40
-50
-60
-70

Groundwater Levels (mMSL)

Land Subsidence (cm)0-10Fig. 1

Groundwater Usage (Million
Cubic Meters/day)Years
1960 1970 1980 1990 2000 2010 2020 2030 2040
0
2.5
0
-5
2.2
0
-10
1.9
0
-15
1.6
0
-20
1.3
0
-25
1.0
0
-30
0.7
0
-35
0.4
0
GWL PD0017
GWL PD0023
GWL PD0034
GWL PD0028
DWL at PD0023
DWL at PD0017
DWL at PD0034
DWL at PD0028
GW Usage
Settlement (CI.8-1)

Following the soft soil thickness of Bangkok clay
as Fig. 2, Bangkok clay was separate to 6 zone withdifferent colors ]1[. Each zone was divided by the soft
clay thickness. The dark red (F) area has soft clay
layer thicker more than 18 m. The red (E) area has
soft clay thickness from 14 m to 18 m. The yellow (D)area has soft clay thickness from10m to 14m. The
blue (C) area has soft clay thickness from 6 m to 10
m. The green(B) area has soft clay thickness from 3
m to 6 m and the grey (A) area has soft clay thickness
less than 3m. The red marks show the data fromboring log by Department of Public Works and Town
& Country Planning. The green marks show theinvestigated holes of DGR. The name of each hole
show as Table 2. This study considers only zone D
because this area is a developed area and have data toevaluate the behavior of soil surface displacement byusing Terzaghi’s theory of consolidation by
conducting the data from Department of Groundwater
Resource and Department of Public Works and Town
& Country Planning [7]. Finally, the calculation
results will be used to evaluate in other areas.

PT-2
PT-1
NB BH-6
BK-2
BH-1
BK-1
BH-2
NP BH-5 BH-4
SS BH-3SP

Groundwater level change and land
subsidence due to groundwater pumping

In present, DGR measures the groundwater levelby using piezometer measurements. The groundwaterlevel situation showed the groundwater level
increasing ratio in each province in Table 1. All of theprovinces that located along the Chao Phraya River

Fig. 2

209

Location of the studied hole (Modified [1])

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 1 Groundwater level situation each hole

Soil parameters
The soil parameters in this research were adopted
from the boring log data of 8 bore holes which locatedin zone D. Soil parameters such as water content,liquid limit, plastic limit, total density, undrained
shear strength and standard penetration test (SPT)
were gathered from each bore hole. Initial void ratio
was calculated by using phase relationship equationfrom bore hole parameters. The soil parameters andthickness of soil layer were calibrated with the datafrom DGR. This study chooses 8 bore holes toevaluate the soil surface displacement. The soil
parameters of each soil layer shown in Table 3. Someparameters cannot be interpreted by using only boringlog data. So, many parameters were adopted fromprevious studies such as the soil specific gravity equal
to 2.7 [6], over consolidation ratio (OCR) [8],effective stress parameter as matric of suction
coefficient () [9] by referred from soil parametersfrom previous study [10].

Groundwater Level Recovery Rate
Province(2009)
Elevation

Minimum

Maximum(m)Rate (m/yr)

Rate (m/yr)

Bangkok

32.86

0.57

2.74

Samutprakan

29.47

1.07

2.45

Pathumthani

19.3

0.48

2.41

Nonthaburi

12.7

0.27

1.06

Ayutthaya

16.3

0.61

1.23

Nakhonpathom

19.18

0.60

1.59

Samutsakhon

32.75

1.94

2.34

Table 2 Location of the marks on the map
Name

Description

NP

NahonPathom

SS

SamutSakhon

BK-1

Bangkok (Taling Chan)

BK-2

Bangkok (Khlong Sam Wa)

NB

Nonthaburi

SP

SamutPrakon

PT-1

PathumThani (KhlongLuang)

PT-2

PathumThani(Thanya)

BH-1

Chatuchak Park

BH-2

Lumphini Park

BH-3

Suea Pa Park

BH-4

Rommaninath Park

BH-5

Rajamangala University

BH-6

Kasetsart University

Theoretical calculation
The soil surface displacement calculation is very
important to evaluate stability of building in eachperiod of time. This study tries to simplify theequation to evaluate the soil surface displacement
during groundwater level change based on timing of
DGR
duringgroundwaterlevel
increase.
Consolidation settlement equation calculate in case ofover-consolidation clay because the maximum
vertical effective stress is more than the final verticaleffective stress, as in Eq. (1). Many parameters were
calculated such as the recompression index (Cr), theinitial void ratio (eo), the maximum vertical effective
stress (’vm), the final vertical effective stress (’vf)

Table 3 Soil parameters for calculation

Soil layers

Bored holes

Soilparameters

Description
NP

SS

NB

BK-1

BK-2

SP

PT-1

PT-2

Wn (%)

Water content

52

38

88

69

72

62

78

65

Very soft to

LL (%)

Liquid Limit

41

43

87

64

48

46

50

38

Medium Clay

PL (%)

Plastic Limit

23

25

67

27

25

37

25

20

(2m. to 13m.)

t (t/m )Total density

1.70

1.84

1.50

1.59

1.58

1.64

1.55

1.62

eo

Initial void ratio

1.40

1.03

2.37

1.86

1.94

1.67

2.10

1.75

Wn (%)

Water content

19

17

-

22

37

34

25

25

Stiff to Very stiff

LL (%)

Liquid Limit

31

35

-

51

50

51

60

40

clay

PL (%)

Plastic Limit

18

17

-

2.06

1.85

1.88

2.01

2.01

(13m. to 25m.)

t (t/m )Total density

2.12

2.16

-

2.06

1.85

1.88

2.01

2.01

eo

Initial void ratio

0.51

0.46

-

0.59

0.99

0.92

0.68

0.68

3

3

210

GEOMATE – Bangkok, Thailand, 22-24 November 2022

NP SK
BK-1 BH-1 SP
Weathered Clay
Soft Clay
Stiff Clay

0m
-10 m
-20 m
-30 m
-40 m
-50 m
-60 m
-70 m
-80 m

BH-2 BH-5
BH-4 BH-1 NB

BH-6 BK-2

PT-1

PT-2

Sand
Hard Clay

170,830 m
Fig. 3

Soil profile layout of zone D

and the thickness of soil or drainage path. Highgroundwater level was considered as a saturated soil
refer to Eq. (2) based on Terzaghi theory ]11[ and low
groundwater level was considered as an unsaturated
soil refer to Eq. (3) following the expression of
Bishop equation ]12]. The total vertical effective
stress (), an effective stress parameter (s), pore-airpressure (ua), pore water pressure (uw) and suction (S)as in Eq. (4).
′
𝜎𝑣𝑓

𝐶

H = 1+𝑒𝑟 log 𝜎′ 𝐻𝑜(1)

σ′ = σ − u

(2)

𝑜

w

(3)

S=u −u

(4)

a

a

w

w

Soil surface displacement𝑣𝑚

σ′ = [(σ − u ) + χ (u − u )] ; 1 > χ > 0a

The variation of each soil layer was interpreted fromsoil boring log that verified by DGR. (BH-1 to BH-6)
again. After verification of 2D soil profile, the 3D soil
profile was prepared as shown in Fig. 4 to Fig. 6. This
study has considered only upper 3 subsoil layers
which have affected to groundwater level change
covering the area of 1334 square meters. All of soil
profile found that each soil layer has different soil
thickness. So, this is the gap to evaluate the surface
settlement in the future.

This study focuses on the soil surface
displacement of the soft Bangkok clay and stiff tovery stiff clay by using theoretical calculation. This
result shows the total displacement at ground surface
and displacement ratio during groundwater increaseor recovery to ground surface. The methodology oftheoretical calculation was considered the time periodof DGR.
So, this study estimates the total displacement
every 5 meters of groundwater level increase. It
represents the groundwater level equal to -25 m. in2001 then groundwater level increase to groundsurface in 2032. The 8 bored holes have similar total
displacement trend and displacement ratio from 2001
to 2032 as in Table 4. The Fig.7 show the relationshipbetween the total displacement and time of all boredhole in zone D. There is the rebound displacement
trend due to groundwater recovery. In the same study
area, the results of this study have the characteristicof soil surface behavior similar to other results suchas the measurement of DGR have displacement ratio
equal to 0.25 cm/yr., the results of the ABAQUS
software have the rebound ratio from 1997 to 2016
equal to 0.26 to 0.32 cm/yr. And the result of
PLAXIS2D evaluate the soil displacement ratio equal
to 0.35 cm/yr. from 1997 to 2037. By the way, theresult of this study was evaluated by using the

RESULS AND DISCUSSIONSThis part shows the results of soil surface
displacement after estimation and interpretation of
the soil parameters, soil profile and groundwater levelrecovery.
3D soil profile
The 8 bored holes was located on the zone D,
yellow area. All bored holes are distributed aroundthe area for evaluate the soil parameters and soil
profiles. The soil layers were specified from soil
parameters in boring log data. The data in boring log
is not enough to determine the soil layer. So, someparameter such as Cc and Cr were adopted from theprevious research and DGR data, [13] – [17]. The datain this research were adopted only the value which
has the R square more than 0.9. Then, this study drew
the 2D soil profile by the AutoCAD software as theFig. 3 and drew the 3D layout by 3ds Max software.
211

GEOMATE – Bangkok, Thailand, 22-24 November 2022

prediction of groundwater level from previous
studies. In present, no one know that when the
groundwater level will increase to ground surface andhow about the soil surface displacement during
groundwater rise up. So, this research tries to simplify
the calculation method. Therefore, the results from
simplified theoretical calculation can be conducted toevaluate or predict soil surface displacement due togroundwater recovery in the future.

PT-2
NP
0m
-2 m

SS

BK-2

BK-1

0m
-2 m
- 9m

SP
Weathered Clay

-12 m
-16 m
-18 m

-23 m

Soft Clay- 30m

Stiff Clay

View B

Sand
114,000 m
PT-2

Fig. 5PT-1

3D of view A

NB
0m
-2 m
BK-2

NB

PT-2

NP

PT-1
Weathered Clay

-12 m
-16 m
-18 m

Soft Clay

- 9m

0m
-2 m

Stiff Clay
Sand

-23 m

BK-1

-30 m

NP
SS

114,000 m

SP
Fig. 6

3D of view B

-50
NP
SS
BK1
BK2
NB
SP
PT1
PT2

View A
-40
Soil profile layout of zone D

Displacement (cm)Fig. 4

Table 4 Total displacement and displacement ratio
Bored
hole

Total displacement(cm)

Displacement Rate
(cm/year)At 2001

At 2032NP-9.07-17.68

-0.28

SS

-7.29

-13.64-0.20

BK1-12.30-23.50-0.36

BK2-17.41-33.31-0.51

NB-18.92-35.03-0.52

SP-17.05-28.52-0.37

PT1-12.63-25.90-0.43

PT2-11.48-20.44

-0.29

-30
-20
-10
0
1990

2010

2030

2050

Time (year)
Fig. 7212

Displacement of each bored hole

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Due to Groundwater Level Rising, Appl. Sci.,
2021.
[6] Department of Groundwater Resources., The
study of the effect of underground structures dueto the recovery of water pressure in the
groundwater layer in Bangkok and its vicinity,
Ministry of Natural Resources and Environment,
2012, pp. 17-62.
[7] Department of Public Works and Town &Country Planning, Soil GIS DPT, [online],
Available from: http://soilgis.dpt.go.th/login.php,
2020, April.
[8] Giao P. H., Paveechana T., and Saowiang K.,
Consolidation Settlement Analysis with
Reference to Groundwater Recovery in The
Bangkok Multi-Aquifer System, Advances inGeotechnical Infrastructure, 2013, pp. 567-573.
[9] Zhou A.N., Sheng D., Scott W. Sloan., and Gens
A., Interpretation of unsaturated soil behavior in
the stress – Saturation space, I: Volume change
and water retention behavior, Computers andGeotechnics 43, 2012, pp. 178–187.
[10] Surarak C., Likitlersuang S., Wanatowski D.,
Balasubramaniam A., Oh E., and Guan H.,
Stiffness and strength parameters for hardening
soil model of soft and stiff Bangkok clays, The
Japanese Geotechnical Society, 2012.
[11] Terzaghi K., The shearing resistance of saturated
soils and the angle between the planes of shear,in The First International Conference on Soil
Mechanics and Foundation Engineering, A.
Casagrande, P. C. Rutledge, and J. D. Watson,
Eds., 1936, pp. 54–56.
[12] Bishop A. W., The principle of effective stress.
Teknisk Ukeblad 106(39), 1959, pp. 859–863.
[13] Adikari, G.S.N., Statistical Evaluations ofStrength and Deformation Characteristics of
Bangkok Clays, MSc Thesis, Asian Institute ofTechnology, Bangkok, Thailand, 1977.
[14] Suthamnatpong S., Engineering Properties andBehaviour of Bangkok Clay Soils, M.Eng.
Thesis Chulalongkorn University, 1977.
[15] Tsai, C.Y., A Monograph on the Engineering
Properties of Bangkok Subsoils. M.Eng. Thesis,
Asian Institute of Technology, Bangkok,1982.
[16] Kusuwan K., Statistical Evaluation ofEngineering Properties of Bangkok Subsoil,
Chulalongkorn University, 1988.
[17] Chuenwattana R., Evaluation of engineering
properties of soft Bangkok clay, M.Thesis,
Kasetsart university, 2002.

CONCLUSIONSThis study summarizes the subsoil layer
characteristic and soil properties to evaluate the soil
surface displacement during groundwater levelchamging. The results were calcalated base on the
Terzaghi’s theory of consolidation. This research tries
to simplified the soil layer characteristic and otherdata from Department of Public Works and Town &Country Planning and Department of Groundwater
Resource )DGR(. The results of this study wereseparated in to two parts. The first part is simplified
the subsoil layers of zone D. The soil schematics haveverified by the subsoil data of DGR. The second partis evaluation of soil surface displacement during
groundwater level recovery by theoretical calculation.
The soil surface displacement has rebound to groundsurface every bored hole was considered. All result of
this calculation shows that the soil surface
displacement characteristic trend similar to theprevious studies which evaluation by anothersoftware such as ABAQUS and PLAXIS2D.
ACKNOWLEDGMENTS
All authors would like to thank Department of
Groundwater Resource to support the instrument data
and Department of Public Works and Town &Country Planning to support the soil boring log data.
That is the necessary data to help this research to get
the result to evaluate the soil surface displacement.
REFERENCES[1] Amornkul C., Engineering Subsoil Database ofLower Central Plain, Thailand, Master ofEngineering, 2010, pp. 3-161.
[2] Intachai K., Assessment on pile capacity in viewof Effects of groundwater drawdown andrebound Associated with deep well pumping in
Bangkok, Asian Institute of Technology, 2018.
[3] Phien-Wej N., Giao P.H., and Nutalaya P., Land
Subsidence in Bangkok, Thailand., Geology, Vol.
82(4), 2006, pp. 187–201.
[4] Saowiang K., and Giao P. H., Numerical analysis
of subsurface deformation induced bygroundwater level changes in the Bangkok
aquifer system, Acta Geotechnica, 2021, pp.
1265–1279.
[5] Phoban H., Seeboonruang U., and Lueprasert P.,
Numerical Modeling of Single Pile Behaviors

213

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

A BASIC STUDY ON ESTIMATING PARTICLE-SIZE DISTRIBUTIONBY AUGER EXCAVATION SOUND USING MACHINE LEARNING
Keito Endo1, Taizo Kobayashi2 and Ryoichi Fukagawa2
1

Graduate School of Science and Engineering, Ritsumeikan University Graduate school, Japan;
2
College of Science and Engineering, Ritsumeikan University, Japan
ABSTRACTThe construction industry is due for a digital renovation. This paper have focused on ground excavation
technology using screw drills, thereby, developing a technology that can evaluate geotechnical properties andclassification in real time using AI based on sound generated around the tip of the drill. In this paper, as afundamental study, the mass ratio of beads of each particle sizes was estimated from excavation sound of a model
ground mixed with artificial beads of different particle sizes. First, we prepared a model ground involving
alumina beads in a circular container and drilled it using an auger with a microphone built into the tip and compared
the excavation sound waveforms and FFT results for each grain size. Second, we estimated the mass ratio of beadsof each grain size in the model ground using AI from the excavation sound of the model ground mixed with beadsof different grain sizes, showing the feasibility of grain size distribution from excavation sound.

Keywords: Particle-size distribution, Machine learning, Excavation, Auger, FFT
INTRODUCTION
EXAMINATION OF EXCAVATION SOUND
USING
MODEL
EXPERIMENT
WITH
ALUMINA BEADSIn recent years, digital technology has beendeveloped in construction business, and technology
using various types information of construction has
been developed, such as to evaluate strata from theelectric current of the drive unit during auger
excavation [1] and to measure ground stiffness and soil
density from acceleration of vibratory roller undercompaction [2].
We are developing a technology for groundexcavation with auger drill, that evaluate soil
properties and strata in real time using AI based on
excavation sound generated by the tip of the auger drill.
There have been studies on using sound and vibration
for geotechnical investigations, such as soil
classification by the sound of friction between thescrew point and soil particles during SWS test (screw
weight penetration test) [3] and assessment of soil on
the sound of cutting prosses by boring machine [4].
However, modeling approach to determining therelationshipbetweensoundandvibration
characteristics (explanatory variables) and particle
size distribution (objective variable) is difficult andhas not put into practical use. On the other hand,because AI derives the correlation between
explanatory variables and objective variables, there isa possibility to realize measuring particle size
distribution without the process of modeling. In thispaper, as a feasibility study, laboratory experiments
using a small excavation machine were conducted tomeasure particle size distribution by auger excavation
sound.

In this study, we performed excavation
experiments using artificial beads (alumina beads) asa simulated soil because of their easily managed grain
size. We prepared a model ground filled with three
types of beads of different particle sizes (D = 1, 3, and5 mm) in a cylindrical container. The model groundwas 120 mm in inside diameter and 100 mm in height(Fig. 1). We created a 3D(three dimensions)-printed
auger made of plastic (material: PLA (Poly-LacticAcid)) (shaft diameter: 20 mm, blade width: 20 mm),which has a small microphone built into its tip torecord sounds generated near the auger tip during
excavation (Fig. 2). The excavation conditions wereset at a excavation speed of 5 mm/s and a rotation
speed of 0.5 r.p.s., that means 30 r.p.m., and the soundFig. 1 Specimen214GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 2 Cross section of auger tip

Fig. 4 FFT analysis
Table 1 Mass ratio of beads of each grain sizein the model groundFig. 3 Sound wave
was recorded for 10 s from the time the auger tip
reached a depth of 80 mm (sampling frequency: 48
kHz).
Fig. 3 and Fig. 4 show the sound signal, whenbeads of each grain size were excavated, andamplitude spectra obtained by FFT analysis. The lager
grain size, the lager sound signal and the different
frequency characteristics.
ESTIMATION OF MASS MIXING RATIO OFBEADS USING MACHINE LEARNING

different grain size beads. A total of 15 model groundpatterns with different mass mix ratios were preparedusing alumina beads of different grain sizes (D = 1, 3,
and 5 mm). The mass mix ratios of the model grounds
prepared are shown in Table-1. The excavation
conditions were the same as in the section2.

In this section, the mass mix ratio of artificial
beads of each grain size is estimated using AI from the
excavation sound of a model ground mixed with215

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 5 Flow chart of estimation(a) Relationship between the observed andpredicted values(b) Relative frequency of error

Fig. 6 Estimation results from FFT numerical data
(Training data: Excavation sound of a single grain size)
We focused In this aaaaaa
216

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Develop An AI Model Based on Time History
Numerical Datawith a single grain size: (100:0:0), (0:100:0), and(0:0:100). A time data of 11 s obtained during one
drilling cycle was shifted every 12.5 ms and separated
by 100 ms to create 800 training data (total number oftraining data:800 data * 3 model soils = 2400 data).
For the validation data, we used the drilling soundfrom the mixed-model ground. To maintain the ratioof training data to validation data at 8:2–7:2 (training
data: validation data), 68 data points were prepared foreach type of model ground (number of test data:68
data points, 15 types of model ground = 1020 data).

We focused on FFT to extract grain size featuresfrom sound and developped a one-dimensional
convolutional neutral network (1D CNN) that
regressively estimates the mass ratio of each bead (D= 1, 3, and 5 mm) that makes up the ground given FFTprocessed numerical data from the recorded
excavation sound (Fig. 5). First, we provided training
data involving excavation sounds of three model soils(a) Relationship between the observed andpredicted values(b) Relative frequency of error

Fig. 7 Estimation results from FFT numerical data
(Training data: Excavation sound of single grain size and 3types mixed grain size )217

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Additionally, for test data, 50 data points were used foreach type of model ground (training data: validation
data: test data = 2400:1020:750).
When we input the test data into the constructed
learning model, the outputs were the beads mass ratio
for D = 1, 3, and 5 mm. Fig. 6 shows the relationshipbetween the predicted and observed values of the mass
ratio for each grain size output for the 750 test data andthe frequency distribution of the prediction error(correct value (%) – prediction value (%)) for eachmass content of grain size (correct value). From Fig.
6(a), it can be observed that there is no positive
correlation for the grain size of 3 mm and that theestimated values for other grain sizes also exhibit large
errors.
Next, to improve the accuracy of the machinelearning estimation, we input the excavation sounds ofthe mixed model ground into the training data in
addition to the excavation sounds of the single grain
size model ground. The excavation sounds added to(a) Relationship between the observed andpredicted values(b) Relative frequency of error

Fig. 8 Estimation results from sound numerical data
(Training data: Excavation sound of a single grain size)

218

GEOMATE – Bangkok, Thailand, 22-24 November 2022

the training data were (50:25:25), (25:50:25), and(25:25:50) bead mass ratios (%) with D = 1, 3, and 5mm. We selected these three model grounds as
training data because all three types of beads withdifferent grain sizes were present in these three typesof model grounds. The numbers of training data andvalidation data were the same as in the previous
training condition (number of training data:2376,
number of validation data:1020). The estimation
results are shown in Fig. 7. As shown in the figure,there was a positive correlation for all grain sizes, andthe errors were smaller. However, as shown in Figure7(b), the estimation accuracy is not sufficient, witherrors of about 30% for some mass mixing ratios.

(1) First, we compared the waveform data and FFT
results from the drilling beads of different grain
sizes. Consequently, we confirmed the differencein the sound pressure and frequency response
according to the grain size.
(2) We constructed a learning model using FFTprocessed numerical data of the recorded
excavation sounds as input values. The learningmodel using a single grain size model for training
data demonstrated low estimation accuracy;
however, when the number of excavation soundtypes used for training data was increased, theestimation accuracy was improved.
(3) Finally, we have built a AI model using the
excavation sound of a single grain size model
ground as the training data and the excavation
sound data as the input values, which gave thebest estimation accuracy.

Develop An AI Model Based on Time History
Numerical Data
In this section, the unprocessed waveform data
recorded as the teacher data for this study is used toestimate the mass ratio of beads of each particle size.
The excavation sound sources used in the previous
section were used to develop the AI model. We also
used data from a single grain-size ground model as thetraining data. The estimation results of the training
model are shown in Fig. 8. Fig. 8(b) shows that theerror is within 20%, indicating that the estimation
accuracy has improved compared to the FFT results(corresponding to a maximum 95% confidence
interval of 6.0 %). Interestingly, the ratio of each grain
size bead in the mixed model can be estimated bylearning the excavation sound of a single grain sizemodel ground.

REFERENCES[1] Japan Federation of Construction Contractors
and Concrete Pile and Pole Industrial
Technology Association, Kui no sekoukanri
niokeru sizisoutoutatsu no kakunin-nhouhou, pp.
4-13, 2017.
[2] FUJIYAMA, T and TATEYAMA, K, Theevaluation of compacting ground stiffness by theacceleration of vibratory roller, Japan society ofcivil engineers, No. 652, pp. 115-123, 2000.
[3] Yukihiro TSUKADA, Noriaki AOYAMA andTadayoshi YAMAKl, Study of a soil assessment
method based on sounds generated by rotarysounding, Japan Society of Civil Engineers,
No.638, pp.1-10, 1999.
[4] Noriaki SAKO, Shuichi SHIMOMURA,
Masashi
KAWAMURA,
Hiroyoshi
SHIOKAWA, and Shouta KATAOKA, Soilclassification method using screw point-soil
fricative sound by swedish weight sounding. J.
Struct. Constr. Eng. AIJ, Vol. 83, No743, pp.111121, 2018.

CONCLUSION
In this study, we estimated the bead mass ratio of
each particle size in the model ground from excavation
sound of a model ground filled with alumina beads ofdifferent particle sizes using machine learning. The
main conclusions are as follows.

219

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

MODEL TESTS ON CONCRETE BLOCKS SINKING INTO SEABEDGROUND WITH UPWARD SEEPAGE FLOW GENERATED
Tatsuya Matsuda1, Kinya Miura1, Naoto Naito1 and Yuki Ando1
1

Department of Architecture and Civil Engineering, Toyohashi University of Technology, Japan
ABSTRACT

Under stormy weather condition structures on shore and offshore have been severely damaged by heavy sea
wave loading. Many researchers have conducted investigations on the damaged structures and revealed that thedamage of the structures is emphasized by the disintegration of the seabed, caused by sea wave loading on theseafloor. The study has conducted model sea wave loading tests on sand ground and revealed that the disintegrationof seabed and associated scouring are caused by the upward seepage flow as a response to sea wave loading, which
reduces the effective stress in the seabed. The breakwaters which consist of concrete brocks have been monitored
for years, and as a result it is reported that they are sinking every year by a fraction of a meter in accordance withstormy wave condition. This study conducted a series of model tests on a block which is equipped with three-axial
accelerometer to detect its attitude and is rested on the seabed with upward seepage flow generated. The sinking
behavior of the block in the seabed during the gradual increase in hydraulic gradient of seepage flow was carefully
observed to clarify the fundamental mechanism of the sinking of the block. Consequently, this study found thatthe critical condition of the hydraulic gradient under which the sinking of the block is initiated is rather independentof the density of seabed material. In the scheme of the model tests the applicability of the three-axis accelerometerto the detection of the block even under the critical hydraulic gradient attained was demonstrated.
Keywords: Model Test, Upward Seepage flow, Seabed, Concrete Block, Bearing Capacity

INTRODUCTION

and currents. The results showed that upward seepage
increased wave-induced erosion, while downward
seepage stabilized bottom sediments. Matsuda et al.
(2019) [6] have conducted model sea wave loading
tests on sand ground and revealed that thedisintegration of seabed and associated scouring arecaused by the upward seepage flow as a response to
sea wave loading, which reduces the effective stressin the seabed. The wave response of rectangular
blocks on a sandy substrate was also examined.
Accelerometers installed on the blocks showed thatthe blocks on the sandy bottom tended to tilt offshore
under the action of the waves. However, it became
necessary to discuss in more detail the response ofblocks on sand-bed subjected to wave-induced
seepage forces.
In this study, a series of model tests wereconducted for clarify to behavior a block on seabedthat is acting an upward seepage flow. To clarify thefundamental mechanism of block settling, thebehavior of the block settling into the seafloor as thehydrodynamic gradient of the seepage flow gradually
increased was carefully observed using the sensors
for acceleration and displacement.

Under stormy weather condition structures onshore and offshore have been severely damaged byheavy sea wave loading. The breakwaters which
consist of concrete brocks have been monitored foryears, and as a result it is reported that they aresinking every year by a fraction of a meter inaccordance with stormy wave condition. Many
researchers have conducted investigations on thedamaged structures and revealed that the damage ofthe structures is emphasized by the disintegration ofseabed, caused by sea wave loading on the seabed [1].
The fluctuation of effective stress in the seabed iscaused by the upward and downward seepage flow
during cyclic sea wave loading. This mechanism has
been studied theoretically or experimentally. For
example, Yamamoto et al. (1978) [2] has shown theanalytical solution that effective stress response inseabed induced ocean wave. Miura et al. (2004) [3]qualitatively examined the relationship between theeffective stress response of the seabed ground andsediment transport in response to progressive, steadyand irregular waves. Zen and Yamazaki (1990) [4]have shown clarify to mechanism of the waveinduced liquefaction and densification in permeable
seabed using model tests. They also showed that theheavy structures may submerge into the seabed andlight-weight structures may rise up from the seabed.
Baldock and Holme (1998) [5] discussed theeffects of seepage on sediment transport by waves

INTRODUCTION OF MODEL TESTS
Experimental and Measurement Device
Fig. 1 shows a schematic diagram of the onedimensional seepage experimental apparatus system.
220

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 2 Experimental cases

Seepage flow
regulator

Crane

Water tank

Pump

(a)
Displacement
meter

150

150

Pore waterpressure sensor

100

None
None
None
None
Exist
Exist
Exist
Exist

Toyoura sand and silica sand No. 8 (hereinafterreferred to as silica sand #8) were used as thegeomaterials in this test. Table 1 shows the propertiesof the soil materials. The ground was deposited by theunderwater dropping method using a specified
amount of soil measured to obtain target relative
densities of Dr = 40 % and 70 %. In the tests, twocases were considered: a ground only case with noblocks and a case with rectangular blocks placed on
the ground surface. Table 2 shows the experimental
conditions for each case. The rectangular block used
was a cube 100 mm long, 100 mm wide, and 100 mm
high, weighing 2.2 kg.

200

(c)

Table 1 Physical properties of geomaterials
Toyoura sand
0.189
0.999
0.632
2.668

Toyoura sandToyoura sand
Silica sand #8
Silica sand #8
Toyoura sandToyoura sand
Silica sand #8
Silica sand #8

Block

Experimental Conditions

50 50 50

50 50 50

200

370

50

370

100

z

Schematic of experimental apparatus
system (a): Case of ground only condition(b) and Case of rectangular block placed on
the Ground condition (c)Properties
D50 (mm)
emax
emin
Gs

Case-T40
Case-T70
Case-S40
Case-S70
Case-T40B
Case-T70B
Case-S40B
Case-S70B

Relative
density,Dr (%)
40
70
40
70
40
70
40
70

Accelerometer50xFig. 1

Geomaterial

Cylindricalacrylic apparatus

H

(b)

Cases

Silica #8
0.109
1.218
0.670
2.681

External Conditions
The loading conditions for the seepage force were
the same for all experimental cases. The seepage flow
regulator was kept stationary with a zero-water level
difference for up to 60 s after the start of theexperiment, after which the seepage flow regulator
was raised at a rate of 0.05 mm/s and the seepage
force was applied. Thereafter, while maintaining thecritical dynamic hydraulic gradient, the seepage force
was applied until the ground collapsed in the case of
the ground only and until 1200 s in the case of therectangular block.

In this tests, a cylindrical acrylic apparatus with aheight of 370 mm and a diameter of 150 mm was used.
A drainage valve is installed at the bottom of theapparatus, which is connected to the seepage flow
regulator by a hose. The seepage flow regulator isequipped with a drainage weir so that the water levelis always maintained at a constant level in the device.
The height of the apparatus was adjusted by a crane
to change the water level difference ΔH between theapparatus and the experimental apparatus. In all
experiments, a 200 mm thick layer of soil was
prepared to achieve a given density. For measure thepore water pressure in the ground, five sensors of porewater pressure were installed in the soil at on theinterior wall of the apparatus at intervals of 50 mm
from the bottom of the apparatus. In addition, a threeaxis accelerometer and a displacement transducer
were installed in the center of the top of the block tomeasure the behavior of the block.

EXPERIMENTAL RESULTS
Case of Soil Ground Only
This section discusses the seepage failure
phenomenon of the ground in the absence of blocks.
Fig. 2 shows the time histories of the excess porewater pressure for each case. The pore water pressure
sensor at z = 0.02 m, the lowest point of the ground,is lower than the increment of pore water pressurecaused by the difference in hydraulic head. This isdue to the influence of the boundary. The same trend
221

0.15
0.10

Case-T40
0.00 m0.05 m0.10 m0.15 m0.20 m

0.05
0.00
0

0.05
0.00
0

Case-S40
0.00 m0.05 m0.10 m0.15 m0.20 m

0.05
0.00
0

Excess pore water pressure, ue (kPa)

(c)
0.20
0.15
0.10

Case-S70
0.05 m0.10 m0.15 m0.20 mFig. 350 100 150 200 250 300 350 400 450
Time, t (s)Time histories of excess pore waterpressure in case of soil ground only: CaseT40 (a); Case-T70 (b); Case-S40(c) and
Case-S70e z = 0.15

Case-S70
50

100 150 200 250 300 350 400 450
Time, t (s)Time histories of hydraulic gradient in caseof soil ground only: Case-T40 (a); CaseT70 (b); Case-S40(c) and Case-S70

weight of water, Gs is specific gravity weight of soil
particle, and e is void ratio, respectively.
Fig. 3 shows the change of hydraulic gradient in
the ground over time. Hydraulic gradient was nodifferenced depending on the geomaterials, but it wasdifferences around the critical hydraulic gradient withthe soil density. For example, in the loose ground of
40 % relative density, the hydraulic gradient changed
significantly when it reached about 90% of the criticalhydraulic gradient (Fig. 2(a) and (c)). On the other
hand, in the case of the middle dense ground of 70 %relative density, the hydraulic gradient changed after
the critical hydraulic gradient was reached (Fig. 2(b)and (d)). In both cases, there was no significant
difference in the hydraulic gradient depending on thegeomaterials, suggesting that the seepage failure
occurred when the hydraulic gradient value changed
significantly.
Fig. 4 shows the ground conditions at initialcondition (t = 0 s), in the region where the hydraulic

was observed in all cases. In Case S70, the pore waterpressure at z = 0.00 m at the ground surface could not
be measured accurately. Therefore, the followinghydrodynamic gradient values are calculated using
the measured values at z = 0.05 m and 0.15 m, wherethe values are stable, in order to consider spatial
variations in the ground. The hydraulic gradient i andcritical hydraulic gradient icr are calculated by thefollowing equations;

( u

1.0
0.8
0.6
0.4
0.2
0.0
0

Critical hydraulic gradient

(d)

(d)

i=

1.0 Critical hydraulic gradient
0.8
0.6
0.4
0.2
Case-S40
0.0
050 100 150 200 250 300 350 400 450
Time, t (s)

0.05
0.00
0

100 150 200 250 300 350 400 450
Time, t (s)(c)
50 100 150 200 250 300 350 400 450
Time, t (s)Hydraulic gradient, i

Excess pore water pressure, ue (kPa)

0.10

Case-T70
50

(b)

0.20
0.15

Critical hydraulic gradient

50 100 150 200 250 300 350 400 450
Time, t (s)Hydraulic gradient, i

Excess pore water pressure, ue (kPa)

Case-T70
0.00 m0.05 m0.10 m0.15 m0.20 m(b)

Fig. 2

1.0
0.8
0.6
0.4
0.2
0.0
0

(a)

0.20

0.10

1.0 Critical hydraulic gradient
0.8
0.6
0.4
0.2
Case-T40
0.0
050 100 150 200 250 300 350 400 450
Time, t (s)50 100 150 200 250 300 350 400 450
Time, t (s)

(a)

0.15

Hydraulic gradient, i

0.20

Hydraulic gradient, i

Excess pore water pressure, ue (kPa)GEOMATE – Bangkok, Thailand, 22-24 November 2022)

− ue z = 0.05  w(1)0.15 − 0.05
G −1
(2)
icr = s1+ ehere, ue is excess pore water pressure, γw is unit
222

GEOMATE – Bangkok, Thailand, 22-24 November 2022

(a)

(b)

(c)

(d)
Fig. 4

Time histories of soil ground change due to upward seepage flow in case of soil ground only: Case-T40(a); Case-T70 (b); Case-S40(c) and Case-S70

gradient reached 0.5 (t = 250 s), and the near thecritical hydraulic gradient at t = 400 s and 420 s,
respectively. The ground elevation was no changed ata hydraulic gradient of 0.5 in each case. Although thelocation of the ground surface cannot be determinedbecause Case S40 failed at 400 s. In the other cases,
no change from the initial ground surface wasobserved. In Case T40, Case T70, and Case S70,
where the hydraulic gradient changed significantly
near the critical hydraulic gradient and failure
occurred with boiling. This suggests that the
deformation of the ground when seepage forces cause
seepage failure is not gradual, but rather occurs
rapidly when the critical hydrodynamic gradient isreached. This is consistent with the experimental
results of Yoshimi et al. (1973) [7].

shows the time histories of the hydraulic gradient in
the ground, the amount of settlement of the blockmeasured by the displacement meter, and the tilt
angle of a block from the initial state, which is
calculated from the change in the acceleration sensor
in each case. Fig. 6 shows the changes in the block
conditions and sand surface level due to upward
seepage flow for each case at 300 s (around hydraulicgradient i = 0.5), 420 s (around the critical hydraulicgradient icr), and 1,200 s (at the end of the test).
The hydraulic gradient in the ground increased as
the hydraulic head difference increased in each case,and reached the critical hydraulic gradient at a given
time (approximately 420 s). The critical hydraulicgradient is the same as the hydraulic gradient in the
ground only, and does not take into account the effectof the over-load due to the blocks. After the criticalhydraulic gradient was reached, the hydraulic headdifference was kept constant, but seepage failure did
not occur as in the soil-only case. The reason for thisCase of Rectangular Block Placed on the Ground
This section investigates the behavior of theground and blocks subjected to seepage forces. Fig. 5

223

2

Critical hydraulic gradientSettlement,  (mm) Hydraulic gradient, iSettlement,  (mm) Hydraulic gradient, i

1.0

Tilt angle,  (deg.)

GEOMATE – Bangkok, Thailand, 22-24 November 2022

i = 0.5

0.5
0.0
75
50
25

Tilt angle,  (deg.)

0

0
-2

2

400

800

50
25
0
2
0
-2
0

400

800

(a)

(b)
i = 0.5

0.0
75
50
25

Tilt angle,  (deg.)

0

0
-2

Fig. 6

75

Time, t (s)0.5

Fig. 50.0Time, t (s)Critical hydraulic gradient0

i = 0.5

0.5

1200

Settlement,  (mm) Hydraulic gradient, iSettlement,  (mm) Hydraulic gradient, i

1.0

Tilt angle,  (deg.)0

Critical hydraulic gradient
1.0

400

800

Critical hydraulic gradient1.0

i = 0.5

0.5
0.0
75
50
25
0
2
0
-2
0

1200

1200

400

800

Time, t (s)Time, t (s)

(c)

(d)

1200

Time histories of the hydraulic gradient in the ground (top), the amount of settlement of the block(middle), and the tilt angle of a block (low): Case-T40B (a); Case-T70B (b); Case-S40B (c) and CaseS70B (d)

(a)

(b)

(c)

(d)

Time histories of the changes in the block conditions and ground surface level due to upward seepage
flow at 300 s, 420 s and 1,200 s: Case-T40B (a); Case-T70B (b); Case-S40B (c) and Case-S70B (d)is considered to be that the ground resisted theseepage force because the rectangular blocks
increased the effective stress due to the overlying
load. It is also possible that the area where the groundcan flow away was limited between the block and theexperimental apparatus, and therefore, the groundresisted the flow away to some extent.
In the case of 40 % relative density using Toyoura
sand (Case-T40), settlement began at a hydraulicgradient of i = 0.5, and the gradient of the settlement224

GEOMATE – Bangkok, Thailand, 22-24 November 2022

(4) The tilting angle of the concrete block increased
or decreased with settlement progressed,
indicating that the block settled with a rocking
behavior around horizontal axis.

curve became steeper at the point where themaximum hydraulic head difference was reachedaround 400 s, the gradient of settlement curve of thebrock is maximum. On the other hand, in the othercases, the settlement tended to increase slowly. In allcases, however, the settlement of the blocks ceased toa certain degree despite the continuous application ofa seepage force equivalent to the critical dynamic
hydraulic gradient.
The tilt angle of the block from the time of its
initial installation increased or decreased withsettlement, suggesting that the block settled with arocking behavior starting around the y-axis. Thischange in tilt angle was observed at the same time asor even before the response of the block displacement
meter, and is considered to capture the fine behaviorof the block.

ACKNOWLEDGMENTS
This work were supported by JSPS Grant-in-Aid
for Scientific Research (C) 17K06553, JSPS Grantin-Aid for Young Scientists 20K14824. The authors
would like to thank Yudai Fuji for the help with themodel tests.

REFERENCES[1] Oka F., Yashima A., Miura K., Ohmaki S., andKamata A., Settlement of Breakwater on
Submarine Soil Due to Wave-induced
Liquefaction, in Proc. 5th International Societyof Offshore and Polar. Engineers, Vol.2, 1995,
pp.237-242.
[2] Yamamoto T., Koning H. S. L., and Van Hijum
E., On the Response of a Pore-elastic Bed toWater Waves, Journal of Fluid Mechanics, Vol.
87, Part 1, 1978, pp.193-206.
[3] Miura K., Asahara S., Otsuka N., and Ueno K.,
Formulation of ground for coupled analysis ofseabed response to wave loading, Proceedings ofGeotechnical Engineering symposium, Vol.49,
2004, pp. 233-240. (in Japanese)[4] Zen K., and Yamazaki H., Mechanism of waveinduced liquefaction and densification in seabed,
Soils and Foundations, Vol.30, No.4, 1990, pp.
90-104.
[5] Baldock T. E., and Holmes P., Seepage effects on
sediment transport by wave and currents, Coastal
Engineering, 1998, pp.3601-3614.
[6] Matsuda T., Miura K., Takayanagi R., and AnaiK., Wave flume experiment on wave response ofa rectangle block under different seabedcharacteristics, Journal of Japan Society of Civil
Engineers, Ser. B2 (Coastal Engineering),
Vol.75, Issue 2, 2019, pp.I_955-I_960. (inJapanese)[7] Yoshimi Y., Kuwabara F., and Tokimatsu K.
One-dimensional volume change characteristics
of sands under very low confining stresses, Soils
and Foundations, Vol.15, No.3, 1975, pp.51-60.

CONCLUSIONSIn this study we conducted a series of model tests
on the behavior of concrete blocks resting on seafloor,
where the seabed was subjected to upward seepage
flow under different conditions of geomaterials andrelative density of the ground using model tests. The
following conclusions were drawn from the carefulobservation of the behavior of the concrete blocks
sinking.
(1) The critical hidrauric gradient at which effective
stress diminishes due to upward seepage force
was rather dependent on the relative density ofseabed material, and however independent of
the type of seabed mterial with different grain
size distribution. In the case of the seabed
consisted with medium loose sand (Dr = 40 %),the concrete blocks started to sink at thehydraulic gradient of 40 % of critical value. In
the case of dense sand (Dr = 70 %), the concrete
block sank near the critical hydraulic gradient.
(2) While the concrete block resting on seafloor ofloose Toyoura sand sank quickly into the seabedin the manner of progressive failure, in the othercases the concrete block sank rgradually. I wasfound that the settlement of concrete block was
capped at a certain level even when thehiydraulic gradient is reaching the critical value.
(3) The monitoriring of displacement andacceleration by using sensors installed on theblocks showed that the concrete blocks started
to move around the hydraulic gradient of 50 %of the crictical value, regardless of thegeomaterial type and relative density.

225

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

MODEL TESTS ON SAND BOILING AROUND SHEET PILEREGARDING PROPERIES OF GROUND MATERIAL
Tatsuya MATSUDA1, Naoto NAITO1 and Kinya MIURA1
1

Department of Architecture and Civil Engineering, Toyohashi University of Technology, Japan
ABSTRACT

Cofferdams have been widely employed for the construction of structures in rivers or under groundwater tables.
The coffers constructed with sheet piles are sometimes destabilized due to sand boiling, which is caused by the
upward seepage force inside the cofferdams in the vicinity of the sheet piles. A rational design method of cofferdam
is indispensable for ensuring the soundness of the structures resting on integrated foundation ground even in thewater. The integrity of the foundation ground inside the cofferdam is examined usually by assessing exclusively
the balance between the weight of ground material and upward seepage force, where the mechanical properties of
the ground material or the scale effect of soil particle are not sufficiently taken into consideration. In this study weconducted a series of model tests on the occurrence of sand boiling inside the cofferdam, in which two cofferdam
models of different size are prepared, and several ground materials with different grainsize distribution anddifferent relative densities were employed. We carefully observed the process of seepage flow until the sandboiling occurred in every model test and recorded not only the condition of seepage flow for the occurrence ofsand boiling, but also the formation of ground after failure. As a consequence we found perceptible effects ofmechanical properties of ground material and scale of model cofferdam on the critical seepage condition for theoccurrence of boiling.
Keywords: Cofferdams, Seepage failure, Scaling model test, Scale effect

INTRODUCTION

FEM. As a result of the investigation it was found thatthe seepage force is largely dependent on the plane
shape of cofferdam as well as that in vertical section,
the critical hydraulic head difference for rectangularand round-shaped cofferdams is approximately twothird of that for strip-shaped cofferdam.
Tanaka and Verruijt (1999) [4] carried out themodel tests on medium dense sand ground under twodimensional condition to investigate seepage failureof the ground behind sheet piles, and the mechanismof local seepage failure. From the results of the model
tests, they showed that the prismatic failure concept
is effective for evaluating critical hydraulic headdifference for occurrence of sand boiling, where acertain soil prism loses the equilibrium. They also
clarified that the hydraulic head difference forprismatic sand model has approximately 10% freeboard between compared with that for total failure.
Asaoka and Kodaka (1992) [5] classified
saturated soil failure problems into four types; Type
I: The failure of loose and/or normally consolidated
soils under fully drained loading conditions. Type II:
The same soils but under perfectly undrained
conditions. Type III: The failure of dense and/or
overconsolidated soils under fully drained conditions.
Type IV: The same soils but under perfectly
undrained conditions. Based on this classification,
they conducted a series of model tests for the failure
problem due to seepage, and soil-water coupled limit
equilibrium analyses with critical state concept
combined. Their findings for the relationship between

Cofferdams have been widely employed for theconstruction of structure foundations in river or undergroundwater table. The cofferdams constructed withsheet piles are sometimes damaged due to sandboiling and the ground inside the cofferdams are
disintegrated, which is caused by the upward seepage
force inside the cofferdams around the sheet piles.
The disintegration of ground inside cofferdam
probably makes it impossible for the ground toprovide load bearing capacity for structures.
Many researchers have investigated the stabilityof cofferdams regarding seepage force and theassociated sand boiling. The primary design ofcofferdam constructed with sheet piles is conductedbased on the method proposed by Terzaghi and Peck(1948) [1], where the critical hydraulic headdifference between outside and inside the cofferdam
which triggers the occurrence of sand boiling is
simply estimated by using from two-dimensional
static seepage analysis where the cofferdam is
implicitly modeled as a strip-shaped structure. In themethod mechanical properties of ground material
except its density were not considered in thecalculation of critical hydraulic head difference.
Miura, et al. (1999 [2], 2001 [3]) investigatedsome cases where the cofferdams were severely
damaged due to sand boiling during the constructionof bridge piers in river, and they conducted someseries of three-dimensional seepage analysis by using
226

GEOMATE – Bangkok, Thailand, 22-24 November 2022

failure load and failure mode were summarized: (1)the smallest failure load in loose sand under
undrained condition, (2) the largest failure load withthe largest failure region in dense sand under
undrained condition, and (3) the observed no global
deformation before failure with in the soil under fully
drained condition that suggests the development ofvery localized shear deformation in the sand.
Benmebarek et al. (2005) [6] used the FLAC-2D
code to analyze seepage failure of sandy soil in acofferdam subjected to upward seepage flow. The
results showed that the soil dilation angle has asignificant effect on the shape of the failure
mechanism. In particular, it was found that a dilating
material, the failure occurs as a triangular prism due
to heaving, whereas in other cases, the failure occurs
as a rectangular prism and the obtained widths of therectangular prisms were smaller than those obtainedby Terzaghi's method (Terzaghi and Peck, 1948).
Okajima et al. (2009) [7] carried out the model
tests which were designed for studying the seepage
failure of soil behind fixed sheet piles and our elastoplastic finite element method was applied to verify
the effectiveness. The model tests and FE analysis
were conducted the different relative density of theground. Terzaghi’s method also investigated by tests
data. The results reaffirm the effectiveness of
Terzaghi's method for calculating failure headdifferences in loose density ground. It was also shownthat in densely density soils, the shape of the soil
differs from the rectangular shape assumed by
Terzaghi.
Numerical methods have been actively developed
to reproduce seepage failure with large-scale grounddeformation. Maeda and Sakai (1994) [8] developed
Smoothed Particle Hydrodynamics (SPH) method, a
discrete and continuous analysis that considers theinteraction of the three phases of soil, water, and air.
There were successes to simulate seepage failurearound sheet piles and river embankments. Maeda
and Sakai (2010) [9] also investigated a process ofgenerating air bubbles and their effect on seepage
failure using the SPH method and model test.
Fukumoto et al. (2021) [10] coupled the lattice
Boltzmann and distinct element methods for bothseepage flow and soil particle motion to perform twodimensional direct simulations at the scale of a modelexperiment using soil particles of a size equivalent toactual sand. The results of the analysis showed thatthe typical series of seepage failure behaviors whereboiling and heaving at downstream in the vicinity ofthe sheet pile and finally leading to quicksand, could
be reproduced seamlessly.
At present, the stability of the foundation soil
inside cofferdam is usually evaluated only from thebalancing weight of soil material and upward seepage
force, and the mechanical properties of soil material
and the scale effect of soil particles have not been
sufficiently considered. In addition, it is unclear

whether the shape of water channel observed during
sand boiling model cofferdams can be useful topredict the real-scale scour shape. The prediction of
failure mode and scale is rather important forevaluating the performance of cofferdams, especially
if numerical analysis methods that can simulate large
deformation problems can be made more accurate. In
order to validate the accuracy of the numericalanalysis method, it is essential to collect a series ofexperimental data for the evaluation of seepage
failure modes and following erosion process
associated being dependent on geomaterial
properties.
In this study we conducted a series of model tests
on the occurrence of sand boiling inside cofferdam, inwhich two cofferdam models of different size areused, and the model ground was formed with several
ground materials with different grainsize distribution
and different relative densities. In all model tests, theseepage flow and the behavior of ground up to theonset of sand boiling was carefully observed.
INTRODUCTION OF MODEL TESTS
Experimental and Measurement Device
A schematic diagram of the experimental deviceis shown in Fig. 1. Two different scales were used inthis model test. The dimensions of the apparatus areshown in Table 1. Each scale experiment is referred
to as a standard model test and a small model test,with the small model being 1/4 the scale of thestandard model. However, the depth of the small
model experiment was not 1/4 because the apparatus
used in the small model test was a pre-made one.
Therefore, the water injection volume was adjusted tosatisfy the similarity described in the next section.
The experimental apparatus was set up with adivider in the center that simulated a sheet pile in acofferdam. This divider is fixed to both side walls.
Drainage outlets were provided on both sides of theapparatus. During the experiment, the downstream of
the apparatus was opened and drained to maintain aconstant water level at the downstream side. On theother hand, the upstream drain was closed, and apump was used to inject water at a constant flow rate.
A high-speed camera and a video camera wereused to capture the phenomena during the experiment.
In addition, pore pressure gauges were used in thestandard model test to take measurements pore waterpressure change at a total of seven locations along the
sheet pile perimeter.
Experimental condition
Three types of geomaterials were used in thismodel tests: silica sand No. 8, silica sand No. 7, andToyoura sand. The grain size distribution of the227

GEOMATE – Bangkok, Thailand, 22-24 November 2022

The model tests were conducted at relative densities
up-stream

Sheet piledown-stream

250tOutletOutflow

I.W.L

hw

Water level difference, h (mm)ΔhInflow

Dg

D

I.G.L.

GroundGround

W

200

150

Silica #8: Small
Silica #7: Small
Toyoura sand: Small
Silica #8: Standard
Silica #7: Standard
Toyoura sand: Standard

100

50

0

L

0

100

200

300

Time, t (s)Fig. 1

Schematic of model test apparatus: (left)
front view; (right) side viewFig. 3Time history of water level difference at
relative density Dr = 40 %Table 1 The scales of model devices
Small
145
150
39
3
13
38
15

250

Standard
600
600
200
10
50
150
50

Water level difference, h (mm)Model Size
H (mm)W (mm)L (mm)t (mm)D (mm)
Dg (mm)H (mm)

200

150

50

0

Fig. 2

Particle size
geomaterials

distribution

curve

0

100

200

300

Time, t (s)ofFig. 4

100

Time history of water level difference at
relative density Dr = 80 %80

40

Toyoura-sandSilica sand #7
Silica sand #8

20
0
0.01

down-stream

up-stream

60

0.1
1
Grain size, d (mm)ΔhPercent passing (%)

Silica #8: Small
Silica #7: Small
Toyoura sand: Small
Silica #8: Standard
Silica #7: Standard
Toyoura sand: Standard

100

10

I.G.L

Table 2 Physical properties of geomaterials
Properties

Silica #8

Silica #7

D50 (mm)emaxemin
k40 (m/s)
k80 (m/s)

0.109
1.218
0.670
3.02×10-5
1.41×10-5

0.168
1.057
0.650
1.16×10-4
6.96×10-51mm
SeepageToyoura
sand
0.189
0.999
0.632
1.96×10-4
1.18×10-4

Fig. 5GroundSchematic diagram of the condition withdisplacement head differenceof Dr = 40 % and Dr = 80 % for all sedimental groundconditions in order to examine differences in seepage
failure phenomena due to density. Properties k40 and
k80 mean the permeability at 40 % and 80 % relative
density Dr, respectively. The soil ground was
deposited to a given density by the underwater drop
method. In the case of relative density Dr = 80 %, thegeomaterials is shown in Fig. 2, and the main physicalproperties of the geomaterials are shown in Table 2.
228

GEOMATE – Bangkok, Thailand, 22-24 November 2022

hydraulic head difference across the sheet pile
between upstream and downstream. Fig. 3 shows at
relative density Dr = 40 % and Fig. 4 shows at relative
density Dr = 80 %. The trend of the hydraulic headdifference is similar regardless of the experimental
scale, confirming that the external force conditions
given by the water injection rate are similar. The trend
over time shows that the gradient changes after acertain amount of time has elapsed from the initial
gradient. The phenomena at the time of the slopechange were visually confirmed to indicate that
deformation had occurred in the sedimental ground.
The head difference at that point is summarized inTables 3 and 4, and each time to reach each headdifference is summarized in Tables 5 and 6. The time
when the sedimental ground at downstream was
uplifted by 1 mm was defined as the displacement
head difference (Fig. 5), and the time when a major
failure occurred was defined as the failure headdifference. The values outside the parentheses in thetable indicate the values obtained in the standard
model test, and the values inside the parentheses are
the values obtained by converting the results of thesmall model test to scale and adjusting them to thestandard model. In Figs. 3 and 4, the markers arefilled in after the transformed hydraulic headdifference is reached.
Tables 3 and 4 shows that the sedimental groundin small model tends to undergo deformation at alower hydraulic head difference than the sedimental
ground in standard model, leading to seepage failure.
In terms of relative density, in the case of the loosely
packed ground with Dr = 40 % in the standard model
test, the displacement and failure head differences
tended to increase as the grain size of the material
increased, while in the case of the tightly packed
ground with Dr = 80 %, each head difference
increased as the grain size of the material decreased.
On the other hand, the small model test did not showa uniform trend as well as each arrival time.
Tables 5 and 6 shows that for all geomaterials,
regardless of the density, the sedimental ground in thesmall model tends to deform and reach failure earlier
than the sedimental ground in standard model. In
order to relative density, the arrival times of theloosely packed ground with Dr = 40 % in the standard
model tests tended to increase with increasing grainsize, while the arrival times of the densely packed
ground with Dr = 80 % were almost the same
regardless of the material. On the other hand, no such
trend was observed in the small model tests.

Table 3 Displacement head difference
Headdifference(mm)Silica #8
Silica #7
Toyoura sand

Displacement head difference
Dr = 40 %Dr = 80 %154 (142)
159 (160)
184 (160)

212 (188)
189 (156)
188 (170)

Table 4 The time to reach failure head difference
Headdifference(mm)Silica #8
Silica #7
Toyoura sandfailure head difference
Dr = 40 %Dr = 80 %154 (142)
159 (160)
184 (160)

212 (188)
189 (156)
188 (170)

Table 5 The time to reach displacement headdifferenceTime (s)Silica #8
Silica #7
Toyoura sand

Displacement head difference
Dr = 40 %Dr = 80 %
190 (153)
260 (228)
225 (140)
260 (184)
255 (162)
265 (212)

Table 6 The time to reach failure head differenceTime (s)Silica #8
Silica #7
Toyoura sandFailure head difference
Dr = 40 %Dr = 80 %
239 (188)
307 (272)
268 (242)
294 (214)318 (275)
292 (268)

sedimental ground was compacted to construct thedense ground.
Water was injected upstream by a pump. The
pump flow rates were 1.29 cm3/s and 42.9 cm3/s forthe small and medium models, respectively,
according to Froude's law.
EXPERIMENTAL RESULTS
To investigate the similarity, the experimental
data for the small model tests were examined inconjunction with the standard scale, doubling the timeand quadrupling the length. In Tables 3 to 8 the values
in parentheses indicate the experimental results forthe small model and those outside the parentheses forthe standard model.

Scour shape of sediment groundFigs. 6 and 7 shows the scour shapes at the time
when the maximum scour depth under the sheet pile
was reached for each material with a relative densityof Dr = 40 % and Dr = 80 %, respectively. The
maximum scour depth is defined as the depth at which

Fluctuation of hydraulic head difference
Figs. 3 and 4 shows the time histories of the229

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Scour depth, D (mm)up-stream
0
-100

-50

0

50

100

-50

Maximum scour width

Silica #8: Small
Silica #7: Small
Toyoura sand: Small
Silica #8: Standard
Silica #7: Standard
Toyoura sand: Standard

I.G.L
-100

Maximum scour
depthDistance from center of the tank, x (mm)GroundScour shape when maximum scour depth isreached at relative density Dr = 40 %Fig. 8

Schematic diagram defining maximum
scour depth and maximum scour width

Table 7 Maximum scour depth
0
-100

-50

0

50

100

(mm)
B

Scour depth, D (mm)Fig. 6

down-stream-50

Silica #8: Small
Silica #7: Small
Toyoura sand: Small
Silica #8: Standard
Silica #7: Standard
Toyoura sand: Standard

Silica #8
Silica #7
Toyoura sand-100

Maximum scour depth
Dr = 40 %Dr = 80 %87 (80)95 (80)83 (76)75 (72)80 (68)74 (64)

Distance from center of the tank, x (mm)Fig. 7

Table 8 Maximum scour width

Scour shape when maximum scour depth isreached at relative density Dr = 60 %Headdifference(mm)Silica #8
Silica #7
Toyoura sandthe scour depth around the sheet pile reaches its
maximum during the seepage failure process, and the
maximum scour width is defined as the horizontal
distance between the upstream and downstream sidesof the sheet pile at that time. Table 5 shows the valuesof maximum scour depth and maximum scour width
for the standard model test and the small model test,
respectively. Fig. 8 shows the schematic diagram
defining maximum scour depth and maximum scour
width. The maximum scour width is defined as thewidth at the maximum scour depth.
The maximum scour depth of each material issimilar regardless of the relative density. On the other
hand, the maximum scouring width varies dependingon the relative density, and the loosely packed soil
with a relative density of Dr = 40 % tends to have awider scouring width, indicating that the scouring
area is wider. In addition, the scour profile of theloose soil with Dr = 40 % is different from that of thedense soil with Dr = 80%, indicating that the scour
shape varies depending on the geomaterial.
Focus on the scale effects of model test, the resultsof the small model were similar to or slightly
underestimated compared to the results of thestandard model in terms of maximum scour depth andscour shape in Table 7. On the other hand, the small
model overestimated the maximum scour widthcompared to the standard model in Table 8.

Maximum scour width
Dr = 40 %Dr = 80 %64 (56)114 (124)
79 (88)76 (80)62 (80)50 (68)CONCLUSIONSThis study we conducted seepage flow tests on
the model cofferdams to observing the behavior of
seepage flow and ground deformation, in which
two model cofferdam with different model scaleand several ground materials with different
grainsize distribution and different relative
densities were employed. The conclusions of thisstudy can be summarized as follows:
・ The perceptive effect of the model scale wasobserved on the critical hydraulic headdifference between inside and outside
cofferdam. The critical hydraulic headdifference for small-scaled model was smaller
than that for standard-scaled model.
・ We found that the shape of water channel fromoutside to inside cofferdam during sandboiling was rather influenced by the grainsizeof ground material and relative density. The
shape of the channel was scarcely influenced
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

by the model scale of cofferdam.

sand behind sheet piles -The mechanism andpractical approach to analyze-, Soils andFoundations, Vol. 39, Issue 2, 1999, pp. 27-35.
[5] Asaoka A., and Kodaka T., Seepage failure
experiment and their analysis of loose andmedium dense sands. Soils and Foundations, Vol.
32, Issue 3, 1992, pp.117-129.
[6] Benmebarek N., Benmebarek S., and Kastner R.,
Numerical studies of seepage failure of sandwithin a cofferdam, Computers and Geotechnics,
Vol. 32, 2005, pp. 264-273.
[7] Okajima K., Tanaka T., Zhang S., and KomatsuT., Model experiments and elasto-plastic finite
element analysis about seepage failure of sandbehind fixed sheet pile, Transaction of the
JSIDRE, Vol.260, 2009, pp.107-112.
[8] Maeda K., and Sakai M., Development of
seepage failure analysis procedure of granularground with Smoothed Particle Hydrodynamics(SPH) method, Journal of Applied Mechanics,
JSCE, Vol.7, 2004, pp.775-786.
[9] Maeda K., and Sakai M., Seepage failure anderosion of ground with air bubble dynamics,
ASCE, Geoenvironmental Engineering andGeotechnics, 2010, pp.261-266.
[10] Fukumoto Y., Yang H., Hosoyamada T., andOhtsuka S., 2-D coupled fluid-particle numericalanalysis of seepage failure of saturated granularsoils around an embedded sheet pile with nomacroscopic assumptions, Computers andGeotechnics, 2021, Vol.136.

ACKNOWLEDGMENTS
This work were supported by JSPS Grant-in-Aid
for Scientific Research (C) 17K06553, JSPS Grantin-Aid for Young Scientists 20K14824, The Geoscience Center Foundation, and Research Funding
Granted by Toyohashi University of Technology
President. We would like to thanks Erika Yagami,
Yuki Ando and Nur Atiqah Syazrina Binti Aziz for
their help to conduct the model tests.
REFERENCES[1] Terzaghi K., and Peck R. B., Soil Mechanics inEngineering Practice, John Wiley and Sone,
1948, pp.218-233.
[2] Miura K., Imafuku M., Furukawa N., andNagasawa M., Ground failure due to seepage
flow in a river cofferdam, Soil mechanics andfoundation engineering, Vol.47 (4), 1999, pp.710. (in Japanese)[3] Miura K., Supachawarote C., Phien-Wej N., and
Lin D. G., Boiling Resistant Design ofCofferdams Regarding the 3-D Effects of
Seepage, The 15th International Conference on
Soil Mechanics and Geotechnical Engineering(15ISSMGE), 2001, pp.2151-2154.
[4] Tanaka T., and Verruijt A., Seepage failure of231

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

DISCRETE ELEMENT MODELING OF SLOPE FLOW BEHAVIORSOF DRY GRANULAR MATERIALS WITH DIFFERENT COLLAPSECONDITIONSNaoto Naito1, Tatsuya Matsuda1, Kinya Miura1, Yasuhiro Yamada1 and Takumu Omura1
1

Department of Architecture and Civil Engineering, Toyohashi University of Technology, Japan
ABSTRACT

There has been a need to improve the accuracy of predicting the arrival distance of granular avalanches
generated by rock slope failures. However, it has not been entirely clarified how the flow behavior depends onproperties of slope and granular material. Although many researchers have conducted the analytical investigation
using discrete element simulation, the numerical models employed were merely confirmed by comparing only the
final deposition shapes between experiments and simulations. When analyzing the flow behavior of granularmaterials, we are required as well to reproduce the velocity distribution of granular materials during slope flow.
In this study was proposed a discrete element modeling method which is based on the results of particle shapemeasurements, restitution tests, and sliding tests, for more reliable the DEM simulation of the flow behavior of
failed debris on slope. As consequence it was found that the proposed discrete element modeling is capable ofreproducing the final deposition and velocity distribution of granular materials during slope flow with practicalaccuracies.
Keywords: rock slope failure, granular avalanche, runout distance, discrete element method

INTRODUCTION

comparison item to validate the numerical method.
In this study we presents an example of a discreteelement modeling method that reproduces not only
the final deposition shape of granular materials, butalso the velocity distribution of the granular materials
from downslope flow to deposition. The validity ofthe modeling method was confirmed by comparison
with the results of slope flow experiments withdifferent slope angles, slope heights, and collapse
volumes.

These days the restoration of aged social
infrastructures is a national-wide pressing issue inJapan. The failure of aged cut slopes are involved tothe issue, and the prevention of slope failure isinevitable problem for the extension service life of thesocial infrastructures. This study aims to propose arational prediction method for evaluation of thedegree of potential of slope failures and the range andintensity of the associated damages.
Numerical simulation by means of discreteelement method (DEM) [1] has been developed as
one of the most promising methods for predicting therunout distance of rock debris during rock slope
failures [2] [3]. It has been reported that thedeposition range can be predicted through thesimulation by using DEM based on the comparison
with in-situ observation and laboratory experiments.
Some investigations have compared the velocity at
the tip of flowing granular materials [4] [5], but theirvalidity is often confirmed only by the final
deposition shape [6] [7]. In the investigations the
granular avalanche experiments that were used forcomparison with the numerical simulations hadconstant slope angles and slope heights. However, topredict the runout distance of granular materials due
to rock slope failure with high accuracy, acombination of parameters such as slope angle, slope
height, granular diameter, and amount of collapse
should be considered. Therefore, not only the final
deposition shape but also the flow velocity
distribution during slope flow must be used as a

OVERVIEW OF EXPERIMENTAL AND
NUMERICAL SIMULATION METHODS
Outline of model tests
For the slope flow model experiment of granularmaterial, we used the apparatus shown in Fig. 1,
which consists of a slope model with a horizontal
surface length of 3 m and a slope length of 2 m witha variable slope angle and a hinge type trigger gate ina soil tank with a depth length of 0.4 m. The granularmaterials used in the experiment were crushed rocks
with grain sizes ranging from 19.0 to 37.5 mm, as
shown in Fig. 2. The granular materials werecollapsed by instantaneously opening the gate using ahinge placed above the initial accumulation positionof the granular materials as the rotation center. During
the experiment, the slope flow behavior was
photographed from the side of the soil tank at 500 fps
using a high-speed camera. After the experiment,
photographs were taken from the side and top of the232GEOMATE – Bangkok, Thailand, 22-24 November 2022

the saltating particles as the maximum runout
distance Lmax. Since the total mass of saltating
particles was less than 1% of the collapse mass, thepresent study focused on the runout distance Ld toevaluate the area where the granular materials collide
as a group with a large mass.

Slope angle
θ = 45, 60°

Initial deposition

M0

Slope heightH = 400, 800mm

2m

Collapse mass
M 0= 40, 80 kg

H

3m

θ

Outline of numerical simulations

Fig.1 Outline of slope and initial deposition

The DEM simulations presented here are
performed using the open-source LIGGGHTS® [8],adopting Hertz contact model where Young’s
modulus, Poisson ratio, the coefficient of friction andthe coefficient of restitution are the major parametersspecified in material properties. There have been
several studies of granular materials using
LIGGGHTS, including studies of the shear behaviorof granular materials [9] and studies of the behaviorof granular materials impinging on rigid barriers [10].

Fig. 2 Photo of granular material
Runout distancex = Ldx=0

Flow direction
Deposition area

Maximumrunout distancex = Lmax

GRANULARPROPERTIESANDITSDISCRETE ELEMENT MODELING METHOD

Saltatingparticle

Particle shape
From the granular materials used in theexperiment, 50 rock blocks were randomly selected
and measured for major axis diameter a, medium axis
diameter b, and minor axis diameter c to classify theparticle shape [11]. Fig. 4 shows the results of the 50
measurements, and the filled plots show the averagevalues. The particle shape is generally close to a lumpshape.
In these simulations, we used clumped particles
whose size ratios are the average of those of the
granular materials used in the experiment, shown in
Fig. 5.

Fig. 3 Definition of runout distance
1.0

Measured value
Average value

b/a

0.8
0.6

c

0.4

a

0.2
0
0

b
b

0.2

0.4 0.6
c/b

0.8

1.0

Fig. 4 Shape classification of granular materialsaaParticle mass
Fifty granular materials were randomly selectedfrom the granular materials used in the experimentand the particle masses were measured, resulting in
the distribution shown in Fig. 6.
In the simulation, five different sizes of particles
were used to obtain the same conditions as the fivelevels of wight distribution shown in Fig. 6. The
particle dimensions at each mass were set to theaverage size ratio shown in Fig. 5, assuming that theparticles were ellipsoids with a unit volume mass of
2700 kg/m3.

bcFig. 5 Rock particle model in DEM
soil tank to confirm the runout distance of the
granular materials and the deposition.
In this study, the final deposition of the granularavalanche has been divided into depositional area
where the rock particle is stacked in two or morelevels and saltating particles as shown in Fig. 3. The
distance x in the flow direction was defined as the
distance to the depositional area starting from the toeof the slope as the runout distance Ld, and the tip ofRestitution coefficient
To investigate the restitution coefficient ofparticles, 50 randomly selected rock particles were
impacted with the slope model and the particle fixed
on the slope model from a height of 500 mm, as
shown in Fig. 7, and the rebound heights were
233

50

100

40

80

30

60

20

40

10

20

0

7.86

15.96

24.06

32.16

40.26

～

～

～

～

～

15.96

24.06

32.16

40.26

48.36

0

simulation was performed for a total of 20 cases of
initial postures, each rotated 18 degrees around the xaxis and y-axis. The results of the rebound simulation
set the restitution coefficient between the rockparticle and the slope model to 0.64 and the restitution
coefficient between the rock particles to 0.26.

Accumulated percentage (%)

Percentage of mass (%)

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Friction coefficient
The friction coefficient between the rock particle
and the slope model was measured using the sliding
test shown in Fig. 8. The static friction coefficient wasmeasured on 50 randomly selected rock particles
from the angle at which the slope was inclined to startsliding. The coefficient of static friction was 0.60.
Since this study is concerned with slope flow
phenomena, it was decided to use the dynamic
friction coefficient in the simulation. Referring to theprevious literature [12], where the dynamic friction
coefficient is approximately 52% of the static friction
coefficient, 0.32 was selected as a candidate friction
coefficient to be used in the simulation. For thefriction coefficient between rock particles, 0.47,
which corresponds to a friction angle of 25° between
particles, was selected as a candidate for thesimulation based on previous measurements of thefriction angle between particles [13], which ranges
from about 22-30°. The validity of the friction
coefficient setting method was confirmed throughsensitivity analysis of the parameters and comparison
with experimental results, as described below.

Particle mass (g)Fig. 6 Particle mass distribution
Rock particle
Rock particle

Rock particle（fix）

Rebound height

Rebound height
Slope model

Slope modelFig. 7 Outline of rebound test
Rock particle

Slope model

Slope angle

Fig. 8 Outline of sliding test
Table 1 DEM input parameters
Unit
Basic
setting
Young’s modulus
Pa
5.0×107
Poisson’s ratio

0.32

Restitution coefficientparticle-particle

0.26

Restitution coefficientparticle-wall

0.64

Friction coefficientparticle-particle

0.466

Friction coefficientparticle-wall

0.325

Timesteps

Poisson’s ratio and Young’s modulus
Poisson's ratio was set to 0.32 in the analysis,referring to the values of 0.29-0.34 given in theliterature of limestone association of Japan [14].
Young's modulus was set to a value that considers
the computational cost, based on previous researchshowing that its contribution to slope flow behavior
is relatively small compared to other parameters [15].
The validity of the set values was confirmed bysensitivity analysis of the parameters and comparison
with experimental results, as described below.
PARAMETER SENSITIVITY ANALYSIS FORSLOPE FLOW SIMULATION

1.0×10-6

In this study, sensitivity analyses of theparameters Young's modulus, computation timeincrements, coefficient of friction between particles,
and coefficient of friction between particles and slope
were conducted. The purpose was to set values for
Young's modulus and computation time incrementsthat consider computational cost, and to validate thesetting method for the friction coefficient. The effectsof various analytical parameters on the final
deposition shape were investigated and comparedwith experimental results. The experimental
conditions for comparison were a slope angle of 45°,

measured from images. The median rebound height
between the rock particle and the slope model was 4mm, and the median rebound height between the rockparticles was 26 mm.
In the simulation, the restitution coefficients were
set such that the median rebound height agreed withthe experiment. In the simulation of the rebound test,
the rock particle model in Fig. 5 was used to measure
the rebound height by dropping the rock particle froma height of 500 mm onto a flat surface. Since one rockparticle geometry was used in the simulation, rebound234

GEOMATE – Bangkok, Thailand, 22-24 November 2022

1.6

1.8

0.2

0.4

0.6 0.8 1.0 1.2
X-coordinate, x (m)
Exp.
Sim.

1.4

1.6

1.8

0.2

0.4

0.6 0.8 1.0 1.2
X-coordinate, x (m)

1.4

1.6

1.8

Y-coordinate, y (m)0.4

0.6 0.8 1.0 1.2
X-coordinate, x (m)

1.4

1.6

1.8

2.0

-5

Y-coordinate, y (m)1.0×10-6-7

1.0×10

2.0×10[ θ = 45°, H = 800mm, M0 = 80kg ]0.1
0-0.4 -0.2

0

0.2

0.4

0.6 0.8 1.0 1.2
X-coordinate, x (m)

1.4

1.6

1.8

2.0

Y-coordinate, y (m)

0.404

0.2

0.466

0.577

1.000

[ θ = 45°, H = 800mm, M0 = 80kg ]0.1
0-0.4 -0.2

0

0.2

0.4

0.6 0.8 1.0 1.2
X-coordinate, x (m)

1.4

1.6

1.8

2.0

Y-coordinate, y (m)

Exp.
0.338

0.276
0.351

0.2

0.300
0.364

0.312
0.378

0.325
0.392

[ θ = 45°, H = 800mm, M0 = 80kg ]0.1
0-0.4 -0.2

0

0.2

0.4

0.6 0.8 1.0 1.2
X-coordinate, x (m)

1.4

1.6

1.8

2.0

Y-coordinate, y (m)Y-coordinate, y (m)(d) Friction coefficient between particles and slope
Fig. 9 Final deposition shapes with different
parameters
0.4
0.4 0.3
0.3 0.2
0.2 0.1
0.1 0
0 -0.4 -0.2
-0.4 -0.2 0

2.0

0.4
0.3
0.2
0.1
0
-0.4 -0.2

2.0

0.4
0.3
0.2
0.1
0
-0.4 -0.2

2.0

0.4
0.3
0.2
0.1
0
-0.4 -0.2[ θ = 45°, H = 800mm, M0 = 80kg ]00.2(c) Friction coefficient between particles[ θ = 45°, H = 400mm, M0 = 80kg ]

0

0

0.2

Y-coordinate, y (m)

0.4
0.3
0.2
0.1
0
-0.4 -0.2

1.4

0-0.4 -0.2

Exp.

Sim.
Sim.

0.6 0.8 1.0 1.2
X-coordinate, x (m)
Exp.
Sim.

[ θ = 45°, H = 800mm, M0 = 80kg ](b) Calculation timestep

0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0
0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0
X-coordinate, x (m)X-coordinate, x (m)
Exp.
Sim.

0.4

7

5.0×10
1.0×1090.1

Exp.

[ θ = 45°, H = 400mm, M0 = 40kg ]0.2

7

1.0×10
5.0×108(a)Young’s modulus[ θ = 45°, H = 800mm, M0 = 40kg ]0

5.0×10
1.0×108

0.2

Y-coordinate, y (m)

0.4
0.3
0.2
0.1
0
-0.4 -0.2

Exp.
Exp.

6

Exp.

Y-coordinate, y (m)Y-coordinate, y (m)Y-coordinate, y (m)

0.4
0.3
0.2
0.1
0
-0.4 -0.2

Y-coordinate, y (m)0.4
0.4 0.3
0.3 0.2
0.2 0.1
0.1 0
0 -0.4 -0.2
-0.4 -0.2 0Y-coordinate, y (m)Y-coordinate, y (m)a drop height of 800 mm, and a collapse mass of 80kg. Table 1 shows the basic setting value ofparameters.
Fig. 9(a) shows the simulation result of final
deposition shape with different Young's modulus andits experimental results. As in a previous study [15],Young's modulus was found to have little effect on
the deposition shape, so a Young's modulus of 5.0 ×
107 Pa was set for this simulation.
Fig. 9(b) shows the simulation result of final
deposition shape with different calculation timestep
and its experimental results. Since the calculation
timestep has little effect on the runout distance in therange of 1.0 × 10-5 s or less, the calculation timestep
was set to 1.0 × 10-6 s in this simulation, referring
also to the values in previous studies [10] [16].
Fig. 9(c) shows the simulation result of final
deposition shape with different friction coefficient
between particles and its experimental results. Since
the friction coefficient between particles was found tohave little effect on the deposition shape, it was set to0.466, which corresponds to an inter-particle friction
angle of 25°, referring to a previous study [13].
Fig. 9(d) shows the simulation result of final
deposition shape with different friction coefficient
between the particles and the slope and its
experimental results. The effect of the friction
coefficient between the particles and the slope on thedeposition shape is very significant. The value mostconsistent with the experimental results is a friction
coefficient of 0.325, and we decided to use a setting
method that uses a value of 52% of the static friction
coefficient, based on previous research [12].

Exp.
Exp.

Sim.
Sim.

[ θ = 60°, H = 400mm, M0 = 40kg ]0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0
0.2 0.4 0.6
0.8 1.0 x (m)1.2 1.4 1.6 1.8 2.0
X-coordinate,X-coordinate, x (m)
Exp.
Sim.

[ θ = 60°, H = 800mm, M0 = 40kg ]

0

0.2

0.4

0.6 0.8 1.0 1.2
X-coordinate, x (m)
Exp.
Sim.

1.4

1.6

2.0

[ θ = 60°, H = 400mm, M0 = 80kg ]

0

0.2

0.4

0.6 0.8 1.0 1.2
X-coordinate, x (m)
Exp.
Sim.

1.4

1.6

1.8

2.0

[ θ = 60°, H = 800mm, M0 = 80kg ]

0

0.2

0.4

0.6 0.8 1.0 1.2
X-coordinate, x (m)

1.4

1.6

Fig. 10 Final deposition shapes with different slope angles, slope heights amd collapse masses

235

1.8

1.8

2.0

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Velocity[m/s]

Velocity[m/s]

0

t=0ms

2

0

4

0.5m

t=0ms

2

Velocity[m/s]

Velocity[m/s]

4

0

0.5m

t=0ms

0

4.7

0.5m

t=0ms

t=540ms

t=540ms

t=438ms

t=438ms

t=714ms

t=714ms

t=592ms

t=592mst=944mst=944ms

t=728ms

t=728ms(a)Experiment(b)DEM simulation
Fig. 11 Flow velocity distribution in the case ofθ = 45 °, H = 800mm, M0 = 80 kg

4.7

0.5m

(a)Experiment(b)DEM simulation
Fig. 12 Flow velocity distribution in the case ofθ = 60 °, H = 800mm, M0 = 80 kg

COMPARISON
OF
SLOPE
FLOW
EXPERIMENTS AND SIMULATION RESULTS

simulation results were consistent with theexperimental results and reproduced the difference inthe flow mode at the toe of slope, which varied withdifferent slope angles.
The discrete element modeling method based onshape measurements of granular materials, reboundand sliding tests, and parameter sensitivity analysis
can reproduce not only the final deposition shape of
granular materials, but also the velocity distribution
during slope flow. Although it is sometimes difficult
to judge whether the slope flow process is reproducedby comparing only the final deposition shape because
there are conditions in which the flow modes differ,at least the modeling method in this study can
reproduce differences in flow modes.

Final deposition shape
To confirm the validity of the discrete element
modeling method based on rock particle shapemeasurements, rebound and sliding tests, andparameter sensitivity analysis, we compared theexperimental and analytical results for a total of eightcases with different slope angles θ = 45 and 60 °,slope heights H = 400 and 800 mm, and collapse
masses M0 = 40 and 80 kg. Fig. 10 shows the final
deposition shapes obtained from the experiments andanalyses for a total of 8 cases. By setting parameters
based on the discrete element modeling method
presented in this paper, the DEM simulation
accurately reproduces the final deposition shapes
obtained from the experiments.

CONCLUSIONSThis study presented an example of a parametersetting method based on rock particle shapemeasurements, rebound tests, sliding tests, andparameter sensitivity analysis, so that the modeling ofrock debris grains in DEM reproduces not only the
final deposition shape of granular materials, but also
the velocity distribution of the granular materials
from downslope flow to deposition. It was shown that
this numerical modeling can reproduce the final
deposition shape observed in the experiments on rockdebris flow experiments induced by slope failure withdifferent slope angle, slope height, and collapse
volume, as well as the velocity distribution duringslope flow and different flow mode at the toe of slope.

Granular flow velocity
Fig. 11 and Fig. 12 show experimental andanalytical flow velocity distributions for slope angles
θ = 45 and 60°, slope height H = 800 mm, andcollapse mass M0 = 80 kg. Time t is given in Fig. witht = 0 ms as the start of slope flow. In the experimentwith a slope angle of θ = 45°, the flow mode was thatthe following granular materials pushes out thepreceding granular materials. For the experiment witha slope angle of θ = 60 °, the flow mode was that thefollowing granular materials get over the preceding
granular materials and the velocity of granularmaterials becomes zero around the slope toe. The
236

GEOMATE – Bangkok, Thailand, 22-24 November 2022

We are planning to conduct numericalexperiments using this modeling method in
combination with various parameters such as rockparticle shape, amount of collapse, and slope angle,
and to clarify the relationship between internal
behavior of granular materials and runout distance.

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ACKNOWLEDGMENTS
The authors are grateful for the financial support
provided by the Japan Society for the Promotion of
Science (Grant-in-Aid for Young Scientists
21K14239) and Toyohashi University of Technology(Grant-in-Aid for activating education and research).
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12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

MODEL ROCK-SLOPE FAILURE TESTS ON FINAL RUNOUTDISTANCE OF DRY GRANULAR AVALANCHE WITHSECONDARY SLOPE FAILURE
Naoto Naito1, Tatsuya Matsuda1, Kinya Miura1, Takumu Omura1 and Arif Daniel Bin Azmi1
1

Department of Architecture and Civil Engineering, Toyohashi University of Technology, Japan
ABSTRACT

To rationalize the evaluation method for the runout distance of rock-slope failure debris, it is necessary to
clarify the relationship between the runout distance of granular materials and their flow behavior. In this study, we
conducted a series of model tests of dry granular avalanches with test parameters of slope angle, flow height, andgranular diameter comprehensively changed. Thus, we investigated the effect of granular diameter on the runout
distance from the comparative examination of the test results. The test results showed that the runout distance isdependent on the behavior of granular materials. If the following granular materials pushes out the preceding
granular materials, the increase in particle diameter produces the longer runout distance. On the other hand, if thefollowing granular materials get over the preceding granular materials and the velocity of granular materials
becomes zero around the slope toe, the runout distance of the largest granular diameter may not be the maximum.
To investigate the runout distance by the secondary slope failure, we conducted an additional series of model testsof dry granular avalanches, in which the rock masses generated by the primary slope failure were deposited at thetoe of slope. The test results showed that the runout distance of the secondary slope failure was the same as that ofthe primary slope failure, regardless of the particle size.
Keywords: rock slope failure, dry granular material, granular avalanche, runout distance, model test

INTRODUCTION

parameters. Watanabe et al. [7] conducted also an
numerical analysis by means of DEM to clarify theeffects of the grain size distribution and grain shapeon the runout distance, and suggested the relationshipbetween segregation during granular avalanche andthe runout distance has been reported. However, theseresearcher investigated the runout distance under acertain slope angle conditions. Almost all theinvestigations on the flow behavior of rock debris
have focused only on the flow on slopes [8] [9], anddiscussed the behavior from the viewpoint of energy
attenuation [6]. There have been few studies on flowmodes around the toe of slope.
In this study we conducted a series of model tests
on rock-slope failure and carefully observed therunout behavior of rock debris, and comparatively
examined the effects of slope angle, flow height, andgranular diameter on the runout distance.

To rationalize the evaluation method for the
runout distance of rock-slope failure debris, we needto clarify the relationship between the runout distanceof granular materials and their flow behavior. These
years Discrete Element Method (DEM) [1] has been
widely used in Japan as one of the most promising
numerical analysis methods to predict the runout
distance of rock debris in rock slope failures [2]. The
DEM has been employed to evaluate the safety ofcritical structures such as nuclear power plants [3].
This study aims to clarify the slope flow behavior of
the rock masses and obtain fundamental dates forvalidating the results of DEM analyses.
Many researchers have focused on the granular
diameter as one of the primary parameters that affects
the runout distance of rock debris. Tochigi et al. [4]conducted a series of experiments parametrically withdifferent grain diameter and different collapse
volume, and reported that rock debris with larger
grain diameter runout through longer distance. Bartali
et al. [5] conducted a series of model tests on the
runout distance with rock debris mixture of coarse
grains and fine grains, and reported that the observed
runout distance reaches maximum value when thevolume ratio of coarse grains exceeds 50 percent.
Mollon et al. [6] conducted numerical analysis bymeans of DEM to examine the runout distance andenergy attenuation mechanism with granular
diameter, particle shape, and slope undulation as

CONDITIONS OF
FAILURE TESTS

MODEL

ROCK-SLOPE

Outline of model tests
Model tests have been conducted using theapparatus shown in Fig. 1, which consists of a slopemodel with a horizontal surface length of 3 m and aslope length of 2 m with a variable slope angle and a
trigger gate installed in a 0.4 m wide earthen tank.
Shutter-type gate has been used for the M0 = 40 kg
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Initial deposition

Weight M 0= 40 kg

Slope angle
θ = 30, 45, 60°

Height of slope
H = 0, 400, 800mm
2m

H
θ

3m

Fig. 2 Photo of granular materials

1.0
0.8
1.0

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33

Table 1 Conditions of model tests
Type
θ
H
M0
Granular
of
(°)
(mm)
(kg)
diametertriggergate
Shutter 30
0
40
Small
type
Medium
gate
Large
400
Small
Medium
Large
800
Small
Medium
Large
45
0
Small
Medium
Large
400
Small
Medium
Large
800
Small
Medium
Large
60
0
Small
Medium
Large
400
Small
Medium
Large
800
Small
Medium
Large
Hinge
45
800
80
Small
type
Medium
gate
Large
40×2
Small
(totalMedium
80)
Large

b/a

Case
No.

b/a

Fig.1 Outline of slope and initial deposition

0.6
0.8
0.4
0.6
0.2
0.4
0
0.20

0.2

0.4 0.6
c/b

0.8

1.0

0
0

0.2

0.4 0.6
c/b

0.8

1.0

Large
MediumSmall
Average-Large
Large
Average-Medium
MediumAverage-Small
Small
Average-Largec
Average-Medium
Average-SmallabbFig. 3 Shape classification of granular materials
x=0

Runout distancex = LdFlow direction

Deposition area

Maximumrunout distancex = Lmax

Saltatingparticle

Fig. 4 Definition of runout distancex = -100x=0

x = 200yx = 400[ Unit : mm ]x
O

Fig. 5 Time history velocity measurement area
In the model tests, granular materials have been
deposited in a storage container on the slope, and thetrigger gate has been opened to collapse the granularmaterials. In the case of the shutter-type gate, theshutter has been pulled vertically upward at a velocity
of about 1.6 m/s to collapse the granular materials. In
the case of the hinge-type gate, the hinge placed
above the initial deposition of the granular materials
has been used as the center of rotation to instantly
release the sedimentary soil pressure, causing the
granular materials to collapse. During the model tests,
the slope flow behaviors have been photographed at
500 fps using a high-speed camera from theorthogonal direction of the flow direction. After theexperiment, photographs have been taken
orthogonally to the flow direction and from the top of
the soil tank to confirm the runout distance of the
granular avalanche and the final deposition shapes.

condition, and hinge-type gate has been used for the
80 kg case. Crushed rocks with grain sizes ranging
from 4.75 to 9.50 mm (Small), 9.50 to 19.0 mm(Medium), and 19.0 to 37.5 mm (Large) were used as
the granular material. Details of the granular shapeand other characteristics are described below.
239

GEOMATE – Bangkok, Thailand, 22-24 November 2022
30°-800mm-Large
30°-800mm-Medium
30°-800mm-Small

30°-400mm-Large
30°-400mm-Medium
30°-400mm-Small

30°-0mm-Large
30°-0mm-Medium
30°-0mm-Small

Y-coordinate, y (m)Y-coordinate, y (m)Y-coordinate, y (m)

y (m)
y (m)
Y-coordinate,(m)Y-coordinate,
Y-coordinate, yy (m)Y-coordinate,

t=0s

t=0s

t = 0.12 s

t = 0.12 s

t = 0.30 s

t = 0.30 s
1

Y-coordinate, y (m)Y-coordinate, y (m)Y-coordinate, y (m)Y-coordinate, y (m)Y-coordinate, y (m)Y-coordinate, y (m)Y-coordinate, y (m)Y-coordinate, y (m)Y-coordinate, y (m)Y-coordinate, y (m)Y-coordinate, y (m)Y-coordinate, y (m)Y-coordinate, y (m)Y-coordinate, y (m)Y-coordinate, y (m)30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
X-coordinate,X-coordinate,X-coordinate,x (m)x (m) xx(m)X-coordinate,X-coordinate,X-coordinate,x (m) x (m) x (m)X-coordinate,
(m)
0.8
Fig.
deposition shapes;
θ = 30° (case1-9)30°-800mm-Large
30°-400mm-Large
30°-0mm-Large
0.7 6 Final
30°-800mm-Medium
30°-400mm-Medium
30°-0mm-Medium
30°-800mm-Large
30°-400mm-Large
30°-0mm-Large
0.6
t = 0.48 s
t = 0.48 s
30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
30°-800mm-Medium
30°-400mm-Medium
30°-0mm-Medium
30°-800mm-Large
30°-400mm-Large
30°-0mm-Large
30°-800mm-Large
30°-400mm-Large
30°-0mm-Large
0.5
0.8
Large
MediumSmall
30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
30°-800mm-Large
30°-400mm-Large
30°-0mm-Large
30°-800mm-Medium
30°-400mm-Medium
30°-0mm-Medium
30°-800mm-Medium
30°-400mm-Medium
30°-0mm-Medium
30°-800mm-Medium
30°-400mm-Medium
30°-0mm-Medium
30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
0.830°-800mm-Large
0.4
30°-800mm-Large
30°-400mm-Large
30°-0mm-Large
0.7
30°-800mm-Large
30°-800mm-Large
30°-400mm-Large
30°-400mm-Large
30°-400mm-Large
30°-0mm-Large
30°-0mm-Large
30°-0mm-Large
30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
0.8
30°-800mm-Medium
30°-400mm-Medium
[
θ
=
45°,
H
=30°-0mm-Medium
0mm ] 30°-400mm-Medium
30°-800mm-Medium
30°-800mm-Medium
30°-800mm-Medium
30°-400mm-Medium
30°-400mm-Medium
30°-0mm-Medium
30°-0mm-Medium
30°-0mm-Medium
0.7
0.3
0.8
0.6
0.8
30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
30°-800mm-Small
30°-800mm-Small
30°-800mm-Small
30°-400mm-Small
30°-400mm-Small
30°-400mm-Small
30°-0mm-Small
30°-0mm-Small
30°-0mm-Small
0.7
0.6 0.8
0.2
0.7
0.5
0.8
0.8
0.8
0.7
0.6
0.5
(a)Medium(case14)(b)Large(case15)
0.1
0.6
0.4
0.7
0.7 0.7 0.7
0.6
0.5
0.4
0.5
0.3
45°-400mm-Large
45°-0mm-Large
0.6045°-800mm-Large
Fig.
9
Velocity
distribution;
θ= 45°，H = 400 mm
0.6
0.6
0.6
0.5 -0.6 -0.4 -0.2
-1.2
-1
-0.8
0 [ θ0.2
0.4
0.6
1
0.4
45°-400mm-Medium
45°-0mm-Medium= 45°,
H = 400mm]0.8
0.3 0.5
0.4
0.2
0.545°-800mm-Medium
0.5 0.5
0.4
45°-800mm-Small
45°-400mm-Small
45°-0mm-Small
0.3 X-coordinate,(m)
0.2x 0.4
0.3
0.1
0.4
0.4
0.4 0.4
0.3
0.2
0.1
0.2
0
0.3
0.3
0.3 0.3 0.3
0.2
-1.2
-1
-0.8 -0.6 0.1-0.4 -0.2
0 [ θ0.2
0.6
1= 45°,0.4
H = 800mm]0.8
0.1
Granulardiameterandshapeclassification
0.2
0.2
0.2 0.2 0 0.2
0.1-0.8 -0.6 -0.4 -0.2
0
0.2
0.4
0.6
0.8
1
0 X-coordinate,-1.2
x (m) -1
0
0.1
0.1
0.1
0.1
0.1
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
0
x
(m)
-1.2
-1
-0.8-1.2 -0.6-1 -0.4-0.8 -0.2-0.6 0 -0.4 0.2-0.2 0.4 0 0.60.2 0.80.4X-coordinate,
1 0.6
0.8
1
00
0
0
0
X-coordinate, x (m)
-1.2
-1
-0.8
-0.4
-0.2
0x-1(m)
0.2
0.4
0.6
0.8
1 0 -0.2
X-coordinate,
Fig.
shows
of1 small, medium, and-0.6 30°-800mm-Large-0.4-0.2 -0.6
0
0.2
0.4
0.6
0.8
1-0.6
1.2
1.4
-1.2 -1.2-1.2-1-0.8 -1-0.8-0.6-0.4 -0.6-0.4-0.2 -0.4
-0.2
00.2
0.2
0 0.42 0.2
0.4
0.6 0.4
0.60.8examples
0.6
0.8 1 0.8
1
X-coordinate,
x-0.8
(m)
30°-400mm-Large
30°-0mm-Large
30°-800mm-MediumX-coordinate,30°-400mm-Medium
30°-0mm-Medium
X-coordinate,
x (m)
x (m)
X-coordinate,X-coordinate,X-coordinate,x large(m)x (m)granularx (m)material,withthe mean mass of each
30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
Fig.
0.8 7 Final deposition shapes; θ = 45° (case10-18)
particle diameter being 0.36 g, 4.03 g, and 19.85 g,
30°-800mm-Large
30°-400mm-Large
30°-0mm-Large
0.7respectively. Fig. 3 shows the shape classification
30°-800mm-Medium
30°-400mm-Medium
30°-0mm-Medium
30°-800mm-Large
30°-400mm-Large
30°-0mm-Large
0.6
30°-800mm-Small
30°-400mm-Small
30°-0mm-Smallobtainedfrom30°-0mm-Medium
50 randomly selected major axis
30°-800mm-Medium
30°-400mm-Medium
30°-800mm-Large
30°-400mm-Large
30°-0mm-Large
30°-800mm-Large
30°-400mm-Large
30°-0mm-Large
0.5
0.8
Large
MediumSmall
30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
30°-800mm-Large
30°-400mm-Large
30°-0mm-Large
30°-800mm-Medium
30°-400mm-Medium
30°-0mm-Medium
diametera,mediumaxis diameter b, and minor axis
30°-800mm-Medium
30°-400mm-Medium
30°-0mm-Medium
30°-800mm-Medium
30°-400mm-Medium
30°-0mm-Medium
30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
0.8
30°-800mm-Large
30°-400mm-Large
30°-0mm-Large
0.4
0.7
30°-800mm-Large
30°-800mm-Large
30°-800mm-Large
30°-400mm-Large
30°-400mm-Large
30°-400mm-Large
30°-0mm-Large
30°-0mm-Large
30°-0mm-Large
30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
0.8
30°-800mm-Medium
30°-400mm-Medium[ θ30°-800mm-Medium=
60°,
H =30°-0mm-Medium
0mm ] 30°-400mm-Medium
30°-800mm-Medium
30°-400mm-Medium
30°-400mm-Medium
30°-0mm-Medium
30°-0mm-Medium
30°-0mm-Medium
diameter
c
from
each
particle, and the filled plots
0.730°-800mm-Medium
0.3
0.8
0.6
0.8
30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
30°-800mm-Small
30°-800mm-Small
30°-800mm-Small
30°-400mm-Small
30°-400mm-Small
30°-400mm-Small
30°-0mm-Small
30°-0mm-Small
30°-0mm-Small
0.7
0.6
0.2
show
their
mean
values.
All of the granular diameters
0.7
0.8
0.5
0.8 0.8 0.8
0.7
0.6
0.5 0.7
0.1
0.6
0.7
0.4
0.7 0.7
aregenerallyclose
tomassivein shape.
0.6
0.5
0.4 0.6
0.5
0.6045°-800mm-Large
0.3
45°-0mm-Large
0.6 0.6
0.5 -0.6 45°-400mm-Large
-1.2
-1
-0.8
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
0.4
45°-400mm-Medium
45°-0mm-Medium[ θ = 60°,
H = 400mm ]0.3 0.5
0.4
0.545°-800mm-Medium
0.2
0.5 0.5
0.4
45°-800mm-Small
45°-400mm-Small
45°-0mm-Small
0.3
X-coordinate,
x (m)
0.2
0.3
0.4
0.1
0.4
0.4
0.4
0.4
Data organization methods
0.3
0.2
0.1 0.3
0.2
0.3
0
0.3
0.3 0.3
0.2-1.2-1-0.8 -0.6 0.1-0.4 -0.2
0 [ θ0.2
0.6
1= 60°,0.4
H = 800mm
]0.8
0.1
0.2
0.2
0.2 0.2 0 0.2
0.1-0.8 -0.6 -0.4 -0.2
0
0.2
0.4
0.6
0.8
1
0 X-coordinate,-1.2
x (m) -1
0
0.1
0.1
-1.2 0.1-1 0.1-0.80.1 -0.6 -0.4 -0.2
00.2 Runout
0.4
0.6distance
0.8
1
0x (m) 1-1.2
-1-0.8-1.2 -0.6-1 -0.4-0.8 -0.2-0.6 0 -0.4 0.2-0.2 0.4 0 0.60.2 0.80.4X-coordinate,
1 0.6
0.8
00
In this study, the final deposition of the granular000X-coordinate,x (m)1
-1.2
-1
-0.8
-0.4
-0.2
0x-1(m)
0.2
0.4
0.6
0.8
X-coordinate,-0.6 -0.4-0.2 -0.6
0
0.2
0.4
0.6
0.8
1-0.6x-0.8
1.2
1.4-1.2 -1.2-1.2-1-0.8 -1-0.8-0.6-0.4 -0.6-0.4-0.2 -0.4-0.20 -0.2
00.2 0.2
0 0.4 0.2
0.40.6 0.4
0.8 0.6
0.8 1 0.8
1
1
X-coordinate,(m)avalanche
has 0.6
been
dividedinto depositional area
X-coordinate,x (m)X-coordinate,x (m)X-coordinate,X-coordinate,X-coordinate,x (m)x (m) x (m)y (m)Y-coordinate,y (m)Y-coordinate,
y (m)
y (m)
Y-coordinate,
Y-coordinate,

Y-coordinate, y (m)0.80.7X-coordinate,0
0.6
0.6 0.6 0.1
0.6 0.6 x (m)
0.6
0.6
-1.2
-1
-0.8 -0.6 -0.4 -0.2
00.2 [ θ0.4
0.6 800mm
0.8 ] 1
= 30°,
0
0.5 0.5 0.5
0.5
0.5 H =0.5
0.5
-1.2
-1
-0.8 -0.6 -0.4X-coordinate,-0.2
0 x (m)0.2
0.4
0.6
0.8
1
0.4 0.4 0.4
0.4 0.4 0.4
0.4
X-coordinate, x (m)0.3 0.3 0.3
0.3 0.3 0.3
0.3
0.2 0.2 0.2
0.2 0.2 0.2
0.2
0.1 0.1 0.1
0.1 0.1 0.1
0.1
30°-800mm-Large
30°-400mm-Large
30°-0mm-Large000000 30°-0mm-Medium
030°-800mm-Medium
30°-400mm-Medium
000.4 0.2
0.4
0.6
0.8
1-0.6
-1.2 -1.2-1 -1.2-1-0.8 -1-0.8-0.6-0.4 -0.6-0.4-0.2 -0.4-0.2
0 0.2 0.2
0.4
0.6 0.6-1.2
0.8 -1.2
0.8-11 -1.2
1-1-0.8 -1-0.8-0.6-0.4 -0.6-0.4-0.2 -0.4-0.20 -0.2
00.2 00.2
0.4 0.2
0.4
0.6 0.4
0.6
0.8 0.6
0.8
1 0.81-1.2-1-0.6 -0.8
-0.8
-0.6
-0.40 -0.2
-0.2
0.2
0.4
0.6
0.8
1 -0.8y (m)Y-coordinate,y (m)Y-coordinate,
y (m)
y (m)
Y-coordinate,
Y-coordinate,

Y-coordinate, y (m)Y-coordinate, y (m)0.8
0.7
30°-800mm-Large
30°-400mm-Large
30°-0mm-Large
0.6
30°-800mm-Medium
30°-400mm-Medium
30°-0mm-Medium
30°-800mm-Large
30°-400mm-Large
30°-0mm-Large
0.5
Large
MediumSmall
30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
30°-800mm-Large
30°-400mm-Large
30°-0mm-Large
30°-800mm-Medium
30°-400mm-Medium
30°-0mm-Medium
Velocity (m/s)
30°-800mm-Medium
30°-400mm-Medium
30°-0mm-Medium
30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
0.8
0.4
3.0
0
30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
0.8
[
θ
=
30°,
H
=
0mm
]
0.7
30°-800mm-Large
30°-400mm-Large
30°-0mm-Large
0.3
0.8
30°-800mm-Medium
30°-400mm-Medium
30°-0mm-Medium
0.7
30°-800mm-Large
30°-400mm-Large
30°-0mm-Large
0.6
0.2
30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
0.7
30°-800mm-Medium
30°-0mm-Medium
0.630°-400mm-Medium
0.5
0.1
0.8
30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
0.6
0.5
0.4
0.8
0.70
-1.2
-1
-0.8 0.5-0.6 -0.40.4 -0.2
0 0.3 0.2 [ θ0.4
0.8 ] 1
= 30°,0.6
H = 400mm
0.7
0.6
0.4
0.3
X-coordinate, x0.2
(m)
0.6
0.5
0.3
0.2
0.1 30°-0mm-Large
0.5
30°-800mm-Large
30°-400mm-Large
30°-0mm-Large
30°-800mm-Large
30°-400mm-Large
30°-0mm-Large
30°-800mm-Large
30°-800mm-Large
30°-800mm-Large
30°-400mm-Large
30°-400mm-Large
30°-400mm-Large
30°-0mm-Large
30°-0mm-Large
30°-0mm-Large
30°-800mm-Large
30°-400mm-Large
30°-0mm-Large
0.4 30°-800mm-Large
0.2 30°-400mm-Large
0.1 30°-400mm-Medium
30°-800mm-Medium
30°-0mm-Medium
30°-800mm-Medium
30°-800mm-Medium
30°-400mm-Medium
30°-400mm-Medium
30°-0mm-Medium
30°-800mm-Medium
30°-800mm-Medium
30°-800mm-Medium
30°-400mm-Medium
30°-400mm-Medium
30°-400mm-Medium
30°-0mm-Medium
30°-0mm-Medium
30°-0mm-Medium
30°-800mm-Medium
30°-400mm-Medium
30°-0mm-Medium
0 30°-0mm-Medium
0.4
0.3
0.1 30°-400mm-Small
30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
30°-800mm-Small
30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
30°-0mm-Small
30°-800mm-Small
30°-800mm-Small
30°-800mm-Small
30°-400mm-Small
30°-400mm-Small
30°-400mm-Small
30°-0mm-Small
30°-0mm-Small
30°-0mm-Small
30°-800mm-Small
30°-400mm-Small
30°-0mm-Small
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
0
0.2
0.8
0.8 0.3
0.8 -0.4
0.8 0.8
0.8-1.2-1-0.8 -0.6-0.2
0 X-coordinate,
0.2
0.4x (m)0.6
0.8
1
0
-1.2
-1
-0.8 -0.6 -0.4 -0.2
0.4
0.6
0.8
1
0.1
0.7
0.7 0.2
0.7 0.70X-coordinate,
0.70.20.7
x (m)where the rock particle is stacked in two or morelevels and saltating particles as shown in Fig. 4. The
distance x in the flow direction was defined as the
distance to the depositional area starting from the toeof the slope as the runout distance Ld, and the tip of
the saltating particles as the maximum runout
distance Lmax. Since the total mass of saltating
particles was less than 1% of the collapse weight, thepresent study focused on the runout distance Ld toevaluate the area where the granular materials collide
as a group with a large mass.

Fig. 8 Final deposition shapes; θ = 60° (case19-27)
Model test caseIn this model tests, a total of 27 cases of rockslope failure tests have been conducted using shuttertype open devices with a combination of parametersof granular diameter (small, medium, and large
granular materials), slope angle θ (30, 45, 60°), andheight of slope H (0 , 400, 800 mm). Model tests havebeen also conducted under the conditions of a firstly
failure followed by a secondary slope failure and its
total collapse volume at one time. Table 1 lists theconditions of model tests.

Granular flow velocity
The velocity vector of the granular avalanche has
been calculated from the image taken by the highspeed camera using PIV analysis. Then, time240

GEOMATE – Bangkok, Thailand, 22-24 November 2022

waveforms of the velocity in the measurement area
shown by the red frame in Fig. 5 were output.

why the condition with the largest granular diameter
does not maximize the runout distance under thecondition with the slope angle θ =60°.
First, we focus on the case θ = 45°, H = 400 mm
as the flow behavior under the conditions that resulted
in a longer runout distance for larger granular
diameters. Fig. 9 shows the distribution of flow
velocity for medium and large granular material. It isfound that the thickness of the flow layer tends tobecome thicker as the granular diameter increases.
The reason for the longer runout distance for larger

EFFECT OF GRANULAR DIAMETER ON
RUNOUT DISTANCE
Final deposition shape
Fig. 6, Fig. 7, and Fig. 8 show the final deposition
shapes summarized for each slope angle θ = 30, 45,
and 60°, respectively. There is no significant
difference in the depositional shape at a slope heightof H = 0 mm. A possible reason for the small effectof granular diameter is that the flow height H = 0 mm
is a test condition that only collapses as in the dambreak type angle of repose test, and the short flow
distance reduces the number of collisions between
granular materials. At the slope height H = 400 and
800 mm, the larger granular diameter tends toincrease the runout distance under the conditions ofslope angle θ = 30 and 45°. This trend is consistent
with the results of previous studies [4] that the larger
the granular diameter, the longer the distance attained.
However, under the condition of slope θ = 60°, therunout distance of the large rock mass is not
maximum, which is different from the previous result[4] that the larger the granular diameter, the longer the
runout distance.

Velocity (m/s)
6.0

0

t=0s

t=0s

t = 0.12 s

t = 0.12 s
Dead zonet = 0.30 s

t = 0.30 s

t = 0.48 s

t = 0.48 s

Distribution of flow velocity
In contrast to the previous finding that the larger
the granular diameter, the longer the runout distance[4], the flow velocity distribution of granularavalanche was investigated in this study to clarify(a)Medium(case23)(b)Large(case24)
Fig. 10 Velocity distribution; θ = 60°，H = 400 mm

Velocity (m/s)
0Velocity (m/s)
3.5

0

300

x = -100mm

200

100

0

0
x = 0mm100
0x = 200mm

200

x = 0mm200
Y-coordinate (mm)200
Y-coordinate (mm)x = -100mm

200

100

100

100
0
x = 200mm

200
100
0

0
x = 400mm

200

x = 400mm

200

100

100

0

5.5

300

0

0.2

0.4

0.6

0.8

0

0

0.2

0.4
Time (s)Time (s)(a) θ = 45°(case15)(b) θ = 60°(case24)
Fig. 11 time contours of flow velocity; H = 400 mm, Large granular material
241

0.6

0.8

GEOMATE – Bangkok, Thailand, 22-24 November 2022

granular material may be that the momentum of thefollowing granular materials pushing the preceding
granular materials is greater and the velocity is lesslikely to decrease as the layer thickness increases, inaddition to the fewer collisions per particle aspreviously found [10].
We then turn our attention to the case θ = 60°, H= 400 mm, where the large granular material did nothave the maximum runout distance. Fig. 10 shows theflow velocity distribution for medium and large
granular material. Under the condition of large
granular material, a dead zone occurs immediately
after the granular avalanche impact on the horizontal
plane, where the velocity near the toe of slope
becomes almost zero. In particular, the dead zones oflarge granular material occur earlier than those of
medium granular material, indicating that the deadzones have a wider developmental area than those of
medium granular material.
Fig. 11 shows time contours of flow velocity formedium and large granular materials at θ = 60°. The
vertical axis is the vertical layer thickness, and thehorizontal axis is the time. In the figures, t = 0 s is themoment when the leading edge of the granularavalanche impacts the horizontal plane. These figuresindicate that the occurrence of the dead zone is notinstantaneous but is a continuous phenomenon that
occurs immediately after the impact of the granularavalanche on the horizontal plane.
Comparing Fig. 9 and Fig. 10, at θ = 45°, where
no dead zone develops, the granular materials on thehorizontal plane x ≥ 0 have a small velocity gradientfrom the bottom to the surface, indicating that thesliding mode is such that the preceding granularmaterials are pushed out by the following granularmaterials. On the other hand, at θ = 60°, where thedead zone develops significantly, the granularmaterials on the horizontal plane have a large velocity
slope angle from the bottom to the surface, and themode is such that the following granular materials
overtakes the preceding granular materials in thevicinity of the toe of slope.

Y-coordinate, y (m)Firstly(40kg):
Secondary(40kg):
0.4
0.3
0.2
0.1
0
-0.4

Large
Large

MediumMedium

Small
Small

[ θ = 45°, H = 800mm, M0 = 40kg×2 ]

-0.2

0

0.2

0.4
0.6
0.8
X-coordinate, x (m)

1.0

1.2

1.4

1.6

Fig. 12 Final deposition shapes;
θ = 45°, H = 800 mm, M0 = 40 kg×2 (case31-33)

Y-coordinate, y (m)

Large
0.4
0.3
0.2
0.1
0
-0.4

MediumSmall[ θ = 45°, H = 800mm, M0 = 80kg ]

-0.2

0

0.2

0.4
0.6
0.8
X-coordinate, x (m)

1.0

1.2

1.4

1.6

Fig. 13 Final deposition shapes;
θ = 45°, H = 800 mm, M0 = 80 kg (case28-30)
slope and thus increased the frictional resistance.
Fig. 13 shows the final depositional shape for aslope angle θ = 45°, slope height H = 800 mm, andcollapse mass M0 = 80 kg. Both experimental
conditions in Fig. 12 and Fig. 13 are for a totalcollapse mass of 80 kg. It can be seen from this thatan 80 kg collapse at one time has a greater runout
distance than a collapse divided into primary andsecondary slope failures. As discussed in Fig. 9, thismay be due to the possibility that the runout distanceis greater under the condition where the slope failure
occurs all at once due to the greater thickness of theflow layer.
Fig. 12 and Fig. 13 show the final depositional
shape for a slope angle θ = 45°, slope height H = 800mm, and collapse mass M0 = 80 kg. Bothexperimental conditions have been for a total collapse
mass of 80 kg. It can be seen from this that an 80 kg
collapse at one time, in Fig. 13, has a greater runout
distance than a collapse divided into primary andsecondary slope failures, in Fig. 12. As discussed inFig. 9, this may be due to the possibility that therunout distance is greater under the condition where
the slope failure occurs all at once due to the greater
thickness of the flow layer.

EFFECT OF GRANULAR DIAMETER ON
RUNOUT DISTANCE WITH SECONDARYSLOPE FAILURE
Fig. 12 shows the final depositional shape of theexperimental case in which a 40 kg granular material
was collapsed at a slope angle of θ = 45° and a slope
height of H = 800 mm, followed by another 40 kg
granular material. The first slope failure resulted in alonger runout distance for larger granular diameters,
like the condition of Fig. 7 for slope height H = 800
mm. On the other hand, in the second slope failure,
the runout distance was not longer than in the first
failure, regardless of the granular diameter. This may
be because the granular materials deposited in thefirst collapse increased the roughness of the toe of

CONCLUSIONSIn this study we investigated the effect of granular
diameter on the runout distance from the comparative
examination of the test results. The observed behavior
can be summarized as follows:
1) The observed runout behavior of rock debris isaccordance with previous findings that the rockdebris with larger grain diameter runout throughthe greater runout distance.
2) In the case of sliding mode in which the flowing
242

GEOMATE – Bangkok, Thailand, 22-24 November 2022

rock debris push out the preceding ones at the toeof slope, the change in the flow layer thicknessof the rock debris with different grain diameter
contributes to the velocity decay on thehorizontal plane and may be an importantparameter in the prediction of runout distance.
3) The runout distance does not increase withincreasing granular diameter under the condition
that a dead zone develops at the toe of slope,which is a mode where the preceding granularmaterials are surmounted by the following ones.
4) In the slope failure in which the same rock debris
volumes flow separately in the two steps,
primary and secondary slope failures, the runout
distance of the secondary slope failure is notlarger than that of the primary slope failure,
regardless of the granular diameter.
5) Even if the total collapsing volume is same, therunout distance became greater in the case where
the entire mass collapsed quickly, compared withthe case where the rock mass collapsed in the
double process of primary and secondary slope
failures.

[3] Nakase, H., Cao, G., Tochigi, H., Tabei, K.: A
method to access collision hazard of falling rockdue to slope collapse application of DEM onmodeling of earthquake triggered slope failure
for nuclear power plants, JSCE Journal of
Earthquake Engineering, Vol.71，No.4，I_476I_492，2015.
[4] Tochigi, H.: Investigation of Influence of Falling
Rock Size and Shape on Traveling Distance due
to Earthquake, N09021, 2010.
[5] Bartali, R., Rodríguez Liñán, G.M., TorresCisneros, L., Pérez-Ángel, G., Nahmad-Molinari,Y.: Runout transition and clustering instability
observed in binary-mixture avalanche deposits,
Granular Matter, Vol.22, No.30, 2020.
[6] Mollon, G., Richefeu, V., Villard, P., Daudon,
D.: Discrete modelling of rock avalanches:sensitivity to block and slope geometries,
Granular Matter, Vol.17, No.5, pp.645–666,
2015.
[7] Watanabe, D, Moriguchi, S., Terada, S.: Effectof particle size distributions on granular flow
simulations using discrete element method,
JSCE Journal of Geosphere Engineering, Vol.77,
No.4, I_392-I_402, 2021.
[8] Bryant, S.K., Take, W.A., Bowman, E.T.:
Observations of grain-scale interactions andsimulation of dry granular flows in a large-scale
flume, Canadian Geotechnical Journal, Vol.52,
pp.638-655, 2015.
[9] Zhou, G.G.D., Sun, Q.C.: Three-dimensional
numerical study on flow regimes of dry granular
flows by DEM, Powder Technology, Vol.239,
pp.115-127, 2013.
[10] Naito, N., Yoshida, I., Nakase, H., Tochigi, H.:
Probability Distribution of Falling Rock byExperiments and a Method to Assess Collision
Hazard, 6th International Conference on
Earthquake Geotechnical Engineering Paper,
No.279, 2015.

ACKNOWLEDGMENTS
The authors are grateful for the financial support
provided by the Japan Society for the Promotion of
Science (Grant-in-Aid for Young Scientists
21K14239) and Toyohashi University of Technology(Grant-in-Aid for activating education and research).
REFERENCES[1] Cundall, P. A. and O. D. L. Stack.: A Discrete
Models for Granular Assemblies, Geotechnique,Vol.29, No.1, pp.47-65, 1979.
[2] Nakase, H., Iwamoto, T., Cao, G., Tabei, K.,
Sakaguchi, H., Matsushima, T.: Reproduction
analysis of actual slope collapse and parametricstudy for evaluation of the deposit volume by asimple model of distinct element method, JSCE
Journal of Earthquake Engineering, Vol.73, No.4,
I_694-I_703, 2017.

243

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

INFLUENCE OF TEMPERATURE ON ELASTIC STIFFNESS AND
TIME-DEPENDENT DEFORMATION BEHAVIOURS OF HOSTUN
SAND IN TRIAXIAL COMPRESSION TEST
Kosit Jariyatatsakorn1 and Warat Kongkitkul2
1,2

Department of Civil Engineering, Faculty of Engineering,
King Mongkut’s University of Technology Thonburi, Thailand
ABSTRACT
Elastic stiffness and creep deformation of Hostun sand, at different shear stress levels and temperatures, were
investigated in this study. Hostun sand is widely treated as a standard sand used in geotechnical engineeringresearch. A comprehensive series of temperature-controlled triaxial compression tests were performed on air-driedsamples. During the drained heating stage, the sand sample was heated to different target temperatures (i.e., 30,
45 and 60°C), after which the temperature is held constant. In the shearing stages, the small strain-amplitude cyclicloading (CL) was applied for evaluating the equivalent elastic Young’s modulus (Eeq ) at different shear stresslevels. Sustained loading (SL) was also applied to investigate the creep behaviour. The results from the experimentare as follows. Shear strengths, both at the peak sand residual stages, decrease with increasing temperature. The
Eeq increases with increasing shear stress level, but at the same shear stress level, it decreases with increasing
temperature. The Eeq can be expressed as a function of the shear stress level and temperature. The axial creep
strain increases with increasing shear stress level and temperature.
Keywords: Creep, Elasticity, Hostun sand, Temperature, Triaxial compression test

INTRODUCTION

deformation. The increment of inelastic strain isdeveloped by plastic yielding that is controlled by theviscous effect and inviscid cyclic loading effect [15].
Therefore, to precisely predict the long-term
deformation of soils, Both elastic and viscous
properties should be concerned.
The elastic properties of geomaterials were
investigated with various aspects by plenty of studies.
For example, Hoque and Tatsuoka [16] performed aseries of the large triaxial specimen on various
granular materials (e.g., Toyoura sand, Hostun sand,Ticino sand) and found that elasticity is a stressdependent property. This result is corresponding to
the study of Kohata et al. [17], in which the tests on
small triaxial apparatuses with air-dried Hostun sandwere performed. Moreover, a pair of local
deformation transducers (LDTs) was attached to thesurface of the specimen for precise measurement ofaxial strain to compare with the elastic modulus
obtained from an external measurement. Duttine et al.
[18] studied anisotropic small strain elastic properties
on Hostun and Toyoura sands in triaxial compression
test. The elastic properties were measured by both
static and dynamic methods (i.e., small cyclicloadings and shear, and compression wave
propagations). The test results showed that the elastic
properties are non-linearly related with stress state forboth Hostun and Toyoura sands. Thus, the elastic
property from numerous previous studies is stressstate-dependent or it can be understood that the elastic
property exhibited a hypo-elastic manner.
Nevertheless, the study of elastic property with the

Nowadays, the geotechnical engineering projects
tend to be more related to the temperature, forexample, energy piles [1]-[4], the energy-efficienttechniques for space heating and cooling via boreholeheat exchangers (BHE) [5]. Thermally-active MSE
wall with additional plumbing for heat exchange [6],and nuclear waste disposal [7]. Therefore, soil andgeogrid are likely subjected to non-isothermal
conditions because of cyclical changed intemperature. From previous studies, number of
research on thermo-mechanical behaviour of clay soil
under various temperature-controlled tests has
gradually increased over the years, for instance,
triaxial compression (TC) test, one-dimensional
compression, and isotropic compression [8]-[11].
Conversely, the thermal effect on the behaviour of
granular materials, especially sand, has been rarely
reported, for example, the stress-strain behaviour ofdensely compacted sand at high pressure andtemperature to fill containers of waste nuclear fuel,thermal effect on the shear strength and volume
change behaviour of saturated sand [12]-[14].
Particularly, the temperature effect combined with thetime effect (e.g., creep behaviour) has rarely been
comprehensively investigated.
For long-term settlement, known as secondary
consolidation, the classical consolidation theory iscommonly applied to determine the settlement ofsoils. In fact, the deformation of the soils has occurred
as not only the elastic deformation but also inelastic
244

GEOMATE – Bangkok, Thailand, 22-24 November 2022

temperature effect are rarely limited. Mitchell [19]showed that the elastic modulus of most engineering
materials is temperature dependent. Therefore, theelastic characteristics of geomaterials are likely
temperature-dependent too. Punya-in and Kongkitkul[20] performed a series of triaxial compression test on
a clean sand. The elastic property was evaluated interms of Eeq by performing small unload-reload
cycles after SL test. The results revealed that the Eeq
decreased with increasing temperature. However, inthe past studies, the thermal effect on the elastic
modulus of geomaterials is still ambiguous becauseof different experimental conditions (e.g., the studies
of Cekerevac and Laloui, Liu et al., and Murayama[9], [13], [21])Moreover, when geomaterials are subjected to SL,
the developing deformation is called creep. There areseveral factors which affect to the creep deformation.
Enomoto et al. [22] revealed that the creep
deformation depends on loading rate, particle shape,stress level and so on. These factors can be explainedby the viscous properties of geomaterials. Tatsuoka etal. [23] explained the creep deformation as a responseby the viscous properties of geomaterials with an aid
of non-linear three-component (NTC) model.
However, the studies of thermal effect on creep
behaviour of geomaterials are limited. For example,
Tsutsumi and Tanaka [11] investigated the combined
effects of temperature and strain rate of clayey soils
and found that the clay specimen exhibited peculiar
viscous behaviour (i.e., non-isotach viscosity type) ofclay when the temperature is high, and the strain rate
is low. In addition, Kaddouri et al. [24] performed aseries of creep test by using the temperaturecontrolled oedometer on a saturated compacted
clayey soil. The test results showed that the creep
deformation increases with stress level andtemperature.
In this study, the creep deformation and elastic
Young’s modulus of Hostun sand were investigatedin temperature-controlled triaxial compression test.
Air-dried sand specimens were used in this study toeliminate the difference of thermal expansion
between the solid and liquid in the specimen, and to
avoid the pore pressure induced by the heating,consolidation, and shearing stages. The elastic
properties were presented in terms of Eeq by

2.584, an effective particle size (D50) of 0.302 mm,
and a coefficient of uniformity (Cu ) of 1.999. The
maximum and minimum void ratios are 0.854 and0.522, respectively, which are determined inaccordance with JIS A 1224:2009 [25]. Its gradation
curve, index properties, and particle shape are shownin Fig. 1.
Test apparatus
A temperature-controlled triaxial compression
test apparatus (Fig. 2), used in this study wasdeveloped by Punya-in and Kongkitkul [20]. The
temperature was measured by two sets of the k-type
thermocouple. One of them was installed inside thetriaxial cell for measuring the temperaturesurrounding the specimen (No. in Fig. 2), and theother (No. in Fig. 2) at the outflow of thermal
controller for the feedback control of the circulatingwater. The temperature value of No. was recorded
by a computer. The cell water temperature waselevated to the target temperature by using a heaterand a pair of diaphragm pumps, which were used tocirculate the water. The cell pressure is automaticallycontrolled by an electro-pneumatic (E/P) transducer.
The complicated shear loading type (e.g., small-strain
cyclic loading, sustained loading) can be achieved by
the precise gear loading system, which isdisplacement-controlled. It can respond sharply tocontrol the loading directions and speed [26].
In this study, some parts of this apparatus were
improved. A pair of diaphragm pumps were used tosolve the durability problem due to the long-term
operation during SL test. And the heat insulator(No. in Fig. 2) was installed below the outside loadcell to eliminate the effect of heat transferred from theinside of the cell chamber.
Specimen preparation
The specimen was basically cylindrical in shape (150mm in height and 70 mm in diameter). Hostun sandsample was prepared by pluviating the sand particles
through the air into a split mould, adhered which arubber membrane to the inner surface by suction
around 20 kPa. The multiple-sieving apparatus wasused to achieve high uniformity. At the top cap andpedestal, one layer of silicone grease having athickness of 50 m is placed to reduce friction at theends of sample [28]. After sealing the membrane withthe top cap, a partial vacuuming with 20 kPa wasapplied to the specimen. Then, the split mould wasdissembled. The specimen was left for reaching anequilibrium stage for 30 minutes (see Fig. 3). Notethat the dummy load cell was installed inside thetriaxial chamber to extend the length of piston. Then,the thermocouple was installed Next, after sealing thetriaxial chamber, the partial vacuuming was replaced

performing the CL test, while the creep strain was
obtained by performing SL test at different stresslevels and temperatures.
EXPERIMENTAL SETUP
Test material
Hostun sand is widely used in the geotechnicalengineering research in France. The particle shape is
sub-angular to angular. It has specific gravity (Gs ) of245

Percent finer than D (%)

100

constant at the target temperature since the first 40
minutes, the axial strain of the specimen continued to
expand until reaching equilibrium.

No.4

No.200GEOMATE – Bangkok, Thailand, 22-24 November 2022

80

Test program

60

Hostun sandGs = 2.584;
D50 = 0.302 mm;
Cu = 1.999

40

20

0

In this study, a comprehensive series of shear
loading histories were employed to examine theelastic and creep deformation behaviours of Hostun
sand. The loading type consists of two types, which
can be described as follows.
Loading type a) is continuous monotonic loading(ML) at a constant strain rate. The basic strain rate is
0.075 %/min for each target temperature.
Loading type b) is sustained loading and cyclicloading (SL-CL) test. It starts from ML at the basicstrain rate until reaching the target stress ratio (R),which is defined as the ratio of the major to minor
principal effective stress (i.e., R= σ'1 ⁄σ'3 ). At thetarget stress ratio, SL is hold for three hours to
observe the creep deformation. Then, the CL with adouble amplitude (∆q) of 25 kPa is applied for 10
cycles to evaluate the Eeq . After finishing in CL, ML
is restarted to the next target R value. The target R
value in this loading type consists of 2.0, 2.5, 3.0, 3.5,
4.0, and 5.0. To avoid the diaphragm pumpoverloading, this loading type was divided into two
sets using two different specimens. SL-CL tests were
performed at three different R values in a single
specimen as shown in Fig. 5 and listed in Table 1.

(Source: Duttine et al., 2008)

0.1

1

10

Particle size diameter, D (mm)Fig. 1

Gradation curve and particle shape of
Hostun sand used in present study (modified
from Duttine et al. [27]).

by the cell water pressure until the cell water pressure
reached 20 kPa. Then, the specimen was isotropicallyconsolidated until the confining pressure achieved 30
kPa. The next stage is drained heating stage. In thisstage, the cell pressure was maintained constant at 30
kPa, while the cell water was circulated to the heaterby a pair of diaphragm pumps until reaching the target
temperature. After that, the waiting period was
required for allowing the specimen to achieve an
equilibrium. The time spent for this period was one
hour (60 mins), which is verified by the measured
axial strain during drained heating as shown in Fig. 4.
Even though the cell water temperature had been kept

1

3
1

Load Cell

2

Heat insulator

3

Cell
External displacement
pressuretransducer

4Thermal Controller

5

Thermocouple
1.Temp. Control

6

Thermocouple
2.Temp. Measure (in chamber)

2Computer(-)

Inflow(+)

Heater
6

PV
SV

4

5

Pump
1

Pump
2

Outflow

Diaphragm pump

Fig. 2Temperature-controlled triaxial compression test apparatus developed by Punya-in and Kongkitkul [20]246GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 1 Test program and details of Hostun sand inpresent study
Loading
type

T
(°C)

Target
R

(-)

Dr
(%)

1
2

a) ML

30
45

-

0.63710.6232

86.00
89.82

0.650382.37

0.64030.63950.63910.6426
0.63730.6462

85.13
85.35
85.45
84.49
85.94
83.52

3
4
5
6
7
8
9

0.20
0.15

70
Hostun sandDrained Heating - Target T = 60°C
(keep q=0)

65
60

0.10
55
0.05

50

0.00

45
40

-0.05

35
-0.10
30
-0.15
-0.20

25
0

20

40

60

60
b) SL-CL-1b) SL-CL-2

30
45
60
30
45
60

2, 3, 42.5,
3.5, 5For each loading type mentioned above, the cellwater temperature is kept constant at either 30°C,
45°C, or 60°C throughout the shearing process. This
range of temperature is consistent with the value usedin the previous studies with sands by Liu et al. [13],[14], He et al. [29], and Punya-in and Kongkitkul[20]. Moreover, there are also limitation of thetemperature-controlled triaxial testing in present
study in that: i) the cell pressure was only 30 kPa foravoiding any damage to the heater and diaphragmpumps, ii) there was no system to cool down the cellwater temperature to achieve the value below theroom temperature, and iii) the maximum temperature
was not exceeded 60°C for avoiding any damage tothe measuring devices such as pressure transducer.

Preparation of a Hostun sand sample fortriaxial compression test during applying
partial vacuuming of 20 kPa.

Temperature, T (°C)

Axial strain during drained heating, a (%)

Fig. 3

e0

Test
No.

20

TEST RESULTS AND DISCUSSION

Time (min)Influence of temperature on shear strength
Fig. 4

Time histories of axial strain and cell watertemperature during drained heating stage.

Figure 6 shows the relationship between stressratio (R) and axial strain (a) obtained by continuous
monotonic loading (ML) (i.e., loading type a)) atconstant strain rate for different temperatures. It canbe readily seen that the specimens were degraded by
the increasing of temperature. The shear strength of
specimen can be expressed in terms of internal
friction angle (  ), which is shown in Table 2. The 
values can be determined in Eq. (1) because the sandsample in experiment was clean without any fines andthe cohesion was assumed null. The  peak value was
the internal friction angle at the peak state, while theres value the internal friction angle at the axial strainof 14%.

Stress ratio, R

10
9

Loading histories

8

a) ML

7

T=30°, 45°, and 60°Cb) SL-CL-2
T=30°, 45°, and 60°C

65

Creep (SL) for 3 hrs.

4
3
2
1
0

b) SL-CL-1
T=30°, 45°, and 60°C
Cyclic loading (CL), Dq = 25 kPa, 10 times

Elapsed time, t (not to scale)Fig. 5

 R −1  R +1 = sin −1 Illustration of time histories of stress ratio(R) for loading types a) and b) according to
the test program.

247

(1)

GEOMATE – Bangkok, Thailand, 22-24 November 2022

6

T = 30°C

5Stress ratio, R[31], was used to calculate the volumetric thermal
expansion.
During the drained heating, the volumetric
thermal expansion ( DVs ) of the sand solids can be
defined as follows:

T = 45°C
T = 60°C

4

DVs = s (T − T0 )Vs

Hostun sand (air-dried)
Confining pressure: 30 kPa

3

2

1
0

2

4

6

Test no.
1
2
3

e00.63710.62320.650381210where  s is the volumetric coefficient of thermal
expansion of solid skeleton, Vs is the volume of thesolid skeleton, T is the target temperature, and T0 is
the reference temperature, which was 30°C in thisstudy. The  s value can be estimated from linear
thermal expansion (  ) as  s  3 . Thus, the  sequal to 3.12 10−5 / C was used in this study. Thisvalue is nearly the same as the one reported in
Liu et al. [14], Campanella and Mitchell [32]. From
Eqs. (2) and (3), the void ratio after drained heating
can be determined by the phase calculation.

Dr (%)
86.00
89.82
82.37
14

16

Axial strain, a (%)

Fig. 6R −  a relationships by ML tests fordifferent constant temperatures.

Table 2 The internal friction angle of Hostun sandfrom continuous ML test with different
constant temperature
T (°C)

Rpeak

Rres

peak

res

30
45
60

6.068
5.969
5.888

5.378
5.110
5.149

45.81
45.48
45.20

43.35
42.27
42.43

Internal friction angle decreased as the initial void
ratio decreased
As mentioned above, the thermal expansion
induced the decreasing of void ratio during drained
heating prior to the shearing stage. From the studies
in the past, it is known that the peak internal friction
angle decrease with increasing initial void ratio ( e0 )(with decreasing relative density, Dr ) at the start ofshearing as shown in Fig. 7. From the collected datafrom many studies of several sand, a regression lineof the peak − Dr relationship can be proposed toestimate the reduction of  peak value by thedecreasing of relative density.
Figure 8 shows the relationship between the axial
strain at the equilibrium stage after drained heating
and temperature. The reference temperature was
equal to 30°C. It can be found that the specimens weredilated by thermal expansion as a rate of -0.00656%/°C. From this information, the reduction of peakinternal friction angle at any temperature ( peak ,T ),can be calculated, and then the reduction of peakstress ratio ( R peak ) can be realised. To consider theeffects of temperature increase on the reduction ofstrength on sand, the temperature effect ( A f ) was
proposed in this study, which was modified from
Chantachot et al. [33], as expressed in Eq. (4)Influence of thermal expansion during drained
heating stage
Determination of final void ratio after drained
heating
Thermal expansion is obviously related to thechange in temperature. Therefore, the thermal
expansion of sand solids should partially be
responsible to the temperature effects. During drained
heating, the thermal expansion can occur from thesolid skeleton and the air in the specimen. Due to thespecimens were air-dried, the volume change could
not be directly measured during drained heating.
Thus, the measured axial strain was used to determine
the specimen volume change. The volumetric strain( D vol ) can be estimated from the axial strain ( D a )as expressed in Eq. (2)D vol  3D a

(3)

(2)

A f = f (T ) =Benjelloun et al. [30] showed that silicon dioxide(Silica, SiO2) is the main chemical composition of
Hostun sand by using the X-ray fluorescence
spectrometry. Therefore, the linear thermal expansion(  ) of silica sand, which is equal to 10.4 10−6 / C

Rpeak − 1

 T − T0 
= 1− a Rpeak ,0 − 1
 T0 

b(4)

where R peak is the peak stress ratio at given
temperature, R peak ,0 is the peak stress ratio at thereference temperature, and a and b are constant. The
values of A f obtained from i) the reduction of peak248

GEOMATE – Bangkok, Thailand, 22-24 November 2022

(loading type a) ML), were plotted together against
the temperature as shown in Fig. 9. The thermal
expansion is only a part of temperature effects. Thatis, the effect of temperature on the reduction of shear
strength of Hostun sand cannot just simply be
explained by the effect of thermal expansion. There is
still true effect of temperature existing as depicted inFig. 9.

'(deg)
Peak internal friction angle, peak

60
55
50

95% C.I.

Triaxial compression (TC) test:
Pradhan et al. (1988) - Toyoura sand, Japan
Park et al. (2008) - Jumunjin sand, Korea
Singh and Chung (2013) - Nakdong River sand, Korea
Park and Jeong (2015) - Nakdong River sand, Korea
Amirpour et al. (2017) - Péribonka sand, Canada

45
40
35

Stress-strain relationship during cyclic loading

30
Regression curve for TC
25
100

80

60

40

20

Figure 10 shows the relationships between stressratio, R , and axial strain,  a , for loading type b) SLCL compared with the continuous ML at temperature(T) is equal to 30°C. SLs were performed for threehours and then, CLs were performed by unloadingreloading for 10 cycles with Dq of 25 kPa at different
target R values. It can be found that the R −  arelationships exhibited a high stiffness zone when the
ML was restarted after the finishing of CL for eachstress ratio. The stiffness value in this zone is very
close to the elastic stiffness. Moreover, the R −  arelationship shows a tendency to re-join therespective ones that could be obtained by continuous
ML tests. Therefore, it can be interpreted that the peak
shear strength is maintained, or in others word,
independent of loading historied applied before
reaching the peak. Similar trend of behaviours canalso be observed for the tests performed with the othertemperatures0

Relative density, Dr (%)

Axial strain at the end of drained heating, a (%)

Fig. 7

Variation of peak internal friction angle withrelative density of various sands collectedfrom the literature.

Hostun sand

0.00

Da = - 0.00656 %/°C

-0.05

-0.10

-0.15

-0.20

-0.25

30

35

40

45

50

55

60

65

Determination of elastic stiffness

Temperature, T (°C)

Temperature effect parameter, Af

Fig. 8

To determine Eeq , the stress-strain during CL wasplotted in the relationship of major principal stress(  1 ) against with axial strain (  a ), which theunloading for the last five loops for the respective CLstages were selected as shown in Fig. 11. From
Fig. 11, it can be found that the unloading branch of
1 −  a relationship is quite linear, which exhibits ahighly linear-elastic behaviour. The slope of thislinear relation for each CL loop is the Eeq. The Eeq
value is quite constant during the last five unloadreload cycles. Moreover, the R-square values
obtained by linear regression were presented forrespective  1 −  a relationships. This fact shows thatthe deformation during these unloading branches isessential elastic. The Eeq values from each loop wereaveraged for respective stress ratios, and then it wasplotted in the full-log scale against the vertical stressratio (  1 /  0 ) for respective temperatures as shownin Fig. 12. Note that  0 is the reference stress equal
to 100 kPa. It can be readily seen that the Eeq is notconstant but increases with an increase in stress level,
which the dependency of elasticity with the stresslevel can be expressed in Eq. (5).

Relationship between the axial strain at theend of drained heating and temperature.

1.00

Due to thermal expansion of solid
Af = f(T) = 1-0.0218((T-T0)/T0)1.01093

0.99

0.98
True effect oftemperature
Af = f(T) = 1-0.03515((T-T0)/T0)0.8

0.97

0.96

30

35

40

45

50

55

60

Temperature, T (°C)

Fig. 9Temperature effect parameter (Af) as afunction of temperature.

stress ratio due to the thermal expansion by thecomputation of changing void ratio; and ii) the peakstress ratio at given temperature by the experiment249

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Eeq = E0 (1  0 )

m

(5)

6

Hostun sand (air-dried)
Drained TC ('h = 30 kPa)
0 = 0.075 %/min

5Stress ratio, R

where m is constant.
Comparison the elastic stiffness to the previous
studies
Elastic Young’s modulus of standard sands at
small-strain level obtained from the static method arecompared as shown in Fig. 13. Punya-in andKongkitkul [20] investigated the elastic stiffness of ariverbed sand in Thailand by performing thetemperature-controlled triaxial compression tests
with varying temperatures, which are the same as theones in this study. The increasing temperature
decreases the elastic stiffness of granular materials.
Hoque and Tatsuoka [16] performed TC tests on
Toyoura and Hostun sands at room temperature(25°C) to evaluate the anisotropic elasticity of
granular materials. The axial strain was measured byusing a pair of local deformation transducers (LDTs).
LDTs measure the axial deformation locally, and thus
the measured value is free from bedding errors andsystem compliance [17]. However, due to theincreasing of temperature in the chamber, it isinevitable to evaluate the axial strain by the externalmeasurement (e.g., a linearly variable differential
transformer, LVDT) for avoiding the measuring
errors by the fact that the temperature alters the
LDT’s strain measured by the attached strain gauges.
In summary, the trend of variation of Eeq with thestress level of Hostun sand determined in this study
agree well with the one reported in Hoque andTatsuoka [16]. However, at the same stress level, theEeq determined in this study is lower because an
LVDT was used to externally measure the axial
deformation, while there was no LDTs installed.
Although different experimental devices were used
for measuring the axial strain, the elastic properties
obtained from all the collected data show that theelasticity of different granular materials is of hypoelasticity.

4
Drained creep for 3 hours
Cyclic loading: 10 times, Dq = 25 kPa
Temperature: 30 oC

3

2

1
0

Test no.
47

e00.64030.64262

3

1

Dr (%)
85.13
84.49
4

Axial strain, a (%)

Fig. 10 R −  a relationships with loading type b)
SL-CL with T = 30°C.

Major principal stress, 1 (kPa)

Cyclic loading100

y =a+bEquationsigma_1
sigma_1
sigma_Plot
No Weigh
Weight-604.25844 ± 14.3293 -607.45676 ± 20.7441 -578.92789 ±Intercept
1509.80671 ± 31.2467 1514.73071 ± 45.1859 1452.0963 ± 3
Slope
22.69
42.3015434.0116
Residual Sum of Squares
0.98967
0.97972
0.9822
Pearson's r
0.979440.959850.9648R-Square (COD)
0.97902
0.959
0.9641
Adj. R-Square

Test name: SL-1T30
Hostun sand (air-dried)
Drained TC ('h = 30 kPa)
10 times, Dq = 25 kPa
0 = 0.075 %/min

90Stress ratio: 3.5
Eeq,avg = 146,135 kPa
Loop
6
7
8
9
10

80

70

0.450

0.455

Eeq (kPa)
118,155
120,760
118,334
119,818
119,0280.460

R20.98740.98420.98970.98740.9901

0.465

0.470

Axial strain, a (%)

Equivalent vertical elastic
Young's modulus, Ev,eq (MPa)Fig. 11 R −  a relationships during the last five
unloading branches in the CL course at R =3.5 and T = 30°C.

Creep deformation with temperature changes
Figure 14 shows the time histories of axial strain
during creep for 3 hours at different stress ratio values
for the temperature of 30°C. It can be obviously seenthat the creep strain increase with increasing stresslevel. Moreover, Fig. 15 presents the axial strain at
the end of SL (3 hours) for different stress ratio values
and different temperatures together with its
cumulative. It can be found that not only the stresslevel but also the temperature result in an increase in
the creep strain. However, the influence of stress levelis much more than that of temperature.

260
240
220
200
180

Hostun sand (air-dried)
Hypo-elasticity
Ev,eq = E0(1/0)m

160
140
120
100
80
0 = 100 kPa
60

0.6

0.8

T (°C)

E0

m

R2

30

147.679

0.615

0.931445

136.057

0.611

0.936660

124.994

0.607

0.9988

1

1.2

1.4

1.6 1.8

2

Vertical stress ratio, 1/0

Fig. 12 Dependency of Eeq on the stress level andtemperature with respectively fitted lines.

250

GEOMATE – Bangkok, Thailand, 22-24 November 2022

CONCLUSIONS

1000

Equivalent vertical elastic
Young's modulus, Ev,eq (MPa)Ev,eq = 219.300(1/0)0.46

The following conclusions can be derived from theresults of temperature-controlled triaxial compression
test on Hostun sand performed in this study.

Hostun sand, T = 25 °C(Hoque and Tatsuoka, 1998)

Ev,eq = 278.96(1/0)0.486
Toyoura sand, T = 25 °C(Hoque and Tatsuoka, 1998)
Punya-in andKongkitkul, 2021
(KMUTT sand)1.

The peak internal friction angle decreases withincreasing temperature.

2.

Thermal expansion induces the dilation of sandsolids when drained heating, which results in an
increase in initial void ratio before shearing, andthus a reduction of the shear strength. However,its influence is only a part of temperature effects
on the reduction of shear strength.

3.

Elastic stiffness of Hostun sand clearly increases
with the stress level, which is of hypo-elastic.
Moreover, at the same stress level, the elastic
stiffness decreases with increasing temperature.

4.

Creep strain increases with increasing stress level
and temperature. However, the influence of stresslevel on the creep strain development seems to be
more dominant that of temperature.

T = 30 °C
T = 45 °C
T = 60 °C

T = 30 °C
T = 45 °C
T = 60 °C

This study(Hostun sand)100

0 = 100 kPa
1

Vertical stress ratio, 1/0

Fig. 13 Comparison of variation of Eeq of granularmaterials with the stress level andtemperature.

Axial strain during creep, Da,CP (%)

0.12

R = 5.0
0.10

R = 4.5

0.08
0.06

R = 4.0
R = 3.5
R = 3.0
R = 2.5

0.04
0.02

ACKNOWLEDGMENTS
The authors are grateful to King Mongkut’s
University of Technology Thonburi (KMUTT) for
the financial support granted to this research via The
Petchra Pra Jom Klao PhD scholarship under contract
Grant No. 7/2563. The authors would like to extend
the appreciation to the Prof. Fumio Tatsuoka andProf. Yoshiaki Kikuchi, Tokyo University of Science(TUS), Japan and Prof. Reiko Kuwano, Institute ofIndustrial Science (IIS), University of Tokyo, Japanfor supporting the Hostun sand and others granularmaterials.

Hostun sand (air-dried)
0.00

Temperature: 30°C

0

2000

4000

6000

8000 10000 12000 14000

Elapsed time during creep, DtCP (sec)

Hostun sand (air-dried)

0.5
T = 60°C

0.4
0.3

T = 45°C

0.2

Axial strain during creep,Da,CP (%)

T = 30°C

0.1
0.0

0.25
Creep strain at the end of SL (180 min/stage)

0.20

Cumulative axial strain
during creep (%)

Fig. 14 Time histories of axial strain during creep by
3-hr SL at different stress ratio values with
T = 30°C.

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NUMERICAL ANALYSIS FOR VERTICAL RESPONSE OF SHALLOWSUCTION PILE
Donghyun Lee1, Jaewoo Jung1, Yoowon Lee1, Jaehun Ahn2 and Jongwon Jung3
1

Graduate Student, Department of Civil and Environmental Engineering, Pusan National University, Busan
46241, Republic of South Korea;
2
Prof, Department of Civil and Environmental Engineering, Pusan National University, Busan 46241,
Republic of South Korea, Tel: +82-51-510-7627, Fax: +82-51-513-9596, jahn@pusan.ac.kr, Corresponding
author)3
Associate Prof, School of Civil Engineering, Chungbuk National University, Cheongju 28644, Republic ofSouth Korea;
ABSTRACTTerritory of human has been evolving and being expanded as technology and need for space grows. In civilengineering, the use of tunnel and underground spaces has a long history and now it is a vital component of life
especially in urban area. Space travel and space terminal may become reality in a future, and the industry istrying to make it happen soon. Subsea space can also be an alternative to expansion of human territory. If
engineers can develop and use undergound space, why not underwater base. Recently, a project to build anunderwater base has been initiated in Korea. As there is very few examples and application of underwater space,
there is no well-developed guideline for design of underwater base. This study discusses a preliminary analysis
of a shallow suction pile which may be used for the foundation system of underwater bases. The verticalresponse of a shallow suction caisson embedded in clay soils is mainly discussed here. Wider ranges offoundation types and dimensions will be investigated for fulfillment of the project.
Keywords: Subsea space, Underground space, Underwater base, Offshore structure, Foundation

INTRODUCTION

Here, most of the subsea structures fixed to thesubsea ground use suction piles. Suction piles havebeen mainly used as the basis of platforms for deepsea oil extraction in Northern Europe, such as the
Netherlands and Norway, and are now widely used
in offshore platforms and gravity infrastructure [2],and deep-sea subsea structure [3]. The suction pile isa cylinder-shaped structure with the upper partclosed and the lower part open. It is a type offoundation that penetrates the ground using thedifference of pressure. Since the foundation ispenetrated using the pressure difference, the deeper
the water depth, the greater the penetration force canbe secured, and it is known as an excellent
construction method for offshore workability andeconomy compared to other foundations because
excavation and driving equipment are unnecessary.
In this study, the vertical bearing capacity of thesuction pile according to the undrained shear
strength of the cohesive soil was investigated by
performing a three-dimensional finite element
analysis to investigate the bearing capacity of thesuction pile in various clay soils.

While the development of space such as space
exploration, the development of the space shuttle,
and the construction of a space station is active, thedevelopment of the subsea space has not progressed
relatively. Recently, as the need for securing futureliving spaces and developing deep-sea resources has
been raised, technologies for subsea structures arebeing studied in developed countries. Currently,
representative examples of subsea structures include
offshore plant facilities for oil and gas production,and it consists of a platform exposed to the sea andvarious production and processing facilities installed
below the sea level (Fig. 1). Unlike onshore
structures, this subsea structure has a relatively smallload due to their own weight, and repeated loads
such as waves, tidal waves, earthquakes, currents,
and currents act as the main loads, so considerable
moments should be considered.

FINITE ELEMENT MODEL
Fig. 1. Subsea structure: A. Manifold, B. Subsea tree,
C. Subsea separator [1]In this study, Abaqus software [4] was used forthe finite element analysis. The analysis target is asshown in Fig. 2, which is composed of the ground
254

GEOMATE – Bangkok, Thailand, 22-24 November 2022

the analysis target is the bearing capacity of thesuction pile, not the deformation of the ground.
Since the modulus of elasticity of the ground issufficiently smaller than the modulus of elasticity ofthe pile, this modulus of elasticity is not significant.

and a cylindrical suction pile penetrated into theground. In Fig. 2, D is the diameter of suction pile, L
is the length of suction pile, W is the width ofelement model, and H is the depth of element model.

Table 1. Soil parameters used for numerical analysis
Value
Parameter
Soft
Medium
Stiff
Clay
Clay
Clay
𝜸𝜸𝒔𝒔𝒔𝒔𝒔𝒔 (𝐤𝐤𝐤𝐤/𝐦𝐦𝟐𝟐 )
20
𝑬𝑬 (𝐤𝐤𝐤𝐤𝐤𝐤)
20,000𝒗𝒗
0.45
𝒔𝒔𝒖𝒖 (𝐤𝐤𝐤𝐤𝐤𝐤)
50
100
200

VERTICAL RESPONSE OF SUCTION PILE
Fig. 2. Dimensions of finite element model

In order to investigate the difference in verticalbearing capacity of the suction pile according to theundrained shear strength of the clay soil, a certain
displacement was given to the upper part of thefoundation to obtain a reaction force-displacement
curve for the foundation. Based on this, the verticalbearing capacity of the foundation was calculated.
Fig. 4 is the reaction force-displacement curve
according to the undrained shear strength. Fig. As
shown in Fig. 4, it can be seen that the bearing
capacity of the foundation also increases as theundrained shear strength of the ground increases.
This shows that when an additional load is applied to
the foundation, the clay ground is consolidated,
increasing the strength of the ground and affecting
the vertical bearing capacity of the foundation.

Fig. 3. Finite element model (𝐿𝐿/𝐷𝐷 = 1)

For the overall size of the analysis model, thewidth was 5 times the pile penetration depth, that is,
𝑊𝑊 = 5𝐿𝐿 was used, and the depth was used 𝐻𝐻 =2.5𝐷𝐷. Based on the symmetry of the analysis target
and load conditions, the finite element analysis used
only the half-section of the 3D (3-dimensional)
model. The finite element model used for the
analysis is shown in Fig. 3 is given. The material
properties of the suction pile model applied to theanalysis were assumed to be linear elasticity and acircular cross-section with a diameter (𝐷𝐷) of 5 m,
and a length (𝐿𝐿) to diameter ratio 𝐿𝐿/𝐷𝐷 = 1 was used
as a basic condition.
The ground for the installation of the suction pile
was assumed to be undrained clay, and as shown inthe von Mises fracture criteria and Table 1,
undrained shear strength 𝑆𝑆𝑢𝑢 corresponding to soft,
medium, and hard clay, elastic modulus 𝐸𝐸 =20,000 kPa , poisson ratio 𝑣𝑣 = 0.45 was applied.
There is a relationship of 𝜎𝜎𝑦𝑦 = √3 × 𝑆𝑆𝑢𝑢 between the
Von Mises yield stresses 𝜎𝜎𝑦𝑦 and 𝑆𝑆𝑢𝑢 . Therefore, in
the actual analysis, yield stresses of 86 kPa ,173 kPa , 346 kPa were used to simulate theundrained shear strengths of 50 kPa , 100 kPa ,200 kPa . The elastic modulus of the ground is
20,000 kPa and the elastic modulus of the suction
pile is much larger (107 times), but in this research,

Fig. 4. Vertical displacement and reaction
CONCLUSIONSIn this study, the vertical bearing capacity of thesuction pile was calculated according to theundrained shear strength in the undrained clay
ground using the finite element analysis program.
The length (𝐿𝐿) to diameter (𝐷𝐷) ratio of the suction
pile was used under the condition of 𝐿𝐿/𝐷𝐷 = 1, andthe vertical bearing capacity of the foundation was255

GEOMATE – Bangkok, Thailand, 22-24 November 2022

compared using the undrained shear strength of
50 kPa, 100 kPa, and 200 kPa.
The vertical bearing capacity of the suction pileis affected by the undrained shear strength of thesurrounding ground, and as the undrained shear
strength increases, the vertical bearing capacity of
the foundation increases.
In order to calculate bearing capacity in morediverse environments, finite element analysis
according to various variables such as depth ofpenetration of the foundation and drainage
conditions of the ground is additionally needed.

funded by the Korea government (MSIT, No.
2020R1A2C1012072).
REFERENCES[1] Ji, S.-H., et al. “Development of Marine Plant
TrendsandSubseaFoundation
Technology”, Geotechnical Engineering, 2014,30.11: 12-17.
[2] Tjelta, T., et al. “The Skirt Piled Gullfaks C
Platform Installation”, Proceedings, Offshore
Technology Conference, 1990, Houston, USA,OTC 6473, pp.452-462.
[3] Hefer, P. “Oil and Gas Floating Facility to be
Anchored in 390 m Deep Timor Sea”, Civil
Engineers Australia, December 1998.
[4] Abaqus.
https://www.simuleon.com/simuliaabaqus/, Accessed in June 2021.

ACKNOWLEDGMENTS
This research was a part of the project titled'Development of Underwater Space Resource
Creation Technologies' (No. 20220364) funded by
the Ministry of Oceans and Fisheries, Korea, and the
National Research Foundation of Korea (NRF) grant256

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

MODELLING LIGHTWEIGHT DEFLECTOMETER BASED ON
ANALYTICAL AND NUMERICAL MODELS
Tram Huyen Nguyen1, Yunje Lee2, Hoa Phuong Thi Nguyen3, Sangwook Kang4, Jaehun Ahn5
1,2,3,4

Graduate and Bachelor Student, Department of Civil and Environmental Engineering, Pusan National
University, Busan 46241, Republic of South Korea;
5
Professor, Department of Civil and Environmental Engineering, Pusan National University, Busan 46241,
Republic of South Korea, Tel: +82-51-510-7627, Fax: +82-51-513-9596, jahn@pusan.ac.kr, Corresponding
author
ABSTRACTLightweight Deflectometer (LWD) is a novel device which can determine the elastic moduli of soils. This canmake a valuable tool for quality control of soil compaction enabling modulus-based control. There are a numberof manufacturers and models of LWD with difference specifications out there, but then the devices do not giveconsistent results with sometimes high level of variations. As such, at this point, the quality control based on LWD
has limitations in practice. In general, the modulus is evaluated from a simple equation based on statics. It may benecessary to consider dynamics involved in LWD operation to estimate better the modulus of soil hopefully
reducing inconsistency among different LWDs. This study aims at simulating LWD, considering its dynamic
behavior and investigating the results from it.
Keywords: LWD, Elastic modulus, Dynamic, Simulating

INTRODUCTION

feasibility of implementing dynamics solutions in
calculating soil's young modulus.

The Lightweight Deflectometer (LWD) is known
as a Portable or Light Falling Weight Deflectometerand is widely used as a portable device for quality
control of soil compaction enabling modulus-based
control. LWDs provide reasonable results and are
gradually being accepted all over the world inearthwork construction. In the LWD test, a dynamic
force is imparted via a drop mass to a plate resting on
the ground, which is assumed as an elastic half-space.
The estimation of elastic modulus (𝐸LWD ) is often
based on the Boussinesq solution in Eq. (1) [1].
𝐸LWD =𝐴(1−𝜇 2 )𝑃peak
𝜋𝑟𝑈peak

ANALYTICAL MODELS
Model Based on Elastic Half-Space
Veletsos and Verbic [2] provide solutions forvibrating massless disks resting in an elastic halfspace as a model of LWD dynamic behavior (Fig.1).
The LWD tests dynamic response can be calculated
in the frequency domain and transformed into thetime domain by applying the Fourier Transform
algorithms:(1)

U(𝜔) = 𝐻(𝜔) ∗ 𝑃(𝜔)where 𝐴 is stress distribution factor (Zorn LWD
software assumes 𝐴 = 𝜋/2), 𝜇 is Poisson’s ratio, 𝑟 isplate radius, 𝑃peak is peak impact force, and 𝑈peak ispeak deflection.
However, the results of LWD testing are used to
calculate a dynamic modulus of the soil based on
static Boussinesq theory for plate loading of an elastic
half-space, while the operation of LWD involves
dynamic behavior with a very high loading rate;statics may not be the most dependable theory for
analysis. The study is an attempt to implement
performance-based assessments of designed forproperties such as the resilient modulus and stiffness.
This study presents the results of an investigation
into the influence of evaluation methods on measured
modulus based on the LWD test and shows the(2)where 𝐻(𝜔) , 𝑃(𝜔) are detailed in Veletsos andVerbic [2]. The deflection evaluated in this way isreferred to 𝑈V here.

Fig. 1

257

Schematic of LWD resting on elastic halfspace.

GEOMATE – Bangkok, Thailand, 22-24 November 2022

The impact force in the model is simulated as ahaversine curve with a time step value of 0.05 ms,
and its maximum amplitude value of 7.07 kN is taken(by Zorn LWD datasheet). The impacted force time
history is numerically simulated, as shown in Fig. 2.

Fig. 2FINITE ELEMENT MODEL
To investigate the influence of different
calculation methods on resulting elastic moduli,frequency domain-based finite element method(FEM) analysis was performed by using Abaqus. The
FEM in Abaqus is axisymmetric with a radius of
sufficient size in 5 m to prevent wave reflections
from effect results. An alternative parametric wasimplemented to ensure that the mesh size, soil
dimension, and a number of input data sample points
did not affect the analyzed model. In the study, theFEM model is simplified as the circular plate restingon the soil. This is a reasonable model for simulating
the dynamic operation of the LWD, but not therealistic one.

Force time history applied by LWD

Model Based on 2DOF (Two Degree of Freedom)
The mechanical system of LWD can be modeled
as a 2DOF system and evaluated in the time domain
by using a state-space solution, which consists ofplate mass 𝑚1 = 30 kg and drop mass 𝑚2 = 10 kg
connected with soil and spring [3]. Values of soil
stiffness are 𝐾soil = 30.94; 14.67; 38.63 kN/m andvaluesof
soil
damping
are𝐶soil =
30.32; 20.88; 33.88 kNs/m respectively, for threetests. The spring is designed as buffer with value ofstiffness 𝐾buffer = 35.384 kN/m and damping
coefficient 𝐶buffer = 33.65 Ns/m . The system
performs linear motion in the vertical direction ofsprings and dampers axes. Figure 3 illustrated the
LWD dynamic model as the 2DOF system. The
impacted variable force as Fig. 2, is used for themodel excitation.
The solution was implemented in Matlab by using
Simulink. The deflection calculated by convolution
integral is referred to 𝑈2DOF in this article.

Fig. 4

Schematic of FEM for LWD in Abaqus.

All materials used in the study are assumed to belinear elastic, with their properties shown in Table 1.
The LWD (Zorn) load plate is 15 mm thick, 150 mm
radius, and it houses a 36 mm in diameter and
32 mm tall on-board accelerometer at the central [1].
The time history of response displacement wasmeasured underneath the center of the plate and along
the soil surface with the time step value of 0.05 ms
and the impacted variable force as Fig. 2, is also used
for the model.
Fig. 3

LWD modeling as 2DOF system.
258

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 1 Summary of material properties assigned in
the models [1].
Material Property
Density,kg/m3
LWD mass, kg
Poisson’s ratio(𝜇)Modulus ofelasticity, MPa
Damping factor
( )

Soil

LWD
Plate

2400

7850

--

15

0.35

0.28

95.3, 46.7, 118.4

205,000

0.05

0

.

A comparison of the analytical model response in
Abaqus, three different numerical model responses
with test results were implemented. These test data
for three different soil specimens were taken on a
vertically homogeneous granular base material;
therefore, the model is assumed as an elastic
homogeneous elastic half-space [4].
CALCULATIONS OF DEFLECTION
Field test data were taken on three soil specimens
for analysis. Results summarized in Table 2 show thepeak deflection of each method.

Fig. 5

Fig. 6

Deflection time histories from Abaqus

Figure 5 shows the deflections of the point at thecenter of the LWD plate and soil base interface withdifferent values of soil elastic moduli implemented by
FEM method in Abaqus, which are used as a baseline
for an assessment.

The deflection time histories from different
methods. (a) 𝐸 = 95.3 MPa , (b) 𝐸 =46.7 MPa, (c) 𝐸 = 118.4 MPa.

As it is shown, there are discrepancy andinconsistency among deflection evaluated bydifferent LWD calculation models for the same soil
specimen. The peak deflection values of each method
are summarized in Table 2.

Comparison of presented models
A comparison of the FEM model’s responses andthe other three different models’ responses is shownin Fig. 6.

259

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 2 Deflection.
Case
No.
1
2
3

𝑈FEM
(mm)
0.241
0.464
0.199

𝑈2DOF
(mm)
0.244
0.500
0.195

𝑈V
(mm)
0.217
0.443
0.175

results of this investigation illustrate that each of thecalculations will always produce different deflection
values for the same ground conditions.
Employment of a more realistic analytical model
would help minimize the issue of incorrect moduli
estimation. More realistic modelling based on Abaqus
is planned to test the viabilities of different analyticalsolutions, which will help to make LWD a morereliable tool for compaction control.

𝑈LWD
(mm)
0.236
0.418
0.190

BACK CALCULATION OF MODULUS
ACKNOWLEDGMENTS
The back calculation was conducted to determine
the moduli of three specimens with the threemeasurement deflection values of 𝑈1 = 0.236 mm,
𝑈2 = 0.481 mm , 𝑈3 = 0.190 mm respectively
were inversely calculated based on one static andthree dynamic models, and FEM model is addressed.
Results are summarized in Table 3.

This research was supported by Basic Science
Research Program through the National Research
Foundation of Korea (NRF) funded by the Ministryof Education (No. 2022R1I1A3069043).
REFERENCES

Table 3 Elastic modulus.
Case
No.
1
2
3

𝐸FEM
(MPa)
97.8
44.9
125.0

𝐸2DOF
(MPa)
98.3
48.7
123.0

𝐸V
(MPa)
87.7
43.0
109.0

[1] Stamp, Dave and Michael A. Mooney. “Influenceof Lightweight Deflectometer Characteristics on
Deflection Measurement.” Geotechnical Testing
Journal 36 (2013): 216-226.
[2] Veletsos, Anestis S. and Branislav Verbic. “Basic
Response Functions for Elastic Foundations.”
Journal of Engineering Mechanics-asce 100(1974): 189-202.
[3] Das, Braja M., and Zhe Luo. “Principles of soil
dynamics. Cengage Learning.” (2016).
[4] Tirado, Cesar, Mehran Mazari, César Carrasco
and Soheil Nazarian. “Simulating Response ofDifferent Lightweight Deflectometer Testing
Using Finite Element Modeling.” (2015).

𝐸LWD
(MPa)
95.3
46.7
118.4

CONCLUSIONS
An investigation was conducted on several
dynamic solutions to compute the deflection ofcompacted soil under the impacted load caused by
LWD based on impact force and displacement. The

260

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

FEASIBILITY STUDY ON SOIL CLASSIFICATION FROM SOIL
IMAGES USING DEEP LEARNING
Tomoki Abe1 and Taizo Kobayashi2
1

Graduate School of Science and Engineering, Ritsumeikan University Graduate school, Japan;
2
College of Science and Engineering, Ritsumeikan University, Japan

ABSTRACT
Although a large amount of training data is required for deep learning, it is difficult to obtain sufficient data by
manually capturing pictures. Therefore, we have devised a method for estimating real soil particle images using a
deep learning model trained using virtual soil particle images created on a computer. The particle shapes of Vimage are circular, square, and equilateral triangle. We also created a data set that mixes images of all shapes. As
a result of verifying real images, we obtained a maximum accuracy rate of 78%.

Keywords: Deep Learning, classification of geomaterials for engineering

INTRODUCTION

diameter. In this study, grain size was defined by thearea in the image. Particles within 250 px in area were
defined as fine (F) particles, and those withinapproximately 251–500 and 501–750 px were
defined as medium (M) particles and coarse (C)particles, respectively. The soil classification of wasbased on the F, M, and C particle contents. The soil
classification uses mass content; however, in thisstudy, machine learning was used to estimate thenumber of particles in the image and content based on
the number of particles.

The classification of geomaterials for engineering
requires numerous soil tests and expertise, including
sedimentation analyses. We wondered if it would bepossible to realize a method for instantaneousclassification of geomaterials for engineering using
deep learning (AI) from images of soil. This study
reports the results of a feasibility examination
conducted on a trial basis.
VALIDATION

Image Photograph Method
Method of Classification of Geomaterials forEngineering

As a feasibility study, we photographed a type ofcrushed stones under the condition that they did not
overlap. Using a general digital camera, we captured

As a basic trial study, we attempted to estimate thegrain size from images of crushed stones with amaximum grain size of 15 mm and classify
geomaterials for engineering purposes. Figure 1
shows a breakdown of the classification ofgeomaterials used in this study. In the sieve analysis,
the grain size was defined as the smallest particle

Fig.1

Classification criteria of geomaterials
defined in this study

Fig.2
261

Acquired image and the R-image

GEOMATE – Bangkok, Thailand, 22-24 November 2022

images on a light table within a range of 250 mm ×
250 mm. The image size was 512 px×512 px.
Subsequently, we binarized the images by ImageJ[1].
The processed image is called a real image (R-image)in this study. Figure 2 shows an acquired image andthe R-image. Left image is acquired image. Right
image is R-image.

in the image is too small.; therefore, we set the total
number of particles in the V-image to 70–100.
Deep learning network
We used a neural network console (NNC) [3] tocreate a deep learning model. The NNC is anapplication provided by the SONY Corporation. We
can easily build machine learning network usingdrag-and-drop in the NNC. From the various types ofmachinelearning
processes,
weselected
convolutional neural network (CNN). CNN are
frequently used in image and video recognition. We
used a network that connects three units with two
convolution layers, two batch normalization layers,
two rectified linear unit (ReLU) layers, and a max
pooling layer as one unit. We used the squared error
as the loss function. Figure 4 shows the deep learning
network. The role of each layer is described asfollows. In the convolutional layer, layer plays therole of reacting to a specific shape called a filter.
When it reacts to a particular shape, it is weighted andpassed to the next layer. The content of the filter is
optimized as learning progressed. In the batchnormalization layer, this effect prevents vanishinggradient [4]. ReLU is an activation function. Whenthe input value is zero or less, the output value
becomes zero. When the input value is one or more,
the output value is the same. ReLU has good
calculation efficiency. In the max-pooling layer, themaximum value among the input values obtained
within a range of 2 × 2 is used as the output value.
This has the effect of dealing with positional
deviations in the image and reducing the number ofcalculations required. In the affine layer, the output
value is obtained by multiplying the input value by
the weight parameter. The weight parameters were
optimized using repeated learning. For the squared
error, the square of the difference between the correct
answer and the predicted values is calculated using
machine learning. The smaller the obtained numericalvalue is, the higher is the accuracy of deep learning.

Method of Making Teaching Data
Machine learning generally requires a large
amount training datasets to achieve high accuracy.
However, it requires considerable time to manually
collect a large amount several datasets. In this study,
we devised a method for collecting numerous datasets.
We programmed and created a virtual soil-particle
image (V-image) using a PC. Currently, tens of
thousands of images can be created within a few
minutes. Figure 3 shows the V-image. We used theprogramming language Processing [2] for visualdesign. The grain shapes are circle, equilateraltriangle, and square. The V-image was a binary image
comprising a white background and black particles.
The positions and contents of the particles are
randomly set. We considered that learning cannot be
performed efficiently if the total number of particles(a)circle(b)square

Fig.4

(c)
Fig.3

Deep learning network used in this study

Results

equilateral triangle

The deep learning network that trained the Vimage verified the V- and R-images. The number ofVirtual soil particle images (V-image)262

GEOMATE – Bangkok, Thailand, 22-24 November 2022

training data points was 20,000. The number of test
data points the V-image was 5,000 and the number oftest data points in the R-image was 100. We created amixed dataset in addition to circular, equilateraltriangle, and square datasets; it contained 1/3 of eachshape. We used a deep learning model shape dataset
to validate the V and R-images. We used the accuracy
rate for indicator of the accuracy of the deep-learningmodel. We defined the accuracy rate as thepercentage of correct answers that matched theestimate of the correct answer of the deep-learningmodel. Tables 1 and 2 present the validation results
obtained using deep learning.

horizontal axis of the graph represents the number ofparticles in the image. The red, blue, and green plotsshow the F, M, and C particles. The line drawn at 45 °indicates that the estimated and real counts have thesame value; the better the accuracy, the closer the plot
approaches this line. When a deep learning modeltrained on a circle validates a circle dataset, it obtains
a high accuracy rate. However, when the deeplearning model trained on a circle validates the squareand equilateral triangle datasets, the accuracy rate isextremely low. Fine particles tend to be estimated ingreater numbers than the actual count, and coarse
particles tend to be estimated fewer than the actual
count. We considered that this is due to the fact thatthe learning process is circle-specific and does notallow for different shapes. The particles in the Rimage have a variety of shapes, we assumed that acircle-specific model would be unable to correctly

Table 1 Accuracy rate of deep learning model 1
Validation data

accuracy rate
(%)

●

■

▲

●

88.9

21.2

55.2

140

■

68.5

88.4

63.1

120

▲

53.5

50.6

89.3

Mix

86.2

83.0

89.7

Estimated particle countTraining
data

Fine Particle
Medium Particle
Coarse Particle

Table 2 Accuracy rate of deep learning model 2
Validation data

accuracy rate
(%)

Mix

R-image

●

54.8

56.0

■

68.5

75.0

▲

62.8

77.0

Mix

86.4

78.0

Training
data

100
80
60
40
20
0
0

20

40

60

80

100

120

140

Particle count in image

Fig. 5

Verification result for circle grain shape
Fine Particle
Medium Particle
Coarse Particle

140

Estimated particle countTable 1 shows that when the grain shapes learned
by the deep learning model and the verified grain
shape were the same, a high accuracy rate of over
88% could be obtained. However, when the learned
grain shape of the deep learning model differed fromthe verified grain shape, only a 63.1% accuracy could
be obtained. The details of the validation results of the
deep learning model learned by the circles areprovided below. Figure 5 shows the results when themodel trained on the circle dataset validated the circle
dataset. Figure 6 shows the results when the modeltrained on the circle dataset validated the squaredataset. Figure 7 depicts the results when the modeltrained on the circle dataset validated the equilateraltriangle dataset. The vertical axis in the graphs
represents the estimated particle count. The

120
100
80
60
40
20
0
0

20

40

60

80

100

120

140

Particle count in image

Fig. 6
263

Verification result for square grain shapeGEOMATE – Bangkok, Thailand, 22-24 November 2022

Fine Particle
Medium Particle
Coarse Particle

140

Estimated particle countEstimated particle count140

Fine Particle
Medium Particle
Coarse Particle

120
100
80
60
40
20
0

120

100

80

60

40

20

0

0

20

40

60

80

100

120

140

0

Fig. 7 Verification result for equilateral triangle
grain shape

Estimated particle count

40

60

80

100

120

140

Fig. 9 Verification result for R-image dataset
Thus, the mixed dataset is considered effective forvalidating the R-image. However, an accuracy rate of
78% is not sufficient for practical use. Therefore, it isnecessary to further improve the accuracy rate in the
future.

estimate the R-image. Therefore, we reviewed themixed datasets in detail. Figure 8 shows the results
when the model trained on the mixed dataset
validated the mixed dataset. The deep learning modeltrained with the mixed dataset could validate themixed dataset with high accuracy. As shown in Table
2, the accuracy rate was high (86.4 %). Thus, weconsidered that it is possible to deal with various
shapes and obtain a high accuracy rate. Figure 9shows the results when the model trained on themixed dataset validates the R-image dataset. As canbe observed from the graph, the plot approached the
45° line. In Table 2, the accuracy rate when the Rimage dataset was validated using a model trainedwith a mixed dataset was 78%, which is higher thanthe deep-learning models trained on other datasets.

CONCLUSION
In this study, we proposed a method for theclassification of geomaterials for engineering using
deep learning of particle images. Considering thedifficulty of creating a large amount of teaching data,
we attempted to learn using virtual soil-particle
images created on a PC. The model trained with thecircle dataset was specialized for the circle, and theaccuracy rate for the R-image was only 56%.
However, the deep learning model trained with themixed dataset was versatile, and the accuracy rate of
the R-image improved to 78%. In the future, wewould like to study teaching data with higher
estimation accuracies.

Fine Particle
Medium Particle
Coarse Particle

140

20

Particle count in image

Particle count in image

120

100

references
80[1] ImageJ HP https://imagej.nih.gov/ij 08/13/2022[2] ProcessingHP
https://processing.org
06/10/2022[3] NeuralNetworkConsoleHP
https://dl.sony.com/ja/ 06/10/2022[4] Sergey loffe and Christian Szegedy, Batch
Normalization: Accelerating Deep Network
Training by Reducing Internal Covariate Shift,
arXiv:1052.03167, 02/11/2015

60

40

20

0
0

20

40

60

80

100

120

140

Particle count in image

Fig. 8 Verification result for mixed dataset

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12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

A COMPARISON BETWEEN PAVEMENT RESPONSES FROM THE
FALLING WEIGHT DEFLECTOMETER AND THOSE FROM TRUCKLOADING BASED ON THE LAYERED ELASTIC ANALYSIS
Thinn Thinn1 and Auckpath Sawangsuriya2
1

Department of Transportation Engineering, Asian Institute of Technology, Thailand.
2
Bureau of Road Research and Development, Department of Highways, Thailand.
ABSTRACT

Pavement responses e.g., surface deflections, tensile strains, and compressive strains etc. were determined fromthe multi-layered elastic analysis (LEA). The LEA has been widely accepted in most mechanistic design andperformance analysis of the road pavements, where their structures were assumed to be homogenous, isotropic,
linear-elastic, and finite thickness with modulus of elasticity and Poisson's ratio. The falling weight deflectometer(FWD) is a traditional tool for the structural condition evaluation of road pavements. According to the FWD testingprocedure, three magnitudes of load: 40, 53, 70 kN (e.g., 575, 750, 1,000 kPa) were applied on the road pavements.
In Thailand, a typical loading stress of 700-800 kPa was practically adopted by the Department of Highways, while
a tandem axle-dual wheel having a 690 kPa (100 psi) tire pressure and a 100 kN (10 metric tons) single axle load
generated by the standard 10-wheel Thai truck (e.g., legal load permit of 25-ton gross weight) was considered in
the pavement design and analysis. The objective of this paper is therefore to compare the pavement responses fromthe FWD and the standard Thai truck loads based on the LEA. Comparison results indicated that the average error
was about 10% for 800 kPa FWD-standard Thai truck deflections, while 21% for 700 kPa FWD-standard Thai
truck deflections. In case of tensile strain at the bottom of thin asphalt surface, the average error was respectively
2% and 14% for 800 kPa FWD-standard Thai truck and 700 kPa FWD-standard Thai truck. However, for thickasphalt surface, the average error was respectively 38% and 21% for 800 kPa FWD-standard Thai truck and 700
kPa FWD-standard Thai truck. In case of the compressive strain above the subgrade, the average error ranged from
7 to 11% for 800 kPa FWD-standard Thai truck and 8 to 12% for 700 kPa FWD-standard Thai truck.
Keywords: pavement response, layered elastic analysis, falling weight deflectometer, truck loading

INTRODUCTION

In Thailand, the Department of Highways (DOH)
adopted both empirical and mechanistic (analytical)
methods for the pavement analysis and design. A 10wheel 25-ton truck was typically considered as thestandard traffic load application in the design
procedure. Recently, the deflection-based design
approach has been comprehensively reviewed by the
DOH for the possible adoption of local design
standard and practice. One of the main reasons wasthat Thailand road authorities e.g., DOH and theDepartment of Rural Roads (DRR), consider thefalling weight deflectometer (FWD) for the new
construction and rehabilitation of road pavement. In
addition, such the FWD has been globally accepted as
the non-destructive test for deflection measurement
and structural capacity evaluation [5]. Ultimately, theimplication of FWD deflections for localizedpavement analysis and design shall be developed andproposed to Thailand road authorities. Due to theaforementioned reasons, it was important that such aload magnitude of FWD shall be determined andverified with the 25-ton 10-wheel standard Thai
truck. Therefore, this study presents the comparisonof pavement responses from the FWD and those fromthe standard Thai truck based on the LEA.

In recent years, the road pavement design has
evolved from a purely empirical approach tomechanistic-empirical methods, which requirecomprehensive knowledge of material behavior andtheir responses e.g., stress, strain, and deflection,
under traffic loads [1]. The newly evolved
mechanistic-empirical design method involved thephysical relationship between causes (wheel loads,
material properties) and effects (stress, strains,deflections) and developed mathematical models to
relate these effects to failure modes [2]. The main
structural response models used in pavement analysis
and design are the finite element and layered elastic
analysis (LEA). KENLAYER computer program wasoriginally developed by Professor Yang H. Huang at
the University of Kentucky [3]. KENLAYER reliedon the mathematical models to predict road pavement
structural responses using the layered elastic theory.
This theory is based on Burmister’s [4] equations forthe solutions of stresses, strains, and deflections inlayered systems under traffic loads. Certain generalassumptions and generalizations were incorporated
into the layered elastic theory, including material
isotropy and homogeneity.
265

GEOMATE – Bangkok, Thailand, 22-24 November 2022

LAYERED ELASTIC ANALYSIS

software tool named Michigan Flexible Pavement
Design software (MFPDS) and KENLAYER
computer program to evaluate the pavement response.
Results obtained from both programs appeared to beequivalent for linear analysis, but there was asignificant difference in tensile strain when thesurface thickness was less than 75 mm. In case of
nonlinear analysis, the results did not correspondwell.
Based on the past studies, the LEA was suitable
for analyzing pavement responses because it gave
equivalent pavement responses to FEA, but lessanalysis time.

A flexible pavement has been considered an ideal
elastic body due to its relatively ease of calculation ofstresses, strains, and deflections. The elastic theory
has been used extensively in the computation ofstresses, strains, and deflections. This involved thestresses in a homogeneous, isotropic linearly elastic
solid of semi-infinite extent when subjected to a load
applied normally to the surface. The assumptions ofhomogeneity and isotropy specify that the elastic
properties in any part of a material were identical in
all directions, regardless of location within thematerial. In this context, elasticity referred to a
relationship between stress and strain that had no
relationship with time, loading path, or other factors.
The linearly elastic solid was the most convenient tohandle, and superposition can be employed [6].
Elastic solutions were developed from Boussinesq’s[7] one-layer system to Burmister’s two [8] and threelayer system [4] to multi-layered elastic theories [3],to finite element techniques for the pavement
performance prediction based on material properties
and traffic conditions.

METHODOLOGY
Material Properties and Layer Thicknesses
Flexible pavement structure in this study werecomprised of different layer materials, e.g., asphalt
surface, crushed rock base, soil-aggregate subbase,selected material, above natural subgrade. According
to AASHTO [11], a minimum thickness of the asphalt
surface and aggregate base shall be 2.54 cm (1 inch)
and 10 cm (4 inches), respectively. Thus, theminimum thicknesses of asphalt surface andaggregate base were considered to be 50 mm (2inches) and 100 mm (4 inches), respectively.
A total of 625 pavement sections was selected inthis study. Five asphalt surface thicknesses included
50, 100, 150, 200, and 250 mm. Five crushed rockbase thicknesses included 100, 150, 200, 250, and 300
mm. Five soil-aggregate subbase thicknesses
included 100, 200, 300, 400, and 500 mm. Fourselected material thicknesses included 100, 200, 300,
and 400 mm as it was not existed in some sections.
Table 1 summarized the elastic moduli and Poisson’s
ratio for pavement layer materials according to the
DOH’s pavement design practice [5].

Pavement Responses
Pavement responses are results of the combined
effects of loading, environment condition, subgrade,and pavement material characteristics. With respect
to flexible pavements, the current failure criteria are
the tensile strain at the bottom of the asphalt surface
and the compressive strain at the top of the subgrade.
Tensile strain at the bottom of asphalt surface is used
to predict and control fatigue cracking of the asphalt
surface. The compressive strain at the top of thesubgrade is used to predict and control the permanent
deformation of the pavement structure due to shear
deformation in the upper subgrade. Surface deflection
is an indicator of the strength and stiffness of theentire pavement structure, which can be also related
to the pavement design life.
Sawangsuriya et al. [9] utilized three-dimensional(3-D) finite-element analysis (FEA) model and LEA
to examine the structural responses of flexible
pavement under different types of axle group loads,
e.g., single axle-dual wheel, tandem axle-dual wheel,
and tridem axle-dual wheel and compared with thefield measurement data. The results indicated thatboth FEA and LEA were in good agreement with thefield measurement results with some exceptions forstrains under the asphalt surface. The structural
responses in terms of vertical stresses, vertical strains,and horizontal strains from LEA were identical with
the FEA results. Thus, both FEA and LEA
approaches could be applied for estimating thepavement structural responses.
Srikanth [10] conducted linear and nonlinear
analysis for pavement layers using a finite element

Table 1 Pavement Material Properties
Elastic
Pavement
ModulusMaterials(MPa)
Asphalt Surface
2,500
Crushed Rock Base
350
Soil-aggregate
150
Subbase
Selected Material
100
Subgrade
40

Poisson's
Ratio
0.35
0.35
0.35
0.35
0.40

Load Applications
The study considered two load applications, FWD
and standard Thai truck. According to the FWD
testing protocol by the DOH, a loading pressure
ranging from 700 to 800 kPa was typically adopted
along with a plate radius of 0.15 m. The standard 10266

GEOMATE – Bangkok, Thailand, 22-24 November 2022

330 mm1300 mmwheel 25-ton truck was adopted for Thailand
pavement design and analysis. Having a tandem axledual wheel configuration, this standard Thai truck hada tire pressure of 690 kPa (100 psi) and a single axle
load of 100 kN (10 metric tons). A tire contact radiusof 0.11 m was determined by dividing a wheel load of
2.5 tons (approximately 25 kN) by a tire pressure of
690 kPa. Table 2 summarized the pressure and radiusof contact for the FWD and the standard Thai truck.
Table 2 Pressure and Radius of Contact for the FWD
and the Standard Thai Truck
Description

Pressure (kPa)Radius ofContact (m)FWD

700, 800

0.15

Truck

690

0.11

Fig. 1

Positions of pavement responses andaxle/wheel load configurations of standard
Thai truck

RESULTS AND DISCUSSIONKENLAYER Analysis

Pavement responses e.g., surface deflections,
tensile strains, and compressive strains of flexible
pavement under 700 kPa FWD, 800 kPa FWD, and690 kPa standard Thai truck loads were presented
herein. Only responses under the applied loadingpressure were reported for the FWD, while themaximum responses were reported for the standard
Thai truck.

The LEA by KENLAYER computer program wasused to determine the pavement responses under
FWD and standard Thai truck load applications. A
layered elastic model required a minimum number of
inputs to adequately characterize a pavement
structure and its response to loading. The essential
input parameters were the material properties for eachlayer (e.g., elastic modulus and Poisson’s ratio), layer
thicknesses, load configurations, numbers of loadgroups, x, y, and z coordinates for loads andresponses.
Pavement responses under the FWD load
application was determined right at the center ofloading plate, while the pavement responses under the
standard Thai truck were determined at four
positions, e.g., under the wheel load, between the
dual-wheel load, between the tandem-axle load, andbetween the dual-wheel and tandem-axle load etc.
The standard Thai truck had a center-to-center
spacing of 1,300 mm between the axles and 330 mm
between the wheels. Figure 1 illustrates the locationof four pavement responses as well as axle and wheel
configurations of standard Thai truck.
The outputs from KENLAYER computer
program were surface deflection, tensile strain at thebottom of the asphalt surface, and compressive strain
at the top of the subgrade.

Comparison between FWD and standard Thai
Truck
-Deflections
Comparison between the FWD and the standard
Thai truck deflections is shown in Fig. 2. Based on
the comparison, 800 kPa FWD-standard Thai truck
deflections yielded smaller error than 700 kPa FWDstandard Thai truck deflections. An average error of
10% was obtained for 800 kPa FWD-standard Thai
truck deflections, whereas an average error of 21%
was obtained for 700 kPa FWD-standard Thai truck
deflections. As a consequence, 800 kPa FWD
deflection was closer to 690 kPa standard Thai truck
deflection than 700 kPa FWD deflection.

Fig. 2

267

Comparison between FWD and standard
Thai truck deflections

GEOMATE – Bangkok, Thailand, 22-24 November 2022

-Tensile Strains at the Bottom of Asphalt Surface-Compressive Strain above Subgrade

Comparison between the FWD and the standard
Thai truck tensile strain at the bottom of asphalt
surface is shown in Fig. 3. In cases of thin asphalt
surfaces with thickness less than 100 mm, 800 kPa
FWD-standard Thai truck tensile strains yielded
smaller error than 700 kPa FWD-standard Thai truck
tensile strains. An average error of 2% was obtainedfor 800 kPa FWD-standard Thai truck tensile strains,
whereas an average error of 14% was obtained for
700 kPa FWD-standard Thai truck tensile strains. As
a consequence, for thin asphalt surface with thickness
less than 100 mm, 800 kPa FWD tensile strain wascloser to 690 kPa standard Thai truck tensile strain
than 700 kPa FWD tensile strain.
However, in cases of thick asphalt surfaces withthickness greater than or equal to 100 mm, 700 kPa
FWD-standard Thai truck tensile strains yielded
smaller error than 800 kPa FWD-standard Thai truck
tensile strains. An average error of 21% was obtainedfor 700 kPa FWD-standard Thai truck tensile strains,
whereas an average error of 38% was obtained for
800 kPa FWD-standard Thai truck tensile strains. As
a consequence, for thick asphalt surfaces withthickness greater than or equal to 100 mm, 700 kPa
FWD tensile strain was closer to 690 kPa standard
Thai truck tensile strain than 800 kPa FWD tensile
strain.

Comparison between the FWD and the standard
Thai truck compressive strain above the subgrade isshown in Fig. 4. In cases of compressive strains lessthan 400 microstrains, 800 kPa FWD-standard truck
compressive strains yielded smaller error than 700
kPa FWD-standard Thai truck compressive strains.
An average error of 7% was obtained for 800 kPa
FWD-standard Thai truck compressive strains,
whereas an average error of 12% was obtained for
700 kPa FWD-standard Thai truck compressive
strains. As a consequence, for small compressive
strains less than 400 microstrains, 800 kPa FWD
compressive strain was closer to 690 kPa standard
Thai truck compressive strain than 700 kPa FWD
compressive strain.
However, in cases of compressive strains between
400 and 2,900 microstrains, 700 kPa FWD-standard
Thai truck compressive strains yielded smaller error
than 800 kPa FWD-standard Thai truck compressive
strains. An average error of 8% was obtained for 700
kPa FWD-standard Thai truck compressive strains,
whereas an average error of 11% was obtained for
800 kPa FWD-standard Thai truck compressive
strains. As a consequence, for the compressive strains
between 400 and 2,900 microstrains, 700 kPa FWD
compressive strain was closer to 690 kPa standard
Thai truck compressive strain than 800 kPa FWD
compressive strain.

Fig. 4
Fig. 3Comparison between FWD and standard
Thai truck tensile strain at the bottom ofasphalt surface

268

Comparison between FWD and standard
Thai truck compressive strain above
subgradeGEOMATE – Bangkok, Thailand, 22-24 November 2022

Thailand under H.M. The King’s Patronage(EIT), Vol.31, Issue 3, 2019, pp.51-54[6] Morgan J.R., Flexible Pavement Behaviour andApplication of Elastic Theory – A Review, Partof the Australian Road Research Board’s
Research Programme, Vol. 4, Issue 2, 1968,
pp.1201-1205[7] Boussinesq J., Application Des Potentiels ÀL'étude De L'équilibre Et Du Mouvement Des
Solides Élastiques, Gauhier-Villars, Paris, 1885.
[8] Burmister D.M., The Theory of Stresses andDisplacements in Layered Systems andApplications to the Design of Airport Runways,
Highway Research Board, National ResearchCouncil, Washington, DC, USA, Vol. 23, 1944,
pp. 126-144.
[9] Sawangsuriya
A.,
Imjai
T.,
and
Malaikrisanachalee S., Structural Responses of
Flexible Pavement Subjected to Different Axle
Group Loads, Walailak Journal of Science andTechnology, Vol. 17, Issue 12, 2020, pp.13561366.
[10] Srikanth M. R., Study on Analysis of Flexible
Pavement using Finite Element based Software
Tool, International Journal of Engineering
Research and Technology, Vol. 4, Issue 9, 2015,
pp.912-916.
[11] Official, T., AASHTO Guide for Design ofPavement Structures. The American Associationof State Highway and Transportation Officials,
Washington, DC, USA, 1986.

CONCLUSION AND RECOMMENDATIONSThis study examined the structural responses of
625 flexible pavements under 700 kPa FWD, 800 kPa
FWD, and 690 kPa standard Thai truck loads. A totalof 625 LEA cases was performed to investigate thepavement structural responses. The responses fromboth 700 kPa and 800 kPa FWD were compared withstandard Thai truck in order to determine the mostsuitable loading pressure of FWD.
The comparison results indicated that the average
error was about 10% for 800 kPa FWD-standard Thai
truck deflections, while 21% for 700 kPa FWDstandard Thai truck deflections. The average error
was respectively 2% and 14% for 800 kPa FWDstandard Thai truck and 700 kPa FWD-standard Thai
truck tensile strain at the bottom of thin asphalt
surface. The average error was respectively 38% and
21% for 800 kPa FWD-standard Thai truck and 700
kPa FWD-standard Thai truck tensile strain at thebottom of thick asphalt surface. The average error
ranged from 7% to 11% for 800 kPa FWD-standard
Thai truck and 8% to 12% for 700 kPa FWD-standard
Thai truck compressive strain above the subgrade.
Based on the LEA, this study suggested that 800
kPa FWD deflection was closer to 690 kPa standard
Thai truck deflection than 700 kPa FWD deflection.
Therefore, it is recommended that 800 kPa FWD
could be considered in the development of future
deflection-based design approach in Thailand. The
nonlinear analysis could be further investigated toverify with the linear analysis.
REFERENCES[1] Ekwulo E.O., and Eme D.B., Fatigue and Rutting
Strain Analysis of Flexible Pavements Designusing CBR Methods, African Journal ofEnvironmental Science and Technology, Vol. 3,
Issue 12, 2009, pp.412-421.
[2] Mathew T.V., and Rao. K.V.K., Introduction toTransportation Engineering, Civil EngineeringTransportation Engineering, IIT Bombay,
NPTEL, 2006, pp. 1-8.
[3] Haung Y.H., Pavement Analysis and Design,
2nd Edition, Upper Saddle River, Pearson
Prentice Hall, New Jersey, USA, 2004, pp.57-76,
pp.94-143.
[4] Burmister D.M., The General Theory of Stresses
and Displacement in Layered Soil Systems,
Journal of Applied Physics, Vol.16, Issue 2,1945,
pp. 89.
[5] Sawangsuriya A., Sirirattanachat, T., Taworn, D.,
and Yuayong, S., Application of Falling WeightDeflectometer to Access Road Structural
Condition for Thailand Pavement Management
System, Engineering Journal of Research andDevelopment, The Engineering Institute of269

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

PERFORMANCE OF BEARING LAYER CONSTRUCTED USING
LIGHTLY CEMENTED CLAY
Juan Wei Koh1, Soon Hoe Chew1, Yeow Chong Tan2, Cheng Soon Teo2, Shanyin Kee2 and Danette S.E.
Tan2
1

Department of Civil and Environmental Engineering, National University of Singapore, Singapore,2
Housing & Development Board, Singapore
ABSTRACT

Tons of excess clayey soils are excavated yearly from Singapore’s underground construction activities. This
clayey soil is often repurposed as infill material in land reclamation projects. The issue with using clayey soil as
infill material is that ground improvement works, such as surcharge preloading with prefabricated vertical drains,are required to treat the clayey infill material due to its highly compressible and low-strength properties.
Traditionally for this treatment, a bearing layer made from sandy soil must be laid on top of the infill material tosupport the machinery for the subsequent ground improvement work. In a Singapore land reclamation project, itis proposed to partially replace this sandy soil layer with lightly cemented clay layer. The performance of thislightly cemented clay layer was evaluated based on the unconfined compressive strength (UCS) of undisturbed
samples and the ultimate bearing capacity estimated from the plate load test. In addition, the coefficient of
permeability determined from the double-ring infiltration test was assessed. It was found that the average UCS
was about 55 kPa, the ultimate bearing capacity was about 280 kPa, and the in-situ permeability was in the orderof 1×10-7 m/s. Finally, the lightly cemented clay-based bearing layer was proved to be robust when loaded with areal partition bund of 6m high with a base width of around 15 m.
Keywords: Clayey soil, Land reclamation, Bearing layer, Lightly cemented clay

INTRODUCTION

check its quality and performance. Eventually, thecemented clay layer was loaded with a trapezoidal
shape partition bund, which is 6 m high, and basewidth of about 15 m. This partition bund was made ofstacking geotextile tubes filled with cemented clayeysoil. The detail of this partition bund can be found in[1, 2]. The function of this bund is to form a partition
for the infilling process for a large reclaimed area. In
addition, this bund serves as the “loading test” for the
cemented clay-bearing layer and enables the realistic
evaluation of the overall stability and soundness ofthe constructed cemented clay-bearing layer.
Introducing cement into in-situ clayey soil would
improve its strength and stiffness. In this project, only
the top layer shallow portion of the clayey soil isrequired to be improved with cement to form the loadbearing layer. Thus, the conventional deep mixing
method, which is for greater depth, is unsuitable forthis application. Instead, a shallow soil mixing
technique was explored in this application.
The early strength development of the cemented
soil is the crucial criterion for field implementation.
The factors that affect the early strength developmentof the cemented soil such as type of cement, cement
dosage [3], cement dosing method [4], mixing
loops/passes [5], mixing blade design [4] and others,
have been discussed. Based on a field pilot trial
conducted prior to this project, it was concluded thatthe soil mixed with CEM 1 cement (C=7 % by

Numerous underground construction activities,
such as basement excavations and tunnelling works,
take place in Singapore every year. These
underground construction works produce tons ofexcess excavated clayey soil. This clayey soil isrepurposed as fill material in land reclamation
projects. The problem with using clayey soil as aninfill material is that it will undergo excessive
settlement and is highly unstable when loaded due toits highly compressible and low-strength properties.
Thus, ground improvement works, such as surcharge
preloading with prefabricated vertical drains, arerequired to treat the clayey infill material.
Traditionally for this treatment, a load-bearing layer
made from sandy soil must be laid on top of the infill
material to support the weights of machinery,material and workforce during ground improvement
work. However, it was proposed to research further topartially replace this sandy soil with lightly cemented
clay to form the load-bearing layer.
A 0.5 m thick lightly cemented clay layer wasconstructed in this project. Fig. 1 shows the schematic
diagram of the constructed cemented clay layer laidon the clayey infill soil. After completing thecemented clay layer, in-situ and laboratory tests,
including unconfined compression test of coredsamples and laboratory cured samples, plate load test
and double-ring infiltration test, were carried out to270GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 1

Schematic diagram of the 0.5 m thick cemented clay layer and the 6 m height bund.

weight) in 3-passes is an optimised operation for thisproject, and the final product can provide adequate
stability for subsequent construction work [5]. Early
study also shows that 3-passes of mixing are neededto achieve a satisfactory uniformity of the cemented
clay layer.
Before the field pilot trial, a laboratory test thatsimulates the shallow mixing operation was carriedout and established that a 2-layer dosing method andan overlapping mixing blade design would produce amore uniform cemented clay layer [4]. These findings
were then applied on-site.
In actual field work, 7 % of CEM 1 cement dosage
by weight and 3-passes of mixing were employed toconstruct the bearing layer. Fig. 2 shows theconstruction of the bearing layer using a mixing
bucket attached to an amphibious excavator.

Fig. 2PERFORMANCE OF THE CEMENTED CLAY
LAYER
In order to ensure the quality of the load-bearing
layer, a series of QA/QC tests, which compriseunconfined compression tests (UCT), plate load tests
and double-ring infiltration tests, were conducted at
designated locations.
Unconfined Compressive Strength (UCS)The unconfined compressive strength (UCS) of
the cemented clay bearing layer was determinedthrough three types of samples. First is the in-situ
cored sample, where the cemented clay was mixed insitu and cured in the ground; and was cored on the 3rd,
7th, and 28th Days of curing for the UCS test. The
second type of sample was mixed on-site using the insitu mixing bucket; the slurry state cemented clay wasthen poured into a UCS mould of 50 mm in diameterand 100 mm in height. Subsequently, these samples
were cured in a controlled laboratory condition for
wet curing. The third sample type is the same as thesecond type, except it used dry curing. Wet curingmeans the sample is submerged in water until thetesting date, while dry curing means leaving thesample cured in air. Comparing the UCS under thedifferent curing methods would assist in determining
which laboratory curing method would more
represent the in-situ curing conditions of thecemented clay for UCS determination.
The UCS result of the cemented clay layer isshown in Fig. 3. All the samples show an increase instrength from the 3rd to the 28th Day. On the 28th Day,
the in-situ samples’ UCS range from 27 – 90 kPa,
where the mean and standard deviation is 55 kPa and16.4 kPa, respectively. The high standard deviationshows some non-uniformity within the in-situ
cemented clay layer. In addition, the laboratory wet
curing samples show a lower UCS than the laboratory
dry curing samples. The wet curing condition madethe samples saturated as there was excess water
available during hydration, while the dry curingcondition resulted in the sample being partially(a) The amphibious excavator with soil
mixing bucket; (b) The close-up view of thesoil mixing bucket.
271

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Unconfined Compressive Strength, UCS (kPa)saturated as the water added during the initial mix wasused for hydration. This difference in the degree ofsaturation would induce a different strength ofcementitious products [6].
Comparing the in-situ samples with the laboratory
cured samples revealed that the laboratory wet curingmethod would get a closer UCS value to the in-situ
sample at an average of 61 kPa (28th Day), whilelaboratory dry curing produced a higher UCS value atan average of 99 kPa (28th Day). This result
concluded that the in-situ cemented clay condition
was very likely to be watery, which could be due tothe exposure to rainwater.
120
100
80
60
40
20
0

to three times the working load (213 kPa). The platesize is 300 mm in diameter, and 25 mm in thickness,the influence zone underneath the plate is up to three(3) times the plate diameter, which is 900 mm, whichis larger than the thickness of the bearing layer. The
plate load test setup is shown in Fig. 4. The
counterweight for the test was provided by two (2)water tanks, and (3) three LVDT (linear variation
displacement transducers) were installed to monitor
settlement.

(a) In-situ Sample
55

3

120
100
80
60
40
20
0

7

28

(b) Laboratory Wet Curing Sample
61

3

120
100
80
60
40
20
0

7

28

(c) Laboratory Dry Curing Sample
99

0

3 5 7

10

15

20

25 28 30

Fig. 4Curing Days

Fig. 3

Unconfined compressive strength (UCS) of
the cemented clay: (a) In-situ Sample; (b)Laboratory Wet Curing Sample; and (c)Laboratory Dry Curing Sample.

(a) Plate load test setup; and (b) Close-up
look at the 300 mm diameter plate.

The pressure-settlement curve for plate load tests
is shown in Fig. 5. Based on the figure, the measured
settlement was 3.033 mm at 3 × working load for Test
1, and 2.79 mm at 2.5 × working load for Test 2. Bothmaximum settlements are considered minimal andsatisfying for serviceability criteria.
In addition, hyperbolic method was used toestimate the ultimate bearing capacity of the bearing
layer from the load-settlement data of the plate load
tests. In this method, the graph of settlement overpressure (x/q) against settlement (x) was plotted, as
shown in Fig. 6. The ultimate bearing capacity, qult,
which is the inversed gradient of the graph, was foundto be 270 kPa and 285 kPa for Plate Load Test 1 and
2, respectively.

Ultimate Bearing Capacity
As the UCS strength checks via cored samples canonly capture a particular localised area of thecemented clay bearing layer, plate load tests at two
different locations were planned and conducted to
examine the bearing capacity of the cemented clay
layer on a broader area.
The designed allowable bearing capacity of the
bearing layer to support the bund is 71 kPa, which isalso called the working load (WL). The plate load test
was designed to determine the settlement of the
bearing layer under this working load (71 kPa) and up
272

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Conservatively, the estimated ultimate bearing
capacity of 270 kPa was used for the bearing layer’s
safety factor check against the 6 m high bund. Threelayer of geotextile tubes were stacked together toform the 6 m height bund. Thus, the surchargepressure on the bearing layer due to the weight of thebund is 102 kPa. Based on this bund’s pressure andthe calculated ultimate bearing capacity, the factor ofsafety of the bearing layer is found to be 2.6.
Therefore, the bearing layer is safe and adequate forconstructing the 6 m high bund.

Pressure on Soil, q (kPa)
0

50

100

150

200

250

300

Settlement, x (mm)

0
2
4
6

Hyperbolic
Estimation

8(a) Plate Load Test 1

Undrained Shear Strength

10

Pressure on Soil, q (kPa)
0

50

100

150

200

250

300

The undrained shear strength of the cemented clay
layer bearing layer can be calculated from UCS
strength. The undrained shear strength can also be
estimated by back-analysing the ultimate bearing
capacity value.
In this application, the stronger cemented claybearing layer is laid above the weaker infilling clay
layer to form a two-layered system. Two failuremodes during the plate load test may occur in thissystem, depending on the depth of this bearing layer.
Based on Meyerhof and Hanna’s [7] descriptions,
it was found that the failure mode of this cemented
clay layer during the plate load test was located in the
top layer. Hence, the ultimate bearing capacity (qult)of the bearing layer, which is considered undrained
saturated clay, can be estimated via the equation
below:

Settlement, x (mm)

0
2
4
6

Hyperbolic
Estimation

8(b) Plate Load Test 2
10

Fig. 5Pressure-settlement curves: (a) Plate load
test 1; and (b) Plate load test 2.

0.020

x/q (mm/kPa)(a) Plate Load Test 1
0.015
0.010

𝑞𝑢𝑙𝑡 = 1.3𝑐𝑢 𝑁𝑐

x/q = 0.0037x + 0.0046

where, cu is the undrained shear strength, and N c is theundrained shear strength coefficient (Nc = 5.14).
Fig. 7 shows the undrained shear strength of thecemented clay bearing layer obtained by these two
methods. As the UCS of the cemented clay has a wide
range between samples, the undrained shear strength
estimated via UCS is also scatter, where the upperbound value is 45 kPa and the lower bound value is
14 kPa. Nevertheless, most of the samples’ undrained
shear strength falls around 25 – 30 kPa, which is thefirm clay category [8]. Meanwhile, from the two
ultimate bearing capacity values obtained from thetest, the undrained shear strength was back-calculated
to be 40 kPa and 42 kPa. Hence, a reasonable
undrained shear strength should be in the range of 25– 40 kPa.

0.005
0.000
0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

3.5

4.0

Settlement, x (mm)0.020

x/q (mm/kPa)(b) Plate Load Test 2
0.015
0.010

x/q = 0.0035x + 0.0065

0.005
0.000
0.0

0.5

1.0

1.5

2.0

2.5

3.0

Settlement, x (mm)Fig. 6(circular base)

Settlement over pressure against settlement
plots: (a) Plate load test 1; and (b) Plate load
test 2.

273

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 8Lightly cemented clay layer loaded with thehalf-height of partition bund.

CONCLUSIONS
Fig. 7 Undrained shear strength of the cemented clay
layer.

This project proposed the replacement of theconventional top-sandy soil layer with a layer oflightly cemented clayey soil layer. This layer
functions as a weight-bearing layer to support themachinery and human weight during groundtreatment works. The lightly cemented clay wasconstructed using 7 % CEM I cement dosage and 3passes mixing sequence. Its unconfined compressive
strength, bearing capacity, undrained shear strengthand permeability were evaluated through in-situ andlaboratory tests. Afterwards, the layer was loadedwith the 6 m height partition bund construction using
geotextile tubes. The key findings and conclusions
are summarised as follows:

Permeability
Besides the strength check, the in-situ
permeability of the bearing layer at two random
locations was determined using the double-ring
infiltrometer. The double ring infiltrometer is a
simple instrument used for determining water
infiltration of the soil (Measurements according to
ASTM D3385 standard test method). The rings are
500 mm in height, 300 mm (inner ring) and 600 mm(outer ring) in diameter. The infiltration rate isexpressed in the volume of water per ground surface
and per unit of time (L/T). The infiltration rate will
reduce with time. The stable infiltration rate at a fully
saturated state can be taken as permeability. The
permeability of the bearing layer was found to be at
1 − 2 × 10−7 𝑚/𝑠, which can be considered as silty
soil permeabilitya)Unconfined compressive strength (UCS) of thein-situ cored samples showed a wide range ofscattering between 27 kPa to 90 kPa on the 28th
Day. This result implies the non-uniformity ofthe mixed in-situ cemented clay layer. It also
showed that the wet cured laboratory samples
were close to field data, indicating that the in-situ
cemented clay layer curing was continuously
exposed to moister conditions.
b) The ultimate bearing capacity of the cemented
clay layer was 270 – 285 kPa. Based on the lower
bound of the ultimate bearing capacity, the safety
factor of the bearing layer to support the partitionbund is 2.6.
c) The undrained shear strength of the cemented
clay layer ranged from 14 – 45 kPa, while themajority was at 25 – 30 kPa. This undrained
shear strength is classified as firm clay.
d) The permeability of the cemented clay was on theorder of 10−7 𝑚/𝑠, which is close to silty soil
permeability.

Stability and Structural Soundness of Cemented
Clay Layer
After assessing the quality of the cemented clay
layer through the various tests, it was deemed to besafe to construct the 6 m partition bund on top of it.
Fig. 8 shows the photo of the cemented clay layer
with half of the partition bund constructed. Visualobservation during and after the partition bund wasconstructed revealed that the lightly cemented clay
layer was stable and did not have any damage. Aftercompleting the containment bund construction, no
apparent settlement or crack was found on the bearing
layer.

Based on the results and visual inspection during
and after the construction partition bund, thecemented clay layer is verified to be firm enough to
support the weight of the partition bund.

274

GEOMATE – Bangkok, Thailand, 22-24 November 2022

ACKNOWLEDGMENTS

of
GeotechnicalandGeoenvironmental
Engineering, Vol. 135, Issue 4, 2009, pp. 573–
589.
[4] Yim H. M. A., Koh J. W., Chew S. H., Chua K.
E., and Tan S. E. D., Study on the Performance of
Shallow Soil Mixing with Cement. International
Journal of GEOMATE, Vol. 19, Issue 74, 2020,
pp. 15–21.
[5] Chew S. H., Eng Z. X., Chua K. E., Lim T. Y.,
and Tan S. E. D., Pilot Field Test on Shallow
MixingofLightly-Cemented-Clay,in
Proceedings of the 70th Canadian GeotechnicalConference, 2017.
[6] Subramanian S., Khan Q., and Ku T., Strength
Development and Prediction of Calcium
Sulfoaluminate Treated Sand with Optimised
Gypsum for Replacing OPC in Ground
Improvement. Construction and Building
Materials, Vol. 202, 2019, pp. 308-318.
[7] Meyerhof G. G., and Hanna A. M. Ultimate
Bearing Capacity of Foundations on Layered
Soils
under
Inclined
Load.
Canadian
Geotechnical Journal, Vol. 15, 1978, pp. 565–
572.
[8] Look BG, Handbook of Geotechnical
Investigation and Design Table, 2007, pp. 54.

The authors would like to thank Housing andDevelopment Board (HDB), Singapore, for funding
the “Joint Study on the Reuse of Excavated Soft Clay
Material for Land Reclamation Works” research
project. In addition, the authors gratefully
acknowledge the support of Hyundai Engineering &Construction Co., Ltd. and Geoharbour Co., Ltd. for
coordinating and carrying out the field tests reported
here.
REFERENCES[1] Chew S. H., Yim H. M. A., Koh J. W., Eng Z. X.,
Chua K. E., and Tan S. E. D., Performance of Pilot
Test of Geotextile Tube Filled with Lightly
Cemented Clay, in Proceedings of the 11th
International Conference on Geosynthetics, 2018.
[2] Koh J. W., Chew S. H., Chua K. E., Yim H. M.
A., and Gng Z. X., Effect of Construction
Sequence on the Performance of Geotextile Tubes
in a Containment Bund. International Journal of
GEOMATE, Vol. 19, Issue 74, 2020, pp. 1–7.
[3] Kamruzzaman A. H., Chew S. H., and Lee F. H.,
Structuration and Destructuration Behavior of
Cement-Treated Singapore Marine Clay. Journal275

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

CORROSION OF UNDERGROUND STEEL BETWEEN SOIL AND
CLAY AND ITS PREVENTION
Keiyu Kawaai1 and Takahiro Nishida2
12

Civil and Environmental Engineering, Ehime University, Japan;
Civil Engineering, Shizuoka Institute of Science and Technology, Japan
ABSTRACTThis study reports on the corrosion of underground steel such as pipelines located especially between soil andclay. Macro-cell corrosion tended to occur at the steel in the clay owing to the variations of oxygen permeability.
This led to localized corrosion thus suggesting that higher corrosion rate tended to occur in the clay regions with
reduction of oxygen in the sand regions. This can be explained by the electro-chemical measurements such as halfcell potentials and cathodic polarization curves. To prevent such corrosion of underground steel between sand andclay, this study explored techniques to prevent the localized corrosion taking place in underground steel. Based on
the results obtained, the use of oxygen reducing materials could be effective in preventing those localized corrosion
by inhibiting cathodic reactions in the sand regions. This provides insights into the anti-corrosive techniques inenhancing corrosion resistance of underground steel such as pipelines by reducing oxygen concentrations in thesand regions.
Keywords: Oxygen diffusion, Cathodic inhibitor, Macro-cell corrosion, Underground steel

INTRODUCTION

Based on the research, this study explored theapplication of oxygen reducing materials including
the bio-based materials to unerground steel in soil andclay with respect to prevention of localised corrosion.

Recently, it has been reported that deteriorating
pipelines have increased owing to corrosion of steel.
This led to water leakage from cracking of steel andrupture of pipelines if severe. On the other hand, thevisual inspection is often difficult on the buried
pipelines in soil. It is widely known that deteriorationof underground steel associated with corrosion iscaused by electrolytic corrosion and macro-cell
corrosion leading to localized corrosion. Especially,steel located between soil and clay tends to sufferfrom the localized corrosion owing to differences ofoxygen permeability in which steel in clay leads toanodic region owing to the lower permeability of clay
compared to that of sand as illustrated in Fig. 1 [1]. In
order to prevent such localized corrosion, anticorrosive techniques in preventing cathodic reactions
as cathodic inhibitor in macro-cell corrosion
formation was explored in this study.
For steel bars in concrete structures, macro-cell
corrosion tended to occur leading to re-deteriotaiton
after patch repair method is carried out, which is
caused by the chloride ions left ramained in theexsiting regions of concrete [2]. Our recent study
showed that the reduction of oxygen concentration inrepaired parts of concrete is higly effective to preventthe macro-cell corrosion, which can be obtained bybio-based repair materials using Bacillus Subtilis [3].
Macro-cell corrosion which is often controlled bydiffusion of oxygen could be inhibited by reductionof oxygen concentration leading to enhancement ofcorrosion resistance pertaining to the cathodic
reactions by the consumption of water and oxygen.

Fig. 1

Schematic of corrosion of underground steel
by differences of oxygen concentration

METHODOLOGYThis study explored the anti-corrosive techniques
using oxygen reducing materials including blast
furnace slag powder and Bacillus Subtilis. The former
is advantageous in adsorption of heavy metals in
addition to reduction of oxygen concentration and thelatter could be possibly available in soil. In this study,
the Bacillus Subtilis natto is specified as the microorganism mixed in soil. And segmented steel bars areprepared to investigate the localized corrosion byelectro-chemical measurements.
Materials
This section reports on the materials properties ofclay and soil used in this study. And specimen details
276

GEOMATE – Bangkok, Thailand, 22-24 November 2022

comprised of segmented steel bars in soil aredescribed.

Fig. 2. The sand parts are fully saturated with tap
water or culture solution of Bacillus Subtilis natto,
and clay is consolidated up to 40 kPa before theaccelerated corrosion test started. For the cases of
SCS specimens, the center part is comprised of clay
and the both sides are comprised of silica sandwith/without BFS and N. Segmented steel bars
comprised of three steel elements were embedded in
the specimens as shown in Fig. 2. The steel bar with
13 and length of 20 mm were connected by epoxy
resin and lead wires were attached with each steel
element to measure the electrical reactions on thesteel bars.
The experimental cases are summarized in Table
1. Two types of specimens are tested such as SS
specimens and SCS specimens. SS specimens aremonolithic specimens consisted of silica sand tested
under chloride environment. BFS and N are testing
parameters for oxygen reducing materials in sandparts. On the other hand, the SCS specimens arecomprised of clay (center part) and sand (side parts)
in simulating the sandwich underground environment.
Totally, 6 cases were tested for accelerated corrosion
monitoring.

Soil and clay
The sand used in this study is silica sand (No.5)
with apparent density of 1.50 g/cm3 obtained fromTohoku region in Japan. And the specific gravity ofclay is 2.45 g/cm3 obtained from Okayama prefecture
in Japan.
Blast furnace slag (BFS)
Blast furnace slag powder, a by-product ofsteelmaking, has been used as construction materials
especially in corrosive environment, which is
attributed to the enhanced resistance against ingressof chloride ions in concrete. This material is produced
under reducible environment and could reduce
oxygen concentration when contacted with water.
The specific gravity of BFS is 2.91 g/cm3.
Bacillus subtilis natto (N)The rod-shaped (1 μm width and 2-3 μm length)
Bacillus Subtilis natto is resistant to unfavorable
environmental conditions including salinity andextreme pH through the formation of an endospore attimes of nutritional stress until conditions becomefavorable. The aerobic respiration essentiallyconsumes oxygen in water in which oxygen
concentration is significantly decreased. The culture
solution containing 5.96 x 104 CFU/mL is preparedand mixed in distilled water.

Table 1 Experimental cases

Specimens and experimental cases
Specimens are of cross section 100 x 125 mm, andthe length of 200 mm in acrylic container as shown in

Fig. 2

Cases

Specimen details

SS-Cl
SS-BFS
SS-NSCS
SCS-BFS
SCS-N

Silica sand with ClSilica sand with BFS and ClSilica sand with N and ClClay (center), Silica sand (sides)
Clay (center), BFS (sides)
Clay (center), N (sides)

Note: BFS is mixed in silica sand with a partial replacement ratioof 1.5%. Bacillus Subtilis natto is mixed in tap water and poured
into the sand parts.

Schematic of specimens and segmented steel bars in clay and sand277

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Electro-chemical measurements

the cathodic region, thus macro-cell corrosion took
place between clay and sand owing to the differencesin oxygen permeability as reported by past research[1]. This can be explained by the changes of anodic
polarization curves in Fig. 4. In particular, the current
density in anodic region is likely to increase as
illustrated by the curve measured on the steel B
compared to that of steel C. This led to localized
corrosion especially taking place in the steel B.

This section describes the electro-chemicalmeasurements for determining the corrosion
properties of steel in soil. In particular, the rate ofoxygen permeability in soil was examined bycathodic polarization techniques in this study.
Macro-cell corrosion
Macro-cell corrosion current density isdetermined by the current flows measured byammeters connected with each segmented steel. The
current flowing through steel A to B and B to C was
simultaneously recorded by data logger and current
density was calculated by dividing the current flows
with surface area of a steel element. The
measurements were carried out for three weeks at
most for all the testing cases.

SS specimens (SS-Cl, SS-BFS and SS-N)
This section reports on the effect of oxygen
reducing materials in preventing corrosion of steel insoil. First, the corrosion properties under chloride
environment were examined in the case of SS-Cl. Fig.
5 shows the results of macro-cell corrosion on
average in each steel bar. As can be seen in the figure,macro-cell corrosion took place in the steel A withrespect to anodic current density, which wasincreased up to 0.15 A/cm2. And the steel bars B and
C formed cathodic regions in the macro-cell
corrosion. This is attributed to the variations ofchloride ions around the steel bars in the sand.
For the case of SS-N specimen, the macro-cell
corrosion current density was further increased up to
about 0.5 A/cm2 although the oxygen concentrationsin the soil could be reduced owing to the metabolic
activity of Bacillus subtilis natto. This could bepossibly attributed to the pH drop in the soil
environment. In general, the cathodic reactions takeplace by the reduction of oxygen in the neutral toalkaline environment. However, the reactions arevaried especially in acidic environment. In addition to
the reduction of oxygen, generation of hydrogen
could occur in the acidic environment, which is
caused by the metabolic products such as organic acid.
As can be seen in the figure, the macro-cell
corrosion current density is marginal in the case of
SS-BFS. It is worth to note that the addition of BFS

Polarization measurements
Polarization tests were carried out on each steel
element in clay or sand. The rate of polarization was
specified as 1 mV/s and Ag/AgCl reference electrodewas used for the measurements. The counterelectrode was stainless steel located on the clay orsand. In this study, anodic and cathodic polarization
curves are depicted based on the results and theintegrity of passive films and oxygen permeability incathodic regions were evaluated.
RESULTS AND DICUSSIONSCS without oxygen reducing materials
Figure 3 shows the results of macro-cell corrosion
current density measured in the case of SCS
specimen. As can be seen in the figure, the anodic
regions were formed in the clay parts. On the other
hand, the steel bars located in the sand tended to form

Fig. 3Fig. 4

Macro-cell corrosion current density in thecase of SCS specimen
278

Anodic polarization curves in the case of
SCS specimen

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 5Macro-cell corrosion current density in thecases of SS-Cl, SS-BFS and SS-N

Fig. 6

Cathodic polarization curves in the cases of
SS-Cl, SS-BFS and SS-N

sand in which cathodic reactions took place in thesand parts. If the oxygen concentration in sand partsis reduced, the cathodic reaction associated with
reduction of oxygen could be decreased. The
reduction of macro-cell corrosion current density in
the SCS-BFS is significant until 3 weeks after tested
as shown in Fig. 7. And the corrosion products
generated in anodic region i.e., steel B is evidently
distributed in smaller area in the case of SCS-BFS
compared to those of SCS-N as shown in Fig. 8.
CONCLUSIONS
Fig. 7

Macro-cell corrosion current density in thecases of SCS-BFS up to 3 weeks

The use of oxygen reducing materials could be
effective in preventing localized corrosion by
inhibiting cathodic reactions in the soil. This provides
insights into the anti-corrosive techniques inenhancing corrosion resistance of underground steel
such as pipelines by reducing oxygen concentrationsin the soil.
ACKNOWLEDGMENTS

Fig. 8 Corrosion products of steel bars
in the cases of SCS-BFS and SCS-N

This study is financially supported by JSPS
KAKENHI Grant No.20K04657.

is highly effective in preventing the macro-cell
corrosion formation. This can be explained by the factthat the rate of oxygen permeability is significantly
controlled by the availability of oxygen i.e. oxygen diffusion controlled condition as illustrated by thecathodic polarization curve as shown in Fig. 6. The
effect of oxygen reducing materials in preventing
corrosion observed in SCS specimen is further
examined in subsequent section.

REFERENCES[1] Ito, H., Suzuki, T., Kohno, E., and Aoki, M.,
Characteristic Evaluation of Macrocell Corrosion
based on Concrete Soil relations in Service Steel
Pipeline System, Journal of Environmental
Information Science, Vol.23, 2009, pp.505-510.
[2] Kawaai et al., Application of bio-based materials
to crack and patch repair methods in concrete,
Construction and Building Materials, 340(7),2022, 127718.
[3] Kawaai et al., Corrosion resistance of steel bars inmortar mixtures mixed with organic matter,microbial or other, Cement and Concrete
Research, 124, 2019, 105822.

SCS with oxygen reducing materials
As mentioned in the previous section, macro-cell
corrosion occurred in the SCS specimens owing tovariations of oxygen permeability between clay and279

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

PILE INSTALLATION EFFECTS ON THE STRESS AND DEFORMATION STATE OF SURROUNDING SOIL: REVIEWWorku Firomsa Kabeta
Department of Geotechnical and Hydraulic Engineering, Faculty of Civil and Environmental Engineering,Gdańsk University of Technology, G. Narutowicza 11/12, 80-233 Gdańsk, Poland
ABSTRACT
To investigate the impact of pile installation on bearing capacity, several field and model tests were conducted.
However, little is known about how piles behave during installation, how they interact with the surrounding soil,
and how this affects soil properties. This review paper, investigates the effect of pile driving on surrounding soil
as its compact surrounding sandy soil was investigated. For this purpose, various related literature was studied
based on the observation of the pile installation effect on earth pressure or lateral stress, relative density, and porewater pressure in the soil. A change in the deformation and stress state of surrounding soil due to pile driving waspresented. The installation of fully displacement piles can lead to significant stresses and deformations in thesurrounding soil. This is one of the main causes of uncertainty in the design and analysis of pile foundations.
According to this study, the soil around the pile is compacted during pile driving, resulting in lateral and upward
displacement. This leads to the densification effect of pile driving on loose sandy soil. Soil improvement withdriven piles depends on pile shape, installation method, and pile driving sequences. Therefore, the driven pile canbe used as a new method to improve the loose to medium-density subsoil.
Keywords: Driven pile; Soil improvement; Pore water pressure; Lateral stress; Bearing capacity

INTRODUCTION

study, which takes into account changes in stress stateand displacement field, soil density, and pore waterpressure

Loose sandy deposits should be improved to meetthe subsoil requirements of large infrastructuralprojects, reducing soil settlements and structuraldeformations. Soil densification may also result in a
reduction of liquefaction risk in saturated sandy soils[1]. There are many methods of deep soil compaction,
including dynamic compaction, vibro- flotation or
vibro-driving, sand pile compaction, compaction
grouting, compaction by blasting, and the pile
compaction method. Steel, concrete, timber, orcomposite piles arranged in a grid can be used for thelatter. The piles with different shapes, either constantwith depth or tapered, can be installed in loose sandby driving, vibro-driving, cyclic jacking, or staticallypushing in. Pile driving has unquestionably beenfavored among the various forms of pile installation
due to its numerous advantages, including highbearing capacity, rapid construction speed, andsuitability for a wide range of soil conditions [2]. Due
to noise and vibrations, their use in urban areas ornear existing structures is, however, limited. Vibrodriving can reduce the force needed for installation
decreasing the shaft friction. The pile base resistancecould, however, increase during the vibro-driving
process [3]. Recently developed technologies ofcyclic jacking and pushed-in piles can overcome thedifficulties related to noise and vibration level ofpreviously described installation methods. The
current knowledge about the effect of pile driving on
the surrounding soil is reviewed and presented in thisEffect of pile driving on the surrounding soil
The full displacement pile installation process cancause significant stress and deformation in thesurrounding soil. This is a key source of uncertainty
in pile foundation analysis and design. A drive or
jacking operation used to install a displacement pile
generates changes in the soil characteristics andstress-strain states that affect pile settlement andbearing capacity [4].
Effect on the stress and deformation state
During pile driving, the soil is vertically andradially pushed out to allow the pile penetration. As aresult, the final lateral stress will be quite different
from the in-situ stress in the free field (Figure 1). The
pile installation method has a significant impact on
the vertical capacity of the soil due to the high levelof energy induced on it during pile diving. The entire
stress field around the piles must be established tounderstand and model pile soil-structure interaction,load-displacement response, group action, and timedependent behavior. In comparison to bored piles,
driven piles produce a pre-stressed soil mass aroundthe pile base, resulting in a stiffer pile base response.
The mechanism of pile base penetration is similar to280

GEOMATE – Bangkok, Thailand, 22-24 November 2022

that of a cone penetration test, and the cone resistanceis related to the ultimate base capacity [5].

Stress measurements are frequently taken in the fieldusing a neighboring pile in which stiffness alters thestress field beneath the soil. To quantify thisinaccuracy, precise stresses measurements caused bya single pile installation in laboratory conditions are
required. Jacked pile experimental results [7] for sandshow that with increasing h/R, the radial effective
stress decreases, where h is the distance above the pile
tip and R is the pile radius. During the jacked
installation of a cylindrical pile in dry sand in acentrifuge, D’Arezzo et al. [11] used null gauges tomeasure horizontal stress changes. Stress is measured
using both an adjacent pre-installed square pile andin-soil sensors. The normalized depth of the pile tip(h/R) and the ratio of the increment of horizontal
stress (Δσh) to the pile base stress (qb) were
calculated. As a result, the maximum horizontal stressincrement at 9D from the pile is 0.5 percent of thebase resistance, indicating that horizontal stressdecreases with increasing distance from the pile. The
maximum changes in horizontal pressure occur when
the pile tip is slightly above sensor level; the distance
below the pile tip at which the maximum stress is
detected is greater for shallower depths ofpenetration. Jardine et al. [12] obtained similar results
at the same distance during the installation of acylindrical pile in a calibration chamber.
Hughes and Robertson et al. [13] discovered thatwhen a cone penetration test (CPT) probe penetrates
sand, a highly stressed, rigid ring of sand forms
around the tip. During pile penetration, a friction ofloose sand forms around the pile. A thick cylinder ofsand encircles the loosened sands and prevents theformation of complete lateral earth pressure on thepile through arching [14]. Arching is as a result of thesand compressibility relative to the piles, whichcauses friction between the soil and the piles (Figure2). They also use information from the cone
resistance data and the shape of the pressure
expansion curve to determine in-situ stressconditions. Physical model of driven displacement
piles can also be created using the full-displacement′
probe. After installation, the radial stress, 𝜎𝑟𝑐, actingaround the pile at any depth (z), was found to varies
with cone penetration test resistance, which reflects
stiffness of sand and conditions, and to decrease as
the pile installed and the relative depth above the tip
increased. There was also a small reliance on the′effective stress, 𝜎𝑧𝑜.
Bond and Jardine et al. [16] proposed the equation(1) for radial stress expression for the design ofcylindrical piles driven in sands.

Fig. 1. Stress distribution near single pile.
As a result of the installation process, the soil
properties at the pile-soil interface have also been
observed to change. The particle size distribution near
the pile can change, with fines reducing the interface
friction angle at large deformations, according tolaboratory experiments [6]. The behavior of the soil
surrounding the pile during the pile installation iscomplex, and different laboratory tests on soil
properties, interface parameters, and experiments,
including calibration chamber or centrifuge test,
should be used to model the entire system [7].
The effects of different installation methods,
jacking and impact driving, are investigated for threedifferent initial relative densities of sand [8].
According to their findings, the soil state after several
method of installation (impact driving and pile
jacking) varies significantly, according to theirfindings, and is also dependent on the sand initial
density. The horizontal stress surrounding the pileincreases with both impact driving and pile jacking.
In jacked piles, however, the increase in horizontal
stress is much greater than in impact-driven piles.
Henke and Grabe [9] found that in a moderate-density
sample of sand having relative density of 51–55%, theexternal horizontal stress in the vicinity of the pile tip
is much lower after impact driving than jacking.
Higher vertical stress near the pile tip is expected after
jacking than after impact driving.
However, the degree of soil densification is higher
after impact driving than after jacking, especially near
the top of the pile. As a result of impact driving, thehorizontal stress in the area below the pile tip
increases. Improved soil strength is the result of the
combined effects of increased stress and decreased
void ratio (soil compaction). The magnitude of thepeak horizontal stress increases as the initial relative
density of sand increases. Jacked piles are also prone
to soil arching more than impact-driven piles. Duringpile jacking, stress changes in the soil are significant
near the pile, but they also extend a significant radial
distance. Model tests and field measurements havebeen conducted during pile installation with pressure
cells in the soil mass [3], [10].

′ = 𝑓(𝑧) = 0.029𝑞 (𝜎 ′ /𝑃 )0.13 (ℎ/𝑅)−0.38
𝜎𝑟𝑐
𝑐 𝑧𝑜 𝐴(1)

In which PA is the atmospheric pressure. The h/R andqc variables have significant effects, but a change in′′
𝜎𝑧𝑜
only results in a 35 % change in 𝜎𝑟𝑐.

281

GEOMATE – Bangkok, Thailand, 22-24 November 2022

determined by CPT tip resistance and spatial position
relative to the pile tip (h/R and r/R), as expressed by:𝜎′𝑞𝑐

The stresses in the pile vicinity fluctuate
dramatically as the distance between the pile axis andthe radial distance increases, and as the distance
between the pile tip and the top of the pile, according
to all of the trials. When these data are combined withthe field trends captured in the above equation (1), it
appears that 𝜎𝑢′ , and 𝜎𝑟′ may be stabilized for′fluctuations in free-field vertical stress 𝜎𝑟𝑧
and local
qc, and represented by 2D functions of the form.
(𝜎 ′ /𝑞𝑐)ℎ 𝑟= 𝑓(𝑅 , 𝐻 )(3)

Where h/R is the (positive) relative height aboveor (negative) relative depth below the pile tip, and r/R
is the relative radius from the pile axis.
The soil stresses vary spatially in relation to the pile
tip location. The stress measurements performed by
Jardine et al. [12] revealed that soil stresses developed
during installation were directly correlated with local
qc values; normalization by qc reduced the impact oftest arrangement variations. Although the datasets
have similar initial 𝜎 ′ rs/qc maxima as the pile tip
passes and fall to similar final low 𝜎 ′ rs/qc ratios, thedecay curves applied between these limits converge
better when plotted against h/R than when plottedagainst the number of cycles (N), indicating a closer
correlation with relative pile tip depth than with thenumber of cycles. On-pile measurements show that
doubling N did not result in steeper radial stress
reductions for fixed h/R ratios. Soil mass stresses
were unaffected by the number of jacking cycles.
These characteristics, on the other hand, may be more
important at the pile/soil interface, during dynamic
installation, and over extended periods of time.
During both the penetration and pause stages,
stress distributions established in the soil below andto either side of the tip level stress are roughly
spherically symmetrical, though there are minor
differences between the stress decay curves
established on vertically and horizontally projecteddistributions.
Close to the pile tip, very steep stress gradients
apply, with triaxial test dominating directly under the′′tip, where 𝜎𝑧′ = 𝜎1′ ≈ 𝑞𝑐 and 𝜎𝑟𝑚= 𝜎𝜃𝑚≈ 𝑞𝑐 /𝐾𝑝,in which 𝐾𝑝 is Rankine coefficient of passive earthpressure.
After installation, the radial stress level reachpeaks at normalized around significant radial
distances r/R = 3, which are roughly double those
operating on the shaft (r/R = 1). At r/R > 3,′
𝜎𝑟𝑚
/𝑞𝑐 declines consistently with h/R for any givenr/R and all radial stresses decompose with radius.
Circumferential stresses vary at 1 < r/R < 5 range,
according to the radial equations of equilibrium and
′
′
𝜎𝑟𝑠
< 𝜎𝜃𝑚
at 1 < r/R < 3 once installed. Here, one
should notice that the maximum radial stress is not on
the pile shaft, but at some distance. In low to medium
density quartz sand, Lehane et al. [7] took extensive
measurements of the effective stresses generated
during installation, equalization, and load testing ofdisplacement piles. They demonstrated that thestresses that develop at any given soil horizon arestrongly influenced by the horizon's distance from thepile tip as well as the soil's initial state. As eachjacking stage was started, the effective radial stresses

Figure 2. Stress redistribution caused byarching [15].

′ /𝑃 )0.13
(𝜎𝑧𝑜
𝐴

ℎ 𝑟

= 𝑓(𝑅 , 𝐻)

(2)

Yang et al. [6] performed experimental
investigations and three computational evaluations ofstresses created around piles penetrating into sand.
The cylindrical stresses surrounding piles intruding
into normally consolidated quartz sand were
investigated using a combination of threeexperimental studies and three numerical analyses.
The interpretation summarizes predicted andmeasurement made at points between 10 and 550 kPaof the actual initial stress and between the relaxed andstressed states, which are related to stresses with Conepenetration test and dimensionless ratio of vertical′stress (𝜎𝑧𝑜/𝑃𝐴 )0.13 and dimensionless spatial
coordinates (h=R, r=R), where the top of the pile isthe origin of the coordinates, represent the final statesand (h=Rp, r=Rp) represent the stresses around theexposed pile at moderate radial distances.
Jardine et al. [12] interpreted stress measurements
and presented the distribution of circumferential orvertical stress and radial stress as a function of
distance to the penetrating pile in dense sand. They
jacked up instrumented model piles in the calibrationchamber with soil mass equipped with stress sensors.
Emphasis was placed on the effective radial stresses
occurring around the axes of the columns, but
circumferential and vertical stresses were also taken
into account. They interpreted detailed stressmeasurements taken on and around closed-ended
model displacement piles in pressurized silica sand.
The stresses that develop at any point are primarily282

GEOMATE – Bangkok, Thailand, 22-24 November 2022

( 𝜎𝑟′ ) rised by 5 to 20 kPa, causing the stationary′
profiles of 𝜎𝑟′ , known as 𝜎𝑟𝑠, to separate from thoserecorded during the pile was moving. The long-term,′
fully equalized values ( 𝜎𝑟𝑐) for a pile that has
penetrated to the same tip depth but no further are thesame as the stationary radial stresses recorded during′′
installation pause periods ( 𝜎𝑟𝑠), where 𝜎𝑟𝑐
is the′radial effective stress after equalization and 𝜎𝑟𝑠
is thestationary radial effective stress.
Another study on the change in horizontal stressduring pile driving were conducted by D'Arezzo et al.
[17]. Null-gauges were used to measure horizontal
stress changes during the jacked installation of acylindrical pile in dry sand. They used an adjacentpre-installed square pile and in-soil sensors tomeasure stress, and then compared the centrifuge
results to the radial stress distribution estimated using
traditional methods. Their findings show that when
the pile is at an equal distance between the pile nullgauges and the soil null gauges, the pile experiences
higher stresses due to its stiffness. For thedetermination of stress state near pile base [18]conducted field tests on piles equipped with surface
stress transducers and discovered that severe stressfluctuations occur during sand penetration,
particularly around the tips.
Beijer Lundberg et al. [19] conducted a series ofgeotechnical centrifuge tests of displacement pile
installation in sand with various initial relative
densities. The stress measurements included
horizontal and axial contact stresses. The deformation
measurements were displayed as displacement
trajectories, with incremental stresses and soil
displacements analyzed. The effect of load cycles andthe influence of initial relative density were the focusof the measurement's interpretation. During pile
installation and removal, the initial relative density
was shown to have a significant impact on thehorizontal contact stress. The horizontal displacement
measurements revealed a similar impact, with denser
soil samples exhibiting higher
horizontal
displacement. It was discovered that the soil
compaction during cyclic loading shows in decreased
horizontal contact stress. Dense, medium dense andloose sand with initial relative densities 0.4, 0.6 and0.8 respectively were tested. The installation effects
are strongly influenced by the relative density of thesoil at the start of the experiment, according to theresults. The measurements were compared tonumerical models that produced similar results.
Lehane et al. [18] also used a drum centrifuge totest model piles in normally consolidated sand to see
how the method of pile installation, level of stress,
and aspect ratio of pile affected the increase in lateralstress on the pile while load testing. They consider
pile aspect ratio (elongation) in the model tests as 1
for 14 model piles, 2 for two model piles and 6 forother two model piles. The ratios of lateral stressincreases (the ratio of lateral stress in pseudo-

dynamic mode to jacked mode) measured during
installation for h/D =3 is higher than for h/D=6, where
D is pile breadth. The difference between pseudodynamic and jacking installation mode is that piles
area installed in a sequence of jacking strokes during
jacked installation. For each stroke, the piles were
pushed at 0.2 mm/s for 2 mm, then extracted at 0.005
mm/s until the pile head load was zero. During
pseudo-dynamic installation, the piles were installed
in a series of 2 mm downward jacking increments at
0.2 mm/s, followed by 1.5 mm extraction at 0.2 mm/s.
Furthermore, due to soil compaction near the pile toe,base resistance increases with the number of cycles
applied, whereas shaft load decreases with thenumber of cycles applied due to soil densificationnext to the pile shaft [17].
Figure 3 describes the effect of pile installation
mode on lateral stress for both stationary and moving
pile conditions. Accordingly, stationary lateral pilestresses (σhc) are reduced as the distance above thepile tip (h) increases. For the same type of installation
method, lateral stresses when the pile is moving are
greater than the lateral stresses when the pile isstationary [18]. They also discovered that themagnitude of lateral stress decreases as the distance
between the pile tip and the pile diameter (h/D)
increases for both methods of installation.
Different measurement techniques are used tomeasure the deformation field around installed model
piles. Otani et al. [20] investigated the deformation ofsand surrounding a laterally loaded pile using X-ray
computed tomography. Due to the high cost of CT
scanners, it has limited application in geotechnicalengineering. Particle image velocimetry (PIV), a
cost-effective, full-field image correlation approach,and accurate has been employed in geotechnicalengineering since the invention of digital image
process [21]. In geotechnical research laboratories,
PIV techniques have been used to measure soil
deformations around a driven pile in sand ingeotechnical models [22], [23]. According to study byWhite et al. [22], PIV is a velocimetry method thattracks textured particles in a series of images. Image
processing algorithms have been written to apply the
PIV principle to surface images. The resulting
software is capable of tracking the movement of
natural sand or soil to an accuracy of 1/15 pixel. The
combination of digital imaging and PIV analysis
makes it possible to calculate strain fields consistingof thousands of displacement vectors with greater
accuracy than the averaging method.
Figure 4 shows the displacement contours after
installation and displacement paths. Figure 4 (a)shows the boundary where there are no displacements
as a thick dark line. These influence zones show
where the soil is moved during the installation.
According to the measurements, a pile model shouldat the very least include the boundaries of nodisplacements. The soil sample container should also283

GEOMATE – Bangkok, Thailand, 22-24 November 2022

∆𝑢𝑎 = (1 − 𝐾𝑜 )𝜎1′
∆𝑢𝑠 = (∆𝑢⁄𝑝)𝑚 𝜎1′

be wider, according to these measurements. Figure 4(b) also shows the displacement path for loose soil
and dense soil, showing that the initial density of thesoil sample has a relatively large effect, with thedense soil sample experiencing more heaving verticaldisplacement than the loose soil sample.

(4)
(5)

Where p is the consolidation pressure, ∆𝑢𝑎 is thechange in pore water pressure from total ambientpressure, ∆𝑢𝑠 the change in porewater pressure fromshearing and (∆𝑢⁄𝑝)𝑚 the maximum pore pressure
ratio. Hence,∆𝑢𝑚 = ∆𝑢𝑎 + ∆𝑢𝑠 = [(1 − 𝐾𝑜 ) + (∆𝑢⁄𝑝)𝑚 ]𝜎1′

(6)

The pore-pressure ratio (∆𝑢⁄𝑝) rises with theapplied stress differential, reaches its maximum(∆𝑢⁄𝑝)𝑚 , and then remains constant when a specificstrain is reached [25].
The extra pore water pressure generated below thepile toe while driving in fine sandy soil wasmonitored [26], and the magnitude level of applied
force and the strength level of soil material are theconditions affecting the level of excess pore waterpressure. If these two parameters are at high level, theexcess pore water pressure is considered to increase
to the point of destruction of soil texture, and thenproduce the liquefaction phenomenon. A highpressure zone of excess pore water was forming under
the pile toe. This high-pressure zone operated as abarrier to pile driving, making further pile insertion
impossible. The pile toe generated excessive porewater pressure, which spread throughout thesurrounding area, with this pressure being transferredto the pile toe of the pre-driven pile.
At various distances from driven piles, variationsin pore water pressures were measured in sandy andclayey soil layers by Hwang et al. [27]. To control the
response of soil pore water pressure caused by piledriving, they used electrical piezometers. The porewater pressure of soil positioned at a radial distanceof 3D from the center of the pile began to climb when
the pile tip advanced to an elevation of 4 to 7D (D isthe pile diameter) above the piezometer site, andreached its maximum value when the pile tip passed
4D below the piezometer location. At a distance of3D from the pile's center, the extra pore waterpressure in a sandy layer at 6 m below ground reached
1.5 times the effective overburden pressure, resulting
in liquefaction conditions.
According to Fattah and Mustafa [28], twelve
experiments with piles installed by overdriving werecarried out on a fine sandy soil of medium densitywith a relative density of 60%. The changes in excess
pore water pressure were measured at both the pile tip
and the pile middle. The study reveals that pile
slenderness ratio, machine operation frequency, andsoil permeability are the parameters that influence theformation of extra pore water pressure. The excess
pore water pressure generated during operation,

Figure 3. Lateral stresses (a) jacking installation; (b)installation by pseudo-dynamic [18], 𝜎ℎ𝑐 𝑖𝑠 lateralstresses when pile is stationary and 𝜎ℎ𝑚 is lateralstresses when pile is moving.

Figure 4. Displacement contours and paths (a)displacement contours and (b) vertical arrays ofdisplacement path for typical location [24]Effect on Pore water pressure
Because of soil compaction, pore water pressure
rises during pile driving in loose sands and sandy
silts, causing a reduction in effective soil strength.
Numerous studies have tried to model the pore water
response to pile installation or even other construction
vibrations in the near-field analytically [4]. The initialstresses in the soil before the pile is driven are thehorizontal effective stress (𝜎3′ ), the vertical effective
stress (𝜎1′ ), and the pore pressure (𝑢0 ).
The horizontal effective stress ( 𝜎3′ ), verticaleffective stress (𝜎1′ ), and pore pressure (𝑢0 ) are theinitial stresses in the soil before the pile is driven.
Vertical and horizontal stresses are also the majorstresses. Because the horizontal strain in a naturaldeposit is zero, 𝜎3′ equals 𝐾0 𝜎3′ where 𝐾0 is thecoefficient at rest earth pressure [25].
Radial stress becomes the most critical stresswithin the failure zone during pile driving. The
maximum excess pore pressure (∆𝑢𝑚 ) that results
will be made up of two parts, one from the change intotal ambient pressure (∆𝜎3 ) , and the other fromshearing, as follows [25]:284GEOMATE – Bangkok, Thailand, 22-24 November 2022

however, was found to be less than 20% of the initialhydrostatic pressure in all cases.
As the pile group installation is considered [25],the pore pressures due to the driving of each pile arequite easily discernible. For piezometers placed close
to the pile, the maximum pore pressure is recorded
about 20 minutes after the pile reaches the level of thepiezometer tip. There is a slight effect of porepressure redistribution from regions of high pressures
to regions of low pressures for piezometers locatedfurther away from the pile.
Pore-water pressures were measured by insertingtwo piezometers at 9 and 18 meters below the groundsurface during the inspection of piling activities [29].
Prior to the piling operations, readings from all
piezometer were taken on a regular basis, at least oncea day. As pile driving got closer, the piezometers
readings were taken before and after each pile wasdriven. A significant increase in pore water pressure
was generated at both the shallow and deeppiezometers during pile section drive into very dense
sand and gravel.
The amount of excess pore water induced duringpile driving can be reduced by applying additional
drains installed in the timber piles [30]. The extra
pore pressure generated by 48 test piles with drains
and 13 test piles without drains was measured. Whenthe drain was used, the data showed that excess porepressure was reduced by at least 50%. Furthermore,
the procedure proved much less expensive than othermethods for dealing with dangerously high pore waterpressures caused by piling in similar soils. Pestana etal. [31] performed a geotechnical site investigation
and a site control testing to measure horizontal loads
and pore water pressure at three radial distances fromlarge closed piles. Their findings show that piledriving causes significant excess pore pressures,
which are related to the distance from the pile wall.
Due to the excessive pore pressure generated by thepile installation, the initial effective vertical stress ofthe soil is slightly exceeded within one pile diameter.
The generated excess pore pressure decreased as
distance from the pile–soil interface (1/R2, where R isthe distance from the pile's center) increased. These
pore pressures dissipate over time, and soils morethan one diameter away from the pile wall achieve
80% consolidation between 50 and 80 days. Initial
inclinometer measurements of outward radial
deformations were very close to predictions based on
cylindrical cavity expansion theory. Lateraldeflection measurements show a return to the pile as
the excess pore pressure dissipates, with a decreasing
magnitude as the distance from the pile wall
increases.

compacted, and lateral and upward displacement will
occur. Several scholars and practicing engineers [2],[27], [32]–[34] have described the improved
conditions that resulted from pile driving. The
volume and shape of the pile, as well as the methodof drive, determine the amount of this compacted area
and the characteristics of the soil within it.
Depending on the grain size characteristics of thesoil, on-site soil density, pile spacing, and pile
diameter, the installation process can cause visible
compaction and an increase in lateral stress [33]. It is
well known that loose, clean, granular soils contract
when displacement piles are installed [14]. Whenpiles are installed in loose sand, some compaction
occurs up to 3.5 to 6 pile diameters away from the pile
shaft [35]. Szechy [36] also found that the void ratio
decreased by around 5% near the lower half of driven
piles. Unfortunately, few studies have been
conducted since then on the extent and distribution ofcompaction around displacement piles.
The development of displacements around a full
displacement pile causes loose sand to compact. The
degree of sand compaction induced by driving has
been related to bearing capacity improvement for pile[35]. Previous bearing capacity theories were mainly
founded on the assumption that the soil conditions did
not change as the pile was driven. However, piledriving not only increases the relative density of loose
sand but also the horizontal stress, and the influenceof compaction on the soil parameters cannot beignored. The compaction increase caused by piledriving in loose sands can be related to the shift in theinternal friction angle of sand and factored into pile
ultimate bearing capacity estimates.
Meyerhof [35] examined the installation of adriven pile, resulting in compaction and a rise in the
major stress ratio. He measured the width of thecompacted zone around a single driven pile, andfound that it was around 6 to 8 times the pile width.
In model pile testing, Kishida [37] discovered that the
distribution of displacement stresses caused by sanddisplacement as a result of pile driving was
proportional to the width of the compacted zone.
Based on their findings, the author determined thatthe width of this zone as b = 7D, as shown in figure
5.

Effect of pile driving on the surrounding soil
Figure 5. Effect on friction angle (ϕ) [37]

There is the densification due driving pile on
sandy soil. As a result, soil around the pile is285

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Kishida [37] assumed that the internal friction angle
changes linearly with distance from the pile (where ϕ= ϕ2) to a radius of around 3.5 times the pile diameter(where ϕ = ϕ1) in the compacted zone thus established.
In the sand, the link between ϕ1 and ϕ2 can be stated[35], [37] as:
ϕ2 =𝜙1 +400
2

Dyn. Earthq. Eng., vol. 118, no. January, pp.
134–143,2019,
doi:
10.1016/j.soildyn.2018.12.014.
[3] S. Moriyasu, S. ichi Kobayashi, and T.
Matsumoto, “Experimental study on friction
fatigue of vibratory driven piles by in situ model
tests,” Soils Found., vol. 58, no. 4, pp. 853–865,
2018, doi: 10.1016/j.sandf.2018.03.010.
[4] M. F. Randolph, J. P. Carter, and C. P. Wroth,“Driven Piles in clay—The Effects of Installationand Subsequent Consolidation,” Geotechnique,vol. 29, no. 4, pp. 361–393, 1979, doi:
10.1680/geot.1979.29.4.361.
[5] J. H. Lee and R. Salgado, “Determination of Pile
Base Resistance in Sands,” J. Geotech.
Geoenvironmental Eng., vol. 125, no. 8, pp. 673–
683,
1999,
doi:
10.1061/(asce)10900241(1999)125:8(673).
[6] Z. X. Yang, R. J. Jardine, B. T. Zhu, and S. Rimoy,“Stresses Developed around Displacement Piles
Penetrationin
Sand,”J.
Geotech.
Geoenvironmental Eng., vol. 140, no. 3, p.
04013027, 2014, doi: 10.1061/(asce)gt.19435606.0001022.
[7] B. M. Lehane, R. J. Jardine, A. J. Bond, and R.
Frank, “Mechanisms of shaft friction in sandfrom instrumented pile tests,” J. Geotech. Eng.,
vol. 120, no. 8, pp. 1450–1452, 1994, doi:
10.1061/(ASCE)0733-9410(1994)120:8(1450).
[8] S. Fan, B. Bienen, and M. F. Randolph, “Effectsof Monopile Installation on Subsequent Lateral
Response in Sand. II: Lateral Loading,” J.
Geotech. Geoenvironmental Eng., vol. 147, no.
5,
p.
04021022,
2021,
doi:
10.1061/(asce)gt.1943-5606.0002504.
[9] S. Henke and J. Grabe, “Numerical investigationof soil plugging inside open-ended piles withrespect to the installation method,” Acta
Geotech., vol. 3, no. 3, pp. 215–223, 2008, doi:
10.1007/s11440-008-0079-7.
[10] K. Żarkiewicz and W. Qatrameez, “Assessmentof stress in the soil surrounding the axially loadedmodel pile by thin, flexible sensors,” Sensors,
vol. 21, no. 21, 2021, doi: 10.3390/s21217214.
[11] F. B. d’Arezzo, S. Haigh, M. Talesnick, and Y.
Ishihara, “Measuring horizontal stresses during
jacked pile installation,” Proc. Inst. Civ. Eng.
Geotech. Eng., vol. 168, no. 4, pp. 306–318,
2015, doi: 10.1680/geng.14.00069.
[12] R. J. Jardine, B. T. Zhu, P. Foray, and Z. X.
Yang, “Measurement of stresses around closedended
displacement
piles
insand,”
Geotechnique, vol. 63, no. 1, pp. 1–17, 2013, doi:
10.1680/geot.9.P.137.
[13] J. M. O. Hughes and P. K. Robertson, “Fulldisplacement pressuremeter testing in sand.,”
Can. Geotech. J., vol. 22, no. 3, pp. 298–307,
1985, doi: 10.1139/t85-043.
[14] E. I. Robinsky and C. F. Morrison, “Sand(1)

where ϕ1= Initial value friction angle.
ϕ2 = frictional angle after pile driving.
CONCLUSIONS
Fully displacement piles can cause significant
stresses and deformations in the soil surrounding
them. This is one of the main causes of uncertainty inthe design and analysis of pile foundations. During
driving pile in loose sands and sandy silts, pore waterpressure rises due to soil compaction. Highcompressive stresses arise in the adjacent soils during
the driving of high displacement piles, resulting in abuildup of large effective lateral stresses. The
compaction of the soil mass surrounding a pileincreases its bearing capacity. As a result, the driven
pile can be utilized as a soil compactor to enhance thesoil surrounding the pile. The degree of sandcompaction caused by driving the pile was related tothe increase in bearing capacity of the driven pile insand. The shape of driven piles has a significant
impact on the degree of ground improvement
achieved, revealing that driven tapered piles are more
effective in densifying the ground than straightcylindrical ones. The spacing of displacement piles
and the installation sequence affect their drivability.
Unfortunately, few studies have been conducted sincethen on the extent and distribution of compaction
around displacement piles. As the effect of pile shapeon stress and deformation state is not well known inthe case of tapered piles, the soil deformation andstress fields around constant diameter piles andtapered piles will be studied in a series of tests
planned in the centrifuge at Gustave Eiffel University
in Nantes within the Geolab project. The bearing
capacity of constant diameter piles and tapered piles
will be compared for the installation mode withdriving and jacking. The soil deformation fields willbe estimated using the PIV technique.
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[30] R. Holtz and P. Boman, “A New Technique for
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Driving,” Can. Geotech. J., vol. 11, pp. 423–430,
Jan. 2011, doi: 10.1139/t74-043.
[31] J. M. Pestana, C. E. Hunt, and J. D. Bray, “SoilDeformation and Excess Pore Pressure Field
around a Closed-Ended Pile,” J. Geotech.
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10.1061/(asce)10900241(2002)128:1(1).
[32] M. S. Nataraja and B. E. Cook, “Increase in SPT
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resultingfrominstallationofdrilleddisplacement piles,” Proc. DFI’s 32nd Annu.
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[35] G. G. Meyerhof, “Compaction of Sands and Bearing
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[36] C. Szechy, “The Effects of Vibration and Driving
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287

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

A CORRELATION BETWEEN ONE-DIMENSIONALCONSOLIDATION COEFFICIENTS WITH BASALT FIBER LENGTH,
RHA-CEMENT CONTENT IN FIBER-REINFORCED STABILIZEDEXPANSIVE SOILS
1

Alex Otieno Owino1, and Zakaria Hossain1
Graduate School of Bioresources, Mie University, Japan
ABSTRACTThe consolidation behavior of reinforced and stabilized soils is essential in geotechnical engineering
applications. Recently, environmentally friendly soil reinforcement and stabilization techniques to reconstituteweak expansive soils are on the rise, calling for an in-depth analysis of the consolidation projections on theengineering structures built on them. This study investigated the one-dimensional consolidation coefficients byconducting a series of odometer tests on expansive soil reinforced with different basalt fibers lengths and stabilizedwith rice husk ash (RHA)-cement mixtures in their specified combinations. A correlation between the coefficientof consolidation (cv), coefficient of volume change (mv), and permeability coefficient (k) with the length of thebasalt fibers and RHA-cement contents in the ultimate soil composite material was quantified using equations andgraphical forms. Furthermore, scanning electron microscopic imagery (SEM) was conducted to examine thestructural modifications within the reinforced and stabilized soil composite upon one-dimensional consolidation.
The results showed that basalt fiber reinforced specimens with 5%RHA-3%cement mixtures showed the lowest
one-dimensional consolidation coefficients with a notable reduction at high-stress states than the control specimen.
Additionally, the coefficient of volume change (mv) and permeability coefficient (k) decreased with increased
compactive effort, with a significant reduction evident on the basalt fiber reinforced stabilized soil composites.
The study demonstrated that basalt fiber, RHA, and cement could enhance the consolidation characteristics of
weak expansive soils. This study also proposed analytical equations for evaluating cv, mv, and k considering basalt
fiber lengths. The resulting soil composites had superior properties and can be used as a fill or subbase material
for engineering structures such as embankments, pavements, and foundations.
Keywords: Soil consolidation, Basalt fiber, RHA, Coefficient of consolidation, Coefficient of volume change,
Permeability coefficient, SEM
INTRODUCTION

environment, raising environmental concerns such as
CO2 emission, global warming, natural resources
depletion, and dust generation from their
manufacturing processes [7]. In addition to theenvironmental drawbacks, cement and lime aresusceptible to high plastic shrinkage and cracking,affecting the stabilized soil's mechanical strength [8].
To reduce these environmental concerns and enhance
the geotechnical properties of expansive soils, thepartial replacement of cement and lime withpozzolanic materials such as rice husk ash (RHA) is
gaining research attention. The potential use of RHA
is due to the abundance of rice husks generated
worldwide from rice processing [9] and particularly
the presence of highly active amorphous silica SiO2
produced during the ash production process [10].
However, the pozzolanic reactions when theamorphous silica-rich RHA is used to stabilize
expansive soils form cementitious systems that needfurther reinforcements to avoid cracking andexcessive settlement problems when subjected toextensive loads. Recently, natural fibers such as
basalt fibers are being used together with the
pozzolanic additives to arrest the formation of weakplanes in the stabilized soil and control theWith the increase in built environment due tourbanization and industrialization, the demand forinfrastructure development in marginalized areas
with weak expansive soils, which were consideredunsuitable, is rising. Expansive soils have been
analyzed to possess weak shear strength and highcompressibility and pose numerous challenges tocivil and geotechnical engineers while constructing
any engineering structures. This instability of the
expansive soil is due to the high clay contents, which
tend to shrink or expand when subjected to moisture
content variation. Building on expansive soils canlead to considerable damage to foundations,embankments, and pavements, raising theconstruction costs to construct durable and safe
structures.
Stabilizing and reinforcing the expansive soils toimprove their geotechnical properties is vital to curbthese challenges. Conventionally, the addition of
additives such as cement, lime, and lime-cement
mixtureshas
beenpracticed
extensively[1][2][3][4][5][6]. However, the over-dependence on
cement and lime has proven to degrade the288

GEOMATE – Bangkok, Thailand, 22-24 November 2022

consolidation rate while improving the strength whenbuilt upon [11].
To validate the reinforcement and stabilization
techniques using basalt fiber and RHA, understanding
the consolidation parameters such as the coefficientof consolidation, coefficient of volume change, andthe ultimate permeability coefficient is critical. The
consolidation rate is related to the coefficient ofconsolidation (cv), while the settlement amount isrelated to the coefficient of volume change (mv).
Based on experimental tests, researchers haverealized that stabilization and fiber reinforcement
techniques have increased strength, enhanced
stability, and substantially reduced the rate of
consolidation settlements. While working on fiberreinforced soil, Abdi (2008) found that increasing thefiber content and length significantly reduced theconsolidation settlements, swelling, and crack
formation [12]. Das and Pal (2012) investigated theconsolidation responses on silty clayey stabilizedwith fly ash in another study. They noted that thecoefficient of consolidation (cv) increased with anincrease in the fly ash content [13].
Regarding the coefficient of volume change (mv),
Kar and Pradhan (2011) studied the compressibility
trends of polypropylene and coir fiber reinforced
local cohesive soils and noted that the mv decreased
significantly with an increase in fiber content. In
addition, they concluded that the coefficient ofconsolidation increased with the fiber content [14].
Eberemu and Sada (2013), in their study on onedimensional consolidation of expansive soils treatedwith RHA only, found out that the coefficient ofvolume change (mv) and coefficient of consolidation(cv) generally decreased with increased loadingpressure and RHA treatment for up to 8%. Also, inthe same study, the permeability coefficientdecreased with increased RHA content for up to 8%[15].
The use of RHA as a stabilizing agent and fibers
as a reinforcing agent has attracted the attention ofresearchers who have found out that they have an
advantageous effect in developing the geotechnicalproperties of the soils under study [16] [17] [18] [19][20]. However, in literature, most of the available
research is limited to the reinforcement material used,
the soil tested and the type of stabilizing agent used.
Moreover, the testing procedures have been limited tounconfined compression, triaxial, direct shear, and
CBR tests. To date, few pieces of research have been
carried out on expansive soils with no scientificstudies on the combined action of RHA and basalt
fiber, particularly its effect on consolidation
settlement, volume change, and permeability.
In this context, the present study aims toinvestigate a correlation between the consolidation
coefficients (coefficient of consolidation, coefficientof volume change, permeability coefficient) and the
addition of different basalt fiber lengths and RHA-

cement mixtures to expansive soils as the reinforcing
and stabilizing agents respectively. A series ofodometer tests were conducted on expansive soil
reinforced with different basalt fiber lengths (3mm,6mm, and 12mm) and stabilized with RHA(5%,10%,
and 15%)-cement (3%) mixtures in their specified
combinations. A correlation between the coefficientof consolidation (cv), coefficient of volume change(mv), and permeability coefficient (k) with the lengthof the basalt fibers and RHA-cement mixtures in theultimate soil composite material was determined.
Furthermore, scanning electron microscopic imagery(SEM) was conducted to examine the structuralmodifications within the stabilized and reinforced soil
composite
uponcomplete
one-dimensional
consolidation. The results were presented graphicallywith correlation equations and pictorial forms to solve
design challenges encountered by civil, geotechnical,
and geo-environmental engineers working with the
new composite material.
MATERIALS
Soil
The soil used in this study was obtained from theHanda area, Mie prefecture, Japan. The index
properties of the soil are shown in Table 1.
Additionally, the particle size distribution is
illustrated in Fig. 1. Based on the American
Association of State Highway and Transportation
Officials (AASHTO), the soil was classified as A-7–
5(2) clayey soils.
Table 1 Properties of soil
Property
Specific gravity
Sand (75 μm - 2 mm), (%)
Silt (5 μm -75 μm), (%)
Sand (<5 μm), (%)
Liquid limit, LL, (%)
Plastic limit, PL, (%)
Plasticity index, PI, (%)
Optimum water content, (%)
Maximum dry density, (g/cm3)
AASHTO classification

Value
2.74
6.14
51.24
38.86
58.21
31.05
27.16
26.02
1.65
A-7–5(2)

Rice husk ash (RHA)
Rice husk ash is obtained from burning rice husks,
a byproduct of rice milling. The ash was prepared byburning rice husks at temperatures between
650~700°C for 27 hours. The RHA production
temperatures and time produced high levels of
amorphous silica (91.10%), a valuable element for
pozzolanic reactions. Make Integrated Technology
Co., Ltd, Osaka, Japan, provided the controlled burnt
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

RHA in this study. The properties of the RHA and theparticle size distribution are shown in Table 2 andFig. 1, respectively.

DensityTensile strength, (MPa)Elastic modulus, (GPa)Fracture elongation rate, (%)
Thermal properties
Heat resistant temperature, (°C)
Melting temperature, (°C)
Softening temperature, (°C)
Chemical properties
Oxides composition
Silicon oxide (SiO2)
Aluminum oxide (Al2O3)
Iron oxides
Calcium oxide (CaO)
Magnesium oxide (MgO)
OthersTable 2 Physical and chemical properties of RHA
Physical properties
ValuesSpecific gravity
1.47
Average Particle Size, (mm)0.001 to 0.30
Loss of Ignition
4.00-6.00
Chemical properties
Oxide compounds
Values (%)
Silica (SiO2),
91.10
Carbon dioxide (CO2)
4.35
Potassium Oxide (K2O)
2.40
Calcium Oxide (CaO)
0.57
Iron Oxide (Fe2O3)
0.05
Alumina (Al2O3)
0.03
Others1.50

100% Finner

Table 4 Properties of OPC
Physical properties
Initial setting time, (minutes)
Final setting time, (minutes)
Specific gravity, (g/cm3)
Specific surface area, (m2/kg)
28-day compressive strength, (MPa)
Loss of ignition, (%)
Chemical properties
Oxides composition
Calcium Oxide (CaO)
Silicon dioxide (SiO2)Iron Oxide (Fe2O3)
Alumina (Al2O3)
Magnesium oxide (MgO)
Others60

40

200.01000.10001.0000Particle Size, D (mm)Fig. 1 Particle size distribution for soil, RHA, and
OPC
Basalt fibers (BF)
Basalt fiber is a material made from fine fibers ofnatural basalt rock. The production of BF is a onestage process involving: the melting andhomogenization of basalt rock and extrusion throughsmall nozzles to produce continuous filaments ofbasalt fiber [21] [22]. Basalt fibers were used as theprimary reinforcing material in this study. The
superiority of BF compared to the other conventional
fibers is derived from the high tensile strength(4100~4840MPa),highelastic
modulus(93.1~111.0GPa), long-term durability, thermal
stability, and alkali resistance [23]. A detailed
physical, thermal and chemical properties of the BF
used in this study are shown in Table 3.
Table 3 Properties of basalt fibers
Physical Properties
Fiber diameter, (μm)
Fiber length, (mm)Value (%)
51.6 ～ 59.3
14.6 ～ 18.3
9.0 ～ 14.0
5.9 ～ 9.4
3.0 ～ 5.3
0.09 ～ 0.13

The properties of OPC are shown in Table 4. Also,a detailed particle size distribution is shown in Fig. 1.
Minimal dosages if OPC was used as the pozzolanic
reaction activator by providing the calcium oxide ionsand as a binder element.

A-7-5(2) (Clayey Soil)
Rice Husk Ash (RHA)
Cement (OPC)

0.0010-269～650
1450
1050

Ordinary Portland Cement (OPC)80

00.00012.63～2.84100～4840
93.1～110
3.1

170
225
3.15
340
33–53
<4
Values (%)
63.40
21.60
5.35
4.45
1.65
3.55

EXPERIMENTAL APPARATUS ANDTECHNIQUES
Oedometric cell apparatus
The experimental equipment used in this study ispresented in Figure 2. It consisted of an oedometriccell on a rigid base, a loading lever arm, and a dialgauge combined with LVDT (Linear Variable
Differential Transformer). The oedometric cell
included a water container, a pressurizing ring fittedwith a porous plate at the bottom, a guide ring, aconsolidation ring, and a bottom plate equipped witha porous media. The guide ring and the consolidation
rings were made of stainless steel with a uniform

6~30
3, 6 & 12
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

7

diameter of 60 mm and a height of 20 mm. They wereinserted into the bottom plate to ensure high rigidity,
capable of minimizing the expansions in the radial
direction during loading. The porous media on thepressurizing plate and the bottom plates allowed
proper drainage through the specimens during onedimensional consolidation.

8
9
10

100%S
+15%RHA+3%C+1%BF6
100%S+5%RHA+3%C+1%BF12
100%S+10%RHA+3%C+1%BF12
100%S+15%RHA+3%C+1%BF12

S:15R:3C:1BF6
S:5R:3C:1BF12
S:10R:3C:1BF12
S:15R:3C:1BF12

Note: S=Soil, RHA=Rice husk ash, C=Cement, BF3=Basalt fiber
3mm, BF6=Basalt fiber 6mm, BF12=Basalt fiber 12mm

Experimental procedure
The general experimental procedure is explainedin this subsection. After transferring the specimen
into the consolidation rings, the initial weight anddimensions were determined, and the oedometric cell
was assembled and filled with water. Afterward, themechanical loading-unloading cycles were performed
in steps with incremental loads of 9.8, 19.6, 39.2,
78.5, 157, 314, 628, and 1256 kN/ m2 during theloading cycle and 628, 314, 157, 78.5, 39.2, 19.6 and9.8 kN/m2 during the unloading cycle. All the loadincrements/reductions were kept constant for 24hours until the primary consolidation had seized andthe excess pore water pressure dissipated completely[25]. Meanwhile, the settlement of the specimen withtime was measured using an electronic dial gauge
connected to a data logger to maintain accuracy in thedata sets obtained. Each specimen was consolidated
for 16 days, after which it was weighed and ovendried to calculate the ultimate water content. Finally,a piece of each specimen was collected for SEM
analysis. The testing procedures followed the Japan
Industrial Standards [26,27].

Fig. 2 Components of the oedometric cell apparatus
Specimen preparation procedure
During specimen preparation, one of the primaryconsiderations was achieving the maximum effects ofthe fibers and ensuring their random installation in thesoil composite. The dry materials were mixed
manually before adding water. Each specimen was
prepared using soil (200g), RHA (5%, 10%, or 15%
dry weight of soil), cement (3% dry weight of soil),and basalt fiber (3, 6, or 12mm length) in their
specified combination schemes as shown in Table 5.
Water was added to an initial water content of 45%,
50%, and 55% for 5%RHA, 10%RHA, and 15%
RHA, respectively. The difference in the initial watercontents was due to the high-water affinity for thehigher percentages of RHA. The added water
increased the water content of the dry soil to slightly
above the liquid limit hence simulating pore waterpressures similar to clay in its natural state [11] [24].
The materials were mixed for 10 minutes using aspatula to obtain a homogeneous mix, trimmed thentransferred to the soil consolidation rings. Filter
papers with similar dimensions to the consolidation
rings were placed at the top and bottom of thespecimen to prevent the soil composite from clogging
the porous plates during the loading and drainage
processes.

RESULTS AND DISCUSSIONFirstly, the effects of basalt fiber reinforcements
and RHA-cement stabilization on the onedimensional consolidation coefficients of expansive
soils were evaluated using the time-compression
curves from the oedometer test. The curves were used
to assess the coefficient of consolidation (c v),
followed by a series of equations applied to determine
the coefficient of volume change (mv) and thepermeability coefficient (k). Subsequently, acorrelation analysis was done between the onedimensional consolidation coefficients, basalt fibers'
length, and the RHA-cement content. Additionally,
SEM analyses quantified the structural modificationsin the specimen upon consolidation, considering thesoil structure, fiber orientation, and the porosity of thespecimen combination under study, as discussed in
the next section.

Table 5 Specimen combination schemes
No
1
2
3
4
5
6

Combinations
100% Soil
100%S+5%RHA+3%C+1%BF3
100%S+10%RHA+3%C+1%BF3
100%S+15%RHA+3%C+1%BF3
100%S+5%RHA+3%C+1%BF6
100%S+10%RHA+3%C+1%BF6

CodeS
S:5R:3C:1BF3
S:10R:3C:1BF3
S:15R:3C:1BF3Time-compression curves

S:5R:3C:1BF6

According to the theory of one-dimensional
consolidation, soil undergoes a time-dependent
displacement/deformation when acted upon by an

S:10R:3C:1BF6

291

GEOMATE – Bangkok, Thailand, 22-24 November 2022

external load. These deformations can lead to thedraining of excess pore water pressure due to thegradual transition of the applied loads from the porewater to the soil particles under steady pressure. The
notable assumptions in this theory include; (1) soil
grains and water are incompressible, (2) the load isapplied in the vertical direction only, (3) the drainageof the pore water is one-dimensional in the directionof the load, (4) the coefficient of consolidation, thecoefficient of volume change and the permeability
coefficients are constant, (5) the time lag of
consolidation is dependent on the low permeability of
expansive soils [28] [29].
Based on this theory, the time-displacement plotsof all the ten specimen combinations for the loadincrements in the present study are shown in Fig. 3(ag). Notably, there was no clear distinction between
the primary and secondary consolidation phases at
low compression pressures, as shown in Fig. 3a.
However, as the consolidation pressures increased, aclear contrast between the primary and secondary
consolidation phases in the time-displacement curves
was evident, owing to the generated exponential
curve forms. From these plots, the time T 50 required
to reach 50% primary consolidation was evaluated bydefining the 0% primary consolidation stage from theearly portion of the curve and 100% primaryconsolidation stage from the point of intersection
between primary and secondary consolidation
segments using the logarithm of time method [30]. It
was noteworthy that the time T50 was highly
dependent on the RHA-cement content, as illustrated
in Fig. 3h. Increasing the RHA content from 5% to
15% decreased the time required to achieve a certain
degree of consolidation under the same effective
stress. Similar results were also recorded by Jain andPuri (2013) [31]. It was also apparent that for basalt
fiber reinforced and RHA- cement stabilized
expansive soils, the T50 reduced significantly with anincrease in consolidation pressure. This decrease
prompted a uniform secondary consolidation phase,
preventing structural failures from prolonged primaryconsolidation. Moreover, the addition of different
lengths of basalt fibers significantly reduced the T 50
values compared to the control specimen.

Time (min)

3b
0.1

1

0.1
0.0

1

10

100

100

1000

Displacement (mm)

0.5

1.0

1.5
Consolidation Pressure:39.2 kN/m2
2.0

S(Control)
S:5R:3C:1BF3
S:5R:3C:1BF6
S:5R:3C:1BF12

S:10R:3C:1BF3
S:10R:3C:1BF6
S:10R:3C:1BF12

S:15R:3C:1BF3
S:15R:3C:1BF6
S:15R:3C:1BF12

2.5Time (min)

3c
0.1

1

10

100

1000

0.5

Displacement (mm)

1.0
1.5
2.0
Consolidation Pressure:78.5 kN/m2

2.5

S(Control)
S:5R:3C:1BF3
S:5R:3C:1BF6
S:5R:3C:1BF12

3.0

S:10R:3C:1BF3
S:10R:3C:1BF6
S:10R:3C:1BF12

S:15R:3C:1BF3
S:15R:3C:1BF6
S:15R:3C:1BF12

3.5Time (min)

3d
0.1

1

10

100

1000

1.0

Displacement (mm)

1.5
2.0
2.5
3.0
3.5

Consolidation Pressure:
157 kN/m2
S(Control)
S:5R:3C:1BF3
S:5R:3C:1BF6
S:5R:3C:1BF12

4.0

S:10R:3C:1BF3
S:10R:3C:1BF6
S:10R:3C:1BF12

S:15R:3C:1BF3
S:15R:3C:1BF6
S:15R:3C:1BF12

4.5Time (min)3a10Time (min)

3e

1000

0.1
1.5

1

10

100

1000

2.5

0.5

1.0

Displacement (mm)Displacement (mm)2.0

Consolidation Pressure:19.6 kN/m2
S(Control)
S:5R:3C:1BF3
S:5R:3C:1BF6
S:5R:3C:1BF12

3.0
3.5
4.0
4.5

S:10R:3C:1BF3
S:10R:3C:1BF6
S:10R:3C:1BF12

S:15R:3C:1BF3
S:15R:3C:1BF6
S:15R:3C:1BF12

5.0

1.5

5.5

292

Consolidation Pressure:
314 kN/m2
S(Control)
S:5R:3C:1BF3
S:5R:3C:1BF6
S:5R:3C:1BF12

S:10R:3C:1BF3
S:10R:3C:1BF6
S:10R:3C:1BF12

S:15R:3C:1BF3
S:15R:3C:1BF6
S:15R:3C:1BF12

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Time (min)

3f
0.1

1

10

100

̅ = √(𝐩 ⋅ 𝐩′)𝐩(1)
Where: p is the pressure increment and p', theconsolidation pressure at the previous loading stage
in kN/m2. The data points corresponding to 14kN/m2
were omitted in Fig. 4 owing to the non-uniform
vertical stress-time responses at low consolidationpressure. The cv data points were defined by Eq. (2)in cm2/day for all the ten oedometer tests.

1000

2.5

Displacement (mm)

3.0
3.5
4.0
4.5
5.0
5.5

𝐇̅ 𝟐

S(Control)
S:5R:3C:1BF3
S:5R:3C:1BF6
S:5R:3C:1BF12

6.0

S:10R:3C:1BF3
S:10R:3C:1BF6
S:10R:3C:1BF12

1

10

100

1000

Displacement (mm)

4.0
4.5
5.0
5.5

Consolidation Pressure:1256 kN/m2

6.0

S(Control)
S:5R:3C:1BF3
S:5R:3C:1BF6
S:5R:3C:1BF12

S:10R:3C:1BF3
S:10R:3C:1BF6
S:10R:3C:1BF12

S:5R:3C:1BF

S:10R:3C:1BF

S:15R:3C:1BF3
S:15R:3C:1BF6
S:15R:3C:1BF12

6.5

3.0
3h

T50 (minutes)

2.5
2.0

Control(2)

Where; cv is the coefficient of consolidation in
cm2/day, H̅ is the average height of specimen, 0.197
is the value of the time coefficient at 50%
consolidation, and 1440 is the unit conversion factor
to cm2/day. For the ten oedometer tests, the variationof cv with p̅ for the fiber-RHA-cement specimen
showed a similar trend to that observed for the control
specimen (soil only), as shown in Fig. 4a. During thefirst loading phase, it was observed that the cv
increased as the p̅ approached the yield stress,
whereas afterward, the value remained approximately
constant in the post-yield phase. The observed
approximate constant values of cv revealed that creep
effects should be carefully considered while working
with basalt fiber reinforced and RHA stabilized
expansive soils. This trend agreed with the results
Ferrari (2016) observed while working on the onedimensional consolidation of shales [32].
Additionally, the increasing cv trend with p̅ was
attributed to the enhanced chemical and mechanicalproperties derived from RHA stabilization and basalt
fiber reinforcements. Robinson and Allam (1998) andSridharan and Nagaraj (2004) also recorded a similar
trend of increasing cv with consolidation pressure)when analyzing the role of mechanical and physiochemical factors on the cv trend with p̅. It was also
evident that the RHA content played a major role in
influencing the trend of the consolidation rate. For
example, specimen S:5R:3C:1BF12 with 5% RHA
content produced the lowest cv values compared to
specimen S:15R:3C:1BF3 (15%RHA content) that
had the highest cv values. This increase in the cv
values at 15% RHA was due to the increased
hydraulic conductivity associated with the porous
structure of RHA and the additional voids created byrandom fiber intersections. A comparable increase in
cv value with increasing hydraulic conductivity was
also recorded by Yeo (2005) [35]. It is also
noteworthy that the RHA content influenced the cv
value depending on the degree of pozzolanic activity
in the specimen. The complete utilization of RHA
during pozzolanic reactions for specimens with 5%
and 10% RHA developed the mechanical propertiesof the specimens by improving the compressibility
behavior hence the low cv values. Moreover, as
observed for all the specimens tested, the cv value
varied at the same p̅ depending on the RHA content.
Furthermore, to correlate coefficient ofconsolidation (cv) and basalt fiber reinforcements, all

S:15R:3C:1BF3
S:15R:3C:1BF6
S:15R:3C:1BF12Time (min)

0.1
3.5

𝟏𝟒𝟒𝟎
𝟓𝟎

6.5
3g

𝟐

𝐜𝐯 = 𝟎. 𝟏𝟗𝟕 ( 𝟐 ) ( 𝐓 )Consolidation Pressure:628 kN/m2

39.2 kPa78.5 kPa
157 kPa
314 kPa
628 kPa
1256 kPa
S:15R:3C:1BF

1.5
1.0
0.5
rol) :1BF3 :1BF6 BF12 :1BF3 :1BF6 BF12 :1BF3 :1BF6 BF12
ont
:1
:1
:1
S(C :5R:3C:5R:3C R:3C 0R:3C 0R:3C R:3C 5R:3C 5R:3C R:3C
SS
S:5 S:1 S:1 S:10 S:1 S:1 S:15

Fig. 3 (a ) to (g) Time-displacement curves, (h)Relationship between T50 and specimen
combinations schemes.
Coefficient of consolidation (cv)
The coefficient of consolidation (cv) measures therate at which the soil consolidation process proceeds,a basic differential equation developed by Terzaghi(1943). A presentation of the cv as a function of theaverage vertical consolidation pressure (p̅) is shownin Fig. 4a. The vertical axis (cv) was on a logarithmic
scale against the horizontal axis, plotted as thegeometric mean average consolidation pressure (p̅)(with values 14, 28, 55, 111, 222, 444, 888 kN/m2)
calculated by Eq. (1). All the analytical equations
followed the Japanese Industrial Standards 1217 and
1227.
293

GEOMATE – Bangkok, Thailand, 22-24 November 2022

the values of cv were plotted against the lengths ofbasalt fibers, as shown in Fig. 4b-d. In general, thelength of basalt fibers significantly influenced the cv
value. The longer fibers reduced the coefficient ofconsolidation for the different RHA contents, as
shown in the correlation plots. This reduction in cv
was more significant in the specimen containing
12mm basalt fibers, as shown in Fig. 4b and Fig. 4c,
representing RHA compositions of 5% and 10%,respectively. For example, considering the maximump̅ of 888kPa, with the basalt fiber length increased
from 3mm to 12mm, the cv values for the 5%RHA
specimen reduced from 321.02cm2/d to 147cm2/d. A
similar reduction in the cv with increasing basalt fiber
length was further reported for the 10%RHA
specimens with excerptions in the 15%RHA
specimens at 12mm basalt fiber length, as revealed inFig. 4d. Also, it was evident that the coefficient ofconsolidation
wasdependent
onaverageconsolidation pressure during the loading stage, with
an apparent inverse proportionality.
Further, using the cv versus length of basalt fiberplots, the curve fitting correlation equations forpredicting cv showed better correlation coefficients(R2=1.00). Hence can be used as design charts forpredicting the cv if the length of basalt fiber and theconsolidation pressure is known. It can be concluded
that the longer basalt fibers provided sufficient
reinforcements in the specimen by resisting excessive
compression during the loading phase, resulting in the
reduced c:onsolidation rate.

4c

103

2

p̅ = 28 kPa: cv = 1.89+0.05(lbf)-0.0021(lbf)2 ;R2=1.00p̅ = 55 kPa: cv = 2.32-0.002(lbf)+0.0002(lbf)2 ;R2=1.00p̅ = 111 kPa: cv = 2.56-0.05(lbf)+0.003(lbf)2 ;R2=1.00p̅ = 222 kPa: cv = 2.62-0.07(lbf)+0.003(lbf)2 ;R2=1.00

cv (cm2/d)p̅ = 444 kPa: cv = 2.68-0.06(lbf)+0.003(lbf)2 ;R2=1.00p̅ = 888 kPa: cv = 2.74-0.07(lbf)+0.004(lbf)2 ;R2=1.00

102

2

4

6

8

10

12

Basalt fiber length, lbf (mm)4d

S:15R:3C:1BF

p̅ = 28 kPa: cv = 1.74+0.13(lbf)-0.007(lbf)2 ;R2=1.00p̅ = 55 kPa: cv = 2.76-0.10(lbf)+0.006(lbf)2 ;R2=1.00p̅ = 111 kPa: cv = 2.78-0.09(lbf)+0.005(lbf)2 ;R2=1.00p̅ = 222 kPa: cv = 2.89-0.12(lbf)+0.008(lbf)2 ;R2=1.00

103

cv (cm2/d)p̅ = 444 kPa: cv = 3.04-0.07(lbf)+0.009(lbf)2 ;R2=1.00p̅ = 888 kPa: cv = 2.76-0.06(lbf)+0.004(lbf)2 ;R2=1.00

102

2

4

6

8

10

12

Basalt fiber length, lbf (mm)Fig. 4 (a) Relationship between cv and p̅, (b)Correlation between cv and lbf for 5%RHA
specimens, (c) Correlation between cv and lbf for
10%RHA specimens, (d) Correlation between cv
and lbf for 15%RHA specimens.

; cv = 1.64+0.28(p̅)-0.04(p̅) ;R =0.97
S:5R:3C:1BF ; cv = 0.65+1.32(p̅)-0.24(p̅)2 ;R2=0.97

S(Control)

S:10R:3C:1BF

2

S:10R:3C:1BF ; cv = 1.20+0.88(p̅)-0.16(p)2 ;R2=0.91
S:15R:3C:1BF ; cv = 1.16+1.07(p̅)-0.20(p̅)2 ;R2=0.98

103

Pre-Yied Phase

Coefficient of volume change (mv)cv (cm2/d

Post-Yied Phase

In the construction industry, foundation settlement
is one of the safety considerations used in thegeotechnical assessment of engineering structures.
Excessive settlement can result in structural damage
or significantly reduce the structure's durability.
Evaluating the coefficient of volume change (mv) has
been demonstrated to be a suitable parameter incalculating the consolidation settlement, providing
essential measures to counter the projected soil
settlement. Coefficient of volume change can be
defined as the change in volume of a soil mass perunit of initial volume when subjected to an effective
pressure increase. According to the Japan Industrial
Standards 1217 and 1227, mv is the ratio of thevolume change of soil to the difference in theeffective pressure and is calculated using Eq. (3).

102
S(Control)
S:5R:3C:1BF3
S:5R:3C:1BF6
S:5R:3C:1BF12

10

S:10R:3C:1BF3
S:10R:3C:1BF6
S:10R:3C:1BF12

S:15R:3C:1BF3
S:15R:3C:1BF6
S:15R:3C:1BF12

100

1000

Average consolidation pressure, p̅ (kN/m 2)
4b

S:5R:3C:1BF

103p̅ = 28 kPa: cv = 2.23-0.03(lbf)+0.0003(lbf)2 ;R2=1.00p̅ = 55 kPa: cv = 2.79-0.14(lbf)+0.0063(lbf)2 ;R2=1.00p̅ = 111 kPa: cv = 2.47-0.01(lbf)-0.0013(lbf)2 ;R2=1.00p̅ = 222 kPa: cv = 2.74-0.08(lbf)+0.0021(lbf)2 ;R2=1.00

cv (cm2/d)p̅ = 444 kPa: cv = 2.70-0.04(lbf)+0.0001(lbf)2 ;R2=1.00p̅ = 888 kPa: cv = 2.63-0.04(lbf)+0.0003(lbf)2 ;R2=1.00

𝐦𝐯 =

4

6

8

10

(3)

Where; Δε is the compression strain generated ateach loading stage [the ratio of the consolidation
amount (ΔH) to average specimen height (H̅)], and Δp
is the consolidation pressure increment. The variationof mv at different compactive efforts for specimens

1022𝚫𝛆∕𝟏𝟎𝟎𝚫𝐩12Basalt fiber length, lbf (mm)294

GEOMATE – Bangkok, Thailand, 22-24 November 2022with different basalt fiber lengths and various RHA
contents is shown in Fig. 5a. The coefficient ofvolume change could not be evaluated at low
consolidation pressures (14kPa) due to the highswelling pressures in the expansive soil compared tothe applied load. Eberemu (2016) reported a similar
response to low consolidation loads [36]. In general,
irrespective of the fiber reinforcement length, andRHA stabilization, the mv value showed a reducing
trend as the average consolidation pressure increased.
This reduction was induced by the compressive forces
rearranging and closely packing the composite
materials, increasing the bonding forces and yieldstress in the specimen, thereby reducing the mv
considerably. A relatable reduction in mv withincreasing pressure was also realized by Santagata(2008) [37].
Additionally, it was noteworthy that the mv value
was dependent on the specimen composition in the
pre-yield and post-yield phases. For instance,
considering the basalt fiber reinforcement and RHAcement content, specimen S:5R:3C:1BF3 showed thelowest mv values of 1.20*10-4 m2/kN compared to
specimen S:15R:3C:1BF3 with 1.07*10-3 m2/kN
during the pre-yield phase. This difference wasprobably due to the enhanced stiffness and resistanceto compressive forces obtained from the complete
pozzolanic reactions at 5%RHA than at 15%RHA.
Moreover, the study showed that basalt fiber
reinforcement length's effects differed for each RHA
content. Fiber-reinforced specimen with 5%RHA
portrayed a direct relationship, with the coefficient ofvolume change increasing from 1.20x10 -4 m2/kN to
1.41x10-4 m2/kN to 2.19x10-4 m2/kN for basalt fiber
lengths 3mm, 6mm and 12mm respectively. On theother hand, an inverse relationship was observed forhigher RHA-cement contents.
It was also observed that RHA and basalt fiber
reinforcements increased the mv with compactive
force across all specimens during the pre-yield phase.
This slight increase was due to the additional
displacements required to reduce the voids created by
the porous RHA and the random distribution of basalt
fibers. However, in the post-yield state, the ultimate
compact structure in the specimen provided theresistance to compressive forces leading to thesignificant reduction in mv with average
consolidation pressure. Similar responses have also
been observed by Xu (2020) [38], Eberemu (2011)[39], Eberemu (2016) [36], and Laskar and Pal (2013)[40].
Besides, a plot of mv on the log scale against basalt
fiber length (lbf) was considered to correlate therelationship between the mv and the length of basalt
fiber, as illustrated in Fig. 5b, 5c, and 5d for specimen
S:5R:3C:1BF, S:10R:3C:1BF, and S:15R:3C:1BF
respectively. It was evident that increasing the lengthof basalt fibers in specimens had minimal effects on
mv with increasing consolidation pressures from 55 to

888 kPa.
10-2; mv = -2.26+0.10(p̅)-0.18(p̅) ;R =0.99
S:5R:3C:1BF ; mv = -5.56+2.54(p̅)-0.70(p̅)2 ;R2=0.92
2

S(Control)

2

S:10R:3C:1BF ; mv = -3.86+1.13(p̅)-0.41(p̅)2 ;R2=0.94
S:15R:3C:1BF ; mv = -2.81+0.21(p̅)-0.22(p̅)2 ;R2=0.95

mv (m2/kN)

Pre-Yied Phase10

Post-Yied Phase-3

S(Control)
S:5R:3C:1BF3
S:5R:3C:1BF6
S:5R:3C:1BF12

10-4

S:10R:3C:1BF3
S:10R:3C:1BF6
S:10R:3C:1BF12

10

S:15R:3C:1BF3
S:15R:3C:1BF6
S:15R:3C:1BF12

100

1000

Average consolidation pressure, p̅ (kN/m 2)
05b 10

S:5R:3C:1BF
p̅ = 28 kPa: mv = -2.97-0.05(lbf)+0.002(lbf)2 ;R2=1.00p̅ = 55 kPa: mv = -3.02-0.04(lbf)+0.002(lbf)2 ;R2=1.00

10-1

mv (m2/kN)

p̅ = 111 kPa: mv = -3.21-0.02(lbf)+0.001(lbf)2 ;R2=1.00p̅ = 222 kPa: mv = -3.44-0.02(lbf)+0.001(lbf)2 ;R2=1.00p̅ = 444 kPa: mv = -3.70-0.01(lbf)+0.001(lbf)2 ;R2=1.00p̅ = 888 kPa: mv = -3.94-0.02(lbf)+0.001(lbf)2 ;R2=1.00

10-2

10-3

10-4
2

4

6

0
5c 10

10

12

S:10R:3C:1BF
p̅ = 28 kPa: mv = -2.42-0.19(lbf)+0.011(lbf)2 ;R2=1.00p̅ = 55 kPa: mv = -3.01-0.03(lbf)+0.002(lbf)2 ;R2=1.00

10-1

mv (m2/kN)

8

Basalt fiber length, lbf (mm)p̅ = 111 kPa: mv = -3.40+0.02(lbf)-0.001(lbf)2 ;R2=1.00p̅ = 222 kPa: mv = -3.58+0.01(lbf)-0.001(lbf)2 ;R2=1.00p̅ = 444 kPa: mv = -3.90+0.03(lbf)-0.002(lbf)2 ;R2=1.00p̅ = 888 kPa: mv = -4.07+0.01(lbf)-0.001(lbf)2 ;R2=1.00

10-2

10-3

10-4
2

4

6

8

10

12

Basalt fiber length, lbf (mm)5d

S:15R:3C:1BF

10-1p̅ = 28 kPa: mv = -2.55-0.09(lbf)+0.004(lbf)2 ;R2=1.00p̅ = 55 kPa: mv = -2.98-0.04(lbf)+0.003(lbf)2 ;R2=1.00p̅ = 111 kPa: mv = -3.43+0.03(lbf)-0.002(lbf)2 ;R2=1.00p̅ = 222 kPa: mv = -3.60+0.01(lbf)-0.001(lbf)2 ;R2=1.00

mv (m2/kN)

10-2p̅ = 444 kPa: mv = -3.91+0.04(lbf)-0.002(lbf)2 ;R2=1.00p̅ = 888 kPa: mv = -3.94-0.03(lbf)+0.002(lbf)2 ;R2=1.00

10-3

10-4

2

4

6

8

10

Basalt fiber length, lbf (mm)

295

12

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 5 (a) Relationship between mv and p̅, (b)Correlation between mv and lbf for 5%RHA
specimens, (c) Correlation between mv and lbf for
10%RHA specimens, (d) Correlation between mv
and lbf for 15%RHA specimens.

coefficient due to pozzolanic reaction has also beenrecorded by other studies, such as Onyelowe(2021)[41], Jamil (2013)[42], Gupta and Kumar(2017)[43], and Nshimiyimana (2018)[44].
However, as the average consolidation pressure
increased, the k value reduced to an almost constantvalue of order 10-10 for all the tested specimens due tothe complete removal of the voids and sufficient
rearrangement of soil-RHA-cement and fibers. This
relationship between voids and permeability
indicated that voids played the dominant role inaffecting the permeability coefficient. Cheng (2020)
also observed a similar relationship while
experimenting on the consolidation properties ofreconstituted clay soils [45].
Supplementary to the analyses discussed, acorrelation plot was considered regarding theinfluence of the basalt fiber length on thepermeability coefficient, as shown in Fig. 6b-d. In
Fig. 6b representing basalt fiber reinforced specimen
with 5%RHA and 3%cement, the permeability
coefficient reduced with increasing fiber length,
whereas in the case of 10%RHA-3C and 15%RHA3C, shown in Fig. 6c and Fig. 6d, respectively, a veryminimal reduction in the k value was recorded for all
the average consolidation pressure increments. The
notable reducing trend of k with the length of basalt
fiber for the 5%RHA-3C specimens can be attributed
to the well-defined structural development due tocementation from adequate pozzolanic reaction andthe enhanced reinforcement. However, a further
increase in the basalt fiber length to 12mm increased
the permeability due to the longer paths for water todrain quicker through the specimen. The rise inpermeability coefficient with the length of fiber
reinforcements was also observed by Abdi (2008)
while investigating the effects of fiber inclusion on
the permeability in clay soils [46]. From these
correlation plots, curve fitting equations with an
R2=1.00 were formulated to assist design engineers in
predicting the projected permeability coefficients for
RHA-basalt fiber stabilized soils for a given average
consolidation pressure and length of basalt fiber.

However, it was observed that mv values wereslightly higher for the specimen containing 15%RHA
due to the extra volume changes initiated by the highvoid ratios. Additionally, from the mv versus lengthof basalt fiber (lbf) plots, the curve fitting correlation
equations to predict mv showed better correlation
coefficients (R2=1.00) across all specimens. Hencecan be used to predict the coefficient of volume
change (mv) if the length of basalt fiber (lbf) and theaverage consolidation pressures are provided.
Permeability coefficient (k)This section highlights the effects of basalt fiber
length and RHA-cement contents on the permeability
coefficient (k) of stabilized expansive soils
undergoing
one-dimensional
consolidation.
Permeability describes the rate at which water canmove through the pores within a soil medium and has
proven to be an essential parameter influencing soil's
consolidation and strength. Following the Japan
Industrial Standards 1217 and 1227, the permeability
coefficient can be calculated using Eq. (4).
𝐜 𝐦 𝛄

𝐯 𝐯 𝐰
𝐤 = 𝟖.𝟔𝟒
𝐱 𝟏𝟎𝟖

(4)

Where k is the permeability coefficient in m/s, cv
is the coefficient of consolidation, mv is thecoefficient of volume change, γw is the unit volume
weight of water (9.81kN/m3), and 8.64x108 is theconversion parameter of k to m/s. Fig. 6a compared
the pressure dependence of permeability coefficientof the reinforced and stabilized expansive soil, taking
into consideration the following factors; length ofbasalt fibers, RHA-cement content, and the average
consolidation pressure. It was found that thepermeability coefficient varied in the order of 10-10 to
10-9 and was greatly influenced by the average
consolidation pressure. Also, the value of k increased
during the pre-yield phase and decreased significantlyin the post-yield phase for all the specimens tested.
However, the value of k in the observed trend
depended on the specimen composition.
For instance, the permeability coefficientincreased with RHA content for all the individual
average consolidation pressures, as shown in Fig. 6a.
This increase was due to the high porosity caused by
the excess RHA particles and the voids networks
created by basalt fibers in the composite specimen.
Additionally, the pozzolanic compounds due to RHA
and cement precipitation enhanced cementation at
low RHA-cement contents hence the low k values at
5%RHA. A similar reduction in the permeability

S(Control); k = -8.19-0.16(p̅)-0.14(p̅)2 ;R2=0.99
S:5R:3C:1BF ; k = -11.19+2.22(p̅)-0.60(p̅)2 ;R2=0.94
S:10R:3C:1BF ; k = -9.29+0.80(p̅)-0.31(p̅)2 ;R2=0.99
S:15R:3C:1BF ; k = -8.83+0.51(p̅)-0.25(p̅)2 ;R2=0.99

10-8

k (m/s)

Pre-Yied Phase

Post-Yied Phase

10-9

S(Control)
S:5R:3C:1BF3
S:5R:3C:1BF6
S:5R:3C:1BF12

S:10R:3C:1BF3
S:10R:3C:1BF6
S:10R:3C:1BF12

S:15R:3C:1BF3
S:15R:3C:1BF6
S:15R:3C:1BF12

10-10
10

100
Average conslidation pressure (kN/m2)

296

1000

GEOMATE – Bangkok, Thailand, 22-24 November 2022

6b 10-7

S:5R:3C:1BF

the tested specimen combinations are shown in Fig.
7-9. These SEM images represented thecorresponding specimen states at the end of theconsolidation process (Average consolidationpressure = 888kPa). In this study, a detailed analysis
to justify the trends of the coefficient of consolidation(cv) and the permeability coefficient (k) werediscussed, as explained in this section.
It can be observed from Fig. 7a, 8a, and 9a that themobilization of basalt fiber reinforcements was
driven by the interfacial contacts developed during
the physical connection between the cementitious soil
matrix and the fiber surface. The interfacial contacts
were more significant when the length of basalt fibers
increased from 3mm to 12mm due to theinterconnectivity arising from the intense fiber
network. Additionally, a clear indication of acemented compact structure was evident due to thecomplete utilization of 5%RHA and cement during
the pozzolanic reactions. The two states describedabove (i.e., fiber-soil interfacial contacts and thecementitious structure) reduced the coefficient ofconsolidation (cv) and the permeability (k)significantly by resisting compressibility andeliminating excess voids, respectively, as shown in
Fig. 4a and 6a. The tendency of fiber reinforcements
to improve the bonding strength of expansive soil was
also observed by Syed (2020) when stabilizing
expansive clays using geo-polymerization and fiber
reinforcement [47].
Contrarily, the rising trends of cv and k for thefiber-reinforced specimen containing 10%RHAand
3%C were justified by the occurrence of planes of
weakness and voids in the specimens as displayed inFig. 7b, 8b, and 9b. However, the discrete
distributions of high tensile strength basalt fibers inthe specimen structure interlocked the separate
cemented blocks, restricting further cracks duringconsolidation. The interlocking mechanism was moresignificant with increasing basalt fiber length.
It was also noteworthy that the developments of voidsin the specimens were more significant in specimens
containing 15%RHA and basalt fiber reinforcements,
as illustrated in Fig. 7c, 8c, and 9c. The more notably
porous structure developed due to the reduced
pozzolanic activity that led to the balling of RHA andsoil particles around the basalt fibers. This balling
effect developed a highly compressible and porous
specimen structure, increasing the cv and k values.

p̅ = 28 kPa: k = -8.20-0.29(lbf)+0.02(lbf)2 ;R2=1.00p̅ = 55 kPa: k = -7.84-0.32(lbf)+0.02(lbf)2 ;R2=1.00p̅ = 111 kPa: k = -8.19-0.24(lbf)+0.02(lbf)2 ;R2=1.00p̅ = 222 kPa: k = -8.08-0.34(lbf)+0.02(lbf)2 ;R2=1.00p̅ = 444 kPa: k = -8.31-0.33(lbf)+0.02(lbf)2 ;R2=1.00p̅ = 888 kPa: k = -8.74-0.33(lbf)+0.02(lbf)2 ;R2=1.00

k (m/s)

10-8

10-9

10-10
2

4

6

8

10

12

Basalt fiber length, lbf (mm)6c 10-7

S:10R:3C:1BF

p̅ = 28 kPa: k = -8.17-0.22(lbf)+0.014(lbf)2 ;R2=1.00p̅ = 55 kPa: k = -8.64-0.03(lbf)+0.002(lbf)2 ;R2=1.00p̅ = 111 kPa: k = -8.79-0.03(lbf)+0.001(lbf)2 ;R2=1.00p̅ = 222 kPa: k = -8.90-0.05(lbf)+0.003(lbf)2 ;R2=1.00p̅ = 444 kPa: k = -9.16-0.03(lbf)+0.001(lbf)2 ;R2=1.00p̅ = 888 kPa: k = -9.28-0.06(lbf)+0.003(lbf)2 ;R2=1.00

k (m/s)

10-8

10-9

10-10
2

4

6

8

10

12

Basalt fiber length, lbf (mm)
-7

6d 10

S:15R:3C:1BF

p̅ = 28 kPa: k = -8.80+0.06(lbf)+0.005(lbf)2 ;R2=1.00p̅ = 55 kPa: k = -8.20-0.12(lbf)+0.007(lbf)2 ;R2=1.00p̅ = 111 kPa: k = -8.63-0.04(lbf)+0.002(lbf)2 ;R2=1.00p̅ = 222 kPa: k = -8.70-0.09(lbf)+0.005(lbf)2 ;R2=1.00p̅ = 444 kPa: k = -8.85-0.10(lbf)+0.006(lbf)2 ;R2=1.00p̅ = 888 kPa: k = -9.14-0.09(lbf)+0.005(lbf)2 ;R2=1.00

k (m/s)

10-8

10-9

10-10
2

4

6

8

10

12

Basalt fiber length, lbf (mm)Fig. 6 (a) Relationship between k and p̅, (b)Correlation between k and lbf for 5%RHA
specimens, (c) Correlation between k and lbf for
10%RHA specimens, (d) Correlation between k andlbf for 15%RHA specimens.
Microstructural analyses

CONCLUSIONSThe effect of basalt fibers reinforcements andstabilization using RHA-cement mixtures on the
consolidation coefficients of reconstituted expansive
soils can be explained by capturing the
microstructural development through a scanningelectron microscope (SEM). SEM micrographs
showing the coupling effect of the basalt fibers withthe compact cementitious composite soil matrix forThis paper presented an experimental analysis
based on a series of oedometer tests to study thecorrelation
betweenthe
one-dimensional
consolidation coefficients and the length of basalt
fiber, RHA-cement content in fiber-reinforced RHAtreated expansive soils. An effort has been made to
explain more rationally the influence basalt fiber
297

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 7: SEM imagery (a) Specimen S:5R:3C:1BF3, (b) Specimen S:10R:3C:1BF3, (c) Specimen S:10R:3C:1BF3

Fig. 8: (a) SEM imagery (a) Specimen S:5R:3C:1BF6, (b) Specimen S:10R:3C:1BF6, (c) Specimen
S:10R:3C:1BF6

Fig. 9: (a) SEM imagery (a) Specimen S:5R:3C:1BF12, (b) Specimen S:10R:3C:1BF12, (c) Specimen
S:10R:3C:1BF12
length and RHA-cement content have on thecoefficient of consolidation (cv), coefficient ofvolume change (mv), and the permeability coefficient(k), drawing further justifications through SEM
imagery. The following conclusions can be made:1. Increasing the length of basalt fibers for each RHA
content reduced the coefficient of consolidation(cv). This reduction was more significant for fiberreinforced specimens constituting 5%RHA and
10%RHA, with reduction percentages of 54.21%
and 30% at the end of consolidation. This response
indicated that the degree of consolidation depended
on the specimen composition and the degree ofreinforcement.
2. The coefficient of volume change (mv) was greatly
influenced by the average consolidation pressure
rather than the specimen composition (fiber length
and RHA content). However, at low consolidationpressures during the pre-yield stage, fiber-RHA
reinforced specimens depicted lower volume
changes than the control specimen. Also, a slightincrease of mv with average consolidation pressure
was evident compared to the control specimen.

3. The permeability coefficient (k) of the fiberreinforced specimen with 5%RHA-3%C reducedwith increasing fiber length, whereas, in the case of
10%RHA-3C and 15%RHA-3C, very minimal
reduction in the k value was recorded withincreased fiber length.
4. The curve fitting methods shown in Figures 4a, 5a,and 6a, were used to propose analytical equations
for evaluating cv, mv, and k for all known basalt
fiber lengths, as indicated in equations 5,6 and 7.
cv = 0.31+1.64(lbf)-0.30(lbf)2 ;R2=0.96
mv = -3.85+1.13(lbf)-0.41(lbf)2 ;R2=0.93
k = -11.41+2.57(lbf)-0.66(lbf)2 ;R2=0.96
5. The SEM imagery showed that the random
distribution of basalt fibers in the stabilizedcomposite enhanced the reinforcing network within
the specimens. Increasing the basalt fiber lengthfrom 3mm to 12mm provided a more robust
reinforcing phenomenon in the stabilizedcomposite, thereby controlling the interfacial
strength. This strength enhancement contributed
significantly to the trend of the consolidation
coefficients.
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

6. The relationships discussed demonstrated thatreinforcing expansive soils with 12mm basalt
fibers, 5%RHA and 3%cement, can be considered
the optimal value for use in engineering practice to
avoid the over-settlement of engineering structures.
7. The experimental and analytical methodologies
presented in this paper are thus believed to be ofgreat significance for quantifying and interpreting
one-dimensional consolidation coefficients andtheir correlation to fiber reinforcement and RHA
stabilization techniques. Also, the correlation
equation provided in this study can be used as
design charts regarding the consolidation andsettlement trends of basalt fiber-RHA-cement
stabilized expansive soils.

[7] Ghadir, P. and Ranjbar, N., Clayey soil
stabilization using geopolymer and Portland
cement. Construction and Building Materials,
188,
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pp.361-371.
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07[8] Biswal, D.R., Sahoo, U.C. and Dash, S.R.,
Durability and shrinkage studies of cement
stabilized granular lateritic soils. International
Journal of Pavement Engineering, 20(12), 2019,
pp.1451-1462.
https://doi.org/10.1080/10298436.2018.1433830[9] Sandhu, R.K. and Siddique, R., Influence of ricehusk ash (RHA) on the properties of selfcompacting concrete: A review. Construction
and Building Materials, 153, 2017, pp.751-764.
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65[10] Chen, R., Congress, S.S.C., Cai, G., Duan, W.
and Liu, S., Sustainable utilization of biomass
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686[12] Abdi, M.R., Parsapazhouh, A. and Arjmand, M.,"Effects of Random Fiber Inclusion on
Consolidation, Hydraulic Conductivity, Swelling,
Shrinkage Limit and Desiccation Cracking ofClays, 2008, pp.284-292.
[13] Das, S. and Pal, S.K., Consolidation
Characteristics of Silty-Clay Soil Mixed withClass F Indian Fly Ash. In Indian GeotechnicalConference, December 2012, pp. 13-45.
[14] Kar, R. and Pradhan, P., Strength andcompressibility characteristics of randomly
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treated with rice husk ash. Nigerian Journal ofTechnology, 32(3), 2013, pp.507-521.
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International Journal of Engineering Science andTechnology, 3(11), 2011, pp.8076-8085.

ACKNOWLEDGMENTS
The authors would like to acknowledge the Japan
Ministry of Education, Culture, Sports, Science andTechnology for providing the MEXT scholarship
through Mie University, where this study was
conducted. This research was also supported by Make
Integrated Technology Limited, which provided
experimental materials.
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301

Construction Materials

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12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

PARAMETRIC MODELING OF RECYCLED BRICK AGGREGATE
CONCRETE (RBAC) USING ARTIFICIAL NEURAL NETWORK (ANN)AND MULTIPLE LINEAR REGRESSION (MLR)
Albert A. Griño Jr1
1

Faculty, Adamson University, Philippines
ABSTRACTThis study aims to assess and compare the performance of Artificial Neural Network (ANN) and Multiple
Linear Regression (MLR) model in predicting the compressive strength of concrete with recycled clay and bricktile. A proposed prediction model of artificial neural network (ANN) was generated using Matlab software for thecompressive strength of Recycled Brick Aggregate Concrete (RBAC). The proposed model is tested, trained, andvalidated using the available test data of 147 concretes with various mixture proportions collected from different
technical journals. The collected data is composed of eight input variables (parameters) and one output variable.
The input parameters are the following: cement, water cement ratio, crushed tile ratio (fine), crushed tile ratio(coarse), crushed brick ratio (fine), crushed brick ratio (coarse), natural aggregate (fine), and natural aggregate(coarse) while the output parameter is the compressive strength. The result of the back-propagation algorithm in
ANN model showed that Bayesian Regularization, with nine (9) neurons, is the best mathematical model with aRegression (R) and Mean Squared Error (MSE) values of 0.86499 and 0.007996756, respectively. Whereas, thecorrelation coefficient for the MLR model was 0.6508. The results clearly showed that the prediction using neuralnetwork model is more accurate than using multiple-linear regression model. Based on the result of the parametricstudy, the recommended amount for each material are in the following ranges: cement = 350 to 400 kg, water
cement ratio = 0.3 to 0.4, recycled = 20 to 40 % and natural aggregates = 60 to 80 %.
Keywords: parametric modeling, recycled brick aggregate, artificial neural network, multiple linear regression,
compressive strength

INTRODUCTION

hazardous/non-toxic waste.
Concrete is considered as one of the commonlyused materials in construction. It is composed of
cement, water, and natural (fine and coarse)
aggregates. Recycled aggregates can also be used topartially or completely replace the natural aggregates.
In order to determine the optimum amount ofrecycled material there is a need to apply a numericalmodelling such as artificial neural network that cansimulate and generate a formula to predict thecompressive strength of concrete with natural andrecycled aggregates. Artificial neural network (ANN)
had been widely used as a tool to create a numericalmodel for the experimental data. However, there are
few literatures that focused on modelling thecompressive strength of concrete with recycled
aggregates. In that case, this study was establishedand focused on four (4) methods in the prediction of
the compressive strength of Recycled Brick
Aggregate Concrete (RBAC). The three methods in
ANN are the following: (1) Levenberg-Marquardt,(2) Bayesian Regularization, (3) Scaled Conjugate
Gradient and the fourth method is using Multiple
Linear Regression (MLR) using Microsoft Excel.
This paper is an extension of the previous study of
Sipos (2017) to further explore the applicability ofusing artificial neural network (ANN) and Multiple
Linear Regression (MLR) with parametric study. ThisThe construction industry plays a significant roleto the growth and development of nation. Generally,
construction industry uses three types of aggregatesin construction projects (Ameri and Behnood, 2012;
Behnood and Ameri, 2012): (a) virgin (natural), (b)artificial, and (c) recycled.
In recent years, a lot of attention has been given to
the utilization of recycled aggregates to concrete due
to depleting natural resources and in order to reduce
environmental wastes. There are a lot of wastematerials that can be generated from the construction
activities which may include the following materials:
concrete, tiles, metal, paper, wood, glass and mixed
wastes such as trash and organic wastes. As such,these waste materials are considered as construction
and demolition waste. Construction and demolition
wastes (C&DW) are usually recognized as notdangerous, but their accumulation can generate
serious environmental problems (Simion et al., 2013).
It is interesting to note that around 90% of theconstruction waste can be reused and recycled. The
construction waste is under the definition of a solid
waste. Solid waste can be defined as all discarded
household, commercial waste, non-hazardous
institutional and industrial waste, street sweepings,
construction debris, agricultural waste, and other non303

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study is significant since it can be used to determine
the optimum amount of recycled tile/brick as
partial/full replacement to natural aggregates in
concrete mixture. An ANN model with one hidden
layer is developed by using a MATLAB software to
generate an appropriate model for predicting thecompressive strength of Recycled Brick Aggregate
Concrete (RBAC).

As seen in Figure 1 the theoretical framework of
the study. All the data came from the author, Sipos(2017) were normalized using Equation 1 in order tostandardized the input and output values ranging fromzero to one. The normalized data were subjected to
ANN and MLR.
(1)METHODOLOGY
Under the process of ANN, three algorithm
methods were used such as Levenberg Marquardt,
Bayesian Regularization and Scaled Conjugate
Gradient using Matlab 2019a. For each method, all
the data were trained, validated and tested. On theother hand, Multiple Linear Regression was also usedin order to determine whether there is a linear
relationship between the independent and dependent
variables using Microsoft Excel. All the generated
models from ANN and MLR were evaluated bycomparing their regression values. The higher thevalue of regression (R) means that the model is abetter representation among the generated models.
The best model was subjected to parametric study inorder to determine the effect of each independent
variable to dependent variable by varying the amountof one independent variable while the other variables
were held constant. Lastly, from the result of theparametric study a conclusion can be made bydetermining the optimum range of recycled brick/tilethat can be added to the concrete matrix to attain asuitable compressive strength.

In this study, four different models have beendeveloped using both ANN and MLR techniques. A
total of 147 data sets were collected from 10
international published literatures to examine
whether the ANN and MLR is better in predicting thecompressive strength of Recycled Brick Aggregate
Concrete (RBAC). The ANN training algorithms
used are the following: (1) Levenberg-Marquardt (2)Bayesian Regularization and (3) Scaled Conjugate
Gradient. A feed-forward back-propagation neuralnetwork model is developed to predict thecompressive strength in 147 sets of experimental data.
From these, 89 randomly selected data were used fortraining the network and the remaining 58 samples
were equally divided for the ANN validation andtesting processes (29 of them were selected as thetesting data sets and the other 29 were used forvalidation of network).
A number of iterations with one hidden layer weretried in the ANN structure. The Regression (R) values
were compared against various neurons (1,3,5,7,9 and
11) in the hidden layer in order to determine the most
optimal topology (maximum R value) for thecompressive strength.
The eight (8) input variables to be used in thisstudy are the following: (a) cement (b)water/cement(c)Crushed Tile Ratio (CT) 0-4 (%) (d) Crushed Tile
Ratio (CT) 4-16 (%) (e) Crushed Brick Ratio (CT) 04 (%) (f) Crushed Brick Ratio (CT) 4-16 (%) (g)Natural Aggregate 0-4 (%) and (h) Natural Aggregate
4-16 (%). The output parameter is the compressive
strength, Fc28(MPa).

Fig. 2

Flowchart for optimizing the neuralnetwork model
It can be seen in Figure 2 the optimization for thethree methods under the ANN. A number of hidden
neurons were selected ranging from 1 to 11 with anincrement of 2. For each iteration process, all the data
were trained, validated and tested. The regression
results of each algorithm were recorded andcompared. The highest value of regression can betreated as the best model to predict the compressive
strength of RBAC.
Fig. 1

Theoretical Framework
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be the most optimal topology for compressive
strength of RBAC.

RESULTS AND DISCUSSION
Table 1 Characteristics of the data set

Table 2 Multiple Linear Regression (MLR)
Result

It can be seen in Table 1 the statistical data of
input and output variables. Statistical data includes
the following parameters: maximum, minimum,
mean, median and standard deviation. It can beobserved that the water cement ratio has a range of
0.4 to 1.08 and relative to the other variables thesevalues are very small. Thus, there is a need tonormalize the data so that all the values in thevariables are within the range of zero to one.

Using Microsoft Excel, the Multiple Linear
Regression (MLR) was carried out and the result canbe seen in Table 2 in which the regression value is0.650811271.
Table 3 Regression Value of ANN and MLR
models

The comparison of the regression value from fourmodels developed using both Artificial Neural
Network and Multiple Regression Analysis in
Microsoft Excel can be seen in Table 3. It can beobserved that the goodness-of-fit of the ANN models
is superior when compared to the regression model.
The outcome is analogous to the result of Bingol et al.
in 2013 which states the superiority of the ANN in
predicting the compressive strength of concrete.
The higher values of R implies that the obtained
data corresponds well to the experimental data.
Comparing all the models, it can be concluded thatthe BR_9 model (Bayesian Regularization with 9neurons) is the best model among the generated
models from ANN in Matlab and MLR using
Microsoft Excel.

Fig. 4 Variation of Regression with respect to thenumber of neurons

Table 4 Coefficients of all the input variables using
Multiple Linear Regression (MLR)
Fig. 5 Variation of Mean Squared Error(MSE) withrespect to the number of neurons
It can be seen in Figure 4 and 5 the comparison of theregression and mean squared error for the threealgorithms in ANN. It can be observed that BR_9
model (with 9 neurons) gained the highest regression
value (0.86499) and minimum MSE value of0.007996756. For Bayesian Regularization algorithm,
the ANN with 9 neurons in hidden layer appeared to305

GEOMATE – Bangkok, Thailand, 22-24 November 2022

2009; Dhir et al. 2004; Schulze 1999). An increase in
the cement content results to an increase of strength
in concrete for a given workability in a concrete
matrix. Based on Figure 6, it can be seen that as theamount of cement increases, the compressive strength
also increases up to 500 kg of cement. This shows thatat 500 kg cement content, it is more likely that the
mixture already reached its optimum workability,
hence any cement added to the mixture would
substantially decrease the concrete strength. A
maximum compressive strength increase of 11.25%
can be obtained within the range of 350 to 400 kg.
This range reflect a suitable range to be used in orderto obtain an optimum strength in concrete mixture.

From Table 4, it can be seen that the largest factor forthe compressive strength of concrete is the cement
followed by the natural aggregate and the crushed tile
ratio 0-4. On the other hand, water/cement ratio andcrushed tile ratio 4-16 have a negative effect to thestrength of concrete while the crushed brick ratio does
not have any significant effect to the strength.
The equation that can be generated using MLR in
Excel is(2)

Water Cement Ratio
Parametric Study for the Components of ConcreteThis is defined as the mass of water divided by themass of cement in the concrete mix and this is usuallyabbreviated as w/c. A simulation of water cement
influence was carried out with a cement content of
400 kg, and the content of water cement ratio is varied
from 0.4 to 1.2 with constant value of crushed brickand natural aggregate content.

A parametric study was done in order to determine
the effect of each independent variable to thecompressive strength of concrete by varying theamount of one independent variable and setting theother independent variables to be a constant value.
The constant value selected is the median value foreach variable involved in the parametric study. The
model adapted in the parametric study is the BR_9
model (Bayesian Regularization with 9 neurons)since this is the best model among the generated
models from ANN and MLR.
CementThis component is generally used as a medium tobind all of the materials in concrete mixture throughthe process of hydration. Generally, increasing thecement content would increase the unit weight andworkability of the concrete mixture. A simulation of
cement type influence was carried out with a w/c ratioof 0.5, and the content of cement is varied from 250
to 550 kg with constant value of crushed brick andnatural aggregate content.

Fig. 7 Compressive strength of RBAC with varying
amount of water cement ratio
Strength is primarily a function of w/c as long as
there is sufficient paste to fill the voids between theaggregate particles and the mixture is adequately
consolidated (Mindess et al. 2003; Kosmatka et al.
2002; Mehta and Monteiro, 1993). One theory thatwould relate water cement ratio to the strength of
concrete is the Abrams' law. This law states that thestrength of a concrete mix is inversely related to themass ratio of water to cement (Punmia, 2003). As thewater content increases in the concrete mix, thecompressive strength of concrete decreases. It can beseen in Figure 7 that the compressive strength
decreases as water cement ratio increases. The
maximum decrease of strength can be observed from
1.0 to 1.2 with a 5% decrease. Thus, the maximum
value of water cement ratio should be 0.4 only inorder to obtain a desirable concrete mix. This value isalso the recommended by Adnan (2007) in which theFig. 6 Compressive strength of RBAC with varying
amount of cement
The strength of concrete is independent to thecement content for a given w/c: increasing cement
content does not affect strength (Wassermann et al.
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

natural aggregate type influence was carried out witha concrete mixture of 400 kg cement and w/c ratio of
0.5, and the content of natural aggregate is varied
from 0 to 50% with decreasing crushed brick(fine &coarse) and natural aggregate content.

authors states that Recycled Aggregate Concrete(RAC) with water-cement ratio 0.4 had the highest
strength at the age of 28 days.
It can be noted that as the water-cement ratio
increases, the porosity of the cement paste would also
increase. Hence, the compressive strength will
decrease because the porosity will increase as thevoid in the concrete matrix will be filled by the wateror air.
Recycled Fine/Coarse Aggregates
Recycled concrete, or crushed waste concrete, issimply an old concrete that has been crushed to
produce new aggregate. Concrete made with recycledcoarse aggregates and conventional fine aggregate
can obtain an adequate compressive strength(Kosmatka, 2011). A simulation of tile aggregate typeinfluence was carried out with a concrete mixture of
400 kg cement and w/c ratio of 0.5, and the content ofcrushed tile aggregate is varied from 0 to 40% withdecreasing crushed brick and natural aggregate
content. Similarly, a simulation of brick aggregate
type influence was carried out with a concrete
mixture of 400 kg cement and w/c ratio of 0.5, andthe content of crushed brick aggregate is varied from0 to 44.4% with decreasing crushed brick and natural
aggregate content.

Fig. 9 Compressive Strength of RBAC with varying
percentage of Natural Aggregate
The larger the aggregate percentage in concrete
mix makes it to contribute a lot to its strength (Waziri
et. al., 2011). Figure 9 shows the variation of thecompressive strength with the natural aggregates. It
can be noted that this is similar to Figure 8a for clay
tile in which compressive strength increase as thepercent of aggregates increases up to 50%. Also, thecompressive strength of concrete with coarse
aggregate is consistently higher than the concretewith fine aggregate for percentage up to 37.5% sinceaggregate size has a significant impact to the strengthof concrete. The larger the size of the aggregates, thelesser will be the presence of voids in the concrete
matrix which would increase the value of thecompressive strength. This affirms the study made byVilane(2016) that states the mean concrete
compressive strength increased with increasing
aggregates size.
Based on the result of the parametric study, theoptimum amount for each material are as follows:
cement = 400 kg, water cement ratio = 0.5, Recycled
Tile (Fine) = 0%, Recycled Tile (Coarse) = 40%,
Recycled Brick (Fine)=10%, Recycled Brick(Coarse) = 10%, NA (Fine) = 20%, NA (Coarse) =20%. This is similar to the study made by Aliabdo etal. in 2014 in which they state that in order to avoid
strength reduction, it is strongly recommended tolimit the percentage of coarse aggregate replacement
by recycled aggregate to 25% and 50% for concrete
containing 350 and 250 kg/m3, respectively, of
cement content.

Fig. 8 Compressive Strength of RBAC with varying
percentage of Recycled Aggregate
It can be observed in Figure 8 that thecompressive strength increases as the percentage ofrecycled clay tile increases for both fine and coarse
aggregates up to 40%. However, for brick tile there isa substantial decrease of strength after 24.2% percent
and a constant value of compressive strength can benoticed in the same figure for the recycled brick tile
within the range of 13.8 to 24.2 percent for both fineand coarse aggregates. The recommended percentage
replacement of the recycled clay and brick tile iswithin the range of 15 to 25 percent. This is similar tothe study made by Tsoumani (2014) which states thatthe optimal percentage is 25%.

CONCLUSION
The compressive strength of RBAC can bepredicted using ANN and MLR models with aregression value ranging from 0.53 to 0.86, and MSE
values ranging between 0.008 to 0.02. The result of
this study proved that Artificial Neural Network(ANN) models gave a more accurate prediction forthe compressive strength of RBAC because of the

Natural (Fine/Coarse) AggregatesAround 50 to 60 percent in the concrete mixture
made up of natural aggregates. The natural aggregate
can be classified as fine or coarse. A simulation of307

GEOMATE – Bangkok, Thailand, 22-24 November 2022

higher correlation value relative to the MLR in Excel.
The ANN and MLR models developed in this study
indicates an accurate prediction of compressive
strength of RBAC. The best result for mathematicalmodel belonged to BR_9 model (BayesianRegularization with nine (9) neurons) with aRegression(R) and Mean Squared Error (MSE)
values of 0.86499 and 0.007996756, respectively.
The best ANN for data training is BR_7 model(Bayesian Regularization and 7 neurons). With thisoptimized network, Regression and Mean Squared
Error (MSE) were 0.93918 and 0.0090454755,
respectively.

[5] Bingöl, A. F., Tortum, A., & Gül, R. (2013).
Neural networks analysis of compressive
strength of lightweight concrete after hightemperatures. Materials & Design (1980-2015),
52, 258–264. doi:10.1016/j.matdes.2013.05.022.
[6] Wassermann, R., Katz, A., and Bentur, A. (2009).
Minimum cement content requirements: a mustor a myth?. Materials and Structures, No. 42,
973–982.
[7] Dhir, R. K., McCarthy, M. J., Zhou, S., and Tittle,
P. A. J. (2004). Role of cement content inspecifications for concrete durability: cement
type influences. Structures and Buildings, No.
157, Institution of Civil Engineers, U.K., 113–
127.
[8] Schulze, J. (1999). Influence of water-cement
ratio and cement content on the properties ofpolymer-modified mortars. Cement andConcrete Research, Vol. 29, 909–915.
[9] Mindess, S., Young, J. F., and Darwin, D. (2003).
Concrete. 2nd Ed., Prentice-Hall Inc.,
Englewood Cliffs, New Jersey.
[10] Kosmatka, S., Kerkhoff, B., and Panarese, W.C.
(2002). Design and control of concrete mixtures,
14th Ed., Portland Cement Association, Skokie,
IL, USA.
[11] Mehta, K. P., and Monteiro, P. J. M. (1993).
Concrete structure, properties, and materials. 2ndEd., Prentice Hall, New Jersey.
[12] Punmia, Dr B. C.; Jain, Ashok Kumar; Jain, Arun
Kr (2003-05-01). Basic Civil Engineering.
Firewall Media. ISBN 9788170084037.
[13] Adnan, Suraya Hani & Lee, Yee Loon & Abdul
Rahman, Ismail & Mohd Saman, Hamidah &Wimala, Mia. (2007). Compressive strength ofrecycled aggregate concrete with various
percentage of recycled aggregate.
[14] Kosmatka, Steven H., , Kerkhof, Beatrix. (2011).
Design and control of concrete mixtures.
Portland Cement Association(PCA) USA.
[15] Tsoumani, A. A., Matikas, T. E., Barkoula, N.
M., (2014). Influence of recycled aggregates oncompressive strength of concrete, Proceedings ofthe 2nd International Conference on Sustainable
Solid Waste Management, Athens.
[16] Waziri B. S.; Bukar, A. G and Gaji, Y. Z. (2011).
Applicability of quarry sand as a fine aggregate.
Continental J. Engineering Science.
[17] Vilane, Bruce Roy. (2016). The Effect ofAggregate Size on the Compressive Strength ofConcrete. Journal of Agricultural Science andEngineering. Vol. 2 (6). 66 - 69.

The effect (weight factor) of each concrete
components can be obtained from the generated ANN
matrix function. From the MLR result, cement, andnatural aggregates are the most significant contributor
to the compressive strength of the RBAC. Meanwhile,
the result of the parametric study revealed that inorder to optimize the strength of concrete mixture
with recycled brick/clay, it is recommended that theamount for each material are in the following ranges:
cement=350 to 400 kg, water cement ratio=0.3 to
0.4, %recycled= 20 to 40 and %natural aggregates=
60 to 80 be adopted.
REFERENCES[1] Ameri, M., Behnood, A., 2012. Laboratory
studies to investigate the properties of CIR mixes
containing steel slag as a substitute for virgin
aggregates. Constr. Build. Mater. 26, 475e480.
https://doi.org/10.1016/j.conbuildmat.2011.06.0
47.
[2] Behnood, A., Ameri, M., 2012. Experimental
investigation of stone matrix asphalt mixturescontaining steel slag. Sci. Iran. 19 (5),1214e1219.
https://doi.org/
10.1016/j.scient.2012.07.007.
[3] Simion, Isabela Maria; Fortuna, Maria Emiliana;
Bonoli, Alessandra; Gavrilescu, Maria (2013).
Comparing environmental impacts of naturalinert and recycled construction and demolition
waste processing using LCA. Journal ofEnvironmental Engineering and LandscapeManagement,
21(4),
273–287.
doi:10.3846/16486897.2013.852558[4] Kalman Šipoš, Tanja; Miličević, Ivana; Siddique,Rafat (2017). Model for mix design of brickaggregate concrete based on neural networkmodelling. Construction and Building Materials,
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757–769.
doi:10.1016/j.conbuildmat.2017.05.111
308

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

RAW MATERIAL OPTIMIZATION WITH NEURAL NETWORKMETHOD IN CONCRETE PRODUCTIONON PRECAST INDUSTRY
1,2

Ranti Hidayawanti1, Yusuf Latief2
Faculty of Engineering, University of Indonesia, Indonesia
ABSTRACTThe development of construction is presently experiencing rapid growth in Indonesia, leading to therequirement of the right materials for infrastructural enhancements, such as roads, bridges, high-rise residentialbuildings, and housing. From the existing infrastructure, concrete innovations such as precasts are needed withgood quality materials, for the quick completion of constructions. This is because the need for good quality andsmooth material helps to determine the success of a building project, with the use of technology through precast
being a problem-solving process. Therefore, this study aims to analyze the patterns by which inventory
procurement predictions produce precasts with good quality, using the e readiness framework concept of the neuralnetwork through appropriate decision-making processes. It also focuses on innovating technological products usedin the Indonesian precast industry. The Neural Network was used to produce the best target quality time and precast
commodities. The result indicated two outputs from 2 neural network models, using five similar input-value
variables. Based on the Adaline neural network, the outputs were observed as the highest sales-cost predictions
for precast products, which often occurred in 1, 5, 6 and 9 months. Besides this, production activities were also
normally operated at level (1), with profit optimization being highly considered before months 1, 5, 6 and 9. For
the LVQ neural network, the result was a predictive classification of class intensity levels, where fast decisionmaking processes occurred in months 1, 6 and 9. Cost optimization was also carried out by ordering raw materials
several months in advance, considering the trend in material prices and logistics.
Keywords: Raw Material, Neural Network, Concrete, Precast.
INTRODUCTION(4) damage to raw materials, and (5) large sizes andheavy precast components and coordination in thebad project site. Besides these conditions, the key
issues also contributed to negative consequences on
the efficiency, productivity and effectiveness of
precast delivery [2]. After procurement, the damages
to raw materials are often found to affect the qualityof the process and precast production during theinventory phase (initial stage). This explains that the
procurement division needs to be able to provide thecertainty of scheduling receipts for efficient projectcompletion when ordering raw materials. Ordering of
raw materials is carried out by considering the bestprice pattern for each item including cement, graveland sand from several different vendors. Time
Prediction of determining the peak sales pattern ofprecast products is carried out to find margins basedon the difference in price patterns which will affectmanagement's decision to choose the right vendor
partner at the right time. Irrespective of these
conditions, practical raw material orders andassembly time have still not been highly considered,leading to the probable effects and implications ofexcess inventory occurrences and additional projectfinancing increment, respectively. Therefore, amethodology should be determined for the effective,
efficient, and economical control of precast plants’inventory management [3].
The utilization of technology has reportedly been
implemented widely, to support the management of

The Indonesian government has been undergoingmassive infrastructural development since 2019, with
an effect observed in the significant increase in
precast products' demand in 2022. Based on these
data, precast production was carried out by 76
registered factories, which were distributed
throughout the country. Each factory had an increasein production, which varied between 210,000500,000 tons yearly, to serve the increasing demand.
This indicated that the average monthly production ofeach organization needs to reach 45,000 tons.
In Indonesia, efficiency is often measured from acost and time perspective, showing that the use of
precast concrete is more efficient than conventional
methods [1]. Although this utilization is more
efficient, technology-based precast supply chain
parameters still need to become effective support.
This supply chain is classified into various phases,
namely planning, designing, manufacturing,transportation, installation, and construction. To
achieve an integrated construction, the parties in thesephases need to have efficient communication andeffective collaboration in providing accurate and upto-date information. According to the governmental
data, the main problems in the precast supply chain
phases began from the following, (1) poor planning,(2) ineffective communication between designers andmanufacturers, (3) incompetent employees/workers,
309

GEOMATE – Bangkok, Thailand, 22-24 November 2022

raw materials during the inventory processes. Thiswas in line with the raw material control for precast
tunnelling projects in China [4], where manybusinesses were leveraging historical sales anddemand data to implement intelligent inventory
management systems. Demand forecasting involves
predicting/ensuring the consumption/collection of
precast raw materials. This plays an important role in
the area of inventory control and supply chain, due toenabling production and distribution planning. It isalso conditioned to reduce raw material delivery
times and optimize decisions on the supply chain [5].
This is to help the developers and operators of
inventory management systems in improving
efficiency, maximizing productivity, and minimizing
material losses [6].
Many studies have also evaluated smart
inventory implementation, namely the dynamic
brick-and-mortar supply chain analysis. Thisevaluated the benefits of implementing smart
applications and systems to improve Vendor
Managed Inventory (VMI) efficiency. In the supply
chain mechanism, the manufacturer configured the
production level and replenished the inventory at theretailer's store, where prices were set up to affect sales
and inventory. In this condition, the company also
shared the revenue and inventory costs through anagreement. This condition was very dynamic wheninventory increased and decreased at production andsales levels respectively, with periodical variations
observed according to several stochastic errors [7]. In
this case, the need for accurate predictions led to amore effective and cheap supply chain, as well as
allowed companies to provide quality, quantity,
periodical, and low-production cost products [8].
Many studies also used other machine learning
approaches to map prediction patterns, such as fuzzy
subtractive clustering [9].Therefore, this study aims
to analyze the patterns by which inventory
procurement predictions produce precast productswith good quality, using the e-readiness framework
concept of the neural network method throughappropriate decision-making processes. In thiscondition, prediction modelling was prepared as partof the application of e-readiness in raw material
management. The pattern of obtaining these materials
was also used as the best test data, to assess the
management model in smart inventory.

utilization level of information systems in all business
operations [11]. It is also one of the innovativereferences used in managing highly efficient logistics.
In addition, TRI is related to the Global
Competitiveness and Logistics Performance Indexes(GCI & LPI), as well as other similar supportive
dimensions.

OptimismInnovativenessTechnology Readiness

Discomfort

Insecurity

Fig. 1 E-Readiness Technology
In precast manufacturing companies, technology
is also used in raw material management, by
arranging and using a very suitable proceduralschedule and method, respectively. Using linear
programming methods, Markov models, and genetic
algorithms, scheduling often emphasizes the
management of time to handle and obtain raw
materials [12]–[14]. In this condition, a goodinventory receipt system is needed to provide morevalue during the prediction process, where efficientand periodical systematic performance is a functionof operational activities. This helps to reduce time
consumption in determining optimal operations invarious parameters [15]. Additionally, process
quality problems and production cost efficiency areadequately maintained [16]–[18].
Neural Network Utilization
The amount of inventory is often related to thecompany's profit and the entire supply chain's
survival. This indicates that prediction processes needto increase the company's ability to prevent risks,
improve profits, and reduce losses during theacquisition of inventory, using the backpropagationneural network (BP) method [19]-[20]. Some reports
were also observed based on the development oftechnology readiness, such as [21]-[22]. Thisemphasized determining the optimization value ofmaterial handling, using a neural network with 2algorithm methods, namely ALN and LVQ (Adaptive
Linear Neuron and Linear Vector Quantization).
These methods led to the prediction of cost-benefit

LITERATURE REVIEW
E-ReadinessTechnology Readiness Index (TRI) 1.0 isconstructed based on four-dimensional aspects,
namely Optimism, Innovation, Discomfort, andInsecurity [10], as shown in Fig. 1. This is often
applied to a company with the Strategic Alignment
Maturity Model (SAMM), to determine the310GEOMATE – Bangkok, Thailand, 22-24 November 2022

into 3 categorical levels, namely high, medium, andlow demand, as shown in Fig. 2.
Class

→

→

→

→

wk  wk +  .( x − wk ) , when cm =/≠ y, it is close

Input

Linear Vector
Quantization (LVQ)
Model

Wk (weight improvement) is also used to determine
the weight (w) with the smallest distance value (d) as
follows,

to each other or part of the set, respectively.
Adaptive Linear (Adaline)
Model

METHODOLOGY

Cost

Fig. 2 Neural Network Model based on E Readiness
AdalineADALINE (Adaptive Linear Neuron or later
Adaptive Linear Element) is an early single-layer
artificial neural network, which is implemented as analgorithm to predict outputs with an automatic
controller. Although the accuracy obtained is notsatisfactory, the value still changes and becomes
highly precise during more data analyses [23]. In thefollowing equation, an input vector (K) is observedwith the pattern.
Xk=[x0, x1k, x2k, ..., xnk]T(1)
Where Xk = the components of the weights andcoefficients. Moreover, a weight vector (Wk) isobserved in the Eq. (2) as follows,
Wk = [wx0, w1k, w2k, ..., wnk]T(2),

Fig. 3 Study Methodology
The e-readiness technology emphasized thefollowing factors, (1) security, (2) technical issues,(3) software reliability, (4) digital operations forinternet usage, and (5) technical skill utilization [25].
The concept of this technical influence also originatedfrom internal and external organizations, as shown in
Fig. 3.

where y K = W X K .
TKOutputnyk =  X W + 
k k
k =1

Adaptive learning ruleLearning is also known as the Least Mean Square(LMS), whose rules in this process are observed asfollows,
W ← W + Ƞ(d – o)x(3)

Control Variable- Size- Age- Ownership- Industry TypeE Business
Adoption

Linear Vector Quantization (LVQ) ModelE readiness InternalThis is one of the widely used ANN models(Artificial Neural Network), which emphasizes the
prototype of a supervised learning classification
algorithm and its network. These are trained througha competitive method similar to the Self-Organizing
Map. The clustering technique is also used as aclassifier to evaluate the deviations in the data sample
through a random or specific density. This shows that
performance remains the same with almost all
combinations of training and testing [24]. Based on
the following formula, learning is conducted by
calculating the euclidian distance,→ →Relative Advantage
Top Management Beliefs
Technology Resources

Market Foces E readinessSupporting Industri E
Readiness

Fig. 4 The concept of e-readiness influence
Based on the external conditions, e-readinessemphasized many factors regarding the caseperspective of each corporation in its respective
business field. In this study, these factors werelimited, including the IT technology infrastructure
supporting the precast industry and the vendor market
for raw materials. Meanwhile, the internal conditionsof this technology focused on related technical→ →d ( x , wk ) = min d ( x , w k )E readiness External

(4)

311

GEOMATE – Bangkok, Thailand, 22-24 November 2022

improvements, using neural network methods forprediction processes.

Data Test
This emphasized the data of sand, gravel andcement, which were mixed based on the best quality
standard of Indonesian concrete category K 500-K
600. These data were obtained according to the orderfor 12 months, as shown in Table 1.

Feature Extraction
The internal data sources were the direct
measurement of the goods’ receipts, regarding theyearly production of raw materials at precast
organizations. In this condition, the raw material
parameters included cement, sand, and gravel. In
preparation for the precast products, a value
extraction was also observed for the contributions of
the materials and costs, as shown in Fig. 5. This
showed that the cement and gravel costs and materials
were the largest/lowest and smallest/highest
contributions, respectively.

Table 1 Precast raw material cost
No
1
2
3

Normalized data
The nominal unit of numeric data was normalized
to facilitate data processing in the neural networkarchitecture. This indicated that normalization was
carried out by mapping into numbers between 0 and
1, as shown in the following formula,

COST PRECAST PRODUCTION

COST PRECAST PRODUCTION

Sand

Sand
39%
39%

X Map =

CementCement
61%
61%

Split
Split
0%

0%

(a)

X Original − X min(4)

X max − X min

Where :
Xmap = Normalization ValueX Original = Original Value
X max = Maksimum ValueX Min = Minimum Value

MATERIAL CONTRIBUTION

MATERIAL CONTRIBUTION

CementCement
30%
30%

Split
Split
0,04%
0,04%

Cost IDR (m3)242,000
200,000
715,000

Materials
Sand
Split
CementIn 2021, the normalization of input variables werealso carried out on the price of raw materials,
frequency of intermediaries, and volume oftransaction costs. Moreover, the target data originatedfrom the average total sales of precast products in thesame year.

Sand
Sand
70%

70%

(b)

Fig. 5 Cost Contribution (a) production (m3) andmaterial contribution (b) precast productTarget data
The target data contained three vectors, namely
the minimum, maximum, and median sales values of
the total cost, as shown in Fig. 7.

The second parameter focused on the monthlysupply behaviour pattern of each raw material for ayear, as shown in Fig. 6.

Selling Precast Pattern
4000000000
4.000.000.000

Precast Supply Materials Patterns

3500000000
3.500.000.000

1200

3000000000
3.000.000.000

Cement
IDR

1000

Ton

800
i
Split

600
400

2500000000
2.500.000.000
2000000000
2.000.000.000
1500000000
1.500.000.000

1.000.000.000
1000000000

Sand

500000000
500.000.000

200

0
1

0
1

2

3

4

5

6

7

8

9

10

11

12

2

3

4

5

6

7

8

9

10

11

12

13

Month

Month

Fig.7 Total precast unit cost for the year.

Fig. 6 Annual supply pattern of precast raw
materials

Based on Fig 7, the optimization patterns of the
raw material supply and sales profits were observed
312

GEOMATE – Bangkok, Thailand, 22-24 November 2022

when the production target need to achieve 45,000
tons monthly with a minimum unit cost of IDR800
million.

In Fig. 9. five defined input values were also
observed, where a linear classification produced 3
cluster categories.

DISCUSSION

Simulation Result
Adaline Simulation Output

Based on the external conditions, the system input
parameters included the readiness of IT technology
infrastructure, which supported the precast industry
and market vendors providing raw materials. In thisanalysis, the final output was a value within a
specified range. Meanwhile, the internal input factors
included the monthly frequency of raw material
supplies in a year (Tons). Table 2 shows the input andtarget variables of this analysis.

1,2Normalized

1

2.
3.

1

4.

5.

2

3

4

5

6

7

8

9

10

11

12

Month

Fig. 10 Adaline Method simulation results
According to Fig. 10, the pattern of obtaining raw
materials for precast products fluctuated based on thetest data from 2020, through the Adaline method
learning for a year. In this condition, the lowest orders
were in the 3rd, 8th, 11th, and 12th months when 5parameters were inputted into this method.

Prediction Parameter
Adaline
LVQ

Monthly
PrecastSelling
Patterns

0,40

LVQ Simulation Output

Decision
Classification
Level

3,5

Normalized

1.

Input
Parameter
IT Readiness
Infrastructure
Level Market
VendorCementContributions(monthly)
Split
Contributions(monthly)
Sand
Contributions(monthly)

0,6

0,2

Table 2 Input Parameter Identification andPrediction
No

0,8

3
2,5
2

1,5
1
0,5
0
1

2

3

4

5

6

7

8

9

10

11

12

Month

Architecture Neural Network Adaline

Fig. 11 LVQ Method Simulation Result
Based on Fig. 11, the pattern of obtaining raw
materials for precast products also fluctuatedregarding the test data from 2020, through the LVQ
method learning for a year. This proved that thehighest classes and the best values occurred in the 1st,
6th, and 9th months when 5 parameters were inputted
into this method, with the lowest orders observed on
the 2nd, 3rd, 4th, 7th, 8th, 10th, 11th, and 12th period.
In the 5th month, the values obtained were also found
not to be very high or low. These actions emphasized
the option of maintaining existing raw materials or
placing orders regarding the increment of theprevious month.

Fig. 8 Adaline Architecture
Based on Fig. 8, five defined input values wereobserved, indicating a linear activation function
between 0 and 1.
Prototype
Layer

outputX1

Table 3. Class and Cost Relation

X2
Class 1
X3
Class 2

No.
1
2
3
4
5

X4

.
.
.

Class 3

Xn
Input

Competitive layer

Linear layer

Fig. 9 Linear Vector Quantization Architecture

313

Month

Classes

Jan
Feb
Mar
Apr
May

3
1
1
1
2

Cost (IDR)
3,644,810
1,829,060
804,661
1,724,870
2,097,578

GEOMATE – Bangkok, Thailand, 22-24 November 2022

6
7
8
9
10
11
12

Jun
Jul
Aug
Sep
Oct
Nov
Dec

3
1
1
3
1
1
1

Sepuluh Nop., 2017.pp 39 - 67
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Santoso, and Z. A. Hasibuan., Businessinformation systems strategic alignment readinessmaturity level: Corporate and businesstechnology driver perspective, J. Eng. Sci.
Technol., vol. 14, no. 3, 2019, pp. 1260 - 1270.
[12] C. F. Motta Toledo, L. De Oliveira, R. De Freitas
Pereira, P. M. França, and R. Morabito., A genetic
algorithm/mathematical programming approach
to solve a two-level soft drink production
problem, Comput. Oper. Res., vol. 48, 2014, pp.
40-52.
[13] Z. Citra, B. Susetyo, and P. Wibowo., Optimasi
Kinerja Proyek dengan Penerapan Metode2,872,875
2,370,019
1,170,018
2,464,231
2,010,972
895,467
942,555

According to Table 3, the second and third
months had different advantages, although they werein class (1). This was in line with the eighth andeleventh months. The midpoint was also observed inclass (2), which occurred in the 5th month. However,the 1st, 6th, and 9th months exhibited quite a large
amount of transactions, leading to the significant
effects on the order of raw materials and logistics
financing considerations.
CONCLUSIONS
Based on these results, cost optimization was
conducted by accepting and creating new orders when
the conditions were found in class (2). This action
was often carried out by observing the trend of theprevious month. Due to the high-order rate, theclasses also showed that the level of operations needbe accelerated and periodically limited when theconditions were categorized in class (3). For class (2),the order for raw materials was only performed byobserving the Adaline method simulation, since atendency was found for the market to absorb precast
products in the following month. Furthermore, theapplication of the neural network method wasappropriately implemented when supported byexternal e-readiness factors, including the which
include infrastructure preparedness and many
material vendor options. The implementation of thisconceptual technology also used 2 neural networkmodels for precast products. This involved theprocessing and production of similar input values anddifferent decision model simulation, respectively.
Irrespective of these differences, a strong correlation
was still observed with the time efficiency of thedecision-making process. Therefore, bother LVQ and
Adaline contributed 50% to this decision approach.

ACKNOWLEDGMENTS
The author is grateful to the Rector and LPPM IT
PLN, for the experimental grant for Research Centre.
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Dari Segi Waktu dan Biaya Pada Proyek My
Tower Apartement Surabaya, Inst. Teknol.
314

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Crashing dan Linear Programming pada Proyek
Bulk Godown, Rekayasa Sipil, vol. 7, no. 2, 2019,
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[14] M. R. Bordón, J. M. Montagna, and G. Corsano.,
Mixed integer linear programming approaches forsolving the raw material allocation, routing andscheduling problems in the forest industry, Int. J.
Ind. Eng. Comput., vol. 11, no. 4, 2020, pp. 525548.
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Artificial intelligence for time-efficient prediction
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[16] R. Hidayawanti, S. Legino, I. Sangadji, and R. P.
A. Widodo., The efficiency of fly ash and cement
slag to development building,Int. J. GEOMATE,
vol. 16, Issue. 57, 2019, pp. 95-100.
[17] R. Hidayawanti, Y. Latief, Y. Rahayu, F. I.
Wachid, and R. P. A. Widodo., Utilization Of
Ggbfs As Cement Substitute To Reduce
Production Cost Of Construction Project, Int. J.
GEOMATE, vol. 21, Issue. 86, 2021, pp 9-16.
[18] R. Hidayawanti, D. D. Purnama, T. Iduwin, S.
Legino, and F. I. Wachid., The impact aggregate
quality material as a linear regression study on
mixture concrete, Int. J. GEOMATE, vol. 18,
Issue. 70, 2020, pp. 23-29.
[19] L. Gu, Y. Han, C. Wang, G. Shu, J. Feng, and C.
Wang., Inventory prediction based on
backpropagationneuralnetwork,”
NeuroQuantology, vol. 16, no. 6, 2018, pp. 664-

673.
[20] F. Bin Pan., Inventory prediction research basedon the improved BP neural network algorithm,
Int. J. Grid Distrib. Comput., vol. 9, no. 9, 2016,
pp. 307 - 316.
[21] L. Gao and H. Dou., Inventory management ofrailway logistics park based on artificial neuralnetwork, J. Eur. des Syst. Autom., vol. 53, no. 5,
2020, pp. 715 - 723.
[22] M. Du, J. Luo, S. Wang, and S. Liu, Genetic
algorithm combined with BP neural network in
hospital drug inventory management system,
Neural Comput. Appl., vol. 32, no. 7, 2020, pp. 114.
[23] P. C. Siswipraptini, R. N. Aziza, I. Sangadji, and
I. Indrianto., The design of a smart home
controller based on ADALINE, Telkomnika(Telecommunication Comput. Electron. Control.,vol. 18, no. 4, 2020, pp. 2177 - 2185.
[24] S. Sahoo, S. Mishra, S. K. Mohapatra, and B. K.
Mishra., Clustering deviation analysis on breast
cancer using linear vector quantization technique,Int. J. Control Theory Appl., vol. 9, no. 23, 2016,
pp. 313 - 324.
[25] M. O. M. Baeuo, N. Z. B. Nor Zairah, and A. A.
M. Alaraibi., Technology factors influencing egovernment readiness, J. Theor. Appl. Inf.
Technol., vol. 95, no. 8, 2017, pp. 1637 - 1645.

315

12h Int. Conf. on Geotechnique, Construction Materials & Environment (GEOMATE),
Bangkok, Thailand, Nov.22-24, 2022, ISBN: 978-4-909106025

EFFECT OF ADMIXED POLYPROPYLENE FIBERS ON CONCRETE
PROPERTIES AND SHEAR STRENGTH OF REINFORCED
CONCRETE BEAMS

1,2

Osama M. A. Daoud 1, Reham Ibrahim Ahmed Ibrahim 2
Department of Building and Roads Research Institute, University of Khartoum, Sudan2

ABSTRACTThe main objective of this paper is to study the effects of adding various proportions of polypropylene fiberson the fresh and hardened properties of normal concrete. Also, the research aimed at investigating the effect ofpolypropylene fibers on shear strength of reinforced concrete beams. Compressive, flexural and split tensile
strengths of concrete were investigated of for grade 25 concrete for different ratios of polypropylene
fiber/cement (0.0%, 0.25%, 0.35%, and 0.5% by weight). The mix without polypropylene fiber was considered
as a control mix for comparison. Eight specimens of reinforced concrete beams (100mm×200mm in crosssection and 1200mm in length) were investigated for ductility shear strength with and without polypropylene
fibers. Regarding workability of concrete, results obtained have shown that the slump value was reduced with
the increase of fiber content of the mix. The compressive strength of concrete had increased with about 50% fora fiber/cement content of 0.25% by weight, after that the increase in compressive strength had reduced to 20%
compared to the control mix. For the mix with 0.5% of fiber/cement by weight the compressive strength slightly
affected by addition of polypropylene fibers. For the spilt tensile strength, tests results have shown that tensile
strength had increased gradually from 16% up to about 35% with the increase of fiber content. Similarly, theflexural strength had increased from 27% up to about 42%. Regarding the shear strength for the tested reinforced
concrete beams, it was found that the polypropylene fiber didn’t improve the shear strength. Slight reduction in
the shear strength of the tested beams was noticed with the increase in fiber content more than 0.25%. Deflectionof the tested beams had increased with the increase of fiber content for the same failure load which indicated thatthe fiber had improved the ductility of the beams. It was concluded that the polypropylene fiber clearly improved
tensile and flexural strength of concrete, but the compressive strength slightly effected. Shear strength of thetested reinforced concrete beams didn’t improved by the addition of polypropylene fiber but the ductility of thetested beams had increased according to the increase of deflection.
Key words: Polypropylene fibers; Compressive strength; Tensile strength; Ductility; Shear strength.

strength of concrete many attempts have been made.
One of the successful and most commonly usedmethods is providing steel reinforcement. Steel bars,
however, reinforce concrete against local tension

INTRODUCTION
Concrete is a composite material containing
hydraulic cement, water, coarse aggregate and fineaggregate. The resulting material is a stone likestructure which is formed by the chemical reactionof the cement and water. This stone like material isa brittle material which is strong in compression butvery weak in tension. This weakness in the concrete
makes it crack under small loads, at the tensile end.
These cracks gradually propagate to thecompression end of the member and finally, themember breaks. The formation of cracks in theconcrete may also occur due to the drying shrinkage.
These cracks are basically micro cracks. These
cracks increase in size and magnitude as the time
elapses and the finally makes the concrete to fail.
The formation of cracks is the main reason for the
failure of the concrete. To increase the tensile

Only. Cracks in reinforced concrete members
extend freely until encountering the bar. Thus, needfor multidirectional and closely spaced steel
reinforcement arises. That cannot be practicallypossible. Fiber reinforcement gives the solution forthis problem so to increase the tensile strength of
concrete a technique of introduction of fibers inconcrete is being used. These fibers act as crackarrestors and prevent the propagation of the cracks.
These fibers are uniformly distributed and randomly
arranged. This concrete is named “fiber reinforced
concrete [1].

316SEE - Bangkok, Thailand, Nov.11-13, 2022

effects on fresh properties (workability) andhardened properties (Compressive, spit tensile andflexural strength) the result of slump test indicatedthat workability of concrete is decreased withaddition of polypropylene fibers.
Reduction of
65% had been measured with the highest fiber
content of 1.5% [4].

OBIECTIVES
The objective of this research is to study
the effect of polypropylene plast fiber onmechanical properties of fresh(slump test)andhardened concrete (compressive ,tensile, flexuralstrength) with variation of fiber content (0.25%,
0.35%, and 0.5%) as well as a control mix without
fiber.
Another objective of the study is to explore theeffect of fiber on the shear behavior and strength ofreinforced concrete beam.

Anthony and Abimbola found from destructive
compressive strength test results that it is observedthat addition of polypropylene in low 29
percentages,
about 0.25%,
increases
thecompressive strength by about 5% to 10%, while
addition of 0.5% increases the compressive by
about 3% to 5%. However, the compressive
strength decreases by about 2% to 4% when 0.75%
of polypropylene fibers are added and by 4 to 8%
when 1% of polypropylene fibers are added. The
results of the non-destructive tests follows the same
patter, a dosage of 0.25% increases the strength by
5 to 9%; dosage of 0.5% increased the strengthfrom 3 to 5%. The strength decreases by about 2 to
4% when 0.75% of polypropylene fibers are added
and by 4 to 8% when 1% of polypropylene fibers
are added [5].

PROBLEM STATEMENTS
Portland cement concrete is considered to
be a relatively brittle material which is strong incompression but very weak in tension. Thisweakness in the concrete makes it crack undersmall loads so, use of fiber in concrete is toimprove its properties and prevent the propagationof crack and enhance its compressive, tensile andflexural strength and Improves toughness anddurability of concrete. The effect of fiber on theshear behavior and strength of concrete beams had
not been covered in details it the literature.
LITERATURE REVIEW

MATERIALS

Mohamed R.A.S have presented results ofexperimental work on the effect of polypropylene
fiber on compressive strength, flexural strength andspilt tensile strength. in the compressive strength
increased upon 0.5%polyprplene fiber content in
1% polypropylene fiber content the compressive
strength was reduced .in the flexural strength in
0.5 %the strength was reduced. In spilt tensile
strength increased upon 0.5% of polypropylene
fiber content, and in 1%the value of spilt tensile
strength was reduced [2].

A Materials used
Cement The Portland Cement of 42 Grade wasused in this study.
The initial and final setting times were 1:25 hr. and4:33 hr. respectively, compressive strength 2days
and 28 days were 19.65 and 42.66 respectively.
Fine Aggregate uncrushed aggregate whichobtained from Omdurman-Khartoum stateSpecific gravity 2.85, silt and clay content 0.98%,
sieve analysis according to (B.S 882:1992)

Thirumurgan and Siva Kumar used crimped
polypropylene fibers for reinforcing high strength
concrete, according to find the compressive
strength index to this fiber reinforced concrete, theirwork reported that the workability of concretedecreased with the addition of polypropylene fibers,in their research they overcome this problem byaddition of High Range Water Reducing
Admixtures [3].

Coarse Aggregate Crushed aggregate that wasused has two size (10mm and 20 mm) wereobtained from the local market (Torya mountain)
specific gravity for 20mm and 10mm 2.88 and 2.85
respectively, sieve analysis according to (B.S
882:1992)
Water Ordinary portable water (tap water) was
obtained from Khartoum area

M.Marghani carried out an experimental study on
the effects of adding polypropylene fiber on
concrete properties .A mix of 0.0 fiber content had
been set as control mix. polypropylene fiber wereadded to concrete in six different percentages of
cementofweightasfollows
0.25%,
0.5%0.75%,1% .1.25% and 1.5%.an experimental
programme was carried out to investigates PP fiber

Fibre Polypropylene Fibre was used. Density –bulk
900 kg/m 3, Length 12mm, specific gravity 0.91,
tensile strength 0.67Kn/mm2

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SEE - Bangkok, Thailand, Nov.11-13, 2022

METHODOLOGY
The methodology adopted in this thesis to achieve
the above objectives is divided into two parts;
theoretical review and laboratory work.
A. Theoretical review part:
1. Investigate topic area to define the thesis problem.
2. Study theoretical Background (Literature survey
on basic requirements of PPFRC and properties offresh and hardened PPFRC).
B. Experimental work part:
1. Selection and preparation the suitable constituent
materials.
2. Investigating the physical and mechanicalproperties of constituent materials (specific gravity,and sieve analysis for aggregates-initial, final
setting time and compressive test for cement).
3. Developing a suitable mix design using local
materials through conducting a series of trial mixes.
4. Slump flow test at fresh state of PPFRC concrete
mixes to study the effect of increasing fiber content
on workability.
5. Preparing, casting specimens, curing and testing
after curing to get hardened state parameters; suchas compressive, tensile, and flexural strengths,
dynamic modulus of elasticity in addition shear
behavior and strength of reinforced concrete beams
6. Analysis and discussion of experimental test’s
results. 15
7. Summary of all stages of research.

Fig. 1

Relation between Fiber content % andSlump in mm

Compressive Strength
Compressive strength increases for all fiber
reinforced mixes compared to the plain concrete.
The optimum compressive strength was reached atapercentageof
0.25%
polypropylene
fiber .decreased the compressive strength due to thedecrease of the cohesion of the concrete.

RESULTS AND DISCUSSIONS
Workability with addition of fibers (slump test):
The effect of fiber content on the slump of fresh
concrete. It is clear from this Figure that the slump
was decreased with the increase in fiber
content.polyprplene fiber has hdrophic property [6].
This property is attracting the moisture content.
higher workability can be achieved with theaddition of admixtures (super- plasticizer).

Fig. 2

Table 2 Compressive strength results
Mix
0.0%
0.25%
0.35%
0.5%

Table 1 The results of Slump test
Mix
G 25
G25
G25

Polypropylene
fiber
0.0%
0.25%
0.35%

Slump
(mm)
110
95
75

Compressive strength Vs Fiber content

Mix

7 days21
24
21
21

14days25
29
26
25

28 days
26
39
31
26

Mode of failure for cubes:

G 25
G25
G25

Observation of the mode of failure of the cube in
the control mix compared with the 0.25% fiber
control mix.
In the control mix the standard shape of failuremode shown in Figure (3a) was easy to achieve. But
for the 0.25% fiber content mix the shape of failuremode show in Figure (3b), the standard shape of
failure mode was not achieved.

318

SEE - Bangkok, Thailand, Nov.11-13, 2022

Table 4 Flexural strength results
Mix
0.0%
0.25%
0.35%
0.5%
a) Control Mix
Fig. 328 days
7.41
9.45
9.78
10.56

b) 0.25% fiber Content

Mode of Failure for Mixes

Split tensile strength
The split tensile strength increased with the increasein polypropylene fiber content (tensile strength had
increased gradually from 16% up to about 35%) andthe mechanism of failure of concrete cylinders
under split tensile strength was better in 0.25% and0.35%, and 0.5% fiber content compared to thecontrol mix.

Fig. 5

Table 3 Split tensile strength results
Mix
0.0%

Tensile strength 28 days (N/mm2)
2.14

0.25%
0.35%

2.48
2.48
2.84

0.5%

for

different

fiber

Modulus of elasticity
There is a slight variation in the results, but inoverall, the addition of polypropylene fiber resulted
in a decrease of the modulus of elasticity
Table 5 Modulus of elasticity results
Mix
0.0%
0.25%
0.35%
0.5%

Fig. 4

Flexural strength
content%

Modulus of elasticity 28 days
42.6
40.8
41.6
39

Spilt tensile strength for different fiber
content%

Flexural strength
The flexural strength increases with the increase inpolypropylene fiber content

Fig. 6319

Graph of modulus elasticity

SEE - Bangkok, Thailand, Nov.11-13, 2022

Shear strength for reinforced concrete beams
Failure load for shear strength
The shear strength for the tested reinforced beams,
it was found that the polypropylene fiber didn’t
improve the shear strength. Slight reduction in the
failure load of the tested beams
Table 6 Failure load for shear strength results
Mix
0.0%

Failure load / 90 days(KN)
90.73

0.25%

86

0.35%

69

0.5%

66

Fig. 7

Fig. 8

Shear behavior of beam 0.0% fiber content

Fig. 9

Shear behavior of beam 0.25% fiber
content

Failure load for different fiber content

Mode of failure for reinforced beam concrete
The addition of polypropylene fibers does notchange the crack pattern
Table 7 Type of failure for different fiber content%
Failure load for shear strength results
Mix
0.0%
0.25%
0.35%
0.5%

Sample 1
Shear tension
Shear tension
Shear tension
Shear tension

Sample 2
Shear tension
Shear tension
Shear tension
Shear tension

Fig. 10 Shear behavior of beam 0.35% fiber
content

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SEE - Bangkok, Thailand, Nov.11-13, 2022

Polypropylene fibers reduced slump value.
Workability of concrete decreases with theincrease in polypropylene fiber.



Proper feeding and mixing can virtuallyeliminate the problem of balling, thepolypropylene fiber is added to the mixture toachieve a uniform distribution throughout theconcrete, this helps to prevent balling.

The polypropylene fibers are hydrophobic andtherefore give poor bond characteristics withthe cement matrix.

Maximum compressive strength slightly
increased with increasing the content ofpolypropylene fibers up to o.5 vol. % in thetested concrete.



PFRC shows improved tensile and flexuralstrengthcomparedtoconventionallyreinforced( brittle concrete).

Fig. 11 Shear behavior of beam 0.0% fiberContent

Deflection of reinforced concrete beam fordifferent fiber content

 Polypropylene fiber doesn’t improve theconcrete mix in the shear strength of reinforced
concrete beams

Deflection of the tested beams had increased with
the increase of fiber content for the same failureload which indicated that the fiber had improved theductility of the beams. The maximum value fordeflection at 0.35% polypropylene fiber content.
But the reduction occurred in 0.5% polypropylene
fiber content

REFERENCES[1]http://www.grietinfo.in/projects/MAIN/CIVIL20
13/cd-27-FRC PROJECT REPORT (accessed 2010-2014).
[2] Mohamed R.A.S.,”Effect of polypropylene fiber
on the mechanical properties of Normal Concrete “,
Journal of Engineering sciences, Assiut university,
Vol.43, No.4, pp.1049-1059, July 2006.
[3] S. K. . Thirumurugan.S, “Compressive Strength
Index of Crimped Polypropylene Fibers in HighStrength Cementitious Matrix,” World Appl. Sci. J.,
vol. 24, no. 6, pp. 698–702, 2013.
[4] M.Marghni “Evaluation of fresh and hardened
properties of polypropylene fiber reinforced
concrete” thesis for MS.c of bulding technology inuniversity of Khartoum , feb 2016
[5] Anthony Nkem Ede and AbimbolaOluwabambi
Ige, “Optimal Polypropylene Fiber Content for
Improved Compressive and Flexural Strength ofConcrete \n,” IOSR J. Mech. Civ. Eng., vol. 11, no.
3, pp. 129–135, 2014.

Fig. 12 load _deflection diagram for mixes

CONCLUSIONS[6]http://www.syntechfibres.compolypropylene/pro
perties-of-polypropylen-fibres/ (Accessed 21-92015).

Based on the test results presented in this researchin chapter four it can be concluded that,321

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

ADDITION OF SEWAGE SLUDGE ASH TO CONCRETE AS
SUBSTITUTE FOR PORTLAND CEMENT
Jean-Frank Wagner
Geology Department, University of Trier, Germany

ABSTRACT
Sewage sludge ash (SSA) is a waste product derived from the incineration of sewage sludge. Common
management practices of sewage sludge ash are dumping into landfills and application on agricultural land. Basedon the documented silica, alumina and iron contents of SSA, the present study investigates its pozzolanic potential
by replacing cement with varying proportions of 2 different SSA in the production of mortar blocks. Rapid
Hardening Cement (HRC) and Ordinary Portland Cement (OPC) respectively were replaced by 5, 10 and 15% bymass of SSA. The block units were cured for 7 and 28 days after which their compressive strength was determined.
Results showed that SSA has a positive long-term effect on block shrinkage for both HRC and OPC. The blockunits cured for 28 days exhibit similar compressive strengths for HRC without SSA and with 5-15% of HRC
substitution. Substitution of OPC by SSA showed a decrease in compressive strength of 20% for 5% and 40-60%
for 15% OPC replacement.

Keywords Sewage sludge ash, Rapid Hardening Cement, Ordinary Portland Cement, Compressive strength

INTRODUCTION

compressive strength. Only a few authors reported aslight increase of mortar strength by adding SSA [11],[12]. Pinarli & Kaymal [13] and Ing et al. [14] haveshown a positive long-term effect, whereby thecompressive strength of mortar at lower curing agesof 3, 7 and 14 days were lower than the reference
mortar while at 28 days of curing the compressive
strength was higher than the control strength. Thissuggests that a longer period of curing is required forthe pozzolanic reaction to effectively take place.
The main objective of this study was to determine
the compressive strengths of cement-based concrete
blocks with different cements (Rapid Hardening
Cement (HRC) and Ordinary Portland Cement(OPC)) and blended with different SSA. Testing with
Rapid Hardening Cement (HRC) had been previously
published by Wagner & Tabe Array Egbe [15].

Sewage sludge incineration becomes more andmore important. Sewage sludge ash (SSA) is the end
product of incineration and there are large quantities
which require disposal. However, as SSA has highconcentrations of silica, alumina and iron, it can beused as pozzolan to replace cement in the productionof cement-based blocks [1], [2]. The potential for
SSA to be used as a pozzolan has become an
importantarea of research in the last years because it
may provide an adequate route for SSA recycling.
The pozzolanicity of SSA has been shown by anumber of authors [3], [4], [5]. In addition, replacing
Portland cement by SSA will also reduce CO2
emissions which are generated by the cement industry
in huge quantities. One of the ways by which the
pozzolanic potential of SSA is determined is by
substituting different proportions of cement with SSA
to make concrete or mortar blocks and testing theseblocks for their mechanical performance. In somestudies, it was observed that the compressive strengthof SSA blended blocks were generally lower than thestrength of the control and the compressive strength
decreased with an increase in the amount of SSA [1],[6], [7], [8]. Pérez-Carrióna, et al. [9] reported a 25%
increase and a 5% decrease of compressive strengthfor 10 and 20% SSA substitution respectively whencompared to the control block. Tay & Show [10]reported similar results showing a 10% SSA concrete
block to have the same compressive strength as thereference. Subsequent increase in SSA content by 20,
30 and 40% led to a successive decrease in

MATERIALS AND METHODS
Sewage sludge ashes
The SSA was collected from 2 wastewater
treatment plants in Germany.
SSA1: Sludge is first drained by a screw press,
conditioned with flocculation aids and dried to a dry
matter content of over 85%. The dried sewage sludge
is then fed continuously to a PYREG® double reactor
and incompletely combusted at 650°C. By thisprocessing Phosphorus in the sludge is normallyconcentrated to 10-20%.
The main chemical elements in the ash are SiO2
(28.1%), Al2O5 (8.7%), Fe2O3 (25.3%) and P2O5322

GEOMATE – Bangkok, Thailand, 22-24 November 2022

(18.2%).
SSA2: Here, too, a PYREG system is used toprocess the sewage sludge. After dewatering, thesludge runs through the PYREG® reactor, where it ismineralized at around 700 - 720 °C in the absence ofoxygen.
The main chemical elements are SiO2 (38.6%),
Al2O5 (15.3%), Fe2O3 (12.7%) and P2O5 (18,1%).

block lengths depends on the time of curing and mostimportantly on the concentration of SSA1 and RHC
in the blocks. The blocks generally showed a lower
shrinkage with an increase in SSA content; therefore,
SSA significantly reduces the rate of block shrinkage.
The same pattern was consistent for mortar blocks
with OPC and both SSA. Even a slight increase involume was observed after 28 days of curing at 40°C.

Cements
The first cement used in the investigation is Rapid
Hardening Portland Cement (HRC) bearing thetrade name “Knaufschnellzement” produced by
Knauf Bauprodukte GmbH & Co.KG. It is made fromdry cement mortar, quarry sand and contains
limestone powder as additive.
The second cement is "CEM I 42.5 R CE PM-CP2
NF" and comes from the company Cimalux. This ispure Ordinary Portland Cement (OPC) made from
100% Portland cement clinker without any secondary
components.
Cement-based block production process
The materials which were used in the productionof the cement-based blocks were cement, SSA andwater. SSA was dried at 105°C for 24 h.
Four sample categories of mortar blocks were
produced. The reference sample contained no SSA(0%). In the other samples, 5, 10 and 15% by mass of
cement was replaced successively by SSA. The
amount of water used was kept constant for all
samples with RHC and the water to cement ratio was
0.25. For the samples with OPC the water to cement
ratio had to be increased to 0.35.
The mortar blocks were cured for 7 and 28 days atambient temperature (20°C). Additionally, for OPC
and both SSA further test series were cured at 40°C.

Fig. 1 Percent of shrinkage of the cement–based
blocks at different % of SSA 1 substituting RHC andmeasured after 7 and 28 days of curing.
Compressive strength
For RHC the control block exhibited acompressive strength of 27.06 MPa after 7 days ofcuring, while the block with the highest content by
weight of SSA1 (15% SSA) showed the lowest
compressive strength of 26.83 MPa. There was a veryslight decrease in compressive strength of the blocks
with an increase in SSA content (Fig. 2).
After 28 days of curing, the control block of RHC
exhibited a compressive strength of 27.11 MPa. The
compressive strength exhibited by the block with 5%
ash was a little bit lower than the strength of thecontrol block (0.67 %) but for the blocks containing
10% and 15% SSA1 the strength increased by 0.18%
and 0.37% respectively.
The trend of decrease in strength with increase in
SSA content exhibited by blocks cured for 7 days
correlates with most of the studies found in literature[1], [6], [7], [8], [13].
The trend of a slight increase in compressive
strength with an increase in the quantity of SSA
amount in the blocks as exhibited by the blocks cured
for 28 days in the present study is rarely observed inliterature. However, this result correlates with studies
by Monzó et al. [12] and Nazierah et al. [16].
All in all, the change in compressive strength isvery low, so that it can be concluded that asubstitution of RHC with SSA1 (up to 15%) has at

Compressive strength testingCompressive strength testing was conductedusing a FHF Strassentest machine manufactured inaccordance with DIN 51223. The machine is a point
load machine, a 98 x 98 mm metal plate of weighing
1.4 kg was adapted to it to ensure uniform distributionof the pressure applied by the testing machine. The
blocks (73x39x39 mm) were placed verticallyupwards, and load was applied to the blocks by theequipment. The load was left to increase to block
failure. The maximum load at which the blocks failed
was recorded by the equipment.
RESULTS AND DISCUSSION
Block shrinkage
As shown by the results in Fig. 1 the reduction in
323

GEOMATE – Bangkok, Thailand, 22-24 November 2022

only a slight further decrease, for SSA2 the strength
is reduced significantly by 57%.

compressive strength (%)

least no negative effect on the compressive strength.

Fig. 2 Compressive strengths of blocks containing
rapid hardening cement RHC and varying
concentrations of sewage sludge ash SSA1 cured for
7 and 28 days.

100%
80%
60%
40%
20%
0%

0

5

10

15

percent SSA
7 days 20°C

For testing with OPC the compression strength of
the reference mortar blocks (without SSA) was 20.5
and 31 MPa respectively after 7 and 28 days of curing.
To better compare the strength decrease with theaddition of SSA, these values are shown as 100% in
Fig. 3 and 4.
With both sewage sludge ashes, a reduction instrength can be observed when replacing OPC by
SSA, except for SSA1 with 5 and 10% substitution at
40°C and a curing time of 7 days. There is even aslight increase. After 28 days, however, a significant
decrease is recorded in all samples.

7 days 40°C

28 days 40°C

Fig. 4 Compressive strengths of blocks containing
Ordinary Portland cement OPC and varying
concentrations of sewage sludge ash SSA1 cured for
7 and 28 days.
CONCLUSIONS
Mortar block shrinkage was found to correlate
positively with the amount of SSA. A 15%
substitution of rapid hardening cement as well as
Portland cement by SSA significantly reduces the rateof block shrinkage.
The blocks containing SSA1 and substituting
rapid hardening cement RHC exhibit similar
compressive strengths after 28 days curing as thereference without SSA.
However, substitution of OPC by SSA showed adecrease in compressive strength of 20% for 5%
substitution and 40-60% for 15% OPC replacement.

SSA1 OPC
100%

compressive strength (%)

SSA2 OPC

80%
60%
40%

REFERENCES

20%
0%
0

5

10

[1] Halliday, J. E., Dyer, T. D., Jones, M. R., andDhir, R. K., Potential use of UK sewage sludge
ash in cement-based concrete. Waste andresource management, Vol. 165, 2012, pp. 57-66.
[2] Donatello, S., and Cheeseman, C. R., Recycling
and recovery routes for incinerated sewage
sludge ash (ISSA): A review. Waste
Management, Vol. 33, 2013, pp. 2328-2340.
[3] Lin, K., Chiang, K., and Lin, C., Hydration
characteristics of waste sludge ash that is reusedin eco-cement clinkers. Cement and Concrete
Research, Vol. 35, No. 6, 2005, pp. 1074–1081.
[4] Lin, K.-L., Lin, D., and Luo, H., Influence ofphosphate of the waste sludge on the hydration
characteristics of eco-cement. Journal ofHazardous Materials, Vol. 168, Issue 2-3, 2009,15

percent SSA
7 days 20°C

7 days 40°C

28 days 40°C

Fig. 3 Compressive strengths of blocks containing
Ordinary Portland cement OPC and varying
concentrations of sewage sludge ash SSA1 cured for
7 and 28 days.
If you look at the increase in curing temperaturefrom 20° to 40°C, the positive effect (small decreaseor even increase in strength) is more pronounced with
SSA1 than with SSA2. The final strength after 28
days is similar for both ashes with 5% substitution(20% decrease in strength) and with 10% (37.5%
decrease). For SSA1 with 15% replacement there is324

GEOMATE – Bangkok, Thailand, 22-24 November 2022

pp. 1105–1110.
[5] Tantawy, M. A., El-Roudi, A., Abdalla, E. M.,
and Abdelzaher, M. A., Evaluation of the
Pozzolanic Activity of Sewage Sludge Ash.
ISRN Chemical Engineering, 2012, pp. 1-3.
[6] Cyr, M., Coutand, M., and Clastres, P.,
Technological and environmental behavior of
sewage sludge ash (SSA) in cement-basedmaterials. Cement and Concrete Research, Vol.
37, 2007, pp. 1278–1289.
[7] Lisk, D. J., Compressive Strength of Cement
Containing Ash from Municipal Refuse or
Sewage Sludge Incinerators. Bulletin ofenvironmental contamination and toxicology,
Vol. 42, No. 4, 1989, pp. 540-543.
[8] Piasta, W., and Lukawska, M., The Effect of
Sewage Sludge Ash on Properties of Cement
Composites. Procedia Engineering, Vol. 161,
2016, pp. 1018-1024.
[9] Pérez-Carrióna, M., Baeza-Brotonsa, F., Payáb,J., Savala, J., Zornozaa, E., and Borracherob, M.,
Potential use of sewage sludge ash (SSA) as a
cement replacement in precast concrete blocks.
Materiales de Construcción, Vol. 64, 2014, pp. 13.
[10] Tay, J.-H., and Show, K.-Y., Utilization ofmunicipal wastewater sludge as building andconstruction materials. Resources, Conservation
and Recycling, Vol. 6, 1992, pp. 191-204.
[11] Fontes, C., Barbosa, M., Filho, R. T., andGonçalves, J., Potentiality of sewage sludge ash
as mineral additive in cement mortar and highperformance concrete. International RILEM
conference on the use of recycled materials inbuildings
andstructures,
Conference
proceedings, 2004, pp. 797-806.
[12] Monzó, J., Paya, J., Borrachero, M., andCórcoles, A., Use of sewage sludge ash (SSA)
cement admixtures in mortars. Cement andConcrete Research, Vol. 26, No. 9, 1996, pp.
1389-1398.
[13] Pinarli, V., and Kaymal, G., An innovative
sludge disposal option-reuse of sludge ash byincorporation in construction materials.
Environmental Technology, Vol. 15, No. 9, 1994,
pp. 843-852.
[14] Ing, D. S., Chin, S. C., Guan, T. K., and Suil, A.,
The Use of Sewage Sludge Ash (SSA) as Partial
Replacement of Cement in Concrete. ARPN
Journal of Engineering and Applied Sciences,Vol. 11, No. 6, 2016, pp. 3371-3375.
[15] Wagner, J.-F. and Tabe Arrey Egbe, T., Sewage
sludge ash as cement replacement in concrete
blocks.
Proceedings
SUM2018,
FourthSymposium on Urban Mining, 2018, 6 p.
[16] Nazierah, M. Y., Kartini, R., Hamidah, M. S.,
and Nuraini, T., Compressive Strength andWater Absorption of Sewage Sludge Ash (SSA)
Mortar. CIEC, 2015, pp.199-207.

325

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

STUDY OF THE HEAT EQUATION AND THE EFFECT OF
TEMPERATURE INSIDE AN ELECTRIC CABLE CONSISTING OF
ALUMINUM AND COPPER METALS* Dalal Adnan Maturi 1
1

Departement of Mathematics, Faculty of Science, King Abdulaziz University, P.O.Box 42664, Jeddah 21551,
Saudi Arabia
ABSTRACTIn this paper, we study the mathematical physics model; partial discharge (PD) mapping and power cable fault
location are approaches based on the principle of time domain reflection (TDR) measurement, influenced by thepower cable's height Frequency wave propagation properties. Furthermore, power cables. They're increasingly
being used to transmit electrical energy and communication signals simultaneously. Smart grids are an example
of such technology. Power cables are exposed to a variety of temperatures when in use. Changes in the load current
flowing through the cable cause differences. As a result, it is essential to Recognize how temperature affects thehigh-frequency characteristics of power wires. Temperature changes advance Errors in TDR measurements,
according to simulations, and result in numerical solutions undertaken in this study. It's also shown that when thecable's temperature rises from25°C to 60°C, the attenuation increases by 1° and the propagation velocity rises by
1°.a 4-percentage-point average The phase constant, on the other hand, drops by order of magnitude on average.
The findings imply that temperature effects must be considered while making decisions. Communication channels
in power cables are designed.
Keywords: Heat Equation, Electric Cable, Aluminum, Copper, MATLAB.

INTRODUCTION

could be separated by friction and neutralized eachother when combined [9]. The "two fluid" theory ofelectricity proposed by du Fay, which was based on
experiments with the attraction and repulsion ofdifferent electrified substances, was later contrasted
with the "one-fluid" theory proposed by American
scientist Benjamin Franklin (1706-1790), whichassumed only one fluid was present, either positively
or negatively charged [10]. The basis for the behaviorof the new property was established with theclassification of substances as conductors or
insulators and the establishment of the electrical flow
direction from positive to negative.

In the eighteenth century, the idea of electricalconductivity was developed, as were the firstinvestigations on its temperature dependence.
Although the German physicist Paul Karl Ludwig
Drude (1863-1906) made the first serious theoreticaladvances on electrical conductivity of metals, Thediscovery of electricity as an imponderable elastic
fluid transferable from one body to another by British
scientist Stephen Gray (1666-1736) around one
hundred and eighty years before, sweeping away theold idea of an electrical effluvia inseparably attached
to a body in which they were excited [1-4]. Gray, adyer by trade and an amateur naturalist by inclination,
carried out a series of experiments using a hemp
thread in 1720, inspired by the accounts of his
physicist and compatriot Francis Hawksbee (16661713) [5,6], and after some related experiences as an
assistant to the also British natural philosopher Jean
Theophilus Desaguliers (1683-1744), Gray, a dyer bytrade and an amateur naturalist by inclination, carriedout a series. The early Gray's theories on electricalcommunication were practically ignored at first, butnine years later they returned to the fore and gained
public domain with a fresh publication in 1731 [7,8].
No significant progress was made on this subject until
1734, when Charles Fran cisde Cisternay du Fay(1698-1739), a French scientist and Superintendant of
the Jardins du Roi, proposed the existence of two
types of electric fluid, vitreous and resinous, which

RATING FOR DYNAMIC CABLES
Due to the fluctuation of the conductor temperatureduring the useful life of the cable according to the
inputs of the thermal models, the application of thesteady-state current rating of cables may be cautious
in some circumstances. As a result, throughout time,
the assessment of a line's thermal stress has shiftedfrom a steady-state thermal rating to a more general
Dynamic Line Rating (DLR) or Dynamic Thermal
Rating (DTR), which may better characterize thermaltransients and their implications. The DLR is used todetermine the line's actual current rating based oncontinuous measurements or the solution of thecable's thermal model. Assumptions on input
variables that are conservative, such as those used in
the steady-state model.
326

GEOMATE – Bangkok, Thailand, 22-24 November 2022

𝜕𝑢𝜕𝑡

THERMAL ANALYSIS OF POWER CABLES
USING HEAT TRANSFER CONCEPTS

𝑎2 𝜕

𝜕𝑢

= 𝐴 + 𝑟 𝜕𝑟 (𝑟 𝜕𝑟 ) , 0 ≤ 𝑟 < 𝑏, 0 < 𝑡(2)where 𝐴 = 𝐼 2 𝑅𝑚/((𝜋𝑏2 𝑐𝜌),The nonhomogeneous heat equation for an
infinitely long, axisymmetric cylinder is 8.3.139. We
know that we must write the temperature as the sumof a steady-state and transient solution because of𝑢(𝑟, 𝑡) = 𝑤(𝑟) + 𝑣 (𝑟, 𝑡).

The current traveling through a power cablegenerates heat, which is released into the environment
through the cable's metallic layers and insulation. In
the presence of a temperature gradient, heat is anenergy form that is transmitted from one system toanother. Conduction, convection, and radiation are
the three heat transfer mechanisms involved in thisprocess. If the cable is buried, exposed to the air, or
submerged in water, the conduction phenomena
happens through the cable's metallic layers andinsulation. From the cable surface to the outside
world, convection and radiation occur. Convection isclassified as natural convection or forced convection
depending on the nature of the heat movement.
Natural convection occurs when the flow is producedby buoyant forces caused by density differences in the
air caused by temperature gradients. Forced
convection is caused by external factors such as a
pump, a fan, or the wind. In comparison to cables
buried underground, heat transmission processes forcables deployed in open air or water are more
complex due to interactions with the ambientenvironment. Electromagnetic waves, or photons, arereferred to as radiation. Radiation does not require a
medium and is greatly influenced by temperature [8].
Only for electrical cables located in air, heat istransported from the cable surface to its surroundings
by convection and radiation. If the power wire in the
air is exposed to solar radiation, it is considered an
extra energy source [9].

1 𝑑
𝑟 𝑑𝑟

𝑑𝑤

𝐴

(𝑟 𝑑𝑟 ) = − 𝑎2

(3)

or
𝐴𝑟 2

𝑤(𝑟) = 𝑇𝑐 − 4𝑎2,

(4)

The steady-state solution 𝑤(𝑟) meets therequirements. 𝑇𝑐 is the (as-yet-unknown) temperaturein the cable's center 𝑣(𝑟, 𝑡) is a transient solution
governed by
𝜕𝑣
𝜕𝑡

1 𝜕

𝜕𝑣

= 𝑎2 𝑟 𝜕𝑟 (𝑟 𝜕𝑟 ) , 0 ≤ 𝑟 < 𝑏, 0 < 𝑡,(5)𝑢(𝑟, 0) = 𝑇𝑐 − 𝐴𝑟 2 /(𝐴𝑎2 ) + 𝑣(0, 𝑡) = 0 is theinitial condition. 𝑟 = 𝑏 at the surface
The boundary condition is 𝑢𝑟 = − ℎ𝑢, where ℎis the surface conductance, because heat radiates tofree space. Because after all transient effects passaway, the temperature equals the steady-state solution,𝑤(𝑟) must fulfill this radiation boundary condition
independent of the temporary solution. This
necessitates that
𝐴

𝑏2

𝑏

𝑇𝑐 = 𝑎2 ( 4 + 2ℎ)

TEMPERATURE WITHIN AN ELECTRICALCABLE
The temperature achieved within an electricalcable as a function of current and other parameters is
required in the design of cable systems. Let us solve
the nonhomogeneous heat equation in cylindricalcoordinates with a radiation boundary condition to do
this. The heat equation is derived from the law ofconservation of energy
Heat generated=heat dissipated + heat stored𝜕𝑢𝜕𝑢
𝐼 2 𝑅𝑁𝑑𝑡 = −𝑘 [2𝜋𝑟 | − 2𝜋 (𝑟 + ∆𝑟) |
] 𝑑𝑡
𝜕𝑟 𝑟
𝜕𝑟 𝑟+∆𝑟+ 2𝜋𝑟∆𝑟𝑐𝜌𝑑𝑢,(1)
where 𝐼 is the current through each wire, 𝑅 is theresistance of each conductor, 𝑁 is the number ofconductors in the shell between radii r and 𝑟 + ∆ 𝑟 =2𝜋𝑚∆𝑟/(𝜋𝑏2 ), 𝑏 is the cable radius, 𝑚 is the total
number of conductors in the cable,𝜅 is the thermal
conductivity, 𝑐 is the average specific heat, and 𝑢 is
the temperature. Equation ( 1 )becomes in the limit of
∆𝑟 →0

(6)

As a result, at 𝒓 = 𝒃, 𝒗(𝒓, 𝒕) must satisfy
𝒗𝒓 (𝒃, 𝒕) = −𝒉𝒗(𝒃, 𝒕)
By separating the variables 𝑣 (𝑟, 𝑡) = 𝑅(𝑟)𝑇(𝑡), we
can get the transient solution 𝑣 (𝑟, 𝑡)Using Equation(5) as a starting point
1 𝑑
𝑑𝑅
1 𝑑𝑇
(𝑟 𝑑𝑟 ) = 𝑎2𝑇 𝑑𝑡 = −𝑘 2 ,
(7)
𝑟𝑅 𝑑𝑟
or
𝑑
𝑑𝑟

𝑑𝑅

(𝑟 𝑑𝑟 ) + 𝑘 2 𝑟𝑅 = 0,𝑑𝑇
𝑑𝑡

+ 𝑘 2 𝑎2 𝑇 = 0,

(8)
(9)

with 𝑹′ (𝒃) = −𝒉𝑹(𝒃). 𝑱𝟎 (𝒌𝒓)where 𝑱𝟎 is the zeroorder Bessel function of the first kind, is the only
solution to Equation (8) that stays finite at 𝒓 = 𝟎 andsatisfies the boundary condition. The transcendental
equation I is obtained by substituting 𝑱𝟎 (𝒌𝒓) into theboundary condition
𝑘𝑏𝐽1 (𝑘𝑏) − ℎ𝑏𝐽0 (𝑘𝑏) = 0(10)Equation (10) gives an infinite number of uniquezeros 𝑘𝑛 for a given value of ℎ and 𝑏.
327

GEOMATE – Bangkok, Thailand, 22-24 November 2022

2.2747 𝐶/𝑠 , and 𝑇𝑐 = 23.94 𝐶 . A represents 37
wires of #6 AWG aluminum wire in a cable providing
a 22 amp current.

The problem's associated temporal solution
𝑇𝑛 (𝑡) = 𝐴𝑛 exp(−𝑎2 𝑘𝑛2 𝑡),(11)

as a result, the total number of product solutions
2 2
𝑣 (𝑟, 𝑡) = ∑∞𝑛=1 𝐴𝑛 𝐽0 (𝑘𝑛 𝑟 ) exp(−𝑎 𝑘𝑛 𝑡 ). (12)The final challenge is to calculate An. Equation (12)is evaluated at 𝒕 = 𝟎
𝐴𝑟 2

𝑣 (𝑟, 0) = 4𝑎2 − 𝑇𝑐 = ∑∞𝑛=1 𝐴𝑛 𝐽0 (𝑘𝑛 𝑟 ),(13)

In 𝑱𝟎 , there is a Fourier-Bessel series (𝒌𝒏 𝒓). We
demonstrated how to compute the coefficient of a
Fourier-Bessel series expanded in 𝑱𝟎 (𝒌𝒏 𝒓) with aboundary condition of the type we've arrived to thefollowing conclusion
2𝑘 2𝑏Fig. 2

At various points 𝑟/𝑏 and times 𝑎2 𝑡/𝑏2 thetemperature field (in degrees Celsius) within
an electric aluminum cable with 37 wires
and a current of 22 amperes.The temperature
was initially set to zero, and then the cable
was allowed to cool naturally as it washeatedℎ𝑏 = 1 is the value of theparameter, and the value of the parameter isthe value of the parameter. The cable's radius
is 𝑏 = 4 𝑐𝑚.

Fig. 3

Analytic solution of Electric Equation

𝐴𝑟 2

𝐴𝑛 = (𝑘 2 𝑏2+ℎ2𝑏𝑛2)𝐽2(𝑘 𝑏) ∫0 𝑟 (4𝑎2 − 𝑇𝑐 ) 𝐽0 (𝑘𝑛 𝑟)𝑑𝑟
𝑛

0

𝑛

(14)
Performing the integrations that have been specified
𝐴𝑛 =

2

[(

𝐴𝑘𝑛𝑏

2 +ℎ 2 )𝐽 2 (𝑘 𝑏)
(𝑘𝑛
4𝑎 2
0 𝑛
𝑇𝑐𝑘𝑛
𝐴
) 𝐽1 (𝑘𝑛 𝑏) + 2𝑎2 𝐽0 (𝑘𝑛 𝑏)]𝑏−𝐴
𝑘𝑛 𝑏𝑎 2−(15)

consider the following values:𝑏 = 4 𝑐𝑚 , ℎ𝑏 = 1, 𝑎2 = 1.14 𝑐𝑚2 /𝑠 , 𝐴 =2.2747 𝐶/𝑠 , and 𝑇𝑐 = 23.94 𝐶 . A represents 37
wires of #6 AWG copper wire in a cable providing a
22 amp current.

At various points 𝑟/𝑏 and times 𝑎2 𝑡/𝑏2 thetemperature field (in degrees Celsius) within
an electric copper cable with 37 wires and acurrent of 22 amperes.The temperature wasinitially set to zero, and then the cable wasallowed to cool naturally as it was heatedℎ𝑏 = 1 is the value of the parameter, andthe value of the parameter is the value of theparameter. The cable's radius is 𝑏 = 4 𝑐𝑚.
consider the following values:𝑏 = 4 𝑐𝑚 , ℎ𝑏 = 1, 𝑎2 = 0.86 𝑐𝑚2 /𝑠 , 𝐴 =Fig. 1

Fig. 4328

Numerical solution of Electric Equation

GEOMATE – Bangkok, Thailand, 22-24 November 2022

to upgrade the installed cables, is a topic that is
becoming more appealing.
ACKNOWLEDGMENTS
This paper was supported by the Deanship ofScientific Research (DSR), King Abdulaziz
University, Jeddah.
REFERENCES

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Finite Element Method of Electric Equation

CONCLUSIONSThe thermal models of subterranean cables werediscussed in this study, starting with simple models
with general hypotheses and progressing to morecomprehensive specifications to meet practicalinstances. The heat transport concepts needed toderive an underground cable's thermal model havebeen summarized, and applications to specificscenarios have been defined (e.g., the non-infinite
dimension of the soil, cable with a finite length).
Furthermore, because the cable thermal model iscommonly utilized in the sphere of power systems,
the electrothermal analogy has been applied to it. To
provide an overview of the primary contributions, themethodologies utilized to model heat transfer in thecables and in the external medium have beensummarized. Finally, the cable thermal models havebeen used to calculate the cable current rating.
Underground power cables have been widely
investigated and modelled for decades, and as aresult, there are several contributions in the literature.
The historical components of this review paper havebeen presented, and the latest evolutions have beendelineated. According to the literature assessment,
there are a number of ongoing advancements thatneed to be improved. Due to the processing speed
available, detailed FEM models of cables with thesurrounding soil and ambient, including in 3D, arebecoming feasible in non–uniform circumstances.
However, there has been a fascinating developmentof improved and simplified models that can produce
results that are comparable to FEM. The applicationof realistic thermal models of cables in the field of
dynamic line rating, recognized as an activity thatallows greater utilization of cable lines in various
scenarios and is also useful for deferring expenditures

329

GEOMATE – Bangkok, Thailand, 22-24 November 2022

[15] Maturi A.D., Adomian Decomposition Method
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[16] Maturi A.D., Application of Adomian
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[17] Sorkun H.H., Yalcinbas S., Approximate
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2186-2982 (P), 2186-2990 (O), Japan.
[21] Maturi A.D., Finite Difference Method For
Solving Heat Conduction Equation Of The Brick,
International Journal of GEOMATE, April,
Vol.18, Issue 68, 2020, pp. 114-119.
[22] Maturi A.D., Simbawa A.E., The Modified
Decomposition Method For Solving Volterra
Fredholm Integro-Differential Equation Using
Maple, International Journal of GEOMATE,
March Vol.18, Issue 67,2020, pp. 84-89.

330

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

ANALYSIS OF RESOURCE REQUIREMENTS IN A CONSTRUCTION
SAFETY PROGRAM TO ESTIMATE COST OF ARCHITECTURALWORK IN FLATS BUILDING
Rossy Armyn Machfudiyanto 1, Yusuf Latief1 , Ratih Fitriani1 and Amira Syifa1
1

Department of Civil and Environmental Engineering, Faculty of Engineering, Universitas Indonesia,
Kampus Baru UI Depok, West Java - 16424

ABSTRACT
According to the world meter’s report in December 2020, Indonesia is ranked as the fourth most populated
country globally. Previous reports also showed that the demand for basic human needs such as housing increases
along with the population. Based on the Association of Indonesian Housing and Settlement Developers in 2021,
the housing backlog reaches 8.2 million with an annual increase of approximately 500 thousand/year. To resolve
this problem, The Ministry of Public Works and Housing in Indonesia implement the program "1000 Tower
Project" to build vertical housing in form of flats building but several construction accidents occurred during its
implementation. In this kind of flats building project, architectural works have the highest number of work items
compared to other types, which makes further analysis necessary to mitigate construction accidents. Therefore,
this study aimed to identify the list of architectural work, potential hazards, risk factors, and their controls for goals
and programs to be drawn up with plans for resource needs. The method used was qualitative with secondary dataor literature study, which involved the estimation of construction safety costs for architectural work in Flatsprojects by identifying resource requirements on safety programs.
Keywords: Construction Safety Cost, Flats Building, Resource Requirement, Safety Program

INTRODUCTION

with the lowest prices, which leads to low
construction safety budgets [2]. According to aprevious study [3], the most optimal construction
safety cost for buildings is 1% of the total projectbudget. It was estimated [4] that construction safety
costs on building projects are 1.92% of the total costs.
Based on the calculation of the cost of eachconstruction safety component in a building project,
it was discovered that workers' training and PPE are
the biggest [5]. Therefore, this study aims to conduct
a resource analysis to estimate the cost of
construction safety on the apartment building project.

According to the International Labour Organization(ILO), the impact of work accidents is divided into 3
levels, namely the micro, mezzo, and macro.
Accidents at the micro level will affect human safety,
project delays, and the total project cost. At the mezzo
level, it affects the company's performance andreputation, which negatively influence thecompetitiveness index of a country at the macro level[1]. The Ministry of Public Work and Public Housing(PUPR) stated that the impact of construction
accidents will be felt by 5 subjects, namely labor,
community, projects, companies, and the state(Construction 2020). The effect on the workforce canbe in form of injury or death, causing doubts for otherworkers. The impact on the community includes
injury, death, and feeling uncomfortable living
around the project area. Meanwhile, the effect on the
project is in form of a temporary halt in the project,
delayed use of construction results, idle labor, andequipment, as well as decreased return on investment.
The influence of the construction accident on thecompany is incompetence and a reduction in stock
value. The wider impact is on the state, where there isan inability to carry out development safely. As in
Indonesia, most of the construction work in the public
sector in many countries uses a competitive tenderingsystem. This is implemented to select contractors

THEORETICAL STUDY
Construction Safety Resource
Safety Resources in the Architectural Work of Flats
Buildings is a potential capacity that can be used forconstruction activities. It requires a goodmanagement system for optimal use. Resources
consist of the means that are necessary to carry out
the project and achieve the goals and objectives
efficiently and effectively. In the architectural workof high-rise flats buildings, safety resources areneeded to achieve the project objectives. Based on aprevious study [6], it was stated that the required
resources are as follows:

331

GEOMATE – Bangkok, Thailand, 22-24 November 2022

20
Table 1. Construction Safety Resource TypeNo Construction
Safety Resource Type

Protective

Labor

Officer
Signs

Resource Component
Personal

Safety Expert and Safety

Equipment

Equipment

21

Hint Sign

Equipment

22

Prohibition Sign

Equipment

1

Safety Helmet

Equipment

23

Warning sign

Equipment

2

Goggles, Spectacles

Equipment

24

Obligation Sign

Equipment

3

Face Shield

Equipment

4

Ear Plug, Ear Muff

Equipment

5

Masks

Equipment

6

Safety Gloves

Equipment

7

Safety Shoes

Equipment

8

Full Body Harness

Equipment

9

Safety Vest

Equipment

10

Fall Arrester

Equipment

SafetyOthers Related to Risk
Control
25

Related to Risk Control)
Ear protector

Equipment

2

Noise measurement

Equipment

3

Material transport cart

Equipment

Based on the table, additional construction safety
resources such as ear protectors and noise
measurements are used only for Exposed Concrete
Ceiling Finishing work. This is because the work uses
a grinding tool with a loud sound in the intensity of along duration compared to other jobs. There is also
the provision of material transport carts that are onlyused for masonry and ceramic masonry, consideringthat the volume of work is significantly large and theload transported is also heavy. From the analysis of
the need for the construction safety resource plan, thecosts of each architectural work activity of high-risebuildings for the flats or apartment projects can becalculated.

ofManuals,

Procedures,

Work

Instructions,
Permits

Labor

and

Forms
Safety Socialization andPromotion
12

1

Plan

Manufacture

Work

Equipment

Additional (including Others

Development
11

Light Fire Extinguisher

Safety Briefing, Safety

Labor

Meeting
13

Safety Simulation

METHODOLOGYWork

Based on the flow of the study stages, the first step is
to identify the targets and programs in eachconstruction activity in the apartment building,specifically in the field of Architecture. The method
used can be determined, namely expert validation
with the Delphi approach, with approximately 3-5
experts. Subsequently, the analysis of experts’validation results is carried out to obtain the output ofarchitectural work descriptions on the flats orapartment projects.
This is followed by a resource analysis using theneeds matrix developed by the Minister of Public
Works and Public Housing Regulation No. 10 of
2021, to estimate the safety cost in the apartmentbuilding.

Protective

Equipment
14

Safety Net

Equipment

15

Safety Rope

Equipment

16

Safety Deck

Equipment

17

Safety Fence

Equipment

Insurance and Licensing
18

Equipment

Eligibility

Labor

Permit19

Operator Permit

Labor

Safety Personnel

332

GEOMATE – Bangkok, Thailand, 22-24 November 2022

construction.
Risk
AnalysisWork
Breakdown
Structure

Based on the results of expert validation, 2 additional
potential OHS hazards for architectural work in flatsthat were obtained include:
1. Fire, this potential risk can occur inalternative designs/methods of Waterproof
Membrane Concrete Flooring, where it uses
fire burning from a gas cylinder fire source.
2. Overload of Vertical Transport Equipment.
This potential risk can occur in masonrywork, considering the volume of wall work
is significantly large with the load beingtransported and also heavy compared toother architectural work.

Goals and
ProgramResource
AnalysisFig. 1. Study Flow Methodology

Therefore, the potential risks of safety hazards forarchitectural work in buildings are obtained, namely:
1. Fall from a height.
2. Hit, caused by objects that fall or move andothers.
3. Collision, contact with sharp/hard objects
from cuts, scratches, etc.
4. Inhalation/absorption of harmful substances
into the body through inhalation or skin.
5. Slipping.
6. Exposed to electric current.
7. Movement beyond capacity (sprain).
8. Eyes exposed to/splashed with spray.
9. Caught on, in, and between objects (pinched,buried, drowned, etc.).
10. Exposure (temperature, air pressure,vibration, radiation, sound, light, etc.).
11. Fire.
12. Overload of Vertical Transport Equipment.

RESULT AND DISCUSSION
From previous studies on WBS standards forarchitectural work for rented flats [7], there wereadditional 16 to 258 activities (originally 242)
including:
1. Installation of Moisture Resistant Gypsum
on an Alternative Design of the Gypsum
Wet Resistant Ceiling.
2. Practical Column Work on Alternative
Designs for the Walls of Interior Paint Brick,
Wet Area Ceramic Brick, Brick Parapet,
Fire Rated Fire Stairs, and Pantry Ceramic.
3. Floor Screeding Work on an Alternative
Floor Hardener Floor design.
4. Waterproofing and Testing Work on
Alternative Designs for Wet Area Ceramic
Floors.
5. Screed Leveling Coating Work on an
Alternative Roll Carpet Floor Design.
6. Concrete Finishing Work on Alternative
Concrete Embankment Designs.
7. The Work of Installing the Hanging Frame
and Automatic Door Machine on the
Alternative Design of the Automatic
Entrance Glass Window Door.
8. Installation of Door Closers on Alternative
Designs of Fire Ladder Doors.
9. Marking Work on Alternative Parking
Marking Paint Designs.
10. Glass and Sealant Installation Work on
Alternative Curtain Wall Designs.
The last stage of this study is to carry out the final
validation of the WBS-based OHS cost components
which are presented in the implementation of risk
control. Therefore, a cost must be budgeted,consisting of the objectives and control programs foreach activity. The WBS-based cost components thatare compiled will increase the accuracy of safety costcalculations. They are also expected to increase theapplication of safety to reduce or eliminate thenumber of work accidents that occur in the flats

From the results of these potential hazards, thefrequency and impact of risks for architectural work
based on the WBS (Work Breakdown Structure) of
the Construction Work of the Rental Flats Building
Project are obtained. Based on the WBS architectural
work standards, potential OHS risks were identified
from each alternative design/method, and 52 risks
were in the high-level category.
The resources needed for hazard control in thearchitectural work of flats are as follows:
1. Protective Hat
2. Eye Protection (Goggles, Spectacles)
3. Face Shield
4. Ear Protection (Ear Plug, Earmuff)
5. Respiratory and Mouth Protection (Masks)
6. Gloves (Safety Gloves)
7. Safety Shoes (Safety Shoes)
8. Full Body Harness
9. Safety Vest (Safety Vest)
10. Fall Arrester
11. Preparation of Manuals, Procedures, Work333

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Instructions, Work Permits and Forms
12. Safety Briefing (Safety Briefing),
13. Safety Meeting (Safety Talk and/or Tool
Box Meeting)
14. OHS Simulation
15. Safety Net
16. Safety Line
17. Fall Protection (Safety Deck)
18. Safety Fence (Guard Railing)
19. Equipment Eligibility Permit
20. Operator Permit
21. Safety Expert and/or Safety Officer
22. Prohibition Sign
23. Warning Sign
24. Obligation Sign
25. Information Sign
26. Light Fire Extinguisher
27. Procurement of Material Transport Carts
28. Lighting
29. Communication Tools/Handy Talkies
30. Noise Measurement
31. Manufacture of Work Benches/Platforms
32. Create a Grounding Path
33. Creating a special area for placing B3

Safety Patrol

Construction
Safety Training

HIV/AIDS
outreachConstruction
Safety Simulation
Banners(Banners)
PosterOHS information
board

3

SafetyEquipment

Safety Net

Safety Rope (LifeLine)
Fall Protection(Safety Deck)

Table 2 showed that the analysis results are carriedout to calculate the need for construction safety
resources in the construction of apartments, which
will be used to estimate safety costs.
Table 2. Analysis of Construction Safety Resource
No

Construction Safety Resource

Items

Qty

1

SafetyPlan
Develop
ment

Paper
and
Modul
e
Paper
and
Modul
e
Paper
and
Modul
e

1 Ream of Paper
5 Volumes Module

Construction
Safety Plan
document creation
Preparation ofwork procedures
and instructions
Preparation ofreports on theimplementation of
SafetyManagement
System2

Socialization,
Promotion andTraining

Construction
Safety Induction

Miner
al
Water
and
Snack

Safety Fence(Guard Railing)

1 Ream of Paper
1 Volume Module

Restricted Area
(Restricted Area)

2 Reams of Paper(how many times
the compilationtime) x Module

Fall arrester

Disaster safety
equipment

Number of Workers+ 10% (guests)

Construction
Safety Briefing(Safety Briefing)

The number ofworkers

Meetings on
safety (Safety
Meeting, Safety
Talk, and/or Tool
Box Meeting)

The number ofworkers

4

334

PPE

Protective Helmet(Safety Helmet)(number of patrol
times) x Number of
Stakeholders
Instructor
Semin
ar kit
Consu
mptio
n

1 Instructor
Number of Seminar
Kits andConsumption =Number of Workers

Banne
rs
Poster

4 pieces for eachfloor
5 pieces for eachfloor
1 piece

Infor
matio
n
boardsSafetyNet

Along the side of
the project that is
directly adjacent tothe community

Lifelin
e
Wire
mesh
3x2 m
Iron
Pipe
Frame
s
Guard
Rail

2 x building length

Restri
cted
Area
Board
Fall
Arrester

1 piece

Stretcher
Ambu
lance,
Emerg
ency
Light,
Flashli
ght,
Body
Bag
SafetyHelmetEach Tool @ 2
piecesAlong the longest
side of the building

Length of building
perimeter x Numberof floors

Number of workers
working at height

Number of Workers+ 10% (guests)

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Eye Protection(Goggles,Spectacles)
Face Shield (FaceShield)Diving Mask
(Breathing
Apparatus)
Ear Protection(Ear Plug, Ear
Muff)
Respiratory andMouth Protection(Masks)Gloves (Safety
Gloves)Safety Shoes(Safety Shoes)

56Insurance andLicensingConstru
ction
SafetyPersonnelSafety
Goggl
es
Face
ShieldBreath
ing
Appar
atus
Ear
Plug

The number ofworkersFace
Mask

Number of Workers
per box + 30%(guests and in
COVID-19
condition)
The number ofworkers

SafetyGloves
SafetyShoes

Construction
Safety Officer
and/orConstruction
Safety Officer
Emergency
response officer
First Aid Officer

The number ofworkers
Min. 1 piece

The number ofworkers

Total Stakeholders+ Workers
7

Safety Shoes(Rubber Safety
Shoes and Toe
Cap)Full Body
Harness

Rubbe
r
Shoes

The number ofworkers

SafetyHarne
ss

Number of workers
working at height

Life Jacket (Life
Vest)
Safety Vest
(Safety Vest)

Life
Vest
SafetyVest

Aprons(Aprons/Coveralls
)

Cover
all
SafetyA number of
welding workers

fall protection(fall arrester)

Fall
Arrester

Number of workers
working at height

Insurance

Insura
nce
Docu
ments
Certificate/P
ermit
Docu
ments
SafetyExpert/Safety
Officer

The number ofworkers

Checking or
testing the fitnessof the tool to get
the license of thetoolConstruction
Safety expert orconstruction
safety expert

Traffic control
officer/coordinator/
flagmanMedical and/or
health personnel

HealthFacilitiesEnvironmental
Management
OfficerFirst aid kit

First aid room
Number of Workers+ 10% (guests)

Fogging
Equipment

Cost of infectious
outbreak healthprotocol (COVID19)

A number of heavyequipment to beinspected/tested

The Need for safety
Experts/safety
Officers(comparison ofsafetyExperts/safety
Officers with
Workers)
Low Risk = 1:60
Medium Risk =8
1:50
High Risk = 1: 40

335

Psychotropic andHIV examination

Adequate medical
equipment for
self-isolation

Ambulance

Signs

Hint Sign

firemanFirst
Aid
Officer
Flagm
an

1 personMedicalpersonnel
Janitor1 person

first
aid

3 types of first aid
packages:
Package A: for 25
peoplePackage B: for 50
peoplePackage C: for 100
people1 room

First
Aid
Room
Foggi
ng
Equip
ment
Hand
washi
ng
facilities
Antige
n
Swabs
/PCR
Vitam
in
Medic
al
check
up
Patient's bedOxyge
n tube
Oxime
ter
Ambu
lance
Car
Hint
Sign

1 person1 person

2 persons1 piece

Handwashing place:1 piece at theentrance gate
Swab Antigen/PCR:
number of workers
and stakeholders per
monthVitamins: numberof workers andstakeholders
The number ofworkers

Each item @ 3
pieces for the room
area (± 16 m2)

1 piece1 pieceGEOMATE – Bangkok, Thailand, 22-24 November 2022

Prohibition Sign

Prohib
ition
Sign
Warni
ng
sign
Obliga
tion
Sign
Infor
matio
n Sign
Temp
orary
Job
Sign
Evacu
ation
Route
Sign
Traffi
c
Contr
ol
Stick
Traffic
Cone
RotaryLamp
Signs,
marki
ngs
and
controldevices
ExpertsExperts

1 pieceWork
environment
inspection

ReportDocu
ment

1 x inspection per
document

Light Fire
Extinguisher

fire
extinguishersiren

2 x per floor

Lightn
ing
rod
Anem
omete
r
Safety
flag
Emerg
ency
Lamp

1 piece

Warning sign

Obligation Sign

Information Sign

Temporary Job
Sign

Evacuation route

Traffic Control
Stick

Traffic Cone
(Traffic Cone)
Rotary Lamp
Temporary roadequipment

910Consult
ation
withExperts
relatedto safety
Activities andequipment
relatedtoconstruction
safetyrisk
control

Environment
expertBuilding engineer

sirenLightning rod

Anemometer

Safety flag
Emergency lamp
(Emergency
Lamp)

1 piece

Environmental
inspection andtesting/Sampling
test
Making Worker
Identity Card
(KIP)

1 piece

External audit

1 pieceCCTV

1 piece1 piece1 x as much as thenumber of security

10 pieces

1 piece1 piece each1 x consultation
1 x consultation

1 piece1 piece

ReportDocu
ment

1 inspection/test x
per document

Worker
Identity Card(KIP)
Report
Docu
ment
CCTV

The number ofworkers

1 audit x per
document
4 pieces (gate,board of directors,
field area andstockyard/warehouse)Based on the final validation, the results anddiscussions obtained are stated below:
1. The cost components of ear protection andnoise measurement are used only for
Exposed Concrete Ceiling Finishing work.
This is because the work uses a grinding toolwith a loud sound in the intensity of a long
duration compared to other jobs.
2. The cost component for the procurement ofmaterial transport carts is only used formasonry and ceramic masonry, consideringthat the volume of work is significantly large
and the load transported is also heavy.
3. The vibration measurement component isomitted. This is because for architecturalwork, the intensity of the vibration generated
is generally small and the duration of thework is not too long.
4. The procurement component for fire sensing
facilities is omitted because the work withthe potential to be a fire hazard is only
Waterproof Membrane Concrete Flooring,where at the time the work is carried out,
electrical installations are not yet available.
The table above is a recapitulation of the required
safety cost components and arranged based on theircontrol for each activity in the work of thesuperstructure of the building. The next stage can bearranged according to the format of the Circular
Letter of the Minister of Public Works and Public
Housing Number 66/SE/M/2015 concerning the Costof Implementing an Occupational Health and Safety
Management System Construction in the Public
Works Sector.
A previous investigation [8] stated that the safety cost
component consists of several types of equipment
such as personal protective materials, plans, training,
work protective equipment, personnel, healthfacilities, signs, and security. It was also reported thatthe components of safety costs in building projects for
all activities are personal protective equipment,

1 piece1 piece336

GEOMATE – Bangkok, Thailand, 22-24 November 2022

training, work protective equipment, personnel,
health facilities, and security [9].

Community Engagement (DRPM) Ministry of
Research and Technology/National Research andInnovation Agency.

This study developed the safety cost component
based on the component contained in the Circular of
the Minister of Public Works and Public Housing
Number 66/SE/M/2015 concerning the Cost of
Implementing an Occupational Health and Safety
Management System Construction in the Public
Works Sector. During its application, not all
equipment and components of safety costs areincluded in the preparation to avoid an increase inproject cost. Before compiling the OHS costs on the
project, there is a need to review the existing risk
response to calculate the level of risk value to a low
or acceptable level. After the implementation of therisk control response based on the review, it is
discovered that the risk value has decreased from highor moderate to low and become acceptable.
Therefore, the control and its components are
sufficient. This is carried out to avoid theimplementation of potential risk control which isfeared to cost more than when the risk is accepted at
an already low level.

REFERENCES[1] Latief, Y., Machfudiyanto, R. A., Arifuddin, R.,
Setiawan, R. M. F., & Yogiswara, Y. (2017, July).
Study of evaluation OSH management system
policy based on safety culture dimensions inconstruction project. In Journal of Physics:
Conference Series (Vol. 877, No. 1, p. 012028).
IOP Publishing.
[2] Choi, T. N., Chan, D. W., & Chan, A. P. (2011).
Perceived benefits of applying Pay for Safety
Scheme (PFSS) in construction–A factor analysis
approach. Safety Science, 49(6), 813-823.
[3] Shohet, I. M., Luzi, M., & Tarshish, M. (2018).
Optimal allocation of resources in construction
safety:Analytical-empiricalmodel. Safety
science, 104, 231-238.
[4] Gurcanli, G. E., Bilir, S., & Sevim, M. (2015).
Activity based risk assessment and safety costestimation for residential building construction
projects. Safety Science, 80, 1-12.
[5] Hamid, A. R. A., Singh, B., & Mohd, A. S. (2014,
April). Cost of compliance with health and safety
management system among contractor inconstruction industry. In National Seminar on
Civil Engineering Research (pp. 1-10).
[6] Ferakhim, D., & Latief, Y. (2019). Developmentof safety cost for architectural works in rental
apartments building construction project based on
work breakdown structure. International Journalof Engineering and Advanced Technology, 8(5C),
16-27.
[7] Rianty, M., Latief, Y., & Riantini, L. S. (2018).
Development of risk-based standardized WBS(Work Breakdown Structure) for quality planningof high rise building architectural works.
In MATEC Web of Conferences (Vol. 159, p.
01019). EDP Sciences.
[8] Gunduz, M., & Ahsan, B. (2018). Construction
safety factors assessment through frequency
adjusted importance index. International Journalof Industrial Ergonomics, 64, 155-162.
[9] Yılmaz, M., & Kanıt, R. (2018). A practical toolfor estimating compulsory OHS costs ofresidential building construction projects in
Turkey. Safety science, 101, 326-331.

CONCLUSIONSThis study is expected to be used by project owners
and contractors to understand and apply the flow ofsafety cost calculations in the construction of flats andimplement regulations related to the location andheight of the building. Resource analysis which is themost important process in estimating construction
safety costs will also be a guide for job owners inassessing the fairness of the bid price submitted bybidders for safety costs in the process of selecting
construction work service providers. This makes it
necessary to develop a simpler method to calculate
the safety cost in construction work, specifically flatprojects, and focus more on its application in the field.
Meanwhile, there is a need to develop an application
for calculating construction safety costs forstakeholders to easily estimate safety costs,
specifically in the construction of flats without losing
any substance.
ACKNOWLEDGMENTS
The authors are grateful for the financial support
provided by the Ministry of Research andTechnology/National Research and Innovation
Agency through PDUPT Grant 2020 with contract
number: NKB- 2875/UN2.RST/HKP.05.00/2020
managed by the Directorate for Research and337

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

EXPERIMENTAL STUDY ON SURFACE PROTECTION OF
RESTORATIVE BRICK FOR CULTURAL PROPERTIES INAYUTTHAYA
1

Yuko Ishida1, Masaru Koemoto 2 , Mika Yamada3 and Hajime Ito4
Department of technological systems, Kindai University Technical College, Japan;
2
Osaka Bousui Construction Co., Ltd. , Japan;
3
Faculty of Science and Engineering, Waseda University, Japan;
4
JIP Techno Science Corporation, Japan;
ABSTRACT

Since early times, several bricks have been used as common building materials. Bricks deteriorate significantly intropical climates because of high temperatures and humidity. Therefore, bricks used in historic structures are
discussed to develop conservation measures. This study focuses on the bricks currently used for restoration at
Ayutthaya in Thailand. These are locally hand-made and are sintered at low temperatures. The physical propertiesof the restorative bricks were investigated to consider better conservation measures. Moisture is a major cause ofbrick deterioration, and protection of the brick surface is expected to inhibit rainwater penetration into the interior.
Therefore, an improved methodology of a protective coating to the bricks was provided, and its waterproofing
performance was investigated. Water absorption characteristics were evaluated by wet-dry cycle tests. The
specimens were immersed in water for 30 min before drying at 40°C for 23.5 h in one cycle. The cycle wasdesigned to simulate the local rainy season. The coatings used were (1) plaster, (2) oil mixed plaster, and (3) silanebased surface impregnating material. The initial water adsorption ratio for the bricks with plaster was inhibited byapproximately 1/3 compared to the uncoated bricks. Furthermore, the use of oil mixed with plaster can provide
high waterproofing performance with less than 1% water absorption. However, the adhesive performance of theplaster to the bricks was decreased by mixing oil.
Keywords: Historical brick building, Low-temperature sintering brick, Deterioration, Wet-dry cycle test, Plaster

INTRODUCTION

bricks as materials have been considered. It is
desirable to consider extending the life of material byremoving the cause of deterioration, as a basic idea ofpreserving cultural properties, rather thanstrengthening the deteriorated and vulnerable parts[4]. Therefore, understanding the physical andchemical deterioration characteristics of brick anddeveloping deterioration prevention measures for thematerial could help establish continuous maintenancemethods for historical brick buildings.

Bricks have long been used as a common building
material in many parts of the world. Therefore, manyold brick structures exist, as do many culturalbuildings with heritage value. These historical brickbuildings have problems such as weathering of thebrick and peeling of the bond between individual
bricks due to joint mortar expansion. Brickdeterioration is often more severe in tropical climates
because of temperature, moisture, high humidity,
solar radiation, insects, micro-organisms, and salt
effects [1]. In addition, biological deterioration due tothe propagation of algae, lichen, and plants is aproblem [2]. Some principles must be followed while
repairing cultural properties, such as world heritage
sites; the status quo will not be changed, original
materials and construction methods will be used, andso on. Because of the individual uniqueness of each
cultural property or differences in each surrounding
environment, no established standardized repair
method for historical brick buildings has beenreported. From a global perspective, many cases ofconservation and restoration of brick culturalproperties exist [3]. However, it is often a method for
replacing deteriorated bricks with new bricks fromthe viewpoint of preserving the structure, and there
have been few cases where measures for preserving

RESTORATION WORK OF WAT KRASAIPAGODAThe authors have studied the conservation of abrick-inclined pagoda at Wat Krasai temple in
Ayutthaya, Thailand. This pagoda was restored in2013 by covering the surface with new restorative
bricks below the midsection and recreating thebottom base. In general, historic brick structures in
Ayutthaya were stuccoed when they were first built.
However, no surface protection was applied in thisrestoration. Therefore, the deterioration of therestorative bricks had already been confirmed by thetime of the survey in August 2014 (Fig. 1). Based on
our on-site investigation, we assumed the following
causes of deterioration [5].
1. The bricks were stacked at an angle to match the338

GEOMATE – Bangkok, Thailand, 22-24 November 2022

inclination of the pagoda, resulting in areas where
rainwater collected and ran through the joint.
2. Rainwater is collected on the north and west
faces, making drying difficult because of low sunlight.
3. Bricks on the part where rainwater collected
deteriorate to soil and propagates plants on the soil,
and plants also hold water.
It is desirable to protect the surface to prevent
deterioration of the brick. However, applying a purewhite stucco would drastically change the appearance,as the bricks are exposed in large areas of the pagoda.
Changing the current situation requires discussion, in
terms of cultural property protection. It is necessary
to consider the actual status of originality, as well asa suitable protection method for restorative brick.

Fig.2 Cross -section of the restorative brick
Water absorption and bulk density tests were
performed on five specimens of 50 × 50 × 20 mm
restorative bricks. The water absorption test was
based on JIS R 1250. First, the specimens were ovendried at 105°C–120°C for 24 h, and after being cooled
to room temperature in a desiccator, the dry mass of
m1 was measured. Second, the specimens were
immersed in distilled water at 20°C ± 5°C for 24 h.
After the surface water was wiped out, the saturated
mass of m2 was immediately measured. The water
absorption of restorative brick calculated using Eq.
(1) is very high, exceeding 20% as shown in Table 1.
However, according to previous studies [6], nearly
20% of water absorption is consistent with similar
bricks in the Ayutthaya.
𝑎=(𝑚2 −𝑚1 )𝑚1× 100(%)(1)

The bulk density of restorative brick was calculatedusing the mass in the air-dried state and the volume.
The volume of the specimens was measured using
Archimedes' principle. After soaking the specimen in
distilled water at 20°C until saturated, the mass wasmeasured in a state of receiving buoyancy and wasconverted to volume. The results of bulk density inthe air-dried state are shown in Table 2.
Table 1
Fig.1 Deterioration of the restorative brick(Photograph taken in August 2014)

Specimendry mass

saturated mass

Absorption

No.

(g)

(g)

(%)

1

83.58

101.19

21.07

2

77.38

94.50

22.12

3

79.27

96.93

22.28

4

82.28

100.07

21.62

5

88.55

106.98

20.81

Average

82.21

99.93

21.58

WATER ABSORPTION COEFFICIENT AND
BULK DENSITY OF RESTORATIVE BRICKS
The restorative bricks for Ayutthaya's culturalproperties used in the experiment were obtained froma local brick manufacturing plant in Ayutthaya in
February 2020. The size of the bricks isapproximately 200 × 100 × 50 mm, but the shape isdistorted because of drying shrinkage and the surface
is very uneven, making measurement difficult andallowing for large individual differences. A cracked
cross-section is shown in Fig. 2. Many voids arefound inside the brick, as well as organic matter.

Table 2

approximately 50mm

approximately 100mm

Water absorption of restorative brick

Bulk density of restorative brickSpecimen

air-dried mass

VolumeBulk density

No.

(g)

(cm3)

(g/cm3)

1

83.94

51.61

1.63

2

77.81

48.55

1.60

3

79.82

49.49

1.61

4

82.94

50.71

1.64

5

89.29

53.75

1.66

Average

82.76

50.82

1.63

COMPOSITION OF RESTORATIVE BRICKS
AND IGNITION LOSS TEST
A scanning X-ray fluorescence spectrometer
339

GEOMATE – Bangkok, Thailand, 22-24 November 2022

(ZSX-Primus II) was used to provide the chemicalcomposition of the bricks in terms of oxide. The mainchemical composition, excluding trace elements, isshown in Table 3. The composition of the restorative
bricks is mostly SiO2, Al2O3, and Fe2O3. Silica helps
control shrinkage and excessive consolidation,
whereas alumina provides moderate viscosity [7].
The silica content in bricks is typically 50%–60% andthe alumina content is typically 20%–30%. Thus, thecomposition of restorative brick is within the standard
range.

UNIAXIAL COMPRESSIVE STRENGTH TEST
According to a standard method for uniaxial
compression test of ordinary fire brick based on JIS R
1250, the shape of the specimen is 105 × 100 × 60mm, and the specimen is pressed until failure at aloading rate of 0.5 to 1.0 N/mm2 per second with asurface of approximately 105 × 100 mm as thepressing surface. However, two specimens ofapproximately 10 × 10 × 20 mm were used in thisstudy for compression tests at axial strain rates of
0.01% to 0.1% per minute. The average uniaxial
compressive strength was 11.74 MPa.

Table 3 Composition of the restorative brickChemical composition

Content ratio(%)

SiO2

58.36

Al2O3

23.56

Fe2O3

8.92

CaO

1.31

MgO

0.99

SO3

0.21

Na2O

0.55

K 2O

4.42

TiO2

1.23

ACCELERATED SLAKING TEST
An accelerated slaking test was performed basedon JGS 2125-2020. First, the weight of specimens(m1) was measured, followed by air-drying for at least
24 h, and oven-drying at 40°C ± 5°C for 48 h. Aftercooling to room temperature, the mass (m2) was
measured. The specimens were also photographed sothat their shape, color, cracks, and voids could be
confirmed. Moreover, the specimens were immersed
in distilled water at approximately the same as room
temperature, and the shape changes of the specimens
were observed and photographed within 1 min of
immersion, 30 min later, 1, 2, 4, 6, and 24 h later todetermine each stage of slaking classification, from 0
to 4 (Fig. 4). After 24 h of immersion, the slaked
material was drained, and its mass (m3) was measured.
Finally, the slaked material was oven-dried at 110°C± 5°C until its mass remained constant, and driedmass (m4) was measured. The preceding process was
repeated thrice.
When the specimen is immersed in distilled water,
large bubbles are ejected from the voids with great
force immediately after immersion, and numerous
fine bubbles are generated along with them (Fig. 5).
The generation of fine bubbles decreased with timeand continued until approximately 1.5 hours after
immersion. The bubble generation was similar in all
three slaking tests. In addition, when the specimens
were immersed in distilled water, fine brick debris
adhered to the surface fell into the water because ofslight peeling or chipping. However, the amount ofsuch debris decreased with each test pass. Aftercompletion of the test, the specimen did not slake andremained in the 0 categories for all three test cycles.
The moisture content at each stage was calculatedusing m1, m2, m3, and m4. The variation in results waslarge, and clear trends such as increases, decreases, orcorrelations were not confirmed about changes inmoisture contents.

Natthanan et al. [6] analyzed the physical andengineering properties of ancient and substitution
bricks for the preservation of historical buildings in
Thailand. They obtained substitution bricks fromrestoration sites in Ayutthaya Province and ancient
bricks from historic sites in Bangkok and Ayutthaya
Province. They found that both ancient andsubstitution bricks contained SiO2 in the range of
60.3% to 68.2%, Al2O3 in the range of 60.3% to68.2%, and Fe2O3 in the range of 7.25% to 8.74%.
The restorative bricks we obtained contained slightly
less SiO2 than in the previous study, and slightly
higher Al2O3 and Fe2O3. The component contents ofthe substitution (S) and restoration bricks (R) are
shown in Fig. 3.
In addition, an ignition loss test was performed
based on JIS A 1226. The organic matter content in
restorative brick is very low (Table. 4).

Fig.3 Chemical composition content ratio( The component of S is from reference value [6] )Table 4 Ignition loss of the restorative brickNo.

ignition loss (%)

1

1.44

2

1.29

3

1.19

Average

0

1.31

340

1

2

3

4

GEOMATE – Bangkok, Thailand, 22-24 November 2022

and 200 × 50 mm × 2 were covered with caulking
material to prevent water absorption.
Table 5 Material of surface protection

Fig.4 Classification of slaking level [8]Surface protection
Case-1None

Case-2

PlasterCase-3

Oil mixed plaster

Case-4

Silane-based surface impregnant

Experiment method
First, the initial mass of the specimen in the airdried state was measured (m0). Then, the wet-dry
cycle test was performed based on the following
procedure.
1. Measure the mass of the specimen before watersupply (m1).
2. Spread the felt on the tray and soak it in distilledwater at 20°C ± 5°C.
3. Place the specimen with the protective material
coated surface down on the felt, allow it to absorb
water for 30 ± 5 min, then measure the mass of thespecimen (m2).
4. After drying at 40°C ± 5°C for 23.5 h ± 10 min,measure the mass of the specimen (m3).
5. Repeat wet and dry with 1 to 4 as one cycle.
The above experimental conditions were determinedconsidering the local rainy season environment. A
schematic of water absorption is shown in Fig. 6.

Fig.5 The generation of large and fine bubbles
WET-DRY CYCLE TEST
Purpose of the test
The existing historical brick buildings of the
Ayutthaya Dynasty were covered with decorative
stucco on the surface. However, much of the stucco
has been peeled away, exposing the brick. Rainwater
penetration through unprotected brick surfaces is amajor factor in accelerated weathering. Therefore, awet-dry cycle test was performed to investigate theeffect of brick surface protection on rainwater
infiltration.

Surface protection

Hydroscopic felt

Preparation of the specimens
Restorative brick

The size of the restoration brick used as the
specimens was approximately 200 × 100 × 50 mm.
Three types of protective materials were applied to
one side of the 200 × 100 mm specimens, and fourcases were prepared (Table 5). Three specimens wereprepared per case, and the averages were used for the
results and discussion, except when special changes
occurred in the specimens or an obvious defect in theexperimental conditions. The plaster was mixed in a
1:1:1 ratio with Japanese commercial slaked lime,
powder sanded from the restoration bricks and passed
through a 0.15-mm sieve, and distilled water. An oil
mixed plaster is prepared by mixing commercial saladoil, 1/4 of the total mass of slaked lime and brickpowder, to the above plaster. The silane-based surface
impregnating material using this experiment
penetrates the capillaries of the bricks and forms awater-repellent layer inside the bricks. The
impregnating material prevents water ingress, butwater vapor passes through. The silane-based surface
impregnating material was absorbed eight times thestandard amount because of the high-water
absorbency of the restorative brick.
The four sides of all specimens, 100 × 50 mm × 2

Fig.6 Schematic diagram of water supply
Results and considerations
Twenty-six cycles of wetting and drying were
performed. The average water absorption for the firsttime was 6.74% when the specimens were air-dried
without surface protection and watered from one sidefor 30 min. The average water absorption was 21.58%
after 24 h of immersion in the dry state, as mentioned
in the previous section. Approximately 30% of thewater-saturated state corresponds to 6.74% averagewater absorption. The water absorption during thefirst 30 min of water supply was very large, although
the water content before water absorption, water
absorption area, and water absorption conditions weredifferent. Applying this result to the local situation, areasonable amount of rainwater will permeate thebricks by a squall lasting 30 min, and the weight ofthe brick structure will increase by the amount of341

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 12. One of the three specimens with oil mixed
plaster peeled off from the brick after 12 cycles andanother after 21 cycles. This could be due to the oil
mixed with the plaster weakening the adhesion
between the brick and plaster.
Water absorption(%)

rainwater that permeated. The increased weight acts
as a compressive force on the lower bricks, whichhave absorbed water and become soft. Many verticalcracks were observed in the severely deteriorated
areas of the bricks as shown in Fig.7. As cracks passthrough the voids, water is likely to accumulate
further in the voids due to infiltration.

Many cracks of thedeterioration area

8.00
6.00
4.00
2.00
0.00
4

8

12

16

20

24

Cycle number

Water absorption(%)

Fig. 8 Transition of water absorption (Case-1)-Without surface protectionSalt weathering

Fig. 7 Condition of the deterioration bricks
Figs.8–11 show the cycle variation in water
absorption and a regression curve for each case. The
vertical axis was set to range from 0% to 8% for Case1 and 2, while the other cases were set to 0%–1%
because of their low water absorption. The large
variation in water absorption in each case depends onthe error in water absorption time of 5 minutes in eachexperiment and the impregnation condition of the felt.
When the surface was protected with plaster, themaximum water absorption was 2.27%, which wasapproximately 1/3 compared with the uncoated bricks.
When the plaster was mixed with oil, the maximum
water absorption was 0.66%, indicating a highpenetration prevention effect. The silane-based
surface impregnating material allowed almost no
water to pass through, with a water absorption rate ofless than 0.1%; additionally, its waterproofing
performance did not deteriorate after 26 cycles. The
water absorption rate decreased with each wet-dry
cycle in Case1-3 and became almost stationary
approximately after the 17th drying cycle. The bricks
were relatively dry before the start of the experimentand initially absorbed a large amount of water,
however, it was supposed that the bricks did not drycompletely under the drying condition of 40°C for
23.5 hours. Therefore, the absorbed water gradually
decreased as the non-drying water accumulated in the
bricks through repeated wet and dry cycles. Finally, itis thought that the balance between the amount ofwater absorption for 30 minutes and the amount ofdry water for 23.5 hours became constant.
The waterproofing effect after the plaster is peelingis discussed. The mass gain before and after water
absorption of all specimens in each cycle is shown in

8.00
7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00
4

8

12

16

20

24

Cycle number

Water absorption(%)

Fig. 9 Transition of water absorption (Case-2)-With plaster1.00

0.800
0.600
0.400
0.200
0.00
4

8

12

16

20

24

Cycle number

Water absorption(%)

Fig. 10 Transition of water absorption (Case-3)-With oil mixed plaster.
1.00

0.800
0.600
0.400
0.200
0.00
4

8

12

16

Cycle number

342

20

24

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 11 Transition of water absorption (Case-4)-With silane-based surface impregnating material –By continuing the experiment after the loss of the oil
mixed plaster, the amount of water absorption
increased, but it was less than that in the Case-1
specimen without surface protection, implying thatthe waterproofing effect will last. There is apossibility of the oil mixed plaster adhered andremained in the microscopic voids on the bricksurface.

ACKNOWLEDGMENTS
We would like to thank Veerasak Sansaard
belongings the Aytthaya historical park office for
useful information about the restorative brick in
Ayutthaya. The specimen preparation to apply thisuniaxial compression test was supported by Prof.
Hailong Wang in Faculty of Science and Engineering,
Waseda University. ZSX-Primus II was theequipment shared in MEXT Project for promoting
public utilization of advanced research infrastructure
Grant Number JPMXS0440500021). The surface
impregnating agents was supplied by Kinki KabezaiCo., Ltd., and we are also grateful to Mr. Junji
Hamaoka CEO for his advice about plaster making.

Case3-1
Case3-2
Case3-3
Case4-1
Case4-2
Case4-3

Case1-1
Case1-2
Case1-3
Case2-1
Case2-2
Case2-3(Legend)is necessary to improve the adhesion performance ofthe plaster with oil to the bricks as a future study issue.

120
After peelingAmount of mass increase (g)100

REFERENCES

After peeling

80[1] Webb, T.L. and van Aardt, J.H.P., Deteriorationof materials under tropical conditions,
Proceedings of the symposium on design fortropical living, Durban, South Africa, 1957, p.620.
[2] Yoshida Y., Hokoi S., and Ogura D.,
Degradation of a big buddha in Sukhothai ruins,
Journal of Environ. Engineering AIJ,Vol.78,
No.689,2013, pp.561-567. (in Japanese)[3] Warren J., Conservation of Brick, Oxford,
butterworth-Heinemann,1999,p.294.
[4] Kuchitsu N., Deterioration of bricks as a materialof cultural properties, Materiaru Raifu
Gakkaishi, Vol.17, No.1, 2005, pp.7-11. (inJapanese)[5] Ishida Y., Ito H. and Fukagawa R., Consideration
on Conservation of the Wat Krasai Pagoda in
Ayutthaya, Journal of Disaster Mitigation for
Historical Cities, Vol.15,2021, pp.225-232. (inJapanese)[6] Natthanan W., Chainarong A., Peerasit M.,
Weerachart T., Raktipong S. and Sutat L.,
Ancient materials and substitution materials usedin Thai historical masonry structure preservation,
Journal of renewable materials, Vol.9, No.2,
2021, pp.179-204.
[7] Nishimura A., Fujii M., Minato T., Morikawa H.
and Kagayama T, Latest Civil engineering
material, Morikita Publishing Co., Ltd.,
2014,p.225. (in Japanese)[8] The Japanese Geotechnical Society, Methods and
Description of Geotechnical Materials Testing(1st Revised Edition), 2021, p.299.

60
40
20
0
-20

0

5

10

15

20

25

Cycle number

Fig. 12Mass gain before and after water
absorption in each cycle
CONCLUSIONSThis study clarified the basic physical properties ofrestorative bricks used in historical brick structures in
Ayutthaya. The restorative bricks sintered at low
temperature is absorbed an amount of water by 30
min of water supply that cannot dry out in a rainyseason day cycle. It is possible that moisture remains
in the restorative brick due to daily squalls. The
results of the wet-dry cycle test showed that the initialwater absorption rate of the plastered bricks was
reduced to about 1/3 of that of the uncoated bricks. It
is desirable that the surface of restoration brick iscovered with something to prevent water infiltration.
The use of oil mixed with plaster can provide highwaterproofing performance at a low cost; however, it343

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

SELECTION OF RAILWAY BALLAST BASED ON CEMENTINGPOTENTIAL: A CASE STUDY IN THAILAND
Phitsanu Pholkainuwatra1, Sitthiphat Eua-apiwatch1, and Siam Yimsiri2
12

Department of Civil Engineering, Faculty of Engineering, Burapha University, Thailand;
Sustainable Civil Engineering and Infrastructure Research Unit, Burapha University, Thailand.
ABSTRACT

Properties of fresh ballasts collected from five quarries around Thailand were investigated in the laboratory.
The investigation includes properties specified by the State Railway of Thailand (SRT) for ballast, i.e. rock type,sieve analysis, flat and elongation, and Los Angeles abrasion. Furthermore, cementing value, water absorption,
specific gravity, sulfate soundness, and petrographic analysis were also conducted. The obtained rock properties
were then compared with the requirement for using as a ballast specified by various national standards, including
the Australian Rail Track Corporation (ARTC), the American Railway Engineering and Maintenance of Way
Association (AREMA), and the State Railway of Thailand (SRT). The results show that all rocks are more or lesscomply with all standards. However, Buriram basalt is considered as the best. Saraburi limestone has low abrasion
resistance and high cementing value. The carbonate rock tends to give a higher cementing strength than igneousrock and metamorphic rock, and in terms of parent mineral, plagioclase usually found in igneous and metamorphic
rock has poor cementation potential. The large abundance of plagioclase in igneous rock, such as Buriram basalt,
Nakhon Sawan dacite, and Prachinburi andesite, resulting in lower cementing values. The large cementing valueof Saraburi limestone may hinder its usage as a railway ballast. Therefore, the cementation properties of ballast
by cementing value test, originally suggested by Raymond [1], is strongly recommended on the selection of ballast.
Keywords: Ballast, Cementing, Rock, Railway

INTRODUCTION

Ballast is often made from hard rock, especially
igneous rock, such as granite or even quartzite
metamorphic rocks, because it is durable and hassuitable drainage characteristics. Ballast should be (i)a non-porous and non-absorbent material, (ii)
resistant to abrasion and corrosion, (iii) durable - will
not crush or decompose under changing weather
condition, and (iv) economically worthwhile. Federal
Track Safety Standards (FTSS) classifies the mostimportant functions of ballast as follows, (i)transmiting and distributing the load of the track andrailroad rolling equipment to the subgrade, (ii)
restraining the track laterally, longitudinally, andvertically under dynamic loads imposed by railroad
rolling equipment and thermal stresses exerted by therail, (iii) providing adequate drainage for the track,and (iv) maintaining proper track cross-level, surface,and alingment [4].
Moreover, the sub-ballast layer must be suitablefor preventing an upward migration of fine particles
from the sub-grade layer into the ballast layer's void.
Ballast fouling is defined as a condition when finerparticles in the ballast exceeds 2% by weight [5].
Ballast fouling may occur as a result of one or the
combination of five different mechanisms, which
include (i) fragmentation of ballast, (ii) migration ofunderlying sub-ballast materials, (iii) surface
infiltration of weathered particles and coal droplets,(iv) upward migration of fines from subgrade
formations, and (v) sleeper wearing [3].

In the past, cargo ships used stones as a ballast to
weigh itself when empty ships travel. These stones
had to be disposed of, so it was used as a substructureof railway. Until now, it is still called this stone a“ballast,” even though, in the later stage, stone has
already been specifically produced for railway
purpose [2]. Ballast is a selected material used tosprinkle and compact the top layer of a railway
substructure in which the sleepers are embedded.
Ballast can be subdivided into four zones as shown in
Fig. 1, i.e. (i) crib - between sleepers, (ii) shoulder beyond sleeper ends down to bottom of ballast layer,(iii) top ballast - upper portion of supporting ballast
layer, and (iv) bottom ballast - lower portion of
supporting ballast layer [3].

Fig. 1 Railway track structure components. [3]344

GEOMATE – Bangkok, Thailand, 22-24 November 2022

The effect of ballast fouling is that thecontaminated part prevents the ballast fromperforming its functions. Its severity depends on theamount and cause of contamination. Contamination
caused by sedimentary and clay materials is mainly
found in the field. Clay alone will not cause attrition;
however, sediment particles can cause attrition. Bothclay and sediment interfere with the drainage systemof the tracks and, consequently, cause thedeterioration of ballast due to the poor drainage of therailway tracks. Contamination from fine sand andgravel particles increases the shear strength and thestability of ballast but decrease drainage performanceof ballast. Contamination of fine sand and gravel is
not a significant problem in maintenance.
The basic properties of ballast are set by various
countries. Australia standard is set by the AustralianRail Track Corporation (ARTC). US standard is set
by the American Railway Engineering and
Maintenance of Way Association (AREMA), andThai standard is set by the State Railway of Thailand(SRT). The basic properties requirement consist of
specific gravity, absorption, particle shape, flakinessindex, Aggregate Compression Value (ACV), and
Los Angeles Abrasion (LAA). Table 1 summarizes
the properties of ballast specified by these standards.

gravity, sulfate soundness, sieve analysis, andpetrographic analysis.

Table 1 Comparison of ballast standards by different
countries.
Properties

AREMA

ARTC

SRT

Specific Gravity

2.60-2.65

2.50-

Absorption

1-2%

-

-

Particle shape
Flakiness Index
ACV
LAA

5%
25-40%<30%<30%
25% max
25% max<30%
25% max(a) Chonburi gneissb) Prachinburi andesite(c) Nakhon Sawan dacite(d) Buriram basalt(e) Saraburi limestoneFig. 2 Ballast used in this study.

Table 2 Ballast testing program
Properties

SRT also recommends ballast selection in
Thailand to be made from hard rocks, including
granite, rhyolite, quartzite, dacite, gabbro, basalt,
andesite, diorite, and ballast. Ballast shall not containany harmful chemicals and has no cementation force
when powdered. This paper investigates cementing
characteristics of ballasts collected from various
quarries in Thailand.

MethodLos Angeles abrasion (LAA)

ASTM C535

Flat & elongation

ASTM D4791

Specific gravity & absorption

ASTM C127

Sulfate soundness

ASTM C88

Sieve analysis

ASTM D422, D1140

Rock type

Petrographic analysis

Ballast cementing value
The ballast cementing value test is a test of
coagulation of the formed rock due to degradation,
originally suggested by Raymond [1]. Feng et al [6]explained the term "cemented ballast" as a condition
where the ballast voids are filled with fine particles
and bond the coarse aggregate together, resulting in a
hard-to-break-up matrix of ballast particles foulingmaterial.
This condition can happen when asignificant proportion of the fine particles are silt- or
clay-size (<0.075 mm) and the ballast has become wet
and then dried. This test has not been standardized;
therefore, this study employed a ballast cementing

MATERIALS AND METHODS
Physical and engineering properties
Ballasts collected from five quarries in Thailand
are used in this study. Fig. 2 shows photos of ballasts,
which are gneiss from Chonburi, andesite from
Prachinburi, dacite from Nakhon Sawan, basalt fromBuriram, and limestone from Saraburi. Table 2
shows testing program which includes Los Angeles
abrasion, flat & elongation, absorption, specific345

GEOMATE – Bangkok, Thailand, 22-24 November 2022

value test procedure as suggested by Boucher and
Selig [7].
A ballast cementing value test can be conductedby crushing the rock using the LAA machine to obtainthe fine particles passing #200 sieve. After that, thedough is formed using distilled water with a watercontent above the liquid limit and it is left overnight.
A cylindrical briquette of 25 mm in diameter and 25
mm in height is prepared using a steel cylindricalmold. The pressure of 12.9 MPa is applied to themold with a consolidation time is 10 min. The
briquette is then extruded and left air-dried for 20
hours, then oven-dried for 4 hours. Fig. 3 shows
photos of sample preparation. After that, the samples
are placed in the desiccator until they are tested. Fivesamples of each rock are tested by an unconfinedcompression machine at the rate of 0.0335 in/min to
obtain their maximum strength. The cementing value
is an average of maximum strength of tested
briquettes. Fig. 4 shows photos of unconfinedcompression test on tested briquettes.

(a) Unconfined compression test.

(b) Failure of the sample.
Fig. 4 Unconfined compression test of a sample.

RESULTS AND DISCUSSIONSPetrographic analysis
The purposes of petrographic examination is toclassify and determine the relative amount of theconstituent of the rock sample. A thin section of 0.03
mm thick of ballast from various sites is prepared andtransmitted by light polarized microscope. The
petrographic analysis results are shown in Table 3.
The predominant minerals in Chonburi gneiss arefeldspar and quartz.
Plagioclase is the main
constituent in extrusive igneous, such as Prachinburi
andesite, Nakhon Sawan decite, and Buriram basalt.
Calcite, which is considered a carbonate mineral, ispresented in Saraburi limestone.

(a) Apply pressure to a sample.

Physical and engineering properties
Sieve analyses are conducted to investigate theparticle size distribution of ballast. Effective particles
size corresponding to 10%, 30%, 50%, and 60% are
obtained and the coefficient of uniformity (Cu) andcoefficient of curvature (Cc) of ballast is thencalculated. The sieve analysis result is presented inFig. 5. The average particle sizes of the ballast are(b) Sample briquettes after extruded from the mold.
Fig. 3Sample preparation for ballast cementing
value test.

346

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 4 Average particle sizes of ballast.

presented in Table 4.
Sieve analysis results show that the nominal sizeof ballast (D50) varied from 40.1 to 45.0 mm.
Chonburi gneiss contains more stones of larger
diameter than other ballast. The coefficient of
uniformity ranges from 1.3 to 1.6 and the coefficientof curvature ranges from 0.6 to 1.2. The grain size
distribution of all ballasts can be classified as uniform
ballasts (poorly graded gravel).
Table 3 Ballast petrographic analysis results
BallastChonburigneissPrachinburi
andesite

NakhonSawan
daciteBurirambasaltSaraburi
limestone

Rock type

Metamorphic

Extrusive
igneous

Extrusive
igneous

Extrusive
igneous
Sedimentary

Predominant
minerals
FeldsparPercent)%(
60

Quartz25

Muscovite

10

Chlorite

5

Plagioclase

50

Chlorite

30

Olivine15

Pyroxene

5

Plagioclase

40

Feldspar25

Amphibole

20

Pyroxene

15

Plagioclase

55

Pyroxene

20

Olivine20

Calcite

100

Particlesize(mm)GneissAndesite

DaciteBasaltLimestone

D10

32.0

30.2

33.0

27.0

30.1

D30

31.0

40.0

40.0

36.0

38.0

D50

45.0

43.0

42.3

40.1

41.0

D60

50.0

44.4

43.3

41.0

42.0

Cu

1.6

1.5

1.3

1.5

1.4

Cc

0.6

1.2

1.1

1.2

1.1

Table 5 presents physical and engineering
properties of ballasts. LAA index of tested ballast
ranged from 8.0 to 29.7% and most of the ballast
comply with the ARTC (LAA<25%), AREMA (LAA
25-40%), and SRT (<25%), except Saraburilimestone with the LAA of 29.7%. ARTC and SRT
recommended flat & elongation (1:3) values smaller
than 30%, and only Nakhon Sawan dacite does notcomply with these standards. AREMA required flat
& elongation (1:2) value smaller than 5%, Nakhon
Sawan dacite and Prachinburi andesite do not comply
with AREMA requirement.
AREMA required
absorption smaller than 1 %, and the other two
standards do not mention water absorption value. The
result showed that all ballast comply with AREMA.
The specific gravity of all the ballast varies from 2.68
to 2.87. The sulfate soundness values of all ballast
pass the requirement of AREMA and SRT.

Fig. 5 Ballast particle size gradation results.

347

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Properties

GneissAndesite

DaciteBasaltLimestone

LAA (%)

24.1

19.3

8.0

14.1

29.7

19.8

29.7

33.7

24.1

15.9

0.8

5.9

5.4

3.2

1.7

0.9

0.4

0.2

0.8

0.9

2.68

2.70

2.84

2.87

2.68

metamorphic rock has low cementation potential.
The large cementing value of Saraburi limestone mayhinder its usage as a railway ballast.
Cementation properties of ballast by cementing
value test, originally suggested by Raymond [1], is
recommended on the selection of ballast. If this fineparticle has the high cementation potential, it may
bond the coarse aggregate and resulting in hard to
break up the matrix of ballast. This hard spot cannot
absorb the wheel/rail impact forces, and on wetting,the cemented fines may become soft and cause mud
pumping.

2.6

1.0

1.2

1.5

2.7

ACKNOWLEDGMENTS

Table 5 Ballast basic properties.

Flat &
elong.
(1:2) (%)
Flat &
elong.
(1:3) (%)
Absorption(%)
Gs
Sulfatesoundness(%)

This work was supported by Burapha University
and Thailand Science Research and Innovation(TSRI) (Grant #63/2565) and by the Research andDevelopment Fund of Burapha University to the
Sustainable Civil Engineering and Infrastructure
Research Unit.

Ballast cementing value test results
Table 6 summarizes ballast cementing value test
results.
Saraburi limestone has the greatest
cementing strength compared to igneous rock andmetamorphic rock. The higher strength may result
from the rich in calcite which is a carbonate material.
Plagioclase is usually found in igneous andmetamorphic rock and has low cementation
properties. The abundance of plagioclase in igneousrock, such as Buriram basalt, Nakhon Sawan dacite,
and Prachinburi andesite, resulting in lower
cementing strength. The lowest cementing strengthof Chonburi gneiss may result from the appearance ofquartz. Cementing strength of Saraburi limestone
agrees well with the result from Raymond [1] who
reported that the cementing strength of limestonevaried from 1.0 to 2.8 MPa with an average of 1.7
MPa.

REFERENCES[1] Raymond R.P., Examination of Degraded
Aggregate Cement Value Test, Transportation
Engineering Journal of ASCE, Vol. 105, Issue 3,
1979, pp. 329-340.
[2] Chantasorn N., Chapter 2 – Railway Track,
Introduction to Railway Engineering andModernization of Thailand’s Rail Transportation
and Related Industries, 3rd ed., The National
Science and Technology Development Agency,
2016, pp. 31-62., in Thai.
[3] Selig E.T. and Waters J.M., Chapter 2 – Track
Components and Loading, Track Geotechnology
and Substructure Management, Thomas Telford
Publishing, 1994, pp. 2.1-2.15.
[4] Federal Track Safety Standards (FTSS), Part 213,
Subpart D, Office of Safety, FRA, U.S.
Department of Transportation, 2014.
[5] Indraratna B. and Salim W., Chapter 1 Introduction, Mechanics of Ballasted Rail TracksA Geotechnical Perspective, Taylor and Francis,
2005, pp. 1-5.
[6] Feng D.M., Boucher D.L. and Selig E.T.,
Laboratory Study of Ballast Cementing,
Proceedings of 5th International Heavy Haul
Railway Conference, 1993, pp. 207-214.
[7] Boucher D.L. and Selig E.T., Application ofPetrographic Analysis to Ballast Performance
Evaluation, Transportation Research Record 1131,
1987, pp. 15-25[8] Bold R.D., Non-Destructive Evaluation ofRailway Tracked Ballast, Ph.D. Thesis, Institutefor Infrastructure and Environment, School ofEngineering, University of Edinburgh, 2011, UK

Table 6 Ballast cementing value.
Rock types

Cementing value (MPa)

Gneiss0.2

Andesite

0.4

Dacite0.4

Basalt0.4

Limestone

2.5

CONCLUSIONS
Ballasts collected from five quarries in Thailand
are investigated in this study. The results show that
all rocks are more or less comply with all standards.
However, Buriram Basalt is considered as the best.
Saraburi limestone has lower abrasion resistance andhigh cementing value. The carbonate rock tends togive a higher cementing strength than igneous rockand metamorphic rock, and in terms of parent mineral,plagioclase usually found in igneous and348

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

RECOVERY AND VALORIZATION OF DEMOLITION AND
CONSTRUCTION WASTES SPREAD OVER ALL THE CITY OF
CASABLANCA (MOROCCO)
Mourad MORSLI1, Mohamed TAHIRI1, Youssef HALIMI1, and Mehdi MAANANE2
1

Organic Synthesis, Extraction and Valorization Laboratory, Ain Chock’s Faculty of Sciences,
Hassan II University of Casablanca, Morocco, email: mourad.morsli@hotmail.fr;
2
Geosciences Laboratory, Ain Chock Faculty of Sciences, Hassan II University ofCasablanca, Morocco

ABSTRACT
Morocco produces around 9 million tons per year of building waste (demolition and construction) [1]. The
territorial network of recovery solutions for this waste is therefore crucial, but difficult to optimize because thewaste disposal sites are temporary and present a great diversity of size and concentration. Waste collection
centers must reconcile the needs of small players, the majority in the end-of-life quarry sector. It is not clear how
this enormous amount of material can be obtained, especially as quarries are starting to suffer from excessive
resource extraction; which results in a very negative impact on the environment
In this context, our project aims to recover and valorize this waste. In this present work, we present concrete
results in recycling and exploitation of this abundant and permanent deposit and also the geolocation with GIS of
the areas where are mainly deposited the demolition and construction wastes.
We have collected this waste by type and listed the elements that constitute it. After crushing and grinding
the waste, we designed the representative sample. We characterized this sample by X-ray fluorescence andseparated the different particle sizes by sieving. We have thus exploited each type of powder obtained to develop
a suitable material.
We developed research works and obtained comforting results in particular in: The implementation of
ecological and economical materials dedicated to sustainable buildings; Production from these lightweightcement materials requires only a new supplement to serve as an efficient and effective binder; The preparation of
coatings, The remaining residue can finally be used in the inhibition of quarries and general construction work
(Roads, bridges, rainwater storage basins, etc.)
The materials produced have been tested and characterized by X-ray fluorescence, specific surface area,
resistance to compression and bending and crushing, etc. This work will be detailed in the article.
Keywords: Demolition and construction waste materials, Recovery and recycling, lightweight cement, Coatings,
ecological materials, Morocco.

INTRODUCTION

and/or civil engineering infrastructure.
Construction waste management is a complex
activity. There is no single recipe because suchmanagement varies from one case to another andfrom one trade to another. Moreover, themanagement of construction waste is extremely
different depending on whether the work is new
construction or conversion, rehabilitation, or
renovation. Other decisive elements are the size ofthe construction site and its location (dense urban
area or not, industrial area, rural area).
In this sense, we have realized the geolocation of
the main sites of demolition and construction waste
disposal. These wastes are crushed and thencharacterized and sifted to have a granular
distribution of our sample on which we will base

The building industry in general and theconstruction industry, in particular, are major
consumers of natural resources and energy [2]. In
addition, the construction sector generates
unacceptable levels of waste. In recent years, theconstruction industry has become increasingly awareof the significant environmental impacts associatedwith waste generated during both the construction of
new buildings and the demolition of old structures.
The materials used in this study cover a wide range
obtained from demolition and construction waste.
Indeed, they can be classified as follows:- Wastes from the total or partial demolition ofbuildings and/or civil engineering infrastructure.
- Waste resulting from the construction of buildings
349

GEOMATE – Bangkok, Thailand, 22-24 November 2022

ourselves to determine their way of valorization.

waste for the preparation of mortars. In this sense, we
conducted a series of tests, with variations in the rateof substitution of cement by these wastes ranging
from 5 to 20% of the substituent.
The mechanical properties are analyzed and thencompared with a control mortar.

MATERIALS ET METHODS
Geographical location of demolition andconstruction waste depots in Casablanca
To have an idea of the quantity of demolition andconstruction waste, we have chosen the city ofCasablanca, the largest city in Morocco, and taking
into account different construction sites there, tomake geolocation of the main sites where demolitionand construction materials are thrown for a possible
statistical study on this important deposit [3].

RESULTS AND DISCUSSION
Geolocation
A motorized team with smartphones equippedwith a mobile application of Geolocation was
mobilized to crisscross the territory of the 8
prefectures of the municipality of Casablanca and to
take note of the Cartesian coordinates (X, Y) with theinsertion of the exact address, the photo of the point
and the approximate estimate of the existing tonnage.
At the end of this mission, a global database of
the spatial distribution of inert waste black spots inthe municipality of Casablanca was generated withmaps and Excel tables for each prefecture.

Presentation of the materials studied
Demolition and construction waste
We have selected representative samples fromdifferent sites. These wastes (Figure 1) generally
consist of cement blocks and rubble, sand, plaster,
ceramic tiles, red bricks, and plaster, ... The limestoneaggregates are substituted by deconstructed concrete
aggregates to realize concrete formulations. A
characterization study of the raw materials was
carried out.

Fig. 2: Black spots of inert waste identified at thelevel of the City of Casablanca
Fig. 1: representative samples of construction waste
before crushing and grinding.

Figure 2 shows the main sites where demolitionand construction materials are discarded. Note:

Cement used as an additional binder forconcrete:❖ A large number of sites where demolition andconstruction waste are dumped, something that
implies all stakeholders to give more interest to
the recovery of these wastes.
❖ A concentration of these sites on the outskirts ofthe city, knowing that each site contains a verylarge quantity of waste.

The cement used is portland cement (CPJ 45) by
the Moroccan standard NM 10.1.004 [4] produced by
the Société Ciment du Maroc.
The binder for coatings
Mortar is obtained by mixing a binder (lime or
cement), sand, water, and possibly additives [5].
Multiple compositions of mortar can be obtained byplaying on the different parameters: binder (type anddosage), additives, and water dosage.
As far as the binder is concerned, all cement andlimes can be used; their choice and dosage depend on
the work to be carried out and its environment. [5]The purpose of this formulation is to highlight thepossibility of using demolition and construction

Physico-chemical characterization ofdemolition concrete waste and construction
sites
After the collection of the demolition andconstruction waste, it was crushed and analyzed todefine its physical and chemical properties. Tables 1
and 2 present the chemical composition by X-ray
fluorescence and the analysis of heavy metals. A
particle size analysis (Table 3) was performed onthese samples to determine their recovery pathway.

350

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 1Elements
SiO2
Al2O3
Fe2O3
CaO
MgO
K 2O
Na2O
P2O5
MnO
SO3
TiO2
PAF
Table 2Elements
As
Ba
Cd
Co
Cr
Cu
Mn
Mo
Ni
Pb
Se
Sr
Zn

Chemical composition by FX

2,5
1,25
0,63
0,315
0,16
0,08

Waste concrete deconstruction%
17,36
4,14
2,16
58,94
0,89
0,47
0,15
0,08
0,02
2,33
0,27
13

❖
D (mm) < 5❖ 8 < D (mm) < 5

Lightweight cement
Coating

Heavy metal analysis
Recovery of demolition and constructionwaste: "Lean cement"

Waste concrete deconstruction %0,0006
0,0057000,00070,00050,01530,00010,00070,00080,0001
0,0217
0,0024A large part of this experimental study was
devoted to the selection of the appropriate mixing
procedure. Samples were prepared by varying the
proportion of demolition and construction waste into
two substitution percentages of 20% and 40% whichindicate the proportion of demolition andconstruction waste. For each formulation, wedeveloped three specimens. In the first step, we
determined the chemical composition by X-ray
fluorescence (Table 4) and then analyzed the samples
for heavy metals (Table 5). Density and compressive
strength measurements were performed (Table 7).
Table 4

Particle size analysis of raw materials
The dimensional analysis of the aggregates isessential to characterize the material and to know theperformance of the concrete to be obtained. The test
consists in classifying the different grains constituting
the sample by using a series of sieves, nested one on
the other, whose opening dimensions are decreasingfrom the top to the bottom [6].
The material studied is placed in the upper part ofthe sieves and the classification of the grains is
obtained by the vibration of the sieve column.
Dimensional analysis of aggregates

Sieve mesh in (mm)
8
5

20,31
16,16
9,69
4,96
2,31
1,10

According to the dimensional analysis of theaggregates, we note that a great part of our sample(approximately 50%) contains aggregates withdiameters (D) varying between 5 and 8mm.
To valorize this waste of BTP, we based
ourselves according to the need for a granular
distribution of our sample:

Particle size analysis was carried out on thesamples characterized to determine their recovery
pathway.

Table 3

1760
1400
840
430
200
95

Accumulated sieves
mass (g)
%
2110
1830

22,35
21,12

351

Chemical composition by FX

Elements
SiO2
Al2O3
Fe2O3
CaO
MgO
K2O
Na2O
P2O5
MnO
SO3
TiO2
PAF

F 1 (20%)
19,25
4,37
2,38
56,34
1,13
0,48
0,17
0,08
0,02
2,32
0,30
12,56

F 2 (40%)
22,29
4,52
2,32
55,45
0,94
0,56
0,16
0,08
0,02
2,36
0,32
10,79

Table 5

Heavy metal analysis

Elements

F 1 (20%)F 2 (40%)

As
Ba
Cd

0,00030,006800,00050,00890

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Co
Cr
Cu
Mn
Mo
Ni
Pb
Se
Sr
Zn

0
0,00060,00050,016800,00080,00080,00020,02230,0019

Table 6
Resultsofcharacterization of materials

tests

Natural aggregates
CrushedGI gravel
sand

0/5
13,1
***
***
1,76
2,74
0,19

Granular class
Fine ratio (%) (case of sand)Superficial cleanliness (%)
Flattening coefficient (%)
Bulk density (t/m3)
Actual density (t/m3)
Water absorption coefficient (%)

Table 7
Formulations
F1 (20%)F2 (40%)The cement, sand, water, and equipment must beat laboratory temperature for homogenization
purposes.
Each batch is mixed mechanically in the mixer.
The mixer is in running position.
For this experimental approach, 4 formulations
with percentages of addition going from 0 to 20% of
demolition waste were prepared.
The tests were carried out using specimens withdimensions of 40×40×160 mm (Figure 3).
The different compositions of the mortars aregrouped in the table below:

00,00080,00040,014400,00070,00050,00010,01350,0021
for

Table 8theMortars

Construction waste

8/16
***
0,5
5
1,40
2,71
1,37

G1 (0%)
G2 (5%)
G3 (10%)G4 (20%)

GII gravel Gravel

14/20
***
0,3
6
1,40
2,71
1,05

0/20
***
3,0
20
1,42
2,69
0,42

Composition of mortars
Sand
(g)
1350
1350
1350
1350

Cement
(g)
450
427.5
405
360

Water
(g)
225
225
225
225

Constructionwaste (g)
0
22,5
45
90

Fig. 3: Specimens of dimensions: 40×40×160 mm

Density and compressive strength
Density (g/cm3)

Compressive strength (MPa)Proofnumber

7 da

28 da

7 da Average

28jrs Average

1
2
3
1
2
3

2,37
2,37
2,37
2,37
2,36
2,36

2,38
2,38
2,39
2,38
2,38
2,37

21,5
21
22
19
18
18,5

27,5
27
28
24,5
23,5
24

21,5

18,5

27,5

24

The specimens emerged without delay, suitably,
either horizontally or vertically, in water at
20°C±1°C and in suitable containers.
The age of the specimens is counted from themoment the cement and water are mixed until the
beginning of the test.

Reference formulation : Rc28= 28 MPa. [NM 10.1.008: Rc7 =19-20MPa; Rc28=28MPa]❖ The densities of the F1 (2.39 g/cm3) and F2(2.38 g/cm3) formulations at 28 days of age
are similar to the density at 28 days of thereference formulation (2.39 g/cm3);

Intake tests
The objective is to determine the start and end
times of the setting of the dough using a Vicat device.
When the moving part is released the Vicat
needle stops at a distance from the bottom of themold such that d = 4 mm ±1 mm.
The end of the set corresponds to the moment
when the dough has become a rigid block, themoment when the needle is only 0.5 mm deep in thedough [9].
The results are grouped in the following tables(9 and 10):
Table 9Results tests start taking❖ The compressive strength at 28 days of the ageof the F1 mix (20%) is acceptable compared to
the NM 10.1.008 standard [7] for a class B25
concrete;❖ When the substitution of natural aggregates by
waste limestone aggregate concrete exceeds
20%, the compressive strength does not meetthe requirement of the said standard.
Recovery of demolition and constructionwaste: "Coating"

Formulation
G1 (0%)
G2 (5%)
G3 (10%)G4 (20%)Experimental program
A classical mortar should be composed of one
part cement, thre parts sand, and half a part water [8].

352Start of taking (min)
205
199
201
195

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 10

Results tests end of the intake

Formulation
G1 (0%)
G2 (5%)
G3 (10%)G4 (20%)

Table 12Results
compressive strength test

End of taking (min)
275
263
272
260

Mortars

theflexuralandFlexion(Pa)

2 daysCompression(MPa)

Flexion(Pa)

7 daysCompression
(MPa)

4.2
3,9
3,7
2,7

19,5
17,4
15,3
12,8

5,3
5,7
4,9
3,9

31,1
27,3
23,7
18,9

G1(0%)
G2(5%)
G3(10%)
G4(20%)

According to the experimental results, thesubstitution of cement with construction waste has no
considerable effect on the beginning or the end of themortar.

ofFig. 4: Graph of the flexural strength test at the agesof 2d and 7dSpecific surface
The mineral additives are currently among themost recent developments in the production of
cement because their uses provide an improvement in
the mechanical and chemical properties (durability)of cementitious materials (mortar and concrete) [10].
On the other hand, their use has the objective of
reducing the consumption of clinker, contributing
simply and economically to solving environmental
problems.
The specific surface of the different
formulations has been determined by the Blaine
method (Blaine Permeabilities) [11]. This fineness is
an important characteristic, during the mixing
process, the greater the surface area of the cement incontact with the water, the faster and more complete
the hydration. (Table 11)
Table 11

6

4

2
G1

G2

G3

G4

Fig. 5: Graph of the compressive strength test at ages
2d and 7dcompressive strengths (MPa) 2 Days
compressive strengths (MPa) 7 Days
35
25

Results specific area measurement

Formulation
G1 (0%)
G2 (5%)
G3 (10%)G4 (20%)flexural strengths (Pa) 2 Days
flexural strengths (Pa) 7 Days

Specific area (cm²/g)
3565
3570
3567
3572

15
5
G1

G2

G3

G4

Comparing the flexural and compressive strengths
of mortars prepared with different additions ofdemolition waste, we can observe that the strengths
drop considerably from the second day compared to
the reference mortar without the addition ofdemolition waste.
Thus, the higher the substitution rate ofdemolition waste, the lower the strength.
To correct the revealed strength defects, aplasticizing admixture is used to improve themechanical properties of the mortars [13].
Taking into account these results, tests with theaddition of admixture estimated at 0.1% were carriedout only on the reference formulations (G1: No
addition) and on tone substitution of 20% of cement
by demolition waste.
Since 20% substitution of demolition waste gives
the lowest results, we believe that if the results with
admixture are improved on this sample, they will also
be improved for the other tests.
According to the datasheet of the admixture used,

By comparing the specific surfaces of theformulations with different additions of thedemolition waste, we can consider that the variationin the values of the specific surface is negligible,
indeed the substitution of cement with constructionwaste does not have a remarkable influence on thespecific surface.
Laboratory testing
The mechanical properties of the preparedmortars are determined by the method of determining
the compressive and flexural strengths of a cement
mortar according to the Moroccan Standard NM
10.1.005 [12].
The half-prisms obtained in the bending test must
be tested in compression on the lateral faces of thecasting under a section of 40×40 mm.
The results of the flexural and compressive
strength test at the ages of 2d and 7d are presented in
the following table 12:353

GEOMATE – Bangkok, Thailand, 22-24 November 2022

it is a plasticizer that increases the mechanicalresistance, reduces the cement dosage, and increases
the setting time at 20°C (a delay in the setting ofabout 3 to 4 hours is obtained with 0.1%).
The results are grouped in the following table 13:

granulats d'origine volcanique de Goma et son
influence dans la formulation du béton. International
journal of innovation and applied studies, 15(1), 141.
[7] Institut Marocain de Normalisation (IMANOR),NM 10.1.008, Bétons - Spécification, performances,production et conformité, 2010.

Table 13 The results of the flexural andcompressive strength test at the ages of 2 and 7 ( withadjuvant addition)
Mortars

G1 (0%)
G2 (20%)Flexion(Pa)

2 daysCompression(MPa)

Flexion(Pa)

7 daysCompression
(MPa)

4.2
4,8

19,5
25,8

5,3
7,1

31,1
41,7

[8] GUERBAS, T. (2015). Etude du comportementd'un mortier a base de sable mixte (sable de dune etsable concassé) armé de fibres synthétiques (Doctoral
dissertation, university of M'sila).
[9],[10] Zeyneb, B. (2016). Influence de la surface
spécifique des ciments aux ajouts minéraux (calcaire)
sur le comportement mécanique du mortier (Doctoral
dissertation, Université De Mohamed Boudiaf M'sila
Faculté De Technologie).

By comparing the results of the bending andcompressive strengths of the different formulations
with the reference formulation without demolition
waste and adjuvant, we find that the use of adjuvant
gives very beneficial and better results even than thestrength of the reference mortar (without addition:
G1).

[11] Blaine, R. L. (1941). Etudes de la mesure desurface spécifique par perméabilité à l’air. Société
américaine pour le matériel d’essai.
[12] Institut Marocain de Normalisation (IMANOR),NM 10.1.005, Liants hydrauliques, techniques desessais, 2008.

CONCLUSIONS[13] Belferrag, A. (2016). Contribution àl’amélioration des propriétés mécaniques etrhéologiques des bétons de sable de dunes (Doctoral
dissertation, Université Mohamed Khider-Biskra).

We proposed and compared different tests on
concrete mortars. The first results show that for the
manufacture of lean cement or coatings by thesubstitution of natural aggregates up to 20% of theconcrete waste of limestone aggregates gives very
conclusive consequences that can be further
improved with the addition of admixtures.
REFERENCES[1] CETEMCO. (2020). CETEMCO. Valorisation
des déchets de déconstruction (V2D).
https://cetemco.org/rd/valorisation-des-dechets-dedeconstruction-v2d/[2] Lieberherr-Gardiol, F. (2007). Durabilité urbaine
et gouvernance, enjeux du xxie siècle. Revueinternationale des sciences sociales, 193194 (3), 373385.
[3] Zarouali, M. J. E. (2014). Le Secteur des BTP au
Maroc : Aspects économiques et Sociaux. Dossiersde Recherches en Économie et Gestion, 3(1), 31-46.
[4] Institut Marocain de Normalisation (IMANOR),NM 10.1.004, Ciment et constituants des ciments,
Partie 1 : composition, spécifications et critères deconformité, 2019.
[5] Ahmed Abdo, A. E. (2017). Valorisation dudéchet de marbre et de verre comme additif dans la
production du mortier (Doctoral dissertation,
Université Mohamed Boudiaf-M’sila).
[6] Biryondeke, C. B., Ngapgue, F., & Musangi, G.
O. (2016). Etude du coefficient d'absorption d'eau des354

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

THE EFFECT OF LDPE PLASTIC WASTE ON THE COMPRESSIVE
STRENGTH OF PAVING BLOCKS
Fauzan1, Rani Fahmi Zakaria1, Dyan Adhitya Nugraha M1 and Zev Al Jauhari2
12Engineering Faculty, Andalas University, Indonesia
Department of Civil Engineering, Bengkalis Polythecnic, Indonesia
ABSTRACT

Plastic is one of the most common environmental problems in the world. One way to reduce plastic waste isby reusing it to produce other materials such as partial replacement of sand in paving blocks mixture. Paving block(concrete brick) is a building material made of a mixture of water, aggregate and portland cement. An experimental
study was carried out to investigate the effect of LDPE and PET plastics as a substitute for fine aggregates on thecompressive strength of paving blocks. In this study, paving blocks are planned to be of plan C quality with aminimum compressive strength of 12.5 MPa, which is usually used as a pedestrian facility. LDPE and PET plastics
waste is used as partial replacement of fine aggregates, with variations of 0%, 5%, 10% and 15% of the fineaggregates volume. The compressive strength test was carried out on cylinder specimens of 150mm diameter and
300 mm length. The results shows that the use of LDPE and PET plastics waste as a partial replacement of fineaggregate decreased the compressive strength of paving blocks, in which the use of 5%, 10% and 15% of plastic
reduced the compressive strength by 35.26%; 37.69%; 40.68% for LDPE, 34.15%; 52.22%; 56.53% for PET, and23.14%; 18.01%; 24.65% for LDPEF, respectively. However, the compressive strength result of the tested paving
blocks predominantly exceeds the minimum requirement for compressive strength of paving block quality C,
which is 12.5 MPa.
Keywords: Plastic waste, Paving block, Concrete bricks, Compressive strength, Fine aggregates

INTRODUCTION

addition to waste plastic, many other materials havebeen trialled as replacement materials in concrete
mixes, including recycled electrical cable rubber [6]waste polystyrene [7], and scrap-tire rubber. The
construction industry can use a large amount of solid
waste, one of which is in the production of paving
blocks [8]. The use of plastic in the manufacture ofpaving blocks can increase compressive strength andreduce the use of raw materials, the most important
thing is that it is environmentally friendly and helps
to reduce environmental problems [9], [10].
Currently, many studies are being carried out
regarding the use of plastic waste as paving blocks. In
each year 60million barrels of oil and another type of
insulation would be safe by exploiting of plastic foam
insulation in buildings [11]. The compressive
strength value of using 10% LDPE plastic as a
substitute for sand in paving blocks is greater than the
0% variant (without plastic waste mixture) which is
23.98 MPa and 23.68 Mpa, respectively [4]. The
application of pavement blocks is perfect on
pathways and street ways as it is a simple way offinishing and laying, also cost-effective compared toconservative paving blocks [12], [13].
Based on the background above and also theconcern about the increasing amount of plastic day by
day while decomposition takes a very long time, anexperimental study was carried out to investigate theeffect of using LDPE and PET plastic waste as apartial substitute for fine aggregates on the

Paving block or concrete brick is a composition ofbuilding materials made from a mixture of water,
aggregate and Portland cement or similar hydraulicadhesive materials, with or without other additives
that do not reduce the quality of paving blocks or
concrete bricks [1].
To produce paving blocks that are moreenvironmentally friendly and economical, othermaterials can be used as a substitute for paving block
mixtures, one of which is plastic waste. Plastic is oneof the most common environmental problems in thecontemporary world. The disposal of this plastic is
considered a major challenge due to its nonbiodegradable nature and pollutant [2], [3].
According to the Central Pollution Control Board,
the world produces nearly 150 million tons of plastic
per year, equivalent to 4.8 tons per second [4].
Meanwhile, in Indonesia itself, according to theSecretary General of the Indonesian Olefin, Aromaticand Plastic Industry Association (Inaplas), plastic
consumption has grown from 4.5 million tons in 2015
to 4.8 million tons in 2016, or grew by 5.2%. This
increase in consumption is mainly driven by thegrowth of the food and beverage industry, where theindustry uses a lot of plastic for its product packaging[5].
One alternative to reduce this plastic waste is thereuse of plastic to produce other materials [3]. In
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

compressive strength of paving blocks.

waste. The process of PET plastic waste to be fineaggregate is shown in Fig. 2.

MATERIALS AND METHODS
Table 3 Properties of PET plastic
Ingredients Used
Characteristic
Size
Shape of PETSpecific gravity

Cement
The cement used in this study is Portland
Composite Cement (PCC) produced by PT Semen
Padang in Indonesia.

Value
0.1 mm - 4.75 mm
Irregular
1.38 g/mL

Fine aggregates
Fine aggregates in the form of locally available
sand are used, with a maximum size of 4.75mm. theproportion of the fine aggregate can be seen in Table
1.
Table 1 Properties of fine aggregates
Characteristic
Fine modulus (FM)Rate of water
Fill weight
Specific gravity
Absorption

Value
2.85
1.42%
1.41 kg/L
2.50
3.09%

Fig. 1

PET plastic.

Fig. 2

PET plastic processing.

Coarse aggregates
Locally available coarse aggregates of spilt areused Ø5-10 and Ø10-20. The properties of coarse
aggregates can be seen in Table 2.
Table 2 Properties of coarse aggregates
Characteristic
Fine modulus (FM)Rate of water
Fill weight
Specific gravity
Absorption

Value
Ø5-10
5.34
1.32%
1.33 kg/L
2.61
3.20%

Ø10-206.94
0.77%
1.38 kg/L
2.53
2.81%

Water
The water used for the mixture of paving blocks
and curing is tap water that does not contain acids ororganic substances.
PET plastic
LDPE plastic
The PET plastic used comes from plastic wastefrom mineral drink bottles, as shown in Fig. 1. ThisPET plastic is processed into fine aggregates bymelting and then crushing a maximum of 4.75mm[14]. Table 3 shows the properties of PET Plastic

Recycled LDPE plastic seeds are shown in Figs. 3
and 4. The properties of LDPE Plastic wastes isshown in Table 4.

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 4 Proportions of LDPE plastic
Characteristic
Size
Shape of LDPE
Specific gravity

content used is 15% by replacing the weight of
cement. The composition of the volume and weightof the material in 1m3 mixture of paving blocks with
PET, LDPE and LDPEF are shown in Tables 5 to 10.

Value(3.0 - 4.75) mm
Pellets, Flattened
round
0.94 g/mL

Table 5 The composition of the material volume in
1m3 mixture of paving blocks with LDPE

Material
Cement
Sand
Split
Water
Air
LDPE
Fig. 3

Shape of plastic LDPE pellet.

Composition (%)
LDPE
LDPE
5%
10%
0.10
0.10
0.34
0.32
0.33
0.33
0.20
0.20
0.02
0.02
0.02
0.04

LDPE
15%
0.10
0.30
0.33
0.20
0.02
0.05

Table 6 The composition of the material weight in
1m3 mixture of paving blocks with LDPE

Material
Cement
Sand
Split
Water
LDPE
Fig. 4

LDPE
0%
0.10
0.35
0.33
0.20
0.02
-

LDPE
0%
298.30887.47836.96203.14-Composition (kg)
LDPE
LDPE
5%
10%
298.30 298.30
843.10 798.73
836.96 836.96
203.14 203.14
16.68
33.37

LDPE
15%
298.30754.35836.96203.1450.05

Shape of plastic LDPE flattened round.
Table 7 The composition of the material volume in
1m3 mixture of paving blocks with PET

Fly ashThe fly ash used comes from the SBB Thamrin
Nine Batching Plant, Jakarta, Indonesia.

Material

Implementation Methods

Cement
Sand
Split
Water
Air
PET

Mix design
Mix design is calculated based on (American
Concrete Institute) ACI-211.4R-93. Paving blocks
are planned to be of plan C quality with a minimum
compressive strength of 12.5 MPa. The ratio of
cement volume : sand : split is (1 : 3.75 : 3.46) with
w/c 0.681.
LDPE and PET plastic waste serves as a partial
substitute for fine aggregates, where the amount ofplastic added to the paving block mixture is based on
the fine aggregate volume of concrete paving blocks(cement: quarry dust: sand = 1:1:2) by weight orvolume were produced to serve as control having
tested the compressive strength and water absorption
properties [15].
There are three variations of paving blocks,
namely PET paving blocks, LDPE paving blocks and
LDPE fly ash (LDPEF) paving blocks. The fly ash

PET
0%
0.10
0.35
0.33
0.20
0.02
-

Composition (%)
PET
PET
5%
10%
0.10
0.10
0.34
0.32
0.33
0.33
0.20
0.20
0.02
0.02
0.02
0.04

PET
15%
0.10
0.30
0.33
0.20
0.02
0.05

Table 8 The composition of the material weight in
1m3 mixture of paving blocks with PET

Material
Cement
Sand
Split
Water
PET
357

PET
0%
298.30887.47836.96203.14-Composition (kg)
PET
PET
5%
10%
298.30 298.30
843.10 798.73
836.96 836.96
203.14 203.14
24.49
48.99

PET
15%
298.30754.35836.96203.1473.48

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 9 The composition of the material volume in
1m3 mixture of paving blocks with LDPE fly
ash (LDPEF)
Composition (%)
Material
Cement

LDPEF
0%
0.08

LDPEF
5%
0.08

LDPEF LDPEF
10%
15%
0.08
0.08

Sand

0.35

0.34

0.32

0.30

Split

0.33

0.33

0.33

0.33

Water

0.20

0.20

0.20

0.20

LDPE

-

0.02

0.04

0.05

Fly Ash

0.01

0.01

0.01

0.01

Fig. 5The molding of test specimens.

Fig. 6The curing of test specimens.

Table 10 The composition of the material weight in
1m3 mixture of paving blocks with LDPE
fly ash (LDPEF)
Composition (kg)Material
CementLDPEF
0%253.55LDPEF
5%253.55LDPEF LDPEF
10%
15%
253.55 253.55

Sand

887.47843.10798.73754.35

Split

836.96836.96836.96836.96

Water

203.14203.14203.14203.14

LDPE

-

16.68

33.37

50.05

Fly Ash

44.74

44.74

44.74

44.74

The testing of test specimens
Testing the compressive strength of the paving
block was carried out using a Universal Testing
Machine at the Material, Soil Mechanics andHighway Laboratory, Department of Civil
Engineering, Padang State University, as shown in
Fig. 7.

Manufacture of test specimen
Totally, 36 cylindrical specimens of 150mm
diameter and 300mm were prepared for compressive
strength tests, as shown in Table 11 and Fig. 5.
The curing of test specimens.
The curing of the test specimen is carried out in awater bath available at the Materials and Structural
Laboratory of the Department of Civil Engineering,
Andalas University, as shown in Fig. 6. The curing
process used is to curing water for 28 days.
Table 11 The number of test specimens

Fig. 7
Test specimen
type

The number of test specimen

LDPEF

12

PET

12

LDPE

12

Compressive strength test of cylinder test
specimens.

RESULTS AND DISCUSSION
The results of compressive tests performed on
paving blocks with variations in the LDPE mixture
are shown in Tables 12, 13 and Fig. 8.

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 12 Compressive strength test results
LDPE
(%)
0
5
10
15

Compressive strength (MPa)
LDPE
PET
LDPEF
19.32
19.32
15.36
12.51
12.72
13.55
12.04
9.23
14.46
11.46
8.40
13.29

Table 13 Percentage decreased of the compressive
test results
LDPE
(%)
0
5
10
15

Compressive strength (%)
LDPE
PET
LDPEF
35.26
34.15
23.14
37.69
52.22
18.01
40.68
56.53
24.65

Fig. 8Compressive strength of plastic use as a fineaggregate paving block.

CONCLUSION
The use of LDPE and PET plastic waste as thepartial replacement of fine aggregate decreased thecompressive strength of paving blocks with theincrease of plastic waste content. For paving blocks
containing LDPE plastic waste, the use of 5%, 10%
and 15% of the plastic waste, decrease thecompressive strength by 35.26%, 37.69%, and40.68%, respectively, while it decreased 34.15%,
52.22%, and 56.53%, respectively for paving blocks
containing PET plastic waste. As for the use of
combined fly ash and LDPE pastic waste (LDPEF) in
paving block mixture, the decrease of thecompressive strength was respectively 23.14%,
18.01%, and 24.65%. However, the compressive
strength result of tested specimens predominantly
exceeds the minimum requirement for compressive
strength of paving block quality C, 12.5 MPa, so it
can be used for pedestrian facilities.

As can be seen in Table 12, the result of thecompressive strength of the cylinder test specimen
without the addition of LDPE plastic waste is 19.32
MPa. The lowest compressive strength value is 11.46
MPa when replacing 15% of the sand volume with
LDPE. Replacement of 5% and 10% volume of sandwith LDPE results in the compressive strength of
12.51 MPa and 12.04 MPa, respectively. Meanwhile,for specimen without the PET plastic waste content,
the compressive strength value is 19.32 MPa. The
compressive strength results by replacing 5%, 10%
and 15% of the volume of sand with PET is 12.72
MPa, 9.23 MPa and 8.40 MPa, respectively. For
paving blocks containing combined LDPE and fly ash(LDPEF), on the other hand, the lowest compressive
strength value is 13.29 MPa when replacing 15% of
the volume of sand with LDPE, which is slightly
decreased compared with the specimen without theaddition of LDPE plastic waste content (15.36 MPa).
The replacement of 5% and 10% of sand volume withLDPE results in the compressive strength of 13.55
MPa and 14.46 MPa, respectively.
The decrease in the lowest compressive strength
value occurred when the LDPEF was 10% with adecrease of 18.01%, while the decrease incompressive strength value was greatest when PET
was 15% with a large decrease of 56.53%.
Based on Fig. 8, it can be seen that the
replacement of sand by LDPE and PET plastics
causes a decrease in the compressive strength ofpaving blocks. The reason for this reduction instrength is associated with the smooth surface of theplastic that reduce the adhesion between plastic
particles and cement paste [2], [16].

ACKNOWLEDGMENTS
The authors gratefully acknowledge the financial
support from Andalas University. The author also
would like to thank the staff of the Material and
Structural Laboratory, of the Department of Civil
Engineering, Andalas University for their help andsupport.
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Block), Indonesian National Standardization
Agency, pp.1-9, 1996.
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Polyethylene in High Strengths Concrete
Pavement Blocks Production, Civil andEnvironmental Research, Vol.6, No.5, 2014,
pp.126-135.
359

GEOMATE – Bangkok, Thailand, 22-24 November 2022

[3] Ghuge J., Surale S., Patil B.M. and Bhutekar
S.B., Utilization of Waste Plastic in
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[5] Indrawijaya B., Wibisana A., Setyowati A. D.,
Iwadi D., Naufal D. P., and Pratiwi D.,
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waste electrical cable rubber, IOP Conf. Ser.
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[7] Herki B.A. and Khatib J.M., Valorisation ofwaste expanded polystyrene in concrete using anovel recycling technique, Eur. J. Environ. Civil
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characteristics of self-compacting concretes
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[13] Pandey A.N., Yadav A. and Chaudhary D, Waste
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[14] Thorneycroft J., Orr J., Savoikar P., and Ball R.
J., Performance of Structural Concrete with
Recycled Plastic Waste as A Partial Replacement
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[16] Ebrahim R., Mehdi D., Morteza H. B., Hamed A.
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2013, pp.1302-1308.

360

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

FRAGILITY CURVE DEVELOPMENT OF SCHOOL BUILDING IN
PADANG CITY WITH AND WITHOUT RETROFITTING DUE TO
EARTHQUAKE AND TSUNAMI LOADS
Fauzan1, Ruddy Kurniawan2, Nandaria Syahdiza3 and Zev Al Jauhari4
1,2,3

4

Engineering Faculty, Andalas University, Indonesia
Department of Civil Engineering, Bengkalis Polythecnic, Indonesia
ABSTRACTSMPN 25 building in Padang City is a junior high school building that located in the high seismic zone andprone to tsunami. Based on the results of the initial analysis of the existing building using the current Indonesianbuilding codes, this building have not been able to withstand the working earthquake and tsunami loads. In thisstudy, the retrofitting of the building was designed using concrete jacketing that is with additional dimensions andreinforcement, aimed at making the building function as a vertical evacuation. The structural fragility curve of thebuilding is determined for before and after retrofitting for both earthquake and tsunami loads. The fragility curveis determined from the ductility of the building for each variation of earthquake acceleration based on the Hazus
standard. The first yield displacement was determined from the pushover analysis, and the ultimate displacement
was determined from the time history analysis. The earthquake acceleration records used were the El-cento
earthquake, the Northridge earthquake, the Kobe earthquake and the Padang earthquake. The results of the analysis
show that the retrofit of the building structure using concrete jacketing reduce the probability of damage to thebuilding structure by 42.06% at the level of extensive damage and by 4.42% at the level of complete damage at
PGA 0.6g.
Keywords: Fragility curves, School building, Tsunami loads, Earthquake, Retrofit

INTRODUCTION

withstand a combination of earthquake and tsunami
loads. The results obtained from the structural
analysis of the existing building shows that several
structural elements are not able to withstand thetsunami loads acting on the building structure, so it isnecessary to design the retrofit of the building
structure so that the building can be used as a tsunami
vertical evacuation.
In order to observe the probability of theoccurrence structural damage of the building, afragility curve of the school building with and without
retrofitting due to earthquake loads and tsunami loads
was developed.

The tsunami disaster hit West Sumatra in 1797 in
Siberut and in 1833 in Sipora-Pagai, Mentawai
Island, West Sumatra Province, Indonesia. These two
tsunamis had different effects, both in terms of time
to reach and inundation height [1].
Singh's (2008) research stated that the timeinterval between the occurrence of the first strong
earthquake and the onset of the tsunami that hit the
Padang coast was about 20-30 minutes. Judging fromthe map of the Padang city earthquake inundation,
people must save themselves with a distance of 3-5
km to get to a safe area. With this data, it is estimatedthat the community does not have enough time toevacuate horizontally so that it will be more effective
to evacuate vertically [2].
Vertical evacuation must be supported by a strong
building structure that is resistant to earthquake andtsunami loads. In addition to the structure of thebuilding must be strong enough to withstandearthquake loads that occur, the building must be able
to withstand the load caused by the tsunami wave [2].
SMPN 25 Padang is an existing building in the
Padang city which is located in the tsunami red zone
area. An analysis of the existing building of SMPN
25 Padang based on the current Indonesian seismic
standard (SNI 1726-2019) [3] and tsunami loads(FEMA P-646_508) [4] had been carried out whichaims to see the ability of the building structure to

ANALYSIS OF THE STRUCTURE
Structural Modelling
SMPN 25 building is an existing building in the
Padang city which is located in a tsunami inundationarea, around 0.97 km from the coastal line (Fig. 1).
The building is located on Beringin Raya street,
Lolong Belanti, North Padang, Padang City, West
Sumatra, with coordinates latitude -0.919197 andlongtitude 100.357238. The building is four-story
reinforced concrete structure with 15 m height.
In this study, the building structure was modeledwith and without retrofitting using ETABS v.18.
Plans and 3D modeling of building structures isshown in Figs. 2 and 3.
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

ground motion earthquake data uses 4 ground motion
earthquake recording data, as shown in Table 1. Eachground motion earthquake data is matched with theearthquake characteristics of the Padang city formedium soil types using Seismomatch software. Eachground motion earthquake data is scaled with avariation of 0.2 to 2.0 with an interval of 0.2.
Table 1 Earthquake peak ground acceleration
Earthquake
El-Centro 1940
Northridge 1994
Kobe 1995
Padang 2009

Original
PGA
0.36 g0.60 g0.34 g0.60 g

PGA matched
0.63 g0.76 g0.60 g0.60 gTsunami Loads
Fig. 1

The front view and the location of SMPN 25
building in Google Earth software.

Fig. 2The plan of SMPN 25 Padang building.

The tsunami load was calculated based on the
FEMAP-646-508 2012 with an inundation depth of
4.5 meters based on the inundation map of the Padangcity. In this study, the calculation result of the tsunami
loads on the building structure are hydrostatic force(49.166 kN), buoyant force (5.396 kN),hydrodynamic force (12.947 KN), impulse loads(19.421 kN), debris impact loads (401.431 kN),debris damming loads. (5244.426 kN), uplift load
(0.001 kN) and extra gravity load (5.396 kN). These
loads are applied to structural components such ascolumns and slabs.
FRAGILITY CURVE DEVELOPMENT
In this study, an analytical method was carried out
to obtain the fragility curve of the building with andwithout retrofitting due to earthquake and tsunami
loads.
Existing Building Analysis
The analysis of the existing building includes
checking the cross-sectional capacity of the column
and beam elements of the building against earthquake
loads and combined earthquake and tsunami loads
acting on the structure [5].

Fig. 3

The 3-D modeling of SMPN 25 building
using ETABS.

Structural Retrofitting
An alternative design for retrofitting the building
structure against tsunami loads is by using theconcrete jacketing method, that is a method byenlarging the column cross section and adding
flexural reinforcement and column shear around theold column [6]. In this study, retrofitting using
concrete jacketing was designed for several structuralelements in the building structure which have notbeen able to carry the load acting on the building
structure due to the addition of the tsunami loads.

Earthquake Load
The earthquake load used in this study is the
response spectrum based on SNI 1726-2019 forbuilding analysis with and without retrofitting andground motion earthquake data (time history) for nonlinear time history analysis which is needed as anengineering demand parameter (EDP) in thecalculation of the fragility curve. In this study, the362

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Analytical Fragility Curve Development
The development of the fragility curve wascarried out based on the Hazus standard [7], [8] andis developed using the Probabilistic Seismic Demand
Model (PSDM) method with a cloud approach. The
parameters used in the calculation of the fragility
curve are obtained from the results of pushover
analysis in the form of drift yield values and from theresults of non-linear time history analysis in the formof maximum drift values [9].

Fig. 4Structural elements that have not been able
to withstand the tsunami load.

Concrete Jacketing Retrofit

RESULTS AND DISCUSSION

Retrofitting design using concrete jacketing on
weak structural elements was carried out based on the
results of existing building analysis and the minimum
requirementsforaddingdimensions
andreinforcement for jacketing.
Dimensions details of the retrofitted structuralelements is shown in Table 2 and Fig. 5. Fig. 6 shows
the modeling of the retrofitted building structure.

Load Bearing Capacities of Existing Building
Structure
The analysis of the flexural cross-sectional
capacity of the existing building structure can be seenin the axial-flexural (P-M) interaction diagram of thecolumn (Fig. 7). Table 3 shows the shear capacity ofthe existing building column. The beam flexural
cross-sectional capacity is shown in Table 4.
Based on the analysis of the existing building of
SMPN 25 Padang against earthquake and tsunami
loads, there are several structural elements that havenot been able to withstand the tsunami loads such as
the 2nd floor columns and the 1st floor beams. The
structural elements that have not been able towithstand the tsunami loads is shown in Fig. 4.

Fig. 5

Steel reinforcement details of the concrete
jacketed columns.
Table 2 Concrete jacketing dimension of retrofitted SMPN 25 Padang building
Existing columns
Dimension
Number ofColumn type
Ø (mm)(cm)
rebar
1-K1 (C01)
45x45
22
12
Story 1 1-K2 (C29)
45x35
22
12
1-K3 (C38)
35x35
22
12
2-K1 (C01)
45x45
22
8
Story 2 2-K2 (C29)
45x35
22
8
1-K3 (C38)
35x35
22
8
Story

Concrete jacketed columns
Dimension Ø Number of jacket
Column type
(cm)
(mm)
rebar
1-K1 retrofit (C01) 60x60
22121-K2 retrofit (C29) 50x60
22
121-K3 retrofit (C38) 50 x50
22122-K1 retrofit (C01) 60x60
22
122-K2 retrofit (C29) 50x60
22
122-K3 retrofit (C38) 50x50
22
12Load Bearing Capacities of Structure With andWithout Retrofitting

Fig. 6

The P-M interaction diagram is used to describe
the capacity of the column to withstand the working
load. Figure 7 shows the comparison of the P-M
interaction diagram obtained from the results of thestructural analysis of the building with and without
retrofitting. From the figure, it can be seen that
retrofitting on columns improves the capacity of thebuilding structure, in which columns of the retrofitted
building have been able to withstand tsunami loads
acting on the structure. The comparison of the shear

The 3-D modeling of retrofitted SMPN 25
Padang building with concrete jacketing.

363

GEOMATE – Bangkok, Thailand, 22-24 November 2022

capacity of the column with and without retrofit isshown in Table 3. As can be seen from the table, theshear capacity of the columns improves after the
retrofitting, in which the beam shear capacity of theretrofitted building can resist the applied working
loads including tsunami loads.

Fig. 7The comparison of the flexural capacity of thebeam with and without retrofit is shown in Table 4.
As seen in the table that retrofitting on columns
improves the capacity of the beam, so the beams areable to withstand the tsunami loads. The beam shear
capacity is also able to withstand the tsunami loads.

column without retrofit (2-K1:C1)

column with retrofit (2-K1 retrofit:C1)

column without retrofit (2-K2:C29)

column with retrofit (2-K2 retrofit:C29)

column without retrofit (2-K3:C38)

column with retrofit (2-K3 retrofit:C38)

P-M interaction diagram of the 2nd Floor column with and without retrofitting.

Table 3 Comparison of the shear capacity of the column with and without retrofitting
Without retrofittingWith retrofitting
ColumnColumnVr
Vu’
Shear rebar
Vr
Vu’
Mu ≤
Shear rebar
Mu ≤ Mn(kN) (kN)
(kN)
(kN)
Mn
Section Code
Section Code design2-K1
Ø10-100
2-K1 retrofit
C01
293.43 1037.01 Not Ok
C01 Ø13-50 mm 1060.98 1037.01 Ok(45x45)mm(60x60)2-K2
Ø10-100
2-K2 retrofit
C29
270.09 1180.61 Not Ok
C29 Ø16-50 mm 1476.79 1180.61 Ok(45x35)mm(60x50)2-K3
Ø10-100
2-K3 retrofit
C38
204.21 1185.72 Not Ok
C38 Ø16-50 mm 1213.08 1185.72 Ok(35x25)mm(50x50)364

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 4 Comparison of the flexural capacity of the beam with and without retrofitting
Beam
Section
Code
1-B2
B01
30/55
1-B2
B14
30/55
1-B3
B59
30/70

Number of rebar
Tens.
Comp.
3D19
3D19
3D19
3D19
3D19
3D19
3D19
3D19
3D19
3D19
3D19
3D19

φMn
(kNm)
119.09119.09119.09119.09106.65106.65

Without retrofitting
Mu (kNm)
Mu ≤ Mn
170.89P
Not Ok
120.99M
Ok
175.8
P
Not Ok
85.21
M
Ok
197.19P
Not Ok
120.86
M
Ok

Pushover Analysis Result

With retrofitting
Mu (kNm) Mu ≤ Mn
115.26
P
Ok
58.83
M
Ok
115.26
P
Ok
58.83
M
Ok
106.06
P
Ok
66.59
M
Ok

describes the probability of exceeding certain
structural damage conditions by taking into accountirregularities related to the capacity, demand anddamage conditions (Hazus MH MR 5) [7]. The
fragility curve depicts the relationship between theprobability of the degree of structural damage and thehazard intensity measure (IM).
The general equation for the development of thefragility curve is shown in Eq. (1).

The capacity curve from the pushover analysis
illustrates the relationship between base shear androoftop displacement. In this study, the capacity curvefrom the pushover analysis is used to check the
performance level of the existing structure andfunctions as an engineering demand parameter (EDP)
in the calculation of the building fragility curve withand without retrofitting is given through the drift
yield value.
There are 3 methods used to determine
displacement yield; first yield is based on the firstyield deviation that occurs, significant yield is basedon the intersection between the elastic displacement
and the equivalent load at the time of failure andbased on the same energy absorption capacity
between the elasto-plastic system with the actual
structure at the same ultimate load. In this study, thedetermination of the displacement yield value for thedevelopment of the fragility curve uses a significant
yield. The displacement yield value obtained for the
existing building was 67.30mm and the displacement
yield value for the retrofitted building was 38.98mm.
The drift yield value used in calculating thefragility curve is obtained from the comparison of thedisplacement yield value with the building height. So
based on the results of the pushover analysis, the drift
yield of the building structure is 0.45% for the
existing structure and 0.26% for the retrofitted
structure.

𝐅𝐫𝐚𝐠𝐢𝐥𝐢𝐭𝐲 = 𝐏[𝐋𝐒|𝐈𝐌] = 𝐲(1)

Where LS is the boundary condition or damage
condition (DS), IM is a measure of ground motion
intensity, and y is the state reached due to groundmotion intensity (IM).
The equation is then idealized into a lognormal
distribution, as shown in Eq. (2).
𝒍𝒏(𝒙)−𝝀𝑷(𝒙) = 𝚽 [

𝜻

(2)

]

Where P(x) is a probability function, x is a randomvariable, 𝜻 is the standard deviation value of ln x, 𝝀 isthe mean value of ln x, 𝚽 and is the form of the
cumulative normalized distribution. Lognormal isused because it is very suitable to determine various
types of damage ranging from failure of structuralcomponents, non-structural components, building
collapse, and the probability of zero on EDP equal tozero and below zero.
The fragility curve developed in this study is thefragility curve of the existing building (Fig. 8) and thefragility curve of the retrofitted building (Fig. 9).
The parameters for fragility functions obtained
from the calculation results is used to describe thefragility curve for the building with and without
retrofitting, as shown in Table 4.

Non-Linier Time History Result
In this study, the non-linear time history analysis
uses 4 types of ground motions earthquake data (timehistory) which are scaled on a 0.2–2.0 scale with aninterval of 0.2. So there are 40 ground motion data
used in the calculation of the fragility curve. The
Engineering Demand Parameter (EDP) value
obtained from the non-liner time history analysis forthis fragility curve calculation has 40 maximum
displacement values per each fragility curve before
and after retrofitting.

Table 4 Parameters for fragility functions
Without retrofit
λζ
Slight Damage
-2.55
0.28
Moderate Damage -1.83
0.28
Extensive Damage -0.69
0.50
Complete Damage 0.32
0.50
Damage level

Fragility Function and Fragility Curve
The fragility curve is a long normal function that
365

With retrofit
λζ
-1.99 0.28
-1.27 0.28
-0.13 0.50
0.89 0.50

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 8existing building analysis show that several columnson the 2nd floor and several beams on the 1st floor
have not been able to withstand the tsunami loads.
Retrofitting of the building using the concrete
jacketing method on columns improves the capacityof the building structure, so the building are able towithstand the tsunami loads.
Based on the fragility curve developed, thepercentage of the structural damage probability
decreases after the structure was retrofitted. The
retrofit of the building structure using concrete
jacketing reduce the probability of damage to thebuilding structure by 42.06% at the level of extensive
damage and by 4.42% at the level of complete
damage at PGA 0.6g.

Fragility curve of the existing building

REFERENCES

Fig. 9[1] Borrero, J.C., Tsunami Inundation Modeling forWestern Sumatra. PNAS vol. 103 no. 52. 19677,
2006, pp. 19673–19677.
[2] F. Ashar, D. Amaratunga, and R. Haigh, The
Analysis of Tsunami Vertical Shelter in Padang
City, 4th International Conference On Building
Resilience, Salford Quays, United Kingdom,
2014, pp. 916-923.
[3] Indonesian National Standardization Agency,
Procedures for Planning Earthquake Resistancefor Building and Non-Building Structures SNI
1726-2019, Jakarta, Indonesia, 2019, pp.1-248.
[4] Federal Emergency Management Agency, FEMA
P646: Guidelines for Design of Structure for
Vertical Evacuation from Tsunami 2nd edition,
Washington D.C, USA, 2012, pp.1-174.
[5] Fauzan, Febrin A.I, and Zev A.J, Seismic
Retrofitting Analysis Using Concrete Jacketingand Shear Wall on Dental Hospital Building ofAndalas University, IOP Conf. Series: Materials
Science and Engineering 602, 012108, 2019,
pp.1-11.
[6] Fauzan, Febrin A.I., Nurpadila S., and Zev A.J,
The Effect of Tsunami Loads on Pasar Raya
Inpres Block III Building in Padang City Based on
FEMA P-646, MATEC Web of Conferences 258,
03020, SCESCM 2018, 2019, pp.1-9.
[7] HAZUS,
Earthquake
Loss
Estimation
Methodology, Washington, D.C.: Federal
Emergency Management Agency, 2002, pp.1121.
[8] HAZUS, Hazus Tsunami Model Technical
Guidance, Washington, D.C.: Federal Emergency
Management Agency, 2017, pp. 1-171.
[9] A.C Utami, R Kurniawan and Fauzan, Analytical
Fragility Curve Development of Maternity andChildren’s M. Djamil Hospital Building Padang
due to Earthquake and Tsunami, IOP Conf.
Series: Earth and Environmental Science 708,
012014, 2021, pp.1-10.

Fragility curve of the retrofitted building

The probability of damage level to the building
structure with and without retrofit during anearthquake with a PGA value of 0.6g is shown in
Table 5.
Table 5 Comparation of structural damage levelprobability on the building with and without
retrofit during an earthquake with a PGA
value of 0.6 g
Damage level
Slight Damage
Moderate Damage
Extensive Damage
Complete Damage

Percentage of probability
Without retrofit
With retrofit
100.00%100.00%99.99%
99.66%
64.33%
22.27%
4.67%
0.25%

From the fragility curves of the building with andwithout retrofitting shown in Figs. 8 and 9 show thatthe percentage of the structural damage probability
decreases after the structure was retrofitted. The
retrofit of the building structure using concrete
jacketing reduce the probability of damage to thebuilding structure by 42.06% at the level of extensive
damage and by 4.42% at the level of complete
damage at PGA 0.6g.
CONCLUSION
The structure of the SMPN 25 Padang building
was modeled with and without retrofit against
earthquake and tsunami loads. The results of the366

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

REMEDIATION OF AL-QATIF EXPANSIVE CLAY USING CEMENTKILN DUST, INITIAL ASSESSMENT
Sultan Abdulaziz Almuaythir 1 and Mohamed Farid Abbas 1,2
1

College of Engineering, Prince Sattam Bin Abdulaziz University, Saudi Arabia; 2 Soil Mechanics andGeotechnical Engineering Research Institute, Housing and Building National Research Center (HBRC),
Egypt

ABSTRACT
Expansive soils exist naturally or may be used as a compacted material in different engineering projects.
Uneven ground movements (heave) of expansive soils typically inflict damage to light structures, pavements,
retaining structures and shallow tunnels. Given the high cost of damage inflicted to such structures, it is of aparamount importance to examine the probable approaches to reduce damage associated with its use. This study
examines the feasibility of using Cement Kiln Dust (CKD) for Al-Qatif expansive clay stabilization. Geotechnicalproperties including compaction parameters, swell potential and unconfined compressive strength for different
mixtures containing different doses of CKD were examined. The experimental results of the experimental programindicates that CKD be beneficially used to reduce the swell potential of Al-Qatif expansive clay as well as increase
its strength.
Keywords: Expansive soil, Cement Kiln Dust, Stabilization, Swell Potential, Unconfined Compressive Strength

INTRODUCTION

especially for treating expansive soil with high swellpotential.
Several researchers examined the use of CKD instabilization of expansive soils [16]–[21]. They
reported an improvement in geotechnical properties
with adding CKD and suggested use of CKD for
expansive soil stabilization where it is more
economical and is an environmental solution to othersoil stabilizer agents such as lime and cement.
The main objective of this study is toexperimentally examine the feasibility of using
Cement Kiln Dust (CKD) for Al-Qatif expansive clay
stabilization in view of deformation and shear
strength aspects.

Expansive soils are extended worldwide
especially in arid and semi-arid zones such as Saudi
Arabia. Its nature in undergoing swelling and loss ofshear strength, as a result of moisture change, resultsin structural damage that may cause huge economic
losses as well as hampered development. In thisregard, numerous studies have focused onstabilization of expansive soils by using additives
such as lime [1]–[3] and cement [4]–[7]. However,stabilization of this soil with cement or lime
admixture is effective but very costly and hence thereis need for utilizing potentially cost-effective
additives. Consequently, attention has been turned
towards using industrial waste, such as fly ash [8][10], coal ash [11]–[13], husk ash [14]–[15] andCement Kiln Dust [16]–[21], as a chemical stabilizer.
Potential use of industrial waste as a chemical
stabilizer will not only reduce the cost of disposal but
also will reduce the environmental hazards caused bysuch wastes [19].
Cement Kiln Dust (CKD) is a waste by-productthat is produced during manufacturing process ofPortland cement. It is generated from burning the raw
materials in a rotary kiln to produce clinker. CKD
poses a storage trouble and considered a source ofpotential pollution. Consumption of such material insoil stabilization processes might solve some of these
problems. However, researches that deal with use of
CKD in expansive soil remediation is limited,

MATERIALS
In this section, a brief description of the physicaland chemical properties for the studied expansive soil
and selected stabilizer are presented.
Al-Qatif expansive soil
Al-Qatif expansive soil was selected for thecurrent study since it is a typical expansive soil thatare located in in the Arabian Gulf coastal region,
Saudi Arabia, where the main oil-industrial activatesare located. Also, Al-Qatif expansive soil isconsidered to be a highly expansive soil and hence
remediation of this issue will be more significant.
The sampled soil is a calcareous grayish silty clay
367

GEOMATE – Bangkok, Thailand, 22-24 November 2022

obtained from a field test pit at a depth of about threemeters. Bulk disturbed specimens were air dried, thenpulverized and screened through sieve No. 40. A
summary of physical properties of selected soil areprovided in Table 1.

geotechnical material is based on its deformation andstability properties, current experimental program
involves oedometer (OED) and unconfinedcompressive (UCS) tests. Both testing groups weredivided to subgroups based on the CKD% added. Aswell, Oedometer and UCS tests were executed for
different curing periods (1, 7, 14 and 28 days). Actual
curing period may slightly differ according to theavailability of utilized devices and working days.
Each tested sample has been labelled to infer the
CKD% added and actual curing period.

Table 1 Physical properties of Al–Qatif soil
Physical Property

Value

Specific Gravity, Gs

2.74

Liquid Limit, LL (%)

140

Plastic Limit, PL (%)

45

Shrinkage Limit, SL (%)

18

Plasticity Index, PI (%)

95% Finer than 200 μm

70

USCS Classification

CH

Dry density, gd (kN/m3 )

1.22
1.17
1.12
Untreated
2%
4%
6%
8%

1.07
1.02

Cement Kiln Dust (CKD)

25

In the current study the Cement Kiln Dust wasused for stabilizing the Al-Qatif soil in order to reduce
its swell potential and improve its shear strength.
Current CKD is by product of Yamama cement plant,
Riyadh, Saudi Arabia. Table 2 summarizes theaverage chemical composition of CKD, showing
huge percentage of calcium oxide as well as highpercentages of silica and aluminum oxides.

Fig. 1
CKD %

CaO

SiO2

Al2O3

K2O

%

67.47

14.37

4.21

2.54

Mineral

Fe2O3

SO3

Cr

Loss onIgnition

%

2.50

2.13

0.42

3.79

55

Standard compaction tests for examined

In order to prepare OED specimens, dry soil was
mixed with respective CKD% and optimum moisture
content and thoroughly mixed. Thereafter, mixture
was compacted in oedometer ring to the desired dry
density (i.e., maximum dry density at selected
CKD%), wrapped in thin plastic wrap and cured to
the respected curing period in wet-desiccator. Sameis adopted for UCS specimens, except that compacted
specimen is dismantled after compaction instead ofleft in the compaction ring.
OED testing was adopted with respect to ASTM
D4546-03 [23], method B. Once cured specimen was
installed in oedometer device, 50 kPa applied stress(comparable to stress applied at foundation level ofresidential buildings) was applied in step loading andeach load was left for five minutes, thereafter,
specimen was inundated and deformation due towetting was recorded for 24 hours. The final wettingstrain was labelled as wetting potential (w).
Specimens for the unconfined compressive
strength test (38 mm in diameter and 78 mm in high)were constituted by statically compacting the mixtureof Al-Qatif soil with specific CKD% with respect tocompaction characteristics obtained from Fig.1.
Strain rate of 0.25%/min. is selected for shearing
tested specimens to ensure undrained condition andavoids probable moisture evaporation from cured
specimens.

Table 2 Chemical composition of Cement Kiln Dust
percent by weight of dry mass
Mineral35
45
Moisture content, wc (%)

METHODS
To study the feasibility of using CKD as astabilizer of Al-Qatif soil, four percentages of CKD
by dry weight (2.0, 4.0, 6.0 and 8.0 %) were used.
Standard compaction tests, in accordance to ASTM
D698 [22], were executed to determine thecompaction characteristics of the selected mixtures.
Fig. 1 shows the standard compaction test results for
untreated and treated Al-Qatif soil. As CKD%
increase, the maximum dry density decreases andoptimum moisture content increase which iscomparable with previous researches either for use of
CKD [20] or other stabilizer.
As it is well known that characterization of any
368

GEOMATE – Bangkok, Thailand, 22-24 November 2022

RESULTS AND ANALYSIS
Oedometer testing, OED

1d

7d

14d

28d

Swell reduction (%)

100

Table 3 shows that untreated specimens showedsmall swell (w=6.60%) under 50 kPa applied stress.
Fig. 2 shows that wetting strain (w) reduce withincrease of CKD%. Fig. 3 depicts clearly the impact
of CKD% on swell reduction. CKD%=6.0% is almost
sufficient to remove the swell potential related towetting. As well, it is seen that curing period is not aneffective remediation factor of swell for all CKD%
besides that curing period results in loss ofremediation for 2.0% dose of CKD. This observation
is more clearly seen in Fig. 4. As it is known fromCKD properties, it has fast seating and 7 days curing
is sufficient to ensure the mobilization of moststabilization.

90

80
70
60
50
2

Fig. 3Swell reduction versus CKD %7.0

Wetting strain, w (%)

Table 3 Summary of oedometer testing results

4
68
Cement kiln dust, CKD (%)

Untreated
1d
7d
14d
30d

6.0
5.0

ID

Wetting
strain, w (%)

Swellreduction (%)

CKD_0

6.60

0

CKD_2_1d
CKD_2_7d

1.02
1.47

85
78

CKD_2_15d
CKD_2_31d

1.50
2.53

77
62

0.0

CKD_4_1d
CKD_4_9d
CKD_4_15d
CKD_4_31d

1.41
0.75
0.85
0.59

79
89
87
91

Fig. 4

CKD_6_1d0.18

97

Unconfined compressive strength, UCS

CKD_6_7d
CKD_6_15d
CKD_6_28d

0.45
0.45
0.39

93
93
94

CKD_8_7d
CKD_8_14d
CKD_8_28d

0.22
0.19
0.10

97
97
98

Wetting strain, w (%)

7.0

5.0
4.0

2.0
1.0
0.0

Fig. 210
20
30
Curing period, t (days)

2.0
1.0
2
4
6
8
Cement kiln dust, CKD (%)

10

Variation of wetting strain with CKD %for different curing periods

Evaluation of soil stability under undrained
condition is often estimated in terms of unconfinedcompressive strength (UCS). The unconfinedcompressive strength is one of the key variable for
estimating the undrained shear strength of finegrained soils. The obtained results, see Table 4 andFigs. 5 and 6, reveal that CKD addition is effective in
improve stability of Al-Qatif soil. Moreover, curing
period has a pronounced impact on UCS, Fig. 5, incontrast to its effect on swell reduction. Fig. 6 showsthat CKD% greatly enhance the undrained strength of
Al-Qatif soil.
Figure 7 shows the improvement percentage dueto the addition of CKD and shows that UCS
improvement continuously increase with CKD for
examined CKD% and adding 8.0% of CKD increases
the undrained stability by four times, indicating its
improvement effectiveness.

3.0

0

3.0

0

Untreated
2%
4%
6%
8%

6.0

4.0

40

Variation of wetting strain with curing period
369

GEOMATE – Bangkok, Thailand, 22-24 November 2022

1400
1200

UCS (kN/m2 )Current study investigated experimentally thepossibility of using Cement Kiln Dust (CKD) for AlQatif expansive clay stabilization, as a typical
expansive soil encountered in region that embrace themain oil-industrial activates in Saudi Arabia.
Evaluation of CKD performance is determined inview of deformation and shear strength aspects.
The results showed the fast efficiency of the
reduction of swell potential related to wetting. On theother hand, CKD additave exhibited a continously
improve of undrained shear strength either withcuring period or CKD%. In conclusion, use of CKD
additave for Al-Qatif soil is recommended in view ofdeformation and strength aspects.

Untreated
2%
4%
6%
8%

1000
800
600
400
200
0
0

Fig. 510
20
30
Curing period, t (days)

40

Variation of UCS with curing time for
examined CKD %1400

Untreated
1d
7d
14 d
28 d

1200
UCS (kN/m2 )Table 4 Summary of unconfined compressive testing
results

1000
800
600

ID

UCS
(kN/m2)

Strength
Increase (%)

CKD_0

231.8

0.0

200

CKD_2_1d

231.6

0.0

0

CKD_2_8d
CKD_2_15d
CKD_2_29d

245.0
265.0
282.0

5.7
14.3
21.7

CKD_4_2d
CKD_4_7d
CKD_4_15d
CKD_4_29d

273.9
427.2
535.0
582.2

18.2
84.3
130.8
151.2

CKD_6_1d
CKD_6_8d
CKD_6_14d
CKD_6_28d

261.4
556.4
698.4
891.0

12.8
140.1
201.3
284.5

CKD_8_1d
CKD_8_8d
CKD_8_15d

427.1
754.6
923.0

84.3
225.6
298.3

CKD_8_28d1148.2

395.4

400

0

Fig. 62

4
6
8
10
Cement kiln dust, CKD (%)

Variation of UCS with CKD %Improvement percentage (%)

1d

7d

14d

28d

400
300
200
100
0
2

Fig. 7

4
6
8
Cement kiln dust, CKD (%)

UCS improvement versus CKD %CONCLUSIONS
Expansive soils exist worldwide extensively invadose zones such as Saudi Arabia. The structural
damage accompained with construction of light
structure over such expansive soils cause hugeeconomic losses as well as hampered development.
Given the high cost of damage inflicted to such
structures, it is of a very important to study mosteconomic and effective approaches to reduce damage
associated with its use.

ACKNOWLEDGMENTS
The authors extend their appreciation to the
Deputyship for Research & Innovation, Ministry of
Education in Saudi Arabia for funding this research
work through the project number (IF-PSAU2021/01/19009).

370

GEOMATE – Bangkok, Thailand, 22-24 November 2022

REFERENCES

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and Building Materials, Vol. 239, 2020,
pp.117848.
[13] Mansouri, S., Nasiri, M., and Modarres, A.,
Technical and Environmental Impacts of Coal
Waste Used as a Soil Stabilizer in Construction
Projects of Forest Roads. Croatian Journal ofForest Engineering, Vol. 42, 2021.
[14] Ali, F.H., Adnan, A., and Choy, C.K., Use of ricehusk ash to enhance lime treatment of soil.
Canadian Geotechnical Journal, Vol. 29, 1992,
pp. 843-852.
[15] Vattimalla, V., and Sandela, R., A comparative
study of soil stabilization using fly ash and ricehusk ash. Int. J. of Innov. in Eng. Res. and Tech.,Vol. 8, Issue 7, 2021, pp. 161–166.
[16] Miller G.A., Azad S., Influence of soil type on
stabilization with cement kiln dust. J Constr
Build Mater, Vol. 14, Issue 2, 2000, pp. 89–97.
[17] Hossain K.M.A., Mol L., Some engineering
properties of stabilized clayey soils incorporating
natural pozzolans and industrial wastes. J Constr
Build Mater, Vol. 25, Issue 8, 2011, pp. 3495–
3501.
[18] Ismaiel H.A.H., Cement kiln dust chemical
stabilization of expansive soil exposed at ElKawther Quarter, Sohag Region, Egypt. Int J
Geosci, Vol. 4, Issue 10, 2013, pp. 1416–1424.
[19] Salahudeen, A.B., Eberemu, A.O., and Osinubi,
K.J., Assessment of Cement Kiln Dust-Treated
Expansive Soil for the Construction of Flexible
Pavements. Geotechnical and GeologicalEngineering, Vol. 32, 2014, pp. 923-931.
[20] Ismail A.I.M. and Belal Z.L., Use of cement kiln
dust on the engineering modification of soilmaterials, Nile Delta, Egypt. J Geotech Geol Eng,
Vol. 34, 2016, pp. 463–469.
[21] Ogila, W.A., Effectiveness of fresh cement kiln
dust as a soil stabilizer and stabilization
mechanismofhighswelling
clays.
Environmental Earth Sciences, Vol. 80, 2021, 124.
[22] ASTM D698, Standard test methods forlaboratory compaction characteristics of soil
using standard effort (12,400 ft-lbf/ft3 (600 kNm/m3)). ASTM International, 2000, WestConshohocken, PA, USA.
[23] ASTM D4546-03, Standard test methods for
one-dimensional swell or settlement potential ofcohesive soils. ASTM International, 2003, WestConshohocken, PA, USA.

[1] Khattab, S.I., Al-Mukhtar, M., and Fleureau,
J.M., Long-Term Stability Characteristics of a
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Association.
[5] Kim, W., Tam, N.M., and Jung, D.H.,
Experimental study on strength of cement
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[7] Yong-hui, S., Linrong, X., and Yu, C. Study on
dynamic characteristics of cement-stabilized
expansive soil subgrade of heavy-haul railway
under immersed environment. Rock and Soil
Mechanics, Vol. 41, 2020, pp. 2739.
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methods to treat soft expansive soils. In Softground technology, Reston, VA: ASCE, 2001, pp.
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[9] Hoyos, L.R., Puppala, A.J., and Ordóñez, C.,
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Reclaimed Asphalt Pavement Aggregates:
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[10] Mahedi, M., and Cetin, B., Leaching of elementsfrom cement activated fly ash and slag amended
soils. Chemosphere, Vol. 235, 2019, pp. 565-574.
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12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

EFFECT OF MACRO-SYNTHETIC POLYPROPYLENE FIBER ONDRYING SHRINKAGE OF SELF-CONSOLIDATING CONCRETE
Aris Aryanto1 and Andy Muliohardjo2
1,2

Faculty of Civil and Environmental Engineering, Bandung Institute of Technology, Indonesia
ABSTRACT

Development in the concrete industry led to an increasing need for faster construction, high performance,energy-saving, and eco-friendly concrete technology. Self-consolidating concrete (SCC) has become a promisingconcrete technology to shorten construction time and reduce labor needs. However, SCC has susceptible toshrinkage cracking due to high binder and low aggregate content. On the other hand, fiber has been used to control
cracking and increase post-cracking behavior and toughness. Fiber addition into the SCC mix has become acommon method to improve cracking resistance, even though it may reduce the workability. In this study, theeffect of macro-synthetic fiber on the drying shrinkage of SCC is evaluated using free and restrained shrinkage
tests. SCC mixtures include concrete containing different percentage of polypropylene fiber content (0.25, 0.5,
0.75 and 1.0% by volume). Besides that, compressive strength, splitting tensile strength, and workability in termsof Slump flow, J-Ring, and V-Funnel of fiber-reinforced SCC are also investigated. The results show that theaddition of macro-synthetic fibers can delay crack formation due to drying shrinkage compared to plain SCC.
Keywords: Self-consolidating concrete, Synthetic fiber, Workability, Shrinkage
such as straight monofilament, multifilament,
crimped, embossed, or with an end-hook to improve
its anchorage to concrete [7]. Fiber size can be
categorized as macro- and micro-fiber depending on
the aspect ratio, i.e., diameter to length ratio. In thisstudy, only macro fibers with polypropylenematerials are used.
This study investigated the effect of macrosynthetic polypropylene fibers to SCC on fresh andhardened properties, especially on drying shrinkage.
Two drying shrinkage tests were carried out using
free and restrained shrinkage tests according to
ASTM C341 [8] and ASTM C1581 [9]. The main
parameter is the variation in fiber content of 0, 0.5,
0.75, and 1.0% by volume. Based on the test results,
the effect of fiber content on drying shrinkage
cracking was confirmed.

INTRODUCTION
Self-consolidating concrete (SCC) has become
more frequently used in concrete construction due toits better workability and efficiency in construction
time and labor needs [1]. However, SCC is prone toshrinkage cracking due to high binder and low
aggregate content [2],[3]. Shrinkage cracking on
concrete structures may reduce structural
performance and durability. On the other hand,
introducing fiber into a concrete mixture has known
to be used to control cracking and increase postcracking behavior. At the same time, fibers may
replace some minimum steel reinforcement. The
addition of fiber into the SCC mixture is expected to
improve cracking resistance and increase thehardened properties of concrete, such as tensile,
flexural, and toughness. However, in high fiber
content, it may also reduce the concrete workability,
which may discard the beneficial factor of SCC.
Hence, the effect of fibers on SCC, namely fiberreinforced self-consolidating concrete (FRSCC),
needs to be investigated to obtain an optimum dosage
resulting in the best performance of FRSCC. The
synergetic effect between fiber and SCC has also
been investigated by several researchers [4],[5],[6].
However, the effect of the fiber content of macrosynthetic polypropylene fibers on drying shrinkage
still needs to be investigated.
Various type of fiber has been used in the concretemix, such as steel fiber, glass fiber, or synthetic fiber,e.g., polyester, polypropylene, polyethylene, or
natural fiber. Among them, steel fiber is the mostcommon and widely used. The fiber shape also varies,

EXPERIMENTAL PROGRAM
Material and Mixture Proportion
Type I cement of Portland cement composite(PCC) was used as a binder for the SCC mixture. To
achieve the workability of SCC, a polycarboxylate
high-range water reducer (HRWR) was added into the
mixture without additional shrinkage reducing agent
or mineral pozzolan. The maximum aggregate size
was limited to 12.5 mm for coarse aggregate with a
specific gravity of 2.73 and 1.09% of absorption.
Natural sand finer than 2.4 mm was used with a
specific gravity of 2.39 and 5.5% absorption as a fineaggregate. The mixture proportion was designed forspecified concrete strength of 50 MPa, and a low
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

water-to-cement ratio of 0.32 was used to achieve
higher concrete strength. The concrete mixture
proportion is given in Table 1. The properties ofpolypropylene fibers used in this study are given in
Table 2 and Fig.1. The fiber percentage of volume
fractions of 0, 0.25, 0.5, 0.75, and 1.0. All concrete
mixture parameters keep constant for all specimens,
except for the coarse aggregate content adjusted to thefiber content.

C1611 [10]. The filling ability or flowability isexpressed in terms of the slump flow value of thediameter of 450-760 mm (18 – 30 inches). The
passing ability test was conducted using the J-Ring
test specified in ASTM C1621 [11]. The segregation
resistance test was carried out using the V-funnel test
according to EFNARC [12] and was also visually
examined during the slump flow test.
Mechanical Concrete Test

Table 1 Concrete mix proportions
Mix
No.

w/c

Cement(kg/m3)Coarse

Fine

NF

0.32

618

781

801

0.0

F0.5

0.32

618

776

801

0.5

F0.75

0.32

618

774

801

0.75

F1.0

0.32

618

772

801

1.0

Aggregate(kg/m3)Compressive, splitting tensile, and modulus ofelasticity tests were undertaken to measure themechanical properties of concrete. The test
procedures for compressive, splitting tensile, andmodulus of elasticity tests follow the procedure as
specified in ASTM C39 [13], ASTM C496 [14], andASTM C469 [15], respectively. Compressive
strength was tested at 3, 7, 14, 28, and 59 days. A
concrete cylinder with a diameter of 150 mm and aheight of 300 mm is used for compressive, splitting
tensile, and modulus of elasticity tests. For each test
result, an average of three samples were reported.

Fiber
content(%)

Table 2 Polypropylene fiber properties
Fiber shape

Specific Gravity

0.92

Length, L (mm)60

Diameter, d (mm)0.4

Aspect ratio (L/d)

150

Modulus elasticity (GPa)10

Tensile strength (MPa)640

Fig. 1

Free and Restrained Shrinkage Test

Monofilament
with continuous
embossedThis study carried out two types of shrinkage
tests: free and restrained shrinkage ring tests. The
main parameter of this study was the percentage ofpolypropylene fibers by volume (0.5%, 0.75%, and1%), including plain concrete (0% fibers) as control
specimens. Two specimens for each parameter weremade for free drying and restrained shrinkage ringtests. The free drying shrinkage test procedures were
specified in ASTM C 341 [8]. The specimen’s size
was 100 x 100 x 400 mm, and the embedded studgauges were placed at two locations and the middle
along the longitudinal direction of the specimens as
reference points. The extensometer measured thelength change or shrinkage strain between two
reference points. The specimen and test setup areshown in Fig. 2 (a).
The restrained shrinkage test aims to examine thecracks that occur due to the drying shrinkage ofconcrete. The shrinkage ring test was conductedaccording to ASTM C1581 [9]. The test configurationof the shrinkage ring test is shown in Fig. 2(b).
Shrinkage strain of concrete was measured from thestress that occurred at the inner steel ring determinedby using four strain gauges attached to the internal
steel ring. Strains in the steel rings are monitored atintervals of 30 minutes and recorded with the dataacquisition system. The strain recording started on thesecond day after concrete casting and removing theouter steel mold. The top and bottom of the concretering surface were sealed with silicone rubber and aplastic base. Therefore, concrete drying only
proceeds on the exposed surface, the outercircumferential surface of the concrete. The concrete

Polypropylene fibers.

Fresh Concrete Test
Workability is crucial in the fresh concrete
properties, especially for self-consolidating concrete.
As mentioned in the introduction, the fiber addition
will affect the workability performance of SCC.
There are three aspects of workability performances,i.e., filling ability, passing ability, and segregation
resistance, that should be evaluated. The filling ability
was measured by slump flow test according to ASTM
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

cracking was visually inspected every day, marked
the time and the strain, and mapped the pattern.

Extensometer(a) Free shrinkage test

Samples

Ring test samples

Data logger

Mix
No.

Fresh concrete properties
Slump
Flow
(mm)

J-Ring(mm)

V-funnel
(sec)

NF

626

575

F0.5

550

464

5.43
Blockage

F0.75

434

368

Blockage

F1.0

362.5

315

Blockage(b) Ring restrained shrinkage test
Fig. 2 Test setup for shrinkage test
RESULTS AND DISCUSSIONSFresh Properties
Table 1 summarizes the test results of the fresh
concrete properties of SCC with various
polypropylene fiber content investigated in this study.
Fiber addition from 0.5% to 1% by volume to the
SCC mixture decreases the flowability of SCC, as
shown in Fig. 3. Adding 0.75% fiber or more caused
the slump flow value to be less than the minimum
slump flow for SCC of 450 mm. These findings alignwith previous research by Nehdi M. and Ladanchuk J.
D. [1]. As mentioned by Forgeron D. and Omer A.
[16] in their study, the macro-synthetic fiber volume
fraction below 0.5% still has an insignificant impact
on the flowability of SCC. Besides fiber content,
fiber length also plays a significant role in the
flowability of SCC to move around or throughobstacles [16]. As large macro-synthetic fibers with alength of 60 mm and aspect ratio of 150 were used inthis study, the impact on flowability and passing
ability will be significant.
Macro-synthetic fibers also decrease the passing
ability of SCC through the J-Ring test, as shown in
Fig.4. Fiber increases passing resistance when the
SCC mixture flows through the reinforcement gap.
Macro-synthetic fiber addition to SCC also causes
blockage in the V-funnel test, as presented in Table 1.
A blockage is found in the narrowed area in the Vfunnel. This was also found by Forgeron D. and Omer
A. [16] in their research. Usage of smaller fiber
lengths or micro-synthetic fibers may reduce thisblockage.

Fig. 3

Effect of Polypropylene fibers on slump
flow

700

J-Ring Flow (mm)

600
500
400
300

y = -266.29x + 580.29
R² = 0.99

200
100
0
0

Fig. 4

0.25

0.5

0.75

1

1.25

Fiber Content (%)
Effect of Polypropylene fibers on J-ring flow

Mechanical Properties
The compressive test results at 3, 7, 14, 28, and 59
days are given in Fig. 5. It was observed that thecompressive strength of FRSCC is mostly lesser thanthat of the control specimens or plain concrete. As
shown in Fig. 5, the compressive strength alsoslightly decreases by increasing the fiber volume,
except for 28 days. A lower concrete strength is also
found by others [17] and [1] when using
polypropylene fibers.

Table 3 Fresh properties of various FRSCC

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

modulus of elasticity of propylene fibers compared tothe aggregate.
Free Shrinkage

Effect of Polypropylene
compressive strength

fiberson

Fig. 6

Effect of Polypropylene fibers on splitting
strength

350

Free Shrinkage strain (10-6)

Fig. 5The effect of polypropylene fiber addition on freeshrinkage can be seen in Fig. 8. It can be seen fromthe figure that the addition of polypropylene fiberslightly alters the free shrinkage strain of SCC.
Similar behavior in that polypropylene fiber has an
insignificant effect on free shrinkage strain reported
by Grzybowski M. and Shah S. P. [18] andSarigaphuti M., Shah S.P., and Vinson K.D. [19] fornormal-weight concrete, and Daneti S. B., Wee T. andThangayah T. [20] for lightweight concrete.
Compared with the steel fiber addition reported by
Daneti S. B., Wee T., and Thangayah T. [20], the steel
fiber contributes to a 12% reduction in free shrinkage
strain. This could be due to the steel fiber having
higher stiffness and strength than polypropylene
fibers resulting in higher restraining against shrinkage.
In Fig. 8, the polypropylene-FRSCC produces ahigher free shrinkage strain than plain SCC. This
attribute to aggregate content in mixture proportion
was reduced with fiber's addition to having a constantcement to coarse aggregate ratio or filler content.
Coarse aggregate content has a significant effect on
shrinkage. Thus, the addition of fiber causes a
decrease in shrinkage resistance when coarse
aggregate content is reduced.

300
250
200
150

0% Fiber
0.5% Fiber
0.75% Fiber
1% Fiber

100
50
0
0

4

8

12

16

20

24

28

Time (days)
Fig. 7

Fig. 8

Effect of Polypropylene fibers on themodulus of rupture

Effect of Polypropylene fibers on freeshrinkage

Restrained Shrinkage

The splitting tensile test results for different fiber
volume fractions are presented in Fig.6. The results
indicate that adding the polypropylene fiber caused anincrease in splitting tensile strength (3.2 to 34.6 %)compared to plain SCC. The maximum increase intensile strength occurs at 1% of fiber volume fraction.
As shown in Fig. 7, the addition of macro-synthetic
fibers on the modulus of elasticity of SCC tends todecrease (9 to 19%). This is correlated to the small

The deformation development obtained from therestrained shrinkage test measured in the steel ring isshown in Fig. 9. The time to cracking tcr values indays are also indicated in the figure for each fiber
volume fraction. When tensile stress resulting fromthe restrained shrinkage in the concrete exceeds thetensile strength, cracks can develop. As shown in Fig.
9, crack with a complete fracture or full-depth crack
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

are associated with a sudden drop or a compatible
drop in the strain. Fiber addition to the SCC mixture
can reduce cracking potential and delay crack
formation, as indicated in Table 4 and Fig. 10. Thisagrees with previous findings comparing plain SCC
and FR-SCC [21].

effect of fiber volume fraction on cracking timeshows an unclear trend. However, the measured strain
at cracking time increased with increasing fiber
content, as shown in Table 4. Macro-synthetic
polypropylene fiber addition also has smaller crack
width than plain SCC, as shown in Fig. 11.

Table 4 Cracking age and measure strain
Time tocracking, tcr(days)

Measuredstrain at
tcr (x10-6)

Cracknumber

NF
F0.5
F0.75
F1.0

3.25
12.0
4.0
9.75

38.39
69.77
72.87
81.9

1 to 2
2
2
2

21
12

14
7

9.75
4

3.25

0
0

0.5

0.75

1.0

Moderate Moderate High
-low
-high

28

(a)

(b)

Fig. 11 Restrained shrinkage cracking (a) plain
SCC (b) Fiber-Reinforced SCC.

Potetial for Cracking (ASTM C1581)

35

Low

Time to cracking, tcr (days)

Mix No.

CONCLUSIONSThis study evaluated the effect of macro-synthetic
polypropylene fibers on the fresh and hardened
concrete properties, including drying shrinkage ofself-consolidating concrete. The main parameters inthis study are a variation of the volume fraction offibers. The following conclusions can be drawn: Addition of macro-synthetic polypropylene
fibers into the SCC mixture reduces theworkability performance in terms of Slump flow,
J-Ring, and V-Funnel values of SCC. The
reduction increase with the increase in fiber
volume fraction.
 Fiber’s addition from 0.5 to 1% of volume
fraction can increase the splitting tensile strength(3.2 to 34.6 %) and can decrease the compressive

Fiber Content (%)
Fig. 10 Time to cracking of restrained shrinkage.
Cracking age for polypropylene fiber addition(0.5% to 1.0% fiber) increase from 4 to 12 days. The

20
0
Restrained Strain (10-6)0

4 0% Fiber, 6

8

10

12

tcr = 3.35 days

-40
-60
-80
-100
-120

Fig. 92-20

0.75% Fiber,
tcr = 4.0 days

1.0% Fiber,
tcr = 9.75 days
Time After Casting (Days)

Effect of Polypropylene fibers on restrained shrinkage
376

0.5% Fiber,
tcr = 12.0 days

14

GEOMATE – Bangkok, Thailand, 22-24 November 2022

strength (5.7 to 14.6%).and modulus of elasticity(9 to 19%) compared to plain SCC.
From the restrained shrinkage test, adding 0.5 to
1% of polypropylene fibers in the SCC mixture
can lower the cracking potential, with thin cracks
appearing within 4 to 12 days.

Conshohocken, 2006.
[9] ASTM C1581/1581M-09a, Standard Test Method
for Determining Age at Cracking and Induced
Tensile Stress Characteristic of Mortar andConcrete under Restrained Shrinkage, ASTM
International, West Conshohocken, 2009.
[10] ASTM, C1611/C1611M-14, Standard Test
Method for Slump Flow of Self-Consolidating
Concrete,
ASTM
International,
West
Conshohocken PA, 2014.
[11] ASTM C1621/C1621M-09a, Standard Test
Method for Passing Ability of Self-Consolidating
Concrete by J-Ring, ASTM International, WestConshohocken PA, 2009.
[12] EFNARC, The European Guidelines for SelfCompacting Concrete. Surrey, United Kingdom,
2005[13] ASTM C39/C39M-09a, Standard Test Method
for Compressive Strength of Cylindrical Concrete
Specimens,
ASTM
International,
West
Conshohocken PA, 2009.
[14] ASTM C496/C496M-04, Standard Test Method
for Splitting Tensile Strength of Cylindrical
Concrete Specimens, ASTM International, WestConshohocken PA, 2004.
[15] ASTM C469/C469M-02, Standard Test Method
for Static Modulus of Elasticity and Poisson’s
ratio of Concrete in Compression, ASTM
International, West Conshohocken PA, 2002.
[16] Forgeron D. and Omer A., Flow Characteristicsof Macro-Synthetic Fiber-Reinforced SelfConsolidating Concrete, ACI Symposium Paper,Vol 274(1), 2010, pp.1-14[17] Yao W., Li J., and Wu K., Mechanical Properties
of Hybrid Fiber-Reinforced Concrete at Low
Fiber Volume Fraction, Cement and Concrete
Research, Vol. 33, No. 1, 2003, pp. 27-30[18] Grzybowski M., and Shah S. P., Shrinkage
cracking of fiber reinforced concrete. ACIMaterials Journal, Vol.87(2), 1990, pp.138–148.
[19] Sarigaphuti M., Shah S.P., and Vinson K.D.,
Shrinkage cracking and durability characteristics
of cellulose fiber reinforced concrete, ACI
Materials Journal, Vol.90 (4), 1993, pp.309-318[20] Daneti S. B., Wee T. and Thangayah T., Effectof polypropylene fibers on the shrinkage cracking
behaviour of lightweight concrete, Magazine ofConcrete Research, Vol. 63(11), 2011, pp.871–
881[21] Kassimi F. and Khayat K. H., Strategies to
Mitigate Craking of Self-Consolidating Concrete,
ACI Materials Journal, Vol. 116, No. 3, 2019,
pp.73-83.

ACKNOWLEDGMENTS
The authors acknowledge the financial support
from Bandung Institute of Technology (ITB),
Indonesia to carry out the research under the Program
Penelitian, Pengabdian pada Masyarakat dan Inovasi(P3MI). The authors are also thankful to PT Abel
Grup Indonesia for supplying the polypropylene
fibers used in this research.
REFERENCES[1] Nehdi M. and Ladanchuk J. D., Fiber Synergy in
Fiber-Reinforced Self-Consolidating Concrete.
ACI Materials Journal, Vol. 101, No. 6, 2004,
pp.508-517[2] Loser R. and Leemann A., Shrinkage andrestrained shrinkage cracking of self-compacting
concrete compared to conventionally vibrated
concrete. Mater. Struct., Vol.42 (1), 2009, pp. 7182
[3] Leemann, A., Lura P. and Loser R., Shrinkage
and creep of SCC–The influence of paste volume
and binder composition. Constr. Build.
Mater., Vol.25 (5), 2011, pp. 2283-2289[4] Wani T. A. and Ganesh S., Study on fresh
properties,
mechanicalproperties
andmicrostructure behavior of fiber-reinforced selfcompacting concrete: A review, Materials Today:
Proceedings, 2022, pp.1-14[5] Li K.F., Yang C.Q., Wei Huang, Zhao Y.B., Yi
Wang, Yong Pan, and Xu F. Effects of hybrid
fibers on workability, mechanical, and timedependent properties of high strength fiberreinforced self-consolidating concrete, Constr.
and Build. Mater. Vol.277 (3), 2021, pp.1-12[6] Drago S., Branko, B., Jakob Š., Jože , L. and Franc
S., Shrinkage of polypropylene fiber reinforced
high-performance concrete. Journal of materials
in civil engineering, Vol. 23, Issue. 7, 2011, pp.
941-952.
[7] Brown M. C., Ozyildirim H. C., and Duke W. L.,
Investigation of Steel and Polymer Fiber
Reinforced Self-Consolidating Concrete, ACI
Symposium Paper, Vol 274(5), 2010, pp.69-78[8] ASTM C 341/341M-06, Standard Practice forLength Change of Cast, Drilled, or Sawed
Specimens of Hydraulic-Cement Mortar andConcrete,
ASTM
International,
West

377

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

ACTIVATION ENERGIES OF CHLORIDE INDUCED CORROSION
OF STEEL IN CONCRETE
Takahiro Nishida1, Keiyu Kawaai2 and Nobuaki Otsuki3
1

Department of Civil Engineering, Shizuoka Institute of Science and Technology, Japan; 2 , Civil andEnvironmental Engineering, Ehime University, Japan; 3 Tokyo Institute of Technology, Japan
ABSTRACTThe corrosion reaction of steel in concrete occurs sequentially, governed by elementary reactions such as therate of dissolution of steel into the solution, the rate of diffusion of oxygen in Nernst diffusion layer, and the flowof electricity etc. Understanding and controlling the elementary reaction can reduce the rate of corrosion reaction
induced by chloride attack in concrete. One way to understand the rate-limiting reactions of steel corrosion in
concrete is to determine the activation energy of steel corrosion based on reaction kinetics and identify the ratelimiting process by figuring out which elementary reaction's activation energy is equal to the overall one. In thisstudy, the activation energies related to chloride-induced corrosion in concrete, such as chloride ion/oxygen
diffusion and micro-cell/macro-cell corrosion rates, were obtained from literature review and experiments todiscuss the rate-limiting reaction of the corrosion reaction of steel in concrete based on reaction kinetics. As theresults, the influence of temperature on deterioration due to chloride attack is explained by the Arrhenius theory
from the viewpoint of practice and the activation energies are obtained. Also, the lifetime of structures in several
cities in severe marine environment are estimated using obtained activation energies. And the lifetime of thestructures in south Asian cities are estimated relatively short.
Keywords: Temperature, Chloride induced corrosion, Activation energies, Diffusion, Steel corrosion
INTRODUCTION

is one of the most serious deteriorations of reinforced
concrete. The corrosion reaction of steel occurs
sequentially, governed by elementary reactions suchas the rate of dissolution of steel into the solution, therate of diffusion of oxygen in Nernst diffusion layer,
and the flow of electricity etc. Understanding andcontrolling the elementary reaction can reduce therate of corrosion reaction induced by chloride attackin concrete. One way to understand the rate-limiting
reactions of steel corrosion in concrete is to determine
the activation energy of steel corrosion based onreaction kinetics and identify the rate-limiting process
by figuring out which elementary reaction's activationenergy is equal to the overall one. In this study, theactivation energies related to chloride-induced
corrosion in concrete, such as chloride ion/oxygen
diffusion and micro-cell/macro-cell corrosion rates,
were obtained from literature review and experiments
to discuss the rate-limiting reaction of the corrosion
reaction of steel in concrete based on reaction kinetics.
Finally, temperature effects were introduced into thelifetime prediction of reinforced concrete members.

Reinforced concrete is currently facing a major
challenge in its maintenance worldwide due to thelack of supply of sufficient quality materials andlimited construction budgets. Chloride induced
corrosion of steel bars in concrete is one of the
durability problems [1], [2], [3] especially in coastal
and marine areas. Since concrete deterioration isbased on diffusion and chemical reactions, theprogressing speed of deterioration depends on
temperature. It is generally known that the rate ofmaterial diffusions or the chemical reactions rises
with the temperature elevation [4].
In developed countries such as the United States,
the European Union, and Japan, standards forreinforced concrete construction materials andmethods are set based on research and experience
under mild temperature conditions (around 20°C).
These standards are used and followed in somedeveloping countries without sufficient local
investigations. When conducting a survey using
standards established in other countries, temperature
is one of the most important parameters, as well as thematerials used.
In above viewpoints, it is necessary to determine
the influence of temperature on the deterioration
process of reinforced concrete due to chloride attackand discuss the appropriate predicting or maintenancemethod under several temperature conditions.
Especially Steel corrosion due to chloride in concrete

METHODOLOGY
Materials Used and Environmental Conditions
Ordinary Portland cement (density: 3.16 g/cm3,
specific surface area: 3 270 cm2/g) was used as binder.
River sand (density (SSD): 2.60 g/cm3, F.M.: 2.59)
and crashed stones (density (SSD): 2.64 g/cm3, F.M.:
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 2 Outline of concrete specimen and divided
steel bar.
steel bars. In macrocell corrosion the anode and thecathode exist non-uniformly, while, in the case of
microcell corrosion the anode and the cathode exist
uniformly. The detail of reinforced concrete specimen
is shown in Fig. 2. The size of the specimen was 1500
x 300 x 100 mm. This specimen was embedded witha divided steel bar, which was composed of divided
steel elements as shown in Fig. 2 for the purpose of
making separate measurements for the rates ofmacrocell corrosion and microcell corrosion. The
divided steel bars were electrically connected using
lead wires soldered on each steel element. The
measurement methods of microcell and macrocell
corrosion rate are same with literature survey [6].

Fig. 1 Outline of minute diffusion cell test.
7.00) were used as fine and coarse aggregates
respectively. Water-reducing and air-entraining
agents were used as chemical admixtures. The type ofsteel embedded in the concrete specimen was SD 345
A (D16, deformed bar). Water cement ratio ofconcrete was set at 0.55.
The temperature during the experiments wascontrolled at 20ºC, 30ºC or 40ºC in an environmental
control chamber. Also, the relative humidity wascontrolled and maintained at 60 %.

RESULTS AND DISCUSSIONS
Theoretical Background
Generally, the diffusion of substances and thechemical reactions rise with the temperature elevation.
Since diffusion and chemical reactions are the basis
by which most cases of concrete deterioration occur,then the increase in concrete deterioration with theincrease in temperature must be considered.
When the influences of temperature on the
diffusion of substance or the chemical reaction are
discussed, the activation energy derived from the
Arrhenius equation is the most important value.
Therefore, in this chapter, the deterioration progressof reinforced concrete is discussed based on theapparent activation energy calculated by Arrhenius
equation. In general, the influences of temperature on
diffusion or chemical reaction are theoreticallyillustrated using the Arrhenius equation as shown
below.

Investigated Items
To evaluate the temperature effect on Cldiffusivity of minute region in concrete, the minutediffusion test [5] was conducted under three levels of
fixed exposure temperatures (20 ºC, 30 ºC or 40 ºC).
A small test piece (5 x 5 x 0.5mm) taken fromconcrete specimen (dia.100 x 200 mm) was placed
inside an acrylic cylinder cell as shown in Fig. 1. One
side of the cell was filled with NaCl solution (3.0
wt %) and the other side was filled with saturated
Ca(OH)2 solution. To measure the time-dependentchanges of Cl- concentrations in the Ca(OH)2 solutionside, the sample solution was taken every day. The
concentration of Cl- was then measured using ion
chromatography. The diffusion coefficient wascalculated using the Fick’s law of diffusion as shownin Fig. 1.
Also, to evaluate the temperature effect on the
corrosion rate of steel bars in concrete, a corrosion
monitoring test was conducted under the same treelevels of fixed temperature (20 ºC, 30 ºC or 40 ºC). In
this study, macrocell corrosion and microcell
corrosion were separately evaluated using divided

 E 
k  a exp  RT 

(1)

here, k is Rate Constant, a is Frequency Factor(Constant), E is Activation Energy (cal/mol), R is
Ideal Gas Constant [1.99×10-3] (cal/K/mol) and T is
Absolute Temperature (K).
From equation (1), the increase in rate of the379

GEOMATE – Bangkok, Thailand, 22-24 November 2022

diffusion or chemical reaction with the temperatureelevation can be theoretically confirmed. During theprocess of chemical reactions, the reactants seem toshift to the products side through an active state withhigh energy. The difference energy between theactive state and the reactant is called as the activationenergy [4]. In the process, where multiple chemical
reactions are sequentially generated, the activationenergy follows the reaction which determines the rateof reaction.
On the other hand, by taking the logarithm of thegiven equation, equation (1) will be transformed into
the following equation. From experimental results,
the relationship between logarithm of the rate
constant and the reciprocal of absolute temperature is
generally called the Arrhenius plot.

Table 1 Activation energies in literature.
Kinds

Authors

Cl- Diffusivity in
Cement Past
Carbonation ofConcrete
O2 Diffusivity in
Cement Past
Steel Corrosionin Cl- Solution
Steel Corrosion
in CO3 Solution

C. L. Page etal，1981
Uomoto et al ，
1992
C. L. Page etal ，1987
J. J. Podesta’ etal ，1965
C. De Waard C
et al ，1975

15.0

(2)

Cl- Diffusivity x 10-7 (cm 2/sec)  E 1 
log k  
 log10 e    log a
 R
 T 

The activation energies reported in literatures asshown in Table 1.
Temperature Effect on Cl- Diffusivity in Concrete
The influence of temperature on diffusivity of Clin concrete is shown in Fig. 3. From this graph, therate of Cl- diffusion in concrete rises up with theincrease in temperature. Also, the Arrhenius plots of
the Cl- diffusivity in concrete are shown in Fig. 4.
From this, the relationship between logarithms of the
diffusivity and the reciprocal of absolute temperature
can be linearly distributed. It can be concluded thatthe diffusion of Cl- in concrete follows Arrhenius
theory. Moreover, the apparent activation energies of
the Cl- diffusivity in concrete obtained from
Arrhenius plots are shown in Table 2. From this table,
the apparent activation energies obtained in this study
are a little high comparing with that obtained inliterature survey [7]. This is because activationenergies obtained in the literature survey are based on
the data of cement paste. On the other hand, the Cldiffusivities of minute test pieces taken from concrete
containing interfacial transition zone between
aggregate and cement matrix are used in this study.
That is, it is considered that the pore structure of
cement matrix, especially the property of theinterfacial transition zone, influences on the apparent
activation energy and diffusion phenomenon.
Fig. 5 shows the relationship between Cldiffusivity of concrete and water cement ratio
proposed by JSCE standard specification [8]. And thebroken lines in the graph show the Cl- diffusivities
considering with ± 10 ºC difference against JSCE
equation using obtained apparent activation energy.
From this graph it is observed that the scattering of
Cl- diffusivity in the literature data seems to be
explained by temperature effect. Therefore, it can beconcluded that the temperature profile is one of the

Activation
Energy（kcal/mol）
7.7～10.0
Around 5.1
3.6～5.3
Around 11.5
Around 10.7

Bleeding Ratio
0.18%
0.51%
4.58%

10.0

5.0

0.0
10

20

30

40

50

3.3

3.4

3.5

Temperature (℃)
Fig. 3 Influence of temperature on Cl- diffusivity
in concrete.
0Bleeding Ratio
0.18%

Log (Cl- diffusivity )

-2

0.51%
4.58%

-4

-6

-8

-10
3.1

3.2

1000/Temperature(1/K)

Fig. 4: Arrhenius plots of Cl- diffusivity inconcretemost important factors when we check the diffusion
coefficient of Cl- in design stage.
380

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 2 Apparent activation energies of Cl- diffusivity in concrete obtained from Arrhenius plots.
Activation Energy (kcal/mol)Present study: containing aggregate
16.8-32.2
Literature survey [7]: cement paste
7.7-10.0
Temperature Effect on Corrosion in Concrete

Macrocell Corrosion Rate (mm/year)

0.0

The influences of temperature on macrocell andmicrocell corrosion rate in concrete are shown in Fig.
6. From these graphs, the macrocell and microcell
corrosion rate in concrete rise with temperature
Diffusion Coefficient (cm 2/year)

1.E+02+10ºC

1.E+01

-1.0
-2.0
-3.0
-4.0
-5.0

Position of Steel Bar
Upper

Middle

3.2

3.3

-6.0

- 10ºC

3.1

1.E+003.4

1000/Temperature(1/K)

3.5(a) Macrocell corrosion rate
1.E-01

Microcell Corrosion Rate (mm/year)

0.0

Equation proposed by JSCElog D = -3.9(W/C)2+7.2(W/C)-2.5

1.E-02
0.2

0.3

0.4

0.5

W/C

0.6

0.7

0.8

Fig. 5 Relationship between Cl- Diffusion Coefficientand W/C and temperature effect obtained in present
study
Macrocell Corrosion Rate (mm/year)

0.03

Position of Steel Bar
Upper

-4.0
-5.0

Position of Steel Bar
Upper

Middle

3.2

3.3

Lower
3.4

1000/Temperature(1/K)

3.5(b) Microcell corrosion rate

Fig. 7: Arrhenius plots of corrosion rate of steel
bars in concrete
elevation. Also, the Arrhenius plots of the corrosion
rate in concrete are shown in Fig. 7. From thesefigures, the relationship between the logarithms ofcorrosion rate and the reciprocal of absolutetemperature can be linearly distributed. Therefore, it
can be concluded that the behavior of steel corrosionin concrete follows Arrhenius theory. Moreover, theapparent activation energies of macrocell andmicrocell corrosion in concrete obtained from
Arrhenius plots are shown in Fig. 8. From thesefigures, macrocell and microcell corrosion in concrete
has different temperature dependency. That is to say,the apparent activation energy of macrocell corrosion
increases with the increase in specific concrete
resistance. On the other hand, the apparent activationenergy of microcell corrosion is constant regardlessof the specific concrete resistance. The activationenergy corresponds to ion diffusion phenomena inconcrete, rather than O2 diffusivity as shown in Table
1. Therefore, the corrosion rate of microcells inconcrete was thought to be controlled by ion
migration, such as Fe2+, when the steel was exposed
to chloride attack in atmospheric condition.

0.00
10

20

30

40

Temperature (℃)50(a) Macrocell corrosion rate
Microcell Corrosion Rate (mm/year)

-3.0

3.1

Lower

0.01

0.003

-2.0

-6.0

Middle0.02-1.0

Position of Steel Bar
Upper
Middle
Lower

0.002

0.001

0.000
10

20

30

40

Temperature (℃)50(b) Microcell corrosion rate
Fig. 6 Influence of temperature on corrosion rate ofsteel bars in concrete.

381

GEOMATE – Bangkok, Thailand, 22-24 November 2022

the effect of the temperature.
The steel bar in concrete starts to corrode after the
Cl- concentration over the certain value (thresholdconcentration).
“TechnicalStandards
andCommentaries for Port and Harbour Facilities inJapan” describes that the threshold concentration of
Cl- can be set at around 2.0 kg/m3 in port and habour
port concrete facilities. In this study, the period until
the Cl- concentration around steel bar reaches to 2.0
kg/m3 is calculated by numerical analysis as
incubation period.

25

Apparent Activation Energy(kcal/mol)

Macrocell Corrosion
Microcell Corrosion

20

15

10

5

Prediction of Propagation and Acceleration
Periods

0
0

5

10

15

Specific Concrete Resistance (kΩ・cm)

To predict the propagation and acceleration
periods, the rate of steel corrosion in concreteconsidering temperature effect was investigated. The
total quantity of corrosion was calculated by taking
the sum of the quantity of corrosion in each period.
The method of prediction for steel corrosion in
concrete considering temperature effect is explainedin the following.
Considering the influence of temperature on
deterioration of reinforced concrete, the rate ofcorrosion of steel bar (macrocell and microcell) in
concrete is different from each period. Especially it
was confirmed that the influence of temperature on
macrocell and microcell corrosion is different inprevious chapters. Therefore, in case of predicting the
propagation and acceleration period, it is necessary toconsider the influence of temperature on macrocell
and microcell corrosion rate separately. In this study
the quantity of corrosion was calculated byintegration of the rate of macrocell or microcell
corrosion. The total quantity of corrosion was thencalculated by the sum of the quantities of macrocell
and microcell corrosion.
In the case of predicting the process of corrosionin concrete, it is necessary to know the basic rate ofcorrosion at a certain temperature. The data ofcorrosion rate at 20 ºC in Table 3 were used as areference for the basic rate of steel corrosion in
concrete. In the case of corrosion rate of steel bar inconcrete before cracking, the experimental results
obtained in the previous section were used. On theother hand, in the case of the corrosion rate after
cracking, the experimental results obtained by Otsuki[6] were used.
The total quantity of corrosion is calculated bytaking the sum of the quantity of corrosion rates
obtained in each period. Here it is reported that the
total quantity of corrosion for crack propagation isequal to 10 mg/cm2. Also it is reported that thebending property of reinforced concrete becomes
70% of the initial property when 15 % of the steel bar
decreases due to corrosion [9]. According to thisliterature survey the quantity of corrosion at 15 %degradation of strength in case of steel bar with 16mm

Fig. 8 Apparent activation energy of corrosion
rate of steel bar.
PREDICTION
OF
DETERIORATIONPROCESS CONSIDERING TEMPERATURE
EFFECT
Lifetime of Reinforced Concrete due to SteelCorrosion
The determination of the lifetime of reinforced
concrete is a difficult task because it includes manyfactors such as the importance of structures, part ofthe member or financial issue. However, it isimportant to discuss the lifetime of structures basedon several assumptions from the engineering point ofview. In this study, the lifetime was defined as thesum of the terms of prediction results of the
incubation, propagation and acceleration period, andthe influence of temperature on the lifetime ofreinforced concrete was discussed.
The water cement ratio, water content in mixture
proportion and concrete cover were set at 0.55, 175
kg/m3 and 7.0 cm respectively. The environmental
condition considered in this prediction was tidal zone
which is considered as the most severe environmentthat induces chloride attack. Also, in this prediction,
the data of temperatures for 55 cities around the world
was used.
Prediction of Incubation Periods
To predict the incubation periods, the rate of Cldiffusion in reinforced concrete considering thetemperature was investigated using a numericalanalysis. In this numerical analysis, Nernst-Planck
equation considering with Debye-Hückel equationand electro-neutrality condition was used. By
considering these equation and condition, theinfluence of co-existing ions in pore solution of
concrete could be simulated. The effectiveness of thisnumerical analysis was already confirmed in theliterature survey done by authors [9]. In this study theabove numerical analysis was modified to consider
382

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 3 Data of corrosion rate at 20 ºC.
Corrosion rate
at 20 ºC (mm/year)
Macrocell
Microcell
Before cracking
5.5 x 10-3
2.6 x 10-4
After cracking [6] 2.0 x 10-2
6.0 x 10-2
120

Japan
Other Asian Countries
U.S.A. Continent
Europe100

Life Time (year)

2)

REFERENCES[1] Leeming, M. B., 1983. Corrosion of Steel
Reinforcementin
Off-shore
Concrete
Experience from the Concrete-in-the-Oceans
Programme, Corrosion of Reinforcement inConcrete Construction, Edited by. A.P. Crane.
Ellis Horwood Ltd., Chichester, UK, 59-78.
[2] Sørensen, B., and Maahn, E., 1982. Penetration
Rate of Chloride in Marine Concrete Structures,
Nordic Concrete Research, Publication No.1, 24.
[3] Popovics, S., Simeonov, Y., Boshinov, G., andBarovsky, N., 1983. Durability of Reinforced
Concrete in Sea Water, Corrosion of
Reinforcement in Concrete Construction, Edited
by. A.P. Crane. Ellis Horwood Ltd., Chichester,UK, 19-38.
[4] Ohki, M., Ohsawa, T., Tanaka, M., and Tihara,
H., 1994. Chemical Dictionary, 275.
[5] Otsuki, N., Yodsudjai, W., Nishida, T., andYamane, H., 2004. New Test Methods for
Measuring Strength and Chloride Ion Diffusion
Coefficient of Minute Region in Concrete, ACI
Materials Journal, V. 101, No. 2, 146-153.
[6] Otsuki, N., Miyazato, S., Diola, N. B., andSuzuki, H., 2000. Influences of Bending Crackand Water-Cement Ratio on Chloride-Induced
Corrosion of Main Reinforcing Bars andStirrups, ACI Materials Journal, V. 97, No. 4,454-464.
[7] Page, C. L., Short, N. R., and Tarras, A. El,“Diffusion of chloride ions in hardened cement
pastes.” Chemical and Concrete Research, 11,1981, pp.395-406.
[8] Japan Society of Civil Engineers, 2001.
Standard
SpecificationforConcrete,
Maintenance, 97-112.
[9] Minagawa, H., Otsuki, N., Miyazato, S., andNishida, T., 2002. Establishment of Numerical
Analysis Method For the Prediction of
Leaching of Calcium from Concrete
Considering Migration of Multiple Ions,
Concrete Library of JSCE, No.40, 253-268.
[10] Maruya, T., Tangtermsirikul, S., Matsuoka, Y.,
1992. Simulation of Chloride Movement inHardened Concrete, Journal of Materials,
Concrete Structures and Pavements, Japan
Society of Civil Engineers, No.442/V-16,
pp.81-90.

80
60South‐East Asia
40

50%
20

70%

0
0

10

20

Average Temperature(ºC)

due to chloride attack is explained by the
Arrhenius theory from the viewpoint of practiceand the activation energies are obtained.
The lifetime of structures in several cities insevere marine environment are estimated using
obtained activation energies. And the lifetime ofthe structures in south Asian cities are estimatedrelatively short.

30

Fig. 9 Predicted results of lifetime of reinforced
concrete using the 55 cities of temperature profile.
diameter is about 510 mg/cm2 [10]. Therefore, thequantities of corrosion at 10 mg/cm2 and 510 mg/cm2
were used in order to determine the periods ofcracking (end of propagation period) and structural
deterioration (end of acceleration period) respectively.
Predicted Results
Fig. 9 shows the relationship between the lifetimeof reinforced concrete and the average temperature.
From this figure it can be confirmed that the period of
incubation, propagation and acceleration varied withthe regional and seasonal temperature, and thelifetime of reinforced concrete exponentiallydecreased as the average temperature of the city. It istherefore necessary to consider temperature effect on
the design and maintenance of reinforced concrete,especially in the key cities of South-East Asian
countries, where deterioration was most severe as
indicated by reinforced concrete having only 70 % of
the lifetime compared to other countries with mild
temperature environment (20 ºC) Also it can beconsidered that the lifetime of the reinforced concretedecreases 50% with 10 °C increase of temperature.
CONCLUSIONSIn present study, the influence of the temperatureon the deterioration progress of RC due to chloride
attack is theoretically and experimentallyinvestigated. From the investigations the following
conclusions are obtained.
1) The influence of temperature on deterioration383

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

APPLICATION OF DIGITAL IMAGE CORRELATION METHOD IN
RC AND FRC BEAMS UNDER BENDING TEST
Messa Revolis1, Aris Aryanto2, Yuki Oribe3 and Hibino Yo4
1,2

Faculty of Civil and Environmental Engineering, Bandung Institute of Technology, Indonesia; 3,4Graduate
School of Engineering, Hiroshima University, Japan
ABSTRACTTechniques to monitor the strain field and crack development in the experimental testing are continuously
improved. Digital Image Correlation (DIC) technique is recently developed and is a powerful tool for measuring
object deformation. Using image correlation, this method can correlate the digital images of the undeformed anddeformed objects to determine the displacement and strain field. Compared to conventional techniques (such as
strain gauges), DIC has superiority due to computational accuracy, efficiency, and non-contact measurement. In
this study, DIC is applied to analyze the mechanics of plain and fibrous concrete beams under the 4-point bending
test in the laboratory. Four test specimens were cast: a plain and fibrous concrete with and without longitudinal
reinforcement. Displacement and strain fields derived from digital image correlations are analyzed in crack
detection, deflection, and curvature of beams. DIC analysis results show good agreement with experimental
measurement and are suitable for early crack detection and measurement. Monitoring strain fields and cracks using
DIC confirms the influence of fiber engagement on load resistance, cracking, and flexural behavior.
Keywords: Digital Image Correlation (DIC), Concrete beams, Strain, Crack
DIC is highly dependent on image texture or contrast[4], [5], and image resolution [6].
In concrete structural engineering, numerous
concrete material and element testing is commonly
carried out, e.g., compressive, tensile, flexure, andfracture tests. Those testing may utilize DIC
technology to measure deformation and strain
variation. For example, Sindu B. S. and Sasmal S. [7],Verbruggen S., DeSutter S., Iliopoulos S., Aggelis
D.G., and Tysmans T. [8], and Lacidogna G., Piana
G., Accornero F., and Carpinteri A. [9] use the DIC
method for beam testing. Gali S. and Subramaniam
K. V. L. [10] investigate the shear behavior of fiberreinforced beams using the DIC technique. The
application of the DIC method on fracture and crack
was conducted by Fayyad T. M. and Lees J. M [11]and Bhosale A. B. and Prakash S.S. [12], Filho J,
Xavier J, and Nunes L. [13], respectively.
Meanwhile, Gentruck et al. [14] use DIC for fullscale testing of the prestressed concrete structures.
Moreover, the extent of DIC's capabilities and theaccuracy of results still need to be studied andverified.
In this study, the DIC method will be used in the
4-point bending testing of beams to evaluate
deformation behavior and crack propagation bycomparing the results from the strain gauge readings.
Four concrete beams were cast using normal (plain)
or fibrous concrete with or without longitudinal
reinforcement. In addition, fiber’s addition to
concrete mixture has been known to change the postcracking behavior of the concrete elements.

INTRODUCTION
Reliable deformation measurement is essential forexperimental test measurements in concrete
structures. The most common conventional technique
to measure deformation and strain field is strain
gauges attached to the specimen’s surface, e.g.,concrete or steel reinforcements. However,measuring displacement and strain fields to capture
the structural behavior requires many gages, which
will be expensive.
Recently, a method known as Digital Image
Correlation (DIC) has been developed to measure
deformation and strain variation and crack patterns on
an element's surface plane. This is based on advancesin digital technology that capture high-resolution
images supported by an advanced computation thatcan process the data from a digital image correlation[1]. DIC is relatively easy to operate, reduces researchcosts, and is considered more accurate in results thanconventional measurements. It can also be performedfor various potential applications and in different
materials [2]. Several open-source and noncommercial DIC processing software are available
nowadays [3].
The DIC technique is a non-contact opticalmeasurement technique that can be applied to the test
object's surface. It requires a continuous digital image
that captures during the test. The displacement field
is determined from the correlation between two
images, undeformed and deformed images. Thismethod can be applied from a small to a large-scale
test. The accuracy of displacement measurement by
384

GEOMATE – Bangkok, Thailand, 22-24 November 2022

strength of 400 MPa (BJD40). All specimens tested
in this study are summarized in Table 1. Fig.1 below
shows the beam dimensions and reinforcement details.
To evaluate the strain and curvature in concretebeams, the strain gauges were attached to the surface
of one side of the shaft, and another surface side wasused as a surface object for DIC measurement. Strain
gages configuration is shown in Fig. 2 for
unreinforced and reinforced beam specimens.

EXPERIMENTAL PROGRAM
Four concrete beams were cast and tested toinvestigate load-deformation and crack behavior. All
beams have similar dimensions, specified concrete
strength, and loading arrangement but differ in fiber
andlongitudinal
reinforcement
addition.
Polypropylene fiber with a 0.5% of volume fraction
was used in this experiment. Specified concrete
strength is 60 MPa, and the average measured
concrete strength from compressive testing is 62 MPafor normal concrete (NC) and fiber concrete (FC).

DIC Experimental Setup
All specimens were tested using a four-point
bending test. Each beam was supported at both ends
by two roller supports. The two-roller support was
kept to have a clear distance of 450 mm. The load wasapplied at two-point that have a distance of 150 mm
from each support. The beams were loaded using auniversal testing machine (UTM) with displacement
control. Three linear variable displacementtransducers (LVDT) were installed under beams at
1/3, 1/2 (midspan), and 2/3 of the beam span tomeasure the deflection. A micro-camera with thecrack width measurement was used to measure thecrack opening.
A DLSR camera with a maximum image
resolution of 5184 x 3456 pixels and a focal length of
55 mm was used to record the digital images underloading. The camera was put on a tripod dan fixedaway from the specimen and has a perpendicular axis
to the surface of interest. Surface displacements wereobtained by DIC using digital image cross-correlation.
The image must have a particular texture fromspeckle patterns. To have speckle patterns, the one
surface of beams was spayed by applying a uniform
white paint followed by a mist of black paint from thespray nozzle. The speckle patterns were created on
the side without strain gauge attachment.

Table 1 Specimen details
Specimen

Concrete type

NC
FC
NC-S
FC-S

Plain
Fiber
Plain
Fiber

Fig. 1

Deformedsteel bars
No
No
2D13
2D13

Beam dimensions (in mm).
Load
Actuator
Specimens
LVDT
Computer &DIC software

Fig. 2

DigitalcamaraStrain gages setup configurations.

This study uses a deformed steel bar with anominal diameter of 13 mm with a specified yieldFig. 3385

DIC and loading test setup.

GEOMATE – Bangkok, Thailand, 22-24 November 2022

vs. FC (b) NC-S vs. FC-S.
Table 2 Summary of beams test

RESULTS AND DISCUSSION
Load-deflection RelationshipSpecimen

Beam’s load-deflection responses are shown in
Fig.4. Measured beam deflections shown in the figure
are obtained at the midspan location. Beams’ load
capacity increases with the presence of fibers andlongitudinal bars. The load response could not be
controlled after the peak load in a beam without fibers
and steel bars (NC), even with displacement control.
With fiber addition in the concrete beams (FC andFC-S), there is an increase in load capacity indicatedby the peak load. The peak load and corresponding
deflections of all beams are listed in Table 2. The
post-peak load response in fiber concrete beams
showed a larger deflection than in non-fibers concretebeams. In FC specimens, beam with fibers andwithout longitudinal bars, there is a second peak load
after the first peak or cracking load. This indicatesthat fibers significantly affect post-cracking behavior
as crack-bridging increases deformation andtoughness capacity. As shown in Fig.4(b) for FC-S
specimens, there is no second peak loa. However,there is a steady decrease, almost constant, in the loadwith increasing deflection. The effect of fibers is also
noticed when comparing the peak load with the beam
without fibers (NC-S).

NC
FC
NC-S
FC-S

DIC was used to evaluate the beams’ crack patternand crack progression. Field strain measurement in
the horizontal direction identifies the crack on theconcrete beam surface. To identify cracks in thesurface, a localized swarm of strain contours in asmall region can indicate the crack. The crack pattern
and the cracking sequence can be observed using thisprocedure. The DIC analysis results can have moreaccurate than visual observation.
The formation and progression of cracks in beams
with fibers and longitudinal bars (FC-S) are shown in
Fig. 5. Flexural crack was initiated in the middle ofthe beam at a load of 35.26 kN. Then, a shear crack
located near the support was formed, as shown in Fig.
5(a). The shear crack was formed at approximately
45-degree of the height of the beam. Then anothershear crack that originated from the flexural crack
was also developed. This shear crack extended to theload point and became a primary failure mode. The
shear failure mode of beams is due to longitudinalbars that increase the flexural capacity. All beams
without longitudinal steel bars fail in flexure.
Comparing FC-S and NC-S, a beam without fibers(NC-S) not only exhibits shear cracking but also
experiences bond-splitting cracking, as shown in Fig.
6(c). This is also identified in the load-deflection
response in Fig.4(b) that the beam exhibits small
deflection. This also indicates that fibers significantly
increase the bond splitting capacity of beams. DIC
results can generally capture the crack pattern andprogression, which is essential for evaluating
structural behavior and mode of failure.

Load (kN)20
NC - LVDT
NC - DIC
FC - LVDTFC - DIC

0
2

3

4

Midspan Deflection (mm)
200

(b)

175
150
Load (kN)

125
100

NC-S - LVDT
NC-S - DIC
FC-S - LVDTFC-S - DIC

75
50
25
0
0

1

2

3

4

5

Midspan Deflection (mm)Fig. 4

FlexureFlexure
Shear
Shear

Crack pattern and crack progression(a)1Mode offailureThe images taken under different load conditions
were analyzed using DIC software for each beamspecimen using cross-image correlations. The
displacement vectors of the DIC analysis results
determined the beam deflection and crack opening.
The displacement value from the DIC analysis wasverified against the displacement from LVDTs. As
shown in Fig. 4, the DIC approach effectively
measures the displacement of beam elements.

30

0Percent
increase inpeak load
27.6%
27%

Note: comparison was made only between non-fiber and fiber
concrete.

40

10

Peak
Load
(kN)
31.8
35.12
145.7
185.08Load-deflection responses of beams (a) NC
386

GEOMATE – Bangkok, Thailand, 22-24 November 2022

splitting cracks that lead to failure, can be identified
through DIC analysis.

(a)Crack opening measurement
Crack opening of shear and flexural cracks were
measured at several locations in the primary crack, asshown in Fig. 7. Fig. 7 indicates the beams’ crack
opening calculated using DIC and crack meter
methods under different loading rates. The crack
opening was measured through field displacementsfrom the DIC analysis. Cracks can be identified
accurately from displacement contours after crack
formation and can be observed much earlier thanvisual observation. A pair of gauge points wereplaced on either side of the crack to evaluate thedisplacement across the crack. The line joining thegauge points should be perpendicular to the crack.
The gauge points should be set in fixed positions inprocessing digital images at different loads then thehorizontal and vertical displacements will beobtained. The crack opening was obtained from
relative displacement between two-gauge points
across the crack.
The crack openings measured across the primarycrack from all beams are shown in Fig. 7. With theaddition of fibers in unreinforced beams, there is adelay in flexural crack formation indicated by ahigher load when compared to a normal concrete
beam (NC). It also shows an increase in crack
opening throughout the load response. Fiber addition
for reinforced beams that fail in shear affects thecrack opening, which continues to open even in the
post-peak as the load decreases. This indicates thatfibers control the shear crack opening or dilatancy,
which may lead to an alternate failure mode. A
normal beam without fibers (NC-S) leads to bondsplitting failure as a horizontal crack formed at thelongitudinal bar axis near the support.

(b)

(c)

(d)

(e)

(f)

Fig. 5Strain contours of FC-S beam at loads (a)35.26 kN (b) 77.14 kN (c) 106.05 kN (d)139.37 kN (e) 185.08 kN (f) failure.

(a)

CONCLUSIONS(b)

Digital image correlation (DIC) in this study isused to observe deformation, crack patterns, crack
progression and crack opening of all beam specimens
under the bending test. From this limited
investigation, some conclusions can be drawn.
 DIC produces a closely load-deflection curve
compared to LVDT measurement. The beam’s
load-carrying capacity and ductility increase
with fibers addition, indicated by peak load andpost-peak displacement from load-deflection
responses.
 The DIC method effectively determines the crack
pattern and the crack progression in the bending
test of unreinforced and reinforced concretebeams with or without fiber addition. Using the
DIC method, the failure mode of beams can be
captured from the crack pattern and crack

(c)

(d)

Fig. 6

Strain contours at peak load of beams: (a)NC (b) FC (c) NC-S (d) FC-S.

The crack patterns at the peak load for all beams
are shown in Fig. 6. It is clearly demonstrated that thecritical crack patterns, e.g., shear, flexure, and bond387

GEOMATE – Bangkok, Thailand, 22-24 November 2022

progression. Unreinforced beams (NC and FC)
exhibit flexural failure, and reinforced beams(NC-S and FC-S) produce shear failure.
 The crack opening at selected locations in the
beams extracted from DIC analysis was used toevaluate the effect of fiber addition andlongitudinal bars as beam parameters. Fiber
addition affects the crack opening, which cancontinue to grow even in the post-peak as theload decreases. Fibers in concrete seem to play arole in the crack progression and crack bridging.

REFERENCES[1] Supriya S. Gadhe S.S. and Navthar R. R., Digital
Image Correlation Technique for Strain
Measurement of Aluminium Plate, International
Journal of Engineering Trends and Technology,
Vol. 39, Issue. 6, 2016, pp.306-311.
[2] McCormick N. and Lord J., Digital Image
Correlation, Materials today, Vol.13, Issue. 12,
2010. pp.52-54.
[3] Belloni V., Ravanelli R., Nascetti A., Di Rita M.
Mattei D., and Crespi M., py2DIC: A New Free
and OpenSource Software for Displacement andStrain Measurements in the Field of Experimental
Mechanics, Sensors, Vo.19. 3832, 2019. pp. 1-19.
[4] Mauroux T., Benboudjema F. Turcry P., AïtMokhtar A., and Deves O., Study of Cracking Due

ACKNOWLEDGMENTS
This study is part of a research collaboration
program between Bandung Institute of Technology(ITB), Indonesia, and Hiroshima University, Japan.
Support from both institutions is acknowledged.

40

(a)

35

35

30

30

25

25

(b)

Load (kN)

Load (kN)

40

20

20

15

15

10

10
A
A'

Measured :
DIC :

5
0
0.0

0.2
0.4
Crack opening (mm)Measured :

5

B
B'

DIC :

0

0.6

0.0

2.0
4.0
6.0
Crack opening (mm)

200

200

(d)

(c)
160

Load (kN)

Load (kN)

160

120

80
Measured :
1
2
3

40

DIC :

1'
2'
3'

120

80
Measured :
1
2
3

40

DIC :
1'
2'
3'

0

0
0.0

Fig. 7

8.0

1.0
2.0
Crack opening (mm)

3.0

0.0

1.0
2.0
Crack opening (mm)Crack opening with manual and DIC measurement (a) NC (b) FC (c) NC-S (d) FC-S.

388

3.0

GEOMATE – Bangkok, Thailand, 22-24 November 2022

to Drying in Coating Mortars by Digital Image
Correlation, Cement and Concrete Research, Vol.
42, No. 7, 2012, pp. 1014-1023.
[5] Lecompte D, Smits A., Bossuyt S., Sol H.,
Vantomme J., Van Hemelrijck D., Habraken
A.M., Quality assessment of speckle patterns for
digital image correlation, Optics and Lasers inEngineering Vol. 44,2006, pp. 1132–1145.
[6] Sun Z., Lyons J. S., and McNeill S. R., Measuring
Microscopic Deformations with Digital Image
Correlation, Optic and Lasers in Engineering Vol.
27, 1997, pp. 409-428[7] Sindu B. S., and Sasmal S., Multi-scale Abridged
Cement Composite with Enhanced Mechanical
Properties, ACI Materials Journal, Vol. 117, No.
4, 2020, pp.105-117[8] Verbruggen S., DeSutter S.,
Iliopoulos
S.,
Aggelis D.G., and Tysmans T., Experimental
structural analysis of hybrid composite-concretebeams by digital image correlation (DIC) andacoustic emission (AE), J. Nondestruct.
Eval., 35, 2016, pp. 1-10
[9] Lacidogna G., Piana G., Accornero F., andCarpinteri
A.,
Multi-techniquedamagemonitoring of concrete beams: acoustic emission,
digital image correlation, dynamic identification,

Constr. Build. Mater., 242, 2020. pp.1-18[10] Gali S. and Subramaniam K. V. L., Shear
behavior of slender and Non-slender Steel FiberReinforced Concrete Beams, ACI Structural
Journal, Vol. 116, No. 3, 2019, pp.149-158.
[11] Fayyad T. M. and Lees J. M., Application of
Digital Image Correlation to reinforced concrete
fracture, Procedia Materials Science, 20th
European Conference on Fracture (ECF20), 2014,
pp. 1585 – 1590
[12] Bhosale A. B. and Prakash S.S., Crack
Propagation Analysis of Synthetic vs. Steel vs.
Hybrid Fibre‑Reinforced Concrete Beams Using
Digital Image Correlation Technique, Int J Concr.
Struct. Mater., Vol 14:57, 2020, pp.1-19[13] Filho J, Xavier J, and Nunes L., An alternative
digital image correlation-based experimental
approach to estimate fracture parameters infibrous soft materials. Materials;15(7). 2022,
pp.1-12[14] Genctrurk B., Hossain K., Kapadia A., Labib E.,
Mo Y. Use of digital image correlation techniquein full-scale testing of prestressed concrete
structures, Measurement Vol. 47, 2014, pp. 505515

389

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

A STUDY ON METHODS OF SELF-HEALING ASPHALT PAVEMENT
Enohmbey Chubisong Moses1, and Yoshitaka Kajita2
1

Graduate School of Engineering, Tokai University, Japan 2,3
ABSTRACTAsphalt is widely used in paving roads, parking lots, and other projects that require a smooth level surface.
Asphalt is susceptible to cracking due to a variety of circumstances such as sun damage, water infiltration, etc.
When a crack occurs on asphalt pavement, it gets widened by continuous wear and tear produced by automobiles
leading to potholes and other defects, thereby preventing smooth traffic circulation. According to previous studies,
researchers have discovered remarkable healing methods for asphalt in recent decades and suggested a variety ofcreative approaches to inspire and improve its self-healing capacity and extend the life cycle of asphalt pavement.
The purpose of this study is to investigate the three main self-healing methods of asphalt pavement design. Which
are Nanoparticles, Induction heating, and Rejuvenation. In summary, self-healing methods are techniques thatinvestigate the properties of asphalt that has the potential to delay pavement cracking and increase its service life.
Keywords: Self-healing, Asphalt pavement, Induction heating, Rejuvenation, Nanoparticles
INTRODUCTION
Rejuvenation and heating are two conventional
techniques for achieving self-healing asphalt
pavements. Typically, capsules, hollow fibers, orvascular fibers are used to integrate rejuvenators. Whencracks occur, the healing agent flows out and heals thecrack. Induction and microwave energy are usually used
to heat the asphalt and heal the crack. While metallic
additions are required for induction heating, no additives
are necessary for microwave heating. Additives like
nanoparticles, ionomers, supramolecular polymers,
shape memory polymers, and other common polymer
additives like crumb rubber are also being researched toprovide asphalt intrinsic self-healing characteristics.
Each of these technologies has advantages anddisadvantages of its own [1]. This paper investigates themain self-healing methods available for asphalt
pavement design.
SELF-HEALING ASPHALT PAVEMENT
Erik Schlangen created a new kind of pavement
called a “self-healing asphalt machine" that can repair
itself over time. It is made of a porous material thatenables water to drain through the surfaces and either goto the side of the road or toward collection equipment.
Due to its shape, noise would disappear to the surface[1]. Erik Schlangen has used Five criteria to categorize
the healing mechanism, or fatigue crack growth delay
Plasticity-induced: during crack propagation, thematerial in the asphalt pavement is plastically deformedat the crack tip; once the load is removed, residual
stresses in the matrix induce micro-closures and reduce
the theoretical opening of the crack front.
(i) Oxide-induced: deposit of debris oxide on freshly
broken surfaces especially in humid conditions; thethickness of this oxide layer may be large enough390

to prevent total closure changing the propagation
kinetics of the crack.
(ii) Roughness- induced: the irregularities and themicro-movements of the fracture surfaces preventthe total closure in absence of load(iii) Viscous fluid-induced: a viscous fluid penetrates
within a growing fatigue crack affecting its
propagation rate, and finally,(iv) Transformation-induced: phase transformations at
the tip of the fatigue crack can delay the crack
growth rate [2].
Asphalt Pavement
Asphalt pavement is a Self-healing material. Whenasphalt is given rest intervals, it can restore its stiffness
and strength by sealing the microcracks that excessive
traffic loads and adverse weather have caused on thematerial. The wetting and inter-diffusion of material
between two faces of a microcrack cause crack repair inasphalt pavement systems, which restores the original
material's characteristics. When materials are moistened
and mixed between two faces of a micro-crack, the crack
in the asphalt pavement system is repaired and theoriginal material's qualities are restored. To explain how
asphalt materials heal, Little and Bhasin presented athree-step model: (i)wetting the two faces of a nanocrack. (ii)diffusion of the molecules from one face to theother; and (iii)randomization of the diffused molecules
to restore the material's original strength [3].
Types of Asphalt mix
There are three main types of asphalt mix: Hot
Asphalt, MC Cold Mix, and UPM. Moreso, there aredifferent varieties of these asphalts for summer andwinter use [4]. When passing a construction site, we
typically observe hot asphalt. Hot Asphalt, as its nameGEOMATE – Bangkok, Thailand, 22-24 November 2022

suggests, is mostly used for patching and paving.
Practically it must be utilized immediately after
purchase because it is easier to deal with while theasphalt is hot. As it becomes cooler, the more difficult it
is to work with. MC Cold Mix is asphalt that can beused as a temporary fix. Since the asphalt is used at cold
temperatures, it is slow to cure and is best used in areasthat have little to no traffic. UPM is also a cold mix
asphalt, but unlike MC Cold Mix, it can be used as apermanent fix to any asphalt or concrete problem, it isdesigned to work in any weather condition, UPM can beused to fix both wet and dry holes, allowing us to makeany repair in any situation. Once it has been compacted,
it is immediately ready to be tread upon.

elimination of cracks during the self-healing process
was caused by the combined action of asphalt molecule
stretching and model volume compression. Self-healing
is greatly impacted by the van der Waals forces that exist
between the molecules. Recycled asphalt's ability to
self-heal is strongly linked to its ROSL content; thehigher the ROSL concentration, the higher the diffusion
coefficient, which is more suitable for self-healing.
Recycled asphalt's ability to self-heal is strongly
correlated with its ROSL content; the higher the ROSL
concentration, the higher the diffusion coefficient,which is more conducive to self-healing. However, theysuggest that the ideal ROSL content be set at 10%
because ROSL-10 and ROSL-15 take roughly the sameamounts of time to reach the equilibrium distribution ofrelative concentration and density stability, also because
ROSL-10's diffusion coefficient is roughly equivalent tothat of virgin asphalt [7].

PREVIOUS STUDIES:
Researchers have recently discovered incredible
healing potentials of asphalt and suggested several
unique techniques to encourage and enhance the selfhealing capacity of asphalt to increase its lifespan. using
capsules containing waste oil. Their findings
demonstrate that capsules can survive the mixing andcompacting procedures and release the waste oils they
contain when subjected to mechanical loads.
Furthermore, it was discovered that samples of asphalt
mastic with capsules showed self-healing levels thatwere 80% higher than samples without capsules, and thedegree of self-healing was dependent on the ratio of
capsules added to the mixtures and the curing
temperature employed [5]. Tabacovic et al. 2022
investigated the impact of HealRoad capsules on themechanical performance of 10 mm stone mastic asphalt
mix and measured the damage repair (healing)effectiveness of the capsules in an asphalt mix. The
results showed that HealRoad capsules do not negatively
affect mix performance (indirect tensile strength andrutting resistance) when used in small amounts (>1percent), and in certain circumstances, they evenenhance it, as seen, for example, in the indirect tensile
strength ratio (water sensitivity). However, because
there are so few capsules in the mixture, the HealRoad
capsules are unable to promote induction healing.
Further research revealed that raising the capsulecontent to >5% of the mix can effectively activate
induction heating [6]. Using molecular dynamics
simulation, Zheng et al. 2022 examined the self-healing
behaviour of recycled asphalt created by the liquefactionof straw oil. In their investigation, five ROSL contents
designated as ROSL-2, ROSL-4, ROSL-6, ROSL-10,
and ROSL-1, of 2 percent, 4 percent, 6 percent, 10
percent, and 15 percent respectively, were added to theaged asphalt to represent recycled asphalt. The
molecular simulation was utilized to simulate thebehaviour of Nanocracks during mending. They decided
to make a three-layer structure with an outside layer ofasphalt molecules and an inner layer of 30 vacuum
molecules. The vacuum layer represented the asphalt's
internal nanocracks. They discovered that the

RESEARCH METHODOLOGY:
This paper studies some self-healing methods
available for asphalt pavement design and will focus on
three main types which are induction heating,rejuvenation, and nanoparticles
Types of Self-healing Agents
Healing agents mostly consist of softening
additives with a high concentration of light components.
Rheological and technical properties of hard binders areenhanced by the addition of these additives. Researchers
have come out with self-healing methods where they
employed sunflower oil, used vegetable oils, waste
mineral oils, epoxy resins, waste oils from repair plants,
and green bio-oils as healing agents. Asphalt has beenutilized for self-healing using a few commerciallyavailable rejuvenators. The type of rejuvenator used has
a big influence on how effectively the self-healing
process works, so it's essential to choose one that can
restore asphalt's lost properties and keep working after
healing [8]Induction Heating
Michael Faraday introduced the fundamental ideas
behind electromagnetic induction in the 1930s. In
induction heating, the material is placed in a magnetic
field that has been created artificially, and as eddy
currents pass through it, the substance is heated.
According to researchers, temperature has a significant
impact on the self-healing abilities of asphalt concrete.
Raising the test temperature accelerates self-healing andsimultaneously reduces the time required for complete
healing. Induction heating improves asphalt concrete's
potential for self-healing by raising the temperature. In
this method, metallic fibers are heated by induced eddy
currents and the heat energy diffuses into the bitumen toincrease the temperature. The bitumen begins acting like391

GEOMATE – Bangkok, Thailand, 22-24 November 2022

a Newtonian fluid when the asphalt mixture temperature
exceeds the bitumen's softening point, allowing theasphalt mixture to mend quickly [9].
Some researchers have done experiments on the
Preparation of asphalt concrete containing steel fibers
where Conductive fibers were typically added to asphalt
concrete to make it electrically conductive and suited for
induction heating. Typically, without altering the
mixture's graduation, steel fibers were added straight
while mixing. The typical distribution of steel fibers in
the mixture is depicted in Figure 1, where the steel fibers
act as the heating elements and produce conductive
routes. Long steel fibers are more efficient in improving
asphalt mixture conductivity and induction heating
speed, but they are very difficult to mix and have apropensity to form clusters that will absorb too much
bitumen and reduce the mixture's mechanical qualities.
Short and thick steel wool fibers with a diameter of 70–
130 μm and a length of 4.2 mm are recommended in thelatest research. These steel wool fibers are relatively
simple to blend using the standard mixing technique,and its ideal bitumen concentration is 6% by volume.
The Marshall stability, residual Marshall stability ratio,
water stability, raveling resistance, fatigue resistance,and low-temperature properties of the asphalt mixture
can all be improved by the steel wool fiber. The
induction heating speed, heating homogeneity, andtherefore the induction healing ratio of the asphalt
mixture can all be improved by using a composite ofsteel fibers and steel slag in place of some mineralaggregates [10]. Also, Induction Healing can be used toextend the fatigue life of asphalt concrete using threesteps;(v) The first step entails fatigue testing at 15 °C, 10 Hz,
and with 600 macrostrains perform on asphalt
beams (380 mm × 63.5 mm × 50 mm) until thecomplex modulus drops to half of its initial value.
(vi) The second step entails heating the fatiguedamaged beams to different temperatures, and(vii) finally, the heated beams are cooled to 15 °C andtested again under fatigue until the complex
modulus drops to the same value as the first fatigue
testing [9].

WCO to rejuvenate the aged asphalt binder and improveits characteristics up to the scale of a virgin binder. The
WCO can be applied directly to the asphalt binder (newor old), or after the suspension of pollutants has been
filtered; in other cases, it is used following chemicaltreatment, which has shown improved results (Azahar etal., 2017). Unsaturated fatty acids, like the lower
molecular weight components (maltenes) in the asphalt
binder, are abundant in WCO. The asphalt binder
experiences a loss of light oil components (maltenes)
during its service life and an increase in the compositionof heavy oil components (asphaltenes); the WCO
replaces the lost maltenes in the aged binder (Azahar etal., 2016a). It is a good option for asphalt rejuvenation
because of its copious availability. The risk of thermal
cracking increases with the viscosity of the binder, while
rutting and fatigue are increased by low viscosity. The
addition of WCO to the binder softens the binder,
enhances non-recoverable creep compliance, andreduces the resistance to permanent deformation.
Accordingly, rutting resistance should be considered
before adding WCO to the binder with caution
depending on the site's climatic conditions. [11].
Microcapsules can also be used as asphalt rejuvenators.
In the past, rejuvenators were spread onto the pavement
surfaces and allowed to penetrate the pavement forhealing. However, it was frequently challenging in thefield to accomplish successful healing agent penetration
for successful rejuvenation. Moreover, the pavements'
ability to resist slipping was seriously affected, and thechemicals were bad for the environment. As a result,
rejuvenator-containing microcapsules were madeavailable. Later, further techniques for embedding thehealing agents into the asphalt were created, including
hollow fibers and the microvascular system. The crack
is repaired when the healing agents solidify after being
activated during cracking and being poured into thecracks by capillary action. [12]. The rheologicalcharacteristics of capsules containing asphalt binder
were examined by Zhang et al [13]. The results of the
DSR and bending beam rheometer (BBR) tests showedthat the capsule integration marginally increases thehigh-temperature stability while slightly decreasing theasphalt's resistance to cracking at low temperatures.
Additionally, it was discovered that self-healing asphalt
has better anti-aging qualities than virgin asphalt. In
terms of complex modulus and fatigue life, thesecapsules also considerably enhanced the asphalt binder'sself-healing capabilities. Broken capsules are filled withthe bitumen-rejuvenator mixture and do not leave any
gap in the asphalt binder. The cracked capsules have noeffect on the binder's mechanical characteristics. [12].

Rejuvenation
Rejuvenation is the process of reviving theproperties
byrestoringtheratio
of asphaltenes and maltenes in the aged binder with theuse of a rejuvenator. An asphalt rejuvenator penetrates
the asphalt well below the surface to chemicallyrevitalize and protect the asphalt binder by replacing thetar and oil lost due to oxidation. Waste cooking oil canbe used as an asphalt rejuvenator. WCO should betreated and managed properly as it is considered harmful
to human health and the environment when discarded by
the users into kitchen sinks, waste bins, seweragesystems, or directly to land and water bodies. Many
researchers have explored the opportunity of utilizing

Effect of capsules on the asphalt mix
The distribution of the capsules, their durability,
and their strength can all be used to evaluate how wellcapsules operate in asphalt. It was discovered that the
number of loading cycles used influenced how quickly
392

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 1 Microcapsules made from Sunflower oil andcalcium alginate.

Fig. 2 Materials used to produce homemade capsulesfrom sunflower oil.

Figure 3 Manufacturing process of polymeric capsules by ionotropic gelation of alginate. [Adopted from
Mansoori et al, 2018]the healing agent was released under repeated loading[14]. Micaelo et al. [15]investigated the fatigue
performance of asphalt mixtures containing sunflower oil
encapsulated in calcium-alginate and coated with an
epoxy-cement composite. Complex stiffness modulus
and phase angle decreased because of the presence of
capsules. Following a period of rest, the asphalt mixture
with capsule inclusions showed increased rutting
resistance. The fatigue performance, however, showed no
obvious differences. Vegetable oil was employed by
Mansoori et al. [12] as the self-healing agent with thesame calcium alginate encapsulating medium. According
to the performance studies, rutting and moisture damage
showed positive results, however, stiffness modulus andfatigue parameters were affected negatively. Further,
Mansoori et al. discovered that at temperatures above 40
C, the healing in asphalt with and without self-healingchemicals is extremely comparable. Incorporating
capsules in hot environments was not advised by thestudy. In-depth research was conducted by Garcia et al.
[16] on the healing properties of several asphalt

combinations, including dense asphalt, stone matrix
asphalt (SMA), and porous asphalt (PA). According to
the study, porous asphalt has a slow release of healingchemicals, whereas denser mixes release them more
quickly
The capsules in fig 1 are home made using calcium
lactate, Sodium Alginate, and sunflower oil. 1 teaspoon
full of calcium lactate was put in a beaker containing
250ml of water it was stirred manually for about 5 mins
and 1 teaspoon full of sodium Alginate was put in anotherbeaker with 250ml of water and stirred for 5 minutes ona magnetic stir plate then we added one teaspoon full ofsunflower oil in the sodium alginate mixture as it keep on
stirring on the magnetic stir plate with a the aid of a stirrod. We then added the calcium lactate solution into thesodium alginate containing the sunflower oil using asyringe. Capsules are seen to form as more calcium
lactate solution is added with the syringe. The solution islater sieved, and capsules are selected. Some capsuleseizes were larger than required so capsules of 2.5 mm
and less were selected to be mixed with our Asphalt in393

GEOMATE – Bangkok, Thailand, 22-24 November 2022

the laboratory. These capsules will serve as the
rejuvenation agents in the asphalt.

such as: (1) inorganic-based nanomaterials; (2)carbon-based
nanomaterials;(3)organic-based
nanomaterials; and (4) composite-based nanomaterials.
Generally, inorganic-based nanomaterials include
different metal and metal oxide nanomaterials.
The use of nanoparticles is an innovative andfascinating approach to creating self-healing materials to
repair cracks, it does not involve the destruction andrebonding of polymer chains If a crack develops, thismethod uses a dispersed particulate phase to fill it. Lee etal [18] created a hybrid nanoparticle that actively
responded to injury and has the potential to cure itself
several times. Tyagi et al. [19] indicated that
nanoparticles may move toward the damaged area via apolymer-induced depletion attraction.
The tip of the surface notch may show a clear stressconcentration, which may eventually cause cracks tospread throughout the system and degrade mechanicalbehaviour. The morphology obtained from the MD
simulation serves as the input to the lattice spring modeland determines the mechanical properties of thenanocomposite-coated surface. These nanocomposite
coating nanocomposite coatings could produce flawlesssurfaces with improved mechanical qualities. The input
for the lattice spring model, that establishes themechanical characteristics of the nanocomposite coating
surface, was the morphology modeled using MD. By
applying this nanocomposite coating, we can create asurface devoid of flaws and improve the mechanicalqualities. Experiments have been done to support certain
features of the computer simulation. A multi-layered
composite structure's fissures were used as a model by
Gupta et al. [20] to show how embedded nanoparticles
could migrate and cluster. The result showed that in theinterfaces of the matrix and additive layer, thenanoparticles are evenly and preferentially separated.

Nanoparticles.

Table 1. Shows a study on the advantages anddisadvantages of asphalt self-healing techniques [11].
Techniques

Advantages

Disadvantages

Can heatmaterials with
low thermal
conductivity.

No data about
in situ
progress.

Transferable
readily.
Fast, selective,
and uniform
heating.
Induction
HeatingFits
environmental
issues.
Multi-time
recovery.

Encapsulation

High recovery
ratio.
Good
compatibilityHighmanufacturingcosts.
It has a limited
recovery ratio.
The heating
mechanism ismore complex
thantraditional
methods.
Depends onthe electricalconductivityof thematerial.
Only work in
the top layer.
Helpful only
once.

CONCLUSIONS
The methods discussed in this paper are a revolution
in road construction and maintenance; they have beensuccessfully implemented in the field, and it isanticipated that they will cut maintenance costs bydoubling the lifespan of new pavements. Induction
heating enhances local and rapid heating inside thematerial and increases the healing rates of bitumen.
micro-encapsulation restores damage and increases
durability, by reducing pavement rigidity and sealing
cracks. However, there are still several issues that need tobe resolved. The design of nanocapsules can withstandthe high temperature and pressure during mixing andcompaction processes, as well as obtain adequate
dispersions of additives in the asphalt binder and asphalt
concrete, which should provide homogenous qualities.
Additionally, there are many models to measure binder(asphalt) healing, but more work is still required to model
this process in asphalt composites.

Blend
Ionomers

Multi-time
recovery.
Stable at hightemperature.
Good
compatibility

Only applies
to early cracks

Note: From Benefits and drawbacks of asphalt self-healing techniques.
Agzenai et al, 2015, University Carlos III de madride-Achivo

Nanoparticles are tiny particles with sizes between
1 and 100 manometers. Undetectable by the human eye,nanoparticles can exhibit significantly different physicaland chemical properties to their larger material
counterparts. Nanomaterials can be categorized into fourtypes [17] [7]394

GEOMATE – Bangkok, Thailand, 22-24 November 2022

REFERENCES

at the nano level and new repairing
methodologies,” Recent Pat Nanotechnol, 2015.

[1] A. Tabaković and E. Schlangen, “Self-Healing
Technology for Asphalt Pavements,” Springer
Nature, vol. Volume 273, p. 285–306, 2016.
[2] D. Sun and et al, “A comprehensive review on
self-healing of asphalt materials: Mechanism,
model, characterization and enhancement,”
ScienceDirect, Vols. 256,, pp. 65-93, June 2018,.

[12] T. Mansoori, “Self-healing of asphalt mastic by
the action of polymeric capsules containing
rejuvenators,” Construction Materials, vol. 161,
pp. 330-339, 10 February 2018.
[13] M. Baqersad, “Rheological and chemical
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12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

PRODUCING VALUABLE ‘SAND-LIKE’ MATERIAL FROMEXCAVATED MARINE CLAY USING MID-SIZED ROTARY KILN
Sathya Subramnian1, Juan Wei Koh1 and Soon Hoe Chew1, Y. C. Tan2, C. S. Teo2, M. Y. C. Koh2, T. H. H.
Cheung2, H.B. G. Foo2
1

Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117578;
2
Building and Infrastructure Group, Housing and Development Board, Singapore 310480
ABSTRACT

Traditional land reclamation relies heavily on sand. Since excessive extraction of sand may post environmental
and ecological concerns, it is important to come up with a sustainable solution to reduce the reliance on sand. Oneof the sustainable solutions is to recycle and reuse the excavated marine clay obtained from excavation andunderground constructions. However, this soft marine clay must be improved or treated before it can be used.
‘Sintering’ is a thermal process where a granulated clay is subject to heat treatment, causing an irreversible mineralchange in the clay and thereby converting the soft marine clay to a useful ‘sand-like’ material. Preliminary
laboratory research conducted on ‘clay sintering’ had been successful, which proved that the sintered product has‘sand-like’ properties. This research aims to develop further into industry scale by using industry scale mid-sizedrotary kiln to save on time and energy and thereby help in mass producing the sintered product. Parametric studies
were conducted to identify the optimum operating conditions of the rotary kiln. Visual observations indicated thatclay balls sintered at 800oC were uniformly sintered while clay balls sintered at 900oC had a well sintered surface
with a black core. Further mineralogical studies were conducted to investigate the nature of this black coreformation.
Keywords: black core formation, land reclamation, rotary kiln, ‘sand-like’ material and sintering
INTRODUCTION

at high temperature [1–3]. At a temperature of about
800-850oC, the kaolinite transforms to metakaolinite,and upon further heating to 1100oC, the metakaolinite
is transformed into mullite [4–6]. Both the formationof metakaolinite and mullite are irreversible process.
It was found that the formation of metakaolinite issufficient for the current study, because laboratory
scale testing conducted using a static furnace showedthat this mineralogical change is possible and thestrength & permeability was similar to granularmaterial [7]. The static furnace is suitable for acontrolled laboratory testing purpose, but a rotary kiln
would be more suitable for industrial scale production.
The clay balls in a static furnace are not subjected toany movement but in the case of rotary kiln withinclined axis, the heat required for sintering isdelivered to the material by lifting and overturning
each sample moving through the kiln. The sample in
the kiln moves both radially and axially. The radial
motion is controlled by rotational speed (rpm), thediameter of the kiln and the amount of sample
inserted, while the axial motion is governed by thekiln’s inclination angle. Heat transfer to the material
in the rotary kiln occurs in three different ways: (a)heat transfer to material by convection and gas
radiation, (b) heat transfer to material by radiation,and (c) conductive heat transfer to material from kiln
wall [8]. The more dynamic process of movement ofclay balls and the multiple modes of heat transfer
makes rotary kiln idle for sintering process. Hence, it

Sand is always a preferred filling material for land
reclamation. Excessive extraction of sand from riveror seabed always posed environmental and ecologicalconcerns. Hence there is an urgent need to develop
alternative filling materials which could be used forland reclamation. On the other hand, geology ofSingapore indicates an abundant marine clay in the
downtown area, where a lot of deep excavation andtunnelling works are under construction. Marine clay
excavated during tunnelling or undergroundconstruction has poor engineering properties due to
its low shear strength and high compressibility. In fact,in Singapore additional cost is needed to safely
dispose the excavated marine clay. Furthermore, theneed for finding a suitable dumping site for such
marine clay is also challenging. One sustainable wayof tackling the ‘no or low economic valued’ marine
clay and the sand shortage problem is to convert the
excavated marine to ‘sand-like’ material, thereby
increasing economic value to the recycled and reused
excavated clay. This would also reduce the relianceon imported sand. Singapore marine clay minerology
predominantly consist of kaolinite. An irreversible
mineralogical transformation is required for a
successful conversion of soft marine clay to ‘sandlike’ material.
Sintering is a thermal process, which could be
used to trigger the mineralogical change of kaolinite
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

is important to obtain the optimum operating
conditions for the mid-sized industry scale rotary kiln
to save energy. The end result of this production willthen be tested from a mid-sized rotary kiln.
The objective of the current study is to use a midsized rotary kiln with 240mm in diameter and
2200mm long for the sintering process to obtain the
optimum operating parameters and check the qualityof the sintered product through visual observation andstatic compressive strength.

(I) and the kiln rotation frequency (R) could becontrolled by the user. The target sintering
temperature based on literature review should be
800oC and higher for kaolinite to irreversibly convert
to metakaolinite. The kiln inclination and rotation
frequency are important parameters that control theamount of time the clay balls are retained in the rotarykiln. In other words, the parameters R and I control
the retention time (t) of the clay balls in the rotary kiln.
It is important to find the optimum operating
parameters for the rotary kiln to make the processenergy efficient. The pictures of the end product ateach stage is shown in Fig. 2.

MATERIALS AND METHODOLOGY
Soil containing marine clay obtained fromexcavation site is used in this study. The soil is sieved
through 1mm sieve to remove shells, roots, andcoarse sand particles. The liquid limit and plastic limitof the clay used in this study are 80% and 36%,
respectively. The soil classifies as ‘CH’ clay of highplasticity according to Universal Soil Classification
System (USCS).

Fig. 2 Pictures of end-product at each stage.

Methodology

RESULT AND DISCUSSION

The marine clay obtained after initial screening
has a moisture content of 80-100%. The clay isdewatered to reduce the moisture content closer toplastic limit (~35%) to make it suitable for next stage,
where the lumpy clay is turned into smaller clay balls.
Low moisture content would result in formation of
crack and high moisture content would make the clay
balls sticky. The dewatered clay is then put inside an
extruder with cutting blades to produce clay balls of
3mm size.
The moisture content of the clay balls is further
reduced in the next pre-heating stage. The pre-heatedclay balls are then sintered using mid-sized rotary kiln
to produce the sintered product. The mid-sized rotarykiln used in this study has a rotary furnace which is
2.2m in length and 240 mm in diameter, as shown in
Fig. 1.

Target Sintering Temperature = 800oC
First series (series A) of testing was carried out by
maintaining the target temperature at 800oC andvarying the angle of inclination (I=2.0o, 3.0o, 3.6o,
4.2o & 5.6o) and rotation frequency (R = 8, 10 & 12rpm). Table 1 summarizes the visual observation of
the sintered product obtained from the parametricstudy.
Table 1. Summary of the parametric study (series A,
T=800oC)
Test
Rt
VisualIo
S/UNo.
rpm mins
Inspection
A1

4.2

8

6.0

U

A2

3.6

8

7.67

U

A3

4.2

10

5.0

U

A4

3.6

10

6.0

U

A5

3.0

10

8.5

S

A6

5.2

12

3.0

U

A7

2.2

12

10.5

U

Note: S=Successful and U=Unsuccessful

Fig. 1 Mid-sized industry scale rotary kiln
Target sintering temperature (T), kiln inclination
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

needed to identify the black core in the sinteredsamples.

The retention time (t) defined here is thedifference between the time of arrival of first sinteredproduct and the time of arrival of last sintered product.
In these series of testing, 50g of clay balls were used.
For example, when the 50g of clay balls are put inside
the rotary kiln, the stopwatch was started. The firstsintered product would arrive at 2 mins elapsed time
and the last sintered product would arrive at 12 mins
elapsed time. Thereby the retention time would be 10
mins.
Comparing test A1 and A2, an increase in
inclination angle (I) reduces the retention time (t) ofclay balls in the kiln. Similar trends are observed
while comparing A3, A4 and A5, where the increase
in inclination angle reduced the retention time of theclay balls.
Similarly, higher kiln rotation frequency (R)results in shorter retention time of clay balls in therotary kiln (comparing A1 & A3 and A2 & A4). In
some series of tests, there is a black core observed
when the sintered product is cut open. It was thoughtin the beginning that this black core could be due toincomplete sintering process, where the clay balls did
not have sufficient exposure to the heat energy. A
more detailed analysis on the black core is carried out
in the later section. It is important to find a suitablecombination of inclination and rotation frequency
thereby optimizing the retention time that produces
well sintered clay. Shorter retention time would resultin less energy consumption of the rotary kiln. Basedon visual observations, Test A5 produces wellsintered clay.

Table 2. Summary of the parametric study (series B,
T=900oC)
Test
Rt
VisualIo
S/UNo.
rpm mins
Inspection
B1

4.2

6

6.67

U

B2

3.6

6

7.83

U

B3

3.0

6

10.50

U

B4

1.6

14

9.50

U

B5

1.6

8

13.16

U

B6

1.3

12

14.83

U

B7

1.0

10

>26

U

Microscopic Analysis
A new series of testing is carried out (Series C) to
conduct the microscopic analysis. The parametersconsidered are summarized in Table 3.
Test
No.
C1
C2
C3
C4

Target Sintering Temperature = 900oC
Series A focusses on the importance of theretention time of the clay balls in the rotary kiln.
Apart from retention time, the other way to achieve
better sintering while keeping the retention timeshorter is to increase the sintering temperature.
Increasing sintering temperature would expose clay
balls to more heat energy thereby reducing the timerequired for complete sintering. Series B testing iscarried out with a sintering temperature of 900oC andthe results are tabulated in Table 2.
The visual inspection of the 900oC sinteredsamples show black coring as well. Hence a detailed
investigation of this black core is needed.
It is important to conduct a microscopic
investigation to find the composition of the black corematerial. This could help conclude whether the ‘black
core’ is acceptable (maybe a by-product of sintering)or unacceptable (unsintered product). Hence,microscopic investigations (TGA and XRD) are

Table 3. Summary of testing series C
Mass
R
t
Io
T oC
g
rpm
mins
50
12.67
800
100
14.67
1.56
10
50
13.00
900
100
15.00

Thermogravimetric analysis (TGA) and X-Ray
diffraction (XRD) testing are conducted to study themineralogical composition of the black core and thesintered product. The results obtained from TGA andXRD analysis are shown in Fig. 3 and Fig. 4
respectively.

Fig. 3 TGA analysis on un-sintered marine clay
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

A rapid loss of weight occurs between 350oC 700oC and a peak in the derivative of weight loss
indicates that decomposition occurs at approximately
500°C (Fig. 3). The change in mass is attributed to theprocess of dehydroxylation, where evaporation ofchemically bounded water occurs during thedecomposition of kaolinite to metakaolinite. The
reduction in mass stabilizes at 800°C, indicating thecompletion of dehydroxylation process. Since acomplete dehydroxylation is necessary to prevent thepotential disintegration of clay in water, a minimum
temperature of 800°C must be used for sintering.
Sintered clay without sufficient resistance against
water would be unusable at land reclamation sites due
to exposure to seawater and rain.

Compressive Strength Testing
The compressive strength of individual sinteredproduct was tested in a Zwick compression machineat a strain rate of 1mm/minute. Fig. 5 shows thecompressive strength of individual sintered product.
The strength of sintered product obtained from therotary kiln is 7 MPa higher than that of sinteredproduct obtained from static furnace. This could be
the result of more effective heat transfer occurring inrotary kiln.

Fig. 5 Compressive strength of sintered productproduced from static furnace and mid-sized rotarykiln.
CONCLUSIONS
Fig. 4 XRD results on S1 sample (unsintered claybody), S2 sample (Sintered clay body at 800oC), S3
sample (Sintered clay surface at 900oC) and S4
sample (Black core at 900oC). (Note: Q=Quartz,
I=Illite, K=Kaolinite and H=Hematite).

This study shows that the lumpy excavated soil
containing marine clay can be convered to ‘sand-like’material through sintering process using a mid-sizedrotary kiln. TGA analysis showed that a minimum of
800oC is required for the marine clay to undergo
complete dehydroxylation process, which results inevaporation of chemically bound water in kaolinite
and converstion of kaolinite to metakaolinite occurs.
This study also established, based on this particluar
rotary kiln model that;
1) The optimal retention time is achieved by a
combination of kiln rotating frequency (10 rpm)
and inclination angle (1.56o).
2) The red appearacne of sintered product indicatesthe presence of metakaolinite with hametite,while the absence of hametite is responsible forthe black core formation.
3) The black core formation at 900oC does notindicate that marine clay is un-sintered, through
XRD analysis it was proved that the kaolinite had
converted to metakaolinite.
4) The black core formed will not dissolve in water
as the conversion of kaolinite has taken place,hence, it is considered as a stable sinteredproduct.

Fig. 4 (S2 & S3) shows that at sintering
temperatures of 800°C and 900°C dehydroxylation
has occurred, converting kaolinite into metakaolinite.
Note that metakaolinite is undetectable by XRD due
to it being an amorphous aluminosilicate. The
absence of hematite in the black core in S4 and its
presence in S2 and S3 samples validates that thehematite gives the red appearance in thermallytreated clay. Furthermore, the absence of kaolinite in
the black core in S4 sample indicates that a certain
degree of heat treatment has occurred in the blackcore of the clay, bringing about the process ofdehydroxylation. Therefore, the presence of a blackcore is not due to incomplete sintering. Even with thepresence of black core the sintered product would notdisintergate in water (dehydeoxylation has takenplace).
The microscopic analysis through TGA and XRD
has proved that the black core formation does notindicate unsintered product. Kaolinite has converted
to metakaolinte in the black core, thereby resulting ina stable sintered product.
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ACKNOWLEDGMENTS
This research is supported by the National
Research Foundation, Singapore, and Ministry of
National Development, Singapore, under its Cities ofTomorrow R&D Programme (CoT Award No. COTV3-2019-2). Any opinions, findings, conclusions, orrecommendations expressed in this material are thoseof the author(s) and do not reflect the views of
National Research Foundation, Singapore, andMinistry of National Development, Singapore.

[5]

[6]

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833–837.
https://doi.org/10.2109/jcersj2.16014.
Y.F. Chen, M.C. Wang, M.H. Hon, Phasetransformation and growth of mullite inkaolin ceramics, J. Eur. Ceram. Soc. 24(2004)
2389–2397.
https://doi.org/10.1016/S09552219(03)00631-9.
Y.F. Chen, M.C. Wang, M.H. Hon,
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1355–1362.
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Y. Jorand, R. Doufnoune, G. Fantozzi,
Mullite fabrication from natural kaolin andaluminium slag, Bol. La Soc. Esp. Ceram. y
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https://doi.org/10.1016/j.bsecv.2018.01.001.
J.W. Cogswell, H. Neff, M.D. Glascock, The
effect of firing temperature on the elemental
characterization of pottery, J. Archaeol. Sci.
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283–287.
https://doi.org/10.1006/jasc.1996.0026.
K. Srikrishna, G. Thomas, R. Martinez, M.P.
Corral, J.S. Moya, Kaolinite-mullite reaction
series: a TEM study, J. Mater. Sci. 25 (1990)
607–612.
J.W. Koh, S. Subramanian, K.E. Low, S.H.
Chew, K.E. Chua, T. Yeow Chong, S. Teo
Cheng, L. Ji Mei Jamie, K. Meng Yang
Charmaine, Laboratory-Scale Production of‘Sand-like’ Material from Excavated Soft
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Environment401

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

MATERIAL SOURCE OF MUARAJAMBI TEMPLES, INDONESIA
Sondang Martini Siregar1, Edy Sutriyono2, Ari Siswanto3, Agus Aris Munandar4
1
Environmental Science Faculty; 2 Geological Engineering; 3Architectural Engineer, Sriwijaya University;
4
Faculty of Cultural Sciences, Indonesia University, Indonesia
ABSTRACTThe Muarajambi temples are on a fluvial landform, especially in a linear position along the Batanghari River.
Inside the temples are the main building, the ancillary building, the fence, and the statue, which are made fromvarious materials and remained the same as today. The problems are a) how is the regional location of the
Muarajambi temple area? b) what are the types of building materials used for Muarajambi temples? c) where are
the materials for the Muarajambi temples obtained? This study used a qualitative method with a petrographic
analysis. The data collection was done by conducting a field survey, and then the samples were sent to thelaboratory to determine the mineral content. The results showed that the Muarajambi temple area was on the rocks
South Sumatra basin and in the Muara Enim formation. The materials used for building the temples are bricks(claystone), sandstones, and tuffs, while the materials used for the statues were tuffs, sandstone, and andesite stone.
People used to have a policy of building temples on the riverbank by choosing bricks (claystone), tuffs, andsandstones as building materials because they were not waterproof and came from a local area. Meanwhile, thereason for using andesite stones for the statue material is that they are easy to be sculpted and more durable, eventhough the andesite materials are from outside the Muarajambi temple area.
Keywords: Material, Source, Temple, Stone
temples were from the 10th BC on the swamp
landscape that is often swamped due to both the tides
in a river and rainfall. The interesting point is that the
temple building can remain up until now. The arisingproblems are a) how is the regional location of the
Muarajambi temple area? b) what are the types ofbuilding materials used for Muarajambi temples? c)
Where are the materials for the Muarajambi temples
obtained? This study aimed to find out the location of
Muarajambi temples, the stratigraphy of the rockformations in the Muarajambi temple area, and thesource/location of Muarajambi temple materials.

INTRODUCTION
A temple is a building founded as a worship placefor Hindus/Buddhists [1]. Inside the temples are thestatues as the embodiment of deities worshipped byHindus and Buddhists. So, the temple building is built
from special materials. Manasara silpasastra
mentioned that the temple materials are from 1 type,2 types, or more than 2 material types [2].
In Indonesia, many temples are found in hilly
places, mountains, or lowland areas near or far froma river [3]. In general, the temple material in Central
Java is andesite stone, while the temples in East java
are from bricks (claystone) [4]. The temples in theMusirawas temple area are made from bricks(claystone); they are the materials found around the
local [5]The temple buildings are mostly found in South
Sumatera, such as temples in Bumiayu, Telukkijing,
Binginjungut, Lesungbatu, Tingkip, and Jepara thatare found in the watershed [6]. The existence of those
temples is indicated due to the previous trading
contact with India. Along with the trading activity, it
stimulates the arrival Hindu-Buddhist influence todevelop in South Sumatra, particularly in the locationnear the river bank [7].
One of the temples in the Batanghari watershed is
Muarajambi temple, particularly located on the northand the south side of Batanghari River [8]. The
temples in a linear position against the BatanghariRiver are divided into some groups having aboundary with the channels or tributaries of
Bantanghari River. The temples have an area of 3,000
acres that has a boundary with the channels or
tributaries of Bantanghari River [9]. The Muarajambi

METHOD
Location Analysis
The method used here was conducting a survey
and mapping by referring to the ArcGIS software, theBIG data of the geological map of the Lahat andJambi quadrangles. The coordinate point is, then,placed on a basic geological mapping. Based on the
map integration and the previous literature review,the name and the administrative boundary are
adjusted. It becomes the baseline to make a map offeature placement in the research location.
Petrographic Analysis
A petrographic analysis is a branch of petrology
as the study of rocks, especially rock contents and the
texture relations. This analysis aimed to discover thename of each rock sample collected in the location
based on the texture, structure, and the mineralcontent of rocks available in the research location.
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

The rock sample used in this study was a fresh rockbecause the rock sample needed to be prepared first
until it became a thin incision that would be analyzed
using a microscope. The name classification of
sedimentary rocks was performed based on theclassification by Pettijohn (1986) and Fisher (1966).
At first, an observation survey on the rock exposureand the macroscopic description of hand specimen.
The instrument used here was a polarizing
microscope that can analyze the optical mineralogy ofa thin incision in detail and the micro texture, and thestructure were vital to know the origin of the rock.

Muara Enim formation was deposited at the Late
Miocene to Pliocene in the form of the second
regression cycle with a shallow marine deposition
environment up to continental sands, delta, andclaystone. The first (Air Benakat formation) and thesecond regression cycle are distinguished by theirabsence of glauconitic sandstone and the existence ofa relatively thick coal layer in this Muara Enim
formation. The precipitation is initiated in the swamp
area of a coastal plain, and then it continues to thedelta plain area with a local shale sequence
development and thick sandstone. In this formation,
there is iron-oxide in the form of concretions andsilicified wood. Meanwhile, the coal available in thisformation is generally in the form of lignite. The
thickness in this formation is categorized as thin in
the north part and the maximum is in the southern partwith a thickness of 750 m [11]The Kasai formation is deposited from the Pliocen
to Pleistocene. This formation is the result of an
erosion of lifting the Bukit Barisan and Tigapuluh
mountain. The Kasai formation has a contact with
Muara Enim formation that is marked by the lithologyof tuffaceous sandstones. This formation is depositedin the third regression cycle marked by the occurrenceof volcanic products. The Kasai formation iscomposed of continental sandstones, claystone, andpyroclastic materials (pumice tuff, tuffaceous
sandstones, and tuffaceous claystone). This formation
finalized the oceanic tide cycle. The lower partconsists of tuffaceous sandstone with some variationsof tuffaceous claystone and loose sandstone layers,
and the upper part consists of a layer of tuffs, pumicethat contains the remnant of plants and wood in across-bedding structure. There is also a lens-shaped
lignite in sandstones containing tuffs. The deposition
environment of this formation is fluvial with athickness of >200 m. Alluvial sediment is a material
deposit coming from a rock disintegration available
in an area that is usually in either a lowland area orriver. The alluvial sediment generally consists ofblocks, pebbles or gravels, sands up to mud. [10]The Muarajambi temple area in the line-up
position above the stratigraphy of Muara Enim,
Kasai, and Alluvial Sediment formations. The Muara
Enim formation is marked green on the map. The
stratigraphy of this formation contains iron-oxide in
the form of concretions and silicified wood. In thewestern side, there is alluvial sediment containing
blocks, pebbles, sands, and mud. The westernmost
part of the temple area is located on the stratigraphyof Kasai formation that contains continental
sandstones, claystone, and pyroclastic materials(pumice tuff, tuffaceous sandstones, and tuffaceous
claystone).

Material Source Analysis
The material source analysis is known by carryingout an interpretation of the relationship between theplacement of artifacts/features and the stratigraphy ofrock formation at a regional scale; therefore, it can
indicate the material source of the temple building if
it is from the inside or near the site location or thematerial taken from outside the location/region.
RESULT AND DISCUSSION
The Regional Location of Muarajambi Temple
Area
The South Sumatra basin is a result of tectonic
activities that are strongly related to Indo-Australianplate subduction that moves to the northern part to thenortheastern part against the Eurasian plate that is
relatively static. The plate subduction zone includes
the western area of Sumatra Island and the easternarea of Java Island. Several microplates in between
those zones also move and result in a convergence invarious shapes and directions. The Indo-Australianplate subduction can affect the rock state,morphology, tectonic, and the structure in South
Sumatra. The South Sumatra basin is a relatively
large basin. The difference in the relief of bedrocks
is due to the fault of bedrocks in blocks, it results in a
bedrock elevation model and depression. Unevenrelief and the reactivation of the block fault control
the sedimentation and the fold in the tertiary layer inthe basin. This basin consists of tertiary sediments
unconformity located on the surface of metamorphic
rocks and pre-tertiary igneous rocks. The South
Sumatra basin is located elongating in an NWSE
direction in the southern part of Sumatra Island. The
area of this basin is around 85,670 km2 consisting of
2 sub-basins, namely Jambi sub-basin and Palembang
sub-basin. The research location was in the South
Sumatra basin, specifically in Jambi sub-basin The
stratigraphy of South Sumatra basin consisted of thefollowing formations: Kikim, Telisa, Lahat, Talang
Akar, Gumai, Baturaja and the equivalent, Air
Benakat, Muara Enim, Kasai and the equivalent, andthe alluvial sediment on the upper surface [10]403GEOMATE – Bangkok, Thailand, 22-24 November 2022

The brick sample of Candi Teluk temple is whiteand brown-orange. The rock composition consists of
30% quartz, 2% lithics, and 68% clay cement.

Qtz

Cly
Cly
L

Fig.2 The Distribution of Muarajambi Temples (red)lining up above the stratigraphy of Muara Enim,
Kasai, and Alluvial Sediment

Cly

L

Fig.4 Brick Sample of Candi Teluk containing 30%
quartz, 2% lithics, and 68% clay cement

Types of Material for Temple Building

Sandstone
The material sample of Kedaton temple showed
dull white color from a group of colorless minerals.
The rock composition consists of 90%
monocrystalline quartz, 2% lithic fragments, and 8%
felspars. Through a calculation of rock content, therock name can be determined as quartz arenite.

Nine samples were taken from the Muarajambi
temple area, especially the samples from the temple
building. Those samples were then analyzed using apetrographic analysis by cutting the stone to know themineral content. After cutting the sample, theidentification of rock sample was done by using a
microscope at 40x magnification.
Claystone
Based on the analysis result, 3 samples taken from
the Muarajambi temple area were known to be madefrom claystone, namely 1 sample from Kedaton
temple and Teluk 1 temple. The material sample from
Kedaton temple was brown-orange, gray, and brown.
The rock composition consists of quartz andlithic/rock fragments of sedimentary rocks as a
fragment and cement in the form of clay. The rockcomposition is 18% quartz, 12% lithics, and 70% clay
cement. Therefore, the most rock content is clay.

Qtz
Qtz

Qtz

L

Qtz

Fig.5 Sandstone Sample of Kedaton temple
containing 90% quartz, 2% lithics, and 8% felspars
The rock sample of Gedong temple 1 contains
92.3% quartz matrix, 5.1% feldspars, and 2.6% lithics(sediments). Through the calculation of rock content,
the rock name can be known as quartz arenite [12]Fig 3. Brick Sample of Candi Kedaton containing
18% quartz, 12% lithics, and 70% clay cement404

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Lit

Fsp
Qz

GI

Qz

Fsp

Qz

Por
Qz

Fig.6 Sandstone Sample ofGedong 1 temple containing 92.3% quartz, 5.1%
feldspars, and 2.6% lithics

Lit

Fig.8 Tuff Sample of Gumpung temple containing
6% quartz, 1% biotite, 42% lithics, 25% glass, and
26% porosity

Limestone
The sample of Kedaton temple is cream/white andpearly. The rock contains 100% colorless calcite. This
sample is classified as carbonate rocks and known as
Calcite Sandstone [13]

Cal

Bt

The rock sample of Kembarbatu temple is brown.
This sample contains 10% colorless quartz fragments,
85% grayish cream and dark gray ground-glass mass
(PPL), and 5% porous rocks. Based on the pyroclasticrock classification, then rock name is known as
Tuffaceous Sandstone/Vitric Tuff [14]

Cal

Cal
Fig.7 Limestone Sample of Kedaton temple
containing 100% calcite sandstoneTuffs
The rock sample of Gumpung temple is cloudy
white and brownish cream. The rock contains 6%
colorless quartz having 1% brown biotite, and 42%
colorless lithics. The basic mass is in the form of 25%
grayish cream and dark gray glass (PPL) and 26%
porosity. Through the pyroclastic rock classification,
the rock name is known as Tuffaceous
Sandstone/Vitric Tuff [14]Fig.9 Tuff Sample of Kembarbatu temple
containing 10% quartz, 85% ground-glass mass,
and 5% porous rocks
Igneous Rocks
The igneous rock sample of Kedaton temple iscloudy white with yellowish and brownish gray spots.
This rock contains 46.1% black quartz, 40.4%
plagioclase, and 13.5% alkali feldspars. Based on theclassification of intrusive igneous rock, the rock nameis known as Granodiorite[15]405GEOMATE – Bangkok, Thailand, 22-24 November 2022

India, contains bricks (claystone). The temple in alining-up position from the northern to the southernside is surrounded by a fence made from bricks(claystone) [19].
The Muarajambi temple area generally uses
claystone as the material for the temple building andfloor. It is because claystone is suitable for buildings
established on a wetland as it is waterproof, heatabsorbent, and durable [10].
Claystone used for temple buildings indicates that
it is light and non-waterproof, so it is suitable forbuildings established on a wetland [20]. The staircase
and its balustrade are made from sandstone (thedecoration on the balustrade tip). Sandstones andtuffs are used as statue material, such as Dualapara
statue from Gedong temple that is made fromsandstone, and Prajnaparamitha statue from
Gumpung temple that is made from tuffs. The
consideration of making statues from sandstone andtuffs is that those materials are easy to sculpt,indestructible, and durable [21].
Besides, there are temples that use 2 temple
materials, such as Muaratakus temples that are madefrom the claystone of sandstone [14], Lesungbatu 1
temples that are made from bricks (claystone) andtuffs as well as Lesungbatu 2 temples that are madefrom tuffs and wood. Temples consist of acomposition of big tuffs in a square shape, and there
is a hole in between as a place to store the pegs ofwooden poles. The wooden poles function to support
the temple roof [22].
Wood is a natural material of tree trunks forhuman life needs as it is strong, able to support loads,
durable, and easy to sculpt. The wooden material isused for building the construction, accessories, andtools. Since around the 665-57 BC, the woodenmaterial has been used in Japan and Koreas a material
for building temples, Buddhist temples, and worshipplaces for Shintoism [23].
Gravels and pebbles are the temple materials. The
main room of Kedaton temples is made from gravels
and pebbles in 2 meters height. Similarly, the gravels
and pebbles are found after the height of 1,5 m after
the Tuo Sumay temple excavation. The gravels andpebbles used as the temple materials aimed tomaintain the stability of temple buildings [24].

Plg
Afs
Qz Chl Qz
Fig.10 Igneous Rock Sample of Kedaton temple
containing 46.1 %, 40.4% plagioclase, and 13.5%
alkali feldspars
The rock sample of Gedong 1 temple is blackish
gray with a hint of brown (XPL). The primary mineralconsists of 32.3% colorless quartz, 12.9% colorless(cloudy) plagioclase, and 54.8% colorless alkali
feldspars. Based on the classification of intrusive
igneous rock, the rock name is known as
Syenogranite [15]Plg Afs
Opq BtQz

Plg
Qz

Fig.11 Andesite Rock Sample of Kutomahligai
containing 40% quartz, 47.4% plagioclase, and12.6% alkali feldspars.
The petrographic analysis result indicated that the
Muarajambi temple materials consisted of claystonetaken from Kedaton and Teluk temples, sandstones
taken from Kedaton and Gedong 1 temples, tuffs
taken from Kembarbatu and Gumpung temples,
Limestone taken from Kedaton temple, and granite
taken from Kedaton and Gedong 1 temples, andandesite rock taken from Kutomahlogai temple.
In South Sumatra, claystone generally becomes
the temple material, such as Tingkip, Lesungbatu, andBinginjungut temples [16]. It also includes Bumiayu,
Muarajambi, Bahal, and Padangroco temples [17].
The material of Batujaya temple is also claystone(bricks). The temples on a wetland, specificallyaround rice field areas [18]. Nalanda temples, Bihar

Material Sources of Muarajambi Temples
The Muarajambi temples are at the stratigraphy of
Muara Enim, Kasai, and Alluvial Sedimentformations.Based on the result of the petrographic
analysis on the material sample, the Muarajambi
temples are made from bricks (claystone). The
relatively unique rocks are found in this area as norock formation around the area, such as granite that isfound in Kedaton and Gedong 1 temples, and andesitethat is found in Astano and Kutomahligai temples.
Besides, iron oxide rock is also found in Gedong 1
406

GEOMATE – Bangkok, Thailand, 22-24 November 2022

temple; the volcanic rock is found in Kembarbatu
temple, and calcite sandstone found in Kedaton
temple.
In general, the materials of Muarajambi temples,such as bricks (claystone), tuffs, sandstones, arecollected from the local areas; they are the rockcontent obtained from the stratigraphy of Muara Enimand Kasai formations. The existence of other rocks,
such as limestone, granites, and andesites, are takenfrom outside as they are the paramount materials forbuilding statues, stupas, temple gates, and balustradedecorations.
Similarly, in Bumiayu temples, there are statuesof 3 characters that are made from sandstones [25],Buddhist statues and Awalokiteswara statue that are
made from andesite stones are found in Lesungbatu
temple [16], and the Buddhist statue of Tingkip
temple is made from sandstones [26]. The statuematerials are not the rock constituents taken fromlocal areas, but they are imported from outside thearea for the worshipping purpose done inside thetemple.

[3] V. Myriam and Y. Degroot, Candi Space andLandscape: A Study on DIstribution, Orientationand Spatial Organization of Central Javanese
Tmeple Remains. 2009.
[4] D. P. C. B. dan Permuseuman, Candi Indonesia:
Seri Sumatera Kalimantan Bali Sumbawa. 2014.
[5] S. M. Siregar, “Faktor-Faktor Penempatan
Percandian di Lahan Basah (Studi Kasus
KawasanPercandian
Muarajambi
danBumiayu),” 2020.
[6] S. M. Siregar, “Situs-Situs Klasik di Sumatera
Selatan,” Siddhayatra, vol. 8, no. 2, pp. 84–93,
2003.
[7] S. M. Siregar, “Persebaran Situs-Situs HinduBuddha dan Jalur Perdagangan di Daerah
Sumatera
Selatan(Indikasi
Jejak-Jejak
Perdagangan di Daerah Aliran Sungai Musi),”
Kindai Etam, vol. 2, no. 1, pp. 4–10, 2016.
[8] E. Parwati and A. Suariasi, “Konsep Kosmologi
Candi Empat Muarajambi sebagai Sumber
Pembelajaran Sejarah,” vol. 1, pp. 105–112, 2019.
[9] C. N. Andarsih, Hamzah, and M. Safri, “Pemetaan
Risiko Pemanfaatan Kawasan Cagar Budaya
Nasional Muarajambi,” Siddhayatra, vol. 26, no.
2, pp. 13–26, 2021.
[10]G. L. De Coster, “The Geology of the Central andSouth Sumatra Basins,” in 3rd Annual
Convention Proceedings, 1974, pp. 74–109, doi:
10.29118/ipa.670.77.110.
[11]M. G. Bishop, “South Sumatra Basin Province,
Indonesia: The Lahat/Talang Akar-Cenozoic
Total Petroleum System,” USGS Open File Rep.,
no. 99-50-S, p. 22, 2001.
[12]F. J. Pettijohn, Sedimentary Rocks, Third Edition,
vol. 2, no. 4. 1975.
[13]M. A. Perras and M. S. Diederichs, “Theimportance of classification for carbonates andmudrocks in engineering,” in Proceedings of the2011
Pan-AmCanadian
GeotechnicalConference, 2011, no. January 2011, pp. 2668–
2675.
[14]R.V.Fisher and H.U.Schmincke, Pyroclastic
Rocks, vol. 59. 1984.
[15]M. J. Le Bas and A. L. Streckeisen, “The IUGS
systematics of igneous rocks,” J. Geol. Soc.
London., vol. 148, no. 5, pp. 825–833, 1991, doi:
10.1144/gsjgs.148.5.0825.
[16]S. M. Siregar, M. E. Armanto, and L. R. Susanti,“Penempatan Bangunan Candi Tingkip, Lesung
Batu dan Bingin Jungut pada Bentang Lahan
Fluvial di Musi Rawas, Propinsi Sumatera
Selatan,” Naditira Widya, vol. 11, no. 1, pp. 31–
46, 2017, doi: 10.24832/nw.v11i1.192.
[17]A. Siswanto, A. Ardiansyah, and F. Farida,“Pendekatan Lingkungan Lokasi Candi-candi
Masa Kerajaan Sriwijaya di Sumatera,” in Temu
Ilmiah Ikatan Penenliti Lingkungan Binaan
Indonesia (IPLBI), 2018, pp. H087–H093, doi:
10.32315/ti.7.h087.

CONCLUSION
The Muarajambi Temple area is located in therock basin of South Sumatra, namely the Muara Enim
formation. Generally, temple building materials are
made of bricks (claystone) containing 8%-30%
quartz, 2%-12% lithics, and 68%-70% clay cement.
In addition, there are temples that use additional
materials such as sandstone containing 90%-92.3%
quartz, 5.1%-8% feldspars, 2%-2.6% lithics and tuff
containing 6%-10% quartz, 1% biotite, 42% lithics,
25%-85% glass, and 5%-26% porosity. The material
was obtained from the Muarajambi temple area.
The use of andesite, limestone, and calcite wereconsidered important as they are used for making the
god statues and the important components of thetemples, so they are obtained from outside theMuarajambi temples.
ACKNOWLEDGMENTS
The researchers would like to express gratitude to
the South Sumatra Archeology Center, which has
funded the research and petrographic analysis ofrocks taken from the Muajambi temple areas. The
researchers would also thank Dio Irawan who has
helped analyze the rock samples and the locals who
have granted us permission to conduct research andcollect samples in the Muajambi temple areas.
REFERENCES[1] Soekmono, Candi Fungsi dan Pengertiannya.
Yogyakarta: Ombak, 2017.
[2] A. A. Munandar, Antarala Arkeologi. Jakarta
Selatan: Wedatama Widya Sastra, 2018.
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[18]M. Mansur, “Situs Percandian Batujaya di
Karawang Jawa Barat: Analisis Manajemen
Sumber Daya Arkeologi,” Etnohistori, vol. II, no.
2, pp. 175–184, 2015.
[19]S. K. Tiwary, “Stucco Art of Nalanda ExcavatedSite, District Nalanda , Bihar : An Archaeological
Overview,” Ann. Archaeol., vol. 1, no. 1, pp. 13–
21, 2018.
[20]A. . Gde Bagus and H. Prihatmoko, “Kearifan
Lokal Dalam Pembangunan Kompleks Candi
Gunung Kawi,” Forum Arkeol., vol. 29, no. 2, pp.
105–116, 2017, doi: 10.24832/fa.v29i2.186.
[21]T. M. H. Lelono, “Bahan Dan Cara Pembuatan
Arca Batu Sebagai Komponen Penting CandiCandi Masa Klasik Di Jawa Material and Methodof Making Stone Statue As a Key Component
Classical Temple in Java,” Berk. Arkeol., vol. 33,
no. 1, pp. 93–108, 2013.
[22]S. M. Siregar, “Faktor-Faktor Pertimbangandalam Penempatan Bangunan Candi di KawasanMusi Rawas,” in Peradaban Masa Lalu di
Sumatera Selatan, 2015, pp. 57–76.

[23]G. Suharjanto, “Bahan Bangunan dalam
Peradaban Manusia: Sebuah Tinjauan dalamSejarah Peradaban Manusia,” Humaniora, vol. 2,
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1,
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814,
2011,
doi:
10.21512/humaniora.v2i1.3100.
[24]S. M. Siregar, “Fondasi Bangunan Candi Tuo
Sumay,” Siddhayatra, vol. 9, no. 2, pp. 64–68,
2004.
[25]S. M. Siregar, “Candi Bumiayu: A Magical
Touch from the Past,” in Candi Bumiayu,
Muaraenim, 2012.
[26]S. Widyawati and S. M. Siregar, “Candi Tingkip
dan Lingkungannya,” Siddhayatra, vol. 23, no. 2,
pp. 124–135, 2018.

408

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

DESALINATION BEHAVIORS FROM SEAWATER USING NATURALZEOLITE AND CALCINED CA-FE LAYERED DOUBLE HYDROXIDEFOR CULTIVATION
Takaaki Wajima1 and Fumika Sekihata1
1

Graduate School of Science and Engineering, Chiba University, Japan
ABSTRACTIn recent years, it has been considered to secure water and food using seawater desalination technology, and a
new simple desalination material for reducing high concentration sodium chloride in seawater is required. In thisstudy, desalting agents were prepared from natural zeolite, obtained from Kagoshima prefecture, Japan, withaddition of calcined Ca-Fe type layered double hydroxide (LDH), to desalinate seawater for cultivation. By treatingwith the mixture of natural zeolite and calcined Ca-Fe LDH at a mixing ratio of 5: 4, the salinity could be reduced
from 3.61% in seawater to 0.97% by cation exchange reaction of natural zeolite and reconstruction reaction of
calcined Ca-Fe LDH, and the pH of the obtained solution is 8.4-9.0. The desalination reaction using this mixture
was almost same regardless of temperature. Radish sprouts, bean sprouts and mung bean could be harvested using
seawater treated with a mixture of natural zeolite and calcined Ca-Fe LDH, while it was not possible to harvestusing seawater.
Keywords: Natural zeolite, Calcined Ca-Fe LDH, Desalination, Growth test

INTRODUCTION

can be classified into two main categories. Thermal–
based technologies, such as multi-stage flash (MSF)
and multi-effect distillation (MED), and membranebased technologies, such as reverse osmosis (RO),
together constitute more than 80% of the global
desalination capacity currently in operationworldwide [4]. However, these technologies are
expensive for agricultural use because they produce
high-quality fresh water for domestic or industrial use.
Therefore, the development of technology for
obtaining agricultural water from high-salinity water,such as seawater, is desired to secure food.
In previous studies, desalination behaviors ofnatural zeolite and synthetic zeolite in seawater were
examined [4-7], and seawater was treated withcalcined Mg-Al layered double hydroxide (LDH) andnatural zeolite in two steps to obtain a solution thatcan be used for germination of radish sprouts fromseawater [8-12]. Furthermore, we have succeeded incultivating radish sprouts using seawater treated witha mixture of mordenite-type natural zeolite andcalcined Ca-Fe LDH (calcination at 500 oC) (mixing
ratio of zeolite to LDH =5 : 4), which are cheapmaterials [13-16]. There is a possibility to develop a
new desalination material that reduces the highsalinity in seawater, causing salt damage to a level
where crops can grow, using natural zeolite withcalcined LDH. However, little information can beavailable on the desalination behaviors of naturalzeolite with calcined Ca-Fe LDH.
In this study, the desalination behaviors of naturalzeolite, calcined Ca-Fe LDH and the mixture of thesein seawater were examined to produce the solution forplant growth from seawater.

Water is not only an essential material for humanbeings (70% of our bodies by weight) but also an
important resource for human activities, such as
agriculture, commerce, industry, domestic use and soon. In the 21st century, water scarcity is one of themost serious global challenges. Now, over one-thirdof the world’s population lives in water-stressedcountries, and, by 2025, this is predicted to rise tonearly two-thirds [1, 2]. The challenges of providing
ample and safe water resources are further
complicated by population growth, industrialization,
contamination of available freshwater resources andclimate change.
For water resource on earth, 70 % of Earth’s
surface is covered with water, and the total amount ofwater resources on the earth is about 1.3 billion km3.
In water resources, seawater is about 97.5%,
freshwater is about 2.5%, and human beings can only
use about 0.01% of the total water resources, due tothe most freshwater present as glacier, cloud, vaporand so on. According to the Food and Agriculture
Organization (FAO) of the United Nation (UN), theannual water withdrawal worldwide from natural
water bodies is estimated to be around 4250 km3, of
which agricultural use is the largest consumer ataround 71.7% of the total withdrawal [3]. This
portion is even higher in arid and semi-arid countries.
Therefore, the supply of agricultural water as
freshwater resource is one of the important issue inthe 21st century.
One way of supplying water resources is seawater
desalination. Commercial desalination technologies
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

EXPERIMENTAL
Growth test
Samples
Radish sprouts, bean sprouts and mung bean wereused for 10 days growth test. Tap water, seawater orseawater treated with the mixtures of natural zeoliteand calcined Ca-Fe LDH was given by a spray every
day, and germinations of radish sprouts, bean sprouts
and mung bean were observed.

Mordenite-type natural zeolite obtained from
Koriyama mine, Kagoshima Prefecture, Japan wasused as a natural zeolite sample. The exchangeable
cations (ECs) and cation exchange capacity (CEC) of
natural zeolite were shown in Table 1. The sample
was ground and sieved under 250 μm, and dried to be
used for the experiment.

RESULTS AND DISCUSSION

Table 1 ECs and CEC of natural zeolite sample
EC (mmol/g)
CEC
Na+
K+
Mg2+
Ca2+ (mmol/g)
0.52
0.25
0.01
0.18
1.95

Figure 1 shows the salinity and pH of seawater
treated with natural zeolite during the treatment at 20,
50 and 80 oC. Regardless of temperatures, the salinity
rapidly decreased to be almost constant to about 2.4%. With increasing the temperature, the time forsalinity decrease to about 2.4 % was faster. At 20 oC
and 50 oC, pH of seawater (8.0) decreased to about
7.5, while pH of seawater decreased to 5.8 at 80 oC.

The calcined Ca-Fe LDH was prepared as follows.
A mixed solution of Ca2+ and Fe3+ (Ca/Fe = 2) (200mL) was prepared from 0.2 M CaCl2 solution and 0.1
M FeCl3 solution, and dropwise to 0.3 M NaClsolution (100 mL) at 4 mL/min. The stirring was
carried out for 1 h while bubbling nitrogen gas, andthe pH of the NaCl solution was maintained at 12.5
by dropping NaOH solution during the stirring. After
stirring, the mixture was filtered, dried, and calcinedin an electric furnace at 500°C for 1 h to obtaincalcined Ca-Fe LDH.
Seawater used in this study was collected from thesurface layer of Imari Bay, Saga prefecture, Japan.
Chemical composition, salinity and pH of seawater
were shown in Table 2.

Fig. 1

Salinity and pH of seawater treated withnatural zeolite during the treatment at 20, 50
and 80 oC

Desalination treatment
Figure 2 shows the salinity and pH of seawater
treated with calcined Ca-Fe LDH during the treatment
at 20, 50 and 80 oC. Regardless of temperatures, thebehaviors of salinity decrease are almost same, andthe salinity of seawater (3.61%) rapidly decreased to
be almost constant to about 3.3 %. At 20 oC, pH ofseawater increased to about 10.5, while pH ofseawater increased to about 11 at 50 oC and 80 oC.

The desalting abilities of natural zeolite, calcined
Ca-Fe LDH, and the mixture of natural zeolite andcalcined Ca-Fe LDH were evaluated as follows.
Natural zeolite sample (15 g), calcined Ca-FeLDH (12 g) or the mixture of natural zeolite (15 g)
and calcined Ca-Fe LDH (12 g) was added toseawater (40 mL) heated at 20, 50 and 80 oC, andstirred. After stirring for 0 - 60 min, the solution was
filtered, the pH of the filtrate was measured by pH
meter (Horiba, F-72), salinity was measured bysalinity meter (LAQUA, ES-71), the concentrationsof SO42-, Cl-, Na+, K+, Mg2+ and Ca2+ in the filtrate
were measured with an ion chromatograph (Tosho,
IC-2010) and those of Fe3+, Si4+ and Al3+ were
determinedusing
anatomicabsorption
spectrophotometer (Perkin Elmer, AAnalyst 200).
The residue was dried, and the mineralogicalstructure of natural zeolite, Ca-Fe LDH and theresidue obtained at each temperatures was confirmed
by a powder X-ray diffraction apparatus (Rigaku,
MiniFlex 600).

Fig. 2

Salinity and pH of seawater treated withcalcined Ca-Fe LDH during the treatment at
20, 50 and 80 oC

From these results for natural zeolite and calcined
Ca-Fe LDH, with increasing the temperature, the410

GEOMATE – Bangkok, Thailand, 22-24 November 2022

change of solution pH was larger and the desalination
reaction was faster.

Mixture

0.99

0.95

Figure 4 shows the concentrations of (a) SO42-, (b)Cl , (c) Na+, (d) K+, (e) Mg2+, (f) Ca2+, (g) Fe3+, (h)Si4+ and (i) Al3+ during the treatment with the mixtureof natural zeolite and calcined Ca-Fe LDH at 20 oC,
50 oC and 80 oC. Regardless of temperatures,concentrations of Cl-, SO42-, Na+, K+ and Mg2+
decreased and those of Ca2+, Fe3+ and Si4+ increased
by addition of the mixture. It was considered that thedecreases of Cl- and SO42- was caused by calcined CaFe LDH, those of Na+ and K+ and increase of Ca2+ bynatural zeolite, and the increase of Fe3+ and Si4+ by
the dissolution from calcined Ca-Fe LDH and naturalzeolite, respectively. It is noted that Al concentrationis almost zero without the dissolution from the
mixture.
Table 3 shows the chemical compositions,salinities and pHs of seawater and the seawater
treated with the mixture of natural zeolite andcalcined Ca-Fe LDH at 20, 50 and 80 oC after 60 min
treatment. Seawater used in this study indicate thatchemical composition, salinity and pH are typicalvalues of seawater [17, 18]. After treatment ofseawater with the mixture of natural zeolite andcalcined Ca-Fe LDH at 20, 50 and 80 oC, regardlessof temperatures, the salinity decreased to lower than
1 % (0.95 - 0.99 %) and pH are 8.0 - 9.1, which canbe used for plant cultivation. The contents of SO42-,
Cl-, Na+, K+ and Mg2+ decreased, while that of Ca2+
increased.

Figure 3 shows the salinity and pH of seawater
treated with the mixture of natural zeolite andcalcined Ca-Fe LDH during the treatment at 20, 50
and 80 oC. Regardless of temperatures, the behaviors
of salinity decrease are almost same, and the salinity
of seawater gradually decreased to be 0.95 - 0.99 %,while pH of seawater were almost constant (8 - 9).

Fig. 3

0.97-

Salinity and pH of seawater treated with the
mixture of natural zeolite and calcined CaFe LDH during the treatment at 20, 50 and
80 oC

Table 2 shows the salinities of seawater and theseawater treated with natural zeolite, calcined Ca-Fe
LDH, and the mixture of natural zeolite and calcined
Ca-Fe LDH at 20, 50 and 80 oC after 60 min
treatment. Natural zeolite can reduce the salinity
due to the cation exchange reaction, but the reaction
causes pH decrease to increase H+, which iscompetitor for cation exchange reaction, therefore thedecrease of salinity stopped to be about 2.4% of
salinity. Calcined Ca-Fe LDH can reduce the salinity
due to the reconstruction reaction, but the reaction
causes pH increase to increase OH-, which iscompetitor for removal anions by reconstruction
reaction of calcined Ca-Fe LDH, therefore thedecrease of salinity stopped to be about 3.3 % of
salinity. The mixture of natural zeolite and calcined
Ca-Fe LDH can reduce the salinity due to thereactions of both cation exchange of natural zeolite
and reconstruction of calcined Ca-Fe LDH, thesereactions simultaneously supply H+ from naturalzeolite and OH- from calcined Ca-Fe LDH to be
neutralized, and smoothly occurs to decrease thesalinity od seawater.

Table 3 Chemical compositions, salinities and pHs ofseawater and the seawater treated with the
mixture of natural zeolite and calcined CaFe LDH at 20, 50 and 80 oC after 60 min
treatment
Seawater 20oC
50oC
80oC
Content(mmol/L)
SO4242
5
4
7
Cl
904
222
219
221
Na+
594
67
70
72
K+
14
3
3
3
Mg2+
75
2
0
0
2+
Ca
17
656
650
674
Salinity
3.61
0.97
0.99
0.95
(%)
pH
8.0
8.44
9.08
8.88

Table 2 Salinities of seawater and the seawater treatedwith natural zeolite (NZ), calcined Ca-FeLDH (CFL), and the mixture of naturalzeolite and calcined Ca-Fe LDH (Mixture) at
20, 50 and 80 oC after 60 min treatment
Salinity Seawater
20oC
50oC
80oC
(%)
NZ
2.35
2.44
2.42
3.61
CFL
3.29
3.26
3.28
411

GEOMATE – Bangkok, Thailand, 22-24 November 2022

100

20℃

50℃

Mg²⁺ contact (mmol/L)

90

80℃

80
70

60
50

70
60
50
40
30

10

30

0
0

10

20

10

20

30
40
Time (min)30
40Time (min)

10

0
0

10

20

30
40
Time (min)

50

50

(f)

60

800
700

(b)

600

Ca²⁺ contact (mmol/L)

1000
900
800

Cl⁻ contact (mmol/L)

80℃

20

40

20

700
600
500

500
400
300
200

400

100

300

20℃

0

200

0

100

20℃

0
0

10

50℃
20

80℃

30
40
Time (min)

50

50℃
50

80℃
60

(g)

60

0.05
20℃

(c)
700

50℃

80℃

Fe³⁺ contact (mmol/L)

0.04

600
500

400
300

0.03

0.02

0.01

200

0.00

100

20℃

50℃

0

80℃

10

20

0
0

10

20

30
40
Time (min)

50

60

30
40
Time (min)

50

60

(h)
0.5
20℃

(d)
100

50℃

80℃

0.4
20℃

50℃

80℃

Si⁴⁺ contact (mmol/L)

90

80
70
60
50
40

0.3

0.2

0.1

30
20

0.0

10

0

10

20

0
0

10

20

30
40
Time (min)

50

60

30
40
Time (min)

50

60

(i)
0.02

(e)

20℃

Al³⁺ contact (mmol/L)

K⁺ contact(mmol/L)

Na⁺ contact(mmol/L)

50℃

80

100

SO₄²⁻ contact (mmol/L)

20℃

90

(a)

50℃

80℃

0.01

0.00
0

412

10

20

30
40
Time (min)

50

60

60

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 4 concentrations of (a) SO42-, (b) Cl-, (c) Na+,(d) K+, (e) Mg2+, (f) Ca2+, (g) Fe3+, (h) Si4+ and (i) Al3+
during the treatment with the mixture of naturalzeolite and calcined Ca-Fe LDH at 20 oC, 50 oC and
80 oC

calcined Ca-Fe LDH. The germination of radish
sprouts, bean sprouts and mung bean could be
confirmed in tap water and the seawater treated witha mixture of natural zeolite and calcined Ca-Fe LDH(Fig. 6 (a), (c)), while these plants cannot begerminated in seawater (Fig. 6 (b)).

Figure 5 shows the XRD patterns of (a) Ca-Fe
LDH, (b) calcined Ca-Fe LDH, (c) the mixture ofnatural zeolite and calcined Ca-Fe LDH, and theresidue of the mixture of natural zeolite and calcined
Ca-Fe LDH after 60 min treatment at (d) 20 oC, (e) 50oC and (f) 80 oC. Ca-Fe LDH phase was transformed
into amorphous and calcite phase (Fig. 5 (a), (b)).
After desalination treatment, all residues have naturalzeolite mineral phases, mordenite, and calcite, and
Ca-Fe LDH phases which was formed from calinced
Ca-Fe LDH by reconstruction reaction (Fig. 5 (c)-(f)).
These results indicate that desalination of seawater by
the mixture of natural zeolite and calcined Ca-Fe
LDH was caused by cation exchange of zeolite andreconstruction of calcined Ca-Fe LDH.

Radish sprouts

Bean sprouts

Mung bean■: Mordenite●: Calcite◆: Ca-Fe LDH■■●● ● ● ● ●■ ■◆■■ ■ ■●
■■ ■
●
●

(f)

Intensity (a.u.)

(e)

■■
●
● ● ● ● ●■ ■◆■■ ■ ■●■■ ■●Fig. 6(d)■■■ ■◆■■ ■ ■●●■■ ■● ● ● ● ●
■ ●
(c) ■
■
■
■■ ■
■
■ ● ● ● ●
●
■
■
●
●
●

(b)

●

(a)

0

◆

10

◆

20

CONCLUSIONS●● ● ● ● ●
●
◆

◆ ◆

30

40

50

For the development of desalination material toobtain agriculturally usable freshwater from seawater
for stable supply of water resources, desalination
behaviors of the mixture of natural zeolite andcalcined Ca-Fe LDH in seawater were examined for
producing cultivatin water from seawater.
Using natural zeolite, the salinity decreased from
3.5 % to about 2.5 %, and desalination speed was
faster and pH was lower with increasing thetemperature. Using calcined Ca-Fe LDH, salinity
was littile low (about 3.3%), and pH rises to about
10.5 - 11.0 from 8.0. Regaradless of temperatures,
when seawater were treated with the mixture ofnatural zeolite and calcined Ca-Fe LDH, the salinity
decreased to 0.95 - 0.99% below the available salinity
for agriculture (about 1.0%) and pH is neutral (8 - 9).
When using the seawater treated with a mixture of60

2θ [CuKα (degree)]Fig. 5Observation of radish sprouts, bean sprouts
and mung bean after 10 days growth using(a) tap water, (b) seawater and (c) the
seawater treated with the mixture of naturalzeolite and calcined Ca-Fe LDH.

XRD patterns of (a) Ca-Fe LDH, (b)calcined Ca-Fe LDH, (c) the mixture ofnatural zeolite and calcined Ca-Fe LDH, andthe residue of the mixture of natural zeoliteand calcined Ca-Fe LDH after 60 min
treatment at (d) 20 oC, (e) 50 oC and (f) 80
oC.

Figure 6 shows the observation of radish sprouts,
bean sprouts and mung bean after 10 days’ growthusing (a) tap water, (b) seawater and (c) the seawater
treated with the mixture of natural zeolite and413

GEOMATE – Bangkok, Thailand, 22-24 November 2022

natural zeolite and calcined Ca-Fe LDH, thegermination of radish sprouts, bean sprous and mungbean was confirmed.

preparation of an agricultural cultivation solution
from seawater, J. Ion. Exch., Vol. 18, 2007, pp.
201-202.
[10] Wajima T., Shimizu T., and Yamato T., Removalof NaCl from seawater using natural zeolite, Int. J.
Toxicol. and Environ. Chem., Vol. 91, Issue 1,
2010, pp. 21-26.
[11] Wajima T., Shimizu T., Yamamoto T., andIkegami Y., Simple two-step process for making
agricultural cultivation solution from seawaterusing calcined hydrotalcite and natural zeolite,
Clay Sci., Vol. 14, 2008, pp. 7-12.
[12] Wajima T., Desalination behavior of calcinedhydrotalcite from seawater for preparation ofagricultural cultivation solution using naturalzeolite, Energy Environ. Res, Vol. 4, 2014, pp. 310.
[13] Sekihata F., and Wajima T., Preparation ofdesalination agent from Ca-type clay minerals,
Int. J. GEOMATE, Vol. 19, 2020, pp. 123-129.
[14] Tsutsui D., and Wajima T., Desalination propertyof various calcined layered double hydroxides
from seawater, Int. J. Environ. Agri. Res., Vol. 4,
Issue 11, 2018, pp. 15-21.
[15] Wajima T., Desalination of seawater using
natural zeolite for agricultural utilization, Int. J.
GEOMATE, Vol. 16, 2019, pp. 21-26.
[16] Sekihata, F., and and Wajima, T., Desalination
behavior of natural zeolite in seawater, Int. J.
GEOMATE, Vol. 20, 2021, pp. 82-88.
[17] The society of Sea Water Science, Japan and The
Salt Science Research Foundation, Sea Water, its
Property and Technology, Tokai University Press,
Tokyo, 1994.
[18] Nakajima T., Utilization of deep seawater,
Midori Shobo, Tokyo, 2002.

ACKNOWLEDGMENTS
This study was performed under the Cooperative
Research Program of the Institute of Ocean Energy,
Saga University (22E03). This work partially
supported by Japan Health Foundation.
REFERENCES[1] Service R.F., Desalination freshens up, Sci. Vol.
313, 2006, pp. 1088.
[2] Elimelech M., and Phillip P.W., The future of
seawater desalination: Energy, technology, andthe environment, Sci. Vol. 333, 2011, pp. 712717.
[3] FAO 2021, AQUASTAT data base.
[4] Mezher T., Fath H., Abbas Z., and Khaled A.,
Techno-economic assessment and environmental
impacts of desalination technologies, Desalin.
Vol. 266, 2011, pp. 263-273.
[5] Wajima T., Ion exchange properties of Japanesenatural zeolite in seawater, Anal. Sci., Vol. 29,
2013, pp. 139-141.
[6] Wajima T., Shimizu T., Yamato T., and Ikegami
Y., Effect of HNO3 and H3PO4 on ion exchange ofnatural zeolite for making agricultural cultivation
solution from seawater, Int. J. Soc. Mater. Eng.
Resour., Vol. 20, 2014, pp. 109-112.
[7] Wajima T., Desalination of seawater using Ca-A
zeolite for agricultural utilization, Bull. Soc. Sea
Water Sci. Jpn., Vol. 72, 2018, pp. 325-328.
[8] Wajima T., Shimizu T., and Ikegami Y., New
simple proceed of making agricultural cultivation
solution from seawater, Bull. Soc. Sea Water Sci.
Jpn., Vol. 60, 2006, pp. 201-202.
[9] Wajima T., Shimizu T., and Ikegami Y., Ion
exchange properties of natural zeolite in the414

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

MEASUREMENT OF CRACK DISPLACEMENT USING DIGITAL
PHOTOGRAMMETRY
1

Afia S. Boney1, Satoshi Nishiyama1 and Teruyuki Kikuchi2
Graduate School of Environmental and Life Sciences, Okayama University, Japan; 2J-POWER Design Co
Ltd, Japan
ABSTRACTTo evaluate the soundness of civil engineering structures constructed on rock slopes, new measurement
techniques are needed to determine the relationship between structures and rock behaviour with high accuracy andaccuracy. This paper describes an example of measuring the displacement of a structure related to a landslide ona slope. The proposed technique is measured by using two image processing, one is the computation of the target
centroid with intensity value and the other one is perspective projection. Thus, it is possible to measure thedisplacement in an arbitrary camera position. For verifying the accuracy and precision of this technique, theexperiments were performed, and the result indicated that the authors could measure approximately 0.1mm of thedisplacement of the crack with high accuracy and precision when use the digital image taken from 0.5m of thetarget. In addition, the authors tried to apply this measurement system proposed herein to an actual tunnel for
estimating the serviceability and it was concluded that the result of this measurement has relationship with the
behaviour of the rock slope.
Keywords: Digital Photogrammetry, Displacement, Image Processing, Reflective Targets

INTRODUCTION

principles for measuring cracks on a captured digital
image [1]. This technique proposed herein measures
the width and length of the crack without detecting
the crack. Figure 1(a) shows the reflective target usedin this research, and these targets consist of
microscopic glass beads arranged in circles, to induce
a strong diffused reflection of incident light. There are
four circles on this target. The authors measure thedistance of one pair of circles on both side of the crack.
To measure the distance, a few processes were
performed. Firstly, a pair of reflective targets isarranged around the crack as gauges and a digital
image of the targets, taken from an arbitrary camera
position, as shown in Fig. 1(b), is captured. Secondly,
the centre of gravity in the two-dimensional
coordinate is decided by calculating the centroid of
the targets with its intensity value. Figure 2 shows anexample of the distribution of the intensity value ofthe circle.

Numerous civil engineering structures such as
tunnels have been built on rock slopes. These
infrastructures must be monitored to ascertain its
safety. In the case of concrete structures such as dams
and tunnels, width and patterns of cracks which occur
on the surfaces of these, are critical to evaluate theirsoundness.
This
will
determinewhethercountermeasures such as reinforcement, repair andreconstruction are required. Most existing methods
employ manual inspection, with specific tools, toexamine deficiencies in structures [1]. However, thistraditional method is time-consuming, subjective andsusceptible to human error. Furthermore, financial
difficulties and worker shortages have becomechallenges in recent years, and it is necessary toestablish a more affordable and versatile monitoring
method. More recently, image-based methods ofcrack detection have been utilized. Although existing
research has already proposed new, innovative
methods, these utilize expensive equipment, complex
experimental set up and image segmentation [2]. In
this research, the authors propose a measurement
technique which uses photogrammetry and digital
image processing to measure the crack width. Thismethod is fast, less expensive, reproducible andobject. In addition, by using a Digital Single-lensReflex (DSLR) camera, the author has control overthe quality of the image.

Fig. 1 Example photographs of (a) the reflective target
installed on the crack surface and (b) camera position
when taking an image of (a)PRINCIPLE OF CRACK MEASUREMENT

In this paper, the authors define intensity value as
weight and calculate computation of the target
centroid. The centre of gravity in the two-dimensional

In this section, the authors explain techniques and415

GEOMATE – Bangkok, Thailand, 22-24 November 2022

EXPERIMENTS AND DISCUSSION
For evaluating the accuracy and the precision ofthis technique proposed, the authors performedexperiments. The accuracy and precision are defined𝒙yFig. 2 An example of the distribution of the（a）明度分布の3次元表示
intensity valueof the circle of the target. The
vertical axis takes intensity value.

（b）明度分布の2次元表示

coordinate is defined by the origin of the image
coordinate system x0 and y0, the pixel size ax and ay,
and the intensity value of the pixel (i, j) q (i, j) as
shown in Eq. (1).

Fig. 3 Concept of the collinearity condition.

𝑥 = 𝑥 0 + 𝑎𝑥

𝑦 = 𝑦0 + 𝑎𝑦

∑𝑛𝑖=1 ∑𝑚
𝑗=1(𝑞 (𝑖, 𝑗) × 𝑥𝑖𝑗 )𝑛∑𝑖=1 ∑𝑚
𝑗=1 𝑞(𝑖, 𝑗)𝑚∑𝑛
𝑖=1 ∑𝑗=1(𝑞(𝑖,𝑗)×𝑦𝑖𝑗 )𝑚∑𝑛𝑖=1 ∑𝑗=1 𝑞(𝑖,𝑗)(1)

After defining the centre of gravity of four circles,
the perspective projection was adopted. This allowed
the digital image, which was taken from an arbitrarycamera position, to be converted to an image facing
the target. It is then possible to measure thedisplacement of the crack. Additionally, the authors
adopted collinearity condition as shown in Fig.3,
which means that a measurement point, the camera(centre of lens) and the measurement point imagery
which appears on the images taken, can be connectedby a straight line. The perspective projection, as
shown in Fig.4, is based on the collinearity condition
and the authors defined z as constant in this study.
Consequently, the perspective projection in a twodimensional coordinate system is obtained.
Furthermore, by calculating with the least-squares
method, Eq. (2) is obtained, and the authors adopted
perspective projection. Equation (2) has 8 unknowns
in total: bi (i=1~8). This requires the establishment of
one known point and the measurement of at least four
known points to solve the equation. The residuals forthe least square solutions were 0.10 to 0.05 of a pixel.

Fig. 4 Concept of the perspective projection.
in Eq. (3) and (4). Experimentally (Fig. 5(a)), one pairof reflective targets is attached at a fixed position, andanother pair, to a micrometer as shown in Fig. 5(b).
accuracy: √precision: √(3)𝑛∑(measured value−(∑ni=1 measured value)
𝑛

2

(4)

To verify the influence of the camera position
length, the micrometer was moved and that changed
the displacement of the target. The image was takenfrom a different camera position and the displacementof the target was measured. The digital camera used
had a resolution of 13 million pixels. The size oftargets used in this experiment is 50mm by 50mm andfocal length is 50mm. The result of the experiment isshown in Fig.6(a) and in Table 1. From the result of
the experiment, the authors found that as L increases,
the accuracy and precision of the measurement value
deteriorates. Regardless of this result, it was also
found that when the digital image was taken up to 1m
away from the targets, the displacement was𝑥𝑖 𝑏1 + 𝑦𝑖 𝑏2 + 𝑏3 − 𝑥𝑖 𝑥 ′ 𝑖 𝑏7 − 𝑦𝑖 𝑥′𝑖 𝑏8 = 𝑥′𝑖𝑦𝑖 𝑏4 + 𝑦𝑖 𝑏5 + 𝑏6 − 𝑥𝑖 𝑥 ′ 𝑖 𝑏7 − 𝑦𝑖 𝑥′𝑖 𝑏8 = 𝑦′𝑖∑(measured value−true value)2

(2)

416

GEOMATE – Bangkok, Thailand, 22-24 November 2022

カメラ

(a)

camera

(b)

撮
影
距L
離
Ｌ

anothertargetattached
to a micrometer
Target #1
attached
to a micrometre

撮影角度α
α : angle

movingone
targetthedeskdesk
Target#2 fixed
fixed atatthetargets
target

ターゲット

直角
90°
90°
α: camera
positionangle
α: camera position
angle
L: L:
cameraposition
lengthcamera position
lengthFig. 5 (a) Schematic diagram of the small-scale laboratory experiment and (b) actual test set-up in thelaboratory
measured with an accuracy and precision of less than
0.02mm.
Additionally, to evaluate the influence of thecamera position angle, the digital images were takenfrom an arbitrary camera position and from thecamera length 0.36m. Table 1 and Fig. 6(b)summarizes the results of the experiment. When thecamera angle (α) is at less than 20°, the displacementof the targets can be detected with a good accuracy
and precision of 0.01 mm. At 30° the accuracy
decreased to 0.03 mm

displacement of the cracks. By using extensometers,
the movement per year is observed to be 2-3mm. The
diameter of the tunnel is 2m (Fig. 7).
1.0

measured value (mm)(a)

Table 1 Influence of camera position length (L) on
crack width measurement
L
0.5m
1m
accuracy
0.02
0.04
precision
0.01
0.02

0.8
0.6
0.5m
1m
1:1 line

0.4
0.2
0.0
0.0

Table 2 Influence of camera position angle (α) on
crack width measurement
α
0°
10°
20°
30°
accuracy
0.01
0.01
0.01
0.03
precision
0.01
0.01
0.01
0.01

measured value (mm)1.0EXAMPLE OF MEASUREMENT IN ACTUALTUNNELTo demonstrate the serviceability of thismeasurement system, the authors measured thedisplacement of the crack on an actual tunnel. Thistunnel was selected as it was constructed twenty years
ago, which makes it an ideal example tunnel thatshould be monitored for cracks, to prevent landslides
occurring, from the groundwater in the slope. The
purpose of this experiment is to investigate thesoundness of this tunnel in a quantitative manner andto find the relationship between the landslide and the

0.2
0.4 0.6
0.8 1.0
displacement of targets (mm)
(b)

0.8
0°

0.6
10°
0.4

20°

0.2

30°

0.0
0.0

0.2
0.4 0.6 0.8
1.0
displacement of targets (mm)Fig. 6 (a) and (b) show the relationship between
the displacement of targets and measured values.
Figure (a) shows the result when L is changed and(b) shows the result when α is changed.
417

GEOMATE – Bangkok, Thailand, 22-24 November 2022

the other measurement system. At first, the authors
showed the result of the measured values of targets
No.1 and No.11 as summarized in Figure 10 (a) andfound that the displacements of targets No.1 andNo.11 agree with the displacement of the shallow
landslide by using the electro-optical distancemeasurements. Figure 10 (b) shows the result of themeasured values of target No.8 and ones of theinclinometer, and it was concluded that by the resultof target No.8 the displacement was 2.14mm for 21
months. Therefore, the trend of the measured value oftarget No.8 also agrees with one of the measured
values of the inclinometer. In addition, thedisplacement of target No.8 decomposed into thetensile displacement and the shear displacement.
From the results, the movement of target No.8
corresponds with the direction of the landslide. Insummary the authors can monitor the behavior of the
deep sliding by measuring the displacement of target
No.8 [3].

Fig. 7 Example photograph of the tunnel’sinterior
As a result of landslides in the tunnel, there are morethan three hundred cracks present. The authors
arranged thirty targets in the tunnel at various
locations as shown in Fig. 8. For one year and ninemonths, the cracks were measured every two months
and the result of the displacements of all the targets
were recorded. It was found that the displacements
per month were more than 0.2mm in 6 targets, though
in most targets the displacements per month were lessthan 0.2mm. In addition, the displacements per month
were more than 0.1mm in targets No.1, No.8 andNo.11. Targets No.1 and No.11 were arranged in theshallow landslide and these targets measured thesame crack in the tunnel. Target No.1 was in the side
wall, target No.11 was located on the ceiling, andtarget No.8 was arranged in the deep sliding. As aresult, the displacement of the targets arranged in thearea with unsymmetrical earth pressure was less than
0.02mm per month.
In this paper, the authors focused on targets No.1,
No. 8 and No.11 and compared the results by using

Fig. 9 The sketch of the tunnel and slope, and theimage of the targets No.1, No.8 and No.11.

Fig.8 Schematic diagram of the tunnel alignment and the location and arrangement of the installed 30 targets

418

GEOMATE – Bangkok, Thailand, 22-24 November 2022

In this technique, the authors establish reflective
targets around both sides of the crack as gauges andtook a digital image of the reflective targets. The
digital image adopted image processing, and thedisplacement of the targets was measured. From theresult of the experiments, it was found that theaccuracy and precision of the measurement techniqueare about 0.2mm. In addition, the authors
demonstrated this measurement technique to a tunnel
and measured the displacement of the cracks every
two months. By comparing these results with themeasurement system that uses extensometer, it was
found that the trend of the displacement is consistent.

REFERENCES[1] Munawar, H.S., Hammad, A.W.A., Haddad, A.,
Soares, C.A.P. and Waller, S.T., Image-Based
Crack Detection Methods: A Review,
Infrastructures, 2021, 6, 115.
[2] Kaneko, S., Oka, S., and Matsumiya, N.,
Detection of Cracks in Concrete Structures from
Digital Camera Images, NTT Technical Review,Vol. 10, No. 2, 2012.
[3] Hattori, S., Akimoto, Y., Ohnishi, Y., and Miura,
S., Semi-automated tunnel measurement by vision
metrology
using
coded-targets,
ModernTunneling Science and Technology, 1: 285-288,
2001.

Fig. 10 (a) shows the values of the change of cracks’width and (b) shows the result of the displacement of
No.8 and the measured value of the clinometer
CONCLUSION

419

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

STABILITY OF THE MUNICIPAL SOLID WASTE LANDFILL ON SLOPING
LAND IN BATU CITY, EAST JAVA, INDONESIA
A Rachmansyah1, A P Putra2, A Darmawan3, Harimurti1
1

Civil Engineering Department Brawijaya University Indonesia, ariefftub@yahoo.com
Magister Program, Civil Engineering Department, Brawijaya University, Indonesia
3
Geocentris Consulting, Malang, Indonesia

2

ABSTRACT
It is difficult to find a location for the final disposal site of municipal solid waste that meets the technical andenvironmental requirements. Due to this reason Government of Batu City Indonesia places final disposal facility
on land with a steep slope, so the risk of landslides increases. This paper will be discussed the results of the slide
hazard analysis of the landfill of municipal solid waste on sloping land and different condition. Slope stability
analysis was used the Limit Equilibrium Method (LEM) also the Finite Element Method (FEM), where theresults of the analysis were in the form of slope failure that be reflexed by value of the safety factor (SF).
Furthermore, the stability analysis will be modeled into two conditions, namely conditions during the summer
so that the groundwater level is normal and the rainy season conditions with very high rainfall, so that the
groundwater level is assumed to increase to the middle part of waste landfill. Under normal conditions, theslope safety number in the LEM calculation is 0.8 and when the rainfall conditions are high it is 0.75. By using
FEM, the average safety factor for normal conditions is 1.117 and for high rainfall conditions it is 1.021. The
decreasing of safety factor value on high rainfall is 5% by the LEM and 10% by the FEM.
Keywords: slope stability, solid waste, aerial survey, limit equilibrium, finite element

INTRODUCTION

season with high rainfall, so that the groundwaterlevel is assumed to rise to the bottom of the domestic
waste heap.
Slope stability analysis most often uses the limit
equilibrium method, because it is easy to understandand does not require a lot of data [3]. This method
describes the ratio between the material force that will
move and the resisting force. This comparison figure
is known as the safety factor [4].
The general Limit Equilibrium Method (LEM)
calculation formula for slope stability analysis
requires several assumptions [5]. The first assumption
is to form a slip plane. This method generally uses
two types of slip planes, namely: circular and noncircular (can also be planar). The LEM calculation
provides a Safety Factor value based on thecalculation of the moment equilibrium (Fm) and thehorizontal force balance (Ff). Morgenstern and Price
proposed an equation to calculate the shear force
between layers in the LEM that satisfies the balance
between moments and forces. In calculating thesafety factor, it is done by making an incision, only
landslides that have a slip surface can be counted.
Many experts propose a calculation formula with thiscalculation method, including Fellinius or Bishop [6].
Finite Element Method (FEM) is a calculation
procedure used to get an approach to mathematicalproblems that often arise in engineering with various
approaches and a series of algebraic equations
involving values at discrete points in the part being
evaluated. The finite element method equation is

Batu City, East Java Province, Indonesia which islocated at the conjunction of three active volcanoes,
is one of the most attractive tourist cities. In the city
there are dozens of springs that emerge from thefissures of quaternary volcanic rocks. This condition
makes it difficult to find a location for the processing
facility of municipal solid waste that meets thetechnical and environmental requirements. Therefore,
the city government places it on land with a steep
slope which there are no aquifers or springs, but therisk of landslides increases.
Landfills are very susceptible to landslides
because the shear angle is very small, it can become
very extreme again if the rainy season arrives [1].
This paper will discuss the possibility of landslides
in the landfill area. The analysis is carried out withthe assumption that domestic waste is like soilmaterial with certain characteristics. Thus, slope
stability analysis was carried out on three layers ofsoil with different physical and mechanicalproperties [2].
The analysis of slope failure was carried out
using the Limit Equilibrium Method (LEM) and
Finite Element Method (FEM) whose results
describe the value of safety factor (SF).
Furthermore, the stability analysis will be modeled
into two conditions, namely conditions during thedry season, where the groundwater level is normal.
The second model is carried out during the wet
420GEOMATE – Bangkok, Thailand, 22-24 November 2022

created, and the solution is sought as well as possible
to avoid errors in the result. The net consists ofelements that are connected by nodes. Nodes arepoints on the net where the value of the primaryvariable is calculated. For example, for displacement
analysis, the value of the primary variable is the valueof displacement. Nodal displacement values areinterpolated on the elements to obtain algebraic
equations for displacement, and strain, through thenets formed [7].

RESULT AND DICUSSION
Geology and Topographyc Profiles
The location of the Processing Facility of
Municipal Waste on Tlekung Batu City is located on
the slopes of Mount Butak which is part of thePanderman Volcano Complex. The rock around thelandfill site is composed of tuff and andesite breccia.
At the bottom of the landfill area there is anintermittent river.
In this study, three slope profile models werecreated that represent the condition of the landfill,symbolized by pieces B1-B2, B1-B3, and B1-B4. In
addition, a slope profile is also needed when there is
no waste pile symbolized by the A1-A2 slices (Figure
1 to Figure 5). These three profiles will be used forslope stability analysis of domestic waste piles.

LOCATION AND METHOD
The modeling of the slope stability of the solid
waste landfill was carried out at the Processing
Facility of Municipal Waste in Tlekung Village,
Junrejo District, Batu City. Landfill of solid waste isone of the facilities there.
The research that has been carried out is divided
into several stages, namely:
1. Topographic survey by aerial method and slope
profiling before and after the landfill
2. Sampling of soil that has not been contaminatedand contaminated by leachate
3. Laboratory testing to determine the physical andmechanical properties of the soil
4. Analysis of slope stability of landfills using
LEM and FEM. Slope stability analysis with
FEM is used software Plaxis 2 dimensions.

Fig. 2 Profil A1-A2 before solid waste landfilling
solid waste

For slope stability analysis, using either LEM or
FEM a topographic profile is required [8]. The
elevation contour map is made in conditions after
there is a solid waste landfill, so the aerial method wasused. This was done by considering the risk ofaccidents and the health of the surveyor. The
topographyc profiles before the lanfillling wasmodeled from a topographic map that is published by
the Indonesian Geospatial Information Agency.

original slope

Fig. 2 Profil B1-B2 after solid waste landfilling
solid waste

original slope

Fig. 3 Profil B1-B3 after solid waste landfilling
solid waste

original slope

Fig. 4 Profil B1-B4 after solid waste landfillingFig 1. Aerial Photograph of the Processing Facility of
Municipal Waste in Tlekung, Batu City

After these profiles the average height of solid
waste landfill on the slopes is approximately 25
meters. This model is obtained from the height
difference between the conditions before and after
solid waste landfilling.

421

GEOMATE – Bangkok, Thailand, 22-24 November 2022shows that the slope before the landfill was stable.
Characteristic of Soil and Solid Waste
Physical and mechanical characteristics of soil
and waste are important factors that must be known
before modeling landslides, both with LEM and with
FEM. Parameters to look for cohesion, internal
friction angle, and unit weight.

Fig. 6. Slope stability before solid waste landfilling

Characteristic Of Soil

Slope Stability after Solid Waste landfilling

Soil samples were taken at 3 points. Two samples
represent soil that has not been contaminated byleachate taken at the upper and lower of the landfill
area. One sample represents contaminated soil istaken on a slope adjacent to the landfill. The
mechanical properties of the soil were determined bydirect shear test (Table 2).

The slope stability after the landfilling is modeledby 3 layers of material, from top to bottom are:domestic waste layer, soil contaminated by leachate,and soil that has not been contaminated. This canhappen, because before the garbage is stockpiled ageomembrane is not installed, so that leachate canseep into the ground.
Simulations are carried out in two conditions,
namely during the dry season or normal conditions,and in the rainy season or the water level rises. Under
normal conditions the thickness of polluted soil is 2
meters (Figure 7). During the rainy season, moreleachate is formed, so that the thickness of thepolluted zone increases to 3 meters (Figure 8).

Table 1. Characteristic of Soil Bedding
Sampel Tanah
Unit
Cohesionweight(c)(γd)
gr/cm3kg/cm2Upper landfill area
Contaminated soil
Lower landfill area

2,619
2,118
2,654

0,374
0,504
0,354

Internal
friction
angle
(φ)
°
22
20
23

Based on these characteristics, the soil found isclassified as sand, while the contaminated soil
contains more organic and clay.
Characteristic of Solid Waste
Fig. 7. Stabilitas lereng potongan B1-B2 kondisi
normalUntil now in Indonesia, there is no standardized
method for testing the physical and mechanicalproperties of solid waste. Therefore, the physicalcharacteristics of waste are based on previous
research. Several researchers in Indonesia have tested
the physical and mechanical properties of domestic
waste, one of the results is presented in Table 3 [9].
Table 3. Physical and Mechanical Properties ofSolid Waste
Parameter
Internal friction angle (φ)
Cohesion (c)Wet specific gravity (γwet)

Number15 - 20
1,2
3,16

Fig 8. Stabilitas lereng potongan B1-B2 kondisi curahhujan tinggi

Unit°
kg/cm2gr/cm3Slope stability analysis on profiles B1-B2 showsthat the safety value is 0.818 on the normal condition,
while it decreases to 0.774 when in the raini season.
The the results of the slope stability analysis on otherprofile paths is shown in Table 4.
Based on the classification of the safety factor
values [10], the slope stability of the landfill is in thecritical category, because its value is less than 1.25.
Based on this analysis, it shows that increasing water
due to rain will reduce the value of the safety factor
or stability. However, in all conditions, landslides
only occur in domestic waste landfill.

Slope Stability Analysis by Using Limit
Equilibrium Method (LEM)
Slope Stability before Solid Waste Landfilling
Slope stability analysis before domestic waste is
piled up is needed as a comparison (Figure 6). From
the analysis, the safety factor value is 1.734. This
422

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 4. Value of safety factor that analyzed by
LEM
Slope
Profiles
B1-B2
B1-B3
B1-B4

Safety Factor
Dry
Wet
SeasonSeason
0,811
0,758
0,914
0,836
0,671
0,662

Slope Stability after Solid Waste landfilling

Stability

Slope modeling after landfilling of solid waste
using the finite element method was carried out under
the same conditions as the limit equilibrium method.
The slope model and the results of its analysis in thedry season are presented in Figures 12 and 13, whilein the rainy season in Figures 14 and 15.

CriticalCriticalCriticalSlope Stability Analysis by Using Finite Element
Method (FEM)
Slope stability analysis of solid waste landfill
using Finite Element Method (FEM) was conducted
to determine the prominent differences from theanalysis using the LEM method. Thus, the advantages
and disadvantages of both methods for the analysis ofdomestic waste piles can be identified.

Fig. 12. Slope model on normal condition along B1B2 profile

Table 5. Parameter of soil and solid waste foranalyzing by FEM
Material

Unitweight(γd)

Cohesio
n (c)

kN/m

kN/m2

Internal Poisson
friction Ratio
angle
(v)
(φ)
°

Modulus
Elastisity
(E)
MPa

3

Solid Waste
ContaminatedSoil
Non-contaminated Soil

21,60
31,18

20,24
34,40

23
18

0,2
0,4

2000
3000

26,19

22,14

21

0,3

5500

Fig. 13. Slope failure model on normal condition
along B1-B2 profile

Slope Stability before Solid Waste Landfilling
Slope stability analysis before landfill is presented
in Figures 9 and 10. The safety value of this analysis
is 1.721 and is included in the safe category.
Fig 14. Slope model on wet condition along B1-B2
profileFig. 10 Model of Slope failure before solid waste
landfillingFig 15. Slope failure model on normal condition
along B1-B2 profile
In Figure 12 and Figure 14 the waste landfill is
depicted by a layer of green material, the soil
contaminated with leachate is light blue, and theuncontaminated soil is represented by a brown layer.
The blue line on the waste pile layer in Figure 14
represents the rise in ground water level during therainy season. The two figures show that the slip plane

Fig. 11. Curve of safety factor value before solid
waste landfilling

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

in both the dry and rainy seasons is located above theboundary between the soil and the waste heap. In
other words, landslides occur only in domestic wastematerials.

Table 6. Decreasing percentage of slope stability by
LEM analysis
Slope
Safety Factor (SF)
Decreasing
Profilepercentage
Dry
Wet
of SF
seasonseason
B1-B2
0,811
0,758
5,3
B1-B3
0,914
0,836
7,8
B1-B4
0,671
0,662
0,9

Table 7. Decreasing percentage of slope stability by
FEM analysis
Slope
Safety Factor (SF)
Decreasing
Profilepercentage
Dry
Rainy
of SF
seasonseason
B1-B2
1,132
1,029
10,3
B1-B3
1,126
1,038
8,8
B1-B4
1,093
0,995
9,8

Fig 16. Curve of safety factor value of solid waste
landfillingOn the Figure 16 the slope safety factor when
under normal conditions is 1.132, while on the wet
season decreased to 1.029. This results value ofsafety factor number, the slope can be categorized as
critical. The analysis of safety factors on other slope
profiles can be seen in Table 5.

From the two tables, it can be concluded that the
average decrease in slope safety in the LEM
calculation is 5% and in the FEM calculation it is 10%.

Table 5. The value of safety factor by FEM analysis
SlopeProfile
B1-B2
B1-B3
B1-B4

Safety Factor
Dry
Rainy
seasonseason
1,132
1,029
1,126
1,038
1,093
0,995

CONCLUSIONS

Stability

Based on the results of the analysis of the stabilityof the slopes of the waste heap at the TPA Tlekung,Batu City, it can be concluded that:
1. The slope before the landfill was classified asstable, as the number of safety factors generated
from the analysis with LEM was 1.734 andanalysis with FEM was 1.721. The two methods
did not show a significant difference
2. When there is a pile of garbage on the slope, thevalue of the slope stability safety factor
decreases. The calculation of the factor of safetywith LEM is 0.799 during the dry season, andduring the rainy season it decreases to 0.752.
Analysis by FEM the average safety score undernormal conditions is 1.117 and when conditions
are high rainfall it is 1.021.
3. The presence of rainwater can reduce the valueof the slope safety factor, so that the slope
becomes critical or unstable. The decrease in thevalue of the safety factor is 5% in calculations
with LEM and 10% with FEM.

CriticalCriticalCriticalBased on the results of the analysis using FEM,
slopes with waste piles in the dry season have a safety
rating between 1.093 to 1.132 which is classified as
critical. In the rainy season the value of the safety
factor is between 0.995 to 1.029.

Impact of Water on Slope Stability of Solid
Waste Landfilling
Slope stability analysis with waste piles thathave been carried out using the Limit Equilibrium
Method (LEM) and the Finite Element Method(FEM) shows that the value of the safety factor isrelatively small and can be classified in a criticalcondition. The increase in water in the landfill isreflected by a decrease in the value of the safety factor.
This is probably caused by a decrease in the internal
shear angle, while the cohesion is likely to berelatively constant. The percentage decrease in thestability of the landfill due to the influence ofrainwater can be seen in Table 6. and Table 7.

Based on these conclusions, it can berecommended that to prevent landslides in domestic
waste piles, it can be done by preventing water fromentering the landfill or draining water as soon as
possible to get it out of the landfill.
ACKNOWLEDGMENTS
Acknowledgments were conveyed by the Headof the Faculty of Engineering, Universitas Brawijaya
who provided in research and conference costs.

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Thanks, are also conveyed to the Geotechnical
Compartment of Civil Engineering Universitas
Brawijaya for the input during the discussion.

Analysis Solutions of Earth Pressure Problems,
Fritz Engineering Lab. Report, Lehigh University,
53 pp 11
[6] Salhana Ismail, S., Taib, A M., Abd. Rahman, N.,
Hasbollah, D Z A., Ramli, A B., (2019): Slope
Stability of Landfill with Waste Degradation,
International Journal of Innovative Technology
and Exploring Engineering, , Volume-9 Issue-1, p2278-3075.
[7] Das, Braja M (2011). Principles Of Foundation
Engineering, Seventh Editon. Stamford: Cengage
Learning.
[8] Hardiyatmo, H. C., (2012). Tanah Longsor dan
Erosi - Kejadian dan Penanganan. Yogyakarta:
Gadjah Mada University Press.
[9] Saman, 2018. Penentuan Nilai Kohesi Dan Sudut
Geser Sampah Kota Studi Kasus: Landfill SkalaLaboratorium.
Jurnal,SeminarNasional
Teknologi dan Rekayasa (SENTRA), vol. II,
halaman 82.
[10] Bowles, Joseph (translated by Sinaban Pantur),(1999), “Analisis dan Disain Pondasi” edisi ketiga jilid 2. Jakarta: Erlangga.

REFERENCES[1] Shi-Jin Feng, S J., Wu, S J., Fu, W D., Zheng, Q
T., and Zhang, X L (2021): Slope Stability
Analysis Of A Landfll Subjected To Leachate
Recirculation And Aeration Considering BioHydro Coupled Processes, Geoenvironmental
Disasters, https://doi.org/10.1186/s40677-02100201-2[2] Eid, H T., Timothy D. Stark, TD., Evans, W D.,
and Paul E. Sherry, (2000): Municipal Solid
Waste Slope Failure. I: Waste And Foundation
Soil Properties, Journal Of Geotechnical And
Geoenvironmental Engineering, May 2000, p
397-407[3] Cheng, Y M and Lau, C K. (2014): Slope Stability
Analysis and Stabilization, CRC Press[4] Huang, Y H (1983). Stability Analysis of EarthSlopes, Van Nostrand Reinhold Comp. Inc., NY.
8.
[5] Rosenfarb, J.L and Chen, W.F. (1972), Limit425

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

FACTORS INFLUEANCING ANAEROBIC DIGESTION PROCESSES
OF MARKET WASTE AND COW DUNG AS RAW MATERIAL FOR
RENEWABLE ENERGYEllina Sitepu Pandebesie, Susi Agustina Wilujeng and Trifena Karunia Marbun
Department of Environmental Engineering, Faculty of Civil, Environmental and Geo- Engineering,
Institut Teknologi Sepuluh Nopember (ITS)ABSTRACT:
Activities in traditional markets produce organic waste that often consists of vegetable and fruit waste. Thisbiomass has the potential to be used as raw material for renewable energy. One of the technologies that can beused is anaerobic digestion to produce biogas. Because market waste is organic material with highlignocellulose content, it is necessary to investigate what factors influence the process of utilizing market waste
in the formation of biogas. The research was conducted on a laboratory scale by mixing market waste and cow
dung. The study was carried out for 30 days. Parameters tested were lignocellulose content, Volatile Fatty Acid
(VFA), Total Solid (TS), Volatile Solid (VS), C, N, and biogas production. The results obtained indicate thatmarket waste has the potential to be used as raw material for biogas production if it is mixed with cow dung.
Factors that affect biogas produces were lignocellulose content, Volatile Fatty Acid, Total solid and Volatile
Solid.
Keywords: Anaerobic Digestion, Cow Dung, Lignocellulose, Market Waste, Volatile Solid

INTRODUCTION

biodegradable waste and 33,5% difficult todecompose. Meanwhile, based on the source,households contributed 38,3% of waste generationand the traditional market as much as 17,2%.
Household and market activities generate waste that
contains a lot of vegetable and food residue. The
large volume of waste from households and markets
can be used as raw material for renewable energy.
This utilization will reduce the cost of waste
management, especially the cost of transporting andprocessing waste. Market waste contains highlignocellulose and a C/N ratio of around 32,82 [5].
To process market waste containing high C content,
it is necessary to mix it with materials containing
high N, to obtain the optimum C/N ratio for
anaerobic processes. One of the wastes that have notbeen treated properly in Surabaya is cow dung,where the production of cow dung reaches 5,26
tons/day and the C/N ratio is 16,6 [6]. In thisresearch, market waste and cow dung will be mixed
with a certain ratio to obtain the optimum C/N ratio
in the anaerobic digestion process. The purpose ofthis research is to determine the potential of market
waste as raw material for biogas production and thefactors that influence the formation of biogas in theanaerobic digester process.

Biomass has the potential to be used as arenewable and environmental friendly energy
source. An Anaerobic Digestion (AD) technology
can convert biomass into energy, by utilizing theactivity of microorganisms [1], which will produce
biogas. This technology is easy to implement andsustainable and can use a variety of wastes such as
agricultural and livestock waste, household organic
waste, and food as row materials [2]. To obtain
optimum results, anaerobic digestion operations
must meet several requirements, among others thecharacteristics of raw material, Total Solid andVolatile Solid, the rate of VFA formation, and C/Nratio must be accordant with the growth conditionsof microorganisms.
The characteristics of raw materials are
determined by how easy they are to decompose by
microorganisms. In particular, high-fiber raw
materials such as vegetable residues and leaves are
hard to degrade by their lignin content. According
to [3], the higher the lignin content of organic
material, the more difficult it is to decompose. The
C/N ratio of the substrate should be around 20-30[4]. If the C/N ratio is very high (>30), it will inhibit
the growth of bacteria, so that the biogas production
rate is low. Meanwhile, if the C/N ratio is too low,
it causes the accumulation of nitrogen which is toxic
to microorganisms [1].
According to the Ministry of Environment andForestry Indonesia, the composition of waste inIndonesia in 2020, consisting of 66,5% easily

RESEARCH MATERIALS AND METHOD
Materials
The materials used are market waste from Pasar
Keputih Surabaya and cow dung taken from the426

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Surabaya Slaughterhouse.

RESULTS AND DISCUSSIONS

Experimental Conditions

Operation Conditions

In this study, market waste was mixed with cow
dung. For each experiment, the comparison ofmarket waste and cow dung is R1 (0:100); R2(30:70); R3 (50:50) and R4 (100:0). After mixing
the composition, the acclimatization process was
carried out in a 60 L drum before being divided into
a 19 L reactor. This was done to obtain stablemicroorganisms that were able to adapt to the waste.
To determine the stable condition, gases production
volume measurements were carried out until thereactor has produced gases. After theacclimatization process, the substrate wastransferred to a 19 L reactor. Each reactor was filledwith 18 L of substrate (mixed waste). An
intermittent process was carried out, in which a
mixture of raw materials is added every 3 days, asmuch as 1.8 L. Sampling is done before adding thesubstrate. The intermittent operation consists ofstopping the feeding process for some time, whichallows a more optimal degradation of theaccumulated substrate [7], because the contact time
between microorganisms and the substrate is longer.
The frequency of stirring was carried out 12 times aday, each for 5 minutes. The research was carriedout for 30 days. The measured parameters aretemperature, pH, volume of biogas, water content,
lignin, cellulose, hemicellulose TS, VS, C, N,
Alkalinity, and VFA. The parameter analysis
method can be seen in Table 1.

Temperature
Temperature has an important role in theformation of biogas, where the higher thetemperature, the better the methane gas yield.
According to [3], temperatures between 25-35 °C
are generally more optimum to support the rate of
biological reactions. The temperature in eachreactor is included in the mesophilic temperature
range (25-35 oC). Based on observations, allreactors have the same pattern of temperaturechanges. On the 12th day, the temperature of theentire reactor rose to a temperature range of 30-32
oC. Then on the 16th day, the temperature of eachreactor dropped to the range of 27-28 oC. On the
18th day, the temperature in all reactors increased
to the range of 29-31,5 oC. After that, thetemperature in the reactor was relatively stable,with a range of 28-31 oC. The temperature increaseindicated micro-organisms growth rapidly and aprocess of decomposition of organic matter occuredby microorganisms [8].
pH
Based on Fig.2, only reactor with acomposition of 100% cow feces (R2) was at the
optimum pH (around 7) for the methanogenesisstage. While the other reactors pH below 6,5 are at
the optimum pH for acidification. Therefore,
methanogenic bacteria cannot optimally convert
organic matter into methane. It is recommended thata buffer is needed to increase the pH to neutral sothat the degradation process can run well.
Intermittent treatment causes the pH value in eachreactor to fluctuate. Because of the raw materials
that are entered every three days with slightly
different pH values. This can also be caused by the
pH of market waste which tends to be acidic.

ParameterpH
Temperature
BiogasVolumeVolatile
Solid, Total
Solid
Lignin,Cellulose,
Hemicellulose
C-organic
N-organic
Alkalinity
Volatile Fatty
Acid

Sampling
Time
Every 3 days
Every dayEvery dayEvery 3 days

Analysis
MethodpH meter
Thermometer
Volume Gas
Gauge
Gravimetres

Every 3 days

Chesson-Datta

Every 6 days

Walkey andBlack
Kjeldahl
Titration
Distillation

Every 6 days
Every 6 days
Every 6 days

pH

Table 1 Parameters Analysis Method

8.00
7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00
0

10

20

30

40

Time (days)
pH-R1

pH-R2

Fig 2. pH Conditions
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pH-R3

pH-R4

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Alkalinity (mg/L)

Alkalinity
Alkalinity is considered very important as it isrelated to the reactions that occur in AD. It indicatesthe ability of AD to neutralize the increase in acid,especially at the acetogenesis stage [9]. If thealkalinity
increases,
thepopulationofmethanogenic bacteria will also increase, while
when the alkalinity concentration decreases, acidity
will make volatile acid production faster than theability of methanogenic bacteria to convert volatile
acids into methane [10]. On the 12th day, thealkalinity value in almost all reactors increased or
was the largest. It proves that at that time the abilityof methanogens to convert volatile acids intomethane was excellent. Simultaneously, theacidification process begins to occur, namely theprocess of formation of volatile acids by acidifying
bacteria. Thus, the digester tries neutralizing theincrease in acid that occurs. However, on the 18th
to 30th days, the alkalinity of the entire reactor
decreased. It proves that the production of volatile
acids is faster than the ability of methane to convert
volatile acids into methane. So the ability of AD toneutralize acid also decreases. However, reactor R1
on the 24th day started to increase again, until thealkalinity value decreased again on the 30th day.
This could be due to the absence of the acidformation process.

(R1, R2, R3 and R4) were 38,9; 33,05; 29,41 and
32,39 respectively. This value has met the optimum
criteria for the anaerobic digestion process as can beseen in Fig. 4. The C/N ratio decreased in all
reactors after 30 days of fermentation. This iscaused by the element carbon and other organic
materials have been decomposed by microorganism.
45.00
40.00

C/N Ratio

35.00
30.00
25.00
20.00
15.00
10.00
5.00
0.00
0

6

12

18

24

30

Time (days)
R1

R2

R3

R4

2500

Fig. 4 C/N Ratio During Experiments

2000

Lignin, Hemicellulose and Cellulose Content
influencing biogas produced
Based on Table 2, it concludes that lignin
content fluctuated but decreased. This is because
the polymer content in lignin has been converted
into simpler components (monomers). This makes
the degradation process easier. Lignin content on
6th days, R1 (2,94%) have the highest averagelignin content compared to other reactors and has
the highest overall lignin content degradation value(63,08%). The R4 reactor has the smallest averagelignin content (2,55%). Hemicellulose content
tends to rise, somehow may be due to the lack ofenzymes needed for hemicellulose to be fullyhydrolyzed into monomers. Reactor R4 with 100%Market Waste has the highest average
hemicellulose content of 15,2% at the end of theprocess. The cellulose content showed an increase
until the 30th day. The reactor with 100% Market
Waste (R4)
has the highest average cellulose
content, where the initial content 23,51% increased
to 25,88% at the end of the process.

1500
1000
500
0
0

20

40

Time (days)
R1

R2

R3

R4

Fig. 3 Alkalinity
C/N Ratio
It indicates the ability of AD to neutralize theincrease in acid, especially at the acetogenesis. In
the formation of biogas, the C/N ratio is a criticalfactor, because it determines the life and activity of
microorganisms. Initial C/N ratio of each reactor

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Tabel 2 Lignin, Hemicellulose and Cellulose Content
Time

Lignin (mg/L)

Hemicelluloce (mg/L)

Celluloce (mg/L)(days)

L-R1

L-R2

L-R3

L-R4

H-R1

H-R2

H-R3

H-R4

C-R1

C-R2

C-R3

C-R4

6

2,90

2,86

2,64

2,55

13,08

13,24

13,38

13,62

22,18

22,34

23,86

23,51

12

2,01

1,98

1,92

1,85

13,31

14,05

14,20

14,40

23,05

24,61

23,76

23,88

18

1,34

1,22

1,11

1,01

14,05

14,21

14,65

14,58

23,90

24,09

24,39

24,82

24

1,89

1,68

1,59

1,32

13,58

14,11

14,23

14,36

23,18

24,05

24,21

24,48

30

0,96

0,95

0,90

0,72

14,81

15,01

15,14

15,20

25,05

25,24

25,51

25,88

Note: L= lignin; H= hemicellulose; C= cellulose

The process of breaking down lignocellulose
into lignin, hemicellulose, and cellulose
biologically required the right types of
microorganisms and takes a long time.
Lignocellulose carries the structure of the plantbiomass and is difficult substrate to degrade.
Therefore, thermo-chemical and enzymatic
pretreatments are necessary for lignocellulose
degradation to make the monomers available forfurther processing [11]. After the hemicellulose andcellulose are formed, then a hydrolysis process
occurs to form fructose and glucose. The next stepis the process of asedogenesis, then the methaneforming microorganisms are active in forming
biogas. With the addition of raw materials andsampling every three days, it seems that it is notenough time for microorganisms to move from thebreakdown of lignocellulosic to biogas. The
explanation above concludes that raw materials
containing high lignocellulose are more difficult tobe metabolized by microorganisms to produce
biogas.
Total Solid and Volatile Solid Influencing Biogas
Produced
Total Solid (TS) and Volatile Solid (VS) arevery influential on biogas production. The higher
the TS and VS values, the higher the biogas
formation that occurs [12]. decrease in TS and VS
values indicates how much organic matter isconverted into biogas. The role of the TS value on
the work of AD is to determine the optimum gas
production conditions. Research conducted by [13],showed that total methane production decreased
with increasing TS value. Systems with high solid
content are able to make the rate of methane
production high [14]. Total solid and volatile solid
content shown in Table 3.

Table 3. Total Solid and Volatile Solid Content
Time

Volatile Solid (mg/L)

Total Solid (mg/L)(days)

VS-R1

VS-R2

VS-R3

VS-R4

TS-R1

TS-R2

TS-R3

TS-R4

6

12.070

8.550

7.910

8.080

73.21030.10031.11019.29012

10.060

6.850

6.440

6.120

58.63023.17027.59014.89018

10.210

8.130

5.550

6.020

53.38032.74022.87014.690

24

9.630

4.850

6.310

4.410

53.27018.41028.15012.260

30

9.140

5.290

6.840

3.790

53.72024.28035.64012.360Note: VS=Volatile solid; TS = Total solid429

Table 3 shows the total solid and volatile solid
content of R1 is much higher than the other reactors.
Raw materials with high solid content are able toincrease the rate of methane production [14],because they generally have a high volatile solid
content. This is shown by reactor R1 which can
produce much higher biogas than other reactors.
However, in the anaerobic digestion process, theoptimum solid content is 22% in high rate digestion[3]. Total methane production decreases withincreasing TS value which exceeds the optimum
value [13]. In this study, the total solid cow dung
was 19% while the market waste was 22%. At thissolid content, the biogas formation process shouldbe able to proceed well, but the measured biogas
volume is not in accordance with the organic matter
content in the reactor. This can occur due to leakageof gas pipes or other inhibiting factors, such as thefast VFA production rate, so that the rate ofconversion of methane is hampered. It could also bebecause the raw material contains highlignocellulose, which takes longer in thedecomposition process.

2500

34.00
32.00
30.00
28.00
26.00
24.00
22.00
20.00
18.00
16.00
14.00
12.00
10.00
8.00
6.00
4.00
2.00
0.00

VFA (mg/L)

2000
1500
1000
500
0
0

6

12

18

24

30

Time (days)
VFA-R1pH-R1
VG-R1

VFA-R2pH-R2VG-R2VFA-R3
pH-R3
VG-R3

pH and biogas produced (mL)

GEOMATE – Bangkok, Thailand, 22-24 November 2022

VFA-R4pH-R4Fig. 5 pH and VFA influencing biogas production
CONCLUSION
The factors that influence the formation of
biogas in the anaerobic digestion process in thisstudy are the characteristics of the raw material(lignocellulose content), pH, Volatile Fatty Acid,
Total Solid, and Volatile Solid. This research showsthat market waste has the potential as raw material
for biogas production, which can be done by adding
co-substrates such as cow dung.

The effect of pH and VFA formation on biogas
production
At the initial of the fermentation process, theaddition of a large amount of material will cause asignificant change in the digester environment anda temporary inhibition of microbial activity [15]. It
is because hydrolysis/acidification microbes can
produce many volatile fatty acids (VFA) due toexcess substrate in a short time. The decreased pH
value can also be caused by the formation of organic
acids that accumulate due to the reaction of highorganic content [16]. As a consequence, the pH will
drop, and the methane-forming microbes are unable
to convert much of the methane to acid [17]. The
optimum pH value at the hydrolysis andacidification stages is 5,5-6,5 [18]. According to[19], the optimum pH value for the methanogenesisstage is 6,5-8,2. The best pH value for the digester
process is 7,0, if the pH is below 6,5 the activity ofmethanogenic bacteria will decrease. Based on
observations, only reactors with a composition of
100% cow feces were at the optimum pH (around7) for the methanogenesis stage. From Fig. 5 it canbe seen that pH and VFA formation affect gas
production. The pH of R2, R3, and R4 did not meetthe growth conditions of methanogenic
microorganisms, so that biogas was not formed
optimally. Mixing cow dung co-substrate canincrease the pH of market waste so thatmicroorganisms can still work even though they are
not optimal.

ACKNOWLEDGMENTS
This research was funded by the Directorate
General of Higher Education of the Indonesian
Ministry of Education and Culture and partly by theInstitut Teknologi Sepuluh Nopember.
CONFLICT OF INTEREST
I declare that this publication has no conflictwith any party and is the result of research by theauthor team.
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[2] Cavinato, C., Ros, C. D., Pavan, P. andBolzonella, D. 2016. Influence Of Temperatureand Hydraulic Retention on The Production of
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[3] Tchobanoglous, G., Theisen, H. and Vigil, S.

430

GEOMATE – Bangkok, Thailand, 22-24 November 2022

1993. Integrated Solid
Waste
Management. Mc Graw Hill, Singapore.
[4] Reisberg K., Sun L., Leven L., Horn S.J., andSchnurer A. 2013. Biogas Production from
Wheat Straw And Manure –Impact ofPretreatment
andProcess
OperatingParameters. Bioresource Technology. 149:
232-237.
[5] Abdullah, N.O. and Pandebesie, E.S. 2018. The
Influences of Stirring and Cow Manure Added
on Biogas Production from Vegetable Waste
Using Anaerobic Digester. IOP Conf. Series:
Earth and Environmental Science. 135: 012005.
[6] Trihadiningrum, Y., Ratnawati, R., Wulandari,R.A, Radita, D.R., Juliastuti, S.R. (2015).
Comparison of Slaughterhouse Solid Waste
Treatment Using Anaerobic-Anoxic-Oxic andAerobic Composting Methods. The 5th
Environmental Technology and Management
Conference “Green Technology towards
Sustainable Environment”. ISBN: 978-97998278-5-2.
[7] Nadais, H., Barbosa, M., Capela, I., Arroja, L.,
Ramos, C.G., Grilo, A., Sousa, S.A. and Leitao,
J. H. 2011. Enhancing Wastewater Degradation
and Biogas Production by Intermittent
Operation of UASB Reactors. Energy. 21642168[8] Wellinger, J., Arthur, M. And Baxter, D. 2014.
The Biogas Handbook: Science, Production
and Applications. Woodhead Publishing,Cambridge.
[9] Zhiying, Y. 2015. The Effects of Initial
Substrate Concentration, C/N Ratio, andTemperature. Bioresource Technology. 17(1):266-273.
[10] Dong, X., Shao, L., Wang, Y., Kou, W., Cao,Y., Zhang D. 2015. Biogas By Two-Stage
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Digestion of Chinese Cabbage Waste, Chinese
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[11] Rozenfelde, L., Mâris, P., Irçna, K., Ieva, P.,Nataïja, M., Nikolajs, V., and Alexander R.
2017. Enzymatic Hydrolysis Of Lignocellulose
For Bioethanol Production. Proceedings Of

The Latvian Academy Of Sciences. Section B,
Vol.71, No. 4 (709): 275–279.
[12] Zarkadas, I. S., Sofikiti, A. S., Voudrias, E. A.,
and Pilidis, G. A. 2015. Thermophilic
Anaerobic Digestion of Pasteurised Food
Wastes and Dairy Cattle Manure in Batch and
LargeVolumeLaboratory
Digesters:
Focussing on Mixing Ratios. Renewable
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[13] Abbassi-Guendouz, A., Brockmann, D., Trably,E., Dumas, C. And Delgene`s, J.P. 2012. Total
Solids Content Drives High Solid Anaerobic
Digestion Via Mass Transfer Limitation.
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[15] Golueke, C. G., Oswald, W. J. and Gotaas, H.
B. 1957. Anaerobic Digestion of Algae.
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[16] Yang, L., Huang, Y., Zhao, M., Miao, H. and
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[17] Kwietniewska, E. and Tys, J. 2014. Process
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[19] Lee, D. H., Behera, S. K., Kim, J. W., and Park,
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431

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

TiO2 PHOTOCATALYST SUPPORTED ON FLY ASH
PARTICLES USED IN WASTEWATER TREATMENT LOADEDWITH FOOD DYES
M. Visa1, I. Visa1
Faculty of Product and Environmental Design, Transilvania University of Brasov, Romania

1

ABSTRACTThe paper reports on a novel TiO2-fly ash (TiO2-FA) composite obtained through mild hydrothermal
synthesis, as a possible adsorption and VIS-activated photocatalysis substrate able to simultaneously remove
pollutants in advanced wastewater treatment processes. Combining FA with TiO2 has the following advantages:(1) the TiO2 crystallites grow on the alkaline activated FA forming a stable composite where photocatalytic VISactive tandem systems can result by associating titania with semiconductor oxides from FA (2) the organic
pollutant molecules migrate to the surface of TiO2 and can be photo catalytically degraded and (3) activated fly
ash substrates can adsorb inorganic (heavy metal) pollutants and organics, thus supporting photocatalysis and thein situ regeneration of the FA matrix.
The composites’ surface properties were evaluated SEM, AFM and BET surface characterisation and PZC
measurements, the phase composition and crystallinity by XRD and the optical band gap was estimated based
UV-VIS spectroscopy. The adsorption and the UV - photocatalytic properties of the composites were evaluatedin two – pollutants systems containing food dyes (Carmine 50% P-WSE-120 and FD&C Blue). Kinetic data
allow the investigation of the simultaneous removal of both dyes in a stepwise mechanism. The kinetic studies
were carried out at the optimum conditions and it was observed that the data were fitting pseudo-second ordermodel. The adsorption efficiency of Brilliant Blue (BB) is over 73.67% and of Carmine (RC) dye is 82.07%.
.
Keywords: Food dyes, photocatalytic, adsorption, wastewater(cancer, high blood pressure, hepatitis) on people
and animals [4]. In 1962, the first EU directive(62/2645 /EEC) approved 36 dyes, of which 20 were
naturally derived and 16 were synthetic. Eachmember state could designate where certain colors
could and could not be used. Every year 7*105 tones
and over 10,000 different types of dyes andpigments are produced in the World [5] and arepresented in the effluents of the food, textile, leather
and dye industries.
Many processes have been applied to remove theharmful pollutants, including adsorption [6], [7],biosorption [8], electrochemical coagulation, electro
flocculation and ozonation [9], photodegradation[10], photo-Fenton [11] or simultaneous methods[12]. The adsorption method is intensely used forremoving soluble and insoluble pollutants from fromwastewater without the generation of hazardous byproducts being inexpensive. Modified fly ash is agood adsorbent materials. Fly ash is one of the most
known waste and it is used as raw material in manyindustries such as cement industry, but its negative
effect on the environment cannot be completely
neutralized by doing this, so reusing it as anadsorbent in the removal of different types ofpollutants from wastewater solves two problems:
water quality and waste management.
The aim of this work is to obtain a new composite
starting from fly ash (FA) and a semiconductor
photocatalysts(e.g.TiO2) which could thus represent

INTRODUCTION
It is believed that the addition of dyes to food
took place in Egyptian cities as early as 1500 BC,
when candy makers added natural extracts to givethe appearance of the products and continued to thepresent day [1].
With the onset of the industrial revolution, people
became dependent on foods including ketchup,mustard, jellies, candies and wine beautifullycolored. Inorganic compounds were used to restore
the color of the food. The red lead (Pb3O4) was
routinely used to color cheese and confectionery,
copper arsenite (CuHAsO3) was used to recolor used
tea leaves for resale [2]. It also caused two deaths
when used to color a dessert in 1860. The first foodlaws created in Augsburg, Germany, in 1531,
concerned colorants and required saffroncounterfeiters to be burned. In 1856, mauveine, thefirst synthetic color, was developed by Sir William
Henry Perkin and by the turn of the century,
unmonitored color additives had spread throughEurope and the United States in all sorts of popular
foods. Food coloring is used in commercial foodproduction and domestic cooking, cosmetics,
pharmaceuticals, home craft projects, and medical
devices [3].
Synthetic dyes are often less costly and technicallysuperior to natural dyes, but many synthesized dyes
have been banned because of their adverse effects
432

GEOMATE – Bangkok, Thailand, 22-24 November 2022

filtration on 0.45mm filter, the supernatant was
analysed. The dyes concentration before and after
removal was evaluated based on absorbance spectra(PerkinElmer UV–VIS spectrophotometer, Lambda950). In dye mixtures, the overlap with and adisplacement of the peaks of a single dye isobserved, and the concentrations were calculated
after calibration, based on the first-order derivativeof the absorption spectrum of each dye in the
mixture, as previously described [15] at the
maximum absorption of BB (λ BB = 630 nm) andRC (λ RC = 495nm). The adsorption andphotodegradation efficiency of the dyes wasevaluated using Eq. (1), where c0 is the initialconcentration and ct the pollutants concentration at
moment t.
(1)
η = (c0 − ct ) ∗100 / c0 [%]an efficient and environmentally friendly solution
for to remove two food dyes: Carmine (C22H20O16),
red color in water, notated (RC) and Brilliant Blue
(C37H34N2Na2O9S3) notated (BB). The chromophoregroups are of anthraquinone type in Carmine and of
methane triaryl group type in Brilliant Blue, bothresistant to biodegradation.
2. EXPERIMENTAL
2.1. Materials and the Substrate’s Preparation
Fly ash collected from CET power plant in Brasov,Romania is type F, according to the ASTM standard
C-618-2a [13]. The total percentage of major oxides(SiO2, Al2O3 and Fe2O3) exceeds 70% and this typeof fly ash FA will not aggregate during long contact
with water, being suitable as substrate in adsorptionof organic and inorganic pollutants [14]. The FA
was washed in ultrapure water by stirring at room
temperature (20–22ºC) till constant pH=10.2 andconductivity values =1710 µS.
The washed fly ash powder was further modified by
24 h contact with NaOH 2N (1g : 10mL) to activate
the surface,as previously described [12]. The 40 µm
fractions were selected after sieving, and used toobtain a novel composite by mixing with the TiO2
photocatalyst (Degussa P25, 80% anatase, and 20%
rutile). The new adsorbent (FLY-D) was obtained in
an autoclave from the slurry of the powder mixture(mass ratio fly ash: TiO2= 3:1) and NaOH 2N, understirring (300 rpm) for 24 h, at 5 atm pressure and150ºC. After filtration, washing and drying at 105ºC
till constant mass, the dried FLY-D substrate wasobtained and further used in photocatalytic andadsorption experiments.

The results of pollutants, RC and BB dyes removalfrom solutions on substrate are presented in Fig1.

80

Efficiency [%]60

BB(BB+RC)/FLY-D (A)RC(RC+BB)/FLY-D (A)
40

20

0
0

20

40

60

80

100

120

140

160

180

Time [min]Fig.1. Adsorption efficiency vs. time forsystem RC +BB dyes

2.2. Adsorption and Photocatalytic Experiments
The optimal adsorption time can be estimated as
90min. when the adsorption equilibrium starts tosettle for system. The adsorption efficiency of RC
molecules (82.07%) and of BB molecules (73.67%)
is very close and the adsorption/desorption increases
in the same way, see Fig.1.
The lower bonding possibilities of RC prove that theadsorption on FLY-D runs on active sites ofalumino-silicate and that the contribution of TiO2 in
its adsorption is less important. Additionally, at theworking alkaline pH (9.62) electrostatic interactions
are less likely as the substrate is negatively charged
and the anionic dyes cannot involve the -SO3groups. During adsorption process the amount ofactive sites and their surface distribution is very
important.

2.2.1 Adsorbtion process on FLY-D substrate
The pollutant systems were prepared usingbidistilled water. Experiments were done on systems
containing a dyes mix of RC 20 mg/L+ BB 20 mg/L.
Two types of processes were investigated:- Adsorption (A) on composite substrate (FLY-D
under visible light, at room temperature (22ºC), by
mixing 0.5 g substrate with 50 mL solution undermechanical stirring;- Photodegradation (F) investigations were done on
FLY-D suspensions with the same composition as in
the adsorption studies, with and without hydrogen
peroxide (30%) addition, under UV irradiation.
The homemade photocatalytic reactor is equippedwith three F18W/T8 black tubes (Philips), emitting
UV-A light in the region of 340–400 nm and λ max
emission =365 nm. During the kinetic studies,aliquots were taken at fixed moments (up to 180min) when stirring was briefly interrupted and, after

2.2.2. Photocatalytic process on FLY-D substrate
The active species generated by photo-irradiation
will attack the pollutant, if this is in the very close433

GEOMATE – Bangkok, Thailand, 22-24 November 2022

vicinity of the substrate. Thus, adsorbed pollutant
molecules will support an efficient photodegradation process [16]. The most active
photocatalytic component of the composite is TiO2
polymorphs have band gaps of 3.0 eV (rutile), 3.2
eV (anatase), thus are active under UV radiation
with wavelengths lower than 413 nm for rutile and,
respectively 387 nm for anatase. As the UV
wavelength used in our experiments was 365 nm, we
may conclude that only anatase and rutile are
actually activated under irradiation and can exhibit
the well-known coupling effect [17]. Once formed
the electron–hole pair, the common mechanisms
involves the holes for hydroxyl radical production.
In alkaline media several other reactions are possible[18] [19], involving the O2/HO-, O2/HOO- and theO2/H2O2 couples. The undesired process is theelectron–hole recombination, and the photocatalytic
efficiency strongly depends on the system’s ability
to limit this process. H2O2 addition is expected tosupport electron trapping.
The results of pollutants, RC and BB dyes
removal from solutions by photo-degradation arepresented in Figs.2 and 3.

Efficiency [%]80

60

RC(RC+BB)/FLY-D (F)BB(BB+RC)/FLY-D (F)40+20µL H2O2
20

0
0

50

100

150

200

350

3. RESULTS AND DISCUSSIONS
3.1. The structure of the nano-composite material
The X-ray diffraction was used in order toinvestigate the crystal structures and phasecomposition of the composites, Fig4.
¤
1400# Na6Al6Si10O32* Al2O3-SiO2¤ Anatase syno Rutile" Kyanite Al2SiO5

1200

Intensity [a.u.]

80
70

Efficiency [%]300

Fig.3. Photodegradation efficiency of RC and BB
from dyes mixtures with 20 µL H2O2 addition

90

60
50

1000
800

#

600
400

RC(RC+BB)/FLY-D (F)BB(BB+RC)/FLY-D (F)

40

250

Time [min]

#

*

#

¤

FLY-D

¤
o

"

¤
o

200

30
10

+10µL H2O2

20

20

30

40

50

60

70

80

2thete [degree]

Fig.4. X-ray difractogram of FLY-D

10
0
0

50

100

150

200

250

300

350

The XRD spectra, Fig.4, show overall crystalline
percentages of 72% in FLY-D composite. As
expected, the composite Fly-D contains large
amounts of crystalline TiO2 polymorphs from
Degussa P25 (anatase and rutile). Likewise, thecomposite shows new crystalline alumino-silicates
important in adsorption process of dyes
(Na6Al6Si10O32, Al2O3-SiO2 and Kyanite (AlSiO5).
The mainly processes are: re-precipitation or
polymorph transformations spend in mildhydrothermal conditions:
1. The TiO2 crystallites grow on the alkaline
activated FA forming a stable composite;
2. Tandem systems by associating titania oxide withsemiconductor oxides from fly ash.
Information on the composite morphology/
topography, respectively surface characteristics wereobtained from the SEM micrographs, Fig.5 and
AFM images.

Time [min]Fig.2. Photodegradation efficiency of RC and BB
from dyes mixtures with 10 µL H2O2 addition
In the working conditions, the HO· production
from the direct hemolytic break of the HO–OH bondis unlikely, as higher energy is needed (l < 254 nm).
The substrate material can also interact with theelectrons, contributing to the electron trapping
mechanism. Once produced, the HO· radicals will
attack the organic dye and several mechanisms are
possible, [20]:1. Hydrogen abstraction: RH + HO·→ R· + H2O
2. Redox reactions: RH + HO· → RH+· + HO·
3. Addition to the π-bonds in the pollutant molecule434

GEOMATE – Bangkok, Thailand, 22-24 November 2022

The re-organizing process is confirmed by the
AFM pictures. The surface roughness increases from36.22nm to 45.53nm, and the drop-shaped
aggregates, Fig. 6b, are assembled into roughstructures.
The surface charge depends on pH. The working
pH values over pHPZC, explain well the highadsorption efficiency of cationic dyes (crystal violet,
methylene blue, etc.) [21].
When pH > pHPZC the surface is negatively
charged because in aqueous solution can exist theforms TiIV– O- (Eq.2).
TiIV– OH + HOTiIV – O- + H2O(2)When working pH is over 7.97 values and theadsorbent surface is negative the dyes cations andother hydrated species or precipitate are adsorbed.
Several PZC points were identified Fig.7 due to thecomplex composition of the substrate with moreoxides, SiO2, and TiO2 (anatasse and rutile).

Fig.5. SEM images of the FLY--D composite
The SEM image of FLY-D shows a high degreeof homogeneity of the particles. The TiO2 particles
are embeded on fly ash particles. The elemental
EDX analysis of the Fly-D shows a high intensity
signal for the titanium atoms.
Table 1. Quantitative results of FLY-D particles

The crystalline modifications are accompanied by asignificant increase in the BET surface, from
6.14m2/g in FA to 69.75m2/g for FLY-D.

Fig.7. The isoelectric point of Fly-D composite
4. ADSORPTION KINETICS MODELING
4.1. Adsorption Kinetics Models Analysis
The amount of RC and BB dyes adsorbed at any
time, qt (mg.g -1), was calculated by Eq. (3):qt = (c0 − ct )× Vsol / ms

(a)

(3)

The kinetics models are fitted to experimental
data by nonlinear regression analysis. To determine
the uptake kinetic mechanisms of pollutants (RC andBB) from systems three kinetic models were
investigated:
pseudo-firstorder(Lagergren
equation), pseudo-second order and intraparticle
diffusion. The pseudo-second order kinetic (Eq.4) is
the model which best describes the adsorption of thepollutants on the composite FLY-D.

t
1
t
=
+
q t k 2q e2 q e(b)Fig.6. AFM topography of (a) washed FA; (b)FLYD
Where:
435(4)

GEOMATE – Bangkok, Thailand, 22-24 November 2022

k2- the equilibrium rate constant for the pseudosecond order adsorption (g·mg-1 min-1), qe- theadsorption capacity (mg·g-1). The results arepresented in table 2.

effective within the simultaneous processes ofadsorption and photodegradation of food dyes.
3. The results show that the adsorption efficiency
significantly depends on the surface properties.
4. Therefore, the data indicate that a large volume offood dyes are rapidly absorbed, leading to higher
rate constants (15,204 and 17,492 g mg-1min-1).
5. The pseudo-second order kinetic is the model thatgoverns the entire process of removing food dyes
from mixed systems.
6. The addition of H2O2 in the alkaline working
conditions increased the efficiency of the pollutant
removal process, but on the other hand would
increase sustainability and cost issues.

Table 2 Kinetic parameters of dyes removal inadsorption process
Pseudo-second order
Pollutantk2
qe[g mg-1minR2[mg/g]
1]
RC(RC+BB)A 15.204
0.897
12.59
BB(BB+RC/A 17.492
20.56
0.972

4.2. Photocatalytic Kinetics Models Analysis
Acknowledgements
For the photocatalysis process, the LangmuirHinshelwood mechanism simplified for low
concentration as a pseudo first order kinetics wastested, Eq. (5):(5)
ln c/co= - kt

This paper was supported by a grant of the
Romanian National Authority for Scientific
Research, CNCS – UEFISCDI, PNII -PT-PCCA2013-4-0726-phase 2017. The structural funds
project PRO-DD (ID 123, SMIS 2637, 11/2009) is
acknowledged for providing the infrastructure usedin this work.

The kinetic parameters and the correlation
coefficients calculated from the linear form of the
kinetic laws are given in Fig.8.

REFERENCES[1] Downham A., Collins P., Colouring our foods inthe last and next millennium, Int. J. of Food Sci. andTechno, Vol.35, Issue 1, 2000, pp. 5-22.
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Issue 3, 2012, pp. 220-246.
[3] Rafii F., Hall .JD., Cerniglia C.E., Mutagenicityof azo dyes used in foods, drugs and cosmetics
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from the human intestinal tract, Food Chem Toxicol,
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dispersed TiO2 –fly ash, Environmental Engineeringand Management, Vol. 7, Issue.4, 2008, pp.373-378.
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1-3, 2011, pp. 112-118.

Fig.8. Pseudo-first order kinetics of dyes/FLY-D(Langmuir – Hinshelwood model)
The photo degradation efficiency with 20 µL
H2O2 addition is favorable for removal the food dyes
(RC and BB).
Parallel kinetic mechanisms may occur,
especially inter-particle scattering, due to bulky dyes
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5. CONCLUSIONS
1. A novel nanocomposite with a large specificsurface, high roughness, and high crystallinity wasdeveloped under mild conditions by hydrothermal
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2. The TiO2 –Degussa P25 imbedded on fly ash
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

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437

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

A NOVEL ROUGH FUZZY BASED DELPHI METHOD FOR
HIGHWAY PROJECTS RISK ANALYSIS: THE SOE ASSIGNMENTSCHEME CASE STUDY
1

Gilang Ardi Pratama1,2, Yusuf Latief1 and Lukas Beladi Sihombing3
Department Of Civil And Environmental Engineering, Universitas Indonesia, Indonesia
2
Department Of Industrial Engineering, Universitas Pamulang, Indonesia
3
Department Of Civil Engineering, Universitas Pembangunan Jaya, Indonesia
ABSTRACTIn accelerating the development of the area in Sumatra, the Indonesia Government assigned to PT. X to manage
toll road concessions on 24 Sumatra toll roads. PT X is an enterprise with full ownership by the government.
Because Sumatra toll roads Development are economically viable but not financially viable. This scheme was
implemented to avoid abuse of authority and legal uncertainty issues from government administration
administrators. Then, the Indonesian government was named it "The State-Owned Enterprise (SOE) Assignment
Scheme." But this project has several technical problems and non-technical problems. The technical problems are
Material, Geotechnical Condition, Equipment, and others. The non-technical problems are policy, financial, andothers. Those problems may impact the time and cost of the Sumatran toll road development. This study aims tolist the risks and evaluate each factor corresponding to the Sumatran toll roads development based on The RoughFuzzy of Delphi method. Questioners have been collected from 6 experts. There are 12 critical risk factors in thisstudy that will impact the project. The proposed method can reduce the level of uncertainty and subjectivity thatexists in the process of risk identification and analysis. The results of this study increase our understanding of theimportance of the impact of each criterion which can be valuable for future studies on toll road projects with the
SOE assignment scheme.
Keywords: Rough Numbers, Fuzzy, Risk, Delphi, State-Owned Enterprise
INTRODUCTION

combination of rough set and fuzzy set numbers forthe Delphi method application that is used for
decreasing the ambiguous and subjectivity levels
done by the experts who participated in thecompletion of this study.

Based on Presidential Regulation No. 117 of 2015 on
Amendments to Presidential Regulation No. 100 of2014 on the Acceleration of Toll Road Developmentin Sumatra, TSTR (Trans-Sumatra Toll Road) is oneof the National Strategic Projects (NSP). TSTR willconnect Lampung Province to Aceh Province through24 toll roads totaling 2,988 km, consisting of main
corridors (backbone) with 2,069 km in length andsupport corridors with 919 km in length[1].
The construction of the trans-Sumatra toll road is notfinancially feasible, even though it is economicallyfeasible. Consequently, no business entity isinterested, which causes a business entity cannot
realize the development of the toll road network. The
government can take several actions in accordancewith its authority because no Private business entity
interested with this project, such as carrying out theconstruction of toll roads partially or entirely by itself,then delegating the operation and maintenance to abusiness entity selected through an auction process[1].
In the implementation of this SOE assignment, many
problems affect the cost overrun and delay; when theissue limitation on this task was extremely limited,
this paper, therefore, aimed to identify, evaluate, andrank the critical factors that affected the Sumatera tollroad construction with this assignment scheme using
the rough fuzzy Delphi method. This method is a

MATERIAL & METHODSBackGround of Case Study
In order to accelerate regional development in
Sumatra, Presidential Regulation No. 100 of 2014 has
been amended by Presidential Regulation No. 117 of2015 concerning Amendments to Presidential
Regulation No. 100 of 2015 concerning the
Acceleration of Toll Road Development in Sumatra
are using SOE assignment scheme [2].
Several studies have investigated issues related to theimplementation of toll road projects and theirperformance. The initial step in risk management is to
identify and categorize the risks.
According to Likhitruangsilp [3] risks indevelopment projects are divided into two broadlines: general risks such as policy risks, financing
risks, environmental risks, and stakeholder risks, andproject-specific risks such as pre-construction,
Construction Risk and operating risks

438

GEOMATE – Bangkok, Thailand, 22-24 November 2022̅̅̅̅̅̅̅̅ (𝒍𝒌𝒊𝒋 )]𝑹𝑵 (𝒍𝒌𝒊𝒋 ) = [𝑳𝒊𝒎𝒊𝒕 (𝒍𝒌𝒊𝒋 ), 𝑳𝒊𝒎𝒊𝒕(6)𝒌𝒌 ̅̅̅̅̅̅̅̅𝒌
𝑹𝑵(𝒎𝒊𝒋 ) = [𝑳𝒊𝒎𝒊𝒕 (𝒎𝒊𝒋 ), 𝑳𝒊𝒎𝒊𝒕 (𝒎𝒊𝒋 )](7)̅̅̅̅̅̅̅̅ (𝒖𝒌𝒊𝒋 )]𝑹𝑵 (𝒖𝒌𝒊𝒋 ) = [𝑳𝒊𝒎𝒊𝒕 (𝒖𝒌𝒊𝒋 ), 𝑳𝒊𝒎𝒊𝒕(8)
Therefore, the average rough number of
𝑹𝑵 (𝒍𝒌𝒊𝒋 ), 𝑹𝑵(𝒖𝒌𝒊𝒋 ) 𝐚𝐧𝐝 𝑹𝑵 (𝒖𝒌𝒊𝒋 ) can be written as
follows [4]:Rough Number
The assumption from the response giver, 𝒓𝒌𝒊𝒋 can be
expressed as lower limit 𝑳𝒊𝒎𝒊𝒕 (𝒓𝒍𝒊𝒋 ) and upper limit̅̅̅̅̅̅̅̅
𝑳𝒊𝒎𝒊𝒕 (𝒓𝑼𝒊𝒋 ) with the following formulation [4].
𝒏𝒊𝒋𝑳

𝑳𝒊𝒎𝒊𝒕 (𝒓𝒍𝒊𝒋 ) = (∏ 𝒙𝒊𝒋 )
𝑲=𝟏
𝒏𝒊𝒋𝑼

𝟏
𝑹

𝑹

𝟏
𝑵𝒊𝒋𝒍

𝒔𝒖
𝑹𝑵(𝒍̂𝒊𝒋 ) = [(∏ 𝒍𝒔𝒍𝒊𝒋 ) , (∏ 𝒍𝒊𝒋 ) ]

(𝟏)

𝑺=𝟏

(𝟗)

𝑺=𝟏
𝟏
𝑹

𝑹

𝟏
𝑵𝒊𝒋𝒍

̅̅̅̅̅̅̅̅
𝑳𝒊𝒎𝒊𝒕 (𝒓𝑼𝒊𝒋 ) = (∏ 𝒙𝒊𝒋 )

𝟏
𝑹

𝑹

𝟏
𝑹

𝑹

𝒔𝒖
̂ 𝒊𝒋 ) = [(∏ 𝒎𝒔𝒍
𝑹𝑵(𝒎𝒊𝒋 ) , (∏ 𝒎𝒊𝒋 ) ]

(𝟐)

𝑺=𝟏

𝑲=𝟏

Thus, it can be expressed by the equation below𝒌̅̅̅̅̅̅̅̅ 𝒌
𝑹𝑵 ( 𝒓𝒌(3)𝒊𝒋 ) = [ 𝑳𝒊𝒎𝒊𝒕 (𝒓𝒊𝒋 ), 𝑳𝒊𝒎𝒊𝒕 (𝒓𝒊𝒋 )]Therefore:
𝟏𝑼
𝟐𝑳 𝟐𝑼
𝑴𝑳 𝑴𝑼
𝑹𝑵(𝒓𝑘𝒊𝒋 ) = ([𝒓𝟏𝑳
𝒊𝒋 , 𝒓𝒊𝒋 ], [𝒓𝒊𝒋 , 𝒓𝒊𝒋 ], . , [𝒓𝒊𝒋 , 𝒓𝒊𝒋 ])(𝟒)

(𝟏𝟎)

𝑺=𝟏
𝟏
𝑹

𝑹

𝟏
𝑹

𝑹

𝒔𝒖
̂ 𝒊𝒋 ) = [(∏ 𝒖𝒔𝒍
𝑹𝑵(𝒖𝒊𝒋 ) , (∏ 𝒖𝒊𝒋 ) ]
𝑺=𝟏

(𝟏𝟏)

𝑺=𝟏

Thus, the rough triangular fuzzy number can be
expressed with the following equation:

Fuzzy Number
Fuzzy set theory is a mathematical theory designed tomodel the ambiguity or imprecision of human[5], A
triangular fuzzy number is denoted as (l,m,u) whose
membership function is 𝑭𝒂̃ (𝑿) as followst:
𝟎, 𝒙 < 𝒍 𝒐𝒓 𝒙 > 𝒖𝑭𝒂̃ (𝑿)𝒙−𝒍,𝒍 ≤ 𝒙 ≤ 𝒎
𝒎−𝒍

(𝟏𝟏)

̂ 𝒍𝒊𝒋 =[𝒍𝒍𝒊𝒋 , 𝒎𝒍𝒊𝒋 , 𝒖𝒍𝒊𝒋 ], 𝒅𝒂𝒏 𝒁̂ 𝒖𝒊𝒋 =[𝒍𝒖𝒊𝒋 , 𝒎𝒖𝒊𝒋 , 𝒖𝒖𝒊𝒋 ]𝒁(12)Step 4. Defuzzification of fuzzy numbers using theBest Non Fuzzy Performance (BNP) method, when̂ 𝒍𝒊𝒋 and 𝒁̂ 𝒖𝒊𝒋 then defuzcation can be performed with:𝒁(𝟓)
̂ 𝒍𝒊𝒋 ) =BNP (𝒁𝒖 −𝒙 ,𝒎 ≤ 𝒙 ≤ 𝒖{𝒖 − 𝒎 ,(𝑼𝒍𝒊𝒋 −𝒍𝒍𝒊𝒋 )−(𝒎𝒍𝒊𝒋 −𝒍𝒍𝒊𝒋 )𝟑+ 𝒍𝒍𝒊𝒋(13)(𝑼𝒖 −𝒍𝒖 )−(𝒎𝒖 −𝒍𝒖 )𝒊𝒋 𝒊𝒋
̂ 𝒖𝒊𝒋 ) = 𝒊𝒋 𝒊𝒋
BNP (𝒁+ 𝒍𝒖𝒊𝒋(14)
𝟑
Step 5. Determining the crisp value of the roughnumber is done in a way according to normalization[6]:
̃ 𝒍𝒊 = (𝒁̃ 𝒍𝒊 − 𝒎𝒊𝒏𝒊 𝒛𝑳𝒊 /∆𝑴𝒂𝒙
𝒁
(𝟏𝟓)
𝑴𝒊𝒏 )
𝒖
̃ 𝒊 = (𝒁̃ 𝒖𝒊 − 𝒎𝒊𝒏𝒊 𝒛𝑳𝒊 /∆𝑴𝒂𝒙
𝒁
)
(𝟏𝟔)
𝑴𝒊𝒏
𝒖
𝒍
∆𝑴𝒂𝒙
(17)
𝑴𝒊𝒏 = 𝑴𝒂𝒙𝒊 𝒛𝒊 - 𝑴𝒊𝒏𝒊 𝒛𝒊
The determination of the total of crisp value withequation:
̃ 𝒍𝒊 × (𝟏 − 𝒁̃ 𝒍𝒊 ) + 𝒁
̃ 𝒖𝒊 × 𝒁
̃ 𝒖𝒊
𝒁
𝜷𝒊 =
(𝟏𝟖)
̃ 𝒍𝒊 + 𝒁̃ 𝒖𝒊
𝟏−𝒁Then calculate with the crisp value for the lastvalue.
̃ 𝒅𝒆𝒓𝒁= 𝒎𝒊𝒏𝒊 𝒛𝑳𝒊 + 𝜷𝒊 ∆𝑴𝒂𝒙
(19)
𝒊
𝑴𝒊𝒏
Step 6. Determine whether the risk is important in theconstruction of the trans-Sumatra toll road withreference to the table below.

Proposed Rough Fuzzy Delphi Method
In this section a Rough Fuzzy Delphy method is
proposed to obtain the objective of this study. Hereare the steps:
Step 1. Form a group of n experts to evaluate risk
factors. At least 5 experts for evaluation of therecommended criteria with minimum qualification ofbachelor degree and 10 years of experience.
Step 2. In this step, the n expert team evaluates thecriteria by means of the triangular fuzzy number
values specified in the table below.
Table 1. Fuzzy Scales
No Linguistic Term
1
Strongly Disagree
2
Disagree3
Neutral4
Agree5Strongly Agreê 𝒍𝒊𝒋 , 𝒁
̂ 𝒖𝒊𝒋 ]
𝑹𝑵(𝒛̂𝒊𝒋 ) = [𝒁Fuzzy Scales
0,0,0.25
0,0.25,0.5
0.25,0.5,0.75
0.5,0.75,1
0.75,1,1

Table 2. Criteria Scale
No Reference
Criteria1
Low
0≤CV<0.61
Mild
0.6 ≤CV<0.8
1
Critical
0. 8 ≤CV<1
Step 7. Rank each risk for each indicator of the risk,

Step 3. The rough number formulation of𝑹𝑵 (𝒍𝒌𝒊𝒋 ), 𝑹𝑵(𝒖𝒌𝒊𝒋 ) 𝐚𝐧𝐝 𝑹𝑵 (𝒖𝒌𝒊𝒋 ) can be expressedwith the following equation [4]:439GEOMATE – Bangkok, Thailand, 22-24 November 2022

in order to determine the priority risk of eachindicator according to expert opinion.
No

RESULT AND DISCUSSION

1
2
3
4
5
6
7

By reviewing the previous studies on a case study ontoll roads and an in-depth interview with experienced
professionals, 104 risks were collected.
The common risk is divided into Policy, Financing,
Environment, and stakeholders. The specific risks ofthe project are, furthermore, divided into thefollowing categories: design and procurement,
construction, and operation.
By distributing the questionnaire, seven experts who
filled out the questionnaire had the following criteria.

Table 3. Respondent Criteria
Experience
DegreeInstitution(Year)
Master Degree
17
PublicMaster Degree
20
PublicMaster Degree
37
SOE
Bachelor Degree
15
SOE
Bachelor Degree
11
SOE
Master Degree
40
Consultant
Doctoral Degree30
Academic

The result of the Rough Fuzzy Delphi for Risk
analysis can be seen in the table below:

Table.4 Result of Risk Analysis
̃ 𝒍𝒊
𝒁

̃ 𝒖𝒊
𝒁

Crisp
Value

Criteria

Rank

Contract Terminated By Government [7]Change In Regulation [7-9]

0.54
0.71

0.74
0.79

0.59
0.75

Low
Mild

14

1.3

Late on Project Permit And Approval [3, 10]

0.62

0.80

0.69

Mild

8

1.4

Asset Ownership [7, 10]

0.53

0.69

0.61

Mild

12

1.5

Lack Of Standard Contract Agreement [9]

0.58

0.74

0.68

Mild

9

1.6

Government Intervention [11]

0.68

0.74

0.70

Mild

5

1.7

Corruption [3, 12]

0.67

0.81

0.73

Mild

4

1.8

Lack Of Regulatory And Supervisory System [3, 12]

0.58

0.74

0.64

Mild

11

1.9

No

Indicator[reference]

X.1
1.1
1.2

POLICY FACTOR
3

Domestic Political Situation [8, 10]

0.75

0.75

0.75

Mild

2

1.10

Bureaucratic of Tender System [3, 10]

0.45

0.69

0.54

Low

16

1.11

Concession Period [9, 13]

0.52

0.74

0.65

Mild

10

1.12

Lack Of Policy Support For Investors[9]

0.69

0.84

0.76

Mild

1

1.13

Nationalization Of Projects/Assets[8, 9]

0.49

0.69

0.53

Low

17

1.14

Strict Import-Export Permits[14]

0.37

0.63

0.45

Low

18

1.15

Wrong Decision By The Government[12]

0.49

0.67

0.54

Low

15

1.16

SPV Violate Policies[14]

0.68

0.74

0.70

Mild

5

1.17

Conflict On Contract[3, 7]

0.68

0.74

0.70

Mild

5

1.18

Difference Between Specification And Code[8]

0.48

0.72

0.61

Mild

13

X.2
2.1

Land Cost Increase [7, 8]

FINANCING FACTOR
0.72

0.82

0.77

Mild

9

2.2

Inflation [3, 8, 10]

0.77

0.81

0.78

Mild

5

2.3

Financial Difficulties [7, 10, 13]

0.78

0.84

0.80

Critical4

2.4Interest Rate Fluctuation [3, 9]

0.66

0.77

0.71

Mild

12

2.5

Foreign Currency Fluctuation [3, 10]

0.64

0.75

0.68

Mild

14

2.6

Lack Of Investor Interest In Project Finance [15]

0.76

0.78

0.76

Mild

10

2.7

High Cost Of Funding [14]

0.64

0.75

0.68

Mild

14

2.8

Additional Tax Fee[10]

0.56

0.67

0.59

Low

19

2.9

Late Payments On Completed Work [9, 16]

0.84

0.89

0.85

Critical2

2.10

Construction Cost Estimation Error [16]

0.87

0.90

0.92

Critical1

2.11

Delay Financial Closure [10]

0.77

0.81

0.78

Mild

5

2.12

Difficulty Getting A Bank Loan [9]

0.77

0.81

0.78

Mild

5

2.13

Rising Material Costs [9]

0.66

0.77

0.71

Mild

12

440

GEOMATE – Bangkok, Thailand, 22-24 November 2022

2.14

Financing Delays From The Government [8]

0.80

0.87

Crisp
Value
0.83

2.15

Government Credit Risk [12]

0.60

0.73

0.64

Mild

17

2.16

Spv Change [12]

0.47

0.66

0.49

Low

22

2.17

Profits That Are Not As Expected [9]

0.77

0.81

0.78

Mild

5

2.18

Devaluation [17]

0.64

0.75

0.68

Mild

16

2.19

Liquidity [17]

0.60

0.69

0.63

Mild

18

2.20

Changes In Discriminatory Tax On Projects [17]

0.55

0.68

0.58

Low

2.21
2.22
X.3
3.1
3.2
3.3
3.4
3.5
X.4
4.1
4.2
4.3
4.4
4.5
4.6

Financial Unclosed [in-depth interview]
0.70 0.76
World Economic Crisis [in-depth interview]
0.43 0.71
ENVIRONMENTAL FACTOR
Bad Weather[16]0.47 0.69
Natural Disasters[8]0.68 0.74
There Are Important Things[15]0.66 0.79
Unexpected Field Conditions[9]0.71 0.79
Environmental Protection [13]0.54 0.72
STAKEHOLDER FACTOR
Political Agenda Related To Project Decision[18]0.54 0.69
Lack Of Information Dissemination [18]0.60 0.69
Lack Of Attention From The Government [18]0.53 0.72
Lack Of Staff Capacity In Project Implementation [19]0.71 0.79
Lack Of Efficient Conflict Management[18]0.80 0.87
Difficulty In Assessing The Expectations Of Each
0.58 0.70
Stakeholder [19]Lack Of Coordination Between The Stakeholders Involved 0.71 0.79
[19]
PRE-CONSTRUCTRION FACTOR
Tender System Risk [10]0.41 0.66
Poor Feasibility Study [3, 10]0.67 0.80
Not Good Risk Allocation [3, 9]
0.55 0.75
Lack Of SPV Ability [3]0.78 0.84
Late Land Acquisition [3, 10, 16]
0.78 0.84
Late Compensation Affected By The Project [3, 15]
0.77 0.81
Late Clearing And Rehabilitating Workplace [15]0.44 0.66
Government Breach Of Contract [3]0.61 0.79
Many Of Land Brokers [8]0.78 0.84
Conflicts On Design And Specifications [10]0.66 0.79
Long Planning Time[14, 16]
0.63 0.76
Less Detailed Design [13]0.55 0.75
Lack Of Consulting Experience [13]0.71 0.79
Land Status [8]0.66 0.77
Rejection By Project-Affected Communities [8]0.54 0.77
Delay In Selecting Contractors During Tender Period[14]0.64 0.75
CONSTRUCTION FACTOR
Lack Of Contractor's Technical Ability[3, 10]0.84 0.89
Lack Of Supporting Infrastructure[14]0.76 0.78
Limited Amount Of Material [3, 10]0.70 0.76
Limited Number Of Workers [10, 14]0.70 0.76
Many Changes In The Scope Of Work[7, 15]0.71 0.79
Problems Due To Differences Of Opinion In Practice[3]0.54 0.72
Construction Price Escalation [9]0.77 0.81
Material Procurement Delay [8]0.79 0.84
Mismanagement By The Contractor [10]0.76 0.78
Design Error [10]0.64 0.76
Geotechnical Condition [13]0.72 0.81

0.73
0.54

Mild
Low

20
11
21

0.58
0.70
0.74
0.75
0.64

Low
Mild
Mild
Mild
Mild

5
3
2
1
4

0.58
0.63
0.56
0.74
0.83
0.62

Low
Mild
Low
Mild
Critical
Mild

6
4
7
3
1

0.75

Mild

No

4.7
X.5
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
5.10
5.11
5.12
5.13
5.14
5.15
5.16
X.6
6.1
6.2
6.3
6.4
6.5
6.6
6.7
6.8
6.9
6.10
6.11

Indicator[reference]

441

̃ 𝒍𝒊
𝒁

̃ 𝒖𝒊
𝒁

Criteria

Rank

Critical

3

5
2
0.46
0.73
0.59
0.80
0.80
0.78
0.55
0.65
0.80
0.67
0.67
0.63
0.75
0.71
0.63
0.68

Low
Mild
Low
CriticalCritical
Mild
Low
Mild
Critical
Mild
Mild
Mild
Mild
Mild
Mild
Mild

16
6
14
1
1
4
15
11
1
9
10
13
5
7
12
8

0.85
0.76
0.73
0.73
0.75
0.64
0.78
0.81
0.76
0.68
0.73

Critical
Mild
Mild
Mild
Mild
Mild
Mild
Critical
Mild
Mild
Mild

1
5
9
9
7
15
3
2
5
13
8

GEOMATE – Bangkok, Thailand, 22-24 November 2022

̃ 𝒍𝒊
𝒁

̃ 𝒖𝒊
𝒁

6.12
6.13
6.14
6.15
6.16
6.17
6.18
6.19
6.20
6.21
X.7
7.1
7.2
7.3
7.4
7.5
7.6
7.7

Theft [9]0.51Subcon Ability[9]0.72Construction Delay Risk [9, 13]0.75
Lack Of equipment Capability [13]0.57Lack Of Construction Quality [14]0.72Lack Of Procurement Of Tools [8, 13]
0.57
Specification Change [12]0.55
Lack Of Labor Productivity [9, 13]
0.57
Re-Work [13]0.57
Contractor Change [in-depth interview]
0.57
OPERATION FACTOR
Default Operators [3, 7]
0.57
Excess Operating Costs [7, 8, 15]
0.72High Maintenance Cost [7]0.73
Tariff Adjustment [7]0.59
There Are Infrastructure Defects [3, 12]
0.72
Income Risk [3, 12]
0.76
Demand Risk [3, 10]
0.60

0.66
0.78
0.86
0.74
0.78
0.74
0.78
0.74
0.72
0.74

Crisp
Value
0.55
0.72
0.77
0.63
0.72
0.63
0.64
0.63
0.61
0.63

0.74
0.81
0.84
0.80
0.81
0.89
0.72

7.8
7.9
7.10

0.48
0.57
0.57

7.11

Lack Of Operator Ability[15]Lack Of Service Quality[12, 15]
Not Effective And Efficient During Operation And
Maintenance[9, 14]
Many Traffic Accidents [8, 9]

7.12
7.13
7.14
7.15
7.16

Toll Road Disruption Due To Demonstrations [8, 9]Overload Risk[8, 9]
Low Traffic Volume[8, 9]Competitive Route [8, 9]
Vandalism [8, 9]

No

Indicator[reference]

The critical risks in the financing indicator are theerror in construction estimation, Late Payments on
Completed Work, dan Financing Delays from theGovernment. The State-Owned Corporation carriedout the construction estimation in preparing theconstruction by coordinating with the government
party[20], The late payments and financing delays
happened because the assignment scheme financing
for the equity part mostly relied on the State Equity
Participation scheme; according to Fakhrin [20] it isimportant to find another financing scheme to preventthe financing delays. Then, there is a critical risk
known as Lack of Efficient Conflict Management in
the stakeholder indicator. It is because of many
stakeholders are identified in the execution of theSumatera toll road construction project. [20].
In the pre-construction factor, the critical risk is in theLack of SPV Ability. Because the SPV recruitment in
the State-Owned Corporate is extremely limited inthis assignment scheme, not many other alternatives
involve the private sectors. In addition, it is the firsttoll road construction project with the assignment
scheme, so adaptation is needed. Moreover, Late
Land Acquisition and Many Land Brokers also
became the critical factors in the land clearing processthat minimized the project performance, inhibiting
the Sumatera toll road construction. This is because

Criteria

Rank

Low
Mild
Mild
Mild
Mild
Mild
Mild
Mild
Mild
Mild

21
11
4
16
11
16
14
16
20
16

0.63
0.73
0.75
0.68
0.73
0.80
0.63

Mild
Mild
Mild
Mild
Mild
Critical
Mild

16
4
3
7
4
1
9

0.69
0.72
0.72

0.55
0.61
0.61

Low
Mild
Mild

13
10

0.52

0.69

0.57

Low

0.49
0.62
0.71
0.54
0.49

0.67
0.76
0.87
0.79
0.64

0.54
0.70
0.80
0.65
0.55

Low
Mild
Critical
Mild
Low

10
12
15
6
2
8
14

the land clearing for toll areas is strongly different
from other projects; in the toll road project, the area
needed to be cleared quite large, and not all areasfrom the affected owners can be used or can only be
used partially [3].The critical risks during theconstruction that hinders the TranSumatera toll road
construction with the assignment scheme are the Lackof Contractor's Technical Ability and Material
Procurement Delay. It is similar to the TransJawa tollroad construction, specifically in Semarang – Solo,with the PPP scheme based on the owner’s perception[16]. In the operational and risk factor maintenanceperiod, the risk factors with the critical value were theincome risk and the low traffic volume. It isinterrelated as the small volume of the vehicles thatcrosses the Sumatra toll road will result in a low
income for the toll road. The risk factors included inthis group are crucial in the toll road construction
project with the SOE assignment scheme and theymust get a special attention.The failure in dealing
with this risk is the main cause that results in adding
a significant cost and the decrease in the targeted
performance of the toll road construction with thisState-Owned Corporate assignment scheme.

442

GEOMATE – Bangkok, Thailand, 22-24 November 2022

CONCLUSIONS[10] Pai, S., et al., Identification of risks causing timeand cost overrun in roads and highway projectsin
India. International Journal of Civil Engineeringand Technology, 2018. 9(3): p. 683-697.
[11] Castelblanco, G., et al., Risk allocation in
unsolicited and solicited road public-private
partnerships: Sustainability and management
implications. Sustainability, 2020. 12(11): p.
4478.
[12] Jokar, E., B. Aminnejad, and A. Lorak, Risk
prioritization and selection of contractor
participating in Public-Private Partnership (PPP)
infrastructure project using Hybrid Fuzzy AHP
and Fuzzy TOPSIS (Case Study: SavehSalafchegan Freeway Project). Journal ofConstruction Engineering, Management &Innovation, 2020. 3(1): p. 1-16.
[13] Patel, T.D., T.C. Haupt, and T. Bhatt, Fuzzy
probabilistic approach for risk assessment of
BOT toll roads in Indian context. Journal ofEngineering, Design and Technology, 2019.
[14] Ahmadabadi, A.A. and G. Heravi, Risk
assessment framework of PPP-megaprojects
focusing on risk interaction and project success.
Transportation research part a: policy andpractice, 2019. 124: p. 169-188.
[15] Deshpande, P. and S. Rokade, Prioritization andassessment of critical risks of public private
partnership highway projects in India using
analytical hierarchical process. International
Journal of Civil Engineering and Technology,
2017. 8(6): p. 605-620.
[16] Mochamad, A.W., Risk Management inIndonesia Construction Project: A Case Study ofa Toll Road Project. 2018.
[17] Suseno, Y.H., M.A. Wibowo, and B.H. Setiadji,
Risk analysis of BOT scheme on postconstruction toll road. Procedia Engineering,2015. 125: p. 117-123.
[18] Johnston, J. and A. Kouzmin, Addressing
governance, accountability and performancemonitoring
issuesinpartnerships:
can'infrastructure Australia'provide a strategic
response? Public administration quarterly, 2010:
p. 513-551.
[19] De Schepper, S., M. Dooms, and E.
Haezendonck, Stakeholder dynamics andresponsibilities in Public–Private Partnerships: A
mixed experience. International Journal of
Project Management, 2014. 32(7): p. 1210-1222.
[20] Fakhrin, M.R., L.S. Riantini, and Y. Latief. RiskBased Institutional Evaluation of Trans Sumatera
Toll Road Infrastructure Development to
Improve Time Performance. in International
Conference on Global Optimization and Its
Applications 2021. 2021.

From the research above, it can be concluded that :1. The purpose of this paper was to identify therisk factors responsible for the excessive
time and cost on the SOE assignment
scheme-based toll road project and identify
which of the factors above has a significant
impact on excessive time and cost on the
SOE assignment scheme-based toll roadproject.
2. The analysis of the survey result and theprocessing using the Rough Fuzzy Delphi
reveals that 12 major risk factors
significantly impact the SOE assignment
scheme-based highway and toll roadprojects in terms of excessive time and cost.
REFERENCES[1] BPJT-PU. Tahapan Makro Pengusahaan Jalan
Tol.
[cited 2020 02-08]; Available from:http://bpjt.pu.go.id/konten/investasi/tahapanmakro-pengusahaan-jalan-tol.
[2] Indonesia, R., Peraturan Presiden tentang
Perubahan atas Peraturan Presiden Nomor 100
Tahun 2014 Tentang Percepatan Pembangunan
Jalan Tol Sumatera, Nomor Perpres Nomor 117
Tahun 2015. 2015.
[3] Likhitruangsilp, V., S.T. Do, and M. Onishi, A
comparative study on the risk perceptions of the
public and private sectors in public-private
partnership (PPP) transportation projects in
Vietnam. Engineering Journal, 2017. 21(7): p.
213-231.
[4] Chen, Z., et al., A rough-fuzzy DEMATEL-ANP
method for evaluating sustainable value
requirement of product service system. Journalof Cleaner Production, 2019. 228: p. 485-508.
[5] Zadeh, L.A., Fuzzy sets. Information and control,
1965. 8(3): p. 338-353.
[6] Song, W. and J. Cao, A rough DEMATEL-based
approach for evaluating interaction between
requirements of product-service system.
Computers & Industrial Engineering, 2017. 110:
p. 353-363.
[7] Valipour, A., et al., A fuzzy analytic network
process method for risk prioritization in freeway
PPP projects: an Iranian case study. Journal of
Civil Engineering and Management, 2015. 21(7):p. 933-947.
[8] BPJT-PU, Analisis resiko investasi jalan tol, D.P.
UMUM, Editor. 2005, Kementerian Pekerjaan
Umum: Jakarta.
[9] Wirahadikusumah, R.D., B. Susanti, and B.W.
Soemardi, Risk in government’s estimate for tollroad: based on investors’ perspective.
International Journal on Advanced Science,
Engineering and Information Technology, 2018.
8(2): p. 475-482.

Copyright © Int. J. of GEOMATE All rights reserved,
including making copies unless permission is
obtained from the copyright properties.
443

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

RESULTS OF RECOVERY PROJECT ON WETLANDS WITH LOST
BIODIVERSITY
Michiko Masuda1, Koichi Nagarekawa2 and Fumitake Nishimura 3
1,2

Faculty of Engineering, Nagoya Institute of Technology, Japan; 3 Faculty of Engineering, Kyoto
University, Japan
ABSTRACT

Many wetlands have been lost by vegetation succession and construction development. From the point of SDGs
views, the disappearance of wetland ecosystem is a serious loss to the value of biodiversity. But all wetland will
disappear due to vegetation succession before long. Until recently 20 century the new wetlands were born fromslope collapse or flood ever. Now the phenomenon is prevented for safety people life. In order to maintain thewetland, we must protect the wetland from the vegetation succession. Wetland desiccation has changed thevegetation and led to a decrease in biodiversity. Therefore, in order to restore the wetland, we stripped thevegetation and investigated the flora and environmental factors that regenerate there. And we measured theenvironmental factors before stripping and after it. As a result, vegetation stripping restored the wetland flora over
5 times, and brought about a resurgence of biodiversity. In addition, vegetation stripping resulted in a decrease innutrients, a decrease in pH, and a decrease in electro conductivity. To conserve the wetland biodiversity stripping
method is very effective.
Keywords: Vegetation succession, biodiversity, environmental condition, poor nutrition

INTRODUCTION

on a trial basis from 2013 in the Imou Wetland (3.2ha)
located in Toyohashi City, Aichi Pref., in order tostop forestation and restore the good wetlands on thepast. In this operation, organic matter was removed
by cutting trees that were once wetlands and stripping
the topsoil at a part of wetland. In this way, thepurpose of removing organic matter is to suppress
forestation to rescure sunshine and to suppress
eutrophication to make it oligotrophic, thereby
creating an ideal environment for spring wetlands.
This is because the buried seeds in the soil can be used
to regenerate the wetland. In facts some vanishedspecies recovered from the soil. Though the recovermethod is very attractive for conservation, there are
no analysis and survey. And we cannot know that allarea with recovery method can be revived, then it isvery danger to apply the method to small wetland.
Therefore, in this research, we focused on thefollowing three points. First, how has the physicalenvironment changed due to logging and stripping of
topsoil? Second, How did the vegetation change
before and after the recovery method? Third, are there
enough buried seeds even in a wetland with a small
area of about <10 area?

In Japan, wetlands are likely to the target ofdevelopment such as reclamation and landfill. The
result of that many wetlands have disappeared so far.
According to the Geospatial Information Authority ofJapan’s Lake Wetland Survey 2000, 61.1% of
wetland area has disappeared in Japan for 150 years[1]. In Aichi Prefecture with Tokai Group whichcontain argillaceous layer, wetlands used to exist
everywhere in this area. But many spring wetlands areused as catchment areas for residential landdevelopment, agricultural land, road construction, etc.
It has disappeared with the forest and its area has
shrunk. In addition, many wetlands in Aichi Pref. aresmall size under 10 are, many of them disappeared
due to vegetation succession. Furthermore, in recent
years, new wetlands are rarely formed due toimprovements in civil engineering technology anddisaster prevention measures.
There are many conservationally importantbiological communities including endangered species
in these declining spring wetlands. The spring
wetlands in the Tokai region (that contains Aichi
Pref.) grow mainly in the spring wetlands in the Zhou
Ise Bay region. It is strongly associated with Tokai
hill element plants, which are a classification groupof endemic, quasi-indigenous and isolated plants, andacademic research on relatively large number ofspring wetlands has been carried out in the Tokai
region [2]-[4].
Amid the problem of wetland reduction, a largescale vegetation restoration operation was carried out

MATERIALS AND METHODS
Study site
The two places of the investigation were locatedin Nagakute City, Aichi Pref. Site A is on the campusof Aichi University of Arts (35o16’73” north latitude,
137o16’97” east longitude) and site B is on444

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Yamanouchi (35o18’67” north latitude, 137o07’97”
east longitude) at center of Japan (Fig. 1). Site A wasa wetland that has begun to dry and shrubs haveinvaded. Site B was once a wetland, but vegetation
succession has progressed and it has become forested.
Although it gets a little damp, no spring water can be
seen.

camera (Ricoh theta V) with CanopOn software.
Statistical Analysis

At each site, eight 1m x1m quadrats were placed
at 1m intervals along the contour lines in 2 row and 4
columns. The numbers are 1 to 4 from the left on theupper side of the slope and 5 to 8 from the left on thelower side of the slope. The following surveys wereconducted on each of the installed quadrats.
In order to investigate the effect of the recovery
method, logging and topsoil was stripped on 23th and24th Jan. 2019.

Two-way ANOVA was performed to investigate
the annual variation and homogeneity of each plant.
There were 43 species of plants identified through theresearch time, but non wetland plants and woody
plants that were not conservation targets, such as fern,
Rhododendron kaempferi, Pleioblstus chino var.
viridis, Ilex pedunculosa, I. crenata var. crenata,
Almus japoinica, Eurya japoinica var. japonica,
Akebia trifoliata, Rosa multiflora etc…16 species
combined into one as a non-conservation plant, andTokai hill element plants, and wetland plants with alarge number of samples are tested.
The dependent variable was the vegetation
coverage area of each plant, and the two qualitative
factors were the year and the quadrat, respectively. It
is assumed that the population distribution of eachgroup follows the normal distribution N (µ, 𝜎2), andthe population variance σ2 is unknown, but all are
equal. Null hypothesis: the mean value between eachlevel is equal. Alternative hypothesis: at least one of
the mean values between levels is not equal, thesignificance level is set to 0.05 and the rejection area
R is set as follows. F0 is F distribution (normal), and
𝜑A is the difference between quadrats and 𝜑E is thedifference between years, 𝛼 is a critical ratio(p<0.05)

Vegetation survey

R: F0 > F (𝜑A, 𝜑E; 𝛼)From April to December 2018, 2019 and 2020, thevegetation coverage area was calculated for eachplant species that grows in quadrats once a month. To
calculate the cover area, each quadrate was divided
5cm x 5cm and counting the number of mini squares
with occupied each species at 3 levels (full, half, non).

If null hypothesis is in the rejection area R, it isjudged to be not significant and null hypothesis isrejected. Analysis of variance shows that if
significant, there is a difference between any one ormore groups, but no information is available as towhich group there is a difference.
We performed analysis using GLM (Generlized
Linier Model, using quick-R: correspondence
analysis program) to elucidate the cover area ratio of
each plant species and its relationship toenvironmental factors between October 2019 and2020. Since the probability distribution of the
response variable follows the binomial distribution(ratio distribution so the binominal distribution), thelink function is a logistic function.

siteB
siteA

Fig. 1

The map of study site

Environmental Factor Measurement
We measured each environmental factor at eachquadrat once a month from January 2019 to
December 2020. The first measurement is thecondition before the recovery method. Fourenvironmental parameters, loss on ignition (IL), pH(ph), electrical conductivity (ec) and soil hardness(sh) were measured. Loss on ignition was measured
by sampling center and four corner at each quadrat.
After sampling the materials was into an incubator at
120oC for 2days in order to loss the water. pH andelectrical conductivity was measured 5 point at eachquadrat (center and four corner) with mobile
measurement machine (Eranear SZTZ2SET). Soilhardness were measured by Yamanaka method soil
hardness meter(KamiyamaCo 401-0055) at eachquadrat. And sky openness was measured at January,
October 2019, June and October 2020 using by 360o

𝜂 = log(

y
1-y

)

(1)

(2)

𝜂 is a function of the explanatory variable, and in thecase of a generalized linear model, it is represented
by a linear function of the explanatory variable.
The environmental factors are five types ofmeasurement data obtained by measuring, loss on
ignition (hl), pH (ph), electrical conductivity (ec), soil
hardness (sh) and sky openness (op).
In this study, the variable reduction method is445

GEOMATE – Bangkok, Thailand, 22-24 November 2022

adopted the method of selecting the variables used in
the model. This is a method of inputting all variables
into the model and excluding those with weak
explanatory power. Akaike’s Information Criterion(AIC) was used as a criterion for evaluating the fit of
the model variables.
AIC = -2ln L + 2kchanging seasonally, but the decline was observed.
Fig.4 showed the electrical conductivity. This value
showed the water dirt, so the value is high in winterand gradually decline to the summer. Fig. 5 showed
the value of sky openness. The value of median waschanging but there were not differences about the sky
openness.

(3)
Table 2 Total vegetation coverage of each species atquadrat (cm2) in siteB

L is the maximum likelihood and k is the number ofparameters. A model with the smallest AIC value wasadopted.
RESULTS
The stripped conservation method was shown
surprising vegetation changes. Table 1and 2 showed
the change before and after topsoil stripping
experiment. The structure of plant species waschanging after the stripping, many wetland species
emerged and some dry area species decreased.
Table 1 Total vegetation coverage of each species at
each quadrat (cm2) in siteABlue is a wetland species, red is a forest species.

50 40 -

Ignition loss (%)

30 20 10 050 40 30 20 -Blue is a wetland species, and red is a non-wetland

10 -Fig. 2, 3 and 4 showed the environmental factor
changing after stripping. Fig 2 showed the ignition
loss. Before the stripping the value of IL is very high,and after the experiment, seasonal change wasobserved but the value was not over 20%. Fig. 3
showed the value of pH. The value of pH was

0Ctl J F M A M J J A S O N D F M A M J J A S O N D
2019
2020

Fig. 2 The seasonally change of Ignition loss atsite A and B
446

GEOMATE – Bangkok, Thailand, 22-24 November 2022

7.0 -grow. But the roots penetrate to the lower soil layer.

Site A

Table 3 Mean and SD of soil hardness at each sitemean
SDsite A
6.212
3.185
site B
5.107
7.062

6.0 5.0 -pH

4.0 3.0 7.0 -The result of ANOVA among the distribution ofheterogeneity and years was shown in Table 4. The
species of P < 0.05 was shown in Table 5.

Site B

6.0 -Table 4 The value of ANOVA test between quadrat
and year

5.0 4.0 3.0 -FMAMJ J A SO NDFMAM J J A S OND
2019
2020

Fig. 3 Seasonal change of pH at each site
0.10-

Site A
0.060.040.0200.10-

Site B

0.080.060.040.020-

J J
2019

A

S

O

N

D J
2020

A

S O

N

D

Fig. 4 The seasonal change of electrical conductivity
at each site
Site BSky Openness (%)

Electrical conductivity (S/m)

0.08-

Site A
19Jan. Oct. 20Jun. Oct

From the data of field census, the number of 11
species was sufficient to create new models, one
species non a wetland species Ixeris dentata, ten
wetlandspecies,
Moliniopsis
japonica,
Rhynchosporachinensis,
Scirpuswichurae,
Eleocharis wichurae, Juncus papillous, Carex
capillacea, Utricularia bifida, Utricularia nipponica,

19Jan. Oct. 20Jun. Oct

Fig. 5 The Sky openness at each site
Table 3 showed the soil hardness of each site. The
value of the soil hardness was 0 to 17.63mm thatshowed the soil hardness enough for the plants to447

GEOMATE – Bangkok, Thailand, 22-24 November 2022

𝛼 in Table 5 is initialization parameter.
The result of the GLM, six species, M. japonica,
S. wichurae, E. wichurae, C. capillacea, D. tokaiensis
and I. dentata, are easy to grow in an environment
rich with large value of ignition loss (parameter hl).
And among these species, low pH condition is harshenvironment for plant roots, but four species, M.
japonica, E. wichurae, D. tokaiensis and I. dentata,that prefer it.

Dorosera tokaiensis and Eriocaulon nudicuspe.
Table 5 The GLM model of 11species of eachparamer

DISCUSSION
Many researchers worry about the extinction ofendangered species [5]-[7]. The United Nation
Summit calls for the protection of living organisms
from the SDGs aim, but the rate of extinction has notdecreased [8]. In Japan many endangered species thatgrow in wetlands have been reported, and it isrequired to propose a method for conserving species
in wetlands [9]. It is very important to establish amethod for wetland regeneration. Some people
showed the experiment of recover wetland for
logging and stripping top soil, there have been no
examples analysis of changes in environmental
condition or the quantitative status of restoration.
This study is the first example of an analysis of theenvironmental effects of logging and topsoil removal,
and the amount of emergence of what plantcontinuously.
According to the vegetation survey, the treatmentof experiment restored the lost wetland plants andrestored species that had not been reported in the last
20 years. Wetland restoration was shown to bepossible regardless of the level of vegetation
succession. However, the recovery of wetland plants
has been shown to be significantly heterogeneous
spatially and temporally. This showed that it is notalways possible to restore all species in a small area.
We must think about the amount of area in order torestore the wetland with high biodiversity.
When all ground cover removed, burial seeds
emerged and make the population of wetland. And
physical environment has also changed, in particular,
ignition loss reduced. It was shown that once theorganic matter is removed, ignition loss can be kept
low, but pH and electro conductivity changed
seasonally. Environmental factors change seasonally,
it is difficult to find out the relationships between
environmental factors and plant species. GLM is very
convenient to find out the relationships [10]-[11]. The
models show that some endangered species (M.
japonica, R. chinensis, J. pappillous, two Utricularia
speices, D. tokaiensis , E. nudicuspeI) have low pH
and low nutrient (electro conductivity) even withlarge ignition loss.
Four species, M. japonica, S. wichurae E.
wichurae and C. capillacea, that prefer a place withhigh ignition loss and abundant nutrients are
considered to be able to grow even if the wetland
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

transitions. Because of these species with a high rateof population expansion form a patch community, we
propose that these species preferentially removed aswell as other no wetland species when the
eutrophication of wetland progresses.

REFERENCES[1] Geospatial Information Authority of Japan.,
Survey results of changes in wetland area
throughout Japan, 2000, https://www.gsi.go.jp/
kankyochiri/shicchimenseki2.html.
[2] Ueda K., Phytogeography of Tokai Hilly landelement I. Definition, Acta Phytotaxonomy
Geobotany, Vol. 10, 1989, pp. 190-202.
[3] Nakamura T. and Ueda K., Phytogeography ofTokai Hilly land element II. Taxonomic study ofDrosera tokaiensis (Komiya & C. Shibata) T.
Nakamura & Ueda (Doroseraceae), Acta
Phytotaxonomy Geobotany, Vol. 42, Issue 2,
1991, pp. 125-137.
[4] Tomita K., How well have we understood theenvironment of the seepage marsh in the Tokai
area of Japan?, Wetland Research, Vol. 8, 2018,
pp. 63-79.
[5] Dyke V.D. and Lamb R.L., Conservation
Biology: Foundation, Concepts, Applications,
Springer, 2020. pp. 11-58.
[6] Primack R.B., Essentials of Conservation Biology,
6th edition, Sinauer, 2017, pp. 3-78.
[7] Meffe G.K. and Carroll C.R., Principles ofConservation Biology, Sinauer, 1997, pp. 3-158.
[8] IUCN, Summary Statistics, 2021,
https://www.iucnredlist.org/resources/summarystatistics.
[9] Washitani I., Floodplain wetland loss andregeneration: efforts to utilize paddy fields aswetland, Earth Environment, Vol 12, 2007, pp.
3-6.
[10] Sugiyama Y., Nakamura M., Senda S, andMasuda M., 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[11] 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.

CONCLUSIONS
The following conclusions are drawn from thestudy.
1) The physical environment changed tologging and stripping of topsoil, especiallyloss ignition. After stripping, loss ignition
stable at low level, but pH and electro
conductivity were changing seasonally. pH
was high after stripping and gradually lower
with high level observed in winter. Electro
conductivity was high in winter because of
the deciduous leaves.
2) Vegetation was changed after the recovery
method. Especially half of non-wetland
species was extinct from the wetland. And
some endangered species restored in somequadrats.
3) In small wetland (under 10 area), there wereenough burial seeds to restore the wetland,
but the heterogeneity was observed among
quadrats.
4) Wetland can be restored by recovery
measures even if the vegetation succession
progressed and wetland has alreadydisappeared.
The logging and stripping top topsoil are useful torestore the wetland in small wetland. But the burial
seeds are unevenly distributed, so it is necessary toconsider the area to be restoration treatment.
ACKNOWLEDGMENTS
We send thanks to Prof. T. Kitano who advised
the analysis of statistical method. We also thank to thecolleague at our laboratory, Nagakute city at Aichi
Pref. and the group of ‘the Nagakute wetland
conservation group’, they helped the field census.

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Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

PREPARATION OF GEOPOLYMER CEMENT FROM LUNAR ROCKSAND USING ALKALI FUSION, AND ITS EVALUATION OF
RADIATION SHIELDING ABILITY
Osamu Toda1, Takaaki Wajima2
1,2

Graduate School of Science and Engineering, Chiba University, Japan

ABSTRACTIn this study, simulated lunar rock sand was converted into geopolymer cement by alkali fusion with sodiumhydroxide, and its radiation shielding ability was estimated. Space development is currently being conducted invarious countries. For construction on the moon, it is difficult to bring all the construction materials from the earth,and construction materials prepared from lunar resources are required. In addition, it is necessary to develop
construction materials with radiation shielding ability since the lunar surface is directly affected by space radiation.
In a previous study, it has succeeded in making geopolymer cement from simulated lunar rock sand using alkali
fusion. Therefore, there is a possibility that geopolymer cement used for construction material can be preparedfrom lunar rock sand, abundantly present on the lunar surface.
In this experiment, geopolymer cement was prepared from simulated lunar rock sand by changing the mixing
ratio of the sand, sodium hydroxide and water, and strength of the cement was examined to obtain high-strength
geopolymer. In addition, the resulting high-strength geopolymer cement was subjected to radiation shielding tests
to confirm its radiation shielding ability. As a result, it was found that the high-strength geopolymer cement withradiation shielding ability can be prepared from simulated lunar rock sand using alkali fusion with sodiumhydroxide.
Keywords: Simulant, Alkali fusion, Geopolymer cement, Radiation shielding, Maximum breaking load

INTRODUCTION

present on the moon by alkali fusion of lunar rocksand to supply silicate ions and metal ions.
Therefore, in this study, we attempted to convert
simulated lunar rock sand into geopolymer cement byalkali fusion to be mixed with water setting in vacuum,and evaluated its strength and radiation shielding
ability.

Space exploration with unmanned spacecraft forspace development is currently active around theworld. The moon is satellite of the planet earth, thenearest astronomical object, and development as abase for space development is planned. If all
construction materials for the base construction were
to be transported from the earth, the cost would beenormous (approximately 100 million yen/kg).
Therefore, the development of construction materials
made from lunar resources is required.
In this study, we focused on lunar rock sand(regolith) which is abundant on the lunar surface.
Regolith is the sand that covers the surface of themoon with a thickness of several meters and is mainlycomposed of SiO2 and Al2O3 in the form of aluminosilicate minerals, such as anorthite, albite and so on.
In our previous study, the authors have succeeded in
making geopolymer cements from lunar simulated
sand (simulant) using alkali fusion [1]. Althoughordinary Portland cement is mainly solidified by theformation of needle-like calcium silicate hydrate (CS-H), geopolymer cement is mainly solidified by thepolymerization of silicate ions bridged by metal ions,such as Al3+, Fe3+ and so on, which is similar to thereaction of zeolite synthesis [2, 3]. It is possible to
make geopolymer cement which can be used as abuilding material from lunar rock sand abundantly

EXPERIMENT
SampleSimulated lunar rock sand (Month soil simulation
FJS-1, CSP Japan, Inc.) was used in this study. The
chemical composition is mainly composed of 42% of
SiO2, 14% of Al2O3, 14% of CaO, 13% of Fe2O3 and
3% of MgO.
Geopolymer preparation
The precursor of geopolymer are made by mixing
the sample with sodium hydroxide powder in aweight ratio of 6: 1 to 3: 1 (17%～33%), put in aplatinum crucible, and heated up to setting
temperature (500 °C) for 10 min in vacuum
atmosphere. Thereafter, geopolymer cement are
made by mixing the precursor of geopolymer withwater in a weight ratio 6 : 1 to 3 : 1 (17%～33%),
forming into a mold (φ5 mm × 20 mm), heating in
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vacuum atmosphere at 120 °C for 1 to 24 h to obtain
the product.
The precursor of geopolymer (1 g), which was
subjected to alkali fusion at each mixing ratio of lunar
simulated sand with sodium hydroxide, was put into
1 mol/L hydrochloric acid (20 mL) and shaken for 6hours, followed by filtration. The concentrations ofsilicon, aluminum, iron, and calcium in the filtrate
wereanalyzedbyatomicabsorption
spectrophotometry (Perkin Elmer, AAnalyst 200) andthe elution amounts (S) (mg/g) were calculated using
the following equation.

the distance between the radiation source and the
instrument is changed, and the radiation dose at eachpoint is recorded.

𝑆 = 𝐶 × 𝑉/𝑤

RESULTS AND DISCUSSION

Fig. 2(1)

Radiation shielding measurement method

Maximum breaking load

where C is metal concentrations (mg/L), V isfiltrate volume (L) and w is the weight for precursorof geopolymer (g).

The maximum breaking loads for the products
after 24 h heating on various conditions are shown in
Table 1. The product with the highest breaking load
was obtained when the weight ratio of lunarsimulated sand to sodium hydroxide was 4: 1 (25 %)and the weight ratio of the precursor to water was 5:
1 (20 %), indicating 584 N. It is noted that no brittle
fracture can be observed on the conditions indicating
N.D. As a comparison, the strength of ordinary
Portland cement indicates higher than 1000 N. The
product has a strength less than that of ordinary
Portland cement.
When the water ratio is higher than 25 %, thestrength is week (lower than 150 N). It is consideredthat an increase in the amount of water in the mixturecauses insufficient hardening by decreasing thebonding of silicates and metal ions due to the dilution,
resulting in low strength.

Strength test
The maximum breaking load of the products wasmeasured using a strength tester (ZT series ZTA-500
N, IMADA), referring to the compression test of thestrength test method in JIS Z 8841[4]. Strength test
method is shown in Fig. 1. The cross section after
breaking was observed using SEM(JSM-6510A,

JEOL) to analyzed the morphology of each sample.

Table1
The maximum breaking loads for the
products after 24 h heating on various conditions
Fig. 1

Strength test method

Alkali ratio (NaOH/Simulant)
Water ratio(Water/Precursor)

Radiation shielding ability evaluation
The product for shielding test (5 cm × 5 cm × 5mm) was prepared under the conditions to obtain the
product with the highest strength, and its radiation
shielding ability for γ-rays was evaluated using aradiation measuring instrument (KIND-pro, JSF) anda radiation source (uranium). Ordinary Portland
cement (W/C = 50 %, 14 days) was used forcomparison of radiation shielding performance.
Radiation measurement method is shown in Fig. 2. Aradiation source (uranium) is placed on a flat surface,and each shielding and measuring instrument isplaced on a straight line. By moving the instrument,

(%)

17

20

25

33

17

421 N

310 N

363 N

319 N

20

382 N

464 N

584 N

360 N

25

N.D.

N.D.

N.D.

123 N

33

N.D.

N.D.

N.D.

N.D.

N.D.: Not detected

The cross section of samples after strength test is
shown in Fig.3. Samples with water ratios greaterthan 25% showed some large holes in the crosssection, while those with water ratios less than 25%
were almost filling overall.

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig.3 The cross section of samples after strength
test; (a) water ratios greater than 25% and (b) water
ratios less than 25%
Elution of Si, Al, Fe and Ca from precursor of
geopolymer with various NaOH addition is shown in
Fig.4. The amount of silicon elution increased as the
addition of sodium hydroxide increased. The elutionof aluminum and iron were nearly constant, while
calcium was almost completely undissolved
regardless of the addition of sodium hydroxide.

Fig.5 Ratio of the amount of metals dissolved fromthe precursor of geopolymer with various NaOH
addition and the strength of their prepared products
The solidification of the samples was examinedby heating the mixture of lunar-simulated sand andsodium hydroxide at a weight ratio of 4: 1 (25 %) andthe precursor and water at a weight ratio of 5: 1 (20 %)under a vacuum atmosphere for 1 h to 3 h at 120 ℃.
It is noted that 120 ℃ is the average of surface
temperature on the moon during the sunshine. The
maximum breaking load of the prepared geopolymer
cement as a function of heating time is shown in Fig.6.
The strength is small from 0 to 2 h and increases tobecomes constant (about 200 N) over 2 h.

Fig.4 Elution of Si, Al, Fe and Ca from precursorof geopolymer with various NaOH addition
The ratio of the amount of metal dissolved fromthe precursor of geopolymer with various NaOH
addition and the strength of their prepared products
are shown in Fig.5. It is noted that calcium is notincluded in the metal ratio because very little calcium
is dissolved from the precursor. The dissolution ofaluminum and iron relative to the dissolution ofsilicon (Si/(Al+Fe)) is smaller, the strength is higher.
This is because in the solidification of geopolymer
cement, trivalent cations, such as iron and aluminum
ions, react with silicate monomers to cross-link eachother to form polymers, and when the ratio is smaller,
more cross-linking promotes to form polymers to beharden.

Fig.6 The maximum breaking load of the preparedgeopolymer cement as a function of heating time
Furthermore, wet areas were observed on thesurface of the sample after heating at lower than 2h,
and the wooden needle pick was stuck on the surfaceof the sample, while the wooden needles was notstuck on the samples after heating over 2 h. It isconsidered that insufficient solidification occurred
when the heating time was less than 2h due toinsufficient dehydration reaction during theformation of the geopolymer, and solidified
sufficiently after 2 h heating.

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After
2 hours

Within2 hours

Fig.7 Photo after heating for 1 hourFig.9 Radiation dose of the product and ordinary
portland cement

SEM photos for cross sections of the productprepared by heating at 1 - 3 h are shown in Fig.8. At
1 h and 1.5 h, many gaps were observed in various
places, while at 2 h and 3 h, no large gaps wereobserved, and the gaps were generally filled. It isconsidered that gaps were filled by the geopolymer
due to the dehydration of water.

CONCLUSIONSAn attempt was made to prepare geopolymer
cement by alkali fusion of simulated lunar rock sandwith sodium hydroxide. The highest strength was
found when the ratio of lunar simulated sand to
sodium hydroxide and water was 4 : 1 : 1 (the mixtureof lunar-simulated sand and sodium hydroxide at aweight ratio of 4: 1 (25 %) and the precursor andwater at a weight ratio of 5: 1 (20 %)), and 2 h heatingtime in vacuum at 120 ℃ was found to be sufficientfor the dehydration reaction in the formation ofgeopolymer.
The obtained geopolymer cement has theradiation shielding capacity and its capacity was
found to be comparable to that of commerciallyavailable ordinary portland cement.
REFERENCES[1] Sakamoto K. and Wajima T., Preparation ofgeopolmer cement from simulated lunar rock sandusing alkali fusion, International Journal of
Geomate, vol.18, 2020, pp.62-67.
[2] Melkon T., Gunther M., and Stefan K. H.,
Relation of water adsorption capacities of zeolites
with their structural properties, Microporous andMesoporous Materials, vol.264, 2018, pp. 70–75.
[3] Habbib G., Oliver S., Yvan M. and Philippe K.,
The Reconstruction of Natural Zeolites, Kluwer
Academic Publishers, Dordrecht, 2003, pp. 1-5.
[4] Sekiguchi I. Test methods for evaluating thestrength of granules and agglomerates, 1994.

Fig.8 SEM photos for cross sections of products
prepared by heating at (a)1 h, (b)1.5 h, (c)2 h, and (d)3hRadiation shielding ability
The radiation doses of the prepared geopolymer
cement (GP) product, ordinary portland cement (PC),no shielding, and background (BG) are shown in
Fig.9. Geopolymer cement has radiation shielding
ability since it is lower than no shielding. The
radiation shielding capacities of geopolymer cement
product were almost same as those of portland cement.

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12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

PROPOSAL OF A FLOATING OFFSHORE BASE FOR DISASTER
PREVENTION AND MULTIPURPOSE USE
Shinji Sato1 and Kai Nagatomi2
1

Department of Oceanic Architecture and Engineering, College of Science and Technology, Nihon
University, Japan; 2 JR East Design Corporation, Japan
ABSTRACTTo preserve sustainable environment, protect resources, and adhere to the basic rule of the international society
in the marine environment, the installation of offshore floating fishery bases and the operation of suchcomprehensive facility are essential. Japan’s exclusive economic zone is the 6 th widest in the world. The mosturgent issue is the Tokai earthquake that is predicted to occur within the next 30 years with a probability of 87%
and associated tsunamis. Such tsunami damage is feared to impact not only Japan but many countries facing the
Pacific Ocean. Thus, the installation of a survey base to accurately capture an occurrence and the scale of tsunami
would be an effective measure. This facility is expected to be used for multiple purposes during regular times. Oneof the functions is a multipurpose harbor. By fishing, processing, and exporting all in fishing grounds in the ocean,work efficiency is improved and greenhouse gas emission is reduced. Furthermore, the present plan is based near
the Ogasawara Islands, an area with limited tsunami impact, proposing to be an innovation base that incorporates
exploration of various untapped ocean floor resources, ocean thermal energy conversion, and tidal power
generation research.
Keywords: floating base, tsunami monitoring, resource management, marine research, integrated research

BACKGROUND
Reviewing Marine Resources and Promoting New
Development
Use of marine resources has been mainly catchingof fish and shellfish as part of fisheries. However, in
recent years, wind over the sea is being utilized
worldwide. With a goal of reducing greenhouse gas
emissions to counter climate change, use of seawater
itself is being promoted. In addition, there isworldwide research and development on power
generation using ocean current and seawatertemperature difference, and there is an urgent
establishment of technologies and profitability forrare metals in seawater and minerals on the seafloor.
As such, there are many marine resources that are notdependent of fish and shellfish, and theirdevelopment is anticipated.
Global Fisheries and Aquaculture Production
Global yield in fisheries has dramaticallyincreased since 1990s. In 2000s, the yield continued
to grow in China and other Asian countries. China,
especially, accounted for 15% of the world’s yield at
about 14 million tons in 2010 to 2018. In terms of theaquaculture production, China and Indonesia showedmost notable increase. China accounts for about 68
million tons while other Asian countries account for
about 20 tons. The total accounts for over 70% of theworld’s yield (Fig. 1).

Fig. 1

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Changes in the global yield and aquaculture
production [1]GEOMATE – Bangkok, Thailand, 22-24 November 2022

Relationship Between Decreasing Yield andDecreasing Number of Fishery Workers in Japan

Under such a circumstance, in this Chapter, we
propose the following five items as the plan policies.
It means that it is time to stop considering the ocean
as the biological resource only.

Around Japan, there are marine areas with highpotential for fishing grounds, such as Northwest andMidwest Pacific Ocean. Furthermore, Japanese EEZ
extends about 4.47 million km2, which is 12 times
Japan’s land area, which is the 6th widest in the world.
With such favorable fishing grounds, Japan has
been operating diverse fishery work on open seas,
offshore areas, and coastlines. However, number offishery workers in Japan was about 152,000 in 2018:
14,000 reduction in the last 20 years. Thus, theaverage age of fisher workers is high, 56.9 years,
clearing showing aging (Fig. 2).
A possible cause of this is the low productivity of
the fishing industry. When compared to othercountries (2013 fishery census by Ministry ofAgriculture, Forestry and Fisheries and 2014 fisheries
employment survey)—production per fishing boat of
791.7 (ton/boat) for Iceland, 637.9 (ton/boat) for
Norway, and 404.2 (ton/boat) for New Zealand—
Japan’s production is about 20 times lower at 31.2
(ton/boat). As production per a fishery worker, thedifference is about 10 folds as well. As such, this islinked to a decrease in the number of fishery workers.
Consumption of Fish and Shellfish as Food inJapan
Changes in consumption of fish and shellfish as
food per person per year in each major country andregion (Fig. 3) shows that Japan is the only country
that shows a decrease in the entire graph. In otherwords, in the last 30 years, the consumption of fishand shellfish in Japan has decreased and work
environment for fishery workers has not improved.
Hence, fishery workers cannot feel hopeful in theindustry.

Fig. 2

Changes in the fishery workers [2]Fig. 3

Changes in the consumption of fish andshellfish per person per year in each majorcountry and region [3]Current Situation of the Fishing Industry andDistribution
One of the reasons the future of Japanese fishingindustry is grim is the distribution style of fish andshellfish. It is part of Japanese culture to eat fish andshellfish raw, and the main style of distribution is tokeep the products raw. This is because, in Japanesefood culture, people consume fish and shellfish near
the sea where such products are caught. However,today, the complex and diverse distribution structure
suppresses profits for fishery workers, where
products only arrive to consumers at a high price.
PLAN POLICY
As such, Japan has considered marine life, such as
fish and shellfish, as resources for a long time, but it
has become difficult to continue in the same manner.
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Proposal to Consider the Ocean as a Place ofComplex Resources and Use Such Resources
Sustainably

waters next to the EEZ based on bilateral agreements.
A floating resource-management base for sustainablefishing industry that handles resource management
and illegal operation on the sea is necessary.

In this paper, we propose to consider diverse
marine resources as comprehensive resources. We
must build a sustainable system in a comprehensive
manner.
As comprehensive marine resources, the energy
potential of wind and current might be used. Recently,
wind power is being used worldwide. Furthermore,
tidal and ocean current power generations are being
studied, and Japan’s Mitsui O.S.K. Lines, Ltd. isplanning to start an operation of an “ocean thermalenergy conversion (ocean thermal energy conversion
is a renewable power that generates power through adramatic change in the seawater temperature between
the surface and depth)” power plant with a 1,000-kW
output by around 2025.

Proposal for the Fishing Industry and Handling ofEnvironmental Burden
Many ships today were built when there was noenvironmental measure and have poor fuel efficiency.
International Maritime Organization (IMO)
adopted “greenhouse gas reduction strategy” in
April 2018. This strategy sets the following numericalgoals with 2008 as the reference: (1) improve the fuelefficiency (greenhouse gas emission per amounttransported) of the entire international marinetransport by at least 40% by 2030, (2) reduce the total
emission of greenhouse gases from internationaltransport by at least 50% by 2050, and (3) achievegreenhouse gas emission of zero as early as possible
in the present century.
However, demand for marine transport by ships is
expected to increase further, where a challenge is how
to reduce the environmental burden from ships.
Meanwhile, about half (45%) of fishing boats of 5 toless than 9 tons are at least 30 years old as of 2018
(Fig. 4), associated with high environmental burden.
In the future, new ships will be built as marinetransport increases; thus, the use of small ships should
be reduced and transitioned to large ships with lessenvironmental burden.

Toward Sustainable Fishery Environment
We propose to change from the traditional
fisheries that only caught fish and shellfish to
fisheries that starts from production and ends with
distribution. This is because wasteless use of fish andshellfish is needed in terms of resource protection.
Thus, management based on Hazard Analysis andCritical Control Point (HACCP: A hygiene
management in which food businesses understand therisk factors (hazards) such as contaminations andforeign objects and especially manage important
processes to remove or minimize risk factors in theentire process from the procurement of raw materials
to the shipment of products for securing the safety of
products) as a highly hygienic system is needed for
production, processing, and distribution. It aims tomaintain the entire process as one, from sealed area
for cargo handling, cooling, and freezing facilities. In
addition, drones are used to shorten the duration ofshipping process. In this manner, making the fisheries
well managed, fisheries that were unstable due toweather becomes a routine task with stable income.

Installation of a Research Base with a New Use on
the Sea
While considering the diverse marine resources ascomprehensive resources and building a sustainablesystem, functions and facilities to research anddevelop such acquisitions are necessary. In the pastmarine research, surveys were conducted in field, but
experiments were conducted and papers wereprepared on land. When researchers andentrepreneurs are present in field, it would promote
open innovation to develop marine resources.
For marine development, a facility that functions
as a comprehensive center is desired. Some survey
ships have such functions, but considering thelivability, floating base would be effective.

Centralized Management of Fishery Resources
In the past 20 years, Asian countries have
expanded fishing greatly. When comparing the
production per fishery worker, large purse-seine, atype of offshore fishery, is about 200 (ton/person),while large fixed-net, a type of coast fishery, is only
about 34 (ton/person). Thus, fishery workers appear
to be working offshores. To make fisheries asustainable industry, marine resources must be
sustainably maximized, while evaluating resources
based on science and appropriately managing theyield based on the evaluation.
In waters around Japan, foreign boats operate in
the EEZ while many foreign boats operate in theBASIC PROPOSAL PLAN
Advantage of Integrating Functions (Environment,
Safety, Disaster Prevention, and Securing ofResources)
Going ahead, a place for open innovation byvarious countries and organizations is essential. To
that end, not only diverse researchers and developers
but sometimes tourists should also become involved
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 4

Percentage of ships by age [4]Fig. 5

Relationship between planned area, Japanese EEZ, and fishing grounds [7]in discussions. The livability of the facility must be
improved so that a wide variety of people can visit
and stay at the facility

fishing industry, which has been regarded as the coreindustry thus far. Functions should be integrated sothat oceans are widely researched and research results
are applied to the industry. To that end, we set the
Ogasawara Islands and surrounding areas as theplanned site to serve as the relay point connecting theNorthwest Pacific and Midwest Pacific Oceans, sothat people with various purposes can easily accessthe base (Fig. 5).

Role as a Comprehensive Base That Connects theNorthwest Pacific and Midwest Pacific Oceans
To manage and utilize the diverse resources ofmassive oceans, it is important to cooperate with the457

GEOMATE – Bangkok, Thailand, 22-24 November 2022

below the draft, which makes such rigs lesssusceptible to waves compared with other ships,
providing structural stability. In particular, those built
in the 1990s and later can accommodate water depths
of 1,000 m or more, allowing for a comfortable living
environment well above the surface of the water thatis not impacted by waves. A good living environmentis essential for attracting various people to the facility[6].
Response to Typhoons, Earthquakes, and Other
Disasters (Eruption of Submarine Volcanoes)

Fig. 6

The planned area is close to the S-net (Seafloor
observation network for earthquakes and tsunamis
along the Japan Trench) and DONET (Dense
Oceanfloor Network system for Earthquakes andTsunamis), Japan’s submarine earthquake survey
systems. These are inline seafloor observation
devices to monitor earthquakes and tsunamis in the
Pacific Ocean. By working with such observation
devices, the facility plays a role as a relay base toshare information on submarine earthquakes, crustal
movements, and water level changes to prevent
disasters.

Locations of ocean floor resources andcomprehensive base

The fishing grounds around the Ogasawara
Islands are scattered and distributed around the
Mukojima Islands, Chichijima Islands, Hahajima
Islands, and Io Islands. These areas are blessed withrich fishing grounds, but there is inadequate
infrastructure for the fishery industry. Furthermore,these areas are suited for marine renewable energy
through ocean thermal energy conversion where thetemperature difference between surface water anddeep water remains at least 20°C. These areas also
have a high reserve potential for ocean floor minerals
and natural gas (Fig. 6).
To improve access to these areas, the access by air
must be secured. To that end, runways for planes will
be secured by connecting floating structures. In thismanner, we aim to further expand the flow of people,
items, energy, and information.

Functions Introduced and Scale Calculation of the
Facility
In addition to its main functions as a fishery base,this facility will be equipped with a marine resource
management research wing, aquaculture researchwing, and marine energy resource research wing as
research functions. In addition, there will be an added
salon function that connects these functions (Fig. 7).

Reusing Used Semisubmersible Rigs
Since the 2000s, the number of oil drilling rigs has
dramatically increased with the rise in crude oil
prices; however, in the 2010s, older types of drillingrigs have been replaced due to performance. We
propose reusing such used drilling rigs. This would be
advantageous in terms of the initial investment,
leading to environmental considerations as well.
The total number of jack-up rigs, semisubmersible
rigs, and drilling ships in operation in the world was
822 as of March 2020. Their operation rate has
decreased in recent years due to the spread of
COVID-19 and plunging oil prices. Among those,
there are about 163 semisubmersible rigs [5].
The pontoon structure of semisubmersible rigs is

Fig. 7458

Plan for the arrangement of introduced
functions.

GEOMATE – Bangkok, Thailand, 22-24 November 2022

REFERENCES
CONCLUSIONS[1] The Food and Agriculture Organization (FAO),
The State of World Fisheries and Aquaculture
2020, p. 5.
[2] Fisheries Agency of Japan, 2019 White Paper on
Fisheries Trends, Section 2, Changes in Fisheries
Structure, 2020, p. 51.
[3] Fisheries Agency of Japan, 2019 White Paper on
Fisheries Trends, Section 1, Transitions inJapan’s Fishing Industry, 2020, p. 17.
[4] Fisheries Agency of Japan, 2019 White Paper on
Fisheries Trends, Section 2, Changes in Fisheries
Structure, 2020, p. 56.
[5] Maeda H., The technological history of offshore
drilling rigs and the outlook of the future. Journalof the Japanese Association for Petroleum
Technology. Vol.82, No.5, 2017, pp. 346-354.
[6] Japan Oil, Gas and Metals National Corporation,
Marine Engineering Handbook (5th ed.), 2010.
[7] Nikkei Business November 12, 2012, Nikkei
Business Publications, Inc., 2012, pp. 40-43.
[8] Nagatomi K., Sato S., Proposal of floating
fishery base for offshore fishery Design oflogistics and tourism base off Chichijima in
Ogasawara Islands, Summaries of design worksof annual meeting, Architectural Institute ofJapan, 2019, pp. 430-431.
[9] Sato S., and Deyama R., Floating AutomatedFactory System for Effective Utilization of
Seabed Resources and Reduction of Greenhouse
Gas Emissions, International Journal of
GEOMATE, In press.
[10] Sato S., and Yamakage Y., Proposal of a
Floating-Type
Environmental
Purification
Facility Incorporating the Function of Tideland,International Journal of GEOMATE, Vol. 14,
Issue 45, 2018, pp. 78-83.

1. When considering the issue of climate change asa contemporary challenge, as its impact is wideranging, it is necessary for various research results
to be cross-sectionally analyzed and examined.
By bringing together researchers from various
fields and holding comprehensive discussions, theenvironmental impact can be understood fromvarious aspects.
2. By opening a research venue and providing aplace for more comprehensive discussions, joint
research and development with various countries
will become possible. This, in turn, will lead to thecentralized protection of marine resources.
3. By reutilizing used semisubmersible rigs, theinitial cost and disposal materials can be reduced.
This will also reduce greenhouse gas emissions
during the construction of new rigs, inconsideration of the environmental issues.
4. By living in a comfortable environment directly
connected to marine research areas for a long
period of time, research will be further deepened.
In addition, we will create a salon facility at thecenter where researchers from unfamiliar fields
and other people can interact with each other,where such interaction with tourists and fishery
workers might lead to new ideas.
5. In the future, a network will be created by building
many offshore facilities similar to this one. In
addition to its role as a fishery evacuation base, itwill be possible for it to serve as a disaster
response base against tsunamis.

459

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

STUDY ON THE ALLEY IN THE CITYBASED ON PEDESTRIAN’S IMAGE
Shonosuke Kajita 1 and Kazunari Tanaka1
1

Faculty of Engineer, Osaka Institute of Technology, Japan
ABSTRACTIn recent years, the depopulation of local cities has become a serious problem. Depopulated areas areexperiencing problems such as a decline in economic and social sustainability, stagnation of migration andinteraction, and difficulties for tourists and residents to move around. However, due to the impact of the novel
coronavirus pandemic since 2020, there is a growing interest in rural migration. Under these circumstances, it isnecessary to propose the creation of spaces where people who have migrated to local areas can settle down.
Therefore, our initial hypothesis is that a city with many places where people are easily attached would be easierto settle down. In this study, we focused on "streets" in the city and aimed to quantify some of the attachments thatpeople have to the streets. As a research method, we conducted a factor analysis using the Semantic Differential(SD) method to find out what kind of psychology people have toward streets, where the factors that wereconsidered to be related to the “attachment” were determined. In addition, to make the factors more concrete, we
defined items that were thought to constitute the factors and conducted a path choice experiment using the one-toone comparison method for these items. The items the number of people and wall irregularity revealed by thesurvey were analyzed and discussed, and attempts were made to quantify them.
Keywords: road, attachment, rural depopulation, rural emigration
INTRODUCTION

easier to settle in. In this study, we focused on the"streets" within the city, which "are the dominantelement of the image for many people, and people
observe the city as they move about, and otherelements are placed and associated along their paths".
And by focusing on the attachment that people haveto the streets, we aimed to quantify some of theattachments that people have to the streets.
As a research method, we conducted a factor
analysis using the Semantic Differential (SD) method
to find out what kind of psychology people havetoward streets, where the factors that were considered
to be related to the “attachment” were determined. In
addition, to make the factors more concrete, we
defined items that were thought to constitute thefactors and conducted a path choice experiment using
the one-to-one comparison method for these entities.
The items revealed by the survey were analyzed andquantified.
RESEARCH USING FACTOR ANALYSISIn recent years, the depopulation of regional cities
and their concentration in the Tokyo metropolitan
area has become more serious. As a result, urban
areas are experiencing various overcrowding
phenomena, congestion, problems with waiting
children, accidents during events and disasters, andthe weakening of local communities. Conversely,
depopulated areas are experiencing a decline ineconomic and social sustainability and difficulties inmobility for tourists and residents due to population
outflow. However, the impact of the novel
coronavirus infection since 2020 has increased
interest in rural migration. By considering the local
environment, we believe it is necessary to propose thecreation of spaces that will encourage immigrants tosettle in rural areas.
PURPOSEANDRESEARCH

METHODOLOGY

OF

Using nine photographs of actual roads andeleven different rating scales, we conducted afactor analysis survey using the SD method on 97
university students attending the Osaka Institute ofTechnology. The rating scale used is shown in
Table 1, and the factors got by varimax rotation are
shown in Table 2.

Creating cities that are attractive and livable forpeople and where they want to continue to live for along time is a challenge in suppressing ruraldepopulation. To create a city that people want tosettle in, we thought that "attachment" would benecessary. Attachment means something that people
are deeply attracted to, and what they are familiarwith. Our initial hypothesis is that a city with many
places where people are easily attached would be
460

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 1

symbolism, the elements are related to each other. In
this study, we focused on the relational symbolism
and quantified the symbolism. The photos with highrelational symbolism had four things in common: few
people, plants, shade, and uneven walls, so weinvestigated these items.

Rting scale
Narrow-

Wide

Unapproachable-

Approachable

Irritating

-

Fresh

Inconvenience-

Convenience

Secular-

Tasteful

Old

-

New

Local

-

Urban

Noisy

-

Quiet

Ugly

-

Beautiful

Pale

-

Dense

Feeble-StrongTable 2

Factors obtained
Functional Emotional
ity
ity
Convenien
0.738
0.288
ce

INVESTIGATION BY ROUTE SELECTION
For the four items identified from the discussion
in the previous section, we conducted a routeselection experiment using a pairwise comparison
method. In this experiment, three different photos
were prepared for each item, and for each photo, acontrasting photo was prepared. For example, for thenumber of people, we prepared three images with few
people and high symbolism. Then, we increased thenumber of people and created contrasting photos withlow relational symbolism using Photoshop. Figure 1,
Figure 2, Figure 3, and Figure 4 show the results ofthe path selection process for these six photos.
1

Symbolis
m
0.200

Wide

0.705

0.159

0.195

Quiet

-0.4810.391-0.448

Beautiful

0.002

0.744

0.214

Fresh

0.155

0.683

-0.012

Tasteful-0.4640.490-0.146

Approachable-0.187

0.416

0.055

Strong

0.169

0.232

0.727

Dense

0.143

-0.030

0.600

Urban

0.568

0.135

0.570

-1.00

Fig. 1

Fig. 2

Fig. 3

Based on the structure of the rating scales foreach factor, the first factor was "Functionality"
because it asks about the convenience and ease ofuse of the road, the second factor was"Emotionality" because it asks about the aestheticsof the road, and the third factor was "Symbolism"
because it is a sensory factor such as powerful andthick. In this study, we focused on "Symbolism" toquantify the emotions of the parts of humanattachment that are not related to function or
aesthetics.

Fig. 4

5

0.00

8

9

-0.50

10

6

0.50
11

0.00

1.00

12

0.50

1.00

Presence of plantings
14

15

-0.50

16

0.00

17

18

0.50

1.00

Presence of shade
19

-1.00

4

Number of people

13

-1.00

3

-0.50
7

-1.00

2

20

-0.50

21

22

0.00

23

写真24

0.50

1.00

Presence of wall irregularities

The first three photos in the first half of the six
photos in each category were considered to have highrelational symbolism before processing, and the latter
three photos were contrasting. As a result, it was
found that there are certain tendencies in the numberof people and the unevenness of the wall. First, "thenumber of people" indicates that the fewer thenumber of people, the easier the path is to pass, andWe named the higher symbolism values as"independent symbolism" and the lower values as"relational symbolism". The reason for this is that instreets with high symbolism, the elements of the city
are individually symbolic, while in streets with low
461

GEOMATE – Bangkok, Thailand, 22-24 November 2022

the fewer the number of people in front of you, theeasier the path is to pass. Secondly, the"presence/absence of uneven walls" was rated higher
for the before and after photos, indicating that thepath with uneven walls is more desirable.
For the "people" item, we found that the actual
number of people and the percentage of people in thephoto had nothing to do with the index we wanted tofind. Therefore, we hypothesized that the "percentageof people on the street" might be related to relational
symbolism. This time, as a "road" setting we searched
for a correlation with a place that could be perceived
as a "close space". The reason for this setting was
because it was thought that the walls of the street
space would be affected, and conversely, people
present at the back in the picture would not beaffected.
This time, I captured the location from a depression
angle. The depression angle is the angle between the
direction of gaze and the horizontal plane at eye level
when looking down at an object. According to
Higuchi, "the visual sense of unity with the harbor orlake is affected by whether or not the line of sight near
the 10° angle of elevation stays on the water surface”.
From this, we hypothesized that the area below the
10° angle of depression could be regarded as a "close
space" and examined the relationship between thepercentage of people existing in the "close space" andthe selection rate. In this study, " close space" isinvestigated in the range of 10° to 30° depression
angle, which is defined as the lower limit ofdepression angle. In this study, the depression angle
is defined as being equally spaced. Since thephotograph used in this study had a vertical angle ofview of 57.6°, we calculated the angle of elevation in
the photograph. Figure5 shows the relationship withthe selection rate.

As a result, there was a correlation between the"percentage of people who do not exist in the area
below 10° angle of depression" and the "selection rateby scale value," indicating that the percentage of
people who exist in the area below 10° angle ofdepression is related to relational symbolism. From
this, we hypothesized that the simplicity of the groundin "close space" is related to relational symbolism.
Next, for the "unevenness of the wall", we thoughtthat it would be better to have relative complexity
above 10° angle of depression than "simplicity below
10° angle of depression". In other words, the more
complex the wall was in relation to the ground, thehigher the relational symbolism would be.
EXPERIMENT ON WALL IRREGULARITIESIn the previous chapter, "wall irregularities" could
be regarded as "the complexity of materials other than
the ground". Next, we will clarify "people's
perception of the unevenness of walls".
Wall irregularities can be expressed in various
ways, such as color, texture, size, and proportion. In
this experiment, we focused on proportion.
In this experiment, we used a video of road spacewith varying proportions of wall irregularities andasked the participants to stop the screen at the location
where they felt the most change while moving around.
The video was created so that the subjects would
move through the area between the uneven walls, andthe experimenter's eye level was set at the height ofthe center of the wall. Figure 6 shows a portion of thesimulated video.

100.00Percentage of people
in the near space

80.00
60.00

Fig. 6

40.00

The length of one convex side is 10% of the heightof the wall surface, and the number of convex sides
within a unit of 10 convex sides and 10 convex sides
are varied. The number of convexities was varied infive steps (5, 10, 15, 20, and 25). The positions of theconvexities were determined by using random
numbers.
Two types of paths were prepared, one with thesame width as the wall height (D/H=1) and the otherwith twice the width (D/H=2), and the effect of thepath width was also examined.

20.00

-1.00

-0.50

0.00

0.00

0.50

1.00

Selection rate by scale value
Fig. 5

Simulation for road space experiments

Percentage of people and selection rate462

GEOMATE – Bangkok, Thailand, 22-24 November 2022

The results of the average and standard deviation of
the percentage change in convexity are shown in
Table 3 below.
Table 3
Percentage change in convexity

D/H=1

D/H=2

Average14.25%

15.50%

Standard
Deviation

4.26

5.22

addition, I would like to quantify the complexity of
the wall by examining the photographs and the wordsof the questions in the one-to-one comparison method,
and to quantify the relational symbolism moreaccurately.
REFERENCES[1] Current regional and local conditions andissueshttps://www.soumu.go.jp/main_content/0006
29037.pdf[2] Measures to correct the concentration ofpeople in Tokyo,
Ministry of Land, Infrastructure, Transport
and Tourism
https://www.mlit.go.jp/policy/shingikai/conte
nt/001374933.pdf[3] The Image of the City, Kevin Lynch,translated by Kenzo Tange and Reiko Tomita,
Iwanami Shoten, 1968, pp.56[4] Evaluation experiment by SD method
http://lbm.ab.a.utokyo.ac.jp/~omori/sd_assess.html[5] Table of Contents for Using SPSS
http://www2.kokugakuin.ac.jp/~ogiso/spss/in
dex.html[6] iid pair-wise comparison
https://www.iid.co.jp/service/tool/tool10.htm
l[7] pair-wise comparison
Psychostatistics by Dr. Sirius
http://daas.la.coocan.jp/toukei_hosoku/paired_comparison.htm[8] Structure of Landscape, Tadahiko Higuchi,
Gihodo Publishing,1975, pp.48

The results show that most people perceive the mostchange around 15% regardless of the width of theroad.
Furthermore, when examining the position at which
the video was stopped, it was found that most people(70%) stopped the video when the point of change
had passed, that is when they were at the edge of theirline of sight. It can be read that most people perceive
the convexity to have changed by about 15% at theirstanding position.
CONCLUSION
In this study, we used the SD method to analyze theimages people have of roads, and three factors wereused to express their feelings toward roads. Amongthem, we examined the attachment to symbolism. In
order to quantify "relational symbolism" among thesymbolism, we identified items that were considered
to be related to it, and conducted a path selection
experiment. We found that people were more likely to
be attached to paths with fewer people and unevenwalls.
In addition, the percentage of people with below the
10° angle of depression on the street was considered
to be related to the relational symbolism, as was thecomplexity of the wall in relation to the ground when
it was associated with the unevenness of the wall. In

463

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

A STUDY ON THE THERMAL SENSATION OF SHORT-TERM
RESIDENTS IN TROPICAL REGION
Kenta Fukagawa1, Yoshihito Kurazumi2 Ariya Aruninta3 and Yoshiaki Yamato41

Faculty of Science and Technology, Meijo University, Japan; 2School of Life Studies, Sugiyama Jogakuen
University, Japan; 3Faculty of Architecture, Chulalongkorn University, Thailand; 4Faculty of Architecture &Structural Engineering, Kure National College of Technology, Japan
ABSTRACT
Climate change is one of the most serious issues in the recent world. Climate change is known as caused by
the increase of carbon dioxide and many of the countries are forced to take measures. The first big measure wasthe Paris Agreement in 2015, and many countries joined the agreement. According to the agreement, many of thecountries declared the major shifts, such as the shit to electric vehicle in the car industry. The biggest concern
about the climate change for this moment is the rise of the temperature and it causes more death by heat stroke insummer. It also causes the more usage of air conditioning system and emits more carbon dioxide. It became aserious vicious circle. For that reason, it is important to analyze the mechanism of the human thermal sensation,
which can be affected by the environmental history of the person, such as the residential area, the length of stay,and so on. In addition, it is expected that the effect of these histories especially appears strongly outdoors. For thatreason, the outdoor experiment targeting the short-term residents was carried out in Bangkok. For the experiment,
5 measuring points were selected. As a result, a different tendency was grasped at one of the measuring points
with open sky.
Keywords: Global warming, Thermal sensation, Thermal discomfort, Image evaluation

INTRODUCTION

For instance, when the thermal sensation reaches
to hot, the human body starts to sweat to control the
body temperature, then the body temperature is to be
adjusted. For that reason, to estimate the thermal
sensation is important. However, the thermal
sensation can be affected by both physical andpsychological factors.
There are many researches targeted physicalthermal factors. On the contrary, there are not many
researches targeted the psychological factors.
Matsubara et al. [1] focused on sight sense as
psychological effect and had experiment on hue-heatand clarified that the effect of visual stimulus causes
0.3 degree centigrade difference.
Kurazumi et al. [2] had research on the effect of
artificial and non artificial visual stimulus on humanthermal sensation, and mentioned the effect onhuman’s neutral temperature.
Ohno et al. [3] had research on the composite
environment of thermal and hue-heat impression, andgrasped the interaction on the subject.
Ahmed [4] studied the thermal comfort for thetropical urban environments and showed theimportance of development of outdoor comfort model.
According to those researches, it is important toclarify the evaluation tendency on outdoor thermal
environment from the psychological aspect, which ishuman thermal sensation.
When considering the improvement of the

In recent years, high temperatures in urban areas
have become a serious problem. Higher temperaturesin urban areas are generally referred to as the heatisland effect, and are said to be due to the effects ofincreased man-made land surfaces and increase of
exhaust heat.
In addition, due to the effect of global warming,
people in some urban areas are forced to live under
extremely high temperature during the summer.
As a countermeasure to this worsening thermal
environment, there is an urgent need to curb carbon
dioxide emissions on a global scale.
Looking at measures by region, Singapore, forexample, is promoting greening under thegovernment's initiative.
It is also considered
mandating the installation of solar panels on new
detached houses in Tokyo to decrease fossil fuelconsumption. However, the clear effects of thesemeasures are still expected to take time.
Focusing on the negative effects of this higher
temperature in urban areas, the number of deaths dueto heat stroke is on the rise and has become a serious
problem, especially in Japan.
The death by the heat stroke is caused by losing
the balance of body temperature. In addition, thebalance of body temperature of human is mainly
affected by the heat transfer between human body andthe air. However, the thermal sensation of human is
also important factor.
464

GEOMATE – Bangkok, Thailand, 22-24 November 2022

thermal environment, it is common to consider fromthe perspective of long-term residents. However,
especially in a tourism-oriented country like Thailand,
it is difficult to reduce accidents caused by heat stroke
without considering the thermal environment ofshort-term residents. In addition, when the residents
evaluate the outdoor thermal environment, it is said
that the influence of the environmental record up tothat point strongly appears.
For that reason, the purpose of this study is toclarify the evaluation tendency of the short-term
residents on outdoor thermal environment in
Bangkok.
EXPERIMENT OVERVIEW
The research experiment took place on campus of
Chulalongkorn university in 2016 (rainy/wet season)with a group of Japanese university students who justvisited Bangkok as a first time as the subjects. The
subjects were 11 males and 5 females, for a total of
16 subjects with the clo-value of between about 0.3 to
0.5.
The experiment performed on August 29th and 30th,
which were sunny fine days, during the daytime only
from before noon to afternoon. The observation
points were selected in the university premises. 4
points were selected by considering surrounding
environment such as surface ground condition andsky factors.

Figure1 Measuring points

Measurement Procedures
The observation points and route patterns areshown in Fig.1. The subject moved to these four
observation points and filled out the declaration voteafter staying for 10 minutes in each point. The
movement patterns were the two patterns are asshown in Fig. 1. A total of four experiments were
performed.
To analyze the sky factors of each observation
points, all sky photos are taken in each point. The
photos and the sky factors are shown in Fig.2. andTab.1, and the outline of each measurement points are
as follows.
The observation point No.0 is located the semioutdoor space inside the university building and thesky ratio is very low. The point No.1 is located besidea pond where many trees around so the sky ratio is
low but the green ratio is high.
The point No.2 is in the soccer field so the sky
ratio is high. The point No.3 is located beside theengineer department building so the sky ratio is low.
However, many trees are planted along the building
so the green ratio is quite high.
The point No.4 is located near the auditorium
where has an artificial square in front. For that reason,
the ratio of green is low.

Figure 2 All sky photos of eachobservation points
Table 1 Ratio of sky factors
Sky

Non
Green
Sky

⓪Building court

0.14

0.52

0.34

①Pond side

0.25

0.09

0.66

②North playfield

0.88

0.03

0.09

③Engineering bldg side

0.16

0.28

0.56

④Auditorium plaza

0.42

0.22

0.36

Sky Factor
Measuring Point

465

GEOMATE – Bangkok, Thailand, 22-24 November 2022

-1

0

1

2

3

35.0

Relaxing

34.0

Annoying่ )Breezy ( สดชืน

33.0

Disattractive

Attractive

Unfamiliar

Familiar

Poor

Rich

Useless

UsefulDiscomfortable

Confortable

Boaring

Enjoyable

Temperature [℃]

Beautiful

Unrelaxing

32.0
31.0
30.0
29.0

GentleStrictGracefulMorbid ( น่ากลัว)Healthful

26.0

Fresh

25.0

Cramped

27.0

Pure

Artificial

NaturalLess Green

Slow

Fast

Dark

BrightMild

ActiveClosedDisclosed

Dislikable

LikableAggressive

Passive

Solemn ( เคร่งขรึม )CasualNoisy

Tranquil

Invariant ( คงที)่

2

3

4

1.0

0.5

Changeable

Cold

Hot

Far

Close
Coexistence ( การอยูร่ ่วมกัน )Exclusive

-2

-1

0

1

2

Cool

Details of the declaration vote

1

2

3

4

40.0

35.0

30.0

25.0

20.0

0.0
0

1

2

3

4

0

1

2

3

4

Measuring Points

Measuring Points

Sensation
Figure 3Thermal
Declaration
Vote
-3

0

Measuring Points

1.5Wind Speed [m/s]New

1

Measuring Points

Fully Green

Old

55.0
0

Majestic

Cloudy

60.0

28.0

Wild

Dry

65.0

Humidity [%]-2

Ground Surface Temperatuer [℃]

-3
Ugly

Figure 4 Thermal condition

3
H ot

Thermal Comfort

EXPERIMENT RESULTS

-2
-1
0
1
2was3
The-3 declaration
voteutilized for thisdi s comf ort
Comf orta bl e
experiment. The vote is composed of image
evaluation part by using SD method and thermal
sensation part. SD method part contains 30 adjective
pairs with 7 level rating scales by giving the score of+3 to -3 as shown in Fig.3. Line rating scale wasutilized for thermal sensation and thermal comfort
parts.

In this chapter, the results of image evaluation by
the SD method and the evaluation results of thermal
sensation and thermal discomfort are analyzed.
Image evaluation result
The result of the image evaluation is shown in
Fig.5. The image evaluation scores were aggregatedand averaged for each observation point.
The observation point No. 2 shows characteristic
results, especially for items such as Bright, Disclosed,and Hot that are presumed to affect the feeling ofwarmth and cold. This is estimated that the influenceof the magnitude of the sky ratio which is the largest
at the observation point No.2.
The Observation point No.3 also shows a different
tendency from the other observation points in items
such as Cramped, Dark, and Closed that express afeeling of openness in space. It is considered that thisis due to the small sky factor and the low ability in thevisual field range at that point.
The result of the image evaluation suggests thatthe sky factor, brightness, and the feeling of opennessof the space at the time of evaluation may affect theevaluation results.

Thermal condition overview
For the experiment, temperature, wind speed,
humidity, and ground surface temperature were
measured as physical factors. All data were measured
at 1minute intervals. Fig.4 shows the averaged valuesof each measurement point.
Looking at the temperature data at the time of themeasurement, although the highest value is shown atmeasurement point 2, the difference between themeasurement points is extremely small.
Humidity data shows that there is a difference ofup to 3% between the measurement points. However,like the temperature, the difference is extremely small.
The wind data of the point No. 2 marked thestrongest value, where the sky factor was the largest.
Looking at the value of the ground surface
temperature, at the observation point No. 2 was thehighest, where the sky factor was the largest. Thenfollowed by the highest value at the point No. 4 where
located in the center of artificial square..
By the above results, it is clear that there is no
extreme thermal environment at the observation
points selected for this experiment.

Results of thermal
discomfort

sensation

andthermalThe result of averaging the declared values of466

GEOMATE – Bangkok, Thailand, 22-24 November 2022

-3

-2

-1

0

1

2

①

0

1

2

3
Beautiful
Relaxing

StrictMobid

Graceful
Healthful

Dry
CrampedFresh
Majestic

CloudyArtificial

Pure
NaturalBreezyAttractive
FamiliarRich
UsefulConfortable
Enjoyable
GentleLess Green

Fully Green

Old
Slow

New
Fast

Dark

BrightActiveMild
ClosedDislikable

Disclosed
LikableAggressive

Passive

SolemnNoisy

CasualTranquil

Invariant

Changeable
Hot

Cold
Far
Exclusive

Close
Coexixten

Average②-1③

Average④

Figure 5 Results of image evaluation

Hot
Discomfort

FT

MT

FDC

AT

MDC

120.0

120.0

n=19

110.0

120.0

n=44

110.0
100.0

100.0

90.0

90.0

90.0

80.0

80.0

80.0

70.0

70.0

70.0

60.0

60.0

60.0

50.0

50.0

50.0

40.0

40.0
1

2

3

4

n=63

110.0

100.0

0

AC

130.0

130.0

130.0

Thermal Sensation
Thermal Discomfort

-2

Ugly
Beautiful
Unrelaxing
Relaxing
Annoying
BreezyDisattractive
Attractive
Unfamiliar
Familiar
Poor
Rich
Useless
UsefulConfortable Discomfortable
Boaring
Enjoyable
Wild
GentleGraceful
HealthfulFresh
M ajestic
Pure
Natural
Fully GreenNew
FastBrightActiveDisclosed
LikablePassiveCasualTranquil
Changeable
Hot
Close
Coexixten⓪

Cool
Comfort

-3

3

Ugly
Unrelaxing
Annoying
Disattractive
Unfamiliar
Poor
Useless
Discomfortable
Boaring
Wild
StrictM obidDry
CrampedCloudyArtificial
Less Green
Old
Slow
Dark
M ild
ClosedDislikable
Aggressive
Solemn
Noisy
Invariant
Cold
Far
Exclusive

40.0
0

Observation Point No.

1

2

3

Observation Point No.

4

0

1

2

3

4

Observation Point No.

Figure 6 Results of thermal sensation and thermal discomfort vote
※F: Female, M: Male, A: Average, T: Thermal Sensation, DC: Thermal Discomfort

thermal sensation and thermal discomfort by genderand the overall average value is shown in Fig.6. Since
the declaration method used in this measurement is
the linear rating scale, the fluctuation tendency ismore focused rather than the value itself.
Looking at the results declared by females, it canbe seen that the results of Thermal sensation anddiscomfort are generally similar. However, thedifference between those results get wider at
observation point No.2.
Looking at the results declared by males, thetendency of thermal sensation and discomfort are
generally similar.
The results of the male showed the similartendency to that of female, except for observation
point No.2. At this point, the thermal comfort

deteriorates, but the thermal sensation improves.
By the results shown in Fig. 4, there is no
significant difference found in physical indicatorssuch as temperature at observation point No. 2
compared to other points.
Therefore, it is considered that the difference of
the image evaluation results is influenced bypsychological factors rather than physical factors.
ANALYSIS ON THE IMAGE EVALUATIONSTRUCTURE
To analyze the tendency of the image evaluation
results by the subjects, factor analysis was taken. For
the factor analysis, varimax rotation method was467

DGEOMATE – Bangkok, Thailand, 22-24 November 2022

1.50

Table 2 Factor loading matrix

Unattractive - Attractive
Strict - Graceful
Unrelaxing-Relaxing
Annoying - BreezyUnfamiliar - Familiar
Ugly - Beautiful
Poor - Rich
Wild - Gentle
Dislikable - Likable
Mobid - Healthful
Discomfortable - Confortable
Boaring - Enjoyable
Useless - Useful
Cloudy - Pure
Dry - Fresh
Exclusive - Coexistent
Old - New
Noisy - Tranquil
Dark - Bright
Closed - Disclosed
Cramped - Majestic
Mild - Active
Aggressive - Passive
Cold - Hot
Solemn - CasualLess Green - Fully Green
Slow - Fast
Invariant - Changeable
Far - Close
Artificial - Natural

1st
Comfort
0.83
0.76
0.72
0.72
0.72
0.71
0.71
0.70
0.68
0.67
0.67
0.63
0.55
0.51
0.50
0.42
0.36
0.35
0.06
0.09
0.17
-0.05
0.25
-0.03
0.40
0.37
0.16
0.14
0.23
0.22

2nd
Sensation
0.18
0.10
-0.12
0.06
0.12
0.30
0.31
-0.09
0.25
0.30
-0.10
0.19
0.39
0.40
-0.03
0.36
0.20
0.07
0.85
0.84
0.82
0.54
0.51
0.51
0.49
0.39
0.17
0.11
-0.23
0.30

3rd
Impression
0.23
-0.02
0.19
0.24
0.19
0.07
0.00
-0.17
0.20
-0.06
0.29
0.29
0.19
0.07
0.08
0.19
0.29
-0.19
0.10
0.00
-0.12
0.41
0.36
0.00
0.15
-0.24
0.53
0.46
0.38
-0.37

※The number represents
the observation points.
※F: Female, M: Male

M 1

2nd Factor

Factor

4
0.00

F
0
3

-1.50
-0.80

0.00

0.80

1st Factor
1.50※The number represents
the observation points.
※F: Female, M: Male4
3

3rd Factor

Adjective Pairs

2

M

0.00

0

F
2

-1.50
-0.80

1

0.00

0.80

1st Factor

Figure 7 Average factor score bygender and observation point* Factor scores of higher than 0.37are highlighted.

Analysis on the average factor score

taken. Factor analysis was performed on the basis ofan eigenvalue of 1.0 or higher, and 7 factors wereextracted. Since there was a problem in theinterpretation of the 7 factors, the number of factors
was fixed at 3 to 6 and the factor analysis was
performed again.
The most interpret result was obtained when thefactor number is set as 3. The result of setting thefactor number as 3 is shown in Tab.2. The factor
scores of more than 0.37 are highlighted.
Focusing on the items marked high factor, the first
factor is named as “Familiarity” because the items
which are relating to comfort, such as “Unattractive Attractive”, “Strict - Graceful” and “Unrelaxing Relaxing”, are categorized.
The second factor is named as “Sensation”
because the selected items, such as “Dark - Bright”,“Closed – Disclosed” and “Cramped -Majestic”
which emphasize human sensation are relating.
The third factor is named as “Impression” because
the items such as “Aggressive - Passive” “Slow –Fast”, and “Invariant – Changeable” are related.

In order to analyze the tendency of each of theextracted factors by observation point and by gender,
the average points were calculated and plotted.
The results are shown in Fig.7.
By this result, the observation point No. 2 tends tobe different from the other points. Furthermore, thedifferences of the tendency are greater than those of
the differences by the gender.
The difference between the items is more obvious
between the 1st factor and the 2nd d factor.
In particular, the 2nd factor has a large tendency,
suggesting that it may affect the thermal sensationand discomfort.
Analysis on the correlation of the image evaluation
factors and the thermal discomfort and sensation
In the result of the average factor score, thedifference at each point was larger than the genderdifference. Therefore, in this chapter, the data is notclassified by the gender, but use all the data to
examine the relationship between each factor and thethermal discomfort and sensation.

468

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 3 Result of correlation analysis
ThermalSensation
ThermalDiscomfort

Pearson Correlation
Coefficient
Sample Number

ThermalSensation

1st Factor

2nd Factor

**

-.158

197

196

-.684

Pearson Correlation
Coefficient.306

Sample Number

*

**

193

3rd Factor
**

.041

196

196

**

-.002

.358

-.204

193

193

-.080

1st Factor

Pearson Correlation
Coefficient-.231

Sample Number

201

201
-.057

2nd Factor

Pearson Correlation
Coefficient
Sample Number

201

**

**shows advantage at 1 % level. * shows advantage at 5% level.

The result is shown in Tab.3.
Focusing on Thermal discomfort, the 1st factor
showed a significant negative correlation at the 5%
level, and the 2nd factor showed a significant positive
correlation at the 1% level.
The results of Thermal sensation showedsignificant results at the 1% level for both the 1st
factor and the 2nd factor. In addition, the 1st factor
showed the positive correlation and the second factor
showed the negative correlation.
The 3rd factor did not show a significant
correlation with thermal discomfort or thermal
sensation.

ACKNOWLEDGEMENTS
I would like to show my gratitude to whom joined
the experiment for your support.
REFERENCES[1] N.Matsubara et al., Kyoto Prefecture University,
The Japanese Psychological Association
Conference pp.570-571, 2004,[2] Y.Kurazumi et al., Sugiyama Jogakuen University,
Japanese society of Biometeorology Refereed
Journal Vol.48-4 pp.129-144, Psychologicaleffects of the environmental stimuli on thermal
sense in outdoor spaces, 2011[3]H.Ohno H et al., Nagoyaeiyou college,
Architectural Institute of Japan Refereed Journal
No.374 pp. 8-18, The Interactions between
Thermal Sensation and Hue-Heat Impression
Under Various Thermal Conditions, 1987
[4]K.Ahmed, University of Engineering andTechnology of Dhaka, Energy and Buildings Vol
35, Issue 1, pp.103-110, Comfort in urban spaces:
defining the boundaries of outdoor thermal
comfort for the tropical urban environments, 2003
[5]K.Fukagawa et al. Hiroshima International
University, Architectural Institute of Japan
Refereed Journal Vol. 605 pp.91-98, A study on
the differences on the formation of thetemperature among urban area, rural area andaround irrigation ponds in a developing city, 2005
[6] K.Fukagawa et al. Kyushu Sangyo University,
Ecology
Environment&
Conservation
International Journal Vol 23, Suppl. Issue pp.
S31-S36, A study on the effect of visual stimuli
on human thermal sensation – targeting waterlandscape, 2017

CONCLUSION
In this study, short time resident was targeted toclarify the evaluation tendency of thermal
environment. The obtained results are as below.
1) Comparing the results of image evaluation of eachmeasuring point, point “2” showed obviously
different tendency.
2) By the factor analysis, 3 factors were obtained. In
addition, the averaged factor scores of eachmeasuring point, the measuring point “2” showed
the highest value in the 2nd factor. Furthermore, themeasuring point showed the lowest value in the 2nd
factor.
3) By the correlation analysis between the obtained 3
factors and the thermal discomfort and sensation,
the 1st and the 2nd factor showed significant
correlation.
.
In summary of the above results, it is confirmedthat by understanding the elements that affect the 2nd
factor, it is possible to have some influence on
thermal discomfort and thermal sensation of theshort-term residents.

469

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

THE RECYCLING BIOCHAR BASED-MUSHROOM GROWINGMEDIA FOR SOIL ENRICHMENT IN CORN CULTIVATION
Ambar Pertiwiningrum1, Margaretha Arnita Wuri2, Alva Edy Tontowi3 and Andang Widi Harto4
1

Faculty of Animal Science, Universitas Gadjah Mada, Indonesia; 2Center for Energy Studies, Universitas
Gadjah Mada, Indonesia; 3,4Faculty of Engineering, Universitas Gadjah Mada, Indonesia
ABSTRACT

Climate change and global warming change new farming systems and technologies. Today, urban soil isamongst the most chemically contaminated soil and hence lacks fertility. Soil enrichment through theimplementation of charcoal has been introduced for several years. This paper presented investigation results in theimplementation of biochar from mushroom growing media for soil enrichment in corn cultivation. The aim of thisstudy is to investigate the use of biochar as a substitute for inorganic fertilizer so that it can give a good long-term
impact on the soil compared to inorganic fertilizer. The mushroom growing media was converted to biochar bypyrolysis at a temperature of 500oC for six hours. Biochar is used as soil enrichment with the different addition insoil: P1 (1%), P2 (2.5%), and P3 (5%). These compositions were carried out in polybags with a total mass of 6000
grams of soil for cultivating corn. The growth observation was carried out for 8 weeks until harvesting time. The
stem length, root length, and the number of leaves were measured by every week. The addition of 1% of biocharshows the best performance in corn cultivation with a stem length of 125.3 cm or grows two times longer than
without the addition of biochar. It’s also the best performance in the root length and the number of leaves of 48
cm and 16 cm respectively (more than two times better than without the addition of biochar). It’s related to theorganic content and the characteristics of biochar in the growing media of corn cultivation.
Keywords: Biochar, Recycle, Soil Enrichment, Cultivation.

INTRODUCTION

accelerates soil acidification [2], and climate change.
Agricultural N2O emissions are predicted to increase
up to 35-60% in 2030 due to the increase in nitrogen
fertilizer use [3]. It means to prevent the impact of theincrease in nitrogen fertilizer on the environment, asolution to overcome the problem must be required.
There are many technological developments to
reduce GHG emissions in the agricultural sector,
especially the use of nitrogen fertilizer, byenvironmentally friendly agriculture practices.
Anthropogenic
soil
improvementbythe
implementation of charcoal has been introduced. The
charcoal that is mixed with soil plays a very importantrole in the maintenance of soil fertility. Biochar,biologically active charcoal, is an environmentallyfriendly and low-cost porous material that has been
widely applied as a soil amendment to increase soil
fertility [4]. Compared to other soil amendments,
biochar has a high surface area and porosity to absorb
and retain nutrients and water and also provides
beneficial habitat for microorganisms [5].
The aim of this study is to investigate the use ofmushroom-growing media-based biochar as abiofertilizer and its impact. Converting biomass into
biochar for land application has been purposed as apossible strategy for climate change mitigation due tothe carbon present in the biomass being transformed
into a more stable form, thereby resulting in lower
CO2 emissions [6].
In this study, recycling biomass by converting

Approximately 40 to 50% of the Earth’s land
surface uses for agriculture activities (for production,
cropland, managed grassland, and permanent crops
including agroforestry and bio-energy crops). As weknow, agriculture releases a significant amount ofgreenhouse gas emissions (GHG): (1) CO2 is released
from microbial decay or burning of plant litter andsoil organic materials, (2) CH4 is released whenorganic materials decompose in oxygen-deprived
conditions, and (3) N2O is released by the microbial
transformation in soils and manures. Agriculturalland expanded during the last four decades and was
driven by the increase in food demand and a growing
population. Based on that fact, annual GHG
emissions from agriculture are expected to increase
the in coming decades. In 2005, agriculture
contributed 10-12% of total global anthropogenic
GHG emissions [1]. In addition, in 2030, agriculturalemissions, especially N2O emissions are predicted toincrease by 35-60% [1]. N-fertilizer application tointensive agricultural production causes the surplus N
in soil and consequently increases N2O emissions.
Vialeaching,
run-off,
drainage,erosion,volatilization, and nutrients of fertilizer entering the
groundwater and surface water that can endanger the
ecosystems. Loss of soil particles and nutrients offertilizer not only affects the quality of surface waterand groundwater but also depletes soil fertility,
470

GEOMATE – Bangkok, Thailand, 22-24 November 2022

temperature of 500 oC for six hours. In this study,
biochar was analyzed the water content because waterand ash become a good habitat for microorganisms
for plant growth. Based on the result of water content
analysis, biochar has a water content and ash content
of 14.85 and 5.01% respectively. Compared to theother report [7], there is no significant difference inwhich biochar usually has water and ash contents in
the range of 25-65% and 1-13% respectively. Biochar
also has a total C-organic of 41.02% where the value
is equivalent to the standard compost for fertilizer.
Although biochar is not a product of the compostingprocess, its total C-organic is suitable as an additional
fertilizer in the growing media [8]. It means that theaddition of biochar in soil amendment has thepotential to improve soil quality.
To investigate the impact of the addition ofbiochar on the plants, the experiment by the additionof biochar in soil for Zea mays growth was conducted.
We maintained the growth of Zea mays by watering
twice a day. Sirait [9] reported that Zea mays requires
adequate amounts of water for the growth anddevelopment process. Observation has been done fortwo months to investigate the growth processrepresented by height, length of root, and the numberof leaves [10]. The results in the height of Zea mays
plants are shown in Table 1.

mushroom-growing media into biochar also leads toa circular economy concept in agriculture. It’s
different from a linear economy that uses the concept“take-make-dispose”, in a circular economy, the useof resources, energy, and waste can be minimized
METHODOLOGYThis study was conducted in Centre forInnovation of Agrotechnology, Universitas GadjahMada. The material used in this study were the usedbaglog mushroom or mushroom growing media, Zea
mays seeds, pyrolysis reactor, and polybags.
The Converting Mushroom-Growing Media Into
Biochar
The first, mushroom baglogs were separated fromthe packaging. Then mushroom-growing media wasplaced in the container and stirred until homogenous.
The homogenous media was put in th e pyrolysis
reactor and converted into biochar at 500 oC for six
hours.
The Application Mushroom-Growing MediaBased Biochar as Soil Amendment
Samples were treated and divided into four groups
media with three replications. Treatment P0 as
control has a soil composition of 6000 grams of puresoil without the addition of biochar. Treatment P1,
P2, P3 has soil composition by adding biochar 1% (60grams), 2.5% (150 grams), and 5% (300 grams) ofbiochar respectively (Fig. 1). After that, Zea mays
seeds (every three seeds) were grown on these soil
samples. These samples were maintained by watering
every 07.00 AM o’clock and 05.00 PM 0’clock foreight weeks. Observation by the measurement of root
length and number of leaves was conducted every
week. The measurement of water content, ash
content, and C/N ratio of biochar also were carriedout in this study. Total C-organic also was carried out
by Walkey and Black method.

Fig. 1

Table 1 The height of Zea mays plants in the
different soil treatments
Week
1st
2st
3rd
4th
5th
6th
7th
8th

P0
20.3
30.6
47.3
51.6
53.6
57
61.3
63

P1
18.6
34.3
55.6
69.6
82/6
97
111
125.3

P2
19.6
30.6
54.3
64.6
68.3
77.3
86
89.6

P3
20.6
28
48
62
66.6
72.6
81.3
86

The height was measured from the butt of the stemto the internode of the last trunk before flowers. The
data in Table 1 is then plotted the in graphic in Fig. 2
so that the growth pattern is visible clearly. All
samples show growth day by day for eight weeks. For
P0, Zea mays that were applied with pure soil havethe slowest growth compared to other samples. The
growth of Zea mays underwent accelerating growth
after applying biochar. The fastest and highest growth
was performed by Zea mays that were applied to thesoil with the addition of 1% of biochar. After eightweeks of growth time, Zea mays in P1 (with theaddition of 1% biochar) grew up significantly with alength of 125.3 cm. The second fastest and highest
growth was performed by sample P2 with a length of

The growing media samples

RESULTS AND DISCUSSION
The conversion of mushroom-growing media into
biochar by pyrolysis has been conducted at a
471

GEOMATE – Bangkok, Thailand, 22-24 November 2022

89.6 cm after eight weeks of growth time. Almost thesame pattern results also occurred in the sample P3
with a length of 86 cm after eight weeks.

addition of biochar composition impacted themicroorganism abundance and community involvedin enzyme synthesis [12]. It also can be explained due
to biochar amendment contributes to the high carbon
in soil composition dan promotes enzyme synthesis
and stimulates seed germination [13].

CONCLUSIONSIn this study, the use of 1% biochar shows the best
performance in the growth of Zea mays plants. But,
with the addition of 2.5% and 5% biochar (P2 and P3)
the growth capability reduced if the results werecompared to P1. The addition of 2.5% and 5% shows
results that are not quite different from each othereven though it still showed better results than thecontrol (P0).
The conversion of mushroom-growing media into
biochar is a promising material to be used inagriculture, due to the possibility of biochar’s
properties to retain water, and nutrients, and give abeneficial impact on the growth rate of Zea mays
plant. Despite the study being a preliminary study, theresults concluded that the amendment soil strategy
can be considered the use of waste-based biochar. The
utilization of waste-based products as soil
amendment also leads to accelerating circular
economy implementation in agriculture. The impactof biochar on enzymatic activities in soil compositionand crop yield must be investigated in further study.

Fig. 2 The length of Zea mays plants in the
different soil treatment
The measurement of the number of leaves and thelength of the roots also was carried out in this study
as an indicator of the positive impact of the additionof biochar. The results showed in Table 2.
Table 2 The number of leaves and length of roots of
Zea mays plants in the different soil
treatments after eight weeks of growth time
Growthindicator
NumberofleavesLengthof roots

P0

P1

P2

P3

19

48

31

21
ACKNOWLEDGMENTS

5

16

11

In this study, it is impossible to thank everyone
individually, so we give our thanks to many people
and individuals who have assisted us over the years,
and to the colleagues, students, and volunteers who
have similarly offered their support. We also wish to
express our sincere thanks to the Directorate Generalof Higher Education, Ministry of Education andCulture for its funding and support grant.

8

After eight weeks of growth time, samples P0, P1,
P2, and P3 have 19, 48, 31, and 21 leaves
respectively. The value is twice sampled P0. The
same phenomenon also occurred in the length ofroots. After eight weeks of growth time, samples P0,
P1, P2, and P3 have lengths of the root of 5, 16, 11,
and 8 cm respectively. Based on Table 2, the mostleaves number and the longest root were found insample P1 which was given the addition of 1%
biochar. These values, both the number of leaves andthe length of roots, are twice sampled P0.
The results showed that there is a correlation
between the addition of biochar and the growth Zea
mays. The addition of biochar in the soil has animportant role to enhance soil fertility. Leila [11]analyzed the impact of biochar addition on enzymatic
activities. Leila’s report said that biochar increased
the enzymatic activities due to the increase in organic
C and/or total N. the increase in the enzymatic
activities is an indicator of microbial biomass
stimulation since most of the enzymes are produced
by soil bacteria. It can also be concluded that the

REFERENCES[1] Pete S. and Daniel M., Agriculture in Climate
Change 2007: Mitigation, 1st ed. Cambridge
University Press, 2007, pp.499-540.
[2] Chia-Hsing L., Chung-Chi W., Huan-Hsuan L.,
Sang S.L., Daniel C.W.T., Shih-Hao J., and Yong
S.O., In-situ Biochar Application Conzz serves
Nutrients while Simultaneously Mitigating
Runoff and Erosion of a Fe-Oxide-Enriched
Tropical Soil. Science of The Total Environment,
Vol. 619-620, 2018, pp.665-671.
[3] Food Agriculture Organization (FAO), World
Agriculture: Towards 2015/2030 an FAO
Perspective, 2003, pp.97.
[4] Mao Y., Mingming S., Yanfang F., Jinzhong W.,
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Shanni X., Da T., Yu Z., Jun W., Huixin L., andXi J., Effect of Biochar Amendment on The
Control of Soil Sulfonamides, AntibioticResistance Bacteria, and Gene Enrichment in
Lettuce Tissues. Journal of Hazardous Materials,
Vol. 309, 2016, pp.219-227.
[5] Josiah H., Micahel D., Dwight S., and Andrew K.,
The Basics of Biochar: A Natural Soil
Amendment. Soil and Crop Management, 2010,
pp.1-6.
[6] Fernanda R.O., Anil K.P., Deb P.J., Sushil A., Hui
L., and Samir K.K., Environmental Application of
Biochar: Current Status and Perspectives.
Bioresource Technology, Vol. 246, 2017, pp.110122.
[7] Neneng L.N., Potency of Utilizing Biochar for
Dryland Rehabilitation in Indonesia. Journal ofLand Resources Special Edition, 2014, pp.57-68.
[8] A. Soleymani, Safflower (Carthamus tinctorius
L.) Seed Vigor Tests for The Prediction of Field
Emergence. Industrial Crops and Products, Vol.
131, 2019, pp.378-386.
[9] Juniar S. and Kriston S., A Selected Stenotaphrum
secundatum as Superior Shade Tolerant Forage
Resource. Indonesian Bulletin of Animal and
Veterinary Sciences, Vol.30, Issue 2, 2020,
pp.103-111.
[10] Diah E. and Mochamad N., The Growth of Maize

Crop (Zea mays L.) BISI-2 Variety on Non
Rejected Sand at Pantai Trisik Kulon Progo.
Journal of Human and Environment, Vol.18, Issue
3, 2011, pp.220-230.
[11] Leila E., Ahmed A.A., Salah J., Hanene A., Evan
A.N.M., Camelia M.G., and Mejdi J., Applicationof Olive Mill Waste-Based Biochar in
Agriculture: Impact on Soil Properties, Enzymatic
Activities and Tomato Growth. Science of The
Total Environment, Vol. 755, 2021, pp.1-11.
[12] Martin S., Andreas S., Kilian E.C.Smith., Moritz
N., Martina R., and Joost T.V.D., Comparing
Straw, Compost, and Biochar Regarding Their
Suitability as Agricultural Soil Amendments to
Affect Soil Structure, Nutrient Leaching,Microbial Communities, and The Fate ofPesticides. Science of Total Environment, Vol.
751, 2021, pp.1-19.
[13] Sandeep K.V., Ashok K.D., Saikat G., Vinay K.,
and Ekrem G., Applications of Carbon
Nanomaterials in The Plant System: A
Perspective View on The Pros and Cons. Scienceof The Total Environment, Vol. 667, 2019,
pp.485-499.

473

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

STRATEGIES FOR INCREASING ACCESS TO WATER AND
SANITATION IN A WATER-SENSITIVE AREA
Shella Zahrawani1, Ahmad Soleh Setiyawan1, Prasanti Widyasih Sarli1, Prayatni Soewondo 1, and Dion Awfa2
1
Faculty of Civil and Environmental Engineering, Institut Teknologi Bandung, Indonesia;
2
Department of Environmental Engineering, Institut Teknologi Sumatera, Indonesia
ABSTRACT
Manado city, Indonesia is a water-sensitive area because it is a disaster-prone area located on the riverside,seaside, and hills and has low access to water and sanitation. This study aims to develop a strategies to increase
access to safe and sustainable water and sanitation in water-sensitive areas. The methods of EHRA (Environmental
Health Risk Assessment) and SWOT (Strength, Weakness, Opportunity, and Threat) were used to analyze existing
conditions and to develop strategies in the study area. As results, 76.33% of the community used refilled water for
drinking, 37.67% of the community used portable water for bathing and washing, 73% of the community usedlatrine for feces collection, and 91% of the community managed solid waste by collecting and transporting it to
the landfill. The results of the SWOT analysis show that the weights of strengths, weaknesses, opportunities, andthreats were 3.632, 0.787, 1.756, and 1.594, respectively. Strategies to increase access to water and sanitation in awater-sensitive area were enforcing regulations, establishing a special supervisory, building facilities andinfrastructure, and conducting socialization with the community. Implementing strategies based on strengths andopportunities is the best approach in increasing access to safe and sustainable water and sanitation in watersensitive areas.
Keywords: Increasing strategies, Water-sensitive area, Water, Sanitation

1. INTRODUCTION

2. STUDY AREA

The high rate of urbanization, increasing
population growth, and changes in water
consumption patterns have resulted in a decrease ofwater quality and quantity [1] can threaten thesustainability of an area. This phenomenon will also
have an impact on decreasing environmental
sustainability. It is trigger the emergence of watersensitive areas. Water-sensitive areas arecharacterized by areas sensitive to water sources,
sanitation, disaster-prone areas, and densely
populated communities with an informal economy [2].
This of course will have an impact on health.
Manado City is a disaster-prone area because it islocated on the riverside, seaside, and hills. Data
related to water and sanitation management in watersensitive areas are relatively few, not up-to-date, andnot discuss in detail. In 2019, the access to improved
drinking water and sanitation were only 13.81% and88.72% of households [3]. It is a challenge for
Manado City Government to achieve SDGs 2030.
One of the ways to achieve the target is to develop a
strategies based on existing conditions.
One of the methods for developing strategies is
the Strength, Weakness, Opportunity, and Threat(SWOT) analysis. SWOT analysis is the method mostoften used in formulating strategies. Therefore, thisstudy will be carried out identification of the existing
condition of water and sanitation in water-sensitive
areas in Manado City as the basis for developing astrategy to increase access to water and sanitation.

The study was conducted in three water-sensitive
areas in Manado City, namely (1) the Singkil Satu
Area (Lingkungan 2) in Singkil Subdistrict is the hills
area, thus, it is prone to erosion and is a densely
populated, (2) the Wawonasa Area (Lingkungan 2) in
Singkil Subdistrict is the riverside area of Tondano
river, thus, it is prone to flooding and is a densely
populated, and (3) the Titiwungen Utara Area
(Lingkungan 2 and 3) in Sario Subdistrict is theseaside area of Sulawesi sea, thus, the potential for
ROB flooding. The selection of the study areas are
based on (1) the Decree of the Mayor of Manado
Number 163/KEP/LT.02/Bappeda/2015, which areslum areas in Manado City, and (2) Deltares data
where the areas are in a water-sensitive area.
3. METHODOLOGY
3.1 Data Collection
Primary data was conducted using a random
sampling. The secondary data was used to support the
primary data, such as demographic and regulations.
The questionnaire was adopted from the EHRA study[4] to describe water and sanitation management.
Yamane formula was used to determine sample size:𝑁n = sample size (93 households)𝑛=1 + 𝑁𝑒 2 N = population size (1,345
households)
e = margin of error (10%)
474GEOMATE – Bangkok, Thailand, 22-24 November 2022

SWOT analysis was used to develop a strategy toincrease access to water and sanitation. SWOT
analysis was a management tool usually carried out ina corporate strategic plan to develop a company’s
strategic plan [5]. The SWOT analysis was usedbecause this tool helps to identify the positive points
or strengths, overcome weaknesses, increase
opportunities, and check risks to avoid threats,
therefore, allowing for better decision making [6].
SWOT analysis was carried out through a SWOT
analysis questionnaire. Therefore, thirteen (13)stakeholders who responded were know about waterand sanitation management in the study area. Strategy
determination was obtained by assessing eachstrength and weakness which were included in theInternal Factor Analysis Summary (IFAS) matrix aswell as opportunities and threats which were includedin the External Factor Analysis Summary (EFAS)
matrix. Assessment of each SWOT aspect was carriedout by weighting of 1-5 and rating 1-4 for each IFAS
and EFAS. The weight was the weight total of each
aspects divided the weight total and the final total
weight must be 1. The rating was the rating value ofeach aspects divided the respondents total. The total
score for each factors was obtained by multiplying theweights and ratings. In the SWOT analysis diagram,
there is an x-axis (IFAS) was the difference between
the score of strengths and weaknesses, and a y-axis(EFAS) was the difference between the score ofopportunities and threats. The coordinates of the x
and y axes were plotted to a SWOT analysis diagram
to know the quadrant position to formulate a strategy[7].

of clean water and drinking water sources in the studysite can be seen in Figure 1 and Figure 2.
Figure 1, it shows that the main sources of cleanwater for hygiene (washing and bathing) were
portable water in the hills area (49%) and riverside
area (48%). Meanwhile, in the seaside area was bore
well water (36%). However, in the riverside andseaside area, there were 4% and 3% of the community
use unprotected dug well water. Figure 2, it showsthat the main source of drinking water was refilledwater (75%). However, in the seaside area, there was
3% of the community use unprotected dug well water.
Access to basic drinking water in hills, riverside,and seaside area were 90%, 95%, and 95%,
respectively. Access to limited drinking water in hills,
riverside, and seaside area were 10%, 5%, and 2%,
respectively. In seaside area, there was 3%
unimproved access to drinking water. The riversideand seaside areas has better basic access to drinking
water. The community living on the riverside andseaside areas has easier access to get-and it is closer
to the water sources. Meanwhile, the community inthe hills area must try harder to get water sources. In
addition, the soil structure of the hills area in the studysite was dominated by rocks.
100%

Clean Water Source (%)

3.2 Data Analysis

Dug Well Water Is Not
Self Protected
Dug Well Water Protects
Neighbors
Self Protected Dug Well
Water
Bore Well Water

80%
70%
60%
50%
40%

Portable Water

30%
20%

Refilled Water

10%

Bottled Water0%
Singkil Satu
(Hills)Close-ended questions data and SWOT results
were analyzed using descriptive statistics, processed,
and illustrated using Microsoft Excel. The
information gained, supported with documentationfrom direct observation, was used to describe theexisting condition of water-sensitive areas.

Fig. 1

Drinking

TitiwungenUtara (Seaside)

Clean water sources in the study area
Dug Well Water Is
Not Self Protected

90%
Drinking Water Source (%)

andWawonasa(Riverside)
Area

100%

4. RESULTS AND DISCUSSION
4.1 Clean
Water
Management

Rain Water

90%

Water

The Joint Monitoring Program (JMP) divided
water access into drinking water from improved
sources or unimproved sources [8]. Improved water
sources categories: (1) safely managed: “drinking
water from an improved water source which was
located on-premises, available when needed and freefrom fecal and priority chemical contamination”, (2)basic: “drinking water from an improved source, thecollection time was not more than 30 minutes”, and(3) limited: “drinking water from an improved source,
the collection time exceeds 30 minutes” [8]. The use

80%
70%

Self Protected Dug
Well Water

60%

Bore Well Water

50%

Portable Water

40%
30%

Refilled Water

20%
10%

Bottled Water0%
Singkil Satu
(Hills)Wawonasa(Riverside)

TitiwungenUtara (Seaside)

Area

Fig. 2

Drinking water sources in the study area

4.2 Domestic Wastewater Management
Wastewater management that was inadequate andmaintained can caused pollution of surface andgroundwater with serious health impacts [9]. The
475

GEOMATE – Bangkok, Thailand, 22-24 November 2022

construction of toilets was not a solution to achieving
the SDGs, but the strategy must focus on thecollection and treatment of human feces to achieve asafe sanitation system [10]. When onsite sanitation
technology was full, then there were several
considerations: (1) emptying it, (2) stopping using theonsite sanitation technology, and (3) constructing a
new onsite sanitation technology [10]. Domestic
wastewater management can be seen in Table 1.
In the study site, more than 95% of the community
had a private toilet. However, only 21-25% of thecommunity who has self-construction septic tanks
carried out periodic desludging. There were two waysof emptying, namely manual emptying andmechanical emptying [10]. In the study area, theemptying of fecal sludge using mechanical emptying
from privately owned feces trucks. In the seaside area,
there were 10% of the community discharged
wastewater to the Wastewater Treatment Plant(WWTP), because the location was close to the
WWTP in Megamas Area in Manado City. However,
WWTP was also not functioning optimally. Access tobasic sanitation in the hills, riverside, and seaside area
were 96%, 95%, and 100%, respectively. In hills area,
there was 4% unimproved access to sanitation and inriverside area, there was 5% limited access or shared
to sanitation. In general, fecal sludge management in
the study site was unsafely managed sanitation.
4.3 Solid Waste Management
The unsorted solid waste at the source, social
taboo, citizen’s attitude, poor assessment, inadequate
potential strategies unorganized informal sector ofwaste, unplanned fiscal, and poor implementation of
government policies were the challenges of municipal
solid waste management (MSW) [11].
In general, the solid waste was managed bycollecting and transporting it to the landfill (85100%). However, in the hills area, there were 10% of
the community burned solid waste and in the seaside
area, there were 8% of the community disposed to thehospital because the location was very close to
Pancaran Kasih Hospital. The frequency of solid
waste transported was carried out every day (92.33%).
The solid waste sorting was carried out in the hills
area (20%) and the seaside area (7%). The solid waste
retribution set by the Manado City government was
IDR 20,000-50,000 per month according to thebuilding type.

Table 1. Domestic wastewater management in the study area

Sanitation Chain

Criteria

User interface

Toilet ownership
Toilet type
Defecation location

Containment

Final disposal offeces

Emptying/
Collection
Transport
Treatment andReuse/Disposal

Periodic desludging
of feces-

Singkil Satu
(Hills)96% private toilet
100% gooseneck 96% private toilet
 4% neighbor/relative toilet
 92% latrine
 8% septic tank notimpermeable

25%

Area
Wawonasa(Riverside)
95% private toilet

Titiwungen Utara(Seaside)
100% private toilet

 95% private toilet
 5% MCK/public
toilet 65% latrine 35% septic tank
not impermeable

100% private toilet

21%

25%

 58% latrine 30% septic tank
not impermeable 10% WWTP
 2% unknown

Privately owned feces trucks on-call
Discharged into the ground or river without adequate treatment
unit

Table 2. Flood incidences in the study area

Criteria
Flood incidences
Flood height
The flood dries up

Singkil Satu
(Hills)98% never
Adult heels
1-3 hours

Area
Wawonasa(Riverside)
60% several times a year
Adult knee
Half-day476

Titiwungen Utara(Seaside)
40% several times a year
Adult heels
1-3 hours

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 3. IFAS matrix calculation in SWOT analysis in the study area
INTERNAL FACTORS

WeightRating

Score

0.129
0.158

3.615
3.692

0.465
0.583

0.117
0.155

3.308
3.846

0.387
0.596

0.143
0.167

3.538
3.769

0.507
0.628

0.132
3.538
Total Strengths

0.466
3.632

0.105
0.137

3.385
3.462

0.354
0.473

0.121

3.077

0.371

0.129

3.231

0.416

0.115
0.129

3.385
3.308

0.390
0.426

0.139
3.308
0.126
3.308
Total Weaknesses
Total Strengths-Weaknesses (x-axis)

0.461
0.417
0.787
2.845

STRENGTHS
Clean Water and Drinking Water
1 The community has access to water sources
2 There are regional regulations related to clean water/drinking water
Domestic Wastewater
1 Almost the community (97%) has a private toilet
2 There are local regulations related to wastewater management
Solid Waste
1 The community is willing to pay solid waste retribution
2 There are local regulations regarding solid waste management
Drainage
1 Adequate drainage construction
WEAKNESSES
Clean Water and Drinking Water
1 The water sources that are not pipelines are not protected
2 The community uses groundwater as the main source of water for hygiene
Domestic Wastewater
1 The fecal sludge treatment system still uses latrine and self-constructed
septic tanks that are not impermeable
2 Grey water is discharged into the drainage
Solid Waste
1 All solid waste is directly transported to the TPA
2 There is no processing of solid waste before it is disposed into the TPA
Drainage
1 Grey water mixes with rainwater runoff
2 Insufficient drainage capacity

Table 4. EFAS matrix calculation in SWOT analysis in the study area
EXTERNAL FACTORS
Weight Rating
OPPORTUNITIES
Clean Water and Drinking Water
1 The community is willing to pay clean water fees
0.172
3.538
2 The community is willing to be given socialization and training
0.186
3.692
OPPORTUNITIESDomestic Wastewater
1 The community knows the dangers of feces without proper management
0.155
3.538
3.385
2 Some communities have carried out feces desludging
0.155
Solid Waste
1 Some communities do solid waste sorting
0.162
3.385
Drainage
1 The WSUD urban planning concept integrates the sustainable water cycle
0.172
3.462
Total Opportunities

477

Score

0.608
0.685

0.547
0.523
0.547
0.595
1.756

GEOMATE – Bangkok, Thailand, 22-24 November 2022

EXTERNAL FACTORS (Continued)
THREATS
Clean Water and Drinking Water
1 Prone to floods and landslides
2 Water continuity is not guaranteed
Domestic Wastewater
1 Limited land to build a septic tank
Solid Waste
1 Difficult road access
2 Solid waste production continues to increase
Drainage
1 Area contour

Quadrant I(2.845, 0.162)

weakness

IFAS

strength

Quadrant IV

Quadrant II

threatFig. 3Rating

Score

0.170
0.159

3.538
3.385

0.600
0.539

0.156

2.923

0.455

0.156
0.187

3.154
3.385

0.491
0.632

0.173
3.385
Total Threats
Total Opportunities-Threats (y-axis)

0.586
1.594
0.162

calculation were plotted into a SWOT analysis
diagram as shown in Figure 3.
The results of the SWOT analysis diagram showthat the strategy for increasing access to water andsanitation in the study area was in quadrant I(progressive), thus, it can take advantage of allstrengths and opportunities. The formulation of astrategy for increasing access to water and sanitationin water-sensitive areas was as follows:
1. Strategy 1, the enforcement of domestic
wastewater regulations according to the Manado
City Regional Regulation [12]. It has regulated
increasing and developing access to domestic
wastewater
facilities
andinfrastructure,strengthening
institutions
andincreasing
management capacity, as well as increasing publicawareness through triggering by providing
economic benefits [13]. Domestic wastewater
facilities and infrastructure can be started with theconstruction of a SNI septic tank and a fecal
sludge treatment plant. Colouring project is also
one possible strategy [14].
2. Strategy 2, create catchment areas and applied theconcept of Water Sensitive Urban Design(WSUD) to reduce flooding. In Manado City
Regional Regulation [15], it has planned to build
infiltration ponds in flood-prone areas to control
rainwater runoff.
3. Strategy 3, reducing solid waste by sorting it fromthe source, through a solid waste bank, TPS 3R or
TPST. In Manado City Regional Regulation [16]stated that everyone who produce solid waste wasobliged to reduce and handle solid waste in anenvironmentally manner, which is one way tochange people’s behavior [13].
4. Strategy 4, increasing water treatment capacity
through PDAM Manado City. Manado City has
Manado City Regional Regulation [17]. The aims
to increase the productivity and performance of
PDAMs in serving and increase the efficiency andeffectiveness.

EFAS
opportunity
Quadrant III

Weight

The results of the SWOT analysis diagram

4.4 Drainage Management
Urban water management was carried out by
integrating water services, including water supply,
sewerage, drainage and flood management, andenvironmental protection [2]. The drainage system inthe study area was a mixed system. More than 90% of
the community in the study site discharged grey water
into the drainage, either through open or closed
channels. Flood incidences in the study area can beseen in Table 2. In the riverside and seaside areas,
flood incidences more often occurred several times ayear. Meanwhile, in the hills area, landslides more
often occurred during the rainy season.
4.5 The Strategies to Increase Access to Water andSanitation
The strategies to increase access to water andsanitation that applied in the study area was analyzed
based on IFAS and EFAS matrix using SWOT
analysis. IFAS and EFAS matrix calculations can beseen in Table 3 and Table 4. The total score of internal
factors (x-axis) was 2.845 and the total value of
external factors (y-axis) was 0.162. The results of the478

GEOMATE – Bangkok, Thailand, 22-24 November 2022

academic research from the last decade,”
Journal of Strategy and Management, vol. 3, no.
Aug.
2010,
doi:
3,
pp.
215–251,
10.1108/17554251011064837.
[6] T. J. Chermack and B. K. Kasshanna, “The Useand Misuse of SWOT Analysis and Implicationsfor HRD Professionals,” Human Resource
Development International, vol. 10, no. 4, pp.
383–399,
Dec.
2007,
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10.1080/13678860701718760.
[7] Sardjito, E. B. Santoso, K. D. M. E Handayeni,
and N. Farikha, “Quality improvement strategy
of slum settlement in Kingking sub district,
Tuban Regency,” IOP Conf. Ser.: EarthEnviron. Sci., vol. 202, p. 012070, Nov. 2018,
doi: 10.1088/1755-1315/202/1/012070.
[8] WHO and UNICEF, “Progress on Drinking
Water, Sanitation and Hygiene: 2017 Update and
SDG Baselines,” Geneva: WHO; New York:UNICEF, 2017.
[9] W. Bank, “Improving On-site Sanitation andConnections to Sewers in Southeast Asia
Insights from Indonesia and Vietnam [Research
Brief].,” 2022.
[10] N. Chandana and B. Rao, “A critical review on
sludge management from onsite sanitation
systems: A knowledge to be revised in thecurrent situation,” Environmental Research, vol.
203,
p.
111812,
Jan.
2022,
doi:
10.1016/j.envres.2021.111812.
[11] A. Kumar and A. Agrawal, “Recent trends insolid waste management status, challenges, andpotential for the future Indian cities – A review,”
Current
ResearchinEnvironmental
Sustainability, vol. 2, p. 100011, Dec. 2020, doi:
10.1016/j.crsust.2020.100011.
[12] Manado City Regional Regulation, Domestic
Wastewater Management, vol. 9 of 2016.
[13] P. W. Sarli and P. Soewondo, “Strategic
Thinking to Change Behavior and Improve
Sanitation in Jodipan and Kesatrian, Malang,
East Java, Indonesia,” vol. 11, no. 10, p. 6, 2017.
[14] P. Soewondo et al., “Visual Improvement of
Slum Areas to Accelerate Universal Access to
Domestic Wastewater Treatment (Case study of
Yogyakarta, Semarang and Manado),” IOP
Conf. Ser.: Earth Environ. Sci., vol. 409, no. 1,
p. 012006, Jan. 2020, doi: 10.1088/17551315/409/1/012006.
[15] Manado City Regional Regulation, Manado City
Spatial Plan for 2014-2034, vol. 1 of 2014.
[16] Manado City Regional Regulation, Solid Waste
Management, vol. 1 of 2021.
[17] Manado City Regional Regulation, The Capital
Investment of the Manado City government to
PDAM Manado City, vol. 6 of 2016.

5. CONCLUSION
There were three specific areas in a watersensitive area, namely hills, riverside, and seaside
areas. The riverside and seaside areas has better
access to water and sanitation with 95% access toimproved drinking water, 95-100% access to basic
sanitation, and 85-100% to the solid waste collecting
and transporting. More than 90% of the community
discharged grey water into the drainage. The riversideand seaside areas was more sensitive to flooding. The
strategies for increasing access to water and sanitation
were based on SWOT analysis, namely strengths(3.632), weaknesses (0.787), opportunities (1.756),
and threats (1.594). therefore, the strategies toincrease access to water and sanitation in watersensitive areas were enforcing regulations,establishing a special supervisory, building facilities
and infrastructure, and conducting socialization withthe community. Implementing strategies based onstrengths and opportunities is the best approach inincreasing access to safe and sustainable water andsanitation in water-sensitive areas.
6. ACKNOWLEDGMENTS
This research was supported by Netherlands
Organization for Scientific (NWO) and Indonesian
Ministry of Education, Culture, Research, andTechnology (KEMENDIKBUDRISTEK), and also
Global Sanitation Graduate School (GSGS) program.
This research was part of the Resilient Indonesia
Slum Envisioned project (RISE).
7. REFERENCES[1] R. R. Rahmat and S. H. Djoeffan, “Penerapan
Water
Sensitive Urban Design Pada
Permukiman DAS Cikapundung (Studi Kasus:
Kelurahan Pasirluyu Kota Bandung),” Jurnal
Perencanaan Wilayah dan Kota, vol. XVI, no. 1,
p. 9, 2019.
[2] T. H. F. Wong, B. C. Rogers, and R. R. Brown,“Transforming Cities through Water-Sensitive
Principles and Practices,” One Earth, vol. 3, no.
4,
pp.
436–447,
Oct.
2020,
doi:
10.1016/j.oneear.2020.09.012.
[3] Pemerintah Kota Manado, Kajian Lingkungan
Hidup Strategis RPJMD Kota Manado Tahun
2021-2026. 2021.
[4] Kelompok Kerja Perumahan, Permukiman, Air
Minum, dan Sanitasi (Pokja PPAS), Panduan
Praktis Pelaksanaan EHRA (Environmental
Health Risk Assessment/Penilaian RisikoKesehatan
Lingkungan).
Kementerian
Kesehatan Republik Indonesia, 2020.
[5] M. M. Helms and J. Nixon, “Exploring SWOT
analysis – where are we now?: A review of479

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

DETERMINATION FACTORS FOR SELLING LOCATIONS, GENDER,
AND MIGRATION STATUS OF STREET VENDORS IN EAST JAKARTA IN
ACHIEVING ECONOMIC RESILIENCE DURING THE COVID-19 PANDEMIC
Beti Nurbaiti1, Chotib2, Kemas Ridwan K3, Mia Siscawati4 and Elisabeth Ratu Rante Allo5
1,2,3,4,5

School of Strategic and Global Studies, University of Indonesia

ABSTRACTThe purpose of this study was to analyze the economic resilience of street vendors (street vendors) households
in East Jakarta in terms of selling locations, gender, and migration status. The selling locations are divided into 4categories, namely local government-assisted location, temporary location, base /fixed location, and hawkers forboth female and male street vendors with the status of lifetime migrant and non-lifetime migrant. The researchmethod used is a quantitative approach by processing primary data for 420 street vendors in East Jakarta using
logistic regression. The results show that street vendors selling in local government Assisted locations, female
street vendors, and non-lifetime migrant street vendors tend to have high economic resilience, while the othergroups are in base / fixed locations, male street vendors, and lifetime migrants are in moderate economic
resilience. For street vendors who sell in temporary locations and lifetime migrants, they are in low economic
resilience.
Keywords: Street Vendors; Economic Resilience; Covid-19 pandemic; East Jakarta

I.

and (3) recent migration status with economic
resilience street vendors.

INTRODUCTION

The COVID-19 pandemic has had a tremendous
impact on all aspects of life, including social andhealth aspects, but the hardest hit is the economic
aspect of all levels of world society (Safitri et.al,2021). The impact of this pandemic is also felt by
street vendors who work in the urban informal sector
with a drastic decline in trade turnover. The decreasein street vendors' income has an impact on theirability to fulfill the basic aspects of daily life, health,and education. Most of the street vendors are
migrants or newcomers who have lived and settled
in Jakarta for a long time. The flow of rural-urban
migration is unstoppable, especially with advancesin transportation, technology, and community
services in urban areas. These migrants argue thatthe city can provide a better life than when living in
the village (Nurbaiti, 2017) and (Horiuchi and
Takahashi, 2016). Migrants who are not ready withknowledge and capital will enter the informal sector,
one of which is street vendors (Nurbaiti, 2016).

II.

LITERATURE REVIEWThe informal sector has advantages in adapting
flexibility and reducing poverty even though theincome is uncertain (ILO, 2020) and (Priyono,2015). Social capital often helps street vendors
struggling and make a living in the city based onfamily and kinship relationships. Social capital also
often helps market access, trading methods, to theease of street vendors getting business capital
without going through financial institutions(Nurbaiti and Chotib, 2020). Based on thephenomena mentioned above, the authors would liketo examine the extent to which: (1) the correlation
between trading locations and economic resilience;(2) gender with economic resilience street vendors;

The rural-urban wage gap is the main cause of
the rapid flow of rural-urban migration and willcontinue to grow as long as migrants feel a positive
impact (increased utility) in a new place. The main
driving factor is the scarcity of jobs in the area oforigin, but migration often occurs as a result of
natural disasters, wars, conflicts, and the need for abetter life. The attractiveness factor can also be thereason people migrate, including complete
educational, health, and entertainment facilities(Todaro, 2000). Referring to the results of Nurbaiti
and Chotib’s research (2020), the informal sector
develops because of financial and non-financial

Each individual has their reasons for being amigrant, moving from one place to another, between
villages and cities to between countries. The biggest
factor causing someone to become a migrant is toimprove their standard of living (Nurbaiti, 2017). To
become a migrant requires sacrifice to bear the risks
of uncertainty and pressures of city life, being far
away from family and relatives, especially in a
metropolitan city like Jakarta (Chotib and Nurbaiti,2018). The definition of migrant when referring to
the explanation from the Central Statistics Agency(BPS, 2000) administratively can be divided into
three, namely: (1) lifetime migration, where thecurrent place of residence is different from the placeof birth, has settled in the current place for more than
five years. year; (2) total migration, where thecurrent residence is different from the previous placeof residence; and (3) recent migrants (recent
migration), if a person.

480

GEOMATE – Bangkok, Thailand, 22-24 November 2022

benefits. In addition, the need for employment
opportunities to generate income and reduce poverty
by absorbing migrant workers is the main reason forentering the urban sector. Thus, it can be understood
why the population continues to increase in cities,
especially in DKI Jakarta as a result of urban-rural
migration from year to year.

and/or wife have a fixed income, primary needs aremet such as clothing, food, and housing); (3)financing of children's education; and (4) family
financial security. Resilience or family resiliencemust be considered as a concept that is 'created
together' by all actors to be able to get throughdifficult times, especially when the Covid-19
pandemic hits parts of the world in various walks oflife and increases the number of poverty rates(Lopez and Castro, 2021). Family resilience will becreated if the family which generally consists of ahusband, wife, and children carries out the eightfamily functions in a harmonious, harmonious, andbalanced manner (REACH, 2021).

The existence of street vendors often uses spacethat is not intended to sell officially, most of which
are in public spaces such as road shoulders,sidewalks, and even green open spaces. Street
vendors produce urban social spaces that wereoriginally abstract into real ones, which cannot be
separated from social reality based on the control
and domination of the parties involved (Lefebvre,
1991). The construction of entertainment facilities,
as well as factories or offices, will produce a newsocial space, namely the rampant presence of street
vendors around it. The phenomenon of street
vendors refers to concrete spatial practices because
in it there is a network of interaction andcommunication between individual street vendors
daily. Space is formed by individuals with all theiractivities, and vice versa, humans act because they
are formed by that space. street vendors, throughbuying and selling activities, form space according
to their wishes.

The eight functions of the family in question
include: (1) the function of religion, where thefamily is a forum for introducing, teaching, andpracticing religious values; (2) the function ofaffection, where affection is given from the time thechild is born until an indefinite period of time; (3)protection function; (4) socio-cultural functions; (5)reproductive function; (6) socialization andeducation functions; (7) economic function; and (8)the function of environmental development.
Therefore, family economic resilience is directly
proportional to gender equality and justice appliedin the family, where both husband and wife work
hand in hand in domestic affairs and meet the needsof the family together (Aziz and Solikha, 2018).

As time goes by, the process of socialization andinteraction occurs between street vendors, thus
forming a space zone. The socialization lasted a longtime intensively so that a strong interaction wasformed between street vendors. As a result, theprevious street vendors' space zone was gettingstronger, thus forming its power or power. Even
though it looks abstract, the power of spatial
domination by street vendors is real, and it can beseen that the local government/Satpol PP is making
efforts to control street vendors. This can beunderstood as the abstract space of street vendors.
The space production process by street vendors canalso have obstacles such as permits for trading
places, a small number of visitors, small capital, andcompetition between street vendors. The positive
impact of street vendors from a social and economic
point of view can be seen in terms of overcoming
unemployment and poverty. On the other hand, it
can be negative, are the city's social problems suchas environmental cleanliness, conversion of publicland, and congestion. However, the arrangement andempowerment of street vendors have been regulated
in Governor Regulation No. 10 of 2015 where street
vendors are divided into 4 selling locations, namely:(1) Local Government Assisted Locations; (2)Temporary Locations; (3) Base / Fixed Location;and (4) Hawkers.

III.

METHODS, HYPOTHESIS, AND
RESEARCH RESULTS

Early research was conducted for 6 (six) months
starting from November 30, 2021, to the end of May
27, 2022, using a questionnaire instrument for 420
street vendors in East Jakarta. The approach used isquantitative which includes: (1) descriptive
analysis; (2) bivariate analysis; and (3) inferential
analysis of logistic multinomial regression results totest the research hypothesis. Quantitative data
analysis was performed using a multinomial logistic
regression function with the following equation:
𝑙𝑛 (

𝑦=1
𝑦=0

) = 𝛽0 + 𝛽1 ∗ 𝐾𝐿04 + 𝛽2 ∗ 𝐾𝑅08 + 𝛽3 ∗𝐾𝑅12 + 𝑒(1)

𝑦=2

𝑙𝑛 (𝑦=0) = 𝛽0 + 𝛽1 ∗ 𝐾𝐿04 + 𝛽2 ∗ 𝐾𝑅08 + 𝛽3 ∗𝐾𝑅12 + 𝑒(2)

Where :y= street vendors' economic resilience,
which are :𝑦=1
𝑙𝑛 (𝑦=0) is the probability of street vendors to havemoderate economic resilience against street
vendors who have low economic resilience

Economic resilience is measured through 4
aspects, namely: (1) the condition of the family's
place of residence; (2) family income (husband

481

GEOMATE – Bangkok, Thailand, 22-24 November 2022

𝑦=2

Hypothesis 3 (H3): street vendors with lifetime
migrant status have higher economic resilience thanstreet vendors with non-lifetime migrants.

𝑙𝑛 (𝑦=0) is the probability of street vendors to havehigh economic resilience against street
vendors who have low economic resilience
KL04 = types of street vendors at 4 locationsIV.

DISCUSSION
The public space used by street vendors in
trading and daily activities is 68% using
pedestrians/sidewalks. There are 2 (two) types ofsidewalks that are used, namely sidewalks withpedestrian, bicycle, or disabled lanes, as well as
sidewalks specifically for pedestrians. As many as
28% of street vendors use the road area for selling,followed by the use of crossroads as much as 3%.
There are as many as 1% of street vendors using
green open spaces such as parks and or urban forests.
For the respondents surveyed in this study, none ofthem sold using riverbanks, crossing bridges, andover waterways.

KR 08 = gender
KR12 = lifetime migration status
There are 3 research hypotheses, namely:
Hypothesis 1 (H1): street vendors who trade in local
government-assisted locations have higher
economic resilience than other selling locations;Hypothesis 2 (H2): male street vendors have higher
economic resilience than female street vendors;

Table 4.1 Analysis of Economic Resilience by Selling Location
VariableGroup

Selling Location 1.
2.
3.
4.

Local Government Assisted Location
Temporary Location
Base/Fixed Location
Hawkers
Total
Source: processed by the author (2022)

Referring to Table 4.1 regarding the results ofthe cross-tabulation analysis of the category of
economic resilience on the type of street vendors
trading locations, it can be seen that low economic
resilience tends to be in street vendors who trade in

Low
(N %)
9.3%
41.1%
26.0%
27.8%
30.2%

Moderate
(N %)
25.6%
30.1%
57.7%
55.6%
43.6%

High
(N %)
65.1%
28.8%
16.3%
16.7%
26.2%

Total
(N %)
100.0%
100.0%
100.0%
100.0%
100.0%

Temporary Locations. Moderate economic
resilience tends to be in Base/Fixed Locations and
Hawkers, while high economic resilience is found instreet vendors located in Local Government
Assisted Locations.

Table 4.2 Analysis of Economic Resilience by Gender
Variable

Group

Low
(N %)
Gender
1. Male30.0%
2. Female
30.5%
Total
30.2%
Source: processed by the author (2022)

Moderate
(N %)
50.2%
35.3%
43.6%

Based on the results of the cross-tabulation
analysis of the categories of economic resilience inTable 4.2 associated with the sex of street vendors,
it can be seen that low economic resilience can occur
in male and female street vendors, there is no

High
(N %)
19.7%
34.2%
26.2%

Total
(N %)
100.0%
100.0%
100.0%

significant
difference.
Moderate
economic
resilience tends to be in male street vendors, while
those with low and high economic resilience arefound in female street vendors.

Table 4.3 Analysis of Economic Resilience by Migration Status
Variable
Migration StatusGroup

1. Non-Life Time Migrants
2. Life Time Migrants
Total
Source: processed by the author (2022)

Low
(N %)
25.5%
33.9%
30.2%

482

Moderate
(N %)
41.3%
45.3%
43.6%

High
(N %)
33.2%
20.8%
26.2%

Total
(N %)
100.0%
100.0%
100.0%

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Refer to the results of the cross-tabulation
analysis of the categories of economic resilience inTable 4.3, that street vendors with non-lifetime
migrants migration status have high economic

resilience. On the other hand, street vendors with thestatus of lifetime migrants have low to moderate
economic resilience.

Table 4.4 Logistic Regression Multinomial Test Results on Economic Resilience
Independent Variable

Medium to Low
Odds Ratio
Std.
Error
.964
1.174
.823

High to Low
Odds Ratio
Std.
Error
.014
6,901 **)
4.204

Intercept[KL04. Selling location = 1. Local Government
Assisted Location ][KL04. Selling location = 2. Temporary Location ].328 *).622.695[KL04. Selling location = 3. Base / Fixed Location
1.104
.584
.963
]
[KL04. Selling location = 4. Hawkers.
.
.
[KR08. 1=male]
1.009
.272
.591
[KR08. 3 = female].
.
.
[Non Life Time Migrant=.00]
1,810 **)
.266
1.96 3**)[Migrant Life Time=1.00].
.
.
Note: *) Significant at = 10%; **) Significant at = 5%; Source: processed by author (2022)
Based on Table 4.4, it can be explained the levelof economic resilience of street vendors in eachprofile which will help answer the research
hypothesis. There are two conditions analyzed basedon multinomial logistic regression data processing,namely: (1) moderate economic resilience comparedto low economic resilience, and (2) high economic
resilience compared to low economic resilience. For
the first condition, namely calculating theprobability of moderate economic resilience against
low economic resilience, the significant independent
variables include the type of selling location andnon-lifetime migrant status. Meanwhile, for thesecond condition, calculating the probability of higheconomic resilience against low economic
resilience, the independent variables are
significantly the same, namely selling location andnon-lifetime migrant status.

.201
.002
.
2.877
.
5.122
.

The same thing happened to street vendors as nonlifetime migrants who had a 1,963 times higher
chance than street vendors as lifetime migrants. In
other words, street vendors and non-lifetime
migrants are more likely to have high economic
resilience than lifetime migrants. For gender, there
is no significant difference between the economic
resilience of male and female street vendors.
V.

CONCLUSION

The first hypothesis states that street vendors
who trade in local government-assisted locations
have higher economic resilience than other selling
locations, proven or accepted. This is because thestreet vendors at this location are more organizedwith trading facilities, as well as the availability ofelectricity, and clean water and are more
comfortable with tables, chairs, and tents that havebeen arranged neatly as a contribution from the DKI
Jakarta government to street vendors. Therefore, itis necessary to improve similar facilities for street
vendors who sell in other locations so that they
become organized/move to local governmentassisted locations. It is hoped that later street
vendors who do not stay and hawkers can be
gradually facilitated to be able to sell in better
locations.

The opportunity for economic resilience to occur
for street vendors in temporary locations is 0.328
times lower than for street vendors who do not havelocations/hawkers. In other words, street vendors
who do not have a fixed location are more likely tohave moderate economic survival than street
vendors in temporary locations. As for the chance ofmoderate economic resilience, street vendors' nonlifetime migrants are 1,810 times higher than street
vendors' lifetime migrants. Opportunities for higheconomic resilience occur in local governmentassisted locations 6,901 times higher than hawkers,
or in other words, street vendors in local
government-assisted locations have higher
economic resilience than hawkers. It can also be said
that street vendors in local government-assisted
locations have the opportunity to have higheconomic resilience than street vendors hawkers.

The second hypothesis which states that malestreet vendors have higher economic resilience than
women is not proven. Female street vendors have
similar economic resilience to male street vendors,
this shows that women can carry out their dual roles
both in the domestic sphere (taking care of thehousehold) and in the public domain (trading).
Therefore, the empowerment program for street
vendors, in general, has been carried out by the483

GEOMATE – Bangkok, Thailand, 22-24 November 2022

relevant agencies, in this case, the Small andMedium Enterprises Cooperative Trade Industry
Service (SME CTIS), so that it can be focused on
female street vendors to maintain their economic
resilience.

Migrants? A Data Analysis of SUSENAS 2013.
Journal of Strategic and Global Studies 1 (1), pp. 1528.
[4] Horiuchi, S. & Takashi T. (2016). Globalization
and Regional Revitalization in A Local University ofJapan. In : Globalization , Economic, Political andSocial Issues. Nova Publisher, pp. 149 – 159.
[5] International Labour Organization (ILO).
(2020). World Employment and Social OutlookTrend 2020. Geneva, International Labour Office.
[6] Lopez, L. J. R., & Castro, A. I. G. (2021).
Sustainability and Resilience in Smart City
Planning: A Review. Sustainability (Switzerland),13(1), 1–25.
[7] Nurbaiti, Beti. (2016). Pengaruh Status Migrasi
Melalui Karakteristik Sosiodemografi Terhadap
Tingkat Kesejahteraan Pekerja di DKI Jakarta(Analisis Data Cross Sectional SUSENAS 2013).
Disertasi. HAKI No : EC 00201816779, 3 Juli 2018.
[8] Nurbaiti, Beti. (2017). Pengaruh Status Migrasi
Melalui Karakteristik Sosiodemografi Terhadap
Tingkat Kesejahteraan Pekerja di DKI Jakarta (The
InfluenceofMigration
Statusthrough
Sociodemographic Characteristics on the Welfare
Level of Workers in DKI Jakarta). Jurnal Kajian
Ilmiah Universitas Bhayangkara Jakarta Raya, 17(2).
[9] Nurbaiti and Chotib. (2020). The Impact ofSocial Capital On Welfare : The Evidence From
Urban Informal Sector In East Flood Canal (BKT),
Jakarta. IOP Conference Series : Earth andEnvironmental Science, 436012004.
[10] Priyono, Edi. (2015). Memahami Pasar Tenaga
Kerja (Understanding the Labor Market). Pustaka
Lentera, Jakarta.

The third hypothesis which states that street
vendors with the status of lifelong migrants have ahigher economic resilience than non-migrant street
vendors for life is not proven because the findings in
the field are the opposite. This condition shows that
non-migrants or local indigenous people alreadyhave sufficient capital accumulation, and a wider
network than migrants/migrants, so they are better
able to survive in difficult conditions such as during
a pandemic like this.
ACKNOWLEDGEMENTS
This research was supported by Department ofIndustry, Trade, Cooperatives, Small and Medium
Enterprises, DKI Jakarta Province (DPPK UKM
Provinsi DKI Jakarta).
REFERENCES[1] Aziz, F. A., & Sholikha, A. F. (2018). Pengaruh
Wanita dalam Ketahanan Ekonomi Keluarga Studi
Kasus Pada Wanita Pengrajin Tikar Pandan di Desa
Pesahangan Cimanggu Cilacap. Yin Yang, 13(1), 1–
13.
[2] Badan Pusat Statistik (BPS). 2000. Jakarta
Dalam Angka. Badan Pusat Statistik (BPS), Provinsi
DKI Jakarta.
[3] Chotib & Nurbaiti, Beti. (2018). Are MigrantWorkers in DKI Jakarta More Welfare Than Non

484

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

ON MOVEMENT OF PEDESTRIANS IN THE STATION SQUAREHaru Kanda1 and Kazunari Tanaka2
1

Graduate School of Engineering, Osaka Institute of Technology, Japan; 2 Osaka Institute of Technology,
Japan
ABSTRACTIn Japan, the population is declining due to the declining birthrate and the increased proportion of elderly
people, and so the idea of reconstructing cities into compact cities is being considered. By concentrating the main
urban functions in a certain area, the travel distance is shortened, and some regions may become less dependent
on automobiles. The mortality rate of pedestrian-to-vehicle accidents is the highest among accident categories. In
this research, in order to make a basic town redevelopment plan and a design that is safe for pedestrians and to
improve the value of an area, the characteristics of a pedestrian flow line will be clarified. Video was taken toacquire the flow line. The location and number of people were counted using animate software. We investigated
the relationship with other transportation modes, focusing on the movement of automobiles. We analyzed thechange by time and by day. As a result, it was found that there is a relationship between the amount of staying
traffic and the impact of safety control.
Keywords: Compact city, Traffic accident, WalkableRESEARCH BACKGROUNDRESEARCH METHOD

In 2021, 2,636 people died in Japanese roadaccidents [1]. Of the number of fatalities caused bytraffic accidents, the number of fatalities by condition
can be divided into walking, cars, motorcycles, andbicycles. In addition, the elderly (65+) account for thelargest proportion of deaths while walking, exceeding
70% [1]. Of the pedestrian deaths, the percentage ofpedestrians who violated laws and regulations whencrossing exceeded 60%, including ignoring traffic
lights, indicating that there are many problems on thepedestrian side as well. [1]And in Japan’s in the future, as the population
declines and the birthrate declines and the population
ages, the elderly will become the center of the city. In
addition, although intensive urban structures havebeen studied in recent years, it is expected thatwalking and public transportation will be the mainmeans of transportation. In other words, from thesefacts, there is a possibility that the occurrence ofcrossing violations and the mortality rate will be lesslikely to decrease as the number of pedestrians andthe elderly increases.
One of the causes of crossing violations is that theconventional design methods have been planned toprioritize cars, making it difficult for pedestrians touse. Therefore, I thought it was important to focus on
the case where the flow line of the pedestrian could
not be controlled.
Therefore, as basic research for designing andplanning a city that is safe for moving pedestrians,
this research aims to predict the location of crossing
violations, mainly focusing on the flow lines ofpedestrians.

Table 1 Questionnaire survey question content
Questionnaire contents
Attribute

Gender, age, place of residence

Examples ofmain survey
questions

The most frequently usedtransportation when attending an
event or using Eruto Sakurai (acomplex facility adjacent to thestation), whether you want towalk around Sakurai Station, or
why you want to walk etc.

In order to observe changes in the staying
population over a short period of time and obtain data,
it is necessary to conduct the survey in areas where
events are being held, etc. Also, public transportation
and walking are the main modes of transportation in
a compact city, and it is necessary to collect data at
the points where they are concentrated. For these two
reasons, the plaza in front of the station was chosen
as the survey site. Specifically, the target was whenan event was held in front of Sakurai Station in
Sakurai City, Nara Prefecture. (October 29-31, 2021,
November 20-21,2021) This time, in order to extract
the trajectory of pedestrians from the video data, weused a video camera installed in the plaza at 9
locations. Using the recorded video, the location andnumber of people passing through are counted forchanges in the staying population and characteristic
flow lines. All-day events are held during the above

485

GEOMATE – Bangkok, Thailand, 22-24 November 2022

period, but the 30th was the busiest day due to a
special program. We observed changes in thepedestrian population over time and analyzed daily
differences. The above is the survey method used inthis survey, but at the same time, a simple
questionnaire survey was also conducted as apreliminary survey for pedestrians in the plaza, andquestions were asked about the basic attributes andmain survey items as shown.

visitors came from outside Sakurai City.
Figures 3 and 4 show the trajectories (daily
comparisons) taken from the video for the flow lines
other than the sidewalks. The results of this study
show that the flow lines on the 29th, which show thesame level of retention and traffic as in normal times,
have a fixed movement controlled by the design. On
the other hand, on the 30th, when the number oftrajectories is high, the movements are dispersed.
Here, the percentage of people passing outside of the
sidewalk on the 29th was 13.25% of the total and
4.65% on the 30th, indicating that the percentage of
uncontrolled movement lines is smaller when there isa larger staying population. In addition, the 29th,
which shows the same number of dwellings andtraffic as in normal times, shows that the flow line has
a fixed movement that is controlled by the design.
Conversely, on the 30th, when it can be read that there
are many trajectories, the movements are dispersed.

RESULTS OF THE SURVEY
Figure 1 and Figure 2 show a day-by-day
comparison of the 29th and 30th.
20
18
16
14
12
10
8
6
4
2
0

Fig. 1

Residence of participants on October 29

20
18
16
14
12
10
8
6
4
2
0

Fig. 2

Fig. 329

Trajectory of crossing violators in OctoberFig. 430

Trajectory of crossing violators in October

Residence of participants on October 30
ANALYSIS

Preliminary survey results show that only on the
30th, the event attracted many participants from thelocal area, and the number of people was very large
compared to the other survey days; on the other days,
the number of people did not differ significantly fromnormal, and the survey results indicate that many

Two methods were used in the analysis of theresults of this study, ''Variable for Flexible Movement(VFM)'' and ''Density of Stores'', respectively, wereused to analyze simple predictions of the locations ofcrossing violators and the causes of their occurrence.
486

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Variable for Flexible Movement

trajectories together, and it can be seen that thelocations where crossing violations occurred coincidewith the locations where VFM was high.

In the analysis of the survey results, the focus wason the trajectories taken from the videos. The
difference between the direction of the trajectory of
the pedestrian’s crossing violators and the directionof the shortest distance to a destination is the"Variable for Flexible Movement,"(hereinafterreferred to as VFM) and this value will be used in theevaluation. The larger VFM, the more likely it is thatthe sidewalk will not be able to control pedestrians,
i.e., that crossing violations will occur from there if
there are no guardrails, etc.
The analysis method involves fitting a virtual gridto the station square and calculating the trajectory of
the pedestrian’s crossing violators and the VFM of
the applicable mesh. Figure 5 shows the results before,and Figure 6 shows the results after the projective
transformation. In this case, the destination of shortest
distance is the stairway leading to the station.

Fig. 7 Pedestrian VFM

Fig. 8 Where VFM was highFig. 5 Diagram showing locus and gridFig. 9 High VFM and trajectory
Density of Stores
The density of the number of stores is calculated
by Takahashi et al. for each street by multiplying thenumber of stores by the height influence ratio for eachlevel of the hierarchy, summing them, andconsidering the effect of height on pedestrians. The
number of stores divided by the length of each street
is the density of stores, which is evaluated as thecrowding of stores on the street [2]. This value is used
to analyze the impact of the plaza on pedestrians in
the direction of travel from the pedestrian flow line.
The formulas used and the height impact ratios areshown below.

Fig. 6 Mesh for VFM
In order to easily visualize VFM, a threedimensional representation was used in this study.
Figure 7 shows a three-dimensional representation of
VFM, calculated from the trajectories of crossing
violators during a 10-minute period from 12:00 to
12:10 on October 31. The areas shown in Figure 8 arethose where the VFM was actually higher and stillcoincide with the sidewalk. Figure 9 shows theDensity of Stores = X

487

GEOMATE – Bangkok, Thailand, 22-24 November 2022

X={Σ(pn)(1/2) n-1+ Σ(qm)(1/2) m}/d(1)

Table 3 t-test

Height Influence Ratio
Bn floor ratio、m floor ratio=(1/2)(n-1),m

East to west
averagetpn＝Number of stores on n floors above groundqm＝Number of stores in basement n floor
d＝Street DistanceP(T<=t) one side

West to east

20.5

17.5
0.724
0.261

CONCLUSIONSAs shown in Figure 10, the plazas on the west andeast sides of the area where the most crossing
violators occurred were designated as Plaza WEST
and Plaza EAST, respectively, and the density of thenumber of stores in each was determined. The densityof the number of stores was 0.19 × 10 -3 and 0.15 ×
10 -3 for the WEST and EAST plazas, respectively,
indicating that the density of stores was higher in the
WEST plaza.

Plaza WEST

In this study, pedestrian movements were
captured on video, and the characteristics of theirtrajectories were analyzed. As a result, therelationship between the staying population densityand pedestrian flow lines was found. The VFM
intensity, which is a value that controls or promotes
pedestrian flow lines on the sidewalk itself, wasvisualized as a simple predictor of where pedestriancrossing violations would occur. In the future, weplan to increase the number of data samples andanalyze the characteristics of the data over time andthe relationship with other traffic in more detail, as is
the case with the data on accidents involving people
and vehicles.

Plaza EASTREFERENCE[1] National Police Agency (Traffic Bureau): Traffic
Accidents in Japan in 2021, March 3, 2022
[2] Takahashi H., Goto H., Sakuma Y., Saito R., Ishii
T, The relationship between the moments ofvisitor’s walking around and the density of storeson commercial area. – A case study on the areaaround Shimokitazawa-station –, Journal of theCity Planning Institute of Japan, No. 40-3,
October 2005
[3] Mori K., Yokozeki T., Yano N., Road Crossing by
Elderly People and People with Mobility
Constraints in Disregard of Traffic Lights, Journalof Japan Society of Civil Engineers D3, Vol.73,
No.5, I_941-I_947, 2017.
[4] Kotaki S., Takayama J., Nakayama S., Rachi M.,
Study on Actual Situation and Planning Task forEnvironmental Space of Station Square – Case ofCentral Station Square in City –, Journal of Japan
Society of Civil Engineers D3, Vol.71, No.5,
I_247-I_259, 2015.
[5] Matsui Y., Oikawa S., Hitosugi M., Cyclists
Behavior and Mechanism of Traffic Accident
Occurrence involving Cyclists, Journal of theJapanese Council of Traffic Science, Vol.15, No.3,
2015.
[6] Ogawa K., Ishida N., Ahn Y., A Comparative
Analysis of the Traffic Accident Probability ofBicycles by Running Position and Direction
Considering Characteristics of Road Network andTrip Distance, Traffic science, Vol.51, No.1, 2020.

Fig. 10 Target square for density of stores
DISCUSSION
The number of people who violated the crossing
toward Plaza WEST are listed in Table 2 as east to
west, and the number of people in the opposite
direction are listed in Table 2 as west to east. On
average, the density of stores is higher from east to
west, possibly due to the higher number of stores andplaces to stop, and possibly due to VFM.
Table 3 shows the results of the t-test, whichshowed that the P-value was not very low, but since
the sample size was small, we believe that if weincrease the number of samples, we may obtain
results with significant differences.
Table 2 Number of crossing violators by day

East toWest
West to
East

10/29

10/31

11/20

11/21

16

28

7

31

17

15

13

25

488

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

CEMENT SOLIDIFICATION CHARACTERISTICS OF
COAL CINDERS-MIXED SOIL
Shoji Kamao1, Kentaro Ishii1, Satoshi Shigemura and Yuta Murakami2
1

Civil Engineering, Nihon University, Japan; 2 JR East Consultants Company, Japan
ABSTRACT

Coal was used as fuel for steam locomotives, and coal cinders (coal waste) were buried underground for
disposal. In recent years, redevelopment projects around train stations have been planned for the land where coalwaste was once disposed of. In the redevelopment projects, buried coal waste is increasingly being excavated, andthe disposal and treatment of the excavated coal waste are becoming an issue for developers. Excavated coal waste
must be treated and disposed of as industrial waste, not as soil. In other words, even though the soil is excavatedfrom the ground, it cannot, in principle, be used as backfill material to its original location (railroad sites) and is
disposed of as industrial waste at a high disposal cost. Moreover, the high cost of disposal, along with the shortageof final disposal sites, has led to an increase in the project cost of redevelopment projects. For railroad operatingcompanies such as East Japan Railways Company, the increase in development costs poses a major problem forthe implementation of their projects in Japan. Therefore, the purpose of this study is to reuse coal cinders dumped
underground with the backfill material. Laboratory solidification experiments were conducted to determine theinitial water content, the cement added ratio, etc., as design parameters for the strength of backfill materials. Fromthe experimental results, the strength characteristics of the improved soil mixed with coal cinders also changed as
the various mixing ratios were varied- the possibility of applying the improved soil containing coal waste as
backfill materials were clarified.
Keywords: coal cinders, cement solidification, backfill material, unconfined compressive strength

INTRODUCTION

excavation of huge amounts of coal cinders in theconstruction process puts pressure on the projectbudget.
Against this background, this study aims to makeeffective use of coal cinders buried underground as
roadbeds for railway structures [4]. To be used as
roadbeds, the unconfined compressive strength must
be 1,000 kN/m2 or more, so we set this as the target
strength in our attempt to improve the excavated coalcinders mixed soil by cement solidification. The
improved test samples were made by mixing several
types of coal cinders collected in-situ with clay andcement in predetermined proportions and cured themfor a predetermined period of time after setting
several initial water content. Based on themeasurement of unconfined compressive strength of
the improved test samples, we attempted tounderstand the solidification (strength) characteristicsof the samples by using water-cement ratios, amountof cement added, and water content ratios as
parameters.

In Japan, the railroad network was extended
throughout the country with the opening of the
Shinagawa-Yokohama railroad in 1872 [1]. Coalfired steam locomotives operated for about 100 years
from the opening of the railroad until around 1975.
Although coal cinders used for steam locomotives
have been buried underground as land formationmaterial for railroad sites, the extent, amount, anddepth of coal cinders buried are unknown.
In recent years, coal cinders have been
increasingly excavated as a result of the rebuilding ofrailroad facilities and the redevelopment of station
areas. Basically, coal cinders, including mixed soil,
excavated during construction cannot be backfilled as
it is treated as waste (if backfilled, it violates Waste
Management and Public Cleansing Act andconstitutes illegal dumping) or disposed of as general
residual soil (as it requiring disposal as industrial
waste), which increases the cost of construction due
to the high disposal cost [2] [3]. Further, most of the
excavated coal cinders are landfilled at final disposalsites, and the environmental impact of theirtransportation and disposal is also significant. East
Japan Railway Company is currently planning andimplementing large-scale initiatives, including theredevelopment projects in various locations.
However, the urgent issue is to reduce the cost of
disposal of excavated coal cinders since the

THE MAIN CHARACTERISTIC VALUES OF
THE SAMPLES
The coal cinder mixed soil (after this referred toas “coal cinders”) used in this study was collectedfrom the construction site of East Japan Railway
Company and is classified as "coal cinders A" and"coal cinders B" based on the sampling point. The
489

GEOMATE – Bangkok, Thailand, 22-24 November 2022

For the initial water content w0, it required water
addition because the Kasaoka clay used was in a
powdery state with a water content ratio of about
4.7%. w0 set below 30% makes uniform mixing
difficult due to drying. On the contrary, if w0 exceeds
60% (near the liquid limit), the test piece is not strong
enough to stand on its own after being cured, so inthis experiment, the water content ratios were set
within the 40-60% range.

coal cinder mixed soil was collected during
excavation work for a redevelopment project in thestation areas. The main physical characteristic valuesof coal cinders and clay are shown in Table 1. It was
found that the density of soil particles in the coalcinder mixed soil (coal cinders A and B) decreased
with the addition of coal cinders. The results also
showed that the consistency properties and watercontent ratios varied significantly depending on thetype of soil contained in the coal cinder mixed soil.
Table 1 Physical Properties of used coal cinders
and clay𝑎𝑤 =𝑚𝑠′
× 100 (%) ・・・(1)𝑚𝑔 + 𝑚𝑠𝐷𝑤 =𝑚𝑔
× 100 (%) ・・・(2)𝑚𝑔 + 𝑚𝑠

Coal cinders A Coal cinders B Kasaoka clay
3

Soil particle densityρs (Mg/m )

2.44

1.94

2.87

Liquid limit

wL

(%)

NP

50.5

54.7

Plastic limit

wP

(%)

NP

43.1

33.7

Plastisity index

IP

(-)

NP

7.42

21.0

Natural water content

wn

(%)

3.17

18.44

4.65

Ignition loss

Li

(%)

16.71

8.83

5.85

𝑚𝑠′ : Mass of blast furnace cement type B
𝑚𝑠 : Dry mass of Kasaoka clay
𝑚𝑔 : Dry mass of coal cinder mixed soil
Table 2 Test programs of this study
5

Coal cinder addition rate Dｗ (%)

TEST PIECE PREPARATION METHOD
The test piece preparation method was based on
the Japanese Geotechnical Society Standard (JGS
0821 [5]) without compaction of stabilized soil. Coalcinders that passed through a 2 mm sieve were usedas samples. Commercially available Kasaoka clay
(powder) and blast furnace cement type B were usedas additional agents. The test piece was obtained bymixing Kasaoka clay, coal cinders, and blast furnace
cement type B for about 10 minutes using a mixer tomake the mixture uniform. An acrylic mold with adiameter of 3.5 cm and a height of 10 cm was used.
The lower end was sealed with a rubber stopper, andthe test piece was divided into five layers and tapped
100 times per layer to let the air out while filling thesamples. After filling, the upper end was covered withplastic wrap and cured for a predetermined period oftime. Cement and water were added to the dry massof coal cinder mixed soil and Kasaoka clay at apredetermined ratio. The parameters used in thisstudy (cement addition rate aw and coal cinder
addition rate Dw) were defined as shown in formulas(1) and (2) below. The curing period of the test piece
was 7 and 28 days (Tc=7 days and Tc=28 days,
respectively), and they were cured in a constanttemperature room at 20 ℃. The compositions of thetest piece are shown in Table 2. For coal cinders A,
the cement addition rates aw were set to 10% and 35%,
while the coal cinder addition rate Dw was set to only
50% to make the test piece. For coal cinders B, thecement addition rates aw were set to 5%, 10%, 20%,and 35%, while the coal cinder addition rates Dw were
set to 50%, 75%, and 100% to prepare the test piece.

Cement addition rates aw ( % )
10
20

35

50

40
40-60
40
40-60
coal cinders A coal cinders A coal cinders A coal cinders A
coal cinders B
coal cinders B

75

40
40
40
40
coal cinders A coal cinders A coal cinders A coal cinders A

100

40
40
40
40
coal cinders A coal cinders A coal cinders A coal cinders A

EXPERIMENTAL RESULTS AND DISCUSSION

Relationship Between Unconfined Compressive
Strength and Types of Samples
The relationship between unconfined compressive
strength and cement addition rate for coal cinders A
and B is shown in Fig. 1. The unconfined compressive
strength was obtained under the following conditions:curing period Tc is 7 days, initial water content w0 are
40% and 50%, coal cinder addition rate Dw is 50%,
cement addition rate aw is 35%, and water-cement
ratio w/c is 60%. The unconfined compressive
strength was found to vary in the range of 350-4,000
kN/ m2 depending on the cement addition rate. In bothtypes of coal cinders, the unconfined compressive
strength tended to increase linearly with increasing
cement addition rate, and a comparison of coalcinders A and B showed that B had a greater value
than A. The reason for the difference in strength
between the two is considered to be due to the natureof the soil or the water content in the soil. In the same
figure, the standard values for the roadbed are490

GEOMATE – Bangkok, Thailand, 22-24 November 2022

compared, and it is found that the target value (1,000
kN/m2) is met with Dw = 50% and cement addition
rate of 15% or more for coal cinders A, and with aw =10% or more for coal cinders B. The strength varies
depending on the sample used. The observed
differences in strength depending on the samples usedsuggest that unconfined compressive strength isclosely related to the physical characteristic values ofthe samples.

Fig. 2 Relationship between initial water content
rate and unconfined compressive strength(Coal cinders A, w/c=60 and 100%)
Relationship between Water Content Ratio,
Curing Period, and Unconfined Compressive
Strength
Figs. 3 and 4 show the relationship between
unconfined compressive strength and initial watercontent of coal cinders, and Fig. 3 shows theunconfined compressive strength obtained under thefollowing conditions: curing period Tc are 7 and 28
days, initial water content w0 are 40-60%, coal cinder
addition rate Dw is 50%, cement addition rate aw is35%, and water-cement ratio w/c is 100%. Fig. 4
shows the results obtained for a test piece by changing
the water-cement ratio w/c to 60%. 7-day and 28-day
strength comparison tests showed that unconfining
compressive strength was greater at 28 days than at 7days, although some cases could not be performed.
For the 7-day strength, values were obtained byvarying the initial water content, and a similar trend
was observed for the 28-day strength. The 28-day
strength decreased as the initial water content
increased, as did the 7-day strength. Figs. 5 and 6
show the relationship between the rate of increase inunconfined compressive strength and the curing
period from the results of Figs. 3 and 4 above,
showing the percentage increase for each condition at
Tc = 28 days for the unconfined compressive strengthqu (28 days) when qu (7 days) at Tc = 7 days is used
as a reference. Strength increases of 2.0-2.7 times and1.8-1.9 times were observed for w/c=100% andw/c=60%, respectively. The increase in strength was
not affected by changes in the initial water content orother conditions. The increase in strength was greaterin "w/c=100, w0=50%" compared to other conditions,but further study is planned with an increased numberof samples.

Fig. 1 Relationship between cement addition rates
and unconfined compressive strength(Coal cinders A and B, w/c=60%)
Relationship between Initial Water Content andUnconfined Compressive Strength
The relationship between unconfined compressive
strength and initial water content of coal cinders A isshown in Fig. 2. The unconfined compressive
strength was obtained under the following conditions:curing period Tc is 7 days, initial water content w0 are
40-60%, coal cinder addition rate Dw is 50%, cement
addition rate aw is 35%, and water-cement ratios w/c
are 60% and 100%. Comparing the water-cement
ratio of w/c=100% and w/c=60%, the strength was
greater at w/c=60%. In terms of the change in theinitial water content of the clay, the unconfinedcompressive strength tended to decrease with anincrease in the water content ratio for both w/c, and a
20% increase in the water content ratio for w/c=60%
resulted in a decrease in the unconfined compressive
strength by half. The same trend was observed at
w/c=100%. These results show how much watercontent affects the strength of cement solidification.
It is thought that a higher water content facilitates
uniform mixing of
the samples and improves
workability in the field. However, the strength
decreases as water content increases, indicating that it
is important to maintain an appropriate balance
between water content ratio and strength.

491

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 6 Relationship between curing period andgrowth rate of unconfined compressive strength(Coal cinders A, w/c=60%)

Fig. 3 Relationship between initial water content
rate and unconfined compressive strength(Coal cinders A, w/c=100%)

Relationship between Coal Cinder Addition Rateand Unconfined Compressive Strength
The relationship between unconfined compressive
strength and coal cinder addition rate for coal cinders
A is shown in Fig. 7. The unconfined compressive
strength was obtained under the following conditions:curing period Tc is 7 days, initial water content w0 is
50%, coal cinder addition rates Dw are 0%-71%,
cement addition rates aw are 15% and 35%, and watercement ratio w/c is 60%. In the case of coal cinders
A, the unconfined compressive strength of the
improved soil reached a maximum at a coal cinder
addition rate of about 13%, after which the 7-day
strength of the improved soil decreased as the coalcinder addition rate increased.
The strength development of clay alone, i.e.
Dw=0%, was also observed, but the addition of coalcinders increased the strength, albeit slightly. The
relationship between unconfined compressive
strength and coal cinder addition rate for coal cinders
B is shown in Fig. 8. For comparison, data for coalcinders A (curves in Fig.7) are also shown. The
unconfined compressive strength was obtained under
the following conditions: curing period T c is 7 days,
initial water content w0 is 40%, coal cinder addition
rates Dw are 50%-100%, cement addition rates aw are
5-20%, and water-cement ratio w/c is 60%. The dataobtained for coal cinders B is only for those withthe addition rate of 50% or more, but as with coalcinders A, the unconfined compressive strength
decreased with increasing coal cinder addition rate,
but unlike coal cinders A, the strength was found tobe high even at 100% coal cinders content. The
strength of the clay alone is lower than the maximum
strength, which is considered to be due to theincrease in strength caused by the coal cinders acting
as fine aggregate. To increase the coal cinder addition

Fig. 4 Relationship between initial water content
rate and unconfined compressive strength(Coal cinders A, w/c=60%)

Fig. 5 Relationship between curing period andgrowth rate of unconfined compressive strength(Coal cinders A, w/c=60%)492

GEOMATE – Bangkok, Thailand, 22-24 November 2022

rate, it is important to understand the characteristicsof the samples and to determine the composition
based on the optimum water content ratio and cement
addition rate.

The relationship between unconfined compressive
strength and cement addition rate for coal cinders B
is shown in Fig. 9. The unconfined compressive
strength was obtained under the following conditions:curing period Tc is 7 days, initial water content w0 is
40%, coal cinder addition rates Dw are 50-100%,
cement addition rates aw are 5-35%, and watercement ratio w/c is 60%. Although the unconfinedcompressive strength decreased as the coal cinder
addition rate increased, it was found that the target
value of 1,000 kN/m2 could be exceeded by setting
certain conditions. Moreover, it was found that thestrength exceeding the target strength can be obtainedby varying the amount of cement added. The target
strength was not exceeded even when the coal cinder
addition rate was varied at a cement addition rate of
aw=5%, suggesting that a cement addition rate of 15%
or more would be desirable.

Fig. 7 Relationship between cement addition rate
and unconfined compressive strength(Coal cinders A, w/c=60%)

Fig. 9 Relationship between cement addition rate
and unconfined compressive strength(Coal cinders A, w/c=60%)

Relationship between Coal Cinder Addition Rateand Unconfined Compressive Strength

Fig. 8 Relationship between coal cinders addition
rate and unconfined compressive strength(Coal cinders A and B, w/c=60%)

The
relationshipbetweenunconfinedcompressive strength and the coal cinder addition rate
for coal cinders B is shown in Fig. 10. The unconfinedcompressive strength was obtained under thefollowing conditions: curing period Tc is 7 days,
initial water content w0 is 40%, coal cinder addition
rates Dw are 50, 75 and 100%, cement addition rates
aw are 5, 10 and 20%, and water-cement ratio w/c is60%.
As with coal cinders A, there was a tendency forthe strength to decrease as the coal cinder addition
rate Dw increases. A plot of the results under the same
conditions as those of coal cinders A shows a graphindicating a greater decrease in strength with anincreasing coal cinder addition rate compared to thegraph for coal cinders B.

Results and Discussion on Coal Cinders A
Based on the results for coal cinders A as
described above, the water-cement ratio w/c wasfixed at 60% for easy mixing and expected strength,
and the initial water content w0 was fixed at 40% forsufficient mixing and expected strength of the test
piece. The cement addition rate was varied at 35% orless, while test pieces were prepared at coal cinders
site B to compare each condition.
Relationship between cement addition rate andunconfined compressive strength493

GEOMATE – Bangkok, Thailand, 22-24 November 2022

･Unconfined compressive strength increases with alonger curing period.
･ Cement addition rate aw of 10% or more isconsidered desirable.

The reason for this is that unlike coal cinders A,
coal cinders B contain not only coal cinders but also
clay from the extraction site. Therefore, the coalcinders are considered to have a significant effect on
the unconfined compressive strength.

In order to reduce the cost of actual construction,
it is considered necessary to find the most economicalcomposition that meets the target strength of 1,000
kN/ m ２ by reducing the amount of cement andincreasing the content of coal cinders, which arewaste materials. The type of soil and water content
ratio contained in coal cinders also affected theunconfined compressive strength. When the content
of fine particles was small, the addition of fineparticles increase in strength.
Coal contains many harmful substances. Amongthem, hazardous substances specified in the Soil
Contamination Countermeasures Law are Benzene,
lead, cadmium, mercury, cyanide, chromium,selenium, arsenic, fluorine, boron, etc. it is necessary
to discuss detoxification when hazardous substances
are included, too.

Fig. 10 Relationship between coal cinder addition
rate and unconfined compressive strength(Coal cinders A and B, w/c=60%)
Comparison of Coal Cinders Used
The relationship between unconfined compressive
strength and initial water content for coal cinders A
and B is shown in Fig. 11. The unconfinedcompressive strength was obtained under thefollowing conditions: curing period Tc is 7 days,
initial water content w0 are 30-60%, coal cinder
addition rate Dw is 50%, cement addition rate aw is35%, and water-cement ratio w/c is 60%. The
strength of coal cinders B was about 1.8 times greaterthan that of coal cinders A. Although experimentswith coal cinders B under the same conditions ofinitial water content w0 = 50 % and 60 % were notconducted, the trends of the coal cinder addition rateand cement addition rate are similar for both coalcinders A and B. Therefore, the unconfinedcompressive strength is considered to decrease with
the increase of the initial water content, as in the caseof coal cinders A.

Fig. 11 Comparison of unconfined compressive
strength and initial water content(Coal cinders A and B, w/c=60%)
REFERENCES[1]

Japan's

First

Steam

Engines:

http://steam.fan.coocan.jp/beginning/firstmodel.htm

CONCLUSIONS[2]

Based on the experimental results of this study,
the following results were obtained:[3]・ Understanding the characteristic values of thesamples to be used is important, as they affect theunconfined compressive strength.
･The unconfined compressive strength decreases as
the initial water content w0 and water-cement ratio
increase. w0 of 40% or less is considered desirable.

[4]

Waste Management and Public Cleansing Act: Act No. 137
of 1970, (In Japanese)
National Federation of Industrial Waste Management
Associations:
Industrial
Waste
Guidebook
http://www.haikibutu.net/img/PDF/guidebook2200.pdf,
2009
Railway Technical Research Institute：Design standard forrailway earth structures. p.242~p243, 2007. (In Japanese).

[5]Japanese Geotechnical Society: JAPANESE GEOTECHNICALSOCIETY STANDARD laboratory Testing Standards ofGeomaterials Vol.1, p445~p453, 2020. (In Japanese).

494

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

ROAD TRAFFIC EVALUATION FORCUSING ON
VELOCITY AND FORM
Shion Muramoto1 and Kazunari Tanaka2
1

Faculty of Engineering, Osaka Institute of Technology, Japan
ABSTRACTIn recent years, the proportion of traffic accident fatalities has increased despite the declining population. Thisis because the elderly constitute a high of the population. In order to reduce the number of deaths caused by traffic
accidents, it is necessary to prevent accidents before they occur. We need to ensure safety by developing walking
spaces that contribute to the safety of the elderly, disabled, etc. in our aging society. Therefore, we assume that theform and combination of road use affects each other and the ease of passage. The purpose of this study is toquantify the ease of passage using index values focusing on the velocity and the width of road usage patterns. The
research method was to investigate the effect of sidewalk and roadside strip width on ease of passage from thepedestrian's perspective by means of a questionnaire, and to clarify the relationship between width and ease of
passage. The index values were used to quantify the roads. The evaluation was compared with previous studies toanalyze and discuss the effectiveness of the quantification.
Keywords: road, pedestrian, velocity, passage

INTRODUCTION

forms of transportation affect each other, and which
combination of use patterns makes it easier to walkon a given road.

In recent years, the number of traffic accident
fatalities has decreased as the population has
declined. However, as the population ages, theproportion of traffic fatalities among the elderly has
increased. Looking at the causes of fatal accidents,
the proportion of accidents caused by driver
inattention and other factors is relatively high. For
elderly drivers in particular, the percentage ofaccidents caused by mishandling of the steering
wheel, brakes, etc. due to deterioration of physicalfunctions, etc. is high. Under these circumstances, inorder to reduce the number of fatalities from traffic
accidents, it is important to prevent accidents before
they occur and to mitigate the damage. In this aging
society, it is necessary to ensure traffic safety on
roads for daily life through the development ofwalking spaces and other facilities that contribute to
the safety of the elderly, disabled, and other people.
In addition, the ongoing development of public
transportation in contemporary urban spaces and thecontinual urban change have resulted in different
routes chosen by different people when they travel
toward their destinations, even if the starting point is
the same. To identify people's route choices, it isnecessary to identify paths that are considered
walkable. When considering walkability, people
unconsciously choose paths that are safe and secure.
In addition, we consider that people's perception of
walkability may change depending on the type of useof the path and the traffic conditions. In other words,
the ease of passage is considered to differ dependingon the type of use. The purpose of this study is toquantify and evaluate how pedestrians, cars, and otherRESEARCH METHODSFirst, a questionnaire survey was conducted on
the ease of passage provided by wide sidewalks androadside strips, using photographs taken at pedestrianeye level, mainly among students attending theuniversity.
Next, in order to quantify the ease of passage, we
evaluated the ease of passage by using examples ofroads that have been found to be walkable after roadmaintenancebasedonprevious
researchquestionnaires. To quantify the ease of passage, we
focused on the moving velocity of pedestrians andautomobiles, and the spacious width of the road, suchas the occupied width. In this study, the value
obtained by dividing the moving velocity by theoccupied width is used as the index value. This index
value is called the relative velocity index (RVI). The
moving velocity of the road usage patterns wasconsidered to be the average moving velocity, while
the moving velocity of cars and motorcycles wasconsidered to be the speed limit of the road. For thewidth occupied by cars, the road width defined by theRoad Structure Ordinance was used. Road usage
patterns and travel speeds, occupied widths, and RVI
are shown in Tables 1 and 2.

495

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 1 Usage patterns that mainly use the sidewalks
Movingvelocity(m/s)

Occupiedwidth(m)RVI(/s)

Wheelchair
Walking withumbrellas
Stroller

1.19

1.00

1.19

1.30

1.00

1.30

1.19

0.75

1.59

Pedestrian

1.30

0.75

1.73

Bicycle

4.17

1.00

4.17

Usage patternsuse)/(index value for each type of use)}. The street
that a use form passes through is, for example, asidewalk for pedestrians and a roadway for
automobiles. The value that satisfies both of thesemaximum values is the maximum number that eachusage form can pass through. Within this maximum
number, combinations of usage patterns are
considered. The percentages used are calculated
according to Table 3.
Table 3 Calculation Method
Occupied widthTable 2 Usage patterns that mainly use the roadway

RVI

Movingvelocity(km/h)

Occupiedwidth(m)

RVI
(/s)

Area

20

2.75

2.02

30

2.75

3.03

40

3.00

3.70

60

3.25

5.13

20

1.30

4.27

30

1.30

6.41

40

1.30

8.55

60

1.30

12.82

Usage patterns

Car

Motorcycle(Sum of spacious widths of each typeof use) / (width of the entire street)(Sum of RVI for each type of use)/(Maximum RVI value for roads)(Total area of each type of use) / (Areaof roads)

The area in Table 3 is obtained using the sum of
the RVI and occupied width for each type of use.
Starting with the usage form with the smallest RVI,
plot the x-axis as the total occupied width and the yaxis as the RVI. And then, an approximate curve isdrawn from the origin. The value obtained byintegrating over this approximate curve by the roadwidth from the origin is the area that can be allowedon the road. An example of the area for a car and a
motorcycle with a speed limit of 40 (km/h) is shownin Figure 2.

The evaluation using RVI is performed according to
the procedure shown in Figure 1.
Calculate the maximum RVI
Calculate the maximum numberof road usage patterns.
Consider a combination of road usage patterns.
Calculate the percentage used.
Percentage scoring and evaluation
Fig.1 Evaluation Procedure
The maximum RVI is the RVI that can be allowed
for the entire width of the road and is calculated by{(RVI of pedestrians) x (width of roadside strip or
sidewalk) + (RVI of regular vehicles) x (width ofroadway)}. The maximum number of road usage
patterns is calculated by finding the maximum valueof {(width of roadway through each type ofuse)/(occupied width of each type of use)} and{(maximum index value through each type of

Fig.2 Example of area
The area of the color-coded portion of the graph is thearea of the usage form.
Finally, we the percentage scoring and evaluation:
will be discussed. A score of 18 was used as the
maximum score. The relationship between
percentages and scores is shown in Table 4.
496

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Ratio of roadside roads width to road width30%

Table 4 Relationship between evaluation score andpercentage
6 points for
50% or less3 points for
70% to 80

5 points for
50% to 60
2 points for
80% to 90

4 points for
60% to 70
1 point for
90% to 100

Ratio of sidewalk width to road width

17.6% 32.4% 23.5% 26.5% 0.0%

50.0% 40.0% 10.0% 0.0% 0.0%

16%

11.8% 58.8% 23.5% 5.9% 0.0%
9.4% 50.0% 15.6% 15.6% 9.4%

14%
0

6

The percentage of total scores of 4 and 5 forsidewalk photos was highest when the ratio was
42.9%. The maximum total score of 4 and 5 points
was obtained for the roadside strip photo when theratio was 20.8%. This means that the number of
respondents who felt that the road was easy to pass
did not continue to increase even when the percentageof the road width increased. This suggests that thewidth of sidewalks and roadside strips is notproportionally related to the ease of walking.
VERIFICATION BY ROAD CASE STUDY
The results and evaluation patterns for the roadsideand sidewalk road cases are shown in Tables 5 and 6.
The improvement rate is the percentage within theevaluation pattern when the evaluation score
improves by even one point.
Table 5 Results of Roadside Street Case Study
Rate of
Evaluation Patterns
improvement
Walking form 1 person and25%
vehicle5.9% 0.0% 5.9% 58.8% 29.4%

45%
0.0% 0.0% 6.3% 31.3% 62.5%
6.7% 20.0% 20.0% 33.3% 20.0%
0.0% 0.0% 23.5% 47.1% 29.4%
6.4% 38.3% 23.4% 25.5% 6.4%

Walking form
2 persons

6.3% 37.5% 43.8% 12.5% 0.0%

25%
2

4

Fig.3 Relationship between percentage of roadsidestrip and ease of passage

0.0% 5.9% 5.9% 29.4% 58.8%

02Ease of passage

Tables 3 and 4 show results for cases with sidewalks
and roadside strips on one side.

30%

6.3% 46.9% 31.3% 6.3% 9.4%

18%

Fig.2 Example of photo used

35%

12.0% 46.0% 28.0% 14.0% 0.0%

20%

The photographs used were processed for two
types of sidewalk roads: wide and narrow sidewalks,
and three types of roadside roads: wide, narrow, andno roadside roads. A questionnaire was used toevaluate these photographs on a 5-point Liker scale,with 5 being the easiest passage, and 1 being the mostdifficult.

40%

26%

22%

QUESTIONNAIRE SURVEY

50%

14.3% 35.7% 35.7% 14.3% 0.0%

24%

If there is a fence or other means between the
sidewalk and the roadway, each road is differentiatedand given an evaluation score. This average score wasthen used as the evaluation score for the road. If thepercentage exceeded 100% of any one of theoccupied width, RVI, or area, the evaluation scorewas evaluated as Z points. In this case, we considered
the effect on the ease of passage.

55%

28%

4

Ease of passage
Fig.2 Relationship between percentage ofsidewalks and ease of passage

6

Walking form
3 persons

497

Car

20%

Motorcycle0%

BicycleBicycle andMotorcycle

10%

Car

50%

Motorcycle

20%

Bicycle20%

0%GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 6 Sidewalk road case results
Evaluation Patterns
Walking form
2 personsCar

Rate ofimprovement50%

Motorcycle50%

2 cars90%

2 Motorcycles

90%

Car andMotorcycle
Car and Bicycle

50%

Motorcycle andBicycle2 cars and abicycle2 motorcycles
and a bicycle
Car, Motorcycle
and Bicycle
Evaluation Patterns

Walking form
3 personsscores due to the change in the position of bicycles on
the road. In addition, the improvement in scores in theevaluation using RVI is thought to be related to theease of passage.
CONCLUSION
In this study, the questionnaire survey revealed thatthe width of sidewalks and roadside strips are notproportionally related to the ease of passage. In
addition, the RVI rating improves in road cases,
which may be related to the ease of passage. In orderto improve the reliability and accuracy of these resultsin the future, it is necessary to consider the
relationship with the slope of the graph, etc., since thescore increases with the width of the road when thesame combination of usage patterns is used in theevaluation using RVI.In addition, as new personal
mobility devices such as electric kickboards are being
used, it is necessary to consider other forms of road
use.

100%
100%
100%
100%
100%
Rate ofimprovement

Car

25%

Motorcycle

25%

2 cars30%

2 Motorcycles
Car andMotorcycle
Car and Bicycle
Motorcycle andBicycle2 cars and abicycle2 motorcycles
and a bicycle
Car, Motorcycle
and Bicycle

35%

CITATION AND REFERENCE LIST
[1] Status of the Shijo Sidewalk Widening Project
after Completion: Hiroshi Takahashi, Yasuo
Shiromizu, Ryuzi Huziyoshi, Masaki Nkaya,
Syo Wada, Tetuo Hayashi, Shinya Emoto andKatuhiro yamaguchi, 55th Annual Conferenceon CivilEngineering
andPlanning,
Proceedings, 49-04 JSCE, 2017.6[2] Analysis on the Change of Bicycle User's
Attitude and Route Selection Behavior with the
Improvement of Bicycle Passage Environment:
A Case Study of Bicycle Lane on Prefectural
Road Higashi-Kanazawa taxi stand, Masahiro
Rati, Michiaki Yama, Junichi Takayama,
Masahiro Katagishi and Tatsuya Nakano,
Journal of Civil Engineering Planning, Lecture
Collection Vol.43 No.389 p.9, JSCE Societyof Civil Engineering, 2011.5

25%
50%
50%
50%
50%
50%

In the road case of sidewalks, it is thought that theconstruction of bicycle lanes improved the evaluation498

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

METHOD FOR EVALUATING URBAN COMFORT SPACES
FOCUSING ON ENVIRONMENTAL SOUND USING EEG
Shotaro Otsuji1 and Kazunari Tanaka2
1,2

Faculty of Engineering, Osaka Institute of Technology, Japan
ABSTRACTDue to the rapid changes in the living environment caused by the Covid-19 pandemic, our quality of life has
declined significantly. In many cases, it is difficult for individuals to restore the environment itself, and I thought
it was necessary to find a way for anyone to easily reproduce a comfortable space. Focusing on sounds for which
it is easy to secure a medium, in this study, we conducted an electroencephalogram measurement experiment aimed
at clarifying the presence or absence of environmental sounds that listeners unconsciously feel comfortable with,and the tendency of preferred environments. In the experiment, the intensity values in the α-waves frequency band
were compared. As a result of grouping based on the appearance of EEG, it was found that there is a relationshipbetween the preferred environmental sound and the difference in the tendency of EEG intensity. In addition, theelectroencephalogram intensity values of the environmental sounds preferred by each group were Fourier-analyzed.
As a result of these findings, it could be inferred that there are environmental sounds that many people
unconsciously prefer, and that 'Living room" tends to be the closest to them from the experimental results. However,there are many issues to be addressed in the experimental process, as there are large individual differences between
subjects in analyzing the α-waves frequency band. It is necessary to statistically handle more data. Future research
includes verification with visual imagery and psychological response analysis to correctly assess how
environmental sounds are heard.
Keywords: The environment, Sound, EEG, α-waves
INTRODUCTION

environment that the person feels comfortablewithout being aware of it, and the tendency of thatsound.
In this research we focused on sound. This isbecause of the variety of playback media available forlistening to sound, their high prevalence, ease of
location mobility and the fact that they are not
restricted by location or time. We let the subjects
listen to recordings of the original sound in eachenvironment. To identify the classification ofenvironmental sounds that tend to be preferred by the
subjects and their individual differences, the α-waves
frequency band of the brain waves, which is aphysiological response, was compared and analyzed.

From 2020 to the present, the "Covid-19" virus
have been spreading around the world, and theirimpact is immeasurable. As of July 2022, the numberof infected people worldwide was 550 million and thenumber of deaths exceeded 6 million, and ourlifestyles had drastically changed to cope with the
explosive infectivity. As we were forced to refrainfrom most activities that involve going outside, theidea of using remote to improve our living
environment attracted attention. According to Goto's
research, the group that incorporated environmental
music in their offices showed an average increase inmental and functional comfort. According to the"Annual Report of the Committee for the Adjustmentof Pollution, etc., FY 2020" by the Ministry ofInternal Affairs and Communications, "Noise" hasthe highest number of pollution complaints among theseven typical types of pollution. Therefore, I thoughtthat if we could successfully control the surrounding
sound environment, we could maintain a comfortable
space. As part of this experiment, we focused on therelationship between α-waves, which are closely
related to comfort, and reproduced environmental
sounds.

EEG MEASUREMENTS
The equipment used in the experiment was a PCM
recorder (ZoomH2n), a simple EEG measuring
instrument (EMOTIV EPOC X) and monitor
earphones (SHURE SE215). The dedicated software
EMOTIVPRO was used to measure EEG, andphysiological response experiments were conducted
to analyze the measured EEG. Fifteen students wereused as subjects in this research. Each subject wasallocated a letter of the alphabet from “A” to “O”. The
subjects were not told about the research.
In each environment, a sound source of 10 s ofenvironmental sound plus 5 s of rest, for a total of 150s, was created, which the subjects listened to withPURPOSE AND METHOD OF RESEARCH
We aim to clarify the existence of a sound499

GEOMATE – Bangkok, Thailand, 22-24 November 2022

their eyes closed. Ten environments were selected forrecording for audio, which were of varying nature andconditions. The experiment also focused on theoccipital potentials O1 and O2 based on the 10/20
method. Because this electrode is the most capable ofdetecting the α-wave frequency band.

Fig. 1slight differences. It was also found that large
individual differences occurred in the appearance of
the α-wave frequency band. Based on the results, thesubjects were classified into two groups, Pattern-1(high EEG intensity(B,H,I,J,O)) and Pattern-2 (low
EEG intensity (A,C,D,E,F,G,K,L,M,N)). The
classification method was based on whether the
average value of Band power (α-waves intensity)
exceeded 10 or not. Band power is derived by
performing an FFT on the α-waves frequency band.
There is a difference of approximately four times thisvalue between Pattern-1 and Pattern-2.

Placement of electrodes using the 10/20
method.

Table 1 Order in which the audio was played and thename of each environment.
No
1
2
3
4
5
6
7
8
9
10

Environment Name
On the bridge (at night)
Classroom
Park square (in front of shops)
Station premises
Laboratory
Living room
Station squarePark square
Footsteps on the ground
Shopping mall outdoors

Fig. 3

Band power values of subjects in Pattern-1

Fig. 4

Band power values of subjects in Pattern-2

A comparison of the characteristics ofenvironments with high Band power values showsthat Pattern-1 was dominated by lively environmental
sounds associated with human activity and open
spaces, while Pattern-2 was dominated by calm
environments with relatively few people. Overall, theresults show that "Living room" tends to be widelypreferred and "On the bridge (at night)" is lessfavored.
Fig. 2

Table 2 Classification of subjects in Pattern-1 andPattern-2 and their Band power values.

Photograph of a subject undergoing EEG
measurements.

Pattern-1
Park square

The experiment\tal results showed that all
participants detected similar waveforms, albeit with500

Pattern-2
Living room

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Living room
Station premises
Classroom
Park square(in front of shops)
Station square
Footsteps on the
groundShopping malloutdoors
Laboratory
On the bridge(at night)

Laboratory
Station square
Shopping mall outdoors
Park square(in front of shops)
Footsteps on the ground
Station premises
Classroom
Park squareOn the bridge(at night)

Fig. 6

Note: They are ordered from the top to the bottom by the Bandpower value.

The results showed that the waveform trends of bothpatterns were similar. In addition, only Pattern-1
showed variations for each environmental sound.
This is the result of the individual differences of thesubjects belonging to Pattern-1, which were largely
reflected in the results.
Next, we compared the data from 'Park square' and'Research room', which showed the most differencesin the likelihood of each pattern being preferred in the
EEG measurement experiment.
It can be read that the waveform transitions for bothsubjects were different. Despite the experiment being
conducted under the same conditions, there are
significant unconscious individual differences
between subjects in the degree of their response to theenvironmental sounds.

However, there existed environmental sounds forwhich the tendency of the preferred environmental
sound for each pattern was completely different.
From the above, it can be considered that there is arelationship between the amount of detected α-waves
frequency bands and the preferred environmental
sounds, although there are some individual
differences among listeners.
FOURIER ANALYSIS
Fourier Analysis was used to compare theappearance of the α-waves frequency band for
different patterns. The data used were the high valuesof Band power and the time course, as determined in
the EEG measurement experiment.

Fig. 5In Pattern-2, after Fourier Analysis (top 5
Band power values).

Fig. 7In Pattern-1, after Fourier Analysis (top 5
Band power values).

501

Values after Fourier Analysis for eachpattern ("Park square").

GEOMATE – Bangkok, Thailand, 22-24 November 2022

ACKNOWLEDGMENTS

Fig. 8

We would like to express our sincere gratitude to
all the subjects who cooperated with us in carryingout this research. We would like to thank Professor
Kazunari Tanaka of the Department of Urban Design
Engineering, Faculty of Engineering, Osaka Instituteof Technology, for his guidance in all aspects,
including providing us with the impetus for the
research and the starting point for the research. I
would also like to express my sincere gratitude to themembers of the Spatial Design Laboratory who
guided me from the very beginning in the handling of
the machines used in the experiments, thecomposition of the thesis and other difficult questions
at key points, and to the students who worked hard
together on the research and supported each other inmany ways.

Values after Fourier Analysis for each pattern("Laboratory ").

These results suggest that the characteristics ofenvironmental sounds preferred by people can bedivided into two main categories.
First, there are environmental sounds favored bymany people, such as 'Living room' sounds. Calmsounds and familiar sounds may fall into this category.
Second, there are environmental sounds such as 'Park
square' and 'Research room', whose preference varies
greatly depending on the amount of α-waves
frequency band detected. It is necessary to examine
the relevance of these, together with various factorsof the listeners, such as their preconceptions ofenvironmental sounds, such as their preferences andexperiences, visual images, and psychologicalresponses.

REFERENCES[1] Teruo Okuma Clinical Electroencephalography,
5th edition (Igaku Shoin) Kazuei Suenaga,
Yasunori Okada Latest EEG Standard Text
(Medical System Training Institute) P24[2] MinistryofInternal
AffairsandCommunications, "Annual Report of the
Pollution Coordination Committee for FY
2020," p. 21[3] Goto,
Hisaki,Researchon
Positive
Environmental Effects in Office Environments
and its Experimental Investigation.
[4] MinistryofInternal
AffairsandCommunications, "Results of the 2020
Communications Usage Trends Survey".
[5] Yoshida, Michiyuki, 'Objective Measurementand Evaluation of Comfort'.
[6] Shimai, Tetsushi and Tanaka, Masatoshi,'Relationship between pleasant and unpleasantevaluation of environmental sound and soundpressure'.
[7] Hirai, Akiyasu, Koji Yoshida and Isao Miyaji,'Comparative analysis of thought and memory
duringlearningbysimpleelectroencephalography'.
[8] Shun Hirasawa, Tetsuya Watanabe andMotohachiro Tanaka, 'Comfort evaluation ofenvironmental sound using EEG'.
[9] Tahara, Kei, Tsuneo Harashima, Yuko
Kobayashi and Akiyoshi Katada, Research on
Auditory Imagery of Environmental Sounds in
People with Hearing Impairment.

CONCLUSION
The experiment showed that, regardless of theappearance of the α-waves frequency band, thesounds of daily life were the most preferred and thebridge was the least preferred. It was also found that
there were several environmental sounds that showedsignificantly different results for each subject. The
subjects with the highest frequency of appearance
preferred lively environments such as Park square andClassrooms, while the subjects with the lowest
frequencyofappearance
preferredcalmenvironments such as Laboratories and Shopping
mall outdoors. We hypothesized that the loudness ofthe environmental sounds may have a significant
effect on the individual differences in the way
subjects' brain waves appeared.
However, there are many challenges in theexperimental process because of the large individual
differences among subjects in the analysis of α-waves
frequency bands. In order to reduce bias due tosubject characteristics, more data needs to bestatistically handled. In the future, we will conduct
validation using visual imagery and psychologicalresponse analysis in order to correctly assess how
environmental sounds are heard.

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Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

APPLICATION OF THREE-DIMENSIONAL POINT CLOUDSFOR RIVER MANAGEMENT USING DRONE SURVEYING
Nanoka Akiyama1, Satoshi Nishiyama2
1,2

Graduate School of Environmental and Life Science, Okayama University, Japan
ABSTRACTIn recent years, record-breaking rainfall has been increasing due to effects such as global warming, whichcauses extensive damage in many parts of Japan every year. However, in the current river management, this is
only determined by the results of visual inspections on foot or periodic longitudinal and cross-sectional surveys
every 200 m. Therefore, we consider the use of three-dimensional laser point clouds acquired by ICT devices inriver management recently. The 3D data obtained by laser point clouds can visualize the topography as acontinuous "surface," thus improving the efficiency and sophistication of river management. Therefore, thepurpose of this study is to investigate the application of 3D laser point clouds to river management using a drone
survey equipped with a green laser scanner that can penetrate underwater. As a result, we can determine theunderwater transmission capacity, and the depth that can be measured depends on the transparency, but weconsider that it is effective to apply it to survey the underwater topography at shallower than about 2.0m. In
addition, it was found that the surveying with an accuracy of 5 cm could be performed by using the adjustment
points for measuring the height of river levees to identify overtopping hazard areas, which requires an accuracy of
about 50 mm. Moreover, we demonstrated that it is possible to detect the deformation locations in real time bydeveloping a method to superimpose the laser point clouds at two periods.
Keywords: Three-dimensional point clouds, River management, Drone, ICPINTRODUCTION

But this surveying allows visualization of the
topography as a continuous "surface" that can beinspected and surveyed in detail. Including thisadvantage, the application of 3D point cloud data by
ICT devices can improve the efficiency andsophistication of river management [2].
Among the various methods used in point cloudlaser surveying systems, in this paper, we focus ondrone surveying with a green laser scanner. It isequipped with a green laser scanner that penetrates
underwater and measures the terrain on land and in
the water from the ground level below 150 m with ahigh density of more than 100 points/m2. Thesefeatures are garnering attention as promoting Digital
Transformation (DX), and green laser drones are
expected to be utilized for detailed surveying andinspecting of localized areas. Therefore, the purposeof this paper is to examine the application that candemonstrate the characteristics of green laser drone
surveying, which is expected to be a method torealizing DX in the river sector.

Recently, owing to factors, such as global
warming and, frequent short duration heavy rains
with hourly rainfall exceeding 50 mm and heavy rains
with total rainfall exceeding several hundred toseveral thousand millimeters, causing large-scaledisasters throughout Japan every year [1].
Additionally, considering the increasing tendency offactors, such as linear precipitation belts, to cause
heavy rainfall, it is even more important to closely
inspect rivers over vast distances. However, in thecurrent river management system, only the results ofvisual inspections conducted on foot during theoutflow period, typhoon season, and after the outflow,or periodic longitudinal and cross-sectional surveys
conducted every 200 m are considered. Furthermore,
the paucity of engineers and financial resources has
become severe, and it is difficult to work for the detailand frequency of inspections. However, visualinspections rely on soundness judgments based on theexperience of the engineer. Moreover, there is a lackof objective evidence to take measures at necessary
places on a priority basis in the absence ofquantitative data. Therefore, in recent year, weconsidered the application of three-dimensional (3D)
point clouds acquired by Information andCommunication Technology (ICT) devices inperiodic longitudinal and cross-sectional surveying ofrivers. In conventional surveying, the data is createdby connecting "points" that surveyed point by point.

EXPERIMENTAL MEASUREMENT
Used Equipment
As shown in Figure 1 and Table 1, we used adrone equipped with a green laser scanner of the
wavelength of 532 nm, which penetrates underwater.
The drone is equipped with a GNSS system with ±10mm horizontal and ±20 mm altitude positioning
503

GEOMATE – Bangkok, Thailand, 22-24 November 2022

accuracy.

ANALYSIS OF SYSTEM CHARACTERISTICS
Point Density
Table 3 shows the point density at three periods.
In addition, Figures 3 and 4 show the point density
results obtained at first and third periods, respectively.
The point densities in the first, second, and third
periods are different because of the difference inground altitude.
Table 3 Point density at three periods

Fig. 1

Unit: points/m2
First period
Second Period
Third period

Drone equipped with green laser scanner

Table 1 Specifications of the green laser scanner
Laser wavelength
Laser pulse rate
Scan speed
Laser reflective angle
Beam divergence
Weight (main body only)

Point density
100–1200
100–1000
50-400532±1 nm
60,000 Hz
30 scan/s
90°
0.3 mrad2.6 kgMeasurement Site And Experiment Overview
Figure 2 shows an overview of the measurementsite. We measured in an approximately 1.2 km section
from 14.6 to 15.8 km of Asahikawa River, Okayama
Prefecture, as denoted by the red frame in Figure 2, inMarch 2020, October 2020, and April 2021 at three
periods.

Fig. 2

Fig. 3

Point density result at first period

Fig. 4

Point density result at third period

Measurement site

Table 2 shows a summary of the measurement
work. The ground altitude is the height above thewater surface. The measurement method was variedat the third period in order to compare the effect ofpoint cloud density.

Accuracy Verification Results On LandIn the field, adjustment points were established tocorrect the data and verification points were set up to
verify the accuracy of the survey. The accuracy wasverified by comparing the surveying results using the
Total Station (TS) with the drone surveying results.
Table 4 shows the verification results of the surveying
accuracy at three periods. In the table, X and Y referto the horizontal directions whereas Z refers to theTable 2 Measurement work
PeriodGround Altitude
Side lap
Flight speed

First

Second50 m
75 %2.5 m/s

Third
100 m
60 %
4 m/s
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

elevation direction. The values in the table are themean and Root Mean Squared Error (RMSE) of thedifference between the coordinates values of thedrone equipped with the green laser scanner and TS.
In three periods, the mean and RMSE were below 50
mm in the plane and below 30 mm in the elevation,
indicating that good measurements were possible. It
was demonstrated that the desired accuracy of green
laser drone surveying can be ensured by using

arbitrary adjustment points. In addition, the "Manualfor Utilization of 3D Data for River Management(Draft)" states that a measurement accuracy of about
50 mm is required for grasping the height and shapeof levees in order to extract overtopping hazard areas[2]. Therefore, it was found that it is possible tosurvey with an accuracy of 50 mm, which isnecessary to grasp the height and shape of levees byusing the adjustment points.

Table 4 Verification accuracy results at three periods
Unit: mm
Direction
Average difference
RMSE

X
0
43

First period
Y
13
16

Z
8
29

Table 5 shows the transparency measured in thefield and the maximum water depths that could bemeasured. With the FTU turbidity of 0.8, a maximum
water depth of 2.0 m could be measured, but with theFTU turbidity of 3.1, topographic surveying of water
depths greater than 1.0 m was difficult. From theseresults, we consider that it is possible to apply tosurvey the water depth below 2.0 m when the FTU
turbidity is below 1.0. In addition, cross-sectionalviews of the river channel for three periods are shown

Example of river cross-sections 1

Fig. 6

Example of river cross-sections 2

TECHNOLOGY FOR EXTRACTION
DEFORMATION LOCATIONS

X
-4
32

Third period
Y
-14
29

Z
-1
17

in Figure 5 and 6 The point clouds at 1–3 periods are
represented by green, red, and blue point clouds,
respectively. At this location, the accumulation anderosion of approximately 20–70 cm was observed. It
can be noticed that the high-density point clouds canreproduce the detailed topography of the riverbed.

Effect Of River Transparency On Riverbed
Measurement

Fig. 5

Second period
Y
Z
-6
8
14
29

Y
8
29

Table 5 FTU turbidity and maximum water depths
PeriodFirst
SecondThird

FTU turbidity
0.8
3.12
2.45

Maximum water depths
2.0 m
1.0 m
1.7 m

section, the number of point clouds can be more thanseveral hundred thousands, and without dataprocessing technology that can easily detect where
deformations occur, the measurement results cannot be
used effectively even if the detailed topography isreproduced. In this study, we developed laser point
clouds analysis by applying Iterative Closet Points

OF

High density laser point clouds reproduce thetopography of the measured section in detail by thegreen laser drone surveying. Therefore, the drone
survey data is big data. In the long management505

GEOMATE – Bangkok, Thailand, 22-24 November 2022

(ICP) [3], [4]. ICP is a technology used to superimpose
the figure made from the laser point clouds of thesecond period on the figure made from the laser point
clouds of the first period, as shown in Figure 7. The
displacement and direction of points can be regarded
as displacement vectors, so that deformations can be
expressed quantitatively and analyzed as point clouds.
ICP is the process of directly superimposing the dataof two periods without creating a TIN model from thepoint clouds data so that real-time analysis is possible.

Fig. 7

Fig. 8

Example of deformation extraction at thelevee crown 1

Fig. 9

Example of deformation extraction at thelevee crown 2

Concept of data processing by ICP

Figures 8 and 9 shows the results of deformation
extraction at the levee crown along the Z direction.
Deformities are represented on a color scale. The resultof the deformation extraction of the levee slope isshown in Figures 10 and 11. Figure 12 shows the point
clouds for the first and second periods in green andblue, respectively. Slope deformations are represented
by vectors. At the levee crown, since the deformation
was within 5 cm, we considered that the levee has notsettled. Slope deformations are represented by vectors.
In addition, the displacement was more than 2 m on thelevee slope. This is the vegetation growth during thetwo periods from Fig. 12. Thus, it is possible to obtain
deformations in the local area. Furthermore, byfiltering out vegetation, it is possible to capturechanges in the ground surface itself.

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 10 Example of deformation extraction at the levee slope 1

Fig. 11 Example of deformation extraction at the levee slope 2

Fig. 12 Point clouds of levee slope in two periods

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CONCLUSIONSACKNOWLEDGMENTS

In this paper, we examine the application that candemonstrate the characteristics of green laser drone
surveying, which is expected to be a method torealizing DX in the river sector. The following
summary shows applications for river management.
1) The point density obtained depends on the groundaltitude. We need to change the ground altitudedepending on the object to be measured.
2) It is possible to obtain the accuracy within 50 mm
by using arbitrary adjustment points. Therefore, we
can survey with an accuracy, which is necessary to
grasp the height and shape of levees in order to extract
overtopping hazard areas.
3) It is possible to determine underwater permeability
with the FTU turbidity. It is useful for surveying
topography at the FTU turbidity below 1.0 and water
depths shallower than approximately 2.0 m.
4) Because the application of the ICP technology usedin this study can quantitatively represent the
deformation location, it has proven to be capable ofdeformation location in real time.
In the future, it is necessary to study the extracting
deformations of riverbeds and the levees after
vegetation removal by filtering. In addition, we needto study the effect of point density on deformation
extraction.

This study was conducted under the project
research of the Okayama Office of River, Ministry ofLand, Infrastructure, Transport, and Tourism.
REFERENCES[1] Nishimura S., Takeshita Y., Nishiyama S., Suzuki
S., Shibata T., Shuku T., Komatsu M., and Kim B.,
Disaster report of 2018 July heavy rain for geostructures and slopes in Okayama, Soils andFoundations, Vol. 60, Issue 1, 2020, pp.300-314.
[2] Office of River Conservation Planning, River
Environment Division, Water Management andLand Conservation Bureau, Ministry of Land,
Infrastructure and Transport, Manual for
Utilization of 3D Data for River Management(Draft), 2020.
[3] Szymon R. and Marc L., Efficient Variants of the
ICP Algorithm, Proceedings Third International
Conference on 3-D Digital Imaging andModeling, 2001, pp. 145–152.
[4] Dirk H., Alexandru E. I., Federico T., Radu B.,
and Sven B., Registration with the Point Cloud
Library, IEEE Robotics & Automation Magazine,
Vol. 22, Issue 4, 2015, pp.110-124.

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Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

ASSESSMENT OF DOMESTIC WASTEWATER MANAGEMENTPROGRAMS IN RIVERBANK SETTLEMENTS
Moch Zaelani Pebriansyah1, Ahmad Soleh Setiyawan2, Dyah Wulandari Putri3 and Ken Aryu Ruska Yuniar4
1,4

Water Supply and Sanitation Infrastructure Management, Institut Teknologi Bandung, Indonesia;
2,3
Wastewater Engineering Research Group, Institut Teknologi Bandung, Indonesia
ABSTRACTRiverbanks areas have long been public settlements due to increasing population and economic problems.
Bojong Village, Bandung Regency, Indonesia is one of the public settlements along the Cisungala river that haspoor sanitation level. The purpose of this research was to select the most appropriate domestic wastewater
management programs to be implemented in a riverbank area. Identification of domestic wastewater management
practices was carried out by observation, interviews and questionnaires addressed to stakeholders and community.
A pairwise comparison test was performed to assess the criteria on the selection of domestic wastewater
management programs. The sanitation level in the riverbank area was mostly basic sanitation. Many residents
discharged wastewater directly into river without any treatment process. There were various programs regarding
domestic wastewater management, but they were not running effectively. A pairwise comparison test showed theweighting factors on the program selection criteria. The programs were selected based on various aspects, namely
technical, social, institutional, financial, and regulatory aspects. The appropriate programs that increase publicawareness regarding domestic wastewater management along with development onsite wastewater treatment
facilities are needed to improve the level of sanitation and to create sustainable sanitation in riverbank settlements.
Keywords: Assessment, Domestic wastewater, Management program, Riverbank area
1.

INTRODUCTION

health and environment. Lack of sanitation is linked
to reduced health and environmental degradation [7],[8], Error! Reference source not found.. The
importance of sanitation is reflected in Sustainable
Development Goals (SDGs) 6. In slum areas,
economic limitations, socio-cultural factors, and lackof knowledge cause low-income people to have a low
level of concern for the quality of their housing,including water and sanitation [9], Error! Reference
source not found.. Fulfilling basic needs for cleanwater and sanitation is still a challenge faced byIndonesia in achieving SDGs targets [11], Error!
Reference source not found.. In 2019, national
access to basic sanitation was 77.39%, where in WestJava Province only 69.64% of its population has
access to basic sanitation [12].
There is a gap between the government agenda
and existing conditions in the field. This calls for a
strategy for domestic wastewater management in the
Bojong village riverbank, which is part of Majalaya
subdistrict, reaching a target of adequate and safe
sanitation access based on cooperation between
municipal and provincial government. Not only may
it be a physical infrastructure, but it may be a nontechnical program associated with increasing accessto domestic wastewater management facilities in theregion. This study aims to develop strategies forsustainable domestic wastewater management in theriverbank area.

A challenging area is an area that are difficult tobuild or apply affordable systems of sanitation
services because of the geography and the climate
condition, one of which is the riverbank [1]. Theseriverbanks areas also face social and economic
problems, such as the illegal status of the sites and the
low-level education and income of the inhabitants [2],[4]. The buildings along the rivers create numerous
environmental problems, such as waste resulting frommining activities and household waste directly
disposed to the river [3], [4].
Household wastewater management is classified
into two categories: waste-wastewater (MCK) andkitchen wastewater [5]. According to the Regulationof the Governor of the Special Capital Region of
Jakarta Number 41 of 2016 concerning the Master
Plan for the Development of Domestic Wastewater
Management
Facilities
andInfrastructure,
wastewater is water that comes from the rest of the
production process and other business activities thatare not reused. Domestic wastewater, either grey
water or black water, is wastewater derived fromhousehold, housing, apartments, offices, hospitals,
malls, marketplaces, supermarkets, hotels, industry,and schools. By the regulation specified above, grey
water includes kitchen-based non-toilet wastewater,
waste-washing, and used bathing water, while black
water is wastewater containing human waste Error!
Reference source not found..
Sanitation infrastructure is critical for public

2. STUDY AREA

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

The research is conducted in Bojong Village,
Majalaya District, Bandung Regency, Indonesia. The
Cisungala River are located in Bojong Village, one of
the sub-districts in Majalaya and its location can beseen in Figure 1.

2,709 units or 71.2% of the total houses and 11 unitsof public toilets. The Cisungala River is one of thetributaries of Citarum River, which passes throughthe administrative area of Bojong Village. The
number of hamlets (RW) traversed by this river is 4
RW, namely RW 03, RW 06, RW 10 and RW 13.
This research was carried out at those 4 hamlets. The
distribution of each hamlets can be seen in Figure 3.
2.2 Domestic Wastewater Management Programs
Domestic Wastewater Management Programs is alist of programs regarding to the domestic wastewater
management that have been implemented in various
locations, precisely on the banks of rivers inIndonesia. In determining programs on the banks of
the Cisungula river (Table 1), there are several stages
in the program proposal, namely a longlist of program
proposals, criteria for evaluating program proposals,
and a shortlist of program proposals.

Fig. 1 Located of Bojong Village in Majalaya
District, Bandung Regency

3. METHODOLOGYThis research was carried out in three stages,
including problem formulation, program proposals,
and program evaluation using pairwise comparison
test. Primary data were obtained from questionnaire
data on the community and experts in the field ofdomestic wastewater. Meanwhile, secondary data
was obtained by searching for data related to theexisting condition of the study area and the proposed
program, and then conducted an assessment of the
proposed program.

Fig. 2 Existing Condition of Cisungala River in
Bojong Village
Panyadap
VillageCisungala River

Sukamanah
VillageTangsi Mekar
Village

3.1 Identification of Problems
The components in the study were further detailed
to focus more on problems in the study area regarding
domestic wastewater management. A preliminary
study was carried out by conducting brief interviews
with residents around the study area regarding
domestic wastewater management. A literature study
was performed by searching for related problems
from other areas. The problems found were thencollected and matched with the conditions of thestudy area through a preliminary study.
3.2 Proposal of Programs
The program proposal is the stage of seeking
information about domestic wastewater management
programs that have been implemented bygovernments in other areas. There are several
processes, namely longlist, assessment criteria andshortlist of program proposals.

Mekarpawitan
VillageFig. 3 Administrative map of Bojong Village
2.1 Area Description

3.2.1 Longlist of Program Proposals
Longlists of proposed programs or long data on
sanitation programs were obtained by studying
literature from various sources such as research
journals, reports on best practice programs for each

Bojong Village is one of the villages in
Majalaya District, Bandung Regency, Indonesia
which has an area of 175.54 Ha. In 2020, thepopulation of Bojong Village was 14,372 people. In
2019, the number of houses that have latrines was510

GEOMATE – Bangkok, Thailand, 22-24 November 2022

relevant
domestic
wastewater
management
organization, and explored information on sanitation

programs from printed and online media.

Table 1 Shortlist of proposed domestic wastewater management programs in the Cisungala Riverbank area
No.

Aspect1

Technic2

Social Society

3Institutions

4

Financial

5

Regulations

Program
The Construction of Communal WWTP
The Construction of communal Biofilter Septic Tanks
The Construction of Individual Septic Tanks
The Construction of piping network from Septic Tank to Communal WWTP
Optimazing of Regional WWTP
Optimazing of FSTP
General MCK renovation
Sosialization of Sanitasi Masyarakat dan Sekolah (SMS) programme
Stop BABS Program
Training of Technical and Practice Health Latrine
Trisula’s approaches to build awareness of people
Institutional training for community self-help group
Establish cooperartion with other institutions build latrine and communal WWTP
Formation of the sanitation authority
Private financial aid
Periodic community fundraising
Savings and loan money
Savings and loan money from other members
River Normalization regulatory
River cleaning regulation
Regulation for development at the river crossing

3.2.2 Assessment Criteria of Program Proposals
These criteria are recorded and assessed basedon the existing conditions of the study area obtained
from preliminary studies and literature studies. In
determining the assessment criteria used, it must firstbe known the existing condition of the study area
related to the criteria or factors obtained from eachprogram. The criteria consist of the physicalcondition of the area, social, financial and economic
conditions, as well as its institutions.

with the principle of Comparative Judgment. This
principle is carried out by making an assessment of
the relative importance of two elements at a certain
level in relation to the level above it. The assessment
affects the priority of the existing elements. The
results of this assessment are written in a pairwise
comparison matrix [13]. With pairwise comparisons,it can be seen the degree of relative importance
between criteria. In the data processing, used Ms.
excel 2013 and for data validation used SPSS vers.
25.

3.2.3 Shortlist of Program Proposals
Shortlist of program proposals is the result ofsorting several programs that have been obtained
from the longlist of program proposals through anassessment of several predetermined criteria. If the
program passed at least half of the predetermined
criteria, it is included in the shortlist of the proposed
programs.

4. RESULTS AND DISCUSSION
4.1 Existing Condition of Domestic Wastewater
Management
From figure 4, it can be seen that people in thearea along the Cisungala River still use squat toilets
as a user interface. However, there are still many
people who do not have a septic tank and drain theirdomestic waste into the river. Thus, access tosanitation in this area is still relatively low and needs
improvement.
Sanitation level of the community along
Cisungala River was categorized as having basic
sanitation 57.43%, access to proper sanitation 24.3%,
access to safe sanitation 3.24% and no access topersonal sanitation 15.03%. Figure 4 shows a diagramof domestic wastewater management in the area along

3.3 Assessment of Programs
In this stage, programs that have entered theshortlist of proposed programs were then assessed todetermine the weight of the value of each program
which was expected to be used as a reference in
conducting a study of program implementation. For
each aspect of domestic wastewater management, one
program with the highest weight value was selected
through the Pairwise Comparison test.
The Pairwase Comparison test is carried out
511

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Cisungala River.

Fig. 4 Summary of the existing conditions of domestic wastewater management systems
4.2 Evaluation of Existing Programs of Domestic
Wastewater Management
In evaluating the existing domestic wastewater
management program in the area along the Cisungala
River, it was carried out by conducting literaturestudies and field observations with the results ofcommunity questionnaires. From the results of thecommunity questionnaire, there are results regarding
the residents' desire for toilet facilities and also
aspects of community participation in evaluating thecommunal MCK development program.

Furthermore, in assessing community awareness,there are several parameters/sub-criteria that become
questions to the community which consist of subcriteria for community participation in theconstruction of MCK facilities, the form ofcommunity
participation
andcommunity
desires/initiatives in village activities. The results of
the questionnaire can be seen in Figure 5.

21.52%

Private Toilet

21.52%

29.11%

Not both64.56%

Not interested infollowing

49.37%

Public toilet

13.92%

Fig. 5 Respondents' wishes for toilet facilities(n=79)

Fig. 6

From start to finish
16.46%
44.30%

26.58%
7.59%

6.33%

Execution process
only
Just use andmaintain
Just wear

93.67%

Fig. 7 Respondents' willingness in facility building
activities (n=79)

Fig. 8512More often waiting
to be invited

Respondents' Initiative for participating invillage activities (n=79)

5.06%
Just in the beginning

More often on yourown

One of Energy,
Money, Goods andThoughts
Choose more than
one between energy,
money, thoughts andgoods

Respondents' participation inconstruction of facilitie (n=79)

theGEOMATE – Bangkok, Thailand, 22-24 November 2022

From the graph, it can be concluded that
community participation in the Cisungala
Riverbank area is still not too high, there is nocommunity initiative/awareness to help in building
facilities.

Tabel 2 shows evaluation of the existing programthat has been carried out in the Cisungala riverbank
area.

Table 2 Evaluation of existing programs in the area along the Cisungala River
No.
1

2

Programs
Construction of
Public MCK(Communal WC)

Existing Condition
the majority is used for washingclothes for residents
the majority of bathing and latrine
activities are carried out in private
toilets
The majority have private toilets, but
don't have a septic tank/similar
public toilet cleaning is still voluntary
operational and maintenanceactivities are still a formality, after
the handover O&M is not visible

Citarum Harum
ProgramBojong Village is not included in thesanitation management area in thisprogramthere is still a pipeline that flows into
the riverthere are still people who have doubts
about this program
the availability of land in the area
along the Cisungala river is limited
there are still few experts/facilitators
related to sanitation

Table 3

No.

Solutionthe construction of private toilets is
recommended in this area, because of privacy.
Can be equipped with the construction of a
septic tank for each private WC that has been
built.

if there is construction of a public toilet, it isnecessary to carry out maintenance andoperation, it can be handed over to residents
for picket or a special officer designated formaintenance of the facility.
by submitting to related parties or being able
to personally design sanitation activities byutilizing existing sanitation funds or
submitting proposals to private parties
there is a need for socialization in thecommunity to understand the surrounding
residents
the willingness of residents to donate theirland, but if necessary communal facilities canlook for vacant land belonging to the village
cooperation with other parties is needed to
become a facilitator in the sanitation sector

Recapitulation of the results of pairwase comparison testing for domestic wastewater
management programs in the riverbank study area
AspectValue
AspectValue
Program
0.082

ProgramPriority
6

The Construction of communal Biofilter Septic Tanks

0.125

5

The Construction of Individual Septic Tanks
The Construction of communal wastewater piping networkfrom Septic Tank to Communal WWTP
Optimazing of Regional WWTP
Optimazing of FSTP
General MCK renovation
Sosialization of Sanitasi Masyarakat dan Sekolah (SMS)
programme
Stop BABS Programme
Training of Technical and Practice Health Latrine
Trisula’s approaches to build awareness of people
Institutional training for community self-help group
Establish cooperartion with other institutions build latrine
and communal WWTP
Formation of the sanitation authority
Private financial aid
Periodic community fundraising
Savings and loan money
Savings and loan money from other members
River Normalization regulatory
River cleaning regulation
Regulation for development at the river crossing

0.226

1

0.175

2

0.149
0.170
0.073

4
3
7

0.239

2

0.185
0.224
0.352
0.449

4
3
1
1

0.346

2

0.206
0.422
0.277
0.125
0.176
0.428
0.276
0.296

3
1
2
4
3
1
3
2

Programs
The Construction of Communal WWTP

1

Technical

0.243

2

Social Society

0.367

3

Institutions

0.099

4

Financial

0.176

5

Regulations

0.115

513

GEOMATE – Bangkok, Thailand, 22-24 November 2022

4.3 Assessment of Domestic wastewater
Management Program
Tabel 3 shows a recapitulation of the results of the
Pairwase Comparison Testing for domestic
wastewater management programs in the riverbank
study area. The technical aspect of the priority
program was the construction of individual septic
tanks (0.226). In the socio-community aspect, thepriority program was the Trisula Approach to Build
Community Awareness (0.352). In the institutional
aspect, the priority program was Institutional
Training for community self-help group (0.449). In
the financial-financial aspect, the priority program
was financial assistance from the private sector(0.422), and for the regulatory aspect, the priority
program was the river normalization program(0.428).

2010, doi: 0.3362/1756-3488.2010.020.
[2] Sarwadi, A., Tohiguchi, M., Hashimoto, S.,
2001. An analysis of the riverside settlement
inhabitant’s characteristics in relation to an
urban situation, a case study in the musi urbanriverside settlement, palembang city, Sumatra,Indonesia. J. Archit.Plan. Environ. Engin.,AIJ
544, 225e231.
[3] Prayitno, B., 2013. An analysis on spatial
permeability and fluid dynamics of wind andthermal in tropical riverside residential areas of
Banjarmasin city, Indonesia. J. Manusia dan
Lingkungan (J. Hum. Environ.) 20 (2),199e212.
[4] M. V. Michiani and J. Asano, ‘Physicalupgrading plan for slum riverside settlement intraditional area: A case study in Kuin Utara,
Banjarmasin,Indonesia’,
Frontiers ofArchitectural Research, vol. 8, no. 3, pp. 378–
395,
Sep.
2019,
doi:
10.1016/j.foar.2019.03.005.
[5] R. Iskandar, Perilaku rumah tangga dalampengelolaan limbah domestik - kasus desadesa wilayah Jakarta, Depok dan Bogor
sepanjang sungai Ciliwung. Bogor: Institut
Pertanian Bogor, 2010..
[6] S. M. S. Wirawan, ‘Community Preparation
for Domestic Wastewater Management
Development in Jakarta’, vol. 5, no. 2, p. 11,
2020.
[7] G. Hutton and M. Varughese, The Costs of
Meeting the 2030 Sustainable Development
Goal Targets on Drinking Water, Sanitation,and Hygiene. World Bank, Washington, DC,
2016.
[8] M. Calver, A. Lymbery, J. McComb, and M.
Bamford, Environmental biology. Open
Oregon Educational Resources, 2009.
[9] L. P. Defi, I. . Kusumayanti, Wika Maulany
Fatimah, P. W. Sarli, and P. . Soewondo, “TheThe Rise Of Rainbow Village: Optimizing
Aesthetical Program To Accelerate Sanitation
Access”, International Journal Of Geomate,
vol. 22, no. 90, pp. 118–124, Apr. 2022.
[10] B. Marx, T. Stoker, and T. Suri, ‘The
economics of slums in the developing world’,
J. Econ. Perspect., vol. 27, no. 4, pp. 187–210,
2013.
[11] Badan Pusat Statistik, ‘Survei Sanitasi
Nasional’, Jakarta, 2019.
[12] R. Andiyanti, S. Mardiyah, W. S. Purba, and Y.
Yunarsih, Environment Statistics of Indonesia:
Water and Environment 2020. Jakarta,
Indonesia: Badan Pusat Statistik/BPS –Statistics Indonesia, 2020.
[13] Amalia, ‘Analytical Hierarchy Process (AHP) :Multi-Attribute Decision Making (MADM)’,2016.

5. CONCLUSION
This research was identified the sanitation
condition and propose the most appropriate
domestic wastewater management programs in ariverbank area. The sanitation level in the riverbank
area was mostly basic sanitation 57,43% and publicawareness about the importance of sanitation in thatarea was still lacking. Various programs based ontechnical, socio-community, institutional, financial,
and regulatory aspects have been proposed. The
recommended program by the technical aspect isbuilding an individual treatment facility such as
septic tank. The recommended program by thesocio-community aspect is the Trisula Approach tobuild community awareness. The recommended
program by institutional aspect is institutional
training for community self-help group. The
recommended program by financial aspects isfinancial assistance from the private sector. The
recommended program by regulatory aspect ismaking regulation related to river normalization.
The appropriate programs that increase publicawarenessregardingdomestic
wastewater
management along with development onsite
wastewater treatment facilities are needed to
improve the level of sanitation and to create
sustainable sanitation in riverbank settlements.

6. ACKNOWLEDGMENTS
This research was supported by Riset ITB and
the Global Sanitation Graduate School (GSGS)
program.
7. REFERENCES[1] E. R. Djonoputro, I. Blackett, J. W.
Rosenboom, and A. Weitz, ‘Understanding
sanitation
optionsinchallenging
environments’, Waterlines, vol. 29, no. 3, Jul.
514

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

THE IMPACT OF COVID 19 ON CHANGE OF MONTHLY INCOME IN
INDONESIA
Aditya Maulana Mugiraharjo 1, Chotib2
12School of Strategic and Global Studies, Universitas Indonesia, Indonesia;
School of Strategic and Global Studies, Universitas Indonesia, Indonesia,
ABSTRACTThe COVID-19 pandemic has caused several changes and adjustments in various sectors of people's lives. Oneof the sectors affected is the employment sector. Various elements in the employment sector have also been
affected by COVID 19, for example adjusting the number of hours worked. Furthermore, this paper wants to
explain how the impact of COVID 19 affects the lost or reduced working hours and income of informal workers
in Indonesia. This is important as an academic study because it is a new thing in the study of the urban field in
the context of the impact of COVID 19, as well as an academic study in the discussion of the urban economy inIndonesia. The data analyzed is sourced from the 2021 Street Vendors Masterplan Survey, conducted by theIndustrial, Commerce, and SME Agency of DKI Jakarta Province. The approach used is a quantitative approach,and uses a binary logistic regression analysis method. The independent variables in this study were employment
status; reasons for working; work location; number of jobs; gender; age; marital status; household size; andhousehold status.
Keywords: COVID 19, Working hour, Job, Income, Informal Worker
INTRODUCTION

shorter or the status of employee that employed but
not working or temporarily laid off. Second, workers
are forced to leave their active period of work andenter unemployment. The first hal of 2020, it was
found that around 5,4% of global working hours or
the equivalent of 155 million full-time jobs havebeen lost due to COVID 19 (ILO, 2020; Ngadi and
Purba, 2020).
In the case of several countries in theworld, the opportunity to work remotely as acompromise to the COVID-19 pandemic is no more
flexible due to the availability of more limited
resources. In the Southeast Asia region, thepercentage of lost working hours reached 2,1% oraround 6 million equivalent full time jobs in the firstquarter of 2020. Meanwhile, in the second quarter 0f
2020, it was around 12,7% or the equivalent of 35
million time related to work. Loss or reduction in
working hours occur in various ways, mainly as aresult of existing labor market institutions andpolitical decisions made, such as the implementationof large-scale social restriction policy (PSBB and
PPKM) (ILO, 2020; Ngadi and Purba, 2020).
The nowcasting method proposed andused by the ILO identified foru variations in cases of
reduced or lost working hours,: first, shorter working
hours. What happened was a decrease in the average
weekly working hours compared to the pre-COVID
19 situations; second, the employee status isemployed but actually not working. The employee
continue to have an attachment to the employment
relationship, but is not invovel in any work; third,
unemployment, is the labor force available for and

Urban is a space or a place for city
residents to liv, which consists of physical and nonphysical elements. Urban areas have become one of
the areas that have been heavily impacted by
COVID 19 pandemic. This can be understood on thebasis of the concentration of community activities,
economy, entertainment, and services. Various
stepswere taken by the government in an effort toprevent the spread and transmisiion of the COVID19, such as limiting community activities; workfrom home policy; efforts to encourage people
mobilization with the concept of LIT (lightindividualtransport)ornon-motorizedtransportation, such as cycling and walking(International Transport Forum, 2020).
The economy is one of the sectors thathas the biggest impact due to COVID 19, from thenumber of business experiencing a decline tobankruptcy, changes in the scheme of economictransactions in society, technological disruption incommunity activities, several matters related to thelabor sector, such as the number of layoffs, reduced
number of working hours (ILO, 2020; Ngadi and
Purbam 2020; Sobieralski, 2020; Spurk and Straub,2020).
The number of layoffs and the reductionin the number of hours worked are the impact of themany workplace closures as a direct impact of the
COVID 19 pandemic. The report of the International
Labor Organization (ILO) 2929 revealed that there
were several determinant variables led to a decrease
working hours, such as: first, working hours that ryn
515

GEOMATE – Bangkok, Thailand, 22-24 November 2022

looking for work; fourth, inactive is someone if outof the labor force (ILO, 2020).
In addition to formal workers whosechanges can be seen after COVID 19, the weakest
party are informal workers. According to Indonesia
Central Statistics Agency, the number of informal
workers in Indonesia in 2021 will amount to 70,91
million people. 1,81 milllion among them are
informal workers who live or do their job in DKI
Jakarta province.It can be understood because DKI
Jakarta is the epicenter of community activities andeconomic cycles in Indonesia.
One of several type of informal workers
is street vendors or Pedagang Kaki Lima in Bahasa
Indonesia. According to dataindonesia.id, thenumber of street vendors Indonesia estimated in 22
million people. The challenge to get the exact
amount of street vendors is they are mobile and tend
to change where they do their business activity.
Based on this background explanation,
this paper will explain the impact of COVID 19 onchanges in working hours, as well as changes inincome for informal workersm especially street
vendors in DKI Jakarta province.

One of the keys for workers in the issue ortransition period of the COVID 19 situation is how
to use the time that is reduced due to restrictions oncommunity activities. Community adaptation isneeded and demands needed on the basis of changes
in pattern of living and working in the community.
Changes from offline transaction patterns toonline become a disruptor in community activities.
People who are used to sell face to face directly
between sellers and buyers are now forced totransform into online buying and selling activities.
Eichenbaum (2020) in his study revealed thatpolicies that encourage restrictions on activities
outside the home affect the number of deaths.
Unfortunately, the enactment of this policy has aneffect on the economy and recession. As statedearlier, the reduction in working hours was due tothe COVID-19 recession (Beck, and Hensher, 2021)
The post COVID 19 situation may cause
disruptions to the job market. The shift in thecommunity service model and the form of thebusiness model from offline to online has a caused a
lot of disruption to the existing business models. The
offline form of store services has been reducedand
replaced by online marketplace.

CONCEPT AND THEORY

Methods
The author uses data on the street
vendor master plan 2021 DKI Jakarta Province. The
variable used are as listed in table 1.

Change in Work
From the phenomenon of changes in thelength of working hours in Indonesia, we can discussusing the conceptual framework of change in thecontext of working in a crisis situation. This concept
can provide an understanding, such as crises canoccur at anytime and can lead to unpredictable
things. The Impact of the crisis can result in adecrease in the level of employment, and an increasein the unemployment rate (Beck, and Hensher, 2021)
Thus can be understood as the result of adamaged or disrupted supply chain system of
production and distribution on a global scale. One of
the root causes of the crisis that occurred in the
middle of 2020-2022 was the COVID 19 pandemic
which spread widely to several countries in theworld.
The COVID 19 pandemic caused restrictions
on community activities and which suppressed thecommunity and of course disrupted the production
supply chain system. Declining demand cuses
production to decline and leads to reduced financialtransactions in various sectors.
Changes in work models that lead toincreased layoffs and or reduced length of working
hours for workers in Indonesia. Activity restrictions
not only make some workers work from home but
also restrictions for workers working in essential
sectors who are still allow to operate, for example
the limiting of employee who works in one day or
one shift.

Tabel 1. Variables

516

No

Variable

Number of
Question (insurvey data)
KR 16 andKR 17

1Change ofIncome2

Streetvendors type

KL 04

3
4

Age
Sex

KR 07
KR 08

5

Education

KR 10

6

LT-Migrant

KR 11

7

R-Migran

KR 12

Note

The difference
between KR
16-KR17
1. Lokbin
2. Loksem
3. Klaster
Lapak
4. Mobile
Numerik
1. Man3. Woman
Recodingfrom KR 10,
make 3kategori
1. migran, if
was not bornin DKI
0. non migran,if was born in
DKI
1. migran, if

GEOMATE – Bangkok, Thailand, 22-24 November 2022

8
10

11

12

Ownership
StatusBusiness
SectorPU 02

Length ofBusiness
Hour
Sell inmarketplace

PU 21

PU 16

BM 11

in last 5 years
not live inDKI
0, non migran,if live in DKI
in 5 years ago
1. Owner
0. Others
1. Culinary
2. NonCulinary
3. Service
1. Full day
0. other

CONCLUSIONS
Based on the description of the
background of the problem, and looking at thediscussion and discussion sections, it can beconcluded that COVID 19 has a positive
relationship in its influence on changes in theincome of street vendors in DKI Jakarta every
month.
Based on these conclusions, it can thenbe understood that groups of informal workers
such as street vendors will experience the worst
impact in the COVID 19 pandemic. The role of theGovernment is urgently needed in this regard,
namely social assistance that can help reduce theburden on the general population.

1. Yes
3. No

REFERENCES[1] International Transport Forum. COVID 19
Transport Brief: Analysis, Facts, and Figures forTransport Response to the Corona Virus: Respacing Our Cities for Resilience. 2020
[2] Ruth Meilianna Ngadi, Yanti Astrellina Purba.
Dampak Pandemi COVID 19 Terhadap PHK dan
Pendapatan Pekerja di Indonesia. Jurnal
Kependudukan
Indonesia,
Edisi
KhususDemografi dan COVID 19, 2020, pp 43-48.
[3] Joseph B Sobieralski. COVID 19 and airline
employment:
Insights
fromHistorical
Uncertainty
ShockstotheIndustry.
Transportation
Research
Interdisciplinary
Perspectives , Vol 5, 2020.
[4] Daniel Spurk, Caroline Straub. Flexible
Employment Relationship and Careers in Timesof the COVID 19 Pandemic. Journal of
Vocational Behaviour, Vol 119, 2020.
[5] Dataindonesia.id. Pekerja Informal di Indonesia
Mencapai 70,91 Juta Jiwa Penduduk., 2021,
Online,available:
https://dataindonesia.id/sektorriil/detail/pekerja-informal-indonesia-capai7791-juta-pada-2021[6] Statistik.jakarta.go.id. Pekerja di DKI Jakarta
pada Agustus. 2021, online, available:
https://statistik.jakarta.go.id/pekerja-di-dkijakarta-pada-agustus-2021/[7] International Labour Organization. ILO
Glossary of Statistical Term, 2021, online,
available:
https://www.ilo.org/ilostatfiles/Documents/Statistical%20Glossary.pdf[8] Matthew J Beck, David A Hensher. Australia 6
Months after COVID 19 Restrictions Part 2:
The Impact of Working From Home. Transport
Policy,2021,
online,available:
https://doi.org/10.1016/j.tranpol.2021.06.005

RESULT AND DISCUSSION
Based on data analysis based on the 2021
DKI Jakarta Street Vendor Masterplan data,accompanied by a discussion of the relationshipbetween each independent variable and thedependent variable. In the context of the impact of
COVID 19 on changes in the income of street
vendors per month, there are several independent
variables, namely: the type of street vendor; age,gender; level of education; type of population
movement; business ownership status; businesssector; length of trading in a day; join the online
marketplace.
In the first variable, namely the age
variable, it can be explained that the probability
value is equal to 0.025. The basis for decision
making is if the probability <0.05 then Ho isrejected. This means that the variable age of traders,
there is no relationship with changes in income in amonth. In this one variable, we can see that age is
not a modifier or determinant of changes in traders'incomeThe next variable is the gender variable.
Sex. Sex variables are divided into two, namely
male and female. In traders, the sexes are male andfemale. The results of the analysis show that gender
does not have a positive effect on changes inmonthly income.

517

GEOMATE – Bangkok, Thailand, 22-24 November 2022

MULTIMODAL GOODS TRANSPORTATION POLICY MODEL:
TRANSPORTATION POLICY ENHANCEMENT IN NORTH COAST
LINE OF JAVA
Zony Yulfadli1, Achmad Wicaksono2, Ludfi Djakfar3, Muhammad Zainul Arifin4, and Moch. Abdillah Nafis5
1,2,3,4

Civil Engineering, Faculty of Engineering, Universitas Brawijaya, Malang, Indonesia;
Department of Technology Management, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

5

ABSTRACT
Logistics transportation in Java, especially in the North Coast Line of Java (Pantura), is still dominated bytruck use. This situation burdened the capacity of roads, causing traffic problems and the inefficiency of the freighttransportation from the production center to be distributed at Java Island. Likewise, both obstacles to implementing
railroad transport between the ports and the global pandemic COVID-19 that made truck use in freighttransportation increase by a good portion had significant impacts on the length of the freight transportation process
and, therefore, logistics costs. This research will discuss public policy formulation of logistics transportation.
Using the analytical hierarchy process to assess the best policy scenario by comparing some optimizationassessment models done by previous studies helped make a policy prototype. Increasing taxes on trucks used totransport goods and spending more budgets on developing and enhancing advanced traffic management systems
have significantly reduced traffic jams and damaged roads. It was also found that some policies about utilizing
railways in goods distribution have a great potential to reduce overall costs.
Keywords: Logistics, Multimodal, Pantura, Transportations Policy
1.

INTRODUCTION

the recent COVID-19 pandemic. Sustainability oflogistics services helps ensure the provision of foodstocks, medical supplies, and all consumer goods.
Finally, the monetization of this flow can be seen in
the trade value of goods and shipping costs. In thesame way, the concept of logistics refers to all theinterrelated operations and activities required to
distribute goods in a global commodity chain, thus
their central location in a gear diagram [4].
One of the main problems on the Java northerncoastline highway is over dimension & overload(ODOL). Over-dimension & overload (ODOL)
cause losses ranging from road repair costs tofrequent accidents on the north coast lane. The
government is currently running an ATMS (Arterial
Traffic Management System) project, which has
several objectives, namely [5]:1. Improve performance-based transportation
services for traffic signal management on arterial
and inter-city roads to improve planning,
operation, and maintenance, resulting inincreased safety, mobility, and efficiency for alluser.
2. Supportinginter-city
digitalmobility
improvement programs in every city and districtin Indonesia
3. Designing the implementation of ATMS in
Indonesia systematically, standardization andintegration so that the implementation andmanagement of transportation in Indonesia.
4. Building a culture of orderly traffic and the useof public transportation in Indonesian societyJava is an island in Indonesia that is the center of
the Indonesian economy. The development of the
Economic Corridor on the island of Java is criticalbecause the economy on the island of Java has thepotential to develop various types of products. The
connecting road on the island of Java is divided intoa south lane and a north lane (Pantura). With socialand economic development, the number of vehicles
crossing the north lane has increased rapidly. In
contrast, road capacity has remained so that the levelof road service has gradually decreased [1].
Transportation problems in the form of congestion,
delays, and queues are heterogeneous traffic
problems [2]. The problems that arise are theincreasing traffic volume during rush hour andconflicts between motorized and motorized vehicles[3].
The high growth of motorized vehicles,
especially motorcycles in big cities, around 21% per
year, has increased traffic flow on the roads.
However, this increase was not followed by anincrease in road capacity. In addition, landtransportation is often faced with the problem of
decreasing the quality and sustainability of landtransportation infrastructure services (traffic
congestion, high accident rates, pollution, energy
wastage, and inadequate modes of transportation).
Not to mention the added damage to infrastructure,
which is not only caused by overload. Infrastructure
and logistics have become even more important in
518

GEOMATE – Bangkok, Thailand, 22-24 November 2022

The following is an application of ATMS
technology:
1. Advanced Traffic Light System
Smart APILL that is integrated and coordinatedacross intersections and adaptive.
2. Traffic Data Collection
Traffic flow and volume data are stored in a
database and can be retrieved at any time foranalysis and predictive models for better policy
implementation.
3. Traffic Data Dissemination
4. Integrated Traffic Information System
5. Integrated Mobility
6. Integrated Punishment System
It is integrated with an online ticketing system orother systems.
The other problem is traffic accidents whichsometimes involve a truck. Figure 1 shows accident
rate data in Indonesia from 2017 to 2021

are avoided, policies that are shifting, and policies
that are providing new things (improve). The three
types of policies are combined to find the
combination of policies (scenarios) that best suit thescientific priorities obtained in the analyticalhierarchy process method.
The scientific priorities used in this research aretransportation, economic, social, and environmental
systems. Priority determination is obtained based on
the opinion of expert sources related totransportation and road infrastructure. The resource
persons in this study were consultants, businessdevelopment
managers,
andcompetent
academicians related to land transportation policies.
3. METHODSThis research uses Analytic Hierarchy Process(AHP) for scenario decisions. AHP is a general
theory of measurement used to derive ratio scales
from discrete and continuous pairwise comparisons.
AHP is concerned with deviations from consistency,
i.e., measurement and dependencies within andbetween groups of structural elements. It has foundits most comprehensive application in multi-criteria
decision making, resource planning and allocation,and conflict resolution [6].
There are some ideas and principal to using the
AHP method:a. Hierarchical Structuresb. Criteria and Alternative Assessmentc. Prioritizationd. Logical Consistency
The AHP method is used in this study because it
can parse problems with multi-factor and multicriteria. AHP can be used to determine the best
scenario according to the prioritized criteria based on
AHP. To determine the score of each criterion using
pairwise comparisons with the determination of thescoring criteria as follows at Table 1.

Total Incidents on the Road
1500
1000
500
0

2017

2018

2019

Front and Back Hit
Fig. 1

2020

2021

Total Accident

Total Incident on the Road

Based on Fig. 1, it is known that accidents inIndonesia from 2017 to 2021 have decreased
successively. We can conclude, besides the effect of
the Covid pandemic, that the ATMS program has
also made good progress.
So in this study, we will discuss the formulationof public transportation logistics policies using themethod of the Analytical Hierarchy Process to assess
the best policy scenario by comparing several
optimizations carried out by previous studies toassist in making policy prototypes. The resulting
policy must be in accordance with the conditions andis not required to benefit all parties. So with theanalytical hierarchy process method can find the bestpolicy scenario in transportation optimization.

Table 1

Pairwise Comparison Scale

Level of
Importance
1
3
5
7

2. RESEARCH SIGNIFICANCE

9

The significance of this research is that thepolicies are expected to increase the optimization ofland transportation so that unwanted things do nothappen during the delivery of goods. Policies madeusing the analytical hierarchical process method
produce three types of policy properties: policies that

2,4,6,8Definition
Both elements are equallyimportant
One element is slightly more
important than the others
One element is more important
than other elements
One element is clearly more
important than the other elements
One element is absolutely more
important than the other elements
The middle value between two
adjoining ratings

Source: Saaty, 1987

Fig. 2 shows the general hierarchy structure of519

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Policy Scenario.
PolicyTransportation
SystemEconomySocial- Accessibility- Reliability- Flexibility- Productivity- Load Efficiency- Cost Efficiency- K4- Affordability- Justice

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Fig. 2 General Hierarchy Structure of Policy Scenario.
Below is an analysis step using the AHP method:

5. Determining the priority vector of factors fromthe average aggregated judgment matrix, as
shown in Eq. (6):𝑤11 + 𝑤12 + 𝑤13 ⁄3
𝑓1
𝐹𝑠 = [𝑤21 + 𝑤22 + 𝑤23 ⁄3] = [𝑓2 ](6)
𝑓3
𝑤31 + 𝑤32 + 𝑤33 ⁄3 𝑠

3.1 Determining Local Priority Weight of The
Three Factors
This procedure was adopted from the Saaty
method, which was modified to fit the context of thisstudy. The steps for the three individual factors are
as follows:
1. Determining initial individual priority vectors.
With three segments, which are threesubsegments per segment. Priority vector Vs wasgotten from average normalized original
𝛼𝑖𝑗
judgment ( ) matrix as shown in Eq. (1).
𝛼11
𝑠1
𝛼11

+

𝑠𝑗
𝛼12

𝛼

𝑠2
𝛼12

𝑠3
𝛼13

3.2 Determining Local Priority
Scenario Per Factor

𝐶𝑅𝑆 =

+

of

It has the same procedure in section 3.1, but allpriority weights were consolidated in a 3x4 matrix
Ts, where the row represents each factor and thecolumn for each scenario.

+ 13⁄3𝑉1⁄3 = [𝑉2 ]
(1)
𝑠1
𝑠2
𝑠3
𝑉3
𝛼11
𝛼12
𝛼13
[ 𝑠1 + 𝑠2 + 𝑠3 ⁄3]
2. Calculating consistency of judgment matrix per
respondent with Consistency Ratio per segment(CRs) as shown in Eq. (2) and Eq. (3):𝑉𝑆 =Weights+(𝐶𝑠 −𝑛2 )3.3 Determining Global Priority Weights of
Scenario
From dot product from priority vector of factors
and local priority weights of scenario per factor as
shown at Eq. (7) and (8):(2)

𝑛(𝑛−1)𝑅𝐼

𝜞𝒔 = [𝑭𝑺 ]𝑻 𝑻𝑺 = [𝒇𝟏𝛼11 𝛼12 𝛼13 𝑉1
𝐶𝑆 = [𝛼21 𝛼22 𝛼23 ] [𝑉2 ](3)𝛼31 𝛼32 𝛼33 𝑠 𝑉3 𝑠
Random Index (RI) with n=3 is 0.58.
3. Generation of new judgment matrix labeled Ms,
shown in Eq. (4).
𝐴𝑠 ,𝐶𝑅 ≤ 0.10
𝑀𝑠 = {(4)
2 𝑇
𝑉𝑠° ( ) , 𝐶𝑅 > 0.10𝜸𝟏𝜞𝒔 = [ ⋮ ]
𝜸𝟒

⋯ 𝒕𝟏𝟒
⋱
⋮ ]
⋯ 𝒕𝟑𝟒

(7)
(8)

The sum of rankings from each segment wascalculated to obtain the overall ranking for bothfactors and the scenario. The lowest sum denoted thehighest rank, while the highest sum was given thelowest rank.

4. Determining priority vector per subsegment, asaggregated judgment matrix was based on Eq.
(5):
𝑟

𝒕𝟏𝟏
𝒇𝟑 ] [ ⋮𝒕𝟑𝟏

3.4 Determining Overall Ranking of Factors andScenario

𝑉𝑠

𝑊𝑠 = ( √∏𝑟1(𝑚𝑖𝑗 )𝑠 )

𝒇𝟐

(5)

3.5 ASI (Avoid, Shift, and Improve) Policy

Where r is the total respondent.
520

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Providing additional road space may not solve
the pressing problems of high congestion levels,
declining air quality, and increasing greenhouse gas(GHG) emissions in cities. As a result, cities are
becoming less livable, and mobility is far from being
human-centered. Therefore, what is needed is afundamental rethinking of the paradigms that guidemobility and urban planning. Inspired bysustainability principles, our alternative approach
focuses on the mobility needs of people rather thancar infrastructure. The approach, known as A-S-I(from Avoid/Reduce, Shift/Maintain, Improve),seeks to achieve significant GHG emission
reductions, reduced energy consumption, andreduced congestion, with the ultimate goal of
creating more livable cities.
Firstly, "avoid/reduce" refers to the need to
improve the efficiency of the transport system as awhole. The need for motorized travel and the trip
length. Second, the "shift/maintain" instrument
seeks to improve individual travel efficiency. Third,the "improve" pillar focuses on vehicle and fuelefficiency and optimizing public transport's
operational efficiency. Introducing renewableenergy sources into the transportation sector should
be a fundamental principle of motorizedtransportation [7].

the Economic factor was the most influential. In
contrast, a Lecturer stated that the most importantfactor in formulating policies was the Social factor.
The following is the weighting for each criterion.
Transportation System
1
0.8
0.6
0.4
0.2
0
BD

Consultant

Lecturer

Reliability

Accessibility

Flexibility

Fig. 4 Transportation System Criteria Weighting for
Each Expert
Economy1
0.5

4. RESULT0
BD

All inconsistency values from the results of the
AHP analysis shown in this study are less than 0.1.
That is, all the answers to each question on thequestionnaire proved consistent. The following are
the criteria obtained from 3 respondents as follows.

Consultant

Cost Efficiency

Lecturer

Productivity

Load Efficiency
Fig. 5 Economy Criteria Weighting for Each Expert

PolicySocial

1
1

0.8
0.6

0.5

0.4
0.2

0

0
BD

Consultant
Lecturer
Transportation System
EconomySocialFig. 3 Policy Criteria Weighting for Each Expert

BD

Consultant

Justice

K4

Lecturer

Affordability

Fig. 6 Social Criteria Weighting for Each Expert
Fig. (4), (5), and (6) show that there appears to
be a diversity of perceptions of experts regarding theimportance of each sub-criteria in each policy factor.
Each expert assigns a different weight to the mostimportant sub-criteria for each policy criterion. The
following statistics were obtained based on the
combination of the three experts.

From Fig. 3, each expert has a different
perception of the importance of the policy criteria forthe transfer of goods transportation modes by land.
A BD Manager stated that the Transportation System
was the most important, and a Consultant stated that521

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 7 Policy Criteria Combination Weighting

Fig. 8 Transportation System Criteria Combination Weighting (X1)

Fig. 9 Economy Criteria Combination Weighting (X2)

Fig. 10 Social Criteria Combination Weighting (X3)
From Fig. 7, It was found that the Transportation
System is a priority factor for formulating policies
for transferring modes of transportation of goods byland, with an importance weight of almost 50%. The
following is the combined weighting for eachcriterion. From Fig. 8, the Accessibility sub-criteria
is the most important, although there is a slightdifference in Reliability. From Fig. 9, theproductivity sub-criteria is the most important.
Meanwhile, from Fig.10, the Security, Safety,

Health, and Sustainability (labeled by K4) subcriteria is the most important. It should be noted that
this does not mean that the other sub-criteria are notimportant because the nine sub-factors remain areference in determining policy. The four scenarios
based on the preparation of the A-S-I strategy willconsider all the factors involved with the weightingof each sub-criteria at Table 2.

Table 2. Policies Implemented for Each Scenario

1
2
3

4

5
6
7

Policies
Increased tariffs for freight trucks arriving at theirdestination on different days
Reducing the cost of shipping goods by rail with ajourney of more than 600 km
Reducing the cost of shipping goods that fall into thecategory of perishable goods by train with a journeyof more than 200 km
Implement punishment regulations in the form offines for companies that often travel, as in points 14
Provision of subsidies for switching modes to trains
Promoting driver competency check procedures andcompleteness of vehicle facilities
Promoting the use of information technology at
ATMS in order to use information more effectively
and efficiently

Scenario I

Scenario II✓✓

Scenario III

Scenario IV

✓

✓

✓
✓

✓
✓

✓

✓
✓

522

✓

GEOMATE – Bangkok, Thailand, 22-24 November 2022

The following is the weighting for each
respondent's policy scenario.

produce different weights for each respondent.
However, in the weighting of the scenarios, it is
known that there are two respondents with the
majority of the weighting who tend to agree withscenario 1. So that scenario one is chosen for thepolicy prototype, it is clarified by the weighting ofeach scenario based on the three factors, which show
scenario 1 has the highest weight of the three otherscenarios.

Preferred Scenario
1
0.5
0
BD

Consultant
12

3

6. ACKNOWLEDGMENTS

Lecturer

We thank all those who participated in thecompletion of this research. Starting from the three
respondents whose opinions were very helpful for us
in completing this research, we learned many related
policies that must meet various scientific aspects aswell as supervisors who always provide direction tocomplete this research.

4

Fig. 11 Scenario Weighting Every Respondent
The priority weighting of the scenarios in Fig.
(11) is carried out by considering the nine subfactor
weights in Fig. (4), (5), and (6). Based on the resultsof the AHP analysis, a BD Manager and Lecturer
considered that Scenario 1 was the most importantand best scenario to be implemented immediately. A
Consultant indicated that Scenarios 1 and 4 wereequally important. Combining all the existingassessments will show a weighting pattern for eachscenario based on the three existing factors.

7. REFERENCE[1] Qu, Z., Xing, Y., Hu, H., Song, X., & Duan, Y.
(2012). Study on the relationship between urban
road network service level and capacity. The
Twelfth COTA International Conference ofTransportation
Professionals.
Beijing:
American Society of Civil Engineers.
[2] Lubis, A., Muis, Z., & Nasution, T. (2016).
RelationshipofTraffic
Characteristics
Parameters on the Belmera Toll Road. CIVIL
ENGINEERING COMMUNICATION MEDIA,
DOI:10.14710/MKTS.V22I2.12878.
[3]Harahap, E., Suryadi, A., Ridwan, R., Darmawan,
D., & Cecha, R. (2017). The Effectiveness of
Load Balancing in Overcoming Traffic
Congestion. Journal of Theoretical And Applied
Mathematics, DOI: 10.29313/ JMTM. V16I2.
3665.
[4] Rivera, A. (2020). The impact of COVID-19 ontransport and logistics connectivity in thelandlocked countries of South America.
Santiago:
UnitedNations,
Economic
Commission for Latin America and the
Caribbean (ECLAC).
[5] Mulyana, C. (2021). Law Enforcement in ODOLEnforcement and Arterial Traffic Management
System. Jakarta: Ministry of Transportation of
the Republic of Indonesia.
[6] Saaty, R. (1987). The Analytic Hierarchy
Process – What It Is and How It Is Used.
Pittsburgh: Pergamon Journal Ltd.
[7] Bongardt, D., Stiller, L., Swart, A., & Wagner,
A. (2019, March). Sustainable Urban Transport:
Avoid-Shift-Improve (A-S-I). iNUA #9:
Implementing the New Urban Agenda.

Fig. 12 Weighting of Each Scenario Based on The
Three Factors
In the pattern shown in Fig. 12, all existing
factors support that Scenario 1 is a priority Scenariothat needs to be implemented immediately. The
other scenarios still need to be reviewed because, in
addition to their overall factor weights having avalue below Scenario 1, these three scenarios are still
complicated when viewed from economic factors
versus other factors.
5. CONCLUSION
Based on the weighting of the policy criteria, thethree respondents produced different opinions. The
reason could be due to the interests and knowledgeof each respondent, as well as different goals as well.
It can be seen based on the weighting for each subsegment of each policy criterion which tends to523

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

SMART, INTEGRATED SUSTAINABLE, AND ENVIRONMENTFRIENDLY TRANSPORTATION INFRASTRUCTURE
CONNECTIVITY TO THE CAPITAL CITY OF NUSANTARA
Achmad Wicaksono1, Rosa Agustaniah2 and Ludfi Djakfar3
1,2,3

Department of Civil Engineering, Universitas Brawijaya, Indonesia
ABSTRACTThe President of the Republic of Indonesia Joko Widodo issued a presidential decree regarding the stipulationof moving the capital city of Indonesia from Jakarta to North Penajam Paser and Kutai Kertanegara Regencies,
East Kalimantan. The new state capital with the name Nusantara is stated in Law Number 3 of 2022 concerning
the State Capital. The total area of the Capital of the Archipelago is planned at 256,142.74 hectares with a core
city area of 56,180.87 hectares and a government center of 5,644 hectares. The transportation system of aconnected road network to the Capital of the Archipelago and between supporting cities includes aspects ofadaptation to integrated land use and preserving the ecosystem functions of watersheds. Good environmental
quality with the concept of smart transportation infrastructure planning integrated sustainable and environmentallyfriendly with the application of a forest city to reduce the environmental footprint with 50 percent remaining green
open space.
Keywords: infrastructure, sustainable transportation, environment-friendly, road network

INTRODUCTION

State Capital in North Penajam Paser and Kutai
Kertanegara Regencies, East Kalimantan is [14]; 1)
strategic location, located in the central region ofIndonesia; 2) location free from earthquake andtsunami disaster; 3) availability of state-owned land;4) land slope and soil bearing capacity; 5) availabilityof water resources; 6) a location free from floods,
forest, and land fires; 7) close to existing anddeveloping cities; 8) socio-cultural carrying capacity,
especially openness to immigrants; 9) meet thedefense and security parameters.
The existence of a large-scale activity, namely therelocation of the capital city of Indonesia, to the
Regencies of North Penajam Paser and Kutai
Kertanegara, East Kalimantan, will have anenvironmental impact. The policy issue fordeveloping transportation systems now and in the
future is how each country plays its role in theframework of a sustainable transportation system [3].
This discourse originated from concerns about theinteraction between transportation and theenvironment. The realization that the quality of theenvironment has been greatly affected bytransportation activities, which continues toaccumulate over time, has aroused the attention ofmany people to the "mistakes" that have beenpracticed so far in policy making and planning. The
practice of managing transportation infrastructure on
the one hand and the needs of the community to carryout their activities, on the other hand, cannot be
continued as before, but needs to be observed with adifferent "glasses". The costs that must be borne by
the community in traveling are not only out-of-pocket
costs but also the impact on the environment. The

The Government of Indonesia through President
Joko Widodo on 26 August 2019 officiallyannounced the move of the new State Capital, from
Jakarta to Penajam Paser Utara and Kutai
Kertanegara Regencies, East Kalimantan (Fig. 1).
The planned area of the State Capital is 256,142.74
ha, with a core city area of 56,180.87 ha and agovernment center of 5,644 ha [14].

Fig. 1

Location of the State Capital in the
Regencies of North Penajam Paser and Kutai
Kertanegara, East Kalimantan.
Several reasons for choosing the location of the524

GEOMATE – Bangkok, Thailand, 22-24 November 2022

idea of developing sustainable transportation is an
essential part of the problem of sustainable
development. Experience in various countries showsthat continuing to build the required infrastructure is
not always the best solution. Every transportation
infrastructure development brings environmental
impacts, but the region has a certain environmental
capacity limit to accept the impacts that arise. In
addition, the construction of a road network, inparticular, which only follows the demands of theneeds tends to encourage an increase in the use of
inefficient private vehicles. The growth oftransportation needs (demand) needs to be controlled
so that it is balanced with the ability of network
supply and environmental constraints [17].
This paper discusses transportation issues related
to transportation system development policies andstrategies, sustainability development issues that need
to be considered, especially those related totransportation infrastructure development, as well as
proposed strategic steps to overcome transportation
problems in the future.

a manner consistent with human health andecosystems, and with equity within and between
generations; 2) affordable, operate efficiently,
provide a choice of transportation modes, and support
economic development; 3) limiting emissions andwastes that are still within the earth's ability to absorb
them, minimizing consumption of non-renewablesources, using and recycling their components, andminimizing land use and noise production.
The issue of sustainable development is an issue
that every country is required to focus on this global
agenda. Concerning transportation issues, the issue ofsustainability is a logical consequence that is very
directly related, because regional development andland use are fundamentally influenced bytransportation networks. The evolution of thedevelopment of transportation systems provides thebasic shape for land use characteristics, although theprocess is influenced by economic, social, andpolitical considerations. Therefore, the policies
implemented in managing the development of thetransportation system become a central part of thecontext of sustainable development [3].

LITERATURE REVIEWMETHODOLOGY
Put forward that the concept of sustainable
development is trying to simultaneously realize themost basic needs, namely [9] (Fig.2); 1) the need for
economic development to overcome poverty; 2) theneed for environmental protection for air, water, soil,
and biodiversity; 3) the need for social justice andcultural diversity to enable local communities toconvey their values in solving these issues.

The research method is a scientific procedure or
method to obtain data with a specific purpose [16] itis a scientific feature to obtain data with a specificpurpose and use.
The research method on the concept of smart,integrated sustainable, and environment-friendly
transportation infrastructure planning in the newcapital city area uses the synthesis method, which is acomplete article on summaries from various reference
sources regarding understanding or opinions. The
summary is compiled into a new article that contains
a unit that is following the needs of the researcher.
This presentation is obtained from various reference
sources used by researchers in compiling a scientificwork. The results of the synthesis can be in the formof data, facts, information, or new main ideas [13].
RESULTS AND DISCUSSION

Fig. 2No State Capital has yet been moved from island
to island, except in Indonesia. This is the first time
because almost all of the world's eyes are on
Indonesia. The vision of the State Capital 'Smart,
Green, Beautiful, and Sustainable' is translated
through the development of a city that is side by sidewith nature through the concept of a forest city and asmart and intelligent city.
The determination of the area of the State Capitalarea takes into account One River One Management,
upstream-downstream integration, and the characterof the Watershed, as well as the boundary of the Bukit
Soeharto Grand Forest Park (Tahura) which will be
developed and managed in an integrated manner intoThree development processes at the local
level and the concept of sustainable
development

More specifically for the transportation sector, aresearch institute based in Canada that focuses on theissue of sustainable transportation [3], formulates adefinition that sustainable transportation is a systemthat; 1) enables the very basic access needs ofindividuals and communities to be met safely and in525

GEOMATE – Bangkok, Thailand, 22-24 November 2022

a buffer area as well as a biodiversity conservation
area ( Fig. 3). Infrastructure development includingtransportation must be able [15]; 1) maintain andprotect catchment areas and water management
regulators and their land cover in the form of theforest; 2) maintain and protect the karst area aroundthe State Capital area; 3) maintain and protect buffer
and riparian areas in the catchment areas and water
sources (rivers, lakes and sea coasts); 4) support theimplementation of rehabilitation, revegetation, andenrichment of green landscapes to improve thefunction of watersheds; 5) ensure that water
consumption is carried out very efficiently; 6)maintain and protect the quality of surface water andgroundwater.
Fig. 3

regulations; 10) environment-based budgeting; 11)environmental-based risk analysis; 12) environmental
audit; 13) other instruments as needed.
Roadmap Towards a Sustainable Transportation
SystemThe development of sustainable transportation
infrastructure is a comprehensive effort from various
sectoral dimensions, regions, the involvement ofactors, and their substance. (Fig. 4) Presents a
proposed strategic step towards structuring asustainable transport system. The development oftransportation infrastructure is an integral part ofevery element of the realization of the necessary steps
because this will greatly determine the efficiency andeffectiveness of the utilization of the existing system.

Map of the watershed boundaries of the

National Capital of the Archipelago

Arrangements concerning aspects of technology,
regulation, and user behavior need to be given
priority. Implementation strategies need to be
formulated to achieve more sustainable conditions interms of operations, availability of moreenvironmentally friendly systems, and use ofresources.

Instruments for Environmental Protection andManagement (Environmental Safeguards)
The application of environmental protection
instruments (Environmental Safeguard) developmentof smart, integrated sustainable & environmentfriendly transportation infrastructure planning
concepts in the National Capital area to realize
sustainable
andenvironmentallyfriendlytransportation infrastructure is carried out based on
economic democracy with sustainable principles,with environmental insight. Various instruments ofenvironmental protection and management must be
implemented in an integrated manner based on; 1)
strategic environmental studies; 2) spatial planning;3) environmental quality standards; 4) standard
criteria for environmental damage; 5) environmental
impact analysis; 6) environmental management
efforts and environmental monitoring efforts; 7)
licensing; 8) environmental economic instruments; 9)environmental protection and management laws andRecommendations for developing the concept ofsmart, integrated sustainable, and environmentfriendly transportation infrastructure planninga.

Recommendation 1

Transport Infrastructure Development Should
Consider a structured green space network (Fig. 5).
To increase the carrying capacity, a network ofprotected spaces and areas that need careful treatment
are needed that are connected to environmentallyfriendly corridors [15].

526GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 4

Roadmap
towardstransportation systemasustainable

Key wildlife habitat areas and home ranges must
be protected, one of which is to be designated as
primary green space managed by an approach (Fig.
8); 1) avoid clusters: areas/clusters that need to be
avoided from physical development; 2) minimize
cluster: this area/cluster can be physically developedby minimizing the impact (mitigation) on wildlife(green infrastructures); 3) restore cluster is an
area/cluster that needs to be restored as wildlife
habitat; 4) canopy bridges are indispensable forarboreal animals (a.l. gibbons, orangutans, civets).
this solution is very vital for conservation areas thathave arboreal species whose habitats are cut off byroads; 5) arboreal species don't like going down to theforest floor, so their movement is limited from tree totree; 6) underpass-type crossings can be in the formof bridges, flyovers or box culvert tunnels [15].

The primary green space network has a protection
function; 1) conservation forest area (Tahura); 2)
protected forest areas; 3) home range and main
habitat for key animals. Secondary green space
network that has a buffer and aesthetic function; 1)ecoriparian and coastal areas; 2) natural tourism
areas; 3) botanical gardens and nurseries/nursery; 4)areas of high conservation value and carbon pools; 5)
areas for enrichment/revitalization of a tropical rain
forest ecosystem. A water-based open space network(blue space) functions to maintain water management
and prevent landslides and floods; rivers, lakes,
ponds, reservoirs, bays, and other water storage
infrastructure..
b.

Recommendation 2

Table 1. Existing flora and fauna
flora
fauna
▪ Meranti (Shorea spp.)▪ Sambar Deer
▪ Tengkawang (Shorea▪ Proboscis monkey
macrophylla)▪ Enggang Gading▪ Keruing (Dipterocarpus ▪ Orangutans
retusus)▪ Pesut Mahakam▪ Api-api (Avicennia sp.) ▪ Dugong▪ Kijang Kuning▪ Estuary Crocodile▪ Beruang Madu
▪ Babi Berjanggut▪ Merak Kerdil
Kalimantan▪ Kera Ekor Panjang▪ Macan Dahan▪ Perepat (Sonneratia sp.)
▪ Mangroves(Rhizophora)▪ Black Orchid(Coelogyne pandurate)

Development of Transportation Infrastructure in
the National Capital Region must be able to ensure
the creation of good air quality and cool average airtemperature [15] (Fig. 6)
c. Recommendation 3
The development of Transportation Infrastructurein the State Capital area must be able to protect
wildlife habitats. Key wildlife habitats and homeranges should be protected by designating primary
green spaces [15] (Fig. 7).
Green fingers are used as a green infrastructure tobalance the city along with the growth of the city
itself. Green fingers are also used as corridors forgreen mobility, habitat spaces, recreation areas, urban
forests, water run-off management, urban agriculture,etc. Green Fingers also function as habitat corridors,
where existing flora and fauna can still find theirnatural path of movement from one ecosystem toanother [15] (Table 1).
527

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 5Thedevelopmentoftransportation
infrastructure must consider a structured
green space network.

Fig. 6Thedevelopmentoftransportation
infrastructure in the national capital area
must be able to guarantee the creation ofgood air quality and cool average airtemperature

528

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 7Thedevelopmentoftransportation
infrastructure in the national capital area
must be able to protect wildlife habitats.

d. Recommendation 4
The development of transportation infrastructurein the national capital region must be integrated withthe application of smart city elements forenvironmental management [15] (Fig. 9)
e. Recommendation 5
The development of transportation infrastructurein the state capital area must be in line with efforts tocontrol the physical expansion of satellite areas
around the state capital area. The dominance of forest
area space that has high environmental services,
growth centers around the national capital must be
controlled for physical expansion of its growth so as
not to trigger deforestation, land conversion toactivities that encourage damage and potentiallymarginalize communities who depend on forest
sustainability [15] (Fig. 10).

Fig. 8Thedevelopmentoftransportation
infrastructure in the national capital area
must be able to protect wildlife habitats.

529

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 9The
developmentoftransportation
infrastructure in the national capital area
must be integrated with the application ofsmart city elements for environmental
management

g. Recommendation 7
Development of Smart, integrated and sustainable
Transportation Infrastructure in the national capitalregion with the following planning concepts [15](Fig. 12); 1) The concept of a compact city thatminimizes travel; 2) Making public transportation themain choice; 3) Accessible for all community groups;
4) Encourage people to walk and cycle, with people
friendly facilities; 5) Urban transportation isregulated by intelligent transport system; 6)
Separation between the flow of pedestrians andunmotorized vehicles with motorized vehicles; 7) An
integrated transport node between modes oftransportation and land use (Transit Oriented
Development) [1], [7]; 8) Radial corridor entering thecenter of government with Mass Rapid Transit
underground, with a circular line combined
underground and at grade.
h. Recommendation 8
The proposal for a smart, integrated sustainable &environment-friendly transportation infrastructure
planning concept for the nation's capital city is asfollows;
1. Traffic management system & public transport
services (Fig. 13). It is integrated traffic
management to ensure efficient and safe drivingby presenting [18]; 1) smooth traffic flow; 2)
improve the efficient transportation system; 3)
intelligent mobility; 4) reduce travel time; 5)
improve safety; 6) public information; 7)
supporting facilities for transferring charging
stations to the network (activity centers &transportation nodes).

Fig. 10 The
developmentoftransportation
infrastructure in the state capital area must
be in line with efforts to control the physicalexpansion of satellite areas around the state
capital area.
f. Recommendation 6
The development of transportation infrastructurein the national capital area must be in line with therefinement of the Island and Provincial Spatial Plans(RTRW). In addition to the regional spatial plan
(RTRW) which will specifically regulate the nation's
capital city, the Kalimantan Island regional spatialplan (RTRW) and the provincial spatial plan (RTRW)
need to be refined to support efforts to maintain thecontinuity of the world's lungs and control physicalexpansion. which has the potential to cause
deforestation [15] (Fig. 11).
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Supported by reliable transportation (Light RailTransit, toll roads, and bridges) connecting
between airports, and from the airport to thenation's capital city; 3) an integrated intermodaltransportation system that connects airports byland, sea, and rail 4) airport development carries
the aerotropolis concept that is smart, integrated
and pays attention to environmental ethics.

Fig. 11 DevelopmentofTransportation
Infrastructure in the State Capital area must
be in line with the refinement of the Island
and Provincial Spatial Planning (RTRW)CONCLUSIONS
a. Policies in dealing with transportation problems
in the State Capital area need to be approachedboth from the supply side and from the demandside. There is no "panacea" that with one
particular action will be able to solve alltransportation problems, but requires integrated
and sustainable actions. Transportation Demand
Management is a practice that needs to be pursued
more intensively to optimize resource utilization.
b. Methods that are proven to be effective indeveloped countries may not necessarily give thesame results if applied in the State Capital region,
given the different conditions of society,transportation systems, regions, and institutional
readiness. Therefore, the implementation ofpolicies taken in Indonesia needs to be adjusted tothe existing conditions.
c. Issues regarding sustainable development andespecially sustainable transportation have become
global issues for which each country is required todemonstrate its responsibilities following local
problems and needs. Anticipatory measures are
realized through the necessary preparations in theinstitutional, socio-cultural, regulatory, and law
enforcement aspects, as well as human resource
development, all of which are prepared through an
integrated planning framework.

Fig. 12 The
developmentoftransportation
infrastructure in the national capital area
must be in line with the refinement of theIsland and Provincial Spatial Planning(RTRW)
2. The concept of future transportation in the nation's
capital, as follows [18] (Fig. 14); 1) the farthest
airport distance from the city center (the national
capital can be reached in no more than 2 hours
with the concept of a multi-airport system
between Sultan Aji Muhammad Sulaiman
Sepinggan International Airport (SAMS)
Balikpapan, APT. Pranoto Samarinda and Very
Very Important Person (VVIP) Airport; 2)531

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 13Developmentoftransportation
infrastructure for traffic management
systems and public transport services

Fig. 14 The concept of future transportation in thenation's capital

532

GEOMATE – Bangkok, Thailand, 22-24 November 2022

REFERENCES

mobility. IATSS Research, 1998.
[11] Ministerial Regulation of ATR/BPN No. 16 of2017 concerning Guidelines
for theDevelopment of Transit Oriented Areas[12] Rudi Azis, S. T.; Asrul, S. T. Introduction toTransportation
SystemsandPlanning.
Depublish, 2014.
[13] Samin, Annisa Novianti; Chairul, Chairul;
Mukhtar, Erizal. Analysis of coastal vegetationin the Pasir Jambak tourist area, Padang City.
Biocelebes, 2016, 10.2.
[14] The State Capital Infrastructure Development
Planning Task Force of the Ministry of Public
Works and Public Housing and the Planning
Team (2021) Urban Design Development KIPP
[15] Sigit, Hardwinarto, Development of integrated
and environmentally friendly Transportation
Infrastructure in the State Capital area, Director
GeneralofForestry
Planning
andEnvironmental Management, Ministry ofEnvironment and Forestry 2019.
[16] Sugiyono, P. D. Business research methods:
quantitative, qualitative, combined, and R&D
approaches. CV Publisher. Alphabeta: Bandung,
2017, 225.
[17] Syafruddin,
A.
D.
DevelopmentofTransportation Infrastructure to Support
Science-Based Sustainable Development.
Unpublished papers. Faculty of Civil andEnvironmental Engineering. ITB, 2013.
[18] The Secretariat Team of the Balitbang Task
Force, Weaving the Connectivity of the State
Capital Smart And Eco-Friendly Transportation
System, Ministry of Transportation-Republic ofIndonesia, 2019.

[1] Ayuningtias, Sekar Hapsari; Karmilah, Mila.
Application of Transit Oriented Development(TOD) as an Effort to Realize Sustainable
Transportation. Foundation, 2019, 24.1:45-66.
[2] Calthorpe, Peter. The next American
metropolis: Ecology, community, and theAmerican dream. Princeton architectural press,
1993.
[3] Center For Sustainable Transportation.
Definition and vision of sustainabletransportation. 1997.
[4] Cervero, Robert. Transit-oriented developmentin the United States: Experiences, challenges,
and prospects. 2004.
[5] Dikun, S. Main Thoughts on Urban Transport
Policy Direction, MTI Seminar. Transport, 2000.
[6] Sadly, Noer, et al. Development ofTransportation Infrastructure and Spatial
Structure with the Concept of Transit Oriented
Development in Banda Aceh City. 2018.
[7] Huang, Runjie, et al. "Measuring transitoriented
development(TOD)
networkcomplementarity based on TOD nodetypology." Journal of transport and land use 11.1
(2018): 305-324.
[8] Isnaeni, Muhammad; Lubis, H. A. R.
Environmental Effects of Interaction ofTransport and Urban Spatial Planning. Bandung
Institute of Technology, 2003.
[9] Newman,
Peter;
Kenworthy,
Jeffrey.
Sustainability
andcities:overcoming
automobile dependence. Island Press, 1999.
[10] Ohta, Katsutoshi. TDM measures sustainable533

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

EVALUATING IMPACTS OF OVER-DIMENSION AND
OVERLOADING TRUCKS (Case Study in Arterial Roads)
Achmad Wicaksono1, Meriana Wahyu Nugroho2
1,2

Faculty of Engineering Department of Civil Engineering, Brawijaya University, Indonesia; 3 Faculty ofEngineering, Hasyim Asy’ari University, Indonesia
ABSTRACTThe community increasingly needs logistics delivery services; therefore, trucks have an essential economicrole. This situation has caused the volume of truck traffic also to increase. Trucks were the most significant
contributor to accidents, some of which were caused by over-dimension and overloading trucks. The condition oftrucks with loads that exceed the limit caused the speed of the trucks to decrease and caused congestion so that
traffic flow became obstructed. Oversized and overloading trucks would block the view of drivers of othervehicles, thereby impairing visibility while driving. Collecting data in this study used a questionnaire method witha total of 194 respondents using a proportionate stratified random. Accident factors consisted of environmental
factors, drivers, vehicles, and roads with analysis using a multinomial logistic regression method. The multinomial
logistic regression analysis model used the backward stepwise method to obtain significant variables for accidents;
then, significant variables were tested to get a suitable model. This study determined the factors that caused overdimension and overloading truck accidents and produced a truck accident model on the Jombang arterial road. The
modeling results of the factors causing the accident indicated that the significant variables for the occurrence ofaccidents are road surface conditions, traffic flow volume, accident history, vehicle load, vehicle size/dimensions,and speed limit signs. This research is expected to be a solution in determining transportation policies andproviding recommendations for better and safer truck regulations.
Keywords: Accident, Driver Socio-Economic, Over-dimension and Overloading, Regression Logistic Multinomial
1. INTRODUCTION

the losses caused by truck accidents to fatal accidents
were carried out by identifying risk factors and theirimpact on fatal accidents. The risk factors influencing
the severity of an accident are so diverse andcomplex, as well as interact in complex ways;
therefore, it is difficult to define the relationshipamong variables; for example, wet surface conditions
are often seen during bad weather, and sharp turns, aswell as steep slopes, often coincide along roads.
Obtaining relevant risk factors and capturing theinteractions among these variables from acomprehensive perspective would improve theunderstanding of accident mechanisms [5].
ODOL Truck Accidents were the most significant
contributor to the negative impact on logistics
delivery. The negative implications arise when an
ODOL Truck with excess dimensions and loads. The
ODOL Truck’s condition with a load exceeding thelimit caused the truck’s speed to decrease and caused
congestion, so traffic flow became obstructed. Overdimension and overloading trucks would block thevision of other vehicle drivers, thereby interferingwith driving distance. The problem of ODOL trucks
for logistics delivery is important and requires
handling by predicting and classifying accident
fatalities and their causes. This study aimed to
discover the characteristics and factors causing
accidents involving transport truck vehicles in EastJava Province.

The rapid development of transportation will
indirectly increase the risk of the growth of traffic
problems, including congestion and accidents. Traffic
accidents according to Law of RI Number 22 of 2009
concerning Road Traffic and Transportation, namely
an incident on a road that is unexpected andunintentional involving a vehicle with or without
other road users resulting in human casualties and/or
property loss [1].
The province of East Java is located on the islandof Java, which has the largest population among theIndonesian islands. The province has 38 city andregency administrative areas. For several years, thenumber of traffic accidents has been the highest inIndonesia, but efforts to minimize the potential for
accidents are still lacking. The difficulty has been
increased not only by the behavior of the truck drivers
but also because of some poor road geometry, poorroad lighting, and the limited number of rest areas fordrivers to rest [2]. Transporting goods by truck is themost popular because it has load flexibility and a longcruising range. So transportation is very dependent on
the behavior of the driver, who can provide services
in the form of punctuality and safety in driving [3].
Traffic accidents could not only cause property
damage but also impact the incidence of fatalities
which, in this case, were caused by interactions
between vehicles and road users [4]. Efforts to reduce534

GEOMATE – Bangkok, Thailand, 22-24 November 2022

2. TRAFFIC ACCIDENT[15]-[16] as well as the addition to the characteristicsof the driver. Traffic environmental factors could also
be a factor causing traffic accidents; this was
explained in a study conducted [17]-[18]-[3]. The
combination of three factors was also considered; thiscombined driver, vehicle, and road characteristics[19]-[15].
In 2019 there were 1,376,956 traffic violations
and only 136,470 (10%) committed ODOL
violations. Every day an average of 378 goods
transportation violates ODOL. ODOL violations
were ranked 4th out of 11 types of traffic violations.
The PUPR (Public Works and Housing) Ministry said
state losses reached IDR 43 trillion for repairing
national roads due to being passed by ODOL trucks.
As for the performance of the weighbridge in 2019,as many as 1,246 trucks (40%) violated the most overdimensions. The number of Motor Vehicle Periodic
Test Implementing Units (UPUBKB) in Indonesia is
468 (out of 514 regencies/cities), and 186 accredited
units, 267 UPUBKBare still under the process ofaccreditation.

Traffic accidents involve environmental andhuman factors, so to reduce the number of accidents,
these two factors need to be taken into account.
Human factors are related to personality factors
which are more or less permanent and temporary
conditions of road users. Various personality traits
that can invite danger on the road are a lack of senseof moral responsibility, ego-centricity, aggression,lack of emotional stability, and overconfidence. At
the same time, temporary conditions that are
dangerous are psychological conditions such as
stress, anxiety, and boredom, as well as decreased
physical conditions such as illness, fatigue, thirst,
hunger, sleepiness, and drunkenness [6]. The factors
influencing road traffic accidents can be
distinguished into human, vehicle, road, andenvironmental
factors.
Socio-economic
anddemographic characteristics, land use regulations,and changing road network characteristics (design)
could be applied to the transportation system toensure better safety concerning truck accidents within
the city [7].
Characteristics of respondents (duration of work,
period of employment, duration of rest, travel time for
one departure, and working hours), predisposing
factors (level of knowledge), supporting factors(vehicle condition, weather conditions, and income),
and driving factors (motivation/family support).
Violations committed by employees due to work
discipline problems were relatively high. This
statement was evidenced by a survey that said 16% of
employees in America were late for work in 2011 [8].
Driver’s behavior (undisciplined, speeding, drunk),lack of sleep (sleep less), driver skill, work
experience (old/new driver), as well as age and rest
time of the driver were contributed. Two factors cause
a decrease in a person’s performance, namely
individual factors, which include personality,
attitude, physical characteristics, age, and experience,then situational factors, which include administrative
regulations, work environment, work methods, andconditions of work equipment [9].
So every public transport driver must have adriving competency certificate, including safety
driving, defense driving, and emergency response.
Optimizing the function of the weighbridge to enforce
the law for goods vehicles that are indicated to becarrying goods with overloading and over-dimensionof the vehicle is not in accordance with the provisions[10].
However, several previous studies only
considered the driver factor [11]-[12]-[13]-[14], andsome considered only the driver and vehicle aspects

Table 1. Causes of Arterial Road Accidents
No

Year

1
2
3
4
5

2016
2017
2018
2019
2020

Driver
854
729
935
1153
807

Cause of Accident
Vehicle Environment
93
13
39
17
30
13
0
0
5
1

Road
28
2
17
21
16

Based on the table above, it was known that
accidents in 2016-2020, the incidence of traffic
accidents on arterial roads was more dominantly
caused by the driver factor with a percentage of 98%,
while the difference in the number of accidents
caused by environmental and road factors with avehicle factor of 2%.
3. METHOD
3.1 Research location and object
The object of this research was trucks with overdimensions and loading (ODOL) in Jombang
Regency that passed through arterial roads. In
Jombang Regency, there were problems with thetruck’s dimensions and heaviest axle loads (MST),
thus violating spatial regulations and conditions,disrupting goods transport traffic, and disturbing
comfort, safety, and security can cause potential
accidents.

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Figure 1. Research Location Map

The study was carried out at 8 points of thearterial road of Jombang city (Figure 2); datacollection in the area was because the location was
the main road connecting Jombang City with other
cities, including Mojokerto, Lamongan, Gresik,
Kediri, Tuban, and Nganjuk. The objects in this study
included the following:
1. Over-dimension and overloading trucks(ODOL).
2. Truck driver.
The data for ODOL trucks consisted of theIndonesian Trucking Entrepreneurs Association(APTRINDO) East Java, Officers from the
Department of Transportation of the Province ofJombang Regency, and Officers from the Traffic
Directorate of the East Java Regional Police(Ditlantas Polda Jatim), Officers from the Public
Works Department of Highways Jombang Regency.
Based on Figure 1, the number of ODOL truck
accidents on each road section shows that the
Ngusikan road section was 15 vehicles, the Ploso roadsection was 32 vehicles, the Tembelang road section
was 69 vehicles, the Jombang road section was 262
vehicles, the road section was 35 vehicles, and the
Ngoro road section was 106 vehicles.

3.2 Multinomial Logistic Regression
Multinomial logistic regression (polytomy
logistic regression) is a logistic regression modelingused on response predictor data analysis with nominal
non-binary categorical responses. For example,
suppose the data you have is in the form of predictorresponse data with a non-binary nominal scale
response and has M categories. In that case, one
baseline must be chosen. After that, compare theother categories with the predetermined baseline sothat a logistics regression model would be obtained;
if not specified, the baseline is the category with thelowest response value [20].
The triangulation validation test uses theMultinomial Logistics Regression method todetermine the significant factors in accidents causedby oversized and overweight trucks. The samplingtechnique used was the Proportional Stratified
Random Sampling technique on the driver/truck. The
research variables used in the study are as follows.

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 2. Predictor Variables
Predictor Variable
Exogenous Factor
Indicator
X11.Road Type
X1.Road factor
X12.Speed limit
X13. Accident location
X21.Weather
X22.Road Surface
X2.Environmental
X23.Visibility
factors
X24.Time
X25.Day
X31.Duration of driving
X32. Driving Experience
X3.Driver
Health X33. Age
Factor
X34. Bedtime
X35. Distraction activity
X36. Mindfulness
X41.Vehicle tonnage
X4.Vehicle factor
X42. Vehicle Transmission
X43. Vehicle typeThe response variables with four indicators arepresented in Table 3 below:
Table 3. Response Variables
Response Variable
Endogenous Factor
Y.ODOL Truck
Accident Fatality

Variables that were significant as a model,
namely driver’s age, duration of driving, vehicle
dimensions/size, vehicle age, and the location of theaccident (hotspot), could be obtained by two
multinomial logistic regression functions as follows.

Indicator
Y. Fatality rate

g1(x) = - 7.085 + 5.319X2.8 (1) + 3.966X2.8 (2) = 2.2(1)

1. Number of deaths
2. Number of injured
3. Number of vehicles involved
4. Number of damaged vehicles

g2 (x) = - 2.322 + 4.706X2.1(1) + 4.295X2.8(1) - 1.569X3.1(2)- 3.665X3.2(1) + 4.371X4.2(1) = 5.816(2)

The steps taken to analyze this study are as follows.
1. Describe the characteristics of a freight vehicle
accident with over-dimensions and overloading(ODOL) in the Jombang regency.
2. Conducting model analysis between freightvehicle accidents with over-dimensions andoverloading with influencing factors with thefollowing steps:
a. Test the independence of all predictor
variables on the response variable.
b. Conduct multinomial logistic regression
analysis.
c. Conduct simultaneous tests on predictor
variables that have a relationship with the
response variable and partial tests on
predictor variables that have a relationshipwith the response variable.
d. Conduct model fit test
e. Calculating the accuracy of model
classification

Based on the logit 1 equation, the constant value
was 7.085, where a negative value means that if thedriver has high flying hours, the probability of an
accident occurring is very low. Drivers who have highflying hours have the opportunity to increase theoccurrence of accidents by 5.319.
The logit 2 equation had a constant value of 2.322, where a negative value means that if the driver
has high flying hours, the probability of an accident
occurring is very low. Older driver age has theopportunity to increase accidents by 4.295, smaller
vehicle size has the opportunity to reduce accidents
by 1.569, and newer vehicle age has the opportunity
to reduce the chance of accidents by 3.665. Road
conditions prone to accidents such as sharp turns
could increase the probability of accidents amounted
to 4.371.
The probability of over-dimensions andoverloading trucks on the chance of an accident
occurring with the categories of no accident, indirect
accident, and accident occurring is shown in thefollowing equation :4. RESULTS AND DISCUSSION𝟏𝝅𝟏 (𝒙) = 𝟏+𝒆𝒙𝒑 𝒈 (𝒙)+𝒆𝒙𝒑𝒈= 𝟏+𝒆𝒙𝒑(𝟖.𝟐𝟒𝟒)+𝒆𝒙𝒑(𝟗.𝟒𝟓𝟐) = 𝟎. 𝟎𝟐𝟔(𝒙)

𝒆𝒙𝒑 𝒈 (𝒙)𝒆𝒙𝒑(𝟖.𝟐𝟒𝟒)

𝟏

𝟐

(3)

Estimated parameters of the research variables asshown in table 4. Below:
Table 4. Parameter Estimation and Odds Ratio

𝒆𝒙𝒑 𝒈 (𝒙)𝒆𝒙𝒑(𝟗.𝟒𝟓𝟐)𝟐𝝅𝟐 (𝒙) = 𝟏+𝒆𝒙𝒑 𝒈 (𝒙)+𝒆𝒙𝒑= 𝟏+𝒆𝒙𝒑(𝟖.𝟐𝟒𝟒)+𝒆𝒙𝒑(𝟗.𝟒𝟓𝟐) = 𝟎. 𝟗𝟕𝟏
𝒈 (𝒙)
𝟏

(4)

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𝟐

GEOMATE – Bangkok, Thailand, 22-24 November 2022

𝟏

𝟏

5.

𝝅𝟏 (𝒙) = 𝟏+𝒆𝒙𝒑 𝒈 (𝒙)+𝒆𝒙𝒑 𝒈 (𝒙) = 𝟏+𝒆𝒙𝒑(𝟖.𝟐𝟒𝟒)+𝒆𝒙𝒑(𝟗.𝟒𝟓𝟐) =𝟏

𝟐

𝟎. 𝟎𝟎𝟐𝟗 (5)The factors that cause the occurrence of overdimension and overloading truck (ODOL) accidents
are the driver’s age, the duration of driving, thevehicle’s dimensions/size, the vehicle’s age, and thelocation of the accident (hotspot). The modeling forover-dimension and overloading (ODOL) truck
accident on the primary arterial road in Jombang
regency has two logit equations:

The odds ratio value at logit 1 (no accident
category) for drivers with high flying hours was
204.251 less likely to have accidents than drivers withlower flight hours. Alternatively, it could be said thatdrivers with low flying hours were 52.777 times more
likely to have no accidents than drivers with highflying hours.
The second logit explained that the indirect accident
category for older drivers had an indirect accident
probability of 110.555 compared to younger drivers.
Drivers with high flying hours had 73.352 indirect
accidents compared to drivers with lower flight hours.
A truck with a larger size of 0.048 had an indirectchance of having an accident compared to a smaller
truck, or it can be said that a truck with a smaller sizeof 0.208 had an indirect probability of having an
accident compared to a truck with a larger size.
Newer trucks were 0.026 less likely to have anindirect accident than older trucks. Accident-prone
locations had an indirect accident probability of
79.153 compared to places that were not prone, or it
can be said that locations that were not prone had
3.402 times the chance of indirect accidents
compared to accident-prone locations.

𝒈𝟏 (𝒙) = −𝟕. 𝟎𝟖𝟓 + 𝟓. 𝟑𝟏𝟗𝑿𝟐.𝟖 (𝟏) + 𝟑. 𝟗𝟔𝟔𝑿𝟐.𝟖 (𝟐)No accident
Indirect
Accident
OverallaccuracyNo
accident
5
1
0
3.1%

The logit function above was used to generate theprobability of each accident category as follows:
𝒆𝒙𝒑 𝒈𝟏(𝒙)𝒆𝒙𝒑(𝟖. 𝟐𝟒𝟒)=
𝟏 + 𝒆𝒙𝒑 𝒈𝟏(𝒙) + 𝒆𝒙𝒑 𝒈𝟐(𝒙) 𝟏 + 𝒆𝒙𝒑(𝟖. 𝟐𝟒𝟒) + 𝒆𝒙𝒑(𝟗. 𝟒𝟓𝟐)
= 𝟎. 𝟎𝟐𝟔

𝝅𝟏(𝒙) =

𝒆𝒙𝒑 𝒈𝟐(𝒙)𝒆𝒙𝒑(𝟗. 𝟒𝟓𝟐)=
𝟏 + 𝒆𝒙𝒑 𝒈𝟏(𝒙) + 𝒆𝒙𝒑 𝒈𝟐(𝒙) 𝟏 + 𝒆𝒙𝒑(𝟖. 𝟐𝟒𝟒) + 𝒆𝒙𝒑(𝟗. 𝟒𝟓𝟐)
= 𝟎. 𝟗𝟕𝟏

𝝅𝟐(𝒙) =

𝟏𝟏=
𝟏 + 𝒆𝒙𝒑 𝒈𝟏(𝒙) + 𝒆𝒙𝒑 𝒈𝟐(𝒙) 𝟏 + 𝒆𝒙𝒑(𝟖. 𝟐𝟒𝟒) + 𝒆𝒙𝒑(𝟗. 𝟒𝟓𝟐)
= 𝟎. 𝟎𝟎𝟐𝟗

𝝅𝟏(𝒙) =The resulting model had a classification accuracy of
85.1%.
This research requires further research by
calculating the Accident Equivalent Rate (AEK) on
each road section and further research on toll roads. It
is necessary to conduct a test method using binary
logistic regression to determine the reliability of theresulting model.

Prediction
Accuracy(%)
Indirect Accident
1
91
11
53.1%

1
15
69
43.8%

(1)

𝒈𝟐 (𝒙) = −𝟐. 𝟑𝟐𝟐 + 𝟒. 𝟕𝟎𝟔𝑿𝟐.𝟏 (𝟏) + 𝟒. 𝟐𝟗𝟓𝑿𝟐.𝟖 (𝟏) −𝟏. 𝟓𝟔𝟗𝑿𝟑.𝟏 (𝟐) − 𝟑. 𝟔𝟔𝟓𝑿𝟑.𝟐 (𝟏) + 𝟒. 𝟑𝟕𝟏𝑿𝟒.𝟐 (𝟏)(2)Table 5. Model Classification Accuracy
Observation

CONCLUSIONS AND SUGGESTIONS

71.4%
85.0%
86.2%
85.1%

REFERENCES
Table 5 showed the classification accuracy of theformed model was 85.1%, so it can be interpreted thatthe number of accuracy predictions was classified
according to observations (actual conditions were
85.15 with a percentage of misclassification of
14.9%. The accuracy of the number of observations
with no accident was 71.4%, indicating that theclassification of the probability of not having an
accident by 28.6 was incorrect. On the chance of an
indirect accident having a classification accuracy of
85.0% and 15% of the indirect accident category wasincorrect in its classification, as well as in the accident
category classification accuracy of 86.2% indicatedthat 13.8% in the category of accidents was incorrect
in its classification and was predicted to be in anothercategory.

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12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

APPLICATION OF THE IDEAL FLOW NETWORK (IFN) METHODTO EVALUATE THE LEVEL OF SERVICE ARTERIAL ROADS
Susilowati1, Achmad Wicaksono 1, Ludfi Djakfar1 and Solimun2
1

2

Civil Engineering Department, Faculty of Engineering, Universitas Brawijaya, Indonesia;
Statistical Department, Faculty of Mathematics and Science, Brawijaya University, Indonesia
ABSTRACTThe current government of Greater Malang continues to work hard to balance the density of land transport
traffic to create better traffic flow and still encourage and improve its economy. Moreover, in recent years, INRIX
has rated Malang as the third most congested city in Indonesia after Jakarta and Bandung. This study aims toidentify factors and potential points of congestion on complex roads in the research object area. The researchmethodology uses literature studies, spatial databases, and data collection techniques by conducting surveys ateach point of the research location with a daily traffic system (LHR) for a month. The research location is on thearterial road in the city of Malang. The road network system is variable to analyze using the IFN methodology;
the working concept of the Ideal Flow Network (IFN) method uses pre-magic equilibrium as an ideal pattern,where all roads will have the same ideally congestion level. This study provides an alternative solution to
overcoming congestion and determining when and where the congestion will occur in the city of Malang. Basedon the analysis results, it is concluded that the smallest weighting of traffic volume is founded in alternative route
three at each node, almost evenly and with a smaller dominant value. Then the value of the degree of saturation ateach node is smaller so that the traffic flow becomes smoother.
Keywords: Congestion, Ideal Flow Network (IFN), Modes of Transportation
1. INTRODUCTION

vehicle delays when vehicle drivers join the queue
until they cross the stop line, needs in decisionmaking [3]. Incorporating accurate intersection
delays into route choice should make up for thisdifference in travel time calculations [2].
Traditional traffic assignment models use
historical travel demand data, in this particular from
Origin-Destination surveys, while cost more [4].
Traffic assignment can be seen from the supply side(road infrastructure) and the demand side rather thanfrom the demand side only. The uniqueness andnovelty of the IFN model [5]-[6] can be seen in Figure
1.

Malang City is the second most traffic area in EastJava after Surabaya, which is experiencing a hightrend of motorized vehicle growth, both two-wheeled
and four-wheeled vehicles. Malang City Governmentis currently trying hard to balance the balanced ofland transportation traffic to create land traffic byencouraging its economy [1]. Moreover, INRIX had
rated Malang City as the most congested city inIndonesia after Jakarta and Bandung [2].
Incorporating accurate intersection delays into
route choice should make up for this difference intravel time calculations [2]. In addition, the researchanalyzes traditional mindsets related to road widening
needed to overcome traffic congestion. This mindset
occurs because the way of thinking is still locally incongested lanes in one corridor. In contrast, the Ideal
Flow Network (IFN) invites transportation analysts tothink globally across the road network, not just in one
corridor. Using the pre-magic equilibrium benchmark
as the ideal pattern, all roads will reach the same idealcongestion level; we redirect traffic from a congested
route to a less congested one. Thus the difference
between the maximum congestion and the averagecongestion of the entire network can be reduced. This
research was conducted to know the structure of theroad network in detail isn't only in one corridor but
used to find alternative solutions to overcome
congestion.
Several studies on road congestion include clear
criteria between the two control systems, based on

Figure 1. Missing Links in Transport Planning
Address resilience through three reliability
measures, the Network Reserve Capacity, City
Evacuation Capacity, and Network Vulnerability.
For Network Vulnerabilities, they note that the
failure of some road sections can seriously
consequence the overall performance of the intercity
highway network. A network less prone to link failure
is a strong network with vice versa [7].

540GEOMATE – Bangkok, Thailand, 22-24 November 2022

Level Of Service (LOS) or road service level is aparameter that shows the condition of a road segment
as a whole. The level of service of a road segment isdetermined based on quantitative values such as
travel speed and degree of saturation and based on
qualitative values such as freedom of the driver tochoose the speed, degree of traffic resistance, andlevel of traffic comfort (Figure 3).

2. DEFINITION
2.1 Traffic Congestion Theory
Congestion is a condition where the vehicle
experiences various types of obstacles that result in adecrease in vehicle speed below usual conditions.
Congestion will be very detrimental to the road user
because it will hamper their travel time and increase
the number of transportation costs. The word traffic
jam has often been heard in big cities where masstransportation is still less attractive. Congestion
occurs because road capacity is exceeded, as seen in
the equation Level of Service = V/C(Volume/Capacity) close to 1, the ideal value of V/C< 0.75. (MKJI, 2017) [8]. Another cause ofcongestion is caused by many people who preferprivate vehicles over public transportation.
Traffic jams have a negative impact, including thefollowing:
1. Waste of time because the vehicle cannot go
at normal speed.
2. Waste of energy because when stuck, thevehicle will continue to use fuel.
3. Increased air pollution because the energy
consumption is higher at lower speeds, andthe engine does not operate in optimal
conditions.
4. Increased stress for road users.
5. Disrupt the smooth operation of emergency
vehicles such as ambulances, fire engines,
and the like.
Based on the causes of congestion described by
the Federal Highway Administration (2005) [9], eachcause has a different frequency level. The threebiggest causes of congestion are physical bottlenecks
with a percentage of 40%, traffic accidents with apercentage of 25%, and bad weather conditions witha percentage of 15%. Overall, the estimated numberof each source of congestion can be seen in figure 2.

Road Service Level
Capacity

A
B

C

D

E

F

V/C

Figure 3. Relationship of Traffic Flow Service Level
The level of road service is determined on aninterval scale of 6 levels. These levels are A, B, C, D,
E, and F, where A is the highest level [8]. The higher
the traffic volume on certain roads, the level of roadservice will decrease.
3. METHOD
3.1 Research sites
The research location is in Malang City, East Java
Province following are alternative route maps of thearterial road, as shown in Figure 4.

Figure 4. Alternative route map of arterial roadIn the model, a directional graph represents ahighway traffic network where Origin-Destination
represents the origin, destination, and linkintersections represent roads. Link weights, among
others, represent travel time, distance, capacity, andtraffic flow. This graphic representation is the traffic
supply information (i.e., network structure) used to

Figure 2. Causes and Percentage of Congestion
2.2 Road Service Level

541

GEOMATE – Bangkok, Thailand, 22-24 November 2022

make current traffic flow forecasts.

structure (0,1) of the ideal flow matrix is the same as
the pattern (0,1) of the capacity matrix. Since theactual input of the ideal flow matrix is a stochastictransformation matrix of the capacity matrix, therelative proportion of each link capacity to thecapacity of the other links is more important than theactual capacity. The proportion of capacity toabsolute capacity in the real world will be a simple
global scaling factor. Thus, the proportion of thecapacity matrix can be simplified to the simple
number of lanes in each link, where the capacity ofeach lane is assumed to be unchanged.

3.2 Ideal Flow Network (IFN)
Ideal Flow Network (IFN) is a special type of
weighted directed (digraph) where two conditions areheld. First, the digraphs are strongly related, and theweights must be positive, called flow. Second, at eachnode, the flow is balanced. The total inflow must
equal the total outflow. Since network graphs areclosely related to matrices, we can define the Ideal
Flow matrix as a special matrix of the non-negativesquare matrix type, where the structure of the binary
matrix (0,1) is fundamental, and the matrix is premagical [10].

4. ANALYSIS AND DISCUSSION
In this study, we seek to provide an alternative
solution using modern methods, namely bydescribing the level of traffic congestion on a node toother links by describing the traffic flow using the
Ideal Flow Network (IFN) method. As in Figure 5.

3.2.1 IFN As Aggregated Random Cycle
Ideal Flow is the constraint The relative link flow
distribution of random walks in a strongly connected
network with the probability distribution of outflows
at each node. Since Random Walk is closely related
to Markov Chain, we can also define Ideal Flow as
the soft state equilibrium of link flow in a strongly
connected network where current is conserved. Suchan aggregation of random cycle paths over time iscalled a link stream, which will grow to infinity as
time approaches infinity. However, because we
divide the link flow by the flow statistics such as
minimum, average, maximum or total flow, relative
link flow is always combined with unique positive
values, which we refer to as ideal flow.

IFN

SocialRoute
ModaCongestion

Moda
Transportation

Figure 5. IFN Concept Framework
3.2.2 Axiom IFN
The Ideal Flow Network (IFN) method analyzes
the entire road network globally, not just one corridor.
With the policy of implementing the IFN model, theroad network system impacts the socio-economic
community in transportation. People's perceptions
change towards the choice of transportation modes.
This will reduces transportation costs so that the levelof road service can be controlled.
Thinking simple that reducing conflict points atintersections can reduce congestion levels. Reducing
conflict points at intersections can be done in various
ways, including:a. arm reductionb. Lane reduction
c. Controls of the amount of movement in eacharmd. Systems engineering at the intersection
The absolute ideal flow network can be obtained
through the scaling property of the ideal flow matrix.
By limit ideal flow to absolutes capacity will resultsin absolutes link flow. Link speed and link delay canfollow, as in figure 6.

The four axioms of an ideal flow network are
irreducibility, conservation flow, steady-state, andMarkov property.
a. Axiom 1: IFNs are strongly connected (ideal
flow matrices are irreducible)b.
Axiom 2: the flow in the IFN is maintained (theideal flow matrix is pre-magic)c. Axiom 3: flow in IFN represents a steady-state
equilibriumor
constraining
long-run
distribution.
d. Axiom 4: decisions at each IFN node follow theMarkov property (based on local rather thanglobal information).
3.2.3 IFN based on Proportional Capacity
A capacity matrix is one of the simplest ideal flow
model inputs to represent the maximum flow that canpass through the infrastructure. Link Capacity is the
maximum flow the link can accommodate. The link
capacity of a road network is closely related to thenumber of lanes or width of the road. The pattern (0,1)
of the capacity matrix is the same as that of thefundamental adjacency matrix. Similarly, the542

GEOMATE – Bangkok, Thailand, 22-24 November 2022

1200 pcu/hour and the node E-A with the symbol (b')
of 2400 pcu/hour.
Alternative route two bypasses the arterial route
starting from the node A-B-C-D-E-F-G-A for routeone from origin to destination. This alternative
assumes that the amount of capacity at thenode/departure node is divided equally on each road
network whose number will equal the initial capacity,
as shown in Figure 8.

Figure 6. How IFN (Ideal Flow Network) works toanalyze Traffic Assignment
In the Malang City area, several arterial roads
experience congestion with a degree of saturation
greater than the standard set. In this study, thelocation is along the Sukarno Hatta, MT. Haryono
and Tlogomas Street.
Based on LHR survey data Tlogomas Street is67,513 pcu/hour, MT. Haryono Street numbered
84,217 pcu/hour, and then Sukarno Hatta Street is a
total of 91,073 pcu/hour, so the total vehicle on thethree roads was 242,803 pcu/hour, equivalent to242,800 pcu/hour. Then the capacity of the roadsegment is 2400 pcu/hour based on the provisions of
the 1997 MKJI.
The road network structure can experiencechanges in terms of time that are permanent (longterm) or temporary (short term) by changing links or
nodes, either in structure or weight. Changes to thelink can be made by subtracting the link weight fromthe path capacity matrix described with several
alternatives. This analysis starts with breaking down
the number of traffic flows on each link so that the
number of departures and returns from the number oftraffic flows is the same.
Alternative route 1 bypasses the main arterial
route starting from the node A-B-C-D-E-A for one
route from origin to destination. This alternative
assumes that the capacity at the departure node isdivided equally on each road network, where thenumber will equal the initial capacity (Figure 7).

1000

(d)
1200

(a)

1200

1200

200

(e )

(d )

(f)

(h)
1400
(a )

C

1200

200

(c)

200

200

F

(b )

(d)
(i )

(i)

(f )

(a)

1000

1200 (g )A

G1200 (b)Figure 8. Alternative Route 2
The origin of the departure is shown from node
A–B with symbols (a) as much as 1200 pcu/hour andnode A–G with symbols (b) as much as 1200
pcu/hour, then from node B–C with the symbol (c) is
200 pcu/hour and node B–D with the symbol (e) is1000 pcu/hour, at node G it is divided into twointersections, node G–C with the symbol (d) as manyas 200 pcu/hour and node G-F with the symbol (i) as
much as 1000 pcu/hour, node C–E with the symbol(f) as much as 400 pcu/hour, node D–E with thesymbol (g) as much as 1000 pcu/hour. Furthermore,
the node E–F with the symbol (h) has as many as 1400
pcu/hour.
Then the departure destination starts from thenode F and is divided into two intersections, node F–
E with the symbol (a') of 1400 pcu/hour and node F–
G with the symbol (b') of 1000 pcu/hour, node E–C
with the symbol (d') as much as 400 pcu/hour andnode E–D with the symbol (c') as much as 1000
pcu/hour, on the node D–B with the symbol ( h') as
many as 1000 pcu/hour, the node of node C are
divided into two intersections, node C–B with thesymbol (e') as much as 200 pcu/hour and C–G withthe symbol (f') as much as 200 pcu/hour, node G–A
with symbol (g') are 1200 pcu/hour and node B–A
with symbol (i') are 1200 pcu/hour.
Alternative route three by passing the arterial
route starting from the node A-B-C-D-E-F-G-H-I-A
for route one from the departure to the return journey(Origin-Destination) an assuming the amount ofcapacity at the departure node is divided equally oneach road network whose number will be equal to theinitial capacity. As in Figure 9.

D
1200
(a )

2400
A

(e)

400

1000

(e)

(c)

1400
400

B

1200

1200

(g)

1000

(h )

C
(b)

E
1000

1000

1200
B

(c )

D

E
(b )

Figure 7. Alternative Route 1
symbols (a) as much as 1200 pcu/hour and node A –C with symbols (c) as much as 1200 pcu/hour, thenfrom node B–C with the symbol (b) as many as 1200
pcu/hour, at node C is divided into two intersections,node C–D with a symbol (e) as much as 1200
pcu/hour and node C-E with the symbol (d) as muchas 1200 pcu/hour. Then for the departure destination,
starting from the node D-E with the symbol (a') of
543

GEOMATE – Bangkok, Thailand, 22-24 November 2022

1000
D

E

1000
1000
(g )

obtained, as shown in Table 1. The traffic volume
calculation is:

(c )

(i)

1000
(g)

1400
400
(d )

200 (e )

(j)

C

F

Table 1. Traffic Volume Calculation

(l)

400

1400
(a )

Node Capacity (C)
(pcu)

I

1000
200

(h)

200

1200

1200

200

(h )

(d)

(f )

(b )

(f)

A-B
A-C
A-G
A-H
B-A
B-C
B-D
B-G
C-B
C-D
C-E
C-F
D-B
D-C
D-E
E-A
E-C
E-D
E-F
E-I
F-C
F-E
F-G
G-A
G-B
G-C
G-F
G-H
G-I
H-A
H-G
I-E
I-G

(k)
1000

1200 (i )BG1200 (b)
1200
(l )

1200
(a)
1200

1200

1200

(j )

(e)

(k )

A

H
1200 (c)Figure 9. Alternative Route 3
The origin of the departure is shown from node A
which is divided into two intersections, namely node
A–B with the symbol (a) as much as 1200 pcu/hourand node A–H with the symbol (c) as much as 1200
pcu/hour, then from node B it is divided into twointersections, namely node B–C with the symbol (d)600 pcu/hour and node B–G with a symbol (b) 600
pcu/hour, node H-G node points (e) as many as 1200
pcu/hour at node G are divided into two intersections,
namely node G–I with the symbol (k) as many as 600
pcu/hour and node G-F with the symbol (f) as muchas 1200 pcu/hour, node C is divided into twointersections, namely node C–D with the symbol (g)as much as 300 pcu/hour, node C–F with the symbol(h) as much as 300 pcu/ o'clock. Node D–E with asymbol (i) is 300 pcu/hour, node F-E with a symbol(j) is 1500 pcu/hour, and node E-I with a symbol (l)is 1800 pcu/hour.
Then the departure destination starts from node I,
divided into two intersections: node I–E with asymbol (a) of 1200 pcu/hour and node I–G with asymbol (b') of 1200 pcu/hour. Node E is divided into
two intersections, namely node E–D with the symbol(c') as much as 600 pcu/hour and node E–F with thesymbol (d') 600 pcu/hour, at node D–C with thesymbol (g') as many as 600 pcu/hour. Node F is
divided into two intersections, namely node F–C withthe symbol (e') as much as 300 pcu/hour and F–G
with the symbol ( f') is 200 pcu/hour, node C with thesymbol (h') is 900 pcu/hour. Node G is divided into
two intersections: node G-B with the symbol (i') is
900 pcu /hour, and node G-H with the symbol (j') as
many as 600 pcu/hour. The H-A node with thesymbol (k') is 600 pcu/hour, and the B-A node withthe symbol (l') is 1800 pcu/hour.
From the results of the breakdown of the traffic
flow of the three routes, the results of the calculationof traffic volume and the degree of saturation (DS) on
several arterial roads in the Malang City area were

2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400
2400

Volume (V)Degree of(pcu/hour)
Saturation (DS)
Route 1 Route 2 Route 3 Route 1 Route 2 Route 330350 19164 14280 25.29 15.97
11.9
30350
0
0
25.29
0
0
0
19164
0
0
15.97
0
0
0
14280
0
0
11.9
0
19164 21420
015.97 14.87
30350 3194
7140 12.65 15.97 14.87
0
15970
0
0
15.97
0
0
0
7140
0
0
14.87
0
3194 10710
0
5.32
7.44
30350
0
3570 25.290
7.4430350 6388
0
25.29
5.32
0
0
0
3570
0
0
7.44
0
5970
0
0
13.3
0
0
0
7140
0
0
4.46
30350 15970 3570 12.65
13.3
4.46
60700
0
0
12.65
0
0
0
6388
0
0
18.63
0
0
15790 7140
018.63 14.87
022358 7140
018.63 14.87
0
0
21420
0
0
14.87
0
0
3570
0
0
10.41
0
22358 17850
015.97 10.41
015970 3570
015.97 10.41
0
19164
0
0
15.97
0
0
0
10710
0
0
16.36
0
3194
0
0
15.97
0
0
15970 14280
015.97 16.36
0
0
7140
0
0
16.36
0
0
7140
0
0
16.36
0
0
7140
0
0
8.9
0
0
14280
0
0
8.9
0
0
14280
0
0
11.9
0
0
14280
0
0
11.9

Source: Survey, 2022
Traffic Volume Graph

70000

Volume (pcu/hour)

60000
Route I

50000

Route II

40000

Route III
30000
20000
10000
0
0

10

20
Node

30

40

Figure 10. Traffic Volume Graph
Based on the analysis of each route on each roadsection, it was found that the highest volume of544

GEOMATE – Bangkok, Thailand, 22-24 November 2022

vehicles on alternative route 1, the second-orderalternative route 2, and alternative route 3 was inorder 3.
After knowing the amount of traffic volume oneach alternative route, and analyzed using the Ideal
Flow Network (IFN), the value of the degree ofsaturation (DS) on each alternative route is shown infigure 11.

REFERENCES
[1]

[2]

Graph Degree of Saturation[3]30
Route I

Degree of Saturation

25

Route II
20

Route III

15

[4]

10
5
0
0

10

20
Node

30

40

[5]

Figure 11. Saturation Degree Graph[6]

The value of the degree of saturation (DS) on the threealternative routes, which has an ideal value based on
the standard level of road services, is alternative route
3. The value of the degree of saturation from the third
alternative route is 16.36; 14.87; 11.9; 10.41; 8.9;
7.44; 4.46.

[7]

5. CONCLUSION[8]Based on the analysis results, it is concluded thatthe smallest weighting of traffic volume is founded inalternative route three at each node, almost evenly
and with a smaller dominant value. Then the value ofthe degree of saturation at each node is smaller so thatthe traffic flow becomes smoother.

[9]

6. ACKNOWLEDGMENTS
The authors thank Kardi Teknomo, Ph.D., who
has provided direction and guidance regarding the
Ideal Flow Network (IFN) method. And all of hiscolleagues who have helped during theimplementation of this research.

[10]

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Traffic Congestion On Teuku Umar Street,
Jatingaleh Area, Semarang With The Process
Hierarchy Analysis Method (AHP), p. 82,
2011.
Pishue. B. and T. Analyst, Global Traffic
Scorecard,” INRIX Res., 2020.
Koutsopoulos. H. N. and Habbal. M, Effectof Intersection Delay Modeling on The
Performance of Traffic Equilibrium Models,Transp. Res. Part A, vol. 28, no. 2, pp. 133–
149,
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Al-Madani. H. M. N., Dynamic vehiculardelay comparison between a policecontrolled roundabout and a traffic signal,
Transp. Res. Part A, vol. 37, no. 8, pp. 681–
688,
2003,
doi:
10.1016/S09658564(03)00024-7.
Teknomo. K, Gardon. R. W. and Saloma . C.,
Ideal flow traffic analysis: A case study on a
campus road network, Philipp. J. Sci., vol.
148, no. 1, pp. 51–62, 2019.
Teknomo . K and Gardon . R. W, Intersection
analysis using the ideal flow model, IEEE
Conf. Intell. Transp. Syst. Proceedings, ITSC,
vol. 2018-March, pp. 1–6, 2018, doi:
10.1109/ITSC.2017.8317739.
Teknomo. K, Ideal relative flow distribution
on directed network, arXiv, 2018, doi:
10.11175/easts.12.939.
Alexandra Maria Rios. C and Francisco deSousa. R, Cluster analysis of thecompetitiveness of container ports in Brazil,
Transp. Res. Part A Policy Pract., vol. 69, pp.
423–431,2014,
doi:
10.1016/j.tra.2014.09.005.
T. Congestion, Final report of the safetyassessment of methacrylate ester monomers
used in nail enhancement products., Int. J.
Toxicol., vol. 24 Suppl 5, pp. 53–100, 2005,
doi: 10.1080/10915810500434209.
Teknomo. K, Ideal ﬂow of Markov
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1–18,
2018,
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10.1142/S1793830918500738.

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

THE EFFECT OF CHANGES IN LAND USE ON THE PREDICTION
OF CRITICAL LAND DISTRIBUTION IN THE RAWAS
WATERSHED (South Sumatra Province, Indonesia)
Zainuddin Muchtar1), Dinar Dwi Anugerah Putranto 2)*,Febrian Hadinata 2), Lawin Bastian 3), Julian fikri4)
1)
2)

Doctoral Program in Engineering, Postgraduate Program, Faculty of Engineering, Sriwijaya University;
Department of Civil Engineering and Planning, Faculty of Engineering, Sriwijaya University, Indonesia
3)
Ministry of Environment and Forestry of the Republic of Indonesia
4)
Sriwijaya State Polytechnic

ABSTRACTThe current regulation and monitoring of land degradation is an urgent need to increase national capacity
in carrying out quantitative assessments and mapping critical land, as desired in the Sustainable Development
Goals (SDGs). An innovative approach is needed to monitor critical land at the national, provincial, and districtcity scales. Sub-watershed scale analysis related to the distribution of degraded land by utilizing multi-temporalgeospatial layers [1][2] is useful in providing a regionally consistent data set of required land distribution thatcan be used to support agendas aligned with sustainable development goals (SDGs). ) [3][4]. In the analysis of
topographic conditions, types of land cover, duration and time of rain, the magnitude and distribution oferosivity based on soil type and slope, which is modeled using GIS techniques, can support data collection andpredictive information on the distribution of critical land and potential environmental damage in the watershedarea. The analysis was carried out using contour data from DEM NAS with contour intervals of 15 m, addedwith high points every 2.5 m, and analyzed using the Digital Elevation Model (DEM) technique so that thewatershed boundary was obtained as the basic unit in morphometric analysis, river length, land slope, flow
pattern, flow density, watershed shape, watershed area, to understand the hydrogeological behavior of thedrainage basin and reveal the geological, geomorphological and flow direction conditions that occur in the area.
Most of the land in the study area is located on the Alluvial plain. With such conditions, it can be concluded
that the largest run-off coefficient value in the Rawas watershed area affects the condition of water availability
and the carrying capacity of the soil's ability to absorb water; the run-off efficiency value is > 0.5.
Keywords: Erosion, Coefficient infiltration, Land use, Sedimentation, Water balance

1. INTRODUCTION

Meanwhile, during the dry season, land
management that is not suitable will experience awater deficit. Such conditions will cause significant
environmental damage in the watershed area. Basedon data from the Musi Watershed Management
Center (BPDAS MUSI) in 2011 [7], the site ofcritical land which is categorized as very essential
in the Rawas watershed area is 5,810.909 Ha and aplace of 499,399.042 Ha in the moderately criticalcategory, Critical Potential is 443,737.122 Ha,
while the rest is in non-critical condition, covering
an area of 170.986,394 Ha.

Pressure on land resources coupled with theeffect of climate change on intensive rainfall, will
result in a process of land degradation caused byrainfall that exceeds normal rainfall coupled withhuman activities, such as logging and inappropriate
land development, which will increase theoccurrence of run-off and soil erosion [5].
Therefore, to further preserve environmental
quality and improve natural resource management
and land planning, it is essential to monitor changes
in land degradation that occur periodically [6].
Extreme rainfall events, especially at the
beginning of the season, when the land surface does
not have plant or plant cover, will cause significant
run-off and exfoliation of the topsoil layer. Areas
with steep slopes and non-composite soil structures
will cause soil erosion. And if the soil structure isunstable, then the room with a steep slope will cause
the cliff to collapse. Planting practices carried out
by farmers on several types of land slopes, which
are unsuitable, also cause a lot of erosion.

1.1. Research significance
Optimization of land use arrangement is one of
the critical activities that must be carried out if you
want to reduce the essential distribution of land, as
in the case of the Rawas watershed. The analysis of
topographic conditions, types of land cover,duration and time of rain, and the magnitude anddistribution of erosivity, which is modeled using
GIS techniques, can support data collection on
546

GEOMATE – Bangkok, Thailand, 22-24 November 2022

predictions of critical land distribution and potential
environmental damage in the Rawas watershed area.
The critical land analysis method discussed
further in this paper combines land use distribution
data and various additional data sets in a more
flexible essential land estimation technique.
Analysis of the sub-watershed scale related to the
distribution of necessary land utilizing multitemporal geospatial layers [6]. Multitemporalgeospatial layers help provide a regionallyconsistent set of critical land distribution datasetsthat can be used to support agendas aligned withsustainable development goals (SDGs)[8]gold, sand, and Iron ore. Therefore, this problem isfound to disrupt the raw water supply system. The
mining activities in the hills and along the river
basin of Rawas sub-watershed also affect theerosion magnitude and flooding volume in Tugu
Mulyo District, Musi Rawas Regency.
2.2 Methods
Rainfall-run-off modeling using the RUSLE
analysis technique [11] by utilizing GIS techniques
to assess the criticality level of land based on therelationship between soil properties, slope diversity,
land use patterns, and the amount of erosion. The
physical parameters of the watershed are combinedwith time series parameters of high rainfall in the
upstream and middle regions by calculating theamount of kinetic energy (annual Kinetic Energy)
caused by rain intensity (precipitation) within 30
minutes for several return times variations. The
amount of rainfall is the increase in the amount ofKinetic Energy (KE), soil vulnerability index (Kd),and the percentage of rainfall intercept (INT). Using
the RUSLE model [12], all parameters in thewatershed are analyzed to estimate the magnitudeof detachment indexes or the amount of surface soil
detachment, which has the potential to become asediment solution, depending on the slope level soil
structure and land cover.
Land cover data is used to redistribute theaggregate number of critical lands to increase theaccuracy of the crucial land distribution data basedon regional scale sizes [13]. In addition, various
factors in the occurrence of environmental damage
are known to correlate with how humans distributeland use activities in the landscape [14]. Therefore,a large number of land use groups can be used in themodeling to more effectively separate the
distribution area of critical land within thewatershed boundary unit and to better describe the
distribution of degraded land [15].

2. METHODOLOGY
2.1 Study area
Rawas sub-watershed is one of the 36 water
facilities in the South Sumatra Province
Administrative Region and is also part of the Musi
River Basin. This facility has a catchment area
(DAS) of approximately 5,891 Km2, with ageographical location at variational coordinates of
102° 4' 4.8" – 103° 27' 36" east longitude and 2° 19'
15" – 3° 06' 36 "south latitude. The boundary of the
Rawas sub-watershed is also the administrative
limit of North Musi Rawas Regency, which is the
new Autonomous Region. The upstream area of thisfacility is at an altitude of 2,100 m above sea level,
which is one of the highest peaks in the Kerinci
Seblat National Park zone, partly included in the
Administrative Region of North Musi Rawas
Regency (1,884.61 Km2 or 32%). Moreover,Kerinci Seblat National Park (TNKS) is part of the
global warp, designated by UNESCO as a World
Heritage Area, with more than 4000 species ofprimary forest flora and 115 species of
ethnobotanical plants that are very beneficial for thepreservation of forest resources. This focuses on thebenefits of reducing global warming. In addition,
two significant rivers or sub-river systems flow in
the Rawas sub-watershed, namely the Rupit River,which fulfills and empties into the Rawas waterbody.
The water resources in the sub-systems ofRawas and Rupit rivers have benefited the residents
in the watersheds. However, the licensing
development for several lands utilized forplantation activities is increasing massively in thisarea. This indicates an imbalance in water
availability during the rainy and dry seasons, as
observed in other river systems within the Musi
River Basin [9]-[10]. The provincial and district
governments subsequently issued several permits
for oil palm and rubber plantations, mining,
infrastructure, and other economic developments,
as well as many illegal operations in the hills andalong Rawas River, based on the extraction of coal,

2.2.1
Extraction of Land use
The results of the Multispectral image
classification are land cover maps used as the basis
for multitemporal monitoring of land use andlandform maps. Maps of landforms are generated
through the visual interpretation of images. The
relationship between land cover and landform ispresented in a land use matrix, the result of which is
the unit of land use class. For example, therelationship between an area with low-level
vegetation land cover and plain fluvial landforms isa characteristic of paddy land use. Such linkages ofland use can be determined. Other elements ofinterpretation, such as patterns, shapes, sizes, andassociations, are also considered to identify land use
objects. Processing is done digitally by recognizing
objects and processing spectral values.

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

2.2.2 Extraction of drainage network
DEM (Digital Elevation Model) is digital datawith x, y, and z values. The x and y values are thecoordinates of the position/location, and the z
values are the elevation values. DEM can be derived
or used to generate new data that requires altitude
information, for example; contour maps, elevation
maps, land slope class maps, digital terrain
models/Digital Terrain Model (DTM), hill shading,aspect maps, intervisibility (visible), slope length,watershed shape, watershed density, and so on [16].
In this study, the land slope class map was compiledusing DEM processing. DEM data used is DEM
Nas (Digital Elevation Model National Indonesia)
data in the form of raster data with pixel values
indicating location coordinates and altitude values.
The DEM Nas data is interpolated to produce a
contour map. The quality of the SRTM, which has
a spatial resolution of 7.5 meters, is good enough topresent altitude data on a scale of 1: 10,000.
Morphological parameters play an importantrole in watershed features and proper watershedmanagement planning. Bharath [17] has discussed
the importance of quantitative geomorphologicalevaluation in water resource management. Manygeomorphologists
improvewatershedmorphological methods [18]. Most watershedmorphometric parameters are influenced by rockstrata, lithology, and geological construction [16].
Likewise, geological, geomorphological, land cover,and hydrological information is beneficial forunderstanding watershed drainage patterns [16].
Presenting watershed morphometric conditions will
provide a comprehensive picture of the drainage
basin, which is very useful in research, including
prioritizing watershed environmental management,
hydrologicalmodeling,
conservation,andrestoration [19].

erosion and deposition. The average change in soil
loss is predicted through the RUSLE equation
approach [11]:𝑬 = 𝑹. 𝑲. 𝑳. 𝑺. 𝑪. 𝑷

(1)where,
E = soil erosion rate (tonnes/ha/year)
R = Rain Erosivity Index
K = Soil erosion index
LS = Index of slope length and slope
C = Vegetation cover index
P = Index of tillage or soil conservation measures
The LS factor is calculated to predict thestrength/erosivity of the run-off and is expressed as
the ratio of slope-affected soil loss and slope length
to soil loss under standard conditions at a slope ofabout 5o (9%) with a slope length of approximately
22.13 meters ( Wischmeter and Smith, 1958)[21]𝑳𝑺 = (

𝝀)(𝟔𝟓. 𝟒 𝑺𝒊𝒏 𝟐𝜷 + 𝟒. 𝟓𝟔 𝑺𝒊𝒏𝜷 + 𝟎. 𝟎𝟔𝟓𝟒)
𝟐𝟐.𝟏𝟑

(2)
The amount of rainfall (D) is a function ofincreasing kinetic energy (KE), soil vulnerability
index (Kd), and the percentage of rain intercepts
INT. Annual kinetic energy can be calculated fromthe rainfall station graph using an equation or
estimated from rainfall data using an empiricalequation. Soil vulnerability index is interpreted
locally, but values are based on texture. Forinterception with sparse vegetation, it is estimatedusing the values obtained from the intercept loss
measure [22].
𝑫 = 𝑲𝒅 ∗ (𝑲𝑬 ∗ 𝒆𝑰𝑵𝑻 )(3)

Where,

2.2.3 Soil loss modeling
The soil loss determination method consists of
two different base phases. The first one is the waterphase; the second one is the sedimentation phase.
At the water phase, the kinetic energy of rainfall,
overland flow, and annual precipitation values andat the sedimentation phase rate of soil detachment
by raindrop impact and transport capacity ofoverland flow values are calculated for every pixel
by generating maps for each input data [20]. Thispredictive model for assessing soil erosion risk iscalled the Morgan model. The input parameters andoperating functions of this model are given below.
The strength of the energy generated from thevolume of rainfall is half the mass of the particle
times the square area of the soil surface. To
calculate the amount of erosion used EI30, which
was developed by Wischmeir and Smith, 1958 [20].
Model sediment transport flow is carried out by
estimating mass conservation to simulate soil

𝑲𝑬 = 𝑹 ∗ (𝟏𝟏. 𝟗 + 𝟖. 𝟕 ∗ 𝐥𝐨𝐠(𝟏𝟎) 𝑰)(4)

Kd = soil vulnerability index
INT = Interception (%)
I= Rainfall intensity (mm/hour)
2.2.4. Transport Sediment
The model's sediment transport capacity (T) is afunction of the overland flow volume and slope (S)coverage factor. The importance of overland flow(Q) is the derivative of the annual rainfall (R), theratio between soil moisture storage and theseparation of yearly rainfall with the number ofdaily rains per year (n). Soil moisture storage (Rc)
follows field capacity (ms), bulk density (Bd) andtopsoil, root depth and topsoil, and the ratio of
actual evapotranspiration (EPT). Plant factor (c) is
obtained from the land cover sub-factor so that the548

GEOMATE – Bangkok, Thailand, 22-24 November 2022

equation becomes
𝑻 = 𝑪 ∗ 𝑸𝟐 ∗ 𝑺𝒊𝒏 (𝑺) 𝒈/𝒎𝟐
where,
S = Slope (radians)
C = land cover factor (0-1)

where,
Rd = depth of root zone (m)ms = soil moisture at field capacity (w/w)
Bd = bulk density on Top Soil (g/Cm2)
Ea/Ep = ratio of actual and potential
evapotranspiration
Ro = R/n(5)

𝑹𝒄

𝑸 = 𝑹 ∗ 𝒆𝑹𝒐 mm(6)

where,
n = number of rainy days in a year

R = average annual precipitation mm
Rc = 1000 * Rd * Ms * Bd * (Ea/Ep) 0,5

Sentinel 2
Multitemporal
Satellite Image
entinel 2)

Land UseClassification

Geology
Map

National
DEM

Soil
Map

Geomorphology
Map

Watershed
Morphometry

Erosivity Index EI30

Hydrometereology
Data

Hydrometeorological
data analysis (I30)

Watershed
CharacteristicsStrahler
MethodModelling Input &Parameter

RainfallData

Hydrograph Simulation onvarious EI . variations( I10,20,30)

Rainfall-Run-off Set
Up ModelCritical Land
Analysis
SEDIMENTASI

Sediment
Transport

Fig. 1 Research Flowchart
3. EVALUATION OF MORPHOMETRICPARAMETER

inundation area, and the availability of water in the
study area

The morphometric analysis is based on data on
the elevation of the watershed area, the aspect of thesite, and the gradient and slope of the land that
contributes to the drainage basin. Morphometricanalysis to understand the hydrogeological behaviorof drainage basins and reveal climatic, geological,
geomorphological, and structural conditions that
occur in the area.

3.1.1 Flow Length
Flow length is one of the essential hydrologicalcharacteristics in a watershed area because it
provides information about run-off characteristics.
The size of the most extended river flow in the
Rawas sub-watershed is 44.73 Km. And the shortest
river length in the Rawas sub-watershed is 0.53 Km.
All drainage channels in the river sub-system areelongated patterns, and there are not many riverbranches. Thus, it can be concluded that the Rawas
sub-watershed is a basin with alluvial deposits offormer sediment or river overflow.

3.1. Watershed morphometric analysis
Drainage analysis in the watershed system will
determine whether the flow pattern is good, the549

GEOMATE – Bangkok, Thailand, 22-24 November 2022

3.1.2. Average flow length (L)Average flow length is a dimensionless property
that expresses the characteristic size of the drainage
network and watershed surface components that
contribute to the arrival time of water flow fromupstream to downstream. For example, the average
flow length in the Rawas watershed is 15.42 Km.

3.1.6. Drainage density
Drainage density is the flow length per unit areaof a basin or watershed and is another element ofdrainage analysis. Drainage density is a better
quantitative expression for landform discretion andresearch. However, climate functions, lithology andstructure, and the relief history of the area can beused as indirect indicators to explain these variables
and landform morphogenesis. The drainage densityof the Rawas basin is 0.197 Km/Km2.

3.1.3. Basin area (A)The Watershed area is another crucial parameter,
such as the length of river drainage. The Rawas 8
watershed has the largest catchment area (484.96
Km2), and the smallest size is 0.75 Km.

3.2. Topography of the planning area
The planning area is one of the upper reaches ofthe Musi River Basin with an altitude of 2,100 m
above sea level and the lowest area at an altitude of
25 m above sea level. The slope of the Rawas
watershed varies, with a pitch of < 8% being themost expansive area and a hill of > 45% being thesmallest area. The headwaters of the Rawas river isMount Kerinci, which is part of the Kerinci Seblat
National Park (TNKS) which has been designatedas part of the lungs of the world and functions as aprotected forest. The total economic value of the
TNKS area is the direct use value and the residual
water value for the water value. Indirect use value
is the value of carbon sequestration, as well as thevalue of existence (existence) and inheritance(legacy).

3.1.4. Basin perimeter (P)The basin's perimeter is the outermost boundaryof the watershed, commonly called thecircumference of the sub-watershed. Measured
along the border between adjacent watersheds andused as an indicator of the size and shape of thewatershed. The perimeter of the basin of the study
area varies, with the largest circumference of
161.27 Km
3.1.5. Drainage pattern
In the basin, the drainage pattern reflects theinfluence of slope, lithology, and rock structure.
Analysis of drainage patterns helps in identifying
the stages of the erosion cycle. The drainage patternpresents several characteristics of the watershedthrough the drainage pattern and drainage texture. It
is possible to infer the basin's geology, rock strikeand deposition, the presence of faults, and otherinformation about the geological structure of thedrainage patterns. Texture drainage reflects climate,rock permeability, vegetation, and relief ratio byrelating drainage patterns to geological data. The
Rawas watershed has an elongated drainage pattern
and trails. So it can be concluded that the researcharea is a flat and undulating area, with one extensive
river system, a floodplain basin.

Fig. 3 Topography of the research area

3.2.1. Geology
The Rawas watershed has unconsolidated
sediments from clay to sand sequences of different
grades. Geologically, the Rawas watershed includes
highlands and lowlands and is a quarter alluvial
deposit from the Pleistocene to young age. Someparts of the basin have loamy sand formations due
to silt deposits which are sometimes interspersedwith sand. Shallow aquifers occur mainly in clay
river deposits and meandering rivers. The bay has
deep aquifers in thick sand layers and has goodpotential.

Fig. 2 Watershed morphometry and soil texture

550

GEOMATE – Bangkok, Thailand, 22-24 November 2022

3.2.2. Geomorphology
The background of the Rawas river basin isquarterly alluvial deposits consisting of clay, silt,
sand, and gravel of various grades. Based on the
total dissolved solids, two groundwater samples in
the field were deemed unfit for drinking, but allmodels were found helpful for irrigation.

because it is influenced by a different coefficient of
run-off (C) and the percentage of rainfall that enters
the soil (A). From the land use map, the area andrun-off coefficient values will be generated basedon land use.

3.2.3.Hydrology
A hydrogeological analysis is based onunderstanding the nature of the aquifer and themode of occurrence. The availability ofgroundwater in the alluvial zone is controlled by the
thickness of the sand and clay zone. Groundwater
near the surface occurs under free aquifer
conditions, while deeper aquifers arise underconditions of z (depth) limited to confined aquifer
conditions. Rainfall is the primary source forreplenishing groundwater in the basin. The waterlevel shows a deteriorating trend in some parts ofthe study area due to rapid urbanization andintensive pumping. The average hydraulic gradientis 0.35 m/km, indicating the porous nature of theformation near the surface of the watershed.
The water yield of the Rawas watershed in theform of river flow is strongly influenced by thenature of the rain input and the physical parametersof the watershed. The characteristics of eachwatershed will result in different water production
for the same amount of rain input and vice versa.
Daily rainfall in the Rawas watershed is dominatedby a classification of 24.00 - < 46 mm/day. The
highest rainfall is 78.18 - < 121 mm/day. The lowest
rainfall is 0 - < 18.91 mm/day. Based on the resultsof the monthly rainfall analysis for R 80, the highest
average rainfall value occurred in November March, with the maximum rainfall in December(176.97 mm/month), and the lowest rainfall
occurred in September (30.29 mm/month).

Fig. 5 Land use map of Rawas watersheds
From the map above, it can be seen that dense
forest has the largest land use area in the Rawas
watershed, which is 247120.77 Ha. Meanwhile, theminor land use is residential, with an area of 70.57
Ha.
4. RESULTS AND DISCUSSIONS
The formation of critical land is one indicator of
ecological and environmental damage. Land thathas been damaged will automatically reduce its
function and role. The decline in this function iscaused by land use patterns that do not pay attention
to soil conservation techniques. Based on the resultsof the analysis of the amount of run-off and soil
erosion that occurred in the last ten years (20102020), obtained an analysis of critical land in the
Rawas sub-watershed covering an area of
221,530,294 Ha in the crucial category, 1,078,033
Ha in the introductory class, 62,930, 110 Ha in themoderately critical category, 225,692, 387
potentially critical and only 72,587,452 Ha, which
is not critical (6.31%). The most prominent problem
in the watershed is the damage to natural resources(soil, water, vegetation) caused by soil erosion,
landslides, degradation of soil fertility, floods, anddroughts.
General problems in the Rawas watershedenvironment can be grouped into 4 (four) parts
according to their respective specifications, namely:(1) Physiographic problems (relief/mountainousand hilly topography, steep slopes), in the upstream
area (Kerinci Seblat National Park/Mount Kerinci),
there is a lot of illegal forest clearing by residents insearch of gold ore, so the degree of erosion is high.
In addition, high rainfall results in increased surface
run-off, low soil stability, reduced soil fertility, andsoil productivity will rapidly decline.

Fig. 4 1-6 Hour rain intensity design for 2 -50 years
return period
3.6 Land use
Land use dramatically determines themagnitude of the soil loss index (F) and overland
flow (Q). Each type of land use will be different
551

GEOMATE – Bangkok, Thailand, 22-24 November 2022

(2) Problems with the use of natural resources(destruction/burning of forests, shifting cultivation,
grazing, illegal mining of uncontrolled gold ore),land clearing for land conversion for oil palm
plantations, and so on.
(3) Problems with the final process or mechanism(erosion–high sedimentation, floods and droughts,
water pollution or pollution, eutrophication, and soon.
(4) Socio-economic and population problems(illiteracy, underdevelopment, poverty, lack oftrained or skilled workers, status or property rights
to land, lack of facilities and infrastructure, and soon.

pressures can increase population pressure. The
effect of population pressure can eventually
increase the biophysical damage to the watershed(watershed criticality) due to an imbalance between
population and land ownership (land carrying
capacity has been exceeded). Third, departing fromthese problems, a new problem arises, which has
become a common problem in Indonesia, namely
the problem of spatial planning or land use resulting
from land use that is not following the ability of theland. This fact or fact often hinders or becomes an
obstacle in every arrangement of regional spatialplanning because it involves various very complex
problems. There is an overlap between status orproperty rights and community or development
interests.

The four causes are interrelated. First,
socioeconomic pressures and development

Fig. 6 Morphometric condition and criticality level of Rawas watershed due to land use change

5. CONCLUSIONSClassification is essential in areas with the allotment
of Plantation and Production Forest space on a slopeof 8% - 25%. Meanwhile, critical to moderately
critical and potentially critical land are found in
Rupit District on a hill of 8% - 25%. The land
allocation in the area, based on the direction of theSouth Sumatra Province Spatial Plan (RTRWP), is
for food crop agriculture, production forests, andplantations. The dominance of critical land in areas
with slopes of 8% - 25% and >45% indicates thatthe erosion factor caused by high rainfall in the
upstream region, and inappropriate land

The main characteristics of critical land are bare,arid, and even appearing rocks on the groundsurface.
The land's topography is generally hilly or
steeply sloped [23]. Critical land is characterized byreeds vegetation that dominates it with thecharacteristics of grasslands having a relatively low
soil pH of around 4.8-6.2. The largest area of criticalland in the Rawas watershed area is dominated by
Rawas Ulu and Muara Rupit sub-districts.
552

GEOMATE – Bangkok, Thailand, 22-24 November 2022

management, is the leading cause of the discovery
of many critical lands in the Rawas watershed.

indicator 15.3.1. 2021.
Putranto Dinar DA, Titis Pratami,"Assessment of Spatial Distribution of Land
Based on Analysis of Slope and Water
Conservation Programs", in International
Conference on sustainable agriculture(ICOSA), 2017, pp. 1–9.
[10] A. L. Yuono, D. D. A. Putranto, and S.
Tukirun, "Effect of land use changes of
upstream Komering subwatershed on
declining water availability," J. Ecol. Eng.,
vol. 21, no. 2, pp. 126–130, 2020.
[11] P. Panagos et al., "The new assessment of soil
loss by water erosion in Europe," Environ.
Sci. Policy, vol. 54, 2015.
[12] N. Haregeweyn et al., "Comprehensive
assessment of soil erosion risk for better land
use planning in river basins: A case study of
the Upper Blue Nile River," Sci. Total
Environ., vol. 574, 2017.
[13] F. Hu et al., "Soil internal forces initiate
aggregate breakdown and splash erosion,"
Geoderma, vol. 320, 2018.
[14] M. Fernández-Raga et al., "Splash erosion: A
review with unanswered questions," EarthScience Reviews, vol. 171. 2017.
[15] N. Tayebzadeh Moghadam, K. C. Abbaspour,
B. Malekmohammadi, M. Schirmer, and A. R.
Yavari, "Spatiotemporal Modelling of Water
Balance Components in Response to Climate
and Landuse Changes in a Heterogeneous
Mountainous Catchment," Water Resour.
Manag., vol. 35, no. 3, pp. 793–810, 2021.
[16] P. K. Rai, P. Singh, V. N. Mishra, A. Singh,
B. Sajan, and A. P. Shahi, "Geospatial
approachforquantitativedrainagemorphometric analysis of Varuna river basin,
India," J. Landsc. Ecol. Republic), vol. 12, no.
2, pp. 1–25, 2020.
[17] A. Bharath, K. K. Kumar, R. Maddamsetty,
M. Manjunatha, R. B. Tangadagi, and S.
Preethi, "Drainage morphometry based subwatershed prioritization of Kalinadi basinusing geospatial technology," Environ.
Challenges, vol. 5, no. June, p. 100277, 2021.
[18] B. Niyazi, A. A. Khan, M. Masoud, A. Elfeki,
J. Basahi, and S. Zaidi, "Morphologicalhydrologicalrelationships
andthegeomorphologicalinstantaneousunithydrograph of Makkah Al-Mukarramahwatersheds," Arab. J. Geosci., vol. 14, no. 9,
p. 751, 2021.
[19] H. Tansar, H.-F. Duan, and O. Mark,"Catchment-Scale and Local-Scale Based
Evaluation of LID Effectiveness on Urban
Drainage System Performance," Water
Resour. Manag., vol. 36, no. 2, pp. 507–526,
2022.
[20] Y. Shinohara, K. Ichinose, M. Morimoto, T.
[9]

6. ACKNOWLEDGMENTS
The authors are grateful to the Directorate of
Research Technology and Community Service,
Directorate General of Higher Education, Research
and Technology, Ministry of Education, Culture,
Research and Technology, the Republic ofIndonesia, who has provided research funding
through Doctoral Research grants based on No.
SPPK : 142/E5/PG.02.00.PT/2022, Sriwijaya
University College DIPA Date : November 17,
2021 with No. DIPA : SP DIPA023.17.1.690523/2022. Acknowledgments are also
given to the Doctoral Programs in Engineering,
Faculty of Engineering, Sriwijaya University, forproviding doctoral students with the performance ofstudy activities in the Rawas sub-watershed area.
7. REFERENCES
[1]

[2]

[3]

[4]
[5]
[6]

[7]

[8]

C. O. Dumitru et al., "Understanding satellite
images: a data mining module for Sentinel
images," Big Earth Data, vol. 00, no. 00, pp.
1–42, 2020.
L. Gigović, S. Drobnjak, and D. Pamučar,"The application of the hybrid GIS spatial
multi-criteria decision analysis best–worst
methodology for landslide susceptibility
mapping," ISPRS Int. J. Geo-Information, vol.
8, no. 2, 2019.
G. Giuliani, B. Chatenoux, A. Benvenuti, P.
Lacroix, M. Santoro, and P. Mazzetti,"Monitoring land degradation at the national
level using satellite Earth Observation timeseries data to support SDG15 – exploring thepotential of the data cube," Big Earth Data,
vol. 4, no. 1, pp. 3–22, 2020.
PBB, "Sustainable Development Goals," in EHandbook on SDG Indicator, 2018, p. 15.
I. Chabay, Land degradation, and restoration.
2018.
M. Gonzalez-Roglich et al., "Synergizing
global tools to monitor progress towards landdegradation neutrality: Trends.Earth and theWorld Overview of Conservation Approaches
andTechnologies
sustainable
land
management database," Environ. Sci. Policy,
vol. 93, no. November 2018, pp. 34–42, 2019.
Dinar DA Putranto, Yuono AL, Model Medan
Digital untuk Pemodelan Rainfall-Runoff
Analisis Sedimentasi secara Regional pada
DAS Musi. Souith Sumatera Provincy,
Indonesia: Lembaga Penelitian University of
Sriwijaya, 2015.
N. C. Sims et al., Good Practice Guidance for
Sustainable Development Goal (SDG)
553

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Kubota, and K. Nanko, "Factors influencing
the erosivity indices of raindrops in Japanesecypress plantations," Catena, vol. 171, 2018.
[21] O. Articles, E. the L. F. for R. through I. S. L.
P. of D. E. D. within A. Grid, R. D. Van
Remortel, and R. S. & Supp, "Estimating the
LS Factor for RUSLE through Iterative Slope
Length Processing of Digital Elevation Data
within ArcInfo GridNo Title," Cartography,
vol. 30, no. 1, pp. 27–35, 2001.
[22] C. Zheng and L. Jia, "Global canopy rainfall

interception loss derived from satellite earth
observations," Ecohydrology, vol. 13, no. 2,
pp. 1–13, 2020.
[23] W. Zhang, J. Zhou, G. Feng, D. C. Weindorf,
G. Hu, and J. Sheng, "Characteristics of watererosion and conservation practice in arid
regions of Central Asia: Xinjiang, China as an
example," Int. Soil Water Conserv. Res., vol.
3, no. 2, pp. 97–111, 2015.

554

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

COPPER RECOVERY FROM WASTE WIRE HARNESS USINGPOTASSIUM HIDROXIDEKoto Kagawa1, Takaaki Wajima2
1

Graduate School of Science and Engineering, Chiba University, Japan
ABSTRACTThis study aims to recover copper from waste wire harnesses by heating waste wire harnesses with potassiumhydroxide (KOH) without releasing hydrogen chloride (HCl). Wire harnesses are mainly thin copper wires withdiameter of a few mm coated with polyvinyl chloride (PVC). The demand of wire harnesses is increasing due tothe development of electronic information equipment, such as home electric appliances, airplane, electric vehicles
and so on, and the amount of waste wire harnesses will increase in future. Also, Japan relies on imports for 100%of its copper demand, and promotion of copper recycling is required. Therefore, copper recovery from waste wire
harnesses is desired in Japan. Current copper recovery methods from waste wire harnesses include physical,
chemical, and pyrolysis treatments, but these methods are difficult to apply thin wire harnesses, especiallyhydrogen chloride generated from PVC is a problem. In this study, waste wire harness was heated with KOH in
an inert atmosphere to decompose PVC coating without releasing HCl by capturing with KOH, and copper cables
were recovered. As a result, it was found that copper wire can be recovered by carbonizing PVC coating with KOH
without releasing HCl gas, while it is difficult to recover copper wire by pyrolysis without KOH with releasing
HCl gas.
Keywords: Potassium Hydroxide, Wire Harness, Copper Recovery, PVC, Halogen Trap

INTRODUCTION

harnesses using sodium hydroxide (NaOH) as thealkali hydroxide under inert atmosphere, NaOH
captures hydrogen chloride gas, increases gas
production by accelerating the gasification of the
PVC coating, and allows the effective separation ofcopper wire from the waste wire harness [3].
In this study, we attempted to recover copper
wires by pyrolyzing the PVC coating of wire
harnesses with potassium hydroxide (KOH), without
releasing hydrogen chloride (HCl) gas.

Wire harnesses are mainly thin copper wires ofseveral hundred µm to several mm in diameter coatedwith polyvinyl chloride (PVC), and their use isincreasing due to the shift to information technology.
As a result, the amount of waste is expected toincrease. In addition, since Japan relies on imports for
100% of its copper demand [1] , copper recycling isrequired. Hence, recycling copper wire from wire
harnesses has become an important issue in Japan.
Conventional treatments for waste wire harnesses
can be roughly divided into three categories: physical,
chemical, and pyrolysis treatment [2] . The physicaltreatment is mainly used to strip the coating, and thechemical treatment is mainly used to dissolve thecoating in solvent, but the separated PVC coating andthe large amount of leached liquid are problems.
Thermal decomposition treatment is highly versatile,
but the harmful hydrogen chloride gas was generated
from the PVC coating.
Therefore, we focused on the recovery of copper
wire from wire harnesses by heating with alkalihydroxide. In heating with alkali hydroxide, fuelgasification and carbonization of the PVC coating areaccelerated, and the generated hydrogen chloride gas
can be captured as alkali salt. The PVC coating isdetached from the copper wire as fuel gas andcarbonized powder, which enables effective
utilization of the coated part as well as recovery of thecopper fine wire.
In previous studies, by heating with wire

EXPERIMENT
SampleWaste wire harnesses provided by a domestic
company were used in the experiment. Figure 1
shows the photo of the sample used and Table 1
shows their composition. It is noted that the diameterof this sample is 2 mm.

Figure 1
Table 1
Content(wt.%)

555

Photo of the sample

The compositions of the sample

Cu
Wire

Cl

C

Covering
O
Al

Ca

Si

40.5

5.1

38.9

11.3

2.7

0.6

0.7

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Experiment
The experiment was performed as follows (Figure2, 3). 0 - 4 M KOH solution (5 mL) was placed in areactor along with waste wire harness (3 g) and
immersed in the solution. The reactor was heated up
to 140 °C in 15 min and kept for 15 min to evaporate
water to remain 0-20 mmol of KOH solid together
with sample. Then, nitrogen gas was flowed into thereactor at 200 mL/min for 15 min to replace the insideof the reactor with nitrogen gas (Fig. 2). After the
replacement, the flow rate of nitrogen gas waschanged to 20 mL/min, and the temperature wasraised to 500 °C in 45 min under a nitrogen
atmosphere, followed by natural cooling for 30 min.
Gases generated during heating were collected with
0.1 M NaOH solution (200 mL) for halogen gas bydissolving, and non-condensable gases (H₂, CH₄)
were collected with a gas pack. The amount of
halogen gas and non-condensable gas evolved wasmeasured with a chlorine ion meter (CL-10Z, KRK)
and gas chromatograph (GC-8A, Shimadzu),
respectively, to be calculated. The residue after
heating was removed from the reactor and stirred with
100 mL distilled water at 40 °C for 1 hour. After
filtration and drying, the solid residue and copper
wire were separated by sieving with a 2 mm holediameter sieve. The filtrate was measured for chlorine
concentration with a chlorine ion meter and pH witha pH meter (LAQUA, F-72)(Fig. 3).

RESULTS AND DISCUSSION
Figure 4 shows the amount of generated during
heating with KOH of 0 - 20 mmol. Without KOH,hydrogen chloride gas was generated at around 300°C. The amount of hydrogen chloride gas generated
decreased with increasing KOH addition and was
almost zero to 20 mmol.

Figure 2 Coating treatment of potassium hydroxide
for wire harnesses

Figure 4 Hydrogen chloride gas generation during
heating with KOH of (a) 0 mmol, (b) 5 mmol, (c) 10mmol, and (d) 20 mmol
Figure 3

Experimental procedure for pyrolysis.
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Figure 5 shows the amounts of hydrogen andmethane generated during heating with KOH of 0 - 20mmol. The amount of hydrogen and methane
generated at around 500 °C. No significant change in
the amount of hydrogen and methane was observed
for the addition of 0 to 10 mmol, but the amount ofhydrogen gas increased for the addition of 20 mmol.
On the other hand, the amount of methane gas did notchange significantly with increasing the amount of
KOH added.

Figure 6 shows the appearance of the residue after
heating with KOH of 0 mmol and 20 mmol. Without
KOH, the copper wire (indicated by red circles) wascovered with black, fine-grained carbonized residue,
while with 20 mmol KOH, a gray and dry residue
covered the copper wire.

Figure 6 Residue after heating with KOH of (a) 0mmol and (d) 20 mmol
Figure 7 shows the residue with 0 to 20 mmol
KOH in distilled water after stirring for 10 minutes.
The solution with residue without KOH addition wastranslucent because the residue was not completely
dissolved, while the residue with KOH addition
dissolved and the solution become black.

Figure 7 Residue in distilled water after stirring for
10 minutes with (a) 0 mmol and (b) 20 mmol KOH
Figure 8 shows the captured chlorine in thewashing solution and pH of the solution obtained
from washing the residue after pyrolysis. The amountof captured chlorine increased as the amount of KOH
added increased. The pH did not change significantly(pH 8) until the addition of 10 mmol KOH residue,
but increased to pH 10.5 with the addition of 20 mmol
KOH. This is thought that 20 mmol KOH was reactedwith HCl and the surplus unreacted KOH remained in
the residue. It is noted that an increase in hydrogengeneration was observed at 20 mmol (Fig. 4 (a)).

Figure 5
Amount of hydrogen and methane
generated during heating with KOH of (a) 0 mmol,(b) 5 mmol, (c) 10 mmol, and (d) 20 mmol

557

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Figure 10 Copper wire recovered by sieving after
drying of the residue with (a) 0 mmol, and (b) 20
mmol KOH
Figure 11 shows SEM images of the surface of thecopper wire obtained by stirring, drying, and sieving
the residue obtained from pyrolysis with and without
KOH.The surface of the copper wire obtained with
KOH addition had almost smooth without adhering
the object, while that without KOH was covered withsome objects.

Figure 8 Captured chlorine and pH of filtrate ateach addition amount.
Figure 9 shows the XRD pattern of the residue
obtained by drying and sieving after stirring theresidue obtained by pyrolysis with and without KOH.
The residue obtained by 20 mmol KOH addition
before washing showed the sylvite peak, while the
XRD pattern after washing showed no sylvite (KCl)peaks, and the peaks of calcite (CaCO₃) andcorundum (Al₂O₃) clearly appear. It is noted that the
XRD pattern of the residue with and without KOH
after washing are almost same. Potassium hydroxide
captured hydrogen chloride gas to form potassium
chloride, and the elution of potassium chloride
occured by stirring to appear the peaks of calcite andcorundum.

Figure 11 SEM images of the surface of copper
wire obtained by drying and sieving : (a) 0 mmol, (b)20 mmol
CONCLUSIONSIn this study, we attempted to recycle PVC andrecover copper wire while capturing hydrogenchloride by treating wire harnesses with potassiumhydroxide coexistent heating.
The results showed that the addition of KOH
improved the ability to capture hydrogen chloride
gas, that the PVC gasified more efficiently, and thatthe separation of the copper wire from wire harness ismore effectively using pyrolysis with KOH,
compared to using pyrolysis without KOH.
These results suggested that thin copper wire canbe recovered effectively using pyrolysis with KOH,
without releasing HCl gas.
REFERENCES[1] JOGMEC, Mineral Resources Material Flow
2020.
[2] Li L., Liu G., Pan D., Wang W., Wu Y., Zuo T.,
Overview of the recycling technology for copper
containing cables, Resources, Conservation andRecycling, Vol. 126, November 2017, pp. 132140.

Figure 9 XRD pattern of the residue obtained bydrying and sieving After stirring the residue obtainedby pyrolysis with and without KOH.
Figure 10 shows the copper wire recovered fromthe residue with 0 and 20 mmol KOH by sieving after
washing and drying. Without KOH addition, therecovered copper wire had a black surface without
exfoliating the residue, while with KOH addition, theresidue were exfoliated and the metallic luster ofcopper could be observed.

[3] Wajima T, Pyrolysis Behavior of Polyvinyl Chloride
with Sodium Hydroxide and Application to Copper
Recovery from Multiwire Tinned Copper Cables,
International Journal of the Society of Materials
Engineering for Resources, Vol. 25, No.1, April 2022,
pp. 70-77.

558

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

INFLUENCE OF COMPOSITION ANALYSIS ON UNIT WEIGHT OFSYNTHETIC MUNICIPAL SOLID WASTE
Vidit Singh1 and Prof.Taro Uchimura2
1,2

Saitama University; Japan
ABSTRACTThe Density of Municipal solid waste is a critical parameter in engineering analyses of landfills execution,
design, and stability of landfills, but significant uncertainty as of now exists with respect to its value. By usingsynthetic municipal solid waste (SMSW) that replicates the typical composition of waste produced in India. To
determine the relation between Maximum Dry Density (MDD) & Optimum Moisture Content (OMC) of fresh
SMSW “Modified Proctor Test” has been performed. MSW is a very heterogeneous material, numerous efforts
have been made in the past to generalize the density of MSW, but still, the effect of each material on the density
is Obscure. In this paper, the variation in MDD & OMC has been provided depending on the varying percentage
of each material (Paper, Plastic & Organic Content) present in it. As a result of regulations and shifting consumer
habits, waste is continually changing. Engineers must therefore be mindful of the evolving mechanicalcharacteristics of trash, and it's possible that prior findings may not be a reliable indicator of how waste will behavein the future. Therefore, Calculating the variation in unit weight can be useful in the design of engineered MSW
landfills and also useful for future references.
Keywords: Synthetic municipal solid waste, Density, Compaction, Maximum dry density, Optimum moisture
content.
INTRODUCTION

provide high-performance landfills with lesspossibility of failure [2], [3]. The rate of change could
accelerate over the following years as many attempts
are made to recycle and pretreat MSW using
mechanical and biological methods. Additionally, it
is anticipated that degradation changes a deposit's
mechanical properties with time. Due to theheterogeneity of waste, it is impossible to fully
characterize its engineering properties; however, it is
crucial to understand basic behavior and to be awareof the likely ranges of the relevant engineering
properties.

The Municipal solid waste (MSW) landfills is anengineered geotechnical structure. The foundation's
rainfall infiltration, leachate, and slope stability
bearing capacity are some of the problems related tolandfills. Therefore, based on the information
available at the landfill site, a landfill design planshould be chosen based on the appropriate values ofgeotechnical Parameters of MSW such as density,
shear strength, and permeability.
Specific guidelines on the likely range of MSW
characteristics must be provided if the data are
insufficient for analysis in order to evaluate the safetyof MSW slopes and landfill design. The MSW exhibit
a wide range of uncertainty related to unit weight
depending upon the vulnerability related to theheterogeneity of waste, age of waste, different
degrees of deterioration, filling method, construction
practices, compaction method, and leachate levels[1]. Without taking into account, the variability in thegeotechnical parameters, the design of the MSW
landfill could result in stability or slope failure [18].
Even now, it is common practice to access thestability of slopes using the little data that is currently
accessible. Less reliability will be achieved byplanning the landfill using the minimal informationthat is now available. The parameters density (γ),
cohesion (c), and internal friction angle (ϕ) control
the stability of an MSW landfill. Therefore, in thisresearch paper efforts have been given to analyze thecomposition effect on the Unit Weight of MSW.
Providing design parameters with a known range will

UNIT WEIGHT
Laboratory results of MSW show the variation in
Unit Weight of waste ranging from 8-10 kN/m3 and
15 – 20 kN/m3 for dry and degraded waste. Many ofthem have analyzed the effect of degradation on theunit weight of MSW [4], [5]. For fresh waste, the unit
weight is mainly influenced by its composition
calculated the unit weight of waste range from 3 - 18
kN/m3 [6]. The unit weight of MSW landfills also
varies with the depth performed test on bored samples
which reported a range of 10 – 15 kN/m3 [3].
It is quite surprising that there has been so little
thorough research on unit weight because it isimportant information for landfill design. Due to thevarious waste types and dumping techniques, unit
weight varies greatly. Common challenges indetermining MSW unit weight include clearly
separating out the contribution of daily soil cover,determining how unit weight changes over time and559

GEOMATE – Bangkok, Thailand, 22-24 November 2022with depth and assuming that the most of reported
values correspond to waste that is close to the surface[6].

Factors Affecting Unit Weight of MSW
The unit weight of MSW changes due tonumerous reasons a few of them are compaction
effort, layer depth, layer thickness, and overburdenpressure [9]. In contrast to soils, there are multiple
materials present, and the state and level ofdeterioration affect the unit weight. According to thistheory, the fresh waste density mostly depends on thewaste composition, however, when the waste ages
(degrades), the unit weight primarily depends on
depth. It is extremely common to deposit the waste in
the layer thickness of 2 to 3 m, which accomplishes
moderate compaction and results in low poor density.
The layer thickness is 0.5 - 1.0 m, which will achieve
good compaction and therefore it exhibits high unit
weight [4]. Depending on the level of compaction
attained, a statistical analysis of the data is displayedin Table 1.

Material Description
MSW is a collection of waste largely fromhousehold and commercial sources. The two primarycategories of MSW are organic waste and inorganic
garbage. Food trash, paper, garden waste, soil, andtextiles are examples of organic waste, while plastic,
metals, rubber, and glass are examples of inorganic
waste. The content of waste varies greatly dependingon the source of waste formation. The MSW has avast array of particle sizes, from little soil fragments
to large building stones. These materials' composition
varies from location to location, site to site, and evenfrom country to country.
It is necessary to have a classification system tocharacterize component qualities in both their initialstate, or as they are brought to a landfill, and in any
altered states. Due to physical forces including
compaction, overburden, and degradation over time,
dumping waste in a landfill alters component qualities
like size and shape. Materials utilized in thisinvestigation have been classified according to Dixon
and Langer's proposed scheme from 2004 [7].

Table 1 Bulk unit weight of MSW for fresh waste [6]Items(kN/𝐦𝟑 )
Range

Good
Compacti
on
8.8-10.5

Moderate
Compacti
on5.0-7.8

Poor
Compacti
on
3-9

Waste Mechanism

Average

9.6

7.0

5.3

The current awareness of MSW/Waste behavior isrelatively incomplete. Engineers and scholars haverelied on the mechanics of soil for waste disposal [7].
Although this has been beneficial to some level, using
soil behavior and designing the engineered landfill
structure is not the best course of action. The landfill
was designed using the principles of soil theory, andchanges in the geotechnical properties of waste due todegradation over time haven't been considered muchoften, as well as since the lateral pressure of MSW
hasn't been completely predicted the pressure
analysis of MSW is also based on the principles ofsoil theory. Used for vertical extension of the landfill
using retaining walls. The soil homogenous pressure
hypothesis is highly challenging to apply because of
the heterogeneity related to MSW.
Instead, MSW landfills should be designed
using waste geotechnical properties and also
considering the change in properties with respect totime in mind. While analyzing the similarities anddifferences of MSW with other soil materials willoffer assistance to us to create and understand theengineering properties of MSW. It is always
preferable to conduct tests on original materials
in undisturbed conditions [8]. But it is not always
possible to obtain the material in an undisturbed stateand conduct the laboratory test on a real sample.
Additionally, it can be challenging to generalize
waste behavior due to the great variance in the sample.

Standard
Deviation

0.8

0.5

2.5

COV (%)

8

8

48

PURPOSE AND SCOPE OF THE CURRENT
STUDY
The assessment of the literature demonstrates
unequivocally that there is confusion surrounding theunit weight of MSW because of its heterogeneity,
which demands a systematic investigation. It iscurrently unclear how the composition will affect theMSW unit weight. There have been numerous
investigations into the engineering characteristics ofMSW (Fassett et al., 1994; Dixon and Jones, 2005;
Reddy et al., 2009); however, there have been few
investigations into the effects of waste type (synthetic
and natural), age, and waste material (fibrous,organic, and inorganic) on the shear strength behaviorof MSW [9].
The current study suggested calculating theimpact of paper, plastic, and organic material on theunit weight behavior of MSW. The magnitude of
MSW unit weight is very heterogeneous based on thecontent of waste but the behavior with respect to thechange in the composition will follow a trend.
Calculating the influence of material composition on
the unit weight of MSW will help us create a
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

generalized range of values of the unit weight andhelp us in further analysis and design of MSW
landfills.
MATERIAL CHARACTERIZATIONMETHODOLOGY

However, there are too many combinations ofmaterial and component sizes to realistically
represent each with a synthetic alternative. Therefore,
the component fractions had to be minimized in orderto get a simpler but still representative synthetic waste.
The sample of each composition has been made in
Lab and testing. Components such as beverage cans,rigid and flexible plastic packaging paper/cardboard

AND

In total, nine distinct compositions of synthetic
municipal solid waste were created in the lab. By
doing a waste composition analysis on a sample takenfrom the Jawahar Nagar Landfill, Hyderabad India, it
was possible to determine a reference classificationfor "typical" MSW. To choose synthetic wasteelements that can mimic those found in MSW, thisreference MSW has been employed. Following theconstruction of several synthetic wastes with a varietyof categories (see Fig. 1) using these synthetic
components, the relationship between classificationand mechanical behavior was evaluated. Except forthe components that represented the organic
substance described below in Table 2. The
composition of MSW is varied in such a way that itcomprises the maximum range of each material. Ten
different materials were used to make the Synthetic
waste (Kitchen waste, paper, plastic metal, sand,cardboard, metals, textile, wood shaving, peat moss,and medium-grained soil) [10], [11].
The % of each material of the constituted
synthetic waste sample obtained from landfill ispresented below in Table 2. All the % of each test arealso shown in Table 3, Table 4, and Table 5, which
represent Paper, Plastic and Organic matter. To
determine how various materials will affect the unit
weight of MSW by considering various
compositions. The ASTM D1557-91 Proctor test
method has particle-size limitations. Thus, thesynthetic MSW samples were produced according toparticle-size limitations in the laboratory as shown in
Table 2 below [12].

Table 3 Composition used by keeping paper constant
SampleMetal
Paper
Yard- Trimming
Cardboard
Wood
TextilePlastic
Sand
Peat moss
Soil

Fraction %
SW-1
5.10
21.20
2
4.10
6
3.65
15.10
9
30
4
100

SW-3
4.13
30
2.63
14.13
5.03
1.03
3.03
8.03
29.03
3.03

Table 4 Composition used by keeping plastic constant
SampleMetal
Paper
YardTrimming
Cardboard
Wood
TextilePlastic
Sand
Peat Moss
Soil

Table 2 Composition of Jawahar Nagar Dumping
Yard, Hyderabad India
Material
SampleMetal
Paper
Yard- Trimming
Cardboard
Wood
TextilePlastic
Sand
Peat moss
Soil
Total

SW-2
6.34
10
4.84
16.34
7.24
3.24
5.24
10.24
31.24
5.24

SW-4
5.67
21.77
2.57

SW-5
4
20.10
0.90

SW-6
3.45
19.55
0.35

4.67
6.57
4.22
10
9.57
30.57
4.57

3.0
4.90
2.55
25
7.90
28.90
2.90

2.45
4.35
2.0
35
7.35
28.35
2.35

Table 5 Composition used by keeping Organic Matter
Constant

Size (mm)SampleMetal
Paper
YardTrimming
Cardboard
Wood
TextilePlastic
Sand
Peat Moss
Soil

< 40
15 – 200
50 - 100
50 - 100
10 - 150
10 - 100
10 - 150< 4.75 u
40 – 120> 4.75 u

561

SW-7
6.77
22.87
3.67

SW-8
3.43
19.53
0.33

SW-9
2.32
18.22
0

5.67
7.67
5.27
16.77
10.67
15
5.64

2.33
4.33
1.93
13.43
7.55
45
2.33

1.22
3.12
0.82
12.2
6.12
55
1.18

GEOMATE – Bangkok, Thailand, 22-24 November 2022

and textiles can be employed provided that they are
in an unsoiled state [13]. The maximum size of eachmaterial has been taken from the literature review,and each particle has been cut by hand using the
cutting machine to keep the particle size in the given
range. Mineral material was represented by sand,gravel, and soil. Synthetic waste components wereselected based on consideration of both shape andsize Table 2. Fig. 1 shows the different components
used in this experiment. Each material has been cut
and weighted in a particular fraction to simulate thewaste composition as shown in Fig.1.

Metal

Paper

Textile

Wood

Sand

Soil

17.5 cm in height. The drop hammer used has aweight of 2.5 kg and a constant drop height of 45 cm
as shown in Fig.2. Fig.3 shows the weighing of thesample after every compaction of testing material.

Fig. 2 Proctor Test Apparatus

Cardboard

Plastic

Peat-Moss

Fig. 1 Sample preparation of SMSW
From above Table 3, the composition of paper has
been fixed for each sample (i.e., SW-2= Paper 10%
and SW-3= paper 30%) and all the other material
fractions have been calculated depending on the fixed
fraction of paper. A similar, process was followed for
calculating the composition fractions of plastic andorganic matter as can be seen in Table 4 and Table 5.
Some previous studies have also studied theengineering properties of MSW using SMSW. Table
4 shows the composition of SMSW used to find theinfluence of composition on Unit weight by a varying
paper by 10%, 21.20%, and 30%. For Plastic thecomposition varies from 10%, 15%, 25%, and 35%.
For Organic content, the composition varies from
15%, 30%, 45%, and 55%. By utilizing the abovementioned composition, we have tried to analyze theunit weight of MSW by doing the standard proctor
test as shown in Fig.2 and Fig.3.
The SMSW is filled in three layers inside the moldand each layer has been compacted with 25 blows
from a constant height of 18 in. The water has been
added in a range from 15% to 90% for every test. The
testing mold dimensions are 15 cm inner diameter andFig. 3 Sample weighing after Compaction
RESULTS & DISCUSSIONThe Unit weight of SMSW in each sample differs
very much therefore it helped us in creating a rangeof values depending upon the material present on it.
This laboratory-produced SMSW mix fraction waschosen to resemble the typical MSW composition ofIndia. The maximum particle size tested in thisinvestigation is 20 cm, and the average specificgravity was reported to be 1.52. One of the primarycauses of a variation between MDD values is thought
to be a difference in the maximum particle size [14].
As a result, this analysis takes into account the
maximum size and composition of each material. The
compaction curve for SMSW produced from the562

GEOMATE – Bangkok, Thailand, 22-24 November 2022

conventional proctor test resembles the compaction
curve for soils in general shape.

is more % of plastic than 15 – 20 % it will be more
prone to slope failure.

Fig. 4 Dry Density vs Water content for Paper

Fig. 5 Dry Density vs Water content for Plastic

The dry unit weight of SMSW increased as thewater content increased to its maximum and thenbegan to drop as the water content increased further.
Similar to soils, the additional increase in water solid
content per unit volume was contributing tolubrication, which led to a denser arrangement ofparticles. Additionally, as the water content rises,
solid materials become softer, which enhances
deformation, compressibility, and rebound in
response to compaction [1]. “However, compared tosoils, waste does not show a significant decrease
in dry unit weight at high moisture content, because
the relative difference between the unit weight of“waste” and “solid” is lower for waste than for soils”[1].
The effect of increasing paper % on the unit
weight of SMSW is in the range of 2.83 kN/𝐦𝟑 for
10%, 4.01 kN/𝐦𝟑 for 20%, and 3.162 kN/𝐦𝟑 for
30%. As for the paper, % increases the density
increases reached its maximum and then started todecrease with the increase in paper %. With theinclusion of paper, the SMSW mix behaves denser
than other mix proportions, unit weight tends toincrease from 10 – 20 % from 2.83 to 4.01. Larger
and lighter particle sizes make materials morecompressible. Components deform at relatively low
vertical stresses, with component rearrangement
behavior predominating.
Similarly for Plastic, the % increases for the firsttwo cases of unit weight, and with a further increasein % of plastic, the MDD tends to decrease. Plastic
takes more volume compared to other materials insuch as paper, metal, etc. Low unit weight is again
caused by the inclusion of light and highly
compressible material components deformed fromthe starting of the stress application while
compacting. Plastic shows higher nature ofcompressibility and the SMSW mix with more plastic% tends easier to slip, therefore in the landfill if there

For organic content, the effect on unit weight iscalculated as follows 3.30 kN/𝐦𝟑 for 15% peat moss,
4.01 kN/𝐦𝟑 for 30%, 4.18 kN/𝐦𝟑 for 40%, and 3.28
kN/𝐦𝟑 for 50%. Similarly for plastic content 3.47
kN/𝐦𝟑 for 10%, 4.01 kN/𝐦𝟑 for 15%, 3.14 kN/𝐦𝟑
for 25%, and 2.95 kN/𝐦𝟑 for 35%.As can be seen
above Fig. 6 shows the influence of material on theunit weight of SMSW for organic content, as theorganic content increases the MDD also increases
first three tests but with a further increase in % oforganic matter the MDD decreases. The reason forthis behavior is as organic material is very
compressible and also it can break easily while
compacting, due to the presence of water it tends tostick to other materials. Leading to greaterinterlocking of materials.

Fig. 6 Dry Density vs Water content for Peat Moss
A denser packing arrangement was achieved byincreasing the solid content per unit volume, whichassisted in lubricating the particles from the additionof water. Therefore, it exhibits less volume which
563

GEOMATE – Bangkok, Thailand, 22-24 November 2022

results in an increase in MDD reaching its peak at a
certain point i.e., 30 – 35% of organic matter. Despite
the difference in moisture content and overburdenpressure between synthetic waste and real waste, it
has been shown that SMSW used in this study has
shown magnitude and trends that are comparable toreal waste. The MSW is a heterogeneous material it
also shows wide variation in magnitude, but thetrends followed by the MSW are quite similar.

•

The maximum dry density when Organic content% is increased 15 to 30% increased by 21.52%
with further increase 30 to 45% increased by
4.24% and from 45 to 55% it decreased by
24.16% respectively.
For SW-1, SW-7, SW-8, and SW-9
Organic %
15
30
45
MDD
3.30
4.01
4.18
OMC
1.01
0.92
0.93

CONCLUSION

55
3.17
1.10

This article presents the unit weightcharacteristics of SMSW of Hyderabad India. There
are several failures of landfills happening around theworld which have also caused loss of lives. Therefore,much more effort has to be given to analyze the MSW
properties more efficiently and also to design thelandfill in a more efficient way by utilizing the moreelaborated waste properties. The relatively low unit
weight of synthetic municipal solid waste is the resultof the use of light components and minimal
placement compaction forces. From the literature, it
has been demonstrated that for the same landfill
waste at field capacity, the dry unit weight of SMSW
on the order of 2–4 kN/m3 is equivalent to the bulk
unit weight of 6–9 kN/m3 . The following conclusions
can be drawn from the present study:

Nearly every step of landfill design and analysis
uses knowledge of the unit weight of waste. In the
beginning, factors such as waste composition, age,cover system, degree of compaction, andbiodegradation have a greater impact on the unit
weight of garbage. After a certain time, the landfill's
depth primarily influences the unit weight. From theabove discussion, it is clear that the effect of eachindividual material on MSW is very prominent anddoing this composition analysis on fresh SMSW has
provided us with a definite range of unit weightcorresponding to the change in the composition ofmaterials. The findings of this study showed thatwaste unit weight substantially relies on thecomposition of waste, hence a site-specificassessment is advised [15].

•ACKNOWLEDGMENTS•

When the composition of the synthetic waste
varies there are significant changes in the unit
weight of the testing samples.
When paper percent is increased from 10 to 20%, the maximum dry density increases by 41.7%, and when it is increased from 20 to 30 %, it
lowers by 21.2 %.
For SW-1, SW-2, and SW-3
Paper %
10
20
MDD
2.83
4.01
OMC
0.62
0.92

•

The author would like to acknowledge thecontribution of Prof. Taro Uchimura Sensei, for his
guidance. Additionally, I want to express mygratitude to JICA for giving me the opportunity to
study in Japan and also providing a research grant tocontinue my research. The Department of Civil andEnvironmental Engineering at Saitama University inJapan provided the lab equipment for this study.

30
3.16
0.76

REFERENCES[1] Oluwapelumi O. Ojuri and Peter K. Adegoke.,
Geotechnical characteristics of synthetic
municipal solid waste for effective landfill design,
Int.J. of GEOMATE Vol. 9, No. 1, 2015, pp.
1418-1427.
[2] Hasan First pulat and Yeliz yukselen Aksoy.,
Factors affecting the shear strength behavior ofmunicipal solid waste., Waste Management, Vol.
69, 2017, pp. 215-224.
[3] Hossain M.Z. and Awal A.S.M.A., Experimental
Validation of a Theoretical Model for Flexural
Modulus of Elasticity of Thin Cement Composite,
Const. Build. Mat., Vol.25, No.3, 2011, pp.14601465.
[4] Neil Dixon, and D. Russell V. jones.,Engineering properties of municipal solid waste,
Geotextiles & geomembranes, Vol. 25, 2005, pp.

The maximum dry density increased by 15.56 %when the plastic percentage was increased from10 to 15 %; from 15 to 25 %, it decreased by
22.69 %; and from 25 to 35 %, it decreased by
6.45 %.
For SW-4, SW-1, SW-5, and SW-6
Plastic %
10
15
25
MDD
3.47
4.01
3.1
OMC
1.05
0.92
0.80

35
2.9
0.85

The reason for this kind of density variation for
paper and plastic is mainly due to the compressible
nature of the material. The material tends to soften
when water is added, which makes for a dense
arrangement and also reduced the compaction
rebound.
564

GEOMATE – Bangkok, Thailand, 22-24 November 2022

205-233.
[5] Dixon N, Langer U, and Gotteland P.,
Classification
andmechanicalbehavior
relationships for municipal solid waste study
using synthetic waste, Journal Geotechnical GeoEnvironmental Engineering, ASCE, Vol. 134,
2008, pp. 79-90.
[6] Fassett J.B., Leonards, G.A Repetto, P.C.,
Geotechnical properties of municipal solid waste
and their uses in landfill design, Waste
Technology, 1994, pp. 1-31.
[7] Dixon N, Ng’ambi S, and Jones DRV., Structural
performance of a steep slope landfill lining
system, Proceedings of the Institution of Civil
Engineers Geotechnical Engineering, Vol. 157,
2004, pp. 115–125.
[8] Langer U., Shear and compression behavior ofundegraded municipal solid waste, Dissertation,
Doctor of Philosophy, Loughborough University,
2005.
[9] Kavazanjian E., Mechanical properties ofmunicipal solid waste., Proceedings Sardinia,
Eighth International Waste Management andLandfill Symposium, Cagliari, Italy, Vol. 3, 2001,
pp.415–424.
[10] Christoper A. Bareither, craig h. Benson andTuncer B. Edil., Effect of waste composition anddecomposition on shear strength of Municipal

solid waste, Journal of geotechnical and GeoEnvironmental Engineering, Vol. 138, No. 10,
2012, pp. 1161-1174.
[11] Hsin-yu and Tsuo-hsien Fan., In Situ test andslope stability analysis of municipal solid waste
landfills, Int.Symp. on Geoenvironmental Eng,
2009, pp. 8-18.
[12] US.EPA., Municipal solid waste generation,
recycling, and disposal in the United States,
Facts and figures for 2006., http://
www.epa.gov/osw/nonhaz/municipal/pubs/msw
06.pdf, April 20, 2008.
[13] Manassero M, Van Impe WF, Bouazza A., Waste
disposal and containment, Proceedings Second
International Congress on Environmental
Geotechnics”, Osaka, Vol. 3, 1996, pp.193–242.
[14] Krishna R. Reddy, Hiroshan Hettiarachchi,janardhanan Gangathulasi, Jean E. Bogner andThomas lagier., Geotechnical properties ofsynthetic municipal solid waste, International
Journal of geotechnical Engineering Vol.3, 2009,
pp.429-438.
[15] Hisham T. Eid, Timothy D. Stark, W.Douglas
Evans, and Paul E. Sherry., Municipal solid
waste slope failure, Journal of geotechnical andgeo-environmental Engineering, Vol. 126, No. 5,
2000, pp. 164.

565

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

SPATIAL STATISTICS AND PERCOLATION PROBABILITY OF
PORE-NETWORK IN POROUS MEDIA WITH AGGREGATESTRUCTURE
Junichiro Takeuchi1, Yu Song1, Yuto Takeuchi1 and Masayuki Fujihara1
1

Graduate School of Agriculture, Kyoto University, Japan
ABSTRACT

Soil aggregates are clumps of relatively small soil particles bound each other by organic matter. Soil withaggregate structure possesses relatively large pores around the soil clumps, and it possesses relatively small poresin the clumps. This spatial arrangement of pores enables soil with aggregates to have a contradictory feature suchas well drained but high water-retention. In this study, spatial autocorrelation of porous media with aggregates isevaluated with the global Moran’s index, and the percolation probability, which shows the connectivity of a
network, is also investigated. To compare those properties of porous media without aggregates, virtual porous
media models with and without aggregates were generated from spherical grains variously sized by the discreteelement method. The global Moran’s index of porous media with aggregates showed around 0.36, which is morethan double value of porous media without aggregates. This relatively high global Moran’s index shows that porous
media with aggregates have pore clusters formed by large and/or small pores, and the pore clusters by small pores
get the media to retain water. Furthermore, it was shown that from the percolation threshold in porous media withaggregates is around 0.15, while those in porous media without aggregates are around 0.35. This quite small
percolation probability threshold of porous media with aggregates asserts the existence of a sub-network formedby relatively large pores in the pore-network and this sub-network enables high drainability. Through the analysesof the spatial autocorrelation and the percolation probability, the properties of porous media with aggregates wereshown quantitatively, and it leads to better understanding of soil features.
Keywords: Soil aggregates, Spatial autocorrelation, Percolation probability, Water retention curve

INTRODUCTION

Here, virtual porous media are generated, and porenetworks are extracted based on the watershedsegmentation algorithm.

Soil aggregates play a critical role in growing
plants, and those serve air and retained water withnutrients to roots. Such functionality and stability ofsoil aggregates have been studied so far [1], [2].
Recently, microscale 3D structure of soil aggregatesis elucidated by microtomography. Peth et al. [3]investigated 3D structure of soil aggregates fromdifferent fields by synchrotron-radiation-based x-ray
microtomography, and statistical properties areanalyzed of the extracted pore-networks. Song et al.
[4] investigated nanoscopic 3D structure ofclaystones and estimated permeability based on the
Katz-Thompson equation. Jarvis et al. [5] evaluatedconnectivity or percolation probability, which is oneof network properties in the percolation theory, of silt
loam soil from 3D images by x-ray tomography. As
discussed in [11], it is essential to investigate
connectivity of pore-networks for elucidation of theconstitutive relations of porous media such as thepressure-saturation curves, the hydraulic conductivity,
air-permeability, and the air- and water-entry
pressures.
In this study, properties of porous media withaggregates are investigated quantatively from a pointof view of a network in addition to spatial
autocorrelation of pore arrangement in porous media.

POROUS MEDIA MODEL
Various virtual porous media are generated byusing the discrete element method (DEM). In thisstudy, three types of porous media are made: single
particle size, uniform distribution of particle size, andaggregates. Here, these are referred to SIN, UNI, andAGG, respectively. The first and second media aremade by packing particles with single size or various
size randomly, and these do not have aggregate
structure. The third one is made by packing
aggregates of particles with single size, which areprepared according to the JKR (Johnson-KendallRoberts) model [6].
Pore-network models are extracted from each
virtual porous medium based on the watershedsegmentation algorithm, which separates voids inporous media into many parts like watersheds from
3D image of porous media [7]. In this study, the
SNOW algorithm proposed by [8] is used to extract
pore-networks. A pore-network consists of PBs (porebodies) and PTs (pore throats). PBs are relatively
large pores and PTs are relatively small pores thatconnect two PBs. Properties of PBs and PTs such as
566

GEOMATE – Bangkok, Thailand, 22-24 November 2022

autocorrelation, where the obtain Moran’s 𝐼 value iscompared with those of pore-networks whose
attribute values are shuffled randomly [10].

volume, equivalent and inscribed diameters,
coordinate, and length are obtained by the SNOW
algorithm.
To ensure the REV (representative element
volume) of porous media and the quality of porenetworks, the size of the container that packs particles(or the number of particles) and the resolution of thevirtual porous media were examined in the previous
study [9]. Pores at outer edge tend to be irregularbecause of a wall and especially pores in the top ofmedia could be very large because a lid is notconsidered when particles are packed. To remove
such irregular pores, about 30% of marginal pores aretruncated from the extracted pore-networks. The
inside pore-networks are used for the following
analyses.

Percolation Probability
Percolation probability is an index of theconnectivity of a network, and it shows how many
elements of a network is open, how big ‘open’
clusters are formed in the network [11]. In this study,
elements are pores (PTs and PBs) in a pore-network,and an ‘open’ element means that the target pore canbe invaded by air if water-air interface reaches thepore satisfying the conditions described in thesubsection of invasion percolation. Hence, it isdetermined whether a pore is open or not bycomparing the capillary pressure and the air-entry
pressure of the pore. Here the index is referred as
invadability, and the invadability 𝛼 of each pore is
calculated assuming the air-water interface reaches
the target pore satisfying the condition.

ANALYSES
Spatial Statistics
The spatial distribution of pores in porous media
is analyzed by the spatial autocorrelation. Moran’s I
statistic is one of the statistical indexes that examine
spatial autocorrelation of a certain property
distributed spatially [10]. Here, pore size (PB’s
diameter) is a target, and it is examined if clusters oflarge and/or small pores exist in porous media.
The global Moran’s I statistic shows the averagelevel of spatial autocorrelation over the whole porenetwork, and its value takes from -1 to 1. 𝐼 is zero
when pores distribute randomly, 𝐼 is positive whensome clusters exist, and 𝐼 is negative when large andsmall pores distribute alternately. 𝐼 is defined asfollows.
𝐼=∑𝑛𝑖=1 ∑𝑛𝑗=1 𝑤𝑖𝑗 (𝑥𝑖 − 𝑥̅ )(𝑥𝑗 − 𝑥̅ )𝑚2 𝑆0

𝛼 = 𝑃𝐶 − 𝑃𝐴𝐸

where 𝑃𝐶 is the capillary pressure, 𝑃𝐴𝐸 is the pore-airentry pressure, and those are calculated as follows.

(1)

𝑛

(2)

𝑖=1

𝑛

𝑛

𝑆0 = ∑ ∑ 𝑤𝑖𝑗

𝑃𝐶 = 𝑃𝑎𝑖𝑟 − 𝑃𝑤𝑎𝑡𝑒𝑟(5)

𝑃𝑤𝑎𝑡𝑒𝑟 = 𝑃𝑏𝑡𝑚 − 𝜌𝑔Δ𝑧

(6)

𝑃𝐴𝐸 = 𝜌𝑔𝐿𝜎 cos 𝜃/𝐴(7)

where 𝑃𝑎𝑖𝑟 is the air pressure, 𝑃𝑤𝑎𝑡𝑒𝑟 is the waterpressure, 𝑃𝑏𝑡𝑚 is the pressure at the bottom of thepore-network, 𝜌 and 𝜎 is the density and surface
tension of water, 𝑔 is the gravitational acceleration, 𝐿
and 𝐴 are the circumference and cross-section area ofthe pore, 𝜃 is the contact angle, and Δ𝑧 is thedifference of the vertical coordinates between thepore and the bottom.
If 𝛼 is positive, the pore is ‘open’ or invadable.
Otherwise, if 𝛼 is negative, the pore is not ‘open’ or
uninvadable. As the proportion of open pores
increases by decreasing the water pressure imposed tothe bottom in a case of the drainage process, open
clusters also become large. As the open clusters grow,those begin to connect each other, and eventually one
large open cluster is formed. The percolation
probability of a finite network is evaluated by thepercentage of pores in the maximum open cluster tothe pores in the whole pore-network.

where 𝑛 is the number of PBs, 𝑥𝑖 is the attribute value(PB’s diameter) of the 𝑖-th PB, 𝑥̅ is the average of 𝑥,
𝑤𝑖𝑗 is the weight between 𝑖-th and 𝑗-th PBs. In thisstudy, 𝑤𝑖𝑗 = 1when 𝑖-th and 𝑗-th PBs are adjacent,and 𝑤𝑖𝑗 = 0 otherwise. 𝑚2 and 𝑆0 are defined asfollows.
1
𝑚2 = ∑(𝑥𝑖 − 𝑥̅ )2
𝑛

(4)

(3)

𝑖=1 𝑗=1

Invaded Percolation Probability
The statistical significance of the obtained
Moran’s 𝐼 value is tested by the randomization test
under the null hypothesis that there is no spatial

The above two indexes, Moran’s 𝐼 andpercolation probability, are static ones for a network
567

GEOMATE – Bangkok, Thailand, 22-24 November 2022

itself. In addition to the two indexes, water retention
characteristics and invaded percolation probability,
which were proposed by [12], are used to evaluate theproperty of porous media. The former is aconventional one that has been used to know the
relation between the imposed water pressure andretained water in the medium. The latter alsomeasures the relation between pressure and retainedwater, but it evaluates the relation in terms ofprobability in the same way with percolation
probability. For a finite pore-network, the invaded
percolation probability is evaluated by the percentage
of pores that are invaded by air to the pores in thewhole pore-network.

RESULTS AND DISCUSSIONS
Three types of virtual porous media, SIN, UNI,
AGG, are made by DEM, and each type has threesamples to check fluctuation in properties. A sampleof each type and extracted pore-networks after
truncation of outer pores are shown in Fig. 1. Table 1
shows the parameters of the porous media andextracted pore-networks. Figure 2 shows the
distributions of the coordinate number, which is thenumber of PTs that one PB connects with, of porenetworks and inscribed radius of PBs and PTs.
Almost same distributions among three samples ineach virtual porous media shows the volume
employed in this study is enough for REV. There areno major differences in the distributions of thecoordinate number among the virtual porous media.

Invasion Percolation
To simulate a drainage process, invasion
percolation is employed. In the process, invading
fluid invades into pores filled with invaded fluid if theinvadability defined in Eq. (4) of the pores is positive.
To ensure the physical process of drainage, thecontinuity rule for invading and invaded fluids needs
to be satisfied when invading fluid invades into poresfilled with invaded fluid. That is, the fluids on bothsides on an interface that can shift need to connect toeach outer pool through other pores filled with thesame type of fluid.
In the original invasion percolation [13], only one
pore whose invadability is maximum among
candidate pores, whose invadability is positive andsatisfying the continuity rule, in one step. Takeuchi etal. (2018) showed that various invading regimes canbe simulated by changing the number of invading
pores in one step [12].

Table 1 Parameters of porous media and extracted
pore-networks
Items

SIN

UNI

AGG

Diameter (mm)Mean num. ofparticles
Num. of particles
in one aggregate
Edge length ofcontainer bottom(mm×mm)Mean num. of PBs

0.2

0.1-0.5

0.2

3612

3318

2515

-

-

120

2.9×2.9

3.2×3.2

3.0×3.0

22122

17174

14951

6799

5510

4794

Mean num. of PTs

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

With respect to the pore size, percentage of larger
ones increases in UNI and AGG compared with SIN.

Almost same results among samples in each
virtual medium except for IPP in Fig.4 also show thatthe porous media volume is sufficient to evaluate theobjective properties, and that larger volume might beneeded for IPP. When invasion is fingering, theinvasion path strongly depends on the arrangement ofpores. In a case of the drainage process, air proceeds
selectively through larger pores, and about 40 to 50%
of water, or about 75 to 82% of water, are left
undrained in porous media.
Percolation probability and invaded percolation
probability of SIN and UNI are almost same, and thismeans that the difference of particle size distribution
does not affect the pore-network properties such as

Moran’s I
Table 2 shows the calculated global Moran’s 𝐼 ofeach sample. The values of all samples are positive,
which means that there are some clusters of larger
and/or smaller pores in each pore-network. And pvalues of all samples are quite small, which meansthat the realized spatial distribution of pores unlikely
occurs by chance. SIN and UNI show almost same
score, and that of AGG is more than twice as large as
those of SIN and UNI. This shows that AGG tends tohave larger clusters in pore-network.

Table 2 Global Moral’s I of PBs
PP and IPP
Percolation probability and invaded percolation
probability of each sample are shown in Figs. 3 and 4
with water retention property obtained from thesimulation of the drainage process by the invasion
percolation. Figure 3 is the result when the maximum
number of pores invaded in one step is 1000, and this
could reproduce uniform invasion if the imposed
suction is sufficiently small. This can be considered a
replication of the sand (soil) column method. Figure
4 is the one when the maximum number of pores
invaded in one step is 10, and this can be considereda replication of the vacuum method in which suction
is decreased gradually. This gradual invasion forms afingering pattern, and this corresponds to the capillaryfingering.
569

Items

Moran’s I

Z-scorep-value

SIN-1
SIN-2
SIN-3

0.132
0.137
0.143

6.56
6.76
7.10

5.07×10-11
1.42×10-11
1.27×10-12

average

0.137

UNI-1

0.128

5.76

8.45×10-9

UNI-2

0.156

6.95

3.68×10-12

UNI-3

0.163

7.35

2.04×10-13

average

0.149

AGG-1

0.351

13.54

≅𝟎

AGG-2

0.367

13.99

≅𝟎

AGG-3

0.355

13.29

≅𝟎

average

0.358

GEOMATE – Bangkok, Thailand, 22-24 November 2022

connectivity and spatial autocorrelation. In addition,
those graphs rise around 0.35 in all cases, and thismeans that at least one subnetwork that reaches from
top to bottom is formed by 35% pores from the larger
ones and that air begins to enter continuously. This

point is referred to as percolation threshold, and thiscorresponds to beginning of air-entry. Compared tothe percolation threshold of SIN and UNI, that of
AGG is about 0.15, which means that the top 15% ofpores forms at least one subnetwork that connects
570

GEOMATE – Bangkok, Thailand, 22-24 November 2022

from top to bottom. This small percolation threshold
is a notable feature of the porous media withaggregates structure.

[3] Peth S., Horn R., Beckmann F., Donath T.,
Fischer J., and Smucker A. J. M., ThreeDimensional Quantification of Intra-Aggregate
Pore-Space
Features
Using
SyncrotronRadiation-Based Microtomography, Soil Sci. Soc.
Am. J., Vol. 72, doi:10.2136/sssajs007.0130,
2008, pp. 897-907.
[4] Song Y., Davy C. A., Bertier P., and Troadec D.,
Understanding
Fluid
Transport
through
Claystones from Their 3D Nanoscopic Pore
Network, Microporous and Mesoporous Materials,
Vol. 228, 2016, pp. 64-85.
[5] Jarvis N., Larsbo M., and Koesttel J.,
Connectivity and Percolation of Structural Pore
Network in a Cultivated Silt Loam Soil Quantified
by X-ray Tomography, Geoderma, Vol. 287, 2017,
pp. 71-79.
[6] Coetzee C., A Johnson-Kendall-Roberts (JKR)
Contact
Model,
DOI:10.13140/RG.2.2.21772.54404, 2020, p. 20.
[7] Rabbani A., Jamshidi S., and Salehi S., An
Automated Simple Algorithm for Realistic Pore
Network Extraction from Micro-tomography
Images, J. Pet. Sci. Eng., Vol. 123, 2014, pp. 164171.
[8] Gostic J. T., Versatile and Efficient Pore Network
Extraction Metho Using Marker-based Watershed
Segmentation, Phusical Review E, Vol. 96, 2017,
203307.
[9] Song Y., Takeuchi J., Takeuchi Y., and Fujihara
M., Spatial Statistical Analysis of the Porous
Medium Extracted from Virtual Packed Grains
with Random Sizes, Int. J. GEOMATE, Vol. 21,
pp. 68-74.
[10] Okabe A. and Sugihara K., Network Spatial
autocorrelation, Spatial analysis along networks:
statistical and computational methods, John
Wiley and Sons, 2012, pp.137-151.
[11] Hunt A. and Ewing R., Percolation Theory for
Flow in Porous Media, Springer, Heidelberg,
2009, p. 319.
[12] Takeuchi J. and Fujihara M., Evaluation of
Imbibition Process in Porous Media by Invaded
Percolation Probability, Int. J. GEOMATE, Vol.
14, 2018, pp. 1-7.
[13] Wilkinson D. and Willemsen J. F., Invasion
Percolation: A New Form of Percolation”, J. Phys.
A: Math. Gen., Vol. 16, 1983, pp. 3365-3376.

CONCLUSIONSIn this study, spatial autocorrelation and network
property of virtually generated porous media withaggregate structure were investigated. To compare
those of porous media without aggregate structure,porous media with single particle size and uniform
distribution of particle size were generated. Fromeach porous media model, pore-networks wereextracted with the SNOW algorithm. For the spatial
autocorrelation, global Moran’s I was caluculated.
The global Moran’s I of pores in the porous media
with aggregates were more than twice of those
without aggregates. This means that there are moreclusters of relatively large and/or small pores in the
porous media with aggregates than the porous media
without aggregates. From the percolation probability
and invaded percolation probability, it was shownthat the percolation threshold of the porous media
with aggregates is about 0.15, which is quite small
compared with those (0.35) of the porous media
without aggregates. This means that 15% pores fromlarger form subnetworks that spread through thewhole porous media, and this gives porous media
with aggregates high drainablility.
ACKNOWLEDGMENTS
This work was supported by JSPS KAKENHI
Grant Number JP 20H03100.
REFERENCES[1] Beare M. H., Cabrera M. L., Hendrix P. F., andColeman D. C., Aggregate Protected andUnprotected Organic
MatterPools
inConventional and No-tillage Soils, Soil Sci. Soc.
Am. J. Vol. 58, 1994, pp. 787-795.
[2] Krull E. S., Skjemstad J. O., and Baldock J. A.,
Functions of Soil Organic Matter and the Effecton Soil Properties. Grain Research &Development Corporation Report ProjectNo.CSO 00029, 2004, p. 129.

571

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

NONLINEAR TIME SERIES ANALYSIS OF IRREGULAR
OSCILLATION INDUCED BY SALINE INTRUSION IN
GROUNDWATER WITH LAB-SCALE EXPERIMENT
Theara Seng1, Junichiro Takeuchi1 and Masayuki Fujihara1
1

Graduate School of Agriculture, Kyoto University, Japan
ABSTRACT

Density-driven flow that arises in groundwater or saturated porous media is a challenging problem in protecting
freshwater resources, because flow regimes would change depending on parameters such as density difference andpermeability. Especially when a certain condition is satisfied, pollutant would spread widely on the convection
currents induced by the density difference. In this study, the visualization experiment of saltwater intrusion into
homogeneous porous media on a laboratory-scale and the nonlinear time series analysis of measured data wereconducted. In the visualization experiments, it was shown that the flow regime changes from steady flow to
oscillatory flow as the Rayleigh number increase, and that the measured EC data fluctuates erratically. To analyze
the time series data produced by the visualization experiment, the phase space reconstruction, the Lyapunov
exponent and the recurrence plot were conducted after the analyses by autocorrelation. The obtained results suggestthat the irregular fluctuation of the salinity data could be chaotic, and that the series of analyses would help us tounderstand the dynamism of density-driven flow in groundwater.
Keywords: Saltwater Intrusion, Density-driven Flow, Rayleigh Number, Nonlinear Time Series Analysis
INTRODUCTION

measuring the position and configuration of thelayers, gives rise to certain errors. Typically, theaveraged values of these quantities are used toovercome these uncertainties and many researches
have been done mostly on homogenous porous
media.
In both the Elder-problem and Benardconvection, flow dynamism is investigated throughthe state of high Rayleigh number (Ra) [9]. Some
research works were conducted to investigateddensity-driven flow phenomena which is typicallyassociated with Rayleigh number [10]-[12]. From theextensive investigation of Kawabata [10], it revealed
oscillatory flow and its variation associated with theRayleigh number while periodic patterns appeared forthe numerical model. In recent years, numericalanalysis of instability of density-driven flow [13], andstability criterion of it in homogenous porous media[14]-[15] as well as chaos analysis [16] have been
done. As the replication for Kawabata’s work,
Hashimoto [11] conducted visualization experimentand analyzed the time series data of this experimentby using the statistical tests methods. The patterns oftime series data oscillated irregularly and seemed to
be random which could be chaotic. Regarding
irregular oscillation of saltwater data observed by[10] and [11], Theara et al. [12] estimated theoscillatory flow through designated Rayleigh number
in which the dispersion coefficient kept varies. It isfound that the oscillatory flow happened as abifurcation with increase in Ra. However, the timeseries analysis wasn’t conduct in this study.
To analysis the nonlinearity of time series dataobtained from laboratory experiments, there were an

Density-driven flow that arises in groundwater orsaturated porous media is a challenging problem inprotecting freshwater resources, because flow
regimes would change depending on parameters suchas density difference and permeability. Certain
pollutants are soluble in water and can leak into
groundwater systems, such as seawater into coastal
aquifers or wastewater leaks. Indeed, some pollutants
can change the density of a fluid and induce densitydriven flows within the aquifer. This causes fasterpropagation of the contamination due to convection.
Especially when a certain condition is satisfied,
pollutant would spread widely on the convection
currents induced by the density difference. Accurate
prediction of the movement of contamination in
groundwater is essential. Thus, visualized experimentand numerical analysis of this density-driven flowplay an important role in predicting how pollution canmigrate through an aquifer [1]-[2].
In contrast to the transport of pollution bymolecular diffusion, convection due to density-driven
flow is an unstable process that can undergo quite
complicated patterns of distribution. The Elderproblem is a simplified but comprehensive model that
describes the intrusion of salt water from a top
boundary into an aquifer [4]-[6]. Moreover,hydrogeological formations usually possess aheterogeneous and complicated structure, andgeological condition may consist of layers of porous
media with different porosities and permeability
coefficients [7]-[8]. Difficulty in specifyinghydrogeological parameters of the media, as well as572

GEOMATE – Bangkok, Thailand, 22-24 November 2022

increasing number of studies which applied theconcepts such as phase space reconstruction [17][18], the Lyapunov exponent [19]-[22] and [30], therecurrence plot [23]-[24], and the surrogate data test[25]-[29] by autocorrelation and Fast Fourier
Transforms (FFT). Recent years, Regression (R)technique is widely used to analyze the nonlinear timeseries data in most fields including geophysics
domain [31]-[32].
In this study, we aim to extent our investigation
on irregular oscillation induced by density-driven
flow in porous media through the visualization
experiment of saltwater intrusion and conduct thenonlinear time series analysis of the measured EC
data by employing the concepts of analysis mentioned
above.

seconds. We give the name of the probes
corresponding to their order from upstream todownstream as Cell1, Cell2, Cell3, and Cell4,
respectively.
Two types of relative saltwater density areprepared: 1.0105 and 1.0210 to produce relatively
low and high Rayleigh number flow regime. The
hydraulic gradients (∆h) are adjusted to 3.9 cm which
generate high velocity flow of groundwater. The
combinations of relative saltwater density andhydraulic gradients yields two kinds of the Ra fromthe lowest value of 25 to the highest value of 50. The
Ra = 25 is considered to be lower than threshold value(39.5) and Ra = 50 is greater than the threshold one.
Here, 𝑅𝑎 is calculated based on the following
formula.

VISUALIZED EXPERIMENT𝑅𝑎 =In this study, we extended our investigation
through visualization experiments on saltwater
intrusion into homogeneous porous media on alaboratory-scale by prolonging the duration of theobservation in order to obtain enough time series data
for analyzing. The entire experimental times threehours were set in the previous study. For theexperimental setup, the groundwater tank is filled byhomogeneous glass beads and it has internal
dimensions of 130 cm length, 45 cm height, and 5 cm
width as shown in Fig. 1. Deaerated water is utilized
as groundwater into the flow field. Commercial salt
solution dyed with red food color is used as saltwater
intrusion which is injected at the top left of aquifer
model. Two cameras are installed to monitor
saltwater behaviors at every step.

where 𝛾 is the specific weight of saltwater, 𝐾 is thehydraulic conductivity, 𝐻 is the representative length,
𝛼𝐿 is the longitudinal dispersion length, and 𝑣 is thegroundwater velocity.

(𝛾 − 1)𝐾𝐻
𝛼𝐿 𝑣

(1)

NONLINEAR TIME SERIES ANALYSIS OF
IRREGULAR OSCILLATIONIn this study, two cases of time series salinity data
were analyzed to confirm its nonlinearity. For
nonlinear time series analysis, R language is used.
Time Series Analysis
Autocorrelation
The autocorrelation for a periodic signal is also
periodic and for deterministic chaotic systems the
autocorrelation function decays exponentially withincreasing lag [34]. When the autocorrelation
function falls abruptly to zero, that indicates the lackof a deterministic component from the data; a slow
fall to zero is a sign of stochastic or deterministic
behavior; when the data slowly drops to zero andshows periodic behavior then the data is highly
correlated and is either periodic or chaotic in nature.
In this study, autocorrelation is conducted to observe
these kinds of problem, especially to investigate thechaoticity.

Fig. 1

Experimental setup of flow tank

Determination of Embedding Dimension

The current experimental setting aims to measuretime series data of salinity to analyze the irregularoscillation induced by the density-driven flow ofsaltwater intrusion. Hence, the portable electricalconductivity probes are inserted into the flow tank tomeasure the electrical conductivity time series data.
At this time, the entire experimental time is extended
up to 10 hours and the data is measured every 2

False nearest-neighbors
This method is to determine the minimal
sufficient embedding dimension m proposed by
Kennel et al [35]. It is called the false nearest
neighbor method. The idea is quite intuitive. Suppose
the minimal embedding dimension for a given timeseries is 𝑚0 . This means that in a 𝑚0 -dimensional
573

GEOMATE – Bangkok, Thailand, 22-24 November 2022()

Ri , j ( ) =   − xi − x j , i, j = 1,..., ndelay space the reconstructed attractor is a one-to-one
image of the attractor in the original phase space.
Especially, the topological properties are preserved.
Thus, the neighbors of a given point are mapped onto
neighbors in the delay space. In this study, we set the
maximum embedding dimension as 8.

(2)where n is the number of measured points xi , ɛ is a
threshold distance,  () the Heaviside function (i.e., ( x) = 0, if x < 0 , and  ( x) = 1 otherwise) and  isNonlinearity Analysis

a norm. Moreover, the RP is symmetric with respect
to the main diagonal; that is, Ri , j  R j ,i . If the series

Phase space reconstruction

is periodic, with period T, then, for fixed i, there is a
black dot every T − 1 white circles. In our study, thenumber of measured points n = 4,000.

Phase space or state space reconstruction is an
abstract mathematical construction with important
applications in statistical mechanics, where it allows
the time evolution of a dynamical system to be
represented in geometric form [31]. Phase space has
as many dimensions as the number of variables todefine the instantaneous state of the system. The R
package tseriesChaos offers a family of codes for the
analysis of nonlinear time series whose author andmaintainer is Di Narzo [36]. In this study, thedynamics in a 3-D phase space is reconstructed froma single time series data, using a time delay, where atime series data is transformed into a trajectory in the
3-D phase space.

VISUALIZED AND ANALYTICAL RESULTS
Visualization Experiment
Figure 2 (a)-(b) shows the behavior of intruding
salinity when the difference of water level is 3.9 cm.
The time series data for the case 𝜸 = 𝟏. 𝟎𝟎𝟐𝟔 shownin Fig. 3 (Ra=25) indicated the relatively smooth
pattern whereas those of the case 𝜸 = 𝟏. 𝟎𝟎𝟓𝟐 highly
fluctuated until the end. The cause of the large
fluctuation of Cell3 and Cell4 in Fig. 3 (Ra=50) isconvection, which occurred mainly after Cell2
although it is difficult to recognize it in Fig. 2 (b). For
the case 𝜸 = 𝟏. 𝟎𝟎𝟐𝟔 , irregular fluctuation isobserved in Fig. 3 (Ra=25), although Ra is below thethreshold value and no clear convection is observed.

Lyapunov exponent
Lyapunov exponents 𝜆 quantify the rate ofseparation of infinitesimally close trajectories. The
largest Lyapunov exponent is a very useful quantity
to calculate, since if it is positive, it indicates not
simply nonlinearity, but chaos [19]-[22].
The first step of our approach involves
reconstructing the attractor dynamics from a single
time series. We use the method of delays since one
goal of our work is to develop a fast and easily
implemented algorithm. In practice of R, for each of
the reference points, we look for k neighbors that arecloser than radius of the ball inside (𝜀) and have a
temporal separation greater than Theiler window (t).
Then, we compute the log-average distance between
the point and its k neighbors, and we follow it for a
finite number (s) of steps ahead in time. In our study,
we consider 4,000 reference point, each with 100
neighbors inside a ball of radius 0.02. The evolutionof the mean distance is monitored for 50 steps aheadin time and points separated by less than 5 from thereference point in the series are not considered asneighboring points.
Recurrence plots
As our focus is on recurrences of states of a
dynamical system, we define now the tool whichm⃗ 𝒊 ∈ R in phasemeasures recurrences of a trajectory 𝒙
space. The RP efficiently visualizes recurrences andcan be formally expressed by the matrix:

Fig. 2574

Time variation of salinity distribution

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 5 Autocorrelation of Ra=50 (Cell1 and Cell3)8Fig. 6 Nearest neighbors of Ra=25 (Cell1 and Cell3)8Fig. 3 Normalized salinity data under ∆h = 3.9 cm
In summary, in the visualization experiments, it
was shown that the flow regime changes from steadyflow to oscillatory flow as the Rayleigh number
increase. However, most of the measured EC data
fluctuates erratically which are contradictory to theresult of Kawabata [10]. Particularly, the time series
data in the high Ra flow regime are mostly consideredthat these irregular fluctuations are convection. Somereasons could be arisen. To analyze the time series
data produced by this visualization experiment, thephase space reconstruction, the Lyapunov exponent,
and the recurrence plot are used.

Fig. 7 Nearest neighbors of Ra=50 (Cell1 and Cell3)

Analytical Results
The results of autocorrelation plots show various
patterns in Fig. 4 and Fig. 5. The autocorrelation
functions for Cell1 to Cell2 decay slowly to zero (here
only Cell1 is shown in Figs.4 and 5) which indicateda sign of stochastic or deterministic behaviors.
However, the decays are slight faster on other twocells at downstream and they fluctuate around zero.
Therefore, it could be considered a chaotic behavior.
The nearest-neighbors analysis shown in Fig. 6 andFig. 7 indicated that the average minimal sufficientembedding dimensions are more than 3. But wecannot draw figures more than 3 dimensions, 3D plot
is used for phase space reconstruction.

Fig. 8 Phase space reconstruction (time delay d = 40)
with Ra=25 (Cell1 and Cell3)

Fig. 9 Phase space reconstruction (time delay d = 40)
with Ra=50 (Cell1 and Cell3)

The phase space reconstructions were analyzed byaltering time delays in order to search for an optimal
one. The value of time delay varies from 1 to 5. From
the result of numerical experiments, the appropriate
value of the delay is d = 40 as shown in Fig. 8 and Fig.

Fig. 4 Autocorrelation of Ra=25 (Cell1 and Cell3)575

GEOMATE – Bangkok, Thailand, 22-24 November 2022

9. This time delay corresponds to oscillations whosecycle are about 150-200 sampling times shown in Fig.
2. This time delay was applied to seek for the
Lyapunov exponents (Figs. 10 and 11).

the irregular fluctuation of the salinity data is not
chaotic.
CONCLUSIONS
The visualization experiment of saltwater
intrusion into homogeneous groundwater model andthe nonlinear time series analysis of measured data
were conducted in this study. The visualization
experiments, it indicated that the flow regime changes
from steady flow to oscillatory flow as the Rayleighnumber increase. However, most of the measured EC
data fluctuates erratically. According to analyticalresults of nonlinearity testing, the irregularfluctuation by the saltwater intrusion could bechaotic.

Fig. 10 Lyapunov exponent analysis Ra=25

ACKNOWLEDGMENTS
This research was partially funded by a Grant-inAid for Scientific Research(B) JP20H0310.
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Fig. 11 Lyapunov exponent analysis Ra=50

Fig. 12 Recurrence plot with Ra=25

Fig. 13 Recurrence plot with Ra=50

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[31] Huffaker, R. G., Huffaker, R., Bittelli, M., andRosa, R., Nonlinear time series analysis with R,
Oxford University Press, 2017.
[32] Cryer, J. D., and Chan, K. S., Time series
analysis: with applications in R (Vol. 2), New
York: Springer, 2008.
[33] Dimitrova, Z. I., and Vitanov, N. K., Adaptationand its impact on the dynamics of a system ofthree competing populations, Physica A:
Statistical Mechanics and its Applications, 300(12), 2001, pp. 91-115.
[34] Kennel, M. B., Brown, R., and Abarbanel, H. D.,
Determining embedding dimension for phasespace reconstruction using a geometricalconstruction. Physical review A, 45(6), 1992,
3403.
[35] Wallot, S., and Mønster, D., Calculation of average
mutual information (AMI) and false-nearest neighbors(FNN) for the estimation of embedding parameters ofmultidimensional time series in matlab. Frontiers in
psychology, 2018, 1679.
[36] Di Narzo, A. F., Package tseriesChaos: Analysis ofnonlinear time series, 2015.

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Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

MODELLING THE EFFECT OF CLIMATE CHANGES ON COASTALAQUIFERS IN OMAN
Javed Akhtar13, Ahmad Sana2*, Syed Mohammed Tauseef34 and Shakila Javed1
1 Civil & Architectural Engineering Section, Engineering Department, University of Technology and Applied Sciences,
Muscat, Sultanate of Oman.
2 Department of Civil and Architectural Engineering, Sultan Qaboos University, PO Box 33 Muscat, 123, Oman
3 School of Engineering, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India Civil & ArchitecturalEngineering Section, Engineering
4 Centre for Interdisciplinary Research and Innovation (CIDRI), University of Petroleum and Energy Studies, Dehradun,
Uttarakhand, India.

ABSTRACTIn Sultanate of Oman, the only source of recharge for the aquifers, which have been overexploited over the
past few decades, is rainfall, which is short in supply and varies with location and time. As a result, groundwaterlevels decreased, and seawater began to seep into coastal aquifers. The coastal aquifer of Wadi Al Jizi in Al Batinah
region was chosen as the study area for modelling the groundwater levels and the solute transport using
MODFLOW and MT3DMS, respectively. These models were calibrated and validated over a period of seventeen
years using data collected from twenty monitoring wells under stable and transient conditions. Further, calibrated
model was used to integrate future scenarios to simulate effect of climate changes considering two different
parameters: Rainfall and Sea Level Rise (SLR). Ground water heads and salinity intrusion caused by altered rates
of recharge and Sea level rise were simulated. According to these forecasts, if the base recharge rate will reduceby 10% and SLR considered to increase by + 0.50m from base SLR, salinity intrusion contains Total Dissolved
Solids (TDS > 12800 mg/L) will encroach more than one km inland by the year 2040 when compared with 2016
intrusion. Groundwater resources in the Wadi Al-Jizi aquifer in Al Batinah region may be effectively managedwith the use of the models and findings from the current study.
Keywords: Numerical Modelling, Al Batinah, Sea Level Rise, Recharge Rate, Salinity Intrusion
INTRODUCTION

Shibli, 2003).

Sultanate of Oman is an arid country in the vast
Arabian Peninsula suffering from insufficient
freshwater supplies and extreme hot weather
conditions. For the past few years, to meet with theincreased water demand, coastal aquifers wereoverstressed, which leads to the depletion of watertable, further, resulted in the saline intrusion.
Moreover, large coastal area covered with fertile land
become barren along with contaminated ground water
resources.
Sultanate of Oman's seawater intrusion issue isespecially severe in Salalah and the Al Batinah coast,both of which have extensive agricultural land. The
priceless agricultural land has become unusable dueto the seawater intrusion for more than two decades.
The Sultanate is considered a dry region due to its 100
mm annual average rainfall. Continuous population
growth, climate change, heavy urbanization, andchanges in land use have increased the thrust on theconventional surface water source to the extent that it
has forced groundwater extraction as an alternative tosurface water to cope with water scarcity (Akhtar etal. 2022). Over 90% of the year, clear, bright skies
and little wind are the "typical" weather conditions.
The summer is hot and dry, while the winter isdelightfully warm and dry. Rainfall serves as theprimary supply of freshwater in the upper catchments,
replenishing the aquifers in the process (Sana &Al Batinah region, which makes up roughly 4% of
the entire country's territory, is home to about onethird of the inhabitants of the nation. Highevapotranspiration rates and a dry climatecharacterize the eastern coastal plain (Lakey et al.,1995). It is characterized by a warm, low-humidity
winter, a hot, occasionally humid summer, and low,
unpredictable amounts of precipitation. The averageannual air temperature in the coastal region is 28.5°
C, whereas it is 17.8° C in the highlands. High rainfall
is reported in the southern high region compared tothe coastal region (Weyhenmeyer et al., 2002). The
quality of the groundwater is highly diverse along the
Al Batinah region. Water quality in the hilly region isvery good (less than 1500 mg/L) in the flat and low
land regions; water quality reduces (1500 -6500mg/L) as groundwater dissolves salts (primarily
calcium carbonate) on its way to the ocean. Water
quality in the Al Batinah region has significantly
declined (Barwani and Helmi, 2006). This was shownby a 7 percent decline in the regions with suitable
agricultural water (2,000–6,000 S/cm), which
represented the loss of 2,714 hectares of irrigatedland. The saline interface was also discovered 12 km
inland in the Wadi Al Taww, Barka region in Al
Batinah, Sultanate of Oman.
The main objective of the study is to simulate thegroundwater flow with a numerical model to address578

GEOMATE – Bangkok, Thailand, 22-24 November 2022

the seawater intrusion problem. The study isimportant and timely, considering the serious concern
regarding water security in Sultanate of Oman (and in
the world in general), and its significant influence onsustainable development of the country, that isexacerbated by the problem of seawater intrusion,
climate change and population growth.

longitudes 445120.53m E to 474236.85m E covered
in topographic map (Fig.1). The study area isbounded by the Sea of Oman in the north, andWestern Hajar mountain range in the south.

LITERATURE REVIEW
Groundwater use and maintenance in arid andsemi-arid environments are threatened bysalinization, a global issue in coastal aquifers. Manyof the coastal aquifers across the world have seen
rapid declines in water quality over the last few
centuries (Al Taani et al., 2013; Baalousha, 2016).
Shammas & Jack, 2008 used GMS (Groundwater
Modeling System) that included MODFLOW &MT3DMS to represent 3D groundwater flow andsolute transport in Salalah, Oman. In order toreplicate the intricate multi-layer aquifer found in the
Wadi Samail watershed in the Al Batinah region, Al
Hashmi et al. (2020) employed a 3D groundwatermodel (MODFLOW). In order to research the effects
of climate change on the Sumail Lower Catchment(SLC) aquifer and the Jamma aquifer in the Al
Batinah region of Oman, Maktoumi et al. (2018)
employed MODFLOW and MT3DMS. The
watershed seemed to continue groundwater flow inthe direction of the sea despite seawater intrusion,
although the Jamma aquifer was under stress, wasnegatively impacted, and was at risk. Using
MODFLOW and MT3DMS, Chitrakar and Sana
(2015) investigated the groundwater and seawater
intrusion pattern in the Eastern Al Batinah coastal
plain. Chun et al. (2018) used SUTRA (SaturatedUnsaturated Transport) to study the effect of climatechange and SLR on the salinity intrusion in the
coastal groundwater system of South Korea. Theirfindings revealed that for an accurate assessment ofclimate change impacts on seawater intrusion incoastal groundwater systems, both freshwater
recharge rates and SLR should be taken into account.
Ghazaw et al. (2014) used Visual MODFLOW tostudy the depleting level of groundwater of Saq
aquifer located in the Qasim area in Saudi Arabia.
Dibaj et al (2020) to investigate the combined
consequences of over-abstraction and seawater
intrusion in the Pingtung Plain coastal aquifer system
in Taiwan used a three-dimensional variable-density
finite element model. Seawater intrusion models are
therefore a very effective instrument for managing
and protecting coastal aquifers (El Hamidi et al. 2021)

Fig.1 Study area of Wadi Al Jizi
The total area of Wadi Al-Jizi catchment area is
1,154 km2, including 519 km2 of the eastern part. The
eastern part of the aquifer system is studied that has
an area of 123 km2 approximately, extending from thesea towards the mountains. This part of the aquifer
has been selected since most of the agriculturalactivities, pumping and observations wells andsalinity monitoring wells are in this zone.
METHODLOGY
Conceptual Model
A two-layered conceptual model (Fig.2) is
considered
which
is
a
three-dimensional
representation of the groundwater flow based on theavailable geological and hydrogeological data for the
study area.
The next stage was model development, which
was essentially the conversion of the conceptualmodel into the numerical model input files, andcalibration is performed to demonstrate that themodel can generate field determined heads and flowsthat are used as reference or calibration values ortargets. The model is validated after calibration topredict water table fluctuations and salinity intrusion
for a different set of data considering various
scenarios
In the present study the extent of finite difference
grid is 11903 m (x-direction) and 15285 m (ydirection), containing 356 columns and 357 rows.
Since groundwater levels may change dramaticallynear pumping locations, a finer grid size 5.0 × 10.0 m
was used for the areas where pumping wells are

STUDY AREA
The Wadi Al-Jizi catchment located in Al Batinah
region is selected as the study area (located between
latitudes 2685288.72m N to 2702809.30m N and
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

located whereas a maximum grid size of 40.0 × 80.0
m in other parts of the model domain was used.

and W>0.0 for inflow; Ss is the specific storage of theporous material (1/L); and t is time (T). This equation
combined with appropriate boundary and initialconditions, describes transient 3D groundwater flow
in a heterogeneous and anisotropic medium, provided
that the principal axes of hydraulic conductivity arealigned with the coordinate directions.
The detail about the model and information
regarding the studies that utilized MODFLOW can befound in McDonald and Harbaugh (2000). The results
obtained from MODFLOW for groundwater flow
characteristics can be used to run MT3DMS which isan advanced version of 3D Mass Transport Model,
MT3D developed by Zheng (1999). This model iscapable of modelling advection in steady andtransient flow fields, anisotropic dispersion, firstorder decay and production reactions and linear andnonlinear sorption. This model is based on the generalequation of mass transport as Eq. (2).

Fig. 2. Conceptual model of Wadi Al Jizi groundwater flow system

𝑹

The total number of grid cells are 254184 with
219916 active cells and the remaining as inactive. The
boundary on the northeast side of the model domain(coastline) and the southwest boundary (upstream of
the catchment) were set as specific head boundaries.
Initial water heads of upstream boundaries were
determined from water level records of observation
wells located at or near the upper boundary. The east
and west sides of the model domain have beenoriented parallel to the wadi flow direction andtherefore considered as no-flow boundaries. The
inflow to the model was considered from recharge
due to rainfall, while outflow was the discharge to thesea and from the existing pumping wells in Wadi-Al
Jizi region.

𝛛𝐂
𝛛𝟐 𝐂
𝛛𝟐 𝐂
𝛛𝟐 𝐂
𝛛𝐂
= 𝐃𝐱𝐱 𝟐 + 𝐃𝐲𝐲 𝟐 + 𝐃𝐳𝐳 𝟐 − 𝐕
𝛛𝐭
𝛛𝐱
𝛛𝐲
𝛛𝐳
𝛛𝐱
− 𝛍𝐑𝐂

(𝟐)

where, C is the concentration of solute, andDxx , Dyy and Dzz are the dispersion coefficients in
x, y, and z directions, respectively, V is the averagepore water velocity, R, retardation factor, and µ is
decay coefficient. For seawater simulation by
MT3DMS the decay constant is equal to zero,
therefore, µRC = 0.
RESULTS AND DISCUSSION
Steady-State Groundwater Flow simulation
The model was initially calibrated in steady state
for the year 2000. Trial and error adjustments ofhydraulic conductivity values were carried out for thesteady-state model until the observed and computed
value matched in selected nine observation wells,
distributed over the model domain. Hydraulicconductivity, one of the most importanthydrogeological parameters of the aquifer, wascalibrated manually using trial and error procedure.
This input parameter was varied to determine themodel response by conducting sensitivity analysis
and calculating and plotting the groundwaterfluctuations (Akhtar et al. 2022 -In Press)
Fig. 3 shows the calibrated values steady state
results for the year 2000.

Numerical ModelIn the present study the Groundwater Modeling
System (GMS) is used that includes MODFLOW andMT3DMS for groundwater flow and solute transport
processes, respectively. The governing equation in
MODFLOW is shown as Eq. (1).
∂
∂h
∂
∂h
∂
∂h
(K
) + (K yy ) + (K zz ) + W∂x xx ∂x
∂y
∂y
∂z
∂z
∂h
= Ss
(1)
∂t
where, Kxx , K yy and K zz are values of hydraulicconductivity (L/T) along the x, y, and z coordinate
axes (assumed to be parallel to the major axes ofhydraulic conductivity); h is the potentiometric head(L); W is a volumetric flux per unit volume (1/T)
representing sources and/or sinks of water, with
W<0.0 for outflow from the ground-water system,

Transient model calibration and validation
The steady-state result was used in the transientmodel simulation as the initial condition from 2000 to2017. The transient simulation was divided into two
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

parts: Jan. 2000 to Dec. 2009 was used to calibrate themodel and Jan. 2010 to Dec. 2017 was used tovalidate the model. The calibration target in thetransient state model was the groundwater elevationin 9 observations wells.

(a) NJ-1, (b) JA-2, (c) NJ-9, (d) EA-1, (e) JA-5, and(f) DE-5
A total of 18 stress periods were used with 12-time
steps in each month with a multiplier of 1 resulting in
6574 days. In the transient simulation the resultant
hydraulic conductivity and vertical anisotropy fromthe steady state were used. For validation of themodel, calibrated parameters like hydraulicconductivity, specific yield, and specific storage wereused with a new set of pumping rates and recharge
data for the period of 2010 to 2017. Fig.4 shows
comparison of observed and computed groundwater
elevation in the selected observation wells, namely,
NJ-1, JA-2, NJ-9, EA-1, JA-5, and DE-5.
After the validation of the model, various
management scenarios were studied to mitigate theseawater intrusion in Wadi Al-Jizi aquifer.
Effect of Recharge rates on Seawater Intrusion
Scenario-1: Recharge rate reduction and increment
To study the recharge rates impact scenario,
rainfall pattern which was followed from 2000 - 2017
was considered as baseline and an increment ofrecharge rates by 5% and 10% and reduction ofrecharge rates by 5% and 10% were considered in thebaseline recharge rates of 2000- 2017 applied to thescenarios from 2018-2040. For this scenarios, basepumping rates and base sea level rise were consideredand salinity intrusion contour (TDS > 12800 mg/L)
which was taken as a standard measurement by theministry was studied.

Fig.3 Model showing the zone distribution, boundary
conditions & calibrated values in Steady State (Year2000.

Fig.5 Salinity contours for Scenario-1(Recharge
increment by 5%) for the year 2040.
The salinity contours for the year 2040 was plotted toobserve the impact of recharge rates in the study area

Fig.4 Time series plot of selected observation wells
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

recharges rates by 10% showed that salinity intruded
landward at an average distance of 2.67 km in the year
2040. A rise of 0.50 m added to the baseline sea levelcombined with the reduction of baseline recharge
rates by 10% may result in salinity intrusion inland by
2.72 km as shown in Fig. 8.

as shown in Fig. 5 & 6. The salinity intruded inlandsby an average distance of 2.71 km and 2.74 km in year
2040 by reducing the base recharge rate by 5% and
10% respectively. Similarly, having an increase inbase recharge rates by 5% the salinity may retreat by
0.14 km (since the salinity intruded landward at an
average distance of 2.60 km in 2040) respectively.

Fig. 8 Salinity contours for Scenario-3 (Recharge
reduction by 10% & SLR +0.50m) for the year 2040

Fig.6 Salinity contours for Scenario-1(Recharge
reduction by 10%) for the year 2040

The distance by which the salinity may intrude
inland for all the considered eight scenarios in theyear 2040 is shown in Fig. 9.

Scenario-2: Recharge rate reduction and increment
combines with Sea Level Rise
In this scenario, base pumping rates wereconsidered, but the recharge rate has been reduced by
10% to from the base recharge rates and the Sea level
rise of 0.25m and 0.50m were considered to study the
impact of climate changes.

Fig. 9 Salinity intrusion distance (Year 2040) for all
scenario
CONCLUSIONS
If a numerical model is adequately calibratedusing the measurements of head, pumping rates, andboundary conditions that are currently available, it
can simulate and predict the head and quality ofgroundwater in coastal aquifers with a fair degree of
accuracy and economy. A different set of measured
data can then be utilized to evaluate the model using
the calibrated geo-hydrological parameters. Finally,
projections of groundwater quality and elevation canbe produced for each future event that is anticipated.
When compared to previous data, the ground water

Fig. 7 Salinity contours for Scenario-2 (Recharge
reduction by 10% & SLR +0.25m) for the year 2040
Fig. 7 shows the rise of 0.25 m above the baseline
sea level (0.65m) combined with the reduced base582

GEOMATE – Bangkok, Thailand, 22-24 November 2022

salinity have been increased in majority of the salinity
monitoring wells. Salinity intruded landward at an
average distance of 2.67 km in the year 2040, as
determined by a rise of 0.25 m over the baseline sea
level (0.65 m) and the reduction of base recharge rates
by 10%.

[8] Shammas M. I and Jacks G., Seawater intrusionin the Salalah plain aquifer, Oman.
Environmental Geological Journal 2008.
DOI:10.1007/s00254-007-0673-2.
[9] Al‑Hashmi S, Gunawardhana I, Sana A, &Baawain M, Application of groundwater flow
model in assessing aquifer layers interaction inarid catchment area, International Journal ofEnvironmental Science and Technology, 2020
https://doi.org/10.1007/s13762-020-02805-x.
[10] Al-Maktoumi A, Zekri S, El-Rawy M, Abdalla O,
Al-Wardy M, Al-Rawas G. & Charabi
Y,Assessment of the impact of climate change oncoastal aquifers in Oman, Arabian Journal of
Geosciences
11:
501.
2018.
https://doi.org/10.1007/s12517-018-3858-y.
[11] Chitrakar P and Sana A, Ground Water Flow and
Solute Transport Simulation in Eastern Al
Batinah Coastal Plain, Oman: Case Study”,
Journal of Hydrologic Engineering, ASCE.
ISSN: 1084-0699/050150020 (11).
[12] Chun J A, Lim C, Kim D and Kim J S, Assessing
Impacts of Climate Change and Sea-Level Riseon Seawater Intrusion in a Coastal Aquifer,
Water 2018, 10, 357; doi:10.3390/w10040357[13] Ghazaw YM, Gumman A R, Al-Salamah I &Khan Q U Z, Investigations of Impact ofRecharge Wells on Groundwater in Buraydah by
Numerical Modeling, Arab J Sci Eng 2014,
39:713–724.DOI 10.1007/s13369-013-0690-2.
[14] Dibaj M, Javadi A A, Akrami M, Ke K Y,
Farmani R, Tan Y C & Albert S. Chen,
Modelling seawater intrusion in the Pingtung
coastal aquifer in Taiwan, under the influence of
sea-level rise and changing abstraction regime,
Hydrogeology Journal 2022.
[15] EL Hamidi M.J., Larabi A and Faouzi M,
Numerical Modeling of Saltwater Intrusion in theRmel-Oulad Ogbane Coastal Aquifer (Larache,
Morocco) in the Climate Change and Sea-Level
Rise Context (2040), Water 2021, 13, 2167.
https://doi.org/10.3390/w13162167.
[16] McDonald M G and Harbaugh A W, A modular
three-dimensional finite difference groundwaterflow model technique of water resources
investigations of United States geological survey(2000), Vol. 6, USGS, Denver, 586.
[17] Zheng C, & Wang P P, MT3DMS:Transport
Model for Simulation of Advection, Dispersion,and Chemical Reactions of Contaminants in
Groundwater Systems; Documentation andUser's Guide, 1999, Vicksburg, Mississipi:
United State Army Corps of Engineers.
[18] Akhtar, J., Sana, A. and Tauseef, S.M., Using
MODFLOW to Investigate the Effect of
Persistent Water Deficit in Al-Jizi Coastal
Aquifer, Sultanate of Oman, Arabian Journal of
Geosciences 2022. (In press)

ACKNOWLEDGMENTS
The authors acknowledge the Environment
Authority, Sultanate of Oman for providing the
groundwater elevations and quality data.
REFERENCES[1] Akhtar J, Sana A, Tauseef S M, Chellaiah G,
Kaliyaperumal P, Sarkar H, Ayyaperumal R,
Evaluating the groundwater potential of wadi AlJizi, sultanate of Oman by intergrating Remotesensing & GIS techniques, Environmental
Science and Pollution Research 2022,
https://doi.org/10.1007/s11356-021-17848-x[2] Sana A and Al Shibli S H, Modelling of seawater
intrusion into a coastal aquifer in the Sultanate ofOman”, Proceedings of 30th Congress ofInternational
Association
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HydroEnvironment
Research(IAHR2003),
Thessaloniki, Greece, pp.581-588.
[3] Lakey R, Easton P, Hinai A H, Eastern Batinah
Water Resources Assessment- The International
Conference on Water Resources Management inArid Countries Proceedings 1995. Muscat.
Sultanate of Oman. The library of the General
DirectorateofRegional
Municipality,
Environment and Water Resources in North of
Al Batinah, Sohar, Oman.
[4] Weyhenmeyer C E, Stephen J B, Niklaus W H,
Isotope study of moisture sources, recharge
areas, and groundwater flow paths within the
Eastern Batinah coastal plain, Sultanate ofOman, Water Resources Research 2002, Vol. 38,
NO. 10, 1184, DOI: 10.1029/2000WR000149.
[5] Al Barwani A. and Helmi T. (2006), Seawater
intrusion in Coastal Aquifer: A case study for thearea between As Seeb and As Suwaiq (1984–2005)”, International Conference on Economic
Incentives and Water Demand Management,
United Nations Education Scientific and Cultural
Organization (UNESCO) 2006, France.
[6] Al-Taani A A, Batayneh A , Mogren S , Nazzal
Y, Ghrefat H, Zaman H, Elawadi E,
Groundwater Quality of Coastal Aquifer
Systems in the Eastern Coast of the Gulf of
Aqaba, Saudi Arabia, Journal of Applied Scienceand Agriculture, 8(6) November 2013, Pages:
768-778.
[7] Baalousha H M, Development of a groundwaterflow model for the highly parameterized Qatar
aquifers”, Model. Earth Syst. Environ. (2016)
2:67. DOI 10.1007/s40808-016-0124-8.

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Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

SIMULATION OF SMOKE DISPERSION AND TEMPERATURE
DISTRIBUTION ON KEBON MELATI SUB-DISTRICT FIRE USINGCOMPUTATIONAL FLUID DYNAMICS
1

Deffi Ayu Puspito Sari1, Agnes Setioningrum1, Dani Harmanto2
Environmental Engineering, Universitas Bakrie, Indonesia; 2 Aeronautical Engineering, De Montfort University,
United Kingdom
ABSTRACT

Kebon Melati sub-district is one of the most populous sub-district in DKI Jakarta with three cases of dense
settlement fire the last twenty years. Those fire cause fire smoke dispersion and fire propagation as secondary impact.
The aim of this research is to analyze fire smoke dispersion pattern and temperature distribution pattern around hotspots,
also to analyze potential area that has to encounter those secondary impacts. Google Maps, AutoCAD 2015, Revit
2015, Autodesk Flow Design 2015, and Fire Dynamics Simulator are used in this research for modeling of fire andstudying area geometry. Autodesk Flow Design 2015 simulation result wind flow in the west side of Kebon Melati
sub-district is more stable than in the east and middle side which have wind flow turbulence as effect of wind flow
separation by skyscraper buildings. On the outer west side of Kebon Melati sub-district, wind velocity increase up to
9 m/s as effect of wind flow separation by skyscraper buildings. Fire Dynamics Simulator simulation result, in 30 s
simulation with 10.000 kW/m2 Heat Release Rate per unit Area, fire smoke disperse up to 1.440 m in South direction.
The south side of Kebon Melati sub-district is more vulnerable in degradation of visibility, fire smoke concentration
enhancement, and fire propagation.
Keywords: smoke dispersion, fire, CFD, temperature distribution, Fire Dynamics Simulator
INTRODUCTION
Jakarta, the Capital of Indonesia, has dense
population that increase every year. One ofproblems appear from densely populated
settlements is fire case. Cases of fire that occur on
densely populated settlements can come fromhuman error factors (human activities) (Ismawan,2008).
Based on the fire history data by DKI Jakarta
Province Fire and Rescue Service, many cases offire in Jakarta caused by short-circuit electricity.
One of the most densely populated areas that areprone to fire is Kebon Melati Sub-district, locatedin Tanah Abang District, Central Jakarta. In the lasttwenty years, there have been three cases of fire in
Kebon Melati Sub-district.
In DKI Jakarta 2030 Regional Spatial Plan
(RTRW), DKI Jakarta government maps Kebon
Melati area as a middle-class vertical residence
that combines inspection roads, green open spaces,
and adequate transportation rates to Tanah Abang
Market. That plan was formed because thegovernment's target to make Tanah Abang a center
for world trade (Transformasi, 2015).

patterns and temperature distribution patterns. So
that it can be predicted in areas that will experience
air pollution due to fire smoke dispersion and areas
prone to fire propagation in Kebon Melati Subdistrict.
METHODOLOGY
In analyzing Kebon Melati fire case, theComputational Fluid Dynamics (CFD) method canbe used to produce information about fluid flow
and heat transfer patterns on fire conditions in
Kebon Melati Sub-district. CFD is one of thenumerical methods in fluid mechanics whichbasically uses the philosophy of discretization as
an approach in solving mathematical equations.
The use of CFD is very useful in virtual prototype
analysis of a system or tool that will be analyzed
by applying real conditions in the field (Pasha,2011).
According to Tuakia (2008), Computational
Fluid Dynamics/ CFD is the study of how to
predict fluid flow, heat transfer, chemical reactions,and other phenomena by solving mathematicalequations (mathematical models).
According to Mahan (2009), in general, thesimulation process in CFD is divided into threestages, preprocessing, solving, and post processing.
The previous step calculation results areinterpreted into several forms including graphs,
curves, animations, 2D and 3D images.
Preprocessing is the first step in a simulationthat is making a preparation of building’s geometry
model in the form of CAD (Computer Aided
Design), creating a grid or mesh, and determining

OBJECTIVE
The purpose of this study was to determineand explain smoke dispersion patterns andtemperature distribution patterns in Kebon Melati
Sub-district fire simulations using Computational
Fluid Dynamics (CFD). In addition to knowing thesmoke dispersion patterns and temperature
distribution patterns in fire simulation, also
determined factors related to smoke dispersion
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

the boundary conditions of the geometry. Solving
is the calculation process and the equations
contained in the CFD model are solved using acomputer program in accordance with theconditions that have been determined during
preprocessing before.
Postprocessor
displaysvisualizations
including:
1.the results of geometry and cells that have been
formed;2.plots based on vectors;
3.contour based plots; and4.plot based on the surface (two dimensions or
three dimensions) (Marihot, 2015).
One of the CFD method modeling programs infire simulation is Fire Dynamic Simulator (FDS).
FDS is a CFD method modeling program for fluid
flow with buoyancy style (McGrattan, Hostikka,
McDermott, Floyd, Weinschenk, & Overholt,2013).
FDS was developed by the National Institute ofStandards and Technology (NIST) in collaboration
with the VIT Technical Research Center of Finland,and other communities engaged in the field of fire
safety. In visualizing the results of programming,
FDS is continuous with the Smokeview program.
Both programs are free and open-source accessibleon the official NIST website.
FDS completes basic mass conservation,
momentum equations, and energy for thermal
expansion, as well as ideal gases with various
mixture components. FDS applies the Large Eddy
Simulation (LES) and Direct Numerical
Simulation (DNS) turbulent models (Hu et al.,2011).
In its program, FDS requires a basic input line
including building geometry, computational cellsize, the location of the fire source, heat release
rate (HRR) of the fire source, physical properties
and thermal properties of the wall, as well as thesize, location and time of ventilation. will affect thegrowth and spread of fire. (Barowy andMadrzykowski, 2012).
The study area in this simulation is Kebon
Melati Sub-district, Tanah Abang District, Central
Jakarta. The research methodology can beobserved in Figure 1.1. The retrieval of
meteorological data in the form of dominant winddirection and velocity data was carried out inNovember 2017. Three weeks of data collection
was the dry season, and the last one week of datacollection was the rainy season. The
meteorological data obtained was validated withBMKG meteorological data (the Meteorology,
Climatology and Geophysics Agency) in
November 2017 which can be accessed through theofficial BMKG website. Data analysis techniques
used are quantitative data analysis techniques by
Computational Fluid Dynamics (CFD) simulation
method.
The software used in this study includes
Microsoft Office 2010, Microsoft Excel 2010,
AutoCAD 2015, Revit 2015, Autodesk Flow

Design, Fire Dynamics Simulator. In this study, theAutoCAD 2015 license, Revit 2015, and Autodesk
Flow Design were obtained by educational licenses
that have 3 years of validity period.
Meteorological data analysis techniques wereobtained using Microsoft Excel 2010 to determine
the distribution of dominant wind direction andvelocity in the form of tables and charts of Wind
Rose. The analysis phase was also carried out todetermine the flow pattern and temperature
distribution and air rate around the fire simulation
location. Analysis of flow patterns and temperature
distribution and air velocity are obtained through
CFD simulation methods.
Methodology

Field Observation

Meteorological Data
Observation

Simulation Modelling
Analisa Data Meteorologi
Google Maps
Microsoft Excel
AutoCAD 2015
Meteorology
Data Table

2D Maps (.dwg)

Wind Rose
Diagram

Revit 2015

3D Maps (.rvt)

AutoCAD 2015

FDS Input Line

3D Maps(.stl)

Fire Dynamics
Simulator

Autodesk Flow
Design 2015

Fire Simulation

Wind Flow
Simulation

Figure 1.1 Research Methodology

585

GEOMATE – Bangkok, Thailand, 22-24 November 2022

The first stage in CFD simulation is thecreation of location geometry obtained by Google
Maps and converted in the form of twodimensional (2D) line sketches using AutoCAD
2015 with 1:26 scale. The sketch of 2D lines wasconverted back into three-dimensional (3D)
sketches using Revit 2015. The types of buildings
in Revit 2015 modeling were divided into three
types of building models, slums, simple houses,
and high-rise buildings. The determination of thethree types of buildings is based on fieldobservations that have been carried out. Slumhouses concentrated on the West side of Kebon
Melati Sub-district. Multi floor buildings areconcentrated in parts of the North and East sides,
while simple houses are spread on the North,Central and South sides of Kebon Melati Subdistrict.
At Revit 2015, building lines are converted
into walls of varying types and heights. The wallsof the slums are made of wood or plywood, while
the walls of simple houses are made of brickmaterial, and the walls of high-rise buildings are
made of concrete.
The wall thickness of slums and simple houses
in Revit 2015 software modeling is 0.1016 meters
and the wall thickness of high rise buildings is
0.2032 meters. Variations in the type and thicknessof the walls of the building can be arranged in theProperties column of each wall. In the case ofslums, there is no variation in the number of floorsof buildings, slum houses only have one floor of
the building. Whereas a simple house building has
a variety of building floors, one-floor building, andtwo-floors building. Multi floors building havevariations in the number of floors, namely threefloors to twenty floors. The height of each floor of
the building is 3 meters and is set on the Revit 2015
leveling process.
After finishing 3D geometry, it exported into
Fire Dynamics Simulator. In modeling FDS
software, there are several input data that need to
be prepared. The input data includes building
property data in the form of building geometry andits constituent material, meteorological data in theform of wind direction and wind velocity, desired
output parameters, ambient state of theenvironment in the simulation domain.
Setting the ambient state in the simulation
domain includes mesh settings, wind velocity andwind direction, combustion reactions that willcause sparks in the simulation, starting point of fireand fire strength, and the desired simulation Meshused has an X-axis that extends from the North side
to the South side by 76 units, the Y-axis that
extends from the Westside to the Eastside extends
for 72 units, and 4 units of Z-axis. X and Y meshaxes have a unit value of 0.25 m, while the Z-axis
has a unit value of 0.1667 m. So that the total
dimensions of the mesh in this simulation are 18 m
x 19 m x 4 m.
FDS operations require a very large computerstorage capacity in large-scale geometry modeling.

Therefore the 3D sketches that have been formed
are scaled down to a scale of 1: 12,000 using Revit
2015. Fire Dynamics Simulator will also obtained
room scope fire simulation with fire-triggering
specifications, the type and utilization of burnedbuildings.
Through Fire Dynamics Simulator, we canobtain temperature distribution patterns on burned
objects and in the surrounding objects. In
determining the fire smoke dispersion pattern, 3D
geometry is exported to the Autodesk Flow Design
program for further modeling with specificationsof wind direction and velocity.
RESULTS
Fire simulations were carried out at fivedifferent points in Kebon Melati Sub-district, as in
Figure 1.2. The four points are determined with theassumption that they represent the four cardinal
directions, namely North, East, South, West andone point in the middle of Kebon Melati Subdistrict. Based on the field observations that havebeen carried out, the types and shapes of thebuildings at the five points are divided into three
types of buildings namely slum houses, simple
houses, and high-rise buildings.
Point 1 on the north side and Point 3 on thesouth side of Kebon Melati Sub-district represents
simple house buildings. Point 2 on the middle sideand Point 4 on the east side of Kebon Melati Subdistrict represents high-rise buildings, according tothe observations that have been made. While Point
5 on the west side of Kebon Melati Sub-district
represents slums.

1

scale 1:26

5
2

4

3
Figure 1.2 Meteorological Data Observation
Points and Fire Simulation Points
Observation of meteorological data in theform of measurements of velocity and winddirection observations carried out for one month inNovember 2017, the first three weeks are the dry
season and the last one week is the rainy season.
Wind measurements carried out at fivemeasurement points were analyzed using
Microsoft Excel in the form of simple Wind Rosetables and diagrams formed from Radar type
diagrams. Observations of meteorology data canbe observed in Table 1.1.
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Table 1 Meteorological Data of Five Observation
Pointsthe following month. The measured wind velocity
converted in the form of a simple Wind Rose
diagram. The Wind Rose diagram in this study is adiagram that illustrates the distribution of windvelocity and wind direction distribution based on
the number of events.
In the wind velocity conversion process ofWind Rose diagram data in Graph 1.1, windvelocity grouped and the frequency of occurrence
calculated based on sixteen wind directions. The
grouping of wind velocity is divided into six
groups of velocity with a velocity range of eachgroup of 1 m/s.
The wind group consists of groups with windvelocity range of 0 m/s to 1 m/s, groups with windvelocity range of 1 m/s to 2 m/s, groups with windvelocity range of 2 m/s to 3 m/s, groups with windvelocity range of 3 m/s to 4 m/s, groups with windvelocity range of 4 m/s to 5 m/s, and groups withwind velocity of more than 5 m/s.
Combined Wind Rose Diagram of fiveobservation points in Graph 1.1 shows that inNovember 2017 the average wind observed at fiveobservation points tends to blow towards the South
with wind velocity of 1 m/s to 2 m/s. This trend is
obtained from the observation of the number of
events during November 2017, twenty incidents ofwind gusts towards the South with wind velocityof 1 m/s to 2 m/s. So that is determined the windflow modeling in Autodesk Flow Design is carriedout with wind direction control to the South with 2
m/s wind velocity.

Wind Velocity (m/s)
Wind
Direction

Location (Points)

Date
1

2

3

4

5

1
2
3
4
5
6
7
8
9
10
11
12
13

2.1
1.0
2.1
0.8
1.9
1.9
3.5
1.5
0.8
2.4
1.5
2.0
2.2

2.6
1.3
2.4
1.5
1.7
2.5
2.8
1.3
1.7
2.2
1.8
1.9
2.6

2.6
1.1
2.2
1.4
2.1
2.0
2.8
0.9
1.4
2.4
2.0
2.1
2.6

2.2
0.7
1.9
0.9
2.4
1.7
3.1
1.5
1.2
1.9
1.7
2.4
2.2

2.0
0.8
1.9
0.8
1.9
1.1
3.4
1.1
0.9
1.9
1.5
1.9
2.0

SBD
S
S
Ti
SBD
S
SBD
S
Te
S
S
ST
ST

14
15

1.5
2.3

2.5
2.8

2.1
2.8

1.7
2.2

1.1
2.4

Te
Ti

16
17

1.1
2.2

1.7
2.3

1.5
2.1

1.1
1.7

1.5
1.8

BD
TL

18
19

1.1
0.8

0.7
0.8

1.1
0.8

1.4
1.1

0.9
1.0

TT
SBD

20
21

2.2
2.5

2.2
1.9

2.5
2.1

1.9
2.4

1.9
1.9

ST
TTL

22
23

1.9
2.1

1.7
2.5

1.4
2.5

1.8
2.1

1.8
2.4

Ti
Te

24
25
26
27
28
29
30

3.6
5.1
5.4
6.1
5.5
4.3
3.8

3.6
4.3
5.0
6.2
5.5
4.5
4.0

3.2
4.3
4.4
5.9
4.8
4.6
3.8

2.6
3.9
4.4
5.3
5.1
4.2
3.5

2.9
4.6
4.5
6.2
5.4
4.2
3.5

TL
TTL
TTL
TL
TL
TTL
TTL

Av.

North
Northwest
Northwest

North

20
15

WestNorthwest

10

West

0

5

WestSouthwest
Southwest
SouthSouthwest

2.51 2.62 2.52 2.34 2.31
Note:
BD = Southwest
S = South
SBD = South Southwest
ST = South Southeast
Te = Southeast
Ti = East
TL = Northeast
TT =East Southeast
TTL = East- Northeast
In the last week of measurement, there was anincrease in wind velocity blowing in Kebon Melati
Sub-district. This is caused by the transition of theseason between the dry season to the rainy season

North
Northeast
Northeast
EastNortheast
East

1 <= ws < 2
2 <= ws < 3
3 <= ws < 4

EastSoutheastSouth

0 <= ws < 1

Southeast
South
Southeast

4 <= ws < 5
ws >= 5

Fig. 1 Wind Rose Diagram of Five Observation
PointsNote: ws = wind speed (m/s)
Kebon Melati Sub-district area modeling
begins with a depiction of a line sketch map ofKebon Melati Sub-district area obtained from
Google Maps. The map of area was redrawn with
the Rectangle and Polyline commands in the
AutoCAD 2015 so that the final results in the formof a line sketch map of the Kebon Melati Subdistrict area, as in Figure 1.3. The sketch is thenprocessed into a three-dimensional sketch with thehelp of Revit 2015.
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

scale 1:26

scale 1:12,000

Figure 1.5 Low Wind Flow Modelingscale 1:12,000
Figure 1.3 AutoCAD 2015 Results
In Revit 2015, building lines are converted
into building’s wall with varying types and heights.
The walls of the slums are made of wood or
plywood, while the walls of simple houses are
made of brick material, and the walls of high-risebuildings are made of concrete. The height of eachfloor of the building is 3 meters and is set in Revit
2015 leveling process. The three-dimensional
conversion results can be observed in Figure 1.4,
as follows.

scale 1:12,000

Figure 1.4 Revit 2015 Modeling Results
Autodesk Flow Design’s modeling produces
wind flow model with 2 m/s of velocity to theSouth. Modeling is limited to the observation ofvelocity and wind flow patterns. The fluid
modeling used is the Flow Line model with fluidlines and tubes line type to facilitate theobservation of the fluid flows pattern in the formof elongated lines.
Top side observation aims to observe thepattern of wind flow horizontally at different
heights. Top side observation results can beobserved in Figure 1.5 and Figure 1.6. Observation
variations of height include wind flow at altitudes
below 3 meters (low) and wind flow with altitudes
more than 3 meters (high).

588

Figure 1.6 High Wind Flow Modeling
Based on these two modeling, several colorlines can be observed. The difference in the colorof the line illustrates the different of wind velocity.
The red line shows the highest wind velocity while
the dark blue line shows the lowest wind velocity,which is close to 0 m/s on the Southwest side ofKebon Melati Sub-district. Modeling with windflow altitude variations caused differences of windvelocity range despite of equal initial windvelocity, which is 2 m/s. In modeling with low
altitude, the largest wind velocity reached 9 m/s,
which is on the eastern side of the Kebon Melati
Sub-district. This velocity occurs as a result of thewind flow deflection in the area that has tightdistance between buildings.
The largest wind velocity in modeling withhigh wind flow altitudes only reach 6 m/s, with thesame flow location as low wind flow altitude
modeling. The deceleration of wind velocity is dueto the difference in building height in the area withthe distance between buildings that are quite tight.
The difference in height of the building causes a
gap that allowed the wind to flow so that the windflow hits the area is not completely deflected. The
lowest wind velocity can be observed in the areasurrounded by high-rise buildings on the Middle
side of Kebon Melati Sub-district, with the
turbulence of wind flow forming a circular flow
pattern.
In accordance with Hosker analysis in Alamos
et al. (2004), buildings can change wind flow fields
and ward off wind flow, causing wind flow to rise
and wind flow to fall, horizontal and verticalvortices between buildings, on street corners, andother places on the Urban Canopy Layer. The windflow pattern produced is also in accordance withthe research conducted by Oke (1988) and
Grimmond (2002).

GEOMATE – Bangkok, Thailand, 22-24 November 2022

The wind flow around the building is broken
down at the side of the building. In both models,
turbulence can also be observed in the form of acircular flow pattern in an area that is lower thanthe height of the building. This turbulence occurs
with lower wind velocity compared to deflected
wind velocity. In accordance with the experiments
conducted by Hoydysh in Alamos et al. (2004)
wind flow on the street canyon forms a verticalvortex pattern paralleled with the axis to the street
canyon and affects pollutant concentration onstreet canyon and ventilation outside the street
canyon.
Side viewpoint modeling, the East side ofKebon Melati Sub-district, aims to observe thepattern of wind flow vertically in different building
types. Variations in this modeling are carried out
with variants of high-rise buildings, slums andsimple buildings.
The largest wind velocity generated from twomodels has a slight difference. The largest windvelocity range in variants of high-rise buildings ishigher than the variants of the slums and simple
buildings. The largest wind velocity occurs when
the coming wind hits the building so that the windflow deflected toward the building.
In contrast to the research conducted by
Ozmen, Baydar, and van Beeck (2016) on low-risebuildings, in this FDS simulation, the roofs ofslums and simple buildings that have low buildingheight are planar shaped so that the recirculation
area produced due to the sharpness of the roof
cannot be observed. clearly.
Figure 1.7 shows modeling with variants ofbuilding heights. In this modeling, turbulence canbe observed in areas covered by buildings fromwind exposure. Turbulence is in the form of a flowline that rolls up at a velocity lower than thevelocity of the wind that hits building. Thisphenomenon is in accordance with the research
conducted by Grimmond (2002), the wind flow
pattern in the Urban Canopy Layer (low altitude
area with several buildings as a barrier), will
experience turbulence in the form of circular flow
patterns due to interaction with the barrier. The
circular flow pattern can increase pollutant
concentration on the street canyon. The flow will
be stable in the Inertial Sublayer that is above the
Urban Canopy Layer area due to the absence ofbuildings that cause deflection interaction of wind
flow.

Figure 1.7 Wind Flow Modeling on Different
Height of Building
In the high-rise buildings modeling,
turbulence can be observed between its gaps.
Turbulence forms a circular flow pattern with windflow velocity up to 1 m/s. The turbulence of air
flow can increase pollutant concentrations in street
canyon. Increased flow velocities in streams withaltitudes exceeding the height of high risebuildings occur due to the upward flow of windflow by high rise buildings. The highest wind flow
velocity reaches 2.8 m/s which is at the point ofwind flow deflection by high-rise buildings,
illustrated by the red line
Wind flow velocity and flow pattern in slums
and simple house buildings modeling are morestable and constant than high-rise buildings
modeling. This can be observed in Figure 1.8. The
biggest velocity reaches 2.4 m/s on deflected windflow after hit the outermost building from thedirection of the wind coming. The velocity isrepresented by a red line. Wind velocity between
slums and simple buildings is only between 0 m/s
to 1.2 m/s. While the wind velocity at high altitude(more than 3 m) reaches 2.2 m/s which is depictedwith a yellow line.

scale 1:12,000

Figure 1.8 Slums and Simple Buildings Wind
Flow ModelingAfter obtaining Autodesk Flow Designmodeling, the three-dimensional sketches
generated from Revit 2015 software are also stored
in drawing format (.dwg) that supported by
AutoCAD 2015 software. These three-dimensionalsketches are converted in the form of input linesthat correspond to the Fire Dynamic Simulator(FDS) format. The conversion is done with the
3dsolid2fds command line in the AutoCAD 2015
software which was developed separately from thesoftware.
In FDS modeling, there are several input datathat need to be prepared. These input data includes
building properties in the form of building
geometryand its constituent
material,
meteorological data in the form of wind direction
and wind velocity data, desired output parameters,
ambient state of the environment in the simulation
domain.
The mesh used has X dimension that extends
from the North side to the South side for 72 units,
the Y dimension extending from the Westside to
the East side which extends along 76 units, and thescale 1:12,000

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Z dimension representing mesh height for 4 units.
The geometry of the building and its constituent
material has been determined in the previous
3dsolid2fds conversion. The geometry of thesimulation area can be observed in Figure 1.9.

scale 1:12,000

scale 1:12,000(b) Middle Point

scale 1:12,000(c) South Point

Figure 1.9 Five Observation Points Geometry
At Point 1, Point 3, and Point 5, the starting
point of the fire (hotspots) is simulated on a flat
plane located at the top of each building. Unlike
the research conducted by Lin et al. (2016),
hotspots in this simulation building are carried out
on the roof of a building that has direct contact withthe wind flow.
Based on Barowy and Madrzykowski (2012)
complex physical and chemical modeling inelectrical short circuit case occurs on smaller
modeling scale than the computational cell sizeused in FDS simulations. So in this simulation, thesource of the fire is from the planar plane on the
top side of the building.
Fire simulations in FDS software can only be
run on fire generated by a chemical reaction ineach simulation. The reaction can only beprocessed on a flat plane that has been determined
its surface constituent material. Results of five fire
simulation points can be observed in Figure 1.10.
Fire simulations are carried out within a period of
30 seconds. In this period of time, the smoke flow
and radiation patterns around the simulation point
can be clearly observed.
Fire and fire smoke parameters from time totime are then studied by FDS. In contrast a study
conducted by Wahono (2008), with 700 kW/m2 ofHeat Release Rate per small area unit (HRRPUA)
and ignition time of 35 seconds. The fire
simulation in this study uses 10,000 kW/m2 of
HRRPUA. The amount of HRRPUA determines
the magnitude of the starting point (hotpoint) of thesimulated fire, and the period of time determining
the duration of the burning time. The use of large
HRRPUA is intended to facilitate visualization of
the dispersion of smoke produced.

scale 1:12,000(d) East Point

scale 1:12,000(e) West Point
Figure 1.10 Five Points Fire Simulation
Visualization
Based on the visualization of the five firepoints, it can be observed that the resulting fire andthe smoke produced are quite large. The resulting
smoke interacts with the force produced by thewind flow that blows from the North to the Southby 2 m/s. This is in accordance with previous
research conducted by Sujatmiko et al. (2017), thecontinuity of combustion is determined by theavailability of oxygen which is affected by
openings or ventilation around the fire point.
These interactions cause fire smoke
dispersions that are more likely disperse to thesouth side of the fire point. In accordance with thewind flow modeling in Autodesk Flow Design, theresulting fire smoke dispersion flow pattern isquite stable in areas with slums and simple houses.
Fire smoke also tends to move upward, withaltitude exceeding the height of the fire point. The
fire smoke dispersion of the five simulation points
reached a distance of 1,440 m to the south within
30 seconds of simulation.
This was also observed in the simulation of
Point 2 and Point 4 above the high-rise building.
Fire smoke does not experience turbulence as inscale 1:12,000(a) North Point
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

the Urban Canopy Layer, smoke tends to move up
towards the south. So the potential dispersion offire smoke concentrated in areas with high building
density area on the south side of the fire point is
not too significant.
In research conducted by Hu et al. (2011)
scenario of fire with critical wind velocity, firesmoke will be circulated back to street canyon sothat it can endanger pedestrians and potentiallycause traffic accidents due to reduced visibility bysmoke soot. However, these pollutant fumes cangenerally be released from street canyon with thehelp of buoyancy of smoke with strong firesource’s buoyancy or at relatively weak windvelocity.
Visualizing the temperature distribution
around the fire point, in Figure 1.11, it can beobserved that the increase in temperature does not
only occur at its hotspot, but also around the pointof fire. The highest temperature in buildings in the
North Point and the South Point reaches atemperature of 307 ͦ C and the wind that interacts
with the fire produces a temperature distribution up
to 125 ͦ C to the south of the fire point. The fire
simulation of high-rise building in the Middle
Point produced the highest temperature of 570 ͦ C
and the wind that interacted with the fire caused atemperature distribution up to 130 ͦ C to the southof the fire point. The pattern of fire smoke
dispersion at the Middle Point is more likely tomove upwards so that the distribution of air
temperature towards the south does not take a long
period of time.
While the fire simulation at the East Point
produces the highest temperature of 770 ͦ C with atemperature distribution up to 170 ͦ C to the southof the fire point. That was the highest temperature
produced in all five fire simulations. The
achievement of the temperature distribution iscaused by the location of the hotspots above theupper floor of a high-rise building in high-risebuildings area. The wind flow that hit the hotspots
has a constant flow because there is no turbulence
produced on the Urban Canopy Layer. The highest
temperature produced in the fire simulation West
Point only reaches 420 ͦ C with 140 ͦ C to the southof the fire point.

scale 1:12,000(b)scale 1:12,000(c)South Point

scale 1:12,000(d)East Point

scale 1:12,000(e)West Point
Figure 1.11 Temperature Distribution
Visualization of Five Points Simulations
Visualization of wind velocities distribution
around the focal point of the five fire simulations
can be observed in Figure 1.12. In the simulationof the North Point and the West Point, there is anincrease in wind velocity up to 3.6 m/s with the
distribution of wind velocity reaching 2.2 m/s atthe beginning of the simulation. Until the end of
the simulation, there is a decrease in velocity up to
0.45 m/s, high wind velocity are only distributed
around the hotspots.
The increase in wind velocity at the South andCentral Points only reached 3.5 m/s with a windscale 1:12,000(a)Middle Point

North Point

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

velocity distribution reaching 2.1 m/s. While thesimulation at the East Point experienced anincrease in velocity up to 3.0 m/s with a
distribution of wind velocity reaching 1.8 m/s.
There was no significant difference in the patternof wind velocity distribution in the five simulationsof the fire. The high wind distribution at the
beginning of the simulation is caused by the initialwind velocity of 2 m/s used in the FDS simulation
blowing at the beginning of the simulation. Until acertain time the wind exhaled has decreased
velocity causing an increase in wind velocity onlyin the area around the fire point.

scale 1:12,000(e) West Point
Figure 4.12 Wind Velocity Visualization around
Five Simulation Points
Graph 1.2 shows the comparison of the rate ofheat release at the North Point, Middle Point, South
Point, East Point, and West Point of fire
simulations. The graph shows the tendency of
increasing the rate of heat release the same at the
beginning of the fire. But after passing a certain
time, the rate of combustion in the East Point
increased more sharply than the other four points.

scale 1:12,000
Heat Release Rate(kW)200(a) North Point

150

UTARA

100

TENGAHSELATAN

50

TIMUR
0
0

10

20

30

40

Time(second)

Graph 1.2 Heat Release Rate of Five Fire
Simulations

scale 1:12,000(b) Middle Point

In the research conducted by Sujatmiko et al.
(2017) in Rusunami buildings, the higher the windblows, the higher Heat Release Rate produced.
East Point with fire simulation on the top floor ofhigh rise buildings has a higher level of exposure
to wind and oxygen than the other four points.
The Middle Point is also simulated in high-risebuildings, there is a large gap in the surrounding
buildings, causing a turbulence flow at the hotspots.
North Point, South Point, and West Point show
stable heat release rate due to the steady flow ofwind in the three points, according to the modelingin Autodesk Flow Design software.
Graph 1.3 showed the temperature distribution
around the fire point at the five fire simulations.
There was a sharp increase in temperature at Point
A in the middle of the fire point and Point B which
was on the south side of the fire point. The sharp
increase in temperature at Point B shows theinfluence of wind power and wind direction thatblows around the point of fire.
The high temperature produced by the fire will
be distributed in the direction of the wind. In the
Middle Point simulation, the highest temperature
increase occurred at Point C on the East side of thefire point. This shows that the fire propagation

scale 1:12,000(c) South Point

scale 1:12,000(d) East Point

592

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

500
0
0

10
20
Time (second)30

40

Temperature ( ͦC)(a) North Point
1500
1000
500
0
0

10

20
Time (second)

30

40

Titik_A
Titik_B
Titik_C
Titik_D
Titik_E

Temperature ( ͦC)(b) Middle Point
1500
1000
500
0
0

10

20
Time (second)

30

40

Titik_A
Titik_B
Titik_C
Titik_D
Titik_E

Temperature ( Cͦ )(c) South Point
1000
800
600
400
200
0
0

10

20
Time (second)

30

40

Titik_A
Titik_B
Titik_C
Titik_D
Titik_E(d) East Point
Temperature( C
ͦ )

1500
1000
500
0
0

10

20
30
Time (second)

40

Titik_A
Titik_B
Titik_C
Titik_D
Titik_E

8
6
4
2
0
0

10

20
30
Time (second)

40

5
4
3
2
1
0
0

10

20
Time (second)

30

40

Titik_A
Titik_B
Titik_C
Titik_D
Titik_E(b) Middle Point
8

D
EAC
B

6
4
2
0
0

10

20
Time (second)

30

40

Titik_A
Titik_B
Titik_C
Titik_D
Titik_E(c) South Point
6
5
4
3
2
1
0
0

10

20
Time (second)

30

40

Titik_A
Titik_B
Titik_C
Titik_D
Titik_E

40

Titik_A
Titik_B
Titik_C
Titik_D
Titik_E(d) East Point
8
6
4
2
0
0

10

20

30

Time (second)(e) West Point
Graph 1.4 Wind Velocity Distribution of Five
Fire Simulation
593

Titik_A
Titik_B
Titik_C
Titik_D
Titik_E(a) North Point

Wind Velocity (m/s)(e) West Poin
Graph 1.3 Temperature Distribution AroundHotspot of Five Fire Simulation
The distribution of wind velocity that occurs
around each five fire points can be observed in
Figure 1.4. At the beginning of the simulation, thehighest increase rate of wind velocity occurs at thesouth side of the fire point, namely Point B.
Increased wind velocity also occurs at Point A
which is in the middle of the fire point. High windvelocity at Point B is caused by a wind flow
deflection from a high rise building on the east sideof the fire point. Whereas in the South Point

Wind Velocity (m/s)

Titik_A
Titik_B
Titik_C
Titik_D
Titik_E

Wind Velocity (m/s)

1000Wind Velocity (m/s)

Temperature ( C
ͦ )

1500

simulation, the increase in wind velocity at Point B
is caused by the accumulation of wind flow
velocities which are deflected by the building on
the North side of the fire point with a constantvelocity of wind flow on the West side of the firepoint.
In the Middle Point simulation, an increase inwind velocity occurs at Points A and Point C. Highwind velocity at Point A and Point C are affectedby turbulence and deflection of air flow by highrise buildings on the North and East sides of thefire point.

Wind Velocity (m/s)

potential due to the air temperature distribution
around the hotspot is not only influenced by the
direction of the wind that blows but also theturbulence of the wind flow that occurs aroundhigh-rise buildings.

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Implicate with the evacuation process of thecommunity around the fire location, the evacuationroute used should not be on the south side of thefire point. The southern side of the fire point in thefire simulation Kebon Melati Sub-district has thegreatest potential in reducing visibility andincreasing the concentration of fire smoke
pollution as a result of the phenomenon of smoke
dispersion by the wind that is blowing dominantly
towards the South. Increase of temperature on thesouth side of the fire point is also dangerous
because it potentially cause fire propagation.
Decreasing visibility can jeopardize theactivity of transportation users because it canincrease the risk of accidents on the main road or
small roads around the fire point. Fire smoke
pollution concentrated due to air flow turbulenceon street canyon can endanger the health of therespiratory and visual systems of the surrounding
community. According to Alarie's (2002) study,
when nitrogen-containing materials such as nylon
and woolen cloth are burned, the smoke from thefire will contain hydrogen cyanide (HCN).
Hydrogen cyanide can cause several healthproblems such as narrowing of the respiratory tract,headaches, vomiting, even death at high doses of
exposure (Kompasiana, 2011).
The side that can be used as an evacuationroute in this fire simulation is the Westside and the
Middle side of Kebon Melati Sub-district. The
Eastside which is dominated by high rise buildings
has greater potential for the flow of fire smokerecirculation on street canyon due to turbulence
flow by high rise buildings, compared to the Westside which is dominated by slums and simple
houses. Middle Side Kebon Melati Sub-district
which is traversed by the main road can also reducethe concentration of fire smoke with high andstable wind flow velocity due to no buildings orobjects that brush off the wind flow along the mainroad.

point, at an altitude of 3 m to 60 m according to theheight of the fire point, until it reaches a certain
time the smoke tends to move upwards.
Within 30 seconds, the dispersion of fire smokewith 10,000 kW/m2 of Heat Release Rate per unit
Area, reaches 1,440 m to the South of the five fire
simulations.
The highest temperature generated in the five fire
simulations reached 770 ͦ C in the East Point
simulation with a temperature distribution of up to170 ͦ C to the south of the fire point.
The highest wind velocity at five fire simulations
reaches 3.6 m/s at North Point and West Point withthe distribution of wind velocity reaching 2.2 m/saround hotspot.
The highest heat release rate produced in East
Point simulation, reaches 160 kW due to the levelof oxygen exposure that is greater than oxygen
exposure at the North Point, Middle Point, South
Point, and West Point.
Temperature distribution around hot point tends
to increase at Point B which is on the South side offires due to the influence of wind flow strength.
The distribution of wind velocity around hot
point affected by turbulence of wind flow due todeflection of wind flow by high rise buildings on
the North side and the East side of Kebon Melati
Sub-district.
The South Side of Kebon Melati Sub-district has
the greatest potential in reducing visibility andincreasing the concentration of fire smoke
pollution, as well as fire propagation potential.
The side that can be used as an evacuation routeis the Westside and the Middle side of Kebon
Melati Sub-district which has more stable windflow than East side.
Suggestion
Measurement of meteorological data in a longer
period of observation so there will be comparisonof simulation on dry season and rainy season.
Development of FDS software to be able tosimulate the starting point of a fire that is causedby an electrical short circuit.
Simulation with other CFD software, such as
Fluent and OpenFOAM to get a more accuratecomparison of results.

Conclusion
Wind velocity and wind direction of Kebon
Melati Sub-district that used in this study was
obtained from meteorological observations in
November 2017, with an average wind velocity of
2 m/s heading south.
Wind flow on the West side Kebon Melati Subdistrict is more stable than the East side and Middle
side which has turbulence in the flow of wind dueto the bending of wind flow by high-rise buildings.
In Autodesk Flow Design wind flow simulation,wind velocity increase up to 9 m/s on the easternside of the Kebon Melati Sub-district due to a windflow deflection by high-rise buildings.
Wind flow velocities that experience turbulenceon the East side and Middle side of Kebon Melati
Sub-district only reach velocity of 2 m/s, according
to the initial wind velocity.
Fire simulation using Fire Dynamics Simulator
results five-point fire smoke dispersion patternspreads towards the South side of the simulation

References
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Brow. 2004. Urban Dispersion- Challenges for
Fast Response Modeling. 5th AMS Symposium on
the Urban Environment.
Alarie, Yves. 2002. Toxicity of Fire Smoke.
Critical Reviews in Toxicology, (vol. 32, page.
259–289).
Barowy, Adam and Daniel Madrzykowski. 2012.
Simulation of the Dynamics of a Wind-Driven
Fire in a Ranch-Style House - Texas.
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Grimmond, CSB, T. R. Oke. 2002. Turbulent
Heat Fluxes in Urban Areas: Observations and a
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Pertanian Bogor.

Hu, L. H. et al. 2011. Large Eddy Simulation of
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Sianida). 2015. Kompasiana, 26 Juni 2015.
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X.Nugroho Soelami, and Soegijanto. 2017. Studyon Fire Dynamic Development in a Multistory
Building Compartment. Procedia Engineering(vol. 170, hal. 162–68).

Lin, Cherng Shing, Meng Yin Chen, and Lung
Sheng Pan. 2016. Field Model Simulation andAnalysis for a Residential Building Fire. Applied
Mechanics and Materials (vol. 851, hal. 810–15).

Saatnya Merevitalisasi Tanah Abang. 2015.
Transformasi, 9 Maret 2015. Diakses dari
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Ismawan, D. A. 2008. Kajian Kerentanan
Kawasan Pemukiman Padat terhadap Bencana
Kebakaran di Kecamatan Tambora - Jakarta
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dari Emisi Cerobong Menggunakan
Computational Fluid Dynamics. Bogor: Institut
Pertanian Bogor.

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Universitas Indonesia.

Marihot, L. 2015. Pola Aliran dan Sebaran Suhu
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Ozmen, Y., E. Baydar, and J. P. A. J. van Beeck.
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63–74).

595

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

GEOMETRIC SHAPE FOR IRRIGATION SEDIMENT TRAPSVORTEX DESILTING BASIN
Muhammad Isnaeni1, Muhammad Syahril Badri Kusuma2, Joko Nugroho2, Mohammad Farid2, andMuhammad Cahyono2
1

Doctoral Program of Water Resources Engineering, Faculty of Civil and Environmental Engineering,
Institut Teknologi Bandung, Indonesia; 2Water Resources Engineering Research Group, Faculty of Civil andEnvironmental Engineering, Institut Teknologi Bandung, Indonesia
ABSTRACT
Modern irrigation schemes are increasingly demand-based, which means that the crop water requirements
determine the water flow in a canal. The sediment transport aspect is a significant factor in irrigation
development as it determines to a large extent the sustainability of an irrigation scheme, particularly in the caseof unlined canals in alluvial soils, and as a trigger in reducing the wet capacity of the irrigation canal. The
conventional sediment traps with a rectangular shape, rapidly advancing development, are challenging toconstruct because they require adequate space. The vortex desilting basin has proposed replacing therectangular shape with more effectiveness considering the more concise area, deposition rate, removal
efficiency, and minimum human resources as an operator. The method uses numerical methods withcomputational fluid dynamic (CFD) simulations by Ansys R.21 to acquire a geometric shape approach, thenlaboratory experiments with a model scale 1:40 prototype to an undistorted 3-D physical model. This study
aims to develop a two-dimensional numerical model and the optimal regression equations for the determining
settling basin dimension and then simulate and compare the deposition efficiency of the selected settling basins.
The shape of the vortex desilting basin is slimmer at 42% than the conventional shape by optimising the slope
orifice chamber in future research. In comparing the performance of rectangular sediment traps with the appleto-apple hydraulic parameters and sediment variables, the results obtained based on deposition performanceincreased from 69.12% to 84.90% and flushing performance by leaving the minimum sediment fraction
increased from 53.33% to 87.90%.

Keywords: Irrigation, Vortex desilting basin, Sediment traps, 3-D physical model

INTRODUCTION

and biological matter (biofilms, bacteria, viruses,and biomacromolecules). While fresh sedimentdeposits are often close to fluid mud, older anddeeper riverbed sediments tend to be consolidated,with the state of consolidation higher for deeper
sediment. These vertical gradients complicate themodelling of sediment erosion, transport, anddeposition [4], [5], [6]. A considerable effort isrequired to improve irrigation operations andmodernise them. A desilting basin is a temporary
sediment control structure to intercept sedimentladen runoff and retain the sediment. It aims todetain sediment-laden runoff from the disturbed
area for sufficient time and allow most deposits tosettle within the sediment trap [7].
Erosion occurring upstream of Citarum
Watershed must be treated as necessary because thelonger sediment accumulates, the more significant
reduction in water capacity at Citarum. Soil erosion
is a global environmental problem that threatens the
lives of the majority of small farmers.
Approximately 80% of agricultural land is degraded
due to global soil erosion. The practice ofsustainable agriculture is challenged by severe soil

Land-use change on the main island inIndonesia is inevitably increased due to thedemographic growth and regional development
activity in the last two decades. Many areas haveundergone relatively fast demographic, industrial
and urban development [1], [2]. This intense land
occupancy has affected the quality of surface waters,
which became the receptacle of anthropogenic
effluents from various origins. Irrigation
modernisation is necessary for further efforts toovercome threats to the sustainability of irrigation
countries. Indonesia is a developing archipelagic
country where a land-use change of its most rivercatchment area has become a common predominantphenomenon for national agricultural, especiallypaddy, development policy. Land-use change tends
to decrease the dependable flow for irrigation andincrease the extreme discharge so that the annual
yield of paddy fields tends to decrease [3].
River sediments are heterogeneous aggregates,
composite structures composed of amorphous or
poorly crystalline mineral particles, organic matter,
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

erosion and reduced on-farm productivity of thesoil, resulting in food insecurity. On land
cultivation, soil conservation requires specific typesof vegetation supported by an efficient strategy forcontrolling soil loss [5]. This phenomenon results inthe flow of water for irrigation required that isdiverted through the weir, resulting in a decreased
supply of irrigation water.
Water flowing in the canal taking off from headworks on such rivers also carries sediment load. The
canal gets silted if it receives sediment load over its
transporting capacity, and effective measures are
not taken for its control. This results in a decreasein the discharge carrying capacity of the canal.
Further, the canal slope is generally smaller than themain river; hence, sediment always tends to bedeposited in the canal [8].
To address it, the weir as a head structure
requires a sediment trap that can deposit noncohesive type sediments and quickly flush out
because the water required for irrigation iscomplicated to stop during the cropping pattern
because farmers need it. This study examines anddevelops the modern shape of sediment traps as aproposed to replace sediment traps withconventional geometric shapes. In the present
investigation, vortex settling chambers are studied.
The vortex settling chamber was investigated by
Cecen and Bayazit (1975), Curi et al. (1975),
Ogihara and Sakaguchi (1984), Sanmuganathan(1985), Mashauri (1986), Paul et al. (1991), Athar(2000), Athar et al. (2002), Athar et al. (2005),
Keshavarzi et al. (2006), Ansari (2008) and Ansari
and Athar (2013). However, the development by theprevious study is to deposit sediment in a power
plant and electrical energy to avoid a larger
diameter of non-cohesive sediment entering theturbine drive system. In irrigation, the sediment
diameter allowed to enter the irrigation canal iscohesive type sediment whose diameter is <0.06mm. It is challenging to deposit this type of
sediment because it is suspension sediment thatmoves with the water flow.
Different types of ejecting devices are used tocontrol sediment in the canal. These are tunnel-typeejectors, vortex tube types, settling basins, andvortex settling chambers. Settling chambers suffer
two main disadvantages, i.e. requirement of large
dimensions compared with other types and longresidence time. Vortex settling chamber has
overcome disadvantages of conventional settlingchamber treating the same volume of sediment load.
It is a continuous device that applies a certain
fraction of flow for flushing sediment particles. The
vortex settling chambers can also be used toseparate solids from their transporting fluids, suchas in treating sewage and industrial wastes [9].
This study improves the vortex desilting basin
sediment traps that will gradually test their

hydraulic behaviour. Obtaining the nature of
sediment rheology is very important because future
research aims to find out the flushing force to flush
non-cohesive sediment.
RESEARCH SIGNIFICANCEIn Indonesia, irrigation scheme > 3,000Ha(Central Government Authority) = 2,871 units ofweir, 64%=1,834 units of weir built during theColonial-Post-Independence period (1890 - 1960),
and 71% of its 1,304 units did not have sedimenttraps, at that time the river flow has not muchsediment transportation yet, because there was notmuch damage to the watershed. Meanwhile, thecontribution of the assessment of the irrigation
system's performance, 20% of which is the headstructure (consisting of weir+sediment trap), thepresence of sediment trap is an important constrain.
Nowadays, when sediment traps are built, there isnot sufficient space for hydraulic parameters. Could
vortex settling basins with rounded geometricshapes improve deposition and desilting thanconventional shapes?.
METHODS
Research Location
As a case study, this research takes an example
of one weir which does not have a sediment trap.
The location of the study on the contribution of thisstudy is Macan weir, in Subang Regency, see Fig.1
and Fig. 2. This weir services a technical irrigation
area of 9,670 ha, where the current condition of thewet perimeter section area is reduced by ±35%,
caused by sedimentation. The existing conventional
plan will construct rectangular sediment traps
located right side of the head structure.

Fig. 1 Macan Weir, Located in West Java, Indonesia
The flushing way outlet cross over the national
bridge road. This case is an exciting point to597

GEOMATE – Bangkok, Thailand, 22-24 November 2022

improve the shapes of sediment traps.

tested at the Laboratory of Engineering Geology,
Padjajaran University in Bandung, with the known
sedimentation properties and physical properties.
The sediment gradation was taken by comparing thedata around the study site, such as on the Cibeet andCiasem rivers, see Fig.3. The sediment properties in
the Macan weir sediment trap were taken upstream(3 samples) and downstream (3 samples) sampling
points, with laboratory test results at 20°C watertemperatures summarised, see Table 1.

Sediment Gradation Classified
Previously, research has been conducted for
sediment deposition rate at this location bychoosing a Fergusson-Church (2004) that resulted
in 0.822cm/s for particles 0.07mm, see Fig. 4. The
sedimentation deposition rate at the study site has
been calculated at 52.66 m3/day, see Fig. 3 [3].
This research started by taking data on sediment
properties obtained from primary data in 2019a)b)

Fig. 2 a) Layout of Macan weir, 2 b) Propose of vortex desilting basins location at Macan weir
Table 1 Result of sediment laboratory test
No

SampleLocation

Densityρs=kg/m3

1
2
3
4
5
6

u/s S1
u/s S2u/s S3d/s S1d/s S2d/s S3

2672
2681
2699
2710
2716
2739

Sieve Analyzed
Mass
Consolidation#200=
Concentration
Time<0.007mm
S (kg/m3)(minute)
(%)
33.1
910
5
34.3
654
5,10
34.5
577
5
35.6
1011
5
36.5
674
5
39.4
593
5
(Laboratory of Engineering Geology, Padjajaran University, 2019)

VolumeConcentration
Cv

Mud
Densityρs=kg/m3

0.37
0.29
0.22
0.34
0.24
0.19

1498
1543
1511
1412
1432
1411

Fig. 3 Sediment gradation analysis/plot sediment size (left) and sediment transport rate (right)
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

dimension—length, time, mass, force, temperature,etc. These relations must be so structured that
dependent and independent quantities are combinedso as to yield dimensionally correct formulas [9].
The similarities that have been tested in this study
are geometric, kinematic and dynamic.

From the sample data primary as collected, thefollowing soil/sediment property index is obtained:a). Particle size analysis; b). Particle Concentration(Suspended and Bed Load); c). LL & PL obtained
from Atterberg Limit; d). Water content obtains
from Moisture Content; e). Dry density obtains
from compaction test, and; f). Shear stress (Kg/cm²)
was obtained from Mohr Circle-Triaxial Test.
Therefore, the microstructure of mud samples inlaboratory tests may significantly differ from that of
the natural mud deposited in the riverbed.

Experiment Set-up in Laboratory
Physical model tests are performed toinvestigate the hydraulic behaviour of the entire
sediment trap or each component. Physical model
tests often solve fluid mechanics and hydraulics
problems to discover the hydraulic behaviour that is
not obtained in numerical models with CFD [10].
This physical model test is intended to test or
check the performance of sediment traps in theefficiency of deposition and desilting that have beenpreviously tested in numerical analysis with CFD.
3.5.1 Accessories of the Experimental Set-up
This test used a 3D speed meter programmable
Electro-Magnetic Liquid Velocity Meter (PEMS).
Acoustic Velocimeter (ADV) Doppler was
designed to perform the speed of measurement
points in water with scientific accuracy.
Simple ADV performance interface, enabling
fast data collection by a data logger by a computer.
Laboratory test used 2 unit Micro ADV 16MHz
down and side-looking, 1 unit Probe Propeller
Current Meter H33 with data logger and 8 unit
Camera Highspeed DSLR.

Fig. 4 Laboratory activities to observe sediment
properties in ITB
Numerical Analysis by Computational Fluid
Dynamic (CFD)
As a numerical approach, the method used in
Computational Fluid Dynamics (CFD) to analyse
the flow behaviours. This simulation will be carriedout by modelling three fluid phases, namely air,water, and sediment or mud so that the sediment's
flow characteristics can be obtained against time(transient). Because in this simulation, several
fluids have different phases (air, water, and mud),multiphase modelling must be used. For flows witha clear separation between one phase and another,
the Volume of Fluid (VoF) model is used. Thismodel is also relatively simple and efficientcompared to eulerian or mixture [7].

3.5.2 Schematic of Laboratory Test
A 3-D test of this physical model was carried out
in the hydraulics laboratory of the Faculty of Civil
and Environmental Engineering Institut Teknologi
Bandung (ITB), see Fig. 5 and Fig. 6 conductedfrom February to May 2022.

Dimensional Analysis
Dimensional analysis is a process of formulating
fluid mechanics problems in terms of nondimensional variables and parameters. Dimensional
analysis can be used in some cases to provide acomplete set of dimensionless products constructedfrom the pertinent process variables. Similitude bydimensional
analysisrequiresthat
thedimensionless products have the same value in the
prototype as in the model [8].
Physical problems are described by relations,which are determined by quantities having a certain

Fig. 5 Undistorted layout of 3-D physical model test
The determination of the scale model in theanalysis of the relationship between scales
prototype to model, as considering: Space in alaboratory; accuracy; facilities/accessories, and;
Cost

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

may be attained by different combinations of length,
width, and depth [13]. The economic design of thesettling basin with the specific efficiency is thefocus of this study.
Rectangular Shape Sediment Trap
The performance of a sediment trap is expressedin how effectively it is depositing and quickly
flushing out the sediment fraction. As a comparison,in 2018, there was an undistorted 3-D of physicalmodel of the rectangular shape of sediment traps in
the Hydraulics Laboratory of PUSSAIR Bandung,see Fig. 7.
With a similar location and parameters.
However, due to the Macan main canal's alignment
in the irrigation system's direction, if it is built on a
space with curved alignment is not optimal in termsof performance.

Fig. 6 Visual view of the undistorted physical modelwith a scale of 1:40
Experiment Procedure
The sediment removal efficiency of the vortex
settling/desilting basin used was measured bysystematically varying the inlet discharge,
underflow flushing discharge, sediment size,
underflow outlet orifice diameter and the width ofthe channel. The sediment trap efficiency is definedas the ratio of deposited sediment to the total
sediment inflow for a given period within thesediment trap economic operation [11],[12].
Limitations
In this study, the authors limited several
variables so that the focus of the study became clear
and not pseudo; here are the limitations of theresearch: a). Laboratory 3D Physical Model,
Undistorted Scale 1: 40; b). Inlet Irrigation Main
Canal length at Model: 5.5 m equal Prototype 220m and static with 0.2m in model equal to 8.00 m at
prototype; c). A circular cylindrical type of vortex
settling basin having a diameter at model 1.00m
equal to 40m at prototype was used forexperimentation in the present study; d). Sediment
to be deposited cohesionless type > 0.07mm, and<0.07mm neglected and allowable enter to the main
system; e). Water depth has variations based on Q
50%, 100% and 120% by NFR; f). The underflow
outlet orifice static diameter is 0.025 m, equal to adiameter of 1.00m; g). Cohesion-less uniform
sediments having sizes 0.008 mm to 0.825 mm wereused, and: h). Suspended sediment concentration in
the vortex settling basin varied from 11,200 ppm to
190,000 ppm by weight.

Fig. 7 Undistorted physical model test scaling 1:20
for conventional rectangular sediment trap
The results of physical modelling onconventional sediment trap forms with a model
scale of 1:20 are not very optimal, the deposition
efficiency is measured at 69.12%, and thissedimentary fraction that is deposited can only be
flushed out by 53.33%, see Table 2. The alignmentof sediment traps on the curved line leaves many
sedimentary fractions in the inner curve, see Fig. 8.

RESULTS AND DISCUSSIONS
Decrease the flow velocity in the basin to assure
the sediment particle remaining time longer than thesettling time is the main design idea of the settlingbasin. To achieve this goal, the common procedures
are widening the basin width and lowering the basinbottom. However, the same deposition efficiency

Fig. 8 Fraction of sediment on dashed line – break
the red circle in rectangular sediment trap.

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

In the DC-04 design criteria issued by the
Ministry of Public Works and Housing, sediments
deposited in diameter > 0.07mm, for diameters <0.07mm can be neglected and then allowed to enter
the irrigation system with operational and routine
and periodic maintenance conditions [14].

Table 2 Rectangular Conventional Sediment Trap, 2018∂

Simulation for Approach Geometric Shape by
CFD

∂t

⃗⃗ =0
⃗⃗ ∙dA∭Ѵ ρdѴ + ∬A ρV(1)

The continuity equation of the differential form:

For the approach of geometric shapes ofsediment traps with the model of vortex settling/
desilting basins, a numerical approach with the helpof AnSys R.21 R.2 Student. Obtained previously
developed shapes by Ansari (2008) and Ansari andAthar (2013). In all 22 runs were conducted for
sediment
removalefficiency
ofvortexsettling/desilting basin by varying slope chamber ofunderflow outlet, whereas 44 runs were conducted
for this study.
Here are the inputs used in boundary conditions:
Mass-flow-Inlet: a). In the inlet section, it is defined
as a mass flow inlet either for water or mud. b).
Wall: On walls, it is defined as a wall with a no-slip
condition to represent the friction between the fluid
and the wall. c). Surface: At the top of the domain,
the effects of wall friction are removed to represent
atmospheric air, and d). Outlet: The outlet section isdefined as a pressure outlet representing the flow's"exit" [7].
Computational Fluid Dynamics (CFD) is the artof transforming fluid dynamics set equations in theform of integrals and derivatives into discrete
algebraic forms, which a computer can solve toobtain the values of the flow field at a particular
discrete point or time. As for the regulatory
equations in fluid dynamics: the continuity
equation, the momentum equation and the energy
equation [14], [15].
Here are the equations used in CFDs;
Continuity equation of integral forms:

∂ρ
∂t

⃗ ∙V
⃗⃗ =0
+ρ∇

(2)

The equation of momentum in the direction of thex-axis:∂(ρu)∂t⃗ ∙(ρuV⃗⃗ )=- ∂p + ∂τxx + ∂τyx + ∂τzx +ρfx
+∇
∂x
∂x
∂y
∂z

(3)

The equation of momentum in the direction of they-axis:∂(ρv)∂t⃗ ∙(ρvV⃗⃗ )=- ∂p + ∂τxy + ∂τyy + ∂τzy +ρfy
+∇
∂y
∂x
∂y
∂z

(4)

The equation of momentum in the direction of thez-axis:∂(ρw)∂t⃗ ∙(ρwV⃗⃗ )=- ∂p + ∂τxz + ∂τyz + ∂τzz +ρfz
+∇
∂z
∂x
∂y
∂z

(5)

The energy equation is written in the form ofinternal energy:
∂

V2

∂z

601

V2

∂(ρp) ∂(vp)⃗ ∙ [ρ (e+ ) V⃗⃗ ] =ρq̇ [ρ (e+ 2 )] +∇- ∂y 2
∂x

∂t
∂(wp)

⃗⃗
+ρf∙V

(6)

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Where ρ is Liquid density (kg m-3), A is a mass
⃗ is Velocity vector (m s-1) or velocity fluid
area, ⃗V
parcel, e for internal energy, three velocity⃗ vector, f any vector
components are 𝑢,𝑣,𝑤, ∇function [7],[10],[16]. The solution of a partial
differential analytical equation results in acontinuously closed-form dependent variable
expression across domains. In contrast, the solutionof numerical equations can only give values todiscrete points in the domain, also called grid
points. As a shaping approach to the vortex
geometric shape settling/desilting basins proposed,
as shown in Fig. 9, then input all the hydraulicvariables and applying Eq.1 – Eq. 6 above in CFD
resulted in shape as follows:

Fig. 11 Sediment volume fraction – longitudinal
section view (desilting time)
In addition, the rheological process of sedimentary
sediments also exerts an influence on theclassification of this type of sediment. Of the82.11% of sediment deposited, as shown in Fig. 10
and Fig. 11, a total of 69.31% of the sediment
fraction can be drained. Leaving approximately
30.69% in cyan colour cannot be flushed out due tothe position of the sediment in the transition of thefree vortex flow to the forced vortex.
Result Laboratory Undistorted 3-D PhysicalModel Test
The geometric approach of the vortex
settling/desilting basin with a numerical approach
to its results was tested with a physical model in thelaboratory.
After the dimensional analysis, stages arecarried out, and geometric, kinematic, and dynamic
similarity tests and the available space, a model
scale of 1:40 is determined. The following equation
calculates the deposition efficiency value:

Fig. 9 Sediment volume fraction at settling time
As shown in Fig.9, it showed that the sediment
fraction on the free vortex flow along the forced
vortex flow side of the chamber is relatively highand states that sediment is well deposited, thepercentage of sedimentary volume deposited fromsediment transport within continues flow in 7 daysof flow amounting to 82.11%. On the longitudinal
section of this sediment trap is shown a fraction of
sediment deposited present by the predominance of
red in the following Fig.10 below:

V -V

Tef = inV out ×100
in

(7)

Where 𝑇𝑒𝑓 is trap efficiency (%), 𝑉𝑖𝑛 is the volumeof sediment entering the sediment trap, 𝑉𝑜𝑢𝑡 is thevolume of sediment as flushed at the outlet.
To calculate the efficiency of flushing-out/desilting,use the following equation:Ws flush+Ws Settled

ŋ0 = Total Ws Feeding ×100

(8)Where ŋ0 is flushing efficiency (%), Ws flush is theflushed volume of a sediment chamber, and Ws
Settled as sediment deposited. Ws feeding the total
amount feeding of sediment transport rates [17],[18].

Fig. 10 Sediment volume fraction – longitudinal
section view (deposition time)
This shape is also maximal at the time of desilting,although not all sediment fractions can be deposited
because sediment diffusion exerts an influence.

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 14 Flushed – out of sediment fraction

Fig. 12 Settled sediment volume

As shown in Fig. 12, Fig. 13, and Fig.14, 11 runs
were conducted for this study and feeding thesediment through a sediment feeder to determine
the volume of deposition and the efficiency of
flushing used the Eq equation. 7 and Eq.8. then the
results obtained are tabulated in the following table:

Fig. 13 Measurement of sediment fraction
Table 3 Comparison result of running for rectangular in 2018 and vortex settling/desilting basins in 2022

69.12% to 84.90%. Meanwhile, for flushing, thesediment fraction of 53.33% increases to 87.90%
from the deposited can be flushed out. For future
research, it is optimising the geometric shape of thechamber to be optimum based on the diameter ratio
chamber effect.

CONCLUSION
Present research for steady, vortex, and open
channel flows over smooth, rough, and movablebeds, independent and based on Reynolds andFroude numbers. As a comparison of 2 different
geometric shapes of sediment traps with hydraulicparameters such as discharge, the exact
characteristics of sediments to deposition and flush
out, the running results showed an increase in thenumber of volumes that could be settling fromACKNOWLEDGMENTS
This study aims to develop a 3-D rectangularshape sediment trap for a vortex desilting/settlingbasin shape. The authors would like to thank the603

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Ministry of Public Works and Housing for theirassistance regarding the data collection required forthis paper. Furthermore, the authors would also liketo express their gratitude to the research, Water
Resources Engineering Research Group of theFaculty of Civil and Environmental Engineering
Institut Teknologi Bandung, for supporting thispublication.

[9]

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604

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

PROFILING EXHALED VOLATILE ORGANIC COMPOUNDSFROM SEMERU ERUPTION REFUGEES BY USING E-NOSE
Arinto Yudi Ponco Wardoyo1, Eko Teguh Purwito Adi1, Hari Arief Dharmawan1, Susanthy
Djajalaksana2, Arif Budianto3, Ngakan Putu Putra2, Aditya Sri Listyoko2, Fitri Indah Sari2, Raden Dicky2
1

Department of Physics, Brawijaya University, Indonesia; 2Pulmonology And Respiratory Medicine
Department, Faculty Of Medicine, Brawijaya University, Indonesia; 3Physic Study Program, University of
Mataram, Indonesia
ABSTRACT
Semeru Mountain was reported to erupt on December 4, 2021. The eruption caused more than 10.000
people to refuge from their homes. The refugee’s health was reportedly affected by the volcanic eruption,
especially by respiratory-related diseases. In this research, we tried to profile the exhaled breath content fromthe refugees by using an E-Nose-based system. The E-Nose named µBreath developed by Brawijaya
University uses multi-array sensors to detect twenty parameters in terms of volatile organic compounds(VOCs) and other gases. Forty-four refugees were randomly selected by their capability to participate in thisstudy. All of the participant’s health was assessed for Chronically Obstructive Pulmonary Disease (COPD),
tuberculosis, and asthma after a month of the eruption. Then, we build a profile that links the relationshipbetween the measured exhaled content concentrations to the refugee's health condition. The profiling isestablished by using the K-Nearest Neighbor algorithm. We find that thirteen of the exhaled contents have asignificant correlation to the refugee condition. The most distinguished profile is related to the refugee healthcondition of Chronically Obstructive Pulmonary Disease (COPD), tuberculosis, and asthma. In conclusion, it
was found that the profiling of the exhaled content of the Mt. Semeru refugees has significant health impacts
related to respiratory disease.
Keywords: Breath, Exhaled, Profile, E-Nose
INTRODUCTION[6,7]. Although the chemical characteristic of each
mountain was different, the toxic nature of thecomponent remains of concern [8–10]. Many
respiratory diseases associated with exposure tovolcanic emissions support this opinion [11]. In
the case of Semeru, the mountain was reported torelease a persistent concentration of SO2[12]. The
gas was known to be fatal to humans along withthe other gases or particulate [9,11,13,14].
Respiratory-related diseases can be detected
through an analysis of the chemical components
contained in the exhaled air. The presence ofspecific gases was related to diseases, especiallyvolatile gas. Many studies found different gas
components between healthy people and people
with respiratory diseases [15–17]. An exhaledbiomarker has been used to differentiate the humancondition [18]. However, the study to profile
exhaled breath of the people influenced by
Vulcanic Mountain has not been found up to now.
This study is aimed to profile the exhaled breath of
the Mt.Semeru refugees based on the measurementof the biomarker gases.

Semeru Mountain is an active volcanic locatedat the latitude of 8° 06’ 29” S and longitude of
112° 55’ 12”, East Java, Indonesia. The volcano
was reported to erupt since 2014 and finallyreleased a 15-km-high ash plume after the dome
collapsed on December 4, 2021 [1]. During theeruption, the Center for Volcanology andGeological Hazard Mitigation, Department of
Geology, Indonesian Ministry of Energy, andMineral Resources reported a hot cloud avalanche
up to 4 km in the direction of south-east from thesummit [2]. TROPOMI instrument on the sentinel
5P-satellite captured the SO2 emission spread west
and north from the mountain three days after the
eruption [3]. According to the Indonesian Agency
for National Disaster Management, the eruption
killed at least 50 people and forced more than
10.000 people to evacuate [4]. The agency also
stated more than a thousand houses were heavily
damaged by the eruption [5].
The volcanic eruption released a million
chemical compounds when the eruption happened
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

METHOD

environmental condition [29]. The measurement
procedure was that exhaled breath was collectedby blowing a sample bag. The bag was connectedto Ubreath for the gas concentration measurement.
During the measurement, the participant was asked
to fill in the information about their current
condition, consisting of age, gender, disease
complaint, previous diagnosis, and symptoms. The
information was used as a categorical parameterfor further analysis.

Electrical Nose Design
Twenty parameters consisting of temperature,humidity, and eighteen chemical compounds wereobserved using sixteen sensors. The chemicalcompound consisted of Oxygen (O2) [19], Total
Volatile Organic Compound (TVOC) [20], Carbon
Monoxide (CO) [21], Methane (CH4), Dihydrogen Sulfide (H2S), Nitrogen Dioxide (NO2),
Ammonia (NH3) [22], Ozone (O3), Ammonium
(NH4)
[23],
CarbonDioxide(CO2),
Formaldehydes (CH2O) [24], Ethanol (C2H5OH)[25], Propane (C3H8) [26], Acetone (C3H6O) [27],1-Butanol (C4H9OH), Hexane (C6H14), Benzene(C6H6), and Toluene (C7H8) [28] . All of the gasses
were related to human exhaled breath [29]. The
breath temperature and humidity were measured
with SHT-11 sensor, TVOC and CO2 with CS-811
sensor, NO2 and CO with CJMCU-MICS-4541,
and NH4 and H2S with MQ-136 [30]. Meanwhile,the concentration of CH2O, C6H6, and C7H8 wasquantified with the MS-1100 sensor. The
concentration of O2, O3, SO2, NH3, C3H6O, C6H14,
C4H9OH, C3H8, CH4, and C2H5OH, was conductedby using ME-2, ME-3, ME-4, CJMCU-MICS6814, MQ-135, MQ-138, MQ-3, MQ-5, MQ-9,
and TGS-2600 [30–32]. All of these sensors wereapplied in the µBreath breath analyzer device
developed by the laboratory of Air Quality andAstro-imaging, Brawijaya University. The device
was designed to save the measurement data in theform of a .csv file. All parameter was presented on
the scale of ppm (part per million).

Data Evaluation
Eighteen gas concentrations were analyzed
based on their correlation with the participant's
condition. The classified condition consists of ahealth participant, the participant with arespiratory-related disease, and un-related diseases
stated three different categories of the participants.
Health participant was defined as people without
any health problem or symptoms. The participant
with respiratory-related and unrelated diseases wasdetermined by using the previous diagnosis
statement. As for the unknown disease, theparticipant with respiratory-related symptoms suchas breathlessness, cough, dizziness, and chest pain
was categorized into this classification [36–38].
Further, we also dropped the data of the participant
with diseases unrelated to volcanic emissions e.g.,tuberculosis.
The data were evaluated to find the correlation
to respiratory-related diseases. Heat map analysis
was performed to create a gas combination byusing a correlation factor [39]. Then, we conducted
Principal Component Analysis to cluster theselected gases combination. The PCA result was
performed to reevaluate the data in order to assess
the selected gas combination [40]. The final data
combination was used as the reference data in the
K-Nearest Neighbor (K-NN) [40,41]. In the K-NN,
we evaluated the reference data to find theaccuracy and Unknown diseases as test data.

Participant and measurement procedures
Forty-four refugees in the evacuation camps
located on -8° 12’ 42.35” S and 112° 59’ 6.04”(12.9 km the peak of Mt.Semeru) were selected toparticipate on January 29, 2022, or fifty-six days
after the large eruption. Before the test was
conducted, all participants were assessed toclassify their condition during the examination.
Then, we instructed the participant to introduce
their third exhalation breath to a 500 ml exhaled
breath sample bag. The third breath was chosen to
avoid the effects of breathing techniques, mouthodor, dead space air, or any other irrelevant factor[33–35]. The sample was measured immediately
by an E-Nose device after breath collection
procedure to evade the influence of the

RESULT
Volcanic eruptions release a wide variety ofgases that can cause damage to human tissue. In
this study, we measure the various exhaled gasesof the volcanic eruption refugees and relate themto the association with the diseases of asthma,
chronic obstructive pulmonary disease (COPD),
and pharyngeal abscess. The selection of the threediseases was based on the relationship between
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

tissue damage due to exposure to reactive
substances and the disease suffered by theparticipants during the observation. As shown in
Fig.1, the participants were categorized into 7
groups based on their disease. The observation
showed that of the 44 selected participants, 9participants did not show any health problems, 5
people were diagnosed with COPD, 2 people withtuberculosis, 5 people with asthma, 1 person withpharyngeal abscess, and 6 people with unrelateddiseases. Meanwhile, 16 people were with theundiagnosed disease. For participants with
undiagnosed disease, the results of the assessment
showed that 5 people came with the symptoms ofcough, 3 people had shortness of breath, 1 person
was dizzy, 1 person had chest pain and fatigue, 4people coughed with shortness of breath, and 1
person coughed with fever. For asthmatics, it was
known that 1 person had symptoms of shortness ofbreath, 3 people had symptoms of shortness ofbreath accompanied by coughing, and 1 person had
shortness of breath with chest pain. In COPD, 1
person has shortness of breath, 3 people haveshortness of breath with cough, and 1 person is
asymptomatic.
For
Pharyngeal
Abscess,
participants did not show any symptoms.

diseases, Health, Chronic Obstructive Pulmonary
Disease, tuberculosis, Asthma, and represent
Pharyngeal Abscess respectively. Each group has a
specific distribution of gasses and concentration.
As a sample: asthma, COPD, and a pharyngeal
abscess had a significant concentration of C3H6O
and TVOC. However, C2H5OH is found to have nosignificant difference among them, except theconcentration is slightly high for asthma. In termsof O2, it is generally found in each category, andthe concentration is found at least the same. It isinteresting that we find the O2 concentration of thefemales suffering from COPD higher than theasthma groups. Inversely, the CO2 concentration of
COPD group is lower than the CO2 concentrationof asthma. For the CO concentration, it can be seenin the male that the trend of the decreasing
concentration in the order of COPD, Asthma, andPharyngeal Abscess. On the other hand, theconcentration of CO increases for the female. In
the case of the concentration of NH3, CH2O, andC7H8, there is no difference in the COPD, asthma,and pharyngeal abscess groups. The different
concentration of those gasses is found in the caseof COPD for the female and male participant.
Tuberculosis has a similar characteristic based on
the three gasses distribution concentration.
Meanwhile, the concentration of the rest measured
gasses is difficult to differ the group.
In order to differentiate each group, we analyze
the concentration data using a heat map, as seen in
Fig.2, to find the gas configuration based on thecorrelation value of
which is positive, showing
an equivalent change, and is negative, indicating
an oppositely change. The choice of the positive
and negative values is based on the correlation of
the chemical compound formation to therespiratory process.
The presence of oxygen in the air is important
for the metabolism process. Based on this fact, weevaluate the correlation between oxygen and therest of the gases. As a result, we obtain O2 to havea strong negative correlation with CO and a strong
positive correlation with NH3. On the other hand,
O2 has a weak correlation with CO2, TVOC,
C4H9OH, C6H6, H2S, CH2O, and C7H8. Meanwhile,
O2 is found to have no correlation with C3H8,
C6H14, O3, CH4, NO2, C2H5OH, C3H6O, and NH4.
In the case of TVOC, we find the gas to have avery strong positive correlation with CO, CO2, andC4H9OH. A very strong correlation is also obtainedon CO with TVOC, C4H9OH, and CO2; CO2 with

Fig.1 The profile of thirteen gases correlated withthe diseases and the gender of the participant
Fig.1 shows the results of the measured gas
component in each group, such as UN-K, UN-R,
HLT, COPD, TBC, ASTH, and PHRAB represent
Unrelated diseases, which represent Unknown
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

CO, TVOC, and C4H9OH; CH4 with C3H6O, C6H6,and C6H14; C6H6 with C7H8, CH2O, C2H5OH, NH3,C3H6O, CH4, and C6H14; C7H8 with C6H6, C3H6O,
C6H14, C2H5OH, CH4, NH3, and CH2O; C6H14 withC2H5OH, C7H8, CH2O, C6H6, and CH4; C2H5OH
with CH4, C6H14, C6H6, C3H6O, C7H8, and CH2O;
C4H9OH with CO2 and TVOC; C3H6O with CH2O,
C7H8, NH3, C2H5OH, CH4, and C6H6; NO2 with
NH4 and NO2; NH3 with C6H6, C3H6O, CH2O, andC7H8; and NH4 and NO2. Furthermore, a strong
correlation is obtained between TVOC and CH4;
CO with O2 and CH4; CO2 with CH4; CH4 with
NH3, TVOC, C4H9OH, CO, and CO2; C6H14 with
NH3 and C3H6O; C2H5oh with NH3; C4H9OH with
CH4 and CO; C3H6O with C6H14; H2S with NO2
and NH4; NH3 with CH4, C6H14, O2, and C2H5OH;
and NH4 with H2S.

Based on the correlation value on the Fig.2, we
configure the gases with three different categories
consist of very strong correlation, strong
correlation, and weak correlation. In the result, wecategorize C2H5OH, C3H6O, C3H8, C4H9OH, C6H14,
C6H6, C7H8, CH2O, CH4, CO, CO2, H2S, NH3, O2,
and TVOC in the group of gases with the weakcorrelation. The gases of C3H6O, C6H14, CH4, CO,
H2S, NH3, NO2, O2, C2H5OH, NH4, TVOC,
C4H9OH, and CO2 are classified as the gases with astrong correlation. Finally, the gases with a verystrong correlation contain C2H5OH, C3H6O,
C4H9OH, C6H14, C6H6, C7H8, CH2O, CH4, CO,
CO2, NH3, NH4, TVOC, and NO2.
DISCUSSION
Exposure to volcanic gases has been known tohave a significant impact on humans. In the case of
Mt.Semeru, the sulfur component was the main
attention due to the concentration [12,42]. A sulfur
component in volcanic eruptions has been related
to the development of inflammation, tissue damage,and organ failure. In this study, those impacts arelinked with asthma, COPD, and pharyngeal
abscess [43–45].
In this study, we group a number of diseases
consisted of unknown diseases, asthma, COPD,
and a pharyngeal abscess that are related to tissue
damage. Vulcanic emission gases have been
related to the activation of pro-inflammatory
cytokine. The cytokine was responsible for thedevelopment of asthmatic allergy, COPD, and anyother tissue damage [43–45]. Furthermore, thedevelopment of respiratory-related diseases islinked with the component of exhaled breath.
As shown in the result, thirteen gas
concentrations are found differently depending on
the participant condition. Fig.3 shows theconcentration of the gases for COPD, asthma, andPharyngeal Abscess. The different concentrations
are related to a metabolism process causing disease[23]. In the case of the unknownn disease group,
the concentration distribution are similar to thegroup of COPD, asthmatic, and Pharyngeal
Abscess.
For further analysis, we use a Principal
Component Analysis (PCA) to find the data cluster
for each correlation group as a result of the Heat
Analysis. The results that the data cluster are
clearly separated for the group of health, COPD,
and asthma, as seen in Fig 3. However, the cluster

Fig.2 The correlation between the measured gases
resulted in various relationship types consisting ofvery strong ( in the range of -1 to -0.7 and 0.7 to1.0), strong ( in the range of -0.7 to -0.4 and 0.4
to -0.7), weak ( in the range of -0.4 to -0.1 and
0.1 to 0.4), and no correlation ( in the range of 0.1 to 0.1)

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

data still overlap for the group of COPD,
tuberculosis, and unknown. Due to the overlapping
data of tuberculosis and unknown group and we
dismiss those groups from the PCA analysis.
Another reason is that tuberculosis and respiratoryrelated disease are unrelated to volcanic exposures[46,47]. In the case of the Unknown group, we
treat the data as the test data. After dropping thegroup of tuberculosis and unknown, the restgroups are reclassified again. The result is shownin Fig.3(d), where the cluster data are seen moreclearly.

We reevaluate the data using KNN and we
obtain an accuracy more than 80%. The accuracy
value can be trusted when compared to othersimilar E-Nose systems [35,48,49]. Furthermore,
we evaluate the Unknown data with the K-NN
approach to see the data categories based on thegas component. As a result, from the16participants from the unknown group, 4participants have the same gas profile as theasthma group, 10 participants were in the COPD
group, 1 participant in the Pharyngeal Abscessgroup, and 2 participants in the healthy group as
presented in Table 1. The value of the categorized
COPD group is the highest for the participant withan ID of 1, 3, 4, 6, 8, 10, 12, 13, 15, and 16.
Meanwhile, the asthma group is found the highest
value for the participant with the ID of 2, 5, 9, and
14. In the case of participant 14, the value issimilar to the health group as well. The group ofpharyngeal abscess and health has the highest
value for participants 7 and 11. Finally, we obtain
the profile of the exhaled breath related tovolcanic-related diseases.
CONCLUSION
In this study, we successfully profile theexhaled gas content of the Mt. Semeru refugees
related to respiratory disease. We find the mostpossibility of related respiratory diseases are
Chronically Obstructive Pulmonary Disease(COPD), tuberculosis, and asthma, with anaccuracy more than 80 %.

Fig.3 Principal Component Analysis shows thecluster of health participant ( ),COPD ( ),tuberculosis ( ), asthma ( ), Pharyngeal Abscess( ), and Unknown disease ( )Table 1 The results of the evaluation of unknown
data using the K-Nearest neighbor method for
healthy (HLTH), asthma (ASTH), COPD, andpharyngeal abscess (Phr-Ab)
ID
Pred
Hlth
1
COPD
0.20
2
Asthma
0.12
3
COPD
0.25
4
COPD
0.04
5
Asthma
0.16
6
COPD
0.00
7
phr-ab
0.08
8
COPD
0.04
9
Asthma
0.00
10
COPD
0.04
11
Health
0.63
12
COPD
0.12
13
COPD
0.00
14 Asthma/Health 0.42
15
COPD
0.08
16
COPD
0.00

ACKNOWLEDGMENTS
This study was supported by Indonesian
Ministry of Education, Culture, Research, andTechnology. All authors would like to thank the
support from Malang branch of The Indonesian
Association of Pulmonologists. All authors also
acknowledge the kind hand of Azarine A., Maria
P.W.W., Ilham R., Anthony, Yokanan, Adrian Y.,
Muli Y., Juwita F., Silvy A., Naufal A.H.,
Rachmat S., Andrew S., and Uray R.

Asth COPD Phr-Ab
0.16 0.64
0.00
0.44 0.20
0.24
0.13 0.42
0.21
0.04 0.84
0.08
0.36 0.20
0.28
0.12 0.69
0.19
0.12 0.00
0.80
0.04 0.80
0.12
0.92 0.08
0.00
0.08 0.76
0.12
0.25 0.08
0.04
0.32 0.56
0.00
0.56 0.44
0.00
0.42 0.17
0.00
0.00 0.92
0.00
0.08 0.71
0.21

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12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

EFFECT OF DIATOMACEOUS EARTH ON DESICCATIONCRACKING OF EXPANSIVE SOILS
Alemshet B. Tadesse1, Y. Fukubayshi2, A. Koyama3, and D. Suetsugu4
1

Interdiscilpinary Graduate School of Agri. and Eng., University of Miyazaki, Miyazaki, Japan
2,3,4
Faculty of Engineering, University of Miyazaki, Japan
ABSTRACT

Expansive soils shrink and swell are causing considerable density differences as the moisture content varies
and cracks develop as the soil dries. This cracking condition has a detrimental effect on the stability of
infrastructure built on expansive soil, such as road embankments. This paper investigates the influence of
diatomaceous earth (at 5%, 10%, 15%, and 20% DE percent by mass) on the desiccation cracking of expansive
soils. The report demonstrates an image analysis technique to describe soil surface cracking quantitatively. The
geometric features of cracks such as surface crack area and crack connectivity were estimated and examined. The
study considers soil crack area ratio and cracks index to investigate the effect of diatomaceous earth on expansive
soil surface cracking. The crack area ratio and cracking index analysis results show that the expansive soil surface
cracking is uneven and of significant size. The soil matrix containing 10% and 20% diatomaceous earth has
reduced surface cracking and a homogenous radial cracking pattern. The image analysis approach, an essential
method to quantitatively quantify soil desiccation cracking, revealed that diatomaceous earth substantially affects
surface crack reduction.
Keywords: Expansive Soil, Desiccation Cracking, Diatomaceous Earth (DE), Image Analysis

1. INTRODUCTION

Desiccation cracks illustrated in Fig. 2 are mainly
caused by shrinkage due to rapid water evaporationfrom the soil surface. These desiccation cracks induce
an interparticle tension and volume change,
increasing cracking permeability and reducing soil
stability due to the generated flow paths. It has been
noted that the temperature in Ethiopia ranges from27oc to 50oC; because of climatic changes, rapid
water evaporation occurs, leading to desiccation
cracking [2]. In general, rainfall assists in the partial
or full self-recovery of cracks during the wet season,
as shown in Fig. 3 on the surface in shallow andnarrower cracks [3]. Whereas heavy rainfall in
combination with hot weather contributes todesiccation cracking.

Expansive soils tend to swell as they absorb
moisture, whereas they shrink when they losemoisture which encounters volume change because of
the dry and wet seasons in the year. In the dry season,desiccation cracking develops due to the loss of water[1]. Desiccation cracks are more common in tropicalcountries such as Ethiopia due to the prevalentoccurrence of expansive soils (i.e., vertisols that
account for 10% of the soil distribution), as illustrated
in Fig. 1. This natural phenomenon covers a wide
variety of scales, from a fraction of smaller sizes to alarger scale of sizes. As a result, soil desiccation
cracking is getting massive attention in geotechnicalengineering and other related disciplines.

Fig. 2 Desiccation cracking in expansive natural soil
(Source: http://geophilous.blogspot.com)

Fig. 1 Soil map of Ethiopia
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

soils. Hence, further examinations are required tocomprehensively understand, quantitatively analyze,
and express treated soils. Besides, very few studies
have been conducted on the effect of diatomaceous
earth (DE) on desiccation cracking reduction.
Diatomaceous earth is preferred in this study due to
its rich content of silica (SiO2). It is hypotheticallyassumed that the silica and other chemical elementsin the additive will remove and replace the watermolecules of the expansive clay surface and reduceshrinkage and swelling.
Therefore, the objective of this study is toinvestigate the effect of diatomaceous earth ondesiccation cracking of expansive soil by using image
analysis techniques to capture the crack morphology,
mode of cracking, and crack connectivity. The
diatomaceous earth is mixed at 5%, 10%, 15%, and
20% by mass, and the desiccation cracking
experimental parameters were examined duringdrying.

The resulting formation of cracking networks
negatively alters the soil properties and compromises
the integrity of soil structures [3].
Mechanical properties of soils subjected todesiccation cracking are highly influenced byincreased compressibility, excessive deformation,
and weakened strength. The ultimate reduction of
performance and failure of civil infrastructures are
due to the combined effects of desiccation cracking
influence parameters [4]. A critical influence on theperformance of geotechnical engineering structures
such as road embankments occurs due to desiccation
cracking dictating remedial or eradication solutions
such as altering soil property and providing moisturebarrier.

2. MATERIALS AND METHODS
Locally available natural expansive soil was usedfrom the Yanaizu area, Fukushima prefecture, Japan,
and diatomaceous earth was purchased from themarket.
2.1 Materials
2.1.1 Expansive Soil
Fig. 3 Water content profiles inactive zones
Pertinent characteristics of the soil are shown in
Table 1. The soil that passes through a 425μm sieve
was subjected to the free swell index test. The result
was categorized under extremely high expansive soil
as per the Indian Standard. The chemical compositionof materials from energy dispersive spectrometry(EDAX) is presented in Table 2, and the chemicalelement composition of the soil of Japan.

Different research have been investigatedmitigating desiccation cracking by the influence ofadditives such as cement, lime, sand, fibers, and bioenzymes [3],[5]. These studies focus on hydraulicconductivity and volumetric shrinkage of clay soil.
These additives can reduce shrinkage but sometimes
increment in permeability might be observed. For
example, Ahmed F. et al. (2021) [6], studied thepolypropylene fiber effect on desiccation cracking
under multiple wetting-drying cycles on kaolinite
clay, and the reinforcement reduces cracking.
Very recently, Yaxing et al. (2021) [5] studied theuse of fibers (i.e., jute fiber and polyvinyl alcohol
fiber (PVA)) and illustrated the evolution ofdesiccation cracking by applying the digital image
correlation (DIC) method. Experimental results
showed that the presence of fibers significantly
affects the crack evolution and the reinforced soil's
length and area during shrinkage.
Furthermore, many other studies have also been
conducted on reducing desiccation cracking by
utilizing multiple additives. So that in the form offiber or chemicals to improve shrinkage limits, tensile
strength, shear strength, and hydraulic conductivity.
However, adding those additives does not
significantly affect the mechanical behavior of clay

Table 1. Physical Properties of Yanaizu expansive
soil
Soil Property
Liquid Limit, LL (%)
Plastic Limit, PL (%)
Plastic Index, PI (%)
Gravel (%)
Particle
Sand (%)
Size
Silt (%)
Distribution
Clay (%)
Free Swell Index, FSI (%)
Specific Gravity (ρs, g/cm3)
USCS
Soil ClassAASHTO

Value

Standard

134.3
42.3
ASTM D4318
92
13.5
32.2
JGS0131
0.3
54
134
IS 2720-40
2.55
JGS0111CH
ASTM D2487
Clay AASHTOM145

AASHTO: American Association of Highway & Transport Office
ASTM: American Standard Testing Method
IS.: Indian Standard: USCS.: Universal Soil Classification System

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 2. Chemical Composition of Materials
MgO

K2O

Na20

* 72.88
14.86
5.98
2.55
**90.33
5.97
2.5
0.69
0.25
* - Expansive soil, **- Diatomaceous earth

SiO2

Al2O3

Fe2O3

Cao

2.4
0.26

1.33
1.26

The diatomaceous earth and water contents
percentages are defined in Equation. (1) and (2)according to the phase diagram illustrated in Fig. 4
below:

2.1.2 Diatomaceous Earth%ωc =Filter grade diatomaceous earth was used as anadditive in experimental desiccation tests. The fixed
point of the study was to utilize brewery silage
diatomaceous obtained from beer filtration waste.
However, it is not easy to get the waste here in Japan;
therefore, we were looking for a commerciallyavailable filter grade DE and purchased it from theShowa Chemical Industry Co., Ltd of Japan.
Diatomaceous earth was preferred because of its
reach silica content (see Table 2) with a hypothesisthat the water molecules will be replaced by highly Si,
or Al, Fe, and Ca cations due to cation exchange.
These ion exchanges will influence the capillarysuction and tensile strength and later reflect ondesiccation cracking reduction.

(1)

mDE.
×100
mBE. +mEs(2)

where DE= DE content, ωc = water content; mDE=
mass of DE, mES = mass of expansive soil, and mw =mass of water.
Sample preparation was done from the collectednatural soil was cautiously crushed, air-dried, andsieved through a 2mm and 425μm sieve. Next, thesaturated slurry samples were prepared thoroughly by
dry mixing of the expansive soil and diatomaceous
earth as per the mix design (seeTable 3). Then, distilled water close to the liquid limit(LL=134.3%) amount is added to the dry mix, andgreat care was taken to pulverize lumped particles
while mixing and targeting the homogeneity of
mixtures. The slurry was then poured into 92mm
diameter,17mm height, and 2mm thickness size
greased glass containers and de-aired on a horizontal
shaking plate to remove the air bubbles and ensure
full saturation. Then, the slurry was sealed by a plastic
cover and kept for 24 hours to have a homogenous
water distribution before the desiccation experimentstarted.

2.2 Methods
2.2.1Mix Design and Sample Preparation
This study examined five mix designs of one
control (natural soil) and four diatomaceous earthtreated blends, as shown in
Table 3 below. Therefore, the designation coding
system herein, shown in
Table 3, is adopted.

2.3 Desiccation Cracking Experimental Test
The desiccation cracking experiments were carriedout on five saturated slurry state specimens in thelaboratory. Samples were subjected to indoor realtime temperature and relative humidity conditions
from 3rd to 13th October 2021G.C. The sample wasdesiccated in the laboratory at room temperature of 25± 2℃ and relative humidity of 85 ± 5% in warm
environmental conditions. Digital images were taken
at 135mm height above the soil sample, and theirrespective soil mass was measured every 24hr. The
experiment lasted for 12 drying days, and stabilized
mass and cracked morphology were observed.
Obtained cracked images were analyzed using a
digital image analysis tool called ImageJ.

Table 3 Soil mix design of the experiment
Mix Group
Control1

Designation
DE, %ES + 0%DE
0ES + 05%DE
5ES + 10%DE
10DE Treated
ES + 15%DE
15ES + 20%DE
20
1Natural Soil, DE: Diatomaceous Earth

mw
mES

mDE.
×100
mBE. +mEs% DE =

2.4 Image-based crack analysis(a)

Digital images were analyzed using an image
analysis technique and Fiji (ImageJ) software. The
Schematic diagram of the image analysis technique isillustrated as shown in Fig. 5. Crack parameters suchas area, crack intensity (Dc), and crack index (r) wereused and computed to characterize the desiccation

mw
mDE
m (b)
ES

Fig. 4 Untreated and treated soil specimen phases
615

GEOMATE – Bangkok, Thailand, 22-24 November 2022

cracking of untreated and treated soil specimens [7].
The intensity of soil crack and cracking index are theparameters to describe the soil cracking process, andthey are expressed using the linear surface areas of
the formula specified in Equation. (3) and (4).

while cracking in between the clod areas is designatedas the cracked part. The second mode has two crack
area parts, the shrunk area and the uniform cracked
part designated as shrunk part. Also, the crack
connectivity and index parameters are illustrated in
the irregular mode of cracking. The crack segment
which connects the nodes (i.e., crack intersection
point) is labeled as a crack.

naC
DC = ∑( )*100%
A

(3)

i=1

Where Dc is soil crack area density, aci is the sum of
all soil crack areas in the typical cracked soil (mm2),
and A is the total surface area of the ordinary cracked
soil (mm2).
The soil cracks spatial morphological property isexpressed in terms of connectivity which is theconnection between crack topologies.
r=

L
L
=
Lm 3(ν-2)

Shrunk Area

(4)

Crack
Intersection
Point(Node)

Where r is the connectivity index, L is the number ofconnected cracks, Lm is the number of maximum
possible interconnected cracks, and v is the numberof crack nodes. The cracking index r ranges from 0 to
1, and the greater value indicates higher crack
connectivity.

(a)

The characterization method preferred here
considers the mode of cracking observed from theuntreated and treated soil specimens. Among the soil
specimens tested, two specific cases are observed; thefirst case is the irregular desiccated samples in soil
matrices (i.e., DE+0%DE, DE+5%DE, andDE+15%DE), and a regular uniform radial cracking
observed in (i.e., DE+10%DE and DE+20%DE).
Hence, the two cases are exemplified schematicallyshown in Fig. 6.

Shrunk Area

Image Calibration (pixel/length)
Image Binerazation
1

Crack Area Analysis
5

3

(b)

Desiccation Crack Analysis
Technique2

Fig. 6 Crack image processing and geometricaldescription of the areas a. irregular cracking
scenario and b. uniform radial shrinkage
scenario of soil specimens

4 Image Skeletonization
Greyscale Image (8-bit Image)Fig. 5 Schematics of Crack Analysis Technique

Crack networks on the surface of soil specimens
are highly irregular and difficult to measure with amanual measurement technique using a ruler or otherconventional method, which brings an error. Theseerrors can be reduced with the digital image analysis
method, and reliable, realistic data can be obtained.
The image analysis technique is currently utilized byvarious studies to characterize desiccation cracking in
expansive soils quantitatively [7]. The schematics

Fig. 6 presents the designation for the spatial
distribution of surface desiccation cracking area andcracking parameters utilized using the image analysis
technique. The crack area denotation described here
is for two categories depending on cracking modes(see Fig. 6 a. irregular mode of cracking and b. foruniform radial desiccation cracking). The shrunk(clod) area is the final area of the soil shrink part,616

GEOMATE – Bangkok, Thailand, 22-24 November 2022

depicted in Fig. 5 are used in this study. Color imagesof the cracked samples are converted into greyscale
images and later changed into binary black and whitephotos. Proper engineering judgment needs to beconsidered to refine the cracks and shrunk areas. It
should be scaled, reduced to a greyscale image,binarized and skeletonized to identify and quantify
the cracked and shrunk areas analysis using Equation(5).

specimen's top surface. With gradual drying of thesoil specimens, the water is vaporized at deeper
depths in the soil, with the vapor passing through thedry surface layer to the atmosphere, which will, in
turn, affect the interparticle tensile strength [9].
Once the tensile stress is developed within the soil
particles and exceeds the strength of the soil,
desiccation cracks occur with the increment ofsuction. In addition, soil microstructure usuallyshows heterogeneity in soil particle arrangements,
even for slurry specimens signifying that the tensile
bonding strength between the soil particles is not
uniform [9]-[10]-[11]. Under this effect of the tensilestress, the weak part of the lower bonded strength
fails first, leading us to observe a surface crack at the
top surface of the soil specimens. Hence, at themacroscopic level, the tensile strength of the soil iscritical in reflecting the soil's mechanical property at
the weakest zone. Furthermore, with the gradualchange of liquid water to vapor water and later
evaporation via macropores by a diffusion, at somepoint, there will be residual water at the shrinkage
potential limit, which the temperature might notremove at that experimental condition time[12]-[13][14].

nSA = OA - (SP+ ∑ CP)

(5)

i̇=1

Where SA is shrunk (clod) area; OA is the originaluncracked area; SP area of the shrunk part, and CP
area of the cracked part. For irregular cracking mode
shrunk part area will be zero; in the nonuniform
cracking manner cracked part of the area in Equation
Error! Reference source not found. will be zero.
3. RESULTS AND DISCUSSION
3.1 Mechanism of desiccation cracking
The drying test series in an open indoor laboratory
atmosphere and the test results include the evolutionof cracking parameters (i.e., expressed in crack area
ratio, crack connectivity, and cracking index) andwater evaporation rate. In this context, Fig. 7 shows
the overall mechanism of desiccation cracking in clay
soil. The schematics briefly illustrate and explains thestages of cracking and contributing factors fordesiccation cracking. The saturated soil subjected todrying conditions experiences atmospheric air
contact at the specimen's top surface. Later watervapor exchange occurs at the topmost surface, andwater-air menisci happen.
Eventually, at a macroscopic level, the suction
value (i.e., the difference between air pressure andwater pressure) at the beginning of the desaturation
point is referred to as the air entry value. Thissuggests that the negative water pressure plays acritical role in determining when the gas phase
intrudes the saturated porous soil body [8]. Hence,
capillary suction in the upper layer is developed,
which affects the arrangement of soil particles leading
to a volume change due to rearrangement. The
subsequent process includes an interphase transfer ofliquid water, and water vapor develops following
several scenarios [9].
With the complexity of pore size distribution, it
can be viewed that the air intrusion is an undersurface cavity occurring in the liquid water when aconstant temperature is brought to evaporation
through an evolution of the suction (negative)
pressure. As schematically shown in Fig. 7, water
vaporizes in the soil pores and is diffused from theinterior consisting of tiny pores to a larger poreextending upwards and being exposed to the

Top Surface

Interface of Water
and Air/meniscus

Vaporization
StartsSolid Particle
Soil Water(b) Interaction of soil
water and atmospheric air(a) Initial saturated state ofspecimens
Capillary Suction(Ua -Uw = 2Ts/Rs)

Tensile Stress

Adhesion Forces

Rs

Ts

Soil Solid
Particle

Soil Water(C) Capillary Suction between
Soil Particles(d) Tensile Stress develops on the top surface
layer and gradually continues to the bottom
Continuum Gradual entryof air in the macro pores

Surface Crack Initiates

Water Vapor
Liquid Water(e) Initiation of surface cracks starts
on the top layer of the soil samples(f) Cracking propagates with topdown vaporization of waterContinuum crack evolution due to gradual top-down
entry of atmospheric air in the pore spaces
Evaporation into the air
Water Vapor
Vaporization in the soil

Liquid Water(g) Soil water evaporation stages with the gradualentry of air into the soil layers

Water Vapor

Deeper Gradual
Vaporization
Residual Water(h) Final shrinkage potential limit
stage

Fig. 7 Mechanism of desiccation cracking schematics
It has been observed that the crack networks in thenatural soil, ES+5%DE, and ES+15%DE, haveirregularly desiccated segments of cracks. The
propagation of desiccation cracks gradually develops
617

GEOMATE – Bangkok, Thailand, 22-24 November 2022

after shrinkage (mm2), Asi= Area of the initial surface
area of soil specimen before shrinkage (mm 2).
The spatial soil connectivity is calculated using theconnectivity index parameter r among the soil
specimens. Fig. 8 illustrates the soil specimens' soil
crack morphology intensity and connectivity index. It
shows the crack intensity and index from the
beginning of the crack initiation, starting from the 3rd
day to the stability of cracking from the 7th to 11th dayof the desiccation cracking experiment. The cracking
index indicates that the natural soil, ES+5%DE, andES+15%DE have effective connectivity. However,the soil blend ES+10%DE and ES+20%DE doesn't
have a significant cracking index. Notably,
ES+20%DE significantly reduces desiccation
cracking intensity and spatial crack connectivity.

by widening from the preexisting cracks until final
consolidation is observed in consecutive massmeasurements, while ES+10%DE and ES+20% DE
have a uniform radial deformation on the soil blend,
resistance in tensile strength has been kept, whichneeds to be quantified.
3.2 Cracking Initiation Pattern Observation
From the desiccation crack, experimental tests
resulted in those irregular predominant cracks being
developed on the control (natural soil) and soil
slurries blended with 5% and 15 % DE. However,soil slurry specimens combined with 15% and 20%
DE have uniform regular radial shrinkages.
Table 4. Crack Propagation and End of Dried State of
Untreated and Treated Samples
Crack Initiation
State (START)

Complete Dried
State (END)

Crack Area Ratio, %Soil Matrix

ES+0%DE Shrunk Area
ES+0%DE Cracked Part
ES+5%DE Shrunk Area
ES+5%DE Cracked Part
ES+15%DE Shrunk Area
ES+15%DE Cracked Part

100

ES + 0%DE

ES + 5%
DE

80
60
40
20
0
1

ES + 10%
DE

3

5
7
9
Elapsed drying time, Days

11

Fig. 8 Crack area ratio comparison of the irregular
desiccated soil specimens
ES + 15%DE

Fig 8 illustrates the cracking intensity among the soil
matrices, that have irregular cracking modes. The
addition of 5% DE and 15% DE does not influence
the desiccation cracking of the expansive soil.
However, the soil blends at 10%DE and 20%DE havesignificantly reduced the morphology of crack
density and crack connectivity. But as it is alreadycited in Fig. 7, which clearly illustrates themechanism of desiccation cracking and associatedparameters related to that. Therefore, the capillarysuction, which is dependent on cation exchange
capacity, tensile stress in the soil mass which
emanates from the suction development, and shear
strength of the soil, needs to be addressed tocomprehensively understand and reduce desiccation
cracking in expansive soil.

ES + 20%DE

3.3 Quantitative characterization of crack area
The surface crack area ratio, crack connectivity,and cracking index parameters were considered tocharacterize the desiccation cracking in the soil
matrices. The crack area ratio of the soil specimen canbe evaluated using Equation (6).
Rs =

As f
As i

(6)

Where Rs= Area ratio of soil specimens
Asf = Area of the final surface area of soil specimen
618

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Crack Area Ratio, %3.4 Soil water evaporation

ES+0%DE Shrunk Area
ES+0%DE Cracked Part
ES+10%DE Shrunk Area
ES+10%DE Shrunk Part
ES+20%DE Shrunk Area
ES+20%DE Shrunk Part

100
80

Initially, under the saturated condition, the soil
specimens have enough water in the soil pore spaces.
During the drying state of the soil specimens, the soil
water at the topmost surface of the specimen changes
to water vapor and is continuously removed from thesoil pores, which start from the topmost surface andget deeper. Thus, the upward diffusion of vapor
exceeds the vaporization within the soil and the
diffusion of vapor from the underlying layer [15].
When the soil gets dry over the experimental
dates, the evaporation rate calculation in Equation (7)below is adapted from reference [15]:

60
40
20
0
1

3

5
7
9
Elapsed drying time, Days

11

E=

Fig. 9 Crack area ratio comparison of the regular
desiccated soil specimens

1

Cracking Index, r

0.8
0.6
ES + 0%DE
ES + 5%DE
ES + 10%DE
ES + 15%DE
ES + 20%DE

0.2
0
0

2

4
6
8
10
Elapsed drying Time, Day(7)

Where E (mm/h) is the evaporation rate; Δm (g) refers
to the mass change of the tested soil underevaporation within the time interval Δt (h); ρcorresponds to the water density (0.001g/mm 3), andA(mm2) is the area of the magnitude of the bottom
surface of the container obtained from the image
analysis.
From Fig. 11, it can be observed that theevaporation rate evolution in the soil specimen under
the drying desiccation experiment elapsed time andtemperature difference between two consecutive
days. When the desiccation drying process continues,
the temperature difference increases, but on the third
day of the test, the temperature difference will drop,and crack initiation starts. Eventually, the evaporation
rate decreases with different temperatures. According
to Table 4 and Fig. 11, the soil matrices experience a
sudden evaporation rate increment at the temperature
difference drop point and gradually decrease in rate,
but cracks gradually develop and keep a stable crack
evolution.

The result in Fig. 9 presents the crack area ratio
comparison between the natural soil and soil matrices
at 10% and 20% diatomaceous earth-treated samples.
It is observed that the DE at 10% and 20% mix has
significantly reduced the desiccation cracking. The
maximum crack area ratio resistance observed is at
20% DE mixture, which is about a 16% reduction
from the other soil matrices.

0.4

Δm
ρw AΔt

12

0.15

0

0.1

-1

0.05

-2

0.25

Fig. 10 expresses the cracking index comparison; it isobserved that the crack connectivity in 10% and 20%
DE mixed specimens are null. However, the naturalsoil, 5% and 15% DE mixed soils cracking index
result ranges from 0.6 to 0.8. The cracking index
results in the natural soil, 5% and 15% DE has shownthat there is a significant number of interconnectedcracks. Thus, such a scenario has an implication for
the expansive soil in terms of permeability of water
during wet seasons and loss of strength to bear the
self-weight as well as external loads.

oEvaporation Rate (mm/h)

Fig. 10 Soil crack connectivity index

-3
2
4
6
8
10 12
Elapsed drying time,day
Fig. 11 Evaporation rate of tested soil specimens
0

0

619

Temperature Difference ( c)0.2

ES+0%DE
ES+5%DE
ES+10%DE 2
ES+15%DE
ES+20%DE 1

Temperature Difference(oc)GEOMATE – Bangkok, Thailand, 22-24 November 2022

4. CONCLUSIONS

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Inclusion on the Evolution of Desiccation
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1–7, doi:10.1007/s40808-015-0036-z.
8. Hu, L.B.; Hueckel, T.; Péron, H.; Laloui, L.
Modeling Evaporation, Shrinkage and Crackingof Desiccating Soils. 12th Int. Conf. Comput.
Methods Adv. Geomech. 2008 2008, 2, 1083–
1090.
9. Tang, C.S.; Cui, Y.J.; Tang, A.M.; Shi, B.
Experiment Evidence on the Temperature
Dependence of Desiccation Cracking Behavior of
Clayey Soils. Eng. Geol. 2010, 114, 261–266,
doi:10.1016/j.enggeo.2010.05.003.
10. Hamza, O.; Ikin, J. Electrokinetic Treatment of
Desiccated Expansive Clay. Geotechnique 2020,
70, 421–431, doi:10.1680/jgeot.18.P.266.
11. Abbaszadeh, S.; Houston, S.L.; Zapata, C.E.
Effect of Desiccation Cracking on the Swell and
Swell Pressure of Expansive Clay. Geotech.
Struct. Eng. Congr. 2016 - Proc. Jt. Geotech.
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doi:10.1061/9780784479742.176.
12. Merlin, O.; Al Bitar, A.; Rivalland, V.; Béziat, P.;
Ceschia, E.; Dedieu, G. An Analytical Model of
Evaporation Efficiency for Unsaturated Soil
Surfaces with an Arbitrary Thickness. J. Appl.
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13. Tang, C.S.; Shi, B.; Liu, C.; Suo, W. Bin; Gao, L.
Experimental Characterization of Shrinkage and
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Unsaturated Soil Mechanics. Geotech. Eng. 2019,50, 74–80.
15. Zeng, H.; Tang, C.S.; Zhu, C.; Cheng, Q.; Lin, Z.
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Cracking Using an Infrared Thermal Imaging
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doi:10.1029/2021WR030916.

In conclusion, the experimental results showed thatdiatomaceous earth could be used as an additive to
reduce desiccation cracking of expansive soil. The
results show that the addition of diatomaceous earth
affects the desiccation cracking, which in turn has animplication on the physical, mechanical, andchemical properties of expansive soil. The DEexpansive soil matrix with higher DE content spatial
distribution in a desiccation cracking manner.
Particularly, the observed reduction in desiccation
cracking at a mixture of 20% diatomaceous earth
decreased the crack spatial distribution by 16%. Also,with an increase in diatomaceous content, thecracking connectivity at 10% and 20% diatomaceous
earth soil matrix manifested a maximum removal of
crack connectivity. However, it was assumed that thewater would be absorbed with the addition of
diatomaceous earth, but the experimental results
didn't reflect that. This result was due to the high poresize distribution, resulting in a high evaporation rate.
5. FURTHER STUDY
The authors will further study additional
experimental parameters to comprehensively
comprehend and explain the effectiveness ofdiatomaceous earth on desiccation cracking of
expansive soil. Further experiments such as cationexchange capacity, soil suction, tensile strength test,
shear strength, and pore distribution will be tested.
6. ACKNOWLEDGMENTS
This work was supported by JST-JICA,
SATREPS, grant number JPMJSA1807. I would liketo extend my sincere gratitude to my immediate
supervisors Pr. Daisuke Suetsugu and Dr. Fukubayshi
Yoshinori for their relentless guidance and support.
7. REFERENCES
1. Tang, C.S.; Zhu, C.; Cheng, Q.; Zeng, H.; Xu, J.J.;
Tian, B.G.; Shi, B. Desiccation Cracking of Soils:
A Review of Investigation Approaches,
Underlying Mechanisms, and Influencing
Factors. Earth-Science Rev. 2021, 216, 103586,
doi:10.1016/j.earscirev.2021.103586.
2. Walke, B. Seasonal Weather Assessment for
Ethiopia. 2016, 3.
3. Kim, S.; Xue, J.; Xie, Y.; Zhou, L. Controlling
Desiccation Cracks in Clay with Fibre andEnzyme. Proc. 2nd Symp. Coupled Phenom.
Environ. Geotech. 2017.
4. Costa, S.; Kodikara, J.; Barbour, S.L.; Fredlund,
D.G. Theoretical Analysis of Desiccation Crack620

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

URBAN HEAT HAZARD MODEL BASED ON LOCAL CLIMATEZONE
Adi Wibowo1, Nadira Retno Abisha2, Eko Kusratmoko3 and Ratna Saraswati4
1,2,3

Department Geography, Faculty Mathematics and Natural Sciences, University of Indonesia, Indonesia.
ABSTRACT

Urban heat hazard is the temperature in an urban area of more than 30oC and impacts humans. The local climatezone (LCZ) is an area within 100 x100 meters and has a specified climate type. The LCZ is based on thecomposition of developed and vegetated areas. The value of LCZ represents air surface temperature as an effectof land surface temperature on the environment. The study aims to develop an urban heat hazard model based on
the local climate zone. The study used high-resolution multi-spectral imagery collected from remote sensing using
Google Earth Engine, and the result uses to develop LCZ and Land Surface temperatures (LST). Local Climate
Zone in the study area with 1.000 x1.000 m2 generated from 100 x 100 meters based on band thermal Landsat
data. The LST value uses an algorithm to determine the effect on the environment as air surface temperature andthe impact on humans based on the Universal Thermal Climate Index. This study concluded that a local climatezone determines the urban heat hazard in the city regarding urban heat signature. The urban heat hazard affects theenvironment, with air surface temperature getting more than 32 oC, which impacts a human with head headache,sweat, and heatstroke.
Keywords: Land surface temperature, Urban Heat Signature, air surface temperature, local climate zone, urban
heat hazard

1.

INTRODUCTION

Urban Heat Island (UHI) [10], the air temperature inurban areas with dense buildings/populations ishigher than the temperature in the surrounding
areas/outskirts of the city [11]. This explanation
provides an overview of existing research that has not
yet produced a local climate map based on changes inground cover, land surface temperature and airtemperature.
Spatial-temporal analysis of land use can be
carried out with the help of Google Earth Data [12]which deals with land cover changes related to soil
surface heat [13]. Hot temperatures can impact theenvironment [14] [15], that research has been carriedout on the relationship between changes in groundcover and land surface temperature and airtemperature.
Climate is the weather conditions in an area overa long period [16] [17]. The current climateclassifications made for macro-scale (> 10,000 km)
and mesoscale (100 – 1000 km) [18]. Microclimateclassification is not widely used [19]. The division ofclimate into smaller scales, such as in urban areas,
was not widely applied until the emergence of thetheory of Local Climate Zones (LCZ). LCZ is anurban land classification system based on themorphological characteristics of the city, including
buildings and types of land cover [20]. The Local
Climate Zone describes an area with uniform land
cover, structure, building materials and humanactivities within 100 meters to several kilometres[21]. Local climate zones are currently widely used asa reference in studies related to UHI because their

The Changes in land cover impact two aspects,
natural variations of the earth system and humanactivities [1]. Some examples of land cover changes
caused by human activities include bare land surfaces
and vegetation converted into impermeable surfaces
such as asphalt, concrete, and cement or changing
urban morphology such as building areas withvarying heights and densities [2]. This land coverchange causes the temperature on the earth's surface
to change [3].
Land cover change is a global environmental
problem, especially in developing countries [4].
Changes in land cover from vegetation to urban landcause a break in the carbon and water cycle andenergy fluxes between the soil and the atmosphere[5]. Land Surface Temperature (LST) derived fromremote-sensed Thermal Infrared (TIR) satellite
imagery is a crucial variable in understanding theimpact of land use and land cover change (LULC),
which has been linked to urban thermal patterns invarious ways (Soydan, 2020) [6]. LST is also an
important variable related to climate change in urban
areas and indicates ground-level energy balance [7].
Air temperature is essential in explaining theconditions of the terrestrial environment on earth [8].
Land cover types with high vegetation cover areassociated with low temperatures, and conversely,
land cover types with low vegetation cover areassociated with high temperatures [9]. As aconsequence of changes in the heat balance and621

GEOMATE – Bangkok, Thailand, 22-24 November 2022

typology is universal and can classify urban areas
worldwide with the same standard [22]. Local
Climate Zone is the latest method in climateclassification for an urban scale.
The heat effect on an urban area is called an urban
heat hazard based on a threshold. The threshold used
the Universal Temperature Climate Index [14] [15]for assessing the Urban Heat Hazard (UHH). It will
become an urban heat hazard threat whenever urban
heat signatures are above their threshold [13]. The
urban heat hazard could threaten the sustainableurban environmental quality and human well-being.

The result of air surface temperature modelling will
create a surface air temperature map by applying theexisting model using a raster calculator on the
ArcGISPro device.
The study area is Bogor City, located in thewestern part of West Java Province, Indonesia, at
106°43'30" - 106°51'00"E and 6°30'30" - 6°41'00" S(Fig. 1). The result used to develop LCZ and Land
Surface temperatures (LST). Local Climate Zone in
the study area with 1.000 x1.000 m2 generated from100 x 100 meters based on band thermal Landsat dataand Land Cover Type. The LST value uses analgorithm to determine the effect on the environment
as air surface temperature and the impact on humans
based on the Universal Thermal Climate Index.

2. METHOD
The study uses Landsat 8 OLI-TIRS medium
resolution to extract land cover features and generate
temporal LST. Remote sensing data processing,including land cover classification and LST
processing. The satellite image used in this study is
the Landsat 8 OLI/TIRS Satellite Imagery which wastaken in the month with the lowest rainfall with morethan 20% cloud cover. The nomenclature used is the2010 National Standardization Agency nomenclature
which is generalized to land cover. The classification
is divided into five classes: water bodies, built-up
land, open land, non-agricultural vegetation, andagricultural vegetation. The area of each classcalculates every year to determine the changing area.
The Khat Kappa method is a statistical calculation
method used in research to test the accuracy of thedata between land cover classification resulting fromsatellite imagery processing and the results of Google
Earth validation and field surveys. The land surface
temperature was obtained from calculating thesatellite brightness value in identifying the land
surface temperature, namely band 10 on the Landsat
8 OLI/TIRS image, with the formula in Eq. 1.
𝑳 = (𝑴 × 𝑫𝑵) + 𝑨(1)
The first step is to change the image pixel value(DN) to spectral radiance, where L is the spectral
radiance (wm-2SR-1 m-1), M is the multiplicative
digital number in band 10, and DN is the digital
number in band 10. Moreover, A is the additive value
in band 10. After the spectral radiance value isobtained, it is changed to estimate the soil surface
temperature value with the formula in Eq. 2.
𝑻=

𝑲𝟐
𝑲𝟏
+𝟏)
𝑳

𝑰𝒏(

Fig. 1The study area and Sample Location.

The threshold becomes a hazard whenever thetemperature of land cover types is above 32oC withcategory intense heat stress [14]. The threshold isbased on the Universal Temperature Climate Index[14] [15] for assessing the Urban Heat Hazard (UHH).
The UHH uses spatial analysis (spatial pattern) andoverlays between landcover types, LST and Air
Surface temperature (AST). Thus all generated spatialmodels of Urban Heat Hazard based on the Local
Climate Zone in Bogor City in 2022.

(2)

Where T is the temperature of the satellite sensor(Kelvin), K1 is the calibration constant 1 for Landsat
OLI (774853 K), K2 is the calibration constant 2 forLandsat OLI (1321.0789 K), and 𝑳 is the spectral
radiance band 10. Kelvin temperature value
converted to Celsius with the following algorithm in
Eq. 3.

3. RESULT AND DISCUSSION
3.1. Green Open Space
Based on the results of Landsat 8 OLI image
processing in 2022 (Fig. 2), it has known that the total
green open space area is 4,577 ha or 39% of the areaof Bogor City. The area consists of public green open

𝑳𝑺𝑻(𝑪𝒆𝒍𝒄𝒊𝒖𝒔) = 𝑻 − 𝟐𝟕𝟐, 𝟏𝟓(3)
Temp-Kelvin is the estimated land surface
temperature from satellite sensors in Kelvin units.
622

GEOMATE – Bangkok, Thailand, 22-24 November 2022

water body has an area of 408 hectares or 3% of thetotal area of Bogor City (Table 2).

space and private green open space. If it is reviewed
based on Law No. 26 of 2007 concerning spatialplanning, which regulates the area of green open
space in urban areas, it must be at least 30% of thetotal city area. The sub-district with the mostsignificant total green open space is the South Bogor
District, with an area of 1,745 ha of green open space
whose territory has a hilly area. Meanwhile, thedistrict with the slightest green open space is inCentral Bogor District, with an area of 260 ha.

Table 2 The Land Cover Change Between 2018 and2022 in Bogor City
Land Cover
2018
2022
Green Open Space
4.876
4.577
Build-up Area
6.459
6.782
Open Space
72
83
Water Body
443
408
Total Area
11.85011.850

Table 1 Green Open Space in Bogor City, 2022
Green Open SpaceArea
Per cent2(m )(%)
Public2
5
Private1
6

Sources: Data Analysis

Source: Data Processing, 2022

Fig. 3Fig. 2

The Land Cover in Bogor City, 2022

Based on the results of the 2018 Landsat 8 OLI
image analysis, Bogor City's Land Surface
Temperature (LST) ranges from 15 - 35 oC (Fig. 4).
Bogor City during 2018 dominated LST with 26 –30oC an area of 7646.26 Ha or 64.5% of the area ofBogor City. The LST with 15 – 20oC with an area of
29.69 or 2% based on Bogor City (Table 3).

The Green Open Space in Bogor City, 2022

3.2. Local Climate Zone
The land cover map for 2018 and 2022 wasgenerated from processing Landsat 8 OLI satellite
imagery data recorded on 1st April 2018 and 4th April
2022. Land cover in the form of built-up land
occupies about 6,459 hectares, or 55% of the area ofBogor City. Meanwhile, for land cover in the form ofopen land and water bodies, each occupies an area of
1% and 4% of the total area of Bogor City. The builtup land area dominates the Bogor City area with apercentage of 57% or around 6,782 Ha. This increase
was caused by a change in land cover, initially in theform of open land or vegetated land into built-up land.
The area of vegetated land is decreasing over time
because various developments in the city area replaceit. Meanwhile, open land occupies the smallest areaof 83 hectares or 1% of the total area. Meanwhile, theTable 3 The Land Surface Temperature Between
2018 and 2022 in Bogor City
LST (oC)
2018 (m2)2022 (m2)
15 - 2029.69
0
21 - 25
2,714.15
026 - 30
7,646.26
2,640.84
31 – 35
1,459.91
8.882.01> 35
0363.15
Sources: Data Analysis

Areas with high-class LST in 2018 in Bogor City
spread over land cover areas with the built-up area.
Areas with high-temperature LST spread in thenorthern part of Tanah Sareal District, the southern623

GEOMATE – Bangkok, Thailand, 22-24 November 2022

part of North Bogor District, southeast and north ofNorth Bogor District, the northern part of South
Bogor, the northeastern part of Central Bogor, andWest Bogor District (Fig. 4).

Fig. 4

Fig. 5

The Land Surface Temperature in Bogor
City, 2022

Fig. 6

Local Climate Zone In Bogor City, 2022

The Land Surface Temperature in Bogor
City, 2018

Based on the results of the Landsat 8 OLI image
conversion recorded in 2022, the Land Surface
Temperature (LST) of Bogor City is between 25oCmore than 35oC, including in the medium – very highclass (Fig. 5). LST Bogor City in 20202 domination
by high temperature, with an area of 8,882.01 ha or
74.9%, LST with a medium temperature laid on
2,640.84 ha or 22.6%, and very high-temperature is363.15 ha or 3% (Table 3).
Areas with high-class LST in 2022 in Bogor City
are spread over areas with various land covers,
including vegetated land, built-up land, and open
land, except for water bodies. Areas with very highclass LST are spread out in the centre of Bogor City
with land cover, namely built-up land. Areas withmoderate LST in 2022 in Bogor City spread overareas with land cover, namely vegetated land spread
across the south and northwest of Bogor City (Fig. 5).
Based on Land Cover and Land Surface
Temperature, both of them generate a Local Climate
Zone (LCZ) within 1.000 x 1.000 meters. The type of
LCZ is separated into five classes (Fig. 6). LCZ 1 is a
type of land cover with 100 % vegetation, LCZ 2 with
80 % is vegetation-covered, LCZ 3 covers with 60 %,LCZ 4 covers with 40 % vegetation covered and LCZ
5 covers similar or less than 20 % covered byvegetation (Table 4).

Table 4 Local Climate Zone in Bogor City, 2022
LCZ
Total
Area
Percentage
Grid
(m2)
(%)
LCZ 1
25
25.00020
LCZ 2
3131.00024
LCZ 3
2121.00016
LCZ 4
3535.00025
LCZ 5
2020.00015
Sources; Data Analysis

3.3. Urban Heat Hazard
624

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Based on Land Surface Temperature on Universal
temperature Index, Urban Heat Hazard in 2018 only
maximum with Moderate Hazard, and on the other
hand in 2022, the Urban Heat Hazard getting HighHeat Hazard caused the temperature upper than 35oC
(Table 5). The impact of high heat hazards on thehuman body is getting sweat and head Headaches
until heat stroke. Moreover, the distribution of Urban
Heat Hazard spreads around Bogor City (Fig. 7).
A previous study by Ali and Patnaik concluded
that residents in summer could improve the urban
climatic condition through shading and transpiration.
In contrast, plants release water vapour into thesurroundings, increasing relative humidity anddecreasing air temperature [23]. The Urban Heat will
not impact humans if the temperature is lowest,
caused by vegetation-covered (plants) dominant inthat areas. This result had similar to Ali and Patnaik,
where the dense vegetation covered was the lowest
temperature due to urban heat hazards.

signature. The urban heat hazard affects theenvironment, with air surface temperature getting
more than 32oC, which impacts a human with head
headache, sweat, and heatstroke.

Table 5 The Urban Heat Hazard Between 2018 and2022 in Bogor City
LST (oC)
20182022
15 - 20
No Hazard
0
21 - 25
No Hazard
0
26 - 30Low
Low
31 – 35
Moderate
Moderate
> 35
0
High

Fig. 7

Spatial Model of Urban Heat Hazard in
Bogor City, 2022

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Threatening the natural ecosystem creates
vulnerability to an environmental hazard [24]. The
research agrees with Bekele et al., which explained
the land cover changes in Bogor City. These changes
will generate the heat hazard in Bogor City as Urban
Heat Hazard Threat.
Generally, cities face specific urban events dueto the urban setting that can alter the local
microclimate with higher impacts [25]. Urbanization
increases the risk of extreme heat episodes in
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with Ashaolu et al. to detect the land cover change.
The research detecting land cover changed the effecton urban heat and impact on air surface temperatureas thermal comfort.
Finally, the novelty of the result is that the
research improved the Local Climate Zone could
detect urban heat hazards based on a combination ofvegetation covered and build-up area.
CONCLUSIONSThe LST value uses an algorithm to determine
the effect on the environment as air surface
temperature and the impact on humans based on the
Universal Thermal Climate Index. This study
concluded that a local climate zone determines theurban heat hazard in the city regarding urban heat625

GEOMATE – Bangkok, Thailand, 22-24 November 2022

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Manessa M. D. M., and Ristya Y., Variations ofLand Surface Temperature and Its Relationshipwith Land Cover and Changes in IPB Campus,
Dramaga Bogor 2013-2018. E3S Web ofConferences, Vol. 125 Issue 2019, pp.
[9] Wibowo A., Yusoff M. M., and Salleh K. O.,
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[17] Oke T. R., The Energetic Basis of the Urban Heat
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[21] Stewart I. D., and Oke T. R., Local Climate
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[22] Collins J., and Dronova I. Urban Landscape
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[23] Ali S.B., and Patnaik S., Assessment of the
impact of urban tree canopy on microclimate in
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[24] Bekele D., Alamirew T., Kebede A., Zeleke G.,
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[27] Ashaolu E.D., Olorunfemi J. F., and Ifabiyi I. P.
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Geography, Vol. 12, Issue 1, 2019, pp.41-50.

626

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

URBAN HEAT SIGNATURE AS MONITORING OFENVIRONMENTAL HEALTH
Adi Wibowo1, Iqbal Putut Ash Sidiq2, Mariney Binti Mohd Yusoff3 and Tengku Adeline Adura Binti
Tengku Hamzah4
1,2

Department Geography, Faculty Mathematics and Natural Sciences, University of Indonesia, Indonesia.
3,4Department Geography, Faculty Arts and Social Sciences, University of Malaya, Malaysia.
ABSTRACTThe urban area has a variety of land cover with varying surface temperatures. Urban heat signature is a
representation of the temperature of land cover. Sun heating on land cover affects the urban environment, and its
effect on the Urban Heat Signature (UHS) is essential to assess UHS-related environmental health. Even though
the increased temperature is a natural phenomenon in urban areas, it could be a threat in tropical cities when it ishigher than 30oC, such as increased temperatures will impact human life. The method used Landsat TIRS (100 m)
ground resolution to estimate land and air surface temperature. Environmental health monitoring uses Universal
Temperature Climate Index (UTCI) dan Effective Temperature Index (ETI). The result of UHS profiles related tovegetation cover of 25-33oC, with a mean of 28oC as the lowest temperature, and building cover with a profile of33-39oC, with a mean of 35oC as the highest temperatures based on land cover types. The Urban Heat Signature
between 30-33.0°C affect air temperature with a max >33.0°C. The temperature will impact humans with
uncomfortable sensations, and then it will decrease capacity and achievement of work. This result concluded that
UHS's effect on the air temperature and heat intensity impacts human health, indicating that the environmental
condition is unhealthy.
Keywords: Land cover, land surface temperature, air surface temperature, urban heat signature, environmental
health1.

INTRODUCTION
Land cover types and sun radiation will generatea direct heat signature. Various types of land cover,as a representation of land uses [1], [2], could absorb
and radiate sun radiations to generate urban heat [3][4]. According to Taha (1997), heat signature depends
on local climate characteristics, land use, and land
cover types [5]. Therefore, the information ontemperature and land use cover, which provides formonitoring the urban environment and humanactivities, will enhance our understanding of theurban environment [6].
The Changes in land cover impact two aspects,
natural variations of the earth system and humanactivities [7]. Some examples of land cover changes
caused by human activities include bare land surfaces
and vegetation converted into impermeable surfaces
such as asphalt, concrete, and cement or changing
urban morphology such as building areas withvarying heights and densities [8]. This land coverchange causes the temperature on the earth's surface
to change [9].
Land cover change is a global environmental
problem, especially in developing countries [10].
Changes from vegetation to urban land cover cause abreak in the carbon and water cycle and energy fluxes
between the soil and the atmosphere [11]. Land
Surface Temperature (LST) derived from remotesensed Thermal Infrared (TIR) satellite imagery is a

crucial variable in understanding the impact of land
use and land cover change (LULC), which has been
linked to urban thermal patterns in various ways [12].
LST is also an important variable related to climatechange in urban areas and indicates ground-level
energy balance [13].
Air temperature is essential in explaining theconditions of the terrestrial environment on earth[14]. Land cover types with high vegetation cover areassociated with low temperatures, and conversely,
land cover types with low vegetation cover areassociated with high temperatures [15]. As aconsequence of changes in the heat balance and
Urban Heat Island (UHI) [16], the air temperature inurban areas with dense buildings/populations ishigher than the temperature in the surrounding
areas/outskirts of the city [17]. This explanation
provides an overview of existing research that has not
yet produced a local climate map based on changes inground cover, land surface temperature and airtemperature.
Spatial-temporal analysis of land use can be
carried out with the help of Google Earth Data [18]which deals with land cover changes related to soil
surface heat [19]. Hot temperatures can impact theenvironment [20] [21], that research has been carriedout on the relationship between changes in groundcover and land surface temperature and airtemperature.
627

GEOMATE – Bangkok, Thailand, 22-24 November 2022

𝑳𝑺𝑻(𝑪𝒆𝒍𝒄𝒊𝒖𝒔) = 𝑻 − 𝟐𝟕𝟐, 𝟏𝟓

Temperature information provides a powerful
way to monitor the urban environment [6]. Hence,from a satellite image, the thermal infrared (TIR)band generated land surface temperature by gathering
Land Surface Temperature (LST) or UHS [22] [23][24]. The variation of UHS has used spatial-temporal
analysis to generate a spatial pattern related to thehighest vegetation cover with low temperature andwithout vegetation cover with the highest temperature[4]. The daily pattern of UHS during the daytime ischaracteristic of heat signature behaviour related togreenery areas, which behave as a cooler ambient
temp [25].

Temp-Kelvin is the estimated land surface
temperature from satellite sensors in Kelvin units.
The result of surface air temperature modelling will
create a surface air temperature map by applying theexisting model using a raster calculator on the
ArcGISPro device.
The study area is The Bogor Agriculture
University (BAU) campus. BAU laid in the Bogor
Regency area, Dramaga District. BAU campus isgeographically between 06°32'41”-06°33'58” S and
106°42'47”-106°44'07” E, which includes the fort
village on the north end, Babakan village into the East
End and most of the rest area located in the Cibanteng
Village. The total campus area is about ±250 Ha.

2.

METHODThe study uses Landsat 8 OLI-TIRS medium
resolution to extract land cover features and generate
temporal LST. Remote sensing data processing,including land cover classification and LST
processing. The satellite image used in this study is
the Landsat 8 OLI/TIRS Satellite Imagery which wastaken in the month with the lowest rainfall with morethan 20% cloud cover. The nomenclature used is the2010 National Standardization Agency nomenclature
which is generalized to land cover. The classification
is divided into five classes: water bodies, built-up
land, open land, non-agricultural vegetation, andagricultural vegetation. The area of each classcalculates every year to determine the changing area.
The Khat Kappa method is a statistical calculation
method used in research to test the accuracy of thedata between land cover classification resulting fromsatellite imagery processing and the results of Google
Earth validation and field surveys. The land surface
temperature was obtained from calculating thesatellite brightness value in identifying the land
surface temperature, namely band 10 on the Landsat
8 OLI/TIRS image, with the formula in Eq. 1.

3. RESULT AND DISCUSSION
3.1. Land Cover
The Bogor Agriculture University (BAU) campus
had a total percentage of areas of these features forgreenery area with 75.3% and a no-greenery area of
24.7% in 2018. The BAU campus was 16.9% coveredby building cover and 57.3% covered by the densely
vegetated surface in 2018. The rest of the landscapeis presented in Table 1. The Land cover maps show
in Figure 1.
Table 1 Land Cover in BAU Campus, 2018Land Cover
Area
Percentage(hectare)(%)
Paved Open Space
19.34
7.8
Build-up Covered
41.53
16.9
Open Vegetated
41.61
17.0
Water Body
2.38
1.0
Dense Vegetated140.71
57.3
Sources: Data Analysis

𝑳 = (𝑴 × 𝑫𝑵) + 𝑨(1)
The first step is to change the image pixel value(DN) to spectral radiance, where 𝑳 is the spectral
radiance (wm-2SR-m), M is the multiplicative digital
number in band 10, and DN is the digital number inband 10. Moreover, A is the additive value in band 10.
After the spectral radiance value is obtained, it ischanged to estimate the soil surface temperature value
with the formula in Eq. 2.
𝑻=

𝑲𝟐
𝑲𝟏
𝑰𝒏( +𝟏)
𝑳

(3)Land cover from the BAU campus from Google Earthdigitization processing in 2013 and 2018. The
resulting land cover classifications were water bodies,vegetation, built-up land, agricultural land and open
land. The result is that the built-up area is increasing,followed by a decrease in the area of vegetation.
Table 2 shows the land cover area at the BAU
campus.
Table 2 The Land Cover 2013 and 2018 on the BAULand Cover
2013
2018
Green Open Space152.44145.46
Build-up Area
45.70
50.48
Open Space
2.82
4.50
Water Body
1.04
1.04
Open Vegetated
37.73
38.25

(2)

Where T is the temperature of the satellite sensor(Kelvin), K1 is the calibration constant 1 for Landsat
OLI (774853 K), K2 is the calibration constant 2 forLandsat OLI (1321.0789 K), and L is the spectral
radiance band 10. Kelvin temperature value
converted to Celsius with the following algorithm in
Eq. 3.

Sources: Data Analysis

628

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 2. The Land Cover in Bogor Agriculture University

Fig. 3

The Land Surface Temperature in BAU, 2017

Fig. 4

The Land Surface Temperature in BAU, 2021

629

GEOMATE – Bangkok, Thailand, 22-24 November 2022

3.2. Urban Heta Signature
The finding gives a new understanding of urban heatsignature, and urban heat signature behaviour and its
effect and impact become urban heat hazard inanother university. The relation between UHS andland cover types in 2013-2015 (Table 3) had mintemp 25.7C relationship with Paved Open Space,
UHS min temp 23.50C relation with Open Vegetated
Covered, Dense Vegetated Covered relations withmin temp 23.10C. UHS with max temp 32.40C
relation with all land use cover types. UHS with thehighest mean temperature is Paved Open Space, andthe lowest is Water Bodies with a mean temp 24.60C.

Dense Vegetated Covered. UHS with max temp
32.20C relation with all land use cover types. UHS
with the highest mean average temperature is Paved
Open Space, and the lowest is Dense Vegetated
Covered. The index is 1- 5 for high and low
temperatures, the Paved Open Space is the highest,
and the lowest index is Dense Vegetated Covered.
Based on the Universal temperature Index, Urban
Heat Hazard in 2018 only maximum with Moderate
Hazard, and on the other hand, in 2021, the Urban
Heat Hazard getting High Heat Hazard caused atemperature higher than 35oC. The impact of highheat hazards on the human body is getting sweat andhead Headaches until heat stroke.

Table 3 The Land Cover and Urban Heat Signature
Land Cover
2013
2015
2018
Dense vegetated
26.5
25.9
28.9
Build-up Area
27.5
26.8
30.4
Paved Open Space
28.1
27.3
31.1
Water Bodies
25.5
25.9
30.2
Open Vegetated
26.8
11.850 11.850

Table 5 The Air Surface Temperature and Urban
Heat Hazard in BAU Campus
Land Cover
AST(oC)
HazardPaved Open Space
5
Very High
Building Covered
4
High
Open Veg. Covered
3
Moderate
Water Bodies
2
Low
Dense Veg. Covered
1
Verry Low

Sources: Data Analysis

This research used north to south cross-section
directions to describe the dynamic change of surface
temperature across different land-use and land-covertypes (or the UHS). The urban heat signature in 2017is seen in Fig. 3, and the UHS in 2021 is seen in Fig.
4). The minimum land surface temperature in 2017
was 19,8 oC, rising to 23 oC. Similar to the minimum,
the maximum temperature rises to 27.5 in 2021 from
22.5 in 20017.
The relation between UHS and land use cover types
in 2013-2018 (Table 4) had a mean temp 27.00C
relationship with Dense Vegetated Covered. UHS
with max temp 32.20C relation with all land use covertypes. UHS with the highest mean averagetemperature is Paved Open Space, and the lowest is
Dense Vegetated Covered. The index is 1-5 for highand low temperatures, the Paved Open Space has thehighest index, and the lowest is Dense Vegetated
Covered.
Table 4 The Urban Heat Signature on Land Cover in
BAU Campus
Land Cover
Max
Mean
Index
Paved Open Space
32.4
28.3
5
Building Covered
32.4
27.9
4
Open Veg. Covered
32.4
27.2
3
Water Bodies
32.4
27.9
2
Dense Veg. Covered
32.4
27.0
1

Sources: Data Analysis

The urban climatic condition through shading andtranspiration, whereas plants release water vapour
into the surroundings, increasing relative humidityand decreasing air temperature [26]. The temperatureof the narrow green alleys is lower than that of theircounterparts. The research had similar results to Ali
and Patnaik, where the dense vegetation covered was
the lowest temperature due to urban heat signature,
air surface temperature, and perception study. That
condition threatens the natural ecosystem and creates
vulnerability to an environmental hazard [27]. The
research agrees with Bekele et al., explaining thatland cover changes will generate the heat hazard on
the university campus as Urban Heat Hazard Threat.
Generally, cities face specific urban events dueto the urban setting that can alter the local
microclimate with higher impacts [28]. Urbanization
increases the risk of extreme heat episodes in
Europe's cities due to the loss of urban green space[29]. That spatial-temporal detecting land coverchanged much importance [30]. This research agrees
with Ashaolu et al. to detect the land cover changed.
Urban outdoor environments, including university
campuses, particularly in the tropics, largely dependon their frequency of use, which can be profoundly
altered by the level of outdoor thermal comfort [31].
This research agreed with Ghaffarianhoseini et al.
The research detecting land cover changed the effecton urban heat and impact on air surface temperature
as monitoring environmental health on a university
campus.

Sources: Data Analysis

3.3. Environmental Health based on Urban Head
HazardThe relation between AST and land use cover types
in 2018 had a min temp 21.50C relationship with

630

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 5

Urban Heat Hazard in BAU Campus, 2021

CONCLUSIONS
The result of UHS profiles related to vegetation
cover of 25-33oC, with a mean of 28oC as the lowest
temperature, and building cover with a profile of 3339oC, with a mean of 35oC as thehighest temperatures based on land cover types. The
Urban Heat Signature between 30-33.0°C affect air
temperature with a max >33.0°C. The temperature
will impact humans with uncomfortable sensations,and then it will decrease capacity and achievement ofwork. This result concluded that UHS's effect on the
air temperature and heat intensity impacts humanhealth, indicating that the environmental condition isunhealthy.

25, Issue 2, 1997, pp.99-103.
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Marrakech, Morocco, 2003, p5.
[7] Dong S., Yan X., and Xiong Z., Varying
Responses in Mean Surface Air Temperaturefrom Land Use/Cover Change in different seasons
over northern China. Acta Ecologica Sinica, Vol.
33, Issue 3, 2013, pp.167–171.
[8] Adulkongkaew
T.,
Satapanajaru
T.,
Charoenhirunyingyos S., and Singhirunnusorn W.,
Effect of Land Cover Composition and Building
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632

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

STUDY ON THE TEMPERATURE MITIGATION EFFECT BY A
CULTURAL HERITAGE IN JAPAN -TARGETING A SHRINELOCATED NEAR THE CENTER OF FUKUOKA CITY
Kenta Fukagawa1, Yoshihito Kurazumi2 Ariya Aruninta3 and Yoshiaki Yamato4
Faculty of Science and Technology, Meijo University, Japan; 2School of Life Studies, Sugiyama Jogakuen
University, Japan; 3Faculty of Architecture, Chulalongkorn University, Thailand; 4Faculty of Architecture &Structural Engineering, Kure National College of Technology, Japan
1

ABSTRACTIn recent years, many researchers have focused on the urban heat island, caused mainly by the increase in theartificial ground due to the development in urban areas. Several mitigation techniques have been utilized to reduce
the temperature of the surrounding microclimate by installing green spaces in urban areas. To obtain moresignificant benefits, the establishment of large-scale green spaces such as urban parks is more effective. However,designing urban parks in a big city is economically not efficient according to the limited land area and its highprice. For that reason, roof and wall greening are one of the alternatives. Looking at the conditions in Japan, someareas with greens exist in the city, not parks but shrines. Shrines are religious landmarks based on the religion
called Shinto. Shrines are often religiously and publicly protected and preserved in Japan. However, according
to the lack of successors or decrease in the surrounding population, or financial difficulties, the number isdecreasing. The purpose of this study is to clarify the temperature mitigating effect of the greens in a shrine andreevaluate it by the aspects of both cultural and environmental sides for future preservation. For this study, a shrine
called Hakozaki shrine was targeted. It is a cultural heritage and the huge green area which connects the seashore
and the city center. The measurement was carried out in the summer, of 2021. As a result, the mechanism of thetemperature mitigating effect by the shrine was clarified.
Keywords: Land use, Urban Heat Island, Green space, Temperature Mitigating effect
INTRODUCTION

green spaces which mitigate the temperature of thesurroundings in a city.
There are many studies relating to the temperature
mitigating effect of natural resources.
Yokohari et al. [1] studied the effect of reducing
the temperature of rice paddies located in residential
areas in summer. Furthermore, Fukagawa et al. [2]surveyed several land-use types including ricepaddies to determine the effect on microclimate infour seasons in developing areas in Japan. Therefore,
to obtain greater efficacy, it is estimated that theexistence of large-scale green spaces is more effective.
Related examples of studies that examined theeffect of temperature mitigation by urban green areas,such as parks, are as followings.

In recent years, UHI has become a global problem.
Therefore, as a countermeasure, installing green
spaces in urban areas is being taken worldwide. A
typical example is Singapore's national-levelgreening policy. Singapore is focusing on wall
greening and the expansion of urban parks.
Typical examples of green spaces in urban areasof Japan, on the other hand, include parks andreligious facilities, such as shrines and temples.
Shrines generally have large-scale green spaces, sothey are often traditionally used for festivals and otherevents, and so on. They are also utilized as a place
for local gatherings and parks. Therefore, it can besaid that those shrines play many roles other thanreligious symbols. A typical example of a shrine is
Meiji Jingu, which is located in Tokyo. The satellite
photo is shown in Fig.1.
In addition to playing a role as a vast green spacein the city center, it also plays a role as arepresentative religious facility in Japan, with 3.1
million people visiting for worship every year.
Due to changes in the times and the declining
population, some of the shrines have ended their roleand have been abolished or merged, and the number
is decreasing. However, shrines are not only
historical and cultural heritages but also works as

Meiji
Jingu

1000m

GoogleEarth
Earth
GoogleFig.1 Satellite photo of Meiji Jingu in Tokyo
633

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fukuoka city

Hakozaki
Shrine500m

Fig. 2 Location of Fukuoka city
Feyisa et al. [3] examined the temperature
mitigating effect of a park located in Addis Ababa andshowed its influences. Kong et al. [4] examined thepattern of heat islands and the effects of green spaceinside a park in the eastern part of China. Brown et al.
[5] examined the mitigating effect of climate change
by studying an urban park and clarified its effects.
Fukagawa et al. [6] examined the temperature
mitigation effect of an urban park, built in recent
years, with rainwater storage functions.
According to the studies, the occurrence
prediction of heat islands and the temperature
mitigating effect of green areas such as parks wereconfirmed as appropriate case studies. However,since the appearance pattern of heat islands differs
depending on various influences such as city size,
traffic conditions, and climate zones, further
investigation is necessary. It is important to develop
a quantitative understanding of the climate mitigation
effect of green spaces, especially as a heat island
mitigation measure.
Therefore, the purpose of this study is to examine
the impact of the green areas of shrines located inurban areas on the surrounding microclimate and to
re-evaluate the value of the shrine as anenvironmental resource.

Google EarthFig. 3 Satellite photo of Hakozaki shrine

Fig. 4 Measurement instrument
worshipers, and in addition to its religious role, it also
plays a park-like role.
Before the Covid, a festival called “Hojoya” washeld at the shrine in September every year. “Hojoya”
is known as one of the most famous festivals inFukuoka City.
For that reason, it can be said that Hakozaki
Shrine has great value as a religious, environmental,
and tourism resource.
However, due to the influence of theredevelopment plan as a commercial facility on thesite due to the relocation of Kyushu University, which
was located nearby, the redevelopment of thesurrounding area is progressing rapidly. It is
presumed that it is also effective as a wind pathbecause there are abundant trees on the site and thesite is open straight from the coastline to the city area.
Therefore, it is important to understand the impact
on the surrounding microclimate when considering
the conservation of Hakozaki Shrine.

RESEARCH OVERVIEWThis research was conducted at Hakozaki Shrine
located in Fukuoka City. Fukuoka City is the largest
city in Kyushu, located in the southern part of Japanas shown in Fig.2. Fukuoka city has a population of
about 1.5 million, one of the cities with the highest
population growth rate in recent years in Japan.
Therefore, urban redevelopment is being actively
carried out.
Hakozaki Shrine, the site of the survey, wasestablished in 923 and has been a symbol of the region
to this day. The satellite photo is shown in Fig.3.
The site is arranged linearly from the torii gate on
the sandy beach to the main road near the city center.
Most of the site is always open to residents andMeasurement Procedures
The measurement was carried out at 10-minuteintervals by installing temperature loggers (Elitech-5+) in and around Hakozaki Shrine for a total of 27days from July 9 to August 4, 2021.
For temperature measurement, a simple wooden
Stevenson screen was installed at each measurement634

GEOMATE – Bangkok, Thailand, 22-24 November 2022

point. The photo of the measurement instrument isshown in Fig. 4.
For meteorological data other than temperature,measurement data by the Japan Meteorological
Agency was used.

Measurement points ① to ⑨ are located at thelocation that leads to the city area ⑨ from thecoastline of ① through the shrine grounds. Therefore,
it is expected to function as a sea breeze road. In
addition, the amount of greenery in ② to ⑧ locatedon the shrine site is larger than at other points.
Measurement points ⑩ to ⑯ It is installed along
a narrow road in the northern part of the shrine.
Therefore, the sky factor is low. However, because it
is located near the shrine, the proportion of greenery
is relatively high except for ⑮ and ⑯, which areclose to the city area.
Measurement points ⑰ to ㉓ are installed along a
two-lane road with quite heavy traffic in the southernpart of the shrine. Therefore, the ratio of artificial
structures is high. On the other hand, the amount ofgreen is small.

Overview of Measurement Points
The locations of the measurement point are shownin Fig. 5, and the sky photos of the measurement point
are shown in Fig. 6. A total of 23 measurement
points were set up in and around the shrine premises.
However, ④ , ⑤ and ⑯ are lost during themeasurement. For that reason, data from a total of 19
measurement points were used in this study.
The sky factor of each measurement point isshown in Tab. 1.

①

⑩

⑪

②

③

⑰

⑱

⑬

⑭

④

⑤

⑥

⑳

㉑

⑲

⑯

⑮

⑫

⑧

⑨

⑦

㉓

㉒

Fig. 5 Location of each measurement point

Fig. 6 Sky photos of each measurement point
Table 1 Sky factor at each measurement point
Green

Sky

Artifical

Others

Total

Green

Sky

Artifical

Others

Total

①

9.6

structure
30.9
28.7

30.8

100.0

⑬

43.8

structure
23.5
22.9

9.8

100.0

②

47.8

22.0

30.2

0.0

100.0

⑭

3.4

28.9

67.7

0.0

100.0

③

70.3

15.1

14.6

0.0

100.0

⑮

0.7

14.8

84.5

0.0

100.0

④

78.5

18.0

3.5

0.0

100.0

⑯

13.6

12.1

74.3

0.0

100.0

⑤

32.7

5.8

61.5

0.0

100.0

⑰

7.3

26.6

66.1

0.0

100.0

⑥

55.3

14.6

4.0

26.1

100.0

⑱

14.7

32.8

52.5

0.0

100.0

⑦

61.3

4.5

16.5

17.7

100.0

⑲

6.3

26.1

67.6

0.0

100.0

⑧

54.8

3.2

34.4

7.6

100.0

⑳

16.8

23.1

60.1

0.0

100.0

⑨

0.0

12.0

82.7

5.3

100.0

㉑

28.6

19.1

52.3

0.0

100.0

⑩

24.7

20.7

54.3

0.3

100.0

㉒

31.1

16.7

52.2

0.0

100.0

⑪

42.5

14.8

42.6

0.1

100.0

㉓

9.5

24.0

65.7

0.8

100.0

⑫

44.5

21.9

26.4

7.2

100.0

*Gray shadings are lost measuring instruments

635

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Sunny Day Reference Line
90
80
70
60
50
40
30
20
10
0

31
30
29
28
27
26
25

Aug.4

Aug.3

Aug.2

Aug.1

Jul.31

Jul.30Jul.29Jul.28Jul.27

Jul.26

Jul.25

Jul.24

Jul.23

Jul.22

Jul.21

Jul.20

Jul.19Jul.18Jul.17

Jul.16

Jul.15

Jul.14

Jul.13

Jul.12

Jul.11

Jul.1024

Precipitation(mm/Day)

Daily average temperature

32

Jul.9

Average Temperature (Day)

Daily cumulative precipitation

Date

Fig. 7 Weather overview during the measurement period
Wind Speed
8.0

W

7.0

SW

6.0
5.0
4.0

E

3.0

NE

2.0

N

1.0

0:10
0:50
1:30
2:10
2:50
3:30
4:10
4:50
5:30
6:10
6:50
7:30
8:10
8:50
9:30
10:10
10:50
11:30
12:10
12:50
13:30
14:10
14:50
15:30
16:10
16:50
17:30
18:10
18:50
19:30
20:10
20:50
21:30
22:10
22:50
23:30

S
SE

Wind Speed [m/s]Wind Direction

Wind Speed

0:10
0:50
1:30
2:10
2:50
3:30
4:10
4:50
5:30
6:10
6:50
7:30
8:10
8:50
9:30
10:10
10:50
11:30
12:10
12:50
13:30
14:10
14:50
15:30
16:10
16:50
17:30
18:10
18:50
19:30
20:10
20:50
21:30
22:10
22:50
23:30

WInd Direction

Wind Direction
NW

TIME [Jul. 20th]

Fig. 8 Weather overview during the measurement period
MEASUREMENT RESULTTIME [Jul. 24th]

On the other hand, in terms of wind direction, thewind is southward at night and eastward during theday. Even though the measurement points are locatednear the coast, the influence of the sea breeze wassmall during the selected two representative days.

Fig. 7 shows the results of the weather conditions
during the measurement period based on themeasurement data of the Fukuoka MeteorologicalObservatory where locates about 6 km away from theshrine. There was only a little rainfall during most of
the measurement period, and the average daily
temperature remained high.

Temperature Fluctuations at each Measurement
Point
The time fluctuations of each measurement pointon the representative days are shown in Fig. 9 and 10.
In the legend in the figure, 〇 indicates thepremises of the shrine, △ indicates the northern partof the shrine, and □ indicates the southern part of theshrine.
Looking at the daytime data, the temperature washigh at ① which is the closest to the coast, ② which
is close to the highway with heavy traffic, and ⑥,
which is located at a relatively open point in the shrine.
On the other hand, the temperature was low at ⑦
and ⑧, where the amount of green was large among
all the measurement points. In addition, thetemperature tends to be low even in ⑨, which issurrounded by artificial structures and has a low sky
factor.
Looking at the results at night, when looking at
the data on the premises of the shrine, the temperaturetends to be low in ⑦ and ⑧, like the results during
the day. On the other hand, in ① , where theSetting the Representative days
In this study, for the purpose of grasping theinfluence of land use at each measurement point, thetemperature data on the days when the daily
cumulative precipitation is 0 mm for 4 consecutive
days or more and the daily average temperature
exceeds 30 ° C is used.
Then, the above conditions were met for a total of
6 days, July 20, 21 and July 23-26. Of these, thelongest sunshine hours were 12.4 hours on July 20,
followed by 12.1 hours on July 24, so in this study,
these two days were considered as representatives.
Wind Direction and Wind Speed
Fig. 8 shows the time fluctuations of the windspeed and direction on July 20 and July 24, which are
set as representative days.
Looking at the wind speed results, the average isabout 3 m / s.
636

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Temperature [℃]

①

②

③

⑥

⑦

⑧

⑨

⑩

⑪

⑫

⑬

⑭

⑮

40.0

40.0

38.0

38.0

36.0

36.0

34.0

34.0

32.0

32.0

30.0

30.0

28.0

28.0

26.0

26.0

24.0

24.0

22.0

22.0

20.0

20.0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23Time [h]Fig. 9 Hourly Temperature Fluctuation on August 20th
①

②

③

⑥

⑦

⑧

⑩

⑨

⑪

⑰

⑱

⑲

㉑

㉒

㉓

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23Time [h]⑫⑳Time [h]

⑬

⑭

⑮

⑰

⑱

⑲

⑳

㉑

㉒

㉓

40.0
38.0

Temperature [℃]

36.0
34.0
32.0
30.0
28.0
26.0
24.0
22.0
20.0

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23Time [h]Fig. 10 Hourly Temperature Fluctuation on August 24th
temperature was high during the day, thetemperature was low at night. Looking at
the entire measurement points, theresults were low at ⑫, ⑳ and ㉓.
From this result, the daytime results
are strongly influenced by solar
radiation, artificial waste heat, and thegreen space in the shrine. On the other
hand, at night when the atmosphere isstable, it is presumed that the outflow ofcold air from the green space in theshrine to the surrounding area and theinfluence of the sea breeze appear in thetemperature formation.
Analysis of Temperature Formation
Tendency in the Measurement Area

10

1Time [h]Time [h]

2

17

11

12

13

15
8

6

3

18

14

19

20

9

7

23

22

21

Fig. 11 Temperature Contour Map at 14

1

10

11

2

3

12

13

14
6

15
8
7

Fig. 11 and 12 show the results
17
23
created using the data at 14:00 when the
18
22
19
20
21
temperature was the highest in thedaytime, and at 2:00 am, when theFig. 12 Temperature Contour Map at 2
atmosphere is relatively stable 12 hours
after that, and the influence of theon the seaside and along the main road with heavy
surrounding environment is expected to appear
traffic. In addition, the temperature in the green areastrongly.
of the shrine and its eastern side is decreasing.
Looking at the results of the daytime temperature
Looking at the nighttime temperature formation
formation tendency in Fig. 11, the temperature is highresults shown in Fig. 12, a small low-temperature area
637

9

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Wind Speed-0.60

DaytimeLand Breeze

Green

Sky

CONCLUSION

Others

The findings obtained in this study are shown
followings.

0.60

0.00
.387
-.028

R=.4071) In the survey area, the effects of artificial waste
heat centered on the arterial road and hightemperature due to the artificial covering surface
appear strongly during the daytime. Also, even
within the shrine premises, the temperature tends tobe high when the sky factor is high.
2) At night, the effects of greens in the shrine areclearly visible, and when affected by the sea breeze,
it seems that the temperature reduction effect isspreading to the surrounding areas.

.103
.070
.303

DaytimeSea Breeze-.070

R=.312

-.001
-.013
.512

NighttimeLand Breeze-.316

R=.596-.005.119

As mentioned above, it was shown that inaddition to the traditional cultural and historical valueof this shrine that was the subject of this study, thispaper proved the effect of mitigating the surrounding
temperature as wind paths at night.

.434

Nighttime
Sea Breeze-.383

R=.539-.159.015

Fig. 13 Result of Multiple Regression Analysis

ACKNOWLEDGEMENTS

can be confirmed from the seaside to the main road.
In addition, it seems that the low-temperature area
confirmed during the day on the shrine site is moving
to the inland side. It is presumed that this is because
the influence of sea breeze and green space appears
clearly at night when the atmosphere is stable, and at
the same time, it affects the temperature formation in
the surrounding area.

We would show gratitude to everyone at
Hakozaki Shrine for their cooperation in this research
and Johnson Controls Co., Ltd. for providing thescholarship donation.
[1] Yokohari, M., Brown, R. D., Kato, Y. andYamamoto, S. 2001. The cooling effect of paddy
Fields on summertime air temperature in
residential Tokyo, Japan. Landscape and Urban
Planning. 53: 17-27.
[2] Fukagawa, K., Shimazawa, T., Murakawa, S.,
Koshikawa, Y. and Ando, G. 2006. A study of thedifferences on the formation of temperature
among urban areas, rural areas, and aroundirrigation ponds in a developing city.
Architectural Institute of Japan Refereed Journal.
605: 95-102.
[3] Feyisa, G. L., Dons, K. and Meilby, H. 2014.
Efficiency of parks in mitigating urban heat islandeffect: An example from Addis Ababa. Landscapeand Urban Planning. 123: 87-95.
[4] Kong, F., Yin, H., James., Hutyra, L. R. and S.He,
H. 2014. Effects of spatial pattern of greenspaceon urban cooling in a large metropolitan area of
eastern China. Landscape and Urban Planning.
128: 35-47.
[5] Brown, D.R., Vanos, J., Kenny, N. andLenzholzer, S. 2015. Designing urban parks thatameliorate the effects of climate change,
Landscape and Urban Planning, 138: 118-131.
[6] Fukagawa, K., Aruninta, A., 2014. A Study on the
Efficiency of De-signing Urban Park as Cooling
Spot in the Center of Bangkok - Targeting Raining
Season - Ecology, Environment, and conservation.

Analysis of Factors Contributing to Temperature
by Multiple Regression Analysis
To analyze the factors affecting the temperaturein the measurement area centered on the shrine were
examined using multiple regression analysis. The
dependent variable is the 6 days on a clear day shownin the previous chapter, and the independent variables
are wind speed, green amount, sky factor, and othersky factors. A total of 4 patterns were analyzed. The
results are shown in Fig. 13.
From this, the temperature reduction effect due tothe number of green increases at night when theatmosphere is stable. On the other hand, thetemperature tends to rise by the wind speed increases.
However, in the case of sea breeze, the effect ofraising the temperature due to the wind speed isreduced, and the effect of reducing the temperature
due to the amount of green is expanding as comparedwith the case of la and breeze.
From the above, it can be interpreted that thetemperature reduction effect of the site of the shrine,
which has most of the green space in the measurementtarget area, has appeared. In addition, it is presumed
that the effect spreads to the eastern area of the shrine
during the sea breeze and that the most remarkableeffect appears at night.

638

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

SOME QUESTIONS ABOUT GEORGIA'S LANDSCAPES DYNAMICS(ON THE EXAMPLE OF SAMTSKHE-JAVAKHETI)
Maia Tskhavardze1, 2, Dali Nikolaishvili1, Lia Matchavariani1, Lamzira Lagidze1, Vazha Trapaidze1
1Faculty of Exact and Natural Sciences, Ivane Javakhishvili Tbilisi State University, Georgia;
2
Climate Change Division, Ministry of Environmental Protection and Agriculture, Georgia
ABSTRACTThe given paper was based on the concept of spatial-temporal analysis and synthesis of natural territorial
complexes (NTCs) enabling to study of nature components by applying the unified methodology. Daily conditionof landscapes based on meteorological parameters (air temperature, precipitation, snow cover, etc.) applied todetermine the changes in the structure of landscapes. This point is very important for forecast trends of physicalgeographical processes in landscapes. Such issues can help to clarify the following basic questions: which naturalor natural-anthropogenic processes are more obvious? In which landscapes are these processes more intensive andwhich ones will be changed considerably? How much is rejected last year's annual dynamics of the NTC fromlong-term dynamics? All of these issues are very important for determining the resource potential of landscapesand their changes. This issue was investigated according to two stages: I stage – determine season dynamic oflandscapes; II stage – comparing data of two periods (past and nowadays). To achieve the main purpose of researcha great number of meteorological data were analyzed and all data were processed employing GIS technologies.
Thus, according to these data, the degree of landscape changes and its spatial distribution was revealed.
Keywords: Landscapes Dynamics, Landscape ethology, Samtskhe-Javakheti, Georgia
INTRODUCTION
Landscape ethology is a relatively new field oflandscape science that allows us to identify current
trends in the geographical environment through thestudy of elementary parts (geomasses), verticalstructure, and function. Its practical significance lies
primarily in the fact that it forms the theoretical basis
for the spatial-temporal analysis of landscapes andphysical-geographical regions, focusing on the provision of information on the average annual and potential
status of NTCs, and most importantly in the development of environmental management systems [1].
The study of landscape dynamics issues is of great
theoretical and practical importance. It can be used todetermine the physical-geographical or anthropogenic processes taking place in landscapes and in thegeographical environment in general. Their identification plays a certain role in identifying current
trends in the environment and in revealing the main
directions of spatial planning.
The dynamic of landscapes as a whole has been
repeatedly studied in the world. For example, thebooks [2, 3, 4] provides an overview of the ecologicalindicators of landscape dynamics in the context ofgeographical landscape integration. The landscapedynamic in Georgia, both in a single context andagainst the background of separate regions has beenstudied as well [1, 5, 6, 7, 8], etc. In these papers, theissues of landscape dynamics are analyzed according

to the data of landscape types, subtypes, and basic
meteorological stations. Based on this, a fairly clear
and complete picture of the annual dynamics oflandscapes was created. However, it should be also
noted that landscape types/subtypes combine
landscapes that differ from each other in terms ofthermal and hydration conditions. Accordingly, it isnecessary to study these differences as well.
Particularly, it is insufficient to study the issues ofdynamics by low-ranking landscape units. It requires
the selection and analysis of not only the base but also
several meteorological stations/substations. This willallow us to move to a more detailed stage after the
study – to study the issues of dynamics at the level oflandscape genera, species, and types of NTCs.
For our research, in particular, much moreaccurate data is needed to detect climate change –both in terms of time and the abundance ofmeteorological stations/checkpoints. This -is also dueto the spatial stretching factor of landscapes (somelandscapes that follow orthographic structures are ofelongated configuration) and 1 meteorological station
often does not present a complete picture. Therefore,
data from 12 meteorological stations/checkpoints areused for the Samtskhe-Javakheti landscape analysis.
Thus, the analysis of the climatic characteristics of thelandscapes was carried out based on data from several
stations.

639

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Landscape dynamics for the whole Caucasus are
discussed in N. Beruchashvili's monograph [9], wherebased on computer modeling scenarios of thedevelop-ment of landscape-ethological situations
under different physical-geographical or anthropogenic impacts are developed. In particular, what shifts
can occur in the landscape structure during thedevelopment of various scenarios of environmental
change (global climate change, deforestation,
forestation, or minor glaciation)? This is a generalmodel that can be developed as a result of natural or
anthropogenic impacts on the landscapes of the
Caucasus, including Georgia. However, these
impacts may not even be real, at least in the recent
past, for example, defores-tation of the entire
Caucasus, or, conversely, complete forestation oflandscapes, minor glaciation, etc. [6]. Because of theabove, it is also relevant not only to study the changes
caused by the actual (experimental) processes but also
the actual changes in the landscape.

depression with steppe, dry shrub (phrygana), shibliak,
and rarely mountain semi-desert vegetation).
The process of climate change in SamtskheJavakheti and its impact on environmental conditions
were assessed by analyzing the following parameters:
daily average air temperature, frequency of
atmos-pheric precipitation, wind speed, and height ofsustainable snow cover. Based on the multi-year daily
data provided by the National Environment Agency,
the trends of annual, seasonal, and monthly changes
in these parameters were analyzed and evaluated.
Data from 12 meteorological stations are used for thispurpose: Abastumani, Akhalkalaki, Akhaltsikhe,
Bakuriani, Borjomi, Goderdzi, Paravani, and Tsalka,
Aspindza, Jvari, Minadze, and Arali. However, theavailable data can be assessed as partially deficient,since: first, meteorological stations do not function in
the whole area of the study area (in all landscapes);
Second, we do not have complete meteorological data
for the existing stations either. Due to the createdsituation, we considered it expedient to use
extrapolation and interpolation methods, as well as
temperature and plivial gradients. This approach was
based on general geographical regularities, which
took into account the natural conditions ofneighboring landscapes. To reveal a broad
picture/trend, multi-year data were calculated:
initially the average indicators of each year/month,
autumn – 10-year periods. Based on the analysis ofthese periods, the tendency of changes in the climatic
indicators of each station (average, absolute
maximum, absolute minimum) was determined. In
addition, differences between the first and last
decades have been identified. Seasonal data (spring,summer, autumn, winter) were also analyzed andtrends were identified, as well as the frequency of
extremes.
The study was conducted in 2 stages. Dynamics
of daily conditions of landscapes in the first stage
based on average multival annual data. This creates a
general background and gives us an idea of whatfeatures characterize this or that Landscape in general. The second stage is based on a comparisonof specific periods of time (earlier and later).

RESEARCH METHODS AND DATA
Determining the issues of landscape dynamics
requires the analysis of average multi-year data of
meteorological parameters. Of particular importancein this regard is the determination of air temperature,atmospheric precipitation, and snow cover. They
showed what changes have taken place in the phytogenic, pedogenic, or nival structures of landscapes.
The present study is based on the analysis of theseparameters.
For this purpose, data from meteorologicalstations related to Georgian landscape genera wereused, which was carried out using GIS technologies.
However, it is also clear that there is a shortage of data
– not all landscapes are equally secure inmeteorological data. Accordingly, in the absence ofmeteorological stations in a certain landscape, weresorted to extrapolation and interpolation methods.
In Samtskhe-Javakheti, the data of meteorologicalstations, which exist throughout the period of theiroperation, are used to study climate change. The data
for some stations is continuous, though for somestations it is discrete, or in some cases up to themodern period. For some landscapes, we have analmost continuous chain of data. This applies tometeorological stations in Akhaltsikhe, Bakuriani,and Borjomi (landscape: lower mountain erosionaccumulation landscape with hornbeam-oak, oak-pine,
and pine forests, rarely with shibliak; Denudationerosion-accumulation landscape of mountaindepression with steppe, dry shrub (phrygana), shibliak
and rarely mountain semi-desert vegetation; Erosiondenudation landscape of the middle mountain withbeech-oak, in some places pine. The shortest periodic
data contains informa-tion on the years 1944-1963.
Such is the Arali meteorological station (Denudationerosion-accumulation landscape of mountain

RESULTS AND DISCUSSION
Major Landscapes
The territorial distribution of Samtskhe-Javakheti
landscapes is determined by a whole set of factors.
This complex includes oroclimatic barriers, heat andmoisture distribution, vegetation, paleogeographicaldevelopment features of the area, and the degree ofanthropogenic transformation. All of them areimportant and affect the territorial distribution oflandscapes, and their physical-geographical features.
Absolute height plays an important role in the
differentiation of Samtskhe-Javakheti landscapes. In
particular, the 2600 m altitude range generates great640

GEOMATE – Bangkok, Thailand, 22-24 November 2022

contrasts in terms of landscape, which is related to the
sharply defined vertical plane.
The area of Samtskhe-Javakheti is represented bydiverse landscapes: 5 types, 9 subtypes, and 13 genera.
These landscapes are: 1. Mountainous Thermomoderate Humid (Subtypes: Low-mountain forests
Colchik & middle mountain forest); 2. Mountain
Thermo-moderate Semi-humid/Mountainous Thermomoderate Semiarid (Subtype: Transitional to thermomoderate mountainous depression with steppes,
meadow-steppes, dry shrub “frigana” and “shibliak”);
3. Moun-tainous Thermo-moderate Semiarid (Subtype:Transitional to thermo-moderate mountainous
depression with steppes, meadow-steppes, “frigana”
and “shibliak”); 4. The Mountainous Moderately
Cold (Subtype: Middle mountain coniferous forest);
5. High Mountain Meadow (Subtype: High mountain
subalpine forest-shrubland-meadow).

Approximately the same trend will be observed inother landscapes. There is an increase in temperature
everywhere, but with different intensities (Table 2).
The rise in temperature occurs mainly in the summer
and subsequent months – July, Aug., Sept., October.

Multi-year dynamics of air temperature

Quite significant changes took place in the annual
amount of atmospheric precipitation and its seasonal
distribution. For this purpose, we have considered thedata series in 4 periods: I (1937-1976), II (19771986), III (1987-1996), and IV (1997-2006).
The total amount of atmospheric precipitation on
the high mountain plateaus has increased by almost
900 mm (from 5600 mm to 6530 mm), although inperiods II and III its amount has slightly decreased
compared to the period I, although in 1997-2006 –increased. Precipitation during the first period –increased from 245 mm to 1004 mm: in winter – from
245 mm to 274 mm, in spring from 275 mm to 859
mm, in summer from 516 to 1.004 mm, in autumn –from 349 mm to 419 mm. The total precipitation ofthis period is 5633 mm. The same happened in otherperiods, but one important feature was revealed: wehave a min. amount of precipitation in winter inperiod II – 190 mm, and a max. in the period I – 1004
mm. In spring the min. amount of precipitation is inperiod III – 265 mm, and the max. in period IV – 901
mm. In summer, the min. value is 441 mm in period
III, and the max. is 1004 mm in the period I. The min.
magnitude in autumn is 310 mm in the IV period andthe max. is 512 mm in the IV period.
Atmospheric precipitation tends to decrease inhigh mountain subalpine and alpine meadow landscapes. Precipitation during the 30-year period (19631922) During the periods I (1963-1972) and II (19731992) the amount of precipitation decreased by 3,789
mm, from II to III the period increased by 770 mm.
The situation is similar in the high mountainplateau landscapes, where there is also a tendency for
precipitation to decrease, namely that over 40 years
the total precipitation has decreased by 1808 mm.
However, during periods I (1967-1976) and II (19771986) the amount of precipitation decreased by 450
mm, from II to III (1987-1996) it decreased by 1680
mm. The only exception is the period from III to IV(1997-2006) when precipitation increased by 318 mm.

Table 2. Dynamics of air temperature (ºC) according
to landscapes
Landscapes
Lower Mountain
Middle Mountain Forest
Mountain depression
High Plateau
High Mountain
Subalpine & Alpine

Table 1 Air temperature dynamics at certain timeintervals (Mountain depression Landscape)SpringSummerAutumnWinter19862005

-8
19.7
10.2
-1.8

2.39

3

0.6

Perennial dynamics of atmospheric precipitation

The meteorological data given below is mainly
based on the database of the National Environment
Agency, as well as on Georgia's UNFCCC [10], [8], etc.
Analysis of data from 1986-2015 showed that the
average annual air is highest in the lower mountain
forest and mountain hollow landscapes. The averagetemperature increased by 0.47ºC compared to 19561986. Temperature increases were also recorded byseason (Table 1). Separate months analysis showedthat warming is in progress mostly during the summer
months. Maximum warming was recorded in August– at 1.24ºC also warming trend is quite high in
September-October at 0.83ºC and 0.95ºC. The
absolute minimum temperature was recorded on
December 7, 2016 (-20.7ºC), and the maximum
temperature was recorded on July 25, 2018 (36.4ºC).
In 2016-2020, compared to previous years, there is a
noticeable increase in temperature in all seasons.

Seasons

1951- 1981- Increase in air
1980 2010 temperature
9.3
10
0.77
4.71 5.19
0.48
8.85 9.02
0.17
5.6
6.36
0.7

Increase compared
to 1956-1986
0.21
0.88
0.57
0.2

On the High Mountain Plateau increased by 0.47º
in 1986-2015 compared to 1986-2015. Here, the place
has an increase in temperature for all seasons. Here,also the warming takes place mainly in late summer
and early autumn, namely in August the increase is
1.24ºC, and in September-October – 0.9ºC. The
increase in temperature is noticeable in all seasons,especially in winter. The absolute minimum temperature was recorded in December 2016 (-28ºC); The
maximum temperature was 34.3ºC in August 2019.
641

GEOMATE – Bangkok, Thailand, 22-24 November 2022

phytogenic structure are delayed by 5-15 days. It
turns out that in several landscapes in eastern
Georgia, including Samtskhe-Javakheti, spring
conditions are longer than in western Georgia.
Middle mountain forest landscapes, which unite
12 genera of landscapes in Georgia, are spread in therange of 800-2000m altitude above sea level, -both inwestern as well as eastern Georgia – in the Caucasus
as well as the Lesser Caucasus-. Accordingly, thedynamics of the daily states of landscapes proceed
differently in each of the above-named classification
units. In general, it can be said that in the middle
mountain forest landscapes there are 30 types of thedaily condition of landscapes, 17 of which are
dominant and are found every year.
In Samtskhe-Javakheti, in the middle mountain
forest landscapes of 1936-2014, there is a tendency
for a sharp increase in atmospheric precipitation. This
was also reflected in the increase in pluvian states.
Significant growth was observed from 1998-2004.
This increase was also reflected in the increase innival conditions (Fig. 1).

Multi-year dynamics of wind regime
On the territory of Samtskhe-Javakheti winds ofdifferent directions prevail. The peculiar conditionsof the terrain have a great influence. Here the main
wind directions coincide with the direction of theriver valleys. The recurrence of winds in the otherdirections is negligible. For example, the recurrenceof the west direction is only 1%. Akhaltsikhe basin
and Javakheti plateau are distinguished by thepeculiarity of the relief; The first is closed by ridges,
and the second is relatively wide, which gives a
special character to the wind regime.
The average annual wind speed in SamtskheJavakheti is not the same everywhere, in mountaindepressions, it is relatively small and in most parts of
the area does not exceed 2.0-2.5 m/sec. As for thehigh mountain plateau, the wind speed here is relatively high. According to the observation, materials
reaches 4 m/sec. The latter, in its central and southernpart, is dominated by winds from the south-east(28%) and north-west (20-24%) during the year, andfrom the south (32%) and east (18%). South-east winds
are also relatively frequent in this part of the plateau.
The average wind speed is a significant part of
the area is maximum in the winter months, especiallyduring the winter, with relatively weak winds in thespring and summer months.
Strong winds (wind speed 15 m/s and more) aresmall compared to other regions of Georgia. The
average number of strong windy days is relatively
common in the high plateau landscapes, where it
reaches 14 days a year. Such a high number can be
explained by its location on the open ground. In thecentral part of the mentioned plateau, the number ofstrong windy days is much higher in some years. The
maximum number of strong windy days here is 49.

%

25.0
20.0
15.0
10.0
5.0

1936
1941
1946
1951
1956
1961
1966
1971
1976
1981
1986
1991
1996
2001
2006
2011

0.0

Fig. 1. Trend of nival daily conditions (1936-2013)
If we compare different periods, we will see thatthe situation of atmospheric precipitation sums is very
different. There is an increase in precipitation, insome years – on the contrary, a decrease. However
one important trend has been observed, during theperiod 1972-1991, the amount of precipitation
increased sharply during the spring and partly during
the summer. This difference was 200-250 m per year.
Due to the lack of observational data from the 1990s,
it is impossible to judge the trends of the last decades.
During the same periods, there was no sharp change
in air temperature.
It is interesting to note that against the backgroundof the increase in pluripotent and nival conditions,
there is also a tendency for the growth of semiarid
conditions. This seems like an unusual situation. On
the contrary, the share of humid states is narrowed.
This is due to the circumstances of the summer
period.
The longest period of the year, about 1/2 comes inthe stabilization of the winter structure, about 1/3 – inthe stable conditions of the summer phytogenic
structure. Compared to the lower mountain forest

Analysis of daily conditions
Each landscape is characterized by a certain set ofdaily conditions. However, they are distinguished by
the recurrence and duration of daily conditions. In
some landscapes, we find only typical conditions forit. However, conditions that are identical for different
landscapes are quite long. It is very important todetermine the duration and recurrence of this or thatcondition of the NTCs for each landscape.
In most landscapes, most of the time of the year
falls on summer and winter conditions, while thedaily conditions of spring and autumn are ofnegligible duration. This will be observed in almost
all landscapes of Georgia. It is logical that the
duration of the winter, namely the nival states,
increases according to the absolute height, while theduration of the pluvial daily states extends from east
to west.
Sustainable snow cover in western Georgia is
falling relatively slowly. Because of this, the onset
and completion of complications of spring642

GEOMATE – Bangkok, Thailand, 22-24 November 2022

landscapes, there are few summer phytogenic structurestabilization conditions Longevity, which is -logical,
since the increase in absolute height decreases thelength of summer, hence the amount of heat. The
most deficient in duration are the – phytogenic
structural conditions of spring and autumn (-5-5-%).
The plural position is -based -on the total longevity of
the year ¼ [6]. There is, however, a significant
difference according to the genera of the landscapes.
In some landscapes of western Georgia, where theannual amount of precipitation is signifi-cantly higher
than the corresponding rate in eastern Georgia, theduration of these conditions is 34%.
In the lower mountain forest landscapes, the trendof decreasing semiarid conditions is observed in the
period 1936-2013, although in some years it has also
increased. The duration of semiarid conditions
increased significantly between 1990-2004, although
it declined sharply in the mid-2000s after the midperiod. The sharp downward trend is characterized by
the trend of cryo-thermal conditions, although there
is a pronounced upward trend in the period from themid 1980s to the early 1990s (Fig. 2).

months. This rate is high in western Georgia. The
same can be said for other landscapes, which suggeststhat there is no direct link between soil moisture anddaily conditions. In addition, the duration of this orthat daily conditions and the number of days before
the pluvial state should be taken into account.
%

15.0
10.0
5.0

1936
1942
1948
1954
1960
1966
1972
1978
1984
1990
1996
2002
2008

0.0

Fig. 3 Trend of semiarid conditions (1936-2013)
%

12.0
10.0
8.0
6.0
4.0
2.0

%

14.0

1936
1942
1948
1954
1960
1966
1972
1978
1984
1990
1996
2002
2008

9.0
4.0

Fig. 4 Trend of nival states (1936-2013)

1936
1941
1946
1951
1956
1961
1966
1971
1976
1981
1986
1991
1996
2001
2006

-1.0

In winter, due to the dominance of winterstructure stabilization (1H), snowmelt and waterleakage in the soil is delayed until the second decadeof May, so the soil moisture is also low.
In the case of simplification of winter nival
structure and complication of spring phytogenic
structure, due to snowmelt, soil moisture increases to37-42% (in 0-50 cm layer). Therefore, from the endof winter to mid-June, high soil moisture ismaintained [6].
A significant correlation was found between thenumber of phytomasses and the average soil
moisture. In most of the Georgian landscapes (withthe exception of the -high mountain subnival, nivaland semi-arid landscapes of eastern Georgia) theamount of phytomasses reaches a maximum when thesoil moisture is maintained not only in the topsoil but
also deeper, In particular, when the 30% isoline
passes at 25-35 cm, and the 20% isoline – at a depthof 20-25 cm and deeper.
This, obviously, is due to the frequency andduration of humid and pluvial conditions. For
example, in the lower and middle mountain forest
landscapes of Georgia, the amount of phytomass over
250-300 t/ha is observed where the 30% isoline of soil

Fig. 2 Trend of semiarid conditions (1936-2013)
The trend of increasing semiarid states inmountain depression landscapes from 1936 to 2013 ischaracterized by several features: 1. The general trend
throughout this period is a slight decrease in semiarid
conditions, although it varies greatly from period toperiod; 2. Aridity was highest during 1995-2001 andlowest during 1981-1985 (Fig. 3). Also, during 19362013, the duration of nival states varied at a fairly
large interval (Fig. 4). It was maximal in 1938-1940,
1962-1933 and 1987. However, the general trend isdeclining. Thus, it is true that the share of semiarid
states is not increased, although the duration of nival
states is reduced at the expense of humidification.
Landscape-geophysical indicators also change
according to the change in the daily conditions of thelandscapes. In particular, the strength and structure of
the vertical structure, and the number of geomasses(especially phytomasses and hydromassages) undergo
significant changes. Here are some examples.
In mid-montane forest landscapes, soil moisturevaries with changing daily conditions. It is maximumin May and October, and minimum in the summer
643

GEOMATE – Bangkok, Thailand, 22-24 November 2022

moisture passes at a depth of 30 cm, and the 20%
isoline is deeper than 80 cm. [7]. It turned out that it
does not really matter where the 30 cm isoline of soil
moisture passes deeper than 30 cm, since the amountof phytomass in such conditions is quite large almost
everywhere (300-500 t/ha and more).
Different features were revealed in thoselandscapes where the duration of semi-humid, semiarid, and arid conditions is much longer. In this way,
the foothills landscapes of Eastern Georgia can beinhabited. The maximum of phytomass is observed
here where the 30% isoline of the soil passes closer tothe surface – at a depth of 15-20 cm, and the 40%
isoline should not be close to the surface and it should
go much deeper than 15 cm. Where the 30% isoline
passes at a depth of 0-15 m from the surface, theamount of phytomass is only 20-25 t/ha on average.
The capacity of the vertical structure varies
signi-ficantly in some landscapes, and slightly inothers. It has to do with what form of life is dominant
in this or that landscape layer. If trees predominate,
this change is minimal (up to about 1 m) and is mainly
due to changes due to tree pollination and defoliation.
In forestless NTCs, on meadows, on steppes, thecapacity can change 2-3 times and even more.
Among the landscapes of Georgia, the power ofthe vertical structure of BTCs is especiallysignificantly reduced in the high mountain subalpine
meadow landscapes. The degree of reduction here canbe 10 or more. Among non-forested BTUs, capacity
varies least in those landscapes (excluding highmountain subnival and nival landscapes) dominatedby semi-desert vegetation [6].
The complexity of the vertical structure of NTCs
in the average mountain forest landscapes of Georgiais also maximum in summer and minimum in winter.
The difficulty becomes particularly easy in forestless
NTCs, where only 2-3 of the 4-7 geo-horizons present
in the summer season may remain. As for forest
NTCs, here the reduction has a very different
character. The change is particularly significant in thelower and upper mountain forest landscapes. In the
middle part of the hypsometric distribution of middle
mountain forest landscapes, it is relatively small.
However, it is important to consider -which types ofthe vertical structure of NTCs (forest or non-forest)
predominate. The least change is observed in forest
NTCs with dead cover, or Colchic undergrowth, andthe most – in forest NTCs with grassy or deciduous
shrubs.

which landscapes these processes are more
pronounced and which of them are threatened byfunda-mental structural changes. In addition, it became
possible to determine what changes are expectedaccording to the landscape-geophysical characteristics(number of geomasses, strength, and complexity of thevertical structure). This, in the end, allowed us to
identify trends in the resource potential of landscapes.
• In Samtskhe-Javakheti a diverse picture ofchanges in temperature and atmospheric precipitation
was revealed, in particular, there is a sharp variabilityof air temperature and atmospheric precipitation, witha kind of alternation of warming and cooling.
• Observable The increase in average annual
temperatures during the period was observed in thecentral and northern parts of the High Mountain
Plateau landscapes, while the decrease was observedin its southern part. Air temperatures remained almost
unchanged in the lower mountain or middle mountain
forest landscapes from 1962-2004. As foratmospheric precipitation, their size in recent years
has been particularly pronounced in high-mountainplateaus, while diminishing in mountain depression
landscapes.
REFERENCES[1] Beruchashvili N.L. Ethology of the Landscapeand Mapping of the Natural Environment State.
Tb., TSU, 1989, 198 p.
[2] Sahdev S., Singh, R.B., Kumar, M. Geoecology
of Landscape Dynamics. Springer, 2020, 379 p.
[3] Feranec J. European Landscape Dynamics: Corine
Land Cover Data, 2016, 337p.
[4] Farina A. Landscape Dynamics. Oxford Bibliographies, 2017.
[5] Beruchashvili N.L. Four Dimensions of theLandscape. M., Mysl, 1986. 182 p.
[6] Nikolaishvili D. Spatial-Temporal Analysis of theLandscapes of Georgia. Tb., TSU, 2009, 431 p.
[7] Nikolaishvili D., Lagidze L., Tsitsagi M., Tskhvaradze M., Kubetsia M. Climate Change Trends in
Landscapes of Samtkhe-Javakheti. Proceedings ofthe 18th International Multidisplinary Scientific
GeoConference SGEM2018, vol.18(3.2), 2018,
Bulgaria, 765-772.
[8] Lagidze L., Matchavariani L., Paichadze N. The
Influence of Circular Processes on Change in
Precipitation in the Scope of Climate Change.
International Journal of GEOMATE, Japan, 2017,13(39), 213-219.
[9] Beruchashvili N.L. Caucasus: Landscapes, Models,Experiments. Tb., UNEP, GRID, 1995, 310 p.
[10] Georgia’s Third National Communication to theUnited Nations Framework Convention on
Climate Change. National Climate ResearchCentre, Tbilisi, 2015

CONCLUSION•

The study identified some of the dynamic featuresof landscapes not only by average perennials but also
by individual years. This has allowed us to identify
some of the trends that are taking place in this or thatlandscape, as well as the tendency of humidification or
aridization, temperature increase or decrease, and in644

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

STRENGTH PARAMETERS AND THE RATE PROCESS THEORYAPPLIED TO COMPACTED FADAMA SOILS
Ola, Samuel Akinlabi1, Fadugba, Olaolu George2 and *Nnochiri, Emeka Segun3
1,3

2

College of Engineering, Afe Babalola University, Ado-Ekiti, Nigeria
School of Engineering and Engineering Technology, Federal University of Technology Akure, Nigeria.
ABSTRACT

Fadama soils of Northern Nigeria is generally a problem soil for highway and geotechnical engineers. There
has been no consistent conclusion on the effect of the strain rate on shear strength of soils, thus necessitating theneed to clarify this issue with various types of soil. Consolidated undrained tests with pore pressure measurements
were conducted at optimum moisture content and maximum dry density using standard proctor compaction. Backpressures were applied to saturate the soil. The shear strength parameters were determined. Analyzing the results
and model studies using the Rate Process Theory, functional relationships between the deviator stress and strain
rate were determined and expressed mathematically as: deviator stress = β0+ β1 log(strain rate) at each cell pressure
where β0 and β1 are constants. Also, functional relationships between the pore pressure coefficient Ā f and the time
to failure were determined and expressed mathematically as: pore pressure coefficient, Āf = ψ0+ѱ1log (time to
failure) where ψ0 and ѱ1 are constants. For cell pressure between 69 – 310 kN/m2 (10 - 45psi) the constant foundfor Fadama soil in this study are: ψ0=0.17 and ѱ1=0.18. The study also shows the dependence of the angle offriction (ø’) on rate of strain as it increases from 22o to 25o for increase in rate of strain from 0.08%/min to
1.0%/min. Conclusively, the study also shows that within the strain rate utilized in the research, the deviatorstrength increased with strain rate while the excess pore water pressure decreased with increase in rate of strain.

Keywords: Fadama soils; Rate process; Deviator stress; Pore pressure coefficient; Rate of strain.
INTRODUCTIONof the test results gives the constants in the Rate
Process Theory for the Fadama soil.

Fadama soils occur in most of the Savannah zones
of Northern Nigeria either at the banks of rivers or in
depressions along the Savannah plains. It is the best
agricultural land in this areas and it is consequently
intensively cultivated. However, although the
Fadama soil is a blessing to the farmers andAgricultural Engineers, it is a problem soil for
Highway and Geotechnical Engineers. In this study,
the effect of the rate of strain on the shear strength of
the soil has been studied. According to [1], there has
been no consistent conclusion on the effect of strain
rate on the shear strength of soils. For example, [2]discovered that when strain rate was lower than acritical value, peak strength increased with strain rate,
and above that , it decreased. [3] found that the nondrainage shear strength and the elastic modulus ofclayey soils were increased with increased strain
rates. [4] revealed a positive correlation between theultimate strength of clayey soil and strain rate. [5]investigated the effect of strain rate on non-drainage
shear strength and found that non-drainage shear
strength was proportional to strain rate, regardless ofthe consolidation and soil test type employed. [6]working on the red clay soil of Addis Ababa showedthat as the strain increases the strength of the soil also
increases. In this study, computerization and analysis

The Geology of Site
The clay known as Fadama soil from Shika,
Samaru, Zaria Nigeria used in this study was locatedat a distance of about a kilometre off the main ZariaSokoto road at 14 kilometre from Zaria (Fig. 1).
Samples were taken during the wet season in July at
a depth of one metre and below. The water table was
at 0.61 metre below the ground surface.
The soil is greyish brown clay, and silts with little finesand, and is the product of the most recent erosion
cycle formed from alluvial deposits left by flowing
water. These alluvial deposits are known to contain
substantial Aeolian components due to large
quantities of dust spread over the country annually by
the seasonal harmattan winds. Based on the
Pleistocene sequence of alternating humid and arid
climatic periods, these aeolian deposits are known tobe about 7,000 years old [7].
Fadama fall under the classification of mineralhydromorphic soils whose development andcharacteristics are influenced by permanent or
seasonal water logging [8]. This also corroborates theassertion of [9] that the occurrence and distribution ofsoils in nature varies from location to location, also,
the type of soil depends on the rock type, its mineral645

GEOMATE – Bangkok, Thailand, 22-24 November 2022

constituents and the climatic regime of the area.
Variations in the drainage of Fadamas occur becauseof the variations in soil structure, organic matter
content as well as the heights of water table.
Generally, these soils have a weak to moderate, fine
to medium sub-angular blocky structure except for
the flood plain soils which are often structureless,
especially if recently deposited. The soil sample wastaken in an area where the main rainfall is about
1100mm. and this comes mainly between April andOctober and open water evaporation is about
1880mm per annum [10].

Fig 2: Particle size distribution of Fadama soils
Table 1 Physical properties of Shika Fadama soil.
Clay (< 0.002mm.),%Silt(0.06 – 0.002mm.),%Sand (2- 0.06mm.),%Natural water content
Liquid limit,%

46.0 to 47.0
36.0 to 37.5
15.5 to 17.0
42%
46%

Plastic limit, %20%

Plasticity index, %26%

Specific gravity

2.60

Activity (P.I./% of clay)

0.556

Maximum dry density, kg/m3 (pcf)
1660- 1692(104-106)
Fig. 1: Map of Zaria showing location of soil
samples

Optimum moisture content, %GEOTECHNICAL PROPERTIES

19.5-19.7%

The natural water content is 42%, the liquid limit is46%, plasticity index is 26%, and specific gravity is
2.6. The results of the Atterberg limits show the soil
to be inorganic clays of medium plasticity.

Physical Properties

Methods of Testing Soils for Civil Engineering
Purposes B.S.1377 (1990) was used. The Unified Soil
Classification System (USCS) was used to classify
the soils. The results of the grain size analysis for all
the three sites investigated are shown in Figure 2. The
results from the three sites are consistent and showthat the soil contains about 46.0 to 47.0% clay, 36.0
to 37.5% silt and 15.5 to 17.0% fine sand. This is also
shown in Table 1.0 where summary of the physicalproperties are given.

Mineralogy
In order to determine the mineralogy of the soil, xray diffraction analysis was used to identify thevarious minerals. The method was supplemented with
the Differential Thermal Analysis (D.T.A) method,
Cation Exchange Capacity (C.E.C) and Glycol
retention.
Figure 3 shows the x-ray diffraction analysis on
the fraction of the soil passing No 200 sieve. The
presence of kaolinite was identified by the 7.2 Aopeak in the Air Dry Preferred Orientation (ADPO).
646

GEOMATE – Bangkok, Thailand, 22-24 November 2022

The presence of quartz was confirmed by the 4.27Ao
reflection and by a narrow characteristics peak at
3.36Ao.

normally required for construction of embankments
when a highway is to pass through these areas. Hencethe shear strength properties of these compacted soils
are of great importance for construction. Also
important for these embankments are the rate of porepressure dissipation and the effect of the rate ofloading on the shear strength characteristics of thesoil.

Fig. 3: X-Ray Diffraction for Fadama soils
The results of the x-ray were confirmed by the
DTA shown in Figure 4. At 600oC, the slope ratio wasfound to be 1.60 thus placing the sample within therange of 0.78 and 2.34 suggested for kaolinite by [11].
The C.E.C was 4.5 meq/100g for the sample. [12]suggested a range of 3 to 15 meq/100g for kaolinites.
Ethylene glycol retention of 17.1 mg/g was obtained.

Fig. 5: Moisture/Density relationship for Fadama
soils
Consolidated undrained triaxial tests were
performed at cell pressures of 69, 207, and 310 kN/m2(10, 30 and 45 psi). The loading rates utilized were
0.08, 0.2, 0.4 and 1.0%/min. (0.0024, 0.006, 0.012
and 0.03 in/min) respectively. Also some backpressures were applied to saturate the soil. In
analyzing the shear strength of the soil, it was foundthat the total stress criterion using the maximum
deviator stress (σ1 –σ3) max and the effective stresscriterion using the maximum effective principal stressratio (σ’1 / σ’3) gave almost identical results since thecompacted soil was relatively insensitive andconsequently, the maximum deviator stress was usedin the analysis of the shear strength characteristics inthis study.

Fig 4: Differential thermal analysis for Fadama soils
Shear Strength
The moisture density relationship was determinedby the use of standard proctor method. Fig.5
illustrates the results obtained for three different sites
where samples were collected. The maximum dry
density varied from 1660 to 1692kg/m3 (104-106 pcf)
and the optimum moisture content varied from 19.5
to 19.7% (Table 1). All the shear tests were conductedat this optimum moisture content and maximum dry
density using the standard proctor compaction.
Because Fadamas are generally found in lowlying areas, large quantities of fill materials are

RATE PROCESS THEORY
The relationship derived from the rate of strain ε Runder constant shear force considering the net
647

GEOMATE – Bangkok, Thailand, 22-24 November 2022

activation frequency in the direction of distorting
forces was obtained as follows using [13] on Rate
Process Theory.
𝑲𝑻

εR=𝟐𝑿 𝒉 exp(-∆F/RT)sinh(fλ/2KT)

The experimental results showing the influence ofrate of strain on maximum deviator stress forcompacted Fadama soils is presented in Table 2 andFig. 6. When the results are fitted into several models
including linear regression, Quadratic Logarithm,
Reciprocal and Square root models. The Logarithmic
model gives the best fit equation as shown in Table 3.
The constants, C and 2S/b are presented in Table 3 forthe various cell pressures of 68.95, 206.85 and 310.28
kN/m2(10, 30 and 45 psi). The experimental results
showing the influence of rate of strain on the excess
pore water pressure, pore pressure coefficient Af andtime to failure for compacted Fadama soils ispresented in Table 4. When the results are fitted into
several models including Linear Regression,
Quadratic, Logarithmic, Reciprocal and Square root
models, the Logarithmic model gives the best fit
equation as shown in Table 5 for pore pressure
coefficient and time to failure with the equation Af =0.17 + 0.18 log tf for 68.95 and 206.85kN/m2 (10 psi
and 30 psi) and
Af = 0.17 + 0.2 log t f for
310.28kN/m2 (45psi). Fig. 7 shows the influence ofrate of strain on excess pore water pressure at failure
for compacted Fadama soils.
From the model derived in this study the following
conclusions can be made• Deviator stress = βo + β1 log (strain
rate)

(1)

Where: X = function of the number of flow units inthe direction of deformation and the averagecomponent of displacement in the same direction due
to a single surmounting of the energy barrier.
k=Boltzmann’s constant = 1.38x10-16. erg
K-1=3.29x10-24calo K-1
h= Planck’s constant = 1.38x10-16erg
R = universal gas constant = 1.98 calok erg sec -1
T = absolute temperature ok
f = average shear force acting on each flow unit, dynes
λ = separation distance between successive
equilibrium position, cm
εR= rate of strain
The following functions are treated constant with its
equivalent symbols.
𝑲𝑻

a=2x
b=

𝒉

exp(-∆F/RT)

𝝀

(2)
(3)

𝟐 𝑲𝑻

Hence, Eq. (1) becomes
εR=asinhfb

where βo is a constant and β1is the coefficient of thelog term• pore pressure coefficient , Āf =ψo+ѱ1 log (time to failure)(4)

To obtain a significant creep rates in soil, the shear
stresses must be large enough, that is stress level ofabove 25% of the strength or more. Then the value ofthe term (fλ/2 KT) is greater than 1.0. In this case thehyperbolic sine in Eq. (4) can be replaced by a simple
exponential equation:
εR=aexpfb

where ψo is a constant and ѱ1 is the coefficient of thelog term
Table 2.Relationship between Deviator Stress andRate of Strain.

(5)
Cell
pressure
σ3kN/m2(psi)

If the maximum shear stress is D/2, where D is theprincipal stress difference (deviator stress), therelationship between the average shear force per flow
unit and the deviator stress on the plane of maximum
shear stress is approximated by:
f = D/2S, where S is the bonds per unit area acted onby the sustained shear stress. Thus Eq. (5) becomes:
εR=aexpDb/2S(6)
i.e. 1n εR = 1n a + Db/2S
𝟐𝑺
D = 𝒃 ( lnεR - ln a)𝟐𝑺

D = - 𝒃 ln a +𝟐𝑺

D=c+ 𝒃 lnεR𝟐𝑺𝒃69 (10)207 (30)ln εR
310 (45)(7)

In this study, computerization and analysis of thetest results gives the constants in Eq. (7).
648

Rate
of
strain
εR
%/min
0.08
0.2
0.4
1.0
0.08
0.2
0.4
1.0
0.08
0.2
0.4
1.0

Time
to
failuretf(min.)
200
83
43
19.4
230
97
50
23.2
285
115
58
24

Deviatorstresss at
failure(σ1 - σ3)
kN/m2
200
203
210
221
321
329
332
352
407
421
431
476

Failurestrain
εf %

16.0
16.6
17.2
19.4
18.4
19.2
20.0
23.2
22.8
23.0
23.2
24.0

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 3. Equations Representing the Relationship between Deviator Stress andRate of Strain
Cell Pressure,σ3(psi)
Model
10
20
45
R2
%Fit
R2
%Fit
R2
%Fit
Linear
0.876787.7
0.799879.98
0.887788.8
Quadratic
0.861786.2
0.778977.89
0.8917
89.2
Logarithmic
0.993199.3
0.960396.03
0.7228
72.3
Reciprocal
0.744694.5
0.757975.79
0.4313
43.1
Square Root
0.921692.2
0.857385.73
0.852685.3
Best Fit
D=32.8+1.4 log
D=51.9+2.2 log εR
D=71.5+4.8log εREquationεR2
R indicates the proportion of variation of deviator stress explained by the ratestrain.
Table 4 Relationship between Rate of Strain, Pore Pressure Coefficient, Excess
Pore Water Pressure and Time to Failure
Cell
Rate ofPore Pressure
Excess Pore
Time toPressure
StrainCoefficient
Water
Failureσ3 (psi)in/minĀf=∆u/(σ1-σ3)f
Pressuretf,(min)∆u, (psi)
10

30

45

0.0024

0.195

5.7

200

0.006

0.188

5.6

83

0.012

0.180

5.5

43

0.03

0.169

5.4

19.4

0.0024

0.209

9.4

230

0.006

0.199

9.5

97

0.012

0.195

9.4

50

0.03

0.172

8.8

23.2

0.0024

0.220

13.0

285

0.006

0.211

12.9

115

0.012

0.202

12.6

58

0.03

0.177

12.2

24

TABLE 5: Equations Representing the Relationship between Pore Pressure
Coefficient and Time to Failure
Cell Pressure, σ3 (psi)
10
30
45
R2
%Fit
R2
%Fit
R2
%Fit
Linear
0.864986.5
0.765976.6
0.748274.8
Quadratic
0.655065.5
0.545154.5
0.5194
51.9
Logarithmic 0.9298
93.0
0.992199.2
0.9914
99.1
Reciprocal
0.757
72.6
0.877787.8
0.8621
86.2
Square Root 0.9807
98.0
0.939794.0
0.935893.6
Best Fit
Af= 0.17+0.18 log Af= 0.17+0.18 log Af= 0.17+0.2 log
Equationtf
tf
tf
2
R indicates the proportion of variation of deviator stress explained by the time to
failureModel

649

GEOMATE – Bangkok, Thailand, 22-24 November 2022

General Discussion

Strength Parameters c’ and ø’

Deviator stress

A summary of the shear strength parameters are
given in Table 3 and there is a definite trend of anincrease in the effective angle of internal friction forincreasing rate of strain.

Table 2 as well as Fig. 6 shows the effect of rateof strain on the deviator stress of the Fadama soil. The
deviator stress at failure increases with the increase in
the rate of strain and also with increasing cellpressure. Shown in Table 2 are the failure strain εf ,and time to failure, t f . The failure strain increases
with increase in rate of strain and also with increasingcell pressure. The percentage increase in deviator
stress between the two extreme rates of strain (0.08
and 1.0%/min) for cell pressure of 69 and 207kN/m2(10 and 30 psi) is about 10% each

Table 6. Effect of Rate of Strain on Shear Strength
Parameters of Fadama Soils.
Rate of StrainƐR%/min. (in/min.)Effective
Cohesionc’, kN/m2 (psi)0.08(0.0024)

58.6 (8.5)

Effective
Angle ofInternal
Friction, ø’
degree22

0.20(0.0060)

55.2 (8.0)

23

0.40
(0.0120)

56.5

(8.2)

24

1.0
(0.030)

57.2

(8.3)

25

No such trend could be observed for cohesion. The
effective angle of internal friction increased from 22o
at 0.08%/min. (0.0024 in/min) strain to 25o at
1.0%/min (0.03 in/min) strain.

Fig. 6: Influence of the rate of strain on maximum
deviator stress for compacted Fadama soils

CONCLUSION
Excess Pore Water Pressure
The Fadama soil is a greyish brown CLAY andSILT with little fine sand formed from alluvial
deposit. The mineralogy consists mainly of kaolinite
and quartz [14] .From the analysis of the results andmodel studies carried out using the Rate Process
theory the following conclusions can be made for the
Fadama soils.
• Model studies have shown that there is a functional
relationship between the deviator stress and strain
rate which can be expressed mathematically as
follows:• Deviator stress = β0+β1 log (strain rate) at each cellpressure
where β0 and β1 are constants.
• Model studies have also shown that there is afunctional relationship between the pore pressure
coefficient Āf and time to failure which can be
expressed mathematically as follows:• Pore Pressure Coefficient, Āf= ψ0+ѱ1 log(time to
failure)Where ψ0 and ѱ1 are constants.

The influence of rate of strain on the excess
porewater pressure at failure is shown in Fig 7. As therate of strain increases, there is a consistent decreasein the pore water pressure. Similarly, there is aconsistent increase in the excess pore water pressure
at failure for increasing cell pressures

Fig. 7: Influence of the rate of strain on excess porewater pressure at failure for compacted Fadama soils
650

GEOMATE – Bangkok, Thailand, 22-24 November 2022

For cell pressures between 69 – 310 kN/m2 (1045psi) the constants found for Fadama soils in thisstudy are: ψo = 0.17, and ѱ1 = 0.18
The study also shows the dependence of the angleof friction (ø’) on the strain rate. For the Fadama soils,
ø’ increases from 22o to 25o for increase in rate ofstrain from 0.08%/min to 1.0%/min.

Geography, Ahmadu Bello University,
published for the annual conference of the
Nigerian geographical association, Zaria.
[9] Klinkengerg, D., and Higgins, G.M. (1968).
An outline of Northern Nigeria soils. Nigerian
journal of science, 2(2): 91-115[10] Surendra, R. and Sanjeev, K. B., (2017). Role of
Geotechnical Properties of Soil on Civil
Engineering Structures, Resources andEnvironment,Vol.7No. 4, pg. 103-109.doi:
10.5923/j.re.20170704.03.
[11] Ipinmidun, W.B. (1972). A preliminary survey ofthe agriculture potentials of some valleys around
Zaria, Nigeria. Samaru Research Bulletin 175,
I.A.R., Ahmadu Bello University, Zaria, Nigeria.
[12] Bramao, L; Cady, J.G.; Hendricks, S. B; andSwerdlow, M. (1952). Characterization of kaolin
minerals. Soil science 73, pg. 273-287.
[13] Grim, R.E. (1968). Clay mineralogy. 2nd edition,
McGraw Hill, New York.
[13]Scott,R.F., and Ko Hon-Yim (1969). StressDeformation and Strength Characteristics –Stateof the Art Report, 7th I.C.S.M.F.E, Mexico,1969
pg. 1-47.
[14] De Castro, L.C., and Ola, S.A., (1983).
Geotechnical properties of compacted soils in
Tropical soils of Nigeria in Engineering practice(Ola,S.A Ed) A.A. Balkema/Rotterdam pg. 145154.

REFERNCES[1] Tianfei Hu, Dawei Liu, and Jian Change(2020). Experimental study on strain rate effectof strength characteristics of unsaturated silty
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[3] Zhu, J. G., and Yin, J.H. (2000). Strain – ratedependent stress strain behaviour of overconsolidated Hong Kong marine clay. Can.
Geotech. J. ,37(6) pg. 1272 – 1282.
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Eng., ASCE, 122 (2)(1996), pg. 90 - 395.
[6] Gao, Y.B., and Wang, Z.W. (2005). Effect ofstrain rate on undrained shear strength of clays.
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Occasional paper No.4, Department of651

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

AN EMPIRICAL STUDY OF FLEXURAL STRENGTH OF BEAMSMADE OF RECYCLED AGGREGATE CONCRETE FROMCONSTRUCTION AND DEMOLITION WASTE IN HANOI, VIETNAM
Ha Tan NGHIEM1, Tran Viet CUONG1, Nguyen Ngoc TAN1, Phan Quang MINH1, Nguyen Tien DUNG1,
Ken KAWAMOTO2, and Nguyen Hoang GIANG*1
1

Hanoi University of Civil Engineering, 55 Giai Phong Rd., Hanoi, Vietnam; 2 Graduate School of Scienceand Engineering, Saitama University, 225 Shimo-okubo Sakura-ku Saitama, Japan
ABSTRACTThe employment of recycled materials from construction and demolition waste (CDW) for mixing recycled
aggregate concrete (RAC) is widely practiced in developed countries. In Vietnam however, being a developing
country with rapid rate of urbanization, especially in major cities such as Hanoi, the substantial amount of CDW
generated (2,100-2,300 tons/day [1]) is still mainly treated by landfilling. To promote the recycling of CDW, it is
crucial that studies of the performance of recycled products, which is RAC in this paper, are conducted. Adopting
a standard experiment set up of reinforced beams, the authors employed local recycled coarse aggregates (RCA)in Hanoi, Vietnam produced from crushed concrete (CC) and crushed brick (CC – mixed with CB at controlled
percentages) to conduct experiments to observe flexural behaviors with reference to equivalent natural aggregate
concrete (NAC) such as: cracking load, ultimate load, crack pattern, crack width, etc. Initial results show that RAC
beams using 100% RCA from CC have their cracking loads reduced by 17.69%, while the ultimate loads only
reduce by 3.70% (on average – in comparison with NAC beams). Cracking load continues its trend of increasing
reduction with the increasing amount of mixed RCA from CB: with 30% CB and 70% CB, the reductions are
36.46% and 49.87% (in comparison with NAC beams), respectively. Ultimate load experiences a surprisingphenomenon: while Pult of RAC-30B still follows the trend of increasing reduction (9.00% – in comparison with
3.70% of RAC-0B), that of RAC-70B is as strong as NAC.
Keywords: Construction and demolition waste, Recycled aggregate concrete, Flexural strength, Reinforced
concrete beam

INTRODUCTION

beams were 2,400-mm long, and were loaded at equalintervals of 800mm. Values of ultimate load anddeflection, together with observations of crack
formation were recorded. It was found out that theultimate load of RAC beam did not reduce much
(only by 3.5%) in comparison with that of reference
NAC beam. However, the instantaneous deflection of
RAC beam was always larger by 18-100% (i.e.,
double). In this experiment, the authors employed
RCA produced from CC to replace NCA with thereplacement rate of 100%, all other materials
remained natural.
With a similar choice of recycled aggregates to theprevious authors, Arezoumandi et al. [10] conducted
experiments on RAC and NAC beams in the UnitedStates in 2015. Test results showed that the reductionof ultimate load of RAC beam was negligible (lessthan 1%) compared with equivalent NAC beam. Thisbehavior was also reported by Zhao and Sun [11] inChina in 2013. In their experiments employing RCA
produced from CC, ultimate loads of RAC beams
were lower by 2.6-4.1% with increasing replacement
rate from 30-75%.
A number of other publications over the years
shared the same conclusion of ultimate load.
However, it was also observed that the cracking load

Studies of RAC employing coarse and/or fineaggregates produced from recycling CDW in various
countries have shown that given sufficient quality
control, this type of concrete is capable of being
applied in casting structural members. Naturally,
RAC has lower values of mechanical properties(compressive strength, modulus of elasticity, or
flexural strength) than that of NAC. Takecompressive strength for example, studies since the1970s of the past century up until now generally
report an average reduction of 15-20% of thisproperty when replacing 100% natural coarse
aggregate (NCA) with RCA produced from CC ([2]–[8]). However, with such reduction, the strength of
RAC in these studies is still adequate for different
applications in construction. As a result, further
experiments on behavior of RAC beams have been
designed and conducted. For such experiments, there
are considerations of the type of recycled aggregatesto be employed (coarse and/or fine), as well as thetype of source CDW (CC and/or CB).
In Poland, the authors [9] set up a 4-point flexural
test apparatus for rectangular beams of crosssectional dimensions of B×H = 200mm×300mm. The
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

of RAC beam was much lower that of NAC beam –with a maximum reduction of up to 46.3% [12]. Table
1 summarizes these reduction rates.

procedure, with the source material being crushed
brick wall segments from the same demolition site.

Table 1 Reduction rate of cracking load and ultimate
load in a number of papers on RAC beams
Authors

Replacement rateof RCA
(%)

[13]

50
100
100
20
50
100
50
100

[10]
[12]

[8]

Reduction rateofcracking
load (%)
20
20
8-18
10.5-10.9
20.3-45.6
26.4-46.3
14.3
14.3

Reduction rateofultimateload (%)
4.8
5.5
<1
<1
<1
<5
1.5
3.8

Fig. 1

Source CC from the demolition of Tan Mai
Primary School, Hanoi, Vietnam

Fig. 2

Stationary CDW crushing machine in Hanoi

Fig. 3

RCA from CC of size 10-20mm (left), and5-10mm (right)

Note: all studies only employed RCA produced from CC withvarious replacement rates of NCA.

It can be clearly seen that in most previous studies
of RAC beams, the authors opted for RCA produced
from CC – which is the best type of source material
in terms of strength and conformity. The quality of
RCA is also much easier to control in comparison
with recycled fine aggregate. Experimental results of
RAC beams employing materials with lessconformity (i.e., containing a certain content of CB)
are very much desirable. This issue is even more
relevant in developing countries such as Vietnam,
where an effective management system of CDW isstill lacking, hence the major amount of CDW ismixed CC-CB.
To address this need, the authors adopted anempirical method to study the flexural behavior of
RAC beams, in which 100% NCA was replaced by
RCA. The RCA in these experiments consisted of CC
and CB at controlled percentages. Results of cracking
load and ultimate load, together with observations ofcrack patterns shall be reported.
PRODUCTION OF RCA FROM CDW
The source material selected in this study is CDW
from the demolition of Tan Mai Primary School,
Hanoi, Vietnam. Figure 1 shows the concreteboulders resulted from the process of crushing andseparating all reinforcing steel bars. CDW wascollected to a recycling facility, and would go througha stationary production line to be crushed and sieved(Fig. 2).
The RCA collected from this production line wasthen further sieved into the sizes of 10-20mm, and of
5-10mm. Figure 3 presents the final product of RCAfrom CC. RCA from CB was produced in a similar

EXPERIMENT DESIGN
Mix Design of Concrete
NAC and RAC in these experiments were mixed
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

at a constant water/cement ratio of 0.55. The target
cube strength of concrete was fcu = 30MPa for all mix
proportions. A total of four mix proportions wereadopted, denoted by numbers as follows:(1) NAC (100% natural aggregates);(2) 100% RCA from CC, 100% natural sand as
fine aggregates;(3) 70% RCA from CC, 30% RCA from CB,
100% sand as fine aggregates; and(4) 30% RCA from CC, 70% RCA from CB,
100% sand as fine aggregates.
Mix proportion details are presented in Table 2.

experiments. The cross-sectional area of all beams is
B×H = 150mm×200mm, and the length is 1,800mm.
Steel reinforcements are arranged as follows (Φ –diameter of rebar):- Longitudinal reinforcements: 2Φ8 (deformedbar) top reinforcements; 2Φ16 (deformedbar) bottom reinforcements.
- Stirrup: Φ6@100mm (plain bar) throughout,skip at midspan.
Beams are situated on two supports at a distanceof 1,300mm. The pure bending length is 300mm.
Each beam was arranged with the following sensors(Fig. 5):- 03 linear variable differential transformer(LVDT) sensors D1,2,3 for measuring
deflection at designated points (one at
midspan, and two at supports).
- 02 resistance strain gauges DC1,C2 formeasuring strains at top and bottom layers ofconcrete.
- 02 resistance strain gauges DS1,S2 formeasuring strains in bottom rebars.
- 01 resistance strain gauge DS3 for measuring
strain in one of the two top rebars.

Table 2 Mix design of 1m3 concrete
Mix
Mat.
C
S
G1
G2
C1
C2
B1
B2
W
A

(1)

(2)

(3)

(4)

300
850
275
825
–
–
–
–
165
3

300
850
–
–
275
825
–
–
165
3

300
850
–
–
192.5
577.5
82.5
247.5
165
3

300
850
–
–
82.5
247.5
192.5
577.5
165
3

Note: C – cement; S – sand; G1 – NCA size 5-10mm; G2 – NCA
size 10-20mm; C1 – RCA from CC size 5-10mm; C2 – RCA fromCC size 10-20mm; B1 – RCA from CB size 5-10mm; B2 – RCAfrom CB size 10-20mm; W – water; A – additives; all units are in
kilogram (kg).

To enhance the final quality of RAC, the twostage mixing approach (TSMA) was adopted. This
process of concrete mixing was proposed by Tam etal. in 2005 [14], and was reported to be able toimprovetheinstantaneous
strengthandmicrostructure of RAC, as well as its long-term
behaviors such as shrinkage or creep [15]. Figure 4
displays the difference between TSMA and thenormal mixing approach (NMA).
Fig. 5

Beam detail and sensors arrangement

Experiment procedure and apparatus
Experimental beams were cast by the TSMA asshown in Fig. 6. At 3-day old, formworks were
removed and the beams were allowed to be cured atroom temperature. Final products with all strain
gauges installed are presented in Fig. 7. Each beam
was fixed onto the apparatus as shown in Fig. 8.
All results of loading, deflection, and deformation
were recorded by a 30-channel data-logger.
Throughout each experiment, the crack formation
was closely observed. By the end, all crack patterns
were marked on every beam.

Fig. 4 NMA and TSMA
Source: Tam et al. (2005) [14]Beam Model and Configuration
Beam details and setup of sensors
12 reinforced concrete beams of rectangularcross-section (3 for each concrete mix proportion)
were casted for conducting 4-point flexural
654

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Concerning the mix proportion (2) whereby 100%
NCA were replaced by RCA produced from CC, thecracking load is reduced by an average of 17.69% incomparison with the reference NAC beam, while theultimate load is reduced by an average of only 3.7%.
This behavior agrees with previous conclusions ofpublished papers which suggests that cracks would
appear much sooner in RAC beams than in reference
NAC beams, but with the same mix design, their loadbearing capacity are almost identical.
Table 3 Cracking load of experiment beams
BeamFig. 6

(1)1
(1)2
(1)3
(2)1
(2)2
(2)3
(3)1
(3)2
(3)3
(4)1
(4)2
(4)3

Casting of experimental beams

Cracking load (T)
Value
Ave.
3.9
3.8
3.73
3.5
3.3
2.9
3.07
3.0
2.3
2.4
2.37
2.4
1.8
1.8
1.87
2.0

Reduction (%)
Value
Ave.
–
11.53
22.25
19.57
38.34
35.66
35.66
51.74
51.74
46.38

–

17.69

36.46

49.87

Table 4 Ultimate load of experiment beams
Fig. 7

Beam

Beams with installed sensors

(1)1
(1)2
(1)3
(2)1
(2)2
(2)3
(3)1
(3)2
(3)3
(4)1
(4)2
(4)3
Fig. 8Ultimate load (T)
Value
Ave.
10.0
10.3
10.0
9.8
9.7
9.5
9.63
9.7
9.1
9.2
9.10
9.0
10.1
10.0
10.10
10.2

Reduction (%)
Value
Ave.
–
3.00
5.00
3.00
9.00
8.00
10.00
-1.00
0.00
-2.00

–

3.70

9.00

-1.00

It is clearly observed that the cracking load
exhibits a general decrease with increasing
percentage of CB in the mix. A contribution of 70%
CB in RCA results in halving the cracking load. It canalso be said that this observation is expected as CB isa much weaker material in terms of strengthcompared with natural aggregates or CC.
For the ultimate load, however, there was an
unusual point of interest at the 70% CB content in
RCA. The load-bearing capacity of these three beams

4-point flexural test apparatus

RESULTS AND DISCUSSIONS
Monitoring the experiment process, values ofcracking load and ultimate load of each beam wererecorded. These results are presented in Tables 3 and4, respectively. In these tables, the designation (1)1stands for concrete mix proportion (1) – beam number
1, and so on.
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

is as high as the reference NAC beams (value of Pultof around 10 tons). This might be due to thesignificant amount of brick and attached mortar
absorbs a lot of water right at the moment of concrete
mixing – which results in a reduction in W/C ratio.
Nevertheless, this explanation still needs further
confirmation through other means of observation, thedetermination of compressive strength and elastic
modulus, and even through additional experiments.
Crack patterns of beams are as presented in Fig. 9.
It can be seen that most cracks are vertical, and areconcentrated around the segment of pure bending.
Therefore, it can be safely deduced that all
experiment beams fail by flexure, not by shear.

microstructure of concrete might reveal more
explanations. More experiments may also be
conducted on RAC beams cast with RCA having
intermediate percentages of CB (40%, 50%, or 60%)in order to provide a better view on the variation of
the ultimate load.
ACKNOWLEDGMENTS
This research was supported by JST–JICA
Science and Technology Research Partnership for
Sustainable Development Program (SATREPS)
project (No. JPMJSA1701).
REFERENCES(1) NAC(2) RAC 0%CB

(3) RAC 30%CB(4) RAC 70%CB

Fig. 9[1] Ministry of Natural Resources and Environment,
2016 Report on National Environment: Urban
Environment,
NaturalResourcesandEnvironment Publishing House, 2016.
[2] Nixon, P.J., Recycled concrete as an aggregate forconcrete – A review. Mater. Struct., Vol.11, No.5,
1978, pp.371-378.
[3] Ravindrarajah, R.S. and Tam, C.T., Properties ofconcrete made with crushed concrete as coarse
aggregate. Mag. Concrete Res., Vol.37, No.130,
1985, pp.29-38.
[4] de Oliveira, M.B. and Vazquez, E., The influenceof retained moisture in aggregate from recyclingon the properties of new hardened concrete. Waste
Manag., Vol.16, 1996, pp.113-117.
[5] Topçu, I.B. and Sengel, S., Properties of concretes
produced with waste concrete aggregate. Cem.
Concr. Res., Vol.34, 2004, pp.1307-1312[6] Kou, S.C., Reusing recycled aggregates instructural concrete (PhD Thesis), The Hong
Kong Polytechnic University, 2006.
[7] Tong, T.K., Nghiên cứu sử dụng phế thải xâydựng trong chế tạo bê tông (PhD Thesis, inVietnamese), Hanoi University of Civil
Engineering, 2017.
[8] Xiao, J.Z., Recycled Aggregate Concrete
Structures, Springer-Verlag GmbH Germany,
2018.
[9] Ajdukiewicz, A.B. and Kliszczewicz, A.T.,
Comparative Tests of Beams and Columns Madeof Recycled Aggregate Concrete and Natural
Aggregate Concrete. J. Adv. Concr. Technol.,Vol.5, No.2, 2007, pp.259-273.
[10] Arezoumandi, M., Smith, A., Volz, J.S.,
Khayat, K.H., An experimental study on flexuralstrength of reinforced concrete beams with 100%
recycled concrete aggregate. Eng. Struct. Vol.88,
2015, pp.154-162.
[11] Zhao, S. and Sun, C., Experimental Study of the
Recycled Aggregate Concrete Beam Flexural
Performance. Appl. Mech. Mater., Vol.368-370,
2013, pp.1074-1079.

Crack patterns of experiment beams

CONCLUSIONSIn this paper, experiments of reinforced beams
were set up to observe flexural behavior of RAC withreference to equivalent NAC, including: cracking
load, ultimate load, and crack pattern. Results
obtained generally agree with previous studies,
whereby RAC beams cast with 100% RCA produced
from CC would be almost as strong as reference NAC
beams, but cracks would also appear much sooner.
Hence, it is advisable that when designing with RAC,
the serviceability limit state should be considered
carefully. Revisions of design standards may as well
be needed in order to facilitate the application of RAC
in various structures (in this particular case, flexural
members).
The ultimate load of RAC beams cast with RCA
having high content of CB should be further studied
as it displayed a strange behavior that was not totallyconsistent with common predictions. A look into the656

GEOMATE – Bangkok, Thailand, 22-24 November 2022

[12] Seara-Paz, S., González-Fonteboa, B., MartínezAbella, F., Eiras-López, J., Flexural performanceof reinforced concrete beams made with recycledconcrete coarse aggregate. Eng. Struct., Vol.156,
2018, pp.32-45.
[13] Ignjatovic, I.S., Marinkovic, S.B., Miskovic,
Z.M., Savic, A.R., Flexural behavior of reinforced
recycled aggregate concrete beams under shortterm loading. Mater. Struct., Vol.46, 2013,
pp.1045-1059.
[14] Tam, V.W.Y., Gao, X.F., Tam, C.M.,

Microstructural analysis of recycled aggregate
concrete produced from two-stage mixing
approach. Cem. Concr. Res., Vol.35, 2005,
pp.1195-1203.
[15] Tam, V.W.Y. and Tam, C.M., Assessment of
durability of recycled aggregate concrete
produced by two-stage mixing approach. J.
Mater. Sci., Vol.42, 2007, pp.3592-3602

657

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

LEGALIZATION OF THE USE OF MEDICAL MARIJUANA AS A
TREATMENT IN THE EFFORT OF RENEWING THE NATIONALHEALTH LAW IN INDONESIA
Siska Elvandari1, Yandriza, A.1 Irzal Rias1, Tenofrimer1 and Ansiha Nur2
1

Faculty of Law, Universitas Andalas, Indonesia; 2 Faculty of Engineering, Universitas Andalas, Indonesia
ABSTRACTThis study focuses on finding an ideal concept regarding the criteria for legalizing the use of medical marijuana
as a treatment in an effort to reform the national health law in Indonesia, and being able to find an ideal conceptof criminal law policy in an effort to minimize the misuse of medical marijuana use as a treatment in an effort toreform the law health in the future (Ius Constituendum), so that various criminal law policies must be carried out.
Criminal Law Policy in an effort to minimize cases of medical marijuana abuse as an effort to renew health law inthe future (Ius Constituendum). Recommendations to several relevant agencies, legal practitioners, and healthpractitioners are expected to be able to know, review and find a concept regarding the criteria for legalizing theuse of medical marijuana as a treatment in an effort to reform national health law in Indonesia, and how criminallaw policies are in an effort to minimize the misuse of medical marijuana use. as a treatment in an effort to renew
health law in the future (Ius Constituendum). By conducting a normative juridical analysis, using the principles oflaw and legislation as well as several legal principles in the field of health law, in the form of the principles of “ForLife” (Pro-Life), “For Choice” (Pro-Choice), in efforts to reform the national health law.
Keywords: Health, Legalization, Medical marijuana, Treatment

INTRODUCTION

development is an effort carried out by allcomponents of the nation that aims to increaseawareness and the ability to live healthy for everyone
so that the highest level of public health can berealized [3].
In order to increase the degree of public healthwhich can be realized as high as possible startingfrom the improvement of human resources, whichneeds to be done in order to realize the welfare of thepeople, it is necessary to make efforts to improve inthe field of medicine and health services, among
others by seeking the availability of certain types ofnarcotics which are very much needed as a substitutefor drugs. drugs and to prevent and eradicate thedangers of narcotics and narcotic precursors, so that
the Government forms a law, namely Law Number 35
of 2009 concerning Narcotics. In the Consideration of
Law Number 35 of 2009 concerning Narcotics, statesthat [4]: a. to realize a prosperous, just and prosperous
Indonesian society that is materially and spiritually
evenly distributed based on Pancasila and the 1945
Constitution of the Republic of Indonesia, the qualityof Indonesian human resources as one of the national
development capital needs to be maintained andimproved continuously, including the degree of
health; b. In order to improve the health status ofIndonesian human resources in the context ofrealizing the welfare of the people, it is necessary to
make efforts to improve the field of treatment andhealth services, among others by seeking theavailability of certain types of Narcotics which areurgently needed as drugs as well as preventing andEvery human being who is born and lives has
inherent rights as a form of God's gift which is naturaland fundamental. These rights exist since humans areborn, because every person, society, and state must
maintain, protect, and respect each other, these rights
are human rights (HAM). Human rights must be
upheld and protected by the rule of law, so that otherpeople, or the state, are not arbitrary about these
inherent rights. The ratification of the Universal
Declaration of Human Rights by the United Nations
is a real form of awareness of the nations of the world
legally to protect the human rights of every humanbeing. Indonesia as a concrete form of protecting therights of every human being has been stated in the1945 Constitution and the Government has also
ratified Law Number 39 of 1999 concerning Human
Rights (HAM) [1].
One form of human rights protection that must be
realized is the right to health. The right to health ispart of the welfare element of every human being, sothat every human being who lives does not expect toget sick, so health is always well taken care of.
Quality development in the health sector is a very
important national development investment and must
be continuously improved. If the health of thecommunity is disrupted, it will have a large economic
loss for the country. Without the fulfillment of theright to health, development in the health sector
automatically cannot be realized [2]. In the 2009
National Health System, it is stated that health658

GEOMATE – Bangkok, Thailand, 22-24 November 2022

eradicating the dangers of abuse and illicit trafficking
of Narcotics and Narcotics Precursors; c. Narcotics
on the one hand are drugs or materials that are usefulin the field of treatment or health services and thedevelopment of science and on the other hand canalso cause very detrimental dependence if misused orused without strict and careful control andsupervision; d. importing, exporting, producing,planting, storing, distributing, and/or using Narcotics
without strict and thorough control and supervisionand contrary to laws and regulations is a Narcotics
crime because it is very detrimental and is a very big
danger to human life, society, nation and state. andIndonesia's national security; e. Narcotics crime has
been transnational in nature which is carried out using
high modus operandi, advanced technology,
supported by an extensive network of organizations,and has caused many victims, especially among thenation's young generation which is very dangerous to
the life of the community, nation and state so that the
Law Number 22 of 1997 concerning Narcotics is nolonger in accordance with the development of thesituation and conditions that develop to overcome anderadicate these criminal acts; f. that based on theconsiderations as referred to in letter a, letter b, letterc, letter d, and letter e, it is necessary to establish a
Law on Narcotics.
In Law Number 35 of 2009 concerning Narcotics,
it is stated that: narcotics can only be used for the
benefit of health services and/or scientificdevelopment, but not all types and classes of narcotics
are used for health purposes, especially for class I
narcotics which are in large quantities. Even if it islimited, it can only be used for the benefit ofdeveloping science and technology, in addition to its
benefits in the world of medicine, if misused ormisused, narcotics can cause harmful consequences
for the life and values or patterns of the Indonesiannation which serve as the philosophical basis, namely
Pancasila and the 1945 Constitution which in the final
stage leads to a criminal act.
The rampant circulation of narcotics in several
regions in Indonesia shows that there have been
deviations from the use of narcotics, which areusually used by doctors and health professionals for
medical purposes because they have substances thatare able to help the patient's healing process.
However, because the substances contained in thesedrugs can cause addiction or addiction or othernegative effects, the use of which is beyond the
dosage limit has now been discontinued for several
reasons. One of the effects it causes is not light, suchas causing excessive addiction and always wanting toconsume it continuously, even though increasing thedose of its use causes one's own health to bethreatened, such as the death of a drug user.
Another phenomenon that has caught the public's
attention is not only the use of narcotics that leads tonarcotics abuse, such as several cases that the author

has described in the previous section, but there is alsoa case that has greatly attracted public attention whenusing narcotics for the purpose of medical treatment,
namely the case of Fidelis Ari. , with the following
position cases: On February 19, 2017 Fidelis Ari wasarrested by the National Narcotics Agency, forplanting marijuana to treat his wife who had a spinal
cyst, and a month after Fidelis Ari was arrested hiswife was declared dead. The death of his wife because
his wife could not access the medicine he needed tomaintain life in the form of a potion or concoction of
marijuana leaves that Fidelis Ari made himself. Thiscan be seen from a number of Non-Governmental
Organizations (hereinafter referred to as NGO)
regretting the steps taken by the National Narcotics
Agency (hereinafter abbreviated as BNN) to arrestand detain Fidelis Ari. They considered that Fidelis
should not have been arrested for growing marijuana
for his wife's treatment.
This case is one that is said to be contrary tohuman values, so it is felt that it needs to get attention
from various groups, such as the Government, Law
Enforcement Agencies, and several other related
agencies. This can be seen from the various reactions
that were born from various circles, such as:
Chairman of the Nusantara Maritime Circle(hereinafter abbreviated as LGN) Dhira Narayana
said, this is not the first case where someone uses
marijuana as a drug. Since 2010, LGN has
documented many people using marijuana as atreatment and some of them are still in prison [4]. This
does not mean that if BNN does not investigate thiscase, then they violate the law. The termination of theinvestigation can still be carried out as stipulated inarticle 109 paragraph 2 of the Criminal Procedure
Code [5].
For comparison, at the beginning of 2022 a similarcase was repeated, where the figure of Santi Warastuti
was in the spotlight after uploading a photo of his
action in the Car Free Day (CFD) of the Jakarta HI
Roundabout on Sunday (26 June 2022) which went
viral on social media. Through his personal Twitter
account, singer Andien Aisyah uploaded a photo ofSanti carrying a large poster that reads "Please, myson needs medical marijuana" in a crowd of residents.
In the action, Santi was seen accompanied by amiddle-aged man with a child lying weakly in thestroller. Apparently, the child is Pika, the baby ofSanti and her husband who suffers from cerebral
palsy or a disorder that affects one's muscle ability,
movement, and body coordination [6].
Investigate, this action aims to urge the judges of
the Constitutional Court (hereinafter abbreviated asMK) to immediately decide on a judicial review
lawsuit against Law Number 35 of 2009 concerning
Narcotics which he requested. Santi with her husband
Sunarta and their son Pika came from Yogyakarta to
Jakarta to deliver a letter of hope to the Constitutional
Court regarding this. The reason is that it has been659

GEOMATE – Bangkok, Thailand, 22-24 November 2022

almost two years since the Court has not decided on
the matter of judicial review. In fact, Pika who suffersfrom cerebral palsy requires cannabis seed oil or CBD
oil therapy to seek his recovery. Santi filed a lawsuit
for judicial review of Law Number 35 of 2009
concerning Narcotics to the Constitutional Court
along with two other mothers in November 2020. The
children of the two mothers are also not in good healthbecause they suffer from pneumonia and epilepsy
respectively.
In their lawsuit to the Constitutional Court, thethree mothers questioned the explanation of Article 6
Paragraph (1) letter a and Article 8 Paragraph (1) of
the Narcotics Law which prohibits the use of
marijuana for health services. This article isconsidered detrimental to the constitutional rights of
the applicant because it prevents them from getting
treatment for the baby. Therefore, the three wanted
the Court to legalize the use of class I narcotics so that
their children could receive treatment. In addition to
the three women, several other institutions also
became plaintiffs in this case, namely ICJR, LBH
Masyarakat, IJRS, Yakeba, and EJA. Almost two
years since the lawsuit was filed, the Court has yet todecide this case. However, since November 2020, theConstitutional Court has asked the applicant for
information from a number of experts through a seriesof trials. The spokesman for the Constitutional Court,
Fajar Laksono, said that the trial of this case was quite
long because it brought many experts from thelitigants. "Currently, his position is under internal
discussion by a constitutional judge [6].
Based on the explanation above, it caused
reactions from various groups. LBH Community
Narcotics Policy Analyst Yohan Misero said thecurrent Narcotics Law does not accommodate the useof marijuana for medical purposes, so Law Number
35 of 2009 concerning Health must be revised
immediately. This law does not accommodate the useof marijuana for health, even Article 8 of the
Narcotics Law prohibits the use of narcotics class I
for health. The Narcotics Law should not prohibit theuse of any substance or plant for health, and Yohan
also encourages the Government to conduct research
to prove the health benefits of cannabis leaves, andbelieves that current technology and human resources
are capable of producing quality research.
The above phenomenon strongly indicates that thecommunity's sense of legal awareness is decreasingin achieving the right to health in the form of efforts
to achieve optimal health degrees which are based on
respect for and protection of human rights, namely theright to life. Based on Article 28 a of the 1945
Constitution, it is stated that the right to live andmaintain life and life is the right of every person,
while according to Article 4 of Law no. 39 of 1999
concerning Human Rights, states that: The right tolife is a human right that cannot be reduced under any
circumstances and by anyone. The right to life basedon Article 29 in conjunction with Article 1 Paragraph
3 of the Third Amendment of the 1945 Constitution
states that life does not belong to humans. Life is a
gift from God to everyone that must be guarded andmaintained from the beginning to the end (when it istaken back) not indefinitely.
The reactions carried out by the various agencies
above, according to the author, are not the rightsolution. This is because we should look at thephenomena above as a whole which are closely
related in various aspects of life, not see thephenomena above based on the presence or absenceof something to get further regulation in laws andregulations, because the phenomenon is not only
limited to the right to choose or the right to choose to
do something or Pro Choice, but view the above
phenomenon as the right to live and maintain life or
Pro Life as an effort to continue to view humans as
whole humans as an effort to dignify humans andhumanity itself. Based on the foregoing, it is criticalto discuss the legalization of the use of medical
marijuana as a treatment in the effort of renewing thenational health law in Indonesia. The problems thatwill be discussed are 1) the criteria for legalizing theuse of medical marijuana as a treatment in an effort toreform the national health law in Indonesia, and 2) the
criminal law policy in an effort to minimize themisuse of medical marijuana use as a treatment an aneffort to reform health law in the future (Ius
Constituendum)
METHODOLOGY
1. Research Approach.
The method of approach in this research is anormative juridical research. Normative juridical
research is a legal research conducted by researching
and reviewing the literature. According to Soekanto
and Mamudji [7], normative legal research includes
research on legal principles, legal systems, verticaland horizontal level of synchronization, comparative
law and legal history.
2. Research Specification
The type and specification of this research is
descriptive analytical research. This approach is usedbecause this research analyzes the judge’s decisions.
The implementation of this method is not limited only
to data collection and compilation of data, but also
includes the analysis and interpretation of themeaning of the data [8].
3. Research Phase is performed through 2 stages,
namely:a. The literature study phase begins by collecting
secondary data needed in the research, namely
basic materials of normative legal research(literatures), used literature research methods
that include: First, primary legal materials
consisting of Basic Norms or Basic Rules of
the Constitution, Basic Regulations: The Body
660

GEOMATE – Bangkok, Thailand, 22-24 November 2022of the Constitution, Congress Decrees, Laws
and Regulations, namely: Act, Government
Regulations, Presidential Decrees, and Other
Regulations related to narcotics abuse cases,
and existing jurisprudence relevant to thewriting in this study. Second, secondary legal
materials provide explanations about primary
legal materials, such as: papers, workshops,
seminars, symposia, discussions, and research
results, magazines or newspapers that are
related to this research object. Third, tertiary
legal materials that provide guidance andexplanation of primary and secondary legal
materials,
forexampledictionaries,
encyclopedias, and so on.
b. The field study phase is obtained directly fromthe source of information through interview,using the structured questionnaire about thecriteria of narcotics use that could be
categorized for medical treatment, and thecondition in which the narcotics use could be
categorized as narcotics abuse, in perspectiveof health law in West Sumatra; and to know
the Criminal law policy’s effort to minimize
the narcotics abuse case, as reformation effort
for the law of health in the future (Ius
Constituendum).
4. Data collection technique
Based on the problems and the approaches as
described in the previous section, this study uses two
types of tools for data collection, i.e. document
studies and interviews. The documents studies wereused to collect secondary data, in forms of legal
materials as described above; while interviews wereused to collect primary data, both from respondents
and from informants, because the expected data fromthis interview method was an in-depth data, Then, theinterview guidance used is the free guidanceinterview (unstructured interview guidance).

justice system process, namely seeking at least 2(two) two) sufficient evidence and to meet therequirements of sufficient initial evidence.
The age limit and the number limit must be
adjusted to the adage in health law, must pay attention
and consider the same situation and conditions thatare the basis for consideration in determining the rightdose and will be used in narcotics users for medical
purposes. This is because the human condition isbasically not the same because many factors influence
it. Place is also an important factor for theGovernment to monitor the situation and condition of
its people.
Table 1. Data of revealed crime cases of drug abusedin West Sumatera Regional Police Corps year 20202021

Regional
No
Unit
1

DitresNarkoba2

Polresta
PadangPolresBukittinggi
PolresPasamanPolres 50
Kota
Polres Pessel

3
4
5
6

29

36

10

16

19

24

11

15

17

21

11

14

20

24

36

15

21

9

15

10

12

7

12

14

23

10

5

5

6

8

11

Polres Agam

14

15

15

15

12

PolresPadangPanjang
Polres Solsel

14

15

15

15

4

4

8

9

PolresPayakumbuh
Polres Solok
Kota
PolresDhamasraya
PolresMentawai
PolresSijunjung
PolresPariamanPolresPasbar
Total

29

33

32

44

23

32

19

29

16

28

11

14

1

1

2

2

9

14

9

12

21

24

14

21

27

32

17

22

442

587

460

593

9

13
14
15

The criteria that become the benchmark of the useof of medical marijuana as a treatment in an effort toreform the national health law in Indonesia include
Supervision, Age Limits, Number and Place Limits.
Supervision is carried out by the Minister of Health,the Food and Drug Monitoring Agency (BPOM), theNational Police/BNN, and the Health Professionals.
The inclusion of the National Police, BNN, and the
Health Profession as an effort to achieve legal
certainty with full principles of legal protection basedon the existence of duties, principals, functions andauthorities that are closely related to the investigation
process in finding the basis for the start of the criminal

40

10

8

The criteria for legalizing the use of medical
marijuana as a treatment in an effort to reform thenational health law in Indonesia.

27

PolresPadangPariamanPolresSawahlunto
Polres Tanah
Datar
Polres Solok

7

RESULTS AND DISCUSSION

Result for Revealed Case ofDrug Abused Crime
2020
2021
Trend
(Jan-Jun)(Jan-Jun)
Case Suspect Case Suspect
56
67
67
89
Case
increase
by 18
(4%)
131
178
132
156

16
17
18
19
20

Notes : Polres = Resort police

661

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 1 and Table 2 shows the success of the
Narcotics Directorate of Regional Police of West
Sumatra and the society, to minimize the occurrenceof narcotics abuse cases. In the future, it will required
cooperation with several relevant agencies as the
responsible parties, such as: National Agency for
Drugs and Food Control (BPOM), Commission III of
the House of Representatives, Indonesian Doctors
Association (IDI), Pharmacy Experts, and theMinistry of Health, in formulating various policies as
the institutions who have its main duties, functions
and authority, and also as reminder for re-align the
public's understanding on narcotic use, and to define
the criteria between narcotic usage as a narcotics
abuse or as a series of processes in medical treatment.

Fig. 1 Criminal law policy as part of criminal policy
and social policy

Table 2. Comparison in Evidence Revealed on theYear 2020-2021

Figure 2 shows a comparison Scheme of Criminal
Policy Scope. It is shown that crime prevention
efforts could be pursued by: a. criminal law
application; b. Prevention without punishment; and c.
influencing the public's view on crime andpunishment/mass media. Criminal eradication isdivided into two mainlines; "penal" (criminal law)and "non-penal" (non/outside of criminal law). In thedivisions by G. Peter Hoefnagels above, the efforts
mentioned in points (b) and (c) may be included in the"non penal" effort group.

Result of evidence revealed
No

Type of evidence

1

Marijuana

2

Marijuana Plant

3

Methaphetamine

4

Ecstasy

5

Class. IV
Psychotropics

2020
Jan-Jun134.14Kg
70 Plant

2021
Jan-Jun
231,78 Kg

2.017,3Gr
28 pills

2.266,39 Gr-

40.000 pills of
Hexymer brand +600 pills of
Alpazolam brand+ 47 pills Eriminbrand.

60 Plant

18 pills

The criminal law policy in an effort to minimize
the misuse of medical marijuana use as atreatment in an effort to reform health law in the
future (Ius Constituendum)
Figure 1 shows criminal law policy in Indonesia.
The criminal law policy in an effort to minimize themisuse of medical marijuana use as a treatment in aneffort to reform health law in the future (Ius
Constituendum) is closely related to the 4 (four)
criteria mentioned above. Supervision is carried out
by the Minister of Health, the Food and Drug
Monitoring Agency (BPOM), the National
Police/BNN, and Health Professionals. The inclusionof the National Police, BNN, and the HealthProfession as an effort to achieve legal certainty withfull principles of legal protection based on theexistence of duties, principals, functions andauthorities that are closely related to the investigation
process in finding the basis for the start of the criminal
justice system process, namely seeking at least 2(two) two) sufficient evidence and to meet therequirements of sufficient initial evidence.

Fig. 2 Scheme of the criminal policy scope
In comparison with the author's case, this caseshould be a momentum to review existing policies sothat they can be accustomed according to the current
situation and social condition. These policies at once
become the answer for the criteria that will be used asa benchmark on the use of narcotics in medical
treatment and narcotics abused, as described in Table
3. It shows that the formulation policy in the future
will closely related to the four (4) criteria above.
Supervision will be conducted by the Minister of
Health, National Agency for Drugs and Food Control(BPOM), Police / BNN, and Health Professionals.
Inclusion of the Police, BNN, and HealthProfessionals, who act as an effort to achieve legal

662

GEOMATE – Bangkok, Thailand, 22-24 November 2022

certainty within the principle of legal protection. It
was based on the existence of duties, principals,
functions and authority which are closely related tothe process of investigation, searching for basic startup in the process of criminal justice system, bysearching two sufficient evidence, to meetrequirements for sufficient initial proof.
This health profession is aimed at carrying out
medical actions that are still based on 3 (three)standard actions, such as: Standard Operational
Procedure (SOP), Service Standards, andProfessional Standards. This is necessary considering
the health profession in general, and the medical
profession as a noble or honorable profession(Officium Nobile), by placing humans as objects.
Furthermore, it must pay attention and take into
account the same situations and conditions that are
the basis for consideration in determining the rightdose and will be used in medical marijuana users for
medical purposes. This is due to the fact that thehuman condition is not uniform, as many factors
influence it. The location is also an important factor
for the government to monitor the health of its
citizens.

various conditions as long as it is determined by thelaw which is the core of health services, with theultimate aim of dignifying patients as whole humanbeings, to reduce suffering, prolong life, andaccompany the patient until the end of his life.
ACKNOWLEDGMENTS
This research is supported by the funding of
LPPM Universitas Andalas.
REFERENCES[1] Riyanto, S., Establishment of a Special Medical
Court, Yogyakarta: Deepublish, 2012, p. 1.
[2] Riyanto, S., Establishment of a Special Medical
Court, Yogyakarta: Deepublish, 2012, p. 3.
[3] Hartini, Y. S., Pharmacy Practice Review ofRegulations on Pharmacists Occupation,
Yogyakarta, Sanata Dharma, 2010, p. 3.
[4] Law Number 35 of 2009 concerning Narcotics.
[5] Ihsanuddin, Fidelis Case Becomes Momentum toLegalize Marijuana for Medicine, accessed at
http://www.kompas.com, on July 8, 2022.
[6] Elvandari, S., Medical Dispute Resolution Law,
Depok, Rajawali Pers, 2021, p. 3.
[7] Soekanto, S, and Mamudji, S., Research on
Normative Law, A Brief Review, Jakarta,
Rajawali Press, 1985, p. 13.
[8] Moerad, P., Law Formation through Court Ruling
Criminal Case, Bandung, Disertation on Doctoral
Program in Science of Law, Padjajaran University,
2004, p. 43.

CONCLUSIONSThe use of medical marijuana can be regarded asa new breakthrough in treatment in an effort to reform
national health laws in Indonesia. This is enforced inan effort to defend the right to life and the right todefend one's life. This effort is carried out by
improving the medical rehabilitation system, where
patients who use medical marijuana are allowed with663

12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

FLASH CALCINATED SEDIMENT USED IN THE CEM III CEMENTPRODUCTION AND THE POTENTIAL PRODUCTION OF
HYDRAULIC BINDER FOR THE ROAD CONSTRUCTION – PART I:
CHARACTERIZATION OF CEM III CEMENTS
Mahfoud BENZERZOUR(1), Duc Chinh CHU(1), Joelle KLEIB(1), Mouhamadou AMAR(1), Nor-Edine
ABRIAK(1), Jaouad NADAH(2).
(1)

Univ.Lille, IMT Nord Europe, Univ.Artois, Yncrea Hauts-de-France, ULR 4515-LGCgE, Laboratoire deGénie civil et géo-Environnement, F-59000, Lille, France.
(2)
EQIOM Le LAB, CRT 1 Parc Vendôme – 460 Allée de l’Innovation, 59810 LESQUIN, France.
ABSTRACTThe maintenance operation of waterway generates about of 300 Mt of sediments every year in Europe andprovokes a real problem for the environment. The dredged sediments have been used in several application suchas the cement production, the road construction and the supplementary cementitious materials (SCMs) in theconcrete. Knowing that about of 4.6 billion tons of cement are produced every year in the world and the cement
industry releases about of 8% of CO2 global emission. The reduction of CO2 emission amount as well as thereservation of natural resource becomes a challenge in the 21st century. Consequently, in this study, a fluvial
sediment was used to replace a part of traditional raw materials in the clinker production and another fluvial
sediment treated by flash calcination was used to substitute a part of slag for the CEM III production. The CEM
III cements produced can be used as a hydraulic binder in order to treat the soil in the geotechnical application.
The characterization results indicated that the raw materials replacement by sediment did not modify the cement’s
properties compared to the CEM III reference. However, a substitution rate of 25% wt slag by the flash calcination
sediment promoted the cement reactivity as well as the mechanical and microstructural properties of hydrated
cement.
Keywords: Flash calcined sediment, Cement, Hydration, Slag, Compressive strength, Microstructure.
strength of hydrated cement. These results can be
explained by the presence of minor elements in thewaste which can modify the polymorphs of mineralphases in the produced clinker. For example, MgO
promotes the formation of M3 polymorph in C3S
phase, which is well known to be less reactive
compared to the M1 polymorph [4]. Consequently,
the waste recycling in the clinker production must be
performed with caution to ensure the cement quality.
However, this solution can only limit theconsumption of traditional raw materials while not
reduce the CO2 emission during the clinkering
process. To solve this problem, several previous
studies attempted to reduce the clinker content in the
cement by partially or totally the clinker with
supplementary cementitious materials (SCMs).
From the environmental point of view, the SCMs use
not only reduces the quantity of CO2 emission due tothe clinker production, it also eliminates the need ofdisposal for the storage of some residual products ofindustry as granulated blast furnace slag from theiron production, fly ash from the coal combustion orsilica fume generated from the silicon andferrosilicon alloy production. In addition, from thetechnical point of view, the SCMs incorporated in
the concrete can improve the durability against
corrosion, sulfate attack [7] or alkali – silicate

INTRODUCTION
The cement industry produces about 4.6 billion
tons of cement every year, which is an essential
component for the concrete production. However,this
production
activity
also
contributes
approximately 8% of CO2 global emission in theworld. In addition, the extraction and transport
operations of raw materials have the negative
impacts on the environment. The need to limit theCO2 emission and reservation of natural resources
has become the veritable challenge for the cement
industry in 21st century. Several previous studies
have proven the feasibility of waste recycling to
replace a part of traditional raw materials in theclinker production such as the municipal solid waste
incineration [1], steel slag [2], civil constructionwaste [3] or dredged sediment [4, 5]. Although thecharacterization results of clinker made from thetraditional raw materials replacement by the waste in
the raw meal showed that all the four typical phases
were identified, but the cement quality were veryvariant along with the substitution rate and type ofwaste used. In a previous study [6], the content
increase of municipal sewage sludge incorporated in
the raw meal led a longer initial setting time, final
setting time and a slight decrease of compressive664

GEOMATE – Bangkok, Thailand, 22-24 November 2022

reaction [8] as well as the mechanical performanceand the microstructure at long –term. Depending on
the chemical – mineral compositions as well as thephysical properties of SCMs, the hydration reactionsof cement containing the SCMs are very different
than those of pure cement hydration. The principalchange are from the products formed as the lower
Ca/Si ratio in the C-S-H hydrate or the consumption
level of Ca(OH)2 by the SCMs [9].
The slag has successfully been used with thePortland cement to produce the cement blends thatare more economical, more environmentally friendly
and have performance advantages. The replacementlevel of slag can reach 65 wt% in the cement CEM
III/A described in the NF EN 197-1 standard [10]. In
a previous study, a slag containing CaO (noted C),
SiO2 (noted S), MgO (noted M), and Al2O3 (noted A)
with molar proportions of 7.88 : 7.39 : 3 : 1 was used
to study the chemical reaction of slag in the presenceof calcium hydroxide (noted CH). The result showedthat the chemical reaction for the slag/CH could be
written as follows:
C7.88S7.39M3A + 2.6CH + bH
7.39C1.42SHmA0.046 + 0.66M4.6AHd->pozzolanic activity and can be used as SMCs in orderto replace a cement part in the mortar or concrete.
The aims of this research were twofold: thereuse of sediment in the raw meal for the clinker
production at the laboratory was firstly performed,
and then the sediment treated by flash calcination
method was used to replace a slag part in the CEM
III cement production. The properties of clinkers
including the free lime content, the mineral phases
contents, as well as of the water demand and thesetting time of CEM III cements, the compressive
strength and the porosity of mortars were analyzed.
MATERIALS AND METHOD
Materials in the clinker/cement production
In this research, the sediment used in the clinker
production is a fluvial sediment collected at the Air
sur Lys region (referenced ASL) in the North ofFrance, France. The other raw materials, such as the
limestone, clay, sand and iron oxide were extracted
from the natural quarries. All the raw materials weredried to constant mass at 105 °C and ground to reachthe particle size smaller than 200 µm according to
the requirement in the cement industry. Based on thechemical composition analysis of all the raw
materials and the viscosity analysis of the raw meal
containing the ASL sediment, the content of ASL
sediment in the raw meal was limited to 2 wt% toassure the viscosity requirement. In this part, twodifferent formulations of clinker were establishedusing the moduli values habitually used in the
cement industry: Lime Saturation Factor (LSF),
Silica Ratio (SR) and Alumina Ratio (AR). The firstis the reference clinker (referenced OPC TM) madefrom the limestone, the clays and iron oxide only.
The second clinker, named OPC 2%ASL
corresponds to a sediment substitution rate of 2 wt%
in the raw meal. The proportion of each raw
materials in the formulation was calculated using thevalues as follows: LSF = 98, SR = 2.5 and AR = 1.65
respectively. The sample preparation and clinkering
process used in this research was similar than thatused in a previous study [4]. The free lime content
and mineral phases content of both clinkers was
obtained using two different methods: SchlaferBukolowki method and the Bogue formula, theRietveld method based on the XRD analysis. The
results were given in Table 1.

(1)with b = 7.39m+0.66d.
Generally, the C-(A)-S-H gel formed in the Portland
cement –slag blends has a lower Ca/Si ratio and ahigher Al/Si ratio than pure Portland cement.
Although, the CEM III cement – slag has
demonstrated good economical – environmental –mechanical performance, but the worldwide slag
supply is relatively limited when compared to theannual demand for concrete for new construction andrepair [11]. For example, the slag production in
France has gradually decreased due to thedeindustrialization
of
the
steel
industry.
Consequently, all the potential materials must be
study in earnest to reduce the cement content as aw
well as the conventional mineral admixtures.
One potential material that has been
investigated in the recent years to substitute a cement
part in the concrete, is the sediment that generatefrom the dredging operation at ports and waterways
with approximately of 300 Mt of sediments dredged
every year in Europe. Several previous studies [4, 12,13] indicated that the dredged sediments could be
used as the raw material in the clinker production [4,5, 14], as the alternative material in the road
construction [15, 16]. In addition, the sediments
treated by the thermal treatment also present theTable 1 Free lime content and mineral phases content in both clinkers.
MethodClinker

C3S (wt.%)

C2S (wt.%)

C3A (wt.%)

C4AF(wt.%)

CaO(wt.%)

Clinker TM

61.63

15.76

9.14

9.18

1.60

665

free

GEOMATE – Bangkok, Thailand, 22-24 November 2022

SchlaferBukolowki method–Bogue formula
Rietveld method

Clinker
ASL

2%

63.98

12.23

10.06

9.25

1.65

Clinker TM

64.87

12.47

8.83

9.04

1.19

Clinker

66.23

12.90

9.61

8.64

1.48

2% ASL
From the results obtained, some conclusion can bemade as follows:• The clinkering process was complete and thecooling process did not lead the decompositionof C3S into C2S and CaOfree.
• The ASL sediment can be used to replace a partof traditional raw materials and it did not change
the formation of four typical mineral phases.
In next part of this research, a other sedimentcollected from the Noyelle – Sous –Lens region(referenced RS), in the North of France, France. The
RS sediment was firstly dried at 105 °C to constantmass, then ground to reach the particle size smaller
than 250 µm. Finally, it was calcined at 750 °C(named SF) in the flash furnace. The slag used in the
CEM III cement production was provided by
EQIOM Company. The chemical composition of
sediment before and after the flash calcination as
well as the slag obtained by XRF analysis waspresented in Table 2.

Table 2 Chemical composition of sediment before and after the flash calcination and slag obtained by XRF
analysis.
Oxide

CaO

SiO2

Al2O3

Fe2O3

SO3

Na2O

K2O

MgO

ZnO

P2O5

LOI

Total

RS

10.57

39.62

9.64

5.12

0.22

0.69

1.84

0.88

0.27

2.1

27.63

99.17

SF

11.21

49.71

12.00

5.33

0.18

0.74

2.25

1.12

0.31

2.26

13.70

99.49

Slag

40.98

34.27

10.95

0.27

0.43

0.17

0.49

6.56

ND

ND

4.7

99.40

(wt.%)

used in Eq.(1). It can modify the chemical reactionof slag with the portlandite described in the Eq. (1).
In next stage, three different CEM III cements were
produced blending the clinker, gypsum, slag and SF
sediment to achieve a SO3 content of 2.4 %wt. The
proportion of each component in the three CEM III
cements was shown in Table 3.

The XRF result indicates that: the flash calcination
did not modify the chemical composition of
sediment. In addition, the molar proportion ofprincipal oxides of slag as CaO : SiO2 : MgO : Al2O3= 6.8 : 5.3 : 1.5 : 1 which is lower than that of slag

Table 3 Proportion of each component in the three CEM III cements.
CementOPC TM

OPC 2%ASL(wt.%)(wt.%)

CEM III TM

56.55

CEM III 2%ASL
CEM III SF

1.1.

SLAG (wt.%)

GYP (wt.%)

SF (wt.%)

-

37.70

5.76

-

-

56.52

37.68

5.80

-

56.48

-

28.24

5.86

9.41

specific density of all the cements was measured
using the helium pycnometer Accupyc 1330 device
according to the NF EN 1097-7 standard. The
specific surface area was obtained by using the

Cement characterization

Numerous analyses were performed in order tocharacterize the properties of CEM III cement
anhydrous as well as the cement hydrated. The
666

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Blaine surface method [17] and the Brunauer –Emmett – Teller (BET surface area) method. The
initial setting time of all the cements was measured
using the Vicat needle according to the NF EN 196– 6 standard [18] after the determination of water
demand.
Cement pastes were prepared with a W/C ratio of
0.5 in order to study the hydrates formed from thecement hydration reaction. The XRD analysis using
the Bruker D2 Advance type device equipped with a
Cu anode, λ = 1.5406 Å and the angle acquired 2θ
from 5° to 80° was used to identify the hydrates after
28 days of curing.
The mechanical performance is an important
index to investigate the cement quality. In thisresearch, the compressive strength was measured onprismatic mortar samples (4 * 4 *16 cm3) that werepreviously prepared in accordance with standard NF
EN 196-1 [19]. The mechanical test was carried out
with an INSTRON 5500R (loading capacity of 150
KN) after being cured for 2, 7 and 28 days
respectively in the water. The test result is used todetermine the cement class according to the standard
NF EN 197-1 [10].
The porosity and the pore size distribution ofmortars were measured using the Mercury Intrusion
Porosimetry (MIP) technique (Micromertitics

Autopore IV device). Based on a previous research[20], the mortar specimen of 1 * 1 * 1 cm3 was firstly
immersed in a solvent solution for 3 days to stop thehydration, then dried at 40 °C to a constant mass
before the analysis.
RESULTS AND DISCUSSIONS
Cements characteristics
The physical properties of three cements were
presented in Table 4. The results indicate that the
CEM III 2%ASL possesses the properties very
similar to those of reference cement CEM III TM. It
confirms that a sediment substitution ratio of 2 wt%
in the raw meal did not modify the characteristics offinal cement. However, a slag replacement by the
flash calcined sediment ratio of 25 wt% changes all
the properties of cement. For example, the specificdensity of CEM III SF is lower than that of CEM III
TM. It can be explained by a less specific density ofSF sediment compared to the slag (2.65 g/cm3 for SF
sediment and 2.88 g/cm3 for the slag). The water
demand of CEM III SF as well as the initial setting
time of CEM III SF are higher than those of bothcements CEM III TM and CEM III 2% ASL. It canbe due to a higher BET specific surface area of CEM
III SF that leads to a more significant water demand.

Table 4 Physical characteristics of CEM III TM, CEM III 2%ASL and CEM III 10%SF cements
Properties

CEM III TM

CEM III 2%ASL

CEM III SF

Density (g/cm3)

3.02

3.02

2.98

Blaine specific surface (cm2/g)

3860

3840

3980

BET specific surface (m2/g)

1.37

1.36

1.90

Water demand (%)

33.33

33.33

35.00

Initial setting time (minutes)

255

255

280

Hydrates formed from the hydrated cement
pastesidentify by XRD analysis due to amorphous natureof C-S-H.
• The main difference in the XRD pattern of CEM
III SF cement paste compared to that of CEM III
TM and CEM III 2% ASL was the transformationof ettringite (9.1° 2θ) into monosulfate (9.8° 2θ).
This result can be explained by a higher content of
CaCO3 in the SF sediment which will react withthe remaining aluminates to form themonocarbonate (11.7° 2θ) according to the Eq (2)[22]. It prevents the reaction of remaining
aluminate with ettringite to form the monosulfate
in accordance to the Eq (3).

The result of XRD analysis performed on the
cement paste specimen after 28 days of hydration
was presented in Fig.1. From the XRD patterns,
some observations can be made as follows:• The XRD patterns were nearly identical for the
CEM III TM and CEM III 2% ASL cement pastes.
The main hydrates identified were Ettringite (Aft),
Monosulfate(AFm),
Monocarbonate,
Hydrotalcite and porlandite (CH). Although, C-SH is a main hydration product of Portland – slag
cement [21], but it’s very difficult in order to(2)

C3A + CC + 11H -> C4ACH11

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

2C3A + C6AS3H32 (Ettringite) + 4H -> 3C4ASH12 (Monosulfate)•
In addition, the quartz (SiO2) was also
identified in the XRD pattern of CEM III SF cement(3)

paste. It was from the presence of quartz in the SF
sediment, which was generally considered as inert.
mechanical performance compared to that of CEM
III TM and CEM III 2%ASL at early ages as well as
in the long term. Indeed, it can be explained by thehigher specific surface area of CEM III SF, which
improves the cement reactivity. This result also
demonstrates the interest of slag replacement by the
SF sediment in the mechanical development ofmortar. According to standard NF EN 197-1 [10], allthree cements are classified as CEM III 42.5 class.
Porosity of mortars
Fig. 3 presented the relationship between thecompressive strength and the total porosity obtainedby the MIP technique after 2 and 28 days of curing.
The result indicated that:• The evolution of microstructure as well ascompressive strength of mortar confected from the
CEM III 2%ASL was nearly identical to that ofmortar based on CEM III TM.
• As the mechanical test result, the mortar madefrom the CEM III SF also exhibited a lower
porosity compared to that of the CEM III TM andCEM III 2% ASL. This result can suggest that thehydrate amount produced from the CEM III SF
hydration is higher than that formed from the CEM
III TM and CEM III 2% ASL hydration as the law
described in a previous study [23] which
established the dependence of compressive
strength on the hydrate amount formed.

Fig. 1 XRD patterns of all the three cement pastes
with E/C = 0.5 after 28 days of curing. (1) : C3S +C2S, (2) : Ettringite, (3) : Monosulfate, (4) :Portlandite, (6) : Hydrotalcite, (7) Quartz, (8) :Monocarbonate.

Fig. 2 Compressive strength of mortars over time ofhydration.
Compressive strength of mortars
The compressive strength of all the mortars overtime of hydration was shown in Fig. 2. As theprevious analysis results, the mechanicalperformance of CEM III 2%ASL was nearly
identical to that of CEM III TM at early ages andlong term. This result confirms that the cement
quality made from the sediment substitution in the
raw meal was similar to that of cement made fromthe traditional raw materials. However, this result
also indicates that the CEM III SF has a higher

Fig. 3 : Relationship between the compressive
strength and the porosity measured by MIP
technique of all the mortars.
CONCLUSION
The research focus to investigate the feasibilityof dredged sediment recycling as a raw material in
the clinker production as well as SCMs for the slag
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

replacement in the CEM III cement. The main results
obtained were as follows:• The dredged sediment can replace a part of
traditional raw materials in the clinker production.
The properties of clinker and cement made fromthe sediment substitution were identical comparedto those of CEM III TM.
• The evolution of microstructure as well as thecompressive strength development of CEM III 2%
ASL mortar were nearly similar to those of CEM
III TM mortar.
• The slag replacement by the flash calcined
sediment improved the mechanical performanceand the microstructure of mortar at early ages andlong term compared to the CEM III TM and CEM
III 2% ASL cement.
• The flash calcined sediment incorporation
produced a higher amount of hydration comparedto that formed from the Portland – slag cement
only.
• All three cements are classified as CEM III 42.5
class according to standard NF EN 197-1.
AUTHOR STATEMENT
Mahfoud
BENZERZOUR:
Methodology,
Conceptualization,
Supervision,
Validation,
Writing-review & Editing, Resources.
Duc Chinh CHU: Conceptualization, Methodology,
Investigation, Visualization, Writing-original draft
Joelle KLEIB: Supervision, Validation, Writingreview & Editing.
Mouhamadou AMAR: Supervision, Validation,
Writing-review & Editing
Jaouad NADAH: Supervision, Validation.
Nor-Edine ABRIAK: Supervision, Validation,
Resources.
Funding
No applicable
DECLARATIONS OF COMPETING
INTERESTThe authors declare that they have no known
competing financial interests or personal
relationships that could have appeared to influence
the work reported in this paper.
AVAILABILITY OF DATA AND MATERIAL:
We confirm that all results are available in the
database. If necessary, please contact us at theaddress:
mahfoud.benzerzour@imt-nordeurope.fr
ACKNOWLEGMENTS
The authors wish to acknowledge the SEDICIM
project and the FEDER funds.

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12th Int. Conf. on Geotechnique, Construction Materials & Environment,
Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

MICROSCOPIC INVESTIGATION ON ATMOSPHERIC PARTICLESIN CHELYABINSK, SOUTH URAL REGION, RUSSIA
Olga V. Rakova1, Tatyana G. Krupnova1, Kirill A. Bondarenko 1, Svetlana V. Gavrilkina2 and Valerii N.
Udachin2
1

Institute of Natural Sciences and Mathematics, South Ural State University, Russia; 2Ilmen reserve, South
Urals Federal Research Center of Mineralogy and Geoecology of the Urals Branch of the Russian Academyof Sciences, Russia
ABSTRACT
Fine atmospheric particles have physiological toxicity affecting human health. This study investigates thecompositional and morphological properties of the particulate matter smaller than 10 μm (PM10) collected in thewinter season from five different monitoring stations in Chelyabinsk, Russia. The concentration of 20 elements(Al, B, C, Ca, Cl, Cr, Cu, Fe, K, Mg, Mn, N, Na, Ni, O, Pb, S, Si, Ti and Zn) were determined by scanning electron
microscopy-energy dispersive X-ray spectroscopy (SEM-EDS). In order to determine the possible sources ofemissions of atmospheric particulate matter, a principal component analysis (PCA) was performed. The results
revealed the presence atmospheric PM10 such as aluminosilicates, calcium particles, sulphates, and metallic
particles differentiated on the basis of the chemical composition and morphological parameters. Fe-Mg-rich fineparticles and aluminosilicates were observed at all sites and showed a similar nature due to their mainly natural
origin. The metal-containing particles showed a more heterogeneous composition mainly related to humanactivity. The major metal particles were Fe and Zn, Pb, Cu, Mn, Ni and Cr, which were likely emitted from various
industries and coal-fired power plants at high temperatures. S-rich particles originated from incomplete diesel
combustion. Sulfate PM10 with metal inclusions contain Mn, Ti, Zn, Cu, and Cr. The higher content of potentially
toxic elements in traffic-related S-rich particles compare with industrial-related PM and were the result of thesolubilization of metals in airborne particles and increased their toxicity.
Keywords: PM10, scanning electron microscopy, individual characterization, elemental composition,
anthropogenic sources
INTRODUCTION

metallurgy-up to 79% PM2.5 and up to 84% PM10 [3].
Another major source of air pollution in the urban
environment is vehicular transport and it is important
to identify the sources of PM air pollution.
Various factors contribute to the formation of
airborne pollutants, the spatiotemporal variability ofsuch pollutants is expected to be significant
depending on the processes of pollutant formationand local meteorological conditions. The road dust
and PM collected from the industrial city area isenriched with trace elements [4-6]. It seems
reasonable, therefore, to assess potential health risks
associated with the exposure of inhabitants of large
cities.
Scanning electron microscopy analysis is a
useful tool for the characterization of aerosol samples
at the level of individual particles [7]. Scanningelectron microscopy-energy dispersive X-ray
spectroscopy (SEM-EDS) examines particles which
are too small to be studied with conventional optical
microscopes and gives information on the nature andorigin of the particles.
Using SEM-EDS, this work identifies andcharacterizes the morphology and chemicalcomposition of particles present in samples of PM10
collected from the Chelyabinsk urban area. With theControl over air pollution in Russian cities isincreasing every year, as residents are worried about
the increase in haze or smog driven by elevations inatmospheric particulate matter (PM), especiallyparticles with aerodynamic diameters <2.5 μm(PM2.5) and 10 μm (PM10). By entering the body andpenetrating deep into the respiratory tract, small
airborne particles cause significant harm to health.
In 2010, Russia set maximum permissible
concentrations of PM2.5 and PM10 (35 and 60 μg/m3(24-hour mean concentrations), 25 and 40 μg/m3(annual mean concentrations), respectively) [1].
Since then, the number of automatic air monitoring
posts has grown in Russian cities. However, theseconcentrations are higher than PM2.5 and PM10
concentrations recommended by WHO (25 and 50
μg/m3 (24-hour mean concentrations), 10 and
20 μg/m3 (annual mean concentrations), respectively[2].
Industry is one of the main sources of PM
pollution in Russian industrial cities. The presence offine particles in the emissions of various industries
has been confirmed: in mechanical engineering-up to
13% PM2.5 and up to 40% PM10; in ferrous
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

data obtained from the characterization, a principal
component analysis (PCA) was carried out, fromwhich it was possible to identify some of the potential
emitting sources of PM10 in the region.

The samples were characterized by SEM-EDS.
SEM analysis was performed on a Jeol JSM-7001F
Scanning Electron Microscopy Complex. SEM-EDS
is a non-destructive analytical method for surface
elemental analysis, with a potential detection limit of
0.1–0.5 wt.% for most elements.

MATERIALS AND METHODS
Study area, data, and sample collection
The sampling stations were located in
Chelyabinsk (55°09′14″°N, 61°25′44″ E, Elevation:
219 meters) (Fig. 1). Chelyabinsk is located on theeastern slope of the Southern Urals. The city has
a humid continental climate. The averagetemperature in January is -14°C/6.6 °F. July has arelatively cool average of 19°C/66.7 °F, while theannual average is 3°C/37.8 °F. The results of longterm observations show that the prevailing winds arefrom the south, southwest, west, and northwest. In
Chelyabinsk, the lowest frequency is recorded
for east, southeast, and northeast winds. Wind
directions vary slightly throughout the year. The
population of Chelyabinsk was 1,130,132 in 2010.
The city has a land area of roughly 530 km2.
Chelyabinsk is one of the most air-polluted cities
in Russia. According to a report by the Ministry of
Ecology of the Chelyabinsk Region, Chelyabinsk
showed heavy air pollution, having about 110 days
per year when pollution is above the national
standards [8].
The five sampling sites were located in urban
residential areas and the sampling locations areillustrated in Fig. 1. PM10 sample collection was
performed using cascade impactor samplers (Fig. 2).
Impactors were operated at a flow rate of 16 L min−1
for 24 hours.

Fig. 1

Fig. 2

The aerosol cascade impactor sampler

For SEM-EDS analyses, PM10 samples were
mounted with double-sided tape on an aluminum
SEM stub for analysis. With the help of a vacuum
coating unit (Gold Sputter Coater, SPI-MODULE,US), a very thin layer of gold was deposited on thesurface of each sample in order to achieve better
conductivity and less electron charge. 300 particles
were manually analyzed 60 particles selected
randomly from each of the 5 samples.
RESULTS AND DISCUSSION
Overall chemical composition of PM 10
According to the characteristic SEM-EDX
analysis, atmospheric particles collected in winterfrom Chelyabinsk urban area contained 20 elements(Al, B, C, Ca, Cl, Cr, Cu, Fe, K, Mg, Mn, N, Na, Ni,O, Pb, S, Si, Ti and Zn). The single particles weregrouped and labeled as: Fe-containing Fe-Mgcontaining, aluminosilicates, particles containing andrich in S, particles containing С and N, metal particles
including Fe and Zn, Pb, Cu, Mn, Ni and Cr.
The most abundant elements detected in PM10
were Fe and Mg which were present in 80–100% of
the particles. It could be assumed that Fe-Mgcomplexes were included in the composition ofnatural aluminosilicates. Chelyabinsk stands on
sedimentary rocks and granite, typical for the Urals,consisting of oxides Al2O3 (14–15%), SiO2 (70–72%), Fe2O3 (0.7–1.1%), MgO (0.6–1.1%) [9]. Such
particles were found at all sites.
Characteristic PM10 collected from Site 1
Fig. 3a shows the predominant morphologies of
C-rich particles collected at Site 1. They were regularspherical and spheroidal shapes, with somepresenting surface defects such as porosity.

The study area, the location of the fivesampling sites and prevailing winds

Scanning electron microscopy

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 3 a) SEM micrograph (magnification 10,000X) C-rich particles collected in Site 1 and their EDX spectrumb) SEM micrograph (magnification 5,000X) Ti-rich aluminosilicate particles collected in Site 1 and their EDX
spectrum c) SEM micrograph (magnification 1,000X) S-rich particles collected in Site 2 and their EDX spectrumd) SEM micrograph (magnification 5,000X) Fe-rich aluminosilicate particles collected in Site 5 and their EDX
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

The spheres contained sulfur and nitrogen oxides.
The particle size of this type varied in the range of 1–
5 μm. These particles are observed in cities with alarge vehicular traffic and are associated with exhaustemissions from automobiles using gasoline or diesel
combustibles [10].
Particles in the form of filaments containing
aluminosilicate particles enriched with oxides oftitanium and other metals were also found (Fig. 3b).
They were characteristic of the rock wool production
plant located near Site 1.

The soot particles had a spongy aspect andformed spheres of a size in the order of nanometers;
they are united in chains, forming a mass ofamorphous agglomerates, which give rise to larger
particles. They had the structural complexity of theseparticles, and their composition is dominated bycarbon (C) and oxygen (O). The soot particles
appeared as large clusters of small spheres united bychains and were found to be similar to those in studies
of urban regions in Brazil [16]. Such particles are
known to be produced by anthropogenic sources suchas vehicular emissions, and coal burning. At Site 3 thesource of the soot is the heavy traffic of trucks thatrun on a highway through the city. At Site 4 the sourceof soot is the burning of coal at a thermal power plant.

Characteristic PM10 collected from Site 2
Site 2 had the largest number of S-rich particles,with more than half of them containing more than 3 %sulfur. Sulfate PM10 is commonly identified as
markers of secondary aerosols related to longdistance transport [11]. The majority of the sulfate
particles had one or more potentially toxic metal
inclusions. Figure 3c shows typical rod-shaped,
crystalline, and spherical particles. The size of metalcontaining S-rich particles was about 1 µm. Most
metal particles were classified as Fe-rich (e.g.,
hematite), Zn-rich (e.g., zinc sulfate and zinc oxide),
Pb-rich (e.g., anglesite), Mn-, Ti-, Cu-, Cr- or W-rich,
which were likely emitted from road traffic (exhaust
and tire, brake, car body or road surface abrasions)[12]. Site 2 had the highest traffic volume. Zn, Cu,
and Ti are often related to brake-pad erosion; Fe, Cu,
Pb, and Zn from brake-disc wear. To identify PMfrom tire abrasion, Cd, Cu, Pb, and Zn can bemonitored. Metals such as Fe and Zn are also linked
to the corrosion of car-body parts. Road dust androadside soil often contain metals, including Pb, Cu,
Cd, and Zn, indicative of contamination by roadtraffic emissions and the abrasion of road surfaces. It
has been shown that sulfates from aqueous
SO2 (S(IV)) oxidation catalyzed by transition metals
are an important atmospheric process during winter,an alternative to the photochemical pathway that is
highly unlikely because of the ultralow
O3 concentrations [13]. Metal catalysis can promote
the conversion of SO2 to sulfates in fog droplets [14].
The internal mixing of metals and acidic constituentssolubilize metals and modify metal inclusion shapes.
The solubilization of metals in airborne particles canincrease their toxicity in the particles [15].

Characteristic PM10 collected from Site 5
Fig. 3d shows spherical Fe-rich PM10 found at
Site 5. These particles consisted of iron oxide. They
could be steel f dust emitted by the metallurgicalenterprises and placed in numerous slag dumps. Theirsize ranges between 0.5 and 2 μm and they present apeculiar morphology; they are characterized by aperfect sphericity indicating their smelting iron origin
or metallurgical activities in general.
Sources implied by PCA
To increase the plausibility of the found sourcesof aerosol particles, a PCA was performed by the
Varimax Rotated Factor Matrix method. Total 12
principle components (PCs) were extracted(eigenvalue>1) as that explained the 72% of thevariance of the data (Table 1). A large number of PCs
is explained by the large number of different sources
because various industrial enterprises are located in
Chelyabinsk. Atmospheric particles can interact witheach other. PC1 explained 11 % variance and wasloaded with C, O, Na, Si, S, K, Cl, Fe. It indicated that
PM10 contained carbonates, silicates and chlorides of
sodium, potassium and iron which were mainly fromnatural sources. The same can be tell about PC6, PC8
and PC9 explained 6, 4 and 4% of variance,
respectively. They were loaded with aluminosilicates,
chlorides and carbonates indicating also natural
sources. PC2 titled industrial emission (9% of thetotal variance) exhibits high loadings of Fe and may
be associated with steel production activities. PC4,PC5 and PC10 explains 7, 7 and 4 % of the total
variance, respectively, and have high loadings of В,С, O and Fe. They mainly arose from anthropogenic
sources such as burning of coal (B, C and O containedsoot PM corresponding to PC4 and PC5) orvarious transportation fuels (PC10 which has
additional Fe loading).

Characteristic PM10 collected from Site 3 and 4
At Sites 3 and 4, the morphologicalcharacterization results allowed us to classify most of
the observed particles as soot due to the presence oflarge amounts of carbon in the collected sample; theelemental composition spectra were only qualitativeand semiquantitative.
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Table 1.
Results of principal component analysis (varimax rotation with kaiser normalization) at the five study
sites, only factor loadings ≥0.25 are shown, the highest loadings are in bold
Element
Al
B
C
Ca
Cl
Cr
Cu
Fe
K
Mg
Mn
N
Na
Ni
O
Pb
S
Si
Ti
Zn
Eigenvalue
Variance
contribution rate(%)
Cumulative
variance
contribution rate
(%)

PC1

PC2

PC3

-0.50

PC4

PC5

0.91
-0.28

0.33
-0.70

PC6
0.91

PC7

PC8

-0.53

PC9
0.30

PC10

PC11

PC12

0.30

-0.47

-0.43

-0.55

0.35

0.27
0.29
-0.37

0.94
0.56

-0.25
0.25

-0.32

0.96

-0.47

-0.26

0.60
0.80
0.89
0.27

-0.30

3.1
11

2.5
9

2.1
8

2.0
7

1.9
7

1.6
6

0.37
1.4
5

1.2
4

1.2
4

1.1
4

1.0
4

-0.31
0.28
-0.45
0.29
-0.45
-0.33
0.82
0.31
1.0
4

11

20

28

35

42

47

52

56

61

64

68

72

-0.28

-0.33

-0.42
-0.26

0.89

0.67

PC7 was responsible for 5% of the total varianceincludes C, O, Na, Mg, Si, S, Fe and Zn and may belinked to slag dumps dust. PC11 was responsible for
4% of the total variance and high loadings of C, O, Si,
S, Fe and Mn. This source was characterized as
ferromanganese production. PC3 and PC12 were
responsible for 8 and 4 % of the total variancerespectively. They included S and O, besides PC12
was also represented by Na, Si, C, Cr, Ti, Fe, Cu, Ni,
Pb and Zn. They were traffic-related S-rich particles.

-0.34

-0.53

-0.31

-0.26
-0.26

-0.27

-0.34

0.35

-0.41

-0.36
-0.31
-0.25

-0.98

particles included potentially toxic elements fromtraffic related sources. Additioanal it should be notedthat PM10 nature vary depending on the weather andin future studies it would be useful to compare theobtained results with, for example, a summer
sampling.
ACKNOWLEDGMENTS
The research was found by the Russian Science
Foundation (RSF), project number 22-17-20006(https://rscf.ru/en/project/22-17-20006/)
andChelyabinsk region.
The authors wish to thank Ekaterina Yudina for
SEM measurements.

CONCLUSIONSThis work studied the morphological andchemical characteristics of metal-containing
atmospheric particles collected in winter season from
5 sites in the Chelyabinsk urban area. Different
magnifications have been selected for SEM-EDS
analysis to classify the size range of the particles.
SEM-EDS allowed us to provide information about
the sources of PM and to highlight differences andsimilitudes among the sites. Single particle studies
gave information for understanding the formation of
PM and their potential toxic characteristics. The fivesites are mainly characterized by traffic related
pollution and metallurgical activity. Metal particles
were mainly characterized by iron. S-enriched

REFERENCES[1] GN 2.1.6.2604-10, 2010. Hygienic norms
Supplement No. 8 to GN 2.1.6.1338-03.
Maximum permissible concentrations (MPC) of
pollutants in atmospheric air of populated areas.
(Rus).
[2] World Health Organization, Occupational andEnvironmental Health (2005). "WHO Air quality
guidelines for particulate matter, ozone, nitrogendioxide and sulfur dioxide: Global update 2005:
Summary of Risk Assessment".
[3] Zagorodnov S.Yu., May I.V. and Kokoulina A.A.,
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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fine-disperse particles (PM2.5 and PM10) in
atmospheric air of a large industrial region: issues
related to monitoring and standardIzstion ofsuspended particles in industrial emissions,Hygiene & Sanitation, No.98, 2019, pp.142-147.
[4] Krupnova T.G., Rakova O.V., Gavrilkina S.V.,
Antoshkina E.G., Baranov E.O. and Yakimova
O.N., Road dust trace elements contamination,
sources, dispersed composition, and human healthrisk in Chelyabinsk, Russia, Chemosphere, 2020,
p.261.
[5] Krupnova T.G., Rakova O.V., Bondarenko K.A.,
Potgieter-Vermaak S. and Godoi R.H.M.,
Elemental composition of PM2.5 and PM10 and
health risks assessment in the industrial districtsof chelyabinsk, south ural region, Russia
International Journal of Environmental Researchand Public Health, Vol. 18, No.23, 2021, p.12354.
[6] Krupnova T.G., Rakova O.V., Mashkova I.V.,
Artyukov E.V. and Vlasov N.E., Health risk
assessment of metal(loid)s exposure via indoor
dust from urban area in Chelyabinsk, Russia,
International Journal of GEOMATE, Vol.16,
No.55, 2019, pp.1–7.
[7] Ramirez-Leal R., Valle-Martinez M. and CruzCampas M., Chemical and Morphological Studyof PM10 Analysed by SEM-EDS, OJAP, No.3,
2014, pp.121-129.
[8] Krupnova T.G., Rakova O.V., Plaksina A.L.,
Gavrilkina S.V., Baranov E.O. and Abramyan
A.D., Short Communication: Effect of urban
greening and land use on air pollution in
Chelyabinsk, Russia, Biodiversitas, no.21, 2020,
pp.2716-2720.
[9] Baranovsky A.G. Analysis of the variability of thecomposition, structure and physical andmechanical properties of eluvial clay soils and thepossibility of their prediction: on the example of
Chelyabinsk. Dissertation for the degree of Ph.D.,
Moscow, 2017.
[10] González L.T., Longoria Rodríguez F.E.,
Sánchez-Domínguez M., Leyva-Porras C., Silva-

Vidaurri L.G., Acuna-Askar K., Kharisov, B.I.,
and Alfaro Barbosa J.M., Chemical andmorphological characterization of TSP and
PM2.5 by SEM-EDS, XPS and XRD collected in
the metropolitan area of Monterrey, Mexico,
Atmospheric Environment, No.143, 2016,
pp.249-260.
[11] Cozzi F., Pellegrini I., Adami G., Reisenhofer E.,
Bovenzi M. and Barbieri P., Sulphur speciation ofPM10 samples by XANES spectroscopy, Central
European Journal of Chemistry, Vol.7, No.3,
2009, pp.395-401.
[12] Penkała M., Ogrodnik P. and Rogula-Kozłowska
W., Particulate Matter from the Road Surface
Abrasion as a Problem of Non-Exhaust Emission
Control, Environments, Vol. 5, No.1, 2018, p.9.
[13] Li W., Zhou S., Wang X., Xu Z., Yuan C., Yu
Ya., Zhang Q. and Wang W., Integrated
evaluation of aerosols from regional brown hazes
over northern China in winter: Concentrations,sources, transformation, and mixing states,
Journal of Geophysical Research, No.116, 2001,
D09301.
[14] Desboeufs K.V., Sofikitis A., Losno R., Colin
J.L. and Ausset P., Dissolution and solubility oftrace metals from natural and anthropogenic
aerosol particulate matter, Chemosphere, Vol.58,
No.2, 2005, pp.195–203.
[15] Li W., Wang T., Zhou S., Lee S., Huang Y., Gao
Y. and Wang W., Microscopic Observation ofMetal-Containing Particles from ChineseContinental Outflow Observed from a NonIndustrial Site, Environmental Science andTechnology, Vol.47, No.16, 2013, pp.9124–9131.
[16] Ferreira T.M., Forti M.C. and Alcaide R.L.M.,
Inhalable particulate matter characterization in a
medium-sized urban region in Brazil (São José
dos Campos Town) - Part I: Morphology,
Química Nova, Vol.36, No.9, 2013, pp.1380–
1387.

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Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

СAN TREES HELP REDUCE LEAD IN URBAN AIR? А CASE STUDY
OF GREENING IN A RUSSIAN INDUSTRIAL CITY
Tatyana G. Krupnova1, Olga V. Rakova1, Susanna V. Berentseva 1, Svetlana V. Gavrilkina2 and Valerii N.
Udachin2
1

Institute of Natural Sciences and Mathematics, South Ural State University, Russia; 2Ilmen reserve, South
Urals Federal Research Center of Mineralogy and Geoecology of the Urals Branch of the Russian Academyof Sciences, Russia
ABSTRACT
Urban trees can be effectively used as a biomonitor and phytoremediator of air pollution, but not all species
are equally capable of filtering the air. Lead-containing PM2.5 has raised severe public health concerns in industrial
cities. PM2.5 pollution episodes occur in Chelyabinsk (Russia) because of a zinc production plant located in theurban area. During zinc production from lead-zinc concentrates, the environment around the enterprise may
become contaminated with lead. We measured the amount of Pb in PM2.5 and Pb accumulated by the leavesof Betula pubescens, Populus nigra L., Acer negundo L. and Pinus silvestris L. from the 32 sites. The lowest valueof Spearman’s coefficient was found for pine needles. The stomata of the needles were clogged with anti-icing
agents that had accumulated over the winter. Pb found in the tree leaves correlated significantly with that found in
the PM2.5. The correlations of Pb concentration were maximal for birch tree leaves. The leaves of all three species
studied − B. pubescens, P. nigra, A. negundo − can be used as phytoremediators of Pb air pollution in the
Chelyabinsk urban area.
Keywords: PM2.5, air pollution, lead, vegetation, phytoremediation
the sustainability of the urban space is the use ofurban greening. Trees can give a good spatial
coverage and their leaves are excellent air pollutantscollectors, they are frequently used for air quality
assessments. According to the deposition of theatmospheric particulate matter (PM), especiallyparticles with aerodynamic diameters <2.5 μm(PM2.5), on the leaf surface, metals cations are boundby extracellular ligands. The number of studies rosein this field, but the species responded differently tothe atmospheric pollution. This is connected with theability of trees to capture heavy-metal, which has aserious impact on human health [3]. In the present
paper we studied the role of tree leaves and needles in
the remove of lead-containing PM2.5 from theatmosphere. The foliar transfer of metals andmetalloids in plant leaves and needles as a way ofurban air purification remain under-studied. This
study focused on the foliar uptake of metals fromenriched PM2.5 resulting from the different urban
anthropogenic emissions including emissions from azinc production plant located in Chelyabinsk. We
chose lead (Pb) as a model element. Worldwide lead
pollution is on the rise; lead was responsible 900 000
premature deaths [4]. During zinc production fromlead-zinc concentrates, the environment around theenterprise may become contaminated with Pb. Pb isknown to have many toxic effects in humans and isranked second in the priority list of hazardous
substances compiled by the US Environmental
Protection Agency (USEPA) [5]. Lead exposure may
cause changes in the neurologic system, leading toloss of neurological function [6].

INTRODUCTION
The world’s population is expected to increase by
2 billion by 2050 − from 7.7 billion to 9.7 billion −and by the end of the century, despite continuing
declines in fertility, it will peak at nearly 11 billion.
Until 2009, more people lived in rural areas than inurban areas. Today, cities and towns are home toabout 55 percent of the world’s population, withurbanization projected to reach almost 70 percent by
2050 [1].
Russian cities are traditionally well greened.
Chelyabinsk is a typical Russian industrial city withover a million inhabitants. Its urban area ischaracterized by the concentration in a relatively
small space of a large number of ferrous and nonferrous metallurgical industries, a developed
transport network, and rapid growth in urban
construction. All this has led to residents of the city
worrying about “black sky regimes” and theuncontrolled removal of green spaces. Despite theclear contribution of urban greening to improving
urban quality of life and the existence of demands forurban sustainability, there is a methodological gap inevaluating the effectiveness of urban greening in
Russian industrial cities. We used physical andchemical analysis to search for solutions andrecommendations for the modern organization ofurban greening for city authorities. It is also useful because urban greenery shows positive associations
with human health and mortality [2]. Green spaces
play an important role in preserving the health of the
population in cities. One of the modern solutions for677

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 1

Map of Chelyabinsk and location of the research sites

hydrofluoric acid and 6 mL nitric acid), thenmicrowave digested (MWS 4 Speedwave, Berghof,
Germany) for 2 h after the setup routine for Pb
analysis [7]. After digestion, the extracts were ﬁlteredusing a blue-ribbon filter, and distilled water wasadded so that the total volume was 25 ml. Pb wasanalysed using Perkin Elmer ELAN 9000 Inductively
Coupled Plasma Mass Spectrometry (ICP-MS).
Certiﬁed reference material GSO 10413-2014 CO
cespitose and podsolic srednesuglinisty soil (I BEND
Rosselkhozakademiya’s VNIIA, Russia) was used for
quality control. The range of recovery efficiency was
119%. The Pb limit of detection was estimatedconsidering the standard deviation of 10 blankmeasurements (three times the standard deviation wasused). The limit of detection of atmosphericconcentrations obtained for Pb in PM2.5 samples was
4.2 ng m−3.

The Pb-containing PM present on and insideleaves were investigated by scanning electron
microscopy coupled with energy dispersive X-ray
microanalysis (SEM-EDX).
The study addresses the following two researchquestions:1) Can trees help reduce lead in Chelyabinsk
urban air?
2) What tree species are the best Pb accumulators?
MATERIALS AND METHODS
Study area
The present research studies Chelyabinsk urban
greening. Chelyabinsk is the capital of the South
Urals region. Chelyabinsk is one of the largest
industrial cities in Russia, with a population of morethan 1 million people. The city has an unfavourable
situation with air pollution and is included in the
project "Clean Air".
We chose four types of green area (Fig. 1): urban
forest (two sites), the greening of roads (18 sites),
established city parks (eight sites) and public
residential green spaces in new districts (four sites).

Selection of tree types and sampling of leaves andneedles
Objects of research were tree species widely
found in Chelyabinsk: Betula pubescens, Populus
nigra L., Acer negundo L and Pinus silvestris. L.
Selected tree species are most widely used forlandscaping the city. We collected tree leaves andpine needles from 2–3 trees at each site, 20 leaves or
30–40 pine needles from each tree. We collected
samples from the maximum number of branches
available evenly around the tree from the lower partof the crown. Visibly damaged leaves or pine needles
were not used for the analysis. The details of sample

Sampling and analysis of PM2.5
The PM2.5 samples were collected on 37 mm
polycarbonate filters using low volume cascade
impactor samplers operated at a flow rate of
16 l· min−1 at a height of 2 m (72 h of sampling).
These PM2.5 samples were collected at each station in
July 2022. The loaded PM2.5 filters were placed in a
PTFE digestion vessel for acid treatment (2 ml678

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 1.
Atmospheric concentrations (ng·m−3)
obtained for Pb in PM2.5 samples and concentration(mg·kg−1 dw) obtained for Pb in pine needles and tree
leaves collected in the Chelyabinsk urban area(n = 32)

collection and preparing for analysis were given in
our previous work [8].
We washed the leaves and pine needles inrunning distilled water and air-dried them. All
samples (leaf powder and pine needles) were digested
separately into 10 ml of aqua-regia solution (HNO3:
HCl v/v 3:1) on a hot plate at 120°C for 2 h. Aftercooling, the solution was filtered through blue ribbon
filter paper and deionized water was added to makeup a volume of 50 ml. The solutions were analysed
for Pb content by mass spectrometry with inductively
coupled plasma ICP-MS using Perkin Elmer ELAN
9000. The analyses were performed in triplicate. All
reagents were of analytical grade. The Russian
national state standard samples (Vinogradov Instituteof Geochemistry SB RAS, Russia) of birch leaves(GSO 8923-2007) and a mixture of herbs (GSO 89222007) were used to check the accuracy of the Pb
content in the leaves and needles. The ranges ofrecovery efficiency were 98–105 %. The limit ofdetection of concentrations obtained for Pb in leaves
was 0.1 mg kg−1 of dry weight (dw).
Small leaf strips or needles (washed withchloroform for top-part epicuticular wax removal
before SEM analysis) were cut out from the area
between the margin and midrib of leaves. Eachsample was fixed on a metallic pin stub, using
conductive double-sided tape, and left to dry at room
temperature for at least three days. This dehydrationis required to avoid sample damage, as water would
evaporate too fast at low pressure. Before themicroscopic analysis, samples were vacuum-coatedwith Au to make them conductive in order to preventa charge build-up. SEM analyses were performed on
a Jeol JSM-7001F Scanning Electron Microscopy
Complex. Photographs were taken of randomly
chosen spots at 500× magnification, at 5 kV.

Environmental
Max
Mini
Mean
SD
object
PM2.5
35.0
5.0
18.0
0.5
Pb in pine needles
6.10
0.10
2.80
0.05
Pb in poplar leaves
12.30
0.20
5.50
0.05
Pb in maple leaves
9.50
0.20
4.20
0.05
Pb in birch leaves
15.0
0.3
7.1
0.1
Pb in birch leaves
15.0
0.3
7.1
0.1
Note: SD- Standard deviation. It was the amount of variation ordispersion of a values set.

It is known that the foliar pathway for metal
uptake may be predominantly the soil–root pathway
in urban and industrial environments [8]. In thepresent study, some PM are ultrafine, i.e., submicronic and nanoparticles were observed by SEMEDX in leaf surfaces.
The mean concentrations of Pb in different tree
leaves, ranging from 2.8-7.1 mg·kg−1 dw. It is wellknown that there is a well-recognized definition of"phytoremediator" of Pb from soils which the Pb
concentration in plant leaves is higher than 1000
mg·kg−1 dw [9]. In this study, the deposition of leadfrom air is studied, so these approaches are notapplicable. Rachmadiarti et al. [10] showed that
Wedelia triloba (L.) Hitchc. and Syzigium sp. grownon the main roads absorbed 0.146-0.288 μg·g−1 of Pb
by leaf surface area and Pb content of both the plants
was within the normal range, as the accepted Pb
content for many plant species range from 1.03.5 μg·g-1. In our study Pb content in tree leaves washigher than for many plant species.
The Pb content detected in leaf samples(mg·kg−1 dw.) from urban trees in Chelyabinsk
differed by tree species and location (Fig. 2). The
highest Pb content was found in birch leaves collected
near the zinc production plant, followed by the leavesof same species collected from the urban area near the
steel plant. Poplar leaves collected from the Kashtak
forest located in the north of the city showed thelowest Pb content, but samples of the same species
collected from locations with similarly busy traffic
showed intermediate values of Pb content.
Foliar uptake is likely favoured by these PM
morphologies. Comparing the capacity of the leaves
to adsorb Pb particles across the deciduous tree
species investigated, we can state that the mostefficient species was the birch followed by the maple
and then the poplar (see Table 1). Birch tree(accumulated the most PM compared to other tree
species because its leaves contain high amounts ofcuticular wax.

Data processing
Microsoft Excel 2013 and SPSS 24.0 software
were used to organize and analyse the data. The
Kolmogorov-Smirnov test was used to test datanormality. The relationship between the data wasdetermined using Spearmen’s test. MapInfo Pro 2019was used to build the spatial distribution maps of Pb
concentrations.
RESULTS AND DISCUSSION
Lead concentrations in PM2.5, pine needles andtree leaves
The concentrations of Pb were identified in
PM2.5, pine needles, and tree leaves of three species.
The Pb concentrations are shown in Table 1.

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Fig. 2 Spatial distribution maps of the Pb concentrations in (a) pine needles, (b) poplar, (c) maple, and (d) birch
leaves, and (e) Pb in PM2.5 from all sampling sites. The red and yellow colors on each small map represent relatively
high concentration of Pb, while the green and blue colors represent relatively low concentrations, respectively.

Our results confirm Hrotko et al. [11] who
reported large differences between tree species inheavy metal retaining capacity in foliar dust. Schreck
et al. [12] reported that many factors may affect the
air-vegetation transfer including plant characteristics
such as functional type, leaf surface area, cuticular
structure, and leaf longevity. Rough and hairy leaves
accumulate significantly more lead (e.g. birch leaves)than smooth leaves (e.g. poplar leaves). Maple leaves
occupy an intermediate position, they have a larger
leaf surface than birch, but more rare and narrow
stomata. Compared to poplar, maple has a rougher
surface that better holds particles.
It is known that the foliar pathway for metal
uptake may be predominantly the soil–root pathway
in urban and industrial environments [8]. In thepresent study, some PM are ultrafine, i.e., submicronic and nanoparticles were observed by SEMEDX in leaf surfaces.
Morphological factors such as stomatal index,
trichome density, and leaf length affect the efficiencyof dust collection by plants. These hypotheses of
surface structure were confirmed by scanningelectron microscopy (SEM) images of plant surfaces(Fig. 3).
Fig. 3a shows that the lowest concentration oflead is recorded in pine needles. This is because theabsorption of any substances from the surrounding air
by pine needles is significantly reduced due toclogging of the stomata with crystals of anti-icing
agents that accumulate during the winter. This is ingood agreement with the images presented in Fig. 3.

The stomata of needles taken further from majorroads are less clogged (Fig. 3d).
Correlation of lead in РМ2.5 with lead content inleaves and needles
We studied the correlations of Pb in PM2.5 with Pb
concentrations in pine needles and poplar, maple, andbirch leaves. Table 2 presents Spearman’s correlation
coefficients.
Table 2.
Spearman’s correlation coefficients
between lead in РМ2.5 and lead content in leaves andneedles (n=32)
Environmental factor/ Spearman’s coefficient
objectvalue
Pb in pine needles
0.31
Pb in poplar leaves
0.58*
Pb in maple leaves
0.63*
Pb in birch leaves
0.71*
Note: * Correlation is significant at 0.001 (two-tailed)

The lowest value of Spearman’s coefficient was
found for pine needles with Pb in PM2.5. The reason
for the absence of a significant correlation was factthat the stomata of the needles were clogged with
anti-icing agents that had accumulated over the
winter. The correlation coefficients for the leaves fallwithin the range of 0.58–0.71. Table 2 indicates thatPb concentrations in PM2.5 correlate significantlywith those found in the tree leaves at the 0.001 levelof significance.

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GEOMATE – Bangkok, Thailand, 22-24 November 2022

Fig. 3SEM-images of plant surfaces: (a) salt crystal in the stoma of P. silvestris; (b) stomata of pine needles
clogged with reagents; (c) spectrum of the composition of salt crystals in stomata of P. silvestris; (d) the state ofstomata of pine needles collected at a distance from the roads.
The correlation coefficients of Pb concentrationin leaves and in PM2.5 was the highest for birch trees
because the birch leaf surface has hairs, and it mightfavor its metal tolerance and adsorption of fineatmospheric PM2.5. Specific leaf surface areas could
explain differences in rates of uptake between tree
species. Maple leaves exhibit a dissected shape of its
leaves with a folded structure. This leads to theformation of more reliable correlations for maple
compared to poplar. However, the correlation is alsohigh for poplar because the leaves have a high wax
content [13].

protected, and improved. The local city authorities of
Chelyabinsk are not making enough efforts toincrease the urban green cover. It should also be notedthat in the last two years there has been a negative
trend in the era of universal urbanization − population
decline. Among other things, it can be assumed that it
is associated with poor ecology and poor quality oflife in the city. Therefore, work towards effective
greening of the city could improve the situation. The
government and the public should be aware of thescope and function of green spaces and protect themby collecting more data on green cover to enforce
laws, increase the role of public organizations andenterprises, and use modern landscaping methods.
Such approaches are urgently needed before more
green spaces disappear from the cityscape. The lossof green cover may exacerbate the city’senvironmental problems, which are already chronic,
negatively affecting the image and viability of
Chelyabinsk.

CONCLUSIONSThis study showed that tree leaves play animportant role in the absorption of Pb-containing
PM2.5 from the air. Birch leaves have the best
phytoremediation properties of Pb-rich PM2.5 aircontamination. Despite the summer period ofsampling, the stomata of pine needles taken on theroadside were clogged with anti-icing agents, which
prevents the pines from fulfilling theirphytoremediation effects. It shows critical need toreduce use of road salt in winter.
In general, urban greening has various functions.
It not only forms the urban-ecological framework of
the city, but also regulates the urban microclimate,purifying the air in an industrial city, and having anaesthetic, educational, and cultural-historicalfunction. Urban greenery needs to be maintained,

ACKNOWLEDGMENTS
The research was found by the Russian Science
Foundation (RSF), project number 22-17-20006(https://rscf.ru/en/project/22-17-20006/)
andChelyabinsk region.

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REFERENCES[1] United Nations. Shifting Demographics, 2020.
Available: https://www.un.org/en/un75/shiftingdemographics (accessed on 01 October 2022).
[2] Van den Berg M., Wendel-Vos W., Van Poppel
M., Kemper H., Van Mechelen W. and Maas J.
Health benefits of green spaces in the living
environment:
A systematic
reviewofepidemiological studies, Urban Forestry and
Urban Greening, Vol.14, No. 4, 2015, pp.806816.
[3] Krupnova T.G., Rakova O.V., Mashkova I.V.,
Artyukov E.V. and Vlasov N.E., Health risk
assessment of metal(loid)s exposure via indoor
dust from urban area in Chelyabinsk, Russia,
International Journal of GEOMATE, Vol.16,
No.55, 2019, pp.1–7.
[4] Fuller R., Landrigan P.J., Balakrishnan K., Bathan
G. et al., Pollution and health: a progress update.
Lancet, Vol.391, No.10119, 2018, pp.462-512.
[5] Agency for Toxic Substances and Disease
Registry, 2019. Priority List of Hazardous
Substances that Will be the Subject of
Toxicological Profiles. Available online:
https://www.atsdr.cdc.gov/spl/index.html#2019spl (accessed on 01 October 2022).
[6] Agency for Toxic Substances and Disease
Registry, 2020. Toxicological Profile for Lead.
Available
online:https://www.atsdr.cdc.gov/ToxProfiles/tp13.pdf(accessed on 01 October 2022).
[7] Krupnova T.G., Rakova O.V., Bondarenko K.A.,
Potgieter-Vermaak S. and Godoi R.H.M.,
Elemental composition of PM2.5 and PM10 and
health risks assessment in the industrial districtsof chelyabinsk, south ural region, Russia
International Journal of Environmental Researchand Public Health, Vol. 18, No.23, 2021, p.12354.

[8] Krupnova T.G., Rakova O.V., Gavrilkina S.V.,
Antoshkina E.G., Baranov E.O., Dmitrieva A.P.
and Somova A.V., Extremely high concentrationsof zinc in birch tree leaves collected in
Chelyabinsk,
Russia,Environmental
Geochemistry and Health, No. 43, 2021, pp.25512570.
[9] Egendorf S.P., Groffman P., Moore G. and Cheng
Z., The limits of lead (Pb) phytoextraction andpossibilities of phytostabilization in contaminatedsoil: a critical review, International Journal of
Phytoremediation, Vol. 22, No.9, 2020, pp.916930.
[10] Rachmadiarti F., Purnomo T., Azizah D.N. andFascavitri A., Syzigium Oleina and Wedelia
Trilobata for Phytoremediation of Lead Pollution
in the Atmosphere, Nature Environment and
Pollution Technology, Vol.18, No.1, 2019,
pp.157-162.
[11] Hrotkó K., Gyeviki M., Sütöriné D.M., Magyar
L., Mészáros R., Honfi P. and Kardos L., Foliar
dust and heavy metal deposit on leaves of urban
trees in Budapest (Hungary), Environmental
Geochemistry and Health, No.43, 2021, pp.19271940.
[12] Schreck E., Foucault Y., Sarret G., Sobanska S.,
Cécillon L., Castrec-Rouelle M., Uzu G. andDumat C., Metal and metalloid foliar uptake byvarious plant species exposed to atmosphericindustrial fallout: Mechanisms involved for lead,
Science of the Total Environment, Vol.15,
No.427-428, 2012, pp.253-262.
[13] Wei X., Lyu S., Yu Y., Wang Z., Liu H., Pan D.
and Chen J., Phylloremediation of Air Pollutants:
Exploiting the Potential of Plant Leaves and LeafAssociated Microbes, Frontiers in Plant Science,
No.8, 2017, p.1318.

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Bangkok, Thailand, 22-24 November 2022, ISBN: 978-4-909106087 C3051

FUNDAMENTAL PROPERTIES OF SOIL AND RICE HUSK ASH
COMPOSITE FOR GROUND IMPROVEMENT – A REVIEW
Abd Elmageed Atef1, Alex Otieno Owino1, Md. Yachin Islam1, Md. Mahabub Alan2,
Md. Soybur Rahman3 and Zakaria Hossian1
1

Graduate School of Bioresources, Mie University, Japan.
ABSTRACT

Nearly 170 million tons per year of waste rice husks are produced worldwide, which pollutes the environment
and poses health risks. RHA has become a potential threat to the ecosystem and needs real solutions. The direct
disposal of RHA into open land or water bodies causes environmental pollution. One of the possible solutions is
to utilize RHA additive to cement, lime, basalt fibers, Carbide slag, etc. This paper summarized the fundamental
properties of soil and rice husk ash and its application in ground improvement and environmental protection. A
comprehensive review of the literature available on RHA-soil mixture was performed to identify the gaps inunderstanding the behavior of the mix in terms of mechanical properties, durability, environmental impact, andinternal mechanism. The results showed that adding RHA may significantly improve the soil's compressive
strength, shear strength, and CBR value, which would be advantageous for both the economy and the environment.
Additionally, it can significantly enhance soil performance in terms of shrinkage cracking. Generally, the full use
of RHA as an addition for cement/lime, etc., in-ground improvement is promising since it can result in energy
savings, low carbon emissions, and sustainable development. However, using control burned RHA with highamorphous silica content, conducting soaked CBR tests, consolidation tests, and freezing/thawing tests to analyze
the effects of RHA on the geotechnical properties of soil still need to be studied further.
Keywords: Rice husk ash, Environmental impact, Ground improvement, Properties of soil
INTRODUCTION
Soil comprises minerals, soil organic matter (SOM),
water, and air. The composition and proportion ofthese components greatly influence soil physicalproperties, including texture, structure, and porosity,
the fraction of pore space in the soil. In turn, theseproperties affect air and water movement in the soil,
thus the soil’s ability to function [1]. Ground
improvement techniques are often used to increase
the soil strength, lessen the compressibility, enhance
the performance under the load and improve the soil
properties in terms of their bearing capacity, shear
strength, settlement characteristic, and drainage [2].
These techniques are widely used in an enormous
scope of construction, such as industrial, commercial,and housing projects and infrastructure construction
for dams, tunnels, ports, roadways, and barriers.
Ground improvement is an alteration or stabilization
technique to modify the soil's geotechnical properties,
improving the soil's strength and stability. It reduces
the permeability and compressibility of soil [2].
.

25% ash is produced from the whole rice husk when
it is burnt fig. 1 The paddy rice production was 782
million tons worldwide in 2018 (FAOSTAT,2020)
Fig 2, and about 172 million tons of rice husk and 34
million tons of RHA were produced from the paddy
rice in that year. The content of SiO2 in RHA is morethan 70% and can also go up to more than 90%,
especially under controlled combustion temperature
conditions[2].

Fig. 2 Leading rice (paddy) producing countries
worldwide (FAOSTAT2018).
LITERATURE REVIEW
Many studies have shown that RHA has abundantnanopores (including many closed pores). The porous
structure contributes to low dry weight, large specificsurface area, and excellent thermal insulation
properties [3]. In a wide range of building projects,
including those involving industry, commerce,
housing, and infrastructure development for dams,
tunnels, ports, roads, and embankments, stabilized
soil employing additives can be operated successfully.

Fig. 1: Raw Rice to RHA and Synthesized Silica[2].
Rice is one of the most consumed food items
worldwide; during the paddy milling process, 78% of
the weight represents rice, broken rice, and bran 22%of the weight means husk. In this process, 17% to683

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Table 1 literature review
Author(s)Materials
additives

Soil typeMusa, Alhaji. (2007) [4]

Laterite soil

RHA+ Cement

Arabani, et al. (2017) [5]Asphaltic soil

RHA

Kabubo, et al. (2017) [6]

expansive soil

RHA + Limeand

Results
Under the specific cement content,
CBR and UCS increased with theincrease in RHA content, and the
optimum RHA content is between 46%.
RHA reduced the permeability andthermal sensitivity and improved the
mixture's stability, rutting resistance,
and fatigue life. The optimum RHA
dosage is 15%.
The combination of 20% RHA and 2%
lime can improve CBR by 800% andreduce the free expansion rate by 70%.
The optimum strength of the pit sandcement mortar prisms after 1, 2, 7, and28 days was recorded at 11.11% RHA
by the mass of the cement.
It is recommended that 8% of RHA
content with 2% cement is the
optimum amount for practical
purposes.
The results show that the modified soilof MDD and OMC values are
increased due to the increment amountof binder material.

Kofi, et al. (2017) [7]sea sandRHA+ Cement

Ahmad et al. (2018) [8]

Clay soil

RHA + Cement

Daud, et al. (2018) [9]peat soil

RHA+Cement

Ghorbani, et al. (2018) [10]Sand-clay

RHA+Fibre
Polypropylene+cement

The optimum combination is 8%
RHA, 8% cement, and 6mm
polypropylene fiber.

Phanikumar, et al. (2018) [11]Expansive soil

RHA+ Fly ash

The combination of fly ash and RHA
significantly reduced the free
expansion index and increased the
UCS value.

RHA + Cement

The content of cement in the mixture
is more than 10%, and adding 35%,
RHA contributed to improving thestrength.

RHA + Cement

The UCS value of the treated sample
with 6% RHA and 8% cement cured
for 28 days can reach up to 2900 kPa.

Pornkasem, et al. (2018) [12]Soft clay

Rahgozar, et al. (2018) [13]

Clyey
sand

Jafer, et al. (2018) [14]Soft clay

RHA+Flyash+palm
oil fuel ash

Janbuala, et al. (2018) [15]Expansive soil

RhA＋Bagasse ash

J. Idrus, et al. (2018) [16]

sand

RHA

the best percentage mixture potentiallyconsidered to be used for the subbase
is (90% - 10%) sand and RHA.

Arpit Jain. (2019) [17]Black cotton
soil

RHA

RHA reduced the plasticity index andsensitivity to moisture change,
improved CBR, secant modulus, andcohesion of the parent soil.

soil-

684

The UCS of treated soil increased an
additional 200 kPa to 923 KPa
compared to that of untreated soil.
The optimum content of rice husk ashor bagasse ash is 30%.

GEOMATE – Bangkok, Thailand, 22-24 November 2022

Hamzah N, et al. (2019) [18]Peat soil

RHA+
lime

Liu, et al. (2019) [19]expansive soil

RHA+Lime

RHA-lime can inhibit expansion andreduce compression index. The
optimum combination is 16% RHA
and 4% lime.

Philbert, et al. (2019) [20]clayey soil

RhA＋Carbide slag

The addition of RHA accelerated thecuring process and increased the UCSof the mixture.

Tonmoy, et al. (2019) [21]Weak organicsoil

RHA + Nylon fiber

The optimum combination is 15%
RHA and 1.0% nylon fiber. Comparedwith untreated organic soil, the UCS
value increased by 252.48%.

RHA+ Carbide slag

The optimum combination is
10%RHA and 5% calcium carbide.
The 7-day UCS value is close to
2MPa.
Indicated that paste with 15% ricehusk ash improved sulfate resistanceby stabilization of CeSeH and therefinement of pore structure.

Y.Y.liu, et al. (2019) [22]Expansive soil

SawdustHu et al. (2020) [23]-RHA + Cement

Ramesh, Manjunatha. (2020) [24]Black cotton
soil

RHA + lime

Yu, et al. (2020) [25]

Clay

RHA

Nahar, et al. (2021) [26]silty gravel andsand

Sharma, et al. (2021) [27]Owino, et al. (2022) [28]clayey soil

Clay soil+

It can deduce a promising andimproving result in stabilizing the soil
with sawdust and Rice Husk Ash inboth economic and strength capacity.
The optimum combination is 7.5%
RHA and 2% lime, at which the UCS
can reach the maximum value.

The optimum dosage of RHA and CL
was found to be 20% and 8%,
respectively.
RHA can increase the axial failure
stress of soil at an optimum RHA
content of 15%.

RHA + Cement

The mixture of soil with RHA 5% andcement 6% showed the highest CBRvalue and second-highest permeability
in this study.

RHA+Lime

The addition of RHA reduced DFS.
RHA increased the OMC and reduced
the MDD of the soil. RHA (12%) andlime (5%) increased the CBR value.
C&D waste + lime improved the UCS,
CBR and resilient modulus of clayey
soil.

RHA ＋ Cement ＋
basalt fibers

It is demonstrated that a reconstituteclay soil combination of 12 mm basalt
fibers, 5% RHA, and 3% cement,enhanced the ultimate yield pressuresand the resistance to excessive
swelling.

685

GEOMATE – Bangkok, Thailand, 22-24 November 2022

The addition of RHA can significantly increase redclay soil CBR value, according to research examining
the effectiveness of RHA treatment on red clay soil
through compaction and unconfined compression.
CBR tests. The results showed that the optimal
content of RHA is 6%, and the UCS of laterite soils
treated with 6% RHA and 4% lime and cured forseven days can reach up to 1.15 MPa. Musa et al. [4]The maximum dry density of the treated laterite
declined and stabilized as the RHA content rose. Still,
the ideal moisture content increased over time, which
may be connected to the unique pore structure andsubstantial specific surface area of RHA particles.
Additionally, with the particular cement
composition, the CBR and UCS rose andsubsequently fell as the RHA concentration rose.
Between 4 and 6% was discovered to be the ideal
RHA level.

RHA has also made some progress in its explorationof the strengthening of soft soil. Specifically,
Pornkasem [12] used biomass waste ash to partiallyreplace Portland cement in a deep mixing pile to treat
soft clay and analyzed the influence of RHA content,
cement content, curing age, and initial moisture
content and achieved good results.
Fig .4 shows the stress–strain relationship of treatedsoft soil under different RHA contents. It can beobserved that the addition of RHA did not change thestress-strain relationship of soft soil, but anappropriate amount of RHA can significantly
enhance the stress that the soft soil can endure.

Fig 4. Stress-strain relationship of RHA-treated soft
soil [25].

(MDD)

Fig 5. Axial failure stress of RHA-treated softsoil[25].
The optimal dosage of RHA is recommended to be
15%. RHA can solidify problematic soil, subgrade,and pavement components individually or jointly.
The content of RHA is widely dispersed between 6%
and 40%. This may be attributed to the different typesof soil.

(OMC)
Fig. 3 Effect of RHA content on compaction
characteristics of red clay: (Mdd) maximum dry
density (b) Optimum moisture content [4].

686GEOMATE – Bangkok, Thailand, 22-24 November 2022

The research methods of RHA-reinforced soil aremainly focused on the basic physical and mechanicalproperties. Yet, in-depth research on the permeability
characteristics, dynamic characteristics, durability,and environmental impact under complex
environments of RHA-treated soil was hardly
reported. The results showed that adding RHA caneffectively improve the performance of soil related toshrinkage cracking and significantly enhance thecompressive strength, shear strength, and CBR valueof the soil, indicating economic and environmental
benefits.

silica content.
To conduct soaked CBR tests, triaxial test, UCS test,
direct shear test, compaction test, consolidation test,
and freezing/thawing tests to analyze the effects of
RHA on the geotechnical properties of soil.
ACKNOWLEDGMENTS
The authors would like to thank Mie University
Graduate School Doctoral Student Development
Support for their financial assistance and advice.
REFERENCES

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In this study, the research status of fundamental
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specific gravity, large pores, and a large specificsurface area and contains more than 70% SiO2. Itsessential characteristics are related to many factors,
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In general, the appropriate amount of RHA canreduce the maximum dry density, increase theoptimal moisture content, and enhance the UCS,
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FUTURE RESEARCH
To use control burned RHA with high amorphous687

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